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face-api
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face-api/dist/tfjs.esm.js

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/*
Face-API
homepage: <https://github.com/vladmandic/face-api>
author: <https://github.com/vladmandic>'
*/
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o=++this.pendingBackendInitId,s=n.then(i=>o<this.pendingBackendInitId?!1:(this.registry[t]=i,this.pendingBackendInit=null,!0)).catch(i=>(o<this.pendingBackendInitId||(this.pendingBackendInit=null,Li(`Initialization of backend ${t} failed`),Li(i.stack||i.message)),!1));return this.pendingBackendInit=s,{success:s,asyncInit:!0}}else return this.registry[t]=n,{success:!0,asyncInit:!1}}catch(n){return Li(`Initialization of backend ${t} failed`),Li(n.stack||n.message),{success:!1,asyncInit:!1}}}removeBackend(t){if(!(t in this.registryFactory))throw new Error(`${t} backend not found in registry`);this.backendName===t&&this.pendingBackendInit!=null&&this.pendingBackendInitId++,t in this.registry&&(this.disposeRegisteredKernels(t),this.registry[t].dispose(),delete this.registry[t]),delete this.registryFactory[t],this.backendName===t&&(this.pendingBackendInit=null,this.backendName=null,this.backendInstance=null)}getSortedBackends(){if(Object.keys(this.registryFactory).length===0)throw new 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this.scopedRun(()=>this.state.kernelDepth++,()=>this.state.kernelDepth--,()=>{!this.ENV.getBool("DEBUG")&&!this.state.profiling?e=a():(f=this.profiler.profileKernel(l,c,()=>a()),this.ENV.getBool("DEBUG")&&this.profiler.logKernelProfile(f),e=f.outputs)}),o&&this.addTapeNode(l,c,e,m,n,p),this.state.profiling&&this.state.activeProfile.kernels.push({name:l,bytesAdded:this.state.numBytes-s,totalBytesSnapshot:this.state.numBytes,tensorsAdded:this.state.numTensors-i,totalTensorsSnapshot:this.state.numTensors,inputShapes:Object.keys(c).map(d=>c[d]!=null?c[d].shape:null),outputShapes:e.map(d=>d.shape),kernelTimeMs:f.timeMs,extraInfo:f.extraInfo}),Array.isArray(u)?e:e[0]}saveTensorsForBackwardMode(t){return t.map(n=>this.keep(this.clone(n)))}getTensorsForGradient(t,e,n){let o=XC(t);if(o!=null){let s=o.inputsToSave||[],i=o.outputsToSave||[],a;o.saveAllInputs?(A(Array.isArray(e),()=>"saveAllInputs is true, expected inputs to be an array."),a=Object.keys(e).map(l=>e[l])):a=s.map(l=>e[l]);let 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ZH(r,t,e){let n=C(r,"x","batchToSpaceND"),o=t.reduce((a,u)=>a*u);A(n.rank>=1+t.length,()=>`input rank is ${n.rank} but should be > than blockShape.length ${t.length}`),A(e.length===t.length,()=>`crops.length is ${e.length} but should be equal to blockShape.length ${t.length}`),A(n.shape[0]%o===0,()=>`input tensor batch is ${n.shape[0]} but is not divisible by the product of the elements of blockShape ${t.join(" * ")} === ${o}`);let s={x:n},i={blockShape:t,crops:e};return _.runKernel(vi,s,i)}var ja=N({batchToSpaceND_:ZH});function rE(r){let t;return r.rank===0||r.rank===1?t=R(r,[1,1,1,r.size]):r.rank===2?t=R(r,[1,1,r.shape[0],r.shape[1]]):r.rank===3?t=R(r,[1,r.shape[0],r.shape[1],r.shape[2]]):t=r,t}function JH(r,t,e,n,o,s){s==null&&(s=.001);let i=C(r,"x","batchNorm"),a=C(t,"mean","batchNorm"),u=C(e,"variance","batchNorm"),l;o!=null&&(l=C(o,"scale","batchNorm"));let c;n!=null&&(c=C(n,"offset","batchNorm")),A(a.rank===u.rank,()=>"Batch normalization gradient requires mean and variance to 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${l.rank}.`),c!=null&&A(c.rank===2||c.rank===1,()=>`Error in batchNorm2D: offset must be rank 2 or rank 1 but got rank ${c.rank}.`),xo(i,a,u,c,l,s)}var LI=N({batchNorm2d_:QH});function tq(r,t,e,n,o,s){let i=C(r,"x","batchNorm"),a=C(t,"mean","batchNorm"),u=C(e,"variance","batchNorm"),l;o!=null&&(l=C(o,"scale","batchNorm"));let c;return n!=null&&(c=C(n,"offset","batchNorm")),A(i.rank===3,()=>`Error in batchNorm3D: x must be rank 3 but got rank ${i.rank}.`),A(a.rank===3||a.rank===1,()=>`Error in batchNorm3D: mean must be rank 3 or rank 1 but got rank ${a.rank}.`),A(u.rank===3||u.rank===1,()=>`Error in batchNorm3D: variance must be rank 3 or rank 1 but got rank ${u.rank}.`),l!=null&&A(l.rank===3||l.rank===1,()=>`Error in batchNorm3D: scale must be rank 3 or rank 1 but got rank ${l.rank}.`),c!=null&&A(c.rank===3||c.rank===1,()=>`Error in batchNorm3D: offset must be rank 3 or rank 1 but got rank ${c.rank}.`),xo(i,a,u,c,l,s)}var zI=N({batchNorm3d_:tq});function eq(r,t,e,n,o,s){let i=C(r,"x","batchNorm"),a=C(t,"mean","batchNorm"),u=C(e,"variance","batchNorm"),l;o!=null&&(l=C(o,"scale","batchNorm"));let c;return n!=null&&(c=C(n,"offset","batchNorm")),A(i.rank===4,()=>`Error in batchNorm4D: x must be rank 4 but got rank ${i.rank}.`),A(a.rank===4||a.rank===1,()=>`Error in batchNorm4D: mean must be rank 4 or rank 1 but got rank ${a.rank}.`),A(u.rank===4||u.rank===1,()=>`Error in batchNorm4D: variance must be rank 4 or rank 1 but got rank ${u.rank}.`),l!=null&&A(l.rank===4||l.rank===1,()=>`Error in batchNorm4D: scale must be rank 4 or rank 1 but got rank ${l.rank}.`),c!=null&&A(c.rank===4||c.rank===1,()=>`Error in batchNorm4D: offset must be rank 4 or rank 1 but got rank ${c.rank}.`),xo(i,a,u,c,l,s)}var BI=N({batchNorm4d_:eq});function rq(r,t,e){let n=C(r,"x","bincount"),o=C(t,"weights","bincount");A(n.dtype==="int32",()=>`Error in bincount: input dtype must be int32, but got ${n.dtype}`),A(e>=0,()=>`size must be non-negative, but got ${e}.`),A(o.size===n.size||o.size===0,()=>`Error in bincount: weights must have the same size as input or0-length, but got input shape: ${n.shape}, weights shape: ${o.shape}.`);let s={x:n,weights:o},i={size:e};return _.runKernel(Dp,s,i)}var yh=N({bincount_:rq});function nq(r,t){let e=C(r,"s0","broadcastArgs","int32"),n=C(t,"s1","broadcastArgs","int32");if(e.rank!==1)throw new Error(`broadcastArgs(): first input must be a vector (rank=1). Has rank ${e.rank}`);if(n.rank!==1)throw new Error(`broadcastArgs(): second input must be a vector (rank=1). Has rank ${n.rank}`);let o={s0:e,s1:n};return _.runKernel(Fp,o)}var VI=N({broadcastArgs_:nq});function oq(r,t){let e=C(r,"broadcastTo","x"),n=e.shape;if(t.some(l=>!(l>0)||l%1!==0))throw new Error(`broadcastTo(): Invalid broadcast shape [${t}].`);if(t.length<e.rank)throw new Error(`broadcastTo(): shape.length=${t.length} < input.rank=${e.rank}.`);if(t.length>e.rank){let l=e.shape.slice();for(;l.length<t.length;)l.unshift(1);e=R(e,l)}let o=e.shape,s=Array.from(t);for(let l=t.length-1;l>=0;l--)if(o[l]===t[l])s[l]=1;else if(e.shape[l]!==1)throw new Error(`broadcastTo(): [${n}] cannot be broadcast to [${t}].`);if(s.map((l,c)=>l>1?c:-1).filter(l=>l>=0).length===0)return kn(e);let a={x:e},u={reps:s};return _.runKernel(Yn,a,u)}var Xa=N({broadcastTo_:oq});function sq(r){let e={x:C(r,"x","ceil","float32")};return _.runKernel(es,e)}var bh=N({ceil_:sq});function iq(r,t,e){let n=C(r,"x","clipByValue");A(t<=e,()=>`Error in clip: min (${t}) must be less than or equal to max (${e}).`);let o={x:n},s={clipValueMin:t,clipValueMax:e};return _.runKernel(ao,o,s)}var br=N({clipByValue_:iq});function aq(r){return se(r,0)}var GI=N({concat1d_:aq});function lq(r,t){return se(r,t)}var WI=N({concat2d_:lq});function uq(r,t){return se(r,t)}var UI=N({concat3d_:uq});function cq(r,t){return se(r,t)}var HI=N({concat4d_:cq});function pq(r,t,e,n,o="NHWC",s=[1,1],i){let a=C(r,"x","conv2d","float32"),u=C(t,"filter","conv2d","float32"),l=a,c=!1;a.rank===3&&(c=!0,l=R(a,[1,a.shape[0],a.shape[1],a.shape[2]])),A(l.rank===4,()=>`Error in conv2d: input must be rank 4, but got rank ${l.rank}.`),A(u.rank===4,()=>`Error in conv2d: filter must be rank 4, but got rank ${u.rank}.`),_e("conv2d",n,i);let p=o==="NHWC"?l.shape[3]:l.shape[1];A(p===u.shape[2],()=>`Error in conv2d: depth of input (${p}) must match input depth for filter ${u.shape[2]}.`),A(Dr(e,s),()=>`Error in conv2D: Either strides or dilations must be 1. Got strides ${e} and dilations '${s}'`);let m={x:l,filter:u},f={strides:e,pad:n,dataFormat:o,dilations:s,dimRoundingMode:i},d=_.runKernel(rs,m,f);return c?R(d,[d.shape[1],d.shape[2],d.shape[3]]):d}var pn=N({conv2d_:pq});function mq(r,t,e,n,o="NWC",s=1,i){let a=C(r,"x","conv1d"),u=C(t,"filter","conv1d"),l=a,c=!1;a.rank===2&&(c=!0,l=R(a,[1,a.shape[0],a.shape[1]])),A(l.rank===3,()=>`Error in conv1d: input must be rank 3, but got rank ${l.rank}.`),A(u.rank===3,()=>`Error in conv1d: filter must be rank 3, but got rank ${u.rank}.`),_e("conv1d",n,i),A(l.shape[2]===u.shape[1],()=>`Error in conv1d: depth of input (${l.shape[2]}) must match input depth for filter ${u.shape[1]}.`),A(Dr(e,s),()=>`Error in conv1D: Either stride or dilation must be 1. Got stride ${e} and dilation '${s}'`),A(o==="NWC",()=>`Error in conv1d: got dataFormat of ${o} but only NWC is currently supported.`);let p=R(u,[1,u.shape[0],u.shape[1],u.shape[2]]),m=R(l,[l.shape[0],1,l.shape[1],l.shape[2]]),g=pn(m,p,[1,e],n,"NHWC",[1,s],i);return c?R(g,[g.shape[2],g.shape[3]]):R(g,[g.shape[0],g.shape[2],g.shape[3]])}var Qu=N({conv1d_:mq});function fq(r,t,e,n,o,s="NHWC",i){A(r.length===t.rank,()=>`Length of inShape (${r.length}) and rank of dy (${t.rank}) must match`);let a=r,u=t,l=!1;t.rank===3&&(l=!0,u=R(t,[1,t.shape[0],t.shape[1],t.shape[2]]),a=[1,r[0],r[1],r[2]]),A(a.length===4,()=>`Error in conv2dDerInput: inShape must be length 4, but got length ${a.length}.`),A(u.rank===4,()=>`Error in conv2dDerInput: dy must be rank 4, but got rank ${u.rank}`),A(e.rank===4,()=>`Error in conv2dDerInput: filter must be rank 4, but got rank ${e.rank}`);let c=s==="NHWC"?a[3]:a[1],p=s==="NHWC"?u.shape[3]:u.shape[1];A(c===e.shape[2],()=>`Error in conv2dDerInput: depth of input (${c}) must match input depth for filter ${e.shape[2]}.`),A(p===e.shape[3],()=>`Error in conv2dDerInput: depth of output (${p}) must match output depth for filter ${e.shape[3]}.`),_e("conv2dDerInput",o,i);let m={dy:u,filter:e},f={strides:n,pad:o,dataFormat:s,dimRoundingMode:i,inputShape:a},d=_.runKernel(ns,m,f);return l?R(d,[d.shape[1],d.shape[2],d.shape[3]]):d}var wm=N({conv2DBackpropInput_:fq});function dq(r,t,e,n,o,s){let i=C(r,"x","conv2dTranspose"),a=C(t,"filter","conv2dTranspose");return wm(e,i,a,n,o,"NHWC",s)}var tc=N({conv2dTranspose_:dq});function hq(r,t,e,n,o="NDHWC",s=[1,1,1]){let i=C(r,"x","conv3d"),a=C(t,"filter","conv3d"),u=i,l=!1;i.rank===4&&(l=!0,u=R(i,[1,i.shape[0],i.shape[1],i.shape[2],i.shape[3]])),A(u.rank===5,()=>`Error in conv3d: input must be rank 5, but got rank ${u.rank}.`),A(a.rank===5,()=>`Error in conv3d: filter must be rank 5, but got rank ${a.rank}.`),A(u.shape[4]===a.shape[3],()=>`Error in conv3d: depth of input (${u.shape[4]}) must match input depth for filter ${a.shape[3]}.`),A(Dr(e,s),()=>`Error in conv3D: Either strides or dilations must be 1. 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Yq(r,t,e,n="float32"){t==null&&(t=r);let o=Ct([r,t],n),s=r<=t?r:t;for(let a=0;a<s;++a)o.set(1,a,a);let i=R(o.toTensor(),[r,t]);if(e==null)return i;if(e.length===1)return Nr(fr(i,0),[e[0],1,1]);if(e.length===2)return Nr(fr(fr(i,0),0),[e[0],e[1],1,1]);if(e.length===3)return Nr(fr(fr(fr(i,0),0),0),[e[0],e[1],e[2],1,1]);throw new Error(`eye() currently supports only 1D and 2D batchShapes, but received ${e.length}D.`)}var vm=N({eye_:Yq});function Qs(r,t,e){let n={shape:r,value:t,dtype:e};return _.runKernel(Pl,{},n)}function Zq(r){let e={x:C(r,"x","floor","float32")};return _.runKernel(ps,e)}var ti=N({floor_:Zq});function Jq(r,t,e=0,n=0){let o=C(r,"x","gather"),s=C(t,"indices","gather","int32"),i={x:o,indices:s},a={axis:e,batchDims:n};return _.runKernel(Si,i,a)}var bo=N({gather_:Jq});function Qq(r,t){let e=C(r,"a","greater","string_or_numeric"),n=C(t,"b","greater","string_or_numeric");[e,n]=jt(e,n),Bt(e.shape,n.shape);let o={a:e,b:n};return _.runKernel(ba,o)}var Ge=N({greater_:Qq});function 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g=0;g<t.length;g++)t[g]>o&&l.push({score:t[g],boxIndex:g,suppressBeginIndex:0});l.sort(ME);let c=s>0?-.5/s:0,p=[],m=[];for(;p.length<e&&l.length>0;){let g=l.pop(),{score:y,boxIndex:b,suppressBeginIndex:w}=g;if(y<o)break;let v=!1;for(let k=p.length-1;k>=w;--k){let E=R6(r,b,p[k]);if(E>=n){v=!0;break}if(g.score=g.score*O6(n,c,E),g.score<=o)break}g.suppressBeginIndex=p.length,v||(g.score===y?(p.push(b),m.push(g.score)):g.score>o&&OE(l,g,ME))}let f=p.length,d=e-f;a&&d>0&&(p.push(...new Array(d).fill(0)),m.push(...new Array(d).fill(0)));let h={selectedIndices:p};return i&&(h.selectedScores=m),u&&(h.validOutputs=f),h}function R6(r,t,e){let n=r.subarray(t*4,t*4+4),o=r.subarray(e*4,e*4+4),s=Math.min(n[0],n[2]),i=Math.min(n[1],n[3]),a=Math.max(n[0],n[2]),u=Math.max(n[1],n[3]),l=Math.min(o[0],o[2]),c=Math.min(o[1],o[3]),p=Math.max(o[0],o[2]),m=Math.max(o[1],o[3]),f=(a-s)*(u-i),d=(p-l)*(m-c);if(f<=0||d<=0)return 0;let 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i=C(r,"boxes","nonMaxSuppression"),a=C(t,"scores","nonMaxSuppression"),u=Io(i,a,e,n,o,null),l=u.maxOutputSize,c=u.iouThreshold,p=u.scoreThreshold,m={boxes:i,scores:a},f={maxOutputSize:l,iouThreshold:c,scoreThreshold:p,padToMaxOutputSize:s},d=_.runKernel(Aa,m,f);return{selectedIndices:d[0],validOutputs:d[1]}}var BE=N({nonMaxSuppressionPadded_:z6});async function B6(r,t,e,n=.5,o=Number.NEGATIVE_INFINITY,s=!1){let i=C(r,"boxes","nonMaxSuppressionAsync"),a=C(t,"scores","nonMaxSuppressionAsync"),u=Io(i,a,e,n,o,null),l=u.maxOutputSize,c=u.iouThreshold,p=u.scoreThreshold,[m,f]=await Promise.all([i.data(),a.data()]),{selectedIndices:d,validOutputs:h}=ny(m,f,l,c,p,s);return i!==r&&i.dispose(),a!==t&&a.dispose(),{selectedIndices:Fe(d,"int32"),validOutputs:pt(h,"int32")}}var VE=B6;function V6(r,t,e=!1,n=!1){let o=C(r,"images","resizeBilinear");A(o.rank===3||o.rank===4,()=>`Error in resizeBilinear: x must be rank 3 or 4, but got rank ${o.rank}.`),A(t.length===2,()=>`Error in resizeBilinear: new 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WE=N({transform_:H6});function q6(r,t,e){A(t%1===0,()=>`bandPart(): numLower must be an integer, got ${t}.`),A(e%1===0,()=>`bandPart(): numUpper must be an integer, got ${e}.`);let n=C(r,"a","bandPart");A(n.rank>=2,()=>`bandPart(): Rank must be at least 2, got ${n.rank}.`);let o=n.shape,[s,i]=n.shape.slice(-2);if(!(t<=s))throw new Error(`bandPart(): numLower (${t}) must not be greater than the number of rows (${s}).`);if(!(e<=i))throw new Error(`bandPart(): numUpper (${e}) must not be greater than the number of columns (${i}).`);t<0&&(t=s),e<0&&(e=i);let a=R(nl(0,s,1,"int32"),[-1,1]),u=nl(0,i,1,"int32"),l=lt(a,u),c=Tr(_n(l,pt(+t,"int32")),Tn(l,pt(-e,"int32"))),p=we([s,i],n.dtype);return R(Ze(vr(R(n,[-1,s,i])).map(m=>Ee(c,m,p))),o)}var UE=N({bandPart_:q6});function K6(r){let t;if(Array.isArray(r)){t=!1,A(r!=null&&r.length>0,()=>"Gram-Schmidt process: input must not be null, undefined, or empty");let o=r[0].shape[0];for(let s=1;s<r.length;++s)A(r[s].shape[0]===o,()=>`Gram-Schmidt: 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n=C(r,"losses","computeWeightedLoss"),o=null;t!=null&&(o=C(t,"weights","computeWeightedLoss"));let s=o==null?n:M(n,o);if(e===Je.NONE)return s;if(e===Je.SUM)return mt(s);if(e===Je.MEAN){if(o==null)return be(s);{let i=n.size/o.size,a=ct(mt(s),mt(o));return i>1?ct(a,pt(i)):a}}if(e===Je.SUM_BY_NONZERO_WEIGHTS){if(o==null)return ct(mt(s),pt(n.size));{let i=M(o,lr(n.shape)),a=Z(mt(vo(i,pt(0))),"float32");return ct(mt(s),a)}}throw Error(`Unknown reduction: ${e}`)}var Br=N({computeWeightedLoss_:X6});function Y6(r,t,e,n=Je.SUM_BY_NONZERO_WEIGHTS){let o=C(r,"labels","absoluteDifference"),s=C(t,"predictions","absoluteDifference"),i=null;e!=null&&(i=C(e,"weights","absoluteDifference")),Me(o.shape,s.shape,"Error in absoluteDifference: ");let a=Te(lt(o,s));return Br(a,i,n)}var jE=N({absoluteDifference_:Y6});function Z6(r,t,e,n,o=Je.SUM_BY_NONZERO_WEIGHTS){let 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_t(s),e?o:(o.dispose(),null)}get iterations(){return this.iterations_==null&&(this.iterations_=0),this.iterations_}incrementIterations(){this.iterations_=this.iterations+1}computeGradients(t,e){return zx(t,e)}dispose(){this.iterations_!=null&&_t(this.iterations_)}async saveIterations(){return this.iterations_==null&&(this.iterations_=0),{name:"iter",tensor:pt(this.iterations_,"int32")}}async getWeights(){throw new Error("getWeights() is not implemented for this optimizer yet.")}async setWeights(t){throw new Error(`setWeights() is not implemented for this optimizer class ${this.getClassName()}`)}async extractIterations(t){return this.iterations_=(await t[0].tensor.data())[0],t.slice(1)}};Object.defineProperty(Vr,Symbol.hasInstance,{value:r=>r.minimize!=null&&r.computeGradients!=null&&r.applyGradients!=null});var nu=class extends 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f=J(M(p,-this.learningRate),s);s.assign(f)})}),this.incrementIterations()}dispose(){this.accumulatedUpdates!=null&&(_t(this.accumulatedGrads.map(t=>t.variable)),_t(this.accumulatedUpdates.map(t=>t.variable)))}async getWeights(){let t=[...this.accumulatedGrads,...this.accumulatedUpdates];return[await this.saveIterations()].concat(t.map(e=>({name:e.originalName,tensor:e.variable})))}async setWeights(t){t=await this.extractIterations(t);let e=t.length/2,n=!1;this.accumulatedGrads=t.slice(0,e).map(o=>({originalName:o.name,variable:o.tensor.variable(n)})),this.accumulatedUpdates=t.slice(e,e*2).map(o=>({originalName:o.name,variable:o.tensor.variable(n)}))}getConfig(){return{learningRate:this.learningRate,rho:this.rho,epsilon:this.epsilon}}static fromConfig(t,e){return new t(e.learningRate,e.rho,e.epsilon)}};nu.className="Adadelta";Nn(nu);var ou=class extends Vr{constructor(t,e=.1){super(),this.learningRate=t,this.initialAccumulatorValue=e,this.accumulatedGrads=[]}applyGradients(t){(Array.isArray(t)?t.map(n=>n.name):Object.keys(t)).forEach((n,o)=>{let s=_.registeredVariables[n];this.accumulatedGrads[o]==null&&(this.accumulatedGrads[o]={originalName:`${n}/accumulator`,variable:B(()=>Qs(s.shape,this.initialAccumulatorValue).variable(!1))});let i=Array.isArray(t)?t[o].tensor:t[n];if(i==null)return;let a=this.accumulatedGrads[o].variable;B(()=>{let u=J(a,Wt(i));a.assign(u);let l=J(M(ct(i,ye(J(u,_.backend.epsilon()))),-this.learningRate),s);s.assign(l)})}),this.incrementIterations()}dispose(){this.accumulatedGrads!=null&&_t(this.accumulatedGrads.map(t=>t.variable))}async getWeights(){return[await this.saveIterations()].concat(this.accumulatedGrads.map(t=>({name:t.originalName,tensor:t.variable})))}async setWeights(t){t=await this.extractIterations(t);let e=!1;this.accumulatedGrads=t.map(n=>({originalName:n.name,variable:n.tensor.variable(e)}))}getConfig(){return{learningRate:this.learningRate,initialAccumulatorValue:this.initialAccumulatorValue}}static fromConfig(t,e){return new t(e.learningRate,e.initialAccumulatorValue)}};ou.className="Adagrad";Nn(ou);var su=class extends Vr{constructor(t,e,n,o=null){super(),this.learningRate=t,this.beta1=e,this.beta2=n,this.epsilon=o,this.accumulatedFirstMoment=[],this.accumulatedSecondMoment=[],B(()=>{this.accBeta1=pt(e).variable(),this.accBeta2=pt(n).variable()}),o==null&&(this.epsilon=_.backend.epsilon())}applyGradients(t){let e=Array.isArray(t)?t.map(n=>n.name):Object.keys(t);B(()=>{let n=lt(1,this.accBeta1),o=lt(1,this.accBeta2);e.forEach((s,i)=>{let a=_.registeredVariables[s],u=!1;this.accumulatedFirstMoment[i]==null&&(this.accumulatedFirstMoment[i]={originalName:`${s}/m`,variable:B(()=>St(a).variable(u))}),this.accumulatedSecondMoment[i]==null&&(this.accumulatedSecondMoment[i]={originalName:`${s}/v`,variable:B(()=>St(a).variable(u))});let l=Array.isArray(t)?t[i].tensor:t[s];if(l==null)return;let c=this.accumulatedFirstMoment[i].variable,p=this.accumulatedSecondMoment[i].variable,m=J(M(c,this.beta1),M(l,1-this.beta1)),f=J(M(p,this.beta2),M(Wt(l),1-this.beta2)),d=ct(m,n),h=ct(f,o);c.assign(m),p.assign(f);let g=J(M(ct(d,J(ye(h),this.epsilon)),-this.learningRate),a);a.assign(g)}),this.accBeta1.assign(M(this.accBeta1,this.beta1)),this.accBeta2.assign(M(this.accBeta2,this.beta2))}),this.incrementIterations()}dispose(){this.accBeta1.dispose(),this.accBeta2.dispose(),this.accumulatedFirstMoment!=null&&_t(this.accumulatedFirstMoment.map(t=>t.variable)),this.accumulatedSecondMoment!=null&&_t(this.accumulatedSecondMoment.map(t=>t.variable))}async getWeights(){let t=[...this.accumulatedFirstMoment,...this.accumulatedSecondMoment];return[await this.saveIterations()].concat(t.map(e=>({name:e.originalName,tensor:e.variable})))}async setWeights(t){t=await this.extractIterations(t),B(()=>{this.accBeta1.assign(Yr(this.beta1,this.iterations_+1)),this.accBeta2.assign(Yr(this.beta2,this.iterations_+1))});let e=t.length/2,n=!1;this.accumulatedFirstMoment=t.slice(0,e).map(o=>({originalName:o.name,variable:o.tensor.variable(n)})),this.accumulatedSecondMoment=t.slice(e,e*2).map(o=>({originalName:o.name,variable:o.tensor.variable(n)}))}getConfig(){return{learningRate:this.learningRate,beta1:this.beta1,beta2:this.beta2,epsilon:this.epsilon}}static fromConfig(t,e){return new t(e.learningRate,e.beta1,e.beta2,e.epsilon)}};su.className="Adam";Nn(su);var iu=class extends Vr{constructor(t,e,n,o=null,s=0){super(),this.learningRate=t,this.beta1=e,this.beta2=n,this.epsilon=o,this.decay=s,this.accumulatedFirstMoment=[],this.accumulatedWeightedInfNorm=[],B(()=>{this.iteration=pt(0).variable(),this.accBeta1=pt(e).variable()}),o==null&&(this.epsilon=_.backend.epsilon())}applyGradients(t){let e=Array.isArray(t)?t.map(n=>n.name):Object.keys(t);B(()=>{let n=lt(1,this.accBeta1),o=ct(-this.learningRate,J(M(this.iteration,this.decay),1));e.forEach((s,i)=>{let 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Error("getWeights() is not implemented for Adamax yet.")}async setWeights(t){throw new Error("setWeights() is not implemented for Adamax yet.")}getConfig(){return{learningRate:this.learningRate,beta1:this.beta1,beta2:this.beta2,epsilon:this.epsilon,decay:this.decay}}static fromConfig(t,e){return new t(e.learningRate,e.beta1,e.beta2,e.epsilon,e.decay)}};iu.className="Adamax";Nn(iu);var Hi=class extends Vr{constructor(t){super(),this.learningRate=t,this.setLearningRate(t)}applyGradients(t){(Array.isArray(t)?t.map(n=>n.name):Object.keys(t)).forEach((n,o)=>{let s=Array.isArray(t)?t[o].tensor:t[n];if(s==null)return;let i=_.registeredVariables[n];B(()=>{let a=J(M(this.c,s),i);i.assign(a)})}),this.incrementIterations()}setLearningRate(t){this.learningRate=t,this.c!=null&&this.c.dispose(),this.c=Pe(pt(-t))}dispose(){this.c.dispose()}async getWeights(){return[await this.saveIterations()]}async setWeights(t){if(t=await this.extractIterations(t),t.length!==0)throw new Error("SGD optimizer does 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this.saveIterations()].concat(this.accumulations.map(t=>({name:t.originalName,tensor:t.variable})))}async setWeights(t){t=await this.extractIterations(t);let e=!1;this.accumulations=t.map(n=>({originalName:n.name,variable:n.tensor.variable(e)}))}getConfig(){return{learningRate:this.learningRate,momentum:this.momentum,useNesterov:this.useNesterov}}static fromConfig(t,e){return new t(e.learningRate,e.momentum,e.useNesterov)}};au.className="Momentum";Nn(au);var lu=class extends Vr{constructor(t,e=.9,n=0,o=null,s=!1){if(super(),this.learningRate=t,this.decay=e,this.momentum=n,this.epsilon=o,this.accumulatedMeanSquares=[],this.accumulatedMoments=[],this.accumulatedMeanGrads=[],this.centered=s,o==null&&(this.epsilon=_.backend.epsilon()),t==null)throw new Error("learningRate for RMSPropOptimizer must be defined.")}applyGradients(t){(Array.isArray(t)?t.map(n=>n.name):Object.keys(t)).forEach((n,o)=>{let s=_.registeredVariables[n],i=!1;this.accumulatedMeanSquares[o]==null&&(this.accumulatedMeanSquares[o]={originalName:`${n}/rms`,variable:B(()=>St(s).variable(i))}),this.accumulatedMoments[o]==null&&(this.accumulatedMoments[o]={originalName:`${n}/momentum`,variable:B(()=>St(s).variable(i))}),this.accumulatedMeanGrads[o]==null&&this.centered&&(this.accumulatedMeanGrads[o]={originalName:`${n}/mg`,variable:B(()=>St(s).variable(i))});let a=Array.isArray(t)?t[o].tensor:t[n];if(a==null)return;let u=this.accumulatedMeanSquares[o].variable,l=this.accumulatedMoments[o].variable;B(()=>{let c=J(M(u,this.decay),M(Wt(a),1-this.decay));if(this.centered){let p=this.accumulatedMeanGrads[o].variable,m=J(M(p,this.decay),M(a,1-this.decay)),f=ct(M(a,this.learningRate),ye(lt(c,J(Wt(m),this.epsilon)))),d=J(M(l,this.momentum),f);u.assign(c),p.assign(m),l.assign(d);let h=lt(s,d);s.assign(h)}else{let p=J(M(u,this.decay),M(Wt(a),1-this.decay)),m=J(M(l,this.momentum),ct(M(a,this.learningRate),ye(J(p,this.epsilon))));u.assign(p),l.assign(m);let f=lt(s,m);s.assign(f)}})}),this.incrementIterations()}dispose(){this.accumulatedMeanSquares!=null&&_t(this.accumulatedMeanSquares.map(t=>t.variable)),this.accumulatedMeanGrads!=null&&this.centered&&_t(this.accumulatedMeanGrads.map(t=>t.variable)),this.accumulatedMoments!=null&&_t(this.accumulatedMoments.map(t=>t.variable))}async getWeights(){let t=[...this.accumulatedMeanSquares,...this.accumulatedMoments];return this.centered&&t.push(...this.accumulatedMeanGrads),[await this.saveIterations()].concat(t.map(e=>({name:e.originalName,tensor:e.variable})))}async setWeights(t){t=await this.extractIterations(t);let e=this.centered?t.length/3:t.length/2,n=!1;this.accumulatedMeanSquares=t.slice(0,e).map(o=>({originalName:o.name,variable:o.tensor.variable(n)})),this.accumulatedMoments=t.slice(e,e*2).map(o=>({originalName:o.name,variable:o.tensor.variable(n)})),this.centered&&(this.accumulatedMeanGrads=t.slice(e*2,e*3).map(o=>({originalName:o.name,variable:o.tensor.variable(n)})))}getConfig(){return{learningRate:this.learningRate,decay:this.decay,momentum:this.momentum,epsilon:this.epsilon,centered:this.centered}}static fromConfig(t,e){return new t(e.learningRate,e.decay,e.momentum,e.epsilon,e.centered)}};lu.className="RMSProp";Nn(lu);var oi=class{static sgd(t){return new Hi(t)}static momentum(t,e,n=!1){return new au(t,e,n)}static rmsprop(t,e=.9,n=0,o=null,s=!1){return new lu(t,e,n,o,s)}static adam(t=.001,e=.9,n=.999,o=null){return new su(t,e,n,o)}static adadelta(t=.001,e=.95,n=null){return new nu(t,e,n)}static adamax(t=.002,e=.9,n=.999,o=null,s=0){return new iu(t,e,n,o,s)}static 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Its own _trainableWeights must remain an empty Array.");if(!this.trainable)return[];let t=[];for(let e of this.layers)t=t.concat(e.trainableWeights);return t}get nonTrainableWeights(){let t=[];for(let e of this.layers)t.push(...e.nonTrainableWeights);if(!this.trainable){let e=[];for(let n of this.layers)e.push(...n.trainableWeights);return e.concat(t)}return t}get weights(){return this.trainableWeights.concat(this.nonTrainableWeights)}loadWeights(t,e=!0){let n={},o=0;for(let i of this.layers)for(let a of i.weights){if(n[a.originalName]!=null)throw new z(`Duplicate weight name: ${a.originalName}`);n[a.originalName]=a,o++}let s=[];for(let i in t){let a=i;if(n[i]==null){let u=i.split("/");a=u.slice(0,-2).concat([u[u.length-1]]).join("/")}if(n[a]!=null)s.push([n[a],t[i]]);else if(e)throw new z(`Provided weight data has no target variable: ${i}`);delete n[a]}if(e){let i=[];for(let a in n)i.push(a);if(i.length>0)throw new z(`${i.length} of ${o} weights are not set: ${i}`)}Bm(s)}updatedConfig(){let t=this.getConfig(),e={};return e.className=this.getClassName(),e.config=t,e.kerasVersion=`tfjs-layers ${jm}`,e.backend="TensorFlow.js",e}toJSON(t,e=!0){let n=My(this.updatedConfig());return e?JSON.stringify(n):n}call(t,e){return B(()=>{t=ve(t);let n=new Ao;for(let o=0;o<this.inputs.length;++o)n.add(this.inputs[o],t[o]);return Fc(this.outputs,n,e)})}computeMask(t,e){return B(()=>{t=ve(t);let n;return e==null?n=ko(null,t.length):n=ve(e),this.runInternalGraph(t,n)[1]})}computeOutputShape(t){let e=Lm(t);if(e.length!==this.inputLayers.length)throw new z(`Invalid inputShape argument ${t}: model has ${this.inputLayers.length} tensor inputs.`);let n={};for(let a=0;a<e.length;a++){let u=this.inputLayers[a],l=e[a],c=u.name+"_0_0";n[c]=l}let o=Object.keys(this.nodesByDepth).map(a=>parseInt(a,10)).sort(Kh);if(o.length>1)for(let a of o){let u=this.nodesByDepth[a];for(let l of u){let c=l.outboundLayer;if(this.inputLayers.map(h=>h.id).indexOf(c.id)!==-1)continue;let p=[];for(let h=0;h<l.inboundLayers.length;h++){let g=l.inboundLayers[h],y=l.nodeIndices[h],b=l.tensorIndices[h],w=`${g.name}_${y}_${b}`,v=n[w];p.push(v)}let m=c.computeOutputShape(Er(p)),f=Lm(m),d=c.inboundNodes.indexOf(l);for(let h=0;h<f.length;h++){let g=`${c.name}_${d}_${h}`;n[g]=f[h]}}}let s=[],i=[];for(let a=0;a<this.outputLayers.length;a++){let u=this.outputLayers[a],l=this.outputLayersNodeIndices[a],c=this.outputLayersTensorIndices[a],p=`${u.name}_${l}_${c}`;i.push(p)}for(let a=0;a<i.length;a++){let u=i[a];Qn(u in n),s.push(n[u])}return Er(s)}runInternalGraph(t,e){e==null&&(e=ko(null,t.length));let n={};for(let u=0;u<this.inputs.length;++u){let l=this.inputs[u],c=t[u],p=e[u];n[l.id]=[c,p]}let o=Object.keys(this.nodesByDepth).map(u=>parseInt(u,10)).sort(Kh);for(let u of o){let l=this.nodesByDepth[u];for(let c of l){let p=c.outboundLayer,m=c.inputTensors,f=c.outputTensors,d=new Array;for(let h of m)h.id in n&&d.push(n[h.id]);if(d.length===m.length){let h={},g,y,b,w;if(c.callArgs!=null&&(h=c.callArgs),d.length===1){let[v,k]=d[0];h.mask==null&&(h.mask=k),b=ve(p.call(v,h)),w=ve(p.computeMask(v,k)),g=[v],y=[k]}else g=d.map(v=>v[0]),y=d.map(v=>v[1]),h.mask==null&&(h.mask=y),b=ve(p.call(g,h)),w=ve(p.computeMask(g,y));if(p.activityRegularizer)throw new Nt("LayersModel invocation with concrete Tensor value(s) in the presence of activity regularizer(s) is not supported yet.");for(let v=0;v<f.length;++v){let k=f[v],E=b[v],$=w[v];n[k.id]=[E,$]}}}}let s=[],i=[],a=[];for(let u of this.outputs){Qn(u.id in n,`Could not compute output ${u.name} : ${u.id}`);let[l,c]=n[u.id];a.push(l.shape),s.push(l),i.push(c)}return[s,i,a]}buildNodeConversionMap(t){let e={},n;for(let o of this.layers){n=o instanceof Bn?1:0;for(let s=0;s<o.inboundNodes.length;s++){let i=Bn.nodeKey(o,s);this.containerNodes.has(i)&&(e[i]=n,n+=1)}}return e}getLayer(t,e){if(e!=null){if(this.layers.length<=e)throw new z(`Was asked to retrieve layer at index ${e}, but model only has ${this.layers.length} layer(s).`);return this.layers[e]}else if(t==null)throw new z("Provide either a layer name or layer index");for(let n of this.layers)if(n.name===t)return n;throw new z(`No such layer: ${t}`)}calculateLosses(){return B(()=>{let t=[];for(let e of this.layers)for(let n=0;n<e.inboundNodes.length;++n){let o=Bn.nodeKey(e,n);this.containerNodes.has(o)&&t.push(...e.calculateLosses())}return t})}getConfig(){let t={name:this.name},e=this.buildNodeConversionMap(this.layers),n=[];for(let i of this.layers){let a=i.getClassName(),u=i.getConfig(),l=[];for(let p=0;p<i.inboundNodes.length;p++){let m=i.inboundNodes[p],f=Bn.nodeKey(i,p),d={};if(this.containerNodes.has(f)){if(m.callArgs)try{JSON.stringify(m.callArgs),d=m.callArgs}catch(h){console.warn(`Layer ${i.name} was passed non-serializable keyword arguments: ${m.callArgs}. 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t={theta:this.theta},e=super.getConfig();return Object.assign(t,e),t}};ef.className="ThresholdedReLU";et.registerClass(ef);var rf=class extends Vt{constructor(t){super(t==null?{}:t),this.DEFAULT_AXIS=1,t==null&&(t={}),this.softmax=new Ym().apply,this.axis=t.axis==null?this.DEFAULT_AXIS:t.axis}call(t,e){let n=Pt(t);return this.softmax(n,this.axis)}computeOutputShape(t){return t}getConfig(){let t={axis:this.axis},e=super.getConfig();return Object.assign(t,e),t}};rf.className="Softmax";et.registerClass(rf);function du(r,t,e){if(typeof r=="number")return ko(r,t);if(r.length!==t)throw new z(`The ${e} argument must be an integer or tuple of ${t} integers. 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Received: ${JSON.stringify(r)} including a non-integer number ${o}`)}return r}function An(r,t,e,n,o=1){if(r==null)return r;let s=t+(t-1)*(o-1),i;return e==="same"?i=r:i=r-s+1,Math.floor((i+n-1)/n)}function pi(r,t,e,n){if(r==null)return null;if(n==="valid")r=r*t+ai([e-t,0]);else if(n==="same")r=r*t;else throw new z(`Unsupport padding mode: ${n}.`);return r}function sg(r,t){return B(()=>(Le(t),t==="channelsFirst"?Mt(r,[0,2,3,1]):r))}function jS(r,t){return B(()=>(Le(t),t==="channelsFirst"?Mt(r,[0,2,3,4,1]):r))}function G8(r,t,e,n=1,o="valid",s,i=1){return B(()=>{if(s==null&&(s=gn()),Le(s),r.shape.length!==3)throw new z(`The input of a conv1dWithBias operation should be 3, but is ${r.shape.length} instead.`);if(t.shape.length!==3)throw new z(`The kernel for a conv1dWithBias operation should be 3, but is ${t.shape.length} instead`);if(e!=null&&e.shape.length!==1)throw new z(`The bias for a conv1dWithBias operation should be 1, but is ${t.shape.length} instead`);if(s==="channelsFirst"&&(r=Mt(r,[0,2,1])),o==="causal")throw new Nt("The support for CAUSAL padding mode in conv1dWithBias is not implemented yet.");let a=Qu(r,t,n,o==="same"?"same":"valid","NWC",i);return e!=null&&(a=xn(a,e)),a})}function uD(r,t,e,n=[1,1],o="valid",s,i,a=null){return B(()=>{if(s==null&&(s=gn()),Le(s),r.rank!==3&&r.rank!==4)throw new z(`conv2dWithBiasActivation expects input to be of rank 3 or 4, but received ${r.rank}.`);if(t.rank!==3&&t.rank!==4)throw new z(`conv2dWithBiasActivation expects kernel to be of rank 3 or 4, but received ${r.rank}.`);let u=sg(r,s);if(o==="causal")throw new Nt("The support for CAUSAL padding mode in conv1dWithBias is not implemented yet.");return u=Co.conv2d({x:u,filter:t,strides:n,pad:o==="same"?"same":"valid",dilations:i,dataFormat:"NHWC",bias:e,activation:a}),s==="channelsFirst"&&(u=Mt(u,[0,3,1,2])),u})}function W8(r,t,e,n=[1,1,1],o="valid",s,i){return B(()=>{if(s==null&&(s=gn()),Le(s),r.rank!==4&&r.rank!==5)throw new z(`conv3dWithBias expects input to be of rank 4 or 5, but received ${r.rank}.`);if(t.rank!==4&&t.rank!==5)throw new z(`conv3dWithBias expects kernel to be of rank 4 or 5, but received ${r.rank}.`);let a=jS(r,s);if(o==="causal")throw new Nt("The support for CAUSAL padding mode in conv3dWithBias is not implemented yet.");return a=wh(a,t,n,o==="same"?"same":"valid","NDHWC",i),e!=null&&(a=xn(a,e)),s==="channelsFirst"&&(a=Mt(a,[0,4,1,2,3])),a})}var Pc=class extends Vt{constructor(t,e){if(super(e),this.bias=null,this.DEFAULT_KERNEL_INITIALIZER="glorotNormal",this.DEFAULT_BIAS_INITIALIZER="zeros",Pc.verifyArgs(e),this.rank=t,tr(this.rank,"rank"),this.rank!==1&&this.rank!==2&&this.rank!==3)throw new Nt(`Convolution layer for rank other than 1, 2, or 3 (${this.rank}) is not implemented yet.`);if(this.kernelSize=du(e.kernelSize,t,"kernelSize"),this.strides=du(e.strides==null?1:e.strides,t,"strides"),this.padding=e.padding==null?"valid":e.padding,hn(this.padding),this.dataFormat=e.dataFormat==null?"channelsLast":e.dataFormat,Le(this.dataFormat),this.activation=ci(e.activation),this.useBias=e.useBias==null?!0:e.useBias,this.biasInitializer=de(e.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.biasConstraint=Ue(e.biasConstraint),this.biasRegularizer=Ie(e.biasRegularizer),this.activityRegularizer=Ie(e.activityRegularizer),this.dilationRate=du(e.dilationRate==null?1:e.dilationRate,t,"dilationRate"),this.rank===1&&Array.isArray(this.dilationRate)&&this.dilationRate.length!==1)throw new z(`dilationRate must be a number or an array of a single number for 1D convolution, but received ${JSON.stringify(this.dilationRate)}`);if(this.rank===2){if(typeof this.dilationRate=="number")this.dilationRate=[this.dilationRate,this.dilationRate];else if(this.dilationRate.length!==2)throw new z(`dilationRate must be a number or array of two numbers for 2D convolution, but received ${JSON.stringify(this.dilationRate)}`)}else if(this.rank===3){if(typeof this.dilationRate=="number")this.dilationRate=[this.dilationRate,this.dilationRate,this.dilationRate];else if(this.dilationRate.length!==3)throw new z(`dilationRate must be a number or array of three numbers for 3D convolution, but received ${JSON.stringify(this.dilationRate)}`)}}static verifyArgs(t){if(Qn("kernelSize"in t,"required key 'kernelSize' not in config"),typeof t.kernelSize!="number"&&!py(t.kernelSize,"number",1,3))throw new z(`BaseConv expects config.kernelSize to be number or number[] with length 1, 2, or 3, but received ${JSON.stringify(t.kernelSize)}.`)}getConfig(){let t={kernelSize:this.kernelSize,strides:this.strides,padding:this.padding,dataFormat:this.dataFormat,dilationRate:this.dilationRate,activation:ui(this.activation),useBias:this.useBias,biasInitializer:Ae(this.biasInitializer),biasRegularizer:me(this.biasRegularizer),activityRegularizer:me(this.activityRegularizer),biasConstraint:We(this.biasConstraint)},e=super.getConfig();return Object.assign(t,e),t}},hu=class extends Pc{constructor(t,e){super(t,e),this.kernel=null,hu.verifyArgs(e),this.filters=e.filters,tr(this.filters,"filters"),this.kernelInitializer=de(e.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.kernelConstraint=Ue(e.kernelConstraint),this.kernelRegularizer=Ie(e.kernelRegularizer)}build(t){t=Qt(t);let e=this.dataFormat==="channelsFirst"?1:t.length-1;if(t[e]==null)throw new z(`The channel dimension of the input should be defined. Found ${t[e]}`);let n=t[e],o=this.kernelSize.concat([n,this.filters]);this.kernel=this.addWeight("kernel",o,null,this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint),this.useBias&&(this.bias=this.addWeight("bias",[this.filters],null,this.biasInitializer,this.biasRegularizer,!0,this.biasConstraint)),this.inputSpec=[{ndim:this.rank+2,axes:{[e]:n}}],this.built=!0}call(t,e){return B(()=>{t=Pt(t);let n,o=this.bias==null?null:this.bias.read(),s=my(this.activation.getClassName());if(s!=null&&this.rank===2)n=uD(t,this.kernel.read(),o,this.strides,this.padding,this.dataFormat,this.dilationRate,s);else{if(this.rank===1)n=G8(t,this.kernel.read(),o,this.strides[0],this.padding,this.dataFormat,this.dilationRate[0]);else if(this.rank===2)n=uD(t,this.kernel.read(),o,this.strides,this.padding,this.dataFormat,this.dilationRate);else if(this.rank===3)n=W8(t,this.kernel.read(),o,this.strides,this.padding,this.dataFormat,this.dilationRate);else throw new Nt("convolutions greater than 3D are not implemented yet.");this.activation!=null&&(n=this.activation.apply(n))}return n})}computeOutputShape(t){t=Qt(t);let e=[],n=this.dataFormat==="channelsLast"?t.slice(1,t.length-1):t.slice(2);for(let s=0;s<n.length;++s){let i=An(n[s],this.kernelSize[s],this.padding,this.strides[s],typeof this.dilationRate=="number"?this.dilationRate:this.dilationRate[s]);e.push(i)}let o=[t[0]];return this.dataFormat==="channelsLast"?(o=o.concat(e),o.push(this.filters)):(o.push(this.filters),o=o.concat(e)),o}getConfig(){let t={filters:this.filters,kernelInitializer:Ae(this.kernelInitializer),kernelRegularizer:me(this.kernelRegularizer),kernelConstraint:We(this.kernelConstraint)},e=super.getConfig();return Object.assign(t,e),t}static verifyArgs(t){if(!("filters"in t)||typeof t.filters!="number"||t.filters<1)throw new z(`Convolution layer expected config.filters to be a 'number' > 0 but got ${JSON.stringify(t.filters)}`)}},ml=class extends hu{constructor(t){super(2,t),ml.verifyArgs(t)}getConfig(){let t=super.getConfig();return delete t.rank,t}static verifyArgs(t){if(typeof t.kernelSize!="number"&&!py(t.kernelSize,"number",1,2))throw new z(`Conv2D expects config.kernelSize to be number or number[] with length 1 or 2, but received ${JSON.stringify(t.kernelSize)}.`)}};ml.className="Conv2D";et.registerClass(ml);var fl=class extends hu{constructor(t){super(3,t),fl.verifyArgs(t)}getConfig(){let t=super.getConfig();return delete t.rank,t}static verifyArgs(t){if(typeof t.kernelSize!="number"&&!(Array.isArray(t.kernelSize)&&(t.kernelSize.length===1||t.kernelSize.length===3)))throw new z(`Conv3D expects config.kernelSize to be number or [number, number, number], but received ${JSON.stringify(t.kernelSize)}.`)}};fl.className="Conv3D";et.registerClass(fl);var nf=class extends ml{constructor(t){if(super(t),this.inputSpec=[new Ce({ndim:4})],this.padding!=="same"&&this.padding!=="valid")throw new z(`Conv2DTranspose currently supports only padding modes 'same' and 'valid', but received padding mode ${this.padding}`)}build(t){if(t=Qt(t),t.length!==4)throw new z("Input should have rank 4; Received input shape: "+JSON.stringify(t));let e=this.dataFormat==="channelsFirst"?1:t.length-1;if(t[e]==null)throw new z("The channel dimension of the inputs should be defined. Found `None`.");let n=t[e],o=this.kernelSize.concat([this.filters,n]);this.kernel=this.addWeight("kernel",o,"float32",this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint),this.useBias&&(this.bias=this.addWeight("bias",[this.filters],"float32",this.biasInitializer,this.biasRegularizer,!0,this.biasConstraint)),this.inputSpec=[new Ce({ndim:4,axes:{[e]:n}})],this.built=!0}call(t,e){return B(()=>{let n=Pt(t);if(n.shape.length!==4)throw new z(`Conv2DTranspose.call() expects input tensor to be rank-4, but received a tensor of rank-${n.shape.length}`);let o=n.shape,s=o[0],i,a;this.dataFormat==="channelsFirst"?(i=2,a=3):(i=1,a=2);let u=o[i],l=o[a],c=this.kernelSize[0],p=this.kernelSize[1],m=this.strides[0],f=this.strides[1],d=pi(u,m,c,this.padding),h=pi(l,f,p,this.padding),g=[s,d,h,this.filters];this.dataFormat!=="channelsLast"&&(n=Mt(n,[0,2,3,1]));let y=tc(n,this.kernel.read(),g,this.strides,this.padding);return this.dataFormat!=="channelsLast"&&(y=Mt(y,[0,3,1,2])),this.bias!=null&&(y=xn(y,this.bias.read(),this.dataFormat)),this.activation!=null&&(y=this.activation.apply(y)),y})}computeOutputShape(t){t=Qt(t);let e=t.slice(),n,o,s;this.dataFormat==="channelsFirst"?(n=1,o=2,s=3):(n=3,o=1,s=2);let i=this.kernelSize[0],a=this.kernelSize[1],u=this.strides[0],l=this.strides[1];return e[n]=this.filters,e[o]=pi(e[o],u,i,this.padding),e[s]=pi(e[s],l,a,this.padding),e}getConfig(){let t=super.getConfig();return delete t.dilationRate,t}};nf.className="Conv2DTranspose";et.registerClass(nf);var of=class extends fl{constructor(t){if(super(t),this.inputSpec=[new Ce({ndim:5})],this.padding!=="same"&&this.padding!=="valid")throw new z(`Conv3DTranspose currently supports only padding modes 'same' and 'valid', but received padding mode ${this.padding}`)}build(t){if(t=Qt(t),t.length!==5)throw new z("Input should have rank 5; Received input shape: "+JSON.stringify(t));let e=this.dataFormat==="channelsFirst"?1:t.length-1;if(t[e]==null)throw new z("The channel dimension of the inputs should be defined. Found `None`.");let n=t[e],o=this.kernelSize.concat([this.filters,n]);this.kernel=this.addWeight("kernel",o,"float32",this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint),this.useBias&&(this.bias=this.addWeight("bias",[this.filters],"float32",this.biasInitializer,this.biasRegularizer,!0,this.biasConstraint)),this.inputSpec=[new Ce({ndim:5,axes:{[e]:n}})],this.built=!0}call(t,e){return B(()=>{let n=Pt(t);if(n.shape.length!==5)throw new z(`Conv3DTranspose.call() expects input tensor to be rank-4, but received a tensor of rank-${n.shape.length}`);let o=n.shape,s=o[0],i,a,u;this.dataFormat==="channelsFirst"?(u=2,i=3,a=4):(u=1,i=2,a=3);let l=o[u],c=o[i],p=o[a],m=this.kernelSize[0],f=this.kernelSize[1],d=this.kernelSize[2],h=this.strides[0],g=this.strides[1],y=this.strides[2],b=pi(l,h,m,this.padding),w=pi(c,g,f,this.padding),v=pi(p,y,d,this.padding),k=[s,b,w,v,this.filters];this.dataFormat!=="channelsLast"&&(n=Mt(n,[0,2,3,4,1]));let E=qI(n,this.kernel.read(),k,this.strides,this.padding);return this.dataFormat!=="channelsLast"&&(E=Mt(E,[0,4,1,2,3])),this.bias!==null&&(E=xn(E,this.bias.read(),this.dataFormat)),this.activation!==null&&(E=this.activation.apply(E)),E})}computeOutputShape(t){t=Qt(t);let e=t.slice(),n,o,s,i;this.dataFormat==="channelsFirst"?(n=1,o=2,s=3,i=4):(n=4,o=1,s=2,i=3);let a=this.kernelSize[0],u=this.kernelSize[1],l=this.kernelSize[2],c=this.strides[0],p=this.strides[1],m=this.strides[2];return e[n]=this.filters,e[o]=pi(e[o],c,a,this.padding),e[s]=pi(e[s],p,u,this.padding),e[i]=pi(e[i],m,l,this.padding),e}getConfig(){let t=super.getConfig();return delete t.dilationRate,t}};of.className="Conv3DTranspose";et.registerClass(of);var nb=class extends hu{constructor(t,e){if(super(t,e),this.DEFAULT_DEPTHWISE_INITIALIZER="glorotUniform",this.DEFAULT_POINTWISE_INITIALIZER="glorotUniform",this.depthwiseKernel=null,this.pointwiseKernel=null,e.filters==null)throw new z("The `filters` configuration field is required by SeparableConv, but is unspecified.");if(e.kernelInitializer!=null||e.kernelRegularizer!=null||e.kernelConstraint!=null)throw new z("Fields kernelInitializer, kernelRegularizer and kernelConstraint are invalid for SeparableConv2D. Use depthwiseInitializer, depthwiseRegularizer, depthwiseConstraint, pointwiseInitializer, pointwiseRegularizer and pointwiseConstraint instead.");if(e.padding!=null&&e.padding!=="same"&&e.padding!=="valid")throw new z(`SeparableConv${this.rank}D supports only padding modes: 'same' and 'valid', but received ${JSON.stringify(e.padding)}`);this.depthMultiplier=e.depthMultiplier==null?1:e.depthMultiplier,this.depthwiseInitializer=de(e.depthwiseInitializer||this.DEFAULT_DEPTHWISE_INITIALIZER),this.depthwiseRegularizer=Ie(e.depthwiseRegularizer),this.depthwiseConstraint=Ue(e.depthwiseConstraint),this.pointwiseInitializer=de(e.depthwiseInitializer||this.DEFAULT_POINTWISE_INITIALIZER),this.pointwiseRegularizer=Ie(e.pointwiseRegularizer),this.pointwiseConstraint=Ue(e.pointwiseConstraint)}build(t){if(t=Qt(t),t.length<this.rank+2)throw new z(`Inputs to SeparableConv${this.rank}D should have rank ${this.rank+2}, but received input shape: ${JSON.stringify(t)}`);let e=this.dataFormat==="channelsFirst"?1:t.length-1;if(t[e]==null||t[e]<0)throw new z(`The channel dimension of the inputs should be defined, but found ${JSON.stringify(t[e])}`);let n=t[e],o=this.kernelSize.concat([n,this.depthMultiplier]),s=[];for(let a=0;a<this.rank;++a)s.push(1);s.push(n*this.depthMultiplier,this.filters);let i=!0;this.depthwiseKernel=this.addWeight("depthwise_kernel",o,"float32",this.depthwiseInitializer,this.depthwiseRegularizer,i,this.depthwiseConstraint),this.pointwiseKernel=this.addWeight("pointwise_kernel",s,"float32",this.pointwiseInitializer,this.pointwiseRegularizer,i,this.pointwiseConstraint),this.useBias?this.bias=this.addWeight("bias",[this.filters],"float32",this.biasInitializer,this.biasRegularizer,i,this.biasConstraint):this.bias=null,this.inputSpec=[new Ce({ndim:this.rank+2,axes:{[e]:n}})],this.built=!0}call(t,e){return B(()=>{t=Pt(t);let n;if(this.rank===1)throw new Nt("1D separable convolution is not implemented yet.");return this.rank===2&&(this.dataFormat==="channelsFirst"&&(t=Mt(t,[0,2,3,1])),n=Oh(t,this.depthwiseKernel.read(),this.pointwiseKernel.read(),this.strides,this.padding,this.dilationRate,"NHWC")),this.useBias&&(n=xn(n,this.bias.read(),this.dataFormat)),this.activation!=null&&(n=this.activation.apply(n)),this.dataFormat==="channelsFirst"&&(n=Mt(n,[0,3,1,2])),n})}getConfig(){let t=super.getConfig();return delete t.rank,delete t.kernelInitializer,delete t.kernelRegularizer,delete t.kernelConstraint,t.depthwiseInitializer=Ae(this.depthwiseInitializer),t.pointwiseInitializer=Ae(this.pointwiseInitializer),t.depthwiseRegularizer=me(this.depthwiseRegularizer),t.pointwiseRegularizer=me(this.pointwiseRegularizer),t.depthwiseConstraint=We(this.depthwiseConstraint),t.pointwiseConstraint=We(this.pointwiseConstraint),t}};nb.className="SeparableConv";var sf=class extends nb{constructor(t){super(2,t)}};sf.className="SeparableConv2D";et.registerClass(sf);var gu=class extends hu{constructor(t){super(1,t),gu.verifyArgs(t),this.inputSpec=[{ndim:3}]}getConfig(){let t=super.getConfig();return delete t.rank,delete t.dataFormat,t}static verifyArgs(t){if(typeof t.kernelSize!="number"&&!py(t.kernelSize,"number",1,1))throw new z(`Conv1D expects config.kernelSize to be number or number[] with length 1, but received ${JSON.stringify(t.kernelSize)}.`)}};gu.className="Conv1D";et.registerClass(gu);var af=class extends Vt{constructor(t){super(t),typeof t.cropping=="number"?this.cropping=[[t.cropping,t.cropping],[t.cropping,t.cropping]]:typeof t.cropping[0]=="number"?this.cropping=[[t.cropping[0],t.cropping[0]],[t.cropping[1],t.cropping[1]]]:this.cropping=t.cropping,this.dataFormat=t.dataFormat===void 0?"channelsLast":t.dataFormat,this.inputSpec=[{ndim:4}]}computeOutputShape(t){return this.dataFormat==="channelsFirst"?[t[0],t[1],t[2]-this.cropping[0][0]-this.cropping[0][1],t[3]-this.cropping[1][0]-this.cropping[1][1]]:[t[0],t[1]-this.cropping[0][0]-this.cropping[0][1],t[2]-this.cropping[1][0]-this.cropping[1][1],t[3]]}call(t,e){return B(()=>{if(t=Pt(t),this.dataFormat==="channelsLast"){let n=Xh(t,this.cropping[0][0],t.shape[1]-this.cropping[0][0]-this.cropping[0][1],2);return Xh(n,this.cropping[1][0],t.shape[2]-this.cropping[1][1]-this.cropping[1][0],3)}else{let n=Xh(t,this.cropping[0][0],t.shape[2]-this.cropping[0][0]-this.cropping[0][1],3);return Xh(n,this.cropping[1][0],t.shape[3]-this.cropping[1][1]-this.cropping[1][0],4)}})}getConfig(){let t={cropping:this.cropping,dataFormat:this.dataFormat},e=super.getConfig();return Object.assign(t,e),t}};af.className="Cropping2D";et.registerClass(af);var lf=class extends Vt{constructor(t){super(t),this.DEFAULT_SIZE=[2,2],this.inputSpec=[{ndim:4}],this.size=t.size==null?this.DEFAULT_SIZE:t.size,this.dataFormat=t.dataFormat==null?"channelsLast":t.dataFormat,Le(this.dataFormat),this.interpolation=t.interpolation==null?"nearest":t.interpolation,x$(this.interpolation)}computeOutputShape(t){if(this.dataFormat==="channelsFirst"){let e=t[2]==null?null:this.size[0]*t[2],n=t[3]==null?null:this.size[1]*t[3];return[t[0],t[1],e,n]}else{let e=t[1]==null?null:this.size[0]*t[1],n=t[2]==null?null:this.size[1]*t[2];return[t[0],e,n,t[3]]}}call(t,e){return B(()=>{let n=Pt(t),o=n.shape;if(this.dataFormat==="channelsFirst"){n=Mt(n,[0,2,3,1]);let s=this.size[0]*o[2],i=this.size[1]*o[3],a=this.interpolation==="nearest"?dn.resizeNearestNeighbor(n,[s,i]):dn.resizeBilinear(n,[s,i]);return Mt(a,[0,3,1,2])}else{let s=this.size[0]*o[1],i=this.size[1]*o[2];return this.interpolation==="nearest"?dn.resizeNearestNeighbor(n,[s,i]):dn.resizeBilinear(n,[s,i])}})}getConfig(){let t={size:this.size,dataFormat:this.dataFormat,interpolation:this.interpolation},e=super.getConfig();return Object.assign(t,e),t}};lf.className="UpSampling2D";et.registerClass(lf);function U8(r,t,e=[1,1],n="valid",o,s){return B(()=>{o==null&&(o=gn()),Le(o);let i=sg(r,o);if(r.rank!==4)throw new z(`Input for depthwiseConv2d is required to be 4-D, but is instead ${r.rank}-D`);if(t.rank!==4)throw new z(`depthwiseKernel is required to be 4-D, but is instead ${t.rank}-D`);return i=Zs(i,t,e,n==="same"?"same":"valid","NHWC",s),o==="channelsFirst"&&(i=Mt(i,[0,3,1,2])),i})}var uf=class extends Pc{constructor(t){super(2,t),this.depthwiseKernel=null,this.depthMultiplier=t.depthMultiplier==null?1:t.depthMultiplier,this.depthwiseInitializer=de(t.depthwiseInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.depthwiseConstraint=Ue(t.depthwiseConstraint),this.depthwiseRegularizer=Ie(t.depthwiseRegularizer)}build(t){if(t=Qt(t),t.length<4)throw new z(`Inputs to DepthwiseConv2D should have rank 4. Received input shape: ${JSON.stringify(t)}.`);let e=this.dataFormat==="channelsFirst"?1:3;if(t[e]==null||t[e]<0)throw new z(`The channel dimension of the inputs to DepthwiseConv2D should be defined, but is not (${t[e]}).`);let n=t[e],o=[this.kernelSize[0],this.kernelSize[1],n,this.depthMultiplier];this.depthwiseKernel=this.addWeight("depthwise_kernel",o,null,this.depthwiseInitializer,this.depthwiseRegularizer,!0,this.depthwiseConstraint),this.useBias?this.bias=this.addWeight("bias",[n*this.depthMultiplier],null,this.biasInitializer,this.biasRegularizer,!0,this.biasConstraint):this.bias=null,this.built=!0}call(t,e){return B(()=>{t=Pt(t);let n=U8(t,this.depthwiseKernel.read(),this.strides,this.padding,this.dataFormat,null);return this.useBias&&(n=xn(n,this.bias.read(),this.dataFormat)),this.activation!=null&&(n=this.activation.apply(n)),n})}computeOutputShape(t){t=Qt(t);let e=this.dataFormat==="channelsFirst"?t[2]:t[1],n=this.dataFormat==="channelsFirst"?t[3]:t[2],o=this.dataFormat==="channelsFirst"?t[1]*this.depthMultiplier:t[3]*this.depthMultiplier,s=An(e,this.kernelSize[0],this.padding,this.strides[0]),i=An(n,this.kernelSize[1],this.padding,this.strides[1]);return this.dataFormat==="channelsFirst"?[t[0],o,s,i]:[t[0],s,i,o]}getConfig(){let t=super.getConfig();return t.depthMultiplier=this.depthMultiplier,t.depthwiseInitializer=Ae(this.depthwiseInitializer),t.depthwiseRegularizer=me(this.depthwiseRegularizer),t.depthwiseConstraint=We(this.depthwiseRegularizer),t}};uf.className="DepthwiseConv2D";et.registerClass(uf);function XS(r,t,e,n){if(Array.isArray(r)){if(t!=null||e!=null)throw new z("When inputs is an array, neither initialState or constants should be provided");n!=null&&(e=r.slice(r.length-n,r.length),r=r.slice(0,r.length-n)),r.length>1&&(t=r.slice(1,r.length)),r=r[0]}function o(s){return s==null||Array.isArray(s)?s:[s]}return t=o(t),e=o(e),{inputs:r,initialState:t,constants:e}}function YS(r,t,e,n=!1,o,s,i=!1,a=!1){return B(()=>{let u=t.shape.length;if(u<3)throw new z(`Input should be at least 3D, but is ${u}D.`);let l=[1,0].concat(Jr(2,u));if(t=Mt(t,l),s!=null)throw new Nt("The rnn() functoin of the deeplearn.js backend does not support constants yet.");i&&console.warn("Backend rnn(): the unroll = true option is not applicable to the imperative deeplearn.js backend."),o!=null&&(o=Z(Z(o,"bool"),"float32"),o.rank===u-1&&(o=fr(o,-1)),o=Mt(o,l)),n&&(t=ir(t,0),o!=null&&(o=ir(o,0)));let c=[],p,m=e,f=t.shape[0],d=vr(t),h;o!=null&&(h=vr(o));for(let y=0;y<f;++y){let b=d[y],w=B(()=>r(b,m));if(o==null)p=w[0],m=w[1];else{let v=B(()=>{let k=h[y],E=lt(dr(k),k),$=J(M(w[0],k),M(m[0],E)),D=m.map((F,P)=>J(M(w[1][P],k),M(F,E)));return{output:$,newStates:D}});p=v.output,m=v.newStates}a&&c.push(p)}let g;return a&&(g=Ze(c,1)),[p,g,m]})}var $n=class extends Vt{constructor(t){super(t);let e;if(t.cell==null)throw new z("cell property is missing for the constructor of RNN.");if(Array.isArray(t.cell)?e=new Bc({cells:t.cell}):e=t.cell,e.stateSize==null)throw new z("The RNN cell should have an attribute `stateSize` (tuple of integers, one integer per RNN state).");this.cell=e,this.returnSequences=t.returnSequences==null?!1:t.returnSequences,this.returnState=t.returnState==null?!1:t.returnState,this.goBackwards=t.goBackwards==null?!1:t.goBackwards,this._stateful=t.stateful==null?!1:t.stateful,this.unroll=t.unroll==null?!1:t.unroll,this.supportsMasking=!0,this.inputSpec=[new Ce({ndim:3})],this.stateSpec=null,this.states_=null,this.numConstants=null,this.keptStates=[]}getStates(){if(this.states_==null){let t=Array.isArray(this.cell.stateSize)?this.cell.stateSize.length:1;return Jr(0,t).map(e=>null)}else return this.states_}setStates(t){this.states_=t}computeOutputShape(t){vy(t)&&(t=t[0]),t=t;let e=this.cell.stateSize;Array.isArray(e)||(e=[e]);let n=e[0],o;if(this.returnSequences?o=[t[0],t[1],n]:o=[t[0],n],this.returnState){let s=[];for(let i of e)s.push([t[0],i]);return[o].concat(s)}else return o}computeMask(t,e){return B(()=>{Array.isArray(e)&&(e=e[0]);let n=this.returnSequences?e:null;if(this.returnState){let o=this.states.map(s=>null);return[n].concat(o)}else return n})}get states(){if(this.states_==null){let t=Array.isArray(this.cell.stateSize)?this.cell.stateSize.length:1,e=[];for(let n=0;n<t;++n)e.push(null);return e}else return this.states_}set states(t){this.states_=t}build(t){if(this.numConstants!