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this.scopedRun(()=>this.state.kernelDepth++,()=>this.state.kernelDepth--,()=>{!this.ENV.getBool("DEBUG")&&!this.state.profiling?t=i():(p=this.profiler.profileKernel(l,u,()=>i()),this.ENV.getBool("DEBUG")&&this.profiler.logKernelProfile(p),t=p.outputs)}),n&&this.addTapeNode(l,u,t,c,a,d),this.state.profiling&&this.state.activeProfile.kernels.push({name:l,bytesAdded:this.state.numBytes-r,totalBytesSnapshot:this.state.numBytes,tensorsAdded:this.state.numTensors-s,totalTensorsSnapshot:this.state.numTensors,inputShapes:Object.keys(u).map(h=>u[h]!=null?u[h].shape:null),outputShapes:t.map(h=>h.shape),kernelTimeMs:p.timeMs,extraInfo:p.extraInfo}),Array.isArray(o)?t:t[0]}saveTensorsForBackwardMode(e){return e.map(t=>this.keep(this.clone(t)))}getTensorsForGradient(e,t,a){let n=Um(e);if(n!=null){let r=n.inputsToSave||[],s=n.outputsToSave||[],i;n.saveAllInputs?(F(Array.isArray(t),()=>"saveAllInputs is true, expected inputs to be an array."),i=Object.keys(t).map(l=>t[l])):i=r.map(l=>t[l]);let 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i=R(e,"forgetBias","basicLSTMCell"),o=R(t,"lstmKernel","basicLSTMCell"),l=R(a,"lstmBias","basicLSTMCell"),u=R(n,"data","basicLSTMCell"),d=R(r,"c","basicLSTMCell"),c=R(s,"h","basicLSTMCell"),p=at([u,c],1),h=st(p,o),f=xe(h,l),m=f.shape[0],g=f.shape[1]/4,y=[m,g],A=_e(f,[0,0],y),x=_e(f,[0,g],y),b=_e(f,[0,g*2],y),w=_e(f,[0,g*3],y),S=xe(ae(Da(A),Nc(x)),ae(d,Da(xe(i,b)))),C=ae(Nc(S),Da(w));return[S,C]}var Rx=D({basicLSTMCell_:QN});function eE(e,t,a){let n=R(e,"x","batchToSpaceND"),r=t.reduce((o,l)=>o*l);F(n.rank>=1+t.length,()=>`input rank is ${n.rank} but should be > than blockShape.length ${t.length}`),F(a.length===t.length,()=>`crops.length is ${a.length} but should be equal to blockShape.length ${t.length}`),F(n.shape[0]%r===0,()=>`input tensor batch is ${n.shape[0]} but is not divisible by the product of the elements of blockShape ${t.join(" * ")} === ${r}`);let s={x:n},i={blockShape:t,crops:a};return z.runKernel(bl,s,i)}var k2=D({batchToSpaceND_:eE});function tE(e){let t;return e.rank===0||e.rank===1?t=J(e,[1,1,1,e.size]):e.rank===2?t=J(e,[1,1,e.shape[0],e.shape[1]]):e.rank===3?t=J(e,[1,e.shape[0],e.shape[1],e.shape[2]]):t=e,t}function aE(e,t,a,n,r,s){s==null&&(s=.001);let i=R(e,"x","batchNorm"),o=R(t,"mean","batchNorm"),l=R(a,"variance","batchNorm"),u;r!=null&&(u=R(r,"scale","batchNorm"));let d;n!=null&&(d=R(n,"offset","batchNorm")),F(o.rank===l.rank,()=>"Batch normalization gradient requires mean and variance to have equal ranks."),F(d==null||o.rank===d.rank,()=>"Batch normalization gradient requires mean and offset to have equal ranks."),F(u==null||o.rank===u.rank,()=>"Batch normalization gradient requires mean and scale to have equal ranks.");let c={x:tE(i),scale:u,offset:d,mean:o,variance:l},p={varianceEpsilon:s},h=z.runKernel($i,c,p);return J(h,i.shape)}var Qd=D({batchNorm_:aE});function nE(e,t,a,n,r,s){let i=R(e,"x","batchNorm"),o=R(t,"mean","batchNorm"),l=R(a,"variance","batchNorm"),u;r!=null&&(u=R(r,"scale","batchNorm"));let d;return n!=null&&(d=R(n,"offset","batchNorm")),F(i.rank===2,()=>`Error in batchNorm2D: x must be rank 2 but got rank ${i.rank}.`),F(o.rank===2||o.rank===1,()=>`Error in batchNorm2D: mean must be rank 2 or rank 1 but got rank ${o.rank}.`),F(l.rank===2||l.rank===1,()=>`Error in batchNorm2D: variance must be rank 2 or rank 1 but got rank ${l.rank}.`),u!=null&&F(u.rank===2||u.rank===1,()=>`Error in batchNorm2D: scale must be rank 2 or rank 1 but got rank ${u.rank}.`),d!=null&&F(d.rank===2||d.rank===1,()=>`Error in batchNorm2D: offset must be rank 2 or rank 1 but got rank ${d.rank}.`),Qd(i,o,l,d,u,s)}var Mx=D({batchNorm2d_:nE});function rE(e,t,a,n,r,s){let i=R(e,"x","batchNorm"),o=R(t,"mean","batchNorm"),l=R(a,"variance","batchNorm"),u;r!=null&&(u=R(r,"scale","batchNorm"));let d;return n!=null&&(d=R(n,"offset","batchNorm")),F(i.rank===3,()=>`Error in batchNorm3D: x must be rank 3 but got rank ${i.rank}.`),F(o.rank===3||o.rank===1,()=>`Error in batchNorm3D: mean must be rank 3 or rank 1 but got rank ${o.rank}.`),F(l.rank===3||l.rank===1,()=>`Error in batchNorm3D: variance must be rank 3 or rank 1 but got rank ${l.rank}.`),u!=null&&F(u.rank===3||u.rank===1,()=>`Error in batchNorm3D: scale must be rank 3 or rank 1 but got rank ${u.rank}.`),d!=null&&F(d.rank===3||d.rank===1,()=>`Error in batchNorm3D: offset must be rank 3 or rank 1 but got rank ${d.rank}.`),Qd(i,o,l,d,u,s)}var $x=D({batchNorm3d_:rE});function sE(e,t,a,n,r,s){let i=R(e,"x","batchNorm"),o=R(t,"mean","batchNorm"),l=R(a,"variance","batchNorm"),u;r!=null&&(u=R(r,"scale","batchNorm"));let d;return n!=null&&(d=R(n,"offset","batchNorm")),F(i.rank===4,()=>`Error in batchNorm4D: x must be rank 4 but got rank ${i.rank}.`),F(o.rank===4||o.rank===1,()=>`Error in batchNorm4D: mean must be rank 4 or rank 1 but got rank ${o.rank}.`),F(l.rank===4||l.rank===1,()=>`Error in batchNorm4D: variance must be rank 4 or rank 1 but got rank ${l.rank}.`),u!=null&&F(u.rank===4||u.rank===1,()=>`Error in batchNorm4D: scale must be rank 4 or rank 1 but got rank ${u.rank}.`),d!=null&&F(d.rank===4||d.rank===1,()=>`Error in batchNorm4D: offset must be rank 4 or rank 1 but got rank ${d.rank}.`),Qd(i,o,l,d,u,s)}var _x=D({batchNorm4d_:sE});function iE(e,t,a){let n=R(e,"x","bincount"),r=R(t,"weights","bincount");F(n.dtype==="int32",()=>`Error in bincount: input dtype must be int32, but got ${n.dtype}`),F(a>=0,()=>`size must be non-negative, but got ${a}.`),F(r.size===n.size||r.size===0,()=>`Error in bincount: weights must have the same size as input or0-length, but got input shape: ${n.shape}, weights shape: ${r.shape}.`);let s={x:n,weights:r},i={size:a};return z.runKernel(Uc,s,i)}var I2=D({bincount_:iE});function oE(e,t){let a=R(e,"s0","broadcastArgs","int32"),n=R(t,"s1","broadcastArgs","int32");if(a.rank!==1)throw new Error(`broadcastArgs(): first input must be a vector (rank=1). Has rank ${a.rank}`);if(n.rank!==1)throw new Error(`broadcastArgs(): second input must be a vector (rank=1). Has rank ${n.rank}`);let r={s0:a,s1:n};return z.runKernel(jc,r)}var Fx=D({broadcastArgs_:oE});function lE(e,t){let a=R(e,"broadcastTo","x"),n=a.shape;if(t.some(l=>!(l>0)||l%1!==0))throw new Error(`broadcastTo(): Invalid broadcast shape [${t}].`);if(t.lengtha.rank){let l=a.shape.slice();for(;l.length=0;l--)if(r[l]===t[l])s[l]=1;else if(a.shape[l]!==1)throw new Error(`broadcastTo(): [${n}] cannot be broadcast to [${t}].`);if(s.map((l,u)=>l>1?u:-1).filter(l=>l>=0).length===0)return wa(a);let i={x:a},o={reps:s};return z.runKernel(As,i,o)}var ei=D({broadcastTo_:lE});function uE(e){let t={x:R(e,"x","ceil","float32")};return z.runKernel(Qr,t)}var Px=D({ceil_:uE});function nr(e,t,a){let n={shape:e,value:t,dtype:a};return z.runKernel(kl,{},n)}function dE(e,t,a){let n=R(e,"x","clipByValue");if(F(t<=a,()=>`Error in clip: min (${t}) must be less than or equal to max (${a}).`),t===a)return nr(n.shape,t,n.dtype);let r={x:n},s={clipValueMin:t,clipValueMax:a};return z.runKernel(es,r,s)}var Ox=D({clipByValue_:dE});function pE(e){return at(e,0)}var Dx=D({concat1d_:pE});function cE(e,t){return at(e,t)}var ql=D({concat2d_:cE});function hE(e,t){return at(e,t)}var zx=D({concat3d_:hE});function fE(e,t){return at(e,t)}var Lx=D({concat4d_:fE});function mE(e,t,a,n,r="NHWC",s=[1,1],i){let o=R(e,"x","conv2d","float32"),l=R(t,"filter","conv2d","float32"),u=o,d=!1;o.rank===3&&(d=!0,u=J(o,[1,o.shape[0],o.shape[1],o.shape[2]])),F(u.rank===4,()=>`Error in conv2d: input must be rank 4, but got rank ${u.rank}.`),F(l.rank===4,()=>`Error in conv2d: filter must be rank 4, but got rank ${l.rank}.`),Sn("conv2d",n,i);let c=r==="NHWC"?u.shape[3]:u.shape[1];F(c===l.shape[2],()=>`Error in conv2d: depth of input (${c}) must match input depth for filter ${l.shape[2]}.`),F(kr(a,s),()=>`Error in conv2D: Either strides or dilations must be 1. Got strides ${a} and dilations '${s}'`);let p={x:u,filter:l},h={strides:a,pad:n,dataFormat:r,dilations:s,dimRoundingMode:i},f=z.runKernel(Ai,p,h);return d?J(f,[f.shape[1],f.shape[2],f.shape[3]]):f}var ep=D({conv2d_:mE});function gE(e,t,a,n,r="NWC",s=1,i){let o=R(e,"x","conv1d"),l=R(t,"filter","conv1d"),u=o,d=!1;o.rank===2&&(d=!0,u=J(o,[1,o.shape[0],o.shape[1]])),F(u.rank===3,()=>`Error in conv1d: input must be rank 3, but got rank ${u.rank}.`),F(l.rank===3,()=>`Error in conv1d: filter must be rank 3, but got rank ${l.rank}.`),Sn("conv1d",n,i),F(u.shape[2]===l.shape[1],()=>`Error in conv1d: depth of input (${u.shape[2]}) must match input depth for filter ${l.shape[1]}.`),F(kr(a,s),()=>`Error in conv1D: Either stride or dilation must be 1. Got stride ${a} and dilation '${s}'`),F(r==="NWC",()=>`Error in conv1d: got dataFormat of ${r} but only NWC is currently supported.`);let c=J(l,[1,l.shape[0],l.shape[1],l.shape[2]]),p=J(u,[u.shape[0],1,u.shape[1],u.shape[2]]),h=ep(p,c,[1,a],n,"NHWC",[1,s],i);return d?J(h,[h.shape[2],h.shape[3]]):J(h,[h.shape[0],h.shape[2],h.shape[3]])}var Bx=D({conv1d_:gE});function yE(e,t,a,n,r,s="NHWC",i){F(e.length===t.rank,()=>`Length of inShape (${e.length}) and rank of dy (${t.rank}) must match`);let o=e,l=t,u=!1;t.rank===3&&(u=!0,l=J(t,[1,t.shape[0],t.shape[1],t.shape[2]]),o=[1,e[0],e[1],e[2]]),F(o.length===4,()=>`Error in conv2dDerInput: inShape must be length 4, but got length ${o.length}.`),F(l.rank===4,()=>`Error in conv2dDerInput: dy must be rank 4, but got rank ${l.rank}`),F(a.rank===4,()=>`Error in conv2dDerInput: filter must be rank 4, but got rank ${a.rank}`);let d=s==="NHWC"?o[3]:o[1],c=s==="NHWC"?l.shape[3]:l.shape[1];F(d===a.shape[2],()=>`Error in conv2dDerInput: depth of input (${d}) must match input depth for filter ${a.shape[2]}.`),F(c===a.shape[3],()=>`Error in conv2dDerInput: depth of output (${c}) must match output depth for filter ${a.shape[3]}.`),Sn("conv2dDerInput",r,i);let p={dy:l,filter:a},h={strides:n,pad:r,dataFormat:s,dimRoundingMode:i,inputShape:o},f=z.runKernel(xi,p,h);return u?J(f,[f.shape[1],f.shape[2],f.shape[3]]):f}var Wx=D({conv2DBackpropInput_:yE});function AE(e,t,a,n,r,s){let i=R(e,"x","conv2dTranspose"),o=R(t,"filter","conv2dTranspose");return Wx(a,i,o,n,r,"NHWC",s)}var Vx=D({conv2dTranspose_:AE});function xE(e,t,a,n,r="NDHWC",s=[1,1,1]){let i=R(e,"x","conv3d"),o=R(t,"filter","conv3d"),l=i,u=!1;i.rank===4&&(u=!0,l=J(i,[1,i.shape[0],i.shape[1],i.shape[2],i.shape[3]])),F(l.rank===5,()=>`Error in conv3d: input must be rank 5, but got rank ${l.rank}.`),F(o.rank===5,()=>`Error in conv3d: filter must be rank 5, but got rank ${o.rank}.`),F(l.shape[4]===o.shape[3],()=>`Error in conv3d: depth of input (${l.shape[4]}) must match input depth for filter ${o.shape[3]}.`),F(kr(a,s),()=>`Error in conv3D: Either strides or dilations must be 1. Got strides ${a} and dilations '${s}'`),F(r==="NDHWC",()=>`Error in conv3d: got dataFormat of ${r} but only NDHWC is currently supported.`);let d={x:l,filter:o},c={strides:a,pad:n,dataFormat:r,dilations:s},p=z.runKernel(Xc,d,c);return u?J(p,[p.shape[1],p.shape[2],p.shape[3],p.shape[4]]):p}var Gx=D({conv3d_:xE});function bE(e,t,a,n,r){F(e.length===t.rank,()=>`Length of inShape (${e.length}) and rank of dy (${t.rank}) must match`);let s=e,i=t,o=!1;t.rank===4&&(o=!0,i=J(t,[1,t.shape[0],t.shape[1],t.shape[2],t.shape[3]]),s=[1,e[0],e[1],e[2],e[3]]);let l=s[4],u=i.shape[4];F(s.length===5,()=>`Error in conv3dDerInput: inShape must be length 5, but got length ${s.length}.`),F(i.rank===5,()=>`Error in conv3dDerInput: dy must be rank 5, but got rank ${i.rank}`),F(a.rank===5,()=>`Error in conv3dDerInput: filter must be rank 5, but got rank ${a.rank}`),F(l===a.shape[3],()=>`Error in conv3dDerInput: depth of input (${l}) must match input depth for filter ${a.shape[3]}.`),F(u===a.shape[4],()=>`Error in conv3dDerInput: depth of output (${u}) must match output depth for filter ${a.shape[4]}.`);let d={dy:i,filter:a},c={pad:r,strides:n,inputShape:s},p=z.runKernel(Kc,d,c);return o?J(p,[p.shape[1],p.shape[2],p.shape[3],p.shape[4]]):p}var vE=D({conv3DBackpropInput_:bE});function wE(e,t,a,n,r){let s=R(e,"x","conv3dTranspose"),i=R(t,"filter","conv3dTranspose");return vE(a,s,i,n,r)}var Ux=D({conv3dTranspose_:wE});function kE(e){let t={x:R(e,"x","cos","float32")};return z.runKernel(bi,t)}var jx=D({cos_:kE});function IE(e){let t={x:R(e,"x","cosh","float32")};return z.runKernel(vi,t)}var Hx=D({cosh_:IE});function SE(e,t=0,a=!1,n=!1){let r={x:R(e,"x","cumprod")},s={axis:t,exclusive:a,reverse:n};return z.runKernel(wi,r,s)}var qx=D({cumprod_:SE});function TE(e,t=0,a=!1,n=!1){let r={x:R(e,"x","cumsum")},s={axis:t,exclusive:a,reverse:n};return z.runKernel(ki,r,s)}var Xx=D({cumsum_:TE});function CE(e,t,a,n=!1){let r=R(e,"x","denseBincount"),s=R(t,"weights","denseBincount");F(r.dtype==="int32",()=>`Error in denseBincount: input dtype must be int32, but got ${r.dtype}`),F(r.rank<=2,()=>`Error in denseBincount: input must be at most rank 2, but got rank ${r.rank}.`),F(a>=0,()=>`size must be non-negative, but got ${a}.`),F(s.size===r.size||s.size===0,()=>`Error in denseBincount: weights must have the same shape as x or 0-length, but got x shape: ${r.shape}, weights shape: ${s.shape}.`);let i={x:r,weights:s},o={size:a,binaryOutput:n};return z.runKernel(Zc,i,o)}var Kx=D({denseBincount_:CE});function NE(e,t,a="NHWC"){let n=R(e,"x","depthToSpace","float32"),r=a==="NHWC"?n.shape[1]:n.shape[2],s=a==="NHWC"?n.shape[2]:n.shape[3],i=a==="NHWC"?n.shape[3]:n.shape[1];F(t>1,()=>`blockSize should be > 1 for depthToSpace, but was: ${t}`),F(r*t>=0,()=>`Negative dimension size caused by overflow when multiplying ${r} and ${t} for depthToSpace with input shape ${n.shape}`),F(s*t>=0,()=>`Negative dimension size caused by overflow when multiplying ${s} and ${t} for depthToSpace with input shape ${n.shape}`),F(i%(t*t)===0,()=>`Dimension size must be evenly divisible by ${t*t} but is ${i} for depthToSpace with input shape ${n.shape}`);let o={x:n},l={blockSize:t,dataFormat:a};return z.runKernel(Si,o,l)}var Zx=D({depthToSpace_:NE});function EE(e,t,a,n,r="NHWC",s=[1,1],i){let o=R(e,"x","depthwiseConv2d","float32"),l=R(t,"filter","depthwiseConv2d","float32"),u=o,d=!1;o.rank===3&&(d=!0,u=J(o,[1,o.shape[0],o.shape[1],o.shape[2]])),F(u.rank===4,()=>`Error in depthwiseConv2d: input must be rank 4, but got rank ${u.rank}.`),F(l.rank===4,()=>`Error in depthwiseConv2d: filter must be rank 4, but got rank ${l.rank}.`);let c=r==="NHWC"?u.shape[3]:u.shape[1];F(c===l.shape[2],()=>`Error in depthwiseConv2d: number of input channels (${c}) must match the inChannels dimension in filter ${l.shape[2]}.`),Sn("depthwiseConv2d",n,i);let 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Error("getWeights() is not implemented for Adamax yet.")}async setWeights(e){throw new Error("setWeights() is not implemented for Adamax yet.")}getConfig(){return{learningRate:this.learningRate,beta1:this.beta1,beta2:this.beta2,epsilon:this.epsilon,decay:this.decay}}static fromConfig(e,t){return new e(t.learningRate,t.beta1,t.beta2,t.epsilon,t.decay)}};$h.className="Adamax";bs($h);var ip=class extends vs{constructor(e){super(),this.learningRate=e,this.setLearningRate(e)}applyGradients(e){(Array.isArray(e)?e.map(t=>t.name):Object.keys(e)).forEach((t,a)=>{let n=Array.isArray(e)?e[a].tensor:e[t];if(n==null)return;let r=z.registeredVariables[t];Ee(()=>{let s=xe(ae(this.c,n),r);r.assign(s)})}),this.incrementIterations()}setLearningRate(e){this.learningRate=e,this.c!=null&&this.c.dispose(),this.c=qn(Fe(-e))}dispose(){this.c.dispose()}async getWeights(){return[await this.saveIterations()]}async setWeights(e){if(e=await this.extractIterations(e),e.length!==0)throw new Error("SGD optimizer does 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n=z.registeredVariables[t],r=!1;this.accumulatedMeanSquares[a]==null&&(this.accumulatedMeanSquares[a]={originalName:`${t}/rms`,variable:Ee(()=>Xa(n).variable(r))}),this.accumulatedMoments[a]==null&&(this.accumulatedMoments[a]={originalName:`${t}/momentum`,variable:Ee(()=>Xa(n).variable(r))}),this.accumulatedMeanGrads[a]==null&&this.centered&&(this.accumulatedMeanGrads[a]={originalName:`${t}/mg`,variable:Ee(()=>Xa(n).variable(r))});let s=Array.isArray(e)?e[a].tensor:e[t];if(s==null)return;let i=this.accumulatedMeanSquares[a].variable,o=this.accumulatedMoments[a].variable;Ee(()=>{let l=xe(ae(i,this.decay),ae(In(s),1-this.decay));if(this.centered){let u=this.accumulatedMeanGrads[a].variable,d=xe(ae(u,this.decay),ae(s,1-this.decay)),c=fe(ae(s,this.learningRate),Jn(he(l,xe(In(d),this.epsilon)))),p=xe(ae(o,this.momentum),c);i.assign(l),u.assign(d),o.assign(p);let h=he(n,p);n.assign(h)}else{let 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t=this.centered?e.length/3:e.length/2,a=!1;this.accumulatedMeanSquares=e.slice(0,t).map(n=>({originalName:n.name,variable:n.tensor.variable(a)})),this.accumulatedMoments=e.slice(t,t*2).map(n=>({originalName:n.name,variable:n.tensor.variable(a)})),this.centered&&(this.accumulatedMeanGrads=e.slice(t*2,t*3).map(n=>({originalName:n.name,variable:n.tensor.variable(a)})))}getConfig(){return{learningRate:this.learningRate,decay:this.decay,momentum:this.momentum,epsilon:this.epsilon,centered:this.centered}}static fromConfig(e,t){return new e(t.learningRate,t.decay,t.momentum,t.epsilon,t.centered)}};Fh.className="RMSProp";bs(Fh);var Or=class{static sgd(e){return new ip(e)}static momentum(e,t,a=!1){return new _h(e,t,a)}static rmsprop(e,t=.9,a=0,n=null,r=!1){return new Fh(e,t,a,n,r)}static adam(e=.001,t=.9,a=.999,n=null){return new Mh(e,t,a,n)}static adadelta(e=.001,t=.95,a=null){return new Eh(e,t,a)}static adamax(e=.002,t=.9,a=.999,n=null,r=0){return new $h(e,t,a,n,r)}static 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T={};He(T,{ERF_A1:()=>oF,ERF_A2:()=>lF,ERF_A3:()=>uF,ERF_A4:()=>dF,ERF_A5:()=>pF,ERF_P:()=>iF,PARALLELIZE_THRESHOLD:()=>n3,RowPartitionType:()=>Un,SELU_SCALE:()=>sF,SELU_SCALEALPHA:()=>rF,applyActivation:()=>Ch,assertAndGetBroadcastShape:()=>zt,assertAxesAreInnerMostDims:()=>WE,assertParamsConsistent:()=>j_,assignToTypedArray:()=>yF,axesAreInnerMostDims:()=>C2,calculateShapes:()=>ax,checkEinsumDimSizes:()=>kF,checkPadOnDimRoundingMode:()=>Sn,combineLocations:()=>nb,combineRaggedTensorToTensorShapes:()=>q_,complexWithEvenIndex:()=>fF,complexWithOddIndex:()=>mF,computeConv2DInfo:()=>Jd,computeConv3DInfo:()=>Cx,computeDefaultPad:()=>v2,computeDilation2DInfo:()=>BN,computeOptimalWindowSize:()=>Y_,computeOutAndReduceShapes:()=>BE,computeOutShape:()=>H_,computePool2DInfo:()=>Tx,computePool3DInfo:()=>WN,convertConv2DDataFormat:()=>Nx,decodeEinsumEquation:()=>vF,eitherStridesOrDilationsAreOne:()=>kr,expandShapeToKeepDim:()=>tp,exponent:()=>xF,exponents:()=>AF,fromStringArrayToUint8:()=>UF,fromUint8ToStringArray:()=>GF,getAxesPermutation:()=>VE,getBroadcastDims:()=>QA,getComplexWithIndex:()=>gF,getEinsumComputePath:()=>IF,getEinsumPermutation:()=>wF,getFusedBiasGradient:()=>Th,getFusedDyActivation:()=>Sh,getImageCenter:()=>J_,getInnerMostAxes:()=>UE,getPermuted:()=>eF,getRaggedRank:()=>K_,getReductionAxes:()=>c2,getReshaped:()=>Q_,getReshapedPermuted:()=>tF,getRowPartitionTypesHelper:()=>X_,getSliceBeginCoords:()=>aF,getSliceSize:()=>nF,getSparseFillEmptyRowsIndicesDenseShapeMismatch:()=>NF,getSparseFillEmptyRowsNegativeIndexErrorMessage:()=>EF,getSparseFillEmptyRowsOutOfRangeIndexErrorMessage:()=>RF,getSparseReshapeEmptyTensorZeroOutputDimErrorMessage:()=>_F,getSparseReshapeInputOutputMismatchErrorMessage:()=>PF,getSparseReshapeInputOutputMultipleErrorMessage:()=>FF,getSparseReshapeMultipleNegativeOneOutputDimErrorMessage:()=>MF,getSparseReshapeNegativeOutputDimErrorMessage:()=>$F,getSparseSegmentReductionIndicesOutOfRangeErrorMessage:()=>LF,getSparseSegmentReductionNegativeSegmentIdsErrorMessage:()=>OF,getSparseSegmentReductionNonIncreasingSegmentIdsErrorMessage:()=>DF,getSparseSegmentReductionSegmentIdOutOfRangeErrorMessage:()=>zF,getUndoAxesPermutation:()=>GE,isIdentityPermutation:()=>SF,log:()=>sT,mergeRealAndImagArrays:()=>cF,prepareAndValidate:()=>tx,prepareSplitSize:()=>CF,segment_util:()=>R4,shouldFuse:()=>Nh,slice_util:()=>At,splitRealAndImagArrays:()=>hF,tupleValuesAreOne:()=>nd,upcastType:()=>ra,validateDefaultValueShape:()=>Z_,validateInput:()=>y2,validateUpdateShape:()=>g2,warn:()=>Dr});function j_(e,t){let a=e[0].length;e.forEach((r,s)=>{F(r.length===a,()=>`Error in concat${a}D: rank of tensors[${s}] must be the same as the rank of the rest (${a})`)}),F(t>=0&&t`Error in concat${a}D: axis must be between 0 and ${a-1}.`);let n=e[0];e.forEach((r,s)=>{for(let i=0;i`Error in concat${a}D: Shape of tensors[${s}] (${r}) does not match the shape of the rest (${n}) along the non-concatenated axis ${s}.`)})}function H_(e,t){let a=e[0].slice();for(let n=1;n=0)if(o>=0){if(o!==s)throw new Error(`rt input.shape and shape=${t} are incompatible: rt input.shape[${r+e}] = ${s} but shape[${r+e}] = ${o}`)}else n[i]=s}return n}function X_(e){let t={FIRST_DIM_SIZE:Un.FIRST_DIM_SIZE,VALUE_ROWIDS:Un.VALUE_ROWIDS,ROW_LENGTHS:Un.ROW_LENGTHS,ROW_SPLITS:Un.ROW_SPLITS,ROW_LIMITS:Un.ROW_LIMITS,ROW_STARTS:Un.ROW_STARTS},a=[];for(let n of e)if(n in t)a.push(t[n]);else break;return a}function K_(e){return e.length===0?0:e[0]===Un.FIRST_DIM_SIZE?e.length-1:e.length}function 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range of [-${r}, ${r}], but got ${n}`);if(n<0&&(n+=r),n>s)throw new Error(`batchDims (${n}) must be less than rank(x) ( ${s}).`);if(aIc(t))}catch(t){throw new Error(`Failed to decode encoded string bytes into utf-8, error: ${t}`)}}function UF(e){return e.map(t=>Ud(t))}var Tn={};He(Tn,{nonMaxSuppressionV3Impl:()=>b4,nonMaxSuppressionV4Impl:()=>v4,nonMaxSuppressionV5Impl:()=>w4,whereImpl:()=>d4});var jF=V();jF.registerFlag("KEEP_INTERMEDIATE_TENSORS",()=>!1,e=>{e&&console.warn("Keep intermediate tensors is ON. 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e.map(t=>this.read(t))}write(e,t){if(this.closed_)throw new Error(`TensorArray ${this.name} has already been closed.`);if(e<0||!this.dynamicSize&&e>=this.maxSize)throw new Error(`Tried to write to index ${e}, but array is not resizeable and size is: ${this.maxSize}`);let a=this.tensors[e]||{};if(t.dtype!==this.dtype)throw new Error(`TensorArray ${this.name}: Could not write to TensorArray index ${e}, because the value dtype is ${t.dtype}, but TensorArray dtype is ${this.dtype}.`);if(this.size()===0&&(this.elementShape==null||this.elementShape.length===0)&&(this.elementShape=t.shape),kn(this.elementShape,t.shape,`TensorArray ${this.name}: Could not write to TensorArray index ${e}.`),a.read)throw new Error(`TensorArray ${this.name}: Could not write to TensorArray index ${e}, because it has already been read.`);if(a.written)throw new Error(`TensorArray ${this.name}: Could not write to TensorArray index ${e}, because it has already been written.`);a.tensor=t,qn(t),a.written=!0,this.tensors[e]=a}writeMany(e,t){if(e.length!==t.length)throw new Error(`TensorArray ${this.name}: could not write multiple tensors,because the index size: ${e.length} is not the same as tensors size: ${t.length}.`);e.forEach((a,n)=>this.write(a,t[n]))}gather(e,t){if(!!t&&t!==this.dtype)throw new Error(`TensorArray dtype is ${this.dtype} but gather requested dtype ${t}`);if(e)e=e.slice(0,this.size());else{e=[];for(let n=0;n=this.maxSize)throw new Error(`Max index must be < array size (${a} vs. ${this.maxSize})`);this.writeMany(e,Ta(t,0))}split(e,t){if(t.dtype!==this.dtype)throw new Error(`TensorArray dtype is ${this.dtype} but tensor has dtype ${t.dtype}`);let a=0,n=e.map(o=>(a+=o,a));if(a!==t.shape[0])throw new Error(`Expected sum of lengths to be equal to tensor.shape[0], but sum of lengths is ${a}, and tensor's shape is: ${t.shape}`);if(!this.dynamicSize&&e.length!==this.maxSize)throw new Error(`TensorArray's size is not equal to the size of lengths (${this.maxSize} vs. ${e.length}), and the TensorArray is not marked as dynamically resizeable`);let r=a===0?0:t.size/a,s=[];Ee(()=>{t=J(t,[1,a,r]);for(let o=0;o{if(a!==r.dtype)throw new Error(`Invalid data types; op elements ${a}, but list elements ${r.dtype}`);kn(t,r.shape,"TensorList shape mismatch: "),qn(r)}),this.idTensor=Fe(0),this.maxNumElements=n,qn(this.idTensor)}get id(){return this.idTensor.id}copy(){return new ll([...this.tensors],this.elementShape,this.elementDtype)}clearAndClose(e){this.tensors.forEach(t=>{(e==null||!e.has(t.id))&&t.dispose()}),this.tensors.length=0,this.idTensor.dispose()}size(){return this.tensors.length}stack(e,t,a=-1){if(t!==this.elementDtype)throw new Error(`Invalid data types; op elements ${t}, but list elements ${this.elementDtype}`);if(a!==-1&&this.tensors.length!==a)throw new Error(`Operation expected a list with ${a} elements but got a list with ${this.tensors.length} elements.`);kn(e,this.elementShape,"TensorList shape mismatch: ");let n=Fu(this.elementShape,this.tensors,e);return Ee(()=>{let r=this.tensors.map(s=>J(s,n));return sa(r,0)})}popBack(e,t){if(t!==this.elementDtype)throw new Error(`Invalid data types; op elements ${t}, but list elements ${this.elementDtype}`);if(this.size()===0)throw new Error("Trying to pop from an empty list.");let a=Fu(this.elementShape,this.tensors,e),n=this.tensors.pop();return n.kept=!1,kn(n.shape,e,"TensorList shape mismatch: "),J(n,a)}pushBack(e){if(e.dtype!==this.elementDtype)throw new Error(`Invalid data types; op elements ${e.dtype}, but list elements ${this.elementDtype}`);if(kn(e.shape,this.elementShape,"TensorList shape mismatch: "),this.maxNumElements===this.size())throw new Error("Trying to push element into a full list.");qn(e),this.tensors.push(e)}resize(e){if(e<0)throw new Error(`TensorListResize expects size to be non-negative. 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"),qn(t),this.tensors[e]!=null&&(this.tensors[e].kept=!1),this.tensors[e]=t}gather(e,t,a){if(t!==this.elementDtype)throw new Error(`Invalid data types; op elements ${t}, but list elements ${this.elementDtype}`);kn(this.elementShape,a,"TensorList shape mismatch: "),e=e.slice(0,this.size());let n=Fu(this.elementShape,this.tensors,a);return e.length===0?ze([],[0].concat(n)):Ee(()=>{let r=e.map(s=>J(this.tensors[s],n));return sa(r,0)})}concat(e,t){if(!!e&&e!==this.elementDtype)throw new Error(`TensorList dtype is ${this.elementDtype} but concat requested dtype ${e}`);kn(this.elementShape,t,"TensorList shape mismatch: ");let a=Fu(this.elementShape,this.tensors,t);return this.size()===0?ze([],[0].concat(a)):Ee(()=>{let n=this.tensors.map(r=>J(r,a));return at(n,0)})}};function xP(e,t,a){let n=e.dtype;if(e.shape.length<1)throw new Error(`Tensor must be at least a vector, but saw shape: ${e.shape}`);if(e.dtype!==a)throw new Error(`Invalid data types; op elements ${e.dtype}, but list elements ${a}`);let r=e.shape.slice(1);kn(r,t,"TensorList shape mismatch: ");let s=Ta(e);return new ll(s,t,n)}function bP(e,t,a,n){return new ll([],e,t,n)}function vP(e,t,a,n){if(t.length!==e.shape[0])throw new Error(`Expected len(indices) == tensor.shape[0], but saw: ${t.length} vs. ${e.shape[0]}`);let r=Math.max(...t);if(n!=null&&n!==-1&&r>=n)throw new Error(`Max index must be < array size (${r} vs. ${n})`);let s=new ll([],a,e.dtype,n),i=Ta(e,0);return t.forEach((o,l)=>{s.setItem(o,i[l])}),s}function wP(e,t,a){let n=0,r=t.map(d=>(n+=d,n));if(n!==e.shape[0])throw new Error(`Expected sum of lengths to be equal to tensor.shape[0], but sum of lengths is ${n}, and tensor's shape is: ${e.shape}`);let s=e.shape.slice(1),i=g1(s,a),o=n===0?0:e.size/n,l=Ee(()=>{let d=[];e=J(e,[1,n,o]);for(let c=0;c{switch(e.op){case"If":case"StatelessIf":{let n=k("thenBranch",e,t,a),r=k("elseBranch",e,t,a),s=k("cond",e,t,a),i=k("args",e,t,a);return(await s.data())[0]?a.functionMap[n].executeFunctionAsync(i,a.tensorArrayMap,a.tensorListMap):a.functionMap[r].executeFunctionAsync(i,a.tensorArrayMap,a.tensorListMap)}case"While":case"StatelessWhile":{let n=k("body",e,t,a),r=k("cond",e,t,a),s=k("args",e,t,a),i=await a.functionMap[r].executeFunctionAsync(s,a.tensorArrayMap,a.tensorListMap),o=s.map(d=>d.id),l=await i[0].data();i.forEach(d=>{!d.kept&&o.indexOf(d.id)===-1&&d.dispose()});let u=s;for(;l[0];){let d=u;u=await a.functionMap[n].executeFunctionAsync(u,a.tensorArrayMap,a.tensorListMap);let c=u.map(h=>h.id);d.forEach(h=>{!h.kept&&o.indexOf(h.id)===-1&&c.indexOf(h.id)===-1&&h.dispose()});let p=await a.functionMap[r].executeFunctionAsync(u,a.tensorArrayMap,a.tensorListMap);l=await p[0].data(),p.forEach(h=>{!h.kept&&o.indexOf(h.id)===-1&&c.indexOf(h.id)===-1&&h.dispose()})}return u}case"LoopCond":{let n=k("pred",e,t,a);return[fr(n)]}case"Switch":{let n=k("pred",e,t,a),r=k("data",e,t,a);return r.kept||(r=fr(r)),(await n.data())[0]?[void 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n=k("tensorArrayId",e,t,a),r=k("indices",e,t,a),s=k("dtype",e,t,a);return[a.getTensorArray(n.id).gather(r,s)]}case"TensorArrayScatterV3":{let n=k("tensorArrayId",e,t,a),r=k("indices",e,t,a),s=k("tensor",e,t,a),i=a.getTensorArray(n.id);return i.scatter(r,s),[i.idTensor]}case"TensorArrayConcatV3":{let n=k("tensorArrayId",e,t,a),r=a.getTensorArray(n.id),s=k("dtype",e,t,a);return[r.concat(s)]}case"TensorArraySplitV3":{let n=k("tensorArrayId",e,t,a),r=k("tensor",e,t,a),s=k("lengths",e,t,a),i=a.getTensorArray(n.id);return i.split(s,r),[i.idTensor]}case"TensorArraySizeV3":{let n=k("tensorArrayId",e,t,a),r=a.getTensorArray(n.id);return[Fe(r.size(),"int32")]}case"TensorArrayCloseV3":{let n=k("tensorArrayId",e,t,a),r=a.getTensorArray(n.id);return r.clearAndClose(),[r.idTensor]}case"TensorListSetItem":{let n=k("tensorListId",e,t,a),r=k("index",e,t,a),s=k("tensor",e,t,a),i=a.getTensorList(n.id);return i.setItem(r,s),[i.idTensor]}case"TensorListGetItem":{let n=k("tensorListId",e,t,a),r=k("index",e,t,a),s=k("elementShape",e,t,a),i=k("elementDType",e,t,a);return[a.getTensorList(n.id).getItem(r,s,i)]}case"TensorListScatterV2":case"TensorListScatter":{let n=k("indices",e,t,a),r=k("tensor",e,t,a),s=k("elementShape",e,t,a),i=k("numElements",e,t,a),o=vP(r,n,s,i);return a.addTensorList(o),[o.idTensor]}case"TensorListReserve":case"EmptyTensorList":{let n=k("elementShape",e,t,a),r=k("elementDType",e,t,a),s;e.op==="TensorListReserve"?s="numElements":s="maxNumElements";let i=k(s,e,t,a),o=e.op==="TensorListReserve"?-1:i,l=bP(n,r,i,o);return a.addTensorList(l),[l.idTensor]}case"TensorListGather":{let n=k("tensorListId",e,t,a),r=k("indices",e,t,a),s=k("elementShape",e,t,a),i=k("elementDType",e,t,a);return[a.getTensorList(n.id).gather(r,i,s)]}case"TensorListStack":{let n=k("tensorListId",e,t,a),r=k("elementShape",e,t,a),s=k("elementDType",e,t,a),i=k("numElements",e,t,a);return[a.getTensorList(n.id).stack(r,s,i)]}case"TensorListFromTensor":{let n=k("tensor",e,t,a),r=k("elementShape",e,t,a),s=k("elementDType",e,t,a),i=xP(n,r,s);return a.addTensorList(i),[i.idTensor]}case"TensorListConcat":case"TensorListConcatV2":{let n=k("tensorListId",e,t,a),r=a.getTensorList(n.id),s=k("dtype",e,t,a),i=k("elementShape",e,t,a);return[r.concat(s,i)]}case"TensorListPushBack":{let n=k("tensorListId",e,t,a),r=k("tensor",e,t,a),s=a.getTensorList(n.id);return s.pushBack(r),[s.idTensor]}case"TensorListPopBack":{let n=k("tensorListId",e,t,a),r=k("elementShape",e,t,a),s=k("elementDType",e,t,a);return[a.getTensorList(n.id).popBack(r,s)]}case"TensorListSplit":{let n=k("tensor",e,t,a),r=k("elementShape",e,t,a),s=k("lengths",e,t,a),i=wP(n,s,r);return a.addTensorList(i),[i.idTensor]}case"TensorListLength":{let n=k("tensorListId",e,t,a),r=a.getTensorList(n.id);return[Fe(r.size(),"int32")]}case"TensorListResize":{let n=k("tensorListId",e,t,a),r=k("size",e,t,a),s=a.getTensorList(n.id).resize(r);return a.addTensorList(s),[s.idTensor]}default:throw TypeError(`Node type ${e.op} is not implemented`)}};function uy(e,t,a){let[n,r]=k("fusedOps",e,t,a),s=n==="biasadd",i=!s,o=r==="prelu",l=n==="fusedbatchnorm",u=k("numArgs",e,t,a);if(s){if(o&&u!==2)throw new Error("FusedConv2d and DepthwiseConv2d with BiasAdd and Prelu must have two extra arguments: bias and alpha.");if(!o&&s&&u!==1)throw new Error("FusedConv2d and DepthwiseConv2d with BiasAdd must have one extra argument: bias.")}if(l)throw new Error("FusedConv2d and DepthwiseConv2d with FusedBatchNorm is not supported");let d=k("strides",e,t,a),c=hc(e,t,a),p=k("dataFormat",e,t,a).toUpperCase(),h=k("dilations",e,t,a),[f,m]=k("args",e,t,a);i&&(m=f,f=void 0);let g=k("leakyreluAlpha",e,t,a);return{stride:d,pad:c,dataFormat:p,dilations:h,biasArg:f,preluArg:m,activationFunc:r,leakyreluAlpha:g}}var IP=(e,t,a,n=oa)=>{switch(e.op){case"Conv1D":{let r=k("stride",e,t,a),s=k("pad",e,t,a),i=k("dataFormat",e,t,a).toUpperCase(),o=k("dilation",e,t,a);return[n.conv1d(k("x",e,t,a),k("filter",e,t,a),r,s,i,o)]}case"Conv2D":{let r=k("strides",e,t,a),s=hc(e,t,a),i=k("dataFormat",e,t,a).toUpperCase(),o=k("dilations",e,t,a);return[n.conv2d(k("x",e,t,a),k("filter",e,t,a),[r[1],r[2]],s,i,[o[1],o[2]])]}case"_FusedConv2D":{let{stride:r,pad:s,dataFormat:i,dilations:o,biasArg:l,preluArg:u,activationFunc:d,leakyreluAlpha:c}=uy(e,t,a);return[n.fused.conv2d({x:k("x",e,t,a),filter:k("filter",e,t,a),strides:[r[1],r[2]],pad:s,dataFormat:i,dilations:[o[1],o[2]],bias:l,activation:d,preluActivationWeights:u,leakyreluAlpha:c})]}case"FusedDepthwiseConv2dNative":{let{stride:r,pad:s,dataFormat:i,dilations:o,biasArg:l,preluArg:u,activationFunc:d,leakyreluAlpha:c}=uy(e,t,a);return[n.fused.depthwiseConv2d({x:k("x",e,t,a),filter:k("filter",e,t,a),strides:[r[1],r[2]],pad:s,dataFormat:i,dilations:[o[1],o[2]],bias:l,activation:d,preluActivationWeights:u,leakyreluAlpha:c})]}case"Conv2DBackpropInput":case"Conv2dTranspose":{let r=k("outputShape",e,t,a),s=k("strides",e,t,a),i=hc(e,t,a);return[n.conv2dTranspose(k("x",e,t,a),k("filter",e,t,a),r,[s[1],s[2]],i)]}case"DepthwiseConv2dNative":case"DepthwiseConv2d":{let r=k("strides",e,t,a),s=hc(e,t,a),i=k("dilations",e,t,a),o=k("dataFormat",e,t,a).toUpperCase();return[n.depthwiseConv2d(k("input",e,t,a),k("filter",e,t,a),[r[1],r[2]],s,o,[i[1],i[2]])]}case"Conv3D":{let r=k("strides",e,t,a),s=k("pad",e,t,a),i=k("dataFormat",e,t,a).toUpperCase(),o=k("dilations",e,t,a);return[n.conv3d(k("x",e,t,a),k("filter",e,t,a),[r[1],r[2],r[3]],s,i,[o[1],o[2],o[3]])]}case"AvgPool":{let r=k("strides",e,t,a),s=k("pad",e,t,a),i=k("kernelSize",e,t,a);return[n.avgPool(k("x",e,t,a),[i[1],i[2]],[r[1],r[2]],s)]}case"MaxPool":{let r=k("strides",e,t,a),s=k("pad",e,t,a),i=k("kernelSize",e,t,a);return[n.maxPool(k("x",e,t,a),[i[1],i[2]],[r[1],r[2]],s)]}case"MaxPoolWithArgmax":{let 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r=k("logits",e,t,a),s=k("numSamples",e,t,a),i=k("seed",e,t,a);return[n.multinomial(r,s,i)]}case"OneHot":{let r=k("indices",e,t,a),s=k("depth",e,t,a),i=k("onValue",e,t,a),o=k("offValue",e,t,a),l=k("dtype",e,t,a);return[n.oneHot(r,s,i,o,l)]}case"Ones":return[n.ones(k("shape",e,t,a),k("dtype",e,t,a))];case"OnesLike":return[n.onesLike(k("x",e,t,a))];case"RandomStandardNormal":return[n.randomStandardNormal(k("shape",e,t,a),k("dtype",e,t,a),k("seed",e,t,a))];case"RandomUniform":return[n.randomUniform(k("shape",e,t,a),k("minval",e,t,a),k("maxval",e,t,a),k("dtype",e,t,a))];case"Range":{let r=k("start",e,t,a),s=k("stop",e,t,a),i=k("step",e,t,a);return[n.range(r,s,i,k("dtype",e,t,a))]}case"TruncatedNormal":{let r=k("shape",e,t,a),s=k("mean",e,t,a),i=k("stdDev",e,t,a),o=k("seed",e,t,a);return[n.truncatedNormal(r,s,i,k("dtype",e,t,a),o)]}case"Zeros":return[n.zeros(k("shape",e,t,a),k("dtype",e,t,a))];case"ZerosLike":return[n.zerosLike(k("x",e,t,a))];default:throw TypeError(`Node type ${e.op} is not 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this.tensorMap.forEach(n=>n.dispose()),this.tensorMap.clear(),Ee(()=>{let n=Ta(t),r=a.length,s=n.length;v.assert(r===s,()=>`The number of elements doesn't match, keys has ${r} elements, the values has ${s} elements.`);for(let i=0;i{let n=[];for(let r=0;r{switch(e.op){case"HashTable":case"HashTableV2":{let r=n.getHashTableHandleByName(e.name);if(r!=null)return[r];{let s=k("keyDType",e,t,a),i=k("valueDType",e,t,a),o=new EP(s,i);return n.addHashTable(e.name,o),[o.handle]}}case"LookupTableImport":case"LookupTableImportV2":{let r=k("tableHandle",e,t,a,n),s=k("keys",e,t,a),i=k("values",e,t,a);return[await n.getHashTableById(r.id).import(s,i)]}case"LookupTableFind":case"LookupTableFindV2":{let r=k("tableHandle",e,t,a,n),s=k("keys",e,t,a),i=k("defaultValue",e,t,a);return[await n.getHashTableById(r.id).find(s,i)]}case"LookupTableSize":case"LookupTableSizeV2":{let r=k("tableHandle",e,t,a,n);return[n.getHashTableById(r.id).tensorSize()]}default:throw TypeError(`Node type ${e.op} is not 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r=k("x",e,t,a),s=k("weights",e,t,a),i=k("size",e,t,a);return[n.bincount(r,s,i)];case"DenseBincount":{let o=k("x",e,t,a),l=k("weights",e,t,a),u=k("size",e,t,a),d=k("binaryOutput",e,t,a);return[n.denseBincount(o,l,u,d)]}default:throw TypeError(`Node type ${e.op} is not implemented`)}},OP=(e,t,a,n=oa)=>{switch(e.op){case"ConcatV2":case"Concat":{let r=k("n",e,t,a),s=k("axis",e,t,a),i=k("tensors",e,t,a);return i=i.slice(0,r),[n.concat(i,s)]}case"Gather":{let r=k("x",e,t,a),s=k("indices",e,t,a);return[n.gather(r,n.cast(s,"int32"),0)]}case"GatherV2":{let r=k("axis",e,t,a),s=k("batchDims",e,t,a),i=k("x",e,t,a),o=k("indices",e,t,a);return[n.gather(i,n.cast(o,"int32"),r,s)]}case"Reverse":{let r=k("dims",e,t,a),s=[];for(let o=0;o{let r=k("axis",e,t,a),s=k("tensors",e,t,a),i=s[0].shape,o=n.squeeze(s[0]).shape,l=s.map(u=>{let d=v.arraysEqual(u.shape,i);if(!d&&!v.arraysEqual(n.squeeze(u).shape,o))throw new Error("the input tensors shape does not match");return 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r(()=>yP(i,o,l));case"control":return kP(i,o,l);case"convolution":return r(()=>IP(i,o,l));case"creation":return r(()=>SP(i,o,l));case"dynamic":return TP(i,o,l);case"evaluation":return r(()=>CP(i,o,l));case"image":return r(()=>MP(i,o,l));case"graph":return r(()=>NP(i,o,l));case"logical":return r(()=>$P(i,o,l));case"matrices":return r(()=>_P(i,o,l));case"normalization":return r(()=>FP(i,o,l));case"reduction":return r(()=>PP(i,o,l));case"slice_join":return r(()=>OP(i,o,l));case"sparse":return r(()=>DP(i,o,l));case"spectral":return r(()=>zP(i,o,l));case"string":return r(()=>LP(i,o,l));case"transformation":return r(()=>BP(i,o,l));case"hash_table":return RP(i,o,l,n);case"custom":let u=M4(i.op);if(u&&u.customExecutor)return u.customExecutor(new mP(i,o,l));throw TypeError(`Custom op ${i.op} is not registered.`);default:throw TypeError(`Unknown op '${i.op}'. File an issue at https://github.com/tensorflow/tfjs/issues so we can add it, or register a custom execution with tf.registerOp()`)}})(e,t,a);return v.isPromise(s)?s.then(i=>[].concat(i)):[].concat(s)}var py=class{constructor(e={},t={},a={},n={}){this.weightMap=e,this.tensorArrayMap=t,this.tensorListMap=a,this.functionMap=n,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 cy(e,t,a,n){let r=new Set,s=[],i=null,o=null,l=new Set,u=Object.keys(e).map(p=>ja(p)[0]),d=[];n!=null&&(d=n.map(p=>ja(p.name)[0]));let c=[...t];for(;c.length>0;){let p=c.pop();if((Q4(p)||jP(p)||HP(p))&&i==null&&(i=p,o=i.children.map(h=>h.name).filter(h=>r.has(h))),r.add(p.name),a[p.name]==null&&u.indexOf(p.name)===-1&&d.indexOf(p.name)===-1){if(p.inputs.length===0){s.push(p.name);continue}p.inputs.forEach(h=>{l.has(h.name)||(l.add(h.name),c.push(h))})}}return{inputs:e,outputs:t,usedNodes:r,missingInputs:s,dynamicNode:i,syncInputs:o}}function WP(e,t,a){let{usedNodes:n,inputs:r}=a,s=[],i=Object.keys(r).map(d=>ja(d)[0]).map(d=>e.nodes[d]),o=e.initNodes;i.forEach(d=>{n.has(d.name)&&s.push(d)}),e.weights.forEach(d=>{n.has(d.name)&&s.push(d)}),o!=null&&o.forEach(d=>{n.has(d.name)&&s.push(d)});let l=new Set,u=[];for(;s.length>0;){let d=s.pop();l.add(d.name),t[d.name]||u.push(d),d.children.forEach(c=>{!l.has(c.name)&&n.has(c.name)&&c.inputs.every(p=>l.has(p.name))&&s.push(c)})}return u}var VP=["Switch","Merge","Enter","Exit","NextIteration","StatelessIf","StatelessWhile","if","While"],GP=["NonMaxSuppressionV2","NonMaxSuppressionV3","NonMaxSuppressionV5","Where"],UP=["HashTable","HashTableV2","LookupTableImport","LookupTableImportV2","LookupTableFind","LookupTableFindV2","LookupTableSize","LookupTableSizeV2"];function Q4(e){return VP.indexOf(e.op)>=0}function jP(e){return GP.indexOf(e.op)>=0}function HP(e){return UP.indexOf(e.op)>=0}var y1=class{constructor(e,t){this.graph=e,this.parent=t,this.compiledMap=new Map,this._weightMap={},this.SEPERATOR=",",this._functions={},this._functionExecutorMap={},this.intermediateTensors={},this.keepTensorForDebug=!1,this._outputs=e.outputs,this._inputs=e.inputs,this._initNodes=e.initNodes,this._signature=e.signature,this._functions=e.functions,e.functions!=null&&Object.keys(e.functions).forEach(a=>{this._functionExecutorMap[a]=new y1(e.functions[a],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(a=>e[a].map(n=>n.id));this._weightIds=[].concat(...t),this._weightMap=e}set resourceManager(e){this._resourceManager=e}get inputs(){return this._inputs.map(e=>({name:e.name,shape:e.attrParams.shape?e.attrParams.shape.value:void 0,dtype:e.attrParams.dtype?e.attrParams.dtype.value:void 0}))}get outputs(){return this._outputs.map(e=>({name:e.name,shape:e.attrParams.shape?e.attrParams.shape.value:void 0,dtype:e.attrParams.dtype?e.attrParams.dtype.value:void 0}))}get inputNodes(){return this._inputs.map(e=>e.signatureKey||e.name)}get outputNodes(){return this._outputs.map(e=>{let t=e.signatureKey||e.name;return e.defaultOutput?`${t}:${e.defaultOutput}`:t})}get functions(){return Object.keys(this._functions).reduce((e,t)=>(e[t]=this._functions[t].signature,e),{})}getCompilationKey(e,t){let a=e.map(r=>r.name).sort(),n=t.map(r=>r.name).sort();return a.join(this.SEPERATOR)+"--"+n.join(this.SEPERATOR)}compile(e,t){let a=cy(e,t,this.weightMap,this._initNodes),{missingInputs:n,dynamicNode:r,syncInputs:s}=a;if(r!=null)throw new Error(`This execution contains the node '${r.name}', which has the dynamic op '${r.op}'. Please use model.executeAsync() instead. Alternatively, to avoid the dynamic ops, specify the inputs [${s}]`);if(n.length>0){let i=t.map(l=>l.name),o=Object.keys(e);throw new Error(`Cannot compute the outputs [${i}] from the provided inputs [${o}]. Missing the following inputs: [${n}]`)}return WP(this.graph,this.weightMap,a)}execute(e,t){e=this.mapInputs(e);let a=Object.keys(e).sort();this.checkInputs(e),this.checkInputShapeAndType(e),t=this.mapOutputs(t),this.checkOutputs(t);let n=a.map(d=>this.graph.nodes[ja(d)[0]]),r=t.map(d=>ja(d)[0]),s=r.map(d=>this.graph.nodes[d]);this.resetIntermediateTensors(),s.length===0&&(s=this._outputs);let i=this.getCompilationKey(n,s),o=this.compiledMap.get(i);o==null&&(o=this.compile(e,s),this.compiledMap.set(i,o));let l={},u={};return Ee(()=>{let d=new py(this.weightMap,l,u,this.functionExecutorMap),c=Object.assign({},this.weightMap);Object.keys(e).forEach(f=>{let[m,g]=ja(f),y=[];y[g]=e[f],c[m]=y});let p=this.getFrozenTensorIds(c),h={};for(let f=0;fba(f,c,d))})}getFrozenTensorIds(e){let t=[].concat.apply([],Object.keys(e).map(a=>e[a]).map(a=>a.map(n=>n.id)));return new Set(t)}checkTensorForDisposal(e,t,a,n,r,s,i){t.category==="control"||s.indexOf(e)!==-1||(a[e].forEach(o=>{o!=null&&(i[o.id]=(i[o.id]||0)+t.children.length)}),t.inputs.forEach(o=>{if(o.category!=="control"){let l=XF(o.name,a,n);l!