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For multi-output layers, use the functional API.");this.checkShape(e),this.outputs=[e.inboundNodes[0].outputTensors[0]],this.inputs=rv(this.outputs[0])}this.inboundNodes=[],new Nf({outboundLayer:this,inboundLayers:[],nodeIndices:[],tensorIndices:[],inputTensors:this.inputs,outputTensors:this.outputs,inputMasks:oi(null,this.inputs.length),outputMasks:[null],inputShapes:this.inputs.map(s=>s.shape),outputShapes:this.outputs[0].shape})}else{let s=e.apply(this.outputs[0]);if(Array.isArray(s))throw new TypeError("All layers in a Sequential model should have a single output tensor. 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Add some layers first.");this.model=new Wr({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 Ks("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 Ks("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 Ks("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 Ks("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={},s=!1){let r,a={};if(t instanceof Array){if(t[0].className==null||t[0].className==="Merge")throw new G("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,a=t;let o=new e(a);if(!(o instanceof yu))throw new ze(`Sequential.fromConfig called on non-Sequential input: ${o}`);for(let i of r){let u=Qs(i,void 0,s);s&&u.setFastWeightInitDuringBuild(!0),o.add(u)}return o}set stopTraining(e){if(this.model==null)throw new G("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 G("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}}};yu.className="Sequential";ue.registerClass(yu);function XM(e){return new Wr(e)}function KM(e){return new yu(e)}function ZM(e,t){return t==null&&(t={}),GM(e,t)}function $v(e){return av(e)}function YM(e,t){Ps.registerCallbackConstructor(e,t)}var jn=class extends ue.Serializable{getConfig(){return{}}},Fv=class extends jn{apply(e,t=1){return kP(e,t)}};Fv.className="elu";ue.registerClass(Fv);var Ov=class extends jn{apply(e){return Hh(e)}};Ov.className="selu";ue.registerClass(Ov);var Pv=class extends jn{apply(e){return js(e)}};Pv.className="relu";ue.registerClass(Pv);var Mv=class extends jn{apply(e){return H(()=>iu(6,js(e)))}};Mv.className="relu6";ue.registerClass(Mv);var zv=class extends jn{apply(e){return e}};zv.className="linear";ue.registerClass(zv);var Lv=class extends jn{apply(e){return Wn(e)}};Lv.className="sigmoid";ue.registerClass(Lv);var Bv=class extends jn{apply(e){return SP(e)}};Bv.className="hardSigmoid";ue.registerClass(Bv);var Wv=class extends jn{apply(e){return ei(e)}};Wv.className="softplus";ue.registerClass(Wv);var Vv=class extends jn{apply(e){return IP(e)}};Vv.className="softsign";ue.registerClass(Vv);var Uv=class extends jn{apply(e){return Yo(e)}};Uv.className="tanh";ue.registerClass(Uv);var P1=class extends jn{apply(e,t=-1){return si(e,t)}};P1.className="softmax";ue.registerClass(P1);var Hv=class extends jn{apply(e,t=-1){return Mh(e,t)}};Hv.className="logSoftmax";ue.registerClass(Hv);var Gv=class extends jn{apply(e,t=1){return H(()=>z(Wn(z(e,t)),e))}};Gv.className="swish";ue.registerClass(Gv);var jv=class extends jn{apply(e){return H(()=>z(e,Yo(ei(e))))}};jv.className="mish";ue.registerClass(jv);function wa(e){return e.getClassName()}function M1(e,t={}){return td(e,ue.SerializationMap.getMap().classNameMap,t,"activation")}function ka(e){if(e==null){let t={};return t.className="linear",t.config={},M1(t)}if(typeof e=="string"){let t={};return t.className=e,t.config={},M1(t)}else return e instanceof jn?e:M1(e)}function z1(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 qv=class extends ue.Serializable{},dd=class extends qv{constructor(e){super();z1(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 H(()=>{let t=Pt([1]);return this.hasL1&&(t=le(t,we(z(this.l1,Wt(e))))),this.hasL2&&(t=le(t,we(z(this.l2,ad(e))))),V(t,[])})}getConfig(){return{l1:this.l1,l2:this.l2}}static fromConfig(e,t){return new e({l1:t.l1,l2:t.l2})}};dd.className="L1L2";ue.registerClass(dd);function JM(e){return z1(e),new dd({l1:e!=null?e.l1:null,l2:0})}function QM(e){return z1(e),new dd({l2:e!=null?e.l2:null,l1:0})}var Xv={l1l2:"L1L2"};function At(e){return t1(e)}function Kv(e,t={}){return td(e,ue.SerializationMap.getMap().classNameMap,t,"regularizer")}function Tt(e){if(e==null)return null;if(typeof e=="string"){let n={className:e in Xv?Xv[e]:e,config:{}};return Kv(n)}else return e instanceof qv?e:Kv(e)}var L1=class extends Qe{constructor(e){super(e==null?{}:e);this.supportsMasking=!0,e!=null&&(this.maxValue=e.maxValue)}call(e,t){e=We(e);let n=js(e);return this.maxValue!=null&&(n=Vn(n,0,this.maxValue)),n}computeOutputShape(e){return e}getConfig(){let e={maxValue:this.maxValue},t=super.getConfig();return Object.assign(e,t),e}};L1.className="ReLU";ue.registerClass(L1);var B1=class extends Qe{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=We(e);return Vc(n,this.alpha)}computeOutputShape(e){return e}getConfig(){let e={alpha:this.alpha},t=super.getConfig();return Object.assign(e,t),e}};B1.className="LeakyReLU";ue.registerClass(B1);var W1=class extends Qe{constructor(e){super(e==null?{}:e);if(this.DEFAULT_ALPHA_INITIALIZER="zeros",e==null&&(e={}),this.supportsMasking=!0,this.alphaInitializer=Ct(e.alphaInitializer||this.DEFAULT_ALPHA_INITIALIZER),this.alphaRegularizer=Tt(e.alphaRegularizer),this.alphaConstraint=Qt(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 G(`Expected sharedAxes to be a number or an array of numbers, but got ${e.sharedAxes}`)}build(e){e=dt(e);let t=e.slice(1);if(this.sharedAxes!=null)for(let s of this.sharedAxes)t[s-1]=1;this.alpha=this.addWeight("alpha",t,"float32",this.alphaInitializer,this.alphaRegularizer,!0,this.alphaConstraint);let n={};if(this.sharedAxes!=null)for(let s=1;s(Lt(t),t==="channelsFirst"?Ze(e,[0,2,3,1]):e))}function Zv(e,t){return H(()=>(Lt(t),t==="channelsFirst"?Ze(e,[0,2,3,4,1]):e))}function ez(e,t,n,s=1,r="valid",a,o=1){return H(()=>{if(a==null&&(a=Xs()),Lt(a),e.shape.length!==3)throw new G(`The input of a conv1dWithBias operation should be 3, but is ${e.shape.length} instead.`);if(t.shape.length!==3)throw new G(`The kernel for a conv1dWithBias operation should be 3, but is ${t.shape.length} instead`);if(n!=null&&n.shape.length!==1)throw new G(`The bias for a conv1dWithBias operation should be 1, but is ${t.shape.length} instead`);if(a==="channelsFirst"&&(e=Ze(e,[0,2,1])),r==="causal")throw new ze("The support for CAUSAL padding mode in conv1dWithBias is not implemented yet.");let i=Rh(e,t,s,r==="same"?"same":"valid","NWC",o);return n!=null&&(i=Ys(i,n)),i})}function Yv(e,t,n,s=[1,1],r="valid",a,o,i=null){return H(()=>{if(a==null&&(a=Xs()),Lt(a),e.rank!==3&&e.rank!==4)throw new G(`conv2dWithBiasActivation expects input to be of rank 3 or 4, but received ${e.rank}.`);if(t.rank!==3&&t.rank!==4)throw new G(`conv2dWithBiasActivation expects kernel to be of rank 3 or 4, but received ${e.rank}.`);let l=G1(e,a);if(r==="causal")throw new ze("The support for CAUSAL padding mode in conv1dWithBias is not implemented yet.");return l=Aa.conv2d({x:l,filter:t,strides:s,pad:r==="same"?"same":"valid",dilations:o,dataFormat:"NHWC",bias:n,activation:i}),a==="channelsFirst"&&(l=Ze(l,[0,3,1,2])),l})}function tz(e,t,n,s=[1,1,1],r="valid",a,o){return H(()=>{if(a==null&&(a=Xs()),Lt(a),e.rank!==4&&e.rank!==5)throw new G(`conv3dWithBias expects input to be of rank 4 or 5, but received ${e.rank}.`);if(t.rank!==4&&t.rank!==5)throw new G(`conv3dWithBias expects kernel to be of rank 4 or 5, but received ${e.rank}.`);let i=Zv(e,a);if(r==="causal")throw new ze("The support for CAUSAL padding mode in conv3dWithBias is not implemented yet.");return i=wA(i,t,s,r==="same"?"same":"valid","NDHWC",o),n!=null&&(i=Ys(i,n)),a==="channelsFirst"&&(i=Ze(i,[0,4,1,2,3])),i})}var j1=class extends Qe{constructor(e,t){super(t);if(this.bias=null,this.DEFAULT_KERNEL_INITIALIZER="glorotNormal",this.DEFAULT_BIAS_INITIALIZER="zeros",j1.verifyArgs(t),this.rank=e,cn(this.rank,"rank"),this.rank!==1&&this.rank!==2&&this.rank!==3)throw new ze(`Convolution layer for rank other than 1, 2, or 3 (${this.rank}) is not implemented yet.`);if(this.kernelSize=xu(t.kernelSize,e,"kernelSize"),this.strides=xu(t.strides==null?1:t.strides,e,"strides"),this.padding=t.padding==null?"valid":t.padding,bs(this.padding),this.dataFormat=t.dataFormat==null?"channelsLast":t.dataFormat,Lt(this.dataFormat),this.activation=ka(t.activation),this.useBias=t.useBias==null?!0:t.useBias,this.biasInitializer=Ct(t.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.biasConstraint=Qt(t.biasConstraint),this.biasRegularizer=Tt(t.biasRegularizer),this.activityRegularizer=Tt(t.activityRegularizer),this.dilationRate=xu(t.dilationRate==null?1:t.dilationRate,e,"dilationRate"),this.rank===1&&Array.isArray(this.dilationRate)&&this.dilationRate.length!==1)throw new G(`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 G(`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 G(`dilationRate must be a number or array of three numbers for 3D convolution, but received ${JSON.stringify(this.dilationRate)}`)}}static verifyArgs(e){if(mr("kernelSize"in e,"required key 'kernelSize' not in config"),typeof e.kernelSize!="number"&&!s1(e.kernelSize,"number",1,3))throw new G(`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:wa(this.activation),useBias:this.useBias,biasInitializer:_t(this.biasInitializer),biasRegularizer:At(this.biasRegularizer),activityRegularizer:At(this.activityRegularizer),biasConstraint:Jt(this.biasConstraint)},t=super.getConfig();return Object.assign(e,t),e}},pd=class extends j1{constructor(e,t){super(e,t);this.kernel=null,pd.verifyArgs(t),this.filters=t.filters,cn(this.filters,"filters"),this.kernelInitializer=Ct(t.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.kernelConstraint=Qt(t.kernelConstraint),this.kernelRegularizer=Tt(t.kernelRegularizer)}build(e){e=dt(e);let t=this.dataFormat==="channelsFirst"?1:e.length-1;if(e[t]==null)throw new G(`The channel dimension of the input should be defined. Found ${e[t]}`);let n=e[t],s=this.kernelSize.concat([n,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:{[t]:n}}],this.built=!0}call(e,t){return H(()=>{e=We(e);let n,s=this.bias==null?null:this.bias.read(),r=B3(this.activation.getClassName());if(r!=null&&this.rank===2)n=Yv(e,this.kernel.read(),s,this.strides,this.padding,this.dataFormat,this.dilationRate,r);else{if(this.rank===1)n=ez(e,this.kernel.read(),s,this.strides[0],this.padding,this.dataFormat,this.dilationRate[0]);else if(this.rank===2)n=Yv(e,this.kernel.read(),s,this.strides,this.padding,this.dataFormat,this.dilationRate);else if(this.rank===3)n=tz(e,this.kernel.read(),s,this.strides,this.padding,this.dataFormat,this.dilationRate);else throw new ze("convolutions greater than 3D are not implemented yet.");this.activation!=null&&(n=this.activation.apply(n))}return n})}computeOutputShape(e){e=dt(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)}`)}},hd=class extends pd{constructor(e){super(2,e);hd.verifyArgs(e)}getConfig(){let e=super.getConfig();return delete e.rank,e}static verifyArgs(e){if(typeof e.kernelSize!="number"&&!s1(e.kernelSize,"number",1,2))throw new G(`Conv2D expects config.kernelSize to be number or number[] with length 1 or 2, but received ${JSON.stringify(e.kernelSize)}.`)}};hd.className="Conv2D";ue.registerClass(hd);var fd=class extends pd{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 G(`Conv3D expects config.kernelSize to be number or [number, number, number], but received ${JSON.stringify(e.kernelSize)}.`)}};fd.className="Conv3D";ue.registerClass(fd);var q1=class extends hd{constructor(e){super(e);if(this.inputSpec=[new Ht({ndim:4})],this.padding!=="same"&&this.padding!=="valid")throw new G(`Conv2DTranspose currently supports only padding modes 'same' and 'valid', but received padding mode ${this.padding}`)}build(e){if(e=dt(e),e.length!==4)throw new G("Input should have rank 4; Received input shape: "+JSON.stringify(e));let t=this.dataFormat==="channelsFirst"?1:e.length-1;if(e[t]==null)throw new G("The channel dimension of the inputs should be defined. Found `None`.");let n=e[t],s=this.kernelSize.concat([this.filters,n]);this.kernel=this.addWeight("kernel",s,"float32",this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint),this.useBias&&(this.bias=this.addWeight("bias",[this.filters],"float32",this.biasInitializer,this.biasRegularizer,!0,this.biasConstraint)),this.inputSpec=[new Ht({ndim:4,axes:{[t]:n}})],this.built=!0}call(e,t){return H(()=>{let n=We(e);if(n.shape.length!==4)throw new G(`Conv2DTranspose.call() expects input tensor to be rank-4, but received a tensor of rank-${n.shape.length}`);let s=n.shape,r=s[0],a,o;this.dataFormat==="channelsFirst"?(a=2,o=3):(a=1,o=2);let i=s[a],l=s[o],u=this.kernelSize[0],c=this.kernelSize[1],d=this.strides[0],p=this.strides[1],h=yr(i,d,u,this.padding),m=yr(l,p,c,this.padding),f=[r,h,m,this.filters];this.dataFormat!=="channelsLast"&&(n=Ze(n,[0,2,3,1]));let g=Dh(n,this.kernel.read(),f,this.strides,this.padding);return this.dataFormat!=="channelsLast"&&(g=Ze(g,[0,3,1,2])),this.bias!=null&&(g=Ys(g,this.bias.read(),this.dataFormat)),this.activation!=null&&(g=this.activation.apply(g)),g})}computeOutputShape(e){e=dt(e);let t=e.slice(),n,s,r;this.dataFormat==="channelsFirst"?(n=1,s=2,r=3):(n=3,s=1,r=2);let a=this.kernelSize[0],o=this.kernelSize[1],i=this.strides[0],l=this.strides[1];return t[n]=this.filters,t[s]=yr(t[s],i,a,this.padding),t[r]=yr(t[r],l,o,this.padding),t}getConfig(){let e=super.getConfig();return delete e.dilationRate,e}};q1.className="Conv2DTranspose";ue.registerClass(q1);var X1=class extends fd{constructor(e){super(e);if(this.inputSpec=[new Ht({ndim:5})],this.padding!=="same"&&this.padding!=="valid")throw new G(`Conv3DTranspose currently supports only padding modes 'same' and 'valid', but received padding mode ${this.padding}`)}build(e){if(e=dt(e),e.length!==5)throw new G("Input should have rank 5; Received input shape: "+JSON.stringify(e));let t=this.dataFormat==="channelsFirst"?1:e.length-1;if(e[t]==null)throw new G("The channel dimension of the inputs should be defined. Found `None`.");let n=e[t],s=this.kernelSize.concat([this.filters,n]);this.kernel=this.addWeight("kernel",s,"float32",this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint),this.useBias&&(this.bias=this.addWeight("bias",[this.filters],"float32",this.biasInitializer,this.biasRegularizer,!0,this.biasConstraint)),this.inputSpec=[new Ht({ndim:5,axes:{[t]:n}})],this.built=!0}call(e,t){return H(()=>{let n=We(e);if(n.shape.length!==5)throw new G(`Conv3DTranspose.call() expects input tensor to be rank-4, but received a tensor of rank-${n.shape.length}`);let s=n.shape,r=s[0],a,o,i;this.dataFormat==="channelsFirst"?(i=2,a=3,o=4):(i=1,a=2,o=3);let l=s[i],u=s[a],c=s[o],d=this.kernelSize[0],p=this.kernelSize[1],h=this.kernelSize[2],m=this.strides[0],f=this.strides[1],g=this.strides[2],A=yr(l,m,d,this.padding),y=yr(u,f,p,this.padding),x=yr(c,g,h,this.padding),b=[r,A,y,x,this.filters];this.dataFormat!=="channelsLast"&&(n=Ze(n,[0,2,3,4,1]));let v=Bb(n,this.kernel.read(),b,this.strides,this.padding);return this.dataFormat!=="channelsLast"&&(v=Ze(v,[0,4,1,2,3])),this.bias!==null&&(v=Ys(v,this.bias.read(),this.dataFormat)),this.activation!==null&&(v=this.activation.apply(v)),v})}computeOutputShape(e){e=dt(e);let t=e.slice(),n,s,r,a;this.dataFormat==="channelsFirst"?(n=1,s=2,r=3,a=4):(n=4,s=1,r=2,a=3);let o=this.kernelSize[0],i=this.kernelSize[1],l=this.kernelSize[2],u=this.strides[0],c=this.strides[1],d=this.strides[2];return t[n]=this.filters,t[s]=yr(t[s],u,o,this.padding),t[r]=yr(t[r],c,i,this.padding),t[a]=yr(t[a],d,l,this.padding),t}getConfig(){let e=super.getConfig();return delete e.dilationRate,e}};X1.className="Conv3DTranspose";ue.registerClass(X1);var Jv=class extends pd{constructor(e,t){super(e,t);if(this.DEFAULT_DEPTHWISE_INITIALIZER="glorotUniform",this.DEFAULT_POINTWISE_INITIALIZER="glorotUniform",this.depthwiseKernel=null,this.pointwiseKernel=null,t.filters==null)throw new G("The `filters` configuration field is required by SeparableConv, but is unspecified.");if(t.kernelInitializer!=null||t.kernelRegularizer!=null||t.kernelConstraint!=null)throw new G("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 G(`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=Ct(t.depthwiseInitializer||this.DEFAULT_DEPTHWISE_INITIALIZER),this.depthwiseRegularizer=Tt(t.depthwiseRegularizer),this.depthwiseConstraint=Qt(t.depthwiseConstraint),this.pointwiseInitializer=Ct(t.depthwiseInitializer||this.DEFAULT_POINTWISE_INITIALIZER),this.pointwiseRegularizer=Tt(t.pointwiseRegularizer),this.pointwiseConstraint=Qt(t.pointwiseConstraint)}build(e){if(e=dt(e),e.length{e=We(e);let n;if(this.rank===1)throw new ze("1D separable convolution is not implemented yet.");return this.rank===2&&(this.dataFormat==="channelsFirst"&&(e=Ze(e,[0,2,3,1])),n=BA(e,this.depthwiseKernel.read(),this.pointwiseKernel.read(),this.strides,this.padding,this.dilationRate,"NHWC")),this.useBias&&(n=Ys(n,this.bias.read(),this.dataFormat)),this.activation!=null&&(n=this.activation.apply(n)),this.dataFormat==="channelsFirst"&&(n=Ze(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=_t(this.depthwiseInitializer),e.pointwiseInitializer=_t(this.pointwiseInitializer),e.depthwiseRegularizer=At(this.depthwiseRegularizer),e.pointwiseRegularizer=At(this.pointwiseRegularizer),e.depthwiseConstraint=Jt(this.depthwiseConstraint),e.pointwiseConstraint=Jt(this.pointwiseConstraint),e}};Jv.className="SeparableConv";var K1=class extends Jv{constructor(e){super(2,e)}};K1.className="SeparableConv2D";ue.registerClass(K1);var Mf=class extends pd{constructor(e){super(1,e);Mf.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"&&!s1(e.kernelSize,"number",1,1))throw new G(`Conv1D expects config.kernelSize to be number or number[] with length 1, but received ${JSON.stringify(e.kernelSize)}.`)}};Mf.className="Conv1D";ue.registerClass(Mf);var Z1=class extends Qe{constructor(e){super(e);typeof e.cropping=="number"?this.cropping=[[e.cropping,e.cropping],[e.cropping,e.cropping]]:typeof e.cropping[0]=="number"?this.cropping=[[e.cropping[0],e.cropping[0]],[e.cropping[1],e.cropping[1]]]:this.cropping=e.cropping,this.dataFormat=e.dataFormat===void 0?"channelsLast":e.dataFormat,this.inputSpec=[{ndim:4}]}computeOutputShape(e){return this.dataFormat==="channelsFirst"?[e[0],e[1],e[2]-this.cropping[0][0]-this.cropping[0][1],e[3]-this.cropping[1][0]-this.cropping[1][1]]:[e[0],e[1]-this.cropping[0][0]-this.cropping[0][1],e[2]-this.cropping[1][0]-this.cropping[1][1],e[3]]}call(e,t){return H(()=>{if(e=We(e),this.dataFormat==="channelsLast"){let n=mf(e,this.cropping[0][0],e.shape[1]-this.cropping[0][0]-this.cropping[0][1],2);return mf(n,this.cropping[1][0],e.shape[2]-this.cropping[1][1]-this.cropping[1][0],3)}else{let n=mf(e,this.cropping[0][0],e.shape[2]-this.cropping[0][0]-this.cropping[0][1],3);return mf(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}};Z1.className="Cropping2D";ue.registerClass(Z1);var Y1=class extends Qe{constructor(e){super(e);this.DEFAULT_SIZE=[2,2],this.inputSpec=[{ndim:4}],this.size=e.size==null?this.DEFAULT_SIZE:e.size,this.dataFormat=e.dataFormat==null?"channelsLast":e.dataFormat,Lt(this.dataFormat),this.interpolation=e.interpolation==null?"nearest":e.interpolation,gP(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 H(()=>{let n=We(e),s=n.shape;if(this.dataFormat==="channelsFirst"){n=Ze(n,[0,2,3,1]);let r=this.size[0]*s[2],a=this.size[1]*s[3],o=this.interpolation==="nearest"?De.resizeNearestNeighbor(n,[r,a]):De.resizeBilinear(n,[r,a]);return Ze(o,[0,3,1,2])}else{let r=this.size[0]*s[1],a=this.size[1]*s[2];return this.interpolation==="nearest"?De.resizeNearestNeighbor(n,[r,a]):De.resizeBilinear(n,[r,a])}})}getConfig(){let e={size:this.size,dataFormat:this.dataFormat},t=super.getConfig();return Object.assign(e,t),e}};Y1.className="UpSampling2D";ue.registerClass(Y1);function nz(e,t,n=[1,1],s="valid",r,a){return H(()=>{r==null&&(r=Xs()),Lt(r);let o=G1(e,r);if(e.rank!==4)throw new G(`Input for depthwiseConv2d is required to be 4-D, but is instead ${e.rank}-D`);if(t.rank!==4)throw new G(`depthwiseKernel is required to be 4-D, but is instead ${t.rank}-D`);return o=su(o,t,n,s==="same"?"same":"valid","NHWC",a),r==="channelsFirst"&&(o=Ze(o,[0,3,1,2])),o})}var J1=class extends j1{constructor(e){super(2,e);this.depthwiseKernel=null,this.depthMultiplier=e.depthMultiplier==null?1:e.depthMultiplier,this.depthwiseInitializer=Ct(e.depthwiseInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.depthwiseConstraint=Qt(e.depthwiseConstraint),this.depthwiseRegularizer=Tt(e.depthwiseRegularizer)}build(e){if(e=dt(e),e.length<4)throw new G(`Inputs to DepthwiseConv2D should have rank 4. Received input shape: ${JSON.stringify(e)}.`);let t=this.dataFormat==="channelsFirst"?1:3;if(e[t]==null||e[t]<0)throw new G(`The channel dimension of the inputs to DepthwiseConv2D should be defined, but is not (${e[t]}).`);let n=e[t],s=[this.kernelSize[0],this.kernelSize[1],n,this.depthMultiplier];this.depthwiseKernel=this.addWeight("depthwise_kernel",s,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 H(()=>{e=We(e);let n=nz(e,this.depthwiseKernel.read(),this.strides,this.padding,this.dataFormat,null);return this.useBias&&(n=Ys(n,this.bias.read(),this.dataFormat)),this.activation!=null&&(n=this.activation.apply(n)),n})}computeOutputShape(e){e=dt(e);let t=this.dataFormat==="channelsFirst"?e[2]:e[1],n=this.dataFormat==="channelsFirst"?e[3]:e[2],s=this.dataFormat==="channelsFirst"?e[1]*this.depthMultiplier:e[3]*this.depthMultiplier,r=er(t,this.kernelSize[0],this.padding,this.strides[0]),a=er(n,this.kernelSize[1],this.padding,this.strides[1]);return this.dataFormat==="channelsFirst"?[e[0],s,r,a]:[e[0],r,a,s]}getConfig(){let e=super.getConfig();return e.depthMultiplier=this.depthMultiplier,e.depthwiseInitializer=_t(this.depthwiseInitializer),e.depthwiseRegularizer=At(this.depthwiseRegularizer),e.depthwiseConstraint=Jt(this.depthwiseRegularizer),e}};J1.className="DepthwiseConv2D";ue.registerClass(J1);function Qv(e,t,n,s){if(Array.isArray(e)){if(t!=null||n!=null)throw new G("When inputs is an array, neither initialState or constants should be provided");s!=null&&(n=e.slice(e.length-s,e.length),e=e.slice(0,e.length-s)),e.length>1&&(t=e.slice(1,e.length)),e=e[0]}function r(a){return a==null||Array.isArray(a)?a:[a]}return t=r(t),n=r(n),{inputs:e,initialState:t,constants:n}}function ew(e,t,n,s=!1,r,a,o=!1,i=!1){return H(()=>{let l=t.shape.length;if(l<3)throw new G(`Input should be at least 3D, but is ${l}D.`);let u=[1,0].concat(Zs(2,l));if(t=Ze(t,u),a!=null)throw new ze("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."),r!=null&&(r=pe(pe(r,"bool"),"float32"),r.rank===l-1&&(r=zt(r,-1)),r=Ze(r,u)),s&&(t=os(t,0),r!=null&&(r=os(r,0)));let c=[],d,p=n,h=t.shape[0],m=En(t),f;r!=null&&(f=En(r));for(let A=0;Ae(y,p));if(r==null)d=x[0],p=x[1];else{let b=H(()=>{let v=f[A],k=ye(as(v),v),S=le(z(x[0],v),z(p[0],k)),C=p.map((D,O)=>le(z(x[1][O],v),z(D,k)));return{output:S,newStates:C}});d=b.output,p=b.newStates}i&&c.push(d)}let g;return i&&(g=yn(c,1)),[d,g,p]})}var xr=class extends Qe{constructor(e){super(e);let t;if(e.cell==null)throw new G("cell property is missing for the constructor of RNN.");if(Array.isArray(e.cell)?t=new Bf({cells:e.cell}):t=e.cell,t.stateSize==null)throw new G("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 Ht({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 Zs(0,e).map(t=>null)}else return this.states_}setStates(e){this.states_=e}computeOutputShape(e){b1(e)&&(e=e[0]),e=e;let t=this.cell.stateSize;Array.isArray(t)||(t=[t]);let n=t[0],s;if(this.returnSequences?s=[e[0],e[1],n]:s=[e[0],n],this.returnState){let r=[];for(let a of t)r.push([e[0],a]);return[s].concat(r)}else return s}computeMask(e,t){return H(()=>{Array.isArray(t)&&(t=t[0]);let n=this.returnSequences?t:null;if(this.returnState){let s=this.states.map(r=>null);return[n].concat(s)}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;no.shape[o.shape.length-1]),a))throw new G(`An initialState was passed that is not compatible with cell.stateSize. Received stateSpec=${this.stateSpec}; However cell.stateSize is ${this.cell.stateSize}`)}else this.stateSpec=a.map(o=>new Ht({shape:[null,o]}));this.stateful&&this.resetStates()}resetStates(e,t=!1){H(()=>{if(!this.stateful)throw new Lr("Cannot call resetStates() on an RNN Layer that is not stateful.");let n=this.inputSpec[0].shape[0];if(n==null)throw new G("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=>Pt([n,s])):this.states_=[Pt([n,this.cell.stateSize])];else if(e==null)Z(this.states_),this.keptStates!=null&&(Z(this.keptStates),this.keptStates=[]),Array.isArray(this.cell.stateSize)?this.states_=this.cell.stateSize.map(s=>Pt([n,s])):this.states_[0]=Pt([n,this.cell.stateSize]);else{if(Array.isArray(e)||(e=[e]),e.length!==this.states_.length)throw new G(`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()):Z(this.states_);for(let s=0;sun(s.clone()))})}apply(e,t){let n=t==null?null:t.initialState,s=t==null?null:t.constants;t==null&&(t={});let r=Qv(e,n,s,this.numConstants);e=r.inputs,n=r.initialState,s=r.constants;let a=[],o=[];if(n!=null){t.initialState=n,a=a.concat(n),this.stateSpec=[];for(let l of n)this.stateSpec.push(new Ht({shape:l.shape}));o=o.concat(this.stateSpec)}if(s!=null&&(t.constants=s,a=a.concat(s),this.numConstants=s.length),a[0]instanceof Js){let l=[e].concat(a),u=this.inputSpec.concat(o),c=this.inputSpec;this.inputSpec=u;let d=super.apply(l,t);return this.inputSpec=c,d}else return super.apply(e,t)}call(e,t){return H(()=>{let n=t==null?null:t.mask,s=t==null?null:t.training,r=t==null?null:t.initialState;e=We(e),r==null&&(this.stateful?r=this.states_:r=this.getInitialState(e));let a=Array.isArray(this.cell.stateSize)?this.cell.stateSize.length:1;if(r.length!==a)throw new G(`RNN Layer has ${a} 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 o={training:s},l=ew((h,m)=>{let f=this.cell.call([h].concat(m),o);return[f[0],f.slice(1)]},e,r,this.goBackwards,n,null,this.unroll,this.returnSequences),u=l[0],c=l[1],d=l[2];this.stateful&&this.resetStates(d,s);let p=this.returnSequences?c:u;return this.returnState?[p].concat(d):p})}getInitialState(e){return H(()=>{let t=Pt(e.shape);return t=we(t,[1,2]),t=rd(t),Array.isArray(this.cell.stateSize)?this.cell.stateSize.map(n=>n>1?d1(t,[1,n]):t):this.cell.stateSize>1?[d1(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()===xr.className&&(t.cell={className:this.cell.getClassName(),config:n}),Object.assign({},n,e,t)}static fromConfig(e,t,n={}){let s=t.cell,r=Qs(s,n);return new e(Object.assign(t,{cell:r}))}};xr.className="RNN";ue.registerClass(xr);var md=class extends Qe{},zf=class extends md{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,cn(this.units,"units"),this.activation=ka(e.activation==null?this.DEFAULT_ACTIVATION:e.activation),this.useBias=e.useBias==null?!0:e.useBias,this.kernelInitializer=Ct(e.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.recurrentInitializer=Ct(e.recurrentInitializer||this.DEFAULT_RECURRENT_INITIALIZER),this.biasInitializer=Ct(e.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.kernelRegularizer=Tt(e.kernelRegularizer),this.recurrentRegularizer=Tt(e.recurrentRegularizer),this.biasRegularizer=Tt(e.biasRegularizer),this.kernelConstraint=Qt(e.kernelConstraint),this.recurrentConstraint=Qt(e.recurrentConstraint),this.biasConstraint=Qt(e.biasConstraint),this.dropout=fu([1,ba([0,e.dropout==null?0:e.dropout])]),this.recurrentDropout=fu([1,ba([0,e.recurrentDropout==null?0:e.recurrentDropout])]),this.stateSize=this.units,this.dropoutMask=null,this.recurrentDropoutMask=null}build(e){e=dt(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 H(()=>{if(e=e,e.length!==2)throw new G(`SimpleRNNCell expects 2 input Tensors, got ${e.length}.`);let n=e[1];e=e[0];let s=t.training==null?!1:t.training;0as(e),rate:this.dropout,training:s})),0as(n),rate:this.recurrentDropout,training:s}));let r,a=this.dropoutMask,o=this.recurrentDropoutMask;a!=null?r=gr(z(e,a),this.kernel.read()):r=gr(e,this.kernel.read()),this.bias!=null&&(r=Ys(r,this.bias.read())),o!=null&&(n=z(n,o));let i=le(r,gr(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:wa(this.activation),useBias:this.useBias,kernelInitializer:_t(this.kernelInitializer),recurrentInitializer:_t(this.recurrentInitializer),biasInitializer:_t(this.biasInitializer),kernelRegularizer:At(this.kernelRegularizer),recurrentRegularizer:At(this.recurrentRegularizer),biasRegularizer:At(this.biasRegularizer),activityRegularizer:At(this.activityRegularizer),kernelConstraint:Jt(this.kernelConstraint),recurrentConstraint:Jt(this.recurrentConstraint),biasConstraint:Jt(this.biasConstraint),dropout:this.dropout,recurrentDropout:this.recurrentDropout};return Object.assign({},e,t)}};zf.className="SimpleRNNCell";ue.registerClass(zf);var Q1=class extends xr{constructor(e){e.cell=new zf(e);super(e)}call(e,t){return H(()=>{this.cell.dropoutMask!=null&&(Z(this.cell.dropoutMask),this.cell.dropoutMask=null),this.cell.recurrentDropoutMask!=null&&(Z(this.cell.recurrentDropoutMask),this.cell.recurrentDropoutMask=null);let n=t==null?null:t.mask,s=t==null?null:t.training,r=t==null?null:t.initialState;return super.call(e,{mask:n,training:s,initialState:r})})}static fromConfig(e,t){return new e(t)}};Q1.className="SimpleRNN";ue.registerClass(Q1);var Lf=class extends md{constructor(e){super(e);if(this.DEFAULT_ACTIVATION="tanh",this.DEFAULT_RECURRENT_ACTIVATION="hardSigmoid",this.DEFAULT_KERNEL_INITIALIZER="glorotNormal",this.DEFAULT_RECURRENT_INITIALIZER="orthogonal",this.DEFAULT_BIAS_INITIALIZER="zeros",e.resetAfter)throw new G("GRUCell does not support reset_after parameter set to true.");this.units=e.units,cn(this.units,"units"),this.activation=ka(e.activation===void 0?this.DEFAULT_ACTIVATION:e.activation),this.recurrentActivation=ka(e.recurrentActivation===void 0?this.DEFAULT_RECURRENT_ACTIVATION:e.recurrentActivation),this.useBias=e.useBias==null?!0:e.useBias,this.kernelInitializer=Ct(e.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.recurrentInitializer=Ct(e.recurrentInitializer||this.DEFAULT_RECURRENT_INITIALIZER),this.biasInitializer=Ct(e.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.kernelRegularizer=Tt(e.kernelRegularizer),this.recurrentRegularizer=Tt(e.recurrentRegularizer),this.biasRegularizer=Tt(e.biasRegularizer),this.kernelConstraint=Qt(e.kernelConstraint),this.recurrentConstraint=Qt(e.recurrentConstraint),this.biasConstraint=Qt(e.biasConstraint),this.dropout=fu([1,ba([0,e.dropout==null?0:e.dropout])]),this.recurrentDropout=fu([1,ba([0,e.recurrentDropout==null?0:e.recurrentDropout])]),this.implementation=e.implementation,this.stateSize=this.units,this.dropoutMask=null,this.recurrentDropoutMask=null}build(e){e=dt(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 H(()=>{if(e=e,e.length!==2)throw new G(`GRUCell expects 2 input Tensors (inputs, h, c), got ${e.length}.`);let n=t.training==null?!1:t.training,s=e[1];e=e[0],0as(e),rate:this.dropout,training:n,count:3})),0as(s),rate:this.recurrentDropout,training:n,count:3}));let r=this.dropoutMask,a=this.recurrentDropoutMask,o,i,l;0{this.cell.dropoutMask!=null&&(Z(this.cell.dropoutMask),this.cell.dropoutMask=null),this.cell.recurrentDropoutMask!=null&&(Z(this.cell.recurrentDropoutMask),this.cell.recurrentDropoutMask=null);let n=t==null?null:t.mask,s=t==null?null:t.training,r=t==null?null:t.initialState;return super.call(e,{mask:n,training:s,initialState:r})})}static fromConfig(e,t){return t.implmentation===0&&(t.implementation=1),new e(t)}};ey.className="GRU";ue.registerClass(ey);var gd=class extends md{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,cn(this.units,"units"),this.activation=ka(e.activation===void 0?this.DEFAULT_ACTIVATION:e.activation),this.recurrentActivation=ka(e.recurrentActivation===void 0?this.DEFAULT_RECURRENT_ACTIVATION:e.recurrentActivation),this.useBias=e.useBias==null?!0:e.useBias,this.kernelInitializer=Ct(e.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.recurrentInitializer=Ct(e.recurrentInitializer||this.DEFAULT_RECURRENT_INITIALIZER),this.biasInitializer=Ct(e.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.unitForgetBias=e.unitForgetBias,this.kernelRegularizer=Tt(e.kernelRegularizer),this.recurrentRegularizer=Tt(e.recurrentRegularizer),this.biasRegularizer=Tt(e.biasRegularizer),this.kernelConstraint=Qt(e.kernelConstraint),this.recurrentConstraint=Qt(e.recurrentConstraint),this.biasConstraint=Qt(e.biasConstraint),this.dropout=fu([1,ba([0,e.dropout==null?0:e.dropout])]),this.recurrentDropout=fu([1,ba([0,e.recurrentDropout==null?0:e.recurrentDropout])]),this.implementation=e.implementation,this.stateSize=[this.units,this.units],this.dropoutMask=null,this.recurrentDropoutMask=null}build(e){var t;e=dt(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 s;if(this.useBias){if(this.unitForgetBias){let r=this.biasInitializer,a=this.units;s=new(t=class extends Os{apply(i,l){let u=r.apply([a]),c=new Af().apply([a]),d=r.apply([a*2]);return K3(K3(u,c),d)}},t.className="CustomInit",t)}else s=this.biasInitializer;this.bias=this.addWeight("bias",[this.units*4],null,s,this.biasRegularizer,!0,this.biasConstraint)}else this.bias=null;this.built=!0}call(e,t){return H(()=>{let n=t.training==null?!1:t.training;if(e=e,e.length!==3)throw new G(`LSTMCell expects 3 input Tensors (inputs, h, c), got ${e.length}.`);let s=e[1],r=e[2];e=e[0],0as(e),rate:this.dropout,training:n,count:4})),0as(s),rate:this.recurrentDropout,training:n,count:4}));let a=this.dropoutMask,o=this.recurrentDropoutMask,i,l,u,c;0{this.cell.dropoutMask!=null&&(Z(this.cell.dropoutMask),this.cell.dropoutMask=null),this.cell.recurrentDropoutMask!=null&&(Z(this.cell.recurrentDropoutMask),this.cell.recurrentDropoutMask=null);let n=t==null?null:t.mask,s=t==null?null:t.training,r=t==null?null:t.initialState;return super.call(e,{mask:n,training:s,initialState:r})})}static fromConfig(e,t){return t.implmentation===0&&(t.implementation=1),new e(t)}};ty.className="LSTM";ue.registerClass(ty);var Bf=class extends md{constructor(e){super(e);this.cells=e.cells}get stateSize(){let e=[];for(let t of this.cells.slice().reverse())Array.isArray(t.stateSize)?e.push(...t.stateSize):e.push(t.stateSize);return e}call(e,t){return H(()=>{e=e;let n=e.slice(1),s=[];for(let o of this.cells.slice().reverse())Array.isArray(o.stateSize)?s.push(n.splice(0,o.stateSize.length)):s.push(n.splice(0,1));s.reverse();let r=[],a;for(let o=0;o{ui(`RNNCell_${s}`,()=>{n.build(e),Array.isArray(n.stateSize)?t=n.stateSize[0]:t=n.stateSize,e=[e[0],t]})}),this.built=!0}getConfig(){let e=super.getConfig(),t=r=>({className:r.getClassName(),config:r.getConfig()}),s={cells:this.cells.map(t)};return Object.assign({},e,s)}static fromConfig(e,t,n={}){let s=[];for(let r of t.cells)s.push(Qs(r,n));return new e({cells:s})}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 v1(e)}setWeights(e){let t=[];for(let n of this.cells){let s=n.weights.length,r=e.splice(s);for(let a=0;aY3(t(),n),o=()=>od(a,t,s);return!r||r<=1?un(o().clone()):Array(r).fill(void 0).map(o).map(l=>un(l.clone()))}var sz=function(e,t){var n={};for(var s in e)Object.prototype.hasOwnProperty.call(e,s)&&t.indexOf(s)<0&&(n[s]=e[s]);if(e!=null&&typeof Object.getOwnPropertySymbols=="function")for(var r=0,s=Object.getOwnPropertySymbols(e);r{if(this.cell.dropoutMask!=null&&(Z(this.cell.dropoutMask),this.cell.dropoutMask=null),this.cell.recurrentDropoutMask!=null&&(Z(this.cell.recurrentDropoutMask),this.cell.recurrentDropoutMask=null),t&&t.constants)throw new G("ConvRNN2D cell does not support constants");let n=t==null?null:t.mask,s=t==null?null:t.training,r=t==null?null:t.initialState;return super.call(e,{mask:n,training:s,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 H(()=>{let{stateSize:t}=this.cell,n=e.shape,s=this.computeSingleOutputShape(n),r=[s[0],...s.slice(2)],a=Pt(r);return Array.isArray(t)?Array(t.length).fill(a):[a]})}resetStates(e,t=!1){H(()=>{if(!this.stateful)throw new Lr("Cannot call resetStates() on an RNN Layer that is not stateful.");let n=this.inputSpec[0].shape,s=this.computeSingleOutputShape(n),r=[s[0],...s.slice(2)];if(n[0]==null)throw new G("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(()=>Pt(r)):this.states_=[Pt(r)];else if(e==null)Z(this.states_),this.keptStates!=null&&(Z(this.keptStates),this.keptStates=[]),Array.isArray(this.cell.stateSize)?this.states_=this.cell.stateSize.map(()=>Pt(r)):this.states_[0]=Pt(r);else{if(Array.isArray(e)||(e=[e]),e.length!==this.states_.length)throw new G(`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()):Z(this.states_);for(let o=0;oun(o.clone()))})}computeSingleOutputShape(e){let{dataFormat:t,filters:n,kernelSize:s,padding:r,strides:a,dilationRate:o}=this.cell,i=t==="channelsFirst",l=e[i?3:2],u=e[i?4:3],c=er(l,s[0],r,a[0],o[0]),d=er(u,s[1],r,a[1],o[1]);return[...e.slice(0,2),...i?[n,c,d]:[c,d,n]]}};tw.className="ConvRNN2D";var Wf=class extends gd{constructor(e){let{filters:t,kernelSize:n,strides:s,padding:r,dataFormat:a,dilationRate:o}=e;super(Object.assign({},e,{units:t}));this.filters=t,cn(this.filters,"filters"),this.kernelSize=xu(n,2,"kernelSize"),this.kernelSize.forEach(i=>cn(i,"kernelSize")),this.strides=xu(s||1,2,"strides"),this.strides.forEach(i=>cn(i,"strides")),this.padding=r||"valid",bs(this.padding),this.dataFormat=a||"channelsLast",Lt(this.dataFormat),this.dilationRate=xu(o||1,2,"dilationRate"),this.dilationRate.forEach(i=>cn(i,"dilationRate"))}build(e){var t;e=dt(e);let n=this.dataFormat==="channelsFirst"?1:e.length-1;if(e[n]==null)throw new G(`The channel dimension of the input should be defined. Found ${e[n]}`);let s=e[n],r=4,a=this.kernelSize.concat([s,this.filters*r]);this.kernel=this.addWeight("kernel",a,null,this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint);let o=this.kernelSize.concat([this.filters,this.filters*r]);if(this.recurrentKernel=this.addWeight("recurrent_kernel",o,null,this.recurrentInitializer,this.recurrentRegularizer,!0,this.recurrentConstraint),this.useBias){let i;if(this.unitForgetBias){let l=this.biasInitializer,u=this.filters;i=new(t=class extends Os{apply(d,p){let h=l.apply([u]),m=rs([u]),f=l.apply([u*2]);return c1([h,m,f])}},t.className="CustomInit",t)}else i=this.biasInitializer;this.bias=this.addWeight("bias",[this.filters*r],null,i,this.biasRegularizer,!0,this.biasConstraint)}this.built=!0}call(e,t){return H(()=>{if(e.length!==3)throw new G(`ConvLSTM2DCell expects 3 input Tensors (inputs, h, c), got ${e.length}.