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human
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/*
Human
homepage: <https://github.com/vladmandic/human>
author: <https://github.com/vladmandic>'
*/
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Reusing existing backend factory.`),!1):(this.registryFactory[e]={factory:t,priority:n},!0)}async setBackend(e){if(this.registryFactory[e]==null)throw new Error(`Backend name '${e}' not found in registry`);if(this.backendName=e,this.registry[e]==null){this.backendInstance=null;let{success:t,asyncInit:n}=this.initializeBackend(e);if(!(n?await t:t))return!1}return this.backendInstance=this.registry[e],this.setupRegisteredKernels(),this.profiler=new o_(this.backendInstance),!0}setupRegisteredKernels(){cm(this.backendName).forEach(t=>{t.setupFunc!=null&&t.setupFunc(this.backendInstance)})}disposeRegisteredKernels(e){cm(e).forEach(n=>{n.disposeFunc!=null&&n.disposeFunc(this.registry[e])})}initializeBackend(e){let t=this.registryFactory[e];if(t==null)throw new Error(`Cannot initialize backend ${e}, no registration found.`);try{let n=t.factory();if(n&&!(n instanceof il)&&typeof n.then=="function"){let s=++this.pendingBackendInitId,r=n.then(a=>s<this.pendingBackendInitId?!1:(this.registry[e]=a,this.pendingBackendInit=null,!0)).catch(a=>(s<this.pendingBackendInitId||(this.pendingBackendInit=null,ar(`Initialization of backend ${e} failed`),ar(a.stack||a.message)),!1));return this.pendingBackendInit=r,{success:r,asyncInit:!0}}else return this.registry[e]=n,{success:!0,asyncInit:!1}}catch(n){return ar(`Initialization of backend ${e} failed`),ar(n.stack||n.message),{success:!1,asyncInit:!1}}}removeBackend(e){if(!(e in this.registryFactory))throw new Error(`${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:s,asyncInit:r}=this.initializeBackend(n);if(r||s)return{name:n,asyncInit:r}}throw new Error("Could not initialize any backends, all backend initializations failed.")}moveData(e,t){let n=this.state.tensorInfo.get(t),s=n.backend,r=this.readSync(t),a=s.refCount(t);s.disposeData(t,!0),n.backend=e,e.move(t,r,n.shape,n.dtype,a),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 tidy()");t=e}else{if(typeof e!="string"&&!(e instanceof String))throw new Error("When calling with two arguments, the first argument to tidy() must be a string");if(typeof t!="function")throw new Error("When calling with two arguments, the 2nd argument to tidy() must be a function");n=e}let s;return this.scopedRun(()=>this.startScope(n),()=>this.endScope(s),()=>(s=t(),s instanceof Promise&&console.error("Cannot return a Promise inside of tidy."),s))}scopedRun(e,t,n){e();try{let s=n();return t(),s}catch(s){throw t(),s}}nextTensorId(){return pm.nextTensorId++}nextVariableId(){return pm.nextVariableId++}clone(e){let t=z.runKernel(Wa,{x:e}),n={x:e},s=a=>({x:()=>{let o="float32",i={x:a},u={dtype:o};return z.runKernel(Ta,i,u)}}),r=[];return this.addTapeNode(this.state.activeScope.name,n,[t],s,r,{}),t}runKernel(e,t,n){if(this.backendName==null&&this.backend,!(lm(e,this.backendName)!=null))throw new Error(`Kernel '${e}' not registered for backend '${this.backendName}'`);return this.runKernelFunc({kernelName:e,inputs:t,attrs:n})}shouldCheckForMemLeaks(){return this.ENV.getBool("IS_TEST")}checkKernelForMemLeak(e,t,n){let s=this.backend.numDataIds(),r=0;n.forEach(i=>{r+=i.dtype==="complex64"?3:1});let a=this.state.numDataMovesStack[this.state.numDataMovesStack.length-1],o=s-t-r-a;if(o>0)throw new Error(`Backend '${this.backendName}' has an internal memory leak (${o} data ids) after running '${e}'`)}runKernelFunc(e){let t,n=[],s=this.isTapeOn(),r=this.state.numBytes,a=this.state.numTensors;this.shouldCheckForMemLeaks()&&this.state.numDataMovesStack.push(0);let o;this.backendName==null&&this.backend;let i,u=jf(e)?e.kernelName:this.state.activeScope!=null?this.state.activeScope.name:"";if(jf(e)){let{kernelName:h,inputs:f,attrs:m}=e;this.backendName==null&&this.backend;let g=lm(h,this.backendName);O(g!=null,()=>`Cannot find registered kernel '${h}' for backend '${this.backendName}'`),o=()=>{let b=this.backend.numDataIds();i=g.kernelFunc({inputs:f,attrs:m,backend:this.backend});let y=Array.isArray(i)?i:[i];this.shouldCheckForMemLeaks()&&this.checkKernelForMemLeak(h,b,y);let v=y.map(x=>x.rank!=null?x:this.makeTensorFromTensorInfo(x));if(s){let x=this.getTensorsForGradient(h,f,v);n=this.saveTensorsForBackwardMode(x)}return v}}else{let{forwardFunc:h}=e,f=m=>{!s||(n=m.map(g=>this.keep(this.clone(g))))};o=()=>{let m=this.backend.numDataIds();i=this.tidy(()=>h(this.backend,f));let g=Array.isArray(i)?i:[i];return this.shouldCheckForMemLeaks()&&this.checkKernelForMemLeak(u,m,g),g}}let{inputs:l,attrs:c}=e,p=jf(e)?null:e.backwardsFunc,d;return this.scopedRun(()=>this.state.kernelDepth++,()=>this.state.kernelDepth--,()=>{!this.ENV.getBool("DEBUG")&&!this.state.profiling?t=o():(d=this.profiler.profileKernel(u,l,()=>o()),this.ENV.getBool("DEBUG")&&this.profiler.logKernelProfile(d),t=d.outputs)}),s&&this.addTapeNode(u,l,t,p,n,c),this.state.profiling&&this.state.activeProfile.kernels.push({name:u,bytesAdded:this.state.numBytes-r,totalBytesSnapshot:this.state.numBytes,tensorsAdded:this.state.numTensors-a,totalTensorsSnapshot:this.state.numTensors,inputShapes:Object.keys(l).map(h=>l[h]!=null?l[h].shape:null),outputShapes:t.map(h=>h.shape),kernelTimeMs:d.timeMs,extraInfo:d.extraInfo}),Array.isArray(i)?t:t[0]}saveTensorsForBackwardMode(e){return e.map(n=>this.keep(this.clone(n)))}getTensorsForGradient(e,t,n){let s=hx(e);if(s!=null){let r=s.inputsToSave||[],a=s.outputsToSave||[],o;s.saveAllInputs?(O(Array.isArray(t),()=>"saveAllInputs is true, expected inputs to be an array."),o=Object.keys(t).map(u=>t[u])):o=r.map(u=>t[u]);let i=n.filter((u,l)=>a[l]);return o.concat(i)}return[]}makeTensor(e,t,n,s){if(e==null)throw new Error("Values passed to engine.makeTensor() are null");n=n||"float32",s=s||this.backend;let r=e;n==="string"&&or(e[0])&&(r=e.map(i=>zl(i)));let a=s.write(r,t,n),o=new et(t,n,a,this.nextTensorId());if(this.trackTensor(o,s),n==="string"){let i=this.state.tensorInfo.get(a),u=ck(r);this.state.numBytes+=u-i.bytes,i.bytes=u}return o}makeTensorFromDataId(e,t,n,s){n=n||"float32";let r={dataId:e,shape:t,dtype:n};return this.makeTensorFromTensorInfo(r,s)}makeTensorFromTensorInfo(e,t){let{dataId:n,shape:s,dtype:r}=e,a=new et(s,r,n,this.nextTensorId());return this.trackTensor(a,t),a}makeVariable(e,t=!0,n,s){n=n||this.nextVariableId().toString(),s!=null&&s!==e.dtype&&(e=e.cast(s));let r=new kd(e,t,n,this.nextTensorId());if(this.state.registeredVariables[r.name]!=null)throw new Error(`Variable with name ${r.name} was already registered`);return this.state.registeredVariables[r.name]=r,this.incRef(r,this.backend),r}trackTensor(e,t){this.state.numTensors++,e.dtype==="string"&&this.state.numStringTensors++;let n=0;e.dtype!=="complex64"&&e.dtype!=="string"&&(n=e.size*om(e.dtype)),this.state.numBytes+=n,this.state.tensorInfo.has(e.dataId)||(this.state.numDataBuffers++,this.state.tensorInfo.set(e.dataId,{backend:t||this.backend,dtype:e.dtype,shape:e.shape,bytes:n})),e instanceof kd||this.track(e)}incRef(e,t){this.trackTensor(e,t),this.backend.incRef(e.dataId)}removeDataId(e,t){this.state.tensorInfo.has(e)&&this.state.tensorInfo.get(e).backend===t&&(this.state.tensorInfo.delete(e),this.state.numDataBuffers--)}disposeTensor(e){if(!this.state.tensorInfo.has(e.dataId))return;let t=this.state.tensorInfo.get(e.dataId);if(this.state.numTensors--,e.dtype==="string"&&(this.state.numStringTensors--,this.state.numBytes-=t.bytes),e.dtype!=="complex64"&&e.dtype!=="string"){let n=e.size*om(e.dtype);this.state.numBytes-=n}t.backend.disposeData(e.dataId)&&this.removeDataId(e.dataId,t.backend)}disposeVariables(){for(let e in this.state.registeredVariables){let t=this.state.registeredVariables[e];this.disposeVariable(t)}}disposeVariable(e){this.disposeTensor(e),this.state.registeredVariables[e.name]!=null&&delete this.state.registeredVariables[e.name]}memory(){let e=this.backend.memory();return e.numTensors=this.state.numTensors,e.numDataBuffers=this.state.numDataBuffers,e.numBytes=this.state.numBytes,this.state.numStringTensors>0&&(e.unreliable=!0,e.reasons==null&&(e.reasons=[]),e.reasons.push("Memory usage by string tensors is approximate (2 bytes per character)")),e}async profile(e){this.state.profiling=!0;let t=this.state.numBytes,n=this.state.numTensors;this.state.activeProfile.kernels=[],this.state.activeProfile.result=await e(),this.state.profiling=!1,this.state.activeProfile.peakBytes=Math.max(...this.state.activeProfile.kernels.map(s=>s.totalBytesSnapshot)),this.state.activeProfile.newBytes=this.state.numBytes-t,this.state.activeProfile.newTensors=this.state.numTensors-n;for(let s of this.state.activeProfile.kernels)s.kernelTimeMs=await s.kernelTimeMs,s.extraInfo=await s.extraInfo;return this.state.activeProfile}isTapeOn(){return this.state.gradientDepth>0&&this.state.kernelDepth===0}addTapeNode(e,t,n,s,r,a){let o={id:this.state.nextTapeNodeId++,kernelName:e,inputs:t,outputs:n,saved:r},i=hx(e);i!=null&&(s=i.gradFunc),s!=null&&(o.gradient=u=>(u=u.map((l,c)=>{if(l==null){let p=n[c],d=ep(p.size,p.dtype);return this.makeTensor(d,p.shape,p.dtype)}return l}),s(u.length>1?u:u[0],r,a))),this.state.activeTape.push(o)}keep(e){return e.kept=!0,e}startTape(){this.state.gradientDepth===0&&(this.state.activeTape=[]),this.state.gradientDepth++}endTape(){this.state.gradientDepth--}startScope(e){let t={track:[],name:"unnamed scope",id:this.state.nextScopeId++};e&&(t.name=e),this.state.scopeStack.push(t),this.state.activeScope=t}endScope(e){let t=Gg(e),n=new Set(t.map(r=>r.id));for(let r=0;r<this.state.activeScope.track.length;r++){let a=this.state.activeScope.track[r];!a.kept&&!n.has(a.id)&&a.dispose()}let s=this.state.scopeStack.pop();this.state.activeScope=this.state.scopeStack.length===0?null:this.state.scopeStack[this.state.scopeStack.length-1],t.forEach(r=>{!r.kept&&r.scopeId===s.id&&this.track(r)})}gradients(e,t,n,s=!1){if(O(t.length>0,()=>"gradients() received an empty list of xs."),n!=null&&n.dtype!=="float32")throw new Error(`dy must have 'float32' dtype, but has '${n.dtype}'`);let r=this.scopedRun(()=>this.startTape(),()=>this.endTape(),()=>this.tidy("forward",e));O(r instanceof et,()=>"The result y returned by f() must be a tensor.");let a=l_(this.state.activeTape,t,r);if(!s&&a.length===0&&t.length>0)throw new Error("Cannot compute gradient of y=f(x) with respect to x. Make sure that the f you passed encloses all operations that lead from x to y.");return this.tidy("backward",()=>{let o={};o[r.id]=n==null?k_(r.shape):n,c_(o,a,u=>this.tidy(u),S_);let i=t.map(u=>o[u.id]);return this.state.gradientDepth===0&&(this.state.activeTape.forEach(u=>{for(let l of u.saved)l.dispose()}),this.state.activeTape=null),{value:r,grads:i}})}customGrad(e){return O(fr(e),()=>"The f passed in customGrad(f) must be a function."),(...t)=>{O(t.every(o=>o instanceof et),()=>"The args passed in customGrad(f)(x1, x2,...) must all be tensors");let n,s={};t.forEach((o,i)=>{s[i]=o});let r=(o,i)=>(n=e(...t,i),O(n.value instanceof et,()=>"The function f passed in customGrad(f) must return an object where `obj.value` is a tensor"),O(fr(n.gradFunc),()=>"The function f passed in customGrad(f) must return an object where `obj.gradFunc` is a function."),n.value),a=(o,i)=>{let u=n.gradFunc(o,i),l=Array.isArray(u)?u:[u];O(l.length===t.length,()=>"The function f passed in customGrad(f) must return an object where `obj.gradFunc` is a function that returns the same number of tensors as inputs passed to f(...)."),O(l.every(p=>p instanceof et),()=>"The function f passed in customGrad(f) must return an object where `obj.gradFunc` is a function that returns a list of only tensors.");let c={};return l.forEach((p,d)=>{c[d]=()=>p}),c};return this.runKernelFunc({forwardFunc:r,backwardsFunc:a,inputs:s})}}readSync(e){return this.state.tensorInfo.get(e).backend.readSync(e)}read(e){return this.state.tensorInfo.get(e).backend.read(e)}readToGPU(e,t){return this.state.tensorInfo.get(e).backend.readToGPU(e,t)}async time(e){let t=ju(),n=await this.backend.time(e);return n.wallMs=ju()-t,n}track(e){return this.state.activeScope!=null&&(e.scopeId=this.state.activeScope.id,this.state.activeScope.track.push(e)),e}get registeredVariables(){return this.state.registeredVariables}reset(){this.pendingBackendInitId++,this.state.dispose(),this.ENV.reset(),this.state=new bx;for(let e in 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qE(e,t,n){let s=_(e,"x","batchToSpaceND"),r=t.reduce((i,u)=>i*u);O(s.rank>=1+t.length,()=>`input rank is ${s.rank} but should be > than blockShape.length ${t.length}`),O(n.length===t.length,()=>`crops.length is ${n.length} but should be equal to blockShape.length ${t.length}`),O(s.shape[0]%r===0,()=>`input tensor batch is ${s.shape[0]} but is not divisible by the product of the elements of blockShape ${t.join(" * ")} === ${r}`);let a={x:s},o={blockShape:t,crops:n};return z.runKernel(pi,a,o)}var sb=M({batchToSpaceND_:qE});function jE(e){let t;return e.rank===0||e.rank===1?t=U(e,[1,1,1,e.size]):e.rank===2?t=U(e,[1,1,e.shape[0],e.shape[1]]):e.rank===3?t=U(e,[1,e.shape[0],e.shape[1],e.shape[2]]):t=e,t}function KE(e,t,n,s,r,a){a==null&&(a=.001);let o=_(e,"x","batchNorm"),i=_(t,"mean","batchNorm"),u=_(n,"variance","batchNorm"),l;r!=null&&(l=_(r,"scale","batchNorm"));let c;s!=null&&(c=_(s,"offset","batchNorm")),O(i.rank===u.rank,()=>"Batch normalization gradient requires mean and variance to have equal ranks."),O(c==null||i.rank===c.rank,()=>"Batch normalization gradient requires mean and offset to have equal ranks."),O(l==null||i.rank===l.rank,()=>"Batch normalization gradient requires mean and scale to have equal ranks.");let d={x:jE(o),scale:l,offset:c,mean:i,variance:u},h={varianceEpsilon:a},f=z.runKernel(Ba,d,h);return U(f,o.shape)}var Zu=M({batchNorm_:KE});function XE(e,t,n,s,r,a){let o=_(e,"x","batchNorm"),i=_(t,"mean","batchNorm"),u=_(n,"variance","batchNorm"),l;r!=null&&(l=_(r,"scale","batchNorm"));let c;return s!=null&&(c=_(s,"offset","batchNorm")),O(o.rank===2,()=>`Error in batchNorm2D: x must be rank 2 but got rank ${o.rank}.`),O(i.rank===2||i.rank===1,()=>`Error in batchNorm2D: mean must be rank 2 or rank 1 but got rank ${i.rank}.`),O(u.rank===2||u.rank===1,()=>`Error in batchNorm2D: variance must be rank 2 or rank 1 but got rank ${u.rank}.`),l!=null&&O(l.rank===2||l.rank===1,()=>`Error in batchNorm2D: scale must be rank 2 or rank 1 but got rank ${l.rank}.`),c!=null&&O(c.rank===2||c.rank===1,()=>`Error in batchNorm2D: offset must be rank 2 or rank 1 but got rank ${c.rank}.`),Zu(o,i,u,c,l,a)}var YE=M({batchNorm2d_:XE});function QE(e,t,n,s,r,a){let o=_(e,"x","batchNorm"),i=_(t,"mean","batchNorm"),u=_(n,"variance","batchNorm"),l;r!=null&&(l=_(r,"scale","batchNorm"));let c;return s!=null&&(c=_(s,"offset","batchNorm")),O(o.rank===3,()=>`Error in batchNorm3D: x must be rank 3 but got rank ${o.rank}.`),O(i.rank===3||i.rank===1,()=>`Error in batchNorm3D: mean must be rank 3 or rank 1 but got rank ${i.rank}.`),O(u.rank===3||u.rank===1,()=>`Error in batchNorm3D: variance must be rank 3 or rank 1 but got rank ${u.rank}.`),l!=null&&O(l.rank===3||l.rank===1,()=>`Error in batchNorm3D: scale must be rank 3 or rank 1 but got rank ${l.rank}.`),c!=null&&O(c.rank===3||c.rank===1,()=>`Error in batchNorm3D: offset must be rank 3 or rank 1 but got rank ${c.rank}.`),Zu(o,i,u,c,l,a)}var ZE=M({batchNorm3d_:QE});function JE(e,t,n,s,r,a){let o=_(e,"x","batchNorm"),i=_(t,"mean","batchNorm"),u=_(n,"variance","batchNorm"),l;r!=null&&(l=_(r,"scale","batchNorm"));let c;return s!=null&&(c=_(s,"offset","batchNorm")),O(o.rank===4,()=>`Error in batchNorm4D: x must be rank 4 but got rank ${o.rank}.`),O(i.rank===4||i.rank===1,()=>`Error in batchNorm4D: mean must be rank 4 or rank 1 but got rank ${i.rank}.`),O(u.rank===4||u.rank===1,()=>`Error in batchNorm4D: variance must be rank 4 or rank 1 but got rank ${u.rank}.`),l!=null&&O(l.rank===4||l.rank===1,()=>`Error in batchNorm4D: scale must be rank 4 or rank 1 but got rank ${l.rank}.`),c!=null&&O(c.rank===4||c.rank===1,()=>`Error in batchNorm4D: offset must be rank 4 or rank 1 but got rank ${c.rank}.`),Zu(o,i,u,c,l,a)}var eR=M({batchNorm4d_:JE});function tR(e,t,n){let s=_(e,"x","bincount"),r=_(t,"weights","bincount");O(s.dtype==="int32",()=>`Error in bincount: input dtype must be int32, but got ${s.dtype}`),O(n>=0,()=>`size must be non-negative, but got ${n}.`),O(r.size===s.size||r.size===0,()=>`Error in bincount: weights must have the same size as input or0-length, but got input shape: ${s.shape}, weights shape: ${r.shape}.`);let a={x:s,weights:r},o={size:n};return z.runKernel(vg,a,o)}var fS=M({bincount_:tR});function nR(e,t){let n=_(e,"s0","broadcastArgs","int32"),s=_(t,"s1","broadcastArgs","int32");if(n.rank!==1)throw new Error(`broadcastArgs(): first input must be a vector (rank=1). Has rank ${n.rank}`);if(s.rank!==1)throw new Error(`broadcastArgs(): second input must be a vector (rank=1). Has rank ${s.rank}`);let r={s0:n,s1:s};return z.runKernel(xg,r)}var sR=M({broadcastArgs_:nR});function rR(e,t){let n=_(e,"broadcastTo","x"),s=n.shape;if(t.some(l=>!(l>0)||l%1!==0))throw new Error(`broadcastTo(): Invalid broadcast shape [${t}].`);if(t.length<n.rank)throw new Error(`broadcastTo(): shape.length=${t.length} < input.rank=${n.rank}.`);if(t.length>n.rank){let l=n.shape.slice();for(;l.length<t.length;)l.unshift(1);n=U(n,l)}let r=n.shape,a=Array.from(t);for(let l=t.length-1;l>=0;l--)if(r[l]===t[l])a[l]=1;else if(n.shape[l]!==1)throw new Error(`broadcastTo(): [${s}] cannot be broadcast to [${t}].`);if(a.map((l,c)=>l>1?c:-1).filter(l=>l>=0).length===0)return lr(n);let i={x:n},u={reps:a};return z.runKernel(Tr,i,u)}var id=M({broadcastTo_:rR});function aR(e){let n={x:_(e,"x","ceil","float32")};return z.runKernel($a,n)}var oR=M({ceil_:aR});function iR(e,t,n){let s=_(e,"x","clipByValue");O(t<=n,()=>`Error in clip: min (${t}) must be less than or equal to max (${n}).`);let r={x:s},a={clipValueMin:t,clipValueMax:n};return z.runKernel(Nr,r,a)}var Wn=M({clipByValue_:iR});function uR(e){return Ft(e,0)}var lR=M({concat1d_:uR});function cR(e,t){return Ft(e,t)}var dR=M({concat2d_:cR});function pR(e,t){return Ft(e,t)}var hR=M({concat3d_:pR});function fR(e,t){return Ft(e,t)}var mR=M({concat4d_:fR});function gR(e,t,n,s,r="NHWC",a=[1,1],o){let i=_(e,"x","conv2d","float32"),u=_(t,"filter","conv2d","float32"),l=i,c=!1;i.rank===3&&(c=!0,l=U(i,[1,i.shape[0],i.shape[1],i.shape[2]])),O(l.rank===4,()=>`Error in conv2d: input must be rank 4, but got rank ${l.rank}.`),O(u.rank===4,()=>`Error in conv2d: filter must be rank 4, but got rank ${u.rank}.`),fn("conv2d",s,o);let p=r==="NHWC"?l.shape[3]:l.shape[1];O(p===u.shape[2],()=>`Error in conv2d: depth of input (${p}) must match input depth for filter ${u.shape[2]}.`),O(Ps(n,a),()=>`Error in conv2D: Either strides or dilations must be 1. Got strides ${n} and dilations '${a}'`);let d={x:l,filter:u},h={strides:n,pad:s,dataFormat:r,dilations:a,dimRoundingMode:o},f=z.runKernel(_a,d,h);return c?U(f,[f.shape[1],f.shape[2],f.shape[3]]):f}var da=M({conv2d_:gR});function bR(e,t,n,s,r="NWC",a=1,o){let i=_(e,"x","conv1d"),u=_(t,"filter","conv1d"),l=i,c=!1;i.rank===2&&(c=!0,l=U(i,[1,i.shape[0],i.shape[1]])),O(l.rank===3,()=>`Error in conv1d: input must be rank 3, but got rank ${l.rank}.`),O(u.rank===3,()=>`Error in conv1d: filter must be rank 3, but got rank ${u.rank}.`),fn("conv1d",s,o),O(l.shape[2]===u.shape[1],()=>`Error in conv1d: depth of input (${l.shape[2]}) must match input depth for filter ${u.shape[1]}.`),O(Ps(n,a),()=>`Error in conv1D: Either stride or dilation must be 1. Got stride ${n} and dilation '${a}'`),O(r==="NWC",()=>`Error in conv1d: got dataFormat of ${r} but only NWC is currently supported.`);let p=U(u,[1,u.shape[0],u.shape[1],u.shape[2]]),d=U(l,[l.shape[0],1,l.shape[1],l.shape[2]]),g=da(d,p,[1,n],s,"NHWC",[1,a],o);return c?U(g,[g.shape[2],g.shape[3]]):U(g,[g.shape[0],g.shape[2],g.shape[3]])}var mS=M({conv1d_:bR});function yR(e,t,n,s,r,a="NHWC",o){O(e.length===t.rank,()=>`Length of inShape (${e.length}) and rank of dy (${t.rank}) must match`);let i=e,u=t,l=!1;t.rank===3&&(l=!0,u=U(t,[1,t.shape[0],t.shape[1],t.shape[2]]),i=[1,e[0],e[1],e[2]]),O(i.length===4,()=>`Error in conv2dDerInput: inShape must be length 4, but got length ${i.length}.`),O(u.rank===4,()=>`Error in conv2dDerInput: dy must be rank 4, but got rank ${u.rank}`),O(n.rank===4,()=>`Error in conv2dDerInput: filter must be rank 4, but got rank ${n.rank}`);let c=a==="NHWC"?i[3]:i[1],p=a==="NHWC"?u.shape[3]:u.shape[1];O(c===n.shape[2],()=>`Error in conv2dDerInput: depth of input (${c}) must match input depth for filter ${n.shape[2]}.`),O(p===n.shape[3],()=>`Error in conv2dDerInput: depth of output (${p}) must match output depth for filter ${n.shape[3]}.`),fn("conv2dDerInput",r,o);let d={dy:u,filter:n},h={strides:s,pad:r,dataFormat:a,dimRoundingMode:o,inputShape:i},f=z.runKernel(Aa,d,h);return l?U(f,[f.shape[1],f.shape[2],f.shape[3]]):f}var rb=M({conv2DBackpropInput_:yR});function vR(e,t,n,s,r,a){let o=_(e,"x","conv2dTranspose"),i=_(t,"filter","conv2dTranspose");return rb(n,o,i,s,r,"NHWC",a)}var gS=M({conv2dTranspose_:vR});function xR(e,t,n,s,r="NDHWC",a=[1,1,1]){let o=_(e,"x","conv3d"),i=_(t,"filter","conv3d"),u=o,l=!1;o.rank===4&&(l=!0,u=U(o,[1,o.shape[0],o.shape[1],o.shape[2],o.shape[3]])),O(u.rank===5,()=>`Error in conv3d: input must be rank 5, but got rank ${u.rank}.`),O(i.rank===5,()=>`Error in conv3d: filter must be rank 5, but got rank ${i.rank}.`),O(u.shape[4]===i.shape[3],()=>`Error in conv3d: depth of input (${u.shape[4]}) must match input depth for filter ${i.shape[3]}.`),O(Ps(n,a),()=>`Error in conv3D: Either strides or dilations must be 1. 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${r} and ${t} for depthToSpace with input shape
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t=e.length/2,n=!1;this.accumulatedFirstMoment=e.slice(0,t).map(s=>({originalName:s.name,variable:s.tensor.variable(n)})),this.accumulatedSecondMoment=e.slice(t,t*2).map(s=>({originalName:s.name,variable:s.tensor.variable(n)}))}getConfig(){return{learningRate:this.learningRate,beta1:this.beta1,beta2:this.beta2,epsilon:this.epsilon}}static fromConfig(e,t){return new e(t.learningRate,t.beta1,t.beta2,t.epsilon)}};$b.className="Adam";_r($b);var _b=class extends Er{constructor(e,t,n,s=null,r=0){super(),this.learningRate=e,this.beta1=t,this.beta2=n,this.epsilon=s,this.decay=r,this.accumulatedFirstMoment=[],this.accumulatedWeightedInfNorm=[],q(()=>{this.iteration=we(0).variable(),this.accBeta1=we(t).variable()}),s==null&&(this.epsilon=z.backend.epsilon())}applyGradients(e){let t=Array.isArray(e)?e.map(n=>n.name):Object.keys(e);q(()=>{let n=ge(1,this.accBeta1),s=xe(-this.learningRate,oe(V(this.iteration,this.decay),1));t.forEach((r,a)=>{let 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Error("getWeights() is not implemented for Adamax yet.")}async setWeights(e){throw new Error("setWeights() is not implemented for Adamax yet.")}getConfig(){return{learningRate:this.learningRate,beta1:this.beta1,beta2:this.beta2,epsilon:this.epsilon,decay:this.decay}}static fromConfig(e,t){return new e(t.learningRate,t.beta1,t.beta2,t.epsilon,t.decay)}};_b.className="Adamax";_r(_b);var Rp=class extends Er{constructor(e){super(),this.learningRate=e,this.setLearningRate(e)}applyGradients(e){(Array.isArray(e)?e.map(n=>n.name):Object.keys(e)).forEach((n,s)=>{let r=Array.isArray(e)?e[s].tensor:e[n];if(r==null)return;let a=z.registeredVariables[n];q(()=>{let o=oe(V(this.c,r),a);a.assign(o)})}),this.incrementIterations()}setLearningRate(e){this.learningRate=e,this.c!=null&&this.c.dispose(),this.c=qt(we(-e))}dispose(){this.c.dispose()}async getWeights(){return[await this.saveIterations()]}async setWeights(e){if(e=await this.extractIterations(e),e.length!==0)throw new Error("SGD optimizer does 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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)}};Ab.className="Momentum";_r(Ab);var Eb=class extends Er{constructor(e,t=.9,n=0,s=null,r=!1){if(super(),this.learningRate=e,this.decay=t,this.momentum=n,this.epsilon=s,this.accumulatedMeanSquares=[],this.accumulatedMoments=[],this.accumulatedMeanGrads=[],this.centered=r,s==null&&(this.epsilon=z.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,s)=>{let r=z.registeredVariables[n],a=!1;this.accumulatedMeanSquares[s]==null&&(this.accumulatedMeanSquares[s]={originalName:`${n}/rms`,variable:q(()=>je(r).variable(a))}),this.accumulatedMoments[s]==null&&(this.accumulatedMoments[s]={originalName:`${n}/momentum`,variable:q(()=>je(r).variable(a))}),this.accumulatedMeanGrads[s]==null&&this.centered&&(this.accumulatedMeanGrads[s]={originalName:`${n}/mg`,variable:q(()=>je(r).variable(a))});let o=Array.isArray(e)?e[s].tensor:e[n];if(o==null)return;let i=this.accumulatedMeanSquares[s].variable,u=this.accumulatedMoments[s].variable;q(()=>{let l=oe(V(i,this.decay),V(ct(o),1-this.decay));if(this.centered){let c=this.accumulatedMeanGrads[s].variable,p=oe(V(c,this.decay),V(o,1-this.decay)),d=xe(V(o,this.learningRate),dn(ge(l,oe(ct(p),this.epsilon)))),h=oe(V(u,this.momentum),d);i.assign(l),c.assign(p),u.assign(h);let f=ge(r,h);r.assign(f)}else{let c=oe(V(i,this.decay),V(ct(o),1-this.decay)),p=oe(V(u,this.momentum),xe(V(o,this.learningRate),dn(oe(c,this.epsilon))));i.assign(c),u.assign(p);let d=ge(r,p);r.assign(d)}})}),this.incrementIterations()}dispose(){this.accumulatedMeanSquares!=null&&De(this.accumulatedMeanSquares.map(e=>e.variable)),this.accumulatedMeanGrads!=null&&this.centered&&De(this.accumulatedMeanGrads.map(e=>e.variable)),this.accumulatedMoments!=null&&De(this.accumulatedMoments.map(e=>e.variable))}async getWeights(){let e=[...this.accumulatedMeanSquares,...this.accumulatedMoments];return this.centered&&e.push(...this.accumulatedMeanGrads),[await this.saveIterations()].concat(e.map(t=>({name:t.originalName,tensor:t.variable})))}async setWeights(e){e=await this.extractIterations(e);let t=this.centered?e.length/3:e.length/2,n=!1;this.accumulatedMeanSquares=e.slice(0,t).map(s=>({originalName:s.name,variable:s.tensor.variable(n)})),this.accumulatedMoments=e.slice(t,t*2).map(s=>({originalName:s.name,variable:s.tensor.variable(n)})),this.centered&&(this.accumulatedMeanGrads=e.slice(t*2,t*3).map(s=>({originalName:s.name,variable:s.tensor.variable(n)})))}getConfig(){return{learningRate:this.learningRate,decay:this.decay,momentum:this.momentum,epsilon:this.epsilon,centered:this.centered}}static fromConfig(e,t){return new e(t.learningRate,t.decay,t.momentum,t.epsilon,t.centered)}};Eb.className="RMSProp";_r(Eb);var Gr=class{static sgd(e){return new Rp(e)}static momentum(e,t,n=!1){return new Ab(e,t,n)}static rmsprop(e,t=.9,n=0,s=null,r=!1){return new Eb(e,t,n,s,r)}static adam(e=.001,t=.9,n=.999,s=null){return new $b(e,t,n,s)}static adadelta(e=.001,t=.95,n=null){return new Nb(e,t,n)}static adamax(e=.002,t=.9,n=.999,s=null,r=0){return new _b(e,t,n,s,r)}static adagrad(e,t=.1){return new Tb(e,t)}},Mo={sgd:Gr.sgd,momentum:Gr.momentum,adadelta:Gr.adadelta,adagrad:Gr.adagrad,rmsprop:Gr.rmsprop,adamax:Gr.adamax,adam:Gr.adam},dP=(()=>typeof requestAnimationFrame!="undefined"?requestAnimationFrame:typeof setImmediate!="undefined"?setImmediate:e=>e())();function ZS(){return new Promise(e=>dP(()=>e()))}var 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Input received: ${e}`);for(let n=0;n<e.length;n++){let s=e[n],r=t[n];if(r==null)continue;let a=s.rank;if(r.ndim!=null&&a!==r.ndim)throw new G(`Input ${n} is incompatible with layer ${this.name}: expected ndim=${r.ndim}, found ndim=${a}`);if(r.maxNDim!=null&&a>r.maxNDim)throw new G(`Input ${n} is incompatible with layer ${this.name}: expected max_ndim=${r.maxNDim}, found ndim=${a}`);if(r.minNDim!=null&&a<r.minNDim)throw new G(`Input ${n} is incompatible with layer ${this.name}: expected min_ndim=${r.minNDim}, found ndim=${a}.`);if(r.dtype!=null&&s.dtype!==r.dtype)throw new G(`Input ${n} is incompatible with layer ${this.name} : expected dtype=${r.dtype}, found dtype=${s.dtype}.`);if(r.axes){let o=s.shape;for(let i in r.axes){let u=Number(i),l=r.axes[i],c=u>=0?o[u]:o[o.length+u];if(l!=null&&[l,null].indexOf(c)===-1)throw new G(`Input ${n} is incompatible with layer ${this.name}: expected axis ${u} of input shape to have value ${l} but got shape ${o}.`)}}if(r.shape!=null)for(let o=0;o<r.shape.length;++o){let i=r.shape[o],u=s.shape[o];if(i!=null&&u!=null&&i!==u)throw new G(`Input ${n} is incompatible with layer ${this.name}: expected shape=${r.shape}, found shape=${s.shape}.`)}}}call(e,t){return e}invokeCallHook(e,t){this._callHook!=null&&this._callHook(e,t)}setCallHook(e){this._callHook=e}clearCallHook(){this._callHook=null}apply(e,t){t=t||{},this.assertNotDisposed();let n=ht(e),s=!0;for(let a of n)if(!(a instanceof $s)){s=!1;break}let r=!0;for(let a of n)if(a instanceof $s){r=!1;break}if(s===r)throw new G("Arguments to apply() must be all SymbolicTensors or all Tensors");return na(this.name,()=>{if(!this.built){this.assertInputCompatibility(e);let a=[];for(let o of ht(e))a.push(o.shape);this.build(bn(a)),this.built=!0,this.initialWeights&&this.setWeights(this.initialWeights),this._refCount===null&&r&&(this._refCount=1)}if(this.assertInputCompatibility(e),r){let a=this.call(e,t),o=ht(a),i=[];for(let u of o)n.indexOf(u)!==-1&&(u=u.clone()),i.push(u);if(a=bn(i),this.activityRegularizer!=null)throw new Fe("Layer invocation in the presence of activity regularizer(s) is not supported yet.");return a}else{let a=Fz(e),o=this.computeOutputShape(a),i,u=Oz(e);if(this.warnOnIncompatibleInputShape(Array.isArray(e)?a[0]:a),o!=null&&o.length>0&&Array.isArray(o[0])?i=o.map((l,c)=>new $s(u,l,this,ht(e),t,this.name,c)):i=new $s(u,o,this,ht(e),t,this.name),this.addInboundNode(e,i,null,null,a,o,t),this._refCount++,this.activityRegularizer!=null)throw new Fe("Layer invocation in the presence of activity regularizer(s) is not supported yet.");return i}})}warnOnIncompatibleInputShape(e){if(this.batchInputShape!=null)if(e.length!==this.batchInputShape.length)console.warn(`The rank of the input tensor provided (shape: ${JSON.stringify(e)}) does not match that of the batchInputShape (${JSON.stringify(this.batchInputShape)}) of the layer ${this.name}`);else{let t=!1;this.batchInputShape.forEach((n,s)=>{n!=null&&e[s]!=null&&e[s]!==n&&(t=!0)}),t&&console.warn(`The shape of the input tensor (${JSON.stringify(e)}) does not match the expectation of layer ${this.name}: ${JSON.stringify(this.batchInputShape)}`)}}get outputShape(){if(this.inboundNodes==null||this.inboundNodes.length===0)throw new Bs(`The layer ${this.name} has never been called and thus has no defined output shape.`);let e=[];for(let t of this.inboundNodes){let n=JSON.stringify(t.outputShapes);e.indexOf(n)===-1&&e.push(n)}if(e.length===1){let t=this.inboundNodes[0].outputShapes;return Array.isArray(t)&&Array.isArray(t[0])&&t.length===1?t[0]:t}else throw new Bs(`The layer ${this.name} has multiple inbound nodes with different output shapes. 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The following previous layers were accessed without issue: ${m}`);for(let x of y.outputTensors)f.push(x);m.push(v.name)}}this.nodesByDepth=p;let g=this.layers.map(b=>b.name);for(let b of g){let y=g.filter(v=>v===b).length;if(y!==1)throw new fs(`The name "${b}" is used ${y} times in the model. All layer names should be unique. Layer names: `+JSON.stringify(g))}this.outboundNodes=[],this.inboundNodes=[],new Up({outboundLayer:this,inboundLayers:[],nodeIndices:[],tensorIndices:[],inputTensors:this.inputs,outputTensors:this.outputs,inputMasks:this.inputs.map(b=>null),outputMasks:this.outputs.map(b=>null),inputShapes:this.inputs.map(b=>b.shape),outputShapes:this.outputs.map(b=>b.shape)}),this.built=!0,this._refCount=1}assertNotDisposed(){if(this._refCount===0)throw new Error(`Container '${this.name}' is already disposed.`)}dispose(){this.assertNotDisposed();let e={refCountAfterDispose:null,numDisposedVariables:0};if(--this._refCount===0){for(let t of this.layers)e.numDisposedVariables+=t.dispose().numDisposedVariables;for(let t of this.internalContainerRefs)e.numDisposedVariables+=t.dispose().numDisposedVariables}return e.refCountAfterDispose=this._refCount,e}get trainable(){return this.trainable_}set trainable(e){this.layers.forEach(t=>{t._trainableWeights.forEach(n=>n.trainable=e)}),this.trainable_=e}get trainableWeights(){if(this._trainableWeights.length>0)throw new G("Container instance unexpectedly contains _trainableWeights.The trainable weights of a Container are a union of the trainable weights of its consituent Layers. Its own _trainableWeights must remain an empty Array.");if(!this.trainable)return[];let 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={},s=0;for(let a of this.layers)for(let o of a.weights){if(n[o.originalName]!=null)throw new G(`Duplicate weight name: ${o.originalName}`);n[o.originalName]=o,s++}let r=[];for(let a in e){let o=a;if(n[a]==null){let i=a.split("/");o=i.slice(0,-2).concat([i[i.length-1]]).join("/")}if(n[o]!=null)r.push([n[o],e[a]]);else if(t)throw new G(`Provided weight data has no target variable: ${a}`);delete n[o]}if(t){let a=[];for(let o in n)a.push(o);if(a.length>0)throw new G(`${a.length} of ${s} weights are not set: ${a}`)}Ub(r)}updatedConfig(){let e=this.getConfig(),t={};return t.className=this.getClassName(),t.config=e,t.kerasVersion=`tfjs-layers ${NI}`,t.backend="TensorFlow.js",t}toJSON(e,t=!0){let n=Dm(this.updatedConfig());return t?JSON.stringify(n):n}call(e,t){return q(()=>{e=ht(e);let n=new Jr;for(let s=0;s<this.inputs.length;++s)n.add(this.inputs[s],e[s]);return Fu(this.outputs,n,t)})}computeMask(e,t){return q(()=>{e=ht(e);let n;return t==null?n=ma(null,e.length):n=ht(t),this.runInternalGraph(e,n)[1]})}computeOutputShape(e){let t=_d(e);if(t.length!==this.inputLayers.length)throw new G(`Invalid inputShape argument ${e}: model has ${this.inputLayers.length} tensor inputs.`);let n={};for(let o=0;o<t.length;o++){let i=this.inputLayers[o],u=t[o],l=i.name+"_0_0";n[l]=u}let s=Object.keys(this.nodesByDepth).map(o=>parseInt(o,10)).sort(Kc);if(s.length>1)for(let o of s){let i=this.nodesByDepth[o];for(let u of i){let l=u.outboundLayer;if(this.inputLayers.map(f=>f.id).indexOf(l.id)!==-1)continue;let c=[];for(let f=0;f<u.inboundLayers.length;f++){let m=u.inboundLayers[f],g=u.nodeIndices[f],b=u.tensorIndices[f],y=`${m.name}_${g}_${b}`,v=n[y];c.push(v)}let p=l.computeOutputShape(bn(c)),d=_d(p),h=l.inboundNodes.indexOf(u);for(let f=0;f<d.length;f++){let m=`${l.name}_${h}_${f}`;n[m]=d[f]}}}let r=[],a=[];for(let o=0;o<this.outputLayers.length;o++){let i=this.outputLayers[o],u=this.outputLayersNodeIndices[o],l=this.outputLayersTensorIndices[o],c=`${i.name}_${u}_${l}`;a.push(c)}for(let o=0;o<a.length;o++){let i=a[o];Cs(i in n),r.push(n[i])}return bn(r)}runInternalGraph(e,t){t==null&&(t=ma(null,e.length));let n={};for(let i=0;i<this.inputs.length;++i){let u=this.inputs[i],l=e[i],c=t[i];n[u.id]=[l,c]}let s=Object.keys(this.nodesByDepth).map(i=>parseInt(i,10)).sort(Kc);for(let i of s){let u=this.nodesByDepth[i];for(let l of u){let c=l.outboundLayer,p=l.inputTensors,d=l.outputTensors,h=new Array;for(let f of p)f.id in n&&h.push(n[f.id]);if(h.length===p.length){let f={},m,g,b,y;if(l.callArgs!=null&&(f=l.callArgs),h.length===1){let[v,x]=h[0];f.mask==null&&(f.mask=x),b=ht(c.call(v,f)),y=ht(c.computeMask(v,x)),m=[v],g=[x]}else m=h.map(v=>v[0]),g=h.map(v=>v[1]),f.mask==null&&(f.mask=g),b=ht(c.call(m,f)),y=ht(c.computeMask(m,g));if(c.activityRegularizer)throw new Fe("LayersModel invocation with concrete Tensor value(s) in the presence of activity regularizer(s) is not supported yet.");for(let v=0;v<d.length;++v){let x=d[v],k=b[v],I=y[v];n[x.id]=[k,I]}}}}let r=[],a=[],o=[];for(let i of this.outputs){Cs(i.id in n,`Could not compute output ${i.name} : ${i.id}`);let[u,l]=n[i.id];o.push(u.shape),r.push(u),a.push(l)}return[r,a,o]}buildNodeConversionMap(e){let t={},n;for(let s of this.layers){n=s instanceof Is?1:0;for(let r=0;r<s.inboundNodes.length;r++){let a=Is.nodeKey(s,r);this.containerNodes.has(a)&&(t[a]=n,n+=1)}}return t}getLayer(e,t){if(t!=null){if(this.layers.length<=t)throw new G(`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 G("Provide either a layer name or layer index");for(let n of this.layers)if(n.name===e)return n;throw new G(`No such layer: ${e}`)}calculateLosses(){return q(()=>{let e=[];for(let t of this.layers)for(let n=0;n<t.inboundNodes.length;++n){let s=Is.nodeKey(t,n);this.containerNodes.has(s)&&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 o=a.getClassName(),i=a.getConfig(),u=[];for(let c=0;c<a.inboundNodes.length;c++){let p=a.inboundNodes[c],d=Is.nodeKey(a,c),h={};if(this.containerNodes.has(d)){if(p.callArgs)try{JSON.stringify(p.callArgs),h=p.callArgs}catch(f){console.warn(`Layer ${a.name} was passed non-serializable keyword arguments: ${p.callArgs}. They will not be included in the serialized model (and thus will be missing at deserialization time).`),h={}}if(p.inboundLayers.length>0){let f=[];for(let m=0;m<p.inboundLayers.length;m++){let g=p.inboundLayers[m],b=p.nodeIndices[m],y=p.tensorIndices[m],v=Is.nodeKey(g,b),x=t[v];x==null&&(x=0),f.push([g.name,x,y,h])}u.push(f)}}}let l={};l.name=a.name,l.className=o,l.config=i,l.inboundNodes=u,n.push(l)}e.layers=n;let s=[];for(let a=0;a<this.inputLayers.length;a++){let o=this.inputLayers[a],i=this.inputLayersNodeIndices[a],u=Is.nodeKey(o,i);if(!this.containerNodes.has(u))continue;let l=t[u];l==null&&(l=0);let c=this.inputLayersTensorIndices[a];s.push([o.name,l,c])}e.inputLayers=s;let r=[];for(let a=0;a<this.outputLayers.length;a++){let o=this.outputLayers[a],i=this.outputLayersNodeIndices[a],u=Is.nodeKey(o,i);if(!this.containerNodes.has(u))continue;let l=t[u];l==null&&(l=0);let c=this.outputLayersTensorIndices[a];r.push([o.name,l,c])}return e.outputLayers=r,e}static fromConfig(e,t,n={},s=!1){let r={},a={};function o(m,g){m.name in a?a[m.name].push(g):a[m.name]=[g]}function i(m,g){let b=[],y;for(let v of g){let x=v[0],k=v[1],I=v[2];if(y=v[3]==null?{}:v[3],!(x in r)){o(m,g);return}let $=r[x];if($.inboundNodes.length<=k){o(m,g);return}let R=$.inboundNodes[k];b.push(R.outputTensors[I])}b.length>0&&m.apply(bn(b),y)}function u(m){let g=m.name,b=gs(m,t.customObjects!=null?t.customObjects:{});b.setFastWeightInitDuringBuild(s),r[g]=b,m.inboundNodes.forEach(v=>{if(!(v instanceof Array))throw new G(`Corrupted configuration, expected array for nodeData: ${v}`);o(b,v)})}let l=t.name,c=t.layers;for(let m of c)u(m);for(;!iz(a);)for(let m of c){let g=r[m.name];if(g.name in a){let b=a[g.name];delete a[g.name];for(let y of b)i(g,y)}}let p=[],d=[],h=t.inputLayers;for(let m of h){let g=m[0],b=m[1],y=m[2];Cs(g in r);let x=r[g].inboundNodes[b].outputTensors;p.push(x[y])}let f=t.outputLayers;for(let m of f){let g=m[0],b=m[1],y=m[2];Cs(g in r);let x=r[g].inboundNodes[b].outputTensors;d.push(x[y])}return new e({inputs:p,outputs:d,name:l})}get stateful(){if(this._stateful)throw new G("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(){q(()=>{this.layers.forEach(e=>{e.stateful&&e.resetStates()})})}};function GB(e,t,n){let s=t.length;if(e==null||Array.isArray(e)&&e.length===0)return t.map(r=>null);if(s===1)return Array.isArray(e)&&e.length===1?e:typeof e=="object"&&t[0]in e?[e[t[0]]]:[e];if(Array.isArray(e)){if(e.length!==s)throw new Error(`Provided ${n} is an array of ${e.length} element(s), but the model has ${s} outputs. Make sure a set of weights is provided for each model output.`);return e}else if(typeof e=="object"&&Object.keys(e).length>0&&typeof e[Object.keys(e)[0]]=="object"){let r=[];return t.forEach(a=>{a in e?r.push(e[a]):r.push(null)}),r}else throw new Error(`The model has multiple (${s}) outputs, so ${n} must be either an array with ${s} elements or an object with ${t} keys. Provided ${n} not understood: ${JSON.stringify(e)}`)}function TI(e,t){return GB(e,t,"classWeight")}async function $I(e,t,n,s){if(t!=null||s!=null)throw new Error("Support sampleWeight is not implemented yet");if(n!=null){let r=q(()=>{if(e.shape.length===1)return lr(e);if(e.shape.length===2){if(e.shape[1]>1)return Yu(e,1);if(e.shape[1]===1)return U(e,[e.shape[0]]);throw new Error(`Encountered unexpected last-dimension size (${e.shape[1]}) during handling of class weights. The size is expected to be >= 1.`)}else throw new Error(`Unexpected rank of target (y) tensor (${e.rank}) during handling of class weights. The rank is expected to be 1 or 2.`)}),a=Array.from(await r.data());De(r);let o=[];return a.forEach(i=>{if(n[i]==null)throw new Error(`classWeight must contain all classes in the training data. The class ${i} exists in the data but not in classWeight`);o.push(n[i])}),Zt(o,"float32")}else return null}function HB(e,t){return V(e,t)}var qB=32;function _I(e,t){let n,s,r=t;n=r.xs,s=r.ys,w.assert(n!=null&&s!=null,()=>`A Dataset iterator for fitDataset() is expected to generate objects of the form \`{xs: xVal, ys: yVal}\`, where the two values may be \`tf.Tensor\`, an array of Tensors, or a map of string to Tensor. The provided Dataset instead generates ${t}`);let a=qx("input",e.inputNames,n),o=qx("output",e.outputNames,s),i=a[0].shape[0];w.assert(a.length===e.inputs.length,()=>`LayersModel has ${e.inputs.length} inputs, but the dataset provides ${a.length} inputs. (Expected input keys: ${JSON.stringify(e.inputNames)})`),w.assert(o.length===e.outputs.length,()=>`LayersModel has ${e.outputs.length} outputs, but the dataset provides ${o.length} outputs. (Expected output keys: ${JSON.stringify(e.outputNames)})`);for(let u=0;u<a.length;u++)w.assert(a[u].shape[0]===i,()=>`Batch size mismatch: input ${e.inputNames[u]} has ${a[u].shape[0]}; expected ${i} based on input ${e.inputNames[0]}.`);for(let u=0;u<o.length;u++)w.assert(o[u].shape[0]===i,()=>`Batch size mismatch: output ${e.outputNames[u]} has ${o[u].shape[0]}; expected ${i} based on input ${e.inputNames[0]}.`);return{xs:a,ys:o}}function qx(e,t,n){if(n instanceof et)return[n];if(Array.isArray(n))return w.assert(n.length===t.length,()=>`Received an array of ${n.length} Tensors, but expected ${t.length} to match the ${e} keys ${t}.`),n;{let s=[];for(let r of t){if(n[r]==null)throw new G(`The feature data generated by the dataset lacks the required ${e} key '${r}'.`);s.push(n[r])}return s}}function jB(e){if(e.length===3)throw new Fe("Validation with sample weights is not implemented yet.");return{xs:e[0],ys:e[1]}}async function KB(e,t,n){let s=n.batchesPerEpoch!=null;if(w.assert(e.optimizer!=null,()=>"You must compile a model before training/testing. Use LayersModel.compile(modelCompileConfig)."),w.assert(n!=null,()=>"For fitDataset(), the 2nd argument (config) is required, but it is not provided in this call."),w.assert(n.epochs!=null&&n.epochs>0&&Number.isInteger(n.epochs),()=>`For fitDataset(), config.epochs is expected to be a positive integer, but got ${n.epochs}`),w.assert(!s||n.batchesPerEpoch>0&&Number.isInteger(n.batchesPerEpoch),()=>`For fitDataset(), config.batchesPerEpoch is expected to be a positive integer if specified, but got ${n.batchesPerEpoch}`),w.assert(n.validationSplit==null,()=>"`validationSplit` is not supported by `fitDataset()`. Use validationData instead."),e.isTraining)throw new Error("Cannot start training because another fit() call is ongoing.");e.isTraining=!0;try{let r=n.validationData!=null,a,o;if(r)if(jx(n.validationData))w.assert(n.validationBatches==null||n.validationBatches>0&&Number.isInteger(n.validationBatches),()=>`For fitDataset() with dataset-based validation, config.validationBatches is expected not to be provided, or to be a positive integer, but got ${n.validationBatches}`);else{let g=jB(n.validationData);a=g.xs,o=g.ys}let i=e.makeTrainFunction(),u=e.getDedupedMetricsNames(),l;r?l=u.slice().concat(u.map(g=>"val_"+g)):l=u.slice();let c=yI(n.callbacks,n.yieldEvery),p=n.verbose==null?1:n.verbose,{callbackList:d,history:h}=vI(c,p,n.epochs,null,null,XB(t,n),null,r,l);d.setModel(e),e.history=h,await d.onTrainBegin(),e.stopTraining_=!1;let f=n.initialEpoch==null?0:n.initialEpoch,m=await t.iterator();for(;f<n.epochs;){let g={};await d.onEpochBegin(f);let b=0,y=0;for(s||(m=await t.iterator());!s||b<n.batchesPerEpoch;){let v=await m.next();if(s&&v.done){console.warn(`You provided \`batchesPerEpoch\` as ${n.batchesPerEpoch}, but your dataset iterator ran out of data after ${b} batches; interrupting training. Make sure that your dataset can generate at least \`batchesPerEpoch * epochs\` batches (in this case, ${n.batchesPerEpoch*n.epochs} batches). 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e={theta:this.theta},t=super.getConfig();return Object.assign(e,t),e}};uy.className="ThresholdedReLU";re.registerClass(uy);var ly=class extends He{constructor(e){super(e==null?{}:e),this.DEFAULT_AXIS=1,e==null&&(e={}),this.softmax=new ny().apply,this.axis=e.axis==null?this.DEFAULT_AXIS:e.axis}call(e,t){let n=Oe(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}};ly.className="Softmax";re.registerClass(ly);function Jo(e,t,n){if(typeof e=="number")return ma(e,t);if(e.length!==t)throw new G(`The ${n} argument must be an integer or tuple of ${t} integers. Received: ${e.length} elements.`);for(let s=0;s<t;++s){let r=e[s];if(!yz(r))throw new G(`The ${n} argument must be an integer or tuple of ${t} integers. Received: ${JSON.stringify(e)} including a non-integer number ${r}`)}return e}function bs(e,t,n,s,r=1){if(e==null)return e;let a=t+(t-1)*(r-1),o;return n==="same"?o=e:o=e-a+1,Math.floor((o+s-1)/s)}function Ns(e,t,n,s){if(e==null)return null;if(s==="valid")e=e*t+yr([n-t,0]);else if(s==="same")e=e*t;else throw new G(`Unsupport padding mode: ${s}.`);return e}function cy(e,t){return q(()=>(Ct(t),t==="channelsFirst"?Ge(e,[0,2,3,1]):e))}function qI(e,t){return q(()=>(Ct(t),t==="channelsFirst"?Ge(e,[0,2,3,4,1]):e))}function pV(e,t,n,s=1,r="valid",a,o=1){return q(()=>{if(a==null&&(a=vs()),Ct(a),e.shape.length!==3)throw new G(`The input of a conv1dWithBias operation should be 3, but is ${e.shape.length} instead.`);if(t.shape.length!==3)throw new G(`The kernel for a conv1dWithBias operation should be 3, but is ${t.shape.length} instead`);if(n!=null&&n.shape.length!==1)throw new G(`The bias for a conv1dWithBias operation should be 1, but is ${t.shape.length} instead`);if(a==="channelsFirst"&&(e=Ge(e,[0,2,1])),r==="causal")throw new Fe("The support for CAUSAL padding mode in conv1dWithBias is not implemented yet.");let i=mS(e,t,s,r==="same"?"same":"valid","NWC",o);return n!=null&&(i=ks(i,n)),i})}function Jx(e,t,n,s=[1,1],r="valid",a,o,i=null){return q(()=>{if(a==null&&(a=vs()),Ct(a),e.rank!==3&&e.rank!==4)throw new G(`conv2dWithBiasActivation expects input to be of rank 3 or 4, but received ${e.rank}.`);if(t.rank!==3&&t.rank!==4)throw new G(`conv2dWithBiasActivation expects kernel to be of rank 3 or 4, but received ${e.rank}.`);let u=cy(e,a);if(r==="causal")throw new Fe("The support for CAUSAL padding mode in conv1dWithBias is not implemented yet.");return u=fa.conv2d({x:u,filter:t,strides:s,pad:r==="same"?"same":"valid",dilations:o,dataFormat:"NHWC",bias:n,activation:i}),a==="channelsFirst"&&(u=Ge(u,[0,3,1,2])),u})}function hV(e,t,n,s=[1,1,1],r="valid",a,o){return q(()=>{if(a==null&&(a=vs()),Ct(a),e.rank!==4&&e.rank!==5)throw new G(`conv3dWithBias expects input to be of rank 4 or 5, but received ${e.rank}.`);if(t.rank!==4&&t.rank!==5)throw new G(`conv3dWithBias expects kernel to be of rank 4 or 5, but received ${e.rank}.`);let i=qI(e,a);if(r==="causal")throw new Fe("The support for CAUSAL padding mode in conv3dWithBias is not implemented yet.");return i=bS(i,t,s,r==="same"?"same":"valid","NDHWC",o),n!=null&&(i=ks(i,n)),a==="channelsFirst"&&(i=Ge(i,[0,4,1,2,3])),i})}var dy=class extends He{constructor(e,t){if(super(t),this.bias=null,this.DEFAULT_KERNEL_INITIALIZER="glorotNormal",this.DEFAULT_BIAS_INITIALIZER="zeros",dy.verifyArgs(t),this.rank=e,Bt(this.rank,"rank"),this.rank!==1&&this.rank!==2&&this.rank!==3)throw new Fe(`Convolution layer for rank other than 1, 2, or 3 (${this.rank}) is not implemented yet.`);if(this.kernelSize=Jo(t.kernelSize,e,"kernelSize"),this.strides=Jo(t.strides==null?1:t.strides,e,"strides"),this.padding=t.padding==null?"valid":t.padding,Hn(this.padding),this.dataFormat=t.dataFormat==null?"channelsLast":t.dataFormat,Ct(this.dataFormat),this.activation=xr(t.activation),this.useBias=t.useBias==null?!0:t.useBias,this.biasInitializer=ft(t.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.biasConstraint=Pt(t.biasConstraint),this.biasRegularizer=mt(t.biasRegularizer),this.activityRegularizer=mt(t.activityRegularizer),this.dilationRate=Jo(t.dilationRate==null?1:t.dilationRate,e,"dilationRate"),this.rank===1&&Array.isArray(this.dilationRate)&&this.dilationRate.length!==1)throw new G(`dilationRate must be a number or an array of a single number for 1D convolution, but received ${JSON.stringify(this.dilationRate)}`);if(this.rank===2){if(typeof this.dilationRate=="number")this.dilationRate=[this.dilationRate,this.dilationRate];else if(this.dilationRate.length!==2)throw new G(`dilationRate must be a number or array of two numbers for 2D convolution, but received ${JSON.stringify(this.dilationRate)}`)}else if(this.rank===3){if(typeof this.dilationRate=="number")this.dilationRate=[this.dilationRate,this.dilationRate,this.dilationRate];else if(this.dilationRate.length!==3)throw new G(`dilationRate must be a number or array of three numbers for 3D convolution, but received ${JSON.stringify(this.dilationRate)}`)}}static verifyArgs(e){if(Cs("kernelSize"in e,"required key 'kernelSize' not in config"),typeof e.kernelSize!="number"&&!Fb(e.kernelSize,"number",1,3))throw new G(`BaseConv expects config.kernelSize to be number or number[] with length 1, 2, or 3, but received ${JSON.stringify(e.kernelSize)}.`)}getConfig(){let e={kernelSize:this.kernelSize,strides:this.strides,padding:this.padding,dataFormat:this.dataFormat,dilationRate:this.dilationRate,activation:vr(this.activation),useBias:this.useBias,biasInitializer:yt(this.biasInitializer),biasRegularizer:it(this.biasRegularizer),activityRegularizer:it(this.activityRegularizer),biasConstraint:Ot(this.biasConstraint)},t=super.getConfig();return Object.assign(e,t),e}},Xl=class extends dy{constructor(e,t){super(e,t),this.kernel=null,Xl.verifyArgs(t),this.filters=t.filters,Bt(this.filters,"filters"),this.kernelInitializer=ft(t.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.kernelConstraint=Pt(t.kernelConstraint),this.kernelRegularizer=mt(t.kernelRegularizer)}build(e){e=nt(e);let t=this.dataFormat==="channelsFirst"?1:e.length-1;if(e[t]==null)throw new G(`The channel dimension of the input should be defined. Found ${e[t]}`);let n=e[t],s=this.kernelSize.concat([n,this.filters]);this.kernel=this.addWeight("kernel",s,null,this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint),this.useBias&&(this.bias=this.addWeight("bias",[this.filters],null,this.biasInitializer,this.biasRegularizer,!0,this.biasConstraint)),this.inputSpec=[{ndim:this.rank+2,axes:{[t]:n}}],this.built=!0}call(e,t){return q(()=>{e=Oe(e);let n,s=this.bias==null?null:this.bias.read(),r=aI(this.activation.getClassName());if(r!=null&&this.rank===2)n=Jx(e,this.kernel.read(),s,this.strides,this.padding,this.dataFormat,this.dilationRate,r);else{if(this.rank===1)n=pV(e,this.kernel.read(),s,this.strides[0],this.padding,this.dataFormat,this.dilationRate[0]);else if(this.rank===2)n=Jx(e,this.kernel.read(),s,this.strides,this.padding,this.dataFormat,this.dilationRate);else if(this.rank===3)n=hV(e,this.kernel.read(),s,this.strides,this.padding,this.dataFormat,this.dilationRate);else throw new Fe("convolutions greater than 3D are not implemented yet.");this.activation!=null&&(n=this.activation.apply(n))}return n})}computeOutputShape(e){e=nt(e);let t=[],n=this.dataFormat==="channelsLast"?e.slice(1,e.length-1):e.slice(2);for(let r=0;r<n.length;++r){let a=bs(n[r],this.kernelSize[r],this.padding,this.strides[r],typeof this.dilationRate=="number"?this.dilationRate:this.dilationRate[r]);t.push(a)}let s=[e[0]];return this.dataFormat==="channelsLast"?(s=s.concat(t),s.push(this.filters)):(s.push(this.filters),s=s.concat(t)),s}getConfig(){let e={filters:this.filters,kernelInitializer:yt(this.kernelInitializer),kernelRegularizer:it(this.kernelRegularizer),kernelConstraint:Ot(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 G(`Convolution layer expected config.filters to be a 'number' > 0 but got ${JSON.stringify(e.filters)}`)}},jI=class extends Xl{constructor(e){super(2,e),jI.verifyArgs(e)}getConfig(){let e=super.getConfig();return delete e.rank,e}static verifyArgs(e){if(typeof e.kernelSize!="number"&&!Fb(e.kernelSize,"number",1,2))throw new G(`Conv2D expects config.kernelSize to be number or number[] with length 1 or 2, but received ${JSON.stringify(e.kernelSize)}.`)}},qp=jI;qp.className="Conv2D";re.registerClass(qp);var KI=class extends Xl{constructor(e){super(3,e),KI.verifyArgs(e)}getConfig(){let e=super.getConfig();return delete e.rank,e}static verifyArgs(e){if(typeof e.kernelSize!="number"&&!(Array.isArray(e.kernelSize)&&(e.kernelSize.length===1||e.kernelSize.length===3)))throw new G(`Conv3D expects config.kernelSize to be number or [number, number, number], but received ${JSON.stringify(e.kernelSize)}.`)}},jp=KI;jp.className="Conv3D";re.registerClass(jp);var py=class extends qp{constructor(e){if(super(e),this.inputSpec=[new Dt({ndim:4})],this.padding!=="same"&&this.padding!=="valid")throw new G(`Conv2DTranspose currently supports only padding modes 'same' and 'valid', but received padding mode ${this.padding}`)}build(e){if(e=nt(e),e.length!==4)throw new G("Input should have rank 4; Received input shape: "+JSON.stringify(e));let t=this.dataFormat==="channelsFirst"?1:e.length-1;if(e[t]==null)throw new G("The channel dimension of the inputs should be defined. Found `None`.");let n=e[t],s=this.kernelSize.concat([this.filters,n]);this.kernel=this.addWeight("kernel",s,"float32",this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint),this.useBias&&(this.bias=this.addWeight("bias",[this.filters],"float32",this.biasInitializer,this.biasRegularizer,!0,this.biasConstraint)),this.inputSpec=[new Dt({ndim:4,axes:{[t]:n}})],this.built=!0}call(e,t){return q(()=>{let n=Oe(e);if(n.shape.length!==4)throw new G(`Conv2DTranspose.call() expects input tensor to be rank-4, but received a tensor of rank-${n.shape.length}`);let s=n.shape,r=s[0],a,o;this.dataFormat==="channelsFirst"?(a=2,o=3):(a=1,o=2);let i=s[a],u=s[o],l=this.kernelSize[0],c=this.kernelSize[1],p=this.strides[0],d=this.strides[1],h=Ns(i,p,l,this.padding),f=Ns(u,d,c,this.padding),m=[r,h,f,this.filters];this.dataFormat!=="channelsLast"&&(n=Ge(n,[0,2,3,1]));let g=gS(n,this.kernel.read(),m,this.strides,this.padding);return this.dataFormat!=="channelsLast"&&(g=Ge(g,[0,3,1,2])),this.bias!=null&&(g=ks(g,this.bias.read(),this.dataFormat)),this.activation!=null&&(g=this.activation.apply(g)),g})}computeOutputShape(e){e=nt(e);let t=e.slice(),n,s,r;this.dataFormat==="channelsFirst"?(n=1,s=2,r=3):(n=3,s=1,r=2);let a=this.kernelSize[0],o=this.kernelSize[1],i=this.strides[0],u=this.strides[1];return t[n]=this.filters,t[s]=Ns(t[s],i,a,this.padding),t[r]=Ns(t[r],u,o,this.padding),t}getConfig(){let e=super.getConfig();return delete e.dilationRate,e}};py.className="Conv2DTranspose";re.registerClass(py);var hy=class extends jp{constructor(e){if(super(e),this.inputSpec=[new Dt({ndim:5})],this.padding!=="same"&&this.padding!=="valid")throw new G(`Conv3DTranspose currently supports only padding modes 'same' and 'valid', but received padding mode ${this.padding}`)}build(e){if(e=nt(e),e.length!==5)throw new G("Input should have rank 5; Received input shape: "+JSON.stringify(e));let t=this.dataFormat==="channelsFirst"?1:e.length-1;if(e[t]==null)throw new G("The channel dimension of the inputs should be defined. Found `None`.");let n=e[t],s=this.kernelSize.concat([this.filters,n]);this.kernel=this.addWeight("kernel",s,"float32",this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint),this.useBias&&(this.bias=this.addWeight("bias",[this.filters],"float32",this.biasInitializer,this.biasRegularizer,!0,this.biasConstraint)),this.inputSpec=[new Dt({ndim:5,axes:{[t]:n}})],this.built=!0}call(e,t){return q(()=>{let n=Oe(e);if(n.shape.length!==5)throw new G(`Conv3DTranspose.call() expects input tensor to be rank-4, but received a tensor of rank-${n.shape.length}`);let s=n.shape,r=s[0],a,o,i;this.dataFormat==="channelsFirst"?(i=2,a=3,o=4):(i=1,a=2,o=3);let u=s[i],l=s[a],c=s[o],p=this.kernelSize[0],d=this.kernelSize[1],h=this.kernelSize[2],f=this.strides[0],m=this.strides[1],g=this.strides[2],b=Ns(u,f,p,this.padding),y=Ns(l,m,d,this.padding),v=Ns(c,g,h,this.padding),x=[r,b,y,v,this.filters];this.dataFormat!=="channelsLast"&&(n=Ge(n,[0,2,3,4,1]));let k=SR(n,this.kernel.read(),x,this.strides,this.padding);return this.dataFormat!=="channelsLast"&&(k=Ge(k,[0,4,1,2,3])),this.bias!==null&&(k=ks(k,this.bias.read(),this.dataFormat)),this.activation!==null&&(k=this.activation.apply(k)),k})}computeOutputShape(e){e=nt(e);let t=e.slice(),n,s,r,a;this.dataFormat==="channelsFirst"?(n=1,s=2,r=3,a=4):(n=4,s=1,r=2,a=3);let o=this.kernelSize[0],i=this.kernelSize[1],u=this.kernelSize[2],l=this.strides[0],c=this.strides[1],p=this.strides[2];return t[n]=this.filters,t[s]=Ns(t[s],l,o,this.padding),t[r]=Ns(t[r],c,i,this.padding),t[a]=Ns(t[a],p,u,this.padding),t}getConfig(){let e=super.getConfig();return delete e.dilationRate,e}};hy.className="Conv3DTranspose";re.registerClass(hy);var XI=class extends Xl{constructor(e,t){if(super(e,t),this.DEFAULT_DEPTHWISE_INITIALIZER="glorotUniform",this.DEFAULT_POINTWISE_INITIALIZER="glorotUniform",this.depthwiseKernel=null,this.pointwiseKernel=null,t.filters==null)throw new G("The `filters` configuration field is required by SeparableConv, but is unspecified.");if(t.kernelInitializer!=null||t.kernelRegularizer!=null||t.kernelConstraint!=null)throw new G("Fields kernelInitializer, kernelRegularizer and kernelConstraint are invalid for SeparableConv2D. Use depthwiseInitializer, depthwiseRegularizer, depthwiseConstraint, pointwiseInitializer, pointwiseRegularizer and pointwiseConstraint instead.");if(t.padding!=null&&t.padding!=="same"&&t.padding!=="valid")throw new G(`SeparableConv${this.rank}D supports only padding modes: 'same' and 'valid', but received ${JSON.stringify(t.padding)}`);this.depthMultiplier=t.depthMultiplier==null?1:t.depthMultiplier,this.depthwiseInitializer=ft(t.depthwiseInitializer||this.DEFAULT_DEPTHWISE_INITIALIZER),this.depthwiseRegularizer=mt(t.depthwiseRegularizer),this.depthwiseConstraint=Pt(t.depthwiseConstraint),this.pointwiseInitializer=ft(t.depthwiseInitializer||this.DEFAULT_POINTWISE_INITIALIZER),this.pointwiseRegularizer=mt(t.pointwiseRegularizer),this.pointwiseConstraint=Pt(t.pointwiseConstraint)}build(e){if(e=nt(e),e.length<this.rank+2)throw new G(`Inputs to SeparableConv${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 G(`The channel dimension of the inputs should be defined, but found ${JSON.stringify(e[t])}`);let n=e[t],s=this.kernelSize.concat([n,this.depthMultiplier]),r=[];for(let o=0;o<this.rank;++o)r.push(1);r.push(n*this.depthMultiplier,this.filters);let a=!0;this.depthwiseKernel=this.addWeight("depthwise_kernel",s,"float32",this.depthwiseInitializer,this.depthwiseRegularizer,a,this.depthwiseConstraint),this.pointwiseKernel=this.addWeight("pointwise_kernel",r,"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 Dt({ndim:this.rank+2,axes:{[t]:n}})],this.built=!0}call(e,t){return q(()=>{e=Oe(e);let n;if(this.rank===1)throw new Fe("1D separable convolution is not implemented yet.");return this.rank===2&&(this.dataFormat==="channelsFirst"&&(e=Ge(e,[0,2,3,1])),n=F3(e,this.depthwiseKernel.read(),this.pointwiseKernel.read(),this.strides,this.padding,this.dilationRate,"NHWC")),this.useBias&&(n=ks(n,this.bias.read(),this.dataFormat)),this.activation!=null&&(n=this.activation.apply(n)),this.dataFormat==="channelsFirst"&&(n=Ge(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=yt(this.depthwiseInitializer),e.pointwiseInitializer=yt(this.pointwiseInitializer),e.depthwiseRegularizer=it(this.depthwiseRegularizer),e.pointwiseRegularizer=it(this.pointwiseRegularizer),e.depthwiseConstraint=Ot(this.depthwiseConstraint),e.pointwiseConstraint=Ot(this.pointwiseConstraint),e}};XI.className="SeparableConv";var fy=class extends XI{constructor(e){super(2,e)}};fy.className="SeparableConv2D";re.registerClass(fy);var YI=class extends Xl{constructor(e){super(1,e),YI.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"&&!Fb(e.kernelSize,"number",1,1))throw new G(`Conv1D expects config.kernelSize to be number or number[] with length 1, but received ${JSON.stringify(e.kernelSize)}.`)}},my=YI;my.className="Conv1D";re.registerClass(my);var gy=class extends He{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 q(()=>{if(e=Oe(e),this.dataFormat==="channelsLast"){let n=Yc(e,this.cropping[0][0],e.shape[1]-this.cropping[0][0]-this.cropping[0][1],2);return Yc(n,this.cropping[1][0],e.shape[2]-this.cropping[1][1]-this.cropping[1][0],3)}else{let n=Yc(e,this.cropping[0][0],e.shape[2]-this.cropping[0][0]-this.cropping[0][1],3);return Yc(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}};gy.className="Cropping2D";re.registerClass(gy);var by=class extends He{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,Ct(this.dataFormat),this.interpolation=e.interpolation==null?"nearest":e.interpolation,mz(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 q(()=>{let n=Oe(e),s=n.shape;if(this.dataFormat==="channelsFirst"){n=Ge(n,[0,2,3,1]);let r=this.size[0]*s[2],a=this.size[1]*s[3],o=this.interpolation==="nearest"?Kn.resizeNearestNeighbor(n,[r,a]):Kn.resizeBilinear(n,[r,a]);return Ge(o,[0,3,1,2])}else{let r=this.size[0]*s[1],a=this.size[1]*s[2];return this.interpolation==="nearest"?Kn.resizeNearestNeighbor(n,[r,a]):Kn.resizeBilinear(n,[r,a])}})}getConfig(){let e={size:this.size,dataFormat:this.dataFormat,interpolation:this.interpolation},t=super.getConfig();return Object.assign(e,t),e}};by.className="UpSampling2D";re.registerClass(by);function fV(e,t,n=[1,1],s="valid",r,a){return q(()=>{r==null&&(r=vs()),Ct(r);let o=cy(e,r);if(e.rank!==4)throw new G(`Input for depthwiseConv2d is required to be 4-D, but is instead ${e.rank}-D`);if(t.rank!==4)throw new G(`depthwiseKernel is required to be 4-D, but is instead ${t.rank}-D`);return o=kp(o,t,n,s==="same"?"same":"valid","NHWC",a),r==="channelsFirst"&&(o=Ge(o,[0,3,1,2])),o})}var yy=class extends dy{constructor(e){super(2,e),this.depthwiseKernel=null,this.depthMultiplier=e.depthMultiplier==null?1:e.depthMultiplier,this.depthwiseInitializer=ft(e.depthwiseInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.depthwiseConstraint=Pt(e.depthwiseConstraint),this.depthwiseRegularizer=mt(e.depthwiseRegularizer)}build(e){if(e=nt(e),e.length<4)throw new G(`Inputs to DepthwiseConv2D should have rank 4. Received input shape: ${JSON.stringify(e)}.`);let t=this.dataFormat==="channelsFirst"?1:3;if(e[t]==null||e[t]<0)throw new G(`The channel dimension of the inputs to DepthwiseConv2D should be defined, but is not (${e[t]}).`);let n=e[t],s=[this.kernelSize[0],this.kernelSize[1],n,this.depthMultiplier];this.depthwiseKernel=this.addWeight("depthwise_kernel",s,null,this.depthwiseInitializer,this.depthwiseRegularizer,!0,this.depthwiseConstraint),this.useBias?this.bias=this.addWeight("bias",[n*this.depthMultiplier],null,this.biasInitializer,this.biasRegularizer,!0,this.biasConstraint):this.bias=null,this.built=!0}call(e,t){return q(()=>{e=Oe(e);let n=fV(e,this.depthwiseKernel.read(),this.strides,this.padding,this.dataFormat,null);return this.useBias&&(n=ks(n,this.bias.read(),this.dataFormat)),this.activation!=null&&(n=this.activation.apply(n)),n})}computeOutputShape(e){e=nt(e);let t=this.dataFormat==="channelsFirst"?e[2]:e[1],n=this.dataFormat==="channelsFirst"?e[3]:e[2],s=this.dataFormat==="channelsFirst"?e[1]*this.depthMultiplier:e[3]*this.depthMultiplier,r=bs(t,this.kernelSize[0],this.padding,this.strides[0]),a=bs(n,this.kernelSize[1],this.padding,this.strides[1]);return this.dataFormat==="channelsFirst"?[e[0],s,r,a]:[e[0],r,a,s]}getConfig(){let e=super.getConfig();return e.depthMultiplier=this.depthMultiplier,e.depthwiseInitializer=yt(this.depthwiseInitializer),e.depthwiseRegularizer=it(this.depthwiseRegularizer),e.depthwiseConstraint=Ot(this.depthwiseRegularizer),e}};yy.className="DepthwiseConv2D";re.registerClass(yy);function QI(e,t,n,s){if(Array.isArray(e)){if(t!=null||n!=null)throw new G("When inputs is an array, neither initialState or constants should be provided");s!=null&&(n=e.slice(e.length-s,e.length),e=e.slice(0,e.length-s)),e.length>1&&(t=e.slice(1,e.length)),e=e[0]}function r(a){return a==null||Array.isArray(a)?a:[a]}return t=r(t),n=r(n),{inputs:e,initialState:t,constants:n}}function ZI(e,t,n,s=!1,r,a,o=!1,i=!1){return q(()=>{let u=t.shape.length;if(u<3)throw new G(`Input should be at least 3D, but is ${u}D.`);let l=[1,0].concat(ys(2,u));if(t=Ge(t,l),a!=null)throw new Fe("The rnn() functoin of the deeplearn.js backend does not support constants yet.");o&&console.warn("Backend rnn(): the unroll = true option is not applicable to the imperative deeplearn.js backend."),r!=null&&(r=le(le(r,"bool"),"float32"),r.rank===u-1&&(r=Pn(r,-1)),r=Ge(r,l)),s&&(t=Jn(t,0),r!=null&&(r=Jn(r,0)));let c=[],p,d=n,h=t.shape[0],f=Fs(t),m;r!=null&&(m=Fs(r));for(let b=0;b<h;++b){let y=f[b],v=q(()=>e(y,d));if(r==null)p=v[0],d=v[1];else{let x=q(()=>{let k=m[b],I=ge(Zn(k),k),$=oe(V(v[0],k),V(d[0],I)),R=d.map((E,P)=>oe(V(v[1][P],k),V(E,I)));return{output:$,newStates:R}});p=x.output,d=x.newStates}i&&c.push(p)}let g;return i&&(g=es(c,1)),[p,g,d]})}var JI=class extends He{constructor(e){super(e);let t;if(e.cell==null)throw new G("cell property is missing for the constructor of RNN.");if(Array.isArray(e.cell)?t=new Yp({cells:e.cell}):t=e.cell,t.stateSize==null)throw new G("The RNN cell should have an attribute `stateSize` (tuple of integers, one integer per RNN state).");this.cell=t,this.returnSequences=e.returnSequences==null?!1:e.returnSequences,this.returnState=e.returnState==null?!1:e.returnState,this.goBackwards=e.goBackwards==null?!1:e.goBackwards,this._stateful=e.stateful==null?!1:e.stateful,this.unroll=e.unroll==null?!1:e.unroll,this.supportsMasking=!0,this.inputSpec=[new Dt({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 ys(0,e).map(t=>null)}else return this.states_}setStates(e){this.states_=e}computeOutputShape(e){Am(e)&&(e=e[0]),e=e;let t=this.cell.stateSize;Array.isArray(t)||(t=[t]);let n=t[0],s;if(this.returnSequences?s=[e[0],e[1],n]:s=[e[0],n],this.returnState){let r=[];for(let a of t)r.push([e[0],a]);return[s].concat(r)}else return s}computeMask(e,t){return q(()=>{Array.isArray(t)&&(t=t[0]);let n=this.returnSequences?t:null;if(this.returnState){let s=this.states.map(r=>null);return[n].concat(s)}else return n})}get states(){if(this.states_==null){let e=Array.isArray(this.cell.stateSize)?this.cell.stateSize.length:1,t=[];for(let n=0;n<e;++n)t.push(null);return t}else return this.states_}set states(e){this.states_=e}build(e){if(this.numConstants!=null)throw new Fe("Constants support is not implemented in RNN yet.");Am(e)&&(e=e[0]),e=e;let n=this.stateful?e[0]:null,s=e.slice(2);this.inputSpec[0]=new Dt({shape:[n,null,...s]});let r=[e[0]].concat(e.slice(2));this.cell.build(r);let a;if(Array.isArray(this.cell.stateSize)?a=this.cell.stateSize:a=[this.cell.stateSize],this.stateSpec!=null){if(!w.arraysEqual(this.stateSpec.map(o=>o.shape[o.shape.length-1]),a))throw new G(`An initialState was passed that is not compatible with cell.stateSize. Received stateSpec=${this.stateSpec}; However cell.stateSize is ${this.cell.stateSize}`)}else this.stateSpec=a.map(o=>new Dt({shape:[null,o]}));this.stateful&&this.resetStates()}resetStates(e,t=!1){q(()=>{if(!this.stateful)throw new Bs("Cannot call resetStates() on an RNN Layer that is not stateful.");let n=this.inputSpec[0].shape[0];if(n==null)throw new G("If an RNN is stateful, it needs to know its batch size. Specify the batch size of your input tensors: \n- If using a Sequential model, specify the batch size by passing a `batchInputShape` option to your first layer.\n- If using the functional API, specify the batch size by passing a `batchShape` option to your Input layer.");if(this.states_==null)Array.isArray(this.cell.stateSize)?this.states_=this.cell.stateSize.map(s=>$t([n,s])):this.states_=[$t([n,this.cell.stateSize])];else if(e==null)De(this.states_),this.keptStates!=null&&(De(this.keptStates),this.keptStates=[]),Array.isArray(this.cell.stateSize)?this.states_=this.cell.stateSize.map(s=>$t([n,s])):this.states_[0]=$t([n,this.cell.stateSize]);else{if(Array.isArray(e)||(e=[e]),e.length!==this.states_.length)throw new G(`Layer ${this.name} expects ${this.states_.length} state(s), but it received ${e.length} state value(s). Input received: ${e}`);t===!0?this.keptStates.push(this.states_.slice()):De(this.states_);for(let s=0;s<this.states_.length;++s){let r=e[s],a=Array.isArray(this.cell.stateSize)?this.cell.stateSize[s]:this.cell.stateSize,o=[n,a];if(!w.arraysEqual(r.shape,o))throw new G(`State ${s} is incompatible with layer ${this.name}: expected shape=${o}, received shape=${r.shape}`);this.states_[s]=r}}this.states_=this.states_.map(s=>qt(s.clone()))})}apply(e,t){let n=t==null?null:t.initialState,s=t==null?null:t.constants;t==null&&(t={});let r=QI(e,n,s,this.numConstants);e=r.inputs,n=r.initialState,s=r.constants;let a=[],o=[];if(n!=null){t.initialState=n,a=a.concat(n),this.stateSpec=[];for(let u of n)this.stateSpec.push(new Dt({shape:u.shape}));o=o.concat(this.stateSpec)}if(s!=null&&(t.constants=s,a=a.concat(s),this.numConstants=s.length),a[0]instanceof $s){let u=[e].concat(a),l=this.inputSpec.concat(o),c=this.inputSpec;this.inputSpec=l;let p=super.apply(u,t);return this.inputSpec=c,p}else return super.apply(e,t)}call(e,t){return q(()=>{let n=t==null?null:t.mask,s=t==null?null:t.training,r=t==null?null:t.initialState;e=Oe(e),r==null&&(this.stateful?r=this.states_:r=this.getInitialState(e));let a=Array.isArray(this.cell.stateSize)?this.cell.stateSize.length:1;if(r.length!==a)throw new G(`RNN Layer has ${a} state(s) but was passed ${r.length} initial state(s).`);this.unroll&&console.warn("Ignoring unroll = true for RNN layer, due to imperative backend.");let o={training:s},u=ZI((h,f)=>{let m=this.cell.call([h].concat(f),o);return[m[0],m.slice(1)]},e,r,this.goBackwards,n,null,this.unroll,this.returnSequences),l=u[0],c=u[1],p=u[2];this.stateful&&this.resetStates(p,s);let d=this.returnSequences?c:l;return this.returnState?[d].concat(p):d})}getInitialState(e){return q(()=>{let t=$t(e.shape);return t=ve(t,[1,2]),t=Gl(t),Array.isArray(this.cell.stateSize)?this.cell.stateSize.map(n=>n>1?$m(t,[1,n]):t):this.cell.stateSize>1?[$m(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()===JI.className&&(t.cell={className:this.cell.getClassName(),config:n}),{...n,...e,...t}}static fromConfig(e,t,n={}){let s=t.cell,r=gs(s,n);return new e(Object.assign(t,{cell:r}))}},Rr=JI;Rr.className="RNN";re.registerClass(Rr);var Yl=class extends He{},Kp=class extends Yl{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,Bt(this.units,"units"),this.activation=xr(e.activation==null?this.DEFAULT_ACTIVATION:e.activation),this.useBias=e.useBias==null?!0:e.useBias,this.kernelInitializer=ft(e.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.recurrentInitializer=ft(e.recurrentInitializer||this.DEFAULT_RECURRENT_INITIALIZER),this.biasInitializer=ft(e.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.kernelRegularizer=mt(e.kernelRegularizer),this.recurrentRegularizer=mt(e.recurrentRegularizer),this.biasRegularizer=mt(e.biasRegularizer),this.kernelConstraint=Pt(e.kernelConstraint),this.recurrentConstraint=Pt(e.recurrentConstraint),this.biasConstraint=Pt(e.biasConstraint),this.dropout=ni([1,yr([0,e.dropout==null?0:e.dropout])]),this.recurrentDropout=ni([1,yr([0,e.recurrentDropout==null?0:e.recurrentDropout])]),this.dropoutFunc=e.dropoutFunc,this.stateSize=this.units,this.dropoutMask=null,this.recurrentDropoutMask=null}build(e){e=nt(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 q(()=>{if(e=e,e.length!==2)throw new G(`SimpleRNNCell expects 2 input Tensors, got ${e.length}.`);let n=e[1];e=e[0];let s=t.training==null?!1:t.training;0<this.dropout&&this.dropout<1&&this.dropoutMask==null&&(this.dropoutMask=wr({ones:()=>Zn(e),rate:this.dropout,training:s,dropoutFunc:this.dropoutFunc})),0<this.recurrentDropout&&this.recurrentDropout<1&&this.recurrentDropoutMask==null&&(this.recurrentDropoutMask=wr({ones:()=>Zn(n),rate:this.recurrentDropout,training:s,dropoutFunc:this.dropoutFunc}));let r,a=this.dropoutMask,o=this.recurrentDropoutMask;a!=null?r=Es(V(e,a),this.kernel.read()):r=Es(e,this.kernel.read()),this.bias!=null&&(r=ks(r,this.bias.read())),o!=null&&(n=V(n,o));let i=oe(r,Es(n,this.recurrentKernel.read()));return this.activation!=null&&(i=this.activation.apply(i)),[i,i]})}getConfig(){let e=super.getConfig(),t={units:this.units,activation:vr(this.activation),useBias:this.useBias,kernelInitializer:yt(this.kernelInitializer),recurrentInitializer:yt(this.recurrentInitializer),biasInitializer:yt(this.biasInitializer),kernelRegularizer:it(this.kernelRegularizer),recurrentRegularizer:it(this.recurrentRegularizer),biasRegularizer:it(this.biasRegularizer),activityRegularizer:it(this.activityRegularizer),kernelConstraint:Ot(this.kernelConstraint),recurrentConstraint:Ot(this.recurrentConstraint),biasConstraint:Ot(this.biasConstraint),dropout:this.dropout,recurrentDropout:this.recurrentDropout};return{...e,...t}}};Kp.className="SimpleRNNCell";re.registerClass(Kp);var vy=class extends Rr{constructor(e){e.cell=new Kp(e),super(e)}call(e,t){return q(()=>{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,s=t==null?null:t.training,r=t==null?null:t.initialState;return super.call(e,{mask:n,training:s,initialState:r})})}static fromConfig(e,t){return new e(t)}};vy.className="SimpleRNN";re.registerClass(vy);var Xp=class extends Yl{constructor(e){if(super(e),this.DEFAULT_ACTIVATION="tanh",this.DEFAULT_RECURRENT_ACTIVATION="hardSigmoid",this.DEFAULT_KERNEL_INITIALIZER="glorotNormal",this.DEFAULT_RECURRENT_INITIALIZER="orthogonal",this.DEFAULT_BIAS_INITIALIZER="zeros",e.resetAfter)throw new G("GRUCell does not support reset_after parameter set to true.");this.units=e.units,Bt(this.units,"units"),this.activation=xr(e.activation===void 0?this.DEFAULT_ACTIVATION:e.activation),this.recurrentActivation=xr(e.recurrentActivation===void 0?this.DEFAULT_RECURRENT_ACTIVATION:e.recurrentActivation),this.useBias=e.useBias==null?!0:e.useBias,this.kernelInitializer=ft(e.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.recurrentInitializer=ft(e.recurrentInitializer||this.DEFAULT_RECURRENT_INITIALIZER),this.biasInitializer=ft(e.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.kernelRegularizer=mt(e.kernelRegularizer),this.recurrentRegularizer=mt(e.recurrentRegularizer),this.biasRegularizer=mt(e.biasRegularizer),this.kernelConstraint=Pt(e.kernelConstraint),this.recurrentConstraint=Pt(e.recurrentConstraint),this.biasConstraint=Pt(e.biasConstraint),this.dropout=ni([1,yr([0,e.dropout==null?0:e.dropout])]),this.recurrentDropout=ni([1,yr([0,e.recurrentDropout==null?0:e.recurrentDropout])]),this.dropoutFunc=e.dropoutFunc,this.implementation=e.implementation,this.stateSize=this.units,this.dropoutMask=null,this.recurrentDropoutMask=null}build(e){e=nt(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 q(()=>{if(e=e,e.length!==2)throw new G(`GRUCell expects 2 input Tensors (inputs, h, c), got ${e.length}.`);let n=t.training==null?!1:t.training,s=e[1];e=e[0],0<this.dropout&&this.dropout<1&&this.dropoutMask==null&&(this.dropoutMask=wr({ones:()=>Zn(e),rate:this.dropout,training:n,count:3,dropoutFunc:this.dropoutFunc})),0<this.recurrentDropout&&this.recurrentDropout<1&&this.recurrentDropoutMask==null&&(this.recurrentDropoutMask=wr({ones:()=>Zn(s),rate:this.recurrentDropout,training:n,count:3,dropoutFunc:this.dropoutFunc}));let r=this.dropoutMask,a=this.recurrentDropoutMask,o,i,u;0<this.dropout&&this.dropout<1&&(e=V(e,r[0]));let l=Es(e,this.kernel.read());this.useBias&&(l=ks(l,this.bias.read())),0<this.recurrentDropout&&this.recurrentDropout<1&&(s=V(s,a[0]));let c=this.recurrentKernel.read(),[p,d]=Bn(c,[2*this.units,this.units],c.rank-1),h=Es(s,p),[f,m,g]=Bn(l,3,l.rank-1),[b,y]=Bn(h,2,h.rank-1);o=this.recurrentActivation.apply(oe(f,b)),i=this.recurrentActivation.apply(oe(m,y));let v=Es(V(i,s),d);u=this.activation.apply(oe(g,v));let x=oe(V(o,s),V(oe(1,vt(o)),u));return[x,x]})}getConfig(){let e=super.getConfig(),t={units:this.units,activation:vr(this.activation),recurrentActivation:vr(this.recurrentActivation),useBias:this.useBias,kernelInitializer:yt(this.kernelInitializer),recurrentInitializer:yt(this.recurrentInitializer),biasInitializer:yt(this.biasInitializer),kernelRegularizer:it(this.kernelRegularizer),recurrentRegularizer:it(this.recurrentRegularizer),biasRegularizer:it(this.biasRegularizer),activityRegularizer:it(this.activityRegularizer),kernelConstraint:Ot(this.kernelConstraint),recurrentConstraint:Ot(this.recurrentConstraint),biasConstraint:Ot(this.biasConstraint),dropout:this.dropout,recurrentDropout:this.recurrentDropout,implementation:this.implementation,resetAfter:!1};return{...e,...t}}};Xp.className="GRUCell";re.registerClass(Xp);var xy=class extends Rr{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 Xp(e),super(e)}call(e,t){return q(()=>{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,s=t==null?null:t.training,r=t==null?null:t.initialState;return super.call(e,{mask:n,training:s,initialState:r})})}static fromConfig(e,t){return t.implmentation===0&&(t.implementation=1),new e(t)}};xy.className="GRU";re.registerClass(xy);var Ql=class extends Yl{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,Bt(this.units,"units"),this.activation=xr(e.activation===void 0?this.DEFAULT_ACTIVATION:e.activation),this.recurrentActivation=xr(e.recurrentActivation===void 0?this.DEFAULT_RECURRENT_ACTIVATION:e.recurrentActivation),this.useBias=e.useBias==null?!0:e.useBias,this.kernelInitializer=ft(e.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.recurrentInitializer=ft(e.recurrentInitializer||this.DEFAULT_RECURRENT_INITIALIZER),this.biasInitializer=ft(e.