=null&&l.forEach(u=>{if(u&&!u.kept&&!r.has(u.id)){let d=i[u.id];if(d===1){if(!this.keepTensorForDebug)u.dispose();else{let[c,p]=jn(t.name,n);this.intermediateTensors[c]?this.intermediateTensors[c][p]=u:(this.intermediateTensors[c]=[],this.intermediateTensors[c][p]=u)}delete i[u.id]}else d!=null&&i[u.id]--}})}}))}async executeAsync(e,t){return this._executeAsync(e,t)}disposeIntermediateTensors(){!this.intermediateTensors||(Object.keys(this.intermediateTensors).forEach(e=>this.intermediateTensors[e].forEach(t=>t.dispose())),this.disposeTensorsMap())}disposeTensorsMap(){!this.tensorsMap||Object.keys(this.tensorsMap).forEach(e=>{this.tensorsMap[e].forEach(t=>{t&&!t.kept&&!t.isDisposed&&!this.keepIds.has(t.id)&&t.dispose()})})}getIntermediateTensors(){return this.tensorsMap}resetIntermediateTensors(){for(let e in this.intermediateTensors)this.intermediateTensors[e].forEach(t=>t.dispose()),delete this.intermediateTensors[e]}async _executeAsync(e,t,a=!1,n={},r={}){a||(e=this.mapInputs(e),this.checkInputs(e),this.checkInputShapeAndType(e),t=this.mapOutputs(t),this.checkOutputs(t));try{this.keepTensorForDebug=V().getBool("KEEP_INTERMEDIATE_TENSORS")}catch(u){console.warn(u.message)}this.resetIntermediateTensors();let s=new py(this.weightMap,n,r,this.functionExecutorMap);this.tensorsMap=await this.executeWithControlFlow(e,s,t,a);let i=t.map(u=>ba(u,this.tensorsMap,s)),o=i.map(u=>u.id),l=Object.keys(e).map(u=>e[u].id);return this.keepIds=new Set([...o,...l,...this.weightIds]),this.keepTensorForDebug||this.disposeTensorsMap(),this.parent==null&&s.dispose(this.keepIds),i}async executeFunctionAsync(e,t,a){let n=e.reduce((r,s,i)=>(r[this.inputs[i].name]=s,r),{});return this._executeAsync(n,this.outputNodes,!0,t,a)}async executeWithControlFlow(e,t,a,n){let r=Object.keys(e),s=r.map(A=>this.graph.nodes[ja(A)[0]]),i=a.map(A=>ja(A)[0]),o=i.map(A=>this.graph.nodes[A]);o.length===0&&(o=this._outputs);let{usedNodes:l,missingInputs:u,dynamicNode:d,syncInputs:c}=cy(e,o,this.weightMap,this._initNodes),p=[...s,...this.graph.weights,...this._initNodes||[]].map(A=>({node:A,contexts:t.currentContext})),h=Object.assign({},this.weightMap);Object.keys(e).forEach(A=>{let[x,b]=ja(A),w=[];w[b]=e[A],h[x]=w});let f={},m=this.getFrozenTensorIds(h),g={};for(;p.length>0;){let A=this.processStack(s,p,t,h,g,m,i,f,l);await Promise.all(A)}d==null&&!n&&console.warn("This model execution did not contain any nodes with control flow or dynamic output shapes. You can use model.execute() instead.");let y=o.filter(A=>!Q4(A)&&!ba(A.name,h,t)).map(A=>A.name);if(y.length>0){let A="";throw d!=null&&(A=`Alternatively, to avoid the dynamic ops, use model.execute() and specify the inputs [${c}]`),new Error(`Cannot compute the outputs [${y}] from the provided inputs [${r}]. Consider providing the following inputs: [${u}]. ${A}`)}return h}processStack(e,t,a,n,r,s,i,o,l){let u=[];for(;t.length>0;){let d=t.pop();a.currentContext=d.contexts;let c="";if(d.node.op==="Enter"&&k("isConstant",d.node,n,a)&&([c]=jn(d.node.name,a)),n[d.node.name]==null){let p=dy(d.node,n,a,this._resourceManager);c||([c]=jn(d.node.name,a));let h=a.currentContext;v.isPromise(p)?u.push(p.then(f=>(n[c]=f,a.currentContext=h,this.checkTensorForDisposal(c,d.node,n,a,s,i,o),this.processChildNodes(d.node,t,a,n,r,l),f))):(n[c]=p,this.checkTensorForDisposal(c,d.node,n,a,s,i,o),this.processChildNodes(d.node,t,a,n,r,l))}else this.processChildNodes(d.node,t,a,n,r,l)}return u}processChildNodes(e,t,a,n,r,s){e.children.forEach(i=>{let[o]=jn(i.name,a);r[o]||!s.has(i.name)||(i.op==="Merge"?i.inputNames.some(l=>!!ba(l,n,a))&&(r[o]=!0,t.push({contexts:a.currentContext,node:i})):i.inputNames.every(l=>!!ba(l,n,a))&&(r[o]=!0,t.push({contexts:a.currentContext,node:i})))})}dispose(){Object.keys(this.weightMap).forEach(e=>this.weightMap[e].forEach(t=>t.dispose()))}checkInputShapeAndType(e){Object.keys(e).forEach(t=>{let a=e[t],[n]=ja(t),r=this.graph.nodes[n];if(r.attrParams.shape&&r.attrParams.shape.value){let s=r.attrParams.shape.value,i=s.length===a.shape.length&&a.shape.every((o,l)=>s[l]===-1||s[l]===o);v.assert(i,()=>`The shape of dict['${r.name}'] provided in model.execute(dict) must be [${s}], but was [${a.shape}]`)}r.attrParams.dtype&&r.attrParams.dtype.value&&v.assert(a.dtype===r.attrParams.dtype.value,()=>`The dtype of dict['${r.name}'] provided in model.execute(dict) must be ${r.attrParams.dtype.value}, but was ${a.dtype}`)})}mapInputs(e){let t={};for(let a in e)if(this._signature!=null&&this._signature.inputs!=null&&this._signature.inputs[a]!=null){let n=this._signature.inputs[a];t[n.name]=e[a]}else t[a]=e[a];return t}checkInputs(e){let t=Object.keys(e).filter(a=>{let[n]=ja(a);return this.graph.nodes[n]==null});if(t.length>0)throw new Error(`The dict provided in model.execute(dict) has keys: [${t}] that are not part of graph`)}mapOutputs(e){return e.map(t=>this._signature!=null&&this._signature.outputs!=null&&this._signature.outputs[t]!=null?this._signature.outputs[t].name:t,{})}checkOutputs(e){e.forEach(t=>{let[a]=ja(t);if(!this.graph.nodes[a])throw new Error(`The output '${t}' is not found in the graph`)})}},qP=class{constructor(e={},t={}){this.hashTableNameToHandle=e,this.hashTableMap=t}addHashTable(e,t){this.hashTableNameToHandle[e]=t.handle,this.hashTableMap[t.id]=t}getHashTableHandleByName(e){return this.hashTableNameToHandle[e]}getHashTableById(e){return this.hashTableMap[e]}dispose(){for(let e in this.hashTableMap)this.hashTableMap[e].clearAndClose(),delete this.hashTableMap[e];for(let e in this.hashTableNameToHandle)this.hashTableNameToHandle[e].dispose(),delete this.hashTableNameToHandle[e]}},XP="?tfjs-format=file",KP="model.json",op=class{constructor(e,t={},a=Hn){this.modelUrl=e,this.loadOptions=t,this.version="n/a",this.io=a,t==null&&(this.loadOptions={}),this.resourceManager=new qP}get modelVersion(){return this.version}get inputNodes(){return this.executor.inputNodes}get outputNodes(){return this.executor.outputNodes}get inputs(){return this.executor.inputs}get outputs(){return this.executor.outputs}get weights(){return this.executor.weightMap}get metadata(){return this.artifacts.userDefinedMetadata}get modelSignature(){return this.signature}get modelStructuredOutputKeys(){return this.structuredOutputKeys}findIOHandler(){let e=this.modelUrl;if(e.load!=null)this.handler=e;else if(this.loadOptions.requestInit!=null)this.handler=this.io.browserHTTPRequest(e,this.loadOptions);else{let t=this.io.getLoadHandlers(e,this.loadOptions);if(t.length===0)t.push(this.io.browserHTTPRequest(e,this.loadOptions));else if(t.length>1)throw new Error(`Found more than one (${t.length}) load handlers for URL '${[e]}'`);this.handler=t[0]}}load(){if(this.findIOHandler(),this.handler.load==null)throw new Error("Cannot proceed with model loading because the IOHandler provided does not have the `load` method implemented.");let e=this.handler.load();return v.isPromise(e)?e.then(t=>this.loadSync(t)):this.loadSync(e)}loadSync(e){this.artifacts=e;let t=this.artifacts.modelTopology,a=this.artifacts.signature;if(this.artifacts.userDefinedMetadata!=null){let r=this.artifacts.userDefinedMetadata;r.signature!=null&&(a=r.signature),r.structuredOutputKeys!=null&&(this.structuredOutputKeys=r.structuredOutputKeys)}this.signature=a,this.version=`${t.versions.producer}.${t.versions.minConsumer}`;let n=this.io.decodeWeights(this.artifacts.weightData,this.artifacts.weightSpecs);if(this.executor=new y1(iy.Instance.transformGraph(t,this.signature)),this.executor.weightMap=this.convertTensorMapToTensorsMap(n),this.executor.resourceManager=this.resourceManager,e.modelInitializer!=null&&e.modelInitializer.node!=null){let r=iy.Instance.transformGraph(e.modelInitializer);this.initializer=new y1(r),this.initializer.weightMap=this.executor.weightMap,this.initializer.resourceManager=this.resourceManager,this.initializerSignature=e.initializerSignature}return!0}async save(e,t){if(typeof e=="string"){let a=this.io.getSaveHandlers(e);if(a.length===0)throw new Error(`Cannot find any save handlers for URL '${e}'`);if(a.length>1)throw new Error(`Found more than one (${a.length}) save handlers for URL '${e}'`);e=a[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){let a=this.execute(e,this.outputNodes);if(this.structuredOutputKeys){let n=a instanceof dt?[a]:a,r={};return n.forEach((s,i)=>r[this.structuredOutputKeys[i]]=s),r}return a}normalizeInputs(e){if(!(e instanceof dt)&&!Array.isArray(e)){if(this.signature!=null&&this.signature.inputs!=null)for(let n in this.signature.inputs){let r=this.signature.inputs[n];r.resourceId!=null&&(e[n]=this.resourceIdToCapturedInput[r.resourceId])}return e}e=Array.isArray(e)?e:[e];let t=Object.keys(this.resourceIdToCapturedInput).length;if(e.length+t!==this.inputNodes.length)throw new Error(`Input tensor count mismatch, the graph model has ${this.inputNodes.length-t} non-resource placeholders, while there are ${e.length} input tensors provided.`);let a=0;return this.inputNodes.reduce((n,r)=>{let s=this.signature?this.signature.inputs[r]:null;return s!=null&&s.resourceId!=null?n[r]=this.resourceIdToCapturedInput[s.resourceId]:n[r]=e[a++],n},{})}normalizeOutputs(e){return e=e||this.outputNodes,Array.isArray(e)?e:[e]}executeInitializerGraph(){return this.initializer==null?[]:this.initializerSignature==null?this.initializer.execute({},[]):this.initializer.execute({},Object.keys(this.initializerSignature.outputs))}async executeInitializerGraphAsync(){return this.initializer==null?[]:this.initializerSignature==null?this.initializer.executeAsync({},[]):this.initializer.executeAsync({},Object.keys(this.initializerSignature.outputs))}setResourceIdToCapturedInput(e){if(this.resourceIdToCapturedInput={},this.initializerSignature){let t=Object.keys(this.initializerSignature.outputs);for(let a=0;a1?a:a[0]}async executeAsync(e,t){this.resourceIdToCapturedInput==null&&this.setResourceIdToCapturedInput(await this.executeInitializerGraphAsync()),e=this.normalizeInputs(e),t=this.normalizeOutputs(t);let a=await this.executor.executeAsync(e,t);return a.length>1?a:a[0]}getIntermediateTensors(){return this.executor.getIntermediateTensors()}disposeIntermediateTensors(){this.executor.disposeIntermediateTensors()}convertTensorMapToTensorsMap(e){return Object.keys(e).reduce((t,a)=>(t[a]=[e[a]],t),{})}dispose(){this.executor.dispose(),this.initializer&&(this.initializer.dispose(),this.resourceIdToCapturedInput&&Y(this.resourceIdToCapturedInput)),this.resourceManager.dispose()}};async function i3(e,t={},a=Hn){if(e==null)throw new Error("modelUrl in loadGraphModel() cannot be null. Please provide a url or an IOHandler that loads the model");t==null&&(t={}),t.fromTFHub&&typeof e=="string"&&(e=YP(e));let n=new op(e,t,a);return await n.load(),n}function ZP(e){if(e==null)throw new Error("modelUrl in loadGraphModelSync() cannot be null. 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d=T.computePool3DInfo(s.shape,i,o,1,l,u),c=d.strideDepth,p=d.strideHeight,h=d.strideWidth,f=d.filterDepth,m=d.filterHeight,g=d.filterWidth,y=d.dilationDepth,A=d.dilationHeight,x=d.dilationWidth,b=d.effectiveFilterDepth,w=d.effectiveFilterHeight,S=d.effectiveFilterWidth,C=b-1-d.padInfo.front,N=S-1-d.padInfo.left,_=w-1-d.padInfo.top,$=ve(s.shape,"float32"),M=1/(f*m*g),I=a.bufferSync(r);for(let E=0;E=d.outDepth||Math.floor(Z)!==Z))for(let re=0;re=d.outHeight||Math.floor(ee)!==ee))for(let pe=0;pe=d.outWidth||Math.floor(oe)!==oe)continue;let ye=I.get(E,Z,ee,oe,O);W+=ye}}}$.set(W*M,E,L,B,G,O)}return a.makeTensorInfo($.shape,$.dtype,$.values)}var qD={kernelName:H1,backendName:"cpu",kernelFunc:HD};function XD(e){let{inputs:t,backend:a,attrs:n}=e,{dy:r,input:s}=t,i=s;Ae([r,s],"avgPoolGrad");let{filterSize:o,strides:l,pad:u}=n,d=T.computePool2DInfo(i.shape,o,l,1,u),c=d.strideHeight,p=d.strideWidth,h=d.filterHeight,f=d.filterWidth,m=d.dilationHeight,g=d.dilationWidth,y=d.effectiveFilterHeight,A=d.effectiveFilterWidth,x=A-1-d.padInfo.left,b=y-1-d.padInfo.top,w=ve(i.shape,"float32"),S=1/(h*f),C=a.data.get(r.dataId).values,N=ve(r.shape,"float32",C);for(let _=0;_=d.outHeight||Math.floor(G)!==G))for(let j=0;j=d.outWidth||Math.floor(U)!==U)continue;let H=N.get(_,G,U,$);L+=H}}w.set(L*S,_,M,I,$)}return a.makeTensorInfo(w.shape,w.dtype,w.values)}var KD={kernelName:j1,backendName:"cpu",kernelFunc:XD};function ZD(e){let{inputs:t,backend:a,attrs:n}=e,{x:r,scale:s,offset:i,mean:o,variance:l}=t;v.assert(o.shape.length===l.shape.length,()=>"Batch normalization gradient requires mean and variance to have equal ranks."),v.assert(i==null||o.shape.length===i.shape.length,()=>"Batch normalization gradient 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o=s.reduce((y,A)=>y*A),l=T.getReshaped(r.shape,s,o),u=T.getPermuted(l.length,s.length),d=T.getReshapedPermuted(r.shape,s,o),c=T.getSliceBeginCoords(i,s.length),p=T.getSliceSize(d,i,s.length),h=ft({inputs:{x:r},backend:a,attrs:{shape:l}}),f=La({inputs:{x:h},backend:a,attrs:{perm:u}}),m=ft({inputs:{x:f},backend:a,attrs:{shape:d}}),g=li({inputs:{x:m},backend:a,attrs:{begin:c,size:p}});return a.disposeIntermediateTensorInfo(h),a.disposeIntermediateTensorInfo(f),a.disposeIntermediateTensorInfo(m),g}var QD={kernelName:bl,backendName:"cpu",kernelFunc:JD};function ez(e){let{inputs:t,backend:a,attrs:n}=e,{x:r,weights:s}=t,{size:i}=n,o=a.data.get(r.dataId).values,l=a.data.get(s.dataId).values,u=u3(o,l,s.dtype,s.shape,i);return a.makeTensorInfo([i],s.dtype,u)}var tz={kernelName:Uc,backendName:"cpu",kernelFunc:ez};function az(e){let{inputs:t,backend:a}=e,{s0:n,s1:r}=t,s=a.data.get(n.dataId).values,i=a.data.get(r.dataId).values,o=T.assertAndGetBroadcastShape(Array.from(s),Array.from(i));return a.makeTensorInfo([o.length],"int32",Int32Array.from(o))}var nz={kernelName:jc,backendName:"cpu",kernelFunc:az},rz=it(es,(e,t)=>{let a=t;return e>a.clipValueMax?a.clipValueMax:e{let{x:t}=e.inputs,a=e.backend,n=new Float32Array(v.sizeFromShape(t.shape)),r=a.data.get(t.dataId),s=r.complexTensorInfos.real,i=r.complexTensorInfos.imag,o=a.data.get(s.dataId).values,l=a.data.get(i.dataId).values;for(let u=0;um.shape);T.assertParamsConsistent(i,s);let o=T.computeOutShape(t.map(m=>m.shape),s);if(v.sizeFromShape(o)===0)return a.makeTensorInfo(o,t[0].dtype,[]);let l=t.filter(m=>v.sizeFromShape(m.shape)>0);if(l.length===1)return er({inputs:{x:l[0]},backend:a});if(l[0].dtype==="complex64"){let m=l.map(b=>oi({inputs:{input:b},backend:a})),g=l.map(b=>dl({inputs:{input:b},backend:a})),y=pl({inputs:m,backend:a,attrs:{axis:s}}),A=pl({inputs:g,backend:a,attrs:{axis:s}}),x=qa({inputs:{real:y,imag:A},backend:a});return m.forEach(b=>a.disposeIntermediateTensorInfo(b)),g.forEach(b=>a.disposeIntermediateTensorInfo(b)),a.disposeIntermediateTensorInfo(y),a.disposeIntermediateTensorInfo(A),x}let u=l.map(m=>{let g=[-1,v.sizeFromShape(m.shape.slice(s))];return ft({inputs:{x:m},backend:a,attrs:{shape:g}})}),d=u.map(m=>({vals:a.data.get(m.dataId).values,shape:m.shape}));o=T.computeOutShape(u.map(m=>m.shape),1);let c=u[0].shape[0]===1,p=d3(d,o,t[0].dtype,c),h=T.computeOutShape(l.map(m=>m.shape),s),f=a.makeTensorInfo(h,t[0].dtype,p);return u.forEach(m=>a.disposeIntermediateTensorInfo(m)),f}var uz={kernelName:vl,backendName:"cpu",kernelFunc:pl};function q7(e){let{inputs:t,backend:a,attrs:n}=e,{x:r,filter:s}=t,{strides:i,pad:o,dataFormat:l,dilations:u,dimRoundingMode:d}=n;Ae([r,s],"conv2d");let c=T.convertConv2DDataFormat(l),p=T.computeConv2DInfo(r.shape,s.shape,i,u,o,d,!1,c),h=p.filterHeight,f=p.filterWidth,m=p.dilationHeight,g=p.dilationWidth,y=p.padInfo.left,A=p.padInfo.top,x=p.dataFormat==="channelsLast",b=new Mt(p.outShape,r.dtype),w=v.computeStrides(r.shape),S=v.computeStrides(s.shape),C=w[0],N=x?w[1]:w[2],_=x?w[2]:1,$=x?1:w[1],M=b.strides[0],I=x?b.strides[1]:b.strides[2],E=x?b.strides[2]:1,O=x?1:b.strides[1],L=a.data.get(r.dataId).values,B=a.data.get(s.dataId).values,G=b.values;for(let j=0;j=p.inHeight)continue;let pe=re*S[0],oe=U+ee*N;for(let ye=0;ye=p.inWidth)continue;let nt=pe+Ge*S[1],lt=oe+Xe*_,et=nt;for(let rt=0;rt=u.inDepth)continue;let j=B*_[0],U=M+G*N[1];for(let H=0;H=u.inHeight)continue;let ee=j+Z*_[1],pe=U+re*N[2];for(let oe=0;oe=u.inWidth)continue;let Xe=ee+Ne*_[2],nt=pe+Ge*u.inChannels,lt=Xe;for(let et=0;etMath.cos(e)),wz={kernelName:bi,backendName:"cpu",kernelFunc:vz},kz=it(vi,e=>Math.cosh(e)),Iz={kernelName:vi,backendName:"cpu",kernelFunc:kz};function Sz(e){let{inputs:t,backend:a,attrs:n}=e,{image:r,boxes:s,boxInd:i}=t,{cropSize:o,method:l,extrapolationValue:u}=n,[d,c,p,h]=r.shape,f=s.shape[0],[m,g]=o,y=ve([f,m,g,h],"float32"),A=a.data.get(s.dataId).values,x=a.data.get(i.dataId).values,b=a.data.get(r.dataId).values,w=v.computeStrides(r.shape),S=v.computeStrides(y.shape);for(let C=0;C=d)continue;let O=m>1?(M-_)*(c-1)/(m-1):0,L=g>1?(I-$)*(p-1)/(g-1):0;for(let B=0;B1?_*(c-1)+B*O:.5*(_+M)*(c-1);if(G<0||G>c-1){for(let j=0;j1?$*(p-1)+W*L:.5*($+I)*(p-1);if(Q<0||Q>p-1){for(let pe=0;pe1?$*(p-1)+j*L:.5*($+I)*(p-1);if(U<0||U>p-1){for(let Q=0;Qy+f-A-1:(y,A)=>y+A;for(let y=0;yy+f-A-1:(y,A)=>y+A;for(let y=0;y`Only NHWC dataFormat supported on CPU for depthToSpace. Got ${i}`);let o=r.shape[0],l=r.shape[1],u=r.shape[2],d=r.shape[3],c=l*s,p=u*s,h=d/(s*s),f=a.data.get(r.dataId).values,m=new Float32Array(o*c*p*h),g=0;for(let y=0;y`Error in depthwiseConv2d: Either strides or dilations must be 1. Got strides ${i} and dilations '${p}'`);let h=T.computeConv2DInfo(r.shape,s.shape,i,p,o,u,!0),{filterHeight:f,filterWidth:m,dilationHeight:g,dilationWidth:y,padInfo:A}=h,x=A.left,b=A.top,w=h.outChannels/h.inChannels,S=new Mt(h.outShape,r.dtype),C=a.data.get(r.dataId).values,N=a.data.get(s.dataId).values,_=S.values;for(let $=0;$=h.inHeight)continue;let j=B*c[0],U=M+G*d[1];for(let H=0;H=h.inWidth)continue;let ee=j+Z*c[1],pe=U+re*h.inChannels,oe=W,ye=ee;for(let we=0;we{let{x:n,filter:r}=e,{strides:s,pad:i,dilations:o}=a,l=t,u=l.data.get(n.dataId).values,d=n.shape.length,c=l.data.get(r.dataId).values,p=r.shape.length,{batchSize:h,inHeight:f,inWidth:m,inChannels:g,outHeight:y,outWidth:A,padInfo:x,strideHeight:b,strideWidth:w,filterHeight:S,filterWidth:C,dilationHeight:N,dilationWidth:_,outShape:$}=T.computeDilation2DInfo(n.shape,r.shape,s,i,"NHWC",o),M=v.sizeFromShape($),I=$.length,E=v.getArrayFromDType(n.dtype,M);for(let O=0;O=0&&Z=0&&eeH&&(H=ye)}}}let W=v.locToIndex([O,L,G,U],I,v.computeStrides($));E[W]=H}}}return{dataId:l.write(v.toTypedArray(E,n.dtype),$,n.dtype),shape:$,dtype:n.dtype}}},Gz={kernelName:Gm,backendName:"cpu",kernelFunc:({inputs:e,backend:t,attrs:a})=>{let{x:n,filter:r,dy:s}=e,{strides:i,pad:o,dilations:l}=a,u=t,d=v.toNestedArray(n.shape,u.data.get(n.dataId).values),c=v.toNestedArray(r.shape,u.data.get(r.dataId).values),{batchSize:p,inHeight:h,inWidth:f,inChannels:m,outHeight:g,outWidth:y,padInfo:A,strideHeight:x,strideWidth:b,filterHeight:w,filterWidth:S,dilationHeight:C,dilationWidth:N,outShape:_}=T.computeDilation2DInfo(n.shape,r.shape,i,o,"NHWC",l);v.assert(s.rank===_.length,()=>`Error in ${Gm}, dy must have the same rank as output ${_.length}, but got ${s.rank}`);let $=v.toNestedArray(_,u.data.get(s.dataId).values),M=v.makeZerosNestedTypedArray(r.shape,r.dtype);for(let I=0;I=0&&Q=0&&rej&&(j=ee,U=W,H=Z)}}}M[U][H][G]+=$[I][E][L][G]}}}return{dataId:u.write(v.toTypedArray(M,n.dtype),r.shape,r.dtype),shape:r.shape,dtype:r.dtype}}},Uz={kernelName:Vm,backendName:"cpu",kernelFunc:({inputs:e,backend:t,attrs:a})=>{let{x:n,filter:r,dy:s}=e,{strides:i,pad:o,dilations:l}=a,u=t,d=v.toNestedArray(n.shape,u.data.get(n.dataId).values),c=v.toNestedArray(r.shape,u.data.get(r.dataId).values),{batchSize:p,inHeight:h,inWidth:f,inChannels:m,outHeight:g,outWidth:y,padInfo:A,strideHeight:x,strideWidth:b,filterHeight:w,filterWidth:S,dilationHeight:C,dilationWidth:N,outShape:_}=T.computeDilation2DInfo(n.shape,r.shape,i,o,"NHWC",l);v.assert(s.rank===_.length,()=>`Error in ${Vm}, dy must have the same rank as output ${_.length}, but got ${s.rank}`);let $=v.toNestedArray(_,u.data.get(s.dataId).values),M=v.makeZerosNestedTypedArray(n.shape,n.dtype);for(let I=0;I=0&&Q=0&&rej&&(j=ee,U=Q,H=re)}}}M[I][U][H][G]+=$[I][E][L][G]}}}return{dataId:u.write(v.toTypedArray(M,n.dtype),n.shape,n.dtype),shape:n.shape,dtype:n.dtype}}};function lp(e){let{inputs:t,backend:a,attrs:n}=e,{x:r}=t,{axis:s,keepDims:i}=n;Ae(r,"sum");let o;r.dtype==="bool"?o=Zr({inputs:{x:r},backend:a,attrs:{dtype:"int32"}}):o=er({inputs:{x:r},backend:a});let l=o.shape.length,u=v.parseAxisParam(s,o.shape),d=T.getAxesPermutation(u,l),c=u,p=o;d!=null&&(p=La({inputs:{x:o},backend:a,attrs:{perm:d}}),c=T.getInnerMostAxes(c.length,l)),T.assertAxesAreInnerMostDims("sum",c,p.shape.length);let[h,f]=T.computeOutAndReduceShapes(p.shape,c),m=T.upcastType(p.dtype,"int32"),g=Rc(a,h,m),y=v.sizeFromShape(f),A=a.data.get(g.dataId).values,x=a.data.get(p.dataId).values;for(let b=0;b=0&&(p=lp({inputs:{x:p},backend:a,attrs:{axis:u[m]-(i.length-h),keepDims:!1}}),f.push(p)),h--)}for(let m of f)m!==p&&a.disposeIntermediateTensorInfo(m);return p}var qz={kernelName:Sd,backendName:"cpu",kernelFunc:Hz};function Xz(e){let{inputs:t,backend:a}=e,{dy:n,y:r}=t;Ae([n,r],"eluGrad");let s=new Float32Array(v.sizeFromShape(r.shape)),i=a.data.get(r.dataId).values,o=a.data.get(n.dataId).values;for(let l=0;l=1?s[l]=o[l]:s[l]=o[l]*(u+1)}return a.makeTensorInfo(r.shape,"float32",s)}var Kz={kernelName:X1,backendName:"cpu",kernelFunc:Xz},Zz=T.ERF_P,Yz=T.ERF_A1,Jz=T.ERF_A2,Qz=T.ERF_A3,eL=T.ERF_A4,tL=T.ERF_A5,aL=it(Td,e=>{let t=Math.sign(e),a=Math.abs(e),n=1/(1+Zz*a);return t*(1-((((tL*n+eL)*n+Qz)*n+Jz)*n+Yz)*n*Math.exp(-a*a))}),nL={kernelName:Td,backendName:"cpu",kernelFunc:aL};function _c(e){let{inputs:t,backend:a,attrs:n}=e,{input:r}=t,{dim:s}=n,i=r.shape.length,o=r.shape.slice(),l=s;return s<0&&(v.assert(-(i+1)<=s,()=>`Axis must be in the interval [${-(i+1)}, ${i}]`),l=i+s+1),o.splice(l,0,1),ft({inputs:{x:r},backend:a,attrs:{shape:o}})}var rL={kernelName:wl,backendName:"cpu",kernelFunc:_c},sL=Lt((e,t)=>e/t),b3=Kt(Ci,sL),x1={kernelName:Ci,backendName:"cpu",kernelFunc:b3};function K7(e,t,a){let n=e.shape,r=n[0],s=n[1],i=a.data.get(e.dataId),o=i.complexTensorInfos.real,l=i.complexTensorInfos.imag,u=[r,s],d=v.sizeFromShape(u),c=v.getTypedArrayFromDType("float32",d),p=v.getTypedArrayFromDType("float32",d);for(let g=0;g{let{image:n}=e,r=a,s=v.getTypedArrayFromDType(n.dtype,v.sizeFromShape(n.shape)),[i,o,l,u]=n.shape,d=r.data.get(n.dataId).values;for(let c=0;c=0&&AMath.floor(e/t)),mL=Kt(Mi,fL,null,"int32"),gL={kernelName:Mi,backendName:"cpu",kernelFunc:mL};function yL(e){let{inputs:t,backend:a,attrs:n}=e,{x:r,filter:s,bias:i,preluActivationWeights:o}=t,{strides:l,pad:u,dataFormat:d,dilations:c,dimRoundingMode:p,activation:h,leakyreluAlpha:f}=n,m=q7({inputs:{x:r,filter:s},backend:a,attrs:{strides:l,pad:u,dataFormat:d,dilations:c,dimRoundingMode:p}});if(i){let g=m;if(d==="NCHW"&&i.shape.length===1&&i.shape[0]!==1){let y=ft({inputs:{x:i},backend:a,attrs:{shape:[i.shape[0],1,1]}});m=ul({inputs:{a:m,b:y},backend:a}),a.disposeIntermediateTensorInfo(y)}else m=ul({inputs:{a:m,b:i},backend:a});a.disposeIntermediateTensorInfo(g)}if(h){let g=m;if(d==="NCHW"&&h==="prelu"&&o.shape.length===1&&o.shape[0]!==1){let y=ft({inputs:{x:o},backend:a,attrs:{shape:[o.shape[0],1,1]}});m=$c(a,m,h,y,f),a.disposeIntermediateTensorInfo(y)}else m=$c(a,m,h,o,f);a.disposeIntermediateTensorInfo(g)}return m}var AL={kernelName:jr,backendName:"cpu",kernelFunc:yL};function xL(e){let{inputs:t,backend:a,attrs:n}=e,{x:r,filter:s,bias:i,preluActivationWeights:o}=t,{strides:l,pad:u,dataFormat:d,dilations:c,dimRoundingMode:p,activation:h,leakyreluAlpha:f}=n,m=X7({inputs:{x:r,filter:s},backend:a,attrs:{strides:l,pad:u,dataFormat:d,dilations:c,dimRoundingMode:p}});if(i){let g=m;m=ul({inputs:{a:m,b:i},backend:a}),a.disposeIntermediateTensorInfo(g)}if(h){let g=m;m=$c(a,m,h,o,f),a.disposeIntermediateTensorInfo(g)}return m}var bL={kernelName:Hr,backendName:"cpu",kernelFunc:xL};function vL(e){let{inputs:t,backend:a}=e,{params:n,indices:r}=t,s=v.sizeFromShape(n.shape),i=r.shape,o=i[i.length-1],[l,u,d,c]=T.prepareAndValidate(n,r);if(u===0)return a.makeTensorInfo(l,n.dtype,[]);let p=a.data.get(r.dataId).values,h=a.bufferSync(n),f=p7(p,h,n.dtype,u,o,d,c,n.shape,s);return a.makeTensorInfo(l,n.dtype,f.values)}var wL={kernelName:_i,backendName:"cpu",kernelFunc:vL};function kL(e){let{inputs:t,backend:a,attrs:n}=e,{x:r,indices:s}=t,{axis:i,batchDims:o}=n;Ae([r,s],"gatherV2");let l=v.parseAxisParam(i,r.shape)[0],u=a.data.get(s.dataId).values,d=r.shape[l];for(let b=0;b=0,()=>`GatherV2: the index value ${w} is not in [0, ${d-1}]`)}let c=o;o==null&&(c=0);let p=v.sizeFromShape(s.shape),h=T.segment_util.collectGatherOpShapeInfo(r,s,l,c),f=ft({inputs:{x:r},backend:a,attrs:{shape:[h.batchSize,h.outerSize,h.dimSize,h.sliceSize]}}),m=ft({inputs:{x:s},backend:a,attrs:{shape:[h.batchSize,p/h.batchSize]}}),g=[h.batchSize,h.outerSize,p/h.batchSize,h.sliceSize],y=a.bufferSync(m),A=a.bufferSync(f),x=c7(A,y,g);return a.disposeIntermediateTensorInfo(f),a.disposeIntermediateTensorInfo(m),a.makeTensorInfo(h.outputShape,x.dtype,x.values)}var IL={kernelName:Il,backendName:"cpu",kernelFunc:kL};function SL(e){let{inputs:t,backend:a}=e,{input:n}=t,r=v.sizeFromShape(n.shape),s=n.shape[n.shape.length-1],i=r/s,o=ft({inputs:{x:n},backend:a,attrs:{shape:[i,s]}}),l=K7(o,!0,a),u=ft({inputs:{x:l},backend:a,attrs:{shape:n.shape}});return a.disposeIntermediateTensorInfo(o),a.disposeIntermediateTensorInfo(l),u}var TL={kernelName:ah,backendName:"cpu",kernelFunc:SL},CL=it(Nd,e=>Number.isFinite(e)?1:0,"bool"),NL={kernelName:Nd,backendName:"cpu",kernelFunc:CL},EL=it(Ed,e=>Math.abs(e)===1/0?1:0,"bool"),RL={kernelName:Ed,backendName:"cpu",kernelFunc:EL},ML=it(Sl,e=>Number.isNaN(e)?1:0,"bool"),$L={kernelName:Sl,backendName:"cpu",kernelFunc:ML};function _L(e){let{backend:t,attrs:a}=e,{start:n,stop:r,num:s}=a,i=y7(n,r,s);return t.makeTensorInfo([i.length],"float32",i)}var FL={kernelName:nh,backendName:"cpu",kernelFunc:_L},PL=it(Rd,e=>Math.log1p(e)),OL={kernelName:Rd,backendName:"cpu",kernelFunc:PL},DL=Lt((e,t)=>e&&t),zL=Kt(Oi,DL,null,"bool"),LL={kernelName:Oi,backendName:"cpu",kernelFunc:zL},BL=it(Di,e=>e?0:1,"bool"),WL={kernelName:Di,backendName:"cpu",kernelFunc:BL},VL=Lt((e,t)=>e||t),GL=Kt(Tl,VL,null,"bool"),UL={kernelName:Tl,backendName:"cpu",kernelFunc:GL};function jL(e){let{inputs:t,backend:a,attrs:n}=e,{x:r}=t,{depthRadius:s,bias:i,alpha:o,beta:l}=n;Ae(r,"LRN");let u=r.shape[3],d=u-1,c=a.data.get(r.dataId).values,p=v.sizeFromShape(r.shape),h=new Float32Array(p);function f(m){let g=m%u,y=m-g+Math.max(0,g-s),A=m-g+Math.min(g+s,d),x=0;for(;y<=A;y++){let b=c[y];x+=b*b}return x}for(let m=0;m`Error in maxPool: Either strides or dilations must be 1. 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ce(t,()=>t.attachShader(a,this.vertexShader)),ce(t,()=>t.attachShader(a,e)),o6(t,a),this.debug&&fc(t,a),this.vertexAttrsAreBound||(this.setProgram(a),this.vertexAttrsAreBound=B6(t,this.program,this.vertexBuffer)),a}deleteProgram(e){this.throwIfDisposed(),e===this.program&&(this.program=null),e!=null&&ce(this.gl,()=>this.gl.deleteProgram(e))}setProgram(e){this.throwIfDisposed(),this.program=e,this.program!=null&&this.debug&&fc(this.gl,this.program),ce(this.gl,()=>this.gl.useProgram(e))}getUniformLocation(e,t,a=!0){return this.throwIfDisposed(),a?f6(this.gl,e,t):m6(this.gl,e,t)}getAttributeLocation(e,t){return this.throwIfDisposed(),ce(this.gl,()=>this.gl.getAttribLocation(e,t))}getUniformLocationNoThrow(e,t){return 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v.repeatedTry(()=>this.disposed||this.isQueryAvailable(e,V().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION"))),this.getQueryTime(e,V().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION"))}getQueryTime(e,t){if(t===0)return null;if(t===2){let a=this.gl;return a.getQueryParameter(e,a.QUERY_RESULT)/1e6}else{let a=this.getQueryTimerExtensionWebGL1();return a.getQueryObjectEXT(e,a.QUERY_RESULT_EXT)/1e6}}isQueryAvailable(e,t){if(t===0)return!0;if(t===2){let a=this.gl,n=this.getQueryTimerExtensionWebGL2(),r=a.getQueryParameter(e,a.QUERY_RESULT_AVAILABLE);return this.disjoint==null&&(this.disjoint=this.gl.getParameter(n.GPU_DISJOINT_EXT)),r&&!this.disjoint}else{let a=this.getQueryTimerExtensionWebGL1(),n=a.getQueryObjectEXT(e,a.QUERY_RESULT_AVAILABLE_EXT);return this.disjoint==null&&(this.disjoint=this.gl.getParameter(a.GPU_DISJOINT_EXT)),n&&!this.disjoint}}pollFence(e){return new Promise(t=>{this.addItemToPoll(()=>e.isFencePassed(),()=>t())})}pollItems(){let e=EG(this.itemsToPoll.map(t=>t.isDoneFn));for(let t=0;t<=e;++t){let{resolveFn:a}=this.itemsToPoll[t];a()}this.itemsToPoll=this.itemsToPoll.slice(e+1)}addItemToPoll(e,t){if(this.itemsToPoll.push({isDoneFn:e,resolveFn:t}),this.itemsToPoll.length>1)return;let a;"setTimeoutCustom"in V().platform&&(a=V().platform.setTimeoutCustom.bind(V().platform)),v.repeatedTry(()=>(this.pollItems(),this.itemsToPoll.length===0),()=>0,null,a)}bindTextureToFrameBuffer(e){this.throwIfDisposed(),mc(this.gl,e,this.framebuffer),this.debug&&Bu(this.gl)}unbindTextureToFrameBuffer(){this.outputTexture!=null?(mc(this.gl,this.outputTexture,this.framebuffer),this.debug&&Bu(this.gl)):w1(this.gl,this.framebuffer)}downloadMatrixDriver(e,t){this.bindTextureToFrameBuffer(e);let a=t();return this.unbindTextureToFrameBuffer(),a}setOutputMatrixTextureDriver(e,t,a){this.throwIfDisposed();let n=this.gl;mc(n,e,this.framebuffer),this.debug&&Bu(n),this.outputTexture=e,ce(n,()=>n.viewport(0,0,t,a)),ce(n,()=>n.scissor(0,0,t,a))}setOutputMatrixWriteRegionDriver(e,t,a,n){this.throwIfDisposed(),ce(this.gl,()=>this.gl.scissor(e,t,a,n))}throwIfDisposed(){if(this.disposed)throw new Error("Attempted to use disposed GPGPUContext.")}throwIfNoProgram(){if(this.program==null)throw new Error("No GPU program is currently set.")}};function EG(e){let t=0;for(;t`${e}.${a}`)}function va(e,t){return t===1?[e]:Y6(e,t)}function xU(e,t){if(e===1)return"rc";let a="";for(let n=0;n ${this.enableShapeUniforms?"outShape":this.outputShape[0]}`;let t="";for(let a=this.rank-2;a= ${this.enableShapeUniforms?`outShape[${a}]`:this.outputShape[a]}`,a= ${a}; bool rEdge = rp1 >= ${n}; `}getOutput(e){let t=this.getSourceCoordsArr(e);return this.rank===1?`getA(rc), (rc + 1 >= ${this.enableShapeUniforms?"outShape":this.outputShape[0]} ? 0. : getA(rc + 1)), 0, 0`:`getA(${t[0]}), cEdge ? 0. : getA(${t[1]}), rEdge ? 0. 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n===aa.PACKED_2X2_FLOAT32?i=this.gpgpu.createPackedMatrixTexture(e[0],e[1]):n===aa.PACKED_2X2_FLOAT16?i=this.gpgpu.createFloat16PackedMatrixTexture(e[0],e[1]):n===aa.UNPACKED_FLOAT32?i=this.gpgpu.createFloat32MatrixTexture(e[0],e[1]):n===aa.UNPACKED_FLOAT16?i=this.gpgpu.createFloat16MatrixTexture(e[0],e[1]):n===aa.PACKED_4X1_UNSIGNED_BYTE&&(i=this.gpgpu.createUnsignedBytesMatrixTexture(e[0],e[1])),this.usedTextures[r].push(i),this.numUsedTextures++,this._numBytesAllocated+=s,this.log(),i}releaseTexture(e,t,a,n){if(this.freeTextures==null)return;let r=wy(a,n),s=ky(t,r,n);s in this.freeTextures||(this.freeTextures[s]=[]);let i=vy(t,r,this.gpgpu.gl,this.gpgpu.textureConfig,n),o=V().get("WEBGL_DELETE_TEXTURE_THRESHOLD");o!==-1&&this._numBytesAllocated>o?(this.gpgpu.deleteMatrixTexture(e.texture),this._numBytesAllocated-=i):(this.freeTextures[s].push(e),this.numFreeTextures++,this._numBytesFree+=i),this.numUsedTextures--;let l=this.usedTextures[s],u=l.indexOf(e);if(u<0)throw new Error("Cannot release a texture that was never provided by this texture manager");l.splice(u,1),this.log()}log(){if(!this.logEnabled)return;let e=this.numFreeTextures+this.numUsedTextures;console.log("Free/Used",`${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.texture)});for(let e in this.usedTextures)this.usedTextures[e].forEach(t=>{this.gpgpu.deleteMatrixTexture(t.texture)});this.freeTextures=null,this.usedTextures=null,this.numUsedTextures=0,this.numFreeTextures=0,this._numBytesAllocated=0,this._numBytesFree=0}}};function kU(e,t){let a=e;if(t===a.R32F)return 4;if(t===a.R16F)return 2;if(t===a.RGBA32F||t===e.RGBA)return 16;if(t===a.RGBA16F)return 8;if(t===a.RGBA8)return 4;throw new Error(`Unknown internal format ${t}`)}function vy(e,t,a,n,r){let s=IU(t,n),i;if(r){let[l,u]=Yl(e[0],e[1]);i=l*u}else{let[l,u]=up(e[0],e[1]);i=l*u}let o=kU(a,s);return i*o}function IU(e,t){switch(e){case aa.PACKED_2X2_FLOAT32:return R3(t);case aa.PACKED_2X2_FLOAT16:return M3(t);case aa.UNPACKED_FLOAT32:return C3(t);case aa.UNPACKED_FLOAT16:return N3(t);case aa.PACKED_4X1_UNSIGNED_BYTE:return E3(t);default:throw new Error(`Unknown physical texture type ${e}`)}}function SU(e){return V().getBool("WEBGL_RENDER_FLOAT32_ENABLED")?e?aa.PACKED_2X2_FLOAT32:aa.UNPACKED_FLOAT32:e?aa.PACKED_2X2_FLOAT16:aa.UNPACKED_FLOAT16}function wy(e,t){if(e===un.UPLOAD)return aa.PACKED_2X2_FLOAT32;if(e===un.RENDER||e==null)return SU(t);if(e===un.DOWNLOAD||e===un.PIXELS)return aa.PACKED_4X1_UNSIGNED_BYTE;throw new Error(`Unknown logical texture type ${e}`)}function ky(e,t,a){return`${e[0]}_${e[1]}_${t}_${a}`}var gr=class{constructor(e,t){this.variableNames=["A"],this.outputShape=e,this.enableShapeUniforms=Na(this.outputShape.length),this.userCode=` float unaryOperation(float x) { ${t} } void main() { float x = getAAtOutCoords(); float y = unaryOperation(x); setOutput(y); } `}},Cn="if (isnan(x)) return x;",TU="return x;",Iy="return abs(x);",CU="return (x >= 0.0) ? x : (exp(x) - 1.0);",NU=Cn+` return (x < 0.0) ? 0.0 : x; `,EU=Cn+` return (x < 0.0) ? 0.0 : min(6.0, x); `,Uo="return x;",RU="return 1.0 / (1.0 + exp(-1.0 * x));",MU="return x;",$U=` vec4 result; result.r = (x.r >= 0.0) ? x.r : 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t=this.texData.get(e),{values:a,shape:n,slice:r,dtype:s,complexTensorInfos:i,isPacked:o}=t;if(r!=null){let h;o?h=new Js(n,Uo):h=new gr(n,Uo);let f=this.runWebGLProgram(h,[{dataId:e,shape:n,dtype:s}],s),m=this.read(f.dataId);return this.disposeIntermediateTensorInfo(f),m}if(a!=null)return this.convertAndCacheOnCPU(e);if(V().getBool("DEBUG")&&!V().getBool("WEBGL_DOWNLOAD_FLOAT_ENABLED")&&V().getNumber("WEBGL_VERSION")===2)throw new Error("tensor.data() with WEBGL_DOWNLOAD_FLOAT_ENABLED=false and WEBGL_VERSION=2 not yet supported.");let l=null,u;if(s!=="complex64"&&V().get("WEBGL_BUFFER_SUPPORTED")){u=this.decode(e);let h=this.texData.get(u.dataId);l=this.gpgpu.createBufferFromTexture(h.texture.texture,...oc(n))}this.pendingRead.set(e,[]),s!=="complex64"&&await this.gpgpu.createAndWaitForFence();let d;if(s==="complex64"){let h=await Promise.all([this.read(i.real.dataId),this.read(i.imag.dataId)]),f=h[0],m=h[1];d=T.mergeRealAndImagArrays(f,m)}else if(l==null)d=this.getValuesFromTexture(e);else{let h=v.sizeFromShape(n);d=this.gpgpu.downloadFloat32MatrixFromBuffer(l,h)}if(u!=null&&this.disposeIntermediateTensorInfo(u),l!=null){let h=this.gpgpu.gl;ce(h,()=>h.deleteBuffer(l))}let c=this.convertAndCacheOnCPU(e,d),p=this.pendingRead.get(e);return this.pendingRead.delete(e),p.forEach(h=>h(c)),this.pendingDisposal.has(e)&&(this.pendingDisposal.delete(e),this.disposeData(e)&&kt().removeDataId(e,this),this.pendingDeletes--),c}readToGPU(e,t={}){let a=this.texData.get(e),{values:n,shape:r,slice:s,dtype:i,isPacked:o,texture:l}=a;if(i==="complex64")throw new Error("Does not support reading texture for complex64 dtype.");if(s!=null){let p;o?p=new Js(r,Uo):p=new gr(r,Uo);let h=this.runWebGLProgram(p,[{dataId:e,shape:r,dtype:i}],i),f=this.readToGPU(h,t);return this.disposeIntermediateTensorInfo(h),f}if(l==null)throw n!=null?new Error("Data is not on GPU but on CPU."):new Error("There is no data on GPU or CPU.");let u=this.decode(e,t.customTexShape),d=kt().makeTensorFromTensorInfo(u),c=this.texData.get(u.dataId);return Object.assign({tensorRef:d},c.texture)}bufferSync(e){let t=this.readSync(e.dataId);if(e.dtype==="string")try{let a=t.map(n=>v.decodeString(n));return ve(e.shape,e.dtype,a)}catch(a){throw new Error("Failed to decode encoded string bytes into utf-8")}return ve(e.shape,e.dtype,t)}checkNumericalProblems(e){if(e!=null)for(let t=0;t0}time(e){let t=this.activeTimers,a=[],n=!1;this.programTimersStack==null?(this.programTimersStack=a,n=!0):this.activeTimers.push(a),this.activeTimers=a,e();let r=v.flatten(this.activeTimers.map(o=>o.query)).filter(o=>o!=null),s=v.flatten(this.activeTimers.map(o=>o.name)).filter(o=>o!=null);this.activeTimers=t,n&&(this.programTimersStack=null);let i={uploadWaitMs:this.uploadWaitMs,downloadWaitMs:this.downloadWaitMs,kernelMs:null,wallMs:null};return(async()=>{if(V().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE")>0){let o=await Promise.all(r);i.kernelMs=v.sum(o),i.getExtraProfileInfo=()=>o.map((l,u)=>({name:s[u],ms:l})).map(l=>`${l.name}: ${l.ms}`).join(", ")}else i.kernelMs={error:"WebGL query timers are not supported in this environment."};return this.uploadWaitMs=0,this.downloadWaitMs=0,i})()}memory(){return{unreliable:!1,numBytesInGPU:this.numBytesInGPU,numBytesInGPUAllocated:this.textureManager.numBytesAllocated,numBytesInGPUFree:this.textureManager.numBytesFree}}startTimer(){return V().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE")>0?this.gpgpu.beginQuery():{startMs:v.now(),endMs:null}}endTimer(e){return V().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE")>0?(this.gpgpu.endQuery(),e):(e.endMs=v.now(),e)}async getQueryTime(e){if(V().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:a}=this.texData.get(e);return a!=null&&(this.disposeData(a.real.dataId,t),this.disposeData(a.imag.dataId,t)),this.texData.delete(e),!0}releaseGPUData(e){let{texture:t,dtype:a,texShape:n,usage:r,isPacked:s,slice:i}=this.texData.get(e),o=i&&i.origDataId||e,l=this.dataRefCount.get(o);l>1?this.dataRefCount.set(o,l-1):(this.dataRefCount.delete(o),t!=null&&(this.numBytesInGPU-=this.computeBytes(n,a),this.textureManager.releaseTexture(t,n,r,s)));let u=this.texData.get(e);u.texture=null,u.texShape=null,u.isPacked=!1,u.slice=null}getTexture(e){return this.uploadToGPU(e),this.texData.get(e).texture.texture}getDataInfo(e){return this.texData.get(e)}shouldExecuteOnCPU(e,t=WU){return V().getBool("WEBGL_CPU_FORWARD")&&e.every(a=>this.texData.get(a.dataId).texture==null&&v.sizeFromShape(a.shape)0&&v.isString(a[0])){let r=a.map(s=>v.encodeString(s));n=this.write(r,e,t)}else n=this.write(a,e,t);return this.texData.get(n).usage=null,{dataId:n,shape:e,dtype:t}}makeOutput(e,t,a){return kt().makeTensorFromTensorInfo(this.makeTensorInfo(e,t,a),this)}unpackTensor(e){let t=new OU(e.shape);return this.runWebGLProgram(t,[e],e.dtype)}packTensor(e){let t=new bU(e.shape),a=!0;return this.runWebGLProgram(t,[e],e.dtype,null,a)}packedReshape(e,t){let a=[ui(e.shape),...di(e.shape)],n={dtype:e.dtype,shape:a,dataId:e.dataId},r=[ui(t),...di(t)],s=new J6(r,a),i=!0,o=[a],l=this.runWebGLProgram(s,[n],e.dtype,o,i);return{dataId:l.dataId,shape:t,dtype:l.dtype}}decode(e,t){let a=this.texData.get(e),{isPacked:n,shape:r,dtype:s}=a;if(t!=null){let c=v.sizeFromShape(r),p=t[0]*t[1]*4;v.assert(c<=p,()=>"customTexShape is too small. Row * Column * 4 should be equal or larger than the size of the tensor data.")}let i=Wu(r),o;n?o=new IG(i):o=new kG(i);let l=!0,u=[t!=null?t:oc(i)],d=this.runWebGLProgram(o,[{shape:i,dtype:s,dataId:e}],s,u,l,t);return{dtype:s,shape:r,dataId:d.dataId}}runWebGLProgram(e,t,a,n,r=!1,s){let i=this.makeTensorInfo(e.outputShape,a),o=this.texData.get(i.dataId);if(e.packedOutput&&(o.isPacked=!0),e.outPackingScheme===od.DENSE){let g=s!=null?s:oc(e.outputShape);o.texShape=g.map(y=>y*2)}if(e.outTexUsage!=null&&(o.usage=e.outTexUsage),v.sizeFromShape(i.shape)===0)return o.values=v.getTypedArrayFromDType(i.dtype,0),i;let l=[],u=t.map(g=>{if(g.dtype==="complex64")throw new Error("GPGPUProgram does not support complex64 input. For complex64 dtypes, please separate the program into real and imaginary parts.");let y=this.texData.get(g.dataId);if(y.texture==null){if(!e.packedInputs&&v.sizeFromShape(g.shape)<=V().getNumber("WEBGL_SIZE_UPLOAD_UNIFORM"))return{shape:g.shape,texData:null,isUniform:!0,uniformValues:y.values};e.packedInputs&&(y.isPacked=!0,y.shape=g.shape)}if(this.uploadToGPU(g.dataId),!!y.isPacked!=!!e.packedInputs)g=y.isPacked?this.unpackTensor(g):this.packTensor(g),l.push(g),y=this.texData.get(g.dataId);else if(y.isPacked&&!ld(y.shape,g.shape)){let A=g,x=g.shape;g.shape=y.shape,g=this.packedReshape(g,x),l.push(g),y=this.texData.get(g.dataId),A.shape=x}return{shape:g.shape,texData:y,isUniform:!1}});this.uploadToGPU(i.dataId);let d={shape:i.shape,texData:o,isUniform:!1},c=wG(e,u,d),p=this.getAndSaveBinary(c,()=>bG(this.gpgpu,e,u,d)),h=this.activeTimers!=null,f;h&&(f=this.startTimer()),V().get("ENGINE_COMPILE_ONLY")||vG(this.gpgpu,p,u,d,n),l.forEach(g=>this.disposeIntermediateTensorInfo(g)),h&&(f=this.endTimer(f),this.activeTimers.push({name:e.constructor.name,query:this.getQueryTime(f)}));let m=V().get("WEBGL_FLUSH_THRESHOLD");if(m>0){let g=v.now();g-this.lastGlFlushTime>m&&(this.gpgpu.gl.flush(),this.lastGlFlushTime=g)}if(!V().getBool("WEBGL_LAZILY_UNPACK")&&o.isPacked&&r===!1){let g=this.unpackTensor(i);return this.disposeIntermediateTensorInfo(i),g}return i}compileAndRun(e,t,a,n,r=!1){return a=a||t[0].dtype,this.runWebGLProgram(e,t,a,n,r)}getAndSaveBinary(e,t){return e in this.binaryCache||(this.binaryCache[e]=t()),this.binaryCache[e]}getTextureManager(){return this.textureManager}dispose(){this.disposed||(V().getBool("IS_TEST")||Object.keys(this.binaryCache).forEach(e=>{this.gpgpu.deleteProgram(this.binaryCache[e].webGLProgram),delete this.binaryCache[e]}),this.textureManager.dispose(),this.canvas!=null&&typeof HTMLCanvasElement!="undefined"&&this.canvas instanceof HTMLCanvasElement?this.canvas.remove():this.canvas=null,this.gpgpuCreatedLocally&&(this.gpgpu.program=null,this.gpgpu.dispose()),this.disposed=!0)}floatPrecision(){return this.floatPrecisionValue==null&&(this.floatPrecisionValue=Ee(()=>{if(!V().get("WEBGL_RENDER_FLOAT32_ENABLED")){let e=V().getBool("DEBUG");V().set("DEBUG",!1);let t=this.abs(Fe(1e-8)).dataSync()[0];if(V().set("DEBUG",e),t>0)return 32}return 16})),this.floatPrecisionValue}epsilon(){return this.floatPrecision()===32?zU:LU}uploadToGPU(e){let t=this.texData.get(e),{shape:a,dtype:n,values:r,texture:s,usage:i,isPacked:o}=t;if(s!=null)return;let l=this.activeTimers!=null,u;l&&(u=v.now());let d=t.texShape;if(d==null&&(d=x6(a,o),t.texShape=d),r!=null){let c=Wu(a),p,h=d[1],f=d[0],m=r instanceof Uint8Array||r instanceof Uint8ClampedArray;(o||!m)&&([h,f]=Yl(d[0],d[1])),o?p=new NG(c,m):p=new by(c,m);let g=m?[f,h]:d,y=this.makeTensorInfo(g,n),A=this.texData.get(y.dataId);m?A.usage=un.PIXELS:A.usage=un.UPLOAD,A.texShape=g,this.gpgpu.uploadDenseMatrixToTexture(this.getTexture(y.dataId),h,f,r);let x=[[f,h]],b=!0,w=this.runWebGLProgram(p,[y],n,x,b),S=this.texData.get(w.dataId);t.texShape=S.texShape,t.isPacked=S.isPacked,t.usage=S.usage,V().get("ENGINE_COMPILE_ONLY")?this.disposeData(w.dataId):(t.texture=S.texture,t.values=null,this.texData.delete(w.dataId)),this.disposeIntermediateTensorInfo(y),l&&(this.uploadWaitMs+=v.now()-u)}else{let c=this.acquireTexture(d,i,n,o);t.texture=c}}convertAndCacheOnCPU(e,t){let a=this.texData.get(e),{dtype:n}=a;return this.releaseGPUData(e),t!