ambiser
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human
mirror of https://github.com/vladmandic/human
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human/dist/tfjs.esm.js

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/*
Human library
homepage: <https://github.com/vladmandic/human>
author: <https://github.com/vladmandic>'
*/
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o=++this.pendingBackendInitId,s=n.then(a=>o<this.pendingBackendInitId?!1:(this.registry[e]=a,this.pendingBackendInit=null,!0)).catch(a=>(o<this.pendingBackendInitId||(this.pendingBackendInit=null,console.warn(`Initialization of backend ${e} failed`),console.warn(a.stack||a.message)),!1));return this.pendingBackendInit=s,{success:s,asyncInit:!0}}else return this.registry[e]=n,{success:!0,asyncInit:!1}}catch(n){return console.warn(`Initialization of backend ${e} failed`),console.warn(n.stack||n.message),{success:!1,asyncInit:!1}}}removeBackend(e){if(!(e in this.registryFactory))throw new Error(`${e} backend not found in registry`);this.backendName===e&&this.pendingBackendInit!=null&&this.pendingBackendInitId++,e in this.registry&&(this.disposeRegisteredKernels(e),this.registry[e].dispose(),delete this.registry[e]),delete this.registryFactory[e],this.backendName===e&&(this.pendingBackendInit=null,this.backendName=null,this.backendInstance=null)}getSortedBackends(){if(Object.keys(this.registryFactory).length===0)throw new Error("No backend found in registry.");return Object.keys(this.registryFactory).sort((e,t)=>this.registryFactory[t].priority-this.registryFactory[e].priority)}initializeBackendsAndReturnBest(){let e=this.getSortedBackends();for(let t=0;t<e.length;t++){let n=e[t],{success:o,asyncInit:s}=this.initializeBackend(n);if(s||o)return{name:n,asyncInit:s}}throw new Error("Could not initialize any backends, all backend initializations failed.")}moveData(e,t){let n=this.state.tensorInfo.get(t),o=n.backend,s=this.readSync(t);o.disposeData(t),n.backend=e,e.move(t,s,n.shape,n.dtype),this.shouldCheckForMemLeaks()&&this.state.numDataMovesStack[this.state.numDataMovesStack.length-1]++}tidy(e,t){let n=null;if(t==null){if(typeof e!="function")throw new Error("Please provide a function to 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this.ENV.getBool("IS_TEST")}checkKernelForMemLeak(e,t,n){let o=this.backend.numDataIds(),s=0;n.forEach(l=>{s+=l.dtype==="complex64"?3:1});let a=this.state.numDataMovesStack[this.state.numDataMovesStack.length-1],i=o-t-s-a;if(i>0)throw new Error(`Backend '${this.backendName}' has an internal memory leak (${i} data ids) after running '${e}'`)}runKernelFunc(e,t,n,o,s,a,i){let l,u=[],c=this.isTapeOn();o==null&&(o=this.state.activeScope!=null?this.state.activeScope.name:"");let p=this.state.numBytes,m=this.state.numTensors;this.shouldCheckForMemLeaks()&&this.state.numDataMovesStack.push(0);let f;this.backendName==null&&this.backend;let d=wm(o,this.backendName),h;if(d!=null)f=()=>{let x=this.backend.numDataIds();h=d.kernelFunc({inputs:t,attrs:s,backend:this.backend});let b=Array.isArray(h)?h:[h];this.shouldCheckForMemLeaks()&&this.checkKernelForMemLeak(o,x,b);let w=b.map(_=>{if(_.rank!=null)return _;let{dataId:k,shape:E,dtype:T}=_;return this.makeTensorFromDataId(k,E,T)});if(c){let _=this.getTensorsForGradient(o,t,w);if(_==null){i==null&&(i=[]);let k=w.filter((E,T)=>i[T]);_=(a||[]).slice().concat(k)}u=this.saveTensorsForBackwardMode(_)}return w};else{if(e==null)throw new Error(`Error running ${o}: Neither modular kernel nor forward func passed`);let x=b=>{!c||(u=b.map(w=>this.keep(this.clone(w))))};f=()=>{let b=this.backend.numDataIds();h=this.tidy(()=>e(this.backend,x));let w=Array.isArray(h)?h:[h];return this.shouldCheckForMemLeaks()&&this.checkKernelForMemLeak(o,b,w),w}}let g;return this.scopedRun(()=>this.state.kernelDepth++,()=>this.state.kernelDepth--,()=>{!this.ENV.getBool("DEBUG")&&!this.state.profiling?l=f():(g=this.profiler.profileKernel(o,t,()=>f()),this.ENV.getBool("DEBUG")&&this.profiler.logKernelProfile(g),l=g.outputs)}),c&&this.addTapeNode(o,t,l,n,u,s),this.state.profiling&&this.state.activeProfile.kernels.push({name:o,bytesAdded:this.state.numBytes-p,totalBytesSnapshot:this.state.numBytes,tensorsAdded:this.state.numTensors-m,totalTensorsSnapshot:this.state.numTensors,inputShapes:Object.keys(t).map(x=>t[x]!=null?t[x].shape:null),outputShapes:l.map(x=>x.shape),kernelTimeMs:g.timeMs,extraInfo:g.extraInfo}),Array.isArray(h)?l:l[0]}saveTensorsForBackwardMode(e){return e.map(n=>this.keep(this.clone(n)))}getTensorsForGradient(e,t,n){let o=jh(e);if(o!=null){let s=o.inputsToSave||[],a=o.outputsToSave||[],i;o.saveAllInputs?(A(Array.isArray(t),()=>"saveAllInputs is true, expected inputs to be an array."),i=Object.keys(t).map(u=>t[u])):i=s.map(u=>t[u]);let l=n.filter((u,c)=>a[c]);return i.concat(l)}return null}makeTensor(e,t,n,o){if(e==null)throw new Error("Values passed to engine.makeTensor() are null");n=n||"float32",o=o||this.backend;let s=e;n==="string"&&as(e[0])&&(s=e.map(l=>ol(l)));let a=o.write(s,t,n),i=new R(t,n,a,this.nextTensorId());if(this.incRef(i,o),n==="string"){let l=this.state.tensorInfo.get(a),u=Db(s);this.state.numBytes+=u-l.bytes,l.bytes=u}return i}makeTensorFromDataId(e,t,n,o){n=n||"float32";let s=new R(t,n,e,this.nextTensorId());return this.incRef(s,o),s}makeVariable(e,t=!0,n,o){n=n||this.nextVariableId().toString(),o!=null&&o!==e.dtype&&(e=e.cast(o));let s=new sl(e,t,n,this.nextTensorId());if(this.state.registeredVariables[s.name]!=null)throw new Error(`Variable with name ${s.name} was already registered`);return this.state.registeredVariables[s.name]=s,this.incRef(s,this.backend),s}incRef(e,t){let 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n=v(r,"x","batchToSpaceND"),o=e.reduce((i,l)=>i*l);A(n.rank>=1+e.length,()=>`input rank is ${n.rank} but should be > than blockShape.length ${e.length}`),A(t.length===e.length,()=>`crops.length is ${t.length} but should be equal to blockShape.length ${e.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 ${e.join(" * ")} === ${o}`);let s={x:n},a={blockShape:e,crops:t};return D.runKernel(sa,s,a)}var Ca=S({batchToSpaceND_:Nj});function JS(r){let e;return r.rank===0||r.rank===1?e=z(r,[1,1,1,r.size]):r.rank===2?e=z(r,[1,1,r.shape[0],r.shape[1]]):r.rank===3?e=z(r,[1,r.shape[0],r.shape[1],r.shape[2]]):e=r,e}function Tj(r,e,t,n,o,s){s==null&&(s=.001);let a=v(r,"x","batchNorm"),i=v(e,"mean","batchNorm"),l=v(t,"variance","batchNorm"),u;o!=null&&(u=v(o,"scale","batchNorm"));let c;n!=null&&(c=v(n,"offset","batchNorm")),A(i.rank===l.rank,()=>"Batch normalization gradient requires mean and variance to have equal 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${u.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}.`),zo(a,i,l,c,u,s)}var ww=S({batchNorm2d_:Ej});function Aj(r,e,t,n,o,s){let a=v(r,"x","batchNorm"),i=v(e,"mean","batchNorm"),l=v(t,"variance","batchNorm"),u;o!=null&&(u=v(o,"scale","batchNorm"));let c;return n!=null&&(c=v(n,"offset","batchNorm")),A(a.rank===3,()=>`Error in batchNorm3D: x must be rank 3 but got rank ${a.rank}.`),A(i.rank===3||i.rank===1,()=>`Error in batchNorm3D: mean must be rank 3 or rank 1 but got rank ${i.rank}.`),A(l.rank===3||l.rank===1,()=>`Error in batchNorm3D: variance must be rank 3 or rank 1 but got rank ${l.rank}.`),u!=null&&A(u.rank===3||u.rank===1,()=>`Error in batchNorm3D: scale must be rank 3 or rank 1 but got rank ${u.rank}.`),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}.`),zo(a,i,l,c,u,s)}var _w=S({batchNorm3d_:Aj});function Dj(r,e,t,n,o,s){let a=v(r,"x","batchNorm"),i=v(e,"mean","batchNorm"),l=v(t,"variance","batchNorm"),u;o!=null&&(u=v(o,"scale","batchNorm"));let c;return n!=null&&(c=v(n,"offset","batchNorm")),A(a.rank===4,()=>`Error in batchNorm4D: x must be rank 4 but got rank ${a.rank}.`),A(i.rank===4||i.rank===1,()=>`Error in batchNorm4D: mean must be rank 4 or rank 1 but got rank ${i.rank}.`),A(l.rank===4||l.rank===1,()=>`Error in batchNorm4D: variance must be rank 4 or rank 1 but got rank ${l.rank}.`),u!=null&&A(u.rank===4||u.rank===1,()=>`Error in batchNorm4D: scale must be rank 4 or rank 1 but got rank ${u.rank}.`),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}.`),zo(a,i,l,c,u,s)}var vw=S({batchNorm4d_:Dj});function $j(r,e,t){let n=v(r,"x","bincount"),o=v(e,"weights","bincount");A(n.dtype==="int32",()=>`Error in bincount: input dtype must be int32, but got ${n.dtype}`),A(t>=0,()=>`size must be non-negative, but got ${t}.`),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: ${o.shape}.`);let s={x:n,weights:o},a={size:t};return D.runKernel(Hl,s,a)}var kw=S({bincount_:$j});function Rj(r,e){let t=v(r,"broadcastTo","x"),n=t.shape;if(e.some(u=>!(u>0)||u%1!=0))throw new Error(`broadcastTo(): Invalid broadcast shape [${e}].`);if(e.length<t.rank)throw new Error(`broadcastTo(): shape.length=${e.length} < input.rank=${t.rank}.`);if(e.length>t.rank){let u=t.shape.slice();for(;u.length<e.length;)u.unshift(1);t=z(t,u)}let o=t.shape,s=Array.from(e);for(let u=e.length-1;u>=0;u--)if(o[u]===e[u])s[u]=1;else if(t.shape[u]!==1)throw new Error(`broadcastTo(): [${n}] cannot be broadcast to [${e}].`);if(s.map((u,c)=>u>1?c:-1).filter(u=>u>=0).length===0)return Pn(t);let i={x:t},l={reps:s};return D.runKernel(_n,i,l)}var cl=S({broadcastTo_:Rj});function Fj(r){let t={x:v(r,"x","ceil")};return D.runKernel(ti,t)}var Pm=S({ceil_:Fj});function Oj(r,e,t){let n=v(r,"x","clipByValue");A(e<=t,()=>`Error in clip: min (${e}) must be less than or equal to max (${t}).`);let o={x:n},s={clipValueMin:e,clipValueMax:t};return D.runKernel(Fn,o,s)}var ar=S({clipByValue_:Oj});function Pj(r){return et(r,0)}var Cw=S({concat1d_:Pj});function Mj(r,e){return et(r,e)}var Iw=S({concat2d_:Mj});function Lj(r,e){return et(r,e)}var Sw=S({concat3d_:Lj});function zj(r,e){return et(r,e)}var Nw=S({concat4d_:zj});function Bj(r,e,t,n,o="NHWC",s=[1,1],a){let i=v(r,"x","conv2d"),l=v(e,"filter","conv2d"),u=i,c=!1;i.rank===3&&(c=!0,u=z(i,[1,i.shape[0],i.shape[1],i.shape[2]])),A(u.rank===4,()=>`Error in conv2d: input must be rank 4, but got rank ${u.rank}.`),A(l.rank===4,()=>`Error in conv2d: filter must be rank 4, but got rank ${l.rank}.`),a!=null&&A(it(n),()=>`Error in conv2d: pad must be an integer when using, dimRoundingMode ${a} but got pad ${n}.`);let p=o==="NHWC"?u.shape[3]:u.shape[1];A(p===l.shape[2],()=>`Error in conv2d: depth of input (${p}) must match input depth for filter ${l.shape[2]}.`),A(Mt(t,s),()=>`Error in conv2D: Either strides or dilations must be 1. Got strides ${t} and dilations '${s}'`);let m={x:u,filter:l},f={strides:t,pad:n,dataFormat:o,dilations:s,dimRoundingMode:a},d=D.runKernel(Jn,m,f);return c?z(d,[d.shape[1],d.shape[2],d.shape[3]]):d}var Kr=S({conv2d_:Bj});function Vj(r,e,t,n,o="NWC",s=1,a){let i=v(r,"x","conv1d"),l=v(e,"filter","conv1d"),u=i,c=!1;i.rank===2&&(c=!0,u=z(i,[1,i.shape[0],i.shape[1]])),A(u.rank===3,()=>`Error in conv1d: input must be rank 3, but got rank ${u.rank}.`),A(l.rank===3,()=>`Error in conv1d: filter must be rank 3, but got rank ${l.rank}.`),a!=null&&A(it(n),()=>`Error in conv1d: pad must be an integer when using, dimRoundingMode ${a} but got pad ${n}.`),A(u.shape[2]===l.shape[1],()=>`Error in conv1d: depth of input (${u.shape[2]}) must match input depth for filter ${l.shape[1]}.`),A(Mt(t,s),()=>`Error in conv1D: Either stride or dilation must be 1. Got stride ${t} and dilation '${s}'`),A(o==="NWC",()=>`Error in conv1d: got dataFormat of ${o} but only NWC is currently supported.`);let p=z(l,[1,l.shape[0],l.shape[1],l.shape[2]]),m=z(u,[u.shape[0],1,u.shape[1],u.shape[2]]),g=Kr(m,p,[1,t],n,"NHWC",[1,s],a);return c?z(g,[g.shape[2],g.shape[3]]):z(g,[g.shape[0],g.shape[2],g.shape[3]])}var Iu=S({conv1d_:Vj});function Wj(r,e,t,n,o,s="NHWC",a){A(r.length===e.rank,()=>`Length of inShape (${r.length}) and rank of dy (${e.rank}) must match`);let i=r,l=e,u=!1;e.rank===3&&(u=!0,l=z(e,[1,e.shape[0],e.shape[1],e.shape[2]]),i=[1,r[0],r[1],r[2]]),A(i.length===4,()=>`Error in conv2dDerInput: inShape must be length 4, but got length ${i.length}.`),A(l.rank===4,()=>`Error in conv2dDerInput: dy must be rank 4, but got rank ${l.rank}`),A(t.rank===4,()=>`Error in conv2dDerInput: filter must be rank 4, but got rank ${t.rank}`);let c=s==="NHWC"?i[3]:i[1],p=s==="NHWC"?l.shape[3]:l.shape[1];A(c===t.shape[2],()=>`Error in conv2dDerInput: depth of input (${c}) must match input depth for filter ${t.shape[2]}.`),A(p===t.shape[3],()=>`Error in conv2dDerInput: depth of output (${p}) must match output depth for filter ${t.shape[3]}.`),a!=null&&A(it(o),()=>`Error in conv2dDerInput: pad must be an integer when using, dimRoundingMode ${a} but got pad ${o}.`);let m={dy:l,filter:t},f={strides:n,pad:o,dataFormat:s,dimRoundingMode:a,inputShape:i},d=D.runKernel(Qn,m,f);return u?z(d,[d.shape[1],d.shape[2],d.shape[3]]):d}var Kc=S({conv2DBackpropInput_:Wj});function jj(r,e,t,n,o,s){let a=v(r,"x","conv2dTranspose"),i=v(e,"filter","conv2dTranspose");return Kc(t,a,i,n,o,"NHWC",s)}var Su=S({conv2dTranspose_:jj});function Gj(r,e,t,n,o="NDHWC",s=[1,1,1]){let a=v(r,"x","conv3d"),i=v(e,"filter","conv3d"),l=a,u=!1;a.rank===4&&(u=!0,l=z(a,[1,a.shape[0],a.shape[1],a.shape[2],a.shape[3]])),A(l.rank===5,()=>`Error in conv3d: input must be rank 5, but got rank ${l.rank}.`),A(i.rank===5,()=>`Error in conv3d: filter must be rank 5, but got rank ${i.rank}.`),A(l.shape[4]===i.shape[3],()=>`Error in conv3d: depth of input (${l.shape[4]}) must match input depth for filter ${i.shape[3]}.`),A(Mt(t,s),()=>`Error in conv3D: Either strides or dilations must be 1. Got strides ${t} and dilations '${s}'`),A(o==="NDHWC",()=>`Error in conv3d: got dataFormat of ${o} but only NDHWC is currently supported.`);let c={x:l,filter:i},p={strides:t,pad:n,dataFormat:o,dilations:s},m=D.runKernel(aa,c,p);return u?z(m,[m.shape[1],m.shape[2],m.shape[3],m.shape[4]]):m}var Mm=S({conv3d_:Gj});function Uj(r,e,t,n,o){A(r.length===e.rank,()=>`Length of inShape (${r.length}) and rank of dy (${e.rank}) must match`);let s=r,a=e,i=!1;e.rank===4&&(i=!0,a=z(e,[1,e.shape[0],e.shape[1],e.shape[2],e.shape[3]]),s=[1,r[0],r[1],r[2],r[3]]);let l=s[4],u=a.shape[4];A(s.length===5,()=>`Error in conv3dDerInput: inShape must be length 5, but got length ${s.length}.`),A(a.rank===5,()=>`Error in conv3dDerInput: dy must be rank 5, but got rank ${a.rank}`),A(t.rank===5,()=>`Error in conv3dDerInput: filter must be rank 5, but got rank ${t.rank}`),A(l===t.shape[3],()=>`Error in conv3dDerInput: depth of input (${l}) must match input depth for filter ${t.shape[3]}.`),A(u===t.shape[4],()=>`Error in conv3dDerInput: depth of output (${u}) must match output depth for filter ${t.shape[4]}.`);let c={dy:a,filter:t},p={pad:o,strides:n,inputShape:s},m=D.runKernel(Zl,c,p);return i?z(m,[m.shape[1],m.shape[2],m.shape[3],m.shape[4]]):m}var ig=S({conv3DBackpropInput_:Uj});function qj(r,e,t,n,o){let s=v(r,"x","conv3dTranspose"),a=v(e,"filter","conv3dTranspose");return ig(t,s,a,n,o)}var Hj=S({conv3dTranspose_:qj});function Kj(r){let t={x:v(r,"x","cos")};return D.runKernel(eo,t)}var Ia=S({cos_:Kj});function Xj(r){let t={x:v(r,"x","cosh")};return D.runKernel(ri,t)}var Nu=S({cosh_:Xj});function Yj(r,e=0,t=!1,n=!1){let s={x:v(r,"x","cumsum")},a={axis:e,exclusive:t,reverse:n};return D.runKernel(to,s,a)}var Tu=S({cumsum_:Yj});function Zj(r,e,t,n=!1){let o=v(r,"x","denseBincount"),s=v(e,"weights","denseBincount");A(o.dtype==="int32",()=>`Error in denseBincount: input dtype must be int32, but got ${o.dtype}`),A(o.rank<=2,()=>`Error in denseBincount: input must be at most rank 2, but got rank ${o.rank}.`),A(t>=0,()=>`size must be non-negative, but got ${t}.`),A(s.size===o.size||s.size===0,()=>`Error in denseBincount: weights must have the same shape as x or 0-length, but got x shape: ${o.shape}, weights shape: ${s.shape}.`);let a={x:o,weights:s},i={size:t,binaryOutput:n};return D.runKernel(Jl,a,i)}var Tw=S({denseBincount_:Zj});function Jj(r,e,t="NHWC"){let n=v(r,"x","depthToSpace"),o=t==="NHWC"?n.shape[1]:n.shape[2],s=t==="NHWC"?n.shape[2]:n.shape[3],a=t==="NHWC"?n.shape[3]:n.shape[1];A(o*e>=0,()=>`Negative dimension size caused by overflow when multiplying
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${n.shape}`),A(s*e>=0,()=>`Negative dimension size caused by overflow when multiplying
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c?z(f,[f.shape[1],f.shape[2],f.shape[3]]):f}var Ln=S({depthwiseConv2d_:Qj});function eG(r){let t={x:v(r,"x","diag")};return D.runKernel(tu,t)}var tG=S({diag_:eG});function rG(r,e,t,n,o=[1,1],s="NHWC"){let a=v(r,"x","dilation2d"),i=v(e,"filter","dilation2d");A(a.rank===3||a.rank===4,()=>`Error in dilation2d: input must be rank 3 or 4, but got rank ${a.rank}.`),A(i.rank===3,()=>`Error in dilation2d: filter must be rank 3, but got rank ${i.rank}.`),A(s==="NHWC",()=>`Error in dilation2d: Only NHWC is currently supported, but got dataFormat of ${s}`);let l=a,u=!1;a.rank===3&&(l=z(a,[1,a.shape[0],a.shape[1],a.shape[2]]),u=!0);let c={x:l,filter:i},p={strides:t,pad:n,dilations:o},m=D.runKernel(la,c,p);return u?z(m,[m.shape[1],m.shape[2],m.shape[3]]):m}var zm=S({dilation2d_:rG});function nG(r,e){let t=r.length,n=[];for(let o=0;o<t;o++){let s=t-1-o,a=r[s]||1;(e[e.length-1-o]||1)>1&&a===1&&n.unshift(s)}return n}function St(r,e){let t=[];for(let n=0;n<e.length;n++){let 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O=ce(F,Ge(Ge(F,i),T));o=et([Oe(o,[0,0],[t,u]),O],1)}return[i,s,o]}),De([c,p,m])}return!e&&t>n&&(o=Oe(o,[0,0],[t,n]),s=Oe(s,[0,0],[n,n])),[o,s]})}var FN=S({qr_:Pq});var Ut;(function(r){r[r.NONE=0]="NONE",r[r.MEAN=1]="MEAN",r[r.SUM=2]="SUM",r[r.SUM_BY_NONZERO_WEIGHTS=3]="SUM_BY_NONZERO_WEIGHTS"})(Ut||(Ut={}));function Mq(r,e,t=Ut.SUM_BY_NONZERO_WEIGHTS){let n=v(r,"losses","computeWeightedLoss"),o=null;e!=null&&(o=v(e,"weights","computeWeightedLoss"));let s=o==null?n:P(n,o);if(t===Ut.NONE)return s;if(t===Ut.SUM)return we(s);if(t===Ut.MEAN){if(o==null)return yt(s);{let a=n.size/o.size,i=fe(we(s),we(o));return a>1?fe(i,ue(a)):i}}if(t===Ut.SUM_BY_NONZERO_WEIGHTS){if(o==null)return fe(we(s),ue(n.size));{let a=P(o,Tr(n.shape)),i=ne(we(kn(a,ue(0))),"float32");return fe(we(s),i)}}throw Error(`Unknown reduction: ${t}`)}var Ar=S({computeWeightedLoss_:Mq});function Lq(r,e,t,n=Ut.SUM_BY_NONZERO_WEIGHTS){let o=v(r,"labels","absoluteDifference"),s=v(e,"predictions","absoluteDifference"),a=null;t!=null&&(a=v(t,"weights","absoluteDifference")),We(o.shape,s.shape,"Error in absoluteDifference: ");let i=Et(ce(o,s));return Ar(i,a,n)}var ON=S({absoluteDifference_:Lq});function zq(r,e,t,n,o=Ut.SUM_BY_NONZERO_WEIGHTS){let s=v(r,"labels","cosineDistance"),a=v(e,"predictions","cosineDistance"),i=null;n!=null&&(i=v(n,"weights","cosineDistance")),We(s.shape,a.shape,"Error in cosineDistance: ");let l=ue(1),u=ce(l,we(P(s,a),t,!0));return Ar(u,i,o)}var PN=S({cosineDistance_:zq});function Bq(r,e,t,n=Ut.SUM_BY_NONZERO_WEIGHTS){let o=v(r,"labels","hingeLoss"),s=v(e,"predictions","hingeLoss"),a=null;t!=null&&(a=v(t,"weights","hingeLoss")),We(o.shape,s.shape,"Error in hingeLoss: ");let i=ue(1);o=ce(P(ue(2),o),i);let l=Er(ce(i,P(o,s)));return Ar(l,a,n)}var MN=S({hingeLoss_:Bq});function Vq(r,e,t,n=1,o=Ut.SUM_BY_NONZERO_WEIGHTS){let s=v(r,"labels","huberLoss"),a=v(e,"predictions","huberLoss"),i=null;t!=null&&(i=v(t,"weights","huberLoss")),We(s.shape,a.shape,"Error in huberLoss: ");let l=ue(n),u=Et(ce(a,s)),c=Bn(u,l),p=ce(u,c),m=Q(P(ue(.5),Me(c)),P(l,p));return Ar(m,i,o)}var LN=S({huberLoss_:Vq});function Wq(r,e,t,n=1e-7,o=Ut.SUM_BY_NONZERO_WEIGHTS){let s=v(r,"labels","logLoss"),a=v(e,"predictions","logLoss"),i=null;t!=null&&(i=v(t,"weights","logLoss")),We(s.shape,a.shape,"Error in logLoss: ");let l=ue(1),u=ue(n),c=qe(P(s,lr(Q(a,u)))),p=P(ce(l,s),lr(Q(ce(l,a),u))),m=ce(c,p);return Ar(m,i,o)}var zN=S({logLoss_:Wq});function jq(r,e,t,n=Ut.SUM_BY_NONZERO_WEIGHTS){let o=v(r,"labels","meanSquaredError"),s=v(e,"predictions","meanSquaredError"),a=null;t!=null&&(a=v(t,"weights","meanSquaredError")),We(o.shape,s.shape,"Error in meanSquaredError: ");let i=Pa(o,s);return Ar(i,a,n)}var BN=S({meanSquaredError_:jq});function Gq(r,e){let t=v(r,"labels","sigmoidCrossEntropyWithLogits"),n=v(e,"logits","sigmoidCrossEntropyWithLogits");We(t.shape,n.shape,"Error in sigmoidCrossEntropyWithLogits: ");let o=Er(n),s=P(n,t),a=Au(Qt(qe(Et(n))));return Q(ce(o,s),a)}function Uq(r,e,t,n=0,o=Ut.SUM_BY_NONZERO_WEIGHTS){let s=v(r,"multiClassLabels","sigmoidCrossEntropy"),a=v(e,"logits","sigmoidCrossEntropy"),i=null;if(t!=null&&(i=v(t,"weights","sigmoidCrossEntropy")),We(s.shape,a.shape,"Error in sigmoidCrossEntropy: "),n>0){let u=ue(n),c=ue(1),p=ue(.5);s=Q(P(s,ce(c,u)),P(p,u))}let l=Gq(s,a);return Ar(l,i,o)}var VN=S({sigmoidCrossEntropy_:Uq});function qq(r,e,t=-1){if(t===-1&&(t=e.rank-1),t!==e.rank-1)throw Error(`Softmax cross entropy along a non-last dimension is not yet supported. Labels / logits was rank ${e.rank} and dim was ${t}`);return Yr((o,s,a)=>{let l=Um(s,[t],!0),u=ce(ne(s,"float32"),l);a([o,u]);let c=qe(P(u,o));return{value:we(c,[t]),gradFunc:(f,d)=>{let[h,g]=d,x=Vo(f.shape,[t]);return[P(z(f,x),ce(ne(h,"float32"),Qt(g))),P(z(f,x),ce(Qt(g),ne(h,"float32")))]}}})(r,e)}function Hq(r,e,t,n=0,o=Ut.SUM_BY_NONZERO_WEIGHTS){let s=v(r,"onehotLabels","softmaxCrossEntropy"),a=v(e,"logits","softmaxCrossEntropy"),i=null;if(t!=null&&(i=v(t,"weights","softmaxCrossEntropy")),We(s.shape,a.shape,"Error in softmaxCrossEntropy: "),n>0){let u=ue(n),c=ue(1),p=ue(s.shape[1]);s=Q(P(s,ce(c,u)),fe(u,p))}let l=qq(s,a);return Ar(l,i,o)}var WN=S({softmaxCrossEntropy_:Hq});var Kq={fft:Fa,ifft:Bi,rfft:Oa,irfft:Vu},Xq={hammingWindow:yN,hannWindow:gg,frame:xg,stft:bN},$s={flipLeftRight:_N,resizeNearestNeighbor:bg,resizeBilinear:yg,rotateWithOffset:vN,cropAndResize:wN,nonMaxSuppression:kN,nonMaxSuppressionAsync:SN,nonMaxSuppressionWithScore:NN,nonMaxSuppressionWithScoreAsync:TN,nonMaxSuppressionPadded:EN,nonMaxSuppressionPaddedAsync:AN},v_={bandPart:DN,gramSchmidt:$N,qr:FN},Yq={absoluteDifference:ON,computeWeightedLoss:Ar,cosineDistance:PN,hingeLoss:MN,huberLoss:LN,logLoss:zN,meanSquaredError:BN,sigmoidCrossEntropy:VN,softmaxCrossEntropy:WN};var Lr=class extends og{minimize(e,t=!1,n){let{value:o,grads:s}=this.computeGradients(e,n);if(n!=null){let a=n.map(i=>({name:i.name,tensor:s[i.name]}));this.applyGradients(a)}else this.applyGradients(s);return De(s),t?o:(o.dispose(),null)}get iterations(){return this.iterations_==null&&(this.iterations_=0),this.iterations_}incrementIterations(){this.iterations_=this.iterations+1}computeGradients(e,t){return lg(e,t)}dispose(){this.iterations_!=null&&De(this.iterations_)}async saveIterations(){return this.iterations_==null&&(this.iterations_=0),{name:"iter",tensor:ue(this.iterations_,"int32")}}async getWeights(){throw new Error("getWeights() is not implemented for this optimizer yet.")}async setWeights(e){throw new Error(`setWeights() is not implemented for this optimizer class ${this.getClassName()}`)}async extractIterations(e){return this.iterations_=(await e[0].tensor.data())[0],e.slice(1)}};Object.defineProperty(Lr,Symbol.hasInstance,{value:r=>r.minimize!=null&&r.computeGradients!=null&&r.applyGradients!=null});var rp=class extends Lr{constructor(e,t,n=null){super();this.learningRate=e,this.rho=t,this.epsilon=n,this.accumulatedGrads=[],this.accumulatedUpdates=[],n==null&&(this.epsilon=D.backend.epsilon())}applyGradients(e){(Array.isArray(e)?e.map(n=>n.name):Object.keys(e)).forEach((n,o)=>{let s=D.registeredVariables[n],a=!1;this.accumulatedGrads[o]==null&&(this.accumulatedGrads[o]={originalName:`${n}/accum_grad`,variable:V(()=>Se(s).variable(a))}),this.accumulatedUpdates[o]==null&&(this.accumulatedUpdates[o]={originalName:`${n}/accum_var`,variable:V(()=>Se(s).variable(a))});let i=Array.isArray(e)?e[o].tensor:e[n];if(i==null)return;let l=this.accumulatedGrads[o].variable,u=this.accumulatedUpdates[o].variable;V(()=>{let c=Q(P(l,this.rho),P(Me(i),1-this.rho)),p=P(fe(bt(Q(u,this.epsilon)),bt(Q(l,this.epsilon))),i),m=Q(P(u,this.rho),P(Me(p),1-this.rho));l.assign(c),u.assign(m);let f=Q(P(p,-this.learningRate),s);s.assign(f)})}),this.incrementIterations()}dispose(){this.accumulatedUpdates!=null&&(De(this.accumulatedGrads.map(e=>e.variable)),De(this.accumulatedUpdates.map(e=>e.variable)))}async getWeights(){let e=[...this.accumulatedGrads,...this.accumulatedUpdates];return[await this.saveIterations()].concat(e.map(t=>({name:t.originalName,tensor:t.variable})))}async setWeights(e){e=await this.extractIterations(e);let t=e.length/2,n=!1;this.accumulatedGrads=e.slice(0,t).map(o=>({originalName:o.name,variable:o.tensor.variable(n)})),this.accumulatedUpdates=e.slice(t,t*2).map(o=>({originalName:o.name,variable:o.tensor.variable(n)}))}getConfig(){return{learningRate:this.learningRate,rho:this.rho,epsilon:this.epsilon}}static fromConfig(e,t){return new e(t.learningRate,t.rho,t.epsilon)}};rp.className="Adadelta";nn(rp);var np=class extends Lr{constructor(e,t=.1){super();this.learningRate=e,this.initialAccumulatorValue=t,this.accumulatedGrads=[]}applyGradients(e){(Array.isArray(e)?e.map(n=>n.name):Object.keys(e)).forEach((n,o)=>{let s=D.registeredVariables[n];if(this.accumulatedGrads[o]==null){let l=!1;this.accumulatedGrads[o]={originalName:`${n}/accumulator`,variable:V(()=>Sa(s.shape,this.initialAccumulatorValue).variable(l))}}let a=Array.isArray(e)?e[o].tensor:e[n];if(a==null)return;let i=this.accumulatedGrads[o].variable;V(()=>{let l=Q(i,Me(a));i.assign(l);let u=Q(P(fe(a,bt(Q(l,D.backend.epsilon()))),-this.learningRate),s);s.assign(u)})}),this.incrementIterations()}dispose(){this.accumulatedGrads!=null&&De(this.accumulatedGrads.map(e=>e.variable))}async getWeights(){return[await this.saveIterations()].concat(this.accumulatedGrads.map(e=>({name:e.originalName,tensor:e.variable})))}async setWeights(e){e=await this.extractIterations(e);let t=!1;this.accumulatedGrads=e.map(n=>({originalName:n.name,variable:n.tensor.variable(t)}))}getConfig(){return{learningRate:this.learningRate,initialAccumulatorValue:this.initialAccumulatorValue}}static fromConfig(e,t){return new e(t.learningRate,t.initialAccumulatorValue)}};np.className="Adagrad";nn(np);var op=class extends Lr{constructor(e,t,n,o=null){super();this.learningRate=e,this.beta1=t,this.beta2=n,this.epsilon=o,this.accumulatedFirstMoment=[],this.accumulatedSecondMoment=[],V(()=>{this.accBeta1=ue(t).variable(),this.accBeta2=ue(n).variable()}),o==null&&(this.epsilon=D.backend.epsilon())}applyGradients(e){let t=Array.isArray(e)?e.map(n=>n.name):Object.keys(e);V(()=>{let n=ce(1,this.accBeta1),o=ce(1,this.accBeta2);t.forEach((s,a)=>{let i=D.registeredVariables[s],l=!1;this.accumulatedFirstMoment[a]==null&&(this.accumulatedFirstMoment[a]={originalName:`${s}/m`,variable:V(()=>Se(i).variable(l))}),this.accumulatedSecondMoment[a]==null&&(this.accumulatedSecondMoment[a]={originalName:`${s}/v`,variable:V(()=>Se(i).variable(l))});let u=Array.isArray(e)?e[a].tensor:e[s];if(u==null)return;let c=this.accumulatedFirstMoment[a].variable,p=this.accumulatedSecondMoment[a].variable,m=Q(P(c,this.beta1),P(u,1-this.beta1)),f=Q(P(p,this.beta2),P(Me(u),1-this.beta2)),d=fe(m,n),h=fe(f,o);c.assign(m),p.assign(f);let g=Q(P(fe(d,Q(bt(h),this.epsilon)),-this.learningRate),i);i.assign(g)}),this.accBeta1.assign(P(this.accBeta1,this.beta1)),this.accBeta2.assign(P(this.accBeta2,this.beta2))}),this.incrementIterations()}dispose(){this.accBeta1.dispose(),this.accBeta2.dispose(),this.accumulatedFirstMoment!=null&&De(this.accumulatedFirstMoment.map(e=>e.variable)),this.accumulatedSecondMoment!=null&&De(this.accumulatedSecondMoment.map(e=>e.variable))}async getWeights(){let e=[...this.accumulatedFirstMoment,...this.accumulatedSecondMoment];return[await this.saveIterations()].concat(e.map(t=>({name:t.originalName,tensor:t.variable})))}async setWeights(e){e=await this.extractIterations(e),V(()=>{this.accBeta1.assign(_r(this.beta1,this.iterations_+1)),this.accBeta2.assign(_r(this.beta2,this.iterations_+1))});let t=e.length/2,n=!1;this.accumulatedFirstMoment=e.slice(0,t).map(o=>({originalName:o.name,variable:o.tensor.variable(n)})),this.accumulatedSecondMoment=e.slice(t,t*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(e,t){return new e(t.learningRate,t.beta1,t.beta2,t.epsilon)}};op.className="Adam";nn(op);var sp=class extends Lr{constructor(e,t,n,o=null,s=0){super();this.learningRate=e,this.beta1=t,this.beta2=n,this.epsilon=o,this.decay=s,this.accumulatedFirstMoment=[],this.accumulatedWeightedInfNorm=[],V(()=>{this.iteration=ue(0).variable(),this.accBeta1=ue(t).variable()}),o==null&&(this.epsilon=D.backend.epsilon())}applyGradients(e){let t=Array.isArray(e)?e.map(n=>n.name):Object.keys(e);V(()=>{let n=ce(1,this.accBeta1),o=fe(-this.learningRate,Q(P(this.iteration,this.decay),1));t.forEach((s,a)=>{let i=D.registeredVariables[s],l=!1;this.accumulatedFirstMoment[a]==null&&(this.accumulatedFirstMoment[a]={originalName:`${s}/m`,variable:Se(i).variable(l)}),this.accumulatedWeightedInfNorm[a]==null&&(this.accumulatedWeightedInfNorm[a]={originalName:`${s}/v`,variable:Se(i).variable(l)});let u=Array.isArray(e)?e[a].tensor:e[s];if(u==null)return;let c=this.accumulatedFirstMoment[a].variable,p=this.accumulatedWeightedInfNorm[a].variable,m=Q(P(c,this.beta1),P(u,1-this.beta1)),f=P(p,this.beta2),d=Et(u),h=Nr(f,d);c.assign(m),p.assign(h);let g=Q(P(fe(o,n),fe(m,Q(h,this.epsilon))),i);i.assign(g)}),this.iteration.assign(Q(this.iteration,1)),this.accBeta1.assign(P(this.accBeta1,this.beta1))}),this.incrementIterations()}dispose(){this.accBeta1.dispose(),this.iteration.dispose(),this.accumulatedFirstMoment!=null&&De(this.accumulatedFirstMoment.map(e=>e.variable)),this.accumulatedWeightedInfNorm!=null&&De(this.accumulatedWeightedInfNorm.map(e=>e.variable))}async getWeights(){throw new Error("getWeights() is not implemented for Adamax yet.")}async setWeights(e){throw new Error("setWeights() is not implemented for Adamax yet.")}getConfig(){return{learningRate:this.learningRate,beta1:this.beta1,beta2:this.beta2,epsilon:this.epsilon,decay:this.decay}}static fromConfig(e,t){return new e(t.learningRate,t.beta1,t.beta2,t.epsilon,t.decay)}};sp.className="Adamax";nn(sp);var ml=class extends Lr{constructor(e){super();this.learningRate=e,this.setLearningRate(e)}applyGradients(e){(Array.isArray(e)?e.map(n=>n.name):Object.keys(e)).forEach((n,o)=>{let s=Array.isArray(e)?e[o].tensor:e[n];if(s==null)return;let a=D.registeredVariables[n];V(()=>{let i=Q(P(this.c,s),a);a.assign(i)})}),this.incrementIterations()}setLearningRate(e){this.learningRate=e,this.c!=null&&this.c.dispose(),this.c=Dt(ue(-e))}dispose(){this.c.dispose()}async getWeights(){return[await this.saveIterations()]}async setWeights(e){if(e=await this.extractIterations(e),e.length!==0)throw new Error("SGD optimizer does not have settable weights.")}getConfig(){return{learningRate:this.learningRate}}static fromConfig(e,t){return new e(t.learningRate)}};ml.className="SGD";nn(ml);var ip=class extends ml{constructor(e,t,n=!1){super(e);this.learningRate=e,this.momentum=t,this.useNesterov=n,this.accumulations=[],this.m=ue(this.momentum)}applyGradients(e){(Array.isArray(e)?e.map(n=>n.name):Object.keys(e)).forEach((n,o)=>{let s=D.registeredVariables[n];if(this.accumulations[o]==null){let l=!1;this.accumulations[o]={originalName:`${n}/momentum`,variable:V(()=>Se(s).variable(l))}}let a=this.accumulations[o].variable,i=Array.isArray(e)?e[o].tensor:e[n];i!=null&&V(()=>{let l,u=Q(P(this.m,a),i);this.useNesterov?l=Q(P(this.c,Q(i,P(u,this.m))),s):l=Q(P(this.c,u),s),a.assign(u),s.assign(l)})}),this.incrementIterations()}dispose(){this.m.dispose(),this.accumulations!=null&&De(this.accumulations.map(e=>e.variable))}setMomentum(e){this.momentum=e}async getWeights(){return[await this.saveIterations()].concat(this.accumulations.map(e=>({name:e.originalName,tensor:e.variable})))}async setWeights(e){e=await this.extractIterations(e);let t=!1;this.accumulations=e.map(n=>({originalName:n.name,variable:n.tensor.variable(t)}))}getConfig(){return{learningRate:this.learningRate,momentum:this.momentum,useNesterov:this.useNesterov}}static fromConfig(e,t){return new e(t.learningRate,t.momentum,t.useNesterov)}};ip.className="Momentum";nn(ip);var ap=class extends Lr{constructor(e,t=.9,n=0,o=null,s=!1){super();if(this.learningRate=e,this.decay=t,this.momentum=n,this.epsilon=o,this.accumulatedMeanSquares=[],this.accumulatedMoments=[],this.accumulatedMeanGrads=[],this.centered=s,o==null&&(this.epsilon=D.backend.epsilon()),e==null)throw new Error("learningRate for RMSPropOptimizer must be defined.")}applyGradients(e){(Array.isArray(e)?e.map(n=>n.name):Object.keys(e)).forEach((n,o)=>{let 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============================
Hi there \u{1F44B}. Looks like you are running TensorFlow.js in Node.js. To speed things up dramatically, install our node backend, which binds to TensorFlow C++, by running npm i @tensorflow/tfjs-node, or npm i @tensorflow/tfjs-node-gpu if you have CUDA. Then call require('@tensorflow/tfjs-node'); (-gpu suffix for CUDA) at the start of your program. Visit https://github.com/tensorflow/tfjs-node for more details.