=null)throw new Nt("Constants support is not implemented in RNN yet.");vy(t)&&(t=t[0]),t=t;let n=this.stateful?t[0]:null,o=t.slice(2);this.inputSpec[0]=new Ce({shape:[n,null,...o]});let s=[t[0]].concat(t.slice(2));this.cell.build(s);let i;if(Array.isArray(this.cell.stateSize)?i=this.cell.stateSize:i=[this.cell.stateSize],this.stateSpec!=null){if(!x.arraysEqual(this.stateSpec.map(a=>a.shape[a.shape.length-1]),i))throw new z(`An initialState was passed that is not compatible with cell.stateSize. Received stateSpec=${this.stateSpec}; However cell.stateSize is ${this.cell.stateSize}`)}else this.stateSpec=i.map(a=>new Ce({shape:[null,a]}));this.stateful&&this.resetStates()}resetStates(t,e=!1){B(()=>{if(!this.stateful)throw new En("Cannot call resetStates() on an RNN Layer that is not stateful.");let n=this.inputSpec[0].shape[0];if(n==null)throw new z("If an RNN is stateful, it needs to know its batch size. Specify the batch size of your input tensors: \n- If using a Sequential model, specify the batch size by passing a `batchInputShape` option to your first layer.\n- If using the functional API, specify the batch size by passing a `batchShape` option to your Input layer.");if(this.states_==null)Array.isArray(this.cell.stateSize)?this.states_=this.cell.stateSize.map(o=>we([n,o])):this.states_=[we([n,this.cell.stateSize])];else if(t==null)_t(this.states_),this.keptStates!=null&&(_t(this.keptStates),this.keptStates=[]),Array.isArray(this.cell.stateSize)?this.states_=this.cell.stateSize.map(o=>we([n,o])):this.states_[0]=we([n,this.cell.stateSize]);else{if(Array.isArray(t)||(t=[t]),t.length!==this.states_.length)throw new z(`Layer ${this.name} expects ${this.states_.length} state(s), but it received ${t.length} state value(s). Input received: ${t}`);e===!0?this.keptStates.push(this.states_.slice()):_t(this.states_);for(let o=0;o<this.states_.length;++o){let s=t[o],i=Array.isArray(this.cell.stateSize)?this.cell.stateSize[o]:this.cell.stateSize,a=[n,i];if(!x.arraysEqual(s.shape,a))throw new z(`State ${o} is incompatible with layer ${this.name}: expected shape=${a}, received shape=${s.shape}`);this.states_[o]=s}}this.states_=this.states_.map(o=>Pe(o.clone()))})}apply(t,e){let n=e==null?null:e.initialState,o=e==null?null:e.constants;e==null&&(e={});let s=XS(t,n,o,this.numConstants);t=s.inputs,n=s.initialState,o=s.constants;let i=[],a=[];if(n!=null){e.initialState=n,i=i.concat(n),this.stateSpec=[];for(let l of n)this.stateSpec.push(new Ce({shape:l.shape}));a=a.concat(this.stateSpec)}if(o!=null&&(e.constants=o,i=i.concat(o),this.numConstants=o.length),i[0]instanceof Qr){let l=[t].concat(i),c=this.inputSpec.concat(a),p=this.inputSpec;this.inputSpec=c;let m=super.apply(l,e);return this.inputSpec=p,m}else return super.apply(t,e)}call(t,e){return B(()=>{let n=e==null?null:e.mask,o=e==null?null:e.training,s=e==null?null:e.initialState;t=Pt(t),s==null&&(this.stateful?s=this.states_:s=this.getInitialState(t));let i=Array.isArray(this.cell.stateSize)?this.cell.stateSize.length:1;if(s.length!==i)throw new z(`RNN Layer has ${i} state(s) but was passed ${s.length} initial state(s).`);this.unroll&&console.warn("Ignoring unroll = true for RNN layer, due to imperative backend.");let a={training:o},l=YS((d,h)=>{let g=this.cell.call([d].concat(h),a);return[g[0],g.slice(1)]},t,s,this.goBackwards,n,null,this.unroll,this.returnSequences),c=l[0],p=l[1],m=l[2];this.stateful&&this.resetStates(m,o);let f=this.returnSequences?p:c;return this.returnState?[f].concat(m):f})}getInitialState(t){return B(()=>{let e=we(t.shape);return e=mt(e,[1,2]),e=ul(e),Array.isArray(this.cell.stateSize)?this.cell.stateSize.map(n=>n>1?yy(e,[1,n]):e):this.cell.stateSize>1?[yy(e,[1,this.cell.stateSize])]:[e]})}get trainableWeights(){return this.trainable?this.cell.trainableWeights:[]}get nonTrainableWeights(){return this.trainable?this.cell.nonTrainableWeights:this.cell.weights}setFastWeightInitDuringBuild(t){super.setFastWeightInitDuringBuild(t),this.cell!=null&&this.cell.setFastWeightInitDuringBuild(t)}getConfig(){let t=super.getConfig(),e={returnSequences:this.returnSequences,returnState:this.returnState,goBackwards:this.goBackwards,stateful:this.stateful,unroll:this.unroll};this.numConstants!=null&&(e.numConstants=this.numConstants);let n=this.cell.getConfig();return this.getClassName()===$n.className&&(e.cell={className:this.cell.getClassName(),config:n}),Object.assign({},n,t,e)}static fromConfig(t,e,n={}){let o=e.cell,s=vn(o,n);return new t(Object.assign(e,{cell:s}))}};$n.className="RNN";et.registerClass($n);var dl=class extends Vt{},Lc=class extends dl{constructor(t){super(t),this.DEFAULT_ACTIVATION="tanh",this.DEFAULT_KERNEL_INITIALIZER="glorotNormal",this.DEFAULT_RECURRENT_INITIALIZER="orthogonal",this.DEFAULT_BIAS_INITIALIZER="zeros",this.units=t.units,tr(this.units,"units"),this.activation=ci(t.activation==null?this.DEFAULT_ACTIVATION:t.activation),this.useBias=t.useBias==null?!0:t.useBias,this.kernelInitializer=de(t.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.recurrentInitializer=de(t.recurrentInitializer||this.DEFAULT_RECURRENT_INITIALIZER),this.biasInitializer=de(t.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.kernelRegularizer=Ie(t.kernelRegularizer),this.recurrentRegularizer=Ie(t.recurrentRegularizer),this.biasRegularizer=Ie(t.biasRegularizer),this.kernelConstraint=Ue(t.kernelConstraint),this.recurrentConstraint=Ue(t.recurrentConstraint),this.biasConstraint=Ue(t.biasConstraint),this.dropout=Sc([1,ai([0,t.dropout==null?0:t.dropout])]),this.recurrentDropout=Sc([1,ai([0,t.recurrentDropout==null?0:t.recurrentDropout])]),this.dropoutFunc=t.dropoutFunc,this.stateSize=this.units,this.dropoutMask=null,this.recurrentDropoutMask=null}build(t){t=Qt(t),this.kernel=this.addWeight("kernel",[t[t.length-1],this.units],null,this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint),this.recurrentKernel=this.addWeight("recurrent_kernel",[this.units,this.units],null,this.recurrentInitializer,this.recurrentRegularizer,!0,this.recurrentConstraint),this.useBias?this.bias=this.addWeight("bias",[this.units],null,this.biasInitializer,this.biasRegularizer,!0,this.biasConstraint):this.bias=null,this.built=!0}call(t,e){return B(()=>{if(t=t,t.length!==2)throw new z(`SimpleRNNCell expects 2 input Tensors, got ${t.length}.`);let n=t[1];t=t[0];let o=e.training==null?!1:e.training;0<this.dropout&&this.dropout<1&&this.dropoutMask==null&&(this.dropoutMask=gl({ones:()=>dr(t),rate:this.dropout,training:o,dropoutFunc:this.dropoutFunc})),0<this.recurrentDropout&&this.recurrentDropout<1&&this.recurrentDropoutMask==null&&(this.recurrentDropoutMask=gl({ones:()=>dr(n),rate:this.recurrentDropout,training:o,dropoutFunc:this.dropoutFunc}));let s,i=this.dropoutMask,a=this.recurrentDropoutMask;i!=null?s=Eo(M(t,i),this.kernel.read()):s=Eo(t,this.kernel.read()),this.bias!=null&&(s=xn(s,this.bias.read())),a!=null&&(n=M(n,a));let u=J(s,Eo(n,this.recurrentKernel.read()));return this.activation!=null&&(u=this.activation.apply(u)),[u,u]})}getConfig(){let t=super.getConfig(),e={units:this.units,activation:ui(this.activation),useBias:this.useBias,kernelInitializer:Ae(this.kernelInitializer),recurrentInitializer:Ae(this.recurrentInitializer),biasInitializer:Ae(this.biasInitializer),kernelRegularizer:me(this.kernelRegularizer),recurrentRegularizer:me(this.recurrentRegularizer),biasRegularizer:me(this.biasRegularizer),activityRegularizer:me(this.activityRegularizer),kernelConstraint:We(this.kernelConstraint),recurrentConstraint:We(this.recurrentConstraint),biasConstraint:We(this.biasConstraint),dropout:this.dropout,recurrentDropout:this.recurrentDropout};return Object.assign({},t,e)}};Lc.className="SimpleRNNCell";et.registerClass(Lc);var cf=class extends $n{constructor(t){t.cell=new Lc(t),super(t)}call(t,e){return B(()=>{this.cell.dropoutMask!=null&&(_t(this.cell.dropoutMask),this.cell.dropoutMask=null),this.cell.recurrentDropoutMask!=null&&(_t(this.cell.recurrentDropoutMask),this.cell.recurrentDropoutMask=null);let n=e==null?null:e.mask,o=e==null?null:e.training,s=e==null?null:e.initialState;return super.call(t,{mask:n,training:o,initialState:s})})}static fromConfig(t,e){return new t(e)}};cf.className="SimpleRNN";et.registerClass(cf);var zc=class extends dl{constructor(t){if(super(t),this.DEFAULT_ACTIVATION="tanh",this.DEFAULT_RECURRENT_ACTIVATION="hardSigmoid",this.DEFAULT_KERNEL_INITIALIZER="glorotNormal",this.DEFAULT_RECURRENT_INITIALIZER="orthogonal",this.DEFAULT_BIAS_INITIALIZER="zeros",t.resetAfter)throw new z("GRUCell does not support reset_after parameter set to true.");this.units=t.units,tr(this.units,"units"),this.activation=ci(t.activation===void 0?this.DEFAULT_ACTIVATION:t.activation),this.recurrentActivation=ci(t.recurrentActivation===void 0?this.DEFAULT_RECURRENT_ACTIVATION:t.recurrentActivation),this.useBias=t.useBias==null?!0:t.useBias,this.kernelInitializer=de(t.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.recurrentInitializer=de(t.recurrentInitializer||this.DEFAULT_RECURRENT_INITIALIZER),this.biasInitializer=de(t.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.kernelRegularizer=Ie(t.kernelRegularizer),this.recurrentRegularizer=Ie(t.recurrentRegularizer),this.biasRegularizer=Ie(t.biasRegularizer),this.kernelConstraint=Ue(t.kernelConstraint),this.recurrentConstraint=Ue(t.recurrentConstraint),this.biasConstraint=Ue(t.biasConstraint),this.dropout=Sc([1,ai([0,t.dropout==null?0:t.dropout])]),this.recurrentDropout=Sc([1,ai([0,t.recurrentDropout==null?0:t.recurrentDropout])]),this.dropoutFunc=t.dropoutFunc,this.implementation=t.implementation,this.stateSize=this.units,this.dropoutMask=null,this.recurrentDropoutMask=null}build(t){t=Qt(t);let e=t[t.length-1];this.kernel=this.addWeight("kernel",[e,this.units*3],null,this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint),this.recurrentKernel=this.addWeight("recurrent_kernel",[this.units,this.units*3],null,this.recurrentInitializer,this.recurrentRegularizer,!0,this.recurrentConstraint),this.useBias?this.bias=this.addWeight("bias",[this.units*3],null,this.biasInitializer,this.biasRegularizer,!0,this.biasConstraint):this.bias=null,this.built=!0}call(t,e){return B(()=>{if(t=t,t.length!==2)throw new z(`GRUCell expects 2 input Tensors (inputs, h, c), got ${t.length}.`);let n=e.training==null?!1:e.training,o=t[1];t=t[0],0<this.dropout&&this.dropout<1&&this.dropoutMask==null&&(this.dropoutMask=gl({ones:()=>dr(t),rate:this.dropout,training:n,count:3,dropoutFunc:this.dropoutFunc})),0<this.recurrentDropout&&this.recurrentDropout<1&&this.recurrentDropoutMask==null&&(this.recurrentDropoutMask=gl({ones:()=>dr(o),rate:this.recurrentDropout,training:n,count:3,dropoutFunc:this.dropoutFunc}));let s=this.dropoutMask,i=this.recurrentDropoutMask,a,u,l;0<this.dropout&&this.dropout<1&&(t=M(t,s[0]));let c=Eo(t,this.kernel.read());this.useBias&&(c=xn(c,this.bias.read())),0<this.recurrentDropout&&this.recurrentDropout<1&&(o=M(o,i[0]));let p=this.recurrentKernel.read(),[m,f]=ur(p,[2*this.units,this.units],p.rank-1),d=Eo(o,m),[h,g,y]=ur(c,3,c.rank-1),[b,w]=ur(d,2,d.rank-1);a=this.recurrentActivation.apply(J(h,b)),u=this.recurrentActivation.apply(J(g,w));let v=Eo(M(u,o),f);l=this.activation.apply(J(y,v));let k=J(M(a,o),M(J(1,Zt(a)),l));return[k,k]})}getConfig(){let t=super.getConfig(),e={units:this.units,activation:ui(this.activation),recurrentActivation:ui(this.recurrentActivation),useBias:this.useBias,kernelInitializer:Ae(this.kernelInitializer),recurrentInitializer:Ae(this.recurrentInitializer),biasInitializer:Ae(this.biasInitializer),kernelRegularizer:me(this.kernelRegularizer),recurrentRegularizer:me(this.recurrentRegularizer),biasRegularizer:me(this.biasRegularizer),activityRegularizer:me(this.activityRegularizer),kernelConstraint:We(this.kernelConstraint),recurrentConstraint:We(this.recurrentConstraint),biasConstraint:We(this.biasConstraint),dropout:this.dropout,recurrentDropout:this.recurrentDropout,implementation:this.implementation,resetAfter:!1};return Object.assign({},t,e)}};zc.className="GRUCell";et.registerClass(zc);var pf=class extends $n{constructor(t){t.implementation===0&&console.warn("`implementation=0` has been deprecated, and now defaults to `implementation=1`. Please update your layer call."),t.cell=new zc(t),super(t)}call(t,e){return B(()=>{this.cell.dropoutMask!=null&&(_t(this.cell.dropoutMask),this.cell.dropoutMask=null),this.cell.recurrentDropoutMask!=null&&(_t(this.cell.recurrentDropoutMask),this.cell.recurrentDropoutMask=null);let n=e==null?null:e.mask,o=e==null?null:e.training,s=e==null?null:e.initialState;return super.call(t,{mask:n,training:o,initialState:s})})}static fromConfig(t,e){return e.implmentation===0&&(e.implementation=1),new t(e)}};pf.className="GRU";et.registerClass(pf);var hl=class extends dl{constructor(t){super(t),this.DEFAULT_ACTIVATION="tanh",this.DEFAULT_RECURRENT_ACTIVATION="hardSigmoid",this.DEFAULT_KERNEL_INITIALIZER="glorotNormal",this.DEFAULT_RECURRENT_INITIALIZER="orthogonal",this.DEFAULT_BIAS_INITIALIZER="zeros",this.units=t.units,tr(this.units,"units"),this.activation=ci(t.activation===void 0?this.DEFAULT_ACTIVATION:t.activation),this.recurrentActivation=ci(t.recurrentActivation===void 0?this.DEFAULT_RECURRENT_ACTIVATION:t.recurrentActivation),this.useBias=t.useBias==null?!0:t.useBias,this.kernelInitializer=de(t.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.recurrentInitializer=de(t.recurrentInitializer||this.DEFAULT_RECURRENT_INITIALIZER),this.biasInitializer=de(t.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.unitForgetBias=t.unitForgetBias,this.kernelRegularizer=Ie(t.kernelRegularizer),this.recurrentRegularizer=Ie(t.recurrentRegularizer),this.biasRegularizer=Ie(t.biasRegularizer),this.kernelConstraint=Ue(t.kernelConstraint),this.recurrentConstraint=Ue(t.recurrentConstraint),this.biasConstraint=Ue(t.biasConstraint),this.dropout=Sc([1,ai([0,t.dropout==null?0:t.dropout])]),this.recurrentDropout=Sc([1,ai([0,t.recurrentDropout==null?0:t.recurrentDropout])]),this.dropoutFunc=t.dropoutFunc,this.implementation=t.implementation,this.stateSize=[this.units,this.units],this.dropoutMask=null,this.recurrentDropoutMask=null}build(t){var e;t=Qt(t);let n=t[t.length-1];this.kernel=this.addWeight("kernel",[n,this.units*4],null,this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint),this.recurrentKernel=this.addWeight("recurrent_kernel",[this.units,this.units*4],null,this.recurrentInitializer,this.recurrentRegularizer,!0,this.recurrentConstraint);let o;if(this.useBias){if(this.unitForgetBias){let s=this.biasInitializer,i=this.units;o=new(e=class extends bn{apply(u,l){let c=s.apply([i]),p=new pu().apply([i]),m=s.apply([i*2]);return OS(OS(c,p),m)}},e.className="CustomInit",e)}else o=this.biasInitializer;this.bias=this.addWeight("bias",[this.units*4],null,o,this.biasRegularizer,!0,this.biasConstraint)}else this.bias=null;this.built=!0}call(t,e){return B(()=>{let n=e.training==null?!1:e.training;if(t=t,t.length!==3)throw new z(`LSTMCell expects 3 input Tensors (inputs, h, c), got ${t.length}.`);let o=t[1],s=t[2];t=t[0],0<this.dropout&&this.dropout<1&&this.dropoutMask==null&&(this.dropoutMask=gl({ones:()=>dr(t),rate:this.dropout,training:n,count:4,dropoutFunc:this.dropoutFunc})),0<this.recurrentDropout&&this.recurrentDropout<1&&this.recurrentDropoutMask==null&&(this.recurrentDropoutMask=gl({ones:()=>dr(o),rate:this.recurrentDropout,training:n,count:4,dropoutFunc:this.dropoutFunc}));let i=this.dropoutMask,a=this.recurrentDropoutMask,u,l,c,p;0<this.dropout&&this.dropout<1&&(t=M(t,i[0]));let m=Eo(t,this.kernel.read());0<this.recurrentDropout&&this.recurrentDropout<1&&(o=M(o,a[0])),m=J(m,Eo(o,this.recurrentKernel.read())),this.useBias&&(m=xn(m,this.bias.read()));let[f,d,h,g]=ur(m,4,m.rank-1);u=this.recurrentActivation.apply(f),l=this.recurrentActivation.apply(d),c=J(M(l,s),M(u,this.activation.apply(h))),p=this.recurrentActivation.apply(g);let y=M(p,this.activation.apply(c));return[y,y,c]})}getConfig(){let t=super.getConfig(),e={units:this.units,activation:ui(this.activation),recurrentActivation:ui(this.recurrentActivation),useBias:this.useBias,kernelInitializer:Ae(this.kernelInitializer),recurrentInitializer:Ae(this.recurrentInitializer),biasInitializer:Ae(this.biasInitializer),unitForgetBias:this.unitForgetBias,kernelRegularizer:me(this.kernelRegularizer),recurrentRegularizer:me(this.recurrentRegularizer),biasRegularizer:me(this.biasRegularizer),activityRegularizer:me(this.activityRegularizer),kernelConstraint:We(this.kernelConstraint),recurrentConstraint:We(this.recurrentConstraint),biasConstraint:We(this.biasConstraint),dropout:this.dropout,recurrentDropout:this.recurrentDropout,implementation:this.implementation};return Object.assign({},t,e)}};hl.className="LSTMCell";et.registerClass(hl);var mf=class extends $n{constructor(t){t.implementation===0&&console.warn("`implementation=0` has been deprecated, and now defaults to `implementation=1`. Please update your layer call."),t.cell=new hl(t),super(t)}call(t,e){return B(()=>{this.cell.dropoutMask!=null&&(_t(this.cell.dropoutMask),this.cell.dropoutMask=null),this.cell.recurrentDropoutMask!=null&&(_t(this.cell.recurrentDropoutMask),this.cell.recurrentDropoutMask=null);let n=e==null?null:e.mask,o=e==null?null:e.training,s=e==null?null:e.initialState;return super.call(t,{mask:n,training:o,initialState:s})})}static fromConfig(t,e){return e.implmentation===0&&(e.implementation=1),new t(e)}};mf.className="LSTM";et.registerClass(mf);var Bc=class extends dl{constructor(t){super(t),this.cells=t.cells}get stateSize(){let t=[];for(let e of this.cells.slice().reverse())Array.isArray(e.stateSize)?t.push(...e.stateSize):t.push(e.stateSize);return t}call(t,e){return B(()=>{t=t;let n=t.slice(1),o=[];for(let a of this.cells.slice().reverse())Array.isArray(a.stateSize)?o.push(n.splice(0,a.stateSize.length)):o.push(n.splice(0,1));o.reverse();let s=[],i;for(let a=0;a<this.cells.length;++a){let u=this.cells[a];n=o[a],a===0?i=[t[0]].concat(n):i=[i[0]].concat(n),i=u.call(i,e),s.push(i.slice(1))}n=[];for(let a of s.slice().reverse())n.push(...a);return[i[0]].concat(n)})}build(t){vy(t)&&(t=t[0]),t=t;let e;this.cells.forEach((n,o)=>{ii(`RNNCell_${o}`,()=>{n.build(t),Array.isArray(n.stateSize)?e=n.stateSize[0]:e=n.stateSize,t=[t[0],e]})}),this.built=!0}getConfig(){let t=super.getConfig(),e=s=>({className:s.getClassName(),config:s.getConfig()}),o={cells:this.cells.map(e)};return Object.assign({},t,o)}static fromConfig(t,e,n={}){let o=[];for(let s of e.cells)o.push(vn(s,n));return new t({cells:o})}get trainableWeights(){if(!this.trainable)return[];let t=[];for(let e of this.cells)t.push(...e.trainableWeights);return t}get nonTrainableWeights(){let t=[];for(let e of this.cells)t.push(...e.nonTrainableWeights);if(!this.trainable){let e=[];for(let n of this.cells)e.push(...n.trainableWeights);return e.concat(t)}return t}getWeights(){let t=[];for(let e of this.cells)t.push(...e.weights);return Zh(t)}setWeights(t){let e=[];for(let n of this.cells){let o=n.weights.length,s=t.splice(o);for(let i=0;i<n.weights.length;++i)e.push([n.weights[i],s[i]])}Bm(e)}};Bc.className="StackedRNNCells";et.registerClass(Bc);function gl(r){let{ones:t,rate:e,training:n=!1,count:o=1,dropoutFunc:s}=r,i=()=>s!=null?s(t(),e):wy(t(),e),a=()=>cu(i,t,n);return!o||o<=1?Pe(a().clone()):Array(o).fill(void 0).map(a).map(l=>Pe(l.clone()))}var H8=function(r,t){var e={};for(var n in r)Object.prototype.hasOwnProperty.call(r,n)&&t.indexOf(n)<0&&(e[n]=r[n]);if(r!=null&&typeof Object.getOwnPropertySymbols=="function")for(var o=0,n=Object.getOwnPropertySymbols(r);o<n.length;o++)t.indexOf(n[o])<0&&Object.prototype.propertyIsEnumerable.call(r,n[o])&&(e[n[o]]=r[n[o]]);return e};var ob=class extends $n{constructor(t){if(t.unroll)throw new Nt("Unrolling is not possible with convolutional RNNs.");if(Array.isArray(t.cell))throw new Nt("It is not possible at the moment to stack convolutional cells.");super(t),this.inputSpec=[new Ce({ndim:5})]}call(t,e){return B(()=>{if(this.cell.dropoutMask!=null&&(_t(this.cell.dropoutMask),this.cell.dropoutMask=null),this.cell.recurrentDropoutMask!=null&&(_t(this.cell.recurrentDropoutMask),this.cell.recurrentDropoutMask=null),e&&e.constants)throw new z("ConvRNN2D cell does not support constants");let n=e==null?null:e.mask,o=e==null?null:e.training,s=e==null?null:e.initialState;return super.call(t,{mask:n,training:o,initialState:s})})}computeOutputShape(t){let e=this.computeSingleOutputShape(t);return this.returnSequences||(e=[e[0],...e.slice(2)]),this.returnState&&(e=[e,...Array(2).fill([t[0],...e.slice(-3)])]),e}getInitialState(t){return B(()=>{let{stateSize:e}=this.cell,n=t.shape,o=this.computeSingleOutputShape(n),s=[o[0],...o.slice(2)],i=we(s);return Array.isArray(e)?Array(e.length).fill(i):[i]})}resetStates(t,e=!1){B(()=>{if(!this.stateful)throw new En("Cannot call resetStates() on an RNN Layer that is not stateful.");let n=this.inputSpec[0].shape,o=this.computeSingleOutputShape(n),s=[o[0],...o.slice(2)];if(n[0]==null)throw new z("If an RNN is stateful, it needs to know its batch size. Specify the batch size of your input tensors: \n- If using a Sequential model, specify the batch size by passing a `batchInputShape` option to your first layer.\n- If using the functional API, specify the batch size by passing a `batchShape` option to your Input layer.");if(this.getStates()==null)Array.isArray(this.cell.stateSize)?this.states_=this.cell.stateSize.map(()=>we(s)):this.states_=[we(s)];else if(t==null)_t(this.states_),this.keptStates!=null&&(_t(this.keptStates),this.keptStates=[]),Array.isArray(this.cell.stateSize)?this.states_=this.cell.stateSize.map(()=>we(s)):this.states_[0]=we(s);else{if(Array.isArray(t)||(t=[t]),t.length!==this.states_.length)throw new z(`Layer ${this.name} expects ${this.states_.length} state(s), but it received ${t.length} state value(s). Input received: ${t}`);e?this.keptStates.push(this.states_.slice()):_t(this.states_);for(let a=0;a<this.states_.length;++a){let u=t[a],l=s;if(!x.arraysEqual(u.shape,l))throw new z(`State ${a} is incompatible with layer ${this.name}: expected shape=${l}, received shape=${u.shape}`);this.states_[a]=u}}this.states_=this.states_.map(a=>Pe(a.clone()))})}computeSingleOutputShape(t){let{dataFormat:e,filters:n,kernelSize:o,padding:s,strides:i,dilationRate:a}=this.cell,u=e==="channelsFirst",l=t[u?3:2],c=t[u?4:3],p=An(l,o[0],s,i[0],a[0]),m=An(c,o[1],s,i[1],a[1]);return[...t.slice(0,2),...u?[n,p,m]:[p,m,n]]}};ob.className="ConvRNN2D";var Vc=class extends hl{constructor(t){let{filters:e,kernelSize:n,strides:o,padding:s,dataFormat:i,dilationRate:a}=t;super(Object.assign({},t,{units:e})),this.filters=e,tr(this.filters,"filters"),this.kernelSize=du(n,2,"kernelSize"),this.kernelSize.forEach(u=>tr(u,"kernelSize")),this.strides=du(o||1,2,"strides"),this.strides.forEach(u=>tr(u,"strides")),this.padding=s||"valid",hn(this.padding),this.dataFormat=i||"channelsLast",Le(this.dataFormat),this.dilationRate=du(a||1,2,"dilationRate"),this.dilationRate.forEach(u=>tr(u,"dilationRate"))}build(t){var e;t=Qt(t);let n=this.dataFormat==="channelsFirst"?1:t.length-1;if(t[n]==null)throw new z(`The channel dimension of the input should be defined. Found ${t[n]}`);let o=t[n],s=4,i=this.kernelSize.concat([o,this.filters*s]);this.kernel=this.addWeight("kernel",i,null,this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint);let a=this.kernelSize.concat([this.filters,this.filters*s]);if(this.recurrentKernel=this.addWeight("recurrent_kernel",a,null,this.recurrentInitializer,this.recurrentRegularizer,!0,this.recurrentConstraint),this.useBias){let u;if(this.unitForgetBias){let l=this.biasInitializer,c=this.filters;u=new(e=class extends bn{apply(m,f){let d=l.apply([c]),h=lr([c]),g=l.apply([c*2]);return Em([d,h,g])}},e.className="CustomInit",e)}else u=this.biasInitializer;this.bias=this.addWeight("bias",[this.filters*s],null,u,this.biasRegularizer,!0,this.biasConstraint)}this.built=!0}call(t,e){return B(()=>{if(t.length!==3)throw new z(`ConvLSTM2DCell expects 3 input Tensors (inputs, h, c), got ${t.length}.`);let n=e.training||!1,o=t[0],s=t[1],i=t[2],a=4;0<this.dropout&&this.dropout<1&&this.dropoutMask==null&&(this.dropoutMask=gl({ones:()=>dr(o),rate:this.dropout,training:n,count:a,dropoutFunc:this.dropoutFunc}));let u=this.dropoutMask,l=(st,it,ft)=>!it||!it[ft]?st:M(it[ft],st),c=l(o,u,0),p=l(o,u,1),m=l(o,u,2),f=l(o,u,3);0<this.recurrentDropout&&this.recurrentDropout<1&&this.recurrentDropoutMask==null&&(this.recurrentDropoutMask=gl({ones:()=>dr(s),rate:this.recurrentDropout,training:n,count:a,dropoutFunc:this.dropoutFunc}));let d=this.recurrentDropoutMask,h=l(s,d,0),g=l(s,d,1),y=l(s,d,2),b=l(s,d,3),w=3,[v,k,E,$]=ur(this.kernel.read(),a,w),[D,F,P,W]=this.useBias?ur(this.bias.read(),a):[null,null,null,null];c=this.inputConv(c,v,D,this.padding),p=this.inputConv(p,k,F,this.padding),m=this.inputConv(m,E,P,this.padding),f=this.inputConv(f,$,W,this.padding);let[U,q,K,j]=ur(this.recurrentKernel.read(),a,w);h=this.recurrentConv(h,U),g=this.recurrentConv(g,q),y=this.recurrentConv(y,K),b=this.recurrentConv(b,j);let Q=this.recurrentActivation.apply(J(c,h)),rt=this.recurrentActivation.apply(J(p,g)),X=J(M(rt,i),M(Q,this.activation.apply(J(m,y)))),nt=M(this.recurrentActivation.apply(J(f,b)),this.activation.apply(X));return[nt,nt,X]})}getConfig(){let t=super.getConfig(),{units:e}=t,n=H8(t,["units"]),o={filters:this.filters,kernelSize:this.kernelSize,padding:this.padding,dataFormat:this.dataFormat,dilationRate:this.dilationRate,strides:this.strides};return Object.assign({},n,o)}inputConv(t,e,n,o){let s=pn(t,e,this.strides,o||"valid",this.dataFormat==="channelsFirst"?"NCHW":"NHWC",this.dilationRate);return n?xn(s,n,this.dataFormat):s}recurrentConv(t,e){return pn(t,e,1,"same",this.dataFormat==="channelsFirst"?"NCHW":"NHWC")}};Vc.className="ConvLSTM2DCell";et.registerClass(Vc);var ff=class extends ob{constructor(t){let e=new Vc(t);super(Object.assign({},t,{cell:e}))}static fromConfig(t,e){return new t(e)}};ff.className="ConvLSTM2D";et.registerClass(ff);var Gc=class extends Vt{constructor(t){super(t),this.rate=Math.max(Math.min(t.rate,1),0),this.noiseShape=t.noiseShape,this.seed=t.seed,this.supportsMasking=!0}getNoiseShape(t){if(this.noiseShape==null)return this.noiseShape;let e=t.shape,n=[];for(let o=0;o<this.noiseShape.length;++o)n.push(this.noiseShape[o]==null?e[o]:this.noiseShape[o]);return n}call(t,e){return B(()=>{this.invokeCallHook(t,e);let n=Pt(t);if(0<this.rate&&this.rate<1){let o=e.training==null?!1:e.training,s=this.getNoiseShape(n);return cu(()=>wy(n,this.rate,s,this.seed),()=>n,o)}return t})}getConfig(){let t={rate:this.rate,noiseShape:this.noiseShape,seed:this.seed},e=super.getConfig();return Object.assign(t,e),t}dispose(){return super.dispose()}};Gc.className="Dropout";et.registerClass(Gc);var df=class extends Gc{constructor(t){super(t),this.inputSpec=[{ndim:3}]}getNoiseShape(t){let e=t.shape;return[e[0],1,e[2]]}};df.className="SpatialDropout1D";et.registerClass(df);var hf=class extends Vt{constructor(t){if(super(t),this.activation=null,this.useBias=!0,this.kernel=null,this.bias=null,this.DEFAULT_KERNEL_INITIALIZER="glorotNormal",this.DEFAULT_BIAS_INITIALIZER="zeros",t.batchInputShape==null&&t.inputShape==null&&t.inputDim!=null){let e=null;t.batchSize!=null&&(e=t.batchSize),this.batchInputShape=[e,t.inputDim]}this.units=t.units,tr(this.units,"units"),this.activation=ci(t.activation),t.useBias!=null&&(this.useBias=t.useBias),this.kernelInitializer=de(t.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.biasInitializer=de(t.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.kernelConstraint=Ue(t.kernelConstraint),this.biasConstraint=Ue(t.biasConstraint),this.kernelRegularizer=Ie(t.kernelRegularizer),this.biasRegularizer=Ie(t.biasRegularizer),this.activityRegularizer=Ie(t.activityRegularizer),this.supportsMasking=!0,this.inputSpec=[{minNDim:2}]}build(t){t=Qt(t);let e=t[t.length-1];this.kernel==null&&(this.kernel=this.addWeight("kernel",[e,this.units],null,this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint),this.useBias&&(this.bias=this.addWeight("bias",[this.units],null,this.biasInitializer,this.biasRegularizer,!0,this.biasConstraint))),this.inputSpec=[{minNDim:2,axes:{[-1]:e}}],this.built=!0}computeOutputShape(t){t=Qt(t);let e=t.slice();return e[e.length-1]=this.units,e}call(t,e){return B(()=>{this.invokeCallHook(t,e);let n=Pt(t),o=my(this.activation.getClassName()),s;return o!=null?s=Eo(n,this.kernel.read(),o,this.bias?this.bias.read():null):(s=Eo(n,this.kernel.read()),this.bias!=null&&(s=xn(s,this.bias.read())),this.activation!=null&&(s=this.activation.apply(s))),s})}getConfig(){let t={units:this.units,activation:ui(this.activation),useBias:this.useBias,kernelInitializer:Ae(this.kernelInitializer),biasInitializer:Ae(this.biasInitializer),kernelRegularizer:me(this.kernelRegularizer),biasRegularizer:me(this.biasRegularizer),activityRegularizer:me(this.activityRegularizer),kernelConstraint:We(this.kernelConstraint),biasConstraint:We(this.biasConstraint)},e=super.getConfig();return Object.assign(t,e),t}};hf.className="Dense";et.registerClass(hf);var gf=class extends Vt{constructor(t){t=t||{},super(t),this.inputSpec=[{minNDim:3}],this.dataFormat=t.dataFormat}computeOutputShape(t){t=Qt(t);for(let e of t.slice(1))if(e==null)throw new z(`The shape of the input to "Flatten" is not fully defined (got ${t.slice(1)}). 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B(()=>(t=Pt(t),w$(t,this.n)))}getConfig(){let t={n:this.n},e=super.getConfig();return Object.assign(t,e),t}};yf.className="RepeatVector";et.registerClass(yf);var bf=class extends Vt{constructor(t){super(t),this.targetShape=t.targetShape;for(let e=0;e<this.targetShape.length;++e)this.isUnknown(this.targetShape[e])&&(this.targetShape[e]=null)}isUnknown(t){return t<0||t==null}fixUnknownDimension(t,e){let n="Total size of new array must be unchanged.",o=e.slice(),s=1,i=null;for(let u=0;u<o.length;++u){let l=o[u];if(this.isUnknown(l))if(i===null)i=u;else throw new z("Can only specifiy one unknown dimension.");else s*=l}let a=_o(t);if(i!==null){if(s===0||a%s!==0)throw new z(n);o[i]=a/s}else if(a!==s)throw new z(n);return o}computeOutputShape(t){let e=!1;for(let n=0;n<t.length;++n)if(this.isUnknown(t[n])){e=!0;break}return e?t.slice(0,1).concat(this.targetShape):t.slice(0,1).concat(this.fixUnknownDimension(t.slice(1),this.targetShape))}call(t,e){return B(()=>{this.invokeCallHook(t,e);let n=Pt(t),o=n.shape,s=o.slice(0,1).concat(this.fixUnknownDimension(o.slice(1),this.targetShape));return R(n,s)})}getConfig(){let t={targetShape:this.targetShape},e=super.getConfig();return Object.assign(t,e),t}};bf.className="Reshape";et.registerClass(bf);var wf=class extends Vt{constructor(t){if(super(t),t.dims==null)throw new Error("Required configuration field `dims` is missing during Permute constructor call.");if(!Array.isArray(t.dims))throw new Error(`Permute constructor requires \`dims\` to be an Array, but received ${t.dims} instead.`);let e=Jr(1,t.dims.length+1);if(!x.arraysEqual(t.dims.slice().sort(),e))throw new Error("Invalid permutation `dims`: "+JSON.stringify(t.dims)+" `dims` must contain consecutive integers starting from 1.");this.dims=t.dims,this.dimsIncludingBatch=[0].concat(this.dims),this.inputSpec=[new Ce({ndim:this.dims.length+1})]}computeOutputShape(t){t=Qt(t);let e=t.slice();return this.dims.forEach((n,o)=>{e[o+1]=t[n]}),e}call(t,e){return Mt(Pt(t),this.dimsIncludingBatch)}getConfig(){let t={dims:this.dims},e=super.getConfig();return Object.assign(t,e),t}};wf.className="Permute";et.registerClass(wf);var vf=class extends Vt{constructor(t){super(t==null?{}:t),this.supportsMasking=!0,t!=null?this.maskValue=t.maskValue==null?0:t.maskValue:this.maskValue=0}computeOutputShape(t){return t}getConfig(){let t=super.getConfig(),e={maskValue:this.maskValue};return Object.assign(e,t),e}computeMask(t,e){let n=Pt(t),o=-1;return Jl(vo(n,this.maskValue),o)}call(t,e){return B(()=>{this.invokeCallHook(t,e);let n=Pt(t),o=-1,s=!0,i=Jl(vo(n,this.maskValue),o,s);return M(n,Z(i,n.dtype))})}};vf.className="Masking";et.registerClass(vf);var Cf=class extends Vt{constructor(t){if(super(t),this.embeddings=null,this.DEFAULT_EMBEDDINGS_INITIALIZER="randomUniform",t.batchInputShape==null&&t.inputShape==null){let e=null;t.batchSize!=null&&(e=t.batchSize),t.inputLength==null?this.batchInputShape=[e,null]:this.batchInputShape=[e].concat(ve(t.inputLength))}this.inputDim=t.inputDim,tr(this.inputDim,"inputDim"),this.outputDim=t.outputDim,tr(this.outputDim,"outputDim"),this.embeddingsInitializer=de(t.embeddingsInitializer||this.DEFAULT_EMBEDDINGS_INITIALIZER),this.embeddingsRegularizer=Ie(t.embeddingsRegularizer),this.activityRegularizer=Ie(t.activityRegularizer),this.embeddingsConstraint=Ue(t.embeddingsConstraint),this.maskZero=t.maskZero,this.supportsMasking=t.maskZero,this.inputLength=t.inputLength}build(t){this.embeddings=this.addWeight("embeddings",[this.inputDim,this.outputDim],this.dtype,this.embeddingsInitializer,this.embeddingsRegularizer,!0,this.embeddingsConstraint),this.built=!0}warnOnIncompatibleInputShape(t){}computeMask(t,e){return B(()=>this.maskZero?(t=Pt(t),vo(t,St(t))):null)}computeOutputShape(t){if(t=Qt(t),this.inputLength==null)return[...t,this.outputDim];let e=ve(this.inputLength);if(e.length!==t.length-1)throw new z(`"inputLength" is ${this.inputLength}, but received input shape has shape ${t}`);{let n=0;for(let o=0;o<e.length;++o){let s=e[o],i=t[o+1];if(s!=null&&i!=null&&s!==i)throw new z(`"inputLength" is ${this.inputLength}, but received input shape has shape ${t}`);s==null&&(e[n]=i),n++}}return[t[0],...e,this.outputDim]}call(t,e){return B(()=>{this.invokeCallHook(t,e);let n=Pt(t);n.dtype!=="int32"&&(n=kc(n,"int32"));let o=by(this.embeddings.read(),R(n,[n.size]));return R(o,Qt(this.computeOutputShape(n.shape)))})}getConfig(){let t={inputDim:this.inputDim,outputDim:this.outputDim,embeddingsInitializer:Ae(this.embeddingsInitializer),embeddingsRegularizer:me(this.embeddingsRegularizer),activityRegularizer:me(this.activityRegularizer),embeddingsConstraint:We(this.embeddingsConstraint),maskZero:this.maskZero,inputLength:this.inputLength},e=super.getConfig();return Object.assign(t,e),t}};Cf.className="Embedding";et.registerClass(Cf);var xl=class extends Vt{constructor(t){super(t||{}),this.supportsMasking=!0}mergeFunction(t){throw new Nt}computeElementwiseOpOutputShape(t,e){if(t==null||e==null)return null;if(t.length<e.length)return this.computeElementwiseOpOutputShape(e,t);if(e.length===0)return t;let n=t.slice(0,t.length-e.length);for(let o=0;o<e.length;++o){let s=t[t.length-e.length+o],i=e[o];if(s==null||i==null||s<0||i<0)n.push(null);else if(s===1)n.push(i);else if(i===1)n.push(s);else{if(s!==i)throw new z("Operands could not be broadcast together with shapes "+JSON.stringify(t)+" "+JSON.stringify(e));n.push(s)}}return n}build(t){if(Array.isArray(t)&&!Array.isArray(t[0])&&(t=[Qt(t)]),t=t,t.length<2)throw new z(`A merge layer should be called on an Array of at least 2 inputs. 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t={axis:this.axis},e=super.getConfig();return Object.assign(t,e),t}};_f.className="Concatenate";et.registerClass(_f);function ig(r,t){for(;r<0;)r+=t;return r}function q8(r,t,e){if(r.shape.length>3||t.shape.length>3)throw new Nt("batchDot is not implemented for tensors of 4D or higher rank yet");if(x.assert(r.shape.length>=2,()=>`batchDot requires the rank of x to be >= 2, but got ${r.shape.length}`),x.assert(r.shape.length>=2,()=>`batchDot requires the rank of y to be >= 2, but got ${t.shape.length}`),typeof e=="number"&&(e=[e,e]),r.dtype==="complex64"||t.dtype==="complex64")throw new Nt("batchDot is not implemented for complex64-type Tensors yet.");let n=r.shape.length,o=t.shape.length;e==null&&(e=[n-1,o-2]);let s=e;return B(()=>{let i;if(n>o){i=n-o;let u=[];for(let l=0;l<i;++l)u.push(1);t=R(t,t.shape.concat(u))}else if(o>n){i=o-n;let u=[];for(let l=0;l<i;++l)u.push(1);r=R(r,r.shape.concat(u))}else i=0;let a;if(r.shape.length===2&&t.shape.length===2)s[0]===s[1]?a=mt(M(r,t),s[0]):a=mt(M(Mt(r,[1,0]),t),s[1]);else{let u=s[0]!==r.shape.length-1,l=s[1]===t.shape.length-1;a=zt(r,t,u,l)}if(i>0){let u;n>o?u=n+o-3:u=n-1;let l=[];for(let c=u;c<u+i;++c)l.push(c);a=zr(a,l)}return a.shape.length===1&&(a=fr(a,1)),a})}var Ef=class extends xl{constructor(t){super(t),this.axes=t.axes,this.normalize=t.normalize==null?!1:t.normalize,this.supportsMasking=!0,this.reshapeRequired=!1}build(t){x.assert(Array.isArray(t)&&t.length===2&&Array.isArray(t[0])&&Array.isArray(t[1]),()=>"A `Dot` layer should be called on a list of exactly 2 inputs.");let e=t[0],n=t[1];if(e.length>3||n.length>3)throw new Nt("Dot layer does not support tensors of 4D or higher rank yet.");let o=this.interpretAxes(e,n);if(e[o[0]]!==n[o[1]])throw new z(`Dimension incompatibility: ${e[o[0]]} !== ${n[o[1]]}`)}mergeFunction(t){if(t.length!==2)throw new z(`A \`Dot\` layer must be called on exactly 2 inputs, but received ${t.length} input(s).`);let e=t[0],n=t[1],o;return Array.isArray(this.axes)?o=this.axes.map((s,i)=>ig(s,t[i].shape.length)):o=[ig(this.axes,e.shape.length),ig(this.axes,n.shape.length)],this.normalize&&(e=Jh(e,o[0]),n=Jh(n,o[1])),q8(e,n,o)}interpretAxes(t,e){let n;return Array.isArray(this.axes)?n=this.axes:n=[ig(this.axes,t.length),ig(this.axes,e.length)],n}computeOutputShape(t){x.assert(Array.isArray(t)&&t.length===2&&Array.isArray(t[0])&&Array.isArray(t[1]),()=>"A `Dot` layer should be called on a list of exactly 2 inputs.");let e=t[0].slice(),n=t[1].slice();if(e.length>3||n.length>3)throw new Nt("Dot layer does not support tensors of 4D or higher rank yet.");let o=this.interpretAxes(e,n);e.splice(o[0],1),n.splice(o[1],1),n.splice(0,1);let s=e.concat(n);return s.length===1&&s.push(1),s}computeMask(t,e){return null}getConfig(){let t={axes:this.axes,normalize:this.normalize},e=super.getConfig();return Object.assign(t,e),t}};Ef.className="Dot";et.registerClass(Ef);var Af=class extends Vt{constructor(t){super(t),this.supportsMasking=!0,this.stddev=t.stddev}computeOutputShape(t){return t}getConfig(){let t=super.getConfig(),e={stddev:this.stddev};return Object.assign(e,t),e}call(t,e){return B(()=>{this.invokeCallHook(t,e);let n=Pt(t);return cu(()=>J(Am(n.shape,0,this.stddev),n),()=>n,e.training||!1)})}};Af.className="GaussianNoise";et.registerClass(Af);var $f=class extends Vt{constructor(t){super(t),this.supportsMasking=!0,this.rate=t.rate}computeOutputShape(t){return t}getConfig(){let t=super.getConfig(),e={rate:this.rate};return Object.assign(e,t),e}call(t,e){return B(()=>{this.invokeCallHook(t,e);let n=Pt(t);return this.rate>0&&this.rate<1?cu(()=>{let s=Math.sqrt(this.rate/(1-this.rate));return M(n,Am(n.shape,1,s))},()=>n,e.training||!1):n})}};$f.className="GaussianDropout";et.registerClass($f);var Df=class extends Vt{constructor(t){super(t),this.supportsMasking=!0,this.rate=t.rate,this.noiseShape=t.noiseShape}_getNoiseShape(t){return this.noiseShape||Pt(t).shape}computeOutputShape(t){return t}getConfig(){let t=super.getConfig(),e={rate:this.rate};return Object.assign(e,t),e}call(t,e){return B(()=>{if(this.rate<1&&this.rate>0){let n=this._getNoiseShape(t);return cu(()=>{let s=Pt(t),i=1.6732632423543772,a=1.0507009873554805,u=-i*a,l=Tn(ri(n),this.rate);l=kc(l,"float32");let c=((1-this.rate)*(1+this.rate*u**2))**-.5,p=-c*u*this.rate,m=J(M(s,l),M(J(l,-1),u));return J(M(m,c),p)},()=>Pt(t),e.training||!1)}return t})}};Df.className="AlphaDropout";et.registerClass(Df);function ag(r,t,e,n,o,s=.001){let i;if(r.rank===2)i=LI(r,t,e,n,o,s);else if(r.rank===3)i=zI(r,t,e,n,o,s);else if(r.rank===4)i=BI(r,t,e,n,o,s);else throw new Nt(`batchNormalization is not implemented for array of rank ${r.rank} yet`);return i}function K8(r,t,e,n,o=.001){return B(()=>{let s=Cm(r,n),i=s.mean,a=s.variance;return[ag(r,i,a,e,t,o),i,a]})}function j8(r,t,e,n,o=.001){return B(()=>{let s=Cm(r,n),i=s.mean,a=s.variance,u=[];for(let d of Jr(0,r.rank))n.indexOf(d)!==-1?u.push(1):u.push(r.shape[d]);let l=R(i,u),c=R(a,u),p=t==null?null:R(t,u),m=e==null?null:R(e,u);return[ag(r,l,c,m,p,o),i,a]})}function X8(r,t,e,n,o=.001){return x.arraysEqual(n.slice().sort(),Jr(0,r.rank-1))?K8(r,t,e,n,o):j8(r,t,e,n,o)}var Ff=class extends Vt{constructor(t){t==null&&(t={}),super(t),this.supportsMasking=!0,this.axis=t.axis==null?-1:t.axis,this.momentum=t.momentum==null?.99:t.momentum,this.epsilon=t.epsilon==null?.001:t.epsilon,this.center=t.center==null?!0:t.center,this.scale=t.scale==null?!0:t.scale,this.betaInitializer=de(t.betaInitializer||"zeros"),this.gammaInitializer=de(t.gammaInitializer||"ones"),this.movingMeanInitializer=de(t.movingMeanInitializer||"zeros"),this.movingVarianceInitializer=de(t.movingVarianceInitializer||"ones"),this.betaConstraint=Ue(t.betaConstraint),this.gammaConstraint=Ue(t.gammaConstraint),this.betaRegularizer=Ie(t.betaRegularizer),this.gammaRegularizer=Ie(t.gammaRegularizer)}build(t){t=Qt(t);let e=this.axis>=0?this.axis:this.axis+t.length,n=t[e];if(n==null)throw new z(`Axis ${e} of input tensor should have a defined dimension but the layer received an input with shape ${JSON.stringify(t)}.`);this.inputSpec=[new Ce({ndim:t.length,axes:{[e]:n}})];let o=[n];this.scale&&(this.gamma=this.addWeight("gamma",o,null,this.gammaInitializer,this.gammaRegularizer,!0,this.gammaConstraint)),this.center&&(this.beta=this.addWeight("beta",o,null,this.betaInitializer,this.betaRegularizer,!0,this.betaConstraint)),this.movingMean=this.addWeight("moving_mean",o,null,this.movingMeanInitializer,null,!1),this.movingVariance=this.addWeight("moving_variance",o,null,this.movingVarianceInitializer,null,!1),this.built=!0}call(t,e){return B(()=>{let n=e.training==null?!1:e.training,o=Pt(t),s=o.shape,i=s.length,a=Jr(0,i),u=this.axis>=0?this.axis:this.axis+i;a.splice(u,1);let l=ko(1,i);l[u]=s[u];let c=a.slice();c.sort();let p=!x.arraysEqual(c,Jr(0,i).slice(0,i-1)),m=()=>{if(p){let b=R(this.movingMean.read(),l),w=R(this.movingVariance.read(),l),v=this.center?R(this.beta.read(),l):null,k=this.scale?R(this.gamma.read(),l):null;return ag(o,b,w,v,k,this.epsilon)}else return ag(o,this.movingMean.read(),this.movingVariance.read(),this.beta==null?null:this.beta.read(),this.gamma==null?null:this.gamma.read(),this.epsilon)};if(!n)return m();let[f,d,h]=X8(o,this.gamma.read(),this.beta.read(),a,this.epsilon),g=(b,w,v)=>{B(()=>{let k=1-v,E=b.read(),$=M(lt(E,w),k);b.write(lt(E,$))})};return(()=>{g(this.movingMean,d,this.momentum),g(this.movingVariance,h,this.momentum)})(),f})}getConfig(){let t={axis:this.axis,momentum:this.momentum,epsilon:this.epsilon,center:this.center,scale:this.scale,betaInitializer:Ae(this.betaInitializer),gammaInitializer:Ae(this.gammaInitializer),movingMeanInitializer:Ae(this.movingMeanInitializer),movingVarianceInitializer:Ae(this.movingVarianceInitializer),betaRegularizer:me(this.betaRegularizer),gammaRegularizer:me(this.gammaRegularizer),betaConstraint:We(this.betaConstraint),gammaConstraint:We(this.gammaConstraint)},e=super.getConfig();return Object.assign(t,e),t}};Ff.className="BatchNormalization";et.registerClass(Ff);var Rf=class extends Vt{constructor(t){if(t==null&&(t={}),super(t),this.axis=t.axis==null?-1:t.axis,typeof this.axis=="number"){if(!Number.isInteger(this.axis))throw new Error(`Expected axis to be an integer, but received ${this.axis}`)}else if(Array.isArray(this.axis)){for(let e of this.axis)if(!Number.isInteger(e))throw new Error(`Expected axis to be an array of integers, but received ${JSON.stringify(this.axis)}`)}else throw new Error(`Expected axis to be an integer or an array of integers, but received ${JSON.stringify(this.axis)}`);this.epsilon=t.epsilon==null?.001:t.epsilon,this.center=t.center==null?!0:t.center,this.scale=t.scale==null?!0:t.scale,this.betaInitializer=de(t.betaInitializer||"zeros"),this.gammaInitializer=de(t.gammaInitializer||"ones"),this.betaRegularizer=Ie(t.betaRegularizer),this.gammaRegularizer=Ie(t.gammaRegularizer),this.supportsMasking=!0}build(t){t=Qt(t);let e=t.length;typeof this.axis=="number"&&(this.axis=[this.axis]);for(let s=0;s<this.axis.length;++s)this.axis[s]<0&&(this.axis[s]+=e);for(let s of this.axis)if(s<0||s>=e)throw new Error(`Invalid axis: ${s}`);if(this.axis.length!==To(this.axis).length)throw new Error(`Found duplicate axes in: ${this.axis}`);let n=this.axis.map(s=>t[s]),o=!0;this.scale?this.gamma=this.addWeight("gamma",n,"float32",this.gammaInitializer,this.gammaRegularizer,o):this.gamma=null,this.center?this.beta=this.addWeight("beta",n,"float32",this.betaInitializer,this.betaRegularizer,o):this.beta=null,this.built=!0}call(t,e){let n=Pt(t),o=n.shape,s=o.length;return B(()=>{let{mean:a,variance:u}=Cm(n,this.axis,!0),l=ko(1,s);for(let h of this.axis)l[h]=o[h];let c=h=>h!=null&&h.shape.length!==s?R(h,l):h,p=this.scale?c(this.gamma.read()):null,m=this.center?c(this.beta.read()):null,f=[],d=[];for(let h=0;h<s;++h)this.axis.indexOf(h)!==-1?(f.push(o[h]),d.push(1)):(f.push(1),d.push(o[h]));return a=Nr(a,f),u=Nr(u,f),p!=null&&(p=Nr(p,d)),m!=null&&(m=Nr(m,d)),ag(n,a,u,m,p,this.epsilon)})}getConfig(){let t={axis:this.axis,epsilon:this.epsilon,center:this.center,scale:this.scale,betaInitializer:Ae(this.betaInitializer),gammaInitializer:Ae(this.gammaInitializer),betaRegularizer:me(this.betaRegularizer),gammaRegularizer:me(this.gammaRegularizer)},e=super.getConfig();return Object.assign(t,e),t}};Rf.className="LayerNormalization";et.registerClass(Rf);function Y8(r,t,e){return B(()=>{if(r.rank!==4)throw new z(`temporalPadding expects input tensor to be 4-D, but received a ${r.rank}-D tensor.`);if(t==null&&(t=[[1,1],[1,1]]),t.length!==2||t[0].length!==2||t[1].length!==2)throw new z("spatial2dPadding expects `padding` to be an Array of two Arrays, each of which is an Array of two integers.");if(e==null&&(e=gn()),e!=="channelsLast"&&e!=="channelsFirst")throw new z(`Unknown data format: ${e}. 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length-${t.padding[1].length} array.`);n=t.padding[1]}this.padding=[e,n]}this.inputSpec=[new Ce({ndim:4})]}computeOutputShape(t){t=Qt(t);let e,n;return this.dataFormat==="channelsFirst"?(t[2]!=null&&t[2]>=0?e=t[2]+this.padding[0][0]+this.padding[0][1]:e=null,t[3]!=null&&t[3]>=0?n=t[3]+this.padding[1][0]+this.padding[1][1]:n=null,[t[0],t[1],e,n]):(t[1]!=null&&t[1]>=0?e=t[1]+this.padding[0][0]+this.padding[0][1]:e=null,t[2]!=null&&t[2]>=0?n=t[2]+this.padding[1][0]+this.padding[1][1]:n=null,[t[0],e,n,t[3]])}call(t,e){return B(()=>Y8(Pt(t),this.padding,this.dataFormat))}getConfig(){let t={padding:this.padding,dataFormat:this.dataFormat},e=super.getConfig();return Object.assign(t,e),t}};Of.className="ZeroPadding2D";et.registerClass(Of);function cb(r,t,e,n,o,s){return B(()=>{Le(o),$S(s),hn(n),e==null&&(e=[1,1]),n==null&&(n="valid"),o==null&&(o=gn()),s==null&&(s="max"),r=sg(r,o);let i,a=n==="same"?"same":"valid";return s==="max"?i=tl(r,t,e,a):i=Ka(r,t,e,a),o==="channelsFirst"&&(i=Mt(i,[0,3,1,2])),i})}function cD(r,t,e,n,o,s){return B(()=>{Le(o),$S(s),hn(n),e==null&&(e=[1,1,1]),n==null&&(n="valid"),o==null&&(o=gn()),s==null&&(s="max"),r=jS(r,o);let i,a=n==="same"?"same":"valid";return s==="max"?i=$h(r,t,e,a):i=xh(r,t,e,a),o==="channelsFirst"&&(i=Mt(i,[0,4,1,2,3])),i})}var sb=class extends Vt{constructor(t){if(t.poolSize==null&&(t.poolSize=2),super(t),typeof t.poolSize=="number")this.poolSize=[t.poolSize];else if(Array.isArray(t.poolSize)&&t.poolSize.length===1&&typeof t.poolSize[0]=="number")this.poolSize=t.poolSize;else throw new z(`poolSize for 1D convolutional layer must be a number or an Array of a single number, but received ${JSON.stringify(t.poolSize)}`);if(tr(this.poolSize,"poolSize"),t.strides==null)this.strides=this.poolSize;else if(typeof t.strides=="number")this.strides=[t.strides];else if(Array.isArray(t.strides)&&t.strides.length===1&&typeof t.strides[0]=="number")this.strides=t.strides;else throw new z(`strides for 1D convolutional layer must be a number or an Array of a single number, but received ${JSON.stringify(t.strides)}`);tr(this.strides,"strides"),this.padding=t.padding==null?"valid":t.padding,hn(this.padding),this.inputSpec=[new Ce({ndim:3})]}computeOutputShape(t){t=Qt(t);let e=An(t[1],this.poolSize[0],this.padding,this.strides[0]);return[t[0],e,t[2]]}call(t,e){return B(()=>{this.invokeCallHook(t,e),t=ul(Pt(t),2);let n=this.poolingFunction(Pt(t),[this.poolSize[0],1],[this.strides[0],1],this.padding,"channelsLast");return zr(n,[2])})}getConfig(){let t={poolSize:this.poolSize,padding:this.padding,strides:this.strides},e=super.getConfig();return Object.assign(t,e),t}},Mf=class extends sb{constructor(t){super(t)}poolingFunction(t,e,n,o,s){return Le(s),hn(o),cb(t,e,n,o,s,"max")}};Mf.className="MaxPooling1D";et.registerClass(Mf);var Pf=class extends sb{constructor(t){super(t)}poolingFunction(t,e,n,o,s){return Le(s),hn(o),cb(t,e,n,o,s,"avg")}};Pf.className="AveragePooling1D";et.registerClass(Pf);var ib=class extends Vt{constructor(t){if(t.poolSize==null&&(t.poolSize=[2,2]),super(t),this.poolSize=Array.isArray(t.poolSize)?t.poolSize:[t.poolSize,t.poolSize],t.strides==null)this.strides=this.poolSize;else if(Array.isArray(t.strides)){if(t.strides.length!==2)throw new z(`If the strides property of a 2D pooling layer is an Array, it is expected to have a length of 2, but received length ${t.strides.length}.`);this.strides=t.strides}else this.strides=[t.strides,t.strides];tr(this.poolSize,"poolSize"),tr(this.strides,"strides"),this.padding=t.padding==null?"valid":t.padding,this.dataFormat=t.dataFormat==null?"channelsLast":t.dataFormat,Le(this.dataFormat),hn(this.padding),this.inputSpec=[new Ce({ndim:4})]}computeOutputShape(t){t=Qt(t);let e=this.dataFormat==="channelsFirst"?t[2]:t[1],n=this.dataFormat==="channelsFirst"?t[3]:t[2];return e=An(e,this.poolSize[0],this.padding,this.strides[0]),n=An(n,this.poolSize[1],this.padding,this.strides[1]),this.dataFormat==="channelsFirst"?[t[0],t[1],e,n]:[t[0],e,n,t[3]]}call(t,e){return B(()=>(this.invokeCallHook(t,e),this.poolingFunction(Pt(t),this.poolSize,this.strides,this.padding,this.dataFormat)))}getConfig(){let t={poolSize:this.poolSize,padding:this.padding,strides:this.strides,dataFormat:this.dataFormat},e=super.getConfig();return Object.assign(t,e),t}},Lf=class extends ib{constructor(t){super(t)}poolingFunction(t,e,n,o,s){return Le(s),hn(o),cb(t,e,n,o,s,"max")}};Lf.className="MaxPooling2D";et.registerClass(Lf);var zf=class extends ib{constructor(t){super(t)}poolingFunction(t,e,n,o,s){return Le(s),hn(o),cb(t,e,n,o,s,"avg")}};zf.className="AveragePooling2D";et.registerClass(zf);var ab=class extends Vt{constructor(t){if(t.poolSize==null&&(t.poolSize=[2,2,2]),super(t),this.poolSize=Array.isArray(t.poolSize)?t.poolSize:[t.poolSize,t.poolSize,t.poolSize],t.strides==null)this.strides=this.poolSize;else if(Array.isArray(t.strides)){if(t.strides.length!==3)throw new z(`If the strides property of a 3D pooling layer is an Array, it is expected to have a length of 3, but received length ${t.strides.length}.`);this.strides=t.strides}else this.strides=[t.strides,t.strides,t.strides];tr(this.poolSize,"poolSize"),tr(this.strides,"strides"),this.padding=t.padding==null?"valid":t.padding,this.dataFormat=t.dataFormat==null?"channelsLast":t.dataFormat,Le(this.dataFormat),hn(this.padding),this.inputSpec=[new Ce({ndim:5})]}computeOutputShape(t){t=Qt(t);let e=this.dataFormat==="channelsFirst"?t[2]:t[1],n=this.dataFormat==="channelsFirst"?t[3]:t[2],o=this.dataFormat==="channelsFirst"?t[4]:t[3];return e=An(e,this.poolSize[0],this.padding,this.strides[0]),n=An(n,this.poolSize[1],this.padding,this.strides[1]),o=An(o,this.poolSize[2],this.padding,this.strides[2]),this.dataFormat==="channelsFirst"?[t[0],t[1],e,n,o]:[t[0],e,n,o,t[4]]}call(t,e){return B(()=>(this.invokeCallHook(t,e),this.poolingFunction(Pt(t),this.poolSize,this.strides,this.padding,this.dataFormat)))}getConfig(){let t={poolSize:this.poolSize,padding:this.padding,strides:this.strides,dataFormat:this.dataFormat},e=super.getConfig();return Object.assign(t,e),t}},Bf=class extends ab{constructor(t){super(t)}poolingFunction(t,e,n,o,s){return Le(s),hn(o),cD(t,e,n,o,s,"max")}};Bf.className="MaxPooling3D";et.registerClass(Bf);var Vf=class extends ab{constructor(t){super(t)}poolingFunction(t,e,n,o,s){return Le(s),hn(o),cD(t,e,n,o,s,"avg")}};Vf.className="AveragePooling3D";et.registerClass(Vf);var lb=class extends Vt{constructor(t){super(t),this.inputSpec=[new Ce({ndim:3})]}computeOutputShape(t){return[t[0],t[2]]}call(t,e){throw new Nt}},Gf=class extends lb{constructor(t){super(t||{})}call(t,e){return B(()=>{let n=Pt(t);return be(n,1)})}};Gf.className="GlobalAveragePooling1D";et.registerClass(Gf);var Wf=class extends lb{constructor(t){super(t||{})}call(t,e){return B(()=>{let n=Pt(t);return Fr(n,1)})}};Wf.className="GlobalMaxPooling1D";et.registerClass(Wf);var ub=class extends Vt{constructor(t){super(t),this.dataFormat=t.dataFormat==null?"channelsLast":t.dataFormat,Le(this.dataFormat),this.inputSpec=[new Ce({ndim:4})]}computeOutputShape(t){return t=t,this.dataFormat==="channelsLast"?[t[0],t[3]]:[t[0],t[1]]}call(t,e){throw new Nt}getConfig(){let t={dataFormat:this.dataFormat},e=super.getConfig();return Object.assign(t,e),t}},Uf=class extends ub{call(t,e){return B(()=>{let n=Pt(t);return this.dataFormat==="channelsLast"?be(n,[1,2]):be(n,[2,3])})}};Uf.className="GlobalAveragePooling2D";et.registerClass(Uf);var Hf=class extends ub{call(t,e){return B(()=>{let n=Pt(t);return this.dataFormat==="channelsLast"?Fr(n,[1,2]):Fr(n,[2,3])})}};Hf.className="GlobalMaxPooling2D";et.registerClass(Hf);var pb=class extends Vt{constructor(t){super(t),this.layer=t.layer}build(t){this.built=!0}get trainable(){return this.layer!=null?this.layer.trainable:!1}set trainable(t){this.layer!=null&&(this.layer.trainable=t)}get trainableWeights(){return this.layer.trainableWeights}get nonTrainableWeights(){return this.layer.nonTrainableWeights}get updates(){return this.layer._updates}get losses(){return this.layer.losses}getWeights(){return this.layer.getWeights()}setWeights(t){this.layer.setWeights(t)}getConfig(){let t={layer:{className:this.layer.getClassName(),config:this.layer.getConfig()}},e=super.getConfig();return Object.assign(t,e),t}setFastWeightInitDuringBuild(t){super.setFastWeightInitDuringBuild(t),this.layer!