`);let n=t.training||!1,s=e[0],r=e[1],a=e[2],o=4;0as(s),rate:this.dropout,training:n,count:o}));let i=this.dropoutMask,l=(te,ne,se)=>!ne||!ne[se]?te:z(ne[se],te),u=l(s,i,0),c=l(s,i,1),d=l(s,i,2),p=l(s,i,3);0as(r),rate:this.recurrentDropout,training:n,count:o}));let h=this.recurrentDropoutMask,m=l(r,h,0),f=l(r,h,1),g=l(r,h,2),A=l(r,h,3),y=3,[x,b,v,k]=Vt(this.kernel.read(),o,y),[S,C,D,O]=this.useBias?Vt(this.bias.read(),o):[null,null,null,null];u=this.inputConv(u,x,S,this.padding),c=this.inputConv(c,b,C,this.padding),d=this.inputConv(d,v,D,this.padding),p=this.inputConv(p,k,O,this.padding);let[E,R,T,P]=Vt(this.recurrentKernel.read(),o,y);m=this.recurrentConv(m,E),f=this.recurrentConv(f,R),g=this.recurrentConv(g,T),A=this.recurrentConv(A,P);let U=this.recurrentActivation.apply(le(u,m)),j=this.recurrentActivation.apply(le(c,f)),q=le(z(j,a),z(U,this.activation.apply(le(d,g)))),X=z(this.recurrentActivation.apply(le(p,A)),this.activation.apply(q));return[X,X,q]})}getConfig(){let e=super.getConfig(),{units:t}=e,n=sz(e,["units"]),s={filters:this.filters,kernelSize:this.kernelSize,padding:this.padding,dataFormat:this.dataFormat,dilationRate:this.dilationRate,strides:this.strides};return Object.assign({},n,s)}inputConv(e,t,n,s){let r=Fr(e,t,this.strides,s||"valid",this.dataFormat==="channelsFirst"?"NCHW":"NHWC",this.dilationRate);return n?Ys(r,n,this.dataFormat):r}recurrentConv(e,t){return Fr(e,t,1,"same",this.dataFormat==="channelsFirst"?"NCHW":"NHWC")}};Wf.className="ConvLSTM2DCell";ue.registerClass(Wf);var ny=class extends tw{constructor(e){let t=new Wf(e);super(Object.assign({},e,{cell:t}))}static fromConfig(e,t){return new e(t)}};ny.className="ConvLSTM2D";ue.registerClass(ny);var Vf=class extends Qe{constructor(e){super(e);this.rate=Math.max(Math.min(e.rate,1),0),this.noiseShape=e.noiseShape,this.seed=e.seed,this.supportsMasking=!0}getNoiseShape(e){if(this.noiseShape==null)return this.noiseShape;let t=e.shape,n=[];for(let s=0;s{this.invokeCallHook(e,t);let n=We(e);if(0Y3(n,this.rate,r,this.seed),()=>n,s)}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()}};Vf.className="Dropout";ue.registerClass(Vf);var sy=class extends Vf{constructor(e){super(e);this.inputSpec=[{ndim:3}]}getNoiseShape(e){let t=e.shape;return[t[0],1,t[2]]}};sy.className="SpatialDropout1D";ue.registerClass(sy);var ry=class extends Qe{constructor(e){super(e);if(this.activation=null,this.useBias=!0,this.kernel=null,this.bias=null,this.DEFAULT_KERNEL_INITIALIZER="glorotNormal",this.DEFAULT_BIAS_INITIALIZER="zeros",e.batchInputShape==null&&e.inputShape==null&&e.inputDim!=null){let t=null;e.batchSize!=null&&(t=e.batchSize),this.batchInputShape=[t,e.inputDim]}this.units=e.units,cn(this.units,"units"),this.activation=ka(e.activation),e.useBias!=null&&(this.useBias=e.useBias),this.kernelInitializer=Ct(e.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.biasInitializer=Ct(e.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.kernelConstraint=Qt(e.kernelConstraint),this.biasConstraint=Qt(e.biasConstraint),this.kernelRegularizer=Tt(e.kernelRegularizer),this.biasRegularizer=Tt(e.biasRegularizer),this.activityRegularizer=Tt(e.activityRegularizer),this.supportsMasking=!0,this.inputSpec=[{minNDim:2}]}build(e){e=dt(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=dt(e);let t=e.slice();return t[t.length-1]=this.units,t}call(e,t){return H(()=>{this.invokeCallHook(e,t);let n=We(e),s=B3(this.activation.getClassName()),r;return s!=null?r=gr(n,this.kernel.read(),s,this.bias?this.bias.read():null):(r=gr(n,this.kernel.read()),this.bias!=null&&(r=Ys(r,this.bias.read())),this.activation!=null&&(r=this.activation.apply(r))),r})}getConfig(){let e={units:this.units,activation:wa(this.activation),useBias:this.useBias,kernelInitializer:_t(this.kernelInitializer),biasInitializer:_t(this.biasInitializer),kernelRegularizer:At(this.kernelRegularizer),biasRegularizer:At(this.biasRegularizer),activityRegularizer:At(this.activityRegularizer),kernelConstraint:Jt(this.kernelConstraint),biasConstraint:Jt(this.biasConstraint)},t=super.getConfig();return Object.assign(e,t),e}};ry.className="Dense";ue.registerClass(ry);var ay=class extends Qe{constructor(e){e=e||{};super(e);this.inputSpec=[{minNDim:3}],this.dataFormat=e.dataFormat}computeOutputShape(e){e=dt(e);for(let t of e.slice(1))if(t==null)throw new G(`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],xa(e,1)]}call(e,t){return H(()=>{this.invokeCallHook(e,t);let n=We(e);if(this.dataFormat==="channelsFirst"&&n.rank>1){let s=[0];for(let r=2;r{this.invokeCallHook(e,t);let n=We(e);return this.activation.apply(n)})}getConfig(){let e={activation:wa(this.activation)},t=super.getConfig();return Object.assign(e,t),e}};oy.className="Activation";ue.registerClass(oy);var iy=class extends Qe{constructor(e){super(e);this.n=e.n,this.inputSpec=[{ndim:2}]}computeOutputShape(e){return[e[0],this.n,e[1]]}call(e,t){return H(()=>(e=We(e),bP(e,this.n)))}getConfig(){let e={n:this.n},t=super.getConfig();return Object.assign(e,t),e}};iy.className="RepeatVector";ue.registerClass(iy);var ly=class extends Qe{constructor(e){super(e);this.targetShape=e.targetShape;for(let t=0;t{this.invokeCallHook(e,t);let n=We(e),s=n.shape,r=s.slice(0,1).concat(this.fixUnknownDimension(s.slice(1),this.targetShape));return V(n,r)})}getConfig(){let e={targetShape:this.targetShape},t=super.getConfig();return Object.assign(e,t),e}};ly.className="Reshape";ue.registerClass(ly);var uy=class extends Qe{constructor(e){super(e);if(e.dims==null)throw new Error("Required configuration field `dims` is missing during Permute constructor call.");if(!Array.isArray(e.dims))throw new Error(`Permute constructor requires \`dims\` to be an Array, but received ${e.dims} instead.`);let t=Zs(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 Ht({ndim:this.dims.length+1})]}computeOutputShape(e){e=dt(e);let t=e.slice();return this.dims.forEach((n,s)=>{t[s+1]=e[n]}),t}call(e,t){return Ze(We(e),this.dimsIncludingBatch)}getConfig(){let e={dims:this.dims},t=super.getConfig();return Object.assign(e,t),e}};uy.className="Permute";ue.registerClass(uy);var cy=class extends Qe{constructor(e){super(e==null?{}:e);this.supportsMasking=!0,e!=null?this.maskValue=e.maskValue==null?0:e.maskValue:this.maskValue=0}computeOutputShape(e){return e}getConfig(){let e=super.getConfig(),t={maskValue:this.maskValue};return Object.assign(t,e),t}computeMask(e,t){let n=We(e),s=-1;return Mc(ni(n,this.maskValue),s)}call(e,t){return H(()=>{this.invokeCallHook(e,t);let n=We(e),s=-1,r=!0,a=Mc(ni(n,this.maskValue),s,r);return z(n,pe(a,n.dtype))})}};cy.className="Masking";ue.registerClass(cy);var dy=class extends Qe{constructor(e){super(e);if(this.embeddings=null,this.DEFAULT_EMBEDDINGS_INITIALIZER="randomUniform",e.batchInputShape==null&&e.inputShape==null){let t=null;e.batchSize!=null&&(t=e.batchSize),e.inputLength==null?this.batchInputShape=[t,null]:this.batchInputShape=[t].concat(vt(e.inputLength))}this.inputDim=e.inputDim,cn(this.inputDim,"inputDim"),this.outputDim=e.outputDim,cn(this.outputDim,"outputDim"),this.embeddingsInitializer=Ct(e.embeddingsInitializer||this.DEFAULT_EMBEDDINGS_INITIALIZER),this.embeddingsRegularizer=Tt(e.embeddingsRegularizer),this.activityRegularizer=Tt(e.activityRegularizer),this.embeddingsConstraint=Qt(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 H(()=>this.maskZero?(e=We(e),ni(e,Ye(e))):null)}computeOutputShape(e){if(e=dt(e),this.inputLength==null)return[...e,this.outputDim];let t=vt(this.inputLength);if(t.length!==e.length-1)throw new G(`"inputLength" is ${this.inputLength}, but received input shape has shape ${e}`);{let n=0;for(let s=0;s{this.invokeCallHook(e,t);let n=We(e);n.dtype!=="int32"&&(n=ff(n,"int32"));let s=Z3(this.embeddings.read(),V(n,[n.size]));return V(s,dt(this.computeOutputShape(n.shape)))})}getConfig(){let e={inputDim:this.inputDim,outputDim:this.outputDim,embeddingsInitializer:_t(this.embeddingsInitializer),embeddingsRegularizer:At(this.embeddingsRegularizer),activityRegularizer:At(this.activityRegularizer),embeddingsConstraint:Jt(this.embeddingsConstraint),maskZero:this.maskZero,inputLength:this.inputLength},t=super.getConfig();return Object.assign(e,t),e}};dy.className="Embedding";ue.registerClass(dy);var fi=class extends Qe{constructor(e){super(e||{});this.supportsMasking=!0}mergeFunction(e){throw new ze}computeElementwiseOpOutputShape(e,t){if(e==null||t==null)return null;if(e.length1)throw new G(`Can not merge tensors with different batch sizes. 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${e.shape.length}`),w.assert(e.shape.length>=2,()=>`batchDot requires the rank of y to be >= 2, but got ${t.shape.length}`),typeof n=="number"&&(n=[n,n]),e.dtype==="complex64"||t.dtype==="complex64")throw new ze("batchDot is not implemented for complex64-type Tensors yet.");let s=e.shape.length,r=t.shape.length;n==null&&(n=[s-1,r-2]);let a=n;return H(()=>{let o;if(s>r){o=s-r;let l=[];for(let u=0;us){o=r-s;let l=[];for(let u=0;u0){let l;s>r?l=s+r-3:l=s-1;let u=[];for(let c=l;c"A `Dot` layer should be called on a list of exactly 2 inputs.");let t=e[0],n=e[1];if(t.length>3||n.length>3)throw new ze("Dot layer does not support tensors of 4D or higher rank yet.");let s=this.interpretAxes(t,n);if(t[s[0]]!==n[s[1]])throw new G(`Dimension incompatibility: ${t[s[0]]} !== ${n[s[1]]}`)}mergeFunction(e){if(e.length!==2)throw new G(`A \`Dot\` layer must be called on exactly 2 inputs, but received ${e.length} input(s).`);let t=e[0],n=e[1],s;return Array.isArray(this.axes)?s=this.axes.map((r,a)=>Ad(r,e[a].shape.length)):s=[Ad(this.axes,t.shape.length),Ad(this.axes,n.shape.length)],this.normalize&&(t=Ef(t,s[0]),n=Ef(n,s[1])),rz(t,n,s)}interpretAxes(e,t){let n;return Array.isArray(this.axes)?n=this.axes:n=[Ad(this.axes,e.length),Ad(this.axes,t.length)],n}computeOutputShape(e){w.assert(Array.isArray(e)&&e.length===2&&Array.isArray(e[0])&&Array.isArray(e[1]),()=>"A `Dot` layer should be called on a list of exactly 2 inputs.");let t=e[0].slice(),n=e[1].slice();if(t.length>3||n.length>3)throw new ze("Dot layer does not support tensors of 4D or higher rank yet.");let s=this.interpretAxes(t,n);t.splice(s[0],1),n.splice(s[1],1),n.splice(0,1);let r=t.concat(n);return r.length===1&&r.push(1),r}computeMask(e,t){return null}getConfig(){let e={axes:this.axes,normalize:this.normalize},t=super.getConfig();return Object.assign(e,t),e}};yy.className="Dot";ue.registerClass(yy);var xy=class extends Qe{constructor(e){super(e);this.supportsMasking=!0,this.stddev=e.stddev}computeOutputShape(e){return e}getConfig(){let e=super.getConfig(),t={stddev:this.stddev};return Object.assign(t,e),t}call(e,t){return H(()=>{this.invokeCallHook(e,t);let n=We(e);return od(()=>le(gf(n.shape,0,this.stddev),n),()=>n,t.training||!1)})}};xy.className="GaussianNoise";ue.registerClass(xy);var by=class extends Qe{constructor(e){super(e);this.supportsMasking=!0,this.rate=e.rate}computeOutputShape(e){return e}getConfig(){let e=super.getConfig(),t={rate:this.rate};return Object.assign(t,e),t}call(e,t){return H(()=>{this.invokeCallHook(e,t);let n=We(e);return this.rate>0&&this.rate<1?od(()=>{let r=Math.sqrt(this.rate/(1-this.rate));return z(n,gf(n.shape,1,r))},()=>n,t.training||!1):n})}};by.className="GaussianDropout";ue.registerClass(by);var vy=class extends Qe{constructor(e){super(e);this.supportsMasking=!0,this.rate=e.rate,this.noiseShape=e.noiseShape}_getNoiseShape(e){return this.noiseShape||We(e).shape}computeOutputShape(e){return e}getConfig(){let e=super.getConfig(),t={rate:this.rate};return Object.assign(t,e),t}call(e,t){return H(()=>{if(this.rate<1&&this.rate>0){let n=this._getNoiseShape(e);return od(()=>{let r=We(e),a=1.6732632423543772,o=1.0507009873554805,i=-a*o,l=ma(lu(n),this.rate);l=ff(l,"float32");let u=((1-this.rate)*(1+this.rate*i**2))**-.5,c=-u*i*this.rate,d=le(z(r,l),z(le(l,-1),i));return le(z(d,u),c)},()=>We(e),t.training||!1)}return e})}};vy.className="AlphaDropout";ue.registerClass(vy);function yd(e,t,n,s,r,a=.001){let o;if(e.rank===2)o=_b(e,t,n,s,r,a);else if(e.rank===3)o=$b(e,t,n,s,r,a);else if(e.rank===4)o=Fb(e,t,n,s,r,a);else throw new ze(`batchNormalization is not implemented for array of rank ${e.rank} yet`);return o}function az(e,t,n,s,r=.001){return H(()=>{let a=Lh(e,s),o=a.mean,i=a.variance;return[yd(e,o,i,n,t,r),o,i]})}function oz(e,t,n,s,r=.001){return H(()=>{let a=Lh(e,s),o=a.mean,i=a.variance,l=[];for(let m of Zs(0,e.rank))s.indexOf(m)!==-1?l.push(1):l.push(e.shape[m]);let u=V(o,l),c=V(i,l),d=t==null?null:V(t,l),p=n==null?null:V(n,l);return[yd(e,u,c,p,d,r),o,i]})}function iz(e,t,n,s,r=.001){return w.arraysEqual(s.slice().sort(),Zs(0,e.rank-1))?az(e,t,n,s,r):oz(e,t,n,s,r)}var wy=class extends Qe{constructor(e){e==null&&(e={});super(e);this.supportsMasking=!0,this.axis=e.axis==null?-1:e.axis,this.momentum=e.momentum==null?.99:e.momentum,this.epsilon=e.epsilon==null?.001:e.epsilon,this.center=e.center==null?!0:e.center,this.scale=e.scale==null?!0:e.scale,this.betaInitializer=Ct(e.betaInitializer||"zeros"),this.gammaInitializer=Ct(e.gammaInitializer||"ones"),this.movingMeanInitializer=Ct(e.movingMeanInitializer||"zeros"),this.movingVarianceInitializer=Ct(e.movingVarianceInitializer||"ones"),this.betaConstraint=Qt(e.betaConstraint),this.gammaConstraint=Qt(e.gammaConstraint),this.betaRegularizer=Tt(e.betaRegularizer),this.gammaRegularizer=Tt(e.gammaRegularizer)}build(e){e=dt(e);let t=this.axis>=0?this.axis:this.axis+e.length,n=e[t];if(n==null)throw new G(`Axis ${t} of input tensor should have a defined dimension but the layer received an input with shape ${JSON.stringify(e)}.`);this.inputSpec=[new Ht({ndim:e.length,axes:{[t]:n}})];let s=[n];this.scale&&(this.gamma=this.addWeight("gamma",s,null,this.gammaInitializer,this.gammaRegularizer,!0,this.gammaConstraint)),this.center&&(this.beta=this.addWeight("beta",s,null,this.betaInitializer,this.betaRegularizer,!0,this.betaConstraint)),this.movingMean=this.addWeight("moving_mean",s,null,this.movingMeanInitializer,null,!1),this.movingVariance=this.addWeight("moving_variance",s,null,this.movingVarianceInitializer,null,!1),this.built=!0}call(e,t){return H(()=>{let n=t.training==null?!1:t.training,s=We(e),r=s.shape,a=r.length,o=Zs(0,a),i=this.axis>=0?this.axis:this.axis+a;o.splice(i,1);let l=oi(1,a);l[i]=r[i];let u=o.slice();u.sort();let c=!w.arraysEqual(u,Zs(0,a).slice(0,a-1)),d=()=>{if(c){let A=V(this.movingMean.read(),l),y=V(this.movingVariance.read(),l),x=this.center?V(this.beta.read(),l):null,b=this.scale?V(this.gamma.read(),l):null;return yd(s,A,y,x,b,this.epsilon)}else return yd(s,this.movingMean.read(),this.movingVariance.read(),this.beta==null?null:this.beta.read(),this.gamma==null?null:this.gamma.read(),this.epsilon)};if(!n)return d();let[p,h,m]=iz(s,this.gamma.read(),this.beta.read(),o,this.epsilon),f=(A,y,x)=>{H(()=>{let b=1-x,v=A.read(),k=z(ye(v,y),b);A.write(ye(v,k))})};return(()=>{f(this.movingMean,h,this.momentum),f(this.movingVariance,m,this.momentum)})(),p})}getConfig(){let e={axis:this.axis,momentum:this.momentum,epsilon:this.epsilon,center:this.center,scale:this.scale,betaInitializer:_t(this.betaInitializer),gammaInitializer:_t(this.gammaInitializer),movingMeanInitializer:_t(this.movingMeanInitializer),movingVarianceInitializer:_t(this.movingVarianceInitializer),betaRegularizer:At(this.betaRegularizer),gammaRegularizer:At(this.gammaRegularizer),betaConstraint:Jt(this.betaConstraint),gammaConstraint:Jt(this.gammaConstraint)},t=super.getConfig();return Object.assign(e,t),e}};wy.className="BatchNormalization";ue.registerClass(wy);var ky=class extends Qe{constructor(e){e==null&&(e={});super(e);if(this.axis=e.axis==null?-1:e.axis,typeof this.axis=="number"){if(!Number.isInteger(this.axis))throw new Error(`Expected axis to be an integer, but received ${this.axis}`)}else if(Array.isArray(this.axis)){for(let t of this.axis)if(!Number.isInteger(t))throw new Error(`Expected axis to be an array of integers, but received ${JSON.stringify(this.axis)}`)}else throw new Error(`Expected axis to be an integer or an array of integers, but received ${JSON.stringify(this.axis)}`);this.epsilon=e.epsilon==null?.001:e.epsilon,this.center=e.center==null?!0:e.center,this.scale=e.scale==null?!0:e.scale,this.betaInitializer=Ct(e.betaInitializer||"zeros"),this.gammaInitializer=Ct(e.gammaInitializer||"ones"),this.betaRegularizer=Tt(e.betaRegularizer),this.gammaRegularizer=Tt(e.gammaRegularizer),this.supportsMasking=!0}build(e){e=dt(e);let t=e.length;typeof this.axis=="number"&&(this.axis=[this.axis]);for(let r=0;r=t)throw new Error(`Invalid axis: ${r}`);if(this.axis.length!==ya(this.axis).length)throw new Error(`Found duplicate axes in: ${this.axis}`);let n=this.axis.map(r=>e[r]),s=!0;this.scale?this.gamma=this.addWeight("gamma",n,"float32",this.gammaInitializer,this.gammaRegularizer,s):this.gamma=null,this.center?this.beta=this.addWeight("beta",n,"float32",this.betaInitializer,this.betaRegularizer,s):this.beta=null,this.built=!0}call(e,t){let n=We(e),s=n.shape,r=s.length;return H(()=>{let a=!0,{mean:o,variance:i}=Lh(n,this.axis,a),l=oi(1,r);for(let m of this.axis)l[m]=s[m];let u=m=>m!=null&&m.shape.length!==r&&this.axis!==[r-1]?V(m,l):m,c=u(this.gamma.read()),d=u(this.beta.read()),p=[],h=[];for(let m=0;m{if(e.rank!==4)throw new G(`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 G("spatial2dPadding expects `padding` to be an Array of two Arrays, each of which is an Array of two integers.");if(n==null&&(n=Xs()),n!=="channelsLast"&&n!=="channelsFirst")throw new G(`Unknown data format: ${n}. Supported data formats are 'channelsLast' and 'channelsFirst.`);let s;return n==="channelsFirst"?s=[[0,0],[0,0],t[0],t[1]]:s=[[0,0],t[0],t[1],[0,0]],Or(e,s)})}var Iy=class extends Qe{constructor(e){e==null&&(e={});super(e);if(this.dataFormat=e.dataFormat==null?Xs():e.dataFormat,e.padding==null)this.padding=[[1,1],[1,1]];else if(typeof e.padding=="number")this.padding=[[e.padding,e.padding],[e.padding,e.padding]];else{if(e.padding=e.padding,e.padding.length!==2)throw new G(`ZeroPadding2D expects padding to be a length-2 array, but received a length-${e.padding.length} array.`);let t,n;if(typeof e.padding[0]=="number")t=[e.padding[0],e.padding[0]],n=[e.padding[1],e.padding[1]];else{if(e.padding=e.padding,e.padding[0].length!==2)throw new G(`ZeroPadding2D expects height padding to be a length-2 array, but received a length-${e.padding[0].length} array.`);if(t=e.padding[0],e.padding[1].length!==2)throw new G(`ZeroPadding2D expects width padding to be a length-2 array, but received a length-${e.padding[1].length} array.`);n=e.padding[1]}this.padding=[t,n]}this.inputSpec=[new Ht({ndim:4})]}computeOutputShape(e){e=dt(e);let t,n;return this.dataFormat==="channelsFirst"?(e[2]!=null&&e[2]>=0?t=e[2]+this.padding[0][0]+this.padding[0][1]:t=null,e[3]!=null&&e[3]>=0?n=e[3]+this.padding[1][0]+this.padding[1][1]:n=null,[e[0],e[1],t,n]):(e[1]!=null&&e[1]>=0?t=e[1]+this.padding[0][0]+this.padding[0][1]:t=null,e[2]!=null&&e[2]>=0?n=e[2]+this.padding[1][0]+this.padding[1][1]:n=null,[e[0],t,n,e[3]])}call(e,t){return H(()=>lz(We(e),this.padding,this.dataFormat))}getConfig(){let e={padding:this.padding,dataFormat:this.dataFormat},t=super.getConfig();return Object.assign(e,t),e}};Iy.className="ZeroPadding2D";ue.registerClass(Iy);function Uf(e,t,n,s,r,a){return H(()=>{Lt(r),H3(a),bs(s),n==null&&(n=[1,1]),s==null&&(s="valid"),r==null&&(r=Xs()),a==null&&(a="max"),e=G1(e,r);let o,i=s==="same"?"same":"valid";return a==="max"?o=Gc(e,t,n,i):o=Lc(e,t,n,i),r==="channelsFirst"&&(o=Ze(o,[0,3,1,2])),o})}function nw(e,t,n,s,r,a){return H(()=>{Lt(r),H3(a),bs(s),n==null&&(n=[1,1,1]),s==null&&(s="valid"),r==null&&(r=Xs()),a==null&&(a="max"),e=Zv(e,r);let o,i=s==="same"?"same":"valid";return a==="max"?o=FA(e,t,n,i):o=yA(e,t,n,i),r==="channelsFirst"&&(o=Ze(o,[0,4,1,2,3])),o})}var sw=class extends Qe{constructor(e){e.poolSize==null&&(e.poolSize=2);super(e);if(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 G(`poolSize for 1D convolutional layer must be a number or an Array of a single number, but received ${JSON.stringify(e.poolSize)}`);if(cn(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=er(t,this.poolSize[0],this.padding,this.strides[0]),n=er(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 H(()=>(this.invokeCallHook(e,t),this.poolingFunction(We(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}},Ty=class extends rw{constructor(e){super(e)}poolingFunction(e,t,n,s,r){return Lt(r),bs(s),Uf(e,t,n,s,r,"max")}};Ty.className="MaxPooling2D";ue.registerClass(Ty);var Ny=class extends rw{constructor(e){super(e)}poolingFunction(e,t,n,s,r){return Lt(r),bs(s),Uf(e,t,n,s,r,"avg")}};Ny.className="AveragePooling2D";ue.registerClass(Ny);var aw=class extends Qe{constructor(e){e.poolSize==null&&(e.poolSize=[2,2,2]);super(e);if(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 G(`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];cn(this.poolSize,"poolSize"),cn(this.strides,"strides"),this.padding=e.padding==null?"valid":e.padding,this.dataFormat=e.dataFormat==null?"channelsLast":e.dataFormat,Lt(this.dataFormat),bs(this.padding),this.inputSpec=[new Ht({ndim:5})]}computeOutputShape(e){e=dt(e);let t=this.dataFormat==="channelsFirst"?e[2]:e[1],n=this.dataFormat==="channelsFirst"?e[3]:e[2],s=this.dataFormat==="channelsFirst"?e[4]:e[3];return t=er(t,this.poolSize[0],this.padding,this.strides[0]),n=er(n,this.poolSize[1],this.padding,this.strides[1]),s=er(s,this.poolSize[2],this.padding,this.strides[2]),this.dataFormat==="channelsFirst"?[e[0],e[1],t,n,s]:[e[0],t,n,s,e[4]]}call(e,t){return H(()=>(this.invokeCallHook(e,t),this.poolingFunction(We(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}},Ey=class extends aw{constructor(e){super(e)}poolingFunction(e,t,n,s,r){return Lt(r),bs(s),nw(e,t,n,s,r,"max")}};Ey.className="MaxPooling3D";ue.registerClass(Ey);var Ry=class extends aw{constructor(e){super(e)}poolingFunction(e,t,n,s,r){return Lt(r),bs(s),nw(e,t,n,s,r,"avg")}};Ry.className="AveragePooling3D";ue.registerClass(Ry);var ow=class extends Qe{constructor(e){super(e);this.inputSpec=[new Ht({ndim:3})]}computeOutputShape(e){return[e[0],e[2]]}call(e,t){throw new 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o=I("axis",e,t,n),i=I("keepDims",e,t,n);return[jc(I("x",e,t,n),o,i)]}case"Sum":{let o=I("axis",e,t,n),i=I("keepDims",e,t,n);return[we(I("x",e,t,n),o,i)]}case"All":{let o=I("axis",e,t,n),i=I("keepDims",e,t,n);return[Nh(I("x",e,t,n),o,i)]}case"Any":{let o=I("axis",e,t,n),i=I("keepDims",e,t,n);return[Mc(I("x",e,t,n),o,i)]}case"ArgMax":{let o=I("axis",e,t,n);return[Gs(I("x",e,t,n),o)]}case"ArgMin":{let o=I("axis",e,t,n);return[cA(I("x",e,t,n),o)]}case"Prod":{let o=I("axis",e,t,n),i=I("keepDims",e,t,n);return[Bh(I("x",e,t,n),o,i)]}case"Cumsum":{let o=I("axis",e,t,n),i=I("exclusive",e,t,n),l=I("reverse",e,t,n);return[$h(I("x",e,t,n),o,i,l)]}case"Bincount":let s=I("x",e,t,n),r=I("weights",e,t,n),a=I("size",e,t,n);return[xA(s,r,a)];case"DenseBincount":{let o=I("x",e,t,n),i=I("weights",e,t,n),l=I("size",e,t,n),u=I("binaryOutput",e,t,n);return[Wb(o,i,l,u)]}default:throw TypeError(`Node type ${e.op} is not implemented`)}},DB=(e,t,n)=>{switch(e.op){case"ConcatV2":case"Concat":{let s=I("n",e,t,n),r=I("axis",e,t,n),a=I("tensors",e,t,n);return a=a.slice(0,s),[gt(a,r)]}case"Gather":{let s=I("x",e,t,n),r=I("indices",e,t,n);return[Qo(s,pe(r,"int32"),0)]}case"GatherV2":{let s=I("axis",e,t,n),r=I("batchDims",e,t,n),a=I("x",e,t,n),o=I("indices",e,t,n);return[Qo(a,pe(o,"int32"),s,r)]}case"Reverse":{let s=I("dims",e,t,n),r=[];for(let o=0;o{let s=I("axis",e,t,n),r=I("tensors",e,t,n),a=r[0].shape,o=at(r[0]).shape,i=r.map(l=>{let u=w.arraysEqual(l.shape,a);if(!u&&!w.arraysEqual(at(l).shape,o))throw new Error("the input tensors shape does not match");return u?l:V(l,a)});return[yn(i,s)]});case"Unpack":{let s=I("axis",e,t,n),r=I("tensor",e,t,n);return En(r,s)}case"Tile":{let s=I("reps",e,t,n);return[xs(I("x",e,t,n),s)]}case"Split":case"SplitV":{let s=I("axis",e,t,n),r=I("numOrSizeSplits",e,t,n),a=I("x",e,t,n);return Vt(a,r,s)}case"ScatterNd":{let s=I("indices",e,t,n),r=I("values",e,t,n),a=I("shape",e,t,n);return[i3(s,r,a)]}case"GatherNd":{let s=I("x",e,t,n),r=I("indices",e,t,n);return[l3(s,r)]}case"SparseToDense":{let s=I("sparseIndices",e,t,n),r=I("outputShape",e,t,n),a=I("sparseValues",e,t,n),o=I("defaultValue",e,t,n);return[XA(s,a,r,a.dtype===o.dtype?o:pe(o,a.dtype))]}default:throw TypeError(`Node type ${e.op} is not implemented`)}},_B=(e,t,n)=>{switch(e.op){case"SparseFillEmptyRows":{let{outputIndices:s,outputValues:r,emptyRowIndicator:a,reverseIndexMap:o}=Qc.sparseFillEmptyRows(I("indices",e,t,n),I("values",e,t,n),I("denseShape",e,t,n),I("defaultValue",e,t,n));return[s,r,a,o]}case"SparseReshape":{let{outputIndices:s,outputShape:r}=Qc.sparseReshape(I("inputIndices",e,t,n),I("inputShape",e,t,n),I("newShape",e,t,n));return[s,r]}case"SparseSegmentMean":return[Qc.sparseSegmentMean(I("data",e,t,n),I("indices",e,t,n),I("segmentIds",e,t,n))];case"SparseSegmentSum":return[Qc.sparseSegmentSum(I("data",e,t,n),I("indices",e,t,n),I("segmentIds",e,t,n))];default:throw TypeError(`Node type ${e.op} is not implemented`)}},$B=(e,t,n)=>{switch(e.op){case"FFT":return[Yc(I("x",e,t,n))];case"IFFT":return[cu(I("x",e,t,n))];case"RFFT":return[Jc(I("x",e,t,n))];case"IRFFT":return[Kh(I("x",e,t,n))];default:throw TypeError(`Node type ${e.op} is not implemented`)}},FB=(e,t,n)=>{switch(e.op){case"StringNGrams":{let{nGrams:s,nGramsSplits:r}=rf.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,r]}case"StringSplit":{let{indices:s,values:r,shape:a}=rf.stringSplit(I("input",e,t,n),I("delimiter",e,t,n),I("skipEmpty",e,t,n));return[s,r,a]}case"StringToHashBucketFast":return[rf.stringToHashBucketFast(I("input",e,t,n),I("numBuckets",e,t,n))];default:throw TypeError(`Node type ${e.op} is not implemented`)}},OB=(e,t,n)=>{switch(e.op){case"Cast":return[pe(I("x",e,t,n),I("dtype",e,t,n))];case"ExpandDims":{let s=I("axis",e,t,n);return[zt(I("x",e,t,n),s)]}case"Squeeze":{let s=I("axis",e,t,n);return[at(I("x",e,t,n),s)]}case"Reshape":return[V(I("x",e,t,n),I("shape",e,t,n))];case"MirrorPad":return[OA(I("x",e,t,n),I("padding",e,t,n),I("mode",e,t,n))];case"PadV2":case"Pad":return[Or(I("x",e,t,n),I("padding",e,t,n),I("constantValue",e,t,n))];case"SpaceToBatchND":{let s=I("blockShape",e,t,n),r=I("paddings",e,t,n);return[qc(I("x",e,t,n),s,r)]}case"BatchToSpaceND":{let s=I("blockShape",e,t,n),r=I("crops",e,t,n);return[Bc(I("x",e,t,n),s,r)]}case"DepthToSpace":{let s=I("blockSize",e,t,n),r=I("dataFormat",e,t,n).toUpperCase();return[kA(I("x",e,t,n),s,r)]}case"BroadcastTo":return[tu(I("x",e,t,n),I("shape",e,t,n))];case"BroadcastArgs":return[Ob(I("s0",e,t,n),I("s1",e,t,n))];default:throw TypeError(`Node type ${e.op} is not implemented`)}};function jw(e,t,n,s){let r=((a,o,i)=>{switch(a.category){case"arithmetic":return H(()=>dB(a,o,i));case"basic_math":return H(()=>pB(a,o,i));case"control":return yB(a,o,i);case"convolution":return H(()=>xB(a,o,i));case"creation":return H(()=>bB(a,o,i));case"dynamic":return vB(a,o,i);case"evaluation":return H(()=>wB(a,o,i));case"image":return H(()=>CB(a,o,i));case"graph":return H(()=>kB(a,o,i));case"logical":return H(()=>TB(a,o,i));case"matrices":return H(()=>NB(a,o,i));case"normalization":return H(()=>EB(a,o,i));case"reduction":return H(()=>RB(a,o,i));case"slice_join":return H(()=>DB(a,o,i));case"sparse":return H(()=>_B(a,o,i));case"spectral":return H(()=>$B(a,o,i));case"string":return H(()=>FB(a,o,i));case"transformation":return H(()=>OB(a,o,i));case"hash_table":return SB(a,o,i,s);case"custom":let l=bw(a.op);if(l&&l.customExecutor)return l.customExecutor(new cB(a,o,i));throw TypeError(`Custom op ${a.op} is not registered.`);default:throw TypeError(`Unknown op '${a.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(r)?r.then(a=>[].concat(a)):[].concat(r)}var qw=class{constructor(e={},t={},n={},s={}){this.weightMap=e,this.tensorArrayMap=t,this.tensorListMap=n,this.functionMap=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 Xw(e,t,n,s){let r=new Set,a=[],o=null,i=null,l=new Set,u=Object.keys(e).map(p=>is(p)[0]),c=[];s!=null&&(c=s.map(p=>is(p.name)[0]));let d=[...t];for(;d.length>0;){let p=d.pop();if((Kw(p)||BB(p)||WB(p))&&o==null&&(o=p,i=o.children.map(h=>h.name).filter(h=>r.has(h))),r.add(p.name),n[p.name]==null&&u.indexOf(p.name)===-1&&c.indexOf(p.name)===-1){if(p.inputs.length===0){a.push(p.name);continue}p.inputs.forEach(h=>{l.has(h.name)||(l.add(h.name),d.push(h))})}}return{inputs:e,outputs:t,usedNodes:r,missingInputs:a,dynamicNode:o,syncInputs:i}}function PB(e,t,n){let{usedNodes:s,inputs:r}=n,a=[],o=Object.keys(r).map(c=>is(c)[0]).map(c=>e.nodes[c]),i=e.initNodes;o.forEach(c=>{s.has(c.name)&&a.push(c)}),e.weights.forEach(c=>{s.has(c.name)&&a.push(c)}),i!=null&&i.forEach(c=>{s.has(c.name)&&a.push(c)});let l=new Set,u=[];for(;a.length>0;){let c=a.pop();l.add(c.name),t[c.name]||u.push(c),c.children.forEach(d=>{!l.has(d.name)&&s.has(d.name)&&d.inputs.every(p=>l.has(p.name))&&a.push(d)})}return u}var MB=["Switch","Merge","Enter","Exit","NextIteration","StatelessIf","StatelessWhile","if","While"],zB=["NonMaxSuppressionV2","NonMaxSuppressionV3","NonMaxSuppressionV5","Where"],LB=["HashTable","HashTableV2","LookupTableImport","LookupTableImportV2","LookupTableFind","LookupTableFindV2","LookupTableSize","LookupTableSizeV2"];function Kw(e){return MB.indexOf(e.op)>=0}function BB(e){return zB.indexOf(e.op)>=0}function WB(e){return LB.indexOf(e.op)>=0}var e2=class{constructor(e,t){this.graph=e,this.parent=t,this.compiledMap=new Map,this._weightMap={},this.SEPERATOR=",",this._functions={},this._functionExecutorMap={},this._outputs=e.outputs,this._inputs=e.inputs,this._initNodes=e.initNodes,this._signature=e.signature,this._functions=e.functions,e.functions!=null&&Object.keys(e.functions).forEach(n=>{this._functionExecutorMap[n]=new e2(e.functions[n],this)})}get weightIds(){return this.parent?this.parent.weightIds:this._weightIds}get functionExecutorMap(){return this.parent?this.parent.functionExecutorMap:this._functionExecutorMap}get weightMap(){return this.parent?this.parent.weightMap:this._weightMap}set weightMap(e){let t=Object.keys(e).map(n=>e[n].map(s=>s.id));this._weightIds=[].concat(...t),this._weightMap=e}set resourceManager(e){this._resourceManager=e}get inputs(){return this._inputs.map(e=>({name:e.name,shape:e.attrParams.shape?e.attrParams.shape.value:void 0,dtype:e.attrParams.dtype?e.attrParams.dtype.value:void 0}))}get outputs(){return this._outputs.map(e=>({name:e.name,shape:e.attrParams.shape?e.attrParams.shape.value:void 0,dtype:e.attrParams.dtype?e.attrParams.dtype.value:void 0}))}get inputNodes(){return this._inputs.map(e=>e.signatureKey||e.name)}get outputNodes(){return this._outputs.map(e=>{let t=e.signatureKey||e.name;return e.defaultOutput?`${t}:${e.defaultOutput}`:t})}get functions(){return Object.keys(this._functions).reduce((e,t)=>(e[t]=this._functions[t].signature,e),{})}getCompilationKey(e,t){let n=e.map(r=>r.name).sort(),s=t.map(r=>r.name).sort();return n.join(this.SEPERATOR)+"--"+s.join(this.SEPERATOR)}compile(e,t){let n=Xw(e,t,this.weightMap,this._initNodes),{missingInputs:s,dynamicNode:r,syncInputs:a}=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 [${a}]`);if(s.length>0){let o=t.map(l=>l.name),i=Object.keys(e);throw new Error(`Cannot compute the outputs [${o}] from the provided inputs [${i}]. Missing the following inputs: [${s}]`)}return PB(this.graph,this.weightMap,n)}execute(e,t){e=this.mapInputs(e);let n=Object.keys(e).sort();this.checkInputs(e),this.checkInputShapeAndType(e),t=this.mapOutputs(t),this.checkOutputs(t);let s=n.map(c=>this.graph.nodes[is(c)[0]]),r=t.map(c=>is(c)[0]),a=r.map(c=>this.graph.nodes[c]);a.length===0&&(a=this._outputs);let o=this.getCompilationKey(s,a),i=this.compiledMap.get(o);i==null&&(i=this.compile(e,a),this.compiledMap.set(o,i));let l={},u={};return H(()=>{let c=new qw(this.weightMap,l,u,this.functionExecutorMap),d=Object.assign({},this.weightMap);Object.keys(e).forEach(m=>{let[f,g]=is(m),A=[];A[g]=e[m],d[f]=A});let p=this.getFrozenTensorIds(d),h={};for(let m=0;mDn(m,d,c))})}getFrozenTensorIds(e){let t=[].concat.apply([],Object.keys(e).map(n=>e[n]).map(n=>n.map(s=>s.id)));return new Set(t)}checkTensorForDisposal(e,t,n,s,r,a,o){t.category==="control"||a.indexOf(e)!==-1||(n[e].forEach(i=>{i!=null&&(o[i.id]=(o[i.id]||0)+t.children.length)}),t.inputs.forEach(i=>{if(i.category!=="control"){let l=UL(i.name,n,s);l!=null&&l.forEach(u=>{if(u&&!u.kept&&!r.has(u.id)){let c=o[u.id];c===1?(u.dispose(),delete o[u.id]):c!=null&&o[u.id]--}})}}))}async executeAsync(e,t){return this._executeAsync(e,t)}async _executeAsync(e,t,n=!1,s={},r={}){n||(e=this.mapInputs(e),this.checkInputs(e),this.checkInputShapeAndType(e),t=this.mapOutputs(t),this.checkOutputs(t));let a=new qw(this.weightMap,s,r,this.functionExecutorMap),o=await this.executeWithControlFlow(e,a,t,n),i=t.map(d=>Dn(d,o,a)),l=i.map(d=>d.id),u=Object.keys(e).map(d=>e[d].id),c=new Set([...l,...u,...this.weightIds]);return Object.keys(o).forEach(d=>{o[d].forEach(h=>{h&&!h.kept&&!h.isDisposed&&!c.has(h.id)&&h.dispose()})}),this.parent==null&&a.dispose(c),i}async executeFunctionAsync(e,t,n){let s=e.reduce((r,a,o)=>(r[this.inputs[o].name]=a,r),{});return this._executeAsync(s,this.outputNodes,!0,t,n)}async executeWithControlFlow(e,t,n,s){let r=Object.keys(e),a=r.map(y=>this.graph.nodes[is(y)[0]]),o=n.map(y=>is(y)[0]),i=o.map(y=>this.graph.nodes[y]);i.length===0&&(i=this._outputs);let{usedNodes:l,missingInputs:u,dynamicNode:c,syncInputs:d}=Xw(e,i,this.weightMap,this._initNodes),p=[...a,...this.graph.weights,...this._initNodes||[]].map(y=>({node:y,contexts:t.currentContext})),h=Object.assign({},this.weightMap);Object.keys(e).forEach(y=>{let[x,b]=is(y),v=[];v[b]=e[y],h[x]=v});let m={},f=this.getFrozenTensorIds(h),g={};for(;p.length>0;){let y=this.processStack(a,p,t,h,g,f,o,m,l);await Promise.all(y)}c==null&&!s&&console.warn("This model execution did not contain any nodes with control flow or dynamic output shapes. You can use model.execute() instead.");let A=i.filter(y=>!Kw(y)&&!Dn(y.name,h,t)).map(y=>y.name);if(A.length>0){let y="";throw c!=null&&(y=`Alternatively, to avoid the dynamic ops, use model.execute() and specify the inputs [${d}]`),new Error(`Cannot compute the outputs [${A}] from the provided inputs [${r}]. Consider providing the following inputs: [${u}]. ${y}`)}return h}processStack(e,t,n,s,r,a,o,i,l){let u=[];for(;t.length>0;){let c=t.pop();n.currentContext=c.contexts;let d="";if(c.node.op==="Enter"&&I("isConstant",c.node,s,n)&&([d]=Vr(c.node.name,n)),s[c.node.name]==null){let p=jw(c.node,s,n,this._resourceManager);d||([d]=Vr(c.node.name,n));let h=n.currentContext;w.isPromise(p)?u.push(p.then(m=>(s[d]=m,n.currentContext=h,this.checkTensorForDisposal(d,c.node,s,n,a,o,i),this.processChildNodes(c.node,t,n,s,r,l),m))):(s[d]=p,this.checkTensorForDisposal(d,c.node,s,n,a,o,i),this.processChildNodes(c.node,t,n,s,r,l))}else this.processChildNodes(c.node,t,n,s,r,l)}return u}processChildNodes(e,t,n,s,r,a){e.children.forEach(o=>{let[i]=Vr(o.name,n);r[i]||!a.has(o.name)||(o.op==="Merge"?o.inputNames.some(l=>!!Dn(l,s,n))&&(r[i]=!0,t.push({contexts:n.currentContext,node:o})):o.inputNames.every(l=>!!Dn(l,s,n))&&(r[i]=!0,t.push({contexts:n.currentContext,node:o})))})}dispose(){Object.keys(this.weightMap).forEach(e=>this.weightMap[e].forEach(t=>t.dispose()))}checkInputShapeAndType(e){Object.keys(e).forEach(t=>{let n=e[t],[s]=is(t),r=this.graph.nodes[s];if(r.attrParams.shape&&r.attrParams.shape.value){let a=r.attrParams.shape.value,o=a.length===n.shape.length&&n.shape.every((i,l)=>a[l]===-1||a[l]===i);w.assert(o,()=>`The shape of dict['${r.name}'] provided in model.execute(dict) must be [${a}], but was [${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 ${n.dtype}`)})}mapInputs(e){let t={};for(let n in e)if(this._signature!=null&&this._signature.inputs!=null&&this._signature.inputs[n]!=null){let s=this._signature.inputs[n];t[s.name]=e[n]}else t[n]=e[n];return t}checkInputs(e){let t=Object.keys(e).filter(n=>{let[s]=is(n);return this.graph.nodes[s]==null});if(t.length>0)throw new Error(`The dict provided in model.execute(dict) has keys: [${t}] that are not part of graph`)}mapOutputs(e){return e.map(t=>this._signature!=null&&this._signature.outputs!=null&&this._signature.outputs[t]!=null?this._signature.outputs[t].name:t,{})}checkOutputs(e){e.forEach(t=>{let[n]=is(t);if(!this.graph.nodes[n])throw new Error(`The output '${t}' is not found in the graph`)})}},VB=class{constructor(e={},t={}){this.hashTableNameToHandle=e,this.hashTableMap=t}addHashTable(e,t){this.hashTableNameToHandle[e]=t.handle,this.hashTableMap[t.id]=t}getHashTableHandleByName(e){return this.hashTableNameToHandle[e]}getHashTableById(e){return this.hashTableMap[e]}dispose(){for(let e in this.hashTableMap)this.hashTableMap[e].clearAndClose(),delete this.hashTableMap[e];for(let e in this.hashTableNameToHandle)this.hashTableNameToHandle[e].dispose(),delete this.hashTableNameToHandle[e]}},UB="?tfjs-format=file",HB="model.json",Zw=class{constructor(e,t={}){this.modelUrl=e,this.loadOptions=t,this.version="n/a",t==null&&(this.loadOptions={}),this.resourceManager=new VB}get modelVersion(){return this.version}get inputNodes(){return this.executor.inputNodes}get outputNodes(){return this.executor.outputNodes}get inputs(){return this.executor.inputs}get outputs(){return this.executor.outputs}get weights(){return this.executor.weightMap}get metadata(){return this.artifacts.userDefinedMetadata}get modelSignature(){return this.signature}findIOHandler(){let e=this.modelUrl;if(e.load!=null)this.handler=e;else if(this.loadOptions.requestInit!=null)this.handler=Bn.browserHTTPRequest(e,this.loadOptions);else{let t=Bn.getLoadHandlers(e,this.loadOptions);if(t.length===0)t.push(Bn.browserHTTPRequest(e,this.loadOptions));else if(t.length>1)throw new Error(`Found more than one (${t.length}) load handlers for URL '${[e]}'`);this.handler=t[0]}}async load(){if(this.findIOHandler(),this.handler.load==null)throw new Error("Cannot proceed with model loading because the IOHandler provided does not have the `load` method implemented.");let e=await this.handler.load();return this.loadSync(e)}loadSync(e){this.artifacts=e;let t=this.artifacts.modelTopology,n;this.artifacts.userDefinedMetadata!=null&&this.artifacts.userDefinedMetadata.signature!=null?n=this.artifacts.userDefinedMetadata.signature:n=this.artifacts.signature,this.signature=n,this.version=`${t.versions.producer}.${t.versions.minConsumer}`;let s=Bn.decodeWeights(this.artifacts.weightData,this.artifacts.weightSpecs);if(this.executor=new e2(Bw.Instance.transformGraph(t,this.signature)),this.executor.weightMap=this.convertTensorMapToTensorsMap(s),this.executor.resourceManager=this.resourceManager,e.modelInitializer!=null&&e.modelInitializer.node!=null){let r=Bw.Instance.transformGraph(e.modelInitializer);this.initializer=new e2(r),this.initializer.weightMap=this.executor.weightMap,this.initializer.resourceManager=this.resourceManager,this.initializer.executeAsync({},[])}return!0}async save(e,t){if(typeof e=="string"){let n=Bn.getSaveHandlers(e);if(n.length===0)throw new Error(`Cannot find any save handlers for URL '${e}'`);if(n.length>1)throw new Error(`Found more than one (${n.length}) save handlers for URL '${e}'`);e=n[0]}if(e.save==null)throw new Error("GraphModel.save() cannot proceed because the IOHandler provided does not have the `save` attribute defined.");return e.save(this.artifacts)}predict(e,t){return this.execute(e,this.outputNodes)}normalizeInputs(e){if(!