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It=0;for(let Ke=Fe;Ke<kt;Ke++){let Ot=Math.min(ke,g-1)*re,hn=Math.min(ke,x-1)*pe,tr=U[Ot+Ct*X+Ke*oe],gn=Y[Ke*ae+ft*ie+hn];It+=tr*gn}ye[ke*le+(Ct*j+ft)]+=It}}return t.disposeIntermediateTensorInfo(T),t.disposeIntermediateTensorInfo(F),t.makeTensorInfo(_,ge.dtype,ge.values)}var $E={kernelName:Zn,backendName:"cpu",kernelFunc:G_};function oK(r){let{inputs:e,backend:t,attrs:n}=r,{a:o,b:s,bias:a,preluActivationWeights:i}=e,{transposeA:l,transposeB:u,activation:c,leakyreluAlpha:p}=n,m,f,d,h=[];m=G_({inputs:{a:o,b:s},attrs:{transposeA:l,transposeB:u},backend:t}),a&&(f=La({inputs:{a:m,b:a},backend:t}),h.push(m),m=f),c&&(d=cp(t,m,c,i,p),h.push(m),m=d);for(let x of h)t.disposeIntermediateTensorInfo(x);return m}var RE={kernelName:vs,backendName:"cpu",kernelFunc:oK};var sK=Re(Ks,r=>Math.acos(r)),FE={kernelName:Ks,backendName:"cpu",kernelFunc:sK};var iK=Re(Xs,r=>Math.acosh(r)),OE={kernelName:Xs,backendName:"cpu",kernelFunc:iK};function aK(r){let{inputs:e,backend:t}=r,n=e;te(e,"addN");let o=n.map(i=>t.data.get(i.dataId).values),s=Ie(n[0].shape,n[0].dtype),a=s.values;for(let i=0;i<n.length;i++){let l=o[i];for(let u=0;u<a.length;u++)a[u]+=l[u]}return t.makeTensorInfo(s.shape,s.dtype,s.values)}var PE={kernelName:Kn,backendName:"cpu",kernelFunc:aK};function lK(r){let{inputs:e,backend:t,attrs:n}=r,{x:o}=e,{axis:s,keepDims:a}=n;te(o,"all");let i=y.parseAxisParam(s,o.shape),l=i,u=N.getAxesPermutation(l,o.shape.length),c=o;u!=null&&(c=or({inputs:{x:o},backend:t,attrs:{perm:u}}),l=N.getInnerMostAxes(l.length,o.shape.length)),N.assertAxesAreInnerMostDims("all",l,c.shape.length);let[p,m]=N.computeOutAndReduceShapes(c.shape,l),f=y.sizeFromShape(m),d=y.makeZerosTypedArray(y.sizeFromShape(p),c.dtype),h=t.data.get(c.dataId).values;for(let x=0;x<d.length;++x){let b=x*f,w=h[b];for(let _=0;_<f;++_){let k=h[b+_];w=w&&k}d[x]=w}u!=null&&t.disposeIntermediateTensorInfo(c);let g=t.makeTensorInfo(p,c.dtype,d);if(a){let x=N.expandShapeToKeepDim(p,i),b=rt({inputs:{x:g},backend:t,attrs:{shape:x}});return t.disposeIntermediateTensorInfo(g),b}return g}var ME={kernelName:jl,backendName:"cpu",kernelFunc:lK};function uK(r){let{inputs:e,backend:t,attrs:n}=r,{x:o}=e,{axis:s,keepDims:a}=n;te(o,"any");let i=y.parseAxisParam(s,o.shape),l=i,u=N.getAxesPermutation(l,o.shape.length),c=o;u!=null&&(c=or({inputs:{x:o},backend:t,attrs:{perm:u}}),l=N.getInnerMostAxes(l.length,o.shape.length)),N.assertAxesAreInnerMostDims("any",l,c.shape.length);let[p,m]=N.computeOutAndReduceShapes(c.shape,l),f=y.sizeFromShape(m),d=y.makeZerosTypedArray(y.sizeFromShape(p),c.dtype),h=t.data.get(c.dataId).values;for(let x=0;x<d.length;++x){let b=x*f,w=h[b];for(let _=0;_<f;++_){let k=h[b+_];w=w||k}d[x]=w}u!=null&&t.disposeIntermediateTensorInfo(c);let g=t.makeTensorInfo(p,c.dtype,d);if(a){let x=N.expandShapeToKeepDim(p,i),b=rt({inputs:{x:g},backend:t,attrs:{shape:x}});return t.disposeIntermediateTensorInfo(g),b}return g}var LE={kernelName:Gl,backendName:"cpu",kernelFunc:uK};function cK(r){let{inputs:e,backend:t,attrs:n}=r,{x:o}=e,{axis:s}=n;te(o,"argMax");let a=y.parseAxisParam(s,o.shape),i=N.getAxesPermutation(a,o.shape.length),l=o,u=[];i!=null&&(l=or({inputs:{x:o},backend:t,attrs:{perm:i}}),u.push(l),a=N.getInnerMostAxes(a.length,l.shape.length)),a=[a[0]],N.assertAxesAreInnerMostDims("argMax",a,l.shape.length);let[c,p]=N.computeOutAndReduceShapes(l.shape,a),m=y.sizeFromShape(c),f=y.makeZerosTypedArray(m,"int32"),d=y.sizeFromShape(p),h=t.data.get(l.dataId).values;for(let g=0;g<f.length;++g){let x=g*d,b=h[x],w=0;for(let _=0;_<d;++_){let k=h[x+_];k>b&&(b=k,w=_)}f[g]=w}return u.forEach(g=>t.disposeIntermediateTensorInfo(g)),t.makeTensorInfo(c,"int32",f)}var zE={kernelName:Xn,backendName:"cpu",kernelFunc:cK};function pK(r){let{inputs:e,backend:t,attrs:n}=r,{x:o}=e,{axis:s}=n;te(o,"argMin");let a=y.parseAxisParam(s,o.shape),i=N.getAxesPermutation(a,o.shape.length),l=o,u=[];i!=null&&(l=or({inputs:{x:o},backend:t,attrs:{perm:i}}),u.push(l),a=N.getInnerMostAxes(a.length,l.shape.length)),a=[a[0]],N.assertAxesAreInnerMostDims("argMin",a,l.shape.length);let[c,p]=N.computeOutAndReduceShapes(l.shape,a),m=y.sizeFromShape(c),f=y.makeZerosTypedArray(m,"int32"),d=y.sizeFromShape(p),h=t.data.get(l.dataId).values;for(let g=0;g<f.length;++g){let x=g*d,b=h[x],w=0;for(let _=0;_<d;++_){let k=h[x+_];k<b&&(b=k,w=_)}f[g]=w}return u.forEach(g=>t.disposeIntermediateTensorInfo(g)),t.makeTensorInfo(c,"int32",f)}var BE={kernelName:na,backendName:"cpu",kernelFunc:pK};var mK=Re(Ys,r=>Math.asin(r)),VE={kernelName:Ys,backendName:"cpu",kernelFunc:mK};var fK=Re(Zs,r=>Math.asinh(r)),WE={kernelName:Zs,backendName:"cpu",kernelFunc:fK};var dK=Re(Js,r=>Math.atan(r)),jE={kernelName:Js,backendName:"cpu",kernelFunc:dK};var hK=Je((r,e)=>Math.atan2(r,e)),gK=ot(ei,hK),GE={kernelName:ei,backendName:"cpu",kernelFunc:gK};var xK=Re(Qs,r=>Math.atanh(r)),UE={kernelName:Qs,backendName:"cpu",kernelFunc:xK};function pp(r,e,t,n,o,s){let a=o.strideHeight,i=o.strideWidth,l=o.dilationHeight,u=o.dilationWidth,c=o.effectiveFilterHeight,p=o.effectiveFilterWidth,m=o.padInfo.top,f=o.padInfo.left,d=s==="max"?Number.NEGATIVE_INFINITY:Number.POSITIVE_INFINITY,h=Ie(o.outShape,t),g=h.values,x=o.outShape[1]*o.outShape[2]*o.outShape[3],b=o.outShape[2]*o.outShape[3],w=o.outShape[3];for(let _=0;_<o.batchSize;++_){let k=_*x,E=_*n[0];for(let T=0;T<o.inChannels;++T)for(let F=0;F<o.outHeight;++F){let O=F*a-m,M=Math.max(0,O),j=Math.min(o.inHeight,c+O),W=k+F*b;for(let U=0;U<o.outWidth;++U){let Y=U*i-f,K=Math.max(0,Y),Z=Math.min(o.inWidth,p+Y),re=d,X=0,oe=0;for(let ie=M;ie<j;ie+=l){let pe=E+ie*n[1];for(let le=K;le<Z;le+=u){let ge=pe+le*n[2],ye=r[ge+T];s==="max"&&ye>re?re=ye:s==="avg"&&(X+=ye,oe++)}if(isNaN(re))break}let ae=W+U*w+T;g[ae]=s==="avg"?X/oe:re}}}return h}function Sg(r,e,t,n,o=!1,s=!1){let a=Ie(n.outShape,"int32"),i=n.strideHeight,l=n.strideWidth,u=n.dilationHeight,c=n.dilationWidth,p=n.effectiveFilterHeight,m=n.effectiveFilterWidth,f=n.padInfo.top,d=n.padInfo.left,h=Ie(e,t,r);for(let g=0;g<n.batchSize;++g)for(let x=0;x<n.inChannels;++x)for(let b=0;b<n.outHeight;++b){let w=b*i-f,_=w;for(;_<0;)_+=u;let k=Math.min(n.inHeight,p+w);for(let E=0;E<n.outWidth;++E){let T=E*l-d,F=T;for(;F<0;)F+=c;let O=Math.min(n.inWidth,m+T),M=Number.NEGATIVE_INFINITY,j=-1;for(let W=_;W<k;W+=u){let U=W-w;for(let Y=F;Y<O;Y+=c){let K=Y-T,Z=h.get(g,W,Y,x);Z>M&&(M=Z,o?j=s?((g*n.inHeight+W)*n.inWidth+Y)*n.inChannels+x:(W*n.inWidth+Y)*n.inChannels+x:j=U*m+K)}}a.set(j,g,b,E,x)}}return a}function Ng(r,e,t,n,o,s){let a=o.strideDepth,i=o.strideHeight,l=o.strideWidth,u=o.dilationDepth,c=o.dilationHeight,p=o.dilationWidth,m=o.effectiveFilterDepth,f=o.effectiveFilterHeight,d=o.effectiveFilterWidth,h=o.padInfo.front,g=o.padInfo.top,x=o.padInfo.left,b=s==="max"?Number.NEGATIVE_INFINITY:Number.POSITIVE_INFINITY,w=Ie(o.outShape,t),_=w.values,k=o.outShape[1]*o.outShape[2]*o.outShape[3]*o.outShape[4],E=o.outShape[2]*o.outShape[3]*o.outShape[4],T=o.outShape[3]*o.outShape[4],F=o.outShape[4];for(let O=0;O<o.batchSize;++O){let M=O*k,j=O*n[0];for(let W=0;W<o.inChannels;++W)for(let U=0;U<o.outDepth;++U){let Y=U*a-h,K=Y;for(;K<0;)K+=u;let Z=Math.min(o.inDepth,m+Y),re=M+U*E;for(let X=0;X<o.outHeight;++X){let oe=X*i-g,ae=oe;for(;ae<0;)ae+=c;let ie=Math.min(o.inHeight,f+oe),pe=re+X*T;for(let le=0;le<o.outWidth;++le){let ge=le*l-x,ye=ge;for(;ye<0;)ye+=p;let be=Math.min(o.inWidth,d+ge),ke=pe+le*F,Ae=b,$e=0,Fe=0;for(let ct=K;ct<Z;ct+=u){let kt=j+ct*n[1];for(let Ct=ae;Ct<ie;Ct+=c){let ft=kt+Ct*n[2];for(let It=ye;It<be;It+=p){let Ke=ft+It*n[3],Ot=r[Ke+W];if(s==="max"&&Ot>Ae?Ae=Ot:s==="avg"&&($e+=Ot,Fe++),isNaN(Ae))break}if(isNaN(Ae))break}if(isNaN(Ae))break}let He=ke+W;_[He]=s==="avg"?$e/Fe:Ae}}}}return w}function qE(r,e){let t=Ie(e.outShape,"int32"),n=e.strideDepth,o=e.strideHeight,s=e.strideWidth,a=e.dilationDepth,i=e.dilationHeight,l=e.dilationWidth,u=e.effectiveFilterDepth,c=e.effectiveFilterHeight,p=e.effectiveFilterWidth,m=e.padInfo.front,f=e.padInfo.top,d=e.padInfo.left;for(let h=0;h<e.batchSize;++h)for(let g=0;g<e.inChannels;++g)for(let x=0;x<e.outDepth;++x){let b=x*n-m,w=b;for(;w<0;)w+=a;let _=Math.min(e.inDepth,u+b);for(let k=0;k<e.outHeight;++k){let E=k*o-f,T=E;for(;T<0;)T+=i;let F=Math.min(e.inHeight,c+E);for(let O=0;O<e.outWidth;++O){let M=O*s-d,j=M;for(;j<0;)j+=l;let W=Math.min(e.inWidth,p+M),U=Number.NEGATIVE_INFINITY,Y=-1;for(let K=w;K<_;K+=a){let Z=K-b;for(let re=T;re<F;re+=i){let X=re-E;for(let oe=j;oe<W;oe+=l){let ae=oe-M,ie=r.get(h,K,re,oe,g);ie>=U&&(U=ie,Y=Z*c*p+X*c+ae)}}}t.set(Y,h,x,k,O,g)}}}return t}function yK(r){let{inputs:e,backend:t,attrs:n}=r,{x:o}=e;te(o,"avgPool");let{filterSize:s,strides:a,pad:i,dimRoundingMode:l}=n,u=1;y.assert(N.eitherStridesOrDilationsAreOne(a,u),()=>`Error in avgPool: Either strides or dilations must be 1. Got strides ${a} and dilations '${u}'`);let c=N.computePool2DInfo(o.shape,s,a,u,i,l),p;if(c.filterWidth===1&&c.filterHeight===1&&y.arraysEqual(c.inShape,c.outShape))p=$r({inputs:{x:o},backend:t});else{let m=t.data.get(o.dataId).values,f=y.computeStrides(o.shape),d=pp(m,o.shape,o.dtype,f,c,"avg");p=t.makeTensorInfo(c.outShape,o.dtype,d.values)}return p}var HE={kernelName:Yn,backendName:"cpu",kernelFunc:yK};function bK(r){let{inputs:e,backend:t,attrs:n}=r,{x:o}=e,{filterSize:s,strides:a,pad:i,dimRoundingMode:l,dataFormat:u,dilations:c}=n;te(o,"avgPool3d");let p=c;p==null&&(p=[1,1,1]);let m=N.computePool3DInfo(o.shape,s,a,p,i,l,u),f=t.data.get(o.dataId).values,d=Ng(f,o.shape,o.dtype,y.computeStrides(o.shape),m,"avg");return t.makeTensorInfo(d.shape,"float32",d.values)}var KE={kernelName:oa,backendName:"cpu",kernelFunc:bK};function wK(r){let{inputs:e,backend:t,attrs:n}=r,{dy:o,input:s}=e,{filterSize:a,strides:i,pad:l,dilations:u,dimRoundingMode:c}=n;te([o,s],"avgPool3DGrad");let p=N.computePool3DInfo(s.shape,a,i,u,l,c),m=p.strideDepth,f=p.strideHeight,d=p.strideWidth,h=p.filterDepth,g=p.filterHeight,x=p.filterWidth,b=p.dilationDepth,w=p.dilationHeight,_=p.dilationWidth,k=p.effectiveFilterDepth,E=p.effectiveFilterHeight,T=p.effectiveFilterWidth,F=k-1-p.padInfo.front,O=T-1-p.padInfo.left,M=E-1-p.padInfo.top,j=Ie(s.shape,"float32"),W=1/(h*g*x),U=t.bufferSync(o);for(let Y=0;Y<p.batchSize;++Y)for(let K=0;K<p.inChannels;++K)for(let Z=0;Z<p.inDepth;++Z)for(let re=0;re<p.inHeight;++re)for(let X=0;X<p.inWidth;++X){let oe=Z-F,ae=re-M,ie=X-O,pe=0;for(let le=0;le<k;le+=b){let ge=(oe+le)/m;if(!(ge<0||ge>=p.outDepth||Math.floor(ge)!==ge))for(let ye=0;ye<E;ye+=w){let be=(ae+ye)/f;if(!(be<0||be>=p.outHeight||Math.floor(be)!==be))for(let ke=0;ke<T;ke+=_){let Ae=(ie+ke)/d;if(Ae<0||Ae>=p.outWidth||Math.floor(Ae)!==Ae)continue;pe+=U.get(Y,ge,be,Ae,K)}}}j.set(pe*W,Y,Z,re,X,K)}return t.makeTensorInfo(j.shape,j.dtype,j.values)}var XE={kernelName:ql,backendName:"cpu",kernelFunc:wK};function _K(r){let{inputs:e,backend:t,attrs:n}=r,{dy:o,input:s}=e,a=s;te([o,s],"avgPoolGrad");let{filterSize:i,strides:l,pad:u}=n,c=N.computePool2DInfo(a.shape,i,l,1,u),p=c.strideHeight,m=c.strideWidth,f=c.filterHeight,d=c.filterWidth,h=c.dilationHeight,g=c.dilationWidth,x=c.effectiveFilterHeight,b=c.effectiveFilterWidth,w=b-1-c.padInfo.left,_=x-1-c.padInfo.top,k=Ie(a.shape,"float32"),E=1/(f*d),T=t.data.get(o.dataId).values,F=Ie(o.shape,"float32",T);for(let O=0;O<c.batchSize;++O)for(let M=0;M<c.inChannels;++M)for(let j=0;j<c.inHeight;++j)for(let W=0;W<c.inWidth;++W){let U=j-_,Y=W-w,K=0;for(let Z=0;Z<x;Z+=h){let re=(U+Z)/p;if(!(re<0||re>=c.outHeight||Math.floor(re)!==re))for(let X=0;X<b;X+=g){let oe=(Y+X)/m;if(oe<0||oe>=c.outWidth||Math.floor(oe)!==oe)continue;K+=F.get(O,re,oe,M)}}k.set(K*E,O,j,W,M)}return t.makeTensorInfo(k.shape,k.dtype,k.values)}var YE={kernelName:Ul,backendName:"cpu",kernelFunc:_K};function vK(r){let{inputs:e,backend:t,attrs:n}=r,{x:o,scale:s,offset:a,mean:i,variance:l}=e;y.assert(i.shape.length===l.shape.length,()=>"Batch normalization gradient requires mean and variance to have equal ranks."),y.assert(a==null||i.shape.length===a.shape.length,()=>"Batch normalization gradient requires mean and offset to have equal ranks."),y.assert(s==null||i.shape.length===s.shape.length,()=>"Batch normalization gradient requires mean and scale to have equal ranks."),te([o,i,l,s,a],"batchNorm");let{varianceEpsilon:u}=n;u==null&&(u=.001);let c=t.data.get(o.dataId).values,p=t.data.get(i.dataId).values,m=t.data.get(l.dataId).values,f=s?t.data.get(s.dataId).values:new Float32Array([1]),d=a?t.data.get(a.dataId).values:new Float32Array([0]),h=new Float32Array(c.length),g=d.length,x=f.length,b=m.length,w=p.length,_=0,k=0,E=0,T=0;for(let F=0;F<c.length;++F)h[F]=d[_++]+(c[F]-p[k++])*f[E++]/Math.sqrt(m[T++]+u),_>=g&&(_=0),k>=w&&(k=0),E>=x&&(E=0),T>=b&&(T=0);return t.makeTensorInfo(o.shape,o.dtype,h)}var ZE={kernelName:ao,backendName:"cpu",kernelFunc:vK};function kK(r){let{inputs:e,backend:t,attrs:n}=r,{x:o}=e,{blockShape:s,crops:a}=n;te([o],"batchToSpaceND");let i=s.reduce((x,b)=>x*b),l=N.getReshaped(o.shape,s,i),u=N.getPermuted(l.length,s.length),c=N.getReshapedPermuted(o.shape,s,i),p=N.getSliceBeginCoords(a,s.length),m=N.getSliceSize(c,a,s.length),f=rt({inputs:{x:o},backend:t,attrs:{shape:l}}),d=or({inputs:{x:f},backend:t,attrs:{perm:u}}),h=rt({inputs:{x:d},backend:t,attrs:{shape:c}}),g=Ko({inputs:{x:h},backend:t,attrs:{begin:p,size:m}});return t.disposeIntermediateTensorInfo(f),t.disposeIntermediateTensorInfo(d),t.disposeIntermediateTensorInfo(h),g}var JE={kernelName:sa,backendName:"cpu",kernelFunc:kK};function CK(r){let{inputs:e,backend:t,attrs:n}=r,{x:o,weights:s}=e,{size:a}=n,i=t.data.get(o.dataId).values,l=t.data.get(s.dataId).values,u=uf(i,l,s.dtype,s.shape,a);return t.makeTensorInfo([a],s.dtype,u)}var QE={kernelName:Hl,backendName:"cpu",kernelFunc:CK};var IK=Re(Fn,(r,e)=>{let t=e;return r>t.clipValueMax?t.clipValueMax:r<t.clipValueMin?t.clipValueMin:r}),eA={kernelName:Fn,backendName:"cpu",kernelFunc:IK};var SK=r=>{let{x:e}=r.inputs,t=r.backend,n=new Float32Array(y.sizeFromShape(e.shape)),o=t.data.get(e.dataId),s=o.complexTensorInfos.real,a=o.complexTensorInfos.imag,i=t.data.get(s.dataId).values,l=t.data.get(a.dataId).values;for(let u=0;u<i.length;u++){let c=i[u],p=l[u];n[u]=Math.hypot(c,p)}return t.makeOutput(n,e.shape,"float32")},tA={kernelName:ia,backendName:"cpu",kernelFunc:SK};function Wi(r){let{inputs:e,backend:t}=r,{input:n}=e,o=t.data.get(n.dataId).complexTensorInfos.imag,s=t.data.get(o.dataId).values;return t.makeTensorInfo(o.shape,o.dtype,s)}var rA={kernelName:su,backendName:"cpu",kernelFunc:Wi};function dl(r){let{inputs:e,backend:t,attrs:n}=r,{axis:o}=n,s=y.parseAxisParam(o,e[0].shape)[0],a=N.computeOutShape(e.map(h=>h.shape),s);if(y.sizeFromShape(a)===0)return t.makeTensorInfo(a,e[0].dtype,[]);let i=e.filter(h=>y.sizeFromShape(h.shape)>0);if(i.length===1)return $r({inputs:{x:i[0]},backend:t});let l=i.map(h=>h.shape);if(N.assertParamsConsistent(l,s),i[0].dtype==="complex64"){let h=i.map(_=>Go({inputs:{input:_},backend:t})),g=i.map(_=>Wi({inputs:{input:_},backend:t})),x=dl({inputs:h,backend:t,attrs:{axis:s}}),b=dl({inputs:g,backend:t,attrs:{axis:s}}),w=mr({inputs:{real:x,imag:b},backend:t});return h.forEach(_=>t.disposeIntermediateTensorInfo(_)),g.forEach(_=>t.disposeIntermediateTensorInfo(_)),t.disposeIntermediateTensorInfo(x),t.disposeIntermediateTensorInfo(b),w}let u=i.map(h=>{let g=y.sizeFromShape(h.shape.slice(s));return rt({inputs:{x:h},backend:t,attrs:{shape:[-1,g]}})}),c=u.map(h=>({vals:t.data.get(h.dataId).values,shape:h.shape}));a=N.computeOutShape(u.map(h=>h.shape),1);let p=u[0].shape[0]===1,m=cf(c,a,e[0].dtype,p),f=N.computeOutShape(i.map(h=>h.shape),s),d=t.makeTensorInfo(f,e[0].dtype,m);return 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t.makeTensorInfo(h.shape,h.dtype,h.values)}var iA={kernelName:Qn,backendName:"cpu",kernelFunc:TK};function EK(r){let{inputs:e,backend:t,attrs:n}=r,{x:o,filter:s}=e,{strides:a,pad:i,dilations:l}=n;te([o,s],"conv3d");let u=N.computeConv3DInfo(o.shape,s.shape,a,l,i),{filterDepth:c,filterHeight:p,filterWidth:m,dilationDepth:f,dilationHeight:d,dilationWidth:h,padInfo:g}=u,x=g.front,b=g.left,w=g.top,_=new pt(u.outShape,o.dtype),k=t.data.get(o.dataId).values,E=t.data.get(s.dataId).values,T=_.values,F=y.computeStrides(o.shape),O=y.computeStrides(s.shape);for(let M=0;M<u.batchSize;++M){let j=M*F[0],W=M*_.strides[0];for(let U=0;U<u.outDepth;++U){let Y=W+U*_.strides[1],K=U*u.strideDepth-x;for(let Z=0;Z<c;++Z){let re=K+Z*f;if(re<0||re>=u.inDepth)continue;let X=Z*O[0],oe=j+re*F[1];for(let ae=0;ae<u.outHeight;++ae){let ie=Y+ae*_.strides[2],pe=ae*u.strideHeight-w;for(let le=0;le<p;++le){let ge=pe+le*d;if(ge<0||ge>=u.inHeight)continue;let ye=X+le*O[1],be=oe+ge*F[2];for(let ke=0;ke<u.outWidth;++ke){let 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u=y.computeStrides(o.shape),c=y.computeStrides(s.shape),p=N.computeConv3DInfo(l,s.shape,i,1,a),m=new pt(p.inShape,"float32"),f=m.values,[d,h,g,x]=m.strides,b=t.data.get(o.dataId).values,[w,_,k,E]=u,T=t.data.get(s.dataId).values,[F,O,M,j]=c,{batchSize:W,filterDepth:U,filterHeight:Y,filterWidth:K,inChannels:Z,inDepth:re,inHeight:X,inWidth:oe,outChannels:ae,outDepth:ie,outHeight:pe,outWidth:le,strideDepth:ge,strideHeight:ye,strideWidth:be}=p,ke=U-1-p.padInfo.front,Ae=Y-1-p.padInfo.top,$e=K-1-p.padInfo.left;for(let Fe=0;Fe<W;++Fe)for(let He=0;He<Z;++He)for(let ct=0;ct<re;++ct){let kt=ct-ke,Ct=Math.max(0,Math.ceil(kt/ge)),ft=Math.min(ie,(U+kt)/ge);for(let It=0;It<X;++It){let Ke=It-Ae,Ot=Math.max(0,Math.ceil(Ke/ye)),hn=Math.min(pe,(Y+Ke)/ye);for(let tr=0;tr<oe;++tr){let gn=tr-$e,kr=Math.max(0,Math.ceil(gn/be)),Gn=Math.min(le,(K+gn)/be),tn=0;for(let xn=Ct;xn<ft;++xn){let Cr=xn*ge-kt;for(let An=Ot;An<hn;++An){let Un=An*ye-Ke;for(let rn=kr;rn<Gn;++rn){let 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}
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}
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}
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const float FLOAT_MIN = 1.17549435e-38;
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}
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}
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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 Nv=class{constructor(e){this.variableNames=["A"],this.packedInputs=!1,this.packedOutput=!0,this.outPackingScheme=hl.DENSE;let t=gl(e),n=Lt();this.outputShape=e,this.userCode=`
ivec3 outCoordsFromFlatIndex(int index) {
${Fs(["r","c","d"],e)}
return ivec3(r, c, d);
}
void main() {
ivec2 resTexRC = ivec2(resultUV.yx *
vec2(${t[0]}, ${t[1]}));
int index = 4 * (resTexRC.x * ${t[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);
}
${n.output} = result;
}
`}};var Tv=class{constructor(e){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,this.outPackingScheme=hl.DENSE;let t=gl(e),n=Lt();this.outputShape=e,this.userCode=`
ivec3 outCoordsFromFlatIndex(int index) {
${Fs(["r","c","d"],e)}
return ivec3(r, c, d);
}
void main() {
ivec2 resTexRC = ivec2(resultUV.yx *
vec2(${t[0]}, ${t[1]}));
int index = 4 * (resTexRC.x * ${t[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));
}
${n.output} = result;
}
`}};var Ev=class{constructor(e){this.variableNames=["A"],this.outTexUsage=Rr.DOWNLOAD;let t=Lt();this.outputShape=e,this.userCode=`
${Lg}
void main() {
float x = getAAtOutCoords();
${t.output} = encode_float(x);
}
`}};var Av=class{constructor(e){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!1,this.outTexUsage=Rr.DOWNLOAD;let t=Lt();this.outputShape=e,this.userCode=`
${Lg}
void main() {
ivec3 coords = getOutputCoords();
float x = getChannel(getAAtOutCoords(), vec2(coords.y, coords.z));
${t.output} = encode_float(x);
}
`}};var Dv=class{constructor(e,t,n=!1){this.variableNames=["A"];let o=Lt(),[s,a]=t;this.outputShape=e;let i="result";n&&(i="floor(result * 255. + 0.5)"),this.userCode=`
${hp(e)}
void main() {
ivec3 coords = getOutputCoords();
int flatIndex = getFlatIndex(coords);
int offset = imod(flatIndex, 4);
flatIndex = idiv(flatIndex, 4, 1.);
int r = flatIndex / ${a};
int c = imod(flatIndex, ${a});
vec2 uv = (vec2(c, r) + halfCR) / vec2(${a}.0, ${s}.0);
vec4 values = ${o.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];
}
${o.output} = vec4(${i}, 0., 0., 0.);
}
`}};var $v=class{constructor(e,t,n=!1){this.variableNames=["A"],this.packedInputs=!1,this.packedOutput=!0;let o=Lt(),[s,a]=t;this.outputShape=e;let i="",l="result";n&&(l="floor(result * 255. + 0.5)");for(let u=0;u<=1;u++)for(let c=0;c<=1;c++){let p=u*2+c;i+=`
localCoords = coords;
if(localCoords[2] + ${c} < ${e[2]}) {
localCoords[2] += ${c};
if(localCoords[1] + ${u} < ${e[1]}) {
localCoords[1] += ${u};
flatIndex = getFlatIndex(localCoords);
offset = imod(flatIndex, 4);
flatIndex = idiv(flatIndex, 4, 1.);
r = flatIndex / ${a};
c = imod(flatIndex, ${a});
uv = (vec2(c, r) + halfCR) / vec2(${a}.0, ${s}.0);
values = ${o.texture2D}(A, uv);
if(offset == 0) {
result[${p}] = values[0];
} else if(offset == 1) {
result[${p}] = values[1];
} else if(offset == 2) {
result[${p}] = values[2];
} else {
result[${p}] = values[3];
}
}
}
`}this.userCode=`
${hp(e)}
void main() {
ivec3 coords = getOutputCoords();
vec4 result = vec4(0.);
int flatIndex, r, c, offset;
ivec3 localCoords;
vec2 uv;
vec4 values;
${i}
${o.output} = ${l};
}
`}};var yD={};Ze(yD,{bindVertexProgramAttributeStreams:()=>Vv,createBufferFromOutputTexture:()=>Gv,createFloat16MatrixTexture:()=>Mv,createFloat16PackedMatrixTexture:()=>Bv,createFloat32MatrixTexture:()=>Pv,createIndexBuffer:()=>Ov,createPackedMatrixTexture:()=>zv,createUnsignedBytesMatrixTexture:()=>Lv,createVertexBuffer:()=>Fv,createVertexShader:()=>Rv,downloadByteEncodedFloatMatrixFromOutputTexture:()=>qv,downloadFloat32MatrixFromBuffer:()=>Uv,downloadMatrixFromPackedOutputTexture:()=>Kv,downloadPackedMatrixFromBuffer:()=>Hv,getInternalFormatForFloat16MatrixTexture:()=>Bg,getInternalFormatForFloat16PackedMatrixTexture:()=>jg,getInternalFormatForFloat32MatrixTexture:()=>zg,getInternalFormatForPackedMatrixTexture:()=>Wg,getInternalFormatForUnsignedBytesMatrixTexture:()=>Vg,uploadDenseMatrixToTexture:()=>Wv,uploadPixelDataToTexture:()=>jv});function Rv(r){let e=Lt(),t=`${e.version}
precision highp float;
${e.attribute} vec3 clipSpacePos;
${e.attribute} vec2 uv;
${e.varyingVs} vec2 resultUV;
void main() {
gl_Position = vec4(clipSpacePos, 1);
resultUV = uv;
}`;return av(r,t)}function Fv(r){let e=new Float32Array([-1,1,0,0,1,-1,-1,0,0,0,1,1,0,1,1,1,-1,0,1,0]);return pv(r,e)}function Ov(r){let e=new Uint16Array([0,1,2,2,1,3]);return mv(r,e)}function Cf(r,e,t,n,o,s){dv(e,t);let a=fv(r),i=r.TEXTURE_2D;return Ne(r,()=>r.bindTexture(i,a)),Ne(r,()=>r.texParameteri(i,r.TEXTURE_WRAP_S,r.CLAMP_TO_EDGE)),Ne(r,()=>r.texParameteri(i,r.TEXTURE_WRAP_T,r.CLAMP_TO_EDGE)),Ne(r,()=>r.texParameteri(i,r.TEXTURE_MIN_FILTER,r.NEAREST)),Ne(r,()=>r.texParameteri(i,r.TEXTURE_MAG_FILTER,r.NEAREST)),Ne(r,()=>r.texImage2D(i,0,n,e,t,0,o,s,null)),Ne(r,()=>r.bindTexture(r.TEXTURE_2D,null)),a}function zg(r){return r.internalFormatFloat}function Pv(r,e,t,n){let[o,s]=Zu(e,t);return Cf(r,o,s,zg(n),n.textureFormatFloat,r.FLOAT)}function Bg(r){return r.internalFormatHalfFloat}function Mv(r,e,t,n){let[o,s]=Zu(e,t);return Cf(r,o,s,Bg(n),n.textureFormatFloat,n.textureTypeHalfFloat)}function Vg(r){return r.downloadTextureFormat}function Lv(r,e,t,n){let[o,s]=Zu(e,t);return Cf(r,o,s,Vg(n),r.RGBA,r.UNSIGNED_BYTE)}function Wg(r){return r.internalFormatPackedFloat}function zv(r,e,t,n){let[o,s]=ji(e,t);return Cf(r,o,s,Wg(n),r.RGBA,r.FLOAT)}function jg(r){return r.internalFormatPackedHalfFloat}function Bv(r,e,t,n){let[o,s]=ji(e,t);return Cf(r,o,s,jg(n),r.RGBA,n.textureTypeHalfFloat)}function Vv(r,e,t){let n=0,o=3*4,s=3*4+2*4;return Ne(r,()=>r.bindBuffer(r.ARRAY_BUFFER,t)),$g(r,e,"clipSpacePos",t,3,s,n)&&$g(r,e,"uv",t,2,s,o)}function Wv(r,e,t,n,o,s){Ne(r,()=>r.bindTexture(r.TEXTURE_2D,e));let a,i,l;o instanceof Uint8Array?(a=new Uint8Array(t*n*4),i=r.UNSIGNED_BYTE,l=r.RGBA):(a=new Float32Array(t*n*4),i=r.FLOAT,l=s.internalFormatPackedFloat),a.set(o),Ne(r,()=>r.texImage2D(r.TEXTURE_2D,0,l,t,n,0,r.RGBA,i,a)),Ne(r,()=>r.bindTexture(r.TEXTURE_2D,null))}function jv(r,e,t){Ne(r,()=>r.bindTexture(r.TEXTURE_2D,e)),t.data instanceof Uint8Array?Ne(r,()=>r.texImage2D(r.TEXTURE_2D,0,r.RGBA,t.width,t.height,0,r.RGBA,r.UNSIGNED_BYTE,t.data)):Ne(r,()=>r.texImage2D(r.TEXTURE_2D,0,r.RGBA,r.RGBA,r.UNSIGNED_BYTE,t)),Ne(r,()=>r.bindTexture(r.TEXTURE_2D,null))}function Gv(r,e,t,n){let o=r.createBuffer();Ne(r,()=>r.bindBuffer(r.PIXEL_PACK_BUFFER,o));let i=4*4*e*t;return Ne(r,()=>r.bufferData(r.PIXEL_PACK_BUFFER,i,r.STREAM_READ)),Ne(r,()=>r.readPixels(0,0,t,e,r.RGBA,r.FLOAT,0)),Ne(r,()=>r.bindBuffer(r.PIXEL_PACK_BUFFER,null)),o}function Uv(r,e,t){let n=r,o=new Float32Array(t);return n.bindBuffer(n.PIXEL_PACK_BUFFER,e),n.getBufferSubData(n.PIXEL_PACK_BUFFER,0,o),n.bindBuffer(n.PIXEL_PACK_BUFFER,null),o}function qv(r,e,t,n){let[o,s]=Zu(e,t),a=4,i=new Uint8Array(pD(e*t,a));return Ne(r,()=>r.readPixels(0,0,o,s,n.downloadTextureFormat,r.UNSIGNED_BYTE,i)),new Float32Array(i.buffer)}function Hv(r,e,t,n,o,s,a,i){let l=r,u=new Float32Array(mD(s,a));return l.bindBuffer(l.PIXEL_PACK_BUFFER,e),l.getBufferSubData(l.PIXEL_PACK_BUFFER,0,u),l.bindBuffer(l.PIXEL_PACK_BUFFER,null),u}function Kv(r,e,t){let n=new Float32Array(e*t*4);return Ne(r,()=>r.readPixels(0,0,t,e,r.RGBA,r.FLOAT,n)),n}var Gg=class{constructor(e){this.outputTexture=null,this.program=null,this.disposed=!1,this.vertexAttrsAreBound=!1,this.itemsToPoll=[];let t=G().getNumber("WEBGL_VERSION");e!=null?(this.gl=e,sv(t,e)):this.gl=Vn(t);let n="WEBGL_color_buffer_float",o="EXT_color_buffer_half_float";if(G().getNumber("WEBGL_VERSION")===1){let s="OES_texture_float",a="OES_texture_half_float";if(this.textureFloatExtension=fp(this.gl,s),In(this.gl,a))this.textureHalfFloatExtension=fp(this.gl,a);else if(G().get("WEBGL_FORCE_F16_TEXTURES"))throw new Error("GL context does not support half float textures, yet the environment flag WEBGL_FORCE_F16_TEXTURES is set to true.");if(this.colorBufferFloatExtension=this.gl.getExtension(n),In(this.gl,o))this.colorBufferHalfFloatExtension=fp(this.gl,o);else if(G().get("WEBGL_FORCE_F16_TEXTURES"))throw new Error("GL context does not support color renderable half floats, yet the environment flag WEBGL_FORCE_F16_TEXTURES is set to true.")}else if(n="EXT_color_buffer_float",In(this.gl,n))this.colorBufferFloatExtension=this.gl.getExtension(n);else if(In(this.gl,o))this.colorBufferHalfFloatExtension=this.gl.getExtension(o);else throw new Error("GL context does not support color renderable floats");this.vertexBuffer=Fv(this.gl),this.indexBuffer=Ov(this.gl),this.framebuffer=hv(this.gl),this.textureConfig=wf(this.gl,this.textureHalfFloatExtension)}get debug(){return G().getBool("DEBUG")}dispose(){if(this.disposed)return;this.program!=null&&console.warn("Disposing a GPGPUContext that still has a bound WebGLProgram. This is probably a resource leak, delete the program with GPGPUContext.deleteProgram before disposing."),this.outputTexture!=null&&console.warn("Disposing a GPGPUContext that still has a bound output matrix texture. This is probably a resource leak, delete the output matrix texture with GPGPUContext.deleteMatrixTexture before disposing.");let e=this.gl;Ne(e,()=>e.finish()),Ne(e,()=>e.bindFramebuffer(e.FRAMEBUFFER,null)),Ne(e,()=>e.deleteFramebuffer(this.framebuffer)),Ne(e,()=>e.bindBuffer(e.ARRAY_BUFFER,null)),Ne(e,()=>e.bindBuffer(e.ELEMENT_ARRAY_BUFFER,null)),Ne(e,()=>e.deleteBuffer(this.indexBuffer)),this.disposed=!0}createFloat32MatrixTexture(e,t){return this.throwIfDisposed(),Pv(this.gl,e,t,this.textureConfig)}createFloat16MatrixTexture(e,t){return this.throwIfDisposed(),Mv(this.gl,e,t,this.textureConfig)}createUnsignedBytesMatrixTexture(e,t){return this.throwIfDisposed(),Lv(this.gl,e,t,this.textureConfig)}uploadPixelDataToTexture(e,t){this.throwIfDisposed(),jv(this.gl,e,t)}uploadDenseMatrixToTexture(e,t,n,o){this.throwIfDisposed(),Wv(this.gl,e,t,n,o,this.textureConfig)}createFloat16PackedMatrixTexture(e,t){return this.throwIfDisposed(),Bv(this.gl,e,t,this.textureConfig)}createPackedMatrixTexture(e,t){return this.throwIfDisposed(),zv(this.gl,e,t,this.textureConfig)}deleteMatrixTexture(e){this.throwIfDisposed(),this.outputTexture===e&&(Rg(this.gl,this.framebuffer),this.outputTexture=null),Ne(this.gl,()=>this.gl.deleteTexture(e))}downloadByteEncodedFloatMatrixFromOutputTexture(e,t,n){return this.downloadMatrixDriver(e,()=>qv(this.gl,t,n,this.textureConfig))}downloadPackedMatrixFromBuffer(e,t,n,o,s,a){return Hv(this.gl,e,t,n,o,s,a,this.textureConfig)}downloadFloat32MatrixFromBuffer(e,t){return Uv(this.gl,e,t)}createBufferFromTexture(e,t,n){this.bindTextureToFrameBuffer(e);let o=Gv(this.gl,t,n,this.textureConfig);return this.unbindTextureToFrameBuffer(),o}createAndWaitForFence(){let e=this.createFence(this.gl);return this.pollFence(e)}createFence(e){let t,n;if(G().getBool("WEBGL_FENCE_API_ENABLED")){let o=e,s=o.fenceSync(o.SYNC_GPU_COMMANDS_COMPLETE,0);e.flush(),n=()=>{let a=o.clientWaitSync(s,0,0);return a===o.ALREADY_SIGNALED||a===o.CONDITION_SATISFIED},t=s}else G().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION")>0?(t=this.beginQuery(),this.endQuery(),n=()=>this.isQueryAvailable(t,G().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION"))):n=()=>!0;return{query:t,isFencePassed:n}}downloadMatrixFromPackedTexture(e,t,n){return this.downloadMatrixDriver(e,()=>Kv(this.gl,t,n))}createProgram(e){this.throwIfDisposed();let t=this.gl,n=lv(t,e),o=Rv(t),s=uv(t);return Ne(t,()=>t.attachShader(s,o)),Ne(t,()=>t.attachShader(s,n)),cv(t,s),this.debug&&_f(t,s),this.vertexAttrsAreBound||(this.setProgram(s),this.vertexAttrsAreBound=Vv(t,this.program,this.vertexBuffer)),s}deleteProgram(e){this.throwIfDisposed(),e===this.program&&(this.program=null),e!=null&&Ne(this.gl,()=>this.gl.deleteProgram(e))}setProgram(e){this.throwIfDisposed(),this.program=e,this.program!=null&&this.debug&&_f(this.gl,this.program),Ne(this.gl,()=>this.gl.useProgram(e))}getUniformLocation(e,t,n=!0){return this.throwIfDisposed(),n?gv(this.gl,e,t):xv(this.gl,e,t)}getAttributeLocation(e,t){return this.throwIfDisposed(),Ne(this.gl,()=>this.gl.getAttribLocation(e,t))}getUniformLocationNoThrow(e,t){return this.throwIfDisposed(),this.gl.getUniformLocation(e,t)}setInputMatrixTexture(e,t,n){this.throwIfDisposed(),this.throwIfNoProgram(),yv(this.gl,e,t,n)}setOutputMatrixTexture(e,t,n){this.setOutputMatrixTextureDriver(e,n,t)}setOutputPackedMatrixTexture(e,t,n){this.throwIfDisposed();let[o,s]=ji(t,n);this.setOutputMatrixTextureDriver(e,o,s)}setOutputMatrixWriteRegion(e,t,n,o){this.setOutputMatrixWriteRegionDriver(n,e,o,t)}setOutputPackedMatrixWriteRegion(e,t,n,o){throw new Error("setOutputPackedMatrixWriteRegion not implemented.")}debugValidate(){this.program!=null&&_f(this.gl,this.program),dp(this.gl)}executeProgram(){this.throwIfDisposed(),this.throwIfNoProgram();let e=this.gl;this.debug&&this.debugValidate(),Ne(e,()=>e.drawElements(e.TRIANGLES,6,e.UNSIGNED_SHORT,0))}blockUntilAllProgramsCompleted(){this.throwIfDisposed(),Ne(this.gl,()=>this.gl.finish())}getQueryTimerExtension(){return this.disjointQueryTimerExtension==null&&(this.disjointQueryTimerExtension=fp(this.gl,G().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION")===2?"EXT_disjoint_timer_query_webgl2":"EXT_disjoint_timer_query")),this.disjointQueryTimerExtension}getQueryTimerExtensionWebGL2(){return this.getQueryTimerExtension()}getQueryTimerExtensionWebGL1(){return this.getQueryTimerExtension()}beginQuery(){if(G().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION")===2){let n=this.gl,o=this.getQueryTimerExtensionWebGL2(),s=n.createQuery();return n.beginQuery(o.TIME_ELAPSED_EXT,s),s}let e=this.getQueryTimerExtensionWebGL1(),t=e.createQueryEXT();return e.beginQueryEXT(e.TIME_ELAPSED_EXT,t),t}endQuery(){if(G().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION")===2){let t=this.gl,n=this.getQueryTimerExtensionWebGL2();t.endQuery(n.TIME_ELAPSED_EXT);return}let e=this.getQueryTimerExtensionWebGL1();e.endQueryEXT(e.TIME_ELAPSED_EXT)}async waitForQueryAndGetTime(e){return await y.repeatedTry(()=>this.disposed||this.isQueryAvailable(e,G().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION"))),this.getQueryTime(e,G().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION"))}getQueryTime(e,t){if(t===0)return null;if(t===2){let n=this.gl;return n.getQueryParameter(e,n.QUERY_RESULT)/1e6}else{let n=this.getQueryTimerExtensionWebGL1();return n.getQueryObjectEXT(e,n.QUERY_RESULT_EXT)/1e6}}isQueryAvailable(e,t){if(t===0)return!0;if(t===2){let n=this.gl,o=this.getQueryTimerExtensionWebGL2(),s=n.getQueryParameter(e,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(e,n.QUERY_RESULT_AVAILABLE_EXT);return this.disjoint==null&&(this.disjoint=this.gl.getParameter(n.GPU_DISJOINT_EXT)),o&&!this.disjoint}}pollFence(e){return new Promise(t=>{this.addItemToPoll(()=>e.isFencePassed(),()=>t())})}pollItems(){let e=j5(this.itemsToPoll.map(t=>t.isDoneFn));for(let t=0;t<=e;++t){let{resolveFn:n}=this.itemsToPoll[t];n()}this.itemsToPoll=this.itemsToPoll.slice(e+1)}addItemToPoll(e,t){this.itemsToPoll.push({isDoneFn:e,resolveFn:t}),!(this.itemsToPoll.length>1)&&y.repeatedTry(()=>(this.pollItems(),this.itemsToPoll.length===0))}bindTextureToFrameBuffer(e){this.throwIfDisposed(),vf(this.gl,e,this.framebuffer),this.debug&&dp(this.gl)}unbindTextureToFrameBuffer(){this.outputTexture!=null?(vf(this.gl,this.outputTexture,this.framebuffer),this.debug&&dp(this.gl)):Rg(this.gl,this.framebuffer)}downloadMatrixDriver(e,t){this.bindTextureToFrameBuffer(e);let n=t();return this.unbindTextureToFrameBuffer(),n}setOutputMatrixTextureDriver(e,t,n){this.throwIfDisposed();let o=this.gl;vf(o,e,this.framebuffer),this.debug&&dp(o),this.outputTexture=e,Ne(o,()=>o.viewport(0,0,t,n)),Ne(o,()=>o.scissor(0,0,t,n))}setOutputMatrixWriteRegionDriver(e,t,n,o){this.throwIfDisposed(),Ne(this.gl,()=>this.gl.scissor(e,t,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 j5(r){let e=0;for(;e<r.length&&r[e]();++e);return e-1}var{getBroadcastDims:bD}=N;function wD(r,e,t,n){let o=[];r.forEach(d=>{let h=y.sizeFromShape(d.shapeInfo.logicalShape);d.shapeInfo.isUniform?o.push(`uniform float ${d.name}${h>1?`[${h}]`:""};`):(o.push(`uniform sampler2D ${d.name};`),o.push(`uniform int offset${d.name};`))});let s=o.join(`
`),a=r.map(d=>G5(d,e,n)).join(`
`),i=e.texShape,l=Lt(),u=H5(l),c,p,m=Y5(l);return e.isPacked?(c=U5(e.logicalShape,i),p=X5(l)):(c=q5(e.logicalShape,i),p=K5(l)),n&&(m+=Z5),[m,u,p,s,c,a,t].join(`
`)}function gp(r){let e=r.shapeInfo.logicalShape;switch(e.length){case 0:return J5(r);case 1:return Q5(r);case 2:return e8(r);case 3:return t8(r);case 4:return r8(r);case 5:return n8(r);case 6:return o8(r);default:throw new Error(`${e.length}-D input sampling is not yet supported`)}}function _D(r){switch(r.shapeInfo.logicalShape.length){case 0:return s8(r);case 1:return i8(r);case 2:return a8(r);case 3:return l8(r);default:return u8(r)}}function G5(r,e,t=!1){let n="";t?n+=_D(r):n+=gp(r);let o=r.shapeInfo.logicalShape,s=e.logicalShape;return o.length<=s.length&&(t?n+=c8(r,e):n+=p8(r,e)),n}function U5(r,e){switch(r.length){case 0:return vD();case 1:return m8(r,e);case 2:return h8(r,e);case 3:return f8(r,e);default:return d8(r,e)}}function q5(r,e){switch(r.length){case 0:return vD();case 1:return g8(r,e);case 2:return _8(r,e);case 3:return x8(r,e);case 4:return y8(r,e);case 5:return b8(r,e);case 6:return w8(r,e);default:throw new Error(`${r.length}-D output sampling is not yet supported`)}}function H5(r){return`
float sampleTexture(sampler2D textureSampler, vec2 uv) {
return ${r.texture2D}(textureSampler, uv).r;
}
`}function K5(r){return`
void setOutput(float val) {
${r.output} = vec4(val, 0, 0, 0);
}
`}function X5(r){return`
void setOutput(vec4 val) {
${r.output} = val;
}
`}function Y5(r){return`${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);
}
${v8}
${k8}
${C8}
`}var v8=`
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);
}
`,k8=`
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);
}
`,C8=`
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);
}
`,Z5=`
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 vD(){return`
int getOutputCoords() {
return 0;
}
`}function m8(r,e){let t=[Math.ceil(e[0]/2),Math.ceil(e[1]/2)];return t[0]===1?`
int getOutputCoords() {
return 2 * int(resultUV.x * ${t[1]}.0);
}
`:t[1]===1?`
int getOutputCoords() {
return 2 * int(resultUV.y * ${t[0]}.0);
}
`:`
int getOutputCoords() {
ivec2 resTexRC = ivec2(resultUV.yx *
vec2(${t[0]}, ${t[1]}));
return 2 * (resTexRC.x * ${t[1]} + resTexRC.y);
}
`}function g8(r,e){return e[0]===1?`
int getOutputCoords() {
return int(resultUV.x * ${e[1]}.0);
}
`:e[1]===1?`
int getOutputCoords() {
return int(resultUV.y * ${e[0]}.0);
}
`:`
int getOutputCoords() {
ivec2 resTexRC = ivec2(resultUV.yx *
vec2(${e[0]}, ${e[1]}));
return resTexRC.x * ${e[1]} + resTexRC.y;
}
`}function f8(r,e){let t=[Math.ceil(e[0]/2),Math.ceil(e[1]/2)],n=Math.ceil(r[2]/2),o=n*Math.ceil(r[1]/2);return`
ivec3 getOutputCoords() {
ivec2 resTexRC = ivec2(resultUV.yx *
vec2(${t[0]}, ${t[1]}));
int index = resTexRC.x * ${t[1]} + resTexRC.y;
int b = index / ${o};
index -= b * ${o};
int r = 2 * (index / ${n});
int c = imod(index, ${n}) * 2;
return ivec3(b, r, c);
}
`}function x8(r,e){let t=Fs(["r","c","d"],r);return`
ivec3 getOutputCoords() {
ivec2 resTexRC = ivec2(resultUV.yx *
vec2(${e[0]}, ${e[1]}));
int index = resTexRC.x * ${e[1]} + resTexRC.y;
${t}
return ivec3(r, c, d);
}
`}function d8(r,e){let t=[Math.ceil(e[0]/2),Math.ceil(e[1]/2)],n=Math.ceil(r[r.length-1]/2),o=n*Math.ceil(r[r.length-2]/2),s=o,a="",i="b, r, c";for(let l=2;l<r.length-1;l++)s*=r[r.length-l-1],a=`
int b${l} = index / ${s};
index -= b${l} * ${s};
`+a,i=`b${l}, `+i;return`
ivec${r.length} getOutputCoords() {
ivec2 resTexRC = ivec2(resultUV.yx *
vec2(${t[0]}, ${t[1]}));
int index = resTexRC.x * ${t[1]} + resTexRC.y;
${a}
int b = index / ${o};
index -= b * ${o};
int r = 2 * (index / ${n});
int c = imod(index, ${n}) * 2;
return ivec${r.length}(${i});
}
`}function y8(r,e){let t=Fs(["r","c","d","d2"],r);return`
ivec4 getOutputCoords() {
ivec2 resTexRC = ivec2(resultUV.yx *
vec2(${e[0]}, ${e[1]}));
int index = resTexRC.x * ${e[1]} + resTexRC.y;
${t}
return ivec4(r, c, d, d2);
}
`}function b8(r,e){let t=Fs(["r","c","d","d2","d3"],r);return`
ivec5 getOutputCoords() {
ivec2 resTexRC = ivec2(resultUV.yx * vec2(${e[0]},
${e[1]}));
int index = resTexRC.x * ${e[1]} + resTexRC.y;
${t}
ivec5 outShape = ivec5(r, c, d, d2, d3);
return outShape;
}
`}function w8(r,e){let t=Fs(["r","c","d","d2","d3","d4"],r);return`
ivec6 getOutputCoords() {
ivec2 resTexRC = ivec2(resultUV.yx *
vec2(${e[0]}, ${e[1]}));
int index = resTexRC.x * ${e[1]} + resTexRC.y;
${t}
ivec6 result = ivec6(r, c, d, d2, d3, d4);
return result;
}
`}function h8(r,e){let t=[Math.ceil(e[0]/2),Math.ceil(e[1]/2)];if(y.arraysEqual(r,e))return`
ivec2 getOutputCoords() {
return 2 * ivec2(resultUV.yx * vec2(${t[0]}, ${t[1]}));
}
`;let n=Math.ceil(r[1]/2);return`
ivec2 getOutputCoords() {
ivec2 resTexRC = ivec2(resultUV.yx *
vec2(${t[0]}, ${t[1]}));
int index = resTexRC.x * ${t[1]} + resTexRC.y;
int r = 2 * (index / ${n});
int c = imod(index, ${n}) * 2;
return ivec2(r, c);
}
`}function _8(r,e){return y.arraysEqual(r,e)?`
ivec2 getOutputCoords() {
return ivec2(resultUV.yx * vec2(${e[0]}, ${e[1]}));
}
`:r[1]===1?`
ivec2 getOutputCoords() {
ivec2 resTexRC = ivec2(resultUV.yx *
vec2(${e[0]}, ${e[1]}));
int index = resTexRC.x * ${e[1]} + resTexRC.y;
return ivec2(index, 0);
}
`:r[0]===1?`
ivec2 getOutputCoords() {
ivec2 resTexRC = ivec2(resultUV.yx *
vec2(${e[0]}, ${e[1]}));
int index = resTexRC.x * ${e[1]} + resTexRC.y;
return ivec2(0, index);
}
`:`
ivec2 getOutputCoords() {
ivec2 resTexRC = ivec2(resultUV.yx *
vec2(${e[0]}, ${e[1]}));
int index = resTexRC.x * ${e[1]} + resTexRC.y;
int r = index / ${r[1]};
int c = index - r * ${r[1]};
return ivec2(r, c);
}
`}function Ju(r){return`offset${r}`}function s8(r){let e=r.name,t="get"+e.charAt(0).toUpperCase()+e.slice(1),n=Lt();return`
vec4 ${t}() {
return ${n.texture2D}(${e}, halfCR);
}
`}function J5(r){let e=r.name,t="get"+e.charAt(0).toUpperCase()+e.slice(1);if(r.shapeInfo.isUniform)return`float ${t}() {return ${e};}`;let[n,o]=r.shapeInfo.texShape;if(n===1&&o===1)return`
float ${t}() {
return sampleTexture(${e}, halfCR);
}
`;let[s,a]=r.shapeInfo.texShape,i=Ju(e);return`
float ${t}() {
vec2 uv = uvFromFlat(${s}, ${a}, ${i});
return sampleTexture(${e}, uv);
}
`}function i8(r){let e=r.name,t="get"+e.charAt(0).toUpperCase()+e.slice(1),n=r.shapeInfo.texShape,o=[Math.ceil(n[0]/2),Math.ceil(n[1]/2)],s=Lt();return`
vec4 ${t}(int index) {
vec2 uv = packedUVfrom1D(
${o[0]}, ${o[1]}, index);
return ${s.texture2D}(${e}, uv);
}
`}function Q5(r){let e=r.name,t="get"+e.charAt(0).toUpperCase()+e.slice(1);if(r.shapeInfo.isUniform)return`
float ${t}(int index) {
${xp(r)}
}
`;let n=r.shapeInfo.texShape,o=n[0],s=n[1];if(s===1&&o===1)return`
float ${t}(int index) {
return sampleTexture(${e}, halfCR);
}
`;let a=Ju(e);return s===1?`
float ${t}(int index) {
vec2 uv = vec2(0.5, (float(index + ${a}) + 0.5) / ${o}.0);
return sampleTexture(${e}, uv);
}
`:o===1?`
float ${t}(int index) {
vec2 uv = vec2((float(index + ${a}) + 0.5) / ${s}.0, 0.5);
return sampleTexture(${e}, uv);
}
`:`
float ${t}(int index) {
vec2 uv = uvFromFlat(${o}, ${s}, index + ${a});
return sampleTexture(${e}, uv);
}
`}function a8(r){let e=r.shapeInfo.logicalShape,t=r.name,n="get"+t.charAt(0).toUpperCase()+t.slice(1),o=r.shapeInfo.texShape,s=o[0],a=o[1],i=Lt();if(o!=null&&y.arraysEqual(e,o))return`
vec4 ${n}(int row, int col) {
vec2 uv = (vec2(col, row) + halfCR) / vec2(${a}.0, ${s}.0);
return ${i.texture2D}(${t}, uv);
}
`;let l=[Math.ceil(o[0]/2),Math.ceil(o[1]/2)],u=Math.ceil(e[1]/2);return`
vec4 ${n}(int row, int col) {
vec2 uv = packedUVfrom2D(${u}, ${l[0]}, ${l[1]}, row, col);
return ${i.texture2D}(${t}, uv);
}
`}function e8(r){let e=r.shapeInfo.logicalShape,t=r.name,n="get"+t.charAt(0).toUpperCase()+t.slice(1),o=r.shapeInfo.texShape;if(o!=null&&y.arraysEqual(e,o)){let p=o[0],m=o[1];return`
float ${n}(int row, int col) {
vec2 uv = (vec2(col, row) + halfCR) / vec2(${m}.0, ${p}.0);
return sampleTexture(${t}, uv);
}
`}let{newShape:s,keptDims:a}=y.squeezeShape(e),i=s;if(i.length<e.length){let p=yp(r,i),m=["row","col"];return`
${gp(p)}
float ${n}(int row, int col) {
return ${n}(${bp(m,a)});
}
`}if(r.shapeInfo.isUniform)return`
float ${n}(int row, int col) {
int index = round(dot(vec2(row, col), vec2(${e[1]}, 1)));
${xp(r)}
}
`;let l=o[0],u=o[1],c=Ju(t);return u===1?`
float ${n}(int row, int col) {
float index = dot(vec3(row, col, ${c}), vec3(${e[1]}, 1, 1));
vec2 uv = vec2(0.5, (index + 0.5) / ${l}.0);
return sampleTexture(${t}, uv);
}
`:l===1?`
float ${n}(int row, int col) {
float index = dot(vec3(row, col, ${c}), vec3(${e[1]}, 1, 1));
vec2 uv = vec2((index + 0.5) / ${u}.0, 0.5);
return sampleTexture(${t}, uv);
}
`:`
float ${n}(int row, int col) {
// Explicitly use integer operations as dot() only works on floats.
int index = row * ${e[1]} + col + ${c};
vec2 uv = uvFromFlat(${l}, ${u}, index);
return sampleTexture(${t}, uv);
}
`}function l8(r){let e=r.shapeInfo.logicalShape,t=r.name,n="get"+t.charAt(0).toUpperCase()+t.slice(1),o=r.shapeInfo.texShape,s=[Math.ceil(o[0]/2),Math.ceil(o[1]/2)];if(e[0]===1){let p=e.slice(1),m=[1,2],f=yp(r,p),d=["b","row","col"];return`
${_D(f)}
vec4 ${n}(int b, int row, int col) {
return ${n}(${bp(d,m)});
}
`}let a=s[0],i=s[1],l=Math.ceil(e[2]/2),u=l*Math.ceil(e[1]/2),c=Lt();return`
vec4 ${n}(int b, int row, int col) {
vec2 uv = packedUVfrom3D(
${a}, ${i}, ${u}, ${l}, b, row, col);
return ${c.texture2D}(${t}, uv);
}
`}function t8(r){let e=r.shapeInfo.logicalShape,t=r.name,n="get"+t.charAt(0).toUpperCase()+t.slice(1),o=e[1]*e[2],s=e[2],{newShape:a,keptDims:i}=y.squeezeShape(e),l=a;if(l.length<e.length){let d=yp(r,l),h=["row","col","depth"];return`
${gp(d)}
float ${n}(int row, int col, int depth) {
return ${n}(${bp(h,i)});
}
`}if(r.shapeInfo.isUniform)return`
float ${n}(int row, int col, int depth) {
int index = round(dot(vec3(row, col, depth),
vec3(${o}, ${s}, 1)));
${xp(r)}
}
`;let u=r.shapeInfo.texShape,c=u[0],p=u[1],m=r.shapeInfo.flatOffset;if(p===o&&m==null)return`
float ${n}(int row, int col, int depth) {
float texR = float(row);
float texC = dot(vec2(col, depth), vec2(${s}, 1));
vec2 uv = (vec2(texC, texR) + halfCR) /
vec2(${p}.0, ${c}.0);
return sampleTexture(${t}, uv);
}
`;if(p===s&&m==null)return`
float ${n}(int row, int col, int depth) {
float texR = dot(vec2(row, col), vec2(${e[1]}, 1));
float texC = float(depth);
vec2 uv = (vec2(texC, texR) + halfCR) / vec2(${p}.0, ${c}.0);
return sampleTexture(${t}, uv);
}
`;let f=Ju(t);return`
float ${n}(int row, int col, int depth) {
// Explicitly use integer operations as dot() only works on floats.
int index = row * ${o} + col * ${s} + depth + ${f};
vec2 uv = uvFromFlat(${c}, ${p}, index);
return sampleTexture(${t}, uv);
}
`}function u8(r){let e=r.shapeInfo.logicalShape,t=e.length,n=r.name,o="get"+n.charAt(0).toUpperCase()+n.slice(1),s=r.shapeInfo.texShape,a=[Math.ceil(s[0]/2),Math.ceil(s[1]/2)],i=a[0],l=a[1],u=Math.ceil(e[t-1]/2),c=u*Math.ceil(e[t-2]/2),p="int b, int row, int col",m=`b * ${c} + (row / 2) * ${u} + (col / 2)`;for(let d=2;d<t-1;d++)p=`int b${d}, `+p,c*=e[t-d-1],m=`b${d} * ${c} + `+m;let f=Lt();return`
vec4 ${o}(${p}) {
int index = ${m};
int texR = index / ${l};
int texC = index - texR * ${l};
vec2 uv = (vec2(texC, texR) + halfCR) / vec2(${l}, ${i});
return ${f.texture2D}(${n}, uv);
}
`}function r8(r){let e=r.shapeInfo.logicalShape,t=r.name,n="get"+t.charAt(0).toUpperCase()+t.slice(1),o=e[3],s=e[2]*o,a=e[1]*s,{newShape:i,keptDims:l}=y.squeezeShape(e);if(i.length<e.length){let d=yp(r,i),h=["row","col","depth","depth2"];return`
${gp(d)}
float ${n}(int row, int col, int depth, int depth2) {
return ${n}(${bp(h,l)});
}
`}if(r.shapeInfo.isUniform)return`
float ${n}(int row, int col, int depth, int depth2) {
int index = round(dot(vec4(row, col, depth, depth2),
vec4(${a}, ${s}, ${o}, 1)));
${xp(r)}
}
`;let u=r.shapeInfo.flatOffset,c=r.shapeInfo.texShape,p=c[0],m=c[1];if(m===a&&u==null)return`
float ${n}(int row, int col, int depth, int depth2) {
float texR = float(row);
float texC =
dot(vec3(col, depth, depth2),
vec3(${s}, ${o}, 1));
vec2 uv = (vec2(texC, texR) + halfCR) /
vec2(${m}.0, ${p}.0);
return sampleTexture(${t}, uv);
}
`;if(m===o&&u==null)return`
float ${n}(int row, int col, int depth, int depth2) {
float texR = dot(vec3(row, col, depth),
vec3(${e[1]*e[2]}, ${e[2]}, 1));
float texC = float(depth2);
vec2 uv = (vec2(texC, texR) + halfCR) /
vec2(${m}.0, ${p}.0);
return sampleTexture(${t}, uv);
}
`;let f=Ju(t);return`
float ${n}(int row, int col, int depth, int depth2) {
// Explicitly use integer operations as dot() only works on floats.