=null&&this.layer.setFastWeightInitDuringBuild(t)}static fromConfig(t,e,n={}){let o=e.layer,s=vn(o,n);delete e.layer;let i={layer:s};return Object.assign(i,e),new t(i)}},qf=class extends pb{constructor(t){super(t),this.supportsMasking=!0}build(t){if(t=Qt(t),t.length<3)throw new z(`TimeDistributed layer expects an input shape >= 3D, but received input shape ${JSON.stringify(t)}`);this.inputSpec=[{shape:t}];let e=[t[0]].concat(t.slice(2));this.layer.built||(this.layer.build(e),this.layer.built=!0),super.build(t)}computeOutputShape(t){t=Qt(t);let e=[t[0]].concat(t.slice(2)),n=this.layer.computeOutputShape(e),o=t[1];return[n[0],o].concat(n.slice(1))}call(t,e){return B(()=>(t=Pt(t),YS((i,a)=>[Pt(this.layer.call(i,e)),[]],t,[],!1,null,null,!1,!0)[1]))}};qf.className="TimeDistributed";et.registerClass(qf);function Z8(r){Ki(h$,"BidirectionalMergeMode",r)}var J8="concat",Kf=class extends pb{constructor(t){super(t);let e=t.layer.getConfig(),n={};n.className=t.layer.getClassName(),n.config=e,this.forwardLayer=vn(n),e.goBackwards=e.goBackwards!==!0;let o={};if(o.className=t.layer.getClassName(),o.config=e,this.backwardLayer=vn(o),this.forwardLayer.name="forward_"+this.forwardLayer.name,this.backwardLayer.name="backward_"+this.backwardLayer.name,this.mergeMode=t.mergeMode===void 0?J8:t.mergeMode,Z8(this.mergeMode),t.weights)throw new Nt("weights support is not implemented for Bidirectional layer yet.");this._stateful=t.layer.stateful,this.returnSequences=t.layer.returnSequences,this.returnState=t.layer.returnState,this.supportsMasking=!0,this._trainable=!0,this.inputSpec=t.layer.inputSpec,this.numConstants=null}get trainable(){return this._trainable}set trainable(t){this._trainable=t,this.forwardLayer!=null&&(this.forwardLayer.trainable=t),this.backwardLayer!=null&&(this.backwardLayer.trainable=t)}getWeights(){return this.forwardLayer.getWeights().concat(this.backwardLayer.getWeights())}setWeights(t){let e=t.length,n=Math.floor(e/2);this.forwardLayer.setWeights(t.slice(0,n)),this.backwardLayer.setWeights(t.slice(n))}computeOutputShape(t){let e=this.forwardLayer.computeOutputShape(t);Array.isArray(e)&&Array.isArray(e[0])||(e=[e]),e=e;let n,o,s;return this.returnState&&(s=e.slice(1)),n=e[0],n=n,this.mergeMode==="concat"?(n[n.length-1]*=2,o=[n]):this.mergeMode==null?o=[n,n.slice()]:o=[n],this.returnState?this.mergeMode==null?o.concat(s).concat(s.slice()):[n].concat(s).concat(s.slice()):Er(o)}apply(t,e){let n=e==null?null:e.initialState,o=e==null?null:e.constants;e==null&&(e={});let s=XS(t,n,o,this.numConstants);if(t=s.inputs,n=s.initialState,o=s.constants,Array.isArray(t)&&(n=t.slice(1),t=t[0]),(n==null||n.length===0)&&o==null)return super.apply(t,e);let i=[],a=[];if(n!=null){let l=n.length;if(l%2>0)throw new z("When passing `initialState` to a Bidrectional RNN, the state should be an Array containing the states of the underlying RNNs.");e.initialState=n,i.push(...n);let c=n.map(p=>new Ce({shape:p.shape}));this.forwardLayer.stateSpec=c.slice(0,l/2),this.backwardLayer.stateSpec=c.slice(l/2),a.push(...c)}if(o!=null)throw new Nt("Support for constants in Bidirectional layers is not implemented yet.");let u=i[0]instanceof Qr;for(let l of i)if(l instanceof Qr!==u)throw new z("The initial state of a Bidirectional layer cannot be specified as a mix of symbolic and non-symbolic tensors");if(u){let l=[t].concat(i),c=this.inputSpec.concat(a),p=this.inputSpec;this.inputSpec=c;let m=super.apply(l,e);return this.inputSpec=p,m}else return super.apply(t,e)}call(t,e){return B(()=>{let n=e.initialState,o,s;if(n==null)o=this.forwardLayer.call(t,e),s=this.backwardLayer.call(t,e);else{let u=n.slice(0,n.length/2),l=n.slice(n.length/2);o=this.forwardLayer.call(t,Object.assign(e,{initialState:u})),s=this.backwardLayer.call(t,Object.assign(e,{initialState:l}))}let i;this.returnState&&(Array.isArray(o)&&(i=o.slice(1).concat(s.slice(1))),o=o[0],s=s[0]),this.returnSequences&&(s=ir(s,1));let 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o=I("keyDType",r,t,e),s=I("valueDType",r,t,e),i=new Ab(o,s);return n.addHashTable(r.name,i),[i.handle]}case"LookupTableImport":case"LookupTableImportV2":{let o=I("tableHandle",r,t,e,n),s=I("keys",r,t,e),i=I("values",r,t,e);return[await n.getHashTableById(o.id).import(s,i)]}case"LookupTableFind":case"LookupTableFindV2":{let o=I("tableHandle",r,t,e,n),s=I("keys",r,t,e),i=I("defaultValue",r,t,e);return[await n.getHashTableById(o.id).find(s,i)]}case"LookupTableSize":case"LookupTableSizeV2":{let o=I("tableHandle",r,t,e,n);return[n.getHashTableById(o.id).tensorSize()]}default:throw TypeError(`Node type ${r.op} is not implemented`)}};var BD=(r,t,e)=>{switch(r.op){case"ResizeBilinear":{let n=I("images",r,t,e),o=I("size",r,t,e),s=I("alignCorners",r,t,e),i=I("halfPixelCenters",r,t,e);return[dn.resizeBilinear(n,[o[0],o[1]],s,i)]}case"ResizeNearestNeighbor":{let 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VD=(r,t,e)=>{switch(r.op){case"Equal":return[kr(I("a",r,t,e),I("b",r,t,e))];case"NotEqual":return[vo(I("a",r,t,e),I("b",r,t,e))];case"Greater":return[Ge(I("a",r,t,e),I("b",r,t,e))];case"GreaterEqual":return[Tn(I("a",r,t,e),I("b",r,t,e))];case"Less":return[oc(I("a",r,t,e),I("b",r,t,e))];case"LessEqual":return[_n(I("a",r,t,e),I("b",r,t,e))];case"LogicalAnd":return[Tr(I("a",r,t,e),I("b",r,t,e))];case"LogicalNot":return[Qa(I("a",r,t,e))];case"LogicalOr":return[ic(I("a",r,t,e),I("b",r,t,e))];case"Select":case"SelectV2":return[Ee(I("condition",r,t,e),I("a",r,t,e),I("b",r,t,e))];default:throw TypeError(`Node type ${r.op} is not implemented`)}};var 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this.tensorArrayMap[t]}addTensorList(t){this.tensorListMap[t.id]=t}getTensorList(t){return this.tensorListMap[t]}dispose(t){for(let e in this.tensorArrayMap)this.tensorArrayMap[e].clearAndClose(t);for(let e in this.tensorListMap)this.tensorListMap[e].clearAndClose(t)}};function Ik(r,t,e,n){let o=new Set,s=[],i=null,a=null,u=new Set,l=Object.keys(r).map(m=>Cn(m)[0]),c=[];n!=null&&(c=n.map(m=>Cn(m.name)[0]));let p=[...t];for(;p.length>0;){let m=p.pop();if((Sk(m)||vZ(m)||CZ(m))&&i==null&&(i=m,a=i.children.map(f=>f.name).filter(f=>o.has(f))),o.add(m.name),e[m.name]==null&&l.indexOf(m.name)===-1&&c.indexOf(m.name)===-1){if(m.inputs.length===0){s.push(m.name);continue}m.inputs.forEach(f=>{u.has(f.name)||(u.add(f.name),p.push(f))})}}return{inputs:r,outputs:t,usedNodes:o,missingInputs:s,dynamicNode:i,syncInputs:a}}function YD(r,t,e){let{usedNodes:n,inputs:o}=e,s=[],i=Object.keys(o).map(c=>Cn(c)[0]).map(c=>r.nodes[c]),a=r.initNodes;i.forEach(c=>{n.has(c.name)&&s.push(c)}),r.weights.forEach(c=>{n.has(c.name)&&s.push(c)}),a!=null&&a.forEach(c=>{n.has(c.name)&&s.push(c)});let u=new Set,l=[];for(;s.length>0;){let c=s.pop();u.add(c.name),t[c.name]||l.push(c),c.children.forEach(p=>{!u.has(p.name)&&n.has(p.name)&&p.inputs.every(m=>u.has(m.name))&&s.push(p)})}return l}var yZ=["Switch","Merge","Enter","Exit","NextIteration","StatelessIf","StatelessWhile","if","While"],bZ=["NonMaxSuppressionV2","NonMaxSuppressionV3","NonMaxSuppressionV5","Where"],wZ=["HashTable","HashTableV2","LookupTableImport","LookupTableImportV2","LookupTableFind","LookupTableFindV2","LookupTableSize","LookupTableSizeV2"];function Sk(r){return yZ.indexOf(r.op)>=0}function vZ(r){return bZ.indexOf(r.op)>=0}function CZ(r){return wZ.indexOf(r.op)>=0}var Wc=class{constructor(t,e){this.graph=t,this.parent=e,this.compiledMap=new Map,this._weightMap={},this.SEPERATOR=",",this._functions={},this._functionExecutorMap={},this.intermediateTensors={},this.keepTensorForDebug=!1,this._outputs=t.outputs,this._inputs=t.inputs,this._initNodes=t.initNodes,this._signature=t.signature,this._functions=t.functions,t.functions!=null&&Object.keys(t.functions).forEach(n=>{this._functionExecutorMap[n]=new Wc(t.functions[n],this)})}get weightIds(){return this.parent?this.parent.weightIds:this._weightIds}get functionExecutorMap(){return this.parent?this.parent.functionExecutorMap:this._functionExecutorMap}get weightMap(){return this.parent?this.parent.weightMap:this._weightMap}set weightMap(t){let e=Object.keys(t).map(n=>t[n].map(o=>o.id));this._weightIds=[].concat(...e),this._weightMap=t}set resourceManager(t){this._resourceManager=t}get inputs(){return this._inputs.map(t=>({name:t.name,shape:t.attrParams.shape?t.attrParams.shape.value:void 0,dtype:t.attrParams.dtype?t.attrParams.dtype.value:void 0}))}get outputs(){return this._outputs.map(t=>({name:t.name,shape:t.attrParams.shape?t.attrParams.shape.value:void 0,dtype:t.attrParams.dtype?t.attrParams.dtype.value:void 0}))}get inputNodes(){return this._inputs.map(t=>t.signatureKey||t.name)}get outputNodes(){return this._outputs.map(t=>{let e=t.signatureKey||t.name;return t.defaultOutput?`${e}:${t.defaultOutput}`:e})}get functions(){return Object.keys(this._functions).reduce((t,e)=>(t[e]=this._functions[e].signature,t),{})}getCompilationKey(t,e){let n=t.map(s=>s.name).sort(),o=e.map(s=>s.name).sort();return n.join(this.SEPERATOR)+"--"+o.join(this.SEPERATOR)}compile(t,e){let n=Ik(t,e,this.weightMap,this._initNodes),{missingInputs:o,dynamicNode:s,syncInputs:i}=n;if(s!=null)throw new Error(`This execution contains the node '${s.name}', which has the dynamic op '${s.op}'. 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Missing the following inputs: [${o}]`)}return YD(this.graph,this.weightMap,n)}execute(t,e){t=this.mapInputs(t);let n=Object.keys(t).sort();this.checkInputs(t),this.checkInputShapeAndType(t),e=this.mapOutputs(e),this.checkOutputs(e);let o=n.map(p=>this.graph.nodes[Cn(p)[0]]),s=e.map(p=>Cn(p)[0]),i=s.map(p=>this.graph.nodes[p]);this.resetIntermediateTensors(),i.length===0&&(i=this._outputs);let a=this.getCompilationKey(o,i),u=this.compiledMap.get(a);u==null&&(u=this.compile(t,i),this.compiledMap.set(a,u));let l={},c={};return B(()=>{let p=new cg(this.weightMap,l,c,this.functionExecutorMap),m=Object.assign({},this.weightMap);Object.keys(t).forEach(h=>{let[g,y]=Cn(h),b=[];b[y]=t[h],m[g]=b});let f=this.getFrozenTensorIds(m),d={};for(let h=0;h<u.length;h++){let g=u[h];if(!m[g.name]){let y=Ck(g,m,p,this._resourceManager);if(x.isPromise(y))throw new Error(`The execution of the op '${g.op}' returned a promise. 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c}processChildNodes(t,e,n,o,s,i){t.children.forEach(a=>{let[u]=Do(a.name,n);s[u]||!i.has(a.name)||(a.op==="Merge"?a.inputNames.some(l=>!!Cr(l,o,n))&&(s[u]=!0,e.push({contexts:n.currentContext,node:a})):a.inputNames.every(l=>!!Cr(l,o,n))&&(s[u]=!0,e.push({contexts:n.currentContext,node:a})))})}dispose(){Object.keys(this.weightMap).forEach(t=>this.weightMap[t].forEach(e=>e.dispose()))}checkInputShapeAndType(t){Object.keys(t).forEach(e=>{let n=t[e],[o]=Cn(e),s=this.graph.nodes[o];if(s.attrParams.shape&&s.attrParams.shape.value){let i=s.attrParams.shape.value,a=i.length===n.shape.length&&n.shape.every((u,l)=>i[l]===-1||i[l]===u);x.assert(a,()=>`The shape of dict['${s.name}'] provided in model.execute(dict) must be [${i}], but was [${n.shape}]`)}s.attrParams.dtype&&s.attrParams.dtype.value&&x.assert(n.dtype===s.attrParams.dtype.value,()=>`The dtype of dict['${s.name}'] provided in model.execute(dict) must be ${s.attrParams.dtype.value}, but was ${n.dtype}`)})}mapInputs(t){let e={};for(let n in t)if(this._signature!=null&&this._signature.inputs!=null&&this._signature.inputs[n]!=null){let o=this._signature.inputs[n];e[o.name]=t[n]}else e[n]=t[n];return e}checkInputs(t){let e=Object.keys(t).filter(n=>{let[o]=Cn(n);return this.graph.nodes[o]==null});if(e.length>0)throw new Error(`The dict provided in model.execute(dict) has keys: [${e}] that are not part of graph`)}mapOutputs(t){return t.map(e=>this._signature!=null&&this._signature.outputs!=null&&this._signature.outputs[e]!=null?this._signature.outputs[e].name:e,{})}checkOutputs(t){t.forEach(e=>{let[n]=Cn(e);if(!this.graph.nodes[n])throw new Error(`The output '${e}' is not found in the graph`)})}};var $b=class{constructor(t={},e={}){this.hashTableNameToHandle=t,this.hashTableMap=e}addHashTable(t,e){this.hashTableNameToHandle[t]=e.handle,this.hashTableMap[e.id]=e}getHashTableHandleByName(t){return this.hashTableNameToHandle[t]}getHashTableById(t){return this.hashTableMap[t]}dispose(){for(let t in 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this.set(e,this.pop()),n}};var Hc=class extends Xf{constructor(){super(Hc.INITIAL_CAPACITY)}isFull(){return!1}push(t){super.isFull()&&this.expand(),super.push(t)}unshift(t){super.isFull()&&this.expand(),super.unshift(t)}expand(){let t=this.capacity*2,e=new Array(t),n=this.length();for(let o=0;o<n;o++)e[o]=this.get(this.wrap(this.begin+o));this.data=e,this.capacity=t,this.doubledCapacity=2*this.capacity,this.begin=0,this.end=n}};Hc.INITIAL_CAPACITY=32;function qk(r){return new Rk(r)}function pg(r){return new Ok(r)}function bF(r,t){return new Pb(r,t)}function wF(r,t=bl.FAIL){return new Uk(r,t)}var er=class{async toArray(){let t=[],e=await this.next();for(;!e.done;)t.push(e.value),e=await this.next();return t}async toArrayForTest(){let t=this.prefetch(100),e=[],n=await t.next();for(;!n.done;)e.push(n.value),n=await t.next();return e}async resolveFully(){let t=await this.next();for(;!t.done;)t=await this.next()}async resolveWhile(t){let e=await this.next(),n=t(e.value);for(;!e.done&&n;)e=await this.next(),n=t(e.value)}handleErrors(t){return new Gk(this,t)}filter(t){return new Bk(this,t)}map(t){return new Vk(this,t)}mapAsync(t){return new Mb(this,t)}serialMapAsync(t){return new Mb(this,t).serial()}flatmap(t){return new Wk(this,t)}async forEachAsync(t){return this.map(t).resolveFully()}async serialForEach(t){return this.serialMapAsync(t).resolveWhile(e=>e===!0)}rowMajorBatch(t,e=!0){return new zk(this,t,e)}columnMajorBatch(t,e=!0,n=Fk){return this.rowMajorBatch(t,e).map(s=>dF(s,n))}concatenate(t,e){return new Pb(qk([this,t]),e)}take(t){return t<0||t==null?this:new Lk(this,t)}skip(t){return t<0||t==null?this:new Pk(this,t)}prefetch(t){return new Lb(this,t)}shuffle(t,e){return new Hk(this,t,e)}serial(){return new Mk(this)}},Rk=class extends er{constructor(t){super(),this.items=t,this.trav=0}summary(){return`Array of ${this.items.length} items`}async next(){if(this.trav>=this.items.length)return{value:null,done:!0};let t=this.items[this.trav];return this.trav++,{value:xF(t),done:!1}}},Ok=class extends er{constructor(t){super(),this.nextFn=t}summary(){return"Function call"}async next(){try{return this.nextFn()}catch(t){throw t.message=`Error thrown while iterating through a dataset: ${t.message}`,t}}},Mk=class extends er{constructor(t){super(),this.upstream=t,this.lastRead=Promise.resolve({value:null,done:!1})}summary(){return`${this.upstream.summary()} -> Serial`}async next(){return this.lastRead=this.lastRead.then(()=>this.serialNext()),this.lastRead}async serialNext(){return this.upstream.next()}},Pk=class extends er{constructor(t,e){super(),this.upstream=t,this.maxCount=e,this.count=0,this.lastRead=Promise.resolve({value:null,done:!1})}summary(){return`${this.upstream.summary()} -> Skip`}async next(){return this.lastRead=this.lastRead.then(()=>this.serialNext()),this.lastRead}async serialNext(){for(;this.count++<this.maxCount;){let t=await this.upstream.next();if(t.done)return t;_t(t.value)}return this.upstream.next()}},Lk=class extends er{constructor(t,e){super(),this.upstream=t,this.maxCount=e,this.count=0}summary(){return`${this.upstream.summary()} -> Take`}async next(){return this.count++>=this.maxCount?{value:null,done:!0}:this.upstream.next()}},zk=class extends er{constructor(t,e,n=!0){super(),this.upstream=t,this.batchSize=e,this.enableSmallLastBatch=n,this.lastRead=Promise.resolve({value:null,done:!1})}summary(){return`${this.upstream.summary()} -> RowMajorBatch`}async next(){return this.lastRead=this.lastRead.then(()=>this.serialNext()),this.lastRead}async serialNext(){let t=[];for(;t.length<this.batchSize;){let e=await this.upstream.next();if(e.done)return this.enableSmallLastBatch&&t.length>0?{value:t,done:!1}:{value:null,done:!0};t.push(e.value)}return{value:t,done:!1}}},Bk=class extends er{constructor(t,e){super(),this.upstream=t,this.predicate=e,this.lastRead=Promise.resolve({value:null,done:!1})}summary(){return`${this.upstream.summary()} -> Filter`}async next(){return this.lastRead=this.lastRead.then(()=>this.serialNext()),this.lastRead}async serialNext(){for(;;){let t=await this.upstream.next();if(t.done||this.predicate(t.value))return t;_t(t.value)}}},Vk=class extends er{constructor(t,e){super(),this.upstream=t,this.transform=e}summary(){return`${this.upstream.summary()} -> Map`}async next(){let t=await this.upstream.next();if(t.done)return{value:null,done:!0};let e=fo.getTensorsInContainer(t.value),n=this.transform(t.value),o=fo.getTensorsInContainer(n);for(let s of e)fo.isTensorInList(s,o)||s.dispose();return{value:n,done:!1}}},Gk=class extends er{constructor(t,e){super(),this.upstream=t,this.handler=e,this.count=0,this.lastRead=Promise.resolve({value:null,done:!1})}summary(){return`${this.upstream.summary()} -> handleErrors`}async next(){return this.lastRead=this.lastRead.then(()=>this.serialNext()),this.lastRead}async serialNext(){for(;;)try{return await this.upstream.next()}catch(t){if(!this.handler(t))return{value:null,done:!0}}}},Mb=class extends er{constructor(t,e){super(),this.upstream=t,this.transform=e}summary(){return`${this.upstream.summary()} -> AsyncMap`}async next(){let t=await this.upstream.next();if(t.done)return{value:null,done:!0};let e=fo.getTensorsInContainer(t.value),n=await this.transform(t.value),o=fo.getTensorsInContainer(n);for(let s of e)fo.isTensorInList(s,o)||s.dispose();return{value:n,done:!1}}},qc=class extends er{constructor(){super(),this.outputQueue=new Hc,this.lastRead=Promise.resolve({value:null,done:!1})}async next(){return this.lastRead=this.lastRead.then(()=>this.serialNext()),this.lastRead}async serialNext(){for(;this.outputQueue.length()===0;)if(!await this.pump())return{value:null,done:!0};return{value:this.outputQueue.shift(),done:!1}}},Wk=class extends qc{constructor(t,e){super(),this.upstream=t,this.transform=e}summary(){return`${this.upstream.summary()} -> Flatmap`}async pump(){let t=await this.upstream.next();if(t.done)return!1;let e=fo.getTensorsInContainer(t.value),n=this.transform(t.value),o=fo.getTensorsInContainer(n);this.outputQueue.pushAll(n);for(let s of e)fo.isTensorInList(s,o)||s.dispose();return!0}},Pb=class extends er{constructor(t,e){super(),this.baseErrorHandler=e,this.lastRead=null,this.iterator=null,this.moreIterators=t}summary(){return"TODO: fill in upstream of chained summaries -> Chained"}async next(){return this.lastRead=this.readFromChain(this.lastRead),this.lastRead}async readFromChain(t){if(await t,this.iterator==null){let n=await this.moreIterators.next();if(n.done)return{value:null,done:!0};this.iterator=n.value,this.baseErrorHandler!=null&&(this.iterator=this.iterator.handleErrors(this.baseErrorHandler))}let e=await this.iterator.next();return e.done?(this.iterator=null,this.readFromChain(t)):e}},bl;(function(r){r[r.FAIL=0]="FAIL",r[r.SHORTEST=1]="SHORTEST",r[r.LONGEST=2]="LONGEST"})(bl||(bl={}));var Uk=class extends er{constructor(t,e=bl.FAIL){super(),this.iterators=t,this.mismatchMode=e,this.count=0,this.currentPromise=null}summary(){return"{TODO: fill in upstream of zip summaries} -> Zip"}async nextState(t){await t;let e=0,n=0;function o(i){return i instanceof er?{value:i.next().then(u=>(e++,u.done&&n++,u.value)),recurse:!1}:{value:null,recurse:!0}}let s=await Ob(this.iterators,o);if(e===n)return{value:null,done:!0};if(n>0)switch(this.mismatchMode){case bl.FAIL:throw new Error(`Zipped streams should have the same length. Mismatched at element ${this.count}.`);case bl.SHORTEST:return{value:null,done:!0};case bl.LONGEST:default:}return this.count++,{value:s,done:!1}}async next(){return this.currentPromise=this.nextState(this.currentPromise),this.currentPromise}},Lb=class extends er{constructor(t,e){super(),this.upstream=t,this.bufferSize=e,this.buffer=new Xf(e)}summary(){return`${this.upstream.summary()} -> Prefetch`}refill(){for(;!this.buffer.isFull();){let t=this.upstream.next();this.buffer.push(t)}}next(){return this.refill(),this.buffer.shift()}},Hk=class extends Lb{constructor(t,e,n){super(t,e),this.upstream=t,this.windowSize=e,this.upstreamExhausted=!1,this.random=yF.alea(n||x.now().toString()),this.lastRead=Promise.resolve({value:null,done:!1})}async next(){return this.lastRead=this.lastRead.then(()=>this.serialNext()),this.lastRead}randomInt(t){return Math.floor(this.random()*t)}chooseIndex(){return this.randomInt(this.buffer.length())}async serialNext(){for(this.upstreamExhausted||this.refill();!this.buffer.isEmpty();){let t=this.chooseIndex(),e=await this.buffer.shuffleExcise(t);if(e.done)this.upstreamExhausted=!0;else return this.refill(),e}return{value:null,done:!0}}};var fi=class{constructor(){this.size=null}batch(t,e=!0){let n=this;x.assert(t>0,()=>`batchSize needs to be positive, but it is
${t}`);let o;return this.size===1/0||this.size==null?o=this.size:e?o=Math.ceil(this.size/t):o=Math.floor(this.size/t),Dn(async()=>(await n.iterator()).columnMajorBatch(t,e,RZ),o)}concatenate(t){let e=this,n;return this.size===1/0||t.size===1/0?n=1/0:this.size!=null&&t.size!=null?n=this.size+t.size:n=null,Dn(async()=>(await e.iterator()).concatenate(await t.iterator()),n)}filter(t){let e=this,n;return this.size===1/0?n=1/0:n=null,Dn(async()=>(await e.iterator()).filter(o=>B(()=>t(o))),n)}async forEachAsync(t){return(await this.iterator()).forEachAsync(t)}map(t){let e=this;return Dn(async()=>(await e.iterator()).map(n=>B(()=>t(n))),this.size)}mapAsync(t){let e=this;return Dn(async()=>(await e.iterator()).mapAsync(t),this.size)}prefetch(t){if(t==null)throw new RangeError("`Dataset.prefetch()` requires bufferSize to be specified.");let e=this;return Dn(async()=>(await e.iterator()).prefetch(t),this.size)}repeat(t){let e=this,n;return this.size!=null&&t>0?n=this.size*t:t===0?n=0:this.size!=null&&(t===void 0||t<0)?n=1/0:n=null,Dn(async()=>{let o=pg(async()=>({value:await e.iterator(),done:!1}));return bF(o.take(t))},n)}skip(t){let e=this,n;return this.size!=null&&t>=0&&this.size>=t?n=this.size-t:this.size!=null&&(this.size<t||t===void 0||t<0)?n=0:n=null,Dn(async()=>(await e.iterator()).skip(t),n)}shuffle(t,e,n=!0){if(t==null||t<0)throw this.size==null?new RangeError("`Dataset.shuffle()` requires bufferSize to be specified."):new RangeError(`\`Dataset.shuffle()\` requires bufferSize to be specified. If your data fits in main memory (for regular JS objects), and/or GPU memory (for \`tf.Tensor\`s), consider setting bufferSize to the dataset size (${this.size} elements)`);let o=this,s=vF.alea(e||x.now().toString());return Dn(async()=>{let i=s.int32();return n&&(i+=s.int32()),(await o.iterator()).shuffle(t,i.toString())},this.size)}take(t){let e=this,n;return this.size!=null&&this.size>t?n=t:this.size!=null&&this.size<=t?n=this.size:n=null,Dn(async()=>(await e.iterator()).take(t),n)}async toArray(){if(this.size===1/0)throw new Error("Can not convert infinite data stream to array.");return(await this.iterator()).toArray()}async toArrayForTest(){if(this.size===1/0)throw new Error("Can not convert infinite data stream to array.");return(await this.iterator()).toArrayForTest()}};fi.MAX_BUFFER_SIZE=1e4;function Dn(r,t=null){return new class extends fi{constructor(){super(...arguments),this.size=t}async iterator(){return r()}}}function CF(r){return Dn(async()=>qk(r),r.length)}function IF(r){if(!xu(r))throw new Error("The argument to zip() must be an object or array.");let t;if(Array.isArray(r))for(let e=0;e<r.length;e++)t=t==null?r[e].size:Math.min(t,r[e].size);else if(r instanceof Object)for(let e in r)t=t==null?r[e].size:Math.min(t,r[e].size);return Dn(async()=>{let e=await Ob(r,n=>{if(n instanceof fi)return{value:n.iterator(),recurse:!1};if(xu(n))return{value:null,recurse:!0};throw new Error("Leaves of the structure passed to zip() must be Datasets, not primitives.")});return wF(e,bl.SHORTEST)},t)}function RZ(r){if(r===null)return null;let t=r[0];return gF(t)?{value:OZ(r),recurse:!1}:{value:null,recurse:!0}}function OZ(r){if(r.length===0)throw new Error("Can't make a batch of zero elements.");return r[0]instanceof Lt?Ze(r):$r(r)}var Yf=class extends fi{constructor(t){super(),this.input=t}async iterator(){return(await this.input.iterator()).decodeUTF8().split(`
`).map(o=>(o.endsWith("\r")&&(o=o.slice(0,-1)),o))}};var zb='"',mg=Symbol("out"),SF=Symbol("field"),Bb=Symbol("quote"),Kk=Symbol("quoteafterquote"),kF=Symbol("quoteinquote"),Zf=class extends fi{constructor(t,e){super(),this.input=t,this.hasHeader=!0,this.fullColumnNames=null,this.columnNamesValidated=!1,this.columnConfigs=null,this.configuredColumnsOnly=!1,this.delimiter=",",this.delimWhitespace=!1,this.base=new Yf(t),e||(e={}),this.hasHeader=e.hasHeader!==!1,this.fullColumnNames=e.columnNames,this.columnConfigs=e.columnConfigs,this.configuredColumnsOnly=e.configuredColumnsOnly,e.delimWhitespace?(x.assert(e.delimiter==null,()=>"Delimiter should not be provided when delimWhitespace is true."),this.delimWhitespace=!0,this.delimiter=" "):this.delimiter=e.delimiter?e.delimiter:","}async columnNames(){return this.columnNamesValidated||await this.setColumnNames(),this.configuredColumnsOnly?Object.keys(this.columnConfigs):this.fullColumnNames}async setColumnNames(){let t=await this.maybeReadHeaderLine();if(!this.fullColumnNames&&!t)throw new Error("Column names must be provided if there is no header line.");this.fullColumnNames&&t&&x.assert(t.length===this.fullColumnNames.length,()=>"The length of provided columnNames ("+this.fullColumnNames.length.toString()+") does not match the length of the header line read from file ("+t.length.toString()+")."),this.fullColumnNames||(this.fullColumnNames=t);let e=this.fullColumnNames.reduce((o,s)=>(o[s]=o[s]+1||1,o),{}),n=Object.keys(e).filter(o=>e[o]>1);if(x.assert(n.length===0,()=>"Duplicate column names found: "+n.toString()),this.columnConfigs){for(let o of Object.keys(this.columnConfigs))if(this.fullColumnNames.indexOf(o)===-1)throw new Error('The key "'+o+'" provided in columnConfigs does not match any of the column names ('+this.fullColumnNames.toString()+").")}this.columnNamesValidated=!0}async maybeReadHeaderLine(){if(this.hasHeader){let e=await(await this.base.iterator()).next();if(e.done)throw new Error("No data was found for CSV parsing.");let n=e.value;return this.parseRow(n,!1)}else return null}async iterator(){this.columnNamesValidated||await this.setColumnNames();let t=await this.base.iterator();return this.hasHeader&&(t=t.skip(1)),t.map(e=>this.makeDataElement(e))}makeDataElement(t){let e=this.parseRow(t),n={},o={};for(let s=0;s<this.fullColumnNames.length;s++){let i=this.fullColumnNames[s],a=this.columnConfigs?this.columnConfigs[i]:null;if(!(this.configuredColumnsOnly&&!a)){let u=e[s],l=null;if(u==="")if(a&&a.default!==void 0)l=a.default;else{if(a&&(a.required||a.isLabel))throw new Error(`Required column ${i} is empty in this line: ${t}`);l=void 0}else{let c=Number(u);if(isNaN(c))a&&a.dtype==="bool"?l=this.getBoolean(u):l=u;else if(!a||!a.dtype)l=c;else switch(a.dtype){case"float32":l=c;break;case"int32":l=Math.floor(c);break;case"bool":l=this.getBoolean(u);break;default:l=c}}a&&a.isLabel?o[i]=l:n[i]=l}}return Object.keys(o).length===0?n:{xs:n,ys:o}}getBoolean(t){return t==="1"||t.toLowerCase()==="true"?1:0}parseRow(t,e=!0){let n=[],o=0,s=t.length,i=mg;for(let a=0;a<s;a++)switch(i){case mg:switch(t.charAt(a)){case zb:o=a+1,i=Bb;break;case this.delimiter:if(o=a+1,this.delimiter===" "&&this.delimWhitespace)break;n.push(""),i=mg;break;default:i=SF,o=a;break}break;case SF:switch(t.charAt(a)){case this.delimiter:n.push(t.substring(o,a)),i=mg,o=a+1;break;default:}break;case Bb:switch(t.charAt(a)){case zb:i=Kk;break;default:}break;case Kk:switch(t.charAt(a)){case this.delimiter:n.push(t.substring(o,a-1)),i=mg,o=a+1;break;case zb:i=Bb;break;default:i=kF;break}break;case kF:switch(t.charAt(a)){case zb:i=Bb;break;default:}break;default:}if(i===Kk?n.push(t.substring(o,s-1)):n.push(t.substring(o)),e&&n.length!==this.fullColumnNames.length)throw new Error(`Invalid row in csv file. Should have ${this.fullColumnNames.length} elements in a row, but got ${n}`);return n}};var Jf=class extends er{constructor(t){super(),this.microphoneConfig=t,this.isClosed=!1,this.fftSize=t.fftSize||1024;let e=Math.log2(this.fftSize);if(this.fftSize<0||e<4||e>14||!Number.isInteger(e))throw new Error(`Invalid fftSize: it must be a power of 2 between 2 to 4 and 2 to 14, but got ${this.fftSize}`);if(this.numFrames=t.numFramesPerSpectrogram||43,this.sampleRateHz=t.sampleRateHz,this.columnTruncateLength=t.columnTruncateLength||this.fftSize,this.audioTrackConstraints=t.audioTrackConstraints,this.smoothingTimeConstant=t.smoothingTimeConstant||0,this.includeSpectrogram=t.includeSpectrogram!==!1,this.includeWaveform=t.includeWaveform===!0,!this.includeSpectrogram&&!this.includeWaveform)throw new Error("Both includeSpectrogram and includeWaveform are false. At least one type of data should be returned.")}summary(){return"microphone"}static async create(t={}){if(!V().get("IS_BROWSER"))throw new Error("microphone API is only supported in browser environment.");let e=new Jf(t);return await e.start(),e}async start(){try{this.stream=await navigator.mediaDevices.getUserMedia({audio:this.audioTrackConstraints==null?!0:this.audioTrackConstraints,video:!1})}catch(n){throw new Error(`Error thrown while initializing video stream: ${n.message}`)}if(!this.stream)throw new Error("Could not obtain audio from microphone.");let t=window.AudioContext||window.webkitAudioContext;if(this.audioContext=new t,!this.sampleRateHz)this.sampleRateHz=this.audioContext.sampleRate;else if(this.audioContext.sampleRate!==this.sampleRateHz)throw new Error(`Mismatch in sampling rate: Expected: ${this.sampleRateHz}; Actual: ${this.audioContext.sampleRate}`);let e=this.audioContext.createMediaStreamSource(this.stream);this.analyser=this.audioContext.createAnalyser(),this.analyser.fftSize=this.fftSize*2,this.analyser.smoothingTimeConstant=this.smoothingTimeConstant,e.connect(this.analyser),this.freqData=new Float32Array(this.fftSize),this.timeData=new Float32Array(this.fftSize)}async next(){if(this.isClosed)return{value:null,done:!0};let t,e,n=await this.getAudioData();if(this.includeSpectrogram){let o=this.flattenQueue(n.freqDataQueue);t=this.getTensorFromAudioDataArray(o,[this.numFrames,this.columnTruncateLength,1])}if(this.includeWaveform){let o=this.flattenQueue(n.timeDataQueue);e=this.getTensorFromAudioDataArray(o,[this.numFrames*this.fftSize,1])}return{value:{spectrogram:t,waveform:e},done:!1}}async capture(){return(await this.next()).value}async getAudioData(){let t=[],e=[],n=0;return new Promise(o=>{let s=setInterval(()=>{this.includeSpectrogram&&(this.analyser.getFloatFrequencyData(this.freqData),this.freqData[0]===-1/0&&o({freqDataQueue:t,timeDataQueue:e}),t.push(this.freqData.slice(0,this.columnTruncateLength))),this.includeWaveform&&(this.analyser.getFloatTimeDomainData(this.timeData),e.push(this.timeData.slice())),++n===this.numFrames&&(clearInterval(s),o({freqDataQueue:t,timeDataQueue:e}))},this.fftSize/this.sampleRateHz*1e3)})}stop(){this.isClosed||(this.isClosed=!0,this.analyser.disconnect(),this.audioContext.close(),this.stream!=null&&this.stream.getTracks().length>0&&this.stream.getTracks()[0].stop())}toArray(){throw new Error("Can not convert infinite audio stream to array.")}getSampleRate(){return this.sampleRateHz}flattenQueue(t){let e=t[0].length,n=new Float32Array(t.length*e);return t.forEach((o,s)=>n.set(o,s*e)),n}getTensorFromAudioDataArray(t,e){let n=new Float32Array(x.sizeFromShape(e));return n.set(t,n.length-t.length),$r(n,e)}};var Qf=class extends er{constructor(t,e){if(super(),this.webcamVideoElement=t,this.webcamConfig=e,this.isClosed=!0,this.resize=!1,this.needToResize())if(this.resize=!0,this.cropSize=[this.webcamConfig.resizeHeight,this.webcamConfig.resizeWidth],this.cropBoxInd=Fe([0],"int32"),this.webcamConfig.centerCrop){let n=this.webcamConfig.resizeWidth*1/this.webcamVideoElement.width,o=this.webcamConfig.resizeHeight*1/this.webcamVideoElement.height,s=(1-n)/2,i=(1-o)/2,a=s+n,u=o+i;this.cropBox=Ui([i,s,u,a],[1,4])}else this.cropBox=Ui([0,0,1,1],[1,4])}summary(){return"webcam"}static async create(t,e={}){if(!V().get("IS_BROWSER"))throw new Error("tf.data.webcam is only supported in browser environment.");if(!t){if(t=document.createElement("video"),!e.resizeWidth||!e.resizeHeight)throw new Error("Please provide webcam video element, or resizeWidth and resizeHeight to create a hidden video element.");t.width=e.resizeWidth,t.height=e.resizeHeight}let n=new Qf(t,e);return await n.start(),n}async start(){this.webcamConfig.facingMode&&x.assert(this.webcamConfig.facingMode==="user"||this.webcamConfig.facingMode==="environment",()=>`Invalid webcam facing mode: ${this.webcamConfig.facingMode}. Please provide 'user' or 'environment'`);try{this.stream=await navigator.mediaDevices.getUserMedia({video:{deviceId:this.webcamConfig.deviceId,facingMode:this.webcamConfig.facingMode?this.webcamConfig.facingMode:"user",width:this.webcamVideoElement.width,height:this.webcamVideoElement.height}})}catch(t){throw t.message=`Error thrown while initializing video stream: ${t.message}`,t}if(!this.stream)throw new Error("Could not obtain video from webcam.");try{this.webcamVideoElement.srcObject=this.stream}catch(t){console.log(t),this.webcamVideoElement.src=window.URL.createObjectURL(this.stream)}return this.webcamVideoElement.play(),this.isClosed=!1,new Promise(t=>{this.webcamVideoElement.onloadedmetadata=()=>{t()}})}async next(){if(this.isClosed)return{value:null,done:!0};let t;try{t=Fx.fromPixels(this.webcamVideoElement)}catch(e){throw new Error(`Error thrown converting video to pixels: 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v=i?[g,c,m]:[g,m,c],k=a?[y,f,p]:[y,p,f],E=te({inputs:{x:o},backend:e,attrs:{shape:v}}),$=te({inputs:{x:s},backend:e,attrs:{shape:k}}),D=i?E.shape[1]:E.shape[2],F=i?E.shape[2]:E.shape[1],P=a?$.shape[1]:$.shape[2],W=Math.max(g,y),U=e.data.get(E.dataId).values,q=e.data.get($.dataId).values,K=x.computeStrides(E.shape),j=x.computeStrides($.shape),[Q,rt,X]=i?[K[0],1,K[1]]:[K[0],K[1],1],[nt,st,it]=a?[1,j[1],j[0]]:[j[1],1,j[0]],ft=F*P,at=Ct([W,F,P],E.dtype),xt=at.values,dt=e.blockSize;for(let bt=0;bt<W;bt++)for(let kt=0;kt<F;kt+=dt)for(let At=0;At<P;At+=dt)for(let Dt=0;Dt<D;Dt+=dt){let qt=Math.min(kt+dt,F),Kt=Math.min(At+dt,P),ce=Math.min(Dt+dt,D);for(let Ot=kt;Ot<qt;Ot++)for(let $e=At;$e<Kt;$e++){let ke=0;for(let ae=Dt;ae<ce;ae++){let Ke=Math.min(bt,g-1)*Q,Re=Math.min(bt,y-1)*it,rn=U[Ke+Ot*rt+ae*X],ze=q[ae*nt+$e*st+Re];ke+=rn*ze}xt[bt*ft+(Ot*P+$e)]+=ke}}return e.disposeIntermediateTensorInfo(E),e.disposeIntermediateTensorInfo($),e.makeTensorInfo(w,at.dtype,at.values)}var 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c=S.computePool3DInfo(s.shape,i,a,1,u,l),p=c.strideDepth,m=c.strideHeight,f=c.strideWidth,d=c.filterDepth,h=c.filterHeight,g=c.filterWidth,y=c.dilationDepth,b=c.dilationHeight,w=c.dilationWidth,v=c.effectiveFilterDepth,k=c.effectiveFilterHeight,E=c.effectiveFilterWidth,$=v-1-c.padInfo.front,D=E-1-c.padInfo.left,F=k-1-c.padInfo.top,P=Ct(s.shape,"float32"),W=1/(d*h*g),U=e.bufferSync(o);for(let q=0;q<c.batchSize;++q)for(let K=0;K<c.inChannels;++K)for(let j=0;j<c.inDepth;++j)for(let Q=0;Q<c.inHeight;++Q)for(let rt=0;rt<c.inWidth;++rt){let X=j-$,nt=Q-F,st=rt-D,it=0;for(let ft=0;ft<v;ft+=y){let at=(X+ft)/p;if(!(at<0||at>=c.outDepth||Math.floor(at)!==at))for(let xt=0;xt<k;xt+=b){let dt=(nt+xt)/m;if(!(dt<0||dt>=c.outHeight||Math.floor(dt)!==dt))for(let bt=0;bt<E;bt+=w){let kt=(st+bt)/f;if(kt<0||kt>=c.outWidth||Math.floor(kt)!==kt)continue;it+=U.get(q,at,dt,kt,K)}}}P.set(it*W,q,j,Q,rt,K)}return e.makeTensorInfo(P.shape,P.dtype,P.values)}var RR={kernelName:$p,backendName:"cpu",kernelFunc:I9};function S9(r){let{inputs:t,backend:e,attrs:n}=r,{dy:o,input:s}=t,i=s;ot([o,s],"avgPoolGrad");let{filterSize:a,strides:u,pad:l}=n,c=S.computePool2DInfo(i.shape,a,u,1,l),p=c.strideHeight,m=c.strideWidth,f=c.filterHeight,d=c.filterWidth,h=c.dilationHeight,g=c.dilationWidth,y=c.effectiveFilterHeight,b=c.effectiveFilterWidth,w=b-1-c.padInfo.left,v=y-1-c.padInfo.top,k=Ct(i.shape,"float32"),E=1/(f*d),$=e.data.get(o.dataId).values,D=Ct(o.shape,"float32",$);for(let F=0;F<c.batchSize;++F)for(let P=0;P<c.inChannels;++P)for(let W=0;W<c.inHeight;++W)for(let U=0;U<c.inWidth;++U){let q=W-v,K=U-w,j=0;for(let Q=0;Q<y;Q+=h){let rt=(q+Q)/p;if(!(rt<0||rt>=c.outHeight||Math.floor(rt)!==rt))for(let X=0;X<b;X+=g){let nt=(K+X)/m;if(nt<0||nt>=c.outWidth||Math.floor(nt)!==nt)continue;j+=D.get(F,rt,nt,P)}}k.set(j*E,F,W,U,P)}return e.makeTensorInfo(k.shape,k.dtype,k.values)}var OR={kernelName:Ap,backendName:"cpu",kernelFunc:S9};function 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e.makeTensorInfo(o.shape,o.dtype,h)}var MR={kernelName:fs,backendName:"cpu",kernelFunc:k9};function N9(r){let{inputs:t,backend:e,attrs:n}=r,{x:o}=t,{blockShape:s,crops:i}=n;ot([o],"batchToSpaceND");let a=s.reduce((y,b)=>y*b),u=S.getReshaped(o.shape,s,a),l=S.getPermuted(u.length,s.length),c=S.getReshapedPermuted(o.shape,s,a),p=S.getSliceBeginCoords(i,s.length),m=S.getSliceSize(c,i,s.length),f=te({inputs:{x:o},backend:e,attrs:{shape:u}}),d=He({inputs:{x:f},backend:e,attrs:{perm:l}}),h=te({inputs:{x:d},backend:e,attrs:{shape:c}}),g=Mo({inputs:{x:h},backend:e,attrs:{begin:p,size:m}});return e.disposeIntermediateTensorInfo(f),e.disposeIntermediateTensorInfo(d),e.disposeIntermediateTensorInfo(h),g}var PR={kernelName:vi,backendName:"cpu",kernelFunc:N9};function T9(r){let{inputs:t,backend:e,attrs:n}=r,{x:o,weights:s}=t,{size:i}=n,a=e.data.get(o.dataId).values,u=e.data.get(s.dataId).values,l=id(a,u,s.dtype,s.shape,i);return e.makeTensorInfo([i],s.dtype,l)}var LR={kernelName:Dp,backendName:"cpu",kernelFunc:T9};function _9(r){let{inputs:t,backend:e}=r,{s0:n,s1:o}=t,s=e.data.get(n.dataId).values,i=e.data.get(o.dataId).values,a=S.assertAndGetBroadcastShape(Array.from(s),Array.from(i));return e.makeTensorInfo([a.length],"int32",Int32Array.from(a))}var zR={kernelName:Fp,backendName:"cpu",kernelFunc:_9};var E9=Et(ao,(r,t)=>{let e=t;return r>e.clipValueMax?e.clipValueMax:r<e.clipValueMin?e.clipValueMin:r}),BR={kernelName:ao,backendName:"cpu",kernelFunc:E9};var A9=r=>{let{x:t}=r.inputs,e=r.backend,n=new Float32Array(x.sizeFromShape(t.shape)),o=e.data.get(t.dataId),s=o.complexTensorInfos.real,i=o.complexTensorInfos.imag,a=e.data.get(s.dataId).values,u=e.data.get(i.dataId).values;for(let l=0;l<a.length;l++){let c=a[l],p=u[l];n[l]=Math.hypot(c,p)}return e.makeOutput(n,t.shape,"float32")},VR={kernelName:Rl,backendName:"cpu",kernelFunc:A9};function Ji(r){let{inputs:t,backend:e}=r,{input:n}=t,o=e.data.get(n.dataId).complexTensorInfos.imag,s=e.data.get(o.dataId).values;return e.makeTensorInfo(o.shape,o.dtype,s)}var GR={kernelName:qp,backendName:"cpu",kernelFunc:Ji};function bu(r){let{inputs:t,backend:e,attrs:n}=r,{axis:o}=n,s=x.parseAxisParam(o,t[0].shape)[0],i=S.computeOutShape(t.map(h=>h.shape),s);if(x.sizeFromShape(i)===0)return e.makeTensorInfo(i,t[0].dtype,[]);let a=t.filter(h=>x.sizeFromShape(h.shape)>0);if(a.length===1)return qr({inputs:{x:a[0]},backend:e});let u=a.map(h=>h.shape);if(S.assertParamsConsistent(u,s),a[0].dtype==="complex64"){let h=a.map(v=>Fo({inputs:{input:v},backend:e})),g=a.map(v=>Ji({inputs:{input:v},backend:e})),y=bu({inputs:h,backend:e,attrs:{axis:s}}),b=bu({inputs:g,backend:e,attrs:{axis:s}}),w=Ir({inputs:{real:y,imag:b},backend:e});return h.forEach(v=>e.disposeIntermediateTensorInfo(v)),g.forEach(v=>e.disposeIntermediateTensorInfo(v)),e.disposeIntermediateTensorInfo(y),e.disposeIntermediateTensorInfo(b),w}let l=a.map(h=>{let g=x.sizeFromShape(h.shape.slice(s));return te({inputs:{x:h},backend:e,attrs:{shape:[-1,g]}})}),c=l.map(h=>({vals:e.data.get(h.dataId).values,shape:h.shape}));i=S.computeOutShape(l.map(h=>h.shape),1);let p=l[0].shape[0]===1,m=Kc(c,i,t[0].dtype,p),f=S.computeOutShape(a.map(h=>h.shape),s),d=e.makeTensorInfo(f,t[0].dtype,m);return l.forEach(h=>e.disposeIntermediateTensorInfo(h)),d}var WR={kernelName:Ci,backendName:"cpu",kernelFunc:bu};function EN(r){let{inputs:t,backend:e,attrs:n}=r,{x:o,filter:s}=t,{strides:i,pad:a,dataFormat:u,dilations:l,dimRoundingMode:c}=n;ot([o,s],"conv2d");let p=S.convertConv2DDataFormat(u),m=S.computeConv2DInfo(o.shape,s.shape,i,l,a,c,!1,p),f=m.filterHeight,d=m.filterWidth,h=m.dilationHeight,g=m.dilationWidth,y=m.padInfo.left,b=m.padInfo.top,w=m.dataFormat==="channelsLast",v=new 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$9(r){let{inputs:t,backend:e,attrs:n}=r,{x:o,dy:s}=t,{strides:i,pad:a,dataFormat:u,dimRoundingMode:l,filterShape:c}=n;ot([o,s],"conv2dBackpropFilter");let p=S.convertConv2DDataFormat(u),m=S.computeConv2DInfo(o.shape,c,i,1,a,l,!1,p),{strideHeight:f,strideWidth:d,filterHeight:h,filterWidth:g}=m,y=m.dataFormat==="channelsLast",b=new pe(m.filterShape,"float32"),w=m.padInfo.left,v=m.padInfo.top,k=e.data.get(o.dataId).values,E=e.data.get(s.dataId).values,$=new pe(o.shape,o.dtype,k),D=new pe(s.shape,s.dtype,E);for(let F=0;F<h;++F){let P=Math.max(0,Math.ceil((v-F)/f)),W=Math.min(m.outHeight,(m.inHeight+v-F)/f);for(let U=0;U<g;++U){let q=Math.max(0,Math.ceil((w-U)/d)),K=Math.min(m.outWidth,(m.inWidth+w-U)/d);for(let j=0;j<m.inChannels;++j)for(let Q=0;Q<m.outChannels;++Q){let rt=0;for(let X=0;X<m.batchSize;++X)for(let nt=P;nt<W;++nt){let st=F+nt*f-v;for(let it=q;it<K;++it){let ft=U+it*d-w;y?rt+=$.get(X,st,ft,j)*D.get(X,nt,it,Q):rt+=$.get(X,j,st,ft)*D.get(X,Q,nt,it)}}b.set(rt,F,U,j,Q)}}}return e.makeTensorInfo(b.shape,b.dtype,b.values)}var HR={kernelName:Op,backendName:"cpu",kernelFunc:$9};function D9(r){let{inputs:t,backend:e,attrs:n}=r,{dy:o,filter:s}=t,{inputShape:i,strides:a,pad:u,dataFormat:l,dimRoundingMode:c}=n;ot([o,s],"conv2dBackpropInput");let p=x.computeStrides(s.shape),m=x.computeStrides(o.shape),f=S.convertConv2DDataFormat(l),d=S.computeConv2DInfo(i,s.shape,a,1,u,c,!1,f),h=new pe(d.inShape,"float32"),g=h.values,y=e.data.get(o.dataId).values,b=e.data.get(s.dataId).values,[w,v,k]=p,{batchSize:E,filterHeight:$,filterWidth:D,inChannels:F,inHeight:P,inWidth:W,outChannels:U,outHeight:q,outWidth:K,strideHeight:j,strideWidth:Q}=d;f=d.dataFormat;let rt=$-1-d.padInfo.top,X=D-1-d.padInfo.left,nt=f==="channelsLast",st=h.strides[0],it=nt?h.strides[1]:h.strides[2],ft=nt?h.strides[2]:1,at=nt?1:h.strides[1],xt=m[0],dt=nt?m[1]:m[2],bt=nt?m[2]:1,kt=nt?1:m[1];for(let At=0;At<E;++At)for(let Dt=0;Dt<F;++Dt)for(let qt=0;qt<P;++qt){let 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l=x.computeStrides(o.shape),c=x.computeStrides(s.shape),p=S.computeConv3DInfo(o.shape,u,i,1,a),m=p.strideDepth,f=p.strideHeight,d=p.strideWidth,h=p.filterDepth,g=p.filterHeight,y=p.filterWidth,b=new pe(p.filterShape,"float32"),w=b.values,[v,k,E,$]=b.strides,D=e.data.get(s.dataId).values,[F,P,W,U]=c,q=e.data.get(o.dataId).values,[K,j,Q,rt]=l,X=p.padInfo.front,nt=p.padInfo.left,st=p.padInfo.top;for(let it=0;it<h;++it){let ft=Math.max(0,Math.ceil((X-it)/m)),at=Math.min(p.outDepth,(p.inDepth+X-it)/m),xt=it*v;for(let dt=0;dt<g;++dt){let bt=Math.max(0,Math.ceil((st-dt)/f)),kt=Math.min(p.outHeight,(p.inHeight+st-dt)/f),At=dt*k+xt;for(let Dt=0;Dt<y;++Dt){let qt=Math.max(0,Math.ceil((nt-Dt)/d)),Kt=Math.min(p.outWidth,(p.inWidth+nt-Dt)/d),ce=Dt*E+At;for(let Ot=0;Ot<p.inChannels;++Ot){let $e=Ot*$+ce;for(let ke=0;ke<p.outChannels;++ke){let ae=0;for(let Ke=0;Ke<p.batchSize;++Ke){let Re=Ke*K,rn=Ke*F;for(let ze=ft;ze<at;++ze){let Or=(it+ze*m-X)*j+Re,nn=ze*P+rn;for(let on=bt;on<kt;++on){let 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oO={kernelName:zp,backendName:"cpu",kernelFunc:W9};function U9(r){let{inputs:t,backend:e,attrs:n}=r,{dy:o,filter:s}=t,{strides:i,dilations:a,pad:u,dimRoundingMode:l,inputShape:c}=n;ot([o,s],"depthwiseConv2DNativeBackpropInput");let p=x.computeStrides(o.shape),m=x.computeStrides(s.shape),f=S.computeConv2DInfo(c,s.shape,i,a,u,l,!0),d=new pe(f.inShape,"float32"),h=d.values,[g,y,b]=d.strides,w=e.data.get(o.dataId).values,[v,k,E]=p,$=e.data.get(s.dataId).values,[D,F,P]=m,{batchSize:W,filterHeight:U,filterWidth:q,inChannels:K,inHeight:j,inWidth:Q,outChannels:rt,outHeight:X,outWidth:nt,strideHeight:st,strideWidth:it}=f,ft=U-1-f.padInfo.top,at=q-1-f.padInfo.left,xt=rt/K;for(let dt=0;dt<W;++dt)for(let bt=0;bt<K;++bt)for(let kt=0;kt<j;++kt){let At=kt-ft,Dt=Math.max(0,Math.ceil(At/st)),qt=Math.min(X,(U+At)/st);for(let Kt=0;Kt<Q;++Kt){let ce=Kt-at,Ot=Math.max(0,Math.ceil(ce/it)),$e=Math.min(nt,(q+ce)/it),ke=0;for(let ae=Dt;ae<qt;++ae){let Ke=ae*st-At;for(let Re=Ot;Re<$e;++Re){let 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aO={kernelName:Ml,backendName:"cpu",kernelFunc:({inputs:r,backend:t,attrs:e})=>{let{x:n,filter:o}=r,{strides:s,pad:i,dilations:a}=e,u=t,l=u.data.get(n.dataId).values,c=n.shape.length,p=u.data.get(o.dataId).values,m=o.shape.length,{batchSize:f,inHeight:d,inWidth:h,inChannels:g,outHeight:y,outWidth:b,padInfo:w,strideHeight:v,strideWidth:k,filterHeight:E,filterWidth:$,dilationHeight:D,dilationWidth:F,outShape:P}=S.computeDilation2DInfo(n.shape,o.shape,s,i,"NHWC",a),W=x.sizeFromShape(P),U=P.length,q=x.getArrayFromDType(n.dtype,W);for(let j=0;j<f;++j)for(let Q=0;Q<y;++Q){let rt=Q*v-w.top;for(let X=0;X<b;++X){let nt=X*k-w.left;for(let st=0;st<g;++st){let it=Number.MIN_SAFE_INTEGER;for(let at=0;at<E;++at){let xt=rt+at*D;if(xt>=0&&xt<d)for(let dt=0;dt<$;++dt){let bt=nt+dt*F;if(bt>=0&&bt<h){let kt=x.locToIndex([j,xt,bt,st],c,x.computeStrides(n.shape)),At=x.locToIndex([at,dt,st],m,x.computeStrides(o.shape)),Dt=l[kt]+p[At];Dt>it&&(it=Dt)}}}let 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P=x.toNestedArray(F,l.data.get(s.dataId).values),W=x.makeZerosNestedTypedArray(n.shape,n.dtype);for(let q=0;q<m;++q)for(let K=0;K<g;++K){let j=K*w-b.top;for(let Q=0;Q<y;++Q){let rt=Q*v-b.left;for(let X=0;X<h;++X){let nt=Number.MIN_SAFE_INTEGER,st=j<0?0:j,it=rt<0?0:rt;for(let ft=0;ft<k;++ft){let at=j+ft*$;if(at>=0&&at<f)for(let xt=0;xt<E;++xt){let dt=rt+xt*D;if(dt>=0&&dt<d){let bt=c[q][at][dt][X]+p[ft][xt][X];bt>nt&&(nt=bt,st=at,it=dt)}}}W[q][st][it][X]+=P[q][K][Q][X]}}}return{dataId:l.write(x.toTypedArray(W,n.dtype),n.shape,n.dtype),shape:n.shape,dtype:n.dtype}}};function vl(r){let{inputs:t,backend:e,attrs:n}=r,{x:o}=t,{axis:s,keepDims:i}=n;ot(o,"sum");let a;o.dtype==="bool"?a=Ro({inputs:{x:o},backend:e,attrs:{dtype:"int32"}}):a=qr({inputs:{x:o},backend:e});let 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bool isnan_custom(float val) {
uint floatToUint = floatBitsToUint(val);
return (floatToUint & 0x7fffffffu) > 0x7f800000u;
}
bvec4 isnan_custom(vec4 val) {
return bvec4(isnan_custom(val.x),
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}
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#define round(value) newRound(value)
int newRound(float value) {
return int(floor(value + 0.5));
}
ivec4 newRound(vec4 value) {
return ivec4(floor(value + vec4(0.5)));
}
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#define isnan(value) isnan_custom(value)
bool isnan_custom(float val) {
return (val > 0. || val < 1. || val == 0.) ? false : true;
}
bvec4 isnan_custom(vec4 val) {
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}
`,u=`
uniform float INFINITY;
bool isinf(float val) {
return abs(val) == INFINITY;
}
bvec4 isinf(vec4 val) {
return equal(abs(val), vec4(INFINITY));
}
`,l=`
int round(float value) {
return int(floor(value + 0.5));
}
ivec4 round(vec4 value) {
return ivec4(floor(value + vec4(0.5)));
}
`),{version:r,attribute:t,varyingVs:e,varyingFs:n,texture2D:o,output:s,defineOutput:i,defineSpecialNaN:a,defineSpecialInf:u,defineRound:l}}function hi(r,t,e="index"){let n=x.computeStrides(t);return n.map((o,s)=>{let i=`int ${r[s]} = ${e} / ${o}`,a=s===n.length-1?`int ${r[s+1]} = ${e} - ${r[s]} * ${o}`:`index -= ${r[s]} * ${o}`;return`${i}; ${a};`}).join("")}function Qc(r,t,e="index"){let n=x.computeStrides(t);return n.map((o,s)=>{let i=`int ${r[s]} = ${e} / outShapeStrides[${s}]`,a=s===n.length-1?`int ${r[s+1]} = ${e} - ${r[s]} * outShapeStrides[${s}]`:`index -= ${r[s]} * outShapeStrides[${s}]`;return`${i}; ${a};`}).join("")}function JQ(r,t){let e=r.length,n=r.map(s=>`${t}[${s}]`),o=new Array(e-1);o[e-2]=n[e-1];for(let s=e-3;s>=0;--s)o[s]=`(${o[s+1]} * ${n[s+1]})`;return o}function YM(r,t,e="index"){let n=r.map((s,i)=>i),o=JQ(n,t);return o.map((s,i)=>{let a=`int ${r[i]} = ${e} / ${o[i]}`,u=i===o.length-1?`int ${r[i+1]} = ${e} - ${r[i]} * ${o[i]}`:`index -= ${r[i]} * ${o[i]}`;return`${a}; ${u};`}).join("")}function hd(r){let t=x.computeStrides(r).map(e=>e.toString());return`
int getFlatIndex(ivec3 coords) {
return coords.x * ${t[0]} + coords.y * ${t[1]} + coords.z;
}
`}function gd(){return`
int getFlatIndex(ivec3 coords) {
return coords.x * outShapeStrides[0] + coords.y * outShapeStrides[1] + coords.z;
}
`}var gw=`
const float FLOAT_MAX = 1.70141184e38;
const float FLOAT_MIN = 1.17549435e-38;
lowp vec4 encode_float(highp float v) {
if (isnan(v)) {
return vec4(255, 255, 255, 255);
}
highp float av = abs(v);
if(av < FLOAT_MIN) {
return vec4(0.0, 0.0, 0.0, 0.0);
} else if(v > FLOAT_MAX) {
return vec4(0.0, 0.0, 128.0, 127.0) / 255.0;
} else if(v < -FLOAT_MAX) {
return vec4(0.0, 0.0, 128.0, 255.0) / 255.0;
}
highp vec4 c = vec4(0,0,0,0);
highp float e = floor(log2(av));
highp float m = exp2(fract(log2(av))) - 1.0;
c[2] = floor(128.0 * m);
m -= c[2] / 128.0;
c[1] = floor(32768.0 * m);
m -= c[1] / 32768.0;
c[0] = floor(8388608.0 * m);
highp float ebias = e + 127.0;
c[3] = floor(ebias / 2.0);
ebias -= c[3] * 2.0;
c[2] += floor(ebias) * 128.0;
c[3] += 128.0 * step(0.0, -v);
return c / 255.0;
}
`;var{getBroadcastDims:ZM}=S;function JM(r,t,e){let n=[];if(r.forEach(f=>{let d=x.sizeFromShape(f.shapeInfo.logicalShape);if(f.shapeInfo.isUniform?n.push(`uniform float ${f.name}${d>1?`[${d}]`:""};`):(n.push(`uniform sampler2D ${f.name};`),n.push(`uniform int offset${f.name};`)),e.enableShapeUniforms){let{uniformShape:h}=xw(e.packedInputs,f.shapeInfo.logicalShape,f.shapeInfo.texShape);switch(h.length){case 1:n.push(`uniform int ${f.name}Shape;`);break;case 2:n.push(`uniform ivec2 ${f.name}Shape;`);break;case 3:n.push(`uniform ivec3 ${f.name}Shape;`);break;case 4:n.push(`uniform ivec4 ${f.name}Shape;`);break;default:break}n.push(`uniform ivec2 ${f.name}TexShape;`)}}),e.enableShapeUniforms){switch(t.logicalShape.length){case 1:n.push("uniform int outShape;");break;case 2:n.push("uniform ivec2 outShape;"),n.push("uniform int outShapeStrides;");break;case 3:n.push("uniform ivec3 outShape;"),n.push("uniform ivec2 outShapeStrides;");break;case 4:n.push("uniform ivec4 outShape;"),n.push("uniform ivec3 outShapeStrides;");break;default:break}n.push("uniform ivec2 outTexShape;")}e.customUniforms&&e.customUniforms.forEach(f=>{n.push(`uniform ${f.type} ${f.name}${f.arrayIndex?`[${f.arrayIndex}]`:""};`)});let o=n.join(`
`),s=r.map(f=>QQ(f,t,e.packedInputs,e.enableShapeUniforms)).join(`
`),i=t.texShape,a=qe(),u=rtt(a),l,c,p=stt(a);return t.isPacked?(l=ttt(t.logicalShape,i,e.enableShapeUniforms),c=ott(a)):(l=ett(t.logicalShape,i,e.enableShapeUniforms),c=ntt(a)),e.packedInputs&&(p+=utt),[p,u,c,o,l,s,e.userCode].join(`
`)}function yd(r,t=!1){let e=r.shapeInfo.logicalShape;switch(e.length){case 0:return vtt(r,t);case 1:return Itt(r,t);case 2:return ktt(r,t);case 3:return Ttt(r,t);case 4:return Ett(r,t);case 5:return Att(r);case 6:return $tt(r);default:throw new Error(`${e.length}-D input sampling is not yet supported`)}}function QM(r,t){switch(r.shapeInfo.logicalShape.length){case 0:return wtt(r);case 1:return Ctt(r,t);case 2:return Stt(r,t);case 3:return Ntt(r,t);default:return _tt(r,t)}}function QQ(r,t,e=!1,n){let o="";e?o+=QM(r,n):o+=yd(r,n);let s=r.shapeInfo.logicalShape,i=t.logicalShape;return s.length<=i.length&&(e?o+=Dtt(r,t):o+=Ftt(r,t)),o}function ttt(r,t,e){switch(r.length){case 0:return tP();case 1:return ctt(r,t,e);case 2:return ytt(r,t,e);case 3:return mtt(r,t,e);default:return dtt(r,t,e)}}function ett(r,t,e){switch(r.length){case 0:return tP();case 1:return ptt(r,t,e);case 2:return btt(r,t,e);case 3:return ftt(r,t,e);case 4:return htt(r,t,e);case 5:return gtt(r,t);case 6:return xtt(r,t);default:throw new Error(`${r.length}-D output sampling is not yet supported`)}}function rtt(r){return`
float sampleTexture(sampler2D textureSampler, vec2 uv) {
return ${r.texture2D}(textureSampler, uv).r;
}
`}function ntt(r){return`
void setOutput(float val) {
${r.output} = vec4(val, 0, 0, 0);
}
`}function ott(r){return`
void setOutput(vec4 val) {
${r.output} = val;
}
`}function stt(r){return`${r.version}
precision highp float;
precision highp int;
precision highp sampler2D;
${r.varyingFs} vec2 resultUV;
${r.defineOutput}
const vec2 halfCR = vec2(0.5, 0.5);
struct ivec5
{
int x;
int y;
int z;
int w;
int u;
};
struct ivec6
{
int x;
int y;
int z;
int w;
int u;
int v;
};
uniform float NAN;
${r.defineSpecialNaN}
${r.defineSpecialInf}
${r.defineRound}
int imod(int x, int y) {
return x - y * (x / y);
}
int idiv(int a, int b, float sign) {
int res = a / b;
int mod = imod(a, b);
if (sign < 0. && mod != 0) {
res -= 1;
}
return res;
}
//Based on the work of Dave Hoskins
//https://www.shadertoy.com/view/4djSRW
#define HASHSCALE1 443.8975
float random(float seed){
vec2 p = resultUV * seed;
vec3 p3 = fract(vec3(p.xyx) * HASHSCALE1);
p3 += dot(p3, p3.yzx + 19.19);
return fract((p3.x + p3.y) * p3.z);
}
${itt}
${att}
${ltt}
`}var itt=`
vec2 uvFromFlat(int texNumR, int texNumC, int index) {
int texR = index / texNumC;
int texC = index - texR * texNumC;
return (vec2(texC, texR) + halfCR) / vec2(texNumC, texNumR);
}
vec2 packedUVfrom1D(int texNumR, int texNumC, int index) {
int texelIndex = index / 2;
int texR = texelIndex / texNumC;
int texC = texelIndex - texR * texNumC;
return (vec2(texC, texR) + halfCR) / vec2(texNumC, texNumR);
}
`,att=`
vec2 packedUVfrom2D(int texelsInLogicalRow, int texNumR,
int texNumC, int row, int col) {
int texelIndex = (row / 2) * texelsInLogicalRow + (col / 2);
int texR = texelIndex / texNumC;
int texC = texelIndex - texR * texNumC;
return (vec2(texC, texR) + halfCR) / vec2(texNumC, texNumR);
}
`,ltt=`
vec2 packedUVfrom3D(int texNumR, int texNumC,
int texelsInBatch, int texelsInLogicalRow, int b,
int row, int col) {
int index = b * texelsInBatch + (row / 2) * texelsInLogicalRow + (col / 2);
int texR = index / texNumC;
int texC = index - texR * texNumC;
return (vec2(texC, texR) + halfCR) / vec2(texNumC, texNumR);
}
`,utt=`
float getChannel(vec4 frag, vec2 innerDims) {
vec2 modCoord = mod(innerDims, 2.);
return modCoord.x == 0. ?