=null&&(a.values=UU(t,n)),a.values}acquireTexture(e,t,a,n){if(this.numBytesInGPU+=this.computeBytes(e,a),!this.warnedAboutMemory&&this.numBytesInGPU>this.numMBBeforeWarning*1024*1024){let r=(this.numBytesInGPU/1024/1024).toFixed(2);this.warnedAboutMemory=!0,console.warn(`High memory usage in GPU: ${r} MB, most likely due to a memory leak`)}return this.textureManager.acquireTexture(e,t,n)}computeBytes(e,t){return e[0]*e[1]*v.bytesPerElement(t)}checkCompileCompletion(){for(let[,e]of Object.entries(this.binaryCache))this.checkCompletion_(e)}async checkCompileCompletionAsync(){let e=[];if(this.gpgpu.parallelCompilationExtension){for(let[,t]of Object.entries(this.binaryCache))e.push(this.checkCompletionAsync_(t));return Promise.all(e)}else{for(let[,t]of Object.entries(this.binaryCache)){let a=new Promise(n=>{try{this.checkCompletion_(t),n(!0)}catch(r){throw r}});e.push(a)}return Promise.all(e)}}async checkCompletionAsync_(e){return this.gpgpu.gl.getProgramParameter(e.webGLProgram,this.gpgpu.parallelCompilationExtension.COMPLETION_STATUS_KHR)?this.checkCompletion_(e):(await E4(),this.checkCompletionAsync_(e))}checkCompletion_(e){if(this.gpgpu.gl.getProgramParameter(e.webGLProgram,this.gpgpu.gl.LINK_STATUS)===!1)throw console.log(this.gpgpu.gl.getProgramInfoLog(e.webGLProgram)),this.gpgpu.gl.getShaderParameter(e.fragmentShader,this.gpgpu.gl.COMPILE_STATUS)===!1?(k3(e.source,this.gpgpu.gl.getShaderInfoLog(e.fragmentShader)),new Error("Failed to compile fragment shader.")):new Error("Failed to link vertex and fragment shaders.");return!0}getUniformLocations(){for(let[,e]of Object.entries(this.binaryCache)){let{uniformLocations:t,customUniformLocations:a,infLoc:n,nanLoc:r,inShapesLocations:s,inTexShapesLocations:i,outShapeLocation:o,outShapeStridesLocation:l,outTexShapeLocation:u}=R6(this.gpgpu,e.program,e.webGLProgram);e.uniformLocations=t,e.customUniformLocations=a,e.infLoc=n,e.nanLoc=r,e.inShapesLocations=s,e.inTexShapesLocations=i,e.outShapeLocation=o,e.outShapeStridesLocation=l,e.outTexShapeLocation=u}}createTensorFromTexture(e,t,a){let{texture:n,height:r,width:s,channels:i}=e,o=kt().backend;if(!o.gpgpu.gl.isTexture(n))throw new Error("The texture is invalid. Also, please make sure the texture and the TFJS WebGL backend are using the same canvas. If you want to use your own custom canvas, you have to create and use the custom TFJS WebGL backend created from the canvas through 'new tf.MathBackendWebGL(customCanvas)'.");let l=o.writeTexture(n,t,a,r,s,i);return kt().makeTensorFromDataId(l,t,a,o)}};nu.nextDataId=0;function UU(e,t){if(t==="float32"||t==="complex64")return e;if(t==="int32"||t==="bool"){let a=t==="int32"?new Int32Array(e.length):new Uint8Array(e.length);for(let n=0;nnew nu,2);var HU={forceHalfFloat:Q6},_3=` if (isnan(a)) return a; if (isnan(b)) return b; `,cl=class{constructor(e,t,a){this.variableNames=["A","B"],this.outputShape=T.assertAndGetBroadcastShape(t,a),this.enableShapeUniforms=Na(this.outputShape.length),this.userCode=` float binaryOperation(float a, float b) { ${e} } void main() { float a = getAAtOutCoords(); float b = getBAtOutCoords(); setOutput(binaryOperation(a, b)); } `}},pp=` result.r = isNaN.r ? NAN : result.r; result.g = isNaN.g ? NAN : result.g; result.b = isNaN.b ? NAN : result.b; result.a = isNaN.a ? NAN : result.a; `,cp=class{constructor(e,t,a,n=!1){this.variableNames=["A","B"],this.supportsBroadcasting=!0,this.packedInputs=!0,this.packedOutput=!0,this.outputShape=T.assertAndGetBroadcastShape(t,a);let r=this.outputShape.length;this.enableShapeUniforms=Na(r);let s="";if(n)if(r===0||v.sizeFromShape(this.outputShape)===1)s=` result.y = 0.; result.z = 0.; result.w = 0.; `;else if(s=` ${mt(r)} coords = getOutputCoords(); `,r===1)this.enableShapeUniforms?s+=` result.y = (coords + 1) >= outShape ? 0. : result.y; result.z = 0.; result.w = 0.; `:s+=` result.y = (coords + 1) >= ${this.outputShape[0]} ? 0. : result.y; result.z = 0.; result.w = 0.; `;else{let i=va("coords",r);this.enableShapeUniforms?s+=` bool nextRowOutOfBounds = (${i[r-2]} + 1) >= outShape[${r} - 2]; bool nextColOutOfBounds = (${i[r-1]} + 1) >= outShape[${r} - 1]; result.y = nextColOutOfBounds ? 0. : result.y; result.z = nextRowOutOfBounds ? 0. : result.z; result.w = nextColOutOfBounds || nextRowOutOfBounds ? 0. : result.w; `:s+=` bool nextRowOutOfBounds = (${i[r-2]} + 1) >= ${this.outputShape[r-2]}; bool nextColOutOfBounds = (${i[r-1]} + 1) >= ${this.outputShape[r-1]}; result.y = nextColOutOfBounds ? 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int batch = coords[0]; int outIdx = coords[1]; int inOffset = outIdx * ${n}; int bestIndex = inOffset; float bestValue = getA(batch, bestIndex); for (int i = 0; i < ${n}; i++) { int inIdx = ${o}; float candidate = getA(batch, inIdx); if (candidate ${i} bestValue) { bestValue = candidate; bestIndex = inIdx; } } setOutput(float(bestIndex)); } `}},Rj=class{constructor(e,t,a,n){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,v.assert(e.length>2,()=>`Packed arg${a.charAt(0).toUpperCase()+a.slice(1)} supports only inputs with rank above 2.`);let r=e[e.length-1],s=Math.ceil(r/t);this.outputShape=e.slice(0,-1),s>1&&this.outputShape.push(s),n||this.variableNames.push("bestIndicesA");let i=this.outputShape,o=i.length,l=mt(o),u=va("coords",o),d,c;if(s===1){c=o+1;let C=mt(c);d=` ${C} sourceLocR = ${C}(${u.join()}, 0); ++${u[o-1]}; ${C} sourceLocG = ${C}(${u.join()}, 0); ++${u[o-2]}; ${C} sourceLocA = ${C}(${u.join()}, 0); --${u[o-1]}; ${C} sourceLocB = ${C}(${u.join()}, 0); --${u[o-2]};`}else c=o,d=` ${l} sourceLocR = coords; 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} ${S} void main() { ${l} coords = getOutputCoords(); bool hasNextCol = ${u[o-1]} < ${i[o-1]-1}; bool hasNextRow = ${u[o-2]} < ${i[o-2]-1}; ${d} ivec4 srcIdx = ivec4(sourceLocR${h}, sourceLocG${h}, sourceLocB${h}, sourceLocA${h}) * ${t}; ivec4 inIdx = srcIdx; vec4 bestIndex = vec4(inIdx); vec4 bestValue = ${w}; for (int i = 0; i < ${t}; i++) { inIdx = srcIdx; ${b} vec4 candidate = ${w}; bvec4 nan = isnan(candidate); bvec4 replace = bvec4( vec4(${x}(candidate, bestValue)) * (vec4(1.0) - vec4(nan))); bestValue = vec4(replace.x ? candidate.x : bestValue.x, replace.y ? candidate.y : bestValue.y, replace.z ? candidate.z : bestValue.z, replace.w ? candidate.w : bestValue.w); bestIndex = mix(bestIndex, vec4(inIdx), vec4(replace)); srcIdx++; } setOutput(bestIndex); } `}};function i8(e,t,a,n=null){let r=t.shape[0],s=t.shape[1];n!=null&&(r=n.shape[0],s=n.shape[1]);let i=T.computeOptimalWindowSize(s),o={windowSize:i,inSize:s,batchSize:r,outSize:Math.ceil(s/i)},l=new Ej(o,a,n==null),u=[t];n!=null&&u.push(n);let d=e.runWebGLProgram(l,u,"int32");if(d.shape[1]===1)return d;let c=i8(e,t,a,d);return e.disposeIntermediateTensorInfo(d),c}function o8(e,t,a,n=null){let r=n!=null?n.shape:t.shape,s=r[r.length-1],i=T.computeOptimalWindowSize(s),o=new Rj(r,i,a,n==null),l=n==null?[t]:[t,n],u=e.runWebGLProgram(o,l,"int32");if(u.shape.length===t.shape.length){let d=o8(e,t,a,u);return e.disposeIntermediateTensorInfo(u),d}return u}function l8(e,t,a,n){let r=[a];if(T.assertAxesAreInnerMostDims("arg"+n.charAt(0).toUpperCase()+n.slice(1),r,t.shape.length),!V().getBool("WEBGL_PACK_REDUCE")||t.shape.length<=2){let s=[],i=e.texData.get(t.dataId),o=i!==null&&i.isPacked,l=t;o&&(l=e.unpackTensor(t),s.push(l));let[u,d]=T.computeOutAndReduceShapes(l.shape,r),c=v.sizeFromShape(d),p=de({inputs:{x:l},backend:e,attrs:{shape:[-1,c]}});s.push(p);let h=i8(e,p,n);s.push(h);let f=de({inputs:{x:h},backend:e,attrs:{shape:u}});return s.forEach(m=>e.disposeIntermediateTensorInfo(m)),f}return o8(e,t,n)}function Mj(e){let{inputs:t,backend:a,attrs:n}=e,{x:r}=t,{axis:s}=n,i=v.parseAxisParam(s,r.shape),o=T.getAxesPermutation(i,r.shape.length),l=r,u=[];o!=null&&(l=Ia({inputs:{x:r},backend:a,attrs:{perm:o}}),u.push(l),i=T.getInnerMostAxes(i.length,l.shape.length)),T.assertAxesAreInnerMostDims("argMax",[i[0]],l.shape.length);let d=l8(a,l,i[0],"max");return u.forEach(c=>a.disposeIntermediateTensorInfo(c)),d}var $j={kernelName:fi,backendName:"webgl",kernelFunc:Mj};function _j(e){let{inputs:t,backend:a,attrs:n}=e,{x:r}=t,{axis:s}=n,i=v.parseAxisParam(s,r.shape),o=T.getAxesPermutation(i,r.shape.length),l=r,u=[];o!=null&&(l=Ia({inputs:{x:r},backend:a,attrs:{perm:o}}),u.push(l),i=T.getInnerMostAxes(i.length,l.shape.length)),T.assertAxesAreInnerMostDims("argMin",[i[0]],l.shape.length);let d=l8(a,l,i[0],"min");return u.forEach(c=>a.disposeIntermediateTensorInfo(c)),d}var Fj={kernelName:xd,backendName:"webgl",kernelFunc:_j},Pj=Cn+` if (abs(x) > 1.) { return NAN; } return asin(x); `,Oj=Qe({opSnippet:Pj}),Dj={kernelName:bd,backendName:"webgl",kernelFunc:Oj},zj=Cn+"return log(x + sqrt(x * x + 1.0));",Lj=Qe({opSnippet:zj}),Bj={kernelName:vd,backendName:"webgl",kernelFunc:Lj},Wj=Cn+` return atan(x); `,Vj=Qe({opSnippet:Wj}),Gj={kernelName:wd,backendName:"webgl",kernelFunc:Vj},Uj=_3+` return atan(a, b); `,jj=` vec4 result = atan(a, b); bvec4 isNaNA = isnan(a); bvec4 isNaNB = isnan(b); bvec4 isNaN = bvec4(isNaNA.x || isNaNB.x, isNaNA.y || isNaNB.y, isNaNA.z || isNaNB.z, isNaNA.w || isNaNB.w); `+pp+` return result; `,Hj=la({opSnippet:Uj,packedOpSnippet:jj}),qj={kernelName:xl,backendName:"webgl",kernelFunc:Hj},Xj=Cn+` if ((x < -1.0) || (x > 1.0)) return NAN; return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,Kj=Qe({opSnippet:Xj}),Zj={kernelName:kd,backendName:"webgl",kernelFunc:Kj},dd=class{constructor(e,t,a,n=!1,r=!1){if(this.variableNames=["x"],t==="avg"&&a)throw new Error("Cannot compute positions for average pool.");let s=e.filterWidth,i=e.strideHeight,o=e.strideWidth,l=e.dilationHeight,u=e.dilationWidth,d=e.effectiveFilterHeight,c=e.effectiveFilterWidth,p=e.padInfo.top,h=e.padInfo.left;this.outputShape=e.outShape;let f=t==="avg",m=`((batch * ${e.inHeight} + xR) * ${e.inWidth} + xC) * ${e.inChannels} + d`,g=`(xR * ${e.inWidth} + xC) * ${e.inChannels} + d`,y="0.0";if(f||(y="-1.0 / 1e-20"),a){let C=">=";this.userCode=` const ivec2 strides = ivec2(${i}, ${o}); const ivec2 pads = ivec2(${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 < ${d}; wR += ${l}) { int xR = xRCorner + wR; if (xR < 0 || xR >= ${e.inHeight}) { continue; } for (int wC = 0; wC < ${c}; 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 ${C} currMinMaxValue) { minMaxValue = value; minMaxValueFound = 1.0; minMaxPosition = ${n?r?m:g:`wR * ${c} + wC`}; } } } setOutput(float(minMaxPosition)); } `;return}let A="max",x=`${t}(${t}(${t}(minMaxValue[0], minMaxValue[1]), minMaxValue[2]), minMaxValue[3])`;t==="avg"&&(x="avgValue / count");let b=Math.floor(s/4)*4,w=s%4,S=` if (${f}) { avgValue += dot(values, ones); } else { minMaxValue = ${A}(values, minMaxValue); } `;this.userCode=` const ivec2 strides = ivec2(${i}, ${o}); const ivec2 pads = ivec2(${p}, ${h}); const float initializationValue = ${y}; 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 >= ${e.inWidth}) { return initializationValue; } count += 1.0; return getX(batch, xR, xC, d); } void main() { ivec4 coords = getOutputCoords(); int batch = coords[0]; int d = coords[3]; ivec2 xRCCorner = coords.yz * strides - pads; int xRCorner = xRCCorner.x; int xCCorner = xRCCorner.y; // max/min x(?, ?, d) to get y(yR, yC, d). // ? = to be determined vec4 minMaxValue = vec4(${y}); float avgValue = 0.0; count = 0.0; for (int wR = 0; wR < ${d}; 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) ); ${S} } int xC = xCCorner + ${b}; if (${w===1}) { vec4 values = vec4( getValue(batch, xR, xC, d), initializationValue, initializationValue, initializationValue ); ${S} } else if (${w===2}) { vec4 values = vec4( getValue(batch, xR, xC, d), getValue(batch, xR, xC + ${u}, d), initializationValue, initializationValue ); ${S} } else if (${w===3}) { vec4 values = vec4( getValue(batch, xR, xC, d), getValue(batch, xR, xC + ${u}, d), getValue(batch, xR, xC + 2 * ${u}, d), initializationValue ); ${S} } } setOutput(${x}); } `}},P3=class{constructor(e,t,a,n=!1,r=!1){if(this.variableNames=["x"],t==="avg"&&a)throw new Error("Cannot compute positions for average pool.");let s=e.filterWidth,i=e.strideDepth,o=e.strideHeight,l=e.strideWidth,u=e.dilationDepth,d=e.dilationHeight,c=e.dilationWidth,p=e.effectiveFilterDepth,h=e.effectiveFilterHeight,f=e.effectiveFilterWidth,m=e.padInfo.front,g=e.padInfo.top,y=e.padInfo.left;this.outputShape=e.outShape;let A=t==="avg",x="0.0";if(A||(x="-1.0 / 1e-20"),a){let _=">=";this.userCode=` const ivec3 strides = ivec3(${i}, ${o}, ${l}); const ivec3 pads = ivec3(${m}, ${g}, ${y}); 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 += ${d}) { int xR = xRCorner + wR; if (xR < 0 || xR >= ${e.inHeight}) { continue; } for (int wC = 0; wC < ${f}; wC += ${c}) { 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 ${_} currMinMaxValue) { minMaxValue = value; minMaxValueFound = 1.0; minMaxPosition = ${n?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} * ${f} + wR * ${f} + wC`}; } } } } setOutput(float(minMaxPosition)); } `;return}let b="max",w=`${t}(${t}(${t}(minMaxValue[0], minMaxValue[1]), minMaxValue[2]), minMaxValue[3])`;t==="avg"&&(w="avgValue / count");let S=Math.floor(s/4)*4,C=s%4,N=` if (${A}) { avgValue += dot(values, ones); } else { minMaxValue = ${b}(values, minMaxValue); } `;this.userCode=` const ivec3 strides = ivec3(${i}, ${o}, ${l}); const ivec3 pads = ivec3(${m}, ${g}, ${y}); 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 += ${d}) { int xR = xRCorner + wR; if (xR < 0 || xR >= ${e.inHeight}) { continue; } for (int wC = 0; wC < ${S}; wC += 4) { int xC = xCCorner + wC * ${c}; vec4 values = vec4( getValue(batch, xD, xR, xC, ch), getValue(batch, xD, xR, xC + ${c}, ch), getValue(batch, xD, xR, xC + 2 * ${c}, ch), getValue(batch, xD, xR, xC + 3 * ${c}, ch) ); ${N} } int xC = xCCorner + ${S}; if (${C===1}) { vec4 values = vec4( getValue(batch, xD, xR, xC, ch), initializationValue, initializationValue, initializationValue ); ${N} } else if (${C===2}) { vec4 values = vec4( getValue(batch, xD, xR, xC, ch), getValue(batch, xD, xR, xC + ${c}, ch), initializationValue, initializationValue ); ${N} } else if (${C===3}) { vec4 values = vec4( getValue(batch, xD, xR, xC, ch), getValue(batch, xD, xR, xC + ${c}, ch), getValue(batch, xD, xR, xC + 2 * ${c}, ch), initializationValue ); ${N} } } setOutput(${w}); } } `}};function Yj(e){let{inputs:t,backend:a,attrs:n}=e,{x:r}=t;Jl(r,"avgPool");let{filterSize:s,strides:i,pad:o,dimRoundingMode:l}=n,u=1;v.assert(T.eitherStridesOrDilationsAreOne(i,u),()=>`Error in avgPool: Either strides or dilations must be 1. Got strides ${i} and dilations '${u}'`);let d=T.computePool2DInfo(r.shape,s,i,u,o,l);if(d.filterWidth===1&&d.filterHeight===1&&v.arraysEqual(d.inShape,d.outShape))return Za({inputs:{x:r},backend:a});let c=new dd(d,"avg",!1);return a.runWebGLProgram(c,[r],"float32")}var Jj={kernelName:mi,backendName:"webgl",kernelFunc:Yj};function Qj(e){let{inputs:t,backend:a,attrs:n}=e,{x:r}=t,{filterSize:s,strides:i,pad:o,dimRoundingMode:l,dataFormat:u}=n,d=[1,1,1],c=T.computePool3DInfo(r.shape,s,i,d,o,l,u),p=new P3(c,"avg",!1);return a.runWebGLProgram(p,[r],"float32")}var eH={kernelName:Gc,backendName:"webgl",kernelFunc:Qj},tH=class{constructor(e){this.variableNames=["dy"],this.outputShape=e.inShape;let t=e.filterHeight,a=e.filterWidth,n=e.strideHeight,r=e.strideWidth,s=e.dilationHeight,i=e.dilationWidth,o=e.effectiveFilterHeight,l=e.effectiveFilterWidth,u=o-1-e.padInfo.top,d=l-1-e.padInfo.left,c=1/(t*a);this.userCode=` const ivec2 pads = ivec2(${u}, ${d}); const float avgMultiplier = float(${c}); 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 < ${o}; wR += ${s}) { 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 < ${l}; wC+= ${i}) { float dyC = float(dyCCorner + wC) / ${r}.0; if (dyC < 0.0 || dyC >= ${e.outWidth}.0 || fract(dyC) > 0.0) { continue; } int idyC = int(dyC); float dyValue = getDy(b, idyR, idyC, d); dotProd += dyValue * avgMultiplier; } } setOutput(dotProd); } `}},aH=class{constructor(e){this.variableNames=["dy"],this.outputShape=e.inShape;let t=e.filterDepth,a=e.filterHeight,n=e.filterWidth,r=e.strideDepth,s=e.strideHeight,i=e.strideWidth,o=e.dilationDepth,l=e.dilationHeight,u=e.dilationWidth,d=e.effectiveFilterDepth,c=e.effectiveFilterHeight,p=e.effectiveFilterWidth,h=d-1-e.padInfo.front,f=c-1-e.padInfo.top,m=p-1-e.padInfo.left,g=1/(t*a*n);this.userCode=` const ivec3 pads = ivec3(${h}, ${f}, ${m}); const float avgMultiplier = float(${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 < ${d}; wD += ${o}) { 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 < ${c}; wR += ${l}) { 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 < ${p}; wC += ${u}) { float dyC = float(dyCCorner + wC) / ${i}.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 nH(e){let{inputs:t,backend:a,attrs:n}=e,{dy:r,input:s}=t,i=s,{filterSize:o,strides:l,pad:u,dimRoundingMode:d}=n,c=[1,1,1],p=T.computePool3DInfo(i.shape,o,l,c,u,d),h=new aH(p);return a.runWebGLProgram(h,[r],i.dtype)}var rH={kernelName:H1,backendName:"webgl",kernelFunc:nH};function sH(e){let{inputs:t,backend:a,attrs:n}=e,{dy:r,input:s}=t,i=s;Jl([r,s],"avgPoolGrad");let{filterSize:o,strides:l,pad:u}=n,d=T.computePool2DInfo(i.shape,o,l,1,u),c=new tH(d);return a.runWebGLProgram(c,[r],i.dtype)}var iH={kernelName:j1,backendName:"webgl",kernelFunc:sH};function oH(e){let{inputs:t,backend:a,attrs:n}=e,{a:r,b:s}=t,{transposeA:i,transposeB:o}=n;return Pc({a:r,b:s,transposeA:i,transposeB:o,backend:a})}var lH={kernelName:gi,backendName:"webgl",kernelFunc:oH},uH=class{constructor(e,t,a,n,r,s){this.outputShape=[],this.variableNames=["x","mean","variance"],T.assertAndGetBroadcastShape(e,t),T.assertAndGetBroadcastShape(e,a);let i="0.0";n!=null&&(T.assertAndGetBroadcastShape(e,n),this.variableNames.push("offset"),i="getOffsetAtOutCoords()");let o="1.0";r!=null&&(T.assertAndGetBroadcastShape(e,r),this.variableNames.push("scale"),o="getScaleAtOutCoords()"),this.outputShape=e,this.userCode=` void main() { float x = getXAtOutCoords(); float mean = getMeanAtOutCoords(); float variance = getVarianceAtOutCoords(); float offset = ${i}; float scale = ${o}; float inv = scale * inversesqrt(variance + float(${s})); setOutput(dot(vec3(x, -mean, offset), vec3(inv, inv, 1))); } `}},dH=class{constructor(e,t,a,n,r,s){this.packedInputs=!0,this.packedOutput=!0,this.variableNames=["x","mean","variance"],T.assertAndGetBroadcastShape(e,t),T.assertAndGetBroadcastShape(e,a);let i="vec4(0.0)";n!=null&&(T.assertAndGetBroadcastShape(e,n),this.variableNames.push("offset"),i="getOffsetAtOutCoords()");let o="vec4(1.0)";r!=null&&(T.assertAndGetBroadcastShape(e,r),this.variableNames.push("scale"),o="getScaleAtOutCoords()"),this.outputShape=e,this.userCode=` void main() { vec4 offset = ${i}; vec4 scale = ${o}; vec4 x = getXAtOutCoords(); vec4 mean = getMeanAtOutCoords(); vec4 variance = getVarianceAtOutCoords(); vec4 inv = scale * inversesqrt(variance + vec4(${s})); setOutput((x - mean) * inv + offset); } `}},pH=({inputs:e,backend:t,attrs:a})=>{let{x:n,mean:r,variance:s,offset:i,scale:o}=e;v.assert(r.shape.length===s.shape.length,()=>"Batch normalization gradient requires mean and variance to have equal ranks."),v.assert(i==null||r.shape.length===i.shape.length,()=>"Batch normalization gradient requires mean and offset to have equal ranks."),v.assert(o==null||r.shape.length===o.shape.length,()=>"Batch normalization gradient requires mean and scale to have equal ranks.");let{varianceEpsilon:l}=a;l==null&&(l=.001);let u=[n,r,s],d=null;i!=null&&(d=i.shape,u.push(i));let c=null;o!=null&&(c=o.shape,u.push(o));let p=V().getBool("WEBGL_PACK_NORMALIZATION")?new dH(n.shape,r.shape,s.shape,d,c,l):new uH(n.shape,r.shape,s.shape,d,c,l);return t.runWebGLProgram(p,u,u[0].dtype)},cH={kernelName:$i,backendName:"webgl",kernelFunc:pH},hH=class{constructor(e){this.variableNames=["source"],this.outputShape=e,this.rank=e.length;let t=mt(this.rank);this.customUniforms=[{name:"start",arrayIndex:this.rank,type:"int"}];let a=fH(this.rank),n,r=e.map((s,i)=>`sourceLoc.${S1[i]} = start[${i}] + coords.${S1[i]};`);n=` ${t} sourceLoc; 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} for (int d1 = 0; d1 < ${h}; d1 += 4) { vec4 wValues = vec4( getW(wR, wC, d1, d2), getW(wR, wC, d1 + 1, d2), getW(wR, wC, d1 + 2, d2), getW(wR, wC, d1 + 3, d2) ); if (${m}) { 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 (${f===1}) { if (${m}) { dotProd += getX(batch, xR, xC, ${h}) * getW(wR, wC, ${h}, d2); } else { dotProd += getX(batch, ${h}, xR, xC) * getW(wR, wC, ${h}, d2); } } else if (${f===2}) { vec2 wValues = vec2( getW(wR, wC, ${h}, d2), getW(wR, wC, ${h} + 1, d2) ); if (${m}) { 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, ${h} + 1, xR, xC) ); dotProd += dot(xValues, wValues); } } else if (${f===3}) { vec3 wValues = vec3( getW(wR, wC, ${h}, d2), getW(wR, wC, ${h} + 1, d2), getW(wR, wC, ${h} + 2, d2) ); if (${m}) { 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; ${w} ${b} setOutput(result); } `}},UH=class{constructor(e){this.variableNames=["x","W"],this.outputShape=e.outShape;let t=e.padInfo.front,a=e.padInfo.top,n=e.padInfo.left,r=e.strideDepth,s=e.strideHeight,i=e.strideWidth,o=e.dilationDepth,l=e.dilationHeight,u=e.dilationWidth,d=e.filterDepth,c=e.filterHeight,p=e.filterWidth,h=Math.floor(e.inChannels/4)*4,f=e.inChannels%4;this.userCode=` const ivec3 strides = ivec3(${r}, ${s}, ${i}); const ivec3 pads = ivec3(${t}, ${a}, ${n}); 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 < ${d}; wF++) { int xF = xFCorner + wF * ${o}; if (xF < 0 || xF >= ${e.inDepth}) { continue; } for (int wR = 0; wR < ${c}; 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 < ${h}; d1 += 4) { vec4 xValues = vec4( getX(batch, xF, xR, xC, d1), getX(batch, xF, xR, xC, d1 + 1), getX(batch, xF, xR, xC, d1 + 2), getX(batch, xF, xR, xC, d1 + 3) ); vec4 wValues = vec4( getW(wF, wR, wC, d1, d2), getW(wF, wR, wC, d1 + 1, d2), getW(wF, wR, wC, d1 + 2, d2), getW(wF, wR, wC, d1 + 3, d2) ); dotProd += dot(xValues, wValues); } if (${f===1}) { dotProd += getX(batch, xF, xR, xC, ${h}) * getW(wF, wR, wC, ${h}, d2); } else if (${f===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, ${h} + 1, d2) ); dotProd += dot(xValues, wValues); } else if (${f===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); } `}},c8=class{constructor(e,t=!1,a=null,n=!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=Na(this.outputShape.length);let s=e.padInfo.left,i=e.strideWidth,o=e.dilationWidth,l=e.filterHeight,u=e.filterWidth,d=u,c=` int xR; int xC; int xCOffset; vec4 wTexel; vec4 previous; vec4 final;`;for(let m=0;m=0 && xR < inDims[0]) { `;for(let m=0;m<(d+1)/2;m++){let g=m*2;if(c+=` xC = xCCorner + ${g*o}; `,i===1){if(g= 0 && xCOffset < inDims[1] && xTexelC${g}Ready == 0) { xTexelC${g} = getX(batch, xR, xCOffset, d1); // Need to manually clear unused channels in case // we're reading from recycled texture. if (xCOffset + 1 >= inDims[1]) { xTexelC${g}.zw = vec2(0.0); } xTexelC${g}Ready = 1; } `,o===1&&g>0?c+=` xC${g} = vec4(xTexelC${g-2}.zw, xTexelC${g}.xy); `:c+=` 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${g} = vec4(previous.zw, xTexelC${g}.xy); } else { xC${g} = vec4(0.0, 0.0, xTexelC${g}.xy); } `):c+=` if (xC >= 0 && xC < inDims[1] && xTexelC${g}Ready == 0) { xTexelC${g} = getX(batch, xR, xC, d1); if (xC + 1 >= inDims[1]) { xTexelC${g}.zw = vec2(0.0); } xTexelC${g}Ready = 1; } xC${g} = xTexelC${g}; `,g+1= 0 && xCOffset < inDims[1] && xTexelC${g+1}Ready == 0) { xTexelC${g+1} = getX(batch, xR, xCOffset, d1); // Need to manually clear unused channels in case // we're reading from recycled texture. if (xCOffset + 1 >= inDims[1]) { xTexelC${g+1}.zw = vec2(0.0); } xTexelC${g+1}Ready = 1; } `,o>1?c+=` xCOffset -= 2; if (xCOffset >= 0 && xCOffset < inDims[1]) { previous = getX(batch, xR, xCOffset, d1); xC${g+1} = vec4(previous.zw, xTexelC${g+1}.xy); } else { xC${g+1} = vec4(0.0, 0.0, xTexelC${g+1}.xy); } `:c+=` xC${g+1} = vec4(xTexelC${g}.zw, xTexelC${g+1}.xy); `):y===1?c+=` xC${g+1} = xTexelC${g}; `:c+=` xCOffset = xC + ${y}; if (xCOffset >= 0 && xCOffset < inDims[1] && xTexelC${g+1}Ready == 0) { xTexelC${g+1} = getX(batch, xR, xCOffset, d1); if (xCOffset + 1 >= inDims[1]) { xTexelC${g+1}.zw = vec2(0.0); } xTexelC${g+1}Ready = 1; } xC${g+1} = xTexelC${g+1}; `}}else g= 0 && xCOffset < inDims[1] && xTexelC${g}Ready == 0) { xTexelC${g} = getX(batch, xR, xCOffset, d1); // Need to manually clear unused channels in case // we're reading from recycled texture. if (xCOffset + 1 >= inDims[1]) { xTexelC${g}.zw = vec2(0.0); } xTexelC${g}Ready = 1; } if(xC + 1 >= 0 && xC + 1 < inDims[1] && xTexelC${g+1}Ready == 0) { xTexelC${g+1} = getX(batch, xR, xC + 1, d1); // Need to manually clear unused channels in case // we're reading from recycled texture. if (xC + 2 >= inDims[1]) { xTexelC${g+1}.zw = vec2(0.0); } xTexelC${g+1}Ready = 1; } xC${g} = vec4(xTexelC${g}.zw, xTexelC${g+1}.zw); `,g+1= 0 && xCOffset < inDims[1]) { final = getX(batch, xR, xCOffset, d1); } xC${g+1} = vec4(xTexelC${g+1}.xy, final.xy); `)):(c+=` if(xC >= 0 && xC < inDims[1] && xTexelC${g}Ready == 0) { xTexelC${g} = getX(batch, xR, xC, d1); if (xC + 1 >= inDims[1]) { xTexelC${g}.zw = vec2(0.0); } xTexelC${g}Ready = 1; } xCOffset = xC + strides[1]; if(xCOffset >= 0 && xCOffset < inDims[1] && xTexelC${g+1}Ready == 0) { xTexelC${g+1} = getX(batch, xR, xCOffset, d1); if (xCOffset + 1 >= inDims[1]) { xTexelC${g+1}.zw = vec2(0.); } xTexelC${g+1}Ready = 1; } xC${g} = vec4( xTexelC${g}.xy, xTexelC${g+1}.xy); `,g+1= 0) { // Use custom imod instead mod. On Intel GPU, mod may generate // unexpected value. // https://github.com/tensorflow/tfjs/issues/5447 offsetX = imod(blockIndex, outWidth) * stride[1] - pad[1]; d1 = offsetX + dilation[1] * (imod(pos, itemsPerBlockRow) / inChannels); if(d1 < inputShape[${i}] && d1 >= 0) { ch = imod(pos, inChannels); if (${r}) { innerDims = vec2(d1, ch); result[${u*2+d}] = getChannel( getA(rc.x, d0, int(innerDims.x), int(innerDims.y)), innerDims); } else { innerDims = vec2(d0, d1); result[${u*2+d}] = getChannel( getA(rc.x, ch, int(innerDims.x), int(innerDims.y)), innerDims); } } } } `;this.userCode=` void main() { ivec3 rc = getOutputCoords(); vec4 result = vec4(0); int blockIndex, pos, offsetY, d0, offsetX, d1, ch; vec2 innerDims; ${l} ${n.output} = result; } `}};function Oc(e,t){let a=e.length;return a>=3?t?[...e.slice(0,-3),e[a-3]*e[a-2],e[a-1]]:[...e.slice(0,-3),e[a-3],e[a-2]*e[a-1]]:!t&&a===1&&e[0]>1?[e[0],1]:null}function h8({x:e,filter:t,convInfo:a,backend:n,bias:r=null,preluActivationWeights:s=null,leakyreluAlpha:i=0,activation:o=null}){let l=e.shape,u=n.texData.get(e.dataId),d=a.inChannels,c=l[0]*l[1]*l[2],p=a.outChannels,h=a.dataFormat==="channelsLast",f=!1,m=!1,g,y=[];if(s!=null){let A=Oc(s.shape,h);A!=null&&(s=de({inputs:{x:s},backend:n,attrs:{shape:A}}),y.push(s))}if(r!=null){let A=Oc(r.shape,h);A!=null&&(r=de({inputs:{x:r},backend:n,attrs:{shape:A}}),y.push(r))}if(!((c===1||p===1)&&d>s8)&&u.isPacked&&h&&u.texture!=null&&l[2]%2!==0&&v.arraysEqual(u.shape.slice(-3),l.slice(-3))){let A=l[0]*l[1]*(l[2]+1),x={dataId:e.dataId,shape:[1,A,a.inChannels],dtype:e.dtype},b=u.shape;u.shape=u.shape.slice(),u.shape[u.shape.length-2]++,v.assert(ld(u.shape,x.shape),()=>`packed reshape ${u.shape} to ${x.shape} isn't free`);let w=de({inputs:{x:t},backend:n,attrs:{shape:[1,a.inChannels,a.outChannels]}});y.push(w);let S=Pc({a:x,b:w,backend:n,transposeA:f,transposeB:m,bias:r,activation:o,preluActivationWeights:s,leakyreluAlpha:i}),C=n.texData.get(S.dataId);v.assert(C.isPacked,()=>"batchMatMul result is expected to be packed"),u.shape=b,C.shape=a.outShape,g=Za({inputs:{x:S},backend:n}),g.shape=a.outShape,y.push(S)}else{let A=a.outHeight*a.outWidth,x=de({inputs:{x:e},backend:n,attrs:{shape:h?[a.batchSize,A,a.inChannels]:[a.batchSize,a.inChannels,A]}}),b=de({inputs:{x:t},backend:n,attrs:{shape:[1,a.inChannels,a.outChannels]}}),w=Pc({a:h?x:b,b:h?b:x,transposeA:!h,transposeB:m,backend:n,bias:r,activation:o,preluActivationWeights:s,leakyreluAlpha:i});g=de({inputs:{x:w},backend:n,attrs:{shape:a.outShape}}),y.push(x),y.push(b),y.push(w)}for(let A of y)n.disposeIntermediateTensorInfo(A);return g}function f8({x:e,filter:t,convInfo:a,backend:n,bias:r=null,preluActivationWeights:s=null,leakyreluAlpha:i=0,activation:o=null}){let{filterWidth:l,filterHeight:u,inChannels:d,outWidth:c,outHeight:p,dataFormat:h}=a,f=h==="channelsLast",m=l*u*d,g=p*c,y=[a.batchSize,m,g],A=!0,x=!1,b=[];if(s!=null){let j=Oc(s.shape,f);j!=null&&(s=de({inputs:{x:s},backend:n,attrs:{shape:j}}),b.push(s))}if(r!=null){let j=Oc(r.shape,f);j!=null&&(r=de({inputs:{x:r},backend:n,attrs:{shape:j}}),b.push(r))}let w=de({inputs:{x:t},backend:n,attrs:{shape:[1,m,v.sizeFromShape(t.shape)/m]}});b.push(w);let S=new jH(y,a),C=[e.shape,[a.padInfo.top,a.padInfo.left],[a.strideHeight,a.strideWidth],[a.dilationHeight,a.dilationWidth],[a.inChannels],[a.filterWidth*a.inChannels],[a.outWidth]],N=n.runWebGLProgram(S,[e],"float32",C),_=de({inputs:{x:N},backend:n,attrs:{shape:y}});b.push(N),b.push(_);let $=r!=null,M=s!=null,I=o==="leakyrelu",E=o?ud(o,!0):null,O=new r8(f?_.shape:w.shape,f?w.shape:_.shape,f?[a.batchSize,g,a.outChannels]:[a.batchSize,a.outChannels,g],A,x,$,E,M,I),L=f?[_,w]:[w,_];if(r&&L.push(r),M&&L.push(s),I){let j=n.makeTensorInfo([],"float32",v.createScalarValue(i,"float32"));L.push(j),b.push(j)}let B=n.runWebGLProgram(O,L,"float32"),G=de({inputs:{x:B},backend:n,attrs:{shape:a.outShape}});b.push(B);for(let j of b)n.disposeIntermediateTensorInfo(j);return G}function HH(e){let{inputs:t,backend:a,attrs:n}=e,{x:r,filter:s}=t,{strides:i,pad:o,dataFormat:l,dilations:u,dimRoundingMode:d}=n,c=T.convertConv2DDataFormat(l),p=T.computeConv2DInfo(r.shape,s.shape,i,u,o,d,!1,c),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=h8({x:r,filter:s,convInfo:p,backend:a});else if(p.strideWidth<=2&&c==="channelsLast"&&V().getBool("WEBGL_EXP_CONV")){let m=new c8(p),g=[[p.padInfo.top,p.padInfo.left],[p.strideHeight,p.strideWidth],[p.dilationHeight,p.dilationWidth],[p.inHeight,p.inWidth]];h=a.runWebGLProgram(m,[r,s],"float32",g)}else if(V().getBool("WEBGL_CONV_IM2COL"))h=f8({x:r,filter:s,convInfo:p,backend:a});else{let m=new p8(p);h=a.runWebGLProgram(m,[r,s],"float32")}let f=de({inputs:{x:h},backend:a,attrs:{shape:p.outShape}});return a.disposeIntermediateTensorInfo(h),f}var qH={kernelName:Ai,backendName:"webgl",kernelFunc:HH},XH=class{constructor(e){this.variableNames=["x","dy"],this.outputShape=e.filterShape;let t=e.strideHeight,a=e.strideWidth,n=e.padInfo.top,r=e.padInfo.left,s=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} - ${n}; if (xR < 0 || xR >= ${e.inHeight}) { continue; } for (int yC = 0; yC < ${e.outWidth}; yC++) { int xC = wC + yC * ${a} - ${r}; if (xC < 0 || xC >= ${e.inWidth}) { continue; } if (${s}) { 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); } `}},KH=class{constructor(e){this.variableNames=["dy","W"],this.outputShape=e.inShape;let t=e.filterHeight,a=e.filterWidth,n=e.strideHeight,r=e.strideWidth,s=e.dataFormat==="channelsLast",i=t-1-e.padInfo.top,o=a-1-e.padInfo.left,l=s?1:2,u=s?2:3,d=s?3:1;this.userCode=` const ivec2 pads = ivec2(${i}, ${o}); void main() { ivec4 coords = getOutputCoords(); int batch = coords[0]; int d1 = coords[${d}]; 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) / ${n}.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 < ${a}; 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 = ${a} - 1 - wC; for (int d2 = 0; d2 < ${e.outChannels}; d2++) { if (${s}) { 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); } `}},ZH=class{constructor(e){this.variableNames=["x","dy"],this.outputShape=e.filterShape;let t=e.strideDepth,a=e.strideHeight,n=e.strideWidth,r=e.padInfo.front,s=e.padInfo.top,i=e.padInfo.left;this.userCode=` void main() { ivec5 coords = getOutputCoords(); int wF = coords.x; 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 * ${a} - ${s}; if (xR < 0 || xR >= ${e.inHeight}) { continue; } for (int yC = 0; yC < ${e.outWidth}; yC++) { int xC = wC + yC * ${n} - ${i}; if (xC < 0 || xC >= ${e.inWidth}) { continue; } float dyValue = getDy(b, yF, yR, yC, d2); float xValue = getX(b, xF, xR, xC, d1); dotProd += (xValue * dyValue); } } } } setOutput(dotProd); } `}},YH=class{constructor(e){this.variableNames=["dy","W"],this.outputShape=e.inShape;let t=e.filterDepth,a=e.filterHeight,n=e.filterWidth,r=e.strideDepth,s=e.strideHeight,i=e.strideWidth,o=t-1-e.padInfo.front,l=a-1-e.padInfo.top,u=n-1-e.padInfo.left;this.userCode=` const ivec3 pads = ivec3(${o}, ${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 < ${a}; 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 = ${a} - 1 - wR; for (int wC = 0; wC < ${n}; wC++) { float dyC = float(dyCCorner + wC) / ${i}.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++) { float xValue = getDy(batch, idyF, idyR, idyC, d2); float wValue = getW(wFPerm, wRPerm, wCPerm, d1, d2); dotProd += xValue * wValue; } } } } setOutput(dotProd); } `}};function JH(e){let{inputs:t,backend:a,attrs:n}=e,{x:r,dy:s}=t,{strides:i,pad:o,dataFormat:l,dimRoundingMode:u,filterShape:d}=n,c=T.convertConv2DDataFormat(l),p=T.computeConv2DInfo(r.shape,d,i,1,o,u,!1,c),h=new XH(p);return a.runWebGLProgram(h,[r,s],"float32")}var QH={kernelName:qc,backendName:"webgl",kernelFunc:JH};function eq(e){let{inputs:t,backend:a,attrs:n}=e,{dy:r,filter:s}=t,{inputShape:i,strides:o,pad:l,dataFormat:u,dimRoundingMode:d}=n,c=T.convertConv2DDataFormat(u),p=T.computeConv2DInfo(i,s.shape,o,1,l,d,!1,c),h=new KH(p);return a.runWebGLProgram(h,[r,s],"float32")}var tq={kernelName:xi,backendName:"webgl",kernelFunc:eq};function aq(e){let{inputs:t,backend:a,attrs:n}=e,{x:r,filter:s}=t,{strides:i,pad:o,dilations:l}=n,u=T.computeConv3DInfo(r.shape,s.shape,i,l,o),d=new UH(u);return a.runWebGLProgram(d,[r,s],"float32")}var nq={kernelName:Xc,backendName:"webgl",kernelFunc:aq};function rq(e){let{inputs:t,backend:a,attrs:n}=e,{x:r,dy:s}=t,{strides:i,pad:o,filterShape:l}=n,u=T.computeConv3DInfo(r.shape,l,i,1,o),d=new ZH(u);return a.runWebGLProgram(d,[r,s],"float32")}var sq={kernelName:q1,backendName:"webgl",kernelFunc:rq};function iq(e){let{inputs:t,backend:a,attrs:n}=e,{dy:r,filter:s}=t,{pad:i,strides:o,inputShape:l}=n,u=T.computeConv3DInfo(l,s.shape,o,1,i),d=new YH(u);return a.runWebGLProgram(d,[r,s],"float32")}var oq={kernelName:Kc,backendName:"webgl",kernelFunc:iq},lq=ru+` return cos(x); `,uq=Qe({opSnippet:lq}),dq={kernelName:bi,backendName:"webgl",kernelFunc:uq},pq=` float e2x = exp(-x); return (e2x + 1.0 / e2x) / 2.0; `,cq=Qe({opSnippet:pq}),hq={kernelName:vi,backendName:"webgl",kernelFunc:cq},fq=class{constructor(e,t,a,n,r){this.variableNames=["Image","Boxes","BoxInd"],this.outputShape=[];let[s,i,o,l]=e,[u]=t,[d,c]=a;this.outputShape=[u,d,c,l];let p=n==="bilinear"?1:0,[h,f]=[`${i-1}.0`,`${o-1}.0`],[m,g,y]=d>1?[`${(i-1)/(d-1)}`,"(y2-y1) * height_ratio",`y1*${h} + float(y)*(height_scale)`]:["0.0","0.0",`0.5 * (y1+y2) * ${h}`],[A,x,b]=c>1?[`${(o-1)/(c-1)}`,"(x2-x1) * width_ratio",`x1*${f} + float(x)*(width_scale)`]:["0.0","0.0",`0.5 * (x1+x2) * ${f}`];this.userCode=` const float height_ratio = float(${m}); const float width_ratio = float(${A}); 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 >= ${s}) { return; } float height_scale = ${g}; float width_scale = ${x}; float in_y = ${y}; if( in_y < 0.0 || in_y > ${h} ) { setOutput(float(${r})); return; } float in_x = ${b}; if( in_x < 0.0 || in_x > ${f} ) { setOutput(float(${r})); return; } vec2 sourceFracIndexCR = vec2(in_x,in_y); if(${p} == 1) { // Compute the four integer indices. ivec2 sourceFloorCR = ivec2(sourceFracIndexCR); ivec2 sourceCeilCR = ivec2(ceil(sourceFracIndexCR)); float topLeft = getImage(b, sourceFloorCR.y, sourceFloorCR.x, d); float bottomLeft = getImage(b, sourceCeilCR.y, sourceFloorCR.x, d); float topRight = getImage(b, sourceFloorCR.y, sourceCeilCR.x, d); float bottomRight = getImage(b, sourceCeilCR.y, sourceCeilCR.x, d); vec2 fracCR = sourceFracIndexCR - vec2(sourceFloorCR); float top = topLeft + (topRight - topLeft) * fracCR.x; float bottom = bottomLeft + (bottomRight - bottomLeft) * fracCR.x; float newValue = top + (bottom - top) * fracCR.y; setOutput(newValue); } else { // Compute the coordinators of nearest neighbor point. ivec2 sourceNearestCR = ivec2(floor( sourceFracIndexCR + vec2(0.5,0.5))); float newValue = getImage(b, sourceNearestCR.y, sourceNearestCR.x, d); setOutput(newValue); } } `}},mq=e=>{let{inputs:t,backend:a,attrs:n}=e,{image:r,boxes:s,boxInd:i}=t,{cropSize:o,method:l,extrapolationValue:u}=n,d=new fq(r.shape,s.shape,o,l,u);return a.runWebGLProgram(d,[r,s,i],"float32")},gq={kernelName:Ii,backendName:"webgl",kernelFunc:mq},pd;(function(e){e.Prod="*",e.Sum="+"})(pd||(pd={}));var _y=class{constructor(e,t,a,n){this.op=e,this.outputShape=t,this.variableNames=["x"],this.customUniforms=[{name:"index",type:"float"}];let r=this.outputShape.length,s=this.op===pd.Prod?"1.0":"0.0",i=a?s:`getX(${Fy(r,"coords",this.op)})`,o=this.outputShape[this.outputShape.length-1],l="",u="";a?(l=n?`end != ${o-1}`:"end != 0",u=n?"end + 1":"end - 1"):(l=n?`end + pow2 < ${o}`:"end >= pow2",u=n?"end + pow2":"end - pow2"),this.userCode=` void main() { ${mt(r)} coords = getOutputCoords(); int end = ${Py(r,"coords",this.op)}; float val = ${i}; int pow2 = int(pow(2.0, index)); if (${l}) { int idx = ${u}; ${Py(r,"coords",this.op)} = idx; val ${this.op}= getX(${Fy(r,"coords",this.op)}); } setOutput(val); } `}};function Fy(e,t,a){if(e===1)return`${t}`;if(e===2)return`${t}.x, ${t}.y`;if(e===3)return`${t}.x, ${t}.y, ${t}.z`;if(e===4)return`${t}.x, ${t}.y, ${t}.z, ${t}.w`;throw new Error(`Cumulative ${a} for rank ${e} is not yet supported`)}function Py(e,t,a){if(e===1)return`${t}`;if(e===2)return`${t}.y`;if(e===3)return`${t}.z`;if(e===4)return`${t}.w`;throw new Error(`Cumulative ${a} for rank ${e} is not yet supported`)}function m8(e,t,a,n,r,s){let i=t.shape.length,o=T.getAxesPermutation([n],i),l=t;o!=null&&(l=Ia({inputs:{x:t},backend:a,attrs:{perm:o}}));let u=T.getInnerMostAxes(1,i)[0];if(u!==i-1)throw new Error(`WebGL cumprod shader expects an inner-most axis=${t.shape.length-1} but got axis=${n}`);let d=l.shape[u],c=Za({inputs:{x:l},backend:a});for(let p=0;p<=Math.ceil(Math.log2(d))-1;p++){let h=new _y(e,l.shape,!1,s),f=[[p]],m=c;c=a.runWebGLProgram(h,[c],c.dtype,f),a.disposeIntermediateTensorInfo(m)}if(r){let p=new _y(e,l.shape,r,s),h=c;c=a.runWebGLProgram(p,[c],c.dtype),a.disposeIntermediateTensorInfo(h)}if(o!=null){let p=T.getUndoAxesPermutation(o),h=Ia({inputs:{x:c},backend:a,attrs:{perm:p}});return a.disposeIntermediateTensorInfo(c),a.disposeIntermediateTensorInfo(l),h}return c}function yq(e){let{inputs:t,backend:a,attrs:n}=e,{x:r}=t,{axis:s,exclusive:i,reverse:o}=n;return m8(pd.Prod,r,a,s,i,o)}var Aq={kernelName:wi,backendName:"webgl",kernelFunc:yq};function xq(e){let{inputs:t,backend:a,attrs:n}=e,{x:r}=t,{axis:s,exclusive:i,reverse:o}=n;return m8(pd.Sum,r,a,s,i,o)}var bq={kernelName:ki,backendName:"webgl",kernelFunc:xq};function vq(e){let{inputs:t,backend:a,attrs:n}=e,{x:r,weights:s}=t,{size:i,binaryOutput:o}=n;if(r.shape.length===1){let l=a.readSync(r.dataId),u=a.readSync(s.dataId),d=X6(l,u,s.dtype,s.shape,i);return a.makeTensorInfo([i],s.dtype,d)}else if(r.shape.length===2){let l=a.bufferSync(r),u=a.bufferSync(s),d=MG(l,u,i,o);return a.makeTensorInfo(d.shape,s.dtype,d.values)}throw new Error(`Error in denseBincount: input must be at most rank 2, but got rank${r.shape.length}.`)}var wq={kernelName:Zc,backendName:"webgl",kernelFunc:vq},kq=class{constructor(e,t,a){this.variableNames=["x"],this.outputShape=[],this.outputShape=e,this.blockSize=t,this.dataFormat=a,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 Iq(e){let{inputs:t,backend:a,attrs:n}=e,{x:r}=t,{blockSize:s,dataFormat:i}=n,o=r.shape[0],l=i==="NHWC"?r.shape[1]:r.shape[2],u=i==="NHWC"?r.shape[2]:r.shape[3],d=i==="NHWC"?r.shape[3]:r.shape[1],c=l*s,p=u*s,h=d/(s*s),f=i==="NHWC"?[o,c,p,h]:[o,h,c,p],m=new kq(f,s,i);return a.runWebGLProgram(m,[r],r.dtype)}var Sq={kernelName:Si,backendName:"webgl",kernelFunc:Iq},g8=class{constructor(e,t=!1,a=null,n=!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=Na(this.outputShape.length);let s=e.filterHeight,i=e.filterWidth,o=e.outChannels/e.inChannels,l="",u="";a&&(n?l=`float activation(float a) { float b = getPreluActivationWeightsAtOutCoords(); ${a} }`:r?l=`float activation(float a) { float b = getLeakyreluAlphaAtOutCoords(); ${a} }`:l=` float activation(float x) { ${a} } `,u="result = activation(result);");let d=t?"result += getBiasAtOutCoords();":"";t&&this.variableNames.push("bias"),n&&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 / ${o}; int q = d2 - d1 * ${o}; 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 < ${s}; wR++) { int xR = xRCorner + wR * dilations[0]; if (xR < 0 || xR >= inDims[0]) { continue; } for (int wC = 0; wC < ${i}; 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; ${d} ${u} setOutput(result); } `}},y8=class{constructor(e,t=!1,a=null,n=!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=Na(this.outputShape.length);let s=e.outChannels/e.inChannels,i=e.padInfo.left,o=e.strideWidth,l=e.dilationWidth,u=e.filterHeight,d=e.filterWidth,c=d,p=` int xR; int xC; int xCOffset; vec4 wTexel; vec4 previous; vec4 final;`;for(let g=0;g=0 && xR < inDims[0]) { `;for(let g=0;g<(c+1)/2;g++){let y=g*2;if(p+=` xC = xCCorner + ${y*l}; `,o===1){if(y= 0 && xCOffset < inDims[1] && xTexelC${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]) { previous = getX(batch, xR, xCOffset, d1); xC${y+1} = vec4(previous.zw, xTexelC${y+1}.xy); } else { xC${y+1} = vec4(0.0, 0.0, xTexelC${y+1}.xy); } `:p+=` xC${y+1} = vec4(xTexelC${y}.zw, xTexelC${y+1}.xy); `):A===1?p+=` xC${y+1} = xTexelC${y}; `:p+=` xCOffset = xC + ${A}; if (xCOffset >= 0 && xCOffset < inDims[1] && xTexelC${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 ${i} and dilations '${d}'`);let c=T.computeConv2DInfo(r.shape,s.shape,i,d,o,u,!0),p;V().getBool("WEBGL_PACK_DEPTHWISECONV")&&c.strideWidth<=2&&c.outChannels/c.inChannels===1?p=new y8(c):p=new g8(c);let h=[[c.padInfo.top,c.padInfo.left],[c.strideHeight,c.strideWidth],[c.dilationHeight,c.dilationWidth],[c.inHeight,c.inWidth]];return a.runWebGLProgram(p,[r,s],"float32",h)}var Cq={kernelName:Ti,backendName:"webgl",kernelFunc:Tq},Nq=class{constructor(e){this.variableNames=["x","dy"],this.outputShape=e.filterShape;let t=e.strideHeight,a=e.strideWidth,n=e.padInfo.top,r=e.padInfo.left,s=e.outChannels/e.inChannels;this.userCode=` void main() { ivec4 coords = getOutputCoords(); int wR = coords.x; int wC = coords.y; int d1 = coords.z; int dm = coords.w; int d2 = d1 * ${s} + dm; 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} - ${n}; if (xR < 0 || xR >= ${e.inHeight}) { continue; } for (int yC = 0; yC < ${e.outWidth}; yC++) { int xC = wC + yC * ${a} - ${r}; if (xC < 0 || xC >= ${e.inWidth}) { continue; } float dyValue = getDy(b, yR, yC, d2); float xValue = getX(b, xR, xC, d1); dotProd += (xValue * dyValue); } } } setOutput(dotProd); } `}},Eq=class{constructor(e){this.variableNames=["dy","W"],this.outputShape=e.inShape;let t=e.filterHeight,a=e.filterWidth,n=e.strideHeight,r=e.strideWidth,s=t-1-e.padInfo.top,i=a-1-e.padInfo.left,o=e.outChannels/e.inChannels;this.userCode=` const ivec2 pads = ivec2(${s}, ${i}); 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) / ${n}.