int index = row * ${a} + col * ${s} +
depth * ${o} + depth2;
vec2 uv = uvFromFlat(${p}, ${m}, index + ${f});
return sampleTexture(${t}, uv);
}
`}function n8(r){let e=r.shapeInfo.logicalShape,t=r.name,n="get"+t.charAt(0).toUpperCase()+t.slice(1),o=e[4],s=e[3]*o,a=e[2]*s,i=e[1]*a,{newShape:l,keptDims:u}=y.squeezeShape(e);if(l.length<e.length){let h=yp(r,l),g=["row","col","depth","depth2","depth3"];return`
${gp(h)}
float ${n}(int row, int col, int depth, int depth2, int depth3) {
return ${n}(${bp(g,u)});
}
`}if(r.shapeInfo.isUniform)return`
float ${n}(int row, int col, int depth, int depth2, int depth3) {
float index = dot(
vec4(row, col, depth, depth2),
vec4(${i}, ${a}, ${s}, ${o})) +
depth3;
${xp(r)}
}
`;let c=r.shapeInfo.flatOffset,p=r.shapeInfo.texShape,m=p[0],f=p[1];if(f===i&&c==null)return`
float ${n}(int row, int col, int depth, int depth2, int depth3) {
int texR = row;
float texC = dot(vec4(col, depth, depth2, depth3),
vec4(${a}, ${s}, ${o}, 1));
vec2 uv = (vec2(texC, texR) + halfCR) /
vec2(${f}.0, ${m}.0);
return sampleTexture(${t}, uv);
}
`;if(f===o&&c==null)return`
float ${n}(int row, int col, int depth, int depth2, int depth3) {
float texR = dot(
vec4(row, col, depth, depth2),
vec4(${e[1]*e[2]*e[3]},
${e[2]*e[3]}, ${e[3]}, 1));
int texC = depth3;
vec2 uv = (vec2(texC, texR) + halfCR) /
vec2(${f}.0, ${m}.0);
return sampleTexture(${t}, uv);
}
`;let d=Ju(t);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 * ${i} + col * ${a} + depth * ${s} +
depth2 * ${o} + depth3 + ${d};
vec2 uv = uvFromFlat(${m}, ${f}, index);
return sampleTexture(${t}, uv);
}
`}function o8(r){let e=r.shapeInfo.logicalShape,t=r.name,n="get"+t.charAt(0).toUpperCase()+t.slice(1),{newShape:o,keptDims:s}=y.squeezeShape(e);if(o.length<e.length){let g=yp(r,o),x=["row","col","depth","depth2","depth3","depth4"];return`
${gp(g)}
float ${n}(int row, int col, int depth,
int depth2, int depth3, int depth4) {
return ${n}(${bp(x,s)});
}
`}let a=e[5],i=e[4]*a,l=e[3]*i,u=e[2]*l,c=e[1]*u;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}, ${u}, ${l}, ${i})) +
dot(
vec2(depth3, depth4),
vec2(${a}, 1)));
${xp(r)}
}
`;let p=r.shapeInfo.flatOffset,m=r.shapeInfo.texShape,f=m[0],d=m[1];if(d===c&&p==null)return`
float ${n}(int row, int col, int depth,
int depth2, int depth3, int depth4) {
int texR = row;
float texC = dot(vec4(col, depth, depth2, depth3),
vec4(${u}, ${l}, ${i}, ${a})) +
float(depth4);
vec2 uv = (vec2(texC, texR) + halfCR) /
vec2(${d}.0, ${f}.0);
return sampleTexture(${t}, uv);
}
`;if(d===a&&p==null)return`
float ${n}(int row, int col, int depth,
int depth2, int depth3, int depth4) {
float texR = dot(vec4(row, col, depth, depth2),
vec4(${e[1]*e[2]*e[3]*e[4]},
${e[2]*e[3]*e[4]},
${e[3]*e[4]},
${e[4]})) + float(depth3);
int texC = depth4;
vec2 uv = (vec2(texC, texR) + halfCR) /
vec2(${d}.0, ${f}.0);
return sampleTexture(${t}, uv);
}
`;let h=Ju(t);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 * ${u} + depth * ${l} +
depth2 * ${i} + depth3 * ${a} + depth4 + ${h};
vec2 uv = uvFromFlat(${f}, ${d}, index);
return sampleTexture(${t}, uv);
}
`}function xp(r){let e=r.name,t=y.sizeFromShape(r.shapeInfo.logicalShape);return t<2?`return ${e};`:`
for (int i = 0; i < ${t}; i++) {
if (i == index) {
return ${e}[i];
}
}
`}function c8(r,e){let t=r.name,n=t.charAt(0).toUpperCase()+t.slice(1),o="get"+n+"AtOutCoords",s=r.shapeInfo.logicalShape.length,a=e.logicalShape.length,i=bD(r.shapeInfo.logicalShape,e.logicalShape),l=ze(a),u=a-s,c,p=["x","y","z","w","u","v"];s===0?c="":a<2&&i.length>=1?c="coords = 0;":c=i.map(b=>`coords.${p[b+u]} = 0;`).join(`
`);let m="";a<2&&s>0?m="coords":m=r.shapeInfo.logicalShape.map((b,w)=>`coords.${p[w+u]}`).join(", ");let f="return outputValue;",h=y.sizeFromShape(r.shapeInfo.logicalShape)===1,x=y.sizeFromShape(e.logicalShape)===1;if(s===1&&!h&&!x)f=`
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`;else if(h&&!x)a===1?f=`
return vec4(outputValue.x, outputValue.x, 0., 0.);
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vec4 ${o}() {
${l} coords = getOutputCoords();
${c}
vec4 outputValue = get${n}(${m});
${f}
}
`}function p8(r,e){let t=r.name,n=t.charAt(0).toUpperCase()+t.slice(1),o="get"+n+"AtOutCoords",s=e.texShape,a=r.shapeInfo.texShape,i=r.shapeInfo.logicalShape.length,l=e.logicalShape.length;if(!r.shapeInfo.isUniform&&i===l&&r.shapeInfo.flatOffset==null&&y.arraysEqual(a,s))return`
float ${o}() {
return sampleTexture(${t}, resultUV);
}
`;let u=ze(l),c=bD(r.shapeInfo.logicalShape,e.logicalShape),p=l-i,m,f=["x","y","z","w","u","v"];i===0?m="":l<2&&c.length>=1?m="coords = 0;":m=c.map(h=>`coords.${f[h+p]} = 0;`).join(`
`);let d="";return l<2&&i>0?d="coords":d=r.shapeInfo.logicalShape.map((h,g)=>`coords.${f[g+p]}`).join(", "),`
float ${o}() {
${u} coords = getOutputCoords();
${m}
return get${n}(${d});
}
`}function ze(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 ${r} is not yet supported`)}function yp(r,e){let t=JSON.parse(JSON.stringify(r));return t.shapeInfo.logicalShape=e,t}function bp(r,e){return e.map(t=>r[t]).join(", ")}function kD(r,e,t,n){let o=e.userCode,s=t.map((f,d)=>{let h={logicalShape:f.shape,texShape:f.isUniform?null:f.texData.texShape,isUniform:f.isUniform,isPacked:f.isUniform?!1:f.texData.isPacked,flatOffset:null};return f.texData!=null&&f.texData.slice!=null&&f.texData.slice.flatOffset>0&&(h.flatOffset=f.texData.slice.flatOffset),{name:e.variableNames[d],shapeInfo:h}}),a=s.map(f=>f.shapeInfo),i={logicalShape:n.shape,texShape:n.texData.texShape,isUniform:!1,isPacked:n.texData.isPacked,flatOffset:null},l=wD(s,i,o,e.packedInputs),u=r.createProgram(l),c=null,p=r.getUniformLocation(u,"NAN",!1);G().getNumber("WEBGL_VERSION")===1&&(c=r.getUniformLocation(u,"INFINITY",!1));let m={};for(let f=0;f<e.variableNames.length;f++){let d=e.variableNames[f],h=!1;m[d]=r.getUniformLocation(u,d,h),m[`offset${d}`]=r.getUniformLocation(u,`offset${d}`,h)}return{program:e,source:l,webGLProgram:u,uniformLocations:m,inShapeInfos:a,outShapeInfo:i,infLoc:c,nanLoc:p}}function CD(r,e){if(r.length!==e.length)throw Error(`Binary was compiled with ${r.length} inputs, but was executed with ${e.length} inputs`);r.forEach((t,n)=>{let o=t.logicalShape,s=e[n],a=s.shape;if(!y.arraysEqual(o,a))throw Error(`Binary was compiled with different shapes than the current args. Shapes ${o} and ${a} must match`);if(t.isUniform&&s.isUniform)return;let i=t.texShape,l=s.isUniform?null:s.texData.texShape;if(!y.arraysEqual(i,l))throw Error(`Binary was compiled with different texture shapes than the current args. Shape ${i} and ${l} must match`)})}function ID(r,e,t,n,o){CD(e.inShapeInfos,t),CD([e.outShapeInfo],[n]);let s=n.texData.texture,a=n.texData.texShape;n.texData.isPacked?r.setOutputPackedMatrixTexture(s,a[0],a[1]):r.setOutputMatrixTexture(s,a[0],a[1]),r.setProgram(e.webGLProgram),G().getNumber("WEBGL_VERSION")===1&&e.infLoc!==null&&r.gl.uniform1f(e.infLoc,Infinity),e.nanLoc!==null&&r.gl.uniform1f(e.nanLoc,NaN),t.forEach((i,l)=>{let u=e.program.variableNames[l],c=e.uniformLocations[u],p=e.uniformLocations[`offset${u}`];if(c!=null){if(i.isUniform){if(y.sizeFromShape(i.shape)<2)r.gl.uniform1f(c,i.uniformValues[0]);else{let m=i.uniformValues;m instanceof Float32Array||(m=new Float32Array(m)),r.gl.uniform1fv(c,m)}return}i.texData.slice!=null&&p!=null&&r.gl.uniform1i(p,i.texData.slice.flatOffset),r.setInputMatrixTexture(i.texData.texture,c,l)}}),o!=null&&o(r,e.webGLProgram),r.executeProgram()}function SD(r,e,t){let n="";e.concat(t).forEach(a=>{let i=a.texData!=null&&a.texData.slice!=null&&a.texData.slice.flatOffset>0,l=a.isUniform?"uniform":a.texData.texShape;n+=`${a.shape}_${l}_${i}`});let o=r.userCode,s=r.constructor.name;return s+="_"+n+"_"+o,s}var{addImpl:ND,bincountImpl:Ug,bincountReduceImpl:TD,ceilImpl:ED,concatImpl:AD,expImpl:DD,expm1Impl:$D,floorImpl:RD,gatherV2Impl:FD,greaterImpl:OD,lessImpl:PD,linSpaceImpl:MD,logImpl:LD,maxImpl:zD,maximumImpl:BD,minimumImpl:VD,multiplyImpl:WD,negImpl:jD,prodImpl:GD,rangeImpl:UD,rsqrtImpl:qD,simpleAbsImpl:qg,sliceImpl:HD,stridedSliceImpl:KD,subImpl:XD,tileImpl:YD,topKImpl:ZD,transposeImpl:wp,uniqueImpl:JD}=Ig;function Xv(r,e){return["x","y","z","w","u","v"].slice(0,e).map(t=>`${r}.${t}`)}function qt(r,e){return e===1?[r]:Xv(r,e)}function QD(r,e){if(r===1)return"rc";let t="";for(let n=0;n<r;n++)t+=e[n],n<r-1&&(t+=",");return t}var Yv=class{constructor(e){this.variableNames=["A"],this.packedInputs=!1,this.packedOutput=!0,this.outputShape=e;let t=e.length;if(t===0)this.userCode=`
void main() {
setOutput(vec4(getA(), 0., 0., 0.));
}
`;else{let n=qt("rc",t),o=ze(t),s=I8(t,e,n),a=S8(t,e[e.length-1],e[e.length-2],n),i=N8(e,n);this.userCode=`
void main() {
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setOutput(vec4(0));
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${a}
setOutput(vec4(${i}));
}
}
`}}};function T8(r,e){let t=[];for(let n=0;n<=1;n++)for(let o=0;o<=1;o++){let s=`${n===0?"r":"rp1"}, ${o===0?"c":"cp1"}`;for(let a=2;a<r;a++)s=`${e[e.length-1-a]},`+s;t.push(s)}return t}function I8(r,e,t){if(r===1)return`rc > ${e[0]}`;let n="";for(let o=r-2;o<r;o++)n+=`${t[o]} >= ${e[o]}`,o<r-1&&(n+="||");return n}function S8(r,e,t,n){if(r===1)return"";let o=n.slice(-2);return`
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int c = ${o[1]};
int rp1 = r + 1;
int cp1 = c + 1;
bool cEdge = cp1 >= ${e};
bool rEdge = rp1 >= ${t};
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${s}
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result[${o}] =
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${E8(t)}
${hp(e)}
void main() {
ivec3 rc = getOutputCoords();
vec4 result = vec4(0.);
ivec3 thisRC;
int rows = ${e[1]};
int cols = ${e[2]};
${n}
setOutput(result);
}
`}};function E8(r){return`
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${Fs(["r","c","d"],r)}
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${t}
}
void main() {
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float y = unaryOperation(x);
setOutput(y);
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result.b = (x.b >= 0.0) ? x.b : (exp(x.b) - 1.0);
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vec4 result = x * vec4(greaterThanEqual(x, vec4(0.0)));
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vec4 result = min(x, vec4(6.)) * vec4(greaterThanEqual(x, vec4(0.0)));
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${t}
}
void main() {
vec4 x = getAAtOutCoords();
vec4 y = unaryOperation(x);
setOutput(y);
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void main() {
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vec4 packedInput = getA(${s});
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Please use tf.complex(real, imag).");let o={};return this.texData.set(o,{shape:t,dtype:n,values:e,usage:Rr.UPLOAD,refCount:1,complexParentRefCount:0}),o}incRef(e){let t=this.texData.get(e);t.refCount++}decRef(e){if(this.texData.has(e)){let t=this.texData.get(e);t.refCount--}}decComplexRef(e){if(this.texData.has(e)){let t=this.texData.get(e);t.complexParentRefCount>0&&t.refCount--}}move(e,t,n,o){if(G().getBool("DEBUG")&&this.checkNumericalProblems(t),o==="complex64")throw new Error("Cannot write to a complex64 dtype. 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Consider enabling float32 rendering: 'tf.env().set('WEBGL_RENDER_FLOAT32_ENABLED', true);'`):Error(`The value ${n} cannot be represented on this device.`)}}getValuesFromTexture(e){let{shape:t,dtype:n,isPacked:o}=this.texData.get(e),s=y.sizeFromShape(t);if(G().getBool("WEBGL_DOWNLOAD_FLOAT_ENABLED")){let m=this.decode(e),f=this.texData.get(m.dataId),d=this.gpgpu.downloadMatrixFromPackedTexture(f.texture,...gl(t)).subarray(0,s);return this.disposeIntermediateTensorInfo(m),d}let a=G().getBool("WEBGL_PACK")&&o===!0,i=a?kf(t):t,l=a?new Av(i):new Ev(i),u=this.runWebGLProgram(l,[{shape:i,dtype:n,dataId:e}],"float32"),c=this.texData.get(u.dataId),p=this.gpgpu.downloadByteEncodedFloatMatrixFromOutputTexture(c.texture,c.texShape[0],c.texShape[1]).subarray(0,s);return this.disposeIntermediateTensorInfo(u),p}async time(e){let t=this.activeTimers,n=[],o=!1;this.programTimersStack==null?(this.programTimersStack=n,o=!0):this.activeTimers.push(n),this.activeTimers=n,e();let s=y.flatten(this.activeTimers.map(l=>l.query)).filter(l=>l!=null),a=y.flatten(this.activeTimers.map(l=>l.name)).filter(l=>l!=null);this.activeTimers=t,o&&(this.programTimersStack=null);let i={uploadWaitMs:this.uploadWaitMs,downloadWaitMs:this.downloadWaitMs,kernelMs:null,wallMs:null};if(G().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE")>0){let l=await Promise.all(s);i.kernelMs=y.sum(l),i.getExtraProfileInfo=()=>l.map((u,c)=>({name:a[c],ms:u})).map(u=>`${u.name}: ${u.ms}`).join(", ")}else i.kernelMs={error:"WebGL query timers are not supported in this environment."};return this.uploadWaitMs=0,this.downloadWaitMs=0,i}memory(){return{unreliable:!1,numBytesInGPU:this.numBytesInGPU,numBytesInGPUAllocated:this.textureManager.numBytesAllocated,numBytesInGPUFree:this.textureManager.numBytesFree}}startTimer(){return G().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE")>0?this.gpgpu.beginQuery():{startMs:y.now(),endMs:null}}endTimer(e){return G().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE")>0?(this.gpgpu.endQuery(),e):(e.endMs=y.now(),e)}async getQueryTime(e){if(G().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE")>0)return this.gpgpu.waitForQueryAndGetTime(e);let t=e;return t.endMs-t.startMs}disposeData(e){if(this.pendingDisposal.has(e))return;if(this.pendingRead.has(e)){this.pendingDisposal.add(e),this.pendingDeletes++;return}if(!this.texData.has(e))return;if(this.texData.get(e).complexParentRefCount>0){this.texData.get(e).refCount--;return}this.releaseGPUData(e);let{complexTensorInfos:t}=this.texData.get(e);t!=null&&(this.texData.get(t.real.dataId).complexParentRefCount--,this.disposeIntermediateTensorInfo(t.real),this.texData.get(t.imag.dataId).complexParentRefCount--,this.disposeIntermediateTensorInfo(t.imag)),this.texData.delete(e)}releaseGPUData(e){let{texture:t,dtype:n,texShape:o,usage:s,isPacked:a,slice:i}=this.texData.get(e),l=i&&i.origDataId||e,u=this.dataRefCount.get(l);u>1?this.dataRefCount.set(l,u-1):(this.dataRefCount.delete(l),t!=null&&(this.numBytesInGPU-=this.computeBytes(o,n),this.textureManager.releaseTexture(t,o,s,a)));let c=this.texData.get(e);c.texture=null,c.texShape=null,c.isPacked=!1,c.slice=null}getTexture(e){return this.uploadToGPU(e),this.texData.get(e).texture}getDataInfo(e){return this.texData.get(e)}getCPUBackend(){return G().getBool("WEBGL_CPU_FORWARD")?(this.cpuBackend==null&&(this.cpuBackend=Ss().findBackend("cpu")),this.cpuBackend):null}shouldExecuteOnCPU(e,t=M8){let n=this.getCPUBackend();return!G().getBool("IS_TEST")&&!this.warnedAboutCPUBackend&&n==null&&(console.warn("Your application contains ops that are small enough to be executed on the CPU backend, however the CPU backend cannot be found. 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Call tf.whereAsync() instead");let t=e.dataSync();return R8(e.shape,t)}packedUnaryOp(e,t,n){let o=new Os(e.shape,t);return this.compileAndRun(o,[e],n)}abs(e){if(this.shouldExecuteOnCPU([e])&&e.dtype!=="complex64"){let n=qg(this.texData.get(e.dataId).values);return this.makeOutput(e.shape,e.dtype,n)}if(G().getBool("WEBGL_PACK_UNARY_OPERATIONS"))return this.packedUnaryOp(e,Jv,e.dtype);let t=new sn(e.shape,Jv);return this.compileAndRun(t,[e])}makeTensorInfo(e,t,n){let o;if(t==="string"&&n!=null&&n.length>0&&y.isString(n[0])){let s=n.map(a=>y.encodeString(a));o=this.write(s,e,t)}else o=this.write(n,e,t);return this.texData.get(o).usage=null,{dataId:o,shape:e,dtype:t}}makeOutput(e,t,n){let{dataId:o}=this.makeTensorInfo(e,t,n);return Ss().makeTensorFromDataId(o,e,t,this)}unpackTensor(e){let t=new Qv(e.shape);return this.runWebGLProgram(t,[e],e.dtype)}packTensor(e){let t=new Yv(e.shape),n=!0;return this.runWebGLProgram(t,[e],e.dtype,null,n)}packedReshape(e,t){let n=[Ba(e.shape),...Va(e.shape)],o={dtype:e.dtype,shape:n,dataId:e.dataId},s=[Ba(t),...Va(t)],a=new If(s,n),i=!0,l=this.runWebGLProgram(a,[o],e.dtype,null,i);return{dataId:l.dataId,shape:t,dtype:l.dtype}}decode(e){let t=this.texData.get(e),{isPacked:n,shape:o,dtype:s}=t,a=kf(o),i;n?i=new Tv(a):i=new Nv(a);let l=!0,u=this.runWebGLProgram(i,[{shape:a,dtype:s,dataId:e}],s,null,l);return{dtype:s,shape:o,dataId:u.dataId}}runWebGLProgram(e,t,n,o,s=!1){let a=this.makeTensorInfo(e.outputShape,n),i=this.texData.get(a.dataId);if(e.packedOutput&&(i.isPacked=!0),e.outPackingScheme===hl.DENSE){let h=gl(e.outputShape);i.texShape=h.map(g=>g*2)}if(e.outTexUsage!=null&&(i.usage=e.outTexUsage),y.sizeFromShape(a.shape)===0)return i.values=y.getTypedArrayFromDType(a.dtype,0),a;let l=[],u=t.map(h=>{if(h.dtype==="complex64")throw new Error("GPGPUProgram does not support complex64 input. For complex64 dtypes, please separate the program into real and imaginary parts.");let g=this.texData.get(h.dataId);if(g.texture==null){if(!e.packedInputs&&y.sizeFromShape(h.shape)<=G().getNumber("WEBGL_SIZE_UPLOAD_UNIFORM"))return{shape:h.shape,texData:null,isUniform:!0,uniformValues:g.values};e.packedInputs&&(g.isPacked=!0,g.shape=h.shape)}else if(!!g.isPacked!=!!e.packedInputs)h=g.isPacked?this.unpackTensor(h):this.packTensor(h),l.push(h),g=this.texData.get(h.dataId);else if(g.isPacked&&!xl(g.shape,h.shape)){let x=h,b=h.shape;h.shape=g.shape,h=this.packedReshape(h,b),l.push(h),g=this.texData.get(h.dataId),x.shape=b}return this.uploadToGPU(h.dataId),{shape:h.shape,texData:g,isUniform:!1}});this.uploadToGPU(a.dataId);let c={shape:a.shape,texData:i,isUniform:!1},p=SD(e,u,c),m=this.getAndSaveBinary(p,()=>kD(this.gpgpu,e,u,c)),f=this.activeTimers!=null,d;if(f&&(d=this.startTimer()),ID(this.gpgpu,m,u,c,o),l.forEach(h=>this.disposeIntermediateTensorInfo(h)),f&&(d=this.endTimer(d),this.activeTimers.push({name:e.constructor.name,query:this.getQueryTime(d)})),!G().getBool("WEBGL_LAZILY_UNPACK")&&i.isPacked&&s===!1){let h=this.unpackTensor(a);return this.disposeIntermediateTensorInfo(a),h}return a}compileAndRun(e,t,n,o,s=!1){n=n||t[0].dtype;let a=this.runWebGLProgram(e,t,n,o,s);return Ss().makeTensorFromDataId(a.dataId,a.shape,a.dtype)}getAndSaveBinary(e,t){return e in this.binaryCache||(this.binaryCache[e]=t()),this.binaryCache[e]}getTextureManager(){return this.textureManager}dispose(){this.disposed||(G().getBool("IS_TEST")||Object.keys(this.binaryCache).forEach(t=>{this.gpgpu.deleteProgram(this.binaryCache[t].webGLProgram),delete this.binaryCache[t]}),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=V(()=>{if(!G().get("WEBGL_RENDER_FLOAT32_ENABLED")){let e=G().getBool("DEBUG");G().set("DEBUG",!1);let t=this.abs(ue(1e-8)).dataSync()[0];if(G().set("DEBUG",e),t>0)return 32}return 16})),this.floatPrecisionValue}epsilon(){return this.floatPrecision()===32?F8:O8}uploadToGPU(e){let t=this.texData.get(e),{shape:n,dtype:o,values:s,texture:a,usage:i,isPacked:l}=t;if(a!=null)return;let u=this.activeTimers!=null,c;u&&(c=y.now());let p=t.texShape;if(p==null&&(p=bv(n,l),t.texShape=p),s!=null){let m=kf(n),f,d=p[1],h=p[0],g=s instanceof Uint8Array;l?([d,h]=ji(p[0],p[1]),f=new $v(m,[h,d],g)):f=new Dv(m,[h,d],g);let x=this.makeTensorInfo([h,d],o);g?this.texData.get(x.dataId).usage=Rr.PIXELS:this.texData.get(x.dataId).usage=Rr.UPLOAD,this.gpgpu.uploadDenseMatrixToTexture(this.getTexture(x.dataId),d,h,s);let b=!0,w=this.runWebGLProgram(f,[x],o,null,b),_=this.texData.get(w.dataId);t.texture=_.texture,t.texShape=_.texShape,t.isPacked=_.isPacked,t.usage=_.usage,this.disposeIntermediateTensorInfo(x),this.texData.delete(w.dataId),t.values=null,u&&(this.uploadWaitMs+=y.now()-c)}else{let m=this.acquireTexture(p,i,o,l);t.texture=m}}convertAndCacheOnCPU(e,t){let n=this.texData.get(e),{dtype:o}=n;return this.releaseGPUData(e),t!=null&&(n.values=B8(t,o)),n.values}acquireTexture(e,t,n,o){if(this.numBytesInGPU+=this.computeBytes(e,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(e,t,o)}computeBytes(e,t){return e[0]*e[1]*y.bytesPerElement(t)}};function B8(r,e){if(e==="float32"||e==="complex64")return r;if(e==="int32"||e==="bool"){let t=e==="int32"?new Int32Array(r.length):new Uint8Array(r.length);for(let n=0;n<t.length;++n)t[n]=Math.round(r[n]);return t}else throw new Error(`Unknown dtype ${e}`)}var ek="2.8.5";function tk(){G().set("WEBGL_FORCE_F16_TEXTURES",!0)}Vc.isBrowser()&&wu("webgl",()=>new Kg,2);var V8={forceHalfFloat:tk};var Xg=`
if (isnan(a)) return a;
if (isnan(b)) return b;
`;var Xo=class{constructor(e,t,n){this.variableNames=["A","B"],this.outputShape=N.assertAndGetBroadcastShape(t,n),this.userCode=`
float binaryOperation(float a, float b) {
${e}
}
void main() {
float a = getAAtOutCoords();
float b = getBAtOutCoords();
setOutput(binaryOperation(a, b));
}
`}};var yl=`
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 Ps=class{constructor(e,t,n,o=!1){this.variableNames=["A","B"],this.supportsBroadcasting=!0,this.packedInputs=!0,this.packedOutput=!0,this.outputShape=N.assertAndGetBroadcastShape(t,n);let s=this.outputShape.length,a="";if(o)if(s===0||y.sizeFromShape(this.outputShape)===1)a=`
result.y = 0.;
result.z = 0.;
result.w = 0.;
`;else if(a=`
${ze(s)} coords = getOutputCoords();
`,s===1)a+=`
result.y = (coords + 1) >= ${this.outputShape[0]} ? 0. : result.y;
result.z = 0.;
result.w = 0.;
`;else{let l=qt("coords",s);a+=`
bool nextRowOutOfBounds =
(${l[s-2]} + 1) >= ${this.outputShape[s-2]};
bool nextColOutOfBounds =
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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) {
${e}
}
void main() {
vec4 a = getAAtOutCoords();
vec4 b = getBAtOutCoords();
vec4 result = binaryOperation(a, b);
${a}
setOutput(result);
}
`}};function Ht(r){let{inputs:e,backend:t}=r,{x:n}=e;return t.incRef(n.dataId),{dataId:n.dataId,shape:n.shape,dtype:n.dtype}}var p$={kernelName:ms,backendName:"webgl",kernelFunc:Ht};function an(r){let{inputs:e,backend:t}=r,{real:n,imag:o}=e,s=t.makeTensorInfo(n.shape,"complex64"),a=t.texData.get(s.dataId),i=Ht({inputs:{x:n},backend:t}),l=t.texData.get(i.dataId);l.complexParentRefCount++;let u=Ht({inputs:{x:o},backend:t}),c=t.texData.get(u.dataId);return c.complexParentRefCount++,a.complexTensorInfos={real:i,imag:u},s}var m$={kernelName:Kl,backendName:"webgl",kernelFunc:an};var rk="return (a < 0.) ? b * a : a;",nk=`
vec4 aLessThanZero = vec4(lessThan(a, vec4(0.)));
return (aLessThanZero * (b * a)) + ((vec4(1.0) - aLessThanZero) * a);
`;function W8(r){let{inputs:e,backend:t,attrs:n}=r,{x:o}=e,{alpha:s}=n,a=t.makeTensorInfo([],"float32",y.createScalarValue(s,"float32")),i=G().getBool("WEBGL_PACK_BINARY_OPERATIONS")?new Ps(nk,o.shape,a.shape):new Xo(rk,o.shape,a.shape),l=t.runWebGLProgram(i,[o,a],o.dtype);return t.disposeIntermediateTensorInfo(a),l}var f$={kernelName:uo,backendName:"webgl",kernelFunc:W8};var ok="return (a < 0.) ? b * a : a;",sk=`
vec4 aLessThanZero = vec4(lessThan(a, vec4(0.)));
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if (isnan(a)) return a;
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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 Ce({opSnippet:r,packedOpSnippet:e,cpuKernelImpl:t,dtype:n}){return({inputs:o,backend:s})=>{let{x:a}=o,i=s,l=n||a.dtype;if(i.shouldExecuteOnCPU([a])&&t!=null){let p=i.texData.get(a.dataId),m=t(p.values,l);return i.makeTensorInfo(a.shape,l,m)}let u=G().getBool("WEBGL_PACK_UNARY_OPERATIONS")&&e!=null,c;return u?c=new Os(a.shape,e):c=new sn(a.shape,r),i.runWebGLProgram(c,[a],l)}}function at({opSnippet:r,packedOpSnippet:e,checkOutOfBounds:t=!1,supportsComplex:n=!1,cpuKernelImpl:o,dtype:s}){return({inputs:a,backend:i})=>{let{a:l,b:u}=a,c=i;if(n&&l.dtype==="complex64"){let d=c.texData.get(l.dataId),h=c.texData.get(u.dataId),[g,x]=[[d.complexTensorInfos.real,h.complexTensorInfos.real],[d.complexTensorInfos.imag,h.complexTensorInfos.imag]].map(w=>{let[_,k]=w,E={dataId:_.dataId,dtype:_.dtype,shape:l.shape},T={dataId:k.dataId,dtype:k.dtype,shape:u.shape},F=new Xo(r,l.shape,u.shape);return c.runWebGLProgram(F,[E,T],dr(_.dtype,k.dtype))}),b=an({inputs:{real:g,imag:x},backend:c});return c.disposeIntermediateTensorInfo(g),c.disposeIntermediateTensorInfo(x),b}let p=s||dr(l.dtype,u.dtype);if(c.shouldExecuteOnCPU([l,u])&&o!=null){let d=c.texData.get(l.dataId),h=c.texData.get(u.dataId),[g,x]=o(l.shape,u.shape,d.values,h.values,p),b=c.makeTensorInfo(x,p),w=c.texData.get(b.dataId);return w.values=g,b}let m=G().getBool("WEBGL_PACK_BINARY_OPERATIONS")&&e!=null,f;return m?f=new Ps(e,l.shape,u.shape,t):f=new Xo(r,l.shape,u.shape),c.runWebGLProgram(f,[l,u],p)}}function bl(r,e=!1){if(r==="linear")return e?a$:n$;if(r==="relu")return e?u$:s$;if(r==="elu")return e?l$:o$;if(r==="relu6")return e?c$:i$;if(r==="prelu")return e?sk:ok;if(r==="leakyrelu")return e?nk:rk;throw new Error(`Activation ${r} has not been implemented for the WebGL backend.`)}var Nf=class{constructor(e,t,n,o=!1,s=!1,a=!1,i=null,l=!1,u=!1){this.variableNames=["matrixA","matrixB"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=n;let c=o?e[1]:e[2],p=Math.ceil(c/2),m=o?"i * 2, rc.y":"rc.y, i * 2",f=s?"rc.z, i * 2":"i * 2, rc.z",d=o?["a.xxyy","a.zzww"]:["a.xxzz","a.yyww"],h=s?["b.xzxz","b.ywyw"]:["b.xyxy","b.zwzw"],g="",x="";i&&(l?g=`vec4 activation(vec4 a) {
vec4 b = getPreluActivationWeightsAtOutCoords();
${i}
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vec4 b = getLeakyreluAlphaAtOutCoords();
${i}
}`:g=`vec4 activation(vec4 x) {
${i}
}`,x="result = activation(result);");let b=a?"result += getBiasAtOutCoords();":"";a&&this.variableNames.push("bias"),l&&this.variableNames.push("preluActivationWeights"),u&&this.variableNames.push("leakyreluAlpha");let w="rc.x",_="rc.x";e[0]<t[0]?w=`int(min(float(rc.x), ${e[0]-1}.))`:t[0]<e[0]&&(_=`int(min(float(rc.x), ${t[0]-1}.))`),this.userCode=`
${g}
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 = ${_};
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}
${x}
setOutput(result);
}
`}};var ik={REAL:"return areal * breal - aimag * bimag;",IMAG:"return areal * bimag + aimag * breal;"},Zg=class{constructor(e,t,n){this.variableNames=["AReal","AImag","BReal","BImag"],this.outputShape=N.assertAndGetBroadcastShape(t,n),this.userCode=`
float binaryOpComplex(
float areal, float aimag, float breal, float bimag) {
${e}
}
void main() {
float areal = getARealAtOutCoords();
float aimag = getAImagAtOutCoords();
float breal = getBRealAtOutCoords();
float bimag = getBImagAtOutCoords();
setOutput(binaryOpComplex(areal, aimag, breal, bimag));
}
`}};var x$="return a * b;";function ak(r){let{inputs:e,backend:t}=r,{a:n,b:o}=e,s=N.upcastType(n.dtype,o.dtype);if(n.dtype==="complex64"){let i=t.texData.get(n.dataId),l=t.texData.get(o.dataId),u=new Zg(ik.REAL,n.shape,o.shape),c=new Zg(ik.IMAG,n.shape,o.shape),p=[{dataId:i.complexTensorInfos.real.dataId,dtype:i.complexTensorInfos.real.dtype,shape:n.shape},{dataId:i.complexTensorInfos.imag.dataId,dtype:i.complexTensorInfos.imag.dtype,shape:n.shape},{dataId:l.complexTensorInfos.real.dataId,dtype:l.complexTensorInfos.real.dtype,shape:o.shape},{dataId:l.complexTensorInfos.imag.dataId,dtype:l.complexTensorInfos.imag.dtype,shape:o.shape}],m=t.runWebGLProgram(u,p,"float32"),f=t.runWebGLProgram(c,p,"float32"),d=an({inputs:{real:m,imag:f},backend:t});return t.disposeIntermediateTensorInfo(m),t.disposeIntermediateTensorInfo(f),d}if(t.shouldExecuteOnCPU([n,o])){let i=t.texData.get(n.dataId),l=t.texData.get(o.dataId),[u,c]=WD(n.shape,o.shape,i.values,l.values,s),p=t.makeTensorInfo(c,s),m=t.texData.get(p.dataId);return m.values=u,p}let a;return G().getBool("WEBGL_PACK_BINARY_OPERATIONS")?a=new Ps(x$,n.shape,o.shape):a=new Xo(x$,n.shape,o.shape),t.runWebGLProgram(a,[n,o],s)}var y$={kernelName:yo,backendName:"webgl",kernelFunc:ak};function b$(r,e,t){let n=[Ba(r.shape),...Va(r.shape)],o={dtype:r.dtype,shape:n,dataId:r.dataId},s=[Ba(e),...Va(e)],a=new If(s,n),i=!0,l=t.runWebGLProgram(a,[o],r.dtype,null,i);return{dataId:l.dataId,shape:e,dtype:l.dtype}}function me(r){let{inputs:e,backend:t,attrs:n}=r,{x:o}=e,{shape:s}=n,a=t,i=y.sizeFromShape(o.shape),l=y.inferFromImplicitShape(s,i),u=y.sizeFromShape(l);y.assert(i===u,()=>`The new shape (${l}) has ${u} elements and the old shape (${o.shape}) has ${i} elements. The new shape and old shape must have the same number of elements.`);let c=a.texData.get(o.dataId);return c.isPacked&&!xl(o.shape,l)&&!(c.texture!==null&&xl(c.shape,l))?b$(o,l,a):(a.incRef(o.dataId),{dataId:o.dataId,shape:l,dtype:o.dtype})}var w$={kernelName:gs,backendName:"webgl",kernelFunc:me};var Jg=class{constructor(e,t){this.variableNames=["x"];let{windowSize:n,batchSize:o,inSize:s,outSize:a}=e;this.outputShape=[o,a];let i=Math.floor(n/4)*4,l=n%4,u="sumValue += dot(values, ones);";if(t!=null){let p=1/t;u=`sumValue += dot(values * ${y.isInt(p)?p.toPrecision(2):p}, ones);`}let c="";s%n>0&&(c=`
if (inIdx < 0 || inIdx >= ${s}) {
return 0.0;
}
`),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 < ${i}; i += 4) {
int inIdx = inOffset + i;
vec4 values = vec4(
getValue(batch, inIdx),
getValue(batch, inIdx + 1),
getValue(batch, inIdx + 2),
getValue(batch, inIdx + 3)
);
${u}
}
int inIdx = inOffset + ${i};
if (${l===1}) {
vec4 values = vec4(getValue(batch, inIdx), 0.0, 0.0, 0.0);
${u}
} else if (${l===2}) {
vec4 values = vec4(
getValue(batch, inIdx),
getValue(batch, inIdx + 1), 0.0, 0.0);
${u}
} else if (${l===3}) {
vec4 values = vec4(
getValue(batch, inIdx),
getValue(batch, inIdx + 1),
getValue(batch, inIdx + 2), 0.0);
${u}
}
setOutput(sumValue);
}
`}};var lk=class{constructor(e,t){this.variableNames=["x"];let{windowSize:n,batchSize:o,inSize:s,outSize:a}=e;this.outputShape=[o,a];let i="0.0",l="";t==="prod"?i="1.0":t==="min"?(i="1.0 / 1e-20",l="min"):t==="max"&&(i="-1.0 / 1e-20",l="max");let u=`${t}(${t}(${t}(minMaxValue[0], minMaxValue[1]), minMaxValue[2]), minMaxValue[3])`;t==="sum"?u="sumValue":t==="prod"?u="prodValue":t==="all"?u="allValue":t==="any"&&(u="anyValue");let c=Math.floor(n/4)*4,p=n%4,m=`
if (${t==="sum"}) {
sumValue += dot(values, ones);
} else if (${t==="prod"}) {
vec2 tmp = vec2(values[0], values[1]) * vec2(values[2], values[3]);
prodValue *= tmp[0] * tmp[1];
} else {
minMaxValue = ${l}(values, minMaxValue);
}
`,f="vec4";t==="all"?(i="1.0",m=`
bool reducedAllValue = all(values);
float floatedReducedAllValue = float(reducedAllValue);
allValue = float(allValue >= 1.0 && floatedReducedAllValue >= 1.0);
`,f="bvec4"):t==="any"&&(i="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 = ${i};
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 * ${n};
vec4 minMaxValue = vec4(${i});
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
);
${m}
}
setOutput(${u});
}
`}};function G8(r){let e=[];for(;e.length===0||e[e.length-1].outSize!==1;){let t=e.length?e[e.length-1].outSize:r[1],n=N.computeOptimalWindowSize(t);e.push({inSize:t,windowSize:n,outSize:Math.ceil(t/n)})}return e}function Sn(r,e,t,n){let o=G8(r.shape),s=r;for(let a=0;a<o.length;a++){let{inSize:i,windowSize:l,outSize:u}=o[a],c,p;t==="mean"?c=a===0?new Jg({windowSize:l,inSize:i,batchSize:r.shape[0],outSize:u},i):new Jg({windowSize:l,inSize:i,batchSize:r.shape[0],outSize:u}):c=new lk({windowSize:l,inSize:i,batchSize:r.shape[0],outSize:u},t),p=s,s=n.runWebGLProgram(c,[s],e),p.dataId!==r.dataId&&n.disposeIntermediateTensorInfo(p)}return s}var uk=class{constructor(e,t){this.variableNames=["A"];let n=new Array(e.length);for(let a=0;a<n.length;a++)n[a]=e[t[a]];this.outputShape=n,this.rank=n.length;let o=ze(this.rank),s=U8(t);this.userCode=`
void main() {
${o} resRC = getOutputCoords();
setOutput(getA(${s}));
}
`}};function U8(r){let e=r.length;if(e>6)throw Error(`Transpose for rank ${e} is not yet supported`);let t=["resRC.x","resRC.y","resRC.z","resRC.w","resRC.u","resRC.v"],n=new Array(e);for(let o=0;o<r.length;o++)n[r[o]]=t[o];return n.join()}var ck=class{constructor(e,t){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0;let n=new Array(e.length);for(let c=0;c<n.length;c++)n[c]=e[t[c]];if(this.outputShape=n,this.rank=n.length,this.rank>6)throw Error(`Packed transpose for rank ${this.rank} is not yet supported.`);let o=ze(this.rank),s=Xv("rc",this.rank),a=new Array(this.rank);for(let c=0;c<t.length;c++)a[t[c]]=s[c];let i=`vec2(${a.slice(-2).join()})`,l=`++${s[this.rank-1]} < ${n[this.rank-1]}`,u=`getChannel(getA(${a.join()}), ${i})`;this.userCode=`
void main() {
${o} rc = getOutputCoords();
vec4 result = vec4(0.);
result[0] = ${u};
if(${l}) {
result[1] = ${u};
}
--${s[this.rank-1]};
if(++${s[this.rank-2]} < ${n[this.rank-2]}) {
result[2] = ${u};
if(${l}) {
result[3] = ${u};
}
}
setOutput(result);
}
`}};function wl(r,e,t){let n=G().getBool("WEBGL_PACK_ARRAY_OPERATIONS")?new ck(r.shape,e):new uk(r.shape,e);return t.runWebGLProgram(n,[r],r.dtype)}function _$(r,e,t,n){let o=e,s=r.shape.length,a=y.parseAxisParam(o,r.shape),i=a,l=N.getAxesPermutation(i,s),u=l!=null,c=r;u&&(c=wl(r,l,n),i=N.getInnerMostAxes(i.length,s)),N.assertAxesAreInnerMostDims("sum",i,s);let[p,m]=N.computeOutAndReduceShapes(c.shape,i),f=p;t&&(f=N.expandShapeToKeepDim(p,a));let d=y.sizeFromShape(m),g=y.sizeFromShape(r.shape)/d,x=me({inputs:{x:c},attrs:{shape:[g,d]},backend:n}),b=xu(r.dtype),w=Sn(x,b,"sum",n),_=me({inputs:{x:w},attrs:{shape:f},backend:n});return n.disposeIntermediateTensorInfo(x),n.disposeIntermediateTensorInfo(w),u&&n.disposeIntermediateTensorInfo(c),_}function Tf(r){let{inputs:e,backend:t,attrs:n}=r,{x:o}=e,{axis:s,keepDims:a}=n;return _$(o,s,a,t)}var v$={kernelName:$o,backendName:"webgl",kernelFunc:Tf};function Vt(r){let{inputs:e,backend:t,attrs:n}=r,{x:o}=e,{perm:s}=n,a=t,i=o.shape.length,l=new Array(i);for(let c=0;c<l.length;c++)l[c]=o.shape[s[c]];let u;if(a.shouldExecuteOnCPU([o])){let p=a.texData.get(o.dataId).values,m=wp(p,o.shape,o.dtype,s,l);u=a.makeTensorInfo(l,o.dtype);let f=a.texData.get(u.dataId);f.values=m}else u=wl(o,s,a);return u}var k$={kernelName:Mo,backendName:"webgl",kernelFunc:Vt};var pk=1e3;function Qu({a:r,b:e,transposeA:t,transposeB:n,backend:o,bias:s=null,preluActivationWeights:a=null,leakyreluAlpha:i=0,activation:l=null}){let u=r.shape.length,c=e.shape.length,p=t?r.shape[u-2]:r.shape[u-1],m=n?e.shape[c-1]:e.shape[c-2],f=t?r.shape[u-1]:r.shape[u-2],d=n?e.shape[c-2]:e.shape[c-1],h=r.shape.slice(0,-2),g=e.shape.slice(0,-2),x=y.sizeFromShape(h),b=y.sizeFromShape(g),w=x===b||x===1||b===1;y.assert(u>=2&&c>=2&&w,()=>`Error in matMul: the input batch dimensions must either be the same or at least one input batch dimension must be 1. Got input batch dimensions of (${h}) and (${g}).`);let k=(x>b?r.shape.slice(0,-2):e.shape.slice(0,-2)).concat([f,d]);y.assert(p===m,()=>`Error in matMul: inner shapes (${p}) and (${m}) of Tensors with shapes ${r.shape} and ${e.shape} and transposeA=${t} and transposeB=${n} must match.`);let E=t?[x,p,f]:[x,f,p],T=n?[b,d,m]:[b,m,d],F=me({inputs:{x:r},backend:o,attrs:{shape:E}}),O=me({inputs:{x:e},backend:o,attrs:{shape:T}}),M=[F,O],j=Math.max(x,b),W=t?F.shape[1]:F.shape[2],U=s!=null,Y=a!=null,K=l==="leakyrelu",Z=l!=null?bl(l,!0):null,re=U||Y||K||Z!=null,X;if((f===1||d===1)&&W>pk&&re===!1){let ae=F,ie=O;t&&(ae=Vt({inputs:{x:F},backend:o,attrs:{perm:[0,2,1]}}),M.push(ae)),n&&(ie=Vt({inputs:{x:O},backend:o,attrs:{perm:[0,2,1]}}),M.push(ie));let pe=d!==1,le=d===1,ge=ae;pe&&(ge=me({inputs:{x:ae},backend:o,attrs:{shape:[j,W,1]}}),M.push(ge));let ye=d===1?2:1,be=ie;le&&(be=me({inputs:{x:ie},backend:o,attrs:{shape:[j,1,W]}}),M.push(be));let ke=ak({inputs:{a:ge,b:be},backend:o});X=Tf({inputs:{x:ke},backend:o,attrs:{axis:ye,keepDims:!0}}),M.push(ke)}else{let ae=dr(r.dtype,e.dtype),ie=new Nf(E,T,[j,f,d],t,n,U,Z,Y,K),pe=[F,O];if(s!=null&&pe.push(s),Y&&pe.push(a),K){let le=o.makeTensorInfo([],"float32",y.createScalarValue(i,"float32"));pe.push(le),M.push(le)}X=o.runWebGLProgram(ie,pe,ae)}let oe=me({inputs:{x:X},backend:o,attrs:{shape:k}});M.push(X);for(let ae of M)o.disposeIntermediateTensorInfo(ae);return oe}function q8(r){let{inputs:e,backend:t,attrs:n}=r,{a:o,b:s,bias:a,preluActivationWeights:i}=e,{transposeA:l,transposeB:u,activation:c,leakyreluAlpha:p}=n;return Qu({a:o,b:s,transposeA:l,transposeB:u,backend:t,bias:a,preluActivationWeights:i,leakyreluAlpha:p,activation:c})}var C$={kernelName:vs,backendName:"webgl",kernelFunc:q8};var I$="return abs(x);";function H8(r){let{inputs:e,backend:t}=r,{x:n}=e;if(t.shouldExecuteOnCPU([n])&&n.dtype!=="complex64"){let s=t.texData.get(n.dataId),a=qg(s.values);return t.makeTensorInfo(n.shape,n.dtype,a)}let o;return G().getBool("WEBGL_PACK_UNARY_OPERATIONS")?o=new Os(n.shape,I$):o=new sn(n.shape,I$),t.runWebGLProgram(o,[n],n.dtype)}var S$={kernelName:ls,backendName:"webgl",kernelFunc:H8};var K8=gr+`
if (abs(x) > 1.) {
return NAN;
}
return acos(x);
`,X8=Ce({opSnippet:K8}),N$={kernelName:Ks,backendName:"webgl",kernelFunc:X8};var Y8=gr+`
if (x < 1.0) return NAN;
return log(x + sqrt(x * x - 1.0));`,Z8=Ce({opSnippet:Y8}),T$={kernelName:Xs,backendName:"webgl",kernelFunc:Z8};var E$="return a + b;",J8=at({opSnippet:E$,packedOpSnippet:E$,supportsComplex:!0,cpuKernelImpl:ND}),A$={kernelName:wn,backendName:"webgl",kernelFunc:J8};var mk=class{constructor(e,t){this.outputShape=[],this.outputShape=e,this.variableNames=t.map((s,a)=>`T${a}`);let n=[];this.variableNames.forEach(s=>{n.push(`float v${s} = get${s}AtOutCoords();`)});let o=this.variableNames.map(s=>`v${s}`).join(" + ");this.userCode=`
void main() {
${n.join(`
`)}
float result = ${o};
setOutput(result);
}
`}};var fk=class{constructor(e,t){this.outputShape=[],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=e,this.variableNames=t.map((s,a)=>`T${a}`);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 = ${o};
setOutput(result);
}
`}};function Qg(r){let{inputs:e,backend:t}=r,n=e;if(n.length===1)return Ht({inputs:{x:n[0]},backend:t});if(n.length>G().get("WEBGL_MAX_TEXTURES_IN_SHADER")){let l=Math.floor(n.length/2),u=Qg({inputs:n.slice(0,l),backend:t}),c=Qg({inputs:n.slice(l),backend:t});return Qg({inputs:[u,c],backend:t})}let o=n.map(l=>l.dtype).reduce((l,u)=>dr(l,u)),s=n.map(l=>l.shape),i=G().getBool("WEBGL_PACK")?new fk(n[0].shape,s):new mk(n[0].shape,s);return t.runWebGLProgram(i,n,o)}var D$={kernelName:Kn,backendName:"webgl",kernelFunc:Qg};function Q8(r){let{inputs:e,backend:t,attrs:n}=r,{x:o}=e,{axis:s,keepDims:a}=n,i=o.shape.length,l=y.parseAxisParam(s,o.shape),u=l,c=N.getAxesPermutation(u,i),p=o;c!=null&&(p=Vt({inputs:{x:o},backend:t,attrs:{perm:c}}),u=N.getInnerMostAxes(u.length,i)),N.assertAxesAreInnerMostDims("all",u,i);let[m,f]=N.computeOutAndReduceShapes(p.shape,u),d=y.sizeFromShape(f),h=me({inputs:{x:p},backend:t,attrs:{shape:[-1,d]}}),g=Sn(h,h.dtype,"all",t),x;if(a){let b=N.expandShapeToKeepDim(m,l);x=me({inputs:{x:g},backend:t,attrs:{shape:b}})}else x=me({inputs:{x:g},backend:t,attrs:{shape:m}});return t.disposeIntermediateTensorInfo(h),t.disposeIntermediateTensorInfo(g),c!=null&&t.disposeIntermediateTensorInfo(p),x}var $$={kernelName:jl,backendName:"webgl",kernelFunc:Q8};function eX(r){let{inputs:e,backend:t,attrs:n}=r,{x:o}=e,{axis:s,keepDims:a}=n,i=o.shape.length,l=y.parseAxisParam(s,o.shape),u=l,c=N.getAxesPermutation(u,i),p=o;c!=null&&(p=Vt({inputs:{x:o},backend:t,attrs:{perm:c}}),u=N.getInnerMostAxes(u.length,i)),N.assertAxesAreInnerMostDims("any",u,i);let[m,f]=N.computeOutAndReduceShapes(p.shape,u),d=y.sizeFromShape(f),h=me({inputs:{x:p},backend:t,attrs:{shape:[-1,d]}}),g=Sn(h,h.dtype,"any",t),x;if(a){let b=N.expandShapeToKeepDim(m,l);x=me({inputs:{x:g},backend:t,attrs:{shape:b}})}else x=me({inputs:{x:g},backend:t,attrs:{shape:m}});return t.disposeIntermediateTensorInfo(h),t.disposeIntermediateTensorInfo(g),c!=null&&t.disposeIntermediateTensorInfo(p),x}var R$={kernelName:Gl,backendName:"webgl",kernelFunc:eX};var dk=class{constructor(e,t,n){this.variableNames=["A"];let{windowSize:o,batchSize:s,outSize:a}=e;n||this.variableNames.push("bestIndicesA"),this.outputShape=[s,a];let i=t==="max"?">":"<",l=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 = ${l};
float candidate = getA(batch, inIdx);
if (candidate ${i} bestValue) {
bestValue = candidate;
bestIndex = inIdx;
}
}
setOutput(float(bestIndex));
}
`}};var hk=class{constructor(e,t,n,o){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,y.assert(e.length>2,()=>`Packed arg${n.charAt(0).toUpperCase()+n.slice(1)} supports only inputs with rank above 2.`);let s=e[e.length-1],a=Math.ceil(s/t);this.outputShape=e.slice(0,-1),a>1&&this.outputShape.push(a),o||this.variableNames.push("bestIndicesA");let i=this.outputShape,l=i.length,u=ze(l),c=qt("coords",l),p,m;if(a===1){m=l+1;let F=ze(m);p=`
${F} sourceLocR = ${F}(${c.join()}, 0);
++${c[l-1]};
${F} sourceLocG = ${F}(${c.join()}, 0);
++${c[l-2]};
${F} sourceLocA = ${F}(${c.join()}, 0);
--${c[l-1]};
${F} sourceLocB = ${F}(${c.join()}, 0);
--${c[l-2]};`}else m=l,p=`
${u} sourceLocR = coords;
++${c[l-1]};
${u} sourceLocG = coords;
++${c[l-2]};
${u} sourceLocA = coords;
--${c[l-1]};
${u} sourceLocB = coords;
--${c[l-2]};`;let f=["x","y","z","w","u","v"].slice(0,m),d="."+f[m-1],h=f.map(F=>"int "+F),g=qt("sourceLocR",m-1).concat("inIdx.r"),x=qt("sourceLocG",m-1).concat("inIdx.g"),b=qt("sourceLocB",m-1).concat("inIdx.b"),w=qt("sourceLocA",m-1).concat("inIdx.a"),_=n==="max"?"greaterThan":"lessThan",k=o?"":`
inIdx = round(vec4(getBestIndicesAChannel(${g.join()}),
getBestIndicesAChannel(${x.join()}),
getBestIndicesAChannel(${b.join()}),
getBestIndicesAChannel(${w.join()})));`,E=`vec4(
getAChannel(${g.join()}),
hasNextCol ? getAChannel(${x.join()}) : 0.,
hasNextRow ? getAChannel(${b.join()}) : 0.,
hasNextRow && hasNextCol ? getAChannel(${w.join()}) : 0.)`,T=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()}));
}
${T}
void main() {
${u} coords = getOutputCoords();
bool hasNextCol = ${c[l-1]} < ${i[l-1]-1};
bool hasNextRow = ${c[l-2]} < ${i[l-2]-1};
${p}
ivec4 srcIdx = ivec4(sourceLocR${d}, sourceLocG${d},
sourceLocB${d}, sourceLocA${d}) * ${t};
ivec4 inIdx = srcIdx;
vec4 bestIndex = vec4(inIdx);
vec4 bestValue = ${E};
for (int i = 0; i < ${t}; i++) {
inIdx = srcIdx;
${k}
vec4 candidate = ${E};
bvec4 nan = isnan(candidate);
bvec4 replace = bvec4(
vec4(${_}(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 F$(r,e,t,n=null){let o=e.shape[0],s=e.shape[1];n!=null&&(o=n.shape[0],s=n.shape[1]);let a=N.computeOptimalWindowSize(s),i={windowSize:a,inSize:s,batchSize:o,outSize:Math.ceil(s/a)},l=new dk(i,t,n==null),u=[e];n!=null&&u.push(n);let c=r.runWebGLProgram(l,u,"int32");if(c.shape[1]===1)return c;let p=F$(r,e,t,c);return r.disposeIntermediateTensorInfo(c),p}function O$(r,e,t,n=null){let o=n!=null?n.shape:e.shape,s=o[o.length-1],a=N.computeOptimalWindowSize(s),i=new hk(o,a,t,n==null),l=n==null?[e]:[e,n],u=r.runWebGLProgram(i,l,"int32");if(u.shape.length===e.shape.length){let c=O$(r,e,t,u);return r.disposeIntermediateTensorInfo(u),c}return u}function ex(r,e,t,n){let o=[t];if(N.assertAxesAreInnerMostDims("arg"+n.charAt(0).toUpperCase()+n.slice(1),o,e.shape.length),!G().getBool("WEBGL_PACK_REDUCE")||e.shape.length<=2){let s=[],[a,i]=N.computeOutAndReduceShapes(e.shape,o),l=y.sizeFromShape(i),u=me({inputs:{x:e},backend:r,attrs:{shape:[-1,l]}});s.push(u);let c=F$(r,u,n);s.push(c);let p=me({inputs:{x:c},backend:r,attrs:{shape:a}});return s.forEach(m=>r.disposeIntermediateTensorInfo(m)),p}return O$(r,e,n)}function tX(r){let{inputs:e,backend:t,attrs:n}=r,{x:o}=e,{axis:s}=n,a=y.parseAxisParam(s,o.shape),i=N.getAxesPermutation(a,o.shape.length),l=o,u=[];i!=null&&(l=Vt({inputs:{x:o},backend:t,attrs:{perm:i}}),u.push(l),a=N.getInnerMostAxes(a.length,l.shape.length)),N.assertAxesAreInnerMostDims("argMax",[a[0]],l.shape.length);let c=ex(t,l,a[0],"max");return u.forEach(p=>t.disposeIntermediateTensorInfo(p)),c}var P$={kernelName:Xn,backendName:"webgl",kernelFunc:tX};function rX(r){let{inputs:e,backend:t,attrs:n}=r,{x:o}=e,{axis:s}=n,a=y.parseAxisParam(s,o.shape),i=N.getAxesPermutation(a,o.shape.length),l=o,u=[];i!=null&&(l=Vt({inputs:{x:o},backend:t,attrs:{perm:i}}),u.push(l),a=N.getInnerMostAxes(a.length,l.shape.length)),N.assertAxesAreInnerMostDims("argMin",[a[0]],l.shape.length);let c=ex(t,l,a[0],"min");return u.forEach(p=>t.disposeIntermediateTensorInfo(p)),c}var M$={kernelName:na,backendName:"webgl",kernelFunc:rX};var nX=gr+`
if (abs(x) > 1.) {
return NAN;
}
return asin(x);
`,oX=Ce({opSnippet:nX}),L$={kernelName:Ys,backendName:"webgl",kernelFunc:oX};var sX=gr+"return log(x + sqrt(x * x + 1.0));",iX=Ce({opSnippet:sX}),z$={kernelName:Zs,backendName:"webgl",kernelFunc:iX};var aX=gr+`
return atan(x);
`,lX=Ce({opSnippet:aX}),B$={kernelName:Js,backendName:"webgl",kernelFunc:lX};var uX=h$+`
return atan(a, b);
`,cX=`
vec4 result = atan(a, b);
vec4 isNaN = min(vec4(isnan(a)) + vec4(isnan(b)), vec4(1.0));
`+g$+`
return result;
`,pX=at({opSnippet:uX,packedOpSnippet:cX}),V$={kernelName:ei,backendName:"webgl",kernelFunc:pX};var mX=gr+`
if ((x < -1.0) || (x > 1.0)) return NAN;
return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,fX=Ce({opSnippet:mX}),W$={kernelName:Qs,backendName:"webgl",kernelFunc:fX};var Gi=class{constructor(e,t,n,o=!1,s=!1){if(this.variableNames=["x"],t==="avg"&&n)throw new Error("Cannot compute positions for average pool.");let a=e.filterWidth,i=e.strideHeight,l=e.strideWidth,u=e.dilationHeight,c=e.dilationWidth,p=e.effectiveFilterHeight,m=e.effectiveFilterWidth,f=e.padInfo.top,d=e.padInfo.left;this.outputShape=e.outShape;let h=t==="avg",g=`((batch * ${e.inHeight} + xR) * ${e.inWidth} + xC) * ${e.inChannels} + d`,x=`(xR * ${e.inWidth} + xC) * ${e.inChannels} + d`,b="0.0";if(h||(b="-1.0 / 1e-20"),n){let F=">=";this.userCode=`
const ivec2 strides = ivec2(${i}, ${l});
const ivec2 pads = ivec2(${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 += ${u}) {
int xR = xRCorner + wR;
if (xR < 0 || xR >= ${e.inHeight}) {
continue;
}
for (int wC = 0; wC < ${m};
wC += ${c}) {
int xC = xCCorner + wC;
if (xC < 0 || xC >= ${e.inWidth}) {
continue;
}
float value = getX(batch, xR, xC, d);
// If a min / max value has already been found, use it. If not,
// use the current value.
float currMinMaxValue = mix(
value, minMaxValue, minMaxValueFound);
if (value ${F} currMinMaxValue) {
minMaxValue = value;
minMaxValueFound = 1.0;
minMaxPosition = ${o?s?g:x:`wR * ${m} + wC`};
}
}
}
setOutput(float(minMaxPosition));
}
`;return}let w="max",_=`${t}(${t}(${t}(minMaxValue[0], minMaxValue[1]), minMaxValue[2]), minMaxValue[3])`;t==="avg"&&(_="avgValue / count");let k=Math.floor(a/4)*4,E=a%4,T=`
if (${h}) {
avgValue += dot(values, ones);
} else {
minMaxValue = ${w}(values, minMaxValue);
}
`;this.userCode=`
const ivec2 strides = ivec2(${i}, ${l});
const ivec2 pads = ivec2(${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 >= ${e.inWidth}) {
return initializationValue;
}
count += 1.0;
return getX(batch, xR, xC, d);
}
void main() {
ivec4 coords = getOutputCoords();
int batch = coords[0];
int d = coords[3];
ivec2 xRCCorner = coords.yz * strides - pads;
int xRCorner = xRCCorner.x;
int xCCorner = xRCCorner.y;
// max/min x(?, ?, d) to get y(yR, yC, d).
// ? = to be determined
vec4 minMaxValue = vec4(${b});
float avgValue = 0.0;
count = 0.0;
for (int wR = 0; wR < ${p};
wR += ${u}) {
int xR = xRCorner + wR;
if (xR < 0 || xR >= ${e.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)
);
${T}
}
int xC = xCCorner + ${k};
if (${E===1}) {
vec4 values = vec4(
getValue(batch, xR, xC, d),
initializationValue,
initializationValue,
initializationValue
);
${T}
} else if (${E===2}) {
vec4 values = vec4(
getValue(batch, xR, xC, d),
getValue(batch, xR, xC + ${c}, d),
initializationValue,
initializationValue
);
${T}
} else if (${E===3}) {
vec4 values = vec4(
getValue(batch, xR, xC, d),
getValue(batch, xR, xC + ${c}, d),
getValue(batch, xR, xC + 2 * ${c}, d),
initializationValue
);
${T}
}
}
setOutput(${_});
}
`}},ec=class{constructor(e,t,n,o=!1,s=!1){if(this.variableNames=["x"],t==="avg"&&n)throw new Error("Cannot compute positions for average pool.");let a=e.filterWidth,i=e.strideDepth,l=e.strideHeight,u=e.strideWidth,c=e.dilationDepth,p=e.dilationHeight,m=e.dilationWidth,f=e.effectiveFilterDepth,d=e.effectiveFilterHeight,h=e.effectiveFilterWidth,g=e.padInfo.front,x=e.padInfo.top,b=e.padInfo.left;this.outputShape=e.outShape;let w=t==="avg",_="0.0";if(w||(_="-1.0 / 1e-20"),n){let M=">=";this.userCode=`
const ivec3 strides =
ivec3(${i}, ${l}, ${u});
const ivec3 pads = ivec3(${g}, ${x}, ${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 >= ${e.inDepth}) {
continue;
}
for (int wR = 0; wR < ${d};
wR += ${p}) {
int xR = xRCorner + wR;
if (xR < 0 || xR >= ${e.inHeight}) {
continue;
}
for (int wC = 0; wC < ${h};
wC += ${m}) {
int xC = xCCorner + wC;
if (xC < 0 || xC >= ${e.inWidth}) {
continue;
}
float value = getX(batch, xD, xR, xC, ch);
// If a min / max value has already been found, use it. If not,
// use the current value.
float currMinMaxValue = mix(
value, minMaxValue, minMaxValueFound);
if (value ${M} currMinMaxValue) {
minMaxValue = value;
minMaxValueFound = 1.0;
minMaxPosition = ${o?s?`(((batch * ${e.inDepth} + xD) * ${e.inHeight} + xR) * ${e.inWidth} + xC) * ${e.inChannels} + ch`:`((xD * ${e.inHeight} + xR) * ${e.inWidth} + xC) * ${e.inChannels} + ch`:`wD * ${d} * ${h} +
wR * ${h} + wC`};
}
}
}
}
setOutput(float(minMaxPosition));
}
`;return}let k="max",E=`${t}(${t}(${t}(minMaxValue[0], minMaxValue[1]), minMaxValue[2]), minMaxValue[3])`;t==="avg"&&(E="avgValue / count");let T=Math.floor(a/4)*4,F=a%4,O=`
if (${w}) {
avgValue += dot(values, ones);
} else {
minMaxValue = ${k}(values, minMaxValue);
}
`;this.userCode=`
const ivec3 strides =
ivec3(${i}, ${l}, ${u});
const ivec3 pads = ivec3(${g}, ${x}, ${b});
const float initializationValue = ${_};
const vec4 ones = vec4(1.0, 1.0, 1.0, 1.0);
float count = 0.0;
float getValue(int batch, int xD, int xR, int xC, int ch) {
if (xC < 0 || xC >= ${e.inWidth}) {
return initializationValue;
}
count += 1.0;
return getX(batch, xD, xR, xC, ch);
}
void main() {
ivec5 coords = getOutputCoords();
int batch = coords.x;
int ch = coords.u;
ivec3 xCorner = ivec3(coords.y, coords.z, coords.w) * strides - pads;
int xDCorner = xCorner.x;
int xRCorner = xCorner.y;
int xCCorner = xCorner.z;
// max/min x(?, ?, ?, d) to get y(yD, yR, yC, ch).