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.unitForgetBias=e.unitForgetBias,this.kernelRegularizer=mt(e.kernelRegularizer),this.recurrentRegularizer=mt(e.recurrentRegularizer),this.biasRegularizer=mt(e.biasRegularizer),this.kernelConstraint=Pt(e.kernelConstraint),this.recurrentConstraint=Pt(e.recurrentConstraint),this.biasConstraint=Pt(e.biasConstraint),this.dropout=ni([1,yr([0,e.dropout==null?0:e.dropout])]),this.recurrentDropout=ni([1,yr([0,e.recurrentDropout==null?0:e.recurrentDropout])]),this.dropoutFunc=e.dropoutFunc,this.implementation=e.implementation,this.stateSize=[this.units,this.units],this.dropoutMask=null,this.recurrentDropoutMask=null}build(e){var t;e=nt(e);let n=e[e.length-1];this.kernel=this.addWeight("kernel",[n,this.units*4],null,this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint),this.recurrentKernel=this.addWeight("recurrent_kernel",[this.units,this.units*4],null,this.recurrentInitializer,this.recurrentRegularizer,!0,this.recurrentConstraint);let s;if(this.useBias){if(this.unitForgetBias){let r=this.biasInitializer,a=this.units;s=new(t=class extends ns{apply(o,i){let u=r.apply([a]),l=new Pp().apply([a]),c=r.apply([a*2]);return Ex(Ex(u,l),c)}},t.className="CustomInit",t)}else s=this.biasInitializer;this.bias=this.addWeight("bias",[this.units*4],null,s,this.biasRegularizer,!0,this.biasConstraint)}else this.bias=null;this.built=!0}call(e,t){return q(()=>{let n=t.training==null?!1:t.training;if(e=e,e.length!==3)throw new G(`LSTMCell expects 3 input Tensors (inputs, h, c), got ${e.length}.`);let s=e[1],r=e[2];e=e[0],0<this.dropout&&this.dropout<1&&this.dropoutMask==null&&(this.dropoutMask=wr({ones:()=>Zn(e),rate:this.dropout,training:n,count:4,dropoutFunc:this.dropoutFunc})),0<this.recurrentDropout&&this.recurrentDropout<1&&this.recurrentDropoutMask==null&&(this.recurrentDropoutMask=wr({ones:()=>Zn(s),rate:this.recurrentDropout,training:n,count:4,dropoutFunc:this.dropoutFunc}));let a=this.dropoutMask,o=this.recurrentDropoutMask,i,u,l,c;0<this.dropout&&this.dropout<1&&(e=V(e,a[0]));let p=Es(e,this.kernel.read());0<this.recurrentDropout&&this.recurrentDropout<1&&(s=V(s,o[0])),p=oe(p,Es(s,this.recurrentKernel.read())),this.useBias&&(p=ks(p,this.bias.read()));let[d,h,f,m]=Bn(p,4,p.rank-1);i=this.recurrentActivation.apply(d),u=this.recurrentActivation.apply(h),l=oe(V(u,r),V(i,this.activation.apply(f))),c=this.recurrentActivation.apply(m);let g=V(c,this.activation.apply(l));return[g,g,l]})}getConfig(){let e=super.getConfig(),t={units:this.units,activation:vr(this.activation),recurrentActivation:vr(this.recurrentActivation),useBias:this.useBias,kernelInitializer:yt(this.kernelInitializer),recurrentInitializer:yt(this.recurrentInitializer),biasInitializer:yt(this.biasInitializer),unitForgetBias:this.unitForgetBias,kernelRegularizer:it(this.kernelRegularizer),recurrentRegularizer:it(this.recurrentRegularizer),biasRegularizer:it(this.biasRegularizer),activityRegularizer:it(this.activityRegularizer),kernelConstraint:Ot(this.kernelConstraint),recurrentConstraint:Ot(this.recurrentConstraint),biasConstraint:Ot(this.biasConstraint),dropout:this.dropout,recurrentDropout:this.recurrentDropout,implementation:this.implementation};return{...e,...t}}};Ql.className="LSTMCell";re.registerClass(Ql);var wy=class extends Rr{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 Ql(e),super(e)}call(e,t){return q(()=>{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,s=t==null?null:t.training,r=t==null?null:t.initialState;return super.call(e,{mask:n,training:s,initialState:r})})}static fromConfig(e,t){return t.implmentation===0&&(t.implementation=1),new e(t)}};wy.className="LSTM";re.registerClass(wy);var Yp=class extends Yl{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 q(()=>{e=e;let n=e.slice(1),s=[];for(let o of this.cells.slice().reverse())Array.isArray(o.stateSize)?s.push(n.splice(0,o.stateSize.length)):s.push(n.splice(0,1));s.reverse();let r=[],a;for(let o=0;o<this.cells.length;++o){let i=this.cells[o];n=s[o],o===0?a=[e[0]].concat(n):a=[a[0]].concat(n),a=i.call(a,t),r.push(a.slice(1))}n=[];for(let o of r.slice().reverse())n.push(...o);return[a[0]].concat(n)})}build(e){Am(e)&&(e=e[0]),e=e;let t;this.cells.forEach((n,s)=>{na(`RNNCell_${s}`,()=>{n.build(e),Array.isArray(n.stateSize)?t=n.stateSize[0]:t=n.stateSize,e=[e[0],t]})}),this.built=!0}getConfig(){let e=super.getConfig(),t=r=>({className:r.getClassName(),config:r.getConfig()}),s={cells:this.cells.map(t)};return{...e,...s}}static fromConfig(e,t,n={}){let s=[];for(let r of t.cells)s.push(gs(r,n));return new e({cells:s})}get trainableWeights(){if(!this.trainable)return[];let e=[];for(let t of this.cells)e.push(...t.trainableWeights);return e}get nonTrainableWeights(){let e=[];for(let t of this.cells)e.push(...t.nonTrainableWeights);if(!this.trainable){let t=[];for(let n of this.cells)t.push(...n.trainableWeights);return t.concat(e)}return e}getWeights(){let e=[];for(let t of this.cells)e.push(...t.weights);return Em(e)}setWeights(e){let t=[];for(let n of this.cells){let s=n.weights.length,r=e.splice(s);for(let a=0;a<n.weights.length;++a)t.push([n.weights[a],r[a]])}Ub(t)}};Yp.className="StackedRNNCells";re.registerClass(Yp);function wr(e){let{ones:t,rate:n,training:s=!1,count:r=1,dropoutFunc:a}=e,o=()=>a!=null?a(t(),n):pI(t(),n),i=()=>ql(o,t,s);return!r||r<=1?qt(i().clone()):Array(r).fill(void 0).map(i).map(l=>qt(l.clone()))}var e0=class extends Rr{constructor(e){if(e.unroll)throw new Fe("Unrolling is not possible with convolutional RNNs.");if(Array.isArray(e.cell))throw new Fe("It is not possible at the moment to stack convolutional cells.");super(e),this.inputSpec=[new Dt({ndim:5})]}call(e,t){return q(()=>{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 G("ConvRNN2D cell does not support constants");let n=t==null?null:t.mask,s=t==null?null:t.training,r=t==null?null:t.initialState;return super.call(e,{mask:n,training:s,initialState:r})})}computeOutputShape(e){let t=this.computeSingleOutputShape(e);return this.returnSequences||(t=[t[0],...t.slice(2)]),this.returnState&&(t=[t,...Array(2).fill([e[0],...t.slice(-3)])]),t}getInitialState(e){return q(()=>{let{stateSize:t}=this.cell,n=e.shape,s=this.computeSingleOutputShape(n),r=[s[0],...s.slice(2)],a=$t(r);return Array.isArray(t)?Array(t.length).fill(a):[a]})}resetStates(e,t=!1){q(()=>{if(!this.stateful)throw new Bs("Cannot call resetStates() on an RNN Layer that is not stateful.");let n=this.inputSpec[0].shape,s=this.computeSingleOutputShape(n),r=[s[0],...s.slice(2)];if(n[0]==null)throw new G("If an RNN is stateful, it needs to know its batch size. Specify the batch size of your input tensors: \n- If using a Sequential model, specify the batch size by passing a `batchInputShape` option to your first layer.\n- If using the functional API, specify the batch size by passing a `batchShape` option to your Input layer.");if(this.getStates()==null)Array.isArray(this.cell.stateSize)?this.states_=this.cell.stateSize.map(()=>$t(r)):this.states_=[$t(r)];else if(e==null)De(this.states_),this.keptStates!=null&&(De(this.keptStates),this.keptStates=[]),Array.isArray(this.cell.stateSize)?this.states_=this.cell.stateSize.map(()=>$t(r)):this.states_[0]=$t(r);else{if(Array.isArray(e)||(e=[e]),e.length!==this.states_.length)throw new G(`Layer ${this.name} expects ${this.states_.length} state(s), but it received ${e.length} state value(s). Input received: ${e}`);t?this.keptStates.push(this.states_.slice()):De(this.states_);for(let o=0;o<this.states_.length;++o){let i=e[o],u=r;if(!w.arraysEqual(i.shape,u))throw new G(`State ${o} is incompatible with layer ${this.name}: expected shape=${u}, received shape=${i.shape}`);this.states_[o]=i}}this.states_=this.states_.map(o=>qt(o.clone()))})}computeSingleOutputShape(e){let{dataFormat:t,filters:n,kernelSize:s,padding:r,strides:a,dilationRate:o}=this.cell,i=t==="channelsFirst",u=e[i?3:2],l=e[i?4:3],c=bs(u,s[0],r,a[0],o[0]),p=bs(l,s[1],r,a[1],o[1]);return[...e.slice(0,2),...i?[n,c,p]:[c,p,n]]}};e0.className="ConvRNN2D";var Qp=class extends Ql{constructor(e){let{filters:t,kernelSize:n,strides:s,padding:r,dataFormat:a,dilationRate:o}=e;super({...e,units:t}),this.filters=t,Bt(this.filters,"filters"),this.kernelSize=Jo(n,2,"kernelSize"),this.kernelSize.forEach(i=>Bt(i,"kernelSize")),this.strides=Jo(s||1,2,"strides"),this.strides.forEach(i=>Bt(i,"strides")),this.padding=r||"valid",Hn(this.padding),this.dataFormat=a||"channelsLast",Ct(this.dataFormat),this.dilationRate=Jo(o||1,2,"dilationRate"),this.dilationRate.forEach(i=>Bt(i,"dilationRate"))}build(e){var t;e=nt(e);let n=this.dataFormat==="channelsFirst"?1:e.length-1;if(e[n]==null)throw new G(`The channel dimension of the input should be defined. Found ${e[n]}`);let s=e[n],r=4,a=this.kernelSize.concat([s,this.filters*r]);this.kernel=this.addWeight("kernel",a,null,this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint);let o=this.kernelSize.concat([this.filters,this.filters*r]);if(this.recurrentKernel=this.addWeight("recurrent_kernel",o,null,this.recurrentInitializer,this.recurrentRegularizer,!0,this.recurrentConstraint),this.useBias){let i;if(this.unitForgetBias){let u=this.biasInitializer,l=this.filters;i=new(t=class extends ns{apply(c,p){let d=u.apply([l]),h=Ln([l]),f=u.apply([l*2]);return Ob([d,h,f])}},t.className="CustomInit",t)}else i=this.biasInitializer;this.bias=this.addWeight("bias",[this.filters*r],null,i,this.biasRegularizer,!0,this.biasConstraint)}this.built=!0}call(e,t){return q(()=>{if(e.length!==3)throw new G(`ConvLSTM2DCell expects 3 input Tensors (inputs, h, c), got ${e.length}.`);let n=t.training||!1,s=e[0],r=e[1],a=e[2],o=4;0<this.dropout&&this.dropout<1&&this.dropoutMask==null&&(this.dropoutMask=wr({ones:()=>Zn(s),rate:this.dropout,training:n,count:o,dropoutFunc:this.dropoutFunc}));let i=this.dropoutMask,u=(Z,ne,ee)=>!ne||!ne[ee]?Z:V(ne[ee],Z),l=u(s,i,0),c=u(s,i,1),p=u(s,i,2),d=u(s,i,3);0<this.recurrentDropout&&this.recurrentDropout<1&&this.recurrentDropoutMask==null&&(this.recurrentDropoutMask=wr({ones:()=>Zn(r),rate:this.recurrentDropout,training:n,count:o,dropoutFunc:this.dropoutFunc}));let h=this.recurrentDropoutMask,f=u(r,h,0),m=u(r,h,1),g=u(r,h,2),b=u(r,h,3),y=3,[v,x,k,I]=Bn(this.kernel.read(),o,y),[$,R,E,P]=this.useBias?Bn(this.bias.read(),o):[null,null,null,null];l=this.inputConv(l,v,$,this.padding),c=this.inputConv(c,x,R,this.padding),p=this.inputConv(p,k,E,this.padding),d=this.inputConv(d,I,P,this.padding);let[A,D,T,L]=Bn(this.recurrentKernel.read(),o,y);f=this.recurrentConv(f,A),m=this.recurrentConv(m,D),g=this.recurrentConv(g,T),b=this.recurrentConv(b,L);let W=this.recurrentActivation.apply(oe(l,f)),j=this.recurrentActivation.apply(oe(c,m)),Y=oe(V(j,a),V(W,this.activation.apply(oe(p,g)))),X=V(this.recurrentActivation.apply(oe(d,b)),this.activation.apply(Y));return[X,X,Y]})}getConfig(){let{units:e,...t}=super.getConfig(),n={filters:this.filters,kernelSize:this.kernelSize,padding:this.padding,dataFormat:this.dataFormat,dilationRate:this.dilationRate,strides:this.strides};return{...t,...n}}inputConv(e,t,n,s){let r=da(e,t,this.strides,s||"valid",this.dataFormat==="channelsFirst"?"NCHW":"NHWC",this.dilationRate);return n?ks(r,n,this.dataFormat):r}recurrentConv(e,t){return da(e,t,1,"same",this.dataFormat==="channelsFirst"?"NCHW":"NHWC")}};Qp.className="ConvLSTM2DCell";re.registerClass(Qp);var ky=class extends e0{constructor(e){let t=new Qp(e);super({...e,cell:t})}static fromConfig(e,t){return new e(t)}};ky.className="ConvLSTM2D";re.registerClass(ky);var Zp=class extends He{constructor(e){super(e),this.rate=Math.max(Math.min(e.rate,1),0),this.noiseShape=e.noiseShape,this.seed=e.seed,this.supportsMasking=!0}getNoiseShape(e){if(this.noiseShape==null)return this.noiseShape;let t=e.shape,n=[];for(let s=0;s<this.noiseShape.length;++s)n.push(this.noiseShape[s]==null?t[s]:this.noiseShape[s]);return n}call(e,t){return q(()=>{this.invokeCallHook(e,t);let n=Oe(e);if(0<this.rate&&this.rate<1){let s=t.training==null?!1:t.training,r=this.getNoiseShape(n);return ql(()=>pI(n,this.rate,r,this.seed),()=>n,s)}return e})}getConfig(){let e={rate:this.rate,noiseShape:this.noiseShape,seed:this.seed},t=super.getConfig();return Object.assign(e,t),e}dispose(){return super.dispose()}};Zp.className="Dropout";re.registerClass(Zp);var Sy=class extends Zp{constructor(e){super(e),this.inputSpec=[{ndim:3}]}getNoiseShape(e){let t=e.shape;return[t[0],1,t[2]]}};Sy.className="SpatialDropout1D";re.registerClass(Sy);var Iy=class extends He{constructor(e){if(super(e),this.activation=null,this.useBias=!0,this.kernel=null,this.bias=null,this.DEFAULT_KERNEL_INITIALIZER="glorotNormal",this.DEFAULT_BIAS_INITIALIZER="zeros",e.batchInputShape==null&&e.inputShape==null&&e.inputDim!=null){let t=null;e.batchSize!=null&&(t=e.batchSize),this.batchInputShape=[t,e.inputDim]}this.units=e.units,Bt(this.units,"units"),this.activation=xr(e.activation),e.useBias!=null&&(this.useBias=e.useBias),this.kernelInitializer=ft(e.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.biasInitializer=ft(e.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.kernelConstraint=Pt(e.kernelConstraint),this.biasConstraint=Pt(e.biasConstraint),this.kernelRegularizer=mt(e.kernelRegularizer),this.biasRegularizer=mt(e.biasRegularizer),this.activityRegularizer=mt(e.activityRegularizer),this.supportsMasking=!0,this.inputSpec=[{minNDim:2}]}build(e){e=nt(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=nt(e);let t=e.slice();return t[t.length-1]=this.units,t}call(e,t){return q(()=>{this.invokeCallHook(e,t);let n=Oe(e),s=aI(this.activation.getClassName()),r;return s!=null?r=Es(n,this.kernel.read(),s,this.bias?this.bias.read():null):(r=Es(n,this.kernel.read()),this.bias!=null&&(r=ks(r,this.bias.read())),this.activation!=null&&(r=this.activation.apply(r))),r})}getConfig(){let e={units:this.units,activation:vr(this.activation),useBias:this.useBias,kernelInitializer:yt(this.kernelInitializer),biasInitializer:yt(this.biasInitializer),kernelRegularizer:it(this.kernelRegularizer),biasRegularizer:it(this.biasRegularizer),activityRegularizer:it(this.activityRegularizer),kernelConstraint:Ot(this.kernelConstraint),biasConstraint:Ot(this.biasConstraint)},t=super.getConfig();return Object.assign(e,t),e}};Iy.className="Dense";re.registerClass(Iy);var Cy=class extends He{constructor(e){e=e||{},super(e),this.inputSpec=[{minNDim:3}],this.dataFormat=e.dataFormat}computeOutputShape(e){e=nt(e);for(let t of e.slice(1))if(t==null)throw new G(`The shape of the input to "Flatten" is not fully defined (got ${e.slice(1)}). Make sure to pass a complete "input_shape" or "batch_input_shape" argument to the first layer in your model.`);return[e[0],dr(e,1)]}call(e,t){return q(()=>{this.invokeCallHook(e,t);let n=Oe(e);if(this.dataFormat==="channelsFirst"&&n.rank>1){let s=[0];for(let r=2;r<n.rank;++r)s.push(r);s.push(1),n=Ge(n,s)}return wz(n)})}getConfig(){let e={};this.dataFormat!=null&&(e.dataFormat=this.dataFormat);let t=super.getConfig();return Object.assign(e,t),e}};Cy.className="Flatten";re.registerClass(Cy);var Ny=class extends He{constructor(e){super(e),this.supportsMasking=!0,this.activation=xr(e.activation)}call(e,t){return q(()=>{this.invokeCallHook(e,t);let n=Oe(e);return this.activation.apply(n)})}getConfig(){let e={activation:vr(this.activation)},t=super.getConfig();return Object.assign(e,t),e}};Ny.className="Activation";re.registerClass(Ny);var Ty=class extends He{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 q(()=>(e=Oe(e),vz(e,this.n)))}getConfig(){let e={n:this.n},t=super.getConfig();return Object.assign(e,t),e}};Ty.className="RepeatVector";re.registerClass(Ty);var $y=class extends He{constructor(e){super(e),this.targetShape=e.targetShape;for(let t=0;t<this.targetShape.length;++t)this.isUnknown(this.targetShape[t])&&(this.targetShape[t]=null)}isUnknown(e){return e<0||e==null}fixUnknownDimension(e,t){let n="Total size of new array must be unchanged.",s=t.slice(),r=1,a=null;for(let i=0;i<s.length;++i){let u=s[i];if(this.isUnknown(u))if(a===null)a=i;else throw new G("Can only specifiy one unknown dimension.");else r*=u}let o=dr(e);if(a!==null){if(r===0||o%r!==0)throw new G(n);s[a]=o/r}else if(o!==r)throw new G(n);return s}computeOutputShape(e){let t=!1;for(let n=0;n<e.length;++n)if(this.isUnknown(e[n])){t=!0;break}return t?e.slice(0,1).concat(this.targetShape):e.slice(0,1).concat(this.fixUnknownDimension(e.slice(1),this.targetShape))}call(e,t){return q(()=>{this.invokeCallHook(e,t);let n=Oe(e),s=n.shape,r=s.slice(0,1).concat(this.fixUnknownDimension(s.slice(1),this.targetShape));return U(n,r)})}getConfig(){let e={targetShape:this.targetShape},t=super.getConfig();return Object.assign(e,t),e}};$y.className="Reshape";re.registerClass($y);var _y=class extends He{constructor(e){if(super(e),e.dims==null)throw new Error("Required configuration field `dims` is missing during Permute constructor call.");if(!Array.isArray(e.dims))throw new Error(`Permute constructor requires \`dims\` to be an Array, but received ${e.dims} instead.`);let t=ys(1,e.dims.length+1);if(!w.arraysEqual(e.dims.slice().sort(),t))throw new Error("Invalid permutation `dims`: "+JSON.stringify(e.dims)+" `dims` must contain consecutive integers starting from 1.");this.dims=e.dims,this.dimsIncludingBatch=[0].concat(this.dims),this.inputSpec=[new Dt({ndim:this.dims.length+1})]}computeOutputShape(e){e=nt(e);let t=e.slice();return this.dims.forEach((n,s)=>{t[s+1]=e[n]}),t}call(e,t){return Ge(Oe(e),this.dimsIncludingBatch)}getConfig(){let e={dims:this.dims},t=super.getConfig();return Object.assign(e,t),e}};_y.className="Permute";re.registerClass(_y);var Ay=class extends He{constructor(e){super(e==null?{}:e),this.supportsMasking=!0,e!=null?this.maskValue=e.maskValue==null?0:e.maskValue:this.maskValue=0}computeOutputShape(e){return e}getConfig(){let e=super.getConfig(),t={maskValue:this.maskValue};return Object.assign(t,e),t}computeMask(e,t){let n=Oe(e),s=-1;return Sm(el(n,this.maskValue),s)}call(e,t){return q(()=>{this.invokeCallHook(e,t);let n=Oe(e),s=-1,r=!0,a=Sm(el(n,this.maskValue),s,r);return V(n,le(a,n.dtype))})}};Ay.className="Masking";re.registerClass(Ay);var Ey=class extends He{constructor(e){if(super(e),this.embeddings=null,this.DEFAULT_EMBEDDINGS_INITIALIZER="randomUniform",e.batchInputShape==null&&e.inputShape==null){let t=null;e.batchSize!=null&&(t=e.batchSize),e.inputLength==null?this.batchInputShape=[t,null]:this.batchInputShape=[t].concat(ht(e.inputLength))}this.inputDim=e.inputDim,Bt(this.inputDim,"inputDim"),this.outputDim=e.outputDim,Bt(this.outputDim,"outputDim"),this.embeddingsInitializer=ft(e.embeddingsInitializer||this.DEFAULT_EMBEDDINGS_INITIALIZER),this.embeddingsRegularizer=mt(e.embeddingsRegularizer),this.activityRegularizer=mt(e.activityRegularizer),this.embeddingsConstraint=Pt(e.embeddingsConstraint),this.maskZero=e.maskZero,this.supportsMasking=e.maskZero,this.inputLength=e.inputLength}build(e){this.embeddings=this.addWeight("embeddings",[this.inputDim,this.outputDim],this.dtype,this.embeddingsInitializer,this.embeddingsRegularizer,!0,this.embeddingsConstraint),this.built=!0}warnOnIncompatibleInputShape(e){}computeMask(e,t){return q(()=>this.maskZero?(e=Oe(e),el(e,je(e))):null)}computeOutputShape(e){if(e=nt(e),this.inputLength==null)return[...e,this.outputDim];let t=ht(this.inputLength);if(t.length!==e.length-1)throw new G(`"inputLength" is ${this.inputLength}, but received input shape has shape ${e}`);{let n=0;for(let s=0;s<t.length;++s){let r=t[s],a=e[s+1];if(r!=null&&a!=null&&r!==a)throw new G(`"inputLength" is ${this.inputLength}, but received input shape has shape ${e}`);r==null&&(t[n]=a),n++}}return[e[0],...t,this.outputDim]}call(e,t){return q(()=>{this.invokeCallHook(e,t);let n=Oe(e);n.dtype!=="int32"&&(n=Fp(n,"int32"));let s=dI(this.embeddings.read(),U(n,[n.size]));return U(s,nt(this.computeOutputShape(n.shape)))})}getConfig(){let e={inputDim:this.inputDim,outputDim:this.outputDim,embeddingsInitializer:yt(this.embeddingsInitializer),embeddingsRegularizer:it(this.embeddingsRegularizer),activityRegularizer:it(this.activityRegularizer),embeddingsConstraint:Ot(this.embeddingsConstraint),maskZero:this.maskZero,inputLength:this.inputLength},t=super.getConfig();return Object.assign(e,t),e}};Ey.className="Embedding";re.registerClass(Ey);var xo=class extends He{constructor(e){super(e||{}),this.supportsMasking=!0}mergeFunction(e){throw new Fe}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 s=0;s<t.length;++s){let r=e[e.length-t.length+s],a=t[s];if(r==null||a==null||r<0||a<0)n.push(null);else if(r===1)n.push(a);else if(a===1)n.push(r);else{if(r!==a)throw new G("Operands could not be broadcast together with shapes "+JSON.stringify(e)+" "+JSON.stringify(t));n.push(r)}}return n}build(e){if(Array.isArray(e)&&!Array.isArray(e[0])&&(e=[nt(e)]),e=e,e.length<2)throw new G(`A merge layer should be called on an Array of at least 2 inputs. Got ${e.length} input(s).`);let t=[];for(let r of e)r!=null&&r[0]!==null&&t.push(r[0]);if(t=cr(t),t.length>1)throw new G(`Can not merge tensors with different batch sizes. Got tensors with shapes: ${JSON.stringify(e)}.`);let n=e[0]==null?null:e[0].slice(1);for(let r=1;r<e.length;++r){let a=e[r]==null?null:e[r].slice(1);n=this.computeElementwiseOpOutputShape(n,a)}let s=e.map(r=>r.length);e.indexOf(null)===-1&&cr(s).length===1?this.reshapeRequired=!1:this.reshapeRequired=!0}call(e,t){return q(()=>{if(e=e,this.reshapeRequired){let n=[],s=e.map(r=>r.rank);if(s.indexOf(null)===-1){let r=yr(s);for(let a of e){let o=a.rank;for(let i=0;i<r-o;++i)a=Gl(a,1);n.push(a)}return this.mergeFunction(n)}else{let r=!1;for(let i of e){let u=i.rank;if(u==null){let l=i.shape,c=l[0],p=l.slice(1).concat([c]),d=U(i,[c].concat(dr(l.slice(1))));d=Ge(d,[1,0]),d=U(d,p),n.push(d),r=!0}else if(u>1){let l=ys(1,u).concat([0]);n.push(Ge(i,l)),r=!0}else n.push(i)}let a=this.mergeFunction(n),o=a.rank;if(r){if(o==null){let i=a.shape,u=i.length,l=i[u-1],c=[l].concat(i.slice(0,i.length-1));a=U(Ge(U(a,[-1,l]),[1,0]),c)}else if(o>1){let i=[o-1].concat(ys(0,o-1));a=Ge(a,i)}}return a}}else return this.mergeFunction(e)})}computeOutputShape(e){e=e;let t;e[0]==null?t=null:t=e[0].slice(1);for(let s=1;s<e.length;++s){let r=e[s]==null?null:e[s].slice(1);t=this.computeElementwiseOpOutputShape(t,r)}let n=[];for(let s of e)s!=null&&s[0]!==null&&n.push(s[0]);return n=cr(n),n.length===1?t=n.concat(t):t=[null].concat(t),t}computeMask(e,t){return q(()=>{if(t==null)return null;if(!Array.isArray(t))throw new G("`mask` should be an Array");if(!Array.isArray(e))throw new G("`inputs` should be an Array");if(t.length!==e.length)throw new G(`The Array 'inputs' and 'mask' are expected to have the same length, but have different lengths (${e.length} vs ${t.length})`);if(t.every(s=>s==null))return null;t=t.map(s=>s==null?s:Pn(s,0));let n=t[0];for(let s=1;s<t.length-1;++s)n=Ds(n,t[s]);return n})}},Ry=class extends xo{constructor(e){super(e)}mergeFunction(e){return q(()=>{let t=e[0].clone();for(let n=1;n<e.length;++n)t=oe(t,e[n]);return t})}};Ry.className="Add";re.registerClass(Ry);var Dy=class extends xo{constructor(e){super(e)}mergeFunction(e){return q(()=>{let t=e[0].clone();for(let n=1;n<e.length;++n)t=V(t,e[n]);return t})}};Dy.className="Multiply";re.registerClass(Dy);var Fy=class extends xo{constructor(e){super(e)}mergeFunction(e){return q(()=>{let t=e[0].clone();for(let n=1;n<e.length;++n)t=oe(t,e[n]);return V(1/e.length,t)})}};Fy.className="Average";re.registerClass(Fy);var Oy=class extends xo{constructor(e){super(e)}mergeFunction(e){return q(()=>{let t=e[0];for(let n=1;n<e.length;++n)t=Ar(t,e[n]);return t})}};Oy.className="Maximum";re.registerClass(Oy);var Py=class extends xo{constructor(e){super(e)}mergeFunction(e){return q(()=>{let t=e[0];for(let n=1;n<e.length;++n)t=Np(t,e[n]);return t})}};Py.className="Minimum";re.registerClass(Py);var zy=class extends xo{constructor(e){super(e),this.DEFAULT_AXIS=-1,e==null&&(e={}),this.axis=e.axis==null?this.DEFAULT_AXIS:e.axis,this.supportsMasking=!0,this.reshapeRequired=!1}build(e){if(!(Array.isArray(e)&&Array.isArray(e[0]))||e.length===1)throw new G("A `Concatenate` layer should be called on a list of at least 2 inputs");e=e;let t=!0;for(let s of e)if(s!=null){t=!1;break}if(t)return;let n=[];for(let s=0;s<e.length;++s){let r=e[s].slice();r.splice(this.axis,1);let a=!1;for(let o of n)if(w.arraysEqual(o,r)){a=!0;break}a||n.push(r)}if(n.length>1)throw new G("A `Concatenate` layer requires inputs with matching shapes except for the concat axis. 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e={axis:this.axis},t=super.getConfig();return Object.assign(e,t),e}};zy.className="Concatenate";re.registerClass(zy);function _u(e,t){for(;e<0;)e+=t;return e}function mV(e,t,n){if(e.shape.length>3||t.shape.length>3)throw new Fe("batchDot is not implemented for tensors of 4D or higher rank yet");if(w.assert(e.shape.length>=2,()=>`batchDot requires the rank of x to be >= 2, but got ${e.shape.length}`),w.assert(e.shape.length>=2,()=>`batchDot requires the rank of y to be >= 2, but got ${t.shape.length}`),typeof n=="number"&&(n=[n,n]),e.dtype==="complex64"||t.dtype==="complex64")throw new Fe("batchDot is not implemented for complex64-type Tensors yet.");let s=e.shape.length,r=t.shape.length;n==null&&(n=[s-1,r-2]);let a=n;return q(()=>{let o;if(s>r){o=s-r;let u=[];for(let l=0;l<o;++l)u.push(1);t=U(t,t.shape.concat(u))}else if(r>s){o=r-s;let u=[];for(let l=0;l<o;++l)u.push(1);e=U(e,e.shape.concat(u))}else o=0;let i;if(e.shape.length===2&&t.shape.length===2)a[0]===a[1]?i=ve(V(e,t),a[0]):i=ve(V(Ge(e,[1,0]),t),a[1]);else{let u=a[0]!==e.shape.length-1,l=a[1]===t.shape.length-1;i=We(e,t,u,l)}if(o>0){let u;s>r?u=s+r-3:u=s-1;let l=[];for(let c=u;c<u+o;++c)l.push(c);i=br(i,l)}return i.shape.length===1&&(i=Pn(i,1)),i})}var Ly=class extends xo{constructor(e){super(e),this.axes=e.axes,this.normalize=e.normalize==null?!1:e.normalize,this.supportsMasking=!0,this.reshapeRequired=!1}build(e){w.assert(Array.isArray(e)&&e.length===2&&Array.isArray(e[0])&&Array.isArray(e[1]),()=>"A `Dot` layer should be called on a list of exactly 2 inputs.");let t=e[0],n=e[1];if(t.length>3||n.length>3)throw new Fe("Dot layer does not support tensors of 4D or higher rank yet.");let s=this.interpretAxes(t,n);if(t[s[0]]!==n[s[1]])throw new G(`Dimension incompatibility: ${t[s[0]]} !== ${n[s[1]]}`)}mergeFunction(e){if(e.length!==2)throw new G(`A \`Dot\` layer must be called on exactly 2 inputs, but received ${e.length} 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He{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 q(()=>{this.invokeCallHook(e,t);let n=Oe(e);return ql(()=>oe(Op(n.shape,0,this.stddev),n),()=>n,t.training||!1)})}};My.className="GaussianNoise";re.registerClass(My);var By=class extends He{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 q(()=>{this.invokeCallHook(e,t);let n=Oe(e);return this.rate>0&&this.rate<1?ql(()=>{let r=Math.sqrt(this.rate/(1-this.rate));return V(n,Op(n.shape,1,r))},()=>n,t.training||!1):n})}};By.className="GaussianDropout";re.registerClass(By);var Vy=class extends He{constructor(e){super(e),this.supportsMasking=!0,this.rate=e.rate,this.noiseShape=e.noiseShape}_getNoiseShape(e){return this.noiseShape||Oe(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 q(()=>{if(this.rate<1&&this.rate>0){let n=this._getNoiseShape(e);return ql(()=>{let r=Oe(e),a=1.6732632423543772,o=1.0507009873554805,i=-a*o,u=Zi(Wl(n),this.rate);u=Fp(u,"float32");let l=((1-this.rate)*(1+this.rate*i**2))**-.5,c=-l*i*this.rate,p=oe(V(r,u),V(oe(u,-1),i));return oe(V(p,l),c)},()=>Oe(e),t.training||!1)}return e})}};Vy.className="AlphaDropout";re.registerClass(Vy);function rl(e,t,n,s,r,a=.001){let o;if(e.rank===2)o=YE(e,t,n,s,r,a);else if(e.rank===3)o=ZE(e,t,n,s,r,a);else if(e.rank===4)o=eR(e,t,n,s,r,a);else throw new Fe(`batchNormalization is not implemented for array of rank ${e.rank} yet`);return o}function gV(e,t,n,s,r=.001){return q(()=>{let a=hb(e,s),o=a.mean,i=a.variance;return[rl(e,o,i,n,t,r),o,i]})}function bV(e,t,n,s,r=.001){return q(()=>{let a=hb(e,s),o=a.mean,i=a.variance,u=[];for(let f of ys(0,e.rank))s.indexOf(f)!==-1?u.push(1):u.push(e.shape[f]);let l=U(o,u),c=U(i,u),p=t==null?null:U(t,u),d=n==null?null:U(n,u);return[rl(e,l,c,d,p,r),o,i]})}function yV(e,t,n,s,r=.001){return w.arraysEqual(s.slice().sort(),ys(0,e.rank-1))?gV(e,t,n,s,r):bV(e,t,n,s,r)}var Wy=class extends He{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=ft(e.betaInitializer||"zeros"),this.gammaInitializer=ft(e.gammaInitializer||"ones"),this.movingMeanInitializer=ft(e.movingMeanInitializer||"zeros"),this.movingVarianceInitializer=ft(e.movingVarianceInitializer||"ones"),this.betaConstraint=Pt(e.betaConstraint),this.gammaConstraint=Pt(e.gammaConstraint),this.betaRegularizer=mt(e.betaRegularizer),this.gammaRegularizer=mt(e.gammaRegularizer)}build(e){e=nt(e);let t=this.axis>=0?this.axis:this.axis+e.length,n=e[t];if(n==null)throw new G(`Axis ${t} of input tensor should have a defined dimension but the layer received an input with shape ${JSON.stringify(e)}.`);this.inputSpec=[new Dt({ndim:e.length,axes:{[t]:n}})];let s=[n];this.scale&&(this.gamma=this.addWeight("gamma",s,null,this.gammaInitializer,this.gammaRegularizer,!0,this.gammaConstraint)),this.center&&(this.beta=this.addWeight("beta",s,null,this.betaInitializer,this.betaRegularizer,!0,this.betaConstraint)),this.movingMean=this.addWeight("moving_mean",s,null,this.movingMeanInitializer,null,!1),this.movingVariance=this.addWeight("moving_variance",s,null,this.movingVarianceInitializer,null,!1),this.built=!0}call(e,t){return q(()=>{let n=t.training==null?!1:t.training,s=Oe(e),r=s.shape,a=r.length,o=ys(0,a),i=this.axis>=0?this.axis:this.axis+a;o.splice(i,1);let u=ma(1,a);u[i]=r[i];let l=o.slice();l.sort();let c=!w.arraysEqual(l,ys(0,a).slice(0,a-1)),p=()=>{if(c){let b=U(this.movingMean.read(),u),y=U(this.movingVariance.read(),u),v=this.center?U(this.beta.read(),u):null,x=this.scale?U(this.gamma.read(),u):null;return rl(s,b,y,v,x,this.epsilon)}else return rl(s,this.movingMean.read(),this.movingVariance.read(),this.beta==null?null:this.beta.read(),this.gamma==null?null:this.gamma.read(),this.epsilon)};if(!n)return p();let[d,h,f]=yV(s,this.gamma.read(),this.beta.read(),o,this.epsilon),m=(b,y,v)=>{q(()=>{let x=1-v,k=b.read(),I=V(ge(k,y),x);b.write(ge(k,I))})};return(()=>{m(this.movingMean,h,this.momentum),m(this.movingVariance,f,this.momentum)})(),d})}getConfig(){let e={axis:this.axis,momentum:this.momentum,epsilon:this.epsilon,center:this.center,scale:this.scale,betaInitializer:yt(this.betaInitializer),gammaInitializer:yt(this.gammaInitializer),movingMeanInitializer:yt(this.movingMeanInitializer),movingVarianceInitializer:yt(this.movingVarianceInitializer),betaRegularizer:it(this.betaRegularizer),gammaRegularizer:it(this.gammaRegularizer),betaConstraint:Ot(this.betaConstraint),gammaConstraint:Ot(this.gammaConstraint)},t=super.getConfig();return Object.assign(e,t),e}};Wy.className="BatchNormalization";re.registerClass(Wy);var Uy=class extends He{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=ft(e.betaInitializer||"zeros"),this.gammaInitializer=ft(e.gammaInitializer||"ones"),this.betaRegularizer=mt(e.betaRegularizer),this.gammaRegularizer=mt(e.gammaRegularizer),this.supportsMasking=!0}build(e){e=nt(e);let t=e.length;typeof this.axis=="number"&&(this.axis=[this.axis]);for(let r=0;r<this.axis.length;++r)this.axis[r]<0&&(this.axis[r]+=t);for(let r of this.axis)if(r<0||r>=t)throw new Error(`Invalid axis: ${r}`);if(this.axis.length!==cr(this.axis).length)throw new Error(`Found duplicate axes in: ${this.axis}`);let n=this.axis.map(r=>e[r]),s=!0;this.scale?this.gamma=this.addWeight("gamma",n,"float32",this.gammaInitializer,this.gammaRegularizer,s):this.gamma=null,this.center?this.beta=this.addWeight("beta",n,"float32",this.betaInitializer,this.betaRegularizer,s):this.beta=null,this.built=!0}call(e,t){let n=Oe(e),s=n.shape,r=s.length;return q(()=>{let{mean:o,variance:i}=hb(n,this.axis,!0),u=ma(1,r);for(let f of this.axis)u[f]=s[f];let l=f=>f!=null&&f.shape.length!==r?U(f,u):f,c=this.scale?l(this.gamma.read()):null,p=this.center?l(this.beta.read()):null,d=[],h=[];for(let f=0;f<r;++f)this.axis.indexOf(f)!==-1?(d.push(s[f]),h.push(1)):(d.push(1),h.push(s[f]));return o=hs(o,d),i=hs(i,d),c!=null&&(c=hs(c,h)),p!=null&&(p=hs(p,h)),rl(n,o,i,p,c,this.epsilon)})}getConfig(){let e={axis:this.axis,epsilon:this.epsilon,center:this.center,scale:this.scale,betaInitializer:yt(this.betaInitializer),gammaInitializer:yt(this.gammaInitializer),betaRegularizer:it(this.betaRegularizer),gammaRegularizer:it(this.gammaRegularizer)},t=super.getConfig();return Object.assign(e,t),e}};Uy.className="LayerNormalization";re.registerClass(Uy);function vV(e,t,n){return q(()=>{if(e.rank!==4)throw new G(`temporalPadding expects input tensor to be 4-D, but received a ${e.rank}-D tensor.`);if(t==null&&(t=[[1,1],[1,1]]),t.length!==2||t[0].length!==2||t[1].length!==2)throw new G("spatial2dPadding expects `padding` to be an Array of two Arrays, each of which is an Array of two integers.");if(n==null&&(n=vs()),n!=="channelsLast"&&n!=="channelsFirst")throw new G(`Unknown data format: ${n}. Supported data formats are 'channelsLast' and 'channelsFirst.`);let s;return n==="channelsFirst"?s=[[0,0],[0,0],t[0],t[1]]:s=[[0,0],t[0],t[1],[0,0]],bo(e,s)})}var Gy=class extends He{constructor(e){if(e==null&&(e={}),super(e),this.dataFormat=e.dataFormat==null?vs():e.dataFormat,e.padding==null)this.padding=[[1,1],[1,1]];else if(typeof e.padding=="number")this.padding=[[e.padding,e.padding],[e.padding,e.padding]];else{if(e.padding=e.padding,e.padding.length!==2)throw new G(`ZeroPadding2D expects padding to be a length-2 array, but received a length-${e.padding.length} array.`);let t,n;if(typeof e.padding[0]=="number")t=[e.padding[0],e.padding[0]],n=[e.padding[1],e.padding[1]];else{if(e.padding=e.padding,e.padding[0].length!==2)throw new G(`ZeroPadding2D expects height padding to be a length-2 array, but received a length-${e.padding[0].length} array.`);if(t=e.padding[0],e.padding[1].length!==2)throw new G(`ZeroPadding2D expects width padding to be a length-2 array, but received a length-${e.padding[1].length} array.`);n=e.padding[1]}this.padding=[t,n]}this.inputSpec=[new Dt({ndim:4})]}computeOutputShape(e){e=nt(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 q(()=>vV(Oe(e),this.padding,this.dataFormat))}getConfig(){let e={padding:this.padding,dataFormat:this.dataFormat},t=super.getConfig();return Object.assign(e,t),e}};Gy.className="ZeroPadding2D";re.registerClass(Gy);function Jp(e,t,n,s,r,a){return q(()=>{Ct(r),iI(a),Hn(s),n==null&&(n=[1,1]),s==null&&(s="valid"),r==null&&(r=vs()),a==null&&(a="max"),e=cy(e,r);let o,i=s==="same"?"same":"valid";return a==="max"?o=pb(e,t,n,i):o=nb(e,t,n,i),r==="channelsFirst"&&(o=Ge(o,[0,3,1,2])),o})}function t0(e,t,n,s,r,a){return q(()=>{Ct(r),iI(a),Hn(s),n==null&&(n=[1,1,1]),s==null&&(s="valid"),r==null&&(r=vs()),a==null&&(a="max"),e=qI(e,r);let o,i=s==="same"?"same":"valid";return a==="max"?o=AS(e,t,n,i):o=hS(e,t,n,i),r==="channelsFirst"&&(o=Ge(o,[0,4,1,2,3])),o})}var n0=class extends He{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 G(`poolSize for 1D convolutional layer must be a number or an Array of a single number, but received ${JSON.stringify(e.poolSize)}`);if(Bt(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 G(`strides for 1D convolutional layer must be a number or an Array of a single number, but received ${JSON.stringify(e.strides)}`);Bt(this.strides,"strides"),this.padding=e.padding==null?"valid":e.padding,Hn(this.padding),this.inputSpec=[new Dt({ndim:3})]}computeOutputShape(e){e=nt(e);let t=bs(e[1],this.poolSize[0],this.padding,this.strides[0]);return[e[0],t,e[2]]}call(e,t){return q(()=>{this.invokeCallHook(e,t),e=Gl(Oe(e),2);let n=this.poolingFunction(Oe(e),[this.poolSize[0],1],[this.strides[0],1],this.padding,"channelsLast");return br(n,[2])})}getConfig(){let e={poolSize:this.poolSize,padding:this.padding,strides:this.strides},t=super.getConfig();return Object.assign(e,t),e}},Hy=class extends n0{constructor(e){super(e)}poolingFunction(e,t,n,s,r){return Ct(r),Hn(s),Jp(e,t,n,s,r,"max")}};Hy.className="MaxPooling1D";re.registerClass(Hy);var qy=class extends n0{constructor(e){super(e)}poolingFunction(e,t,n,s,r){return Ct(r),Hn(s),Jp(e,t,n,s,r,"avg")}};qy.className="AveragePooling1D";re.registerClass(qy);var s0=class extends He{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 G(`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];Bt(this.poolSize,"poolSize"),Bt(this.strides,"strides"),this.padding=e.padding==null?"valid":e.padding,this.dataFormat=e.dataFormat==null?"channelsLast":e.dataFormat,Ct(this.dataFormat),Hn(this.padding),this.inputSpec=[new Dt({ndim:4})]}computeOutputShape(e){e=nt(e);let t=this.dataFormat==="channelsFirst"?e[2]:e[1],n=this.dataFormat==="channelsFirst"?e[3]:e[2];return t=bs(t,this.poolSize[0],this.padding,this.strides[0]),n=bs(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 q(()=>(this.invokeCallHook(e,t),this.poolingFunction(Oe(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}},jy=class extends s0{constructor(e){super(e)}poolingFunction(e,t,n,s,r){return Ct(r),Hn(s),Jp(e,t,n,s,r,"max")}};jy.className="MaxPooling2D";re.registerClass(jy);var Ky=class extends s0{constructor(e){super(e)}poolingFunction(e,t,n,s,r){return Ct(r),Hn(s),Jp(e,t,n,s,r,"avg")}};Ky.className="AveragePooling2D";re.registerClass(Ky);var r0=class extends He{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 G(`If the strides property of a 3D pooling layer is an Array, it is expected to have a length of 3, but received length ${e.strides.length}.`);this.strides=e.strides}else this.strides=[e.strides,e.strides,e.strides];Bt(this.poolSize,"poolSize"),Bt(this.strides,"strides"),this.padding=e.padding==null?"valid":e.padding,this.dataFormat=e.dataFormat==null?"channelsLast":e.dataFormat,Ct(this.dataFormat),Hn(this.padding),this.inputSpec=[new Dt({ndim:5})]}computeOutputShape(e){e=nt(e);let t=this.dataFormat==="channelsFirst"?e[2]:e[1],n=this.dataFormat==="channelsFirst"?e[3]:e[2],s=this.dataFormat==="channelsFirst"?e[4]:e[3];return t=bs(t,this.poolSize[0],this.padding,this.strides[0]),n=bs(n,this.poolSize[1],this.padding,this.strides[1]),s=bs(s,this.poolSize[2],this.padding,this.strides[2]),this.dataFormat==="channelsFirst"?[e[0],e[1],t,n,s]:[e[0],t,n,s,e[4]]}call(e,t){return q(()=>(this.invokeCallHook(e,t),this.poolingFunction(Oe(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}},Xy=class extends r0{constructor(e){super(e)}poolingFunction(e,t,n,s,r){return Ct(r),Hn(s),t0(e,t,n,s,r,"max")}};Xy.className="MaxPooling3D";re.registerClass(Xy);var Yy=class extends r0{constructor(e){super(e)}poolingFunction(e,t,n,s,r){return Ct(r),Hn(s),t0(e,t,n,s,r,"avg")}};Yy.className="AveragePooling3D";re.registerClass(Yy);var a0=class extends He{constructor(e){super(e),this.inputSpec=[new Dt({ndim:3})]}computeOutputShape(e){return[e[0],e[2]]}call(e,t){throw new Fe}},Qy=class extends a0{constructor(e){super(e||{})}call(e,t){return q(()=>{let n=Oe(e);return It(n,1)})}};Qy.className="GlobalAveragePooling1D";re.registerClass(Qy);var Zy=class extends a0{constructor(e){super(e||{})}call(e,t){return q(()=>{let n=Oe(e);return As(n,1)})}};Zy.className="GlobalMaxPooling1D";re.registerClass(Zy);var o0=class extends He{constructor(e){super(e),this.dataFormat=e.dataFormat==null?"channelsLast":e.dataFormat,Ct(this.dataFormat),this.inputSpec=[new Dt({ndim:4})]}computeOutputShape(e){return e=e,this.dataFormat==="channelsLast"?[e[0],e[3]]:[e[0],e[1]]}call(e,t){throw new Fe}getConfig(){let e={dataFormat:this.dataFormat},t=super.getConfig();return Object.assign(e,t),e}},Jy=class extends o0{call(e,t){return q(()=>{let n=Oe(e);return this.dataFormat==="channelsLast"?It(n,[1,2]):It(n,[2,3])})}};Jy.className="GlobalAveragePooling2D";re.registerClass(Jy);var ev=class extends o0{call(e,t){return q(()=>{let n=Oe(e);return this.dataFormat==="channelsLast"?As(n,[1,2]):As(n,[2,3])})}};ev.className="GlobalMaxPooling2D";re.registerClass(ev);var i0=class extends He{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 s=t.layer,r=gs(s,n);delete t.layer;let a={layer:r};return Object.assign(a,t),new e(a)}},tv=class extends i0{constructor(e){super(e),this.supportsMasking=!0}build(e){if(e=nt(e),e.length<3)throw new G(`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=nt(e);let t=[e[0]].concat(e.slice(2)),n=this.layer.computeOutputShape(t),s=e[1];return[n[0],s].concat(n.slice(1))}call(e,t){return q(()=>(e=Oe(e),ZI((a,o)=>[Oe(this.layer.call(a,t)),[]],e,[],!1,null,null,!1,!0)[1]))}};tv.className="TimeDistributed";re.registerClass(tv);function xV(e){yo(fz,"BidirectionalMergeMode",e)}var wV="concat",nv=class extends i0{constructor(e){super(e);let t=e.layer.getConfig(),n={};n.className=e.layer.getClassName(),n.config=t,this.forwardLayer=gs(n),t.goBackwards=t.goBackwards!==!0;let s={};if(s.className=e.layer.getClassName(),s.config=t,this.backwardLayer=gs(s),this.forwardLayer.name="forward_"+this.forwardLayer.name,this.backwardLayer.name="backward_"+this.backwardLayer.name,this.mergeMode=e.mergeMode===void 0?wV:e.mergeMode,xV(this.mergeMode),e.weights)throw new Fe("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,s,r;return this.returnState&&(r=t.slice(1)),n=t[0],n=n,this.mergeMode==="concat"?(n[n.length-1]*=2,s=[n]):this.mergeMode==null?s=[n,n.slice()]:s=[n],this.returnState?this.mergeMode==null?s.concat(r).concat(r.slice()):[n].concat(r).concat(r.slice()):bn(s)}apply(e,t){let n=t==null?null:t.initialState,s=t==null?null:t.constants;t==null&&(t={});let r=QI(e,n,s,this.numConstants);if(e=r.inputs,n=r.initialState,s=r.constants,Array.isArray(e)&&(n=e.slice(1),e=e[0]),(n==null||n.length===0)&&s==null)return super.apply(e,t);let a=[],o=[];if(n!=null){let u=n.length;if(u%2>0)throw new G("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 l=n.map(c=>new Dt({shape:c.shape}));this.forwardLayer.stateSpec=l.slice(0,u/2),this.backwardLayer.stateSpec=l.slice(u/2),o.push(...l)}if(s!=null)throw new Fe("Support for constants in Bidirectional layers is not implemented yet.");let i=a[0]instanceof $s;for(let u of a)if(u instanceof $s!==i)throw new G("The initial state of a Bidirectional layer cannot be specified as a mix of symbolic and non-symbolic tensors");if(i){let u=[e].concat(a),l=this.inputSpec.concat(o),c=this.inputSpec;this.inputSpec=l;let p=super.apply(u,t);return this.inputSpec=c,p}else return super.apply(e,t)}call(e,t){return q(()=>{let n=t.initialState,s,r;if(n==null)s=this.forwardLayer.call(e,t),r=this.backwardLayer.call(e,t);else{let i=n.slice(0,n.length/2),u=n.slice(n.length/2);s=this.forwardLayer.call(e,Object.assign(t,{initialState:i})),r=this.backwardLayer.call(e,Object.assign(t,{initialState:u}))}let a;this.returnState&&(Array.isArray(s)&&(a=s.slice(1).concat(r.slice(1))),s=s[0],r=r[0]),this.returnSequences&&(r=Jn(r,1));let o;return this.mergeMode==="concat"?o=Ob([s,r]):this.mergeMode==="sum"?o=oe(s,r):this.mergeMode==="ave"?o=V(.5,oe(s,r)):this.mergeMode==="mul"?o=V(s,r):this.mergeMode==null&&(o=[s,r]),this.returnState?this.mergeMode==null?o.concat(a):[o].concat(a):o})}resetStates(e){this.forwardLayer.resetStates(),this.backwardLayer.resetStates()}build(e){na(this.forwardLayer.name,()=>{this.forwardLayer.build(e)}),na(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 r=this.forwardLayer.states.map(a=>null);return Array.isArray(n)?n.concat(r).concat(r):[n].concat(r).concat(r)}else return n}get trainableWeights(){return this.forwardLayer.trainableWeights.concat(this.backwardLayer.trainableWeights)}get nonTrainableWeights(){return 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o=S("axis",e,t,n),i=S("keepDims",e,t,n);return[It(S("x",e,t,n),o,i)]}case"Min":{let o=S("axis",e,t,n),i=S("keepDims",e,t,n);return[Nm(S("x",e,t,n),o,i)]}case"Sum":{let o=S("axis",e,t,n),i=S("keepDims",e,t,n);return[ve(S("x",e,t,n),o,i)]}case"All":{let o=S("axis",e,t,n),i=S("keepDims",e,t,n);return[lS(S("x",e,t,n),o,i)]}case"Any":{let o=S("axis",e,t,n),i=S("keepDims",e,t,n);return[Sm(S("x",e,t,n),o,i)]}case"ArgMax":{let o=S("axis",e,t,n);return[Yu(S("x",e,t,n),o)]}case"ArgMin":{let o=S("axis",e,t,n);return[wE(S("x",e,t,n),o)]}case"Prod":{let o=S("axis",e,t,n),i=S("keepDims",e,t,n);return[ES(S("x",e,t,n),o,i)]}case"Cumprod":{let o=S("axis",e,t,n),i=S("exclusive",e,t,n),u=S("reverse",e,t,n);return[Cm(S("x",e,t,n),o,i,u)]}case"Cumsum":{let o=S("axis",e,t,n),i=S("exclusive",e,t,n),u=S("reverse",e,t,n);return[xS(S("x",e,t,n),o,i,u)]}case"Bincount":let s=S("x",e,t,n),r=S("weights",e,t,n),a=S("size",e,t,n);return[fS(s,r,a)];case"DenseBincount":{let o=S("x",e,t,n),i=S("weights",e,t,n),u=S("size",e,t,n),l=S("binaryOutput",e,t,n);return[_R(o,i,u,l)]}default:throw TypeError(`Node type ${e.op} is not implemented`)}},H4=(e,t,n)=>{switch(e.op){case"ConcatV2":case"Concat":{let s=S("n",e,t,n),r=S("axis",e,t,n),a=S("tensors",e,t,n);return a=a.slice(0,s),[Ft(a,r)]}case"Gather":{let s=S("x",e,t,n),r=S("indices",e,t,n);return[Ju(s,le(r,"int32"),0)]}case"GatherV2":{let s=S("axis",e,t,n),r=S("batchDims",e,t,n),a=S("x",e,t,n),o=S("indices",e,t,n);return[Ju(a,le(o,"int32"),s,r)]}case"Reverse":{let s=S("dims",e,t,n),r=[];for(let o=0;o<s.length;o++)s[o]&&r.push(o);let a=S("x",e,t,n);return[Jn(a,r)]}case"ReverseV2":{let s=S("axis",e,t,n),r=S("x",e,t,n);return[Jn(r,s)]}case"Slice":{let s=S("begin",e,t,n),r=S("size",e,t,n);return[qe(S("x",e,t,n),s,r)]}case"StridedSlice":{let s=S("begin",e,t,n),r=S("end",e,t,n),a=S("strides",e,t,n),o=S("beginMask",e,t,n),i=S("endMask",e,t,n),u=S("ellipsisMask",e,t,n),l=S("newAxisMask",e,t,n),c=S("shrinkAxisMask",e,t,n),p=S("x",e,t,n);return[nF(p,s,r,a,o,i,u,l,c)]}case"Pack":return q(()=>{let s=S("axis",e,t,n),r=S("tensors",e,t,n),a=r[0].shape,o=br(r[0]).shape,i=r.map(u=>{let l=w.arraysEqual(u.shape,a);if(!l&&!w.arraysEqual(br(u).shape,o))throw new Error("the input tensors shape does not match");return l?u:U(u,a)});return[es(i,s)]});case"Unpack":{let s=S("axis",e,t,n),r=S("tensor",e,t,n);return Fs(r,s)}case"Tile":{let s=S("reps",e,t,n);return[hs(S("x",e,t,n),s)]}case"Split":case"SplitV":{let s=S("axis",e,t,n),r=S("numOrSizeSplits",e,t,n),a=S("x",e,t,n);return Bn(a,r,s)}case"ScatterNd":{let s=S("indices",e,t,n),r=S("values",e,t,n),a=S("shape",e,t,n);return[yF(s,r,a)]}case"GatherNd":{let s=S("x",e,t,n),r=S("indices",e,t,n);return[kF(s,r)]}case"SparseToDense":{let s=S("sparseIndices",e,t,n),r=S("outputShape",e,t,n),a=S("sparseValues",e,t,n),o=S("defaultValue",e,t,n);return[US(s,a,r,a.dtype===o.dtype?o:le(o,a.dtype))]}default:throw TypeError(`Node type ${e.op} is not implemented`)}},q4=(e,t,n)=>{switch(e.op){case"SparseFillEmptyRows":{let{outputIndices:s,outputValues:r,emptyRowIndicator:a,reverseIndexMap:o}=jc.sparseFillEmptyRows(S("indices",e,t,n),S("values",e,t,n),S("denseShape",e,t,n),S("defaultValue",e,t,n));return[s,r,a,o]}case"SparseReshape":{let{outputIndices:s,outputShape:r}=jc.sparseReshape(S("inputIndices",e,t,n),S("inputShape",e,t,n),S("newShape",e,t,n));return[s,r]}case"SparseSegmentMean":return[jc.sparseSegmentMean(S("data",e,t,n),S("indices",e,t,n),S("segmentIds",e,t,n))];case"SparseSegmentSum":return[jc.sparseSegmentSum(S("data",e,t,n),S("indices",e,t,n),S("segmentIds",e,t,n))];default:throw TypeError(`Node type ${e.op} is not implemented`)}},j4=(e,t,n)=>{switch(e.op){case"FFT":return[wb(S("x",e,t,n))];case"IFFT":return[$d(S("x",e,t,n))];case"RFFT":return[kb(S("x",e,t,n))];case"IRFFT":return[MS(S("x",e,t,n))];default:throw TypeError(`Node type ${e.op} is not implemented`)}},K4=(e,t,n)=>{switch(e.op){case"StringNGrams":{let{nGrams:s,nGramsSplits:r}=Yf.stringNGrams(S("data",e,t,n),S("dataSplits",e,t,n),S("separator",e,t,n),S("nGramWidths",e,t,n),S("leftPad",e,t,n),S("rightPad",e,t,n),S("padWidth",e,t,n),S("preserveShortSequences",e,t,n));return[s,r]}case"StringSplit":{let{indices:s,values:r,shape:a}=Yf.stringSplit(S("input",e,t,n),S("delimiter",e,t,n),S("skipEmpty",e,t,n));return[s,r,a]}case"StringToHashBucketFast":return[Yf.stringToHashBucketFast(S("input",e,t,n),S("numBuckets",e,t,n))];default:throw TypeError(`Node type ${e.op} is not implemented`)}},X4=(e,t,n)=>{switch(e.op){case"Cast":return[le(S("x",e,t,n),S("dtype",e,t,n))];case"ExpandDims":{let s=S("axis",e,t,n);return[Pn(S("x",e,t,n),s)]}case"Squeeze":{let s=S("axis",e,t,n);return[br(S("x",e,t,n),s)]}case"Reshape":return[U(S("x",e,t,n),S("shape",e,t,n))];case"MirrorPad":return[jD(S("x",e,t,n),S("padding",e,t,n),S("mode",e,t,n))];case"PadV2":case"Pad":return[bo(S("x",e,t,n),S("padding",e,t,n),S("constantValue",e,t,n))];case"SpaceToBatchND":{let s=S("blockShape",e,t,n),r=S("paddings",e,t,n);return[fb(S("x",e,t,n),s,r)]}case"BatchToSpaceND":{let s=S("blockShape",e,t,n),r=S("crops",e,t,n);return[sb(S("x",e,t,n),s,r)]}case"DepthToSpace":{let s=S("blockSize",e,t,n),r=S("dataFormat",e,t,n).toUpperCase();return[ER(S("x",e,t,n),s,r)]}case"BroadcastTo":return[id(S("x",e,t,n),S("shape",e,t,n))];case"BroadcastArgs":return[sR(S("s0",e,t,n),S("s1",e,t,n))];default:throw TypeError(`Node type ${e.op} is not implemented`)}};function ow(e,t,n,s){let r=((a,o,i)=>{switch(a.category){case"arithmetic":return q(()=>C4(a,o,i));case"basic_math":return q(()=>N4(a,o,i));case"control":return R4(a,o,i);case"convolution":return q(()=>D4(a,o,i));case"creation":return q(()=>F4(a,o,i));case"dynamic":return O4(a,o,i);case"evaluation":return q(()=>P4(a,o,i));case"image":return q(()=>B4(a,o,i));case"graph":return q(()=>z4(a,o,i));case"logical":return q(()=>V4(a,o,i));case"matrices":return q(()=>W4(a,o,i));case"normalization":return q(()=>U4(a,o,i));case"reduction":return q(()=>G4(a,o,i));case"slice_join":return q(()=>H4(a,o,i));case"sparse":return q(()=>q4(a,o,i));case"spectral":return q(()=>j4(a,o,i));case"string":return q(()=>K4(a,o,i));case"transformation":return q(()=>X4(a,o,i));case"hash_table":return M4(a,o,i,s);case"custom":let u=h0(a.op);if(u&&u.customExecutor)return u.customExecutor(new I4(a,o,i));throw TypeError(`Custom op ${a.op} is not registered.`);default:throw TypeError(`Unknown op '${a.op}'. File an issue at https://github.com/tensorflow/tfjs/issues so we can add it, or register a custom execution with tf.registerOp()`)}})(e,t,n);return w.isPromise(r)?r.then(a=>[].concat(a)):[].concat(r)}var iw=class{constructor(e={},t={},n={},s={}){this.weightMap=e,this.tensorArrayMap=t,this.tensorListMap=n,this.functionMap=s,this.rootContext={id:0,frameName:"",iterationId:0},this.contexts=[this.rootContext],this.lastId=0,this.generateCurrentContextIds()}newFrame(e,t){return{id:e,frameName:t,iterationId:0}}set currentContext(e){this.contexts!==e&&(this.contexts=e,this.generateCurrentContextIds())}get currentContext(){return this.contexts}get currentContextId(){return this._currentContextIds[0]}get currentContextIds(){return this._currentContextIds}generateCurrentContextIds(){let e=[];for(let t=0;t<this.contexts.length-1;t++){let n=this.contexts.slice(0,this.contexts.length-t);e.push(this.contextIdforContexts(n))}e.push(""),this._currentContextIds=e}contextIdforContexts(e){return e?e.map(t=>t.id===0&&t.iterationId===0?"":`${t.frameName}-${t.iterationId}`).join("/"):""}enterFrame(e){this.contexts&&(this.lastId++,this.contexts=this.contexts.slice(),this.contexts.push(this.newFrame(this.lastId,e)),this._currentContextIds.unshift(this.contextIdforContexts(this.contexts)))}exitFrame(){if(this.contexts&&this.contexts.length>1)this.contexts=this.contexts.slice(),this.contexts.splice(-1),this.currentContextIds.shift();else throw new Error("Cannot exit frame, the context is empty")}nextIteration(){if(this.contexts&&this.contexts.length>0){this.contexts=this.contexts.slice(),this.lastId++;let e=Object.assign({},this.contexts[this.contexts.length-1]);e.iterationId+=1,e.id=this.lastId,this.contexts.splice(-1,1,e),this._currentContextIds.splice(0,1,this.contextIdforContexts(this.contexts))}else throw new Error("Cannot increase frame iteration, the context is empty")}getWeight(e){return this.weightMap[e]}addTensorArray(e){this.tensorArrayMap[e.id]=e}getTensorArray(e){return this.tensorArrayMap[e]}addTensorList(e){this.tensorListMap[e.id]=e}getTensorList(e){return this.tensorListMap[e]}dispose(e){for(let t in this.tensorArrayMap)this.tensorArrayMap[t].clearAndClose(e);for(let t in this.tensorListMap)this.tensorListMap[t].clearAndClose(e)}};function uw(e,t,n,s){let r=new Set,a=[],o=null,i=null,u=new Set,l=Object.keys(e).map(d=>_n(d)[0]),c=[];s!=null&&(c=s.map(d=>_n(d.name)[0]));let p=[...t];for(;p.length>0;){let d=p.pop();if((O0(d)||eU(d)||tU(d))&&o==null&&(o=d,i=o.children.map(h=>h.name).filter(h=>r.has(h))),r.add(d.name),n[d.name]==null&&l.indexOf(d.name)===-1&&c.indexOf(d.name)===-1){if(d.inputs.length===0){a.push(d.name);continue}d.inputs.forEach(h=>{u.has(h.name)||(u.add(h.name),p.push(h))})}}return{inputs:e,outputs:t,usedNodes:r,missingInputs:a,dynamicNode:o,syncInputs:i}}function Y4(e,t,n){let{usedNodes:s,inputs:r}=n,a=[],o=Object.keys(r).map(c=>_n(c)[0]).map(c=>e.nodes[c]),i=e.initNodes;o.forEach(c=>{s.has(c.name)&&a.push(c)}),e.weights.forEach(c=>{s.has(c.name)&&a.push(c)}),i!=null&&i.forEach(c=>{s.has(c.name)&&a.push(c)});let u=new Set,l=[];for(;a.length>0;){let c=a.pop();u.add(c.name),t[c.name]||l.push(c),c.children.forEach(p=>{!u.has(p.name)&&s.has(p.name)&&p.inputs.every(d=>u.has(d.name))&&a.push(p)})}return l}var Q4=["Switch","Merge","Enter","Exit","NextIteration","StatelessIf","StatelessWhile","if","While"],Z4=["NonMaxSuppressionV2","NonMaxSuppressionV3","NonMaxSuppressionV5","Where"],J4=["HashTable","HashTableV2","LookupTableImport","LookupTableImportV2","LookupTableFind","LookupTableFindV2","LookupTableSize","LookupTableSizeV2"];function O0(e){return Q4.indexOf(e.op)>=0}function eU(e){return Z4.indexOf(e.op)>=0}function tU(e){return J4.indexOf(e.op)>=0}var Xm=class{constructor(e,t){this.graph=e,this.parent=t,this.compiledMap=new Map,this._weightMap={},this.SEPERATOR=",",this._functions={},this._functionExecutorMap={},this.intermediateTensors={},this.keepTensorForDebug=!1,this._outputs=e.outputs,this._inputs=e.inputs,this._initNodes=e.initNodes,this._signature=e.signature,this._functions=e.functions,e.functions!=null&&Object.keys(e.functions).forEach(n=>{this._functionExecutorMap[n]=new Xm(e.functions[n],this)})}get weightIds(){return this.parent?this.parent.weightIds:this._weightIds}get functionExecutorMap(){return this.parent?this.parent.functionExecutorMap:this._functionExecutorMap}get weightMap(){return this.parent?this.parent.weightMap:this._weightMap}set weightMap(e){let t=Object.keys(e).map(n=>e[n].map(s=>s.id));this._weightIds=[].concat(...t),this._weightMap=e}set resourceManager(e){this._resourceManager=e}get inputs(){return this._inputs.map(e=>({name:e.name,shape:e.attrParams.shape?e.attrParams.shape.value:void 0,dtype:e.attrParams.dtype?e.attrParams.dtype.value:void 0}))}get outputs(){return this._outputs.map(e=>({name:e.name,shape:e.attrParams.shape?e.attrParams.shape.value:void 0,dtype:e.attrParams.dtype?e.attrParams.dtype.value:void 0}))}get inputNodes(){return this._inputs.map(e=>e.signatureKey||e.name)}get outputNodes(){return this._outputs.map(e=>{let t=e.signatureKey||e.name;return e.defaultOutput?`${t}:${e.defaultOutput}`:t})}get functions(){return Object.keys(this._functions).reduce((e,t)=>(e[t]=this._functions[t].signature,e),{})}getCompilationKey(e,t){let n=e.map(r=>r.name).sort(),s=t.map(r=>r.name).sort();return n.join(this.SEPERATOR)+"--"+s.join(this.SEPERATOR)}compile(e,t){let n=uw(e,t,this.weightMap,this._initNodes),{missingInputs:s,dynamicNode:r,syncInputs:a}=n;if(r!=null)throw new Error(`This execution contains the node '${r.name}', which has the dynamic op '${r.op}'. Please use model.executeAsync() instead. Alternatively, to avoid the dynamic ops, specify the inputs [${a}]`);if(s.length>0){let o=t.map(u=>u.name),i=Object.keys(e);throw new Error(`Cannot compute the outputs [${o}] from the provided inputs [${i}]. Missing the following inputs: [${s}]`)}return Y4(this.graph,this.weightMap,n)}execute(e,t){e=this.mapInputs(e);let n=Object.keys(e).sort();this.checkInputs(e),this.checkInputShapeAndType(e),t=this.mapOutputs(t),this.checkOutputs(t);let s=n.map(c=>this.graph.nodes[_n(c)[0]]),r=t.map(c=>_n(c)[0]),a=r.map(c=>this.graph.nodes[c]);this.resetIntermediateTensors(),a.length===0&&(a=this._outputs);let o=this.getCompilationKey(s,a),i=this.compiledMap.get(o);i==null&&(i=this.compile(e,a),this.compiledMap.set(o,i));let u={},l={};return q(()=>{let c=new iw(this.weightMap,u,l,this.functionExecutorMap),p={...this.weightMap};Object.keys(e).forEach(f=>{let[m,g]=_n(f),b=[];b[g]=e[f],p[m]=b});let d=this.getFrozenTensorIds(p),h={};for(let f=0;f<i.length;f++){let m=i[f];if(!p[m.name]){let g=ow(m,p,c,this._resourceManager);if(w.isPromise(g))throw new Error(`The execution of the op '${m.op}' returned a promise. Please use model.executeAsync() instead.`);p[m.name]=g,this.checkTensorForDisposal(m.name,m,p,c,d,r,h)}}return this.parent==null&&c.dispose(d),t.map(f=>un(f,p,c))})}getFrozenTensorIds(e){let t=[].concat.apply([],Object.keys(e).map(n=>e[n]).map(n=>n.map(s=>s.id)));return new Set(t)}checkTensorForDisposal(e,t,n,s,r,a,o){t.category==="control"||a.indexOf(e)!==-1||(n[e].forEach(i=>{i!=null&&(o[i.id]=(o[i.id]||0)+t.children.length)}),t.inputs.forEach(i=>{if(i.category!=="control"){let u=s4(i.name,n,s);u!=null&&u.forEach(l=>{if(l&&!l.kept&&!r.has(l.id)){let c=o[l.id];if(c===1){if(!this.keepTensorForDebug)l.dispose();else{let[p,d]=Ts(t.name,s);this.intermediateTensors[p]?this.intermediateTensors[p][d]=l:(this.intermediateTensors[p]=[],this.intermediateTensors[p][d]=l)}delete o[l.id]}else c!=null&&o[l.id]--}})}}))}async executeAsync(e,t){return this._executeAsync(e,t)}disposeIntermediateTensors(){!this.intermediateTensors||(Object.keys(this.intermediateTensors).forEach(e=>this.intermediateTensors[e].forEach(t=>t.dispose())),this.disposeTensorsMap())}disposeTensorsMap(){!this.tensorsMap||Object.keys(this.tensorsMap).forEach(e=>{this.tensorsMap[e].forEach(n=>{n&&!n.kept&&!n.isDisposed&&!this.keepIds.has(n.id)&&n.dispose()})})}getIntermediateTensors(){return this.tensorsMap}resetIntermediateTensors(){for(let e in this.intermediateTensors)this.intermediateTensors[e].forEach(t=>t.dispose()),delete this.intermediateTensors[e]}async _executeAsync(e,t,n=!1,s={},r={}){n||(e=this.mapInputs(e),this.checkInputs(e),this.checkInputShapeAndType(e),t=this.mapOutputs(t),this.checkOutputs(t));try{this.keepTensorForDebug=K().getBool("KEEP_INTERMEDIATE_TENSORS")}catch(l){console.warn(l.message)}this.resetIntermediateTensors();let a=new iw(this.weightMap,s,r,this.functionExecutorMap);this.tensorsMap=await this.executeWithControlFlow(e,a,t,n);let o=t.map(l=>un(l,this.tensorsMap,a)),i=o.map(l=>l.id),u=Object.keys(e).map(l=>e[l].id);return this.keepIds=new Set([...i,...u,...this.weightIds]),this.keepTensorForDebug||this.disposeTensorsMap(),this.parent==null&&a.dispose(this.keepIds),o}async executeFunctionAsync(e,t,n){let s=e.reduce((r,a,o)=>(r[this.inputs[o].name]=a,r),{});return this._executeAsync(s,this.outputNodes,!0,t,n)}async executeWithControlFlow(e,t,n,s){let r=Object.keys(e),a=r.map(y=>this.graph.nodes[_n(y)[0]]),o=n.map(y=>_n(y)[0]),i=o.map(y=>this.graph.nodes[y]);i.length===0&&(i=this._outputs);let{usedNodes:u,missingInputs:l,dynamicNode:c,syncInputs:p}=uw(e,i,this.weightMap,this._initNodes),d=[...a,...this.graph.weights,...this._initNodes||[]].map(y=>({node:y,contexts:t.currentContext})),h={...this.weightMap};Object.keys(e).forEach(y=>{let[v,x]=_n(y),k=[];k[x]=e[y],h[v]=k});let f={},m=this.getFrozenTensorIds(h),g={};for(;d.length>0;){let y=this.processStack(a,d,t,h,g,m,o,f,u);await Promise.all(y)}c==null&&!s&&console.warn("This model execution did not contain any nodes with control flow or dynamic output shapes. You can use model.execute() instead.");let b=i.filter(y=>!O0(y)&&!un(y.name,h,t)).map(y=>y.name);if(b.length>0){let y="";throw c!=null&&(y=`Alternatively, to avoid the dynamic ops, use model.execute() and specify the inputs [${p}]`),new Error(`Cannot compute the outputs [${b}] from the provided inputs [${r}]. 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this.upstream.next();if(e.done)return!1;let t=_s.getTensorsInContainer(e.value),n=this.transform(e.value),s=_s.getTensorsInContainer(n);this.outputQueue.pushAll(n);for(let r of t)_s.isTensorInList(r,s)||r.dispose();return!0}},G0=class extends Ut{constructor(e,t){super(),this.baseErrorHandler=t,this.lastRead=null,this.iterator=null,this.moreIterators=e}summary(){return"TODO: fill in upstream of chained summaries -> Chained"}async next(){return this.lastRead=this.readFromChain(this.lastRead),this.lastRead}async readFromChain(e){if(await e,this.iterator==null){let n=await this.moreIterators.next();if(n.done)return{value:null,done:!0};this.iterator=n.value,this.baseErrorHandler!=null&&(this.iterator=this.iterator.handleErrors(this.baseErrorHandler))}let t=await this.iterator.next();return t.done?(this.iterator=null,this.readFromChain(e)):t}},H0=(e=>(e[e.FAIL=0]="FAIL",e[e.SHORTEST=1]="SHORTEST",e[e.LONGEST=2]="LONGEST",e))(H0||{}),TU=class extends Ut{constructor(e,t=0){super(),this.iterators=e,this.mismatchMode=t,this.count=0,this.currentPromise=null}summary(){return"{TODO: fill in upstream of zip summaries} -> Zip"}async nextState(e){await e;let t=0,n=0;function s(a){return a instanceof Ut?{value:a.next().then(i=>(t++,i.done&&n++,i.value)),recurse:!1}:{value:null,recurse:!0}}let r=await M0(this.iterators,s);if(t===n)return{value:null,done:!0};if(n>0)switch(this.mismatchMode){case 0:throw new Error(`Zipped streams should have the same length. Mismatched at element ${this.count}.`);case 1:return{value:null,done:!0};case 2:default:}return this.count++,{value:r,done:!1}}async next(){return this.currentPromise=this.nextState(this.currentPromise),this.currentPromise}},q0=class extends Ut{constructor(e,t){super(),this.upstream=e,this.bufferSize=t,this.buffer=new B0(t)}summary(){return`${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()}},$U=class extends q0{constructor(e,t,n){super(e,t),this.upstream=e,this.windowSize=t,this.upstreamExhausted=!1,this.random=uU.alea(n||w.now().toString()),this.lastRead=Promise.resolve({value:null,done:!1})}async next(){return this.lastRead=this.lastRead.then(()=>this.serialNext()),this.lastRead}randomInt(e){return Math.floor(this.random()*e)}chooseIndex(){return this.randomInt(this.buffer.length())}async serialNext(){for(this.upstreamExhausted||this.refill();!this.buffer.isEmpty();){let e=this.chooseIndex(),t=await this.buffer.shuffleExcise(e);if(t.done)this.upstreamExhausted=!0;else return this.refill(),t}return{value:null,done:!0}}},su=class{constructor(){this.size=null}batch(e,t=!0){let n=this;w.assert(e>0,()=>`batchSize needs to be positive, but it is
${e}`);let s;return this.size===1/0||this.size==null?s=this.size:t?s=Math.ceil(this.size/e):s=Math.floor(this.size/e),$n(async()=>(await n.iterator()).columnMajorBatch(e,t,EU),s)}concatenate(e){let t=this,n;return this.size===1/0||e.size===1/0?n=1/0:this.size!=null&&e.size!=null?n=this.size+e.size:n=null,$n(async()=>(await t.iterator()).concatenate(await e.iterator()),n)}filter(e){let t=this,n;return this.size===1/0?n=1/0:n=null,$n(async()=>(await t.iterator()).filter(s=>q(()=>e(s))),n)}async forEachAsync(e){return(await this.iterator()).forEachAsync(e)}map(e){let t=this;return $n(async()=>(await t.iterator()).map(n=>q(()=>e(n))),this.size)}mapAsync(e){let t=this;return $n(async()=>(await t.iterator()).mapAsync(e),this.size)}prefetch(e){if(e==null)throw new RangeError("`Dataset.prefetch()` requires bufferSize to be specified.");let t=this;return $n(async()=>(await t.iterator()).prefetch(e),this.size)}repeat(e){let t=this,n;return this.size!=null&&e>0?n=this.size*e:e===0?n=0:this.size!=null&&(e===void 0||e<0)?n=1/0:n=null,$n(async()=>{let s=uv(async()=>({value:await t.iterator(),done:!1}));return mU(s.take(e))},n)}skip(e){let t=this,n;return this.size!=null&&e>=0&&this.size>=e?n=this.size-e:this.size!=null&&(this.size<e||e===void 0||e<0)?n=0:n=null,$n(async()=>(await t.iterator()).skip(e),n)}shuffle(e,t,n=!0){if(e==null||e<0)throw this.size==null?new RangeError("`Dataset.shuffle()` requires bufferSize to be specified."):new RangeError(`\`Dataset.shuffle()\` requires bufferSize to be specified. If your data fits in main memory (for regular JS objects), and/or GPU memory (for \`tf.Tensor\`s), consider setting bufferSize to the dataset size (${this.size} elements)`);let s=this,r=iU.alea(t||w.now().toString());return $n(async()=>{let a=r.int32();return n&&(a+=r.int32()),(await s.iterator()).shuffle(e,a.toString())},this.size)}take(e){let t=this,n;return this.size!=null&&this.size>e?n=e:this.size!=null&&this.size<=e?n=this.size:n=null,$n(async()=>(await t.iterator()).take(e),n)}async toArray(){if(this.size===1/0)throw new Error("Can not convert infinite data stream to array.");return(await this.iterator()).toArray()}async toArrayForTest(){if(this.size===1/0)throw new Error("Can not convert infinite data stream to array.");return(await this.iterator()).toArrayForTest()}};su.MAX_BUFFER_SIZE=1e4;function $n(e,t=null){return new class extends su{constructor(){super(...arguments),this.size=t}async iterator(){return e()}}}function _U(e){return $n(async()=>U0(e),e.length)}function AU(e){if(!ai(e))throw new Error("The argument to zip() must be an object or array.");let t;if(Array.isArray(e))for(let n=0;n<e.length;n++)t=t==null?e[n].size:Math.min(t,e[n].size);else if(e instanceof Object)for(let n in e)t=t==null?e[n].size:Math.min(t,e[n].size);return $n(async()=>{let n=await M0(e,s=>{if(s instanceof su)return{value:s.iterator(),recurse:!1};if(ai(s))return{value:null,recurse:!0};throw new Error("Leaves of the structure passed to zip() must be Datasets, not primitives.")});return gU(n,1)},t)}function EU(e){if(e===null)return null;let t=e[0];return dU(t)?{value:RU(e),recurse:!1}:{value:null,recurse:!0}}function RU(e){if(e.length===0)throw new Error("Can't make a batch of zero elements.");return e[0]instanceof et?es(e):ms(e)}var j0=class extends su{constructor(e){super(),this.input=e}async iterator(){return(await this.input.iterator()).decodeUTF8().split(`
`).map(s=>(s.endsWith("\r")&&(s=s.slice(0,-1)),s))}},Jc='"',Eu=Symbol("out"),cw=Symbol("field"),ed=Symbol("quote"),sm=Symbol("quoteafterquote"),dw=Symbol("quoteinquote"),K0=class extends su{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 j0(e),t||(t={}),this.hasHeader=t.hasHeader!==!1,this.fullColumnNames=t.columnNames,this.columnConfigs=t.columnConfigs,this.configuredColumnsOnly=t.configuredColumnsOnly,t.delimWhitespace?(w.assert(t.delimiter==null,()=>"Delimiter should not be provided when delimWhitespace is true."),this.delimWhitespace=!0,this.delimiter=" "):this.delimiter=t.delimiter?t.delimiter:","}async columnNames(){return this.columnNamesValidated||await this.setColumnNames(),this.configuredColumnsOnly?Object.keys(this.columnConfigs):this.fullColumnNames}async setColumnNames(){let e=await this.maybeReadHeaderLine();if(!this.fullColumnNames&&!e)throw new Error("Column names must be provided if there is no header line.");this.fullColumnNames&&e&&w.assert(e.length===this.fullColumnNames.length,()=>"The length of provided columnNames ("+this.fullColumnNames.length.toString()+") does not match the length of the header line read from file ("+e.length.toString()+")."),this.fullColumnNames||(this.fullColumnNames=e);let t=this.fullColumnNames.reduce((s,r)=>(s[r]=s[r]+1||1,s),{}),n=Object.keys(t).filter(s=>t[s]>1);if(w.assert(n.length===0,()=>"Duplicate column names found: "+n.toString()),this.columnConfigs){for(let s of Object.keys(this.columnConfigs))if(this.fullColumnNames.indexOf(s)===-1)throw new Error('The key "'+s+'" provided in columnConfigs does not match any of the column names ('+this.fullColumnNames.toString()+").")}this.columnNamesValidated=!0}async maybeReadHeaderLine(){if(this.hasHeader){let t=await(await this.base.iterator()).next();if(t.done)throw new Error("No data was found for CSV parsing.");let n=t.value;return this.parseRow(n,!1)}else return null}async iterator(){this.columnNamesValidated||await this.setColumnNames();let e=await this.base.iterator();return this.hasHeader&&(e=e.skip(1)),e.map(t=>this.makeDataElement(t))}makeDataElement(e){let t=this.parseRow(e),n={},s={};for(let r=0;r<this.fullColumnNames.length;r++){let a=this.fullColumnNames[r],o=this.columnConfigs?this.columnConfigs[a]:null;if(!(this.configuredColumnsOnly&&!o)){let i=t[r],u=null;if(i==="")if(o&&o.default!==void 0)u=o.default;else{if(o&&(o.required||o.isLabel))throw new Error(`Required column ${a} is empty in this line: ${e}`);u=void 0}else{let l=Number(i);if(isNaN(l))o&&o.dtype==="bool"?u=this.getBoolean(i):u=i;else if(!o||!o.dtype)u=l;else switch(o.dtype){case"float32":u=l;break;case"int32":u=Math.floor(l);break;case"bool":u=this.getBoolean(i);break;default:u=l}}o&&o.isLabel?s[a]=u:n[a]=u}}return Object.keys(s).length===0?n:{xs:n,ys:s}}getBoolean(e){return e==="1"||e.toLowerCase()==="true"?1:0}parseRow(e,t=!0){let n=[],s=0,r=e.length,a=Eu;for(let o=0;o<r;o++)switch(a){case Eu:switch(e.charAt(o)){case Jc:s=o+1,a=ed;break;case this.delimiter:if(s=o+1,this.delimiter===" "&&this.delimWhitespace)break;n.push(""),a=Eu;break;default:a=cw,s=o;break}break;case cw:switch(e.charAt(o)){case this.delimiter:n.push(e.substring(s,o)),a=Eu,s=o+1;break;default:}break;case ed:switch(e.charAt(o)){case Jc:a=sm;break;default:}break;case sm:switch(e.charAt(o)){case this.delimiter:n.push(e.substring(s,o-1)),a=Eu,s=o+1;break;case Jc:a=ed;break;default:a=dw;break}break;case dw:switch(e.charAt(o)){case Jc:a=ed;break;default:}break;default:}if(a===sm?n.push(e.substring(s,r-1)):n.push(e.substring(s)),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}},X0=class extends Ut{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(!K().get("IS_BROWSER"))throw new Error("microphone API is only supported in browser environment.");let t=new X0(e);return await t.start(),t}async start(){try{this.stream=await navigator.mediaDevices.getUserMedia({audio:this.audioTrackConstraints==null?!0:this.audioTrackConstraints,video:!1})}catch(n){throw new Error(`Error thrown while initializing video stream: ${n.message}`)}if(!this.stream)throw new Error("Could not obtain audio from microphone.");let e=window.AudioContext||window.webkitAudioContext;if(this.audioContext=new e,!this.sampleRateHz)this.sampleRateHz=this.audioContext.sampleRate;else if(this.audioContext.sampleRate!==this.sampleRateHz)throw new Error(`Mismatch in sampling rate: Expected: ${this.sampleRateHz}; Actual: ${this.audioContext.sampleRate}`);let t=this.audioContext.createMediaStreamSource(this.stream);this.analyser=this.audioContext.createAnalyser(),this.analyser.fftSize=this.fftSize*2,this.analyser.smoothingTimeConstant=this.smoothingTimeConstant,t.connect(this.analyser),this.freqData=new Float32Array(this.fftSize),this.timeData=new Float32Array(this.fftSize)}async next(){if(this.isClosed)return{value:null,done:!0};let e,t,n=await this.getAudioData();if(this.includeSpectrogram){let s=this.flattenQueue(n.freqDataQueue);e=this.getTensorFromAudioDataArray(s,[this.numFrames,this.columnTruncateLength,1])}if(this.includeWaveform){let s=this.flattenQueue(n.timeDataQueue);t=this.getTensorFromAudioDataArray(s,[this.numFrames*this.fftSize,1])}return{value:{spectrogram:e,waveform:t},done:!1}}async capture(){return(await this.next()).value}async getAudioData(){let e=[],t=[],n=0;return new Promise(s=>{let r=setInterval(()=>{this.includeSpectrogram&&(this.analyser.getFloatFrequencyData(this.freqData),this.freqData[0]===-1/0&&s({freqDataQueue:e,timeDataQueue:t}),e.push(this.freqData.slice(0,this.columnTruncateLength))),this.includeWaveform&&(this.analyser.getFloatTimeDomainData(this.timeData),t.push(this.timeData.slice())),++n===this.numFrames&&(clearInterval(r),s({freqDataQueue:e,timeDataQueue:t}))},this.fftSize/this.sampleRateHz*1e3)})}stop(){this.isClosed||(this.isClosed=!0,this.analyser.disconnect(),this.audioContext.close(),this.stream!=null&&this.stream.getTracks().length>0&&this.stream.getTracks()[0].stop())}toArray(){throw new Error("Can not convert infinite audio stream to array.")}getSampleRate(){return this.sampleRateHz}flattenQueue(e){let t=e[0].length,n=new Float32Array(e.length*t);return e.forEach((s,r)=>n.set(s,r*t)),n}getTensorFromAudioDataArray(e,t){let n=new Float32Array(w.sizeFromShape(t));return n.set(e,n.length-e.length),ms(n,t)}},Y0=class extends Ut{constructor(e,t){if(super(),this.webcamVideoElement=e,this.webcamConfig=t,this.isClosed=!0,this.resize=!1,this.needToResize())if(this.resize=!0,this.cropSize=[this.webcamConfig.resizeHeight,this.webcamConfig.resizeWidth],this.cropBoxInd=Zt([0],"int32"),this.webcamConfig.centerCrop){let n=this.webcamConfig.resizeWidth*1/this.webcamVideoElement.width,s=this.webcamConfig.resizeHeight*1/this.webcamVideoElement.height,r=(1-n)/2,a=(1-s)/2,o=r+n,i=s+a;this.cropBox=Zo([a,r,i,o],[1,4])}else this.cropBox=Zo([0,0,1,1],[1,4])}summary(){return"webcam"}static async create(e,t={}){if(!K().get("IS_BROWSER"))throw new Error("tf.data.webcam is only supported in browser environment.");if(!e){if(e=document.createElement("video"),!t.resizeWidth||!t.resizeHeight)throw new Error("Please provide webcam video element, or resizeWidth and resizeHeight to create a hidden video element.");e.width=t.resizeWidth,e.height=t.resizeHeight}let n=new Y0(e,t);return await n.start(),n}async start(){this.webcamConfig.facingMode&&w.assert(this.webcamConfig.facingMode==="user"||this.webcamConfig.facingMode==="environment",()=>`Invalid webcam facing mode: ${this.webcamConfig.facingMode}. Please provide 'user' or 'environment'`);try{this.stream=await navigator.mediaDevices.getUserMedia({video:{deviceId:this.webcamConfig.deviceId,facingMode:this.webcamConfig.facingMode?this.webcamConfig.facingMode:"user",width:this.webcamVideoElement.width,height:this.webcamVideoElement.height}})}catch(e){throw e.message=`Error thrown while initializing video stream: ${e.message}`,e}if(!this.stream)throw new Error("Could not obtain video from webcam.");try{this.webcamVideoElement.srcObject=this.stream}catch(e){console.log(e),this.webcamVideoElement.src=window.URL.createObjectURL(this.stream)}return this.webcamVideoElement.play(),this.isClosed=!1,new Promise(e=>{this.webcamVideoElement.onloadedmetadata=()=>{e()}})}async next(){if(this.isClosed)return{value:null,done:!0};let e;try{e=Gk.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: ${t.message}`)}finally{e.dispose()}else return{value:e,done:!1}}needToResize(){return!!(this.webcamConfig.resizeWidth&&this.webcamConfig.resizeHeight&&(this.webcamVideoElement.width!==this.webcamConfig.resizeWidth||this.webcamVideoElement.height!==this.webcamConfig.resizeHeight))}cropAndResizeFrame(e){return q(()=>{let t=Pn(le(e,"float32"),0),n;n=Kn.cropAndResize(t,this.cropBox,this.cropBoxInd,this.cropSize,"bilinear");let s=n.shape;return U(n,s.slice(1))})}async capture(){return(await this.next()).value}stop(){this.stream.getTracks().forEach(t=>t.stop());try{this.webcamVideoElement.srcObject=null}catch(t){console.log(t),this.webcamVideoElement.src=null}this.isClosed=!0}toArray(){throw new Error("Can not convert infinite video stream to array.")}},Q0=class{},Z0=class extends Ut{split(e){return new DU(this,e)}},DU=class extends Z0{constructor(e,t){super(),this.upstream=e,this.impl=new FU(e,t)}summary(){return this.impl.summary()}async next(){return this.impl.next()}},FU=class extends lv{constructor(e,t){super(),this.upstream=e,this.separator=t,this.carryover=""}summary(){return`${this.upstream.summary()} -> Split('${this.separator}')`}async pump(){let e=await this.upstream.next();if(e.done)return this.carryover===""?!1:(this.outputQueue.push(this.carryover),this.carryover="",!0);let t=e.value.split(this.separator);t[0]=this.carryover+t[0];for(let n of t.slice(0,-1))this.outputQueue.push(n);return this.carryover=t[t.length-1],!0}},OU=class extends Ut{decodeUTF8(){return new PU(this)}},PU=class extends Z0{constructor(e){super(),this.upstream=e,this.impl=new zU(e)}summary(){return this.impl.summary()}async next(){return this.impl.next()}},zU=class extends lv{constructor(e){if(super(),this.upstream=e,K().get("IS_BROWSER"))this.decoder=new TextDecoder("utf-8");else{let{StringDecoder:t}=sk();this.decoder=new t("utf8")}}summary(){return`${this.upstream.summary()} -> Utf8`}async pump(){let e=await this.upstream.next(),t;if(e.done)return!1;t=e.value;let n;return K().get("IS_BROWSER")?n=this.decoder.decode(t,{stream:!0}):n=this.decoder.write(Buffer.from(t.buffer)),this.outputQueue.push(n),!0}},J0=class extends OU{constructor(e,t={}){super(),this.file=e,this.options=t,w.assert(e instanceof Uint8Array||(K().get("IS_BROWSER")?e instanceof File||e instanceof Blob:!1),()=>"FileChunkIterator only supports File, Blob and Uint8Array right now."),this.offset=t.offset||0,this.chunkSize=t.chunkSize||1024*1024}summary(){return`FileChunks ${this.file}`}async next(){return this.offset>=(this.file instanceof Uint8Array?this.file.byteLength:this.file.size)?{value:null,done:!0}:{value:await new Promise((t,n)=>{let s=this.offset+this.chunkSize;if(this.file instanceof Uint8Array)t(new Uint8Array(this.file.slice(this.offset,s)));else{let r=new FileReader;r.onload=o=>{let i=r.result;if(i instanceof ArrayBuffer&&(i=new Uint8Array(i)),!(i instanceof Uint8Array))return n(new TypeError("FileReader returned unknown type."));t(i)},r.onabort=o=>n(new Error("Aborted")),r.onerror=o=>n(new Error(o.type));let a=this.file.slice(this.offset,s);r.readAsArrayBuffer(a)}this.offset=s}),done:!1}}};async function LU(e,t={},n){let s,r;typeof e=="string"?s=e:(s=e.url,r=MU(e));let a=await(n||w.fetch)(s,r);if(a.ok){let o=new Uint8Array(await a.arrayBuffer());return new J0(o,t)}else throw new Error(a.statusText)}var MU=e=>({method:e.method,headers:e.headers,body:e.body,mode:e.mode,credentials:e.credentials,cache:e.cache,redirect:e.redirect,referrer:e.referrer,integrity:e.integrity});function eC(e){return typeof e=="string"&&e.slice(0,7)==="file://"}var tC=class extends Q0{constructor(e,t={}){super(),this.input=e,this.options=t}async iterator(){if(eC(this.input)&&K().get("IS_NODE")){let e=pg();this.input=e.readFileSync(this.input.slice(7))}return new J0(this.input,this.options)}},nC=class extends Q0{constructor(e,t={}){super(),this.url=e,this.fileOptions=t}async iterator(){return eC(this.url)?new tC(this.url,this.fileOptions).iterator():LU(this.url,this.fileOptions)}};function BU(e,t={}){return new K0(new nC(e),t)}function VU(e){let t=uv(e);return $n(async()=>t)}function WU(e){return $n(async()=>{let t=await e();return uv(()=>t.next())})}async function UU(e,t){return Y0.create(e,t)}async function GU(e){return X0.create(e)}var HU="0.0.0";function be(e,t){Array.isArray(e)||(e=[e]),e.forEach(n=>{n!=null&&w.assert(n.dtype!=="complex64",()=>`${t} does not support complex64 tensors in the CPU backend.`)})}var qU=ws.whereImpl,sC=class extends il{constructor(){super(),this.blockSize=48,this.firstUse=!0,this.data=new Zd(this,ds())}nextDataId(){return sC.nextDataId++}write(e,t,n){this.firstUse&&(this.firstUse=!1,K().get("IS_NODE")&&C.warn(`
============================
Hi, looks like you are running TensorFlow.js in Node.js. To speed things up dramatically, install our node backend, visit https://github.com/tensorflow/tfjs-node for more details.