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 < ${a}; wC++) { float dyC = float(dyCCorner + wC) / ${r}.0; 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float curVal = neg_infinity; for (int h = 0; h < ${i}; h++) { int hIn = hBeg + h * ${l}; if (hIn >= 0 && hIn < ${t}) { for (int w = 0; w < ${o}; w++) { int wIn = wBeg + w * ${u}; if (wIn >= 0 && wIn < ${a}) { 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 zq(e){let{inputs:t,backend:a,attrs:n}=e,{x:r,filter:s}=t,{strides:i,pad:o,dilations:l}=n,u=T.computeDilation2DInfo(r.shape,s.shape,i,o,"NHWC",l),d,c=new Dq(u);d=a.runWebGLProgram(c,[r,s],"float32");let p=de({inputs:{x:d},backend:a,attrs:{shape:u.outShape}});return a.disposeIntermediateTensorInfo(d),p}var Lq={kernelName:eh,backendName:"webgl",kernelFunc:zq};function Bq(e){let{inputs:t,backend:a,attrs:n}=e,{equation:r}=n,s=t,{allDims:i,summedDims:o,idDims:l}=T.decodeEinsumEquation(r,s.length);T.checkEinsumDimSizes(i.length,l,s);let{path:u,steps:d}=T.getEinsumComputePath(o,l),c=d.length,p=null,h=i.length,f=[];for(let m=0;m=0&&(p=Bh({inputs:{x:p},backend:a,attrs:{axis:u[m]-(i.length-h),keepDims:!1}}),f.push(p)),h--)}for(let m of f)m!==p&&a.disposeIntermediateTensorInfo(m);return p}var Wq={kernelName:Sd,backendName:"webgl",kernelFunc:Bq},Vq="return (x >= 0.0) ? x : (exp(x) - 1.0);",Gq=` vec4 result; 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float unaryOpComplex(float real, float expR, float imag, float expI) { ${i} } float mulMatDFT(int batch, int index) { float indexRatio = float(index) / float(${n}); float exponentMultiplierTimesIndexRatio = exponentMultiplier * indexRatio; float result = 0.0; for (int i = 0; i < ${n}; i++) { // x = (-2|2 * PI / N) * index * i; float x = exponentMultiplierTimesIndexRatio * float(i); float expR = cos(x); float expI = sin(x); float real = getReal(batch, i); float imag = getImag(batch, i); result += unaryOpComplex(real, expR, imag, expI) / ${s}; } return result; } void main() { ivec2 coords = getOutputCoords(); setOutput(mulMatDFT(coords[0], coords[1])); } `}};function x8(e,t,a){let n=a.texData.get(e.dataId),r=v.sizeFromShape(e.shape),s=e.shape[e.shape.length-1],i=r/s,o=de({inputs:{x:e},backend:a,attrs:{shape:[i,s]}}),l=o.shape,u=new Dy("real",l,t),d=new Dy("imag",l,t),c=[{dataId:n.complexTensorInfos.real.dataId,dtype:n.complexTensorInfos.real.dtype,shape:l},{dataId:n.complexTensorInfos.imag.dataId,dtype:n.complexTensorInfos.imag.dtype,shape:l}],p=a.runWebGLProgram(u,c,"float32"),h=a.runWebGLProgram(d,c,"float32"),f=ks({inputs:{real:p,imag:h},backend:a});a.disposeIntermediateTensorInfo(p),a.disposeIntermediateTensorInfo(h);let m=de({inputs:{x:f},backend:a,attrs:{shape:e.shape}});return a.disposeIntermediateTensorInfo(o),a.disposeIntermediateTensorInfo(f),m}function uX(e){let{inputs:t,backend:a}=e,{input:n}=t;return x8(n,!1,a)}var dX={kernelName:th,backendName:"webgl",kernelFunc:uX},pX=class{constructor(e,t){this.outputShape=[],this.customUniforms=[{name:"value",type:"float"}],this.variableNames=["x"],this.outputShape=e,this.userCode=` void main() { // Input can be obtained from uniform value. setOutput(value); 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vec4 values = ${t.texture2D}(A, uv); float value; if (depth == 0) { value = values.r; } else if (depth == 1) { value = values.g; } else if (depth == 2) { value = values.b; } else if (depth == 3) { value = values.a; } setOutput(floor(value * 255.0 + 0.5)); } `}},wX=class{constructor(e){this.variableNames=["A"],this.packedInputs=!1,this.packedOutput=!0;let t=Ca(),[a,n]=e;this.outputShape=e,this.userCode=` void main() { ivec3 coords = getOutputCoords(); int texR = coords[0]; int texC = coords[1]; int depth = coords[2]; vec4 result = vec4(0.); for(int row=0; row<=1; row++) { for(int col=0; col<=1; col++) { texC = coords[1] + row; depth = coords[2] + col; vec2 uv = (vec2(texC, texR) + halfCR) / vec2(${n}.0, ${a}.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); } } ${t.output} = result; } `}},kX={kernelName:Yu,backendName:"webgl",kernelFunc:IX},jo,zm=V().getBool("CANVAS2D_WILL_READ_FREQUENTLY_FOR_GPU");function IX(e){let{inputs:t,backend:a,attrs:n}=e,{pixels:r}=t,{numChannels:s}=n,i=typeof HTMLVideoElement!="undefined"&&r instanceof HTMLVideoElement,o=typeof HTMLImageElement!="undefined"&&r instanceof HTMLImageElement,[l,u]=i?[r.videoWidth,r.videoHeight]:[r.width,r.height],d=[u,l],c=[u,l,s];if(o||i){let m=V().getBool("CANVAS2D_WILL_READ_FREQUENTLY_FOR_GPU");(jo==null||m!==zm)&&(zm=m,jo=document.createElement("canvas").getContext("2d",{willReadFrequently:zm})),jo.canvas.width=l,jo.canvas.height=u,jo.drawImage(r,0,0,l,u),r=jo.canvas}let p=a.makeTensorInfo(d,"int32");a.texData.get(p.dataId).usage=un.PIXELS,a.gpgpu.uploadPixelDataToTexture(a.getTexture(p.dataId),r);let h=V().getBool("WEBGL_PACK")?new wX(c):new vX(c),f=a.runWebGLProgram(h,[p],"int32");return a.disposeData(p.dataId),f}function SX(e){let{inputs:t,backend:a,attrs:n}=e,{x:r,filter:s,bias:i,preluActivationWeights:o}=t,{strides:l,pad:u,dataFormat:d,dilations:c,dimRoundingMode:p,activation:h,leakyreluAlpha:f}=n,m=T.convertConv2DDataFormat(d),g=T.computeConv2DInfo(r.shape,s.shape,l,c,u,p,!1,m),y,A=[],x=i!=null,b=o!=null,w=h==="leakyrelu",S=()=>{let N=[r,s],_=($,M)=>{if(M==="NCHW"&&$.shape.length===1&&$.shape[0]!==1){let I=de({inputs:{x:$},backend:a,attrs:{shape:[$.shape[0],1,1]}});return A.push(I),I}return $};if(x&&N.push(_(i,d)),b&&N.push(_(o,d)),w){let $=a.makeTensorInfo([],"float32",v.createScalarValue(f,"float32"));N.push($),A.push($)}return N};if(g.filterHeight===1&&g.filterWidth===1&&g.dilationHeight===1&&g.dilationWidth===1&&g.strideHeight===1&&g.strideWidth===1&&(g.padInfo.type==="SAME"||g.padInfo.type==="VALID"))y=h8({x:r,filter:s,convInfo:g,backend:a,bias:i,activation:h,preluActivationWeights:o,leakyreluAlpha:f});else if(g.strideWidth<=2&&m==="channelsLast"&&V().getBool("WEBGL_EXP_CONV")){let N=h?ud(h,!0):null,_=new c8(g,x,N,b,w),$=[[g.padInfo.top,g.padInfo.left],[g.strideHeight,g.strideWidth],[g.dilationHeight,g.dilationWidth],[g.inHeight,g.inWidth]],M=S();y=a.runWebGLProgram(_,M,"float32",$)}else if(V().getBool("WEBGL_CONV_IM2COL"))y=f8({x:r,filter:s,convInfo:g,backend:a,bias:i,activation:h,preluActivationWeights:o,leakyreluAlpha:f});else{let N=h?ud(h,!1):null,_=new p8(g,x,N,b,w),$=S();y=a.runWebGLProgram(_,$,"float32")}let C=de({inputs:{x:y},backend:a,attrs:{shape:g.outShape}});return A.push(y),A.forEach(N=>a.disposeIntermediateTensorInfo(N)),C}var TX={kernelName:jr,backendName:"webgl",kernelFunc:SX};function CX(e){let{inputs:t,backend:a,attrs:n}=e,{x:r,filter:s,bias:i,preluActivationWeights:o}=t,{strides:l,pad:u,dilations:d,dimRoundingMode:c,activation:p,leakyreluAlpha:h}=n,f=[],m=d;m==null&&(m=[1,1]),v.assert(T.eitherStridesOrDilationsAreOne(l,m),()=>`Error in depthwiseConv2d: Either strides or dilations must be 1. Got strides ${l} and dilations '${m}'`);let g=T.computeConv2DInfo(r.shape,s.shape,l,m,u,c,!0),y=V().getBool("WEBGL_PACK_DEPTHWISECONV")&&g.strideWidth<=2&&g.outChannels/g.inChannels===1,A=p?ud(p,y):null,x=[r,s],b=i!=null,w=o!=null,S=p==="leakyrelu";if(b&&x.push(i),w&&x.push(o),S){let $=a.makeTensorInfo([],"float32",v.createScalarValue(h,"float32"));x.push($),f.push($)}let C;y?C=new y8(g,b,A,w,S):C=new g8(g,b,A,w,S);let N=[[g.padInfo.top,g.padInfo.left],[g.strideHeight,g.strideWidth],[g.dilationHeight,g.dilationWidth],[g.inHeight,g.inWidth]],_=a.runWebGLProgram(C,x,"float32",N);return f.forEach($=>a.disposeIntermediateTensorInfo($)),_}var NX={kernelName:Hr,backendName:"webgl",kernelFunc:CX},EX=class{constructor(e,t,a,n){this.sliceDim=e,this.strides=t,this.paramsShape=n,this.variableNames=["x","indices"],this.outputShape=a;let r=mt(a.length),s=` int index;`;for(let i=0;i= ${this.paramsShape[i]}; flattenIndex += index * ${this.strides[i]};`;this.userCode=` void main() { ${r} coords = getOutputCoords(); int flattenIndex = 0; bool out_of_bounds = false; ${s} setOutput(out_of_bounds ? 0.0 : getX(flattenIndex, coords[1])); } `}};function RX(e){let{inputs:t,backend:a}=e,{params:n,indices:r}=t,s=r.shape,i=s[s.length-1],o=v.sizeFromShape(n.shape),[l,u,d,c]=T.prepareAndValidate(n,r),p=de({inputs:{x:r},backend:a,attrs:{shape:[u,i]}}),h=de({inputs:{x:n},backend:a,attrs:{shape:[v.sizeFromShape(n.shape)/d,d]}});if(a.shouldExecuteOnCPU([n,r])||n.dtype==="string"){let y=a.readSync(r.dataId),A=a.bufferSync(n),x=LG(y,A,n.dtype,u,i,d,c,n.shape,o);return a.makeTensorInfo(l,n.dtype,x.values)}let f=new EX(i,c,[u,d],n.shape),m=a.runWebGLProgram(f,[h,p],h.dtype),g=de({inputs:{x:m},backend:a,attrs:{shape:l}});return a.disposeIntermediateTensorInfo(p),a.disposeIntermediateTensorInfo(h),a.disposeIntermediateTensorInfo(m),g}var MX={kernelName:_i,backendName:"webgl",kernelFunc:RX},$X=class{constructor(e,t){this.variableNames=["A","indices"],this.outputShape=t,this.rank=t.length;let a=mt(this.rank),n=_X(e,2);this.userCode=` void main() { ${a} resRC = getOutputCoords(); int index = int(getIndices(resRC.x, resRC.z)); float inBounds = (index >= 0) && (index < ${e[2]}) ? 1.0 : 0.0; setOutput(inBounds * getA(${n})); } `}};function _X(e,t){let a=["resRC.x","resRC.y","resRC.z","resRC.w"],n=[];for(let r=0;r=0,()=>`GatherV2: the index value ${w} is not in [0, ${x-1}]`)}}let u=T.segment_util.collectGatherOpShapeInfo(r,s,l,o),d=v.sizeFromShape(s.shape),c=[],p=de({inputs:{x:r},backend:a,attrs:{shape:[u.batchSize,u.outerSize,u.dimSize,u.sliceSize]}}),h=de({inputs:{x:s},backend:a,attrs:{shape:[u.batchSize,d/u.batchSize]}});c.push(p),c.push(h);let f=[u.batchSize,u.outerSize,d/u.batchSize,u.sliceSize];if(a.shouldExecuteOnCPU([r,s])||r.dtype==="string"){let A=a.bufferSync(h),x=a.bufferSync(p),b=BG(x,A,f);return c.forEach(w=>a.disposeIntermediateTensorInfo(w)),a.makeTensorInfo(u.outputShape,b.dtype,b.values)}let m=new $X(p.shape,f),g=a.runWebGLProgram(m,[p,h],p.dtype);c.push(g);let y=de({inputs:{x:g},backend:a,attrs:{shape:u.outputShape}});return c.forEach(A=>a.disposeIntermediateTensorInfo(A)),y}var FX={kernelName:Il,backendName:"webgl",kernelFunc:b8},PX="return float(a > b);",OX=` return vec4(greaterThan(a, b)); `,DX=la({opSnippet:PX,packedOpSnippet:OX,cpuKernelImpl:WG,dtype:"bool"}),zX={kernelName:rs,backendName:"webgl",kernelFunc:DX},LX="return float(a >= b);",BX=` return vec4(greaterThanEqual(a, b)); `,WX=la({opSnippet:LX,packedOpSnippet:BX,dtype:"bool",cpuKernelImpl:VG}),VX={kernelName:ss,backendName:"webgl",kernelFunc:WX};function GX(e){let{inputs:t,backend:a}=e,{input:n}=t;return x8(n,!0,a)}var UX={kernelName:ah,backendName:"webgl",kernelFunc:GX},jX="return float(!isnan(x) && !isinf(x));",HX=Qe({opSnippet:jX,dtype:"bool"}),qX={kernelName:Nd,backendName:"webgl",kernelFunc:HX},XX="return float(isinf(x));",KX=Qe({opSnippet:XX,dtype:"bool"}),ZX={kernelName:Ed,backendName:"webgl",kernelFunc:KX},YX="return float(isnan(x));",JX=Qe({opSnippet:YX,dtype:"bool"}),QX={kernelName:Sl,backendName:"webgl",kernelFunc:JX},eK="return float(a < b);",tK=` return vec4(lessThan(a, b)); `,aK=la({opSnippet:eK,packedOpSnippet:tK,cpuKernelImpl:GG,dtype:"bool"}),nK={kernelName:is,backendName:"webgl",kernelFunc:aK},rK="return float(a <= b);",sK=` return vec4(lessThanEqual(a, b)); `,iK=la({opSnippet:rK,packedOpSnippet:sK,cpuKernelImpl:UG,dtype:"bool"}),oK={kernelName:os,backendName:"webgl",kernelFunc:iK};function lK(e){let{backend:t,attrs:a}=e,{start:n,stop:r,num:s}=a,i=jG(n,r,s);return t.makeTensorInfo([i.length],"float32",i)}var uK={kernelName:nh,backendName:"webgl",kernelFunc:lK},dK=ru+` return x < 0.0 ? 0./0. : log(x); `,pK=` vec4 result = log(x); bvec4 isNaN = isnan(x); result.r = isNaN.r ? x.r : (x.r < 0.0 ? 0./0. : result.r); result.g = isNaN.g ? x.g : (x.g < 0.0 ? 0./0. : result.g); result.b = isNaN.b ? x.b : (x.b < 0.0 ? 0./0. : result.b); result.a = isNaN.a ? x.a : (x.a < 0.0 ? 0./0. : result.a); return result; `,cK=Qe({opSnippet:dK,packedOpSnippet:pK,cpuKernelImpl:HG}),hK={kernelName:ls,backendName:"webgl",kernelFunc:cK},fK=ru+` return log(1.0 + x); `,mK=Qe({opSnippet:fK}),gK={kernelName:Rd,backendName:"webgl",kernelFunc:mK},yK="return float(a >= 1.0 && b >= 1.0);",AK=` return vec4( vec4(greaterThanEqual(a, vec4(1.0))) * vec4(greaterThanEqual(b, vec4(1.0)))); `,xK=la({opSnippet:yK,packedOpSnippet:AK,dtype:"bool"}),bK={kernelName:Oi,backendName:"webgl",kernelFunc:xK},vK="return float(!(x >= 1.0));",wK=Qe({opSnippet:vK}),kK={kernelName:Di,backendName:"webgl",kernelFunc:wK},IK="return float(a >= 1.0 || b >= 1.0);",SK=` return min( vec4(greaterThanEqual(a, vec4(1.0))) + vec4(greaterThanEqual(b, vec4(1.0))), vec4(1.0)); `,TK=la({opSnippet:IK,packedOpSnippet:SK,dtype:"bool"}),CK={kernelName:Tl,backendName:"webgl",kernelFunc:TK},NK=class{constructor(e,t,a,n,r){this.variableNames=["x"],this.outputShape=[];let s=t,i=e[3]-1;this.outputShape=e;let o,l=`float(${a}) + float(${n}) * sum`;r===.5?o=`inversesqrt(${l})`:r===1?o=`1.0/(${l})`:o=`exp(log(${l}) * float(-${r}));`,this.userCode=` void main() { ivec4 coords = getOutputCoords(); int b = coords[0]; int r = coords[1]; int c = coords[2]; int d = coords[3]; float x = getX(b, r, c, d); float sum = 0.0; for (int j = -${s}; j <= ${s}; j++) { int idx = d + j; if (idx >= 0 && idx <= ${i}) { float z = getX(b, r, c, idx); sum += z * z; } } float val = x * ${o}; setOutput(val); } `}},EK=class{constructor(e,t,a,n,r){this.variableNames=["x"],this.outputShape=[],this.packedInputs=!0,this.packedOutput=!0;let s=t,i=e[3]-1;this.outputShape=e;let o,l=`float(${a}) + float(${n}) * sum`;r===.5?o=`inversesqrt(${l})`:r===1?o=`1.0/(${l})`:o=`exp(log(${l}) * float(-${r}));`,this.userCode=` void main() { ivec4 coords = getOutputCoords(); int b = coords.x; 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 - ${s}; 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 = - ${s}; j <= ${s}; j++) { ivec2 idx = depth + j; bvec2 aboveLowerBound = greaterThanEqual(idx, ivec2(0)); bvec2 belowUpperBound = lessThanEqual(idx, ivec2(${i})); 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 * ${o}; setOutput(result); } `}},RK=e=>{let{inputs:t,backend:a,attrs:n}=e,{x:r}=t,{depthRadius:s,bias:i,alpha:o,beta:l}=n,u=V().getBool("WEBGL_PACK_NORMALIZATION")?new EK(r.shape,s,i,o,l):new NK(r.shape,s,i,o,l);return a.runWebGLProgram(u,[r],r.dtype)},MK={kernelName:rh,backendName:"webgl",kernelFunc:RK},$K=class{constructor(e,t,a,n,r){this.variableNames=["inputImage","outputImage","dy"],this.outputShape=[],this.outputShape=e,this.depth=e[3],this.depthRadius=t,this.bias=a,this.alpha=n,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 = ${this.depth}; float norm = 0.0; for (int k = MIN_DEPTH_BEGIN; k < MAX_DEPTH_END; ++k) { if (k < depthBegin){ continue; } else if (k >= depthBegin && k < depthEnd) { norm += getInputImage(b, r, c, k) * getInputImage(b, r, c, k); } else { break; } } norm = float(${n}) * norm + float(${a}); for(int k = MIN_DEPTH_BEGIN; k < MAX_DEPTH_END; ++k){ if (k < depthBegin){ continue; } else if (k >= depthBegin && k < depthEnd){ float dyi = -2.0 * float(${n}) * 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); } `}},_K=e=>{let{inputs:t,backend:a,attrs:n}=e,{x:r,y:s,dy:i}=t,{depthRadius:o,bias:l,alpha:u,beta:d}=n,c=new $K(r.shape,o,l,u,d);return a.runWebGLProgram(c,[r,s,i],r.dtype)},FK={kernelName:K1,backendName:"webgl",kernelFunc:_K};function PK(e,t,a,n){let r=v.sizeFromShape(t),s=v.sizeFromShape(e.shape)/r,i=de({inputs:{x:e},attrs:{shape:[s,r]},backend:n}),o=fo(i,e.dtype,"max",n),l=de({inputs:{x:o},attrs:{shape:a},backend:n});return n.disposeIntermediateTensorInfo(i),n.disposeIntermediateTensorInfo(o),l}function v8(e){let{inputs:t,backend:a,attrs:n}=e,{x:r}=t,{reductionIndices:s,keepDims:i}=n,o=r.shape.length,l=v.parseAxisParam(s,r.shape),u=l,d=T.getAxesPermutation(u,o),c=d!=null,p=a.shouldExecuteOnCPU([r]),h=r;if(c){if(p){let A=a.texData.get(h.dataId).values,x=new Array(o);for(let S=0;S`Error in maxPool: Either strides or dilations must be 1. Got strides ${i} and dilations '${u}'`);let d=T.computePool2DInfo(r.shape,s,i,u,o,l);if(d.filterWidth===1&&d.filterHeight===1&&v.arraysEqual(d.inShape,d.outShape))return Za({inputs:{x:r},backend:a});let c=new dd(d,"max",!1);return a.runWebGLProgram(c,[r],r.dtype)}var VK={kernelName:Li,backendName:"webgl",kernelFunc:WK};function GK(e){let{inputs:t,backend:a,attrs:n}=e,{x:r}=t,{filterSize:s,strides:i,pad:o,dataFormat:l,dimRoundingMode:u}=n,d=[1,1,1],c=T.computePool3DInfo(r.shape,s,i,d,o,u,l),p=new P3(c,"max",!1);return a.runWebGLProgram(p,[r],r.dtype)}var UK={kernelName:sh,backendName:"webgl",kernelFunc:GK},jK=class{constructor(e){this.variableNames=["dy","maxPos"],this.outputShape=e.inShape;let t=e.strideHeight,a=e.strideWidth,n=e.dilationHeight,r=e.effectiveFilterHeight,s=e.effectiveFilterWidth,i=r-1-e.padInfo.top,o=s-1-e.padInfo.left,l=r*s-1;this.userCode=` const ivec2 pads = ivec2(${i}, ${o}); void main() { 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 += ${n}) { 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 < ${s}; wC++) { float dyC = float(dyCCorner + wC) / ${a}.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 * ${s} + wC; float mask = float(maxPosValue == curPosValue ? 1.0 : 0.0); dotProd += dyValue * mask; } } setOutput(dotProd); } `}},HK=class{constructor(e){this.variableNames=["dy","maxPos"],this.outputShape=e.inShape;let t=e.strideDepth,a=e.strideHeight,n=e.strideWidth,r=e.dilationDepth,s=e.dilationHeight,i=e.dilationWidth,o=e.effectiveFilterDepth,l=e.effectiveFilterHeight,u=e.effectiveFilterWidth,d=o-1-e.padInfo.front,c=l-1-e.padInfo.top,p=u-1-e.padInfo.left,h=o*l*u-1;this.userCode=` const ivec3 pads = ivec3(${d}, ${c}, ${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 < ${o}; 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 += ${s}) { 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 < ${u}; wC += ${i}) { 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(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 qK(e){let{inputs:t,backend:a,attrs:n}=e,{dy:r,input:s}=t,i=s,{filterSize:o,strides:l,pad:u,dimRoundingMode:d}=n,c=[1,1,1],p=T.computePool3DInfo(i.shape,o,l,c,u,d),h=new P3(p,"max",!0),f=a.runWebGLProgram(h,[i],i.dtype),m=new HK(p),g=a.runWebGLProgram(m,[r,f],i.dtype);return a.disposeIntermediateTensorInfo(f),g}var XK={kernelName:Y1,backendName:"webgl",kernelFunc:qK};function KK(e){let{inputs:t,backend:a,attrs:n}=e,{dy:r,input:s,output:i}=t,o=s;Jl([s,i],"maxPoolGrad");let{filterSize:l,strides:u,pad:d,dimRoundingMode:c}=n,p=T.computePool2DInfo(o.shape,l,u,1,d,c),h=!0,f=new dd(p,"max",h),m=a.runWebGLProgram(f,[o],o.dtype),g=new jK(p),y=a.runWebGLProgram(g,[r,m],o.dtype);return a.disposeIntermediateTensorInfo(m),y}var ZK={kernelName:Z1,backendName:"webgl",kernelFunc:KK};function YK(e,t,a,n){let r=new dd(a,"max",!1),s=n.runWebGLProgram(r,[e],"float32");r=new dd(a,"max",!0,!0,t);let i=n.runWebGLProgram(r,[e],"float32");return[s,i]}var JK={kernelName:ih,backendName:"webgl",kernelFunc:({inputs:e,attrs:t,backend:a})=>{let{x:n}=e,{filterSize:r,strides:s,pad:i,includeBatchInIndex:o}=t,l=a;v.assert(n.shape.length===4,()=>`Error in maxPool: input must be rank 4 but got rank ${n.shape.length}.`);let u=[1,1];v.assert(T.eitherStridesOrDilationsAreOne(s,u),()=>`Error in maxPool: Either strides or dilations must be 1. Got strides ${s} and dilations '${u}'`);let d=T.computePool2DInfo(n.shape,r,s,u,i),[c,p]=YK(n,o,d,l);return[c,p]}};function QK(e,t,a,n){let r=v.sizeFromShape(t),s=v.sizeFromShape(e.shape)/r,i=de({inputs:{x:e},attrs:{shape:[s,r]},backend:n}),o=fo(i,"float32","mean",n),l=de({inputs:{x:o},attrs:{shape:a},backend:n});return n.disposeIntermediateTensorInfo(i),n.disposeIntermediateTensorInfo(o),l}var eZ={kernelName:Bi,backendName:"webgl",kernelFunc:({inputs:e,attrs:t,backend:a})=>{let{x:n}=e,{keepDims:r,axis:s}=t,i=a,o=n.shape.length,l=v.parseAxisParam(s,n.shape),u=l,d=T.getAxesPermutation(u,o),c=d!=null,p=i.shouldExecuteOnCPU([n]),h=[],f=n;if(c){if(p){let x=i.texData.get(f.dataId).values,b=new Array(o);for(let C=0;Cu[0]+e[d]+u[1]);let n=e.length,r=mt(n),s=t.map(u=>u[0]).join(","),i=t.map((u,d)=>u[0]+e[d]).join(","),o=["coords[0]","coords[1]","coords[2]","coords[3]"].slice(0,n),l=a==="reflect"?0:1;if(n===1){this.userCode=` int start = ${s}; int end = ${i}; 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 = ${r}(${s}); ${r} end = ${r}(${i}); void main() { ${r} outC = getOutputCoords(); for (int i = 0; i < ${n}; 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}; } } ${r} coords = outC - start; setOutput(getX(${o})); } `}},lZ=class{constructor(e,t,a){this.variableNames=["x"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=t.map((h,f)=>h[0]+e[f]+h[1]);let n=e.length,r=mt(n),s=t.map(h=>h[0]).join(","),i=t.map((h,f)=>h[0]+e[f]).join(","),o=va("rc",n),l=va("source",n),u=`${o[n-1]} < ${this.outputShape[n-1]}`,d=n===1?"source":`vec2(${l.slice(-2).join()})`,c=a==="reflect"?0:1,p="";if(n===1){let h=` ${r} source = rc; if (source < start) { source = start * 2 - source - ${c}; } else if (source >= end) { source = (end - 1) * 2 - source + ${c}; } source -= start; `;p=` ${r} rc = outputLoc; ${h} result[0] = getChannel(getX(${l.join()}), ${d}); ${o[n-1]} += 1; if(${u}) { ${h} result[1] = getChannel(getX(${l.join()}), ${d}); } `}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 - ${c}) + gte * ((end - 1) * 2 - source + ${c}); source -= start; `;p=` ${r} rc = outputLoc; ${h} result[0] = getChannel(getX(${l.join()}), ${d}); ${o[n-1]} += 1; if(${u}) { ${h} result[1] = getChannel(getX(${l.join()}), ${d}); } rc = outputLoc; ${o[n-2]} += 1; if(${o[n-2]} < ${this.outputShape[n-2]}) { ${h} result[2] = getChannel(getX(${l.join()}), ${d}); ${o[n-1]} += 1; if(${u}) { ${h} result[3] = getChannel(getX(${l.join()}), ${d}); } } `}this.userCode=` const ${r} start = ${r}(${s}); const ${r} end = ${r}(${i}); void main() { ${r} outputLoc = getOutputCoords(); vec4 result = vec4(0.); ${p} setOutput(result); 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`,uY=` 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; `,dY=Qe({opSnippet:lY,packedOpSnippet:uY}),pY={kernelName:Ki,backendName:"webgl",kernelFunc:dY},cY=Cn+` return (x < 0.0) ? 0.0 : min(6.0, x); `,hY=` 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; `,fY=Qe({opSnippet:cY,packedOpSnippet:hY}),mY={kernelName:Ji,backendName:"webgl",kernelFunc:fY},gY=class{constructor(e,t,a,n,r){this.variableNames=["A"],this.outputShape=[];let[s,i,o,l]=e;this.outputShape=[s,t,a,l];let u=[n&&t>1?i-1:i,n&&a>1?o-1:o],d=[n&&t>1?t-1:t,n&&a>1?a-1:a],c;r?c="(vec2(yRC) + vec2(0.5)) * effectiveInputOverOutputRatioRC - vec2(0.5)":c="vec2(yRC) * effectiveInputOverOutputRatioRC",this.userCode=` const vec2 effectiveInputOverOutputRatioRC = vec2( ${u[0]/d[0]}, ${u[1]/d[1]}); const vec2 inputShapeRC = vec2(${i}.0, ${o}.0); void main() { ivec4 coords = getOutputCoords(); int b = coords[0]; int d = coords[3]; ivec2 yRC = coords.yz; // Fractional source index. vec2 sourceFracIndexRC = ${c}; // 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); } `}},yY=class{constructor(e,t,a,n,r){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=[];let[s,i,o,l]=e;this.outputShape=[s,t,a,l];let u=[n&&t>1?i-1:i,n&&a>1?o-1:o],d=[n&&t>1?t-1:t,n&&a>1?a-1:a],c;r?c="(vec3(yRC) + vec3(0.5)) * effectiveInputOverOutputRatioRC - vec3(0.5)":c="vec3(yRC) * effectiveInputOverOutputRatioRC",this.userCode=` const vec3 effectiveInputOverOutputRatioRC = vec3( ${u[0]/d[0]}, ${u[1]/d[1]}, ${u[1]/d[1]}); const vec3 inputShapeRC = vec3(${i}.0, ${o}.0, ${o}.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 = ${c}; // 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 < ${a-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 AY(e){let{inputs:t,backend:a,attrs:n}=e,{images:r}=t,{alignCorners:s,halfPixelCenters:i,size:o}=n,[l,u]=o,d=V().getBool("WEBGL_PACK_IMAGE_OPERATIONS")?new yY(r.shape,l,u,s,i):new gY(r.shape,l,u,s,i);return a.runWebGLProgram(d,[r],"float32")}var xY={kernelName:Yi,backendName:"webgl",kernelFunc:AY},bY=class{constructor(e,t,a){this.variableNames=["dy"],this.outputShape=[],this.outputShape=t;let[,n,r]=t,[,s,i]=e,o=[a&&s>1?n-1:n,a&&i>1?r-1:r],l=[a&&s>1?s-1:s,a&&i>1?i-1:i],u=o[0]/l[0],d=o[1]/l[1],c=1/u,p=1/d,h=Math.ceil(c)*2+2,f=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(${d}); const float invHeightScale = float(${c}); const float invWidthScale = float(${p}); const int winHeight = int(${h}); const int winWidth = int(${f}); // 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 >= ${s}) { 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 >= ${i}) { continue; } float dxR = float(dyR) * heightScale; int topDxRIndex = int(floor(dxR)); int bottomDxRIndex = int(min(ceil(dxR), ${n-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 vY(e){let{inputs:t,backend:a,attrs:n}=e,{images:r,dy:s}=t,{alignCorners:i}=n,o=new bY(s.shape,r.shape,i);return a.runWebGLProgram(o,[s],s.dtype)}var wY={kernelName:Q1,backendName:"webgl",kernelFunc:vY},kY=class{constructor(e,t,a,n,r){this.variableNames=["A"],this.outputShape=[];let[s,i,o,l]=e;this.outputShape=[s,t,a,l];let u=[n&&t>1?i-1:i,n&&a>1?o-1:o],d=[n&&t>1?t-1:t,n&&a>1?a-1:a],c=n?"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]/d[0]}, ${u[1]/d[1]}); const vec2 inputShapeRC = vec2(${i}.0, ${o}.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 + ${c}))); float newValue = getA(b, sourceNearestRC.x, sourceNearestRC.y, d); setOutput(newValue); } `}},IY=class{constructor(e,t,a,n,r){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=[];let[s,i,o,l]=e;this.outputShape=[s,t,a,l];let u=[n&&t>1?i-1:i,n&&a>1?o-1:o],d=[n&&t>1?t-1:t,n&&a>1?a-1:a],c=n?"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]/d[0]}, ${u[1]/d[1]}, ${u[1]/d[1]}); const vec3 inputShapeRC = vec3(${i}.0, ${o}.0, ${o}.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 + ${c}))); // Should we calculate next column and row elements in 2x2 packed cell. bool hasNextCol = d < ${l-1}; bool hasNextRow = coords.z < ${a-1}; vec4 newValue = vec4( getAValue(b, sourceNearestRC.x, sourceNearestRC.y, d), hasNextCol ? getAValue(b, sourceNearestRC.x, sourceNearestRC.y, d + 1) : 0.0, hasNextRow ? getAValue(b, sourceNearestRC.x, sourceNearestRC.z, d) : 0.0, (hasNextRow && hasNextCol) ? getAValue(b, sourceNearestRC.x, sourceNearestRC.z, d + 1) : 0.0); setOutput(newValue); } `}};function SY(e){let{inputs:t,backend:a,attrs:n}=e,{images:r}=t,{alignCorners:s,halfPixelCenters:i,size:o}=n,[l,u]=o,d=V().getBool("WEBGL_PACK_IMAGE_OPERATIONS")?new IY(r.shape,l,u,s,i):new kY(r.shape,l,u,s,i);return a.runWebGLProgram(d,[r],r.dtype)}var TY={kernelName:Zi,backendName:"webgl",kernelFunc:SY},CY=class{constructor(e,t,a){this.variableNames=["dy"],this.outputShape=[],this.outputShape=t;let[,n,r]=t,[,s,i]=e,o=[a&&s>1?n-1:n,a&&i>1?r-1:r],l=[a&&s>1?s-1:s,a&&i>1?i-1:i],u=o[0]/l[0],d=o[1]/l[1],c=1/u,p=1/d,h=Math.ceil(c)*2+2,f=Math.ceil(p)*2+2;this.userCode=` void main() { ivec4 coords = getOutputCoords(); int b = coords[0]; int d = coords[3]; int r = coords[1]; int c = coords[2]; float accumulator = 0.0; const float heightScale = float(${u}); const float widthScale = float(${d}); const float invHeightScale = float(${c}); const float invWidthScale = float(${p}); const int winHeight = int(${h}); const int winWidth = int(${f}); // 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 >= ${s}) { 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 >= ${i}) { continue; } float sourceFracRow = float(${o[0]}) * (float(dyR) / float(${l[0]})); float sourceFracCol = float(${o[1]}) * (float(dyC) / float(${l[1]})); int sourceNearestRow = int(min( float(int(${n}) - 1), ${a} ? float(round(sourceFracRow)) : float(floor(sourceFracRow)))); int sourceNearestCol = int(min( float(int(${r}) - 1), ${a} ? float(round(sourceFracCol)) : float(floor(sourceFracCol)))); if (r == sourceNearestRow && c == sourceNearestCol) { accumulator += getDy(b, dyR, dyC, d); } } } // End loop over dy setOutput(accumulator); } `}};function NY(e){let{inputs:t,backend:a,attrs:n}=e,{images:r,dy:s}=t,{alignCorners:i}=n,o=new CY(s.shape,r.shape,i);return a.runWebGLProgram(o,[s],s.dtype)}var EY={kernelName:J1,backendName:"webgl",kernelFunc:NY},RY=class{constructor(e,t){this.variableNames=["x"];let a=e.length;if(a>4)throw new Error(`WebGL backend: Reverse of rank-${a} tensor is not yet supported`);if(this.outputShape=e,a===1){this.userCode=` void main() { int coord = getOutputCoords(); setOutput(getX(${e[0]} - coord - 1)); } `;return}let n=i=>t.indexOf(i)!==-1&&e[i]!==1?`${e[i]} - coords[${i}] - 1`:`coords[${i}]`,r=e.map((i,o)=>n(o)).join(","),s=mt(a);this.userCode=` void main() { ${s} coords = getOutputCoords(); setOutput(getX(${r})); } `}},MY=class{constructor(e,t){this.variableNames=["x"],this.packedInputs=!0,this.packedOutput=!0;let a=e.length;if(a>4)throw new Error(`WebGL backend: Reverse of rank-${a} tensor is not yet supported`);this.outputShape=e;let n=va("rc",a),r=`${n[a-1]} + 1 < ${this.outputShape[a-1]}`,s=`${n[a-2]} + 1 < ${this.outputShape[a-2]}`,i=mt(a);a===1?this.userCode=` void main(){ int rc = getOutputCoords(); vec4 result = vec4(0.); result.r = getChannel(getX(${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() { ${i} rc = getOutputCoords(); vec4 result = vec4(0.); result.r = ${o(n.slice())}; if(${r}){ result.g = ${l(n.slice())}; } if(${s}) { result.b = ${u(n.slice())}; if(${r}) { result.a = ${d(n.slice())}; } } setOutput(result); } `;function o(h){return c(h)}function l(h){return h[a-1]="("+h[a-1]+" + 1)",c(h)}function u(h){return h[a-2]="("+h[a-2]+" + 1)",c(h)}function d(h){return h[a-1]="("+h[a-1]+" + 1)",h[a-2]="("+h[a-2]+" + 1)",c(h)}function c(h){let f=e.map((y,A)=>p(A,h)),m=f.join(","),g=f.slice(-2).join(",");return`getChannel(getX(${m}), vec2(${g}))`}function p(h,f){return t.indexOf(h)!==-1&&e[h]!==1?`${e[h]} - ${f[h]} - 1`:`${f[h]}`}}};function $Y(e){let{inputs:t,backend:a,attrs:n}=e,{x:r}=t,{dims:s}=n,i=r.shape.length,o=v.parseAxisParam(s,r.shape);if(i===0)return Za({inputs:{x:r},backend:a});let l=V().getBool("WEBGL_PACK_ARRAY_OPERATIONS")?new MY(r.shape,o):new RY(r.shape,o);return a.runWebGLProgram(l,[r],r.dtype)}var _Y={kernelName:Pl,backendName:"webgl",kernelFunc:$Y},FY=class{constructor(e,t){this.variableNames=["Image"],this.outputShape=[],this.customUniforms=[{name:"params",type:"vec4"}];let a=e[1],n=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 < ${n} && coordY >= 0 && coordY < ${a}) { outputValue = getImage(coords[0], coordY, coordX, coords[3]); } setOutput(outputValue); } `}},PY={kernelName:lo,backendName:"webgl",kernelFunc:({inputs:e,attrs:t,backend:a})=>{let{image:n}=e,{radians:r,fillValue:s,center:i}=t,o=a,l=new FY(n.shape,s),[u,d]=T.getImageCenter(i,n.shape[1],n.shape[2]),c=[[u,d,Math.sin(r),Math.cos(r)]];return o.runWebGLProgram(l,[n],n.dtype,c)}},OY=` // OpenGL ES does not support round function. // The algorithm is based on banker's rounding. float base = floor(x); if ((x - base) < 0.5) { return floor(x); } else if ((x - base) > 0.5) { return ceil(x); } else { if (mod(base, 2.0) == 0.0) { return base; } else { return base + 1.0; } } `,DY=Qe({opSnippet:OY}),zY={kernelName:Ol,backendName:"webgl",kernelFunc:DY},LY="return inversesqrt(x);",BY=Qe({opSnippet:LY,cpuKernelImpl:rU}),WY={kernelName:hs,backendName:"webgl",kernelFunc:BY},N8=class{constructor(e,t,a,n,r,s,i=!0){this.variableNames=["updates","indices","defaultValue"],this.outputShape=s;let o=mt(r.length),l=mt(s.length),u="";a===1?u="i":a===2&&(u="i, j");let d=`getIndices(${u})`,c="";n===1?c="i":n===2&&(c="i, coords[1]");let p=`getUpdates(${c})`,h=t>1?"strides[j]":"strides";this.userCode=` ${o} strides = ${o}(${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 < ${t}; j++) { int index = round(${d}); flattenedIndex += index * ${h}; } if (flattenedIndex == coords[0]) { sum += ${p}; found = true; } } setOutput(mix(getDefaultValue(), sum, float(found))); } `}};function VY(e){let{inputs:t,backend:a,attrs:n}=e,{indices:r,updates:s}=t,{shape:i}=n,{sliceRank:o,numUpdates:l,sliceSize:u,strides:d,outputSize:c}=T.calculateShapes(s,r,i),p=[c/u,u];if(c===0)return a.makeTensorInfo(i,r.dtype);let h=de({inputs:{x:r},backend:a,attrs:{shape:[l,o]}}),f=de({inputs:{x:s},backend:a,attrs:{shape:[l,u]}}),m=a.makeTensorInfo([],"float32",new Float32Array([0])),g=new N8(l,o,h.shape.length,f.shape.length,d,p),y=a.runWebGLProgram(g,[f,h,m],f.dtype),A=de({inputs:{x:y},backend:a,attrs:{shape:i}});return a.disposeIntermediateTensorInfo(h),a.disposeIntermediateTensorInfo(f),a.disposeIntermediateTensorInfo(y),a.disposeIntermediateTensorInfo(m),A}var GY={kernelName:Qi,backendName:"webgl",kernelFunc:VY},UY=class{constructor(e,t,a,n){this.variableNames=["sortedSequence","values"],this.customUniforms=[{name:"numInputs",type:"int"}],this.outputShape=[e,a];let r="while (left < right) {",s=`for (int i = 0; i < ${Math.ceil(Math.log2(t+1))}; ++i) { if (left >= right) break;`,i=V().getNumber("WEBGL_VERSION")===2?r:s,o=n==="left"?"<":"<=";this.userCode=` int findBound(int batch, float value) { int left = 0; int right = numInputs; int mid; ${i} mid = (left + right) / 2; if (getSortedSequence(batch, mid) ${o} value) { left = mid + 1; } else { right = mid; } } return right; } void main() { ivec2 coords = getOutputCoords(); int batch = coords[0]; int valueIndex = coords[1]; float value = getValues(batch, valueIndex); setOutput(float(findBound(batch, value))); } `}};function jY(e){let{inputs:t,backend:a,attrs:n}=e,{sortedSequence:r,values:s}=t,{side:i}=n,o=new UY(r.shape[0],r.shape[1],s.shape[1],i),l=[[r.shape[1]]];return a.runWebGLProgram(o,[r,s],"int32",l)}var HY={kernelName:ph,backendName:"webgl",kernelFunc:jY},qY=class{constructor(e,t,a){this.variableNames=["c","a","b"],this.outputShape=t;let n,r;if(a>4)throw Error(`Where for rank ${a} is not yet supported`);if(a===1)r="resRC",n="resRC";else{let i=["resRC.x","resRC.y","resRC.z","resRC.w"],o=[],l=[];for(let u=0;u= 1.0) { setOutput(getA(${r})); } else { setOutput(getB(${r})); } } `}};function XY(e){let{inputs:t,backend:a}=e,{condition:n,t:r,e:s}=t,i=new qY(n.shape.length,r.shape,r.shape.length);return a.runWebGLProgram(i,[n,r,s],ra(r.dtype,s.dtype))}var KY={kernelName:Dl,backendName:"webgl",kernelFunc:XY},ZY=` // Stable and Attracting Fixed Point (0, 1) for Normalized Weights. // see: https://arxiv.org/abs/1706.02515 float scaleAlpha = ${T.SELU_SCALEALPHA}; float scale = ${T.SELU_SCALE}; return (x >= 0.0) ? scale * x : scaleAlpha * (exp(x) - 1.0); `,YY=Qe({opSnippet:ZY}),JY={kernelName:_d,backendName:"webgl",kernelFunc:YY},QY=ru+` return 1.0 / (1.0 + exp(-1.0 * x)); `,eJ=` vec4 result = 1.0 / (1.0 + exp(-1.0 * x)); bvec4 isNaN = isnan(x); result.r = isNaN.r ? x.r : result.r; result.g = isNaN.g ? x.g : result.g; result.b = isNaN.b ? x.b : result.b; result.a = isNaN.a ? x.a : result.a; return result; `,tJ=Qe({opSnippet:QY,packedOpSnippet:eJ,cpuKernelImpl:iU}),aJ={kernelName:fs,backendName:"webgl",kernelFunc:tJ},nJ=` if (isnan(x)) { return 0.0; } return sign(x); `,rJ=Qe({opSnippet:nJ}),sJ={kernelName:Fd,backendName:"webgl",kernelFunc:rJ},iJ=ru+` return sin(x); `,oJ=Qe({opSnippet:iJ}),lJ={kernelName:eo,backendName:"webgl",kernelFunc:oJ},uJ=` float e2x = exp(x); return (e2x - 1.0 / e2x) / 2.0; `,dJ=Qe({opSnippet:uJ}),pJ={kernelName:Ll,backendName:"webgl",kernelFunc:dJ},cJ=` float epsilon = 1.1920928955078125e-7; float threshold = log(epsilon) + 2.0; 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When we double the index, // we basically interpolate a position, so 2i looks like // | _ | _ | _ | _ | _ | _ | _. We move the | to the first k position // of each 2k positions by - elemIdx % k. 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c=a.texData.get(r.dataId),p=c!==null&&c.isPacked,h=p?a.unpackTensor(r):r,f=v.sizeFromShape(u)/d,m=de({inputs:{x:h},attrs:{shape:[f,d]},backend:a});p&&js(a,h);let g=Vy(s),y=Vy(d),A=null,x=()=>A===null?[m,m]:[m,A],b=(_,$,M)=>{let I=x(),E=new aQ(M),O=[[d],[A===null?1:0],[Number.NEGATIVE_INFINITY],[_],[$]],L=A;A=a.runWebGLProgram(E,I,"int32",O),js(a,L)};for(let _=1;_=1;M/=2)b($,M,[f,y])}for(let _=y;_>g;_/=2){let $=x(),M=new nQ([f,_/2]),I=[[d],[A===null?1:0],[g]],E=A;A=a.runWebGLProgram(M,$,"int32",I),js(a,E);let O=g/2,L=O*2;for(let B=O;B>=1;B/=2)b(L,B,A.shape)}let w=A;A=su({inputs:{x:A},backend:a,attrs:{begin:0,size:[f,s]}}),js(a,w);let S=b8({inputs:{x:m,indices:A},backend:a,attrs:{axis:1,batchDims:1}});js(a,m);let C=u.slice(0,-1);C.push(s),w=A,A=de({inputs:{x:A},attrs:{shape:C},backend:a}),js(a,w);let N=S;return S=de({inputs:{x:S},attrs:{shape:C},backend:a}),js(a,N),[S,A]}var sQ={kernelName:so,backendName:"webgl",kernelFunc:rQ},iQ=class{constructor(e,t,a,n,r,s){this.variableNames=["Image","Transforms"],this.outputShape=s;let i=a==="nearest"?1:2,o;switch(n){case"constant":o=1;break;case"reflect":o=2;break;case"wrap":o=3;break;case"nearest":o=4;break;default:o=1;break}this.userCode=` float mapCoord(float outCoord, float len) { float inCoord = outCoord; if(${o} == 2) { if (inCoord < 0.0) { if (len <= 1.0) { inCoord = 0.0; } else { float sz2 = 2.0 * len; if (inCoord < sz2) { inCoord = sz2 * float(int(float(-inCoord / sz2))) + inCoord; } inCoord = inCoord < -len ? inCoord + sz2 : -inCoord - 1.0; } } else if (inCoord > len - 1.0) { if (len <= 1.0) { inCoord = 0.0; } else { float sz2 = 2.0 * len; inCoord -= sz2 * float(int(float(inCoord / sz2))); if (inCoord >= len) { inCoord = sz2 - inCoord - 1.0; } } } return clamp(inCoord, 0.0, len - 1.0); } else if (${o} == 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 (${o} == 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(${e})); if (${i} == 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), 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y=T.getUndoAxesPermutation(u);g=Yr({inputs:{x:p},attrs:{perm:y},backend:a}),a.disposeData(d.dataId),a.disposeData(p.dataId)}return g}var vee={kernelName:ki,backendName:"wasm",setupFunc:xee,kernelFunc:bee},j8;function wee(e){j8=e.wasm.cwrap(Si,null,["number","number","number","array","number","array","array","number","number"])}function kee(e){let{backend:t,inputs:a,attrs:n}=e,{x:r}=a,{blockSize:s,dataFormat:i}=n,o=r.shape[0],l=i==="NHWC"?r.shape[1]:r.shape[2],u=i==="NHWC"?r.shape[2]:r.shape[3],d=i==="NHWC"?r.shape[3]:r.shape[1],c=l*s,p=u*s,h=d/(s*s),f=i==="NHWC"?[o,c,p,h]:[o,h,c,p],m=t.makeOutput(f,"float32"),g=t.dataIdMap.get(r.dataId).id,y=new Uint8Array(new Int32Array(v.computeStrides(r.shape)).buffer),A=new Uint8Array(new Int32Array(f).buffer),x=new Uint8Array(new Int32Array(v.computeStrides(f)).buffer),b=t.dataIdMap.get(m.dataId).id;return j8(g,s,i==="NHWC"?1:0,y,r.shape.length-1,A,x,f.length,b),m}var Iee={kernelName:Si,backendName:"wasm",setupFunc:wee,kernelFunc:kee},H8;function 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_te={kernelName:Wi,backendName:"wasm",setupFunc:Mte,kernelFunc:$te},Fte=!1,Pte=ua(ds,Fte),R1;(function(e){e[e.reflect=0]="reflect",e[e.symmetric=1]="symmetric"})(R1||(R1={}));var sv;function Ote(e){sv=e.wasm.cwrap(Vi,null,["number","array","number","number","array","array","number","number"])}function Dte(e){let{inputs:{x:t},backend:a,attrs:{paddings:n,mode:r}}=e,s=n.map((f,m)=>f[0]+t.shape[m]+f[1]),i=a.dataIdMap.get(t.dataId).id,o=a.makeOutput(s,t.dtype),l=a.dataIdMap.get(o.dataId).id,u=new Uint8Array(new Int32Array(t.shape).buffer),d=n.map(f=>f[0]),c=n.map(f=>f[1]),p=new Uint8Array(new Int32Array(d).buffer),h=new Uint8Array(new Int32Array(c).buffer);return sv(i,u,t.shape.length,St[t.dtype],p,h,R1[r],l),o}var zte={kernelName:Vi,backendName:"wasm",kernelFunc:Dte,setupFunc:Ote},Lte=!0,Bte=ua(ps,Lte),Wte=Zt(Cl);function O3(e,t){let a=new Int32Array(e.wasm.HEAPU8.buffer,t,4),n=a[0],r=a[1],s=a[2],i=a[3];return e.wasm._free(t),{pSelectedIndices:n,selectedSize:r,pSelectedScores:s,pValidOutputs:i}}var iv;function Vte(e){iv=e.wasm.cwrap(Gi,"number",["number","number","number","number","number"])}function Gte(e){let{backend:t,inputs:a,attrs:n}=e,{iouThreshold:r,maxOutputSize:s,scoreThreshold:i}=n,{boxes:o,scores:l}=a,u=t.dataIdMap.get(o.dataId).id,d=t.dataIdMap.get(l.dataId).id,c=iv(u,d,s,r,i),{pSelectedIndices:p,selectedSize:h,pSelectedScores:f,pValidOutputs:m}=O3(t,c);return t.wasm._free(f),t.wasm._free(m),t.makeOutput([h],"int32",p)}var Ute={kernelName:Gi,backendName:"wasm",setupFunc:Vte,kernelFunc:Gte},ov;function jte(e){ov=e.wasm.cwrap(Nl,"number",["number","number","number","number","number","bool"])}function 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Zte={kernelName:Ui,backendName:"wasm",setupFunc:Xte,kernelFunc:Kte},Yte=!1,Jte=ua(cs,Yte,"bool"),uv;function Qte(e){uv=e.wasm.cwrap(Rl,null,["number","number","number","number","number"])}function eae(e){let{inputs:t,backend:a,attrs:n}=e,{indices:r}=t,{dtype:s,depth:i,onValue:o,offValue:l}=n,u=a.makeOutput([...r.shape,i],s),d=a.dataIdMap.get(u.dataId).id,c=a.dataIdMap.get(r.dataId).id;return uv(c,i,o,l,d),u}var tae={kernelName:Rl,backendName:"wasm",setupFunc:Qte,kernelFunc:eae};function aae(e){let{inputs:{x:t},backend:a}=e,n=a.makeOutput(t.shape,t.dtype);return a.typedArrayFromHeap(n).fill(1),n}var nae={kernelName:El,backendName:"wasm",kernelFunc:aae};function rae(e){let{inputs:t,backend:a,attrs:n}=e,{axis:r}=n;if(t.length===1)return E1({inputs:{input:t[0]},backend:a,attrs:{dim:r}});let s=t[0].shape,i=t[0].dtype;t.forEach(d=>{v.assertShapesMatch(s,d.shape,"All tensors passed to stack must have matching shapes"),v.assert(i===d.dtype,()=>"All tensors passed to stack must have matching 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} fn ${i}Coords(coords : ${u}) -> vec4 { return get${s}(); } `:` fn ${i}Index(globalIndex : i32) -> f32{ return get${s}(); } fn ${i}Coords(coords : ${u}) -> f32{ return get${s}(); } `;l<2&&d.length>=1?p="coords = 0;":p=d.map(g=>`coords.${Ar(g+c)} = 0;`).join(` `);let h="";if(l<2&&o>0)h="coords";else if(l>1){let g=na(o),y=e.shape.map((A,x)=>`coords.${Ar(x+c)}`).join(", ");h=`${g}(${y})`}else h="coords";let f=`uniforms.${r.charAt(0).toLowerCase()+r.slice(1)}Shape`,m=`${o}D`;return a?` fn ${i}Index(globalIndex : i32) -> vec4 { var coords = getCoordsFromIndex(globalIndex); ${p} return ${r}[getIndexFromCoords${m}(${h}, ${f}) / 4]; } fn ${i}Coords(coordsIn : ${u}) -> vec4 { var coords = coordsIn; ${p} return ${r}[getIndexFromCoords${m}(${h}, ${f}) / 4]; } `:` fn ${i}Index(globalIndex : i32) -> f32 { var coords = getCoordsFromIndex(globalIndex); ${p} return f32(${r}[getIndexFromCoords${m}(${h}, ${f})]); } fn ${i}Coords(coordsIn : ${u}) -> f32 { var coords = coordsIn; ${p} return 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dot(coords, vec4( uniforms.outShapeStrides.x, uniforms.outShapeStrides.y, uniforms.outShapeStrides.z, 1)); } `;break;case 5:t+=` fn getOutputIndexFromCoords(coords : vec5) -> i32 { return coords.x * uniforms.outShapeStrides.x + coords.y * uniforms.outShapeStrides.y + coords.z * uniforms.outShapeStrides.z + coords.w * uniforms.outShapeStrides.w + coords.u; } `;break;case 6:t+=` fn getOutputIndexFromCoords(coords : vec6) -> i32 { return coords.x * uniforms.outShapeStrides.x + coords.y * uniforms.outShapeStrides.y + coords.z * uniforms.outShapeStrides.z + coords.w * uniforms.outShapeStrides.w + coords.u * uniforms.outShapeStrides.u + coords.v; } `;break;default:v.assert(!1,()=>`Unsupported ${e}D shape`);break}return t}function Pv(e){return e.dispatch[1]===1&&e.dispatch[2]===1}function Ku(e,t){return e==="float32"?t?"vec4":"f32":e==="int32"||e==="bool"?t?"vec4":"i32":e}function mre(e,t,a){let n=e.length,r=Ku(t,a),s;if(a?s=`fn setOutputAtIndex(flatIndex : i32, value : vec4) { result[flatIndex] = ${r}(value); } fn setOutputAtIndexI32(flatIndex : i32, value : vec4) { result[flatIndex] = ${r}(value); }`:s=`fn setOutputAtIndex(flatIndex : i32, value : f32) { result[flatIndex] = ${r}(value); } fn setOutputAtIndexI32(flatIndex : i32, value : i32) { result[flatIndex] = ${r}(value); }`,n>=2){let i=["d0","d1","d2","d3","d4","d5"].slice(0,n),o=na(n);a?s+=` fn setOutputAtCoords(${i.map(l=>`${l} : i32`).join(", ")}, value : vec4) { let flatIndex = getOutputIndexFromCoords(${o}(${i.join(", ")})); setOutputAtIndex(flatIndex / 4, value); } fn setOutputAtCoordsI32(${i.map(l=>`${l} : i32`).join(", ")}, value : vec4) { let flatIndex = getOutputIndexFromCoords(${o}(${i.join(", ")})); setOutputAtIndexI32(flatIndex / 4, value); } `:s+=` fn setOutputAtCoords(${i.map(l=>`${l} : i32`).join(", ")}, value : f32) { let flatIndex = getOutputIndexFromCoords(${o}(${i.join(", ")})); setOutputAtIndex(flatIndex, value); } fn setOutputAtCoordsI32(${i.map(l=>`${l} : i32`).join(", ")}, value 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n||(e<=8&&(s[1]=1),t<=16&&a<=16&&(r[0]=4)),{workGroupSize:r,elementsPerThread:s}}function z3(e,t,a=!1){if(a)return[8,8,1];let n=ti(e.x.map(s=>t[s])),r=ti(e.y.map(s=>t[s]));return n<=4?