// ? = to be determined
vec4 minMaxValue = vec4(${_});
float avgValue = 0.0;
count = 0.0;
for (int wD = 0; wD < ${f};
wD += ${c}) {
int xD = xDCorner + wD;
if (xD < 0 || xD >= ${e.inDepth}) {
continue;
}
for (int wR = 0; wR < ${d};
wR += ${p}) {
int xR = xRCorner + wR;
if (xR < 0 || xR >= ${e.inHeight}) {
continue;
}
for (int wC = 0; wC < ${T}; 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)
);
${O}
}
int xC = xCCorner + ${T};
if (${F===1}) {
vec4 values = vec4(
getValue(batch, xD, xR, xC, ch),
initializationValue,
initializationValue,
initializationValue
);
${O}
} else if (${F===2}) {
vec4 values = vec4(
getValue(batch, xD, xR, xC, ch),
getValue(batch, xD, xR, xC + ${m}, ch),
initializationValue,
initializationValue
);
${O}
} else if (${F===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
);
${O}
}
}
setOutput(${E});
}
}
`}};function dX(r){let{inputs:e,backend:t,attrs:n}=r,{x:o}=e;Rs(o,"avgPool");let{filterSize:s,strides:a,pad:i,dimRoundingMode:l}=n,u=1;y.assert(N.eitherStridesOrDilationsAreOne(a,u),()=>`Error in avgPool: Either strides or dilations must be 1. Got strides ${a} and dilations '${u}'`);let c=N.computePool2DInfo(o.shape,s,a,u,i,l);if(c.filterWidth===1&&c.filterHeight===1&&y.arraysEqual(c.inShape,c.outShape))return Ht({inputs:{x:o},backend:t});let p=new Gi(c,"avg",!1);return t.runWebGLProgram(p,[o],"float32")}var j$={kernelName:Yn,backendName:"webgl",kernelFunc:dX};function hX(r){let{inputs:e,backend:t,attrs:n}=r,{x:o}=e,{filterSize:s,strides:a,pad:i,dimRoundingMode:l,dataFormat:u}=n,c=[1,1,1],p=N.computePool3DInfo(o.shape,s,a,c,i,l,u),m=new ec(p,"avg",!1);return t.runWebGLProgram(m,[o],"float32")}var G$={kernelName:oa,backendName:"webgl",kernelFunc:hX};var gk=class{constructor(e){this.variableNames=["dy"],this.outputShape=e.inShape;let t=e.filterHeight,n=e.filterWidth,o=e.strideHeight,s=e.strideWidth,a=e.dilationHeight,i=e.dilationWidth,l=e.effectiveFilterHeight,u=e.effectiveFilterWidth,c=l-1-e.padInfo.top,p=u-1-e.padInfo.left,m=1/(t*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 < ${l};
wR += ${a}) {
float dyR = float(dyRCorner + wR) / ${o}.0;
if (dyR < 0.0 || dyR >= ${e.outHeight}.0 || fract(dyR) > 0.0) {
continue;
}
int idyR = int(dyR);
for (int wC = 0; wC < ${u};
wC+= ${i}) {
float dyC = float(dyCCorner + wC) / ${s}.0;
if (dyC < 0.0 || dyC >= ${e.outWidth}.0 ||
fract(dyC) > 0.0) {
continue;
}
int idyC = int(dyC);
float dyValue = getDy(b, idyR, idyC, d);
dotProd += dyValue * avgMultiplier;
}
}
setOutput(dotProd);
}
`}},xk=class{constructor(e){this.variableNames=["dy"],this.outputShape=e.inShape;let t=e.filterDepth,n=e.filterHeight,o=e.filterWidth,s=e.strideDepth,a=e.strideHeight,i=e.strideWidth,l=e.dilationDepth,u=e.dilationHeight,c=e.dilationWidth,p=e.effectiveFilterDepth,m=e.effectiveFilterHeight,f=e.effectiveFilterWidth,d=p-1-e.padInfo.front,h=m-1-e.padInfo.top,g=f-1-e.padInfo.left,x=1/(t*n*o);this.userCode=`
const ivec3 pads = ivec3(${d}, ${h}, ${g});
const float avgMultiplier = float(${x});
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 += ${l}) {
float dyD = float(dyDCorner + wD) / ${s}.0;
if (dyD < 0.0 || dyD >= ${e.outDepth}.0 || fract(dyD) > 0.0) {
continue;
}
int idyD = int(dyD);
for (int wR = 0; wR < ${m};
wR += ${u}) {
float dyR = float(dyRCorner + wR) / ${a}.0;
if (dyR < 0.0 || dyR >= ${e.outHeight}.0 ||
fract(dyR) > 0.0) {
continue;
}
int idyR = int(dyR);
for (int wC = 0; wC < ${f};
wC += ${c}) {
float dyC = float(dyCCorner + wC) / ${i}.0;
if (dyC < 0.0 || dyC >= ${e.outWidth}.0 ||
fract(dyC) > 0.0) {
continue;
}
int idyC = int(dyC);
float dyValue = getDy(batch, idyD, idyR, idyC, ch);
dotProd += dyValue * avgMultiplier;
}
}
}
setOutput(dotProd);
}
`}};function gX(r){let{inputs:e,backend:t,attrs:n}=r,{dy:o,input:s}=e,a=s,{filterSize:i,strides:l,pad:u,dimRoundingMode:c}=n,p=[1,1,1],m=N.computePool3DInfo(a.shape,i,l,p,u,c),f=new xk(m);return t.runWebGLProgram(f,[o],a.dtype)}var U$={kernelName:ql,backendName:"webgl",kernelFunc:gX};function xX(r){let{inputs:e,backend:t,attrs:n}=r,{dy:o,input:s}=e,a=s;Rs([o,s],"avgPoolGrad");let{filterSize:i,strides:l,pad:u}=n,c=N.computePool2DInfo(a.shape,i,l,1,u),p=new gk(c);return t.runWebGLProgram(p,[o],a.dtype)}var q$={kernelName:Ul,backendName:"webgl",kernelFunc:xX};function yX(r){let{inputs:e,backend:t,attrs:n}=r,{a:o,b:s}=e,{transposeA:a,transposeB:i}=n;return Qu({a:o,b:s,transposeA:a,transposeB:i,backend:t})}var H$={kernelName:Zn,backendName:"webgl",kernelFunc:yX};var yk=class{constructor(e,t,n,o,s,a){this.outputShape=[],this.variableNames=["x","mean","variance"],N.assertAndGetBroadcastShape(e,t),N.assertAndGetBroadcastShape(e,n);let i="0.0";o!=null&&(N.assertAndGetBroadcastShape(e,o),this.variableNames.push("offset"),i="getOffsetAtOutCoords()");let l="1.0";s!=null&&(N.assertAndGetBroadcastShape(e,s),this.variableNames.push("scale"),l="getScaleAtOutCoords()"),this.outputShape=e,this.userCode=`
void main() {
float x = getXAtOutCoords();
float mean = getMeanAtOutCoords();
float variance = getVarianceAtOutCoords();
float offset = ${i};
float scale = ${l};
float inv = scale * inversesqrt(variance + float(${a}));
setOutput(dot(vec3(x, -mean, offset), vec3(inv, inv, 1)));
}
`}};var bk=class{constructor(e,t,n,o,s,a){this.packedInputs=!0,this.packedOutput=!0,this.variableNames=["x","mean","variance"],N.assertAndGetBroadcastShape(e,t),N.assertAndGetBroadcastShape(e,n);let i="vec4(0.0)";o!=null&&(N.assertAndGetBroadcastShape(e,o),this.variableNames.push("offset"),i="getOffsetAtOutCoords()");let l="vec4(1.0)";s!=null&&(N.assertAndGetBroadcastShape(e,s),this.variableNames.push("scale"),l="getScaleAtOutCoords()"),this.outputShape=e,this.userCode=`
void main() {
vec4 offset = ${i};
vec4 scale = ${l};
vec4 x = getXAtOutCoords();
vec4 mean = getMeanAtOutCoords();
vec4 variance = getVarianceAtOutCoords();
vec4 inv = scale * inversesqrt(variance + vec4(${a}));
setOutput((x - mean) * inv + offset);
}
`}};var bX=({inputs:r,backend:e,attrs:t})=>{let{x:n,mean:o,variance:s,offset:a,scale:i}=r;y.assert(o.shape.length===s.shape.length,()=>"Batch normalization gradient requires mean and variance to have equal ranks."),y.assert(a==null||o.shape.length===a.shape.length,()=>"Batch normalization gradient requires mean and offset to have equal ranks."),y.assert(i==null||o.shape.length===i.shape.length,()=>"Batch normalization gradient requires mean and scale to have equal ranks.");let{varianceEpsilon:l}=t;l==null&&(l=.001);let u=[n,o,s],c=null;a!=null&&(c=a.shape,u.push(a));let p=null;i!=null&&(p=i.shape,u.push(i));let m=G().getBool("WEBGL_PACK_NORMALIZATION")?new bk(n.shape,o.shape,s.shape,c,p,l):new yk(n.shape,o.shape,s.shape,c,p,l);return e.runWebGLProgram(m,u,u[0].dtype)},K$={kernelName:ao,backendName:"webgl",kernelFunc:bX};var wk=class{constructor(e){this.variableNames=["source"],this.outputShape=e,this.rank=e.length;let t=ze(this.rank),n=`uniform int start[${this.rank}];`,o=wX(this.rank),s,a=e.map((i,l)=>`sourceLoc.${_k[l]} = start[${l}] + coords.${_k[l]};`);s=`
${t} sourceLoc;
${t} coords = getOutputCoords();
${a.join(`
`)}
`,this.userCode=`
${n}
void main() {
${s}
setOutput(getSource(${o}));
}
`}getCustomSetupFunc(e){if(e.length!==this.rank)throw Error(`The rank (${this.rank}) of the program must match the length of start (${e.length})`);return(t,n)=>{this.startLoc==null&&(this.startLoc=t.getUniformLocationNoThrow(n,"start"),this.startLoc==null)||t.gl.uniform1iv(this.startLoc,e)}}},_k=["x","y","z","w","u","v"];function wX(r){if(r===1)return"sourceLoc";if(r<=6)return _k.slice(0,r).map(e=>"sourceLoc."+e).join(",");throw Error(`Slicing for rank ${r} is not yet supported`)}var vk=class{constructor(e){this.variableNames=["source"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=e,this.rank=e.length;let t=ze(this.rank),n=qt("coords",this.rank),o=qt("sourceLoc",this.rank),s=this.rank===1?"sourceLoc":`vec2(${o.slice(-2).join()})`,a=`getChannel(getSource(${o.join()}), ${s})`,i=`
result.x = ${a};
if (++${n[this.rank-1]} < ${e[this.rank-1]}) {
++${o[this.rank-1]};
result.y = ${a};
--${o[this.rank-1]};
}
`,l=this.rank===1?"":`
--${n[this.rank-1]};
if (++${n[this.rank-2]} < ${e[this.rank-2]}) {
++${o[this.rank-2]};
result.z = ${a};
if (++${n[this.rank-1]} < ${e[this.rank-1]}) {
++${o[this.rank-1]};
result.w = ${a};
}
}
`,u=this.rank<=4?`sourceLoc = coords +
${t}(${e.map((c,p)=>`start[${p}]`).join()});`:e.map((c,p)=>`${o[p]} = ${n[p]} + start[${p}];`).join(`
`);this.userCode=`
uniform int start[${this.rank}];
void main() {
${t} coords = getOutputCoords();
${t} sourceLoc;
${u}
vec4 result = vec4(0.);
${i}
${l}
setOutput(result);
}
`}getCustomSetupFunc(e){if(e.length!==this.rank)throw Error(`The rank (${this.rank}) of the program must match the length of start (${e.length})`);return(t,n)=>{this.startLoc==null&&(this.startLoc=t.getUniformLocationNoThrow(n,"start"),this.startLoc==null)||t.gl.uniform1iv(this.startLoc,e)}}};function _X(r,e,t,n){let o=n.texData.get(r.dataId),s=n.makeTensorInfo(t,r.dtype),a=n.texData.get(s.dataId);Object.assign(a,o),a.complexParentRefCount=0,a.refCount=1,a.shape=t,a.dtype=r.dtype;let i=ir.computeFlatOffset(e,y.computeStrides(r.shape));o.slice&&(i+=o.slice.flatOffset),a.slice={flatOffset:i,origDataId:o.slice&&o.slice.origDataId||r.dataId};let l=n.dataRefCount.get(a.slice.origDataId)||1;return n.dataRefCount.set(a.slice.origDataId,l+1),s}function Wa(r){let{inputs:e,backend:t,attrs:n}=r,{x:o}=e,{begin:s,size:a}=n,[i,l]=ir.parseSliceParams(o,s,a);if(ir.assertParamsValid(o,i,l),y.sizeFromShape(l)===0)return t.makeTensorInfo(l,o.dtype,[]);if(t.shouldExecuteOnCPU([o])||o.dtype==="string"){let p=t.texData.get(o.dataId),m=HD(p.values,i,l,o.shape,o.dtype);return t.makeTensorInfo(l,o.dtype,m)}let{isPacked:u}=t.texData.get(o.dataId),c=ir.isSliceContinous(o.shape,i,l);if(u||!c){let p=G().getBool("WEBGL_PACK_ARRAY_OPERATIONS")?new vk(l):new wk(l),m=p.getCustomSetupFunc(i);return t.runWebGLProgram(p,[o],o.dtype,m)}return t.uploadToGPU(o.dataId),_X(o,i,l,t)}var X$={kernelName:ys,backendName:"webgl",kernelFunc:Wa};var vX=r=>{let{inputs:e,backend:t,attrs:n}=r,{x:o}=e,{blockShape:s,crops:a}=n;y.assert(o.shape.length<=4,()=>"batchToSpaceND for rank > 4 with a WebGL backend not implemented yet");let i=s.reduce((b,w)=>b*w),l=N.getReshaped(o.shape,s,i),u=N.getPermuted(l.length,s.length),c=N.getReshapedPermuted(o.shape,s,i),p=N.getSliceBeginCoords(a,s.length),m=N.getSliceSize(c,a,s.length),f=[],d=me({inputs:{x:o},backend:t,attrs:{shape:l}}),h=Vt({inputs:{x:d},backend:t,attrs:{perm:u}}),g=me({inputs:{x:h},backend:t,attrs:{shape:c}}),x=Wa({inputs:{x:g},backend:t,attrs:{begin:p,size:m}});return f.push(d),f.push(h),f.push(g),f.forEach(b=>t.disposeIntermediateTensorInfo(b)),x},Y$={kernelName:sa,backendName:"webgl",kernelFunc:vX};function kX(r){let{inputs:e,backend:t,attrs:n}=r,{x:o,weights:s}=e,{size:a}=n,i=t.readSync(o.dataId),l=t.readSync(s.dataId),u=Ug(i,l,s.dtype,s.shape,a);return t.makeTensorInfo([a],s.dtype,u)}var Z$={kernelName:Hl,backendName:"webgl",kernelFunc:kX};var CX="return float(a != b);",kk=at({opSnippet:CX,dtype:"bool"}),J$={kernelName:wi,backendName:"webgl",kernelFunc:kk};function ja(r){let{inputs:e,backend:t}=r,{input:n}=e,o=t.texData.get(n.dataId);return Ht({inputs:{x:o.complexTensorInfos.real},backend:t})}var Q$={kernelName:mu,backendName:"webgl",kernelFunc:ja};var IX="return float(int(x));";function eR(r,e){let t=new sn(r.shape,IX),n=e.runWebGLProgram(t,[r],"int32");return{dataId:n.dataId,shape:n.shape,dtype:n.dtype}}function Ck(r){let{inputs:e,backend:t,attrs:n}=r,{x:o}=e,{dtype:s}=n;if(s==="complex64"){if(o.dtype==="complex64")return Ht({inputs:{x:o},backend:t});let a=mt(o.shape),i=Ck({inputs:{x:o},backend:t,attrs:{dtype:"float32"}}),l=an({inputs:{real:i,imag:a},backend:t});return a.dispose(),t.disposeIntermediateTensorInfo(i),l}if(o.dtype==="complex64"){let a=ja({inputs:{input:o},backend:t}),i=Ck({inputs:{x:a},backend:t,attrs:{dtype:s}});return t.disposeIntermediateTensorInfo(a),i}if(!y.hasEncodingLoss(o.dtype,s)){let a=Ht({inputs:{x:o},backend:t});return{dataId:a.dataId,shape:a.shape,dtype:s}}if(s==="int32")return eR(o,t);if(s==="bool"){let a=t.makeTensorInfo([],"bool",y.getTypedArrayFromDType("bool",1)),l=kk({inputs:{a:o,b:a},backend:t});return t.disposeIntermediateTensorInfo(a),l}throw new Error(`Error in Cast: failed to cast ${o.dtype} to ${s}`)}var tR={kernelName:Rn,backendName:"webgl",kernelFunc:Ck};var rR="return ceil(x);",SX=Ce({opSnippet:rR,packedOpSnippet:rR,cpuKernelImpl:ED}),nR={kernelName:ti,backendName:"webgl",kernelFunc:SX};var Ik=class{constructor(e){this.variableNames=["A"],this.outputShape=e,this.userCode=`
uniform float minVal;
uniform float maxVal;
void main() {
float value = getAAtOutCoords();
if (isnan(value)) {
setOutput(value);
return;
}
setOutput(clamp(value, minVal, maxVal));
}
`}getCustomSetupFunc(e,t){return(n,o)=>{this.minLoc==null&&(this.minLoc=n.getUniformLocationNoThrow(o,"minVal"),this.maxLoc=n.getUniformLocationNoThrow(o,"maxVal")),n.gl.uniform1f(this.minLoc,e),n.gl.uniform1f(this.maxLoc,t)}}};var Sk=class{constructor(e){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=e,this.userCode=`
uniform float minVal;
uniform float maxVal;
void main() {
vec4 value = getAAtOutCoords();
if (any(isnan(value))) {
setOutput(value);
return;
}
setOutput(clamp(value, vec4(minVal), vec4(maxVal)));
}
`}getCustomSetupFunc(e,t){return(n,o)=>{this.minLoc==null&&(this.minLoc=n.getUniformLocationNoThrow(o,"minVal"),this.maxLoc=n.getUniformLocationNoThrow(o,"maxVal")),n.gl.uniform1f(this.minLoc,e),n.gl.uniform1f(this.maxLoc,t)}}};function NX(r){let{inputs:e,backend:t,attrs:n}=r,{x:o}=e,{clipValueMin:s,clipValueMax:a}=n,i;G().getBool("WEBGL_PACK_CLIP")?i=new Sk(o.shape):i=new Ik(o.shape);let l=i.getCustomSetupFunc(s,a);return t.runWebGLProgram(i,[o],o.dtype,l)}var oR={kernelName:Fn,backendName:"webgl",kernelFunc:NX};var Nk=class{constructor(e){this.variableNames=["real","imag"],this.outputShape=e,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 sR(r,e){return{dataId:e.dataId,dtype:e.dtype,shape:r.shape}}function TX(r){let{inputs:e,backend:t}=r,{x:n}=e,o=t.texData.get(n.dataId),s=new Nk(n.shape),a=[sR(n,o.complexTensorInfos.real),sR(n,o.complexTensorInfos.imag)];return t.runWebGLProgram(s,a,a[0].dtype)}var iR={kernelName:ia,backendName:"webgl",kernelFunc:TX};var Tk=class{constructor(e){this.outputShape=[],this.outputShape=N.computeOutShape(e,1),this.variableNames=e.map((a,i)=>`T${i}`);let t=new Array(e.length-1);t[0]=e[0][1];for(let a=1;a<t.length;a++)t[a]=t[a-1]+e[a][1];let n=[`if (yC < ${t[0]}) setOutput(getT0(yR, yC));`];for(let a=1;a<t.length;a++){let i=t[a-1];n.push(`else if (yC < ${t[a]}) setOutput(getT${a}(yR, yC-${i}));`)}let o=t.length,s=t[t.length-1];n.push(`else setOutput(getT${o}(yR, yC-${s}));`),this.userCode=`
void main() {
ivec2 coords = getOutputCoords();
int yR = coords.x;
int yC = coords.y;
${n.join(`
`)}
}
`}};var Ek=class{constructor(e,t){this.packedInputs=!0,this.packedOutput=!0,this.outputShape=[],this.outputShape=N.computeOutShape(e,t);let n=this.outputShape,o=n.length,s=ze(o),a=qt("coords",o),i=["x","y","z","w","u","v"].slice(0,o);this.variableNames=e.map((h,g)=>`T${g}`);let l=new Array(e.length-1);l[0]=e[0][t];for(let h=1;h<l.length;h++)l[h]=l[h-1]+e[h][t];let u=i[t],c=i.slice(-2),p=i.join(),m=`if (${u} < ${l[0]}) {
return getChannel(
getT0(${p}), vec2(${c.join()}));
}`;for(let h=1;h<l.length;h++){let g=l[h-1];m+=`
if (${u} < ${l[h]} && ${u} >= ${l[h-1]}) {
return getChannel(
getT${h}(${tx(i,u,g)}),
vec2(${tx(c,u,g)}));
}`}let f=l.length,d=l[l.length-1];m+=`
return getChannel(
getT${f}(${tx(i,u,d)}),
vec2(${tx(c,u,d)}));`,this.userCode=`
float getValue(${i.map(h=>"int "+h)}) {
${m}
}
void main() {
${s} coords = getOutputCoords();
vec4 result = vec4(getValue(${a}), 0., 0., 0.);
${a[o-1]} = ${a[o-1]} + 1;
if (${a[o-1]} < ${n[o-1]}) {
result.g = getValue(${a});
}
${a[o-2]} = ${a[o-2]} + 1;
if (${a[o-2]} < ${n[o-2]}) {
result.a = getValue(${a});
}
${a[o-1]} = ${a[o-1]} - 1;
if (${a[o-2]} < ${n[o-2]} &&
${a[o-1]} < ${n[o-1]}) {
result.b = getValue(${a});
}
setOutput(result);
}
`}};function tx(r,e,t){let n=r.indexOf(e);return r.map((s,a)=>a===n?`${s} - ${t}`:s).join()}function tc(r){let{inputs:e,backend:t}=r,{input:n}=e,o=t.texData.get(n.dataId);return Ht({inputs:{x:o.complexTensorInfos.imag},backend:t})}var aR={kernelName:su,backendName:"webgl",kernelFunc:tc};function rc(r,e,t){let n=r[0].dtype;if(n==="complex64"){let u=r.map(d=>ja({inputs:{input:d},backend:t})),c=r.map(d=>tc({inputs:{input:d},backend:t})),p=rc(u,e,t),m=rc(c,e,t),f=an({inputs:{real:p,imag:m},backend:t});return u.forEach(d=>t.disposeIntermediateTensorInfo(d)),c.forEach(d=>t.disposeIntermediateTensorInfo(d)),t.disposeIntermediateTensorInfo(p),t.disposeIntermediateTensorInfo(m),f}if(n==="string"){let{tensors2D:u,outShape:c}=lR(r,e,t),p=u.map(g=>({vals:t.readSync(g.dataId),shape:g.shape})),m=u[0].shape[0]===1,f=AD(p,c,n,m),d=N.computeOutShape(r.map(g=>g.shape),e),h=t.makeTensorInfo(d,n,f);return u.forEach(g=>t.disposeIntermediateTensorInfo(g)),h}if(r.length>G().getNumber("WEBGL_MAX_TEXTURES_IN_SHADER")){let u=Math.floor(r.length/2),c=rc(r.slice(0,u),e,t),p=rc(r.slice(u),e,t),m=rc([c,p],e,t);return t.disposeIntermediateTensorInfo(c),t.disposeIntermediateTensorInfo(p),m}if(G().getBool("WEBGL_PACK_ARRAY_OPERATIONS")&&r[0].shape.length>1){let u=new Ek(r.map(c=>c.shape),e);return t.runWebGLProgram(u,r,n)}let{tensors2D:o,outShape:s}=lR(r,e,t),a=new Tk(o.map(u=>u.shape)),i=t.runWebGLProgram(a,o,n);o.forEach(u=>t.disposeIntermediateTensorInfo(u));let l=me({inputs:{x:i},attrs:{shape:s},backend:t});return t.disposeIntermediateTensorInfo(i),l}function lR(r,e,t){let n=N.computeOutShape(r.map(s=>s.shape),e);return{tensors2D:r.map(s=>me({inputs:{x:s},attrs:{shape:[-1,y.sizeFromShape(s.shape.slice(e))]},backend:t})),outShape:n}}function Ak(r){let{inputs:e,backend:t,attrs:n}=r,{axis:o}=n,s=y.parseAxisParam(o,e[0].shape)[0],a=N.computeOutShape(e.map(u=>u.shape),s);if(y.sizeFromShape(a)===0)return t.makeTensorInfo(a,e[0].dtype,[]);let i=e.filter(u=>y.sizeFromShape(u.shape)>0);if(i.length===1)return Ht({inputs:{x:i[0]},backend:t});let l=i.map(u=>u.shape);return N.assertParamsConsistent(l,s),rc(i,s,t)}var uR={kernelName:us,backendName:"webgl",kernelFunc:Ak};var Ef=class{constructor(e,t=!1,n=null,o=!1,s=!1){this.variableNames=["x","W"],this.outputShape=e.outShape;let a=e.padInfo.top,i=e.padInfo.left,l=e.strideHeight,u=e.strideWidth,c=e.dilationHeight,p=e.dilationWidth,m=e.filterHeight,f=e.filterWidth,d=Math.floor(e.inChannels/4)*4,h=e.inChannels%4,g=e.dataFormat==="channelsLast",x=g?1:2,b=g?2:3,w=g?3:1,_="",k="";n&&(o?_=`float activation(float a) {
float b = getPreluActivationWeightsAtOutCoords();
${n}
}`:s?_=`float activation(float a) {
float b = getLeakyreluAlphaAtOutCoords();
${n}
}`:_=`
float activation(float x) {
${n}
}
`,k="result = activation(result);");let E=t?"result += getBiasAtOutCoords();":"";t&&this.variableNames.push("bias"),o&&this.variableNames.push("preluActivationWeights"),s&&this.variableNames.push("leakyreluAlpha"),this.userCode=`
${_}
const ivec2 strides = ivec2(${l}, ${u});
const ivec2 pads = ivec2(${a}, ${i});
void main() {
ivec4 coords = getOutputCoords();
int batch = coords[0];
int d2 = coords[${w}];
ivec2 xRCCorner =
ivec2(coords[${x}], coords[${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 >= ${e.inHeight}) {
continue;
}
for (int wC = 0; wC < ${f}; wC++) {
int xC = xCCorner + wC * ${p};
if (xC < 0 || xC >= ${e.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}
${k}
setOutput(result);
}
`}},Dk=class{constructor(e){this.variableNames=["x","W"],this.outputShape=e.outShape;let t=e.padInfo.front,n=e.padInfo.top,o=e.padInfo.left,s=e.strideDepth,a=e.strideHeight,i=e.strideWidth,l=e.dilationDepth,u=e.dilationHeight,c=e.dilationWidth,p=e.filterDepth,m=e.filterHeight,f=e.filterWidth,d=Math.floor(e.inChannels/4)*4,h=e.inChannels%4;this.userCode=`
const ivec3 strides = ivec3(${s}, ${a}, ${i});
const ivec3 pads = ivec3(${t}, ${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 * ${l};
if (xF < 0 || xF >= ${e.inDepth}) {
continue;
}
for (int wR = 0; wR < ${m}; wR++) {
int xR = xRCorner + wR * ${u};
if (xR < 0 || xR >= ${e.inHeight}) {
continue;
}
for (int wC = 0; wC < ${f}; wC++) {
int xC = xCCorner + wC * ${c};
if (xC < 0 || xC >= ${e.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 $k=class{constructor(e,t,n){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=e;let{filterWidth:o,inChannels:s,strideWidth:a,strideHeight:i,padInfo:l,outWidth:u,dilationWidth:c,dilationHeight:p,dataFormat:m}=n,{left:f,top:d}=l,h=s*o,g=Lt(),x=m==="channelsLast",b=x?0:1,w=x?1:2,_="";for(let k=0;k<=1;k++)for(let E=0;E<=1;E++)_+=`
blockIndex = rc.y + ${E};
pos = rc.x + ${k};
if(blockIndex < ${e[1]} && pos < ${e[0]}) {
offsetY = int(blockIndex / (${u})) * ${i} - ${d};
d0 = offsetY + ${p} * (pos / ${h});
if(d0 < ${t[b]} && d0 >= 0) {
offsetX = int(mod(float(blockIndex), ${u}.) * ${a}. - ${f}.);
d1 = offsetX + ${c} * (int(mod(float(pos), ${h}.) / ${s}.));
if(d1 < ${t[w]} && d1 >= 0) {
ch = int(mod(float(pos), ${s}.));
if (${x}) {
innerDims = vec2(d1, ch);
result[${k*2+E}] = getChannel(
getA(d0, int(innerDims.x),
int(innerDims.y)), innerDims);
} else {
innerDims = vec2(d0, d1);
result[${k*2+E}] = 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;
${_}
${g.output} = result;
}
`}};function rx({x:r,filter:e,convInfo:t,backend:n,bias:o=null,preluActivationWeights:s=null,leakyreluAlpha:a=0,activation:i=null}){let l=r.shape,u=n.texData.get(r.dataId),c=t.inChannels,p=l[0]*l[1]*l[2],m=t.outChannels,f=t.dataFormat==="channelsLast",d=!1,h=!1,g,x=[],b=(p===1||m===1)&&c>pk,w=l[2]%2!=0&&!!u.isPacked;if(b||!G().getBool("WEBGL_LAZILY_UNPACK")||!G().getBool("WEBGL_PACK_BINARY_OPERATIONS")||!w){let _=f?l[0]*l[1]*l[2]:l[0]*l[2]*l[3],k=me({inputs:{x:r},backend:n,attrs:{shape:[1,_,t.inChannels]}}),E=me({inputs:{x:e},backend:n,attrs:{shape:[1,t.inChannels,t.outChannels]}}),T=Qu({a:k,b:E,transposeA:d,transposeB:h,backend:n,bias:o,activation:i,preluActivationWeights:s,leakyreluAlpha:a});g=me({inputs:{x:T},backend:n,attrs:{shape:t.outShape}}),x.push(k),x.push(E),x.push(T)}else{let _=f?l[0]*l[1]*(l[2]+1):l[0]*l[2]*(l[3]+1),k={dataId:r.dataId,shape:[1,_,t.inChannels],dtype:r.dtype},E=u.shape;u.shape=u.shape.slice(),u.shape[u.shape.length-2]++,y.assert(xl(u.shape,k.shape),()=>`packed reshape ${u.shape} to ${k.shape} isn't free`);let T=me({inputs:{x:e},backend:n,attrs:{shape:[1,t.inChannels,t.outChannels]}});x.push(T);let F=Qu({a:k,b:T,backend:n,transposeA:d,transposeB:h,bias:o,activation:i,preluActivationWeights:s,leakyreluAlpha:a}),O=n.texData.get(F.dataId);y.assert(O.isPacked,()=>"batchMatMul result is expected to be packed"),u.shape=E,O.shape=t.outShape,g=Ht({inputs:{x:F},backend:n}),g.shape=t.outShape,x.push(F)}for(let _ of x)n.disposeIntermediateTensorInfo(_);return g}function nx({x:r,filter:e,convInfo:t,backend:n,bias:o=null,preluActivationWeights:s=null,leakyreluAlpha:a=0,activation:i=null}){let{filterWidth:l,filterHeight:u,inChannels:c,outWidth:p,outHeight:m,dataFormat:f}=t,d=f==="channelsLast",h=l*u*c,g=m*p,x=[h,g],b=!0,w=!1,_=[],k=me({inputs:{x:r},backend:n,attrs:{shape:r.shape.slice(1)}}),E=me({inputs:{x:e},backend:n,attrs:{shape:[1,h,y.sizeFromShape(e.shape)/h]}});_.push(k),_.push(E);let T=new $k(x,k.shape,t),F=n.runWebGLProgram(T,[k],"float32"),O=me({inputs:{x:F},backend:n,attrs:{shape:[1,x[0],x[1]]}});_.push(F),_.push(O);let M=o!=null,j=s!=null,W=i==="leakyrelu",U=i?bl(i,!0):null,Y=new Nf(O.shape,E.shape,[1,g,t.outChannels],b,w,M,U,j,W),K=[O,E];if(o&&K.push(o),j&&K.push(s),W){let oe=n.makeTensorInfo([],"float32",y.createScalarValue(a,"float32"));K.push(oe),_.push(oe)}let Z=n.runWebGLProgram(Y,K,"float32"),re=d?[1,m,p,t.outChannels]:[1,t.outChannels,m,p],X=me({inputs:{x:Z},backend:n,attrs:{shape:re}});_.push(Z);for(let oe of _)n.disposeIntermediateTensorInfo(oe);return X}function EX(r){let{inputs:e,backend:t,attrs:n}=r,{x:o,filter:s}=e,{strides:a,pad:i,dataFormat:l,dilations:u,dimRoundingMode:c}=n,p=N.convertConv2DDataFormat(l),m=N.computeConv2DInfo(o.shape,s.shape,a,u,i,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=rx({x:o,filter:s,convInfo:m,backend:t});else if(G().getBool("WEBGL_CONV_IM2COL")&&o.shape[0]===1)f=nx({x:o,filter:s,convInfo:m,backend:t});else{let h=new Ef(m);f=t.runWebGLProgram(h,[o,s],"float32")}let d=me({inputs:{x:f},backend:t,attrs:{shape:m.outShape}});return t.disposeIntermediateTensorInfo(f),d}var cR={kernelName:Jn,backendName:"webgl",kernelFunc:EX};var Rk=class{constructor(e){this.variableNames=["x","dy"],this.outputShape=e.filterShape;let t=e.strideHeight,n=e.strideWidth,o=e.padInfo.top,s=e.padInfo.left,a=e.dataFormat==="channelsLast";this.userCode=`
void main() {
ivec4 coords = getOutputCoords();
int wR = coords.x;
int wC = coords.y;
int d1 = coords.z;
int d2 = coords.w;
// Convolve x(?, ?, d1) with dy(:, :, d2) to get dw(wR, wC, d1, d2).
// ? = to be determined. : = across all values in that axis.
float dotProd = 0.0;
for (int b = 0; b < ${e.batchSize}; b++) {
for (int yR = 0; yR < ${e.outHeight}; yR++) {
int xR = wR + yR * ${t} - ${o};
if (xR < 0 || xR >= ${e.inHeight}) {
continue;
}
for (int yC = 0; yC < ${e.outWidth}; yC++) {
int xC = wC + yC * ${n} - ${s};
if (xC < 0 || xC >= ${e.inWidth}) {
continue;
}
if (${a}) {
float dyValue = getDy(b, yR, yC, d2);
float xValue = getX(b, xR, xC, d1);
dotProd += (xValue * dyValue);
} else {
float dyValue = getDy(b, d2, yR, yC);
float xValue = getX(b, d1, xR, xC);
dotProd += (xValue * dyValue);
}
}
}
}
setOutput(dotProd);
}
`}},Fk=class{constructor(e){this.variableNames=["dy","W"],this.outputShape=e.inShape;let t=e.filterHeight,n=e.filterWidth,o=e.strideHeight,s=e.strideWidth,a=e.dataFormat==="channelsLast",i=t-1-e.padInfo.top,l=n-1-e.padInfo.left,u=a?1:2,c=a?2:3,p=a?3:1;this.userCode=`
const ivec2 pads = ivec2(${i}, ${l});
void main() {
ivec4 coords = getOutputCoords();
int batch = coords[0];
int d1 = coords[${p}];
ivec2 dyCorner = ivec2(coords[${u}], coords[${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 < ${t}; wR++) {
float dyR = float(dyRCorner + wR) / ${o}.0;
if (dyR < 0.0 || dyR >= ${e.outHeight}.0 || fract(dyR) > 0.0) {
continue;
}
int idyR = int(dyR);
int wRPerm = ${t} - 1 - wR;
for (int wC = 0; wC < ${n}; wC++) {
float dyC = float(dyCCorner + wC) / ${s}.0;
if (dyC < 0.0 || dyC >= ${e.outWidth}.0 ||
fract(dyC) > 0.0) {
continue;
}
int idyC = int(dyC);
int wCPerm = ${n} - 1 - wC;
for (int d2 = 0; d2 < ${e.outChannels}; d2++) {
if (${a}) {
float xValue = getDy(batch, idyR, idyC, d2);
float wValue = getW(wRPerm, wCPerm, d1, d2);
dotProd += xValue * wValue;
} else {
float xValue = getDy(batch, d2, idyR, idyC);
float wValue = getW(wRPerm, wCPerm, d1, d2);
dotProd += xValue * wValue;
}
}
}
}
setOutput(dotProd);
}
`}},Ok=class{constructor(e){this.variableNames=["x","dy"],this.outputShape=e.filterShape;let t=e.strideDepth,n=e.strideHeight,o=e.strideWidth,s=e.padInfo.front,a=e.padInfo.top,i=e.padInfo.left;this.userCode=`
void main() {
ivec5 coords = getOutputCoords();
int wF = coords.x;
int wR = coords.y;
int wC = coords.z;
int d1 = coords.w;
int d2 = coords.u;
float dotProd = 0.0;
for (int b = 0; b < ${e.batchSize}; b++) {
for (int yF = 0; yF < ${e.outDepth}; yF++) {
int xF = wF + yF * ${t} - ${s};
if (xF < 0 || xF >= ${e.inDepth}) {
continue;
}
for (int yR = 0; yR < ${e.outHeight}; yR++) {
int xR = wR + yR * ${n} - ${a};
if (xR < 0 || xR >= ${e.inHeight}) {
continue;
}
for (int yC = 0; yC < ${e.outWidth}; yC++) {
int xC = wC + yC * ${o} - ${i};
if (xC < 0 || xC >= ${e.inWidth}) {
continue;
}
float dyValue = getDy(b, yF, yR, yC, d2);
float xValue = getX(b, xF, xR, xC, d1);
dotProd += (xValue * dyValue);
}
}
}
}
setOutput(dotProd);
}
`}},Pk=class{constructor(e){this.variableNames=["dy","W"],this.outputShape=e.inShape;let t=e.filterDepth,n=e.filterHeight,o=e.filterWidth,s=e.strideDepth,a=e.strideHeight,i=e.strideWidth,l=t-1-e.padInfo.front,u=n-1-e.padInfo.top,c=o-1-e.padInfo.left;this.userCode=`
const ivec3 pads = ivec3(${l}, ${u}, ${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 < ${t}; wF++) {
float dyF = float(dyFCorner + wF) / ${s}.0;
if (dyF < 0.0 || dyF >= ${e.outDepth}.0 || fract(dyF) > 0.0) {
continue;
}
int idyF = int(dyF);
int wFPerm = ${t} - 1 - wF;
for (int wR = 0; wR < ${n}; wR++) {
float dyR = float(dyRCorner + wR) / ${a}.0;
if (dyR < 0.0 || dyR >= ${e.outHeight}.0 ||
fract(dyR) > 0.0) {
continue;
}
int idyR = int(dyR);
int wRPerm = ${n} - 1 - wR;
for (int wC = 0; wC < ${o}; wC++) {
float dyC = float(dyCCorner + wC) / ${i}.0;
if (dyC < 0.0 || dyC >= ${e.outWidth}.0 ||
fract(dyC) > 0.0) {
continue;
}
int idyC = int(dyC);
int wCPerm = ${o} - 1 - wC;
for (int d2 = 0; d2 < ${e.outChannels}; d2++) {
float xValue = getDy(batch, idyF, idyR, idyC, d2);
float wValue = getW(wFPerm, wRPerm, wCPerm, d1, d2);
dotProd += xValue * wValue;
}
}
}
}
setOutput(dotProd);
}
`}};function AX(r){let{inputs:e,backend:t,attrs:n}=r,{x:o,dy:s}=e,{strides:a,pad:i,dataFormat:l,dimRoundingMode:u,filterShape:c}=n,p=N.convertConv2DDataFormat(l),m=N.computeConv2DInfo(o.shape,c,a,1,i,u,!1,p),f=new Rk(m);return t.runWebGLProgram(f,[o,s],"float32")}var pR={kernelName:Xl,backendName:"webgl",kernelFunc:AX};function DX(r){let{inputs:e,backend:t,attrs:n}=r,{dy:o,filter:s}=e,{inputShape:a,strides:i,pad:l,dataFormat:u,dimRoundingMode:c}=n,p=N.convertConv2DDataFormat(u),m=N.computeConv2DInfo(a,s.shape,i,1,l,c,!1,p),f=new Fk(m);return t.runWebGLProgram(f,[o,s],"float32")}var mR={kernelName:Qn,backendName:"webgl",kernelFunc:DX};function $X(r){let{inputs:e,backend:t,attrs:n}=r,{x:o,filter:s}=e,{strides:a,pad:i,dilations:l}=n,u=N.computeConv3DInfo(o.shape,s.shape,a,l,i),c=new Dk(u);return t.runWebGLProgram(c,[o,s],"float32")}var fR={kernelName:aa,backendName:"webgl",kernelFunc:$X};function RX(r){let{inputs:e,backend:t,attrs:n}=r,{x:o,dy:s}=e,{strides:a,pad:i,filterShape:l}=n,u=N.computeConv3DInfo(o.shape,l,a,1,i),c=new Ok(u);return t.runWebGLProgram(c,[o,s],"float32")}var dR={kernelName:Yl,backendName:"webgl",kernelFunc:RX};function FX(r){let{inputs:e,backend:t,attrs:n}=r,{dy:o,filter:s}=e,{pad:a,strides:i,inputShape:l}=n,u=N.computeConv3DInfo(l,s.shape,i,1,a),c=new Pk(u);return t.runWebGLProgram(c,[o,s],"float32")}var hR={kernelName:Zl,backendName:"webgl",kernelFunc:FX};var OX=Yg+`
return cos(x);
`,PX=Ce({opSnippet:OX}),gR={kernelName:eo,backendName:"webgl",kernelFunc:PX};var MX=`
float e2x = exp(-x);
return (e2x + 1.0 / e2x) / 2.0;
`,LX=Ce({opSnippet:MX}),xR={kernelName:ri,backendName:"webgl",kernelFunc:LX};var Mk=class{constructor(e,t,n,o,s){this.variableNames=["Image","Boxes","BoxInd"],this.outputShape=[];let[a,i,l,u]=e,[c]=t,[p,m]=n;this.outputShape=[c,p,m,u];let f=o==="bilinear"?1:0,[d,h]=[`${i-1}.0`,`${l-1}.0`],[g,x,b]=p>1?[`${(i-1)/(p-1)}`,"(y2-y1) * height_ratio",`y1*${d} + float(y)*(height_scale)`]:["0.0","0.0",`0.5 * (y1+y2) * ${d}`],[w,_,k]=m>1?[`${(l-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 >= ${a}) {
return;
}
float height_scale = ${x};
float width_scale = ${_};
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(${f} == 1) {
// Compute the four integer indices.
ivec2 sourceFloorCR = ivec2(sourceFracIndexCR);
ivec2 sourceCeilCR = ivec2(ceil(sourceFracIndexCR));
float topLeft = getImage(b, sourceFloorCR.y, sourceFloorCR.x, d);
float bottomLeft = getImage(b, sourceCeilCR.y, sourceFloorCR.x, d);
float topRight = getImage(b, sourceFloorCR.y, sourceCeilCR.x, d);
float bottomRight = getImage(b, sourceCeilCR.y, sourceCeilCR.x, d);
vec2 fracCR = sourceFracIndexCR - vec2(sourceFloorCR);
float top = topLeft + (topRight - topLeft) * fracCR.x;
float bottom = bottomLeft + (bottomRight - bottomLeft) * fracCR.x;
float newValue = top + (bottom - top) * fracCR.y;
setOutput(newValue);
} else {
// Compute the coordinators of nearest neighbor point.
ivec2 sourceNearestCR = ivec2(floor(
sourceFracIndexCR + vec2(0.5,0.5)));
float newValue = getImage(b, sourceNearestCR.y, sourceNearestCR.x, d);
setOutput(newValue);
}
}
`}};var zX=r=>{let{inputs:e,backend:t,attrs:n}=r,{image:o,boxes:s,boxInd:a}=e,{cropSize:i,method:l,extrapolationValue:u}=n,c=new Mk(o.shape,s.shape,i,l,u);return t.runWebGLProgram(c,[o,s,a],"float32")},yR={kernelName:ni,backendName:"webgl",kernelFunc:zX};var ox=class{constructor(e,t,n){this.variableNames=["x"],this.outputShape=e;let o=e.length,s=t?"0.0":`getX(${bR(o,"coords")})`,a=e[e.length-1],i="",l="";t?(i=n?`end != ${a-1}`:"end != 0",l=n?"end + 1":"end - 1"):(i=n?`end + pow2 < ${a}`:"end >= pow2",l=n?"end + pow2":"end - pow2"),this.userCode=`
uniform float index;
void main() {
${ze(o)} coords = getOutputCoords();
int end = ${wR(o,"coords")};
float val = ${s};
int pow2 = int(pow(2.0, index));
if (${i}) {
int idx = ${l};
${wR(o,"coords")} = idx;
val += getX(${bR(o,"coords")});
}
setOutput(val);
}
`}getCustomSetupFunc(e){return(t,n)=>{this.index==null&&(this.index=t.getUniformLocation(n,"index")),t.gl.uniform1f(this.index,e)}}};function bR(r,e){if(r===1)return`${e}`;if(r===2)return`${e}.x, ${e}.y`;if(r===3)return`${e}.x, ${e}.y, ${e}.z`;if(r===4)return`${e}.x, ${e}.y, ${e}.z, ${e}.w`;throw Error(`Cumulative sum for rank ${r} is not yet supported`)}function wR(r,e){if(r===1)return`${e}`;if(r===2)return`${e}.y`;if(r===3)return`${e}.z`;if(r===4)return`${e}.w`;throw Error(`Cumulative sum for rank ${r} is not yet supported`)}function BX(r){let{inputs:e,backend:t,attrs:n}=r,{x:o}=e,{axis:s,exclusive:a,reverse:i}=n,l=o.shape.length,u=N.getAxesPermutation([s],l),c=o;u!=null&&(c=Vt({inputs:{x:o},backend:t,attrs:{perm:u}}));let p=N.getInnerMostAxes(1,l)[0];if(p!==l-1)throw new Error(`WebGL cumsum shader expects an inner-most axis=${o.shape.length-1} but got axis=${s}`);let m=o.shape[p],f=Ht({inputs:{x:c},backend:t});for(let d=0;d<=Math.ceil(Math.log2(m))-1;d++){let h=new ox(c.shape,!1,i),g=h.getCustomSetupFunc(d),x=f;f=t.runWebGLProgram(h,[f],f.dtype,g),t.disposeIntermediateTensorInfo(x)}if(a){let d=new ox(c.shape,a,i),h=f;f=t.runWebGLProgram(d,[f],f.dtype),t.disposeIntermediateTensorInfo(h)}if(u!=null){let d=N.getUndoAxesPermutation(u),h=Vt({inputs:{x:f},backend:t,attrs:{perm:d}});return t.disposeIntermediateTensorInfo(f),t.disposeIntermediateTensorInfo(c),h}return f}var _R={kernelName:to,backendName:"webgl",kernelFunc:BX};function VX(r){let{inputs:e,backend:t,attrs:n}=r,{x:o,weights:s}=e,{size:a,binaryOutput:i}=n;if(o.shape.length===1){let l=t.readSync(o.dataId),u=t.readSync(s.dataId),c=Ug(l,u,s.dtype,s.shape,a);return t.makeTensorInfo([a],s.dtype,c)}else if(o.shape.length===2){let l=t.bufferSync(o),u=t.bufferSync(s),c=TD(l,u,a,i);return t.makeTensorInfo(c.shape,s.dtype,c.values)}throw new Error(`Error in denseBincount: input must be at most rank 2, but got rank${o.shape.length}.`)}var vR={kernelName:Jl,backendName:"webgl",kernelFunc:VX};var Lk=class{constructor(e,t,n){this.variableNames=["x"],this.outputShape=[],this.outputShape=e,this.blockSize=t,this.dataFormat=n,this.userCode=`
void main() {
ivec4 coords = getOutputCoords();
int b = coords[0];
int h = ${this.getHeightCoordString()};
int w = ${this.getWidthCoordString()};
int d = ${this.getDepthCoordString()};
int in_h = h / ${t};
int offset_h = imod(h, ${t});
int in_w = w / ${t};
int offset_w = imod(w, ${t});
int offset_d = (offset_h * ${t} + offset_w) *
${this.getOutputDepthSize()};
int in_d = d + offset_d;
float result = ${this.getInputSamplingString()};
setOutput(result);
}
`}getHeightCoordString(){return this.dataFormat==="NHWC"?"coords[1]":"coords[2]"}getWidthCoordString(){return this.dataFormat==="NHWC"?"coords[2]":"coords[3]"}getDepthCoordString(){return this.dataFormat==="NHWC"?"coords[3]":"coords[1]"}getOutputDepthSize(){return this.dataFormat==="NHWC"?this.outputShape[3]:this.outputShape[1]}getInputSamplingString(){return this.dataFormat==="NHWC"?"getX(b, in_h, in_w, in_d)":"getX(b, in_d, in_h, in_w)"}};function WX(r){let{inputs:e,backend:t,attrs:n}=r,{x:o}=e,{blockSize:s,dataFormat:a}=n;y.assert(s>1,()=>`blockSize should be > 1 for depthToSpace, but was: ${s}`);let i=o.shape[0],l=a==="NHWC"?o.shape[1]:o.shape[2],u=a==="NHWC"?o.shape[2]:o.shape[3],c=a==="NHWC"?o.shape[3]:o.shape[1],p=l*s,m=u*s,f=c/(s*s),d=a==="NHWC"?[i,p,m,f]:[i,f,p,m],h=new Lk(d,s,a);return t.runWebGLProgram(h,[o],o.dtype)}var kR={kernelName:oi,backendName:"webgl",kernelFunc:WX};var Af=class{constructor(e,t=!1,n=null,o=!1,s=!1){this.variableNames=["x","W"],this.outputShape=e.outShape;let a=e.inHeight,i=e.inWidth,l=e.padInfo.top,u=e.padInfo.left,c=e.strideHeight,p=e.strideWidth,m=e.dilationHeight,f=e.dilationWidth,d=e.filterHeight,h=e.filterWidth,g=e.outChannels/e.inChannels,x="",b="";n&&(o?x=`float activation(float a) {
float b = getPreluActivationWeightsAtOutCoords();
${n}
}`:s?x=`float activation(float a) {
float b = getLeakyreluAlphaAtOutCoords();
${n}
}`:x=`
float activation(float x) {
${n}
}
`,b="result = activation(result);");let w=t?"result += getBiasAtOutCoords();":"";t&&this.variableNames.push("bias"),o&&this.variableNames.push("preluActivationWeights"),s&&this.variableNames.push("leakyreluAlpha"),this.userCode=`
${x}
const ivec2 strides = ivec2(${c}, ${p});
const ivec2 pads = ivec2(${l}, ${u});
void main() {
ivec4 coords = getOutputCoords();
int batch = coords.x;
ivec2 xRCCorner = coords.yz * strides - pads;
int d2 = coords.w;
int d1 = d2 / ${g};
int q = d2 - d1 * ${g};
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 < ${d}; wR++) {
int xR = xRCorner + wR * ${m};
if (xR < 0 || xR >= ${a}) {
continue;
}
for (int wC = 0; wC < ${h}; wC++) {
int xC = xCCorner + wC * ${f};
if (xC < 0 || xC >= ${i}) {
continue;
}
float xVal = getX(batch, xR, xC, d1);
float wVal = getW(wR, wC, d1, q);
dotProd += xVal * wVal;
}
}
float result = dotProd;
${w}
${b}
setOutput(result);
}
`}};var Df=class{constructor(e,t=!1,n=null,o=!1,s=!1){this.variableNames=["x","W"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=e.outShape;let a=e.inHeight,i=e.inWidth,l=e.padInfo.top,u=e.padInfo.left,c=e.strideHeight,p=e.strideWidth,m=e.dilationHeight,f=e.dilationWidth,d=e.filterHeight,h=e.filterWidth,g=h,x="int xR; int xC; int xCOffset;";for(let k=0;k<d;k++)for(let E=0;E<h;E++)x+=`
vec4 xTexelR${k}C${E*2} = vec4(0.);
vec4 wR${k}C${E} = vec4(0.);
vec4 xR${k}C${E} = vec4(0.);`;for(let k=0;k<d;k++)for(let E=0;E<g;E++){let T=E*2;if(x+=`
xR = xRCorner + ${k*m};
xC = xCCorner + ${T*f};
`,p===1){if(T<h&&(u%2==1?x+=`
xCOffset = xC + 1;
if(xR >= 0 && xR < ${a} && xCOffset >= 0 && xCOffset < ${i}) {
xTexelR${k}C${T} = getX(batch, xR, xCOffset, d1);
// Need to manually clear unused channels in case
// we're reading from recycled texture.
if(xCOffset + 1 >= ${i}) {
xTexelR${k}C${T}.zw = vec2(0.);
}
} else {
xTexelR${k}C${T} = vec4(0.);
}
xCOffset = xC + 1 - 2;
if(xR >= 0 && xR < ${a} && xCOffset >= 0 && xCOffset < ${i}) {
vec4 previous = getX(batch, xR, xCOffset, d1);
// Need to manually clear unused channels in case
// we're reading from recycled texture.