(e instanceof He)&&!Array.isArray(e))return e;if(e=Array.isArray(e)?e:[e],e.length!==this.inputNodes.length)throw new Error(`Input tensor count mismatch,the graph model has ${this.inputNodes.length} placeholders, while there are ${e.length} input tensors.`);return this.inputNodes.reduce((t,n,s)=>(t[n]=e[s],t),{})}normalizeOutputs(e){return e=e||this.outputNodes,Array.isArray(e)?e:[e]}execute(e,t){e=this.normalizeInputs(e),t=this.normalizeOutputs(t);let n=this.executor.execute(e,t);return n.length>1?n:n[0]}async executeAsync(e,t){e=this.normalizeInputs(e),t=this.normalizeOutputs(t);let n=await this.executor.executeAsync(e,t);return n.length>1?n:n[0]}convertTensorMapToTensorsMap(e){return Object.keys(e).reduce((t,n)=>(t[n]=[e[n]],t),{})}dispose(){this.executor.dispose(),this.initializer&&this.initializer.dispose(),this.resourceManager.dispose()}};async function st(e,t={}){if(e==null)throw new Error("modelUrl in loadGraphModel() cannot be null. Please provide a url or an IOHandler that loads the model");t==null&&(t={}),t.fromTFHub&&e.load==null&&(e.endsWith("/")||(e=e+"/"),e=`${e}${HB}${UB}`);let n=new Zw(e,t);return await n.load(),n}var GB="3.9.0",Yw={};Le(Yw,{CSVDataset:()=>u7,Dataset:()=>vu,FileDataSource:()=>g7,TextLineDataset:()=>o7,URLDataSource:()=>A7,array:()=>fW,csv:()=>SW,func:()=>CW,generator:()=>TW,microphone:()=>EW,version_data:()=>RW,webcam:()=>NW,zip:()=>mW});var jB=Wa(h5()),qB=Wa(h5());function XB(e,t){return qf(e,t)}function qf(e,t,n=new Map,s=new Set){if(e==null)return null;if(s.has(e))throw new Error("Circular references are not supported.");if(n.has(e))return n.get(e);let r=t(e);if(r.recurse&&r.value!==null)throw new Error("A deep map function may not return both a value and recurse=true.");if(r.recurse)if(bu(e)){let a=Array.isArray(e)?[]:{};s.add(e);for(let o in e){let i=e[o],l=qf(i,t,n,s);a[o]=l}return s.delete(e),a}else throw new Error(`Can't recurse into non-iterable type: ${e}`);else return n.set(e,r.value),r.value}function KB(e,t=Qw){return Jw(e,t)}function Jw(e,t,n=new Set){let s=e[0];if(n.has(s))throw new Error("Circular references are not supported.");let r=t(e);if(r.recurse&&r.value!==null)throw new Error("A deep zip function may not return both a value and recurse=true.");if(r.recurse)if(bu(s)){let a=Array.isArray(s)?[]:{};n.add(s);for(let o in s){let i=e.map(u=>u[o]),l=Jw(i,t,n);a[o]=l}return n.delete(s),a}else throw new Error(`Can't recurse into non-iterable type: ${s}`);else return r.value}function Qw(e){return e===null?null:bu(e[0])?{value:null,recurse:!0}:{value:e,recurse:!1}}async function e7(e,t){let n=new Map;qf(e,t,n);for(let r of Array.from(n.keys())){let a=n.get(r);if(w.isPromise(a)){let o=await a;n.set(r,o)}}return qf(e,t,n)}function bu(e){let t=!1;if(Y().get("IS_BROWSER"))t=e instanceof TextDecoder;else{let{StringDecoder:n}=f5();t=e instanceof n}return e!=null&&!ArrayBuffer.isView(e)&&(Array.isArray(e)||typeof e=="object"&&!(e instanceof He)&&!(e instanceof Promise)&&!t)}function ZB(e){return e==null||YB(e)||Array.isArray(e)||typeof e=="object"&&e instanceof He||w.isTypedArray(e)}function YB(e){return e===null||typeof e!="object"&&typeof e!="function"}function JB(e){return XB(e,QB)}function QB(e){return e instanceof He?{value:e.clone(),recurse:!1}:bu(e)?{value:null,recurse:!0}:{value:e,recurse:!1}}var t7=class{constructor(e){if(this.capacity=e,this.begin=0,this.end=0,e==null)throw new RangeError("Can't create a ring buffer of unknown capacity.");if(e<1)throw new RangeError("Can't create ring buffer of capacity < 1.");this.data=new Array(e),this.doubledCapacity=2*e}wrap(e){for(;e<0;)e+=this.doubledCapacity;return e%this.doubledCapacity}get(e){if(e<0)throw new RangeError("Can't get item at a negative index.");return this.data[e%this.capacity]}set(e,t){if(e<0)throw new RangeError("Can't set item at a negative index.");this.data[e%this.capacity]=t}length(){let e=this.end-this.begin;return e<0&&(e=this.doubledCapacity+e),e}isFull(){return this.length()===this.capacity}isEmpty(){return this.length()===0}push(e){if(this.isFull())throw new RangeError("Ring buffer is full.");this.set(this.end,e),this.end=this.wrap(this.end+1)}pushAll(e){for(let t of e)this.push(t)}pop(){if(this.isEmpty())throw new RangeError("Ring buffer is empty.");this.end=this.wrap(this.end-1);let e=this.get(this.end);return this.set(this.end,void 0),e}unshift(e){if(this.isFull())throw new RangeError("Ring buffer is full.");this.begin=this.wrap(this.begin-1),this.set(this.begin,e)}shift(){if(this.isEmpty())throw new RangeError("Ring buffer is empty.");let e=this.get(this.begin);return this.set(this.begin,void 0),this.begin=this.wrap(this.begin+1),e}shuffleExcise(e){if(this.isEmpty())throw new RangeError("Ring buffer is empty.");let t=this.wrap(this.begin+e),n=this.get(t);return this.set(t,this.pop()),n}},t2=class extends t7{constructor(){super(t2.INITIAL_CAPACITY)}isFull(){return!1}push(e){super.isFull()&&this.expand(),super.push(e)}unshift(e){super.isFull()&&this.expand(),super.unshift(e)}expand(){let e=this.capacity*2,t=new Array(e),n=this.length();for(let s=0;st===!0)}rowMajorBatch(e,t=!0){return new iW(this,e,t)}columnMajorBatch(e,t=!0,n=Qw){return this.rowMajorBatch(e,t).map(r=>KB(r,n))}concatenate(e,t){return new r7(n7([this,e]),t)}take(e){return e<0||e==null?this:new oW(this,e)}skip(e){return e<0||e==null?this:new aW(this,e)}prefetch(e){return new a7(this,e)}shuffle(e,t){return new hW(this,e,t)}serial(){return new rW(this)}},nW=class extends dn{constructor(e){super();this.items=e,this.trav=0}summary(){return`Array of ${this.items.length} items`}async next(){if(this.trav>=this.items.length)return{value:null,done:!0};let e=this.items[this.trav];return this.trav++,{value:JB(e),done:!1}}},sW=class extends dn{constructor(e){super();this.nextFn=e}summary(){return"Function call"}async next(){try{return this.nextFn()}catch(e){throw e.message=`Error thrown while iterating through a dataset: ${e.message}`,e}}},rW=class extends dn{constructor(e){super();this.upstream=e,this.lastRead=Promise.resolve({value:null,done:!1})}summary(){return`${this.upstream.summary()} -> Serial`}async next(){return this.lastRead=this.lastRead.then(()=>this.serialNext()),this.lastRead}async serialNext(){return this.upstream.next()}},aW=class extends dn{constructor(e,t){super();this.upstream=e,this.maxCount=t,this.count=0,this.lastRead=Promise.resolve({value:null,done:!1})}summary(){return`${this.upstream.summary()} -> Skip`}async next(){return this.lastRead=this.lastRead.then(()=>this.serialNext()),this.lastRead}async serialNext(){for(;this.count++ Take`}async next(){return this.count++>=this.maxCount?{value:null,done:!0}:this.upstream.next()}},iW=class extends dn{constructor(e,t,n=!0){super();this.upstream=e,this.batchSize=t,this.enableSmallLastBatch=n,this.lastRead=Promise.resolve({value:null,done:!1})}summary(){return`${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}}},lW=class extends dn{constructor(e,t){super();this.upstream=e,this.predicate=t,this.lastRead=Promise.resolve({value:null,done:!1})}summary(){return`${this.upstream.summary()} -> Filter`}async next(){return this.lastRead=this.lastRead.then(()=>this.serialNext()),this.lastRead}async serialNext(){for(;;){let e=await this.upstream.next();if(e.done||this.predicate(e.value))return e;Z(e.value)}}},uW=class extends dn{constructor(e,t){super();this.upstream=e,this.transform=t}summary(){return`${this.upstream.summary()} -> Map`}async next(){let e=await this.upstream.next();if(e.done)return{value:null,done:!0};let t=Vs.getTensorsInContainer(e.value),n=this.transform(e.value),s=Vs.getTensorsInContainer(n);for(let r of t)Vs.isTensorInList(r,s)||r.dispose();return{value:n,done:!1}}},cW=class extends dn{constructor(e,t){super();this.upstream=e,this.handler=t,this.count=0,this.lastRead=Promise.resolve({value:null,done:!1})}summary(){return`${this.upstream.summary()} -> handleErrors`}async next(){return this.lastRead=this.lastRead.then(()=>this.serialNext()),this.lastRead}async serialNext(){for(;;)try{return await this.upstream.next()}catch(e){if(!this.handler(e))return{value:null,done:!0}}}},s7=class extends dn{constructor(e,t){super();this.upstream=e,this.transform=t}summary(){return`${this.upstream.summary()} -> AsyncMap`}async next(){let e=await this.upstream.next();if(e.done)return{value:null,done:!0};let t=Vs.getTensorsInContainer(e.value),n=await this.transform(e.value),s=Vs.getTensorsInContainer(n);for(let r of t)Vs.isTensorInList(r,s)||r.dispose();return{value:n,done:!1}}},s2=class extends dn{constructor(){super();this.outputQueue=new t2,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}}},dW=class extends s2{constructor(e,t){super();this.upstream=e,this.transform=t}summary(){return`${this.upstream.summary()} -> Flatmap`}async pump(){let e=await this.upstream.next();if(e.done)return!1;let t=Vs.getTensorsInContainer(e.value),n=this.transform(e.value),s=Vs.getTensorsInContainer(n);this.outputQueue.pushAll(n);for(let r of t)Vs.isTensorInList(r,s)||r.dispose();return!0}},r7=class extends dn{constructor(e,t){super();this.baseErrorHandler=t,this.lastRead=null,this.iterator=null,this.moreIterators=e}summary(){return"TODO: fill in upstream of chained summaries 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e7(this.iterators,s);if(t===n)return{value:null,done:!0};if(n>0)switch(this.mismatchMode){case Sa.FAIL:throw new Error(`Zipped streams should have the same length. 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If your data fits in main memory (for regular JS objects), and/or GPU memory (for \`tf.Tensor\`s), consider setting bufferSize to the dataset size (${this.size} elements)`);let s=this,r=jB.alea(t||w.now().toString());return ls(async()=>{let a=r.int32();return n&&(a+=r.int32()),(await s.iterator()).shuffle(e,a.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,ls(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()}};vu.MAX_BUFFER_SIZE=1e4;function ls(e,t=null){return new class extends vu{constructor(){super(...arguments);this.size=t}async iterator(){return e()}}}function fW(e){return ls(async()=>n7(e),e.length)}function mW(e){if(!bu(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 e7(e,s=>{if(s instanceof vu)return{value:s.iterator(),recurse:!1};if(bu(s))return{value:null,recurse:!0};throw new Error("Leaves of the structure passed to zip() must be Datasets, not primitives.")});return tW(n,Sa.SHORTEST)},t)}function gW(e){if(e===null)return null;let t=e[0];return ZB(t)?{value:AW(e),recurse:!1}:{value:null,recurse:!0}}function AW(e){if(e.length===0)throw new Error("Can't make a batch of zero elements.");return e[0]instanceof He?yn(e):ln(e)}var o7=class extends vu{constructor(e){super();this.input=e}async iterator(){return(await this.input.iterator()).decodeUTF8().split(` `).map(s=>(s.endsWith("\r")&&(s=s.slice(0,-1)),s))}},Xf='"',vd=Symbol("out"),i7=Symbol("field"),Kf=Symbol("quote"),r2=Symbol("quoteafterquote"),l7=Symbol("quoteinquote"),u7=class extends vu{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 o7(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 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((s,r)=>(s[r]=s[r]+1||1,s),{}),n=Object.keys(t).filter(s=>t[s]>1);if(w.assert(n.length===0,()=>"Duplicate column names found: "+n.toString()),this.columnConfigs){for(let s of Object.keys(this.columnConfigs))if(this.fullColumnNames.indexOf(s)===-1)throw new Error('The key "'+s+'" provided in columnConfigs does not match any of the column names ('+this.fullColumnNames.toString()+").")}this.columnNamesValidated=!0}async maybeReadHeaderLine(){if(this.hasHeader){let t=await(await this.base.iterator()).next();if(t.done)throw new Error("No data was found for CSV parsing.");let n=t.value;return this.parseRow(n,!1)}else return null}async iterator(){this.columnNamesValidated||await this.setColumnNames();let e=await this.base.iterator();return this.hasHeader&&(e=e.skip(1)),e.map(t=>this.makeDataElement(t))}makeDataElement(e){let t=this.parseRow(e),n={},s={};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(Y().get("IS_NODE"))throw new Error("microphone API is only supported in browser environment.");let t=new c7(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 s=this.flattenQueue(n.freqDataQueue);e=this.getTensorFromAudioDataArray(s,[this.numFrames,this.columnTruncateLength,1])}if(this.includeWaveform){let s=this.flattenQueue(n.timeDataQueue);t=this.getTensorFromAudioDataArray(s,[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(s=>{let r=setInterval(()=>{this.includeSpectrogram&&(this.analyser.getFloatFrequencyData(this.freqData),this.freqData[0]===-1/0&&s({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),s({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((s,r)=>n.set(s,r*t)),n}getTensorFromAudioDataArray(e,t){let n=new Float32Array(w.sizeFromShape(t));return n.set(e,n.length-e.length),ln(n,t)}},d7=class extends dn{constructor(e,t){super();if(this.webcamVideoElement=e,this.webcamConfig=t,this.isClosed=!0,this.resize=!1,this.needToResize())if(this.resize=!0,this.cropSize=[this.webcamConfig.resizeHeight,this.webcamConfig.resizeWidth],this.cropBoxInd=Ut([0],"int32"),this.webcamConfig.centerCrop){let n=this.webcamConfig.resizeWidth*1/this.webcamVideoElement.width,s=this.webcamConfig.resizeHeight*1/this.webcamVideoElement.height,r=(1-n)/2,a=(1-s)/2,o=r+n,i=s+a;this.cropBox=qs([a,r,i,o],[1,4])}else this.cropBox=qs([0,0,1,1],[1,4])}summary(){return"webcam"}static async create(e,t={}){if(Y().get("IS_NODE"))throw new Error("tf.data.webcam is only supported in browser environment.");if(!e){if(e=document.createElement("video"),!t.resizeWidth||!t.resizeHeight)throw new Error("Please provide webcam video element, or resizeWidth and resizeHeight to create a hidden video element.");e.width=t.resizeWidth,e.height=t.resizeHeight}let n=new d7(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=_s.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: ${t.message}`)}finally{e.dispose()}else return{value:e,done:!1}}needToResize(){return!!(this.webcamConfig.resizeWidth&&this.webcamConfig.resizeHeight&&(this.webcamVideoElement.width!==this.webcamConfig.resizeWidth||this.webcamVideoElement.height!==this.webcamConfig.resizeHeight))}cropAndResizeFrame(e){return H(()=>{let t=zt(pe(e,"float32"),0),n;n=De.cropAndResize(t,this.cropBox,this.cropBoxInd,this.cropSize,"bilinear");let s=n.shape;return V(n,s.slice(1))})}async capture(){return(await this.next()).value}stop(){this.stream.getTracks().forEach(t=>t.stop());try{this.webcamVideoElement.srcObject=null}catch(t){console.log(t),this.webcamVideoElement.src=null}this.isClosed=!0}toArray(){throw new Error("Can not convert infinite video stream to array.")}},p7=class{},h7=class extends dn{split(e){return new yW(this,e)}},yW=class extends h7{constructor(e,t){super();this.upstream=e,this.impl=new xW(e,t)}summary(){return this.impl.summary()}async next(){return this.impl.next()}},xW=class extends s2{constructor(e,t){super();this.upstream=e,this.separator=t,this.carryover=""}summary(){return`${this.upstream.summary()} -> Split('${this.separator}')`}async pump(){let e=await this.upstream.next();if(e.done)return this.carryover===""?!1:(this.outputQueue.push(this.carryover),this.carryover="",!0);let t=e.value.split(this.separator);t[0]=this.carryover+t[0];for(let n of t.slice(0,-1))this.outputQueue.push(n);return this.carryover=t[t.length-1],!0}},bW=class extends dn{decodeUTF8(){return new vW(this)}},vW=class extends h7{constructor(e){super();this.upstream=e,this.impl=new wW(e)}summary(){return this.impl.summary()}async next(){return this.impl.next()}},wW=class extends s2{constructor(e){super();if(this.upstream=e,Y().get("IS_BROWSER"))this.decoder=new TextDecoder("utf-8");else{let{StringDecoder:t}=f5();this.decoder=new t("utf8")}}summary(){return`${this.upstream.summary()} -> Utf8`}async pump(){let e=await this.upstream.next(),t;if(e.done)return!1;t=e.value;let n;return Y().get("IS_BROWSER")?n=this.decoder.decode(t,{stream:!0}):n=this.decoder.write(Buffer.from(t.buffer)),this.outputQueue.push(n),!0}},f7=class extends bW{constructor(e,t={}){super();this.file=e,this.options=t,w.assert(e instanceof Uint8Array||(Y().get("IS_BROWSER")?e instanceof File||e instanceof Blob:!1),()=>"FileChunkIterator only supports File, Blob and Uint8Array right now."),this.offset=t.offset||0,this.chunkSize=t.chunkSize||1024*1024}summary(){return`FileChunks ${this.file}`}async next(){return this.offset>=(this.file instanceof Uint8Array?this.file.byteLength:this.file.size)?{value:null,done:!0}:{value:await new Promise((t,n)=>{let s=this.offset+this.chunkSize;if(this.file instanceof Uint8Array)t(new Uint8Array(this.file.slice(this.offset,s)));else{let r=new FileReader;r.onload=o=>{let i=r.result;if(i instanceof 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p7{constructor(e,t={}){super();this.url=e,this.fileOptions=t}async iterator(){return m7(this.url)?new g7(this.url,this.fileOptions).iterator():kW(this.url,this.fileOptions)}};function SW(e,t={}){return new u7(new A7(e),t)}function CW(e){let t=n2(e);return ls(async()=>t)}function TW(e){return ls(async()=>{let t=await e();return n2(()=>t.next())})}async function NW(e,t){return d7.create(e,t)}async function EW(e){return c7.create(e)}var RW="3.9.0";function Se(e,t){Array.isArray(e)||(e=[e]),e.forEach(n=>{n!=null&&w.assert(n.dtype!=="complex64",()=>`${t} does not support complex64 tensors in the CPU backend.`)})}var DW=fr.whereImpl,a2=class extends lc{constructor(){super();this.blockSize=48,this.firstUse=!0,this.data=new Sp(this,Qn())}nextDataId(){return a2.nextDataId++}write(e,t,n){this.firstUse&&(this.firstUse=!1,Y().get("IS_NODE")&&_.warn(` ============================ Hi there \u{1F44B}. 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c=_.computePool3DInfo(a.shape,o,i,1,l,u),d=c.strideDepth,p=c.strideHeight,h=c.strideWidth,m=c.filterDepth,f=c.filterHeight,g=c.filterWidth,A=c.dilationDepth,y=c.dilationHeight,x=c.dilationWidth,b=c.effectiveFilterDepth,v=c.effectiveFilterHeight,k=c.effectiveFilterWidth,S=b-1-c.padInfo.front,C=k-1-c.padInfo.left,D=v-1-c.padInfo.top,O=Ge(a.shape,"float32"),E=1/(m*f*g),R=n.bufferSync(r);for(let T=0;T=c.outDepth||Math.floor(Q)!==Q))for(let ce=0;ce=c.outHeight||Math.floor(de)!==de))for(let fe=0;fe=c.outWidth||Math.floor(xe)!==xe)continue;se+=R.get(T,Q,de,xe,P)}}}O.set(se*E,T,U,j,q,P)}return n.makeTensorInfo(O.shape,O.dtype,O.values)}var yU={kernelName:Dp,backendName:"cpu",kernelFunc:AU};function xU(e){let{inputs:t,backend:n,attrs:s}=e,{dy:r,input:a}=t,o=a;Se([r,a],"avgPoolGrad");let{filterSize:i,strides:l,pad:u}=s,c=_.computePool2DInfo(o.shape,i,l,1,u),d=c.strideHeight,p=c.strideWidth,h=c.filterHeight,m=c.filterWidth,f=c.dilationHeight,g=c.dilationWidth,A=c.effectiveFilterHeight,y=c.effectiveFilterWidth,x=y-1-c.padInfo.left,b=A-1-c.padInfo.top,v=Ge(o.shape,"float32"),k=1/(h*m),S=n.data.get(r.dataId).values,C=Ge(r.shape,"float32",S);for(let D=0;D=c.outHeight||Math.floor(q)!==q))for(let X=0;X=c.outWidth||Math.floor(te)!==te)continue;U+=C.get(D,q,te,O)}}v.set(U*k,D,E,R,O)}return n.makeTensorInfo(v.shape,v.dtype,v.values)}var bU={kernelName:Rp,backendName:"cpu",kernelFunc:xU};function vU(e){let{inputs:t,backend:n,attrs:s}=e,{x:r,scale:a,offset:o,mean:i,variance:l}=t;w.assert(i.shape.length===l.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 requires 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i=a.reduce((A,y)=>A*y),l=_.getReshaped(r.shape,a,i),u=_.getPermuted(l.length,a.length),c=_.getReshapedPermuted(r.shape,a,i),d=_.getSliceBeginCoords(o,a.length),p=_.getSliceSize(c,o,a.length),h=wt({inputs:{x:r},backend:n,attrs:{shape:l}}),m=vs({inputs:{x:h},backend:n,attrs:{perm:u}}),f=wt({inputs:{x:m},backend:n,attrs:{shape:c}}),g=gi({inputs:{x:f},backend:n,attrs:{begin:d,size:p}});return n.disposeIntermediateTensorInfo(h),n.disposeIntermediateTensorInfo(m),n.disposeIntermediateTensorInfo(f),g}var IU={kernelName:tl,backendName:"cpu",kernelFunc:kU};function SU(e){let{inputs:t,backend:n,attrs:s}=e,{x:r,weights:a}=t,{size:o}=s,i=n.data.get(r.dataId).values,l=n.data.get(a.dataId).values,u=i2(i,l,a.dtype,a.shape,o);return n.makeTensorInfo([o],a.dtype,u)}var CU={kernelName:_p,backendName:"cpu",kernelFunc:SU};function TU(e){let{inputs:t,backend:n}=e,{s0:s,s1:r}=t,a=n.data.get(s.dataId).values,o=n.data.get(r.dataId).values,i=_.assertAndGetBroadcastShape(Array.from(a),Array.from(o));return n.makeTensorInfo([i.length],"int32",Int32Array.from(i))}var NU={kernelName:Eg,backendName:"cpu",kernelFunc:TU},EU=pt(aa,(e,t)=>{let n=t;return e>n.clipValueMax?n.clipValueMax:e{let{x:t}=e.inputs,n=e.backend,s=new Float32Array(w.sizeFromShape(t.shape)),r=n.data.get(t.dataId),a=r.complexTensorInfos.real,o=r.complexTensorInfos.imag,i=n.data.get(a.dataId).values,l=n.data.get(o.dataId).values;for(let u=0;uf.shape),a);if(w.sizeFromShape(o)===0)return n.makeTensorInfo(o,t[0].dtype,[]);let i=t.filter(f=>w.sizeFromShape(f.shape)>0);if(i.length===1)return br({inputs:{x:i[0]},backend:n});let l=i.map(f=>f.shape);if(_.assertParamsConsistent(l,a),i[0].dtype==="complex64"){let f=i.map(b=>mi({inputs:{input:b},backend:n})),g=i.map(b=>ku({inputs:{input:b},backend:n})),A=Iu({inputs:f,backend:n,attrs:{axis:a}}),y=Iu({inputs:g,backend:n,attrs:{axis:a}}),x=us({inputs:{real:A,imag:y},backend:n});return f.forEach(b=>n.disposeIntermediateTensorInfo(b)),g.forEach(b=>n.disposeIntermediateTensorInfo(b)),n.disposeIntermediateTensorInfo(A),n.disposeIntermediateTensorInfo(y),x}let u=i.map(f=>{let g=w.sizeFromShape(f.shape.slice(a));return wt({inputs:{x:f},backend:n,attrs:{shape:[-1,g]}})}),c=u.map(f=>({vals:n.data.get(f.dataId).values,shape:f.shape}));o=_.computeOutShape(u.map(f=>f.shape),1);let d=u[0].shape[0]===1,p=l2(c,o,t[0].dtype,d),h=_.computeOutShape(i.map(f=>f.shape),a),m=n.makeTensorInfo(h,t[0].dtype,p);return u.forEach(f=>n.disposeIntermediateTensorInfo(f)),m}var FU={kernelName:nl,backendName:"cpu",kernelFunc:Iu};function d6(e){let{inputs:t,backend:n,attrs:s}=e,{x:r,filter:a}=t,{strides:o,pad:i,dataFormat:l,dilations:u,dimRoundingMode:c}=s;Se([r,a],"conv2d");let d=_.convertConv2DDataFormat(l),p=_.computeConv2DInfo(r.shape,a.shape,o,u,i,c,!1,d),h=p.filterHeight,m=p.filterWidth,f=p.dilationHeight,g=p.dilationWidth,A=p.padInfo.left,y=p.padInfo.top,x=p.dataFormat==="channelsLast",b=new Kt(p.outShape,r.dtype),v=w.computeStrides(r.shape),k=w.computeStrides(a.shape),S=v[0],C=x?v[1]:v[2],D=x?v[2]:1,O=x?1:v[1],E=b.strides[0],R=x?b.strides[1]:b.strides[2],T=x?b.strides[2]:1,P=x?1:b.strides[1],U=n.data.get(r.dataId).values,j=n.data.get(a.dataId).values,q=b.values;for(let X=0;X=p.inHeight)continue;let fe=ce*k[0],xe=te+de*C;for(let Ee=0;Ee=p.inWidth)continue;let mt=fe+Be*k[1],lt=xe+Me*D,ut=mt;for(let ot=0;ot=u.inDepth)continue;let X=j*D[0],te=E+q*C[1];for(let ne=0;ne=u.inHeight)continue;let de=X+Q*D[1],fe=te+ce*C[2];for(let xe=0;xe=u.inWidth)continue;let Me=de+Pe*D[2],mt=fe+Be*u.inChannels,lt=Me;for(let ut=0;utMath.cos(e)),qU={kernelName:Ja,backendName:"cpu",kernelFunc:jU},XU=pt(Qa,e=>Math.cosh(e)),KU={kernelName:Qa,backendName:"cpu",kernelFunc:XU};function ZU(e){let{inputs:t,backend:n,attrs:s}=e,{image:r,boxes:a,boxInd:o}=t,{cropSize:i,method:l,extrapolationValue:u}=s,[c,d,p,h]=r.shape,m=a.shape[0],[f,g]=i,A=Ge([m,f,g,h],"float32"),y=n.data.get(a.dataId).values,x=n.data.get(o.dataId).values,b=n.data.get(r.dataId).values,v=w.computeStrides(r.shape),k=w.computeStrides(A.shape);for(let S=0;S=c)continue;let P=f>1?(E-D)*(d-1)/(f-1):0,U=g>1?(R-O)*(p-1)/(g-1):0;for(let j=0;j1?D*(d-1)+j*P:.5*(D+E)*(d-1);if(q<0||q>d-1){for(let X=0;X1?O*(p-1)+se*U:.5*(O+R)*(p-1);if(ae<0||ae>p-1){for(let fe=0;fe1?O*(p-1)+X*U:.5*(O+R)*(p-1);if(te<0||te>p-1){for(let ae=0;aeA+m-y-1:(A,y)=>A+y;for(let A=0;A`Only NHWC dataFormat supported on CPU for depthToSpace. Got ${o}`),w.assert(a>1,()=>`blockSize should be > 1 for depthToSpace, but was: ${a}`);let i=r.shape[0],l=r.shape[1],u=r.shape[2],c=r.shape[3],d=l*a,p=u*a,h=c/(a*a),m=n.data.get(r.dataId).values,f=new Float32Array(i*d*p*h),g=0;for(let A=0;A`Error in depthwiseConv2d: Either strides or dilations must be 1. Got strides ${o} and dilations '${p}'`);let h=_.computeConv2DInfo(r.shape,a.shape,o,p,i,u,!0),{filterHeight:m,filterWidth:f,dilationHeight:g,dilationWidth:A,padInfo:y}=h,x=y.left,b=y.top,v=h.outChannels/h.inChannels,k=new Kt(h.outShape,r.dtype),S=n.data.get(r.dataId).values,C=n.data.get(a.dataId).values,D=k.values;for(let O=0;O=h.inHeight)continue;let X=j*d[0],te=E+q*c[1];for(let ne=0;ne=h.inWidth)continue;let de=X+Q*d[1],fe=te+ce*h.inChannels,xe=se,Ee=de;for(let Re=0;Re{let{x:s,filter:r}=e,{strides:a,pad:o,dilations:i}=n,l=t,u=l.data.get(s.dataId).values,c=s.shape.length,d=l.data.get(r.dataId).values,p=r.shape.length,{batchSize:h,inHeight:m,inWidth:f,inChannels:g,outHeight:A,outWidth:y,padInfo:x,strideHeight:b,strideWidth:v,filterHeight:k,filterWidth:S,dilationHeight:C,dilationWidth:D,outShape:O}=_.computeDilation2DInfo(s.shape,r.shape,a,o,"NHWC",i),E=w.sizeFromShape(O),R=O.length,T=w.getArrayFromDType(s.dtype,E);for(let U=0;U=0&&ce=0&&fese&&(se=Re)}}}let ae=w.locToIndex([U,j,X,ne],R,w.computeStrides(O));T[ae]=se}}}return{dataId:l.write(w.toTypedArray(T,s.dtype),O,s.dtype),shape:O,dtype:s.dtype}}},pH={kernelName:Vp,backendName:"cpu",kernelFunc:({inputs:e,backend:t,attrs:n})=>{let{x:s,filter:r,dy:a}=e,{strides:o,pad:i,dilations:l}=n,u=t,c=w.toNestedArray(s.shape,u.data.get(s.dataId).values),d=w.toNestedArray(r.shape,u.data.get(r.dataId).values),{batchSize:p,inHeight:h,inWidth:m,inChannels:f,outHeight:g,outWidth:A,padInfo:y,strideHeight:x,strideWidth:b,filterHeight:v,filterWidth:k,dilationHeight:S,dilationWidth:C,outShape:D}=_.computeDilation2DInfo(s.shape,r.shape,o,i,"NHWC",l);w.assert(a.rank===D.length,()=>`Error in ${Vp}, dy must have the same rank as output ${D.length}, but got ${a.rank}`);let O=w.toNestedArray(D,u.data.get(a.dataId).values),E=w.makeZerosNestedTypedArray(r.shape,r.dtype);for(let T=0;T=0&&Q=0&&dete&&(te=fe,ne=ae,se=ce)}}}E[ne][se][X]+=O[T][P][j][X]}}}return{dataId:u.write(w.toTypedArray(E,s.dtype),r.shape,r.dtype),shape:r.shape,dtype:r.dtype}}},hH={kernelName:Wp,backendName:"cpu",kernelFunc:({inputs:e,backend:t,attrs:n})=>{let{x:s,filter:r,dy:a}=e,{strides:o,pad:i,dilations:l}=n,u=t,c=w.toNestedArray(s.shape,u.data.get(s.dataId).values),d=w.toNestedArray(r.shape,u.data.get(r.dataId).values),{batchSize:p,inHeight:h,inWidth:m,inChannels:f,outHeight:g,outWidth:A,padInfo:y,strideHeight:x,strideWidth:b,filterHeight:v,filterWidth:k,dilationHeight:S,dilationWidth:C,outShape:D}=_.computeDilation2DInfo(s.shape,r.shape,o,i,"NHWC",l);w.assert(a.rank===D.length,()=>`Error in ${Wp}, dy must have the same rank as output ${D.length}, but got ${a.rank}`);let O=w.toNestedArray(D,u.data.get(a.dataId).values),E=w.makeZerosNestedTypedArray(s.shape,s.dtype);for(let T=0;T=0&&Q=0&&dete&&(te=fe,ne=Q,se=de)}}}E[T][ne][se][X]+=O[T][P][j][X]}}}return{dataId:u.write(w.toTypedArray(E,s.dtype),s.shape,s.dtype),shape:s.shape,dtype:s.dtype}}};function Id(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{axis:a,keepDims:o}=s;Se(r,"sum");let i;r.dtype==="bool"?i=Ca({inputs:{x:r},backend:n,attrs:{dtype:"int32"}}):i=br({inputs:{x:r},backend:n});let l=i.shape.length,u=w.parseAxisParam(a,i.shape),c=_.getAxesPermutation(u,l),d=u,p=i;c!=null&&(p=vs({inputs:{x:i},backend:n,attrs:{perm:c}}),d=_.getInnerMostAxes(d.length,l)),_.assertAxesAreInnerMostDims("sum",d,p.shape.length);let[h,m]=_.computeOutAndReduceShapes(p.shape,d),f=_.upcastType(p.dtype,"int32"),g=Zf(n,h,f),A=w.sizeFromShape(m),y=n.data.get(g.dataId).values,x=n.data.get(p.dataId).values;for(let b=0;b=0&&(p=Id({inputs:{x:p},backend:n,attrs:{axis:u[f]-(o.length-h),keepDims:!1}}),m.push(p)),h--)}for(let f of m)f!==p&&n.disposeIntermediateTensorInfo(f);return p}var gH={kernelName:Up,backendName:"cpu",kernelFunc:mH};function AH(e){let{inputs:t,backend:n}=e,{dy:s,y:r}=t;Se([s,r],"eluGrad");let a=new Float32Array(w.sizeFromShape(r.shape)),o=n.data.get(r.dataId).values,i=n.data.get(s.dataId).values;for(let l=0;l=1?a[l]=i[l]:a[l]=i[l]*(u+1)}return n.makeTensorInfo(r.shape,"float32",a)}var yH={kernelName:Hp,backendName:"cpu",kernelFunc:AH},xH=_.ERF_P,bH=_.ERF_A1,vH=_.ERF_A2,wH=_.ERF_A3,kH=_.ERF_A4,IH=_.ERF_A5,SH=pt(al,e=>{let t=Math.sign(e),n=Math.abs(e),s=1/(1+xH*n);return t*(1-((((IH*s+kH)*s+wH)*s+vH)*s+bH)*s*Math.exp(-n*n))}),CH={kernelName:al,backendName:"cpu",kernelFunc:SH};function Qf(e){let{inputs:t,backend:n,attrs:s}=e,{input:r}=t,{dim:a}=s,o=r.shape.length,i=r.shape.slice(),l=a;return a<0&&(w.assert(-(o+1)<=a,()=>`Axis must be in the interval [${-(o+1)}, ${o}]`),l=o+a+1),i.splice(l,0,1),wt({inputs:{x:r},backend:n,attrs:{shape:i}})}var TH={kernelName:il,backendName:"cpu",kernelFunc:Qf},NH=Gt((e,t)=>e/t),g2=pn(no,NH),A2={kernelName:no,backendName:"cpu",kernelFunc:g2};function h6(e,t,n){let s=e.shape,r=s[0],a=s[1],o=n.data.get(e.dataId),i=o.complexTensorInfos.real,l=o.complexTensorInfos.imag,u=[r,a],c=w.sizeFromShape(u),d=w.getTypedArrayFromDType("float32",c),p=w.getTypedArrayFromDType("float32",c);for(let g=0;g{let{image:s}=e,r=n,a=w.getTypedArrayFromDType(s.dtype,w.sizeFromShape(s.shape)),[o,i,l,u]=s.shape,c=r.data.get(s.dataId).values;for(let p=0;p=0&&xMath.floor(e/t)),zH=pn(oo,MH,null,"int32"),LH={kernelName:oo,backendName:"cpu",kernelFunc:zH};function BH(e){let{inputs:t,backend:n,attrs:s}=e,{x:r,filter:a,bias:o,preluActivationWeights:i}=t,{strides:l,pad:u,dataFormat:c,dilations:d,dimRoundingMode:p,activation:h,leakyreluAlpha:m}=s,f=d6({inputs:{x:r,filter:a},backend:n,attrs:{strides:l,pad:u,dataFormat:c,dilations:d,dimRoundingMode:p}});if(o){let g=f;f=wd({inputs:{a:f,b:o},backend:n}),n.disposeIntermediateTensorInfo(g)}if(h){let g=f;f=f2(n,f,h,i,m),n.disposeIntermediateTensorInfo(g)}return f}var WH={kernelName:Vo,backendName:"cpu",kernelFunc:BH};function VH(e){let{inputs:t,backend:n,attrs:s}=e,{x:r,filter:a,bias:o,preluActivationWeights:i}=t,{strides:l,pad:u,dataFormat:c,dilations:d,dimRoundingMode:p,activation:h,leakyreluAlpha:m}=s,f=p6({inputs:{x:r,filter:a},backend:n,attrs:{strides:l,pad:u,dataFormat:c,dilations:d,dimRoundingMode:p}});if(o){let g=f;f=wd({inputs:{a:f,b:o},backend:n}),n.disposeIntermediateTensorInfo(g)}if(h){let g=f;f=f2(n,f,h,i,m),n.disposeIntermediateTensorInfo(g)}return f}var UH={kernelName:Uo,backendName:"cpu",kernelFunc:VH};function HH(e){let{inputs:t,backend:n}=e,{params:s,indices:r}=t,a=w.sizeFromShape(s.shape),o=r.shape,i=o[o.length-1],[l,u,c,d]=_.prepareAndValidate(s,r);if(u===0)return n.makeTensorInfo(l,s.dtype,[]);let p=n.data.get(r.dataId).values,h=n.bufferSync(s),m=E7(p,h,s.dtype,u,i,c,d,s.shape,a);return n.makeTensorInfo(l,s.dtype,m.values)}var GH={kernelName:dl,backendName:"cpu",kernelFunc:HH};function jH(e){let{inputs:t,backend:n,attrs:s}=e,{x:r,indices:a}=t,{axis:o,batchDims:i}=s;Se([r,a],"gatherV2");let l=i;i==null&&(l=0);let u=w.sizeFromShape(a.shape),c=w.parseAxisParam(o,r.shape)[0],d=_.segment_util.collectGatherOpShapeInfo(r,a,c,l),p=wt({inputs:{x:r},backend:n,attrs:{shape:[d.batchSize,d.outerSize,d.dimSize,d.sliceSize]}}),h=wt({inputs:{x:a},backend:n,attrs:{shape:[d.batchSize,u/d.batchSize]}}),m=[d.batchSize,d.outerSize,u/d.batchSize,d.sliceSize],f=n.bufferSync(h),g=n.bufferSync(p),A=R7(g,f,m);return n.disposeIntermediateTensorInfo(p),n.disposeIntermediateTensorInfo(h),n.makeTensorInfo(d.outputShape,A.dtype,A.values)}var qH={kernelName:cl,backendName:"cpu",kernelFunc:jH};function XH(e){let{inputs:t,backend:n}=e,{input:s}=t,r=w.sizeFromShape(s.shape),a=s.shape[s.shape.length-1],o=r/a,i=wt({inputs:{x:s},backend:n,attrs:{shape:[o,a]}}),l=h6(i,!0,n),u=wt({inputs:{x:l},backend:n,attrs:{shape:s.shape}});return n.disposeIntermediateTensorInfo(i),n.disposeIntermediateTensorInfo(l),u}var KH={kernelName:jp,backendName:"cpu",kernelFunc:XH},ZH=pt(hl,e=>Number.isFinite(e)?1:0,"bool"),YH={kernelName:hl,backendName:"cpu",kernelFunc:ZH},JH=pt(fl,e=>Math.abs(e)===1/0?1:0,"bool"),QH={kernelName:fl,backendName:"cpu",kernelFunc:JH},eG=pt(ml,e=>Number.isNaN(e)?1:0,"bool"),tG={kernelName:ml,backendName:"cpu",kernelFunc:eG};function nG(e){let{backend:t,attrs:n}=e,{start:s,stop:r,num:a}=n,o=O7(s,r,a);return t.makeTensorInfo([o.length],"float32",o)}var sG={kernelName:Xp,backendName:"cpu",kernelFunc:nG},rG=pt(yl,e=>Math.log1p(e)),aG={kernelName:yl,backendName:"cpu",kernelFunc:rG},oG=Gt((e,t)=>e&&t),iG=pn(xl,oG,null,"bool"),lG={kernelName:xl,backendName:"cpu",kernelFunc:iG},uG=pt(Ac,e=>e?0:1,"bool"),cG={kernelName:Ac,backendName:"cpu",kernelFunc:uG},dG=Gt((e,t)=>e||t),pG=pn(yc,dG,null,"bool"),hG={kernelName:yc,backendName:"cpu",kernelFunc:pG};function fG(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{depthRadius:a,bias:o,alpha:i,beta:l}=s;Se(r,"LRN");let u=r.shape[3],c=u-1,d=n.data.get(r.dataId).values,p=w.sizeFromShape(r.shape),h=new Float32Array(p);function m(f){let g=f%u,A=f-g+Math.max(0,g-a),y=f-g+Math.min(g+a,c),x=0;for(;A<=y;A++){let b=d[A];x+=b*b}return x}for(let f=0;f`Error in maxPool: Either strides or dilations must be 1. Got strides ${o} and dilations '${u}'`);let c=_.computePool2DInfo(r.shape,a,o,u,i,l),d;if(c.filterWidth===1&&c.filterHeight===1&&w.arraysEqual(c.inShape,c.outShape))d=br({inputs:{x:r},backend:n});else{let p=n.data.get(r.dataId).values,h=w.computeStrides(r.shape),m=m2(p,r.shape,r.dtype,h,c,"max");d=n.makeTensorInfo(c.outShape,r.dtype,m.values)}return d}var bG={kernelName:mo,backendName:"cpu",kernelFunc:xG};function vG(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{filterSize:a,strides:o,pad:i,dimRoundingMode:l,dataFormat:u}=s;Se(r,"maxPool3d");let c=_.computePool3DInfo(r.shape,a,o,1,i,l,u),d=n.data.get(r.dataId).values,p=c6(d,r.shape,r.dtype,w.computeStrides(r.shape),c,"max");return n.makeTensorInfo(p.shape,"float32",p.values)}var wG={kernelName:bc,backendName:"cpu",kernelFunc:vG};function kG(e){let{inputs:t,backend:n,attrs:s}=e,{dy:r,input:a}=t,{filterSize:o,strides:i,pad:l,dimRoundingMode:u}=s;Se([r,a],"maxPool3DGrad");let c=_.computePool3DInfo(a.shape,o,i,1,l,u),d=n.bufferSync(a),p=pU(d,c),h=c.strideDepth,m=c.strideHeight,f=c.strideWidth,g=c.dilationDepth,A=c.dilationHeight,y=c.dilationWidth,x=c.effectiveFilterDepth,b=c.effectiveFilterHeight,v=c.effectiveFilterWidth,k=x-1-c.padInfo.front,S=v-1-c.padInfo.left,C=b-1-c.padInfo.top,D=Ge(a.shape,"float32"),O=n.bufferSync(r);for(let E=0;E=c.outDepth||Math.floor(se)!==se))for(let ae=0;ae=c.outHeight||Math.floor(Q)!==Q))for(let ce=0;ce=c.outWidth||Math.floor(de)!==de)continue;let fe=x*b*v-1-p.get(E,se,Q,de,R),xe=ne*b*v+ae*v+ce,Ee=fe===xe?1:0;if(Ee===0)continue;te+=O.get(E,se,Q,de,R)*Ee}}}D.set(te,E,T,P,U,R)}return n.makeTensorInfo(D.shape,D.dtype,D.values)}var IG={kernelName:Yp,backendName:"cpu",kernelFunc:kG};function SG(e){let{inputs:t,backend:n,attrs:s}=e,{dy:r,input:a,output:o}=t,i=a;Se([a,o],"maxPoolGrad");let{filterSize:l,strides:u,pad:c,dimRoundingMode:d}=s,p=_.computePool2DInfo(i.shape,l,u,1,c,d),h=n.data.get(i.dataId).values,m=Ge(p.outShape,i.dtype,u6(h,i.shape,i.dtype,p).values),f=p.strideHeight,g=p.strideWidth,A=p.dilationHeight,y=p.dilationWidth,x=p.effectiveFilterHeight,b=p.effectiveFilterWidth,v=b-1-p.padInfo.left,k=x-1-p.padInfo.top,S=Ge(i.shape,"float32"),C=n.data.get(r.dataId).values,D=Ge(r.shape,"float32",C);for(let O=0;O=p.outHeight||Math.floor(X)!==X))for(let te=0;te=p.outWidth||Math.floor(ne)!==ne)continue;let se=x*b-1-m.get(O,X,ne,E),ae=q*b+te,Q=se===ae?1:0;if(Q===0)continue;j+=D.get(O,X,ne,E)*Q}}S.set(j,O,R,T,E)}return n.makeTensorInfo(S.shape,S.dtype,S.values)}var CG={kernelName:Zp,backendName:"cpu",kernelFunc:SG};function TG(e,t,n,s,r){let a=w.computeStrides(t),o=m2(e,t,n,a,r,"max"),i=u6(e,t,n,r,!0,s);return[o.values,i.values]}var NG={kernelName:Jp,backendName:"cpu",kernelFunc:({inputs:e,attrs:t,backend:n})=>{let{x:s}=e,{filterSize:r,strides:a,pad:o,includeBatchInIndex:i}=t,l=n;Se(s,"MaxPoolWithArgmax");let u=l.data.get(s.dataId).values,c=_.computePool2DInfo(s.shape,r,a,[1,1],o),[d,p]=TG(u,s.shape,s.dtype,i,c),h=l.write(d,c.outShape,s.dtype),m=l.write(p,c.outShape,s.dtype);return[{dataId:h,shape:c.outShape,dtype:s.dtype},{dataId:m,shape:c.outShape,dtype:"int32"}]}};function EG(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{axis:a,keepDims:o}=s,i=w.parseAxisParam(a,r.shape),u=_.computeOutAndReduceShapes(r.shape,i)[1],c=w.sizeFromShape(u),d=[],p=n.makeTensorInfo([],"float32",new Float32Array([c]));d.push(p);let h=Ca({inputs:{x:r},backend:n,attrs:{dtype:"float32"}});d.push(h);let m=g2({inputs:{a:h,b:p},backend:n});d.push(m);let f=Id({inputs:{x:m},backend:n,attrs:{axis:a,keepDims:o}});return d.forEach(g=>n.disposeIntermediateTensorInfo(g)),f}var RG={kernelName:go,backendName:"cpu",kernelFunc:EG};function DG(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{axis:a,keepDims:o}=s;Se(r,"min");let i=w.parseAxisParam(a,r.shape),l=i,u=_.getAxesPermutation(l,r.shape.length),c=r;u!=null&&(c=vs({inputs:{x:r},backend:n,attrs:{perm:u}}),l=_.getInnerMostAxes(l.length,r.shape.length)),_.assertAxesAreInnerMostDims("min",l,c.shape.length);let[d,p]=_.computeOutAndReduceShapes(c.shape,l),h=w.sizeFromShape(p),m=w.makeZerosTypedArray(w.sizeFromShape(d),c.dtype),f=n.data.get(c.dataId).values;for(let A=0;Ax[0]+r.shape[b]+x[1]),l=a.map(x=>x[0]),u=a.map((x,b)=>x[0]+r.shape[b]),c=o==="reflect"?0:1,d=n.data.get(r.dataId).values,p=r.shape.length,h=w.computeStrides(r.shape),m=w.sizeFromShape(i),f=i.length,g=w.computeStrides(i),A=w.getTypedArrayFromDType(r.dtype,m);for(let x=0;x=u[k]&&(b[k]=(u[k]-1)*2-b[k]+c);b=b.map((k,S)=>k-l[S]);let v=w.locToIndex(b,p,h);A[x]=d[v]}return{dataId:n.write(A,i,r.dtype),shape:i,dtype:r.dtype}}var FG={kernelName:xo,backendName:"cpu",kernelFunc:$G},OG=Gt((e,t)=>{let n=e%t;return e<0&&t<0||e>=0&&t>=0?n:(n+t)%t}),PG=pn(bl,OG),MG={kernelName:bl,backendName:"cpu",kernelFunc:PG},zG=Wa(p5());function m6(e){let{inputs:t,backend:n,attrs:s}=e,{logits:r}=t,{dim:a}=s,o=r.shape.length,i=a;if(i===-1&&(i=o-1),i!==o-1)throw Error(`Softmax along a non-last dimension is not yet supported. 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ke(t,()=>t.attachShader(s,this.vertexShader)),ke(t,()=>t.attachShader(s,n)),N6(t,s),this.debug&&s0(t,s),this.vertexAttrsAreBound||(this.setProgram(s),this.vertexAttrsAreBound=i4(t,this.program,this.vertexBuffer)),s}deleteProgram(e){this.throwIfDisposed(),e===this.program&&(this.program=null),e!=null&&ke(this.gl,()=>this.gl.deleteProgram(e))}setProgram(e){this.throwIfDisposed(),this.program=e,this.program!=null&&this.debug&&s0(this.gl,this.program),ke(this.gl,()=>this.gl.useProgram(e))}getUniformLocation(e,t,n=!0){return this.throwIfDisposed(),n?O6(this.gl,e,t):P6(this.gl,e,t)}getAttributeLocation(e,t){return this.throwIfDisposed(),ke(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(),M6(this.gl,e,t,n)}setOutputMatrixTexture(e,t,n){this.setOutputMatrixTextureDriver(e,n,t)}setOutputPackedMatrixTexture(e,t,n){this.throwIfDisposed();let[s,r]=Su(t,n);this.setOutputMatrixTextureDriver(e,s,r)}setOutputMatrixWriteRegion(e,t,n,s){this.setOutputMatrixWriteRegionDriver(n,e,s,t)}setOutputPackedMatrixWriteRegion(e,t,n,s){throw new Error("setOutputPackedMatrixWriteRegion not implemented.")