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zG=Dr(e=>Math.sqrt(e)),LG=st(lo,e=>Math.sqrt(e)),MG={kernelName:lo,backendName:"cpu",kernelFunc:LG},DC=At((e,t)=>{let n=e-t;return n*n}),BG=Gt(ho,DC),VG={kernelName:ho,backendName:"cpu",kernelFunc:BG};function FC(e,t,n,s){let r=Ae(e,t.dtype);for(let a=0;a<r.size;a++){let o=r.indexToLoc(a),i=new Array(o.length);for(let u=0;u<i.length;u++)i[u]=o[u]*n[u]+s[u];r.set(t.get(...i),...o)}return r}var WG=class{constructor(e,t,n,s,r,a){this.separator=w.encodeString(e),this.nGramWidths=t,this.leftPad=w.encodeString(n),this.rightPad=w.encodeString(s),this.padWidth=r,this.preserveShort=a}getPadWidth(e){return Math.min(this.padWidth<0?e-1:this.padWidth,e-1)}getNumNGrams(e,t){let n=this.getPadWidth(t);return Math.max(0,e+2*n-t+1)}createNGrams(e,t,n,s,r,a){for(let o=0;o<r;++o){let i=this.getPadWidth(a),u=Math.max(0,i-o),l=Math.max(0,i-(r-(o+1))),c=a-(u+l),p=t+(u>0?0:o-i),d=0;d+=u*this.leftPad.length;for(let 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JG={kernelName:Na,backendName:"cpu",kernelFunc:KC};function eH(e){let{inputs:t,backend:n,attrs:s}=e,{a:r,b:a,bias:o,preluActivationWeights:i}=t,{transposeA:u,transposeB:l,activation:c,leakyreluAlpha:p}=s,d,h,f,m=[];d=KC({inputs:{a:r,b:a},attrs:{transposeA:u,transposeB:l},backend:n}),o&&(h=oi({inputs:{a:d,b:o},backend:n}),m.push(d),d=h),c&&(f=Wd(n,d,c,i,p),m.push(d),d=f);for(let b of m)n.disposeIntermediateTensorInfo(b);return d}var tH={kernelName:oa,backendName:"cpu",kernelFunc:eH},nH=st(ul,e=>Math.acos(e)),sH={kernelName:ul,backendName:"cpu",kernelFunc:nH},rH=st(ll,e=>Math.acosh(e)),aH={kernelName:ll,backendName:"cpu",kernelFunc:rH};function oH(e){let{inputs:t,backend:n}=e,s=t;be(t,"addN");let r=s.map(i=>n.data.get(i.dataId).values),a=Ae(s[0].shape,s[0].dtype),o=a.values;for(let i=0;i<s.length;i++){let u=r[i];for(let l=0;l<o.length;l++)o[l]+=u[l]}return n.makeTensorInfo(a.shape,a.dtype,a.values)}var iH={kernelName:Sa,backendName:"cpu",kernelFunc:oH};function 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i=w.parseAxisParam(a,r.shape),u=i,l=C.getAxesPermutation(u,r.shape.length),c=r;l!=null&&(c=wn({inputs:{x:r},backend:n,attrs:{perm:l}}),u=C.getInnerMostAxes(u.length,r.shape.length)),C.assertAxesAreInnerMostDims("any",u,c.shape.length);let[p,d]=C.computeOutAndReduceShapes(c.shape,u),h=w.sizeFromShape(d),f=w.makeZerosTypedArray(w.sizeFromShape(p),c.dtype),m=n.data.get(c.dataId).values;for(let b=0;b<f.length;++b){let y=b*h,v=m[y];for(let x=0;x<h;++x){let k=m[y+x];v=v||k}f[b]=v}l!=null&&n.disposeIntermediateTensorInfo(c);let g=n.makeTensorInfo(p,c.dtype,f);if(o){let b=C.expandShapeToKeepDim(p,i),y=pt({inputs:{x:g},backend:n,attrs:{shape:b}});return n.disposeIntermediateTensorInfo(g),y}return g}var dH={kernelName:dl,backendName:"cpu",kernelFunc:cH};function pH(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{axis:a}=s;be(r,"argMax");let o=w.parseAxisParam(a,r.shape),i=C.getAxesPermutation(o,r.shape.length),u=r,l=[];i!=null&&(u=wn({inputs:{x:r},backend:n,attrs:{perm:i}}),l.push(u),o=C.getInnerMostAxes(o.length,u.shape.length)),o=[o[0]],C.assertAxesAreInnerMostDims("argMax",o,u.shape.length);let[c,p]=C.computeOutAndReduceShapes(u.shape,o),d=w.sizeFromShape(c),h=w.makeZerosTypedArray(d,"int32"),f=w.sizeFromShape(p),m=n.data.get(u.dataId).values;for(let g=0;g<h.length;++g){let b=g*f,y=m[b],v=0;for(let x=0;x<f;++x){let k=m[b+x];k>y&&(y=k,v=x)}h[g]=v}return l.forEach(g=>n.disposeIntermediateTensorInfo(g)),n.makeTensorInfo(c,"int32",h)}var hH={kernelName:Ia,backendName:"cpu",kernelFunc:pH};function fH(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{axis:a}=s;be(r,"argMin");let o=w.parseAxisParam(a,r.shape),i=C.getAxesPermutation(o,r.shape.length),u=r,l=[];i!=null&&(u=wn({inputs:{x:r},backend:n,attrs:{perm:i}}),l.push(u),o=C.getInnerMostAxes(o.length,u.shape.length)),o=[o[0]],C.assertAxesAreInnerMostDims("argMin",o,u.shape.length);let[c,p]=C.computeOutAndReduceShapes(u.shape,o),d=w.sizeFromShape(c),h=w.makeZerosTypedArray(d,"int32"),f=w.sizeFromShape(p),m=n.data.get(u.dataId).values;for(let g=0;g<h.length;++g){let b=g*f,y=m[b],v=0;for(let x=0;x<f;++x){let k=m[b+x];k<y&&(y=k,v=x)}h[g]=v}return l.forEach(g=>n.disposeIntermediateTensorInfo(g)),n.makeTensorInfo(c,"int32",h)}var mH={kernelName:pl,backendName:"cpu",kernelFunc:fH},gH=st(hl,e=>Math.asin(e)),bH={kernelName:hl,backendName:"cpu",kernelFunc:gH},yH=st(fl,e=>Math.asinh(e)),vH={kernelName:fl,backendName:"cpu",kernelFunc:yH},xH=st(ml,e=>Math.atan(e)),wH={kernelName:ml,backendName:"cpu",kernelFunc:xH},kH=At((e,t)=>Math.atan2(e,t)),SH=Gt(bl,kH),IH={kernelName:bl,backendName:"cpu",kernelFunc:SH},CH=st(gl,e=>Math.atanh(e)),NH={kernelName:gl,backendName:"cpu",kernelFunc:CH};function vv(e,t,n,s,r,a){let o=r.strideHeight,i=r.strideWidth,u=r.dilationHeight,l=r.dilationWidth,c=r.effectiveFilterHeight,p=r.effectiveFilterWidth,d=r.padInfo.top,h=r.padInfo.left,f=a==="max"?Number.NEGATIVE_INFINITY:Number.POSITIVE_INFINITY,m=Ae(r.outShape,n),g=m.values,b=r.outShape[1]*r.outShape[2]*r.outShape[3],y=r.outShape[2]*r.outShape[3],v=r.outShape[3];for(let x=0;x<r.batchSize;++x){let k=x*b,I=x*s[0];for(let $=0;$<r.inChannels;++$)for(let R=0;R<r.outHeight;++R){let 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ie=te*u-b,ae=ie;for(;ae<0;)ae+=p;let de=Math.min(r.inWidth,f+ie),me=se+te*R,ke=y,Ie=0,Re=0;for(let Xe=W;Xe<j;Xe+=l){let Je=A+Xe*s[1];for(let Ye=ne;Ye<ee;Ye+=c){let tt=Je+Ye*s[2];for(let Ce=ae;Ce<de;Ce+=p){let ut=tt+Ce*s[3],ot=e[ut+D];if(a==="max"&&ot>ke?ke=ot:a==="avg"&&(Ie+=ot,Re++),isNaN(ke))break}if(isNaN(ke))break}if(isNaN(ke))break}let Pe=me+D;x[Pe]=a==="avg"?Ie/Re:ke}}}}return v}function TH(e,t){let n=Ae(t.outShape,"int32"),s=t.strideDepth,r=t.strideHeight,a=t.strideWidth,o=t.dilationDepth,i=t.dilationHeight,u=t.dilationWidth,l=t.effectiveFilterDepth,c=t.effectiveFilterHeight,p=t.effectiveFilterWidth,d=t.padInfo.front,h=t.padInfo.top,f=t.padInfo.left;for(let m=0;m<t.batchSize;++m)for(let g=0;g<t.inChannels;++g)for(let b=0;b<t.outDepth;++b){let y=b*s-d,v=y;for(;v<0;)v+=o;let x=Math.min(t.inDepth,l+y);for(let k=0;k<t.outHeight;++k){let I=k*r-h,$=I;for(;$<0;)$+=i;let R=Math.min(t.inHeight,c+I);for(let E=0;E<t.outWidth;++E){let P=E*a-f,A=P;for(;A<0;)A+=u;let D=Math.min(t.inWidth,p+P),T=Number.NEGATIVE_INFINITY,L=-1;for(let W=v;W<x;W+=o){let j=W-y;for(let Y=$;Y<R;Y+=i){let X=Y-I;for(let Z=A;Z<D;Z+=u){let ne=Z-P,ee=e.get(m,W,Y,Z,g);ee>=T&&(T=ee,L=j*c*p+X*c+ne)}}}n.set(L,m,b,k,E,g)}}}return n}function $H(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t;be(r,"avgPool");let{filterSize:a,strides:o,pad:i,dimRoundingMode:u}=s,l=1;w.assert(C.eitherStridesOrDilationsAreOne(o,l),()=>`Error in avgPool: Either strides or dilations must be 1. 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c=C.computePool3DInfo(a.shape,o,i,1,u,l),p=c.strideDepth,d=c.strideHeight,h=c.strideWidth,f=c.filterDepth,m=c.filterHeight,g=c.filterWidth,b=c.dilationDepth,y=c.dilationHeight,v=c.dilationWidth,x=c.effectiveFilterDepth,k=c.effectiveFilterHeight,I=c.effectiveFilterWidth,$=x-1-c.padInfo.front,R=I-1-c.padInfo.left,E=k-1-c.padInfo.top,P=Ae(a.shape,"float32"),A=1/(f*m*g),D=n.bufferSync(r);for(let T=0;T<c.batchSize;++T)for(let L=0;L<c.inChannels;++L)for(let W=0;W<c.inDepth;++W)for(let j=0;j<c.inHeight;++j)for(let Y=0;Y<c.inWidth;++Y){let X=W-$,Z=j-E,ne=Y-R,ee=0;for(let se=0;se<x;se+=b){let te=(X+se)/p;if(!(te<0||te>=c.outDepth||Math.floor(te)!==te))for(let ie=0;ie<k;ie+=y){let ae=(Z+ie)/d;if(!(ae<0||ae>=c.outHeight||Math.floor(ae)!==ae))for(let de=0;de<I;de+=v){let me=(ne+de)/h;if(me<0||me>=c.outWidth||Math.floor(me)!==me)continue;ee+=D.get(T,te,ae,me,L)}}}P.set(ee*A,T,W,j,Y,L)}return n.makeTensorInfo(P.shape,P.dtype,P.values)}var DH={kernelName:yg,backendName:"cpu",kernelFunc:RH};function FH(e){let{inputs:t,backend:n,attrs:s}=e,{dy:r,input:a}=t,o=a;be([r,a],"avgPoolGrad");let{filterSize:i,strides:u,pad:l}=s,c=C.computePool2DInfo(o.shape,i,u,1,l),p=c.strideHeight,d=c.strideWidth,h=c.filterHeight,f=c.filterWidth,m=c.dilationHeight,g=c.dilationWidth,b=c.effectiveFilterHeight,y=c.effectiveFilterWidth,v=y-1-c.padInfo.left,x=b-1-c.padInfo.top,k=Ae(o.shape,"float32"),I=1/(h*f),$=n.data.get(r.dataId).values,R=Ae(r.shape,"float32",$);for(let E=0;E<c.batchSize;++E)for(let P=0;P<c.inChannels;++P)for(let A=0;A<c.inHeight;++A)for(let D=0;D<c.inWidth;++D){let T=A-x,L=D-v,W=0;for(let j=0;j<b;j+=m){let Y=(T+j)/p;if(!(Y<0||Y>=c.outHeight||Math.floor(Y)!==Y))for(let X=0;X<y;X+=g){let Z=(L+X)/d;if(Z<0||Z>=c.outWidth||Math.floor(Z)!==Z)continue;W+=R.get(E,Y,Z,P)}}k.set(W*I,E,A,D,P)}return n.makeTensorInfo(k.shape,k.dtype,k.values)}var OH={kernelName:bg,backendName:"cpu",kernelFunc:FH};function PH(e){let{inputs:t,backend:n,attrs:s}=e,{x:r,scale:a,offset:o,mean:i,variance:u}=t;w.assert(i.shape.length===u.shape.length,()=>"Batch normalization gradient requires mean and variance to have equal ranks."),w.assert(o==null||i.shape.length===o.shape.length,()=>"Batch normalization gradient requires mean and offset to have equal ranks."),w.assert(a==null||i.shape.length===a.shape.length,()=>"Batch normalization gradient requires mean and scale to have equal ranks."),be([r,i,u,a,o],"batchNorm");let{varianceEpsilon:l}=s;l==null&&(l=.001);let c=n.data.get(r.dataId).values,p=n.data.get(i.dataId).values,d=n.data.get(u.dataId).values,h=a?n.data.get(a.dataId).values:new Float32Array([1]),f=o?n.data.get(o.dataId).values:new Float32Array([0]),m=new Float32Array(c.length),g=f.length,b=h.length,y=d.length,v=p.length,x=0,k=0,I=0,$=0;for(let R=0;R<c.length;++R)m[R]=f[x++]+(c[R]-p[k++])*h[I++]/Math.sqrt(d[$++]+l),x>=g&&(x=0),k>=v&&(k=0),I>=b&&(I=0),$>=y&&($=0);return n.makeTensorInfo(r.shape,r.dtype,m)}var zH={kernelName:Ba,backendName:"cpu",kernelFunc:PH};function LH(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{blockShape:a,crops:o}=s;be([r],"batchToSpaceND");let i=a.reduce((b,y)=>b*y),u=C.getReshaped(r.shape,a,i),l=C.getPermuted(u.length,a.length),c=C.getReshapedPermuted(r.shape,a,i),p=C.getSliceBeginCoords(o,a.length),d=C.getSliceSize(c,o,a.length),h=pt({inputs:{x:r},backend:n,attrs:{shape:u}}),f=wn({inputs:{x:h},backend:n,attrs:{perm:l}}),m=pt({inputs:{x:f},backend:n,attrs:{shape:c}}),g=ba({inputs:{x:m},backend:n,attrs:{begin:p,size:d}});return n.disposeIntermediateTensorInfo(h),n.disposeIntermediateTensorInfo(f),n.disposeIntermediateTensorInfo(m),g}var MH={kernelName:pi,backendName:"cpu",kernelFunc:LH};function BH(e){let{inputs:t,backend:n,attrs:s}=e,{x:r,weights:a}=t,{size:o}=s,i=n.data.get(r.dataId).values,u=n.data.get(a.dataId).values,l=pv(i,u,a.dtype,a.shape,o);return n.makeTensorInfo([o],a.dtype,l)}var VH={kernelName:vg,backendName:"cpu",kernelFunc:BH};function WH(e){let{inputs:t,backend:n}=e,{s0:s,s1:r}=t,a=n.data.get(s.dataId).values,o=n.data.get(r.dataId).values,i=C.assertAndGetBroadcastShape(Array.from(a),Array.from(o));return n.makeTensorInfo([i.length],"int32",Int32Array.from(i))}var UH={kernelName:xg,backendName:"cpu",kernelFunc:WH},GH=st(Nr,(e,t)=>{let n=t;return e>n.clipValueMax?n.clipValueMax:e<n.clipValueMin?n.clipValueMin:e}),HH={kernelName:Nr,backendName:"cpu",kernelFunc:GH},qH=e=>{let{x:t}=e.inputs,n=e.backend,s=new Float32Array(w.sizeFromShape(t.shape)),r=n.data.get(t.dataId),a=r.complexTensorInfos.real,o=r.complexTensorInfos.imag,i=n.data.get(a.dataId).values,u=n.data.get(o.dataId).values;for(let l=0;l<i.length;l++){let c=i[l],p=u[l];s[l]=Math.hypot(c,p)}return n.makeOutput(s,t.shape,"float32")},jH={kernelName:sp,backendName:"cpu",kernelFunc:qH};function ii(e){let{inputs:t,backend:n}=e,{input:s}=t,r=n.data.get(s.dataId).complexTensorInfos.imag,a=n.data.get(r.dataId).values;return n.makeTensorInfo(r.shape,r.dtype,a)}var KH={kernelName:ip,backendName:"cpu",kernelFunc:ii};function ui(e){let{inputs:t,backend:n,attrs:s}=e,{axis:r}=s,a=w.parseAxisParam(r,t[0].shape)[0],o=C.computeOutShape(t.map(m=>m.shape),a);if(w.sizeFromShape(o)===0)return n.makeTensorInfo(o,t[0].dtype,[]);let i=t.filter(m=>w.sizeFromShape(m.shape)>0);if(i.length===1)return Os({inputs:{x:i[0]},backend:n});let u=i.map(m=>m.shape);if(C.assertParamsConsistent(u,a),i[0].dtype==="complex64"){let m=i.map(x=>ga({inputs:{input:x},backend:n})),g=i.map(x=>ii({inputs:{input:x},backend:n})),b=ui({inputs:m,backend:n,attrs:{axis:a}}),y=ui({inputs:g,backend:n,attrs:{axis:a}}),v=En({inputs:{real:b,imag:y},backend:n});return m.forEach(x=>n.disposeIntermediateTensorInfo(x)),g.forEach(x=>n.disposeIntermediateTensorInfo(x)),n.disposeIntermediateTensorInfo(b),n.disposeIntermediateTensorInfo(y),v}let l=i.map(m=>{let g=w.sizeFromShape(m.shape.slice(a));return pt({inputs:{x:m},backend:n,attrs:{shape:[-1,g]}})}),c=l.map(m=>({vals:n.data.get(m.dataId).values,shape:m.shape}));o=C.computeOutShape(l.map(m=>m.shape),1);let p=l[0].shape[0]===1,d=hv(c,o,t[0].dtype,p),h=C.computeOutShape(i.map(m=>m.shape),a),f=n.makeTensorInfo(h,t[0].dtype,d);return l.forEach(m=>n.disposeIntermediateTensorInfo(m)),f}var XH={kernelName:hi,backendName:"cpu",kernelFunc:ui};function QC(e){let{inputs:t,backend:n,attrs:s}=e,{x:r,filter:a}=t,{strides:o,pad:i,dataFormat:u,dilations:l,dimRoundingMode:c}=s;be([r,a],"conv2d");let p=C.convertConv2DDataFormat(u),d=C.computeConv2DInfo(r.shape,a.shape,o,l,i,c,!1,p),h=d.filterHeight,f=d.filterWidth,m=d.dilationHeight,g=d.dilationWidth,b=d.padInfo.left,y=d.padInfo.top,v=d.dataFormat==="channelsLast",x=new Vt(d.outShape,r.dtype),k=w.computeStrides(r.shape),I=w.computeStrides(a.shape),$=k[0],R=v?k[1]:k[2],E=v?k[2]:1,P=v?1:k[1],A=x.strides[0],D=v?x.strides[1]:x.strides[2],T=v?x.strides[2]:1,L=v?1:x.strides[1],W=n.data.get(r.dataId).values,j=n.data.get(a.dataId).values,Y=x.values;for(let X=0;X<d.batchSize;++X){let Z=X*$,ne=X*A;for(let ee=0;ee<d.outHeight;++ee){let se=ne+ee*D,te=ee*d.strideHeight-y;for(let ie=0;ie<h;++ie){let ae=te+ie*m;if(ae<0||ae>=d.inHeight)continue;let de=ie*I[0],me=Z+ae*R;for(let ke=0;ke<d.outWidth;++ke){let Ie=se+ke*T,Re=ke*d.strideWidth-b;for(let Pe=0;Pe<f;++Pe){let Xe=Re+Pe*g;if(Xe<0||Xe>=d.inWidth)continue;let Je=de+Pe*I[1],Ye=me+Xe*E,tt=Je;for(let Ce=0;Ce<d.inChannels;++Ce){let ut=W[Ye+Ce*P];for(let ot=0;ot<d.outChannels;++ot)Y[Ie+ot*L]+=ut*j[tt+ot];tt+=d.outChannels}}}}}}return n.makeTensorInfo(x.shape,x.dtype,Y)}var YH={kernelName:_a,backendName:"cpu",kernelFunc:QC};function QH(e){let{inputs:t,backend:n,attrs:s}=e,{x:r,dy:a}=t,{strides:o,pad:i,dataFormat:u,dimRoundingMode:l,filterShape:c}=s;be([r,a],"conv2dBackpropFilter");let p=C.convertConv2DDataFormat(u),d=C.computeConv2DInfo(r.shape,c,o,1,i,l,!1,p),{strideHeight:h,strideWidth:f,filterHeight:m,filterWidth:g}=d,b=d.dataFormat==="channelsLast",y=new Vt(d.filterShape,"float32"),v=d.padInfo.left,x=d.padInfo.top,k=n.data.get(r.dataId).values,I=n.data.get(a.dataId).values,$=new Vt(r.shape,r.dtype,k),R=new Vt(a.shape,a.dtype,I);for(let E=0;E<m;++E){let P=Math.max(0,Math.ceil((x-E)/h)),A=Math.min(d.outHeight,(d.inHeight+x-E)/h);for(let D=0;D<g;++D){let T=Math.max(0,Math.ceil((v-D)/f)),L=Math.min(d.outWidth,(d.inWidth+v-D)/f);for(let W=0;W<d.inChannels;++W)for(let j=0;j<d.outChannels;++j){let Y=0;for(let X=0;X<d.batchSize;++X)for(let Z=P;Z<A;++Z){let ne=E+Z*h-x;for(let ee=T;ee<L;++ee){let se=D+ee*f-v;b?Y+=$.get(X,ne,se,W)*R.get(X,Z,ee,j):Y+=$.get(X,W,ne,se)*R.get(X,j,Z,ee)}}y.set(Y,E,D,W,j)}}}return n.makeTensorInfo(y.shape,y.dtype,y.values)}var ZH={kernelName:wg,backendName:"cpu",kernelFunc:QH};function JH(e){let{inputs:t,backend:n,attrs:s}=e,{dy:r,filter:a}=t,{inputShape:o,strides:i,pad:u,dataFormat:l,dimRoundingMode:c}=s;be([r,a],"conv2dBackpropInput");let p=w.computeStrides(a.shape),d=w.computeStrides(r.shape),h=C.convertConv2DDataFormat(l),f=C.computeConv2DInfo(o,a.shape,i,1,u,c,!1,h),m=new Vt(f.inShape,"float32"),g=m.values,b=n.data.get(r.dataId).values,y=n.data.get(a.dataId).values,[v,x,k]=p,{batchSize:I,filterHeight:$,filterWidth:R,inChannels:E,inHeight:P,inWidth:A,outChannels:D,outHeight:T,outWidth:L,strideHeight:W,strideWidth:j}=f;h=f.dataFormat;let Y=$-1-f.padInfo.top,X=R-1-f.padInfo.left,Z=h==="channelsLast",ne=m.strides[0],ee=Z?m.strides[1]:m.strides[2],se=Z?m.strides[2]:1,te=Z?1:m.strides[1],ie=d[0],ae=Z?d[1]:d[2],de=Z?d[2]:1,me=Z?1:d[1];for(let ke=0;ke<I;++ke)for(let Ie=0;Ie<E;++Ie)for(let Re=0;Re<P;++Re){let Pe=Re-Y,Xe=Math.max(0,Math.ceil(Pe/W)),Je=Math.min(T,($+Pe)/W);for(let Ye=0;Ye<A;++Ye){let tt=Ye-X,Ce=Math.max(0,Math.ceil(tt/j)),ut=Math.min(L,(R+tt)/j),ot=0;for(let Nt=Xe;Nt<Je;++Nt){let In=Nt*W-Pe;for(let Et=Ce;Et<ut;++Et){let en=Et*j-tt,Cn=ie*ke+ae*Nt+de*Et,Nn=v*($-1-In)+x*(R-1-en)+k*Ie;for(let Yt=0;Yt<D;++Yt){let Dn=b[Cn+me*Yt],tn=y[Nn+Yt];ot+=Dn*tn}}}let Jt=ne*ke+ee*Re+se*Ye+te*Ie;g[Jt]=ot}}return n.makeTensorInfo(m.shape,m.dtype,m.values)}var eq={kernelName:Aa,backendName:"cpu",kernelFunc:JH};function tq(e){let{inputs:t,backend:n,attrs:s}=e,{x:r,filter:a}=t,{strides:o,pad:i,dilations:u}=s;be([r,a],"conv3d");let l=C.computeConv3DInfo(r.shape,a.shape,o,u,i),{filterDepth:c,filterHeight:p,filterWidth:d,dilationDepth:h,dilationHeight:f,dilationWidth:m,padInfo:g}=l,b=g.front,y=g.left,v=g.top,x=new Vt(l.outShape,r.dtype),k=n.data.get(r.dataId).values,I=n.data.get(a.dataId).values,$=x.values,R=w.computeStrides(r.shape),E=w.computeStrides(a.shape);for(let P=0;P<l.batchSize;++P){let A=P*R[0],D=P*x.strides[0];for(let T=0;T<l.outDepth;++T){let L=D+T*x.strides[1],W=T*l.strideDepth-b;for(let j=0;j<c;++j){let Y=W+j*h;if(Y<0||Y>=l.inDepth)continue;let X=j*E[0],Z=A+Y*R[1];for(let ne=0;ne<l.outHeight;++ne){let ee=L+ne*x.strides[2],se=ne*l.strideHeight-v;for(let te=0;te<p;++te){let ie=se+te*f;if(ie<0||ie>=l.inHeight)continue;let ae=X+te*E[1],de=Z+ie*R[2];for(let me=0;me<l.outWidth;++me){let ke=ee+me*l.outChannels,Ie=me*l.strideWidth-y;for(let Re=0;Re<d;++Re){let Pe=Ie+Re*m;if(Pe<0||Pe>=l.inWidth)continue;let Xe=ae+Re*E[2],Je=de+Pe*l.inChannels,Ye=Xe;for(let tt=0;tt<l.inChannels;++tt){let Ce=k[Je+tt];for(let ut=0;ut<l.outChannels;++ut)$[ke+ut]+=Ce*I[Ye+ut];Ye+=l.outChannels}}}}}}}}return n.makeTensorInfo(x.shape,x.dtype,x.values)}var nq={kernelName:rp,backendName:"cpu",kernelFunc:tq};function sq(e){let{inputs:t,backend:n,attrs:s}=e,{x:r,dy:a}=t,{strides:o,pad:i,filterShape:u}=s;be([r,a],"conv3dBackpropFilterV2");let l=w.computeStrides(r.shape),c=w.computeStrides(a.shape),p=C.computeConv3DInfo(r.shape,u,o,1,i),d=p.strideDepth,h=p.strideHeight,f=p.strideWidth,m=p.filterDepth,g=p.filterHeight,b=p.filterWidth,y=new Vt(p.filterShape,"float32"),v=y.values,[x,k,I,$]=y.strides,R=n.data.get(a.dataId).values,[E,P,A,D]=c,T=n.data.get(r.dataId).values,[L,W,j,Y]=l,X=p.padInfo.front,Z=p.padInfo.left,ne=p.padInfo.top;for(let ee=0;ee<m;++ee){let se=Math.max(0,Math.ceil((X-ee)/d)),te=Math.min(p.outDepth,(p.inDepth+X-ee)/d),ie=ee*x;for(let ae=0;ae<g;++ae){let de=Math.max(0,Math.ceil((ne-ae)/h)),me=Math.min(p.outHeight,(p.inHeight+ne-ae)/h),ke=ae*k+ie;for(let Ie=0;Ie<b;++Ie){let Re=Math.max(0,Math.ceil((Z-Ie)/f)),Pe=Math.min(p.outWidth,(p.inWidth+Z-Ie)/f),Xe=Ie*I+ke;for(let Je=0;Je<p.inChannels;++Je){let Ye=Je*$+Xe;for(let tt=0;tt<p.outChannels;++tt){let Ce=0;for(let ut=0;ut<p.batchSize;++ut){let ot=ut*L,Jt=ut*E;for(let Nt=se;Nt<te;++Nt){let Et=(ee+Nt*d-X)*W+ot,en=Nt*P+Jt;for(let Cn=de;Cn<me;++Cn){let Yt=(ae+Cn*h-ne)*j+Et,Dn=Cn*A+en;for(let tn=Re;tn<Pe;++tn){let Ls=(Ie+tn*f-Z)*Y+Yt,Co=tn*D+Dn;Ce+=T[Ls+Je]*R[Co+tt]}}}}v[Ye+tt]=Ce}}}}}return n.makeTensorInfo(y.shape,y.dtype,y.values)}var rq={kernelName:kg,backendName:"cpu",kernelFunc:sq};function aq(e){let{inputs:t,backend:n,attrs:s}=e,{dy:r,filter:a}=t,{pad:o,strides:i,inputShape:u}=s;be([r],"conv3dBackpropInputV2");let l=w.computeStrides(r.shape),c=w.computeStrides(a.shape),p=C.computeConv3DInfo(u,a.shape,i,1,o),d=new Vt(p.inShape,"float32"),h=d.values,[f,m,g,b]=d.strides,y=n.data.get(r.dataId).values,[v,x,k,I]=l,$=n.data.get(a.dataId).values,[R,E,P,A]=c,{batchSize:D,filterDepth:T,filterHeight:L,filterWidth:W,inChannels:j,inDepth:Y,inHeight:X,inWidth:Z,outChannels:ne,outDepth:ee,outHeight:se,outWidth:te,strideDepth:ie,strideHeight:ae,strideWidth:de}=p,me=T-1-p.padInfo.front,ke=L-1-p.padInfo.top,Ie=W-1-p.padInfo.left;for(let Re=0;Re<D;++Re)for(let Pe=0;Pe<j;++Pe)for(let Xe=0;Xe<Y;++Xe){let Je=Xe-me,Ye=Math.max(0,Math.ceil(Je/ie)),tt=Math.min(ee,(T+Je)/ie);for(let Ce=0;Ce<X;++Ce){let ut=Ce-ke,ot=Math.max(0,Math.ceil(ut/ae)),Jt=Math.min(se,(L+ut)/ae);for(let Nt=0;Nt<Z;++Nt){let In=Nt-Ie,Et=Math.max(0,Math.ceil(In/de)),en=Math.min(te,(W+In)/de),Cn=0;for(let Nn=Ye;Nn<tt;++Nn){let Yt=Nn*ie-Je;for(let Dn=ot;Dn<Jt;++Dn){let tn=Dn*ae-ut;for(let zs=Et;zs<en;++zs){let Ls=zs*de-In,Co=v*Re+x*Nn+k*Dn+I*zs,Zs=R*(T-1-Yt)+E*(L-1-tn)+P*(W-1-Ls)+A*Pe;for(let Ms=0;Ms<ne;++Ms){let gu=y[Co+Ms],No=$[Zs+Ms];Cn+=gu*No}}}}h[f*Re+m*Xe+g*Ce+b*Nt+Pe]=Cn}}}return n.makeTensorInfo(d.shape,d.dtype,d.values)}var oq={kernelName:Sg,backendName:"cpu",kernelFunc:aq},iq=st(Ea,e=>Math.cos(e)),uq={kernelName:Ea,backendName:"cpu",kernelFunc:iq},lq=st(Ra,e=>Math.cosh(e)),cq={kernelName:Ra,backendName:"cpu",kernelFunc:lq};function dq(e){let{inputs:t,backend:n,attrs:s}=e,{image:r,boxes:a,boxInd:o}=t,{cropSize:i,method:u,extrapolationValue:l}=s,[c,p,d,h]=r.shape,f=a.shape[0],[m,g]=i,b=Ae([f,m,g,h],"float32"),y=n.data.get(a.dataId).values,v=n.data.get(o.dataId).values,x=n.data.get(r.dataId).values,k=w.computeStrides(r.shape),I=w.computeStrides(b.shape);for(let $=0;$<f;$++){let R=$*4,E=y[R],P=y[R+1],A=y[R+2],D=y[R+3],T=v[$];if(T>=c)continue;let L=m>1?(A-E)*(p-1)/(m-1):0,W=g>1?(D-P)*(d-1)/(g-1):0;for(let j=0;j<m;j++){let Y=m>1?E*(p-1)+j*L:.5*(E+A)*(p-1);if(Y<0||Y>p-1){for(let X=0;X<g;X++)for(let Z=0;Z<h;Z++){let ne=Z+X*I[2]+j*I[1]+$*I[0];b.values[ne]=l}continue}if(u==="bilinear"){let X=Math.floor(Y),Z=Math.ceil(Y),ne=Y-X;for(let ee=0;ee<g;ee++){let se=g>1?P*(d-1)+ee*W:.5*(P+D)*(d-1);if(se<0||se>d-1){for(let de=0;de<h;de++){let me=de+ee*I[2]+j*I[1]+$*I[0];b.values[me]=l}continue}let te=Math.floor(se),ie=Math.ceil(se),ae=se-te;for(let de=0;de<h;de++){let me=de+te*k[2]+X*k[1]+T*k[0],ke=x[me];me=de+ie*k[2]+X*k[1]+T*k[0];let Ie=x[me];me=de+te*k[2]+Z*k[1]+T*k[0];let Re=x[me];me=de+ie*k[2]+Z*k[1]+T*k[0];let Pe=x[me],Xe=ke+(Ie-ke)*ae,Je=Re+(Pe-Re)*ae;me=de+ee*I[2]+j*I[1]+$*I[0],b.values[me]=Xe+(Je-Xe)*ne}}}else for(let X=0;X<g;++X){let Z=g>1?P*(d-1)+X*W:.5*(P+D)*(d-1);if(Z<0||Z>d-1){for(let se=0;se<h;se++){let te=se+X*I[2]+j*I[1]+$*I[0];b.values[te]=l}continue}let ne=Math.round(Z),ee=Math.round(Y);for(let se=0;se<h;se++){let te=se+ne*k[2]+ee*k[1]+T*k[0],ie=se+X*I[2]+j*I[1]+$*I[0];b.values[ie]=x[te]}}}}return n.makeTensorInfo(b.shape,b.dtype,b.values)}var pq={kernelName:mi,backendName:"cpu",kernelFunc:dq};function hq(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{axis:a,exclusive:o,reverse:i}=s;be(r,"cumprod");let u=C.getAxesPermutation([a],r.shape.length),l=r;u!=null&&(l=wn({inputs:{x:r},backend:n,attrs:{perm:u}}));let c=C.getInnerMostAxes(1,r.shape.length)[0];if(c!==l.shape.length-1)throw new Error(`backend.cumprod in CPU expects an inner-most axis=${l.shape.length-1} but got axis=${c}`);let p=cn(l.dtype,"int32"),d=w.makeOnesTypedArray(w.sizeFromShape(l.shape),p),h=n.data.get(l.dataId).values,f=l.shape[l.shape.length-1],m=i?(b,y)=>b+f-y-1:(b,y)=>b+y;for(let b=0;b<h.length;b+=f)for(let y=0;y<f;y++){let v=m(b,y);if(y===0)d[v]=o?1:h[v];else{let x=m(b,y-1);d[v]=o?h[x]*d[x]:h[v]*d[x]}}let g=n.makeTensorInfo(l.shape,p,d);if(u!=null){let b=C.getUndoAxesPermutation(u),y=wn({inputs:{x:g},backend:n,attrs:{perm:b}});return n.disposeIntermediateTensorInfo(g),n.disposeIntermediateTensorInfo(l),y}return g}var fq={kernelName:fi,backendName:"cpu",kernelFunc:hq};function mq(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{axis:a,exclusive:o,reverse:i}=s;be(r,"cumsum");let u=C.getAxesPermutation([a],r.shape.length),l=r;u!=null&&(l=wn({inputs:{x:r},backend:n,attrs:{perm:u}}));let c=C.getInnerMostAxes(1,r.shape.length)[0];if(c!==l.shape.length-1)throw new Error(`backend.cumsum in CPU expects an inner-most axis=${l.shape.length-1} but got axis=${c}`);let p=cn(l.dtype,"int32"),d=w.makeZerosTypedArray(w.sizeFromShape(l.shape),p),h=n.data.get(l.dataId).values,f=l.shape[l.shape.length-1],m=i?(b,y)=>b+f-y-1:(b,y)=>b+y;for(let b=0;b<h.length;b+=f)for(let y=0;y<f;y++){let v=m(b,y);if(y===0)d[v]=o?0:h[v];else{let x=m(b,y-1);d[v]=o?h[x]+d[x]:h[v]+d[x]}}let g=n.makeTensorInfo(l.shape,p,d);if(u!=null){let b=C.getUndoAxesPermutation(u),y=wn({inputs:{x:g},backend:n,attrs:{perm:b}});return n.disposeIntermediateTensorInfo(g),n.disposeIntermediateTensorInfo(l),y}return g}var gq={kernelName:Da,backendName:"cpu",kernelFunc:mq};function bq(e){let{inputs:t,backend:n,attrs:s}=e,{x:r,weights:a}=t,{size:o,binaryOutput:i}=s;if(r.shape.length===1){let u=n.data.get(r.dataId).values,l=n.data.get(a.dataId).values,c=pv(u,l,a.dtype,a.shape,o);return n.makeTensorInfo([o],a.dtype,c)}else if(r.shape.length===2){let u=n.bufferSync(r),l=n.bufferSync(a),c=iC(u,l,o,i);return n.makeTensorInfo(c.shape,a.dtype,c.values)}throw new Error(`Error in denseBincount: input must be at most rank 2, but got rank${r.shape.length}.`)}var yq={kernelName:Ig,backendName:"cpu",kernelFunc:bq};function vq(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{blockSize:a,dataFormat:o}=s;w.assert(o==="NHWC",()=>`Only NHWC dataFormat supported on CPU for depthToSpace. Got ${o}`);let i=r.shape[0],u=r.shape[1],l=r.shape[2],c=r.shape[3],p=u*a,d=l*a,h=c/(a*a),f=n.data.get(r.dataId).values,m=new Float32Array(i*p*d*h),g=0;for(let b=0;b<i;++b)for(let y=0;y<p;++y){let v=Math.floor(y/a),x=y%a;for(let k=0;k<d;++k){let I=Math.floor(k/a),$=k%a,R=(x*a+$)*h;for(let E=0;E<h;++E){let A=E+R+c*(I+l*(v+u*b));m[g++]=f[A]}}}return n.makeTensorInfo([i,p,d,h],r.dtype,m)}var xq={kernelName:gi,backendName:"cpu",kernelFunc:vq};function ZC(e){let{inputs:t,backend:n,attrs:s}=e,{x:r,filter:a}=t,{strides:o,pad:i,dilations:u,dimRoundingMode:l}=s;be([r,a],"depthwiseConv2DNative");let c=w.computeStrides(r.shape),p=w.computeStrides(a.shape),d=u;d==null&&(d=[1,1]),w.assert(C.eitherStridesOrDilationsAreOne(o,d),()=>`Error in depthwiseConv2d: Either strides or dilations must be 1. Got strides ${o} and dilations '${d}'`);let h=C.computeConv2DInfo(r.shape,a.shape,o,d,i,l,!0),{filterHeight:f,filterWidth:m,dilationHeight:g,dilationWidth:b,padInfo:y}=h,v=y.left,x=y.top,k=h.outChannels/h.inChannels,I=new Vt(h.outShape,r.dtype),$=n.data.get(r.dataId).values,R=n.data.get(a.dataId).values,E=I.values;for(let P=0;P<h.batchSize;++P){let A=P*c[0],D=P*I.strides[0];for(let T=0;T<h.outHeight;++T){let L=D+T*I.strides[1],W=T*h.strideHeight-x;for(let j=0;j<f;++j){let Y=W+j*g;if(Y<0||Y>=h.inHeight)continue;let X=j*p[0],Z=A+Y*c[1];for(let ne=0;ne<h.outWidth;++ne){let ee=L+ne*I.strides[2],se=ne*h.strideWidth-v;for(let te=0;te<m;++te){let ie=se+te*b;if(ie<0||ie>=h.inWidth)continue;let ae=X+te*p[1],de=Z+ie*h.inChannels,me=ee,ke=ae;for(let Ie=0;Ie<h.inChannels;++Ie){let Re=$[de+Ie];for(let Pe=0;Pe<k;++Pe)E[me+Pe]+=Re*R[ke+Pe];me+=k,ke+=k}}}}}}return n.makeTensorInfo(I.shape,I.dtype,I.values)}var wq={kernelName:Fa,backendName:"cpu",kernelFunc:ZC};function kq(e){let{inputs:t,backend:n,attrs:s}=e,{x:r,dy:a}=t,{strides:o,dilations:i,pad:u,dimRoundingMode:l,filterShape:c}=s;be([r,a],"depthwiseConv2dNativeBackpropFilter");let p=C.computeConv2DInfo(r.shape,c,o,i,u,l,!0),{strideHeight:d,strideWidth:h,filterHeight:f,filterWidth:m}=p,g=new Vt(p.filterShape,"float32"),b=p.padInfo.left,y=p.padInfo.top,v=p.outChannels/p.inChannels,x=n.data.get(r.dataId).values,k=new Vt(r.shape,r.dtype,x),I=n.data.get(a.dataId).values,$=new Vt(a.shape,a.dtype,I);for(let R=0;R<f;++R){let E=Math.max(0,Math.ceil((y-R)/d)),P=Math.min(p.outHeight,(p.inHeight+y-R)/d);for(let A=0;A<m;++A){let D=Math.max(0,Math.ceil((b-A)/h)),T=Math.min(p.outWidth,(p.inWidth+b-A)/h);for(let L=0;L<p.outChannels;++L){let W=Math.trunc(L/v),j=L%v,Y=0;for(let X=0;X<p.batchSize;++X)for(let Z=E;Z<P;++Z){let ne=R+Z*d-y;for(let ee=D;ee<T;++ee){let se=A+ee*h-b;Y+=k.get(X,ne,se,W)*$.get(X,Z,ee,L)}}g.set(Y,R,A,W,j)}}}return n.makeTensorInfo(g.shape,g.dtype,g.values)}var Sq={kernelName:Cg,backendName:"cpu",kernelFunc:kq};function Iq(e){let{inputs:t,backend:n,attrs:s}=e,{dy:r,filter:a}=t,{strides:o,dilations:i,pad:u,dimRoundingMode:l,inputShape:c}=s;be([r,a],"depthwiseConv2DNativeBackpropInput");let p=w.computeStrides(r.shape),d=w.computeStrides(a.shape),h=C.computeConv2DInfo(c,a.shape,o,i,u,l,!0),f=new Vt(h.inShape,"float32"),m=f.values,[g,b,y]=f.strides,v=n.data.get(r.dataId).values,[x,k,I]=p,$=n.data.get(a.dataId).values,[R,E,P]=d,{batchSize:A,filterHeight:D,filterWidth:T,inChannels:L,inHeight:W,inWidth:j,outChannels:Y,outHeight:X,outWidth:Z,strideHeight:ne,strideWidth:ee}=h,se=D-1-h.padInfo.top,te=T-1-h.padInfo.left,ie=Y/L;for(let ae=0;ae<A;++ae)for(let de=0;de<L;++de)for(let me=0;me<W;++me){let ke=me-se,Ie=Math.max(0,Math.ceil(ke/ne)),Re=Math.min(X,(D+ke)/ne);for(let Pe=0;Pe<j;++Pe){let Xe=Pe-te,Je=Math.max(0,Math.ceil(Xe/ee)),Ye=Math.min(Z,(T+Xe)/ee),tt=0;for(let Ce=Ie;Ce<Re;++Ce){let ut=Ce*ne-ke;for(let ot=Je;ot<Ye;++ot){let 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s=e.shape,r=s[0],a=s[1],o=n.data.get(e.dataId),i=o.complexTensorInfos.real,u=o.complexTensorInfos.imag,l=[r,a],c=w.sizeFromShape(l),p=w.getTypedArrayFromDType("float32",c),d=w.getTypedArrayFromDType("float32",c);for(let g=0;g<r;g++){let b=ba({inputs:{x:i},backend:n,attrs:{begin:[g,0],size:[1,a]}}),y=ba({inputs:{x:u},backend:n,attrs:{begin:[g,0],size:[1,a]}}),v=En({inputs:{real:b,imag:y},backend:n}),{real:x,imag:k}=qq(v,t,n),I=C.mergeRealAndImagArrays(x,k);for(let $=0;$<a;$++){let R=C.getComplexWithIndex(I,$);p[g*a+$]=R.real,d[g*a+$]=R.imag}n.disposeIntermediateTensorInfo(b),n.disposeIntermediateTensorInfo(y),n.disposeIntermediateTensorInfo(v)}let h=n.makeTensorInfo(l,"float32",p),f=n.makeTensorInfo(l,"float32",d),m=En({inputs:{real:h,imag:f},backend:n});return n.disposeIntermediateTensorInfo(h),n.disposeIntermediateTensorInfo(f),m}function qq(e,t,n){let 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Kj={kernelName:Mi,backendName:"cpu",kernelFunc:jj},Xj=C.SELU_SCALEALPHA,Yj=C.SELU_SCALE,Qj=st(Al,e=>e>=0?Yj*e:Xj*(Math.exp(e)-1)),Zj={kernelName:Al,backendName:"cpu",kernelFunc:Qj},Jj=st(El,e=>e<0?-1:e>0?1:0),e5={kernelName:El,backendName:"cpu",kernelFunc:Jj},t5=st(io,e=>Math.sin(e)),n5={kernelName:io,backendName:"cpu",kernelFunc:t5},s5=st(Vi,e=>Math.sinh(e)),r5={kernelName:Vi,backendName:"cpu",kernelFunc:s5},a5=11920928955078125e-23,pw=Math.log(a5)+2,o5=st(Rl,e=>{let t=e>-pw,n=e<pw,s=Math.exp(e),r;return n?r=s:t?r=e:r=Math.log(1+s),r}),i5={kernelName:Rl,backendName:"cpu",kernelFunc:o5};function u5(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{blockShape:a,paddings:o}=s;be([r],"spaceToBatchND");let i=w.sizeFromShape(a),u=[[0,0]];u.push(...o);for(let I=1+a.length;I<r.shape.length;++I)u.push([0,0]);let 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s=w.sizeFromShape(e);if(e.length<=1&&s<=n)return[1,s];if(e.length===2&&e[0]<=n&&e[1]<=n)return e;if(e.length===3&&e[0]*e[1]<=n&&e[2]<=n)return[e[0]*e[1],e[2]];if(e.length===3&&e[0]<=n&&e[1]*e[2]<=n)return[e[0],e[1]*e[2]];if(e.length===4&&e[0]*e[1]*e[2]<=n&&e[3]<=n)return[e[0]*e[1]*e[2],e[3]];if(e.length===4&&e[0]<=n&&e[1]*e[2]*e[3]<=n)return[e[0],e[1]*e[2]*e[3]];if(t){let r=ya(e),a=2,o=2;return e.length&&([a,o]=va(e)),s=r*(a/2)*(o/2),w.sizeToSquarishShape(s).map(i=>i*2)}return w.sizeToSquarishShape(s)}function nd(e){return e%2===0}function al(e,t){if(e=e.slice(-2),t=t.slice(-2),w.arraysEqual(e,t)||!e.length||!t.length||e[0]===0||e[1]===0||t[0]===0||t[1]===0)return!0;if(e.length!==t.length){let n=e.slice(-1)[0],s=t.slice(-1)[0];if(n===s||nd(n)&&nd(s)&&(e[0]===1||t[0]===1))return!0}return e[1]===t[1]&&nd(e[0])&&nd(t[0])}var pd,hd;function S1(e){if(pd==null){let t=xs(e);pd=t.getParameter(t.MAX_TEXTURE_SIZE)}return pd}function mK(){pd=null}function gK(){hd=null}function I1(e){if(hd==null){let t=xs(e);hd=t.getParameter(t.MAX_TEXTURE_IMAGE_UNITS)}return Math.min(16,hd)}function C1(e){if(e===0)return 0;let t,n=xs(e);return Mn(n,"EXT_disjoint_timer_query_webgl2")&&e===2?t=2:Mn(n,"EXT_disjoint_timer_query")?t=1:t=0,t}function Mn(e,t){return e.getExtension(t)!=null}function eg(e){try{if(xs(e)!=null)return!0}catch(t){return console.log("Error when getting WebGL context: ",t),!1}return!1}function N1(e){if(e===0)return!1;let t=xs(e);if(e===1){if(!Mn(t,"OES_texture_float"))return!1}else if(!Mn(t,"EXT_color_buffer_float"))return!1;return tg(t)}function T1(e){if(e===0)return!1;let t=xs(e);if(e===1){if(!Mn(t,"OES_texture_float")||!Mn(t,"WEBGL_color_buffer_float"))return!1}else{if(Mn(t,"EXT_color_buffer_float"))return tg(t);let s="EXT_color_buffer_half_float";if(Mn(t,s)){let r=t.getExtension(s);return bK(t,r)}return!1}return tg(t)}function tg(e){let t=kv(e),n=e.createTexture();e.bindTexture(e.TEXTURE_2D,n);let s=1,r=1;e.texImage2D(e.TEXTURE_2D,0,t.internalFormatFloat,s,r,0,t.textureFormatFloat,t.textureTypeFloat,null);let a=e.createFramebuffer();e.bindFramebuffer(e.FRAMEBUFFER,a),e.framebufferTexture2D(e.FRAMEBUFFER,e.COLOR_ATTACHMENT0,e.TEXTURE_2D,n,0);let o=e.checkFramebufferStatus(e.FRAMEBUFFER)===e.FRAMEBUFFER_COMPLETE;return e.bindTexture(e.TEXTURE_2D,null),e.bindFramebuffer(e.FRAMEBUFFER,null),e.deleteTexture(n),e.deleteFramebuffer(a),o}function bK(e,t){let n=kv(e,t),s=e.createTexture();e.bindTexture(e.TEXTURE_2D,s);let r=1,a=1;e.texImage2D(e.TEXTURE_2D,0,n.internalFormatHalfFloat,r,a,0,n.textureFormatFloat,n.textureTypeHalfFloat,null);let o=e.createFramebuffer();e.bindFramebuffer(e.FRAMEBUFFER,o),e.framebufferTexture2D(e.FRAMEBUFFER,e.COLOR_ATTACHMENT0,e.TEXTURE_2D,s,0);let i=e.checkFramebufferStatus(e.FRAMEBUFFER)===e.FRAMEBUFFER_COMPLETE;return e.bindTexture(e.TEXTURE_2D,null),e.bindFramebuffer(e.FRAMEBUFFER,null),e.deleteTexture(s),e.deleteFramebuffer(o),i}function $1(e){return e!==2?!1:xs(e).fenceSync!=null}function ou(e,t){Array.isArray(e)||(e=[e]),e.forEach(n=>{n!=null&&w.assert(n.dtype!=="complex64",()=>`${t} does not support complex64 tensors in the WebGL backend.`)})}var Ne=K();Ne.registerFlag("HAS_WEBGL",()=>Ne.getNumber("WEBGL_VERSION")>0);Ne.registerFlag("WEBGL_VERSION",()=>eg(2)?2:eg(1)?1:0);Ne.registerFlag("WEBGL_CHECK_NUMERICAL_PROBLEMS",()=>!1);Ne.registerFlag("WEBGL_BUFFER_SUPPORTED",()=>Ne.get("WEBGL_VERSION")===2);Ne.registerFlag("WEBGL_CPU_FORWARD",()=>!0);Ne.registerFlag("WEBGL_FORCE_F16_TEXTURES",()=>!1);Ne.registerFlag("WEBGL_PACK",()=>Ne.getBool("HAS_WEBGL"));Ne.registerFlag("WEBGL_PACK_NORMALIZATION",()=>Ne.getBool("WEBGL_PACK"));Ne.registerFlag("WEBGL_PACK_CLIP",()=>Ne.getBool("WEBGL_PACK"));Ne.registerFlag("WEBGL_PACK_DEPTHWISECONV",()=>Ne.getBool("WEBGL_PACK"));Ne.registerFlag("WEBGL_PACK_BINARY_OPERATIONS",()=>Ne.getBool("WEBGL_PACK"));Ne.registerFlag("WEBGL_PACK_UNARY_OPERATIONS",()=>Ne.getBool("WEBGL_PACK"));Ne.registerFlag("WEBGL_PACK_ARRAY_OPERATIONS",()=>Ne.getBool("WEBGL_PACK"));Ne.registerFlag("WEBGL_PACK_IMAGE_OPERATIONS",()=>Ne.getBool("WEBGL_PACK"));Ne.registerFlag("WEBGL_PACK_REDUCE",()=>Ne.getBool("WEBGL_PACK"));Ne.registerFlag("WEBGL_LAZILY_UNPACK",()=>Ne.getBool("WEBGL_PACK"));Ne.registerFlag("WEBGL_CONV_IM2COL",()=>Ne.getBool("WEBGL_PACK"));Ne.registerFlag("WEBGL_MAX_TEXTURE_SIZE",()=>S1(Ne.getNumber("WEBGL_VERSION")));Ne.registerFlag("WEBGL_MAX_TEXTURES_IN_SHADER",()=>I1(Ne.getNumber("WEBGL_VERSION")));Ne.registerFlag("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION",()=>{let e=Ne.getNumber("WEBGL_VERSION");return e===0?0:C1(e)});Ne.registerFlag("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE",()=>Ne.getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION")>0&&!vp.isMobile());Ne.registerFlag("WEBGL_RENDER_FLOAT32_CAPABLE",()=>N1(Ne.getNumber("WEBGL_VERSION")));Ne.registerFlag("WEBGL_RENDER_FLOAT32_ENABLED",()=>Ne.getBool("WEBGL_FORCE_F16_TEXTURES")?!1:Ne.getBool("WEBGL_RENDER_FLOAT32_CAPABLE"));Ne.registerFlag("WEBGL_DOWNLOAD_FLOAT_ENABLED",()=>T1(Ne.getNumber("WEBGL_VERSION")));Ne.registerFlag("WEBGL_FENCE_API_ENABLED",()=>$1(Ne.getNumber("WEBGL_VERSION")));Ne.registerFlag("WEBGL_SIZE_UPLOAD_UNIFORM",()=>Ne.getBool("WEBGL_RENDER_FLOAT32_ENABLED")?4:0);Ne.registerFlag("WEBGL_DELETE_TEXTURE_THRESHOLD",()=>-1,e=>{if(e<0&&e!==-1)throw new Error(`WEBGL_DELETE_TEXTURE_THRESHOLD must be -1 (indicating never delete) or at least 0, but got ${e}.`)});Ne.registerFlag("WEBGL_FLUSH_THRESHOLD",()=>vp.isMobile()?1:-1,e=>{if(e<0&&e!==-1)throw new Error(`WEBGL_FLUSH_THRESHOLD must be -1 (indicating never manual flush) or at least 0, but got ${e}.`)});Ne.registerFlag("CPU_HANDOFF_SIZE_THRESHOLD",()=>128);Ne.registerFlag("WEBGL_USE_SHAPES_UNIFORMS",()=>!1);Ne.registerFlag("TOPK_LAST_DIM_CPU_HANDOFF_SIZE_THRESHOLD",()=>1e5);Ne.registerFlag("TOPK_K_CPU_HANDOFF_THRESHOLD",()=>128);function mn(){let e,t,n,s,r,a,o,i,u,l;return K().getNumber("WEBGL_VERSION")===2?(e="#version 300 es",t="in",n="out",s="in",r="texture",a="outputColor",o="out vec4 outputColor;",i=`
bool isnan_custom(float val) {
uint floatToUint = floatBitsToUint(val);
return (floatToUint & 0x7fffffffu) > 0x7f800000u;
}
bvec4 isnan_custom(vec4 val) {
return bvec4(isnan_custom(val.x),
isnan_custom(val.y), isnan_custom(val.z), isnan_custom(val.w));
}
#define isnan(value) isnan_custom(value)
`,u="",l=`
#define round(value) newRound(value)
int newRound(float value) {
return int(floor(value + 0.5));
}
ivec4 newRound(vec4 value) {
return ivec4(floor(value + vec4(0.5)));
}
`):(e="",t="attribute",n="varying",s="varying",r="texture2D",a="gl_FragColor",o="",i=`
#define isnan(value) isnan_custom(value)
bool isnan_custom(float val) {
return (val > 0. || val < 1. || val == 0.) ? false : true;
}
bvec4 isnan_custom(vec4 val) {
return bvec4(isnan(val.x), isnan(val.y), isnan(val.z), isnan(val.w));
}
`,u=`
uniform float INFINITY;
bool isinf(float val) {
return abs(val) == INFINITY;
}
bvec4 isinf(vec4 val) {
return equal(abs(val), vec4(INFINITY));
}
`,l=`
int round(float value) {
return int(floor(value + 0.5));
}
ivec4 round(vec4 value) {
return ivec4(floor(value + vec4(0.5)));
}
`),{version:e,attribute:t,varyingVs:n,varyingFs:s,texture2D:r,output:a,defineOutput:o,defineSpecialNaN:i,defineSpecialInf:u,defineRound:l}}function wo(e,t,n="index"){let s=w.computeStrides(t);return s.map((r,a)=>{let o=`int ${e[a]} = ${n} / ${r}`,i=a===s.length-1?`int ${e[a+1]} = ${n} - ${e[a]} * ${r}`:`index -= ${e[a]} * ${r}`;return`${o}; ${i};`}).join("")}function th(e,t,n="index"){let s=w.computeStrides(t);return s.map((r,a)=>{let o=`int ${e[a]} = ${n} / outShapeStrides[${a}]`,i=a===s.length-1?`int ${e[a+1]} = ${n} - ${e[a]} * outShapeStrides[${a}]`:`index -= ${e[a]} * outShapeStrides[${a}]`;return`${o}; ${i};`}).join("")}function yK(e,t){let n=e.length,s=e.map(a=>`${t}[${a}]`),r=new Array(n-1);r[n-2]=s[n-1];for(let a=n-3;a>=0;--a)r[a]=`(${r[a+1]} * ${s[a+1]})`;return r}function vK(e,t,n="index"){let s=e.map((a,o)=>o),r=yK(s,t);return r.map((a,o)=>{let i=`int ${e[o]} = ${n} / ${r[o]}`,u=o===r.length-1?`int ${e[o+1]} = ${n} - ${e[o]} * ${r[o]}`:`index -= ${e[o]} * ${r[o]}`;return`${i}; ${u};`}).join("")}function Iv(e){let t=w.computeStrides(e).map(n=>n.toString());return`
int getFlatIndex(ivec3 coords) {
return coords.x * ${t[0]} + coords.y * ${t[1]} + coords.z;
}
`}function Cv(){return`
int getFlatIndex(ivec3 coords) {
return coords.x * outShapeStrides[0] + coords.y * outShapeStrides[1] + coords.z;
}
`}var _1=`
const float FLOAT_MAX = 1.70141184e38;
const float FLOAT_MIN = 1.17549435e-38;
lowp vec4 encode_float(highp float v) {
if (isnan(v)) {
return vec4(255, 255, 255, 255);
}
highp float av = abs(v);
if(av < FLOAT_MIN) {
return vec4(0.0, 0.0, 0.0, 0.0);
} else if(v > FLOAT_MAX) {
return vec4(0.0, 0.0, 128.0, 127.0) / 255.0;
} else if(v < -FLOAT_MAX) {
return vec4(0.0, 0.0, 128.0, 255.0) / 255.0;
}
highp vec4 c = vec4(0,0,0,0);
highp float e = floor(log2(av));
highp float m = exp2(fract(log2(av))) - 1.0;
c[2] = floor(128.0 * m);
m -= c[2] / 128.0;
c[1] = floor(32768.0 * m);
m -= c[1] / 32768.0;
c[0] = floor(8388608.0 * m);
highp float ebias = e + 127.0;
c[3] = floor(ebias / 2.0);
ebias -= c[3] * 2.0;
c[2] += floor(ebias) * 128.0;
c[3] += 128.0 * step(0.0, -v);
return c / 255.0;
}
`,{getBroadcastDims:A1}=C;function xK(e,t,n){let s=[];if(e.forEach(h=>{let f=w.sizeFromShape(h.shapeInfo.logicalShape);if(h.shapeInfo.isUniform?s.push(`uniform float ${h.name}${f>1?`[${f}]`:""};`):(s.push(`uniform sampler2D ${h.name};`),s.push(`uniform int offset${h.name};`)),n.enableShapeUniforms){let{uniformShape:m}=Nv(n.packedInputs,h.shapeInfo.logicalShape,h.shapeInfo.texShape);switch(m.length){case 1:s.push(`uniform int ${h.name}Shape;`);break;case 2:s.push(`uniform ivec2 ${h.name}Shape;`);break;case 3:s.push(`uniform ivec3 ${h.name}Shape;`);break;case 4:s.push(`uniform ivec4 ${h.name}Shape;`);break;default:break}s.push(`uniform ivec2 ${h.name}TexShape;`)}}),n.enableShapeUniforms){switch(t.logicalShape.length){case 1:s.push("uniform int outShape;");break;case 2:s.push("uniform ivec2 outShape;"),s.push("uniform int outShapeStrides;");break;case 3:s.push("uniform ivec3 outShape;"),s.push("uniform ivec2 outShapeStrides;");break;case 4:s.push("uniform ivec4 outShape;"),s.push("uniform ivec3 outShapeStrides;");break;default:break}s.push("uniform ivec2 outTexShape;")}n.customUniforms&&n.customUniforms.forEach(h=>{s.push(`uniform ${h.type} ${h.name}${h.arrayIndex?`[${h.arrayIndex}]`:""};`)});let r=s.join(`
`),a=e.map(h=>wK(h,t,n.packedInputs,n.enableShapeUniforms)).join(`
`),o=t.texShape,i=mn(),u=IK(i),l,c,p=TK(i);return t.isPacked?(l=kK(t.logicalShape,o,n.enableShapeUniforms),c=NK(i)):(l=SK(t.logicalShape,o,n.enableShapeUniforms),c=CK(i)),n.packedInputs&&(p+=EK),[p,u,c,r,l,a,n.userCode].join(`
`)}function iu(e,t=!1){let n=e.shapeInfo.logicalShape;switch(n.length){case 0:return UK(e,t);case 1:return HK(e,t);case 2:return jK(e,t);case 3:return XK(e,t);case 4:return QK(e,t);case 5:return ZK(e);case 6:return JK(e);default:throw new Error(`${n.length}-D input sampling is not yet supported`)}}function E1(e,t){switch(e.shapeInfo.logicalShape.length){case 0:return WK(e);case 1:return GK(e,t);case 2:return qK(e,t);case 3:return KK(e,t);default:return YK(e,t)}}function wK(e,t,n=!1,s){let r="";n?r+=E1(e,s):r+=iu(e,s);let a=e.shapeInfo.logicalShape,o=t.logicalShape;return a.length<=o.length&&(n?r+=eX(e,t):r+=tX(e,t)),r}function kK(e,t,n){switch(e.length){case 0:return R1();case 1:return RK(e,t,n);case 2:return BK(e,t,n);case 3:return FK(e,t,n);default:return PK(e,t,n)}}function SK(e,t,n){switch(e.length){case 0:return R1();case 1:return DK(e,t,n);case 2:return VK(e,t,n);case 3:return OK(e,t,n);case 4:return zK(e,t,n);case 5:return LK(e,t);case 6:return MK(e,t);default:throw new Error(`${e.length}-D output sampling is not yet supported`)}}function IK(e){return`
float sampleTexture(sampler2D textureSampler, vec2 uv) {
return ${e.texture2D}(textureSampler, uv).r;
}
`}function CK(e){return`
void setOutput(float val) {
${e.output} = vec4(val, 0, 0, 0);
}
`}function NK(e){return`
void setOutput(vec4 val) {
${e.output} = val;
}
`}function TK(e){return`${e.version}
precision highp float;
precision highp int;
precision highp sampler2D;
${e.varyingFs} vec2 resultUV;
${e.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;
${e.defineSpecialNaN}
${e.defineSpecialInf}
${e.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);
}
${$K}
${_K}
${AK}
`}var $K=`
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);
}
`,_K=`
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);
}
`,AK=`
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);
}
`,EK=`
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 R1(){return`
int getOutputCoords() {
return 0;
}
`}function RK(e,t,n){let s=[Math.ceil(t[0]/2),Math.ceil(t[1]/2)];return s[0]===1?n?`
int getOutputCoords() {
return 2 * int(resultUV.x * ceil(float(outTexShape[1]) / 2.0));
}
`:`
int getOutputCoords() {
return 2 * int(resultUV.x * ${s[1]}.0);
}
`:s[1]===1?n?`
int getOutputCoords() {
return 2 * int(resultUV.y * ceil(float(outTexShape[0]) / 2.0));
}
`:`
int getOutputCoords() {
return 2 * int(resultUV.y * ${s[0]}.0);
}
`:n?`
int getOutputCoords() {
ivec2 packedTexShape = ivec2(ceil(float(outTexShape[0]) / 2.0), ceil(float(outTexShape[1]) / 2.0));
ivec2 resTexRC = ivec2(resultUV.yx *
vec2(packedTexShape[0], packedTexShape[1]));
return 2 * (resTexRC.x * packedTexShape[1] + resTexRC.y);
}
`:`
int getOutputCoords() {
ivec2 resTexRC = ivec2(resultUV.yx *
vec2(${s[0]}, ${s[1]}));
return 2 * (resTexRC.x * ${s[1]} + resTexRC.y);
}
`}function DK(e,t,n){return t[0]===1?n?`
int getOutputCoords() {
return int(resultUV.x * float(outTexShape[1]));
}
`:`
int getOutputCoords() {
return int(resultUV.x * ${t[1]}.0);
}
`:t[1]===1?n?`
int getOutputCoords() {
return int(resultUV.y * float(outTexShape[0]));
}
`:`
int getOutputCoords() {
return int(resultUV.y * ${t[0]}.0);
}
`:n?`
int getOutputCoords() {
ivec2 resTexRC = ivec2(resultUV.yx *
vec2(outTexShape[0], outTexShape[1]));
return resTexRC.x * outTexShape[1] + resTexRC.y;
}
`:`
int getOutputCoords() {
ivec2 resTexRC = ivec2(resultUV.yx *
vec2(${t[0]}, ${t[1]}));
return resTexRC.x * ${t[1]} + resTexRC.y;
}
`}function FK(e,t,n){if(n)return`
ivec3 getOutputCoords() {
ivec2 packedTexShape = ivec2(ceil(float(outTexShape[0]) / 2.0), ceil(float(outTexShape[1]) / 2.0));
int texelsInLogicalRow = int(ceil(float(outShape[2]) / 2.0));
int texelsInBatch = texelsInLogicalRow * int(ceil(float(outShape[1]) / 2.0));
ivec2 resTexRC = ivec2(resultUV.yx *
vec2(packedTexShape[0], packedTexShape[1]));
int index = resTexRC.x * packedTexShape[1] + resTexRC.y;
int b = index / texelsInBatch;
index -= b * texelsInBatch;
int r = 2 * (index / texelsInLogicalRow);
int c = imod(index, texelsInLogicalRow) * 2;
return ivec3(b, r, c);
}
`;let s=[Math.ceil(t[0]/2),Math.ceil(t[1]/2)],r=Math.ceil(e[2]/2),a=r*Math.ceil(e[1]/2);return`
ivec3 getOutputCoords() {
ivec2 resTexRC = ivec2(resultUV.yx *
vec2(${s[0]}, ${s[1]}));
int index = resTexRC.x * ${s[1]} + resTexRC.y;
int b = index / ${a};
index -= b * ${a};
int r = 2 * (index / ${r});
int c = imod(index, ${r}) * 2;
return ivec3(b, r, c);
}
`}function OK(e,t,n){if(n)return`
ivec3 getOutputCoords() {
ivec2 resTexRC = ivec2(resultUV.yx *
vec2(outTexShape[0], outTexShape[1]));
int index = resTexRC.x * outTexShape[1] + resTexRC.y;
${th(["r","c","d"],e)}
return ivec3(r, c, d);
}
`;let s=wo(["r","c","d"],e);return`
ivec3 getOutputCoords() {
ivec2 resTexRC = ivec2(resultUV.yx *
vec2(${t[0]}, ${t[1]}));
int index = resTexRC.x * ${t[1]} + resTexRC.y;
${s}
return ivec3(r, c, d);
}
`}function PK(e,t,n){if(n)return`
ivec4 getOutputCoords() {
ivec2 packedTexShape = ivec2(ceil(float(outTexShape[0]) / 2.0), ceil(float(outTexShape[1]) / 2.0));
ivec2 resTexRC = ivec2(resultUV.yx *
vec2(packedTexShape[0], packedTexShape[1]));
int index = resTexRC.x * packedTexShape[1] + resTexRC.y;
int texelsInLogicalRow = int(ceil(float(outShape[3]) / 2.0));
int texelsInBatch = texelsInLogicalRow * int(ceil(float(outShape[2]) / 2.0));
int texelsInBatchN = texelsInBatch * outShape[1];
int b2 = index / texelsInBatchN;
index -= b2 * texelsInBatchN;
int b = index / texelsInBatch;
index -= b * texelsInBatch;
int r = 2 * (index / texelsInLogicalRow);
int c = imod(index, texelsInLogicalRow) * 2;
return ivec4(b2, b, r, c);
}
`;let s=[Math.ceil(t[0]/2),Math.ceil(t[1]/2)],r=Math.ceil(e[e.length-1]/2),a=r*Math.ceil(e[e.length-2]/2),o=a,i="",u="b, r, c";for(let l=2;l<e.length-1;l++)o*=e[e.length-l-1],i=`
int b${l} = index / ${o};
index -= b${l} * ${o};
`+i,u=`b${l}, `+u;return`
ivec${e.length} getOutputCoords() {
ivec2 resTexRC = ivec2(resultUV.yx *
vec2(${s[0]}, ${s[1]}));
int index = resTexRC.x * ${s[1]} + resTexRC.y;
${i}
int b = index / ${a};
index -= b * ${a};
int r = 2 * (index / ${r});
int c = imod(index, ${r}) * 2;
return ivec${e.length}(${u});
}
`}function zK(e,t,n){if(n)return`
ivec4 getOutputCoords() {
ivec2 resTexRC = ivec2(resultUV.yx *
vec2(outTexShape[0], outTexShape[1]));
int index = resTexRC.x * outTexShape[1] + resTexRC.y;
${th(["r","c","d","d2"],e)}
return ivec4(r, c, d, d2);
}
`;let s=wo(["r","c","d","d2"],e);return`
ivec4 getOutputCoords() {
ivec2 resTexRC = ivec2(resultUV.yx *
vec2(${t[0]}, ${t[1]}));
int index = resTexRC.x * ${t[1]} + resTexRC.y;
${s}
return ivec4(r, c, d, d2);
}
`}function LK(e,t){let n=wo(["r","c","d","d2","d3"],e);return`
ivec5 getOutputCoords() {
ivec2 resTexRC = ivec2(resultUV.yx * vec2(${t[0]},
${t[1]}));
int index = resTexRC.x * ${t[1]} + resTexRC.y;
${n}
ivec5 outShape = ivec5(r, c, d, d2, d3);
return outShape;
}
`}function MK(e,t){let n=wo(["r","c","d","d2","d3","d4"],e);return`
ivec6 getOutputCoords() {
ivec2 resTexRC = ivec2(resultUV.yx *
vec2(${t[0]}, ${t[1]}));
int index = resTexRC.x * ${t[1]} + resTexRC.y;
${n}
ivec6 result = ivec6(r, c, d, d2, d3, d4);
return result;
}
`}function BK(e,t,n){let s=[Math.ceil(t[0]/2),Math.ceil(t[1]/2)];if(w.arraysEqual(e,t))return n?`
ivec2 getOutputCoords() {
ivec2 packedTexShape = ivec2(ceil(float(outTexShape[0]) / 2.0), ceil(float(outTexShape[1]) / 2.0));
return 2 * ivec2(resultUV.yx * vec2(packedTexShape[0], packedTexShape[1]));
}
`:`
ivec2 getOutputCoords() {
return 2 * ivec2(resultUV.yx * vec2(${s[0]}, ${s[1]}));
}
`;let r=Math.ceil(e[1]/2);return n?`
ivec2 getOutputCoords() {
ivec2 packedTexShape = ivec2(ceil(float(outTexShape[0]) / 2.0), ceil(float(outTexShape[1]) / 2.0));
int texelsInLogicalRow = int(ceil(float(outShape[1]) / 2.0));
ivec2 resTexRC = ivec2(resultUV.yx *
vec2(packedTexShape[0], packedTexShape[1]));
int index = resTexRC.x * packedTexShape[1] + resTexRC.y;
int r = 2 * (index / texelsInLogicalRow);
int c = imod(index, texelsInLogicalRow) * 2;
return ivec2(r, c);
}
`:`
ivec2 getOutputCoords() {
ivec2 resTexRC = ivec2(resultUV.yx *
vec2(${s[0]}, ${s[1]}));
int index = resTexRC.x * ${s[1]} + resTexRC.y;
int r = 2 * (index / ${r});
int c = imod(index, ${r}) * 2;
return ivec2(r, c);
}
`}function VK(e,t,n){return w.arraysEqual(e,t)?n?`
ivec2 getOutputCoords() {
return ivec2(resultUV.yx * vec2(outTexShape[0], outTexShape[1]));
}
`:`
ivec2 getOutputCoords() {
return ivec2(resultUV.yx * vec2(${t[0]}, ${t[1]}));
}
`:e[1]===1?n?`
ivec2 getOutputCoords() {
ivec2 resTexRC = ivec2(resultUV.yx *
vec2(outTexShape[0], outTexShape[1]));
int index = resTexRC.x * outTexShape[1] + resTexRC.y;
return ivec2(index, 0);
}
`:`
ivec2 getOutputCoords() {
ivec2 resTexRC = ivec2(resultUV.yx *
vec2(${t[0]}, ${t[1]}));
int index = resTexRC.x * ${t[1]} + resTexRC.y;
return ivec2(index, 0);
}
`:e[0]===1?n?`
ivec2 getOutputCoords() {
ivec2 resTexRC = ivec2(resultUV.yx *
vec2(outTexShape[0], outTexShape[1]));
int index = resTexRC.x * outTexShape[1] + resTexRC.y;
return ivec2(0, index);
}
`:`
ivec2 getOutputCoords() {
ivec2 resTexRC = ivec2(resultUV.yx *
vec2(${t[0]}, ${t[1]}));
int index = resTexRC.x * ${t[1]} + resTexRC.y;
return ivec2(0, index);
}
`:n?`
ivec2 getOutputCoords() {
ivec2 resTexRC = ivec2(resultUV.yx *
vec2(outTexShape[0], outTexShape[1]));
int index = resTexRC.x * outTexShape[1] + resTexRC.y;
int r = index / outShape[1];
int c = index - r * outShape[1];
return ivec2(r, c);
}
`:`
ivec2 getOutputCoords() {
ivec2 resTexRC = ivec2(resultUV.yx *
vec2(${t[0]}, ${t[1]}));
int index = resTexRC.x * ${t[1]} + resTexRC.y;
int r = index / ${e[1]};
int c = index - r * ${e[1]};
return ivec2(r, c);
}
`}function ko(e){return`offset${e}`}function WK(e){let t=e.name,n="get"+t.charAt(0).toUpperCase()+t.slice(1),s=mn();return`
vec4 ${n}() {
return ${s.texture2D}(${t}, halfCR);
}
`}function UK(e,t){let n=e.name,s="get"+n.charAt(0).toUpperCase()+n.slice(1);if(e.shapeInfo.isUniform)return`float ${s}() {return ${n};}`;let[r,a]=e.shapeInfo.texShape;if(r===1&&a===1)return`
float ${s}() {
return sampleTexture(${n}, halfCR);
}
`;let o=ko(n);if(t)return`
float ${s}() {
vec2 uv = uvFromFlat(${n}TexShape[0], ${n}TexShape[1], ${o});
return sampleTexture(${n}, uv);
}
`;let[i,u]=e.shapeInfo.texShape;return`
float ${s}() {
vec2 uv = uvFromFlat(${i}, ${u}, ${o});
return sampleTexture(${n}, uv);
}
`}function GK(e,t){let n=e.name,s="get"+n.charAt(0).toUpperCase()+n.slice(1),r=e.shapeInfo.texShape,a=mn();if(t)return`
vec4 ${s}(int index) {
ivec2 packedTexShape = ivec2(ceil(float(${n}TexShape[0]) / 2.0), ceil(float(${n}TexShape[1]) / 2.0));
vec2 uv = packedUVfrom1D(
packedTexShape[0], packedTexShape[1], index);
return ${a.texture2D}(${n}, uv);
}
`;let o=[Math.ceil(r[0]/2),Math.ceil(r[1]/2)];return`
vec4 ${s}(int index) {
vec2 uv = packedUVfrom1D(
${o[0]}, ${o[1]}, index);
return ${a.texture2D}(${n}, uv);
}
`}function HK(e,t){let n=e.name,s="get"+n.charAt(0).toUpperCase()+n.slice(1);if(e.shapeInfo.isUniform)return`
float ${s}(int index) {
${uu(e)}
}
`;let r=e.shapeInfo.texShape,a=r[0],o=r[1];if(o===1&&a===1)return`
float ${s}(int index) {
return sampleTexture(${n}, halfCR);
}
`;let i=ko(n);return o===1?t?`
float ${s}(int index) {
vec2 uv = vec2(0.5, (float(index + ${i}) + 0.5) / float(${n}TexShape[0]));
return sampleTexture(${n}, uv);
}
`:`
float ${s}(int index) {
vec2 uv = vec2(0.5, (float(index + ${i}) + 0.5) / ${a}.0);
return sampleTexture(${n}, uv);
}
`:a===1?t?`
float ${s}(int index) {
vec2 uv = vec2((float(index + ${i}) + 0.5) / float(${n}TexShape[1]), 0.5);
return sampleTexture(${n}, uv);
}
`:`
float ${s}(int index) {
vec2 uv = vec2((float(index + ${i}) + 0.5) / ${o}.0, 0.5);
return sampleTexture(${n}, uv);
}
`:t?`
float ${s}(int index) {
vec2 uv = uvFromFlat(${n}TexShape[0], ${n}TexShape[1], index + ${i});
return sampleTexture(${n}, uv);
}
`:`
float ${s}(int index) {
vec2 uv = uvFromFlat(${a}, ${o}, index + ${i});
return sampleTexture(${n}, uv);
}
`}function qK(e,t){let n=e.shapeInfo.logicalShape,s=e.name,r="get"+s.charAt(0).toUpperCase()+s.slice(1),a=e.shapeInfo.texShape,o=a[0],i=a[1],u=mn();if(a!=null&&w.arraysEqual(n,a))return t?`
vec4 ${r}(int row, int col) {
vec2 uv = (vec2(col, row) + halfCR) / vec2(${s}TexShape[1], ${s}TexShape[0]);
return ${u.texture2D}(${s}, uv);
}
`:`
vec4 ${r}(int row, int col) {
vec2 uv = (vec2(col, row) + halfCR) / vec2(${i}.0, ${o}.0);
return ${u.texture2D}(${s}, uv);
}
`;if(t)return`
vec4 ${r}(int row, int col) {
ivec2 packedTexShape = ivec2(ceil(float(${s}TexShape[0]) / 2.0), ceil(float(${s}TexShape[1]) / 2.0));
int valuesPerRow = int(ceil(float(${s}Shape[1]) / 2.0));
vec2 uv = packedUVfrom2D(valuesPerRow, packedTexShape[0], packedTexShape[1], row, col);
return ${u.texture2D}(${s}, uv);
}
`;let l=[Math.ceil(a[0]/2),Math.ceil(a[1]/2)],c=Math.ceil(n[1]/2);return`
vec4 ${r}(int row, int col) {
vec2 uv = packedUVfrom2D(${c}, ${l[0]}, ${l[1]}, row, col);
return ${u.texture2D}(${s}, uv);
}
`}function jK(e,t){let n=e.shapeInfo.logicalShape,s=e.name,r="get"+s.charAt(0).toUpperCase()+s.slice(1),a=e.shapeInfo.texShape;if(a!=null&&w.arraysEqual(n,a)){if(t)return`
float ${r}(int row, int col) {
vec2 uv = (vec2(col, row) + halfCR) / vec2(${s}TexShape[1], ${s}TexShape[0]);
return sampleTexture(${s}, uv);
}
`;let d=a[0],h=a[1];return`
float ${r}(int row, int col) {
vec2 uv = (vec2(col, row) + halfCR) / vec2(${h}.0, ${d}.0);
return sampleTexture(${s}, uv);
}
`}let{newShape:o,keptDims:i}=w.squeezeShape(n),u=o;if(u.length<n.length){let d=lu(e,u),h=["row","col"];return`
${iu(d,t)}
float ${r}(int row, int col) {
return ${r}(${cu(h,i)});
}
`}if(e.shapeInfo.isUniform)return`
float ${r}(int row, int col) {
int index = round(dot(vec2(row, col), vec2(${n[1]}, 1)));
${uu(e)}
}
`;let l=a[0],c=a[1],p=ko(s);return c===1?t?`
float ${r}(int row, int col) {
float index = dot(vec3(row, col, ${p}), vec3(${s}Shape[1], 1, 1));
vec2 uv = vec2(0.5, (index + 0.5) / float(${s}TexShape[0]));
return sampleTexture(${s}, uv);
}
`:`
float ${r}(int row, int col) {
float index = dot(vec3(row, col, ${p}), vec3(${n[1]}, 1, 1));
vec2 uv = vec2(0.5, (index + 0.5) / ${l}.0);
return sampleTexture(${s}, uv);
}
`:l===1?t?`
float ${r}(int row, int col) {
float index = dot(vec3(row, col, ${p}), vec3(${s}Shape[1], 1, 1));
vec2 uv = vec2((index + 0.5) / float(${s}TexShape[1]), 0.5);
return sampleTexture(${s}, uv);
}
`:`
float ${r}(int row, int col) {
float index = dot(vec3(row, col, ${p}), vec3(${n[1]}, 1, 1));
vec2 uv = vec2((index + 0.5) / ${c}.0, 0.5);
return sampleTexture(${s}, uv);
}
`:t?`
float ${r}(int row, int col) {
// Explicitly use integer operations as dot() only works on floats.