(modCoord.y == 0. ? frag.r : frag.g) :
(modCoord.y == 0. ? frag.b : frag.a);
}
float getChannel(vec4 frag, int dim) {
float modCoord = mod(float(dim), 2.);
return modCoord == 0. ? frag.r : frag.g;
}
`;function tP(){return`
int getOutputCoords() {
return 0;
}
`}function ctt(r,t,e){let n=[Math.ceil(t[0]/2),Math.ceil(t[1]/2)];return n[0]===1?e?`
int getOutputCoords() {
return 2 * int(resultUV.x * ceil(float(outTexShape[1]) / 2.0));
}
`:`
int getOutputCoords() {
return 2 * int(resultUV.x * ${n[1]}.0);
}
`:n[1]===1?e?`
int getOutputCoords() {
return 2 * int(resultUV.y * ceil(float(outTexShape[0]) / 2.0));
}
`:`
int getOutputCoords() {
return 2 * int(resultUV.y * ${n[0]}.0);
}
`:e?`
int getOutputCoords() {
ivec2 packedTexShape = ivec2(ceil(float(outTexShape[0]) / 2.0), ceil(float(outTexShape[1]) / 2.0));
ivec2 resTexRC = ivec2(resultUV.yx *
vec2(packedTexShape[0], packedTexShape[1]));
return 2 * (resTexRC.x * packedTexShape[1] + resTexRC.y);
}
`:`
int getOutputCoords() {
ivec2 resTexRC = ivec2(resultUV.yx *
vec2(${n[0]}, ${n[1]}));
return 2 * (resTexRC.x * ${n[1]} + resTexRC.y);
}
`}function ptt(r,t,e){return t[0]===1?e?`
int getOutputCoords() {
return int(resultUV.x * float(outTexShape[1]));
}
`:`
int getOutputCoords() {
return int(resultUV.x * ${t[1]}.0);
}
`:t[1]===1?e?`
int getOutputCoords() {
return int(resultUV.y * float(outTexShape[0]));
}
`:`
int getOutputCoords() {
return int(resultUV.y * ${t[0]}.0);
}
`:e?`
int getOutputCoords() {
ivec2 resTexRC = ivec2(resultUV.yx *
vec2(outTexShape[0], outTexShape[1]));
return resTexRC.x * outTexShape[1] + resTexRC.y;
}
`:`
int getOutputCoords() {
ivec2 resTexRC = ivec2(resultUV.yx *
vec2(${t[0]}, ${t[1]}));
return resTexRC.x * ${t[1]} + resTexRC.y;
}
`}function mtt(r,t,e){if(e)return`
ivec3 getOutputCoords() {
ivec2 packedTexShape = ivec2(ceil(float(outTexShape[0]) / 2.0), ceil(float(outTexShape[1]) / 2.0));
int texelsInLogicalRow = int(ceil(float(outShape[2]) / 2.0));
int texelsInBatch = texelsInLogicalRow * int(ceil(float(outShape[1]) / 2.0));
ivec2 resTexRC = ivec2(resultUV.yx *
vec2(packedTexShape[0], packedTexShape[1]));
int index = resTexRC.x * packedTexShape[1] + resTexRC.y;
int b = index / texelsInBatch;
index -= b * texelsInBatch;
int r = 2 * (index / texelsInLogicalRow);
int c = imod(index, texelsInLogicalRow) * 2;
return ivec3(b, r, c);
}
`;let n=[Math.ceil(t[0]/2),Math.ceil(t[1]/2)],o=Math.ceil(r[2]/2),s=o*Math.ceil(r[1]/2);return`
ivec3 getOutputCoords() {
ivec2 resTexRC = ivec2(resultUV.yx *
vec2(${n[0]}, ${n[1]}));
int index = resTexRC.x * ${n[1]} + resTexRC.y;
int b = index / ${s};
index -= b * ${s};
int r = 2 * (index / ${o});
int c = imod(index, ${o}) * 2;
return ivec3(b, r, c);
}
`}function ftt(r,t,e){if(e)return`
ivec3 getOutputCoords() {
ivec2 resTexRC = ivec2(resultUV.yx *
vec2(outTexShape[0], outTexShape[1]));
int index = resTexRC.x * outTexShape[1] + resTexRC.y;
${Qc(["r","c","d"],r)}
return ivec3(r, c, d);
}
`;let n=hi(["r","c","d"],r);return`
ivec3 getOutputCoords() {
ivec2 resTexRC = ivec2(resultUV.yx *
vec2(${t[0]}, ${t[1]}));
int index = resTexRC.x * ${t[1]} + resTexRC.y;
${n}
return ivec3(r, c, d);
}
`}function dtt(r,t,e){if(e)return`
ivec4 getOutputCoords() {
ivec2 packedTexShape = ivec2(ceil(float(outTexShape[0]) / 2.0), ceil(float(outTexShape[1]) / 2.0));
ivec2 resTexRC = ivec2(resultUV.yx *
vec2(packedTexShape[0], packedTexShape[1]));
int index = resTexRC.x * packedTexShape[1] + resTexRC.y;
int texelsInLogicalRow = int(ceil(float(outShape[3]) / 2.0));
int texelsInBatch = texelsInLogicalRow * int(ceil(float(outShape[2]) / 2.0));
int texelsInBatchN = texelsInBatch * outShape[1];
int b2 = index / texelsInBatchN;
index -= b2 * texelsInBatchN;
int b = index / texelsInBatch;
index -= b * texelsInBatch;
int r = 2 * (index / texelsInLogicalRow);
int c = imod(index, texelsInLogicalRow) * 2;
return ivec4(b2, b, r, c);
}
`;let n=[Math.ceil(t[0]/2),Math.ceil(t[1]/2)],o=Math.ceil(r[r.length-1]/2),s=o*Math.ceil(r[r.length-2]/2),i=s,a="",u="b, r, c";for(let l=2;l<r.length-1;l++)i*=r[r.length-l-1],a=`
int b${l} = index / ${i};
index -= b${l} * ${i};
`+a,u=`b${l}, `+u;return`
ivec${r.length} getOutputCoords() {
ivec2 resTexRC = ivec2(resultUV.yx *
vec2(${n[0]}, ${n[1]}));
int index = resTexRC.x * ${n[1]} + resTexRC.y;
${a}
int b = index / ${s};
index -= b * ${s};
int r = 2 * (index / ${o});
int c = imod(index, ${o}) * 2;
return ivec${r.length}(${u});
}
`}function htt(r,t,e){if(e)return`
ivec4 getOutputCoords() {
ivec2 resTexRC = ivec2(resultUV.yx *
vec2(outTexShape[0], outTexShape[1]));
int index = resTexRC.x * outTexShape[1] + resTexRC.y;
${Qc(["r","c","d","d2"],r)}
return ivec4(r, c, d, d2);
}
`;let n=hi(["r","c","d","d2"],r);return`
ivec4 getOutputCoords() {
ivec2 resTexRC = ivec2(resultUV.yx *
vec2(${t[0]}, ${t[1]}));
int index = resTexRC.x * ${t[1]} + resTexRC.y;
${n}
return ivec4(r, c, d, d2);
}
`}function gtt(r,t){let e=hi(["r","c","d","d2","d3"],r);return`
ivec5 getOutputCoords() {
ivec2 resTexRC = ivec2(resultUV.yx * vec2(${t[0]},
${t[1]}));
int index = resTexRC.x * ${t[1]} + resTexRC.y;
${e}
ivec5 outShape = ivec5(r, c, d, d2, d3);
return outShape;
}
`}function xtt(r,t){let e=hi(["r","c","d","d2","d3","d4"],r);return`
ivec6 getOutputCoords() {
ivec2 resTexRC = ivec2(resultUV.yx *
vec2(${t[0]}, ${t[1]}));
int index = resTexRC.x * ${t[1]} + resTexRC.y;
${e}
ivec6 result = ivec6(r, c, d, d2, d3, d4);
return result;
}
`}function ytt(r,t,e){let n=[Math.ceil(t[0]/2),Math.ceil(t[1]/2)];if(x.arraysEqual(r,t))return e?`
ivec2 getOutputCoords() {
ivec2 packedTexShape = ivec2(ceil(float(outTexShape[0]) / 2.0), ceil(float(outTexShape[1]) / 2.0));
return 2 * ivec2(resultUV.yx * vec2(packedTexShape[0], packedTexShape[1]));
}
`:`
ivec2 getOutputCoords() {
return 2 * ivec2(resultUV.yx * vec2(${n[0]}, ${n[1]}));
}
`;let o=Math.ceil(r[1]/2);return e?`
ivec2 getOutputCoords() {
ivec2 packedTexShape = ivec2(ceil(float(outTexShape[0]) / 2.0), ceil(float(outTexShape[1]) / 2.0));
int texelsInLogicalRow = int(ceil(float(outShape[1]) / 2.0));
ivec2 resTexRC = ivec2(resultUV.yx *
vec2(packedTexShape[0], packedTexShape[1]));
int index = resTexRC.x * packedTexShape[1] + resTexRC.y;
int r = 2 * (index / texelsInLogicalRow);
int c = imod(index, texelsInLogicalRow) * 2;
return ivec2(r, c);
}
`:`
ivec2 getOutputCoords() {
ivec2 resTexRC = ivec2(resultUV.yx *
vec2(${n[0]}, ${n[1]}));
int index = resTexRC.x * ${n[1]} + resTexRC.y;
int r = 2 * (index / ${o});
int c = imod(index, ${o}) * 2;
return ivec2(r, c);
}
`}function btt(r,t,e){return x.arraysEqual(r,t)?e?`
ivec2 getOutputCoords() {
return ivec2(resultUV.yx * vec2(outTexShape[0], outTexShape[1]));
}
`:`
ivec2 getOutputCoords() {
return ivec2(resultUV.yx * vec2(${t[0]}, ${t[1]}));
}
`:r[1]===1?e?`
ivec2 getOutputCoords() {
ivec2 resTexRC = ivec2(resultUV.yx *
vec2(outTexShape[0], outTexShape[1]));
int index = resTexRC.x * outTexShape[1] + resTexRC.y;
return ivec2(index, 0);
}
`:`
ivec2 getOutputCoords() {
ivec2 resTexRC = ivec2(resultUV.yx *
vec2(${t[0]}, ${t[1]}));
int index = resTexRC.x * ${t[1]} + resTexRC.y;
return ivec2(index, 0);
}
`:r[0]===1?e?`
ivec2 getOutputCoords() {
ivec2 resTexRC = ivec2(resultUV.yx *
vec2(outTexShape[0], outTexShape[1]));
int index = resTexRC.x * outTexShape[1] + resTexRC.y;
return ivec2(0, index);
}
`:`
ivec2 getOutputCoords() {
ivec2 resTexRC = ivec2(resultUV.yx *
vec2(${t[0]}, ${t[1]}));
int index = resTexRC.x * ${t[1]} + resTexRC.y;
return ivec2(0, index);
}
`:e?`
ivec2 getOutputCoords() {
ivec2 resTexRC = ivec2(resultUV.yx *
vec2(outTexShape[0], outTexShape[1]));
int index = resTexRC.x * outTexShape[1] + resTexRC.y;
int r = index / outShape[1];
int c = index - r * outShape[1];
return ivec2(r, c);
}
`:`
ivec2 getOutputCoords() {
ivec2 resTexRC = ivec2(resultUV.yx *
vec2(${t[0]}, ${t[1]}));
int index = resTexRC.x * ${t[1]} + resTexRC.y;
int r = index / ${r[1]};
int c = index - r * ${r[1]};
return ivec2(r, c);
}
`}function tp(r){return`offset${r}`}function wtt(r){let t=r.name,e="get"+t.charAt(0).toUpperCase()+t.slice(1),n=qe();return`
vec4 ${e}() {
return ${n.texture2D}(${t}, halfCR);
}
`}function vtt(r,t){let e=r.name,n="get"+e.charAt(0).toUpperCase()+e.slice(1);if(r.shapeInfo.isUniform)return`float ${n}() {return ${e};}`;let[o,s]=r.shapeInfo.texShape;if(o===1&&s===1)return`
float ${n}() {
return sampleTexture(${e}, halfCR);
}
`;let i=tp(e);if(t)return`
float ${n}() {
vec2 uv = uvFromFlat(${e}TexShape[0], ${e}TexShape[1], ${i});
return sampleTexture(${e}, uv);
}
`;let[a,u]=r.shapeInfo.texShape;return`
float ${n}() {
vec2 uv = uvFromFlat(${a}, ${u}, ${i});
return sampleTexture(${e}, uv);
}
`}function Ctt(r,t){let e=r.name,n="get"+e.charAt(0).toUpperCase()+e.slice(1),o=r.shapeInfo.texShape,s=qe();if(t)return`
vec4 ${n}(int index) {
ivec2 packedTexShape = ivec2(ceil(float(${e}TexShape[0]) / 2.0), ceil(float(${e}TexShape[1]) / 2.0));
vec2 uv = packedUVfrom1D(
packedTexShape[0], packedTexShape[1], index);
return ${s.texture2D}(${e}, uv);
}
`;let i=[Math.ceil(o[0]/2),Math.ceil(o[1]/2)];return`
vec4 ${n}(int index) {
vec2 uv = packedUVfrom1D(
${i[0]}, ${i[1]}, index);
return ${s.texture2D}(${e}, uv);
}
`}function Itt(r,t){let e=r.name,n="get"+e.charAt(0).toUpperCase()+e.slice(1);if(r.shapeInfo.isUniform)return`
float ${n}(int index) {
${bd(r)}
}
`;let o=r.shapeInfo.texShape,s=o[0],i=o[1];if(i===1&&s===1)return`
float ${n}(int index) {
return sampleTexture(${e}, halfCR);
}
`;let a=tp(e);return i===1?t?`
float ${n}(int index) {
vec2 uv = vec2(0.5, (float(index + ${a}) + 0.5) / float(${e}TexShape[0]));
return sampleTexture(${e}, uv);
}
`:`
float ${n}(int index) {
vec2 uv = vec2(0.5, (float(index + ${a}) + 0.5) / ${s}.0);
return sampleTexture(${e}, uv);
}
`:s===1?t?`
float ${n}(int index) {
vec2 uv = vec2((float(index + ${a}) + 0.5) / float(${e}TexShape[1]), 0.5);
return sampleTexture(${e}, uv);
}
`:`
float ${n}(int index) {
vec2 uv = vec2((float(index + ${a}) + 0.5) / ${i}.0, 0.5);
return sampleTexture(${e}, uv);
}
`:t?`
float ${n}(int index) {
vec2 uv = uvFromFlat(${e}TexShape[0], ${e}TexShape[1], index + ${a});
return sampleTexture(${e}, uv);
}
`:`
float ${n}(int index) {
vec2 uv = uvFromFlat(${s}, ${i}, index + ${a});
return sampleTexture(${e}, uv);
}
`}function Stt(r,t){let e=r.shapeInfo.logicalShape,n=r.name,o="get"+n.charAt(0).toUpperCase()+n.slice(1),s=r.shapeInfo.texShape,i=s[0],a=s[1],u=qe();if(s!=null&&x.arraysEqual(e,s))return t?`
vec4 ${o}(int row, int col) {
vec2 uv = (vec2(col, row) + halfCR) / vec2(${n}TexShape[1], ${n}TexShape[0]);
return ${u.texture2D}(${n}, uv);
}
`:`
vec4 ${o}(int row, int col) {
vec2 uv = (vec2(col, row) + halfCR) / vec2(${a}.0, ${i}.0);
return ${u.texture2D}(${n}, uv);
}
`;if(t)return`
vec4 ${o}(int row, int col) {
ivec2 packedTexShape = ivec2(ceil(float(${n}TexShape[0]) / 2.0), ceil(float(${n}TexShape[1]) / 2.0));
int valuesPerRow = int(ceil(float(${n}Shape[1]) / 2.0));
vec2 uv = packedUVfrom2D(valuesPerRow, packedTexShape[0], packedTexShape[1], row, col);
return ${u.texture2D}(${n}, uv);
}
`;let l=[Math.ceil(s[0]/2),Math.ceil(s[1]/2)],c=Math.ceil(e[1]/2);return`
vec4 ${o}(int row, int col) {
vec2 uv = packedUVfrom2D(${c}, ${l[0]}, ${l[1]}, row, col);
return ${u.texture2D}(${n}, uv);
}
`}function ktt(r,t){let e=r.shapeInfo.logicalShape,n=r.name,o="get"+n.charAt(0).toUpperCase()+n.slice(1),s=r.shapeInfo.texShape;if(s!=null&&x.arraysEqual(e,s)){if(t)return`
float ${o}(int row, int col) {
vec2 uv = (vec2(col, row) + halfCR) / vec2(${n}TexShape[1], ${n}TexShape[0]);
return sampleTexture(${n}, uv);
}
`;let m=s[0],f=s[1];return`
float ${o}(int row, int col) {
vec2 uv = (vec2(col, row) + halfCR) / vec2(${f}.0, ${m}.0);
return sampleTexture(${n}, uv);
}
`}let{newShape:i,keptDims:a}=x.squeezeShape(e),u=i;if(u.length<e.length){let m=wd(r,u),f=["row","col"];return`
${yd(m,t)}
float ${o}(int row, int col) {
return ${o}(${vd(f,a)});
}
`}if(r.shapeInfo.isUniform)return`
float ${o}(int row, int col) {
int index = round(dot(vec2(row, col), vec2(${e[1]}, 1)));
${bd(r)}
}
`;let l=s[0],c=s[1],p=tp(n);return c===1?t?`
float ${o}(int row, int col) {
float index = dot(vec3(row, col, ${p}), vec3(${n}Shape[1], 1, 1));
vec2 uv = vec2(0.5, (index + 0.5) / float(${n}TexShape[0]));
return sampleTexture(${n}, uv);
}
`:`
float ${o}(int row, int col) {
float index = dot(vec3(row, col, ${p}), vec3(${e[1]}, 1, 1));
vec2 uv = vec2(0.5, (index + 0.5) / ${l}.0);
return sampleTexture(${n}, uv);
}
`:l===1?t?`
float ${o}(int row, int col) {
float index = dot(vec3(row, col, ${p}), vec3(${n}Shape[1], 1, 1));
vec2 uv = vec2((index + 0.5) / float(${n}TexShape[1]), 0.5);
return sampleTexture(${n}, uv);
}
`:`
float ${o}(int row, int col) {
float index = dot(vec3(row, col, ${p}), vec3(${e[1]}, 1, 1));
vec2 uv = vec2((index + 0.5) / ${c}.0, 0.5);
return sampleTexture(${n}, uv);
}
`:t?`
float ${o}(int row, int col) {
// Explicitly use integer operations as dot() only works on floats.
int index = row * ${n}Shape[1] + col + ${p};
vec2 uv = uvFromFlat(${n}TexShape[0], ${n}TexShape[1], index);
return sampleTexture(${n}, uv);
}
`:`
float ${o}(int row, int col) {
// Explicitly use integer operations as dot() only works on floats.
int index = row * ${e[1]} + col + ${p};
vec2 uv = uvFromFlat(${l}, ${c}, index);
return sampleTexture(${n}, uv);
}
`}function Ntt(r,t){let e=r.shapeInfo.logicalShape,n=r.name,o="get"+n.charAt(0).toUpperCase()+n.slice(1),s=r.shapeInfo.texShape,i=[Math.ceil(s[0]/2),Math.ceil(s[1]/2)];if(e[0]===1){let m=e.slice(1),f=[1,2],d=wd(r,m),h=["b","row","col"];return`
${QM(d,t)}
vec4 ${o}(int b, int row, int col) {
return ${o}(${vd(h,f)});
}
`}let a=qe();if(t)return`
vec4 ${o}(int b, int row, int col) {
ivec2 packedTexShape = ivec2(ceil(float(${n}TexShape[0]) / 2.0), ceil(float(${n}TexShape[1]) / 2.0));
int valuesPerRow = int(ceil(float(${n}Shape[2]) / 2.0));
int texelsInBatch = valuesPerRow * int(ceil(float(${n}Shape[1]) / 2.0));
vec2 uv = packedUVfrom3D(
packedTexShape[0], packedTexShape[1], texelsInBatch, valuesPerRow, b, row, col);
return ${a.texture2D}(${n}, uv);
}
`;let u=i[0],l=i[1],c=Math.ceil(e[2]/2),p=c*Math.ceil(e[1]/2);return`
vec4 ${o}(int b, int row, int col) {
vec2 uv = packedUVfrom3D(
${u}, ${l}, ${p}, ${c}, b, row, col);
return ${a.texture2D}(${n}, uv);
}
`}function Ttt(r,t){let e=r.shapeInfo.logicalShape,n=r.name,o="get"+n.charAt(0).toUpperCase()+n.slice(1),s=e[1]*e[2],i=e[2],{newShape:a,keptDims:u}=x.squeezeShape(e),l=a;if(l.length<e.length){let h=wd(r,l),g=["row","col","depth"];return`
${yd(h,t)}
float ${o}(int row, int col, int depth) {
return ${o}(${vd(g,u)});
}
`}if(r.shapeInfo.isUniform)return`
float ${o}(int row, int col, int depth) {
int index = round(dot(vec3(row, col, depth),
vec3(${s}, ${i}, 1)));
${bd(r)}
}
`;let c=r.shapeInfo.texShape,p=c[0],m=c[1],f=r.shapeInfo.flatOffset;if(m===s&&f==null)return t?`
float ${o}(int row, int col, int depth) {
int stride1 = ${n}Shape[2];
float texR = float(row);
float texC = dot(vec2(col, depth), vec2(stride1, 1));
vec2 uv = (vec2(texC, texR) + halfCR) /
vec2(${n}TexShape[1], ${n}TexShape[0]);
return sampleTexture(${n}, uv);
}
`:`
float ${o}(int row, int col, int depth) {
float texR = float(row);
float texC = dot(vec2(col, depth), vec2(${i}, 1));
vec2 uv = (vec2(texC, texR) + halfCR) /
vec2(${m}.0, ${p}.0);
return sampleTexture(${n}, uv);
}
`;if(m===i&&f==null)return t?`
float ${o}(int row, int col, int depth) {
float texR = dot(vec2(row, col), vec2(${n}Shape[1], 1));
float texC = float(depth);
vec2 uv = (vec2(texC, texR) + halfCR) / vec2(${n}TexShape[1], ${n}TexShape[0]);
return sampleTexture(${n}, uv);
}
`:`
float ${o}(int row, int col, int depth) {
float texR = dot(vec2(row, col), vec2(${e[1]}, 1));
float texC = float(depth);
vec2 uv = (vec2(texC, texR) + halfCR) / vec2(${m}.0, ${p}.0);
return sampleTexture(${n}, uv);
}
`;let d=tp(n);return t?`
float ${o}(int row, int col, int depth) {
// Explicitly use integer operations as dot() only works on floats.
int stride0 = ${n}Shape[1] * ${n}Shape[2];
int stride1 = ${n}Shape[2];
int index = row * ${s} + col * ${i} + depth + ${d};
vec2 uv = uvFromFlat(${n}TexShape[0], ${n}TexShape[1], index);
return sampleTexture(${n}, uv);
}
`:`
float ${o}(int row, int col, int depth) {
// Explicitly use integer operations as dot() only works on floats.
int index = row * ${s} + col * ${i} + depth + ${d};
vec2 uv = uvFromFlat(${p}, ${m}, index);
return sampleTexture(${n}, uv);
}
`}function _tt(r,t){let e=r.name,n="get"+e.charAt(0).toUpperCase()+e.slice(1),o=qe();if(t)return`
vec4 ${n}(int b2, int b, int row, int col) {
int valuesPerRow = int(ceil(float(${e}Shape[3]) / 2.0));
int texelsInBatch = valuesPerRow * int(ceil(float(${e}Shape[2]) / 2.0));
int index = b * texelsInBatch + (row / 2) * valuesPerRow + (col / 2);
texelsInBatch *= ${e}Shape[1];
index = b2 * texelsInBatch + index;
ivec2 packedTexShape = ivec2(ceil(float(${e}TexShape[0]) / 2.0), ceil(float(${e}TexShape[1]) / 2.0));
int texR = index / packedTexShape[1];
int texC = index - texR * packedTexShape[1];
vec2 uv = (vec2(texC, texR) + halfCR) / vec2(packedTexShape[1], packedTexShape[0]); return ${o.texture2D}(${e}, uv);
}
`;let s=r.shapeInfo.logicalShape,i=s.length,a=r.shapeInfo.texShape,u=[Math.ceil(a[0]/2),Math.ceil(a[1]/2)],l=u[0],c=u[1],p=Math.ceil(s[i-1]/2),m=p*Math.ceil(s[i-2]/2),f="int b, int row, int col",d=`b * ${m} + (row / 2) * ${p} + (col / 2)`;for(let h=2;h<i-1;h++)f=`int b${h}, `+f,m*=s[i-h-1],d=`b${h} * ${m} + `+d;return`
vec4 ${n}(${f}) {
int index = ${d};
int texR = index / ${c};
int texC = index - texR * ${c};
vec2 uv = (vec2(texC, texR) + halfCR) / vec2(${c}, ${l});
return ${o.texture2D}(${e}, uv);
}
`}function Ett(r,t){let e=r.shapeInfo.logicalShape,n=r.name,o="get"+n.charAt(0).toUpperCase()+n.slice(1),s=e[3],i=e[2]*s,a=e[1]*i,{newShape:u,keptDims:l}=x.squeezeShape(e);if(u.length<e.length){let b=wd(r,u),w=["row","col","depth","depth2"];return`
${yd(b,t)}
float ${o}(int row, int col, int depth, int depth2) {
return ${o}(${vd(w,l)});
}
`}if(r.shapeInfo.isUniform)return`
float ${o}(int row, int col, int depth, int depth2) {
int index = round(dot(vec4(row, col, depth, depth2),
vec4(${a}, ${i}, ${s}, 1)));
${bd(r)}
}
`;let c=r.shapeInfo.flatOffset,p=r.shapeInfo.texShape,m=p[0],f=p[1],d=`int stride2 = ${n}Shape[3];`,h=`int stride1 = ${n}Shape[2] * stride2;`,g=`int stride0 = ${n}Shape[1] * stride1;`;if(f===a&&c==null)return t?`
float ${o}(int row, int col, int depth, int depth2) {
${d}
${h}
float texR = float(row);
float texC =
dot(vec3(col, depth, depth2),
vec3(stride1, stride2, 1));
vec2 uv = (vec2(texC, texR) + halfCR) /
vec2(${n}TexShape[1], ${n}TexShape[0]);
return sampleTexture(${n}, uv);
}
`:`
float ${o}(int row, int col, int depth, int depth2) {
float texR = float(row);
float texC =
dot(vec3(col, depth, depth2),
vec3(${i}, ${s}, 1));
vec2 uv = (vec2(texC, texR) + halfCR) /
vec2(${f}.0, ${m}.0);
return sampleTexture(${n}, uv);
}
`;if(f===s&&c==null)return t?`
float ${o}(int row, int col, int depth, int depth2) {
float texR = dot(vec3(row, col, depth),
vec3(${n}Shape[1] * ${n}Shape[2], ${n}Shape[2], 1));
float texC = float(depth2);
vec2 uv = (vec2(texC, texR) + halfCR) /
vec2(${n}TexShape[1], ${n}TexShape[0]);
return sampleTexture(${n}, uv);
}
`:`
float ${o}(int row, int col, int depth, int depth2) {
float texR = dot(vec3(row, col, depth),
vec3(${e[1]*e[2]}, ${e[2]}, 1));
float texC = float(depth2);
vec2 uv = (vec2(texC, texR) + halfCR) /
vec2(${f}.0, ${m}.0);
return sampleTexture(${n}, uv);
}
`;let y=tp(n);return t?`
float ${o}(int row, int col, int depth, int depth2) {
// Explicitly use integer operations as dot() only works on floats.
${d}
${h}
${g}
int index = row * stride0 + col * stride1 +
depth * stride2 + depth2;
vec2 uv = uvFromFlat(${n}TexShape[0], ${n}TexShape[1], index + ${y});
return sampleTexture(${n}, uv);
}
`:`
float ${o}(int row, int col, int depth, int depth2) {
// Explicitly use integer operations as dot() only works on floats.
int index = row * ${a} + col * ${i} +
depth * ${s} + depth2;
vec2 uv = uvFromFlat(${m}, ${f}, index + ${y});
return sampleTexture(${n}, uv);
}
`}function Att(r){let t=r.shapeInfo.logicalShape,e=r.name,n="get"+e.charAt(0).toUpperCase()+e.slice(1),o=t[4],s=t[3]*o,i=t[2]*s,a=t[1]*i,{newShape:u,keptDims:l}=x.squeezeShape(t);if(u.length<t.length){let h=wd(r,u),g=["row","col","depth","depth2","depth3"];return`
${yd(h)}
float ${n}(int row, int col, int depth, int depth2, int depth3) {
return ${n}(${vd(g,l)});
}
`}if(r.shapeInfo.isUniform)return`
float ${n}(int row, int col, int depth, int depth2, int depth3) {
float index = dot(
vec4(row, col, depth, depth2),
vec4(${a}, ${i}, ${s}, ${o})) +
depth3;
${bd(r)}
}
`;let c=r.shapeInfo.flatOffset,p=r.shapeInfo.texShape,m=p[0],f=p[1];if(f===a&&c==null)return`
float ${n}(int row, int col, int depth, int depth2, int depth3) {
int texR = row;
float texC = dot(vec4(col, depth, depth2, depth3),
vec4(${i}, ${s}, ${o}, 1));
vec2 uv = (vec2(texC, texR) + halfCR) /
vec2(${f}.0, ${m}.0);
return sampleTexture(${e}, uv);
}
`;if(f===o&&c==null)return`
float ${n}(int row, int col, int depth, int depth2, int depth3) {
float texR = dot(
vec4(row, col, depth, depth2),
vec4(${t[1]*t[2]*t[3]},
${t[2]*t[3]}, ${t[3]}, 1));
int texC = depth3;
vec2 uv = (vec2(texC, texR) + halfCR) /
vec2(${f}.0, ${m}.0);
return sampleTexture(${e}, uv);
}
`;let d=tp(e);return`
float ${n}(int row, int col, int depth, int depth2, int depth3) {
// Explicitly use integer operations as dot() only works on floats.
int index = row * ${a} + col * ${i} + depth * ${s} +
depth2 * ${o} + depth3 + ${d};
vec2 uv = uvFromFlat(${m}, ${f}, index);
return sampleTexture(${e}, uv);
}
`}function $tt(r){let t=r.shapeInfo.logicalShape,e=r.name,n="get"+e.charAt(0).toUpperCase()+e.slice(1),{newShape:o,keptDims:s}=x.squeezeShape(t);if(o.length<t.length){let g=wd(r,o),y=["row","col","depth","depth2","depth3","depth4"];return`
${yd(g)}
float ${n}(int row, int col, int depth,
int depth2, int depth3, int depth4) {
return ${n}(${vd(y,s)});
}
`}let i=t[5],a=t[4]*i,u=t[3]*a,l=t[2]*u,c=t[1]*l;if(r.shapeInfo.isUniform)return`
float ${n}(int row, int col, int depth,
int depth2, int depth3, int depth4) {
int index = round(dot(
vec4(row, col, depth, depth2),
vec4(${c}, ${l}, ${u}, ${a})) +
dot(
vec2(depth3, depth4),
vec2(${i}, 1)));
${bd(r)}
}
`;let p=r.shapeInfo.flatOffset,m=r.shapeInfo.texShape,f=m[0],d=m[1];if(d===c&&p==null)return`
float ${n}(int row, int col, int depth,
int depth2, int depth3, int depth4) {
int texR = row;
float texC = dot(vec4(col, depth, depth2, depth3),
vec4(${l}, ${u}, ${a}, ${i})) +
float(depth4);
vec2 uv = (vec2(texC, texR) + halfCR) /
vec2(${d}.0, ${f}.0);
return sampleTexture(${e}, uv);
}
`;if(d===i&&p==null)return`
float ${n}(int row, int col, int depth,
int depth2, int depth3, int depth4) {
float texR = dot(vec4(row, col, depth, depth2),
vec4(${t[1]*t[2]*t[3]*t[4]},
${t[2]*t[3]*t[4]},
${t[3]*t[4]},
${t[4]})) + float(depth3);
int texC = depth4;
vec2 uv = (vec2(texC, texR) + halfCR) /
vec2(${d}.0, ${f}.0);
return sampleTexture(${e}, uv);
}
`;let h=tp(e);return`
float ${n}(int row, int col, int depth,
int depth2, int depth3, int depth4) {
// Explicitly use integer operations as dot() only works on floats.
int index = row * ${c} + col * ${l} + depth * ${u} +
depth2 * ${a} + depth3 * ${i} + depth4 + ${h};
vec2 uv = uvFromFlat(${f}, ${d}, index);
return sampleTexture(${e}, uv);
}
`}function bd(r){let t=r.name,e=x.sizeFromShape(r.shapeInfo.logicalShape);return e<2?`return ${t};`:`
for (int i = 0; i < ${e}; i++) {
if (i == index) {
return ${t}[i];
}
}
`}function Dtt(r,t){let e=r.name,n=e.charAt(0).toUpperCase()+e.slice(1),o="get"+n+"AtOutCoords",s=r.shapeInfo.logicalShape.length,i=t.logicalShape.length,a=ZM(r.shapeInfo.logicalShape,t.logicalShape),u=Ht(i),l=i-s,c,p=["x","y","z","w","u","v"];s===0?c="":i<2&&a.length>=1?c="coords = 0;":c=a.map(b=>`coords.${p[b+l]} = 0;`).join(`
`);let m="";i<2&&s>0?m="coords":m=r.shapeInfo.logicalShape.map((b,w)=>`coords.${p[w+l]}`).join(", ");let f="return outputValue;",h=x.sizeFromShape(r.shapeInfo.logicalShape)===1,y=x.sizeFromShape(t.logicalShape)===1;if(s===1&&!h&&!y)f=`
return vec4(outputValue.xy, outputValue.xy);
`;else if(h&&!y)i===1?f=`
return vec4(outputValue.x, outputValue.x, 0., 0.);
`:f=`
return vec4(outputValue.x);
`;else if(a.length){let b=s-2,w=s-1;a.indexOf(b)>-1&&a.indexOf(w)>-1?f="return vec4(outputValue.x);":a.indexOf(b)>-1?f="return vec4(outputValue.x, outputValue.y, outputValue.x, outputValue.y);":a.indexOf(w)>-1&&(f="return vec4(outputValue.xx, outputValue.zz);")}return`
vec4 ${o}() {
${u} coords = getOutputCoords();
${c}
vec4 outputValue = get${n}(${m});
${f}
}
`}function Ftt(r,t){let e=r.name,n=e.charAt(0).toUpperCase()+e.slice(1),o="get"+n+"AtOutCoords",s=t.texShape,i=r.shapeInfo.texShape,a=r.shapeInfo.logicalShape.length,u=t.logicalShape.length;if(!r.shapeInfo.isUniform&&a===u&&r.shapeInfo.flatOffset==null&&x.arraysEqual(i,s))return`
float ${o}() {
return sampleTexture(${e}, resultUV);
}
`;let l=Ht(u),c=ZM(r.shapeInfo.logicalShape,t.logicalShape),p=u-a,m,f=["x","y","z","w","u","v"];a===0?m="":u<2&&c.length>=1?m="coords = 0;":m=c.map(h=>`coords.${f[h+p]} = 0;`).join(`
`);let d="";return u<2&&a>0?d="coords":d=r.shapeInfo.logicalShape.map((h,g)=>`coords.${f[g+p]}`).join(", "),`
float ${o}() {
${l} coords = getOutputCoords();
${m}
return get${n}(${d});
}
`}function Ht(r){if(r<=1)return"int";if(r===2)return"ivec2";if(r===3)return"ivec3";if(r===4)return"ivec4";if(r===5)return"ivec5";if(r===6)return"ivec6";throw Error(`GPU for rank ${r} is not yet supported`)}function xw(r,t,e){let{newShape:n,keptDims:o}=x.squeezeShape(t),s=t.length,i=r&&s===3&&t[0]===1,a=i?t.slice(1):n,u=!r&&s>1&&!x.arraysEqual(t,e)&&n.length<s||i;return{useSqueezeShape:u,uniformShape:u?a:t,keptDims:o}}function wd(r,t){let e=JSON.parse(JSON.stringify(r));return e.shapeInfo.logicalShape=t,e}function vd(r,t){return t.map(e=>r[e]).join(", ")}function rP(r,t,e,n){let o=e.map((c,p)=>{let m={logicalShape:c.shape,texShape:c.isUniform?null:c.texData.texShape,isUniform:c.isUniform,isPacked:c.isUniform?!1:c.texData.isPacked,flatOffset:null};return c.texData!=null&&c.texData.slice!=null&&c.texData.slice.flatOffset>0&&(m.flatOffset=c.texData.slice.flatOffset),{name:t.variableNames[p],shapeInfo:m}}),s=o.map(c=>c.shapeInfo),i={logicalShape:n.shape,texShape:n.texData.texShape,isUniform:!1,isPacked:n.texData.isPacked,flatOffset:null},a=JM(o,i,t),u=VN(r.gl,a),l=r.createProgram(u);return V().get("ENGINE_COMPILE_ONLY")?{program:t,fragmentShader:u,source:a,webGLProgram:l,inShapeInfos:s,outShapeInfo:i,uniformLocations:null,customUniformLocations:null,infLoc:null,nanLoc:null,inShapesLocations:null,inTexShapesLocations:null,outShapeLocation:null,outShapeStridesLocation:null,outTexShapeLocation:null}:Object.assign({program:t,fragmentShader:u,source:a,webGLProgram:l,inShapeInfos:s,outShapeInfo:i},sT(r,t,l))}function sT(r,t,e){let n={},o={},s={},i=[],a,u,l,c=null,p=null;p=r.getUniformLocation(e,"NAN",!1),V().getNumber("WEBGL_VERSION")===1&&(c=r.getUniformLocation(e,"INFINITY",!1));let m=!1;for(let f=0;f<t.variableNames.length;f++){let d=t.variableNames[f];n[d]=r.getUniformLocation(e,d,m),n[`offset${d}`]=r.getUniformLocation(e,`offset${d}`,m),t.enableShapeUniforms&&(o[`${d}Shape`]=r.getUniformLocation(e,`${d}Shape`,m),s[`${d}TexShape`]=r.getUniformLocation(e,`${d}TexShape`,m))}return t.enableShapeUniforms&&(a=r.getUniformLocation(e,"outShape",m),l=r.getUniformLocation(e,"outShapeStrides",m),u=r.getUniformLocation(e,"outTexShape",m)),t.customUniforms&&t.customUniforms.forEach((f,d)=>{i[d]=r.getUniformLocation(e,f.name,m)}),{uniformLocations:n,customUniformLocations:i,infLoc:c,nanLoc:p,inShapesLocations:o,inTexShapesLocations:s,outShapeLocation:a,outShapeStridesLocation:l,outTexShapeLocation:u}}function eP(r,t){if(r.length!==t.length)throw Error(`Binary was compiled with ${r.length} inputs, but was executed with ${t.length} inputs`);r.forEach((e,n)=>{let o=e.logicalShape,s=t[n],i=s.shape;if(!x.arraysEqual(o,i))throw Error(`Binary was compiled with different shapes than the current args. Shapes ${o} and ${i} must match`);if(e.isUniform&&s.isUniform)return;let a=e.texShape,u=s.isUniform?null:s.texData.texShape;if(!x.arraysEqual(a,u))throw Error(`Binary was compiled with different texture shapes than the current args. Shape ${a} and ${u} must match`)})}function nP(r,t,e,n,o){t.program.enableShapeUniforms||(eP(t.inShapeInfos,e),eP([t.outShapeInfo],[n]));let s=n.texData.texture,i=n.texData.texShape;n.texData.isPacked?r.setOutputPackedMatrixTexture(s.texture,i[0],i[1]):r.setOutputMatrixTexture(s.texture,i[0],i[1]),r.setProgram(t.webGLProgram),V().getNumber("WEBGL_VERSION")===1&&t.infLoc!==null&&r.gl.uniform1f(t.infLoc,1/0),t.nanLoc!==null&&r.gl.uniform1f(t.nanLoc,NaN),e.forEach((u,l)=>{let c=t.program.variableNames[l],p=t.uniformLocations[c],m=t.uniformLocations[`offset${c}`],f=t.inShapesLocations[`${c}Shape`],d=t.inTexShapesLocations[`${c}TexShape`];if(f){let{uniformShape:h}=xw(t.program.packedInputs,u.shape,u.texData.texShape);switch(h.length){case 1:r.gl.uniform1iv(f,new Int32Array(h));break;case 2:r.gl.uniform2iv(f,new Int32Array(h));break;case 3:r.gl.uniform3iv(f,new Int32Array(h));break;case 4:r.gl.uniform4iv(f,new Int32Array(h));break;default:break}}if(d&&r.gl.uniform2i(d,u.texData.texShape[0],u.texData.texShape[1]),p!=null){if(u.isUniform){if(x.sizeFromShape(u.shape)<2)r.gl.uniform1f(p,u.uniformValues[0]);else{let h=u.uniformValues;h instanceof Float32Array||(h=new Float32Array(h)),r.gl.uniform1fv(p,h)}return}u.texData.slice!=null&&m!=null&&r.gl.uniform1i(m,u.texData.slice.flatOffset),r.setInputMatrixTexture(u.texData.texture.texture,p,l)}});let a=t.outShapeLocation;if(a)switch(n.shape.length){case 1:r.gl.uniform1iv(a,new Int32Array(n.shape));break;case 2:r.gl.uniform2iv(a,new Int32Array(n.shape));break;case 3:r.gl.uniform3iv(a,new Int32Array(n.shape));break;case 4:r.gl.uniform4iv(a,new Int32Array(n.shape));break;default:break}if(t.outShapeStridesLocation){let u=x.computeStrides(n.shape);switch(n.shape.length){case 2:r.gl.uniform1iv(t.outShapeStridesLocation,new Int32Array(u));break;case 3:r.gl.uniform2iv(t.outShapeStridesLocation,new Int32Array(u));break;case 4:r.gl.uniform3iv(t.outShapeStridesLocation,new Int32Array(u));break;default:break}}t.outTexShapeLocation&&r.gl.uniform2i(t.outTexShapeLocation,n.texData.texShape[0],n.texData.texShape[1]),t.program.customUniforms&&o&&t.program.customUniforms.forEach((u,l)=>{let c=t.customUniformLocations[l],p=o[l];if(u.type==="float")r.gl.uniform1fv(c,p);else if(u.type==="vec2")r.gl.uniform2fv(c,p);else if(u.type==="vec3")r.gl.uniform3fv(c,p);else if(u.type==="vec4")r.gl.uniform4fv(c,p);else if(u.type==="int")r.gl.uniform1iv(c,p);else if(u.type==="ivec2")r.gl.uniform2iv(c,p);else if(u.type==="ivec3")r.gl.uniform3iv(c,p);else if(u.type==="ivec4")r.gl.uniform4iv(c,p);else throw Error(`uniform type ${u.type} is not supported yet.`)}),r.executeProgram()}function oP(r,t,e){let n="";t.concat(e).forEach(i=>{let a=i.texData!=null&&i.texData.slice!=null&&i.texData.slice.flatOffset>0;if(r.enableShapeUniforms&&!i.isUniform){let u=i.texData.texShape,{useSqueezeShape:l,uniformShape:c,keptDims:p}=xw(r.packedInputs,i.shape,u),m="",f="",d="";if(c.length===1&&r.packedInputs){let k=[Math.ceil(u[0]/2),Math.ceil(u[1]/2)];m=`${k[0]>1}_${k[1]>1}`}else if(c.length===2&&!r.packedInputs)f=`${c[0]>1}_${c[1]>1}`;else if(c.length>2&&!r.packedInputs){let k=x.computeStrides(c);d=`${k[0]===u[1]}_${k[k.length-1]===u[1]}`}let h=i.shape.length,g=c.length===2&&x.arraysEqual(i.shape,u),y=x.sizeFromShape(i.shape)===1,b=S.getBroadcastDims(i.shape,e.shape),w=!r.packedInputs&&h===e.shape.length&&x.arraysEqual(u,e.texData.texShape),v=r.packedInputs||c.length>2?"":`${u[0]>1}_${u[1]>1}`;n+=`${h}_${w}_${l?p:""}_${c.length}_${y}_${b}_${g}_${m}_${f}_${d}_${v}_${a}`}else{let u=i.isUniform?"uniform":i.texData.texShape;n+=`${i.shape}_${u}_${a}`}});let o=r.userCode,s=r.constructor.name;return s+="_"+n+"_"+o+`${V().getNumber("WEBGL_VERSION")}`,s}function De(r){return V().getBool("WEBGL_USE_SHAPES_UNIFORMS")&&r<=4}var yw=class{constructor(t){this.variableNames=["A"],this.packedInputs=!1,this.packedOutput=!0,this.outPackingScheme=wu.DENSE,this.customUniforms=[{name:"texShape",type:"ivec2"}];let e=qe();this.outputShape=t,this.enableShapeUniforms=De(this.outputShape.length),this.userCode=`
ivec3 outCoordsFromFlatIndex(int index) {
${this.enableShapeUniforms?Qc(["r","c","d"],t):hi(["r","c","d"],t)}
return ivec3(r, c, d);
}
void main() {
ivec2 resTexRC = ivec2(resultUV.yx * vec2(texShape[0], texShape[1]));
int index = 4 * (resTexRC.x * texShape[1] + resTexRC.y);
vec4 result = vec4(0.);
for (int i=0; i<4; i++) {
int flatIndex = index + i;
ivec3 rc = outCoordsFromFlatIndex(flatIndex);
result[i] = getA(rc.x, rc.y, rc.z);
}
${e.output} = result;
}
`}};var bw=class{constructor(t){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,this.outPackingScheme=wu.DENSE,this.customUniforms=[{name:"texShape",type:"ivec2"}];let e=qe();this.outputShape=t,this.enableShapeUniforms=De(this.outputShape.length),this.userCode=`
ivec3 outCoordsFromFlatIndex(int index) {
${this.enableShapeUniforms?Qc(["r","c","d"],t):hi(["r","c","d"],t)}
return ivec3(r, c, d);
}
void main() {
ivec2 resTexRC = ivec2(resultUV.yx * vec2(texShape[0], texShape[1]));
int index = 4 * (resTexRC.x * texShape[1] + resTexRC.y);
vec4 result = vec4(0.);
for (int i=0; i<4; i++) {
int flatIndex = index + i;
ivec3 rc = outCoordsFromFlatIndex(flatIndex);
result[i] = getChannel(getA(rc.x, rc.y, rc.z), vec2(rc.y, rc.z));
}
${e.output} = result;
}
`}};var ww=class{constructor(t){this.variableNames=["A"],this.outTexUsage=Kr.DOWNLOAD;let e=qe();this.outputShape=t,this.userCode=`
${gw}
void main() {
float x = getAAtOutCoords();
${e.output} = encode_float(x);
}
`}};var vw=class{constructor(t){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!1,this.outTexUsage=Kr.DOWNLOAD;let e=qe();this.outputShape=t,this.userCode=`
${gw}
void main() {
ivec3 coords = getOutputCoords();
float x = getChannel(getAAtOutCoords(), vec2(coords.y, coords.z));
${e.output} = encode_float(x);
}
`}};var Cw=class{constructor(t,e=!1){this.variableNames=["A"],this.customUniforms=[{name:"texShape",type:"ivec2"}];let n=qe();this.outputShape=t,this.enableShapeUniforms=De(this.outputShape.length);let o="result";e&&(o="floor(result * 255. + 0.5)"),this.userCode=`
${this.enableShapeUniforms?gd():hd(t)}
void main() {
ivec3 coords = getOutputCoords();
int flatIndex = getFlatIndex(coords);
int offset = imod(flatIndex, 4);
flatIndex = idiv(flatIndex, 4, 1.);
int r = flatIndex / texShape[1];
int c = imod(flatIndex, texShape[1]);
vec2 uv = (vec2(c, r) + halfCR) / vec2(texShape[1], texShape[0]);
vec4 values = ${n.texture2D}(A, uv);
float result;
if(offset == 0) {
result = values[0];
} else if(offset == 1) {
result = values[1];
} else if(offset == 2) {
result = values[2];
} else {
result = values[3];
}
${n.output} = vec4(${o}, 0., 0., 0.);
}
`}};var Iw=class{constructor(t,e=!1){this.variableNames=["A"],this.packedInputs=!1,this.packedOutput=!0,this.customUniforms=[{name:"texShape",type:"ivec2"}];let n=qe();this.outputShape=t,this.enableShapeUniforms=De(this.outputShape.length);let o="",s="result";e&&(s="floor(result * 255. + 0.5)");for(let i=0;i<=1;i++)for(let a=0;a<=1;a++){let u=i*2+a;o+=`
localCoords = coords;
if(localCoords[2] + ${a} < ${this.enableShapeUniforms?"outShape[2]":`${t[2]}`}) {
localCoords[2] += ${a};
if (localCoords[1] + ${i} < ${this.enableShapeUniforms?"outShape[1]":`${t[1]}`}) {
localCoords[1] += ${i};
flatIndex = getFlatIndex(localCoords);
offset = imod(flatIndex, 4);
flatIndex = idiv(flatIndex, 4, 1.);
int r = flatIndex / texShape[1];
int c = imod(flatIndex, texShape[1]);
vec2 uv = (vec2(c, r) + halfCR) / vec2(texShape[1], texShape[0]);
values = ${n.texture2D}(A, uv);
if (offset == 0) {
result[${u}] = values[0];
} else if (offset == 1) {
result[${u}] = values[1];
} else if (offset == 2) {
result[${u}] = values[2];
} else {
result[${u}] = values[3];
}
}
}
`}this.userCode=`
${this.enableShapeUniforms?gd():hd(t)}
void main() {
ivec3 coords = getOutputCoords();
vec4 result = vec4(0.);
int flatIndex, r, c, offset;
ivec3 localCoords;
vec2 uv;
vec4 values;
${o}
${n.output} = ${s};
}
`}};var CT={};Yt(CT,{bindVertexProgramAttributeStreams:()=>dT,createBufferFromOutputTexture:()=>xT,createFloat16MatrixTexture:()=>cT,createFloat16PackedMatrixTexture:()=>fT,createFloat32MatrixTexture:()=>uT,createIndexBuffer:()=>lT,createPackedMatrixTexture:()=>mT,createUnsignedBytesMatrixTexture:()=>pT,createVertexBuffer:()=>aT,createVertexShader:()=>iT,downloadByteEncodedFloatMatrixFromOutputTexture:()=>bT,downloadFloat32MatrixFromBuffer:()=>yT,downloadMatrixFromPackedOutputTexture:()=>vT,downloadPackedMatrixFromBuffer:()=>wT,getInternalFormatForFloat16MatrixTexture:()=>kw,getInternalFormatForFloat16PackedMatrixTexture:()=>_w,getInternalFormatForFloat32MatrixTexture:()=>Sw,getInternalFormatForPackedMatrixTexture:()=>Tw,getInternalFormatForUnsignedBytesMatrixTexture:()=>Nw,uploadDenseMatrixToTexture:()=>hT,uploadPixelDataToTexture:()=>gT});function iT(r){let t=qe(),e=`${t.version}
precision highp float;
${t.attribute} vec3 clipSpacePos;
${t.attribute} vec2 uv;
${t.varyingVs} vec2 resultUV;
void main() {
gl_Position = vec4(clipSpacePos, 1);
resultUV = uv;
}`;return BN(r,e)}function aT(r){let t=new Float32Array([-1,1,0,0,1,-1,-1,0,0,0,1,1,0,1,1,1,-1,0,1,0]);return UN(r,t)}function lT(r){let t=new Uint16Array([0,1,2,2,1,3]);return HN(r,t)}function Tg(r,t,e,n,o,s){KN(t,e);let i=qN(r),a=r.TEXTURE_2D;return vt(r,()=>r.bindTexture(a,i)),vt(r,()=>r.texParameteri(a,r.TEXTURE_WRAP_S,r.CLAMP_TO_EDGE)),vt(r,()=>r.texParameteri(a,r.TEXTURE_WRAP_T,r.CLAMP_TO_EDGE)),vt(r,()=>r.texParameteri(a,r.TEXTURE_MIN_FILTER,r.NEAREST)),vt(r,()=>r.texParameteri(a,r.TEXTURE_MAG_FILTER,r.NEAREST)),V().getNumber("WEBGL_VERSION")===1?vt(r,()=>r.texImage2D(a,0,n,t,e,0,o,s,null)):vt(r,()=>r.texStorage2D(a,1,n,t,e)),vt(r,()=>r.bindTexture(r.TEXTURE_2D,null)),{texture:i,texShape:[e,t]}}function Sw(r){return r.internalFormatFloat}function uT(r,t,e,n){let[o,s]=Jc(t,e);return Tg(r,o,s,Sw(n),n.textureFormatFloat,r.FLOAT)}function kw(r){return r.internalFormatHalfFloat}function cT(r,t,e,n){let[o,s]=Jc(t,e);return 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This is probably a resource leak, delete the output matrix texture with GPGPUContext.deleteMatrixTexture before disposing.");let t=this.gl;vt(t,()=>t.finish()),vt(t,()=>t.bindFramebuffer(t.FRAMEBUFFER,null)),vt(t,()=>t.deleteFramebuffer(this.framebuffer)),vt(t,()=>t.bindBuffer(t.ARRAY_BUFFER,null)),vt(t,()=>t.bindBuffer(t.ELEMENT_ARRAY_BUFFER,null)),vt(t,()=>t.deleteBuffer(this.indexBuffer)),this.disposed=!0}createFloat32MatrixTexture(t,e){return this.throwIfDisposed(),uT(this.gl,t,e,this.textureConfig)}createFloat16MatrixTexture(t,e){return this.throwIfDisposed(),cT(this.gl,t,e,this.textureConfig)}createUnsignedBytesMatrixTexture(t,e){return this.throwIfDisposed(),pT(this.gl,t,e,this.textureConfig)}uploadPixelDataToTexture(t,e){this.throwIfDisposed(),gT(this.gl,t,e)}uploadDenseMatrixToTexture(t,e,n,o){this.throwIfDisposed(),hT(this.gl,t,e,n,o,this.textureConfig)}createFloat16PackedMatrixTexture(t,e){return this.throwIfDisposed(),fT(this.gl,t,e,this.textureConfig)}createPackedMatrixTexture(t,e){return this.throwIfDisposed(),mT(this.gl,t,e,this.textureConfig)}deleteMatrixTexture(t){this.throwIfDisposed(),this.outputTexture===t&&(dw(this.gl,this.framebuffer),this.outputTexture=null),vt(this.gl,()=>this.gl.deleteTexture(t))}downloadByteEncodedFloatMatrixFromOutputTexture(t,e,n){return this.downloadMatrixDriver(t,()=>bT(this.gl,e,n,this.textureConfig))}downloadPackedMatrixFromBuffer(t,e,n,o,s,i){return wT(this.gl,t,e,n,o,s,i,this.textureConfig)}downloadFloat32MatrixFromBuffer(t,e){return yT(this.gl,t,e)}createBufferFromTexture(t,e,n){this.bindTextureToFrameBuffer(t);let o=xT(this.gl,e,n,this.textureConfig);return this.unbindTextureToFrameBuffer(),o}createAndWaitForFence(){let t=this.createFence(this.gl);return this.pollFence(t)}createFence(t){let e,n;if(V().getBool("WEBGL_FENCE_API_ENABLED")){let o=t,s=o.fenceSync(o.SYNC_GPU_COMMANDS_COMPLETE,0);t.flush(),n=()=>{let i=o.clientWaitSync(s,0,0);return i===o.ALREADY_SIGNALED||i===o.CONDITION_SATISFIED},e=s}else V().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION")>0?(e=this.beginQuery(),this.endQuery(),n=()=>this.isQueryAvailable(e,V().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION"))):n=()=>!0;return{query:e,isFencePassed:n}}downloadMatrixFromPackedTexture(t,e,n){return this.downloadMatrixDriver(t,()=>vT(this.gl,e,n))}createProgram(t){this.throwIfDisposed();let e=this.gl;this.vertexShader==null&&(this.vertexShader=iT(e));let n=GN(e);return vt(e,()=>e.attachShader(n,this.vertexShader)),vt(e,()=>e.attachShader(n,t)),WN(e,n),this.debug&&Sg(e,n),this.vertexAttrsAreBound||(this.setProgram(n),this.vertexAttrsAreBound=dT(e,this.program,this.vertexBuffer)),n}deleteProgram(t){this.throwIfDisposed(),t===this.program&&(this.program=null),t!=null&&vt(this.gl,()=>this.gl.deleteProgram(t))}setProgram(t){this.throwIfDisposed(),this.program=t,this.program!=null&&this.debug&&Sg(this.gl,this.program),vt(this.gl,()=>this.gl.useProgram(t))}getUniformLocation(t,e,n=!0){return this.throwIfDisposed(),n?XN(this.gl,t,e):YN(this.gl,t,e)}getAttributeLocation(t,e){return this.throwIfDisposed(),vt(this.gl,()=>this.gl.getAttribLocation(t,e))}getUniformLocationNoThrow(t,e){return this.throwIfDisposed(),this.gl.getUniformLocation(t,e)}setInputMatrixTexture(t,e,n){this.throwIfDisposed(),this.throwIfNoProgram(),ZN(this.gl,t,e,n)}setOutputMatrixTexture(t,e,n){this.setOutputMatrixTextureDriver(t,n,e)}setOutputPackedMatrixTexture(t,e,n){this.throwIfDisposed();let[o,s]=Qi(e,n);this.setOutputMatrixTextureDriver(t,o,s)}setOutputMatrixWriteRegion(t,e,n,o){this.setOutputMatrixWriteRegionDriver(n,t,o,e)}setOutputPackedMatrixWriteRegion(t,e,n,o){throw new Error("setOutputPackedMatrixWriteRegion not implemented.")}debugValidate(){this.program!=null&&Sg(this.gl,this.program),fd(this.gl)}executeProgram(){this.throwIfDisposed(),this.throwIfNoProgram();let t=this.gl;this.debug&&this.debugValidate(),vt(t,()=>t.drawElements(t.TRIANGLES,6,t.UNSIGNED_SHORT,0))}blockUntilAllProgramsCompleted(){this.throwIfDisposed(),vt(this.gl,()=>this.gl.finish())}getQueryTimerExtension(){return this.disjointQueryTimerExtension==null&&(this.disjointQueryTimerExtension=md(this.gl,V().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION")===2?"EXT_disjoint_timer_query_webgl2":"EXT_disjoint_timer_query")),this.disjointQueryTimerExtension}getQueryTimerExtensionWebGL2(){return this.getQueryTimerExtension()}getQueryTimerExtensionWebGL1(){return this.getQueryTimerExtension()}beginQuery(){if(V().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION")===2){let n=this.gl,o=this.getQueryTimerExtensionWebGL2(),s=n.createQuery();return n.beginQuery(o.TIME_ELAPSED_EXT,s),s}let t=this.getQueryTimerExtensionWebGL1(),e=t.createQueryEXT();return t.beginQueryEXT(t.TIME_ELAPSED_EXT,e),e}endQuery(){if(V().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION")===2){let e=this.gl,n=this.getQueryTimerExtensionWebGL2();e.endQuery(n.TIME_ELAPSED_EXT);return}let t=this.getQueryTimerExtensionWebGL1();t.endQueryEXT(t.TIME_ELAPSED_EXT)}async waitForQueryAndGetTime(t){return await x.repeatedTry(()=>this.disposed||this.isQueryAvailable(t,V().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION"))),this.getQueryTime(t,V().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION"))}getQueryTime(t,e){if(e===0)return null;if(e===2){let n=this.gl;return n.getQueryParameter(t,n.QUERY_RESULT)/1e6}else{let n=this.getQueryTimerExtensionWebGL1();return n.getQueryObjectEXT(t,n.QUERY_RESULT_EXT)/1e6}}isQueryAvailable(t,e){if(e===0)return!0;if(e===2){let n=this.gl,o=this.getQueryTimerExtensionWebGL2(),s=n.getQueryParameter(t,n.QUERY_RESULT_AVAILABLE);return this.disjoint==null&&(this.disjoint=this.gl.getParameter(o.GPU_DISJOINT_EXT)),s&&!this.disjoint}else{let n=this.getQueryTimerExtensionWebGL1(),o=n.getQueryObjectEXT(t,n.QUERY_RESULT_AVAILABLE_EXT);return this.disjoint==null&&(this.disjoint=this.gl.getParameter(n.GPU_DISJOINT_EXT)),o&&!this.disjoint}}pollFence(t){return new Promise(e=>{this.addItemToPoll(()=>t.isFencePassed(),()=>e())})}pollItems(){let t=Mtt(this.itemsToPoll.map(e=>e.isDoneFn));for(let e=0;e<=t;++e){let{resolveFn:n}=this.itemsToPoll[e];n()}this.itemsToPoll=this.itemsToPoll.slice(t+1)}addItemToPoll(t,e){this.itemsToPoll.push({isDoneFn:t,resolveFn:e}),!(this.itemsToPoll.length>1)&&x.repeatedTry(()=>(this.pollItems(),this.itemsToPoll.length===0))}bindTextureToFrameBuffer(t){this.throwIfDisposed(),kg(this.gl,t,this.framebuffer),this.debug&&fd(this.gl)}unbindTextureToFrameBuffer(){this.outputTexture!=null?(kg(this.gl,this.outputTexture,this.framebuffer),this.debug&&fd(this.gl)):dw(this.gl,this.framebuffer)}downloadMatrixDriver(t,e){this.bindTextureToFrameBuffer(t);let n=e();return this.unbindTextureToFrameBuffer(),n}setOutputMatrixTextureDriver(t,e,n){this.throwIfDisposed();let o=this.gl;kg(o,t,this.framebuffer),this.debug&&fd(o),this.outputTexture=t,vt(o,()=>o.viewport(0,0,e,n)),vt(o,()=>o.scissor(0,0,e,n))}setOutputMatrixWriteRegionDriver(t,e,n,o){this.throwIfDisposed(),vt(this.gl,()=>this.gl.scissor(t,e,n,o))}throwIfDisposed(){if(this.disposed)throw new Error("Attempted to use disposed GPGPUContext.")}throwIfNoProgram(){if(this.program==null)throw new Error("No GPU program is currently set.")}};function Mtt(r){let t=0;for(;t<r.length&&r[t]();++t);return t-1}var{addImpl:sP,bincountImpl:Ew,bincountReduceImpl:iP,ceilImpl:aP,concatImpl:lP,equalImpl:uP,expImpl:cP,expm1Impl:pP,floorImpl:mP,gatherNdImpl:fP,gatherV2Impl:dP,greaterImpl:hP,greaterEqualImpl:gP,lessImpl:xP,lessEqualImpl:yP,linSpaceImpl:bP,logImpl:wP,maxImpl:vP,maximumImpl:CP,minimumImpl:IP,multiplyImpl:SP,negImpl:kP,notEqualImpl:NP,prodImpl:TP,rangeImpl:_P,rsqrtImpl:EP,scatterImpl:AP,sigmoidImpl:$P,simpleAbsImpl:Aw,sliceImpl:DP,sparseFillEmptyRowsImpl:FP,sparseReshapeImpl:RP,sparseSegmentReductionImpl:$w,sqrtImpl:OP,stridedSliceImpl:MP,stringNGramsImpl:PP,stringSplitImpl:LP,stringToHashBucketFastImpl:zP,subImpl:BP,tileImpl:VP,topKImpl:GP,transposeImpl:rp,uniqueImpl:WP}=ow;function IT(r,t){return["x","y","z","w","u","v"].slice(0,t).map(e=>`${r}.${e}`)}function rr(r,t){return t===1?[r]:IT(r,t)}function UP(r,t){if(r===1)return"rc";let e="";for(let n=0;n<r;n++)e+=t[n],n<r-1&&(e+=",");return e}var Dw=class{constructor(t){if(this.variableNames=["A"],this.packedInputs=!1,this.packedOutput=!0,this.outputShape=t,this.rank=t.length,this.enableShapeUniforms=De(this.outputShape.length),this.rank===0)this.userCode=`
void main() {
setOutput(vec4(getA(), 0., 0., 0.));
}
`;else{let e=rr("rc",this.rank),n=Ht(this.rank),o=this.getOutOfBoundsCondition(e),s=this.getSetup(e),i=this.getOutput(e);this.userCode=`
void main() {
${n} rc = getOutputCoords();
if(${o}) {
setOutput(vec4(0));
} else {
${s}
setOutput(vec4(${i}));
}
}
`}}getSourceCoordsArr(t){let e=[];for(let n=0;n<=1;n++)for(let o=0;o<=1;o++){let s=`${n===0?"r":"rp1"}, ${o===0?"c":"cp1"}`;for(let i=2;i<this.rank;i++)s=`${t[t.length-1-i]},`+s;e.push(s)}return e}getOutOfBoundsCondition(t){if(this.rank===1)return`rc > ${this.enableShapeUniforms?"outShape":this.outputShape[0]}`;let e="";for(let n=this.rank-2;n<this.rank;n++)e+=`${t[n]} >= ${this.enableShapeUniforms?`outShape[${n}]`:this.outputShape[n]}`,n<this.rank-1&&(e+="||");return e}getSetup(t){if(this.rank===1)return"";let e=t.slice(-2),n=this.enableShapeUniforms?`outShape[${this.rank} - 1]`:this.outputShape[this.rank-1],o=this.enableShapeUniforms?`outShape[${this.rank} - 2]`:this.outputShape[this.rank-2];return`
int r = ${e[0]};
int c = ${e[1]};
int rp1 = r + 1;
int cp1 = c + 1;
bool cEdge = cp1 >= ${n};
bool rEdge = rp1 >= ${o};
`}getOutput(t){let e=this.getSourceCoordsArr(t);return this.rank===1?`getA(rc), (rc + 1 >= ${this.enableShapeUniforms?"outShape":this.outputShape[0]} ? 0. : getA(rc + 1)), 0, 0`:`getA(${e[0]}),
cEdge ? 0. : getA(${e[1]}),
rEdge ? 0. : getA(${e[2]}),
rEdge || cEdge ? 0. : getA(${e[3]})`}};var Cd=class{constructor(t,e){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,this.customUniforms=[{name:"inputShape",type:"ivec3"}],this.outputShape=t,this.enableShapeUniforms=De(this.outputShape.length);let n="";for(let o=0;o<4;o++){let s="thisRC = rc;";o%2===1&&(s+="thisRC.z += 1;"),o>1&&(s+="thisRC.y += 1;"),n+=`
${s}
${o>0?"if(thisRC.y < rows && thisRC.z < cols){":""}
int flatIndex = getFlatIndex(thisRC);
ivec3 inputRC = inputCoordsFromReshapedOutCoords(flatIndex);
vec2 inputRCInnerDims = vec2(float(inputRC.y),float(inputRC.z));
result[${o}] =
getChannel(getA(inputRC.x, inputRC.y, inputRC.z), inputRCInnerDims);
${o>0?"}":""}
`}this.userCode=`
${Ptt(e,this.enableShapeUniforms)}
${this.enableShapeUniforms?gd():hd(t)}
void main() {
ivec3 rc = getOutputCoords();
vec4 result = vec4(0.);
ivec3 thisRC;
int rows = ${this.enableShapeUniforms?"outShape[1]":t[1]};
int cols = ${this.enableShapeUniforms?"outShape[2]":t[2]};
${n}
setOutput(result);
}
`}};function Ptt(r,t){return`
ivec3 inputCoordsFromReshapedOutCoords(int index) {
${t?YM(["r","c","d"],"inputShape"):hi(["r","c","d"],r)}
return ivec3(r, c, d);
}