[4,16,1]:r<=4?[16,4,1]:[16,16,1]}function L3(e,t,a=!1){if(a)return[4,4,1];let n=ti(e.x.map(s=>t[s])),r=ti(e.y.map(s=>t[s]));return n<=4?[1,2,1]:r<=4?[2,1,1]:[2,2,1]}function Ye(e){return{x:e.map((t,a)=>a)}}function zv(e){if(e==="float32"||e==="int32"||e==="bool"||e==="string")return 4;if(e==="complex64")return 8;throw new Error(`Unknown dtype ${e}`)}function Lv(e,t){if(t==="float32")return new Float32Array(e);if(t==="int32")return new Int32Array(e);if(t==="bool"||t==="string")return Uint8Array.from(new Int32Array(e));throw new Error(`Unknown dtype ${t}`)}function B3(){return(typeof window!="undefined"||typeof WorkerGlobalScope!="undefined")&&!!navigator.gpu}var Pn;(function(e){e[e.MatMulReduceProgram=0]="MatMulReduceProgram",e[e.MatMulSplitKProgram=1]="MatMulSplitKProgram",e[e.MatMulSmallOutputSizeProgram=2]="MatMulSmallOutputSizeProgram",e[e.MatMulPackedProgram=3]="MatMulPackedProgram",e[e.MatMulMax=4]="MatMulMax"})(Pn||(Pn={}));var Are=V().getNumber("WEBGPU_CPU_HANDOFF_SIZE_THRESHOLD"),xre=(e,t)=>{let a=e.limits.maxComputeWorkgroupsPerDimension,n=t.dispatchLayout,r=t.dispatch;if(r.every(i=>i<=a))return r;v.assert(r[0]>a&&n.y===void 0&&n.z===void 0,()=>"Dispatch size exceeds WebGPU limits in Y or Z dimension.");let s=Math.ceil(Math.sqrt(r[0]));return s>a?(s=Math.ceil(Math.cbrt(r[0])),v.assert(s<=a,()=>"Total dispatch size exceeds WebGPU maximum."),[s,s,s]):[s,s,1]},Uh=class extends fl{constructor(e,t){if(super(),this.commandQueueOwnedIds=new WeakSet,this.dispatchNumberInEncoder=0,this.disposed=!1,this.downloadWaitMs=0,this.tensorDataPendingDisposal=[],this.stagingPendingDisposal=[],this.uniformPendingDisposal=[],this.uploadWaitMs=0,!B3())throw new Error("WebGPU is not supported on this device");this.pipelineCache={},this.device=e,this.queue=e.queue,this.currentCommandEncoder=null,this.currentComputePass=null,this.supportTimeQuery=e.features.has("timestamp-query"),this.adapterInfo=new nre(t),this.bufferManager=new rre(this.device),this.textureManager=new sre(this.device),this.tensorMap=new md(this,kt()),this.supportTimeQuery&&(this.querySet=this.device.createQuerySet({type:"timestamp",count:2})),V().getBool("WEBGPU_USE_PROFILE_TOOL")&&(this.dummyCanvas=document.createElement("canvas"),this.dummyCanvas.width=1,this.dummyCanvas.height=1,this.dummyContext=this.dummyCanvas.getContext("webgpu"),this.dummyContext.configure({device:e,format:"bgra8unorm"}),document.body.appendChild(this.dummyCanvas))}nextDataId(){return Uh.nextDataId++}floatPrecision(){return 32}defaultGpuBufferUsage(){return GPUBufferUsage.STORAGE|GPUBufferUsage.COPY_SRC|GPUBufferUsage.COPY_DST}disposeData(e,t=!1){if(this.tensorDataPendingDisposal.indexOf(e)>=0)return!1;if(!this.tensorMap.has(e))return!0;let a=this.tensorMap.get(e);if(this.decRef(e),!t&&a.refCount>0)return!1;if(this.commandQueueOwnedIds.has(e))return this.tensorDataPendingDisposal.push(e),!1;let{complexTensorInfos:n}=this.tensorMap.get(e);return n!=null&&(this.disposeData(n.real.dataId,t),this.disposeData(n.imag.dataId,t)),this.releaseResource(e),this.tensorMap.delete(e),!0}memory(){return{numBytesInGPU:this.bufferManager.numBytesUsed,numBytesAllocatedInGPU:this.bufferManager.numBytesAllocated,unreliable:!1}}releaseResource(e){let t=this.tensorMap.get(e);if(!(!t||!t.resourceInfo)){if("texture"in t.resourceInfo){let a=t.resourceInfo;a.texture instanceof GPUTexture&&this.textureManager.releaseTexture(a.texture,a.width,a.height,a.format,a.usage),a.texture=null}else{let a=t.resourceInfo;this.bufferManager.releaseBuffer(a.buffer,a.size,a.usage),a.buffer=null}t.resourceInfo=null}}refCount(e){return this.tensorMap.has(e)?this.tensorMap.get(e).refCount:0}incRef(e){let t=this.tensorMap.get(e);t.refCount++}decRef(e){if(this.tensorMap.has(e)){let t=this.tensorMap.get(e);t.refCount--}}write(e,t,a){if(a==="complex64"&&e!=null)throw new Error("Cannot write to a complex64 dtype. Please use tf.complex(real, imag).");let n={id:this.nextDataId()};return this.tensorMap.set(n,{dtype:a,shape:t,values:e,refCount:1}),n}move(e,t,a,n,r){if(n==="complex64")throw new Error("Cannot write to a complex64 dtype. Please use tf.complex(real, imag).");this.tensorMap.set(e,{dtype:n,shape:a,values:t,refCount:r})}submitQueue(){this.ensureComputePassEnded(),this.queue.submit([this.currentCommandEncoder.finish()]),this.currentCommandEncoder=null,this.dispatchNumberInEncoder=0,this.commandQueueOwnedIds=new WeakSet,this.tensorDataPendingDisposal.forEach(e=>{this.releaseResource(e),this.tensorMap.delete(e)}),this.uniformPendingDisposal.forEach(e=>this.bufferManager.releaseBuffer(e.buffer,e.size,e.usage)),this.stagingPendingDisposal.forEach(e=>this.bufferManager.releaseUploadBuffer(e.buffer,e.size,e.usage)),this.tensorDataPendingDisposal=[],this.uniformPendingDisposal=[],this.stagingPendingDisposal=[]}ensureCommandEncoderReady(){this.currentCommandEncoder||(this.currentCommandEncoder=this.device.createCommandEncoder())}ensureComputePassEnded(){this.currentComputePass&&(this.currentComputePass.end(),this.currentComputePass=null)}getComputePass(){return this.currentComputePass||(this.currentComputePass=this.currentCommandEncoder.beginComputePass()),this.currentComputePass}async getBufferData(e,t){let a=this.bufferManager.acquireBuffer(t,GPUBufferUsage.COPY_DST|GPUBufferUsage.MAP_READ);this.ensureCommandEncoderReady(),this.ensureComputePassEnded(),this.currentCommandEncoder.copyBufferToBuffer(e,0,a,0,t),this.submitQueue(),await a.mapAsync(GPUMapMode.READ);let n=a.getMappedRange().slice(0);return a.unmap(),a!=null&&this.bufferManager.releaseBuffer(a,t,GPUBufferUsage.COPY_DST|GPUBufferUsage.MAP_READ),V().getBool("WEBGPU_USE_PROFILE_TOOL")&&(v.assert(this.dummyContext!==void 0,()=>"Fail to get context for profiling tool"),this.dummyContext.getCurrentTexture()),n}convertAndCacheOnCPU(e,t){let a=this.tensorMap.get(e);return this.releaseResource(e),a.values=t,a.values}readSync(e){let t=this.tensorMap.get(e),{values:a}=t;if(a==null)throw new Error("WebGPU readSync is only available for CPU-resident tensors.");return a}async read(e){if(!this.tensorMap.has(e))throw new Error(`Tensor ${e} was not registered!`);let t=this.tensorMap.get(e),{values:a}=t;if(a!=null)return this.convertAndCacheOnCPU(e,a);let n;if(t.dtype==="complex64"){let r=await Promise.all([this.read(t.complexTensorInfos.real.dataId),this.read(t.complexTensorInfos.imag.dataId)]),s=r[0],i=r[1];n=T.mergeRealAndImagArrays(s,i)}else{let r=t.resourceInfo,s=await this.getBufferData(r.buffer,r.size);n=Lv(s,t.dtype)}return this.convertAndCacheOnCPU(e,n),n}readToGPU(e){let t=this.tensorMap.get(e),{values:a,dtype:n,shape:r,resourceInfo:s}=t;if(n==="complex64")throw new Error("Does not support reading buffer for complex64 dtype.");if(s==null)throw a!=null?new Error("Data is not on GPU but on CPU."):new Error("There is no data on GPU or CPU.");let i=s.size,o=this.bufferManager.acquireBuffer(i,s.usage);this.ensureCommandEncoderReady(),this.ensureComputePassEnded(),this.currentCommandEncoder.copyBufferToBuffer(s.buffer,0,o,0,i),this.submitQueue();let l=this.makeTensorInfo(r,n),u=kt().makeTensorFromTensorInfo(l),d=this.tensorMap.get(l.dataId);return d.resourceInfo={size:i,usage:this.defaultGpuBufferUsage(),buffer:o},{tensorRef:u,buffer:o,bufSize:i}}bufferSync(e){let t=this.readSync(e.dataId);if(e.dtype==="string")try{let a=t.map(n=>v.decodeString(n));return ve(e.shape,e.dtype,a)}catch(a){throw new Error("Failed to decode encoded string bytes into utf-8")}return ve(e.shape,e.dtype,t)}async time(e){this.supportTimeQuery||console.warn("This device doesn't support timestamp-query extension. Start Chrome browser with flag --disable-dawn-features=disallow_unsafe_apis then try again. Otherwise, zero will be shown for the kernel time when profiling mode is enabled. Using performance.now is not workable for webgpu since it doesn't support synchronous data read from GPU.");let t=this.activeTimers,a=[],n=!1;this.programTimersStack==null?(this.programTimersStack=a,n=!0):this.activeTimers.push(a),this.activeTimers=a,e();let r=v.flatten(this.activeTimers.map(l=>l.query)).filter(l=>l!=null),s=v.flatten(this.activeTimers.map(l=>l.name)).filter(l=>l!=null);this.activeTimers=t,n&&(this.programTimersStack=null);let i={uploadWaitMs:this.uploadWaitMs,downloadWaitMs:this.downloadWaitMs,kernelMs:null,wallMs:null},o=await Promise.all(r);return i.kernelMs=v.sum(o),i.getExtraProfileInfo=()=>o.map((l,u)=>({name:s[u],ms:l})).map(l=>`${l.name}: ${l.ms}`).join(", "),this.uploadWaitMs=0,this.downloadWaitMs=0,i}makeTensorInfo(e,t,a){return t==="string"&&a!=null&&a.length>0&&v.isString(a[0])&&(a=a.map(n=>v.encodeString(n))),{dataId:this.write(a,e,t),shape:e,dtype:t}}tensorToBinding(e){if(!e)return null;let t=this.tensorMap.get(e.dataId);if("texture"in t.resourceInfo){let n=t.resourceInfo;return n.texture instanceof GPUExternalTexture?n.texture:n.texture.createView()}let a=t.resourceInfo;return{offset:0,size:a.size,buffer:a.buffer}}async getQueryTime(e){return this.supportTimeQuery?this.getTimeFromQuerySet(e):0}uploadToGPU(e){let t=this.tensorMap.get(e);if(t.resourceInfo)return;let a=zv(t.dtype)*v.sizeFromShape(t.shape),n=this.bufferManager.acquireBuffer(a,this.defaultGpuBufferUsage());if(t.resourceInfo={size:a,usage:this.defaultGpuBufferUsage(),buffer:n},t.values){let r=this.bufferManager.acquireUploadBuffer(a,GPUBufferUsage.MAP_WRITE|GPUBufferUsage.COPY_SRC),s=r.getMappedRange();t.dtype==="int32"||t.dtype==="bool"?new Int32Array(s).set(t.values):new Float32Array(s).set(t.values),r.unmap(),this.ensureCommandEncoderReady(),this.ensureComputePassEnded(),this.currentCommandEncoder.copyBufferToBuffer(r,0,n,0,a);let i={size:a,usage:GPUBufferUsage.MAP_WRITE|GPUBufferUsage.COPY_SRC,buffer:r};this.stagingPendingDisposal.push(i)}}makeUniforms(e){let t=0,a=0,n=[];e.forEach(o=>{o.data.length===0&&(o.data=[1]);let l;switch(o.data.length){case 1:l=4;break;case 2:l=8;break;case 3:l=16;break;case 4:l=16;break;case 5:l=16;break;case 6:l=16;break;default:v.assert(!1,()=>`Unsupported ${o.data.length}D shape`)}(a===5||a===6)&&(l=16),t=Math.ceil(t/l)*l,a=o.data.length,n.push(t),t+=o.data.length*4});let r=new ArrayBuffer(t);e.forEach((o,l)=>{let u=n[l];o.type==="int32"?new Int32Array(r,u,o.data.length).set(o.data):o.type==="uint32"?new Uint32Array(r,u,o.data.length).set(o.data):new Float32Array(r,u,o.data.length).set(o.data)});let s=this.bufferManager.acquireBuffer(t,GPUBufferUsage.COPY_DST|GPUBufferUsage.UNIFORM);this.queue.writeBuffer(s,0,r,0,t);let i={size:t,usage:GPUBufferUsage.COPY_DST|GPUBufferUsage.UNIFORM,buffer:s};return this.uniformPendingDisposal.push(i),{offset:0,size:t,buffer:s}}runWebGPUProgram(e,t,a,n,r){if(r||(r=this.makeTensorInfo(e.outputShape,a)),v.sizeFromShape(r.shape)===0)return this.tensorMap.get(r.dataId).values=v.getTypedArrayFromDType(r.dtype,0),r;this.uploadToGPU(r.dataId),e.dispatch=xre(this.device,e);let s=[],i=[];if(!e.isFromPixels){s.push({type:"float32",data:[NaN]}),i=t.concat(r).map(g=>g.shape);let f="int32";i.map(g=>{s.push({type:f,data:g})});let m=v.computeStrides(r.shape);if(s.push({type:f,data:m}),e.size){let g=v.sizeFromShape(e.outputShape);s.push({type:f,data:[e.isVec4?g/4:g]})}}let o=t.map((f,m)=>{if(f.dtype==="complex64")throw new Error("GPGPUProgram does not support complex64 input. For complex64 dtypes, please separate the program into real and imaginary parts.");return this.uploadToGPU(f.dataId),{dtype:this.tensorMap.get(f.dataId).dtype,shape:f.shape,name:e.variableNames[m]}}),l=ure(e,i,o,r),u;l in this.pipelineCache?u=this.pipelineCache[l]:(u=ore(this.device,e,o,r),this.pipelineCache[l]=u),n&&(s=[...s,...n]);let d=[this.tensorToBinding(r),...t.map(f=>this.tensorToBinding(f)),this.makeUniforms(s)],c=this.device.createBindGroup({layout:u.getBindGroupLayout(0),entries:d.map((f,m)=>({binding:m,resource:f}))});this.ensureCommandEncoderReady();let p=this.getComputePass(),h=this.activeTimers!=null;return h&&this.supportTimeQuery&&p.writeTimestamp(this.querySet,0),p.setPipeline(u),p.setBindGroup(0,c),p.dispatchWorkgroups(e.dispatch[0],e.dispatch[1],e.dispatch[2]),h&&this.supportTimeQuery&&p.writeTimestamp(this.querySet,1),this.dispatchNumberInEncoder++,t.forEach(f=>{this.commandQueueOwnedIds.add(f.dataId)}),this.commandQueueOwnedIds.add(r.dataId),V().get("WEBGPU_DEFERRED_SUBMIT_BATCH_SIZE")<=this.dispatchNumberInEncoder&&this.submitQueue(),h&&this.activeTimers.push({name:e.constructor.name,query:this.getQueryTime(this.querySet)}),r}async getTimeFromQuerySet(e){let t=this.bufferManager.acquireBuffer(16,GPUBufferUsage.COPY_SRC|GPUBufferUsage.QUERY_RESOLVE),a=this.bufferManager.acquireBuffer(16,GPUBufferUsage.MAP_READ|GPUBufferUsage.COPY_DST);this.ensureCommandEncoderReady(),this.ensureComputePassEnded(),this.currentCommandEncoder.resolveQuerySet(e,0,2,t,0),this.currentCommandEncoder.copyBufferToBuffer(t,0,a,0,16),this.submitQueue(),await a.mapAsync(GPUMapMode.READ);let n=new BigUint64Array(a.getMappedRange()),r=Number(n[1]-n[0]);return a.unmap(),this.bufferManager.releaseBuffer(a,16,GPUBufferUsage.MAP_READ|GPUBufferUsage.COPY_DST),this.bufferManager.releaseBuffer(t,16,GPUBufferUsage.COPY_SRC|GPUBufferUsage.QUERY_RESOLVE),r/1e6}shouldExecuteOnCPU(e,t=Are){return V().getBool("WEBGPU_CPU_FORWARD")&&e.every(a=>this.tensorMap.get(a.dataId).resourceInfo==null&&v.sizeFromShape(a.shape){V().set("CHECK_COMPUTATION_FOR_ERRORS",!1);let e={powerPreference:V().get("WEBGPU_USE_LOW_POWER_GPU")?"low-power":"high-performance"},t=await navigator.gpu.requestAdapter(e),a=t.limits,n={},r=t.features.has("timestamp-query");n.requiredLimits={maxComputeWorkgroupStorageSize:a.maxComputeWorkgroupStorageSize,maxComputeWorkgroupsPerDimension:a.maxComputeWorkgroupsPerDimension,maxStorageBufferBindingSize:a.maxStorageBufferBindingSize},r&&(n.requiredFeatures=["timestamp-query"]);let s=await t.requestDevice(n),i=await t.requestAdapterInfo();return new Uh(s,i)},3);var Be;(function(e){e[e.MUL=0]="MUL",e[e.ADD=1]="ADD",e[e.ATAN2=2]="ATAN2",e[e.SUB=3]="SUB",e[e.DIV=4]="DIV",e[e.EQUAL=5]="EQUAL",e[e.GREATER=6]="GREATER",e[e.GREATER_EQUAL=7]="GREATER_EQUAL",e[e.LESS=8]="LESS",e[e.LESS_EQUAL=9]="LESS_EQUAL",e[e.LOGICAL_AND=10]="LOGICAL_AND",e[e.NOT_EQUAL=11]="NOT_EQUAL",e[e.SQUARED_DIFFERENCE=12]="SQUARED_DIFFERENCE",e[e.INT_DIV=13]="INT_DIV",e[e.POW=14]="POW",e[e.PRELU=15]="PRELU",e[e.MAX=16]="MAX",e[e.MIN=17]="MIN",e[e.COMPLEX_MULTIPLY_REAL=18]="COMPLEX_MULTIPLY_REAL",e[e.COMPLEX_MULTIPLY_IMAG=19]="COMPLEX_MULTIPLY_IMAG"})(Be||(Be={}));var bre=` if (isnan(a)) { return a; } if (isnan(b)) { return b; } `,Bv=` if (isNaN.r) { resultTemp.r = valueForNaN; } if (isNaN.g) { resultTemp.g = valueForNaN; } if (isNaN.b) { resultTemp.b = valueForNaN; } if (isNaN.a) { resultTemp.a = valueForNaN; } `,Wv=` let isNaN = isnanVec4(a) | isnanVec4(b); ${Bv} `,vre="return a + b;",wre="return areal * breal - aimag * bimag;",kre="return areal * bimag + aimag * breal;",Ire="return a / b;",Sre="return a * b;",Tre="return (a - b) * (a - b);",Cre="return a - b;",Nre="return f32(a == b);",Ere="return vec4(a == b);",Rre="return f32(a > b);",Mre="return vec4(a > b);",$re="return f32(a >= b);",_re="return vec4(a >= b);",Fre="return f32(a < b);",Pre="return vec4(a < b);",Ore="return f32(a <= b);",Dre="return vec4(a <= b);",zre="return f32(f32(a) >= 1.0 && f32(b) >= 1.0);",Lre=`return (vec4(a >= vec4(1.0)) * vec4(b >= vec4(1.0)));`,Bre=` let s = sign(a) * sign(b); let ia = i32(round(a)); let ib = i32(round(b)); return f32(idiv(ia, ib, s)); `,Wre=` let ia = vec4(round(a)); let ib = vec4(round(b)); let cond = ib != vec4(0); var resultTemp = vec4(0); let s = sign(a) * sign(b); // Windows (D3D) wants guaranteed non-zero int division at compile-time. if (cond[0]) { resultTemp[0] = idiv(ia[0], ib[0], s[0]); } if (cond[1]) { resultTemp[1] = idiv(ia[1], ib[1], s[1]); } if (cond[2]) { resultTemp[2] = idiv(ia[2], ib[2], s[2]); } if (cond[3]) { resultTemp[3] = idiv(ia[3], ib[3], s[3]); } return vec4(resultTemp); `,Vre=` if (isnan(a) || isnan(b)) { return 1.0; } return f32(a != b); `,Gre=` var resultTemp = vec4(a != b); let valueForNaN = 1.0; ${Wv} return resultTemp; `,Ure=` if(a < 0.0 && floor(b) < b) { return uniforms.NAN; } if (b == 0.0) { return 1.0; } if (round(abs(b) % 2.0) != 1.0) { return pow(abs(a), b); } return sign(a) * pow(abs(a), b); `,jre=` let isModRound1Bool = vec4(round(abs(b) % vec4(2.0))) == vec4(1); let isModRound1 = vec4(isModRound1Bool); let multiplier = sign(a) * isModRound1 + (vec4(1.0) - isModRound1); var resultTemp = multiplier * pow(abs(a), b); // Ensure that a^0 = 1, including 0^0 = 1 as this correspond to TF and JS let isExpZero = b == vec4(0.0); if (isExpZero.r) { resultTemp.r = 1.0; } if (isExpZero.g) { resultTemp.g = 1.0; } if (isExpZero.b) { resultTemp.b = 1.0; } if (isExpZero.a) { resultTemp.a = 1.0; } let isNaN = (a < vec4(0.0)) & (floor(b) < b); let valueForNaN = uniforms.NAN; ${Bv} return resultTemp; `,Hre="if (a < 0.0) { return b * a; } return a;",qre=` let aLessThanZero = vec4(a < vec4(0.0)); return (aLessThanZero * (b * a)) + ((vec4(1.0) - aLessThanZero) * a); `;function Lm(e,t,a="uniforms.NAN"){let n=t?Wv:bre;return t?` let valueForNaN = ${a}; var resultTemp = vec4(${e}(a, b)); `+n+` return resultTemp; `:n+` return ${e}(a, b); `}function W3(e,t){switch(e){case Be.MUL:return Sre;case Be.ADD:return vre;case Be.ATAN2:return Lm("atan2",t);case Be.SUB:return Cre;case Be.DIV:return Ire;case Be.EQUAL:return t?Ere:Nre;case Be.GREATER:return t?Mre:Rre;case Be.GREATER_EQUAL:return t?_re:$re;case Be.LESS:return t?Pre:Fre;case Be.LESS_EQUAL:return t?Dre:Ore;case Be.LOGICAL_AND:return t?Lre:zre;case Be.NOT_EQUAL:return t?Gre:Vre;case Be.SQUARED_DIFFERENCE:return Tre;case Be.INT_DIV:return t?Wre:Bre;case Be.PRELU:return t?qre:Hre;case Be.MAX:return Lm("max",t);case Be.MIN:return Lm("min",t);case Be.POW:return t?jre:Ure;case Be.COMPLEX_MULTIPLY_REAL:return wre;case Be.COMPLEX_MULTIPLY_IMAG:return kre;default:throw new Error(`BinaryType ${e} is not implemented!`)}}var Se;(function(e){e[e.ABS=0]="ABS",e[e.CEIL=1]="CEIL",e[e.COS=2]="COS",e[e.COSH=3]="COSH",e[e.ELU=4]="ELU",e[e.EXP=5]="EXP",e[e.EXPM1=6]="EXPM1",e[e.FLOOR=7]="FLOOR",e[e.IS_NAN=8]="IS_NAN",e[e.LINEAR=9]="LINEAR",e[e.LOG=10]="LOG",e[e.LOGICAL_NOT=11]="LOGICAL_NOT",e[e.NEG=12]="NEG",e[e.RELU=13]="RELU",e[e.RELU6=14]="RELU6",e[e.LEAKYRELU=15]="LEAKYRELU",e[e.RECIPROCAL=16]="RECIPROCAL",e[e.RSQRT=17]="RSQRT",e[e.SIN=18]="SIN",e[e.SINH=19]="SINH",e[e.SIGMOID=20]="SIGMOID",e[e.SQRT=21]="SQRT",e[e.SQUARE=22]="SQUARE",e[e.TANH=23]="TANH",e[e.TO_INT=24]="TO_INT"})(Se||(Se={}));var Xre="return abs(a);",Kre="return ceil(a);",Zre="return cos(a);",Yre=` let e2x = exp(-a); return (e2x + 1.0 / e2x) / 2.0; `,Jre="return exp(a) - 1.0;",Qre="if (a >= 0.0) { return a; } return (exp(a) - 1.0);",ese=` var resFloat = exp(a) - vec4(1.0); if (a.r >= 0.0) { resFloat.r = a.r; } if (a.g >= 0.0) { resFloat.g = a.g; } if (a.b >= 0.0) { resFloat.b = a.b; } if (a.a >= 0.0) { resFloat.a = a.a; } return resFloat; `,tse="return exp(a);",ase="return floor(a);",nse="return f32(isnan(a));",rse="return a;",sse=`if (a < 0.0) { return uniforms.NAN; } return log(a);`,ise="return f32(!(a >= 1.0));",ose="return -a;",lse="if (a < 0.0) { return uniforms.alpha * a; } return a;",use=` let aLessThanZero = vec4(a < vec4(0.0)); return (aLessThanZero * (uniforms.alpha * a)) + ((vec4(1.0) - aLessThanZero) * a); `,dse="return 1.0 / a;",pse="return select(a, 0.0, a < 0.0);",cse="return clamp(a, 0.0, 6.0);",hse="return clamp(a, vec4(0.0, 0.0, 0.0, 0.0), vec4(6.0, 6.0, 6.0, 6.0));",fse=` return select(a, vec4(0.0), a < vec4(0.0)); `,mse="return 1.0/sqrt(a);",gse="return 1.0 / (1.0 + exp(-1.0 * a));",yse="return sin(a);",Ase=` let e2x = exp(a); return (e2x - 1.0 / e2x) / 2.0; `,xse="return sqrt(a);",bse="return a * a;",vse=` let e2x = exp(-2.0 * abs(a)); return sign(a) * (1.0 - e2x) / (1.0 + e2x); `,wse="return f32(i32((a)));";function qs(e,t){switch(e){case Se.ABS:return Xre;case Se.COS:return Zre;case Se.COSH:return Yre;case Se.CEIL:return Kre;case Se.ELU:return t?ese:Qre;case Se.EXP:return tse;case Se.EXPM1:return Jre;case Se.FLOOR:return ase;case Se.IS_NAN:return nse;case Se.LINEAR:return rse;case Se.LOG:return sse;case Se.LOGICAL_NOT:return ise;case Se.NEG:return ose;case Se.LEAKYRELU:return t?use:lse;case Se.RECIPROCAL:return dse;case Se.RELU:return t?fse:pse;case Se.RELU6:return t?hse:cse;case Se.RSQRT:return mse;case Se.SIGMOID:return gse;case Se.SIN:return yse;case Se.SINH:return Ase;case Se.SQRT:return xse;case Se.SQUARE:return bse;case Se.TANH:return vse;case Se.TO_INT:return wse;default:throw new Error(`BinaryType ${e} is not implemented!`)}}var Rt=e=>{switch(e){case 1:return"f32";case 2:return"vec2";case 3:return"vec3";case 4:return"vec4";default:throw new Error(`${e}-component is not supported.`)}};function Cr(e,t=!1,a=!1,n=3){if(e===null)return"";let r="";if(e==="linear")r=qs(Se.LINEAR);else if(e==="relu")r=qs(Se.RELU,a);else if(e==="elu")r=qs(Se.ELU,a);else if(e==="relu6")r=qs(Se.RELU6,a);else if(e==="prelu")r=W3(Be.PRELU,a);else if(e==="sigmoid")r=qs(Se.SIGMOID,a);else if(e==="leakyrelu")r=qs(Se.LEAKYRELU,a);else throw new Error(`Activation ${e} has not been implemented for the WebGPU backend.`);let s=Rt(a?4:1),i="";return t?i=` fn activation(a : ${s}, coords : vec${n}) -> ${s} { let b = getPreluActivationWeightsByOutputCoords(coords); ${r} }`:i=` fn activation(a : ${s}, coords : vec${n}) -> ${s} { ${r} }`,i}function mo(e,t){return` ${e?"value = value + getBiasByOutputCoords(coords);":""} ${t?"value = activation(value, coords);":""} `}function Vv(e,t,a,n,r=!1,s=!1,i=!1,o=1){v.assert(a&&o===1||!a,()=>`transposeA ${a} is not compatible with component size ${o}`);let l=` let batch = ${e?"0":"batchIn"}; ${a?"value = getA(batch, col, row);":"value = getA(batch, row, col);"} `,u=n?"value = getB(batch, col, row);":"value = getB(batch, row, col);";return` fn mm_readA(batchIn: i32, row: i32, colIn: i32) -> ${Rt(o)} { var value = ${Rt(o)}(0.0); let col = colIn * ${o}; ${r&&i?l:` ${a?"if(row < uniforms.dimAOuter && col < uniforms.dimInner)":"if(row < uniforms.aShape[1] && col < uniforms.aShape[2])"} { ${l} } `} return value; } fn mm_readB(batchIn: i32, row: i32, colIn: i32) -> ${Rt(o)} { let col = colIn * ${o}; let batch = ${t?"0":"batchIn"}; var value = ${Rt(o)}(0.0); ${u} return value; } `}function V3(e,t,a,n,r,s,i=!1,o=!1,l=!1,u=1){return` ${Vv(a,n,r,s,i,o,l,u)} fn mm_write(batch: i32, row: i32, colIn: i32, valueIn: ${Rt(u)}) { let col = colIn * ${u}; ${i&&o?"":"if (row < uniforms.dimAOuter && col < uniforms.dimBOuter)"} { var value = valueIn; let coords = vec3(batch, row, col); ${mo(e,t)} setOutputAtCoords(coords[0], coords[1], coords[2], value); } } `}var kse=e=>e?` mm_Asub[inputRow][inputCol] = mm_readA(batch, kStart + inputRow, globalRowStart / InnerElementSize + inputCol); `:` mm_Asub[inputRow][inputCol] = mm_readA(batch, globalRow + innerRow, kStart / InnerElementSize + inputCol); `,Ise=(e,t)=>e?` let ACached0 = mm_Asub[k * InnerElementSize][localRow]; let ACached1 = mm_Asub[k * InnerElementSize + 1][localRow]; let ACached2 = mm_Asub[k * InnerElementSize + 2][localRow]; ${t===3?"":"let ACached3 = mm_Asub[k * InnerElementSize + 3][localRow];"} for (var i = 0; i < RowPerThread; i = i + 1) { acc[i] = BCached0 * ACached0[i] + acc[i]; acc[i] = BCached1 * ACached1[i] + acc[i]; acc[i] = BCached2 * ACached2[i] + acc[i]; ${t===3?"":"acc[i] = BCached3 * ACached3[i] + acc[i];"} }`:` for (var i = 0; i < RowPerThread; i = i + 1) { let ACached = mm_Asub[tileRow + i][k]; acc[i] = BCached0 * ACached.x + acc[i]; acc[i] = BCached1 * ACached.y + acc[i]; acc[i] = BCached2 * ACached.z + acc[i]; ${t===3?"":"acc[i] = BCached3 * ACached.w + acc[i];"} }`;function jh(e,t,a=!1,n=32,r=!1,s=32,i=!1){let o=t[1]*e[1],l=t[0]*e[0],u=a?o:n,d=a?n:o,c=u/t[0],p=n/t[1];return v.assert((a&&c===4&&e[1]===4||!a&&(c===3||c===4))&&u%t[0]===0&&n%t[1]===0&&e[0]===4,()=>`If transposeA ${a} is true, innerElementSize ${c} and workPerThread[1] ${e[1]} must be 4. Otherwise, innerElementSize ${c} must be 3 or 4. tileAWidth ${u} must be divisible by workGroupSize[0]${t[0]}. tileInner ${n} must be divisible by workGroupSize[1] ${t[1]}. ColPerThread ${e[0]} must be 4.`),` var mm_Asub : array, ${u/c}>, ${d}>; var mm_Bsub : array, ${l/e[0]}>, ${n}>; const RowPerThread = ${e[1]}; const ColPerThread = ${e[0]}; const InnerElementSize = ${c}; const TileInner = ${n}; @compute @workgroup_size(workGroupSizeX, workGroupSizeY, workGroupSizeZ) fn _start(@builtin(local_invocation_id) LocalId : vec3, @builtin(global_invocation_id) GlobalId : vec3, @builtin(num_workgroups) NumWorkgroups: vec3, @builtin(workgroup_id) workgroupId: vec3) { localId = LocalId; globalId = GlobalId; numWorkgroups = NumWorkgroups; let localRow = i32(localId.y); let tileRow = ${i?"0":"localRow * RowPerThread"}; let tileCol = i32(localId.x); let globalRow = ${i?"0":"i32(globalId.y) * RowPerThread"}; let globalCol = i32(globalId.x); let batch = ${r?"0":"i32(globalId.z)"}; let globalRowStart = i32(workgroupId.y) * ${o}; let numTiles = ${r?`${Math.ceil(s/n)}`:"(uniforms.dimInner - 1) / TileInner + 1"}; var kStart = ${r?`i32(globalId.z) * ${s}`:"0"}; var acc: array, RowPerThread>; // Loop over shared dimension. let tileRowB = localRow * ${p}; for (var t = 0; t < numTiles; t = t + 1) { // Load one tile of A into local memory. for (var innerRow = 0; innerRow < RowPerThread; innerRow = innerRow + 1) { let inputRow = tileRow + innerRow; let inputCol = tileCol; ${kse(a)} } // Load one tile of B into local memory. for (var innerRow = 0; innerRow < ${p}; innerRow = innerRow + 1) { let inputRow = tileRowB + innerRow; let inputCol = tileCol; mm_Bsub[inputRow][inputCol] = mm_readB(batch, kStart + inputRow, globalCol); } kStart = kStart + TileInner; workgroupBarrier(); // Compute acc values for a single thread. for (var k = 0; k < TileInner / InnerElementSize; k = k + 1) { let BCached0 = mm_Bsub[k * InnerElementSize][tileCol]; let BCached1 = mm_Bsub[k * InnerElementSize + 1][tileCol]; let BCached2 = mm_Bsub[k * InnerElementSize + 2][tileCol]; ${c===3?"":"let BCached3 = mm_Bsub[k * InnerElementSize + 3][tileCol];"} ${Ise(a,c)} } workgroupBarrier(); } for (var innerRow = 0; innerRow < RowPerThread; innerRow = innerRow + 1) { mm_write(batch, globalRow + innerRow, globalCol, acc[innerRow]); } }`}var Jy=e=>e?` mm_Asub[inputRow][inputCol] = mm_readA(batch, kStart + inputRow, globalRowStart + inputCol); `:` mm_Asub[inputRow][inputCol] = mm_readA(batch, globalRowStart + inputRow, kStart + inputCol); `,Sse=e=>e?"let ACached = mm_Asub[k][tileRow + innerRow];":"let ACached = mm_Asub[tileRow + innerRow][k];";function Hh(e,t,a=!1,n=32,r=!1,s=32,i=!1){let o=e[1]*t[1],l=e[0]*t[0],u=a?o:n,d=a?n:o;v.assert(d%t[1]===0&&u%t[0]===0&&n%t[1]===0,()=>`tileAHight ${d} must be divisible by workGroupSize[1]${t[1]}, tileAWidth ${u} must be divisible by workGroupSize[0]${t[0]}, tileInner ${n} must be divisible by workGroupSize[1]${t[1]}`);let c=d/t[1],p=u/t[0],h=n/t[1],f=i?` let localRow = i32(localId.y); let localCol = i32(localId.x); let globalRowStart = i32(workgroupId.y) * ${o}; let globalColStart = i32(workgroupId.x) * ${l}; // Loop over shared dimension. for (var t = 0; t < numTiles; t = t + 1) { // Load one tile of A into local memory. for (var inputRow = localRow; inputRow < ${d}; inputRow = inputRow + ${t[1]}) { for (var inputCol = localCol; inputCol < ${u}; inputCol = inputCol + ${t[0]}) { ${Jy(a)} } } // Load one tile of B into local memory. for (var inputRow = localRow; inputRow < ${n}; inputRow = inputRow + ${t[1]}) { for (var inputCol = localCol; inputCol < ${l}; inputCol = inputCol + ${t[0]}) { mm_Bsub[inputRow][inputCol] = mm_readB(batch, kStart + inputRow, globalColStart + inputCol); } } kStart = kStart + TileInner; workgroupBarrier(); // Compute acc values for a single thread. var BCached : array; for (var k = 0; k < TileInner; k = k + 1) { for (var inner = 0; inner < ColPerThread; inner = inner + 1) { BCached[inner] = mm_Bsub[k][localCol + inner * ${t[0]}]; } for (var innerRow = 0; innerRow < RowPerThread; innerRow = innerRow + 1) { let ACached = ${a?`mm_Asub[k][localRow + innerRow * ${t[1]}];`:`mm_Asub[localRow + innerRow * ${t[1]}][k];`} for (var innerCol = 0; innerCol < ColPerThread; innerCol = innerCol + 1) { acc[innerRow][innerCol] = acc[innerRow][innerCol] + ACached * BCached[innerCol]; } } } workgroupBarrier(); } for (var innerRow = 0; innerRow < RowPerThread; innerRow = innerRow + 1) { let gRow = globalRowStart + localRow + innerRow * ${t[1]}; for (var innerCol = 0; innerCol < ColPerThread; innerCol = innerCol + 1) { let gCol = globalColStart + localCol + innerCol * ${t[0]}; mm_write(batch, gRow, gCol, acc[innerRow][innerCol]); } } `:` let tileRow = i32(localId.y) * RowPerThread; let tileCol = i32(localId.x) * ColPerThread; let globalRow = i32(globalId.y) * RowPerThread; let globalCol = i32(globalId.x) * ColPerThread; let globalRowStart = i32(workgroupId.y) * ${o}; let tileRowA = i32(localId.y) * ${c}; let tileColA = i32(localId.x) * ${p}; let tileRowB = i32(localId.y) * ${h}; // Loop over shared dimension. for (var t = 0; t < numTiles; t = t + 1) { // Load one tile of A into local memory. for (var innerRow = 0; innerRow < ${c}; innerRow = innerRow + 1) { for (var innerCol = 0; innerCol < ${p}; innerCol = innerCol + 1) { let inputRow = tileRowA + innerRow; let inputCol = tileColA + innerCol; ${Jy(a)} } } // Load one tile of B into local memory. for (var innerRow = 0; innerRow < ${h}; innerRow = innerRow + 1) { for (var innerCol = 0; innerCol < ColPerThread; innerCol = innerCol + 1) { let inputRow = tileRowB + innerRow; let inputCol = tileCol + innerCol; mm_Bsub[inputRow][inputCol] = mm_readB(batch, kStart + inputRow, globalCol + innerCol); } } kStart = kStart + TileInner; workgroupBarrier(); // Compute acc values for a single thread. var BCached : array; for (var k = 0; k < TileInner; k = k + 1) { for (var inner = 0; inner < ColPerThread; inner = inner + 1) { BCached[inner] = mm_Bsub[k][tileCol + inner]; } for (var innerRow = 0; innerRow < RowPerThread; innerRow = innerRow + 1) { ${Sse(a)} for (var innerCol = 0; innerCol < ColPerThread; innerCol = innerCol + 1) { acc[innerRow][innerCol] = acc[innerRow][innerCol] + ACached * BCached[innerCol]; } } } workgroupBarrier(); } for (var innerRow = 0; innerRow < RowPerThread; innerRow = innerRow + 1) { for (var innerCol = 0; innerCol < ColPerThread; innerCol = innerCol + 1) { mm_write(batch, globalRow + innerRow, globalCol + innerCol, acc[innerRow][innerCol]); } } `;return` var mm_Asub : array, ${d}>; var mm_Bsub : array, ${n}>; const RowPerThread = ${e[1]}; const ColPerThread = ${e[0]}; const TileInner = ${n}; @compute @workgroup_size(workGroupSizeX, workGroupSizeY, workGroupSizeZ) fn _start(@builtin(local_invocation_id) LocalId : vec3, @builtin(global_invocation_id) GlobalId : vec3, @builtin(num_workgroups) NumWorkgroups: vec3, @builtin(workgroup_id) workgroupId: vec3) { localId = LocalId; globalId = GlobalId; numWorkgroups = NumWorkgroups; let batch = ${r?"0":"i32(globalId.z)"}; let numTiles = ${r?`${Math.ceil(s/n)}`:"(uniforms.dimInner - 1) / TileInner + 1"}; var kStart = ${r?`i32(globalId.z) * ${s}`:"0"}; var acc : array, RowPerThread>; // Without this initialization strange values show up in acc. for (var innerRow = 0; innerRow < RowPerThread; innerRow = innerRow + 1) { for (var innerCol = 0; innerCol < ColPerThread; innerCol = innerCol + 1) { acc[innerRow][innerCol] = 0.0; } } ${f} } `}var Tse=e=>e?` mm_readA(batch, colA, globalRow), mm_readA(batch, colA + 1, globalRow), mm_readA(batch, colA + 2, globalRow), mm_readA(batch, colA + 3, globalRow) `:` mm_readA(batch, globalRow, colA), mm_readA(batch, globalRow, colA + 1), mm_readA(batch, globalRow, colA + 2), mm_readA(batch, globalRow, colA + 3) `;function Cse(e,t=!1){return v.assert(e[1]===1&&e[2]===1,()=>`A linear work group size is required. But got ${e}.`),` const TileSize = ${e[0]*4}; var mm_Asub : array, ${e[0]}>; ${We()} { let tileCol = i32(localId.x); let globalCol = i32(globalId.x); let globalRow = i32(globalId.y); let numTiles = (uniforms.dimInner - 1) / TileSize + 1; let batch = i32(globalId.z); // Without this initialization strange values show up in acc. var acc = 0.0; // Loop over shared dimension. for (var t = 0; t < numTiles; t = t + 1) { // Load one tile of A into local memory. let colA = t * TileSize + tileCol * 4; mm_Asub[tileCol] = vec4(${Tse(t)}); workgroupBarrier(); // Compute acc values for a single thread. for (var k = 0; k < TileSize / 4; k = k + 1) { let rowB = t * TileSize + k * 4; let BCached = vec4(mm_readB(batch, rowB, globalCol), mm_readB(batch, rowB + 1, globalCol), mm_readB(batch, rowB + 2, globalCol), mm_readB(batch, rowB + 3, globalCol)); let ACached = mm_Asub[k]; acc = acc + dot(ACached, BCached); } workgroupBarrier(); } mm_write(batch, globalRow, globalCol, acc); } `}var Nse=class{constructor(e,t,a,n,r=!1,s=!1,i=null,o=null,l=null,u=!1){this.variableNames=["A","B"],this.uniforms="dimAOuter : i32, dimBOuter : i32, dimInner : i32,",this.outputShape=t,this.dispatchLayout={x:[2],y:[1],z:[0]};let d=r?e[1]:e[2];if(this.isVec4=(d%4===0&&!r||t[1]%4===0&&r)&&t[2]%4===0&&!s,this.isVectorA=t[1]===1&&!r,!this.isVec4&&this.isVectorA)this.elementsPerThread=[1,1,1],this.workGroupSize=[32,1,1];else{let h=Dv(t[1],d,t[2],r);this.workGroupSize=h.workGroupSize,this.elementsPerThread=h.elementsPerThread}this.dispatch=Me(this.dispatchLayout,this.outputShape,this.workGroupSize,this.elementsPerThread);let c=i!=null,p=l!=null;c&&this.variableNames.push("bias"),p&&this.variableNames.push("preluActivationWeights"),this.sequentialAccessByThreads=u,this.transposeA=r,this.transposeB=s,this.addBias=c,this.activation=o,this.hasPreluActivationWeights=p,this.batchAEqualOne=a,this.batchBEqualOne=n,[this.fitAOuter,this.fitBOuter,this.fitInner]=this.getShapeFit(t[1],t[2],d),this.shaderKey=`matMulPacked_${this.elementsPerThread}_${r}_${s}_${this.activation}_${this.fitAOuter}_${this.fitBOuter}_${this.fitInner}_${this.isVec4}_${this.isVectorA}_${this.batchAEqualOne}_${this.batchBEqualOne}_${this.sequentialAccessByThreads}`}getShapeFit(e,t,a){let n=this.workGroupSize[1]*this.elementsPerThread[1],r=this.workGroupSize[0]*this.elementsPerThread[0];!this.isVec4&&this.isVectorA?this.tileInner=this.workGroupSize[0]*4:this.tileInner=r;let s=e%n===0,i=t%r===0,o=a%this.tileInner===0;return[s,i,o]}getUserCode(){return` ${Cr(this.activation,this.hasPreluActivationWeights,this.isVec4)} ${V3(this.addBias,this.activation,this.batchAEqualOne,this.batchBEqualOne,!1,this.transposeB,this.fitAOuter,this.fitBOuter,this.fitInner,this.isVec4?4:1)} ${this.isVec4?jh(this.elementsPerThread,this.workGroupSize,this.transposeA,this.tileInner,!1,null,this.isVectorA):this.isVectorA?Cse(this.workGroupSize,this.transposeA):Hh(this.elementsPerThread,this.workGroupSize,this.transposeA,this.tileInner,!1,null,this.sequentialAccessByThreads)} `}};function Ese(){return` var sumValues : array; ${We()} { let coords = getOutputCoords(); let batch = coords[0]; let row = coords[1]; let col = coords[2]; var sum = 0.0; let Length = uniforms.dimInner; for (var k = i32(localId.x); k < Length; k = k + i32(workGroupSizeX)) { let dataA = mm_readA(batch, row, k); let dataB = mm_readB(batch, k, col); sum = sum + dataA * dataB; } sumValues[localId.x] = sum; workgroupBarrier(); for(var currentSize = workGroupSizeX / 2u; currentSize > 1u; currentSize = currentSize / 2u) { if (localId.x < currentSize) { sumValues[localId.x] = sumValues[localId.x] + sumValues[localId.x + currentSize]; } workgroupBarrier(); } if (localId.x == 0u) { sum = sumValues[0] + sumValues[1]; mm_write(batch, row, col, sum); } } `}var Rse=class{constructor(e,t,a,n=!1,r=!1,s=null,i=null,o=null){this.variableNames=["A","B"],this.uniforms="dimAOuter : i32, dimBOuter : i32, dimInner : i32,",this.workGroupSize=[256,1,1],this.outputShape=e,this.dispatchLayout={x:[],y:[1,2],z:[0]},this.dispatch=Me(this.dispatchLayout,this.outputShape,this.workGroupSize);let l=s!=null,u=o!=null;l&&this.variableNames.push("bias"),u&&this.variableNames.push("preluActivationWeights"),this.transposeA=n,this.transposeB=r,this.addBias=l,this.activation=i,this.hasPreluActivationWeights=u,this.batchAEqualOne=t,this.batchBEqualOne=a,this.shaderKey=`matMulReduce_${this.activation}_${n}_${r}_${this.batchAEqualOne}_${this.batchBEqualOne}`}getUserCode(){return` ${Cr(this.activation,this.hasPreluActivationWeights)} ${V3(this.addBias,this.activation,this.batchAEqualOne,this.batchBEqualOne,this.transposeA,this.transposeB)} ${Ese()} `}};function Mse(e){let t=e[1],a=e[0],n=t>a?t:a;return` var mm_Asub : array, ${t}>; var mm_Bsub : array, ${n}>; // If the output size is small for matrix multiplication, avoid to use vec4 // and handle some elements per thread to optimally utilize the ALU. // Read data from global memory to registers firstly, then store them into // shared memory, so it is instruction-Level parallelism for arithmetic // operations and others handle IO operations between barrier api, makes ALU // and load/store units work simultaneously, could improves the performance. ${We()} { let tileRow = i32(localId.y); let tileCol = i32(localId.x); let globalRow = i32(globalId.y); let globalCol = i32(globalId.x); let batch = i32(globalId.z); // uniforms.dimInner should be greater than 0. let numTiles = (uniforms.dimInner - 1) / ${n} + 1; var acc = 0.0; var globalColA = tileCol; var globalRowB = 0; var regA = mm_readA(batch, globalRow, globalColA); var regB0 = mm_readB(batch, globalRowB + 2 * tileRow, globalCol); var regB1 = mm_readB(batch, globalRowB + 2 * tileRow + 1, globalCol); globalColA = globalColA + ${n}; globalRowB = globalRowB + ${n}; for (var t = 0; t < numTiles; t = t + 1) { mm_Asub[tileRow][tileCol] = regA; mm_Bsub[2 * tileRow][tileCol] = regB0; mm_Bsub[2 * tileRow + 1][tileCol] = regB1; workgroupBarrier(); regA = mm_readA(batch, globalRow, globalColA); regB0 = mm_readB(batch, globalRowB + 2 * tileRow, globalCol); regB1 = mm_readB(batch, globalRowB + 2 * tileRow + 1, globalCol); globalColA = globalColA + ${n}; globalRowB = globalRowB + ${n}; for (var k = 0; k < ${n}; k = k + 1) { acc = acc + mm_Asub[tileRow][k] * mm_Bsub[k][tileCol]; } workgroupBarrier(); } mm_write(batch, globalRow, globalCol, acc); } `}var $se=class{constructor(e,t,a,n=!1,r=!1,s=null,i=null,o=null){this.variableNames=["A","B"],this.uniforms="dimAOuter : i32, dimBOuter : i32, dimInner : i32,",this.workGroupSize=[16,8,1],this.outputShape=a,this.dispatchLayout={x:[2],y:[1],z:[0]},this.dispatch=[Math.ceil(a[2]/this.workGroupSize[0]),Math.ceil(a[1]/this.workGroupSize[1]),a[0]];let l=s!=null;l&&this.variableNames.push("bias");let u=o!=null;u&&this.variableNames.push("preluActivationWeights"),this.transposeA=n,this.transposeB=r,this.addBias=l,this.activation=i,this.hasPreluActivationWeights=u,this.batchAEqualOne=e[0]===1,this.batchBEqualOne=t[0]===1,this.shaderKey=`matMulSmallOutputSize_${this.activation}_${n}_${r}_${this.batchAEqualOne}_${this.batchBEqualOne}`}getUserCode(){return` ${Cr(this.activation,this.hasPreluActivationWeights)} ${V3(this.addBias,this.activation,this.batchAEqualOne,this.batchBEqualOne,this.transposeA,this.transposeB)} ${Mse(this.workGroupSize)} `}},_se=class{constructor(e,t,a,n,r=!1,s=!1){this.variableNames=["A","B"],this.uniforms="dimAOuter : i32, dimBOuter : i32, dimInner : i32,",this.workGroupSize=[8,8,1],this.atomic=!0,this.isVec4=!1,this.splitedDimInner=128,v.assert(e[0]===1,()=>"MatMulSplitKProgram only supports batch = 1."),this.outputShape=e,this.dispatchLayout={x:[2],y:[1],z:[0,3]},this.isVec4=(r&&this.outputShape[1]%4===0||!r&&t%4===0)&&this.outputShape[2]%4===0,this.elementsPerThread=[4,4,this.splitedDimInner],this.isVec4||(this.outputShape[1]<16&&(this.elementsPerThread[1]=1),this.outputShape[2]<16&&(this.elementsPerThread[0]=1)),this.dispatch=Me(this.dispatchLayout,[this.outputShape[0],this.outputShape[1],this.outputShape[2],t],this.workGroupSize,this.elementsPerThread),this.transposeA=r,this.transposeB=s,this.batchAEqualOne=a,this.batchBEqualOne=n,this.shaderKey=`matMulSplitK_${r}_${s}_${a}_${n}_${this.elementsPerThread}_${this.isVec4}`}getUserCode(){let e=a=>` for (var i = 0; i < ${a}; i = i + 1) { var oldValue = atomicLoad(&(result[flatIndex + i])); var exchanged = false; for (; !exchanged;) { let newValueF32 = bitcast(oldValue) + ${a>1?"value[i]":"value"}; let newValue = bitcast(newValueF32); let res = atomicCompareExchangeWeak(&(result[flatIndex + i]), oldValue, newValue); oldValue = res.old_value; exchanged = res.exchanged; } } `,t=this.isVec4?4:1;return` ${Vv(this.batchAEqualOne,this.batchBEqualOne,!1,this.transposeB,!1,!1,!1,t)} fn mm_write(batch: i32, row : i32, colIn : i32, value : ${Rt(t)}) { let col = colIn * ${t}; if (row < uniforms.dimAOuter && col < uniforms.dimBOuter) { let coords = vec3(batch, row, col); let flatIndex = getOutputIndexFromCoords(coords); // The problem is that we should initialize output to zero before using. // Otherwise, the original value will be added to the result. ${e(t)} } } ${this.isVec4?jh(this.elementsPerThread,this.workGroupSize,this.transposeA,32,!0,this.splitedDimInner):Hh(this.elementsPerThread,this.workGroupSize,this.transposeA,32,!0,this.splitedDimInner)} `}},Fse=class{constructor(e,t=null,a=null,n=null){this.uniforms="",this.variableNames=["x"],this.workGroupSize=[64,1,1],this.size=!0,this.outputShape=e,this.dispatchLayout=Ye(this.outputShape),this.dispatch=Me(this.dispatchLayout,this.outputShape,this.workGroupSize),this.addBias=t!=null,this.hasPreluActivationWeights=n!