int index = row * ${s}Shape[1] + col + ${p};
vec2 uv = uvFromFlat(${s}TexShape[0], ${s}TexShape[1], index);
return sampleTexture(${s}, uv);
}
`:`
float ${r}(int row, int col) {
// Explicitly use integer operations as dot() only works on floats.
int index = row * ${n[1]} + col + ${p};
vec2 uv = uvFromFlat(${l}, ${c}, index);
return sampleTexture(${s}, uv);
}
`}function KK(e,t){let n=e.shapeInfo.logicalShape,s=e.name,r="get"+s.charAt(0).toUpperCase()+s.slice(1),a=e.shapeInfo.texShape,o=[Math.ceil(a[0]/2),Math.ceil(a[1]/2)];if(n[0]===1){let d=n.slice(1),h=[1,2],f=lu(e,d),m=["b","row","col"];return`
${E1(f,t)}
vec4 ${r}(int b, int row, int col) {
return ${r}(${cu(m,h)});
}
`}let i=mn();if(t)return`
vec4 ${r}(int b, int row, int col) {
ivec2 packedTexShape = ivec2(ceil(float(${s}TexShape[0]) / 2.0), ceil(float(${s}TexShape[1]) / 2.0));
int valuesPerRow = int(ceil(float(${s}Shape[2]) / 2.0));
int texelsInBatch = valuesPerRow * int(ceil(float(${s}Shape[1]) / 2.0));
vec2 uv = packedUVfrom3D(
packedTexShape[0], packedTexShape[1], texelsInBatch, valuesPerRow, b, row, col);
return ${i.texture2D}(${s}, uv);
}
`;let u=o[0],l=o[1],c=Math.ceil(n[2]/2),p=c*Math.ceil(n[1]/2);return`
vec4 ${r}(int b, int row, int col) {
vec2 uv = packedUVfrom3D(
${u}, ${l}, ${p}, ${c}, b, row, col);
return ${i.texture2D}(${s}, uv);
}
`}function XK(e,t){let n=e.shapeInfo.logicalShape,s=e.name,r="get"+s.charAt(0).toUpperCase()+s.slice(1),a=n[1]*n[2],o=n[2],{newShape:i,keptDims:u}=w.squeezeShape(n),l=i;if(l.length<n.length){let m=lu(e,l),g=["row","col","depth"];return`
${iu(m,t)}
float ${r}(int row, int col, int depth) {
return ${r}(${cu(g,u)});
}
`}if(e.shapeInfo.isUniform)return`
float ${r}(int row, int col, int depth) {
int index = round(dot(vec3(row, col, depth),
vec3(${a}, ${o}, 1)));
${uu(e)}
}
`;let c=e.shapeInfo.texShape,p=c[0],d=c[1],h=e.shapeInfo.flatOffset;if(d===a&&h==null)return t?`
float ${r}(int row, int col, int depth) {
int stride1 = ${s}Shape[2];
float texR = float(row);
float texC = dot(vec2(col, depth), vec2(stride1, 1));
vec2 uv = (vec2(texC, texR) + halfCR) /
vec2(${s}TexShape[1], ${s}TexShape[0]);
return sampleTexture(${s}, uv);
}
`:`
float ${r}(int row, int col, int depth) {
float texR = float(row);
float texC = dot(vec2(col, depth), vec2(${o}, 1));
vec2 uv = (vec2(texC, texR) + halfCR) /
vec2(${d}.0, ${p}.0);
return sampleTexture(${s}, uv);
}
`;if(d===o&&h==null)return t?`
float ${r}(int row, int col, int depth) {
float texR = dot(vec2(row, col), vec2(${s}Shape[1], 1));
float texC = float(depth);
vec2 uv = (vec2(texC, texR) + halfCR) / vec2(${s}TexShape[1], ${s}TexShape[0]);
return sampleTexture(${s}, uv);
}
`:`
float ${r}(int row, int col, int depth) {
float texR = dot(vec2(row, col), vec2(${n[1]}, 1));
float texC = float(depth);
vec2 uv = (vec2(texC, texR) + halfCR) / vec2(${d}.0, ${p}.0);
return sampleTexture(${s}, uv);
}
`;let f=ko(s);return t?`
float ${r}(int row, int col, int depth) {
// Explicitly use integer operations as dot() only works on floats.
int stride0 = ${s}Shape[1] * ${s}Shape[2];
int stride1 = ${s}Shape[2];
int index = row * ${a} + col * ${o} + depth + ${f};
vec2 uv = uvFromFlat(${s}TexShape[0], ${s}TexShape[1], index);
return sampleTexture(${s}, uv);
}
`:`
float ${r}(int row, int col, int depth) {
// Explicitly use integer operations as dot() only works on floats.
int index = row * ${a} + col * ${o} + depth + ${f};
vec2 uv = uvFromFlat(${p}, ${d}, index);
return sampleTexture(${s}, uv);
}
`}function YK(e,t){let n=e.name,s="get"+n.charAt(0).toUpperCase()+n.slice(1),r=mn();if(t)return`
vec4 ${s}(int b2, int b, int row, int col) {
int valuesPerRow = int(ceil(float(${n}Shape[3]) / 2.0));
int texelsInBatch = valuesPerRow * int(ceil(float(${n}Shape[2]) / 2.0));
int index = b * texelsInBatch + (row / 2) * valuesPerRow + (col / 2);
texelsInBatch *= ${n}Shape[1];
index = b2 * texelsInBatch + index;
ivec2 packedTexShape = ivec2(ceil(float(${n}TexShape[0]) / 2.0), ceil(float(${n}TexShape[1]) / 2.0));
int texR = index / packedTexShape[1];
int texC = index - texR * packedTexShape[1];
vec2 uv = (vec2(texC, texR) + halfCR) / vec2(packedTexShape[1], packedTexShape[0]); return ${r.texture2D}(${n}, uv);
}
`;let a=e.shapeInfo.logicalShape,o=a.length,i=e.shapeInfo.texShape,u=[Math.ceil(i[0]/2),Math.ceil(i[1]/2)],l=u[0],c=u[1],p=Math.ceil(a[o-1]/2),d=p*Math.ceil(a[o-2]/2),h="int b, int row, int col",f=`b * ${d} + (row / 2) * ${p} + (col / 2)`;for(let m=2;m<o-1;m++)h=`int b${m}, `+h,d*=a[o-m-1],f=`b${m} * ${d} + `+f;return`
vec4 ${s}(${h}) {
int index = ${f};
int texR = index / ${c};
int texC = index - texR * ${c};
vec2 uv = (vec2(texC, texR) + halfCR) / vec2(${c}, ${l});
return ${r.texture2D}(${n}, uv);
}
`}function QK(e,t){let n=e.shapeInfo.logicalShape,s=e.name,r="get"+s.charAt(0).toUpperCase()+s.slice(1),a=n[3],o=n[2]*a,i=n[1]*o,{newShape:u,keptDims:l}=w.squeezeShape(n);if(u.length<n.length){let y=lu(e,u),v=["row","col","depth","depth2"];return`
${iu(y,t)}
float ${r}(int row, int col, int depth, int depth2) {
return ${r}(${cu(v,l)});
}
`}if(e.shapeInfo.isUniform)return`
float ${r}(int row, int col, int depth, int depth2) {
int index = round(dot(vec4(row, col, depth, depth2),
vec4(${i}, ${o}, ${a}, 1)));
${uu(e)}
}
`;let c=e.shapeInfo.flatOffset,p=e.shapeInfo.texShape,d=p[0],h=p[1],f=`int stride2 = ${s}Shape[3];`,m=`int stride1 = ${s}Shape[2] * stride2;`,g=`int stride0 = ${s}Shape[1] * stride1;`;if(h===i&&c==null)return t?`
float ${r}(int row, int col, int depth, int depth2) {
${f}
${m}
float texR = float(row);
float texC =
dot(vec3(col, depth, depth2),
vec3(stride1, stride2, 1));
vec2 uv = (vec2(texC, texR) + halfCR) /
vec2(${s}TexShape[1], ${s}TexShape[0]);
return sampleTexture(${s}, uv);
}
`:`
float ${r}(int row, int col, int depth, int depth2) {
float texR = float(row);
float texC =
dot(vec3(col, depth, depth2),
vec3(${o}, ${a}, 1));
vec2 uv = (vec2(texC, texR) + halfCR) /
vec2(${h}.0, ${d}.0);
return sampleTexture(${s}, uv);
}
`;if(h===a&&c==null)return t?`
float ${r}(int row, int col, int depth, int depth2) {
float texR = dot(vec3(row, col, depth),
vec3(${s}Shape[1] * ${s}Shape[2], ${s}Shape[2], 1));
float texC = float(depth2);
vec2 uv = (vec2(texC, texR) + halfCR) /
vec2(${s}TexShape[1], ${s}TexShape[0]);
return sampleTexture(${s}, uv);
}
`:`
float ${r}(int row, int col, int depth, int depth2) {
float texR = dot(vec3(row, col, depth),
vec3(${n[1]*n[2]}, ${n[2]}, 1));
float texC = float(depth2);
vec2 uv = (vec2(texC, texR) + halfCR) /
vec2(${h}.0, ${d}.0);
return sampleTexture(${s}, uv);
}
`;let b=ko(s);return t?`
float ${r}(int row, int col, int depth, int depth2) {
// Explicitly use integer operations as dot() only works on floats.
${f}
${m}
${g}
int index = row * stride0 + col * stride1 +
depth * stride2 + depth2;
vec2 uv = uvFromFlat(${s}TexShape[0], ${s}TexShape[1], index + ${b});
return sampleTexture(${s}, uv);
}
`:`
float ${r}(int row, int col, int depth, int depth2) {
// Explicitly use integer operations as dot() only works on floats.
int index = row * ${i} + col * ${o} +
depth * ${a} + depth2;
vec2 uv = uvFromFlat(${d}, ${h}, index + ${b});
return sampleTexture(${s}, uv);
}
`}function ZK(e){let t=e.shapeInfo.logicalShape,n=e.name,s="get"+n.charAt(0).toUpperCase()+n.slice(1),r=t[4],a=t[3]*r,o=t[2]*a,i=t[1]*o,{newShape:u,keptDims:l}=w.squeezeShape(t);if(u.length<t.length){let m=lu(e,u),g=["row","col","depth","depth2","depth3"];return`
${iu(m)}
float ${s}(int row, int col, int depth, int depth2, int depth3) {
return ${s}(${cu(g,l)});
}
`}if(e.shapeInfo.isUniform)return`
float ${s}(int row, int col, int depth, int depth2, int depth3) {
float index = dot(
vec4(row, col, depth, depth2),
vec4(${i}, ${o}, ${a}, ${r})) +
depth3;
${uu(e)}
}
`;let c=e.shapeInfo.flatOffset,p=e.shapeInfo.texShape,d=p[0],h=p[1];if(h===i&&c==null)return`
float ${s}(int row, int col, int depth, int depth2, int depth3) {
int texR = row;
float texC = dot(vec4(col, depth, depth2, depth3),
vec4(${o}, ${a}, ${r}, 1));
vec2 uv = (vec2(texC, texR) + halfCR) /
vec2(${h}.0, ${d}.0);
return sampleTexture(${n}, uv);
}
`;if(h===r&&c==null)return`
float ${s}(int row, int col, int depth, int depth2, int depth3) {
float texR = dot(
vec4(row, col, depth, depth2),
vec4(${t[1]*t[2]*t[3]},
${t[2]*t[3]}, ${t[3]}, 1));
int texC = depth3;
vec2 uv = (vec2(texC, texR) + halfCR) /
vec2(${h}.0, ${d}.0);
return sampleTexture(${n}, uv);
}
`;let f=ko(n);return`
float ${s}(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 * ${o} + depth * ${a} +
depth2 * ${r} + depth3 + ${f};
vec2 uv = uvFromFlat(${d}, ${h}, index);
return sampleTexture(${n}, uv);
}
`}function JK(e){let t=e.shapeInfo.logicalShape,n=e.name,s="get"+n.charAt(0).toUpperCase()+n.slice(1),{newShape:r,keptDims:a}=w.squeezeShape(t);if(r.length<t.length){let g=lu(e,r),b=["row","col","depth","depth2","depth3","depth4"];return`
${iu(g)}
float ${s}(int row, int col, int depth,
int depth2, int depth3, int depth4) {
return ${s}(${cu(b,a)});
}
`}let o=t[5],i=t[4]*o,u=t[3]*i,l=t[2]*u,c=t[1]*l;if(e.shapeInfo.isUniform)return`
float ${s}(int row, int col, int depth,
int depth2, int depth3, int depth4) {
int index = round(dot(
vec4(row, col, depth, depth2),
vec4(${c}, ${l}, ${u}, ${i})) +
dot(
vec2(depth3, depth4),
vec2(${o}, 1)));
${uu(e)}
}
`;let p=e.shapeInfo.flatOffset,d=e.shapeInfo.texShape,h=d[0],f=d[1];if(f===c&&p==null)return`
float ${s}(int row, int col, int depth,
int depth2, int depth3, int depth4) {
int texR = row;
float texC = dot(vec4(col, depth, depth2, depth3),
vec4(${l}, ${u}, ${i}, ${o})) +
float(depth4);
vec2 uv = (vec2(texC, texR) + halfCR) /
vec2(${f}.0, ${h}.0);
return sampleTexture(${n}, uv);
}
`;if(f===o&&p==null)return`
float ${s}(int row, int col, int depth,
int depth2, int depth3, int depth4) {
float texR = dot(vec4(row, col, depth, depth2),
vec4(${t[1]*t[2]*t[3]*t[4]},
${t[2]*t[3]*t[4]},
${t[3]*t[4]},
${t[4]})) + float(depth3);
int texC = depth4;
vec2 uv = (vec2(texC, texR) + halfCR) /
vec2(${f}.0, ${h}.0);
return sampleTexture(${n}, uv);
}
`;let m=ko(n);return`
float ${s}(int row, int col, int depth,
int depth2, int depth3, int depth4) {
// Explicitly use integer operations as dot() only works on floats.
int index = row * ${c} + col * ${l} + depth * ${u} +
depth2 * ${i} + depth3 * ${o} + depth4 + ${m};
vec2 uv = uvFromFlat(${h}, ${f}, index);
return sampleTexture(${n}, uv);
}
`}function uu(e){let t=e.name,n=w.sizeFromShape(e.shapeInfo.logicalShape);return n<2?`return ${t};`:`
for (int i = 0; i < ${n}; i++) {
if (i == index) {
return ${t}[i];
}
}
`}function eX(e,t){let n=e.name,s=n.charAt(0).toUpperCase()+n.slice(1),r="get"+s+"AtOutCoords",a=e.shapeInfo.logicalShape.length,o=t.logicalShape.length,i=A1(e.shapeInfo.logicalShape,t.logicalShape),u=rt(o),l=o-a,c,p=["x","y","z","w","u","v"];a===0?c="":o<2&&i.length>=1?c="coords = 0;":c=i.map(y=>`coords.${p[y+l]} = 0;`).join(`
`);let d="";o<2&&a>0?d="coords":d=e.shapeInfo.logicalShape.map((y,v)=>`coords.${p[v+l]}`).join(", ");let h="return outputValue;",m=w.sizeFromShape(e.shapeInfo.logicalShape)===1,b=w.sizeFromShape(t.logicalShape)===1;if(a===1&&!m&&!b)h=`
return vec4(outputValue.xy, outputValue.xy);
`;else if(m&&!b)o===1?h=`
return vec4(outputValue.x, outputValue.x, 0., 0.);
`:h=`
return vec4(outputValue.x);
`;else if(i.length){let y=a-2,v=a-1;i.indexOf(y)>-1&&i.indexOf(v)>-1?h="return vec4(outputValue.x);":i.indexOf(y)>-1?h="return vec4(outputValue.x, outputValue.y, outputValue.x, outputValue.y);":i.indexOf(v)>-1&&(h="return vec4(outputValue.xx, outputValue.zz);")}return`
vec4 ${r}() {
${u} coords = getOutputCoords();
${c}
vec4 outputValue = get${s}(${d});
${h}
}
`}function tX(e,t){let n=e.name,s=n.charAt(0).toUpperCase()+n.slice(1),r="get"+s+"AtOutCoords",a=t.texShape,o=e.shapeInfo.texShape,i=e.shapeInfo.logicalShape.length,u=t.logicalShape.length;if(!e.shapeInfo.isUniform&&i===u&&e.shapeInfo.flatOffset==null&&w.arraysEqual(o,a))return`
float ${r}() {
return sampleTexture(${n}, resultUV);
}
`;let l=rt(u),c=A1(e.shapeInfo.logicalShape,t.logicalShape),p=u-i,d,h=["x","y","z","w","u","v"];i===0?d="":u<2&&c.length>=1?d="coords = 0;":d=c.map(m=>`coords.${h[m+p]} = 0;`).join(`
`);let f="";return u<2&&i>0?f="coords":f=e.shapeInfo.logicalShape.map((m,g)=>`coords.${h[g+p]}`).join(", "),`
float ${r}() {
${l} coords = getOutputCoords();
${d}
return get${s}(${f});
}
`}function rt(e){if(e<=1)return"int";if(e===2)return"ivec2";if(e===3)return"ivec3";if(e===4)return"ivec4";if(e===5)return"ivec5";if(e===6)return"ivec6";throw Error(`GPU for rank ${e} is not yet supported`)}function Nv(e,t,n){let{newShape:s,keptDims:r}=w.squeezeShape(t),a=t.length,o=e&&a===3&&t[0]===1,i=o?t.slice(1):s,u=!e&&a>1&&!w.arraysEqual(t,n)&&s.length<a||o;return{useSqueezeShape:u,uniformShape:u?i:t,keptDims:r}}function lu(e,t){let n=JSON.parse(JSON.stringify(e));return n.shapeInfo.logicalShape=t,n}function cu(e,t){return t.map(n=>e[n]).join(", ")}function nX(e,t,n,s){let r=n.map((c,p)=>{let d={logicalShape:c.shape,texShape:c.isUniform?null:c.texData.texShape,isUniform:c.isUniform,isPacked:c.isUniform?!1:c.texData.isPacked,flatOffset:null};return c.texData!=null&&c.texData.slice!=null&&c.texData.slice.flatOffset>0&&(d.flatOffset=c.texData.slice.flatOffset),{name:t.variableNames[p],shapeInfo:d}}),a=r.map(c=>c.shapeInfo),o={logicalShape:s.shape,texShape:s.texData.texShape,isUniform:!1,isPacked:s.texData.isPacked,flatOffset:null},i=xK(r,o,t),u=u1(e.gl,i),l=e.createProgram(u);return K().get("ENGINE_COMPILE_ONLY")?{program:t,fragmentShader:u,source:i,webGLProgram:l,inShapeInfos:a,outShapeInfo:o,uniformLocations:null,customUniformLocations:null,infLoc:null,nanLoc:null,inShapesLocations:null,inTexShapesLocations:null,outShapeLocation:null,outShapeStridesLocation:null,outTexShapeLocation:null}:{program:t,fragmentShader:u,source:i,webGLProgram:l,inShapeInfos:a,outShapeInfo:o,...D1(e,t,l)}}function D1(e,t,n){let s={},r={},a={},o=[],i,u,l,c=null,p=null;p=e.getUniformLocation(n,"NAN",!1),K().getNumber("WEBGL_VERSION")===1&&(c=e.getUniformLocation(n,"INFINITY",!1));let d=!1;for(let h=0;h<t.variableNames.length;h++){let f=t.variableNames[h];s[f]=e.getUniformLocation(n,f,d),s[`offset${f}`]=e.getUniformLocation(n,`offset${f}`,d),t.enableShapeUniforms&&(r[`${f}Shape`]=e.getUniformLocation(n,`${f}Shape`,d),a[`${f}TexShape`]=e.getUniformLocation(n,`${f}TexShape`,d))}return t.enableShapeUniforms&&(i=e.getUniformLocation(n,"outShape",d),l=e.getUniformLocation(n,"outShapeStrides",d),u=e.getUniformLocation(n,"outTexShape",d)),t.customUniforms&&t.customUniforms.forEach((h,f)=>{o[f]=e.getUniformLocation(n,h.name,d)}),{uniformLocations:s,customUniformLocations:o,infLoc:c,nanLoc:p,inShapesLocations:r,inTexShapesLocations:a,outShapeLocation:i,outShapeStridesLocation:l,outTexShapeLocation:u}}function fw(e,t){if(e.length!==t.length)throw Error(`Binary was compiled with ${e.length} inputs, but was executed with ${t.length} inputs`);e.forEach((n,s)=>{let r=n.logicalShape,a=t[s],o=a.shape;if(!w.arraysEqual(r,o))throw Error(`Binary was compiled with different shapes than the current args. Shapes ${r} and ${o} must match`);if(n.isUniform&&a.isUniform)return;let i=n.texShape,u=a.isUniform?null:a.texData.texShape;if(!w.arraysEqual(i,u))throw Error(`Binary was compiled with different texture shapes than the current args. Shape ${i} and ${u} must match`)})}function sX(e,t,n,s,r){t.program.enableShapeUniforms||(fw(t.inShapeInfos,n),fw([t.outShapeInfo],[s]));let a=s.texData.texture,o=s.texData.texShape;s.texData.isPacked?e.setOutputPackedMatrixTexture(a.texture,o[0],o[1]):e.setOutputMatrixTexture(a.texture,o[0],o[1]),e.setProgram(t.webGLProgram),K().getNumber("WEBGL_VERSION")===1&&t.infLoc!==null&&e.gl.uniform1f(t.infLoc,1/0),t.nanLoc!==null&&e.gl.uniform1f(t.nanLoc,NaN),n.forEach((u,l)=>{let c=t.program.variableNames[l],p=t.uniformLocations[c],d=t.uniformLocations[`offset${c}`],h=t.inShapesLocations[`${c}Shape`],f=t.inTexShapesLocations[`${c}TexShape`];if(h){let{uniformShape:m}=Nv(t.program.packedInputs,u.shape,u.texData.texShape);switch(m.length){case 1:e.gl.uniform1iv(h,new Int32Array(m));break;case 2:e.gl.uniform2iv(h,new Int32Array(m));break;case 3:e.gl.uniform3iv(h,new Int32Array(m));break;case 4:e.gl.uniform4iv(h,new Int32Array(m));break;default:break}}if(f&&e.gl.uniform2i(f,u.texData.texShape[0],u.texData.texShape[1]),p!=null){if(u.isUniform){if(w.sizeFromShape(u.shape)<2)e.gl.uniform1f(p,u.uniformValues[0]);else{let m=u.uniformValues;m instanceof Float32Array||(m=new Float32Array(m)),e.gl.uniform1fv(p,m)}return}u.texData.slice!=null&&d!=null&&e.gl.uniform1i(d,u.texData.slice.flatOffset),e.setInputMatrixTexture(u.texData.texture.texture,p,l)}});let i=t.outShapeLocation;if(i)switch(s.shape.length){case 1:e.gl.uniform1iv(i,new Int32Array(s.shape));break;case 2:e.gl.uniform2iv(i,new Int32Array(s.shape));break;case 3:e.gl.uniform3iv(i,new Int32Array(s.shape));break;case 4:e.gl.uniform4iv(i,new Int32Array(s.shape));break;default:break}if(t.outShapeStridesLocation){let u=w.computeStrides(s.shape);switch(s.shape.length){case 2:e.gl.uniform1iv(t.outShapeStridesLocation,new Int32Array(u));break;case 3:e.gl.uniform2iv(t.outShapeStridesLocation,new Int32Array(u));break;case 4:e.gl.uniform3iv(t.outShapeStridesLocation,new Int32Array(u));break;default:break}}t.outTexShapeLocation&&e.gl.uniform2i(t.outTexShapeLocation,s.texData.texShape[0],s.texData.texShape[1]),t.program.customUniforms&&r&&t.program.customUniforms.forEach((u,l)=>{let c=t.customUniformLocations[l],p=r[l];if(u.type==="float")e.gl.uniform1fv(c,p);else if(u.type==="vec2")e.gl.uniform2fv(c,p);else if(u.type==="vec3")e.gl.uniform3fv(c,p);else if(u.type==="vec4")e.gl.uniform4fv(c,p);else if(u.type==="int")e.gl.uniform1iv(c,p);else if(u.type==="ivec2")e.gl.uniform2iv(c,p);else if(u.type==="ivec3")e.gl.uniform3iv(c,p);else if(u.type==="ivec4")e.gl.uniform4iv(c,p);else throw Error(`uniform type ${u.type} is not supported yet.`)}),e.executeProgram()}function rX(e,t,n){let s="";t.concat(n).forEach(o=>{let i=o.texData!=null&&o.texData.slice!=null&&o.texData.slice.flatOffset>0;if(e.enableShapeUniforms&&!o.isUniform){let u=o.texData.texShape,{useSqueezeShape:l,uniformShape:c,keptDims:p}=Nv(e.packedInputs,o.shape,u),d="",h="",f="";if(c.length===1&&e.packedInputs){let k=[Math.ceil(u[0]/2),Math.ceil(u[1]/2)];d=`${k[0]>1}_${k[1]>1}`}else if(c.length===2&&!e.packedInputs)h=`${c[0]>1}_${c[1]>1}`;else if(c.length>2&&!e.packedInputs){let k=w.computeStrides(c);f=`${k[0]===u[1]}_${k[k.length-1]===u[1]}`}let m=o.shape.length,g=c.length===2&&w.arraysEqual(o.shape,u),b=w.sizeFromShape(o.shape)===1,y=C.getBroadcastDims(o.shape,n.shape),v=!e.packedInputs&&m===n.shape.length&&w.arraysEqual(u,n.texData.texShape),x=e.packedInputs||c.length>2?"":`${u[0]>1}_${u[1]>1}`;s+=`${m}_${v}_${l?p:""}_${c.length}_${b}_${y}_${g}_${d}_${h}_${f}_${x}_${i}`}else{let u=o.isUniform?"uniform":o.texData.texShape;s+=`${o.shape}_${u}_${i}`}});let r=e.userCode,a=e.constructor.name;return a+="_"+s+"_"+r+`${K().getNumber("WEBGL_VERSION")}`,a}function Sn(e){return K().getBool("WEBGL_USE_SHAPES_UNIFORMS")&&e<=4}var aX=class{constructor(e){this.variableNames=["A"],this.packedInputs=!1,this.packedOutput=!0,this.outPackingScheme=0,this.customUniforms=[{name:"texShape",type:"ivec2"}];let t=mn();this.outputShape=e,this.enableShapeUniforms=Sn(this.outputShape.length),this.userCode=`
ivec3 outCoordsFromFlatIndex(int index) {
${this.enableShapeUniforms?th(["r","c","d"],e):wo(["r","c","d"],e)}
return ivec3(r, c, d);
}
void main() {
ivec2 resTexRC = ivec2(resultUV.yx * vec2(texShape[0], texShape[1]));
int index = 4 * (resTexRC.x * texShape[1] + resTexRC.y);
vec4 result = vec4(0.);
for (int i=0; i<4; i++) {
int flatIndex = index + i;
ivec3 rc = outCoordsFromFlatIndex(flatIndex);
result[i] = getA(rc.x, rc.y, rc.z);
}
${t.output} = result;
}
`}},oX=class{constructor(e){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,this.outPackingScheme=0,this.customUniforms=[{name:"texShape",type:"ivec2"}];let t=mn();this.outputShape=e,this.enableShapeUniforms=Sn(this.outputShape.length),this.userCode=`
ivec3 outCoordsFromFlatIndex(int index) {
${this.enableShapeUniforms?th(["r","c","d"],e):wo(["r","c","d"],e)}
return ivec3(r, c, d);
}
void main() {
ivec2 resTexRC = ivec2(resultUV.yx * vec2(texShape[0], texShape[1]));
int index = 4 * (resTexRC.x * texShape[1] + resTexRC.y);
vec4 result = vec4(0.);
for (int i=0; i<4; i++) {
int flatIndex = index + i;
ivec3 rc = outCoordsFromFlatIndex(flatIndex);
result[i] = getChannel(getA(rc.x, rc.y, rc.z), vec2(rc.y, rc.z));
}
${t.output} = result;
}
`}},iX=class{constructor(e){this.variableNames=["A"],this.outTexUsage=3;let t=mn();this.outputShape=e,this.userCode=`
${_1}
void main() {
float x = getAAtOutCoords();
${t.output} = encode_float(x);
}
`}},uX=class{constructor(e){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!1,this.outTexUsage=3;let t=mn();this.outputShape=e,this.userCode=`
${_1}
void main() {
ivec3 coords = getOutputCoords();
float x = getChannel(getAAtOutCoords(), vec2(coords.y, coords.z));
${t.output} = encode_float(x);
}
`}},lX=class{constructor(e,t=!1){this.variableNames=["A"],this.customUniforms=[{name:"texShape",type:"ivec2"}];let n=mn();this.outputShape=e,this.enableShapeUniforms=Sn(this.outputShape.length);let s="result";t&&(s="floor(result * 255. + 0.5)"),this.userCode=`
${this.enableShapeUniforms?Cv():Iv(e)}
void main() {
ivec3 coords = getOutputCoords();
int flatIndex = getFlatIndex(coords);
int offset = imod(flatIndex, 4);
flatIndex = idiv(flatIndex, 4, 1.);
int r = flatIndex / texShape[1];
int c = imod(flatIndex, texShape[1]);
vec2 uv = (vec2(c, r) + halfCR) / vec2(texShape[1], texShape[0]);
vec4 values = ${n.texture2D}(A, uv);
float result;
if(offset == 0) {
result = values[0];
} else if(offset == 1) {
result = values[1];
} else if(offset == 2) {
result = values[2];
} else {
result = values[3];
}
${n.output} = vec4(${s}, 0., 0., 0.);
}
`}},cX=class{constructor(e,t=!1){this.variableNames=["A"],this.packedInputs=!1,this.packedOutput=!0,this.customUniforms=[{name:"texShape",type:"ivec2"}];let n=mn();this.outputShape=e,this.enableShapeUniforms=Sn(this.outputShape.length);let s="",r="result";t&&(r="floor(result * 255. + 0.5)");for(let a=0;a<=1;a++)for(let o=0;o<=1;o++){let i=a*2+o;s+=`
localCoords = coords;
if(localCoords[2] + ${o} < ${this.enableShapeUniforms?"outShape[2]":`${e[2]}`}) {
localCoords[2] += ${o};
if (localCoords[1] + ${a} < ${this.enableShapeUniforms?"outShape[1]":`${e[1]}`}) {
localCoords[1] += ${a};
flatIndex = getFlatIndex(localCoords);
offset = imod(flatIndex, 4);
flatIndex = idiv(flatIndex, 4, 1.);
int r = flatIndex / texShape[1];
int c = imod(flatIndex, texShape[1]);
vec2 uv = (vec2(c, r) + halfCR) / vec2(texShape[1], texShape[0]);
values = ${n.texture2D}(A, uv);
if (offset == 0) {
result[${i}] = values[0];
} else if (offset == 1) {
result[${i}] = values[1];
} else if (offset == 2) {
result[${i}] = values[2];
} else {
result[${i}] = values[3];
}
}
}
`}this.userCode=`
${this.enableShapeUniforms?Cv():Iv(e)}
void main() {
ivec3 coords = getOutputCoords();
vec4 result = vec4(0.);
int flatIndex, r, c, offset;
ivec3 localCoords;
vec2 uv;
vec4 values;
${s}
${n.output} = ${r};
}
`}},dX={};Ee(dX,{bindVertexProgramAttributeStreams:()=>W1,createBufferFromOutputTexture:()=>H1,createFloat16MatrixTexture:()=>L1,createFloat16PackedMatrixTexture:()=>V1,createFloat32MatrixTexture:()=>z1,createIndexBuffer:()=>P1,createPackedMatrixTexture:()=>B1,createUnsignedBytesMatrixTexture:()=>M1,createVertexBuffer:()=>O1,createVertexShader:()=>F1,downloadByteEncodedFloatMatrixFromOutputTexture:()=>j1,downloadFloat32MatrixFromBuffer:()=>q1,downloadMatrixFromPackedOutputTexture:()=>X1,downloadPackedMatrixFromBuffer:()=>K1,getInternalFormatForFloat16MatrixTexture:()=>$v,getInternalFormatForFloat16PackedMatrixTexture:()=>Ev,getInternalFormatForFloat32MatrixTexture:()=>Tv,getInternalFormatForPackedMatrixTexture:()=>Av,getInternalFormatForUnsignedBytesMatrixTexture:()=>_v,uploadDenseMatrixToTexture:()=>U1,uploadPixelDataToTexture:()=>G1});function F1(e){let t=mn(),n=`${t.version}
precision highp float;
${t.attribute} vec3 clipSpacePos;
${t.attribute} vec2 uv;
${t.varyingVs} vec2 resultUV;
void main() {
gl_Position = vec4(clipSpacePos, 1);
resultUV = uv;
}`;return i1(e,n)}function O1(e){let t=new Float32Array([-1,1,0,0,1,-1,-1,0,0,0,1,1,0,1,1,1,-1,0,1,0]);return d1(e,t)}function P1(e){let t=new Uint16Array([0,1,2,2,1,3]);return p1(e,t)}function ec(e,t,n,s,r,a){f1(t,n);let o=h1(e),i=e.TEXTURE_2D;return fe(e,()=>e.bindTexture(i,o)),fe(e,()=>e.texParameteri(i,e.TEXTURE_WRAP_S,e.CLAMP_TO_EDGE)),fe(e,()=>e.texParameteri(i,e.TEXTURE_WRAP_T,e.CLAMP_TO_EDGE)),fe(e,()=>e.texParameteri(i,e.TEXTURE_MIN_FILTER,e.NEAREST)),fe(e,()=>e.texParameteri(i,e.TEXTURE_MAG_FILTER,e.NEAREST)),K().getNumber("WEBGL_VERSION")===1?fe(e,()=>e.texImage2D(i,0,s,t,n,0,r,a,null)):fe(e,()=>e.texStorage2D(i,1,s,t,n)),fe(e,()=>e.bindTexture(e.TEXTURE_2D,null)),{texture:o,texShape:[n,t]}}function Tv(e){return e.internalFormatFloat}function z1(e,t,n,s){let[r,a]=Jl(t,n);return ec(e,r,a,Tv(s),s.textureFormatFloat,e.FLOAT)}function $v(e){return e.internalFormatHalfFloat}function L1(e,t,n,s){let[r,a]=Jl(t,n);return ec(e,r,a,$v(s),s.textureFormatFloat,s.textureTypeHalfFloat)}function _v(e){return e.downloadTextureFormat}function M1(e,t,n,s){let[r,a]=Jl(t,n);return ec(e,r,a,_v(s),e.RGBA,e.UNSIGNED_BYTE)}function Av(e){return e.internalFormatPackedFloat}function B1(e,t,n,s){let[r,a]=au(t,n);return ec(e,r,a,Av(s),e.RGBA,e.FLOAT)}function Ev(e){return e.internalFormatPackedHalfFloat}function V1(e,t,n,s){let[r,a]=au(t,n);return ec(e,r,a,Ev(s),e.RGBA,s.textureTypeHalfFloat)}function W1(e,t,n){return fe(e,()=>e.bindBuffer(e.ARRAY_BUFFER,n)),Zm(e,t,"clipSpacePos",n,3,20,0)&&Zm(e,t,"uv",n,2,20,12)}function U1(e,t,n,s,r,a){fe(e,()=>e.bindTexture(e.TEXTURE_2D,t));let o,i,u;r instanceof Uint8Array?(o=new Uint8Array(n*s*4),i=e.UNSIGNED_BYTE,u=e.RGBA):(o=new Float32Array(n*s*4),i=e.FLOAT,u=a.internalFormatPackedFloat),o.set(r),K().getNumber("WEBGL_VERSION")===2?fe(e,()=>e.texSubImage2D(e.TEXTURE_2D,0,0,0,n,s,e.RGBA,i,o)):fe(e,()=>e.texImage2D(e.TEXTURE_2D,0,u,n,s,0,e.RGBA,i,o)),fe(e,()=>e.bindTexture(e.TEXTURE_2D,null))}function G1(e,t,n){fe(e,()=>e.bindTexture(e.TEXTURE_2D,t)),n.data instanceof Uint8Array?K().getNumber("WEBGL_VERSION")===2?fe(e,()=>e.texSubImage2D(e.TEXTURE_2D,0,0,0,n.width,n.height,e.RGBA,e.UNSIGNED_BYTE,n.data)):fe(e,()=>e.texImage2D(e.TEXTURE_2D,0,e.RGBA,n.width,n.height,0,e.RGBA,e.UNSIGNED_BYTE,n.data)):K().getNumber("WEBGL_VERSION")===2?fe(e,()=>e.texSubImage2D(e.TEXTURE_2D,0,0,0,e.RGBA,e.UNSIGNED_BYTE,n)):fe(e,()=>e.texImage2D(e.TEXTURE_2D,0,e.RGBA,e.RGBA,e.UNSIGNED_BYTE,n)),fe(e,()=>e.bindTexture(e.TEXTURE_2D,null))}function H1(e,t,n,s){let r=e.createBuffer();fe(e,()=>e.bindBuffer(e.PIXEL_PACK_BUFFER,r));let i=4*4*t*n;return fe(e,()=>e.bufferData(e.PIXEL_PACK_BUFFER,i,e.STREAM_READ)),fe(e,()=>e.readPixels(0,0,n,t,e.RGBA,e.FLOAT,0)),fe(e,()=>e.bindBuffer(e.PIXEL_PACK_BUFFER,null)),r}function q1(e,t,n){let s=e,r=new Float32Array(n);return s.bindBuffer(s.PIXEL_PACK_BUFFER,t),s.getBufferSubData(s.PIXEL_PACK_BUFFER,0,r),s.bindBuffer(s.PIXEL_PACK_BUFFER,null),r}function j1(e,t,n,s){let[r,a]=Jl(t,n),o=4,i=new Uint8Array(oK(t*n,o));return fe(e,()=>e.readPixels(0,0,r,a,s.downloadTextureFormat,e.UNSIGNED_BYTE,i)),new Float32Array(i.buffer)}function K1(e,t,n,s,r,a,o,i){let u=e,l=new Float32Array(iK(a,o));return u.bindBuffer(u.PIXEL_PACK_BUFFER,t),u.getBufferSubData(u.PIXEL_PACK_BUFFER,0,l),u.bindBuffer(u.PIXEL_PACK_BUFFER,null),l}function X1(e,t,n){let s=new Float32Array(t*n*4);return fe(e,()=>e.readPixels(0,0,n,t,e.RGBA,e.FLOAT,s)),s}var am=class{constructor(e){this.outputTexture=null,this.program=null,this.disposed=!1,this.vertexAttrsAreBound=!1,this.itemsToPoll=[];let t=K().getNumber("WEBGL_VERSION");e!=null?(this.gl=e,sK(t,e)):this.gl=xs(t);let n="WEBGL_color_buffer_float",s="EXT_color_buffer_half_float";if(this.parallelCompilationExtension=this.gl.getExtension("KHR_parallel_shader_compile"),K().getNumber("WEBGL_VERSION")===1){let r="OES_texture_float",a="OES_texture_half_float";if(this.textureFloatExtension=Lu(this.gl,r),Mn(this.gl,a))this.textureHalfFloatExtension=Lu(this.gl,a);else if(K().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),Mn(this.gl,s))this.colorBufferHalfFloatExtension=Lu(this.gl,s);else if(K().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",Mn(this.gl,n))this.colorBufferFloatExtension=this.gl.getExtension(n);else if(Mn(this.gl,s))this.colorBufferHalfFloatExtension=this.gl.getExtension(s);else throw new Error("GL context does not support color renderable floats");this.vertexBuffer=O1(this.gl),this.indexBuffer=P1(this.gl),this.framebuffer=m1(this.gl),this.textureConfig=kv(this.gl,this.textureHalfFloatExtension)}get debug(){return K().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;fe(e,()=>e.finish()),fe(e,()=>e.bindFramebuffer(e.FRAMEBUFFER,null)),fe(e,()=>e.deleteFramebuffer(this.framebuffer)),fe(e,()=>e.bindBuffer(e.ARRAY_BUFFER,null)),fe(e,()=>e.bindBuffer(e.ELEMENT_ARRAY_BUFFER,null)),fe(e,()=>e.deleteBuffer(this.indexBuffer)),this.disposed=!0}createFloat32MatrixTexture(e,t){return this.throwIfDisposed(),z1(this.gl,e,t,this.textureConfig)}createFloat16MatrixTexture(e,t){return this.throwIfDisposed(),L1(this.gl,e,t,this.textureConfig)}createUnsignedBytesMatrixTexture(e,t){return this.throwIfDisposed(),M1(this.gl,e,t,this.textureConfig)}uploadPixelDataToTexture(e,t){this.throwIfDisposed(),G1(this.gl,e,t)}uploadDenseMatrixToTexture(e,t,n,s){this.throwIfDisposed(),U1(this.gl,e,t,n,s,this.textureConfig)}createFloat16PackedMatrixTexture(e,t){return this.throwIfDisposed(),V1(this.gl,e,t,this.textureConfig)}createPackedMatrixTexture(e,t){return this.throwIfDisposed(),B1(this.gl,e,t,this.textureConfig)}deleteMatrixTexture(e){this.throwIfDisposed(),this.outputTexture===e&&(Jm(this.gl,this.framebuffer),this.outputTexture=null),fe(this.gl,()=>this.gl.deleteTexture(e))}downloadByteEncodedFloatMatrixFromOutputTexture(e,t,n){return this.downloadMatrixDriver(e,()=>j1(this.gl,t,n,this.textureConfig))}downloadPackedMatrixFromBuffer(e,t,n,s,r,a){return K1(this.gl,e,t,n,s,r,a,this.textureConfig)}downloadFloat32MatrixFromBuffer(e,t){return q1(this.gl,e,t)}createBufferFromTexture(e,t,n){this.bindTextureToFrameBuffer(e);let s=H1(this.gl,t,n,this.textureConfig);return this.unbindTextureToFrameBuffer(),s}createAndWaitForFence(){let e=this.createFence(this.gl);return this.pollFence(e)}createFence(e){let t,n;if(K().getBool("WEBGL_FENCE_API_ENABLED")){let s=e,r=s.fenceSync(s.SYNC_GPU_COMMANDS_COMPLETE,0);e.flush(),n=()=>{let a=s.clientWaitSync(r,0,0);return a===s.ALREADY_SIGNALED||a===s.CONDITION_SATISFIED},t=r}else K().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION")>0?(t=this.beginQuery(),this.endQuery(),n=()=>this.isQueryAvailable(t,K().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION"))):n=()=>!0;return{query:t,isFencePassed:n}}downloadMatrixFromPackedTexture(e,t,n){return this.downloadMatrixDriver(e,()=>X1(this.gl,t,n))}createProgram(e){this.throwIfDisposed();let t=this.gl;this.vertexShader==null&&(this.vertexShader=F1(t));let n=l1(t);return fe(t,()=>t.attachShader(n,this.vertexShader)),fe(t,()=>t.attachShader(n,e)),c1(t,n),this.debug&&ld(t,n),this.vertexAttrsAreBound||(this.setProgram(n),this.vertexAttrsAreBound=W1(t,this.program,this.vertexBuffer)),n}deleteProgram(e){this.throwIfDisposed(),e===this.program&&(this.program=null),e!=null&&fe(this.gl,()=>this.gl.deleteProgram(e))}setProgram(e){this.throwIfDisposed(),this.program=e,this.program!=null&&this.debug&&ld(this.gl,this.program),fe(this.gl,()=>this.gl.useProgram(e))}getUniformLocation(e,t,n=!0){return this.throwIfDisposed(),n?b1(this.gl,e,t):y1(this.gl,e,t)}getAttributeLocation(e,t){return this.throwIfDisposed(),fe(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(),v1(this.gl,e,t,n)}setOutputMatrixTexture(e,t,n){this.setOutputMatrixTextureDriver(e,n,t)}setOutputPackedMatrixTexture(e,t,n){this.throwIfDisposed();let[s,r]=au(t,n);this.setOutputMatrixTextureDriver(e,s,r)}setOutputMatrixWriteRegion(e,t,n,s){this.setOutputMatrixWriteRegionDriver(n,e,s,t)}setOutputPackedMatrixWriteRegion(e,t,n,s){throw new Error("setOutputPackedMatrixWriteRegion not implemented.")}debugValidate(){this.program!=null&&ld(this.gl,this.program),Mu(this.gl)}executeProgram(){this.throwIfDisposed(),this.throwIfNoProgram();let e=this.gl;this.debug&&this.debugValidate(),fe(e,()=>e.drawElements(e.TRIANGLES,6,e.UNSIGNED_SHORT,0))}blockUntilAllProgramsCompleted(){this.throwIfDisposed(),fe(this.gl,()=>this.gl.finish())}getQueryTimerExtension(){return this.disjointQueryTimerExtension==null&&(this.disjointQueryTimerExtension=Lu(this.gl,K().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(K().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION")===2){let n=this.gl,s=this.getQueryTimerExtensionWebGL2(),r=n.createQuery();return n.beginQuery(s.TIME_ELAPSED_EXT,r),r}let e=this.getQueryTimerExtensionWebGL1(),t=e.createQueryEXT();return e.beginQueryEXT(e.TIME_ELAPSED_EXT,t),t}endQuery(){if(K().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION")===2){let t=this.gl,n=this.getQueryTimerExtensionWebGL2();t.endQuery(n.TIME_ELAPSED_EXT);return}let e=this.getQueryTimerExtensionWebGL1();e.endQueryEXT(e.TIME_ELAPSED_EXT)}async waitForQueryAndGetTime(e){return await w.repeatedTry(()=>this.disposed||this.isQueryAvailable(e,K().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION"))),this.getQueryTime(e,K().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION"))}getQueryTime(e,t){if(t===0)return null;if(t===2){let n=this.gl;return n.getQueryParameter(e,n.QUERY_RESULT)/1e6}else{let n=this.getQueryTimerExtensionWebGL1();return n.getQueryObjectEXT(e,n.QUERY_RESULT_EXT)/1e6}}isQueryAvailable(e,t){if(t===0)return!0;if(t===2){let n=this.gl,s=this.getQueryTimerExtensionWebGL2(),r=n.getQueryParameter(e,n.QUERY_RESULT_AVAILABLE);return this.disjoint==null&&(this.disjoint=this.gl.getParameter(s.GPU_DISJOINT_EXT)),r&&!this.disjoint}else{let n=this.getQueryTimerExtensionWebGL1(),s=n.getQueryObjectEXT(e,n.QUERY_RESULT_AVAILABLE_EXT);return this.disjoint==null&&(this.disjoint=this.gl.getParameter(n.GPU_DISJOINT_EXT)),s&&!this.disjoint}}pollFence(e){return new Promise(t=>{this.addItemToPoll(()=>e.isFencePassed(),()=>t())})}pollItems(){let e=pX(this.itemsToPoll.map(t=>t.isDoneFn));for(let t=0;t<=e;++t){let{resolveFn:n}=this.itemsToPoll[t];n()}this.itemsToPoll=this.itemsToPoll.slice(e+1)}addItemToPoll(e,t){this.itemsToPoll.push({isDoneFn:e,resolveFn:t}),!(this.itemsToPoll.length>1)&&w.repeatedTry(()=>(this.pollItems(),this.itemsToPoll.length===0))}bindTextureToFrameBuffer(e){this.throwIfDisposed(),cd(this.gl,e,this.framebuffer),this.debug&&Mu(this.gl)}unbindTextureToFrameBuffer(){this.outputTexture!=null?(cd(this.gl,this.outputTexture,this.framebuffer),this.debug&&Mu(this.gl)):Jm(this.gl,this.framebuffer)}downloadMatrixDriver(e,t){this.bindTextureToFrameBuffer(e);let n=t();return this.unbindTextureToFrameBuffer(),n}setOutputMatrixTextureDriver(e,t,n){this.throwIfDisposed();let s=this.gl;cd(s,e,this.framebuffer),this.debug&&Mu(s),this.outputTexture=e,fe(s,()=>s.viewport(0,0,t,n)),fe(s,()=>s.scissor(0,0,t,n))}setOutputMatrixWriteRegionDriver(e,t,n,s){this.throwIfDisposed(),fe(this.gl,()=>this.gl.scissor(e,t,n,s))}throwIfDisposed(){if(this.disposed)throw new Error("Attempted to use disposed GPGPUContext.")}throwIfNoProgram(){if(this.program==null)throw new Error("No GPU program is currently set.")}};function pX(e){let t=0;for(;t<e.length&&e[t]();++t);return t-1}var{addImpl:hX,bincountImpl:Y1,bincountReduceImpl:fX,ceilImpl:mX,concatImpl:gX,equalImpl:bX,expImpl:yX,expm1Impl:vX,floorImpl:xX,gatherNdImpl:wX,gatherV2Impl:kX,greaterImpl:SX,greaterEqualImpl:IX,lessImpl:CX,lessEqualImpl:NX,linSpaceImpl:TX,logImpl:$X,maxImpl:_X,maximumImpl:AX,minimumImpl:EX,multiplyImpl:RX,negImpl:DX,notEqualImpl:FX,prodImpl:OX,rangeImpl:PX,rsqrtImpl:zX,scatterImpl:LX,sigmoidImpl:MX,simpleAbsImpl:Q1,sliceImpl:BX,sparseFillEmptyRowsImpl:VX,sparseReshapeImpl:WX,sparseSegmentReductionImpl:Z1,sqrtImpl:UX,stridedSliceImpl:GX,stringNGramsImpl:HX,stringSplitImpl:qX,stringToHashBucketFastImpl:jX,subImpl:KX,tileImpl:XX,topKImpl:YX,transposeImpl:Rv,uniqueImpl:QX}=cv;function J1(e,t){return["x","y","z","w","u","v"].slice(0,t).map(n=>`${e}.${n}`)}function ln(e,t){return t===1?[e]:J1(e,t)}function ZX(e,t){if(e===1)return"rc";let n="";for(let s=0;s<e;s++)n+=t[s],s<e-1&&(n+=",");return n}var JX=class{constructor(e){if(this.variableNames=["A"],this.packedInputs=!1,this.packedOutput=!0,this.outputShape=e,this.rank=e.length,this.enableShapeUniforms=Sn(this.outputShape.length),this.rank===0)this.userCode=`
void main() {
setOutput(vec4(getA(), 0., 0., 0.));
}
`;else{let t=ln("rc",this.rank),n=rt(this.rank),s=this.getOutOfBoundsCondition(t),r=this.getSetup(t),a=this.getOutput(t);this.userCode=`
void main() {
${n} rc = getOutputCoords();
if(${s}) {
setOutput(vec4(0));
} else {
${r}
setOutput(vec4(${a}));
}
}
`}}getSourceCoordsArr(e){let t=[];for(let n=0;n<=1;n++)for(let s=0;s<=1;s++){let r=`${n===0?"r":"rp1"}, ${s===0?"c":"cp1"}`;for(let a=2;a<this.rank;a++)r=`${e[e.length-1-a]},`+r;t.push(r)}return t}getOutOfBoundsCondition(e){if(this.rank===1)return`rc > ${this.enableShapeUniforms?"outShape":this.outputShape[0]}`;let t="";for(let n=this.rank-2;n<this.rank;n++)t+=`${e[n]} >= ${this.enableShapeUniforms?`outShape[${n}]`:this.outputShape[n]}`,n<this.rank-1&&(t+="||");return t}getSetup(e){if(this.rank===1)return"";let t=e.slice(-2),n=this.enableShapeUniforms?`outShape[${this.rank} - 1]`:this.outputShape[this.rank-1],s=this.enableShapeUniforms?`outShape[${this.rank} - 2]`:this.outputShape[this.rank-2];return`
int r = ${t[0]};
int c = ${t[1]};
int rp1 = r + 1;
int cp1 = c + 1;
bool cEdge = cp1 >= ${n};
bool rEdge = rp1 >= ${s};
`}getOutput(e){let t=this.getSourceCoordsArr(e);return this.rank===1?`getA(rc), (rc + 1 >= ${this.enableShapeUniforms?"outShape":this.outputShape[0]} ? 0. : getA(rc + 1)), 0, 0`:`getA(${t[0]}),
cEdge ? 0. : getA(${t[1]}),
rEdge ? 0. : getA(${t[2]}),
rEdge || cEdge ? 0. : getA(${t[3]})`}},e2=class{constructor(e,t){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,this.customUniforms=[{name:"inputShape",type:"ivec3"}],this.outputShape=e,this.enableShapeUniforms=Sn(this.outputShape.length);let n="";for(let s=0;s<4;s++){let r="thisRC = rc;";s%2===1&&(r+="thisRC.z += 1;"),s>1&&(r+="thisRC.y += 1;"),n+=`
${r}
${s>0?"if(thisRC.y < rows && thisRC.z < cols){":""}
int flatIndex = getFlatIndex(thisRC);
ivec3 inputRC = inputCoordsFromReshapedOutCoords(flatIndex);
vec2 inputRCInnerDims = vec2(float(inputRC.y),float(inputRC.z));
result[${s}] =
getChannel(getA(inputRC.x, inputRC.y, inputRC.z), inputRCInnerDims);
${s>0?"}":""}
`}this.userCode=`
${e8(t,this.enableShapeUniforms)}
${this.enableShapeUniforms?Cv():Iv(e)}
void main() {
ivec3 rc = getOutputCoords();
vec4 result = vec4(0.);
ivec3 thisRC;
int rows = ${this.enableShapeUniforms?"outShape[1]":e[1]};
int cols = ${this.enableShapeUniforms?"outShape[2]":e[2]};
${n}
setOutput(result);
}
`}};function e8(e,t){return`
ivec3 inputCoordsFromReshapedOutCoords(int index) {
${t?vK(["r","c","d"],"inputShape"):wo(["r","c","d"],e)}
return ivec3(r, c, d);
}
`}var t8=class{constructor(e){this.gpgpu=e,this.numUsedTextures=0,this.numFreeTextures=0,this._numBytesAllocated=0,this._numBytesFree=0,this.freeTextures={},this.logEnabled=!1,this.usedTextures={}}acquireTexture(e,t,n){let s=gw(t,n),r=bw(e,s,n);r in this.freeTextures||(this.freeTextures[r]=[]),r in this.usedTextures||(this.usedTextures[r]=[]);let a=mw(e,s,this.gpgpu.gl,this.gpgpu.textureConfig,n);if(this.freeTextures[r].length>0){this.numFreeTextures--,this.numUsedTextures++,this._numBytesFree-=a,this.log();let i=this.freeTextures[r].shift();return this.usedTextures[r].push(i),i}let o;return s===3?o=this.gpgpu.createPackedMatrixTexture(e[0],e[1]):s===4?o=this.gpgpu.createFloat16PackedMatrixTexture(e[0],e[1]):s===1?o=this.gpgpu.createFloat32MatrixTexture(e[0],e[1]):s===0?o=this.gpgpu.createFloat16MatrixTexture(e[0],e[1]):s===2&&(o=this.gpgpu.createUnsignedBytesMatrixTexture(e[0],e[1])),this.usedTextures[r].push(o),this.numUsedTextures++,this._numBytesAllocated+=a,this.log(),o}releaseTexture(e,t,n,s){if(this.freeTextures==null)return;let r=gw(n,s),a=bw(t,r,s);a in this.freeTextures||(this.freeTextures[a]=[]);let o=mw(t,r,this.gpgpu.gl,this.gpgpu.textureConfig,s),i=K().get("WEBGL_DELETE_TEXTURE_THRESHOLD");i!==-1&&this._numBytesAllocated>i?(this.gpgpu.deleteMatrixTexture(e.texture),this._numBytesAllocated-=o):(this.freeTextures[a].push(e),this.numFreeTextures++,this._numBytesFree+=o),this.numUsedTextures--;let u=this.usedTextures[a],l=u.indexOf(e);if(l<0)throw new Error("Cannot release a texture that was never provided by this texture manager");u.splice(l,1),this.log()}log(){if(!this.logEnabled)return;let e=this.numFreeTextures+this.numUsedTextures;console.log("Free/Used",`${this.numFreeTextures} / ${this.numUsedTextures}`,`(${e})`);let t=this._numBytesFree/this._numBytesAllocated;console.log(`Bytes allocated: ${this._numBytesAllocated}`),console.log(`Bytes unused: ${this._numBytesFree} (${Math.round(100*t)}%)`)}get numBytesAllocated(){return this._numBytesAllocated}get numBytesFree(){return this._numBytesFree}getNumUsedTextures(){return this.numUsedTextures}getNumFreeTextures(){return this.numFreeTextures}dispose(){if(this.freeTextures!=null){for(let e in this.freeTextures)this.freeTextures[e].forEach(t=>{this.gpgpu.deleteMatrixTexture(t.texture)});for(let e in this.usedTextures)this.usedTextures[e].forEach(t=>{this.gpgpu.deleteMatrixTexture(t.texture)});this.freeTextures=null,this.usedTextures=null,this.numUsedTextures=0,this.numFreeTextures=0,this._numBytesAllocated=0,this._numBytesFree=0}}};function n8(e,t){let n=e;if(t===n.R32F)return 4;if(t===n.R16F)return 2;if(t===n.RGBA32F)return 16;if(t===e.RGBA)return 16;if(t===n.RGBA16F)return 8;if(t===n.RGBA8)return 4;throw new Error(`Unknown internal format ${t}`)}function mw(e,t,n,s,r){let a=s8(t,s),o;if(r){let[u,l]=au(e[0],e[1]);o=u*l}else{let[u,l]=Jl(e[0],e[1]);o=u*l}let i=n8(n,a);return o*i}function s8(e,t){switch(e){case 3:return Av(t);case 4:return Ev(t);case 1:return Tv(t);case 0:return $v(t);case 2:return _v(t);default:throw new Error(`Unknown physical texture type ${e}`)}}function r8(e){return K().getBool("WEBGL_RENDER_FLOAT32_ENABLED")?e?3:1:e?4:0}function gw(e,t){if(e===1)return 3;if(e===0||e==null)return r8(t);if(e===3||e===2)return 2;throw new Error(`Unknown logical texture type ${e}`)}function bw(e,t,n){return`${e[0]}_${e[1]}_${t}_${n}`}var Gs=class{constructor(e,t){this.variableNames=["A"],this.outputShape=e,this.enableShapeUniforms=Sn(this.outputShape.length),this.userCode=`
float unaryOperation(float x) {
${t}
}
void main() {
float x = getAAtOutCoords();
float y = unaryOperation(x);
setOutput(y);
}
`}},ss="if (isnan(x)) return x;",a8="return x;",yw="return abs(x);",o8="return (x >= 0.0) ? x : (exp(x) - 1.0);",i8=ss+`
return (x < 0.0) ? 0.0 : x;
`,u8=ss+`
return (x < 0.0) ? 0.0 : min(6.0, x);
`,Vo="return x;",l8="return 1.0 / (1.0 + exp(-1.0 * x));",c8="return x;",d8=`
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;
`,p8=`
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;
`,h8=`
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;
`,f8="return 1.0 / (1.0 + exp(-1.0 * x));",ea=class{constructor(e,t){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=e,this.enableShapeUniforms=Sn(this.outputShape.length),this.userCode=`
vec4 unaryOperation(vec4 x) {
${t}
}
void main() {
vec4 x = getAAtOutCoords();
vec4 y = unaryOperation(x);
setOutput(y);
}
`}},m8=class{constructor(e){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!1,this.outputShape=e,this.enableShapeUniforms=Sn(this.outputShape.length);let t=e.length,n=ln("rc",t),s=rt(t),r=ZX(t,n),a=n.slice(-2),o=t<=1?"rc":`vec2(${a.join(",")})`;this.userCode=`
void main() {
${s} rc = getOutputCoords();
vec4 packedInput = getA(${r});
setOutput(getChannel(packedInput, ${o}));
}
`}},g8=ws.whereImpl,b8=1e-7,y8=1e-4,sd={};function v8(e){return e in sd||(sd[e]={}),sd[e]}var x8=K().getNumber("CPU_HANDOFF_SIZE_THRESHOLD"),w8=600;function k8(){return K().global.screen==null?1024:K().global.screen.height*K().global.screen.width*window.devicePixelRatio*w8/1024/1024}var t2=class extends il{constructor(e){if(super(),this.pendingRead=new WeakMap,this.pendingDisposal=new WeakSet,this.dataRefCount=new WeakMap,this.numBytesInGPU=0,this.uploadWaitMs=0,this.downloadWaitMs=0,this.lastGlFlushTime=0,this.warnedAboutMemory=!1,this.pendingDeletes=0,this.disposed=!1,!K().getBool("HAS_WEBGL"))throw new Error("WebGL is not supported on this device");let t;if(e!=null){if(e instanceof am)t=e;else{let n=xs(K().getNumber("WEBGL_VERSION"),e);t=new am(n)}this.binaryCache={},this.gpgpuCreatedLocally=!1}else{let n=xs(K().getNumber("WEBGL_VERSION"));t=new am(n),this.binaryCache=v8(K().getNumber("WEBGL_VERSION")),this.gpgpuCreatedLocally=!0}this.gpgpu=t,this.canvas=this.gpgpu.gl.canvas,this.textureManager=new t8(this.gpgpu),this.numMBBeforeWarning=k8(),this.texData=new Zd(this,ds())}nextDataId(){return t2.nextDataId++}numDataIds(){return this.texData.numDataIds()-this.pendingDeletes}write(e,t,n){if((K().getBool("WEBGL_CHECK_NUMERICAL_PROBLEMS")||K().getBool("DEBUG"))&&this.checkNumericalProblems(e),n==="complex64"&&e!=null)throw new Error("Cannot write to a complex64 dtype. Please use tf.complex(real, imag).");let s={id:this.nextDataId()};return this.texData.set(s,{shape:t,dtype:n,values:e,usage:1,refCount:1}),s}refCount(e){return this.texData.has(e)?this.texData.get(e).refCount:0}incRef(e){let t=this.texData.get(e);t.refCount++}decRef(e){if(this.texData.has(e)){let t=this.texData.get(e);t.refCount--}}move(e,t,n,s,r){if(K().getBool("DEBUG")&&this.checkNumericalProblems(t),s==="complex64")throw new Error("Cannot write to a complex64 dtype. Please use tf.complex(real, imag).");this.texData.set(e,{shape:n,dtype:s,values:t,usage:1,refCount:r})}disposeIntermediateTensorInfo(e){this.disposeData(e.dataId)}readSync(e){let t=this.texData.get(e),{values:n,dtype:s,complexTensorInfos:r,slice:a,shape:o,isPacked:i}=t;if(a!=null){let p;i?p=new ea(o,Vo):p=new Gs(o,Vo);let d=this.runWebGLProgram(p,[{dataId:e,shape:o,dtype:s}],s),h=this.readSync(d.dataId);return this.disposeIntermediateTensorInfo(d),h}if(n!=null)return this.convertAndCacheOnCPU(e);if(s==="string")return n;let u=this.activeTimers!=null,l;u&&(l=w.now());let c;if(s==="complex64"){let p=this.readSync(r.real.dataId),d=this.readSync(r.imag.dataId);c=C.mergeRealAndImagArrays(p,d)}else c=this.getValuesFromTexture(e);return u&&(this.downloadWaitMs+=w.now()-l),this.convertAndCacheOnCPU(e,c)}async read(e){if(this.pendingRead.has(e)){let h=this.pendingRead.get(e);return new Promise(f=>h.push(f))}let t=this.texData.get(e),{values:n,shape:s,slice:r,dtype:a,complexTensorInfos:o,isPacked:i}=t;if(r!=null){let h;i?h=new ea(s,Vo):h=new Gs(s,Vo);let f=this.runWebGLProgram(h,[{dataId:e,shape:s,dtype:a}],a),m=this.read(f.dataId);return this.disposeIntermediateTensorInfo(f),m}if(n!=null)return this.convertAndCacheOnCPU(e);if(K().getBool("DEBUG")&&!K().getBool("WEBGL_DOWNLOAD_FLOAT_ENABLED")&&K().getNumber("WEBGL_VERSION")===2)throw new Error("tensor.data() with WEBGL_DOWNLOAD_FLOAT_ENABLED=false and WEBGL_VERSION=2 not yet supported.");let u=null,l;if(a!=="complex64"&&K().get("WEBGL_BUFFER_SUPPORTED")){l=this.decode(e);let h=this.texData.get(l.dataId);u=this.gpgpu.createBufferFromTexture(h.texture.texture,...td(s))}this.pendingRead.set(e,[]),a!=="complex64"&&await this.gpgpu.createAndWaitForFence();let c;if(a==="complex64"){let h=await Promise.all([this.read(o.real.dataId),this.read(o.imag.dataId)]),f=h[0],m=h[1];c=C.mergeRealAndImagArrays(f,m)}else if(u==null)c=this.getValuesFromTexture(e);else{let h=w.sizeFromShape(s);c=this.gpgpu.downloadFloat32MatrixFromBuffer(u,h)}if(l!=null&&this.disposeIntermediateTensorInfo(l),u!=null){let h=this.gpgpu.gl;fe(h,()=>h.deleteBuffer(u))}let p=this.convertAndCacheOnCPU(e,c),d=this.pendingRead.get(e);return this.pendingRead.delete(e),d.forEach(h=>h(p)),this.pendingDisposal.has(e)&&(this.pendingDisposal.delete(e),this.disposeData(e)&&ds().removeDataId(e,this),this.pendingDeletes--),p}readToGPU(e,t={}){let n=this.texData.get(e),{values:s,shape:r,slice:a,dtype:o,isPacked:i,texture:u}=n;if(o==="complex64")throw new Error("Does not support reading texture for complex64 dtype.");if(a!=null){let d;i?d=new ea(r,Vo):d=new Gs(r,Vo);let h=this.runWebGLProgram(d,[{dataId:e,shape:r,dtype:o}],o),f=this.readToGPU(h,t);return this.disposeIntermediateTensorInfo(h),f}if(u==null)throw s!=null?new Error("Data is not on GPU but on CPU."):new Error("There is no data on GPU or CPU.");let l=this.decode(e,t.customTexShape),c=ds().makeTensorFromTensorInfo(l),p=this.texData.get(l.dataId);return{tensorRef:c,...p.texture}}bufferSync(e){let t=this.readSync(e.dataId);if(e.dtype==="string")try{let n=t.map(s=>w.decodeString(s));return Ae(e.shape,e.dtype,n)}catch(n){throw new Error("Failed to decode encoded string bytes into utf-8")}return Ae(e.shape,e.dtype,t)}checkNumericalProblems(e){if(e!=null)for(let t=0;t<e.length;t++){let n=e[t];if(!a1(n))throw K().getBool("WEBGL_RENDER_FLOAT32_CAPABLE")?Error(`The value ${n} cannot be represented with your current settings. Consider enabling float32 rendering: 'tf.env().set('WEBGL_RENDER_FLOAT32_ENABLED', true);'`):Error(`The value ${n} cannot be represented on this device.`)}}getValuesFromTexture(e){let{shape:t,dtype:n,isPacked:s}=this.texData.get(e),r=w.sizeFromShape(t);if(K().getBool("WEBGL_DOWNLOAD_FLOAT_ENABLED")){let p=this.decode(e),d=this.texData.get(p.dataId),h=this.gpgpu.downloadMatrixFromPackedTexture(d.texture.texture,...td(t)).subarray(0,r);return this.disposeIntermediateTensorInfo(p),h}let a=K().getBool("WEBGL_PACK")&&s===!0,o=a?dd(t):t,i=a?new uX(o):new iX(o),u=this.runWebGLProgram(i,[{shape:o,dtype:n,dataId:e}],"float32"),l=this.texData.get(u.dataId),c=this.gpgpu.downloadByteEncodedFloatMatrixFromOutputTexture(l.texture.texture,l.texShape[0],l.texShape[1]).subarray(0,r);return this.disposeIntermediateTensorInfo(u),c}timerAvailable(){return K().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE")>0}time(e){let t=this.activeTimers,n=[],s=!1;this.programTimersStack==null?(this.programTimersStack=n,s=!0):this.activeTimers.push(n),this.activeTimers=n,e();let r=w.flatten(this.activeTimers.map(i=>i.query)).filter(i=>i!=null),a=w.flatten(this.activeTimers.map(i=>i.name)).filter(i=>i!=null);this.activeTimers=t,s&&(this.programTimersStack=null);let o={uploadWaitMs:this.uploadWaitMs,downloadWaitMs:this.downloadWaitMs,kernelMs:null,wallMs:null};return(async()=>{if(K().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE")>0){let i=await Promise.all(r);o.kernelMs=w.sum(i),o.getExtraProfileInfo=()=>i.map((u,l)=>({name:a[l],ms:u})).map(u=>`${u.name}: ${u.ms}`).join(", ")}else o.kernelMs={error:"WebGL query timers are not supported in this environment."};return this.uploadWaitMs=0,this.downloadWaitMs=0,o})()}memory(){return{unreliable:!1,numBytesInGPU:this.numBytesInGPU,numBytesInGPUAllocated:this.textureManager.numBytesAllocated,numBytesInGPUFree:this.textureManager.numBytesFree}}startTimer(){return K().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE")>0?this.gpgpu.beginQuery():{startMs:w.now(),endMs:null}}endTimer(e){return K().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE")>0?(this.gpgpu.endQuery(),e):(e.endMs=w.now(),e)}async getQueryTime(e){if(K().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE")>0)return this.gpgpu.waitForQueryAndGetTime(e);let t=e;return t.endMs-t.startMs}disposeData(e,t=!1){if(this.pendingDisposal.has(e))return!1;if(!this.texData.has(e))return!0;if(t?this.texData.get(e).refCount=0:this.texData.get(e).refCount--,!t&&this.texData.get(e).refCount>0)return!1;if(this.pendingRead.has(e))return this.pendingDisposal.add(e),this.pendingDeletes++,!1;this.releaseGPUData(e);let{complexTensorInfos:n}=this.texData.get(e);return n!=null&&(this.disposeData(n.real.dataId,t),this.disposeData(n.imag.dataId,t)),this.texData.delete(e),!0}releaseGPUData(e){let{texture:t,dtype:n,texShape:s,usage:r,isPacked:a,slice:o}=this.texData.get(e),i=o&&o.origDataId||e,u=this.dataRefCount.get(i);u>1?this.dataRefCount.set(i,u-1):(this.dataRefCount.delete(i),t!=null&&(this.numBytesInGPU-=this.computeBytes(s,n),this.textureManager.releaseTexture(t,s,r,a)));let l=this.texData.get(e);l.texture=null,l.texShape=null,l.isPacked=!1,l.slice=null}getTexture(e){return this.uploadToGPU(e),this.texData.get(e).texture.texture}getDataInfo(e){return this.texData.get(e)}shouldExecuteOnCPU(e,t=x8){return K().getBool("WEBGL_CPU_FORWARD")&&e.every(n=>this.texData.get(n.dataId).texture==null&&w.sizeFromShape(n.shape)<t)}getGPGPUContext(){return this.gpgpu}where(e){C.warn("tf.where() in webgl locks the UI thread. Call tf.whereAsync() instead");let t=e.dataSync();return g8(e.shape,t)}packedUnaryOp(e,t,n){let s=new ea(e.shape,t),r=this.compileAndRun(s,[e],n);return ds().makeTensorFromTensorInfo(r)}abs(e){if(this.shouldExecuteOnCPU([e])&&e.dtype!=="complex64"){let s=Q1(this.texData.get(e.dataId).values);return this.makeOutput(e.shape,e.dtype,s)}if(K().getBool("WEBGL_PACK_UNARY_OPERATIONS"))return this.packedUnaryOp(e,yw,e.dtype);let t=new Gs(e.shape,yw),n=this.compileAndRun(t,[e]);return ds().makeTensorFromTensorInfo(n)}makeTensorInfo(e,t,n){let s;if(t==="string"&&n!=null&&n.length>0&&w.isString(n[0])){let r=n.map(a=>w.encodeString(a));s=this.write(r,e,t)}else s=this.write(n,e,t);return this.texData.get(s).usage=null,{dataId:s,shape:e,dtype:t}}makeOutput(e,t,n){return ds().makeTensorFromTensorInfo(this.makeTensorInfo(e,t,n),this)}unpackTensor(e){let t=new m8(e.shape);return this.runWebGLProgram(t,[e],e.dtype)}packTensor(e){let t=new JX(e.shape),n=!0;return this.runWebGLProgram(t,[e],e.dtype,null,n)}packedReshape(e,t){let n=[ya(e.shape),...va(e.shape)],s={dtype:e.dtype,shape:n,dataId:e.dataId},r=[ya(t),...va(t)],a=new e2(r,n),o=!0,i=[n],u=this.runWebGLProgram(a,[s],e.dtype,i,o);return{dataId:u.dataId,shape:t,dtype:u.dtype}}decode(e,t){let n=this.texData.get(e),{isPacked:s,shape:r,dtype:a}=n;if(t!=null){let p=w.sizeFromShape(r),d=t[0]*t[1]*4;w.assert(p<=d,()=>"customTexShape is too small. Row * Column * 4 should be equal or larger than the size of the tensor data.")}let o=dd(r),i;s?i=new oX(o):i=new aX(o);let u=!0,l=[t!=null?t:td(o)],c=this.runWebGLProgram(i,[{shape:o,dtype:a,dataId:e}],a,l,u,t);return{dtype:a,shape:r,dataId:c.dataId}}runWebGLProgram(e,t,n,s,r=!1,a){let o=this.makeTensorInfo(e.outputShape,n),i=this.texData.get(o.dataId);if(e.packedOutput&&(i.isPacked=!0),e.outPackingScheme===0){let g=a!=null?a:td(e.outputShape);i.texShape=g.map(b=>b*2)}if(e.outTexUsage!=null&&(i.usage=e.outTexUsage),w.sizeFromShape(o.shape)===0)return i.values=w.getTypedArrayFromDType(o.dtype,0),o;let u=[],l=t.map(g=>{if(g.dtype==="complex64")throw new Error("GPGPUProgram does not support complex64 input. For complex64 dtypes, please separate the program into real and imaginary parts.");let b=this.texData.get(g.dataId);if(b.texture==null){if(!e.packedInputs&&w.sizeFromShape(g.shape)<=K().getNumber("WEBGL_SIZE_UPLOAD_UNIFORM"))return{shape:g.shape,texData:null,isUniform:!0,uniformValues:b.values};e.packedInputs&&(b.isPacked=!0,b.shape=g.shape)}if(this.uploadToGPU(g.dataId),!!b.isPacked!=!!e.packedInputs)g=b.isPacked?this.unpackTensor(g):this.packTensor(g),u.push(g),b=this.texData.get(g.dataId);else if(b.isPacked&&!al(b.shape,g.shape)){let y=g,v=g.shape;g.shape=b.shape,g=this.packedReshape(g,v),u.push(g),b=this.texData.get(g.dataId),y.shape=v}return{shape:g.shape,texData:b,isUniform:!1}});this.uploadToGPU(o.dataId);let c={shape:o.shape,texData:i,isUniform:!1},p=rX(e,l,c),d=this.getAndSaveBinary(p,()=>nX(this.gpgpu,e,l,c)),h=this.activeTimers!=null,f;h&&(f=this.startTimer()),K().get("ENGINE_COMPILE_ONLY")||sX(this.gpgpu,d,l,c,s),u.forEach(g=>this.disposeIntermediateTensorInfo(g)),h&&(f=this.endTimer(f),this.activeTimers.push({name:e.constructor.name,query:this.getQueryTime(f)}));let m=K().get("WEBGL_FLUSH_THRESHOLD");if(m>0){let g=w.now();g-this.lastGlFlushTime>m&&(this.gpgpu.gl.flush(),this.lastGlFlushTime=g)}if(!K().getBool("WEBGL_LAZILY_UNPACK")&&i.isPacked&&r===!1){let g=this.unpackTensor(o);return this.disposeIntermediateTensorInfo(o),g}return o}compileAndRun(e,t,n,s,r=!1){return n=n||t[0].dtype,this.runWebGLProgram(e,t,n,s,r)}getAndSaveBinary(e,t){return e in this.binaryCache||(this.binaryCache[e]=t()),this.binaryCache[e]}getTextureManager(){return this.textureManager}dispose(){this.disposed||(K().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=q(()=>{if(!K().get("WEBGL_RENDER_FLOAT32_ENABLED")){let e=K().getBool("DEBUG");K().set("DEBUG",!1);let t=this.abs(we(1e-8)).dataSync()[0];if(K().set("DEBUG",e),t>0)return 32}return 16})),this.floatPrecisionValue}epsilon(){return this.floatPrecision()===32?b8:y8}uploadToGPU(e){let t=this.texData.get(e),{shape:n,dtype:s,values:r,texture:a,usage:o,isPacked:i}=t;if(a!=null)return;let u=this.activeTimers!=null,l;u&&(l=w.now());let c=t.texShape;if(c==null&&(c=k1(n,i),t.texShape=c),r!=null){let p=dd(n),d,h=c[1],f=c[0],m=r instanceof Uint8Array||r instanceof Uint8ClampedArray;(i||!m)&&([h,f]=au(c[0],c[1])),i?d=new cX(p,m):d=new lX(p,m);let g=m?[f,h]:c,b=this.makeTensorInfo(g,s),y=this.texData.get(b.dataId);m?y.usage=2:y.usage=1,y.texShape=g,this.gpgpu.uploadDenseMatrixToTexture(this.getTexture(b.dataId),h,f,r);let v=[[f,h]],x=!0,k=this.runWebGLProgram(d,[b],s,v,x),I=this.texData.get(k.dataId);t.texShape=I.texShape,t.isPacked=I.isPacked,t.usage=I.usage,K().get("ENGINE_COMPILE_ONLY")?this.disposeData(k.dataId):(t.texture=I.texture,t.values=null,this.texData.delete(k.dataId)),this.disposeIntermediateTensorInfo(b),u&&(this.uploadWaitMs+=w.now()-l)}else{let p=this.acquireTexture(c,o,s,i);t.texture=p}}convertAndCacheOnCPU(e,t){let n=this.texData.get(e),{dtype:s}=n;return this.releaseGPUData(e),t!=null&&(n.values=S8(t,s)),n.values}acquireTexture(e,t,n,s){if(this.numBytesInGPU+=this.computeBytes(e,n),!this.warnedAboutMemory&&this.numBytesInGPU>this.numMBBeforeWarning*1024*1024){let r=(this.numBytesInGPU/1024/1024).toFixed(2);this.warnedAboutMemory=!0,console.warn(`High memory usage in GPU: ${r} MB, most likely due to a memory leak`)}return this.textureManager.acquireTexture(e,t,s)}computeBytes(e,t){return e[0]*e[1]*w.bytesPerElement(t)}checkCompileCompletion(){for(let[,e]of Object.entries(this.binaryCache))this.checkCompletion_(e)}async checkCompileCompletionAsync(){let e=[];if(this.gpgpu.parallelCompilationExtension){for(let[,t]of Object.entries(this.binaryCache))e.push(this.checkCompletionAsync_(t));return Promise.all(e)}else{for(let[,t]of Object.entries(this.binaryCache)){let n=new Promise(s=>{try{this.checkCompletion_(t),s(!0)}catch(r){throw r}});e.push(n)}return Promise.all(e)}}async checkCompletionAsync_(e){return this.gpgpu.gl.getProgramParameter(e.webGLProgram,this.gpgpu.parallelCompilationExtension.COMPLETION_STATUS_KHR)?this.checkCompletion_(e):(await ZS(),this.checkCompletionAsync_(e))}checkCompletion_(e){if(this.gpgpu.gl.getProgramParameter(e.webGLProgram,this.gpgpu.gl.LINK_STATUS)===!1)throw console.log(this.gpgpu.gl.getProgramInfoLog(e.webGLProgram)),this.gpgpu.gl.getShaderParameter(e.fragmentShader,this.gpgpu.gl.COMPILE_STATUS)===!1?(Sv(e.source,this.gpgpu.gl.getShaderInfoLog(e.fragmentShader)),new Error("Failed to compile fragment shader.")):new Error("Failed to link vertex and fragment shaders.");return!0}getUniformLocations(){for(let[,e]of Object.entries(this.binaryCache)){let{uniformLocations:t,customUniformLocations:n,infLoc:s,nanLoc:r,inShapesLocations:a,inTexShapesLocations:o,outShapeLocation:i,outShapeStridesLocation:u,outTexShapeLocation:l}=D1(this.gpgpu,e.program,e.webGLProgram);e.uniformLocations=t,e.customUniformLocations=n,e.infLoc=s,e.nanLoc=r,e.inShapesLocations=a,e.inTexShapesLocations=o,e.outShapeLocation=i,e.outShapeStridesLocation=u,e.outTexShapeLocation=l}}},n2=t2;n2.nextDataId=0;function S8(e,t){if(t==="float32"||t==="complex64")return e;if(t==="int32"||t==="bool"){let n=t==="int32"?new Int32Array(e.length):new Uint8Array(e.length);for(let s=0;s<n.length;++s)n[s]=Math.round(e[s]);return n}else throw new Error(`Unknown dtype ${t}`)}var Phe="0.0.0";function I8(){K().set("WEBGL_FORCE_F16_TEXTURES",!0)}vp.isBrowser()&&xp("webgl",()=>new n2,2);var zhe={forceHalfFloat:I8},s2=`
if (isnan(a)) return a;
if (isnan(b)) return b;
`,li=class{constructor(e,t,n){this.variableNames=["A","B"],this.outputShape=C.assertAndGetBroadcastShape(t,n),this.enableShapeUniforms=Sn(this.outputShape.length),this.userCode=`
float binaryOperation(float a, float b) {
${e}
}
void main() {
float a = getAAtOutCoords();
float b = getBAtOutCoords();
setOutput(binaryOperation(a, b));
}
`}},nh=`
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;
`,tc=class{constructor(e,t,n,s=!1){this.variableNames=["A","B"],this.supportsBroadcasting=!0,this.packedInputs=!0,this.packedOutput=!0,this.outputShape=C.assertAndGetBroadcastShape(t,n);let r=this.outputShape.length;this.enableShapeUniforms=Sn(r);let a="";if(s)if(r===0||w.sizeFromShape(this.outputShape)===1)a=`
result.y = 0.;
result.z = 0.;
result.w = 0.;
`;else if(a=`
${rt(r)} coords = getOutputCoords();
`,r===1)this.enableShapeUniforms?a+=`
result.y = (coords + 1) >= outShape ? 0. : result.y;
result.z = 0.;
result.w = 0.;
`:a+=`
result.y = (coords + 1) >= ${this.outputShape[0]} ? 0. : result.y;
result.z = 0.;
result.w = 0.;
`;else{let i=ln("coords",r);this.enableShapeUniforms?a+=`
bool nextRowOutOfBounds =
(${i[r-2]} + 1) >= outShape[${r} - 2];
bool nextColOutOfBounds =
(${i[r-1]} + 1) >= outShape[${r} - 1];
result.y = nextColOutOfBounds ? 0. : result.y;
result.z = nextRowOutOfBounds ? 0. : result.z;
result.w = nextColOutOfBounds || nextRowOutOfBounds ? 0. : result.w;
`:a+=`
bool nextRowOutOfBounds =
(${i[r-2]} + 1) >= ${this.outputShape[r-2]};
bool nextColOutOfBounds =
(${i[r-1]} + 1) >= ${this.outputShape[r-1]};
result.y = nextColOutOfBounds ? 0. : result.y;
result.z = nextRowOutOfBounds ? 0. : result.z;
result.w = nextColOutOfBounds || nextRowOutOfBounds ? 0. : result.w;
`}this.userCode=`
vec4 binaryOperation(vec4 a, vec4 b) {
${e}
}
void main() {
vec4 a = getAAtOutCoords();
vec4 b = getBAtOutCoords();
vec4 result = binaryOperation(a, b);
${a}
setOutput(result);
}
`}};function Rn(e){let{inputs:t,backend:n}=e,{x:s}=t;return n.incRef(s.dataId),{dataId:s.dataId,shape:s.shape,dtype:s.dtype}}var C8={kernelName:Wa,backendName:"webgl",kernelFunc:Rn};function Fr(e){let{inputs:t,backend:n}=e,{real:s,imag:r}=t,a=n.makeTensorInfo(s.shape,"complex64"),o=n.texData.get(a.dataId),i=Rn({inputs:{x:s},backend:n}),u=Rn({inputs:{x:r},backend:n});return o.complexTensorInfos={real:i,imag:u},a}var N8={kernelName:np,backendName:"webgl",kernelFunc:Fr},r2="return (a < 0.) ? b * a : a;",a2=`
vec4 aLessThanZero = vec4(lessThan(a, vec4(0.)));
return (aLessThanZero * (b * a)) + ((vec4(1.0) - aLessThanZero) * a);
`;function T8(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{alpha:a}=s,o=n.makeTensorInfo([],"float32",w.createScalarValue(a,"float32")),i=K().getBool("WEBGL_PACK_BINARY_OPERATIONS")?new tc(a2,r.shape,o.shape):new li(r2,r.shape,o.shape),u=n.runWebGLProgram(i,[r,o],"float32");return n.disposeIntermediateTensorInfo(o),u}var $8={kernelName:Ua,backendName:"webgl",kernelFunc:T8},o2="return (a < 0.) ? b * a : a;",i2=`
vec4 aLessThanZero = vec4(lessThan(a, vec4(0.)));
return (aLessThanZero * (b * a)) + ((vec4(1.0) - aLessThanZero) * a);
`;function _8(e){let{inputs:t,backend:n}=e,{x:s,alpha:r}=t,a=K().getBool("WEBGL_PACK_BINARY_OPERATIONS")?new tc(i2,s.shape,r.shape):new li(o2,s.shape,r.shape);return n.runWebGLProgram(a,[s,r],"float32")}var A8={kernelName:to,backendName:"webgl",kernelFunc:_8},du="if (isnan(x)) return x;",E8=`
if (isnan(a)) return a;
if (isnan(b)) return b;
`,R8=`
result.r = isNaN.r > 0. ? NAN : result.r;
result.g = isNaN.g > 0. ? NAN : result.g;
result.b = isNaN.b > 0. ? NAN : result.b;
result.a = isNaN.a > 0. ? NAN : result.a;
`;function Ke({opSnippet:e,packedOpSnippet:t,cpuKernelImpl:n,dtype:s}){return({inputs:r,backend:a})=>{let{x:o}=r,i=a,u=s||o.dtype;if(i.shouldExecuteOnCPU([o])&&n!=null){let p=i.texData.get(o.dataId),d=n(p.values,u);return i.makeTensorInfo(o.shape,u,d)}let l=K().getBool("WEBGL_PACK_UNARY_OPERATIONS")&&t!=null,c;return l?c=new ea(o.shape,t):c=new Gs(o.shape,e),i.runWebGLProgram(c,[o],u)}}function jt({opSnippet:e,packedOpSnippet:t,checkOutOfBounds:n=!1,supportsComplex:s=!1,cpuKernelImpl:r,dtype:a}){return({inputs:o,backend:i})=>{let{a:u,b:l}=o,c=i;if(s&&u.dtype==="complex64"){let f=c.texData.get(u.dataId),m=c.texData.get(l.dataId),[g,b]=[[f.complexTensorInfos.real,m.complexTensorInfos.real],[f.complexTensorInfos.imag,m.complexTensorInfos.imag]].map(v=>{let[x,k]=v,I={dataId:x.dataId,dtype:x.dtype,shape:u.shape},$={dataId:k.dataId,dtype:k.dtype,shape:l.shape},R=new li(e,u.shape,l.shape);return c.runWebGLProgram(R,[I,$],cn(x.dtype,k.dtype))}),y=Fr({inputs:{real:g,imag:b},backend:c});return c.disposeIntermediateTensorInfo(g),c.disposeIntermediateTensorInfo(b),y}let p=a||cn(u.dtype,l.dtype);if((u.dtype==="string"||l.dtype==="string"||c.shouldExecuteOnCPU([u,l]))&&r!=null){let f=c.texData.get(u.dataId).values,m=c.texData.get(l.dataId).values,g=u.dtype==="string"?C.fromUint8ToStringArray(f):f,b=u.dtype==="string"?C.fromUint8ToStringArray(m):m,[y,v]=r(u.shape,l.shape,g,b,p),x=c.makeTensorInfo(v,p),k=c.texData.get(x.dataId);return k.values=y,x}let d=K().getBool("WEBGL_PACK_BINARY_OPERATIONS")&&t!=null,h;return d?h=new tc(t,u.shape,l.shape,n):h=new li(e,u.shape,l.shape),c.runWebGLProgram(h,[u,l],p)}}function sh(e,t=!1){if(e==="linear")return t?c8:a8;if(e==="relu")return t?p8:i8;if(e==="elu")return t?d8:o8;if(e==="relu6")return t?h8:u8;if(e==="prelu")return t?i2:o2;if(e==="leakyrelu")return t?a2:r2;if(e==="sigmoid")return t?f8:l8;throw new Error(`Activation ${e} has not been implemented for the WebGL backend.`)}var u2=class{constructor(e,t,n,s=!1,r=!1,a=!1,o=null,i=!1,u=!1){this.variableNames=["matrixA","matrixB"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=n,this.enableShapeUniforms=Sn(this.outputShape.length);let l=s?e[1]:e[2],c=Math.ceil(l/2),p=s?"i * 2, rc.y":"rc.y, i * 2",d=r?"rc.z, i * 2":"i * 2, rc.z",h=s?["a.xxyy","a.zzww"]:["a.xxzz","a.yyww"],f=r?["b.xzxz","b.ywyw"]:["b.xyxy","b.zwzw"],m="",g="";o&&(i?m=`vec4 activation(vec4 a) {
vec4 b = getPreluActivationWeightsAtOutCoords();
${o}
}`:u?m=`vec4 activation(vec4 a) {
vec4 b = getLeakyreluAlphaAtOutCoords();
${o}
}`:m=`vec4 activation(vec4 x) {
${o}
}`,g="result = activation(result);");let b=a?"result += getBiasAtOutCoords();":"";a&&this.variableNames.push("bias"),i&&this.variableNames.push("preluActivationWeights"),u&&this.variableNames.push("leakyreluAlpha");let y="rc.x",v="rc.x";e[0]<t[0]?y=`int(min(float(rc.x), ${e[0]-1}.))`:t[0]<e[0]&&(v=`int(min(float(rc.x), ${t[0]-1}.))`),this.userCode=`
${m}
// Don't use uniform for sharedDimensionPacked for performance.