`}var Fw=class{constructor(t){this.gpgpu=t,this.numUsedTextures=0,this.numFreeTextures=0,this._numBytesAllocated=0,this._numBytesFree=0,this.freeTextures={},this.logEnabled=!1,this.usedTextures={}}acquireTexture(t,e,n){let o=qP(e,n),s=KP(t,o,n);s in this.freeTextures||(this.freeTextures[s]=[]),s in this.usedTextures||(this.usedTextures[s]=[]);let i=HP(t,o,this.gpgpu.gl,this.gpgpu.textureConfig,n);if(this.freeTextures[s].length>0){this.numFreeTextures--,this.numUsedTextures++,this._numBytesFree-=i,this.log();let u=this.freeTextures[s].shift();return this.usedTextures[s].push(u),u}let a;return o===Rr.PACKED_2X2_FLOAT32?a=this.gpgpu.createPackedMatrixTexture(t[0],t[1]):o===Rr.PACKED_2X2_FLOAT16?a=this.gpgpu.createFloat16PackedMatrixTexture(t[0],t[1]):o===Rr.UNPACKED_FLOAT32?a=this.gpgpu.createFloat32MatrixTexture(t[0],t[1]):o===Rr.UNPACKED_FLOAT16?a=this.gpgpu.createFloat16MatrixTexture(t[0],t[1]):o===Rr.PACKED_4X1_UNSIGNED_BYTE&&(a=this.gpgpu.createUnsignedBytesMatrixTexture(t[0],t[1])),this.usedTextures[s].push(a),this.numUsedTextures++,this._numBytesAllocated+=i,this.log(),a}releaseTexture(t,e,n,o){if(this.freeTextures==null)return;let s=qP(n,o),i=KP(e,s,o);i in this.freeTextures||(this.freeTextures[i]=[]);let a=HP(e,s,this.gpgpu.gl,this.gpgpu.textureConfig,o),u=V().get("WEBGL_DELETE_TEXTURE_THRESHOLD");u!==-1&&this._numBytesAllocated>u?(this.gpgpu.deleteMatrixTexture(t.texture),this._numBytesAllocated-=a):(this.freeTextures[i].push(t),this.numFreeTextures++,this._numBytesFree+=a),this.numUsedTextures--;let l=this.usedTextures[i],c=l.indexOf(t);if(c<0)throw new Error("Cannot release a texture that was never provided by this texture manager");l.splice(c,1),this.log()}log(){if(!this.logEnabled)return;let t=this.numFreeTextures+this.numUsedTextures;console.log("Free/Used",`${this.numFreeTextures} / ${this.numUsedTextures}`,`(${t})`);let e=this._numBytesFree/this._numBytesAllocated;console.log(`Bytes allocated: ${this._numBytesAllocated}`),console.log(`Bytes unused: ${this._numBytesFree} (${Math.round(100*e)}%)`)}get numBytesAllocated(){return this._numBytesAllocated}get numBytesFree(){return this._numBytesFree}getNumUsedTextures(){return this.numUsedTextures}getNumFreeTextures(){return this.numFreeTextures}dispose(){if(this.freeTextures!=null){for(let t in this.freeTextures)this.freeTextures[t].forEach(e=>{this.gpgpu.deleteMatrixTexture(e.texture)});for(let t in this.usedTextures)this.usedTextures[t].forEach(e=>{this.gpgpu.deleteMatrixTexture(e.texture)});this.freeTextures=null,this.usedTextures=null,this.numUsedTextures=0,this.numFreeTextures=0,this._numBytesAllocated=0,this._numBytesFree=0}}};function Ltt(r,t){let e=r;if(t===e.R32F)return 4;if(t===e.R16F)return 2;if(t===e.RGBA32F)return 16;if(t===r.RGBA)return 16;if(t===e.RGBA16F)return 8;if(t===e.RGBA8)return 4;throw new Error(`Unknown internal format ${t}`)}function HP(r,t,e,n,o){let s=ztt(t,n),i;if(o){let[u,l]=Qi(r[0],r[1]);i=u*l}else{let[u,l]=Jc(r[0],r[1]);i=u*l}let a=Ltt(e,s);return i*a}function ztt(r,t){switch(r){case Rr.PACKED_2X2_FLOAT32:return Tw(t);case Rr.PACKED_2X2_FLOAT16:return _w(t);case Rr.UNPACKED_FLOAT32:return Sw(t);case Rr.UNPACKED_FLOAT16:return kw(t);case Rr.PACKED_4X1_UNSIGNED_BYTE:return Nw(t);default:throw new Error(`Unknown physical texture type ${r}`)}}function Btt(r){return V().getBool("WEBGL_RENDER_FLOAT32_ENABLED")?r?Rr.PACKED_2X2_FLOAT32:Rr.UNPACKED_FLOAT32:r?Rr.PACKED_2X2_FLOAT16:Rr.UNPACKED_FLOAT16}function qP(r,t){if(r===Kr.UPLOAD)return Rr.PACKED_2X2_FLOAT32;if(r===Kr.RENDER||r==null)return Btt(t);if(r===Kr.DOWNLOAD||r===Kr.PIXELS)return Rr.PACKED_4X1_UNSIGNED_BYTE;throw new Error(`Unknown logical texture type ${r}`)}function KP(r,t,e){return`${r[0]}_${r[1]}_${t}_${e}`}var en=class{constructor(t,e){this.variableNames=["A"],this.outputShape=t,this.enableShapeUniforms=De(this.outputShape.length),this.userCode=`
float unaryOperation(float x) {
${e}
}
void main() {
float x = getAAtOutCoords();
float y = unaryOperation(x);
setOutput(y);
}
`}},hr="if (isnan(x)) return x;",jP="return x;",ST="return abs(x);";var XP="return (x >= 0.0) ? x : (exp(x) - 1.0);",YP=hr+`
return (x < 0.0) ? 0.0 : x;
`,ZP=hr+`
return (x < 0.0) ? 0.0 : min(6.0, x);
`,np="return x;",JP="return 1.0 / (1.0 + exp(-1.0 * x));";var tL="return x;",eL=`
vec4 result;
result.r = (x.r >= 0.0) ? x.r : (exp(x.r) - 1.0);
result.g = (x.g >= 0.0) ? x.g : (exp(x.g) - 1.0);
result.b = (x.b >= 0.0) ? x.b : (exp(x.b) - 1.0);
result.a = (x.a >= 0.0) ? x.a : (exp(x.a) - 1.0);
return result;
`,rL=`
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;
`,nL=`
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;
`,oL="return 1.0 / (1.0 + exp(-1.0 * x));",eo=class{constructor(t,e){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=t,this.enableShapeUniforms=De(this.outputShape.length),this.userCode=`
vec4 unaryOperation(vec4 x) {
${e}
}
void main() {
vec4 x = getAAtOutCoords();
vec4 y = unaryOperation(x);
setOutput(y);
}
`}};var Rw=class{constructor(t){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!1,this.outputShape=t,this.enableShapeUniforms=De(this.outputShape.length);let e=t.length,n=rr("rc",e),o=Ht(e),s=UP(e,n),i=n.slice(-2),a=e<=1?"rc":`vec2(${i.join(",")})`;this.userCode=`
void main() {
${o} rc = getOutputCoords();
vec4 packedInput = getA(${s});
setOutput(getChannel(packedInput, ${a}));
}
`}};var Gtt=Gr.whereImpl,Wtt=1e-7,Utt=1e-4,Ow={};function Htt(r){return r in Ow||(Ow[r]={}),Ow[r]}var qtt=V().getNumber("CPU_HANDOFF_SIZE_THRESHOLD"),Ktt=600;function jtt(){return V().global.screen==null?1024:V().global.screen.height*V().global.screen.width*window.devicePixelRatio*Ktt/1024/1024}var Cu=class extends jo{constructor(t){if(super(),this.pendingRead=new WeakMap,this.pendingDisposal=new WeakSet,this.dataRefCount=new WeakMap,this.numBytesInGPU=0,this.uploadWaitMs=0,this.downloadWaitMs=0,this.lastGlFlushTime=0,this.warnedAboutMemory=!1,this.pendingDeletes=0,this.disposed=!1,!V().getBool("HAS_WEBGL"))throw new Error("WebGL is not supported on this device");let e;if(t!=null){if(t instanceof ep)e=t;else{let n=Wn(V().getNumber("WEBGL_VERSION"),t);e=new ep(n)}this.binaryCache={},this.gpgpuCreatedLocally=!1}else{let n=Wn(V().getNumber("WEBGL_VERSION"));e=new ep(n),this.binaryCache=Htt(V().getNumber("WEBGL_VERSION")),this.gpgpuCreatedLocally=!0}this.gpgpu=e,this.canvas=this.gpgpu.gl.canvas,this.textureManager=new Fw(this.gpgpu),this.numMBBeforeWarning=jtt(),this.texData=new ta(this,go())}nextDataId(){return Cu.nextDataId++}numDataIds(){return this.texData.numDataIds()-this.pendingDeletes}write(t,e,n){if((V().getBool("WEBGL_CHECK_NUMERICAL_PROBLEMS")||V().getBool("DEBUG"))&&this.checkNumericalProblems(t),n==="complex64"&&t!=null)throw new Error("Cannot write to a complex64 dtype. Please use tf.complex(real, imag).");let o={id:this.nextDataId()};return this.texData.set(o,{shape:e,dtype:n,values:t,usage:Kr.UPLOAD,refCount:1}),o}refCount(t){return this.texData.has(t)?this.texData.get(t).refCount:0}incRef(t){let e=this.texData.get(t);e.refCount++}decRef(t){if(this.texData.has(t)){let e=this.texData.get(t);e.refCount--}}move(t,e,n,o,s){if(V().getBool("DEBUG")&&this.checkNumericalProblems(e),o==="complex64")throw new Error("Cannot write to a complex64 dtype. Please use tf.complex(real, imag).");this.texData.set(t,{shape:n,dtype:o,values:e,usage:Kr.UPLOAD,refCount:s})}disposeIntermediateTensorInfo(t){this.disposeData(t.dataId)}readSync(t){let e=this.texData.get(t),{values:n,dtype:o,complexTensorInfos:s,slice:i,shape:a,isPacked:u}=e;if(i!=null){let m;u?m=new eo(a,np):m=new en(a,np);let f=this.runWebGLProgram(m,[{dataId:t,shape:a,dtype:o}],o),d=this.readSync(f.dataId);return this.disposeIntermediateTensorInfo(f),d}if(n!=null)return this.convertAndCacheOnCPU(t);if(o==="string")return n;let l=this.activeTimers!=null,c;l&&(c=x.now());let p;if(o==="complex64"){let m=this.readSync(s.real.dataId),f=this.readSync(s.imag.dataId);p=S.mergeRealAndImagArrays(m,f)}else p=this.getValuesFromTexture(t);return l&&(this.downloadWaitMs+=x.now()-c),this.convertAndCacheOnCPU(t,p)}async read(t){if(this.pendingRead.has(t)){let d=this.pendingRead.get(t);return new Promise(h=>d.push(h))}let e=this.texData.get(t),{values:n,shape:o,slice:s,dtype:i,complexTensorInfos:a,isPacked:u}=e;if(s!=null){let d;u?d=new eo(o,np):d=new en(o,np);let h=this.runWebGLProgram(d,[{dataId:t,shape:o,dtype:i}],i),g=this.read(h.dataId);return this.disposeIntermediateTensorInfo(h),g}if(n!=null)return this.convertAndCacheOnCPU(t);if(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,c;if(i!=="complex64"&&V().get("WEBGL_BUFFER_SUPPORTED")){c=this.decode(t);let d=this.texData.get(c.dataId);l=this.gpgpu.createBufferFromTexture(d.texture.texture,...Cg(o))}this.pendingRead.set(t,[]),i!=="complex64"&&await this.gpgpu.createAndWaitForFence();let p;if(i==="complex64"){let d=await Promise.all([this.read(a.real.dataId),this.read(a.imag.dataId)]),h=d[0],g=d[1];p=S.mergeRealAndImagArrays(h,g)}else if(l==null)p=this.getValuesFromTexture(t);else{let d=x.sizeFromShape(o);p=this.gpgpu.downloadFloat32MatrixFromBuffer(l,d)}if(c!=null&&this.disposeIntermediateTensorInfo(c),l!=null){let d=this.gpgpu.gl;vt(d,()=>d.deleteBuffer(l))}let m=this.convertAndCacheOnCPU(t,p),f=this.pendingRead.get(t);return this.pendingRead.delete(t),f.forEach(d=>d(m)),this.pendingDisposal.has(t)&&(this.pendingDisposal.delete(t),this.disposeData(t)&&go().removeDataId(t,this),this.pendingDeletes--),m}readToGPU(t,e={}){let n=this.texData.get(t),{values:o,shape:s,slice:i,dtype:a,isPacked:u,texture:l}=n;if(a==="complex64")throw new Error("Does not support reading texture for complex64 dtype.");if(i!=null){let f;u?f=new eo(s,np):f=new en(s,np);let d=this.runWebGLProgram(f,[{dataId:t,shape:s,dtype:a}],a),h=this.readToGPU(d,e);return this.disposeIntermediateTensorInfo(d),h}if(l==null)throw o!=null?new Error("Data is not on GPU but on CPU."):new Error("There is no data on GPU or CPU.");let c=this.decode(t,e.customTexShape),p=go().makeTensorFromTensorInfo(c),m=this.texData.get(c.dataId);return Object.assign({tensorRef:p},m.texture)}bufferSync(t){let e=this.readSync(t.dataId);if(t.dtype==="string")try{let n=e.map(o=>x.decodeString(o));return Ct(t.shape,t.dtype,n)}catch(n){throw new Error("Failed to decode encoded string bytes into utf-8")}return Ct(t.shape,t.dtype,e)}checkNumericalProblems(t){if(t!=null)for(let e=0;e<t.length;e++){let n=t[e];if(!zN(n))throw V().getBool("WEBGL_RENDER_FLOAT32_CAPABLE")?Error(`The value ${n} cannot be represented with your current settings. Consider enabling float32 rendering: 'tf.env().set('WEBGL_RENDER_FLOAT32_ENABLED', true);'`):Error(`The value ${n} cannot be represented on this device.`)}}getValuesFromTexture(t){let{shape:e,dtype:n,isPacked:o}=this.texData.get(t),s=x.sizeFromShape(e);if(V().getBool("WEBGL_DOWNLOAD_FLOAT_ENABLED")){let m=this.decode(t),f=this.texData.get(m.dataId),d=this.gpgpu.downloadMatrixFromPackedTexture(f.texture.texture,...Cg(e)).subarray(0,s);return this.disposeIntermediateTensorInfo(m),d}let i=V().getBool("WEBGL_PACK")&&o===!0,a=i?Ng(e):e,u=i?new vw(a):new ww(a),l=this.runWebGLProgram(u,[{shape:a,dtype:n,dataId:t}],"float32"),c=this.texData.get(l.dataId),p=this.gpgpu.downloadByteEncodedFloatMatrixFromOutputTexture(c.texture.texture,c.texShape[0],c.texShape[1]).subarray(0,s);return this.disposeIntermediateTensorInfo(l),p}timerAvailable(){return V().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE")>0}time(t){let e=this.activeTimers,n=[],o=!1;this.programTimersStack==null?(this.programTimersStack=n,o=!0):this.activeTimers.push(n),this.activeTimers=n,t();let s=x.flatten(this.activeTimers.map(u=>u.query)).filter(u=>u!=null),i=x.flatten(this.activeTimers.map(u=>u.name)).filter(u=>u!=null);this.activeTimers=e,o&&(this.programTimersStack=null);let a={uploadWaitMs:this.uploadWaitMs,downloadWaitMs:this.downloadWaitMs,kernelMs:null,wallMs:null};return(async()=>{if(V().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE")>0){let u=await Promise.all(s);a.kernelMs=x.sum(u),a.getExtraProfileInfo=()=>u.map((l,c)=>({name:i[c],ms:l})).map(l=>`${l.name}: ${l.ms}`).join(", ")}else a.kernelMs={error:"WebGL query timers are not supported in this environment."};return this.uploadWaitMs=0,this.downloadWaitMs=0,a})()}memory(){return{unreliable:!1,numBytesInGPU:this.numBytesInGPU,numBytesInGPUAllocated:this.textureManager.numBytesAllocated,numBytesInGPUFree:this.textureManager.numBytesFree}}startTimer(){return V().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE")>0?this.gpgpu.beginQuery():{startMs:x.now(),endMs:null}}endTimer(t){return V().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE")>0?(this.gpgpu.endQuery(),t):(t.endMs=x.now(),t)}async getQueryTime(t){if(V().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE")>0)return this.gpgpu.waitForQueryAndGetTime(t);let e=t;return e.endMs-e.startMs}disposeData(t,e=!1){if(this.pendingDisposal.has(t))return!1;if(!this.texData.has(t))return!0;if(e?this.texData.get(t).refCount=0:this.texData.get(t).refCount--,!e&&this.texData.get(t).refCount>0)return!1;if(this.pendingRead.has(t))return this.pendingDisposal.add(t),this.pendingDeletes++,!1;this.releaseGPUData(t);let{complexTensorInfos:n}=this.texData.get(t);return n!=null&&(this.disposeData(n.real.dataId,e),this.disposeData(n.imag.dataId,e)),this.texData.delete(t),!0}releaseGPUData(t){let{texture:e,dtype:n,texShape:o,usage:s,isPacked:i,slice:a}=this.texData.get(t),u=a&&a.origDataId||t,l=this.dataRefCount.get(u);l>1?this.dataRefCount.set(u,l-1):(this.dataRefCount.delete(u),e!=null&&(this.numBytesInGPU-=this.computeBytes(o,n),this.textureManager.releaseTexture(e,o,s,i)));let c=this.texData.get(t);c.texture=null,c.texShape=null,c.isPacked=!1,c.slice=null}getTexture(t){return this.uploadToGPU(t),this.texData.get(t).texture.texture}getDataInfo(t){return this.texData.get(t)}shouldExecuteOnCPU(t,e=qtt){return V().getBool("WEBGL_CPU_FORWARD")&&t.every(n=>this.texData.get(n.dataId).texture==null&&x.sizeFromShape(n.shape)<e)}getGPGPUContext(){return this.gpgpu}where(t){S.warn("tf.where() in webgl locks the UI thread. Call tf.whereAsync() instead");let e=t.dataSync();return Gtt(t.shape,e)}packedUnaryOp(t,e,n){let o=new eo(t.shape,e),s=this.compileAndRun(o,[t],n);return go().makeTensorFromTensorInfo(s)}abs(t){if(this.shouldExecuteOnCPU([t])&&t.dtype!=="complex64"){let o=Aw(this.texData.get(t.dataId).values);return this.makeOutput(t.shape,t.dtype,o)}if(V().getBool("WEBGL_PACK_UNARY_OPERATIONS"))return this.packedUnaryOp(t,ST,t.dtype);let e=new en(t.shape,ST),n=this.compileAndRun(e,[t]);return go().makeTensorFromTensorInfo(n)}makeTensorInfo(t,e,n){let o;if(e==="string"&&n!=null&&n.length>0&&x.isString(n[0])){let s=n.map(i=>x.encodeString(i));o=this.write(s,t,e)}else o=this.write(n,t,e);return this.texData.get(o).usage=null,{dataId:o,shape:t,dtype:e}}makeOutput(t,e,n){return go().makeTensorFromTensorInfo(this.makeTensorInfo(t,e,n),this)}unpackTensor(t){let e=new Rw(t.shape);return this.runWebGLProgram(e,[t],t.dtype)}packTensor(t){let e=new Dw(t.shape),n=!0;return this.runWebGLProgram(e,[t],t.dtype,null,n)}packedReshape(t,e){let n=[Il(t.shape),...Sl(t.shape)],o={dtype:t.dtype,shape:n,dataId:t.dataId},s=[Il(e),...Sl(e)],i=new Cd(s,n),a=!0,u=[n],l=this.runWebGLProgram(i,[o],t.dtype,u,a);return{dataId:l.dataId,shape:e,dtype:l.dtype}}decode(t,e){let n=this.texData.get(t),{isPacked:o,shape:s,dtype:i}=n;if(e!=null){let m=x.sizeFromShape(s),f=e[0]*e[1]*4;x.assert(m<=f,()=>"customTexShape is too small. Row * Column * 4 should be equal or larger than the size of the tensor data.")}let a=Ng(s),u;o?u=new bw(a):u=new yw(a);let l=!0,c=[e!=null?e:Cg(a)],p=this.runWebGLProgram(u,[{shape:a,dtype:i,dataId:t}],i,c,l,e);return{dtype:i,shape:s,dataId:p.dataId}}runWebGLProgram(t,e,n,o,s=!1,i){let a=this.makeTensorInfo(t.outputShape,n),u=this.texData.get(a.dataId);if(t.packedOutput&&(u.isPacked=!0),t.outPackingScheme===wu.DENSE){let y=i!=null?i:Cg(t.outputShape);u.texShape=y.map(b=>b*2)}if(t.outTexUsage!=null&&(u.usage=t.outTexUsage),x.sizeFromShape(a.shape)===0)return u.values=x.getTypedArrayFromDType(a.dtype,0),a;let l=[],c=e.map(y=>{if(y.dtype==="complex64")throw new Error("GPGPUProgram does not support complex64 input. For complex64 dtypes, please separate the program into real and imaginary parts.");let b=this.texData.get(y.dataId);if(b.texture==null){if(!t.packedInputs&&x.sizeFromShape(y.shape)<=V().getNumber("WEBGL_SIZE_UPLOAD_UNIFORM"))return{shape:y.shape,texData:null,isUniform:!0,uniformValues:b.values};t.packedInputs&&(b.isPacked=!0,b.shape=y.shape)}if(this.uploadToGPU(y.dataId),!!b.isPacked!=!!t.packedInputs)y=b.isPacked?this.unpackTensor(y):this.packTensor(y),l.push(y),b=this.texData.get(y.dataId);else if(b.isPacked&&!vu(b.shape,y.shape)){let w=y,v=y.shape;y.shape=b.shape,y=this.packedReshape(y,v),l.push(y),b=this.texData.get(y.dataId),w.shape=v}return{shape:y.shape,texData:b,isUniform:!1}});this.uploadToGPU(a.dataId);let p={shape:a.shape,texData:u,isUniform:!1},m=oP(t,c,p),f=this.getAndSaveBinary(m,()=>rP(this.gpgpu,t,c,p)),d=this.activeTimers!=null,h;d&&(h=this.startTimer()),V().get("ENGINE_COMPILE_ONLY")||nP(this.gpgpu,f,c,p,o),l.forEach(y=>this.disposeIntermediateTensorInfo(y)),d&&(h=this.endTimer(h),this.activeTimers.push({name:t.constructor.name,query:this.getQueryTime(h)}));let g=V().get("WEBGL_FLUSH_THRESHOLD");if(g>0){let y=x.now();y-this.lastGlFlushTime>g&&(this.gpgpu.gl.flush(),this.lastGlFlushTime=y)}if(!V().getBool("WEBGL_LAZILY_UNPACK")&&u.isPacked&&s===!1){let y=this.unpackTensor(a);return this.disposeIntermediateTensorInfo(a),y}return a}compileAndRun(t,e,n,o,s=!1){return n=n||e[0].dtype,this.runWebGLProgram(t,e,n,o,s)}getAndSaveBinary(t,e){return t in this.binaryCache||(this.binaryCache[t]=e()),this.binaryCache[t]}getTextureManager(){return this.textureManager}dispose(){this.disposed||(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=B(()=>{if(!V().get("WEBGL_RENDER_FLOAT32_ENABLED")){let t=V().getBool("DEBUG");V().set("DEBUG",!1);let e=this.abs(pt(1e-8)).dataSync()[0];if(V().set("DEBUG",t),e>0)return 32}return 16})),this.floatPrecisionValue}epsilon(){return this.floatPrecision()===32?Wtt:Utt}uploadToGPU(t){let e=this.texData.get(t),{shape:n,dtype:o,values:s,texture:i,usage:a,isPacked:u}=e;if(i!=null)return;let l=this.activeTimers!=null,c;l&&(c=x.now());let p=e.texShape;if(p==null&&(p=JN(n,u),e.texShape=p),s!=null){let m=Ng(n),f,d=p[1],h=p[0],g=s instanceof Uint8Array||s instanceof Uint8ClampedArray;(u||!g)&&([d,h]=Qi(p[0],p[1])),u?f=new Iw(m,g):f=new Cw(m,g);let y=g?[h,d]:p,b=this.makeTensorInfo(y,o),w=this.texData.get(b.dataId);g?w.usage=Kr.PIXELS:w.usage=Kr.UPLOAD,w.texShape=y,this.gpgpu.uploadDenseMatrixToTexture(this.getTexture(b.dataId),d,h,s);let v=[[h,d]],k=!0,E=this.runWebGLProgram(f,[b],o,v,k),$=this.texData.get(E.dataId);e.texShape=$.texShape,e.isPacked=$.isPacked,e.usage=$.usage,V().get("ENGINE_COMPILE_ONLY")?this.disposeData(E.dataId):(e.texture=$.texture,e.values=null,this.texData.delete(E.dataId)),this.disposeIntermediateTensorInfo(b),l&&(this.uploadWaitMs+=x.now()-c)}else{let m=this.acquireTexture(p,a,o,u);e.texture=m}}convertAndCacheOnCPU(t,e){let n=this.texData.get(t),{dtype:o}=n;return this.releaseGPUData(t),e!=null&&(n.values=Xtt(e,o)),n.values}acquireTexture(t,e,n,o){if(this.numBytesInGPU+=this.computeBytes(t,n),!this.warnedAboutMemory&&this.numBytesInGPU>this.numMBBeforeWarning*1024*1024){let s=(this.numBytesInGPU/1024/1024).toFixed(2);this.warnedAboutMemory=!0,console.warn(`High memory usage in GPU: ${s} MB, most likely due to a memory leak`)}return this.textureManager.acquireTexture(t,e,o)}computeBytes(t,e){return t[0]*t[1]*x.bytesPerElement(e)}checkCompileCompletion(){for(let[,t]of Object.entries(this.binaryCache))this.checkCompletion_(t)}async checkCompileCompletionAsync(){let t=[];if(this.gpgpu.parallelCompilationExtension){for(let[,e]of Object.entries(this.binaryCache))t.push(this.checkCompletionAsync_(e));return Promise.all(t)}else{for(let[,e]of Object.entries(this.binaryCache)){let n=new Promise(o=>{try{this.checkCompletion_(e),o(!0)}catch(s){throw s}});t.push(n)}return Promise.all(t)}}async checkCompletionAsync_(t){return this.gpgpu.gl.getProgramParameter(t.webGLProgram,this.gpgpu.parallelCompilationExtension.COMPLETION_STATUS_KHR)?this.checkCompletion_(t):(await Hh(),this.checkCompletionAsync_(t))}checkCompletion_(t){if(this.gpgpu.gl.getProgramParameter(t.webGLProgram,this.gpgpu.gl.LINK_STATUS)===!1)throw console.log(this.gpgpu.gl.getProgramInfoLog(t.webGLProgram)),this.gpgpu.gl.getShaderParameter(t.fragmentShader,this.gpgpu.gl.COMPILE_STATUS)===!1?(mw(t.source,this.gpgpu.gl.getShaderInfoLog(t.fragmentShader)),new Error("Failed to compile fragment shader.")):new Error("Failed to link vertex and fragment shaders.");return!0}getUniformLocations(){for(let[,t]of Object.entries(this.binaryCache)){let{uniformLocations:e,customUniformLocations:n,infLoc:o,nanLoc:s,inShapesLocations:i,inTexShapesLocations:a,outShapeLocation:u,outShapeStridesLocation:l,outTexShapeLocation:c}=sT(this.gpgpu,t.program,t.webGLProgram);t.uniformLocations=e,t.customUniformLocations=n,t.infLoc=o,t.nanLoc=s,t.inShapesLocations=i,t.inTexShapesLocations=a,t.outShapeLocation=u,t.outShapeStridesLocation=l,t.outTexShapeLocation=c}}};Cu.nextDataId=0;function Xtt(r,t){if(t==="float32"||t==="complex64")return r;if(t==="int32"||t==="bool"){let e=t==="int32"?new Int32Array(r.length):new Uint8Array(r.length);for(let n=0;n<e.length;++n)e[n]=Math.round(r[n]);return e}else throw new Error(`Unknown dtype ${t}`)}var sL="3.17.0";function iL(){V().set("WEBGL_FORCE_F16_TEXTURES",!0)}Yl.isBrowser()&&ym("webgl",()=>new Cu,2);var cNe={forceHalfFloat:iL};var Mw=`
if (isnan(a)) return a;
if (isnan(b)) return b;
`;var ro=class{constructor(t,e,n){this.variableNames=["A","B"],this.outputShape=S.assertAndGetBroadcastShape(e,n),this.enableShapeUniforms=De(this.outputShape.length),this.userCode=`
float binaryOperation(float a, float b) {
${t}
}
void main() {
float a = getAAtOutCoords();
float b = getBAtOutCoords();
setOutput(binaryOperation(a, b));
}
`}};var Iu=`
result.r = isNaN.r > 0. ? NAN : result.r;
result.g = isNaN.g > 0. ? NAN : result.g;
result.b = isNaN.b > 0. ? NAN : result.b;
result.a = isNaN.a > 0. ? NAN : result.a;
`;var Po=class{constructor(t,e,n,o=!1){this.variableNames=["A","B"],this.supportsBroadcasting=!0,this.packedInputs=!0,this.packedOutput=!0,this.outputShape=S.assertAndGetBroadcastShape(e,n);let s=this.outputShape.length;this.enableShapeUniforms=De(s);let i="";if(o)if(s===0||x.sizeFromShape(this.outputShape)===1)i=`
result.y = 0.;
result.z = 0.;
result.w = 0.;
`;else if(i=`
${Ht(s)} coords = getOutputCoords();
`,s===1)this.enableShapeUniforms?i+=`
result.y = (coords + 1) >= outShape ? 0. : result.y;
result.z = 0.;
result.w = 0.;
`:i+=`
result.y = (coords + 1) >= ${this.outputShape[0]} ? 0. : result.y;
result.z = 0.;
result.w = 0.;
`;else{let u=rr("coords",s);this.enableShapeUniforms?i+=`
bool nextRowOutOfBounds =
(${u[s-2]} + 1) >= outShape[${s} - 2];
bool nextColOutOfBounds =
(${u[s-1]} + 1) >= outShape[${s} - 1];
result.y = nextColOutOfBounds ? 0. : result.y;
result.z = nextRowOutOfBounds ? 0. : result.z;
result.w = nextColOutOfBounds || nextRowOutOfBounds ? 0. : result.w;
`:i+=`
bool nextRowOutOfBounds =
(${u[s-2]} + 1) >= ${this.outputShape[s-2]};
bool nextColOutOfBounds =
(${u[s-1]} + 1) >= ${this.outputShape[s-1]};
result.y = nextColOutOfBounds ? 0. : result.y;
result.z = nextRowOutOfBounds ? 0. : result.z;
result.w = nextColOutOfBounds || nextRowOutOfBounds ? 0. : result.w;
`}this.userCode=`
vec4 binaryOperation(vec4 a, vec4 b) {
${t}
}
void main() {
vec4 a = getAAtOutCoords();
vec4 b = getBAtOutCoords();
vec4 result = binaryOperation(a, b);
${i}
setOutput(result);
}
`}};function nr(r){let{inputs:t,backend:e}=r,{x:n}=t;return e.incRef(n.dataId),{dataId:n.dataId,shape:n.shape,dtype:n.dtype}}var aL={kernelName:lo,backendName:"webgl",kernelFunc:nr};function Fn(r){let{inputs:t,backend:e}=r,{real:n,imag:o}=t,s=e.makeTensorInfo(n.shape,"complex64"),i=e.texData.get(s.dataId),a=nr({inputs:{x:n},backend:e}),u=nr({inputs:{x:o},backend:e});return i.complexTensorInfos={real:a,imag:u},s}var lL={kernelName:Rp,backendName:"webgl",kernelFunc:Fn};var kT="return (a < 0.) ? b * a : a;",NT=`
vec4 aLessThanZero = vec4(lessThan(a, vec4(0.)));
return (aLessThanZero * (b * a)) + ((vec4(1.0) - aLessThanZero) * a);
`;function Ytt(r){let{inputs:t,backend:e,attrs:n}=r,{x:o}=t,{alpha:s}=n,i=e.makeTensorInfo([],"float32",x.createScalarValue(s,"float32")),a=V().getBool("WEBGL_PACK_BINARY_OPERATIONS")?new Po(NT,o.shape,i.shape):new ro(kT,o.shape,i.shape),u=e.runWebGLProgram(a,[o,i],"float32");return e.disposeIntermediateTensorInfo(i),u}var uL={kernelName:hs,backendName:"webgl",kernelFunc:Ytt};var TT="return (a < 0.) ? b * a : a;",_T=`
vec4 aLessThanZero = vec4(lessThan(a, vec4(0.)));
return (aLessThanZero * (b * a)) + ((vec4(1.0) - aLessThanZero) * a);
`;function Ztt(r){let{inputs:t,backend:e}=r,{x:n,alpha:o}=t,s=V().getBool("WEBGL_PACK_BINARY_OPERATIONS")?new Po(_T,n.shape,o.shape):new ro(TT,n.shape,o.shape);return e.runWebGLProgram(s,[n,o],"float32")}var cL={kernelName:_s,backendName:"webgl",kernelFunc:Ztt};var Lo="if (isnan(x)) return x;",pL=`
if (isnan(a)) return a;
if (isnan(b)) return b;
`,mL=`
result.r = isNaN.r > 0. ? NAN : result.r;
result.g = isNaN.g > 0. ? NAN : result.g;
result.b = isNaN.b > 0. ? NAN : result.b;
result.a = isNaN.a > 0. ? NAN : result.a;
`;function It({opSnippet:r,packedOpSnippet:t,cpuKernelImpl:e,dtype:n}){return({inputs:o,backend:s})=>{let{x:i}=o,a=s,u=n||i.dtype;if(a.shouldExecuteOnCPU([i])&&e!=null){let p=a.texData.get(i.dataId),m=e(p.values,u);return a.makeTensorInfo(i.shape,u,m)}let l=V().getBool("WEBGL_PACK_UNARY_OPERATIONS")&&t!=null,c;return l?c=new eo(i.shape,t):c=new en(i.shape,r),a.runWebGLProgram(c,[i],u)}}function le({opSnippet:r,packedOpSnippet:t,checkOutOfBounds:e=!1,supportsComplex:n=!1,cpuKernelImpl:o,dtype:s}){return({inputs:i,backend:a})=>{let{a:u,b:l}=i,c=a;if(n&&u.dtype==="complex64"){let d=c.texData.get(u.dataId),h=c.texData.get(l.dataId),[g,y]=[[d.complexTensorInfos.real,h.complexTensorInfos.real],[d.complexTensorInfos.imag,h.complexTensorInfos.imag]].map(w=>{let[v,k]=w,E={dataId:v.dataId,dtype:v.dtype,shape:u.shape},$={dataId:k.dataId,dtype:k.dtype,shape:l.shape},D=new ro(r,u.shape,l.shape);return c.runWebGLProgram(D,[E,$],ar(v.dtype,k.dtype))}),b=Fn({inputs:{real:g,imag:y},backend:c});return c.disposeIntermediateTensorInfo(g),c.disposeIntermediateTensorInfo(y),b}let p=s||ar(u.dtype,l.dtype);if((u.dtype==="string"||l.dtype==="string"||c.shouldExecuteOnCPU([u,l]))&&o!=null){let d=c.texData.get(u.dataId).values,h=c.texData.get(l.dataId).values,g=u.dtype==="string"?S.fromUint8ToStringArray(d):d,y=u.dtype==="string"?S.fromUint8ToStringArray(h):h,[b,w]=o(u.shape,l.shape,g,y,p),v=c.makeTensorInfo(w,p),k=c.texData.get(v.dataId);return k.values=b,v}let m=V().getBool("WEBGL_PACK_BINARY_OPERATIONS")&&t!=null,f;return m?f=new Po(t,u.shape,l.shape,e):f=new ro(r,u.shape,l.shape),c.runWebGLProgram(f,[u,l],p)}}function Su(r,t=!1){if(r==="linear")return t?tL:jP;if(r==="relu")return t?rL:YP;if(r==="elu")return t?eL:XP;if(r==="relu6")return t?nL:ZP;if(r==="prelu")return t?_T:TT;if(r==="leakyrelu")return t?NT:kT;if(r==="sigmoid")return t?oL:JP;throw new Error(`Activation ${r} has not been implemented for the WebGL backend.`)}var Id=class{constructor(t,e,n,o=!1,s=!1,i=!1,a=null,u=!1,l=!1){this.variableNames=["matrixA","matrixB"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=n,this.enableShapeUniforms=De(this.outputShape.length);let c=o?t[1]:t[2],p=Math.ceil(c/2),m=o?"i * 2, rc.y":"rc.y, i * 2",f=s?"rc.z, i * 2":"i * 2, rc.z",d=o?["a.xxyy","a.zzww"]:["a.xxzz","a.yyww"],h=s?["b.xzxz","b.ywyw"]:["b.xyxy","b.zwzw"],g="",y="";a&&(u?g=`vec4 activation(vec4 a) {
vec4 b = getPreluActivationWeightsAtOutCoords();
${a}
}`:l?g=`vec4 activation(vec4 a) {
vec4 b = getLeakyreluAlphaAtOutCoords();
${a}
}`:g=`vec4 activation(vec4 x) {
${a}
}`,y="result = activation(result);");let b=i?"result += getBiasAtOutCoords();":"";i&&this.variableNames.push("bias"),u&&this.variableNames.push("preluActivationWeights"),l&&this.variableNames.push("leakyreluAlpha");let w="rc.x",v="rc.x";t[0]<e[0]?w=`int(min(float(rc.x), ${t[0]-1}.))`:e[0]<t[0]&&(v=`int(min(float(rc.x), ${e[0]-1}.))`),this.userCode=`
${g}
// Don't use uniform for sharedDimensionPacked for performance.
const float sharedDimension = ${p}.0;
vec4 dot2x2ARowBCol(ivec3 rc) {
vec4 result = vec4(0);
for (int i = 0; i < ${p}; i++) {
int batchA = ${w};
int batchB = ${v};
vec4 a = getMatrixA(batchA, ${m});
vec4 b = getMatrixB(batchB, ${f});
// These swizzled products need to be separately added.
// See: https://github.com/tensorflow/tfjs/issues/1735
result += (${d[0]} * ${h[0]});
result += (${d[1]} * ${h[1]});
}
return result;
}
void main() {
ivec3 rc = getOutputCoords();
vec4 result = dot2x2ARowBCol(rc);
${b}
${y}
setOutput(result);
}
`}};var ET={REAL:"return areal * breal - aimag * bimag;",IMAG:"return areal * bimag + aimag * breal;"},_g=class{constructor(t,e,n){this.variableNames=["AReal","AImag","BReal","BImag"],this.outputShape=S.assertAndGetBroadcastShape(e,n),this.userCode=`
float binaryOpComplex(
float areal, float aimag, float breal, float bimag) {
${t}
}
void main() {
float areal = getARealAtOutCoords();
float aimag = getAImagAtOutCoords();
float breal = getBRealAtOutCoords();
float bimag = getBImagAtOutCoords();
setOutput(binaryOpComplex(areal, aimag, breal, bimag));
}
`}};var fL="return a * b;";function Eg(r){let{inputs:t,backend:e}=r,{a:n,b:o}=t,s=S.upcastType(n.dtype,o.dtype);if(n.dtype==="complex64"){let a=e.texData.get(n.dataId),u=e.texData.get(o.dataId),l=new _g(ET.REAL,n.shape,o.shape),c=new _g(ET.IMAG,n.shape,o.shape),p=[{dataId:a.complexTensorInfos.real.dataId,dtype:a.complexTensorInfos.real.dtype,shape:n.shape},{dataId:a.complexTensorInfos.imag.dataId,dtype:a.complexTensorInfos.imag.dtype,shape:n.shape},{dataId:u.complexTensorInfos.real.dataId,dtype:u.complexTensorInfos.real.dtype,shape:o.shape},{dataId:u.complexTensorInfos.imag.dataId,dtype:u.complexTensorInfos.imag.dtype,shape:o.shape}],m=e.runWebGLProgram(l,p,"float32"),f=e.runWebGLProgram(c,p,"float32"),d=Fn({inputs:{real:m,imag:f},backend:e});return e.disposeIntermediateTensorInfo(m),e.disposeIntermediateTensorInfo(f),d}if(e.shouldExecuteOnCPU([n,o])){let a=e.texData.get(n.dataId),u=e.texData.get(o.dataId),[l,c]=SP(n.shape,o.shape,a.values,u.values,s),p=e.makeTensorInfo(c,s),m=e.texData.get(p.dataId);return m.values=l,p}let i;return V().getBool("WEBGL_PACK_BINARY_OPERATIONS")?i=new Po(fL,n.shape,o.shape):i=new ro(fL,n.shape,o.shape),e.runWebGLProgram(i,[n,o],s)}var dL={kernelName:Ss,backendName:"webgl",kernelFunc:Eg};function hL(r,t,e){let n=[Il(r.shape),...Sl(r.shape)],o={dtype:r.dtype,shape:n,dataId:r.dataId},s=[Il(t),...Sl(t)],i=new Cd(s,n),a=!0,u=[n],l=e.runWebGLProgram(i,[o],r.dtype,u,a);return{dataId:l.dataId,shape:t,dtype:l.dtype}}function ut(r){let{inputs:t,backend:e,attrs:n}=r,{x:o}=t,{shape:s}=n,i=e,a=x.sizeFromShape(o.shape),u=x.inferFromImplicitShape(s,a),l=x.sizeFromShape(u);x.assert(a===l,()=>`The new shape (${u}) has ${l} elements and the old shape (${o.shape}) has ${a} elements. The new shape and old shape must have the same number of elements.`);let c=i.texData.get(o.dataId);return c.isPacked&&!vu(o.shape,u)&&!(c.texture!==null&&vu(c.shape,u))?hL(o,u,i):(i.incRef(o.dataId),{dataId:o.dataId,shape:u,dtype:o.dtype})}var gL={kernelName:_i,backendName:"webgl",kernelFunc:ut};var Ag=class{constructor(t,e){this.variableNames=["x"];let{windowSize:n,batchSize:o,inSize:s,outSize:i}=t;this.outputShape=[o,i];let a=Math.floor(n/4)*4,u=n%4,l="sumValue += dot(values, ones);";if(e!=null){let p=1/e;l=`sumValue += dot(values * ${x.isInt(p)?p.toPrecision(2):p}, ones);`}let c="";s%n>0&&(c=`
if (inIdx < 0 || inIdx >= ${s}) {
return 0.0;
}
`),this.userCode=`
const vec4 ones = vec4(1.0, 1.0, 1.0, 1.0);
float getValue(int batch, int inIdx) {
${c}
return getX(batch, inIdx);
}
void main() {
ivec2 coords = getOutputCoords();
int batch = coords[0];
int outIdx = coords[1];
int inOffset = outIdx * ${n};
float sumValue = 0.0;
for (int i = 0; i < ${a}; i += 4) {
int inIdx = inOffset + i;
vec4 values = vec4(
getValue(batch, inIdx),
getValue(batch, inIdx + 1),
getValue(batch, inIdx + 2),
getValue(batch, inIdx + 3)
);
${l}
}
int inIdx = inOffset + ${a};
if (${u===1}) {
vec4 values = vec4(getValue(batch, inIdx), 0.0, 0.0, 0.0);
${l}
} else if (${u===2}) {
vec4 values = vec4(
getValue(batch, inIdx),
getValue(batch, inIdx + 1), 0.0, 0.0);
${l}
} else if (${u===3}) {
vec4 values = vec4(
getValue(batch, inIdx),
getValue(batch, inIdx + 1),
getValue(batch, inIdx + 2), 0.0);
${l}
}
setOutput(sumValue);
}
`}};var Pw=class{constructor(t,e){this.variableNames=["x"];let{windowSize:n,batchSize:o,inSize:s,outSize:i}=t;this.outputShape=[o,i];let a="0.0",u="";e==="prod"?a="1.0":e==="min"?(a="1.0 / 1e-20",u="min"):e==="max"&&(a="-1.0 / 1e-20",u="max");let l=`${e}(${e}(${e}(minMaxValue[0], minMaxValue[1]), minMaxValue[2]), minMaxValue[3])`;e==="sum"?l="sumValue":e==="prod"?l="prodValue":e==="all"?l="allValue":e==="any"&&(l="anyValue");let c=Math.floor(n/4)*4,p=n%4,m=`
if (${e==="sum"}) {
sumValue += dot(values, ones);
} else if (${e==="prod"}) {
vec2 tmp = vec2(values[0], values[1]) * vec2(values[2], values[3]);
prodValue *= tmp[0] * tmp[1];
} else {
minMaxValue = ${u}(values, minMaxValue);
if (${e==="min"} || ${e==="max"}) {
minMaxValue = ${u}(values, minMaxValue);
bvec4 isNaN = isnan(values);
if (isNaN.r || isNaN.g || isNaN.b || isNaN.a) {
minMaxValue = vec4(NAN);
}
}
}
`,f="vec4";e==="all"?(a="1.0",m=`
bool reducedAllValue = all(values);
float floatedReducedAllValue = float(reducedAllValue);
allValue = float(allValue >= 1.0 && floatedReducedAllValue >= 1.0);
`,f="bvec4"):e==="any"&&(a="0.0",m=`
bool reducedAnyValue = any(values);
float floatedReducedAnyValue = float(reducedAnyValue);
anyValue = float(anyValue >= 1.0 || floatedReducedAnyValue >= 1.0);
`,f="bvec4");let d="";s%n>0&&(d=`
if (inIdx < 0 || inIdx >= ${s}) {
return initializationValue;
}
`),this.userCode=`
const float initializationValue = ${a};
const vec4 ones = vec4(1.0, 1.0, 1.0, 1.0);
float getValue(int batch, int inIdx) {
${d}
return getX(batch, inIdx);
}
void main() {
ivec2 coords = getOutputCoords();
int batch = coords[0];
int outIdx = coords[1];
int inOffset = outIdx * ${n};
vec4 minMaxValue = vec4(${a});
float prodValue = 1.0;
float sumValue = 0.0;
float allValue = 1.0;
float anyValue = 0.0;
for (int i = 0; i < ${c}; i += 4) {
int inIdx = inOffset + i;
${f} values = ${f}(
getValue(batch, inIdx),
getValue(batch, inIdx + 1),
getValue(batch, inIdx + 2),
getValue(batch, inIdx + 3)
);
${m}
}
int inIdx = inOffset + ${c};
if (${p===1}) {
${f} values = ${f}(
getValue(batch, inIdx),
initializationValue,
initializationValue,
initializationValue
);
${m}
} else if (${p===2}) {
${f} values = ${f}(
getValue(batch, inIdx),
getValue(batch, inIdx + 1),
initializationValue,
initializationValue
);
${m}
} else if (${p===3}) {
${f} values = ${f}(
getValue(batch, inIdx),
getValue(batch, inIdx + 1),
getValue(batch, inIdx + 2),
initializationValue
);
${m}
}
setOutput(${l});
}
`}};function Qtt(r){let t=[];for(;t.length===0||t[t.length-1].outSize!==1;){let e=t.length?t[t.length-1].outSize:r[1],n=S.computeOptimalWindowSize(e);t.push({inSize:e,windowSize:n,outSize:Math.ceil(e/n)})}return t}function Hn(r,t,e,n){let o=Qtt(r.shape),s=r;for(let i=0;i<o.length;i++){let{inSize:a,windowSize:u,outSize:l}=o[i],c,p;e==="mean"?c=i===0?new Ag({windowSize:u,inSize:a,batchSize:r.shape[0],outSize:l},a):new Ag({windowSize:u,inSize:a,batchSize:r.shape[0],outSize:l}):c=new Pw({windowSize:u,inSize:a,batchSize:r.shape[0],outSize:l},e),p=s,s=n.runWebGLProgram(c,[s],t),p.dataId!==r.dataId&&n.disposeIntermediateTensorInfo(p)}return s}var Lw=class{constructor(t,e){this.variableNames=["A"];let n=new Array(t.length);for(let i=0;i<n.length;i++)n[i]=t[e[i]];this.outputShape=n,this.rank=n.length;let o=Ht(this.rank),s=tet(e);this.userCode=`
void main() {
${o} resRC = getOutputCoords();
setOutput(getA(${s}));
}
`}};function tet(r){let t=r.length;if(t>6)throw Error(`Transpose for rank ${t} is not yet supported`);let e=["resRC.x","resRC.y","resRC.z","resRC.w","resRC.u","resRC.v"],n=new Array(t);for(let o=0;o<r.length;o++)n[r[o]]=e[o];return n.join()}var zw=class{constructor(t,e){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0;let n=new Array(t.length);for(let c=0;c<n.length;c++)n[c]=t[e[c]];if(this.outputShape=n,this.rank=n.length,this.rank>6)throw Error(`Packed transpose for rank ${this.rank} is not yet supported.`);let o=Ht(this.rank),s=IT("rc",this.rank),i=new Array(this.rank);for(let c=0;c<e.length;c++)i[e[c]]=s[c];let a=`vec2(${i.slice(-2).join()})`,u=`++${s[this.rank-1]} < ${n[this.rank-1]}`,l=`getChannel(getA(${i.join()}), ${a})`;this.userCode=`
void main() {
${o} rc = getOutputCoords();
vec4 result = vec4(0.);
result[0] = ${l};
if(${u}) {
result[1] = ${l};
}
--${s[this.rank-1]};
if(++${s[this.rank-2]} < ${n[this.rank-2]}) {
result[2] = ${l};
if(${u}) {
result[3] = ${l};
}
}
setOutput(result);
}
`}};function ku(r,t,e){let n=V().getBool("WEBGL_PACK_ARRAY_OPERATIONS")?new zw(r.shape,t):new Lw(r.shape,t);return e.runWebGLProgram(n,[r],r.dtype)}function xL(r,t,e,n){let o=t,s=r.shape.length,i=x.parseAxisParam(o,r.shape),a=i,u=S.getAxesPermutation(a,s),l=u!=null,c=r;l&&(c=ku(r,u,n),a=S.getInnerMostAxes(a.length,s)),S.assertAxesAreInnerMostDims("sum",a,s);let[p,m]=S.computeOutAndReduceShapes(c.shape,a),f=p;e&&(f=S.expandShapeToKeepDim(p,i));let d=x.sizeFromShape(m),g=x.sizeFromShape(r.shape)/d,y=ut({inputs:{x:c},attrs:{shape:[g,d]},backend:n}),b=qu(r.dtype),w=Hn(y,b,"sum",n),v=ut({inputs:{x:w},attrs:{shape:f},backend:n});return n.disposeIntermediateTensorInfo(y),n.disposeIntermediateTensorInfo(w),l&&n.disposeIntermediateTensorInfo(c),v}function op(r){let{inputs:t,backend:e,attrs:n}=r,{x:o}=t,{axis:s,keepDims:i}=n;return xL(o,s,i,e)}var yL={kernelName:zs,backendName:"webgl",kernelFunc:op};function he(r){let{inputs:t,backend:e,attrs:n}=r,{x:o}=t,{perm:s}=n,i=e,a=o.shape.length,u=new Array(a);for(let c=0;c<u.length;c++)u[c]=o.shape[s[c]];let l;if(i.shouldExecuteOnCPU([o])){let p=i.texData.get(o.dataId).values,m=rp(p,o.shape,o.dtype,s,u);l=i.makeTensorInfo(u,o.dtype);let f=i.texData.get(l.dataId);f.values=m}else l=ku(o,s,i);return l}var bL={kernelName:Hs,backendName:"webgl",kernelFunc:he};var AT=1e3;function sp({a:r,b:t,transposeA:e,transposeB:n,backend:o,bias:s=null,preluActivationWeights:i=null,leakyreluAlpha:a=0,activation:u=null}){let l=r.shape.length,c=t.shape.length,p=e?r.shape[l-2]:r.shape[l-1],m=n?t.shape[c-1]:t.shape[c-2],f=e?r.shape[l-1]:r.shape[l-2],d=n?t.shape[c-2]:t.shape[c-1],h=r.shape.slice(0,-2),g=t.shape.slice(0,-2),y=x.sizeFromShape(h),b=x.sizeFromShape(g),v=Sr.assertAndGetBroadcastShape(r.shape.slice(0,-2),t.shape.slice(0,-2)).concat([f,d]);x.assert(p===m,()=>`Error in matMul: inner shapes (${p}) and (${m}) of Tensors with shapes ${r.shape} and ${t.shape} and transposeA=${e} and transposeB=${n} must match.`);let k=e?[y,p,f]:[y,f,p],E=n?[b,d,m]:[b,m,d],$=ut({inputs:{x:r},backend:o,attrs:{shape:k}}),D=ut({inputs:{x:t},backend:o,attrs:{shape:E}}),F=[$,D],P=Math.max(y,b),W=e?$.shape[1]:$.shape[2],U=s!=null,q=i!=null,K=u==="leakyrelu",j=u!=null?Su(u,!0):null,Q=U||q||K||j!=null,rt;if((f===1||d===1)&&W>AT&&Q===!1){let nt=$,st=D;e&&(nt=he({inputs:{x:$},backend:o,attrs:{perm:[0,2,1]}}),F.push(nt)),n&&(st=he({inputs:{x:D},backend:o,attrs:{perm:[0,2,1]}}),F.push(st));let it=d!==1,ft=d===1,at=nt;it&&(at=ut({inputs:{x:nt},backend:o,attrs:{shape:[P,W,1]}}),F.push(at));let xt=d===1?2:1,dt=st;ft&&(dt=ut({inputs:{x:st},backend:o,attrs:{shape:[P,1,W]}}),F.push(dt));let bt=Eg({inputs:{a:at,b:dt},backend:o});rt=op({inputs:{x:bt},backend:o,attrs:{axis:xt,keepDims:!0}}),F.push(bt)}else{let nt=ar(r.dtype,t.dtype),st=new Id(k,E,[P,f,d],e,n,U,j,q,K),it=[$,D];if(s!=null&&it.push(s),q&&it.push(i),K){let ft=o.makeTensorInfo([],"float32",x.createScalarValue(a,"float32"));it.push(ft),F.push(ft)}rt=o.runWebGLProgram(st,it,nt)}let X=ut({inputs:{x:rt},backend:o,attrs:{shape:v}});F.push(rt);for(let nt of F)o.disposeIntermediateTensorInfo(nt);return X}function eet(r){let{inputs:t,backend:e,attrs:n}=r,{a:o,b:s,bias:i,preluActivationWeights:a}=t,{transposeA:u,transposeB:l,activation:c,leakyreluAlpha:p}=n;return sp({a:o,b:s,transposeA:u,transposeB:l,backend:e,bias:i,preluActivationWeights:a,leakyreluAlpha:p,activation:c})}var wL={kernelName:Oi,backendName:"webgl",kernelFunc:eet};var vL="return abs(x);";function ret(r){let{inputs:t,backend:e}=r,{x:n}=t;if(e.shouldExecuteOnCPU([n])&&n.dtype!=="complex64"){let s=e.texData.get(n.dataId),i=Aw(s.values);return e.makeTensorInfo(n.shape,n.dtype,i)}let o;return V().getBool("WEBGL_PACK_UNARY_OPERATIONS")?o=new eo(n.shape,vL):o=new en(n.shape,vL),e.runWebGLProgram(o,[n],n.dtype)}var CL={kernelName:wi,backendName:"webgl",kernelFunc:ret};var net=hr+`
if (abs(x) > 1.) {
return NAN;
}
return acos(x);
`,oet=It({opSnippet:net}),IL={kernelName:ra,backendName:"webgl",kernelFunc:oet};var set=hr+`
if (x < 1.0) return NAN;
return log(x + sqrt(x * x - 1.0));`,iet=It({opSnippet:set}),SL={kernelName:na,backendName:"webgl",kernelFunc:iet};var kL="return a + b;",aet=le({opSnippet:kL,packedOpSnippet:kL,supportsComplex:!0,cpuKernelImpl:sP}),NL={kernelName:Xn,backendName:"webgl",kernelFunc:aet};var Bw=class{constructor(t,e){this.outputShape=[],this.outputShape=t,this.variableNames=e.map((s,i)=>`T${i}`);let n=[];this.variableNames.forEach(s=>{n.push(`float v${s} = get${s}AtOutCoords();`)});let o=this.variableNames.map(s=>`v${s}`).join(" + ");this.userCode=`
void main() {
${n.join(`
`)}
float result = ${o};
setOutput(result);
}
`}};var Vw=class{constructor(t,e){this.outputShape=[],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=t,this.variableNames=e.map((s,i)=>`T${i}`);let n=[];this.variableNames.forEach(s=>{n.push(`vec4 v${s} = get${s}AtOutCoords();`)});let o=this.variableNames.map(s=>`v${s}`).join(" + ");this.userCode=`
void main() {
${n.join(`
`)}
vec4 result = ${o};
setOutput(result);
}
`}};function Gw(r){let{inputs:t,backend:e}=r,n=t;if(n.length===1)return nr({inputs:{x:n[0]},backend:e});if(n.length>V().get("WEBGL_MAX_TEXTURES_IN_SHADER")){let u=Math.floor(n.length/2),l=Gw({inputs:n.slice(0,u),backend:e}),c=Gw({inputs:n.slice(u),backend:e});return Gw({inputs:[l,c],backend:e})}let o=n.map(u=>u.dtype).reduce((u,l)=>ar(u,l)),s=n.map(u=>u.shape),a=V().getBool("WEBGL_PACK")?new Vw(n[0].shape,s):new Bw(n[0].shape,s);return e.runWebGLProgram(a,n,o)}var TL={kernelName:Zo,backendName:"webgl",kernelFunc:Gw};function uet(r){let{inputs:t,backend:e,attrs:n}=r,{x:o}=t,{axis:s,keepDims:i}=n,a=o.shape.length,u=x.parseAxisParam(s,o.shape),l=u,c=S.getAxesPermutation(l,a),p=o;c!=null&&(p=he({inputs:{x:o},backend:e,attrs:{perm:c}}),l=S.getInnerMostAxes(l.length,a)),S.assertAxesAreInnerMostDims("all",l,a);let[m,f]=S.computeOutAndReduceShapes(p.shape,l),d=x.sizeFromShape(f),h=ut({inputs:{x:p},backend:e,attrs:{shape:[-1,d]}}),g=Hn(h,h.dtype,"all",e),y;if(i){let b=S.expandShapeToKeepDim(m,u);y=ut({inputs:{x:g},backend:e,attrs:{shape:b}})}else y=ut({inputs:{x:g},backend:e,attrs:{shape:m}});return e.disposeIntermediateTensorInfo(h),e.disposeIntermediateTensorInfo(g),c!=null&&e.disposeIntermediateTensorInfo(p),y}var _L={kernelName:oa,backendName:"webgl",kernelFunc:uet};function cet(r){let{inputs:t,backend:e,attrs:n}=r,{x:o}=t,{axis:s,keepDims:i}=n,a=o.shape.length,u=x.parseAxisParam(s,o.shape),l=u,c=S.getAxesPermutation(l,a),p=o;c!=null&&(p=he({inputs:{x:o},backend:e,attrs:{perm:c}}),l=S.getInnerMostAxes(l.length,a)),S.assertAxesAreInnerMostDims("any",l,a);let[m,f]=S.computeOutAndReduceShapes(p.shape,l),d=x.sizeFromShape(f),h=ut({inputs:{x:p},backend:e,attrs:{shape:[-1,d]}}),g=Hn(h,h.dtype,"any",e),y;if(i){let b=S.expandShapeToKeepDim(m,u);y=ut({inputs:{x:g},backend:e,attrs:{shape:b}})}else y=ut({inputs:{x:g},backend:e,attrs:{shape:m}});return e.disposeIntermediateTensorInfo(h),e.disposeIntermediateTensorInfo(g),c!=null&&e.disposeIntermediateTensorInfo(p),y}var EL={kernelName:sa,backendName:"webgl",kernelFunc:cet};var Ww=class{constructor(t,e,n){this.variableNames=["A"];let{windowSize:o,batchSize:s,outSize:i}=t;n||this.variableNames.push("bestIndicesA"),this.outputShape=[s,i];let a=e==="max"?">":"<",u=n?"inOffset + i;":"round(getBestIndicesA(batch, inOffset + i));";this.userCode=`
void main() {
ivec2 coords = getOutputCoords();
int batch = coords[0];
int outIdx = coords[1];
int inOffset = outIdx * ${o};
int bestIndex = inOffset;
float bestValue = getA(batch, bestIndex);
for (int i = 0; i < ${o}; i++) {
int inIdx = ${u};
float candidate = getA(batch, inIdx);
if (candidate ${a} bestValue) {
bestValue = candidate;
bestIndex = inIdx;
}
}
setOutput(float(bestIndex));
}
`}};var Uw=class{constructor(t,e,n,o){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,x.assert(t.length>2,()=>`Packed arg${n.charAt(0).toUpperCase()+n.slice(1)} supports only inputs with rank above 2.`);let s=t[t.length-1],i=Math.ceil(s/e);this.outputShape=t.slice(0,-1),i>1&&this.outputShape.push(i),o||this.variableNames.push("bestIndicesA");let a=this.outputShape,u=a.length,l=Ht(u),c=rr("coords",u),p,m;if(i===1){m=u+1;let D=Ht(m);p=`
${D} sourceLocR = ${D}(${c.join()}, 0);
++${c[u-1]};
${D} sourceLocG = ${D}(${c.join()}, 0);
++${c[u-2]};
${D} sourceLocA = ${D}(${c.join()}, 0);
--${c[u-1]};
${D} sourceLocB = ${D}(${c.join()}, 0);
--${c[u-2]};`}else m=u,p=`
${l} sourceLocR = coords;
++${c[u-1]};
${l} sourceLocG = coords;
++${c[u-2]};
${l} sourceLocA = coords;
--${c[u-1]};
${l} sourceLocB = coords;
--${c[u-2]};`;let f=["x","y","z","w","u","v"].slice(0,m),d="."+f[m-1],h=f.map(D=>"int "+D),g=rr("sourceLocR",m-1).concat("inIdx.r"),y=rr("sourceLocG",m-1).concat("inIdx.g"),b=rr("sourceLocB",m-1).concat("inIdx.b"),w=rr("sourceLocA",m-1).concat("inIdx.a"),v=n==="max"?"greaterThan":"lessThan",k=o?"":`
inIdx = round(vec4(getBestIndicesAChannel(${g.join()}),
getBestIndicesAChannel(${y.join()}),
getBestIndicesAChannel(${b.join()}),
getBestIndicesAChannel(${w.join()})));`,E=`vec4(
getAChannel(${g.join()}),
hasNextCol ? getAChannel(${y.join()}) : 0.,
hasNextRow ? getAChannel(${b.join()}) : 0.,
hasNextRow && hasNextCol ? getAChannel(${w.join()}) : 0.)`,$=o?"":`
float getBestIndicesAChannel(${h.join()}) {
return getChannel(getBestIndicesA(${f.join()}),
vec2(${f.slice(-2).join()}));
}`;this.userCode=`
float getAChannel(${h.join()}) {
return getChannel(getA(${f.join()}),
vec2(${f.slice(-2).join()}));
}
${$}
void main() {
${l} coords = getOutputCoords();
bool hasNextCol = ${c[u-1]} < ${a[u-1]-1};
bool hasNextRow = ${c[u-2]} < ${a[u-2]-1};
${p}
ivec4 srcIdx = ivec4(sourceLocR${d}, sourceLocG${d},
sourceLocB${d}, sourceLocA${d}) * ${e};
ivec4 inIdx = srcIdx;
vec4 bestIndex = vec4(inIdx);
vec4 bestValue = ${E};
for (int i = 0; i < ${e}; i++) {
inIdx = srcIdx;
${k}
vec4 candidate = ${E};
bvec4 nan = isnan(candidate);
bvec4 replace = bvec4(
vec4(${v}(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 AL(r,t,e,n=null){let o=t.shape[0],s=t.shape[1];n!=null&&(o=n.shape[0],s=n.shape[1]);let i=S.computeOptimalWindowSize(s),a={windowSize:i,inSize:s,batchSize:o,outSize:Math.ceil(s/i)},u=new Ww(a,e,n==null),l=[t];n!=null&&l.push(n);let c=r.runWebGLProgram(u,l,"int32");if(c.shape[1]===1)return c;let p=AL(r,t,e,c);return r.disposeIntermediateTensorInfo(c),p}function $L(r,t,e,n=null){let o=n!=null?n.shape:t.shape,s=o[o.length-1],i=S.computeOptimalWindowSize(s),a=new Uw(o,i,e,n==null),u=n==null?[t]:[t,n],l=r.runWebGLProgram(a,u,"int32");if(l.shape.length===t.shape.length){let c=$L(r,t,e,l);return r.disposeIntermediateTensorInfo(l),c}return l}function Hw(r,t,e,n){let o=[e];if(S.assertAxesAreInnerMostDims("arg"+n.charAt(0).toUpperCase()+n.slice(1),o,t.shape.length),!V().getBool("WEBGL_PACK_REDUCE")||t.shape.length<=2){let s=[],i=r.texData.get(t.dataId),a=i!==null&&i.isPacked,u=t;a&&(u=r.unpackTensor(t),s.push(u));let[l,c]=S.computeOutAndReduceShapes(u.shape,o),p=x.sizeFromShape(c),m=ut({inputs:{x:u},backend:r,attrs:{shape:[-1,p]}});s.push(m);let f=AL(r,m,n);s.push(f);let d=ut({inputs:{x:f},backend:r,attrs:{shape:l}});return s.forEach(h=>r.disposeIntermediateTensorInfo(h)),d}return $L(r,t,n)}function pet(r){let{inputs:t,backend:e,attrs:n}=r,{x:o}=t,{axis:s}=n,i=x.parseAxisParam(s,o.shape),a=S.getAxesPermutation(i,o.shape.length),u=o,l=[];a!=null&&(u=he({inputs:{x:o},backend:e,attrs:{perm:a}}),l.push(u),i=S.getInnerMostAxes(i.length,u.shape.length)),S.assertAxesAreInnerMostDims("argMax",[i[0]],u.shape.length);let c=Hw(e,u,i[0],"max");return l.forEach(p=>e.disposeIntermediateTensorInfo(p)),c}var DL={kernelName:Jo,backendName:"webgl",kernelFunc:pet};function met(r){let{inputs:t,backend:e,attrs:n}=r,{x:o}=t,{axis:s}=n,i=x.parseAxisParam(s,o.shape),a=S.getAxesPermutation(i,o.shape.length),u=o,l=[];a!=null&&(u=he({inputs:{x:o},backend:e,attrs:{perm:a}}),l.push(u),i=S.getInnerMostAxes(i.length,u.shape.length)),S.assertAxesAreInnerMostDims("argMin",[i[0]],u.shape.length);let c=Hw(e,u,i[0],"min");return l.forEach(p=>e.disposeIntermediateTensorInfo(p)),c}var FL={kernelName:Dl,backendName:"webgl",kernelFunc:met};var fet=hr+`
if (abs(x) > 1.) {
return NAN;
}
return asin(x);
`,det=It({opSnippet:fet}),RL={kernelName:ia,backendName:"webgl",kernelFunc:det};var het=hr+"return log(x + sqrt(x * x + 1.0));",get=It({opSnippet:het}),OL={kernelName:aa,backendName:"webgl",kernelFunc:get};var xet=hr+`
return atan(x);
`,yet=It({opSnippet:xet}),ML={kernelName:la,backendName:"webgl",kernelFunc:yet};var bet=pL+`
return atan(a, b);
`,wet=`
vec4 result = atan(a, b);
vec4 isNaN = min(vec4(isnan(a)) + vec4(isnan(b)), vec4(1.0));
`+mL+`
return result;
`,vet=le({opSnippet:bet,packedOpSnippet:wet}),PL={kernelName:ca,backendName:"webgl",kernelFunc:vet};var Cet=hr+`
if ((x < -1.0) || (x > 1.0)) return NAN;
return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,Iet=It({opSnippet:Cet}),LL={kernelName:ua,backendName:"webgl",kernelFunc:Iet};var gi=class{constructor(t,e,n,o=!1,s=!1){if(this.variableNames=["x"],e==="avg"&&n)throw new Error("Cannot compute positions for average pool.");let i=t.filterWidth,a=t.strideHeight,u=t.strideWidth,l=t.dilationHeight,c=t.dilationWidth,p=t.effectiveFilterHeight,m=t.effectiveFilterWidth,f=t.padInfo.top,d=t.padInfo.left;this.outputShape=t.outShape;let h=e==="avg",g=`((batch * ${t.inHeight} + xR) * ${t.inWidth} + xC) * ${t.inChannels} + d`,y=`(xR * ${t.inWidth} + xC) * ${t.inChannels} + d`,b="0.0";if(h||(b="-1.0 / 1e-20"),n){let D=">=";this.userCode=`
const ivec2 strides = ivec2(${a}, ${u});
const ivec2 pads = ivec2(${f}, ${d});
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 < ${p};
wR += ${l}) {
int xR = xRCorner + wR;
if (xR < 0 || xR >= ${t.inHeight}) {
continue;
}
for (int wC = 0; wC < ${m};
wC += ${c}) {
int xC = xCCorner + wC;
if (xC < 0 || xC >= ${t.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 ${D} currMinMaxValue) {
minMaxValue = value;
minMaxValueFound = 1.0;
minMaxPosition = ${o?s?g:y:`wR * ${m} + wC`};
}
}
}
setOutput(float(minMaxPosition));
}
`;return}let w="max",v=`${e}(${e}(${e}(minMaxValue[0], minMaxValue[1]), minMaxValue[2]), minMaxValue[3])`;e==="avg"&&(v="avgValue / count");let k=Math.floor(i/4)*4,E=i%4,$=`
if (${h}) {
avgValue += dot(values, ones);
} else {
minMaxValue = ${w}(values, minMaxValue);
}
`;this.userCode=`
const ivec2 strides = ivec2(${a}, ${u});
const ivec2 pads = ivec2(${f}, ${d});
const float initializationValue = ${b};
const vec4 ones = vec4(1.0, 1.0, 1.0, 1.0);
float count = 0.0;
float getValue(int batch, int xR, int xC, int d) {
if (xC < 0 || xC >= ${t.inWidth}) {
return initializationValue;
}
count += 1.0;
return getX(batch, xR, xC, d);
}
void main() {
ivec4 coords = getOutputCoords();
int batch = coords[0];
int d = coords[3];
ivec2 xRCCorner = coords.yz * strides - pads;
int xRCorner = xRCCorner.x;
int xCCorner = xRCCorner.y;
// max/min x(?, ?, d) to get y(yR, yC, d).