}debugValidate(){this.program!=null&&s0(this.gl,this.program),Ed(this.gl)}executeProgram(){this.throwIfDisposed(),this.throwIfNoProgram();let e=this.gl;this.debug&&this.debugValidate(),ke(e,()=>e.drawElements(e.TRIANGLES,6,e.UNSIGNED_SHORT,0))}blockUntilAllProgramsCompleted(){this.throwIfDisposed(),ke(this.gl,()=>this.gl.finish())}getQueryTimerExtension(){return this.disjointQueryTimerExtension==null&&(this.disjointQueryTimerExtension=Nd(this.gl,Y().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(Y().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION")===2){let n=this.gl,s=this.getQueryTimerExtensionWebGL2(),r=n.createQuery();return n.beginQuery(s.TIME_ELAPSED_EXT,r),r}let e=this.getQueryTimerExtensionWebGL1(),t=e.createQueryEXT();return e.beginQueryEXT(e.TIME_ELAPSED_EXT,t),t}endQuery(){if(Y().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION")===2){let t=this.gl,n=this.getQueryTimerExtensionWebGL2();t.endQuery(n.TIME_ELAPSED_EXT);return}let e=this.getQueryTimerExtensionWebGL1();e.endQueryEXT(e.TIME_ELAPSED_EXT)}async waitForQueryAndGetTime(e){return await w.repeatedTry(()=>this.disposed||this.isQueryAvailable(e,Y().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION"))),this.getQueryTime(e,Y().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,s=this.getQueryTimerExtensionWebGL2(),r=n.getQueryParameter(e,n.QUERY_RESULT_AVAILABLE);return this.disjoint==null&&(this.disjoint=this.gl.getParameter(s.GPU_DISJOINT_EXT)),r&&!this.disjoint}else{let n=this.getQueryTimerExtensionWebGL1(),s=n.getQueryObjectEXT(e,n.QUERY_RESULT_AVAILABLE_EXT);return this.disjoint==null&&(this.disjoint=this.gl.getParameter(n.GPU_DISJOINT_EXT)),s&&!this.disjoint}}pollFence(e){return new Promise(t=>{this.addItemToPoll(()=>e.isFencePassed(),()=>t())})}pollItems(){let e=VX(this.itemsToPoll.map(t=>t.isDoneFn));for(let t=0;t<=e;++t){let{resolveFn:n}=this.itemsToPoll[t];n()}this.itemsToPoll=this.itemsToPoll.slice(e+1)}addItemToPoll(e,t){this.itemsToPoll.push({isDoneFn:e,resolveFn:t}),!(this.itemsToPoll.length>1)&&w.repeatedTry(()=>(this.pollItems(),this.itemsToPoll.length===0))}bindTextureToFrameBuffer(e){this.throwIfDisposed(),r0(this.gl,e,this.framebuffer),this.debug&&Ed(this.gl)}unbindTextureToFrameBuffer(){this.outputTexture!=null?(r0(this.gl,this.outputTexture,this.framebuffer),this.debug&&Ed(this.gl)):k2(this.gl,this.framebuffer)}downloadMatrixDriver(e,t){this.bindTextureToFrameBuffer(e);let n=t();return this.unbindTextureToFrameBuffer(),n}setOutputMatrixTextureDriver(e,t,n){this.throwIfDisposed();let s=this.gl;r0(s,e,this.framebuffer),this.debug&&Ed(s),this.outputTexture=e,ke(s,()=>s.viewport(0,0,t,n)),ke(s,()=>s.scissor(0,0,t,n))}setOutputMatrixWriteRegionDriver(e,t,n,s){this.throwIfDisposed(),ke(this.gl,()=>this.gl.scissor(e,t,n,s))}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 VX(e){let t=0;for(;t`${e}.${n}`)}function $n(e,t){return t===1?[e]:y4(e,t)}function TK(e,t){if(e===1)return"rc";let n="";for(let s=0;s ${t[0]}`;let s="";for(let r=e-2;r= ${t[r]}`,r= ${t}; bool rEdge = rp1 >= ${n}; `}function _K(e,t){let n=e.length,s=EK(n,t);return n===1?`getA(rc), rc + 1 >= ${e[0]} ? 0. : getA(rc + 1), 0, 0`:`getA(${s[0]}), cEdge ? 0. : getA(${s[1]}), rEdge ? 0. : getA(${s[2]}), rEdge || cEdge ? 0. : getA(${s[3]})`}var x4=class{constructor(e,t){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,this.customUniforms=[{name:"inputShape",type:"ivec3"}],this.outputShape=e,this.enableShapeUniforms=Is(this.outputShape.length);let n="";for(let s=0;s<4;s++){let r="thisRC = rc;";s%2==1&&(r+="thisRC.z += 1;"),s>1&&(r+="thisRC.y += 1;"),n+=` ${r} ${s>0?"if(thisRC.y < rows && thisRC.z < cols){":""} int flatIndex = getFlatIndex(thisRC); ivec3 inputRC = inputCoordsFromReshapedOutCoords(flatIndex); vec2 inputRCInnerDims = vec2(float(inputRC.y),float(inputRC.z)); result[${s}] = getChannel(getA(inputRC.x, inputRC.y, inputRC.z), inputRCInnerDims); ${s>0?"}":""} `}this.userCode=` ${$K(t,this.enableShapeUniforms)} ${this.enableShapeUniforms?T2():C2(e)} void main() { ivec3 rc = getOutputCoords(); vec4 result = vec4(0.); ivec3 thisRC; int rows = ${this.enableShapeUniforms?"outShape[1]":e[1]}; int cols = ${this.enableShapeUniforms?"outShape[2]":e[2]}; ${n} setOutput(result); } `}};function $K(e,t){return` 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s===xn.PACKED_2X2_FLOAT32?o=this.gpgpu.createPackedMatrixTexture(e[0],e[1]):s===xn.PACKED_2X2_FLOAT16?o=this.gpgpu.createFloat16PackedMatrixTexture(e[0],e[1]):s===xn.UNPACKED_FLOAT32?o=this.gpgpu.createFloat32MatrixTexture(e[0],e[1]):s===xn.UNPACKED_FLOAT16?o=this.gpgpu.createFloat16MatrixTexture(e[0],e[1]):s===xn.PACKED_4X1_UNSIGNED_BYTE&&(o=this.gpgpu.createUnsignedBytesMatrixTexture(e[0],e[1])),this.usedTextures[r].push(o),this.numUsedTextures++,this._numBytesAllocated+=a,this.log(),o}releaseTexture(e,t,n,s){if(this.freeTextures==null)return;let r=v4(n,s),a=w4(t,r,s);a in this.freeTextures||(this.freeTextures[a]=[]);let o=b4(t,r,this.gpgpu.gl,this.gpgpu.textureConfig,s),i=Y().get("WEBGL_DELETE_TEXTURE_THRESHOLD");i!==-1&&this._numBytesAllocated>i?(this.gpgpu.deleteMatrixTexture(e),this._numBytesAllocated-=o):(this.freeTextures[a].push(e),this.numFreeTextures++,this._numBytesFree+=o),this.numUsedTextures--;let l=this.usedTextures[a],u=l.indexOf(e);if(u<0)throw new Error("Cannot release a texture that was never provided by this texture manager");l.splice(u,1),this.log()}log(){if(!this.logEnabled)return;let e=this.numFreeTextures+this.numUsedTextures;console.log("Free/Used",`${this.numFreeTextures} / ${this.numUsedTextures}`,`(${e})`);let t=this._numBytesFree/this._numBytesAllocated;console.log(`Bytes allocated: ${this._numBytesAllocated}`),console.log(`Bytes unused: ${this._numBytesFree} (${Math.round(100*t)}%)`)}get numBytesAllocated(){return this._numBytesAllocated}get numBytesFree(){return this._numBytesFree}getNumUsedTextures(){return this.numUsedTextures}getNumFreeTextures(){return this.numFreeTextures}dispose(){if(this.freeTextures!=null){for(let e in this.freeTextures)this.freeTextures[e].forEach(t=>{this.gpgpu.deleteMatrixTexture(t)});for(let e in this.usedTextures)this.usedTextures[e].forEach(t=>{this.gpgpu.deleteMatrixTexture(t)});this.freeTextures=null,this.usedTextures=null,this.numUsedTextures=0,this.numFreeTextures=0,this._numBytesAllocated=0,this._numBytesFree=0}}};function OK(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;throw new Error(`Unknown internal format ${t}`)}function b4(e,t,n,s,r){let a=PK(t,s),o;if(r){let[l,u]=Su(e[0],e[1]);o=l*u}else{let[l,u]=Td(e[0],e[1]);o=l*u}let i=OK(n,a);return o*i}function PK(e,t){switch(e){case xn.PACKED_2X2_FLOAT32:return _2(t);case xn.PACKED_2X2_FLOAT16:return $2(t);case xn.UNPACKED_FLOAT32:return E2(t);case xn.UNPACKED_FLOAT16:return R2(t);case xn.PACKED_4X1_UNSIGNED_BYTE:return D2(t);default:throw new Error(`Unknown physical texture type ${e}`)}}function MK(e){return Y().getBool("WEBGL_RENDER_FLOAT32_ENABLED")?e?xn.PACKED_2X2_FLOAT32:xn.UNPACKED_FLOAT32:e?xn.PACKED_2X2_FLOAT16:xn.UNPACKED_FLOAT16}function v4(e,t){if(e===ws.UPLOAD)return xn.PACKED_2X2_FLOAT32;if(e===ws.RENDER||e==null)return MK(t);if(e===ws.DOWNLOAD||e===ws.PIXELS)return xn.PACKED_4X1_UNSIGNED_BYTE;throw new Error(`Unknown logical texture type ${e}`)}function w4(e,t,n){return`${e[0]}_${e[1]}_${t}_${n}`}var Na=class{constructor(e,t){this.variableNames=["A"],this.outputShape=e,this.enableShapeUniforms=Is(this.outputShape.length),this.userCode=` float unaryOperation(float x) { ${t} } void main() { float x = getAAtOutCoords(); float y = unaryOperation(x); setOutput(y); } `}},nr="if (isnan(x)) return x;",zK="return x;",k4="return abs(x);",LK="return (x >= 0.0) ? x : (exp(x) - 1.0);",BK=nr+` return (x < 0.0) ? 0.0 : x; `,WK=nr+` return (x < 0.0) ? 0.0 : min(6.0, x); `,d0="return x;",VK="return 1.0 / (1.0 + exp(-1.0 * x));",UK="return x;",HK=` vec4 result; result.r = (x.r >= 0.0) ? x.r : (exp(x.r) - 1.0); result.g = (x.g >= 0.0) ? x.g : (exp(x.g) - 1.0); result.b = (x.b >= 0.0) ? x.b : (exp(x.b) - 1.0); result.a = (x.a >= 0.0) ? x.a : (exp(x.a) - 1.0); return result; `,GK=` vec4 result = x * vec4(greaterThanEqual(x, vec4(0.0))); bvec4 isNaN = isnan(x); result.r = isNaN.r ? x.r : result.r; result.g = isNaN.g ? x.g : result.g; result.b = isNaN.b ? x.b : result.b; result.a = isNaN.a ? x.a : result.a; return result; `,jK=` vec4 result = min(x, vec4(6.)) * vec4(greaterThanEqual(x, vec4(0.0))); bvec4 isNaN = isnan(x); result.r = isNaN.r ? x.r : result.r; result.g = isNaN.g ? x.g : result.g; result.b = isNaN.b ? x.b : result.b; result.a = isNaN.a ? x.a : result.a; return result; `,qK="return 1.0 / (1.0 + exp(-1.0 * x));",Du=class{constructor(e,t){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=e,this.enableShapeUniforms=Is(this.outputShape.length),this.userCode=` vec4 unaryOperation(vec4 x) { ${t} } void main() { vec4 x = getAAtOutCoords(); vec4 y = unaryOperation(x); setOutput(y); } `}},XK=class{constructor(e){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!1,this.outputShape=e;let t=e.length,n=$n("rc",t),s=yt(t),r=TK(t,n),a=n.slice(-2),o=t<=1?"rc":`vec2(${a.join(",")})`;this.userCode=` void main() { ${s} rc = getOutputCoords(); vec4 packedInput = getA(${r}); setOutput(getChannel(packedInput, ${o})); } `}},KK=fr.whereImpl,ZK=1e-7,YK=1e-4,p0={};function JK(e){return e in p0||(p0[e]={}),p0[e]}var QK=Y().getNumber("CPU_HANDOFF_SIZE_THRESHOLD"),eZ=600;function tZ(){return Y().global.screen==null?1024:Y().global.screen.height*Y().global.screen.width*window.devicePixelRatio*eZ/1024/1024}var _u=class extends lc{constructor(e){super();if(this.pendingRead=new WeakMap,this.pendingDisposal=new WeakSet,this.dataRefCount=new WeakMap,this.numBytesInGPU=0,this.uploadWaitMs=0,this.downloadWaitMs=0,this.lastGlFlushTime=0,this.warnedAboutMemory=!1,this.pendingDeletes=0,this.disposed=!1,!Y().getBool("HAS_WEBGL"))throw new Error("WebGL is not supported on this device");if(e==null){let t=vr(Y().getNumber("WEBGL_VERSION"));this.binaryCache=JK(Y().getNumber("WEBGL_VERSION")),this.gpgpu=new c0(t),this.canvas=t.canvas,this.gpgpuCreatedLocally=!0}else this.gpgpu=e,this.binaryCache={},this.gpgpuCreatedLocally=!1,this.canvas=e.gl.canvas;this.textureManager=new FK(this.gpgpu),this.numMBBeforeWarning=tZ(),this.texData=new Sp(this,Qn())}nextDataId(){return _u.nextDataId++}numDataIds(){return this.texData.numDataIds()-this.pendingDeletes}write(e,t,n){if((Y().getBool("WEBGL_CHECK_NUMERICAL_PROBLEMS")||Y().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 s={id:this.nextDataId()};return this.texData.set(s,{shape:t,dtype:n,values:e,usage:ws.UPLOAD,refCount:1}),s}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,s,r){if(Y().getBool("DEBUG")&&this.checkNumericalProblems(t),s==="complex64")throw new Error("Cannot write to a complex64 dtype. Please use tf.complex(real, imag).");this.texData.set(e,{shape:n,dtype:s,values:t,usage:ws.UPLOAD,refCount:r})}disposeIntermediateTensorInfo(e){this.disposeData(e.dataId)}readSync(e){let t=this.texData.get(e),{values:n,dtype:s,complexTensorInfos:r,slice:a,shape:o,isPacked:i}=t;if(a!=null){let d;i?d=new Du(o,d0):d=new Na(o,d0);let p=this.runWebGLProgram(d,[{dataId:e,shape:o,dtype:s}],s),h=this.readSync(p.dataId);return this.disposeIntermediateTensorInfo(p),h}if(n!=null)return this.convertAndCacheOnCPU(e);if(s==="string")return n;let l=this.activeTimers!=null,u;l&&(u=w.now());let c;if(s==="complex64"){let d=this.readSync(r.real.dataId),p=this.readSync(r.imag.dataId);c=_.mergeRealAndImagArrays(d,p)}else c=this.getValuesFromTexture(e);return l&&(this.downloadWaitMs+=w.now()-u),this.convertAndCacheOnCPU(e,c)}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:s,slice:r,dtype:a,complexTensorInfos:o,isPacked:i}=t;if(r!=null){let h;i?h=new Du(s,d0):h=new Na(s,d0);let m=this.runWebGLProgram(h,[{dataId:e,shape:s,dtype:a}],a),f=this.read(m.dataId);return this.disposeIntermediateTensorInfo(m),f}if(n!=null)return this.convertAndCacheOnCPU(e);if(!Y().getBool("WEBGL_DOWNLOAD_FLOAT_ENABLED")&&Y().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(a!=="complex64"&&Y().get("WEBGL_BUFFER_SUPPORTED")){u=this.decode(e);let h=this.texData.get(u.dataId);l=this.gpgpu.createBufferFromTexture(h.texture,...n0(s))}this.pendingRead.set(e,[]),a!=="complex64"&&await this.gpgpu.createAndWaitForFence();let c;if(a==="complex64"){let h=await Promise.all([this.read(o.real.dataId),this.read(o.imag.dataId)]),m=h[0],f=h[1];c=_.mergeRealAndImagArrays(m,f)}else if(l==null)c=this.getValuesFromTexture(e);else{let h=w.sizeFromShape(s);c=this.gpgpu.downloadFloat32MatrixFromBuffer(l,h)}if(u!=null&&this.disposeIntermediateTensorInfo(u),l!=null){let h=this.gpgpu.gl;ke(h,()=>h.deleteBuffer(l))}let d=this.convertAndCacheOnCPU(e,c),p=this.pendingRead.get(e);return this.pendingRead.delete(e),p.forEach(h=>h(d)),this.pendingDisposal.has(e)&&(this.pendingDisposal.delete(e),this.disposeData(e)&&Qn().removeDataId(e,this),this.pendingDeletes--),d}bufferSync(e){let t=this.readSync(e.dataId),n=t;if(e.dtype==="string")try{n=t.map(s=>w.decodeString(s))}catch(s){throw new Error("Failed to decode encoded string bytes into utf-8")}return Ge(e.shape,e.dtype,n)}checkNumericalProblems(e){if(e!=null)for(let t=0;t0}async time(e){let t=this.activeTimers,n=[],s=!1;this.programTimersStack==null?(this.programTimersStack=n,s=!0):this.activeTimers.push(n),this.activeTimers=n,e();let r=w.flatten(this.activeTimers.map(i=>i.query)).filter(i=>i!=null),a=w.flatten(this.activeTimers.map(i=>i.name)).filter(i=>i!=null);this.activeTimers=t,s&&(this.programTimersStack=null);let o={uploadWaitMs:this.uploadWaitMs,downloadWaitMs:this.downloadWaitMs,kernelMs:null,wallMs:null};if(Y().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE")>0){let i=await Promise.all(r);o.kernelMs=w.sum(i),o.getExtraProfileInfo=()=>i.map((l,u)=>({name:a[u],ms:l})).map(l=>`${l.name}: ${l.ms}`).join(", ")}else o.kernelMs={error:"WebGL query timers are not supported in this environment."};return this.uploadWaitMs=0,this.downloadWaitMs=0,o}memory(){return{unreliable:!1,numBytesInGPU:this.numBytesInGPU,numBytesInGPUAllocated:this.textureManager.numBytesAllocated,numBytesInGPUFree:this.textureManager.numBytesFree}}startTimer(){return Y().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE")>0?this.gpgpu.beginQuery():{startMs:w.now(),endMs:null}}endTimer(e){return Y().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE")>0?(this.gpgpu.endQuery(),e):(e.endMs=w.now(),e)}async getQueryTime(e){if(Y().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:s,usage:r,isPacked:a,slice:o}=this.texData.get(e),i=o&&o.origDataId||e,l=this.dataRefCount.get(i);l>1?this.dataRefCount.set(i,l-1):(this.dataRefCount.delete(i),t!=null&&(this.numBytesInGPU-=this.computeBytes(s,n),this.textureManager.releaseTexture(t,s,r,a)));let u=this.texData.get(e);u.texture=null,u.texShape=null,u.isPacked=!1,u.slice=null}getTexture(e){return this.uploadToGPU(e),this.texData.get(e).texture}getDataInfo(e){return this.texData.get(e)}shouldExecuteOnCPU(e,t=QK){return Y().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(a=>w.encodeString(a));s=this.write(r,e,t)}else s=this.write(n,e,t);return this.texData.get(s).usage=null,{dataId:s,shape:e,dtype:t}}makeOutput(e,t,n){let{dataId:s}=this.makeTensorInfo(e,t,n);return Qn().makeTensorFromDataId(s,e,t,this)}unpackTensor(e){let t=new XK(e.shape);return this.runWebGLProgram(t,[e],e.dtype)}packTensor(e){let t=new NK(e.shape),n=!0;return this.runWebGLProgram(t,[e],e.dtype,null,n)}packedReshape(e,t){let n=[yi(e.shape),...xi(e.shape)],s={dtype:e.dtype,shape:n,dataId:e.dataId},r=[yi(t),...xi(t)],a=new x4(r,n),o=!0,i=[n],l=this.runWebGLProgram(a,[s],e.dtype,i,o);return{dataId:l.dataId,shape:t,dtype:l.dtype}}decode(e){let t=this.texData.get(e),{isPacked:n,shape:s,dtype:r}=t,a=a0(s),o,i=n0(a);n?o=new MX(a):o=new PX(a);let l=!0,u=[i],c=this.runWebGLProgram(o,[{shape:a,dtype:r,dataId:e}],r,u,l);return{dtype:r,shape:s,dataId:c.dataId}}runWebGLProgram(e,t,n,s,r=!1){let a=this.makeTensorInfo(e.outputShape,n),o=this.texData.get(a.dataId);if(e.packedOutput&&(o.isPacked=!0),e.outPackingScheme===Cd.DENSE){let f=n0(e.outputShape);o.texShape=f.map(g=>g*2)}if(e.outTexUsage!=null&&(o.usage=e.outTexUsage),w.sizeFromShape(a.shape)===0)return o.values=w.getTypedArrayFromDType(a.dtype,0),a;let i=[],l=t.map(f=>{if(f.dtype==="complex64")throw new Error("GPGPUProgram does not support complex64 input. 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NAN : result.r; result.g = isNaN.g > 0. ? NAN : result.g; result.b = isNaN.b > 0. ? NAN : result.b; result.a = isNaN.a > 0. ? 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const ivec2 pads = ivec2(${p}, ${h}); void main() { ivec4 coords = getOutputCoords(); int batch = coords[0]; int d = coords[3]; ivec2 xRCCorner = coords.yz * strides - pads; int xRCorner = xRCCorner.x; int xCCorner = xRCCorner.y; // max/min x(?, ?, d) to get y(yR, yC, d). // ? = to be determined float minMaxValue = 0.0; float minMaxValueFound = 0.0; int minMaxPosition = 0; float avgValue = 0.0; for (int wR = 0; wR < ${c}; wR += ${l}) { int xR = xRCorner + wR; if (xR < 0 || xR >= ${e.inHeight}) { continue; } for (int wC = 0; wC < ${d}; wC += ${u}) { int xC = xCCorner + wC; if (xC < 0 || xC >= ${e.inWidth}) { continue; } float value = getX(batch, xR, xC, d); // If a min / max value has already been found, use it. If not, // use the current value. float currMinMaxValue = mix( value, minMaxValue, minMaxValueFound); if (value ${S} currMinMaxValue) { minMaxValue = value; minMaxValueFound = 1.0; minMaxPosition = ${s?r?f:g:`wR * ${d} + wC`}; } } } setOutput(float(minMaxPosition)); } `;return}let y="max",x=`${t}(${t}(${t}(minMaxValue[0], minMaxValue[1]), minMaxValue[2]), minMaxValue[3])`;t==="avg"&&(x="avgValue / count");let b=Math.floor(a/4)*4,v=a%4,k=` if (${m}) { avgValue += dot(values, ones); } else { minMaxValue = ${y}(values, minMaxValue); } `;this.userCode=` const ivec2 strides = ivec2(${o}, ${i}); const ivec2 pads = ivec2(${p}, ${h}); const float initializationValue = ${A}; 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 >= ${e.inWidth}) { return initializationValue; } count += 1.0; return getX(batch, xR, xC, d); } void main() { ivec4 coords = getOutputCoords(); int batch = coords[0]; int d = coords[3]; ivec2 xRCCorner = coords.yz * strides - pads; int xRCorner = xRCCorner.x; int xCCorner = xRCCorner.y; // max/min x(?, ?, d) to get y(yR, yC, d). // ? = to be determined vec4 minMaxValue = vec4(${A}); float avgValue = 0.0; count = 0.0; for (int wR = 0; wR < ${c}; wR += ${l}) { int xR = xRCorner + wR; if (xR < 0 || xR >= ${e.inHeight}) { continue; } for (int wC = 0; wC < ${b}; wC += 4) { int xC = xCCorner + wC * ${u}; 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) ); ${k} } int xC = xCCorner + ${b}; if (${v===1}) { vec4 values = vec4( getValue(batch, xR, xC, d), initializationValue, initializationValue, initializationValue ); ${k} } else if (${v===2}) { vec4 values = vec4( getValue(batch, xR, xC, d), getValue(batch, xR, xC + ${u}, d), initializationValue, initializationValue ); ${k} } else if (${v===3}) { vec4 values = vec4( getValue(batch, xR, xC, d), getValue(batch, xR, xC + ${u}, d), getValue(batch, xR, xC + 2 * ${u}, d), initializationValue ); ${k} } } setOutput(${x}); } `}},P2=class{constructor(e,t,n,s=!1,r=!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,l=e.strideWidth,u=e.dilationDepth,c=e.dilationHeight,d=e.dilationWidth,p=e.effectiveFilterDepth,h=e.effectiveFilterHeight,m=e.effectiveFilterWidth,f=e.padInfo.front,g=e.padInfo.top,A=e.padInfo.left;this.outputShape=e.outShape;let y=t==="avg",x="0.0";if(y||(x="-1.0 / 1e-20"),n){let D=">=";this.userCode=` const ivec3 strides = ivec3(${o}, ${i}, ${l}); const ivec3 pads = ivec3(${f}, ${g}, ${A}); void main() { ivec5 coords = getOutputCoords(); int batch = coords.x; int ch = coords.u; ivec3 xCorner = ivec3(coords.y, coords.z, coords.w) * strides - pads; int xDCorner = xCorner.x; int xRCorner = xCorner.y; int xCCorner = xCorner.z; // max/min x(?, ?, ?, ch) to get y(yD, yR, yC, ch). // ? = to be determined float minMaxValue = 0.0; float minMaxValueFound = 0.0; int minMaxPosition = 0; for (int wD = 0; wD < ${p}; wD += ${u}) { int xD = xDCorner + wD; if (xD < 0 || xD >= ${e.inDepth}) { continue; } for (int wR = 0; wR < ${h}; wR += ${c}) { int xR = xRCorner + wR; if (xR < 0 || xR >= ${e.inHeight}) { continue; } for (int wC = 0; wC < ${m}; wC += ${d}) { int xC = xCCorner + wC; if (xC < 0 || xC >= ${e.inWidth}) { continue; } float value = getX(batch, xD, xR, xC, ch); // If a min / max value has already been found, use it. If not, // use the current value. float currMinMaxValue = mix( value, minMaxValue, minMaxValueFound); if (value ${D} currMinMaxValue) { minMaxValue = value; minMaxValueFound = 1.0; minMaxPosition = ${s?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 * ${m} + wC`}; } } } } setOutput(float(minMaxPosition)); } `;return}let b="max",v=`${t}(${t}(${t}(minMaxValue[0], minMaxValue[1]), minMaxValue[2]), minMaxValue[3])`;t==="avg"&&(v="avgValue / count");let k=Math.floor(a/4)*4,S=a%4,C=` if (${y}) { avgValue += dot(values, ones); } else { minMaxValue = ${b}(values, minMaxValue); } `;this.userCode=` const ivec3 strides = ivec3(${o}, ${i}, ${l}); const ivec3 pads = ivec3(${f}, ${g}, ${A}); const float initializationValue = ${x}; 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 >= ${e.inWidth}) { return initializationValue; } count += 1.0; return getX(batch, xD, xR, xC, ch); } void main() { ivec5 coords = getOutputCoords(); int batch = coords.x; int ch = coords.u; ivec3 xCorner = ivec3(coords.y, coords.z, coords.w) * strides - pads; int xDCorner = xCorner.x; int xRCorner = xCorner.y; int xCCorner = xCorner.z; // max/min x(?, ?, ?, d) to get y(yD, yR, yC, ch). // ? = to be determined vec4 minMaxValue = vec4(${x}); float avgValue = 0.0; count = 0.0; for (int wD = 0; wD < ${p}; wD += ${u}) { int xD = xDCorner + wD; if (xD < 0 || xD >= ${e.inDepth}) { continue; } for (int wR = 0; wR < ${h}; wR += ${c}) { int xR = xRCorner + wR; if (xR < 0 || xR >= ${e.inHeight}) { continue; } for (int wC = 0; wC < ${k}; wC += 4) { int xC = xCCorner + wC * ${d}; 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) ); ${C} } int xC = xCCorner + ${k}; if (${S===1}) { vec4 values = vec4( getValue(batch, xD, xR, xC, ch), initializationValue, initializationValue, initializationValue ); ${C} } else if (${S===2}) { vec4 values = vec4( getValue(batch, xD, xR, xC, ch), getValue(batch, xD, xR, xC + ${d}, ch), initializationValue, initializationValue ); ${C} } else if (${S===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), initializationValue ); ${C} } } setOutput(${v}); } } `}};function dY(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t;Cu(r,"avgPool");let{filterSize:a,strides:o,pad:i,dimRoundingMode:l}=s,u=1;w.assert(_.eitherStridesOrDilationsAreOne(o,u),()=>`Error in avgPool: Either strides or dilations must be 1. Got strides ${o} and dilations '${u}'`);let c=_.computePool2DInfo(r.shape,a,o,u,i,l);if(c.filterWidth===1&&c.filterHeight===1&&w.arraysEqual(c.inShape,c.outShape))return cs({inputs:{x:r},backend:n});let d=new $d(c,"avg",!1);return n.runWebGLProgram(d,[r],"float32")}var pY={kernelName:ja,backendName:"webgl",kernelFunc:dY};function hY(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{filterSize:a,strides:o,pad:i,dimRoundingMode:l,dataFormat:u}=s,c=[1,1,1],d=_.computePool3DInfo(r.shape,a,o,c,i,l,u),p=new P2(d,"avg",!1);return n.runWebGLProgram(p,[r],"float32")}var fY={kernelName:pc,backendName:"webgl",kernelFunc:hY},mY=class{constructor(e){this.variableNames=["dy"],this.outputShape=e.inShape;let t=e.filterHeight,n=e.filterWidth,s=e.strideHeight,r=e.strideWidth,a=e.dilationHeight,o=e.dilationWidth,i=e.effectiveFilterHeight,l=e.effectiveFilterWidth,u=i-1-e.padInfo.top,c=l-1-e.padInfo.left,d=1/(t*n);this.userCode=` const ivec2 pads = ivec2(${u}, ${c}); const float avgMultiplier = float(${d}); void main() { ivec4 coords = getOutputCoords(); int b = coords[0]; int d = coords[3]; ivec2 dyRCCorner = coords.yz - pads; int dyRCorner = dyRCCorner.x; int dyCCorner = dyRCCorner.y; // 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 < ${i}; wR += ${a}) { float dyR = float(dyRCorner + wR) / ${s}.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+= ${o}) { float dyC = float(dyCCorner + wC) / ${r}.0; if (dyC < 0.0 || dyC >= ${e.outWidth}.0 || fract(dyC) > 0.0) { continue; } int idyC = int(dyC); float dyValue = getDy(b, idyR, idyC, d); dotProd += dyValue * avgMultiplier; } } setOutput(dotProd); } `}},gY=class{constructor(e){this.variableNames=["dy"],this.outputShape=e.inShape;let t=e.filterDepth,n=e.filterHeight,s=e.filterWidth,r=e.strideDepth,a=e.strideHeight,o=e.strideWidth,i=e.dilationDepth,l=e.dilationHeight,u=e.dilationWidth,c=e.effectiveFilterDepth,d=e.effectiveFilterHeight,p=e.effectiveFilterWidth,h=c-1-e.padInfo.front,m=d-1-e.padInfo.top,f=p-1-e.padInfo.left,g=1/(t*n*s);this.userCode=` const ivec3 pads = ivec3(${h}, ${m}, ${f}); const float avgMultiplier = float(${g}); void main() { ivec5 coords = getOutputCoords(); int batch = coords.x; int ch = coords.u; ivec3 dyCorner = ivec3(coords.y, coords.z, coords.w) - pads; int dyDCorner = dyCorner.x; int dyRCorner = dyCorner.y; int dyCCorner = dyCorner.z; // Convolve dy(?, ?, ?, d) with pos mask(:, :, :, ch) to get // dx(xD, xR, xC, ch). // ? = to be determined. : = across all values in that axis. float dotProd = 0.0; for (int wD = 0; wD < ${c}; wD += ${i}) { float dyD = float(dyDCorner + wD) / ${r}.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) / ${a}.0; if (dyR < 0.0 || dyR >= ${e.outHeight}.0 || fract(dyR) > 0.0) { continue; } int idyR = int(dyR); for (int wC = 0; wC < ${p}; wC += ${u}) { float dyC = float(dyCCorner + wC) / ${o}.0; if (dyC < 0.0 || dyC >= ${e.outWidth}.0 || fract(dyC) > 0.0) { continue; } int idyC = int(dyC); float dyValue = getDy(batch, idyD, idyR, idyC, ch); dotProd += dyValue * avgMultiplier; } } } setOutput(dotProd); } `}};function AY(e){let{inputs:t,backend:n,attrs:s}=e,{dy:r,input:a}=t,o=a,{filterSize:i,strides:l,pad:u,dimRoundingMode:c}=s,d=[1,1,1],p=_.computePool3DInfo(o.shape,i,l,d,u,c),h=new gY(p);return n.runWebGLProgram(h,[r],o.dtype)}var yY={kernelName:Dp,backendName:"webgl",kernelFunc:AY};function xY(e){let{inputs:t,backend:n,attrs:s}=e,{dy:r,input:a}=t,o=a;Cu([r,a],"avgPoolGrad");let{filterSize:i,strides:l,pad:u}=s,c=_.computePool2DInfo(o.shape,i,l,1,u),d=new mY(c);return n.runWebGLProgram(d,[r],o.dtype)}var bY={kernelName:Rp,backendName:"webgl",kernelFunc:xY};function vY(e){let{inputs:t,backend:n,attrs:s}=e,{a:r,b:a}=t,{transposeA:o,transposeB:i}=s;return A0({a:r,b:a,transposeA:o,transposeB:i,backend:n})}var wY={kernelName:qa,backendName:"webgl",kernelFunc:vY},kY=class{constructor(e,t,n,s,r,a){this.outputShape=[],this.variableNames=["x","mean","variance"],_.assertAndGetBroadcastShape(e,t),_.assertAndGetBroadcastShape(e,n);let o="0.0";s!=null&&(_.assertAndGetBroadcastShape(e,s),this.variableNames.push("offset"),o="getOffsetAtOutCoords()");let i="1.0";r!=null&&(_.assertAndGetBroadcastShape(e,r),this.variableNames.push("scale"),i="getScaleAtOutCoords()"),this.outputShape=e,this.userCode=` void main() { float x = getXAtOutCoords(); 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= 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}; if (xR < 0 || xR >= ${e.inHeight}) { continue; } for (int wC = 0; wC < ${p}; wC++) { int xC = xCCorner + wC * ${c}; if (xC < 0 || xC >= ${e.inWidth}) { continue; } for (int d1 = 0; d1 < ${h}; d1 += 4) { vec4 wValues = vec4( getW(wR, wC, d1, d2), getW(wR, wC, d1 + 1, d2), getW(wR, wC, d1 + 2, d2), getW(wR, wC, d1 + 3, d2) ); if (${f}) { vec4 xValues = vec4( getX(batch, xR, xC, d1), getX(batch, xR, xC, d1 + 1), getX(batch, xR, xC, d1 + 2), getX(batch, xR, xC, d1 + 3) ); dotProd += dot(xValues, wValues); } else { vec4 xValues = vec4( getX(batch, d1, xR, xC), getX(batch, d1 + 1, xR, xC), getX(batch, d1 + 2, xR, xC), getX(batch, d1 + 3, xR, xC) ); dotProd += dot(xValues, wValues); } } if (${m===1}) { if (${f}) { dotProd += getX(batch, xR, xC, ${h}) * getW(wR, wC, ${h}, d2); } else { dotProd += getX(batch, ${h}, xR, xC) * getW(wR, wC, ${h}, d2); } } else if (${m===2}) { vec2 wValues = vec2( getW(wR, wC, ${h}, d2), getW(wR, wC, ${h} + 1, d2) ); if (${f}) { vec2 xValues = vec2( getX(batch, xR, xC, ${h}), getX(batch, xR, xC, ${h} + 1) ); dotProd += dot(xValues, wValues); } else { vec2 xValues = vec2( getX(batch, ${h}, xR, xC), getX(batch, ${h} + 1, xR, xC) ); dotProd += dot(xValues, wValues); } } else if (${m===3}) { vec3 wValues = vec3( getW(wR, wC, ${h}, d2), getW(wR, wC, ${h} + 1, d2), getW(wR, wC, ${h} + 2, d2) ); if (${f}) { vec3 xValues = vec3( getX(batch, xR, xC, ${h}), getX(batch, xR, xC, ${h} + 1), getX(batch, xR, xC, ${h} + 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) ); dotProd += dot(xValues, wValues); } } } } float result = dotProd; ${v} ${b} setOutput(result); } `}},nJ=class{constructor(e){this.variableNames=["x","W"],this.outputShape=e.outShape;let t=e.padInfo.front,n=e.padInfo.top,s=e.padInfo.left,r=e.strideDepth,a=e.strideHeight,o=e.strideWidth,i=e.dilationDepth,l=e.dilationHeight,u=e.dilationWidth,c=e.filterDepth,d=e.filterHeight,p=e.filterWidth,h=Math.floor(e.inChannels/4)*4,m=e.inChannels%4;this.userCode=` const ivec3 strides = ivec3(${r}, ${a}, ${o}); const ivec3 pads = ivec3(${t}, ${n}, ${s}); void main() { ivec5 coords = getOutputCoords(); int batch = coords.x; int d2 = coords.u; ivec3 xFRCCorner = ivec3(coords.y, coords.z, coords.w) * strides - pads; int xFCorner = xFRCCorner.x; int xRCorner = xFRCCorner.y; int xCCorner = xFRCCorner.z; // Convolve x(?, ?, ?, d1) with w(:, :, :, d1, d2) to get // y(yF, yR, yC, d2). ? = to be determined. : = across all // values in that axis. float dotProd = 0.0; for (int wF = 0; wF < ${c}; wF++) { int xF = xFCorner + wF * ${i}; if (xF < 0 || xF >= ${e.inDepth}) { continue; } for (int wR = 0; wR < ${d}; wR++) { int xR = xRCorner + wR * ${l}; if (xR < 0 || xR >= ${e.inHeight}) { continue; } for (int wC = 0; wC < ${p}; wC++) { int xC = xCCorner + wC * ${u}; if (xC < 0 || xC >= ${e.inWidth}) { continue; } for (int d1 = 0; d1 < ${h}; d1 += 4) { vec4 xValues = vec4( getX(batch, xF, xR, xC, d1), getX(batch, xF, xR, xC, d1 + 1), getX(batch, xF, xR, xC, d1 + 2), getX(batch, xF, xR, xC, d1 + 3) ); vec4 wValues = vec4( getW(wF, wR, wC, d1, d2), getW(wF, wR, wC, d1 + 1, d2), getW(wF, wR, wC, d1 + 2, d2), getW(wF, wR, wC, d1 + 3, d2) ); dotProd += dot(xValues, wValues); } if (${m===1}) { dotProd += getX(batch, xF, xR, xC, ${h}) * getW(wF, wR, wC, ${h}, d2); } else if (${m===2}) { vec2 xValues = vec2( getX(batch, xF, xR, xC, ${h}), getX(batch, xF, xR, xC, ${h} + 1) ); vec2 wValues = vec2( getW(wF, wR, wC, ${h}, d2), getW(wF, wR, wC, ${h} + 1, d2) ); dotProd += dot(xValues, wValues); } else if (${m===3}) { vec3 xValues = vec3( getX(batch, xF, xR, xC, ${h}), getX(batch, xF, xR, xC, ${h} + 1), getX(batch, xF, xR, xC, ${h} + 2) ); vec3 wValues = vec3( getW(wF, wR, wC, ${h}, d2), getW(wF, wR, wC, ${h} + 1, d2), getW(wF, wR, wC, ${h} + 2, d2) ); dotProd += dot(xValues, wValues); } } } } setOutput(dotProd); } `}},sJ=class{constructor(e,t){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,this.customUniforms=[{name:"inputShape",type:"ivec3"},{name:"pad",type:"ivec2"},{name:"stride",type:"ivec2"},{name:"dilation",type:"ivec2"},{name:"inChannels",type:"int"},{name:"itemsPerBlockRow",type:"int"},{name:"outWidth",type:"int"}],this.outputShape=e,this.enableShapeUniforms=Is(this.outputShape.length);let{dataFormat:n}=t,s=_n(),r=n==="channelsLast",a=r?0:1,o=r?1:2,i=this.enableShapeUniforms?"if(blockIndex < outShape[1] && pos < outShape[0]) {":`if(blockIndex < ${e[1]} && pos < ${e[0]}) {`,l="";for(let u=0;u<=1;u++)for(let c=0;c<=1;c++)l+=` blockIndex = rc.y + ${c}; pos = rc.x + ${u}; ${i} offsetY = int(blockIndex / outWidth) * stride[0] - pad[0]; d0 = offsetY + dilation[0] * (pos / itemsPerBlockRow); if(d0 < inputShape[${a}] && d0 >= 0) { // Use custom imod instead mod. On Intel GPU, mod may generate // unexpected value. // https://github.com/tensorflow/tfjs/issues/5447 offsetX = imod(blockIndex, outWidth) * stride[1] - pad[1]; d1 = offsetX + dilation[1] * (imod(pos, itemsPerBlockRow) / inChannels); if(d1 < inputShape[${o}] && d1 >= 0) { ch = imod(pos, inChannels); if (${r}) { innerDims = vec2(d1, ch); result[${u*2+c}] = getChannel( getA(d0, int(innerDims.x), int(innerDims.y)), innerDims); } else { innerDims = vec2(d0, d1); result[${u*2+c}] = getChannel( getA(ch, int(innerDims.x), int(innerDims.y)), innerDims); } } } } `;this.userCode=` void main() { ivec2 rc = getOutputCoords(); vec4 result = vec4(0); int blockIndex, pos, offsetY, d0, offsetX, d1, ch; vec2 innerDims; ${l} ${s.output} = result; } `}};function q4({x:e,filter:t,convInfo:n,backend:s,bias:r=null,preluActivationWeights:a=null,leakyreluAlpha:o=0,activation:i=null}){let l=e.shape,u=s.texData.get(e.dataId),c=n.inChannels,d=l[0]*l[1]*l[2],p=n.outChannels,h=n.dataFormat==="channelsLast",m=!1,f=!1,g,A=[];if(!((d===1||p===1)&&c>P4)&&u.isPacked&&h&&u.texture!=null&&l[2]%2!=0&&w.arraysEqual(u.shape.slice(-3),l.slice(-3))){let b=l[0]*l[1]*(l[2]+1),v={dataId:e.dataId,shape:[1,b,n.inChannels],dtype:e.dtype},k=u.shape;u.shape=u.shape.slice(),u.shape[u.shape.length-2]++,w.assert(Rd(u.shape,v.shape),()=>`packed reshape ${u.shape} to ${v.shape} isn't free`);let S=be({inputs:{x:t},backend:s,attrs:{shape:[1,n.inChannels,n.outChannels]}});A.push(S);let C=A0({a:v,b:S,backend:s,transposeA:m,transposeB:f,bias:r,activation:i,preluActivationWeights:a,leakyreluAlpha:o}),D=s.texData.get(C.dataId);w.assert(D.isPacked,()=>"batchMatMul result is expected to be packed"),u.shape=k,D.shape=n.outShape,g=cs({inputs:{x:C},backend:s}),g.shape=n.outShape,A.push(C)}else{let b=h?l[0]*l[1]*l[2]:l[0]*l[2]*l[3],v=be({inputs:{x:e},backend:s,attrs:{shape:[1,b,n.inChannels]}}),k=be({inputs:{x:t},backend:s,attrs:{shape:[1,n.inChannels,n.outChannels]}}),S=A0({a:v,b:k,transposeA:m,transposeB:f,backend:s,bias:r,activation:i,preluActivationWeights:a,leakyreluAlpha:o});g=be({inputs:{x:S},backend:s,attrs:{shape:n.outShape}}),A.push(v),A.push(k),A.push(S)}for(let b of A)s.disposeIntermediateTensorInfo(b);return g}function X4({x:e,filter:t,convInfo:n,backend:s,bias:r=null,preluActivationWeights:a=null,leakyreluAlpha:o=0,activation:i=null}){let{filterWidth:l,filterHeight:u,inChannels:c,outWidth:d,outHeight:p,dataFormat:h}=n,m=h==="channelsLast",f=l*u*c,g=p*d,A=[f,g],y=!0,x=!1,b=[],v=be({inputs:{x:e},backend:s,attrs:{shape:e.shape.slice(1)}}),k=be({inputs:{x:t},backend:s,attrs:{shape:[1,f,w.sizeFromShape(t.shape)/f]}});b.push(v),b.push(k);let S=new sJ(A,n),C=[v.shape,[n.padInfo.top,n.padInfo.left],[n.strideHeight,n.strideWidth],[n.dilationHeight,n.dilationWidth],[n.inChannels],[n.filterWidth*n.inChannels],[n.outWidth]],D=s.runWebGLProgram(S,[v],"float32",C),O=be({inputs:{x:D},backend:s,attrs:{shape:[1,A[0],A[1]]}});b.push(D),b.push(O);let E=r!=null,R=a!=null,T=i==="leakyrelu",P=i?f0(i,!0):null,U=new D4(O.shape,k.shape,[1,g,n.outChannels],y,x,E,P,R,T),j=[O,k];if(r&&j.push(r),R&&j.push(a),T){let ne=s.makeTensorInfo([],"float32",w.createScalarValue(o,"float32"));j.push(ne),b.push(ne)}let q=s.runWebGLProgram(U,j,"float32"),X=m?[1,p,d,n.outChannels]:[1,n.outChannels,p,d],te=be({inputs:{x:q},backend:s,attrs:{shape:X}});b.push(q);for(let ne of b)s.disposeIntermediateTensorInfo(ne);return te}function rJ(e){let{inputs:t,backend:n,attrs:s}=e,{x:r,filter:a}=t,{strides:o,pad:i,dataFormat:l,dilations:u,dimRoundingMode:c}=s,d=_.convertConv2DDataFormat(l),p=_.computeConv2DInfo(r.shape,a.shape,o,u,i,c,!1,d),h;if(p.filterHeight===1&&p.filterWidth===1&&p.dilationHeight===1&&p.dilationWidth===1&&p.strideHeight===1&&p.strideWidth===1&&(p.padInfo.type==="SAME"||p.padInfo.type==="VALID"))h=q4({x:r,filter:a,convInfo:p,backend:n});else if(Y().getBool("WEBGL_CONV_IM2COL")&&r.shape[0]===1)h=X4({x:r,filter:a,convInfo:p,backend:n});else{let f=new j4(p);h=n.runWebGLProgram(f,[r,a],"float32")}let m=be({inputs:{x:h},backend:n,attrs:{shape:p.outShape}});return n.disposeIntermediateTensorInfo(h),m}var aJ={kernelName:Za,backendName:"webgl",kernelFunc:rJ},oJ=class{constructor(e){this.variableNames=["x","dy"],this.outputShape=e.filterShape;let t=e.strideHeight,n=e.strideWidth,s=e.padInfo.top,r=e.padInfo.left,a=e.dataFormat==="channelsLast";this.userCode=` void main() { ivec4 coords = getOutputCoords(); int wR = coords.x; int wC = coords.y; int d1 = coords.z; int d2 = coords.w; // Convolve x(?, ?, d1) with dy(:, :, d2) to get dw(wR, wC, d1, d2). // ? = 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} - ${s}; if (xR < 0 || xR >= ${e.inHeight}) { continue; } for (int yC = 0; yC < ${e.outWidth}; yC++) { int xC = wC + yC * ${n} - ${r}; if (xC < 0 || xC >= ${e.inWidth}) { continue; } if (${a}) { float dyValue = getDy(b, yR, yC, d2); float xValue = getX(b, xR, xC, d1); dotProd += (xValue * dyValue); } else { float dyValue = getDy(b, d2, yR, yC); float xValue = getX(b, d1, xR, xC); dotProd += (xValue * dyValue); } } } } setOutput(dotProd); } `}},iJ=class{constructor(e){this.variableNames=["dy","W"],this.outputShape=e.inShape;let t=e.filterHeight,n=e.filterWidth,s=e.strideHeight,r=e.strideWidth,a=e.dataFormat==="channelsLast",o=t-1-e.padInfo.top,i=n-1-e.padInfo.left,l=a?1:2,u=a?2:3,c=a?3:1;this.userCode=` const ivec2 pads = ivec2(${o}, ${i}); void main() { ivec4 coords = getOutputCoords(); int batch = coords[0]; int d1 = coords[${c}]; ivec2 dyCorner = ivec2(coords[${l}], coords[${u}]) - 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) / ${s}.0; if (dyR < 0.0 || dyR >= ${e.outHeight}.0 || fract(dyR) > 0.0) { continue; } int idyR = int(dyR); int wRPerm = ${t} - 1 - wR; for (int wC = 0; wC < ${n}; wC++) { float dyC = float(dyCCorner + wC) / ${r}.0; if (dyC < 0.0 || dyC >= ${e.outWidth}.0 || fract(dyC) > 0.0) { continue; } int idyC = int(dyC); int wCPerm = ${n} - 1 - wC; for (int d2 = 0; d2 < ${e.outChannels}; d2++) { if (${a}) { float xValue = getDy(batch, idyR, idyC, d2); float wValue = getW(wRPerm, wCPerm, d1, d2); dotProd += xValue * wValue; } else { float xValue = getDy(batch, d2, idyR, idyC); float wValue = getW(wRPerm, wCPerm, d1, d2); dotProd += xValue * wValue; } } } } setOutput(dotProd); } `}},lJ=class{constructor(e){this.variableNames=["x","dy"],this.outputShape=e.filterShape;let t=e.strideDepth,n=e.strideHeight,s=e.strideWidth,r=e.padInfo.front,a=e.padInfo.top,o=e.padInfo.left;this.userCode=` void main() { ivec5 coords = getOutputCoords(); int wF = coords.x; int wR = coords.y; int wC = coords.z; int d1 = coords.w; int d2 = coords.u; 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}; if (xF < 0 || xF >= ${e.inDepth}) { continue; } for (int yR = 0; yR < ${e.outHeight}; yR++) { 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 * ${s} - ${o}; if (xC < 0 || xC >= ${e.inWidth}) { continue; } float dyValue = getDy(b, yF, yR, yC, d2); float xValue = getX(b, xF, xR, xC, d1); dotProd += (xValue * dyValue); } } } } setOutput(dotProd); } `}},uJ=class{constructor(e){this.variableNames=["dy","W"],this.outputShape=e.inShape;let t=e.filterDepth,n=e.filterHeight,s=e.filterWidth,r=e.strideDepth,a=e.strideHeight,o=e.strideWidth,i=t-1-e.padInfo.front,l=n-1-e.padInfo.top,u=s-1-e.padInfo.left;this.userCode=` const ivec3 pads = ivec3(${i}, ${l}, ${u}); void main() { ivec5 coords = getOutputCoords(); int batch = coords.x; int d1 = coords.u; ivec3 dyCorner = ivec3(coords.y, coords.z, coords.w) - pads; int dyFCorner = dyCorner.x; int dyRCorner = dyCorner.y; int dyCCorner = dyCorner.z; float dotProd = 0.0; for (int wF = 0; wF < ${t}; wF++) { float dyF = float(dyFCorner + wF) / ${r}.0; if (dyF < 0.0 || dyF >= ${e.outDepth}.0 || fract(dyF) > 0.0) { continue; } int idyF = int(dyF); int wFPerm = ${t} - 1 - wF; for (int wR = 0; wR < ${n}; wR++) { float dyR = float(dyRCorner + wR) / ${a}.0; if (dyR < 0.0 || dyR >= ${e.outHeight}.0 || fract(dyR) > 0.0) { continue; } int idyR = int(dyR); int wRPerm = ${n} - 1 - wR; for (int wC = 0; wC < ${s}; wC++) { float dyC = float(dyCCorner + wC) / ${o}.0; if (dyC < 0.0 || dyC >= ${e.outWidth}.0 || fract(dyC) > 0.0) { continue; } int idyC = int(dyC); int wCPerm = ${s} - 1 - wC; for (int d2 = 0; d2 < ${e.outChannels}; d2++) { float xValue = getDy(batch, idyF, idyR, idyC, d2); float wValue = getW(wFPerm, wRPerm, wCPerm, d1, d2); dotProd += xValue * wValue; } } } } setOutput(dotProd); } `}};function cJ(e){let{inputs:t,backend:n,attrs:s}=e,{x:r,dy:a}=t,{strides:o,pad:i,dataFormat:l,dimRoundingMode:u,filterShape:c}=s,d=_.convertConv2DDataFormat(l),p=_.computeConv2DInfo(r.shape,c,o,1,i,u,!1,d),h=new oJ(p);return n.runWebGLProgram(h,[r,a],"float32")}var dJ={kernelName:Fp,backendName:"webgl",kernelFunc:cJ};function pJ(e){let{inputs:t,backend:n,attrs:s}=e,{dy:r,filter:a}=t,{inputShape:o,strides:i,pad:l,dataFormat:u,dimRoundingMode:c}=s,d=_.convertConv2DDataFormat(u),p=_.computeConv2DInfo(o,a.shape,i,1,l,c,!1,d),h=new iJ(p);return n.runWebGLProgram(h,[r,a],"float32")}var hJ={kernelName:Ya,backendName:"webgl",kernelFunc:pJ};function fJ(e){let{inputs:t,backend:n,attrs:s}=e,{x:r,filter:a}=t,{strides:o,pad:i,dilations:l}=s,u=_.computeConv3DInfo(r.shape,a.shape,o,l,i),c=new nJ(u);return n.runWebGLProgram(c,[r,a],"float32")}var mJ={kernelName:fc,backendName:"webgl",kernelFunc:fJ};function gJ(e){let{inputs:t,backend:n,attrs:s}=e,{x:r,dy:a}=t,{strides:o,pad:i,filterShape:l}=s,u=_.computeConv3DInfo(r.shape,l,o,1,i),c=new lJ(u);return n.runWebGLProgram(c,[r,a],"float32")}var AJ={kernelName:Op,backendName:"webgl",kernelFunc:gJ};function yJ(e){let{inputs:t,backend:n,attrs:s}=e,{dy:r,filter:a}=t,{pad:o,strides:i,inputShape:l}=s,u=_.computeConv3DInfo(l,a.shape,i,1,o),c=new uJ(u);return n.runWebGLProgram(c,[r,a],"float32")}var xJ={kernelName:Pp,backendName:"webgl",kernelFunc:yJ},bJ=R4+` return cos(x); `,vJ=tt({opSnippet:bJ}),wJ={kernelName:Ja,backendName:"webgl",kernelFunc:vJ},kJ=` float e2x = exp(-x); return (e2x + 1.0 / e2x) / 2.0; `,IJ=tt({opSnippet:kJ}),SJ={kernelName:Qa,backendName:"webgl",kernelFunc:IJ},CJ=class{constructor(e,t,n,s,r){this.variableNames=["Image","Boxes","BoxInd"],this.outputShape=[];let[a,o,i,l]=e,[u]=t,[c,d]=n;this.outputShape=[u,c,d,l];let p=s==="bilinear"?1:0,[h,m]=[`${o-1}.0`,`${i-1}.0`],[f,g,A]=c>1?