const float sharedDimension = ${c}.0;
vec4 dot2x2ARowBCol(ivec3 rc) {
vec4 result = vec4(0);
for (int i = 0; i < ${c}; i++) {
int batchA = ${y};
int batchB = ${v};
vec4 a = getMatrixA(batchA, ${p});
vec4 b = getMatrixB(batchB, ${d});
// These swizzled products need to be separately added.
// See: https://github.com/tensorflow/tfjs/issues/1735
result += (${h[0]} * ${f[0]});
result += (${h[1]} * ${f[1]});
}
return result;
}
void main() {
ivec3 rc = getOutputCoords();
vec4 result = dot2x2ARowBCol(rc);
${b}
${g}
setOutput(result);
}
`}},vw={REAL:"return areal * breal - aimag * bimag;",IMAG:"return areal * bimag + aimag * breal;"},xw=class{constructor(e,t,n){this.variableNames=["AReal","AImag","BReal","BImag"],this.outputShape=C.assertAndGetBroadcastShape(t,n),this.userCode=`
float binaryOpComplex(
float areal, float aimag, float breal, float bimag) {
${e}
}
void main() {
float areal = getARealAtOutCoords();
float aimag = getAImagAtOutCoords();
float breal = getBRealAtOutCoords();
float bimag = getBImagAtOutCoords();
setOutput(binaryOpComplex(areal, aimag, breal, bimag));
}
`}},ww="return a * b;";function Dv(e){let{inputs:t,backend:n}=e,{a:s,b:r}=t,a=C.upcastType(s.dtype,r.dtype);if(s.dtype==="complex64"){let i=n.texData.get(s.dataId),u=n.texData.get(r.dataId),l=new xw(vw.REAL,s.shape,r.shape),c=new xw(vw.IMAG,s.shape,r.shape),p=[{dataId:i.complexTensorInfos.real.dataId,dtype:i.complexTensorInfos.real.dtype,shape:s.shape},{dataId:i.complexTensorInfos.imag.dataId,dtype:i.complexTensorInfos.imag.dtype,shape:s.shape},{dataId:u.complexTensorInfos.real.dataId,dtype:u.complexTensorInfos.real.dtype,shape:r.shape},{dataId:u.complexTensorInfos.imag.dataId,dtype:u.complexTensorInfos.imag.dtype,shape:r.shape}],d=n.runWebGLProgram(l,p,"float32"),h=n.runWebGLProgram(c,p,"float32"),f=Fr({inputs:{real:d,imag:h},backend:n});return n.disposeIntermediateTensorInfo(d),n.disposeIntermediateTensorInfo(h),f}if(n.shouldExecuteOnCPU([s,r])){let i=n.texData.get(s.dataId),u=n.texData.get(r.dataId),[l,c]=RX(s.shape,r.shape,i.values,u.values,a),p=n.makeTensorInfo(c,a),d=n.texData.get(p.dataId);return d.values=l,p}let o;return K().getBool("WEBGL_PACK_BINARY_OPERATIONS")?o=new tc(ww,s.shape,r.shape):o=new li(ww,s.shape,r.shape),n.runWebGLProgram(o,[s,r],a)}var D8={kernelName:Za,backendName:"webgl",kernelFunc:Dv};function F8(e,t,n){let s=[ya(e.shape),...va(e.shape)],r={dtype:e.dtype,shape:s,dataId:e.dataId},a=[ya(t),...va(t)],o=new e2(a,s),i=!0,u=[s],l=n.runWebGLProgram(o,[r],e.dtype,u,i);return{dataId:l.dataId,shape:t,dtype:l.dtype}}function pe(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{shape:a}=s,o=n,i=w.sizeFromShape(r.shape),u=w.inferFromImplicitShape(a,i),l=w.sizeFromShape(u);w.assert(i===l,()=>`The new shape (${u}) has ${l} elements and the old shape (${r.shape}) has ${i} elements. The new shape and old shape must have the same number of elements.`);let c=o.texData.get(r.dataId);return c.isPacked&&!al(r.shape,u)&&!(c.texture!==null&&al(c.shape,u))?F8(r,u,o):(o.incRef(r.dataId),{dataId:r.dataId,shape:u,dtype:r.dtype})}var O8={kernelName:Oi,backendName:"webgl",kernelFunc:pe},kw=class{constructor(e,t){this.variableNames=["x"];let{windowSize:n,batchSize:s,inSize:r,outSize:a}=e;this.outputShape=[s,a];let o=Math.floor(n/4)*4,i=n%4,u="sumValue += dot(values, ones);";if(t!=null){let c=1/t;u=`sumValue += dot(values * ${w.isInt(c)?c.toPrecision(2):c}, ones);`}let l="";r%n>0&&(l=`
if (inIdx < 0 || inIdx >= ${r}) {
return 0.0;
}
`),this.userCode=`
const vec4 ones = vec4(1.0, 1.0, 1.0, 1.0);
float getValue(int batch, int inIdx) {
${l}
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 < ${o}; 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 + ${o};
if (${i===1}) {
vec4 values = vec4(getValue(batch, inIdx), 0.0, 0.0, 0.0);
${u}
} else if (${i===2}) {
vec4 values = vec4(
getValue(batch, inIdx),
getValue(batch, inIdx + 1), 0.0, 0.0);
${u}
} else if (${i===3}) {
vec4 values = vec4(
getValue(batch, inIdx),
getValue(batch, inIdx + 1),
getValue(batch, inIdx + 2), 0.0);
${u}
}
setOutput(sumValue);
}
`}},P8=class{constructor(e,t){this.variableNames=["x"];let{windowSize:n,batchSize:s,inSize:r,outSize:a}=e;this.outputShape=[s,a];let o="0.0",i="";t==="prod"?o="1.0":t==="min"?(o="1.0 / 1e-20",i="min"):t==="max"&&(o="-1.0 / 1e-20",i="max");let u=`${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 l=Math.floor(n/4)*4,c=n%4,p=`
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 = ${i}(values, minMaxValue);
if (${t==="min"} || ${t==="max"}) {
minMaxValue = ${i}(values, minMaxValue);
bvec4 isNaN = isnan(values);
if (isNaN.r || isNaN.g || isNaN.b || isNaN.a) {
minMaxValue = vec4(NAN);
}
}
}
`,d="vec4";t==="all"?(o="1.0",p=`
bool reducedAllValue = all(values);
float floatedReducedAllValue = float(reducedAllValue);
allValue = float(allValue >= 1.0 && floatedReducedAllValue >= 1.0);
`,d="bvec4"):t==="any"&&(o="0.0",p=`
bool reducedAnyValue = any(values);
float floatedReducedAnyValue = float(reducedAnyValue);
anyValue = float(anyValue >= 1.0 || floatedReducedAnyValue >= 1.0);
`,d="bvec4");let h="";r%n>0&&(h=`
if (inIdx < 0 || inIdx >= ${r}) {
return initializationValue;
}
`),this.userCode=`
const float initializationValue = ${o};
const vec4 ones = vec4(1.0, 1.0, 1.0, 1.0);
float getValue(int batch, int inIdx) {
${h}
return getX(batch, inIdx);
}
void main() {
ivec2 coords = getOutputCoords();
int batch = coords[0];
int outIdx = coords[1];
int inOffset = outIdx * ${n};
vec4 minMaxValue = vec4(${o});
float prodValue = 1.0;
float sumValue = 0.0;
float allValue = 1.0;
float anyValue = 0.0;
for (int i = 0; i < ${l}; i += 4) {
int inIdx = inOffset + i;
${d} values = ${d}(
getValue(batch, inIdx),
getValue(batch, inIdx + 1),
getValue(batch, inIdx + 2),
getValue(batch, inIdx + 3)
);
${p}
}
int inIdx = inOffset + ${l};
if (${c===1}) {
${d} values = ${d}(
getValue(batch, inIdx),
initializationValue,
initializationValue,
initializationValue
);
${p}
} else if (${c===2}) {
${d} values = ${d}(
getValue(batch, inIdx),
getValue(batch, inIdx + 1),
initializationValue,
initializationValue
);
${p}
} else if (${c===3}) {
${d} values = ${d}(
getValue(batch, inIdx),
getValue(batch, inIdx + 1),
getValue(batch, inIdx + 2),
initializationValue
);
${p}
}
setOutput(${u});
}
`}};function z8(e){let t=[];for(;t.length===0||t[t.length-1].outSize!==1;){let n=t.length?t[t.length-1].outSize:e[1],s=C.computeOptimalWindowSize(n);t.push({inSize:n,windowSize:s,outSize:Math.ceil(n/s)})}return t}function So(e,t,n,s){let r=z8(e.shape),a=e;for(let o=0;o<r.length;o++){let{inSize:i,windowSize:u,outSize:l}=r[o],c,p;n==="mean"?c=o===0?new kw({windowSize:u,inSize:i,batchSize:e.shape[0],outSize:l},i):new kw({windowSize:u,inSize:i,batchSize:e.shape[0],outSize:l}):c=new P8({windowSize:u,inSize:i,batchSize:e.shape[0],outSize:l},n),p=a,a=s.runWebGLProgram(c,[a],t),p.dataId!==e.dataId&&s.disposeIntermediateTensorInfo(p)}return a}var L8=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 s=rt(this.rank),r=M8(t);this.userCode=`
void main() {
${s} resRC = getOutputCoords();
setOutput(getA(${r}));
}
`}};function M8(e){let t=e.length;if(t>6)throw Error(`Transpose for rank ${t} is not yet supported`);let n=["resRC.x","resRC.y","resRC.z","resRC.w","resRC.u","resRC.v"],s=new Array(t);for(let r=0;r<e.length;r++)s[e[r]]=n[r];return s.join()}var B8=class{constructor(e,t){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0;let n=new Array(e.length);for(let l=0;l<n.length;l++)n[l]=e[t[l]];if(this.outputShape=n,this.rank=n.length,this.rank>6)throw Error(`Packed transpose for rank ${this.rank} is not yet supported.`);let s=rt(this.rank),r=J1("rc",this.rank),a=new Array(this.rank);for(let l=0;l<t.length;l++)a[t[l]]=r[l];let o=`vec2(${a.slice(-2).join()})`,i=`++${r[this.rank-1]} < ${n[this.rank-1]}`,u=`getChannel(getA(${a.join()}), ${o})`;this.userCode=`
void main() {
${s} rc = getOutputCoords();
vec4 result = vec4(0.);
result[0] = ${u};
if(${i}) {
result[1] = ${u};
}
--${r[this.rank-1]};
if(++${r[this.rank-2]} < ${n[this.rank-2]}) {
result[2] = ${u};
if(${i}) {
result[3] = ${u};
}
}
setOutput(result);
}
`}};function rh(e,t,n){let s=K().getBool("WEBGL_PACK_ARRAY_OPERATIONS")?new B8(e.shape,t):new L8(e.shape,t);return n.runWebGLProgram(s,[e],e.dtype)}function V8(e,t,n,s){let r=t,a=e.shape.length,o=w.parseAxisParam(r,e.shape),i=o,u=C.getAxesPermutation(i,a),l=u!=null,c=e;l&&(c=rh(e,u,s),i=C.getInnerMostAxes(i.length,a)),C.assertAxesAreInnerMostDims("sum",i,a);let[p,d]=C.computeOutAndReduceShapes(c.shape,i),h=p;n&&(h=C.expandShapeToKeepDim(p,o));let f=w.sizeFromShape(d),g=w.sizeFromShape(e.shape)/f,b=pe({inputs:{x:c},attrs:{shape:[g,f]},backend:s}),y=yp(e.dtype),v=So(b,y,"sum",s),x=pe({inputs:{x:v},attrs:{shape:h},backend:s});return s.disposeIntermediateTensorInfo(b),s.disposeIntermediateTensorInfo(v),l&&s.disposeIntermediateTensorInfo(c),x}function ah(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{axis:a,keepDims:o}=s;return V8(r,a,o,n)}var W8={kernelName:co,backendName:"webgl",kernelFunc:ah};function pn(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{perm:a}=s,o=n,i=r.shape.length,u=new Array(i);for(let c=0;c<u.length;c++)u[c]=r.shape[a[c]];let l;if(o.shouldExecuteOnCPU([r])){let p=o.texData.get(r.dataId).values,d=Rv(p,r.shape,r.dtype,a,u);l=o.makeTensorInfo(u,r.dtype);let h=o.texData.get(l.dataId);h.values=d}else l=rh(r,a,o);return l}var U8={kernelName:Hs,backendName:"webgl",kernelFunc:pn},l2=1e3;function Hd({a:e,b:t,transposeA:n,transposeB:s,backend:r,bias:a=null,preluActivationWeights:o=null,leakyreluAlpha:i=0,activation:u=null}){let l=e.shape.length,c=t.shape.length,p=n?e.shape[l-2]:e.shape[l-1],d=s?t.shape[c-1]:t.shape[c-2],h=n?e.shape[l-1]:e.shape[l-2],f=s?t.shape[c-2]:t.shape[c-1],m=e.shape.slice(0,-2),g=t.shape.slice(0,-2),b=w.sizeFromShape(m),y=w.sizeFromShape(g),x=Qi.assertAndGetBroadcastShape(e.shape.slice(0,-2),t.shape.slice(0,-2)).concat([h,f]);w.assert(p===d,()=>`Error in matMul: inner shapes (${p}) and (${d}) of Tensors with shapes ${e.shape} and ${t.shape} and transposeA=${n} and transposeB=${s} must match.`);let k=n?[b,p,h]:[b,h,p],I=s?[y,f,d]:[y,d,f],$=pe({inputs:{x:e},backend:r,attrs:{shape:k}}),R=pe({inputs:{x:t},backend:r,attrs:{shape:I}}),E=[$,R],P=Math.max(b,y),A=n?$.shape[1]:$.shape[2],D=a!=null,T=o!=null,L=u==="leakyrelu",W=u!=null?sh(u,!0):null,j=D||T||L||W!=null,Y;if((h===1||f===1)&&A>l2&&j===!1){let Z=$,ne=R;n&&(Z=pn({inputs:{x:$},backend:r,attrs:{perm:[0,2,1]}}),E.push(Z)),s&&(ne=pn({inputs:{x:R},backend:r,attrs:{perm:[0,2,1]}}),E.push(ne));let ee=f!==1,se=f===1,te=Z;ee&&(te=pe({inputs:{x:Z},backend:r,attrs:{shape:[P,A,1]}}),E.push(te));let ie=f===1?2:1,ae=ne;se&&(ae=pe({inputs:{x:ne},backend:r,attrs:{shape:[P,1,A]}}),E.push(ae));let de=Dv({inputs:{a:te,b:ae},backend:r});Y=ah({inputs:{x:de},backend:r,attrs:{axis:ie,keepDims:!0}}),E.push(de)}else{let Z=cn(e.dtype,t.dtype),ne=new u2(k,I,[P,h,f],n,s,D,W,T,L),ee=[$,R];if(a!=null&&ee.push(a),T&&ee.push(o),L){let se=r.makeTensorInfo([],"float32",w.createScalarValue(i,"float32"));ee.push(se),E.push(se)}Y=r.runWebGLProgram(ne,ee,Z)}let X=pe({inputs:{x:Y},backend:r,attrs:{shape:x}});E.push(Y);for(let Z of E)r.disposeIntermediateTensorInfo(Z);return X}function G8(e){let{inputs:t,backend:n,attrs:s}=e,{a:r,b:a,bias:o,preluActivationWeights:i}=t,{transposeA:u,transposeB:l,activation:c,leakyreluAlpha:p}=s;return Hd({a:r,b:a,transposeA:u,transposeB:l,backend:n,bias:o,preluActivationWeights:i,leakyreluAlpha:p,activation:c})}var H8={kernelName:oa,backendName:"webgl",kernelFunc:G8},Sw="return abs(x);";function q8(e){let{inputs:t,backend:n}=e,{x:s}=t;if(n.shouldExecuteOnCPU([s])&&s.dtype!=="complex64"){let a=n.texData.get(s.dataId),o=Q1(a.values);return n.makeTensorInfo(s.shape,s.dtype,o)}let r;return K().getBool("WEBGL_PACK_UNARY_OPERATIONS")?r=new ea(s.shape,Sw):r=new Gs(s.shape,Sw),n.runWebGLProgram(r,[s],s.dtype)}var j8={kernelName:di,backendName:"webgl",kernelFunc:q8},K8=ss+`
if (abs(x) > 1.) {
return NAN;
}
return acos(x);
`,X8=Ke({opSnippet:K8}),Y8={kernelName:ul,backendName:"webgl",kernelFunc:X8},Q8=ss+`
if (x < 1.0) return NAN;
return log(x + sqrt(x * x - 1.0));`,Z8=Ke({opSnippet:Q8}),J8={kernelName:ll,backendName:"webgl",kernelFunc:Z8},Iw="return a + b;",eY=jt({opSnippet:Iw,packedOpSnippet:Iw,supportsComplex:!0,cpuKernelImpl:hX}),tY={kernelName:Cr,backendName:"webgl",kernelFunc:eY},nY=class{constructor(e,t){this.outputShape=[],this.outputShape=e,this.variableNames=t.map((r,a)=>`T${a}`);let n=[];this.variableNames.forEach(r=>{n.push(`float v${r} = get${r}AtOutCoords();`)});let s=this.variableNames.map(r=>`v${r}`).join(" + ");this.userCode=`
void main() {
${n.join(`
`)}
float result = ${s};
setOutput(result);
}
`}},sY=class{constructor(e,t){this.outputShape=[],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=e,this.variableNames=t.map((r,a)=>`T${a}`);let n=[];this.variableNames.forEach(r=>{n.push(`vec4 v${r} = get${r}AtOutCoords();`)});let s=this.variableNames.map(r=>`v${r}`).join(" + ");this.userCode=`
void main() {
${n.join(`
`)}
vec4 result = ${s};
setOutput(result);
}
`}};function fd(e){let{inputs:t,backend:n}=e,s=t;if(s.length===1)return Rn({inputs:{x:s[0]},backend:n});if(s.length>K().get("WEBGL_MAX_TEXTURES_IN_SHADER")){let u=Math.floor(s.length/2),l=fd({inputs:s.slice(0,u),backend:n}),c=fd({inputs:s.slice(u),backend:n});return fd({inputs:[l,c],backend:n})}let r=s.map(u=>u.dtype).reduce((u,l)=>cn(u,l)),a=s.map(u=>u.shape),i=K().getBool("WEBGL_PACK")?new sY(s[0].shape,a):new nY(s[0].shape,a);return n.runWebGLProgram(i,s,r)}var rY={kernelName:Sa,backendName:"webgl",kernelFunc:fd};function aY(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{axis:a,keepDims:o}=s,i=r.shape.length,u=w.parseAxisParam(a,r.shape),l=u,c=C.getAxesPermutation(l,i),p=r;c!=null&&(p=pn({inputs:{x:r},backend:n,attrs:{perm:c}}),l=C.getInnerMostAxes(l.length,i)),C.assertAxesAreInnerMostDims("all",l,i);let[d,h]=C.computeOutAndReduceShapes(p.shape,l),f=w.sizeFromShape(h),m=pe({inputs:{x:p},backend:n,attrs:{shape:[-1,f]}}),g=So(m,m.dtype,"all",n),b;if(o){let y=C.expandShapeToKeepDim(d,u);b=pe({inputs:{x:g},backend:n,attrs:{shape:y}})}else b=pe({inputs:{x:g},backend:n,attrs:{shape:d}});return n.disposeIntermediateTensorInfo(m),n.disposeIntermediateTensorInfo(g),c!=null&&n.disposeIntermediateTensorInfo(p),b}var oY={kernelName:cl,backendName:"webgl",kernelFunc:aY};function iY(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{axis:a,keepDims:o}=s,i=r.shape.length,u=w.parseAxisParam(a,r.shape),l=u,c=C.getAxesPermutation(l,i),p=r;c!=null&&(p=pn({inputs:{x:r},backend:n,attrs:{perm:c}}),l=C.getInnerMostAxes(l.length,i)),C.assertAxesAreInnerMostDims("any",l,i);let[d,h]=C.computeOutAndReduceShapes(p.shape,l),f=w.sizeFromShape(h),m=pe({inputs:{x:p},backend:n,attrs:{shape:[-1,f]}}),g=So(m,m.dtype,"any",n),b;if(o){let y=C.expandShapeToKeepDim(d,u);b=pe({inputs:{x:g},backend:n,attrs:{shape:y}})}else b=pe({inputs:{x:g},backend:n,attrs:{shape:d}});return n.disposeIntermediateTensorInfo(m),n.disposeIntermediateTensorInfo(g),c!=null&&n.disposeIntermediateTensorInfo(p),b}var uY={kernelName:dl,backendName:"webgl",kernelFunc:iY},lY=class{constructor(e,t,n){this.variableNames=["A"];let{windowSize:s,batchSize:r,outSize:a}=e;n||this.variableNames.push("bestIndicesA"),this.outputShape=[r,a];let o=t==="max"?">":"<",i=n?"inOffset + i;":"round(getBestIndicesA(batch, inOffset + i));";this.userCode=`
void main() {
ivec2 coords = getOutputCoords();
int batch = coords[0];
int outIdx = coords[1];
int inOffset = outIdx * ${s};
int bestIndex = inOffset;
float bestValue = getA(batch, bestIndex);
for (int i = 0; i < ${s}; i++) {
int inIdx = ${i};
float candidate = getA(batch, inIdx);
if (candidate ${o} bestValue) {
bestValue = candidate;
bestIndex = inIdx;
}
}
setOutput(float(bestIndex));
}
`}},cY=class{constructor(e,t,n,s){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,w.assert(e.length>2,()=>`Packed arg${n.charAt(0).toUpperCase()+n.slice(1)} supports only inputs with rank above 2.`);let r=e[e.length-1],a=Math.ceil(r/t);this.outputShape=e.slice(0,-1),a>1&&this.outputShape.push(a),s||this.variableNames.push("bestIndicesA");let o=this.outputShape,i=o.length,u=rt(i),l=ln("coords",i),c,p;if(a===1){p=i+1;let $=rt(p);c=`
${$} sourceLocR = ${$}(${l.join()}, 0);
++${l[i-1]};
${$} sourceLocG = ${$}(${l.join()}, 0);
++${l[i-2]};
${$} sourceLocA = ${$}(${l.join()}, 0);
--${l[i-1]};
${$} sourceLocB = ${$}(${l.join()}, 0);
--${l[i-2]};`}else p=i,c=`
${u} sourceLocR = coords;
++${l[i-1]};
${u} sourceLocG = coords;
++${l[i-2]};
${u} sourceLocA = coords;
--${l[i-1]};
${u} sourceLocB = coords;
--${l[i-2]};`;let d=["x","y","z","w","u","v"].slice(0,p),h="."+d[p-1],f=d.map($=>"int "+$),m=ln("sourceLocR",p-1).concat("inIdx.r"),g=ln("sourceLocG",p-1).concat("inIdx.g"),b=ln("sourceLocB",p-1).concat("inIdx.b"),y=ln("sourceLocA",p-1).concat("inIdx.a"),v=n==="max"?"greaterThan":"lessThan",x=s?"":`
inIdx = round(vec4(getBestIndicesAChannel(${m.join()}),
getBestIndicesAChannel(${g.join()}),
getBestIndicesAChannel(${b.join()}),
getBestIndicesAChannel(${y.join()})));`,k=`vec4(
getAChannel(${m.join()}),
hasNextCol ? getAChannel(${g.join()}) : 0.,
hasNextRow ? getAChannel(${b.join()}) : 0.,
hasNextRow && hasNextCol ? getAChannel(${y.join()}) : 0.)`,I=s?"":`
float getBestIndicesAChannel(${f.join()}) {
return getChannel(getBestIndicesA(${d.join()}),
vec2(${d.slice(-2).join()}));
}`;this.userCode=`
float getAChannel(${f.join()}) {
return getChannel(getA(${d.join()}),
vec2(${d.slice(-2).join()}));
}
${I}
void main() {
${u} coords = getOutputCoords();
bool hasNextCol = ${l[i-1]} < ${o[i-1]-1};
bool hasNextRow = ${l[i-2]} < ${o[i-2]-1};
${c}
ivec4 srcIdx = ivec4(sourceLocR${h}, sourceLocG${h},
sourceLocB${h}, sourceLocA${h}) * ${t};
ivec4 inIdx = srcIdx;
vec4 bestIndex = vec4(inIdx);
vec4 bestValue = ${k};
for (int i = 0; i < ${t}; i++) {
inIdx = srcIdx;
${x}
vec4 candidate = ${k};
bvec4 nan = isnan(candidate);
bvec4 replace = bvec4(
vec4(${v}(candidate, bestValue)) * (vec4(1.0) - vec4(nan)));
bestValue = vec4(replace.x ? candidate.x : bestValue.x,
replace.y ? candidate.y : bestValue.y,
replace.z ? candidate.z : bestValue.z,
replace.w ? candidate.w : bestValue.w);
bestIndex = mix(bestIndex, vec4(inIdx), vec4(replace));
srcIdx++;
}
setOutput(bestIndex);
}
`}};function c2(e,t,n,s=null){let r=t.shape[0],a=t.shape[1];s!=null&&(r=s.shape[0],a=s.shape[1]);let o=C.computeOptimalWindowSize(a),i={windowSize:o,inSize:a,batchSize:r,outSize:Math.ceil(a/o)},u=new lY(i,n,s==null),l=[t];s!=null&&l.push(s);let c=e.runWebGLProgram(u,l,"int32");if(c.shape[1]===1)return c;let p=c2(e,t,n,c);return e.disposeIntermediateTensorInfo(c),p}function d2(e,t,n,s=null){let r=s!=null?s.shape:t.shape,a=r[r.length-1],o=C.computeOptimalWindowSize(a),i=new cY(r,o,n,s==null),u=s==null?[t]:[t,s],l=e.runWebGLProgram(i,u,"int32");if(l.shape.length===t.shape.length){let c=d2(e,t,n,l);return e.disposeIntermediateTensorInfo(l),c}return l}function p2(e,t,n,s){let r=[n];if(C.assertAxesAreInnerMostDims("arg"+s.charAt(0).toUpperCase()+s.slice(1),r,t.shape.length),!K().getBool("WEBGL_PACK_REDUCE")||t.shape.length<=2){let a=[],o=e.texData.get(t.dataId),i=o!==null&&o.isPacked,u=t;i&&(u=e.unpackTensor(t),a.push(u));let[l,c]=C.computeOutAndReduceShapes(u.shape,r),p=w.sizeFromShape(c),d=pe({inputs:{x:u},backend:e,attrs:{shape:[-1,p]}});a.push(d);let h=c2(e,d,s);a.push(h);let f=pe({inputs:{x:h},backend:e,attrs:{shape:l}});return a.forEach(m=>e.disposeIntermediateTensorInfo(m)),f}return d2(e,t,s)}function dY(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{axis:a}=s,o=w.parseAxisParam(a,r.shape),i=C.getAxesPermutation(o,r.shape.length),u=r,l=[];i!=null&&(u=pn({inputs:{x:r},backend:n,attrs:{perm:i}}),l.push(u),o=C.getInnerMostAxes(o.length,u.shape.length)),C.assertAxesAreInnerMostDims("argMax",[o[0]],u.shape.length);let c=p2(n,u,o[0],"max");return l.forEach(p=>n.disposeIntermediateTensorInfo(p)),c}var pY={kernelName:Ia,backendName:"webgl",kernelFunc:dY};function hY(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{axis:a}=s,o=w.parseAxisParam(a,r.shape),i=C.getAxesPermutation(o,r.shape.length),u=r,l=[];i!=null&&(u=pn({inputs:{x:r},backend:n,attrs:{perm:i}}),l.push(u),o=C.getInnerMostAxes(o.length,u.shape.length)),C.assertAxesAreInnerMostDims("argMin",[o[0]],u.shape.length);let c=p2(n,u,o[0],"min");return l.forEach(p=>n.disposeIntermediateTensorInfo(p)),c}var fY={kernelName:pl,backendName:"webgl",kernelFunc:hY},mY=ss+`
if (abs(x) > 1.) {
return NAN;
}
return asin(x);
`,gY=Ke({opSnippet:mY}),bY={kernelName:hl,backendName:"webgl",kernelFunc:gY},yY=ss+"return log(x + sqrt(x * x + 1.0));",vY=Ke({opSnippet:yY}),xY={kernelName:fl,backendName:"webgl",kernelFunc:vY},wY=ss+`
return atan(x);
`,kY=Ke({opSnippet:wY}),SY={kernelName:ml,backendName:"webgl",kernelFunc:kY},IY=E8+`
return atan(a, b);
`,CY=`
vec4 result = atan(a, b);
vec4 isNaN = min(vec4(isnan(a)) + vec4(isnan(b)), vec4(1.0));
`+R8+`
return result;
`,NY=jt({opSnippet:IY,packedOpSnippet:CY}),TY={kernelName:bl,backendName:"webgl",kernelFunc:NY},$Y=ss+`
if ((x < -1.0) || (x > 1.0)) return NAN;
return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,_Y=Ke({opSnippet:$Y}),AY={kernelName:gl,backendName:"webgl",kernelFunc:_Y},ol=class{constructor(e,t,n,s=!1,r=!1){if(this.variableNames=["x"],t==="avg"&&n)throw new Error("Cannot compute positions for average pool.");let a=e.filterWidth,o=e.strideHeight,i=e.strideWidth,u=e.dilationHeight,l=e.dilationWidth,c=e.effectiveFilterHeight,p=e.effectiveFilterWidth,d=e.padInfo.top,h=e.padInfo.left;this.outputShape=e.outShape;let f=t==="avg",m=`((batch * ${e.inHeight} + xR) * ${e.inWidth} + xC) * ${e.inChannels} + d`,g=`(xR * ${e.inWidth} + xC) * ${e.inChannels} + d`,b="0.0";if(f||(b="-1.0 / 1e-20"),n){let $=">=";this.userCode=`
const ivec2 strides = ivec2(${o}, ${i});
const ivec2 pads = ivec2(${d}, ${h});
void main() {
ivec4 coords = getOutputCoords();
int batch = coords[0];
int d = coords[3];
ivec2 xRCCorner = coords.yz * strides - pads;
int xRCorner = xRCCorner.x;
int xCCorner = xRCCorner.y;
// max/min x(?, ?, d) to get y(yR, yC, d).
// ? = to be determined
float minMaxValue = 0.0;
float minMaxValueFound = 0.0;
int minMaxPosition = 0;
float avgValue = 0.0;
for (int wR = 0; wR < ${c};
wR += ${u}) {
int xR = xRCorner + wR;
if (xR < 0 || xR >= ${e.inHeight}) {
continue;
}
for (int wC = 0; wC < ${p};
wC += ${l}) {
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 ${$} currMinMaxValue) {
minMaxValue = value;
minMaxValueFound = 1.0;
minMaxPosition = ${s?r?m:g:`wR * ${p} + wC`};
}
}
}
setOutput(float(minMaxPosition));
}
`;return}let y="max",v=`${t}(${t}(${t}(minMaxValue[0], minMaxValue[1]), minMaxValue[2]), minMaxValue[3])`;t==="avg"&&(v="avgValue / count");let x=Math.floor(a/4)*4,k=a%4,I=`
if (${f}) {
avgValue += dot(values, ones);
} else {
minMaxValue = ${y}(values, minMaxValue);
}
`;this.userCode=`
const ivec2 strides = ivec2(${o}, ${i});
const ivec2 pads = ivec2(${d}, ${h});
const float initializationValue = ${b};
const vec4 ones = vec4(1.0, 1.0, 1.0, 1.0);
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 < ${c};
wR += ${u}) {
int xR = xRCorner + wR;
if (xR < 0 || xR >= ${e.inHeight}) {
continue;
}
for (int wC = 0; wC < ${x}; wC += 4) {
int xC = xCCorner + wC * ${l};
vec4 values = vec4(
getValue(batch, xR, xC, d),
getValue(batch, xR, xC + ${l}, d),
getValue(batch, xR, xC + 2 * ${l}, d),
getValue(batch, xR, xC + 3 * ${l}, d)
);
${I}
}
int xC = xCCorner + ${x};
if (${k===1}) {
vec4 values = vec4(
getValue(batch, xR, xC, d),
initializationValue,
initializationValue,
initializationValue
);
${I}
} else if (${k===2}) {
vec4 values = vec4(
getValue(batch, xR, xC, d),
getValue(batch, xR, xC + ${l}, d),
initializationValue,
initializationValue
);
${I}
} else if (${k===3}) {
vec4 values = vec4(
getValue(batch, xR, xC, d),
getValue(batch, xR, xC + ${l}, d),
getValue(batch, xR, xC + 2 * ${l}, d),
initializationValue
);
${I}
}
}
setOutput(${v});
}
`}},Fv=class{constructor(e,t,n,s=!1,r=!1){if(this.variableNames=["x"],t==="avg"&&n)throw new Error("Cannot compute positions for average pool.");let a=e.filterWidth,o=e.strideDepth,i=e.strideHeight,u=e.strideWidth,l=e.dilationDepth,c=e.dilationHeight,p=e.dilationWidth,d=e.effectiveFilterDepth,h=e.effectiveFilterHeight,f=e.effectiveFilterWidth,m=e.padInfo.front,g=e.padInfo.top,b=e.padInfo.left;this.outputShape=e.outShape;let y=t==="avg",v="0.0";if(y||(v="-1.0 / 1e-20"),n){let E=">=";this.userCode=`
const ivec3 strides =
ivec3(${o}, ${i}, ${u});
const ivec3 pads = ivec3(${m}, ${g}, ${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 < ${d};
wD += ${l}) {
int xD = xDCorner + wD;
if (xD < 0 || xD >= ${e.inDepth}) {
continue;
}
for (int wR = 0; wR < ${h};
wR += ${c}) {
int xR = xRCorner + wR;
if (xR < 0 || xR >= ${e.inHeight}) {
continue;
}
for (int wC = 0; wC < ${f};
wC += ${p}) {
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 ${E} currMinMaxValue) {
minMaxValue = value;
minMaxValueFound = 1.0;
minMaxPosition = ${s?r?`(((batch * ${e.inDepth} + xD) * ${e.inHeight} + xR) * ${e.inWidth} + xC) * ${e.inChannels} + ch`:`((xD * ${e.inHeight} + xR) * ${e.inWidth} + xC) * ${e.inChannels} + ch`:`wD * ${h} * ${f} +
wR * ${f} + wC`};
}
}
}
}
setOutput(float(minMaxPosition));
}
`;return}let x="max",k=`${t}(${t}(${t}(minMaxValue[0], minMaxValue[1]), minMaxValue[2]), minMaxValue[3])`;t==="avg"&&(k="avgValue / count");let I=Math.floor(a/4)*4,$=a%4,R=`
if (${y}) {
avgValue += dot(values, ones);
} else {
minMaxValue = ${x}(values, minMaxValue);
}
`;this.userCode=`
const ivec3 strides =
ivec3(${o}, ${i}, ${u});
const ivec3 pads = ivec3(${m}, ${g}, ${b});
const float initializationValue = ${v};
const vec4 ones = vec4(1.0, 1.0, 1.0, 1.0);
float count = 0.0;
float getValue(int batch, int xD, int xR, int xC, int ch) {
if (xC < 0 || xC >= ${e.inWidth}) {
return initializationValue;
}
count += 1.0;
return getX(batch, xD, xR, xC, ch);
}
void main() {
ivec5 coords = getOutputCoords();
int batch = coords.x;
int ch = coords.u;
ivec3 xCorner = ivec3(coords.y, coords.z, coords.w) * strides - pads;
int xDCorner = xCorner.x;
int xRCorner = xCorner.y;
int xCCorner = xCorner.z;
// max/min x(?, ?, ?, d) to get y(yD, yR, yC, ch).
// ? = to be determined
vec4 minMaxValue = vec4(${v});
float avgValue = 0.0;
count = 0.0;
for (int wD = 0; wD < ${d};
wD += ${l}) {
int xD = xDCorner + wD;
if (xD < 0 || xD >= ${e.inDepth}) {
continue;
}
for (int wR = 0; wR < ${h};
wR += ${c}) {
int xR = xRCorner + wR;
if (xR < 0 || xR >= ${e.inHeight}) {
continue;
}
for (int wC = 0; wC < ${I}; wC += 4) {
int xC = xCCorner + wC * ${p};
vec4 values = vec4(
getValue(batch, xD, xR, xC, ch),
getValue(batch, xD, xR, xC + ${p}, ch),
getValue(batch, xD, xR, xC + 2 * ${p}, ch),
getValue(batch, xD, xR, xC + 3 * ${p}, ch)
);
${R}
}
int xC = xCCorner + ${I};
if (${$===1}) {
vec4 values = vec4(
getValue(batch, xD, xR, xC, ch),
initializationValue,
initializationValue,
initializationValue
);
${R}
} else if (${$===2}) {
vec4 values = vec4(
getValue(batch, xD, xR, xC, ch),
getValue(batch, xD, xR, xC + ${p}, ch),
initializationValue,
initializationValue
);
${R}
} else if (${$===3}) {
vec4 values = vec4(
getValue(batch, xD, xR, xC, ch),
getValue(batch, xD, xR, xC + ${p}, ch),
getValue(batch, xD, xR, xC + 2 * ${p}, ch),
initializationValue
);
${R}
}
}
setOutput(${k});
}
}
`}};function EY(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t;ou(r,"avgPool");let{filterSize:a,strides:o,pad:i,dimRoundingMode:u}=s,l=1;w.assert(C.eitherStridesOrDilationsAreOne(o,l),()=>`Error in avgPool: Either strides or dilations must be 1. Got strides ${o} and dilations '${l}'`);let c=C.computePool2DInfo(r.shape,a,o,l,i,u);if(c.filterWidth===1&&c.filterHeight===1&&w.arraysEqual(c.inShape,c.outShape))return Rn({inputs:{x:r},backend:n});let p=new ol(c,"avg",!1);return n.runWebGLProgram(p,[r],"float32")}var RY={kernelName:Ca,backendName:"webgl",kernelFunc:EY};function DY(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{filterSize:a,strides:o,pad:i,dimRoundingMode:u,dataFormat:l}=s,c=[1,1,1],p=C.computePool3DInfo(r.shape,a,o,c,i,u,l),d=new Fv(p,"avg",!1);return n.runWebGLProgram(d,[r],"float32")}var FY={kernelName:tp,backendName:"webgl",kernelFunc:DY},OY=class{constructor(e){this.variableNames=["dy"],this.outputShape=e.inShape;let t=e.filterHeight,n=e.filterWidth,s=e.strideHeight,r=e.strideWidth,a=e.dilationHeight,o=e.dilationWidth,i=e.effectiveFilterHeight,u=e.effectiveFilterWidth,l=i-1-e.padInfo.top,c=u-1-e.padInfo.left,p=1/(t*n);this.userCode=`
const ivec2 pads = ivec2(${l}, ${c});
const float avgMultiplier = float(${p});
void main() {
ivec4 coords = getOutputCoords();
int b = coords[0];
int d = coords[3];
ivec2 dyRCCorner = coords.yz - pads;
int dyRCorner = dyRCCorner.x;
int dyCCorner = dyRCCorner.y;
// Convolve dy(?, ?, d) with pos mask(:, :, d) to get dx(xR, xC, d).
// ? = to be determined. : = across all values in that axis.
float dotProd = 0.0;
for (int wR = 0; wR < ${i};
wR += ${a}) {
float dyR = float(dyRCorner + wR) / ${s}.0;
if (dyR < 0.0 || dyR >= ${e.outHeight}.0 || fract(dyR) > 0.0) {
continue;
}
int idyR = int(dyR);
for (int wC = 0; wC < ${u};
wC+= ${o}) {
float dyC = float(dyCCorner + wC) / ${r}.0;
if (dyC < 0.0 || dyC >= ${e.outWidth}.0 ||
fract(dyC) > 0.0) {
continue;
}
int idyC = int(dyC);
float dyValue = getDy(b, idyR, idyC, d);
dotProd += dyValue * avgMultiplier;
}
}
setOutput(dotProd);
}
`}},PY=class{constructor(e){this.variableNames=["dy"],this.outputShape=e.inShape;let t=e.filterDepth,n=e.filterHeight,s=e.filterWidth,r=e.strideDepth,a=e.strideHeight,o=e.strideWidth,i=e.dilationDepth,u=e.dilationHeight,l=e.dilationWidth,c=e.effectiveFilterDepth,p=e.effectiveFilterHeight,d=e.effectiveFilterWidth,h=c-1-e.padInfo.front,f=p-1-e.padInfo.top,m=d-1-e.padInfo.left,g=1/(t*n*s);this.userCode=`
const ivec3 pads = ivec3(${h}, ${f}, ${m});
const float avgMultiplier = float(${g});
void main() {
ivec5 coords = getOutputCoords();
int batch = coords.x;
int ch = coords.u;
ivec3 dyCorner = ivec3(coords.y, coords.z, coords.w) - pads;
int dyDCorner = dyCorner.x;
int dyRCorner = dyCorner.y;
int dyCCorner = dyCorner.z;
// Convolve dy(?, ?, ?, d) with pos mask(:, :, :, ch) to get
// dx(xD, xR, xC, ch).
// ? = to be determined. : = across all values in that axis.
float dotProd = 0.0;
for (int wD = 0; wD < ${c};
wD += ${i}) {
float dyD = float(dyDCorner + wD) / ${r}.0;
if (dyD < 0.0 || dyD >= ${e.outDepth}.0 || fract(dyD) > 0.0) {
continue;
}
int idyD = int(dyD);
for (int wR = 0; wR < ${p};
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 < ${d};
wC += ${l}) {
float dyC = float(dyCCorner + wC) / ${o}.0;
if (dyC < 0.0 || dyC >= ${e.outWidth}.0 ||
fract(dyC) > 0.0) {
continue;
}
int idyC = int(dyC);
float dyValue = getDy(batch, idyD, idyR, idyC, ch);
dotProd += dyValue * avgMultiplier;
}
}
}
setOutput(dotProd);
}
`}};function zY(e){let{inputs:t,backend:n,attrs:s}=e,{dy:r,input:a}=t,o=a,{filterSize:i,strides:u,pad:l,dimRoundingMode:c}=s,p=[1,1,1],d=C.computePool3DInfo(o.shape,i,u,p,l,c),h=new PY(d);return n.runWebGLProgram(h,[r],o.dtype)}var LY={kernelName:yg,backendName:"webgl",kernelFunc:zY};function MY(e){let{inputs:t,backend:n,attrs:s}=e,{dy:r,input:a}=t,o=a;ou([r,a],"avgPoolGrad");let{filterSize:i,strides:u,pad:l}=s,c=C.computePool2DInfo(o.shape,i,u,1,l),p=new OY(c);return n.runWebGLProgram(p,[r],o.dtype)}var BY={kernelName:bg,backendName:"webgl",kernelFunc:MY};function VY(e){let{inputs:t,backend:n,attrs:s}=e,{a:r,b:a}=t,{transposeA:o,transposeB:i}=s;return Hd({a:r,b:a,transposeA:o,transposeB:i,backend:n})}var WY={kernelName:Na,backendName:"webgl",kernelFunc:VY},UY=class{constructor(e,t,n,s,r,a){this.outputShape=[],this.variableNames=["x","mean","variance"],C.assertAndGetBroadcastShape(e,t),C.assertAndGetBroadcastShape(e,n);let o="0.0";s!=null&&(C.assertAndGetBroadcastShape(e,s),this.variableNames.push("offset"),o="getOffsetAtOutCoords()");let i="1.0";r!=null&&(C.assertAndGetBroadcastShape(e,r),this.variableNames.push("scale"),i="getScaleAtOutCoords()"),this.outputShape=e,this.userCode=`
void main() {
float x = getXAtOutCoords();
float mean = getMeanAtOutCoords();
float variance = getVarianceAtOutCoords();
float offset = ${o};
float scale = ${i};
float inv = scale * inversesqrt(variance + float(${a}));
setOutput(dot(vec3(x, -mean, offset), vec3(inv, inv, 1)));
}
`}},GY=class{constructor(e,t,n,s,r,a){this.packedInputs=!0,this.packedOutput=!0,this.variableNames=["x","mean","variance"],C.assertAndGetBroadcastShape(e,t),C.assertAndGetBroadcastShape(e,n);let o="vec4(0.0)";s!=null&&(C.assertAndGetBroadcastShape(e,s),this.variableNames.push("offset"),o="getOffsetAtOutCoords()");let i="vec4(1.0)";r!=null&&(C.assertAndGetBroadcastShape(e,r),this.variableNames.push("scale"),i="getScaleAtOutCoords()"),this.outputShape=e,this.userCode=`
void main() {
vec4 offset = ${o};
vec4 scale = ${i};
vec4 x = getXAtOutCoords();
vec4 mean = getMeanAtOutCoords();
vec4 variance = getVarianceAtOutCoords();
vec4 inv = scale * inversesqrt(variance + vec4(${a}));
setOutput((x - mean) * inv + offset);
}
`}},HY=({inputs:e,backend:t,attrs:n})=>{let{x:s,mean:r,variance:a,offset:o,scale:i}=e;w.assert(r.shape.length===a.shape.length,()=>"Batch normalization gradient requires mean and variance to have equal ranks."),w.assert(o==null||r.shape.length===o.shape.length,()=>"Batch normalization gradient requires mean and offset to have equal ranks."),w.assert(i==null||r.shape.length===i.shape.length,()=>"Batch normalization gradient requires mean and scale to have equal ranks.");let{varianceEpsilon:u}=n;u==null&&(u=.001);let l=[s,r,a],c=null;o!=null&&(c=o.shape,l.push(o));let p=null;i!=null&&(p=i.shape,l.push(i));let d=K().getBool("WEBGL_PACK_NORMALIZATION")?new GY(s.shape,r.shape,a.shape,c,p,u):new UY(s.shape,r.shape,a.shape,c,p,u);return t.runWebGLProgram(d,l,l[0].dtype)},qY={kernelName:Ba,backendName:"webgl",kernelFunc:HY},jY=class{constructor(e){this.variableNames=["source"],this.outputShape=e,this.rank=e.length;let t=rt(this.rank);this.customUniforms=[{name:"start",arrayIndex:this.rank,type:"int"}];let n=KY(this.rank),s,r=e.map((a,o)=>`sourceLoc.${ng[o]} = start[${o}] + coords.${ng[o]};`);s=`
${t} sourceLoc;
${t} coords = getOutputCoords();
${r.join(`
`)}
`,this.userCode=`
void main() {
${s}
setOutput(getSource(${n}));
}
`}},ng=["x","y","z","w","u","v"];function KY(e){if(e===1)return"sourceLoc";if(e<=6)return ng.slice(0,e).map(t=>"sourceLoc."+t).join(",");throw Error(`Slicing for rank ${e} is not yet supported`)}var XY=class{constructor(e){this.variableNames=["source"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=e,this.rank=e.length,this.customUniforms=[{name:"start",arrayIndex:this.rank,type:"int"}];let t=rt(this.rank),n=ln("coords",this.rank),s=ln("sourceLoc",this.rank),r=this.rank===1?"sourceLoc":`vec2(${s.slice(-2).join()})`,a=`getChannel(getSource(${s.join()}), ${r})`,o=`
result.x = ${a};
if (++${n[this.rank-1]} < ${e[this.rank-1]}) {
++${s[this.rank-1]};
result.y = ${a};
--${s[this.rank-1]};
}
`,i=this.rank===1?"":`
--${n[this.rank-1]};
if (++${n[this.rank-2]} < ${e[this.rank-2]}) {
++${s[this.rank-2]};
result.z = ${a};
if (++${n[this.rank-1]} < ${e[this.rank-1]}) {
++${s[this.rank-1]};
result.w = ${a};
}
}
`,u=this.rank<=4?`sourceLoc = coords +
${t}(${e.map((l,c)=>`start[${c}]`).join()});`:e.map((l,c)=>`${s[c]} = ${n[c]} + start[${c}];`).join(`
`);this.userCode=`
void main() {
${t} coords = getOutputCoords();
${t} sourceLoc;
${u}
vec4 result = vec4(0.);
${o}
${i}
setOutput(result);
}
`}};function YY(e,t,n,s){let r=s.texData.get(e.dataId),a=s.makeTensorInfo(n,e.dtype),o=s.texData.get(a.dataId);Object.assign(o,r),o.refCount=1,o.shape=n,o.dtype=e.dtype;let i=kt.computeFlatOffset(t,w.computeStrides(e.shape));r.slice&&(i+=r.slice.flatOffset),o.slice={flatOffset:i,origDataId:r.slice&&r.slice.origDataId||e.dataId};let u=s.dataRefCount.get(o.slice.origDataId)||1;return s.dataRefCount.set(o.slice.origDataId,u+1),a}function pu(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{begin:a,size:o}=s,[i,u]=kt.parseSliceParams(r,a,o);if(kt.assertParamsValid(r,i,u),w.sizeFromShape(u)===0)return n.makeTensorInfo(u,r.dtype,[]);if(n.shouldExecuteOnCPU([r])||r.dtype==="string"){let p=n.texData.get(r.dataId),d=BX(p.values,i,u,r.shape,r.dtype);return n.makeTensorInfo(u,r.dtype,d)}let{isPacked:l}=n.texData.get(r.dataId),c=kt.isSliceContinous(r.shape,i,u);if(l||!c){let p=K().getBool("WEBGL_PACK_ARRAY_OPERATIONS")?new XY(u):new jY(u),d=[i];return n.runWebGLProgram(p,[r],r.dtype,d)}return n.uploadToGPU(r.dataId),YY(r,i,u,n)}var QY={kernelName:Bi,backendName:"webgl",kernelFunc:pu},ZY=e=>{let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{blockShape:a,crops:o}=s;w.assert(r.shape.length<=4,()=>"batchToSpaceND for rank > 4 with a WebGL backend not implemented yet");let i=a.reduce((y,v)=>y*v),u=C.getReshaped(r.shape,a,i),l=C.getPermuted(u.length,a.length),c=C.getReshapedPermuted(r.shape,a,i),p=C.getSliceBeginCoords(o,a.length),d=C.getSliceSize(c,o,a.length),h=[],f=pe({inputs:{x:r},backend:n,attrs:{shape:u}}),m=pn({inputs:{x:f},backend:n,attrs:{perm:l}}),g=pe({inputs:{x:m},backend:n,attrs:{shape:c}}),b=pu({inputs:{x:g},backend:n,attrs:{begin:p,size:d}});return h.push(f),h.push(m),h.push(g),h.forEach(y=>n.disposeIntermediateTensorInfo(y)),b},JY={kernelName:pi,backendName:"webgl",kernelFunc:ZY};function e9(e){let{inputs:t,backend:n,attrs:s}=e,{x:r,weights:a}=t,{size:o}=s,i=n.readSync(r.dataId),u=n.readSync(a.dataId),l=Y1(i,u,a.dtype,a.shape,o);return n.makeTensorInfo([o],a.dtype,l)}var t9={kernelName:vg,backendName:"webgl",kernelFunc:e9};function n9(e){let{inputs:t,backend:n}=e,{s0:s,s1:r}=t,a=n.readSync(s.dataId),o=n.readSync(r.dataId),i=C.assertAndGetBroadcastShape(Array.from(a),Array.from(o));return n.makeTensorInfo([i.length],"int32",Int32Array.from(i))}var s9={kernelName:xg,backendName:"webgl",kernelFunc:n9},r9="return float(a != b);",h2=jt({opSnippet:r9,cpuKernelImpl:FX,dtype:"bool"}),a9={kernelName:_i,backendName:"webgl",kernelFunc:h2};function nc(e){let{inputs:t,backend:n}=e,{input:s}=t,r=n.texData.get(s.dataId);return Rn({inputs:{x:r.complexTensorInfos.real},backend:n})}var o9={kernelName:cp,backendName:"webgl",kernelFunc:nc},i9="return float(int(x));";function u9(e,t){let n=new Gs(e.shape,i9),s=t.runWebGLProgram(n,[e],"int32");return{dataId:s.dataId,shape:s.shape,dtype:s.dtype}}function sg(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{dtype:a}=s;if(a==="complex64"){if(r.dtype==="complex64")return Rn({inputs:{x:r},backend:n});let o=$t(r.shape),i=sg({inputs:{x:r},backend:n,attrs:{dtype:"float32"}}),u=Fr({inputs:{real:i,imag:o},backend:n});return o.dispose(),n.disposeIntermediateTensorInfo(i),u}if(r.dtype==="complex64"){let o=nc({inputs:{input:r},backend:n}),i=sg({inputs:{x:o},backend:n,attrs:{dtype:a}});return n.disposeIntermediateTensorInfo(o),i}if(!w.hasEncodingLoss(r.dtype,a)){let o=Rn({inputs:{x:r},backend:n});return{dataId:o.dataId,shape:o.shape,dtype:a}}if(a==="int32")return u9(r,n);if(a==="bool"){let o=n.makeTensorInfo([],"bool",w.getTypedArrayFromDType("bool",1)),u=h2({inputs:{a:r,b:o},backend:n});return n.disposeIntermediateTensorInfo(o),u}throw new Error(`Error in Cast: failed to cast ${r.dtype} to ${a}`)}var l9={kernelName:Ta,backendName:"webgl",kernelFunc:sg},Cw="return ceil(x);",c9=Ke({opSnippet:Cw,packedOpSnippet:Cw,cpuKernelImpl:mX}),d9={kernelName:$a,backendName:"webgl",kernelFunc:c9},p9=class{constructor(e){this.variableNames=["A"],this.customUniforms=[{name:"minVal",type:"float"},{name:"maxVal",type:"float"}],this.outputShape=e,this.userCode=`
void main() {
float value = getAAtOutCoords();
if (isnan(value)) {
setOutput(value);
return;
}
setOutput(clamp(value, minVal, maxVal));
}
`}},h9=class{constructor(e){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,this.customUniforms=[{name:"minVal",type:"float"},{name:"maxVal",type:"float"}],this.outputShape=e,this.userCode=`
void main() {
vec4 value = getAAtOutCoords();
if (any(isnan(value))) {
setOutput(value);
return;
}
setOutput(clamp(value, vec4(minVal), vec4(maxVal)));
}
`}};function f9(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{clipValueMin:a,clipValueMax:o}=s,i;K().getBool("WEBGL_PACK_CLIP")?i=new h9(r.shape):i=new p9(r.shape);let u=[[a],[o]];return n.runWebGLProgram(i,[r],r.dtype,u)}var m9={kernelName:Nr,backendName:"webgl",kernelFunc:f9},g9=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 Nw(e,t){return{dataId:t.dataId,dtype:t.dtype,shape:e.shape}}function b9(e){let{inputs:t,backend:n}=e,{x:s}=t,r=n.texData.get(s.dataId),a=new g9(s.shape),o=[Nw(s,r.complexTensorInfos.real),Nw(s,r.complexTensorInfos.imag)];return n.runWebGLProgram(a,o,o[0].dtype)}var y9={kernelName:sp,backendName:"webgl",kernelFunc:b9},v9=class{constructor(e){this.outputShape=[],this.outputShape=C.computeOutShape(e,1),this.variableNames=e.map((a,o)=>`T${o}`);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 o=t[a-1];n.push(`else if (yC < ${t[a]}) setOutput(getT${a}(yR, yC-${o}));`)}let s=t.length,r=t[t.length-1];n.push(`else setOutput(getT${s}(yR, yC-${r}));`),this.userCode=`
void main() {
ivec2 coords = getOutputCoords();
int yR = coords.x;
int yC = coords.y;
${n.join(`
`)}
}
`}},x9=class{constructor(e,t){this.packedInputs=!0,this.packedOutput=!0,this.outputShape=[],this.outputShape=C.computeOutShape(e,t);let n=this.outputShape,s=n.length,r=rt(s),a=ln("coords",s),o=["x","y","z","w","u","v"].slice(0,s);this.variableNames=e.map((f,m)=>`T${m}`);let i=new Array(e.length-1);i[0]=e[0][t];for(let f=1;f<i.length;f++)i[f]=i[f-1]+e[f][t];let u=o[t],l=o.slice(-2),c=o.join(),p=`if (${u} < ${i[0]}) {
return getChannel(
getT0(${c}), vec2(${l.join()}));
}`;for(let f=1;f<i.length;f++){let m=i[f-1];p+=`
if (${u} < ${i[f]} && ${u} >= ${i[f-1]}) {
return getChannel(
getT${f}(${rd(o,u,m)}),
vec2(${rd(l,u,m)}));
}`}let d=i.length,h=i[i.length-1];p+=`
return getChannel(
getT${d}(${rd(o,u,h)}),
vec2(${rd(l,u,h)}));`,this.userCode=`
float getValue(${o.map(f=>"int "+f)}) {
${p}
}
void main() {
${r} coords = getOutputCoords();
vec4 result = vec4(getValue(${a}), 0., 0., 0.);
${a[s-1]} = ${a[s-1]} + 1;
if (${a[s-1]} < ${n[s-1]}) {
result.g = getValue(${a});
}
${a[s-2]} = ${a[s-2]} + 1;
if (${a[s-2]} < ${n[s-2]}) {
result.a = getValue(${a});
}
${a[s-1]} = ${a[s-1]} - 1;
if (${a[s-2]} < ${n[s-2]} &&
${a[s-1]} < ${n[s-1]}) {
result.b = getValue(${a});
}
setOutput(result);
}
`}};function rd(e,t,n){let s=e.indexOf(t);return e.map((a,o)=>o===s?`${a} - ${n}`:a).join()}function oh(e){let{inputs:t,backend:n}=e,{input:s}=t,r=n.texData.get(s.dataId);return Rn({inputs:{x:r.complexTensorInfos.imag},backend:n})}var w9={kernelName:ip,backendName:"webgl",kernelFunc:oh};function jo(e,t,n){let s=e[0].dtype;if(s==="complex64"){let c=e.map(m=>nc({inputs:{input:m},backend:n})),p=e.map(m=>oh({inputs:{input:m},backend:n})),d=jo(c,t,n),h=jo(p,t,n),f=Fr({inputs:{real:d,imag:h},backend:n});return c.forEach(m=>n.disposeIntermediateTensorInfo(m)),p.forEach(m=>n.disposeIntermediateTensorInfo(m)),n.disposeIntermediateTensorInfo(d),n.disposeIntermediateTensorInfo(h),f}let r=n.shouldExecuteOnCPU(e);if(s==="string"&&(r=!0),r){let c=e.map(b=>{let y=w.sizeFromShape(b.shape.slice(t));return pe({inputs:{x:b},backend:n,attrs:{shape:[-1,y]}})}),p=c.map(b=>({vals:n.readSync(b.dataId),shape:b.shape})),d=C.computeOutShape(c.map(b=>b.shape),1),h=c[0].shape[0]===1,f=gX(p,d,s,h),m=C.computeOutShape(e.map(b=>b.shape),t),g=n.makeTensorInfo(m,s,f);return c.forEach(b=>n.disposeIntermediateTensorInfo(b)),g}if(e.length>K().getNumber("WEBGL_MAX_TEXTURES_IN_SHADER")){let c=Math.floor(e.length/2),p=jo(e.slice(0,c),t,n),d=jo(e.slice(c),t,n),h=jo([p,d],t,n);return n.disposeIntermediateTensorInfo(p),n.disposeIntermediateTensorInfo(d),h}if(K().getBool("WEBGL_PACK_ARRAY_OPERATIONS")&&e[0].shape.length>1){let c=new x9(e.map(p=>p.shape),t);return n.runWebGLProgram(c,e,s)}let{tensors2D:a,outShape:o}=k9(e,t,n),i=new v9(a.map(c=>c.shape)),u=n.runWebGLProgram(i,a,s);a.forEach(c=>n.disposeIntermediateTensorInfo(c));let l=pe({inputs:{x:u},attrs:{shape:o},backend:n});return n.disposeIntermediateTensorInfo(u),l}function k9(e,t,n){let s=C.computeOutShape(e.map(a=>a.shape),t);return{tensors2D:e.map(a=>pe({inputs:{x:a},attrs:{shape:[-1,w.sizeFromShape(a.shape.slice(t))]},backend:n})),outShape:s}}function f2(e){let{inputs:t,backend:n,attrs:s}=e,{axis:r}=s,a=w.parseAxisParam(r,t[0].shape)[0],o=C.computeOutShape(t.map(l=>l.shape),a);if(w.sizeFromShape(o)===0)return n.makeTensorInfo(o,t[0].dtype,[]);let i=t.filter(l=>w.sizeFromShape(l.shape)>0);if(i.length===1)return Rn({inputs:{x:i[0]},backend:n});let u=i.map(l=>l.shape);return C.assertParamsConsistent(u,a),jo(i,a,n)}var S9={kernelName:hi,backendName:"webgl",kernelFunc:f2},m2=class{constructor(e,t=!1,n=null,s=!1,r=!1){this.variableNames=["x","W"],this.outputShape=e.outShape;let a=e.padInfo.top,o=e.padInfo.left,i=e.strideHeight,u=e.strideWidth,l=e.dilationHeight,c=e.dilationWidth,p=e.filterHeight,d=e.filterWidth,h=Math.floor(e.inChannels/4)*4,f=e.inChannels%4,m=e.dataFormat==="channelsLast",g=m?1:2,b=m?2:3,y=m?3:1,v="",x="";n&&(s?v=`float activation(float a) {
float b = getPreluActivationWeightsAtOutCoords();
${n}
}`:r?v=`float activation(float a) {
float b = getLeakyreluAlphaAtOutCoords();
${n}
}`:v=`
float activation(float x) {
${n}
}
`,x="result = activation(result);");let k=t?"result += getBiasAtOutCoords();":"";t&&this.variableNames.push("bias"),s&&this.variableNames.push("preluActivationWeights"),r&&this.variableNames.push("leakyreluAlpha"),this.userCode=`
${v}
const ivec2 strides = ivec2(${i}, ${u});
const ivec2 pads = ivec2(${a}, ${o});
void main() {
ivec4 coords = getOutputCoords();
int batch = coords[0];
int d2 = coords[${y}];
ivec2 xRCCorner =
ivec2(coords[${g}], coords[${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 < ${p}; wR++) {
int xR = xRCorner + wR * ${l};
if (xR < 0 || xR >= ${e.inHeight}) {
continue;
}
for (int wC = 0; wC < ${d}; wC++) {
int xC = xCCorner + wC * ${c};
if (xC < 0 || xC >= ${e.inWidth}) {
continue;
}
for (int d1 = 0; d1 < ${h}; d1 += 4) {
vec4 wValues = vec4(
getW(wR, wC, d1, d2),
getW(wR, wC, d1 + 1, d2),
getW(wR, wC, d1 + 2, d2),
getW(wR, wC, d1 + 3, d2)
);
if (${m}) {
vec4 xValues = vec4(
getX(batch, xR, xC, d1),
getX(batch, xR, xC, d1 + 1),
getX(batch, xR, xC, d1 + 2),
getX(batch, xR, xC, d1 + 3)
);
dotProd += dot(xValues, wValues);
} else {
vec4 xValues = vec4(
getX(batch, d1, xR, xC),
getX(batch, d1 + 1, xR, xC),
getX(batch, d1 + 2, xR, xC),
getX(batch, d1 + 3, xR, xC)
);
dotProd += dot(xValues, wValues);
}
}
if (${f===1}) {
if (${m}) {
dotProd +=
getX(batch, xR, xC, ${h}) *
getW(wR, wC, ${h}, d2);
} else {
dotProd +=
getX(batch, ${h}, xR, xC) *
getW(wR, wC, ${h}, d2);
}
} else if (${f===2}) {
vec2 wValues = vec2(
getW(wR, wC, ${h}, d2),
getW(wR, wC, ${h} + 1, d2)
);
if (${m}) {
vec2 xValues = vec2(
getX(batch, xR, xC, ${h}),
getX(batch, xR, xC, ${h} + 1)
);
dotProd += dot(xValues, wValues);
} else {
vec2 xValues = vec2(
getX(batch, ${h}, xR, xC),
getX(batch, ${h} + 1, xR, xC)
);
dotProd += dot(xValues, wValues);
}
} else if (${f===3}) {
vec3 wValues = vec3(
getW(wR, wC, ${h}, d2),
getW(wR, wC, ${h} + 1, d2),
getW(wR, wC, ${h} + 2, d2)
);
if (${m}) {
vec3 xValues = vec3(
getX(batch, xR, xC, ${h}),
getX(batch, xR, xC, ${h} + 1),
getX(batch, xR, xC, ${h} + 2)
);
dotProd += dot(xValues, wValues);
} else {
vec3 xValues = vec3(
getX(batch, ${h}, xR, xC),
getX(batch, ${h} + 1, xR, xC),
getX(batch, ${h} + 2, xR, xC)
);
dotProd += dot(xValues, wValues);
}
}
}
}
float result = dotProd;
${k}
${x}
setOutput(result);
}
`}},I9=class{constructor(e){this.variableNames=["x","W"],this.outputShape=e.outShape;let t=e.padInfo.front,n=e.padInfo.top,s=e.padInfo.left,r=e.strideDepth,a=e.strideHeight,o=e.strideWidth,i=e.dilationDepth,u=e.dilationHeight,l=e.dilationWidth,c=e.filterDepth,p=e.filterHeight,d=e.filterWidth,h=Math.floor(e.inChannels/4)*4,f=e.inChannels%4;this.userCode=`
const ivec3 strides = ivec3(${r}, ${a}, ${o});
const ivec3 pads = ivec3(${t}, ${n}, ${s});
void main() {
ivec5 coords = getOutputCoords();
int batch = coords.x;
int d2 = coords.u;
ivec3 xFRCCorner = ivec3(coords.y, coords.z, coords.w) * strides - pads;
int xFCorner = xFRCCorner.x;
int xRCorner = xFRCCorner.y;
int xCCorner = xFRCCorner.z;
// Convolve x(?, ?, ?, d1) with w(:, :, :, d1, d2) to get
// y(yF, yR, yC, d2). ? = to be determined. : = across all
// values in that axis.
float dotProd = 0.0;
for (int wF = 0; wF < ${c}; wF++) {
int xF = xFCorner + wF * ${i};
if (xF < 0 || xF >= ${e.inDepth}) {
continue;
}
for (int wR = 0; wR < ${p}; wR++) {
int xR = xRCorner + wR * ${u};
if (xR < 0 || xR >= ${e.inHeight}) {
continue;
}
for (int wC = 0; wC < ${d}; wC++) {
int xC = xCCorner + wC * ${l};
if (xC < 0 || xC >= ${e.inWidth}) {
continue;
}
for (int d1 = 0; d1 < ${h}; d1 += 4) {
vec4 xValues = vec4(
getX(batch, xF, xR, xC, d1),
getX(batch, xF, xR, xC, d1 + 1),
getX(batch, xF, xR, xC, d1 + 2),
getX(batch, xF, xR, xC, d1 + 3)
);
vec4 wValues = vec4(
getW(wF, wR, wC, d1, d2),
getW(wF, wR, wC, d1 + 1, d2),
getW(wF, wR, wC, d1 + 2, d2),
getW(wF, wR, wC, d1 + 3, d2)
);
dotProd += dot(xValues, wValues);
}
if (${f===1}) {
dotProd +=
getX(batch, xF, xR, xC, ${h}) *
getW(wF, wR, wC, ${h}, d2);
} else if (${f===2}) {
vec2 xValues = vec2(
getX(batch, xF, xR, xC, ${h}),
getX(batch, xF, xR, xC, ${h} + 1)
);
vec2 wValues = vec2(
getW(wF, wR, wC, ${h}, d2),
getW(wF, wR, wC, ${h} + 1, d2)
);
dotProd += dot(xValues, wValues);
} else if (${f===3}) {
vec3 xValues = vec3(
getX(batch, xF, xR, xC, ${h}),
getX(batch, xF, xR, xC, ${h} + 1),
getX(batch, xF, xR, xC, ${h} + 2)
);
vec3 wValues = vec3(
getW(wF, wR, wC, ${h}, d2),
getW(wF, wR, wC, ${h} + 1, d2),
getW(wF, wR, wC, ${h} + 2, d2)
);
dotProd += dot(xValues, wValues);
}
}
}
}
setOutput(dotProd);
}
`}},C9=class{constructor(e,t){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,this.customUniforms=[{name:"inputShape",type:"ivec4"},{name:"pad",type:"ivec2"},{name:"stride",type:"ivec2"},{name:"dilation",type:"ivec2"},{name:"inChannels",type:"int"},{name:"itemsPerBlockRow",type:"int"},{name:"outWidth",type:"int"}],this.outputShape=e,this.enableShapeUniforms=Sn(this.outputShape.length);let{dataFormat:n}=t,s=mn(),r=n==="channelsLast",a=r?1:2,o=r?2:3,i=this.enableShapeUniforms?"if(blockIndex < outShape[2] && pos < outShape[1]) {":`if(blockIndex < ${e[2]} && pos < ${e[1]}) {`,u="";for(let l=0;l<=1;l++)for(let c=0;c<=1;c++)u+=`
blockIndex = rc.z + ${c};
pos = rc.y + ${l};
${i}
offsetY = int(blockIndex / outWidth) * stride[0] - pad[0];
d0 = offsetY + dilation[0] * (pos / itemsPerBlockRow);
if(d0 < inputShape[${a}] && d0 >= 0) {
// Use custom imod instead mod. On Intel GPU, mod may generate
// unexpected value.