// ? = to be determined
vec4 minMaxValue = vec4(${b});
float avgValue = 0.0;
count = 0.0;
for (int wR = 0; wR < ${p};
wR += ${l}) {
int xR = xRCorner + wR;
if (xR < 0 || xR >= ${t.inHeight}) {
continue;
}
for (int wC = 0; wC < ${k}; wC += 4) {
int xC = xCCorner + wC * ${c};
vec4 values = vec4(
getValue(batch, xR, xC, d),
getValue(batch, xR, xC + ${c}, d),
getValue(batch, xR, xC + 2 * ${c}, d),
getValue(batch, xR, xC + 3 * ${c}, d)
);
${$}
}
int xC = xCCorner + ${k};
if (${E===1}) {
vec4 values = vec4(
getValue(batch, xR, xC, d),
initializationValue,
initializationValue,
initializationValue
);
${$}
} else if (${E===2}) {
vec4 values = vec4(
getValue(batch, xR, xC, d),
getValue(batch, xR, xC + ${c}, d),
initializationValue,
initializationValue
);
${$}
} else if (${E===3}) {
vec4 values = vec4(
getValue(batch, xR, xC, d),
getValue(batch, xR, xC + ${c}, d),
getValue(batch, xR, xC + 2 * ${c}, d),
initializationValue
);
${$}
}
}
setOutput(${v});
}
`}},Nu=class{constructor(t,e,n,o=!1,s=!1){if(this.variableNames=["x"],e==="avg"&&n)throw new Error("Cannot compute positions for average pool.");let i=t.filterWidth,a=t.strideDepth,u=t.strideHeight,l=t.strideWidth,c=t.dilationDepth,p=t.dilationHeight,m=t.dilationWidth,f=t.effectiveFilterDepth,d=t.effectiveFilterHeight,h=t.effectiveFilterWidth,g=t.padInfo.front,y=t.padInfo.top,b=t.padInfo.left;this.outputShape=t.outShape;let w=e==="avg",v="0.0";if(w||(v="-1.0 / 1e-20"),n){let P=">=";this.userCode=`
const ivec3 strides =
ivec3(${a}, ${u}, ${l});
const ivec3 pads = ivec3(${g}, ${y}, ${b});
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 < ${f};
wD += ${c}) {
int xD = xDCorner + wD;
if (xD < 0 || xD >= ${t.inDepth}) {
continue;
}
for (int wR = 0; wR < ${d};
wR += ${p}) {
int xR = xRCorner + wR;
if (xR < 0 || xR >= ${t.inHeight}) {
continue;
}
for (int wC = 0; wC < ${h};
wC += ${m}) {
int xC = xCCorner + wC;
if (xC < 0 || xC >= ${t.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 ${P} currMinMaxValue) {
minMaxValue = value;
minMaxValueFound = 1.0;
minMaxPosition = ${o?s?`(((batch * ${t.inDepth} + xD) * ${t.inHeight} + xR) * ${t.inWidth} + xC) * ${t.inChannels} + ch`:`((xD * ${t.inHeight} + xR) * ${t.inWidth} + xC) * ${t.inChannels} + ch`:`wD * ${d} * ${h} +
wR * ${h} + wC`};
}
}
}
}
setOutput(float(minMaxPosition));
}
`;return}let k="max",E=`${e}(${e}(${e}(minMaxValue[0], minMaxValue[1]), minMaxValue[2]), minMaxValue[3])`;e==="avg"&&(E="avgValue / count");let $=Math.floor(i/4)*4,D=i%4,F=`
if (${w}) {
avgValue += dot(values, ones);
} else {
minMaxValue = ${k}(values, minMaxValue);
}
`;this.userCode=`
const ivec3 strides =
ivec3(${a}, ${u}, ${l});
const ivec3 pads = ivec3(${g}, ${y}, ${b});
const float initializationValue = ${v};
const vec4 ones = vec4(1.0, 1.0, 1.0, 1.0);
float count = 0.0;
float getValue(int batch, int xD, int xR, int xC, int ch) {
if (xC < 0 || xC >= ${t.inWidth}) {
return initializationValue;
}
count += 1.0;
return getX(batch, xD, xR, xC, ch);
}
void main() {
ivec5 coords = getOutputCoords();
int batch = coords.x;
int ch = coords.u;
ivec3 xCorner = ivec3(coords.y, coords.z, coords.w) * strides - pads;
int xDCorner = xCorner.x;
int xRCorner = xCorner.y;
int xCCorner = xCorner.z;
// max/min x(?, ?, ?, d) to get y(yD, yR, yC, ch).
// ? = to be determined
vec4 minMaxValue = vec4(${v});
float avgValue = 0.0;
count = 0.0;
for (int wD = 0; wD < ${f};
wD += ${c}) {
int xD = xDCorner + wD;
if (xD < 0 || xD >= ${t.inDepth}) {
continue;
}
for (int wR = 0; wR < ${d};
wR += ${p}) {
int xR = xRCorner + wR;
if (xR < 0 || xR >= ${t.inHeight}) {
continue;
}
for (int wC = 0; wC < ${$}; wC += 4) {
int xC = xCCorner + wC * ${m};
vec4 values = vec4(
getValue(batch, xD, xR, xC, ch),
getValue(batch, xD, xR, xC + ${m}, ch),
getValue(batch, xD, xR, xC + 2 * ${m}, ch),
getValue(batch, xD, xR, xC + 3 * ${m}, ch)
);
${F}
}
int xC = xCCorner + ${$};
if (${D===1}) {
vec4 values = vec4(
getValue(batch, xD, xR, xC, ch),
initializationValue,
initializationValue,
initializationValue
);
${F}
} else if (${D===2}) {
vec4 values = vec4(
getValue(batch, xD, xR, xC, ch),
getValue(batch, xD, xR, xC + ${m}, ch),
initializationValue,
initializationValue
);
${F}
} else if (${D===3}) {
vec4 values = vec4(
getValue(batch, xD, xR, xC, ch),
getValue(batch, xD, xR, xC + ${m}, ch),
getValue(batch, xD, xR, xC + 2 * ${m}, ch),
initializationValue
);
${F}
}
}
setOutput(${E});
}
}
`}};function ket(r){let{inputs:t,backend:e,attrs:n}=r,{x:o}=t;di(o,"avgPool");let{filterSize:s,strides:i,pad:a,dimRoundingMode:u}=n,l=1;x.assert(S.eitherStridesOrDilationsAreOne(i,l),()=>`Error in avgPool: Either strides or dilations must be 1. Got strides ${i} and dilations '${l}'`);let c=S.computePool2DInfo(o.shape,s,i,l,a,u);if(c.filterWidth===1&&c.filterHeight===1&&x.arraysEqual(c.inShape,c.outShape))return nr({inputs:{x:o},backend:e});let p=new gi(c,"avg",!1);return e.runWebGLProgram(p,[o],"float32")}var zL={kernelName:Qo,backendName:"webgl",kernelFunc:ket};function Net(r){let{inputs:t,backend:e,attrs:n}=r,{x:o}=t,{filterSize:s,strides:i,pad:a,dimRoundingMode:u,dataFormat:l}=n,c=[1,1,1],p=S.computePool3DInfo(o.shape,s,i,c,a,u,l),m=new Nu(p,"avg",!1);return e.runWebGLProgram(m,[o],"float32")}var BL={kernelName:Fl,backendName:"webgl",kernelFunc:Net};var qw=class{constructor(t){this.variableNames=["dy"],this.outputShape=t.inShape;let e=t.filterHeight,n=t.filterWidth,o=t.strideHeight,s=t.strideWidth,i=t.dilationHeight,a=t.dilationWidth,u=t.effectiveFilterHeight,l=t.effectiveFilterWidth,c=u-1-t.padInfo.top,p=l-1-t.padInfo.left,m=1/(e*n);this.userCode=`
const ivec2 pads = ivec2(${c}, ${p});
const float avgMultiplier = float(${m});
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 < ${u};
wR += ${i}) {
float dyR = float(dyRCorner + wR) / ${o}.0;
if (dyR < 0.0 || dyR >= ${t.outHeight}.0 || fract(dyR) > 0.0) {
continue;
}
int idyR = int(dyR);
for (int wC = 0; wC < ${l};
wC+= ${a}) {
float dyC = float(dyCCorner + wC) / ${s}.0;
if (dyC < 0.0 || dyC >= ${t.outWidth}.0 ||
fract(dyC) > 0.0) {
continue;
}
int idyC = int(dyC);
float dyValue = getDy(b, idyR, idyC, d);
dotProd += dyValue * avgMultiplier;
}
}
setOutput(dotProd);
}
`}},Kw=class{constructor(t){this.variableNames=["dy"],this.outputShape=t.inShape;let e=t.filterDepth,n=t.filterHeight,o=t.filterWidth,s=t.strideDepth,i=t.strideHeight,a=t.strideWidth,u=t.dilationDepth,l=t.dilationHeight,c=t.dilationWidth,p=t.effectiveFilterDepth,m=t.effectiveFilterHeight,f=t.effectiveFilterWidth,d=p-1-t.padInfo.front,h=m-1-t.padInfo.top,g=f-1-t.padInfo.left,y=1/(e*n*o);this.userCode=`
const ivec3 pads = ivec3(${d}, ${h}, ${g});
const float avgMultiplier = float(${y});
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 < ${p};
wD += ${u}) {
float dyD = float(dyDCorner + wD) / ${s}.0;
if (dyD < 0.0 || dyD >= ${t.outDepth}.0 || fract(dyD) > 0.0) {
continue;
}
int idyD = int(dyD);
for (int wR = 0; wR < ${m};
wR += ${l}) {
float dyR = float(dyRCorner + wR) / ${i}.0;
if (dyR < 0.0 || dyR >= ${t.outHeight}.0 ||
fract(dyR) > 0.0) {
continue;
}
int idyR = int(dyR);
for (int wC = 0; wC < ${f};
wC += ${c}) {
float dyC = float(dyCCorner + wC) / ${a}.0;
if (dyC < 0.0 || dyC >= ${t.outWidth}.0 ||
fract(dyC) > 0.0) {
continue;
}
int idyC = int(dyC);
float dyValue = getDy(batch, idyD, idyR, idyC, ch);
dotProd += dyValue * avgMultiplier;
}
}
}
setOutput(dotProd);
}
`}};function Tet(r){let{inputs:t,backend:e,attrs:n}=r,{dy:o,input:s}=t,i=s,{filterSize:a,strides:u,pad:l,dimRoundingMode:c}=n,p=[1,1,1],m=S.computePool3DInfo(i.shape,a,u,p,l,c),f=new Kw(m);return e.runWebGLProgram(f,[o],i.dtype)}var VL={kernelName:$p,backendName:"webgl",kernelFunc:Tet};function _et(r){let{inputs:t,backend:e,attrs:n}=r,{dy:o,input:s}=t,i=s;di([o,s],"avgPoolGrad");let{filterSize:a,strides:u,pad:l}=n,c=S.computePool2DInfo(i.shape,a,u,1,l),p=new qw(c);return e.runWebGLProgram(p,[o],i.dtype)}var GL={kernelName:Ap,backendName:"webgl",kernelFunc:_et};function Eet(r){let{inputs:t,backend:e,attrs:n}=r,{a:o,b:s}=t,{transposeA:i,transposeB:a}=n;return sp({a:o,b:s,transposeA:i,transposeB:a,backend:e})}var WL={kernelName:ts,backendName:"webgl",kernelFunc:Eet};var jw=class{constructor(t,e,n,o,s,i){this.outputShape=[],this.variableNames=["x","mean","variance"],S.assertAndGetBroadcastShape(t,e),S.assertAndGetBroadcastShape(t,n);let a="0.0";o!=null&&(S.assertAndGetBroadcastShape(t,o),this.variableNames.push("offset"),a="getOffsetAtOutCoords()");let u="1.0";s!=null&&(S.assertAndGetBroadcastShape(t,s),this.variableNames.push("scale"),u="getScaleAtOutCoords()"),this.outputShape=t,this.userCode=`
void main() {
float x = getXAtOutCoords();
float mean = getMeanAtOutCoords();
float variance = getVarianceAtOutCoords();
float offset = ${a};
float scale = ${u};
float inv = scale * inversesqrt(variance + float(${i}));
setOutput(dot(vec3(x, -mean, offset), vec3(inv, inv, 1)));
}
`}};var Xw=class{constructor(t,e,n,o,s,i){this.packedInputs=!0,this.packedOutput=!0,this.variableNames=["x","mean","variance"],S.assertAndGetBroadcastShape(t,e),S.assertAndGetBroadcastShape(t,n);let a="vec4(0.0)";o!=null&&(S.assertAndGetBroadcastShape(t,o),this.variableNames.push("offset"),a="getOffsetAtOutCoords()");let u="vec4(1.0)";s!=null&&(S.assertAndGetBroadcastShape(t,s),this.variableNames.push("scale"),u="getScaleAtOutCoords()"),this.outputShape=t,this.userCode=`
void main() {
vec4 offset = ${a};
vec4 scale = ${u};
vec4 x = getXAtOutCoords();
vec4 mean = getMeanAtOutCoords();
vec4 variance = getVarianceAtOutCoords();
vec4 inv = scale * inversesqrt(variance + vec4(${i}));
setOutput((x - mean) * inv + offset);
}
`}};var Aet=({inputs:r,backend:t,attrs:e})=>{let{x:n,mean:o,variance:s,offset:i,scale:a}=r;x.assert(o.shape.length===s.shape.length,()=>"Batch normalization gradient requires mean and variance to have equal ranks."),x.assert(i==null||o.shape.length===i.shape.length,()=>"Batch normalization gradient requires mean and offset to have equal ranks."),x.assert(a==null||o.shape.length===a.shape.length,()=>"Batch normalization gradient requires mean and scale to have equal ranks.");let{varianceEpsilon:u}=e;u==null&&(u=.001);let l=[n,o,s],c=null;i!=null&&(c=i.shape,l.push(i));let p=null;a!=null&&(p=a.shape,l.push(a));let m=V().getBool("WEBGL_PACK_NORMALIZATION")?new Xw(n.shape,o.shape,s.shape,c,p,u):new jw(n.shape,o.shape,s.shape,c,p,u);return t.runWebGLProgram(m,l,l[0].dtype)},UL={kernelName:fs,backendName:"webgl",kernelFunc:Aet};var Yw=class{constructor(t){this.variableNames=["source"],this.outputShape=t,this.rank=t.length;let e=Ht(this.rank);this.customUniforms=[{name:"start",arrayIndex:this.rank,type:"int"}];let n=$et(this.rank),o,s=t.map((i,a)=>`sourceLoc.${$T[a]} = start[${a}] + coords.${$T[a]};`);o=`
${e} sourceLoc;
${e} coords = getOutputCoords();
${s.join(`
`)}
`,this.userCode=`
void main() {
${o}
setOutput(getSource(${n}));
}
`}},$T=["x","y","z","w","u","v"];function $et(r){if(r===1)return"sourceLoc";if(r<=6)return $T.slice(0,r).map(t=>"sourceLoc."+t).join(",");throw Error(`Slicing for rank ${r} is not yet supported`)}var Zw=class{constructor(t){this.variableNames=["source"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=t,this.rank=t.length,this.customUniforms=[{name:"start",arrayIndex:this.rank,type:"int"}];let e=Ht(this.rank),n=rr("coords",this.rank),o=rr("sourceLoc",this.rank),s=this.rank===1?"sourceLoc":`vec2(${o.slice(-2).join()})`,i=`getChannel(getSource(${o.join()}), ${s})`,a=`
result.x = ${i};
if (++${n[this.rank-1]} < ${t[this.rank-1]}) {
++${o[this.rank-1]};
result.y = ${i};
--${o[this.rank-1]};
}
`,u=this.rank===1?"":`
--${n[this.rank-1]};
if (++${n[this.rank-2]} < ${t[this.rank-2]}) {
++${o[this.rank-2]};
result.z = ${i};
if (++${n[this.rank-1]} < ${t[this.rank-1]}) {
++${o[this.rank-1]};
result.w = ${i};
}
}
`,l=this.rank<=4?`sourceLoc = coords +
${e}(${t.map((c,p)=>`start[${p}]`).join()});`:t.map((c,p)=>`${o[p]} = ${n[p]} + start[${p}];`).join(`
`);this.userCode=`
void main() {
${e} coords = getOutputCoords();
${e} sourceLoc;
${l}
vec4 result = vec4(0.);
${a}
${u}
setOutput(result);
}
`}};function Det(r,t,e,n){let o=n.texData.get(r.dataId),s=n.makeTensorInfo(e,r.dtype),i=n.texData.get(s.dataId);Object.assign(i,o),i.refCount=1,i.shape=e,i.dtype=r.dtype;let a=Ve.computeFlatOffset(t,x.computeStrides(r.shape));o.slice&&(a+=o.slice.flatOffset),i.slice={flatOffset:a,origDataId:o.slice&&o.slice.origDataId||r.dataId};let u=n.dataRefCount.get(i.slice.origDataId)||1;return n.dataRefCount.set(i.slice.origDataId,u+1),s}function xi(r){let{inputs:t,backend:e,attrs:n}=r,{x:o}=t,{begin:s,size:i}=n,[a,u]=Ve.parseSliceParams(o,s,i);if(Ve.assertParamsValid(o,a,u),x.sizeFromShape(u)===0)return e.makeTensorInfo(u,o.dtype,[]);if(e.shouldExecuteOnCPU([o])||o.dtype==="string"){let p=e.texData.get(o.dataId),m=DP(p.values,a,u,o.shape,o.dtype);return e.makeTensorInfo(u,o.dtype,m)}let{isPacked:l}=e.texData.get(o.dataId),c=Ve.isSliceContinous(o.shape,a,u);if(l||!c){let p=V().getBool("WEBGL_PACK_ARRAY_OPERATIONS")?new Zw(u):new Yw(u),m=[a];return e.runWebGLProgram(p,[o],o.dtype,m)}return e.uploadToGPU(o.dataId),Det(o,a,u,e)}var HL={kernelName:Ai,backendName:"webgl",kernelFunc:xi};var Fet=r=>{let{inputs:t,backend:e,attrs:n}=r,{x:o}=t,{blockShape:s,crops:i}=n;x.assert(o.shape.length<=4,()=>"batchToSpaceND for rank > 4 with a WebGL backend not implemented yet");let a=s.reduce((b,w)=>b*w),u=S.getReshaped(o.shape,s,a),l=S.getPermuted(u.length,s.length),c=S.getReshapedPermuted(o.shape,s,a),p=S.getSliceBeginCoords(i,s.length),m=S.getSliceSize(c,i,s.length),f=[],d=ut({inputs:{x:o},backend:e,attrs:{shape:u}}),h=he({inputs:{x:d},backend:e,attrs:{perm:l}}),g=ut({inputs:{x:h},backend:e,attrs:{shape:c}}),y=xi({inputs:{x:g},backend:e,attrs:{begin:p,size:m}});return f.push(d),f.push(h),f.push(g),f.forEach(b=>e.disposeIntermediateTensorInfo(b)),y},qL={kernelName:vi,backendName:"webgl",kernelFunc:Fet};function Ret(r){let{inputs:t,backend:e,attrs:n}=r,{x:o,weights:s}=t,{size:i}=n,a=e.readSync(o.dataId),u=e.readSync(s.dataId),l=Ew(a,u,s.dtype,s.shape,i);return e.makeTensorInfo([i],s.dtype,l)}var KL={kernelName:Dp,backendName:"webgl",kernelFunc:Ret};function Oet(r){let{inputs:t,backend:e}=r,{s0:n,s1:o}=t,s=e.readSync(n.dataId),i=e.readSync(o.dataId),a=S.assertAndGetBroadcastShape(Array.from(s),Array.from(i));return e.makeTensorInfo([a.length],"int32",Int32Array.from(a))}var jL={kernelName:Fp,backendName:"webgl",kernelFunc:Oet};var Met="return float(a != b);",DT=le({opSnippet:Met,cpuKernelImpl:NP,dtype:"bool"}),XL={kernelName:_a,backendName:"webgl",kernelFunc:DT};function kl(r){let{inputs:t,backend:e}=r,{input:n}=t,o=e.texData.get(n.dataId);return nr({inputs:{x:o.complexTensorInfos.real},backend:e})}var YL={kernelName:Qp,backendName:"webgl",kernelFunc:kl};var Pet="return float(int(x));";function ZL(r,t){let e=new en(r.shape,Pet),n=t.runWebGLProgram(e,[r],"int32");return{dataId:n.dataId,shape:n.shape,dtype:n.dtype}}function FT(r){let{inputs:t,backend:e,attrs:n}=r,{x:o}=t,{dtype:s}=n;if(s==="complex64"){if(o.dtype==="complex64")return nr({inputs:{x:o},backend:e});let i=we(o.shape),a=FT({inputs:{x:o},backend:e,attrs:{dtype:"float32"}}),u=Fn({inputs:{real:a,imag:i},backend:e});return i.dispose(),e.disposeIntermediateTensorInfo(a),u}if(o.dtype==="complex64"){let i=kl({inputs:{input:o},backend:e}),a=FT({inputs:{x:i},backend:e,attrs:{dtype:s}});return e.disposeIntermediateTensorInfo(i),a}if(!x.hasEncodingLoss(o.dtype,s)){let i=nr({inputs:{x:o},backend:e});return{dataId:i.dataId,shape:i.shape,dtype:s}}if(s==="int32")return ZL(o,e);if(s==="bool"){let i=e.makeTensorInfo([],"bool",x.getTypedArrayFromDType("bool",1)),u=DT({inputs:{a:o,b:i},backend:e});return e.disposeIntermediateTensorInfo(i),u}throw new Error(`Error in Cast: failed to cast ${o.dtype} to ${s}`)}var JL={kernelName:io,backendName:"webgl",kernelFunc:FT};var QL="return ceil(x);",Let=It({opSnippet:QL,packedOpSnippet:QL,cpuKernelImpl:aP}),t3={kernelName:es,backendName:"webgl",kernelFunc:Let};var Jw=class{constructor(t){this.variableNames=["A"],this.customUniforms=[{name:"minVal",type:"float"},{name:"maxVal",type:"float"}],this.outputShape=t,this.userCode=`
void main() {
float value = getAAtOutCoords();
if (isnan(value)) {
setOutput(value);
return;
}
setOutput(clamp(value, minVal, maxVal));
}
`}};var Qw=class{constructor(t){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,this.customUniforms=[{name:"minVal",type:"float"},{name:"maxVal",type:"float"}],this.outputShape=t,this.userCode=`
void main() {
vec4 value = getAAtOutCoords();
if (any(isnan(value))) {
setOutput(value);
return;
}
setOutput(clamp(value, vec4(minVal), vec4(maxVal)));
}
`}};function zet(r){let{inputs:t,backend:e,attrs:n}=r,{x:o}=t,{clipValueMin:s,clipValueMax:i}=n,a;V().getBool("WEBGL_PACK_CLIP")?a=new Qw(o.shape):a=new Jw(o.shape);let u=[[s],[i]];return e.runWebGLProgram(a,[o],o.dtype,u)}var e3={kernelName:ao,backendName:"webgl",kernelFunc:zet};var tv=class{constructor(t){this.variableNames=["real","imag"],this.outputShape=t,this.userCode=`
void main() {
float re = abs(getRealAtOutCoords());
float im = abs(getImagAtOutCoords());
float mx = max(re, im);
// sadly the length function in glsl is not underflow-safe
// (at least not on Intel GPUs). So the safe solution is
// to ensure underflow-safety in all cases.
setOutput(
mx == 0.0 ? 0.0 : mx * length(vec2(1, min(re, im)/mx))
);
}
`}};function r3(r,t){return{dataId:t.dataId,dtype:t.dtype,shape:r.shape}}function Bet(r){let{inputs:t,backend:e}=r,{x:n}=t,o=e.texData.get(n.dataId),s=new tv(n.shape),i=[r3(n,o.complexTensorInfos.real),r3(n,o.complexTensorInfos.imag)];return e.runWebGLProgram(s,i,i[0].dtype)}var n3={kernelName:Rl,backendName:"webgl",kernelFunc:Bet};var ev=class{constructor(t){this.outputShape=[],this.outputShape=S.computeOutShape(t,1),this.variableNames=t.map((i,a)=>`T${a}`);let e=new Array(t.length-1);e[0]=t[0][1];for(let i=1;i<e.length;i++)e[i]=e[i-1]+t[i][1];let n=[`if (yC < ${e[0]}) setOutput(getT0(yR, yC));`];for(let i=1;i<e.length;i++){let a=e[i-1];n.push(`else if (yC < ${e[i]}) setOutput(getT${i}(yR, yC-${a}));`)}let o=e.length,s=e[e.length-1];n.push(`else setOutput(getT${o}(yR, yC-${s}));`),this.userCode=`
void main() {
ivec2 coords = getOutputCoords();
int yR = coords.x;
int yC = coords.y;
${n.join(`
`)}
}
`}};var nv=class{constructor(t,e){this.packedInputs=!0,this.packedOutput=!0,this.outputShape=[],this.outputShape=S.computeOutShape(t,e);let n=this.outputShape,o=n.length,s=Ht(o),i=rr("coords",o),a=["x","y","z","w","u","v"].slice(0,o);this.variableNames=t.map((h,g)=>`T${g}`);let u=new Array(t.length-1);u[0]=t[0][e];for(let h=1;h<u.length;h++)u[h]=u[h-1]+t[h][e];let l=a[e],c=a.slice(-2),p=a.join(),m=`if (${l} < ${u[0]}) {
return getChannel(
getT0(${p}), vec2(${c.join()}));
}`;for(let h=1;h<u.length;h++){let g=u[h-1];m+=`
if (${l} < ${u[h]} && ${l} >= ${u[h-1]}) {
return getChannel(
getT${h}(${rv(a,l,g)}),
vec2(${rv(c,l,g)}));
}`}let f=u.length,d=u[u.length-1];m+=`
return getChannel(
getT${f}(${rv(a,l,d)}),
vec2(${rv(c,l,d)}));`,this.userCode=`
float getValue(${a.map(h=>"int "+h)}) {
${m}
}
void main() {
${s} coords = getOutputCoords();
vec4 result = vec4(getValue(${i}), 0., 0., 0.);
${i[o-1]} = ${i[o-1]} + 1;
if (${i[o-1]} < ${n[o-1]}) {
result.g = getValue(${i});
}
${i[o-2]} = ${i[o-2]} + 1;
if (${i[o-2]} < ${n[o-2]}) {
result.a = getValue(${i});
}
${i[o-1]} = ${i[o-1]} - 1;
if (${i[o-2]} < ${n[o-2]} &&
${i[o-1]} < ${n[o-1]}) {
result.b = getValue(${i});
}
setOutput(result);
}
`}};function rv(r,t,e){let n=r.indexOf(t);return r.map((s,i)=>i===n?`${s} - ${e}`:s).join()}function ip(r){let{inputs:t,backend:e}=r,{input:n}=t,o=e.texData.get(n.dataId);return nr({inputs:{x:o.complexTensorInfos.imag},backend:e})}var o3={kernelName:qp,backendName:"webgl",kernelFunc:ip};function ap(r,t,e){let n=r[0].dtype;if(n==="complex64"){let c=r.map(h=>kl({inputs:{input:h},backend:e})),p=r.map(h=>ip({inputs:{input:h},backend:e})),m=ap(c,t,e),f=ap(p,t,e),d=Fn({inputs:{real:m,imag:f},backend:e});return c.forEach(h=>e.disposeIntermediateTensorInfo(h)),p.forEach(h=>e.disposeIntermediateTensorInfo(h)),e.disposeIntermediateTensorInfo(m),e.disposeIntermediateTensorInfo(f),d}let o=e.shouldExecuteOnCPU(r);if(n==="string"&&(o=!0),o){let c=r.map(y=>{let b=x.sizeFromShape(y.shape.slice(t));return ut({inputs:{x:y},backend:e,attrs:{shape:[-1,b]}})}),p=c.map(y=>({vals:e.readSync(y.dataId),shape:y.shape})),m=S.computeOutShape(c.map(y=>y.shape),1),f=c[0].shape[0]===1,d=lP(p,m,n,f),h=S.computeOutShape(r.map(y=>y.shape),t),g=e.makeTensorInfo(h,n,d);return c.forEach(y=>e.disposeIntermediateTensorInfo(y)),g}if(r.length>V().getNumber("WEBGL_MAX_TEXTURES_IN_SHADER")){let c=Math.floor(r.length/2),p=ap(r.slice(0,c),t,e),m=ap(r.slice(c),t,e),f=ap([p,m],t,e);return e.disposeIntermediateTensorInfo(p),e.disposeIntermediateTensorInfo(m),f}if(V().getBool("WEBGL_PACK_ARRAY_OPERATIONS")&&r[0].shape.length>1){let c=new nv(r.map(p=>p.shape),t);return e.runWebGLProgram(c,r,n)}let{tensors2D:s,outShape:i}=Vet(r,t,e),a=new ev(s.map(c=>c.shape)),u=e.runWebGLProgram(a,s,n);s.forEach(c=>e.disposeIntermediateTensorInfo(c));let l=ut({inputs:{x:u},attrs:{shape:i},backend:e});return e.disposeIntermediateTensorInfo(u),l}function Vet(r,t,e){let n=S.computeOutShape(r.map(s=>s.shape),t);return{tensors2D:r.map(s=>ut({inputs:{x:s},attrs:{shape:[-1,x.sizeFromShape(s.shape.slice(t))]},backend:e})),outShape:n}}function RT(r){let{inputs:t,backend:e,attrs:n}=r,{axis:o}=n,s=x.parseAxisParam(o,t[0].shape)[0],i=S.computeOutShape(t.map(l=>l.shape),s);if(x.sizeFromShape(i)===0)return e.makeTensorInfo(i,t[0].dtype,[]);let a=t.filter(l=>x.sizeFromShape(l.shape)>0);if(a.length===1)return nr({inputs:{x:a[0]},backend:e});let u=a.map(l=>l.shape);return S.assertParamsConsistent(u,s),ap(a,s,e)}var s3={kernelName:Ci,backendName:"webgl",kernelFunc:RT};var Sd=class{constructor(t,e=!1,n=null,o=!1,s=!1){this.variableNames=["x","W"],this.outputShape=t.outShape;let i=t.padInfo.top,a=t.padInfo.left,u=t.strideHeight,l=t.strideWidth,c=t.dilationHeight,p=t.dilationWidth,m=t.filterHeight,f=t.filterWidth,d=Math.floor(t.inChannels/4)*4,h=t.inChannels%4,g=t.dataFormat==="channelsLast",y=g?1:2,b=g?2:3,w=g?3:1,v="",k="";n&&(o?v=`float activation(float a) {
float b = getPreluActivationWeightsAtOutCoords();
${n}
}`:s?v=`float activation(float a) {
float b = getLeakyreluAlphaAtOutCoords();
${n}
}`:v=`
float activation(float x) {
${n}
}
`,k="result = activation(result);");let E=e?"result += getBiasAtOutCoords();":"";e&&this.variableNames.push("bias"),o&&this.variableNames.push("preluActivationWeights"),s&&this.variableNames.push("leakyreluAlpha"),this.userCode=`
${v}
const ivec2 strides = ivec2(${u}, ${l});
const ivec2 pads = ivec2(${i}, ${a});
void main() {
ivec4 coords = getOutputCoords();
int batch = coords[0];
int d2 = coords[${w}];
ivec2 xRCCorner =
ivec2(coords[${y}], coords[${b}]) * strides - pads;
int xRCorner = xRCCorner.x;
int xCCorner = xRCCorner.y;
// Convolve x(?, ?, d1) with w(:, :, d1, d2) to get y(yR, yC, d2).
// ? = to be determined. : = across all values in that axis.
float dotProd = 0.0;
for (int wR = 0; wR < ${m}; wR++) {
int xR = xRCorner + wR * ${c};
if (xR < 0 || xR >= ${t.inHeight}) {
continue;
}
for (int wC = 0; wC < ${f}; wC++) {
int xC = xCCorner + wC * ${p};
if (xC < 0 || xC >= ${t.inWidth}) {
continue;
}
for (int d1 = 0; d1 < ${d}; d1 += 4) {
vec4 wValues = vec4(
getW(wR, wC, d1, d2),
getW(wR, wC, d1 + 1, d2),
getW(wR, wC, d1 + 2, d2),
getW(wR, wC, d1 + 3, d2)
);
if (${g}) {
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 (${h===1}) {
if (${g}) {
dotProd +=
getX(batch, xR, xC, ${d}) *
getW(wR, wC, ${d}, d2);
} else {
dotProd +=
getX(batch, ${d}, xR, xC) *
getW(wR, wC, ${d}, d2);
}
} else if (${h===2}) {
vec2 wValues = vec2(
getW(wR, wC, ${d}, d2),
getW(wR, wC, ${d} + 1, d2)
);
if (${g}) {
vec2 xValues = vec2(
getX(batch, xR, xC, ${d}),
getX(batch, xR, xC, ${d} + 1)
);
dotProd += dot(xValues, wValues);
} else {
vec2 xValues = vec2(
getX(batch, ${d}, xR, xC),
getX(batch, ${d} + 1, xR, xC)
);
dotProd += dot(xValues, wValues);
}
} else if (${h===3}) {
vec3 wValues = vec3(
getW(wR, wC, ${d}, d2),
getW(wR, wC, ${d} + 1, d2),
getW(wR, wC, ${d} + 2, d2)
);
if (${g}) {
vec3 xValues = vec3(
getX(batch, xR, xC, ${d}),
getX(batch, xR, xC, ${d} + 1),
getX(batch, xR, xC, ${d} + 2)
);
dotProd += dot(xValues, wValues);
} else {
vec3 xValues = vec3(
getX(batch, ${d}, xR, xC),
getX(batch, ${d} + 1, xR, xC),
getX(batch, ${d} + 2, xR, xC)
);
dotProd += dot(xValues, wValues);
}
}
}
}
float result = dotProd;
${E}
${k}
setOutput(result);
}
`}},ov=class{constructor(t){this.variableNames=["x","W"],this.outputShape=t.outShape;let e=t.padInfo.front,n=t.padInfo.top,o=t.padInfo.left,s=t.strideDepth,i=t.strideHeight,a=t.strideWidth,u=t.dilationDepth,l=t.dilationHeight,c=t.dilationWidth,p=t.filterDepth,m=t.filterHeight,f=t.filterWidth,d=Math.floor(t.inChannels/4)*4,h=t.inChannels%4;this.userCode=`
const ivec3 strides = ivec3(${s}, ${i}, ${a});
const ivec3 pads = ivec3(${e}, ${n}, ${o});
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 < ${p}; wF++) {
int xF = xFCorner + wF * ${u};
if (xF < 0 || xF >= ${t.inDepth}) {
continue;
}
for (int wR = 0; wR < ${m}; wR++) {
int xR = xRCorner + wR * ${l};
if (xR < 0 || xR >= ${t.inHeight}) {
continue;
}
for (int wC = 0; wC < ${f}; wC++) {
int xC = xCCorner + wC * ${c};
if (xC < 0 || xC >= ${t.inWidth}) {
continue;
}
for (int d1 = 0; d1 < ${d}; d1 += 4) {
vec4 xValues = vec4(
getX(batch, xF, xR, xC, d1),
getX(batch, xF, xR, xC, d1 + 1),
getX(batch, xF, xR, xC, d1 + 2),
getX(batch, xF, xR, xC, d1 + 3)
);
vec4 wValues = vec4(
getW(wF, wR, wC, d1, d2),
getW(wF, wR, wC, d1 + 1, d2),
getW(wF, wR, wC, d1 + 2, d2),
getW(wF, wR, wC, d1 + 3, d2)
);
dotProd += dot(xValues, wValues);
}
if (${h===1}) {
dotProd +=
getX(batch, xF, xR, xC, ${d}) *
getW(wF, wR, wC, ${d}, d2);
} else if (${h===2}) {
vec2 xValues = vec2(
getX(batch, xF, xR, xC, ${d}),
getX(batch, xF, xR, xC, ${d} + 1)
);
vec2 wValues = vec2(
getW(wF, wR, wC, ${d}, d2),
getW(wF, wR, wC, ${d} + 1, d2)
);
dotProd += dot(xValues, wValues);
} else if (${h===3}) {
vec3 xValues = vec3(
getX(batch, xF, xR, xC, ${d}),
getX(batch, xF, xR, xC, ${d} + 1),
getX(batch, xF, xR, xC, ${d} + 2)
);
vec3 wValues = vec3(
getW(wF, wR, wC, ${d}, d2),
getW(wF, wR, wC, ${d} + 1, d2),
getW(wF, wR, wC, ${d} + 2, d2)
);
dotProd += dot(xValues, wValues);
}
}
}
}
setOutput(dotProd);
}
`}};var sv=class{constructor(t,e){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,this.customUniforms=[{name:"inputShape",type:"ivec3"},{name:"pad",type:"ivec2"},{name:"stride",type:"ivec2"},{name:"dilation",type:"ivec2"},{name:"inChannels",type:"int"},{name:"itemsPerBlockRow",type:"int"},{name:"outWidth",type:"int"}],this.outputShape=t,this.enableShapeUniforms=De(this.outputShape.length);let{dataFormat:n}=e,o=qe(),s=n==="channelsLast",i=s?0:1,a=s?1:2,u=this.enableShapeUniforms?"if(blockIndex < outShape[1] && pos < outShape[0]) {":`if(blockIndex < ${t[1]} && pos < ${t[0]}) {`,l="";for(let c=0;c<=1;c++)for(let p=0;p<=1;p++)l+=`
blockIndex = rc.y + ${p};
pos = rc.x + ${c};
${u}
offsetY = int(blockIndex / outWidth) * stride[0] - pad[0];
d0 = offsetY + dilation[0] * (pos / itemsPerBlockRow);
if(d0 < inputShape[${i}] && d0 >= 0) {
// Use custom imod instead mod. On Intel GPU, mod may generate
// unexpected value.
// https://github.com/tensorflow/tfjs/issues/5447
offsetX = imod(blockIndex, outWidth) * stride[1] - pad[1];
d1 = offsetX + dilation[1] * (imod(pos, itemsPerBlockRow) /
inChannels);
if(d1 < inputShape[${a}] && d1 >= 0) {
ch = imod(pos, inChannels);
if (${s}) {
innerDims = vec2(d1, ch);
result[${c*2+p}] = getChannel(
getA(d0, int(innerDims.x),
int(innerDims.y)), innerDims);
} else {
innerDims = vec2(d0, d1);
result[${c*2+p}] = getChannel(
getA(ch, int(innerDims.x),
int(innerDims.y)), innerDims);
}
}
}
}
`;this.userCode=`
void main() {
ivec2 rc = getOutputCoords();
vec4 result = vec4(0);
int blockIndex, pos, offsetY, d0, offsetX, d1, ch;
vec2 innerDims;
${l}
${o.output} = result;
}
`}};function i3(r,t,e,n){let o=r.shape;if(x.assert(o.length<=1||o.length===3,()=>`WebGL conv2d only supports scalar, 1-D Tensor or 3-D Tensor PReLU activation weights but got a tensor of rank-${o.length}.`),o.length===1){let s=e?t[3]:t[1];x.assert(o[0]===1||o[0]===s,()=>`WebGL conv2d PReLU activation weights (${o}) is not compatible with the number of output channels (${s}).`)}else if(o.length===3){try{Sr.assertAndGetBroadcastShape(o,t)}catch(s){let i=`WebGL conv2d PReLU activation weights (${o}) is not compatible with the output shape of the conv2d (${t}).`;throw Error(i)}if(!e)return he({inputs:{x:r},backend:n,attrs:{perm:[1,2,0]}})}return r}function iv({x:r,filter:t,convInfo:e,backend:n,bias:o=null,preluActivationWeights:s=null,leakyreluAlpha:i=0,activation:a=null}){let u=r.shape,l=n.texData.get(r.dataId),c=e.inChannels,p=u[0]*u[1]*u[2],m=e.outChannels,f=e.dataFormat==="channelsLast",d=!1,h=!1,g,y=[];if(s!=null){let v=i3(s,e.outShape,f,n);v.dataId!==s.dataId&&(y.push(v),s=v)}if(!((p===1||m===1)&&c>AT)&&l.isPacked&&f&&l.texture!=null&&u[2]%2!==0&&x.arraysEqual(l.shape.slice(-3),u.slice(-3))){let v=u[0]*u[1]*(u[2]+1),k={dataId:r.dataId,shape:[1,v,e.inChannels],dtype:r.dtype},E=l.shape;l.shape=l.shape.slice(),l.shape[l.shape.length-2]++,x.assert(vu(l.shape,k.shape),()=>`packed reshape ${l.shape} to ${k.shape} isn't free`);let $=ut({inputs:{x:t},backend:n,attrs:{shape:[1,e.inChannels,e.outChannels]}});y.push($);let D=sp({a:k,b:$,backend:n,transposeA:d,transposeB:h,bias:o,activation:a,preluActivationWeights:s,leakyreluAlpha:i}),F=n.texData.get(D.dataId);x.assert(F.isPacked,()=>"batchMatMul result is expected to be packed"),l.shape=E,F.shape=e.outShape,g=nr({inputs:{x:D},backend:n}),g.shape=e.outShape,y.push(D)}else{let v=f?r:he({inputs:{x:r},backend:n,attrs:{perm:[0,2,3,1]}}),k=v.shape,E=k[0]*k[1]*k[2],$=ut({inputs:{x:v},backend:n,attrs:{shape:[1,E,e.inChannels]}}),D=ut({inputs:{x:t},backend:n,attrs:{shape:[1,e.inChannels,e.outChannels]}}),F=sp({a:$,b:D,transposeA:d,transposeB:h,backend:n,bias:o,activation:a,preluActivationWeights:s,leakyreluAlpha:i}),P=[e.batchSize,e.outHeight,e.outWidth,e.outChannels],W=ut({inputs:{x:F},backend:n,attrs:{shape:P}});g=f?W:he({inputs:{x:W},backend:n,attrs:{perm:[0,3,1,2]}}),f||(y.push(v),y.push(W)),y.push($),y.push(D),y.push(F)}for(let v of y)n.disposeIntermediateTensorInfo(v);return g}function av({x:r,filter:t,convInfo:e,backend:n,bias:o=null,preluActivationWeights:s=null,leakyreluAlpha:i=0,activation:a=null}){let{filterWidth:u,filterHeight:l,inChannels:c,outWidth:p,outHeight:m,dataFormat:f}=e,d=f==="channelsLast",h=u*l*c,g=m*p,y=[h,g],b=!0,w=!1,v=[];if(s!=null){let it=i3(s,e.outShape,d,n);it.dataId!==s.dataId&&(v.push(it),s=it)}let k=ut({inputs:{x:r},backend:n,attrs:{shape:r.shape.slice(1)}}),E=ut({inputs:{x:t},backend:n,attrs:{shape:[1,h,x.sizeFromShape(t.shape)/h]}});v.push(k),v.push(E);let $=new sv(y,e),D=[k.shape,[e.padInfo.top,e.padInfo.left],[e.strideHeight,e.strideWidth],[e.dilationHeight,e.dilationWidth],[e.inChannels],[e.filterWidth*e.inChannels],[e.outWidth]],F=n.runWebGLProgram($,[k],"float32",D),P=ut({inputs:{x:F},backend:n,attrs:{shape:[1,y[0],y[1]]}});v.push(F),v.push(P);let W=o!=null,U=s!=null,q=a==="leakyrelu",K=a?Su(a,!0):null,j=new Id(P.shape,E.shape,[1,g,e.outChannels],b,w,W,K,U,q),Q=[P,E];if(o&&Q.push(o),U&&Q.push(s),q){let it=n.makeTensorInfo([],"float32",x.createScalarValue(i,"float32"));Q.push(it),v.push(it)}let rt=n.runWebGLProgram(j,Q,"float32"),X=[1,m,p,e.outChannels],nt=ut({inputs:{x:rt},backend:n,attrs:{shape:X}}),st=d?nt:he({inputs:{x:nt},backend:n,attrs:{perm:[0,3,1,2]}});d||v.push(nt),v.push(rt);for(let it of v)n.disposeIntermediateTensorInfo(it);return st}function Get(r){let{inputs:t,backend:e,attrs:n}=r,{x:o,filter:s}=t,{strides:i,pad:a,dataFormat:u,dilations:l,dimRoundingMode:c}=n,p=S.convertConv2DDataFormat(u),m=S.computeConv2DInfo(o.shape,s.shape,i,l,a,c,!1,p),f;if(m.filterHeight===1&&m.filterWidth===1&&m.dilationHeight===1&&m.dilationWidth===1&&m.strideHeight===1&&m.strideWidth===1&&(m.padInfo.type==="SAME"||m.padInfo.type==="VALID"))f=iv({x:o,filter:s,convInfo:m,backend:e});else if(V().getBool("WEBGL_CONV_IM2COL")&&o.shape[0]===1)f=av({x:o,filter:s,convInfo:m,backend:e});else{let h=new Sd(m);f=e.runWebGLProgram(h,[o,s],"float32")}let d=ut({inputs:{x:f},backend:e,attrs:{shape:m.outShape}});return e.disposeIntermediateTensorInfo(f),d}var a3={kernelName:rs,backendName:"webgl",kernelFunc:Get};var lv=class{constructor(t){this.variableNames=["x","dy"],this.outputShape=t.filterShape;let e=t.strideHeight,n=t.strideWidth,o=t.padInfo.top,s=t.padInfo.left,i=t.dataFormat==="channelsLast";this.userCode=`
void main() {
ivec4 coords = getOutputCoords();
int wR = coords.x;
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 < ${t.batchSize}; b++) {
for (int yR = 0; yR < ${t.outHeight}; yR++) {
int xR = wR + yR * ${e} - ${o};
if (xR < 0 || xR >= ${t.inHeight}) {
continue;
}
for (int yC = 0; yC < ${t.outWidth}; yC++) {
int xC = wC + yC * ${n} - ${s};
if (xC < 0 || xC >= ${t.inWidth}) {
continue;
}
if (${i}) {
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);
}
`}},uv=class{constructor(t){this.variableNames=["dy","W"],this.outputShape=t.inShape;let e=t.filterHeight,n=t.filterWidth,o=t.strideHeight,s=t.strideWidth,i=t.dataFormat==="channelsLast",a=e-1-t.padInfo.top,u=n-1-t.padInfo.left,l=i?1:2,c=i?2:3,p=i?3:1;this.userCode=`
const ivec2 pads = ivec2(${a}, ${u});
void main() {
ivec4 coords = getOutputCoords();
int batch = coords[0];
int d1 = coords[${p}];
ivec2 dyCorner = ivec2(coords[${l}], coords[${c}]) - pads;
int dyRCorner = dyCorner.x;
int dyCCorner = dyCorner.y;
// Convolve dy(?, ?, d2) with w(:, :, d1, d2) to compute dx(xR, xC, d1).
// ? = to be determined. : = across all values in that axis.
float dotProd = 0.0;
for (int wR = 0; wR < ${e}; wR++) {
float dyR = float(dyRCorner + wR) / ${o}.0;
if (dyR < 0.0 || dyR >= ${t.outHeight}.0 || fract(dyR) > 0.0) {
continue;
}
int idyR = int(dyR);
int wRPerm = ${e} - 1 - wR;
for (int wC = 0; wC < ${n}; wC++) {
float dyC = float(dyCCorner + wC) / ${s}.0;
if (dyC < 0.0 || dyC >= ${t.outWidth}.0 ||
fract(dyC) > 0.0) {
continue;
}
int idyC = int(dyC);
int wCPerm = ${n} - 1 - wC;
for (int d2 = 0; d2 < ${t.outChannels}; d2++) {
if (${i}) {
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);
}
`}},cv=class{constructor(t){this.variableNames=["x","dy"],this.outputShape=t.filterShape;let e=t.strideDepth,n=t.strideHeight,o=t.strideWidth,s=t.padInfo.front,i=t.padInfo.top,a=t.padInfo.left;this.userCode=`
void main() {
ivec5 coords = getOutputCoords();
int wF = coords.x;
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 < ${t.batchSize}; b++) {
for (int yF = 0; yF < ${t.outDepth}; yF++) {
int xF = wF + yF * ${e} - ${s};
if (xF < 0 || xF >= ${t.inDepth}) {
continue;
}
for (int yR = 0; yR < ${t.outHeight}; yR++) {
int xR = wR + yR * ${n} - ${i};
if (xR < 0 || xR >= ${t.inHeight}) {
continue;
}
for (int yC = 0; yC < ${t.outWidth}; yC++) {
int xC = wC + yC * ${o} - ${a};
if (xC < 0 || xC >= ${t.inWidth}) {
continue;
}
float dyValue = getDy(b, yF, yR, yC, d2);
float xValue = getX(b, xF, xR, xC, d1);
dotProd += (xValue * dyValue);
}
}
}
}
setOutput(dotProd);
}
`}},pv=class{constructor(t){this.variableNames=["dy","W"],this.outputShape=t.inShape;let e=t.filterDepth,n=t.filterHeight,o=t.filterWidth,s=t.strideDepth,i=t.strideHeight,a=t.strideWidth,u=e-1-t.padInfo.front,l=n-1-t.padInfo.top,c=o-1-t.padInfo.left;this.userCode=`
const ivec3 pads = ivec3(${u}, ${l}, ${c});
void main() {
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 < ${e}; wF++) {
float dyF = float(dyFCorner + wF) / ${s}.0;
if (dyF < 0.0 || dyF >= ${t.outDepth}.0 || fract(dyF) > 0.0) {
continue;
}
int idyF = int(dyF);
int wFPerm = ${e} - 1 - wF;
for (int wR = 0; wR < ${n}; wR++) {
float dyR = float(dyRCorner + wR) / ${i}.0;
if (dyR < 0.0 || dyR >= ${t.outHeight}.0 ||
fract(dyR) > 0.0) {
continue;
}
int idyR = int(dyR);
int wRPerm = ${n} - 1 - wR;
for (int wC = 0; wC < ${o}; wC++) {
float dyC = float(dyCCorner + wC) / ${a}.0;
if (dyC < 0.0 || dyC >= ${t.outWidth}.0 ||
fract(dyC) > 0.0) {
continue;
}
int idyC = int(dyC);
int wCPerm = ${o} - 1 - wC;
for (int d2 = 0; d2 < ${t.outChannels}; d2++) {
float xValue = getDy(batch, idyF, idyR, idyC, d2);
float wValue = getW(wFPerm, wRPerm, wCPerm, d1, d2);
dotProd += xValue * wValue;
}
}
}
}
setOutput(dotProd);
}
`}};function Wet(r){let{inputs:t,backend:e,attrs:n}=r,{x:o,dy:s}=t,{strides:i,pad:a,dataFormat:u,dimRoundingMode:l,filterShape:c}=n,p=S.convertConv2DDataFormat(u),m=S.computeConv2DInfo(o.shape,c,i,1,a,l,!1,p),f=new lv(m);return e.runWebGLProgram(f,[o,s],"float32")}var l3={kernelName:Op,backendName:"webgl",kernelFunc:Wet};function Uet(r){let{inputs:t,backend:e,attrs:n}=r,{dy:o,filter:s}=t,{inputShape:i,strides:a,pad:u,dataFormat:l,dimRoundingMode:c}=n,p=S.convertConv2DDataFormat(l),m=S.computeConv2DInfo(i,s.shape,a,1,u,c,!1,p),f=new uv(m);return e.runWebGLProgram(f,[o,s],"float32")}var u3={kernelName:ns,backendName:"webgl",kernelFunc:Uet};function Het(r){let{inputs:t,backend:e,attrs:n}=r,{x:o,filter:s}=t,{strides:i,pad:a,dilations:u}=n,l=S.computeConv3DInfo(o.shape,s.shape,i,u,a),c=new ov(l);return e.runWebGLProgram(c,[o,s],"float32")}var c3={kernelName:Ol,backendName:"webgl",kernelFunc:Het};function qet(r){let{inputs:t,backend:e,attrs:n}=r,{x:o,dy:s}=t,{strides:i,pad:a,filterShape:u}=n,l=S.computeConv3DInfo(o.shape,u,i,1,a),c=new cv(l);return e.runWebGLProgram(c,[o,s],"float32")}var p3={kernelName:Mp,backendName:"webgl",kernelFunc:qet};function Ket(r){let{inputs:t,backend:e,attrs:n}=r,{dy:o,filter:s}=t,{pad:i,strides:a,inputShape:u}=n,l=S.computeConv3DInfo(u,s.shape,a,1,i),c=new pv(l);return e.runWebGLProgram(c,[o,s],"float32")}var m3={kernelName:Pp,backendName:"webgl",kernelFunc:Ket};var jet=Lo+`
return cos(x);
`,Xet=It({opSnippet:jet}),f3={kernelName:os,backendName:"webgl",kernelFunc:Xet};var Yet=`
float e2x = exp(-x);
return (e2x + 1.0 / e2x) / 2.0;
`,Zet=It({opSnippet:Yet}),d3={kernelName:ss,backendName:"webgl",kernelFunc:Zet};var mv=class{constructor(t,e,n,o,s){this.variableNames=["Image","Boxes","BoxInd"],this.outputShape=[];let[i,a,u,l]=t,[c]=e,[p,m]=n;this.outputShape=[c,p,m,l];let f=o==="bilinear"?1:0,[d,h]=[`${a-1}.0`,`${u-1}.0`],[g,y,b]=p>1?[`${(a-1)/(p-1)}`,"(y2-y1) * height_ratio",`y1*${d} + float(y)*(height_scale)`]:["0.0","0.0",`0.5 * (y1+y2) * ${d}`],[w,v,k]=m>1?[`${(u-1)/(m-1)}`,"(x2-x1) * width_ratio",`x1*${h} + float(x)*(width_scale)`]:["0.0","0.0",`0.5 * (x1+x2) * ${h}`];this.userCode=`
const float height_ratio = float(${g});
const float width_ratio = float(${w});
void main() {
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 >= ${i}) {
return;
}
float height_scale = ${y};
float width_scale = ${v};
float in_y = ${b};
if( in_y < 0.0 || in_y > ${d} ) {
setOutput(float(${s}));
return;
}
float in_x = ${k};
if( in_x < 0.0 || in_x > ${h} ) {
setOutput(float(${s}));
return;
}
vec2 sourceFracIndexCR = vec2(in_x,in_y);
if(${f} == 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);
}
}
`}};var Jet=r=>{let{inputs:t,backend:e,attrs:n}=r,{image:o,boxes:s,boxInd:i}=t,{cropSize:a,method:u,extrapolationValue:l}=n,c=new mv(o.shape,s.shape,a,u,l);return e.runWebGLProgram(c,[o,s,i],"float32")},h3={kernelName:ma,backendName:"webgl",kernelFunc:Jet};var lp;(function(r){r.Prod="*",r.Sum="+"})(lp||(lp={}));var $g=class{constructor(t,e,n,o){this.variableNames=["x"],this.customUniforms=[{name:"index",type:"float"}],this.op=t,this.outputShape=e;let s=e.length,i=this.op===lp.Prod?"1.0":"0.0",a=n?i:`getX(${g3(s,"coords",this.op)})`,u=e[e.length-1],l="",c="";n?(l=o?`end != ${u-1}`:"end != 0",c=o?"end + 1":"end - 1"):(l=o?`end + pow2 < ${u}`:"end >= pow2",c=o?"end + pow2":"end - pow2"),this.userCode=`
void main() {
${Ht(s)} coords = getOutputCoords();
int end = ${x3(s,"coords",this.op)};
float val = ${a};
int pow2 = int(pow(2.0, index));
if (${l}) {
int idx = ${c};
${x3(s,"coords",this.op)} = idx;
val ${this.op}= getX(${g3(s,"coords",this.op)});
}
setOutput(val);
}
`}};function g3(r,t,e){if(r===1)return`${t}`;if(r===2)return`${t}.x, ${t}.y`;if(r===3)return`${t}.x, ${t}.y, ${t}.z`;if(r===4)return`${t}.x, ${t}.y, ${t}.z, ${t}.w`;throw Error(`Cumulative ${e} for rank ${r} is not yet supported`)}function x3(r,t,e){if(r===1)return`${t}`;if(r===2)return`${t}.y`;if(r===3)return`${t}.z`;if(r===4)return`${t}.w`;throw Error(`Cumulative ${e} for rank ${r} is not yet supported`)}function fv(r,t,e,n,o,s){let i=t.shape.length,a=S.getAxesPermutation([n],i),u=t;a!=null&&(u=he({inputs:{x:t},backend:e,attrs:{perm:a}}));let l=S.getInnerMostAxes(1,i)[0];if(l!==i-1)throw new Error(`WebGL cumprod shader expects an inner-most axis=${t.shape.length-1} but got axis=${n}`);let c=u.shape[l],p=nr({inputs:{x:u},backend:e});for(let m=0;m<=Math.ceil(Math.log2(c))-1;m++){let f=new $g(r,u.shape,!1,s),d=[[m]],h=p;p=e.runWebGLProgram(f,[p],p.dtype,d),e.disposeIntermediateTensorInfo(h)}if(o){let m=new $g(r,u.shape,o,s),f=p;p=e.runWebGLProgram(m,[p],p.dtype),e.disposeIntermediateTensorInfo(f)}if(a!=null){let m=S.getUndoAxesPermutation(a),f=he({inputs:{x:p},backend:e,attrs:{perm:m}});return e.disposeIntermediateTensorInfo(p),e.disposeIntermediateTensorInfo(u),f}return p}function Qet(r){let{inputs:t,backend:e,attrs:n}=r,{x:o}=t,{axis:s,exclusive:i,reverse:a}=n;return fv(lp.Prod,o,e,s,i,a)}var y3={kernelName:pa,backendName:"webgl",kernelFunc:Qet};function trt(r){let{inputs:t,backend:e,attrs:n}=r,{x:o}=t,{axis:s,exclusive:i,reverse:a}=n;return fv(lp.Sum,o,e,s,i,a)}var b3={kernelName:is,backendName:"webgl",kernelFunc:trt};function ert(r){let{inputs:t,backend:e,attrs:n}=r,{x:o,weights:s}=t,{size:i,binaryOutput:a}=n;if(o.shape.length===1){let u=e.readSync(o.dataId),l=e.readSync(s.dataId),c=Ew(u,l,s.dtype,s.shape,i);return e.makeTensorInfo([i],s.dtype,c)}else if(o.shape.length===2){let u=e.bufferSync(o),l=e.bufferSync(s),c=iP(u,l,i,a);return e.makeTensorInfo(c.shape,s.dtype,c.values)}throw new Error(`Error in denseBincount: input must be at most rank 2, but got rank${o.shape.length}.`)}var w3={kernelName:Lp,backendName:"webgl",kernelFunc:ert};var dv=class{constructor(t,e,n){this.variableNames=["x"],this.outputShape=[],this.outputShape=t,this.blockSize=e,this.dataFormat=n,this.userCode=`
void main() {
ivec4 coords = getOutputCoords();
int b = coords[0];
int h = ${this.getHeightCoordString()};
int w = ${this.getWidthCoordString()};
int d = ${this.getDepthCoordString()};
int in_h = h / ${e};
int offset_h = imod(h, ${e});
int in_w = w / ${e};
int offset_w = imod(w, ${e});
int offset_d = (offset_h * ${e} + offset_w) *
${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 rrt(r){let{inputs:t,backend:e,attrs:n}=r,{x:o}=t,{blockSize:s,dataFormat:i}=n,a=o.shape[0],u=i==="NHWC"?o.shape[1]:o.shape[2],l=i==="NHWC"?o.shape[2]:o.shape[3],c=i==="NHWC"?o.shape[3]:o.shape[1],p=u*s,m=l*s,f=c/(s*s),d=i==="NHWC"?[a,p,m,f]:[a,f,p,m],h=new dv(d,s,i);return e.runWebGLProgram(h,[o],o.dtype)}var v3={kernelName:fa,backendName:"webgl",kernelFunc:rrt};var kd=class{constructor(t,e=!1,n=null,o=!1,s=!1){this.variableNames=["x","W"],this.customUniforms=[{name:"pads",type:"ivec2"},{name:"strides",type:"ivec2"},{name:"dilations",type:"ivec2"},{name:"inDims",type:"ivec2"}],this.outputShape=t.outShape,this.enableShapeUniforms=De(this.outputShape.length);let i=t.filterHeight,a=t.filterWidth,u=t.outChannels/t.inChannels,l="",c="";n&&(o?l=`float activation(float a) {
float b = getPreluActivationWeightsAtOutCoords();
${n}
}`:s?l=`float activation(float a) {
float b = getLeakyreluAlphaAtOutCoords();
${n}
}`:l=`
float activation(float x) {
${n}
}
`,c="result = activation(result);");let p=e?"result += getBiasAtOutCoords();":"";e&&this.variableNames.push("bias"),o&&this.variableNames.push("preluActivationWeights"),s&&this.variableNames.push("leakyreluAlpha"),this.userCode=`
${l}
void main() {
ivec4 coords = getOutputCoords();
int batch = coords.x;
ivec2 xRCCorner = coords.yz * strides - pads;
int d2 = coords.w;
int d1 = d2 / ${u};
int q = d2 - d1 * ${u};
int 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 < ${i}; wR++) {
int xR = xRCorner + wR * dilations[0];
if (xR < 0 || xR >= inDims[0]) {
continue;
}
for (int wC = 0; wC < ${a}; 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;
${p}
${c}
setOutput(result);
}
`}};var Nd=class{constructor(t,e=!1,n=null,o=!1,s=!1){this.variableNames=["x","W"],this.packedInputs=!0,this.packedOutput=!0,this.customUniforms=[{name:"pads",type:"ivec2"},{name:"strides",type:"ivec2"},{name:"dilations",type:"ivec2"},{name:"inDims",type:"ivec2"}],this.outputShape=t.outShape,this.enableShapeUniforms=De(this.outputShape.length);let i=t.outChannels/t.inChannels,a=t.padInfo.left,u=t.strideWidth,l=t.dilationWidth,c=t.filterHeight,p=t.filterWidth,m=p,f=`
int xR; int xC; int xCOffset;
vec4 wTexel; vec4 previous; vec4 final;`;for(let y=0;y<p;y++)f+=`
vec4 xTexelC${y*2};
int xTexelC${y*2}Ready;
vec4 xTexelC${y*2+1};
int xTexelC${y*2+1}Ready;
vec4 xC${y};`;f+=`
for (int r = 0; r < ${c}; r++) {
`;for(let y=0;y<p;y++)f+=`
xTexelC${y*2} = vec4(0.0);
xTexelC${y*2}Ready = 0;
xTexelC${y*2+1} = vec4(0.0);
xTexelC${y*2+1}Ready = 0;
xC${y} = vec4(0.0);`;f+=`
xR = xRCorner + r * dilations[0];
if (xR >=0 && xR < inDims[0]) {
`;for(let y=0;y<(m+1)/2;y++){let b=y*2;if(f+=`
xC = xCCorner + ${b*l};
`,u===1){if(b<p&&(a%2===1?(f+=`
xCOffset = xC + 1;
if (xCOffset >= 0 && xCOffset < inDims[1] && xTexelC${b}Ready == 0) {
xTexelC${b} = getX(batch, xR, xCOffset, d1);
// Need to manually clear unused channels in case
// we're reading from recycled texture.
if (xCOffset + 1 >= inDims[1]) {
xTexelC${b}.zw = vec2(0.0);
}
xTexelC${b}Ready = 1;
}
`,l===1&&b>0?f+=`
xC${b} = vec4(xTexelC${b-2}.zw, xTexelC${b}.xy);
`:f+=`
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${b} = vec4(previous.zw, xTexelC${b}.xy);
} else {
xC${b} = vec4(0.0, 0.0, xTexelC${b}.xy);
}
`):f+=`
if (xC >= 0 && xC < inDims[1] && xTexelC${b}Ready == 0) {
xTexelC${b} = getX(batch, xR, xC, d1);
if (xC + 1 >= inDims[1]) {
xTexelC${b}.zw = vec2(0.0);
}
xTexelC${b}Ready = 1;
}
xC${b} = xTexelC${b};
`,b+1<p)){let w=a%2===0?x.nearestLargerEven(l):l;l%2===0&&a%2===1||l%2!==0&&a%2!==1?(f+=`
xCOffset = xC + imod(pads[1], 2) + ${w};
if (xCOffset >= 0 && xCOffset < inDims[1] && xTexelC${b+1}Ready == 0) {
xTexelC${b+1} = getX(batch, xR, xCOffset, d1);
// Need to manually clear unused channels in case
// we're reading from recycled texture.
if (xCOffset + 1 >= inDims[1]) {
xTexelC${b+1}.zw = vec2(0.0);
}
xTexelC${b+1}Ready = 1;
}
`,l>1&&(f+=`
xCOffset -= 2;
if (xCOffset >= 0 && xCOffset < inDims[1] && xTexelC${b}Ready == 0) {
xTexelC${b} = getX(batch, xR, xCOffset, d1);
xTexelC${b}Ready = 1;
}
`),f+=`
xC${b+1} = vec4(xTexelC${b}.zw, xTexelC${b+1}.xy);
`):w===1?f+=`
xC${b+1} = xTexelC${b};
`:f+=`
xCOffset = xC + ${w};
if (xCOffset >= 0 && xCOffset < inDims[1] && xTexelC${b+1}Ready == 0) {
xTexelC${b+1} = getX(batch, xR, xCOffset, d1);
if (xCOffset + 1 >= inDims[1]) {
xTexelC${b+1}.zw = vec2(0.0);
}
xTexelC${b+1}Ready = 1;
}
xC${b+1} = xTexelC${b+1};
`}}else b<p&&(a%2===1?(f+=`
xCOffset = xC + 1 - strides[1];
if(xCOffset >= 0 && xCOffset < inDims[1] && xTexelC${b}Ready == 0) {
xTexelC${b} = getX(batch, xR, xCOffset, d1);
// Need to manually clear unused channels in case
// we're reading from recycled texture.
if (xCOffset + 1 >= inDims[1]) {
xTexelC${b}.zw = vec2(0.0);
}
xTexelC${b}Ready = 1;
}
if(xC + 1 >= 0 && xC + 1 < inDims[1] && xTexelC${b+1}Ready == 0) {
xTexelC${b+1} = getX(batch, xR, xC + 1, d1);
// Need to manually clear unused channels in case
// we're reading from recycled texture.