[`${(o-1)/(c-1)}`,"(y2-y1) * height_ratio",`y1*${h} + float(y)*(height_scale)`]:["0.0","0.0",`0.5 * (y1+y2) * ${h}`],[y,x,b]=d>1?[`${(i-1)/(d-1)}`,"(x2-x1) * width_ratio",`x1*${m} + float(x)*(width_scale)`]:["0.0","0.0",`0.5 * (x1+x2) * ${m}`];this.userCode=` const float height_ratio = float(${f}); const float width_ratio = float(${y}); void main() { ivec4 coords = getOutputCoords(); int b = coords[0]; int y = coords[1]; int x = coords[2]; int d = coords[3]; // get box vals float y1 = getBoxes(b,0); float x1 = getBoxes(b,1); float y2 = getBoxes(b,2); float x2 = getBoxes(b,3); // get image in batch index int bInd = round(getBoxInd(b)); if(bInd < 0 || bInd >= ${a}) { return; } float height_scale = ${g}; float width_scale = ${x}; float in_y = ${A}; if( in_y < 0.0 || in_y > ${h} ) { setOutput(float(${r})); return; } float in_x = ${b}; if( in_x < 0.0 || in_x > ${m} ) { setOutput(float(${r})); return; } vec2 sourceFracIndexCR = vec2(in_x,in_y); if(${p} == 1) { // Compute the four integer indices. ivec2 sourceFloorCR = ivec2(sourceFracIndexCR); ivec2 sourceCeilCR = ivec2(ceil(sourceFracIndexCR)); float topLeft = getImage(b, sourceFloorCR.y, sourceFloorCR.x, d); float bottomLeft = getImage(b, sourceCeilCR.y, sourceFloorCR.x, d); float topRight = getImage(b, sourceFloorCR.y, sourceCeilCR.x, d); float bottomRight = getImage(b, sourceCeilCR.y, sourceCeilCR.x, d); vec2 fracCR = sourceFracIndexCR - vec2(sourceFloorCR); float top = topLeft + (topRight - topLeft) * fracCR.x; float bottom = bottomLeft + (bottomRight - bottomLeft) * fracCR.x; float newValue = top + (bottom - top) * fracCR.y; setOutput(newValue); } else { // Compute the coordinators of nearest neighbor point. ivec2 sourceNearestCR = ivec2(floor( sourceFracIndexCR + vec2(0.5,0.5))); float newValue = getImage(b, sourceNearestCR.y, sourceNearestCR.x, d); setOutput(newValue); } } `}},TJ=e=>{let{inputs:t,backend:n,attrs:s}=e,{image:r,boxes:a,boxInd:o}=t,{cropSize:i,method:l,extrapolationValue:u}=s,c=new CJ(r.shape,a.shape,i,l,u);return n.runWebGLProgram(c,[r,a,o],"float32")},NJ={kernelName:sl,backendName:"webgl",kernelFunc:TJ},K4=class{constructor(e,t,n){this.variableNames=["x"],this.customUniforms=[{name:"index",type:"float"}],this.outputShape=e;let s=e.length,r=t?"0.0":`getX(${Z4(s,"coords")})`,a=e[e.length-1],o="",i="";t?(o=n?`end != ${a-1}`:"end != 0",i=n?"end + 1":"end - 1"):(o=n?`end + pow2 < ${a}`:"end >= pow2",i=n?"end + pow2":"end - pow2"),this.userCode=` void main() { ${yt(s)} coords = getOutputCoords(); int end = ${Y4(s,"coords")}; float val = ${r}; int pow2 = int(pow(2.0, index)); if (${o}) { int idx = ${i}; ${Y4(s,"coords")} = idx; val += getX(${Z4(s,"coords")}); } setOutput(val); } `}};function Z4(e,t){if(e===1)return`${t}`;if(e===2)return`${t}.x, ${t}.y`;if(e===3)return`${t}.x, ${t}.y, ${t}.z`;if(e===4)return`${t}.x, ${t}.y, ${t}.z, ${t}.w`;throw Error(`Cumulative sum for rank ${e} is not yet supported`)}function Y4(e,t){if(e===1)return`${t}`;if(e===2)return`${t}.y`;if(e===3)return`${t}.z`;if(e===4)return`${t}.w`;throw Error(`Cumulative sum for rank ${e} is not yet supported`)}function EJ(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{axis:a,exclusive:o,reverse:i}=s,l=r.shape.length,u=_.getAxesPermutation([a],l),c=r;u!=null&&(c=Fn({inputs:{x:r},backend:n,attrs:{perm:u}}));let d=_.getInnerMostAxes(1,l)[0];if(d!==l-1)throw new Error(`WebGL cumsum shader expects an inner-most axis=${r.shape.length-1} but got axis=${a}`);let p=c.shape[d],h=cs({inputs:{x:c},backend:n});for(let m=0;m<=Math.ceil(Math.log2(p))-1;m++){let f=new K4(c.shape,!1,i),g=[[m]],A=h;h=n.runWebGLProgram(f,[h],h.dtype,g),n.disposeIntermediateTensorInfo(A)}if(o){let m=new K4(c.shape,o,i),f=h;h=n.runWebGLProgram(m,[h],h.dtype),n.disposeIntermediateTensorInfo(f)}if(u!=null){let m=_.getUndoAxesPermutation(u),f=Fn({inputs:{x:h},backend:n,attrs:{perm:m}});return n.disposeIntermediateTensorInfo(h),n.disposeIntermediateTensorInfo(c),f}return h}var RJ={kernelName:eo,backendName:"webgl",kernelFunc:EJ};function DJ(e){let{inputs:t,backend:n,attrs:s}=e,{x:r,weights:a}=t,{size:o,binaryOutput:i}=s;if(r.shape.length===1){let l=n.readSync(r.dataId),u=n.readSync(a.dataId),c=m4(l,u,a.dtype,a.shape,o);return n.makeTensorInfo([o],a.dtype,c)}else if(r.shape.length===2){let l=n.bufferSync(r),u=n.bufferSync(a),c=HX(l,u,o,i);return n.makeTensorInfo(c.shape,a.dtype,c.values)}throw new Error(`Error in denseBincount: input must be at most rank 2, but got rank${r.shape.length}.`)}var _J={kernelName:Mp,backendName:"webgl",kernelFunc:DJ},$J=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]; int h = ${this.getHeightCoordString()}; 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) * ${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 FJ(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{blockSize:a,dataFormat:o}=s;w.assert(a>1,()=>`blockSize should be > 1 for depthToSpace, but was: ${a}`);let i=r.shape[0],l=o==="NHWC"?r.shape[1]:r.shape[2],u=o==="NHWC"?r.shape[2]:r.shape[3],c=o==="NHWC"?r.shape[3]:r.shape[1],d=l*a,p=u*a,h=c/(a*a),m=o==="NHWC"?[i,d,p,h]:[i,h,d,p],f=new $J(m,a,o);return n.runWebGLProgram(f,[r],r.dtype)}var OJ={kernelName:rl,backendName:"webgl",kernelFunc:FJ},J4=class{constructor(e,t=!1,n=null,s=!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=Is(this.outputShape.length);let a=e.filterHeight,o=e.filterWidth,i=e.outChannels/e.inChannels,l="",u="";n&&(s?l=`float activation(float a) { float b = getPreluActivationWeightsAtOutCoords(); ${n} }`:r?l=`float activation(float a) { float b = getLeakyreluAlphaAtOutCoords(); ${n} }`:l=` float activation(float x) { ${n} } `,u="result = activation(result);");let c=t?"result += getBiasAtOutCoords();":"";t&&this.variableNames.push("bias"),s&&this.variableNames.push("preluActivationWeights"),r&&this.variableNames.push("leakyreluAlpha"),this.userCode=` ${l} void main() { ivec4 coords = getOutputCoords(); int batch = coords.x; ivec2 xRCCorner = coords.yz * strides - pads; int d2 = coords.w; int d1 = d2 / ${i}; int q = d2 - d1 * ${i}; int xRCorner = xRCCorner.x; int xCCorner = xRCCorner.y; // Convolve x(?, ?, d1) with w(:, :, d1, q) to get y(yR, yC, d2). // ? = 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 < ${a}; wR++) { int xR = xRCorner + wR * dilations[0]; if (xR < 0 || xR >= inDims[0]) { continue; } for (int wC = 0; wC < ${o}; wC++) { int xC = xCCorner + wC * dilations[1]; if (xC < 0 || xC >= inDims[1]) { continue; } float xVal = getX(batch, xR, xC, d1); float wVal = getW(wR, wC, d1, q); dotProd += xVal * wVal; } } float result = dotProd; ${c} ${u} setOutput(result); } `}},Q4=class{constructor(e,t=!1,n=null,s=!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=Is(this.outputShape.length);let a=e.outChannels/e.inChannels,o=e.padInfo.left,i=e.strideWidth,l=e.dilationWidth,u=e.filterHeight,c=e.filterWidth,d=c,p=` int xR; int xC; int xCOffset; vec4 wTexel; vec4 previous; vec4 final;`;for(let g=0;g=0 && xR < inDims[0]) { `;for(let A=0;A<(d+1)/2;A++){let y=A*2;if(p+=` xC = xCCorner + ${y*l}; `,i===1){if(y= 0 && xCOffset < inDims[1] && xTexelC${y}Ready == 0) { xTexelC${y} = getX(batch, xR, xCOffset, d1); // Need to manually clear unused channels in case // we're reading from recycled texture. if (xCOffset + 1 >= inDims[1]) { xTexelC${y}.zw = vec2(0.0); } xTexelC${y}Ready = 1; } `,l===1&&y>0?p+=` xC${y} = vec4(xTexelC${y-2}.zw, xTexelC${y}.xy); `:p+=` xCOffset = xC + 1 - 2; if (xCOffset >= 0 && xCOffset < inDims[1]) { previous = getX(batch, xR, xCOffset, d1); // Need to manually clear unused channels in case // we're reading from recycled texture. if (xCOffset + 1 >= inDims[1]) { previous.zw = vec2(0.0); } xC${y} = vec4(previous.zw, xTexelC${y}.xy); } else { xC${y} = vec4(0.0, 0.0, xTexelC${y}.xy); } `):p+=` if (xC >= 0 && xC < inDims[1] && xTexelC${y}Ready == 0) { xTexelC${y} = getX(batch, xR, xC, d1); if (xC + 1 >= inDims[1]) { xTexelC${y}.zw = vec2(0.0); } xTexelC${y}Ready = 1; } xC${y} = xTexelC${y}; `,y+1= 0 && xCOffset < inDims[1] && xTexelC${y+1}Ready == 0) { xTexelC${y+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${y+1}.zw = vec2(0.0); } xTexelC${y+1}Ready = 1; } `,l>1&&(p+=` xCOffset -= 2; if (xCOffset >= 0 && xCOffset < inDims[1] && xTexelC${y}Ready == 0) { xTexelC${y} = getX(batch, xR, xCOffset, d1); xTexelC${y}Ready = 1; } `),p+=` xC${y+1} = vec4(xTexelC${y}.zw, xTexelC${y+1}.xy); `):x===1?p+=` xC${y+1} = xTexelC${y}; `:p+=` xCOffset = xC + ${x}; if (xCOffset >= 0 && xCOffset < inDims[1] && xTexelC${y+1}Ready == 0) { xTexelC${y+1} = getX(batch, xR, xCOffset, d1); if (xCOffset + 1 >= inDims[1]) { xTexelC${y+1}.zw = vec2(0.0); } xTexelC${y+1}Ready = 1; } xC${y+1} = xTexelC${y+1}; `}}else y= 0 && xCOffset < inDims[1] && xTexelC${y}Ready == 0) { xTexelC${y} = getX(batch, xR, xCOffset, d1); // Need to manually clear unused channels in case // we're reading from recycled texture. if (xCOffset + 1 >= inDims[1]) { xTexelC${y}.zw = vec2(0.0); } xTexelC${y}Ready = 1; } if(xC + 1 >= 0 && xC + 1 < inDims[1] && xTexelC${y+1}Ready == 0) { xTexelC${y+1} = getX(batch, xR, xC + 1, d1); // Need to manually clear unused channels in case // we're reading from recycled texture. if (xC + 2 >= inDims[1]) { xTexelC${y+1}.zw = vec2(0.0); } xTexelC${y+1}Ready = 1; } xC${y} = vec4(xTexelC${y}.zw, xTexelC${y+1}.zw); `,y+1= 0 && xCOffset < inDims[1]) { final = getX(batch, xR, xCOffset, d1); } xC${y+1} = vec4(xTexelC${y+1}.xy, final.xy); `)):(p+=` if(xC >= 0 && xC < inDims[1] && xTexelC${y}Ready == 0) { xTexelC${y} = getX(batch, xR, xC, d1); if (xC + 1 >= inDims[1]) { xTexelC${y}.zw = vec2(0.0); } xTexelC${y}Ready = 1; } xCOffset = xC + strides[1]; if(xCOffset >= 0 && xCOffset < inDims[1] && xTexelC${y+1}Ready == 0) { xTexelC${y+1} = getX(batch, xR, xCOffset, d1); if (xCOffset + 1 >= inDims[1]) { xTexelC${y+1}.zw = vec2(0.); } xTexelC${y+1}Ready = 1; } xC${y} = vec4( xTexelC${y}.xy, xTexelC${y+1}.xy); `,y+1`Error in depthwiseConv2d: Either strides or dilations must be 1. Got strides ${o} and dilations '${c}'`);let d=_.computeConv2DInfo(r.shape,a.shape,o,c,i,u,!0),p;Y().getBool("WEBGL_PACK_DEPTHWISECONV")&&d.strideWidth<=2&&d.outChannels/d.inChannels==1?p=new Q4(d):p=new J4(d);let h=[[d.padInfo.top,d.padInfo.left],[d.strideHeight,d.strideWidth],[d.dilationHeight,d.dilationWidth],[d.inHeight,d.inWidth]];return n.runWebGLProgram(p,[r,a],"float32",h)}var MJ={kernelName:to,backendName:"webgl",kernelFunc:PJ},zJ=class{constructor(e){this.variableNames=["x","dy"],this.outputShape=e.filterShape;let t=e.strideHeight,n=e.strideWidth,s=e.padInfo.top,r=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 * ${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} - ${s}; if (xR < 0 || xR >= ${e.inHeight}) { continue; } for (int yC = 0; yC < ${e.outWidth}; yC++) { int xC = wC + yC * ${n} - ${r}; if (xC < 0 || xC >= ${e.inWidth}) { continue; } float dyValue = getDy(b, yR, yC, d2); float xValue = getX(b, xR, xC, d1); dotProd += (xValue * dyValue); } } } setOutput(dotProd); } `}},LJ=class{constructor(e){this.variableNames=["dy","W"],this.outputShape=e.inShape;let t=e.filterHeight,n=e.filterWidth,s=e.strideHeight,r=e.strideWidth,a=t-1-e.padInfo.top,o=n-1-e.padInfo.left,i=e.outChannels/e.inChannels;this.userCode=` const ivec2 pads = ivec2(${a}, ${o}); void main() { ivec4 coords = getOutputCoords(); int batch = coords[0]; int d1 = coords[3]; ivec2 dyCorner = coords.yz - pads; int dyRCorner = dyCorner.x; int dyCCorner = dyCorner.y; float dotProd = 0.0; for (int wR = 0; wR < ${t}; wR++) { float dyR = float(dyRCorner + wR) / ${s}.0; if (dyR < 0.0 || dyR >= ${e.outHeight}.0 || fract(dyR) > 0.0) { continue; } int idyR = int(dyR); int wRPerm = ${t} - 1 - wR; for (int wC = 0; wC < ${n}; wC++) { float dyC = float(dyCCorner + wC) / ${r}.0; if (dyC < 0.0 || dyC >= ${e.outWidth}.0 || fract(dyC) > 0.0) { continue; } int idyC = int(dyC); int wCPerm = ${n} - 1 - wC; // TO DO: Vec4 over the channelMul for (int dm = 0; dm < ${i}; dm++) { int d2 = d1 * ${i} + dm; float xValue = getDy(batch, idyR, idyC, d2); float wValue = getW(wRPerm, wCPerm, d1, dm); dotProd += xValue * wValue; } } } setOutput(dotProd); } `}};function BJ(e){let{inputs:t,backend:n,attrs:s}=e,{x:r,dy:a}=t,{strides:o,dilations:i,pad:l,dimRoundingMode:u,filterShape:c}=s,d=_.computeConv2DInfo(r.shape,c,o,i,l,u,!0),p=new zJ(d);return n.runWebGLProgram(p,[r,a],"float32")}var WJ={kernelName:zp,backendName:"webgl",kernelFunc:BJ};function VJ(e){let{inputs:t,backend:n,attrs:s}=e,{dy:r,filter:a}=t,{strides:o,dilations:i,pad:l,dimRoundingMode:u,inputShape:c}=s,d=_.computeConv2DInfo(c,a.shape,o,i,l,u,!0),p=new LJ(d);return n.runWebGLProgram(p,[r,a],"float32")}var UJ={kernelName:Lp,backendName:"webgl",kernelFunc:VJ},HJ=class{constructor(e){this.variableNames=["X"],this.outputShape=[e,e],this.userCode=` void main() { ivec2 coords = getOutputCoords(); float val = coords[0] == coords[1] ? getX(coords[0]) : 0.0; setOutput(val); } `}};function GJ(e){let{inputs:t,backend:n}=e,{x:s}=t,r=[...s.shape,...s.shape],a=w.sizeFromShape(s.shape),o=be({inputs:{x:s},backend:n,attrs:{shape:[a]}}),i=new HJ(a),l=n.runWebGLProgram(i,[o],o.dtype),u=be({inputs:{x:l},backend:n,attrs:{shape:r}});return n.disposeIntermediateTensorInfo(o),n.disposeIntermediateTensorInfo(l),u}var jJ={kernelName:Bp,backendName:"webgl",kernelFunc:GJ},qJ=class{constructor(e){this.variableNames=["x","W"],this.outputShape=e.outShape;let{inHeight:t,inWidth:n,padInfo:s,strideHeight:r,strideWidth:a,filterHeight:o,filterWidth:i,dilationHeight:l,dilationWidth:u}=e,{top:c,left:d}=s;this.userCode=` const ivec2 strides = ivec2(${r}, ${a}); const ivec2 pads = ivec2(${c}, ${d}); const float neg_infinity = -3.4e38; void main() { ivec4 coords = getOutputCoords(); int batch = coords.x; int d1 = coords.w; ivec2 outTopLeftCorner = coords.yz * strides - pads; int hBeg = outTopLeftCorner.x; int wBeg = outTopLeftCorner.y; float curVal = neg_infinity; for (int h = 0; h < ${o}; h++) { int hIn = hBeg + h * ${l}; if (hIn >= 0 && hIn < ${t}) { for (int w = 0; w < ${i}; w++) { int wIn = wBeg + w * ${u}; if (wIn >= 0 && wIn < ${n}) { float xVal = getX(batch, hIn, wIn, d1); float wVal = getW(h, w, d1); float val = xVal + wVal; if (val > curVal) { curVal = val; } } } } } float result = curVal; setOutput(result); } `}};function XJ(e){let{inputs:t,backend:n,attrs:s}=e,{x:r,filter:a}=t,{strides:o,pad:i,dilations:l}=s,u=_.computeDilation2DInfo(r.shape,a.shape,o,i,"NHWC",l),c,d=new qJ(u);c=n.runWebGLProgram(d,[r,a],"float32");let p=be({inputs:{x:c},backend:n,attrs:{shape:u.outShape}});return n.disposeIntermediateTensorInfo(c),p}var KJ={kernelName:mc,backendName:"webgl",kernelFunc:XJ};function ZJ(e){let{inputs:t,backend:n,attrs:s}=e,{equation:r}=s,a=t,{allDims:o,summedDims:i,idDims:l}=_.decodeEinsumEquation(r,a.length);_.checkEinsumDimSizes(o.length,l,a);let{path:u,steps:c}=_.getEinsumComputePath(i,l),d=c.length,p=null,h=o.length,m=[];for(let f=0;f=0&&(p=g0({inputs:{x:p},backend:n,attrs:{axis:u[f]-(o.length-h),keepDims:!1}}),m.push(p)),h--)}for(let f of m)f!==p&&n.disposeIntermediateTensorInfo(f);return p}var YJ={kernelName:Up,backendName:"webgl",kernelFunc:ZJ},JJ="return (x >= 0.0) ? x : (exp(x) - 1.0);",QJ=` vec4 result; result.r = (x.r >= 0.0) ? x.r : (exp(x.r) - 1.0); result.g = (x.g >= 0.0) ? x.g : (exp(x.g) - 1.0); result.b = (x.b >= 0.0) ? x.b : (exp(x.b) - 1.0); result.a = (x.a >= 0.0) ? x.a : (exp(x.a) - 1.0); return result; `,eQ=tt({opSnippet:JJ,packedOpSnippet:QJ}),tQ={kernelName:so,backendName:"webgl",kernelFunc:eQ},nQ="return (b >= 1.0) ? a : a * (b + 1.0);",sQ=` vec4 bGTEZero = vec4(greaterThanEqual(b, vec4(0.))); return (bGTEZero * a) + ((vec4(1.0) - bGTEZero) * (a * (b + vec4(1.0)))); `,rQ=e=>{let{inputs:t,backend:n}=e,{dy:s,y:r}=t,a=Y().getBool("WEBGL_PACK_BINARY_OPERATIONS")?new _d(sQ,s.shape,r.shape):new $u(nQ,s.shape,r.shape);return n.runWebGLProgram(a,[s,r],s.dtype)},aQ={kernelName:Hp,backendName:"webgl",kernelFunc:rQ},oQ=` return vec4(equal(a, b)); `,iQ="return float(a == b);",lQ=bn({opSnippet:iQ,packedOpSnippet:oQ,dtype:"bool",cpuKernelImpl:qX}),uQ={kernelName:ol,backendName:"webgl",kernelFunc:lQ},cQ=` // Error function is calculated approximately with elementary function. // See "Handbook of Mathematical Functions with Formulas, // Graphs, and Mathematical Tables", Abramowitz and Stegun. float p = ${_.ERF_P}; 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vec4 values = ${t.texture2D}(A, uv); float value; if (depth == 0) { value = values.r; } else if (depth == 1) { value = values.g; } else if (depth == 2) { value = values.b; } else if (depth == 3) { value = values.a; } setOutput(floor(value * 255.0 + 0.5)); } `}},RQ=class{constructor(e){this.variableNames=["A"],this.packedInputs=!1,this.packedOutput=!0;let t=_n(),[n,s]=e;this.outputShape=e,this.userCode=` void main() { ivec3 coords = getOutputCoords(); int texR = coords[0]; int texC = coords[1]; int depth = coords[2]; vec4 result = vec4(0.); for(int row=0; row<=1; row++) { for(int col=0; col<=1; col++) { texC = coords[1] + row; depth = coords[2] + col; vec2 uv = (vec2(texC, texR) + halfCR) / vec2(${s}.0, ${n}.0); vec4 values = ${t.texture2D}(A, uv); float value; if (depth == 0) { value = values.r; } else if (depth == 1) { value = values.g; } else if (depth == 2) { value = values.b; } else if (depth == 3) { value = values.a; } result[row * 2 + col] = floor(value * 255.0 + 0.5); } } ${t.output} = result; } `}},DQ={kernelName:ph,backendName:"webgl",kernelFunc:_Q},Pu;function _Q(e){let{inputs:t,backend:n,attrs:s}=e,{pixels:r}=t,{numChannels:a}=s,o=typeof HTMLVideoElement!="undefined"&&r instanceof HTMLVideoElement,i=typeof HTMLImageElement!="undefined"&&r instanceof HTMLImageElement,[l,u]=o?[r.videoWidth,r.videoHeight]:[r.width,r.height],c=[u,l],d=[u,l,a];(i||o)&&(Pu==null&&(Pu=document.createElement("canvas").getContext("2d")),Pu.canvas.width=l,Pu.canvas.height=u,Pu.drawImage(r,0,0,l,u),r=Pu.canvas);let p=n.makeTensorInfo(c,"int32");n.texData.get(p.dataId).usage=ws.PIXELS,n.gpgpu.uploadPixelDataToTexture(n.getTexture(p.dataId),r);let h=Y().getBool("WEBGL_PACK")?new RQ(d):new EQ(d),m=n.runWebGLProgram(h,[p],"int32");return n.disposeData(p.dataId),m}function $Q(e){let{inputs:t,backend:n,attrs:s}=e,{x:r,filter:a,bias:o,preluActivationWeights:i}=t,{strides:l,pad:u,dataFormat:c,dilations:d,dimRoundingMode:p,activation:h,leakyreluAlpha:m}=s,f=_.convertConv2DDataFormat(c),g=_.computeConv2DInfo(r.shape,a.shape,l,d,u,p,!1,f),A,y=[];if(g.filterHeight===1&&g.filterWidth===1&&g.dilationHeight===1&&g.dilationWidth===1&&g.strideHeight===1&&g.strideWidth===1&&(g.padInfo.type==="SAME"||g.padInfo.type==="VALID"))A=q4({x:r,filter:a,convInfo:g,backend:n,bias:o,activation:h,preluActivationWeights:i,leakyreluAlpha:m});else if(Y().getBool("WEBGL_CONV_IM2COL")&&r.shape[0]===1)A=X4({x:r,filter:a,convInfo:g,backend:n,bias:o,activation:h,preluActivationWeights:i,leakyreluAlpha:m});else{let b=o!=null,v=i!=null,k=h==="leakyrelu",S=h?f0(h,!1):null,C=new j4(g,b,S,v,k),D=[r,a];if(o&&D.push(o),i&&D.push(i),k){let O=n.makeTensorInfo([],"float32",w.createScalarValue(m,"float32"));D.push(O),y.push(O)}A=n.runWebGLProgram(C,D,"float32")}let x=be({inputs:{x:A},backend:n,attrs:{shape:g.outShape}});return y.push(A),y.forEach(b=>n.disposeIntermediateTensorInfo(b)),x}var FQ={kernelName:Vo,backendName:"webgl",kernelFunc:$Q};function OQ(e){let{inputs:t,backend:n,attrs:s}=e,{x:r,filter:a,bias:o,preluActivationWeights:i}=t,{strides:l,pad:u,dilations:c,dimRoundingMode:d,activation:p,leakyreluAlpha:h}=s,m=[],f=c;f==null&&(f=[1,1]),w.assert(_.eitherStridesOrDilationsAreOne(l,f),()=>`Error in depthwiseConv2d: Either strides or dilations must be 1. 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NAN : result.r; result.g = isNaN.g == 1.0 ? NAN : result.g; result.b = isNaN.b == 1.0 ? NAN : result.b; result.a = isNaN.a == 1.0 ? NAN : result.a; return result; `,bee=tt({opSnippet:yee,packedOpSnippet:xee,cpuKernelImpl:rK}),vee={kernelName:po,backendName:"webgl",kernelFunc:bee},wee="return log(1.0 + x);",kee=tt({opSnippet:wee}),Iee={kernelName:yl,backendName:"webgl",kernelFunc:kee},See="return float(a >= 1.0 && b >= 1.0);",Cee=` return vec4( vec4(greaterThanEqual(a, vec4(1.0))) * vec4(greaterThanEqual(b, vec4(1.0)))); `,Tee=bn({opSnippet:See,packedOpSnippet:Cee,dtype:"bool"}),Nee={kernelName:xl,backendName:"webgl",kernelFunc:Tee},Eee="return float(!(x >= 1.0));",Ree=tt({opSnippet:Eee}),Dee={kernelName:Ac,backendName:"webgl",kernelFunc:Ree},_ee="return float(a >= 1.0 || b >= 1.0);",$ee=` return min( vec4(greaterThanEqual(a, vec4(1.0))) + vec4(greaterThanEqual(b, vec4(1.0))), vec4(1.0)); `,Fee=bn({opSnippet:_ee,packedOpSnippet:$ee,dtype:"bool"}),Oee={kernelName:yc,backendName:"webgl",kernelFunc:Fee},Pee=class{constructor(e,t,n,s,r){this.variableNames=["x"],this.outputShape=[];let a=t,o=e[3]-1;this.outputShape=e;let i,l=`float(${n}) + float(${s}) * sum`;r===.5?i=`inversesqrt(${l})`:r===1?i=`1.0/(${l})`:i=`exp(log(${l}) * float(-${r}));`,this.userCode=` void main() { ivec4 coords = getOutputCoords(); int b = coords[0]; int r = coords[1]; int c = coords[2]; int d = coords[3]; float x = getX(b, r, c, d); float sum = 0.0; for (int j = -${a}; j <= ${a}; j++) { int idx = d + j; if (idx >= 0 && idx <= ${o}) { float z = getX(b, r, c, idx); sum += z * z; } } float val = x * ${i}; setOutput(val); } `}},Mee=class{constructor(e,t,n,s,r){this.variableNames=["x"],this.outputShape=[],this.packedInputs=!0,this.packedOutput=!0;let a=t,o=e[3]-1;this.outputShape=e;let i,l=`float(${n}) + float(${s}) * sum`;r===.5?i=`inversesqrt(${l})`:r===1?i=`1.0/(${l})`:i=`exp(log(${l}) * float(-${r}));`,this.userCode=` void main() { ivec4 coords = getOutputCoords(); int b = coords.x; int r = coords.y; int c = coords.z; int d = coords.w; bool hasNextCol = d < ${this.outputShape[3]}; bool hasNextRow = c < ${this.outputShape[2]}; vec4 sum = vec4(0.); vec4 xFragAtOutputCoords = getX(b, r, c, d); vec4 xAtOutputCoords = vec4( getChannel(xFragAtOutputCoords, vec2(c, d)), hasNextCol ? getChannel(xFragAtOutputCoords, vec2(c, d + 1)) : 0.0, hasNextRow ? getChannel(xFragAtOutputCoords , vec2(c + 1, d)) : 0.0, (hasNextRow && hasNextCol) ? getChannel(xFragAtOutputCoords, vec2(c + 1, d + 1)) : 0.0 ); int firstChannel = d - ${a}; vec2 cache = vec2(0.); if(firstChannel >= 0){ vec4 firstChannelFrag = getX(b, r, c, firstChannel); cache.x = getChannel(firstChannelFrag, vec2(c, firstChannel)); if(hasNextRow){ cache.y = getChannel(firstChannelFrag, vec2(c + 1, firstChannel)); } } ivec2 depth = ivec2(d, d + 1); for (int j = - ${a}; j <= ${a}; j++) { ivec2 idx = depth + j; bvec2 aboveLowerBound = greaterThanEqual(idx, ivec2(0)); bvec2 belowUpperBound = lessThanEqual(idx, ivec2(${o})); bool depthInRange = aboveLowerBound.x && belowUpperBound.x; bool depthPlusOneInRange = aboveLowerBound.y && belowUpperBound.y; if(depthInRange || depthPlusOneInRange){ vec4 z = vec4(0.); vec4 xFragAtCurrentDepth; z.xz = cache.xy; if(depthPlusOneInRange && hasNextCol){ xFragAtCurrentDepth = idx.y != d ? getX(b, r, c, idx.y) : xFragAtOutputCoords; z.y = getChannel(xFragAtCurrentDepth, vec2(c, idx.y)); if(hasNextRow){ z.w = getChannel(xFragAtCurrentDepth, vec2(c + 1, idx.y)); } } cache.xy = z.yw; sum += z * z; } } vec4 result = xAtOutputCoords * ${i}; setOutput(result); } `}},zee=e=>{let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{depthRadius:a,bias:o,alpha:i,beta:l}=s,u=Y().getBool("WEBGL_PACK_NORMALIZATION")?new Mee(r.shape,a,o,i,l):new Pee(r.shape,a,o,i,l);return n.runWebGLProgram(u,[r],r.dtype)},Lee={kernelName:xc,backendName:"webgl",kernelFunc:zee},Bee=class{constructor(e,t,n,s,r){this.variableNames=["inputImage","outputImage","dy"],this.outputShape=[],this.outputShape=e,this.depth=e[3],this.depthRadius=t,this.bias=n,this.alpha=s,this.beta=r,this.userCode=` void main() { ivec4 coords = getOutputCoords(); int b = coords[0]; int r = coords[1]; int c = coords[2]; float result = 0.0; for (int d = 0; d < ${this.depth}; ++d) { int depthBegin = int(max(0.0, float(d - ${t}))); int depthEnd = int(min(float(${this.depth}), float(d + ${t} + 1))); const int MIN_DEPTH_BEGIN = 0; const int MAX_DEPTH_END = ${this.depth}; float norm = 0.0; for (int k = MIN_DEPTH_BEGIN; k < MAX_DEPTH_END; ++k) { if (k < depthBegin){ continue; } else if (k >= depthBegin && k < depthEnd) { norm += getInputImage(b, r, c, k) * getInputImage(b, r, c, k); } else { break; } } norm = float(${s}) * norm + float(${n}); for(int k = MIN_DEPTH_BEGIN; k < MAX_DEPTH_END; ++k){ if (k < depthBegin){ continue; } else if (k >= depthBegin && k < depthEnd){ float dyi = -2.0 * float(${s}) * float(${r}) * getInputImage(b ,r ,c, k) * getOutputImage(b, r, c, d) / norm; if (k == d) { dyi += pow(norm, -1.0 * ${r}); } if (k == coords[3]) { dyi *= getDy(b, r, c, d); result += dyi; } } else { break; } } } setOutput(result); } `}},Wee=e=>{let{inputs:t,backend:n,attrs:s}=e,{x:r,y:a,dy:o}=t,{depthRadius:i,bias:l,alpha:u,beta:c}=s,d=new Bee(r.shape,i,l,u,c);return n.runWebGLProgram(d,[r,a,o],r.dtype)},Vee={kernelName:Kp,backendName:"webgl",kernelFunc:Wee};function Uee(e,t,n,s){let r=w.sizeFromShape(t),o=w.sizeFromShape(e.shape)/r,i=be({inputs:{x:e},attrs:{shape:[o,r]},backend:s}),l=wi(i,e.dtype,"max",s),u=be({inputs:{x:l},attrs:{shape:n},backend:s});return s.disposeIntermediateTensorInfo(i),s.disposeIntermediateTensorInfo(l),u}function ik(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{reductionIndices:a,keepDims:o}=s,i=r.shape.length,l=w.parseAxisParam(a,r.shape),u=l,c=_.getAxesPermutation(u,i),d=c!=null,p=n.shouldExecuteOnCPU([r]),h=r;if(d){if(p){let x=n.texData.get(h.dataId).values,b=new Array(i);for(let S=0;S`Error in maxPool: Either strides or dilations must be 1. Got strides ${o} and dilations '${u}'`);let c=_.computePool2DInfo(r.shape,a,o,u,i,l);if(c.filterWidth===1&&c.filterHeight===1&&w.arraysEqual(c.inShape,c.outShape))return cs({inputs:{x:r},backend:n});let d=new $d(c,"max",!1);return n.runWebGLProgram(d,[r],r.dtype)}var Zee={kernelName:mo,backendName:"webgl",kernelFunc:Kee};function Yee(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{filterSize:a,strides:o,pad:i,dataFormat:l,dimRoundingMode:u}=s,c=[1,1,1],d=_.computePool3DInfo(r.shape,a,o,c,i,u,l),p=new P2(d,"max",!1);return n.runWebGLProgram(p,[r],r.dtype)}var Jee={kernelName:bc,backendName:"webgl",kernelFunc:Yee},Qee=class{constructor(e){this.variableNames=["dy","maxPos"],this.outputShape=e.inShape;let t=e.strideHeight,n=e.strideWidth,s=e.dilationHeight,r=e.effectiveFilterHeight,a=e.effectiveFilterWidth,o=r-1-e.padInfo.top,i=a-1-e.padInfo.left,l=r*a-1;this.userCode=` const ivec2 pads = ivec2(${o}, ${i}); void main() { ivec4 coords = getOutputCoords(); int b = coords[0]; int d = coords[3]; ivec2 dyRCCorner = coords.yz - pads; int dyRCorner = dyRCCorner.x; int dyCCorner = dyRCCorner.y; // 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 += ${s}) { float dyR = float(dyRCorner + wR) / ${t}.0; if (dyR < 0.0 || dyR >= ${e.outHeight}.0 || fract(dyR) > 0.0) { continue; } int idyR = int(dyR); for (int wC = 0; wC < ${a}; wC++) { float dyC = float(dyCCorner + wC) / ${n}.0; if (dyC < 0.0 || dyC >= ${e.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)); // Get the current value, check it against the value from the // position matrix. int curPosValue = wR * ${a} + wC; float mask = float(maxPosValue == curPosValue ? 1.0 : 0.0); dotProd += dyValue * mask; } } setOutput(dotProd); } `}},ete=class{constructor(e){this.variableNames=["dy","maxPos"],this.outputShape=e.inShape;let t=e.strideDepth,n=e.strideHeight,s=e.strideWidth,r=e.dilationDepth,a=e.dilationHeight,o=e.dilationWidth,i=e.effectiveFilterDepth,l=e.effectiveFilterHeight,u=e.effectiveFilterWidth,c=i-1-e.padInfo.front,d=l-1-e.padInfo.top,p=u-1-e.padInfo.left,h=i*l*u-1;this.userCode=` const ivec3 pads = ivec3(${c}, ${d}, ${p}); void main() { ivec5 coords = getOutputCoords(); int batch = coords.x; int ch = coords.u; ivec3 dyCorner = ivec3(coords.y, coords.z, coords.w) - pads; int dyDCorner = dyCorner.x; int dyRCorner = dyCorner.y; int dyCCorner = dyCorner.z; // Convolve dy(?, ?, ?, ch) with pos mask(:, :, :, d) to get // dx(xD, xR, xC, ch). // ? = to be determined. : = across all values in that axis. float dotProd = 0.0; for (int wD = 0; wD < ${i}; wD += ${r}) { float dyD = float(dyDCorner + wD) / ${t}.0; if (dyD < 0.0 || dyD >= ${e.outDepth}.0 || fract(dyD) > 0.0) { continue; } int idyD = int(dyD); for (int wR = 0; wR < ${l}; wR += ${a}) { float dyR = float(dyRCorner + wR) / ${n}.0; if (dyR < 0.0 || dyR >= ${e.outHeight}.0 || fract(dyR) > 0.0) { continue; } int idyR = int(dyR); 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) { continue; } int idyC = int(dyC); float dyValue = getDy(batch, idyD, idyR, idyC, ch); int maxPosValue = ${h} - 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; float mask = float(maxPosValue == curPosValue ? 1.0 : 0.0); dotProd += dyValue * mask; } } } setOutput(dotProd); } `}};function tte(e){let{inputs:t,backend:n,attrs:s}=e,{dy:r,input:a}=t,o=a,{filterSize:i,strides:l,pad:u,dimRoundingMode:c}=s,d=[1,1,1],p=_.computePool3DInfo(o.shape,i,l,d,u,c),h=new P2(p,"max",!0),m=n.runWebGLProgram(h,[o],o.dtype),f=new ete(p),g=n.runWebGLProgram(f,[r,m],o.dtype);return n.disposeIntermediateTensorInfo(m),g}var nte={kernelName:Yp,backendName:"webgl",kernelFunc:tte};function ste(e){let{inputs:t,backend:n,attrs:s}=e,{dy:r,input:a,output:o}=t,i=a;Cu([a,o],"maxPoolGrad");let{filterSize:l,strides:u,pad:c,dimRoundingMode:d}=s,p=_.computePool2DInfo(i.shape,l,u,1,c,d),h=!0,m=new $d(p,"max",h),f=n.runWebGLProgram(m,[i],i.dtype),g=new Qee(p),A=n.runWebGLProgram(g,[r,f],i.dtype);return n.disposeIntermediateTensorInfo(f),A}var rte={kernelName:Zp,backendName:"webgl",kernelFunc:ste};function ate(e,t,n,s){let r=new $d(n,"max",!1),a=s.runWebGLProgram(r,[e],"float32");r=new $d(n,"max",!0,!0,t);let o=s.runWebGLProgram(r,[e],"float32");return[a,o]}var ote={kernelName:Jp,backendName:"webgl",kernelFunc:({inputs:e,attrs:t,backend:n})=>{let{x:s}=e,{filterSize:r,strides:a,pad:o,includeBatchInIndex:i}=t,l=n;w.assert(s.shape.length===4,()=>`Error in maxPool: input must be rank 4 but got rank ${s.shape.length}.`);let u=[1,1];w.assert(_.eitherStridesOrDilationsAreOne(a,u),()=>`Error in maxPool: Either strides or dilations must be 1. Got strides ${a} and dilations '${u}'`);let c=_.computePool2DInfo(s.shape,r,a,u,o),[d,p]=ate(s,i,c,l);return[d,p]}};function ite(e,t,n,s){let r=w.sizeFromShape(t),o=w.sizeFromShape(e.shape)/r,i=be({inputs:{x:e},attrs:{shape:[o,r]},backend:s}),l=wi(i,"float32","mean",s),u=be({inputs:{x:l},attrs:{shape:n},backend:s});return s.disposeIntermediateTensorInfo(i),s.disposeIntermediateTensorInfo(l),u}var lte={kernelName:go,backendName:"webgl",kernelFunc:({inputs:e,attrs:t,backend:n})=>{let{x:s}=e,{keepDims:r,axis:a}=t,o=n,i=s.shape.length,l=w.parseAxisParam(a,s.shape),u=l,c=_.getAxesPermutation(u,i),d=c!