if(xCOffset + 1 >= ${i}) {
previous.zw = vec2(0.);
}
xR${k}C${T} = vec4(previous.zw, xTexelR${k}C${T}.xy);
} else {
xR${k}C${T} = vec4(0, 0, xTexelR${k}C${T}.xy);
}
`:x+=`
if(xR >= 0 && xR < ${a} && xC >= 0 && xC < ${i}) {
xTexelR${k}C${T} = getX(batch, xR, xC, d1);
} else {
xTexelR${k}C${T} = vec4(0.);
}
xR${k}C${T} = xTexelR${k}C${T};
`,T+1<h)){let F=u%2==0?y.nearestLargerEven(f):f;f%2==0&&u%2==1||f%2!=0&&u%2!=1?(x+=`
xCOffset = xC + ${u%2} + ${F};
if(xR >= 0 && xR < ${a} &&
xCOffset >= 0 && xCOffset < ${i}) {
xTexelR${k}C${T+2} = getX(batch, xR, xCOffset, d1);
}
`,f>1&&(x+=`
xCOffset -= 2;
if(xR >= 0 && xR < ${a} &&
xCOffset >= 0 && xCOffset < ${i}) {
xTexelR${k}C${T} = getX(batch, xR, xCOffset, d1);
} else {
xTexelR${k}C${T} = vec4(0.);
}
`),x+=`
xR${k}C${T+1} = vec4(
xTexelR${k}C${T}.zw, xTexelR${k}C${T+2}.xy);
`):x+=`
xCOffset = xC + ${F};
if(xR >= 0 && xR < ${a} &&
xCOffset >= 0 && xCOffset < ${i}) {
xTexelR${k}C${T+2} = getX(batch, xR, xCOffset, d1);
}
xR${k}C${T+1} = xTexelR${k}C${T+2};
`}}else T<h&&(x+=`
if(xR >= 0 && xR < ${a}) {
`,u%2==1?(x+=`
xCOffset = xC + 1 - ${p};
if(xCOffset >= 0 && xCOffset < ${i}) {
xTexelR${k}C${T} = getX(batch, xR, xCOffset, d1);
} else {
xTexelR${k}C${T} = vec4(0.);
}
if(xC + 1 >= 0 && xC + 1 < ${i}) {
xTexelR${k}C${T+2} = getX(batch, xR, xC + 1, d1);
} else {
xTexelR${k}C${T+2} = vec4(0.);
}
xR${k}C${T} = vec4(
xTexelR${k}C${T}.zw, xTexelR${k}C${T+2}.zw);
`,T+1<h&&(x+=`
vec4 final = vec4(0.);
xCOffset = xC + 1 + ${p};
if(xCOffset >= 0 && xCOffset < ${i}) {
final = getX(batch, xR, xCOffset, d1);
}
xR${k}C${T+1} = vec4(xTexelR${k}C${T+2}.xy, final.xy);
`)):(x+=`
if(xC >= 0 && xC < ${i}) {
xTexelR${k}C${T} = getX(batch, xR, xC, d1);
} else {
xTexelR${k}C${T} = vec4(0.);
}
xCOffset = xC + ${p};
if(xCOffset >= 0 && xCOffset < ${i}) {
xTexelR${k}C${T+2} = getX(batch, xR, xCOffset, d1);
} else {
xTexelR${k}C${T+2} = vec4(0.);
}
xR${k}C${T} = vec4(
xTexelR${k}C${T}.xy, xTexelR${k}C${T+2}.xy);
`,T+1<h&&(x+=`
xR${k}C${T+1} = vec4(
xTexelR${k}C${T}.zw, xTexelR${k}C${T+2}.zw);
`)),x+="}");T<h&&(x+=`
vec4 wTexelR${k}C${T} = getW(${k}, ${T}, d1, q);
wR${k}C${T} = vec4(wTexelR${k}C${T}.xz, wTexelR${k}C${T}.xz);
`,T+1<h&&(x+=`
vec4 wTexelR${k}C${T+1} = getW(${k}, ${T+1}, d1, q);
wR${k}C${T+1} =
vec4(wTexelR${k}C${T+1}.xz, wTexelR${k}C${T+1}.xz);`))}for(let k=0;k<d;k++)for(let E=0;E<h;E++)x+=`dotProd += xR${k}C${E} * wR${k}C${E};`;let b="",w="";n&&(o?b=`vec4 activation(vec4 a) {
vec4 b = getPreluActivationWeightsAtOutCoords();
${n}
}`:s?b=`vec4 activation(vec4 a) {
vec4 b = getLeakyreluAlphaAtOutCoords();
${n}
}`:b=`vec4 activation(vec4 x) {
${n}
}`,w="result = activation(result);");let _=t?"result += getBiasAtOutCoords();":"";t&&this.variableNames.push("bias"),o&&this.variableNames.push("preluActivationWeights"),s&&this.variableNames.push("leakyreluAlpha"),this.userCode=`
${b}
const ivec2 strides = ivec2(${c}, ${p});
const ivec2 pads = ivec2(${l}, ${u});
void main() {
ivec4 coords = getOutputCoords();
int batch = coords.x;
ivec2 xRCCorner = coords.yz * strides - pads;
int d2 = coords.w;
int d1 = d2;
int q = 0;
int xRCorner = xRCCorner.x;
int xCCorner = xRCCorner.y;
vec4 dotProd = vec4(0.);
${x}
vec4 result = dotProd;
${_}
${w}
setOutput(result);
}
`}};function jX(r){let{inputs:e,backend:t,attrs:n}=r,{x:o,filter:s}=e,{strides:a,pad:i,dilations:l,dimRoundingMode:u}=n,c=l;c==null&&(c=[1,1]),y.assert(N.eitherStridesOrDilationsAreOne(a,c),()=>`Error in depthwiseConv2d: Either strides or dilations must be 1. Got strides ${a} and dilations '${c}'`);let p=N.computeConv2DInfo(o.shape,s.shape,a,c,i,u,!0),m;return G().getBool("WEBGL_PACK_DEPTHWISECONV")&&p.strideWidth<=2&&p.outChannels/p.inChannels==1?m=new Df(p):m=new Af(p),t.runWebGLProgram(m,[o,s],"float32")}var CR={kernelName:ro,backendName:"webgl",kernelFunc:jX};var zk=class{constructor(e){this.variableNames=["x","dy"],this.outputShape=e.filterShape;let t=e.strideHeight,n=e.strideWidth,o=e.padInfo.top,s=e.padInfo.left,a=e.outChannels/e.inChannels;this.userCode=`
void main() {
ivec4 coords = getOutputCoords();
int wR = coords.x;
int wC = coords.y;
int d1 = coords.z;
int dm = coords.w;
int d2 = d1 * ${a} + dm;
float dotProd = 0.0;
// TO DO: Vec4 over the batch size
for (int b = 0; b < ${e.batchSize}; b++) {
for (int yR = 0; yR < ${e.outHeight}; yR++) {
int xR = wR + yR * ${t} - ${o};
if (xR < 0 || xR >= ${e.inHeight}) {
continue;
}
for (int yC = 0; yC < ${e.outWidth}; yC++) {
int xC = wC + yC * ${n} - ${s};
if (xC < 0 || xC >= ${e.inWidth}) {
continue;
}
float dyValue = getDy(b, yR, yC, d2);
float xValue = getX(b, xR, xC, d1);
dotProd += (xValue * dyValue);
}
}
}
setOutput(dotProd);
}
`}},Bk=class{constructor(e){this.variableNames=["dy","W"],this.outputShape=e.inShape;let t=e.filterHeight,n=e.filterWidth,o=e.strideHeight,s=e.strideWidth,a=t-1-e.padInfo.top,i=n-1-e.padInfo.left,l=e.outChannels/e.inChannels;this.userCode=`
const ivec2 pads = ivec2(${a}, ${i});
void main() {
ivec4 coords = getOutputCoords();
int batch = coords[0];
int d1 = coords[3];
ivec2 dyCorner = coords.yz - pads;
int dyRCorner = dyCorner.x;
int dyCCorner = dyCorner.y;
float dotProd = 0.0;
for (int wR = 0; wR < ${t}; wR++) {
float dyR = float(dyRCorner + wR) / ${o}.0;
if (dyR < 0.0 || dyR >= ${e.outHeight}.0 || fract(dyR) > 0.0) {
continue;
}
int idyR = int(dyR);
int wRPerm = ${t} - 1 - wR;
for (int wC = 0; wC < ${n}; wC++) {
float dyC = float(dyCCorner + wC) / ${s}.0;
if (dyC < 0.0 || dyC >= ${e.outWidth}.0 ||
fract(dyC) > 0.0) {
continue;
}
int idyC = int(dyC);
int wCPerm = ${n} - 1 - wC;
// TO DO: Vec4 over the channelMul
for (int dm = 0; dm < ${l}; dm++) {
int d2 = d1 * ${l} + dm;
float xValue = getDy(batch, idyR, idyC, d2);
float wValue = getW(wRPerm, wCPerm, d1, dm);
dotProd += xValue * wValue;
}
}
}
setOutput(dotProd);
}
`}};function GX(r){let{inputs:e,backend:t,attrs:n}=r,{x:o,dy:s}=e,{strides:a,dilations:i,pad:l,dimRoundingMode:u,filterShape:c}=n,p=N.computeConv2DInfo(o.shape,c,a,i,l,u,!0),m=new zk(p);return t.runWebGLProgram(m,[o,s],"float32")}var IR={kernelName:Ql,backendName:"webgl",kernelFunc:GX};function UX(r){let{inputs:e,backend:t,attrs:n}=r,{dy:o,filter:s}=e,{strides:a,dilations:i,pad:l,dimRoundingMode:u,inputShape:c}=n,p=N.computeConv2DInfo(c,s.shape,a,i,l,u,!0),m=new Bk(p);return t.runWebGLProgram(m,[o,s],"float32")}var SR={kernelName:eu,backendName:"webgl",kernelFunc:UX};var Vk=class{constructor(e){this.variableNames=["X"],this.outputShape=[e,e],this.userCode=`
void main() {
ivec2 coords = getOutputCoords();
float val = coords[0] == coords[1] ? getX(coords[0]) : 0.0;
setOutput(val);
}
`}};function qX(r){let{inputs:e,backend:t}=r,{x:n}=e,o=[...n.shape,...n.shape],s=y.sizeFromShape(n.shape),a=me({inputs:{x:n},backend:t,attrs:{shape:[s]}}),i=new Vk(s),l=t.runWebGLProgram(i,[a],a.dtype),u=me({inputs:{x:l},backend:t,attrs:{shape:o}});return t.disposeIntermediateTensorInfo(a),t.disposeIntermediateTensorInfo(l),u}var NR={kernelName:tu,backendName:"webgl",kernelFunc:qX};var Wk=class{constructor(e){this.variableNames=["x","W"],this.outputShape=e.outShape;let{inHeight:t,inWidth:n,padInfo:o,strideHeight:s,strideWidth:a,filterHeight:i,filterWidth:l,dilationHeight:u,dilationWidth:c}=e,{top:p,left:m}=o;this.userCode=`
const ivec2 strides = ivec2(${s}, ${a});
const ivec2 pads = ivec2(${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 < ${i}; h++) {
int hIn = hBeg + h * ${u};
if (hIn >= 0 && hIn < ${t}) {
for (int w = 0; w < ${l}; 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 HX(r){let{inputs:e,backend:t,attrs:n}=r,{x:o,filter:s}=e,{strides:a,pad:i,dilations:l}=n,u=N.computeDilation2DInfo(o.shape,s.shape,a,i,"NHWC",l),c,p=new Wk(u);c=t.runWebGLProgram(p,[o,s],"float32");let m=me({inputs:{x:c},backend:t,attrs:{shape:u.outShape}});return t.disposeIntermediateTensorInfo(c),m}var TR={kernelName:la,backendName:"webgl",kernelFunc:HX};var KX="return (x >= 0.0) ? x : (exp(x) - 1.0);",XX=`
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;
`,YX=Ce({opSnippet:KX,packedOpSnippet:XX}),ER={kernelName:si,backendName:"webgl",kernelFunc:YX};var ZX="return (b >= 1.0) ? a : a * (b + 1.0);",JX=`
vec4 bGTEZero = vec4(greaterThanEqual(b, vec4(0.)));
return (bGTEZero * a) + ((vec4(1.0) - bGTEZero) * (a * (b + vec4(1.0))));
`,QX=r=>{let{inputs:e,backend:t}=r,{dy:n,y:o}=e,s=G().getBool("WEBGL_PACK_BINARY_OPERATIONS")?new Ps(JX,n.shape,o.shape):new Xo(ZX,n.shape,o.shape);return t.runWebGLProgram(s,[n,o],n.dtype)},AR={kernelName:ru,backendName:"webgl",kernelFunc:QX};var e7=`
return vec4(equal(a, b));
`,t7="return float(a == b);",r7=at({opSnippet:t7,packedOpSnippet:e7,dtype:"bool"}),DR={kernelName:ai,backendName:"webgl",kernelFunc:r7};var n7=`
// Error function is calculated approximately with elementary function.
// See "Handbook of Mathematical Functions with Formulas,
// Graphs, and Mathematical Tables", Abramowitz and Stegun.
float p = ${N.ERF_P};
float a1 = ${N.ERF_A1};
float a2 = ${N.ERF_A2};
float a3 = ${N.ERF_A3};
float a4 = ${N.ERF_A4};
float a5 = ${N.ERF_A5};
float sign = sign(x);
x = abs(x);
float t = 1.0 / (1.0 + p * x);
return sign * (1.0 - (((((a5*t + a4)*t) + a3)*t + a2)*t + a1)*t*exp(-x*x));
`,o7=Ce({opSnippet:n7}),$R={kernelName:ii,backendName:"webgl",kernelFunc:o7};var RR="return exp(x);",jk=Ce({opSnippet:RR,packedOpSnippet:RR,cpuKernelImpl:DD}),FR={kernelName:oo,backendName:"webgl",kernelFunc:jk};function sx(r){let{inputs:e,attrs:t,backend:n}=r,{dim:o}=t,{input:s}=e,a=s.shape.length,i=s.shape.slice(),l=o;return o<0&&(y.assert(-(a+1)<=o,()=>`Axis must be in the interval [${-(a+1)}, ${a}]`),l=a+o+1),i.splice(l,0,1),me({inputs:{x:s},backend:n,attrs:{shape:i}})}var OR={kernelName:cs,backendName:"webgl",kernelFunc:sx};var PR="return exp(x) - 1.0;",s7=Ce({opSnippet:PR,packedOpSnippet:PR,cpuKernelImpl:$D}),MR={kernelName:li,backendName:"webgl",kernelFunc:s7};var ix=class{constructor(e,t,n){this.variableNames=["real","imag"];let o=t[1];this.outputShape=t;let s=n?`2.0 * ${Math.PI}`:`-2.0 * ${Math.PI}`,a=n?`${o}.0`:"1.0",i;if(e==="real")i="return real * expR - imag * expI;";else if(e==="imag")i="return real * expI + imag * expR;";else throw new Error(`FFT component must be either "real" or "imag", got ${e}.`);this.userCode=`
const float exponentMultiplier = ${s};
float unaryOpComplex(float real, float expR, float imag, float expI) {
${i}
}
float mulMatDFT(int batch, int index) {
float indexRatio = float(index) / float(${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) / ${a};
}
return result;
}
void main() {
ivec2 coords = getOutputCoords();
setOutput(mulMatDFT(coords[0], coords[1]));
}
`}};function ax(r,e,t){let n=t.texData.get(r.dataId),o=y.sizeFromShape(r.shape),s=r.shape[r.shape.length-1],a=o/s,i=me({inputs:{x:r},backend:t,attrs:{shape:[a,s]}}),l=i.shape,u=new ix("real",l,e),c=new ix("imag",l,e),p=[{dataId:n.complexTensorInfos.real.dataId,dtype:n.complexTensorInfos.real.dtype,shape:l},{dataId:n.complexTensorInfos.imag.dataId,dtype:n.complexTensorInfos.imag.dtype,shape:l}],m=t.runWebGLProgram(u,p,"float32"),f=t.runWebGLProgram(c,p,"float32"),d=an({inputs:{real:m,imag:f},backend:t});t.disposeIntermediateTensorInfo(m),t.disposeIntermediateTensorInfo(f);let h=me({inputs:{x:d},backend:t,attrs:{shape:r.shape}});return t.disposeIntermediateTensorInfo(i),t.disposeIntermediateTensorInfo(d),h}function i7(r){let{inputs:e,backend:t}=r,{input:n}=e;return ax(n,!1,t)}var LR={kernelName:nu,backendName:"webgl",kernelFunc:i7};var Gk=class{constructor(e,t){this.outputShape=[],this.variableNames=["x"],this.outputShape=e,this.userCode=`
uniform float value;
void main() {
// Input can be obtained from uniform value.
setOutput(value);
}
`}getCustomSetupFunc(e){return(t,n)=>{this.valueLoc==null&&(this.valueLoc=t.getUniformLocationNoThrow(n,"value")),t.gl.uniform1f(this.valueLoc,e)}}};function $f(r){let{backend:e,attrs:t}=r,{shape:n,value:o}=t,{dtype:s}=t;if(s=s||y.inferDtype(o),s==="string"){let a=y.getArrayFromDType(s,y.sizeFromShape(n));return a.fill(o),e.makeTensorInfo(n,s,a)}else{let a=new Gk(n,o),i=a.getCustomSetupFunc(o);return e.runWebGLProgram(a,[],s,i)}}var zR={kernelName:ua,backendName:"webgl",kernelFunc:$f};var Uk=class{constructor(e){this.variableNames=["Image"],this.outputShape=[];let t=e[2];this.outputShape=e,this.userCode=`
void main() {
ivec4 coords = getOutputCoords();
int x = coords[2];
int coordX = ${t} - x;
float outputValue;
if(coordX >= 0 && coordX < ${t}) {
outputValue = getImage(coords[0], coords[1], coordX, coords[3]);
} else {
outputValue = getImage(coords[0], coords[1], coords[2], coords[3]);
}
setOutput(outputValue);
}
`}};var BR={kernelName:ui,backendName:"webgl",kernelFunc:({inputs:r,backend:e})=>{let{image:t}=r,n=e,o=new Uk(t.shape);return n.runWebGLProgram(o,[t],t.dtype)}};var VR="return floor(x);",a7=Ce({opSnippet:VR,packedOpSnippet:VR,cpuKernelImpl:RD}),WR={kernelName:so,backendName:"webgl",kernelFunc:a7};var l7=`
float s = sign(a) * sign(b);
int ia = round(a);
int ib = round(b);
if (ib != 0) {
// Windows (D3D) wants guaranteed non-zero int division at compile-time.
return float(idiv(ia, ib, s));
} else {
return NAN;
}
`,u7=`
ivec4 ia = round(a);
ivec4 ib = round(b);
bvec4 cond = notEqual(ib, ivec4(0));
ivec4 result = ivec4(0);
vec4 s = sign(a) * sign(b);
// Windows (D3D) wants guaranteed non-zero int division at compile-time.
if (cond[0]) {
result[0] = idiv(ia[0], ib[0], s[0]);
}
if (cond[1]) {
result[1] = idiv(ia[1], ib[1], s[1]);
}
if (cond[2]) {
result[2] = idiv(ia[2], ib[2], s[2]);
}
if (cond[3]) {
result[3] = idiv(ia[3], ib[3], s[3]);
}
return vec4(result);
`,c7=at({opSnippet:l7,packedOpSnippet:u7,dtype:"int32"}),jR={kernelName:io,backendName:"webgl",kernelFunc:c7};var qk=class{constructor(e){this.variableNames=["A"];let t=Lt(),[n,o]=e;this.outputShape=e,this.userCode=`
void main() {
ivec3 coords = getOutputCoords();
int texR = coords[0];
int texC = coords[1];
int depth = coords[2];
vec2 uv = (vec2(texC, texR) + halfCR) / vec2(${o}.0, ${n}.0);
vec4 values = ${t.texture2D}(A, uv);
float value;
if (depth == 0) {
value = values.r;
} else if (depth == 1) {
value = values.g;
} else if (depth == 2) {
value = values.b;
} else if (depth == 3) {
value = values.a;
}
setOutput(floor(value * 255.0 + 0.5));
}
`}};var Hk=class{constructor(e){this.variableNames=["A"],this.packedInputs=!1,this.packedOutput=!0;let t=Lt(),[n,o]=e;this.outputShape=e,this.userCode=`
void main() {
ivec3 coords = getOutputCoords();
int texR = coords[0];
int texC = coords[1];
int depth = coords[2];
vec4 result = vec4(0.);
for(int row=0; row<=1; row++) {
for(int col=0; col<=1; col++) {
texC = coords[1] + row;
depth = coords[2] + col;
vec2 uv = (vec2(texC, texR) + halfCR) /
vec2(${o}.0, ${n}.0);
vec4 values = ${t.texture2D}(A, uv);
float value;
if (depth == 0) {
value = values.r;
} else if (depth == 1) {
value = values.g;
} else if (depth == 2) {
value = values.b;
} else if (depth == 3) {
value = values.a;
}
result[row * 2 + col] = floor(value * 255.0 + 0.5);
}
}
${t.output} = result;
}
`}};var GR={kernelName:Pc,backendName:"webgl",kernelFunc:p7},_p;function p7(r){let{inputs:e,backend:t,attrs:n}=r,{pixels:o}=e,{numChannels:s}=n,a=typeof HTMLVideoElement!="undefined"&&o instanceof HTMLVideoElement,i=typeof HTMLImageElement!="undefined"&&o instanceof HTMLImageElement,l=typeof ImageBitmap!="undefined"&&o instanceof ImageBitmap,[u,c]=a?[o.videoWidth,o.videoHeight]:[o.width,o.height],p=[c,u],m=[c,u,s];(i||a||l)&&(_p==null&&(_p=document.createElement("canvas").getContext("2d")),_p.canvas.width=u,_p.canvas.height=c,_p.drawImage(o,0,0,u,c),o=_p.canvas);let f=t.makeTensorInfo(p,"int32");t.texData.get(f.dataId).usage=Rr.PIXELS,t.gpgpu.uploadPixelDataToTexture(t.getTexture(f.dataId),o);let d=G().getBool("WEBGL_PACK")?new Hk(m):new qk(m),h=t.runWebGLProgram(d,[f],"int32");return t.disposeData(f.dataId),h}function m7(r){let{inputs:e,backend:t,attrs:n}=r,{x:o,filter:s,bias:a,preluActivationWeights:i}=e,{strides:l,pad:u,dataFormat:c,dilations:p,dimRoundingMode:m,activation:f,leakyreluAlpha:d}=n,h=N.convertConv2DDataFormat(c),g=N.computeConv2DInfo(o.shape,s.shape,l,p,u,m,!1,h),x,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"))x=rx({x:o,filter:s,convInfo:g,backend:t,bias:a,activation:f,preluActivationWeights:i,leakyreluAlpha:d});else if(G().getBool("WEBGL_CONV_IM2COL")&&o.shape[0]===1)x=nx({x:o,filter:s,convInfo:g,backend:t,bias:a,activation:f,preluActivationWeights:i,leakyreluAlpha:d});else{let _=a!=null,k=i!=null,E=f==="leakyrelu",T=f?bl(f,!1):null,F=new Ef(g,_,T,k,E),O=[o,s];if(a&&O.push(a),i&&O.push(i),E){let M=t.makeTensorInfo([],"float32",y.createScalarValue(d,"float32"));O.push(M),b.push(M)}x=t.runWebGLProgram(F,O,"float32")}let w=me({inputs:{x},backend:t,attrs:{shape:g.outShape}});return b.push(x),b.forEach(_=>t.disposeIntermediateTensorInfo(_)),w}var UR={kernelName:ks,backendName:"webgl",kernelFunc:m7};function f7(r){let{inputs:e,backend:t,attrs:n}=r,{x:o,filter:s,bias:a,preluActivationWeights:i}=e,{strides:l,pad:u,dilations:c,dimRoundingMode:p,activation:m,leakyreluAlpha:f}=n,d=[],h=c;h==null&&(h=[1,1]),y.assert(N.eitherStridesOrDilationsAreOne(l,h),()=>`Error in depthwiseConv2d: Either strides or dilations must be 1. Got strides ${l} and dilations '${h}'`);let g=N.computeConv2DInfo(o.shape,s.shape,l,h,u,p,!0),x=G().getBool("WEBGL_PACK_DEPTHWISECONV")&&g.strideWidth<=2&&g.outChannels/g.inChannels==1,b=m?bl(m,x):null,w=[o,s],_=a!=null,k=i!=null,E=m==="leakyrelu";if(_&&w.push(a),k&&w.push(i),E){let O=t.makeTensorInfo([],"float32",y.createScalarValue(f,"float32"));w.push(O),d.push(O)}let T;x?T=new Df(g,_,b,k,E):T=new Af(g,_,b,k,E);let F=t.runWebGLProgram(T,w,"float32");return d.forEach(O=>t.disposeIntermediateTensorInfo(O)),F}var qR={kernelName:Cs,backendName:"webgl",kernelFunc:f7};var Kk=class{constructor(e,t,n){this.sliceDim=e,this.strides=t,this.variableNames=["x","indices"],this.outputShape=n;let o=ze(t.length),s=ze(n.length),a=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 * ${a};
}
setOutput(getX(flattenIndex, coords[1]));
}
`}};function d7(r){let{inputs:e,backend:t}=r,{params:n,indices:o}=e,s=o.shape,a=s[s.length-1],[i,l,u,c]=N.prepareAndValidate(n,o),p=me({inputs:{x:o},backend:t,attrs:{shape:[l,a]}}),m=me({inputs:{x:n},backend:t,attrs:{shape:[y.sizeFromShape(n.shape)/u,u]}}),f=new Kk(a,c,[l,u]),d=t.runWebGLProgram(f,[m,p],m.dtype),h=me({inputs:{x:d},backend:t,attrs:{shape:i}});return t.disposeIntermediateTensorInfo(p),t.disposeIntermediateTensorInfo(m),t.disposeIntermediateTensorInfo(d),h}var HR={kernelName:ci,backendName:"webgl",kernelFunc:d7};var Xk=class{constructor(e,t){this.variableNames=["A","indices"],this.outputShape=t,this.rank=t.length;let n=ze(this.rank),o=h7(e,2);this.userCode=`
void main() {
${n} resRC = getOutputCoords();
setOutput(getA(${o}));
}
`}};function h7(r,e){let t=["resRC.x","resRC.y","resRC.z","resRC.w"],n=[];for(let o=0;o<r.length;o++)o===2?n.push("int(getIndices(resRC.x, resRC.z))"):n.push(`${t[o]}`);return n.join()}function g7(r){let{inputs:e,backend:t,attrs:n}=r,{x:o,indices:s}=e,{axis:a,batchDims:i}=n,l=y.parseAxisParam(a,o.shape)[0],u=N.segment_util.collectGatherOpShapeInfo(o,s,l,i),c=y.sizeFromShape(s.shape),p=[],m=me({inputs:{x:o},backend:t,attrs:{shape:[u.batchSize,u.outerSize,u.dimSize,u.sliceSize]}}),f=me({inputs:{x:s},backend:t,attrs:{shape:[u.batchSize,c/u.batchSize]}});p.push(m),p.push(f);let d=[u.batchSize,u.outerSize,c/u.batchSize,u.sliceSize];if(t.shouldExecuteOnCPU([o,s])||o.dtype==="string"){let b=t.bufferSync(f),w=t.bufferSync(m),_=FD(w,b,d);return p.forEach(k=>t.disposeIntermediateTensorInfo(k)),t.makeTensorInfo(u.outputShape,_.dtype,_.values)}let h=new Xk(m.shape,d),g=t.runWebGLProgram(h,[m,f],m.dtype);p.push(g);let x=me({inputs:{x:g},backend:t,attrs:{shape:u.outputShape}});return p.forEach(b=>t.disposeIntermediateTensorInfo(b)),x}var KR={kernelName:ps,backendName:"webgl",kernelFunc:g7};var x7="return float(a > b);",y7=`
return vec4(greaterThan(a, b));
`,b7=at({opSnippet:x7,packedOpSnippet:y7,cpuKernelImpl:OD,dtype:"bool"}),XR={kernelName:pi,backendName:"webgl",kernelFunc:b7};var w7="return float(a >= b);",_7=`
return vec4(greaterThanEqual(a, b));
`,v7=at({opSnippet:w7,packedOpSnippet:_7,dtype:"bool"}),YR={kernelName:lo,backendName:"webgl",kernelFunc:v7};function k7(r){let{inputs:e,backend:t}=r,{input:n}=e;return ax(n,!0,t)}var ZR={kernelName:ou,backendName:"webgl",kernelFunc:k7};var C7="return float(!isnan(x) && !isinf(x));",I7=Ce({opSnippet:C7,dtype:"bool"}),JR={kernelName:mi,backendName:"webgl",kernelFunc:I7};var S7="return float(isinf(x));",N7=Ce({opSnippet:S7,dtype:"bool"}),QR={kernelName:fi,backendName:"webgl",kernelFunc:N7};var T7="return float(isnan(x));",E7=Ce({opSnippet:T7,dtype:"bool"}),eF={kernelName:di,backendName:"webgl",kernelFunc:E7};var A7="return float(a < b);",D7=`
return vec4(lessThan(a, b));
`,$7=at({opSnippet:A7,packedOpSnippet:D7,cpuKernelImpl:PD,dtype:"bool"}),tF={kernelName:hi,backendName:"webgl",kernelFunc:$7};var R7="return float(a <= b);",F7=`
return vec4(lessThanEqual(a, b));
`,O7=at({opSnippet:R7,packedOpSnippet:F7,dtype:"bool"}),rF={kernelName:gi,backendName:"webgl",kernelFunc:O7};function P7(r){let{backend:e,attrs:t}=r,{start:n,stop:o,num:s}=t,a=MD(n,o,s);return e.makeTensorInfo([a.length],"float32",a)}var nF={kernelName:iu,backendName:"webgl",kernelFunc:P7};var M7=`if (x < 0.0) return NAN;
return log(x);`,L7=`
vec4 result = log(x);
vec4 isNaN = vec4(lessThan(x, vec4(0.0)));
result.r = isNaN.r == 1.0 ? NAN : result.r;
result.g = isNaN.g == 1.0 ? NAN : result.g;
result.b = isNaN.b == 1.0 ? NAN : result.b;
result.a = isNaN.a == 1.0 ? NAN : result.a;
return result;
`,z7=Ce({opSnippet:M7,packedOpSnippet:L7,cpuKernelImpl:LD}),oF={kernelName:co,backendName:"webgl",kernelFunc:z7};var B7="return log(1.0 + x);",V7=Ce({opSnippet:B7}),sF={kernelName:xi,backendName:"webgl",kernelFunc:V7};var W7="return float(a >= 1.0 && b >= 1.0);",j7=`
return vec4(
vec4(greaterThanEqual(a, vec4(1.0))) *
vec4(greaterThanEqual(b, vec4(1.0))));
`,G7=at({opSnippet:W7,packedOpSnippet:j7,dtype:"bool"}),iF={kernelName:yi,backendName:"webgl",kernelFunc:G7};var U7="return float(!(x >= 1.0));",q7=Ce({opSnippet:U7}),aF={kernelName:tl,backendName:"webgl",kernelFunc:q7};var H7="return float(a >= 1.0 || b >= 1.0);",K7=`
return min(
vec4(greaterThanEqual(a, vec4(1.0))) +
vec4(greaterThanEqual(b, vec4(1.0))),
vec4(1.0));
`,X7=at({opSnippet:H7,packedOpSnippet:K7,dtype:"bool"}),lF={kernelName:rl,backendName:"webgl",kernelFunc:X7};var Yk=class{constructor(e,t,n,o,s){this.variableNames=["x"],this.outputShape=[];let a=t,i=e[3]-1;this.outputShape=e;let l,u=`float(${n}) + float(${o}) * sum`;s===.5?l=`inversesqrt(${u})`:s===1?l=`1.0/(${u})`:l=`exp(log(${u}) * float(-${s}));`,this.userCode=`
void main() {
ivec4 coords = getOutputCoords();
int b = coords[0];
int r = coords[1];
int c = coords[2];
int d = coords[3];
float x = getX(b, r, c, d);
float sum = 0.0;
for (int j = -${a}; j <= ${a}; j++) {
int idx = d + j;
if (idx >= 0 && idx <= ${i}) {
float z = getX(b, r, c, idx);
sum += z * z;
}
}
float val = x * ${l};
setOutput(val);
}
`}};var Zk=class{constructor(e,t,n,o,s){this.variableNames=["x"],this.outputShape=[],this.packedInputs=!0,this.packedOutput=!0;let a=t,i=e[3]-1;this.outputShape=e;let l,u=`float(${n}) + float(${o}) * sum`;s===.5?l=`inversesqrt(${u})`:s===1?l=`1.0/(${u})`:l=`exp(log(${u}) * 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 - ${a};
vec2 cache = vec2(0.);
if(firstChannel >= 0){
vec4 firstChannelFrag = getX(b, r, c, firstChannel);
cache.x = getChannel(firstChannelFrag, vec2(c, firstChannel));
if(hasNextRow){
cache.y = getChannel(firstChannelFrag, vec2(c + 1, firstChannel));
}
}
ivec2 depth = ivec2(d, d + 1);
for (int j = - ${a}; j <= ${a}; j++) {
ivec2 idx = depth + j;
bvec2 aboveLowerBound = greaterThanEqual(idx, ivec2(0));
bvec2 belowUpperBound = lessThanEqual(idx, ivec2(${i}));
bool depthInRange = aboveLowerBound.x && belowUpperBound.x;
bool depthPlusOneInRange = aboveLowerBound.y && belowUpperBound.y;
if(depthInRange || depthPlusOneInRange){
vec4 z = vec4(0.);
vec4 xFragAtCurrentDepth;
z.xz = cache.xy;
if(depthPlusOneInRange && hasNextCol){
xFragAtCurrentDepth = idx.y != d ?
getX(b, r, c, idx.y) : xFragAtOutputCoords;
z.y = getChannel(xFragAtCurrentDepth, vec2(c, idx.y));
if(hasNextRow){
z.w = getChannel(xFragAtCurrentDepth, vec2(c + 1, idx.y));
}
}
cache.xy = z.yw;
sum += z * z;
}
}
vec4 result = xAtOutputCoords * ${l};
setOutput(result);
}
`}};var Y7=r=>{let{inputs:e,backend:t,attrs:n}=r,{x:o}=e,{depthRadius:s,bias:a,alpha:i,beta:l}=n,u=G().getBool("WEBGL_PACK_NORMALIZATION")?new Zk(o.shape,s,a,i,l):new Yk(o.shape,s,a,i,l);return t.runWebGLProgram(u,[o],o.dtype)},uF={kernelName:ca,backendName:"webgl",kernelFunc:Y7};var Jk=class{constructor(e,t,n,o,s){this.variableNames=["inputImage","outputImage","dy"],this.outputShape=[],this.outputShape=e,this.depth=e[3],this.depthRadius=t,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 - ${t})));
int depthEnd = int(min(float(${this.depth}),
float(d + ${t} + 1)));
const int MIN_DEPTH_BEGIN = 0;
const int MAX_DEPTH_END = ${this.depth};
float norm = 0.0;
for (int k = MIN_DEPTH_BEGIN; k < MAX_DEPTH_END; ++k) {
if (k < depthBegin){
continue;
}
else if (k >= depthBegin && k < depthEnd) {
norm += getInputImage(b, r, c, k) * getInputImage(b, r, c, k);
}
else {
break;
}
}
norm = float(${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 Z7=r=>{let{inputs:e,backend:t,attrs:n}=r,{x:o,y:s,dy:a}=e,{depthRadius:i,bias:l,alpha:u,beta:c}=n,p=new Jk(o.shape,i,l,u,c);return t.runWebGLProgram(p,[o,s,a],o.dtype)},cF={kernelName:au,backendName:"webgl",kernelFunc:Z7};function pF(r,e,t,n){let o=y.sizeFromShape(e),a=y.sizeFromShape(r.shape)/o,i=me({inputs:{x:r},attrs:{shape:[a,o]},backend:n}),l=Sn(i,r.dtype,"max",n),u=me({inputs:{x:l},attrs:{shape:t},backend:n});return n.disposeIntermediateTensorInfo(i),n.disposeIntermediateTensorInfo(l),u}function Qk(r){let{inputs:e,backend:t,attrs:n}=r,{x:o}=e,{reductionIndices:s,keepDims:a}=n,i=o.shape.length,l=y.parseAxisParam(s,o.shape),u=l,c=N.getAxesPermutation(u,i),p=c!=null,m=t.shouldExecuteOnCPU([o]),f=o;if(p){if(m){let w=t.texData.get(f.dataId).values,_=new Array(i);for(let T=0;T<_.length;T++)_[T]=o.shape[c[T]];let k=wp(w,o.shape,o.dtype,c,_);f=t.makeTensorInfo(_,o.dtype);let E=t.texData.get(f.dataId);E.values=k}else f=wl(o,c,t);u=N.getInnerMostAxes(u.length,i)}N.assertAxesAreInnerMostDims("max",u,i);let[d,h]=N.computeOutAndReduceShapes(f.shape,u),g=d;a&&(g=N.expandShapeToKeepDim(d,l));let x;if(m){let w=t.texData.get(f.dataId).values,_=zD(w,y.sizeFromShape(h),g,o.dtype);x=t.makeTensorInfo(g,o.dtype);let k=t.texData.get(x.dataId);k.values=_}else x=pF(f,h,g,t);return p&&t.disposeIntermediateTensorInfo(f),x}var mF={kernelName:po,backendName:"webgl",kernelFunc:Qk};var J7=Xg+`
return max(a, b);
`,Q7=`
vec4 result = vec4(max(a, b));
vec4 isNaN = min(vec4(isnan(a)) + vec4(isnan(b)), vec4(1.0));
`+yl+`
return result;
`,eY=at({opSnippet:J7,packedOpSnippet:Q7,cpuKernelImpl:BD}),fF={kernelName:mo,backendName:"webgl",kernelFunc:eY};function tY(r){let{inputs:e,backend:t,attrs:n}=r,{x:o}=e;Rs(o,"maxPool");let{filterSize:s,strides:a,pad:i,dimRoundingMode:l}=n,u=1;y.assert(N.eitherStridesOrDilationsAreOne(a,u),()=>`Error in maxPool: Either strides or dilations must be 1. Got strides ${a} and dilations '${u}'`);let c=N.computePool2DInfo(o.shape,s,a,u,i,l);if(c.filterWidth===1&&c.filterHeight===1&&y.arraysEqual(c.inShape,c.outShape))return Ht({inputs:{x:o},backend:t});let p=new Gi(c,"max",!1);return t.runWebGLProgram(p,[o],o.dtype)}var dF={kernelName:fo,backendName:"webgl",kernelFunc:tY};function rY(r){let{inputs:e,backend:t,attrs:n}=r,{x:o}=e,{filterSize:s,strides:a,pad:i,dataFormat:l,dimRoundingMode:u}=n,c=[1,1,1],p=N.computePool3DInfo(o.shape,s,a,c,i,u,l),m=new ec(p,"max",!1);return t.runWebGLProgram(m,[o],o.dtype)}var hF={kernelName:pa,backendName:"webgl",kernelFunc:rY};var e0=class{constructor(e){this.variableNames=["dy","maxPos"],this.outputShape=e.inShape;let t=e.strideHeight,n=e.strideWidth,o=e.dilationHeight,s=e.effectiveFilterHeight,a=e.effectiveFilterWidth,i=s-1-e.padInfo.top,l=a-1-e.padInfo.left,u=s*a-1;this.userCode=`
const ivec2 pads = ivec2(${i}, ${l});
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) / ${t}.0;
if (dyR < 0.0 || dyR >= ${e.outHeight}.0 || fract(dyR) > 0.0) {
continue;
}
int idyR = int(dyR);
for (int wC = 0; wC < ${a}; wC++) {
float dyC = float(dyCCorner + wC) / ${n}.0;
if (dyC < 0.0 || dyC >= ${e.outWidth}.0 ||
fract(dyC) > 0.0) {
continue;
}
int idyC = int(dyC);
float dyValue = getDy(b, idyR, idyC, d);
int maxPosValue = ${u} - int(getMaxPos(b, idyR, idyC, d));
// Get the current value, check it against the value from the
// position matrix.
int curPosValue = wR * ${a} + wC;
float mask = float(maxPosValue == curPosValue ? 1.0 : 0.0);
dotProd += dyValue * mask;
}
}
setOutput(dotProd);
}
`}},t0=class{constructor(e){this.variableNames=["dy","maxPos"],this.outputShape=e.inShape;let t=e.strideDepth,n=e.strideHeight,o=e.strideWidth,s=e.dilationDepth,a=e.dilationHeight,i=e.dilationWidth,l=e.effectiveFilterDepth,u=e.effectiveFilterHeight,c=e.effectiveFilterWidth,p=l-1-e.padInfo.front,m=u-1-e.padInfo.top,f=c-1-e.padInfo.left,d=l*u*c-1;this.userCode=`
const ivec3 pads = ivec3(${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 < ${l};
wD += ${s}) {
float dyD = float(dyDCorner + wD) / ${t}.0;
if (dyD < 0.0 || dyD >= ${e.outDepth}.0 || fract(dyD) > 0.0) {
continue;
}
int idyD = int(dyD);
for (int wR = 0; wR < ${u};
wR += ${a}) {
float dyR = float(dyRCorner + wR) / ${n}.0;
if (dyR < 0.0 || dyR >= ${e.outHeight}.0 ||
fract(dyR) > 0.0) {
continue;
}
int idyR = int(dyR);
for (int wC = 0; wC < ${c};
wC += ${i}) {
float dyC = float(dyCCorner + wC) / ${o}.0;
if (dyC < 0.0 || dyC >= ${e.outWidth}.0 ||
fract(dyC) > 0.0) {
continue;
}
int idyC = int(dyC);
float dyValue = getDy(batch, idyD, idyR, idyC, ch);
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 * ${u} * ${c} +
wR * ${c} + wC;
float mask = float(maxPosValue == curPosValue ? 1.0 : 0.0);
dotProd += dyValue * mask;
}
}
}
setOutput(dotProd);
}
`}};function nY(r){let{inputs:e,backend:t,attrs:n}=r,{dy:o,input:s}=e,a=s,{filterSize:i,strides:l,pad:u,dimRoundingMode:c}=n,p=[1,1,1],m=N.computePool3DInfo(a.shape,i,l,p,u,c),f=new ec(m,"max",!0),d=t.runWebGLProgram(f,[a],a.dtype),h=new t0(m),g=t.runWebGLProgram(h,[o,d],a.dtype);return t.disposeIntermediateTensorInfo(d),g}var gF={kernelName:uu,backendName:"webgl",kernelFunc:nY};function oY(r){let{inputs:e,backend:t,attrs:n}=r,{dy:o,input:s,output:a}=e,i=s;Rs([s,a],"maxPoolGrad");let{filterSize:l,strides:u,pad:c,dimRoundingMode:p}=n,m=N.computePool2DInfo(i.shape,l,u,1,c,p),f=!0,d=new Gi(m,"max",f),h=t.runWebGLProgram(d,[i],i.dtype),g=new e0(m),x=t.runWebGLProgram(g,[o,h],i.dtype);return t.disposeIntermediateTensorInfo(h),x}var xF={kernelName:lu,backendName:"webgl",kernelFunc:oY};function yF(r,e,t,n){let o=new Gi(t,"max",!1),s=n.runWebGLProgram(o,[r],"float32");o=new Gi(t,"max",!0,!0,e);let a=n.runWebGLProgram(o,[r],"float32");return[s,a]}var bF={kernelName:cu,backendName:"webgl",kernelFunc:({inputs:r,attrs:e,backend:t})=>{let{x:n}=r,{filterSize:o,strides:s,pad:a,includeBatchInIndex:i}=e,l=t;y.assert(n.shape.length===4,()=>`Error in maxPool: input must be rank 4 but got rank ${n.shape.length}.`);let u=[1,1];y.assert(N.eitherStridesOrDilationsAreOne(s,u),()=>`Error in maxPool: Either strides or dilations must be 1. Got strides ${s} and dilations '${u}'`);let c=N.computePool2DInfo(n.shape,o,s,u,a),[p,m]=yF(n,i,c,l);return[p,m]}};function wF(r,e,t,n){let o=y.sizeFromShape(e),a=y.sizeFromShape(r.shape)/o,i=me({inputs:{x:r},attrs:{shape:[a,o]},backend:n}),l=Sn(i,"float32","mean",n),u=me({inputs:{x:l},attrs:{shape:t},backend:n});return n.disposeIntermediateTensorInfo(i),n.disposeIntermediateTensorInfo(l),u}var _F={kernelName:ho,backendName:"webgl",kernelFunc:({inputs:r,attrs:e,backend:t})=>{let{x:n}=r,{keepDims:o,axis:s}=e,a=t,i=n.shape.length,l=y.parseAxisParam(s,n.shape),u=l,c=N.getAxesPermutation(u,i),p=c!=null,m=a.shouldExecuteOnCPU([n]),f=[],d=n;if(p){if(m){let _=a.texData.get(d.dataId).values,k=new Array(i);for(let F=0;F<k.length;F++)k[F]=n.shape[c[F]];let E=wp(_,n.shape,n.dtype,c,k);d=a.makeTensorInfo(k,n.dtype);let T=a.texData.get(d.dataId);T.values=E}else d=wl(n,c,a);f.push(d),u=N.getInnerMostAxes(u.length,i)}N.assertAxesAreInnerMostDims("sum",u,i);let[h,g]=N.computeOutAndReduceShapes(d.shape,u),x=h;o&&(x=N.expandShapeToKeepDim(h,l));let b=wF(d,g,x,a);for(let w of f)a.disposeIntermediateTensorInfo(w);return b}};function sY(r){let{inputs:e,backend:t,attrs:n}=r,{x:o}=e,{axis:s,keepDims:a}=n,i=o.shape.length,l=y.parseAxisParam(s,o.shape),u=l,c=N.getAxesPermutation(u,i),p=o;c!=null&&(p=Vt({inputs:{x:o},backend:t,attrs:{perm:c}}),u=N.getInnerMostAxes(u.length,o.shape.length)),N.assertAxesAreInnerMostDims("min",u,i);let[m,f]=N.computeOutAndReduceShapes(p.shape,u),d=y.sizeFromShape(f),h=me({inputs:{x:p},backend:t,attrs:{shape:[-1,d]}}),g=Sn(h,h.dtype,"min",t),x;if(a){let b=N.expandShapeToKeepDim(m,l);x=me({inputs:{x:g},backend:t,attrs:{shape:b}})}else x=me({inputs:{x:g},backend:t,attrs:{shape:m}});return t.disposeIntermediateTensorInfo(h),t.disposeIntermediateTensorInfo(g),c!=null&&t.disposeIntermediateTensorInfo(p),x}var vF={kernelName:go,backendName:"webgl",kernelFunc:sY};var iY=Xg+`
return min(a, b);
`,aY=`
vec4 result = vec4(min(a, b));
vec4 isNaN = min(vec4(isnan(a)) + vec4(isnan(b)), vec4(1.0));
`+yl+`
return result;
`,lY=at({opSnippet:iY,packedOpSnippet:aY,cpuKernelImpl:VD}),kF={kernelName:xo,backendName:"webgl",kernelFunc:lY};var r0=class{constructor(e,t,n){this.variableNames=["x"],this.outputShape=t.map((c,p)=>c[0]+e[p]+c[1]);let o=e.length,s=ze(o),a=t.map(c=>c[0]).join(","),i=t.map((c,p)=>c[0]+e[p]).join(","),l=["coords[0]","coords[1]","coords[2]","coords[3]"].slice(0,o),u=n==="reflect"?0:1;if(o===1){this.userCode=`
int start = ${a};
int end = ${i};
void main() {
int outC = getOutputCoords();
if (outC < start) {
outC = start * 2 - outC - ${u};
} else if(outC >= end) {
outC = (end - 1) * 2 - outC + ${u};
}
setOutput(getX(outC - start));
}
`;return}this.userCode=`
${s} start = ${s}(${a});
${s} end = ${s}(${i});
void main() {
${s} outC = getOutputCoords();
for (int i = 0; i < ${o}; i++) {
if (outC[i] < start[i]) {
outC[i] = start[i] * 2 - outC[i] - ${u};
} else if(outC[i] >= end[i]) {
outC[i] = (end[i] - 1) * 2 - outC[i] + ${u};
}
}
${s} coords = outC - start;
setOutput(getX(${l}));
}
`}};var n0=class{constructor(e,t,n){this.variableNames=["x"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=t.map((d,h)=>d[0]+e[h]+d[1]);let o=e.length,s=ze(o),a=t.map(d=>d[0]).join(","),i=t.map((d,h)=>d[0]+e[h]).join(","),l=qt("rc",o),u=qt("source",o),c=`${l[o-1]} < ${this.outputShape[o-1]}`,p=o===1?"source":`vec2(${u.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;
${d}
result[0] = getChannel(getX(${u.join()}), ${p});
${l[o-1]} += 1;
if(${c}) {
${d}
result[1] = getChannel(getX(${u.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;
${d}
result[0] = getChannel(getX(${u.join()}), ${p});
${l[o-1]} += 1;
if(${c}) {
${d}
result[1] = getChannel(getX(${u.join()}), ${p});
}
rc = outputLoc;
${l[o-2]} += 1;
if(${l[o-2]} < ${this.outputShape[o-2]}) {
${d}
result[2] = getChannel(getX(${u.join()}), ${p});
${l[o-1]} += 1;
if(${c}) {
${d}
result[3] = getChannel(getX(${u.join()}), ${p});
}
}
`}this.userCode=`
const ${s} start = ${s}(${a});
const ${s} end = ${s}(${i});
void main() {
${s} outputLoc = getOutputCoords();
vec4 result = vec4(0.);
${f}
setOutput(result);
}
`}};var uY=({inputs:r,backend:e,attrs:t})=>{let{x:n}=r,{paddings:o,mode:s}=t,a=G().getBool("WEBGL_PACK_ARRAY_OPERATIONS")?new n0(n.shape,o,s):new r0(n.shape,o,s);return e.runWebGLProgram(a,[n],n.dtype)},CF={kernelName:ma,backendName:"webgl",kernelFunc:uY};var cY=`if (b == 0.0) return NAN;
return mod(a, b);`,pY=`
vec4 result = mod(a, b);
vec4 isNaN = vec4(equal(b, vec4(0.0)));
`+yl+`
return result;
`,mY=at({opSnippet:cY,packedOpSnippet:pY}),IF={kernelName:bi,backendName:"webgl",kernelFunc:mY};var o0=class{constructor(e,t,n){this.variableNames=["probs"],this.outputShape=[e,n],this.userCode=`
uniform float seed;
void main() {
ivec2 coords = getOutputCoords();
int batch = coords[0];
float r = random(seed);
float cdf = 0.0;
for (int i = 0; i < ${t-1}; i++) {
cdf += getProbs(batch, i);
if (r < cdf) {
setOutput(float(i));
return;
}
}
// If no other event happened, last event happened.