// https://github.com/tensorflow/tfjs/issues/5447
offsetX = imod(blockIndex, outWidth) * stride[1] - pad[1];
d1 = offsetX + dilation[1] * (imod(pos, itemsPerBlockRow) /
inChannels);
if(d1 < inputShape[${o}] && d1 >= 0) {
ch = imod(pos, inChannels);
if (${r}) {
innerDims = vec2(d1, ch);
result[${l*2+c}] = getChannel(
getA(rc.x, d0, int(innerDims.x),
int(innerDims.y)), innerDims);
} else {
innerDims = vec2(d0, d1);
result[${l*2+c}] = getChannel(
getA(rc.x, ch, int(innerDims.x),
int(innerDims.y)), innerDims);
}
}
}
}
`;this.userCode=`
void main() {
ivec3 rc = getOutputCoords();
vec4 result = vec4(0);
int blockIndex, pos, offsetY, d0, offsetX, d1, ch;
vec2 innerDims;
${u}
${s.output} = result;
}
`}};function qd(e,t){let n=e.length;return n>=3?t?[...e.slice(0,-3),e[n-3]*e[n-2],e[n-1]]:[...e.slice(0,-3),e[n-3],e[n-2]*e[n-1]]:!t&&n===1&&e[0]>1?[e[0],1]:null}function g2({x:e,filter:t,convInfo:n,backend:s,bias:r=null,preluActivationWeights:a=null,leakyreluAlpha:o=0,activation:i=null}){let u=e.shape,l=s.texData.get(e.dataId),c=n.inChannels,p=u[0]*u[1]*u[2],d=n.outChannels,h=n.dataFormat==="channelsLast",f=!1,m=!1,g,b=[];if(a!=null){let x=qd(a.shape,h);x!=null&&(a=pe({inputs:{x:a},backend:s,attrs:{shape:x}}),b.push(a))}if(r!=null){let x=qd(r.shape,h);x!=null&&(r=pe({inputs:{x:r},backend:s,attrs:{shape:x}}),b.push(r))}if(!((p===1||d===1)&&c>l2)&&l.isPacked&&h&&l.texture!=null&&u[2]%2!==0&&w.arraysEqual(l.shape.slice(-3),u.slice(-3))){let x=u[0]*u[1]*(u[2]+1),k={dataId:e.dataId,shape:[1,x,n.inChannels],dtype:e.dtype},I=l.shape;l.shape=l.shape.slice(),l.shape[l.shape.length-2]++,w.assert(al(l.shape,k.shape),()=>`packed reshape ${l.shape} to ${k.shape} isn't free`);let $=pe({inputs:{x:t},backend:s,attrs:{shape:[1,n.inChannels,n.outChannels]}});b.push($);let R=Hd({a:k,b:$,backend:s,transposeA:f,transposeB:m,bias:r,activation:i,preluActivationWeights:a,leakyreluAlpha:o}),E=s.texData.get(R.dataId);w.assert(E.isPacked,()=>"batchMatMul result is expected to be packed"),l.shape=I,E.shape=n.outShape,g=Rn({inputs:{x:R},backend:s}),g.shape=n.outShape,b.push(R)}else{let x=n.outHeight*n.outWidth,k=pe({inputs:{x:e},backend:s,attrs:{shape:h?[n.batchSize,x,n.inChannels]:[n.batchSize,n.inChannels,x]}}),I=pe({inputs:{x:t},backend:s,attrs:{shape:[1,n.inChannels,n.outChannels]}}),$=Hd({a:h?k:I,b:h?I:k,transposeA:!h,transposeB:m,backend:s,bias:r,activation:i,preluActivationWeights:a,leakyreluAlpha:o});g=pe({inputs:{x:$},backend:s,attrs:{shape:n.outShape}}),b.push(k),b.push(I),b.push($)}for(let x of b)s.disposeIntermediateTensorInfo(x);return g}function b2({x:e,filter:t,convInfo:n,backend:s,bias:r=null,preluActivationWeights:a=null,leakyreluAlpha:o=0,activation:i=null}){let{filterWidth:u,filterHeight:l,inChannels:c,outWidth:p,outHeight:d,dataFormat:h}=n,f=h==="channelsLast",m=u*l*c,g=d*p,b=[n.batchSize,m,g],y=!0,v=!1,x=[];if(a!=null){let X=qd(a.shape,f);X!=null&&(a=pe({inputs:{x:a},backend:s,attrs:{shape:X}}),x.push(a))}if(r!=null){let X=qd(r.shape,f);X!=null&&(r=pe({inputs:{x:r},backend:s,attrs:{shape:X}}),x.push(r))}let k=pe({inputs:{x:t},backend:s,attrs:{shape:[1,m,w.sizeFromShape(t.shape)/m]}});x.push(k);let I=new C9(b,n),$=[e.shape,[n.padInfo.top,n.padInfo.left],[n.strideHeight,n.strideWidth],[n.dilationHeight,n.dilationWidth],[n.inChannels],[n.filterWidth*n.inChannels],[n.outWidth]],R=s.runWebGLProgram(I,[e],"float32",$),E=pe({inputs:{x:R},backend:s,attrs:{shape:b}});x.push(R),x.push(E);let P=r!=null,A=a!=null,D=i==="leakyrelu",T=i?sh(i,!0):null,L=new u2(f?E.shape:k.shape,f?k.shape:E.shape,f?[n.batchSize,g,n.outChannels]:[n.batchSize,n.outChannels,g],y,v,P,T,A,D),W=f?[E,k]:[k,E];if(r&&W.push(r),A&&W.push(a),D){let X=s.makeTensorInfo([],"float32",w.createScalarValue(o,"float32"));W.push(X),x.push(X)}let j=s.runWebGLProgram(L,W,"float32"),Y=pe({inputs:{x:j},backend:s,attrs:{shape:n.outShape}});x.push(j);for(let X of x)s.disposeIntermediateTensorInfo(X);return Y}function N9(e){let{inputs:t,backend:n,attrs:s}=e,{x:r,filter:a}=t,{strides:o,pad:i,dataFormat:u,dilations:l,dimRoundingMode:c}=s,p=C.convertConv2DDataFormat(u),d=C.computeConv2DInfo(r.shape,a.shape,o,l,i,c,!1,p),h;if(d.filterHeight===1&&d.filterWidth===1&&d.dilationHeight===1&&d.dilationWidth===1&&d.strideHeight===1&&d.strideWidth===1&&(d.padInfo.type==="SAME"||d.padInfo.type==="VALID"))h=g2({x:r,filter:a,convInfo:d,backend:n});else if(K().getBool("WEBGL_CONV_IM2COL"))h=b2({x:r,filter:a,convInfo:d,backend:n});else{let m=new m2(d);h=n.runWebGLProgram(m,[r,a],"float32")}let f=pe({inputs:{x:h},backend:n,attrs:{shape:d.outShape}});return n.disposeIntermediateTensorInfo(h),f}var T9={kernelName:_a,backendName:"webgl",kernelFunc:N9},$9=class{constructor(e){this.variableNames=["x","dy"],this.outputShape=e.filterShape;let t=e.strideHeight,n=e.strideWidth,s=e.padInfo.top,r=e.padInfo.left,a=e.dataFormat==="channelsLast";this.userCode=`
void main() {
ivec4 coords = getOutputCoords();
int wR = coords.x;
int wC = coords.y;
int d1 = coords.z;
int d2 = coords.w;
// Convolve x(?, ?, d1) with dy(:, :, d2) to get dw(wR, wC, d1, d2).
// ? = to be determined. : = across all values in that axis.
float dotProd = 0.0;
for (int b = 0; b < ${e.batchSize}; b++) {
for (int yR = 0; yR < ${e.outHeight}; yR++) {
int xR = wR + yR * ${t} - ${s};
if (xR < 0 || xR >= ${e.inHeight}) {
continue;
}
for (int yC = 0; yC < ${e.outWidth}; yC++) {
int xC = wC + yC * ${n} - ${r};
if (xC < 0 || xC >= ${e.inWidth}) {
continue;
}
if (${a}) {
float dyValue = getDy(b, yR, yC, d2);
float xValue = getX(b, xR, xC, d1);
dotProd += (xValue * dyValue);
} else {
float dyValue = getDy(b, d2, yR, yC);
float xValue = getX(b, d1, xR, xC);
dotProd += (xValue * dyValue);
}
}
}
}
setOutput(dotProd);
}
`}},_9=class{constructor(e){this.variableNames=["dy","W"],this.outputShape=e.inShape;let t=e.filterHeight,n=e.filterWidth,s=e.strideHeight,r=e.strideWidth,a=e.dataFormat==="channelsLast",o=t-1-e.padInfo.top,i=n-1-e.padInfo.left,u=a?1:2,l=a?2:3,c=a?3:1;this.userCode=`
const ivec2 pads = ivec2(${o}, ${i});
void main() {
ivec4 coords = getOutputCoords();
int batch = coords[0];
int d1 = coords[${c}];
ivec2 dyCorner = ivec2(coords[${u}], coords[${l}]) - pads;
int dyRCorner = dyCorner.x;
int dyCCorner = dyCorner.y;
// Convolve dy(?, ?, d2) with w(:, :, d1, d2) to compute dx(xR, xC, d1).
// ? = to be determined. : = across all values in that axis.
float dotProd = 0.0;
for (int wR = 0; wR < ${t}; wR++) {
float dyR = float(dyRCorner + wR) / ${s}.0;
if (dyR < 0.0 || dyR >= ${e.outHeight}.0 || fract(dyR) > 0.0) {
continue;
}
int idyR = int(dyR);
int wRPerm = ${t} - 1 - wR;
for (int wC = 0; wC < ${n}; wC++) {
float dyC = float(dyCCorner + wC) / ${r}.0;
if (dyC < 0.0 || dyC >= ${e.outWidth}.0 ||
fract(dyC) > 0.0) {
continue;
}
int idyC = int(dyC);
int wCPerm = ${n} - 1 - wC;
for (int d2 = 0; d2 < ${e.outChannels}; d2++) {
if (${a}) {
float xValue = getDy(batch, idyR, idyC, d2);
float wValue = getW(wRPerm, wCPerm, d1, d2);
dotProd += xValue * wValue;
} else {
float xValue = getDy(batch, d2, idyR, idyC);
float wValue = getW(wRPerm, wCPerm, d1, d2);
dotProd += xValue * wValue;
}
}
}
}
setOutput(dotProd);
}
`}},A9=class{constructor(e){this.variableNames=["x","dy"],this.outputShape=e.filterShape;let t=e.strideDepth,n=e.strideHeight,s=e.strideWidth,r=e.padInfo.front,a=e.padInfo.top,o=e.padInfo.left;this.userCode=`
void main() {
ivec5 coords = getOutputCoords();
int wF = coords.x;
int wR = coords.y;
int wC = coords.z;
int d1 = coords.w;
int d2 = coords.u;
float dotProd = 0.0;
for (int b = 0; b < ${e.batchSize}; b++) {
for (int yF = 0; yF < ${e.outDepth}; yF++) {
int xF = wF + yF * ${t} - ${r};
if (xF < 0 || xF >= ${e.inDepth}) {
continue;
}
for (int yR = 0; yR < ${e.outHeight}; yR++) {
int xR = wR + yR * ${n} - ${a};
if (xR < 0 || xR >= ${e.inHeight}) {
continue;
}
for (int yC = 0; yC < ${e.outWidth}; yC++) {
int xC = wC + yC * ${s} - ${o};
if (xC < 0 || xC >= ${e.inWidth}) {
continue;
}
float dyValue = getDy(b, yF, yR, yC, d2);
float xValue = getX(b, xF, xR, xC, d1);
dotProd += (xValue * dyValue);
}
}
}
}
setOutput(dotProd);
}
`}},E9=class{constructor(e){this.variableNames=["dy","W"],this.outputShape=e.inShape;let t=e.filterDepth,n=e.filterHeight,s=e.filterWidth,r=e.strideDepth,a=e.strideHeight,o=e.strideWidth,i=t-1-e.padInfo.front,u=n-1-e.padInfo.top,l=s-1-e.padInfo.left;this.userCode=`
const ivec3 pads = ivec3(${i}, ${u}, ${l});
void main() {
ivec5 coords = getOutputCoords();
int batch = coords.x;
int d1 = coords.u;
ivec3 dyCorner = ivec3(coords.y, coords.z, coords.w) - pads;
int dyFCorner = dyCorner.x;
int dyRCorner = dyCorner.y;
int dyCCorner = dyCorner.z;
float dotProd = 0.0;
for (int wF = 0; wF < ${t}; wF++) {
float dyF = float(dyFCorner + wF) / ${r}.0;
if (dyF < 0.0 || dyF >= ${e.outDepth}.0 || fract(dyF) > 0.0) {
continue;
}
int idyF = int(dyF);
int wFPerm = ${t} - 1 - wF;
for (int wR = 0; wR < ${n}; wR++) {
float dyR = float(dyRCorner + wR) / ${a}.0;
if (dyR < 0.0 || dyR >= ${e.outHeight}.0 ||
fract(dyR) > 0.0) {
continue;
}
int idyR = int(dyR);
int wRPerm = ${n} - 1 - wR;
for (int wC = 0; wC < ${s}; wC++) {
float dyC = float(dyCCorner + wC) / ${o}.0;
if (dyC < 0.0 || dyC >= ${e.outWidth}.0 ||
fract(dyC) > 0.0) {
continue;
}
int idyC = int(dyC);
int wCPerm = ${s} - 1 - wC;
for (int d2 = 0; d2 < ${e.outChannels}; d2++) {
float xValue = getDy(batch, idyF, idyR, idyC, d2);
float wValue = getW(wFPerm, wRPerm, wCPerm, d1, d2);
dotProd += xValue * wValue;
}
}
}
}
setOutput(dotProd);
}
`}};function R9(e){let{inputs:t,backend:n,attrs:s}=e,{x:r,dy:a}=t,{strides:o,pad:i,dataFormat:u,dimRoundingMode:l,filterShape:c}=s,p=C.convertConv2DDataFormat(u),d=C.computeConv2DInfo(r.shape,c,o,1,i,l,!1,p),h=new $9(d);return n.runWebGLProgram(h,[r,a],"float32")}var D9={kernelName:wg,backendName:"webgl",kernelFunc:R9};function F9(e){let{inputs:t,backend:n,attrs:s}=e,{dy:r,filter:a}=t,{inputShape:o,strides:i,pad:u,dataFormat:l,dimRoundingMode:c}=s,p=C.convertConv2DDataFormat(l),d=C.computeConv2DInfo(o,a.shape,i,1,u,c,!1,p),h=new _9(d);return n.runWebGLProgram(h,[r,a],"float32")}var O9={kernelName:Aa,backendName:"webgl",kernelFunc:F9};function P9(e){let{inputs:t,backend:n,attrs:s}=e,{x:r,filter:a}=t,{strides:o,pad:i,dilations:u}=s,l=C.computeConv3DInfo(r.shape,a.shape,o,u,i),c=new I9(l);return n.runWebGLProgram(c,[r,a],"float32")}var z9={kernelName:rp,backendName:"webgl",kernelFunc:P9};function L9(e){let{inputs:t,backend:n,attrs:s}=e,{x:r,dy:a}=t,{strides:o,pad:i,filterShape:u}=s,l=C.computeConv3DInfo(r.shape,u,o,1,i),c=new A9(l);return n.runWebGLProgram(c,[r,a],"float32")}var M9={kernelName:kg,backendName:"webgl",kernelFunc:L9};function B9(e){let{inputs:t,backend:n,attrs:s}=e,{dy:r,filter:a}=t,{pad:o,strides:i,inputShape:u}=s,l=C.computeConv3DInfo(u,a.shape,i,1,o),c=new E9(l);return n.runWebGLProgram(c,[r,a],"float32")}var V9={kernelName:Sg,backendName:"webgl",kernelFunc:B9},W9=du+`
return cos(x);
`,U9=Ke({opSnippet:W9}),G9={kernelName:Ea,backendName:"webgl",kernelFunc:U9},H9=`
float e2x = exp(-x);
return (e2x + 1.0 / e2x) / 2.0;
`,q9=Ke({opSnippet:H9}),j9={kernelName:Ra,backendName:"webgl",kernelFunc:q9},K9=class{constructor(e,t,n,s,r){this.variableNames=["Image","Boxes","BoxInd"],this.outputShape=[];let[a,o,i,u]=e,[l]=t,[c,p]=n;this.outputShape=[l,c,p,u];let d=s==="bilinear"?1:0,[h,f]=[`${o-1}.0`,`${i-1}.0`],[m,g,b]=c>1?[`${(o-1)/(c-1)}`,"(y2-y1) * height_ratio",`y1*${h} + float(y)*(height_scale)`]:["0.0","0.0",`0.5 * (y1+y2) * ${h}`],[y,v,x]=p>1?[`${(i-1)/(p-1)}`,"(x2-x1) * width_ratio",`x1*${f} + float(x)*(width_scale)`]:["0.0","0.0",`0.5 * (x1+x2) * ${f}`];this.userCode=`
const float height_ratio = float(${m});
const float width_ratio = float(${y});
void main() {
ivec4 coords = getOutputCoords();
int b = coords[0];
int y = coords[1];
int x = coords[2];
int d = coords[3];
// get box vals
float y1 = getBoxes(b,0);
float x1 = getBoxes(b,1);
float y2 = getBoxes(b,2);
float x2 = getBoxes(b,3);
// get image in batch index
int bInd = round(getBoxInd(b));
if(bInd < 0 || bInd >= ${a}) {
return;
}
float height_scale = ${g};
float width_scale = ${v};
float in_y = ${b};
if( in_y < 0.0 || in_y > ${h} ) {
setOutput(float(${r}));
return;
}
float in_x = ${x};
if( in_x < 0.0 || in_x > ${f} ) {
setOutput(float(${r}));
return;
}
vec2 sourceFracIndexCR = vec2(in_x,in_y);
if(${d} == 1) {
// Compute the four integer indices.
ivec2 sourceFloorCR = ivec2(sourceFracIndexCR);
ivec2 sourceCeilCR = ivec2(ceil(sourceFracIndexCR));
float topLeft = getImage(b, sourceFloorCR.y, sourceFloorCR.x, d);
float bottomLeft = getImage(b, sourceCeilCR.y, sourceFloorCR.x, d);
float topRight = getImage(b, sourceFloorCR.y, sourceCeilCR.x, d);
float bottomRight = getImage(b, sourceCeilCR.y, sourceCeilCR.x, d);
vec2 fracCR = sourceFracIndexCR - vec2(sourceFloorCR);
float top = topLeft + (topRight - topLeft) * fracCR.x;
float bottom = bottomLeft + (bottomRight - bottomLeft) * fracCR.x;
float newValue = top + (bottom - top) * fracCR.y;
setOutput(newValue);
} else {
// Compute the coordinators of nearest neighbor point.
ivec2 sourceNearestCR = ivec2(floor(
sourceFracIndexCR + vec2(0.5,0.5)));
float newValue = getImage(b, sourceNearestCR.y, sourceNearestCR.x, d);
setOutput(newValue);
}
}
`}},X9=e=>{let{inputs:t,backend:n,attrs:s}=e,{image:r,boxes:a,boxInd:o}=t,{cropSize:i,method:u,extrapolationValue:l}=s,c=new K9(r.shape,a.shape,i,u,l);return n.runWebGLProgram(c,[r,a,o],"float32")},Y9={kernelName:mi,backendName:"webgl",kernelFunc:X9},Tw=class{constructor(e,t,n,s){this.op=e,this.outputShape=t,this.variableNames=["x"],this.customUniforms=[{name:"index",type:"float"}];let r=this.outputShape.length,a=this.op==="*"?"1.0":"0.0",o=n?a:`getX(${$w(r,"coords",this.op)})`,i=this.outputShape[this.outputShape.length-1],u="",l="";n?(u=s?`end != ${i-1}`:"end != 0",l=s?"end + 1":"end - 1"):(u=s?`end + pow2 < ${i}`:"end >= pow2",l=s?"end + pow2":"end - pow2"),this.userCode=`
void main() {
${rt(r)} coords = getOutputCoords();
int end = ${_w(r,"coords",this.op)};
float val = ${o};
int pow2 = int(pow(2.0, index));
if (${u}) {
int idx = ${l};
${_w(r,"coords",this.op)} = idx;
val ${this.op}= getX(${$w(r,"coords",this.op)});
}
setOutput(val);
}
`}};function $w(e,t,n){if(e===1)return`${t}`;if(e===2)return`${t}.x, ${t}.y`;if(e===3)return`${t}.x, ${t}.y, ${t}.z`;if(e===4)return`${t}.x, ${t}.y, ${t}.z, ${t}.w`;throw new Error(`Cumulative ${n} for rank ${e} is not yet supported`)}function _w(e,t,n){if(e===1)return`${t}`;if(e===2)return`${t}.y`;if(e===3)return`${t}.z`;if(e===4)return`${t}.w`;throw new Error(`Cumulative ${n} for rank ${e} is not yet supported`)}function y2(e,t,n,s,r,a){let o=t.shape.length,i=C.getAxesPermutation([s],o),u=t;i!=null&&(u=pn({inputs:{x:t},backend:n,attrs:{perm:i}}));let l=C.getInnerMostAxes(1,o)[0];if(l!==o-1)throw new Error(`WebGL cumprod shader expects an inner-most axis=${t.shape.length-1} but got axis=${s}`);let c=u.shape[l],p=Rn({inputs:{x:u},backend:n});for(let d=0;d<=Math.ceil(Math.log2(c))-1;d++){let h=new Tw(e,u.shape,!1,a),f=[[d]],m=p;p=n.runWebGLProgram(h,[p],p.dtype,f),n.disposeIntermediateTensorInfo(m)}if(r){let d=new Tw(e,u.shape,r,a),h=p;p=n.runWebGLProgram(d,[p],p.dtype),n.disposeIntermediateTensorInfo(h)}if(i!=null){let d=C.getUndoAxesPermutation(i),h=pn({inputs:{x:p},backend:n,attrs:{perm:d}});return n.disposeIntermediateTensorInfo(p),n.disposeIntermediateTensorInfo(u),h}return p}function Q9(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{axis:a,exclusive:o,reverse:i}=s;return y2("*",r,n,a,o,i)}var Z9={kernelName:fi,backendName:"webgl",kernelFunc:Q9};function J9(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{axis:a,exclusive:o,reverse:i}=s;return y2("+",r,n,a,o,i)}var eQ={kernelName:Da,backendName:"webgl",kernelFunc:J9};function tQ(e){let{inputs:t,backend:n,attrs:s}=e,{x:r,weights:a}=t,{size:o,binaryOutput:i}=s;if(r.shape.length===1){let u=n.readSync(r.dataId),l=n.readSync(a.dataId),c=Y1(u,l,a.dtype,a.shape,o);return n.makeTensorInfo([o],a.dtype,c)}else if(r.shape.length===2){let u=n.bufferSync(r),l=n.bufferSync(a),c=fX(u,l,o,i);return n.makeTensorInfo(c.shape,a.dtype,c.values)}throw new Error(`Error in denseBincount: input must be at most rank 2, but got rank${r.shape.length}.`)}var nQ={kernelName:Ig,backendName:"webgl",kernelFunc:tQ},sQ=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 rQ(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{blockSize:a,dataFormat:o}=s,i=r.shape[0],u=o==="NHWC"?r.shape[1]:r.shape[2],l=o==="NHWC"?r.shape[2]:r.shape[3],c=o==="NHWC"?r.shape[3]:r.shape[1],p=u*a,d=l*a,h=c/(a*a),f=o==="NHWC"?[i,p,d,h]:[i,h,p,d],m=new sQ(f,a,o);return n.runWebGLProgram(m,[r],r.dtype)}var aQ={kernelName:gi,backendName:"webgl",kernelFunc:rQ},v2=class{constructor(e,t=!1,n=null,s=!1,r=!1){this.variableNames=["x","W"],this.customUniforms=[{name:"pads",type:"ivec2"},{name:"strides",type:"ivec2"},{name:"dilations",type:"ivec2"},{name:"inDims",type:"ivec2"}],this.outputShape=e.outShape,this.enableShapeUniforms=Sn(this.outputShape.length);let a=e.filterHeight,o=e.filterWidth,i=e.outChannels/e.inChannels,u="",l="";n&&(s?u=`float activation(float a) {
float b = getPreluActivationWeightsAtOutCoords();
${n}
}`:r?u=`float activation(float a) {
float b = getLeakyreluAlphaAtOutCoords();
${n}
}`:u=`
float activation(float x) {
${n}
}
`,l="result = activation(result);");let c=t?"result += getBiasAtOutCoords();":"";t&&this.variableNames.push("bias"),s&&this.variableNames.push("preluActivationWeights"),r&&this.variableNames.push("leakyreluAlpha"),this.userCode=`
${u}
void main() {
ivec4 coords = getOutputCoords();
int batch = coords.x;
ivec2 xRCCorner = coords.yz * strides - pads;
int d2 = coords.w;
int d1 = d2 / ${i};
int q = d2 - d1 * ${i};
int xRCorner = xRCCorner.x;
int xCCorner = xRCCorner.y;
// Convolve x(?, ?, d1) with w(:, :, d1, q) to get y(yR, yC, d2).
// ? = to be determined. : = across all values in that axis.
float dotProd = 0.0;
// TO DO(dsmilkov): Flatten the two for loops and vec4 the operations.
for (int wR = 0; wR < ${a}; wR++) {
int xR = xRCorner + wR * dilations[0];
if (xR < 0 || xR >= inDims[0]) {
continue;
}
for (int wC = 0; wC < ${o}; wC++) {
int xC = xCCorner + wC * dilations[1];
if (xC < 0 || xC >= inDims[1]) {
continue;
}
float xVal = getX(batch, xR, xC, d1);
float wVal = getW(wR, wC, d1, q);
dotProd += xVal * wVal;
}
}
float result = dotProd;
${c}
${l}
setOutput(result);
}
`}},x2=class{constructor(e,t=!1,n=null,s=!1,r=!1){this.variableNames=["x","W"],this.packedInputs=!0,this.packedOutput=!0,this.customUniforms=[{name:"pads",type:"ivec2"},{name:"strides",type:"ivec2"},{name:"dilations",type:"ivec2"},{name:"inDims",type:"ivec2"}],this.outputShape=e.outShape,this.enableShapeUniforms=Sn(this.outputShape.length);let a=e.outChannels/e.inChannels,o=e.padInfo.left,i=e.strideWidth,u=e.dilationWidth,l=e.filterHeight,c=e.filterWidth,p=c,d=`
int xR; int xC; int xCOffset;
vec4 wTexel; vec4 previous; vec4 final;`;for(let g=0;g<c;g++)d+=`
vec4 xTexelC${g*2};
int xTexelC${g*2}Ready;
vec4 xTexelC${g*2+1};
int xTexelC${g*2+1}Ready;
vec4 xC${g};`;d+=`
for (int r = 0; r < ${l}; r++) {
`;for(let g=0;g<c;g++)d+=`
xTexelC${g*2} = vec4(0.0);
xTexelC${g*2}Ready = 0;
xTexelC${g*2+1} = vec4(0.0);
xTexelC${g*2+1}Ready = 0;
xC${g} = vec4(0.0);`;d+=`
xR = xRCorner + r * dilations[0];
if (xR >=0 && xR < inDims[0]) {
`;for(let g=0;g<(p+1)/2;g++){let b=g*2;if(d+=`
xC = xCCorner + ${b*u};
`,i===1){if(b<c&&(o%2===1?(d+=`
xCOffset = xC + 1;
if (xCOffset >= 0 && xCOffset < inDims[1] && xTexelC${b}Ready == 0) {
xTexelC${b} = getX(batch, xR, xCOffset, d1);
// Need to manually clear unused channels in case
// we're reading from recycled texture.
if (xCOffset + 1 >= inDims[1]) {
xTexelC${b}.zw = vec2(0.0);
}
xTexelC${b}Ready = 1;
}
`,u===1&&b>0?d+=`
xC${b} = vec4(xTexelC${b-2}.zw, xTexelC${b}.xy);
`:d+=`
xCOffset = xC + 1 - 2;
if (xCOffset >= 0 && xCOffset < inDims[1]) {
previous = getX(batch, xR, xCOffset, d1);
// Need to manually clear unused channels in case
// we're reading from recycled texture.
if (xCOffset + 1 >= inDims[1]) {
previous.zw = vec2(0.0);
}
xC${b} = vec4(previous.zw, xTexelC${b}.xy);
} else {
xC${b} = vec4(0.0, 0.0, xTexelC${b}.xy);
}
`):d+=`
if (xC >= 0 && xC < inDims[1] && xTexelC${b}Ready == 0) {
xTexelC${b} = getX(batch, xR, xC, d1);
if (xC + 1 >= inDims[1]) {
xTexelC${b}.zw = vec2(0.0);
}
xTexelC${b}Ready = 1;
}
xC${b} = xTexelC${b};
`,b+1<c)){let y=o%2===0?w.nearestLargerEven(u):u;u%2===0&&o%2===1||u%2!==0&&o%2!==1?(d+=`
xCOffset = xC + imod(pads[1], 2) + ${y};
if (xCOffset >= 0 && xCOffset < inDims[1] && xTexelC${b+1}Ready == 0) {
xTexelC${b+1} = getX(batch, xR, xCOffset, d1);
// Need to manually clear unused channels in case
// we're reading from recycled texture.
if (xCOffset + 1 >= inDims[1]) {
xTexelC${b+1}.zw = vec2(0.0);
}
xTexelC${b+1}Ready = 1;
}
`,u>1&&(d+=`
xCOffset -= 2;
if (xCOffset >= 0 && xCOffset < inDims[1] && xTexelC${b}Ready == 0) {
xTexelC${b} = getX(batch, xR, xCOffset, d1);
xTexelC${b}Ready = 1;
}
`),d+=`
xC${b+1} = vec4(xTexelC${b}.zw, xTexelC${b+1}.xy);
`):y===1?d+=`
xC${b+1} = xTexelC${b};
`:d+=`
xCOffset = xC + ${y};
if (xCOffset >= 0 && xCOffset < inDims[1] && xTexelC${b+1}Ready == 0) {
xTexelC${b+1} = getX(batch, xR, xCOffset, d1);
if (xCOffset + 1 >= inDims[1]) {
xTexelC${b+1}.zw = vec2(0.0);
}
xTexelC${b+1}Ready = 1;
}
xC${b+1} = xTexelC${b+1};
`}}else b<c&&(o%2===1?(d+=`
xCOffset = xC + 1 - strides[1];
if(xCOffset >= 0 && xCOffset < inDims[1] && xTexelC${b}Ready == 0) {
xTexelC${b} = getX(batch, xR, xCOffset, d1);
// Need to manually clear unused channels in case
// we're reading from recycled texture.
if (xCOffset + 1 >= inDims[1]) {
xTexelC${b}.zw = vec2(0.0);
}
xTexelC${b}Ready = 1;
}
if(xC + 1 >= 0 && xC + 1 < inDims[1] && xTexelC${b+1}Ready == 0) {
xTexelC${b+1} = getX(batch, xR, xC + 1, d1);
// Need to manually clear unused channels in case
// we're reading from recycled texture.
if (xC + 2 >= inDims[1]) {
xTexelC${b+1}.zw = vec2(0.0);
}
xTexelC${b+1}Ready = 1;
}
xC${b} = vec4(xTexelC${b}.zw, xTexelC${b+1}.zw);
`,b+1<c&&(d+=`
final = vec4(0.0);
xCOffset = xC + 1 + strides[1];
if(xCOffset >= 0 && xCOffset < inDims[1]) {
final = getX(batch, xR, xCOffset, d1);
}
xC${b+1} = vec4(xTexelC${b+1}.xy, final.xy);
`)):(d+=`
if(xC >= 0 && xC < inDims[1] && xTexelC${b}Ready == 0) {
xTexelC${b} = getX(batch, xR, xC, d1);
if (xC + 1 >= inDims[1]) {
xTexelC${b}.zw = vec2(0.0);
}
xTexelC${b}Ready = 1;
}
xCOffset = xC + strides[1];
if(xCOffset >= 0 && xCOffset < inDims[1] && xTexelC${b+1}Ready == 0) {
xTexelC${b+1} = getX(batch, xR, xCOffset, d1);
if (xCOffset + 1 >= inDims[1]) {
xTexelC${b+1}.zw = vec2(0.);
}
xTexelC${b+1}Ready = 1;
}
xC${b} = vec4(
xTexelC${b}.xy, xTexelC${b+1}.xy);
`,b+1<c&&(d+=`
xC${b+1} = vec4(xTexelC${b}.zw, xTexelC${b+1}.zw);
`)));b<c&&(d+=`
wTexel = getW(r, ${b}, d1, q);
dotProd += xC${b} * vec4(wTexel.xz, wTexel.xz);
`,b+1<c&&(d+=`
wTexel = getW(r, ${b+1}, d1, q);
dotProd += xC${b+1} * vec4(wTexel.xz, wTexel.xz);
`))}d+=`
}
`,d+=`
}
`;let h="",f="";n&&(s?h=`vec4 activation(vec4 a) {
vec4 b = getPreluActivationWeightsAtOutCoords();
${n}
}`:r?h=`vec4 activation(vec4 a) {
vec4 b = getLeakyreluAlphaAtOutCoords();
${n}
}`:h=`vec4 activation(vec4 x) {
${n}
}`,f="result = activation(result);");let m=t?"result += getBiasAtOutCoords();":"";t&&this.variableNames.push("bias"),s&&this.variableNames.push("preluActivationWeights"),r&&this.variableNames.push("leakyreluAlpha"),this.userCode=`
${h}
void main() {
ivec4 coords = getOutputCoords();
int batch = coords.x;
ivec2 xRCCorner = coords.yz * strides - pads;
int d2 = coords.w;
int d1 = d2 / ${a};
int q = d2 - d1 * ${a};
int xRCorner = xRCCorner.x;
int xCCorner = xRCCorner.y;
//intialize dotProd with a small epsilon seems to reduce GPU accuracy loss.
vec4 dotProd = vec4(0.000000000000001);
${d}
vec4 result = dotProd - vec4(0.000000000000001);
${m}
${f}
setOutput(result);
}
`}};function oQ(e){let{inputs:t,backend:n,attrs:s}=e,{x:r,filter:a}=t,{strides:o,pad:i,dilations:u,dimRoundingMode:l}=s,c=u;c==null&&(c=[1,1]),w.assert(C.eitherStridesOrDilationsAreOne(o,c),()=>`Error in depthwiseConv2d: Either strides or dilations must be 1. Got strides ${o} and dilations '${c}'`);let p=C.computeConv2DInfo(r.shape,a.shape,o,c,i,l,!0),d;K().getBool("WEBGL_PACK_DEPTHWISECONV")&&p.strideWidth<=2&&p.outChannels/p.inChannels===1?d=new x2(p):d=new v2(p);let h=[[p.padInfo.top,p.padInfo.left],[p.strideHeight,p.strideWidth],[p.dilationHeight,p.dilationWidth],[p.inHeight,p.inWidth]];return n.runWebGLProgram(d,[r,a],"float32",h)}var iQ={kernelName:Fa,backendName:"webgl",kernelFunc:oQ},uQ=class{constructor(e){this.variableNames=["x","dy"],this.outputShape=e.filterShape;let t=e.strideHeight,n=e.strideWidth,s=e.padInfo.top,r=e.padInfo.left,a=e.outChannels/e.inChannels;this.userCode=`
void main() {
ivec4 coords = getOutputCoords();
int wR = coords.x;
int wC = coords.y;
int d1 = coords.z;
int dm = coords.w;
int d2 = d1 * ${a} + dm;
float dotProd = 0.0;
// TO DO: Vec4 over the batch size
for (int b = 0; b < ${e.batchSize}; b++) {
for (int yR = 0; yR < ${e.outHeight}; yR++) {
int xR = wR + yR * ${t} - ${s};
if (xR < 0 || xR >= ${e.inHeight}) {
continue;
}
for (int yC = 0; yC < ${e.outWidth}; yC++) {
int xC = wC + yC * ${n} - ${r};
if (xC < 0 || xC >= ${e.inWidth}) {
continue;
}
float dyValue = getDy(b, yR, yC, d2);
float xValue = getX(b, xR, xC, d1);
dotProd += (xValue * dyValue);
}
}
}
setOutput(dotProd);
}
`}},lQ=class{constructor(e){this.variableNames=["dy","W"],this.outputShape=e.inShape;let t=e.filterHeight,n=e.filterWidth,s=e.strideHeight,r=e.strideWidth,a=t-1-e.padInfo.top,o=n-1-e.padInfo.left,i=e.outChannels/e.inChannels;this.userCode=`
const ivec2 pads = ivec2(${a}, ${o});
void main() {
ivec4 coords = getOutputCoords();
int batch = coords[0];
int d1 = coords[3];
ivec2 dyCorner = coords.yz - pads;
int dyRCorner = dyCorner.x;
int dyCCorner = dyCorner.y;
float dotProd = 0.0;
for (int wR = 0; wR < ${t}; wR++) {
float dyR = float(dyRCorner + wR) / ${s}.0;
if (dyR < 0.0 || dyR >= ${e.outHeight}.0 || fract(dyR) > 0.0) {
continue;
}
int idyR = int(dyR);
int wRPerm = ${t} - 1 - wR;
for (int wC = 0; wC < ${n}; wC++) {
float dyC = float(dyCCorner + wC) / ${r}.0;
if (dyC < 0.0 || dyC >= ${e.outWidth}.0 ||
fract(dyC) > 0.0) {
continue;
}
int idyC = int(dyC);
int wCPerm = ${n} - 1 - wC;
// TO DO: Vec4 over the channelMul
for (int dm = 0; dm < ${i}; dm++) {
int d2 = d1 * ${i} + dm;
float xValue = getDy(batch, idyR, idyC, d2);
float wValue = getW(wRPerm, wCPerm, d1, dm);
dotProd += xValue * wValue;
}
}
}
setOutput(dotProd);
}
`}};function cQ(e){let{inputs:t,backend:n,attrs:s}=e,{x:r,dy:a}=t,{strides:o,dilations:i,pad:u,dimRoundingMode:l,filterShape:c}=s,p=C.computeConv2DInfo(r.shape,c,o,i,u,l,!0),d=new uQ(p);return n.runWebGLProgram(d,[r,a],"float32")}var dQ={kernelName:Cg,backendName:"webgl",kernelFunc:cQ};function pQ(e){let{inputs:t,backend:n,attrs:s}=e,{dy:r,filter:a}=t,{strides:o,dilations:i,pad:u,dimRoundingMode:l,inputShape:c}=s,p=C.computeConv2DInfo(c,a.shape,o,i,u,l,!0),d=new lQ(p);return n.runWebGLProgram(d,[r,a],"float32")}var hQ={kernelName:Ng,backendName:"webgl",kernelFunc:pQ},fQ=class{constructor(e){this.variableNames=["X"],this.outputShape=[e,e],this.userCode=`
void main() {
ivec2 coords = getOutputCoords();
float val = coords[0] == coords[1] ? getX(coords[0]) : 0.0;
setOutput(val);
}
`}};function mQ(e){let{inputs:t,backend:n}=e,{x:s}=t,r=[...s.shape,...s.shape],a=w.sizeFromShape(s.shape),o=pe({inputs:{x:s},backend:n,attrs:{shape:[a]}}),i=new fQ(a),u=n.runWebGLProgram(i,[o],o.dtype),l=pe({inputs:{x:u},backend:n,attrs:{shape:r}});return n.disposeIntermediateTensorInfo(o),n.disposeIntermediateTensorInfo(u),l}var gQ={kernelName:Tg,backendName:"webgl",kernelFunc:mQ},bQ=class{constructor(e){this.variableNames=["x","W"],this.outputShape=e.outShape;let{inHeight:t,inWidth:n,padInfo:s,strideHeight:r,strideWidth:a,filterHeight:o,filterWidth:i,dilationHeight:u,dilationWidth:l}=e,{top:c,left:p}=s;this.userCode=`
const ivec2 strides = ivec2(${r}, ${a});
const ivec2 pads = ivec2(${c}, ${p});
const float neg_infinity = -3.4e38;
void main() {
ivec4 coords = getOutputCoords();
int batch = coords.x;
int d1 = coords.w;
ivec2 outTopLeftCorner =
coords.yz * strides - pads;
int hBeg = outTopLeftCorner.x;
int wBeg = outTopLeftCorner.y;
float curVal = neg_infinity;
for (int h = 0; h < ${o}; h++) {
int hIn = hBeg + h * ${u};
if (hIn >= 0 && hIn < ${t}) {
for (int w = 0; w < ${i}; w++) {
int wIn = wBeg + w * ${l};
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 yQ(e){let{inputs:t,backend:n,attrs:s}=e,{x:r,filter:a}=t,{strides:o,pad:i,dilations:u}=s,l=C.computeDilation2DInfo(r.shape,a.shape,o,i,"NHWC",u),c,p=new bQ(l);c=n.runWebGLProgram(p,[r,a],"float32");let d=pe({inputs:{x:c},backend:n,attrs:{shape:l.outShape}});return n.disposeIntermediateTensorInfo(c),d}var vQ={kernelName:ap,backendName:"webgl",kernelFunc:yQ};function xQ(e){let{inputs:t,backend:n,attrs:s}=e,{equation:r}=s,a=t,{allDims:o,summedDims:i,idDims:u}=C.decodeEinsumEquation(r,a.length);C.checkEinsumDimSizes(o.length,u,a);let{path:l,steps:c}=C.getEinsumComputePath(i,u),p=c.length,d=null,h=o.length,f=[];for(let m=0;m<p;++m){for(let g of c[m]){let{permutationIndices:b,expandDims:y}=C.getEinsumPermutation(h,u[g]),v;C.isIdentityPermutation(b)?v=a[g]:(v=pn({inputs:{x:a[g]},backend:n,attrs:{perm:b}}),f.push(v));let x=v.shape.slice();for(let k=0;k<y.length;++k)x.splice(y[k],0,1);w.arraysEqual(v.shape,x)||(v=pe({inputs:{x:v},backend:n,attrs:{shape:x}}),f.push(v)),d===null?d=v:(d=Dv({inputs:{a:v,b:d},backend:n}),f.push(d))}m<p-1&&(l[m]>=0&&(d=ah({inputs:{x:d},backend:n,attrs:{axis:l[m]-(o.length-h),keepDims:!1}}),f.push(d)),h--)}for(let m of f)m!==d&&n.disposeIntermediateTensorInfo(m);return d}var wQ={kernelName:op,backendName:"webgl",kernelFunc:xQ},kQ="return (x >= 0.0) ? x : (exp(x) - 1.0);",SQ=`
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;
`,IQ=Ke({opSnippet:kQ,packedOpSnippet:SQ}),CQ={kernelName:Pa,backendName:"webgl",kernelFunc:IQ},NQ="return (b >= 1.0) ? a : a * (b + 1.0);",TQ=`
vec4 bGTEZero = vec4(greaterThanEqual(b, vec4(0.)));
return (bGTEZero * a) + ((vec4(1.0) - bGTEZero) * (a * (b + vec4(1.0))));
`,$Q=e=>{let{inputs:t,backend:n}=e,{dy:s,y:r}=t,a=K().getBool("WEBGL_PACK_BINARY_OPERATIONS")?new tc(TQ,s.shape,r.shape):new li(NQ,s.shape,r.shape);return n.runWebGLProgram(a,[s,r],s.dtype)},_Q={kernelName:$g,backendName:"webgl",kernelFunc:$Q},AQ=`
return vec4(equal(a, b));
`,EQ="return float(a == b);",RQ=jt({opSnippet:EQ,packedOpSnippet:AQ,dtype:"bool",cpuKernelImpl:bX}),DQ={kernelName:bi,backendName:"webgl",kernelFunc:RQ},FQ=`
// Error function is calculated approximately with elementary function.
// See "Handbook of Mathematical Functions with Formulas,
// Graphs, and Mathematical Tables", Abramowitz and Stegun.
float p = ${C.ERF_P};
float a1 = ${C.ERF_A1};
float a2 = ${C.ERF_A2};
float a3 = ${C.ERF_A3};
float a4 = ${C.ERF_A4};
float a5 = ${C.ERF_A5};
float sign = sign(x);
x = abs(x);
float t = 1.0 / (1.0 + p * x);
return sign * (1.0 - (((((a5*t + a4)*t) + a3)*t + a2)*t + a1)*t*exp(-x*x));
`,OQ=Ke({opSnippet:FQ}),PQ={kernelName:yl,backendName:"webgl",kernelFunc:OQ},zQ=du+`
return exp(x);
`,LQ=`
vec4 result = exp(x);
bvec4 isNaN = isnan(x);
result.r = isNaN.r ? x.r : result.r;
result.g = isNaN.g ? x.g : result.g;
result.b = isNaN.b ? x.b : result.b;
result.a = isNaN.a ? x.a : result.a;
return result;
`,w2=Ke({opSnippet:zQ,packedOpSnippet:LQ,cpuKernelImpl:yX,dtype:"float32"}),MQ={kernelName:za,backendName:"webgl",kernelFunc:w2};function rg(e){let{inputs:t,attrs:n,backend:s}=e,{dim:r}=n,{input:a}=t,o=a.shape.length,i=a.shape.slice(),u=r;return r<0&&(w.assert(-(o+1)<=r,()=>`Axis must be in the interval [${-(o+1)}, ${o}]`),u=o+r+1),i.splice(u,0,1),pe({inputs:{x:a},backend:s,attrs:{shape:i}})}var BQ={kernelName:yi,backendName:"webgl",kernelFunc:rg},Aw="return exp(x) - 1.0;",VQ=Ke({opSnippet:Aw,packedOpSnippet:Aw,cpuKernelImpl:vX}),WQ={kernelName:vi,backendName:"webgl",kernelFunc:VQ},Ew=class{constructor(e,t,n){this.variableNames=["real","imag"];let s=t[1];this.outputShape=t;let r=n?`2.0 * ${Math.PI}`:`-2.0 * ${Math.PI}`,a=n?`${s}.0`:"1.0",o;if(e==="real")o="return real * expR - imag * expI;";else if(e==="imag")o="return real * expI + imag * expR;";else throw new Error(`FFT component must be either "real" or "imag", got ${e}.`);this.userCode=`
const float exponentMultiplier = ${r};
float unaryOpComplex(float real, float expR, float imag, float expI) {
${o}
}
float mulMatDFT(int batch, int index) {
float indexRatio = float(index) / float(${s});
float exponentMultiplierTimesIndexRatio =
exponentMultiplier * indexRatio;
float result = 0.0;
for (int i = 0; i < ${s}; 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 k2(e,t,n){let s=n.texData.get(e.dataId),r=w.sizeFromShape(e.shape),a=e.shape[e.shape.length-1],o=r/a,i=pe({inputs:{x:e},backend:n,attrs:{shape:[o,a]}}),u=i.shape,l=new Ew("real",u,t),c=new Ew("imag",u,t),p=[{dataId:s.complexTensorInfos.real.dataId,dtype:s.complexTensorInfos.real.dtype,shape:u},{dataId:s.complexTensorInfos.imag.dataId,dtype:s.complexTensorInfos.imag.dtype,shape:u}],d=n.runWebGLProgram(l,p,"float32"),h=n.runWebGLProgram(c,p,"float32"),f=Fr({inputs:{real:d,imag:h},backend:n});n.disposeIntermediateTensorInfo(d),n.disposeIntermediateTensorInfo(h);let m=pe({inputs:{x:f},backend:n,attrs:{shape:e.shape}});return n.disposeIntermediateTensorInfo(i),n.disposeIntermediateTensorInfo(f),m}function UQ(e){let{inputs:t,backend:n}=e,{input:s}=t;return k2(s,!1,n)}var GQ={kernelName:_g,backendName:"webgl",kernelFunc:UQ},HQ=class{constructor(e,t){this.outputShape=[],this.customUniforms=[{name:"value",type:"float"}],this.variableNames=["x"],this.outputShape=e,this.userCode=`
void main() {
// Input can be obtained from uniform value.
setOutput(value);
}
`}};function sc(e){let{backend:t,attrs:n}=e,{shape:s,value:r}=n,{dtype:a}=n;if(a=a||w.inferDtype(r),a==="string"){let o=w.getArrayFromDType(a,w.sizeFromShape(s));return o.fill(r),t.makeTensorInfo(s,a,o)}else{let o=new HQ(s,r),i=[[r]];return t.runWebGLProgram(o,[],a,i)}}var qQ={kernelName:vl,backendName:"webgl",kernelFunc:sc},jQ=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 - 1;
float outputValue;
if(coordX >= 0 && coordX < ${t}) {
outputValue = getImage(coords[0], coords[1], coordX, coords[3]);
} else {
outputValue = getImage(coords[0], coords[1], coords[2], coords[3]);
}
setOutput(outputValue);
}
`}},KQ={kernelName:xi,backendName:"webgl",kernelFunc:({inputs:e,backend:t})=>{let{image:n}=e,s=t,r=new jQ(n.shape);return s.runWebGLProgram(r,[n],n.dtype)}},Rw="return floor(x);",XQ=Ke({opSnippet:Rw,packedOpSnippet:Rw,cpuKernelImpl:xX}),YQ={kernelName:La,backendName:"webgl",kernelFunc:XQ},QQ=`
float s = sign(a) * sign(b);
int ia = round(a);
int ib = round(b);
if (ib != 0) {
// Windows (D3D) wants guaranteed non-zero int division at compile-time.
return float(idiv(ia, ib, s));
} else {
return NAN;
}
`,ZQ=`
ivec4 ia = round(a);
ivec4 ib = round(b);
bvec4 cond = notEqual(ib, ivec4(0));
ivec4 result = ivec4(0);
vec4 s = sign(a) * sign(b);
// Windows (D3D) wants guaranteed non-zero int division at compile-time.
if (cond[0]) {
result[0] = idiv(ia[0], ib[0], s[0]);
}
if (cond[1]) {
result[1] = idiv(ia[1], ib[1], s[1]);
}
if (cond[2]) {
result[2] = idiv(ia[2], ib[2], s[2]);
}
if (cond[3]) {
result[3] = idiv(ia[3], ib[3], s[3]);
}
return vec4(result);
`,JQ=jt({opSnippet:QQ,packedOpSnippet:ZQ,dtype:"int32"}),eZ={kernelName:Ma,backendName:"webgl",kernelFunc:JQ},tZ=class{constructor(e){this.variableNames=["A"];let t=mn(),[n,s]=e;this.outputShape=e,this.userCode=`
void main() {
ivec3 coords = getOutputCoords();
int texR = coords[0];
int texC = coords[1];
int depth = coords[2];
vec2 uv = (vec2(texC, texR) + halfCR) / vec2(${s}.0, ${n}.0);
vec4 values = ${t.texture2D}(A, uv);
float value;
if (depth == 0) {
value = values.r;
} else if (depth == 1) {
value = values.g;
} else if (depth == 2) {
value = values.b;
} else if (depth == 3) {
value = values.a;
}
setOutput(floor(value * 255.0 + 0.5));
}
`}},nZ=class{constructor(e){this.variableNames=["A"],this.packedInputs=!1,this.packedOutput=!0;let t=mn(),[n,s]=e;this.outputShape=e,this.userCode=`
void main() {
ivec3 coords = getOutputCoords();
int texR = coords[0];
int texC = coords[1];
int depth = coords[2];
vec4 result = vec4(0.);
for(int row=0; row<=1; row++) {
for(int col=0; col<=1; col++) {
texC = coords[1] + row;
depth = coords[2] + col;
vec2 uv = (vec2(texC, texR) + halfCR) /
vec2(${s}.0, ${n}.0);
vec4 values = ${t.texture2D}(A, uv);
float value;
if (depth == 0) {
value = values.r;
} else if (depth == 1) {
value = values.g;
} else if (depth == 2) {
value = values.b;
} else if (depth == 3) {
value = values.a;
}
result[row * 2 + col] = floor(value * 255.0 + 0.5);
}
}
${t.output} = result;
}
`}},sZ={kernelName:xd,backendName:"webgl",kernelFunc:rZ},Wo;function rZ(e){let{inputs:t,backend:n,attrs:s}=e,{pixels:r}=t,{numChannels:a}=s,o=typeof HTMLVideoElement!="undefined"&&r instanceof HTMLVideoElement,i=typeof HTMLImageElement!="undefined"&&r instanceof HTMLImageElement,[u,l]=o?[r.videoWidth,r.videoHeight]:[r.width,r.height],c=[l,u],p=[l,u,a];(i||o)&&(Wo==null&&(Wo=document.createElement("canvas").getContext("2d")),Wo.canvas.width=u,Wo.canvas.height=l,Wo.drawImage(r,0,0,u,l),r=Wo.canvas);let d=n.makeTensorInfo(c,"int32");n.texData.get(d.dataId).usage=2,n.gpgpu.uploadPixelDataToTexture(n.getTexture(d.dataId),r);let h=K().getBool("WEBGL_PACK")?new nZ(p):new tZ(p),f=n.runWebGLProgram(h,[d],"int32");return n.disposeData(d.dataId),f}function aZ(e){let{inputs:t,backend:n,attrs:s}=e,{x:r,filter:a,bias:o,preluActivationWeights:i}=t,{strides:u,pad:l,dataFormat:c,dilations:p,dimRoundingMode:d,activation:h,leakyreluAlpha:f}=s,m=C.convertConv2DDataFormat(c),g=C.computeConv2DInfo(r.shape,a.shape,u,p,l,d,!1,m),b,y=[];if(g.filterHeight===1&&g.filterWidth===1&&g.dilationHeight===1&&g.dilationWidth===1&&g.strideHeight===1&&g.strideWidth===1&&(g.padInfo.type==="SAME"||g.padInfo.type==="VALID"))b=g2({x:r,filter:a,convInfo:g,backend:n,bias:o,activation:h,preluActivationWeights:i,leakyreluAlpha:f});else if(K().getBool("WEBGL_CONV_IM2COL"))b=b2({x:r,filter:a,convInfo:g,backend:n,bias:o,activation:h,preluActivationWeights:i,leakyreluAlpha:f});else{let x=o!=null,k=i!=null,I=h==="leakyrelu",$=h?sh(h,!1):null,R=new m2(g,x,$,k,I),E=[r,a],P=(A,D)=>{if(D==="NCHW"&&A.shape.length===1&&A.shape[0]!==1){let T=pe({inputs:{x:A},backend:n,attrs:{shape:[A.shape[0],1,1]}});return y.push(T),T}return A};if(x&&E.push(P(o,c)),k&&E.push(P(i,c)),I){let A=n.makeTensorInfo([],"float32",w.createScalarValue(f,"float32"));E.push(A),y.push(A)}b=n.runWebGLProgram(R,E,"float32")}let v=pe({inputs:{x:b},backend:n,attrs:{shape:g.outShape}});return y.push(b),y.forEach(x=>n.disposeIntermediateTensorInfo(x)),v}var oZ={kernelName:ia,backendName:"webgl",kernelFunc:aZ};function iZ(e){let{inputs:t,backend:n,attrs:s}=e,{x:r,filter:a,bias:o,preluActivationWeights:i}=t,{strides:u,pad:l,dilations:c,dimRoundingMode:p,activation:d,leakyreluAlpha:h}=s,f=[],m=c;m==null&&(m=[1,1]),w.assert(C.eitherStridesOrDilationsAreOne(u,m),()=>`Error in depthwiseConv2d: Either strides or dilations must be 1. Got strides ${u} and dilations '${m}'`);let g=C.computeConv2DInfo(r.shape,a.shape,u,m,l,p,!0),b=K().getBool("WEBGL_PACK_DEPTHWISECONV")&&g.strideWidth<=2&&g.outChannels/g.inChannels===1,y=d?sh(d,b):null,v=[r,a],x=o!=null,k=i!=null,I=d==="leakyrelu";if(x&&v.push(o),k&&v.push(i),I){let P=n.makeTensorInfo([],"float32",w.createScalarValue(h,"float32"));v.push(P),f.push(P)}let $;b?$=new x2(g,x,y,k,I):$=new v2(g,x,y,k,I);let R=[[g.padInfo.top,g.padInfo.left],[g.strideHeight,g.strideWidth],[g.dilationHeight,g.dilationWidth],[g.inHeight,g.inWidth]],E=n.runWebGLProgram($,v,"float32",R);return f.forEach(P=>n.disposeIntermediateTensorInfo(P)),E}var uZ={kernelName:ua,backendName:"webgl",kernelFunc:iZ},lZ=class{constructor(e,t,n,s){this.sliceDim=e,this.strides=t,this.paramsShape=s,this.variableNames=["x","indices"],this.outputShape=n;let r=rt(t.length),a=rt(n.length),o=this.sliceDim>1?"strides[j]":"strides",i=rt(s.length),u=s.length>1?"paramsShape[j]":"paramsShape";this.userCode=`
${r} strides = ${r}(${this.strides});
${i} paramsShape = ${i}(${this.paramsShape});
void main() {
${a} coords = getOutputCoords();
int flattenIndex = 0;
bool out_of_bounds = false;
for (int j = 0; j < ${this.sliceDim}; j++) {
int index = round(getIndices(coords[0], j));
out_of_bounds = out_of_bounds || index < 0;
out_of_bounds = out_of_bounds || index >= ${u};
flattenIndex += index * ${o};
}
setOutput(out_of_bounds ? 0.0 : getX(flattenIndex, coords[1]));
}
`}};function cZ(e){let{inputs:t,backend:n}=e,{params:s,indices:r}=t,a=r.shape,o=a[a.length-1],i=w.sizeFromShape(s.shape),[u,l,c,p]=C.prepareAndValidate(s,r),d=pe({inputs:{x:r},backend:n,attrs:{shape:[l,o]}}),h=pe({inputs:{x:s},backend:n,attrs:{shape:[w.sizeFromShape(s.shape)/c,c]}});if(n.shouldExecuteOnCPU([s,r])||s.dtype==="string"){let b=n.readSync(r.dataId),y=n.bufferSync(s),v=wX(b,y,s.dtype,l,o,c,p,s.shape,i);return n.makeTensorInfo(u,s.dtype,v.values)}let f=new lZ(o,p,[l,c],s.shape),m=n.runWebGLProgram(f,[h,d],h.dtype),g=pe({inputs:{x:m},backend:n,attrs:{shape:u}});return n.disposeIntermediateTensorInfo(d),n.disposeIntermediateTensorInfo(h),n.disposeIntermediateTensorInfo(m),g}var dZ={kernelName:ki,backendName:"webgl",kernelFunc:cZ},pZ=class{constructor(e,t){this.variableNames=["A","indices"],this.outputShape=t,this.rank=t.length;let n=rt(this.rank),s=hZ(e,2);this.userCode=`
void main() {
${n} resRC = getOutputCoords();
int index = int(getIndices(resRC.x, resRC.z));
float inBounds = (index >= 0) && (index < ${e[2]}) ? 1.0 : 0.0;
setOutput(inBounds * getA(${s}));
}
`}};function hZ(e,t){let n=["resRC.x","resRC.y","resRC.z","resRC.w"],s=[];for(let r=0;r<e.length;r++)r===2?s.push("index"):s.push(`${n[r]}`);return s.join()}function S2(e){let{inputs:t,backend:n,attrs:s}=e,{x:r,indices:a}=t,{axis:o,batchDims:i}=s,u=w.parseAxisParam(o,r.shape)[0];if(K().get("DEBUG")){let y=n.readSync(a.dataId),v=r.shape[u];for(let x=0;x<y.length;++x){let k=y[x];w.assert(k<=v-1&&k>=0,()=>`GatherV2: the index value ${k} is not in [0, ${v-1}]`)}}let l=C.segment_util.collectGatherOpShapeInfo(r,a,u,i),c=w.sizeFromShape(a.shape),p=[],d=pe({inputs:{x:r},backend:n,attrs:{shape:[l.batchSize,l.outerSize,l.dimSize,l.sliceSize]}}),h=pe({inputs:{x:a},backend:n,attrs:{shape:[l.batchSize,c/l.batchSize]}});p.push(d),p.push(h);let f=[l.batchSize,l.outerSize,c/l.batchSize,l.sliceSize];if(n.shouldExecuteOnCPU([r,a])||r.dtype==="string"){let y=n.bufferSync(h),v=n.bufferSync(d),x=kX(v,y,f);return p.forEach(k=>n.disposeIntermediateTensorInfo(k)),n.makeTensorInfo(l.outputShape,x.dtype,x.values)}let m=new pZ(d.shape,f),g=n.runWebGLProgram(m,[d,h],d.dtype);p.push(g);let b=pe({inputs:{x:g},backend:n,attrs:{shape:l.outputShape}});return p.forEach(y=>n.disposeIntermediateTensorInfo(y)),b}var fZ={kernelName:wi,backendName:"webgl",kernelFunc:S2},mZ="return float(a > b);",gZ=`
return vec4(greaterThan(a, b));
`,bZ=jt({opSnippet:mZ,packedOpSnippet:gZ,cpuKernelImpl:SX,dtype:"bool"}),yZ={kernelName:Si,backendName:"webgl",kernelFunc:bZ},vZ="return float(a >= b);",xZ=`
return vec4(greaterThanEqual(a, b));
`,wZ=jt({opSnippet:vZ,packedOpSnippet:xZ,dtype:"bool",cpuKernelImpl:IX}),kZ={kernelName:Va,backendName:"webgl",kernelFunc:wZ};function SZ(e){let{inputs:t,backend:n}=e,{input:s}=t;return k2(s,!0,n)}var IZ={kernelName:Ag,backendName:"webgl",kernelFunc:SZ},CZ="return float(!isnan(x) && !isinf(x));",NZ=Ke({opSnippet:CZ,dtype:"bool"}),TZ={kernelName:xl,backendName:"webgl",kernelFunc:NZ},$Z="return float(isinf(x));",_Z=Ke({opSnippet:$Z,dtype:"bool"}),AZ={kernelName:wl,backendName:"webgl",kernelFunc:_Z},EZ="return float(isnan(x));",RZ=Ke({opSnippet:EZ,dtype:"bool"}),DZ={kernelName:kl,backendName:"webgl",kernelFunc:RZ},FZ="return float(a < b);",OZ=`
return vec4(lessThan(a, b));
`,PZ=jt({opSnippet:FZ,packedOpSnippet:OZ,cpuKernelImpl:CX,dtype:"bool"}),zZ={kernelName:Ii,backendName:"webgl",kernelFunc:PZ},LZ="return float(a <= b);",MZ=`
return vec4(lessThanEqual(a, b));
`,BZ=jt({opSnippet:LZ,packedOpSnippet:MZ,cpuKernelImpl:NX,dtype:"bool"}),VZ={kernelName:Ci,backendName:"webgl",kernelFunc:BZ};function WZ(e){let{backend:t,attrs:n}=e,{start:s,stop:r,num:a}=n,o=TX(s,r,a);return t.makeTensorInfo([o.length],"float32",o)}var UZ={kernelName:Eg,backendName:"webgl",kernelFunc:WZ},GZ=du+`
return x < 0.0 ? 0./0. : log(x);
`,HZ=`
vec4 result = log(x);
bvec4 isNaN = isnan(x);
result.r = isNaN.r ? x.r : (x.r < 0.0 ? 0./0. : result.r);
result.g = isNaN.g ? x.g : (x.g < 0.0 ? 0./0. : result.g);
result.b = isNaN.b ? x.b : (x.b < 0.0 ? 0./0. : result.b);
result.a = isNaN.a ? x.a : (x.a < 0.0 ? 0./0. : result.a);
return result;
`,qZ=Ke({opSnippet:GZ,packedOpSnippet:HZ,cpuKernelImpl:$X}),jZ={kernelName:Ga,backendName:"webgl",kernelFunc:qZ},KZ=du+`
return log(1.0 + x);
`,XZ=Ke({opSnippet:KZ}),YZ={kernelName:Sl,backendName:"webgl",kernelFunc:XZ},QZ="return float(a >= 1.0 && b >= 1.0);",ZZ=`
return vec4(
vec4(greaterThanEqual(a, vec4(1.0))) *
vec4(greaterThanEqual(b, vec4(1.0))));
`,JZ=jt({opSnippet:QZ,packedOpSnippet:ZZ,dtype:"bool"}),e7={kernelName:Ni,backendName:"webgl",kernelFunc:JZ},t7="return float(!(x >= 1.0));",n7=Ke({opSnippet:t7}),s7={kernelName:Ti,backendName:"webgl",kernelFunc:n7},r7="return float(a >= 1.0 || b >= 1.0);",a7=`
return min(
vec4(greaterThanEqual(a, vec4(1.0))) +
vec4(greaterThanEqual(b, vec4(1.0))),
vec4(1.0));
`,o7=jt({opSnippet:r7,packedOpSnippet:a7,dtype:"bool"}),i7={kernelName:Il,backendName:"webgl",kernelFunc:o7},u7=class{constructor(e,t,n,s,r){this.variableNames=["x"],this.outputShape=[];let a=t,o=e[3]-1;this.outputShape=e;let i,u=`float(${n}) + float(${s}) * sum`;r===.5?i=`inversesqrt(${u})`:r===1?i=`1.0/(${u})`:i=`exp(log(${u}) * float(-${r}));`,this.userCode=`
void main() {
ivec4 coords = getOutputCoords();
int b = coords[0];
int r = coords[1];
int c = coords[2];
int d = coords[3];
float x = getX(b, r, c, d);
float sum = 0.0;
for (int j = -${a}; j <= ${a}; j++) {
int idx = d + j;
if (idx >= 0 && idx <= ${o}) {
float z = getX(b, r, c, idx);
sum += z * z;
}
}
float val = x * ${i};
setOutput(val);
}
`}},l7=class{constructor(e,t,n,s,r){this.variableNames=["x"],this.outputShape=[],this.packedInputs=!0,this.packedOutput=!0;let a=t,o=e[3]-1;this.outputShape=e;let i,u=`float(${n}) + float(${s}) * sum`;r===.5?i=`inversesqrt(${u})`:r===1?i=`1.0/(${u})`:i=`exp(log(${u}) * float(-${r}));`,this.userCode=`
void main() {
ivec4 coords = getOutputCoords();
int b = coords.x;
int r = coords.y;
int c = coords.z;
int d = coords.w;
bool hasNextCol = d < ${this.outputShape[3]};
bool hasNextRow = c < ${this.outputShape[2]};
vec4 sum = vec4(0.);
vec4 xFragAtOutputCoords = getX(b, r, c, d);
vec4 xAtOutputCoords = vec4(
getChannel(xFragAtOutputCoords, vec2(c, d)),
hasNextCol ?
getChannel(xFragAtOutputCoords, vec2(c, d + 1)) : 0.0,
hasNextRow ?
getChannel(xFragAtOutputCoords , vec2(c + 1, d)) : 0.0,
(hasNextRow && hasNextCol) ?
getChannel(xFragAtOutputCoords, vec2(c + 1, d + 1)) : 0.0
);
int firstChannel = d - ${a};
vec2 cache = vec2(0.);
if(firstChannel >= 0){
vec4 firstChannelFrag = getX(b, r, c, firstChannel);
cache.x = getChannel(firstChannelFrag, vec2(c, firstChannel));
if(hasNextRow){
cache.y = getChannel(firstChannelFrag, vec2(c + 1, firstChannel));
}
}
ivec2 depth = ivec2(d, d + 1);
for (int j = - ${a}; j <= ${a}; j++) {
ivec2 idx = depth + j;
bvec2 aboveLowerBound = greaterThanEqual(idx, ivec2(0));
bvec2 belowUpperBound = lessThanEqual(idx, ivec2(${o}));
bool depthInRange = aboveLowerBound.x && belowUpperBound.x;
bool depthPlusOneInRange = aboveLowerBound.y && belowUpperBound.y;
if(depthInRange || depthPlusOneInRange){
vec4 z = vec4(0.);
vec4 xFragAtCurrentDepth;
z.xz = cache.xy;
if(depthPlusOneInRange && hasNextCol){
xFragAtCurrentDepth = idx.y != d ?
getX(b, r, c, idx.y) : xFragAtOutputCoords;
z.y = getChannel(xFragAtCurrentDepth, vec2(c, idx.y));
if(hasNextRow){
z.w = getChannel(xFragAtCurrentDepth, vec2(c + 1, idx.y));
}
}
cache.xy = z.yw;
sum += z * z;
}
}
vec4 result = xAtOutputCoords * ${i};
setOutput(result);
}
`}},c7=e=>{let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{depthRadius:a,bias:o,alpha:i,beta:u}=s,l=K().getBool("WEBGL_PACK_NORMALIZATION")?new l7(r.shape,a,o,i,u):new u7(r.shape,a,o,i,u);return n.runWebGLProgram(l,[r],r.dtype)},d7={kernelName:up,backendName:"webgl",kernelFunc:c7},p7=class{constructor(e,t,n,s,r){this.variableNames=["inputImage","outputImage","dy"],this.outputShape=[],this.outputShape=e,this.depth=e[3],this.depthRadius=t,this.bias=n,this.alpha=s,this.beta=r,this.userCode=`
void main() {
ivec4 coords = getOutputCoords();
int b = coords[0];
int r = coords[1];
int c = coords[2];
float result = 0.0;
for (int d = 0; d < ${this.depth}; ++d) {
int depthBegin = int(max(0.0, float(d - ${t})));
int depthEnd = int(min(float(${this.depth}),
float(d + ${t} + 1)));
const int MIN_DEPTH_BEGIN = 0;
const int MAX_DEPTH_END = ${this.depth};
float norm = 0.0;
for (int k = MIN_DEPTH_BEGIN; k < MAX_DEPTH_END; ++k) {
if (k < depthBegin){
continue;
}
else if (k >= depthBegin && k < depthEnd) {
norm += getInputImage(b, r, c, k) * getInputImage(b, r, c, k);
}
else {
break;
}
}
norm = float(${s}) * norm + float(${n});
for(int k = MIN_DEPTH_BEGIN; k < MAX_DEPTH_END; ++k){
if (k < depthBegin){
continue;
}
else if (k >= depthBegin && k < depthEnd){
float dyi = -2.0 * float(${s})
* float(${r})
* getInputImage(b ,r ,c, k) * getOutputImage(b, r, c, d)
/ norm;
if (k == d) {
dyi += pow(norm, -1.0 * ${r});
}
if (k == coords[3]) {
dyi *= getDy(b, r, c, d);
result += dyi;
}
}
else {
break;
}
}
}
setOutput(result);
}
`}},h7=e=>{let{inputs:t,backend:n,attrs:s}=e,{x:r,y:a,dy:o}=t,{depthRadius:i,bias:u,alpha:l,beta:c}=s,p=new p7(r.shape,i,u,l,c);return n.runWebGLProgram(p,[r,a,o],r.dtype)},f7={kernelName:Rg,backendName:"webgl",kernelFunc:h7};function m7(e,t,n,s){let r=w.sizeFromShape(t),o=w.sizeFromShape(e.shape)/r,i=pe({inputs:{x:e},attrs:{shape:[o,r]},backend:s}),u=So(i,e.dtype,"max",s),l=pe({inputs:{x:u},attrs:{shape:n},backend:s});return s.disposeIntermediateTensorInfo(i),s.disposeIntermediateTensorInfo(u),l}function I2(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{reductionIndices:a,keepDims:o}=s,i=r.shape.length,u=w.parseAxisParam(a,r.shape),l=u,c=C.getAxesPermutation(l,i),p=c!=null,d=n.shouldExecuteOnCPU([r]),h=r;if(p){if(d){let v=n.texData.get(h.dataId).values,x=new Array(i);for(let $=0;$<x.length;$++)x[$]=r.shape[c[$]];let k=Rv(v,r.shape,r.dtype,c,x);h=n.makeTensorInfo(x,r.dtype);let I=n.texData.get(h.dataId);I.values=k}else h=rh(r,c,n);l=C.getInnerMostAxes(l.length,i)}C.assertAxesAreInnerMostDims("max",l,i);let[f,m]=C.computeOutAndReduceShapes(h.shape,l),g=f;o&&(g=C.expandShapeToKeepDim(f,u));let b;if(d){let v=n.texData.get(h.dataId).values,x=_X(v,w.sizeFromShape(m),g,r.dtype);b=n.makeTensorInfo(g,r.dtype);let k=n.texData.get(b.dataId);k.values=x}else b=m7(h,m,g,n);return p&&n.disposeIntermediateTensorInfo(h),b}var g7={kernelName:Ha,backendName:"webgl",kernelFunc:I2},b7=s2+`
return max(a, b);
`,y7=`
vec4 result = vec4(max(a, b));
vec4 isNaN = min(vec4(isnan(a)) + vec4(isnan(b)), vec4(1.0));
`+nh+`
return result;
`,v7=jt({opSnippet:b7,packedOpSnippet:y7,cpuKernelImpl:AX}),x7={kernelName:qa,backendName:"webgl",kernelFunc:v7};function w7(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t;ou(r,"maxPool");let{filterSize:a,strides:o,pad:i,dimRoundingMode:u}=s,l=1;w.assert(C.eitherStridesOrDilationsAreOne(o,l),()=>`Error in maxPool: Either strides or dilations must be 1. Got strides ${o} and dilations '${l}'`);let c=C.computePool2DInfo(r.shape,a,o,l,i,u);if(c.filterWidth===1&&c.filterHeight===1&&w.arraysEqual(c.inShape,c.outShape))return Rn({inputs:{x:r},backend:n});let p=new ol(c,"max",!1);return n.runWebGLProgram(p,[r],r.dtype)}var k7={kernelName:ja,backendName:"webgl",kernelFunc:w7};function S7(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{filterSize:a,strides:o,pad:i,dataFormat:u,dimRoundingMode:l}=s,c=[1,1,1],p=C.computePool3DInfo(r.shape,a,o,c,i,l,u),d=new Fv(p,"max",!1);return n.runWebGLProgram(d,[r],r.dtype)}var I7={kernelName:lp,backendName:"webgl",kernelFunc:S7},C7=class{constructor(e){this.variableNames=["dy","maxPos"],this.outputShape=e.inShape;let t=e.strideHeight,n=e.strideWidth,s=e.dilationHeight,r=e.effectiveFilterHeight,a=e.effectiveFilterWidth,o=r-1-e.padInfo.top,i=a-1-e.padInfo.left,u=r*a-1;this.userCode=`
const ivec2 pads = ivec2(${o}, ${i});
void main() {
ivec4 coords = getOutputCoords();
int b = coords[0];
int d = coords[3];
ivec2 dyRCCorner = coords.yz - pads;
int dyRCorner = dyRCCorner.x;
int dyCCorner = dyRCCorner.y;
// Convolve dy(?, ?, d) with pos mask(:, :, d) to get dx(xR, xC, d).
// ? = to be determined. : = across all values in that axis.
float dotProd = 0.0;
for (int wR = 0; wR < ${r};
wR += ${s}) {
float dyR = float(dyRCorner + wR) / ${t}.0;
if (dyR < 0.0 || dyR >= ${e.outHeight}.0 || fract(dyR) > 0.0) {
continue;
}
int idyR = int(dyR);
for (int wC = 0; wC < ${a}; wC++) {
float dyC = float(dyCCorner + wC) / ${n}.0;
if (dyC < 0.0 || dyC >= ${e.outWidth}.0 ||
fract(dyC) > 0.0) {
continue;
}
int idyC = int(dyC);
float dyValue = getDy(b, idyR, idyC, d);
int maxPosValue = ${u} - int(getMaxPos(b, idyR, idyC, d));
// Get the current value, check it against the value from the
// position matrix.
int curPosValue = wR * ${a} + wC;
float mask = float(maxPosValue == curPosValue ? 1.0 : 0.0);
dotProd += dyValue * mask;
}
}
setOutput(dotProd);
}
`}},N7=class{constructor(e){this.variableNames=["dy","maxPos"],this.outputShape=e.inShape;let t=e.strideDepth,n=e.strideHeight,s=e.strideWidth,r=e.dilationDepth,a=e.dilationHeight,o=e.dilationWidth,i=e.effectiveFilterDepth,u=e.effectiveFilterHeight,l=e.effectiveFilterWidth,c=i-1-e.padInfo.front,p=u-1-e.padInfo.top,d=l-1-e.padInfo.left,h=i*u*l-1;this.userCode=`
const ivec3 pads = ivec3(${c}, ${p}, ${d});
void main() {
ivec5 coords = getOutputCoords();
int batch = coords.x;
int ch = coords.u;
ivec3 dyCorner = ivec3(coords.y, coords.z, coords.w) - pads;
int dyDCorner = dyCorner.x;
int dyRCorner = dyCorner.y;
int dyCCorner = dyCorner.z;
// Convolve dy(?, ?, ?, ch) with pos mask(:, :, :, d) to get
// dx(xD, xR, xC, ch).