if (xC + 2 >= inDims[1]) {
xTexelC${b+1}.zw = vec2(0.0);
}
xTexelC${b+1}Ready = 1;
}
xC${b} = vec4(xTexelC${b}.zw, xTexelC${b+1}.zw);
`,b+1<p&&(f+=`
final = vec4(0.0);
xCOffset = xC + 1 + strides[1];
if(xCOffset >= 0 && xCOffset < inDims[1]) {
final = getX(batch, xR, xCOffset, d1);
}
xC${b+1} = vec4(xTexelC${b+1}.xy, final.xy);
`)):(f+=`
if(xC >= 0 && xC < inDims[1] && xTexelC${b}Ready == 0) {
xTexelC${b} = getX(batch, xR, xC, d1);
if (xC + 1 >= inDims[1]) {
xTexelC${b}.zw = vec2(0.0);
}
xTexelC${b}Ready = 1;
}
xCOffset = xC + strides[1];
if(xCOffset >= 0 && xCOffset < inDims[1] && xTexelC${b+1}Ready == 0) {
xTexelC${b+1} = getX(batch, xR, xCOffset, d1);
if (xCOffset + 1 >= inDims[1]) {
xTexelC${b+1}.zw = vec2(0.);
}
xTexelC${b+1}Ready = 1;
}
xC${b} = vec4(
xTexelC${b}.xy, xTexelC${b+1}.xy);
`,b+1<p&&(f+=`
xC${b+1} = vec4(xTexelC${b}.zw, xTexelC${b+1}.zw);
`)));b<p&&(f+=`
wTexel = getW(r, ${b}, d1, q);
dotProd += xC${b} * vec4(wTexel.xz, wTexel.xz);
`,b+1<p&&(f+=`
wTexel = getW(r, ${b+1}, d1, q);
dotProd += xC${b+1} * vec4(wTexel.xz, wTexel.xz);
`))}f+=`
}
`,f+=`
}
`;let d="",h="";n&&(o?d=`vec4 activation(vec4 a) {
vec4 b = getPreluActivationWeightsAtOutCoords();
${n}
}`:s?d=`vec4 activation(vec4 a) {
vec4 b = getLeakyreluAlphaAtOutCoords();
${n}
}`:d=`vec4 activation(vec4 x) {
${n}
}`,h="result = activation(result);");let g=e?"result += getBiasAtOutCoords();":"";e&&this.variableNames.push("bias"),o&&this.variableNames.push("preluActivationWeights"),s&&this.variableNames.push("leakyreluAlpha"),this.userCode=`
${d}
void main() {
ivec4 coords = getOutputCoords();
int batch = coords.x;
ivec2 xRCCorner = coords.yz * strides - pads;
int d2 = coords.w;
int d1 = d2 / ${i};
int q = d2 - d1 * ${i};
int xRCorner = xRCCorner.x;
int xCCorner = xRCCorner.y;
//intialize dotProd with a small epsilon seems to reduce GPU accuracy loss.
vec4 dotProd = vec4(0.000000000000001);
${f}
vec4 result = dotProd - vec4(0.000000000000001);
${g}
${h}
setOutput(result);
}
`}};function nrt(r){let{inputs:t,backend:e,attrs:n}=r,{x:o,filter:s}=t,{strides:i,pad:a,dilations:u,dimRoundingMode:l}=n,c=u;c==null&&(c=[1,1]),x.assert(S.eitherStridesOrDilationsAreOne(i,c),()=>`Error in depthwiseConv2d: Either strides or dilations must be 1. Got strides ${i} and dilations '${c}'`);let p=S.computeConv2DInfo(o.shape,s.shape,i,c,a,l,!0),m;V().getBool("WEBGL_PACK_DEPTHWISECONV")&&p.strideWidth<=2&&p.outChannels/p.inChannels===1?m=new Nd(p):m=new kd(p);let f=[[p.padInfo.top,p.padInfo.left],[p.strideHeight,p.strideWidth],[p.dilationHeight,p.dilationWidth],[p.inHeight,p.inWidth]];return e.runWebGLProgram(m,[o,s],"float32",f)}var C3={kernelName:as,backendName:"webgl",kernelFunc:nrt};var hv=class{constructor(t){this.variableNames=["x","dy"],this.outputShape=t.filterShape;let e=t.strideHeight,n=t.strideWidth,o=t.padInfo.top,s=t.padInfo.left,i=t.outChannels/t.inChannels;this.userCode=`
void main() {
ivec4 coords = getOutputCoords();
int wR = coords.x;
int wC = coords.y;
int d1 = coords.z;
int dm = coords.w;
int d2 = d1 * ${i} + dm;
float dotProd = 0.0;
// TO DO: Vec4 over the batch size
for (int b = 0; b < ${t.batchSize}; b++) {
for (int yR = 0; yR < ${t.outHeight}; yR++) {
int xR = wR + yR * ${e} - ${o};
if (xR < 0 || xR >= ${t.inHeight}) {
continue;
}
for (int yC = 0; yC < ${t.outWidth}; yC++) {
int xC = wC + yC * ${n} - ${s};
if (xC < 0 || xC >= ${t.inWidth}) {
continue;
}
float dyValue = getDy(b, yR, yC, d2);
float xValue = getX(b, xR, xC, d1);
dotProd += (xValue * dyValue);
}
}
}
setOutput(dotProd);
}
`}},gv=class{constructor(t){this.variableNames=["dy","W"],this.outputShape=t.inShape;let e=t.filterHeight,n=t.filterWidth,o=t.strideHeight,s=t.strideWidth,i=e-1-t.padInfo.top,a=n-1-t.padInfo.left,u=t.outChannels/t.inChannels;this.userCode=`
const ivec2 pads = ivec2(${i}, ${a});
void main() {
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 < ${e}; wR++) {
float dyR = float(dyRCorner + wR) / ${o}.0;
if (dyR < 0.0 || dyR >= ${t.outHeight}.0 || fract(dyR) > 0.0) {
continue;
}
int idyR = int(dyR);
int wRPerm = ${e} - 1 - wR;
for (int wC = 0; wC < ${n}; wC++) {
float dyC = float(dyCCorner + wC) / ${s}.0;
if (dyC < 0.0 || dyC >= ${t.outWidth}.0 ||
fract(dyC) > 0.0) {
continue;
}
int idyC = int(dyC);
int wCPerm = ${n} - 1 - wC;
// TO DO: Vec4 over the channelMul
for (int dm = 0; dm < ${u}; dm++) {
int d2 = d1 * ${u} + dm;
float xValue = getDy(batch, idyR, idyC, d2);
float wValue = getW(wRPerm, wCPerm, d1, dm);
dotProd += xValue * wValue;
}
}
}
setOutput(dotProd);
}
`}};function ort(r){let{inputs:t,backend:e,attrs:n}=r,{x:o,dy:s}=t,{strides:i,dilations:a,pad:u,dimRoundingMode:l,filterShape:c}=n,p=S.computeConv2DInfo(o.shape,c,i,a,u,l,!0),m=new hv(p);return e.runWebGLProgram(m,[o,s],"float32")}var I3={kernelName:zp,backendName:"webgl",kernelFunc:ort};function srt(r){let{inputs:t,backend:e,attrs:n}=r,{dy:o,filter:s}=t,{strides:i,dilations:a,pad:u,dimRoundingMode:l,inputShape:c}=n,p=S.computeConv2DInfo(c,s.shape,i,a,u,l,!0),m=new gv(p);return e.runWebGLProgram(m,[o,s],"float32")}var S3={kernelName:Bp,backendName:"webgl",kernelFunc:srt};var xv=class{constructor(t){this.variableNames=["X"],this.outputShape=[t,t],this.userCode=`
void main() {
ivec2 coords = getOutputCoords();
float val = coords[0] == coords[1] ? getX(coords[0]) : 0.0;
setOutput(val);
}
`}};function irt(r){let{inputs:t,backend:e}=r,{x:n}=t,o=[...n.shape,...n.shape],s=x.sizeFromShape(n.shape),i=ut({inputs:{x:n},backend:e,attrs:{shape:[s]}}),a=new xv(s),u=e.runWebGLProgram(a,[i],i.dtype),l=ut({inputs:{x:u},backend:e,attrs:{shape:o}});return e.disposeIntermediateTensorInfo(i),e.disposeIntermediateTensorInfo(u),l}var k3={kernelName:Vp,backendName:"webgl",kernelFunc:irt};var yv=class{constructor(t){this.variableNames=["x","W"],this.outputShape=t.outShape;let{inHeight:e,inWidth:n,padInfo:o,strideHeight:s,strideWidth:i,filterHeight:a,filterWidth:u,dilationHeight:l,dilationWidth:c}=t,{top:p,left:m}=o;this.userCode=`
const ivec2 strides = ivec2(${s}, ${i});
const ivec2 pads = ivec2(${p}, ${m});
const float neg_infinity = -3.4e38;
void main() {
ivec4 coords = getOutputCoords();
int batch = coords.x;
int d1 = coords.w;
ivec2 outTopLeftCorner =
coords.yz * strides - pads;
int hBeg = outTopLeftCorner.x;
int wBeg = outTopLeftCorner.y;
float curVal = neg_infinity;
for (int h = 0; h < ${a}; h++) {
int hIn = hBeg + h * ${l};
if (hIn >= 0 && hIn < ${e}) {
for (int w = 0; w < ${u}; w++) {
int wIn = wBeg + w * ${c};
if (wIn >= 0 && wIn < ${n}) {
float xVal = getX(batch, hIn, wIn, d1);
float wVal = getW(h, w, d1);
float val = xVal + wVal;
if (val > curVal) {
curVal = val;
}
}
}
}
}
float result = curVal;
setOutput(result);
}
`}};function art(r){let{inputs:t,backend:e,attrs:n}=r,{x:o,filter:s}=t,{strides:i,pad:a,dilations:u}=n,l=S.computeDilation2DInfo(o.shape,s.shape,i,a,"NHWC",u),c,p=new yv(l);c=e.runWebGLProgram(p,[o,s],"float32");let m=ut({inputs:{x:c},backend:e,attrs:{shape:l.outShape}});return e.disposeIntermediateTensorInfo(c),m}var N3={kernelName:Ml,backendName:"webgl",kernelFunc:art};function lrt(r){let{inputs:t,backend:e,attrs:n}=r,{equation:o}=n,s=t,{allDims:i,summedDims:a,idDims:u}=S.decodeEinsumEquation(o,s.length);S.checkEinsumDimSizes(i.length,u,s);let{path:l,steps:c}=S.getEinsumComputePath(a,u),p=c.length,m=null,f=i.length,d=[];for(let h=0;h<p;++h){for(let g of c[h]){let{permutationIndices:y,expandDims:b}=S.getEinsumPermutation(f,u[g]),w;S.isIdentityPermutation(y)?w=s[g]:(w=he({inputs:{x:s[g]},backend:e,attrs:{perm:y}}),d.push(w));let v=w.shape.slice();for(let k=0;k<b.length;++k)v.splice(b[k],0,1);x.arraysEqual(w.shape,v)||(w=ut({inputs:{x:w},backend:e,attrs:{shape:v}}),d.push(w)),m===null?m=w:(m=Eg({inputs:{a:w,b:m},backend:e}),d.push(m))}h<p-1&&(l[h]>=0&&(m=op({inputs:{x:m},backend:e,attrs:{axis:l[h]-(i.length-f),keepDims:!1}}),d.push(m)),f--)}for(let h of d)h!==m&&e.disposeIntermediateTensorInfo(h);return m}var T3={kernelName:Gp,backendName:"webgl",kernelFunc:lrt};var urt="return (x >= 0.0) ? x : (exp(x) - 1.0);",crt=`
vec4 result;
result.r = (x.r >= 0.0) ? x.r : (exp(x.r) - 1.0);
result.g = (x.g >= 0.0) ? x.g : (exp(x.g) - 1.0);
result.b = (x.b >= 0.0) ? x.b : (exp(x.b) - 1.0);
result.a = (x.a >= 0.0) ? x.a : (exp(x.a) - 1.0);
return result;
`,prt=It({opSnippet:urt,packedOpSnippet:crt}),_3={kernelName:us,backendName:"webgl",kernelFunc:prt};var mrt="return (b >= 1.0) ? a : a * (b + 1.0);",frt=`
vec4 bGTEZero = vec4(greaterThanEqual(b, vec4(0.)));
return (bGTEZero * a) + ((vec4(1.0) - bGTEZero) * (a * (b + vec4(1.0))));
`,drt=r=>{let{inputs:t,backend:e}=r,{dy:n,y:o}=t,s=V().getBool("WEBGL_PACK_BINARY_OPERATIONS")?new Po(frt,n.shape,o.shape):new ro(mrt,n.shape,o.shape);return e.runWebGLProgram(s,[n,o],n.dtype)},E3={kernelName:Wp,backendName:"webgl",kernelFunc:drt};var hrt=`
return vec4(equal(a, b));
`,grt="return float(a == b);",xrt=le({opSnippet:grt,packedOpSnippet:hrt,dtype:"bool",cpuKernelImpl:uP}),A3={kernelName:ha,backendName:"webgl",kernelFunc:xrt};var yrt=`
// Error function is calculated approximately with elementary function.
// See "Handbook of Mathematical Functions with Formulas,
// Graphs, and Mathematical Tables", Abramowitz and Stegun.
float p = ${S.ERF_P};
float a1 = ${S.ERF_A1};
float a2 = ${S.ERF_A2};
float a3 = ${S.ERF_A3};
float a4 = ${S.ERF_A4};
float a5 = ${S.ERF_A5};
float sign = sign(x);
x = abs(x);
float t = 1.0 / (1.0 + p * x);
return sign * (1.0 - (((((a5*t + a4)*t) + a3)*t + a2)*t + a1)*t*exp(-x*x));
`,brt=It({opSnippet:yrt}),$3={kernelName:da,backendName:"webgl",kernelFunc:brt};var wrt=Lo+`
return exp(x);
`,vrt=`
vec4 result = exp(x);
bvec4 isNaN = isnan(x);
result.r = isNaN.r ? x.r : result.r;
result.g = isNaN.g ? x.g : result.g;
result.b = isNaN.b ? x.b : result.b;
result.a = isNaN.a ? x.a : result.a;
return result;
`,OT=It({opSnippet:wrt,packedOpSnippet:vrt,cpuKernelImpl:cP,dtype:"float32"}),D3={kernelName:cs,backendName:"webgl",kernelFunc:OT};function bv(r){let{inputs:t,attrs:e,backend:n}=r,{dim:o}=e,{input:s}=t,i=s.shape.length,a=s.shape.slice(),u=o;return o<0&&(x.assert(-(i+1)<=o,()=>`Axis must be in the interval [${-(i+1)}, ${i}]`),u=i+o+1),a.splice(u,0,1),ut({inputs:{x:s},backend:n,attrs:{shape:a}})}var F3={kernelName:Ii,backendName:"webgl",kernelFunc:bv};var R3="return exp(x) - 1.0;",Crt=It({opSnippet:R3,packedOpSnippet:R3,cpuKernelImpl:pP}),O3={kernelName:ga,backendName:"webgl",kernelFunc:Crt};var Dg=class{constructor(t,e,n){this.variableNames=["real","imag"];let o=e[1];this.outputShape=e;let s=n?`2.0 * ${Math.PI}`:`-2.0 * ${Math.PI}`,i=n?`${o}.0`:"1.0",a;if(t==="real")a="return real * expR - imag * expI;";else if(t==="imag")a="return real * expI + imag * expR;";else throw new Error(`FFT component must be either "real" or "imag", got ${t}.`);this.userCode=`
const float exponentMultiplier = ${s};
float unaryOpComplex(float real, float expR, float imag, float expI) {
${a}
}
float mulMatDFT(int batch, int index) {
float indexRatio = float(index) / float(${o});
float exponentMultiplierTimesIndexRatio =
exponentMultiplier * indexRatio;
float result = 0.0;
for (int i = 0; i < ${o}; 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) / ${i};
}
return result;
}
void main() {
ivec2 coords = getOutputCoords();
setOutput(mulMatDFT(coords[0], coords[1]));
}
`}};function wv(r,t,e){let n=e.texData.get(r.dataId),o=x.sizeFromShape(r.shape),s=r.shape[r.shape.length-1],i=o/s,a=ut({inputs:{x:r},backend:e,attrs:{shape:[i,s]}}),u=a.shape,l=new Dg("real",u,t),c=new Dg("imag",u,t),p=[{dataId:n.complexTensorInfos.real.dataId,dtype:n.complexTensorInfos.real.dtype,shape:u},{dataId:n.complexTensorInfos.imag.dataId,dtype:n.complexTensorInfos.imag.dtype,shape:u}],m=e.runWebGLProgram(l,p,"float32"),f=e.runWebGLProgram(c,p,"float32"),d=Fn({inputs:{real:m,imag:f},backend:e});e.disposeIntermediateTensorInfo(m),e.disposeIntermediateTensorInfo(f);let h=ut({inputs:{x:d},backend:e,attrs:{shape:r.shape}});return e.disposeIntermediateTensorInfo(a),e.disposeIntermediateTensorInfo(d),h}function Irt(r){let{inputs:t,backend:e}=r,{input:n}=t;return wv(n,!1,e)}var M3={kernelName:Up,backendName:"webgl",kernelFunc:Irt};var vv=class{constructor(t,e){this.outputShape=[],this.customUniforms=[{name:"value",type:"float"}],this.variableNames=["x"],this.outputShape=t,this.userCode=`
void main() {
// Input can be obtained from uniform value.
setOutput(value);
}
`}};function Nl(r){let{backend:t,attrs:e}=r,{shape:n,value:o}=e,{dtype:s}=e;if(s=s||x.inferDtype(o),s==="string"){let i=x.getArrayFromDType(s,x.sizeFromShape(n));return i.fill(o),t.makeTensorInfo(n,s,i)}else{let i=new vv(n,o),a=[[o]];return t.runWebGLProgram(i,[],s,a)}}var P3={kernelName:Pl,backendName:"webgl",kernelFunc:Nl};var Cv=class{constructor(t){this.variableNames=["Image"],this.outputShape=[];let e=t[2];this.outputShape=t,this.userCode=`
void main() {
ivec4 coords = getOutputCoords();
int x = coords[2];
int coordX = ${e} - x - 1;
float outputValue;
if(coordX >= 0 && coordX < ${e}) {
outputValue = getImage(coords[0], coords[1], coordX, coords[3]);
} else {
outputValue = getImage(coords[0], coords[1], coords[2], coords[3]);
}
setOutput(outputValue);
}
`}};var L3={kernelName:xa,backendName:"webgl",kernelFunc:({inputs:r,backend:t})=>{let{image:e}=r,n=t,o=new Cv(e.shape);return n.runWebGLProgram(o,[e],e.dtype)}};var z3="return floor(x);",Srt=It({opSnippet:z3,packedOpSnippet:z3,cpuKernelImpl:mP}),B3={kernelName:ps,backendName:"webgl",kernelFunc:Srt};var krt=`
float s = sign(a) * sign(b);
int ia = round(a);
int ib = round(b);
if (ib != 0) {
// Windows (D3D) wants guaranteed non-zero int division at compile-time.
return float(idiv(ia, ib, s));
} else {
return NAN;
}
`,Nrt=`
ivec4 ia = round(a);
ivec4 ib = round(b);
bvec4 cond = notEqual(ib, ivec4(0));
ivec4 result = ivec4(0);
vec4 s = sign(a) * sign(b);
// Windows (D3D) wants guaranteed non-zero int division at compile-time.
if (cond[0]) {
result[0] = idiv(ia[0], ib[0], s[0]);
}
if (cond[1]) {
result[1] = idiv(ia[1], ib[1], s[1]);
}
if (cond[2]) {
result[2] = idiv(ia[2], ib[2], s[2]);
}
if (cond[3]) {
result[3] = idiv(ia[3], ib[3], s[3]);
}
return vec4(result);
`,Trt=le({opSnippet:krt,packedOpSnippet:Nrt,dtype:"int32"}),V3={kernelName:ms,backendName:"webgl",kernelFunc:Trt};var Iv=class{constructor(t){this.variableNames=["A"];let e=qe(),[n,o]=t;this.outputShape=t,this.userCode=`
void main() {
ivec3 coords = getOutputCoords();
int texR = coords[0];
int texC = coords[1];
int depth = coords[2];
vec2 uv = (vec2(texC, texR) + halfCR) / vec2(${o}.0, ${n}.0);
vec4 values = ${e.texture2D}(A, uv);
float value;
if (depth == 0) {
value = values.r;
} else if (depth == 1) {
value = values.g;
} else if (depth == 2) {
value = values.b;
} else if (depth == 3) {
value = values.a;
}
setOutput(floor(value * 255.0 + 0.5));
}
`}};var Sv=class{constructor(t){this.variableNames=["A"],this.packedInputs=!1,this.packedOutput=!0;let e=qe(),[n,o]=t;this.outputShape=t,this.userCode=`
void main() {
ivec3 coords = getOutputCoords();
int texR = coords[0];
int texC = coords[1];
int depth = coords[2];
vec4 result = vec4(0.);
for(int row=0; row<=1; row++) {
for(int col=0; col<=1; col++) {
texC = coords[1] + row;
depth = coords[2] + col;
vec2 uv = (vec2(texC, texR) + halfCR) /
vec2(${o}.0, ${n}.0);
vec4 values = ${e.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);
}
}
${e.output} = result;
}
`}};var G3={kernelName:Xd,backendName:"webgl",kernelFunc:_rt},Td;function _rt(r){let{inputs:t,backend:e,attrs:n}=r,{pixels:o}=t,{numChannels:s}=n,i=typeof HTMLVideoElement!="undefined"&&o instanceof HTMLVideoElement,a=typeof HTMLImageElement!="undefined"&&o instanceof HTMLImageElement,[u,l]=i?[o.videoWidth,o.videoHeight]:[o.width,o.height],c=[l,u],p=[l,u,s];(a||i)&&(Td==null&&(Td=document.createElement("canvas").getContext("2d")),Td.canvas.width=u,Td.canvas.height=l,Td.drawImage(o,0,0,u,l),o=Td.canvas);let m=e.makeTensorInfo(c,"int32");e.texData.get(m.dataId).usage=Kr.PIXELS,e.gpgpu.uploadPixelDataToTexture(e.getTexture(m.dataId),o);let f=V().getBool("WEBGL_PACK")?new Sv(p):new Iv(p),d=e.runWebGLProgram(f,[m],"int32");return e.disposeData(m.dataId),d}function Ert(r){let{inputs:t,backend:e,attrs:n}=r,{x:o,filter:s,bias:i,preluActivationWeights:a}=t,{strides:u,pad:l,dataFormat:c,dilations:p,dimRoundingMode:m,activation:f,leakyreluAlpha:d}=n,h=S.convertConv2DDataFormat(c),g=S.computeConv2DInfo(o.shape,s.shape,u,p,l,m,!1,h),y,b=[];if(g.filterHeight===1&&g.filterWidth===1&&g.dilationHeight===1&&g.dilationWidth===1&&g.strideHeight===1&&g.strideWidth===1&&(g.padInfo.type==="SAME"||g.padInfo.type==="VALID"))y=iv({x:o,filter:s,convInfo:g,backend:e,bias:i,activation:f,preluActivationWeights:a,leakyreluAlpha:d});else if(V().getBool("WEBGL_CONV_IM2COL")&&o.shape[0]===1)y=av({x:o,filter:s,convInfo:g,backend:e,bias:i,activation:f,preluActivationWeights:a,leakyreluAlpha:d});else{let v=i!=null,k=a!=null,E=f==="leakyrelu",$=f?Su(f,!1):null,D=new Sd(g,v,$,k,E),F=[o,s];if(i&&F.push(i),a&&F.push(a),E){let P=e.makeTensorInfo([],"float32",x.createScalarValue(d,"float32"));F.push(P),b.push(P)}y=e.runWebGLProgram(D,F,"float32")}let w=ut({inputs:{x:y},backend:e,attrs:{shape:g.outShape}});return b.push(y),b.forEach(v=>e.disposeIntermediateTensorInfo(v)),w}var W3={kernelName:Mi,backendName:"webgl",kernelFunc:Ert};function Art(r){let{inputs:t,backend:e,attrs:n}=r,{x:o,filter:s,bias:i,preluActivationWeights:a}=t,{strides:u,pad:l,dilations:c,dimRoundingMode:p,activation:m,leakyreluAlpha:f}=n,d=[],h=c;h==null&&(h=[1,1]),x.assert(S.eitherStridesOrDilationsAreOne(u,h),()=>`Error in depthwiseConv2d: Either strides or dilations must be 1. Got strides ${u} and dilations '${h}'`);let g=S.computeConv2DInfo(o.shape,s.shape,u,h,l,p,!0),y=V().getBool("WEBGL_PACK_DEPTHWISECONV")&&g.strideWidth<=2&&g.outChannels/g.inChannels===1,b=m?Su(m,y):null,w=[o,s],v=i!=null,k=a!=null,E=m==="leakyrelu";if(v&&w.push(i),k&&w.push(a),E){let P=e.makeTensorInfo([],"float32",x.createScalarValue(f,"float32"));w.push(P),d.push(P)}let $;y?$=new Nd(g,v,b,k,E):$=new kd(g,v,b,k,E);let D=[[g.padInfo.top,g.padInfo.left],[g.strideHeight,g.strideWidth],[g.dilationHeight,g.dilationWidth],[g.inHeight,g.inWidth]],F=e.runWebGLProgram($,w,"float32",D);return d.forEach(P=>e.disposeIntermediateTensorInfo(P)),F}var U3={kernelName:Pi,backendName:"webgl",kernelFunc:Art};var kv=class{constructor(t,e,n){this.sliceDim=t,this.strides=e,this.variableNames=["x","indices"],this.outputShape=n;let o=Ht(e.length),s=Ht(n.length),i=this.sliceDim>1?"strides[j]":"strides";this.userCode=`
${o} strides = ${o}(${this.strides});
void main() {
${s} coords = getOutputCoords();
int flattenIndex = 0;
for (int j = 0; j < ${this.sliceDim}; j++) {
int index = round(getIndices(coords[0], j));
flattenIndex += index * ${i};
}
setOutput(getX(flattenIndex, coords[1]));
}
`}};function $rt(r){let{inputs:t,backend:e}=r,{params:n,indices:o}=t,s=o.shape,i=s[s.length-1],a=x.sizeFromShape(n.shape),[u,l,c,p]=S.prepareAndValidate(n,o),m=ut({inputs:{x:o},backend:e,attrs:{shape:[l,i]}}),f=ut({inputs:{x:n},backend:e,attrs:{shape:[x.sizeFromShape(n.shape)/c,c]}});if(e.shouldExecuteOnCPU([n,o])||n.dtype==="string"){let y=e.readSync(o.dataId),b=e.bufferSync(n),w=fP(y,b,n.dtype,l,i,c,p,n.shape,a);return e.makeTensorInfo(u,n.dtype,w.values)}let d=new kv(i,p,[l,c]),h=e.runWebGLProgram(d,[f,m],f.dtype),g=ut({inputs:{x:h},backend:e,attrs:{shape:u}});return e.disposeIntermediateTensorInfo(m),e.disposeIntermediateTensorInfo(f),e.disposeIntermediateTensorInfo(h),g}var H3={kernelName:ya,backendName:"webgl",kernelFunc:$rt};var Nv=class{constructor(t,e){this.variableNames=["A","indices"],this.outputShape=e,this.rank=e.length;let n=Ht(this.rank),o=Drt(t,2);this.userCode=`
void main() {
${n} resRC = getOutputCoords();
int index = int(getIndices(resRC.x, resRC.z));
float inBounds = (index >= 0) && (index < ${t[2]}) ? 1.0 : 0.0;
setOutput(inBounds * getA(${o}));
}
`}};function Drt(r,t){let e=["resRC.x","resRC.y","resRC.z","resRC.w"],n=[];for(let o=0;o<r.length;o++)o===2?n.push("index"):n.push(`${e[o]}`);return n.join()}function MT(r){let{inputs:t,backend:e,attrs:n}=r,{x:o,indices:s}=t,{axis:i,batchDims:a}=n,u=x.parseAxisParam(i,o.shape)[0];if(V().get("DEBUG")){let b=e.readSync(s.dataId),w=o.shape[u];for(let v=0;v<b.length;++v){let k=b[v];x.assert(k<=w-1&&k>=0,()=>`GatherV2: the index value ${k} is not in [0, ${w-1}]`)}}let l=S.segment_util.collectGatherOpShapeInfo(o,s,u,a),c=x.sizeFromShape(s.shape),p=[],m=ut({inputs:{x:o},backend:e,attrs:{shape:[l.batchSize,l.outerSize,l.dimSize,l.sliceSize]}}),f=ut({inputs:{x:s},backend:e,attrs:{shape:[l.batchSize,c/l.batchSize]}});p.push(m),p.push(f);let d=[l.batchSize,l.outerSize,c/l.batchSize,l.sliceSize];if(e.shouldExecuteOnCPU([o,s])||o.dtype==="string"){let b=e.bufferSync(f),w=e.bufferSync(m),v=dP(w,b,d);return p.forEach(k=>e.disposeIntermediateTensorInfo(k)),e.makeTensorInfo(l.outputShape,v.dtype,v.values)}let h=new Nv(m.shape,d),g=e.runWebGLProgram(h,[m,f],m.dtype);p.push(g);let y=ut({inputs:{x:g},backend:e,attrs:{shape:l.outputShape}});return p.forEach(b=>e.disposeIntermediateTensorInfo(b)),y}var q3={kernelName:Si,backendName:"webgl",kernelFunc:MT};var Frt="return float(a > b);",Rrt=`
return vec4(greaterThan(a, b));
`,Ort=le({opSnippet:Frt,packedOpSnippet:Rrt,cpuKernelImpl:hP,dtype:"bool"}),K3={kernelName:ba,backendName:"webgl",kernelFunc:Ort};var Mrt="return float(a >= b);",Prt=`
return vec4(greaterThanEqual(a, b));
`,Lrt=le({opSnippet:Mrt,packedOpSnippet:Prt,dtype:"bool",cpuKernelImpl:gP}),j3={kernelName:ds,backendName:"webgl",kernelFunc:Lrt};function zrt(r){let{inputs:t,backend:e}=r,{input:n}=t;return wv(n,!0,e)}var X3={kernelName:Hp,backendName:"webgl",kernelFunc:zrt};var Brt="return float(!isnan(x) && !isinf(x));",Vrt=It({opSnippet:Brt,dtype:"bool"}),Y3={kernelName:wa,backendName:"webgl",kernelFunc:Vrt};var Grt="return float(isinf(x));",Wrt=It({opSnippet:Grt,dtype:"bool"}),Z3={kernelName:va,backendName:"webgl",kernelFunc:Wrt};var Urt="return float(isnan(x));",Hrt=It({opSnippet:Urt,dtype:"bool"}),J3={kernelName:Ca,backendName:"webgl",kernelFunc:Hrt};var qrt="return float(a < b);",Krt=`
return vec4(lessThan(a, b));
`,jrt=le({opSnippet:qrt,packedOpSnippet:Krt,cpuKernelImpl:xP,dtype:"bool"}),Q3={kernelName:Ia,backendName:"webgl",kernelFunc:jrt};var Xrt="return float(a <= b);",Yrt=`
return vec4(lessThanEqual(a, b));
`,Zrt=le({opSnippet:Xrt,packedOpSnippet:Yrt,cpuKernelImpl:yP,dtype:"bool"}),tz={kernelName:Sa,backendName:"webgl",kernelFunc:Zrt};function Jrt(r){let{backend:t,attrs:e}=r,{start:n,stop:o,num:s}=e,i=bP(n,o,s);return t.makeTensorInfo([i.length],"float32",i)}var ez={kernelName:Kp,backendName:"webgl",kernelFunc:Jrt};var Qrt=Lo+`
return x < 0.0 ? 0./0. : log(x);
`,tnt=`
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;
`,ent=It({opSnippet:Qrt,packedOpSnippet:tnt,cpuKernelImpl:wP}),rz={kernelName:gs,backendName:"webgl",kernelFunc:ent};var rnt=Lo+`
return log(1.0 + x);
`,nnt=It({opSnippet:rnt}),nz={kernelName:ka,backendName:"webgl",kernelFunc:nnt};var ont="return float(a >= 1.0 && b >= 1.0);",snt=`
return vec4(
vec4(greaterThanEqual(a, vec4(1.0))) *
vec4(greaterThanEqual(b, vec4(1.0))));
`,int=le({opSnippet:ont,packedOpSnippet:snt,dtype:"bool"}),oz={kernelName:Na,backendName:"webgl",kernelFunc:int};var ant="return float(!(x >= 1.0));",lnt=It({opSnippet:ant}),sz={kernelName:Lu,backendName:"webgl",kernelFunc:lnt};var unt="return float(a >= 1.0 || b >= 1.0);",cnt=`
return min(
vec4(greaterThanEqual(a, vec4(1.0))) +
vec4(greaterThanEqual(b, vec4(1.0))),
vec4(1.0));
`,pnt=le({opSnippet:unt,packedOpSnippet:cnt,dtype:"bool"}),iz={kernelName:zu,backendName:"webgl",kernelFunc:pnt};var Tv=class{constructor(t,e,n,o,s){this.variableNames=["x"],this.outputShape=[];let i=e,a=t[3]-1;this.outputShape=t;let u,l=`float(${n}) + float(${o}) * sum`;s===.5?u=`inversesqrt(${l})`:s===1?u=`1.0/(${l})`:u=`exp(log(${l}) * float(-${s}));`,this.userCode=`
void main() {
ivec4 coords = getOutputCoords();
int b = coords[0];
int r = coords[1];
int c = coords[2];
int d = coords[3];
float x = getX(b, r, c, d);
float sum = 0.0;
for (int j = -${i}; j <= ${i}; j++) {
int idx = d + j;
if (idx >= 0 && idx <= ${a}) {
float z = getX(b, r, c, idx);
sum += z * z;
}
}
float val = x * ${u};
setOutput(val);
}
`}};var _v=class{constructor(t,e,n,o,s){this.variableNames=["x"],this.outputShape=[],this.packedInputs=!0,this.packedOutput=!0;let i=e,a=t[3]-1;this.outputShape=t;let u,l=`float(${n}) + float(${o}) * sum`;s===.5?u=`inversesqrt(${l})`:s===1?u=`1.0/(${l})`:u=`exp(log(${l}) * float(-${s}));`,this.userCode=`
void main() {
ivec4 coords = getOutputCoords();
int b = coords.x;
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 - ${i};
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 = - ${i}; j <= ${i}; j++) {
ivec2 idx = depth + j;
bvec2 aboveLowerBound = greaterThanEqual(idx, ivec2(0));
bvec2 belowUpperBound = lessThanEqual(idx, ivec2(${a}));
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 * ${u};
setOutput(result);
}
`}};var mnt=r=>{let{inputs:t,backend:e,attrs:n}=r,{x:o}=t,{depthRadius:s,bias:i,alpha:a,beta:u}=n,l=V().getBool("WEBGL_PACK_NORMALIZATION")?new _v(o.shape,s,i,a,u):new Tv(o.shape,s,i,a,u);return e.runWebGLProgram(l,[o],o.dtype)},az={kernelName:Ll,backendName:"webgl",kernelFunc:mnt};var Ev=class{constructor(t,e,n,o,s){this.variableNames=["inputImage","outputImage","dy"],this.outputShape=[],this.outputShape=t,this.depth=t[3],this.depthRadius=e,this.bias=n,this.alpha=o,this.beta=s,this.userCode=`
void main() {
ivec4 coords = getOutputCoords();
int b = coords[0];
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 - ${e})));
int depthEnd = int(min(float(${this.depth}),
float(d + ${e} + 1)));
const int MIN_DEPTH_BEGIN = 0;
const int MAX_DEPTH_END = ${this.depth};
float norm = 0.0;
for (int k = MIN_DEPTH_BEGIN; k < MAX_DEPTH_END; ++k) {
if (k < depthBegin){
continue;
}
else if (k >= depthBegin && k < depthEnd) {
norm += getInputImage(b, r, c, k) * getInputImage(b, r, c, k);
}
else {
break;
}
}
norm = float(${o}) * norm + float(${n});
for(int k = MIN_DEPTH_BEGIN; k < MAX_DEPTH_END; ++k){
if (k < depthBegin){
continue;
}
else if (k >= depthBegin && k < depthEnd){
float dyi = -2.0 * float(${o})
* float(${s})
* getInputImage(b ,r ,c, k) * getOutputImage(b, r, c, d)
/ norm;
if (k == d) {
dyi += pow(norm, -1.0 * ${s});
}
if (k == coords[3]) {
dyi *= getDy(b, r, c, d);
result += dyi;
}
}
else {
break;
}
}
}
setOutput(result);
}
`}};var fnt=r=>{let{inputs:t,backend:e,attrs:n}=r,{x:o,y:s,dy:i}=t,{depthRadius:a,bias:u,alpha:l,beta:c}=n,p=new Ev(o.shape,a,u,l,c);return e.runWebGLProgram(p,[o,s,i],o.dtype)},lz={kernelName:jp,backendName:"webgl",kernelFunc:fnt};function uz(r,t,e,n){let o=x.sizeFromShape(t),i=x.sizeFromShape(r.shape)/o,a=ut({inputs:{x:r},attrs:{shape:[i,o]},backend:n}),u=Hn(a,r.dtype,"max",n),l=ut({inputs:{x:u},attrs:{shape:e},backend:n});return n.disposeIntermediateTensorInfo(a),n.disposeIntermediateTensorInfo(u),l}function PT(r){let{inputs:t,backend:e,attrs:n}=r,{x:o}=t,{reductionIndices:s,keepDims:i}=n,a=o.shape.length,u=x.parseAxisParam(s,o.shape),l=u,c=S.getAxesPermutation(l,a),p=c!=null,m=e.shouldExecuteOnCPU([o]),f=o;if(p){if(m){let w=e.texData.get(f.dataId).values,v=new Array(a);for(let $=0;$<v.length;$++)v[$]=o.shape[c[$]];let k=rp(w,o.shape,o.dtype,c,v);f=e.makeTensorInfo(v,o.dtype);let E=e.texData.get(f.dataId);E.values=k}else f=ku(o,c,e);l=S.getInnerMostAxes(l.length,a)}S.assertAxesAreInnerMostDims("max",l,a);let[d,h]=S.computeOutAndReduceShapes(f.shape,l),g=d;i&&(g=S.expandShapeToKeepDim(d,u));let y;if(m){let w=e.texData.get(f.dataId).values,v=vP(w,x.sizeFromShape(h),g,o.dtype);y=e.makeTensorInfo(g,o.dtype);let k=e.texData.get(y.dataId);k.values=v}else y=uz(f,h,g,e);return p&&e.disposeIntermediateTensorInfo(f),y}var cz={kernelName:xs,backendName:"webgl",kernelFunc:PT};var dnt=Mw+`
return max(a, b);
`,hnt=`
vec4 result = vec4(max(a, b));
vec4 isNaN = min(vec4(isnan(a)) + vec4(isnan(b)), vec4(1.0));
`+Iu+`
return result;
`,gnt=le({opSnippet:dnt,packedOpSnippet:hnt,cpuKernelImpl:CP}),pz={kernelName:ys,backendName:"webgl",kernelFunc:gnt};function xnt(r){let{inputs:t,backend:e,attrs:n}=r,{x:o}=t;di(o,"maxPool");let{filterSize:s,strides:i,pad:a,dimRoundingMode:u}=n,l=1;x.assert(S.eitherStridesOrDilationsAreOne(i,l),()=>`Error in maxPool: Either strides or dilations must be 1. Got strides ${i} and dilations '${l}'`);let c=S.computePool2DInfo(o.shape,s,i,l,a,u);if(c.filterWidth===1&&c.filterHeight===1&&x.arraysEqual(c.inShape,c.outShape))return nr({inputs:{x:o},backend:e});let p=new gi(c,"max",!1);return e.runWebGLProgram(p,[o],o.dtype)}var mz={kernelName:bs,backendName:"webgl",kernelFunc:xnt};function ynt(r){let{inputs:t,backend:e,attrs:n}=r,{x:o}=t,{filterSize:s,strides:i,pad:a,dataFormat:u,dimRoundingMode:l}=n,c=[1,1,1],p=S.computePool3DInfo(o.shape,s,i,c,a,l,u),m=new Nu(p,"max",!1);return e.runWebGLProgram(m,[o],o.dtype)}var fz={kernelName:zl,backendName:"webgl",kernelFunc:ynt};var Av=class{constructor(t){this.variableNames=["dy","maxPos"],this.outputShape=t.inShape;let e=t.strideHeight,n=t.strideWidth,o=t.dilationHeight,s=t.effectiveFilterHeight,i=t.effectiveFilterWidth,a=s-1-t.padInfo.top,u=i-1-t.padInfo.left,l=s*i-1;this.userCode=`
const ivec2 pads = ivec2(${a}, ${u});
void main() {
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 < ${s};
wR += ${o}) {
float dyR = float(dyRCorner + wR) / ${e}.0;
if (dyR < 0.0 || dyR >= ${t.outHeight}.0 || fract(dyR) > 0.0) {
continue;
}
int idyR = int(dyR);
for (int wC = 0; wC < ${i}; wC++) {
float dyC = float(dyCCorner + wC) / ${n}.0;
if (dyC < 0.0 || dyC >= ${t.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 * ${i} + wC;
float mask = float(maxPosValue == curPosValue ? 1.0 : 0.0);
dotProd += dyValue * mask;
}
}
setOutput(dotProd);
}
`}},$v=class{constructor(t){this.variableNames=["dy","maxPos"],this.outputShape=t.inShape;let e=t.strideDepth,n=t.strideHeight,o=t.strideWidth,s=t.dilationDepth,i=t.dilationHeight,a=t.dilationWidth,u=t.effectiveFilterDepth,l=t.effectiveFilterHeight,c=t.effectiveFilterWidth,p=u-1-t.padInfo.front,m=l-1-t.padInfo.top,f=c-1-t.padInfo.left,d=u*l*c-1;this.userCode=`
const ivec3 pads = ivec3(${p}, ${m}, ${f});
void main() {
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 < ${u};
wD += ${s}) {
float dyD = float(dyDCorner + wD) / ${e}.0;
if (dyD < 0.0 || dyD >= ${t.outDepth}.0 || fract(dyD) > 0.0) {
continue;
}
int idyD = int(dyD);
for (int wR = 0; wR < ${l};
wR += ${i}) {
float dyR = float(dyRCorner + wR) / ${n}.0;
if (dyR < 0.0 || dyR >= ${t.outHeight}.0 ||
fract(dyR) > 0.0) {
continue;
}
int idyR = int(dyR);
for (int wC = 0; wC < ${c};
wC += ${a}) {
float dyC = float(dyCCorner + wC) / ${o}.0;
if (dyC < 0.0 || dyC >= ${t.outWidth}.0 ||
fract(dyC) > 0.0) {
continue;
}
int idyC = int(dyC);
float dyValue = getDy(batch, idyD, idyR, idyC, ch);
int maxPosValue = ${d} -
int(getMaxPos(batch, idyD, idyR, idyC, ch));
// Get the current value, check it against the value from the
// position matrix.
int curPosValue =
wD * ${l} * ${c} +
wR * ${c} + wC;
float mask = float(maxPosValue == curPosValue ? 1.0 : 0.0);
dotProd += dyValue * mask;
}
}
}
setOutput(dotProd);
}
`}};function bnt(r){let{inputs:t,backend:e,attrs:n}=r,{dy:o,input:s}=t,i=s,{filterSize:a,strides:u,pad:l,dimRoundingMode:c}=n,p=[1,1,1],m=S.computePool3DInfo(i.shape,a,u,p,l,c),f=new Nu(m,"max",!0),d=e.runWebGLProgram(f,[i],i.dtype),h=new $v(m),g=e.runWebGLProgram(h,[o,d],i.dtype);return e.disposeIntermediateTensorInfo(d),g}var dz={kernelName:Yp,backendName:"webgl",kernelFunc:bnt};function wnt(r){let{inputs:t,backend:e,attrs:n}=r,{dy:o,input:s,output:i}=t,a=s;di([s,i],"maxPoolGrad");let{filterSize:u,strides:l,pad:c,dimRoundingMode:p}=n,m=S.computePool2DInfo(a.shape,u,l,1,c,p),f=!0,d=new gi(m,"max",f),h=e.runWebGLProgram(d,[a],a.dtype),g=new Av(m),y=e.runWebGLProgram(g,[o,h],a.dtype);return e.disposeIntermediateTensorInfo(h),y}var hz={kernelName:Xp,backendName:"webgl",kernelFunc:wnt};function gz(r,t,e,n){let o=new gi(e,"max",!1),s=n.runWebGLProgram(o,[r],"float32");o=new gi(e,"max",!0,!0,t);let i=n.runWebGLProgram(o,[r],"float32");return[s,i]}var xz={kernelName:Zp,backendName:"webgl",kernelFunc:({inputs:r,attrs:t,backend:e})=>{let{x:n}=r,{filterSize:o,strides:s,pad:i,includeBatchInIndex:a}=t,u=e;x.assert(n.shape.length===4,()=>`Error in maxPool: input must be rank 4 but got rank ${n.shape.length}.`);let l=[1,1];x.assert(S.eitherStridesOrDilationsAreOne(s,l),()=>`Error in maxPool: Either strides or dilations must be 1. Got strides ${s} and dilations '${l}'`);let c=S.computePool2DInfo(n.shape,o,s,l,i),[p,m]=gz(n,a,c,u);return[p,m]}};function yz(r,t,e,n){let o=x.sizeFromShape(t),i=x.sizeFromShape(r.shape)/o,a=ut({inputs:{x:r},attrs:{shape:[i,o]},backend:n}),u=Hn(a,"float32","mean",n),l=ut({inputs:{x:u},attrs:{shape:e},backend:n});return n.disposeIntermediateTensorInfo(a),n.disposeIntermediateTensorInfo(u),l}var bz={kernelName:ws,backendName:"webgl",kernelFunc:({inputs:r,attrs:t,backend:e})=>{let{x:n}=r,{keepDims:o,axis:s}=t,i=e,a=n.shape.length,u=x.parseAxisParam(s,n.shape),l=u,c=S.getAxesPermutation(l,a),p=c!=null,m=i.shouldExecuteOnCPU([n]),f=[],d=n;if(p){if(m){let v=i.texData.get(d.dataId).values,k=new Array(a);for(let D=0;D<k.length;D++)k[D]=n.shape[c[D]];let E=rp(v,n.shape,n.dtype,c,k);d=i.makeTensorInfo(k,n.dtype);let $=i.texData.get(d.dataId);$.values=E}else d=ku(n,c,i);f.push(d),l=S.getInnerMostAxes(l.length,a)}S.assertAxesAreInnerMostDims("sum",l,a);let[h,g]=S.computeOutAndReduceShapes(d.shape,l),y=h;o&&(y=S.expandShapeToKeepDim(h,u));let b=yz(d,g,y,i);for(let w of f)i.disposeIntermediateTensorInfo(w);return b}};function vnt(r){let{inputs:t,backend:e,attrs:n}=r,{x:o}=t,{axis:s,keepDims:i}=n,a=o.shape.length,u=x.parseAxisParam(s,o.shape),l=u,c=S.getAxesPermutation(l,a),p=o;c!=null&&(p=he({inputs:{x:o},backend:e,attrs:{perm:c}}),l=S.getInnerMostAxes(l.length,o.shape.length)),S.assertAxesAreInnerMostDims("min",l,a);let[m,f]=S.computeOutAndReduceShapes(p.shape,l),d=x.sizeFromShape(f),h=ut({inputs:{x:p},backend:e,attrs:{shape:[-1,d]}}),g=Hn(h,h.dtype,"min",e),y;if(i){let b=S.expandShapeToKeepDim(m,u);y=ut({inputs:{x:g},backend:e,attrs:{shape:b}})}else y=ut({inputs:{x:g},backend:e,attrs:{shape:m}});return e.disposeIntermediateTensorInfo(h),e.disposeIntermediateTensorInfo(g),c!=null&&e.disposeIntermediateTensorInfo(p),y}var wz={kernelName:vs,backendName:"webgl",kernelFunc:vnt};var Cnt=Mw+`
return min(a, b);
`,Int=`
vec4 result = vec4(min(a, b));
vec4 isNaN = min(vec4(isnan(a)) + vec4(isnan(b)), vec4(1.0));
`+Iu+`
return result;
`,Snt=le({opSnippet:Cnt,packedOpSnippet:Int,cpuKernelImpl:IP}),vz={kernelName:Cs,backendName:"webgl",kernelFunc:Snt};var Dv=class{constructor(t,e,n){this.variableNames=["x"],this.outputShape=e.map((c,p)=>c[0]+t[p]+c[1]);let o=t.length,s=Ht(o),i=e.map(c=>c[0]).join(","),a=e.map((c,p)=>c[0]+t[p]).join(","),u=["coords[0]","coords[1]","coords[2]","coords[3]"].slice(0,o),l=n==="reflect"?0:1;if(o===1){this.userCode=`
int start = ${i};
int end = ${a};
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=`
${s} start = ${s}(${i});
${s} end = ${s}(${a});
void main() {
${s} outC = getOutputCoords();
for (int i = 0; i < ${o}; 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};
}
}
${s} coords = outC - start;
setOutput(getX(${u}));
}
`}};var Fv=class{constructor(t,e,n){this.variableNames=["x"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=e.map((d,h)=>d[0]+t[h]+d[1]);let o=t.length,s=Ht(o),i=e.map(d=>d[0]).join(","),a=e.map((d,h)=>d[0]+t[h]).join(","),u=rr("rc",o),l=rr("source",o),c=`${u[o-1]} < ${this.outputShape[o-1]}`,p=o===1?"source":`vec2(${l.slice(-2).join()})`,m=n==="reflect"?0:1,f="";if(o===1){let d=`
${s} source = rc;
if (source < start) {
source = start * 2 - source - ${m};
} else if (source >= end) {
source = (end - 1) * 2 - source + ${m};
}
source -= start;
`;f=`
${s} rc = outputLoc;
${d}
result[0] = getChannel(getX(${l.join()}), ${p});
${u[o-1]} += 1;
if(${c}) {
${d}
result[1] = getChannel(getX(${l.join()}), ${p});
}
`}else{let d=`
${s} source = rc;
${s} lt = ${s}(lessThan(source, start));
${s} gte = ${s}(greaterThanEqual(source, end));
${s} orig = 1 - (lt + gte);
source = orig * source +
lt * (start * 2 - source - ${m}) +
gte * ((end - 1) * 2 - source + ${m});
source -= start;
`;f=`
${s} rc = outputLoc;
${d}
result[0] = getChannel(getX(${l.join()}), ${p});
${u[o-1]} += 1;
if(${c}) {
${d}
result[1] = getChannel(getX(${l.join()}), ${p});
}
rc = outputLoc;
${u[o-2]} += 1;
if(${u[o-2]} < ${this.outputShape[o-2]}) {
${d}
result[2] = getChannel(getX(${l.join()}), ${p});
${u[o-1]} += 1;
if(${c}) {
${d}
result[3] = getChannel(getX(${l.join()}), ${p});
}
}
`}this.userCode=`
const ${s} start = ${s}(${i});
const ${s} end = ${s}(${a});
void main() {
${s} outputLoc = getOutputCoords();
vec4 result = vec4(0.);
${f}
setOutput(result);
}
`}};var knt=({inputs:r,backend:t,attrs:e})=>{let{x:n}=r,{paddings:o,mode:s}=e,i=V().getBool("WEBGL_PACK_ARRAY_OPERATIONS")?new Fv(n.shape,o,s):new Dv(n.shape,o,s);return t.runWebGLProgram(i,[n],n.dtype)},Cz={kernelName:Is,backendName:"webgl",kernelFunc:knt};var Nnt=`if (b == 0.0) return NAN;
return mod(a, b);`,Tnt=`
vec4 result = mod(a, b);
vec4 isNaN = vec4(equal(b, vec4(0.0)));
`+Iu+`
return result;
`,_nt=le({opSnippet:Nnt,packedOpSnippet:Tnt}),Iz={kernelName:Ta,backendName:"webgl",kernelFunc:_nt};var Rv=class{constructor(t,e,n){this.variableNames=["probs"],this.customUniforms=[{name:"seed",type:"float"}],this.outputShape=[t,n],this.userCode=`
void main() {
ivec2 coords = getOutputCoords();
int batch = coords[0];
float r = random(seed);
float cdf = 0.0;
for (int i = 0; i < ${e-1}; i++) {
cdf += getProbs(batch, i);
if (r < cdf) {
setOutput(float(i));
return;
}
}
// If no other event happened, last event happened.
setOutput(float(${e-1}));
}
`}};var Ent=`
if (a == b) {
return 1.0;
};
return a / b;`,Ant=`
// vec4 one = vec4(equal(a, b));
// return one + (vec4(1.0) - one) * a / b;
vec4 result = a / b;
if(a.x == b.x) {
result.x = 1.;
}
if(a.y == b.y) {
result.y = 1.;
}
if(a.z == b.z) {
result.z = 1.;
}
if(a.w == b.w) {
result.w = 1.;
}
return result;
`,LT=le({opSnippet:Ent,packedOpSnippet:Ant,checkOutOfBounds:!0}),Sz={kernelName:ls,backendName:"webgl",kernelFunc:LT};var kz="return a - b;",zT=le({opSnippet:kz,packedOpSnippet:kz,supportsComplex:!0,cpuKernelImpl:BP}),Nz={kernelName:Gs,backendName:"webgl",kernelFunc:zT};function BT(r){let{inputs:t,backend:e,attrs:n}=r,{logits:o}=t,{dim:s}=n,i=x.parseAxisParam([s],o.shape),a=PT({inputs:{x:o},backend:e,attrs:{reductionIndices:i,keepDims:!1}}),u=S.expandShapeToKeepDim(a.shape,i),l=ut({inputs:{x:a},backend:e,attrs:{shape:u}}),c=zT({inputs:{a:o,b:l},backend:e}),p=OT({inputs:{x:c},backend:e}),m=op({inputs:{x:p},backend:e,attrs:{axis:i,keepDims:!1}}),f=ut({inputs:{x:m},backend:e,attrs:{shape:u}}),d=LT({inputs:{a:p,b:f},backend:e});return e.disposeIntermediateTensorInfo(a),e.disposeIntermediateTensorInfo(l),e.disposeIntermediateTensorInfo(c),e.disposeIntermediateTensorInfo(p),e.disposeIntermediateTensorInfo(m),e.disposeIntermediateTensorInfo(f),d}var Tz={kernelName:Bs,backendName:"webgl",kernelFunc:BT};function $nt(r){let{inputs:t,backend:e,attrs:n}=r,{logits:o}=t,{numSamples:s,seed:i,normalized:a}=n,u=a?o:BT({inputs:{logits:o},backend:e,attrs:{dim:o.shape.length-1}}),l=u.shape[0],c=u.shape[1],p=new Rv(l,c,s),m=[[i]],f=e.runWebGLProgram(p,[u],"int32",m);return a||e.disposeIntermediateTensorInfo(u),f}var _z={kernelName:Jp,backendName:"webgl",kernelFunc:$nt};var Dnt=hr+`
return -x;
`,Fnt=`
vec4 result = -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;
`;function Rnt(r){let{inputs:t,backend:e}=r,{x:n}=t;if(e.shouldExecuteOnCPU([n])){let s=e.texData.get(n.dataId),[i,a]=kP(s.values,n.shape,n.dtype);return e.makeTensorInfo(a,n.dtype,i)}let o;return V().getBool("WEBGL_PACK_UNARY_OPERATIONS")?o=new eo(n.shape,Fnt):o=new en(n.shape,Dnt),e.runWebGLProgram(o,[n],n.dtype)}var Ez={kernelName:ki,backendName:"webgl",kernelFunc:Rnt};var Ont=Gr.nonMaxSuppressionV3Impl;function Mnt(r){S.warn("tf.nonMaxSuppression() in webgl locks the UI thread. Call tf.nonMaxSuppressionAsync() instead");let{inputs:t,backend:e,attrs:n}=r,{boxes:o,scores:s}=t,{maxOutputSize:i,iouThreshold:a,scoreThreshold:u}=n,l=e.readSync(o.dataId),c=e.readSync(s.dataId),{selectedIndices:p}=Ont(l,c,i,a,u);return e.makeTensorInfo([p.length],"int32",new Int32Array(p))}var Az={kernelName:Ea,backendName:"webgl",kernelFunc:Mnt};var Pnt=Gr.nonMaxSuppressionV4Impl;function Lnt(r){S.warn("tf.nonMaxSuppression() in webgl locks the UI thread. Call tf.nonMaxSuppressionAsync() instead");let{inputs:t,backend:e,attrs:n}=r,{boxes:o,scores:s}=t,{maxOutputSize:i,iouThreshold:a,scoreThreshold:u,padToMaxOutputSize:l}=n,c=e.readSync(o.dataId),p=e.readSync(s.dataId),{selectedIndices:m,validOutputs:f}=Pnt(c,p,i,a,u,l);return[e.makeTensorInfo([m.length],"int32",new Int32Array(m)),e.makeTensorInfo([],"int32",new Int32Array([f]))]}var $z={kernelName:Aa,backendName:"webgl",kernelFunc:Lnt};var znt=Gr.nonMaxSuppressionV5Impl;function Bnt(r){S.warn("tf.nonMaxSuppression() in webgl locks the UI thread. Call tf.nonMaxSuppressionAsync() instead");let{inputs:t,backend:e,attrs:n}=r,{boxes:o,scores:s}=t,{maxOutputSize:i,iouThreshold:a,scoreThreshold:u,softNmsSigma:l}=n,c=e.readSync(o.dataId),p=e.readSync(s.dataId),m=i,f=a,d=u,h=l,{selectedIndices:g,selectedScores:y}=znt(c,p,m,f,d,h);return[e.makeTensorInfo([g.length],"int32",new Int32Array(g)),e.makeTensorInfo([y.length],"float32",new Float32Array(y))]}var Dz={kernelName:$a,backendName:"webgl",kernelFunc:Bnt};var Ov=class{constructor(t,e,n,o){this.variableNames=["indices"],this.outputShape=[t,e],this.userCode=`
void main() {
ivec2 coords = getOutputCoords();
int index = round(getIndices(coords.x));
setOutput(mix(float(${o}), float(${n}),
float(index == coords.y)));
}
`}};var Vnt=r=>{let{inputs:t,backend:e,attrs:n}=r,{indices:o}=t,{depth:s,onValue:i,offValue:a}=n,u=x.sizeFromShape(o.shape),l=new Ov(u,s,i,a),c=ut({inputs:{x:o},backend:e,attrs:{shape:[u]}}),p=e.runWebGLProgram(l,[c],o.dtype);e.disposeIntermediateTensorInfo(c);let m=[...o.shape,s],f=ut({inputs:{x:p},backend:e,attrs:{shape:m}});return e.disposeIntermediateTensorInfo(p),f},Fz={kernelName:ks,backendName:"webgl",kernelFunc:Vnt};function Fg(r){let{inputs:t,backend:e}=r,{x:n}=t;if(n.dtype==="complex64"){let o=kl({inputs:{input:n},backend:e}),s=Fg({inputs:{x:o},backend:e}),i=ip({inputs:{input:n},backend:e}),a=Fg({inputs:{x:i},backend:e}),u=Fn({inputs:{real:s,imag:a},backend:e});return e.disposeIntermediateTensorInfo(o),e.disposeIntermediateTensorInfo(s),e.disposeIntermediateTensorInfo(i),e.disposeIntermediateTensorInfo(a),u}else return Nl({attrs:{shape:n.shape,dtype:n.dtype,value:n.dtype==="string"?"":0},backend:e})}var Rz={kernelName:Ri,backendName:"webgl",kernelFunc:Fg};function Oz(r){let{inputs:t,backend:e}=r,{x:n}=t;if(n.dtype==="string")throw new Error("onesLike is not supported under string dtype");if(n.dtype==="complex64"){let o=kl({inputs:{input:n},backend:e}),s=Oz({inputs:{x:o},backend:e}),i=ip({inputs:{input:n},backend:e}),a=Fg({inputs:{x:i},backend:e}),u=Fn({inputs:{real:s,imag:a},backend:e});return e.disposeIntermediateTensorInfo(o),e.disposeIntermediateTensorInfo(s),e.disposeIntermediateTensorInfo(i),e.disposeIntermediateTensorInfo(a),u}else return Nl({attrs:{shape:n.shape,dtype:n.dtype,value:1},backend:e})}var Mz={kernelName:Ni,backendName:"webgl",kernelFunc:Oz};function Gnt(r){let{inputs:t,backend:e,attrs:n}=r,{axis:o}=n;if(t.length===1)return bv({inputs:{input:t[0]},backend:e,attrs:{dim:o}});let s=t[0].shape,i=t[0].dtype;t.forEach(c=>{x.assertShapesMatch(s,c.shape,"All tensors passed to stack must have matching shapes"),x.assert(i===c.dtype,()=>"All tensors passed to stack must have matching dtypes")});let a=[],u=t.map(c=>{let p=bv({inputs:{input:c},backend:e,attrs:{dim:o}});return a.push(p),p}),l=RT({inputs:u,backend:e,attrs:{axis:o}});return a.forEach(c=>e.disposeIntermediateTensorInfo(c)),l}var Pz={kernelName:Ti,backendName:"webgl",kernelFunc:Gnt};var Mv=class{constructor(t,e,n){this.variableNames=["x"],this.customUniforms=[{name:"value",type:"float"}],this.outputShape=e.map((l,c)=>l[0]+t[c]+l[1]);let o=t.length,s=Ht(o),i=e.map(l=>l[0]).join(","),a=e.map((l,c)=>l[0]+t[c]).join(","),u=["coords[0]","coords[1]","coords[2]","coords[3]"].slice(0,o);if(o===1){this.userCode=`
int start = ${i};
int end = ${a};
void main() {
int outC = getOutputCoords();
if (outC < start || outC >= end) {
setOutput(value);
} else {
setOutput(getX(outC - start));
}
}
`;return}this.userCode=`
${s} start = ${s}(${i});
${s} end = ${s}(${a});
void main() {
${s} outC = getOutputCoords();
if (any(lessThan(outC, start)) || any(greaterThanEqual(outC, end))) {
setOutput(value);
} else {
${s} coords = outC - start;
setOutput(getX(${u}));
}
}
`}};var Pv=class{constructor(t,e,n){this.variableNames=["x"],this.packedInputs=!0,this.packedOutput=!0,this.customUniforms=[{name:"value",type:"float"}],this.outputShape=e.map((h,g)=>h[0]+t[g]+h[1]);let o=t.length,s=Ht(o),i=e.map(h=>h[0]).join(","),a=e.map((h,g)=>h[0]+t[g]).join(","),u=rr("rc",o),l=rr("source",o),c=`${u[o-1]} < ${this.outputShape[o-1]}`,p=o===1?"source":`vec2(${l.slice(-2).join()})`,m=[`${s} rc = outputLoc;`,`${u[o-1]} += 1;
if(${c}) {
`,o===1?"":`}
rc = outputLoc;
${u[o-2]} += 1;
if(${u[o-2]} < ${this.outputShape[o-2]}) {`,o===1?"":` ${u[o-1]} += 1;
if(${c}) {`],f=o===1?"rc < start || rc >= end":"any(lessThan(rc, start)) || any(greaterThanEqual(rc, end))",d="";for(let h=0,g=o===1?2:4;h<g;h++)d+=`
${m[h]}
if (${f}) {
result[${h}] = float(value);
} else {
${s} source = rc - start;
result[${h}] = getChannel(getX(${l.join()}), ${p});
}
`;d+=o===1?"} ":"}}",this.userCode=`
const ${s} start = ${s}(${i});
const ${s} end = ${s}(${a});
void main() {
${s} outputLoc = getOutputCoords();
vec4 result = vec4(0.);
${d}
setOutput(result);
}
`}};var VT=r=>{let{inputs:t,backend:e,attrs:n}=r,{x:o}=t,{paddings:s,constantValue:i}=n;if(x.sizeFromShape(o.shape)===0){let l=s.map((c,p)=>c[0]+o.shape[p]+c[1]);return Nl({backend:e,attrs:{shape:l,value:i,dtype:o.dtype}})}let a=V().getBool("WEBGL_PACK_ARRAY_OPERATIONS")?new Pv(o.shape,s,i):new Mv(o.shape,s,i),u=[[i]];return e.runWebGLProgram(a,[o],o.dtype,u)},Lz={kernelName:Ns,backendName:"webgl",kernelFunc:VT};var Wnt=`
if(a < 0.0 && floor(b) < b){
return NAN;
}
if (b == 0.0) {
return 1.0;
}
return (round(mod(b, 2.0)) != 1) ?
pow(abs(a), b) : sign(a) * pow(abs(a), b);
`,Unt=`
// isModRound1 has 1 for components with round(mod(b, 2.0)) == 1, 0 otherwise.
vec4 isModRound1 = vec4(equal(round(mod(b, 2.0)), ivec4(1)));
vec4 multiplier = sign(a) * isModRound1 + (vec4(1.0) - isModRound1);
vec4 result = multiplier * pow(abs(a), b);
// Ensure that a^0 = 1, including 0^0 = 1 as this correspond to TF and JS
bvec4 isExpZero = equal(b, vec4(0.0));
result.r = isExpZero.r ? 1.0 : result.r;
result.g = isExpZero.g ? 1.0 : result.g;
result.b = isExpZero.b ? 1.0 : result.b;
result.a = isExpZero.a ? 1.0 : result.a;
vec4 isNaN = vec4(lessThan(a, vec4(0.0))) * vec4(lessThan(floor(b), b));
`+Iu+`
return result;
`,Hnt=le({opSnippet:Wnt,packedOpSnippet:Unt}),zz={kernelName:Ts,backendName:"webgl",kernelFunc:Hnt};function qnt(r){let{inputs:t,backend:e,attrs:n}=r,{x:o}=t,{axis:s,keepDims:i}=n,a=o.shape.length,u=[],l=x.parseAxisParam(s,o.shape),c=l,p=S.getAxesPermutation(c,a),m=o;p!=null&&(m=he({inputs:{x:o},backend:e,attrs:{perm:p}}),c=S.getInnerMostAxes(c.length,a),u.push(m)),S.assertAxesAreInnerMostDims("prod",c,a);let f;if(e.shouldExecuteOnCPU([m])){let d=e.texData.get(m.dataId).values,{outVals:h,outShape:g,outDtype:y}=TP(m.shape,m.dtype,d,c);f=e.makeTensorInfo(g,y,h)}else{let[d,h]=S.computeOutAndReduceShapes(m.shape,c),g=x.sizeFromShape(h),y=ut({inputs:{x:m},backend:e,attrs:{shape:[-1,g]}}),b=qu(o.dtype),w=Hn(y,b,"prod",e);f=ut({inputs:{x:w},backend:e,attrs:{shape:d}}),u.push(y),u.push(w)}if(i){u.push(f);let d=S.expandShapeToKeepDim(f.shape,l);f=ut({inputs:{x:f},backend:e,attrs:{shape:d}})}return u.forEach(d=>e.disposeIntermediateTensorInfo(d)),f}var Bz={kernelName:Es,backendName:"webgl",kernelFunc:qnt};var GT=r=>{let{backend:t,attrs:e}=r,{start:n,stop:o,step:s,dtype:i}=e,a=_P(n,o,s,i);return t.makeTensorInfo([a.length],i,a)},Vz={kernelName:Bl,backendName:"webgl",kernelFunc:GT};var Knt="return 1.0 / x;",jnt=It({opSnippet:Knt}),Gz={kernelName:Da,backendName:"webgl",kernelFunc:jnt};var Xnt=hr+`
return (x < 0.0) ? 0.0 : x;
`,Ynt=`
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;
`,Znt=It({opSnippet:Xnt,packedOpSnippet:Ynt}),Wz={kernelName:As,backendName:"webgl",kernelFunc:Znt};var Jnt=hr+`
return (x < 0.0) ? 0.0 : min(6.0, x);
`,Qnt=`
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;
`,tot=It({opSnippet:Jnt,packedOpSnippet:Qnt}),Uz={kernelName:Ds,backendName:"webgl",kernelFunc:tot};var Lv=class{constructor(t,e,n,o,s){this.variableNames=["A"],this.outputShape=[];let[i,a,u,l]=t;this.outputShape=[i,e,n,l];let c=[o&&e>1?a-1:a,o&&n>1?u-1:u],p=[o&&e>1?e-1:e,o&&n>1?n-1:n],m;s?m="(vec2(yRC) + vec2(0.5)) * effectiveInputOverOutputRatioRC - vec2(0.5)":m="vec2(yRC) * effectiveInputOverOutputRatioRC",this.userCode=`
const vec2 effectiveInputOverOutputRatioRC = vec2(
${c[0]/p[0]},
${c[1]/p[1]});
const vec2 inputShapeRC = vec2(${a}.0, ${u}.0);
void main() {
ivec4 coords = getOutputCoords();
int b = coords[0];
int d = coords[3];
ivec2 yRC = coords.yz;
// Fractional source index.
vec2 sourceFracIndexRC = ${m};
// 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);
}
`}};var zv=class{constructor(t,e,n,o,s){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=[];let[i,a,u,l]=t;this.outputShape=[i,e,n,l];let c=[o&&e>1?a-1:a,o&&n>1?u-1:u],p=[o&&e>1?e-1:e,o&&n>1?n-1:n],m;s?m="(vec3(yRC) + vec3(0.5)) * effectiveInputOverOutputRatioRC - vec3(0.5)":m="vec3(yRC) * effectiveInputOverOutputRatioRC",this.userCode=`
const vec3 effectiveInputOverOutputRatioRC = vec3(
${c[0]/p[0]},
${c[1]/p[1]},
${c[1]/p[1]});
const vec3 inputShapeRC = vec3(${a}.0, ${u}.0,
${u}.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 = ${m};
// Compute the four integer indices.
ivec3 sourceFloorRC = ivec3(max(sourceFracIndexRC, vec3(0.0)));
ivec3 sourceCeilRC = ivec3(
min(inputShapeRC - 1.0, ceil(sourceFracIndexRC)));
// Should we calculate next column and row elements in 2x2 packed cell.