=null,p=o.shouldExecuteOnCPU([s]),h=[],m=s;if(d){if(p){let b=o.texData.get(m.dataId).values,v=new Array(i);for(let C=0;Cu[0]+e[c]+u[1]);let s=e.length,r=yt(s),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,s),l=n==="reflect"?0:1;if(s===1){this.userCode=` int start = ${a}; int end = ${o}; void main() { int outC = getOutputCoords(); if (outC < start) { outC = start * 2 - outC - ${l}; } else if(outC >= end) { outC = (end - 1) * 2 - outC + ${l}; } setOutput(getX(outC - start)); } `;return}this.userCode=` ${r} start = ${r}(${a}); ${r} end = ${r}(${o}); void main() { ${r} outC = getOutputCoords(); for (int i = 0; i < ${s}; i++) { if (outC[i] < start[i]) { outC[i] = start[i] * 2 - outC[i] - ${l}; } else if(outC[i] >= end[i]) { outC[i] = (end[i] - 1) * 2 - outC[i] + ${l}; } } ${r} coords = outC - start; setOutput(getX(${i})); } `}},gte=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 s=e.length,r=yt(s),a=t.map(h=>h[0]).join(","),o=t.map((h,m)=>h[0]+e[m]).join(","),i=$n("rc",s),l=$n("source",s),u=`${i[s-1]} < ${this.outputShape[s-1]}`,c=s===1?"source":`vec2(${l.slice(-2).join()})`,d=n==="reflect"?0:1,p="";if(s===1){let h=` ${r} source = rc; if (source < start) { source = start * 2 - source - ${d}; } else if (source >= end) { source = (end - 1) * 2 - source + ${d}; } source -= start; `;p=` ${r} rc = outputLoc; ${h} result[0] = getChannel(getX(${l.join()}), ${c}); ${i[s-1]} += 1; if(${u}) { ${h} result[1] = getChannel(getX(${l.join()}), ${c}); } `}else{let h=` ${r} source = rc; ${r} lt = ${r}(lessThan(source, start)); ${r} gte = ${r}(greaterThanEqual(source, end)); ${r} orig = 1 - (lt + gte); source = orig * source + lt * (start * 2 - source - ${d}) + gte * ((end - 1) * 2 - source + ${d}); source -= start; `;p=` ${r} rc = outputLoc; ${h} result[0] = getChannel(getX(${l.join()}), ${c}); ${i[s-1]} += 1; if(${u}) { ${h} result[1] = getChannel(getX(${l.join()}), ${c}); } rc = outputLoc; ${i[s-2]} += 1; if(${i[s-2]} < ${this.outputShape[s-2]}) { ${h} result[2] = getChannel(getX(${l.join()}), ${c}); ${i[s-1]} += 1; if(${u}) { ${h} result[3] = getChannel(getX(${l.join()}), ${c}); } } `}this.userCode=` const ${r} start = ${r}(${a}); const ${r} end = ${r}(${o}); void main() { ${r} outputLoc = getOutputCoords(); vec4 result = vec4(0.); ${p} setOutput(result); } `}},Ate=({inputs:e,backend:t,attrs:n})=>{let{x:s}=e,{paddings:r,mode:a}=n,o=Y().getBool("WEBGL_PACK_ARRAY_OPERATIONS")?new gte(s.shape,r,a):new mte(s.shape,r,a);return t.runWebGLProgram(o,[s],s.dtype)},yte={kernelName:xo,backendName:"webgl",kernelFunc:Ate},xte=`if (b == 0.0) return NAN; return mod(a, b);`,bte=` vec4 result = mod(a, b); vec4 isNaN = vec4(equal(b, vec4(0.0))); `+h0+` return result; `,vte=bn({opSnippet:xte,packedOpSnippet:bte}),wte={kernelName:bl,backendName:"webgl",kernelFunc:vte},kte=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]; float r = random(seed); float cdf = 0.0; for (int i = 0; i < ${t-1}; i++) { cdf += getProbs(batch, i); if (r < cdf) { setOutput(float(i)); return; } } // If no other event happened, last event happened. setOutput(float(${t-1})); } `}},Ite=` if (a == b) { return 1.0; }; return a / b;`,Ste=` // vec4 one = vec4(equal(a, b)); // return one + (vec4(1.0) - one) * a / b; vec4 result = a / b; if(a.x == b.x) { result.x = 1.; } if(a.y == b.y) { result.y = 1.; } if(a.z == b.z) { result.z = 1.; } if(a.w == b.w) { result.w = 1.; } return result; `,lk=bn({opSnippet:Ite,packedOpSnippet:Ste,checkOutOfBounds:!0}),Cte={kernelName:no,backendName:"webgl",kernelFunc:lk},uk="return a - b;",ck=bn({opSnippet:uk,packedOpSnippet:uk,supportsComplex:!0,cpuKernelImpl:kK}),Tte={kernelName:Mo,backendName:"webgl",kernelFunc:ck};function dk(e){let{inputs:t,backend:n,attrs:s}=e,{logits:r}=t,{dim:a}=s,o=w.parseAxisParam([a],r.shape),i=ik({inputs:{x:r},backend:n,attrs:{reductionIndices:o,keepDims:!1}}),l=_.expandShapeToKeepDim(i.shape,o),u=be({inputs:{x:i},backend:n,attrs:{shape:l}}),c=ck({inputs:{a:r,b:u},backend:n}),d=tk({inputs:{x:c},backend:n}),p=g0({inputs:{x:d},backend:n,attrs:{axis:o,keepDims:!1}}),h=be({inputs:{x:p},backend:n,attrs:{shape:l}}),m=lk({inputs:{a:d,b:h},backend:n});return n.disposeIntermediateTensorInfo(i),n.disposeIntermediateTensorInfo(u),n.disposeIntermediateTensorInfo(c),n.disposeIntermediateTensorInfo(d),n.disposeIntermediateTensorInfo(p),n.disposeIntermediateTensorInfo(h),m}var Nte={kernelName:Oo,backendName:"webgl",kernelFunc:dk};function Ete(e){let{inputs:t,backend:n,attrs:s}=e,{logits:r}=t,{numSamples:a,seed:o,normalized:i}=s,l=i?r:dk({inputs:{logits:r},backend:n,attrs:{dim:r.shape.length-1}}),u=l.shape[0],c=l.shape[1],d=new kte(u,c,a),p=[[o]],h=n.runWebGLProgram(d,[l],"int32",p);return i||n.disposeIntermediateTensorInfo(l),h}var Rte={kernelName:Qp,backendName:"webgl",kernelFunc:Ete},pk="return -x;";function Dte(e){let{inputs:t,backend:n}=e,{x:s}=t;if(n.shouldExecuteOnCPU([s])){let a=n.texData.get(s.dataId),[o,i]=uK(a.values,s.shape,s.dtype);return n.makeTensorInfo(i,s.dtype,o)}let r;return Y().getBool("WEBGL_PACK_UNARY_OPERATIONS")?r=new Du(s.shape,pk):r=new Na(s.shape,pk),n.runWebGLProgram(r,[s],s.dtype)}var _te={kernelName:vl,backendName:"webgl",kernelFunc:Dte},$te=fr.nonMaxSuppressionV3Impl;function Fte(e){_.warn("tf.nonMaxSuppression() in webgl locks the UI thread. Call tf.nonMaxSuppressionAsync() instead");let{inputs:t,backend:n,attrs:s}=e,{boxes:r,scores:a}=t,{maxOutputSize:o,iouThreshold:i,scoreThreshold:l}=s,u=n.readSync(r.dataId),c=n.readSync(a.dataId),{selectedIndices:d}=$te(u,c,o,i,l);return n.makeTensorInfo([d.length],"int32",new Int32Array(d))}var Ote={kernelName:kl,backendName:"webgl",kernelFunc:Fte},Pte=fr.nonMaxSuppressionV4Impl;function Mte(e){_.warn("tf.nonMaxSuppression() in webgl locks the UI thread. Call tf.nonMaxSuppressionAsync() instead");let{inputs:t,backend:n,attrs:s}=e,{boxes:r,scores:a}=t,{maxOutputSize:o,iouThreshold:i,scoreThreshold:l,padToMaxOutputSize:u}=s,c=n.readSync(r.dataId),d=n.readSync(a.dataId),{selectedIndices:p,validOutputs:h}=Pte(c,d,o,i,l,u);return[n.makeTensorInfo([p.length],"int32",new Int32Array(p)),n.makeTensorInfo([],"int32",new Int32Array([h]))]}var zte={kernelName:Il,backendName:"webgl",kernelFunc:Mte},Lte=fr.nonMaxSuppressionV5Impl;function Bte(e){_.warn("tf.nonMaxSuppression() in webgl locks the UI thread. Call tf.nonMaxSuppressionAsync() instead");let{inputs:t,backend:n,attrs:s}=e,{boxes:r,scores:a}=t,{maxOutputSize:o,iouThreshold:i,scoreThreshold:l,softNmsSigma:u}=s,c=n.readSync(r.dataId),d=n.readSync(a.dataId),p=o,h=i,m=l,f=u,{selectedIndices:g,selectedScores:A}=Lte(c,d,p,h,m,f);return[n.makeTensorInfo([g.length],"int32",new Int32Array(g)),n.makeTensorInfo([A.length],"float32",new Float32Array(A))]}var Wte={kernelName:Sl,backendName:"webgl",kernelFunc:Bte},Vte=class{constructor(e,t,n,s){this.variableNames=["indices"],this.outputShape=[e,t],this.userCode=` void main() { ivec2 coords = getOutputCoords(); int index = round(getIndices(coords.x)); setOutput(mix(float(${s}), float(${n}), float(index == coords.y))); } `}},Ute=e=>{let{inputs:t,backend:n,attrs:s}=e,{indices:r}=t,{depth:a,onValue:o,offValue:i}=s,l=w.sizeFromShape(r.shape),u=new Vte(l,a,o,i),c=be({inputs:{x:r},backend:n,attrs:{shape:[l]}}),d=n.runWebGLProgram(u,[c],r.dtype);n.disposeIntermediateTensorInfo(c);let p=[...r.shape,a],h=be({inputs:{x:d},backend:n,attrs:{shape:p}});return n.disposeIntermediateTensorInfo(d),h},Hte={kernelName:vo,backendName:"webgl",kernelFunc:Ute};function v0(e){let{inputs:t,backend:n}=e,{x:s}=t;if(s.dtype==="complex64"){let r=Fd({inputs:{input:s},backend:n}),a=v0({inputs:{x:r},backend:n}),o=b0({inputs:{input:s},backend:n}),i=v0({inputs:{x:o},backend:n}),l=Ea({inputs:{real:a,imag:i},backend:n});return n.disposeIntermediateTensorInfo(r),n.disposeIntermediateTensorInfo(a),n.disposeIntermediateTensorInfo(o),n.disposeIntermediateTensorInfo(i),l}else return Od({attrs:{shape:s.shape,dtype:s.dtype,value:s.dtype==="string"?"":0},backend:n})}var Gte={kernelName:Hl,backendName:"webgl",kernelFunc:v0};function hk(e){let{inputs:t,backend:n}=e,{x:s}=t;if(s.dtype==="string")throw new Error("onesLike is not supported under string dtype");if(s.dtype==="complex64"){let r=Fd({inputs:{input:s},backend:n}),a=hk({inputs:{x:r},backend:n}),o=b0({inputs:{input:s},backend:n}),i=v0({inputs:{x:o},backend:n}),l=Ea({inputs:{real:a,imag:i},backend:n});return n.disposeIntermediateTensorInfo(r),n.disposeIntermediateTensorInfo(a),n.disposeIntermediateTensorInfo(o),n.disposeIntermediateTensorInfo(i),l}else return Od({attrs:{shape:s.shape,dtype:s.dtype,value:1},backend:n})}var jte={kernelName:Cl,backendName:"webgl",kernelFunc:hk};function qte(e){let{inputs:t,backend:n,attrs:s}=e,{axis:r}=s;if(t.length===1)return L2({inputs:{input:t[0]},backend:n,attrs:{dim:r}});let a=t[0].shape,o=t[0].dtype;t.forEach(c=>{w.assertShapesMatch(a,c.shape,"All tensors passed to stack must have matching shapes"),w.assert(o===c.dtype,()=>"All tensors passed to stack must have matching dtypes")});let i=[],l=t.map(c=>{let d=L2({inputs:{input:c},backend:n,attrs:{dim:r}});return i.push(d),d}),u=G4({inputs:l,backend:n,attrs:{axis:r}});return i.forEach(c=>n.disposeIntermediateTensorInfo(c)),u}var Xte={kernelName:Tl,backendName:"webgl",kernelFunc:qte},Kte=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 s=e.length,r=yt(s),a=t.map(l=>l[0]).join(","),o=t.map((l,u)=>l[0]+e[u]).join(","),i=["coords[0]","coords[1]","coords[2]","coords[3]"].slice(0,s);if(s===1){this.userCode=` int start = ${a}; int end = ${o}; void main() { int outC = getOutputCoords(); if (outC < start || outC >= end) { setOutput(value); } else { setOutput(getX(outC - start)); } } `;return}this.userCode=` ${r} start = ${r}(${a}); ${r} end = ${r}(${o}); void main() { ${r} outC = getOutputCoords(); if (any(lessThan(outC, start)) || any(greaterThanEqual(outC, end))) { setOutput(value); } else { ${r} coords = outC - start; setOutput(getX(${i})); } } `}},Zte=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 s=e.length,r=yt(s),a=t.map(m=>m[0]).join(","),o=t.map((m,f)=>m[0]+e[f]).join(","),i=$n("rc",s),l=$n("source",s),u=`${i[s-1]} < ${this.outputShape[s-1]}`,c=s===1?"source":`vec2(${l.slice(-2).join()})`,d=[`${r} rc = outputLoc;`,`${i[s-1]} += 1; if(${u}) { `,s===1?"":`} rc = outputLoc; ${i[s-2]} += 1; if(${i[s-2]} < ${this.outputShape[s-2]}) {`,s===1?"":` ${i[s-1]} += 1; if(${u}) {`],p=s===1?"rc < start || rc >= end":"any(lessThan(rc, start)) || any(greaterThanEqual(rc, end))",h="";for(let m=0,f=s===1?2:4;m{let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{paddings:a,constantValue:o}=s;if(w.sizeFromShape(r.shape)===0){let u=a.map((c,d)=>c[0]+r.shape[d]+c[1]);return Od({backend:n,attrs:{shape:u,value:o,dtype:r.dtype}})}let i=Y().getBool("WEBGL_PACK_ARRAY_OPERATIONS")?new Zte(r.shape,a,o):new Kte(r.shape,a,o),l=[[o]];return n.runWebGLProgram(i,[r],r.dtype,l)},Yte={kernelName:wo,backendName:"webgl",kernelFunc:fk},Jte=` if(a < 0.0 && floor(b) < b){ return NAN; } if (b == 0.0) { return 1.0; } return (round(mod(b, 2.0)) != 1) ? pow(abs(a), b) : sign(a) * pow(abs(a), b); `,Qte=` // 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); vec4 result = multiplier * pow(abs(a), b); // Ensure that a^0 = 1, including 0^0 = 1 as this correspond to TF and JS bvec4 isExpZero = equal(b, vec4(0.0)); result.r = isExpZero.r ? 1.0 : result.r; result.g = isExpZero.g ? 1.0 : result.g; result.b = isExpZero.b ? 1.0 : result.b; result.a = isExpZero.a ? 1.0 : result.a; vec4 isNaN = vec4(lessThan(a, vec4(0.0))) * vec4(lessThan(floor(b), b)); `+h0+` return result; `,ene=bn({opSnippet:Jte,packedOpSnippet:Qte}),tne={kernelName:ko,backendName:"webgl",kernelFunc:ene};function nne(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{axis:a,keepDims:o}=s,i=r.shape.length,l=[],u=w.parseAxisParam(a,r.shape),c=u,d=_.getAxesPermutation(c,i),p=r;d!=null&&(p=Fn({inputs:{x:r},backend:n,attrs:{perm:d}}),c=_.getInnerMostAxes(c.length,i),l.push(p)),_.assertAxesAreInnerMostDims("prod",c,i);let h;if(n.shouldExecuteOnCPU([p])){let m=n.texData.get(p.dataId).values,{outVals:f,outShape:g,outDtype:A}=dK(p.shape,p.dtype,m,c);h=n.makeTensorInfo(g,A,f)}else{let[m,f]=_.computeOutAndReduceShapes(p.shape,c),g=w.sizeFromShape(f),A=be({inputs:{x:p},backend:n,attrs:{shape:[-1,g]}}),y=yh(r.dtype),x=wi(A,y,"prod",n);h=be({inputs:{x},backend:n,attrs:{shape:m}}),l.push(A),l.push(x)}if(o){l.push(h);let m=_.expandShapeToKeepDim(h.shape,u);h=be({inputs:{x:h},backend:n,attrs:{shape:m}})}return l.forEach(m=>n.disposeIntermediateTensorInfo(m)),h}var sne={kernelName:Nl,backendName:"webgl",kernelFunc:nne},mk=e=>{let{backend:t,attrs:n}=e,{start:s,stop:r,step:a,dtype:o}=n,i=pK(s,r,a,o);return t.makeTensorInfo([i.length],o,i)},rne={kernelName:vc,backendName:"webgl",kernelFunc:mk},ane="return 1.0 / x;",one=tt({opSnippet:ane}),ine={kernelName:El,backendName:"webgl",kernelFunc:one},lne=nr+` return (x < 0.0) ? 0.0 : x; `,une=` vec4 result = x * vec4(greaterThanEqual(x, vec4(0.0))); bvec4 isNaN = isnan(x); result.r = isNaN.r ? x.r : result.r; result.g = isNaN.g ? x.g : result.g; result.b = isNaN.b ? x.b : result.b; result.a = isNaN.a ? x.a : result.a; return result; `,cne=tt({opSnippet:lne,packedOpSnippet:une}),dne={kernelName:So,backendName:"webgl",kernelFunc:cne},pne=nr+` return (x < 0.0) ? 0.0 : min(6.0, x); `,hne=` vec4 result = min(x, vec4(6.)) * vec4(greaterThanEqual(x, vec4(0.0))); bvec4 isNaN = isnan(x); result.r = isNaN.r ? x.r : result.r; result.g = isNaN.g ? x.g : result.g; result.b = isNaN.b ? x.b : result.b; result.a = isNaN.a ? x.a : result.a; return result; `,fne=tt({opSnippet:pne,packedOpSnippet:hne}),mne={kernelName:To,backendName:"webgl",kernelFunc:fne},gne=class{constructor(e,t,n,s,r){this.variableNames=["A"],this.outputShape=[];let[a,o,i,l]=e;this.outputShape=[a,t,n,l];let u=[s&&t>1?o-1:o,s&&n>1?i-1:i],c=[s&&t>1?t-1:t,s&&n>1?n-1:n],d;r?d="(vec2(yRC) + vec2(0.5)) * effectiveInputOverOutputRatioRC - vec2(0.5)":d="vec2(yRC) * effectiveInputOverOutputRatioRC",this.userCode=` const vec2 effectiveInputOverOutputRatioRC = vec2( ${u[0]/c[0]}, ${u[1]/c[1]}); const vec2 inputShapeRC = vec2(${o}.0, ${i}.0); void main() { ivec4 coords = getOutputCoords(); int b = coords[0]; int d = coords[3]; ivec2 yRC = coords.yz; // Fractional source index. vec2 sourceFracIndexRC = ${d}; // Compute the four integer indices. ivec2 sourceFloorRC = ivec2(max(sourceFracIndexRC, vec2(0.0))); ivec2 sourceCeilRC = ivec2( min(inputShapeRC - 1.0, ceil(sourceFracIndexRC))); float topLeft = getA(b, sourceFloorRC.x, sourceFloorRC.y, d); float bottomLeft = getA(b, sourceCeilRC.x, sourceFloorRC.y, d); float topRight = getA(b, sourceFloorRC.x, sourceCeilRC.y, d); float bottomRight = getA(b, sourceCeilRC.x, sourceCeilRC.y, d); vec2 fracRC = sourceFracIndexRC - vec2(sourceFloorRC); float top = topLeft + (topRight - topLeft) * fracRC.y; float bottom = bottomLeft + (bottomRight - bottomLeft) * fracRC.y; float newValue = top + (bottom - top) * fracRC.x; setOutput(newValue); } `}},Ane=class{constructor(e,t,n,s,r){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=[];let[a,o,i,l]=e;this.outputShape=[a,t,n,l];let u=[s&&t>1?o-1:o,s&&n>1?i-1:i],c=[s&&t>1?t-1:t,s&&n>1?n-1:n],d;r?d="(vec3(yRC) + vec3(0.5)) * effectiveInputOverOutputRatioRC - vec3(0.5)":d="vec3(yRC) * effectiveInputOverOutputRatioRC",this.userCode=` const vec3 effectiveInputOverOutputRatioRC = vec3( ${u[0]/c[0]}, ${u[1]/c[1]}, ${u[1]/c[1]}); const vec3 inputShapeRC = vec3(${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)); } void main() { ivec4 coords = getOutputCoords(); int b = coords[0]; int d = coords[3]; // Calculate values for next column in yRC.z. ivec3 yRC = coords.yzz + ivec3(0, 0, 1); // Fractional source index. vec3 sourceFracIndexRC = ${d}; // Compute the four integer indices. ivec3 sourceFloorRC = ivec3(max(sourceFracIndexRC, vec3(0.0))); ivec3 sourceCeilRC = ivec3( 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}; // In parallel, construct four corners for all four components in // packed 2x2 cell. vec4 topLeft = vec4( getAValue(b, sourceFloorRC.x, sourceFloorRC.y, d), hasNextCol ? getAValue(b, sourceFloorRC.x, sourceFloorRC.y, d + 1) : 0.0, hasNextRow ? getAValue(b, sourceFloorRC.x, sourceFloorRC.z, d) : 0.0, (hasNextRow && hasNextCol) ? getAValue(b, sourceFloorRC.x, sourceFloorRC.z, d + 1) : 0.0); vec4 bottomLeft = vec4( getAValue(b, sourceCeilRC.x, sourceFloorRC.y, d), hasNextCol ? getAValue(b, sourceCeilRC.x, sourceFloorRC.y, d + 1) : 0.0, hasNextRow ? getAValue(b, sourceCeilRC.x, sourceFloorRC.z, d) : 0.0, (hasNextRow && hasNextCol) ? getAValue(b, sourceCeilRC.x, sourceFloorRC.z, d + 1) : 0.0); vec4 topRight = vec4( getAValue(b, sourceFloorRC.x, sourceCeilRC.y, d), hasNextCol ? getAValue(b, sourceFloorRC.x, sourceCeilRC.y, d + 1) : 0.0, hasNextRow ? getAValue(b, sourceFloorRC.x, sourceCeilRC.z, d) : 0.0, (hasNextRow && hasNextCol) ? getAValue(b, sourceFloorRC.x, sourceCeilRC.z, d + 1) : 0.0); vec4 bottomRight = vec4( getAValue(b, sourceCeilRC.x, sourceCeilRC.y, d), hasNextCol ? getAValue(b, sourceCeilRC.x, sourceCeilRC.y, d + 1) : 0.0, hasNextRow ? getAValue(b, sourceCeilRC.x, sourceCeilRC.z, d) : 0.0, (hasNextRow && hasNextCol) ? getAValue(b, sourceCeilRC.x, sourceCeilRC.z, d + 1) : 0.0); vec3 fracRC = sourceFracIndexRC - vec3(sourceFloorRC); vec4 top = mix(topLeft, topRight, fracRC.yyzz); vec4 bottom = mix(bottomLeft, bottomRight, fracRC.yyzz); vec4 newValue = mix(top, bottom, fracRC.x); setOutput(newValue); } `}};function yne(e){let{inputs:t,backend:n,attrs:s}=e,{images:r}=t,{alignCorners:a,halfPixelCenters:o,size:i}=s,[l,u]=i,c=Y().getBool("WEBGL_PACK_IMAGE_OPERATIONS")?new Ane(r.shape,l,u,a,o):new gne(r.shape,l,u,a,o);return n.runWebGLProgram(c,[r],"float32")}var xne={kernelName:Co,backendName:"webgl",kernelFunc:yne},bne=class{constructor(e,t,n){this.variableNames=["dy"],this.outputShape=[],this.outputShape=t;let[,s,r]=t,[,a,o]=e,i=[n&&a>1?s-1:s,n&&o>1?r-1:r],l=[n&&a>1?a-1:a,n&&o>1?o-1:o],u=i[0]/l[0],c=i[1]/l[1],d=1/u,p=1/c,h=Math.ceil(d)*2+2,m=Math.ceil(p)*2+2;this.userCode=` void main() { ivec4 coords = getOutputCoords(); int b = coords[0]; int d = coords[3]; int r = coords[1]; int c = coords[2]; float accumulator = 0.0; const float heightScale = float(${u}); const float widthScale = float(${c}); const float invHeightScale = float(${d}); const float invWidthScale = float(${p}); const int winHeight = int(${h}); const int winWidth = int(${m}); // Compute bounds for where in dy we will look float startRLerp = floor(float(r) * invHeightScale); int startDyR = int(startRLerp - float(winHeight / 2)); float startCLerp = floor(float(c) * invWidthScale); int startDyC = int(startCLerp - float(winWidth / 2)); // Loop over dy for (int dyROffset = 0; dyROffset < winHeight; dyROffset++) { int dyR = dyROffset + startDyR; // Guard against the window exceeding the bounds of dy if (dyR < 0 || dyR >= ${a}) { continue; } for (int dyCOffset = 0; dyCOffset < winWidth; dyCOffset++) { int dyC = dyCOffset + startDyC; // Guard against the window exceeding the bounds of dy if (dyC < 0 || dyC >= ${o}) { continue; } float dxR = float(dyR) * heightScale; int topDxRIndex = int(floor(dxR)); int bottomDxRIndex = int(min(ceil(dxR), ${s-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)); float dxCLerp = dxC - float(leftDxCIndex); float inverseDxCLerp = 1.0 - dxCLerp; if (r == topDxRIndex && c == leftDxCIndex) { // topLeft accumulator += getDy(b, dyR, dyC, d) * inverseDxRLerp * inverseDxCLerp; } if (r == topDxRIndex && c == rightDxCIndex) { // topRight accumulator += getDy(b, dyR, dyC, d) * inverseDxRLerp * dxCLerp; } if (r == bottomDxRIndex && c == leftDxCIndex) { // bottomLeft accumulator += getDy(b, dyR, dyC, d) * dxRLerp * inverseDxCLerp; } if (r == bottomDxRIndex && c == rightDxCIndex) { // bottomRight accumulator += getDy(b, dyR, dyC, d) * dxRLerp * dxCLerp; } } } // End loop over dy setOutput(accumulator); } `}};function vne(e){let{inputs:t,backend:n,attrs:s}=e,{images:r,dy:a}=t,{alignCorners:o}=s,i=new bne(a.shape,r.shape,o);return n.runWebGLProgram(i,[a],a.dtype)}var wne={kernelName:nh,backendName:"webgl",kernelFunc:vne},kne=class{constructor(e,t,n,s,r){this.variableNames=["A"],this.outputShape=[];let[a,o,i,l]=e;this.outputShape=[a,t,n,l];let u=[s&&t>1?o-1:o,s&&n>1?i-1:i],c=[s&&t>1?t-1:t,s&&n>1?n-1:n],d=s?"0.5":"0.0",p;r?p="max((vec2(yRC) + vec2(0.5)) * effectiveInputOverOutputRatioRC, vec2(0.0))":p="vec2(yRC) * effectiveInputOverOutputRatioRC",this.userCode=` const vec2 effectiveInputOverOutputRatioRC = vec2( ${u[0]/c[0]}, ${u[1]/c[1]}); const vec2 inputShapeRC = vec2(${o}.0, ${i}.0); void main() { ivec4 coords = getOutputCoords(); int b = coords[0]; int d = coords[3]; ivec2 yRC = coords.yz; // Fractional source index. vec2 sourceFracIndexRC = ${p}; // Compute the coordinators of nearest neighbor point. ivec2 sourceNearestRC = ivec2( min(inputShapeRC - 1.0, floor(sourceFracIndexRC + ${d}))); float newValue = getA(b, sourceNearestRC.x, sourceNearestRC.y, d); setOutput(newValue); } `}},Ine=class{constructor(e,t,n,s,r){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=[];let[a,o,i,l]=e;this.outputShape=[a,t,n,l];let u=[s&&t>1?o-1:o,s&&n>1?i-1:i],c=[s&&t>1?t-1:t,s&&n>1?n-1:n],d=s?"0.5":"0.0",p;r?p="max((vec3(yRC) + vec3(0.5)) * effectiveInputOverOutputRatioRC, vec3(0.0))":p="vec3(yRC) * effectiveInputOverOutputRatioRC",this.userCode=` const vec3 effectiveInputOverOutputRatioRC = vec3( ${u[0]/c[0]}, ${u[1]/c[1]}, ${u[1]/c[1]}); const vec3 inputShapeRC = vec3(${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)); } void main() { ivec4 coords = getOutputCoords(); int b = coords[0]; int d = coords[3]; // Calculate values for next column in yRC.z. ivec3 yRC = coords.yzz + ivec3(0, 0, 1); // Fractional source index. vec3 sourceFracIndexRC = ${p}; // Compute the coordinators of nearest neighbor point. ivec3 sourceNearestRC = ivec3( min(inputShapeRC - 1.0, floor(sourceFracIndexRC + ${d}))); // Should we calculate next column and row elements in 2x2 packed cell. bool hasNextCol = d < ${l-1}; bool hasNextRow = coords.z < ${n-1}; vec4 newValue = vec4( getAValue(b, sourceNearestRC.x, sourceNearestRC.y, d), hasNextCol ? getAValue(b, sourceNearestRC.x, sourceNearestRC.y, d + 1) : 0.0, hasNextRow ? getAValue(b, sourceNearestRC.x, sourceNearestRC.z, d) : 0.0, (hasNextRow && hasNextCol) ? getAValue(b, sourceNearestRC.x, sourceNearestRC.z, d + 1) : 0.0); setOutput(newValue); } `}};function Sne(e){let{inputs:t,backend:n,attrs:s}=e,{images:r}=t,{alignCorners:a,halfPixelCenters:o,size:i}=s,[l,u]=i,c=Y().getBool("WEBGL_PACK_IMAGE_OPERATIONS")?new Ine(r.shape,l,u,a,o):new kne(r.shape,l,u,a,o);return n.runWebGLProgram(c,[r],r.dtype)}var Cne={kernelName:wc,backendName:"webgl",kernelFunc:Sne},Tne=class{constructor(e,t,n){this.variableNames=["dy"],this.outputShape=[],this.outputShape=t;let[,s,r]=t,[,a,o]=e,i=[n&&a>1?s-1:s,n&&o>1?r-1:r],l=[n&&a>1?a-1:a,n&&o>1?o-1:o],u=i[0]/l[0],c=i[1]/l[1],d=1/u,p=1/c,h=Math.ceil(d)*2+2,m=Math.ceil(p)*2+2;this.userCode=` void main() { ivec4 coords = getOutputCoords(); int b = coords[0]; int d = coords[3]; int r = coords[1]; int c = coords[2]; float accumulator = 0.0; const float heightScale = float(${u}); const float widthScale = float(${c}); const float invHeightScale = float(${d}); const float invWidthScale = float(${p}); const int winHeight = int(${h}); const int winWidth = int(${m}); // Compute bounds for where in dy we will look float startRLerp = floor(float(r) * invHeightScale); int startDyR = int(floor(startRLerp - float(winHeight / 2))); float startCLerp = floor(float(c) * invWidthScale); int startDyC = int(floor(startCLerp - float(winWidth / 2))); // Loop over dy for (int dyROffset = 0; dyROffset < winHeight; dyROffset++) { int dyR = dyROffset + startDyR; // Guard against the window exceeding the bounds of dy if (dyR < 0 || dyR >= ${a}) { continue; } for (int dyCOffset = 0; dyCOffset < winWidth; dyCOffset++) { int dyC = dyCOffset + startDyC; // Guard against the window exceeding the bounds of dy if (dyC < 0 || dyC >= ${o}) { continue; } float sourceFracRow = float(${i[0]}) * (float(dyR) / float(${l[0]})); float sourceFracCol = float(${i[1]}) * (float(dyC) / float(${l[1]})); int sourceNearestRow = int(min( float(int(${s}) - 1), ${n} ? float(round(sourceFracRow)) : float(floor(sourceFracRow)))); int sourceNearestCol = int(min( float(int(${r}) - 1), ${n} ? float(round(sourceFracCol)) : float(floor(sourceFracCol)))); if (r == sourceNearestRow && c == sourceNearestCol) { accumulator += getDy(b, dyR, dyC, d); } } } // End loop over dy setOutput(accumulator); } `}};function Nne(e){let{inputs:t,backend:n,attrs:s}=e,{images:r,dy:a}=t,{alignCorners:o}=s,i=new Tne(a.shape,r.shape,o);return n.runWebGLProgram(i,[a],a.dtype)}var Ene={kernelName:th,backendName:"webgl",kernelFunc:Nne},Rne=class{constructor(e,t){this.variableNames=["x"];let n=e.length;if(n>4)throw new Error(`WebGL backend: Reverse of rank-${n} tensor is not yet supported`);if(this.outputShape=e,n===1){this.userCode=` void main() { int coord = getOutputCoords(); setOutput(getX(${e[0]} - coord - 1)); } `;return}let s=o=>t.indexOf(o)!==-1&&e[o]!==1?`${e[o]} - coords[${o}] - 1`:`coords[${o}]`,r=e.map((o,i)=>s(i)).join(","),a=yt(n);this.userCode=` void main() { ${a} coords = getOutputCoords(); setOutput(getX(${r})); } `}},Dne=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-${n} tensor is not yet supported`);this.outputShape=e;let s=$n("rc",n),r=`${s[n-1]} + 1 < ${this.outputShape[n-1]}`,a=`${s[n-2]} + 1 < ${this.outputShape[n-2]}`,o=yt(n);n===1?this.userCode=` void main(){ int rc = getOutputCoords(); vec4 result = vec4(0.); result.r = getChannel(getX(${e[0]} - rc - 1), ${e[0]} - rc - 1); if(${r}){ result.g = getChannel(getX(${e[0]} - (rc + 1) - 1), ${e[0]} - (rc + 1) - 1); } setOutput(result); } `:this.userCode=` void main() { ${o} rc = getOutputCoords(); vec4 result = vec4(0.); result.r = ${i(s.slice())}; if(${r}){ result.g = ${l(s.slice())}; } if(${a}) { result.b = ${u(s.slice())}; if(${r}) { result.a = ${c(s.slice())}; } } setOutput(result); } `;function i(h){return d(h)}function l(h){return h[n-1]="("+h[n-1]+" + 1)",d(h)}function u(h){return h[n-2]="("+h[n-2]+" + 1)",d(h)}function c(h){return h[n-1]="("+h[n-1]+" + 1)",h[n-2]="("+h[n-2]+" + 1)",d(h)}function d(h){let m=e.map((A,y)=>p(y,h)),f=m.join(","),g=m.slice(-2).join(",");return`getChannel(getX(${f}), vec2(${g}))`}function p(h,m){return t.indexOf(h)!==-1&&e[h]!==1?`${e[h]} - ${m[h]} - 1`:`${m[h]}`}}};function _ne(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{dims:a}=s,o=r.shape.length,i=w.parseAxisParam(a,r.shape);if(o===0)return cs({inputs:{x:r},backend:n});let l=Y().getBool("WEBGL_PACK_ARRAY_OPERATIONS")?new Dne(r.shape,i):new Rne(r.shape,i);return n.runWebGLProgram(l,[r],r.dtype)}var $ne={kernelName:No,backendName:"webgl",kernelFunc:_ne},Fne=class{constructor(e,t){this.variableNames=["Image"],this.outputShape=[],this.customUniforms=[{name:"params",type:"vec4"}];let n=e[1],s=e[2];this.outputShape=e;let r="";typeof t=="number"?r=`float outputValue = ${t.toFixed(2)};`:r=` vec3 fill = vec3(${t.join(",")}); float outputValue = fill[coords[3]];`,this.userCode=` void main() { ivec4 coords = getOutputCoords(); int x = coords[2]; int y = coords[1]; float coordXFloat = (float(x) - params[0]) * params[3] - (float(y) - params[1]) * params[2]; float coordYFloat = (float(x) - params[0]) * params[2] + (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 < ${s} && coordY >= 0 && coordY < ${n}) { outputValue = getImage(coords[0], coordY, coordX, coords[3]); } setOutput(outputValue); } `}},One={kernelName:Gl,backendName:"webgl",kernelFunc:({inputs:e,attrs:t,backend:n})=>{let{image:s}=e,{radians:r,fillValue:a,center:o}=t,i=n,l=new Fne(s.shape,a),[u,c]=_.getImageCenter(o,s.shape[1],s.shape[2]),d=[[u,c,Math.sin(r),Math.cos(r)]];return i.runWebGLProgram(l,[s],s.dtype,d)}},Pne=` // OpenGL ES does not support round function. // The algorithm is based on banker's rounding. float base = floor(x); if ((x - base) < 0.5) { return floor(x); } else if ((x - base) > 0.5) { return ceil(x); } else { if (mod(base, 2.0) == 0.0) { return base; } else { return base + 1.0; } } `,Mne=tt({opSnippet:Pne}),zne={kernelName:Eo,backendName:"webgl",kernelFunc:Mne},Lne="return inversesqrt(x);",Bne=tt({opSnippet:Lne,cpuKernelImpl:hK}),Wne={kernelName:Ro,backendName:"webgl",kernelFunc:Bne},gk=class{constructor(e,t,n,s,r,a,o=!0){this.variableNames=["updates","indices","defaultValue"],this.outputShape=a;let i=yt(r.length),l=yt(a.length),u="";n===1?u="i":n===2&&(u="i, j");let c=`getIndices(${u})`,d="";s===1?d="i":s===2&&(d="i, coords[1]");let p=`getUpdates(${d})`,h=t>1?"strides[j]":"strides";this.userCode=` ${i} strides = ${i}(${r}); void main() { ${l} coords = getOutputCoords(); float sum = 0.0; bool found = false; for (int i = 0; i < ${e}; i++) { int flattenedIndex = 0; for (int j = 0; j < ${t}; j++) { int index = round(${c}); flattenedIndex += index * ${h}; } if (flattenedIndex == coords[0]) { sum += ${p}; found = true; } } setOutput(mix(getDefaultValue(), sum, float(found))); } `}};function Vne(e){let{inputs:t,backend:n,attrs:s}=e,{indices:r,updates:a}=t,{shape:o}=s,{sliceRank:i,numUpdates:l,sliceSize:u,strides:c,outputSize:d}=_.calculateShapes(a,r,o),p=[d/u,u];if(d===0)return n.makeTensorInfo(o,r.dtype);let h=be({inputs:{x:r},backend:n,attrs:{shape:[l,i]}}),m=be({inputs:{x:a},backend:n,attrs:{shape:[l,u]}}),f=n.makeTensorInfo([],"float32",new Float32Array([0])),g=new gk(l,i,h.shape.length,m.shape.length,c,p),A=n.runWebGLProgram(g,[m,h,f],m.dtype),y=be({inputs:{x:A},backend:n,attrs:{shape:o}});return n.disposeIntermediateTensorInfo(h),n.disposeIntermediateTensorInfo(m),n.disposeIntermediateTensorInfo(A),n.disposeIntermediateTensorInfo(f),y}var Une={kernelName:Dl,backendName:"webgl",kernelFunc:Vne},Hne=class{constructor(e,t,n){this.variableNames=["c","a","b"],this.outputShape=t;let s,r;if(n>4)throw Error(`Where for rank ${n} is not yet supported`);if(n===1)r="resRC",s="resRC";else{let o=["resRC.x","resRC.y","resRC.z","resRC.w"],i=[],l=[];for(let u=0;u= 1.0) { setOutput(getA(${r})); } else { setOutput(getB(${r})); } } `}};function Gne(e){let{inputs:t,backend:n}=e,{condition:s,t:r,e:a}=t,o=new Hne(s.shape.length,r.shape,r.shape.length);return n.runWebGLProgram(o,[s,r,a],Ds(r.dtype,a.dtype))}var jne={kernelName:_l,backendName:"webgl",kernelFunc:Gne},qne=` // Stable and Attracting Fixed Point (0, 1) for Normalized Weights. // see: https://arxiv.org/abs/1706.02515 float scaleAlpha = ${_.SELU_SCALEALPHA}; float scale = ${_.SELU_SCALE}; return (x >= 0.0) ? scale * x : scaleAlpha * (exp(x) - 1.0); `,Xne=tt({opSnippet:qne}),Kne={kernelName:$l,backendName:"webgl",kernelFunc:Xne},Ak="return 1.0 / (1.0 + exp(-1.0 * x));",Zne=tt({opSnippet:Ak,packedOpSnippet:Ak,cpuKernelImpl:fK}),Yne={kernelName:_o,backendName:"webgl",kernelFunc:Zne},Jne=` if (isnan(x)) { return 0.0; } return sign(x); `,Qne=tt({opSnippet:Jne}),ese={kernelName:Pl,backendName:"webgl",kernelFunc:Qne},tse=R4+` return sin(x); `,nse=tt({opSnippet:tse}),sse={kernelName:Do,backendName:"webgl",kernelFunc:nse},rse=` float e2x = exp(x); return (e2x - 1.0 / e2x) / 2.0; `,ase=tt({opSnippet:rse}),ose={kernelName:Ol,backendName:"webgl",kernelFunc:ase},ise=` float epsilon = 1.1920928955078125e-7; float threshold = log(epsilon) + 2.0; bool too_large = x > -threshold; bool too_small = x < threshold; float result; float exp_x = exp(x); if (too_large){ result = x; } else if (too_small){ result = exp_x; } else{ result = log(exp_x + 1.0); } return result; `,lse=tt({opSnippet:ise}),use={kernelName:Ml,backendName:"webgl",kernelFunc:lse},cse=e=>{let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{blockShape:a,paddings:o}=s;w.assert(r.shape.length<=4,()=>"spaceToBatchND for rank > 4 with a WebGL backend not implemented yet");let i=a.reduce((A,y)=>A*y),l=[[0,0]];l.push(...o);for(let A=1+a.length;An.disposeIntermediateTensorInfo(A)),g},dse={kernelName:zl,backendName:"webgl",kernelFunc:cse};function pse(e){let{inputs:t,backend:n}=e,{indices:s,values:r,denseShape:a,defaultValue:o}=t;if(a.shape.length!==1)throw new Error(`Dense shape must be a vector, saw: ${a.shape}`);if(s.shape.length!==2)throw new Error(`Indices must be a matrix, saw: ${s.shape}`);if(r.shape.length!==1)throw new Error(`Values must be a vector, saw: ${r.shape}`);if(o.shape.length!==0)throw new Error(`Default value must be a scalar, saw: ${o.shape}`);let i=n.readSync(s.dataId),l=n.readSync(r.dataId),u=n.readSync(a.dataId),c=n.readSync(o.dataId)[0],[d,p,h,m,f]=gK(i,s.shape,s.dtype,l,r.dtype,u,c);return[n.makeTensorInfo(p,s.dtype,d),n.makeTensorInfo([p[0]],r.dtype,h),n.makeTensorInfo([m.length],"bool",new Uint8Array(m.map(g=>Number(g)))),n.makeTensorInfo([f.length],s.dtype,new Int32Array(f))]}var hse={kernelName:sh,backendName:"webgl",kernelFunc:pse};function fse(e){let{inputs:t,backend:n}=e,{inputIndices:s,inputShape:r,newShape:a}=t;if(s.shape.length!==2)throw new Error(`Input indices should be a matrix but received shape ${s.shape}`);if(r.shape.length!==1)throw new Error(`Input shape should be a vector but received shape ${r.shape}`);if(a.shape.length!==1)throw new Error(`Target shape should be a vector but received shape ${a.shape}`);let o=Array.from(n.readSync(r.dataId)),i=n.readSync(s.dataId),l=Array.from(n.readSync(a.dataId)),[u,c,d]=AK(i,s.shape,s.dtype,o,l);return[n.makeTensorInfo(c,s.dtype,u),n.makeTensorInfo([d.length],a.dtype,new Int32Array(d))]}var mse={kernelName:rh,backendName:"webgl",kernelFunc:fse};function gse(e){let{inputs:t,backend:n}=e,{data:s,indices:r,segmentIds:a}=t;if(s.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(a.shape.length!==1)throw new Error(`Segment ids should be a vector but received shape ${a.shape}`);let o=n.readSync(s.dataId),i=n.readSync(r.dataId),l=n.readSync(a.dataId),[u,c]=A4(o,s.shape,s.dtype,i,l,!0);return n.makeTensorInfo(c,s.dtype,u)}var Ase={kernelName:ah,backendName:"webgl",kernelFunc:gse};function yse(e){let{inputs:t,backend:n}=e,{data:s,indices:r,segmentIds:a}=t;if(s.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(a.shape.length!==1)throw new Error(`Segment ids should be a vector but received shape ${a.shape}`);let o=n.readSync(s.dataId),i=n.readSync(r.dataId),l=n.readSync(a.dataId),[u,c]=A4(o,s.shape,s.dtype,i,l);return n.makeTensorInfo(c,s.dtype,u)}var xse={kernelName:oh,backendName:"webgl",kernelFunc:yse};function bse(e){let{inputs:t,backend:n,attrs:s}=e,{sparseIndices:r,sparseValues:a,defaultValue:o}=t,{outputShape:i}=s,{sliceRank:l,numUpdates:u,strides:c,outputSize:d}=_.calculateShapes(a,r,i),p=!1,h=new gk(u,l,r.shape.length,a.shape.length,c,[d,1],p),m=n.runWebGLProgram(h,[a,r,o],a.dtype),f=be({inputs:{x:m},backend:n,attrs:{shape:i}});return n.disposeIntermediateTensorInfo(m),f}var vse={kernelName:ih,backendName:"webgl",kernelFunc:bse};function wse(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{numOrSizeSplits:a,axis:o}=s,i=w.parseAxisParam(o,r.shape)[0],l=_.prepareSplitSize(r,a,i),u=r.shape.length,c=new Array(u).fill(0),d=r.shape.slice();return l.map(p=>{let h=[...d];h[i]=p;let m=Fu({inputs:{x:r},backend:n,attrs:{begin:c,size:h}});return c[i]+=p,m})}var kse={kernelName:Ll,backendName:"webgl",kernelFunc:wse},yk="return sqrt(x);",Ise=tt({opSnippet:yk,packedOpSnippet:yk,cpuKernelImpl:yK}),Sse={kernelName:$o,backendName:"webgl",kernelFunc:Ise},Cse="return x * x;",Tse=tt({opSnippet:Cse}),Nse={kernelName:kc,backendName:"webgl",kernelFunc:Tse},xk="return (a - b) * (a - b);",Ese=bn({opSnippet:xk,packedOpSnippet:xk}),Rse={kernelName:Po,backendName:"webgl",kernelFunc:Ese};function Dse({inputs:e,attrs:t,backend:n}){let{x:s}=e,r=nr+` return x > 0.0 ? 1.0 : float(${t.alpha}); `,a=new Na(s.shape,r);return n.runWebGLProgram(a,[s],s.dtype)}var _se={kernelName:ia,backendName:"webgl",kernelFunc:Dse},$se=class{constructor(e,t,n){this.variableNames=["x"],this.outputShape=n;let s=n.length,r=yt(n.length),a=yt(n.length),o="";if(s===1)o="coords * strides + begin";else{let i=0;o=n.map((l,u)=>(i++,n.length===1?`coords * strides[${u}] + begin[${u}]`:`coords[${i-1}] * strides[${u}] + begin[${u}]`)).join(",")}this.userCode=` ${r} begin = ${r}(${e}); ${r} strides = ${r}(${t}); void main() { ${a} coords = getOutputCoords(); setOutput(getX(${o})); } `}};function Fse(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{begin:a,end:o,strides:i,beginMask:l,endMask:u,ellipsisMask:c,newAxisMask:d,shrinkAxisMask:p}=s,{nonStrided:h,$begin:m,$strides:f,size:g,newShape:A,outShape:y}=Nn.sliceInfo(r.shape,a,o,i,l,u,c,d,p),x=be({inputs:{x:r},backend:n,attrs:{shape:A}}),b;if(h){let k=Fu({inputs:{x},backend:n,attrs:{begin:m,size:g}});b=be({inputs:{x:k},backend:n,attrs:{shape:y}}),n.disposeIntermediateTensorInfo(k)}else if(y.some(k=>k===0))b=n.makeTensorInfo(y,r.dtype,[]);else if(n.shouldExecuteOnCPU([x])){let C=n.texData.get(x.dataId).values,D=Ge(x.shape,x.dtype,C),O=xK(y,D,f,m);b=n.makeTensorInfo(y,x.dtype,O.values)}else{let S=new $se(m,f,y);b=n.runWebGLProgram(S,[x],x.dtype)}let v=be({inputs:{x:b},backend:n,attrs:{shape:y}});return n.disposeIntermediateTensorInfo(x),n.disposeIntermediateTensorInfo(b),v}var Ose={kernelName:Bl,backendName:"webgl",kernelFunc:Fse};function Pse(e){let{inputs:t,backend:n,attrs:s}=e,{separator:r,nGramWidths:a,leftPad:o,rightPad:i,padWidth:l,preserveShortSequences:u}=s,{data:c,dataSplits:d}=t,p=n.readSync(c.dataId),h=n.readSync(d.dataId),[m,f]=bK(p,h,r,a,o,i,l,u);return[n.makeTensorInfo([m.length],"string",m),n.makeTensorInfo(d.shape,"int32",f)]}var Mse={kernelName:lh,backendName:"webgl",kernelFunc:Pse};function zse(e){let{inputs:t,backend:n,attrs:s}=e,{skipEmpty:r}=s,{input:a,delimiter:o}=t;if(a.dtype!=="string")throw new Error("Input must be of datatype string");if(a.shape.length!==1)throw new Error(`Input must be a vector, got shape: ${a.shape}`);if(o.shape.length!==0)throw new Error(`Delimiter must be a scalar, got shape: ${o.shape}`);let i=n.readSync(a.dataId),l=n.readSync(o.dataId)[0],[u,c,d]=vK(i,l,r),p=c.length;return[n.makeTensorInfo([p,2],"int32",u),n.makeTensorInfo([p],"string",c),n.makeTensorInfo([2],"int32",new Int32Array(d))]}var Lse={kernelName:uh,backendName:"webgl",kernelFunc:zse};function Bse(e){let{inputs:t,backend:n,attrs:s}=e,{numBuckets:r}=s,{input:a}=t;if(a.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 o=n.readSync(a.dataId),i=wK(o,r);return n.makeTensorInfo(a.shape,"int32",i)}var Wse={kernelName:ch,backendName:"webgl",kernelFunc:Bse},Vse="return tan(x);",Use=tt({opSnippet:Vse}),Hse={kernelName:zo,backendName:"webgl",kernelFunc:Use},Gse=` float e2x = exp(-2.0 * abs(x)); return sign(x) * (1.0 - e2x) / (1.0 + e2x); `,jse=tt({opSnippet:Gse}),qse={kernelName:Lo,backendName:"webgl",kernelFunc:jse},Xse=class{constructor(e,t){this.variableNames=["A"];let n=new Array(e.length);for(let a=0;a5)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"],s=[];for(let r=0;r5){let l=n.readSync(r.dataId),u=r.dtype==="string"?l.map(p=>w.decodeString(p)):l,c=Ge(r.shape,r.dtype,u),d=IK(c,a);return n.makeTensorInfo(d.shape,d.dtype,d.values)}let o=new Xse(r.shape,a);return n.runWebGLProgram(o,[r],r.dtype)}var Zse={kernelName:oa,backendName:"webgl",kernelFunc:bk},Yse=class{constructor(e){this.variableNames=["x","indices"],this.customUniforms=[{name:"n",type:"int"},{name:"firstPass",type:"int"},{name:"negativeInf",type:"float"},{name:"dir",type:"int"},{name:"inc",type:"int"}],this.outputShape=e,this.userCode=` void main() { ivec2 coords = getOutputCoords(); int batch = coords[0]; int elemIdx = coords[1]; // We compare elements pair-wise within a group of size 2 * inc. // The comparing rule for each group alternates between ascending // and descending. Within each group, we compare each pair at // positions i and i+inc. To decide whether an element at position i // is x0 or x1, we mod it by 2 * inc, if the result is smaller than // inc, it is in the first half of the group, we denote it as x0, // otherwise we denote it as x1. // For example, as shown in the Bitonic top K paper referenced above, // Figure5(a) shows that element[1] is in the // second half of the group when group size is 2, but it is in the // first half of the group when group size is 4. bool isFirstInPair = imod(elemIdx, 2 * inc) < inc; int i = isFirstInPair ? elemIdx : elemIdx - inc; int i0 = firstPass == 1 ? i : int(getIndices(batch, i)); int i1 = firstPass == 1 ? i + inc : int(getIndices(batch, i + inc)); float x0 = i0 < n ? getX(batch, i0) : negativeInf; float x1 = i1 < n ? getX(batch, i1) : negativeInf; // Denotes which direction indices are in (ascending or descending). bool reverse = imod(elemIdx, 2 * dir) >= dir; bool isGreater = x0 > x1 || (x0 == x1 && i1 > i0); if (reverse == isGreater) { // Elements in opposite order of direction int iTemp = i0; i0 = i1; i1 = iTemp; } if (isFirstInPair) { setOutput(float(i0)); } else { setOutput(float(i1)); } } `}},Jse=class{constructor(e){this.variableNames=["x","indices"],this.customUniforms=[{name:"n",type:"int"},{name:"firstPass",type:"int"},{name:"k",type:"int"}],this.outputShape=e,this.userCode=` void main() { // Takes max of indices (0, k), (1, k + 1), (2, k + 2) ... ivec2 coords = getOutputCoords(); int batch = coords[0]; int elemIdx = coords[1]; // The output size is half of the previous size. // If the previous sequence is | | | | _ _ _ _ | | | | _ _ _ _ (k=4), // we only need to output the indices at positions |, the indices at // positions _ can be thrown away, see Figure5(b) After Phase 2 // (Merge phase) in the Bitonic Top K paper referenced above. // For example, the paper shows we only need to output the orange bars. // The output sequence should look like this | | | | | | | |. // Because the sequence is halved, to map the output index back // to the previous sequence to find the corresponding value, // we need to double the index. When we double the index, // we basically interpolate a position, so 2i looks like // | _ | _ | _ | _ | _ | _ | _. We move the | to the first k position // of each 2k positions by - elemIdx % k. E.g. for output at // index 4,5,6,7, we want to get the corresponding element at // original index 8,9,10,11, for output at index 8,9,10,11, // we want to get the corresponding element at original index // 16,17,18,19, so on and so forth. int i = elemIdx < k ? elemIdx : (elemIdx * 2 - imod(elemIdx, k)); int i0 = firstPass == 1 ? i : int(getIndices(batch, i)); int i1 = firstPass == 1 ? i + k : int(getIndices(batch, i + k)); float x0 = getX(batch, i0); float x1 = i1 < n ? getX(batch, i1) : x0; setOutput(x0 >= x1 ? float(i0) : float(i1)); } `}};function ki(e,t){t!==null&&e.disposeIntermediateTensorInfo(t)}function vk(e){let t=1;for(;tl){let O=n.readSync(r.dataId),[E,R]=SK(O,u,r.dtype,a,o);return[n.makeTensorInfo(E.shape,E.dtype,E.values),n.makeTensorInfo(R.shape,R.dtype,R.values)]}if(a===0)return u[u.length-1]=0,[n.makeTensorInfo(u,r.dtype,[]),n.makeTensorInfo(u,"int32",[])];if(c===1)return[r,Od({attrs:{shape:u,dtype:"int32",value:0},backend:n})];let d=n.texData.get(r.dataId),p=d!==null&&d.isPacked,h=p?n.unpackTensor(r):r,f=w.sizeFromShape(u)/c,g=be({inputs:{x:h},attrs:{shape:[f,c]},backend:n});p&&ki(n,h);let A=vk(a),y=vk(c),x=null,b=()=>x===null?