// ? = to be determined. : = across all values in that axis.
float dotProd = 0.0;
for (int wD = 0; wD < ${i};
wD += ${r}) {
float dyD = float(dyDCorner + wD) / ${t}.0;
if (dyD < 0.0 || dyD >= ${e.outDepth}.0 || fract(dyD) > 0.0) {
continue;
}
int idyD = int(dyD);
for (int wR = 0; wR < ${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 < ${l};
wC += ${o}) {
float dyC = float(dyCCorner + wC) / ${s}.0;
if (dyC < 0.0 || dyC >= ${e.outWidth}.0 ||
fract(dyC) > 0.0) {
continue;
}
int idyC = int(dyC);
float dyValue = getDy(batch, idyD, idyR, idyC, ch);
int maxPosValue = ${h} -
int(getMaxPos(batch, idyD, idyR, idyC, ch));
// Get the current value, check it against the value from the
// position matrix.
int curPosValue =
wD * ${u} * ${l} +
wR * ${l} + wC;
float mask = float(maxPosValue == curPosValue ? 1.0 : 0.0);
dotProd += dyValue * mask;
}
}
}
setOutput(dotProd);
}
`}};function T7(e){let{inputs:t,backend:n,attrs:s}=e,{dy:r,input:a}=t,o=a,{filterSize:i,strides:u,pad:l,dimRoundingMode:c}=s,p=[1,1,1],d=C.computePool3DInfo(o.shape,i,u,p,l,c),h=new Fv(d,"max",!0),f=n.runWebGLProgram(h,[o],o.dtype),m=new N7(d),g=n.runWebGLProgram(m,[r,f],o.dtype);return n.disposeIntermediateTensorInfo(f),g}var $7={kernelName:Fg,backendName:"webgl",kernelFunc:T7};function _7(e){let{inputs:t,backend:n,attrs:s}=e,{dy:r,input:a,output:o}=t,i=a;ou([a,o],"maxPoolGrad");let{filterSize:u,strides:l,pad:c,dimRoundingMode:p}=s,d=C.computePool2DInfo(i.shape,u,l,1,c,p),h=!0,f=new ol(d,"max",h),m=n.runWebGLProgram(f,[i],i.dtype),g=new C7(d),b=n.runWebGLProgram(g,[r,m],i.dtype);return n.disposeIntermediateTensorInfo(m),b}var A7={kernelName:Dg,backendName:"webgl",kernelFunc:_7};function E7(e,t,n,s){let r=new ol(n,"max",!1),a=s.runWebGLProgram(r,[e],"float32");r=new ol(n,"max",!0,!0,t);let o=s.runWebGLProgram(r,[e],"float32");return[a,o]}var R7={kernelName:Og,backendName:"webgl",kernelFunc:({inputs:e,attrs:t,backend:n})=>{let{x:s}=e,{filterSize:r,strides:a,pad:o,includeBatchInIndex:i}=t,u=n;w.assert(s.shape.length===4,()=>`Error in maxPool: input must be rank 4 but got rank ${s.shape.length}.`);let l=[1,1];w.assert(C.eitherStridesOrDilationsAreOne(a,l),()=>`Error in maxPool: Either strides or dilations must be 1. Got strides ${a} and dilations '${l}'`);let c=C.computePool2DInfo(s.shape,r,a,l,o),[p,d]=E7(s,i,c,u);return[p,d]}};function D7(e,t,n,s){let r=w.sizeFromShape(t),o=w.sizeFromShape(e.shape)/r,i=pe({inputs:{x:e},attrs:{shape:[o,r]},backend:s}),u=So(i,"float32","mean",s),l=pe({inputs:{x:u},attrs:{shape:n},backend:s});return s.disposeIntermediateTensorInfo(i),s.disposeIntermediateTensorInfo(u),l}var F7={kernelName:Ka,backendName:"webgl",kernelFunc:({inputs:e,attrs:t,backend:n})=>{let{x:s}=e,{keepDims:r,axis:a}=t,o=n,i=s.shape.length,u=w.parseAxisParam(a,s.shape),l=u,c=C.getAxesPermutation(l,i),p=c!=null,d=o.shouldExecuteOnCPU([s]),h=[],f=s;if(p){if(d){let x=o.texData.get(f.dataId).values,k=new Array(i);for(let R=0;R<k.length;R++)k[R]=s.shape[c[R]];let I=Rv(x,s.shape,s.dtype,c,k);f=o.makeTensorInfo(k,s.dtype);let $=o.texData.get(f.dataId);$.values=I}else f=rh(s,c,o);h.push(f),l=C.getInnerMostAxes(l.length,i)}C.assertAxesAreInnerMostDims("sum",l,i);let[m,g]=C.computeOutAndReduceShapes(f.shape,l),b=m;r&&(b=C.expandShapeToKeepDim(m,u));let y=D7(f,g,b,o);for(let v of h)o.disposeIntermediateTensorInfo(v);return y}};function O7(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{axis:a,keepDims:o}=s,i=r.shape.length,u=w.parseAxisParam(a,r.shape),l=u,c=C.getAxesPermutation(l,i),p=r;c!=null&&(p=pn({inputs:{x:r},backend:n,attrs:{perm:c}}),l=C.getInnerMostAxes(l.length,r.shape.length)),C.assertAxesAreInnerMostDims("min",l,i);let[d,h]=C.computeOutAndReduceShapes(p.shape,l),f=w.sizeFromShape(h),m=pe({inputs:{x:p},backend:n,attrs:{shape:[-1,f]}}),g=So(m,m.dtype,"min",n),b;if(o){let y=C.expandShapeToKeepDim(d,u);b=pe({inputs:{x:g},backend:n,attrs:{shape:y}})}else b=pe({inputs:{x:g},backend:n,attrs:{shape:d}});return n.disposeIntermediateTensorInfo(m),n.disposeIntermediateTensorInfo(g),c!=null&&n.disposeIntermediateTensorInfo(p),b}var P7={kernelName:Xa,backendName:"webgl",kernelFunc:O7},z7=s2+`
return min(a, b);
`,L7=`
vec4 result = vec4(min(a, b));
vec4 isNaN = min(vec4(isnan(a)) + vec4(isnan(b)), vec4(1.0));
`+nh+`
return result;
`,M7=jt({opSnippet:z7,packedOpSnippet:L7,cpuKernelImpl:EX}),B7={kernelName:Ya,backendName:"webgl",kernelFunc:M7},V7=class{constructor(e,t,n){this.variableNames=["x"],this.outputShape=t.map((l,c)=>l[0]+e[c]+l[1]);let s=e.length,r=rt(s),a=t.map(l=>l[0]).join(","),o=t.map((l,c)=>l[0]+e[c]).join(","),i=["coords[0]","coords[1]","coords[2]","coords[3]"].slice(0,s),u=n==="reflect"?0:1;if(s===1){this.userCode=`
int start = ${a};
int end = ${o};
void main() {
int outC = getOutputCoords();
if (outC < start) {
outC = start * 2 - outC - ${u};
} else if(outC >= end) {
outC = (end - 1) * 2 - outC + ${u};
}
setOutput(getX(outC - start));
}
`;return}this.userCode=`
${r} start = ${r}(${a});
${r} end = ${r}(${o});
void main() {
${r} outC = getOutputCoords();
for (int i = 0; i < ${s}; i++) {
if (outC[i] < start[i]) {
outC[i] = start[i] * 2 - outC[i] - ${u};
} else if(outC[i] >= end[i]) {
outC[i] = (end[i] - 1) * 2 - outC[i] + ${u};
}
}
${r} coords = outC - start;
setOutput(getX(${i}));
}
`}},W7=class{constructor(e,t,n){this.variableNames=["x"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=t.map((h,f)=>h[0]+e[f]+h[1]);let s=e.length,r=rt(s),a=t.map(h=>h[0]).join(","),o=t.map((h,f)=>h[0]+e[f]).join(","),i=ln("rc",s),u=ln("source",s),l=`${i[s-1]} < ${this.outputShape[s-1]}`,c=s===1?"source":`vec2(${u.slice(-2).join()})`,p=n==="reflect"?0:1,d="";if(s===1){let h=`
${r} source = rc;
if (source < start) {
source = start * 2 - source - ${p};
} else if (source >= end) {
source = (end - 1) * 2 - source + ${p};
}
source -= start;
`;d=`
${r} rc = outputLoc;
${h}
result[0] = getChannel(getX(${u.join()}), ${c});
${i[s-1]} += 1;
if(${l}) {
${h}
result[1] = getChannel(getX(${u.join()}), ${c});
}
`}else{let h=`
${r} source = rc;
${r} lt = ${r}(lessThan(source, start));
${r} gte = ${r}(greaterThanEqual(source, end));
${r} orig = 1 - (lt + gte);
source = orig * source +
lt * (start * 2 - source - ${p}) +
gte * ((end - 1) * 2 - source + ${p});
source -= start;
`;d=`
${r} rc = outputLoc;
${h}
result[0] = getChannel(getX(${u.join()}), ${c});
${i[s-1]} += 1;
if(${l}) {
${h}
result[1] = getChannel(getX(${u.join()}), ${c});
}
rc = outputLoc;
${i[s-2]} += 1;
if(${i[s-2]} < ${this.outputShape[s-2]}) {
${h}
result[2] = getChannel(getX(${u.join()}), ${c});
${i[s-1]} += 1;
if(${l}) {
${h}
result[3] = getChannel(getX(${u.join()}), ${c});
}
}
`}this.userCode=`
const ${r} start = ${r}(${a});
const ${r} end = ${r}(${o});
void main() {
${r} outputLoc = getOutputCoords();
vec4 result = vec4(0.);
${d}
setOutput(result);
}
`}},U7=({inputs:e,backend:t,attrs:n})=>{let{x:s}=e,{paddings:r,mode:a}=n,o=K().getBool("WEBGL_PACK_ARRAY_OPERATIONS")?new W7(s.shape,r,a):new V7(s.shape,r,a);return t.runWebGLProgram(o,[s],s.dtype)},G7={kernelName:Qa,backendName:"webgl",kernelFunc:U7},H7=`if (b == 0.0) return NAN;
return mod(a, b);`,q7=`
vec4 result = mod(a, b);
vec4 isNaN = vec4(equal(b, vec4(0.0)));
`+nh+`
return result;
`,j7=jt({opSnippet:H7,packedOpSnippet:q7}),K7={kernelName:Cl,backendName:"webgl",kernelFunc:j7},X7=class{constructor(e,t,n){this.variableNames=["probs"],this.customUniforms=[{name:"seed",type:"float"}],this.outputShape=[e,n],this.userCode=`
void main() {
ivec2 coords = getOutputCoords();
int batch = coords[0];
float r = random(seed);
float cdf = 0.0;
for (int i = 0; i < ${t-1}; i++) {
cdf += getProbs(batch, i);
if (r < cdf) {
setOutput(float(i));
return;
}
}
// If no other event happened, last event happened.
setOutput(float(${t-1}));
}
`}},Y7=`
if (a == b) {
return 1.0;
};
return a / b;`,Q7=`
// 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;
`,C2=jt({opSnippet:Y7,packedOpSnippet:Q7,checkOutOfBounds:!0}),Z7={kernelName:Oa,backendName:"webgl",kernelFunc:C2},Dw="return a - b;",N2=jt({opSnippet:Dw,packedOpSnippet:Dw,supportsComplex:!0,cpuKernelImpl:KX}),J7={kernelName:fo,backendName:"webgl",kernelFunc:N2};function T2(e){let{inputs:t,backend:n,attrs:s}=e,{logits:r}=t,{dim:a}=s,o=w.parseAxisParam([a],r.shape),i=I2({inputs:{x:r},backend:n,attrs:{reductionIndices:o,keepDims:!1}}),u=C.expandShapeToKeepDim(i.shape,o),l=pe({inputs:{x:i},backend:n,attrs:{shape:u}}),c=N2({inputs:{a:r,b:l},backend:n}),p=w2({inputs:{x:c},backend:n}),d=ah({inputs:{x:p},backend:n,attrs:{axis:o,keepDims:!1}}),h=pe({inputs:{x:d},backend:n,attrs:{shape:u}}),f=C2({inputs:{a:p,b:h},backend:n});return n.disposeIntermediateTensorInfo(i),n.disposeIntermediateTensorInfo(l),n.disposeIntermediateTensorInfo(c),n.disposeIntermediateTensorInfo(p),n.disposeIntermediateTensorInfo(d),n.disposeIntermediateTensorInfo(h),f}var eJ={kernelName:po,backendName:"webgl",kernelFunc:T2};function tJ(e){let{inputs:t,backend:n,attrs:s}=e,{logits:r}=t,{numSamples:a,seed:o,normalized:i}=s,u=i?r:T2({inputs:{logits:r},backend:n,attrs:{dim:r.shape.length-1}}),l=u.shape[0],c=u.shape[1],p=new X7(l,c,a),d=[[o]],h=n.runWebGLProgram(p,[u],"int32",d);return i||n.disposeIntermediateTensorInfo(u),h}var nJ={kernelName:Pg,backendName:"webgl",kernelFunc:tJ},sJ=ss+`
return -x;
`,rJ=`
vec4 result = -x;
bvec4 isNaN = isnan(x);
result.r = isNaN.r ? x.r : result.r;
result.g = isNaN.g ? x.g : result.g;
result.b = isNaN.b ? x.b : result.b;
result.a = isNaN.a ? x.a : result.a;
return result;
`;function aJ(e){let{inputs:t,backend:n}=e,{x:s}=t;if(n.shouldExecuteOnCPU([s])){let a=n.texData.get(s.dataId),[o,i]=DX(a.values,s.shape,s.dtype);return n.makeTensorInfo(i,s.dtype,o)}let r;return K().getBool("WEBGL_PACK_UNARY_OPERATIONS")?r=new ea(s.shape,rJ):r=new Gs(s.shape,sJ),n.runWebGLProgram(r,[s],s.dtype)}var oJ={kernelName:$i,backendName:"webgl",kernelFunc:aJ},iJ=ws.nonMaxSuppressionV3Impl;function uJ(e){C.warn("tf.nonMaxSuppression() in webgl locks the UI thread. Call tf.nonMaxSuppressionAsync() instead");let{inputs:t,backend:n,attrs:s}=e,{boxes:r,scores:a}=t,{maxOutputSize:o,iouThreshold:i,scoreThreshold:u}=s,l=n.readSync(r.dataId),c=n.readSync(a.dataId),{selectedIndices:p}=iJ(l,c,o,i,u);return n.makeTensorInfo([p.length],"int32",new Int32Array(p))}var lJ={kernelName:Ai,backendName:"webgl",kernelFunc:uJ},cJ=ws.nonMaxSuppressionV4Impl;function dJ(e){C.warn("tf.nonMaxSuppression() in webgl locks the UI thread. Call tf.nonMaxSuppressionAsync() instead");let{inputs:t,backend:n,attrs:s}=e,{boxes:r,scores:a}=t,{maxOutputSize:o,iouThreshold:i,scoreThreshold:u,padToMaxOutputSize:l}=s,c=n.readSync(r.dataId),p=n.readSync(a.dataId),{selectedIndices:d,validOutputs:h}=cJ(c,p,o,i,u,l);return[n.makeTensorInfo([d.length],"int32",new Int32Array(d)),n.makeTensorInfo([],"int32",new Int32Array([h]))]}var pJ={kernelName:Nl,backendName:"webgl",kernelFunc:dJ},hJ=ws.nonMaxSuppressionV5Impl;function fJ(e){C.warn("tf.nonMaxSuppression() in webgl locks the UI thread. Call tf.nonMaxSuppressionAsync() instead");let{inputs:t,backend:n,attrs:s}=e,{boxes:r,scores:a}=t,{maxOutputSize:o,iouThreshold:i,scoreThreshold:u,softNmsSigma:l}=s,c=n.readSync(r.dataId),p=n.readSync(a.dataId),d=o,h=i,f=u,m=l,{selectedIndices:g,selectedScores:b}=hJ(c,p,d,h,f,m);return[n.makeTensorInfo([g.length],"int32",new Int32Array(g)),n.makeTensorInfo([b.length],"float32",new Float32Array(b))]}var mJ={kernelName:Ei,backendName:"webgl",kernelFunc:fJ},gJ=class{constructor(e,t,n,s){this.variableNames=["indices"],this.outputShape=[e,t],this.userCode=`
void main() {
ivec2 coords = getOutputCoords();
int index = round(getIndices(coords.x));
setOutput(mix(float(${s}), float(${n}),
float(index == coords.y)));
}
`}},bJ=e=>{let{inputs:t,backend:n,attrs:s}=e,{indices:r}=t,{depth:a,onValue:o,offValue:i}=s,u=w.sizeFromShape(r.shape),l=new gJ(u,a,o,i),c=pe({inputs:{x:r},backend:n,attrs:{shape:[u]}}),p=n.runWebGLProgram(l,[c],r.dtype);n.disposeIntermediateTensorInfo(c);let d=[...r.shape,a],h=pe({inputs:{x:p},backend:n,attrs:{shape:d}});return n.disposeIntermediateTensorInfo(p),h},yJ={kernelName:Di,backendName:"webgl",kernelFunc:bJ};function jd(e){let{inputs:t,backend:n}=e,{x:s}=t;if(s.dtype==="complex64"){let r=nc({inputs:{input:s},backend:n}),a=jd({inputs:{x:r},backend:n}),o=oh({inputs:{input:s},backend:n}),i=jd({inputs:{x:o},backend:n}),u=Fr({inputs:{real:a,imag:i},backend:n});return n.disposeIntermediateTensorInfo(r),n.disposeIntermediateTensorInfo(a),n.disposeIntermediateTensorInfo(o),n.disposeIntermediateTensorInfo(i),u}else return sc({attrs:{shape:s.shape,dtype:s.dtype,value:s.dtype==="string"?"":0},backend:n})}var vJ={kernelName:Xi,backendName:"webgl",kernelFunc:jd};function $2(e){let{inputs:t,backend:n}=e,{x:s}=t;if(s.dtype==="string")throw new Error("onesLike is not supported under string dtype");if(s.dtype==="complex64"){let r=nc({inputs:{input:s},backend:n}),a=$2({inputs:{x:r},backend:n}),o=oh({inputs:{input:s},backend:n}),i=jd({inputs:{x:o},backend:n}),u=Fr({inputs:{real:a,imag:i},backend:n});return n.disposeIntermediateTensorInfo(r),n.disposeIntermediateTensorInfo(a),n.disposeIntermediateTensorInfo(o),n.disposeIntermediateTensorInfo(i),u}else return sc({attrs:{shape:s.shape,dtype:s.dtype,value:1},backend:n})}var xJ={kernelName:Ri,backendName:"webgl",kernelFunc:$2};function wJ(e){let{inputs:t,backend:n,attrs:s}=e,{axis:r}=s;if(t.length===1)return rg({inputs:{input:t[0]},backend:n,attrs:{dim:r}});let a=t[0].shape,o=t[0].dtype;t.forEach(c=>{w.assertShapesMatch(a,c.shape,"All tensors passed to stack must have matching shapes"),w.assert(o===c.dtype,()=>"All tensors passed to stack must have matching dtypes")});let i=[],u=t.map(c=>{let p=rg({inputs:{input:c},backend:n,attrs:{dim:r}});return i.push(p),p}),l=f2({inputs:u,backend:n,attrs:{axis:r}});return i.forEach(c=>n.disposeIntermediateTensorInfo(c)),l}var kJ={kernelName:Fi,backendName:"webgl",kernelFunc:wJ},SJ=class{constructor(e,t,n){this.variableNames=["x"],this.customUniforms=[{name:"value",type:"float"}],this.outputShape=t.map((u,l)=>u[0]+e[l]+u[1]);let s=e.length,r=rt(s),a=t.map(u=>u[0]).join(","),o=t.map((u,l)=>u[0]+e[l]).join(","),i=["coords[0]","coords[1]","coords[2]","coords[3]"].slice(0,s);if(s===1){this.userCode=`
int start = ${a};
int end = ${o};
void main() {
int outC = getOutputCoords();
if (outC < start || outC >= end) {
setOutput(value);
} else {
setOutput(getX(outC - start));
}
}
`;return}this.userCode=`
${r} start = ${r}(${a});
${r} end = ${r}(${o});
void main() {
${r} outC = getOutputCoords();
if (any(lessThan(outC, start)) || any(greaterThanEqual(outC, end))) {
setOutput(value);
} else {
${r} coords = outC - start;
setOutput(getX(${i}));
}
}
`}},IJ=class{constructor(e,t,n){this.variableNames=["x"],this.packedInputs=!0,this.packedOutput=!0,this.customUniforms=[{name:"value",type:"float"}],this.outputShape=t.map((f,m)=>f[0]+e[m]+f[1]);let s=e.length,r=rt(s),a=t.map(f=>f[0]).join(","),o=t.map((f,m)=>f[0]+e[m]).join(","),i=ln("rc",s),u=ln("source",s),l=`${i[s-1]} < ${this.outputShape[s-1]}`,c=s===1?"source":`vec2(${u.slice(-2).join()})`,p=[`${r} rc = outputLoc;`,`${i[s-1]} += 1;
if(${l}) {
`,s===1?"":`}
rc = outputLoc;
${i[s-2]} += 1;
if(${i[s-2]} < ${this.outputShape[s-2]}) {`,s===1?"":` ${i[s-1]} += 1;
if(${l}) {`],d=s===1?"rc < start || rc >= end":"any(lessThan(rc, start)) || any(greaterThanEqual(rc, end))",h="";for(let f=0,m=s===1?2:4;f<m;f++)h+=`
${p[f]}
if (${d}) {
result[${f}] = float(value);
} else {
${r} source = rc - start;
result[${f}] = getChannel(getX(${u.join()}), ${c});
}
`;h+=s===1?"} ":"}}",this.userCode=`
const ${r} start = ${r}(${a});
const ${r} end = ${r}(${o});
void main() {
${r} outputLoc = getOutputCoords();
vec4 result = vec4(0.);
${h}
setOutput(result);
}
`}},_2=e=>{let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{paddings:a,constantValue:o}=s;if(w.sizeFromShape(r.shape)===0){let l=a.map((c,p)=>c[0]+r.shape[p]+c[1]);return sc({backend:n,attrs:{shape:l,value:o,dtype:r.dtype}})}let i=K().getBool("WEBGL_PACK_ARRAY_OPERATIONS")?new IJ(r.shape,a,o):new SJ(r.shape,a,o),u=[[o]];return n.runWebGLProgram(i,[r],r.dtype,u)},CJ={kernelName:Ja,backendName:"webgl",kernelFunc:_2},NJ=`
if(a < 0.0 && floor(b) < b){
return NAN;
}
if (b == 0.0) {
return 1.0;
}
return (round(mod(b, 2.0)) != 1) ?
pow(abs(a), b) : sign(a) * pow(abs(a), b);
`,TJ=`
// isModRound1 has 1 for components with round(mod(b, 2.0)) == 1, 0 otherwise.
vec4 isModRound1 = vec4(equal(round(mod(b, 2.0)), ivec4(1)));
vec4 multiplier = sign(a) * isModRound1 + (vec4(1.0) - isModRound1);
vec4 result = multiplier * pow(abs(a), b);
// Ensure that a^0 = 1, including 0^0 = 1 as this correspond to TF and JS
bvec4 isExpZero = equal(b, vec4(0.0));
result.r = isExpZero.r ? 1.0 : result.r;
result.g = isExpZero.g ? 1.0 : result.g;
result.b = isExpZero.b ? 1.0 : result.b;
result.a = isExpZero.a ? 1.0 : result.a;
vec4 isNaN = vec4(lessThan(a, vec4(0.0))) * vec4(lessThan(floor(b), b));
`+nh+`
return result;
`,$J=jt({opSnippet:NJ,packedOpSnippet:TJ}),_J={kernelName:eo,backendName:"webgl",kernelFunc:$J};function AJ(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{axis:a,keepDims:o}=s,i=r.shape.length,u=[],l=w.parseAxisParam(a,r.shape),c=l,p=C.getAxesPermutation(c,i),d=r;p!=null&&(d=pn({inputs:{x:r},backend:n,attrs:{perm:p}}),c=C.getInnerMostAxes(c.length,i),u.push(d)),C.assertAxesAreInnerMostDims("prod",c,i);let h;if(n.shouldExecuteOnCPU([d])){let f=n.texData.get(d.dataId).values,{outVals:m,outShape:g,outDtype:b}=OX(d.shape,d.dtype,f,c);h=n.makeTensorInfo(g,b,m)}else{let[f,m]=C.computeOutAndReduceShapes(d.shape,c),g=w.sizeFromShape(m),b=pe({inputs:{x:d},backend:n,attrs:{shape:[-1,g]}}),y=yp(r.dtype),v=So(b,y,"prod",n);h=pe({inputs:{x:v},backend:n,attrs:{shape:f}}),u.push(b),u.push(v)}if(o){u.push(h);let f=C.expandShapeToKeepDim(h.shape,l);h=pe({inputs:{x:h},backend:n,attrs:{shape:f}})}return u.forEach(f=>n.disposeIntermediateTensorInfo(f)),h}var EJ={kernelName:no,backendName:"webgl",kernelFunc:AJ},A2=e=>{let{backend:t,attrs:n}=e,{start:s,stop:r,step:a,dtype:o}=n,i=PX(s,r,a,o);return t.makeTensorInfo([i.length],o,i)},RJ={kernelName:Tl,backendName:"webgl",kernelFunc:A2},DJ="return 1.0 / x;",FJ=Ke({opSnippet:DJ}),OJ={kernelName:$l,backendName:"webgl",kernelFunc:FJ},PJ=ss+`
return (x < 0.0) ? 0.0 : x;
`,zJ=`
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;
`,LJ=Ke({opSnippet:PJ,packedOpSnippet:zJ}),MJ={kernelName:so,backendName:"webgl",kernelFunc:LJ},BJ=ss+`
return (x < 0.0) ? 0.0 : min(6.0, x);
`,VJ=`
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;
`,WJ=Ke({opSnippet:BJ,packedOpSnippet:VJ}),UJ={kernelName:ao,backendName:"webgl",kernelFunc:WJ},GJ=class{constructor(e,t,n,s,r){this.variableNames=["A"],this.outputShape=[];let[a,o,i,u]=e;this.outputShape=[a,t,n,u];let l=[s&&t>1?o-1:o,s&&n>1?i-1:i],c=[s&&t>1?t-1:t,s&&n>1?n-1:n],p;r?p="(vec2(yRC) + vec2(0.5)) * effectiveInputOverOutputRatioRC - vec2(0.5)":p="vec2(yRC) * effectiveInputOverOutputRatioRC",this.userCode=`
const vec2 effectiveInputOverOutputRatioRC = vec2(
${l[0]/c[0]},
${l[1]/c[1]});
const vec2 inputShapeRC = vec2(${o}.0, ${i}.0);
void main() {
ivec4 coords = getOutputCoords();
int b = coords[0];
int d = coords[3];
ivec2 yRC = coords.yz;
// Fractional source index.
vec2 sourceFracIndexRC = ${p};
// Compute the 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);
}
`}},HJ=class{constructor(e,t,n,s,r){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=[];let[a,o,i,u]=e;this.outputShape=[a,t,n,u];let l=[s&&t>1?o-1:o,s&&n>1?i-1:i],c=[s&&t>1?t-1:t,s&&n>1?n-1:n],p;r?p="(vec3(yRC) + vec3(0.5)) * effectiveInputOverOutputRatioRC - vec3(0.5)":p="vec3(yRC) * effectiveInputOverOutputRatioRC",this.userCode=`
const vec3 effectiveInputOverOutputRatioRC = vec3(
${l[0]/c[0]},
${l[1]/c[1]},
${l[1]/c[1]});
const vec3 inputShapeRC = vec3(${o}.0, ${i}.0,
${i}.0);
float getAValue(int b, int r, int c, int d) {
return getChannel(getA(b, r, c, d), vec2(c, d));
}
void main() {
ivec4 coords = getOutputCoords();
int b = coords[0];
int d = coords[3];
// Calculate values for next column in yRC.z.
ivec3 yRC = coords.yzz + ivec3(0, 0, 1);
// Fractional source index.
vec3 sourceFracIndexRC = ${p};
// Compute the 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 qJ(e){let{inputs:t,backend:n,attrs:s}=e,{images:r}=t,{alignCorners:a,halfPixelCenters:o,size:i}=s,[u,l]=i,c=K().getBool("WEBGL_PACK_IMAGE_OPERATIONS")?new HJ(r.shape,u,l,a,o):new GJ(r.shape,u,l,a,o);return n.runWebGLProgram(c,[r],"float32")}var jJ={kernelName:ro,backendName:"webgl",kernelFunc:qJ},KJ=class{constructor(e,t,n){this.variableNames=["dy"],this.outputShape=[],this.outputShape=t;let[,s,r]=t,[,a,o]=e,i=[n&&a>1?s-1:s,n&&o>1?r-1:r],u=[n&&a>1?a-1:a,n&&o>1?o-1:o],l=i[0]/u[0],c=i[1]/u[1],p=1/l,d=1/c,h=Math.ceil(p)*2+2,f=Math.ceil(d)*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(${l});
const float widthScale = float(${c});
const float invHeightScale = float(${p});
const float invWidthScale = float(${d});
const int winHeight = int(${h});
const int winWidth = int(${f});
// Compute bounds for where in dy we will look
float startRLerp = floor(float(r) * invHeightScale);
int startDyR = int(startRLerp - float(winHeight / 2));
float startCLerp = floor(float(c) * invWidthScale);
int startDyC = int(startCLerp - float(winWidth / 2));
// Loop over dy
for (int dyROffset = 0; dyROffset < winHeight; dyROffset++) {
int dyR = dyROffset + startDyR;
// Guard against the window exceeding the bounds of dy
if (dyR < 0 || dyR >= ${a}) {
continue;
}
for (int dyCOffset = 0; dyCOffset < winWidth; dyCOffset++) {
int dyC = dyCOffset + startDyC;
// Guard against the window exceeding the bounds of dy
if (dyC < 0 || dyC >= ${o}) {
continue;
}
float dxR = float(dyR) * heightScale;
int topDxRIndex = int(floor(dxR));
int bottomDxRIndex = int(min(ceil(dxR), ${s-1}.0));
float dxRLerp = dxR - float(topDxRIndex);
float inverseDxRLerp = 1.0 - dxRLerp;
float dxC = float(dyC) * widthScale;
int leftDxCIndex = int(floor(dxC));
int rightDxCIndex = int(min(ceil(dxC), ${r-1}.0));
float dxCLerp = dxC - float(leftDxCIndex);
float inverseDxCLerp = 1.0 - dxCLerp;
if (r == topDxRIndex && c == leftDxCIndex) {
// topLeft
accumulator +=
getDy(b, dyR, dyC, d) * inverseDxRLerp * inverseDxCLerp;
}
if (r == topDxRIndex && c == rightDxCIndex) {
// topRight
accumulator += getDy(b, dyR, dyC, d) * inverseDxRLerp * dxCLerp;
}
if (r == bottomDxRIndex && c == leftDxCIndex) {
// bottomLeft
accumulator += getDy(b, dyR, dyC, d) * dxRLerp * inverseDxCLerp;
}
if (r == bottomDxRIndex && c == rightDxCIndex) {
// bottomRight
accumulator += getDy(b, dyR, dyC, d) * dxRLerp * dxCLerp;
}
}
}
// End loop over dy
setOutput(accumulator);
}
`}};function XJ(e){let{inputs:t,backend:n,attrs:s}=e,{images:r,dy:a}=t,{alignCorners:o}=s,i=new KJ(a.shape,r.shape,o);return n.runWebGLProgram(i,[a],a.dtype)}var YJ={kernelName:Lg,backendName:"webgl",kernelFunc:XJ},QJ=class{constructor(e,t,n,s,r){this.variableNames=["A"],this.outputShape=[];let[a,o,i,u]=e;this.outputShape=[a,t,n,u];let l=[s&&t>1?o-1:o,s&&n>1?i-1:i],c=[s&&t>1?t-1:t,s&&n>1?n-1:n],p=s?"0.5":"0.0",d;r?d="max((vec2(yRC) + vec2(0.5)) * effectiveInputOverOutputRatioRC, vec2(0.0))":d="vec2(yRC) * effectiveInputOverOutputRatioRC",this.userCode=`
const vec2 effectiveInputOverOutputRatioRC = vec2(
${l[0]/c[0]},
${l[1]/c[1]});
const vec2 inputShapeRC = vec2(${o}.0, ${i}.0);
void main() {
ivec4 coords = getOutputCoords();
int b = coords[0];
int d = coords[3];
ivec2 yRC = coords.yz;
// Fractional source index.
vec2 sourceFracIndexRC = ${d};
// Compute the coordinators of nearest neighbor point.
ivec2 sourceNearestRC = ivec2(
min(inputShapeRC - 1.0, floor(sourceFracIndexRC + ${p})));
float newValue = getA(b, sourceNearestRC.x, sourceNearestRC.y, d);
setOutput(newValue);
}
`}},ZJ=class{constructor(e,t,n,s,r){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=[];let[a,o,i,u]=e;this.outputShape=[a,t,n,u];let l=[s&&t>1?o-1:o,s&&n>1?i-1:i],c=[s&&t>1?t-1:t,s&&n>1?n-1:n],p=s?"0.5":"0.0",d;r?d="max((vec3(yRC) + vec3(0.5)) * effectiveInputOverOutputRatioRC, vec3(0.0))":d="vec3(yRC) * effectiveInputOverOutputRatioRC",this.userCode=`
const vec3 effectiveInputOverOutputRatioRC = vec3(
${l[0]/c[0]},
${l[1]/c[1]},
${l[1]/c[1]});
const vec3 inputShapeRC = vec3(${o}.0, ${i}.0,
${i}.0);
float getAValue(int b, int r, int c, int d) {
return getChannel(getA(b, r, c, d), vec2(c, d));
}
void main() {
ivec4 coords = getOutputCoords();
int b = coords[0];
int d = coords[3];
// Calculate values for next column in yRC.z.
ivec3 yRC = coords.yzz + ivec3(0, 0, 1);
// Fractional source index.
vec3 sourceFracIndexRC = ${d};
// Compute the coordinators of nearest neighbor point.
ivec3 sourceNearestRC = ivec3(
min(inputShapeRC - 1.0, floor(sourceFracIndexRC + ${p})));
// Should we calculate next column and row elements in 2x2 packed cell.
bool hasNextCol = d < ${u-1};
bool hasNextRow = coords.z < ${n-1};
vec4 newValue = vec4(
getAValue(b, sourceNearestRC.x, sourceNearestRC.y, d),
hasNextCol ? getAValue(b, sourceNearestRC.x, sourceNearestRC.y, d + 1)
: 0.0,
hasNextRow ? getAValue(b, sourceNearestRC.x, sourceNearestRC.z, d)
: 0.0,
(hasNextRow && hasNextCol) ?
getAValue(b, sourceNearestRC.x, sourceNearestRC.z, d + 1) : 0.0);
setOutput(newValue);
}
`}};function JJ(e){let{inputs:t,backend:n,attrs:s}=e,{images:r}=t,{alignCorners:a,halfPixelCenters:o,size:i}=s,[u,l]=i,c=K().getBool("WEBGL_PACK_IMAGE_OPERATIONS")?new ZJ(r.shape,u,l,a,o):new QJ(r.shape,u,l,a,o);return n.runWebGLProgram(c,[r],r.dtype)}var eee={kernelName:_l,backendName:"webgl",kernelFunc:JJ},tee=class{constructor(e,t,n){this.variableNames=["dy"],this.outputShape=[],this.outputShape=t;let[,s,r]=t,[,a,o]=e,i=[n&&a>1?s-1:s,n&&o>1?r-1:r],u=[n&&a>1?a-1:a,n&&o>1?o-1:o],l=i[0]/u[0],c=i[1]/u[1],p=1/l,d=1/c,h=Math.ceil(p)*2+2,f=Math.ceil(d)*2+2;this.userCode=`
void main() {
ivec4 coords = getOutputCoords();
int b = coords[0];
int d = coords[3];
int r = coords[1];
int c = coords[2];
float accumulator = 0.0;
const float heightScale = float(${l});
const float widthScale = float(${c});
const float invHeightScale = float(${p});
const float invWidthScale = float(${d});
const int winHeight = int(${h});
const int winWidth = int(${f});
// Compute bounds for where in dy we will look
float startRLerp = floor(float(r) * invHeightScale);
int startDyR = int(floor(startRLerp - float(winHeight / 2)));
float startCLerp = floor(float(c) * invWidthScale);
int startDyC = int(floor(startCLerp - float(winWidth / 2)));
// Loop over dy
for (int dyROffset = 0; dyROffset < winHeight; dyROffset++) {
int dyR = dyROffset + startDyR;
// Guard against the window exceeding the bounds of dy
if (dyR < 0 || dyR >= ${a}) {
continue;
}
for (int dyCOffset = 0; dyCOffset < winWidth; dyCOffset++) {
int dyC = dyCOffset + startDyC;
// Guard against the window exceeding the bounds of dy
if (dyC < 0 || dyC >= ${o}) {
continue;
}
float sourceFracRow =
float(${i[0]}) *
(float(dyR) / float(${u[0]}));
float sourceFracCol =
float(${i[1]}) *
(float(dyC) / float(${u[1]}));
int sourceNearestRow = int(min(
float(int(${s}) - 1),
${n} ? float(round(sourceFracRow)) :
float(floor(sourceFracRow))));
int sourceNearestCol = int(min(
float(int(${r}) - 1),
${n} ? float(round(sourceFracCol)) :
float(floor(sourceFracCol))));
if (r == sourceNearestRow && c == sourceNearestCol) {
accumulator += getDy(b, dyR, dyC, d);
}
}
}
// End loop over dy
setOutput(accumulator);
}
`}};function nee(e){let{inputs:t,backend:n,attrs:s}=e,{images:r,dy:a}=t,{alignCorners:o}=s,i=new tee(a.shape,r.shape,o);return n.runWebGLProgram(i,[a],a.dtype)}var see={kernelName:zg,backendName:"webgl",kernelFunc:nee},ree=class{constructor(e,t){this.variableNames=["x"];let n=e.length;if(n>4)throw new Error(`WebGL backend: Reverse of rank-${n} tensor is not yet supported`);if(this.outputShape=e,n===1){this.userCode=`
void main() {
int coord = getOutputCoords();
setOutput(getX(${e[0]} - coord - 1));
}
`;return}let s=o=>t.indexOf(o)!==-1&&e[o]!==1?`${e[o]} - coords[${o}] - 1`:`coords[${o}]`,r=e.map((o,i)=>s(i)).join(","),a=rt(n);this.userCode=`
void main() {
${a} coords = getOutputCoords();
setOutput(getX(${r}));
}
`}},aee=class{constructor(e,t){this.variableNames=["x"],this.packedInputs=!0,this.packedOutput=!0;let n=e.length;if(n>4)throw new Error(`WebGL backend: Reverse of rank-${n} tensor is not yet supported`);this.outputShape=e;let s=ln("rc",n),r=`${s[n-1]} + 1 < ${this.outputShape[n-1]}`,a=`${s[n-2]} + 1 < ${this.outputShape[n-2]}`,o=rt(n);n===1?this.userCode=`
void main(){
int rc = getOutputCoords();
vec4 result = vec4(0.);
result.r = getChannel(getX(${e[0]} - rc - 1),
${e[0]} - rc - 1);
if(${r}){
result.g = getChannel(getX(${e[0]} - (rc + 1) - 1),
${e[0]} - (rc + 1) - 1);
}
setOutput(result);
}
`:this.userCode=`
void main() {
${o} rc = getOutputCoords();
vec4 result = vec4(0.);
result.r = ${i(s.slice())};
if(${r}){
result.g = ${u(s.slice())};
}
if(${a}) {
result.b = ${l(s.slice())};
if(${r}) {
result.a = ${c(s.slice())};
}
}
setOutput(result);
}
`;function i(h){return p(h)}function u(h){return h[n-1]="("+h[n-1]+" + 1)",p(h)}function l(h){return h[n-2]="("+h[n-2]+" + 1)",p(h)}function c(h){return h[n-1]="("+h[n-1]+" + 1)",h[n-2]="("+h[n-2]+" + 1)",p(h)}function p(h){let f=e.map((b,y)=>d(y,h)),m=f.join(","),g=f.slice(-2).join(",");return`getChannel(getX(${m}), vec2(${g}))`}function d(h,f){return t.indexOf(h)!==-1&&e[h]!==1?`${e[h]} - ${f[h]} - 1`:`${f[h]}`}}};function oee(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{dims:a}=s,o=r.shape.length,i=w.parseAxisParam(a,r.shape);if(o===0)return Rn({inputs:{x:r},backend:n});let u=K().getBool("WEBGL_PACK_ARRAY_OPERATIONS")?new aee(r.shape,i):new ree(r.shape,i);return n.runWebGLProgram(u,[r],r.dtype)}var iee={kernelName:Pi,backendName:"webgl",kernelFunc:oee},uee=class{constructor(e,t){this.variableNames=["Image"],this.outputShape=[],this.customUniforms=[{name:"params",type:"vec4"}];let n=e[1],s=e[2];this.outputShape=e;let r="";typeof t=="number"?r=`float outputValue = ${t.toFixed(2)};`:r=`
vec3 fill = vec3(${t.join(",")});
float outputValue = fill[coords[3]];`,this.userCode=`
void main() {
ivec4 coords = getOutputCoords();
int x = coords[2];
int y = coords[1];
float coordXFloat = (float(x) - params[0]) * params[3] -
(float(y) - params[1]) * params[2];
float coordYFloat = (float(x) - params[0]) * params[2] +
(float(y) - params[1]) * params[3];
int coordX = int(round(coordXFloat + params[0]));
int coordY = int(round(coordYFloat + params[1]));
${r}
if(coordX >= 0 && coordX < ${s} && coordY >= 0 && coordY < ${n}) {
outputValue = getImage(coords[0], coordY, coordX, coords[3]);
}
setOutput(outputValue);
}
`}},lee={kernelName:Yi,backendName:"webgl",kernelFunc:({inputs:e,attrs:t,backend:n})=>{let{image:s}=e,{radians:r,fillValue:a,center:o}=t,i=n,u=new uee(s.shape,a),[l,c]=C.getImageCenter(o,s.shape[1],s.shape[2]),p=[[l,c,Math.sin(r),Math.cos(r)]];return i.runWebGLProgram(u,[s],s.dtype,p)}},cee=`
// OpenGL ES does not support round function.
// The algorithm is based on banker's rounding.
float base = floor(x);
if ((x - base) < 0.5) {
return floor(x);
} else if ((x - base) > 0.5) {
return ceil(x);
} else {
if (mod(base, 2.0) == 0.0) {
return base;
} else {
return base + 1.0;
}
}
`,dee=Ke({opSnippet:cee}),pee={kernelName:zi,backendName:"webgl",kernelFunc:dee},hee="return inversesqrt(x);",fee=Ke({opSnippet:hee,cpuKernelImpl:zX}),mee={kernelName:oo,backendName:"webgl",kernelFunc:fee},E2=class{constructor(e,t,n,s,r,a,o=!0){this.variableNames=["updates","indices","defaultValue"],this.outputShape=a;let i=rt(r.length),u=rt(a.length),l="";n===1?l="i":n===2&&(l="i, j");let c=`getIndices(${l})`,p="";s===1?p="i":s===2&&(p="i, coords[1]");let d=`getUpdates(${p})`,h=t>1?"strides[j]":"strides";this.userCode=`
${i} strides = ${i}(${r});
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 < ${t}; j++) {
int index = round(${c});
flattenedIndex += index * ${h};
}
if (flattenedIndex == coords[0]) {
sum += ${d};
found = true;
}
}
setOutput(mix(getDefaultValue(), sum, float(found)));
}
`}};function gee(e){let{inputs:t,backend:n,attrs:s}=e,{indices:r,updates:a}=t,{shape:o}=s,{sliceRank:i,numUpdates:u,sliceSize:l,strides:c,outputSize:p}=C.calculateShapes(a,r,o),d=[p/l,l];if(p===0)return n.makeTensorInfo(o,r.dtype);let h=pe({inputs:{x:r},backend:n,attrs:{shape:[u,i]}}),f=pe({inputs:{x:a},backend:n,attrs:{shape:[u,l]}}),m=n.makeTensorInfo([],"float32",new Float32Array([0])),g=new E2(u,i,h.shape.length,f.shape.length,c,d),b=n.runWebGLProgram(g,[f,h,m],f.dtype),y=pe({inputs:{x:b},backend:n,attrs:{shape:o}});return n.disposeIntermediateTensorInfo(h),n.disposeIntermediateTensorInfo(f),n.disposeIntermediateTensorInfo(b),n.disposeIntermediateTensorInfo(m),y}var bee={kernelName:Li,backendName:"webgl",kernelFunc:gee},yee=class{constructor(e,t,n,s){this.variableNames=["sortedSequence","values"],this.customUniforms=[{name:"numInputs",type:"int"}],this.outputShape=[e,n];let r="while (left < right) {",a=`for (int i = 0; i < ${Math.ceil(Math.log2(t+1))}; ++i) { if (left >= right) break;`,o=K().getNumber("WEBGL_VERSION")===2?r:a,i=s==="left"?"<":"<=";this.userCode=`
int findBound(int batch, float value) {
int left = 0;
int right = numInputs;
int mid;
${o}
mid = (left + right) / 2;
if (getSortedSequence(batch, mid) ${i} value) {
left = mid + 1;
} else {
right = mid;
}
}
return right;
}
void main() {
ivec2 coords = getOutputCoords();
int batch = coords[0];
int valueIndex = coords[1];
float value = getValues(batch, valueIndex);
setOutput(float(findBound(batch, value)));
}
`}};function vee(e){let{inputs:t,backend:n,attrs:s}=e,{sortedSequence:r,values:a}=t,{side:o}=s,i=new yee(r.shape[0],r.shape[1],a.shape[1],o),u=[[r.shape[1]]];return n.runWebGLProgram(i,[r,a],"int32",u)}var xee={kernelName:Mg,backendName:"webgl",kernelFunc:vee},wee=class{constructor(e,t,n){this.variableNames=["c","a","b"],this.outputShape=t;let s,r;if(n>4)throw Error(`Where for rank ${n} is not yet supported`);if(n===1)r="resRC",s="resRC";else{let o=["resRC.x","resRC.y","resRC.z","resRC.w"],i=[],u=[];for(let l=0;l<t.length;l++)u.push(`${o[l]}`),l<e&&i.push(`${o[l]}`);s=i.join(),r=u.join()}let a=rt(n);this.userCode=`
void main() {
${a} resRC = getOutputCoords();
float cVal = getC(${s});
if (cVal >= 1.0) {
setOutput(getA(${r}));
} else {
setOutput(getB(${r}));
}
}
`}};function kee(e){let{inputs:t,backend:n}=e,{condition:s,t:r,e:a}=t,o=new wee(s.shape.length,r.shape,r.shape.length);return n.runWebGLProgram(o,[s,r,a],cn(r.dtype,a.dtype))}var See={kernelName:Mi,backendName:"webgl",kernelFunc:kee},Iee=`
// Stable and Attracting Fixed Point (0, 1) for Normalized Weights.
// see: https://arxiv.org/abs/1706.02515
float scaleAlpha = ${C.SELU_SCALEALPHA};
float scale = ${C.SELU_SCALE};
return (x >= 0.0) ? scale * x : scaleAlpha * (exp(x) - 1.0);
`,Cee=Ke({opSnippet:Iee}),Nee={kernelName:Al,backendName:"webgl",kernelFunc:Cee},Tee=du+`
return 1.0 / (1.0 + exp(-1.0 * x));
`,$ee=`
vec4 result = 1.0 / (1.0 + exp(-1.0 * x));
bvec4 isNaN = isnan(x);
result.r = isNaN.r ? x.r : result.r;
result.g = isNaN.g ? x.g : result.g;
result.b = isNaN.b ? x.b : result.b;
result.a = isNaN.a ? x.a : result.a;
return result;
`,_ee=Ke({opSnippet:Tee,packedOpSnippet:$ee,cpuKernelImpl:MX}),Aee={kernelName:uo,backendName:"webgl",kernelFunc:_ee},Eee=`
if (isnan(x)) { return 0.0; }
return sign(x);
`,Ree=Ke({opSnippet:Eee}),Dee={kernelName:El,backendName:"webgl",kernelFunc:Ree},Fee=du+`
return sin(x);
`,Oee=Ke({opSnippet:Fee}),Pee={kernelName:io,backendName:"webgl",kernelFunc:Oee},zee=`
float e2x = exp(x);
return (e2x - 1.0 / e2x) / 2.0;
`,Lee=Ke({opSnippet:zee}),Mee={kernelName:Vi,backendName:"webgl",kernelFunc:Lee},Bee=`
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;
`,Vee=Ke({opSnippet:Bee}),Wee={kernelName:Rl,backendName:"webgl",kernelFunc:Vee},Uee=e=>{let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{blockShape:a,paddings:o}=s;w.assert(r.shape.length<=4,()=>"spaceToBatchND for rank > 4 with a WebGL backend not implemented yet");let i=a.reduce((b,y)=>b*y),u=[[0,0]];u.push(...o);for(let b=1+a.length;b<r.shape.length;++b)u.push([0,0]);let l=[],c=_2({inputs:{x:r},backend:n,attrs:{paddings:u,constantValue:0}}),p=C.getReshaped(c.shape,a,i,!1),d=C.getPermuted(p.length,a.length,!1),h=C.getReshapedPermuted(c.shape,a,i,!1),f=pe({inputs:{x:c},backend:n,attrs:{shape:p}}),m=pn({inputs:{x:f},backend:n,attrs:{perm:d}}),g=pe({inputs:{x:m},backend:n,attrs:{shape:h}});return l.push(c),l.push(f),l.push(m),l.forEach(b=>n.disposeIntermediateTensorInfo(b)),g},Gee={kernelName:Wi,backendName:"webgl",kernelFunc:Uee};function Hee(e){let{inputs:t,backend:n}=e,{indices:s,values:r,denseShape:a,defaultValue:o}=t;if(a.shape.length!==1)throw new Error(`Dense shape must be a vector, saw:
${a.shape}`);if(s.shape.length!==2)throw new Error(`Indices must be a matrix, saw:
${s.shape}`);if(r.shape.length!==1)throw new Error(`Values must be a vector, saw:
${r.shape}`);if(o.shape.length!==0)throw new Error(`Default value must be a scalar, saw:
${o.shape}`);let i=n.readSync(s.dataId),u=n.readSync(r.dataId),l=n.readSync(a.dataId),c=n.readSync(o.dataId)[0],[p,d,h,f,m]=VX(i,s.shape,s.dtype,u,r.dtype,l,c);return[n.makeTensorInfo(d,s.dtype,p),n.makeTensorInfo([d[0]],r.dtype,h),n.makeTensorInfo([f.length],"bool",new Uint8Array(f.map(g=>Number(g)))),n.makeTensorInfo([m.length],s.dtype,new Int32Array(m))]}var qee={kernelName:dp,backendName:"webgl",kernelFunc:Hee};function jee(e){let{inputs:t,backend:n}=e,{inputIndices:s,inputShape:r,newShape:a}=t;if(s.shape.length!==2)throw new Error(`Input indices should be a matrix but received shape ${s.shape}`);if(r.shape.length!==1)throw new Error(`Input shape should be a vector but received shape ${r.shape}`);if(a.shape.length!==1)throw new Error(`Target shape should be a vector but received shape ${a.shape}`);let o=Array.from(n.readSync(r.dataId)),i=n.readSync(s.dataId),u=Array.from(n.readSync(a.dataId)),[l,c,p]=WX(i,s.shape,s.dtype,o,u);return[n.makeTensorInfo(c,s.dtype,l),n.makeTensorInfo([p.length],a.dtype,new Int32Array(p))]}var Kee={kernelName:Dl,backendName:"webgl",kernelFunc:jee};function Xee(e){let{inputs:t,backend:n}=e,{data:s,indices:r,segmentIds:a}=t;if(s.shape.length<1)throw new Error("Data should be at least 1 dimensional but received scalar");if(r.shape.length!==1)throw new Error(`Indices should be a vector but received shape
${r.shape}`);if(a.shape.length!==1)throw new Error(`Segment ids should be a vector but received shape
${a.shape}`);let o=n.readSync(s.dataId),i=n.readSync(r.dataId),u=n.readSync(a.dataId),[l,c]=Z1(o,s.shape,s.dtype,i,u,!0);return n.makeTensorInfo(c,s.dtype,l)}var Yee={kernelName:pp,backendName:"webgl",kernelFunc:Xee};function Qee(e){let{inputs:t,backend:n}=e,{data:s,indices:r,segmentIds:a}=t;if(s.shape.length<1)throw new Error("Data should be at least 1 dimensional but received scalar");if(r.shape.length!==1)throw new Error(`Indices should be a vector but received shape
${r.shape}`);if(a.shape.length!==1)throw new Error(`Segment ids should be a vector but received shape
${a.shape}`);let o=n.readSync(s.dataId),i=n.readSync(r.dataId),u=n.readSync(a.dataId),[l,c]=Z1(o,s.shape,s.dtype,i,u);return n.makeTensorInfo(c,s.dtype,l)}var Zee={kernelName:hp,backendName:"webgl",kernelFunc:Qee};function Jee(e){let{inputs:t,backend:n,attrs:s}=e,{sparseIndices:r,sparseValues:a,defaultValue:o}=t,{outputShape:i}=s,{sliceRank:u,numUpdates:l,sliceSize:c,strides:p,outputSize:d}=C.calculateShapes(a,r,i),h=!1;if(a.dtype==="string"){let b=n.bufferSync(r),y=n.bufferSync(a),v=w.decodeString(n.readSync(o.dataId)[0]),x=LX(b,y,i,d,c,l,u,p,v,h);return n.makeTensorInfo(i,x.dtype,x.values)}let f=new E2(l,u,r.shape.length,a.shape.length,p,[d,1],h),m=n.runWebGLProgram(f,[a,r,o],a.dtype),g=pe({inputs:{x:m},backend:n,attrs:{shape:i}});return n.disposeIntermediateTensorInfo(m),g}var ete={kernelName:fp,backendName:"webgl",kernelFunc:Jee};function tte(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{numOrSizeSplits:a,axis:o}=s,i=w.parseAxisParam(o,r.shape)[0],u=C.prepareSplitSize(r,a,i),l=r.shape.length,c=new Array(l).fill(0),p=r.shape.slice();return u.map(d=>{let h=[...p];h[i]=d;let f=pu({inputs:{x:r},backend:n,attrs:{begin:c,size:h}});return c[i]+=d,f})}var nte={kernelName:Ui,backendName:"webgl",kernelFunc:tte},Fw="return sqrt(x);",ste=Ke({opSnippet:Fw,packedOpSnippet:Fw,cpuKernelImpl:UX}),rte={kernelName:lo,backendName:"webgl",kernelFunc:ste},ate="return x * x;",ote=Ke({opSnippet:ate}),ite={kernelName:Fl,backendName:"webgl",kernelFunc:ote},Ow="return (a - b) * (a - b);",ute=jt({opSnippet:Ow,packedOpSnippet:Ow}),lte={kernelName:ho,backendName:"webgl",kernelFunc:ute};function cte({inputs:e,attrs:t,backend:n}){let{x:s}=e,r=ss+`
return x > 0.0 ? 1.0 : float(${t.alpha});
`,a=new Gs(s.shape,r);return n.runWebGLProgram(a,[s],s.dtype)}var dte={kernelName:go,backendName:"webgl",kernelFunc:cte},pte=class{constructor(e,t,n){this.variableNames=["x"],this.outputShape=n;let s=n.length,r=rt(n.length),a=rt(n.length),o="";if(s===1)o="coords * strides + begin";else{let i=0;o=n.map((u,l)=>(i++,n.length===1?`coords * strides[${l}] + begin[${l}]`:`coords[${i-1}] * strides[${l}] + begin[${l}]`)).join(",")}this.userCode=`
${r} begin = ${r}(${e});
${r} strides = ${r}(${t});
void main() {
${a} coords = getOutputCoords();
setOutput(getX(${o}));
}
`}};function hte(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{begin:a,end:o,strides:i,beginMask:u,endMask:l,ellipsisMask:c,newAxisMask:p,shrinkAxisMask:d}=s,{finalShapeSparse:h,finalShape:f,isIdentity:m,sliceDim0:g,isSimpleSlice:b,begin:y,end:v,strides:x}=kt.sliceInfo(r.shape,a,o,i,u,l,c,p,d),k;if(m)k=pe({inputs:{x:r},backend:n,attrs:{shape:f}});else if(g||b){w.assert(r.shape.length>=1,()=>`Input must have rank at least 1, got: ${r.shape.length}`);let $=kt.computeOutShape(y,v,x),R=pu({inputs:{x:r},backend:n,attrs:{begin:y,size:$}});k=pe({inputs:{x:R},backend:n,attrs:{shape:f}}),n.disposeIntermediateTensorInfo(R)}else if(n.shouldExecuteOnCPU([r])){let R=n.readSync(r.dataId),E=Ae(r.shape,r.dtype,R),P=GX(h,E,x,y);k=n.makeTensorInfo(f,r.dtype,P.values)}else{let R=new pte(y,x,h);k=n.runWebGLProgram(R,[r],r.dtype)}let I=pe({inputs:{x:k},backend:n,attrs:{shape:f}});return n.disposeIntermediateTensorInfo(k),I}var fte={kernelName:Gi,backendName:"webgl",kernelFunc:hte};function mte(e){let{inputs:t,backend:n,attrs:s}=e,{separator:r,nGramWidths:a,leftPad:o,rightPad:i,padWidth:u,preserveShortSequences:l}=s,{data:c,dataSplits:p}=t,d=n.readSync(c.dataId),h=n.readSync(p.dataId),[f,m]=HX(d,h,r,a,o,i,u,l);return[n.makeTensorInfo([f.length],"string",f),n.makeTensorInfo(p.shape,"int32",m)]}var gte={kernelName:mp,backendName:"webgl",kernelFunc:mte};function bte(e){let{inputs:t,backend:n,attrs:s}=e,{skipEmpty:r}=s,{input:a,delimiter:o}=t;if(a.dtype!=="string")throw new Error("Input must be of datatype string");if(a.shape.length!==1)throw new Error(`Input must be a vector, got shape: ${a.shape}`);if(o.shape.length!==0)throw new Error(`Delimiter must be a scalar, got shape: ${o.shape}`);let i=n.readSync(a.dataId),u=n.readSync(o.dataId)[0],[l,c,p]=qX(i,u,r),d=c.length;return[n.makeTensorInfo([d,2],"int32",l),n.makeTensorInfo([d],"string",c),n.makeTensorInfo([2],"int32",new Int32Array(p))]}var yte={kernelName:Bg,backendName:"webgl",kernelFunc:bte};function vte(e){let{inputs:t,backend:n,attrs:s}=e,{numBuckets:r}=s,{input:a}=t;if(a.dtype!=="string")throw new Error("Input must be of datatype string");if(r<=0)throw new Error("Number of buckets must be at least 1");let o=n.readSync(a.dataId),i=jX(o,r);return n.makeTensorInfo(a.shape,"int32",i)}var xte={kernelName:Vg,backendName:"webgl",kernelFunc:vte},wte="return tan(x);",kte=Ke({opSnippet:wte}),Ste={kernelName:Hi,backendName:"webgl",kernelFunc:kte},Ite=`
float e2x = exp(-2.0 * abs(x));
return sign(x) * (1.0 - e2x) / (1.0 + e2x);
`,Cte=Ke({opSnippet:Ite}),Nte={kernelName:mo,backendName:"webgl",kernelFunc:Cte},Tte=class{constructor(e,t){this.variableNames=["A"];let n=new Array(e.length);for(let a=0;a<n.length;a++)n[a]=e[a]*t[a];this.outputShape=n,this.rank=n.length;let s=rt(this.rank),r=$te(e);this.userCode=`
void main() {
${s} resRC = getOutputCoords();
setOutput(getA(${r}));
}
`}};function $te(e){let t=e.length;if(t>5)throw Error(`Tile for rank ${t} is not yet supported`);if(t===1)return`imod(resRC, ${e[0]})`;let n=["resRC.x","resRC.y","resRC.z","resRC.w","resRC.u"],s=[];for(let r=0;r<e.length;r++)s.push(`imod(${n[r]}, ${e[r]})`);return s.join()}function R2(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{reps:a}=s;if(r.dtype==="string"||r.shape.length>5){let u=n.readSync(r.dataId),l=r.dtype==="string"?u.map(d=>w.decodeString(d)):u,c=Ae(r.shape,r.dtype,l),p=XX(c,a);return n.makeTensorInfo(p.shape,p.dtype,p.values)}let o=new Tte(r.shape,a);return n.runWebGLProgram(o,[r],r.dtype)}var _te={kernelName:Tr,backendName:"webgl",kernelFunc:R2},Ate=class{constructor(e){this.variableNames=["x","indices"],this.customUniforms=[{name:"n",type:"int"},{name:"firstPass",type:"int"},{name:"negativeInf",type:"float"},{name:"dir",type:"int"},{name:"inc",type:"int"}],this.outputShape=e,this.userCode=`
void main() {
ivec2 coords = getOutputCoords();
int batch = coords[0];
int elemIdx = coords[1];
// We compare elements pair-wise within a group of size 2 * inc.
// The comparing rule for each group alternates between ascending
// and descending. Within each group, we compare each pair at
// positions i and i+inc. To decide whether an element at position i
// is x0 or x1, we mod it by 2 * inc, if the result is smaller than
// inc, it is in the first half of the group, we denote it as x0,
// otherwise we denote it as x1.
// For example, as shown in the Bitonic top K paper referenced above,
// Figure5(a) shows that element[1] is in the
// second half of the group when group size is 2, but it is in the
// first half of the group when group size is 4.
bool isFirstInPair = imod(elemIdx, 2 * inc) < inc;
int i = isFirstInPair ? elemIdx : elemIdx - inc;
int i0 = firstPass == 1 ? i : int(getIndices(batch, i));
int i1 = firstPass == 1 ? i + inc : int(getIndices(batch, i + inc));
float x0 = i0 < n ? getX(batch, i0) : negativeInf;
float x1 = i1 < n ? getX(batch, i1) : negativeInf;
// Denotes which direction indices are in (ascending or descending).
bool reverse = imod(elemIdx, 2 * dir) >= dir;
bool isGreater = x0 > x1 || (x0 == x1 && i1 > i0);
if (reverse == isGreater) { // Elements in opposite order of direction
int iTemp = i0;
i0 = i1;
i1 = iTemp;
}
if (isFirstInPair) {
setOutput(float(i0));
} else {
setOutput(float(i1));
}
}
`}},Ete=class{constructor(e){this.variableNames=["x","indices"],this.customUniforms=[{name:"n",type:"int"},{name:"firstPass",type:"int"},{name:"k",type:"int"}],this.outputShape=e,this.userCode=`
void main() {
// Takes max of indices (0, k), (1, k + 1), (2, k + 2) ...
ivec2 coords = getOutputCoords();
int batch = coords[0];
int elemIdx = coords[1];
// The output size is half of the previous size.
// If the previous sequence is | | | | _ _ _ _ | | | | _ _ _ _ (k=4),
// we only need to output the indices at positions |, the indices at
// positions _ can be thrown away, see Figure5(b) After Phase 2
// (Merge phase) in the Bitonic Top K paper referenced above.
// For example, the paper shows we only need to output the orange bars.
// The output sequence should look like this | | | | | | | |.
// Because the sequence is halved, to map the output index back
// to the previous sequence to find the corresponding value,
// we need to double the index. When we double the index,
// we basically interpolate a position, so 2i looks like
// | _ | _ | _ | _ | _ | _ | _. We move the | to the first k position
// of each 2k positions by - elemIdx % k. E.g. for output at
// index 4,5,6,7, we want to get the corresponding element at
// original index 8,9,10,11, for output at index 8,9,10,11,
// we want to get the corresponding element at original index
// 16,17,18,19, so on and so forth.
int i = elemIdx < k ? elemIdx : (elemIdx * 2 - imod(elemIdx, k));
int i0 = firstPass == 1 ? i : int(getIndices(batch, i));
int i1 = firstPass == 1 ? i + k : int(getIndices(batch, i + k));
float x0 = getX(batch, i0);
float x1 = i1 < n ? getX(batch, i1) : x0;
setOutput(x0 >= x1 ? float(i0) : float(i1));
}
`}};function Hr(e,t){t!==null&&e.disposeIntermediateTensorInfo(t)}function Pw(e){let t=1;for(;t<e;)t*=2;return t}function Rte(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{k:a,sorted:o}=s,i=K().getNumber("TOPK_LAST_DIM_CPU_HANDOFF_SIZE_THRESHOLD"),u=K().getNumber("TOPK_K_CPU_HANDOFF_THRESHOLD"),l=r.shape,c=l[l.length-1];if(n.shouldExecuteOnCPU([r])||c<i||a>u){let P=n.readSync(r.dataId),[A,D]=YX(P,l,r.dtype,a,o);return[n.makeTensorInfo(A.shape,A.dtype,A.values),n.makeTensorInfo(D.shape,D.dtype,D.values)]}if(a===0)return l[l.length-1]=0,[n.makeTensorInfo(l,r.dtype,[]),n.makeTensorInfo(l,"int32",[])];if(c===1)return[r,sc({attrs:{shape:l,dtype:"int32",value:0},backend:n})];let p=n.texData.get(r.dataId),d=p!==null&&p.isPacked,h=d?n.unpackTensor(r):r,m=w.sizeFromShape(l)/c,g=pe({inputs:{x:h},attrs:{shape:[m,c]},backend:n});d&&Hr(n,h);let b=Pw(a),y=Pw(c),v=null,x=()=>v===null?[g,g]:[g,v],k=(P,A,D)=>{let T=x(),L=new Ate(D),j=[[c],[v===null?1:0],[Number.NEGATIVE_INFINITY],[P],[A]],Y=v;v=n.runWebGLProgram(L,T,"int32",j),Hr(n,Y)};for(let P=1;P<b;P*=2){let A=P*2;for(let D=P;D>=1;D/=2)k(A,D,[m,y])}for(let P=y;P>b;P/=2){let A=x(),D=new Ete([m,P/2]),L=[[c],[v===null?1:0],[b]],W=v;v=n.runWebGLProgram(D,A,"int32",L),Hr(n,W);let j=b/2,Y=j*2;for(let X=j;X>=1;X/=2)k(Y,X,v.shape)}let I=v;v=pu({inputs:{x:v},backend:n,attrs:{begin:0,size:[m,a]}}),Hr(n,I);let $=S2({inputs:{x:g,indices:v},backend:n,attrs:{axis:1,batchDims:1}});Hr(n,g);let R=l.slice(0,-1);R.push(a),I=v,v=pe({inputs:{x:v},attrs:{shape:R},backend:n}),Hr(n,I);let E=$;return $=pe({inputs:{x:$},attrs:{shape:R},backend:n}),Hr(n,E),[$,v]}var Dte={kernelName:qi,backendName:"webgl",kernelFunc:Rte},Fte=class{constructor(e,t,n,s,r,a){this.variableNames=["Image","Transforms"],this.outputShape=a;let o=n==="nearest"?1:2,i;switch(s){case"constant":i=1;break;case"reflect":i=2;break;case"wrap":i=3;break;case"nearest":i=4;break;default:i=1;break}this.userCode=`
float mapCoord(float outCoord, float len) {
float inCoord = outCoord;
if(${i} == 2) {
if (inCoord < 0.0) {
if (len <= 1.0) {
inCoord = 0.0;
} else {
float sz2 = 2.0 * len;
if (inCoord < sz2) {
inCoord = sz2 * float(int(float(-inCoord / sz2))) +
inCoord;
}
inCoord = inCoord < -len ? inCoord + sz2 : -inCoord - 1.0;
}
} else if (inCoord > len - 1.0) {
if (len <= 1.0) {
inCoord = 0.0;
} else {
float sz2 = 2.0 * len;
inCoord -= sz2 * float(int(float(inCoord / sz2)));
if (inCoord >= len) {
inCoord = sz2 - inCoord - 1.0;
}
}
}
return clamp(inCoord, 0.0, len - 1.0);
} else if (${i} == 3) {
if (inCoord < 0.0) {
if (len <= 1.0) {
inCoord = 0.0;
} else {
float sz = len - 1.0;
inCoord += len * (float(int(float(-inCoord / sz))) + 1.0);
}
} else if (inCoord > len - 1.0) {
if (len <= 1.0) {
inCoord = 0.0;
} else {
float sz = len - 1.0;
inCoord -= len * float(int(float(inCoord / sz)));
}
}
return clamp(inCoord, 0.0, len - 1.0);
} else if (${i} == 4) {
return clamp(outCoord, 0.0, len - 1.0);
} else {
return outCoord;
}
}
float readWithFillValue(int batch, int coordY, int coordX,
int channel) {
float outputValue;
if (0 <= coordY && coordY < ${e} && 0 <= coordX && coordX < ${t}) {
outputValue = getImage(batch, coordY, coordX, channel);
} else {
outputValue = float(${r});
}
return outputValue;
}
void main() {
ivec4 coords = getOutputCoords();
float outputValue;
int batch = coords[0];
int x = coords[2];
int y = coords[1];
int channel = coords[3];
float xf = float(x);
float yf = float(y);
float a1 = getTransforms(batch, 0);
float a2 = getTransforms(batch, 1);
float a3 = getTransforms(batch, 2);
float b1 = getTransforms(batch, 3);
float b2 = getTransforms(batch, 4);
float b3 = getTransforms(batch, 5);
float c1 = getTransforms(batch, 6);
float c2 = getTransforms(batch, 7);
float projection = c1 * xf + c2 * yf + 1.0;
if (projection == 0.0) {
outputValue = float(${r});
} else {
float inX = (a1 * xf + a2 * yf + a3) / projection;
float inY = (b1 * xf + b2 * yf + b3) / projection;
float mapX = mapCoord(inX, float(${t}));
float mapY = mapCoord(inY, float(${e}));
if (${o} == 1) {
int coordY = int(round(mapY));
int coordX = int(round(mapX));
outputValue = readWithFillValue(batch, coordY, coordX,
channel);
} else {
float yFloor = floor(mapY);
float xFloor = floor(mapX);
float yCeil = yFloor + 1.0;
float xCeil = xFloor + 1.0;
float valueYFloor = (xCeil - mapX) *
readWithFillValue(batch, int(yFloor), int(xFloor), channel) +
(mapX - xFloor) *
readWithFillValue(batch, int(yFloor), int(xCeil), channel);
float valueYCeil = (xCeil - mapX) *
readWithFillValue(batch, int(yCeil), int(xFloor), channel) +
(mapX - xFloor) *
readWithFillValue(batch, int(yCeil), int(xCeil), channel);
outputValue = (yCeil - mapY) * valueYFloor +
(mapY - yFloor) * valueYCeil;
}
}
setOutput(outputValue);
}
`}};function Ote(e){let{inputs:t,backend:n,attrs:s}=e,{image:r,transforms:a}=t,{interpolation:o,fillMode:i,fillValue:u,outputShape:l}=s,[c,p,d,h]=r.shape,[f,m]=l!=null?l:[p,d],g=[c,f,m,h],b=new Fte(p,d,o,i,u,g);return n.runWebGLProgram(b,[r,a],"float32")}var Pte={kernelName:ji,backendName:"webgl",kernelFunc:Ote};function zte(e){let{inputs:t,attrs:n,backend:s}=e,{axis:r}=n,{x:a}=t;ou(a,"unique"),console.warn("WARNING: ","UI might be locked temporarily as data is being downloaded");let o=s.readSync(a.dataId),{outputValues:i,outputShape:u,indices:l}=QX(o,r,a.shape,a.dtype);return[s.makeTensorInfo(u,a.dtype,i),s.makeTensorInfo([l.length],"int32",l)]}var Lte={kernelName:Wg,backendName:"webgl",kernelFunc:zte};function Mte(e){let{inputs:t,backend:n,attrs:s}=e,{value:r}=t,{axis:a}=s;a<0&&(a+=r.shape.length);let o=r,i=o.shape.length,u=r.shape[a],l=new Array(i-1),c=0;for(let m=0;m<i;m++)m!==a&&(l[c++]=o.shape[m]);let p=[],d=new Array(i).fill(0),h=o.shape.slice();h[a]=1;let f=new Array(u);for(let m=0;m<f.length;m++){d[a]=m;let g=pu({inputs:{x:o},backend:n,attrs:{begin:d,size:h}}),b=pe({inputs:{x:g},backend:n,attrs:{shape:l}});f[m]=b,p.push(g)}return p.forEach(m=>n.disposeIntermediateTensorInfo(m)),f}var Bte={kernelName:Ki,backendName:"webgl",kernelFunc:Mte},Vte=class{constructor(e,t){this.variableNames=["x","segmentIds"];let n=e.windowSize,s=e.batchSize,r=e.inSize,a=e.numSegments,o=a*Math.ceil(r/n);this.outputShape=[s,o];let i="0.0",u="sumValue",l=Math.floor(n/4)*4,c=n%4,p=`
sumValue += dot(values, segFilter);
`,d="";r%n>0&&(d=`
if (inIdx < 0 || inIdx >= ${r}) {
return initializationValue;
}
`);let h="";r%n>0&&(h=`
if (inIdx < 0 || inIdx >= ${r}) {
return -1.0;
}
`),this.userCode=`
const float initializationValue = ${i};
float getValue(int batch, int inIdx) {
${d}
return getX(batch, inIdx);
}
float getSegmentIdAtIndex(int inIdx) {
${h}
return getSegmentIds(inIdx);
}
void main() {
ivec2 coords = getOutputCoords();
int batch = coords[0];
int outIdx = coords[1];
int inOffset = int(floor(float(outIdx) / float(
${a})) * float(${n}));
int currentSeg = int(mod(float(outIdx), float(${a})));
float sumValue = 0.0;
for (int i = 0; i < ${l}; 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
);
${p}
}
int inIdx = inOffset + ${l};
if (${c===1}) {
vec4 values = vec4(
getValue(batch, inIdx),
initializationValue,
initializationValue,
initializationValue
);
int inIdxSeg = int(getSegmentIdAtIndex(inIdx));
vec4 segFilter = vec4(
int(getSegmentIdAtIndex(inIdx)) == currentSeg ? 1 : 0,
0,
0,
0
);
${p}
} else if (${c===2}) {
vec4 values = vec4(
getValue(batch, inIdx),
getValue(batch, inIdx + 1),
initializationValue,
initializationValue
);
vec4 segFilter = vec4(
int(getSegmentIdAtIndex(inIdx)) == currentSeg ? 1 : 0,
int(getSegmentIdAtIndex(inIdx + 1)) == currentSeg ? 1 : 0,
0,
0
);
${p}
} else if (${c===3}) {
vec4 values = vec4(
getValue(batch, inIdx),
getValue(batch, inIdx + 1),
getValue(batch, inIdx + 2),
initializationValue
);
vec4 segFilter = vec4(
int(getSegmentIdAtIndex(inIdx)) == currentSeg ? 1 : 0,
int(getSegmentIdAtIndex(inIdx + 1)) == currentSeg ? 1 : 0,
int(getSegmentIdAtIndex(inIdx + 2)) == currentSeg ? 1 : 0,
0
);
${p}
}
setOutput(${u});
}
`}};function Wte(e){let{inputs:t,backend:n,attrs:s}=e,{x:r,segmentIds:a}=t,{numSegments:o}=s,i=r.shape.length,u=[],l=0,c=C.getAxesPermutation([l],i),p=r;c!=null&&(p=pn({inputs:{x:r},backend:n,attrs:{perm:c}}),u.push(p),l=C.getInnerMostAxes(1,i)[0]);let d=C.segment_util.computeOutShape(p.shape,l,o),h=w.sizeFromShape([p.shape[l]]),f=pe({inputs:{x:p},backend:n,attrs:{shape:[-1,h]}});u.push(f);let m=yp(r.dtype),g=(x,k,I,$,R)=>{let E=x.shape[0],P=x.shape[1],A=C.segment_util.segOpComputeOptimalWindowSize(P,R),D={windowSize:A,inSize:P,batchSize:E,numSegments:R},T=new Vte(D,k),L=n.compileAndRun(T,[x,I],$);if(u.push(L),L.shape[1]===R)return L;let W=A2({backend:n,attrs:{start:0,stop:R,step:1,dtype:"float32"}}),j=R2({inputs:{x:W},backend:n,attrs:{reps:[P/A]}});return u.push(W),u.push(j),g(L,k,j,$,R)},b=g(f,"unsortedSegmentSum",a,m,o),y=pe({inputs:{x:b},backend:n,attrs:{shape:d}}),v=y;if(c!=null){u.push(y);let x=C.getUndoAxesPermutation(c);v=pn({inputs:{x:v},backend:n,attrs:{perm:x}})}return u.forEach(x=>n.disposeIntermediateTensorInfo(x)),v}var Ute={kernelName:gp,backendName:"webgl",kernelFunc:Wte},Gte=[H8,j8,Y8,J8,tY,rY,oY,uY,pY,fY,bY,xY,SY,TY,AY,RY,FY,LY,BY,WY,qY,JY,t9,s9,l9,d9,m9,N8,y9,S9,T9,D9,O9,z9,M9,V9,G9,j9,Y9,Z9,eQ,nQ,aQ,iQ,dQ,hQ,gQ,vQ,wQ,CQ,_Q,DQ,PQ,MQ,BQ,WQ,GQ,qQ,KQ,YQ,eZ,sZ,oZ,uZ,dZ,fZ,yZ,kZ,C8,IZ,w9,TZ,AZ,DZ,$8,zZ,VZ,UZ,jZ,YZ,e7,s7,i7,d7,f7,g7,x7,k7,I7,$7,A7,R7,F7,P7,B7,G7,K7,nJ,D8,oJ,lJ,pJ,mJ,a9,yJ,xJ,kJ,CJ,_J,A8,EJ,RJ,o9,Z7,OJ,MJ,UJ,O8,jJ,YJ,eee,see,iee,lee,pee,mee,bee,xee,See,Nee,Aee,Dee,Pee,Mee,QY,eJ,Wee,Gee,qee,Kee,Yee,Zee,ete,nte,rte,ite,lte,dte,fte,gte,yte,xte,J7,W8,Ste,Nte,_te,Dte,Pte,U8,Lte,Bte,Ute,vJ];for(let e of Gte)Ol(e);var Or=K();Or.registerFlag("WEBGPU_DEFERRED_SUBMIT_BATCH_SIZE",()=>15);Or.registerFlag("WEBGPU_CPU_FORWARD",()=>!0);Or.registerFlag("WEBGPU_MATMUL_WORK_PER_THREAD",()=>4);Or.registerFlag("WEBGPU_USE_NAIVE_CONV2D_TRANSPOSE",()=>!1);Or.registerFlag("WEBGPU_USE_LOW_POWER_GPU",()=>!1);Or.registerFlag("WEBGPU_CPU_HANDOFF_SIZE_THRESHOLD",()=>1e3);Or.registerFlag("WEBGPU_USE_PROFILE_TOOL",()=>!1);Or.registerFlag("WEBGPU_USE_IMPORT",()=>!1);var Hte="return a + b;",qte="return areal * breal - aimag * bimag;",jte="return areal * bimag + aimag * breal;",Kte="return a / b;",Xte="return a * b;",Yte="return (a - b) * (a - b);",Qte="return a - b;",Zte="return f32(a == b);",Jte="return vec4<f32>(a == b);",ene="return f32(a > b);",tne="return vec4<f32>(a > b);",nne="return f32(a >= b);",sne="return vec4<f32>(a >= b);",rne="return f32(a < b);",ane="return vec4<f32>(a < b);",one="return f32(a <= b);",ine="return vec4<f32>(a <= b);",une="return f32(f32(a) >= 1.0 && f32(b) >= 1.0);",lne=`return (vec4<f32>(a >= vec4<f32>(1.0)) *
vec4<f32>(b >= vec4<f32>(1.0)));`,cne=`
if (isnan(a)) { return a; }
if (isnan(b)) { return b; }
`,D2=`
if (isNaN.r) {
resultTemp.r = uniforms.NAN;
}
if (isNaN.g) {
resultTemp.g = uniforms.NAN;
}
if (isNaN.b) {
resultTemp.b = uniforms.NAN;
}
if (isNaN.a) {
resultTemp.a = uniforms.NAN;
}
`,dne=`
let s = sign(a) * sign(b);
let ia = i32(round(a));
let ib = i32(round(b));
return f32(idiv(ia, ib, s));
`,pne=`
let ia = vec4<i32>(round(a));
let ib = vec4<i32>(round(b));
let cond = ib != vec4<i32>(0);
var resultTemp = vec4<i32>(0);
let s = sign(a) * sign(b);
// Windows (D3D) wants guaranteed non-zero int division at compile-time.