setOutput(float(${t-1}));
}
`}getCustomSetupFunc(e){return(t,n)=>{this.seedLoc==null&&(this.seedLoc=t.getUniformLocation(n,"seed")),t.gl.uniform1f(this.seedLoc,e)}}};var fY=`
if (a == b) {
return 1.0;
};
return a / b;`,dY=`
// 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;
`,s0=at({opSnippet:fY,packedOpSnippet:dY,checkOutOfBounds:!0}),SF={kernelName:no,backendName:"webgl",kernelFunc:s0};var NF="return a - b;",i0=at({opSnippet:NF,packedOpSnippet:NF,supportsComplex:!0,cpuKernelImpl:XD}),TF={kernelName:Oo,backendName:"webgl",kernelFunc:i0};function a0(r){let{inputs:e,backend:t,attrs:n}=r,{logits:o}=e,{dim:s}=n,a=y.parseAxisParam([s],o.shape),i=Qk({inputs:{x:o},backend:t,attrs:{reductionIndices:a,keepDims:!1}}),l=N.expandShapeToKeepDim(i.shape,a),u=me({inputs:{x:i},backend:t,attrs:{shape:l}}),c=i0({inputs:{a:o,b:u},backend:t}),p=jk({inputs:{x:c},backend:t}),m=Tf({inputs:{x:p},backend:t,attrs:{axis:a,keepDims:!1}}),f=me({inputs:{x:m},backend:t,attrs:{shape:l}}),d=s0({inputs:{a:p,b:f},backend:t});return t.disposeIntermediateTensorInfo(i),t.disposeIntermediateTensorInfo(u),t.disposeIntermediateTensorInfo(c),t.disposeIntermediateTensorInfo(p),t.disposeIntermediateTensorInfo(m),t.disposeIntermediateTensorInfo(f),d}var EF={kernelName:Ro,backendName:"webgl",kernelFunc:a0};function hY(r){let{inputs:e,backend:t,attrs:n}=r,{logits:o}=e,{numSamples:s,seed:a,normalized:i}=n,l=i?o:a0({inputs:{logits:o},backend:t,attrs:{dim:o.shape.length-1}}),u=l.shape[0],c=l.shape[1],p=new o0(u,c,s),m=p.getCustomSetupFunc(a),f=t.runWebGLProgram(p,[l],"int32",m);return i||t.disposeIntermediateTensorInfo(l),f}var AF={kernelName:pu,backendName:"webgl",kernelFunc:hY};var DF="return -x;";function gY(r){let{inputs:e,backend:t}=r,{x:n}=e;if(t.shouldExecuteOnCPU([n])){let s=t.texData.get(n.dataId),[a,i]=jD(s.values,n.shape,n.dtype);return t.makeTensorInfo(i,n.dtype,a)}let o;return G().getBool("WEBGL_PACK_UNARY_OPERATIONS")?o=new Os(n.shape,DF):o=new sn(n.shape,DF),t.runWebGLProgram(o,[n],n.dtype)}var $F={kernelName:fs,backendName:"webgl",kernelFunc:gY};var xY=Dr.nonMaxSuppressionV3Impl;function yY(r){N.warn("tf.nonMaxSuppression() in webgl locks the UI thread. Call tf.nonMaxSuppressionAsync() instead");let{inputs:e,backend:t,attrs:n}=r,{boxes:o,scores:s}=e,{maxOutputSize:a,iouThreshold:i,scoreThreshold:l}=n,u=t.readSync(o.dataId),c=t.readSync(s.dataId),{selectedIndices:p}=xY(u,c,a,i,l);return t.makeTensorInfo([p.length],"int32",new Int32Array(p))}var RF={kernelName:_i,backendName:"webgl",kernelFunc:yY};var bY=Dr.nonMaxSuppressionV4Impl;function wY(r){N.warn("tf.nonMaxSuppression() in webgl locks the UI thread. Call tf.nonMaxSuppressionAsync() instead");let{inputs:e,backend:t,attrs:n}=r,{boxes:o,scores:s}=e,{maxOutputSize:a,iouThreshold:i,scoreThreshold:l,padToMaxOutputSize:u}=n,c=t.readSync(o.dataId),p=t.readSync(s.dataId),{selectedIndices:m,validOutputs:f}=bY(c,p,a,i,l,u);return[t.makeTensorInfo([m.length],"int32",new Int32Array(m)),t.makeTensorInfo([],"int32",new Int32Array([f]))]}var FF={kernelName:vi,backendName:"webgl",kernelFunc:wY};var _Y=Dr.nonMaxSuppressionV5Impl;function vY(r){N.warn("tf.nonMaxSuppression() in webgl locks the UI thread. Call tf.nonMaxSuppressionAsync() instead");let{inputs:e,backend:t,attrs:n}=r,{boxes:o,scores:s}=e,{maxOutputSize:a,iouThreshold:i,scoreThreshold:l,softNmsSigma:u}=n,c=t.readSync(o.dataId),p=t.readSync(s.dataId),m=a,f=i,d=l,h=u,{selectedIndices:g,selectedScores:x}=_Y(c,p,m,f,d,h);return[t.makeTensorInfo([g.length],"int32",new Int32Array(g)),t.makeTensorInfo([x.length],"float32",new Float32Array(x))]}var OF={kernelName:ki,backendName:"webgl",kernelFunc:vY};var l0=class{constructor(e,t,n,o){this.variableNames=["indices"],this.outputShape=[e,t],this.userCode=`
void main() {
ivec2 coords = getOutputCoords();
int index = round(getIndices(coords.x));
setOutput(mix(float(${o}), float(${n}),
float(index == coords.y)));
}
`}};var kY=r=>{let{inputs:e,backend:t,attrs:n}=r,{indices:o}=e,{depth:s,onValue:a,offValue:i}=n,l=y.sizeFromShape(o.shape),u=new l0(l,s,a,i),c=me({inputs:{x:o},backend:t,attrs:{shape:[l]}}),p=t.runWebGLProgram(u,[c],o.dtype);t.disposeIntermediateTensorInfo(c);let m=[...o.shape,s],f=me({inputs:{x:p},backend:t,attrs:{shape:m}});return t.disposeIntermediateTensorInfo(p),f},PF={kernelName:bo,backendName:"webgl",kernelFunc:kY};function Rf(r){let{inputs:e,backend:t}=r,{x:n}=e;if(n.dtype==="complex64"){let o=ja({inputs:{input:n},backend:t}),s=Rf({inputs:{x:o},backend:t}),a=tc({inputs:{input:n},backend:t}),i=Rf({inputs:{x:a},backend:t}),l=an({inputs:{real:s,imag:i},backend:t});return t.disposeIntermediateTensorInfo(o),t.disposeIntermediateTensorInfo(s),t.disposeIntermediateTensorInfo(a),t.disposeIntermediateTensorInfo(i),l}else return $f({attrs:{shape:n.shape,dtype:n.dtype,value:n.dtype==="string"?"":0},backend:t})}var MF={kernelName:_s,backendName:"webgl",kernelFunc:Rf};function LF(r){let{inputs:e,backend:t}=r,{x:n}=e;if(n.dtype==="string")throw new Error("onesLike is not supported under string dtype");if(n.dtype==="complex64"){let o=ja({inputs:{input:n},backend:t}),s=LF({inputs:{x:o},backend:t}),a=tc({inputs:{input:n},backend:t}),i=Rf({inputs:{x:a},backend:t}),l=an({inputs:{real:s,imag:i},backend:t});return t.disposeIntermediateTensorInfo(o),t.disposeIntermediateTensorInfo(s),t.disposeIntermediateTensorInfo(a),t.disposeIntermediateTensorInfo(i),l}else return $f({attrs:{shape:n.shape,dtype:n.dtype,value:1},backend:t})}var zF={kernelName:ds,backendName:"webgl",kernelFunc:LF};function CY(r){let{inputs:e,backend:t,attrs:n}=r,{axis:o}=n;if(e.length===1)return sx({inputs:{input:e[0]},backend:t,attrs:{dim:o}});let s=e[0].shape,a=e[0].dtype;e.forEach(c=>{y.assertShapesMatch(s,c.shape,"All tensors passed to stack must have matching shapes"),y.assert(a===c.dtype,()=>"All tensors passed to stack must have matching dtypes")});let i=[],l=e.map(c=>{let p=sx({inputs:{input:c},backend:t,attrs:{dim:o}});return i.push(p),p}),u=Ak({inputs:l,backend:t,attrs:{axis:o}});return i.forEach(c=>t.disposeIntermediateTensorInfo(c)),u}var BF={kernelName:hs,backendName:"webgl",kernelFunc:CY};var u0=class{constructor(e,t,n){this.variableNames=["x"],this.outputShape=t.map((u,c)=>u[0]+e[c]+u[1]);let o=e.length,s=ze(o),a=t.map(u=>u[0]).join(","),i=t.map((u,c)=>u[0]+e[c]).join(","),l=["coords[0]","coords[1]","coords[2]","coords[3]"].slice(0,o);if(o===1){this.userCode=`
int start = ${a};
int end = ${i};
void main() {
int outC = getOutputCoords();
if (outC < start || outC >= end) {
setOutput(float(${n}));
} else {
setOutput(getX(outC - start));
}
}
`;return}this.userCode=`
${s} start = ${s}(${a});
${s} end = ${s}(${i});
void main() {
${s} outC = getOutputCoords();
if (any(lessThan(outC, start)) || any(greaterThanEqual(outC, end))) {
setOutput(float(${n}));
} else {
${s} coords = outC - start;
setOutput(getX(${l}));
}
}
`}};var c0=class{constructor(e,t,n){this.variableNames=["x"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=t.map((h,g)=>h[0]+e[g]+h[1]);let o=e.length,s=ze(o),a=t.map(h=>h[0]).join(","),i=t.map((h,g)=>h[0]+e[g]).join(","),l=qt("rc",o),u=qt("source",o),c=`${l[o-1]} < ${this.outputShape[o-1]}`,p=o===1?"source":`vec2(${u.slice(-2).join()})`,m=[`${s} rc = outputLoc;`,`${l[o-1]} += 1;
if(${c}) {
`,o===1?"":`}
rc = outputLoc;
${l[o-2]} += 1;
if(${l[o-2]} < ${this.outputShape[o-2]}) {`,o===1?"":` ${l[o-1]} += 1;
if(${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[${h}] = float(${n});
} else {
${s} source = rc - start;
result[${h}] = getChannel(getX(${u.join()}), ${p});
}
`;d+=o===1?"} ":"}}",this.userCode=`
const ${s} start = ${s}(${a});
const ${s} end = ${s}(${i});
void main() {
${s} outputLoc = getOutputCoords();
vec4 result = vec4(0.);
${d}
setOutput(result);
}
`}};var p0=r=>{let{inputs:e,backend:t,attrs:n}=r,{x:o}=e,{paddings:s,constantValue:a}=n,i=G().getBool("WEBGL_PACK_ARRAY_OPERATIONS")?new c0(o.shape,s,a):new u0(o.shape,s,a);return t.runWebGLProgram(i,[o],o.dtype)},VF={kernelName:wo,backendName:"webgl",kernelFunc:p0};var IY=`
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);
`,SY=`
// 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));
`+yl+`
return result;
`,NY=at({opSnippet:IY,packedOpSnippet:SY}),WF={kernelName:_o,backendName:"webgl",kernelFunc:NY};function TY(r){let{inputs:e,backend:t,attrs:n}=r,{x:o}=e,{axis:s,keepDims:a}=n,i=o.shape.length,l=[],u=y.parseAxisParam(s,o.shape),c=u,p=N.getAxesPermutation(c,i),m=o;p!=null&&(m=Vt({inputs:{x:o},backend:t,attrs:{perm:p}}),c=N.getInnerMostAxes(c.length,i),l.push(m)),N.assertAxesAreInnerMostDims("prod",c,i);let f;if(t.shouldExecuteOnCPU([m])){let d=t.texData.get(m.dataId).values,{outVals:h,outShape:g,outDtype:x}=GD(m.shape,m.dtype,d,c);f=t.makeTensorInfo(g,x,h)}else{let[d,h]=N.computeOutAndReduceShapes(m.shape,c),g=y.sizeFromShape(h),x=me({inputs:{x:m},backend:t,attrs:{shape:[-1,g]}}),b=xu(o.dtype),w=Sn(x,b,"prod",t);f=me({inputs:{x:w},backend:t,attrs:{shape:d}}),l.push(x),l.push(w)}if(a){l.push(f);let d=N.expandShapeToKeepDim(f.shape,u);f=me({inputs:{x:f},backend:t,attrs:{shape:d}})}return l.forEach(d=>t.disposeIntermediateTensorInfo(d)),f}var jF={kernelName:Ci,backendName:"webgl",kernelFunc:TY};var m0=r=>{let{backend:e,attrs:t}=r,{start:n,stop:o,step:s,dtype:a}=t,i=UD(n,o,s,a);return e.makeTensorInfo([i.length],a,i)},GF={kernelName:fa,backendName:"webgl",kernelFunc:m0};var EY="return 1.0 / x;",AY=Ce({opSnippet:EY}),UF={kernelName:Ii,backendName:"webgl",kernelFunc:AY};var DY=gr+`
return (x < 0.0) ? 0.0 : x;
`,$Y=`
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;
`,RY=Ce({opSnippet:DY,packedOpSnippet:$Y}),qF={kernelName:ko,backendName:"webgl",kernelFunc:RY};var FY=gr+`
return (x < 0.0) ? 0.0 : min(6.0, x);
`,OY=`
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;
`,PY=Ce({opSnippet:FY,packedOpSnippet:OY}),HF={kernelName:Io,backendName:"webgl",kernelFunc:PY};var f0=class{constructor(e,t,n,o,s){this.variableNames=["A"],this.outputShape=[];let[a,i,l,u]=e;this.outputShape=[a,t,n,u];let c=[o&&t>1?i-1:i,o&&n>1?l-1:l],p=[o&&t>1?t-1:t,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]},
${c[1]/p[1]});
const vec2 inputShapeRC = vec2(${i}.0, ${l}.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 d0=class{constructor(e,t,n,o,s){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=[];let[a,i,l,u]=e;this.outputShape=[a,t,n,u];let c=[o&&t>1?i-1:i,o&&n>1?l-1:l],p=[o&&t>1?t-1:t,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]},
${c[1]/p[1]});
const vec3 inputShapeRC = vec3(${i}.0, ${l}.0,
${l}.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 < ${u-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 MY(r){let{inputs:e,backend:t,attrs:n}=r,{images:o}=e,{alignCorners:s,halfPixelCenters:a,size:i}=n,[l,u]=i,c=G().getBool("WEBGL_PACK_IMAGE_OPERATIONS")?new d0(o.shape,l,u,s,a):new f0(o.shape,l,u,s,a);return t.runWebGLProgram(c,[o],"float32")}var KF={kernelName:Co,backendName:"webgl",kernelFunc:MY};var h0=class{constructor(e,t,n){this.variableNames=["dy"],this.outputShape=[],this.outputShape=t;let[,o,s]=t,[,a,i]=e,l=[n&&a>1?o-1:o,n&&i>1?s-1:s],u=[n&&a>1?a-1:a,n&&i>1?i-1:i],c=l[0]/u[0],p=l[1]/u[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 >= ${a}) {
continue;
}
for (int dyCOffset = 0; dyCOffset < winWidth; dyCOffset++) {
int dyC = dyCOffset + startDyC;
// Guard against the window exceeding the bounds of dy
if (dyC < 0 || dyC >= ${i}) {
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 LY(r){let{inputs:e,backend:t,attrs:n}=r,{images:o,dy:s}=e,{alignCorners:a}=n,i=new h0(s.shape,o.shape,a);return t.runWebGLProgram(i,[s],s.dtype)}var XF={kernelName:du,backendName:"webgl",kernelFunc:LY};var g0=class{constructor(e,t,n,o,s){this.variableNames=["A"],this.outputShape=[];let[a,i,l,u]=e;this.outputShape=[a,t,n,u];let c=[o&&t>1?i-1:i,o&&n>1?l-1:l],p=[o&&t>1?t-1:t,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]},
${c[1]/p[1]});
const vec2 inputShapeRC = vec2(${i}.0, ${l}.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 + ${m})));
float newValue = getA(b, sourceNearestRC.x, sourceNearestRC.y, d);
setOutput(newValue);
}
`}};function zY(r){let{inputs:e,backend:t,attrs:n}=r,{images:o}=e,{alignCorners:s,halfPixelCenters:a,size:i}=n,[l,u]=i,c=new g0(o.shape,l,u,s,a);return t.runWebGLProgram(c,[o],o.dtype)}var YF={kernelName:da,backendName:"webgl",kernelFunc:zY};var x0=class{constructor(e,t,n){this.variableNames=["dy"],this.outputShape=[],this.outputShape=t;let[,o,s]=t,[,a,i]=e,l=[n&&a>1?o-1:o,n&&i>1?s-1:s],u=[n&&a>1?a-1:a,n&&i>1?i-1:i],c=l[0]/u[0],p=l[1]/u[1],m=1/c,f=1/p,d=Math.ceil(m)*2+2,h=Math.ceil(f)*2+2;this.userCode=`
void main() {
ivec4 coords = getOutputCoords();
int b = coords[0];
int d = coords[3];
int r = coords[1];
int c = coords[2];
float accumulator = 0.0;
const float heightScale = float(${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 >= ${a}) {
continue;
}
for (int dyCOffset = 0; dyCOffset < winWidth; dyCOffset++) {
int dyC = dyCOffset + startDyC;
// Guard against the window exceeding the bounds of dy
if (dyC < 0 || dyC >= ${i}) {
continue;
}
float sourceFracRow =
float(${l[0]}) *
(float(dyR) / float(${u[0]}));
float sourceFracCol =
float(${l[1]}) *
(float(dyC) / float(${u[1]}));
int sourceNearestRow = int(min(
float(int(${o}) - 1),
${n} ? float(round(sourceFracRow)) :
float(floor(sourceFracRow))));
int sourceNearestCol = int(min(
float(int(${s}) - 1),
${n} ? float(round(sourceFracCol)) :
float(floor(sourceFracCol))));
if (r == sourceNearestRow && c == sourceNearestCol) {
accumulator += getDy(b, dyR, dyC, d);
}
}
}
// End loop over dy
setOutput(accumulator);
}
`}};function BY(r){let{inputs:e,backend:t,attrs:n}=r,{images:o,dy:s}=e,{alignCorners:a}=n,i=new x0(s.shape,o.shape,a);return t.runWebGLProgram(i,[s],s.dtype)}var ZF={kernelName:fu,backendName:"webgl",kernelFunc:BY};var y0=class{constructor(e,t){this.variableNames=["x"];let n=e.length;if(n>4)throw new Error(`WebGL backend: Reverse of rank-${n} tensor is not yet supported`);if(this.outputShape=e,n===1){this.userCode=`
void main() {
int coord = getOutputCoords();
setOutput(getX(${e[0]} - coord - 1));
}
`;return}let o=i=>t.indexOf(i)!==-1&&e[i]!==1?`${e[i]} - coords[${i}] - 1`:`coords[${i}]`,s=e.map((i,l)=>o(l)).join(","),a=ze(n);this.userCode=`
void main() {
${a} coords = getOutputCoords();
setOutput(getX(${s}));
}
`}};var b0=class{constructor(e,t){this.variableNames=["x"],this.packedInputs=!0,this.packedOutput=!0;let n=e.length;if(n>4)throw new Error(`WebGL backend: Reverse of rank-${n} tensor is not yet supported`);this.outputShape=e;let o=qt("rc",n),s=`${o[n-1]} + 1 < ${this.outputShape[n-1]}`,a=`${o[n-2]} + 1 < ${this.outputShape[n-2]}`,i=ze(n);n===1?this.userCode=`
void main(){
int rc = getOutputCoords();
vec4 result = vec4(0.);
result.r = getChannel(getX(${e[0]} - rc - 1),
${e[0]} - rc - 1);
if(${s}){
result.g = getChannel(getX(${e[0]} - (rc + 1) - 1),
${e[0]} - (rc + 1) - 1);
}
setOutput(result);
}
`:this.userCode=`
void main() {
${i} rc = getOutputCoords();
vec4 result = vec4(0.);
result.r = ${l(o.slice())};
if(${s}){
result.g = ${u(o.slice())};
}
if(${a}) {
result.b = ${c(o.slice())};
if(${s}) {
result.a = ${p(o.slice())};
}
}
setOutput(result);
}
`;function l(d){return m(d)}function u(d){return d[n-1]="("+d[n-1]+" + 1)",m(d)}function c(d){return d[n-2]="("+d[n-2]+" + 1)",m(d)}function p(d){return d[n-1]="("+d[n-1]+" + 1)",d[n-2]="("+d[n-2]+" + 1)",m(d)}function m(d){let h=e.map((b,w)=>f(w,d)),g=h.join(","),x=h.slice(-2).join(",");return`getChannel(getX(${g}), vec2(${x}))`}function f(d,h){return t.indexOf(d)!==-1&&e[d]!==1?`${e[d]} - ${h[d]} - 1`:`${h[d]}`}}};function VY(r){let{inputs:e,backend:t,attrs:n}=r,{x:o}=e,{dims:s}=n,a=o.shape.length,i=y.parseAxisParam(s,o.shape);if(a===0)return Ht({inputs:{x:o},backend:t});let l=G().getBool("WEBGL_PACK_ARRAY_OPERATIONS")?new b0(o.shape,i):new y0(o.shape,i);return t.runWebGLProgram(l,[o],o.dtype)}var JF={kernelName:So,backendName:"webgl",kernelFunc:VY};var w0=class{constructor(e,t,n,o){this.variableNames=["Image"],this.outputShape=[];let s=e[1],a=e[2],i=Math.sin(t).toFixed(3),l=Math.cos(t).toFixed(3);this.outputShape=e;let[u,c]=N.getImageCenter(o,s,a),p=u.toFixed(3),m=c.toFixed(3),f="";typeof n=="number"?f=`float outputValue = ${n.toFixed(2)};`:f=`
vec3 fill = vec3(${n.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) - ${p}) * ${l} - (float(y) - ${m}) * ${i};
float coordYFloat = (float(x) - ${p}) * ${i} + (float(y) - ${m}) * ${l};
int coordX = int(round(coordXFloat + ${p}));
int coordY = int(round(coordYFloat + ${m}));
${f}
if(coordX >= 0 && coordX < ${a} && coordY >= 0 && coordY < ${s}) {
outputValue = getImage(coords[0], coordY, coordX, coords[3]);
}
setOutput(outputValue);
}
`}};var QF={kernelName:Fi,backendName:"webgl",kernelFunc:({inputs:r,attrs:e,backend:t})=>{let{image:n}=r,{radians:o,fillValue:s,center:a}=e,i=t,l=new w0(n.shape,o,s,a);return i.runWebGLProgram(l,[n],n.dtype)}};var WY=`
// OpenGL ES does not support round function.
// The algorithm is based on banker's rounding.
float base = floor(x);
if ((x - base) < 0.5) {
return floor(x);
} else if ((x - base) > 0.5) {
return ceil(x);
} else {
if (mod(base, 2.0) == 0.0) {
return base;
} else {
return base + 1.0;
}
}
`,jY=Ce({opSnippet:WY}),eO={kernelName:No,backendName:"webgl",kernelFunc:jY};var GY="return inversesqrt(x);",UY=Ce({opSnippet:GY,cpuKernelImpl:qD}),tO={kernelName:To,backendName:"webgl",kernelFunc:UY};var Ff=class{constructor(e,t,n,o,s,a,i=!0){this.variableNames=["updates","indices","defaultValue"],this.outputShape=a;let l=ze(s.length),u=ze(a.length),c="";n===1?c="i":n===2&&(c="i, j");let p=`getIndices(${c})`,m="";o===1?m="i":o===2&&(m="i, coords[1]");let f=`getUpdates(${m})`,d=t>1?"strides[j]":"strides";this.userCode=`
${l} strides = ${l}(${s});
void main() {
${u} coords = getOutputCoords();
float sum = 0.0;
bool found = false;
for (int i = 0; i < ${e}; i++) {
int flattenedIndex = 0;
for (int j = 0; j < ${t}; j++) {
int index = round(${p});
flattenedIndex += index * ${d};
}
if (flattenedIndex == coords[0]) {
sum += ${f};
found = true;
}
}
setOutput(mix(getDefaultValue(), sum, float(found)));
}
`}};function qY(r){let{inputs:e,backend:t,attrs:n}=r,{indices:o,updates:s}=e,{shape:a}=n,{sliceRank:i,numUpdates:l,sliceSize:u,strides:c,outputSize:p}=N.calculateShapes(s,o,a),m=[p/u,u];if(p===0)return t.makeTensorInfo(a,o.dtype);let f=me({inputs:{x:o},backend:t,attrs:{shape:[l,i]}}),d=me({inputs:{x:s},backend:t,attrs:{shape:[l,u]}}),h=t.makeTensorInfo([],"float32",new Float32Array([0])),g=new Ff(l,i,f.shape.length,d.shape.length,c,m),x=t.runWebGLProgram(g,[d,f,h],d.dtype),b=me({inputs:{x},backend:t,attrs:{shape:a}});return t.disposeIntermediateTensorInfo(f),t.disposeIntermediateTensorInfo(d),t.disposeIntermediateTensorInfo(x),t.disposeIntermediateTensorInfo(h),b}var rO={kernelName:Si,backendName:"webgl",kernelFunc:qY};var _0=class{constructor(e,t,n){this.variableNames=["c","a","b"],this.outputShape=t;let o,s;if(n>4)throw Error(`Where for rank ${n} is not yet supported`);if(n===1)s="resRC",o="resRC";else{let i=["resRC.x","resRC.y","resRC.z","resRC.w"],l=[],u=[];for(let c=0;c<t.length;c++)u.push(`${i[c]}`),c<e&&l.push(`${i[c]}`);o=l.join(),s=u.join()}let a=ze(n);this.userCode=`
void main() {
${a} resRC = getOutputCoords();
float cVal = getC(${o});
if (cVal >= 1.0) {
setOutput(getA(${s}));
} else {
setOutput(getB(${s}));
}
}
`}};function HY(r){let{inputs:e,backend:t}=r,{condition:n,t:o,e:s}=e,a=new _0(n.shape.length,o.shape,o.shape.length);return t.runWebGLProgram(a,[n,o,s],dr(o.dtype,s.dtype))}var nO={kernelName:xs,backendName:"webgl",kernelFunc:HY};var KY=`
// Stable and Attracting Fixed Point (0, 1) for Normalized Weights.
// see: https://arxiv.org/abs/1706.02515
float scaleAlpha = ${N.SELU_SCALEALPHA};
float scale = ${N.SELU_SCALE};
return (x >= 0.0) ? scale * x : scaleAlpha * (exp(x) - 1.0);
`,XY=Ce({opSnippet:KY}),oO={kernelName:Ni,backendName:"webgl",kernelFunc:XY};var YY="return 1.0 / (1.0 + exp(-1.0 * x));",ZY=Ce({opSnippet:YY}),sO={kernelName:Ao,backendName:"webgl",kernelFunc:ZY};var JY=`
if (isnan(x)) { return 0.0; }
return sign(x);
`,QY=Ce({opSnippet:JY}),iO={kernelName:Ei,backendName:"webgl",kernelFunc:QY};var e9=Yg+`
return sin(x);
`,t9=Ce({opSnippet:e9}),aO={kernelName:Eo,backendName:"webgl",kernelFunc:t9};var r9=`
float e2x = exp(x);
return (e2x - 1.0 / e2x) / 2.0;
`,n9=Ce({opSnippet:r9}),lO={kernelName:Ti,backendName:"webgl",kernelFunc:n9};var o9=`
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;
`,s9=Ce({opSnippet:o9}),uO={kernelName:Ai,backendName:"webgl",kernelFunc:s9};var i9=r=>{let{inputs:e,backend:t,attrs:n}=r,{x:o}=e,{blockShape:s,paddings:a}=n;y.assert(o.shape.length<=4,()=>"spaceToBatchND for rank > 4 with a WebGL backend not implemented yet");let i=s.reduce((x,b)=>x*b),l=[[0,0]];l.push(...a);for(let x=1+s.length;x<o.shape.length;++x)l.push([0,0]);let u=[],c=p0({inputs:{x:o},backend:t,attrs:{paddings:l,constantValue:0}}),p=N.getReshaped(c.shape,s,i,!1),m=N.getPermuted(p.length,s.length,!1),f=N.getReshapedPermuted(c.shape,s,i,!1),d=me({inputs:{x:c},backend:t,attrs:{shape:p}}),h=Vt({inputs:{x:d},backend:t,attrs:{perm:m}}),g=me({inputs:{x:h},backend:t,attrs:{shape:f}});return u.push(c),u.push(d),u.push(h),u.forEach(x=>t.disposeIntermediateTensorInfo(x)),g},cO={kernelName:ha,backendName:"webgl",kernelFunc:i9};function a9(r){let{inputs:e,backend:t,attrs:n}=r,{sparseIndices:o,sparseValues:s,defaultValue:a}=e,{outputShape:i}=n,{sliceRank:l,numUpdates:u,strides:c,outputSize:p}=N.calculateShapes(s,o,i),m=!1,f=new Ff(u,l,o.shape.length,s.shape.length,c,[p,1],m),d=t.runWebGLProgram(f,[s,o,a],s.dtype),h=me({inputs:{x:d},backend:t,attrs:{shape:i}});return t.disposeIntermediateTensorInfo(d),h}var pO={kernelName:hu,backendName:"webgl",kernelFunc:a9};function l9(r){let{inputs:e,backend:t,attrs:n}=r,{x:o}=e,{numOrSizeSplits:s,axis:a}=n,i=y.parseAxisParam(a,o.shape)[0],l=N.prepareSplitSize(o,s,i),u=o.shape.length,c=new Array(u).fill(0),p=o.shape.slice();return l.map(m=>{let f=[...p];f[i]=m;let d=Wa({inputs:{x:o},backend:t,attrs:{begin:c,size:f}});return c[i]+=m,d})}var mO={kernelName:bs,backendName:"webgl",kernelFunc:l9};var u9="return sqrt(x);",c9=Ce({opSnippet:u9}),fO={kernelName:Do,backendName:"webgl",kernelFunc:c9};var p9="return x * x;",m9=Ce({opSnippet:p9}),dO={kernelName:ga,backendName:"webgl",kernelFunc:m9};var hO="return (a - b) * (a - b);",f9=at({opSnippet:hO,packedOpSnippet:hO}),gO={kernelName:Fo,backendName:"webgl",kernelFunc:f9};function d9({inputs:r,attrs:e,backend:t}){let{x:n}=r,o=gr+`
return x > 0.0 ? 1.0 : float(${e.alpha});
`,s=new sn(n.shape,o);return t.runWebGLProgram(s,[n],n.dtype)}var xO={kernelName:On,backendName:"webgl",kernelFunc:d9};var v0=class{constructor(e,t,n){this.variableNames=["x"],this.outputShape=n;let o=n.length,s=ze(n.length),a=ze(n.length),i="";if(o===1)i="coords * strides + begin";else{let l=0;i=n.map((u,c)=>(l++,n.length===1?`coords * strides[${c}] + begin[${c}]`:`coords[${l-1}] * strides[${c}] + begin[${c}]`)).join(",")}this.userCode=`
${s} begin = ${s}(${e});
${s} strides = ${s}(${t});
void main() {
${a} coords = getOutputCoords();
setOutput(getX(${i}));
}
`}};function h9(r){let{inputs:e,backend:t,attrs:n}=r,{x:o}=e,{begin:s,end:a,strides:i,beginMask:l,endMask:u,ellipsisMask:c,newAxisMask:p,shrinkAxisMask:m}=n,{nonStrided:f,$begin:d,$strides:h,size:g,newShape:x,outShape:b}=ir.sliceInfo(o.shape,s,a,i,l,u,c,p,m),w=me({inputs:{x:o},backend:t,attrs:{shape:x}}),_;if(f){let E=Wa({inputs:{x:w},backend:t,attrs:{begin:d,size:g}});_=me({inputs:{x:E},backend:t,attrs:{shape:b}}),t.disposeIntermediateTensorInfo(E)}else if(b.some(E=>E===0))_=t.makeTensorInfo(b,o.dtype,[]);else if(t.shouldExecuteOnCPU([w])){let F=t.texData.get(w.dataId).values,O=Ie(w.shape,w.dtype,F),M=KD(b,O,h,d);_=t.makeTensorInfo(b,w.dtype,M.values)}else{let T=new v0(d,h,b);_=t.runWebGLProgram(T,[w],w.dtype)}let k=me({inputs:{x:_},backend:t,attrs:{shape:b}});return t.disposeIntermediateTensorInfo(w),t.disposeIntermediateTensorInfo(_),k}var yO={kernelName:Di,backendName:"webgl",kernelFunc:h9};var g9="return tan(x);",x9=Ce({opSnippet:g9}),bO={kernelName:$i,backendName:"webgl",kernelFunc:x9};var y9=`
float e2x = exp(-2.0 * abs(x));
return sign(x) * (1.0 - e2x) / (1.0 + e2x);
`,b9=Ce({opSnippet:y9}),wO={kernelName:Po,backendName:"webgl",kernelFunc:b9};var k0=class{constructor(e,t){this.variableNames=["A"];let n=new Array(e.length);for(let a=0;a<n.length;a++)n[a]=e[a]*t[a];this.outputShape=n,this.rank=n.length;let o=ze(this.rank),s=w9(e);this.userCode=`
void main() {
${o} resRC = getOutputCoords();
setOutput(getA(${s}));
}
`}};function w9(r){let e=r.length;if(e>5)throw Error(`Tile for rank ${e} is not yet supported`);if(e===1)return`imod(resRC, ${r[0]})`;let t=["resRC.x","resRC.y","resRC.z","resRC.w","resRC.u"],n=[];for(let o=0;o<r.length;o++)n.push(`imod(${t[o]}, ${r[o]})`);return n.join()}function C0(r){let{inputs:e,backend:t,attrs:n}=r,{x:o}=e,{reps:s}=n;if(o.dtype==="string"){let u=t.readSync(o.dataId).map(m=>y.decodeString(m)),c=Ie(o.shape,o.dtype,u),p=YD(c,s);return t.makeTensorInfo(p.shape,p.dtype,p.values)}let a=new k0(o.shape,s);return t.runWebGLProgram(a,[o],o.dtype)}var _O={kernelName:_n,backendName:"webgl",kernelFunc:C0};function _9(r){let{inputs:e,backend:t,attrs:n}=r,{x:o}=e,{k:s,sorted:a}=n,i=t.readSync(o.dataId),[l,u]=ZD(i,o.shape,o.dtype,s,a);return[t.makeTensorInfo(l.shape,l.dtype,l.values),t.makeTensorInfo(u.shape,u.dtype,u.values)]}var vO={kernelName:Ri,backendName:"webgl",kernelFunc:_9};function v9(r){let{inputs:e,attrs:t,backend:n}=r,{axis:o}=t,{x:s}=e;Rs(s,"unique"),console.warn("WARNING: ","UI might be locked temporarily as data is being downloaded");let a=n.readSync(s.dataId),{outputValues:i,outputShape:l,indices:u}=JD(a,o,s.shape,s.dtype);return[n.makeTensorInfo(l,s.dtype,i),n.makeTensorInfo([u.length],"int32",u)]}var kO={kernelName:gu,backendName:"webgl",kernelFunc:v9};function k9(r){let{inputs:e,backend:t,attrs:n}=r,{value:o}=e,{axis:s}=n;s<0&&(s+=o.shape.length);let a=o,i=a.shape.length,l=o.shape[s],u=new Array(i-1),c=0;for(let h=0;h<i;h++)h!==s&&(u[c++]=a.shape[h]);let p=[],m=new Array(i).fill(0),f=a.shape.slice();f[s]=1;let d=new Array(l);for(let h=0;h<d.length;h++){m[s]=h;let g=Wa({inputs:{x:a},backend:t,attrs:{begin:m,size:f}}),x=me({inputs:{x:g},backend:t,attrs:{shape:u}});d[h]=x,p.push(g)}return p.forEach(h=>t.disposeIntermediateTensorInfo(h)),d}var CO={kernelName:ws,backendName:"webgl",kernelFunc:k9};var I0=class{constructor(e,t){this.variableNames=["x","segmentIds"];let n=e.windowSize,o=e.batchSize,s=e.inSize,a=e.numSegments,i=a*Math.ceil(s/n);this.outputShape=[o,i];let l="0.0",u="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 = ${l};
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(
${a})) * float(${n}));
int currentSeg = int(mod(float(outIdx), float(${a})));
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
);
${m}
}
setOutput(${u});
}
`}};function C9(r){let{inputs:e,backend:t,attrs:n}=r,{x:o,segmentIds:s}=e,{numSegments:a}=n,i=o.shape.length,l=[],u=0,c=N.getAxesPermutation([u],i),p=o;c!=null&&(p=Vt({inputs:{x:o},backend:t,attrs:{perm:c}}),l.push(p),u=N.getInnerMostAxes(1,i)[0]);let m=N.segment_util.computeOutShape(p.shape,u,a),f=y.sizeFromShape([p.shape[u]]),d=me({inputs:{x:p},backend:t,attrs:{shape:[-1,f]}});l.push(d);let h=xu(o.dtype),g=(_,k,E,T,F)=>{let O=_.shape[0],M=_.shape[1],j=N.segment_util.segOpComputeOptimalWindowSize(M,F),W={windowSize:j,inSize:M,batchSize:O,numSegments:F},U=new I0(W,k),Y=t.compileAndRun(U,[_,E],T);if(l.push(Y),Y.shape[1]===F)return Y;let K=m0({backend:t,attrs:{start:0,stop:F,step:1,dtype:"float32"}}),Z=C0({inputs:{x:K},backend:t,attrs:{reps:[M/j]}});return l.push(K),l.push(Z),g(Y,k,Z,T,F)},x=g(d,"unsortedSegmentSum",s,h,a),b=me({inputs:{x},backend:t,attrs:{shape:m}}),w=b;if(c!=null){l.push(b);let _=N.getUndoAxesPermutation(c);w=Vt({inputs:{x:w},backend:t,attrs:{perm:_}})}return 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Found: ${this.outputs.map(b=>b.name)}`),this.inputLayers=[],this.inputLayersNodeIndices=[],this.inputLayersTensorIndices=[],this.outputLayers=[],this.outputLayersNodeIndices=[],this.outputLayersTensorIndices=[],this.layers=[],this.internalContainerRefs=[];for(let b of this.outputs){let w=b.sourceLayer,_=b.nodeIndex,k=b.tensorIndex;this.outputLayers.push(w),this.outputLayersNodeIndices.push(_),this.outputLayersTensorIndices.push(k)}for(let b of this.inputs){let w=b.sourceLayer,_=b.nodeIndex,k=b.tensorIndex;Wn(_===0,"input layer has >1 nodes"),Wn(k===0,"input layer has >1 tensors"),this.inputLayers.push(w),this.inputLayersNodeIndices.push(_),this.inputLayersTensorIndices.push(k)}this.inputNames=[],this.outputNames=[],this.feedInputShapes=[],this.feedInputNames=[],this.feedOutputNames=[];for(let b=0;b<this.inputLayers.length;b++){let w=this.inputLayers[b];if(!(w instanceof Hi))throw new TypeError(`Input layers to a LayersModel must be InputLayer objects. Received inputs: ${e.inputs}. Input ${b} (0-based) originates from layer type ${w.getClassName()}.`);this.inputNames.push(w.name),this.feedInputShapes.push(w.batchInputShape),this.feedInputNames.push(w.name)}for(let b of this.outputLayers)this.outputNames.push(b.name);this.internalInputShapes=this.inputs.map(b=>b.shape),this.internalOutputShapes=this.outputs.map(b=>b.shape);let t={},n={},o={},s={},a={},i=[],l=(b,w,_,k,E,T)=>{(k==null||E==null||T==null)&&(k=b.sourceLayer,E=b.nodeIndex,T=b.tensorIndex);let F=k.inboundNodes[E];if(_.indexOf(F)!==-1)throw new Br(`The tensor ${b.name} at layer "${k.name}" is part of a cycle.`);if(w.indexOf(F)!==-1)return;this.containerNodes.add(jn.nodeKey(k,E)),k.id in a||(a[k.id]=Object.keys(a).length),_.indexOf(F)===-1&&_.push(F);let O=F.inboundLayers.length;for(let M=0;M<O;M++){let j=F.inputTensors[M],W=F.inboundLayers[M],U=F.nodeIndices[M],Y=F.tensorIndices[M];l(j,w,_,W,U,Y)}for(w.push(F);_.indexOf(F)>=0;)_.splice(_.indexOf(F),1);i.push(F)},u=[],c=[];for(let b of this.outputs)l(b,u,c);let p=i.slice().reverse();for(let b of p){n[b.id]=b,b.id in t||(t[b.id]=0);let w=t[b.id],_=o[b.outboundLayer.id]==null?0:o[b.outboundLayer.id];w=Math.max(w,_),o[b.outboundLayer.id]=w,s[b.outboundLayer.id]=b.outboundLayer,t[b.id]=w;for(let k=0;k<b.inboundLayers.length;k++){let E=b.inboundLayers[k],T=b.nodeIndices[k],F=E.inboundNodes[T],O=t[F.id]==null?0:t[F.id];t[F.id]=Math.max(w+1,O),n[F.id]=F}}let m={};for(let b in t){let w=t[b];w in m||(m[w]=[]),m[w].push(n[b])}let f={};for(let b in o){let w=o[b];w in f||(f[w]=[]),f[w].push(s[b])}let d=Object.keys(f).map(b=>parseInt(b,10)).sort(zf);this.layers=[];for(let b of d){let w=f[b];w.sort((_,k)=>{let E=a[_.id],T=a[k.id];return E<T?-1:E>T?1:0});for(let _ of w)_ instanceof jn&&this.internalContainerRefs.push(_),this.layers.push(_)}this.layersByDepth=f,d=Object.keys(m).map(b=>parseInt(b,10)).sort(zf);let h=this.inputs.slice(),g=[];for(let b of d)for(let w of m[b]){let _=w.outboundLayer;if(_!=null){for(let k of w.inputTensors)if(h.indexOf(k)===-1)throw new Br(`Graph disconnected: cannot obtain value for tensor ${k} at layer "${_.name}". 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Its own _trainableWeights must remain an empty Array.");if(!this.trainable)return[];let e=[];for(let t of this.layers)e=e.concat(t.trainableWeights);return e}get nonTrainableWeights(){let e=[];for(let t of this.layers)e.push(...t.nonTrainableWeights);if(!this.trainable){let t=[];for(let n of this.layers)t.push(...n.trainableWeights);return t.concat(e)}return e}get weights(){return this.trainableWeights.concat(this.nonTrainableWeights)}loadWeights(e,t=!0){let n={},o=0;for(let a of this.layers)for(let i of a.weights){if(n[i.originalName]!=null)throw new B(`Duplicate weight name: ${i.originalName}`);n[i.originalName]=i,o++}let s=[];for(let a in e){let i=a;if(n[a]==null){let l=a.split("/");i=l.slice(0,-2).concat([l[l.length-1]]).join("/")}if(n[i]!=null)s.push([n[i],e[a]]);else if(t)throw new B(`Provided weight data has no target variable: ${a}`);delete n[i]}if(t){let a=[];for(let i in n)a.push(i);if(a.length>0)throw new B(`${a.length} of ${o} weights are not set: ${a}`)}Lp(s)}updatedConfig(){let e=this.getConfig(),t={};return t.className=this.getClassName(),t.config=e,t.kerasVersion=`tfjs-layers ${Wp}`,t.backend="TensorFlow.js",t}toJSON(e,t=!0){let n=Ox(this.updatedConfig());return t?JSON.stringify(n):n}call(e,t){return V(()=>{e=_t(e);let n=new zs;for(let o=0;o<this.inputs.length;++o)n.add(this.inputs[o],e[o]);return pc(this.outputs,n,t)})}computeMask(e,t){return V(()=>{e=_t(e);let n;return t==null?n=Zo(null,e.length):n=_t(t),this.runInternalGraph(e,n)[1]})}computeOutputShape(e){let t=Pp(e);if(t.length!==this.inputLayers.length)throw new B(`Invalid inputShape argument ${e}: model has ${this.inputLayers.length} tensor inputs.`);let n={};for(let i=0;i<t.length;i++){let l=this.inputLayers[i],u=t[i],c=l.name+"_0_0";n[c]=u}let o=Object.keys(this.nodesByDepth).map(i=>parseInt(i,10)).sort(zf);if(o.length>1)for(let i of o){let l=this.nodesByDepth[i];for(let u of l){let c=u.outboundLayer;if(this.inputLayers.map(h=>h.id).indexOf(c.id)!==-1)continue;let p=[];for(let h=0;h<u.inboundLayers.length;h++){let g=u.inboundLayers[h],x=u.nodeIndices[h],b=u.tensorIndices[h],w=`${g.name}_${x}_${b}`,_=n[w];p.push(_)}let m=c.computeOutputShape(xr(p)),f=Pp(m),d=c.inboundNodes.indexOf(u);for(let h=0;h<f.length;h++){let g=`${c.name}_${d}_${h}`;n[g]=f[h]}}}let s=[],a=[];for(let i=0;i<this.outputLayers.length;i++){let l=this.outputLayers[i],u=this.outputLayersNodeIndices[i],c=this.outputLayersTensorIndices[i],p=`${l.name}_${u}_${c}`;a.push(p)}for(let i=0;i<a.length;i++){let l=a[i];Wn(l in n),s.push(n[l])}return xr(s)}runInternalGraph(e,t){t==null&&(t=Zo(null,e.length));let n={};for(let l=0;l<this.inputs.length;++l){let u=this.inputs[l],c=e[l],p=t[l];n[u.id]=[c,p]}let o=Object.keys(this.nodesByDepth).map(l=>parseInt(l,10)).sort(zf);for(let l of o){let u=this.nodesByDepth[l];for(let c of u){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,x,b,w;if(c.callArgs!=null&&(h=c.callArgs),d.length===1){let[_,k]=d[0];h.mask==null&&(h.mask=k),b=_t(p.call(_,h)),w=_t(p.computeMask(_,k)),g=[_],x=[k]}else g=d.map(_=>_[0]),x=d.map(_=>_[1]),h.mask==null&&(h.mask=x),b=_t(p.call(g,h)),w=_t(p.computeMask(g,x));if(p.activityRegularizer)throw new Te("LayersModel invocation with concrete Tensor value(s) in the presence of activity regularizer(s) is not supported yet.");for(let _=0;_<f.length;++_){let k=f[_],E=b[_],T=w[_];n[k.id]=[E,T]}}}}let s=[],a=[],i=[];for(let l of this.outputs){Wn(l.id in n,`Could not compute output ${l.name} : ${l.id}`);let[u,c]=n[l.id];i.push(u.shape),s.push(u),a.push(c)}return[s,a,i]}buildNodeConversionMap(e){let t={},n;for(let o of this.layers){n=o instanceof jn?1:0;for(let s=0;s<o.inboundNodes.length;s++){let a=jn.nodeKey(o,s);this.containerNodes.has(a)&&(t[a]=n,n+=1)}}return t}getLayer(e,t){if(t!=null){if(this.layers.length<=t)throw new B(`Was asked to retrieve layer at index ${t}, but model only has ${this.layers.length} layer(s).`);return this.layers[t]}else if(e==null)throw new B("Provide either a layer name or layer index");for(let n of this.layers)if(n.name===e)return n;throw new B(`No such layer: ${e}`)}calculateLosses(){return V(()=>{let e=[];for(let t of this.layers)for(let n=0;n<t.inboundNodes.length;++n){let o=jn.nodeKey(t,n);this.containerNodes.has(o)&&e.push(...t.calculateLosses())}return e})}getConfig(){let e={name:this.name},t=this.buildNodeConversionMap(this.layers),n=[];for(let a of this.layers){let i=a.getClassName(),l=a.getConfig(),u=[];for(let p=0;p<a.inboundNodes.length;p++){let m=a.inboundNodes[p],f=jn.nodeKey(a,p),d={};if(this.containerNodes.has(f)){if(m.callArgs)try{JSON.stringify(m.callArgs),d=m.callArgs}catch(h){console.warn(`Layer ${a.name} was passed non-serializable keyword arguments: ${m.callArgs}. They will not be included in the serialized model (and thus will be missing at deserialization time).`),d={}}if(m.inboundLayers.length>0){let h=[];for(let g=0;g<m.inboundLayers.length;g++){let x=m.inboundLayers[g],b=m.nodeIndices[g],w=m.tensorIndices[g],_=jn.nodeKey(x,b),k=t[_];k==null&&(k=0),h.push([x.name,k,w,d])}u.push(h)}}}let c={};c.name=a.name,c.className=i,c.config=l,c.inboundNodes=u,n.push(c)}e.layers=n;let o=[];for(let a=0;a<this.inputLayers.length;a++){let i=this.inputLayers[a],l=this.inputLayersNodeIndices[a],u=jn.nodeKey(i,l);if(!this.containerNodes.has(u))continue;let c=t[u];c==null&&(c=0);let p=this.inputLayersTensorIndices[a];o.push([i.name,c,p])}e.inputLayers=o;let s=[];for(let a=0;a<this.outputLayers.length;a++){let i=this.outputLayers[a],l=this.outputLayersNodeIndices[a],u=jn.nodeKey(i,l);if(!this.containerNodes.has(u))continue;let c=t[u];c==null&&(c=0);let p=this.outputLayersTensorIndices[a];s.push([i.name,c,p])}return e.outputLayers=s,e}static fromConfig(e,t,n={},o=!1){let s={},a={};function i(g,x){g.name in a?a[g.name].push(x):a[g.name]=[x]}function l(g,x){let b=[],w;for(let _ of x){let k=_[0],E=_[1],T=_[2];if(w=_[3]==null?{}:_[3],!(k in s)){i(g,x);return}let F=s[k];if(F.inboundNodes.length<=E){i(g,x);return}let O=F.inboundNodes[E];b.push(O.outputTensors[T])}b.length>0&&g.apply(xr(b),w)}function u(g){let x=g.name,b=Qr(g,t.customObjects!=null?t.customObjects:{});b.setFastWeightInitDuringBuild(o),s[x]=b,g.inboundNodes.forEach(_=>{if(!(_ instanceof Array))throw new B(`Corrupted configuration, expected array for nodeData: ${_}`);i(b,_)})}let c=t.name,p=t.layers;for(let g of p)u(g);for(;!tL(a);)for(let g of p){let x=s[g.name];if(x.name in a){let b=a[x.name];delete a[x.name];for(let w of b)l(x,w)}}let m=[],f=[],d=t.inputLayers;for(let g of d){let x=g[0],b=g[1],w=g[2];Wn(x in s);let k=s[x].inboundNodes[b].outputTensors;m.push(k[w])}let h=t.outputLayers;for(let g of h){let x=g[0],b=g[1],w=g[2];Wn(x in s);let k=s[x].inboundNodes[b].outputTensors;f.push(k[w])}return new e({inputs:m,outputs:f,name:c})}get stateful(){if(this._stateful)throw new B("Container instance unexpectedly has _stateful = true. The statefulness of a Container is determined by the Layers it contains. Its _stateful property must remain the default false.");for(let e of this.layers)if(e.stateful)return!0;return!1}resetStates(){V(()=>{this.layers.forEach(e=>{e.stateful&&e.resetStates()})})}};function VQ(r,e,t){let n=e.length;if(r==null||Array.isArray(r)&&r.length===0)return e.map(o=>null);if(n===1)return Array.isArray(r)&&r.length===1?r:typeof r=="object"&&e[0]in r?[r[e[0]]]:[r];if(Array.isArray(r)){if(r.length!==n)throw new Error(`Provided ${t} is an array of ${r.length} element(s), but the model has ${n} outputs. Make sure a set of weights is provided for each model output.`);return r}else if(typeof r=="object"&&Object.keys(r).length>0&&typeof r[Object.keys(r)[0]]=="object"){let o=[];return e.forEach(s=>{s in r?o.push(r[s]):o.push(null)}),o}else throw new Error(`The model has multiple (${n}) outputs, so ${t} must be either an array with ${n} elements or an object with ${e} keys. Provided ${t} not understood: ${JSON.stringify(r)}`)}function Px(r,e){return VQ(r,e,"classWeight")}async function Mx(r,e,t,n){if(e!=null||n!=null)throw new Error("Support sampleWeight is not implemented yet");if(t!=null){let o=V(()=>{if(r.shape.length===1)return r.clone();if(r.shape.length===2)if(r.shape[1]>1){let i=1;return r.argMax(i)}else{if(r.shape[1]===1)return r.reshape([r.shape[0]]);throw new Error(`Encountered unexpected last-dimension size (${r.shape[1]}) during handling of class weights. The size is expected to be >= 1.`)}else throw new Error(`Unexpected rank of target (y) tensor (${r.rank}) during handling of class weights. The rank is expected to be 1 or 2.`)}),s=Array.from(await o.data());De(o);let a=[];return s.forEach(i=>{if(t[i]==null)throw new Error(`classWeight must contain all classes in the training data. 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(Expected output keys: ${JSON.stringify(r.outputNames)})`);for(let l=0;l<s.length;l++)y.assert(s[l].shape[0]===i,()=>`Batch size mismatch: input ${r.inputNames[l]} has ${s[l].shape[0]}; expected ${i} based on input ${r.inputNames[0]}.`);for(let l=0;l<a.length;l++)y.assert(a[l].shape[0]===i,()=>`Batch size mismatch: output ${r.outputNames[l]} has ${a[l].shape[0]}; expected ${i} based on input ${r.inputNames[0]}.`);return{xs:s,ys:a}}function zL(r,e,t){if(t instanceof R)return[t];if(Array.isArray(t))return y.assert(t.length===e.length,()=>`Received an array of ${t.length} Tensors, but expected ${e.length} to match the ${r} keys ${e}.`),t;{let n=[];for(let o of e){if(t[o]==null)throw new B(`The feature data generated by the dataset lacks the required ${r} key '${o}'.`);n.push(t[o])}return n}}function jQ(r){if(r.length===3)throw new Te("Validation with sample weights is not implemented yet.");return{xs:r[0],ys:r[1]}}async function WL(r,e,t){let n=t.batchesPerEpoch!=null;if(y.assert(r.optimizer!=null,()=>"You must compile a model before training/testing. Use LayersModel.compile(modelCompileConfig)."),y.assert(t!=null,()=>"For fitDataset(), the 2nd argument (config) is required, but it is not provided in this call."),y.assert(t.epochs!=null&&t.epochs>0&&Number.isInteger(t.epochs),()=>`For fitDataset(), config.epochs is expected to be a positive integer, but got ${t.epochs}`),y.assert(!n||t.batchesPerEpoch>0&&Number.isInteger(t.batchesPerEpoch),()=>`For fitDataset(), config.batchesPerEpoch is expected to be a positive integer if specified, but got ${t.batchesPerEpoch}`),y.assert(t.validationSplit==null,()=>"`validationSplit` is not supported by `fitDataset()`. Use validationData instead."),r.isTraining)throw new Error("Cannot start training because another fit() call is ongoing.");r.isTraining=!0;try{let o=t.validationData!=null,s,a;if(o)if(VL(t.validationData))y.assert(t.validationBatches==null||t.validationBatches>0&&Number.isInteger(t.validationBatches),()=>`For fitDataset() with dataset-based validation, config.validationBatches is expected not to be provided, or to be a positive integer, but got ${t.validationBatches}`);else{let g=jQ(t.validationData);s=g.xs,a=g.ys}let i=r.makeTrainFunction(),l=r.getDedupedMetricsNames(),u;o?u=l.slice().concat(l.map(g=>"val_"+g)):u=l.slice();let c=Tx(t.callbacks,t.yieldEvery),p=t.verbose==null?1:t.verbose,{callbackList:m,history:f}=Ex(c,p,t.epochs,null,null,GQ(e,t),null,o,u);m.setModel(r),r.history=f,await m.onTrainBegin(),r.stopTraining_=!1;let d=t.initialEpoch==null?0:t.initialEpoch,h=await e.iterator();for(;d<t.epochs;){let g={};await m.onEpochBegin(d);let x=0,b=0;for(n||(h=await e.iterator());n?x<t.batchesPerEpoch:!0;){let w=await h.next();if(n&&w.done){console.warn(`You provided \`batchesPerEpoch\` as ${t.batchesPerEpoch}, but your dataset iterator ran out of data after ${x} batches; interrupting training. 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e={theta:this.theta},t=super.getConfig();return Object.assign(e,t),e}};fd.className="ThresholdedReLU";ee.registerClass(fd);var dd=class extends Le{constructor(e){super(e==null?{}:e);this.DEFAULT_AXIS=1,e==null&&(e={}),this.softmax=new ld().apply,this.axis=e.axis==null?this.DEFAULT_AXIS:e.axis}call(e,t){let n=Pe(e);return this.softmax(n,this.axis)}computeOutputShape(e){return e}getConfig(){let e={axis:this.axis},t=super.getConfig();return Object.assign(e,t),e}};dd.className="Softmax";ee.registerClass(dd);function Tl(r,e,t){if(typeof r=="number")return Zo(r,e);if(r.length!==e)throw new B(`The ${t} argument must be an integer or tuple of ${e} integers. Received: ${r.length} elements.`);for(let n=0;n<e;++n){let o=r[n];if(!dL(o))throw new B(`The ${t} argument must be an integer or tuple of ${e} integers. Received: ${JSON.stringify(r)} including a non-integer number ${o}`)}return r}function mn(r,e,t,n,o=1){if(r==null)return r;let s=e+(e-1)*(o-1),a;return t==="same"?a=r:a=r-s+1,Math.floor((a+n-1)/n)}function hd(r,e,t,n){if(r==null)return null;if(n==="valid")r=r*e+Ls([t-e,0]);else if(n==="same")r=r*e;else throw new B(`Unsupport padding mode: ${n}.`);return r}function gd(r,e){return V(()=>(Ft(e),e==="channelsFirst"?Ue(r,[0,2,3,1]):r))}function xC(r,e){return V(()=>(Ft(e),e==="channelsFirst"?Ue(r,[0,2,3,4,1]):r))}function eee(r,e,t,n=1,o="valid",s,a=1){return V(()=>{if(s==null&&(s=Zr()),Ft(s),r.shape.length!==3)throw new B(`The input of a conv1dWithBias operation should be 3, but is ${r.shape.length} instead.`);if(e.shape.length!==3)throw new B(`The kernel for a conv1dWithBias operation should be 3, but is ${e.shape.length} instead`);if(t!=null&&t.shape.length!==1)throw new B(`The bias for a conv1dWithBias operation should be 1, but is ${e.shape.length} instead`);if(s==="channelsFirst"&&(r=Ue(r,[0,2,1])),o==="causal")throw new Te("The support for CAUSAL padding mode in conv1dWithBias is not implemented yet.");let i=Iu(r,e,n,o==="same"?"same":"valid","NWC",a);return t!=null&&(i=ln(i,t)),i})}function oz(r,e,t,n=[1,1],o="valid",s,a,i=null){return V(()=>{if(s==null&&(s=Zr()),Ft(s),r.rank!==3&&r.rank!==4)throw new B(`conv2dWithBiasActivation expects input to be of rank 3 or 4, but received ${r.rank}.`);if(e.rank!==3&&e.rank!==4)throw new B(`conv2dWithBiasActivation expects kernel to be of rank 3 or 4, but received ${r.rank}.`);let l=gd(r,s);if(o==="causal")throw new Te("The support for CAUSAL padding mode in conv1dWithBias is not implemented yet.");return l=Wo.conv2d({x:l,filter:e,strides:n,pad:o==="same"?"same":"valid",dilations:a,dataFormat:"NHWC",bias:t,activation:i}),s==="channelsFirst"&&(l=Ue(l,[0,3,1,2])),l})}function tee(r,e,t,n=[1,1,1],o="valid",s,a){return V(()=>{if(s==null&&(s=Zr()),Ft(s),r.rank!==4&&r.rank!==5)throw new B(`conv3dWithBias expects input to be of rank 4 or 5, but received ${r.rank}.`);if(e.rank!==4&&e.rank!==5)throw new B(`conv3dWithBias expects kernel to be of rank 4 or 5, but received ${r.rank}.`);let i=xC(r,s);if(o==="causal")throw new Te("The support for CAUSAL padding mode in conv3dWithBias is not implemented yet.");return i=Mm(i,e,n,o==="same"?"same":"valid","NDHWC",a),t!=null&&(i=ln(i,t)),s==="channelsFirst"&&(i=Ue(i,[0,4,1,2,3])),i})}var Gp=class extends Le{constructor(e,t){super(t);if(this.bias=null,this.DEFAULT_KERNEL_INITIALIZER="glorotNormal",this.DEFAULT_BIAS_INITIALIZER="zeros",Gp.verifyArgs(t),this.rank=e,Kt(this.rank,"rank"),this.rank!==1&&this.rank!==2&&this.rank!==3)throw new Te(`Convolution layer for rank other than 1, 2, or 3 (${this.rank}) is not implemented yet.`);if(this.kernelSize=Tl(t.kernelSize,e,"kernelSize"),this.strides=Tl(t.strides==null?1:t.strides,e,"strides"),this.padding=t.padding==null?"valid":t.padding,Jr(this.padding),this.dataFormat=t.dataFormat==null?"channelsLast":t.dataFormat,Ft(this.dataFormat),this.activation=Vs(t.activation),this.useBias=t.useBias==null?!0:t.useBias,this.biasInitializer=gt(t.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.biasConstraint=Bt(t.biasConstraint),this.biasRegularizer=vt(t.biasRegularizer),this.activityRegularizer=vt(t.activityRegularizer),this.dilationRate=Tl(t.dilationRate==null?1:t.dilationRate,e,"dilationRate"),this.rank===1&&Array.isArray(this.dilationRate)&&this.dilationRate.length!==1)throw new B(`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 B(`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 B(`dilationRate must be a number or array of three numbers for 3D convolution, but received ${JSON.stringify(this.dilationRate)}`)}}static verifyArgs(e){if(Wn("kernelSize"in e,"required key 'kernelSize' not in config"),typeof e.kernelSize!="number"&&!fx(e.kernelSize,"number",1,3))throw new B(`BaseConv expects config.kernelSize to be number or number[] with length 1, 2, or 3, but received ${JSON.stringify(e.kernelSize)}.`)}getConfig(){let e={kernelSize:this.kernelSize,strides:this.strides,padding:this.padding,dataFormat:this.dataFormat,dilationRate:this.dilationRate,activation:Bs(this.activation),useBias:this.useBias,biasInitializer:Nt(this.biasInitializer),biasRegularizer:ut(this.biasRegularizer),activityRegularizer:ut(this.activityRegularizer),biasConstraint:zt(this.biasConstraint)},t=super.getConfig();return Object.assign(e,t),e}},fc=class extends Gp{constructor(e,t){super(e,t);this.kernel=null,fc.verifyArgs(t),this.filters=t.filters,Kt(this.filters,"filters"),this.kernelInitializer=gt(t.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.kernelConstraint=Bt(t.kernelConstraint),this.kernelRegularizer=vt(t.kernelRegularizer)}build(e){e=tt(e);let t=this.dataFormat==="channelsFirst"?1:e.length-1;if(e[t]==null)throw new B(`The channel dimension of the input should be defined. Found ${e[t]}`);let n=e[t],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:{[t]:n}}],this.built=!0}call(e,t){return V(()=>{e=Pe(e);let n,o=this.bias==null?null:this.bias.read(),s=dx(this.activation.getClassName());if(s!