[g,g]:[g,x],v=(O,E,R)=>{let T=b(),P=new Yse(R),j=[[c],[x===null?1:0],[Number.NEGATIVE_INFINITY],[O],[E]],q=x;x=n.runWebGLProgram(P,T,"int32",j),ki(n,q)};for(let O=1;O=1;R/=2)v(E,R,[f,y])}for(let O=y;O>A;O/=2){let E=b(),R=new Jse([f,O/2]),P=[[c],[x===null?1:0],[A]],U=x;x=n.runWebGLProgram(R,E,"int32",P),ki(n,U);let j=A/2,q=j*2;for(let X=j;X>=1;X/=2)v(q,X,x.shape)}let k=x;x=Fu({inputs:{x},backend:n,attrs:{begin:0,size:[f,a]}}),ki(n,k);let S=ok({inputs:{x:g,indices:x},backend:n,attrs:{axis:1,batchDims:1}});ki(n,g);let C=u.slice(0,-1);C.push(a),k=x,x=be({inputs:{x},attrs:{shape:C},backend:n}),ki(n,k);let D=S;return S=be({inputs:{x:S},attrs:{shape:C},backend:n}),ki(n,D),[S,x]}var ere={kernelName:Wl,backendName:"webgl",kernelFunc:Qse},tre=class{constructor(e,t,n,s,r,a){this.variableNames=["Image","Transforms"],this.outputShape=a;let o=n==="nearest"?1:2,i;switch(s){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(${i} == 2) { if (inCoord < 0.0) { if (len <= 1.0) { inCoord = 0.0; } else { float sz2 = 2.0 * len; if (inCoord < sz2) { inCoord = sz2 * float(int(float(-inCoord / sz2))) + inCoord; } inCoord = inCoord < -len ? inCoord + sz2 : -inCoord - 1.0; } } else if (inCoord > len - 1.0) { if (len <= 1.0) { inCoord = 0.0; } else { float sz2 = 2.0 * len; inCoord -= sz2 * float(int(float(inCoord / sz2))); if (inCoord >= len) { inCoord = sz2 - inCoord - 1.0; } } } return clamp(inCoord, 0.0, len - 1.0); } else if (${i} == 3) { if (inCoord < 0.0) { if (len <= 1.0) { inCoord = 0.0; } else { float sz = len - 1.0; inCoord += len * (float(int(float(-inCoord / sz))) + 1.0); } } else if (inCoord > len - 1.0) { if (len <= 1.0) { inCoord = 0.0; } else { float sz = len - 1.0; inCoord -= len * float(int(float(inCoord / sz))); } } return clamp(inCoord, 0.0, len - 1.0); } else if (${i} == 4) { return clamp(outCoord, 0.0, len - 1.0); } else { return outCoord; } } float readWithFillValue(int batch, int coordY, int coordX, int channel) { float outputValue; if (0 <= coordY && coordY < ${e} && 0 <= coordX && coordX < ${t}) { outputValue = getImage(batch, coordY, coordX, channel); } else { outputValue = float(${r}); } return outputValue; } void main() { ivec4 coords = getOutputCoords(); float outputValue; int batch = coords[0]; int x = coords[2]; int y = coords[1]; int channel = coords[3]; float xf = float(x); float yf = float(y); float a1 = getTransforms(batch, 0); float a2 = getTransforms(batch, 1); float a3 = getTransforms(batch, 2); float b1 = getTransforms(batch, 3); float b2 = getTransforms(batch, 4); float b3 = getTransforms(batch, 5); float c1 = getTransforms(batch, 6); float c2 = getTransforms(batch, 7); float projection = c1 * xf + c2 * yf + 1.0; if (projection == 0.0) { outputValue = float(${r}); } else { float inX = (a1 * xf + a2 * yf + a3) / projection; float inY = (b1 * xf + b2 * yf + b3) / projection; float mapX = mapCoord(inX, float(${t})); float mapY = mapCoord(inY, float(${e})); if (${o} == 1) { int coordY = int(round(mapY)); int coordX = int(round(mapX)); outputValue = readWithFillValue(batch, coordY, coordX, channel); } else { float yFloor = floor(mapY); float xFloor = floor(mapX); float yCeil = yFloor + 1.0; float xCeil = xFloor + 1.0; float valueYFloor = (xCeil - mapX) * readWithFillValue(batch, int(yFloor), int(xFloor), channel) + (mapX - xFloor) * readWithFillValue(batch, int(yFloor), int(xCeil), channel); float valueYCeil = (xCeil - mapX) * readWithFillValue(batch, int(yCeil), int(xFloor), channel) + (mapX - xFloor) * readWithFillValue(batch, int(yCeil), int(xCeil), channel); outputValue = (yCeil - mapY) * valueYFloor + (mapY - yFloor) * valueYCeil; } } setOutput(outputValue); } `}};function nre(e){let{inputs:t,backend:n,attrs:s}=e,{image:r,transforms:a}=t,{interpolation:o,fillMode:i,fillValue:l,outputShape:u}=s,[c,d,p,h]=r.shape,[m,f]=u!=null?u:[d,p],g=[c,m,f,h],A=new tre(d,p,o,i,l,g);return n.runWebGLProgram(A,[r,a],"float32")}var sre={kernelName:Vl,backendName:"webgl",kernelFunc:nre};function rre(e){let{inputs:t,attrs:n,backend:s}=e,{axis:r}=n,{x:a}=t;Cu(a,"unique"),console.warn("WARNING: ","UI might be locked temporarily as data is being downloaded");let o=s.readSync(a.dataId),{outputValues:i,outputShape:l,indices:u}=CK(o,r,a.shape,a.dtype);return[s.makeTensorInfo(l,a.dtype,i),s.makeTensorInfo([u.length],"int32",u)]}var are={kernelName:dh,backendName:"webgl",kernelFunc:rre};function ore(e){let{inputs:t,backend:n,attrs:s}=e,{value:r}=t,{axis:a}=s;a<0&&(a+=r.shape.length);let o=r,i=o.shape.length,l=r.shape[a],u=new Array(i-1),c=0;for(let f=0;fn.disposeIntermediateTensorInfo(f)),m}var ire={kernelName:Ul,backendName:"webgl",kernelFunc:ore},lre=class{constructor(e,t){this.variableNames=["x","segmentIds"];let n=e.windowSize,s=e.batchSize,r=e.inSize,a=e.numSegments,o=a*Math.ceil(r/n);this.outputShape=[s,o];let i="0.0",l="sumValue",u=Math.floor(n/4)*4,c=n%4,d=` sumValue += dot(values, segFilter); `,p="";r%n>0&&(p=` if (inIdx < 0 || inIdx >= ${r}) { return initializationValue; } `);let h="";r%n>0&&(h=` if (inIdx < 0 || inIdx >= ${r}) { return -1.0; } `),this.userCode=` const float initializationValue = ${i}; float getValue(int batch, int inIdx) { ${p} return getX(batch, inIdx); } float getSegmentIdAtIndex(int inIdx) { ${h} return getSegmentIds(inIdx); } void main() { ivec2 coords = getOutputCoords(); int batch = coords[0]; int outIdx = coords[1]; int inOffset = int(floor(float(outIdx) / float( ${a})) * float(${n})); int currentSeg = int(mod(float(outIdx), float(${a}))); float sumValue = 0.0; for (int i = 0; i < ${u}; i += 4) { int inIdx = inOffset + i; vec4 values = vec4( getValue(batch, inIdx), getValue(batch, inIdx + 1), getValue(batch, inIdx + 2), getValue(batch, inIdx + 3) ); vec4 segFilter = vec4( int(getSegmentIdAtIndex(inIdx)) == currentSeg ? 1 : 0, int(getSegmentIdAtIndex(inIdx + 1)) == currentSeg ? 1 : 0, int(getSegmentIdAtIndex(inIdx + 2)) == currentSeg ? 1 : 0, int(getSegmentIdAtIndex(inIdx + 3)) == currentSeg ? 1 : 0 ); ${d} } int inIdx = inOffset + ${u}; if (${c===1}) { vec4 values = vec4( getValue(batch, inIdx), initializationValue, initializationValue, initializationValue ); int inIdxSeg = int(getSegmentIdAtIndex(inIdx)); vec4 segFilter = vec4( int(getSegmentIdAtIndex(inIdx)) == currentSeg ? 1 : 0, 0, 0, 0 ); ${d} } else if (${c===2}) { vec4 values = vec4( getValue(batch, inIdx), getValue(batch, inIdx + 1), initializationValue, initializationValue ); vec4 segFilter = vec4( int(getSegmentIdAtIndex(inIdx)) == currentSeg ? 1 : 0, int(getSegmentIdAtIndex(inIdx + 1)) == currentSeg ? 1 : 0, 0, 0 ); ${d} } else if (${c===3}) { vec4 values = vec4( getValue(batch, inIdx), getValue(batch, inIdx + 1), getValue(batch, inIdx + 2), initializationValue ); vec4 segFilter = vec4( int(getSegmentIdAtIndex(inIdx)) == currentSeg ? 1 : 0, int(getSegmentIdAtIndex(inIdx + 1)) == currentSeg ? 1 : 0, int(getSegmentIdAtIndex(inIdx + 2)) == currentSeg ? 1 : 0, 0 ); ${d} } setOutput(${l}); } `}};function ure(e){let{inputs:t,backend:n,attrs:s}=e,{x:r,segmentIds:a}=t,{numSegments:o}=s,i=r.shape.length,l=[],u=0,c=_.getAxesPermutation([u],i),d=r;c!=null&&(d=Fn({inputs:{x:r},backend:n,attrs:{perm:c}}),l.push(d),u=_.getInnerMostAxes(1,i)[0]);let p=_.segment_util.computeOutShape(d.shape,u,o),h=w.sizeFromShape([d.shape[u]]),m=be({inputs:{x:d},backend:n,attrs:{shape:[-1,h]}});l.push(m);let f=yh(r.dtype),g=(b,v,k,S,C)=>{let D=b.shape[0],O=b.shape[1],E=_.segment_util.segOpComputeOptimalWindowSize(O,C),R={windowSize:E,inSize:O,batchSize:D,numSegments:C},T=new lre(R,v),P=n.compileAndRun(T,[b,k],S);if(l.push(P),P.shape[1]===C)return P;let U=mk({backend:n,attrs:{start:0,stop:C,step:1,dtype:"float32"}}),j=bk({inputs:{x:U},backend:n,attrs:{reps:[O/E]}});return l.push(U),l.push(j),g(P,v,j,S,C)},A=g(m,"unsortedSegmentSum",a,f,o),y=be({inputs:{x:A},backend:n,attrs:{shape:p}}),x=y;if(c!=null){l.push(y);let b=_.getUndoAxesPermutation(c);x=Fn({inputs:{x},backend:n,attrs:{perm:b}})}return l.forEach(b=>n.disposeIntermediateTensorInfo(b)),x}var cre={kernelName:Ic,backendName:"webgl",kernelFunc:ure},dre=[Lee,Vee,SZ,TZ,RZ,$Z,OZ,zZ,BZ,VZ,jZ,XZ,YZ,eY,iY,sY,cY,fY,pY,yY,bY,wY,CY,$Y,OY,WY,UY,qY,ZY,oZ,tJ,dJ,hJ,aJ,AJ,xJ,mJ,wJ,SJ,NJ,RJ,_J,OJ,WJ,UJ,MJ,jJ,KJ,YJ,tQ,aQ,uQ,pQ,hQ,fQ,gQ,yQ,bQ,wQ,IQ,NQ,DQ,FQ,PQ,LQ,VQ,jQ,ZQ,aZ,JQ,QY,tee,ree,iee,lZ,dee,mee,Aee,Iee,vee,Nee,Dee,Oee,Hee,Jee,Zee,nte,rte,ote,Xee,lte,cte,fte,yte,wte,Rte,hZ,_te,Ote,zte,Wte,MY,Hte,jte,Xte,Yte,tne,cZ,sne,rne,zY,Cte,ine,mne,dne,mZ,xne,wne,Cne,Ene,$ne,One,zne,Wne,Une,jne,Kne,Yne,ese,sse,ose,DY,Nte,use,dse,hse,mse,Ase,xse,vse,kse,Sse,Nse,Rse,_se,Ose,Mse,Lse,Wse,Tte,wZ,Hse,qse,Zse,ere,sre,kZ,are,ire,cre,Gte];for(let e of dre)la(e);var qn;(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"})(qn||(qn={}));var Pd;(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"})(Pd||(Pd={}));var wk;function pre(e){wk=e.wasm.cwrap(Wo,null,["number","array","number","number","array","number","number","number","number","number","number","number","number"])}function hre(e){let{inputs:t,backend:n,attrs:s}=e,{a:r,b:a,bias:o,preluActivationWeights:i}=t;if(r.dtype!=="float32"||a.dtype!=="float32")throw new Error("_FusedMatMul for non non-float32 tensors not yet supported.");let{transposeA:l,transposeB:u,activation:c,leakyreluAlpha:d}=s,p=n.dataIdMap.get(r.dataId).id,h=n.dataIdMap.get(a.dataId).id,m=0;if(o!=null){let C=n.dataIdMap.get(o.dataId);if(C.shape.length!==1)throw new Error(`_FusedMatMul only supports rank-1 bias but got rank ${C.shape.length}.`);m=C.id}let f=i==null?0:n.dataIdMap.get(i.dataId).id,g=Pd[c];if(g==null)throw new Error(`${c} activation not yet supported for FusedConv2D in the wasm backend.`);let A=l?r.shape[2]:r.shape[1],y=u?a.shape[1]:a.shape[2],x=r.shape[0],b=n.makeOutput([x,A,y],r.dtype),v=n.dataIdMap.get(b.dataId).id,k=new Uint8Array(new Int32Array(r.shape).buffer),S=new Uint8Array(new Int32Array(a.shape).buffer);return wk(p,k,r.shape.length,h,S,a.shape.length,l,u,g,m,f,d||0,v),b}var fre={kernelName:Wo,backendName:"wasm",setupFunc:pre,kernelFunc:hre};function vn(e){let t;function n(r){t=r.wasm.cwrap(e,null,["number","number"])}function s(r){let{backend:a,inputs:{x:o}}=r,i=a.dataIdMap.get(o.dataId).id,l=a.makeOutput(o.shape,o.dtype),u=a.dataIdMap.get(l.dataId).id;return w.sizeFromShape(l.shape)===0||t(i,u),l}return{kernelName:e,backendName:"wasm",setupFunc:n,kernelFunc:s}}var mre=vn(Gi);function On(e,t,n){let s;function r(o){s=o.wasm.cwrap(e,null,["number","array","number","number","array","number","number","number"])}function a(o){let{backend:i,inputs:l}=o,{a:u,b:c}=l,d=i.dataIdMap.get(u.dataId).id,p=i.dataIdMap.get(c.dataId).id,h=n!=null?n:u.dtype,m=_.assertAndGetBroadcastShape(u.shape,c.shape),f=i.makeOutput(m,h);if(w.sizeFromShape(m)===0)return f;let g=new Uint8Array(new Int32Array(u.shape).buffer),A=new Uint8Array(new Int32Array(c.shape).buffer),y=i.dataIdMap.get(f.dataId).id,x=()=>s(d,g,u.shape.length,p,A,c.shape.length,qn[u.dtype],y);if(t&&u.dtype==="float32")return x(),f;let b=_.getBroadcastDims(u.shape,m),v=_.getBroadcastDims(c.shape,m),k=b.every((C,D)=>C===D),S=v.every((C,D)=>C===D);if(k&&S)return x(),f;throw new Error(`Broadcasting along outer dims is not yet supported for ${u.dtype} ${e}.`)}return{kernelName:e,backendName:"wasm",setupFunc:r,kernelFunc:a}}var gre=!0,Are=On(ra,gre),kk;function yre(e){kk=e.wasm.cwrap(Ha,null,["array","number","number","number"])}function xre(e){let{inputs:t,backend:n}=e,s=n.makeOutput(t[0].shape,t[0].dtype);if(w.sizeFromShape(s.shape)===0)return s;let r=t.map(i=>n.dataIdMap.get(i.dataId).id),a=new Uint8Array(new Int32Array(r).buffer),o=n.dataIdMap.get(s.dataId).id;return kk(a,r.length,qn[s.dtype],o),s}var bre={kernelName:Ha,backendName:"wasm",setupFunc:yre,kernelFunc:xre};function w0(e){let{inputs:{x:t},backend:n}=e,s=n.makeOutput(t.shape,t.dtype),r=n.typedArrayFromHeap(t);return n.typedArrayFromHeap(s).set(r),s}var vre={kernelName:uo,backendName:"wasm",kernelFunc:w0},Ik;function wre(e){Ik=e.wasm.cwrap(Bo,null,["number","array","number","number","number","array","number"])}function Mu(e){let{inputs:t,backend:n,attrs:s}=e,[r,a]=Ire(t.x.shape,s.perm),o=!0;for(let m=0;m=r&&(a===-1||s[a]>s[o])&&(a=o);s[a]=r}return[n,s]}var Sre={kernelName:Bo,backendName:"wasm",kernelFunc:Mu,setupFunc:wre};function Ra(e,t,n){let s=e.shape,r=e.shape.length,a=w.parseAxisParam(t,s),o=a,i=_.getAxesPermutation(o,r),l=null,u=!1;if(i!=null){let c=new Array(r);for(let h=0;h`new shape: ${o}, old shape: ${s.shape}. New shape and old shape must have the same number of elements.`),e.backend.incRef(s.dataId),{dataId:s.dataId,shape:o,dtype:s.dtype}}var zre={kernelName:Rl,backendName:"wasm",kernelFunc:Xn},Ek;function Lre(e){Ek=e.wasm.cwrap(qa,null,["number","array","number","number","array","number","number","number","number"])}function Bre(e){let{inputs:t,backend:n,attrs:s}=e,{a:r,b:a}=t,{transposeA:o,transposeB:i}=s;if(r.dtype!=="float32"||a.dtype!=="float32")throw new Error("BatchMatMul for non non-float32 tensors not yet supported.");let l=r.shape.length,u=a.shape.length,c=o?r.shape[l-2]:r.shape[l-1],d=i?a.shape[u-1]:a.shape[u-2],p=o?r.shape[l-1]:r.shape[l-2],h=i?a.shape[u-2]:a.shape[u-1],m=r.shape.slice(0,-2),f=a.shape.slice(0,-2),g=w.sizeFromShape(m),A=w.sizeFromShape(f),y=g===A||g===1||A===1;w.assert(l>=2&&u>=2&&y,()=>`Error in matMul: the input batch dimensions must either be the same or at least one input batch dimension must be 1. Got input batch dimensions of (${m}) and (${f}).`);let b=(g>A?r.shape.slice(0,-2):a.shape.slice(0,-2)).concat([p,h]);w.assert(c===d,()=>`Error in matMul: inner shapes (${c}) and (${d}) of Tensors with shapes ${r.shape} and ${a.shape} and transposeA=${o} and transposeB=${i} must match.`);let v=o?[g,c,p]:[g,p,c],k=i?[A,h,d]:[A,d,h],S=Xn({inputs:{x:r},backend:n,attrs:{shape:v}}),C=Xn({inputs:{x:a},backend:n,attrs:{shape:k}}),D=n.dataIdMap.get(S.dataId).id,O=n.dataIdMap.get(C.dataId).id,E=o?S.shape[2]:S.shape[1],R=i?C.shape[1]:C.shape[2],T=Math.max(g,A),P=n.makeOutput([T,E,R],S.dtype),U=n.dataIdMap.get(P.dataId).id,j=new Uint8Array(new Int32Array(S.shape).buffer),q=new Uint8Array(new Int32Array(C.shape).buffer);return Ek(D,j,S.shape.length,O,q,C.shape.length,o,i,U),n.disposeData(S.dataId),n.disposeData(C.dataId),P.shape=b,P}var Wre={kernelName:qa,backendName:"wasm",setupFunc:Lre,kernelFunc:Bre};function Md(e){let{inputs:{x:t},attrs:{begin:n,size:s},backend:r}=e,[a,o]=Nn.parseSliceParams(t,n,s),i=Nn.isSliceContinous(t.shape,a,o),l=r.readSync(t.dataId),u=r.makeOutput(o,t.dtype),c=w.computeStrides(t.shape),d=r.dataIdMap.get(u.dataId);if(i){let m=Nn.computeFlatOffset(a,c);return t.dtype==="string"?d.stringBytes=l.slice(m,m+w.sizeFromShape(o)):r.typedArrayFromHeap(u).set(l.subarray(m,m+w.sizeFromShape(o))),u}if(t.dtype==="string"){let m=Jf(l,a,o,t.shape,t.dtype);return d.stringBytes=m,u}let p=r.typedArrayFromHeap(u),h=t.shape.length;if(h===2)Vre(l,c[0],p,a,o);else if(h===3)Ure(l,c[0],c[1],p,a,o);else if(h===4)Hre(l,c[0],c[1],c[2],p,a,o);else{let m=Jf(l,a,o,t.shape,t.dtype);p.set(m)}return u}function Vre(e,t,n,s,r){let a=0,o=s[0],i=s[1],l=o+r[0];for(let u=o;uA*y),l=_.getReshaped(r.shape,a,i),u=_.getPermuted(l.length,a.length),c=_.getReshapedPermuted(r.shape,a,i),d=_.getSliceBeginCoords(o,a.length),p=_.getSliceSize(c,o,a.length),h=Xn({inputs:{x:r},backend:n,attrs:{shape:l}}),m=Mu({inputs:{x:h},backend:n,attrs:{perm:u}}),f=Xn({inputs:{x:m},backend:n,attrs:{shape:c}}),g=Md({inputs:{x:f},backend:n,attrs:{begin:d,size:p}});return n.disposeData(h.dataId),n.disposeData(m.dataId),n.disposeData(h.dataId),g}var qre={kernelName:tl,backendName:"wasm",kernelFunc:jre};function k0(e){let{inputs:{x:t},attrs:{dtype:n},backend:s}=e,r=s.makeOutput(t.shape,n),a=s.typedArrayFromHeap(t);return s.typedArrayFromHeap(r).set(a),r}var Xre={kernelName:Xa,backendName:"wasm",kernelFunc:k0},Kre=vn(Ka),Rk;function Zre(e){Rk=e.wasm.cwrap(aa,null,["number","number","number","number"])}function Yre(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{clipValueMin:a,clipValueMax:o}=s,i=n.dataIdMap.get(r.dataId).id,l=n.makeOutput(r.shape,r.dtype),u=n.dataIdMap.get(l.dataId).id;return Rk(i,a,o,u),l}var Jre={kernelName:aa,backendName:"wasm",setupFunc:Zre,kernelFunc:Yre};function Dk(e){let{inputs:t,backend:n}=e,s=w.parseAxisParam(e.attrs.axis,t[0].shape)[0],r=_.computeOutShape(t.map(h=>h.shape),s),a=t.filter(h=>w.sizeFromShape(h.shape)>0);if(a.length===1)return w0({inputs:{x:a[0]},backend:n});let o=n.makeOutput(r,t[0].dtype);if(w.sizeFromShape(r)===0)return o;let i=a.map(h=>h.shape);if(_.assertParamsConsistent(i,s),a[0].dtype==="string"){let h=a.map(x=>{let b=w.sizeFromShape(x.shape.slice(s));return Xn({inputs:{x},backend:n,attrs:{shape:[-1,b]}})}),m=h.map(x=>({vals:n.readSync(x.dataId),shape:x.shape}));r=_.computeOutShape(h.map(x=>x.shape),1);let f=h[0].shape[0]===1,g=l2(m,r,t[0].dtype,f),A=_.computeOutShape(a.map(x=>x.shape),s);o.shape=A;let y=n.dataIdMap.get(o.dataId);return y.stringBytes=_.fromStringArrayToUint8(g),h.forEach(x=>n.disposeData(x.dataId)),o}let l=w.sizeFromShape(a[0].shape.slice(0,s)),u=0,c=a.map(h=>{let m=w.sizeFromShape(h.shape.slice(s));return u+=m,m}),d=a.map(h=>n.typedArrayFromHeap(h)),p=n.typedArrayFromHeap(o);for(let h=0;h`cumsum does not support ${r.dtype} tensors in the WASM backend`);let u=_.getAxesPermutation([a],l),c=r;u!==null&&(c=Mu({inputs:{x:r},attrs:{perm:u},backend:n}));let d=_.getInnerMostAxes(1,l)[0];_.assertAxesAreInnerMostDims("cumsum",[d],l);let p=n.makeOutput(c.shape,c.dtype),h=c.shape[d],m=n.dataIdMap.get(c.dataId).id,f=n.dataIdMap.get(p.dataId).id;Ok(m,o?1:0,i?1:0,h,f,qn[r.dtype]);let g=p;if(u!==null){let A=_.getUndoAxesPermutation(u);g=Mu({inputs:{x:p},attrs:{perm:A},backend:n}),n.disposeData(c.dataId),n.disposeData(p.dataId)}return g}var hae={kernelName:eo,backendName:"wasm",setupFunc:dae,kernelFunc:pae},Pk;function fae(e){Pk=e.wasm.cwrap(rl,null,["number","number","number","array","number","array","array","number","number"])}function mae(e){let{backend:t,inputs:n,attrs:s}=e,{x:r}=n,{blockSize:a,dataFormat:o}=s;w.assert(a>1,()=>`blockSize should be > 1 for depthToSpace, but was: ${a}`);let i=r.shape[0],l=o==="NHWC"?r.shape[1]:r.shape[2],u=o==="NHWC"?r.shape[2]:r.shape[3],c=o==="NHWC"?r.shape[3]:r.shape[1],d=l*a,p=u*a,h=c/(a*a),m=o==="NHWC"?[i,d,p,h]:[i,h,d,p],f=t.makeOutput(m,"float32"),A=t.dataIdMap.get(r.dataId).id,y=new Uint8Array(new Int32Array(w.computeStrides(r.shape)).buffer),x=new Uint8Array(new Int32Array(m).buffer),b=new Uint8Array(new Int32Array(w.computeStrides(m)).buffer),v=t.dataIdMap.get(f.dataId).id;return Pk(A,a,o==="NHWC"?1:0,y,r.shape.length-1,x,b,m.length,v),f}var gae={kernelName:rl,backendName:"wasm",setupFunc:fae,kernelFunc:mae},Mk;function Aae(e){Mk=e.wasm.cwrap(to,null,["number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number"])}function yae(e){let{inputs:t,attrs:n,backend:s}=e,{x:r,filter:a}=t,o=s.dataIdMap.get(r.dataId).id,i=s.dataIdMap.get(a.dataId).id,{strides:l,dilations:u,pad:c,dimRoundingMode:d}=n,p=u==null?[1,1]:u,h=_.computeConv2DInfo(r.shape,a.shape,l,p,c,d,!0),m=h.filterHeight,f=h.filterWidth,g=h.padInfo.top,A=h.padInfo.right,y=h.padInfo.bottom,x=h.padInfo.left,b=h.dilationHeight,v=h.dilationWidth,k=h.strideHeight,S=h.strideWidth,C=h.inChannels,D=h.outChannels,O=h.padInfo.type==="SAME"?1:0;if(h.dataFormat!=="channelsLast")throw new Error(`wasm backend DepthwiseConv2dNative does not support dataFormat:'${h.dataFormat}'. Please use 'channelsLast'.`);let E=s.makeOutput(h.outShape,"float32"),R=s.dataIdMap.get(E.dataId).id;return Mk(o,r.shape[0],r.shape[1],r.shape[2],i,m,f,g,A,y,x,O,b,v,k,S,C,D,R),E}var xae={kernelName:to,backendName:"wasm",setupFunc:Aae,kernelFunc:yae},bae=vn(so),vae=!1,wae=On(ol,vae,"bool"),kae=vn(ro);function W2(e){let{inputs:t,attrs:n,backend:s}=e,{input:r}=t,{dim:a}=n,o=r.shape.length,i=r.shape.slice(),l=a;return a<0&&(w.assert(-(o+1)<=a,()=>`Axis must be in the interval [${-(o+1)}, ${o}]`),l=o+a+1),i.splice(l,0,1),Xn({inputs:{x:r},backend:s,attrs:{shape:i}})}var Iae={kernelName:il,backendName:"wasm",kernelFunc:W2};function zk(e){let{attrs:{shape:t,value:n,dtype:s},backend:r}=e,a=r.makeOutput(t,s);return r.typedArrayFromHeap(a).fill(n),a}var Sae={kernelName:gc,backendName:"wasm",kernelFunc:zk},Lk;function Cae(e){Lk=e.wasm.cwrap(ul,null,["number","number","number","number","number","number"])}function Tae(e){let{inputs:t,backend:n}=e,{image:s}=t,r=n.makeOutput(s.shape,s.dtype),a=n.dataIdMap.get(s.dataId).id,o=n.dataIdMap.get(r.dataId).id,[i,l,u,c]=s.shape;return Lk(a,i,l,u,c,o),r}var Nae={kernelName:ul,backendName:"wasm",kernelFunc:Tae,setupFunc:Cae},Eae=vn(ao),Rae=!1,Dae=On(oo,Rae),Bk;function _ae(e){Bk=e.wasm.cwrap(io,null,["number","number","number","number","number","number","number"])}function $ae(e){let{backend:t,inputs:n,attrs:s}=e,{varianceEpsilon:r}=s,{x:a,mean:o,variance:i,offset:l,scale:u}=n,c=t.dataIdMap.get(a.dataId).id,d=t.dataIdMap.get(o.dataId).id,p=t.dataIdMap.get(i.dataId).id,h=l!=null?t.dataIdMap.get(l.dataId).id:0,m=u!=null?t.dataIdMap.get(u.dataId).id:0,f=t.makeOutput(a.shape,a.dtype);if(w.sizeFromShape(a.shape)===0)return f;let g=t.dataIdMap.get(f.dataId).id;return Bk(c,d,p,h,m,r,g),f}var Fae={kernelName:io,backendName:"wasm",setupFunc:_ae,kernelFunc:$ae},Wk;function Oae(e){Wk=e.wasm.cwrap(Vo,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 Pae(e){let{inputs:t,attrs:n,backend:s}=e,{x:r,filter:a,bias:o,preluActivationWeights:i}=t,{strides:l,pad:u,dilations:c,dataFormat:d,dimRoundingMode:p,activation:h,leakyreluAlpha:m}=n,f=_.computeConv2DInfo(r.shape,a.shape,l,c,u,p),g=Pd[h];if(g==null)throw new Error(`${h} activation not yet supported for FusedConv2D in the wasm backend.`);let A=s.dataIdMap.get(r.dataId).id,y=s.dataIdMap.get(a.dataId).id,x=f.outChannels,b=0;if(o!=null){let Q=s.dataIdMap.get(o.dataId);if(Q.shape.length!==1)throw new Error(`FusedConv2D only supports rank-1 bias but got rank ${Q.shape.length}.`);if(Q.shape[0]!==x)throw new Error(`FusedConv2D bias shape (${Q.shape}) does not match the number of output channels (${x})`);b=Q.id}let v=f.filterHeight,k=f.filterWidth,S=f.padInfo.top,C=f.padInfo.right,D=f.padInfo.bottom,O=f.padInfo.left,E=f.dilationHeight,R=f.dilationWidth,T=f.strideHeight,P=f.strideWidth,U=f.inChannels,j=f.padInfo.type==="SAME"?1:0,q=f.batchSize,X=f.inHeight,te=f.inWidth;if(d!=="NHWC")throw new Error(`wasm backend FusedConv2D does not support dataFormat:'${d}'. Please use 'NHWC'.`);let ne=s.makeOutput(f.outShape,"float32"),se=s.dataIdMap.get(ne.dataId).id,ae=i==null?0:s.dataIdMap.get(i.dataId).id;return Wk(A,q,X,te,y,v,k,b,S,C,D,O,j,E,R,T,P,U,x,g,ae,m||0,se),ne}var Mae={kernelName:Vo,backendName:"wasm",setupFunc:Oae,kernelFunc:Pae},Vk;function zae(e){Vk=e.wasm.cwrap(Uo,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 Lae(e){let{inputs:t,attrs:n,backend:s}=e,{x:r,filter:a,bias:o,preluActivationWeights:i}=t,{strides:l,pad:u,dilations:c,dataFormat:d,dimRoundingMode:p,activation:h,leakyreluAlpha:m}=n,f=_.computeConv2DInfo(r.shape,a.shape,l,c,u,p,!0),g=Pd[h];if(g==null)throw new Error(`${h} activation not yet supported for FusedDepthwiseConv2D in the wasm backend.`);let A=s.dataIdMap.get(r.dataId).id,y=s.dataIdMap.get(a.dataId).id,x=f.outChannels,b=0;if(o!=null){let Q=s.dataIdMap.get(o.dataId);if(Q.shape.length!==1)throw new Error(`FusedDepthwiseConv2D only supports rank-1 bias but got rank ${Q.shape.length}.`);if(Q.shape[0]!==x)throw new Error(`FusedDepthwiseConv2D bias shape (${Q.shape}) does not match the number of output channels (${x})`);b=Q.id}let v=f.filterHeight,k=f.filterWidth,S=f.padInfo.top,C=f.padInfo.right,D=f.padInfo.bottom,O=f.padInfo.left,E=f.dilationHeight,R=f.dilationWidth,T=f.strideHeight,P=f.strideWidth,U=f.inChannels,j=f.padInfo.type==="SAME"?1:0,q=f.batchSize,X=f.inHeight,te=f.inWidth;if(d!=="NHWC")throw new Error(`wasm backend FusedDepthwiseConv2D does not support dataFormat:'${d}'. Please use 'NHWC'.`);let ne=s.makeOutput(f.outShape,"float32"),se=s.dataIdMap.get(ne.dataId).id,ae=i==null?0:s.dataIdMap.get(i.dataId).id;return Vk(A,q,X,te,y,v,k,b,S,C,D,O,j,E,R,T,P,U,x,g,ae,m||0,se),ne}var Bae={kernelName:Uo,backendName:"wasm",setupFunc:zae,kernelFunc:Lae},Uk;function Wae(e){Uk=e.wasm.cwrap(dl,null,["number","number","number","number","number","number","array","number"])}function Vae(e){let{backend:t,inputs:n}=e,{params:s,indices:r}=n,[a,o,i,l]=Qg.prepareAndValidate(s,r),u=t.makeOutput(a,s.dtype);if(o===0)return u;let c=r.shape,d=c[c.length-1],h=t.dataIdMap.get(s.dataId).id,f=t.dataIdMap.get(r.dataId).id,g=new Uint8Array(new Int32Array(l).buffer),A=t.dataIdMap.get(u.dataId).id;return Uk(h,qn[s.dtype],f,o,d,i,g,A),u}var Uae={kernelName:dl,backendName:"wasm",setupFunc:Wae,kernelFunc:Vae},Hk;function Hae(e){Hk=e.wasm.cwrap("Gather",null,["number","number","array","number","number","number","array","number"])}function Gae(e){let{backend:t,inputs:n,attrs:s}=e,{x:r,indices:a}=n,{axis:o,batchDims:i}=s,l=w.parseAxisParam(o,r.shape)[0],u=_.segment_util.collectGatherOpShapeInfo(r,a,l,i),c=Xn({inputs:{x:r},attrs:{shape:[u.batchSize,u.outerSize,u.dimSize,u.sliceSize]},backend:t}),d=w.sizeFromShape(a.shape),p=Xn({inputs:{x:a},attrs:{shape:[u.batchSize,d/u.batchSize]},backend:t}),h=[u.batchSize,u.outerSize,d/u.batchSize,u.sliceSize],m=t.makeOutput(h,r.dtype);if(w.sizeFromShape(r.shape)===0)return m;let f=c.shape.length-1,A=t.dataIdMap.get(c.dataId).id,x=t.dataIdMap.get(p.dataId).id,b=t.dataIdMap.get(m.dataId).id,v=new Uint8Array(new Int32Array(w.computeStrides(c.shape)).buffer),k=new Uint8Array(new Int32Array(w.computeStrides(h)).buffer);return Hk(A,qn[r.dtype],v,f,x,u.batchSize,k,b),t.disposeData(c.dataId),t.disposeData(p.dataId),m.shape=u.outputShape,m}var jae={kernelName:cl,backendName:"wasm",setupFunc:Hae,kernelFunc:Gae},qae=!1,Xae=On(pl,qae,"bool"),Kae=!1,Zae=On(lo,Kae,"bool"),Gk;function Yae(e){Gk=e.wasm.cwrap(co,null,["number","number","number"])}function Jae(e){let{inputs:{x:t},attrs:{alpha:n},backend:s}=e,r=s.dataIdMap.get(t.dataId).id,a=s.makeOutput(t.shape,t.dtype);if(w.sizeFromShape(t.shape)!==0){let o=s.dataIdMap.get(a.dataId).id;Gk(r,n,o)}return a}var Qae={kernelName:co,backendName:"wasm",setupFunc:Yae,kernelFunc:Jae},eoe=!1,toe=On(gl,eoe,"bool"),noe=!1,soe=On(Al,noe,"bool"),roe=vn(po),aoe=!1,ooe=On(xl,aoe,"bool"),jk;function ioe(e){jk=e.wasm.cwrap(ho,null,["number, number, number"])}function loe(e){let{backend:t,inputs:n,attrs:s}=e,{reductionIndices:r,keepDims:a}=s,{x:o}=n,l=t.dataIdMap.get(o.dataId).id,u=o,{transposed:c,axes:d,originalAxes:p,inputWasTransposed:h}=Ra(o,r,t);if(h){let x=t.dataIdMap.get(c.dataId).id;u=c,l=x}let m=u.shape.length;_.assertAxesAreInnerMostDims("max",d,m);let[f,g]=_.computeOutAndReduceShapes(u.shape,d),A=w.sizeFromShape(g),y=t.makeOutput(f,o.dtype);if(w.sizeFromShape(u.shape)!==0){let x=t.dataIdMap.get(y.dataId).id;jk(l,A,x)}if(h&&t.disposeData(c.dataId),a){let x=_.expandShapeToKeepDim(y.shape,p);y.shape=x}return y}var uoe={kernelName:ho,backendName:"wasm",setupFunc:ioe,kernelFunc:loe},coe=!1,doe=On(fo,coe),qk;function poe(e){qk=e.wasm.cwrap(mo,null,["number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number"])}function hoe(e){let{inputs:t,attrs:n,backend:s}=e,r=t.x,a=s.dataIdMap.get(r.dataId).id,{filterSize:o,strides:i,pad:l,dimRoundingMode:u}=n,c=_.computePool2DInfo(r.shape,o,i,1,l,u),d=c.filterHeight,p=c.filterWidth,h=c.padInfo.top,m=c.padInfo.right,f=c.padInfo.bottom,g=c.padInfo.left,A=c.dilationHeight,y=c.dilationWidth,x=c.strideHeight,b=c.strideWidth,v=c.inChannels,k=c.outChannels;if(c.dataFormat!=="channelsLast")throw new Error(`wasm backend does not support dataFormat:'${c.dataFormat}'. Please use 'channelsLast'.`);let S=s.makeOutput(c.outShape,"float32"),C=s.dataIdMap.get(S.dataId).id;return qk(a,r.shape[0],r.shape[1],r.shape[2],d,p,h,m,f,g,A,y,x,b,v,k,C),S}var foe={kernelName:mo,backendName:"wasm",setupFunc:poe,kernelFunc:hoe},Xk;function moe(e){Xk=e.wasm.cwrap(go,null,["number, number, number"])}function goe(e){let{backend:t,inputs:n,attrs:s}=e,{axis:r,keepDims:a}=s,{x:o}=n,i=t.dataIdMap.get(o.dataId).id,l=i,u=o,{transposed:c,axes:d,originalAxes:p,inputWasTransposed:h}=Ra(o,r,t),m=d;if(h){let b=t.dataIdMap.get(c.dataId).id;b!==i&&(u=c,l=b,m=_.getInnerMostAxes(m.length,u.shape.length))}_.assertAxesAreInnerMostDims("mean",m,u.shape.length);let[f,g]=_.computeOutAndReduceShapes(u.shape,m),A=w.sizeFromShape(g),y=u;u.dtype!=="float32"&&(y=k0({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 b=t.dataIdMap.get(x.dataId).id;Xk(l,A,b)}if(h&&t.disposeData(c.dataId),a){let 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o=De.resizeBilinear(e,[ps.inputs[0].shape[2],ps.inputs[0].shape[1]]),i=ye(he(pe(o,"float32"),127.5),1),u=ps.execute(i,Xle).map(c=>at(c,[0]));return u[1]=u[1].sigmoid(),u}),s=await Promise.all(n.map(o=>o.buffer()));for(let o of n)Z(o);let r=await eue(s[0],s[1],s[2],s[3],t.body.maxDetected,t.body.minConfidence);return ps.inputs[0].shape?eI(r,[e.shape[1],e.shape[2]],[ps.inputs[0].shape[2],ps.inputs[0].shape[1]]):[]}async function rI(e){return!ps||oe.initial?(ps=await st(rt(e.modelBasePath,e.body.modelPath||"")),!ps||!ps.modelUrl?re("load model failed:",e.body.modelPath):e.debug&&re("load model:",ps.modelUrl)):e.debug&&re("cached model:",ps.modelUrl),ps}function F0(e){return[Math.abs(e.endPoint[0]-e.startPoint[0]),Math.abs(e.endPoint[1]-e.startPoint[1])]}function Kd(e){return[e.startPoint[0]+(e.endPoint[0]-e.startPoint[0])/2,e.startPoint[1]+(e.endPoint[1]-e.startPoint[1])/2]}function aI(e,t,n){let s=t.shape[1],r=t.shape[2],a=[[e.startPoint[1]/s,e.startPoint[0]/r,e.endPoint[1]/s,e.endPoint[0]/r]];return De.cropAndResize(t,a,[0],n)}function oI(e,t){let n=[e.startPoint[0]*t[0],e.startPoint[1]*t[1]],s=[e.endPoint[0]*t[0],e.endPoint[1]*t[1]],r=e.palmLandmarks.map(a=>[a[0]*t[0],a[1]*t[1]]);return{startPoint:n,endPoint:s,palmLandmarks:r,confidence:e.confidence}}function O0(e,t=1.5){let n=Kd(e),s=F0(e),r=[t*s[0]/2,t*s[1]/2],a=[n[0]-r[0],n[1]-r[1]],o=[n[0]+r[0],n[1]+r[1]];return{startPoint:a,endPoint:o,palmLandmarks:e.palmLandmarks}}function P0(e){let t=Kd(e),n=F0(e),r=Math.max(...n)/2,a=[t[0]-r,t[1]-r],o=[t[0]+r,t[1]+r];return{startPoint:a,endPoint:o,palmLandmarks:e.palmLandmarks}}var 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l.palmLandmarks.array();Z(l.box),Z(l.palmLandmarks),i.push(oI({startPoint:c,endPoint:d,palmLandmarks:p,confidence:l.confidence},[r/this.inputSize,s/this.inputSize]))}return i}};function tue(e){return e-2*Math.PI*Math.floor((e+Math.PI)/(2*Math.PI))}function lI(e,t){let n=Math.PI/2-Math.atan2(-(t[1]-e[1]),t[0]-e[0]);return tue(n)}var uI=(e,t)=>[[1,0,e],[0,1,t],[0,0,1]];function _a(e,t){let n=0;for(let s=0;so[0]),s=t.map(o=>o[1]),r=[Math.min(...n),Math.min(...s)],a=[Math.max(...n),Math.max(...s)];return{startPoint:r,endPoint:a}}getBoxForPalmLandmarks(t,n){let s=t.map(a=>yx([...a,1],n)),r=this.calculateLandmarksBoundingBox(s);return O0(P0(r),sue)}getBoxForHandLandmarks(t){let n=this.calculateLandmarksBoundingBox(t),s=O0(P0(n),pI);s.palmLandmarks=[];for(let r=0;r[o[0]*(h[0]-this.inputSize/2),o[1]*(h[1]-this.inputSize/2),o[2]*h[2]]),l=Ax(s,[0,0]),u=i.map(h=>[...yx(h,l),h[2]]),c=dI(r),d=[...Kd(n),1],p=[_a(d,c[0]),_a(d,c[1])];return u.map(h=>[Math.trunc(h[0]+p[0]),Math.trunc(h[1]+p[1]),Math.trunc(h[2])])}async estimateHands(t,n){let s=!1,r;(this.skipped===0||this.skipped>n.hand.skipFrames||!n.hand.landmarks||!n.skipFrame)&&(r=await this.handDetector.estimateHandBounds(t,n),this.skipped=0),n.skipFrame&&this.skipped++,r&&r.length>0&&(r.length!==this.detectedHands&&this.detectedHands!==n.hand.maxDetected||!n.hand.landmarks)&&(this.detectedHands=0,this.storedBoxes=[...r],this.storedBoxes.length>0&&(s=!0));let a=[];for(let o=0;o=n.hand.minConfidence/4){let x=V(A,[-1,3]),b=await x.array();Z(A),Z(x);let v=this.transformRawCoords(b,h,l,p),k=this.getBoxForHandLandmarks(v);this.storedBoxes[o]={...k,confidence:y};let S={landmarks:v,confidence:y,boxConfidence:i.confidence,fingerConfidence:y,box:{topLeft:k.startPoint,bottomRight:k.endPoint}};a.push(S)}else this.storedBoxes[o]=null;Z(A)}else{let 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g=Math.sqrt(r*r+i*i),A=Math.sqrt(a*a+l*l),y=Math.sqrt(o*o+u*u),x=Math.max(g,A,y),b=e[0],v=e[1],k=n[0],S=n[1];x===g?(k=n[0],S=n[1]):x===y&&(b=t[0],v=t[1]);let O=gI([b,v],[k,S]),E=AI(O,Ti.TOTAL_ANGLE_VOTE_POWER);p+=E[0],h+=E[1],m+=E[2];for(let T of s){let P=AI(T,Ti.SINGLE_ANGLE_VOTE_POWER);p+=P[0],h+=P[1],m+=P[2]}let R;return p===Math.max(p,h,m)?R=xI(l,i,u,d):m===Math.max(h,m)?R=yI(a,r,o,c):R=lue(l,i,u,d,a,r,o,c),R}function bI(e){let t=[],n=[],s=[],r=[];if(!e)return{curls:s,directions:r};for(let a of Xe.all){let o=Xe.getPoints(a),i=[],l=[];for(let u of o){let c=e[u[0]],d=e[u[1]],p=gI(c,d),h=p[0],m=p[1];i.push(h),l.push(m)}t.push(i),n.push(l)}for(let a of Xe.all){let o=a===Xe.thumb?1:0,i=Xe.getPoints(a),l=e[i[o][0]],u=e[i[o+1][1]],c=e[i[3][1]],d=iue(l,u,c),p=uue(l,u,c,t[a].slice(o));s[a]=d,r[a]=p}return{curls:s,directions:r}}function z0(e){if(!e||e.length===0)return null;let t=bI(e),n={};for(let s of 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i=n[r].box?[Math.trunc(Math.max(0,n[r].box.topLeft[0])),Math.trunc(Math.max(0,n[r].box.topLeft[1])),Math.trunc(Math.min(e.shape[2]||0,n[r].box.bottomRight[0])-Math.max(0,n[r].box.topLeft[0])),Math.trunc(Math.min(e.shape[1]||0,n[r].box.bottomRight[1])-Math.max(0,n[r].box.topLeft[1]))]:[0,0,0,0],l=[n[r].box.topLeft[0]/(e.shape[2]||0),n[r].box.topLeft[1]/(e.shape[1]||0),(n[r].box.bottomRight[0]-n[r].box.topLeft[0])/(e.shape[2]||0),(n[r].box.bottomRight[1]-n[r].box.topLeft[1])/(e.shape[1]||0)];let u=z0(o);s.push({id:r,score:Math.round(100*n[r].confidence)/100,boxScore:Math.round(100*n[r].boxConfidence)/100,fingerScore:Math.round(100*n[r].fingerConfidence)/100,label:"hand",box:i,boxRaw:l,keypoints:o,annotations:a,landmarks:u})}return s}async function vx(e){var n,s,r,a,o,i;oe.initial&&(Gr=null,jr=null),!Gr||!jr?([Gr,jr]=await Promise.all([e.hand.enabled?st(rt(e.modelBasePath,((n=e.hand.detector)==null?void 0:n.modelPath)||""),{fromTFHub:(((s=e.hand.detector)==null?void 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s=[e.map(c=>c[0]),e.map(c=>c[1])],r=[Math.max(...s[0]),Math.min(...s[0]),Math.max(...s[1]),Math.min(...s[1])],a=[(r[0]+r[1])/2,(r[2]+r[3])/2],o=Math.max(a[0]-r[1],a[1]-r[3],-a[0]+r[0],-a[1]+r[2])*t,i=[Math.trunc(a[0]-o),Math.trunc(a[1]-o),Math.trunc(2*o),Math.trunc(2*o)],l=[i[0]/n[0],i[1]/n[1],i[2]/n[0],i[3]/n[1]],u=[l[1],l[0],l[3]+l[1],l[2]+l[0]];return{box:i,boxRaw:l,yxBox:u}}var $t={name:"humangl",priority:999,canvas:null,gl:null,extensions:[],webGLattr:{alpha:!1,antialias:!1,premultipliedAlpha:!1,preserveDrawingBuffer:!1,depth:!1,stencil:!1,failIfMajorPerformanceCaveat:!1,desynchronized:!0}};function cue(){let e=$t.gl;!e||($t.extensions=e.getSupportedExtensions())}async function II(e){var t;if(e.config.backend==="humangl"&&($t.name in Qn().registry&&(!$t.gl||!$t.gl.getParameter($t.gl.VERSION))&&(re("error: humangl backend invalid context"),wx(e)),!iA($t.name))){try{$t.canvas=await ds(100,100)}catch(s){re("error: cannot create canvas:",s);return}try{$t.gl=(t=$t.canvas)==null?void 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n=Object.values(Bt[0].modelSignature.inputs);qr[0][0]=Array.isArray(n)?parseInt(n[0].tensorShape.dim[1].size):0,qr[0][1]=Array.isArray(n)?parseInt(n[0].tensorShape.dim[2].size):0,!Bt[0]||!Bt[0].modelUrl?re("load model failed:",e.object.modelPath):e.debug&&re("load model:",Bt[0].modelUrl)}return Bt[0]}async function TI(e){var t;if(oe.initial&&(Bt[1]=null),Bt[1])e.debug&&re("cached model:",Bt[1].modelUrl);else{Bt[1]=await st(rt(e.modelBasePath,((t=e.hand.skeleton)==null?void 0:t.modelPath)||""));let n=Object.values(Bt[1].modelSignature.inputs);qr[1][0]=Array.isArray(n)?parseInt(n[0].tensorShape.dim[1].size):0,qr[1][1]=Array.isArray(n)?parseInt(n[0].tensorShape.dim[2].size):0,!Bt[1]||!Bt[1].modelUrl?re("load model failed:",e.object.modelPath):e.debug&&re("load model:",Bt[1].modelUrl)}return Bt[1]}async function hue(e,t){let n=[];if(!e||!Bt[0])return n;let s={},r=(e.shape[2]||1)/(e.shape[1]||1),a=Math.min(Math.round((e.shape[1]||0)/8)*8,512),o=Math.round(a*r/8)*8;s.resize=De.resizeBilinear(e,[a,o]),s.cast=pe(s.resize,"int32"),[s.rawScores,s.rawBoxes]=await Bt[0].executeAsync(s.cast,due),s.boxes=at(s.rawBoxes,[0,2]),s.scores=at(s.rawScores,[0]);let i=En(s.scores,1),l=0;for(let u=0;uZ(u)),Object.keys(s).forEach(u=>Z(s[u])),n.sort((u,c)=>c.score-u.score),n.length>(t.hand.maxDetected||1)&&(n.length=t.hand.maxDetected||1),n}async function Ix(e,t,n){let s={id:t.id,score:Math.round(100*t.score)/100,boxScore:Math.round(100*t.score)/100,fingerScore:0,box:t.box,boxRaw:t.boxRaw,label:t.label,keypoints:[],landmarks:{},annotations:{}};if(e&&Bt[1]&&n.hand.landmarks){let r={};if(!t.yxBox)return s;r.crop=De.cropAndResize(e,[t.yxBox],[0],[qr[1][0],qr[1][1]],"bilinear"),r.cast=pe(r.crop,"float32"),r.div=he(r.cast,255),[r.score,r.keypoints]=Bt[1].execute(r.div);let a=(await r.score.data())[0],o=(100-Math.trunc(100/(1+Math.exp(a))))/100;if(o>=(n.hand.minConfidence||0)){s.fingerScore=o,r.reshaped=V(r.keypoints,[-1,3]);let i=await r.reshaped.array();s.keypoints=i.map(c=>[t.box[2]*c[0]/qr[1][0]+t.box[0],t.box[3]*c[1]/qr[1][1]+t.box[1],(t.box[2]+t.box[3])/2/qr[1][0]*(c[2]||0)]);let l=L0(s.keypoints,Zd,Uu);t.box=l.box,t.boxRaw=l.boxRaw,t.yxBox=l.yxBox,s.box=t.box,s.landmarks=z0(s.keypoints);for(let c of Object.keys(SI))s.annotations[c]=SI[c].map(d=>s.landmarks&&s.keypoints[d]?s.keypoints[d]:null);Math.min(t.box[2]/(e.shape[2]||1),t.box[3]/(e.shape[1]||1))>.05&&rr.tmpBoxes.push(t)}Object.keys(r).forEach(i=>Z(r[i]))}return s}async function Sx(e,t){Uu=[e.shape[2]||0,e.shape[1]||0];let n=[];return rr.tmpBoxes=[],t.hand.landmarks||(rr.fingerBoxes=rr.handBoxes),t.skipFrame||(rr.fingerBoxes=[]),kx<(t.hand.skipFrames||0)&&t.skipFrame?(kx++,n=await Promise.all(rr.fingerBoxes.map(s=>Ix(e,s,t)))):(kx=0,n=await Promise.all(rr.fingerBoxes.map(s=>Ix(e,s,t))),n.length!==t.hand.maxDetected&&(rr.handBoxes=await hue(e,t),n=await Promise.all(rr.handBoxes.map(s=>Ix(e,s,t))))),rr.fingerBoxes=[...rr.tmpBoxes],n}var NI=["nose","leftEyeInside","leftEye","leftEyeOutside","rightEyeInside","rightEye","rightEyeOutside","leftEar","rightEar","leftMouth","rightMouth","leftShoulder","rightShoulder","leftElbow","rightElbow","leftWrist","rightWrist","leftPalm","rightPalm","leftIndex","rightIndex","leftPinky","rightPinky","leftHip","rightHip","leftKnee","rightKnee","leftAnkle","rightAnkle","leftHeel","rightHeel","leftFoot","rightFoot","midHip","forehead","leftThumb","leftHand","rightThumb","rightHand"],EI=["nose","leftEyeInside","leftEye","leftEyeOutside","rightEyeInside","rightEye","rightEyeOutside","leftEar","rightEar","leftMouth","rightMouth","leftShoulder","rightShoulder","leftElbow","rightElbow","left:15","right:16","left:17","right:18","left:19","right:20","left:21","right:22","leftChest","rightChest","neck","forehead","left:27","right:28","left:29","right:30"];var hn=[null,null],fue=["ld_3d","activation_segmentation","activation_heatmap","world_3d","output_poseflag"],Hu=[[0,0],[0,0]],Cx=[0,0];async function RI(e){var t;if(oe.initial&&(hn[0]=null),hn[0])e.debug&&re("cached model:",hn[0].modelUrl);else{hn[0]=await st(rt(e.modelBasePath,((t=e.body.detector)==null?void 0:t.modelPath)||""));let n=Object.values(hn[0].modelSignature.inputs);Hu[0][0]=Array.isArray(n)?parseInt(n[0].tensorShape.dim[1].size):0,Hu[0][1]=Array.isArray(n)?parseInt(n[0].tensorShape.dim[2].size):0,!hn[0]||!hn[0].modelUrl?re("load model failed:",e.object.modelPath):e.debug&&re("load model:",hn[0].modelUrl)}return hn[0]}async function DI(e){if(oe.initial&&(hn[1]=null),hn[1])e.debug&&re("cached model:",hn[1].modelUrl);else{hn[1]=await st(rt(e.modelBasePath,e.body.modelPath||""));let t=Object.values(hn[1].modelSignature.inputs);Hu[1][0]=Array.isArray(t)?parseInt(t[0].tensorShape.dim[1].size):0,Hu[1][1]=Array.isArray(t)?parseInt(t[0].tensorShape.dim[2].size):0,!hn[1]||!hn[1].modelUrl?re("load model failed:",e.object.modelPath):e.debug&&re("load model:",hn[1].modelUrl)}return hn[1]}async function mue(e,t){var p;let n={};n.resize=De.resizeBilinear(e,[Hu[1][0],Hu[1][1]]),[n.ld,n.segmentation,n.heatmap,n.world,n.poseflag]=await((p=hn[1])==null?void 0:p.execute(n.resize,fue));let s=await n.ld.data(),r=[],a=(s==null?void 0:s.length)===195?NI:EI,o=5;for(let h=0;h(t.body.minConfidence||0)&&r.push({part:a[h],position:[Math.trunc(Cx[0]*s[o*h+0]/255),Math.trunc(Cx[1]*s[o*h+1]/255),Math.trunc(s[o*h+2])+0],positionRaw:[s[o*h+0]/255,s[o*h+1]/255,s[o*h+2]+0],score:m})}let i=r.map(h=>h.position[0]),l=r.map(h=>h.position[1]),u=[Math.min(...i),Math.min(...l),Math.max(...i)-Math.min(...i),Math.max(...l)-Math.min(...i)],c=[0,0,0,0],d=r.reduce((h,m)=>m.score>h?m.score:h,0);return Object.keys(n).forEach(h=>Z(n[h])),{id:0,score:d,box:u,boxRaw:c,keypoints:r}}async function Tx(e,t){Cx=[e.shape[2]||0,e.shape[1]||0];let n=[],s=await mue(e,t);return n.push(s),n}var tn,Ir=[],Nx=[0,0,0,0],Ex=[0,0,0,0],W0=0,Rx=Number.MAX_SAFE_INTEGER,gue=["head","neck","rightShoulder","rightElbow","rightWrist","chest","leftShoulder","leftElbow","leftWrist","pelvis","rightHip","rightKnee","rightAnkle","leftHip","leftKnee","leftAnkle"];async function Dx(e){return oe.initial&&(tn=null),tn?e.debug&&re("cached model:",tn.modelUrl):(tn=await st(rt(e.modelBasePath,e.body.modelPath||"")),!tn||!tn.modelUrl?re("load model failed:",e.body.modelPath):e.debug&&re("load model:",tn.modelUrl)),tn}function Aue(e,t){let[n,s]=e.shape;return H(()=>{let r=(i,l)=>ye(i,z(he(i,Ce(l,"int32")),Ce(l,"int32"))),a=V(e,[s*n]),o=ss(a,0).dataSync()[0];if(o>t){let i=Gs(a,0),l=r(i,n).dataSync()[0],u=he(i,Ce(n,"int32")).dataSync()[0];return[l,u,o]}return[0,0,o]})}async function _x(e,t){var n;return Rx<(((n=t.body)==null?void 0:n.skipFrames)||0)&&t.skipFrame&&Object.keys(Ir).length>0?