=null,this.activation=a,this.addBias&&this.variableNames.push("bias"),this.hasPreluActivationWeights&&this.variableNames.push("preluActivationWeights"),this.shaderKey=`biasActivation_${a}`}getUserCode(){return` ${Cr(this.activation,this.hasPreluActivationWeights)} ${We("index")} { if (index < uniforms.size) { let coords = getCoordsFromIndex(index); var value = getXByOutputIndex(index); ${mo(this.addBias,this.activation)} setOutputAtIndex(index, value); } } `}},Pse=class{constructor(e){this.variableNames=[],this.outputShape=[],this.uniforms="value : f32,",this.workGroupSize=[64,1,1],this.size=!0,this.outputShape=e,this.dispatchLayout=Ye(this.outputShape),this.dispatch=Me(this.dispatchLayout,this.outputShape,this.workGroupSize),this.shaderKey="fill"}getUserCode(){return` ${We("index")} { if (index < uniforms.size) { setOutputAtIndex(index, uniforms.value); } } `}};function go(e){let{backend:t,attrs:a}=e,{shape:n,value:r}=a,{dtype:s}=a;if(s=s||v.inferDtype(r),s==="string"){let i=v.getArrayFromDType(s,v.sizeFromShape(n));return i.fill(r),t.makeTensorInfo(n,s,i)}else{let i=new Pse(n),o=[{type:"float32",data:[r]}];return t.runWebGPUProgram(i,[],s,o)}}var Ose={kernelName:kl,backendName:"webgpu",kernelFunc:go};function Re(e){let{inputs:t,attrs:a}=e,{x:n}=t,{shape:r}=a,s=v.sizeFromShape(n.shape),i=v.inferFromImplicitShape(r,s),o=v.sizeFromShape(i);return v.assert(s===o,()=>`The new shape (${i}) has ${o} elements and the old shape (${n.shape}) has ${s} elements. The new shape and old shape must have the same number of elements.`),e.backend.incRef(n.dataId),{dataId:n.dataId,shape:i,dtype:n.dtype}}var Dse={kernelName:Fl,backendName:"webgpu",kernelFunc:Re};function G3({a:e,b:t,transposeA:a,transposeB:n,backend:r,bias:s=null,preluActivationWeights:i=null,leakyreluAlpha:o=0,activation:l=null}){let u=e.shape.length,d=t.shape.length,c=a?e.shape[u-2]:e.shape[u-1],p=n?t.shape[d-1]:t.shape[d-2],h=a?e.shape[u-1]:e.shape[u-2],f=n?t.shape[d-2]:t.shape[d-1],m=e.shape.slice(0,-2),g=t.shape.slice(0,-2),y=v.sizeFromShape(m),A=v.sizeFromShape(g),x=po.assertAndGetBroadcastShape(e.shape.slice(0,-2),t.shape.slice(0,-2)).concat([h,f]);v.assert(c===p,()=>`Error in matMul: inner shapes (${c}) and (${p}) of Tensors with shapes ${e.shape} and ${t.shape} and transposeA=${a} and transposeB=${n} must match.`);let b=a?[y,c,h]:[y,h,c],w=n?[A,f,p]:[A,p,f],S=Re({inputs:{x:e},backend:r,attrs:{shape:b}}),C=Re({inputs:{x:t},backend:r,attrs:{shape:w}}),N=[S,C],_=Math.max(y,A),$=y===1,M=A===1,I=[S,C],E=[{type:"int32",data:[h]},{type:"int32",data:[f]},{type:"int32",data:[c]}],O,L,B=[_,h,f],G=V().get("WEBGPU_MATMUL_PROGRAM_TYPE");switch(G<0&&(h*f<=128?G=Pn.MatMulReduceProgram:_===1&&h<=128&&f<=48&&p>=2e3?G=Pn.MatMulSplitKProgram:h<=16&&(f<=512||p>=2*f)||f<=16&&(h<=512||c>=2*h)?G=Pn.MatMulSmallOutputSizeProgram:G=Pn.MatMulPackedProgram),G){case Pn.MatMulReduceProgram:O=new Rse(B,$,M,a,n,s,l,i);break;case Pn.MatMulSplitKProgram:{if(L=go({backend:r,attrs:{shape:B,value:0,dtype:e.dtype}}),O=new _se(B,p,$,M,a,n),s||l){L=r.runWebGPUProgram(O,I,e.dtype,E,L);let H=new Fse(L.shape,s,l,i),W=null,Q=[L];s&&Q.push(s),i&&Q.push(i),l==="leakyrelu"&&(W=[{type:"float32",data:[o]}],H.uniforms+=" alpha : f32,");let Z=r.runWebGPUProgram(H,Q,L.dtype,W);N.push(L);let re=Re({inputs:{x:Z},backend:r,attrs:{shape:x}});N.push(Z);for(let ee of N)r.disposeData(ee.dataId);return re}break}case Pn.MatMulSmallOutputSizeProgram:O=new $se(b,w,B,a,n,s,l,i);break;case Pn.MatMulPackedProgram:let U=r.adapterInfo.isIntel();O=new Nse(b,B,$,M,a,n,s,l,i,U);break;default:throw new Error(`Unsupported MatMulProgramType ${G}.`)}s&&I.push(s),i&&I.push(i),l==="leakyrelu"&&(E.push({type:"float32",data:[o]}),O.uniforms+=" alpha : f32,"),L=r.runWebGPUProgram(O,I,e.dtype,E,L);let j=Re({inputs:{x:L},backend:r,attrs:{shape:x}});N.push(L);for(let U of N)r.disposeData(U.dataId);return j}function zse(e){let{inputs:t,backend:a,attrs:n}=e,{a:r,b:s,bias:i,preluActivationWeights:o}=t,{transposeA:l,transposeB:u,activation:d,leakyreluAlpha:c}=n;return G3({a:r,b:s,transposeA:l,transposeB:u,backend:a,bias:i,preluActivationWeights:o,leakyreluAlpha:c,activation:d})}var Lse={kernelName:Ur,backendName:"webgpu",kernelFunc:zse},Qy=class{constructor(e,t,a){this.variableNames=["AReal","AImag","BReal","BImag"],this.workGroupSize=[128,1,1],this.size=!0,this.outputShape=T.assertAndGetBroadcastShape(t,a),this.dispatchLayout=Ye(this.outputShape),this.dispatch=Me(this.dispatchLayout,this.outputShape,this.workGroupSize),this.shaderKey=`binaryOpComplex_${e}`,this.op=e}getUserCode(){return` fn binaryOpComplex( areal : f32, aimag : f32, breal : f32, bimag : f32) -> f32 { ${W3(this.op,!1)} } ${We("index")} { if(index < uniforms.size) { let areal = getARealByOutputIndex(index); let aimag = getAImagByOutputIndex(index); let breal = getBRealByOutputIndex(index); let bimag = getBImagByOutputIndex(index); setOutputAtIndex(index, binaryOpComplex(areal, aimag, breal, bimag)); } } `}},_1=class{constructor(e,t,a){this.size=!0,this.variableNames=["A","B"],this.outputShape=T.assertAndGetBroadcastShape(t,a),this.dispatchLayout=Ye(this.outputShape),this.op=e,this.useSharedMemoryWithA=t.length<=1&&a.length>1&&t[0]<128,this.useSharedMemoryWithB=a.length<=1&&t.length>1&&a[0]<128,this.useSharedMemoryWithA||this.useSharedMemoryWithB?(this.isVec4=!1,this.lastDimensionSize=this.useSharedMemoryWithB?a[0]:t[0],this.shaderKey=`binary_${this.type}_${e}_${this.lastDimensionSize}_${this.useSharedMemoryWithB}`,this.type="shared",this.workGroupSize=[256,1,1],this.workPerThread=1):(v.arraysEqual(t,a)&&v.sizeFromShape(t)%4===0?(this.isVec4=!0,this.type="vec4",this.workPerThread=4):(this.isVec4=!1,this.type="plain",this.workPerThread=1),this.shaderKey=`binary_${this.type}_${e}`,this.workGroupSize=[128,1,1]),this.dispatch=Me(this.dispatchLayout,this.outputShape,this.workGroupSize,[this.workPerThread,1,1])}getUserCode(){let e,t=this.isVec4?"vec4":"f32",a=` fn binaryOperation(a : ${t}, b : ${t}) -> ${t} { ${W3(this.op,this.isVec4)} }; `;if(this.type==="shared"){let n=this.lastDimensionSize>1?`coords[${this.outputShape.length-1}]`:"0",r=this.useSharedMemoryWithB?`let a = getAByOutputIndex(index); let b = sharedBuf[${n}];`:`let a = sharedBuf[${n}]; let b = getBByOutputIndex(index);`;e=` ${a} var sharedBuf : array; ${We("index")} { // Fill in the shared memory buffer. let localIndex = i32(localId.x); if(localIndex < ${this.lastDimensionSize}) { sharedBuf[localIndex] = f32(${this.useSharedMemoryWithB?"B":"A"}[localIndex]); } workgroupBarrier(); if(index < uniforms.size) { let coords = getCoordsFromIndex(index); ${r} setOutputAtIndex(index, binaryOperation(a, b)); } } `}else e=` ${a} ${We("index")} { if (index < uniforms.size) { let a = getAByOutputIndex(index); let b = getBByOutputIndex(index); setOutputAtIndex(index, binaryOperation(a, b)); } } `;return e}};function cn(e){let{inputs:t}=e,{x:a}=t;return e.backend.incRef(a.dataId),{dataId:a.dataId,shape:a.shape,dtype:a.dtype}}var Bse={kernelName:Fi,backendName:"webgpu",kernelFunc:cn};function ou(e){let{inputs:t,backend:a}=e,{real:n,imag:r}=t,s=a.makeTensorInfo(n.shape,"complex64"),i=a.tensorMap.get(s.dataId),o=cn({inputs:{x:n},backend:a}),l=cn({inputs:{x:r},backend:a});return i.complexTensorInfos={real:o,imag:l},s}var Wse={kernelName:Id,backendName:"webgpu",kernelFunc:ou},mp=class{constructor(e,t){this.variableNames=["A"],this.size=!0;let a=128;this.workGroupSize=[a,1,1],this.outputShape=e,this.dispatchLayout=Ye(this.outputShape),this.dispatch=Me(this.dispatchLayout,this.outputShape,this.workGroupSize),this.op=t,this.shaderKey=`unary_${t}`}getUserCode(){return` fn unaryOperation(a : f32) -> f32 { ${qs(this.op,!1)} } ${We("index")} { if (index < uniforms.size) { let a = getAByOutputIndex(index); setOutputAtIndex(index, unaryOperation(a)); } } `}};function Ht({opType:e,cpuKernelImpl:t,dtype:a}){return({inputs:n,backend:r})=>{let{x:s}=n,i=r,o=a||s.dtype;if(i.shouldExecuteOnCPU([s])&&t!=null){let u=i.tensorMap.get(s.dataId),d=t(u.values,o);return i.makeTensorInfo(s.shape,o,d)}let l=new mp(s.shape,e);return i.runWebGPUProgram(l,[s],o)}}function ca({opType:e,cpuKernelImpl:t,supportsComplex:a=!1,dtype:n}){return({inputs:r,backend:s})=>{let{a:i,b:o}=r,l=s;if(a&&i.dtype==="complex64"){let c=l.tensorMap.get(i.dataId),p=l.tensorMap.get(o.dataId),h,f;if(e!==Be.MUL)[h,f]=[[c.complexTensorInfos.real,p.complexTensorInfos.real],[c.complexTensorInfos.imag,p.complexTensorInfos.imag]].map(g=>{let[y,A]=g,x={dataId:y.dataId,dtype:y.dtype,shape:i.shape},b={dataId:A.dataId,dtype:A.dtype,shape:o.shape},w=new _1(e,i.shape,o.shape);return l.runWebGPUProgram(w,[x,b],ra(y.dtype,A.dtype))});else{let g=new Qy(Be.COMPLEX_MULTIPLY_REAL,i.shape,o.shape),y=new 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Gv={};He(Gv,{addImpl:()=>jv,bincountImpl:()=>jse,bincountReduceImpl:()=>Hse,castImpl:()=>Uv,ceilImpl:()=>qv,concatImpl:()=>qse,equalImpl:()=>Xv,expImpl:()=>Kv,expm1Impl:()=>Zv,floorImpl:()=>Yv,gatherNdImpl:()=>Xse,gatherV2Impl:()=>Kse,greaterEqualImpl:()=>Qv,greaterImpl:()=>Jv,lessEqualImpl:()=>t9,lessImpl:()=>e9,linSpaceImpl:()=>Zse,logImpl:()=>a9,maxImpl:()=>Yse,maximumImpl:()=>n9,minimumImpl:()=>r9,multiplyImpl:()=>q3,negImpl:()=>Qse,notEqualImpl:()=>s9,prodImpl:()=>tie,raggedGatherImpl:()=>lie,raggedTensorToTensorImpl:()=>uie,rangeImpl:()=>die,rsqrtImpl:()=>i9,scatterImpl:()=>pie,sigmoidImpl:()=>cie,simpleAbsImpl:()=>Vse,sliceImpl:()=>hie,sparseFillEmptyRowsImpl:()=>fie,sparseReshapeImpl:()=>mie,sparseSegmentReductionImpl:()=>gie,sqrtImpl:()=>yie,squaredDifferenceImpl:()=>o9,stridedSliceImpl:()=>Aie,stringNGramsImpl:()=>bie,stringSplitImpl:()=>wie,stringToHashBucketFastImpl:()=>kie,subImpl:()=>l9,tileImpl:()=>Sie,topKImpl:()=>Tie,transposeImpl:()=>eie,uniqueImpl:()=>Cie});function U3(e,t){Array.isArray(e)||(e=[e]),e.forEach(a=>{a!=null&&v.assert(a.dtype!=="complex64",()=>`${t} does not support complex64 tensors in the CPU backend.`)})}function Vse(e){let t=new Float32Array(e.length);for(let a=0;a{let i=T.assertAndGetBroadcastShape(t,a),o=i.length,l=v.computeStrides(i),u=v.sizeFromShape(i),d=v.getTypedArrayFromDType(s,u),c=t.length,p=a.length,h=v.computeStrides(t),f=v.computeStrides(a),m=T.getBroadcastDims(t,i),g=T.getBroadcastDims(a,i);if(m.length+g.length===0)for(let y=0;yx[C]=0);let b=v.locToIndex(x,c,h),w=A.slice(-p);g.forEach(C=>w[C]=0);let S=v.locToIndex(w,p,f);d[y]=e(n[b],r[S])}return[d,i]}}function j3(e){let{inputs:t,backend:a}=e,{real:n,imag:r}=t,s=a.data.get(n.dataId).values,i=a.data.get(r.dataId).values,o=a.makeTensorInfo(n.shape,"complex64"),l=a.data.get(o.dataId);return l.complexTensorInfos={real:a.makeTensorInfo(n.shape,"float32",s),imag:a.makeTensorInfo(r.shape,"float32",i)},o}function F1(e,t,a="float32"){if(a==="complex64"){let 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o=T.assertAndGetBroadcastShape(t,a),l=v.sizeFromShape(o),u=o.length,d=v.computeStrides(o),c=v.getTypedArrayFromDType("float32",l),p=v.getTypedArrayFromDType("float32",l),h=T.getBroadcastDims(t,o),f=T.getBroadcastDims(a,o),m=T.mergeRealAndImagArrays(n,r),g=T.mergeRealAndImagArrays(s,i),y=t.length,A=v.computeStrides(t),x=a.length,b=v.computeStrides(a);if(h.length+f.length===0)for(let w=0;wC[I]=0);let N=v.locToIndex(C,y,A),_=S.slice(-x);f.forEach(I=>_[I]=0);let $=v.locToIndex(_,x,b),M=e(m[N*2],m[N*2+1],g[$*2],g[$*2+1]);c[w]=M.real,p[w]=M.imag}return[c,p,o]}}var jv=fn((e,t)=>e+t),Use=H3((e,t,a,n)=>({real:e+a,imag:t+n})),E0e=Nn(vr,jv,Use);function jse(e,t,a,n,r){let s=v.sizeFromShape(n),i=v.makeZerosTypedArray(r,a);for(let o=0;o=r||(s>0?i[l]+=t[o]:i[l]+=1)}return i}function Hse(e,t,a,n=!1){let r=e.shape[0],s=e.shape[1],i=ve([r,a],t.dtype);for(let o=0;o=a||(n?i.set(1,o,u):t.size>0?i.set(i.get(o,u)+t.get(o,l),o,u):i.set(i.get(o,u)+1,o,u))}return i}function Ss(e){return(t,a,n)=>{let r=v.getTypedArrayFromDType(a,t.length);for(let s=0;s{let{x:i}=n;if(U3(i,e),i.dtype==="string"||a==="string")throw new Error("unaryKernelFunc does not support string input/output");let o=s,l=o.data.get(i.dataId).values,u=v.sizeFromShape(i.shape),d=a||i.dtype,c=v.getArrayFromDType(d,u);for(let p=0;p{let{x:i}=n;if(U3(i,e),i.dtype==="string"||a==="string")throw new Error("unaryKernelFunc does not support string input/output");let o=s,l=o.data.get(i.dataId).values,u=a||i.dtype,d=t(l,u,r);return o.makeTensorInfo(i.shape,u,d)}}var qv=Ss(e=>Math.ceil(e)),R0e=lu(Qr,qv);function qse(e,t,a,n){let r=v.getArrayFromDType(a,v.sizeFromShape(t));if(n&&a!=="string"){let s=0;e.forEach(i=>{let o=v.sizeFromShape(i.shape);r.set(i.vals,s),s+=o})}else{let s=0;e.forEach(i=>{let o=a==="string"?T.fromUint8ToStringArray(i.vals):i.vals,l=0;for(let u=0;ue===t?1:0),M0e=Nn(ts,Xv,null,"bool"),Kv=Ss(e=>Math.exp(e)),$0e=lu(as,Kv,"float32"),Zv=Ss(e=>Math.expm1(e)),_0e=lu(Ei,Zv),Yv=Ss(e=>Math.floor(e)),F0e=lu(ns,Yv);function Xse(e,t,a,n,r,s,i,o,l){let u=ve([n,s],a);for(let d=0;d=l/s)throw new Error(`Invalid indices: ${c} does not index into ${o}`);for(let h=0;he>t?1:0),P0e=Nn(rs,Jv,null,"bool"),Qv=fn((e,t)=>e>=t?1:0),O0e=Nn(ss,Qv,null,"bool"),e9=fn((e,t)=>ee<=t?1:0),z0e=Nn(os,t9,null,"bool");function Zse(e,t,a){let n=(t-e)/(a-1),r=v.makeZerosTypedArray(a,"float32");r[0]=e;for(let s=1;sMath.log(e)),L0e=lu(ls,a9);function Yse(e,t,a,n){let r=v.getTypedArrayFromDType(n,v.sizeFromShape(a));for(let s=0;so)&&(o=u)}r[s]=o}return r}var n9=fn((e,t)=>Math.max(e,t)),B0e=Nn(us,n9),r9=fn((e,t)=>Math.min(e,t)),W0e=Nn(ds,r9),q3=fn((e,t)=>e*t),Jse=H3((e,t,a,n)=>({real:e*a-t*n,imag:e*n+t*a})),V0e=Nn(ps,q3,Jse);function Qse(e,t,a){let n=v.createScalarValue(-1,a);return q3([],t,n,e,a)}var s9=fn((e,t)=>e!==t?1:0),G0e=Nn(cs,s9,null,"bool");function eie(e,t,a,n,r){let s=t.length,i=v.sizeFromShape(t),o=v.computeStrides(t),l=v.computeStrides(r),u=v.getTypedArrayFromDType(a,v.sizeFromShape(r));for(let d=0;d{if(n<0||n>=a){let s=v.indexToLoc(r,t.length,v.computeStrides(t)).join(",");throw new Error(`indices[${s}] = ${n} is not in [0, ${a})`)}})}function nie(e,t){for(let a=0;ar)throw new Error("Ragged splits must not point past values");for(let s=1;sn[s])throw new Error("Ragged splits must be sorted in ascending order")}}function rie(e,t,a,n){let r=[],s=0,i=t.length-1+a.length,o=new Array(i).fill(null).map(()=>[0]);nie(a,n);let l=1;for(let u=0;u=0){let m=o[f],g=m[m.length-1]-h[d];for(let y=d;yr[i]=s)}return t}function tA(e,t){let a=e.slice(0,t);for(;a.lengtha&&(a=r)}return a}static getMaxWidthValueRowID(e){let t=e.length;if(t===0)return 0;let a=0,n=e[0],r=0;for(let s=1;s"Final length of result must be equal to firstDimension."),r}calculateOutputIndexRowSplit(e,t,a,n){let r=e.length,s=[];for(let i=0;i0&&s.length!==e[r-1])throw new Error("Invalid row split size.");return s}calculateOutputIndexValueRowID(e,t,a,n){let r=e.length,s=[];if(r===0)return[];let i=0,o=e[0];if(o>=t.length)throw new Error(`Got currentValueRowId=${o}, which is not less than ${t.length}`);let l=t[o];s.push(l);for(let u=1;u=0&&(++i,i=t.length)throw new Error(`Got nextValueRowId=${d} which is not less than ${t.length}`);l=t[d]}s.push(l)}if(s.length!==e.length)throw new Error("Invalid row ids.");return s}calculateOutputIndex(e,t,a,n){let r=this.getRowPartitionTensor(e),s=this.getRowPartitionTypeByDimension(e);switch(s){case vn.VALUE_ROWIDS:return this.calculateOutputIndexValueRowID(r,t,a,n);case vn.ROW_SPLITS:if(r.length-1>t.length)throw new Error(`Row partition size is greater than output size: ${r.length-1} > ${t.length}`);return this.calculateOutputIndexRowSplit(r,t,a,n);default:throw new Error(`Unsupported partition type: ${vn[s]}`)}}getFirstDimensionSize(){let e=this.rowPartitionValues[0];if(this.rowPartitionTypes.length===0)throw new Error("No row_partition_types given.");let t=this.rowPartitionTypes[0];switch(t){case vn.FIRST_DIM_SIZE:return e[0];case vn.VALUE_ROWIDS:throw new Error("Cannot handle VALUE_ROWIDS in first dimension.");case vn.ROW_SPLITS:return this.rowPartitionValuesShapes[0][0]-1;default:throw new Error(`Cannot handle type ${vn[t]}`)}}compute(){if(this.rowPartitionValues[0].length<=0)throw new Error("Invalid first partition input. 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let batch = coords[0]; let d = coords[3]; let xRCCorner = coords.yz * uniforms.stride; let xRCorner = xRCCorner.x; let xCCorner = xRCCorner.y; let value = getX(batch, xRCorner, xCCorner, d); setOutputAtIndex(index, value); } } `}},Soe=class{constructor(e,t){this.workGroupSize=[64,1,1],this.variableNames=["x"],this.uniforms="reduceSize : i32,",this.size=!0,this.inputShape=[e.batchSize,e.inSize];let[a]=T.computeOutAndReduceShapes(this.inputShape,[1]);this.outputShape=a.length===0?[1]:a,this.dispatchLayout=Ye(this.outputShape),this.dispatch=Me(this.dispatchLayout,this.outputShape,[1,1,1]),this.reduceType=t,this.shaderKey=`reduce_${t}`}getUserCode(){let e="",t="0.0";this.reduceType==="min"||this.reduceType==="max"?(e=` if (isnan(candidate)) { bestValue = uniforms.NAN; } else if (!isnan(bestValue) && candidate ${this.reduceType==="min"?"<":">"} bestValue) { bestValue = candidate; }`,t="f32(x[offset])"):this.reduceType==="sum"||this.reduceType==="mean"?e=" bestValue = bestValue + candidate; ":this.reduceType==="prod"&&(e=" bestValue = bestValue * candidate; ",t="1.0");let a=this.reduceType==="mean"?"setOutputAtIndex(outputIndex, bestValue / f32(uniforms.reduceSize));":"setOutputAtIndex(outputIndex, bestValue);";return` fn DIV_CEIL(a : u32, b : u32) -> u32 { return ((a - 1u) / b + 1u); } ${` var xBestValues : array; `} fn getOffset(outputIndex : i32) -> i32 { let outputCoords = getCoordsFromIndex(outputIndex); let offset = ${this.outputShape.length===1?"outputCoords":"outputCoords[0]"} * uniforms.reduceSize; return offset; } ${We("index")} { let outputIndex = index / i32(workGroupSizeX); let offset = getOffset(outputIndex); var bestValue = ${t}; let Length = uniforms.reduceSize; let WorkPerThread = DIV_CEIL(u32(Length), workGroupSizeX); for (var k = i32(localId.x); k < Length && outputIndex < uniforms.size; k = k + i32(workGroupSizeX)) { let candidate = f32(x[offset + k]); ${e} } xBestValues[localId.x] = bestValue; workgroupBarrier(); var reduceSize = min(u32(Length), workGroupSizeX); for (var currentSize = reduceSize / 2u; reduceSize > 1u; currentSize = reduceSize / 2u) { let interval = DIV_CEIL(reduceSize, 2u); if (localId.x < currentSize) { let candidate = xBestValues[localId.x + interval]; ${e} xBestValues[localId.x] = bestValue; } reduceSize = interval; workgroupBarrier(); } if (localId.x == 0u && outputIndex < uniforms.size) { ${a} } } `}};function gp(e,t,a,n,r){let s=e.shape.length,i=[],o=v.parseAxisParam(t,e.shape),l=o,u=T.getAxesPermutation(l,s),d=e;u!=null&&(d=br({inputs:{x:e},attrs:{perm:u},backend:r}),l=T.getInnerMostAxes(l.length,s),i.push(d)),T.assertAxesAreInnerMostDims(n,l,s);let[c,p]=T.computeOutAndReduceShapes(d.shape,l),h=c;a&&(h=T.expandShapeToKeepDim(c,o));let f;if((n==="max"||n==="prod")&&r.shouldExecuteOnCPU([d])){let m=r.tensorMap.get(d.dataId).values;switch(n){case"max":let g=Gie(m,v.sizeFromShape(p),h,e.dtype);f=r.makeTensorInfo(h,e.dtype,g);break;case"prod":let{outVals:y,outShape:A,outDtype:x}=Kie(d.shape,d.dtype,m,l);f=r.makeTensorInfo(A,x,y);break;default:throw new Error(`${n} CPU implementation is not yet supported.`)}}else{let m=v.sizeFromShape(p),g=v.sizeFromShape(d.shape)/m,y={windowSize:m,inSize:m,batchSize:g,outSize:1},A=n==="mean"?"float32":jd(e.dtype),x=[{type:"int32",data:[m]}],b=new Soe(y,n),w=r.runWebGPUProgram(b,[d],A,x);i.push(w),f=Re({inputs:{x:w},attrs:{shape:h},backend:r})}return i.forEach(m=>r.disposeData(m.dataId)),f}function X3(e){let{inputs:t,backend:a,attrs:n}=e,{x:r}=t,{reductionIndices:s,keepDims:i}=n;return gp(r,s,i,"max",a)}var Toe={kernelName:zi,backendName:"webgpu",kernelFunc:X3};function p9(e){let{inputs:t,backend:a,attrs:n}=e,{x:r}=t,{keepDims:s,axis:i}=n;return gp(r,i,s,"mean",a)}var Coe={kernelName:Bi,backendName:"webgpu",kernelFunc:p9};function c9(e,t,a,n){if(t.filterWidth===1&&t.filterHeight===1&&v.arraysEqual(t.inShape,t.outShape))return cn({inputs:{x:e},backend:n});if(t.filterWidth===t.inWidth&&t.filterHeight===t.inHeight&&t.batchSize===1&&t.padInfo.type==="VALID"){let i=e.shape.length,o=Re({inputs:{x:e},backend:n,attrs:{shape:[e.shape[i-3]*e.shape[i-2],e.shape[i-1]]}}),l;a==="avg"?l=p9({inputs:{x:o},backend:n,attrs:{axis:0,keepDims:!1}}):(v.assert(a==="max",()=>`Invalid pool type ${a}`),l=X3({inputs:{x:o},backend:n,attrs:{reductionIndices:0,keepDims:!1}}));let u=Re({inputs:{x:l},backend:n,attrs:{shape:t.outShape}});return n.disposeData(o.dataId),n.disposeData(l.dataId),u}let r,s=[{type:"int32",data:[t.strideHeight,t.strideWidth]}];return t.filterHeight===1&&t.filterWidth===1?r=new Ioe(t):(a==="avg"?r=new rA(t,"avg"):(v.assert(a==="max",()=>`Invalid pool type ${a}`),r=new rA(t,"max")),s.push({type:"int32",data:[t.padInfo.top,t.padInfo.left]},{type:"int32",data:[t.dilationHeight,t.dilationWidth]},{type:"int32",data:[t.inHeight,t.inWidth]},{type:"int32",data:[t.effectiveFilterHeight,t.effectiveFilterWidth]})),n.runWebGPUProgram(r,[e],e.dtype,s)}function Noe(e){let{inputs:t,backend:a,attrs:n}=e,{x:r}=t,{filterSize:s,strides:i,pad:o,dimRoundingMode:l}=n,u=1,d=T.computePool2DInfo(r.shape,s,i,u,o,l);return c9(r,d,"avg",a)}var Eoe={kernelName:mi,backendName:"webgpu",kernelFunc:Noe};function Roe(e){let{inputs:t,backend:a,attrs:n}=e,{a:r,b:s}=t,{transposeA:i,transposeB:o}=n;return G3({a:r,b:s,transposeA:i,transposeB:o,backend:a})}var Moe={kernelName:gi,backendName:"webgpu",kernelFunc:Roe},$oe=class{constructor(e,t){this.variableNames=["source"],this.workPerThread=1,this.workGroupSize=[64,1,1],this.size=!0,this.outputShape=t,this.rank=t.length,this.dispatchLayout=Ye(this.outputShape),this.dispatch=Me(this.dispatchLayout,this.outputShape,this.workGroupSize,[this.workPerThread,1,1]),this.start=e,this.uniforms=`start : ${na(e.length)}, `,this.shaderKey="slice"}getUserCode(){let e=na(this.rank),t=_oe(this.rank),a;return this.start.length===1?a=this.outputShape.map((n,r)=>"sourceLoc = uniforms.start + coords;"):a=this.outputShape.map((n,r)=>`sourceLoc.${O1[r]} = uniforms.start.${Ar(r)} + coords.${O1[r]};`),` ${We("index")} { if (index < uniforms.size) { var sourceLoc : ${e}; let coords = getCoordsFromIndex(index); ${a.join(` `)} setOutputAtIndex(index, getSource(${t})); } } `}},O1=["x","y","z","w","u","v"];function _oe(e){if(e===1)return"sourceLoc";if(e<=6)return O1.slice(0,e).map(t=>`sourceLoc.${t}`).join(",");throw Error(`Slicing for rank ${e} is not yet supported`)}function uu(e){let{inputs:t,backend:a,attrs:n}=e,{x:r}=t,{begin:s,size:i}=n,[o,l]=At.parseSliceParams(r,s,i);if(At.assertParamsValid(r,o,l),a.shouldExecuteOnCPU([r])||r.dtype==="string"){let c=a.tensorMap.get(r.dataId),p=eoe(c.values,o,l,r.shape,r.dtype);return a.makeTensorInfo(l,r.dtype,p)}if(v.sizeFromShape(l)===0)return a.makeTensorInfo(l,r.dtype,[]);let u=new $oe(o,l),d=[{type:"int32",data:o}];return a.runWebGPUProgram(u,[r],r.dtype,d)}var Foe={kernelName:zl,backendName:"webgpu",kernelFunc:uu},Poe=e=>{let{inputs:t,backend:a,attrs:n}=e,{x:r}=t,{blockShape:s,crops:i}=n;v.assert(r.shape.length<=4,()=>"batchToSpaceND for rank > 4 with a WebGPU backend not implemented yet");let o=s.reduce((A,x)=>A*x),l=T.getReshaped(r.shape,s,o),u=T.getPermuted(l.length,s.length),d=T.getReshapedPermuted(r.shape,s,o),c=T.getSliceBeginCoords(i,s.length),p=T.getSliceSize(d,i,s.length),h=[],f=Re({inputs:{x:r},backend:a,attrs:{shape:l}}),m=br({inputs:{x:f},backend:a,attrs:{perm:u}}),g=Re({inputs:{x:m},backend:a,attrs:{shape:d}}),y=uu({inputs:{x:g},backend:a,attrs:{begin:c,size:p}});return h.push(f),h.push(m),h.push(g),h.forEach(A=>a.disposeData(A.dataId)),y},Ooe={kernelName:bl,backendName:"webgpu",kernelFunc:Poe},h9=ca({opType:Be.NOT_EQUAL,dtype:"bool",cpuKernelImpl:Xie}),Doe={kernelName:cs,backendName:"webgpu",kernelFunc:h9};function yp(e){let{inputs:t,backend:a}=e,{input:n}=t,r=a.tensorMap.get(n.dataId);return cn({inputs:{x:r.complexTensorInfos.real},backend:a})}var zoe={kernelName:$d,backendName:"webgpu",kernelFunc:yp};function Loe(e,t){let a=new mp(e.shape,Se.TO_INT),n=t.runWebGPUProgram(a,[e],"int32");return{dataId:n.dataId,shape:n.shape,dtype:n.dtype}}function D1(e){let{inputs:t,backend:a,attrs:n}=e,{x:r}=t,{dtype:s}=n;if(s==="complex64"){if(r.dtype==="complex64")return cn({inputs:{x:r},backend:a});let i=pn(r.shape),o=D1({inputs:{x:r},backend:a,attrs:{dtype:"float32"}}),l=ou({inputs:{real:o,imag:i},backend:a});return i.dispose(),a.disposeData(o.dataId),l}if(r.dtype==="complex64"){let i=yp({inputs:{input:r},backend:a}),o=D1({inputs:{x:i},backend:a,attrs:{dtype:s}});return a.disposeData(i.dataId),o}if(!v.hasEncodingLoss(r.dtype,s)){let i=cn({inputs:{x:r},backend:a});return{dataId:i.dataId,shape:i.shape,dtype:s}}if(a.shouldExecuteOnCPU([r])){let i=a.tensorMap.get(r.dataId).values,[o,l,u]=Eie(i,r.shape,r.dtype,s);return a.makeTensorInfo(o,l,u)}if(s==="int32")return Loe(r,a);if(s==="bool"){let i=a.makeTensorInfo([],"bool",v.getTypedArrayFromDType("bool",1)),o=h9({inputs:{a:r,b:i},backend:a});return a.disposeData(i.dataId),o}throw new Error(`Error in Cast: failed to cast ${r.dtype} to ${s}`)}var Boe={kernelName:yi,backendName:"webgpu",kernelFunc:D1},Woe=Ht({opType:Se.CEIL,cpuKernelImpl:Rie}),Voe={kernelName:Qr,backendName:"webgpu",kernelFunc:Woe},Goe=class{constructor(e){this.variableNames=["A"],this.uniforms="minVal : f32, maxVal : f32,",this.workPerThread=4,this.workGroupSize=[64,1,1],this.isVec4=!0,this.size=!0,this.outputShape=e,this.dispatchLayout=Ye(this.outputShape),this.dispatch=Me(this.dispatchLayout,this.outputShape,this.workGroupSize,[this.workPerThread,1,1]),this.shaderKey="clipVec4"}getUserCode(){return` ${We("index")} { if(index < uniforms.size) { let value = getAByOutputIndex(index); var clampedValue : vec4; for (var i = 0; i < 4; i = i + 1) { if (isnan(value[i])) { clampedValue[i] = value[i]; } else { clampedValue[i] = clamp(value[i], uniforms.minVal, uniforms.maxVal); } } setOutputAtIndex(index, clampedValue); } } `}},Uoe=class{constructor(e){this.variableNames=["A"],this.uniforms="minVal : f32, maxVal : f32,",this.workGroupSize=[64,1,1],this.size=!0,this.outputShape=e,this.dispatchLayout=Ye(this.outputShape),this.dispatch=Me(this.dispatchLayout,this.outputShape,this.workGroupSize),this.shaderKey="clip"}getUserCode(){return` ${We("index")} { if(index < uniforms.size) { let value = getAByOutputIndex(index); if (isnan(value)) { setOutputAtIndex(index, value); return; } setOutputAtIndex(index, clamp(value, uniforms.minVal, uniforms.maxVal)); } } `}};function joe(e){let{inputs:t,backend:a,attrs:n}=e,{x:r}=t,{clipValueMin:s,clipValueMax:i}=n,o,l=[{type:"float32",data:[s]},{type:"float32",data:[i]}];return v.sizeFromShape(r.shape)%4===0?o=new Goe(r.shape):o=new Uoe(r.shape),a.runWebGPUProgram(o,[r],r.dtype,l)}var Hoe={kernelName:es,backendName:"webgpu",kernelFunc:joe},qoe=class{constructor(e){this.uniforms="",this.workPerThread=1,this.workGroupSize=[64,1,1],this.size=!0,this.outputShape=T.computeOutShape(e,1),this.variableNames=e.map((t,a)=>`T${a}`),this.dispatchLayout=Ye(this.outputShape),this.dispatch=Me(this.dispatchLayout,this.outputShape,this.workGroupSize,[this.workPerThread,1,1]),this.offsetLength=e.length-1;for(let t=0;t0){e.push("if (yC < uniforms.offset0){ setOutputAtCoords(coords.x, coords.y, getT0(yR, yC)); }");for(let n=1;nyp({inputs:{input:x},backend:a})),m=e.map(x=>qh({inputs:{input:x},backend:a})),g=ju(f,t,a),y=ju(m,t,a),A=ou({inputs:{real:g,imag:y},backend:a});return f.forEach(x=>a.disposeData(x.dataId)),m.forEach(x=>a.disposeData(x.dataId)),a.disposeData(g.dataId),a.disposeData(y.dataId),A}let r=a.shouldExecuteOnCPU(e);if(n==="string"&&(r=!0),r){let f=e.map(w=>{let S=[-1,v.sizeFromShape(w.shape.slice(t))];return Re({inputs:{x:w},backend:a,attrs:{shape:S}})}),m=f.map(w=>({vals:a.readSync(w.dataId),shape:w.shape})),g=T.computeOutShape(f.map(w=>w.shape),1),y=f[0].shape[0]===1,A=Mie(m,g,n,y),x=T.computeOutShape(e.map(w=>w.shape),t),b=a.makeTensorInfo(x,n,A);return f.forEach(w=>a.disposeData(w.dataId)),b}let s=a.device.limits.maxStorageBuffersPerShaderStage-1;if(e.length>s){let f=[];for(let g=0;gf.shape),u=new qoe(l),d=[],c=new Array(l.length-1);if(c.length>0){c[0]=l[0][1],d.push({type:"int32",data:[c[0]]});for(let f=1;fa.disposeData(f.dataId));let h=Re({inputs:{x:p},backend:a,attrs:{shape:o}});return a.disposeData(p.dataId),h}function Koe(e,t,a){let n=T.computeOutShape(e.map(r=>r.shape),t);return{tensors2D:e.map(r=>Re({inputs:{x:r},backend:a,attrs:{shape:[v.sizeFromShape(r.shape.slice(0,t)),v.sizeFromShape(r.shape.slice(t))]}})),outShape:n}}function f9(e){let{inputs:t,backend:a,attrs:n}=e,{axis:r}=n,s=v.parseAxisParam(r,t[0].shape)[0],i=t.map(u=>u.shape);T.assertParamsConsistent(i,s);let o=T.computeOutShape(t.map(u=>u.shape),s);if(v.sizeFromShape(o)===0)return a.makeTensorInfo(o,t[0].dtype,[]);let l=t.filter(u=>v.sizeFromShape(u.shape)>0);return l.length===1?cn({inputs:{x:l[0]},backend:a}):ju(l,s,a)}var Zoe={kernelName:vl,backendName:"webgpu",kernelFunc:f9};function Yoe(e,t,a,n,r=!1,s=null,i=!1,o=4,l=4,u=4){let d=N=>{switch(N){case 1:return"resData = x[xIndex];";case 3:return"resData = vec3(x[xIndex], x[xIndex + 1], x[xIndex + 2]);";case 4:return"resData = x[xIndex / 4];";default:throw new Error(`innerElementSize ${N} is not supported.`)}},c=N=>{switch(N){case 1:return"return W[row * uniforms.wShape[3] + colIn];";case 4:return"return W[row * uniforms.wShape[3] / 4 + colIn];";default:throw new Error(`innerElementSize ${N} is not supported.`)}},p=e?` let coord = vec4(batch, xRow, xCol, xCh); `:` let coord = vec4(batch, xCh, xRow, xCol); `,h=e?` let coords = vec4( batch, row / outWidth, row % outWidth, col); `:` let coords = vec4( batch, row, col / outWidth, col % outWidth); `,f=e?"uniforms.xShape[1]":"uniforms.xShape[2]",m=e?"uniforms.xShape[2]":"uniforms.xShape[3]",g=e?"row":"col",y=e?"col":"row",A=` let inChannels = uniforms.wShape[2]; let outWidth = ${e?"uniforms.outShape[2]":"uniforms.outShape[3]"}; let outRow = ${g} / outWidth; let outCol = ${g} % outWidth; let WRow = ${y} / (uniforms.filterDims[1] * inChannels); let WCol = ${y} / inChannels % uniforms.filterDims[1]; let xRow = outRow * uniforms.stride[0] + uniforms.dilation[0] * WRow - uniforms.pad[0]; let xCol = outCol * uniforms.stride[1] + uniforms.dilation[1] * WCol - uniforms.pad[1]; let xCh = ${y} % inChannels; var resData = ${Rt(o)}(0.0); // The bounds checking is always needed since we use it to pad zero for // the 'same' padding type. if (xRow >= 0 && xRow < ${f} && xCol >= 0 && xCol < ${m}) { ${p} let xIndex = getIndexFromCoords4D(coord, uniforms.xShape); ${d(o)} } return resData;`,x=e?t&&n?` let col = colIn * ${o}; ${A}`:` let col = colIn * ${o}; if (row < uniforms.dimAOuter && col < uniforms.dimInner) { ${A} } return ${Rt(o)}(0.0);`:n&&a?` let col = colIn * ${o}; ${A}`:` let col = colIn * ${o}; if (row < uniforms.dimInner && col < uniforms.dimBOuter) { ${A} } return ${Rt(o)}(0.0);`,b=`${c(l)}`,w=Rt(u),S=Rt(e?o:l),C=Rt(e?l:o);return` ${Cr(s,i,u===4,4)} fn mm_readA(batch: i32, row : i32, colIn : i32) -> ${S} { ${e?x:b} } fn mm_readB(batch: i32, row : i32, colIn : i32) -> ${C} { ${e?b:x} } fn mm_write(batch: i32, row : i32, colIn : i32, valueIn : ${w}) { let col = colIn * ${u}; if (row < uniforms.dimAOuter && col < uniforms.dimBOuter) { var value = valueIn; let outWidth = ${e?"uniforms.outShape[2]":"uniforms.outShape[3]"}; ${h} ${mo(r,s)} setOutputAtCoords(coords[0], coords[1], coords[2], coords[3], value); } }`}var Joe=class{constructor(e,t,a,n,r=!1,s=null,i=!1,o=!1){this.variableNames=["x","W"],this.uniforms="filterDims : vec2, pad : vec2, stride : vec2, dilation : vec2, dimAOuter : i32, dimBOuter : i32, dimInner : i32,",this.outputShape=e.outShape,this.isChannelsLast=e.dataFormat==="channelsLast",this.isVec4=((e.inChannels%4===0||e.inChannels%3===0)&&this.isChannelsLast||e.outWidth%4===0&&!this.isChannelsLast)&&e.outChannels%4===0,this.dispatchLayout=this.isChannelsLast?{x:[3],y:[1,2],z:[0]}:{x:[2,3],y:[1],z:[0]},this.workGroupSize=z3(this.dispatchLayout,this.outputShape,this.isVec4),this.elementsPerThread=L3(this.dispatchLayout,this.outputShape,this.isVec4),this.dispatch=Me(this.dispatchLayout,this.outputShape,this.workGroupSize,this.elementsPerThread),this.isVec4?(this.isChannelsLast&&e.inChannels%4!==0?(this.innerElementSize=3,this.variableTypes=["f32","vec4"]):(this.innerElementSize=4,this.variableTypes=["vec4","vec4"]),r&&(this.variableNames.push("bias"),this.variableTypes.push("vec4")),i&&(this.variableNames.push("preluActivationWeights"),this.variableTypes.push("vec4"))):(this.innerElementSize=this.elementsPerThread[0],r&&this.variableNames.push("bias"),i&&this.variableNames.push("preluActivationWeights")),this.sequentialAccessByThreads=o,this.addBias=r,this.activation=s,this.hasPreluActivationWeights=i,this.tileAOuter=this.workGroupSize[1]*this.elementsPerThread[1],this.tileBOuter=this.workGroupSize[0]*this.elementsPerThread[0],this.tileInner=Math.max(this.workGroupSize[0]*this.innerElementSize,this.workGroupSize[1]),this.fitAOuter=t%this.tileAOuter===0,this.fitBOuter=a%this.tileBOuter===0,this.fitInner=n%this.tileInner===0,this.shaderKey=`conv2DMM_${this.elementsPerThread}_${this.activation}}_${this.fitAOuter}_${this.fitBOuter}_${this.fitInner}_${this.isVec4}_${this.innerElementSize}_${this.isChannelsLast}_${this.sequentialAccessByThreads}`}getUserCode(){let e=this.isVec4?jh(this.elementsPerThread,this.workGroupSize,!this.isChannelsLast,this.tileInner):Hh(this.elementsPerThread,this.workGroupSize,!this.isChannelsLast,this.tileInner,!1,null,this.sequentialAccessByThreads),t=this.isVec4?[this.innerElementSize,4,4]:[1,1,1];return` ${Yoe(this.isChannelsLast,this.fitAOuter,this.fitBOuter,this.fitInner,this.addBias,this.activation,this.hasPreluActivationWeights,t[0],t[1],t[2])} ${e} `}},Qoe=class{constructor(e,t=!1,a=null,n=!1){this.variableNames=["x","W"],this.uniforms="filterDims: vec2, pad: vec2, stride: vec2, dilation: vec2,",this.workGroupSize=[4,4,8],this.outputShape=e.outShape,this.isChannelsLast=e.dataFormat==="channelsLast",this.dispatchLayout=this.isChannelsLast?{x:[2],y:[1],z:[0,3]}:{x:[3],y:[2],z:[0,1]},this.dispatch=Me(this.dispatchLayout,this.outputShape,this.workGroupSize),this.addBias=t,this.activation=a,this.hasPreluActivationWeights=n,t&&this.variableNames.push("bias"),n&&this.variableNames.push("preluActivationWeights"),this.shaderKey=`conv2dnaive_${this.activation}_${this.isChannelsLast}`}getUserCode(){return` ${Cr(this.activation,this.hasPreluActivationWeights,!1,4)} fn readInp(batch : i32, row : i32, col : i32, chan : i32) -> f32{ let coords = vec4(batch, row, col, chan); if (coordsInBounds4D(coords, uniforms.xShape)) { return getX(batch, row, col, chan); } else { return 0.0; } } fn readFilt(row : i32, col : i32, xChannel : i32, outChannel : i32) -> f32{ let coords = vec4(row, col, xChannel, outChannel); if(coordsInBounds4D(coords, uniforms.wShape)) { return getW(row, col, xChannel, outChannel); } else { return 0.0; } } fn writeResult(batch : i32, row : i32, col : i32, chan : i32, valueIn : f32) { let coords = ${this.isChannelsLast?"vec4(batch, row, col, chan);":"vec4(batch, chan, row, col);"} if (coordsInBounds4D(coords, uniforms.outShape)) { var value = valueIn; ${mo(this.addBias,this.activation)} setOutputAtCoords(coords.x, coords.y, coords.z, coords.w, value); } } ${We("index")} { let coords = getOutputCoords(); let batch = coords[0]; let outChannel = ${this.isChannelsLast?"coords[3];":"coords[1];"} let outRow = ${this.isChannelsLast?"coords[1];":"coords[2];"} let outCol = ${this.isChannelsLast?"coords[2];":"coords[3];"} var acc : f32 = 0.0; for (var row = 0; row < uniforms.filterDims[0]; row = row + 1) { for (var col = 0; col < uniforms.filterDims[1]; col = col + 1) { let xRow = outRow * uniforms.stride[0] + uniforms.dilation[0] * row - uniforms.pad[0]; let xCol = outCol * uniforms.stride[1] + uniforms.dilation[1] * col - uniforms.pad[1]; for (var xChannel = 0; xChannel < ${this.isChannelsLast?"uniforms.xShape[3];":"uniforms.xShape[1];"} xChannel = xChannel + 1) { ${this.isChannelsLast?"let v = readInp(batch, xRow, xCol, xChannel);":"let v = readInp(batch, xChannel, xRow, xCol);"} let f = readFilt(row, col, xChannel, outChannel); acc = acc + v * f; } } } writeResult(batch, outRow, outCol, outChannel, acc); } `}};function sA(e,t){let a=e.length;return a>=3?t?[...e.slice(0,-3),e[a-3]*e[a-2],e[a-1]]:[...e.slice(0,-3),e[a-3],e[a-2]*e[a-1]]:!t&&a===1&&e[0]>1?[e[0],1]:null}function ele({x:e,filter:t,convInfo:a,backend:n,bias:r=null,preluActivationWeights:s=null,leakyreluAlpha:i=0,activation:o=null}){let l=a.dataFormat==="channelsLast",u=!l,d=!1,c=l&&a.filterHeight===a.inHeight&&a.filterWidth===a.inWidth&&a.padInfo.type==="VALID",p=[],h,f;if(c){let y=a.inHeight*a.inWidth*a.inChannels;h=Re({inputs:{x:e},backend:n,attrs:{shape:[1,a.batchSize,y]}}),f=Re({inputs:{x:t},backend:n,attrs:{shape:[1,y,a.outChannels]}})}else h=Re({inputs:{x:e},backend:n,attrs:{shape:l?[a.batchSize,a.inHeight*a.inWidth,a.inChannels]:[a.batchSize,a.inChannels,a.inHeight*a.inWidth]}}),f=Re({inputs:{x:t},backend:n,attrs:{shape:[1,a.inChannels,a.outChannels]}});if(p.push(h),p.push(f),s!=null){let y=sA(s.shape,l);y!=null&&(s=Re({inputs:{x:s},backend:n,attrs:{shape:y}}),p.push(s))}if(r!=null){let y=sA(r.shape,l);y!=null&&(r=Re({inputs:{x:r},backend:n,attrs:{shape:y}}),p.push(r))}let m=G3({a:l?h:f,b:l?f:h,transposeA:u,transposeB:d,backend:n,bias:r,activation:o,preluActivationWeights:s,leakyreluAlpha:i}),g=Re({inputs:{x:m},backend:n,attrs:{shape:a.outShape}});p.push(m);for(let y of p)n.disposeData(y.dataId);return g}function m9({x:e,filter:t,convInfo:a,backend:n,bias:r=null,preluActivationWeights:s=null,leakyreluAlpha:i=0,activation:o=null}){let l=r!=null,u=s!=null,d=a.dataFormat==="channelsLast",c=d&&a.filterHeight===a.inHeight&&a.filterWidth===a.inWidth&&a.padInfo.type==="VALID",p=V().getBool("WEBGPU_USE_NAIVE_CONV2D_DEBUG");if(!p&&(c||a.filterHeight===1&&a.filterWidth===1&&a.dilationHeight===1&&a.dilationWidth===1&&a.strideHeight===1&&a.strideWidth===1&&(a.padInfo.type==="SAME"||a.padInfo.type==="VALID")))return ele({x:e,filter:t,convInfo:a,backend:n,bias:r,activation:o,preluActivationWeights:s,leakyreluAlpha:i});let h,f=[a.padInfo.top,a.padInfo.left],m=[{type:"int32",data:[a.filterHeight,a.filterWidth]},{type:"int32",data:[...f]},{type:"int32",data:[a.strideHeight,a.strideWidth]},{type:"int32",data:[a.dilationHeight,a.dilationWidth]}];if(p)h=new Qoe(a,l,o,u);else{let x=d?a.outHeight*a.outWidth:a.outChannels,b=d?a.outChannels:a.outHeight*a.outWidth,w=a.filterHeight*a.filterWidth*a.inChannels;m.push({type:"int32",data:[x]},{type:"int32",data:[b]},{type:"int32",data:[w]});let S=n.adapterInfo.isIntel();h=new Joe(a,x,b,w,l,o,u,S)}let g=[],y=[e,t];l&&(!d&&r.shape.length===1&&(r=Re({inputs:{x:r},backend:n,attrs:{shape:[r.shape[0],1,1]}}),g.push(r)),y.push(r)),u&&(!d&&s.shape.length===1&&(s=Re({inputs:{x:s},backend:n,attrs:{shape:[s.shape[0],1,1]}}),g.push(s)),y.push(s)),o==="leakyrelu"&&(m.push({type:"float32",data:[i]}),h.uniforms+=" alpha : f32,");let A=n.runWebGPUProgram(h,y,e.dtype,m);for(let x of g)n.disposeData(x.dataId);return A}function tle(e){let{inputs:t,attrs:a,backend:n}=e,{x:r,filter:s}=t,{strides:i,pad:o,dataFormat:l,dilations:u,dimRoundingMode:d}=a,c=T.convertConv2DDataFormat(l),p=T.computeConv2DInfo(r.shape,s.shape,i,u,o,d,!1,c);return m9({x:r,filter:s,convInfo:p,backend:n})}var ale={kernelName:Ai,backendName:"webgpu",kernelFunc:tle};function nle(e=4){let t=n=>{switch(n){case 1:return"return W[getIndexFromCoords4D(coord, uniforms.wShape)];";case 4:return` let coord1 = vec4(coordX, coordY, col + 1, rowInner); let coord2 = vec4(coordX, coordY, col + 2, rowInner); let coord3 = vec4(coordX, coordY, col + 3, rowInner); let v0 = W[getIndexFromCoords4D(coord, uniforms.wShape)]; let v1 = W[getIndexFromCoords4D(coord1, uniforms.wShape)]; let v2 = W[getIndexFromCoords4D(coord2, uniforms.wShape)]; let v3 = W[getIndexFromCoords4D(coord3, uniforms.wShape)]; return vec4(v0, v1, v2, v3); `;default:throw new Error(`innerElementSize ${n} is not supported.`)}},a=`if (row < uniforms.dimAOuter && col < uniforms.dimInner) { ${` let outRow = row / uniforms.outShape[2]; let outCol = row % uniforms.outShape[2]; let WRow = col / (uniforms.filterDims[1] * uniforms.outBackprop[3]); let WCol = col / uniforms.outBackprop[3] % uniforms.filterDims[1]; let xR = f32(outRow - uniforms.pads[0] + WRow) / f32(uniforms.stride[0]); let xC = f32(outCol - uniforms.pads[1] + WCol) / f32(uniforms.stride[1]); if (xR < 0.0 || xR >= f32(uniforms.outBackprop[1]) || fract(xR) > 0.0) { return ${Rt(e)}(0.0); } if (xC < 0.0 || xC >= f32(uniforms.outBackprop[2]) || fract(xC) > 0.0) { return ${Rt(e)}(0.0); } let coord = vec4( batch, i32(xR), i32(xC), col % uniforms.outBackprop[3]); return x[getIndexFromCoords4D(coord, uniforms.xShape)/${e}];`} } return ${Rt(e)}(0.0);`;return` fn mm_readA(batch: i32, row : i32, colIn : i32) -> ${Rt(e)} { let col = colIn * ${e}; ${a} } fn mm_readB(batch: i32, row : i32, colIn : i32) -> ${Rt(e)} { let col = colIn * ${e}; let coordX = uniforms.filterDims.x - 1 - row / (uniforms.filterDims[1] * uniforms.outBackprop[3]); let coordY = uniforms.filterDims.y - 1 - (row / uniforms.outBackprop[3]) % uniforms.filterDims[1]; if (row < uniforms.dimInner && col < uniforms.dimBOuter && coordX >= 0 && coordY >= 0) { let rowInner = row % uniforms.outBackprop[3]; let coord = vec4(coordX, coordY, col, rowInner); ${t(e)} } return ${Rt(e)}(0.0); } fn mm_write(batch: i32, row : i32, colIn : i32, valueInput : ${Rt(e)}) { let col = colIn * ${e}; if (row < uniforms.dimAOuter && (col + ${e-1}) < uniforms.dimBOuter) { var value = valueInput; let outCoord = vec4( batch, row / uniforms.outShape[2], row % uniforms.outShape[2], col); result[getIndexFromCoords4D(outCoord, uniforms.outShape)/${e}] = value; } }`}var rle=class{constructor(e){this.variableNames=["x","W"],this.uniforms="filterDims : vec2, pads : vec2, stride : vec2, outBackprop : vec4, dimAOuter : i32, dimBOuter : i32, dimInner : i32,",this.outputShape=e.inShape,v.assert(e.dataFormat==="channelsLast",()=>"TODO: NCHW is unimplemented"),this.isVec4=e.inChannels%4===0&&e.outChannels%4===0,this.dispatchLayout={x:[3],y:[1,2],z:[0]},this.workGroupSize=z3(this.dispatchLayout,this.outputShape,this.isVec4),this.elementsPerThread=L3(this.dispatchLayout,this.outputShape,this.isVec4),this.dispatch=Me(this.dispatchLayout,this.outputShape,this.workGroupSize,this.elementsPerThread),this.isVec4&&(this.variableTypes=["vec4","f32"]),this.shaderKey=`conv2DDerInputMM_${this.isVec4}_${this.elementsPerThread}`}getUserCode(){let e=this.isVec4?jh(this.elementsPerThread,this.workGroupSize):Hh(this.elementsPerThread,this.workGroupSize);return` ${nle(this.isVec4?4:1)} ${e} `}},sle=class{constructor(e){this.variableNames=["dy","W"],this.uniforms="filterDims : vec2, pads : vec2, stride : vec2, outBackprop : vec4,",this.workGroupSize=[64,1,1],this.size=!0,this.outputShape=e.inShape,this.dispatchLayout=Ye(this.outputShape),this.dispatch=Me(this.dispatchLayout,this.outputShape,this.workGroupSize),this.isChannelsLast=e.dataFormat==="channelsLast",this.shaderKey=`conv2DDerInput_${this.isChannelsLast}`}getUserCode(){let e=this.isChannelsLast?1:2,t=this.isChannelsLast?2:3,a=this.isChannelsLast?3:1;return` ${We("index")} { if(index < uniforms.size) { let coords = getCoordsFromIndex(index); let batch = coords[0]; let d1 = coords[${a}]; let dyCorner = vec2(coords[${e}], coords[${t}]) - uniforms.pads; let dyRCorner = dyCorner.x; let 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. var dotProd = 0.0; for (var wR = 0; wR < uniforms.filterDims.x; wR = wR + 1) { let dyR = (f32(dyRCorner) + f32(wR)) / f32(uniforms.stride.x); let wRPerm = uniforms.filterDims.x - 1 - wR; if (dyR < 0.0 || dyR >= f32(uniforms.outBackprop[1]) || fract(dyR) > 0.0 || wRPerm < 0) { continue; } let idyR = i32(dyR); for (var wC = 0; wC < uniforms.filterDims.y; wC = wC + 1) { let dyC = (f32(dyCCorner) + f32(wC)) / f32(uniforms.stride.y); let wCPerm = uniforms.filterDims.y - 1 - wC; if (dyC < 0.0 || dyC >= f32(uniforms.outBackprop[2]) || fract(dyC) > 0.0 || wCPerm < 0) { continue; } let idyC = i32(dyC); for (var d2 = 0; d2 < uniforms.outBackprop[3]; d2 = d2 + 1) { if (${this.isChannelsLast}) { let xValue = getDy(batch, idyR, idyC, d2); let wValue = getW(wRPerm, wCPerm, d1, d2); dotProd = dotProd + xValue * wValue; } else { let xValue = getDy(batch, d2, idyR, idyC); let wValue = getW(wRPerm, wCPerm, d1, d2); dotProd = dotProd + xValue * wValue; } } } } setOutputAtIndex(index, dotProd); } } `}};function ile(e){let{inputs:t,backend:a,attrs:n}=e,{dy:r,filter:s}=t,{inputShape:i,strides:o,pad:l,dataFormat:u,dimRoundingMode:d}=n,c=T.convertConv2DDataFormat(u),p=T.computeConv2DInfo(i,s.shape,o,1,l,d,!1,c),h=[{type:"int32",data:[p.filterHeight,p.filterWidth]},{type:"int32",data:[p.filterHeight-1-p.padInfo.top,p.filterWidth-1-p.padInfo.left]},{type:"int32",data:[p.strideHeight,p.strideWidth]},{type:"int32",data:[p.batchSize,p.outHeight,p.outWidth,p.outChannels]}],f;if(V().getBool("WEBGPU_USE_NAIVE_CONV2D_TRANSPOSE")||p.filterHeight<=2&&p.filterWidth<=2&&p.outChannels<=16&&p.inChannels===1)f=new sle(p);else{f=new rle(p);let m=p.inHeight*p.inWidth,g=p.inChannels,y=p.filterHeight*p.filterWidth*p.outChannels;h.push({type:"uint32",data:[m]},{type:"uint32",data:[g]},{type:"uint32",data:[y]})}return a.runWebGPUProgram(f,[r,s],"float32",h)}var ole={kernelName:xi,backendName:"webgpu",kernelFunc:ile},lle=Ht({opType:Se.COS}),ule={kernelName:bi,backendName:"webgpu",kernelFunc:lle},dle=Ht({opType:Se.COSH}),ple={kernelName:vi,backendName:"webgpu",kernelFunc:dle},cle=class{constructor(e,t,a,n){this.variableNames=["Image","Boxes","BoxInd"],this.uniforms="extrapolationValue : f32,",this.workGroupSize=[64,1,1],this.size=!0;let[r]=t;this.outputShape=[r,a[0],a[1],e],this.dispatchLayout=Ye(this.outputShape),this.dispatch=Me(this.dispatchLayout,this.outputShape,this.workGroupSize),this.methodId=n==="bilinear"?1:0,this.cropHeightBiggerThan1=this.outputShape[1]>1,this.cropWidthBiggerThan1=this.outputShape[2]>1,this.shaderKey=`cropAndResize_${this.methodId}_${this.cropHeightBiggerThan1}_${this.cropWidthBiggerThan1}`}getUserCode(){let[e,t]=["f32(uniforms.imageShape[1] - 1)","f32(uniforms.imageShape[2] - 1)"],[a,n,r]=this.cropHeightBiggerThan1?