=null&&this.rank===2)n=oz(e,this.kernel.read(),o,this.strides,this.padding,this.dataFormat,this.dilationRate,s);else{if(this.rank===1)n=eee(e,this.kernel.read(),o,this.strides[0],this.padding,this.dataFormat,this.dilationRate[0]);else if(this.rank===2)n=oz(e,this.kernel.read(),o,this.strides,this.padding,this.dataFormat,this.dilationRate);else if(this.rank===3)n=tee(e,this.kernel.read(),o,this.strides,this.padding,this.dataFormat,this.dilationRate);else throw new Te("convolutions greater than 3D are not implemented yet.");this.activation!=null&&(n=this.activation.apply(n))}return n})}computeOutputShape(e){e=tt(e);let t=[],n=this.dataFormat==="channelsLast"?e.slice(1,e.length-1):e.slice(2);for(let s=0;s<n.length;++s){let a=mn(n[s],this.kernelSize[s],this.padding,this.strides[s],typeof this.dilationRate=="number"?this.dilationRate:this.dilationRate[s]);t.push(a)}let o=[e[0]];return this.dataFormat==="channelsLast"?(o=o.concat(t),o.push(this.filters)):(o.push(this.filters),o=o.concat(t)),o}getConfig(){let e={filters:this.filters,kernelInitializer:Nt(this.kernelInitializer),kernelRegularizer:ut(this.kernelRegularizer),kernelConstraint:zt(this.kernelConstraint)},t=super.getConfig();return Object.assign(e,t),e}static verifyArgs(e){if(!("filters"in e)||typeof e.filters!="number"||e.filters<1)throw new B(`Convolution layer expected config.filters to be a 'number' > 0 but got ${JSON.stringify(e.filters)}`)}},El=class extends fc{constructor(e){super(2,e);El.verifyArgs(e)}getConfig(){let e=super.getConfig();return delete e.rank,e}static verifyArgs(e){if(typeof e.kernelSize!="number"&&!fx(e.kernelSize,"number",1,2))throw new B(`Conv2D expects config.kernelSize to be number or number[] with length 1 or 2, but received ${JSON.stringify(e.kernelSize)}.`)}};El.className="Conv2D";ee.registerClass(El);var dc=class extends fc{constructor(e){super(3,e);dc.verifyArgs(e)}getConfig(){let e=super.getConfig();return delete e.rank,e}static verifyArgs(e){if(typeof e.kernelSize!="number"&&!(Array.isArray(e.kernelSize)&&(e.kernelSize.length===1||e.kernelSize.length===3)))throw new B(`Conv3D expects config.kernelSize to be number or [number, number, number], but received ${JSON.stringify(e.kernelSize)}.`)}};dc.className="Conv3D";ee.registerClass(dc);var xd=class extends El{constructor(e){super(e);if(this.inputSpec=[new At({ndim:4})],this.padding!=="same"&&this.padding!=="valid")throw new B(`Conv2DTranspose currently supports only padding modes 'same' and 'valid', but received padding mode ${this.padding}`)}build(e){if(e=tt(e),e.length!==4)throw new B("Input should have rank 4; Received input shape: "+JSON.stringify(e));let t=this.dataFormat==="channelsFirst"?1:e.length-1;if(e[t]==null)throw new B("The channel dimension of the inputs should be defined. Found `None`.");let n=e[t],o=this.kernelSize.concat([this.filters,n]);this.kernel=this.addWeight("kernel",o,"float32",this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint),this.useBias&&(this.bias=this.addWeight("bias",[this.filters],"float32",this.biasInitializer,this.biasRegularizer,!0,this.biasConstraint)),this.inputSpec=[new At({ndim:4,axes:{[t]:n}})],this.built=!0}call(e,t){return V(()=>{let n=Pe(e);if(n.shape.length!==4)throw new B(`Conv2DTranspose.call() expects input tensor to be rank-4, but received a tensor of rank-${n.shape.length}`);let o=n.shape,s=o[0],a,i;this.dataFormat==="channelsFirst"?(a=2,i=3):(a=1,i=2);let l=o[a],u=o[i],c=this.kernelSize[0],p=this.kernelSize[1],m=this.strides[0],f=this.strides[1],d=hd(l,m,c,this.padding),h=hd(u,f,p,this.padding),g=[s,d,h,this.filters];this.dataFormat!=="channelsLast"&&(n=Ue(n,[0,2,3,1]));let x=Su(n,this.kernel.read(),g,this.strides,this.padding);return this.dataFormat!=="channelsLast"&&(x=Ue(x,[0,3,1,2])),this.bias!=null&&(x=ln(x,this.bias.read(),this.dataFormat)),this.activation!=null&&(x=this.activation.apply(x)),x})}computeOutputShape(e){e=tt(e);let t=e.slice(),n,o,s;this.dataFormat==="channelsFirst"?(n=1,o=2,s=3):(n=3,o=1,s=2);let a=this.kernelSize[0],i=this.kernelSize[1],l=this.strides[0],u=this.strides[1];return t[n]=this.filters,t[o]=hd(t[o],l,a,this.padding),t[s]=hd(t[s],u,i,this.padding),t}getConfig(){let e=super.getConfig();return delete e.dilationRate,e}};xd.className="Conv2DTranspose";ee.registerClass(xd);var yC=class extends fc{constructor(e,t){super(e,t);if(this.DEFAULT_DEPTHWISE_INITIALIZER="glorotUniform",this.DEFAULT_POINTWISE_INITIALIZER="glorotUniform",this.depthwiseKernel=null,this.pointwiseKernel=null,t.filters==null)throw new B("The `filters` configuration field is required by SeparableConv, but is unspecified.");if(t.kernelInitializer!=null||t.kernelRegularizer!=null||t.kernelConstraint!=null)throw new B("Fields kernelInitializer, kernelRegularizer and kernelConstraint are invalid for SeparableConv2D. Use depthwiseInitializer, depthwiseRegularizer, depthwiseConstraint, pointwiseInitializer, pointwiseRegularizer and pointwiseConstraint instead.");if(t.padding!=null&&t.padding!=="same"&&t.padding!=="valid")throw new B(`SeparableConv${this.rank}D supports only padding modes: 'same' and 'valid', but received ${JSON.stringify(t.padding)}`);this.depthMultiplier=t.depthMultiplier==null?1:t.depthMultiplier,this.depthwiseInitializer=gt(t.depthwiseInitializer||this.DEFAULT_DEPTHWISE_INITIALIZER),this.depthwiseRegularizer=vt(t.depthwiseRegularizer),this.depthwiseConstraint=Bt(t.depthwiseConstraint),this.pointwiseInitializer=gt(t.depthwiseInitializer||this.DEFAULT_POINTWISE_INITIALIZER),this.pointwiseRegularizer=vt(t.pointwiseRegularizer),this.pointwiseConstraint=Bt(t.pointwiseConstraint)}build(e){if(e=tt(e),e.length<this.rank+2)throw new B(`Inputs to SeparableConv${this.rank}D should have rank ${this.rank+2}, but received input shape: ${JSON.stringify(e)}`);let t=this.dataFormat==="channelsFirst"?1:e.length-1;if(e[t]==null||e[t]<0)throw new B(`The channel dimension of the inputs should be defined, but found ${JSON.stringify(e[t])}`);let n=e[t],o=this.kernelSize.concat([n,this.depthMultiplier]),s=[];for(let i=0;i<this.rank;++i)s.push(1);s.push(n*this.depthMultiplier,this.filters);let a=!0;this.depthwiseKernel=this.addWeight("depthwise_kernel",o,"float32",this.depthwiseInitializer,this.depthwiseRegularizer,a,this.depthwiseConstraint),this.pointwiseKernel=this.addWeight("pointwise_kernel",s,"float32",this.pointwiseInitializer,this.pointwiseRegularizer,a,this.pointwiseConstraint),this.useBias?this.bias=this.addWeight("bias",[this.filters],"float32",this.biasInitializer,this.biasRegularizer,a,this.biasConstraint):this.bias=null,this.inputSpec=[new At({ndim:this.rank+2,axes:{[t]:n}})],this.built=!0}call(e,t){return V(()=>{e=Pe(e);let n;if(this.rank===1)throw new Te("1D separable convolution is not implemented yet.");return this.rank===2&&(this.dataFormat==="channelsFirst"&&(e=Ue(e,[0,2,3,1])),n=Ym(e,this.depthwiseKernel.read(),this.pointwiseKernel.read(),this.strides,this.padding,this.dilationRate,"NHWC")),this.useBias&&(n=ln(n,this.bias.read(),this.dataFormat)),this.activation!=null&&(n=this.activation.apply(n)),this.dataFormat==="channelsFirst"&&(n=Ue(n,[0,3,1,2])),n})}getConfig(){let e=super.getConfig();return delete e.rank,delete e.kernelInitializer,delete e.kernelRegularizer,delete e.kernelConstraint,e.depthwiseInitializer=Nt(this.depthwiseInitializer),e.pointwiseInitializer=Nt(this.pointwiseInitializer),e.depthwiseRegularizer=ut(this.depthwiseRegularizer),e.pointwiseRegularizer=ut(this.pointwiseRegularizer),e.depthwiseConstraint=zt(this.depthwiseConstraint),e.pointwiseConstraint=zt(this.pointwiseConstraint),e}};yC.className="SeparableConv";var yd=class extends yC{constructor(e){super(2,e)}};yd.className="SeparableConv2D";ee.registerClass(yd);var hc=class extends fc{constructor(e){super(1,e);hc.verifyArgs(e),this.inputSpec=[{ndim:3}]}getConfig(){let e=super.getConfig();return delete e.rank,delete e.dataFormat,e}static verifyArgs(e){if(typeof e.kernelSize!="number"&&!fx(e.kernelSize,"number",1,1))throw new B(`Conv1D expects config.kernelSize to be number or number[] with length 1, but received ${JSON.stringify(e.kernelSize)}.`)}};hc.className="Conv1D";ee.registerClass(hc);var bd=class extends Le{constructor(e){super(e);typeof e.cropping=="number"?this.cropping=[[e.cropping,e.cropping],[e.cropping,e.cropping]]:typeof e.cropping[0]=="number"?this.cropping=[[e.cropping[0],e.cropping[0]],[e.cropping[1],e.cropping[1]]]:this.cropping=e.cropping,this.dataFormat=e.dataFormat===void 0?"channelsLast":e.dataFormat,this.inputSpec=[{ndim:4}]}computeOutputShape(e){return this.dataFormat==="channelsFirst"?[e[0],e[1],e[2]-this.cropping[0][0]-this.cropping[0][1],e[3]-this.cropping[1][0]-this.cropping[1][1]]:[e[0],e[1]-this.cropping[0][0]-this.cropping[0][1],e[2]-this.cropping[1][0]-this.cropping[1][1],e[3]]}call(e,t){return V(()=>{if(e=Pe(e),this.dataFormat==="channelsLast"){let n=Uf(e,this.cropping[0][0],e.shape[1]-this.cropping[0][0]-this.cropping[0][1],2);return Uf(n,this.cropping[1][0],e.shape[2]-this.cropping[1][1]-this.cropping[1][0],3)}else{let n=Uf(e,this.cropping[0][0],e.shape[2]-this.cropping[0][0]-this.cropping[0][1],3);return Uf(n,this.cropping[1][0],e.shape[3]-this.cropping[1][1]-this.cropping[1][0],4)}})}getConfig(){let e={cropping:this.cropping,dataFormat:this.dataFormat},t=super.getConfig();return Object.assign(e,t),e}};bd.className="Cropping2D";ee.registerClass(bd);var wd=class extends Le{constructor(e){super(e);this.DEFAULT_SIZE=[2,2],this.inputSpec=[{ndim:4}],this.size=e.size==null?this.DEFAULT_SIZE:e.size,this.dataFormat=e.dataFormat==null?"channelsLast":e.dataFormat,Ft(this.dataFormat),this.interpolation=e.interpolation==null?"nearest":e.interpolation,pL(this.interpolation)}computeOutputShape(e){if(this.dataFormat==="channelsFirst"){let t=e[2]==null?null:this.size[0]*e[2],n=e[3]==null?null:this.size[1]*e[3];return[e[0],e[1],t,n]}else{let t=e[1]==null?null:this.size[0]*e[1],n=e[2]==null?null:this.size[1]*e[2];return[e[0],t,n,e[3]]}}call(e,t){return V(()=>{let n=Pe(e),o=n.shape;if(this.dataFormat==="channelsFirst"){n=Ue(n,[0,2,3,1]);let s=this.size[0]*o[2],a=this.size[1]*o[3],i=this.interpolation==="nearest"?n.resizeNearestNeighbor([s,a]):n.resizeBilinear([s,a]);return Ue(i,[0,3,1,2])}else{let s=this.size[0]*o[1],a=this.size[1]*o[2];return this.interpolation==="nearest"?n.resizeNearestNeighbor([s,a]):n.resizeBilinear([s,a])}})}getConfig(){let e={size:this.size,dataFormat:this.dataFormat},t=super.getConfig();return Object.assign(e,t),e}};wd.className="UpSampling2D";ee.registerClass(wd);function ree(r,e,t=[1,1],n="valid",o,s){return V(()=>{o==null&&(o=Zr()),Ft(o);let a=gd(r,o);if(r.rank!==4)throw new B(`Input for depthwiseConv2d is required to be 4-D, but is instead ${r.rank}-D`);if(e.rank!==4)throw new B(`depthwiseKernel is required to be 4-D, but is instead ${e.rank}-D`);return a=Ln(a,e,t,n==="same"?"same":"valid","NHWC",s),o==="channelsFirst"&&(a=Ue(a,[0,3,1,2])),a})}var _d=class extends Gp{constructor(e){super(2,e);this.depthwiseKernel=null,this.depthMultiplier=e.depthMultiplier==null?1:e.depthMultiplier,this.depthwiseInitializer=gt(e.depthwiseInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.depthwiseConstraint=Bt(e.depthwiseConstraint),this.depthwiseRegularizer=vt(e.depthwiseRegularizer)}build(e){if(e=tt(e),e.length<4)throw new B(`Inputs to DepthwiseConv2D should have rank 4. Received input shape: ${JSON.stringify(e)}.`);let t=this.dataFormat==="channelsFirst"?1:3;if(e[t]==null||e[t]<0)throw new B(`The channel dimension of the inputs to DepthwiseConv2D should be defined, but is not (${e[t]}).`);let n=e[t],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(e,t){return V(()=>{e=Pe(e);let n=ree(e,this.depthwiseKernel.read(),this.strides,this.padding,this.dataFormat,null);return this.useBias&&(n=ln(n,this.bias.read(),this.dataFormat)),this.activation!=null&&(n=this.activation.apply(n)),n})}computeOutputShape(e){e=tt(e);let t=this.dataFormat==="channelsFirst"?e[2]:e[1],n=this.dataFormat==="channelsFirst"?e[3]:e[2],o=this.dataFormat==="channelsFirst"?e[1]*this.depthMultiplier:e[3]*this.depthMultiplier,s=mn(t,this.kernelSize[0],this.padding,this.strides[0]),a=mn(n,this.kernelSize[1],this.padding,this.strides[1]);return this.dataFormat==="channelsFirst"?[e[0],o,s,a]:[e[0],s,a,o]}getConfig(){let e=super.getConfig();return e.depthMultiplier=this.depthMultiplier,e.depthwiseInitializer=Nt(this.depthwiseInitializer),e.depthwiseRegularizer=ut(this.depthwiseRegularizer),e.depthwiseConstraint=zt(this.depthwiseRegularizer),e}};_d.className="DepthwiseConv2D";ee.registerClass(_d);function bC(r,e,t,n){if(Array.isArray(r)){if(e!=null||t!=null)throw new B("When inputs is an array, neither initialState or constants should be provided");n!=null&&(t=r.slice(r.length-n,r.length),r=r.slice(0,r.length-n)),r.length>1&&(e=r.slice(1,r.length)),r=r[0]}function o(s){return s==null||Array.isArray(s)?s:[s]}return e=o(e),t=o(t),{inputs:r,initialState:e,constants:t}}function wC(r,e,t,n=!1,o,s,a=!1,i=!1){return V(()=>{let l=e.shape.length;if(l<3)throw new B(`Input should be at least 3D, but is ${l}D.`);let u=[1,0].concat(Vr(2,l));if(e=Ue(e,u),s!=null)throw new Te("The rnn() functoin of the deeplearn.js backend does not support constants yet.");a&&console.warn("Backend rnn(): the unroll = true option is not applicable to the imperative deeplearn.js backend."),o!=null&&(o=o.asType("bool").asType("float32"),o.rank===l-1&&(o=wr(o,-1)),o=Ue(o,u)),n&&(e=Zt(e,0),o!=null&&(o=Zt(o,0)));let c=[],p,m=t,f=e.shape[0],d=pr(e),h;o!=null&&(h=pr(o));for(let x=0;x<f;++x){let b=d[x],w=V(()=>r(b,m));if(o==null)p=w[0],m=w[1];else{let _=V(()=>{let k=h[x],E=nr(k).sub(k),T=w[0].mul(k).add(m[0].mul(E)),F=m.map((O,M)=>w[1][M].mul(k).add(O.mul(E)));return{output:T,newStates:F}});p=_.output,m=_.newStates}i&&c.push(p)}let g;return i&&(g=jt(c,1)),[p,g,m]})}var fn=class extends Le{constructor(e){super(e);let t;if(e.cell==null)throw new B("cell property is missing for the constructor of RNN.");if(Array.isArray(e.cell)?t=new Up({cells:e.cell}):t=e.cell,t.stateSize==null)throw new B("The RNN cell should have an attribute `stateSize` (tuple of integers, one integer per RNN state).");this.cell=t,this.returnSequences=e.returnSequences==null?!1:e.returnSequences,this.returnState=e.returnState==null?!1:e.returnState,this.goBackwards=e.goBackwards==null?!1:e.goBackwards,this._stateful=e.stateful==null?!1:e.stateful,this.unroll=e.unroll==null?!1:e.unroll,this.supportsMasking=!0,this.inputSpec=[new At({ndim:3})],this.stateSpec=null,this.states_=null,this.numConstants=null,this.keptStates=[]}getStates(){if(this.states_==null){let e=Array.isArray(this.cell.stateSize)?this.cell.stateSize.length:1;return Vr(0,e).map(t=>null)}else return this.states_}setStates(e){this.states_=e}computeOutputShape(e){vx(e)&&(e=e[0]),e=e;let t=this.cell.stateSize;Array.isArray(t)||(t=[t]);let n=t[0],o;if(this.returnSequences?o=[e[0],e[1],n]:o=[e[0],n],this.returnState){let s=[];for(let a of t)s.push([e[0],a]);return[o].concat(s)}else return o}computeMask(e,t){return V(()=>{Array.isArray(t)&&(t=t[0]);let n=this.returnSequences?t: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 e=Array.isArray(this.cell.stateSize)?this.cell.stateSize.length:1,t=[];for(let n=0;n<e;++n)t.push(null);return t}else return this.states_}set states(e){this.states_=e}build(e){let t=null;if(this.numConstants!=null)throw new Te("Constants support is not implemented in RNN yet.");vx(e)&&(e=e[0]),e=e;let n=this.stateful?e[0]:null,o=e.slice(2);this.inputSpec[0]=new At({shape:[n,null,...o]});let s=[e[0]].concat(e.slice(2));if(t!=null)throw new Te("Constants support is not implemented in RNN yet.");this.cell.build(s);let a;if(Array.isArray(this.cell.stateSize)?a=this.cell.stateSize:a=[this.cell.stateSize],this.stateSpec!=null){if(!y.arraysEqual(this.stateSpec.map(i=>i.shape[i.shape.length-1]),a))throw new B(`An initialState was passed that is not compatible with cell.stateSize. Received stateSpec=${this.stateSpec}; However cell.stateSize is ${this.cell.stateSize}`)}else this.stateSpec=a.map(i=>new At({shape:[null,i]}));this.stateful&&this.resetStates()}resetStates(e,t=!1){V(()=>{if(!this.stateful)throw new Nn("Cannot call resetStates() on an RNN Layer that is not stateful.");let n=this.inputSpec[0].shape[0];if(n==null)throw new B("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=>mt([n,o])):this.states_=[mt([n,this.cell.stateSize])];else if(e==null)De(this.states_),this.keptStates!=null&&(De(this.keptStates),this.keptStates=[]),Array.isArray(this.cell.stateSize)?this.states_=this.cell.stateSize.map(o=>mt([n,o])):this.states_[0]=mt([n,this.cell.stateSize]);else{if(Array.isArray(e)||(e=[e]),e.length!==this.states_.length)throw new B(`Layer ${this.name} expects ${this.states_.length} state(s), but it received ${e.length} state value(s). Input received: ${e}`);t===!0?this.keptStates.push(this.states_.slice()):De(this.states_);for(let o=0;o<this.states_.length;++o){let s=e[o],a=Array.isArray(this.cell.stateSize)?this.cell.stateSize[o]:this.cell.stateSize,i=[n,a];if(!y.arraysEqual(s.shape,i))throw new B(`State ${o} is incompatible with layer ${this.name}: expected shape=${i}, received shape=${s.shape}`);this.states_[o]=s}}this.states_=this.states_.map(o=>Dt(o.clone()))})}apply(e,t){let n=t==null?null:t.initialState,o=t==null?null:t.constants;t==null&&(t={});let s=bC(e,n,o,this.numConstants);e=s.inputs,n=s.initialState,o=s.constants;let a=[],i=[];if(n!=null){t.initialState=n,a=a.concat(n),this.stateSpec=[];for(let u of n)this.stateSpec.push(new At({shape:u.shape}));i=i.concat(this.stateSpec)}if(o!=null&&(t.constants=o,a=a.concat(o),this.numConstants=o.length),a[0]instanceof jr){let u=[e].concat(a),c=this.inputSpec.concat(i),p=this.inputSpec;this.inputSpec=c;let m=super.apply(u,t);return this.inputSpec=p,m}else return super.apply(e,t)}call(e,t){return V(()=>{let n=t==null?null:t.mask,o=t==null?null:t.training,s=t==null?null:t.initialState;e=Pe(e),s==null&&(this.stateful?s=this.states_:s=this.getInitialState(e));let a=Array.isArray(this.cell.stateSize)?this.cell.stateSize.length:1;if(s.length!==a)throw new B(`RNN Layer has ${a} 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 i={training:o},u=wC((d,h)=>{let g=this.cell.call([d].concat(h),i);return[g[0],g.slice(1)]},e,s,this.goBackwards,n,null,this.unroll,this.returnSequences),c=u[0],p=u[1],m=u[2];this.stateful&&this.resetStates(m,o);let f=this.returnSequences?p:c;return this.returnState?[f].concat(m):f})}getInitialState(e){return V(()=>{let t=mt(e.shape);return t=we(t,[1,2]),t=qa(t),Array.isArray(this.cell.stateSize)?this.cell.stateSize.map(n=>n>1?xx(t,[1,n]):t):this.cell.stateSize>1?[xx(t,[1,this.cell.stateSize])]:[t]})}get trainableWeights(){return this.trainable?this.cell.trainableWeights:[]}get nonTrainableWeights(){return this.trainable?this.cell.nonTrainableWeights:this.cell.weights}setFastWeightInitDuringBuild(e){super.setFastWeightInitDuringBuild(e),this.cell!=null&&this.cell.setFastWeightInitDuringBuild(e)}getConfig(){let e=super.getConfig(),t={returnSequences:this.returnSequences,returnState:this.returnState,goBackwards:this.goBackwards,stateful:this.stateful,unroll:this.unroll};this.numConstants!=null&&(t.numConstants=this.numConstants);let n=this.cell.getConfig();return this.getClassName()===fn.className&&(t.cell={className:this.cell.getClassName(),config:n}),Object.assign({},n,e,t)}static fromConfig(e,t,n={}){let o=t.cell,s=Qr(o,n);return new e(Object.assign(t,{cell:s}))}};fn.className="RNN";ee.registerClass(fn);var Al=class extends Le{},qp=class extends Al{constructor(e){super(e);this.DEFAULT_ACTIVATION="tanh",this.DEFAULT_KERNEL_INITIALIZER="glorotNormal",this.DEFAULT_RECURRENT_INITIALIZER="orthogonal",this.DEFAULT_BIAS_INITIALIZER="zeros",this.units=e.units,Kt(this.units,"units"),this.activation=Vs(e.activation==null?this.DEFAULT_ACTIVATION:e.activation),this.useBias=e.useBias==null?!0:e.useBias,this.kernelInitializer=gt(e.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.recurrentInitializer=gt(e.recurrentInitializer||this.DEFAULT_RECURRENT_INITIALIZER),this.biasInitializer=gt(e.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.kernelRegularizer=vt(e.kernelRegularizer),this.recurrentRegularizer=vt(e.recurrentRegularizer),this.biasRegularizer=vt(e.biasRegularizer),this.kernelConstraint=Bt(e.kernelConstraint),this.recurrentConstraint=Bt(e.recurrentConstraint),this.biasConstraint=Bt(e.biasConstraint),this.dropout=ic([1,Ls([0,e.dropout==null?0:e.dropout])]),this.recurrentDropout=ic([1,Ls([0,e.recurrentDropout==null?0:e.recurrentDropout])]),this.stateSize=this.units,this.dropoutMask=null,this.recurrentDropoutMask=null}build(e){e=tt(e),this.kernel=this.addWeight("kernel",[e[e.length-1],this.units],null,this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint),this.recurrentKernel=this.addWeight("recurrent_kernel",[this.units,this.units],null,this.recurrentInitializer,this.recurrentRegularizer,!0,this.recurrentConstraint),this.useBias?this.bias=this.addWeight("bias",[this.units],null,this.biasInitializer,this.biasRegularizer,!0,this.biasConstraint):this.bias=null,this.built=!0}call(e,t){return V(()=>{if(e=e,e.length!==2)throw new B(`SimpleRNNCell expects 2 input Tensors, got ${e.length}.`);let n=e[1];e=e[0];let o=t.training==null?!1:t.training;0<this.dropout&&this.dropout<1&&this.dropoutMask==null&&(this.dropoutMask=Ka({ones:()=>nr(e),rate:this.dropout,training:o})),0<this.recurrentDropout&&this.recurrentDropout<1&&this.recurrentDropoutMask==null&&(this.recurrentDropoutMask=Ka({ones:()=>nr(n),rate:this.recurrentDropout,training:o}));let s,a=this.dropoutMask,i=this.recurrentDropoutMask;a!=null?s=rs(P(e,a),this.kernel.read()):s=rs(e,this.kernel.read()),this.bias!=null&&(s=ln(s,this.bias.read())),i!=null&&(n=P(n,i));let l=Q(s,rs(n,this.recurrentKernel.read()));return this.activation!=null&&(l=this.activation.apply(l)),[l,l]})}getConfig(){let e=super.getConfig(),t={units:this.units,activation:Bs(this.activation),useBias:this.useBias,kernelInitializer:Nt(this.kernelInitializer),recurrentInitializer:Nt(this.recurrentInitializer),biasInitializer:Nt(this.biasInitializer),kernelRegularizer:ut(this.kernelRegularizer),recurrentRegularizer:ut(this.recurrentRegularizer),biasRegularizer:ut(this.biasRegularizer),activityRegularizer:ut(this.activityRegularizer),kernelConstraint:zt(this.kernelConstraint),recurrentConstraint:zt(this.recurrentConstraint),biasConstraint:zt(this.biasConstraint),dropout:this.dropout,recurrentDropout:this.recurrentDropout};return Object.assign({},e,t)}};qp.className="SimpleRNNCell";ee.registerClass(qp);var vd=class extends fn{constructor(e){e.cell=new qp(e),super(e)}call(e,t){return V(()=>{this.cell.dropoutMask!=null&&(De(this.cell.dropoutMask),this.cell.dropoutMask=null),this.cell.recurrentDropoutMask!=null&&(De(this.cell.recurrentDropoutMask),this.cell.recurrentDropoutMask=null);let n=t==null?null:t.mask,o=t==null?null:t.training,s=t==null?null:t.initialState;return super.call(e,{mask:n,training:o,initialState:s})})}static fromConfig(e,t){return new e(t)}};vd.className="SimpleRNN";ee.registerClass(vd);var Hp=class extends Al{constructor(e){super(e);if(this.DEFAULT_ACTIVATION="tanh",this.DEFAULT_RECURRENT_ACTIVATION="hardSigmoid",this.DEFAULT_KERNEL_INITIALIZER="glorotNormal",this.DEFAULT_RECURRENT_INITIALIZER="orthogonal",this.DEFAULT_BIAS_INITIALIZER="zeros",e.resetAfter)throw new B("GRUCell does not support reset_after parameter set to true.");this.units=e.units,Kt(this.units,"units"),this.activation=Vs(e.activation===void 0?this.DEFAULT_ACTIVATION:e.activation),this.recurrentActivation=Vs(e.recurrentActivation===void 0?this.DEFAULT_RECURRENT_ACTIVATION:e.recurrentActivation),this.useBias=e.useBias==null?!0:e.useBias,this.kernelInitializer=gt(e.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.recurrentInitializer=gt(e.recurrentInitializer||this.DEFAULT_RECURRENT_INITIALIZER),this.biasInitializer=gt(e.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.kernelRegularizer=vt(e.kernelRegularizer),this.recurrentRegularizer=vt(e.recurrentRegularizer),this.biasRegularizer=vt(e.biasRegularizer),this.kernelConstraint=Bt(e.kernelConstraint),this.recurrentConstraint=Bt(e.recurrentConstraint),this.biasConstraint=Bt(e.biasConstraint),this.dropout=ic([1,Ls([0,e.dropout==null?0:e.dropout])]),this.recurrentDropout=ic([1,Ls([0,e.recurrentDropout==null?0:e.recurrentDropout])]),this.implementation=e.implementation,this.stateSize=this.units,this.dropoutMask=null,this.recurrentDropoutMask=null}build(e){e=tt(e);let t=e[e.length-1];this.kernel=this.addWeight("kernel",[t,this.units*3],null,this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint),this.recurrentKernel=this.addWeight("recurrent_kernel",[this.units,this.units*3],null,this.recurrentInitializer,this.recurrentRegularizer,!0,this.recurrentConstraint),this.useBias?this.bias=this.addWeight("bias",[this.units*3],null,this.biasInitializer,this.biasRegularizer,!0,this.biasConstraint):this.bias=null,this.built=!0}call(e,t){return V(()=>{if(e=e,e.length!==2)throw new B(`GRUCell expects 2 input Tensors (inputs, h, c), got ${e.length}.`);let n=t.training==null?!1:t.training,o=e[1];e=e[0],0<this.dropout&&this.dropout<1&&this.dropoutMask==null&&(this.dropoutMask=Ka({ones:()=>nr(e),rate:this.dropout,training:n,count:3})),0<this.recurrentDropout&&this.recurrentDropout<1&&this.recurrentDropoutMask==null&&(this.recurrentDropoutMask=Ka({ones:()=>nr(o),rate:this.recurrentDropout,training:n,count:3}));let s=this.dropoutMask,a=this.recurrentDropoutMask,i,l,u;0<this.dropout&&this.dropout<1&&(e=P(e,s[0]));let c=rs(e,this.kernel.read());this.useBias&&(c=ln(c,this.bias.read())),0<this.recurrentDropout&&this.recurrentDropout<1&&(o=P(o,a[0]));let p=this.recurrentKernel.read(),[m,f]=cr(p,[2*this.units,this.units],p.rank-1),d=rs(o,m),[h,g,x]=cr(c,3,c.rank-1),[b,w]=cr(d,2,d.rank-1);i=this.recurrentActivation.apply(Q(h,b)),l=this.recurrentActivation.apply(Q(g,w));let _=rs(P(l,o),f);u=this.activation.apply(Q(x,_));let k=Q(P(i,o),P(Q(1,qe(i)),u));return[k,k]})}getConfig(){let e=super.getConfig(),t={units:this.units,activation:Bs(this.activation),recurrentActivation:Bs(this.recurrentActivation),useBias:this.useBias,kernelInitializer:Nt(this.kernelInitializer),recurrentInitializer:Nt(this.recurrentInitializer),biasInitializer:Nt(this.biasInitializer),kernelRegularizer:ut(this.kernelRegularizer),recurrentRegularizer:ut(this.recurrentRegularizer),biasRegularizer:ut(this.biasRegularizer),activityRegularizer:ut(this.activityRegularizer),kernelConstraint:zt(this.kernelConstraint),recurrentConstraint:zt(this.recurrentConstraint),biasConstraint:zt(this.biasConstraint),dropout:this.dropout,recurrentDropout:this.recurrentDropout,implementation:this.implementation,resetAfter:!1};return Object.assign({},e,t)}};Hp.className="GRUCell";ee.registerClass(Hp);var kd=class extends fn{constructor(e){e.implementation===0&&console.warn("`implementation=0` has been deprecated, and now defaults to `implementation=1`. Please update your layer call."),e.cell=new Hp(e),super(e)}call(e,t){return V(()=>{this.cell.dropoutMask!=null&&(De(this.cell.dropoutMask),this.cell.dropoutMask=null),this.cell.recurrentDropoutMask!=null&&(De(this.cell.recurrentDropoutMask),this.cell.recurrentDropoutMask=null);let n=t==null?null:t.mask,o=t==null?null:t.training,s=t==null?null:t.initialState;return super.call(e,{mask:n,training:o,initialState:s})})}static fromConfig(e,t){return t.implmentation===0&&(t.implementation=1),new e(t)}};kd.className="GRU";ee.registerClass(kd);var Dl=class extends Al{constructor(e){super(e);this.DEFAULT_ACTIVATION="tanh",this.DEFAULT_RECURRENT_ACTIVATION="hardSigmoid",this.DEFAULT_KERNEL_INITIALIZER="glorotNormal",this.DEFAULT_RECURRENT_INITIALIZER="orthogonal",this.DEFAULT_BIAS_INITIALIZER="zeros",this.units=e.units,Kt(this.units,"units"),this.activation=Vs(e.activation===void 0?this.DEFAULT_ACTIVATION:e.activation),this.recurrentActivation=Vs(e.recurrentActivation===void 0?this.DEFAULT_RECURRENT_ACTIVATION:e.recurrentActivation),this.useBias=e.useBias==null?!0:e.useBias,this.kernelInitializer=gt(e.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.recurrentInitializer=gt(e.recurrentInitializer||this.DEFAULT_RECURRENT_INITIALIZER),this.biasInitializer=gt(e.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.unitForgetBias=e.unitForgetBias,this.kernelRegularizer=vt(e.kernelRegularizer),this.recurrentRegularizer=vt(e.recurrentRegularizer),this.biasRegularizer=vt(e.biasRegularizer),this.kernelConstraint=Bt(e.kernelConstraint),this.recurrentConstraint=Bt(e.recurrentConstraint),this.biasConstraint=Bt(e.biasConstraint),this.dropout=ic([1,Ls([0,e.dropout==null?0:e.dropout])]),this.recurrentDropout=ic([1,Ls([0,e.recurrentDropout==null?0:e.recurrentDropout])]),this.implementation=e.implementation,this.stateSize=[this.units,this.units],this.dropoutMask=null,this.recurrentDropoutMask=null}build(e){var t;e=tt(e);let n=e[e.length-1];this.kernel=this.addWeight("kernel",[n,this.units*4],null,this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint),this.recurrentKernel=this.addWeight("recurrent_kernel",[this.units,this.units*4],null,this.recurrentInitializer,this.recurrentRegularizer,!0,this.recurrentConstraint);let o;if(this.useBias){if(this.unitForgetBias){let s=this.biasInitializer,a=this.units;o=new(t=class extends un{apply(l,u){let c=s.apply([a]),p=new lc().apply([a]),m=s.apply([a*2]);return G0(G0(c,p),m)}},t.className="CustomInit",t)}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(e,t){return V(()=>{let n=t.training==null?!1:t.training;if(e=e,e.length!==3)throw new B(`LSTMCell expects 3 input Tensors (inputs, h, c), got ${e.length}.`);let o=e[1],s=e[2];e=e[0],0<this.dropout&&this.dropout<1&&this.dropoutMask==null&&(this.dropoutMask=Ka({ones:()=>nr(e),rate:this.dropout,training:n,count:4})),0<this.recurrentDropout&&this.recurrentDropout<1&&this.recurrentDropoutMask==null&&(this.recurrentDropoutMask=Ka({ones:()=>nr(o),rate:this.recurrentDropout,training:n,count:4}));let a=this.dropoutMask,i=this.recurrentDropoutMask,l,u,c,p;0<this.dropout&&this.dropout<1&&(e=P(e,a[0]));let m=rs(e,this.kernel.read());0<this.recurrentDropout&&this.recurrentDropout<1&&(o=P(o,i[0])),m=Q(m,rs(o,this.recurrentKernel.read())),this.useBias&&(m=ln(m,this.bias.read()));let[f,d,h,g]=cr(m,4,m.rank-1);l=this.recurrentActivation.apply(f),u=this.recurrentActivation.apply(d),c=Q(P(u,s),P(l,this.activation.apply(h))),p=this.recurrentActivation.apply(g);let x=P(p,this.activation.apply(c));return[x,x,c]})}getConfig(){let e=super.getConfig(),t={units:this.units,activation:Bs(this.activation),recurrentActivation:Bs(this.recurrentActivation),useBias:this.useBias,kernelInitializer:Nt(this.kernelInitializer),recurrentInitializer:Nt(this.recurrentInitializer),biasInitializer:Nt(this.biasInitializer),unitForgetBias:this.unitForgetBias,kernelRegularizer:ut(this.kernelRegularizer),recurrentRegularizer:ut(this.recurrentRegularizer),biasRegularizer:ut(this.biasRegularizer),activityRegularizer:ut(this.activityRegularizer),kernelConstraint:zt(this.kernelConstraint),recurrentConstraint:zt(this.recurrentConstraint),biasConstraint:zt(this.biasConstraint),dropout:this.dropout,recurrentDropout:this.recurrentDropout,implementation:this.implementation};return Object.assign({},e,t)}};Dl.className="LSTMCell";ee.registerClass(Dl);var Cd=class extends fn{constructor(e){e.implementation===0&&console.warn("`implementation=0` has been deprecated, and now defaults to `implementation=1`. Please update your layer call."),e.cell=new Dl(e),super(e)}call(e,t){return V(()=>{this.cell.dropoutMask!=null&&(De(this.cell.dropoutMask),this.cell.dropoutMask=null),this.cell.recurrentDropoutMask!=null&&(De(this.cell.recurrentDropoutMask),this.cell.recurrentDropoutMask=null);let n=t==null?null:t.mask,o=t==null?null:t.training,s=t==null?null:t.initialState;return super.call(e,{mask:n,training:o,initialState:s})})}static fromConfig(e,t){return t.implmentation===0&&(t.implementation=1),new e(t)}};Cd.className="LSTM";ee.registerClass(Cd);var Up=class extends Al{constructor(e){super(e);this.cells=e.cells}get stateSize(){let e=[];for(let t of this.cells.slice().reverse())Array.isArray(t.stateSize)?e.push(...t.stateSize):e.push(t.stateSize);return e}call(e,t){return V(()=>{e=e;let n=e.slice(1),o=[];for(let i of this.cells.slice().reverse())Array.isArray(i.stateSize)?o.push(n.splice(0,i.stateSize.length)):o.push(n.splice(0,1));o.reverse();let s=[],a;for(let i=0;i<this.cells.length;++i){let l=this.cells[i];n=o[i],i===0?a=[e[0]].concat(n):a=[a[0]].concat(n),a=l.call(a,t),s.push(a.slice(1))}n=[];for(let i of s.slice().reverse())n.push(...i);return[a[0]].concat(n)})}build(e){vx(e)&&(e=e[0]),e=e;let t;this.cells.forEach((n,o)=>{Ms(`RNNCell_${o}`,()=>{n.build(e),Array.isArray(n.stateSize)?t=n.stateSize[0]:t=n.stateSize,e=[e[0],t]})}),this.built=!0}getConfig(){let e=super.getConfig(),t=s=>({className:s.getClassName(),config:s.getConfig()}),o={cells:this.cells.map(t)};return Object.assign({},e,o)}static fromConfig(e,t,n={}){let o=[];for(let s of t.cells)o.push(Qr(s,n));return new e({cells:o})}get trainableWeights(){if(!this.trainable)return[];let e=[];for(let t of this.cells)e.push(...t.trainableWeights);return e}get nonTrainableWeights(){let e=[];for(let t of this.cells)e.push(...t.nonTrainableWeights);if(!this.trainable){let t=[];for(let n of this.cells)t.push(...n.trainableWeights);return t.concat(e)}return e}getWeights(){let e=[];for(let t of this.cells)e.push(...t.weights);return ed(e)}setWeights(e){let t=[];for(let n of this.cells){let o=n.weights.length,s=e.splice(o);for(let a=0;a<n.weights.length;++a)t.push([n.weights[a],s[a]])}Lp(t)}};Up.className="StackedRNNCells";ee.registerClass(Up);function Ka(r){let{ones:e,rate:t,training:n=!1,count:o=1}=r,s=()=>bx(e(),t),a=()=>vl(s,e,n);return!o||o<=1?Dt(a().clone()):Array(o).fill(void 0).map(a).map(l=>Dt(l.clone()))}var nee=function(r,e){var t={};for(var n in r)Object.prototype.hasOwnProperty.call(r,n)&&e.indexOf(n)<0&&(t[n]=r[n]);if(r!=null&&typeof Object.getOwnPropertySymbols=="function")for(var o=0,n=Object.getOwnPropertySymbols(r);o<n.length;o++)e.indexOf(n[o])<0&&Object.prototype.propertyIsEnumerable.call(r,n[o])&&(t[n[o]]=r[n[o]]);return t};var _C=class extends fn{constructor(e){if(e.unroll)throw new Te("Unrolling is not possible with convolutional RNNs.");if(Array.isArray(e.cell))throw new Te("It is not possible at the moment to stack convolutional cells.");super(e);this.inputSpec=[new At({ndim:5})]}call(e,t){return V(()=>{if(this.cell.dropoutMask!=null&&(De(this.cell.dropoutMask),this.cell.dropoutMask=null),this.cell.recurrentDropoutMask!=null&&(De(this.cell.recurrentDropoutMask),this.cell.recurrentDropoutMask=null),t&&t.constants)throw new B("ConvRNN2D cell does not support constants");let n=t==null?null:t.mask,o=t==null?null:t.training,s=t==null?null:t.initialState;return super.call(e,{mask:n,training:o,initialState:s})})}computeOutputShape(e){let t=this.computeSingleOutputShape(e);return this.returnSequences||(t=[t[0],...t.slice(2)]),this.returnState&&(t=[t,...Array(2).fill([e[0],...t.slice(-3)])]),t}getInitialState(e){return V(()=>{let{stateSize:t}=this.cell,n=e.shape,o=this.computeSingleOutputShape(n),s=[o[0],...o.slice(2)],a=mt(s);return Array.isArray(t)?Array(t.length).fill(a):[a]})}resetStates(e,t=!1){V(()=>{if(!this.stateful)throw new Nn("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 B("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(()=>mt(s)):this.states_=[mt(s)];else if(e==null)De(this.states_),this.keptStates!=null&&(De(this.keptStates),this.keptStates=[]),Array.isArray(this.cell.stateSize)?this.states_=this.cell.stateSize.map(()=>mt(s)):this.states_[0]=mt(s);else{if(Array.isArray(e)||(e=[e]),e.length!==this.states_.length)throw new B(`Layer ${this.name} expects ${this.states_.length} state(s), but it received ${e.length} state value(s). Input received: ${e}`);t?this.keptStates.push(this.states_.slice()):De(this.states_);for(let i=0;i<this.states_.length;++i){let l=e[i],u=s;if(!y.arraysEqual(l.shape,u))throw new B(`State ${i} is incompatible with layer ${this.name}: expected shape=${u}, received shape=${l.shape}`);this.states_[i]=l}}this.states_=this.states_.map(i=>Dt(i.clone()))})}computeSingleOutputShape(e){let{dataFormat:t,filters:n,kernelSize:o,padding:s,strides:a,dilationRate:i}=this.cell,l=t==="channelsFirst",u=e[l?3:2],c=e[l?4:3],p=mn(u,o[0],s,a[0],i[0]),m=mn(c,o[1],s,a[1],i[1]);return[...e.slice(0,2),...l?[n,p,m]:[p,m,n]]}};_C.className="ConvRNN2D";var Kp=class extends Dl{constructor(e){let{filters:t,kernelSize:n,strides:o,padding:s,dataFormat:a,dilationRate:i}=e;super(Object.assign({},e,{units:t}));this.filters=t,Kt(this.filters,"filters"),this.kernelSize=Tl(n,2,"kernelSize"),this.kernelSize.forEach(l=>Kt(l,"kernelSize")),this.strides=Tl(o||1,2,"strides"),this.strides.forEach(l=>Kt(l,"strides")),this.padding=s||"valid",Jr(this.padding),this.dataFormat=a||"channelsLast",Ft(this.dataFormat),this.dilationRate=Tl(i||1,2,"dilationRate"),this.dilationRate.forEach(l=>Kt(l,"dilationRate"))}build(e){var t;e=tt(e);let n=this.dataFormat==="channelsFirst"?1:e.length-1;if(e[n]==null)throw new B(`The channel dimension of the input should be defined. Found ${e[n]}`);let o=e[n],s=4,a=this.kernelSize.concat([o,this.filters*s]);this.kernel=this.addWeight("kernel",a,null,this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint);let i=this.kernelSize.concat([this.filters,this.filters*s]);if(this.recurrentKernel=this.addWeight("recurrent_kernel",i,null,this.recurrentInitializer,this.recurrentRegularizer,!0,this.recurrentConstraint),this.useBias){let l;if(this.unitForgetBias){let u=this.biasInitializer,c=this.filters;l=new(t=class extends un{apply(m,f){let d=u.apply([c]),h=Tr([c]),g=u.apply([c*2]);return Tp([d,h,g])}},t.className="CustomInit",t)}else l=this.biasInitializer;this.bias=this.addWeight("bias",[this.filters*s],null,l,this.biasRegularizer,!0,this.biasConstraint)}this.built=!0}call(e,t){return V(()=>{if(e.length!==3)throw new B(`ConvLSTM2DCell expects 3 input Tensors (inputs, h, c), got ${e.length}.`);let n=t.training||!1,o=e[0],s=e[1],a=e[2],i=4;0<this.dropout&&this.dropout<1&&this.dropoutMask==null&&(this.dropoutMask=Ka({ones:()=>nr(o),rate:this.dropout,training:n,count:i}));let l=this.dropoutMask,u=(ae,ie,pe)=>!ie||!ie[pe]?ae:P(ie[pe],ae),c=u(o,l,0),p=u(o,l,1),m=u(o,l,2),f=u(o,l,3);0<this.recurrentDropout&&this.recurrentDropout<1&&this.recurrentDropoutMask==null&&(this.recurrentDropoutMask=Ka({ones:()=>nr(s),rate:this.recurrentDropout,training:n,count:i}));let d=this.recurrentDropoutMask,h=u(s,d,0),g=u(s,d,1),x=u(s,d,2),b=u(s,d,3),w=3,[_,k,E,T]=cr(this.kernel.read(),i,w),[F,O,M,j]=this.useBias?cr(this.bias.read(),i):[null,null,null,null];c=this.inputConv(c,_,F,this.padding),p=this.inputConv(p,k,O,this.padding),m=this.inputConv(m,E,M,this.padding),f=this.inputConv(f,T,j,this.padding);let[W,U,Y,K]=cr(this.recurrentKernel.read(),i,w);h=this.recurrentConv(h,W),g=this.recurrentConv(g,U),x=this.recurrentConv(x,Y),b=this.recurrentConv(b,K);let Z=this.recurrentActivation.apply(Q(c,h)),re=this.recurrentActivation.apply(Q(p,g)),X=Q(P(re,a),P(Z,this.activation.apply(Q(m,x)))),oe=P(this.recurrentActivation.apply(Q(f,b)),this.activation.apply(X));return[oe,oe,X]})}getConfig(){let e=super.getConfig(),{units:t}=e,n=nee(e,["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(e,t,n,o){let s=Kr(e,t,this.strides,o||"valid",this.dataFormat==="channelsFirst"?"NCHW":"NHWC",this.dilationRate);return n?ln(s,n,this.dataFormat):s}recurrentConv(e,t){return Kr(e,t,1,"same",this.dataFormat==="channelsFirst"?"NCHW":"NHWC")}};Kp.className="ConvLSTM2DCell";ee.registerClass(Kp);var Id=class extends _C{constructor(e){let t=new Kp(e);super(Object.assign({},e,{cell:t}))}static fromConfig(e,t){return new e(t)}};Id.className="ConvLSTM2D";ee.registerClass(Id);var Xp=class extends Le{constructor(e){super(e);this.rate=Math.max(Math.min(e.rate,1),0),this.noiseShape=e.noiseShape,this.seed=e.seed,this.supportsMasking=!0}getNoiseShape(e){if(this.noiseShape==null)return this.noiseShape;let t=e.shape,n=[];for(let o=0;o<this.noiseShape.length;++o)n.push(this.noiseShape[o]==null?t[o]:this.noiseShape[o]);return n}call(e,t){return V(()=>{this.invokeCallHook(e,t);let n=Pe(e);if(0<this.rate&&this.rate<1){let o=t.training==null?!1:t.training,s=this.getNoiseShape(n);return vl(()=>bx(n,this.rate,s,this.seed),()=>n,o)}return e})}getConfig(){let e={rate:this.rate,noiseShape:this.noiseShape,seed:this.seed},t=super.getConfig();return Object.assign(e,t),e}dispose(){return super.dispose()}};Xp.className="Dropout";ee.registerClass(Xp);var Sd=class extends Xp{constructor(e){super(e);this.inputSpec=[{ndim:3}]}getNoiseShape(e){let t=e.shape;return[t[0],1,t[2]]}};Sd.className="SpatialDropout1D";ee.registerClass(Sd);var Nd=class extends Le{constructor(e){super(e);if(this.activation=null,this.useBias=!0,this.kernel=null,this.bias=null,this.DEFAULT_KERNEL_INITIALIZER="glorotNormal",this.DEFAULT_BIAS_INITIALIZER="zeros",e.batchInputShape==null&&e.inputShape==null&&e.inputDim!=null){let t=null;e.batchSize!=null&&(t=e.batchSize),this.batchInputShape=[t,e.inputDim]}this.units=e.units,Kt(this.units,"units"),this.activation=Vs(e.activation),e.useBias!=null&&(this.useBias=e.useBias),this.kernelInitializer=gt(e.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.biasInitializer=gt(e.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.kernelConstraint=Bt(e.kernelConstraint),this.biasConstraint=Bt(e.biasConstraint),this.kernelRegularizer=vt(e.kernelRegularizer),this.biasRegularizer=vt(e.biasRegularizer),this.activityRegularizer=vt(e.activityRegularizer),this.supportsMasking=!0,this.inputSpec=[{minNDim:2}]}build(e){e=tt(e);let t=e[e.length-1];this.kernel==null&&(this.kernel=this.addWeight("kernel",[t,this.units],null,this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint),this.useBias&&(this.bias=this.addWeight("bias",[this.units],null,this.biasInitializer,this.biasRegularizer,!0,this.biasConstraint))),this.inputSpec=[{minNDim:2,axes:{[-1]:t}}],this.built=!0}computeOutputShape(e){e=tt(e);let t=e.slice();return t[t.length-1]=this.units,t}call(e,t){return V(()=>{this.invokeCallHook(e,t);let n=Pe(e),o=dx(this.activation.getClassName()),s;return o!=null?s=rs(n,this.kernel.read(),o,this.bias?this.bias.read():null):(s=rs(n,this.kernel.read()),this.bias!=null&&(s=ln(s,this.bias.read())),this.activation!=null&&(s=this.activation.apply(s))),s})}getConfig(){let e={units:this.units,activation:Bs(this.activation),useBias:this.useBias,kernelInitializer:Nt(this.kernelInitializer),biasInitializer:Nt(this.biasInitializer),kernelRegularizer:ut(this.kernelRegularizer),biasRegularizer:ut(this.biasRegularizer),activityRegularizer:ut(this.activityRegularizer),kernelConstraint:zt(this.kernelConstraint),biasConstraint:zt(this.biasConstraint)},t=super.getConfig();return Object.assign(e,t),e}};Nd.className="Dense";ee.registerClass(Nd);var Td=class extends Le{constructor(e){e=e||{},super(e),this.inputSpec=[{minNDim:3}],this.dataFormat=e.dataFormat}computeOutputShape(e){e=tt(e);for(let t of e.slice(1))if(t==null)throw new B(`The shape of the input to "Flatten" is not fully defined (got ${e.slice(1)}). Make sure to pass a complete "input_shape" or "batch_input_shape" argument to the first layer in your model.`);return[e[0],ts(e,1)]}call(e,t){return V(()=>{this.invokeCallHook(e,t);let n=Pe(e);if(this.dataFormat==="channelsFirst"&&n.rank>1){let o=[0];for(let s=2;s<n.rank;++s)o.push(s);o.push(1),n=n.transpose(o)}return yL(n)})}getConfig(){let e={};this.dataFormat!=null&&(e.dataFormat=this.dataFormat);let t=super.getConfig();return Object.assign(e,t),e}};Td.className="Flatten";ee.registerClass(Td);var Ed=class extends Le{constructor(e){super(e);this.supportsMasking=!0,this.activation=Vs(e.activation)}call(e,t){return V(()=>{this.invokeCallHook(e,t);let n=Pe(e);return this.activation.apply(n)})}getConfig(){let e={activation:Bs(this.activation)},t=super.getConfig();return Object.assign(e,t),e}};Ed.className="Activation";ee.registerClass(Ed);var Ad=class extends Le{constructor(e){super(e);this.n=e.n,this.inputSpec=[{ndim:2}]}computeOutputShape(e){return[e[0],this.n,e[1]]}call(e,t){return V(()=>(e=Pe(e),gL(e,this.n)))}getConfig(){let e={n:this.n},t=super.getConfig();return Object.assign(e,t),e}};Ad.className="RepeatVector";ee.registerClass(Ad);var Dd=class extends Le{constructor(e){super(e);this.targetShape=e.targetShape;for(let t=0;t<this.targetShape.length;++t)this.isUnknown(this.targetShape[t])&&(this.targetShape[t]=null)}isUnknown(e){return e<0||e==null}fixUnknownDimension(e,t){let n="Total size of new array must be unchanged.",o=t.slice(),s=1,a=null;for(let l=0;l<o.length;++l){let u=o[l];if(this.isUnknown(u))if(a===null)a=l;else throw new B("Can only specifiy one unknown dimension.");else s*=u}let i=ts(e);if(a!==null){if(s===0||i%s!=0)throw new B(n);o[a]=i/s}else if(i!==s)throw new B(n);return o}computeOutputShape(e){let t=!1;for(let n=0;n<e.length;++n)if(this.isUnknown(e[n])){t=!0;break}return t?e.slice(0,1).concat(this.targetShape):e.slice(0,1).concat(this.fixUnknownDimension(e.slice(1),this.targetShape))}call(e,t){return V(()=>{this.invokeCallHook(e,t);let n=Pe(e),o=n.shape,s=o.slice(0,1).concat(this.fixUnknownDimension(o.slice(1),this.targetShape));return n.reshape(s)})}getConfig(){let e={targetShape:this.targetShape},t=super.getConfig();return Object.assign(e,t),e}};Dd.className="Reshape";ee.registerClass(Dd);var $d=class extends Le{constructor(e){super(e);if(e.dims==null)throw new Error("Required configuration field `dims` is missing during Permute constructor call.");if(!Array.isArray(e.dims))throw new Error(`Permute constructor requires \`dims\` to be an Array, but received ${e.dims} instead.`);let t=Vr(1,e.dims.length+1);if(!y.arraysEqual(e.dims.slice().sort(),t))throw new Error("Invalid permutation `dims`: "+JSON.stringify(e.dims)+" `dims` must contain consecutive integers starting from 1.");this.dims=e.dims,this.dimsIncludingBatch=[0].concat(this.dims),this.inputSpec=[new At({ndim:this.dims.length+1})]}computeOutputShape(e){e=tt(e);let t=e.slice();return this.dims.forEach((n,o)=>{t[o+1]=e[n]}),t}call(e,t){return 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t=_t(this.inputLength);if(t.length!==e.length-1)throw new B(`"inputLength" is ${this.inputLength}, but received input shape has shape ${e}`);{let n=0;for(let o=0;o<t.length;++o){let s=t[o],a=e[o+1];if(s!=null&&a!=null&&s!==a)throw new B(`"inputLength" is ${this.inputLength}, but received input shape has shape ${e}`);s==null&&(t[n]=a),n++}}return[e[0],...t,this.outputDim]}call(e,t){return V(()=>{this.invokeCallHook(e,t);let n=Pe(e);return n.dtype!=="int32"&&(n=Ua(n,"int32")),yx(this.embeddings.read(),n.as1D()).reshape(tt(this.computeOutputShape(n.shape)))})}getConfig(){let e={inputDim:this.inputDim,outputDim:this.outputDim,embeddingsInitializer:Nt(this.embeddingsInitializer),embeddingsRegularizer:ut(this.embeddingsRegularizer),activityRegularizer:ut(this.activityRegularizer),embeddingsConstraint:zt(this.embeddingsConstraint),maskZero:this.maskZero,inputLength:this.inputLength},t=super.getConfig();return Object.assign(e,t),e}};Fd.className="Embedding";ee.registerClass(Fd);var $l=class extends Le{constructor(e){super(e||{});this.supportsMasking=!0}mergeFunction(e){throw new Te}computeElementwiseOpOutputShape(e,t){if(e==null||t==null)return null;if(e.length<t.length)return this.computeElementwiseOpOutputShape(t,e);if(t.length===0)return e;let n=e.slice(0,e.length-t.length);for(let o=0;o<t.length;++o){let s=e[e.length-t.length+o],a=t[o];if(s==null||a==null||s<0||a<0)n.push(null);else if(s===1)n.push(a);else if(a===1)n.push(s);else{if(s!==a)throw new B("Operands could not be broadcast together with shapes "+JSON.stringify(e)+" "+JSON.stringify(t));n.push(s)}}return n}build(e){if(Array.isArray(e)&&!Array.isArray(e[0])&&(e=[tt(e)]),e=e,e.length<2)throw new B(`A merge layer should be called on an Array of at least 2 inputs. 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e={axis:this.axis},t=super.getConfig();return Object.assign(e,t),e}};Bd.className="Concatenate";ee.registerClass(Bd);function Vd(r,e){for(;r<0;)r+=e;return r}function oee(r,e,t){if(r.shape.length>3||e.shape.length>3)throw new Te("batchDot is not implemented for tensors of 4D or higher rank yet");if(y.assert(r.shape.length>=2,()=>`batchDot requires the rank of x to be >= 2, but got ${r.shape.length}`),y.assert(r.shape.length>=2,()=>`batchDot requires the rank of y to be >= 2, but got ${e.shape.length}`),typeof t=="number"&&(t=[t,t]),r.dtype==="complex64"||e.dtype==="complex64")throw new Te("batchDot is not implemented for complex64-type Tensors yet.");let n=r.shape.length,o=e.shape.length;t==null&&(t=[n-1,o-2]);let s=t;return V(()=>{let a;if(n>o){a=n-o;let l=[];for(let u=0;u<a;++u)l.push(1);e=e.reshape(e.shape.concat(l))}else if(o>n){a=o-n;let l=[];for(let u=0;u<a;++u)l.push(1);r=r.reshape(r.shape.concat(l))}else a=0;let i;if(r.shape.length===2&&e.shape.length===2)s[0]===s[1]?i=r.mul(e).sum(s[0]):i=r.transpose([1,0]).mul(e).sum(s[1]);else{let l=s[0]!