bool hasNextCol = d < ${l-1};
bool hasNextRow = coords.z < ${n-1};
// In parallel, construct four corners for all four components in
// packed 2x2 cell.
vec4 topLeft = vec4(
getAValue(b, sourceFloorRC.x, sourceFloorRC.y, d),
hasNextCol ? getAValue(b, sourceFloorRC.x, sourceFloorRC.y, d + 1)
: 0.0,
hasNextRow ? getAValue(b, sourceFloorRC.x, sourceFloorRC.z, d)
: 0.0,
(hasNextRow && hasNextCol) ?
getAValue(b, sourceFloorRC.x, sourceFloorRC.z, d + 1) : 0.0);
vec4 bottomLeft = vec4(
getAValue(b, sourceCeilRC.x, sourceFloorRC.y, d),
hasNextCol ? getAValue(b, sourceCeilRC.x, sourceFloorRC.y, d + 1)
: 0.0,
hasNextRow ? getAValue(b, sourceCeilRC.x, sourceFloorRC.z, d)
: 0.0,
(hasNextRow && hasNextCol) ?
getAValue(b, sourceCeilRC.x, sourceFloorRC.z, d + 1) : 0.0);
vec4 topRight = vec4(
getAValue(b, sourceFloorRC.x, sourceCeilRC.y, d),
hasNextCol ? getAValue(b, sourceFloorRC.x, sourceCeilRC.y, d + 1)
: 0.0,
hasNextRow ? getAValue(b, sourceFloorRC.x, sourceCeilRC.z, d)
: 0.0,
(hasNextRow && hasNextCol) ?
getAValue(b, sourceFloorRC.x, sourceCeilRC.z, d + 1) : 0.0);
vec4 bottomRight = vec4(
getAValue(b, sourceCeilRC.x, sourceCeilRC.y, d),
hasNextCol ? getAValue(b, sourceCeilRC.x, sourceCeilRC.y, d + 1)
: 0.0,
hasNextRow ? getAValue(b, sourceCeilRC.x, sourceCeilRC.z, d)
: 0.0,
(hasNextRow && hasNextCol) ?
getAValue(b, sourceCeilRC.x, sourceCeilRC.z, d + 1) : 0.0);
vec3 fracRC = sourceFracIndexRC - vec3(sourceFloorRC);
vec4 top = mix(topLeft, topRight, fracRC.yyzz);
vec4 bottom = mix(bottomLeft, bottomRight, fracRC.yyzz);
vec4 newValue = mix(top, bottom, fracRC.x);
setOutput(newValue);
}
`}};function eot(r){let{inputs:t,backend:e,attrs:n}=r,{images:o}=t,{alignCorners:s,halfPixelCenters:i,size:a}=n,[u,l]=a,c=V().getBool("WEBGL_PACK_IMAGE_OPERATIONS")?new zv(o.shape,u,l,s,i):new Lv(o.shape,u,l,s,i);return e.runWebGLProgram(c,[o],"float32")}var Hz={kernelName:$s,backendName:"webgl",kernelFunc:eot};var Bv=class{constructor(t,e,n){this.variableNames=["dy"],this.outputShape=[],this.outputShape=e;let[,o,s]=e,[,i,a]=t,u=[n&&i>1?o-1:o,n&&a>1?s-1:s],l=[n&&i>1?i-1:i,n&&a>1?a-1:a],c=u[0]/l[0],p=u[1]/l[1],m=1/c,f=1/p,d=Math.ceil(m)*2+2,h=Math.ceil(f)*2+2;this.userCode=`
void main() {
ivec4 coords = getOutputCoords();
int b = coords[0];
int d = coords[3];
int r = coords[1];
int c = coords[2];
float accumulator = 0.0;
const float heightScale = float(${c});
const float widthScale = float(${p});
const float invHeightScale = float(${m});
const float invWidthScale = float(${f});
const int winHeight = int(${d});
const int winWidth = int(${h});
// 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 >= ${i}) {
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 >= ${a}) {
continue;
}
float dxR = float(dyR) * heightScale;
int topDxRIndex = int(floor(dxR));
int bottomDxRIndex = int(min(ceil(dxR), ${o-1}.0));
float dxRLerp = dxR - float(topDxRIndex);
float inverseDxRLerp = 1.0 - dxRLerp;
float dxC = float(dyC) * widthScale;
int leftDxCIndex = int(floor(dxC));
int rightDxCIndex = int(min(ceil(dxC), ${s-1}.0));
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 rot(r){let{inputs:t,backend:e,attrs:n}=r,{images:o,dy:s}=t,{alignCorners:i}=n,a=new Bv(s.shape,o.shape,i);return e.runWebGLProgram(a,[s],s.dtype)}var qz={kernelName:em,backendName:"webgl",kernelFunc:rot};var Vv=class{constructor(t,e,n,o,s){this.variableNames=["A"],this.outputShape=[];let[i,a,u,l]=t;this.outputShape=[i,e,n,l];let c=[o&&e>1?a-1:a,o&&n>1?u-1:u],p=[o&&e>1?e-1:e,o&&n>1?n-1:n],m=o?"0.5":"0.0",f;s?f="max((vec2(yRC) + vec2(0.5)) * effectiveInputOverOutputRatioRC, vec2(0.0))":f="vec2(yRC) * effectiveInputOverOutputRatioRC",this.userCode=`
const vec2 effectiveInputOverOutputRatioRC = vec2(
${c[0]/p[0]},
${c[1]/p[1]});
const vec2 inputShapeRC = vec2(${a}.0, ${u}.0);
void main() {
ivec4 coords = getOutputCoords();
int b = coords[0];
int d = coords[3];
ivec2 yRC = coords.yz;
// Fractional source index.
vec2 sourceFracIndexRC = ${f};
// Compute the coordinators of nearest neighbor point.
ivec2 sourceNearestRC = ivec2(
min(inputShapeRC - 1.0, floor(sourceFracIndexRC + ${m})));
float newValue = getA(b, sourceNearestRC.x, sourceNearestRC.y, d);
setOutput(newValue);
}
`}};var Gv=class{constructor(t,e,n,o,s){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=[];let[i,a,u,l]=t;this.outputShape=[i,e,n,l];let c=[o&&e>1?a-1:a,o&&n>1?u-1:u],p=[o&&e>1?e-1:e,o&&n>1?n-1:n],m=o?"0.5":"0.0",f;s?f="max((vec3(yRC) + vec3(0.5)) * effectiveInputOverOutputRatioRC, vec3(0.0))":f="vec3(yRC) * effectiveInputOverOutputRatioRC",this.userCode=`
const vec3 effectiveInputOverOutputRatioRC = vec3(
${c[0]/p[0]},
${c[1]/p[1]},
${c[1]/p[1]});
const vec3 inputShapeRC = vec3(${a}.0, ${u}.0,
${u}.0);
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 = ${f};
// Compute the coordinators of nearest neighbor point.
ivec3 sourceNearestRC = ivec3(
min(inputShapeRC - 1.0, floor(sourceFracIndexRC + ${m})));
// Should we calculate next column and row elements in 2x2 packed cell.
bool hasNextCol = d < ${l-1};
bool hasNextRow = coords.z < ${n-1};
vec4 newValue = vec4(
getAValue(b, sourceNearestRC.x, sourceNearestRC.y, d),
hasNextCol ? getAValue(b, sourceNearestRC.x, sourceNearestRC.y, d + 1)
: 0.0,
hasNextRow ? getAValue(b, sourceNearestRC.x, sourceNearestRC.z, d)
: 0.0,
(hasNextRow && hasNextCol) ?
getAValue(b, sourceNearestRC.x, sourceNearestRC.z, d + 1) : 0.0);
setOutput(newValue);
}
`}};function not(r){let{inputs:t,backend:e,attrs:n}=r,{images:o}=t,{alignCorners:s,halfPixelCenters:i,size:a}=n,[u,l]=a,c=V().getBool("WEBGL_PACK_IMAGE_OPERATIONS")?new Gv(o.shape,u,l,s,i):new Vv(o.shape,u,l,s,i);return e.runWebGLProgram(c,[o],o.dtype)}var Kz={kernelName:Vl,backendName:"webgl",kernelFunc:not};var Wv=class{constructor(t,e,n){this.variableNames=["dy"],this.outputShape=[],this.outputShape=e;let[,o,s]=e,[,i,a]=t,u=[n&&i>1?o-1:o,n&&a>1?s-1:s],l=[n&&i>1?i-1:i,n&&a>1?a-1:a],c=u[0]/l[0],p=u[1]/l[1],m=1/c,f=1/p,d=Math.ceil(m)*2+2,h=Math.ceil(f)*2+2;this.userCode=`
void main() {
ivec4 coords = getOutputCoords();
int b = coords[0];
int d = coords[3];
int r = coords[1];
int c = coords[2];
float accumulator = 0.0;
const float heightScale = float(${c});
const float widthScale = float(${p});
const float invHeightScale = float(${m});
const float invWidthScale = float(${f});
const int winHeight = int(${d});
const int winWidth = int(${h});
// 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 >= ${i}) {
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 >= ${a}) {
continue;
}
float sourceFracRow =
float(${u[0]}) *
(float(dyR) / float(${l[0]}));
float sourceFracCol =
float(${u[1]}) *
(float(dyC) / float(${l[1]}));
int sourceNearestRow = int(min(
float(int(${o}) - 1),
${n} ? float(round(sourceFracRow)) :
float(floor(sourceFracRow))));
int sourceNearestCol = int(min(
float(int(${s}) - 1),
${n} ? float(round(sourceFracCol)) :
float(floor(sourceFracCol))));
if (r == sourceNearestRow && c == sourceNearestCol) {
accumulator += getDy(b, dyR, dyC, d);
}
}
}
// End loop over dy
setOutput(accumulator);
}
`}};function oot(r){let{inputs:t,backend:e,attrs:n}=r,{images:o,dy:s}=t,{alignCorners:i}=n,a=new Wv(s.shape,o.shape,i);return e.runWebGLProgram(a,[s],s.dtype)}var jz={kernelName:tm,backendName:"webgl",kernelFunc:oot};var Uv=class{constructor(t,e){this.variableNames=["x"];let n=t.length;if(n>4)throw new Error(`WebGL backend: Reverse of rank-${n} tensor is not yet supported`);if(this.outputShape=t,n===1){this.userCode=`
void main() {
int coord = getOutputCoords();
setOutput(getX(${t[0]} - coord - 1));
}
`;return}let o=a=>e.indexOf(a)!==-1&&t[a]!==1?`${t[a]} - coords[${a}] - 1`:`coords[${a}]`,s=t.map((a,u)=>o(u)).join(","),i=Ht(n);this.userCode=`
void main() {
${i} coords = getOutputCoords();
setOutput(getX(${s}));
}
`}};var Hv=class{constructor(t,e){this.variableNames=["x"],this.packedInputs=!0,this.packedOutput=!0;let n=t.length;if(n>4)throw new Error(`WebGL backend: Reverse of rank-${n} tensor is not yet supported`);this.outputShape=t;let o=rr("rc",n),s=`${o[n-1]} + 1 < ${this.outputShape[n-1]}`,i=`${o[n-2]} + 1 < ${this.outputShape[n-2]}`,a=Ht(n);n===1?this.userCode=`
void main(){
int rc = getOutputCoords();
vec4 result = vec4(0.);
result.r = getChannel(getX(${t[0]} - rc - 1),
${t[0]} - rc - 1);
if(${s}){
result.g = getChannel(getX(${t[0]} - (rc + 1) - 1),
${t[0]} - (rc + 1) - 1);
}
setOutput(result);
}
`:this.userCode=`
void main() {
${a} rc = getOutputCoords();
vec4 result = vec4(0.);
result.r = ${u(o.slice())};
if(${s}){
result.g = ${l(o.slice())};
}
if(${i}) {
result.b = ${c(o.slice())};
if(${s}) {
result.a = ${p(o.slice())};
}
}
setOutput(result);
}
`;function u(d){return m(d)}function l(d){return d[n-1]="("+d[n-1]+" + 1)",m(d)}function c(d){return d[n-2]="("+d[n-2]+" + 1)",m(d)}function p(d){return d[n-1]="("+d[n-1]+" + 1)",d[n-2]="("+d[n-2]+" + 1)",m(d)}function m(d){let h=t.map((b,w)=>f(w,d)),g=h.join(","),y=h.slice(-2).join(",");return`getChannel(getX(${g}), vec2(${y}))`}function f(d,h){return e.indexOf(d)!==-1&&t[d]!==1?`${t[d]} - ${h[d]} - 1`:`${h[d]}`}}};function sot(r){let{inputs:t,backend:e,attrs:n}=r,{x:o}=t,{dims:s}=n,i=o.shape.length,a=x.parseAxisParam(s,o.shape);if(i===0)return nr({inputs:{x:o},backend:e});let u=V().getBool("WEBGL_PACK_ARRAY_OPERATIONS")?new Hv(o.shape,a):new Uv(o.shape,a);return e.runWebGLProgram(u,[o],o.dtype)}var Xz={kernelName:Fs,backendName:"webgl",kernelFunc:sot};var qv=class{constructor(t,e){this.variableNames=["Image"],this.outputShape=[],this.customUniforms=[{name:"params",type:"vec4"}];let n=t[1],o=t[2];this.outputShape=t;let s="";typeof e=="number"?s=`float outputValue = ${e.toFixed(2)};`:s=`
vec3 fill = vec3(${e.join(",")});
float outputValue = fill[coords[3]];`,this.userCode=`
void main() {
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]));
${s}
if(coordX >= 0 && coordX < ${o} && coordY >= 0 && coordY < ${n}) {
outputValue = getImage(coords[0], coordY, coordX, coords[3]);
}
setOutput(outputValue);
}
`}};var Yz={kernelName:Ga,backendName:"webgl",kernelFunc:({inputs:r,attrs:t,backend:e})=>{let{image:n}=r,{radians:o,fillValue:s,center:i}=t,a=e,u=new qv(n.shape,s),[l,c]=S.getImageCenter(i,n.shape[1],n.shape[2]),p=[[l,c,Math.sin(o),Math.cos(o)]];return a.runWebGLProgram(u,[n],n.dtype,p)}};var iot=`
// OpenGL ES does not support round function.
// The algorithm is based on banker's rounding.
float base = floor(x);
if ((x - base) < 0.5) {
return floor(x);
} else if ((x - base) > 0.5) {
return ceil(x);
} else {
if (mod(base, 2.0) == 0.0) {
return base;
} else {
return base + 1.0;
}
}
`,aot=It({opSnippet:iot}),Zz={kernelName:Rs,backendName:"webgl",kernelFunc:aot};var lot="return inversesqrt(x);",uot=It({opSnippet:lot,cpuKernelImpl:EP}),Jz={kernelName:Os,backendName:"webgl",kernelFunc:uot};var _d=class{constructor(t,e,n,o,s,i,a=!0){this.variableNames=["updates","indices","defaultValue"],this.outputShape=i;let u=Ht(s.length),l=Ht(i.length),c="";n===1?c="i":n===2&&(c="i, j");let p=`getIndices(${c})`,m="";o===1?m="i":o===2&&(m="i, coords[1]");let f=`getUpdates(${m})`,d=e>1?"strides[j]":"strides";this.userCode=`
${u} strides = ${u}(${s});
void main() {
${l} coords = getOutputCoords();
float sum = 0.0;
bool found = false;
for (int i = 0; i < ${t}; i++) {
int flattenedIndex = 0;
for (int j = 0; j < ${e}; j++) {
int index = round(${p});
flattenedIndex += index * ${d};
}
if (flattenedIndex == coords[0]) {
sum += ${f};
found = true;
}
}
setOutput(mix(getDefaultValue(), sum, float(found)));
}
`}};function cot(r){let{inputs:t,backend:e,attrs:n}=r,{indices:o,updates:s}=t,{shape:i}=n,{sliceRank:a,numUpdates:u,sliceSize:l,strides:c,outputSize:p}=S.calculateShapes(s,o,i),m=[p/l,l];if(p===0)return e.makeTensorInfo(i,o.dtype);let f=ut({inputs:{x:o},backend:e,attrs:{shape:[u,a]}}),d=ut({inputs:{x:s},backend:e,attrs:{shape:[u,l]}}),h=e.makeTensorInfo([],"float32",new Float32Array([0])),g=new _d(u,a,f.shape.length,d.shape.length,c,m),y=e.runWebGLProgram(g,[d,f,h],d.dtype),b=ut({inputs:{x:y},backend:e,attrs:{shape:i}});return e.disposeIntermediateTensorInfo(f),e.disposeIntermediateTensorInfo(d),e.disposeIntermediateTensorInfo(y),e.disposeIntermediateTensorInfo(h),b}var Qz={kernelName:Fa,backendName:"webgl",kernelFunc:cot};var Kv=class{constructor(t,e,n,o){this.variableNames=["sortedSequence","values"],this.customUniforms=[{name:"numInputs",type:"int"}],this.outputShape=[t,n];let s="while (left < right) {",i=`for (int i = 0; i < ${Math.ceil(Math.log2(e+1))}; ++i) { if (left >= right) break;`,a=V().getNumber("WEBGL_VERSION")===2?s:i,u=o==="left"?"<":"<=";this.userCode=`
int findBound(int batch, float value) {
int left = 0;
int right = numInputs;
int mid;
${a}
mid = (left + right) / 2;
if (getSortedSequence(batch, mid) ${u} 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 pot(r){let{inputs:t,backend:e,attrs:n}=r,{sortedSequence:o,values:s}=t,{side:i}=n,a=new Kv(o.shape[0],o.shape[1],s.shape[1],i),u=[[o.shape[1]]];return e.runWebGLProgram(a,[o,s],"int32",u)}var tB={kernelName:rm,backendName:"webgl",kernelFunc:pot};var jv=class{constructor(t,e,n){this.variableNames=["c","a","b"],this.outputShape=e;let o,s;if(n>4)throw Error(`Where for rank ${n} is not yet supported`);if(n===1)s="resRC",o="resRC";else{let a=["resRC.x","resRC.y","resRC.z","resRC.w"],u=[],l=[];for(let c=0;c<e.length;c++)l.push(`${a[c]}`),c<t&&u.push(`${a[c]}`);o=u.join(),s=l.join()}let i=Ht(n);this.userCode=`
void main() {
${i} resRC = getOutputCoords();
float cVal = getC(${o});
if (cVal >= 1.0) {
setOutput(getA(${s}));
} else {
setOutput(getB(${s}));
}
}
`}};function mot(r){let{inputs:t,backend:e}=r,{condition:n,t:o,e:s}=t,i=new jv(n.shape.length,o.shape,o.shape.length);return e.runWebGLProgram(i,[n,o,s],ar(o.dtype,s.dtype))}var eB={kernelName:Ei,backendName:"webgl",kernelFunc:mot};var fot=`
// Stable and Attracting Fixed Point (0, 1) for Normalized Weights.
// see: https://arxiv.org/abs/1706.02515
float scaleAlpha = ${S.SELU_SCALEALPHA};
float scale = ${S.SELU_SCALE};
return (x >= 0.0) ? scale * x : scaleAlpha * (exp(x) - 1.0);
`,dot=It({opSnippet:fot}),rB={kernelName:Ra,backendName:"webgl",kernelFunc:dot};var hot=Lo+`
return 1.0 / (1.0 + exp(-1.0 * x));
`,got=`
vec4 result = 1.0 / (1.0 + exp(-1.0 * x));
bvec4 isNaN = isnan(x);
result.r = isNaN.r ? x.r : result.r;
result.g = isNaN.g ? x.g : result.g;
result.b = isNaN.b ? x.b : result.b;
result.a = isNaN.a ? x.a : result.a;
return result;
`,xot=It({opSnippet:hot,packedOpSnippet:got,cpuKernelImpl:$P}),nB={kernelName:Ps,backendName:"webgl",kernelFunc:xot};var yot=`
if (isnan(x)) { return 0.0; }
return sign(x);
`,bot=It({opSnippet:yot}),oB={kernelName:Ma,backendName:"webgl",kernelFunc:bot};var wot=Lo+`
return sin(x);
`,vot=It({opSnippet:wot}),sB={kernelName:Ms,backendName:"webgl",kernelFunc:vot};var Cot=`
float e2x = exp(x);
return (e2x - 1.0 / e2x) / 2.0;
`,Iot=It({opSnippet:Cot}),iB={kernelName:Oa,backendName:"webgl",kernelFunc:Iot};var Sot=`
float epsilon = 1.1920928955078125e-7;
float threshold = log(epsilon) + 2.0;
bool too_large = x > -threshold;
bool too_small = x < threshold;
float result;
float exp_x = exp(x);
if (too_large){
result = x;
}
else if (too_small){
result = exp_x;
}
else{
result = log(exp_x + 1.0);
}
return result;
`,kot=It({opSnippet:Sot}),aB={kernelName:Pa,backendName:"webgl",kernelFunc:kot};var Not=r=>{let{inputs:t,backend:e,attrs:n}=r,{x:o}=t,{blockShape:s,paddings:i}=n;x.assert(o.shape.length<=4,()=>"spaceToBatchND for rank > 4 with a WebGL backend not implemented yet");let a=s.reduce((y,b)=>y*b),u=[[0,0]];u.push(...i);for(let y=1+s.length;y<o.shape.length;++y)u.push([0,0]);let l=[],c=VT({inputs:{x:o},backend:e,attrs:{paddings:u,constantValue:0}}),p=S.getReshaped(c.shape,s,a,!1),m=S.getPermuted(p.length,s.length,!1),f=S.getReshapedPermuted(c.shape,s,a,!1),d=ut({inputs:{x:c},backend:e,attrs:{shape:p}}),h=he({inputs:{x:d},backend:e,attrs:{perm:m}}),g=ut({inputs:{x:h},backend:e,attrs:{shape:f}});return l.push(c),l.push(d),l.push(h),l.forEach(y=>e.disposeIntermediateTensorInfo(y)),g},lB={kernelName:$i,backendName:"webgl",kernelFunc:Not};function Tot(r){let{inputs:t,backend:e}=r,{indices:n,values:o,denseShape:s,defaultValue:i}=t;if(s.shape.length!==1)throw new Error(`Dense shape must be a vector, saw:
${s.shape}`);if(n.shape.length!==2)throw new Error(`Indices must be a matrix, saw:
${n.shape}`);if(o.shape.length!==1)throw new Error(`Values must be a vector, saw:
${o.shape}`);if(i.shape.length!==0)throw new Error(`Default value must be a scalar, saw:
${i.shape}`);let a=e.readSync(n.dataId),u=e.readSync(o.dataId),l=e.readSync(s.dataId),c=e.readSync(i.dataId)[0],[p,m,f,d,h]=FP(a,n.shape,n.dtype,u,o.dtype,l,c);return[e.makeTensorInfo(m,n.dtype,p),e.makeTensorInfo([m[0]],o.dtype,f),e.makeTensorInfo([d.length],"bool",new Uint8Array(d.map(g=>Number(g)))),e.makeTensorInfo([h.length],n.dtype,new Int32Array(h))]}var uB={kernelName:Gl,backendName:"webgl",kernelFunc:Tot};function _ot(r){let{inputs:t,backend:e}=r,{inputIndices:n,inputShape:o,newShape:s}=t;if(n.shape.length!==2)throw new Error(`Input indices should be a matrix but received shape ${n.shape}`);if(o.shape.length!==1)throw new Error(`Input shape should be a vector but received shape ${o.shape}`);if(s.shape.length!==1)throw new Error(`Target shape should be a vector but received shape ${s.shape}`);let i=Array.from(e.readSync(o.dataId)),a=e.readSync(n.dataId),u=Array.from(e.readSync(s.dataId)),[l,c,p]=RP(a,n.shape,n.dtype,i,u);return[e.makeTensorInfo(c,n.dtype,l),e.makeTensorInfo([p.length],s.dtype,new Int32Array(p))]}var cB={kernelName:La,backendName:"webgl",kernelFunc:_ot};function Eot(r){let{inputs:t,backend:e}=r,{data:n,indices:o,segmentIds:s}=t;if(n.shape.length<1)throw new Error("Data should be at least 1 dimensional but received scalar");if(o.shape.length!==1)throw new Error(`Indices should be a vector but received shape
${o.shape}`);if(s.shape.length!==1)throw new Error(`Segment ids should be a vector but received shape
${s.shape}`);let i=e.readSync(n.dataId),a=e.readSync(o.dataId),u=e.readSync(s.dataId),[l,c]=$w(i,n.shape,n.dtype,a,u,!0);return e.makeTensorInfo(c,n.dtype,l)}var pB={kernelName:Wl,backendName:"webgl",kernelFunc:Eot};function Aot(r){let{inputs:t,backend:e}=r,{data:n,indices:o,segmentIds:s}=t;if(n.shape.length<1)throw new Error("Data should be at least 1 dimensional but received scalar");if(o.shape.length!==1)throw new Error(`Indices should be a vector but received shape
${o.shape}`);if(s.shape.length!==1)throw new Error(`Segment ids should be a vector but received shape
${s.shape}`);let i=e.readSync(n.dataId),a=e.readSync(o.dataId),u=e.readSync(s.dataId),[l,c]=$w(i,n.shape,n.dtype,a,u);return e.makeTensorInfo(c,n.dtype,l)}var mB={kernelName:Ul,backendName:"webgl",kernelFunc:Aot};function $ot(r){let{inputs:t,backend:e,attrs:n}=r,{sparseIndices:o,sparseValues:s,defaultValue:i}=t,{outputShape:a}=n,{sliceRank:u,numUpdates:l,sliceSize:c,strides:p,outputSize:m}=S.calculateShapes(s,o,a),f=!1;if(s.dtype==="string"){let y=e.bufferSync(o),b=e.bufferSync(s),w=x.decodeString(e.readSync(i.dataId)[0]),v=AP(y,b,a,m,c,l,u,p,w,f);return e.makeTensorInfo(a,v.dtype,v.values)}let d=new _d(l,u,o.shape.length,s.shape.length,p,[m,1],f),h=e.runWebGLProgram(d,[s,o,i],s.dtype),g=ut({inputs:{x:h},backend:e,attrs:{shape:a}});return e.disposeIntermediateTensorInfo(h),g}var fB={kernelName:nm,backendName:"webgl",kernelFunc:$ot};function Dot(r){let{inputs:t,backend:e,attrs:n}=r,{x:o}=t,{numOrSizeSplits:s,axis:i}=n,a=x.parseAxisParam(i,o.shape)[0],u=S.prepareSplitSize(o,s,a),l=o.shape.length,c=new Array(l).fill(0),p=o.shape.slice();return u.map(m=>{let f=[...p];f[a]=m;let d=xi({inputs:{x:o},backend:e,attrs:{begin:c,size:f}});return c[a]+=m,d})}var dB={kernelName:Di,backendName:"webgl",kernelFunc:Dot};var hB="return sqrt(x);",Fot=It({opSnippet:hB,packedOpSnippet:hB,cpuKernelImpl:OP}),gB={kernelName:Ls,backendName:"webgl",kernelFunc:Fot};var Rot="return x * x;",Oot=It({opSnippet:Rot}),xB={kernelName:Hl,backendName:"webgl",kernelFunc:Oot};var yB="return (a - b) * (a - b);",Mot=le({opSnippet:yB,packedOpSnippet:yB}),bB={kernelName:Vs,backendName:"webgl",kernelFunc:Mot};function Pot({inputs:r,attrs:t,backend:e}){let{x:n}=r,o=hr+`
return x > 0.0 ? 1.0 : float(${t.alpha});
`,s=new en(n.shape,o);return e.runWebGLProgram(s,[n],n.dtype)}var wB={kernelName:uo,backendName:"webgl",kernelFunc:Pot};var Xv=class{constructor(t,e,n){this.variableNames=["x"],this.outputShape=n;let o=n.length,s=Ht(n.length),i=Ht(n.length),a="";if(o===1)a="coords * strides + begin";else{let u=0;a=n.map((l,c)=>(u++,n.length===1?`coords * strides[${c}] + begin[${c}]`:`coords[${u-1}] * strides[${c}] + begin[${c}]`)).join(",")}this.userCode=`
${s} begin = ${s}(${t});
${s} strides = ${s}(${e});
void main() {
${i} coords = getOutputCoords();
setOutput(getX(${a}));
}
`}};function Lot(r){let{inputs:t,backend:e,attrs:n}=r,{x:o}=t,{begin:s,end:i,strides:a,beginMask:u,endMask:l,ellipsisMask:c,newAxisMask:p,shrinkAxisMask:m}=n,{finalShapeSparse:f,finalShape:d,isIdentity:h,sliceDim0:g,isSimpleSlice:y,begin:b,end:w,strides:v}=Ve.sliceInfo(o.shape,s,i,a,u,l,c,p,m),k;if(h)k=ut({inputs:{x:o},backend:e,attrs:{shape:d}});else if(g||y){x.assert(o.shape.length>=1,()=>`Input must have rank at least 1, got: ${o.shape.length}`);let $=Ve.computeOutShape(b,w,v),D=xi({inputs:{x:o},backend:e,attrs:{begin:b,size:$}});k=ut({inputs:{x:D},backend:e,attrs:{shape:d}}),e.disposeIntermediateTensorInfo(D)}else if(e.shouldExecuteOnCPU([o])){let D=e.readSync(o.dataId),F=Ct(o.shape,o.dtype,D),P=MP(f,F,v,b);k=e.makeTensorInfo(d,o.dtype,P.values)}else{let D=new Xv(b,v,f);k=e.runWebGLProgram(D,[o],o.dtype)}let E=ut({inputs:{x:k},backend:e,attrs:{shape:d}});return e.disposeIntermediateTensorInfo(k),E}var vB={kernelName:za,backendName:"webgl",kernelFunc:Lot};function zot(r){let{inputs:t,backend:e,attrs:n}=r,{separator:o,nGramWidths:s,leftPad:i,rightPad:a,padWidth:u,preserveShortSequences:l}=n,{data:c,dataSplits:p}=t,m=e.readSync(c.dataId),f=e.readSync(p.dataId),[d,h]=PP(m,f,o,s,i,a,u,l);return[e.makeTensorInfo([d.length],"string",d),e.makeTensorInfo(p.shape,"int32",h)]}var CB={kernelName:om,backendName:"webgl",kernelFunc:zot};function Bot(r){let{inputs:t,backend:e,attrs:n}=r,{skipEmpty:o}=n,{input:s,delimiter:i}=t;if(s.dtype!=="string")throw new Error("Input must be of datatype string");if(s.shape.length!==1)throw new Error(`Input must be a vector, got shape: ${s.shape}`);if(i.shape.length!==0)throw new Error(`Delimiter must be a scalar, got shape: ${i.shape}`);let a=e.readSync(s.dataId),u=e.readSync(i.dataId)[0],[l,c,p]=LP(a,u,o),m=c.length;return[e.makeTensorInfo([m,2],"int32",l),e.makeTensorInfo([m],"string",c),e.makeTensorInfo([2],"int32",new Int32Array(p))]}var IB={kernelName:sm,backendName:"webgl",kernelFunc:Bot};function Vot(r){let{inputs:t,backend:e,attrs:n}=r,{numBuckets:o}=n,{input:s}=t;if(s.dtype!=="string")throw new Error("Input must be of datatype string");if(o<=0)throw new Error("Number of buckets must be at least 1");let i=e.readSync(s.dataId),a=zP(i,o);return e.makeTensorInfo(s.shape,"int32",a)}var SB={kernelName:im,backendName:"webgl",kernelFunc:Vot};var Got="return tan(x);",Wot=It({opSnippet:Got}),kB={kernelName:Ws,backendName:"webgl",kernelFunc:Wot};var Uot=`
float e2x = exp(-2.0 * abs(x));
return sign(x) * (1.0 - e2x) / (1.0 + e2x);
`,Hot=It({opSnippet:Uot}),NB={kernelName:Us,backendName:"webgl",kernelFunc:Hot};var Yv=class{constructor(t,e){this.variableNames=["A"];let n=new Array(t.length);for(let i=0;i<n.length;i++)n[i]=t[i]*e[i];this.outputShape=n,this.rank=n.length;let o=Ht(this.rank),s=qot(t);this.userCode=`
void main() {
${o} resRC = getOutputCoords();
setOutput(getA(${s}));
}
`}};function qot(r){let t=r.length;if(t>5)throw Error(`Tile for rank ${t} is not yet supported`);if(t===1)return`imod(resRC, ${r[0]})`;let e=["resRC.x","resRC.y","resRC.z","resRC.w","resRC.u"],n=[];for(let o=0;o<r.length;o++)n.push(`imod(${e[o]}, ${r[o]})`);return n.join()}function WT(r){let{inputs:t,backend:e,attrs:n}=r,{x:o}=t,{reps:s}=n;if(o.dtype==="string"||o.shape.length>5){let u=e.readSync(o.dataId),l=o.dtype==="string"?u.map(m=>x.decodeString(m)):u,c=Ct(o.shape,o.dtype,l),p=VP(c,s);return e.makeTensorInfo(p.shape,p.dtype,p.values)}let i=new Yv(o.shape,s);return e.runWebGLProgram(i,[o],o.dtype)}var TB={kernelName:Yn,backendName:"webgl",kernelFunc:WT};var Zv=class{constructor(t){this.variableNames=["x","indices"],this.customUniforms=[{name:"n",type:"int"},{name:"firstPass",type:"int"},{name:"negativeInf",type:"float"},{name:"dir",type:"int"},{name:"inc",type:"int"}],this.outputShape=t,this.userCode=`
void main() {
ivec2 coords = getOutputCoords();
int batch = coords[0];
int elemIdx = coords[1];
// We compare elements pair-wise within a group of size 2 * inc.
// The comparing rule for each group alternates between ascending
// and descending. Within each group, we compare each pair at
// positions i and i+inc. To decide whether an element at position i
// is x0 or x1, we mod it by 2 * inc, if the result is smaller than
// inc, it is in the first half of the group, we denote it as x0,
// otherwise we denote it as x1.
// For example, as shown in the Bitonic top K paper referenced above,
// Figure5(a) shows that element[1] is in the
// second half of the group when group size is 2, but it is in the
// first half of the group when group size is 4.
bool isFirstInPair = imod(elemIdx, 2 * inc) < inc;
int i = isFirstInPair ? elemIdx : elemIdx - inc;
int i0 = firstPass == 1 ? i : int(getIndices(batch, i));
int i1 = firstPass == 1 ? i + inc : int(getIndices(batch, i + inc));
float x0 = i0 < n ? getX(batch, i0) : negativeInf;
float x1 = i1 < n ? getX(batch, i1) : negativeInf;
// Denotes which direction indices are in (ascending or descending).
bool reverse = imod(elemIdx, 2 * dir) >= dir;
bool isGreater = x0 > x1 || (x0 == x1 && i1 > i0);
if (reverse == isGreater) { // Elements in opposite order of direction
int iTemp = i0;
i0 = i1;
i1 = iTemp;
}
if (isFirstInPair) {
setOutput(float(i0));
} else {
setOutput(float(i1));
}
}
`}},Jv=class{constructor(t){this.variableNames=["x","indices"],this.customUniforms=[{name:"n",type:"int"},{name:"firstPass",type:"int"},{name:"k",type:"int"}],this.outputShape=t,this.userCode=`
void main() {
// Takes max of indices (0, k), (1, k + 1), (2, k + 2) ...
ivec2 coords = getOutputCoords();
int batch = coords[0];
int elemIdx = coords[1];
// The output size is half of the previous size.
// If the previous sequence is | | | | _ _ _ _ | | | | _ _ _ _ (k=4),
// we only need to output the indices at positions |, the indices at
// positions _ can be thrown away, see Figure5(b) After Phase 2
// (Merge phase) in the Bitonic Top K paper referenced above.
// For example, the paper shows we only need to output the orange bars.
// The output sequence should look like this | | | | | | | |.
// Because the sequence is halved, to map the output index back
// to the previous sequence to find the corresponding value,
// we need to double the index. When we double the index,
// we basically interpolate a position, so 2i looks like
// | _ | _ | _ | _ | _ | _ | _. We move the | to the first k position
// of each 2k positions by - elemIdx % k. E.g. for output at
// index 4,5,6,7, we want to get the corresponding element at
// original index 8,9,10,11, for output at index 8,9,10,11,
// we want to get the corresponding element at original index
// 16,17,18,19, so on and so forth.
int i = elemIdx < k ? elemIdx : (elemIdx * 2 - imod(elemIdx, k));
int i0 = firstPass == 1 ? i : int(getIndices(batch, i));
int i1 = firstPass == 1 ? i + k : int(getIndices(batch, i + k));
float x0 = getX(batch, i0);
float x1 = i1 < n ? getX(batch, i1) : x0;
setOutput(x0 >= x1 ? float(i0) : float(i1));
}
`}};function up(r,t){t!==null&&r.disposeIntermediateTensorInfo(t)}function _B(r){let t=1;for(;t<r;)t*=2;return t}function Kot(r){let{inputs:t,backend:e,attrs:n}=r,{x:o}=t,{k:s,sorted:i}=n,a=V().getNumber("TOPK_LAST_DIM_CPU_HANDOFF_SIZE_THRESHOLD"),u=V().getNumber("TOPK_K_CPU_HANDOFF_THRESHOLD"),l=o.shape,c=l[l.length-1];if(e.shouldExecuteOnCPU([o])||c<a||s>u){let P=e.readSync(o.dataId),[W,U]=GP(P,l,o.dtype,s,i);return[e.makeTensorInfo(W.shape,W.dtype,W.values),e.makeTensorInfo(U.shape,U.dtype,U.values)]}if(s===0)return l[l.length-1]=0,[e.makeTensorInfo(l,o.dtype,[]),e.makeTensorInfo(l,"int32",[])];if(c===1)return[o,Nl({attrs:{shape:l,dtype:"int32",value:0},backend:e})];let p=e.texData.get(o.dataId),m=p!==null&&p.isPacked,f=m?e.unpackTensor(o):o,h=x.sizeFromShape(l)/c,g=ut({inputs:{x:f},attrs:{shape:[h,c]},backend:e});m&&up(e,f);let y=_B(s),b=_B(c),w=null,v=()=>w===null?[g,g]:[g,w],k=(P,W,U)=>{let q=v(),K=new Zv(U),Q=[[c],[w===null?1:0],[Number.NEGATIVE_INFINITY],[P],[W]],rt=w;w=e.runWebGLProgram(K,q,"int32",Q),up(e,rt)};for(let P=1;P<y;P*=2){let W=P*2;for(let U=P;U>=1;U/=2)k(W,U,[h,b])}for(let P=b;P>y;P/=2){let W=v(),U=new Jv([h,P/2]),K=[[c],[w===null?1:0],[y]],j=w;w=e.runWebGLProgram(U,W,"int32",K),up(e,j);let Q=y/2,rt=Q*2;for(let X=Q;X>=1;X/=2)k(rt,X,w.shape)}let E=w;w=xi({inputs:{x:w},backend:e,attrs:{begin:0,size:[h,s]}}),up(e,E);let $=MT({inputs:{x:g,indices:w},backend:e,attrs:{axis:1,batchDims:1}});up(e,g);let D=l.slice(0,-1);D.push(s),E=w,w=ut({inputs:{x:w},attrs:{shape:D},backend:e}),up(e,E);let F=$;return $=ut({inputs:{x:$},attrs:{shape:D},backend:e}),up(e,F),[$,w]}var EB={kernelName:Ba,backendName:"webgl",kernelFunc:Kot};var Qv=class{constructor(t,e,n,o,s,i){this.variableNames=["Image","Transforms"],this.outputShape=i;let a=n==="nearest"?1:2,u;switch(o){case"constant":u=1;break;case"reflect":u=2;break;case"wrap":u=3;break;case"nearest":u=4;break;default:u=1;break}this.userCode=`
float mapCoord(float outCoord, float len) {
float inCoord = outCoord;
if(${u} == 2) {
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 (${u} == 3) {
if (inCoord < 0.0) {
if (len <= 1.0) {
inCoord = 0.0;
} else {
float sz = len - 1.0;
inCoord += len * (float(int(float(-inCoord / sz))) + 1.0);
}
} else if (inCoord > len - 1.0) {
if (len <= 1.0) {
inCoord = 0.0;
} else {
float sz = len - 1.0;
inCoord -= len * float(int(float(inCoord / sz)));
}
}
return clamp(inCoord, 0.0, len - 1.0);
} else if (${u} == 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 < ${t} && 0 <= coordX && coordX < ${e}) {
outputValue = getImage(batch, coordY, coordX, channel);
} else {
outputValue = float(${s});
}
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(${s});
} else {
float inX = (a1 * xf + a2 * yf + a3) / projection;
float inY = (b1 * xf + b2 * yf + b3) / projection;
float mapX = mapCoord(inX, float(${e}));
float mapY = mapCoord(inY, float(${t}));
if (${a} == 1) {
int coordY = int(round(mapY));
int coordX = int(round(mapX));
outputValue = readWithFillValue(batch, coordY, coordX,
channel);
} else {
float yFloor = floor(mapY);
float xFloor = floor(mapX);
float yCeil = yFloor + 1.0;
float xCeil = xFloor + 1.0;
float valueYFloor = (xCeil - mapX) *
readWithFillValue(batch, int(yFloor), int(xFloor), channel) +
(mapX - xFloor) *
readWithFillValue(batch, int(yFloor), int(xCeil), channel);
float valueYCeil = (xCeil - mapX) *
readWithFillValue(batch, int(yCeil), int(xFloor), channel) +
(mapX - xFloor) *
readWithFillValue(batch, int(yCeil), int(xCeil), channel);
outputValue = (yCeil - mapY) * valueYFloor +
(mapY - yFloor) * valueYCeil;
}
}
setOutput(outputValue);
}
`}};function jot(r){let{inputs:t,backend:e,attrs:n}=r,{image:o,transforms:s}=t,{interpolation:i,fillMode:a,fillValue:u,outputShape:l}=n,[c,p,m,f]=o.shape,[d,h]=l!=null?l:[p,m],g=[c,d,h,f],y=new Qv(p,m,i,a,u,g);return e.runWebGLProgram(y,[o,s],"float32")}var AB={kernelName:Va,backendName:"webgl",kernelFunc:jot};function Xot(r){let{inputs:t,attrs:e,backend:n}=r,{axis:o}=e,{x:s}=t;di(s,"unique"),console.warn("WARNING: ","UI might be locked temporarily as data is being downloaded");let i=n.readSync(s.dataId),{outputValues:a,outputShape:u,indices:l}=WP(i,o,s.shape,s.dtype);return[n.makeTensorInfo(u,s.dtype,a),n.makeTensorInfo([l.length],"int32",l)]}var $B={kernelName:am,backendName:"webgl",kernelFunc:Xot};function Yot(r){let{inputs:t,backend:e,attrs:n}=r,{value:o}=t,{axis:s}=n;s<0&&(s+=o.shape.length);let i=o,a=i.shape.length,u=o.shape[s],l=new Array(a-1),c=0;for(let h=0;h<a;h++)h!==s&&(l[c++]=i.shape[h]);let p=[],m=new Array(a).fill(0),f=i.shape.slice();f[s]=1;let d=new Array(u);for(let h=0;h<d.length;h++){m[s]=h;let g=xi({inputs:{x:i},backend:e,attrs:{begin:m,size:f}}),y=ut({inputs:{x:g},backend:e,attrs:{shape:l}});d[h]=y,p.push(g)}return p.forEach(h=>e.disposeIntermediateTensorInfo(h)),d}var DB={kernelName:Fi,backendName:"webgl",kernelFunc:Yot};var t0=class{constructor(t,e){this.variableNames=["x","segmentIds"];let n=t.windowSize,o=t.batchSize,s=t.inSize,i=t.numSegments,a=i*Math.ceil(s/n);this.outputShape=[o,a];let u="0.0",l="sumValue",c=Math.floor(n/4)*4,p=n%4,m=`
sumValue += dot(values, segFilter);
`,f="";s%n>0&&(f=`
if (inIdx < 0 || inIdx >= ${s}) {
return initializationValue;
}
`);let d="";s%n>0&&(d=`
if (inIdx < 0 || inIdx >= ${s}) {
return -1.0;
}
`),this.userCode=`
const float initializationValue = ${u};
float getValue(int batch, int inIdx) {
${f}
return getX(batch, inIdx);
}
float getSegmentIdAtIndex(int inIdx) {
${d}
return getSegmentIds(inIdx);
}
void main() {
ivec2 coords = getOutputCoords();
int batch = coords[0];
int outIdx = coords[1];
int inOffset = int(floor(float(outIdx) / float(
${i})) * float(${n}));
int currentSeg = int(mod(float(outIdx), float(${i})));
float sumValue = 0.0;
for (int i = 0; i < ${c}; i += 4) {
int inIdx = inOffset + i;
vec4 values = vec4(
getValue(batch, inIdx),
getValue(batch, inIdx + 1),
getValue(batch, inIdx + 2),
getValue(batch, inIdx + 3)
);
vec4 segFilter = vec4(
int(getSegmentIdAtIndex(inIdx)) == currentSeg ? 1 : 0,
int(getSegmentIdAtIndex(inIdx + 1)) == currentSeg ? 1 : 0,
int(getSegmentIdAtIndex(inIdx + 2)) == currentSeg ? 1 : 0,
int(getSegmentIdAtIndex(inIdx + 3)) == currentSeg ? 1 : 0
);
${m}
}
int inIdx = inOffset + ${c};
if (${p===1}) {
vec4 values = vec4(
getValue(batch, inIdx),
initializationValue,
initializationValue,
initializationValue
);
int inIdxSeg = int(getSegmentIdAtIndex(inIdx));
vec4 segFilter = vec4(
int(getSegmentIdAtIndex(inIdx)) == currentSeg ? 1 : 0,
0,
0,
0
);
${m}
} else if (${p===2}) {
vec4 values = vec4(
getValue(batch, inIdx),
getValue(batch, inIdx + 1),
initializationValue,
initializationValue
);
vec4 segFilter = vec4(
int(getSegmentIdAtIndex(inIdx)) == currentSeg ? 1 : 0,
int(getSegmentIdAtIndex(inIdx + 1)) == currentSeg ? 1 : 0,
0,
0
);
${m}
} else if (${p===3}) {
vec4 values = vec4(
getValue(batch, inIdx),
getValue(batch, inIdx + 1),
getValue(batch, inIdx + 2),
initializationValue
);
vec4 segFilter = vec4(
int(getSegmentIdAtIndex(inIdx)) == currentSeg ? 1 : 0,
int(getSegmentIdAtIndex(inIdx + 1)) == currentSeg ? 1 : 0,
int(getSegmentIdAtIndex(inIdx + 2)) == currentSeg ? 1 : 0,
0
);
${m}
}
setOutput(${l});
}
`}};function Zot(r){let{inputs:t,backend:e,attrs:n}=r,{x:o,segmentIds:s}=t,{numSegments:i}=n,a=o.shape.length,u=[],l=0,c=S.getAxesPermutation([l],a),p=o;c!=null&&(p=he({inputs:{x:o},backend:e,attrs:{perm:c}}),u.push(p),l=S.getInnerMostAxes(1,a)[0]);let m=S.segment_util.computeOutShape(p.shape,l,i),f=x.sizeFromShape([p.shape[l]]),d=ut({inputs:{x:p},backend:e,attrs:{shape:[-1,f]}});u.push(d);let h=qu(o.dtype),g=(v,k,E,$,D)=>{let F=v.shape[0],P=v.shape[1],W=S.segment_util.segOpComputeOptimalWindowSize(P,D),U={windowSize:W,inSize:P,batchSize:F,numSegments:D},q=new t0(U,k),K=e.compileAndRun(q,[v,E],$);if(u.push(K),K.shape[1]===D)return K;let j=GT({backend:e,attrs:{start:0,stop:D,step:1,dtype:"float32"}}),Q=WT({inputs:{x:j},backend:e,attrs:{reps:[P/W]}});return u.push(j),u.push(Q),g(K,k,Q,$,D)},y=g(d,"unsortedSegmentSum",s,h,i),b=ut({inputs:{x:y},backend:e,attrs:{shape:m}}),w=b;if(c!=null){u.push(b);let v=S.getUndoAxesPermutation(c);w=he({inputs:{x:w},backend:e,attrs:{perm:v}})}return 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Qot(r){RB=r.wasm.cwrap(Oi,null,["number","array","number","number","array","number","number","number","number","number","number","number","number"])}function tst(r){let{inputs:t,backend:e,attrs:n}=r,{a:o,b:s,bias:i,preluActivationWeights:a}=t;if(o.dtype!=="float32"||s.dtype!=="float32")throw new Error("_FusedMatMul for non non-float32 tensors not yet supported.");let{transposeA:u,transposeB:l,activation:c,leakyreluAlpha:p}=n,m=e.dataIdMap.get(o.dataId).id,f=e.dataIdMap.get(s.dataId).id,d=0;if(i!=null){let D=e.dataIdMap.get(i.dataId);if(D.shape.length!==1)throw new Error(`_FusedMatMul only supports rank-1 bias but got rank ${D.shape.length}.`);d=D.id}let h=a==null?0:e.dataIdMap.get(a.dataId).id,g=Tu[c];if(g==null)throw new Error(`${c} activation not yet supported for FusedConv2D in the wasm backend.`);let y=u?o.shape[2]:o.shape[1],b=l?s.shape[1]:s.shape[2],w=Sr.assertAndGetBroadcastShape(o.shape.slice(0,-2),s.shape.slice(0,-2)),v=e.makeOutput([...w,y,b],o.dtype),k=e.dataIdMap.get(v.dataId).id,E=new Uint8Array(new Int32Array(o.shape).buffer),$=new Uint8Array(new Int32Array(s.shape).buffer);return RB(m,E,o.shape.length,f,$,s.shape.length,u,l,g,d,h,p||0,k),v}var OB={kernelName:Oi,backendName:"wasm",setupFunc:Qot,kernelFunc:tst};function ue(r,t){let e;function n(s){e=s.wasm.cwrap(r,null,["number","number","number"])}function o(s){let{backend:i,inputs:{x:a}}=s,u=i.dataIdMap.get(a.dataId).id,l=i.makeOutput(a.shape,t||a.dtype),c=i.dataIdMap.get(l.dataId).id;return x.sizeFromShape(l.shape)===0||e(u,Xt[a.dtype],c),l}return{kernelName:r,backendName:"wasm",setupFunc:n,kernelFunc:o}}var MB=ue(wi);function Se(r,t,e){let n;function o(i){n=i.wasm.cwrap(r,null,["number","array","number","number","array","number","number","number"])}function 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Sst(r){let{inputs:t,attrs:e,backend:n}=r,{x:o,filter:s}=t,i=n.dataIdMap.get(o.dataId).id,a=n.dataIdMap.get(s.dataId).id,{strides:u,dilations:l,pad:c,dimRoundingMode:p,dataFormat:m}=e,f=S.convertConv2DDataFormat(m),d=S.computeConv2DInfo(o.shape,s.shape,u,l,c,p,!1,f),h=d.filterHeight,g=d.filterWidth,y=d.padInfo.top,b=d.padInfo.right,w=d.padInfo.bottom,v=d.padInfo.left,k=d.dilationHeight,E=d.dilationWidth,$=d.strideHeight,D=d.strideWidth,F=d.inChannels,P=d.outChannels,W=d.padInfo.type==="SAME"?1:0;if(d.dataFormat!=="channelsLast")throw new Error(`wasm backend Conv2D does not support dataFormat:'${d.dataFormat}'. 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HT;(function(r){r[r.bilinear=0]="bilinear",r[r.nearest=1]="nearest"})(HT||(HT={}));var fV;function Tst(r){fV=r.wasm.cwrap(ma,null,["number","number","number","number","array","number","number","number","number","number"])}function _st(r){let{backend:t,inputs:e,attrs:n}=r,{method:o,extrapolationValue:s,cropSize:i}=n,{image:a,boxes:u,boxInd:l}=e,c=u.shape[0],[p,m]=i,f=[c,p,m,a.shape[3]],d=t.dataIdMap.get(a.dataId),h;a.dtype!=="float32"&&(h=Tl({backend:t,inputs:{x:a},attrs:{dtype:"float32"}}),d=t.dataIdMap.get(h.dataId));let g=d.id,y=t.dataIdMap.get(u.dataId).id,b=t.dataIdMap.get(l.dataId).id,w=t.makeOutput(f,"float32"),v=t.dataIdMap.get(w.dataId).id,k=new Uint8Array(new Int32Array(a.shape).buffer);return fV(g,y,b,c,k,p,m,HT[o],s,v),h!=null&&t.disposeData(h.dataId),w}var dV={kernelName:ma,backendName:"wasm",setupFunc:Tst,kernelFunc:_st};var hV;function Est(r){hV=r.wasm.cwrap(pa,null,["number","number","number","number","number","number"])}function Ast(r){let{inputs:t,backend:e,attrs:n}=r,{x:o}=t,{axis:s,exclusive:i,reverse:a}=n,u=o.shape.length;x.assert(o.dtype==="float32"||o.dtype==="int32",()=>`cumprod does not support ${o.dtype} tensors in the WASM backend`);let l=S.getAxesPermutation([s],u),c=o;l!==null&&(c=no({inputs:{x:o},attrs:{perm:l},backend:e}));let p=S.getInnerMostAxes(1,u)[0];S.assertAxesAreInnerMostDims("cumprod",[p],u);let m=e.makeOutput(c.shape,c.dtype),f=c.shape[p],d=e.dataIdMap.get(c.dataId).id,h=e.dataIdMap.get(m.dataId).id;hV(d,i?1:0,a?1:0,f,h,Xt[o.dtype]);let g=m;if(l!==null){let y=S.getUndoAxesPermutation(l);g=no({inputs:{x:m},attrs:{perm:y},backend:e}),e.disposeData(c.dataId),e.disposeData(m.dataId)}return g}var gV={kernelName:pa,backendName:"wasm",setupFunc:Est,kernelFunc:Ast};var xV;function $st(r){xV=r.wasm.cwrap(is,null,["number","number","number","number","number","number"])}function 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Rst(r){let{backend:t,inputs:e,attrs:n}=r,{x:o}=e,{blockSize:s,dataFormat:i}=n,a=o.shape[0],u=i==="NHWC"?o.shape[1]:o.shape[2],l=i==="NHWC"?o.shape[2]:o.shape[3],c=i==="NHWC"?o.shape[3]:o.shape[1],p=u*s,m=l*s,f=c/(s*s),d=i==="NHWC"?[a,p,m,f]:[a,f,p,m],h=t.makeOutput(d,"float32"),y=t.dataIdMap.get(o.dataId).id,b=new Uint8Array(new Int32Array(x.computeStrides(o.shape)).buffer),w=new Uint8Array(new Int32Array(d).buffer),v=new Uint8Array(new Int32Array(x.computeStrides(d)).buffer),k=t.dataIdMap.get(h.dataId).id;return bV(y,s,i==="NHWC"?1:0,b,o.shape.length-1,w,v,d.length,k),h}var wV={kernelName:fa,backendName:"wasm",setupFunc:Fst,kernelFunc:Rst};var vV;function Ost(r){vV=r.wasm.cwrap(as,null,["number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number"])}function Mst(r){let{inputs:t,attrs:e,backend:n}=r,{x:o,filter:s}=t,i=n.dataIdMap.get(o.dataId).id,a=n.dataIdMap.get(s.dataId).id,{strides:u,dilations:l,pad:c,dimRoundingMode:p}=e,m=l==null?[1,1]:l,f=S.computeConv2DInfo(o.shape,s.shape,u,m,c,p,!0),d=f.filterHeight,h=f.filterWidth,g=f.padInfo.top,y=f.padInfo.right,b=f.padInfo.bottom,w=f.padInfo.left,v=f.dilationHeight,k=f.dilationWidth,E=f.strideHeight,$=f.strideWidth,D=f.inChannels,F=f.outChannels,P=f.padInfo.type==="SAME"?1:0;if(f.dataFormat!=="channelsLast")throw new Error(`wasm backend DepthwiseConv2dNative does not support dataFormat:'${f.dataFormat}'. 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BatchMatMul,vi as BatchToSpaceND,Dp as Bincount,Fp as BroadcastArgs,E1 as BroadcastTo,fb as Callback,Ny as CallbackList,io as Cast,es as Ceil,ao as ClipByValue,Rp as Complex,Rl as ComplexAbs,Ci as Concat,rs as Conv2D,Op as Conv2DBackpropFilter,ns as Conv2DBackpropInput,Ol as Conv3D,Mp as Conv3DBackpropFilterV2,Pp as Conv3DBackpropInputV2,os as Cos,ss as Cosh,ma as CropAndResize,pa as Cumprod,is as Cumsum,_y as CustomCallback,ta as DataStorage,Lp as DenseBincount,fa as DepthToSpace,as as DepthwiseConv2dNative,zp as DepthwiseConv2dNativeBackpropFilter,Bp as DepthwiseConv2dNativeBackpropInput,Vp as Diag,Ml as Dilation2D,jd as Dilation2DBackpropFilter,Kd as Dilation2DBackpropInput,qC as ENV,db as EarlyStopping,Gp as Einsum,us as Elu,Wp as EluGrad,Hd as Environment,ha as Equal,da as Erf,cs as Exp,Ii as ExpandDims,ga as Expm1,Up as FFT,Pl as Fill,xa as FlipLeftRight,ps as Floor,ms as FloorDiv,Xd as FromPixels,fs as FusedBatchNorm,Mi as FusedConv2D,Pi as FusedDepthwiseConv2D,ep as GPGPUContext,ya as GatherNd,Si as GatherV2,Db as GraphModel,ba as Greater,ds as GreaterEqual,Ty as History,Hp as IFFT,lo as Identity,qp as Imag,Ce as InputSpec,wa as IsFinite,va as IsInf,Ca as IsNan,jo as KernelBackend,Ll as LRN,jp as LRNGrad,Yh as LayerVariable,Vn as LayersModel,hs as LeakyRelu,Ia as Less,Sa as LessEqual,Kp as LinSpace,gs as Log,ka as Log1p,A1 as LogSoftmax,Na as LogicalAnd,Lu as LogicalNot,zu as LogicalOr,Hat as LowerBound,Cu as MathBackendWebGL,xs as Max,bs as MaxPool,zl as MaxPool3D,Yp as MaxPool3DGrad,Xp as MaxPoolGrad,Zp as MaxPoolWithArgmax,ys as Maximum,ws as Mean,vs as Min,Cs as Minimum,Is as MirrorPad,Ta as Mod,au as MomentumOptimizer,Jp as Multinomial,Ss as Multiply,ki as Neg,Ea as NonMaxSuppressionV3,Aa as NonMaxSuppressionV4,$a as NonMaxSuppressionV5,_a as NotEqual,o_ as OP_SCOPE_SUFFIX,ks as OneHot,Ni as OnesLike,Vr as Optimizer,oi as OptimizerConstructors,Ti as Pack,Ns as PadV2,qat as Pool,Ts as Pow,_s as Prelu,Es as Prod,lu as RMSPropOptimizer,$n as RNN,Bl as Range,nI as Rank,Qp as Real,ls as RealDiv,Da as Reciprocal,Je as Reduction,As as Relu,Ds as Relu6,_i as Reshape,$s as ResizeBilinear,em as ResizeBilinearGrad,Vl as ResizeNearestNeighbor,tm as ResizeNearestNeighborGrad,Fs as Reverse,Ga as RotateWithOffset,Rs as Round,Os as Rsqrt,Hi as SGDOptimizer,Fa as ScatterNd,rm as SearchSorted,Ei as Select,Ra as Selu,Yi as Sequential,Ps as Sigmoid,Ma as Sign,Ms as Sin,Oa as Sinh,Ai as Slice,Bs as Softmax,Pa as Softplus,$i as SpaceToBatchND,Gl as SparseFillEmptyRows,La as SparseReshape,Wl as SparseSegmentMean,Ul as SparseSegmentSum,nm as SparseToDense,Di as SplitV,Ls as Sqrt,Hl as Square,Vs as SquaredDifference,uo as Step,za as StridedSlice,om as StringNGrams,sm as StringSplit,im as StringToHashBucketFast,Gs as Sub,zs as Sum,Qr as SymbolicTensor,Ws as Tan,Us as Tanh,Lt as Tensor,pe as TensorBuffer,Yn as Tile,Ba as TopK,Va as Transform,Hs as Transpose,am as Unique,Fi as Unpack,ql as UnsortedSegmentSum,Kat as UpperBound,Wa as Variable,Ri as ZerosLike,Oi as _FusedMatMul,Te as abs,uh as acos,ch as acosh,J as add,RI as addN,Yu as all,Jl as any,Xs as argMax,ph as argMin,mh as asin,fh as asinh,dh as atan,hh as atan2,gh as atanh,Ka as avgPool,xh as avgPool3d,Q_ as backend,S as backend_util,YH as basicLSTMCell,xo as batchNorm,LI as batchNorm2d,zI as batchNorm3d,BI as batchNorm4d,ja as batchToSpaceND,yh as bincount,Z1t as booleanMaskAsync,VI as broadcastArgs,Xa as broadcastTo,Sr as broadcast_util,Fx as browser,Ct as buffer,_7 as callbacks,Z as cast,bh as ceil,br as clipByValue,kn as clone,zn as complex,se as concat,GI as concat1d,WI as concat2d,UI as concat3d,HI as concat4d,M$ as constraints,Qu as conv1d,pn as conv2d,tc as conv2dTranspose,wh as conv3d,qI as conv3dTranspose,tlt as copyRegisteredKernels,Ya as cos,ec as cosh,Zx as cosineWindow,Ql as cumprod,rc as cumsum,mn as customGrad,DF as data,KI as denseBincount,FI as deprecationWarn,vh as depthToSpace,Zs as depthwiseConv2d,D7 as deregisterOp,Yl as device_util,Nq as diag,Ch as dilation2d,Ipt as disableDeprecationWarnings,_t as dispose,Spt as disposeVariables,ct as div,Ih as divNoNan,jI as dot,IE as dropout,XI as einsum,Js as elu,Cpt as enableDebugMode,vpt as enableProdMode,SE as enclosingPowerOfTwo,go as engine,V as env,kr as equal,Sh as erf,Nh as euclideanNorm,Ye as exp,fr as expandDims,Th as expm1,vm as eye,sl as fft,Qs as fill,Apt as findBackend,$pt as findBackendFactory,ti as floor,Xu as floorDiv,iL as forceHalfFloat,Co as fused,bo as gather,vE as gatherND,Rx as gather_util,_pt as getBackend,XC as getGradient,Zd as getKernel,bx as getKernelsForBackend,_at as getThreadsCount,CT as gpgpu_util,pK as grad,mK as grads,Ge as greater,Tn as greaterEqual,Wi as ifft,nc as imag,dn as image,V_t as inTopKAsync,P$ as initializers,HS as input,Pr as io,gc as irfft,tS as isFinite,eS as isInf,_h as isNaN,Pe as keep,Gr as kernel_impls,hD as layers,Za as leakyRelu,oc as less,_n as lessEqual,uA as linalg,rS as linspace,kZ as loadGraphModel,z8 as loadLayersModel,Eh as localResponseNormalization,wr as log,Ja as log1p,nS as logSigmoid,sc as logSoftmax,Ah as logSumExp,Tr as logicalAnd,Qa as logicalNot,ic as logicalOr,oS as logicalXor,yOt as losses,sS as lowerBound,zt as matMul,R_ as math,Fr as max,tl as maxPool,$h as maxPool3d,iS as maxPoolWithArgmax,fn as maximum,be as mean,lh as memory,$K as meshgrid,gD as metrics,tu as min,ei as minimum,Dh as mirrorPad,Fh as mod,P8 as model,xD as models,Cm as moments,l_t as movingAverage,M as mul,PK as multiRNNCell,aS as multinomial,Zt as neg,Hh as nextFrame,eu as norm,vo as notEqual,js as oneHot,lr as ones,dr as onesLike,N as op,GK as outerProduct,Zr as pad,HK as pad1d,KK as pad2d,XK as pad3d,ZK as pad4d,lS as pool,Yr as pow,rl as prelu,vI as print,ac as prod,kpt as profile,sj as rand,fj as randomGamma,Kx as randomNormal,ri as randomUniform,nl as range,Tpt as ready,ru as real,Rh as reciprocal,ym as registerBackend,B8 as registerCallbackConstructor,D1 as registerGradient,Bu as registerKernel,$7 as registerOp,yD as regularizers,_r as relu,cc as relu6,Ept as removeBackend,R as reshape,ir as reverse,Cj as reverse1d,Sj as reverse2d,Nj as reverse3d,_j as reverse4d,il as rfft,pc as round,mc as rsqrt,pt as scalar,bE as scatterND,ih as scatter_util,Gx as searchSorted,fc as selu,Oh as separableConv2d,L8 as sequential,et as serialization,yH as setBackend,Dpt as setPlatform,Tat as setThreadsCount,kat as setWasmPath,Nat as setWasmPaths,MN as setWebGLContext,xS as setdiff1dAsync,Lr as sigmoid,Mh as sign,HRt as signal,dc as sin,hc as sinh,Ft as slice,Ph as slice1d,jx as slice2d,Lh as slice3d,Im as slice4d,Ve as slice_util,ol as softmax,wo as softplus,el as spaceToBatchND,Uh as sparse,Yx as sparseToDense,BRt as spectral,ur as split,ye as sqrt,Wt as square,xc as squaredDifference,zr as squeeze,Ze as stack,ni as step,zh as stridedSlice,ay as string,lt as sub,mt as sum,qu as sumOutType,Bh as tan,Ys as tanh,$r as tensor,Fe as tensor1d,Ui as tensor2d,TI as tensor3d,Qj as tensor4d,t6 as tensor5d,e6 as tensor6d,fo as tensor_util,Z_ as test_util,B as tidy,Nr as tile,Npt as time,Vh as topk,Ic as train,Mt as transpose,yc as truncatedNormal,Sm as unique,Qat as unregisterGradient,Jat as unregisterKernel,Gh as unsortedSegmentSum,vr as unstack,ar as upcastType,yS as upperBound,x as util,fK as valueAndGrad,dK as valueAndGrads,bS as variable,zx as variableGrads,zat as version,ZD as version_converter,J_ as version_core,jm as version_layers,Eat as version_wasm,sL as version_webgl,cNe as webgl,dd as webgl_util,Ee as where,Wh as whereAsync,we as zeros,St as zerosLike};
/**
* @license
* Copyright 2017 Google LLC. All Rights Reserved.
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
* =============================================================================
*/
/**
* @license
* Copyright 2018 Google LLC
*
* Use of this source code is governed by an MIT-style
* license that can be found in the LICENSE file or at
* https://opensource.org/licenses/MIT.
* =============================================================================
*/
/**
* @license
* Copyright 2018 Google LLC. All Rights Reserved.
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
*
* =============================================================================
*/
/**
* @license
* Copyright 2018 Google LLC. All Rights Reserved.
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
* =============================================================================
*/
/**
* @license
* Copyright 2019 Google LLC
*
* Use of this source code is governed by an MIT-style
* license that can be found in the LICENSE file or at
* https://opensource.org/licenses/MIT.
* =============================================================================
*/
/**
* @license
* Copyright 2019 Google LLC. All Rights Reserved.
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
*
* =============================================================================
*/
/**
* @license
* Copyright 2019 Google LLC. All Rights Reserved.
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
* =============================================================================
*/
/**
* @license
* Copyright 2020 Google Inc. All Rights Reserved.
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
* =============================================================================
*/
/**
* @license
* Copyright 2020 Google LLC
*
* Use of this source code is governed by an MIT-style
* license that can be found in the LICENSE file or at
* https://opensource.org/licenses/MIT.
* =============================================================================
*/
/**
* @license
* Copyright 2020 Google LLC. All Rights Reserved.
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
* =============================================================================
*/
/**
* @license
* Copyright 2020 Google LLC. All Rights Reserved.
* Licensed under the Apache License, Version 2.0 (the License);
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an AS IS BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
* =============================================================================
*/
/**
* @license
* Copyright 2021 Google LLC. All Rights Reserved.
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
* =============================================================================
*/
/**
* @license
* Copyright 2021 Google LLC. All Rights Reserved.
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* https://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
* =============================================================================
*/
/**
* @license
* Copyright 2022 Google Inc. All Rights Reserved.
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
* =============================================================================
*/
/**
* @license
* Copyright 2022 Google LLC
*
* Use of this source code is governed by an MIT-style
* license that can be found in the LICENSE file or at
* https://opensource.org/licenses/MIT.
* =============================================================================
*/
/**
* @license
* Copyright 2022 Google LLC. All Rights Reserved.
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
* =============================================================================
*/
/**
* @license
* Copyright 2022 Google LLC. All Rights Reserved.
* Licensed under the Apache License, Version 2.0 (the 'License');
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an 'AS IS' BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
* =============================================================================
*/
/**
* @license
* Copyright 2018 Google LLC. All Rights Reserved.
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
* =============================================================================
*/
/** @license See the LICENSE file. */
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