[`(${e} / f32(uniforms.outShape[1] - 1))`,"(y2-y1) * height_ratio",`y1*${e} + f32(y)*(height_scale)`]:["0.0","0.0",`0.5 * (y1+y2) * ${e}`],[s,i,o]=this.cropWidthBiggerThan1?[`(${t} / f32(uniforms.outShape[2] - 1))`,"(x2-x1) * width_ratio",`x1*${t} + f32(x)*(width_scale)`]:["0.0","0.0",`0.5 * (x1+x2) * ${t}`];return` ${We("index")} { if (index < uniforms.size) { let coords = getCoordsFromIndex(index); let height_ratio = f32(${a}); let width_ratio = f32(${s}); let b = coords[0]; let y = coords[1]; let x = coords[2]; let d = coords[3]; // get box vals let y1 = getBoxes(b, 0); let x1 = getBoxes(b, 1); let y2 = getBoxes(b, 2); let x2 = getBoxes(b, 3); // get image in batch index let bInd = i32(round(getBoxInd(b))); if(bInd < 0 || bInd >= uniforms.outShape[0]) { return; } let height_scale = ${n}; let width_scale = ${i}; let in_y = ${r}; if( in_y < 0.0 || in_y > ${e} ) { setOutputAtIndex(index, uniforms.extrapolationValue); return; } let in_x = ${o}; if( in_x < 0.0 || in_x > ${t} ) { setOutputAtIndex(index, uniforms.extrapolationValue); return; } let sourceFracIndexCR = vec2(in_x,in_y); if(${this.methodId} == 1) { // Compute the four integer indices. let sourceFloorCR = vec2(sourceFracIndexCR); let sourceCeilCR = vec2(ceil(sourceFracIndexCR)); let topLeft = getImage(bInd, sourceFloorCR.y, sourceFloorCR.x, d); let bottomLeft = getImage(bInd, sourceCeilCR.y, sourceFloorCR.x, d); let topRight = getImage(bInd, sourceFloorCR.y, sourceCeilCR.x, d); let bottomRight = getImage(bInd, sourceCeilCR.y, sourceCeilCR.x, d); let fracCR = sourceFracIndexCR - vec2(sourceFloorCR); let top = topLeft + (topRight - topLeft) * fracCR.x; let bottom = bottomLeft + (bottomRight - bottomLeft) * fracCR.x; let newValue = top + (bottom - top) * fracCR.y; setOutputAtIndex(index, newValue); } else { // Compute the coordinators of nearest neighbor point. let sourceNearestCR = vec2(floor( sourceFracIndexCR + vec2(0.5,0.5))); let newValue = getImage( bInd, sourceNearestCR.y, sourceNearestCR.x, d); setOutputAtIndex(index, newValue); } } } `}},hle=e=>{let{inputs:t,backend:a,attrs:n}=e,{image:r,boxes:s,boxInd:i}=t,{cropSize:o,method:l,extrapolationValue:u}=n,d=new cle(r.shape[3],s.shape,o,l),c=[{type:"float32",data:[u]}];return a.runWebGPUProgram(d,[r,s,i],"float32",c)},fle={kernelName:Ii,backendName:"webgpu",kernelFunc:hle},fd;(function(e){e.Prod="*",e.Sum="+"})(fd||(fd={}));var iA=class{constructor(e,t,a,n){this.variableNames=["x"],this.uniforms="index : f32,",this.size=!0;let r=128;this.workGroupSize=[r,1,1],this.outputShape=t,this.dispatchLayout=Ye(this.outputShape),this.dispatch=Me(this.dispatchLayout,this.outputShape,this.workGroupSize),this.exclusive=a,this.reverse=n,this.op=e,this.shaderKey=`cum_${this.op}_${this.exclusive}_${this.reverse}`}getUserCode(){let e=this.outputShape.length,t=this.op===fd.Prod?"1.0":"0.0",a=this.exclusive?t:`getX(${oA(e,"coords",this.op)})`,n=this.outputShape[this.outputShape.length-1],r="",s="";return this.exclusive?(r=this.reverse?`end != ${n-1}`:"end != 0",s=this.reverse?"end + 1":"end - 1"):(r=this.reverse?`end + pow2 < ${n}`:"end >= pow2",s=this.reverse?"end + pow2":"end - pow2"),` ${We("index")} { if (index < uniforms.size) { var coords = getCoordsFromIndex(index); let end = ${lA(e,"coords",this.op)}; var val = ${a}; let pow2 = i32(pow(2.0, uniforms.index)); if (${r}) { let idx = ${s}; ${lA(e,"coords",this.op)} = idx; val ${this.op}= getX(${oA(e,"coords",this.op)}); } setOutputAtIndex(index, val); } } `}};function oA(e,t,a){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 ${a} for rank ${e} is not yet supported`)}function lA(e,t,a){if(e===1)return`${t}`;if(e===2)return`${t}.y`;if(e===3)return`${t}.z`;if(e===4)return`${t}.w`;throw Error(`Cumulative ${a} for rank ${e} is not yet supported`)}function g9(e,t,a,n,r,s){let i=t.shape.length,o=T.getAxesPermutation([n],i),l=t;o!=null&&(l=br({inputs:{x:t},backend:a,attrs:{perm:o}}));let u=T.getInnerMostAxes(1,i)[0];if(u!==i-1)throw new Error(`WebGPU cumprod shader expects an inner-most axis=${t.shape.length-1} but got axis=${n}`);let d=l.shape[u],c=cn({inputs:{x:l},backend:a});for(let p=0;p<=Math.ceil(Math.log2(d))-1;p++){let h=new iA(e,l.shape,!1,s),f=c,m=[{type:"float32",data:[p]}];c=a.runWebGPUProgram(h,[c],c.dtype,m),a.disposeData(f.dataId)}if(r){let p=new iA(e,l.shape,r,s),h=c,f=[{type:"float32",data:[0]}];c=a.runWebGPUProgram(p,[c],c.dtype,f),a.disposeData(h.dataId)}if(o!=null){let p=T.getUndoAxesPermutation(o),h=br({inputs:{x:c},backend:a,attrs:{perm:p}});return a.disposeData(c.dataId),a.disposeData(l.dataId),h}return c}function mle(e){let{inputs:t,backend:a,attrs:n}=e,{x:r}=t,{axis:s,exclusive:i,reverse:o}=n;return g9(fd.Prod,r,a,s,i,o)}var gle={kernelName:wi,backendName:"webgpu",kernelFunc:mle};function yle(e){let{inputs:t,backend:a,attrs:n}=e,{x:r}=t,{axis:s,exclusive:i,reverse:o}=n;return g9(fd.Sum,r,a,s,i,o)}var Ale={kernelName:ki,backendName:"webgpu",kernelFunc:yle},xle=class{constructor(e,t){this.variableNames=["x"],this.workGroupSize=[64,1,1],this.size=!0,this.uniforms="blockSize : i32,",this.outputShape=e,this.dispatchLayout=Ye(this.outputShape),this.dispatch=Me(this.dispatchLayout,this.outputShape,this.workGroupSize),this.shaderKey=`depthToSpace_${t}`,this.dataFormat=t}getUserCode(){return` ${We("index")} { if (index < uniforms.size) { let coords = getCoordsFromIndex(index); let b = coords[0]; let h = ${this.getHeightCoordString()}; let w = ${this.getWidthCoordString()}; let d = ${this.getDepthCoordString()}; let in_h = h / uniforms.blockSize; let offset_h = h % uniforms.blockSize; let in_w = w / uniforms.blockSize; let offset_w = w % uniforms.blockSize; let offset_d = (offset_h * uniforms.blockSize + offset_w) * ${this.getOutputDepthSize()}; let in_d = d + offset_d; let rlt = ${this.getInputSamplingString()}; setOutputAtIndex(index, rlt); } }`}getHeightCoordString(){return this.dataFormat==="NHWC"?"coords[1]":"coords[2]"}getWidthCoordString(){return this.dataFormat==="NHWC"?"coords[2]":"coords[3]"}getDepthCoordString(){return this.dataFormat==="NHWC"?"coords[3]":"coords[1]"}getOutputDepthSize(){return this.dataFormat==="NHWC"?"uniforms.outShape[3]":"uniforms.outShape[1]"}getInputSamplingString(){return this.dataFormat==="NHWC"?"getX(b, in_h, in_w, in_d)":"getX(b, in_d, in_h, in_w)"}};function ble(e){let{inputs:t,backend:a,attrs:n}=e,{x:r}=t,{blockSize:s,dataFormat:i}=n,o=r.shape[0],l=i==="NHWC"?r.shape[1]:r.shape[2],u=i==="NHWC"?r.shape[2]:r.shape[3],d=i==="NHWC"?r.shape[3]:r.shape[1],c=l*s,p=u*s,h=d/(s*s),f=i==="NHWC"?[o,c,p,h]:[o,h,c,p],m=[{type:"int32",data:[s]}],g=new xle(f,i);return a.runWebGPUProgram(g,[r],r.dtype,m)}var vle={kernelName:Si,backendName:"webgpu",kernelFunc:ble},wle=class{constructor(e,t,a,n=!1,r=null,s=!1){this.variableNames=["x","W"],this.uniforms="pad : vec2, inDims : vec2,",this.workGroupSize=[16,16,1],this.outputShape=e,this.dispatchLayout={x:[3],y:[2],z:[0,1]},this.dispatch=Me(this.dispatchLayout,this.outputShape,this.workGroupSize),n&&this.variableNames.push("bias"),s&&this.variableNames.push("preluActivationWeights"),this.addBias=n,this.activation=r,this.hasPreluActivation=s,this.filterHeight=t,this.filterWidth=a,this.shaderKey=`depthwiseNCHW_${this.activation}_${this.filterHeight}_${this.filterWidth}`}getUserCode(){let e=this.filterWidth*this.filterHeight,t=this.workGroupSize[0]*this.workGroupSize[1]*this.workGroupSize[2],a=this.workGroupSize[1]+this.filterHeight-1,n=this.workGroupSize[0]+this.filterWidth-1;return` ${Cr(this.activation,this.hasPreluActivation,!1,4)} var mm_Asub : array, ${a}>; var mm_Bsub : array, ${this.filterHeight}>; fn readX(batch : i32, channel : i32, row : i32, col : i32) -> f32 { var value = 0.0; if (row >=0 && row < uniforms.inDims[0] && col >=0 && col < uniforms.inDims[1]) { value = getX(batch, channel, row, col); } return value; } ${hd()} fn _start(@builtin(local_invocation_id) LocalId : vec3, @builtin(global_invocation_id) GlobalId : vec3, @builtin(local_invocation_index) LocalIndex: u32, @builtin(num_workgroups) NumWorkgroups: vec3) { localId = LocalId; globalId = GlobalId; let localIndex = i32(LocalIndex); numWorkgroups = NumWorkgroups; let coords = getOutputCoords(); let batch = coords[0]; let xRCCorner = vec2(coords.zw) - uniforms.pad; let channelMul = uniforms.wShape[3]; let d1 = coords[1] / channelMul; let q = coords[1] % channelMul; let inputRowStart = xRCCorner.x; let inputColStart = xRCCorner.y; let localRow = i32(localId.y); let localCol = i32(localId.x); // Load one tile of X into local memory. for (var inputRow = localRow; inputRow < ${a}; inputRow = inputRow + ${this.workGroupSize[1]}) { for (var inputCol = localCol; inputCol < ${n}; inputCol = inputCol + ${this.workGroupSize[0]}) { let rowOffset = inputRow - localRow; let colOffset = inputCol - localCol; mm_Asub[inputRow][inputCol] = readX(batch, d1, inputRowStart + rowOffset, inputColStart + colOffset); } } // Load one tile of W into local memory. var wIndex = localIndex; ${e, inDims : vec2,",this.workGroupSize=[4,4,4],this.workPerThread=4,this.isVec4=!0,this.outputShape=e.outShape,this.dispatchLayout={x:[3],y:[2],z:[0,1]},this.dispatch=Me(this.dispatchLayout,this.outputShape,this.workGroupSize,[4,this.workPerThread,1]),v.assert(e.dataFormat==="channelsLast",()=>"TODO: NCHW is unimplemented"),t&&this.variableNames.push("bias"),n&&this.variableNames.push("preluActivationWeights"),this.convInfo=e,this.addBias=t,this.activation=a,this.hasPreluActivation=n,this.shaderKey=`depthwiseVec4_${a}_${this.convInfo.filterHeight}_${this.convInfo.filterWidth}_${this.convInfo.strideHeight}_${this.convInfo.strideWidth}_${this.workPerThread}`}getUserCode(){let e=(this.workPerThread-1)*this.convInfo.strideWidth+this.convInfo.filterWidth;return` ${Cr(this.activation,this.hasPreluActivation,!0,4)} fn readX(batch : i32, row : i32, col : i32, channel : i32) -> vec4 { var value = vec4(0.0); if (col >=0 && col < uniforms.inDims[1]) { value = getX(batch, row, col, channel); } return value; } const strideHeight = ${this.convInfo.strideHeight}; const strideWidth = ${this.convInfo.strideWidth}; ${hd()} fn _start(@builtin(global_invocation_id) globalId: vec3) { let batch = i32(globalId.z) / uniforms.outShape[1]; let r = i32(globalId.z) % uniforms.outShape[1]; let c = i32(globalId.y) * ${this.workPerThread}; let d1 = i32(globalId.x) * 4; let xRCCorner = vec2(r, c) * vec2(strideHeight, strideWidth) - uniforms.pad; let xRCorner = xRCCorner.x; let xCCorner = xRCCorner.y; var xVals : array, ${e}>; var dotProd : array, ${this.workPerThread}>; for (var i = 0; i < ${this.workPerThread}; i++) { dotProd[i] = vec4(0.0); } // Use constant instead of uniform can give better performance. for (var wR = 0; wR < ${this.convInfo.filterHeight}; wR = wR + 1) { let xR = xRCorner + wR; if (xR >=0 && xR < uniforms.inDims[0]) { for (var i = 0; i < ${e}; i++) { xVals[i] = readX(batch, xR, xCCorner + i, d1); } for (var wC = 0; wC < ${this.convInfo.filterWidth}; wC = wC + 1) { let wValue = getW(wR, wC, d1, 0); for (var i = 0; i < ${this.workPerThread}; i++) { dotProd[i] = fma(xVals[i * strideWidth + wC], wValue, dotProd[i]); } } } } for (var i = 0; i < ${this.workPerThread}; i = i + 1) { let coords = vec4(batch, r, c + i, d1); if (coordsInBounds4D(coords, uniforms.outShape)) { var value = dotProd[i]; ${mo(this.addBias,this.activation)} setOutputAtCoords(coords[0], coords[1], coords[2], coords[3], value); } } } `}},A9=class{constructor(e,t=!1,a=null,n=!1){this.variableNames=["x","W"],this.uniforms=`pad : vec2, inDims : vec2, filterHeight : i32, filterWidth : i32, stride : vec2, dilation : vec2,`,this.workGroupSize=[256,1,1],this.outputShape=e.outShape,this.dispatchLayout=Ye(this.outputShape),this.dispatch=Me(this.dispatchLayout,this.outputShape,this.workGroupSize),this.isChannelsLast=e.dataFormat==="channelsLast",t&&this.variableNames.push("bias"),n&&this.variableNames.push("preluActivationWeights"),this.convInfo=e,this.addBias=t,this.activation=a,this.hasPreluActivation=n,this.shaderKey=`depthwise_${this.activation}_${this.isChannelsLast}`}getUserCode(){let e=this.isChannelsLast?"getX(batch, xR, xC, d1);":"getX(batch, d1, xR, xC);";return` ${Cr(this.activation,this.hasPreluActivation,!1,4)} ${We()} { let coords = getOutputCoords(); let batch = coords[0]; let xRCCorner = vec2(coords.${this.isChannelsLast?"yz":"zw"}) * uniforms.stride - uniforms.pad; let d2 = coords[${this.isChannelsLast?3:1}]; let channelMul = uniforms.wShape[3]; let d1 = d2 / channelMul; let q = d2 % channelMul; let inputRowStart = xRCCorner.x; let inputColStart = xRCCorner.y; let inputRowEnd = inputRowStart + uniforms.filterHeight * uniforms.dilation[0]; let inputColEnd = inputColStart + uniforms.filterWidth * uniforms.dilation[1]; // Convolve x(?, ?, d1)|x(d1, ?, ?) with w(:, :, d1, q) to get // y(yR, yC, d2)|y(d2, yR, yC). ? = to be determined. : = across all // values in that axis. x(?, ?, d1) and y(yR, yC, d2) is for NHWC. // x(d1, ?, ?) and y(d2, yR, yC) is for NCHW. var value = 0.0; // Extract if checking out of for loop for performance. if (inputRowStart >= 0 && inputColStart >= 0 && inputRowEnd < uniforms.inDims[0] && inputColEnd < uniforms.inDims[1]) { for (var wR = 0; wR < uniforms.filterHeight; wR = wR + 1) { let xR = inputRowStart + wR * uniforms.dilation[0]; for (var wC = 0; wC < uniforms.filterWidth; wC = wC + 1) { let xC = inputColStart + wC * uniforms.dilation[1]; let xVal = ${e}; let wVal = getW(wR, wC, d1, q); value = value + xVal * wVal; } } } else { for (var wR = 0; wR < uniforms.filterHeight; wR = wR + 1) { let xR = inputRowStart + wR * uniforms.dilation[0]; if (xR < 0 || xR >= uniforms.inDims[0]) { continue; } for (var wC = 0; wC < uniforms.filterWidth; wC = wC + 1) { let xC = inputColStart + wC * uniforms.dilation[1]; if (xC < 0 || xC >= uniforms.inDims[1]) { continue; } let xVal = ${e}; let wVal = getW(wR, wC, d1, q); value = value + xVal * wVal; } } } ${mo(this.addBias,this.activation)} if (coordsInBounds4D(coords, uniforms.outShape)) { setOutputAtCoords(coords[0], coords[1], coords[2], coords[3], value); } } `}};function kle(e){let{inputs:t,backend:a,attrs:n}=e,{x:r,filter:s}=t,{strides:i,pad:o,dataFormat:l,dilations:u,dimRoundingMode:d}=n,c=T.convertConv2DDataFormat(l),p=u;p==null&&(p=[1,1]);let h=T.computeConv2DInfo(r.shape,s.shape,i,p,o,d,!0,c),f=[{type:"int32",data:[h.padInfo.top,h.padInfo.left]},{type:"int32",data:[h.inHeight,h.inWidth]}],m=h.dataFormat==="channelsLast",g;return!m&&h.inHeight>16&&h.inWidth>16&&h.strideHeight===1&&h.strideWidth===1&&h.dilationWidth===1&&h.dilationHeight===1&&h.inChannels===h.outChannels?g=new wle(h.outShape,h.filterHeight,h.filterWidth):m&&h.inHeight>4&&h.inWidth>4&&h.strideWidth<=2&&h.inChannels===h.outChannels&&h.dilationHeight===1&&h.dilationWidth===1&&h.inChannels%4===0?g=new y9(h):(g=new A9(h),f.push({type:"int32",data:[h.filterHeight]},{type:"int32",data:[h.filterWidth]},{type:"int32",data:[h.strideHeight,h.strideWidth]},{type:"int32",data:[h.dilationHeight,h.dilationWidth]})),a.runWebGPUProgram(g,[r,s],r.dtype,f)}var Ile={kernelName:Ti,backendName:"webgpu",kernelFunc:kle},x9=ca({opType:Be.MUL,cpuKernelImpl:Hie,supportsComplex:!0}),Sle={kernelName:ps,backendName:"webgpu",kernelFunc:x9};function K3(e){let{inputs:t,backend:a,attrs:n}=e,{x:r}=t,{axis:s,keepDims:i}=n;return gp(r,s,i,"sum",a)}var Tle={kernelName:to,backendName:"webgpu",kernelFunc:K3};function Cle(e){let{inputs:t,backend:a,attrs:n}=e,{equation:r}=n,s=t,{allDims:i,summedDims:o,idDims:l}=T.decodeEinsumEquation(r,s.length);T.checkEinsumDimSizes(i.length,l,s);let{path:u,steps:d}=T.getEinsumComputePath(o,l),c=d.length,p=null,h=i.length,f=[];for(let m=0;m=0&&(p=K3({inputs:{x:p},backend:a,attrs:{axis:u[m]-(i.length-h),keepDims:!1}}),f.push(p)),h--)}for(let m of f)m!==p&&a.disposeData(m.dataId);return p}var Nle={kernelName:Sd,backendName:"webgpu",kernelFunc:Cle},Ele=Ht({opType:Se.ELU}),Rle={kernelName:Ni,backendName:"webgpu",kernelFunc:Ele},Mle=ca({opType:Be.EQUAL,dtype:"bool",cpuKernelImpl:$ie}),$le={kernelName:ts,backendName:"webgpu",kernelFunc:Mle},b9=Ht({opType:Se.EXP,cpuKernelImpl:_ie,dtype:"float32"}),_le={kernelName:as,backendName:"webgpu",kernelFunc:b9};function z1(e){let{inputs:t,attrs:a,backend:n}=e,{dim:r}=a,{input:s}=t,i=s.shape.length,o=s.shape.slice(),l=r;return r<0&&(v.assert(-(i+1)<=r,()=>`Axis must be in the interval [${-(i+1)}, ${i}]`),l=i+r+1),o.splice(l,0,1),Re({inputs:{x:s},backend:n,attrs:{shape:o}})}var Fle={kernelName:wl,backendName:"webgpu",kernelFunc:z1},Ple=Ht({opType:Se.EXPM1,cpuKernelImpl:Fie}),Ole={kernelName:Ei,backendName:"webgpu",kernelFunc:Ple},Dle=class{constructor(e){this.outputShape=[],this.variableNames=["x"],this.workGroupSize=[64,1,1],this.size=!0,this.outputShape=e,this.dispatchLayout=Ye(this.outputShape),this.dispatch=Me(this.dispatchLayout,this.outputShape,this.workGroupSize),this.shaderKey="flipLeftRight"}getUserCode(){return` ${We("index")} { if (index < uniforms.size) { let coords = getCoordsFromIndex(index); let coordX = uniforms.xShape[2] - coords[2] - 1; let outputValue = getX(coords[0], coords[1], coordX, coords[3]); setOutputAtIndex(index, outputValue); } } `}},zle={kernelName:Ri,backendName:"webgpu",kernelFunc:({inputs:e,backend:t})=>{let{image:a}=e,n=t,r=new Dle(a.shape);return n.runWebGPUProgram(r,[a],a.dtype)}},Lle=Ht({opType:Se.FLOOR,cpuKernelImpl:Pie}),Ble={kernelName:ns,backendName:"webgpu",kernelFunc:Lle},Wle=ca({opType:Be.INT_DIV,dtype:"int32"}),Vle={kernelName:Mi,backendName:"webgpu",kernelFunc:Wle},Gle=class{constructor(e,t,a=!1){this.isFromPixels=!0,this.outputShape=[0],this.variableNames=[],this.workGroupSize=[256,1,1],this.outputShape=e,this.dispatchLayout=Ye(this.outputShape),this.dispatch=Me(this.dispatchLayout,this.outputShape,this.workGroupSize,[t,1,1]),this.importVideo=a,this.shaderKey=`fromPixels_${this.importVideo}`}getUserCode(){let e=this.importVideo?"textureLoad(src, vec2(coords.yx));":"textureLoad(src, vec2(coords.yx), 0)";return` @binding(1) @group(0) var src: ${this.importVideo?"texture_external":"texture_2d"}; ${We("index")} { let flatIndex = index * uniforms.numChannels; if (flatIndex < uniforms.size) { let coords = getCoordsFromIndex(flatIndex); let values = ${e}; for (var i = 0; i < uniforms.numChannels; i = i + 1) { result[flatIndex + i] = i32(floor(255.0 * values[i])); } } } `}},Ule={kernelName:Yu,backendName:"webgpu",kernelFunc:jle},Ho,Bm=V().getBool("CANVAS2D_WILL_READ_FREQUENTLY_FOR_GPU"),dc=new Map;function jle(e){let{inputs:t,backend:a,attrs:n}=e,{pixels:r}=t,{numChannels:s}=n;if(r==null)throw new Error("pixels passed to tf.browser.fromPixels() can not be null");let i=typeof HTMLVideoElement!="undefined"&&r instanceof HTMLVideoElement,o=typeof HTMLImageElement!="undefined"&&r instanceof HTMLImageElement,l=typeof HTMLCanvasElement!="undefined"&&r instanceof HTMLCanvasElement||typeof OffscreenCanvas!="undefined"&&r instanceof OffscreenCanvas,u=typeof ImageBitmap!="undefined"&&r instanceof ImageBitmap,[d,c]=i?[r.videoWidth,r.videoHeight]:[r.width,r.height],p=[c,d,s],h=!1,f=i||o;if(u||l||f){let A;if(h){let $=r;if(!dc.has($)||dc.get($).expired){let M={source:$};dc.set($,a.device.importExternalTexture(M))}A={width:d,height:c,format:null,usage:null,texture:dc.get($)}}else{if(f){let E=V().getBool("CANVAS2D_WILL_READ_FREQUENTLY_FOR_GPU");(Ho==null||E!==Bm)&&(Bm=E,Ho=document.createElement("canvas").getContext("2d",{willReadFrequently:Bm})),Ho.canvas.width=d,Ho.canvas.height=c,Ho.drawImage(r,0,0,d,c),r=Ho.canvas}let $=GPUTextureUsage.COPY_DST|GPUTextureUsage.RENDER_ATTACHMENT|GPUTextureUsage.TEXTURE_BINDING,M="rgba8unorm",I=a.textureManager.acquireTexture(p[1],p[0],M,$);a.queue.copyExternalImageToTexture({source:r},{texture:I},[p[1],p[0]]),A={width:d,height:c,format:M,usage:$,texture:I}}let x=v.sizeFromShape(p),b=v.computeStrides(p),w=new Gle(p,s,h),S=[{type:"uint32",data:[x]},{type:"uint32",data:[s]},{type:"uint32",data:[...b]}],C=a.makeTensorInfo([c,d],"int32"),N=a.tensorMap.get(C.dataId);N.resourceInfo=A;let _=a.runWebGPUProgram(w,[C],"int32",S);return a.disposeData(C.dataId),_}let m=r.data,g=m;if(s!=null&&s!==4){g=new Uint8Array(r.width*r.height*s);let A=m.length,x=0;for(let b=0;b(xValue, -meanValue, offsetValue), vec3(inv, inv, 1.0))); } } `}},qle={kernelName:$i,backendName:"webgpu",kernelFunc:({inputs:e,attrs:t,backend:a})=>{let{x:n,scale:r,offset:s,mean:i,variance:o}=e,{varianceEpsilon:l}=t,u=a,d=[n,i,o],c=null;s!=null&&(c=s.shape,d.push(s));let p=null;r!=null&&(p=r.shape,d.push(r));let h=new Hle(n.shape,i.shape,o.shape,c,p),f=[{type:"float32",data:[l]}];return u.runWebGPUProgram(h,d,n.dtype,f)}};function Xle(e){let{inputs:t,backend:a,attrs:n}=e,{x:r,filter:s,bias:i,preluActivationWeights:o}=t,{strides:l,pad:u,dataFormat:d,dilations:c,dimRoundingMode:p,activation:h,leakyreluAlpha:f}=n,m=T.convertConv2DDataFormat(d),g=T.computeConv2DInfo(r.shape,s.shape,l,c,u,p,!1,m);return m9({x:r,filter:s,convInfo:g,backend:a,bias:i,preluActivationWeights:o,leakyreluAlpha:f,activation:h})}var Kle={kernelName:jr,backendName:"webgpu",kernelFunc:Xle};function Zle(e){let{inputs:t,backend:a,attrs:n}=e,{x:r,filter:s,bias:i,preluActivationWeights:o}=t,{strides:l,pad:u,dilations:d,dimRoundingMode:c,activation:p,leakyreluAlpha:h}=n,f=d;f==null&&(f=[1,1]),v.assert(T.eitherStridesOrDilationsAreOne(l,f),()=>`Error in depthwiseConv2d: Either strides or dilations must be 1. 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var flattenIndex = 0; for (var j = 0; j < uniforms.sliceDim; j = j + 1) { let indexTemp = i32(round(getIndices(coords[0], j))); let strideNum = ${e}; flattenIndex = flattenIndex + indexTemp * strideNum; } setOutputAtIndex(index, getA(flattenIndex, coords[1])); } } `}};function Qle(e){let{inputs:t,backend:a}=e,{params:n,indices:r}=t,s=r.shape,i=s[s.length-1],o=v.sizeFromShape(n.shape),[l,u,d,c]=T.prepareAndValidate(n,r),p=Re({inputs:{x:r},backend:a,attrs:{shape:[u,i]}}),h=Re({inputs:{x:n},backend:a,attrs:{shape:[v.sizeFromShape(n.shape)/d,d]}});if(a.shouldExecuteOnCPU([n,r])||n.dtype==="string"){let A=a.readSync(r.dataId),x=a.bufferSync(n),b=Oie(A,x,n.dtype,u,i,d,c,n.shape,o);return a.makeTensorInfo(l,n.dtype,b.values)}let f=new Jle(i,[u,d]),m=[{type:"int32",data:[i]},{type:"int32",data:c}],g=a.runWebGPUProgram(f,[h,p],h.dtype,m),y=Re({inputs:{x:g},backend:a,attrs:{shape:l}});return a.disposeData(p.dataId),a.disposeData(h.dataId),a.disposeData(g.dataId),y}var eue={kernelName:_i,backendName:"webgpu",kernelFunc:Qle},tue=class{constructor(e,t){this.variableNames=["A","indices"],this.workGroupSize=[64,1,1],this.size=!0,this.outputShape=e.slice(),this.aShape=e,this.outputShape=t,this.dispatchLayout=Ye(this.outputShape),this.dispatch=Me(this.dispatchLayout,this.outputShape,this.workGroupSize),this.shaderKey="gather"}getUserCode(){let e=aue(this.aShape);return` ${We("index")} { if (index < uniforms.size) { let resRC = getCoordsFromIndex(index); let indexZ = i32(getIndices(resRC.x, resRC.z)); let inBounds = select(0.0, 1.0, indexZ >= 0 && indexZ < uniforms.aShape[2]); setOutputAtIndex(index, inBounds * getA(${e})); } } `}};function aue(e){let t=["resRC.x","resRC.y","resRC.z","resRC.w"],a=[];for(let n=0;na.disposeData(C.dataId)),a.makeTensorInfo(u.outputShape,S.dtype,S.values)}let m=new tue(p.shape,f),g=a.runWebGPUProgram(m,[p,h],p.dtype);c.push(g);let y=Re({inputs:{x:g},backend:a,attrs:{shape:u.outputShape}});return c.forEach(A=>a.disposeData(A.dataId)),y}var nue={kernelName:Il,backendName:"webgpu",kernelFunc:v9},rue=ca({opType:Be.GREATER,cpuKernelImpl:Lie,dtype:"bool"}),sue={kernelName:rs,backendName:"webgpu",kernelFunc:rue},iue=ca({opType:Be.GREATER_EQUAL,dtype:"bool",cpuKernelImpl:zie}),oue={kernelName:ss,backendName:"webgpu",kernelFunc:iue},lue=Ht({opType:Se.IS_NAN,dtype:"bool"}),uue={kernelName:Sl,backendName:"webgpu",kernelFunc:lue};function due(e){let{inputs:t,backend:a,attrs:n}=e,{x:r}=t,{alpha:s}=n,i=[{type:"float32",data:[s]}],o=new mp(r.shape,Se.LEAKYRELU);return o.uniforms="alpha : f32,",a.runWebGPUProgram(o,[r],"float32",i)}var pue={kernelName:Pi,backendName:"webgpu",kernelFunc:due},cue=ca({opType:Be.LESS,dtype:"bool",cpuKernelImpl:Wie}),hue={kernelName:is,backendName:"webgpu",kernelFunc:cue},fue=ca({opType:Be.LESS_EQUAL,dtype:"bool",cpuKernelImpl:Bie}),mue={kernelName:os,backendName:"webgpu",kernelFunc:fue},gue=Ht({opType:Se.LOG,cpuKernelImpl:Vie}),yue={kernelName:ls,backendName:"webgpu",kernelFunc:gue},Aue=ca({opType:Be.LOGICAL_AND,dtype:"bool"}),xue={kernelName:Oi,backendName:"webgpu",kernelFunc:Aue},bue=Ht({opType:Se.LOGICAL_NOT}),vue={kernelName:Di,backendName:"webgpu",kernelFunc:bue},wue=ca({opType:Be.MAX,cpuKernelImpl:Uie}),kue={kernelName:us,backendName:"webgpu",kernelFunc:wue};function Iue(e){let{inputs:t,backend:a,attrs:n}=e,{x:r}=t,{filterSize:s,strides:i,pad:o,dimRoundingMode:l}=n,u=1,d=T.computePool2DInfo(r.shape,s,i,u,o,l);return c9(r,d,"max",a)}var Sue={kernelName:Li,backendName:"webgpu",kernelFunc:Iue};function Tue(e){let{inputs:t,backend:a,attrs:n}=e,{x:r}=t,{axis:s,keepDims:i}=n;return gp(r,s,i,"min",a)}var Cue={kernelName:Wi,backendName:"webgpu",kernelFunc:Tue},Nue=ca({opType:Be.MIN,cpuKernelImpl:jie}),Eue={kernelName:ds,backendName:"webgpu",kernelFunc:Nue},Rue=class{constructor(e,t,a){this.uniforms="",this.variableNames=["x"],this.workGroupSize=[64,1,1],this.size=!0,this.outputShape=t.map((n,r)=>n[0]+e[r]+n[1]),this.dispatchLayout=Ye(this.outputShape),this.dispatch=Me(this.dispatchLayout,this.outputShape,this.workGroupSize),this.xShape=e,t.map((n,r)=>{this.uniforms+=` pad${r} : vec2,`}),this.offset=a==="reflect"?0:1,this.shaderKey=`mirrorPad_${a}`}getUserCode(){let e=this.xShape.length,t=this.xShape.map((l,u)=>`uniforms.pad${u}[0]`).join(","),a=this.xShape.map((l,u)=>`uniforms.pad${u}[0] + uniforms.xShape${e>1?`[${u}]`:""}`).join(","),n=e===1?"start":"start[i]",r=e===1?"end":"end[i]",s=e===1?"outC":"outC[i]",i=na(e),o=e>1?["coords[0]","coords[1]","coords[2]","coords[3]"].slice(0,e):"coords";return` ${We("index")} { if (index < uniforms.size) { let start = ${i}(${t}); let end = ${i}(${a}); var outC = getCoordsFromIndex(index); for (var i = 0; i < ${e}; i = i + 1) { if (${s} < ${n}) { ${s} = ${n} * 2 - ${s} - ${this.offset}; } else if(${s} >= ${r}) { ${s} = (${r} - 1) * 2 - ${s} + ${this.offset}; } } let coords = outC - start; setOutputAtIndex(index, getX(${o})); } } `}},Mue={kernelName:Vi,backendName:"webgpu",kernelFunc:({inputs:e,attrs:t,backend:a})=>{let{x:n}=e,{paddings:r,mode:s}=t,i=a,o=r.map(u=>({type:"int32",data:[u[0],u[1]]})),l=new Rue(n.shape,r,s);return i.runWebGPUProgram(l,[n],n.dtype,o)}};function $ue(e){let{inputs:t,backend:a}=e,{x:n}=t;if(a.shouldExecuteOnCPU([n])){let s=a.tensorMap.get(n.dataId),[i,o]=qie(s.values,n.shape,n.dtype);return a.makeTensorInfo(o,n.dtype,i)}let r=new mp(n.shape,Se.NEG);return a.runWebGPUProgram(r,[n],n.dtype)}var _ue={kernelName:Cl,backendName:"webgpu",kernelFunc:$ue};function Fue(e){console.warn("tf.nonMaxSuppression() in webgpu locks the UI thread. Call tf.nonMaxSuppressionAsync() instead");let{inputs:t,backend:a,attrs:n}=e,{boxes:r,scores:s}=t,{maxOutputSize:i,iouThreshold:o,scoreThreshold:l}=n,u=a.readSync(r.dataId),d=a.readSync(s.dataId),{selectedIndices:c}=Tn.nonMaxSuppressionV3Impl(u,d,i,o,l);return a.makeTensorInfo([c.length],"int32",new Int32Array(c))}var Pue={kernelName:Gi,backendName:"webgpu",kernelFunc:Fue};function Oue(e){console.warn("tf.nonMaxSuppression() in webgpu locks the UI thread. Call tf.nonMaxSuppressionAsync() instead");let{inputs:t,backend:a,attrs:n}=e,{boxes:r,scores:s}=t,{maxOutputSize:i,iouThreshold:o,scoreThreshold:l,softNmsSigma:u}=n,d=a.readSync(r.dataId),c=a.readSync(s.dataId),p=i,h=o,f=l,m=u,{selectedIndices:g,selectedScores:y}=Tn.nonMaxSuppressionV5Impl(d,c,p,h,f,m);return[a.makeTensorInfo([g.length],"int32",new Int32Array(g)),a.makeTensorInfo([y.length],"float32",new Float32Array(y))]}var Due={kernelName:Ui,backendName:"webgpu",kernelFunc:Oue};function Bc(e){let{inputs:t,backend:a}=e,{x:n}=t;if(n.dtype==="complex64"){let r=yp({inputs:{input:n},backend:a}),s=Bc({inputs:{x:r},backend:a}),i=qh({inputs:{input:n},backend:a}),o=Bc({inputs:{x:i},backend:a}),l=ou({inputs:{real:s,imag:o},backend:a});return a.disposeData(r.dataId),a.disposeData(s.dataId),a.disposeData(i.dataId),a.disposeData(o.dataId),l}else return go({attrs:{shape:n.shape,dtype:n.dtype,value:n.dtype==="string"?"":0},backend:a})}var zue={kernelName:Hl,backendName:"webgpu",kernelFunc:Bc};function w9(e){let{inputs:t,backend:a}=e,{x:n}=t;if(n.dtype==="string")throw new Error("onesLike is not supported under string dtype");if(n.dtype==="complex64"){let r=yp({inputs:{input:n},backend:a}),s=w9({inputs:{x:r},backend:a}),i=qh({inputs:{input:n},backend:a}),o=Bc({inputs:{x:i},backend:a}),l=ou({inputs:{real:s,imag:o},backend:a});return a.disposeData(r.dataId),a.disposeData(s.dataId),a.disposeData(i.dataId),a.disposeData(o.dataId),l}else return go({attrs:{shape:n.shape,dtype:n.dtype,value:1},backend:a})}var Lue={kernelName:El,backendName:"webgpu",kernelFunc:w9};function Bue(e){let{inputs:t,backend:a,attrs:n}=e,{axis:r}=n;if(t.length===1)return z1({inputs:{input:t[0]},backend:a,attrs:{dim:r}});let s=t[0].shape,i=t[0].dtype;t.forEach(d=>{v.assertShapesMatch(s,d.shape,"All tensors passed to stack must have matching shapes"),v.assert(i===d.dtype,()=>"All tensors passed to stack must have matching dtypes")});let o=[],l=t.map(d=>{let c=z1({inputs:{input:d},backend:a,attrs:{dim:r}});return o.push(c),c}),u=f9({inputs:l,backend:a,attrs:{axis:r}});return o.forEach(d=>a.disposeData(d.dataId)),u}var Wue={kernelName:Ml,backendName:"webgpu",kernelFunc:Bue},Vue=class{constructor(e,t){this.variableNames=["x"],this.uniforms="constantValue : f32,",this.workGroupSize=[64,1,1],this.size=!0,this.outputShape=t.map((a,n)=>a[0]+e[n]+a[1]),this.dispatchLayout=Ye(this.outputShape),this.dispatch=Me(this.dispatchLayout,this.outputShape,this.workGroupSize),t.map((a,n)=>{this.uniforms+=` pad${n} : vec2,`}),this.xShape=e,this.shaderKey="pad"}getUserCode(){let e=this.xShape.length,t=na(e),a=this.xShape.map((u,d)=>`uniforms.pad${d}[0]`).join(","),n=this.xShape.map((u,d)=>`uniforms.pad${d}[0] + uniforms.xShape${e>1?`[${d}]`:""}`).join(","),r=e>1?`${t}(${a})`:`${a}`,s=e>1?`${t}(${n})`:`${n}`,i=e>1?"any(outC < start)":"outC < start",o=e>1?"any(outC >= end)":"outC >= end",l=e>1?["coords[0]","coords[1]","coords[2]","coords[3]"].slice(0,e):"coords";return` ${We("index")} { if (index < uniforms.size) { let start = ${r}; let end = ${s}; let outC = getCoordsFromIndex(index); if (${i} || ${o}) { setOutputAtIndex(index, uniforms.constantValue); } else { let coords = outC - start; setOutputAtIndex(index, getX(${l})); } } } `}},k9=e=>{let{inputs:t,backend:a,attrs:n}=e,{x:r}=t,{paddings:s,constantValue:i}=n;if(s.every(u=>v.arraysEqual(u,[0,0])))return cn({inputs:{x:r},backend:a});if(v.sizeFromShape(r.shape)===0){let u=s.map((d,c)=>d[0]+r.shape[c]+d[1]);return go({backend:a,attrs:{shape:u,value:i,dtype:r.dtype}})}let o=[{type:"float32",data:[i]}];s.map(u=>o.push({type:"int32",data:[u[0],u[1]]}));let l=new Vue(r.shape,s);return a.runWebGPUProgram(l,[r],r.dtype,o)},Gue={kernelName:ji,backendName:"webgpu",kernelFunc:k9},Uue=ca({opType:Be.POW}),jue={kernelName:Hi,backendName:"webgpu",kernelFunc:Uue};function Hue(e){let{inputs:t,backend:a}=e,{x:n,alpha:r}=t,s=new _1(Be.PRELU,n.shape,r.shape);return a.runWebGPUProgram(s,[n,r],"float32")}var que={kernelName:qi,backendName:"webgpu",kernelFunc:Hue};function Xue(e){let{inputs:t,backend:a,attrs:n}=e,{x:r}=t,{axis:s,keepDims:i}=n;return gp(r,s,i,"prod",a)}var Kue={kernelName:Xi,backendName:"webgpu",kernelFunc:Xue},Zue=e=>{let{backend:t,attrs:a}=e,{start:n,stop:r,step:s,dtype:i}=a,o=Zie(n,r,s,i);return t.makeTensorInfo([o.length],i,o)},Yue={kernelName:$l,backendName:"webgpu",kernelFunc:Zue},I9=ca({opType:Be.DIV}),Jue={kernelName:Ci,backendName:"webgpu",kernelFunc:I9},Que=Ht({opType:Se.RECIPROCAL}),ede={kernelName:_l,backendName:"webgpu",kernelFunc:Que},tde=Ht({opType:Se.RELU}),ade={kernelName:Ki,backendName:"webgpu",kernelFunc:tde},nde=Ht({opType:Se.RELU6}),rde={kernelName:Ji,backendName:"webgpu",kernelFunc:nde},sde=class{constructor(e,t,a){this.variableNames=["x"],this.uniforms="adjustHeightWidth : vec2, halfPixelCenters : f32,",this.workGroupSize=[64,1,1],this.size=!0,this.outputShape=[e[0],t,a,e[3]],this.dispatchLayout=Ye(this.outputShape),this.dispatch=Me(this.dispatchLayout,this.outputShape,this.workGroupSize),this.shaderKey="resizeBilinear"}getUserCode(){return` ${We("index")} { if (index < uniforms.size) { let coords = getCoordsFromIndex(index); let b = coords[0]; let d = coords[3]; let rc = coords.yz; let effectiveInSize = vec2( f32(uniforms.xShape.y) - uniforms.adjustHeightWidth[0], f32(uniforms.xShape.z) - uniforms.adjustHeightWidth[1]); let effectiveOutSize = vec2( f32(uniforms.outShape.y) - uniforms.adjustHeightWidth[0], f32(uniforms.outShape.z) - uniforms.adjustHeightWidth[1]); let effectiveInputOverOutputRatioRC = effectiveInSize / effectiveOutSize; // Fractional source index let sourceFracIndexRC = (vec2(rc) + vec2(uniforms.halfPixelCenters)) * effectiveInputOverOutputRatioRC - vec2(uniforms.halfPixelCenters); // Compute the four integer indices. let sourceFloorRC = vec2(sourceFracIndexRC); let sourceCeilRC = vec2( min(vec2(uniforms.xShape.yz) - vec2(1.0), ceil(sourceFracIndexRC))); let topLeft = getX(b, sourceFloorRC.x, sourceFloorRC.y, d); let bottomLeft = getX(b, sourceCeilRC.x, sourceFloorRC.y, d); let topRight = getX(b, sourceFloorRC.x, sourceCeilRC.y, d); let bottomRight = getX(b, sourceCeilRC.x, sourceCeilRC.y, d); let fracRC = sourceFracIndexRC - vec2(sourceFloorRC); let top = topLeft + (topRight - topLeft) * fracRC.y; let bottom = bottomLeft + (bottomRight - bottomLeft) * fracRC.y; let newValue = top + (bottom - top) * fracRC.x; setOutputAtIndex(index, newValue); } } `}};function ide(e){let{inputs:t,backend:a,attrs:n}=e,{images:r}=t,{alignCorners:s,size:i,halfPixelCenters:o}=n,[l,u]=i,d=s&&l>1?1:0,c=s&&u>1?1:0,p=[{type:"float32",data:[d,c]},{type:"float32",data:[o?.5:0]}],h=new sde(r.shape,l,u);return a.runWebGPUProgram(h,[r],"float32",p)}var ode={kernelName:Yi,backendName:"webgpu",kernelFunc:ide},lde=class{constructor(e,t,a,n){this.variableNames=["x"],this.uniforms="adjustHeightWidth : vec2, roundBase : f32,",this.workGroupSize=[64,1,1],this.size=!0,this.outputShape=[e[0],t,a,e[3]],this.dispatchLayout=Ye(this.outputShape),this.dispatch=Me(this.dispatchLayout,this.outputShape,this.workGroupSize),this.halfPixelCenters=n,this.shaderKey=`resizeNearest_${n}`}getUserCode(){let e;return this.halfPixelCenters?e="max((vec2(rc) + vec2(0.5)) * effectiveInputOverOutputRatioRC, vec2(0.0))":e="vec2(rc) * effectiveInputOverOutputRatioRC",` ${We("index")} { if (index < uniforms.size) { let coords = getCoordsFromIndex(index); let b = coords[0]; let d = coords[3]; let rc = coords.yz; let effectiveInSize = vec2( f32(uniforms.xShape.y) - uniforms.adjustHeightWidth[0], f32(uniforms.xShape.z) - uniforms.adjustHeightWidth[1]); let effectiveOutSize = vec2( f32(uniforms.outShape.y) - uniforms.adjustHeightWidth[0], f32(uniforms.outShape.z) - uniforms.adjustHeightWidth[1]); let effectiveInputOverOutputRatioRC = effectiveInSize / effectiveOutSize; // Fractional source index let sourceFracIndexRC = ${e}; // Compute the coordinators of nearest neighbor point. let inputShapeRC = vec2(f32(uniforms.xShape.y), f32(uniforms.xShape.z)); let sourceNearestRC = vec2( min(inputShapeRC - 1.0, floor(sourceFracIndexRC + uniforms.roundBase))); let newValue = getX(b, sourceNearestRC.x, sourceNearestRC.y, d); setOutputAtIndex(index, newValue); } } `}};function ude(e){let{inputs:t,backend:a,attrs:n}=e,{images:r}=t,{alignCorners:s,halfPixelCenters:i,size:o}=n,[l,u]=o,d=s&&l>1?1:0,c=s&&u>1?1:0,p=[{type:"float32",data:[d,c]},{type:"float32",data:[s?.5:0]}],h=new lde(r.shape,l,u,i);return a.runWebGPUProgram(h,[r],r.dtype,p)}var dde={kernelName:Zi,backendName:"webgpu",kernelFunc:ude},pde=class{constructor(e,t){this.outputShape=[],this.variableNames=["x"],this.workGroupSize=[64,1,1],this.size=!0,this.outputShape=e,this.dispatchLayout=Ye(this.outputShape),this.dispatch=Me(this.dispatchLayout,this.outputShape,this.workGroupSize),this.uniforms=`centerX : f32, centerY : f32, sinRadians : f32, cosRadians : f32,`,this.shaderKey="rotate",this.outputShape=e,typeof t=="number"?(this.uniforms+=" fillValue : f32,",this.fillSnippet="var outputValue = uniforms.fillValue;",this.shaderKey+="_float"):(this.uniforms+=" fillValue : vec3,",this.fillSnippet="var outputValue = uniforms.fillValue[coords[3]];",this.shaderKey+="_vec3")}getUserCode(){return` ${We("index")} { if (index < uniforms.size) { let coords = getCoordsFromIndex(index); let coordXFloat = (f32(coords[2]) - uniforms.centerX) * uniforms.cosRadians - (f32(coords[1]) - uniforms.centerY) * uniforms.sinRadians; let coordYFloat = (f32(coords[2]) - uniforms.centerX) * uniforms.sinRadians + (f32(coords[1]) - uniforms.centerY) * uniforms.cosRadians; let coordX = i32(round(coordXFloat + uniforms.centerX)); let coordY = i32(round(coordYFloat + uniforms.centerY)); ${this.fillSnippet} if(coordX >= 0 && coordX < uniforms.xShape[2] && coordY >= 0 && coordY < uniforms.xShape[1]) { outputValue = getX(coords[0], coordY, coordX, coords[3]); } setOutputAtIndex(index, outputValue); } } `}},cde={kernelName:lo,backendName:"webgpu",kernelFunc:({inputs:e,attrs:t,backend:a})=>{let{image:n}=e,{radians:r,fillValue:s,center:i}=t,o=a,l=new pde(n.shape,s),[u,d]=T.getImageCenter(i,n.shape[1],n.shape[2]),c=[{type:"float32",data:[u]},{type:"float32",data:[d]},{type:"float32",data:[Math.sin(r)]},{type:"float32",data:[Math.cos(r)]}];return typeof s=="number"?c.push({type:"float32",data:[Number.parseFloat(s.toFixed(2))]}):c.push({type:"float32",data:s}),o.runWebGPUProgram(l,[n],n.dtype,c)}},hde=Ht({opType:Se.RSQRT,cpuKernelImpl:Yie}),fde={kernelName:hs,backendName:"webgpu",kernelFunc:hde},xc=class{constructor(e,t,a,n,r,s,i,o=!0){this.variableNames=["updates","indices"],this.workGroupSize=[64,1,1],this.atomic=!0,this.outputShape=s,this.type=i,this.sumDupeIndices=o,this.dispatchLayout=Ye(e),this.dispatch=Me(this.dispatchLayout,e,this.workGroupSize),this.sliceDimGreaterThanOne=t>1,this.shaderKey=`scatter_${a}_${n}_${this.sliceDimGreaterThanOne}_${i}_${o}`;let l=na(r.length);this.uniforms=`sliceDim : i32, strides: ${l}, size: i32,`,this.updatesRank=n,this.indicesRank=a}getUserCode(){let e="";this.indicesRank===1?e="coords[0]":this.indicesRank===2&&(e="coords[0], j");let t=`getIndices(${e})`,a=this.sliceDimGreaterThanOne?"uniforms.strides[j]":"uniforms.strides",n="",r="";this.dispatchLayout.x.length===1?