==r.shape.length-1,u=s[1]===e.shape.length-1;i=r.matMul(e,l,u)}if(a>0){let l;n>o?l=n+o-3:l=n-1;let u=[];for(let c=l;c<l+a;++c)u.push(c);i=i.squeeze(u)}return i.shape.length===1&&(i=i.expandDims(1)),i})}var Wd=class extends $l{constructor(e){super(e);this.axes=e.axes,this.normalize=e.normalize==null?!1:e.normalize,this.supportsMasking=!0,this.reshapeRequired=!1}build(e){y.assert(Array.isArray(e)&&e.length===2&&Array.isArray(e[0])&&Array.isArray(e[1]),()=>"A `Dot` layer should be called on a list of exactly 2 inputs.");let t=e[0],n=e[1];if(t.length>3||n.length>3)throw new Te("Dot layer does not support tensors of 4D or higher rank yet.");let o=this.interpretAxes(t,n);if(t[o[0]]!==n[o[1]])throw new B(`Dimension incompatibility: ${t[o[0]]} !== ${n[o[1]]}`)}mergeFunction(e){if(e.length!==2)throw new B(`A \`Dot\` layer must be called on exactly 2 inputs, but 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jd=class extends Le{constructor(e){super(e);this.supportsMasking=!0,this.stddev=e.stddev}computeOutputShape(e){return e}getConfig(){let e=super.getConfig(),t={stddev:this.stddev};return Object.assign(t,e),t}call(e,t){return V(()=>{this.invokeCallHook(e,t);let n=Pe(e);return vl(()=>Ep(n.shape,0,this.stddev).add(n),()=>n,t.training||!1)})}};jd.className="GaussianNoise";ee.registerClass(jd);var Gd=class extends Le{constructor(e){super(e);this.supportsMasking=!0,this.rate=e.rate}computeOutputShape(e){return e}getConfig(){let e=super.getConfig(),t={rate:this.rate};return Object.assign(t,e),t}call(e,t){return V(()=>{this.invokeCallHook(e,t);let n=Pe(e);return this.rate>0&&this.rate<1?vl(()=>{let s=Math.sqrt(this.rate/(1-this.rate));return n.mul(Ep(n.shape,1,s))},()=>n,t.training||!1):n})}};Gd.className="GaussianDropout";ee.registerClass(Gd);var Ud=class extends Le{constructor(e){super(e);this.supportsMasking=!0,this.rate=e.rate,this.noiseShape=e.noiseShape}_getNoiseShape(e){return this.noiseShape||Pe(e).shape}computeOutputShape(e){return e}getConfig(){let e=super.getConfig(),t={rate:this.rate};return Object.assign(t,e),t}call(e,t){return V(()=>{if(this.rate<1&&this.rate>0){let n=this._getNoiseShape(e);return vl(()=>{let s=Pe(e),a=1.6732632423543772,i=1.0507009873554805,l=-a*i,u=Pr(As(n),this.rate);u=Ua(u,"float32");let c=((1-this.rate)*(1+this.rate*l**2))**-.5,p=-c*l*this.rate;return s.mul(u).add(u.add(-1).mul(l)).mul(c).add(p)},()=>Pe(e),t.training||!1)}return e})}};Ud.className="AlphaDropout";ee.registerClass(Ud);function qd(r,e,t,n,o,s=.001){let a;if(r.rank===2)a=ww(r,e,t,n,o,s);else if(r.rank===3)a=_w(r,e,t,n,o,s);else if(r.rank===4)a=vw(r,e,t,n,o,s);else throw new Te(`batchNormalization is not implemented for array of rank ${r.rank} yet`);return a}function see(r,e,t,n,o=.001){return V(()=>{let s=Yc(r,n),a=s.mean,i=s.variance;return[qd(r,a,i,t,e,o),a,i]})}function iee(r,e,t,n,o=.001){return V(()=>{let s=Yc(r,n),a=s.mean,i=s.variance,l=[];for(let d of Vr(0,r.rank))n.indexOf(d)!==-1?l.push(1):l.push(r.shape[d]);let u=a.reshape(l),c=i.reshape(l),p=e==null?null:e.reshape(l),m=t==null?null:t.reshape(l);return[qd(r,u,c,m,p,o),a,i]})}function aee(r,e,t,n,o=.001){return y.arraysEqual(n.slice().sort(),Vr(0,r.rank-1))?see(r,e,t,n,o):iee(r,e,t,n,o)}var Hd=class extends Le{constructor(e){e==null&&(e={}),super(e),this.supportsMasking=!0,this.axis=e.axis==null?-1:e.axis,this.momentum=e.momentum==null?.99:e.momentum,this.epsilon=e.epsilon==null?.001:e.epsilon,this.center=e.center==null?!0:e.center,this.scale=e.scale==null?!0:e.scale,this.betaInitializer=gt(e.betaInitializer||"zeros"),this.gammaInitializer=gt(e.gammaInitializer||"ones"),this.movingMeanInitializer=gt(e.movingMeanInitializer||"zeros"),this.movingVarianceInitializer=gt(e.movingVarianceInitializer||"ones"),this.betaConstraint=Bt(e.betaConstraint),this.gammaConstraint=Bt(e.gammaConstraint),this.betaRegularizer=vt(e.betaRegularizer),this.gammaRegularizer=vt(e.gammaRegularizer)}build(e){e=tt(e);let t=this.axis>=0?this.axis:this.axis+e.length,n=e[t];if(n==null)throw new B(`Axis ${t} of input tensor should have a defined dimension but the layer received an input with shape ${JSON.stringify(e)}.`);this.inputSpec=[new At({ndim:e.length,axes:{[t]: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(e,t){return V(()=>{let n=t.training==null?!1:t.training,o=Pe(e),s=o.shape,a=s.length,i=Vr(0,a),l=this.axis>=0?this.axis:this.axis+a;i.splice(l,1);let u=Zo(1,a);u[l]=s[l];let c=i.slice();c.sort();let p=!y.arraysEqual(c,Vr(0,a).slice(0,a-1)),m=()=>{if(p){let b=this.movingMean.read().reshape(u),w=this.movingVariance.read().reshape(u),_=this.center?this.beta.read().reshape(u):null,k=this.scale?this.gamma.read().reshape(u):null;return qd(o,b,w,_,k,this.epsilon)}else return qd(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]=aee(o,this.gamma.read(),this.beta.read(),i,this.epsilon),g=(b,w,_)=>{V(()=>{let k=1-_,E=b.read(),T=E.sub(w).mul(k);b.write(E.sub(T))})};return(()=>{g(this.movingMean,d,this.momentum),g(this.movingVariance,h,this.momentum)})(),f})}getConfig(){let e={axis:this.axis,momentum:this.momentum,epsilon:this.epsilon,center:this.center,scale:this.scale,betaInitializer:Nt(this.betaInitializer),gammaInitializer:Nt(this.gammaInitializer),movingMeanInitializer:Nt(this.movingMeanInitializer),movingVarianceInitializer:Nt(this.movingVarianceInitializer),betaRegularizer:ut(this.betaRegularizer),gammaRegularizer:ut(this.gammaRegularizer),betaConstraint:zt(this.betaConstraint),gammaConstraint:zt(this.gammaConstraint)},t=super.getConfig();return Object.assign(e,t),e}};Hd.className="BatchNormalization";ee.registerClass(Hd);var Kd=class extends Le{constructor(e){if(e==null&&(e={}),super(e),this.axis=e.axis==null?-1:e.axis,typeof this.axis=="number"){if(!Number.isInteger(this.axis))throw new Error(`Expected axis to be an integer, but received ${this.axis}`)}else if(Array.isArray(this.axis)){for(let t of this.axis)if(!Number.isInteger(t))throw new Error(`Expected axis to be an array of integers, but received ${JSON.stringify(this.axis)}`)}else throw new Error(`Expected axis to be an integer or an array of integers, but received ${JSON.stringify(this.axis)}`);this.epsilon=e.epsilon==null?.001:e.epsilon,this.center=e.center==null?!0:e.center,this.scale=e.scale==null?!0:e.scale,this.betaInitializer=gt(e.betaInitializer||"zeros"),this.gammaInitializer=gt(e.gammaInitializer||"ones"),this.betaRegularizer=vt(e.betaRegularizer),this.gammaRegularizer=vt(e.gammaRegularizer),this.supportsMasking=!0}build(e){e=tt(e);let t=e.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]+=t);for(let s of this.axis)if(s<0||s>=t)throw new Error(`Invalid axis: ${s}`);if(this.axis.length!==es(this.axis).length)throw new Error(`Found duplicate axes in: ${this.axis}`);let n=this.axis.map(s=>e[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(e,t){let n=Pe(e),o=n.shape,s=o.length;return V(()=>{let a=!0,{mean:i,variance:l}=Yc(n,this.axis,a),u=Zo(1,s);for(let h of this.axis)u[h]=o[h];let c=h=>h!=null&&h.shape.length!==s&&this.axis!==[s-1]?h.reshape(u):h,p=c(this.gamma.read()),m=c(this.beta.read()),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 i=i.tile(f),l=l.tile(f),p=p.tile(d),m=m.tile(d),qd(n,i,l,m,p,this.epsilon)})}getConfig(){let e={axis:this.axis,epsilon:this.epsilon,center:this.center,scale:this.scale,betaInitializer:Nt(this.betaInitializer),gammaInitializer:Nt(this.gammaInitializer),betaRegularizer:ut(this.betaRegularizer),gammaRegularizer:ut(this.gammaRegularizer)},t=super.getConfig();return Object.assign(e,t),e}};Kd.className="LayerNormalization";ee.registerClass(Kd);function lee(r,e,t){return V(()=>{if(r.rank!==4)throw new B(`temporalPadding expects input tensor to be 4-D, but received a ${r.rank}-D tensor.`);if(e==null&&(e=[[1,1],[1,1]]),e.length!==2||e[0].length!==2||e[1].length!==2)throw new B("spatial2dPadding expects `padding` to be an Array of two Arrays, each of which is an Array of two integers.");if(t==null&&(t=Zr()),t!=="channelsLast"&&t!=="channelsFirst")throw new B(`Unknown data format: ${t}. Supported data formats are 'channelsLast' and 'channelsFirst.`);let n;return t==="channelsFirst"?n=[[0,0],[0,0],e[0],e[1]]:n=[[0,0],e[0],e[1],[0,0]],Mr(r,n)})}var Xd=class extends Le{constructor(e){if(e==null&&(e={}),super(e),this.dataFormat=e.dataFormat==null?Zr():e.dataFormat,e.padding==null)this.padding=[[1,1],[1,1]];else if(typeof e.padding=="number")this.padding=[[e.padding,e.padding],[e.padding,e.padding]];else{if(e.padding=e.padding,e.padding.length!==2)throw new B(`ZeroPadding2D expects padding to be a length-2 array, but received a length-${e.padding.length} array.`);let t,n;if(typeof e.padding[0]=="number")t=[e.padding[0],e.padding[0]],n=[e.padding[1],e.padding[1]];else{if(e.padding=e.padding,e.padding[0].length!==2)throw new B(`ZeroPadding2D expects height padding to be a length-2 array, but received a length-${e.padding[0].length} array.`);if(t=e.padding[0],e.padding[1].length!==2)throw new B(`ZeroPadding2D expects width padding to be a length-2 array, but received a length-${e.padding[1].length} array.`);n=e.padding[1]}this.padding=[t,n]}this.inputSpec=[new At({ndim:4})]}computeOutputShape(e){e=tt(e);let t,n;return this.dataFormat==="channelsFirst"?(e[2]!=null&&e[2]>=0?t=e[2]+this.padding[0][0]+this.padding[0][1]:t=null,e[3]!=null&&e[3]>=0?n=e[3]+this.padding[1][0]+this.padding[1][1]:n=null,[e[0],e[1],t,n]):(e[1]!=null&&e[1]>=0?t=e[1]+this.padding[0][0]+this.padding[0][1]:t=null,e[2]!=null&&e[2]>=0?n=e[2]+this.padding[1][0]+this.padding[1][1]:n=null,[e[0],t,n,e[3]])}call(e,t){return V(()=>lee(Pe(e),this.padding,this.dataFormat))}getConfig(){let e={padding:this.padding,dataFormat:this.dataFormat},t=super.getConfig();return Object.assign(e,t),e}};Xd.className="ZeroPadding2D";ee.registerClass(Xd);function Wx(r,e,t,n,o,s){return V(()=>{Ft(o),W0(s),Jr(n),t==null&&(t=[1,1]),n==null&&(n="valid"),o==null&&(o=Zr()),s==null&&(s="max"),r=gd(r,o);let a,i=n==="same"?"same":"valid";return s==="max"?a=Aa(r,e,t,i):a=ka(r,e,t,i),o==="channelsFirst"&&(a=Ue(a,[0,3,1,2])),a})}function sz(r,e,t,n,o,s){return V(()=>{Ft(o),W0(s),Jr(n),t==null&&(t=[1,1,1]),n==null&&(n="valid"),o==null&&(o=Zr()),s==null&&(s="max"),r=xC(r,o);let a,i=n==="same"?"same":"valid";return s==="max"?a=qm(r,e,t,i):a=Om(r,e,t,i),o==="channelsFirst"&&(a=Ue(a,[0,4,1,2,3])),a})}var vC=class extends Le{constructor(e){if(e.poolSize==null&&(e.poolSize=2),super(e),typeof e.poolSize=="number")this.poolSize=[e.poolSize];else if(Array.isArray(e.poolSize)&&e.poolSize.length===1&&typeof e.poolSize[0]=="number")this.poolSize=e.poolSize;else throw new B(`poolSize for 1D convolutional layer must be a number or an Array of a single number, but received ${JSON.stringify(e.poolSize)}`);if(Kt(this.poolSize,"poolSize"),e.strides==null)this.strides=this.poolSize;else if(typeof e.strides=="number")this.strides=[e.strides];else if(Array.isArray(e.strides)&&e.strides.length===1&&typeof e.strides[0]=="number")this.strides=e.strides;else throw new B(`strides for 1D convolutional layer must be a number or an Array of a single number, but received ${JSON.stringify(e.strides)}`);Kt(this.strides,"strides"),this.padding=e.padding==null?"valid":e.padding,Jr(this.padding),this.inputSpec=[new At({ndim:3})]}computeOutputShape(e){e=tt(e);let t=mn(e[1],this.poolSize[0],this.padding,this.strides[0]);return[e[0],t,e[2]]}call(e,t){return V(()=>{this.invokeCallHook(e,t),e=qa(Pe(e),2);let n=this.poolingFunction(Pe(e),[this.poolSize[0],1],[this.strides[0],1],this.padding,"channelsLast");return Cn(n,[2])})}getConfig(){let e={poolSize:this.poolSize,padding:this.padding,strides:this.strides},t=super.getConfig();return Object.assign(e,t),e}},Yd=class extends vC{constructor(e){super(e)}poolingFunction(e,t,n,o,s){return Ft(s),Jr(o),Wx(e,t,n,o,s,"max")}};Yd.className="MaxPooling1D";ee.registerClass(Yd);var Zd=class extends vC{constructor(e){super(e)}poolingFunction(e,t,n,o,s){return Ft(s),Jr(o),Wx(e,t,n,o,s,"avg")}};Zd.className="AveragePooling1D";ee.registerClass(Zd);var kC=class extends Le{constructor(e){if(e.poolSize==null&&(e.poolSize=[2,2]),super(e),this.poolSize=Array.isArray(e.poolSize)?e.poolSize:[e.poolSize,e.poolSize],e.strides==null)this.strides=this.poolSize;else if(Array.isArray(e.strides)){if(e.strides.length!==2)throw new B(`If the strides property of a 2D pooling layer is an Array, it is expected to have a length of 2, but received length ${e.strides.length}.`);this.strides=e.strides}else this.strides=[e.strides,e.strides];Kt(this.poolSize,"poolSize"),Kt(this.strides,"strides"),this.padding=e.padding==null?"valid":e.padding,this.dataFormat=e.dataFormat==null?"channelsLast":e.dataFormat,Ft(this.dataFormat),Jr(this.padding),this.inputSpec=[new At({ndim:4})]}computeOutputShape(e){e=tt(e);let t=this.dataFormat==="channelsFirst"?e[2]:e[1],n=this.dataFormat==="channelsFirst"?e[3]:e[2];return t=mn(t,this.poolSize[0],this.padding,this.strides[0]),n=mn(n,this.poolSize[1],this.padding,this.strides[1]),this.dataFormat==="channelsFirst"?[e[0],e[1],t,n]:[e[0],t,n,e[3]]}call(e,t){return V(()=>(this.invokeCallHook(e,t),this.poolingFunction(Pe(e),this.poolSize,this.strides,this.padding,this.dataFormat)))}getConfig(){let e={poolSize:this.poolSize,padding:this.padding,strides:this.strides,dataFormat:this.dataFormat},t=super.getConfig();return Object.assign(e,t),e}},Jd=class extends kC{constructor(e){super(e)}poolingFunction(e,t,n,o,s){return Ft(s),Jr(o),Wx(e,t,n,o,s,"max")}};Jd.className="MaxPooling2D";ee.registerClass(Jd);var Qd=class extends kC{constructor(e){super(e)}poolingFunction(e,t,n,o,s){return Ft(s),Jr(o),Wx(e,t,n,o,s,"avg")}};Qd.className="AveragePooling2D";ee.registerClass(Qd);var CC=class extends Le{constructor(e){if(e.poolSize==null&&(e.poolSize=[2,2,2]),super(e),this.poolSize=Array.isArray(e.poolSize)?e.poolSize:[e.poolSize,e.poolSize,e.poolSize],e.strides==null)this.strides=this.poolSize;else if(Array.isArray(e.strides)){if(e.strides.length!==3)throw new B(`If the strides property of a 3D pooling layer is an Array, it is expected to have a length of 3, but received length ${e.strides.length}.`);this.strides=e.strides}else this.strides=[e.strides,e.strides,e.strides];Kt(this.poolSize,"poolSize"),Kt(this.strides,"strides"),this.padding=e.padding==null?"valid":e.padding,this.dataFormat=e.dataFormat==null?"channelsLast":e.dataFormat,Ft(this.dataFormat),Jr(this.padding),this.inputSpec=[new At({ndim:5})]}computeOutputShape(e){e=tt(e);let t=this.dataFormat==="channelsFirst"?e[2]:e[1],n=this.dataFormat==="channelsFirst"?e[3]:e[2],o=this.dataFormat==="channelsFirst"?e[4]:e[3];return t=mn(t,this.poolSize[0],this.padding,this.strides[0]),n=mn(n,this.poolSize[1],this.padding,this.strides[1]),o=mn(o,this.poolSize[2],this.padding,this.strides[2]),this.dataFormat==="channelsFirst"?[e[0],e[1],t,n,o]:[e[0],t,n,o,e[4]]}call(e,t){return V(()=>(this.invokeCallHook(e,t),this.poolingFunction(Pe(e),this.poolSize,this.strides,this.padding,this.dataFormat)))}getConfig(){let e={poolSize:this.poolSize,padding:this.padding,strides:this.strides,dataFormat:this.dataFormat},t=super.getConfig();return Object.assign(e,t),e}},eh=class extends CC{constructor(e){super(e)}poolingFunction(e,t,n,o,s){return Ft(s),Jr(o),sz(e,t,n,o,s,"max")}};eh.className="MaxPooling3D";ee.registerClass(eh);var th=class extends CC{constructor(e){super(e)}poolingFunction(e,t,n,o,s){return Ft(s),Jr(o),sz(e,t,n,o,s,"avg")}};th.className="AveragePooling3D";ee.registerClass(th);var IC=class extends Le{constructor(e){super(e);this.inputSpec=[new At({ndim:3})]}computeOutputShape(e){return[e[0],e[2]]}call(e,t){throw new Te}},rh=class extends IC{constructor(e){super(e||{})}call(e,t){return V(()=>{let n=Pe(e);return yt(n,1)})}};rh.className="GlobalAveragePooling1D";ee.registerClass(rh);var nh=class extends IC{constructor(e){super(e||{})}call(e,t){return V(()=>{let n=Pe(e);return ur(n,1)})}};nh.className="GlobalMaxPooling1D";ee.registerClass(nh);var SC=class extends Le{constructor(e){super(e);this.dataFormat=e.dataFormat==null?"channelsLast":e.dataFormat,Ft(this.dataFormat),this.inputSpec=[new At({ndim:4})]}computeOutputShape(e){return e=e,this.dataFormat==="channelsLast"?[e[0],e[3]]:[e[0],e[1]]}call(e,t){throw new Te}getConfig(){let e={dataFormat:this.dataFormat},t=super.getConfig();return Object.assign(e,t),e}},oh=class extends SC{call(e,t){return V(()=>{let n=Pe(e);return this.dataFormat==="channelsLast"?yt(n,[1,2]):yt(n,[2,3])})}};oh.className="GlobalAveragePooling2D";ee.registerClass(oh);var sh=class extends SC{call(e,t){return V(()=>{let n=Pe(e);return this.dataFormat==="channelsLast"?ur(n,[1,2]):ur(n,[2,3])})}};sh.className="GlobalMaxPooling2D";ee.registerClass(sh);var NC=class extends Le{constructor(e){super(e);this.layer=e.layer}build(e){this.built=!0}get trainable(){return this.layer!=null?this.layer.trainable:!1}set trainable(e){this.layer!=null&&(this.layer.trainable=e)}get trainableWeights(){return this.layer.trainableWeights}get nonTrainableWeights(){return this.layer.nonTrainableWeights}get updates(){return this.layer._updates}get losses(){return this.layer.losses}getWeights(){return this.layer.getWeights()}setWeights(e){this.layer.setWeights(e)}getConfig(){let e={layer:{className:this.layer.getClassName(),config:this.layer.getConfig()}},t=super.getConfig();return Object.assign(e,t),e}setFastWeightInitDuringBuild(e){super.setFastWeightInitDuringBuild(e),this.layer!=null&&this.layer.setFastWeightInitDuringBuild(e)}static fromConfig(e,t,n={}){let o=t.layer,s=Qr(o,n);delete t.layer;let a={layer:s};return Object.assign(a,t),new e(a)}},ih=class extends NC{constructor(e){super(e);this.supportsMasking=!0}build(e){if(e=tt(e),e.length<3)throw new B(`TimeDistributed layer expects an input shape >= 3D, but received input shape ${JSON.stringify(e)}`);this.inputSpec=[{shape:e}];let t=[e[0]].concat(e.slice(2));this.layer.built||(this.layer.build(t),this.layer.built=!0),super.build(e)}computeOutputShape(e){e=tt(e);let t=[e[0]].concat(e.slice(2)),n=this.layer.computeOutputShape(t),o=e[1];return[n[0],o].concat(n.slice(1))}call(e,t){return V(()=>(e=Pe(e),wC((a,i)=>[Pe(this.layer.call(a,t)),[]],e,[],!1,null,null,!1,!0)[1]))}};ih.className="TimeDistributed";ee.registerClass(ih);function uee(r){qi(cL,"BidirectionalMergeMode",r)}var cee="concat",ah=class extends NC{constructor(e){super(e);let t=e.layer.getConfig(),n={};n.className=e.layer.getClassName(),n.config=t,this.forwardLayer=Qr(n),t.goBackwards=t.goBackwards!==!0;let o={};if(o.className=e.layer.getClassName(),o.config=t,this.backwardLayer=Qr(o),this.forwardLayer.name="forward_"+this.forwardLayer.name,this.backwardLayer.name="backward_"+this.backwardLayer.name,this.mergeMode=e.mergeMode===void 0?cee:e.mergeMode,uee(this.mergeMode),e.weights)throw new Te("weights support is not implemented for Bidirectional layer yet.");this._stateful=e.layer.stateful,this.returnSequences=e.layer.returnSequences,this.returnState=e.layer.returnState,this.supportsMasking=!0,this._trainable=!0,this.inputSpec=e.layer.inputSpec,this.numConstants=null}get trainable(){return this._trainable}set trainable(e){this._trainable=e,this.forwardLayer!=null&&(this.forwardLayer.trainable=e),this.backwardLayer!=null&&(this.backwardLayer.trainable=e)}getWeights(){return this.forwardLayer.getWeights().concat(this.backwardLayer.getWeights())}setWeights(e){let t=e.length,n=Math.floor(t/2);this.forwardLayer.setWeights(e.slice(0,n)),this.backwardLayer.setWeights(e.slice(n))}computeOutputShape(e){let t=this.forwardLayer.computeOutputShape(e);Array.isArray(t)&&Array.isArray(t[0])||(t=[t]),t=t;let n,o,s;return this.returnState&&(s=t.slice(1)),n=t[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()):xr(o)}apply(e,t){let n=t==null?null:t.initialState,o=t==null?null:t.constants;t==null&&(t={});let s=bC(e,n,o,this.numConstants);if(e=s.inputs,n=s.initialState,o=s.constants,Array.isArray(e)&&(n=e.slice(1),e=e[0]),(n==null||n.length===0)&&o==null)return super.apply(e,t);let a=[],i=[];if(n!=null){let u=n.length;if(u%2>0)throw new B("When passing `initialState` to a Bidrectional RNN, the state should be an Array containing the states of the underlying RNNs.");t.initialState=n,a.push(...n);let c=n.map(p=>new At({shape:p.shape}));this.forwardLayer.stateSpec=c.slice(0,u/2),this.backwardLayer.stateSpec=c.slice(u/2),i.push(...c)}if(o!=null)throw new Te("Support for constants in Bidirectional layers is not implemented yet.");let l=a[0]instanceof jr;for(let u of a)if(u instanceof jr!==l)throw new B("The initial state of a Bidirectional layer cannot be specified as a mix of symbolic and non-symbolic tensors");if(l){let u=[e].concat(a),c=this.inputSpec.concat(i),p=this.inputSpec;this.inputSpec=c;let m=super.apply(u,t);return this.inputSpec=p,m}else return super.apply(e,t)}call(e,t){return V(()=>{let n=t.initialState,o,s;if(n==null)o=this.forwardLayer.call(e,t),s=this.backwardLayer.call(e,t);else{let l=n.slice(0,n.length/2),u=n.slice(n.length/2);o=this.forwardLayer.call(e,Object.assign(t,{initialState:l})),s=this.backwardLayer.call(e,Object.assign(t,{initialState:u}))}let a;this.returnState&&(Array.isArray(o)&&(a=o.slice(1).concat(s.slice(1))),o=o[0],s=s[0]),this.returnSequences&&(s=Zt(s,1));let i;return this.mergeMode==="concat"?i=Tp([o,s]):this.mergeMode==="sum"?i=Q(o,s):this.mergeMode==="ave"?i=P(.5,Q(o,s)):this.mergeMode==="mul"?i=P(o,s):this.mergeMode==null&&(i=[o,s]),this.returnState?this.mergeMode==null?i.concat(a):[i].concat(a):i})}resetStates(e){this.forwardLayer.resetStates(),this.backwardLayer.resetStates()}build(e){Ms(this.forwardLayer.name,()=>{this.forwardLayer.build(e)}),Ms(this.backwardLayer.name,()=>{this.backwardLayer.build(e)}),this.built=!0}computeMask(e,t){Array.isArray(t)&&(t=t[0]);let n;if(this.returnSequences?this.mergeMode==null?n=[t,t]:n=t:this.mergeMode==null?n=[null,null]:n=null,this.returnState){let s=this.forwardLayer.states.map(a=>null);return Array.isArray(n)?n.concat(s).concat(s):[n].concat(s).concat(s)}else return n}get trainableWeights(){return this.forwardLayer.trainableWeights.concat(this.backwardLayer.trainableWeights)}get nonTrainableWeights(){return 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Fz=(r,e,t)=>{switch(r.op){case"Equal":return[Xr(C("a",r,e,t),C("b",r,e,t))];case"NotEqual":return[kn(C("a",r,e,t),C("b",r,e,t))];case"Greater":return[Yt(C("a",r,e,t),C("b",r,e,t))];case"GreaterEqual":return[Pr(C("a",r,e,t),C("b",r,e,t))];case"Less":return[Ta(C("a",r,e,t),C("b",r,e,t))];case"LessEqual":return[on(C("a",r,e,t),C("b",r,e,t))];case"LogicalAnd":return[hr(C("a",r,e,t),C("b",r,e,t))];case"LogicalNot":return[Ea(C("a",r,e,t))];case"LogicalOr":return[$u(C("a",r,e,t),C("b",r,e,t))];case"Select":case"SelectV2":return[$t(C("condition",r,e,t),C("a",r,e,t),C("b",r,e,t))];default:throw TypeError(`Node type ${r.op} is not implemented`)}};var 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Vz(r,e,t){let{usedNodes:n,inputs:o}=t,s=[],a=Object.keys(o).map(c=>en(c)[0]).map(c=>r.nodes[c]),i=r.initNodes;a.forEach(c=>{n.has(c.name)&&s.push(c)}),r.weights.forEach(c=>{n.has(c.name)&&s.push(c)}),i!=null&&i.forEach(c=>{n.has(c.name)&&s.push(c)});let l=new Set,u=[];for(;s.length>0;){let c=s.pop();l.add(c.name),e[c.name]||u.push(c),c.children.forEach(p=>{!l.has(p.name)&&n.has(p.name)&&p.inputs.every(m=>l.has(m.name))&&s.push(p)})}return u}var ire=["Switch","Merge","Enter","Exit","NextIteration","StatelessIf","StatelessWhile","if","While"],are=["NonMaxSuppressionV2","NonMaxSuppressionV3","NonMaxSuppressionV5","Where"],lre=["HashTable","HashTableV2","LookupTableImport","LookupTableImportV2","LookupTableFind","LookupTableFindV2"];function sI(r){return ire.indexOf(r.op)>=0}function ore(r){return are.indexOf(r.op)>=0}function sre(r){return lre.indexOf(r.op)>=0}var Yp=class{constructor(e,t){this.graph=e,this.parent=t,this.compiledMap=new 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c}processChildNodes(e,t,n,o,s,a){e.children.forEach(i=>{let[l]=Ws(i.name,n);s[l]||!a.has(i.name)||(i.op==="Merge"?i.inputNames.some(u=>!!yr(u,o,n))&&(s[l]=!0,t.push({contexts:n.currentContext,node:i})):i.inputNames.every(u=>!!yr(u,o,n))&&(s[l]=!0,t.push({contexts:n.currentContext,node:i})))})}dispose(){Object.keys(this.weightMap).forEach(e=>this.weightMap[e].forEach(t=>t.dispose()))}checkInputShapeAndType(e){Object.keys(e).forEach(t=>{let n=e[t],[o]=en(t),s=this.graph.nodes[o];if(s.attrParams.shape&&s.attrParams.shape.value){let a=s.attrParams.shape.value,i=a.length===n.shape.length&&n.shape.every((l,u)=>a[u]===-1||a[u]===l);y.assert(i,()=>`The shape of dict['${s.name}'] provided in model.execute(dict) must be [${a}], but was [${n.shape}]`)}s.attrParams.dtype&&s.attrParams.dtype.value&&y.assert(n.dtype===s.attrParams.dtype.value,()=>`The dtype of dict['${s.name}'] provided in model.execute(dict) must be ${s.attrParams.dtype.value}, but was ${n.dtype}`)})}mapInputs(e){let t={};for(let n 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e;if(e=Array.isArray(e)?e:[e],e.length!==this.inputNodes.length)throw new Error(`Input tensor count mismatch,the graph model has ${this.inputNodes.length} placeholders, while there are ${e.length} input tensors.`);return this.inputNodes.reduce((t,n,o)=>(t[n]=e[o],t),{})}normalizeOutputs(e){return e=e||this.outputNodes,Array.isArray(e)?e:[e]}execute(e,t){e=this.normalizeInputs(e),t=this.normalizeOutputs(t);let n=this.executor.execute(e,t);return n.length>1?n:n[0]}async executeAsync(e,t){e=this.normalizeInputs(e),t=this.normalizeOutputs(t);let n=await this.executor.executeAsync(e,t);return n.length>1?n:n[0]}convertTensorMapToTensorsMap(e){return Object.keys(e).reduce((t,n)=>(t[n]=[e[n]],t),{})}dispose(){this.executor.dispose(),this.initializer&&this.initializer.dispose(),this.resourceManager.dispose()}};async function Wz(r,e={}){if(r==null)throw new Error("modelUrl in loadGraphModel() cannot be null. 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Mismatched at element ${this.count}.`);case Xa.SHORTEST:return{value:null,done:!0};case Xa.LONGEST:default:}return this.count++,{value:s,done:!1}}async next(){return this.currentPromise=this.nextState(this.currentPromise),this.currentPromise}},bI=class extends Jt{constructor(e,t){super();this.upstream=e,this.bufferSize=t,this.buffer=new uh(t)}summary(){return`${this.upstream.summary()} -> Prefetch`}refill(){for(;!this.buffer.isFull();){let e=this.upstream.next();this.buffer.push(e)}}next(){return this.refill(),this.buffer.shift()}},C3=class extends bI{constructor(e,t,n){super(e,t);this.upstream=e,this.windowSize=t,this.upstreamExhausted=!1,this.random=c3.alea(n||y.now().toString()),this.lastRead=Promise.resolve({value:null,done:!1})}async next(){return this.lastRead=this.lastRead.then(()=>this.serialNext()),this.lastRead}randomInt(e){return Math.floor(this.random()*e)}chooseIndex(){return this.randomInt(this.buffer.length())}async serialNext(){for(this.upstreamExhausted||this.refill();!this.buffer.isEmpty();){let e=this.chooseIndex(),t=await this.buffer.shuffleExcise(e);if(t.done)this.upstreamExhausted=!0;else return this.refill(),t}return{value:null,done:!0}}};var Zi=class{constructor(){this.size=null}batch(e,t=!0){let n=this;y.assert(e>0,()=>`batchSize needs to be positive, but it is
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`).map(o=>(o.endsWith("\r")&&(o=o.slice(0,-1)),o))}};var cy='"',mh=Symbol("out"),T3=Symbol("field"),py=Symbol("quote"),wI=Symbol("quoteafterquote"),E3=Symbol("quoteinquote"),fh=class extends Zi{constructor(e,t){super();this.input=e,this.hasHeader=!0,this.fullColumnNames=null,this.columnNamesValidated=!1,this.columnConfigs=null,this.configuredColumnsOnly=!1,this.delimiter=",",this.delimWhitespace=!1,this.base=new ph(e),t||(t={}),this.hasHeader=t.hasHeader!==!1,this.fullColumnNames=t.columnNames,this.columnConfigs=t.columnConfigs,this.configuredColumnsOnly=t.configuredColumnsOnly,t.delimWhitespace?(y.assert(t.delimiter==null,()=>"Delimiter should not be provided when delimWhitespace is true."),this.delimWhitespace=!0,this.delimiter=" "):this.delimiter=t.delimiter?t.delimiter:","}async columnNames(){return this.columnNamesValidated||await this.setColumnNames(),this.configuredColumnsOnly?Object.keys(this.columnConfigs):this.fullColumnNames}async setColumnNames(){let e=await this.maybeReadHeaderLine();if(!this.fullColumnNames&&!e)throw new Error("Column names must be provided if there is no header line.");this.fullColumnNames&&e&&y.assert(e.length===this.fullColumnNames.length,()=>"The length of provided columnNames ("+this.fullColumnNames.length.toString()+") does not match the length of the header line read from file ("+e.length.toString()+")."),this.fullColumnNames||(this.fullColumnNames=e);let t=this.fullColumnNames.reduce((o,s)=>(o[s]=o[s]+1||1,o),{}),n=Object.keys(t).filter(o=>t[o]>1);if(y.assert(n.length===0,()=>"Duplicate column names found: "+n.toString()),this.columnConfigs){for(let o of Object.keys(this.columnConfigs))if(this.fullColumnNames.indexOf(o)===-1)throw new Error('The key "'+o+'" provided in columnConfigs does not match any of the column names ('+this.fullColumnNames.toString()+").")}this.columnNamesValidated=!0}async maybeReadHeaderLine(){if(this.hasHeader){let t=await(await this.base.iterator()).next();if(t.done)throw new Error("No data was found for CSV parsing.");let n=t.value;return this.parseRow(n,!1)}else return null}async iterator(){this.columnNamesValidated||await this.setColumnNames();let e=await this.base.iterator();return this.hasHeader&&(e=e.skip(1)),e.map(t=>this.makeDataElement(t))}makeDataElement(e){let t=this.parseRow(e),n={},o={};for(let s=0;s<this.fullColumnNames.length;s++){let a=this.fullColumnNames[s],i=this.columnConfigs?this.columnConfigs[a]:null;if(!(this.configuredColumnsOnly&&!i)){let l=t[s],u=null;if(l==="")if(i&&i.default!==void 0)u=i.default;else{if(i&&(i.required||i.isLabel))throw new Error(`Required column ${a} is empty in this line: ${e}`);u=void 0}else{let c=Number(l);if(isNaN(c))i&&i.dtype==="bool"?u=this.getBoolean(l):u=l;else if(!i||!i.dtype)u=c;else switch(i.dtype){case"float32":u=c;break;case"int32":u=Math.floor(c);break;case"bool":u=this.getBoolean(l);break;default:u=c}}i&&i.isLabel?o[a]=u:n[a]=u}}return Object.keys(o).length===0?n:{xs:n,ys:o}}getBoolean(e){return e==="1"||e.toLowerCase()==="true"?1:0}parseRow(e,t=!0){let n=[],o=0,s=e.length,a=mh;for(let i=0;i<s;i++)switch(a){case mh:switch(e.charAt(i)){case cy:o=i+1,a=py;break;case this.delimiter:if(o=i+1,this.delimiter===" "&&this.delimWhitespace)break;n.push(""),a=mh;break;default:a=T3,o=i;break}break;case T3:switch(e.charAt(i)){case this.delimiter:n.push(e.substring(o,i)),a=mh,o=i+1;break;default:}break;case py:switch(e.charAt(i)){case cy:a=wI;break;default:}break;case wI:switch(e.charAt(i)){case this.delimiter:n.push(e.substring(o,i-1)),a=mh,o=i+1;break;case cy:a=py;break;default:a=E3;break}break;case E3:switch(e.charAt(i)){case cy:a=py;break;default:}break;default:}if(a===wI?n.push(e.substring(o,s-1)):n.push(e.substring(o)),t&&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 dh=class extends Jt{constructor(e){super();this.microphoneConfig=e,this.isClosed=!1,this.fftSize=e.fftSize||1024;let t=Math.log2(this.fftSize);if(this.fftSize<0||t<4||t>14||!Number.isInteger(t))throw new Error(`Invalid fftSize: it must be a power of 2 between 2 to 4 and 2 to 14, but got ${this.fftSize}`);if(this.numFrames=e.numFramesPerSpectrogram||43,this.sampleRateHz=e.sampleRateHz,this.columnTruncateLength=e.columnTruncateLength||this.fftSize,this.audioTrackConstraints=e.audioTrackConstraints,this.smoothingTimeConstant=e.smoothingTimeConstant||0,this.includeSpectrogram=e.includeSpectrogram!==!1,this.includeWaveform=e.includeWaveform===!0,!this.includeSpectrogram&&!this.includeWaveform)throw new Error("Both includeSpectrogram and includeWaveform are false. At least one type of data should be returned.")}summary(){return"microphone"}static async create(e={}){if(G().get("IS_NODE"))throw new Error("microphone API is only supported in browser environment.");let t=new dh(e);return await t.start(),t}async start(){try{this.stream=await navigator.mediaDevices.getUserMedia({audio:this.audioTrackConstraints==null?!0:this.audioTrackConstraints,video:!1})}catch(n){throw new Error(`Error thrown while initializing video stream: ${n.message}`)}if(!this.stream)throw new Error("Could not obtain audio from microphone.");let e=window.AudioContext||window.webkitAudioContext;if(this.audioContext=new e,!this.sampleRateHz)this.sampleRateHz=this.audioContext.sampleRate;else if(this.audioContext.sampleRate!==this.sampleRateHz)throw new Error(`Mismatch in sampling rate: Expected: ${this.sampleRateHz}; Actual: ${this.audioContext.sampleRate}`);let t=this.audioContext.createMediaStreamSource(this.stream);this.analyser=this.audioContext.createAnalyser(),this.analyser.fftSize=this.fftSize*2,this.analyser.smoothingTimeConstant=this.smoothingTimeConstant,t.connect(this.analyser),this.freqData=new Float32Array(this.fftSize),this.timeData=new Float32Array(this.fftSize)}async next(){if(this.isClosed)return{value:null,done:!0};let e,t,n=await this.getAudioData();if(this.includeSpectrogram){let o=this.flattenQueue(n.freqDataQueue);e=this.getTensorFromAudioDataArray(o,[this.numFrames,this.columnTruncateLength,1])}if(this.includeWaveform){let o=this.flattenQueue(n.timeDataQueue);t=this.getTensorFromAudioDataArray(o,[this.numFrames*this.fftSize,1])}return{value:{spectrogram:e,waveform:t},done:!1}}async capture(){return(await this.next()).value}async getAudioData(){let e=[],t=[],n=0;return new Promise(o=>{let s=setInterval(()=>{this.includeSpectrogram&&(this.analyser.getFloatFrequencyData(this.freqData),this.freqData[0]===-Infinity&&o({freqDataQueue:e,timeDataQueue:t}),e.push(this.freqData.slice(0,this.columnTruncateLength))),this.includeWaveform&&(this.analyser.getFloatTimeDomainData(this.timeData),t.push(this.timeData.slice())),++n===this.numFrames&&(clearInterval(s),o({freqDataQueue:e,timeDataQueue:t}))},this.fftSize/this.sampleRateHz*1e3)})}stop(){this.isClosed||(this.isClosed=!0,this.analyser.disconnect(),this.audioContext.close(),this.stream!=null&&this.stream.getTracks().length>0&&this.stream.getTracks()[0].stop())}toArray(){throw new Error("Can not convert infinite audio stream to array.")}getSampleRate(){return this.sampleRateHz}flattenQueue(e){let t=e[0].length,n=new Float32Array(e.length*t);return e.forEach((o,s)=>n.set(o,s*t)),n}getTensorFromAudioDataArray(e,t){let n=new Float32Array(y.sizeFromShape(t));return n.set(e,n.length-e.length),Or(n,t)}};var hh=class extends Jt{constructor(e,t){super();if(this.webcamVideoElement=e,this.webcamConfig=t,this.isClosed=!0,this.resize=!1,this.needToResize())if(this.resize=!0,this.cropSize=[this.webcamConfig.resizeHeight,this.webcamConfig.resizeWidth],this.cropBoxInd=Gt([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,a=(1-o)/2,i=s+n,l=o+a;this.cropBox=Vi([a,s,l,i],[1,4])}else this.cropBox=Vi([0,0,1,1],[1,4])}summary(){return"webcam"}static async create(e,t={}){if(G().get("IS_NODE"))throw new Error("tf.data.webcam is only supported in browser environment.");if(!e){if(e=document.createElement("video"),!t.resizeWidth||!t.resizeHeight)throw new Error("Please provide webcam video element, or resizeWidth and resizeHeight to create a hidden video element.");e.width=t.resizeWidth,e.height=t.resizeHeight}let n=new hh(e,t);return await n.start(),n}async start(){this.webcamConfig.facingMode&&y.assert(this.webcamConfig.facingMode==="user"||this.webcamConfig.facingMode==="environment",()=>`Invalid webcam facing mode: ${this.webcamConfig.facingMode}. Please provide 'user' or 'environment'`);try{this.stream=await navigator.mediaDevices.getUserMedia({video:{deviceId:this.webcamConfig.deviceId,facingMode:this.webcamConfig.facingMode?this.webcamConfig.facingMode:"user",width:this.webcamVideoElement.width,height:this.webcamVideoElement.height}})}catch(e){throw e.message=`Error thrown while initializing video stream: ${e.message}`,e}if(!this.stream)throw new Error("Could not obtain video from webcam.");try{this.webcamVideoElement.srcObject=this.stream}catch(e){console.log(e),this.webcamVideoElement.src=window.URL.createObjectURL(this.stream)}return this.webcamVideoElement.play(),this.isClosed=!1,new Promise(e=>{this.webcamVideoElement.onloadedmetadata=()=>{e()}})}async next(){if(this.isClosed)return{value:null,done:!0};let e;try{e=Qh.fromPixels(this.webcamVideoElement)}catch(t){throw new Error(`Error thrown converting video to pixels: ${JSON.stringify(t)}`)}if(this.resize)try{return{value:this.cropAndResizeFrame(e),done:!1}}catch(t){throw new Error(`Error thrown cropping the video: ${t.message}`)}finally{e.dispose()}else return{value:e,done:!1}}needToResize(){return!!(this.webcamConfig.resizeWidth&&this.webcamConfig.resizeHeight&&(this.webcamVideoElement.width!==this.webcamConfig.resizeWidth||this.webcamVideoElement.height!==this.webcamConfig.resizeHeight))}cropAndResizeFrame(e){return V(()=>{let t=e.toFloat().expandDims(0),n;n=$s.cropAndResize(t,this.cropBox,this.cropBoxInd,this.cropSize,"bilinear");let o=n.shape;return n.reshape(o.slice(1))})}async capture(){return(await this.next()).value}stop(){this.stream.getTracks().forEach(t=>t.stop());try{this.webcamVideoElement.srcObject=null}catch(t){console.log(t),this.webcamVideoElement.src=null}this.isClosed=!0}toArray(){throw new Error("Can not convert infinite video stream to array.")}};var gh=class{};var my=class extends Jt{split(e){return new A3(this,e)}},A3=class extends my{constructor(e,t){super();this.upstream=e,this.impl=new D3(e,t)}summary(){return this.impl.summary()}async next(){return this.impl.next()}},D3=class extends Jp{constructor(e,t){super();this.upstream=e,this.separator=t,this.carryover=""}summary(){return`${this.upstream.summary()} -> Split('${this.separator}')`}async pump(){let e=await this.upstream.next();if(e.done)return this.carryover===""?!1:(this.outputQueue.push(this.carryover),this.carryover="",!0);let t=e.value.split(this.separator);t[0]=this.carryover+t[0];for(let n of t.slice(0,-1))this.outputQueue.push(n);return this.carryover=t[t.length-1],!0}};var _I=class extends Jt{decodeUTF8(){return new R3(this)}},R3=class extends my{constructor(e){super();this.upstream=e,this.impl=new F3(e)}summary(){return this.impl.summary()}async next(){return this.impl.next()}},F3=class extends Jp{constructor(e){super();if(this.upstream=e,G().get("IS_BROWSER"))this.decoder=new TextDecoder("utf-8");else{let{StringDecoder:t}=$3();this.decoder=new t("utf8")}}summary(){return`${this.upstream.summary()} -> Utf8`}async pump(){let e=await this.upstream.next(),t;if(e.done)return!1;t=e.value;let n;return G().get("IS_BROWSER")?n=this.decoder.decode(t,{stream:!0}):n=this.decoder.write(Buffer.from(t.buffer)),this.outputQueue.push(n),!0}};var xh=class extends _I{constructor(e,t={}){super();this.file=e,this.options=t,y.assert(e instanceof Uint8Array||(G().get("IS_BROWSER")?e instanceof File||e instanceof Blob:!1),()=>"FileChunkIterator only supports File, Blob and Uint8Array right now."),this.offset=t.offset||0,this.chunkSize=t.chunkSize||1024*1024}summary(){return`FileChunks ${this.file}`}async next(){return this.offset>=(this.file instanceof Uint8Array?this.file.byteLength:this.file.size)?{value:null,done:!0}:{value:await new Promise((t,n)=>{let o=this.offset+this.chunkSize;if(this.file instanceof Uint8Array)t(new Uint8Array(this.file.slice(this.offset,o)));else{let s=new FileReader;s.onload=i=>{let l=s.result;if(l instanceof ArrayBuffer&&(l=new Uint8Array(l)),!(l instanceof Uint8Array))return n(new TypeError("FileReader returned unknown type."));t(l)},s.onabort=i=>n(new Error("Aborted")),s.onerror=i=>n(new Error(i.type));let a=this.file.slice(this.offset,o);s.readAsArrayBuffer(a)}this.offset=o}),done:!1}}};async function O3(r,e={}){let t,n;typeof r=="string"?t=r:(t=r.url,n=_re(r));let o=await y.fetch(t,n);if(o.ok){let s=new Uint8Array(await o.arrayBuffer());return new xh(s,e)}else throw new Error(o.statusText)}var _re=r=>({method:r.method,headers:r.headers,body:r.body,mode:r.mode,credentials:r.credentials,cache:r.cache,redirect:r.redirect,referrer:r.referrer,integrity:r.integrity});function fy(r){return typeof r=="string"&&r.substr(0,7)==="file://"}var yh=class extends gh{constructor(e,t={}){super();this.input=e,this.options=t}async iterator(){if(fy(this.input)&&G().get("IS_NODE")){let e=require("fs");this.input=e.readFileSync(this.input.substr(7))}return new xh(this.input,this.options)}};var bh=class extends gh{constructor(e,t={}){super();this.url=e,this.fileOptions=t}async iterator(){return fy(this.url)?new yh(this.url,this.fileOptions).iterator():O3(this.url,this.fileOptions)}};function P3(r,e={}){return new fh(new bh(r),e)}function M3(r){let e=ch(r);return dn(async()=>e)}function L3(r){return dn(async()=>{let e=await r();return ch(()=>e.next())})}async function z3(r,e){return hh.create(r,e)}async function B3(r){return dh.create(r)}var V3="2.8.5";var vre={tfjs:RI,"tfjs-core":FI,"tfjs-data":OI,"tfjs-layers":PI,"tfjs-converter":MI,"tfjs-backend-cpu":L_,"tfjs-backend-webgl":ek,"tfjs-backend-wasm":O0};export{ls as Abs,Ks as Acos,Xs as Acosh,rp as AdadeltaOptimizer,np as AdagradOptimizer,op as AdamOptimizer,sp as AdamaxOptimizer,wn as Add,Kn as AddN,jl as All,Gl as Any,Xn as ArgMax,na as ArgMin,Ys as Asin,Zs as Asinh,Js as Atan,ei as Atan2,Qs as Atanh,Yn as AvgPool,oa as AvgPool3D,ql as AvgPool3DGrad,Ul as AvgPoolGrad,px as BackendWasm,Zn as BatchMatMul,sa as BatchToSpaceND,Hl as Bincount,Pb as BroadcastTo,jx as Callback,Ix as CallbackList,Rn as Cast,ti as Ceil,Fn as ClipByValue,Kl as Complex,ia as ComplexAbs,us as Concat,Jn as Conv2D,Xl as Conv2DBackpropFilter,Qn as Conv2DBackpropInput,aa as Conv3D,Yl as Conv3DBackpropFilterV2,Zl as Conv3DBackpropInputV2,eo as Cos,ri as Cosh,ni as CropAndResize,to as Cumsum,Nx as CustomCallback,el as DataStorage,Jl as DenseBincount,oi as DepthToSpace,ro as DepthwiseConv2dNative,Ql as DepthwiseConv2dNativeBackpropFilter,eu as DepthwiseConv2dNativeBackpropInput,tu as Diag,la as Dilation2D,Oc as Dilation2DBackpropFilter,Fc as Dilation2DBackpropInput,$b as ENV,Ux as EarlyStopping,si as Elu,ru as EluGrad,Wh as Environment,ai as Equal,ii as Erf,oo as Exp,cs as ExpandDims,li as Expm1,nu as FFT,ua as Fill,ui as FlipLeftRight,so as Floor,io as FloorDiv,Pc as FromPixels,ao as FusedBatchNorm,ks as FusedConv2D,Cs as FusedDepthwiseConv2D,Gg as GPGPUContext,ci as GatherNd,ps as GatherV2,iy as GraphModel,pi as Greater,lo as GreaterEqual,Sx as History,ou as IFFT,ms as Identity,su as Imag,At as InputSpec,mi as IsFinite,fi as IsInf,di as IsNan,Us as KernelBackend,ca as LRN,au as LRNGrad,Qf as LayerVariable,Tn as LayersModel,uo as LeakyRelu,hi as Less,gi as LessEqual,iu as LinSpace,co as Log,xi as Log1p,Mb as LogSoftmax,yi as LogicalAnd,tl as LogicalNot,rl as LogicalOr,kg as MathBackendCPU,Kg as MathBackendWebGL,po as Max,fo as MaxPool,pa as MaxPool3D,uu as MaxPool3DGrad,lu as MaxPoolGrad,cu as MaxPoolWithArgmax,mo as Maximum,ho as Mean,go as Min,xo as Minimum,ma as MirrorPad,bi as Mod,ip as MomentumOptimizer,pu as Multinomial,yo as Multiply,fs as Neg,_i as NonMaxSuppressionV3,vi as NonMaxSuppressionV4,ki as NonMaxSuppressionV5,wi as NotEqual,rS as OP_SCOPE_SUFFIX,bo as OneHot,ds as OnesLike,Lr as Optimizer,hs as Pack,wo as PadV2,NV as Pool,_o as Pow,vo as Prelu,Ci as Prod,ap as RMSPropOptimizer,fn as RNN,fa as Range,Wb as Rank,mu as Real,no as RealDiv,Ii as Reciprocal,Ut as Reduction,ko as Relu,Io as Relu6,gs as Reshape,Co as ResizeBilinear,du as ResizeBilinearGrad,da as ResizeNearestNeighbor,fu as ResizeNearestNeighborGrad,So as Reverse,Fi as RotateWithOffset,No as Round,To as Rsqrt,ml as SGDOptimizer,Si as ScatterNd,xs as Select,Ni as Selu,Yi as Sequential,Ao as Sigmoid,Ei as Sign,Eo as Sin,Ti as Sinh,ys as Slice,Ro as Softmax,Ai as Softplus,ha as SpaceToBatchND,hu as SparseToDense,bs as SplitV,Do as Sqrt,ga as Square,Fo as SquaredDifference,On as Step,Di as StridedSlice,Oo as Sub,$o as Sum,jr as SymbolicTensor,$i as Tan,Po as Tanh,R as Tensor,pt as TensorBuffer,_n as Tile,Ri as TopK,Mo as Transpose,gu as Unique,ws as Unpack,xa as UnsortedSegmentSum,sl as Variable,_s as ZerosLike,vs as _FusedMatMul,Et as abs,Nm as acos,Tm as acosh,Q as add,gw as addN,s_ as addStrict,vu as all,ll as any,ul as argMax,Em as argMin,Am as asin,Dm as asinh,$m as atan,Rm as atan2,Fm as atanh,ka as avgPool,Om as avgPool3d,hw as backend,N as backend_util,Sj as basicLSTMCell,zo as batchNorm,ww as batchNorm2d,_w as batchNorm3d,vw as batchNorm4d,Ca as batchToSpaceND,kw as bincount,FU as booleanMaskAsync,cl as broadcastTo,Qh as browser,Ie as buffer,pz as callbacks,ne as cast,Pm as ceil,ar as clipByValue,Pn as clone,vn as complex,et as concat,Cw as concat1d,Iw as concat2d,Sw as concat3d,Nw as concat4d,V0 as constraints,Iu as conv1d,Kr as conv2d,Su as conv2dTranspose,Mm as conv3d,Hj as conv3dTranspose,AV as copyRegisteredKernels,Ia as cos,Nu as cosh,af as cosineWindow,Tu as cumsum,Yr as customGrad,dy as data,Tw as denseBincount,Pt as deprecationWarn,Lm as depthToSpace,Ln as depthwiseConv2d,dz as deregisterOp,Vc as device_util,tG as diag,zm as dilation2d,zW as disableDeprecationWarnings,De as dispose,BW as disposeVariables,fe as div,Bm as divNoNan,i_ as divStrict,Ew as dot,g_ as dropout,Ns as elu,LW as enableDebugMode,MW as enableProdMode,x_ as enclosingPowerOfTwo,Ss as engine,G as env,Xr as equal,Qw as equalStrict,Vm as erf,Qt as exp,wr as expandDims,Wm as expm1,Xc as eye,Fa as fft,Sa as fill,qW as findBackend,HW as findBackendFactory,Ts as floor,_u as floorDiv,tk as forceHalfFloat,Wo as fused,Bo as gather,h_ as gatherND,eg as gather_util,GW as getBackend,jh as getGradient,wm as getKernel,_m as getKernelsForBackend,yD as gpgpu_util,AG as grad,DG as grads,Yt as greater,Pr as greaterEqual,e_ as greaterEqualStrict,t_ as greaterStrict,Bi as ifft,Eu as imag,$s as image,oq as inTopKAsync,q0 as initializers,Vx as input,Sr as io,Vu as irfft,Aw as isFinite,Dw as isInf,$w as isNaN,Dt as keep,Dr as kernel_impls,TC as layers,Na as leakyRelu,Ta as less,on as lessEqual,r_ as lessEqualStrict,n_ as lessStrict,v_ as linalg,Rw as linspace,Wz as loadGraphModel,JL as loadLayersModel,jm as localResponseNormalization,lr as log,Au as log1p,Fw as logSigmoid,Du as logSoftmax,Um as logSumExp,hr as logicalAnd,Ea as logicalNot,$u as logicalOr,Lw as logicalXor,Yq as losses,Ge as matMul,RS as math,ur as max,Aa as maxPool,qm as maxPool3d,zw as maxPoolWithArgmax,Nr as maximum,a_ as maximumStrict,yt as mean,qc as memory,$C as metrics,zi as min,Bn as minimum,l_ as minimumStrict,Hm as mirrorPad,Ru as mod,u_ as modStrict,YL as model,RC as models,Yc as moments,JU as movingAverage,P as mul,c_ as mulStrict,i4 as multiRNNCell,Bw as multinomial,qe as neg,lf as nextFrame,ju as norm,kn as notEqual,o_ as notEqualStrict,Is as oneHot,Tr as ones,nr as onesLike,S as op,p4 as outerProduct,Mr as pad,d4 as pad1d,g4 as pad2d,y4 as pad3d,w4 as pad4d,Vw as pool,_r as pow,p_ as powStrict,$a as prelu,sw as print,Fu as prod,VW as profile,E4 as rand,M4 as randomGamma,mg as randomNormal,As as randomUniform,Jc as range,jW as ready,pl as real,Km as reciprocal,wu as registerBackend,QL as registerCallbackConstructor,zb as registerGradient,nl as registerKernel,fz as registerOp,FC as regularizers,Er as relu,Pu as relu6,UW as removeBackend,z as reshape,Zt as reverse,q4 as reverse1d,K4 as reverse2d,Y4 as reverse3d,J4 as reverse4d,Oa as rfft,Xm as round,Mu as rsqrt,ue as scalar,d_ as scatterND,tg as scatter_util,Lu as selu,Ym as separableConv2d,ZL as sequential,ee as serialization,XS as setBackend,KW as setPlatform,OJ as setWasmPath,PJ as setWasmPaths,sv as setWebGLContext,Yw as setdiff1dAsync,Ig as shared,Hr as sigmoid,Zm as sign,Xq as signal,zu as sin,Bu as sinh,Oe as slice,Jm as slice1d,fg as slice2d,Qm as slice3d,Qc as slice4d,ir as slice_util,Ra as softmax,Es as softplus,Da as spaceToBatchND,sf as sparseToDense,Kq as spectral,cr as split,bt as sqrt,Me as square,Pa as squaredDifference,m_ as squaredDifferenceStrict,Cn as squeeze,jt as stack,Ds as step,ef as stridedSlice,ce as sub,f_ as subStrict,we as sum,xu as sumOutType,tf as tan,Li as tanh,Or as tensor,Gt as tensor1d,Vi as tensor2d,uw as tensor3d,CU as tensor4d,IU as tensor5d,SU as tensor6d,Lo as tensor_util,qS as test_util,V as tidy,zn as tile,WW as time,rf as topk,fl as train,Ue as transpose,Wu as truncatedNormal,ep as unique,EV as unregisterGradient,TV as unregisterKernel,nf as unsortedSegmentSum,pr as unstack,dr as upcastType,y as util,$G as valueAndGrad,RG as valueAndGrads,Zw as variable,lg as variableGrads,vre as version,jz as version_converter,PW as version_core,L_ as version_cpu,Wp as version_layers,O0 as version_wasm,ek as version_webgl,V8 as webgl,fD as webgl_util,$t as where,of as whereAsync,mt as zeros,Se 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 See the LICENSE file. */
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