(Rx++,[{id:0,score:W0,box:Nx,boxRaw:Ex,keypoints:Ir}]):(Rx=0,new Promise(async s=>{var c;let r=H(()=>{if(!(tn==null?void 0:tn.inputs[0].shape))return null;let d=De.resizeBilinear(e,[tn.inputs[0].shape[2],tn.inputs[0].shape[1]],!1);return z(d,2).sub(1)}),a;if(t.body.enabled&&(a=await(tn==null?void 0:tn.predict(r))),Z(r),a){Ir.length=0;let d=a.squeeze();Z(a);let p=d.unstack(2);Z(d);for(let h=0;h(((c=t.body)==null?void 0:c.minConfidence)||0)&&Ir.push({score:Math.round(100*g)/100,part:gue[h],positionRaw:[m/tn.inputs[0].shape[2],f/tn.inputs[0].shape[1]],position:[Math.round(e.shape[2]*m/tn.inputs[0].shape[2]),Math.round(e.shape[1]*f/tn.inputs[0].shape[1])]})}p.forEach(h=>Z(h))}W0=Ir.reduce((d,p)=>p.score>d?p.score:d,0);let o=Ir.map(d=>d.position[0]),i=Ir.map(d=>d.position[1]);Nx=[Math.min(...o),Math.min(...i),Math.max(...o)-Math.min(...o),Math.max(...i)-Math.min(...i)];let l=Ir.map(d=>d.positionRaw[0]),u=Ir.map(d=>d.positionRaw[1]);Ex=[Math.min(...l),Math.min(...u),Math.max(...l)-Math.min(...l),Math.max(...u)-Math.min(...u)],s([{id:0,score:W0,box:Nx,boxRaw:Ex,keypoints:Ir}])}))}var nn,Ni=0,Fa=[],$x=Number.MAX_SAFE_INTEGER,Oa=[],_I=["nose","leftEye","rightEye","leftEar","rightEar","leftShoulder","rightShoulder","leftElbow","rightElbow","leftWrist","rightWrist","leftHip","rightHip","leftKnee","rightKnee","leftAnkle","rightAnkle"];async function $I(e){return oe.initial&&(nn=null),nn?e.debug&&re("cached model:",nn.modelUrl):(Vu(["size"],e),nn=await st(rt(e.modelBasePath,e.body.modelPath||"")),!nn||!nn.modelUrl?re("load model failed:",e.body.modelPath):e.debug&&re("load model:",nn.modelUrl)),Ni=nn.inputs[0].shape?nn.inputs[0].shape[2]:0,Ni===-1&&(Ni=256),nn}function yue(e){let t=e.map(i=>i.position[0]),n=e.map(i=>i.position[1]),s=[Math.min(...t),Math.min(...n),Math.max(...t)-Math.min(...t),Math.max(...n)-Math.min(...n)],r=e.map(i=>i.positionRaw[0]),a=e.map(i=>i.positionRaw[1]),o=[Math.min(...r),Math.min(...a),Math.max(...r)-Math.min(...r),Math.max(...a)-Math.min(...a)];return[s,o]}async function FI(e,t,n,s){let r=e[0][0];Oa.length=0;let a=0;for(let u=0;ut.body.minConfidence){let c=[(s[3]-s[1])*r[u][1]+s[1],(s[2]-s[0])*r[u][0]+s[0]];Oa.push({score:Math.round(100*a)/100,part:_I[u],positionRaw:c,position:[Math.round((n.shape[2]||0)*c[0]),Math.round((n.shape[1]||0)*c[1])]})}a=Oa.reduce((u,c)=>c.score>u?c.score:u,0);let o=[],[i,l]=yue(Oa);return o.push({id:0,score:a,box:i,boxRaw:l,keypoints:Oa}),o}async function OI(e,t,n,s){let r=[];for(let a=0;at.body.minConfidence){Oa.length=0;for(let c=0;c<17;c++){let d=o[3*c+2];if(d>t.body.minConfidence){let p=[(s[3]-s[1])*o[3*c+1]+s[1],(s[2]-s[0])*o[3*c+0]+s[0]];Oa.push({part:_I[c],score:Math.round(100*d)/100,positionRaw:p,position:[Math.round((n.shape[2]||0)*p[0]),Math.round((n.shape[1]||0)*p[1])]})}}let l=[o[51+1],o[51+0],o[51+3]-o[51+1],o[51+2]-o[51+0]],u=[Math.trunc(l[0]*(n.shape[2]||0)),Math.trunc(l[1]*(n.shape[1]||0)),Math.trunc(l[2]*(n.shape[2]||0)),Math.trunc(l[3]*(n.shape[1]||0))];r.push({id:a,score:i,boxRaw:l,box:u,keypoints:[...Oa]})}}return r.sort((a,o)=>o.score-a.score),r.length>t.body.maxDetected&&(r.length=t.body.maxDetected),r}async function Fx(e,t){return!nn||!(nn==null?void 0:nn.inputs[0].shape)?[]:new Promise(async n=>{let s={},r=[];t.skipFrame||(Fa.length=0),$x++;for(let a=0;aZ(s[l]))}if(r.length!==t.body.maxDetected&&$x>(t.body.skipFrames||0)){s.resized=De.resizeBilinear(e,[Ni,Ni],!1),s.cast=pe(s.resized,"int32"),s.res=await(nn==null?void 0:nn.predict(s.cast));let a=await s.res.array();r=s.res.shape[2]===17?await FI(a,t,e,[0,0,1,1]):await OI(a,t,e,[0,0,1,1]),Object.keys(s).forEach(o=>Z(s[o])),Fa.length=0,$x=0}if(t.skipFrame){Fa.length=0;for(let a=0;a10){let o=r[a].keypoints.map(l=>l.position),i=L0(o,1.5,[e.shape[2],e.shape[1]]);Fa.push([...i.yxBox])}}n(r)})}var Gu=[{class:1,label:"person"},{class:2,label:"bicycle"},{class:3,label:"car"},{class:4,label:"motorcycle"},{class:5,label:"airplane"},{class:6,label:"bus"},{class:7,label:"train"},{class:8,label:"truck"},{class:9,label:"boat"},{class:10,label:"traffic 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drier"},{class:80,label:"toothbrush"}];var hs,V0=[],Ox=Number.MAX_SAFE_INTEGER,U0=2.5;async function PI(e){if(!hs||oe.initial){hs=await st(rt(e.modelBasePath,e.object.modelPath||""));let t=Object.values(hs.modelSignature.inputs);if(hs.inputSize=Array.isArray(t)?parseInt(t[0].tensorShape.dim[2].size):null,!hs.inputSize)throw new Error(`cannot determine model inputSize: ${e.object.modelPath}`);!hs||!hs.modelUrl?re("load model failed:",e.object.modelPath):e.debug&&re("load model:",hs.modelUrl)}else e.debug&&re("cached model:",hs.modelUrl);return hs}async function xue(e,t,n,s){let r=0,a=[];for(let u of[1,2,4])H(async()=>{var g,A;let c=u*13,d=(g=e.find(y=>y.shape[1]===c**2&&y.shape[2]===Gu.length))==null?void 0:g.squeeze(),p=(A=e.find(y=>y.shape[1]===c**2&&y.shape[2]s.object.minConfidence&&x!==61){let v=(.5+Math.trunc(y%c))/c,k=(.5+Math.trunc(y/c))/c,S=m[y].map(U=>U*(c/u/t)),[C,D]=[v-U0/u*S[0],k-U0/u*S[1]],[O,E]=[v+U0/u*S[2]-C,k+U0/u*S[3]-D],R=[C,D,O,E];R=R.map(U=>Math.max(0,Math.min(U,1)));let T=[R[0]*n[0],R[1]*n[1],R[2]*n[0],R[3]*n[1]],P={id:r++,score:Math.round(100*b)/100,class:x+1,label:Gu[x].label,box:T.map(U=>Math.trunc(U)),boxRaw:R};a.push(P)}}});e.forEach(u=>Z(u));let o=a.map(u=>[u.boxRaw[1],u.boxRaw[0],u.boxRaw[3],u.boxRaw[2]]),i=a.map(u=>u.score),l=[];if(o&&o.length>0){let u=await De.nonMaxSuppressionAsync(o,i,s.object.maxDetected,s.object.iouThreshold,s.object.minConfidence);l=await u.data(),Z(u)}return a=a.filter((u,c)=>l.includes(c)).sort((u,c)=>c.score-u.score),a}async function Px(e,t){return Ox<(t.object.skipFrames||0)&&t.skipFrame&&V0.length>0?(Ox++,V0):(Ox=0,!oe.kernels.includes("mod")||!oe.kernels.includes("sparsetodense")?V0:new Promise(async n=>{let s=[e.shape[2],e.shape[1]],r=De.resizeBilinear(e,[hs.inputSize,hs.inputSize],!1),a=he(r,255),o=a.transpose([0,3,1,2]);Z(a),Z(r);let i;t.object.enabled&&(i=await hs.predict(o)),Z(o);let l=await xue(i,hs.inputSize,s,t);V0=l,n(l)}))}var Bs,Ei=0,H0=[],Mx=Number.MAX_SAFE_INTEGER;async function MI(e){if(oe.initial&&(Bs=null),Bs)e.debug&&re("cached model:",Bs.modelUrl);else{Vu(["floormod"],e),Bs=await st(rt(e.modelBasePath,e.object.modelPath||""));let t=Object.values(Bs.modelSignature.inputs);Ei=Array.isArray(t)?parseInt(t[0].tensorShape.dim[2].size):0,!Bs||!Bs.modelUrl?re("load model failed:",e.object.modelPath):e.debug&&re("load model:",Bs.modelUrl)}return Bs}async function bue(e,t,n){if(!e)return[];let s=[],r=await e.array(),a=at(e);Z(e);let o=Vt(a,6,1);Z(a);let i=yn([o[1],o[0],o[3],o[2]],1),l=at(i);Z(i);let u=at(o[4]),c=at(o[5]);o.forEach(m=>Z(m));let d=await De.nonMaxSuppressionAsync(l,u,n.object.maxDetected,n.object.iouThreshold,n.object.minConfidence);Z(l),Z(u),Z(c);let p=await d.data();Z(d);let h=0;for(let m of p){let f=Math.trunc(100*r[0][m][4])/100,g=r[0][m][5],A=Gu[g].label,[y,x]=[r[0][m][0]/Ei,r[0][m][1]/Ei],b=[y,x,r[0][m][2]/Ei-y,r[0][m][3]/Ei-x],v=[Math.trunc(b[0]*t[0]),Math.trunc(b[1]*t[1]),Math.trunc(b[2]*t[0]),Math.trunc(b[3]*t[1])];s.push({id:h++,score:f,class:g,label:A,box:v,boxRaw:b})}return s}async function zx(e,t){return Mx<(t.object.skipFrames||0)&&t.skipFrame&&H0.length>0?(Mx++,H0):(Mx=0,!oe.kernels.includes("mod")||!oe.kernels.includes("sparsetodense")?H0:new Promise(async n=>{let s=[e.shape[2],e.shape[1]],r=De.resizeBilinear(e,[Ei,Ei]),a=t.object.enabled?Bs==null?void 0:Bs.execute(r,["tower_0/detections"]):null;Z(r);let o=await bue(a,s,t);H0=o,n(o)}))}var Ts,Lx=!1;async function Bx(e){return!Ts||oe.initial?(Ts=await st(rt(e.modelBasePath,e.segmentation.modelPath||"")),!Ts||!Ts.modelUrl?re("load model failed:",e.segmentation.modelPath):e.debug&&re("load model:",Ts.modelUrl)):e.debug&&re("cached model:",Ts.modelUrl),Ts}async function zI(e,t,n){var f,g;if(Lx)return{data:[],canvas:null,alpha:null};Lx=!0,Ts||await Bx(n);let s=zu(e,n),r=((f=s.canvas)==null?void 0:f.width)||0,a=((g=s.canvas)==null?void 0:g.height)||0;if(!s.tensor)return{data:[],canvas:null,alpha:null};let o={};o.resize=De.resizeBilinear(s.tensor,[Ts.inputs[0].shape?Ts.inputs[0].shape[1]:0,Ts.inputs[0].shape?Ts.inputs[0].shape[2]:0],!1),Z(s.tensor),o.norm=he(o.resize,255),o.res=Ts.predict(o.norm),o.squeeze=at(o.res,0),o.squeeze.shape[2]===2?(o.softmax=si(o.squeeze),[o.bg,o.fg]=En(o.softmax,2),o.expand=zt(o.fg,2),o.pad=zt(o.expand,0),o.crop=De.cropAndResize(o.pad,[[0,0,.5,.5]],[0],[r,a]),o.data=at(o.crop,0)):o.data=De.resizeBilinear(o.squeeze,[a,r]);let i=Array.from(await o.data.data());if(oe.node&&!oe.Canvas&&typeof ImageData=="undefined")return n.debug&&re("canvas support missing"),Object.keys(o).forEach(A=>Z(o[A])),{data:i,canvas:null,alpha:null};let l=ds(r,a);await _s.toPixels(o.data,l);let u=l.getContext("2d");n.segmentation.blur&&n.segmentation.blur>0&&(u.filter=`blur(${n.segmentation.blur}px)`);let c=u.getImageData(0,0,r,a),d=ds(r,a),p=d.getContext("2d");s.canvas&&p.drawImage(s.canvas,0,0),p.globalCompositeOperation="darken",n.segmentation.blur&&n.segmentation.blur>0&&(p.filter=`blur(${n.segmentation.blur}px)`),p.drawImage(l,0,0),p.globalCompositeOperation="source-over",p.filter="none";let h=p.getImageData(0,0,r,a);for(let A=0;AZ(o[A])),Lx=!1,{data:i,canvas:m||d,alpha:l}}var Pa;var epe=Number.MAX_SAFE_INTEGER;async function LI(e){return oe.initial&&(Pa=null),Pa?e.debug&&re("cached model:",Pa.modelUrl):(Pa=await st(rt(e.modelBasePath,e.face.agegenderrace.modelPath)),!Pa||!Pa.modelUrl?re("load model failed:",e.face.agegenderrace.modelPath):e.debug&&re("load model:",Pa.modelUrl)),Pa}var Yd=class{constructor(){Ne(this,"age",null);Ne(this,"agegenderrace",null);Ne(this,"blazeposedetect",null);Ne(this,"blazepose",null);Ne(this,"centernet",null);Ne(this,"efficientpose",null);Ne(this,"embedding",null);Ne(this,"emotion",null);Ne(this,"facedetect",null);Ne(this,"faceiris",null);Ne(this,"facemesh",null);Ne(this,"faceres",null);Ne(this,"gender",null);Ne(this,"handpose",null);Ne(this,"handskeleton",null);Ne(this,"handtrack",null);Ne(this,"movenet",null);Ne(this,"nanodet",null);Ne(this,"posenet",null);Ne(this,"segmentation",null)}};function wx(e){for(let t of Object.keys(e.models))e.models[t]=null}async function BI(e){var t,n,s,r,a,o,i,l,u,c,d,p,h,m,f,g,A,y,x,b,v,k,S,C,D,O,E,R,T,P;oe.initial&&wx(e),e.config.hand.enabled&&(!e.models.handpose&&((n=(t=e.config.hand.detector)==null?void 0:t.modelPath)==null?void 0:n.includes("handdetect"))&&([e.models.handpose,e.models.handskeleton]=await vx(e.config)),!e.models.handskeleton&&e.config.hand.landmarks&&((r=(s=e.config.hand.detector)==null?void 0:s.modelPath)==null?void 0:r.includes("handdetect"))&&([e.models.handpose,e.models.handskeleton]=await vx(e.config))),e.config.face.enabled&&!e.models.facedetect&&(e.models.facedetect=O8(e.config)),e.config.face.enabled&&((a=e.config.face.mesh)==null?void 0:a.enabled)&&!e.models.facemesh&&(e.models.facemesh=H8(e.config)),e.config.face.enabled&&((o=e.config.face.iris)==null?void 0:o.enabled)&&!e.models.faceiris&&(e.models.faceiris=M8(e.config)),e.config.hand.enabled&&!e.models.handtrack&&((l=(i=e.config.hand.detector)==null?void 0:i.modelPath)==null?void 0:l.includes("handtrack"))&&(e.models.handtrack=CI(e.config)),e.config.hand.enabled&&e.config.hand.landmarks&&!e.models.handskeleton&&((c=(u=e.config.hand.detector)==null?void 0:u.modelPath)==null?void 0:c.includes("handtrack"))&&(e.models.handskeleton=TI(e.config)),e.config.body.enabled&&!e.models.posenet&&((p=(d=e.config.body)==null?void 0:d.modelPath)==null?void 0:p.includes("posenet"))&&(e.models.posenet=rI(e.config)),e.config.body.enabled&&!e.models.efficientpose&&((m=(h=e.config.body)==null?void 0:h.modelPath)==null?void 0:m.includes("efficientpose"))&&(e.models.efficientpose=Dx(e.config)),e.config.body.enabled&&!e.models.blazepose&&((g=(f=e.config.body)==null?void 0:f.modelPath)==null?void 0:g.includes("blazepose"))&&(e.models.blazepose=DI(e.config)),e.config.body.enabled&&!e.models.blazeposedetect&&((A=e.config.body.detector)==null?void 0:A.modelPath)&&((x=(y=e.config.body)==null?void 0:y.modelPath)==null?void 0:x.includes("blazepose"))&&(e.models.blazeposedetect=RI(e.config)),e.config.body.enabled&&!e.models.efficientpose&&((v=(b=e.config.body)==null?void 0:b.modelPath)==null?void 0:v.includes("efficientpose"))&&(e.models.efficientpose=Dx(e.config)),e.config.body.enabled&&!e.models.movenet&&((S=(k=e.config.body)==null?void 0:k.modelPath)==null?void 0:S.includes("movenet"))&&(e.models.movenet=$I(e.config)),e.config.object.enabled&&!e.models.nanodet&&((D=(C=e.config.object)==null?void 0:C.modelPath)==null?void 0:D.includes("nanodet"))&&(e.models.nanodet=PI(e.config)),e.config.object.enabled&&!e.models.centernet&&((E=(O=e.config.object)==null?void 0:O.modelPath)==null?void 0:E.includes("centernet"))&&(e.models.centernet=MI(e.config)),e.config.face.enabled&&((R=e.config.face.emotion)==null?void 0:R.enabled)&&!e.models.emotion&&(e.models.emotion=Y8(e.config)),e.config.face.enabled&&((T=e.config.face.description)==null?void 0:T.enabled)&&!e.models.faceres&&(e.models.faceres=X8(e.config)),e.config.segmentation.enabled&&!e.models.segmentation&&(e.models.segmentation=Bx(e.config)),e.config.face.enabled&&((P=e.config.face.agegenderrace)==null?void 0:P.enabled)&&!e.models.agegenderrace&&(e.models.agegenderrace=LI(e.config));for await(let U of Object.keys(e.models))e.models[U]&&typeof e.models[U]!="undefined"&&(e.models[U]=await e.models[U])}async function WI(e){let t=["const","placeholder","noop","pad","squeeze","add","sub","mul","div"];for(let n of Object.keys(e.models))if(e.models[n]){let s=[];Array.isArray(e.models[n])?s=e.models[n].filter(r=>r!==null).map(r=>r&&r.executor?r:r.model):s=[e.models[n]];for(let r of s){if(!r){e.config.debug&&re("model marked as loaded but not defined:",n);continue}let a=[],o=r==null?void 0:r.executor;if(o&&o.graph.nodes)for(let l of Object.values(o.graph.nodes)){let u=l.op.toLowerCase();a.includes(u)||a.push(u)}else!o&&e.config.debug&&re("model signature not determined:",n);let i=[];for(let l of 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h=[d[0],d[1],d[2],c[0],c[1],c[2],p[0],p[1],p[2]],m=a(h),f=i.length===478?vue(e):{bearing:0,strength:0};return{angle:m,matrix:h,gaze:f}};var Wx=async(e,t)=>{var d,p,h,m;let n,s,r,a,o,i,l,u=[];e.state="run:face",n=et();let c=await U8(t,e.config);if(e.performance.face=Math.trunc(et()-n),!t.shape||t.shape.length!==4)return[];if(!c)return[];for(let f=0;f0&&c[f].annotations.rightEyeIris.length>0&&c[f].annotations.leftEyeIris[0]!==null&&c[f].annotations.rightEyeIris[0]!==null?Math.max(Math.abs(c[f].annotations.leftEyeIris[3][0]-c[f].annotations.leftEyeIris[1][0]),Math.abs(c[f].annotations.rightEyeIris[4][1]-c[f].annotations.rightEyeIris[2][1]))/t.shape[2]:0,y=e.config.face.detector.return?at(c[f].tensor):null;Z(c[f].tensor),c[f].tensor&&delete c[f].tensor,u.push({...c[f],id:f,age:l.age,gender:l.gender,genderScore:l.genderScore,embedding:l.descriptor,emotion:o,iris:A!==0?Math.trunc(500/A/11.7)/100:0,rotation:g,tensor:y}),e.analyze("End Face")}return e.analyze("End 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d=Math.abs(u.annotations.leftEyeIris[3][0]-u.annotations.leftEyeIris[1][0])/2,p=Math.abs(u.annotations.leftEyeIris[4][1]-u.annotations.leftEyeIris[2][1])/2;r.ellipse(u.annotations.leftEyeIris[0][0],u.annotations.leftEyeIris[0][1],d,p,0,0,2*Math.PI),r.stroke(),s.fillPolygons&&(r.fillStyle=s.useDepth?"rgba(255, 255, 200, 0.3)":s.color,r.fill())}if(u.annotations&&u.annotations.rightEyeIris&&u.annotations.rightEyeIris[0]){r.strokeStyle=s.useDepth?"rgba(255, 200, 255, 0.3)":s.color,r.beginPath();let d=Math.abs(u.annotations.rightEyeIris[3][0]-u.annotations.rightEyeIris[1][0])/2,p=Math.abs(u.annotations.rightEyeIris[4][1]-u.annotations.rightEyeIris[2][1])/2;r.ellipse(u.annotations.rightEyeIris[0][0],u.annotations.rightEyeIris[0][1],d,p,0,0,2*Math.PI),r.stroke(),s.fillPolygons&&(r.fillStyle=s.useDepth?"rgba(255, 255, 200, 0.3)":s.color,r.fill())}if(s.drawGaze&&((o=(a=u.rotation)==null?void 0:a.gaze)==null?void 0:o.strength)&&((l=(i=u.rotation)==null?void 0:i.gaze)==null?void 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o=0;ou.part==="leftShoulder"),i&&l.push([i.position[0],i.position[1]]),i=t[o].keypoints.find(u=>u.part==="rightShoulder"),i&&l.push([i.position[0],i.position[1]]),Qd(r,l,s),l.length=0,i=t[o].keypoints.find(u=>u.part==="rightShoulder"),i&&l.push([i.position[0],i.position[1]]),i=t[o].keypoints.find(u=>u.part==="rightHip"),i&&l.push([i.position[0],i.position[1]]),i=t[o].keypoints.find(u=>u.part==="leftHip"),i&&l.push([i.position[0],i.position[1]]),i=t[o].keypoints.find(u=>u.part==="leftShoulder"),i&&l.push([i.position[0],i.position[1]]),l.length===4&&Ux(r,l,s),l.length=0,i=t[o].keypoints.find(u=>u.part==="leftHip"),i&&l.push([i.position[0],i.position[1]]),i=t[o].keypoints.find(u=>u.part==="leftKnee"),i&&l.push([i.position[0],i.position[1]]),i=t[o].keypoints.find(u=>u.part==="leftAnkle"),i&&l.push([i.position[0],i.position[1]]),i=t[o].keypoints.find(u=>u.part==="leftHeel"),i&&l.push([i.position[0],i.position[1]]),i=t[o].keypoints.find(u=>u.part==="leftFoot"),i&&l.push([i.position[0],i.position[1]]),Qd(r,l,s),l.length=0,i=t[o].keypoints.find(u=>u.part==="rightHip"),i&&l.push([i.position[0],i.position[1]]),i=t[o].keypoints.find(u=>u.part==="rightKnee"),i&&l.push([i.position[0],i.position[1]]),i=t[o].keypoints.find(u=>u.part==="rightAnkle"),i&&l.push([i.position[0],i.position[1]]),i=t[o].keypoints.find(u=>u.part==="rightHeel"),i&&l.push([i.position[0],i.position[1]]),i=t[o].keypoints.find(u=>u.part==="rightFoot"),i&&l.push([i.position[0],i.position[1]]),Qd(r,l,s),l.length=0,i=t[o].keypoints.find(u=>u.part==="leftShoulder"),i&&l.push([i.position[0],i.position[1]]),i=t[o].keypoints.find(u=>u.part==="leftElbow"),i&&l.push([i.position[0],i.position[1]]),i=t[o].keypoints.find(u=>u.part==="leftWrist"),i&&l.push([i.position[0],i.position[1]]),i=t[o].keypoints.find(u=>u.part==="leftPalm"),i&&l.push([i.position[0],i.position[1]]),Qd(r,l,s),l.length=0,i=t[o].keypoints.find(u=>u.part==="rightShoulder"),i&&l.push([i.position[0],i.position[1]]),i=t[o].keypoints.find(u=>u.part==="rightElbow"),i&&l.push([i.position[0],i.position[1]]),i=t[o].keypoints.find(u=>u.part==="rightWrist"),i&&l.push([i.position[0],i.position[1]]),i=t[o].keypoints.find(u=>u.part==="rightPalm"),i&&l.push([i.position[0],i.position[1]]),Qd(r,l,s)}}}async function qx(e,t,n){let s=rn(Xr,n);if(!t||!e)return;let r=Ri(e);r.lineJoin="round",r.font=s.font;for(let a of t){if(s.drawBoxes&&(r.strokeStyle=s.color,r.fillStyle=s.color,Jd(r,a.box[0],a.box[1],a.box[2],a.box[3],s),s.drawLabels&&(s.shadowColor&&s.shadowColor!==""&&(r.fillStyle=s.shadowColor,r.fillText(`hand:${Math.trunc(100*a.score)}%`,a.box[0]+3,1+a.box[1]+s.lineHeight,a.box[2])),r.fillStyle=s.labelColor,r.fillText(`hand:${Math.trunc(100*a.score)}%`,a.box[0]+2,0+a.box[1]+s.lineHeight,a.box[2])),r.stroke()),s.drawPoints&&a.keypoints&&a.keypoints.length>0)for(let o of a.keypoints)r.fillStyle=s.useDepth?`rgba(${127.5+2*(o[2]||0)}, ${127.5-2*(o[2]||0)}, 255, 0.5)`:s.color,Vx(r,o[0],o[1],0,s);if(s.drawLabels&&a.annotations){let o=(i,l)=>{!i||i.length===0||!i[0]||(r.fillStyle=s.useDepth?`rgba(${127.5+2*i[i.length-1][2]}, ${127.5-2*i[i.length-1][2]}, 255, 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Fe={face:[],body:[],hand:[],gesture:[],object:[],persons:[],performance:{},timestamp:0};function YI(e){var s,r,a,o,i,l,u,c,d,p,h,m,f,g,A,y,x,b,v,k,S;if(!e)return{face:[],body:[],hand:[],gesture:[],object:[],persons:[],performance:{},timestamp:0};let t=Date.now()-e.timestamp,n=t<1e3?8-Math.log(t+1):1;if(Fe.canvas=e.canvas,!Fe.body||e.body.length!==Fe.body.length)Fe.body=JSON.parse(JSON.stringify(e.body));else for(let 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e.detect(r,e.config),r.close()}return s}async function Iue(e){return new Promise(t=>{let n;switch(e.config.warmup){case"face":n="data:image/jpeg;base64,"+j0;break;case"full":case"body":n="data:image/jpeg;base64,"+q0;break;default:n=null}let s;typeof Image!="undefined"?s=new Image:oe.Image&&(s=new oe.Image),s.onload=async()=>{let r=ds(s.naturalWidth,s.naturalHeight);if(!r)re("Warmup: Canvas not found"),t({});else{let a=r.getContext("2d");a&&a.drawImage(s,0,0);let o=await e.image(r),i=await e.detect(o.tensor,e.config);t(i)}},n?s.src=n:t(null)})}async function Sue(e){let t=r=>Buffer.from(r,"base64"),n;if(e.config.warmup==="face"&&(n=t(j0)),(e.config.warmup==="body"||e.config.warmup==="full")&&(n=t(q0)),!n)return null;let s;if(typeof void 0!="undefined"){let r=(void 0).decodeJpeg(n),a=r.expandDims(0);e.tf.dispose(r),s=await e.detect(a,e.config),e.tf.dispose(a)}else e.config.debug&&re("Warmup tfjs-node not loaded");return s}async function JI(e,t){let n=et();if(e.state="warmup",t&&(e.config=rn(e.config,t)),!e.config.warmup||e.config.warmup==="none")return{error:"null"};let s;return new Promise(async r=>{typeof createImageBitmap=="function"?s=await kue(e):typeof Image!="undefined"||oe.Canvas!==void 0?s=await Iue(e):s=await Sue(e);let a=et();e.config.debug&&re("Warmup",e.config.warmup,Math.round(a-n),"ms"),e.emit("warmup"),r(s)})}var ju,ep,tp,X0,eS=class{constructor(t){Ne(this,"version");Ne(this,"config");Ne(this,"result");Ne(this,"state");Ne(this,"process");Ne(this,"tf");Ne(this,"env");Ne(this,"draw");Ne(this,"models");Ne(this,"events");Ne(this,"faceTriangulation");Ne(this,"faceUVMap");Ne(this,"performance");ac(this,ju,void 0);ac(this,ep,void 0);ac(this,tp,void 0);Ne(this,"gl");Ne(this,"analyze",(...t)=>{if(!rc(this,ep))return;let n=this.tf.engine().state.numTensors,s=rc(this,ju);oc(this,ju,n);let r=n-s;r!==0&&re(...t,r)});ac(this,X0,t=>{if(!rc(this,tp))return null;if(!t)return"input is not defined";if(this.env.node&&!(t instanceof He))return"input must be a tensor";try{this.tf.getBackend()}catch(n){return"backend not loaded"}return null});Ne(this,"emit",t=>{var n;return(n=this.events)==null?void 0:n.dispatchEvent(new Event(t))});E0(),this.env=oe,ta.wasmPath=`https://cdn.jsdelivr.net/npm/@tensorflow/tfjs-backend-wasm@${kh}/dist/`,ta.modelBasePath=this.env.browser?"../models/":"file://models/",ta.backend=this.env.browser?"humangl":"tensorflow",this.version=Kx,Object.defineProperty(this,"version",{value:Kx}),this.config=JSON.parse(JSON.stringify(ta)),Object.seal(this.config),t&&(this.config=rn(this.config,t)),this.tf=Ii,this.state="idle",oc(this,ju,0),oc(this,ep,!1),oc(this,tp,!1),this.performance={backend:0,load:0,image:0,frames:0,cached:0,changed:0,total:0,draw:0},this.events=new EventTarget,this.models=new Yd,this.draw={options:Xr,canvas:(n,s)=>XI(n,s),face:(n,s,r)=>Gx(n,s,r),body:(n,s,r)=>jx(n,s,r),hand:(n,s,r)=>qx(n,s,r),gesture:(n,s,r)=>Hx(n,s,r),object:(n,s,r)=>Xx(n,s,r),person:(n,s,r)=>qI(n,s,r),all:(n,s,r)=>KI(n,s,r)},this.result={face:[],body:[],hand:[],gesture:[],object:[],performance:{},timestamp:0,persons:[]},this.process={tensor:null,canvas:null},this.faceTriangulation=G8,this.faceUVMap=j8,this.gl=$t,this.emit("create")}reset(){let t=this.config.backend;this.config=JSON.parse(JSON.stringify(ta)),this.config.backend=t}validate(t){return wg(ta,t||this.config)}image(t){return zu(t,this.config)}similarity(t,n){return rx(t,n)}async segmentation(t,n){return zI(t,n,this.config)}enhance(t){return ax(t)}match(t,n,s=0){return K8(t,n,s)}async init(){await B0(this,!0),await this.tf.ready(),_8(this.env)}async load(t){this.state="load";let n=et(),s=Object.values(this.models).filter(o=>o).length;t&&(this.config=rn(this.config,t)),oe.initial&&(this.config.debug&&re(`version: ${this.version}`),this.config.debug&&re(`tfjs version: ${this.tf.version_core}`),await B0(this)||re("error: backend check failed"),await Sh(),this.env.browser&&(this.config.debug&&re("configuration:",this.config),this.config.debug&&re("tf flags:",this.tf.ENV.flags))),await BI(this),oe.initial&&this.config.debug&&re("tf engine state:",this.tf.engine().state.numBytes,"bytes",this.tf.engine().state.numTensors,"tensors"),oe.initial=!1,Object.values(this.models).filter(o=>o).length!==s&&(await WI(this),this.emit("load"));let a=Math.trunc(et()-n);a>(this.performance.load||0)&&(this.performance.load=a)}next(t=this.result){return YI(t)}async warmup(t){return JI(this,t)}async detect(t,n){return this.state="detect",new Promise(async s=>{var A,y,x,b,v,k,S,C,D,O,E,R,T,P,U,j,q,X,te,ne,se,ae;this.state="config";let r,a;this.config=rn(this.config,n),this.state="check";let o=rc(this,X0).call(this,t);o&&(re(o,t),s({error:o}));let i=et();await B0(this),await this.load(),r=et(),this.state="image";let l=zu(t,this.config);if(this.process=l,this.performance.image=Math.trunc(et()-r),this.analyze("Get Image:"),!l.tensor){this.config.debug&&re("could not convert input to tensor"),s({error:"could not convert input to tensor"});return}this.emit("image"),r=et(),this.config.skipFrame=await D8(this.config,l.tensor),this.performance.frames||(this.performance.frames=0),this.performance.cached||(this.performance.cached=0),this.performance.frames++,this.config.skipFrame&&this.performance.cached++,this.performance.changed=Math.trunc(et()-r),this.analyze("Check Changed:");let u=[],c=[],d=[],p=[];this.state="detect:face",this.config.async?(u=this.config.face.enabled?Wx(this,l.tensor):[],this.performance.face&&delete this.performance.face):(r=et(),u=this.config.face.enabled?await Wx(this,l.tensor):[],a=Math.trunc(et()-r),a>0&&(this.performance.face=a)),this.config.async&&(this.config.body.maxDetected===-1||this.config.hand.maxDetected===-1)&&(u=await u),this.analyze("Start Body:"),this.state="detect:body";let h=this.config.body.maxDetected===-1?rn(this.config,{body:{maxDetected:this.config.face.enabled?1*u.length:1}}):this.config;this.config.async?(((A=this.config.body.modelPath)==null?void 0:A.includes("posenet"))?c=this.config.body.enabled?mx(l.tensor,h):[]:((y=this.config.body.modelPath)==null?void 0:y.includes("blazepose"))?c=this.config.body.enabled?Tx(l.tensor,h):[]:((x=this.config.body.modelPath)==null?void 0:x.includes("efficientpose"))?c=this.config.body.enabled?_x(l.tensor,h):[]:((b=this.config.body.modelPath)==null?void 0:b.includes("movenet"))&&(c=this.config.body.enabled?Fx(l.tensor,h):[]),this.performance.body&&delete this.performance.body):(r=et(),((v=this.config.body.modelPath)==null?void 0:v.includes("posenet"))?c=this.config.body.enabled?await mx(l.tensor,h):[]:((k=this.config.body.modelPath)==null?void 0:k.includes("blazepose"))?c=this.config.body.enabled?await Tx(l.tensor,h):[]:((S=this.config.body.modelPath)==null?void 0:S.includes("efficientpose"))?c=this.config.body.enabled?await _x(l.tensor,h):[]:((C=this.config.body.modelPath)==null?void 0:C.includes("movenet"))&&(c=this.config.body.enabled?await Fx(l.tensor,h):[]),a=Math.trunc(et()-r),a>0&&(this.performance.body=a)),this.analyze("End Body:"),this.analyze("Start Hand:"),this.state="detect:hand";let m=this.config.hand.maxDetected===-1?rn(this.config,{hand:{maxDetected:this.config.face.enabled?2*u.length:1}}):this.config;this.config.async?(((O=(D=this.config.hand.detector)==null?void 0:D.modelPath)==null?void 0:O.includes("handdetect"))?d=this.config.hand.enabled?bx(l.tensor,m):[]:((R=(E=this.config.hand.detector)==null?void 0:E.modelPath)==null?void 0:R.includes("handtrack"))&&(d=this.config.hand.enabled?Sx(l.tensor,m):[]),this.performance.hand&&delete this.performance.hand):(r=et(),((P=(T=this.config.hand.detector)==null?void 0:T.modelPath)==null?void 0:P.includes("handdetect"))?d=this.config.hand.enabled?await bx(l.tensor,m):[]:((j=(U=this.config.hand.detector)==null?void 0:U.modelPath)==null?void 0:j.includes("handtrack"))&&(d=this.config.hand.enabled?await Sx(l.tensor,m):[]),a=Math.trunc(et()-r),a>0&&(this.performance.hand=a)),this.analyze("End Hand:"),this.analyze("Start Object:"),this.state="detect:object",this.config.async?(((q=this.config.object.modelPath)==null?void 0:q.includes("nanodet"))?p=this.config.object.enabled?Px(l.tensor,this.config):[]:((X=this.config.object.modelPath)==null?void 0:X.includes("centernet"))&&(p=this.config.object.enabled?zx(l.tensor,this.config):[]),this.performance.object&&delete this.performance.object):(r=et(),((te=this.config.object.modelPath)==null?void 0:te.includes("nanodet"))?p=this.config.object.enabled?await Px(l.tensor,this.config):[]:((ne=this.config.object.modelPath)==null?void 0:ne.includes("centernet"))&&(p=this.config.object.enabled?await zx(l.tensor,this.config):[]),a=Math.trunc(et()-r),a>0&&(this.performance.object=a)),this.analyze("End Object:"),this.state="detect:await",this.config.async&&([u,c,d,p]=await Promise.all([u,c,d,p])),this.state="detect:gesture";let f=[];this.config.gesture.enabled&&(r=et(),f=[...HI(u),...UI(c),...jI(d),...GI(u)],this.config.async?this.performance.gesture&&delete this.performance.gesture:this.performance.gesture=Math.trunc(et()-r)),this.performance.total=Math.trunc(et()-i);let g=((ae=(se=this.process)==null?void 0:se.tensor)==null?void 0:ae.shape)||[];this.result={face:u,body:c,hand:d,gesture:f,object:p,performance:this.performance,canvas:this.process.canvas,timestamp:Date.now(),get persons(){return ZI(u,c,d,f,g)}},Z(l.tensor),this.emit("detect"),this.state="idle",s(this.result)})}};ju=new WeakMap,ep=new WeakMap,tp=new WeakMap,X0=new WeakMap;return Cue;})(); /** * @license * Copyright 2017 Google LLC. All Rights Reserved. * Licensed under the Apache License, Version 2.0 (the "License"); * you may not use this file except in compliance with the License. * You may obtain a copy of the License at * * http://www.apache.org/licenses/LICENSE-2.0 * * Unless required by applicable law or agreed to in writing, software * distributed under the License is distributed on an "AS IS" BASIS, * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. * See the License for the specific language governing permissions and * limitations under the License. * ============================================================================= */ /** * @license * Copyright 2018 Google LLC * * Use of this source code is governed by an MIT-style * license that can be found in the LICENSE file or at * https://opensource.org/licenses/MIT. * ============================================================================= */ /** * @license * Copyright 2018 Google LLC. All Rights Reserved. * Licensed under the Apache License, Version 2.0 (the "License"); * you may not use this file except in compliance with the License. * You may obtain a copy of the License at * * http://www.apache.org/licenses/LICENSE-2.0 * * Unless required by applicable law or agreed to in writing, software * distributed under the License is distributed on an "AS IS" BASIS, * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. * See the License for the specific language governing permissions and * limitations under the License. * * ============================================================================= */ /** * @license * Copyright 2018 Google LLC. All Rights Reserved. * Licensed under the Apache License, Version 2.0 (the "License"); * you may not use this file except in compliance with the License. * You may obtain a copy of the License at * * http://www.apache.org/licenses/LICENSE-2.0 * * Unless required by applicable law or agreed to in writing, software * distributed under the License is distributed on an "AS IS" BASIS, * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. * See the License for the specific language governing permissions and * limitations under the License. * ============================================================================= */ /** * @license * Copyright 2019 Google LLC * * Use of this source code is governed by an MIT-style * license that can be found in the LICENSE file or at * https://opensource.org/licenses/MIT. * ============================================================================= */ /** * @license * Copyright 2019 Google LLC. All Rights Reserved. * Licensed under the Apache License, Version 2.0 (the "License"); * you may not use this file except in compliance with the License. * You may obtain a copy of the License at * * http://www.apache.org/licenses/LICENSE-2.0 * * Unless required by applicable law or agreed to in writing, software * distributed under the License is distributed on an "AS IS" BASIS, * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. * See the License for the specific language governing permissions and * limitations under the License. * * ============================================================================= */ /** * @license * Copyright 2019 Google LLC. All Rights Reserved. * Licensed under the Apache License, Version 2.0 (the "License"); * you may not use this file except in compliance with the License. * You may obtain a copy of the License at * * http://www.apache.org/licenses/LICENSE-2.0 * * Unless required by applicable law or agreed to in writing, software * distributed under the License is distributed on an "AS IS" BASIS, * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. * See the License for the specific language governing permissions and * limitations under the License. * ============================================================================= */ /** * @license * Copyright 2020 Google Inc. All Rights Reserved. * Licensed under the Apache License, Version 2.0 (the "License"); * you may not use this file except in compliance with the License. * You may obtain a copy of the License at * * http://www.apache.org/licenses/LICENSE-2.0 * * Unless required by applicable law or agreed to in writing, software * distributed under the License is distributed on an "AS IS" BASIS, * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. * See the License for the specific language governing permissions and * limitations under the License. * ============================================================================= */ /** * @license * Copyright 2020 Google LLC * * Use of this source code is governed by an MIT-style * license that can be found in the LICENSE file or at * https://opensource.org/licenses/MIT. * ============================================================================= */ /** * @license * Copyright 2020 Google LLC. All Rights Reserved. * Licensed under the Apache License, Version 2.0 (the "License"); * you may not use this file except in compliance with the License. * You may obtain a copy of the License at * * http://www.apache.org/licenses/LICENSE-2.0 * * Unless required by applicable law or agreed to in writing, software * distributed under the License is distributed on an "AS IS" BASIS, * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. * See the License for the specific language governing permissions and * limitations under the License. * ============================================================================= */ /** * @license * Copyright 2020 Google LLC. All Rights Reserved. * Licensed under the Apache License, Version 2.0 (the License); * you may not use this file except in compliance with the License. * You may obtain a copy of the License at * * http://www.apache.org/licenses/LICENSE-2.0 * * Unless required by applicable law or agreed to in writing, software * distributed under the License is distributed on an AS IS BASIS, * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. * See the License for the specific language governing permissions and * limitations under the License. * ============================================================================= */ /** * @license * Copyright 2021 Google LLC. All Rights Reserved. * Licensed under the Apache License, Version 2.0 (the "License"); * you may not use this file except in compliance with the License. * You may obtain a copy of the License at * * http://www.apache.org/licenses/LICENSE-2.0 * * Unless required by applicable law or agreed to in writing, software * distributed under the License is distributed on an "AS IS" BASIS, * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. * See the License for the specific language governing permissions and * limitations under the License. * ============================================================================= */ /** * @license * Copyright 2021 Google LLC. All Rights Reserved. * Licensed under the Apache License, Version 2.0 (the "License"); * you may not use this file except in compliance with the License. * You may obtain a copy of the License at * * https://www.apache.org/licenses/LICENSE-2.0 * * Unless required by applicable law or agreed to in writing, software * distributed under the License is distributed on an "AS IS" BASIS, * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. * See the License for the specific language governing permissions and * limitations under the License. * ============================================================================= */ /** * @license * Copyright 2018 Google LLC. All Rights Reserved. * Licensed under the Apache License, Version 2.0 (the "License"); * you may not use this file except in compliance with the License. * You may obtain a copy of the License at * * http://www.apache.org/licenses/LICENSE-2.0 * * Unless required by applicable law or agreed to in writing, software * distributed under the License is distributed on an "AS IS" BASIS, * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. * See the License for the specific language governing permissions and * limitations under the License. * ============================================================================= */ /** @license See the LICENSE file. */