(n="flattenedIndex",r=` fn getUpdatesCoordsFromFlatIndex(index : i32) -> i32 { return index; } `):this.dispatchLayout.x.length===2&&(n="vec2(flattenedIndex, coords[1])",r=` fn getUpdatesCoordsFromFlatIndex(index : i32) -> vec2 { // N.B. |updates| could be a scalar tensor, conceptually representing a // 2D tensor with all values equal to that. By design, its size must be // the same as |outShape[1]| in one dimension, and |indicesShape[0]| // gives the other. let sliceSize = uniforms.outShape[1]; let d0 = index / sliceSize; let d1 = index - d0 * sliceSize; return vec2(d0, d1); } `);let s=`getUpdates(${Array.from({length:this.updatesRank},(o,l)=>`coords[${l}]`).join(", ")})`,i=(o,l)=>{let u=`atomicAdd(${o}, bitcast(${l}))`;this.type==="float32"&&(u=` { var oldBits = 0; var newBits = bitcast(${l}); loop { let info = atomicCompareExchangeWeak(${o}, oldBits, newBits); if (info.exchanged) { break; } oldBits = info.old_value; let oldValue = bitcast(oldBits); let newValue = oldValue + (${l}); newBits = bitcast(newValue); } } `);let d=`atomicStore(${o}, bitcast(${l}));`;return this.sumDupeIndices?u:d};return` ${r} ${We("index")} { if (index < uniforms.size) { let coords = getUpdatesCoordsFromFlatIndex(index); var flattenedIndex = 0; for (var j = 0; j < uniforms.sliceDim; j = j + 1) { let indexInside = i32(round(${t})); flattenedIndex = flattenedIndex + indexInside * ${a}; } let updateValue = ${Ku(this.type,!1)}(${s}); let flatIndex = getOutputIndexFromCoords(${n}); ${i("&result[flatIndex]","updateValue")}; } }`}};function mde(e){let{inputs:t,backend:a,attrs:n}=e,{indices:r,updates:s}=t,{shape:i}=n,{sliceRank:o,numUpdates:l,sliceSize:u,strides:d,outputSize:c}=T.calculateShapes(s,r,i),p=[c/u,u];if(c===0)return a.makeTensorInfo(i,r.dtype);let h=Re({inputs:{x:r},backend:a,attrs:{shape:[l,o]}}),f=Re({inputs:{x:s},backend:a,attrs:{shape:[l,u]}}),m=f.dtype,g=go({backend:a,attrs:{shape:p,value:0,dtype:m}}),y=v.sizeFromShape(f.shape),A=[{type:"int32",data:[o]},{type:"int32",data:d},{type:"int32",data:[y]}],x=new xc(f.shape,o,h.shape.length,f.shape.length,d,p,m),b=a.runWebGPUProgram(x,[f,h],m,A,g),w=Re({inputs:{x:b},backend:a,attrs:{shape:i}});return a.disposeData(h.dataId),a.disposeData(f.dataId),a.disposeData(b.dataId),w}var gde={kernelName:Qi,backendName:"webgpu",kernelFunc:mde},yde=class{constructor(e,t,a){this.variableNames=["c","a","b"],this.workGroupSize=[64,1,1],this.size=!0,this.outputShape=t,this.dispatchLayout=Ye(this.outputShape),this.dispatch=Me(this.dispatchLayout,this.outputShape,this.workGroupSize),this.cRank=e,this.rank=a,this.shaderKey="select"}getUserCode(){let e,t;if(this.rank>4)throw Error(`Where for rank ${this.rank} is not yet supported`);if(this.rank===1)t="resRC",e="resRC";else{let a=["resRC.x","resRC.y","resRC.z","resRC.w"],n=[],r=[];for(let s=0;s= 1.0) { setOutputAtIndex(index, getA(${t})); } else { setOutputAtIndex(index, getB(${t})); } } } `}};function Ade(e){let{inputs:t,backend:a}=e,{condition:n,t:r,e:s}=t,i=new yde(n.shape.length,r.shape,r.shape.length);return a.runWebGPUProgram(i,[n,r,s],ra(r.dtype,s.dtype))}var xde={kernelName:Dl,backendName:"webgpu",kernelFunc:Ade},bde=Ht({opType:Se.SIGMOID}),vde={kernelName:fs,backendName:"webgpu",kernelFunc:bde},wde=Ht({opType:Se.SIN}),kde={kernelName:eo,backendName:"webgpu",kernelFunc:wde},Ide=Ht({opType:Se.SINH}),Sde={kernelName:Ll,backendName:"webgpu",kernelFunc:Ide},S9=ca({opType:Be.SUB,cpuKernelImpl:noe,supportsComplex:!0}),Tde={kernelName:ys,backendName:"webgpu",kernelFunc:S9};function Cde(e){let{inputs:t,backend:a,attrs:n}=e,{logits:r}=t,{dim:s}=n,i=v.parseAxisParam([s],r.shape),o=X3({inputs:{x:r},backend:a,attrs:{reductionIndices:i,keepDims:!1}}),l=T.expandShapeToKeepDim(o.shape,i),u=Re({inputs:{x:o},backend:a,attrs:{shape:l}}),d=S9({inputs:{a:r,b:u},backend:a}),c=b9({inputs:{x:d},backend:a}),p=K3({inputs:{x:c},backend:a,attrs:{axis:i,keepDims:!1}}),h=Re({inputs:{x:p},backend:a,attrs:{shape:l}}),f=I9({inputs:{a:c,b:h},backend:a});return a.disposeData(o.dataId),a.disposeData(u.dataId),a.disposeData(d.dataId),a.disposeData(c.dataId),a.disposeData(p.dataId),a.disposeData(h.dataId),f}var Nde={kernelName:ao,backendName:"webgpu",kernelFunc:Cde},Ede=e=>{let{inputs:t,backend:a,attrs:n}=e,{x:r}=t,{blockShape:s,paddings:i}=n;v.assert(r.shape.length<=4,()=>"spaceToBatchND for rank > 4 with a WebGPU backend not implemented yet");let o=s.reduce((y,A)=>y*A),l=[[0,0]];l.push(...i);for(let y=1+s.length;ya.disposeData(y.dataId)),g},Rde={kernelName:Bl,backendName:"webgpu",kernelFunc:Ede},Mde=class{constructor(e,t){this.variableNames=["A"],this.workGroupSize=[64,1,1],this.size=!0;let a=new Array(e.length);for(let n=0;n=5)throw Error(`Tile for rank ${e} is not yet supported`);if(e===1)return`(resRC % ${t}aShape)`;let a=["resRC.x","resRC.y","resRC.z","resRC.w"],n=[];for(let r=0;r=5){let o=a.readSync(r.dataId),l=r.dtype==="string"?o.map(c=>v.decodeString(c)):o,u=ve(r.shape,r.dtype,l),d=roe(u,s);return a.makeTensorInfo(d.shape,d.dtype,d.values)}let i=new Mde(r.shape,s);return a.runWebGPUProgram(i,[r],r.dtype)}var _de={kernelName:As,backendName:"webgpu",kernelFunc:T9};function Fde(e){let{inputs:t,backend:a,attrs:n}=e,{sparseIndices:r,sparseValues:s,defaultValue:i}=t,{outputShape:o}=n,{sliceRank:l,numUpdates:u,sliceSize:d,strides:c,outputSize:p}=T.calculateShapes(s,r,o),h=!1;if(s.dtype==="string"){let N=a.bufferSync(r),_=a.bufferSync(s),$=v.decodeString(a.readSync(i.dataId)[0]),M=Jie(N,_,o,p,d,u,l,c,$,h);return a.makeTensorInfo(o,M.dtype,M.values)}let f=[p/d,d],m=Re({inputs:{x:r},backend:a,attrs:{shape:[u,l]}}),g=s.shape.length?Re({inputs:{x:s},backend:a,attrs:{shape:[u,d]}}):cn({inputs:{x:s},backend:a}),y=g.dtype,A=a.makeTensorInfo([],y,v.makeZerosTypedArray(1,y)),x=Re({inputs:{x:i},backend:a,attrs:{shape:Array(f.length).fill(1)}}),b=T9({inputs:{x},backend:a,attrs:{reps:f}}),w=v.sizeFromShape([u,d]),S=[{type:"int32",data:[l]},{type:"int32",data:c},{type:"int32",data:[w]}];switch(u){case 0:break;case 1:{let N=new xc([u,d],l,m.shape.length,g.shape.length,c,f,y,h);a.runWebGPUProgram(N,[g,m],y,S,b)}break;default:{let N=new xc([u,d],l,m.shape.length,A.shape.length,c,f,y,h);a.runWebGPUProgram(N,[A,m],y,S,b)}{let N=new xc([u,d],l,m.shape.length,g.shape.length,c,f,y);a.runWebGPUProgram(N,[g,m],y,S,b)}}let C=Re({inputs:{x:b},backend:a,attrs:{shape:o}});return a.disposeData(m.dataId),a.disposeData(g.dataId),a.disposeData(x.dataId),a.disposeData(A.dataId),a.disposeData(b.dataId),C}var Pde={kernelName:Ld,backendName:"webgpu",kernelFunc:Fde};function Ode(e){let{inputs:t,backend:a,attrs:n}=e,{x:r}=t,{numOrSizeSplits:s,axis:i}=n,o=v.parseAxisParam(i,r.shape)[0],l=T.prepareSplitSize(r,s,o),u=r.shape.length,d=new Array(u).fill(0),c=r.shape.slice();return l.map(p=>{let h=[...c];h[o]=p;let f=uu({inputs:{x:r},backend:a,attrs:{begin:d,size:h}});return d[o]+=p,f})}var Dde={kernelName:Wl,backendName:"webgpu",kernelFunc:Ode},zde=Ht({opType:Se.SQRT}),Lde={kernelName:ms,backendName:"webgpu",kernelFunc:zde},Bde={kernelName:Bd,backendName:"webgpu",kernelFunc:({inputs:e,backend:t})=>{let{x:a}=e,n=t,r=new mp(a.shape,Se.SQUARE);return n.runWebGPUProgram(r,[a],a.dtype)}},Wde=ca({opType:Be.SQUARED_DIFFERENCE}),Vde={kernelName:gs,backendName:"webgpu",kernelFunc:Wde},Gde=class{constructor(e){this.variableNames=["x"],this.workPerThread=1,this.workGroupSize=[64,1,1],this.size=!0,this.outputShape=e,this.dispatchLayout=Ye(this.outputShape),this.dispatch=Me(this.dispatchLayout,this.outputShape,this.workGroupSize,[this.workPerThread,1,1]);let t=na(this.outputShape.length);this.uniforms=`begin : ${t}, strides : ${t}, `,this.shaderKey="stridedSlice"}getUserCode(){let e=this.outputShape.length,t="";if(e===1)t="coords * uniforms.strides + uniforms.begin";else{let a=0;t=this.outputShape.map((n,r)=>(a++,this.outputShape.length===1?`coords * uniforms.strides[${r}] + uniforms.begin[${r}]`:`coords[${a-1}] * uniforms.strides[${r}] + uniforms.begin[${r}]`)).join(",")}return` ${We("index")} { if (index < uniforms.size) { let coords = getCoordsFromIndex(index); setOutputAtIndex(index, getX(${t})); } } `}};function Ude(e){let{inputs:t,backend:a,attrs:n}=e,{x:r}=t,{begin:s,end:i,strides:o,beginMask:l,endMask:u,ellipsisMask:d,newAxisMask:c,shrinkAxisMask:p}=n,{finalShapeSparse:h,finalShape:f,isIdentity:m,sliceDim0:g,isSimpleSlice:y,begin:A,end:x,strides:b}=At.sliceInfo(r.shape,s,i,o,l,u,d,c,p),w;if(m)w=Re({inputs:{x:r},backend:a,attrs:{shape:f}});else if(g||y){v.assert(r.shape.length>=1,()=>`Input must have rank at least 1, got: ${r.shape.length}`);let S=At.computeOutShape(A,x,b),C=uu({inputs:{x:r},backend:a,attrs:{begin:A,size:S}});w=Re({inputs:{x:C},backend:a,attrs:{shape:f}}),a.disposeData(C.dataId)}else if(a.shouldExecuteOnCPU([r])){let S=a.readSync(r.dataId),C=ve(r.shape,r.dtype,S),N=toe(h,C,b,A);w=a.makeTensorInfo(f,r.dtype,N.values)}else{let S=new Gde(h),C=[{type:"int32",data:A},{type:"int32",data:b}],N=a.runWebGPUProgram(S,[r],r.dtype,C);w=Re({inputs:{x:N},backend:a,attrs:{shape:f}}),a.disposeData(N.dataId)}return w}var jde={kernelName:no,backendName:"webgpu",kernelFunc:Ude};function Hde(e){let{inputs:t,backend:a,attrs:n}=e,{separator:r,nGramWidths:s,leftPad:i,rightPad:o,padWidth:l,preserveShortSequences:u}=n,{data:d,dataSplits:c}=t,p=a.readSync(d.dataId),h=a.readSync(c.dataId),[f,m]=aoe(p,h,r,s,i,o,l,u);return[a.makeTensorInfo([f.length],"string",f),a.makeTensorInfo(c.shape,"int32",m)]}var qde={kernelName:Gl,backendName:"webgpu",kernelFunc:Hde},Xde=Ht({opType:Se.TANH}),Kde={kernelName:ro,backendName:"webgpu",kernelFunc:Xde},Zde=class{constructor(e){this.variableNames=["x","indices"],this.workGroupSize=[256,1,1],this.size=!0,this.outputShape=e,this.dispatchLayout=Ye(this.outputShape),this.dispatch=Me(this.dispatchLayout,this.outputShape,this.workGroupSize),this.uniforms=`inputSize : i32, firstPass : i32, negativeInf : f32, dir : i32, inc : i32,`,this.shaderKey="swap"}getUserCode(){return` ${We("index")} { if (index < uniforms.size) { let outC = getCoordsFromIndex(index); let batch = outC[0]; let elemIdx = outC[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. let isFirstInPair = elemIdx % (2 * uniforms.inc) < uniforms.inc; var i = 0; if (isFirstInPair) { i = elemIdx; } else { i = elemIdx - uniforms.inc; } var i0 = 0; if (uniforms.firstPass == 1) { i0 = i; } else { i0 = i32(getIndices(batch, i)); } var i1 = 0; if (uniforms.firstPass == 1) { i1 = i + uniforms.inc; } else { i1 = i32(getIndices(batch, i + uniforms.inc)); } var x0 = f32(0.0); var x1 = f32(0.0); if (i0 < uniforms.inputSize) { x0 = getX(batch, i0); } else { x0 = uniforms.negativeInf; } if (i1 < uniforms.inputSize) { x1 = getX(batch, i1); } else { x1 = uniforms.negativeInf; } let reverse = elemIdx % (2 * uniforms.dir) >= uniforms.dir; let isGreater = x0 > x1 || (x0 == x1 && i1 > i0); if (reverse == isGreater) { // Elements in opposite order of direction let iTemp = i0; i0 = i1; i1 = iTemp; } if (isFirstInPair) { setOutputAtIndex(index, f32(i0)); } else { setOutputAtIndex(index, f32(i1)); } } } `}},Yde=class{constructor(e){this.variableNames=["x","indices"],this.workGroupSize=[256,1,1],this.size=!0,this.outputShape=e,this.dispatchLayout=Ye(this.outputShape),this.dispatch=Me(this.dispatchLayout,this.outputShape,this.workGroupSize),this.uniforms="inputSize : i32, firstPass : i32, k : i32,",this.shaderKey="merge"}getUserCode(){return` ${We("index")} { if (index < uniforms.size) { let outC = getCoordsFromIndex(index); let batch = outC[0]; let elemIdx = outC[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. var i = 0; if (elemIdx < uniforms.k) { i = elemIdx; } else { i = elemIdx * 2 - elemIdx % uniforms.k; } var i0 = 0; if (uniforms.firstPass == 1) { i0 = i; } else { i0 = i32(getIndices(batch, i)); } var i1 = 0; if (uniforms.firstPass == 1) { i1 = i + uniforms.k; } else { i1 = i32(getIndices(batch, i + uniforms.k)); } let x0 = getX(batch, i0); var x1 = f32(0.0); if (i1 < uniforms.inputSize) { x1 = getX(batch, i1); } else { x1 = x0; } if (x0 >= x1) { setOutputAtIndex(index, f32(i0)); } else { setOutputAtIndex(index, f32(i1)); } } } `}};function qo(e,t){t!==null&&e.disposeData(t.dataId)}function uA(e){let t=1;for(;th===null?[d,d]:[d,h],m=(b,w,S)=>{let C=f(),N=new 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(f32(i32(f32(-inCoord / sz))) + 1.0); } } else if (inCoord > len - 1.0) { if (len <= 1.0) { inCoord = 0.0; } else { let sz = len - 1.0; inCoord = inCoord - len * f32(i32(f32(inCoord / sz))); } } return clamp(inCoord, 0.0, len - 1.0); } else if (uniforms.fillModeId == 4) { return clamp(outCoord, 0.0, len - 1.0); } return outCoord; } fn readWithFillValue(batch : i32, coordY : i32, coordX : i32, channel : i32) -> f32 { var outputValue : f32; if (0 <= coordY && coordY < uniforms.imageShape[1] && 0 <= coordX && coordX < uniforms.imageShape[2]) { outputValue = getImage(batch, coordY, coordX, channel); } else { outputValue = uniforms.fillValue; } return outputValue; } ${We("index")} { if (index < uniforms.size) { let coords = getCoordsFromIndex(index); var outputValue : f32; let batch = coords[0]; let x = coords[2]; let y = coords[1]; let channel = coords[3]; let xf = f32(x); let yf = f32(y); let a1 = getTransforms(batch, 0); let a2 = getTransforms(batch, 1); let a3 = getTransforms(batch, 2); let b1 = getTransforms(batch, 3); let b2 = getTransforms(batch, 4); let b3 = getTransforms(batch, 5); let c1 = getTransforms(batch, 6); let c2 = getTransforms(batch, 7); let projection = c1 * xf + c2 * yf + 1.0; if (projection == 0.0) { outputValue = uniforms.fillValue; } else { let inX = (a1 * xf + a2 * yf + a3) / projection; let inY = (b1 * xf + b2 * yf + b3) / projection; let mapX = mapCoord(inX, f32(uniforms.imageShape[2])); let mapY = mapCoord(inY, f32(uniforms.imageShape[1])); if (uniforms.interpolationModeId == 1) { let coordY = i32(round(mapY)); let coordX = i32(round(mapX)); outputValue = readWithFillValue(batch, coordY, coordX, channel); } else { let yFloor = floor(mapY); let xFloor = floor(mapX); let yCeil = yFloor + 1.0; let xCeil = xFloor + 1.0; let valueYFloor = (xCeil - mapX) * readWithFillValue(batch, i32(yFloor), i32(xFloor), channel) + (mapX - xFloor) * readWithFillValue(batch, i32(yFloor), i32(xCeil), channel); let valueYCeil = (xCeil - mapX) * 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yo={backend:"",modelBasePath:"",cacheModels:!0,validateModels:!0,wasmPath:"",wasmPlatformFetch:!1,debug:!1,async:!0,warmup:"full",cacheSensitivity:.7,skipAllowed:!1,deallocate:!1,flags:{},softwareKernels:!1,filter:{enabled:!0,equalization:!1,width:0,height:0,flip:!1,return:!0,autoBrightness:!0,brightness:0,contrast:0,sharpness:0,blur:0,saturation:0,hue:0,negative:!1,sepia:!1,vintage:!1,kodachrome:!1,technicolor:!1,polaroid:!1,pixelate:0},gesture:{enabled:!0},face:{enabled:!0,detector:{modelPath:"blazeface.json",rotation:!0,maxDetected:1,skipFrames:99,skipTime:2500,minConfidence:.2,iouThreshold:.1,mask:!1,return:!1},mesh:{enabled:!0,modelPath:"facemesh.json",keepInvalid:!1},attention:{enabled:!1,modelPath:"facemesh-attention.json"},iris:{enabled:!0,modelPath:"iris.json"},emotion:{enabled:!0,minConfidence:.1,skipFrames:99,skipTime:1500,modelPath:"emotion.json"},description:{enabled:!0,modelPath:"faceres.json",skipFrames:99,skipTime:3e3,minConfidence:.1},antispoof:{enabled:!1,skipFrames:99,skipTime:4e3,modelPath:"antispoof.json"},liveness:{enabled:!1,skipFrames:99,skipTime:4e3,modelPath:"liveness.json"}},body:{enabled:!0,modelPath:"movenet-lightning.json",maxDetected:-1,minConfidence:.3,skipFrames:1,skipTime:200},hand:{enabled:!0,rotation:!0,skipFrames:99,skipTime:1e3,minConfidence:.5,iouThreshold:.2,maxDetected:-1,landmarks:!0,detector:{modelPath:"handtrack.json"},skeleton:{modelPath:"handlandmark-full.json"}},object:{enabled:!1,modelPath:"centernet.json",minConfidence:.2,iouThreshold:.4,maxDetected:10,skipFrames:99,skipTime:2e3},segmentation:{enabled:!1,modelPath:"rvm.json",ratio:.5,mode:"default"}};var 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a={audio:!1,video:{facingMode:this.config.mode==="front"?"user":"environment",resizeMode:this.config.crop?"crop-and-scale":"none",width:{ideal:this.config.width>0?this.config.width:window.innerWidth},height:{ideal:this.config.height>0?this.config.height:window.innerHeight}}};if(this.config.id&&(a.video.deviceId=this.config.id),this.element.addEventListener("play",()=>{this.config.debug&&K("webcam","play")}),this.element.addEventListener("pause",()=>{this.config.debug&&K("webcam","pause")}),this.element.addEventListener("click",async()=>{!this.element||!this.stream||(this.element.paused?await this.element.play():this.element.pause())}),!(navigator!=null&&navigator.mediaDevices)){this.config.debug&&K("webcam","no devices");return}try{this.stream=await navigator.mediaDevices.getUserMedia(a)}catch(r){K("webcam",r);return}if(!this.stream){this.config.debug&&K("webcam","no stream");return}this.element.srcObject=this.stream,await new 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m=0;mte()-ir.timestamp,n=ir.skipped<(((u=t.face.detector)==null?void 0:u.skipFrames)||0);!t.skipAllowed||!a||!n||ir.boxes.length===0?(ir.boxes=await Aw(e,t),ir.timestamp=te(),ir.skipped=0):ir.skipped++;let r=[],s=[],i=0,o=kp;for(let A=0;A$.shape[$.shape.length-1]===1).data();if(S.faceScore=Math.round(100*_[0])/100,S.faceScore<(((f=t.face.detector)==null?void 0:f.minConfidence)||1)){if(x.confidence=S.faceScore,t.face.mesh.keepInvalid){S.box=f0(x,e),S.boxRaw=m0(x,e),S.score=S.boxScore,S.mesh=x.landmarks.map($=>[(x.startPoint[0]+x.endPoint[0])/2+(x.endPoint[0]+x.startPoint[0])*$[0]/fu(),(x.startPoint[1]+x.endPoint[1])/2+(x.endPoint[1]+x.startPoint[1])*$[1]/fu()]),S.meshRaw=S.mesh.map($=>[$[0]/(e.shape[2]||1),$[1]/(e.shape[1]||1),($[2]||0)/o]);for(let $ of Object.keys(bo))S.annotations[$]=[S.mesh[bo[$]]]}}else{let $=C.find(O=>O.shape[O.shape.length-1]===1404),M=J($,[-1,3]),I=await M.array();Y(M),(m=t.face.attention)!=null&&m.enabled?I=await 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p={age:0};if((h=t.face.ssrnet)!=null&&h.enabled&&(c.age=Ja.execute(c.enhance)),c.age){let f=await c.age.data();p.age=Math.trunc(10*f[0])/10}Object.keys(c).forEach(f=>Y(c[f])),w0[a]=p,Jw=n,Qw=te(),d(p)}))}var _n,k0=[],ak=0,nk=0,Vg=Number.MAX_SAFE_INTEGER,Gg=[.2989,.587,.114];async function rk(e){var t;return ne.initial&&(_n=null),_n?e.debug&&K("cached model:",_n.modelUrl):_n=await Ce((t=e.face.ssrnet)==null?void 0:t.modelPathGender),_n}async function Ug(e,t,a,n){var i,o,l,u;if(!_n)return{gender:"unknown",genderScore:0};let r=Vg<(((i=t.face.ssrnet)==null?void 0:i.skipFrames)||0),s=(((o=t.face.ssrnet)==null?void 0:o.skipTime)||0)>te()-nk;return t.skipAllowed&&r&&s&&ak===n&&((l=k0[a])==null?void 0:l.gender)&&((u=k0[a])==null?void 0:u.genderScore)>0?(Vg++,k0[a]):(Vg=0,new Promise(async d=>{var f;if(!(_n!=null&&_n.inputs[0].shape))return;let c={};c.resize=me.resizeBilinear(e,[_n.inputs[0].shape[2],_n.inputs[0].shape[1]],!1),c.enhance=Ee(()=>{let[m,g,y]=ka(c.resize,3,3),A=ae(m,Gg[0]),x=ae(g,Gg[1]),b=ae(y,Gg[2]),w=mh([A,x,b]);return ae(he(w,Oe.tf05),2)});let p={gender:"unknown",genderScore:0};(f=t.face.ssrnet)!=null&&f.enabled&&(c.gender=_n.execute(c.enhance));let h=await c.gender.data();p.gender=h[0]>h[1]?"female":"male",p.genderScore=h[0]>h[1]?Math.trunc(100*h[0])/100:Math.trunc(100*h[1])/100,Object.keys(c).forEach(m=>Y(c[m])),k0[a]=p,ak=n,nk=te(),d(p)}))}var Qa,jg=[],ik=0,ok=0,lk=Number.MAX_SAFE_INTEGER;async function uk(e){var t;return ne.initial&&(Qa=null),Qa?e.debug&&K("cached model:",Qa.modelUrl):Qa=await Ce((t=e.face.mobilefacenet)==null?void 0:t.modelPath),Qa}async function Hg(e,t,a,n){var i,o;if(!(Qa!=null&&Qa.executor))return[];let r=lk<(((i=t.face.mobilefacenet)==null?void 0:i.skipFrames)||0),s=(((o=t.face.mobilefacenet)==null?void 0:o.skipTime)||0)>te()-ok;return t.skipAllowed&&s&&r&&ik===n&&jg[a]?(lk++,jg[a]):new Promise(async l=>{var d;let u=[];if(((d=t.face.mobilefacenet)==null?void 0:d.enabled)&&(Qa==null?void 0:Qa.inputs[0].shape)){let c={};c.crop=me.resizeBilinear(e,[Qa.inputs[0].shape[2],Qa.inputs[0].shape[1]],!1),c.data=Qa.execute(c.crop);let p=await c.data.data();u=Array.from(p),Object.keys(c).forEach(h=>Y(c[h]))}jg[a]=u,ik=n,ok=te(),l(u)})}var en,qg=[],pk=0,ck=0,hk=Number.MAX_SAFE_INTEGER;async function fk(e){return ne.initial&&(en=null),en?e.debug&&K("cached model:",en.modelUrl):en=await Ce(e.face.insightface.modelPath),en}async function Xg(e,t,a,n){var i,o;if(!(en!=null&&en.executor))return[];let r=hk<(((i=t.face.insightface)==null?void 0:i.skipFrames)||0),s=(((o=t.face.insightface)==null?void 0:o.skipTime)||0)>te()-ck;return t.skipAllowed&&s&&r&&pk===n&&qg[a]?(hk++,qg[a]):new Promise(async l=>{var d;let u=[];if(((d=t.face.insightface)==null?void 0:d.enabled)&&(en==null?void 0:en.inputs[0].shape)){let c={};c.crop=me.resizeBilinear(e,[en.inputs[0].shape[2],en.inputs[0].shape[1]],!1),c.data=en.execute(c.crop);let p=await c.data.data();u=Array.from(p),Object.keys(c).forEach(h=>Y(c[h]))}qg[a]=u,pk=n,ck=te(),l(u)})}var Mhe=e=>{let t=(c,p)=>Math.atan2(c[1]-p[1],c[0]-p[0]);if(!e.annotations.rightEyeIris||!e.annotations.leftEyeIris)return{bearing:0,strength:0};let a=[0,-.1],n=1,r=(e.mesh[33][2]||0)>(e.mesh[263][2]||0),s=r?e.mesh[473]:e.mesh[468],i=r?[(e.mesh[133][0]+e.mesh[33][0])/2,(e.mesh[133][1]+e.mesh[33][1])/2]:[(e.mesh[263][0]+e.mesh[362][0])/2,(e.mesh[263][1]+e.mesh[362][1])/2],o=r?[e.mesh[133][0]-e.mesh[33][0],e.mesh[23][1]-e.mesh[27][1]]:[e.mesh[263][0]-e.mesh[362][0],e.mesh[253][1]-e.mesh[257][1]],l=[(i[0]-s[0])/o[0]-a[0],n*(s[1]-i[1])/o[1]-a[1]],u=Math.sqrt(l[0]*l[0]+l[1]*l[1]);return u=Math.min(u,e.boxRaw[2]/2,e.boxRaw[3]/2),{bearing:(t([0,0],l)+Math.PI/2)%Math.PI,strength:u}},gk=(e,t)=>{let a=m=>{let g=Math.sqrt(m[0]*m[0]+m[1]*m[1]+m[2]*m[2]);return 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0:B.enabled)&&r&&(c={...c,age:r.age,gender:r.gender,genderScore:r.genderScore,race:r.race}),((G=e.config.face.mobilefacenet)==null?void 0:G.enabled)&&o&&(c.descriptor=o),((j=e.config.face.insightface)==null?void 0:j.enabled)&&l&&(c.descriptor=l);let Z=(U=e.config.face.iris)!=null&&U.enabled?yk(h[W],t.shape[2]):0,re=(H=e.config.face.detector)!=null&&H.return?$e(h[W].tensor):null;Y(h[W].tensor),h[W].tensor&&delete h[W].tensor;let ee={...h[W],id:W};c.age&&(ee.age=c.age),c.gender&&(ee.gender=c.gender),c.genderScore&&(ee.genderScore=c.genderScore),c.descriptor&&(ee.embedding=c.descriptor),c.race&&(ee.race=c.race),i&&(ee.emotion=i),u&&(ee.real=u),d&&(ee.live=d),Z>0&&(ee.distance=Z),Q&&(ee.rotation=Q),re&&(ee.tensor=re),p.push(ee),e.analyze("End Face")}return e.analyze("End FaceMesh:"),e.config.async&&(e.performance.face&&delete e.performance.face,e.performance.age&&delete e.performance.age,e.performance.gender&&delete e.performance.gender,e.performance.emotion&&delete e.performance.emotion),p};var Ma={thumb:0,index:1,middle:2,ring:3,pinky:4,all:[0,1,2,3,4],nameMapping:{0:"thumb",1:"index",2:"middle",3:"ring",4:"pinky"},pointsMapping:{0:[[0,1],[1,2],[2,3],[3,4]],1:[[0,5],[5,6],[6,7],[7,8]],2:[[0,9],[9,10],[10,11],[11,12]],3:[[0,13],[13,14],[14,15],[15,16]],4:[[0,17],[17,18],[18,19],[19,20]]},getName:e=>Ma.nameMapping[e],getPoints:e=>Ma.pointsMapping[e]},Fs={none:0,half:1,full:2,nameMapping:{0:"none",1:"half",2:"full"},getName:e=>Fs.nameMapping[e]},Nt={verticalUp:0,verticalDown:1,horizontalLeft:2,horizontalRight:3,diagonalUpRight:4,diagonalUpLeft:5,diagonalDownRight:6,diagonalDownLeft:7,nameMapping:{0:"verticalUp",1:"verticalDown",2:"horizontalLeft",3:"horizontalRight",4:"diagonalUpRight",5:"diagonalUpLeft",6:"diagonalDownRight",7:"diagonalDownLeft"},getName:e=>Nt.nameMapping[e]},_s=class{constructor(t){le(this,"name");le(this,"curls");le(this,"directions");le(this,"weights");le(this,"weightsRelative");this.name=t,this.curls={},this.directions={},this.weights=[1,1,1,1,1],this.weightsRelative=[1,1,1,1,1]}curl(t,a,n){typeof this.curls[t]=="undefined"&&(this.curls[t]=[]),this.curls[t].push([a,n])}direction(t,a,n){this.directions[t]||(this.directions[t]=[]),this.directions[t].push([a,n])}weight(t,a){this.weights[t]=a;let n=this.weights.reduce((r,s)=>r+s,0);this.weightsRelative=this.weights.map(r=>r*5/n)}matchAgainst(t,a){let n=0;for(let r in t){let s=t[r],i=this.curls[r];if(typeof i=="undefined"){n+=this.weightsRelative[r];continue}for(let[o,l]of i)if(s===o){n+=l*this.weightsRelative[r];break}}for(let r in a){let s=a[r],i=this.directions[r];if(typeof i=="undefined"){n+=this.weightsRelative[r];continue}for(let[o,l]of i)if(s===o){n+=l*this.weightsRelative[r];break}}return n/10}};var{thumb:Ln,index:Rr,middle:Mr,ring:Co,pinky:No}=Ma,{none:Bn,half:_he,full:Wn}=Fs,{verticalUp:Au,verticalDown:s5e,horizontalLeft:Zg,horizontalRight:Fhe,diagonalUpRight:Phe,diagonalUpLeft:xu,diagonalDownRight:i5e,diagonalDownLeft:o5e}=Nt,Ps=new _s("thumbs up");Ps.curl(Ln,Bn,1);Ps.direction(Ln,Au,1);Ps.direction(Ln,xu,.25);Ps.direction(Ln,Phe,.25);for(let e of[Ma.index,Ma.middle,Ma.ring,Ma.pinky])Ps.curl(e,Wn,1),Ps.direction(e,Zg,1),Ps.direction(e,Fhe,1);var Bt=new _s("victory");Bt.curl(Ln,_he,.5);Bt.curl(Ln,Bn,.5);Bt.direction(Ln,Au,1);Bt.direction(Ln,xu,1);Bt.curl(Rr,Bn,1);Bt.direction(Rr,Au,.75);Bt.direction(Rr,xu,1);Bt.curl(Mr,Bn,1);Bt.direction(Mr,Au,1);Bt.direction(Mr,xu,.75);Bt.curl(Co,Wn,1);Bt.direction(Co,Au,.2);Bt.direction(Co,xu,1);Bt.direction(Co,Zg,.2);Bt.curl(No,Wn,1);Bt.direction(No,Au,.2);Bt.direction(No,xu,1);Bt.direction(No,Zg,.2);Bt.weight(Rr,2);Bt.weight(Mr,2);var Os=new _s("point");Os.curl(Ln,Wn,1);Os.curl(Rr,Bn,.5);Os.curl(Mr,Wn,.5);Os.curl(Co,Wn,.5);Os.curl(No,Wn,.5);Os.weight(Rr,2);Os.weight(Mr,2);var Ds=new _s("middle finger");Ds.curl(Ln,Bn,1);Ds.curl(Rr,Wn,.5);Ds.curl(Mr,Wn,.5);Ds.curl(Co,Wn,.5);Ds.curl(No,Wn,.5);Ds.weight(Rr,2);Ds.weight(Mr,2);var bu=new _s("open palm");bu.curl(Ln,Bn,.75);bu.curl(Rr,Bn,.75);bu.curl(Mr,Bn,.75);bu.curl(Co,Bn,.75);bu.curl(No,Bn,.75);var Ak=[Ps,Bt,Os,Ds,bu];var Ohe=.7,Eo={HALF_CURL_START_LIMIT:60,NO_CURL_START_LIMIT:130,DISTANCE_VOTE_POWER:1.1,SINGLE_ANGLE_VOTE_POWER:.9,TOTAL_ANGLE_VOTE_POWER:1.6};function xk(e,t,a,n){let r=(t-n)/(e-a),s=Math.atan(r)*180/Math.PI;return s<=0?s=-s:s>0&&(s=180-s),s}function vk(e,t){if(!e||!t)return[0,0];let a=xk(e[0],e[1],t[0],t[1]);if(e.length===2)return a;let n=xk(e[1],e[2],t[1],t[2]);return[a,n]}function bk(e,t=1){let a=0,n=0,r=0;return e>=75&&e<=105?a=1*t:e>=25&&e<=155?n=1*t:r=1*t,[a,n,r]}function Dhe(e,t,a){let n=e[0]-t[0],r=e[0]-a[0],s=t[0]-a[0],i=e[1]-t[1],o=e[1]-a[1],l=t[1]-a[1],u=e[2]-t[2],d=e[2]-a[2],c=t[2]-a[2],p=Math.sqrt(n*n+i*i+u*u),h=Math.sqrt(r*r+o*o+d*d),f=Math.sqrt(s*s+l*l+c*c),m=(f*f+p*p-h*h)/(2*f*p);m>1?m=1:m<-1&&(m=-1);let g=Math.acos(m);g=57.2958*g%180;let y;return g>Eo.NO_CURL_START_LIMIT?y=Fs.none:g>Eo.HALF_CURL_START_LIMIT?y=Fs.half:y=Fs.full,y}function wk(e,t,a,n){let r;return n===Math.abs(e)?e>0?r=Nt.horizontalLeft:r=Nt.horizontalRight:n===Math.abs(t)?t>0?r=Nt.horizontalLeft:r=Nt.horizontalRight:a>0?r=Nt.horizontalLeft:r=Nt.horizontalRight,r}function kk(e,t,a,n){let r;return n===Math.abs(e)?e<0?r=Nt.verticalDown:r=Nt.verticalUp:n===Math.abs(t)?t<0?r=Nt.verticalDown:r=Nt.verticalUp:a<0?r=Nt.verticalDown:r=Nt.verticalUp,r}function zhe(e,t,a,n,r,s,i,o){let l,u=kk(e,t,a,n),d=wk(r,s,i,o);return u===Nt.verticalUp?d===Nt.horizontalLeft?l=Nt.diagonalUpLeft:l=Nt.diagonalUpRight:d===Nt.horizontalLeft?l=Nt.diagonalDownLeft:l=Nt.diagonalDownRight,l}function Lhe(e,t,a,n){let r=e[0]-t[0],s=e[0]-a[0],i=t[0]-a[0],o=e[1]-t[1],l=e[1]-a[1],u=t[1]-a[1],d=Math.max(Math.abs(r),Math.abs(s),Math.abs(i)),c=Math.max(Math.abs(o),Math.abs(l),Math.abs(u)),p=0,h=0,f=0,m=c/(d+1e-5);m>1.5?p+=Eo.DISTANCE_VOTE_POWER:m>.66?h+=Eo.DISTANCE_VOTE_POWER:f+=Eo.DISTANCE_VOTE_POWER;let g=Math.sqrt(r*r+o*o),y=Math.sqrt(s*s+l*l),A=Math.sqrt(i*i+u*u),x=Math.max(g,y,A),b=e[0],w=e[1],S=a[0],C=a[1];x===g?(S=a[0],C=a[1]):x===A&&(b=t[0],w=t[1]);let $=vk([b,w],[S,C]),M=bk($,Eo.TOTAL_ANGLE_VOTE_POWER);p+=M[0],h+=M[1],f+=M[2];for(let E of n){let O=bk(E,Eo.SINGLE_ANGLE_VOTE_POWER);p+=O[0],h+=O[1],f+=O[2]}let I;return p===Math.max(p,h,f)?I=kk(l,o,u,c):f===Math.max(h,f)?I=wk(s,r,i,d):I=zhe(l,o,u,c,s,r,i,d),I}function Ik(e){let t=[],a=[],n=[],r=[];if(!e)return{curls:n,directions:r};for(let s of Ma.all){let i=Ma.getPoints(s),o=[],l=[];for(let u of i){let d=e[u[0]],c=e[u[1]],p=vk(d,c),h=p[0],f=p[1];o.push(h),l.push(f)}t.push(o),a.push(l)}for(let s of Ma.all){let i=s===Ma.thumb?1:0,o=Ma.getPoints(s),l=e[o[i][0]],u=e[o[i+1][1]],d=e[o[3][1]],c=Dhe(l,u,d),p=Lhe(l,u,d,t[s].slice(i));n[s]=c,r[s]=p}return{curls:n,directions:r}}function I0(e){if(!e||e.length===0)return null;let t=Ik(e),a={};for(let n of Ma.all)a[Ma.getName(n)]={curl:Fs.getName(t.curls[n]),direction:Nt.getName(t.directions[n])};return a}function Sk(e){let t=[];if(!e||e.length===0)return t;let a=Ik(e);for(let n of Ak){let r=n.matchAgainst(a.curls,a.directions);r>=Ohe&&t.push({name:n.name,confidence:r})}return t}var Tk=e=>{if(!e)return[];let t=[];for(let a=0;al.part==="leftWrist"),r=e[a].keypoints.find(l=>l.part==="rightWrist"),s=e[a].keypoints.find(l=>l.part==="nose");s&&n&&r&&n.position[1]l.part==="leftShoulder"),o=e[a].keypoints.find(l=>l.part==="rightShoulder");i&&o&&Math.abs(i.positionRaw[1]-o.positionRaw[1])>.1&&t.push({body:a,gesture:`leaning ${i.position[1]>o.position[1]?"left":"right"}`})}return t},Ck=e=>{if(!e)return[];let t=[];for(let a=0;a450){let n=(e[a].mesh[33][2]||0)-(e[a].mesh[263][2]||0),r=e[a].mesh[33][0]-e[a].mesh[263][0];Math.abs(n/r)<=.15?t.push({face:a,gesture:"facing center"}):t.push({face:a,gesture:`facing ${n<0?"left":"right"}`}),Math.abs(e[a].mesh[374][1]-e[a].mesh[386][1])/Math.abs(e[a].mesh[443][1]-e[a].mesh[450][1])<.2&&t.push({face:a,gesture:"blink left eye"}),Math.abs(e[a].mesh[145][1]-e[a].mesh[159][1])/Math.abs(e[a].mesh[223][1]-e[a].mesh[230][1])<.2&&t.push({face:a,gesture:"blink right eye"});let o=Math.min(100,500*Math.abs(e[a].mesh[13][1]-e[a].mesh[14][1])/Math.abs(e[a].mesh[10][1]-e[a].mesh[152][1]));o>10&&t.push({face:a,gesture:`mouth ${Math.trunc(o)}% open`});let l=e[a].mesh[152][2]||0;Math.abs(l)>10&&t.push({face:a,gesture:`head ${l<0?"up":"down"}`})}return t},Nk=e=>{var a,n,r,s;if(!e)return[];let t=[];for(let i=0;i.06||g>.06)&&(h=!1),m>g?m>.05&&t.push({iris:i,gesture:"looking right"}):g>.05&&t.push({iris:i,gesture:"looking left"});let y=Math.abs(e[i].mesh[145][1]-e[i].annotations.rightEyeIris[0][1])/e[i].box[3],A=Math.abs(e[i].mesh[374][1]-e[i].annotations.leftEyeIris[0][1])/e[i].box[3];(A<.01||y<.01||A>.022||y>.022)&&(h=!1),(A<.01||y<.01)&&t.push({iris:i,gesture:"looking down"}),(A>.022||y>.022)&&t.push({iris:i,gesture:"looking up"}),h&&t.push({iris:i,gesture:"looking center"})}return t},Ek=e=>{if(!e)return[];let t=[];for(let a=0;a0){let r=n.reduce((i,o)=>(i.position[2]||0)<(o.position[2]||0)?i:o);t.push({hand:a,gesture:`${r.name} forward`});let s=n.reduce((i,o)=>i.position[1][s[0]*t[0],s[1]*t[1]]);return{startPoint:a,endPoint:n,palmLandmarks:r,confidence:e.confidence}}function T0(e,t=1.5){let a=Ip(e),n=S0(e),r=[t*n[0]/2,t*n[1]/2],s=[a[0]-r[0],a[1]-r[1]],i=[a[0]+r[0],a[1]+r[1]];return{startPoint:s,endPoint:i,palmLandmarks:e.palmLandmarks}}function C0(e){let t=Ip(e),a=S0(e),r=Math.max(...a)/2,s=[t[0]-r,t[1]-r],i=[t[0]+r,t[1]+r];return{startPoint:s,endPoint:i,palmLandmarks:e.palmLandmarks}}function Whe(e){return e-2*Math.PI*Math.floor((e+Math.PI)/(2*Math.PI))}function Fk(e,t){let a=Math.PI/2-Math.atan2(-(t[1]-e[1]),t[0]-e[0]);return Whe(a)}var Rk=(e,t)=>[[1,0,e],[0,1,t],[0,0,1]];function zs(e,t){let a=0;for(let n=0;n[i.x,i.y]),this.anchorsTensor=Kn(this.anchors),this.inputSize=((s=(r=(n=(a=this==null?void 0:this.model)==null?void 0:a.inputs)==null?void 0:n[0])==null?void 0:r.shape)==null?void 0:s[2])||0,this.inputSizeTensor=Ut([this.inputSize,this.inputSize]),this.doubleInputSizeTensor=Ut([this.inputSize*2,this.inputSize*2])}normalizeBoxes(t){let a={};a.boxOffsets=_e(t,[0,0],[-1,2]),a.boxSizes=_e(t,[0,2],[-1,2]),a.div=fe(a.boxOffsets,this.inputSizeTensor),a.boxCenterPoints=xe(a.div,this.anchorsTensor),a.halfBoxSizes=fe(a.boxSizes,this.doubleInputSizeTensor),a.sub=he(a.boxCenterPoints,a.halfBoxSizes),a.startPoints=ae(a.sub,this.inputSizeTensor),a.add=xe(a.boxCenterPoints,a.halfBoxSizes),a.endPoints=ae(a.add,this.inputSizeTensor);let n=ql([a.startPoints,a.endPoints],1);return Object.keys(a).forEach(r=>Y(a[r])),n}normalizeLandmarks(t,a){let n={};n.reshape=J(t,[-1,7,2]),n.div=fe(n.reshape,this.inputSizeTensor),n.landmarks=xe(n.div,this.anchors[a]?this.anchors[a]:0);let r=ae(n.landmarks,this.inputSizeTensor);return Object.keys(n).forEach(s=>Y(n[s])),r}async predict(t,a){var o;let n={};n.resize=me.resizeBilinear(t,[this.inputSize,this.inputSize]),n.div=fe(n.resize,Oe.tf127),n.image=he(n.div,Oe.tf1),n.batched=this.model.execute(n.image),n.predictions=$e(n.batched),n.slice=_e(n.predictions,[0,0],[-1,1]),n.sigmoid=Da(n.slice),n.scores=$e(n.sigmoid);let r=await n.scores.data();n.boxes=_e(n.predictions,[0,1],[-1,4]),n.norm=this.normalizeBoxes(n.boxes),n.nms=await me.nonMaxSuppressionAsync(n.norm,n.scores,3*(((o=a.hand)==null?void 0:o.maxDetected)||1),a.hand.iouThreshold,a.hand.minConfidence);let s=await n.nms.array(),i=[];for(let l of s){let u={};u.box=_e(n.norm,[l,0],[1,-1]),u.slice=_e(n.predictions,[l,5],[1,14]),u.norm=this.normalizeLandmarks(u.slice,l),u.palmLandmarks=J(u.norm,[-1,2]);let d=await u.box.data(),c=d.slice(0,2),p=d.slice(2,4),h=await u.palmLandmarks.array(),f={startPoint:c,endPoint:p,palmLandmarks:h,confidence:r[l]},m=_k(f,[(t.shape[2]||1)/this.inputSize,(t.shape[1]||0)/this.inputSize]);i.push(m),Object.keys(u).forEach(g=>Y(u[g]))}return Object.keys(n).forEach(l=>Y(n[l])),i}};var jhe=5,zk=1.65,Lk=[0,5,9,13,17,1,2],Hhe=0,qhe=2,Bk=0,E0=class{constructor(t,a){le(this,"handDetector");le(this,"handPoseModel");le(this,"inputSize");le(this,"storedBoxes");le(this,"skipped");le(this,"detectedHands");var n,r,s;this.handDetector=t,this.handPoseModel=a,this.inputSize=((s=(r=(n=this.handPoseModel)==null?void 0:n.inputs)==null?void 0:r[0].shape)==null?void 0:s[2])||0,this.storedBoxes=[],this.skipped=Number.MAX_SAFE_INTEGER,this.detectedHands=0}calculateLandmarksBoundingBox(t){let a=t.map(i=>i[0]),n=t.map(i=>i[1]),r=[Math.min(...a),Math.min(...n)],s=[Math.max(...a),Math.max(...n)];return{startPoint:r,endPoint:s}}getBoxForPalmLandmarks(t,a){let n=t.map(s=>Qg([...s,1],a)),r=this.calculateLandmarksBoundingBox(n);return T0(C0(r),jhe)}getBoxForHandLandmarks(t){let a=this.calculateLandmarksBoundingBox(t),n=T0(C0(a),zk);n.palmLandmarks=[];for(let r=0;r[i[0]*(h[0]-this.inputSize/2),i[1]*(h[1]-this.inputSize/2),i[2]*h[2]]),l=Jg(n,[0,0]),u=o.map(h=>[...Qg(h,l),h[2]]),d=Pk(r),c=[...Ip(a),1],p=[zs(c,d[0]),zs(c,d[1])];return u.map(h=>[Math.trunc(h[0]+p[0]),Math.trunc(h[1]+p[1]),Math.trunc(h[2])])}async estimateHands(t,a){let n=!1,r,s=(a.hand.skipTime||0)>te()-Bk,i=this.skipped<(a.hand.skipFrames||0);a.skipAllowed&&s&&i&&(r=await this.handDetector.predict(t,a),this.skipped=0),a.skipAllowed&&this.skipped++,r&&r.length>0&&(r.length!==this.detectedHands&&this.detectedHands!==a.hand.maxDetected||!a.hand.landmarks)&&(this.detectedHands=0,this.storedBoxes=[...r],this.storedBoxes.length>0&&(n=!0));let o=[];for(let l=0;l=a.hand.minConfidence/4){let w=J(x,[-1,3]),S=await w.array();Y(x),Y(w);let C=this.transformRawCoords(S,m,d,f),N=this.getBoxForHandLandmarks(C);this.storedBoxes[l]={...N,confidence:b};let _={landmarks:C,confidence:b,boxConfidence:u.confidence,fingerConfidence:b,box:{topLeft:N.startPoint,bottomRight:N.endPoint}};o.push(_)}else this.storedBoxes[l]=null;Y(x)}else{let d=T0(C0(u),zk),c={confidence:u.confidence,boxConfidence:u.confidence,fingerConfidence:0,box:{topLeft:d.startPoint,bottomRight:d.endPoint},landmarks:[]};o.push(c)}}return this.storedBoxes=this.storedBoxes.filter(l=>l!==null),this.detectedHands=o.length,o.length>a.hand.maxDetected&&(o.length=a.hand.maxDetected),o}};var Wk={thumb:[1,2,3,4],index:[5,6,7,8],middle:[9,10,11,12],ring:[13,14,15,16],pinky:[17,18,19,20],palm:[0]},Ro,Mo,Vk;async function e5(e,t){let a=await Vk.estimateHands(e,t);if(!a)return[];let n=[];for(let r=0;ra[r].landmarks[c]);let i=a[r].landmarks,o=[Number.MAX_SAFE_INTEGER,Number.MAX_SAFE_INTEGER,0,0],l=[0,0,0,0];if(i&&i.length>0){for(let d of i)d[0]o[2]&&(o[2]=d[0]),d[1]>o[3]&&(o[3]=d[1]);o[2]-=o[0],o[3]-=o[1],l=[o[0]/(e.shape[2]||0),o[1]/(e.shape[1]||0),o[2]/(e.shape[2]||0),o[3]/(e.shape[1]||0)]}else o=a[r].box?[Math.trunc(Math.max(0,a[r].box.topLeft[0])),Math.trunc(Math.max(0,a[r].box.topLeft[1])),Math.trunc(Math.min(e.shape[2]||0,a[r].box.bottomRight[0])-Math.max(0,a[r].box.topLeft[0])),Math.trunc(Math.min(e.shape[1]||0,a[r].box.bottomRight[1])-Math.max(0,a[r].box.topLeft[1]))]:[0,0,0,0],l=[a[r].box.topLeft[0]/(e.shape[2]||0),a[r].box.topLeft[1]/(e.shape[1]||0),(a[r].box.bottomRight[0]-a[r].box.topLeft[0])/(e.shape[2]||0),(a[r].box.bottomRight[1]-a[r].box.topLeft[1])/(e.shape[1]||0)];let u=I0(i);n.push({id:r,score:Math.round(100*a[r].confidence)/100,boxScore:Math.round(100*a[r].boxConfidence)/100,fingerScore:Math.round(100*a[r].fingerConfidence)/100,label:"hand",box:o,boxRaw:l,keypoints:i,annotations:s,landmarks:u})}return n}async function Gk(e){var a,n;ne.initial&&(Ro=null,Mo=null),!Ro||!Mo?[Ro,Mo]=await Promise.all([e.hand.enabled?Ce((a=e.hand.detector)==null?void 0:a.modelPath):null,e.hand.landmarks?Ce((n=e.hand.skeleton)==null?void 0:n.modelPath):null]):(e.debug&&K("cached model:",Ro.modelUrl),e.debug&&K("cached model:",Mo.modelUrl));let t=Ro?new N0(Ro):void 0;return t&&Mo&&(Vk=new E0(t,Mo)),[Ro,Mo]}var Ft=[null,null],Khe=["StatefulPartitionedCall/Postprocessor/Slice","StatefulPartitionedCall/Postprocessor/ExpandDims_1"],Ls=[[0,0],[0,0]],Zhe=["hand","fist","pinch","point","face","tip","pinchtip"],jk=4,Hk=1.6,Yhe=512,Jhe=1.4,R0=Number.MAX_SAFE_INTEGER,t5=0,$r=[0,0],_t={boxes:[],hands:[]},qk={thumb:[1,2,3,4],index:[5,6,7,8],middle:[9,10,11,12],ring:[13,14,15,16],pinky:[17,18,19,20],base:[0],palm:[0,17,13,9,5,1,0]};async function Xk(e){var t;if(ne.initial&&(Ft[0]=null),Ft[0])e.debug&&K("cached model:",Ft[0].modelUrl);else{e0(["tensorlistreserve","enter","tensorlistfromtensor","merge","loopcond","switch","exit","tensorliststack","nextiteration","tensorlistsetitem","tensorlistgetitem","reciprocal","shape","split","where"],e),Ft[0]=await Ce((t=e.hand.detector)==null?void 0:t.modelPath);let a=Ft[0].executor?Object.values(Ft[0].modelSignature.inputs):void 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n={id:t.id,score:Math.round(100*t.score)/100,boxScore:Math.round(100*t.score)/100,fingerScore:0,box:t.box,boxRaw:t.boxRaw,label:t.label,keypoints:[],landmarks:{},annotations:{}};if(e&&Ft[1]&&a.hand.landmarks&&t.score>(a.hand.minConfidence||0)){let r={},s=[t.boxRaw[1],t.boxRaw[0],t.boxRaw[3]+t.boxRaw[1],t.boxRaw[2]+t.boxRaw[0]];r.crop=me.cropAndResize(e,[s],[0],[Ls[1][0],Ls[1][1]],"bilinear"),r.div=fe(r.crop,Oe.tf255),[r.score,r.keypoints]=Ft[1].execute(r.div,["Identity_1","Identity"]);let i=(await r.score.data())[0],o=(100-Math.trunc(100/(1+Math.exp(i))))/100;if(o>=(a.hand.minConfidence||0)){n.fingerScore=o,r.reshaped=J(r.keypoints,[-1,3]);let d=(await r.reshaped.array()).map(c=>[c[0]/Ls[1][1],c[1]/Ls[1][0],c[2]||0]).map(c=>[c[0]*t.boxRaw[2],c[1]*t.boxRaw[3],c[2]||0]);n.keypoints=d.map(c=>[$r[0]*(c[0]+t.boxRaw[0]),$r[1]*(c[1]+t.boxRaw[1]),c[2]||0]),n.landmarks=I0(n.keypoints);for(let c of 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a=te(),n=Object.values(this.models).filter(i=>i).length;t&&(this.config=Tt(this.config,t)),this.env.initial&&(await bp(this,!1)||K("error: backend check failed"),await Kd(),this.env.browser&&(this.config.debug&&K("configuration:",this.config),this.config.debug&&K("tf flags:",this.tf.ENV.flags))),await this.models.load(),this.env.initial&&this.config.debug&&K("tf engine state:",this.tf.engine().state.numBytes,"bytes",this.tf.engine().state.numTensors,"tensors"),this.env.initial=!1,Object.values(this.models).filter(i=>i).length!==n&&(this.models.validate(),this.emit("load"));let s=Math.trunc(te()-a);s>(this.performance.loadModels||0)&&(this.performance.loadModels=this.env.perfadd?(this.performance.loadModels||0)+s:s)}next(t=this.result){return Yk(t,this.config)}async warmup(t){let a=te(),n=await SI(this,t),r=te();return this.performance.warmup=Math.trunc(r-a),n}async profile(t,a){let n=await this.tf.profile(()=>this.detect(t,a)),r={},s=0;for(let o of n.kernels){let 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P9(this.config,o.tensor),this.config.filter.autoBrightness=(this.config.filter.autoBrightness||!1)&&this.config.skipAllowed,this.performance.totalFrames||(this.performance.totalFrames=0),this.performance.cachedFrames||(this.performance.cachedFrames=0),this.performance.totalFrames++,this.config.skipAllowed&&this.performance.cachedFrames++,this.performance.cacheCheck=this.env.perfadd?(this.performance.cacheCheck||0)+Math.trunc(te()-r):Math.trunc(te()-r),this.analyze("Check Changed:");let l=[],u=[],d=[],c=[];this.state="detect:face",this.config.async?(l=this.config.face.enabled?Kg(this,o.tensor):[],this.performance.face&&delete this.performance.face):(r=te(),l=this.config.face.enabled?await Kg(this,o.tensor):[],this.performance.face=this.env.perfadd?(this.performance.face||0)+Math.trunc(te()-r):Math.trunc(te()-r)),this.config.async&&(this.config.body.maxDetected===-1||this.config.hand.maxDetected===-1)&&(l=await l),this.analyze("Start Body:"),this.state="detect:body";let p=this.config.body.maxDetected===-1?Tt(this.config,{body:{maxDetected:this.config.face.enabled?1*l.length:1}}):this.config;this.config.async?((g=this.config.body.modelPath)!=null&&g.includes("posenet")?u=this.config.body.enabled?x5(o.tensor,p):[]:(y=this.config.body.modelPath)!=null&&y.includes("blazepose")?u=this.config.body.enabled?hg(o.tensor,p):[]:(A=this.config.body.modelPath)!=null&&A.includes("efficientpose")?u=this.config.body.enabled?bg(o.tensor,p):[]:(x=this.config.body.modelPath)!=null&&x.includes("movenet")&&(u=this.config.body.enabled?c5(o.tensor,p):[]),this.performance.body&&delete this.performance.body):(r=te(),(b=this.config.body.modelPath)!=null&&b.includes("posenet")?u=this.config.body.enabled?await x5(o.tensor,p):[]:(w=this.config.body.modelPath)!=null&&w.includes("blazepose")?u=this.config.body.enabled?await hg(o.tensor,p):[]:(S=this.config.body.modelPath)!=null&&S.includes("efficientpose")?u=this.config.body.enabled?await bg(o.tensor,p):[]:(C=this.config.body.modelPath)!=null&&C.includes("movenet")&&(u=this.config.body.enabled?await c5(o.tensor,p):[]),this.performance.body=this.env.perfadd?(this.performance.body||0)+Math.trunc(te()-r):Math.trunc(te()-r)),this.analyze("End Body:"),this.analyze("Start Hand:"),this.state="detect:hand";let h=this.config.hand.maxDetected===-1?Tt(this.config,{hand:{maxDetected:this.config.face.enabled?2*l.length:1}}):this.config;this.config.async?((_=(N=this.config.hand.detector)==null?void 0:N.modelPath)!=null&&_.includes("handdetect")?d=this.config.hand.enabled?e5(o.tensor,h):[]:(M=($=this.config.hand.detector)==null?void 0:$.modelPath)!=null&&M.includes("handtrack")&&(d=this.config.hand.enabled?n5(o.tensor,h):[]),this.performance.hand&&delete this.performance.hand):(r=te(),(E=(I=this.config.hand.detector)==null?void 0:I.modelPath)!=null&&E.includes("handdetect")?d=this.config.hand.enabled?await e5(o.tensor,h):[]:(L=(O=this.config.hand.detector)==null?void 0:O.modelPath)!=null&&L.includes("handtrack")&&(d=this.config.hand.enabled?await n5(o.tensor,h):[]),this.performance.hand=this.env.perfadd?(this.performance.hand||0)+Math.trunc(te()-r):Math.trunc(te()-r)),this.analyze("End Hand:"),this.analyze("Start Object:"),this.state="detect:object",this.config.async?((B=this.config.object.modelPath)!=null&&B.includes("nanodet")?c=this.config.object.enabled?f5(o.tensor,this.config):[]:(G=this.config.object.modelPath)!=null&&G.includes("centernet")&&(c=this.config.object.enabled?gg(o.tensor,this.config):[]),this.performance.object&&delete this.performance.object):(r=te(),(j=this.config.object.modelPath)!=null&&j.includes("nanodet")?c=this.config.object.enabled?await f5(o.tensor,this.config):[]:(U=this.config.object.modelPath)!=null&&U.includes("centernet")&&(c=this.config.object.enabled?await gg(o.tensor,this.config):[]),this.performance.object=this.env.perfadd?(this.performance.object||0)+Math.trunc(te()-r):Math.trunc(te()-r)),this.analyze("End Object:"),this.state="detect:await",this.config.async&&([l,u,d,c]=await Promise.all([l,u,d,c])),this.state="detect:gesture";let f=[];this.config.gesture.enabled&&(r=te(),f=[...Ck(l),...Tk(u),...Ek(d),...Nk(l)],this.config.async?this.performance.gesture&&delete this.performance.gesture:this.performance.gesture=this.env.perfadd?(this.performance.gesture||0)+Math.trunc(te()-r):Math.trunc(te()-r)),this.performance.total=this.env.perfadd?(this.performance.total||0)+Math.trunc(te()-i):Math.trunc(te()-i);let m=((H=this.process.tensor)==null?void 0:H.shape)||[0,0,0,0];this.result={face:l,body:u,hand:d,gesture:f,object:c,performance:this.performance,canvas:this.process.canvas,timestamp:Date.now(),error:null,width:m[2],height:m[1],get persons(){return II(l,u,d,f,m)}},Y(o.tensor),this.emit("detect"),this.state="idle",n(this.result)})}async sleep(t){return new Promise(a=>{setTimeout(a,t)})}async video(t,a=!0,n=0){a?(Gn(this,Bs)[t.id]||(this.config.debug&&K("video start",t.id),Gn(this,Bs)[t.id]=!0),!t.paused&&Gn(this,Bs)[t.id]&&t.readyState>=2&&await this.detect(t),n>0&&await this.sleep(n),Gn(this,Bs)[t.id]&&requestAnimationFrame(()=>this.video(t,a,n))):(this.config.debug&&K("video stop",t.id),Gn(this,Bs)[t.id]=!1)}};wu=new WeakMap,Ep=new WeakMap,Rp=new WeakMap,G0=new WeakMap,Bs=new WeakMap;return dS(C0e);})();