if (cond[0]) {
resultTemp[0] = idiv(ia[0], ib[0], s[0]);
}
if (cond[1]) {
resultTemp[1] = idiv(ia[1], ib[1], s[1]);
}
if (cond[2]) {
resultTemp[2] = idiv(ia[2], ib[2], s[2]);
}
if (cond[3]) {
resultTemp[3] = idiv(ia[3], ib[3], s[3]);
}
return vec4<f32>(resultTemp);
`,hne="return f32(a != b);",fne="return vec4<f32>(a != b);",mne=`
if(a < 0.0 && floor(b) < b) {
return uniforms.NAN;
}
if (b == 0.0) {
return 1.0;
}
if (round(abs(b) % 2.0) != 1.0) {
return pow(abs(a), b);
}
return sign(a) * pow(abs(a), b);
`,gne=`
let isModRound1Bool = vec4<i32>(round(abs(b) % vec4<f32>(2.0))) == vec4<i32>(1);
let isModRound1 = vec4<f32>(isModRound1Bool);
let multiplier = sign(a) * isModRound1 + (vec4<f32>(1.0) - isModRound1);
var resultTemp = multiplier * pow(abs(a), b);
// Ensure that a^0 = 1, including 0^0 = 1 as this correspond to TF and JS
let isExpZero = b == vec4<f32>(0.0);
if (isExpZero.r) {
resultTemp.r = 1.0;
}
if (isExpZero.g) {
resultTemp.g = 1.0;
}
if (isExpZero.b) {
resultTemp.b = 1.0;
}
if (isExpZero.a) {
resultTemp.a = 1.0;
}
let isNaN = a < vec4<f32>(0.0) & floor(b) < b;
${D2}
return resultTemp;
`,bne="if (a < 0.0) { return b * a; } return a;",yne=`
let aLessThanZero = vec4<f32>(a < vec4<f32>(0.0));
return (aLessThanZero * (b * a)) + ((vec4<f32>(1.0) - aLessThanZero) * a);
`;function zw(e,t){let n=t?D2:cne;return t?`
var resultTemp = vec4<f32>(${e}(a, b));
let isNaN = isnanVec4(a) | isnanVec4(b);
`+n+`
return resultTemp;
`:n+`
return ${e}(a, b);
`}function rc(e,t){switch(e){case 0:return Xte;case 1:return Hte;case 2:return Qte;case 3:return Kte;case 4:return t?Jte:Zte;case 5:return t?tne:ene;case 6:return t?sne:nne;case 7:return t?ane:rne;case 8:return t?ine:one;case 9:return t?lne:une;case 10:return t?fne:hne;case 11:return Yte;case 12:return t?pne:dne;case 14:return t?yne:bne;case 15:return zw("max",t);case 16:return zw("min",t);case 13:return t?gne:mne;case 17:return qte;case 18:return jte;default:throw new Error(`BinaryType ${e} is not implemented!`)}}var vne="return abs(a);",xne="return ceil(a);",wne="return cos(a);",kne=`
let e2x = exp(-a);
return (e2x + 1.0 / e2x) / 2.0;
`,Sne="return exp(a) - 1.0;",Ine="if (a >= 0.0) { return a; } return (exp(a) - 1.0);",Cne=`
var resFloat = exp(a) - vec4<f32>(1.0);
if (a.r >= 0.0) {
resFloat.r = a.r;
}
if (a.g >= 0.0) {
resFloat.g = a.g;
}
if (a.b >= 0.0) {
resFloat.b = a.b;
}
if (a.a >= 0.0) {
resFloat.a = a.a;
}
return resFloat;
`,Nne="return exp(a);",Tne="return floor(a);",$ne="return a;",_ne=`if (a < 0.0) { return 1.0/0.0; }
return log(a);`,Ane="return f32(!(a >= 1.0));",Ene="return -a;",Rne="if (a < 0.0) { return uniforms.alpha * a; } return a;",Dne=`
let aLessThanZero = vec4<f32>(a < vec4<f32>(0.0));
return (aLessThanZero * (uniforms.alpha * a)) + ((vec4<f32>(1.0) - aLessThanZero) * a);
`,Fne="return select(a, 0.0, a < 0.0);",One="return clamp(a, 0.0, 6.0);",Pne="return clamp(a, vec4<f32>(0.0, 0.0, 0.0, 0.0), vec4<f32>(6.0, 6.0, 6.0, 6.0));",zne=`
return select(a, vec4<f32>(0.0), a < vec4<f32>(0.0));
`,Lne="return 1.0/sqrt(a);",Mne="return 1.0 / (1.0 + exp(-1.0 * a));",Bne="return sin(a);",Vne=`
let e2x = exp(a);
return (e2x - 1.0 / e2x) / 2.0;
`,Wne="return sqrt(a);",Une="return a * a;",Gne=`
let e2x = exp(-2.0 * abs(a));
return sign(a) * (1.0 - e2x) / (1.0 + e2x);
`,Hne="return f32(i32((a)));";function jr(e,t){switch(e){case 0:return vne;case 2:return wne;case 3:return kne;case 1:return xne;case 4:return t?Cne:Ine;case 5:return Nne;case 6:return Sne;case 7:return Tne;case 8:return $ne;case 9:return _ne;case 10:return Ane;case 11:return Ene;case 14:return t?Dne:Rne;case 12:return t?zne:Fne;case 13:return t?Pne:One;case 15:return Lne;case 18:return Mne;case 16:return Bne;case 17:return Vne;case 19:return Wne;case 20:return Une;case 21:return Gne;case 22:return Hne;default:throw new Error(`BinaryType ${e} is not implemented!`)}}function Io(e,t=!1){if(e===null)return null;if(e==="linear")return jr(8);if(e==="relu")return jr(12,t);if(e==="elu")return jr(4,t);if(e==="relu6")return jr(13,t);if(e==="prelu")return rc(14,t);if(e==="sigmoid")return jr(18,t);if(e==="leakyrelu")return jr(14,t);throw new Error(`Activation ${e} has not been implemented for the WebGPU backend.`)}var F2={};Ee(F2,{ArrayBufferToTypedArray:()=>P2,GPUBytesPerElement:()=>md,computeDispatch:()=>_e,computeWorkGroupSizeForConv2d:()=>Ov,computeWorkGroupSizeForMatMul:()=>O2,computeWorkPerThreadForConv2d:()=>Pv,flatDispatchLayout:()=>Ve,isWebGPUSupported:()=>zv,tilesFitEvenlyIntoShape:()=>qne});var ra=e=>{let t=1;for(let n=0;n<e.length;n++)t*=e[n];return t};function qne(e,t){if(e.length!==t.length)throw new Error(`Cannot compute whether rank ${e.length} tiles fit evenly into rank ${t.length} shape - ranks must match.`);return t.every((n,s)=>n%e[s]===0)}function _e(e,t,n=[1,1,1],s=[1,1,1]){let[r,a,o]=[Math.ceil(ra(e.x.map(i=>t[i]))/(n[0]*s[0])),e.y?Math.ceil(ra(e.y.map(i=>t[i]))/(n[1]*s[1])):1,e.z?Math.ceil(ra(e.z.map(i=>t[i]))/(n[2]*s[2])):1];return[r,a,o]}function Ov(e,t,n=!1){if(n)return[8,8,1];let s=ra(e.x.map(a=>t[a])),r=ra(e.y.map(a=>t[a]));return s<=4?[4,16,1]:r<=4?[16,4,1]:[16,16,1]}function O2(e,t,n){return e===1?[32,1,1]:n===1?[1,32,1]:[8,8,1]}function Pv(e,t,n=!1){if(n)return[4,4,1];let s=ra(e.x.map(a=>t[a])),r=ra(e.y.map(a=>t[a]));return s<=4?[1,2,1]:r<=4?[2,1,1]:[2,2,1]}function Ve(e){return{x:e.map((t,n)=>n)}}function md(e){if(e==="float32"||e==="int32"||e==="bool"||e==="string")return 4;if(e==="complex64")return 8;throw new Error(`Unknown dtype ${e}`)}function P2(e,t){if(t==="float32")return new Float32Array(e);if(t==="int32")return new Int32Array(e);if(t==="bool"||t==="string")return Uint8Array.from(new Int32Array(e));throw new Error(`Unknown dtype ${t}`)}function zv(){return(typeof window!="undefined"||typeof WorkerGlobalScope!="undefined")&&!!navigator.gpu}var jne=(e,t)=>e?`
mm_Asub[inputRow][inputCol] = mm_readA(
t * TileInner + inputRow,
globalRowStart / ${t} + inputCol, globalId);
`:`
mm_Asub[inputRow][inputCol] = mm_readA(
globalRow + innerRow,
t * TileInner / ${t} + inputCol, globalId);
`,Kne=(e,t)=>e?`
let ACached0 = mm_Asub[k * InnerElementSize][localRow];
let ACached1 = mm_Asub[k * InnerElementSize + 1][localRow];
let ACached2 = mm_Asub[k * InnerElementSize + 2][localRow];
${t===3?"":"let ACached3 = mm_Asub[k * InnerElementSize + 3][localRow];"}
for (var i = 0; i < RowPerThread; i = i + 1) {
acc[i] = BCached[0] * ACached0[i] + acc[i];
acc[i] = BCached[1] * ACached1[i] + acc[i];
acc[i] = BCached[2] * ACached2[i] + acc[i];
${t===3?"":"acc[i] = BCached[3] * ACached3[i] + acc[i];"}
}`:`
for (var i = 0; i < RowPerThread; i = i + 1) {
let ACached = mm_Asub[tileRow + i][k];
acc[i] = BCached[0] * ACached.x + acc[i];
acc[i] = BCached[1] * ACached.y + acc[i];
acc[i] = BCached[2] * ACached.z + acc[i];
${t===3?"":"acc[i] = BCached[3] * ACached.w + acc[i];"}
}`;function z2(e,t,n,s,r=4,a=!1){let o=a?t:s,i=a?s:t,u=a?e[1]:r;return w.assert((a&&t===n||s%4===0||s%3===0)&&e[0]===4&&(r===3||r===4),()=>`tileInner ${s} must be divisible by 4|3. ColPerThread ${e[0]} must be 4.
innerElementSize ${r} must be 3|4.`),`
var<workgroup> mm_Asub : array<array<vec${u}<f32>, ${o/u}>, ${i}>;
var<workgroup> mm_Bsub : array<array<vec4<f32>, ${n/e[0]}>, ${s}>;
let RowPerThread = ${e[1]};
let ColPerThread = ${e[0]};
let InnerElementSize = ${r};
let TileInner = ${s};
@stage(compute) @workgroup_size(workGroupSizeX, workGroupSizeY, workGroupSizeZ)
fn main(@builtin(local_invocation_id) LocalId : vec3<u32>,
@builtin(global_invocation_id) GlobalId : vec3<u32>,
@builtin(num_workgroups) NumWorkgroups: vec3<u32>,
@builtin(workgroup_id) workgroupId: vec3<u32>) {
localId = LocalId;
globalId = GlobalId;
numWorkgroups = NumWorkgroups;
let localRow = i32(localId.y);
let tileRow = ${t===1?"0":"localRow * RowPerThread"};
let tileCol = i32(localId.x);
let globalRow = ${t===1?"0":"i32(globalId.y) * RowPerThread"};
let globalCol = i32(globalId.x);
let globalRowStart = i32(workgroupId.y) * ${t};
let numTiles = (uniforms.dimInner - 1) / TileInner + 1;
var acc: array<vec4<f32>, RowPerThread>;
var BCached : array<vec4<f32>, 4>;
// Loop over shared dimension.
let RowPerThreadB = TileInner / i32(workGroupSizeY);
let tileRowB = localRow * RowPerThreadB;
for (var t = 0; t < numTiles; t = t + 1) {
// Load one tile of A into local memory.
for (var innerRow = 0; innerRow < RowPerThread; innerRow = innerRow + 1) {
let inputRow = tileRow + innerRow;
let inputCol = tileCol;
${jne(a,u)}
}
// Load one tile of B into local memory.
for (var innerRow = 0; innerRow < RowPerThreadB; innerRow = innerRow + 1) {
let inputRow = tileRowB + innerRow;
let inputCol = tileCol;
mm_Bsub[inputRow][inputCol] = mm_readB(t * TileInner + inputRow, globalCol, globalId);
}
workgroupBarrier();
// Compute acc values for a single thread.
for (var k = 0; k < TileInner / InnerElementSize; k = k + 1) {
BCached[0] = mm_Bsub[k * InnerElementSize][tileCol];
BCached[1] = mm_Bsub[k * InnerElementSize + 1][tileCol];
BCached[2] = mm_Bsub[k * InnerElementSize + 2][tileCol];
${r===3?"":"BCached[3] = mm_Bsub[k * InnerElementSize + 3][tileCol];"}
${Kne(a,r)}
}
workgroupBarrier();
}
for (var innerRow = 0; innerRow < RowPerThread; innerRow = innerRow + 1) {
mm_write(globalRow + innerRow,
globalCol,
acc[innerRow], globalId);
}
}`}var Xne=class{constructor(e,t,n,s,r=!1,a=null,o=null,i=null){this.variableNames=["A","B"],this.uniforms="dimAOuter : i32, dimBOuter : i32, dimInner : i32,",this.workGroupSize=[8,8,1],this.isVec4=!0,this.outputShape=t,this.dispatchLayout={x:[2],y:[1],z:[0]},t[1]===1&&!r?this.elementsPerThread=[4,1,1]:this.elementsPerThread=[4,4,1],this.dispatch=_e(this.dispatchLayout,this.outputShape,this.workGroupSize,this.elementsPerThread);let u=a!=null,l=i!=null;u&&this.variableNames.push("bias"),l&&this.variableNames.push("preluActivationWeights"),this.tileAOuter=t[1]===1&&!r?1:this.workGroupSize[1]*this.elementsPerThread[1],this.tileBOuter=this.workGroupSize[0]*this.elementsPerThread[0],this.tileInner=this.tileBOuter,this.aShape=e,this.addBias=u,this.activation=o,this.hasPreluActivationWeights=l,this.batchAEqualOne=n,this.batchBEqualOne=s,this.transposeA=r;let c=r?e[1]:e[2];this.fitAOuter=t[1]%this.tileAOuter===0,this.fitBOuter=t[2]%this.tileBOuter===0,this.fitInner=c%this.tileInner===0,this.shaderKey=`matMulPackedVec4_${this.activation}_${this.fitAOuter}_${this.fitBOuter}_${this.fitInner}_${this.elementsPerThread}_${this.batchAEqualOne}_${this.batchBEqualOne}_${this.transposeA}`}getUserCode(){let e=this.fitAOuter&&this.fitInner?"return A[batch * batchASize + row * uniforms.aShape[2] / 4 + col]":`if (coordsInBounds2D(vec2<i32>(row, col * 4), vec2<i32>(uniforms.aShape[1], uniforms.aShape[2]))) {
return A[batch * batchASize + row * uniforms.aShape[2] / 4 + col];
}
return vec4<f32>(0.0)`,t=this.fitInner&&this.fitBOuter?"return B[batch * batchBSize + row * uniforms.dimBOuter / 4 + col]":`if(coordsInBounds2D(vec2<i32>(row, col * 4), vec2<i32>(uniforms.dimInner, uniforms.dimBOuter))) {
return B[batch * batchBSize + row * uniforms.dimBOuter / 4 + col];
}
return vec4<f32>(0.0)`,n="",s="";if(this.activation){let o=Io(this.activation,this.isVec4);this.hasPreluActivationWeights?n=`fn activation(a : vec4<f32>, outCoord : vec3<i32>) -> vec4<f32> {
let b = getPreluActivationWeightsByOutputCoords(outCoord);
${o}
}`:n=`
fn activation(a : vec4<f32>, outCoord : vec3<i32>) -> vec4<f32> {
${o}
}`,s="value = activation(value, outCoord);"}let r=this.addBias?"value = value + getBiasByOutputCoords(outCoord);":"";return`
${n}
fn mm_readA(row : i32, col : i32, globalId : vec3<u32>) -> vec4<f32> {
${this.batchAEqualOne?`
let batchASize = 0;
let batch = 0;
`:`
let batchASize = uniforms.aShape[1] * uniforms.aShape[2] / 4;
let batch = i32(globalId.z);
`}
${e};
}
fn mm_readB(row : i32, col : i32, globalId : vec3<u32>) -> vec4<f32> {
${this.batchBEqualOne?`
let batchBSize = 0;
let batch = 0;
`:`
let batchBSize = uniforms.bShape[1] * uniforms.bShape[2] / 4;
let batch = i32(globalId.z);
`}
${t};
}
fn mm_write(row : i32, col : i32, valueIn : vec4<f32>, globalId : vec3<u32>) {
if (row < uniforms.dimAOuter && col * 4 < uniforms.dimBOuter)
{
var value = valueIn;
let batch = i32(globalId.z);
let outCoord = vec3<i32>(batch, row, col * 4);
${r}
${s}
setOutputAtCoords(outCoord[0], outCoord[1], outCoord[2], value);
}
}
${z2(this.elementsPerThread,this.tileAOuter,this.tileBOuter,this.tileInner,4,this.transposeA)}
`}};function Yne(e,t){if(Math.max(...e)>3)throw new Error("Cannot symbolically compute strides for rank > 4 tensor.");let n=e.length,s=e.map(a=>`${t}[${a}]`),r=new Array(n-1);r[n-2]=s[n-1];for(let a=n-3;a>=0;--a)r[a]=`(${r[a+1]} * ${s[a+1]})`;return r}var Lw=(e,t,n,s,r,a=!1)=>{let o={dtype:r.dtype,shape:r.shape},i=Qne(s,o,t,a),u=e.createShaderModule({code:i,label:t.constructor.name});return e.createComputePipeline({layout:n,compute:{module:u,entryPoint:"main"},label:t.constructor.name})};function Wt(e){if(e<=1)return"i32";if(e===2)return"vec2<i32>";if(e===3)return"vec3<i32>";if(e===4)return"vec4<i32>";if(e===5)return"vec5";if(e===6)return"vec6";throw Error(`GPU for rank ${e} is not yet supported`)}function hr(e){if(e===0)return"x";if(e===1)return"y";if(e===2)return"z";if(e===3)return"w";if(e===4)return"u";if(e===5)return"v";throw Error(`Index ${e} is not yet supported`)}function Ue(){return`
${ac()}
let index = getGlobalIndex();
`}function ac(){return`
${Lv()}
fn main(@builtin(local_invocation_id) LocalId : vec3<u32>,
@builtin(global_invocation_id) GlobalId : vec3<u32>,
@builtin(num_workgroups) NumWorkgroups: vec3<u32>) {
localId = LocalId;
globalId = GlobalId;
numWorkgroups = NumWorkgroups;
`}function Lv(){return`
@stage(compute) @workgroup_size(workGroupSizeX, workGroupSizeY, workGroupSizeZ)
`}function Qne(e,t,n,s=!1){let r=[];if(r.push(`
let workGroupSizeX = ${n.workGroupSize[0]}u;
let workGroupSizeY = ${n.workGroupSize[1]}u;
let workGroupSizeZ = ${n.workGroupSize[2]}u;
var<private> localId: vec3<u32>;
var<private> globalId: vec3<u32>;
var<private> numWorkgroups: vec3<u32>;
// Only used when the y/z dimension of workgroup size is 1.
fn getGlobalIndex() -> i32 {
${L2(n)?" return i32(globalId.x);":` let localInvocationIndex = localId.z * workGroupSizeX * workGroupSizeY +
localId.y * workGroupSizeX + localId.x;
let workGroupID = (globalId - localId)/vec3<u32>(
workGroupSizeX, workGroupSizeY, workGroupSizeZ);
return i32((workGroupID.z * numWorkgroups.x * numWorkgroups.y +
workGroupID.y * numWorkgroups.x + workGroupID.x) *
(workGroupSizeX * workGroupSizeY * workGroupSizeZ) +
localInvocationIndex);
`}
}
`),s===!0)return r.push(`
struct Uniform {
size : i32,
numChannels : i32,
outShapeStrides : vec2<i32>,
dispatchSize : vec3<u32>,
};
@group(0) @binding(0) var<storage, write> result: array<${gd(t.dtype,n.isVec4)}>;
@group(0) @binding(2) var<uniform> uniforms: Uniform;
`),[Bw,r.join(`
`),Vw(t.shape),n.getUserCode()].join(`
`);let a=!1,o=!1,i="struct Uniforms { NAN : f32, ";n.variableNames.forEach((m,g)=>{let b=Wt(e[g].shape.length);(b==="vec5"||b==="vec6")&&(o=!0),(a||o)&&(i+="@align(16) "),a=o,i+=`${m.charAt(0).toLowerCase()+m.slice(1)}Shape : ${b}, `});let u=Wt(t.shape.length);o=u==="vec5"||u==="vec6",(a||o)&&(i+="@align(16) "),a=o,i+=`outShape : ${u}, `;let l=t.shape.length-1,c=Wt(l);o=c==="vec5"||c==="vec6",(a||o)&&(i+="@align(16) "),a=o,i+=`
outShapeStrides: ${c}, `,n.size&&(a&&(i+="@align(16) "),a=!1,i+="size : i32, "),n.uniforms&&(a&&(i+="@align(16) "),i+=n.uniforms),i+="};",r.push(i),n.atomic?r.push(`
@group(0) @binding(0) var<storage, read_write> result: array<atomic<i32>>;
`):r.push(`
@group(0) @binding(0) var<storage, write> result: array<${gd(t.dtype,n.isVec4)}>;
`),n.variableNames.forEach((m,g)=>{r.push(`
@group(0) @binding(${1+g}) var<storage, read> ${m}: array<${n.variableTypes?n.variableTypes[g]:gd(e[g].dtype,n.isVec4)}>;
`)}),i!==""&&r.push(`
@group(0) @binding(${1+n.variableNames.length}) var<uniform> uniforms: Uniforms;
`);let[p,d]=tse(t.shape,n.dispatchLayout),h=[Bw,r.join(`
`),Vw(t.shape),p,nse(t.shape.length)];if(n.atomic||h.push(sse(t.shape,t.dtype,n.isVec4)),d===t.shape.length){let m=e.map((g,b)=>ese(g,t.shape,n.variableTypes?n.variableTypes[b]==="vec4<f32>":n.isVec4,n.dispatchLayout.x.length===t.shape.length)).join(`
`);h.push(m)}return h.push(n.getUserCode()),h.join(`
`)}function Mw(e,t,n=[],s="",r=""){let a=L2(e)?"flatDispatch":"";return e.shaderKey+"_"+(e.workGroupSize?e.workGroupSize.join(","):"")+t.map(i=>i.length).join(",")+n.join(",")+e.variableNames.join(",")+s+r+a}var Bw=`
struct vec5 {x: i32, y: i32, z: i32, w: i32, u: i32};
struct vec6 {x: i32, y: i32, z: i32, w: i32, u: i32, v: i32};
// Checks whether coordinates lie within the bounds of the shape.
fn coordsInBounds2D(coord : vec2<i32>, shape : vec2<i32>) -> bool {
return all(coord >= vec2<i32>(0)) && all(coord < shape);
}
fn coordsInBounds3D(coord : vec3<i32>, shape : vec3<i32>) -> bool {
return all(coord >= vec3<i32>(0)) && all(coord < shape);
}
fn coordsInBounds4D(coord : vec4<i32>, shape : vec4<i32>) -> bool {
return all(coord >= vec4<i32>(0)) && all(coord < shape);
}
fn getIndexFromCoords1D(coord : i32, shape : i32) -> i32 {
return coord;
}
fn getIndexFromCoords2D(coords : vec2<i32>, shape : vec2<i32>) -> i32 {
return dot(coords, vec2<i32>(shape.y, 1));
}
fn getIndexFromCoords3D(coords : vec3<i32>, shape : vec3<i32>) -> i32 {
return dot(coords, vec3<i32>(shape.y * shape.z, shape.z, 1));
}
fn getIndexFromCoords4D(coords : vec4<i32>, shape : vec4<i32>) -> i32 {
return dot(coords, vec4<i32>(
shape.y * shape.z * shape.w, shape.z * shape.w, shape.w, 1));
}
fn getIndexFromCoords5D(coords : vec5, shape : vec5) -> i32 {
let shapeStrides: vec5 = vec5(shape.y * shape.z * shape.w * shape.u, shape.z * shape.w * shape.u, shape.w * shape.u, shape.u, 1);
return coords.x*shapeStrides.x + coords.y*shapeStrides.y + coords.z*shapeStrides.z + coords.w*shapeStrides.w + coords.u*shapeStrides.u;
}
fn getIndexFromCoords6D(coords : vec6, shape : vec6) -> i32 {
let shapeStrides: vec6 = vec6(shape.y * shape.z * shape.w * shape.u * shape.v, shape.z * shape.w * shape.u * shape.v, shape.w * shape.u * shape.v, shape.u * shape.v, shape.v, 1);
return coords.x*shapeStrides.x + coords.y*shapeStrides.y + coords.z*shapeStrides.z + coords.w*shapeStrides.w + coords.u*shapeStrides.u + coords.v*shapeStrides.v;
}
fn idiv(a: i32, b: i32, sign: f32) -> i32 {
var res: i32 = a / b;
let mod: i32 = a % b;
if (sign < 0. && mod != 0) {
res = res - 1;
}
return res;
}
// NaN defination in IEEE 754-1985 is :
// - sign = either 0 or 1.
// - biased exponent = all 1 bits.
// - fraction = anything except all 0 bits (since all 0 bits represents infinity).
// https://en.wikipedia.org/wiki/IEEE_754-1985#Representation_of_non-numbers
fn isnan(val: f32) -> bool {
let floatToUint: u32 = bitcast<u32>(val);
return (floatToUint & 0x7fffffffu) > 0x7f800000u;
}
fn isnanVec4(val : vec4<f32>) -> vec4<bool> {
return vec4<bool>(isnan(val[0]), isnan(val[1]), isnan(val[2]), isnan(val[3]));
}
`;function Vw(e){let t=e.length;if(t<=1)return"fn getCoordsFromIndex(index : i32) -> i32 { return index; }";let n=w.computeStrides(e),s=Wt(t),r=[];for(let o=0;o<t;o++)r.push(`d${o}`);if(n.length===1)return` fn getCoordsFromIndex(index : i32) -> vec2<i32> {
let d0 = index / uniforms.outShapeStrides; let d1 = index - d0 * uniforms.outShapeStrides;
return vec2<i32>(d0, d1);
}`;let a;return a="var index2 = index;"+n.map((o,i)=>{let u=`let ${r[i]} = index2 / uniforms.outShapeStrides.${hr(i)}`,l=i===n.length-1?`let ${r[i+1]} = index2 - ${r[i]} * uniforms.outShapeStrides.${hr(i)}`:`index2 = index2 - ${r[i]} * uniforms.outShapeStrides.${hr(i)}`;return`${u}; ${l};`}).join(""),`
fn getCoordsFromIndex(index : i32) -> ${s} {
${a}
return ${s}(${r.join(",")});
}
`}function Zne(e,t){let n=e.name,s=e.shape.length,r=Wt(s),a="get"+n.charAt(0).toUpperCase()+n.slice(1),o=["d0","d1","d2","d3","d4","d5"].slice(0,s),i=o.map(c=>`${c} : i32`).join(", ");if(s<1)return t?`
fn ${a}() -> vec4<f32> {
return vec4<f32>(${n}[0]);
}
`:`
fn ${a}() ->f32 {
return f32(${n}[0]);
}
`;let u=`uniforms.${n.charAt(0).toLowerCase()+n.slice(1)}Shape`,l=`${s}D`;return s===0&&(l="1D"),t?`
fn ${a}(${i}) -> vec4<f32> {
return vec4<f32>(${n}[getIndexFromCoords${l}(${r}(${o.join(",")}),
${u}) / 4]);
}
`:`
fn ${a}(${i}) -> f32 {
return f32(${n}[getIndexFromCoords${l}(${r}(${o.join(",")}),
${u})]);
}
`}function Jne(e,t,n,s){let r=e.name,a=r.charAt(0).toUpperCase()+r.slice(1),o="get"+a+"ByOutput",i=e.shape.length,u=t.length,l=Wt(u);if(w.arraysEqual(e.shape,t)&&s)return n?`
fn ${o}Index(globalIndex : i32) -> vec4<f32> {
return vec4<f32>(${r}[globalIndex]);
}
fn ${o}Coords(coords : ${l}) -> vec4<f32> {
return vec4<f32>(${r}[${u>1?"getOutputIndexFromCoords(coords)":"coords"} / 4]);
}
`:`
fn ${o}Index(globalIndex : i32) -> f32 {
return f32(${r}[globalIndex]);
}
fn ${o}Coords(coords : ${l}) -> f32 {
return f32(${r}[${u>1?"getOutputIndexFromCoords(coords)":"coords"}]);
}
`;let c=C.getBroadcastDims(e.shape,t),p=u-i,d="";if(i===0)return n?`
fn ${o}Index(globalIndex : i32) -> vec4<f32> {
return get${a}();
}
fn ${o}Coords(coords : ${l}) -> vec4<f32> {
return get${a}();
}
`:`
fn ${o}Index(globalIndex : i32) -> f32{
return get${a}();
}
fn ${o}Coords(coords : ${l}) -> f32{
return get${a}();
}
`;u<2&&c.length>=1?d="coords = 0;":d=c.map(g=>`coords.${hr(g+p)} = 0;`).join(`
`);let h="";if(u<2&&i>0)h="coords";else if(u>1){let g=Wt(i),b=e.shape.map((y,v)=>`coords.${hr(v+p)}`).join(", ");h=`${g}(${b})`}else h="coords";let f=`uniforms.${r.charAt(0).toLowerCase()+r.slice(1)}Shape`,m=`${i}D`;return n?`
fn ${o}Index(globalIndex : i32) -> vec4<f32> {
var coords = getCoordsFromIndex(globalIndex);
${d}
return ${r}[getIndexFromCoords${m}(${h}, ${f}) / 4];
}
fn ${o}Coords(coordsIn : ${l}) -> vec4<f32> {
var coords = coordsIn;
${d}
return ${r}[getIndexFromCoords${m}(${h}, ${f}) / 4];
}
`:`
fn ${o}Index(globalIndex : i32) -> f32 {
var coords = getCoordsFromIndex(globalIndex);
${d}
return f32(${r}[getIndexFromCoords${m}(${h}, ${f})]);
}
fn ${o}Coords(coordsIn : ${l}) -> f32 {
var coords = coordsIn;
${d}
return f32(${r}[getIndexFromCoords${m}(${h}, ${f})]);
}
`}function ese(e,t,n,s){let r=Zne(e,n);return e.shape.length<=t.length&&(r+=Jne(e,t,n,s)),r}function tse(e,t){let{x:n,y:s=[],z:r=[]}=t,a=e.length;if(n.length===a)return[`fn getOutputCoords() -> ${Wt(a)}{
let globalIndex = getGlobalIndex();
return getCoordsFromIndex(globalIndex);
}
`,a];let o="",i=[n,s,r],u=0;for(let d=0;d<i.length;d++){let h=i[d];if(h.length!==0)if(u+=h.length,h.length===1)o+=`let d${h[0]} = i32(globalId[${d}]);`;else{let f=Yne(h,"uniforms.outShape");o+=`var index${d} = i32(globalId[${d}]);`;for(let m=0;m<f.length;m++)o+=`let d${h[m]} = index${d} / ${f[m]};`,m===f.length-1?o+=`let d${h[m+1]} = index${d} - d${h[m]} * ${f[m]};`:o+=`index${d} = index${d} - d${h[m]} * ${f[m]};`}}let l=[];for(let d=0;d<u;d++)l.push(`d${d}`);let c=Wt(u),p=`fn getOutputCoords() -> ${c} {
${o}
`;return l.length===0?p+=`return ${c}(0); }`:p+=`return ${c}(${l.join(",")}); }`,[p,u]}function nse(e){let t="";switch(e){case 0:case 1:t+=`
fn getOutputIndexFromCoords(coords : i32) -> i32 {
return coords;
}
`;break;case 2:t+=`
fn getOutputIndexFromCoords(coords : vec2<i32>) -> i32 {
return dot(coords, vec2<i32>(uniforms.outShapeStrides, 1));
}
`;break;case 3:t+=`
fn getOutputIndexFromCoords(coords : vec3<i32>) -> i32 {
return dot(coords, vec3<i32>(uniforms.outShapeStrides.x, uniforms.outShapeStrides.y, 1));
}
`;break;case 4:t+=`
fn getOutputIndexFromCoords(coords : vec4<i32>) -> i32 {
return dot(coords, vec4<i32>(
uniforms.outShapeStrides.x, uniforms.outShapeStrides.y, uniforms.outShapeStrides.z, 1));
}
`;break;case 5:t+=`
fn getOutputIndexFromCoords(coords : vec5) -> i32 {
return coords.x * uniforms.outShapeStrides.x +
coords.y * uniforms.outShapeStrides.y +
coords.z * uniforms.outShapeStrides.z +
coords.w * uniforms.outShapeStrides.w +
coords.u;
}
`;break;case 6:t+=`
fn getOutputIndexFromCoords(coords : vec6) -> i32 {
return coords.x * uniforms.outShapeStrides.x +
coords.y * uniforms.outShapeStrides.y +
coords.z * uniforms.outShapeStrides.z +
coords.w * uniforms.outShapeStrides.w +
coords.u * uniforms.outShapeStrides.u +
coords.v;
}
`;break;default:w.assert(!1,()=>`Unsupported ${e}D shape`);break}return t}function L2(e){return e.dispatch[1]===1&&e.dispatch[2]===1}function gd(e,t){return e==="float32"?t?"vec4<f32>":"f32":e==="int32"||e==="bool"?t?"vec4<i32>":"i32":e}function sse(e,t,n){let s=e.length,r=gd(t,n),a;if(n?a=`fn setOutputAtIndex(flatIndex : i32, value : vec4<f32>) {
result[flatIndex] = ${r}(value);
}
fn setOutputAtIndexI32(flatIndex : i32, value : vec4<i32>) {
result[flatIndex] = ${r}(value);
}`:a=`fn setOutputAtIndex(flatIndex : i32, value : f32) {
result[flatIndex] = ${r}(value);
}
fn setOutputAtIndexI32(flatIndex : i32, value : i32) {
result[flatIndex] = ${r}(value);
}`,s>=2){let o=["d0","d1","d2","d3","d4","d5"].slice(0,s),i=Wt(s);n?a+=`
fn setOutputAtCoords(${o.map(u=>`${u} : i32`).join(", ")}, value : vec4<f32>) {
let flatIndex = getOutputIndexFromCoords(${i}(${o.join(", ")}));
setOutputAtIndex(flatIndex / 4, value);
}
fn setOutputAtCoordsI32(${o.map(u=>`${u} : i32`).join(", ")}, value : vec4<i32>) {
let flatIndex = getOutputIndexFromCoords(${i}(${o.join(", ")}));
setOutputAtIndexI32(flatIndex / 4, value);
}
`:a+=`
fn setOutputAtCoords(${o.map(u=>`${u} : i32`).join(", ")}, value : f32) {
let flatIndex = getOutputIndexFromCoords(${i}(${o.join(", ")}));
setOutputAtIndex(flatIndex, value);
}
fn setOutputAtCoordsI32(${o.map(u=>`${u} : i32`).join(", ")}, value : i32) {
let flatIndex = getOutputIndexFromCoords(${i}(${o.join(", ")}));
setOutputAtIndexI32(flatIndex, value);
}
`}return a}var rse=e=>e?`
mm_Asub[inputRow][inputCol] = mm_readA(
t * TileInner + inputRow,
globalRowStart + inputCol, globalId);
`:`
mm_Asub[inputRow][inputCol] = mm_readA(
globalRowStart + inputRow,
t * TileInner + inputCol, globalId);
`,ase=e=>e?"let ACached = mm_Asub[k][tileRow + innerRow];":"let ACached = mm_Asub[tileRow + innerRow][k];";function Mv(e,t,n=!1,s=32){let r=e[1]*t[1],a=e[0]*t[0],o=n?r:s,i=n?s:r;w.assert(i%t[1]===0&&o%t[0]===0&&s%t[1]===0,()=>`tileAHight ${i} must be divisible by workGroupSize[1]${t[1]}, tileAWidth ${o} must be divisible by workGroupSize[0]${t[0]}, tileInner ${s} must be divisible by workGroupSize[1]${t[1]}`);let u=i/t[1],l=o/t[0],c=s/t[1];return`
var<workgroup> mm_Asub : array<array<f32, ${o}>, ${i}>;
var<workgroup> mm_Bsub : array<array<f32, ${a}>, ${s}>;
let RowPerThread = ${e[1]};
let ColPerThread = ${e[0]};
let TileInner = ${s};
@stage(compute) @workgroup_size(workGroupSizeX, workGroupSizeY, workGroupSizeZ)
fn main(@builtin(local_invocation_id) LocalId : vec3<u32>,
@builtin(global_invocation_id) GlobalId : vec3<u32>,
@builtin(num_workgroups) NumWorkgroups: vec3<u32>,
@builtin(workgroup_id) workgroupId: vec3<u32>) {
localId = LocalId;
globalId = GlobalId;
numWorkgroups = NumWorkgroups;
let tileRow = i32(localId.y) * RowPerThread;
let tileCol = i32(localId.x) * ColPerThread;
let globalRow = i32(globalId.y) * RowPerThread;
let globalCol = i32(globalId.x) * ColPerThread;
let globalRowStart = i32(workgroupId.y) * ${r};
let numTiles = (uniforms.dimInner - 1) / TileInner + 1;
var acc : array<array<f32, ColPerThread>, RowPerThread>;
// Without this initialization strange values show up in acc.
for (var innerRow = 0; innerRow < RowPerThread; innerRow = innerRow + 1) {
for (var innerCol = 0; innerCol < ColPerThread; innerCol = innerCol + 1) {
acc[innerRow][innerCol] = 0.0;
}
}
let tileRowA = i32(localId.y) * ${u};
let tileColA = i32(localId.x) * ${l};
let tileRowB = i32(localId.y) * ${c};
// Loop over shared dimension.
for (var t = 0; t < numTiles; t = t + 1) {
// Load one tile of A into local memory.
for (var innerRow = 0; innerRow < ${u}; innerRow = innerRow + 1) {
for (var innerCol = 0; innerCol < ${l}; innerCol = innerCol + 1) {
let inputRow = tileRowA + innerRow;
let inputCol = tileColA + innerCol;
${rse(n)}
}
}
// Load one tile of B into local memory.
for (var innerRow = 0; innerRow < ${c}; innerRow = innerRow + 1) {
for (var innerCol = 0; innerCol < ColPerThread; innerCol = innerCol + 1) {
let inputRow = tileRowB + innerRow;
let inputCol = tileCol + innerCol;
mm_Bsub[inputRow][inputCol] = mm_readB(
t * ${s} + inputRow,
globalCol + innerCol, globalId);
}
}
workgroupBarrier();
// Compute acc values for a single thread.
var BCached : array<f32, ColPerThread>;
for (var k = 0; k < TileInner; k = k + 1) {
for (var inner = 0; inner < ColPerThread; inner = inner + 1) {
BCached[inner] = mm_Bsub[k][tileCol + inner];
}
for (var innerRow = 0; innerRow < RowPerThread; innerRow = innerRow + 1) {
${ase(n)}
for (var innerCol = 0; innerCol < ColPerThread; innerCol = innerCol + 1) {
acc[innerRow][innerCol] = acc[innerRow][innerCol] + ACached * BCached[innerCol];
}
}
}
workgroupBarrier();
}
for (var innerRow = 0; innerRow < RowPerThread; innerRow = innerRow + 1) {
for (var innerCol = 0; innerCol < ColPerThread; innerCol = innerCol + 1) {
mm_write(globalRow + innerRow,
globalCol + innerCol,
acc[innerRow][innerCol], globalId);
}
}
}
`}var ose=e=>e?`
mm_readA(colA, globalRow, globalId),
mm_readA(colA + 1, globalRow, globalId),
mm_readA(colA + 2, globalRow, globalId),
mm_readA(colA + 3, globalRow, globalId)
`:`
mm_readA(globalRow, colA, globalId),
mm_readA(globalRow, colA + 1, globalId),
mm_readA(globalRow, colA + 2, globalId),
mm_readA(globalRow, colA + 3, globalId)
`;function ise(e,t=!1){return w.assert(e[1]===1&&e[2]===1,()=>`A linear work group size is required. But got ${e}.`),`
let TileSize = ${e[0]*4};
var<workgroup> mm_Asub : array<vec4<f32>, ${e[0]}>;
${ac()}
let tileCol = i32(localId.x);
let globalCol = i32(globalId.x);
let globalRow = i32(globalId.y);
let numTiles = (uniforms.dimInner - 1) / TileSize + 1;
// Without this initialization strange values show up in acc.
var acc = 0.0;
// Loop over shared dimension.
for (var t = 0; t < numTiles; t = t + 1) {
// Load one tile of A into local memory.
let colA = t * TileSize + tileCol * 4;
mm_Asub[tileCol] = vec4<f32>(${ose(t)});
workgroupBarrier();
// Compute acc values for a single thread.
for (var k = 0; k < TileSize / 4; k = k + 1) {
let rowB = t * TileSize + k * 4;
let BCached = vec4<f32>(mm_readB(rowB, globalCol, globalId),
mm_readB(rowB + 1, globalCol, globalId),
mm_readB(rowB + 2, globalCol, globalId),
mm_readB(rowB + 3, globalCol, globalId));
let ACached = mm_Asub[k];
acc = acc + dot(ACached, BCached);
}
workgroupBarrier();
}
mm_write(globalRow, globalCol, acc, globalId);
}
`}var use=class{constructor(e,t,n,s,r,a=!1,o=!1,i=null,u=null,l=null){this.variableNames=["A","B"],this.uniforms="dimAOuter : i32, dimBOuter : i32, dimInner : i32,",this.workGroupSize=[16,16,1],this.outputShape=t,this.dispatchLayout={x:[2],y:[1],z:[0]};let c=a?e[1]:e[2];this.workGroupSize=O2(t[1],c,t[2]),(t[1]===1||t[2]===1)&&(n=1),this.dispatch=_e(this.dispatchLayout,this.outputShape,this.workGroupSize,[n,n,1]),w.arraysEqual(this.dispatch,[1,1,1])&&(n=1,this.dispatch=_e(this.dispatchLayout,this.outputShape,this.workGroupSize,[n,n,1]));let p=i!=null,d=l!=null;p&&this.variableNames.push("bias"),d&&this.variableNames.push("preluActivationWeights"),this.workPerThread=n,this.transposeA=a,this.transposeB=o,this.addBias=p,this.activation=u,this.hasPreluActivationWeights=d,this.batchAEqualOne=s,this.batchBEqualOne=r,[this.fitAOuter,this.fitBOuter,this.fitInner]=this.getShapeFit(t[1],t[2],c),this.shaderKey=`matMulPacked_${this.workPerThread}_${a}_${o}_${this.activation}_${this.fitAOuter}_${this.fitBOuter}_${this.fitInner}_${this.outputShape[1]>1}_${this.batchAEqualOne}_${this.batchBEqualOne}`}getShapeFit(e,t,n){let s=this.workGroupSize[1]*this.workPerThread,r=this.workGroupSize[0]*this.workPerThread;this.tileInner=32,this.outputShape[1]===1&&(this.tileInner=this.workGroupSize[0]*4);let a=e%s===0,o=t%r===0,i=n%this.tileInner===0;return[a,o,i]}getUserCode(){let e=this.fitAOuter&&this.fitInner?"return A[batch * batchASize + row * uniforms.aShape[2] + col];":`
if(row < uniforms.aShape[1] && col < uniforms.aShape[2]) {
return A[batch * batchASize + row * uniforms.aShape[2] + col];
}
return 0.0;
`,t;this.transposeB===!1?t="return B[batch * batchBSize + row * uniforms.dimBOuter + col];":t="return B[batch * batchBSize + col * uniforms.dimInner + row];";let n="",s="";if(this.activation){let o=Io(this.activation,!1);this.hasPreluActivationWeights?n=`fn activation(a : f32, outCoord : vec3<i32>) -> f32 {
let b = getPreluActivationWeightsByOutputCoords(outCoord);
${o}
}`:n=`
fn activation(a : f32, outCoord : vec3<i32>) -> f32 {
${o}
}
`,s="value = activation(value, outCoord);"}let r=this.addBias?"value = value + getBiasByOutputCoords(outCoord);":"";return`
${n}
fn mm_readA(row : i32, col : i32, globalId : vec3<u32>) -> f32 {
${this.batchAEqualOne?`
let batch = 0;
let batchASize = 0;
`:`
let batch = i32(globalId.z);
let batchASize = uniforms.aShape[1] * uniforms.aShape[2];
`}
${e}
}
fn mm_readB(row : i32, col : i32, globalId : vec3<u32>) -> f32 {
${this.batchBEqualOne?`
let batch = 0;
let batchBSize = 0;
`:`
let batch = i32(globalId.z);
let batchBSize = uniforms.bShape[1] * uniforms.bShape[2];
`}
${t}
}
fn mm_write(row : i32, col : i32, valueIn : f32, globalId : vec3<u32>) {
${this.fitAOuter&&this.fitBOuter?"":"if (row < uniforms.dimAOuter && col < uniforms.dimBOuter)"}
{
var value = valueIn;
let batch = i32(globalId.z);
let outCoord = vec3<i32>(batch, row, col);
${r}
${s}
setOutputAtCoords(batch, row, col, value);
}
}
${this.outputShape[1]>1?Mv([this.workPerThread,this.workPerThread,1],this.workGroupSize,this.transposeA,this.tileInner):ise(this.workGroupSize,this.transposeA)}
`}};function lse(){return`
var<workgroup> sumValues : array<f32, workGroupSizeX>;
${ac()}
let coords = getOutputCoords();
let batch = coords[0];
let row = coords[1];
let col = coords[2];
var sum = 0.0;
let Length = uniforms.dimInner;
for (var k = i32(localId.x); k < Length; k = k + i32(workGroupSizeX)) {
let dataA = mm_readA(batch, row, k);
let dataB = mm_readB(batch, k, col);
sum = sum + dataA * dataB;
}
sumValues[localId.x] = sum;
workgroupBarrier();
for(var currentSize = workGroupSizeX / 2u; currentSize > 1u;
currentSize = currentSize / 2u) {
if (localId.x < currentSize)
{
sumValues[localId.x] = sumValues[localId.x] + sumValues[localId.x + currentSize];
}
workgroupBarrier();
}
if (localId.x == 0u) {
sum = sumValues[0] + sumValues[1];
mm_write(batch, row, col, sum);
}
}
`}var cse=class{constructor(e,t,n,s=!1,r=!1,a=null,o=null,i=null){this.variableNames=["A","B"],this.uniforms="dimAOuter : i32, dimBOuter : i32, dimInner : i32,",this.workGroupSize=[256,1,1],this.outputShape=e,this.dispatchLayout={x:[],y:[1,2],z:[0]},this.dispatch=_e(this.dispatchLayout,this.outputShape,this.workGroupSize);let u=a!=null,l=i!=null;u&&this.variableNames.push("bias"),l&&this.variableNames.push("preluActivationWeights"),this.transposeA=s,this.transposeB=r,this.addBias=u,this.activation=o,this.hasPreluActivationWeights=l,this.batchAEqualOne=t,this.batchBEqualOne=n,this.shaderKey=`matMulReduce_${this.activation}_${s}_${r}_${this.batchAEqualOne}_${this.batchBEqualOne}`}getUserCode(){let e;this.transposeA===!1?e="return f32(A[batch * batchASize + row * uniforms.dimInner + col]);":e="return f32(A[batch * batchASize + col * uniforms.dimAOuter + row]);";let t;this.transposeB===!1?t="return f32(B[batch * batchBSize + row * uniforms.dimBOuter + col]);":t="return f32(B[batch * batchBSize + col * uniforms.dimInner + row]);";let n="",s="";if(this.activation){let o=Io(this.activation,!1);this.hasPreluActivationWeights?n=`fn activation(a : f32, outCoord : vec3<i32>) -> f32 {
let b = getPreluActivationWeightsByOutputCoords(outCoord);
${o}
}`:n=`
fn activation(a : f32, outCoord : vec3<i32>) -> f32 {
${o}
}
`,s="value = activation(value, outCoord);"}let r=this.addBias?"value = value + getBiasByOutputCoords(outCoord);":"";return`
${n}
fn mm_readA(batchIn: i32, row : i32, col : i32) -> f32 {
${this.batchAEqualOne?`
let batchASize = 0;
let batch = 0;
`:`
let batchASize = uniforms.aShape[1] * uniforms.aShape[2];
let batch = batchIn;
`}
${e}
}
fn mm_readB(batchIn: i32, row : i32, col : i32) -> f32 {
${this.batchBEqualOne?`
let batch = 0;
let batchBSize = 0;
`:`
let batch = batchIn;
let batchBSize = uniforms.bShape[1] * uniforms.bShape[2];
`}
${t}
}
fn mm_write(batch: i32, row : i32, col : i32, valueIn : f32) {
var value = valueIn;
let outCoord = vec3<i32>(batch, row, col);
${r}
${s}
setOutputAtCoords(batch, row, col, value);
}
${lse()}
`}};function dse(e){let t=e[1]/2,n=e[0],s=t>n?t:n;return`
var<workgroup> mm_Asub1 : array<array<f32, ${s}>, ${t}>;
var<workgroup> mm_Bsub1 : array<array<f32, ${n}>, ${s}>;
var<workgroup> mm_Asub2 : array<array<f32, ${s}>, ${t}>;
var<workgroup> mm_Bsub2 : array<array<f32, ${n}>, ${s}>;
// If the output size is small for matrix multiplication, avoid to use vec4
// and handle some elements per thread to optimally utilize the ALU.
// Introduces two shared memory buffers, some logical threads could handle
// arithmetic operations and others handle IO operations between barrier api,
// makes ALUs and load/store units work simultaneously, could improves
// the performance.
${ac()}
let tileRow = i32(localId.y);
let tileCol = i32(localId.x);
let globalRow = i32(globalId.y);
let globalCol = i32(globalId.x);
// uniforms.dimInner should be greater than 0.
let numTiles = (uniforms.dimInner - 1) / ${s} + 1;
var acc = 0.0;
var globalColA = tileCol;
var globalRowB = tileRow;
for (var t = 0; t < numTiles; t = t + 1) {
if (t == 0) {
if (tileRow < ${t}) {
// Load one tile of A and B into local memory.
// globalRow is always greater than or equal tileRow.
mm_Asub1[tileRow][tileCol] =
mm_readA((globalRow - tileRow) / 2 + tileRow, globalColA, globalId);
globalColA = globalColA + ${s};
mm_Bsub1[tileRow][tileCol] = mm_readB(globalRowB, globalCol, globalId);
globalRowB = globalRowB + ${s};
}
} else {
if (tileRow < ${t}) {
// Load one tile of A and B into local memory.
// globalRow is always greater than or equal tileRow.
mm_Asub1[tileRow][tileCol] =
mm_readA((globalRow - tileRow) / 2 + tileRow, globalColA, globalId);
globalColA = globalColA + ${s};
mm_Bsub1[tileRow][tileCol] = mm_readB(globalRowB, globalCol, globalId);
globalRowB = globalRowB + ${s};
} else {
// Compute acc values for a single thread.
for (var k = 0; k < ${s}; k = k + 1) {
let subRow = tileRow - ${t};
if (subRow < 0) {
continue;
}
acc = acc + mm_Asub2[subRow][k] * mm_Bsub2[k][tileCol];
}
}
}
workgroupBarrier();
if (t != 0) {
t = t + 1;
}
if (t < numTiles) {
if (tileRow < ${t}) {
// Load one tile of A and B into local memory.
// globalRow is always greater than or equal tileRow.
mm_Asub2[tileRow][tileCol] =
mm_readA((globalRow - tileRow) / 2 + tileRow, globalColA, globalId);
globalColA = globalColA + ${s};
mm_Bsub2[tileRow][tileCol] = mm_readB(globalRowB, globalCol, globalId);
globalRowB = globalRowB + ${s};
} else {
// Compute acc values for a single thread.
for (var k = 0; k < ${s}; k = k + 1) {
let subRow = tileRow - ${t};
if (subRow < 0) {
continue;
}
acc = acc + mm_Asub1[subRow][k] * mm_Bsub1[k][tileCol];
}
}
}
workgroupBarrier();
}
let writeCol = (globalRow - tileRow) / 2 + tileRow - ${t};
if (tileRow >= ${t} && writeCol >= 0) {
mm_write(writeCol, globalCol, acc, globalId);
}
}
`}var pse=class{constructor(e,t,n,s=null,r=null,a=null){this.variableNames=["A","B"],this.uniforms="dimAOuter : i32, dimBOuter : i32, dimInner : i32,",this.workGroupSize=[8,16,1],w.assert(e[1]<=16||t[2]<=16,()=>"This program can be only used when A width or B Height are small"),this.outputShape=n,this.dispatchLayout={x:[2],y:[1],z:[0]},this.dispatch=[Math.ceil(n[2]/this.workGroupSize[0]),Math.ceil(n[1]*2/this.workGroupSize[1]),n[0]];let o=s!=null;o&&this.variableNames.push("bias");let i=a!=null;i&&this.variableNames.push("preluActivationWeights"),this.addBias=o,this.activation=r,this.hasPreluActivationWeights=i,this.batchAEqualOne=e[0]===1,this.batchBEqualOne=t[0]===1,this.shaderKey=`matMulSmallOutputSize_${this.activation}_${this.batchAEqualOne}_${this.batchBEqualOne}`}getUserCode(){let e=`if (coordsInBounds2D(vec2<i32>(row, col), vec2<i32>(uniforms.dimAOuter, uniforms.dimInner))) {
return A[batch * batchASize + row * uniforms.dimInner + col];
}
return 0.0;`,t=`if (coordsInBounds2D(vec2<i32>(row, col), vec2<i32>(uniforms.dimInner, uniforms.dimBOuter))) {
return B[batch * batchBSize + row * uniforms.dimBOuter + col];
}
return 0.0;`,n="",s="";if(this.activation){let o=Io(this.activation,!1);this.hasPreluActivationWeights?n=`fn activation(a : f32, outCoord : vec3<i32>) -> f32 {
let b = getPreluActivationWeightsByOutputCoords(outCoord);
${o}
}`:n=`fn activation(a : f32, outCoord : vec3<i32>) -> f32 {
${o}
}`,s="value = activation(value, outCoord);"}let r=this.addBias?"value = value + getBiasByOutputCoords(outCoord);":"";return`
${n}
fn mm_readA(row : i32, col : i32, globalId : vec3<u32>) -> f32 {
${this.batchAEqualOne?`
let batch = 0;
let batchASize = 0;
`:`
let batchASize = uniforms.aShape[1] * uniforms.aShape[2];
let batch = i32(globalId.z);
`}
${e}
}
fn mm_readB(row : i32, col : i32, globalId : vec3<u32>) -> f32 {
${this.batchBEqualOne?`
let batch = 0;
let batchBSize = 0;
`:`
let batch = i32(globalId.z);
let batchBSize = uniforms.bShape[1] * uniforms.bShape[2];
`}
${t}
}
fn mm_write(row : i32, col : i32, valueIn : f32, globalId : vec3<u32>) {
if (coordsInBounds2D(vec2<i32>(row, col), vec2<i32>(uniforms.dimAOuter, uniforms.dimBOuter))) {
let batch = i32(globalId.z);
let outCoord = vec3<i32>(batch, row, col);
var value = valueIn;
${r}
${s}
setOutputAtCoords(batch, row, col, value);
}
}
${dse(this.workGroupSize)}
`}};function Le(e){let{inputs:t,attrs:n}=e,{x:s}=t,{shape:r}=n,a=w.sizeFromShape(s.shape),o=w.inferFromImplicitShape(r,a),i=w.sizeFromShape(o);return w.assert(a===i,()=>`The new shape (${o}) has ${i} elements and the old shape (${s.shape}) has ${a} elements. The new shape and old shape must have the same number of elements.`),e.backend.incRef(s.dataId),{dataId:s.dataId,shape:o,dtype:s.dtype}}var hse={kernelName:Oi,backendName:"webgpu",kernelFunc:Le};function Bv({a:e,b:t,transposeA:n,transposeB:s,backend:r,bias:a=null,preluActivationWeights:o=null,leakyreluAlpha:i=0,activation:u=null}){let l=e.shape.length,c=t.shape.length,p=n?e.shape[l-2]:e.shape[l-1],d=s?t.shape[c-1]:t.shape[c-2],h=n?e.shape[l-1]:e.shape[l-2],f=s?t.shape[c-2]:t.shape[c-1],m=e.shape.slice(0,-2),g=t.shape.slice(0,-2),b=w.sizeFromShape(m),y=w.sizeFromShape(g),x=Qi.assertAndGetBroadcastShape(e.shape.slice(0,-2),t.shape.slice(0,-2)).concat([h,f]);w.assert(p===d,()=>`Error in matMul: inner shapes (${p}) and (${d}) of Tensors with shapes ${e.shape} and ${t.shape} and transposeA=${n} and transposeB=${s} must match.`);let k=n?[b,p,h]:[b,h,p],I=s?[y,f,d]:[y,d,f],$=Le({inputs:{x:e},backend:r,attrs:{shape:k}}),R=Le({inputs:{x:t},backend:r,attrs:{shape:I}}),E=[$,R],P=Math.max(b,y),A=b===1,D=y===1,T=(p%4===0&&!n||h%4===0&&n)&&f%4===0&&!s,L;h*f<=32?L=new cse([P,h,f],A,D,n,s,a,u,o):!n&&!s&&(h<=16&&(f<=512||d>=2*f)||f<=16&&(h<=512||p>=2*h))?L=new pse(k,I,[P,h,f],a,u,o):T?L=new Xne(k,[P,h,f],A,D,n,a,u,o):L=new use(k,[P,h,f],K().get("WEBGPU_MATMUL_WORK_PER_THREAD"),A,D,n,s,a,u,o);let W=[$,R];a&&W.push(a),o&&W.push(o);let j=[{type:"int32",data:[h]},{type:"int32",data:[f]},{type:"int32",data:[p]}];u==="leakyrelu"&&(j.push({type:"float32",data:[i]}),L.uniforms+=" alpha : f32,");let Y=r.runWebGPUProgram(L,W,e.dtype,j),X=Le({inputs:{x:Y},backend:r,attrs:{shape:x}});E.push(Y);for(let Z of E)r.disposeData(Z.dataId);return X}function fse(e){let{inputs:t,backend:n,attrs:s}=e,{a:r,b:a,bias:o,preluActivationWeights:i}=t,{transposeA:u,transposeB:l,activation:c,leakyreluAlpha:p}=s;return Bv({a:r,b:a,transposeA:u,transposeB:l,backend:n,bias:o,preluActivationWeights:i,leakyreluAlpha:p,activation:c})}var mse={kernelName:oa,backendName:"webgpu",kernelFunc:fse},Ww=class{constructor(e,t,n){this.variableNames=["AReal","AImag","BReal","BImag"],this.workGroupSize=[128,1,1],this.size=!0,this.outputShape=C.assertAndGetBroadcastShape(t,n),this.dispatchLayout=Ve(this.outputShape),this.dispatch=_e(this.dispatchLayout,this.outputShape,this.workGroupSize),this.shaderKey=`binaryOpComplex_${e}`,this.op=e}getUserCode(){return`
fn binaryOpComplex(
areal : f32, aimag : f32, breal : f32, bimag : f32) -> f32 {
${rc(this.op,!1)}
}
${Ue()}
if(index < uniforms.size) {
let areal = getARealByOutputIndex(index);
let aimag = getAImagByOutputIndex(index);
let breal = getBRealByOutputIndex(index);
let bimag = getBImagByOutputIndex(index);
setOutputAtIndex(index, binaryOpComplex(areal, aimag, breal, bimag));
}
}
`}},gse=class{constructor(e,t,n,s){this.variableNames=["A","B"],this.size=!0;let r=256;this.workGroupSize=[r,1,1],this.outputShape=C.assertAndGetBroadcastShape(t,n),this.dispatchLayout=Ve(this.outputShape),this.lastDimensionSize=s?n[0]:t[0],this.lastDimensionSize<256?this.workPerThread=1:this.lastDimensionSize<512?this.workPerThread=2:this.workPerThread=4,this.dispatch=_e(this.dispatchLayout,this.outputShape,this.workGroupSize,[this.workPerThread,1,1]),this.useSharedMemoryWithB=s,this.op=e,this.shaderKey=`binaryShared_${e}_${this.lastDimensionSize}_${this.useSharedMemoryWithB}`}getUserCode(){let e=this.lastDimensionSize>1?`coords[${this.outputShape.length-1}]`:"0",t=this.useSharedMemoryWithB?`let a = getAByOutputCoords(coords);
let b = sharedBuf[${e}];`:`let a = sharedBuf[${e}];
let b = getBByOutputCoords(coords);`;return`
fn binaryOperation(a : f32, b : f32) -> f32 {
${rc(this.op,!1)}
}
var<workgroup> sharedBuf : array<f32, ${this.lastDimensionSize}>;
${Ue()}
// Fill in the shared memory buffer. Here we need a loop to make sure
// that all data in A|B are uploaded when |sharedMemorySize| is larger
// than work group size.
for(var localIndex = i32(localId.x); localIndex < ${this.lastDimensionSize}; localIndex = localIndex + ${this.workGroupSize[0]}) {
sharedBuf[localIndex] = f32(${this.useSharedMemoryWithB?"B":"A"}[localIndex]);
}
workgroupBarrier();
for(var i = 0; i < ${this.workPerThread}; i = i + 1) {
let flatIndex = index * ${this.workPerThread} + i;
if(flatIndex < uniforms.size) {
let coords = getCoordsFromIndex(flatIndex);
${t}
setOutputAtIndex(flatIndex, binaryOperation(a, b));
}
}
}
`}},bse=class{constructor(e,t,n){this.variableNames=["A","B"],this.workPerThread=4,this.isVec4=!0,this.size=!0;let s=128;this.workGroupSize=[s,1,1],this.outputShape=C.assertAndGetBroadcastShape(t,n),this.dispatchLayout=Ve(this.outputShape),this.dispatch=_e(this.dispatchLayout,this.outputShape,this.workGroupSize,[this.workPerThread,1,1]),this.op=e,this.shaderKey=`binaryVec4_${e}`}getUserCode(){return`
fn binaryOperation(a : vec4<f32>, b : vec4<f32>) -> vec4<f32> {
${rc(this.op,this.isVec4)}
}
${Ue()}
if (index < uniforms.size) {
let a = getAByOutputIndex(index);
let b = getBByOutputIndex(index);
setOutputAtIndex(index, binaryOperation(a, b));
}
}
`}},M2=class{constructor(e,t,n){this.variableNames=["A","B"],this.size=!0;let s=128;this.workGroupSize=[s,1,1],this.outputShape=C.assertAndGetBroadcastShape(t,n),this.dispatchLayout=Ve(this.outputShape),this.dispatch=_e(this.dispatchLayout,this.outputShape,this.workGroupSize),this.shaderKey=`binary_${e}`,this.op=e}getUserCode(){return`
fn binaryOperation(a : f32, b : f32) -> f32 {
${rc(this.op,!1)}
}
${Ue()}
if (index < uniforms.size) {
let a = getAByOutputIndex(index);
let b = getBByOutputIndex(index);
setOutputAtIndex(index, binaryOperation(a, b));
}
}
`}};function Uw(e,t,n){if(w.arraysEqual(t,n)&&w.sizeFromShape(t)%4===0)return new bse(e,t,n);let r=t.length===1&&n.length>1&&t[0]<1024,a=n.length===1&&t.length>1&&n[0]<1024;return r||a?new gse(e,t,n,a):new M2(e,t,n)}function Un(e){let{inputs:t}=e,{x:n}=t;return e.backend.incRef(n.dataId),{dataId:n.dataId,shape:n.shape,dtype:n.dtype}}var yse={kernelName:Wa,backendName:"webgpu",kernelFunc:Un};function hu(e){let{inputs:t,backend:n}=e,{real:s,imag:r}=t,a=n.makeTensorInfo(s.shape,"complex64"),o=n.tensorMap.get(a.dataId),i=Un({inputs:{x:s},backend:n}),u=Un({inputs:{x:r},backend:n});return o.complexTensorInfos={real:i,imag:u},a}var vse={kernelName:np,backendName:"webgpu",kernelFunc:hu},oc=class{constructor(e,t){this.variableNames=["A"],this.size=!0;let n=128;this.workGroupSize=[n,1,1],this.outputShape=e,this.dispatchLayout=Ve(this.outputShape),this.dispatch=_e(this.dispatchLayout,this.outputShape,this.workGroupSize),this.op=t,this.shaderKey=`unary_${t}`}getUserCode(){return`
fn unaryOperation(a : f32) -> f32 {
${jr(this.op,!1)}
}
${Ue()}
if (index < uniforms.size) {
let a = getAByOutputIndex(index);
setOutputAtIndex(index, unaryOperation(a));
}
}
`}};function Kt({opType:e,cpuKernelImpl:t,dtype:n}){return({inputs:s,backend:r})=>{let{x:a}=s,o=r,i=n||a.dtype;if(o.shouldExecuteOnCPU([a])&&t!=null){let l=o.tensorMap.get(a.dataId),c=t(l.values,i);return o.makeTensorInfo(a.shape,i,c)}let u=new oc(a.shape,e);return o.runWebGPUProgram(u,[a],i)}}function gn({opSnippet:e,cpuKernelImpl:t,supportsComplex:n=!1,dtype:s}){return({inputs:r,backend:a})=>{let{a:o,b:i}=r,u=a;if(n&&o.dtype==="complex64"){let p=u.tensorMap.get(o.dataId),d=u.tensorMap.get(i.dataId),h,f;if(e!==0)[h,f]=[[p.complexTensorInfos.real,d.complexTensorInfos.real],[p.complexTensorInfos.imag,d.complexTensorInfos.imag]].map(g=>{let[b,y]=g,v={dataId:b.dataId,dtype:b.dtype,shape:o.shape},x={dataId:y.dataId,dtype:y.dtype,shape:i.shape},k=Uw(e,o.shape,i.shape);return u.runWebGPUProgram(k,[v,x],cn(b.dtype,y.dtype))});else{let g=new Ww(17,o.shape,i.shape),b=new Ww(18,o.shape,i.shape),y=[{dataId:p.complexTensorInfos.real.dataId,dtype:p.complexTensorInfos.real.dtype,shape:o.shape},{dataId:p.complexTensorInfos.imag.dataId,dtype:p.complexTensorInfos.imag.dtype,shape:o.shape},{dataId:d.complexTensorInfos.real.dataId,dtype:d.complexTensorInfos.real.dtype,shape:i.shape},{dataId:d.complexTensorInfos.imag.dataId,dtype:d.complexTensorInfos.imag.dtype,shape:i.shape}];h=u.runWebGPUProgram(g,y,"float32"),f=u.runWebGPUProgram(b,y,"float32")}let m=hu({inputs:{real:h,imag:f},backend:u});return u.disposeData(h.dataId),u.disposeData(f.dataId),m}let l=s||cn(o.dtype,i.dtype);if((o.dtype==="string"||i.dtype==="string"||u.shouldExecuteOnCPU([o,i]))&&t!=null){let p=u.tensorMap.get(o.dataId).values,d=u.tensorMap.get(i.dataId).values,h=o.dtype==="string"?C.fromUint8ToStringArray(p):p,f=o.dtype==="string"?C.fromUint8ToStringArray(d):d,[m,g]=t(o.shape,i.shape,h,f,l);return u.makeTensorInfo(g,l,m)}let c=Uw(e,o.shape,i.shape);return u.runWebGPUProgram(c,[o,i],l)}}var{addImpl:xse,ceilImpl:wse,concatImpl:kse,equalImpl:Sse,expImpl:Ise,expm1Impl:Cse,floorImpl:Nse,gatherNdImpl:Tse,gatherV2Impl:$se,greaterEqualImpl:_se,greaterImpl:Ase,lessEqualImpl:Ese,lessImpl:Rse,logImpl:Dse,maxImpl:Fse,maximumImpl:Ose,minimumImpl:Pse,multiplyImpl:zse,negImpl:Lse,notEqualImpl:Mse,prodImpl:Bse,rangeImpl:Vse,rsqrtImpl:Wse,scatterImpl:Use,simpleAbsImpl:Gse,sliceImpl:Hse,stridedSliceImpl:qse,stringNGramsImpl:jse,subImpl:Kse,tileImpl:Xse,topKImpl:Yse,transposeImpl:Qse,uniqueImpl:Lhe}=cv,Zse=Kt({opType:0,cpuKernelImpl:Gse}),Jse={kernelName:di,backendName:"webgpu",kernelFunc:Zse},ere=gn({opSnippet:1,cpuKernelImpl:xse,supportsComplex:!0}),tre={kernelName:Cr,backendName:"webgpu",kernelFunc:ere},nre=class{constructor(e){this.workPerThread=4,this.workGroupSize=[64,1,1],this.size=!0,this.outputShape=e[0],this.variableNames=e.map((t,n)=>`T${n}`),this.dispatchLayout=Ve(this.outputShape),this.dispatch=_e(this.dispatchLayout,this.outputShape,this.workGroupSize,[this.workPerThread,1,1]),this.shaderKey="addN"}getUserCode(){let e=[];this.variableNames.forEach(s=>{e.push(`let v${s} = get${s}ByOutputCoords(coords);`)});let t=this.variableNames.map(s=>`v${s}`).join(" + ");return`
${Ue()}
for (var i = 0; i < ${this.workPerThread}; i = i + 1) {
let flatIndex = index * ${this.workPerThread} + i;
if (flatIndex < uniforms.size) {
let coords = getCoordsFromIndex(flatIndex);
${e.join(`
`)}
setOutputAtIndex(flatIndex, ${t});
}
}
}
`}};function sre(e){let{inputs:t,backend:n}=e,s=t;if(s.length===1)return Un({inputs:{x:s[0]},backend:n});let r=s.map(i=>i.dtype).reduce((i,u)=>cn(i,u)),a=s.map(i=>i.shape),o=new nre(a);return n.runWebGPUProgram(o,s,r)}var rre={kernelName:Sa,backendName:"webgpu",kernelFunc:sre},B2=class{constructor(e,t,n){this.workGroupSize=[64,1,1],this.variableNames=["x"],this.uniforms="infinityValue : f32,",this.size=!0;let s=[t];C.assertAxesAreInnerMostDims("arg"+n.charAt(0).toUpperCase()+n.slice(1),s,e.length),this.op=n==="min"?"<":">";let[r]=C.computeOutAndReduceShapes(e,s);this.outputShape=r.length===0?[1]:r,this.dispatchLayout=Ve(this.outputShape),this.dispatch=_e(this.dispatchLayout,this.outputShape,[1,1,1]),this.inputShape=e,this.shaderKey=`argMinMax${this.op}`}getUserCode(){let e=`
var<workgroup> xBestIndices : array<i32, ${this.workGroupSize[0]}>;
var<workgroup> xBestValues : array<f32, ${this.workGroupSize[0]}>;
`,t=()=>this.inputShape.length===1?"uniforms.xShape":`uniforms.xShape.${hr(this.inputShape.length-1)}`,n=()=>{let r="";if(this.outputShape.length===1)this.inputShape.length!==1&&(r+="outputCoords,");else for(let a=0;a<this.outputShape.length;a++)r+=`outputCoords.${hr(a)},`;return r};return`
fn DIV_CEIL(a : u32, b : u32) -> u32 {
return ((a - 1u) / b + 1u);
}
${e}
${Ue()}
let outputIndex = index / i32(workGroupSizeX);
let reduceLength = ${t()};
var bestIndex = i32(localId.x);
var bestValue = uniforms.infinityValue;
let outputCoords = getCoordsFromIndex(outputIndex);
for (var k = i32(localId.x); k < reduceLength && outputIndex < uniforms.size;
k = k + i32(workGroupSizeX)) {
let candidate = getX(${n()} k);
if (!isnan(candidate) && candidate ${this.op} bestValue) {
bestValue = candidate;
bestIndex = k;
}
}
xBestValues[localId.x] = bestValue;
xBestIndices[localId.x] = bestIndex;
workgroupBarrier();
var reduceSize = min(u32(reduceLength), workGroupSizeX);
for (var currentSize = reduceSize / 2u; reduceSize > 1u;
currentSize = reduceSize / 2u) {
let interval = DIV_CEIL(reduceSize, 2u);
if (localId.x < currentSize) {
let candidate = xBestValues[localId.x + interval];
if (candidate ${this.op} bestValue) {
bestValue = candidate;
xBestValues[localId.x] = bestValue;
xBestIndices[localId.x] = xBestIndices[localId.x + interval];
}
}
reduceSize = interval;
workgroupBarrier();
}
if (localId.x == 0u && outputIndex < uniforms.size) {
setOutputAtIndexI32(outputIndex, xBestIndices[localId.x]);
}
}
`}},are=class{constructor(e,t){this.variableNames=["A"],this.workGroupSize=[16,16,1];let n=new Array(e.length);for(let s=0;s<n.length;s++)n[s]=e[t[s]];this.outputShape=n,this.dispatchLayout={x:[0],y:[1]},this.dispatch=_e(this.dispatchLayout,this.outputShape,this.workGroupSize,[1,1,1]),this.shaderKey="transposeShared"}getUserCode(){return`
let TILE_DIM = ${this.workGroupSize[0]};
var<workgroup> tile : array<array<f32, ${this.workGroupSize[0]+1}>, ${this.workGroupSize[0]}>;
${Lv()}
fn main(@builtin(local_invocation_id) localId : vec3<u32>,
@builtin(workgroup_id) workgroupId : vec3<u32>) {
var x = i32(workgroupId.x) * TILE_DIM + i32(localId.x);
var y = i32(workgroupId.y) * TILE_DIM + i32(localId.y);
let width = uniforms.outShape[0];
let height = uniforms.outShape[1];
if (x < width && y < height) {
tile[localId.y][localId.x] = A[y * width + x];
}
workgroupBarrier();
x = i32(workgroupId.y) * TILE_DIM + i32(localId.x);
y = i32(workgroupId.x) * TILE_DIM + i32(localId.y);
if (x < height && y < width) {
setOutputAtIndex((y * height + x), tile[localId.x]
[localId.y]);
}
}
`}},ore=class{constructor(e,t){this.variableNames=["A"],this.workPerThread=4,this.workGroupSize=[64,1,1],this.size=!0;let n=new Array(e.length);for(let s=0;s<n.length;s++)n[s]=e[t[s]];this.outputShape=n,this.dispatchLayout=Ve(this.outputShape),this.dispatch=_e(this.dispatchLayout,this.outputShape,this.workGroupSize,[this.workPerThread,1,1]),this.newDim=t,this.shaderKey=`transpose_${t}`}getUserCode(){let e=Wt(this.outputShape.length),t=ire(this.newDim);return`
${Ue()}
for(var i = 0; i < ${this.workPerThread}; i = i + 1) {
let flatIndex = index * ${this.workPerThread} + i;
if(flatIndex < uniforms.size) {
let resRC = getCoordsFromIndex(flatIndex);
setOutputAtIndex(flatIndex, A[getIndexFromCoords${this.outputShape.length}D(
${e}(${t}), uniforms.aShape)]);
}
}
}
`}};function ire(e){let t=e.length;if(t>6)throw Error(`Transpose for rank ${t} is not yet supported`);let n=new Array(t);for(let s=0;s<e.length;s++)n[e[s]]=`resRC.${hr(s)}`;return n.join()}function Ks(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{perm:a}=s,o=n,i=r.shape.length,u=new Array(i);for(let c=0;c<u.length;c++)u[c]=r.shape[a[c]];if(n.shouldExecuteOnCPU([r])){let p=o.tensorMap.get(r.dataId).values,d=Qse(p,r.shape,r.dtype,a,u);return n.makeTensorInfo(u,r.dtype,d)}if(r.shape.length===2&&w.arraysEqual(a,[1,0])){let c=new are(r.shape,a);return o.runWebGPUProgram(c,[r],r.dtype)}let l=new ore(r.shape,a);return o.runWebGPUProgram(l,[r],r.dtype)}var ure={kernelName:Hs,backendName:"webgpu",kernelFunc:Ks};function lre(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{axis:a}=s,o=w.parseAxisParam(a,r.shape),i=C.getAxesPermutation(o,r.shape.length),u=r,l=[];i!=null&&(u=Ks({inputs:{x:r},backend:n,attrs:{perm:i}}),l.push(u),o=C.getInnerMostAxes(o.length,u.shape.length)),C.assertAxesAreInnerMostDims("argMax",[o[0]],u.shape.length);let c=new B2(u.shape,o[0],"max"),p=[{type:"float32",data:[Number.NEGATIVE_INFINITY]}],d=n.runWebGPUProgram(c,[u],"int32",p);return l.forEach(h=>n.disposeData(h.dataId)),d}var cre={kernelName:Ia,backendName:"webgpu",kernelFunc:lre};function dre(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{axis:a}=s,o=w.parseAxisParam(a,r.shape),i=C.getAxesPermutation(o,r.shape.length),u=r,l=[];i!=null&&(u=Ks({inputs:{x:r},backend:n,attrs:{perm:i}}),l.push(u),o=C.getInnerMostAxes(o.length,u.shape.length)),C.assertAxesAreInnerMostDims("argMin",[o[0]],u.shape.length);let c=new B2(u.shape,o[0],"min"),p=[{type:"float32",data:[Number.POSITIVE_INFINITY]}],d=n.runWebGPUProgram(c,[u],"int32",p);return l.forEach(h=>n.disposeData(h.dataId)),d}var pre={kernelName:pl,backendName:"webgpu",kernelFunc:dre},V2=class{constructor(e,t){this.variableNames=["x"],this.uniforms="stride : vec2<i32>, pad : vec2<i32>, dilation : vec2<i32>, convDims : vec2<i32>, filterDims : vec2<i32>,",this.workGroupSize=[128,1,1],this.size=!0,this.outputShape=e.outShape,this.dispatchLayout=Ve(this.outputShape),this.dispatch=_e(this.dispatchLayout,this.outputShape,this.workGroupSize),this.shaderKey=`pool2D_${t}`,this.poolType=t}getUserCode(){let e="resultValue = max(value, resultValue);";this.poolType==="avg"&&(e="resultValue = resultValue + value; count = count + 1.0;");let t="resultValue";return this.poolType==="avg"&&(t="resultValue / count"),`
${Ue()}
if (index < uniforms.size) {
let coords = getCoordsFromIndex(index);
let batch = coords[0];
let xRCCorner = vec2<i32>(coords.yz) * uniforms.stride - uniforms.pad;
let xRCorner = xRCCorner.x;
let xCCorner = xRCCorner.y;
var resultValue = ${this.poolType==="avg"?"0.0":"-1.0 / pow(10.0, -20.0)"};
var count = 0.0;
for (var wR = 0; wR < uniforms.filterDims.x; wR = wR + uniforms.dilation.x) {
let xR = xRCorner + wR;
if (xR < 0 || xR >= uniforms.convDims.x) {
continue;
}
for (var wC = 0; wC < uniforms.filterDims.y; wC = wC + uniforms.dilation.y) {
let xC = xCCorner + wC;
if (xC < 0 || xC >= uniforms.convDims.y) {
continue;
}
let value = getX(batch, xR, xC, coords[3]);
${e}
}
}
setOutputAtIndex(index, ${t});
}
}
`}},W2=class{constructor(e){this.variableNames=["x"],this.uniforms="stride : vec2<i32>,",this.workGroupSize=[256,1,1],this.size=!0,this.outputShape=e.outShape,this.dispatchLayout=Ve(this.outputShape),this.dispatch=_e(this.dispatchLayout,this.outputShape,this.workGroupSize),this.shaderKey="poolWithFilterSizeEqualsOne"}getUserCode(){return`
${Ue()}
if (index < uniforms.size) {
let coords = getCoordsFromIndex(index);
let batch = coords[0];
let d = coords[3];
let xRCCorner = coords.yz * uniforms.stride;
let xRCorner = xRCCorner.x;
let xCCorner = xRCCorner.y;
let value = getX(batch, xRCorner, xCCorner, d);
setOutputAtIndex(index, value);
}
}
`}};function hre(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{filterSize:a,strides:o,pad:i,dimRoundingMode:u}=s,l=1,c=C.computePool2DInfo(r.shape,a,o,l,i,u);if(c.filterWidth===1&&c.filterHeight===1&&w.arraysEqual(c.inShape,c.outShape))return Un({inputs:{x:r},backend:n});let p,d=[{type:"int32",data:[c.strideHeight,c.strideWidth]}];return c.filterHeight===1&&c.filterWidth===1?p=new W2(c):(p=new V2(c,"avg"),d.push({type:"int32",data:[c.padInfo.top,c.padInfo.left]},{type:"int32",data:[c.dilationHeight,c.dilationWidth]},{type:"int32",data:[c.inHeight,c.inWidth]},{type:"int32",data:[c.effectiveFilterHeight,c.effectiveFilterWidth]})),n.runWebGPUProgram(p,[r],r.dtype,d)}var fre={kernelName:Ca,backendName:"webgpu",kernelFunc:hre};function mre(e){let{inputs:t,backend:n,attrs:s}=e,{a:r,b:a}=t,{transposeA:o,transposeB:i}=s;return Bv({a:r,b:a,transposeA:o,transposeB:i,backend:n})}var gre={kernelName:Na,backendName:"webgpu",kernelFunc:mre},bre=class{constructor(e,t){this.variableNames=["source"],this.workPerThread=1,this.workGroupSize=[64,1,1],this.size=!0,this.outputShape=t,this.rank=t.length,this.dispatchLayout=Ve(this.outputShape),this.dispatch=_e(this.dispatchLayout,this.outputShape,this.workGroupSize,[this.workPerThread,1,1]),this.start=e,this.uniforms=`start : ${Wt(e.length)}, `,this.shaderKey="slice"}getUserCode(){let e=Wt(this.rank),t=yre(this.rank),n;return this.start.length===1?n=this.outputShape.map((r,a)=>"sourceLoc = uniforms.start + coords;"):n=this.outputShape.map((r,a)=>`sourceLoc.${ag[a]} = uniforms.start[${a}] + coords.${ag[a]};`),`
${Ue()}
if (index < uniforms.size) {
var sourceLoc : ${e};
let coords = getCoordsFromIndex(index);
${n.join(`
`)}
setOutputAtIndex(index, getSource(${t}));
}
}
`}},ag=["x","y","z","w","u","v"];function yre(e){if(e===1)return"sourceLoc";if(e<=6)return ag.slice(0,e).map(t=>`sourceLoc.${t}`).join(",");throw Error(`Slicing for rank ${e} is not yet supported`)}function fu(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{begin:a,size:o}=s,[i,u]=kt.parseSliceParams(r,a,o);if(kt.assertParamsValid(r,i,u),n.shouldExecuteOnCPU([r])||r.dtype==="string"){let p=n.tensorMap.get(r.dataId),d=Hse(p.values,i,u,r.shape,r.dtype);return n.makeTensorInfo(u,r.dtype,d)}if(w.sizeFromShape(u)===0)return n.makeTensorInfo(u,r.dtype,[]);let l=new bre(i,u),c=[{type:"int32",data:i}];return n.runWebGPUProgram(l,[r],r.dtype,c)}var vre={kernelName:Bi,backendName:"webgpu",kernelFunc:fu},xre=e=>{let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{blockShape:a,crops:o}=s;w.assert(r.shape.length<=4,()=>"batchToSpaceND for rank > 4 with a WebGPU backend not implemented yet");let i=a.reduce((y,v)=>y*v),u=C.getReshaped(r.shape,a,i),l=C.getPermuted(u.length,a.length),c=C.getReshapedPermuted(r.shape,a,i),p=C.getSliceBeginCoords(o,a.length),d=C.getSliceSize(c,o,a.length),h=[],f=Le({inputs:{x:r},backend:n,attrs:{shape:u}}),m=Ks({inputs:{x:f},backend:n,attrs:{perm:l}}),g=Le({inputs:{x:m},backend:n,attrs:{shape:c}}),b=fu({inputs:{x:g},backend:n,attrs:{begin:p,size:d}});return h.push(f),h.push(m),h.push(g),h.forEach(y=>n.disposeData(y.dataId)),b},wre={kernelName:pi,backendName:"webgpu",kernelFunc:xre},U2=gn({opSnippet:10,dtype:"bool",cpuKernelImpl:Mse}),kre={kernelName:_i,backendName:"webgpu",kernelFunc:U2};function ic(e){let{inputs:t,backend:n}=e,{input:s}=t,r=n.tensorMap.get(s.dataId);return Un({inputs:{x:r.complexTensorInfos.real},backend:n})}var Sre={kernelName:cp,backendName:"webgpu",kernelFunc:ic};function Ire(e,t){let n=new oc(e.shape,22),s=t.runWebGPUProgram(n,[e],"int32");return{dataId:s.dataId,shape:s.shape,dtype:s.dtype}}function og(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{dtype:a}=s;if(a==="complex64"){if(r.dtype==="complex64")return Un({inputs:{x:r},backend:n});let o=$t(r.shape),i=og({inputs:{x:r},backend:n,attrs:{dtype:"float32"}}),u=hu({inputs:{real:i,imag:o},backend:n});return o.dispose(),n.disposeData(i.dataId),u}if(r.dtype==="complex64"){let o=ic({inputs:{input:r},backend:n}),i=og({inputs:{x:o},backend:n,attrs:{dtype:a}});return n.disposeData(o.dataId),i}if(!w.hasEncodingLoss(r.dtype,a)){let o=Un({inputs:{x:r},backend:n});return{dataId:o.dataId,shape:o.shape,dtype:a}}if(a==="int32")return Ire(r,n);if(a==="bool"){let o=n.makeTensorInfo([],"bool",w.getTypedArrayFromDType("bool",1)),u=U2({inputs:{a:r,b:o},backend:n});return n.disposeData(o.dataId),u}throw new Error(`Error in Cast: failed to cast ${r.dtype} to ${a}`)}var Cre={kernelName:Ta,backendName:"webgpu",kernelFunc:og},Nre=Kt({opType:1,cpuKernelImpl:wse}),Tre={kernelName:$a,backendName:"webgpu",kernelFunc:Nre},$re=class{constructor(e){this.variableNames=["A"],this.uniforms="minVal : f32, maxVal : f32,",this.workPerThread=4,this.workGroupSize=[64,1,1],this.isVec4=!0,this.size=!0,this.outputShape=e,this.dispatchLayout=Ve(this.outputShape),this.dispatch=_e(this.dispatchLayout,this.outputShape,this.workGroupSize,[this.workPerThread,1,1]),this.shaderKey="clipVec4"}getUserCode(){return`
${Ue()}
if(index < uniforms.size) {
let value = getAByOutputIndex(index);
var clampedValue : vec4<f32>;
for (var i = 0; i < 4; i = i + 1) {
if (isnan(value[i])) {
clampedValue[i] = value[i];
} else {
clampedValue[i] = clamp(value[i], uniforms.minVal, uniforms.maxVal);
}
}
setOutputAtIndex(index, clampedValue);
}
}
`}},_re=class{constructor(e){this.variableNames=["A"],this.uniforms="minVal : f32, maxVal : f32,",this.workGroupSize=[64,1,1],this.size=!0,this.outputShape=e,this.dispatchLayout=Ve(this.outputShape),this.dispatch=_e(this.dispatchLayout,this.outputShape,this.workGroupSize),this.shaderKey="clip"}getUserCode(){return`
${Ue()}
if(index < uniforms.size) {
let value = getAByOutputIndex(index);
if (isnan(value)) {
setOutputAtIndex(index, value);
return;
}
setOutputAtIndex(index, clamp(value, uniforms.minVal, uniforms.maxVal));
}
}
`}};function Are(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{clipValueMin:a,clipValueMax:o}=s,i,u=[{type:"float32",data:[a]},{type:"float32",data:[o]}];return w.sizeFromShape(r.shape)%4===0?i=new $re(r.shape):i=new _re(r.shape),n.runWebGPUProgram(i,[r],r.dtype,u)}var Ere={kernelName:Nr,backendName:"webgpu",kernelFunc:Are},Rre=class{constructor(e){this.uniforms="",this.workPerThread=4,this.workGroupSize=[64,1,1],this.size=!0,this.outputShape=C.computeOutShape(e,1),this.variableNames=e.map((t,n)=>`T${n}`),this.dispatchLayout=Ve(this.outputShape),this.dispatch=_e(this.dispatchLayout,this.outputShape,this.workGroupSize,[this.workPerThread,1,1]),this.offsetLength=e.length-1;for(let t=0;t<this.offsetLength;t++)this.uniforms+=`offset${t} : i32,`;this.shaderKey="concat"}getUserCode(){let e=[];if(this.offsetLength>0){e.push("if (yC < uniforms.offset0){ setOutputAtCoords(coords.x, coords.y, getT0(yR, yC)); }");for(let r=1;r<this.offsetLength;r++)e.push(`else if (yC < uniforms.offset${[r]}){ setOutputAtCoords(coords.x, coords.y, getT${r}(yR, yC - uniforms.offset${r-1})); }`);let n=this.offsetLength,s=this.offsetLength-1;e.push(`else { setOutputAtCoords(coords.x, coords.y, getT${n}(yR, yC - uniforms.offset${s})); }`)}else e.push("setOutputAtCoords(coords.x, coords.y, getT0(yR, yC));");return`
${Ue()}
for(var i = 0; i < ${this.workPerThread}; i = i + 1) {
let flatIndex = index * ${this.workPerThread} + i;
if(flatIndex < uniforms.size) {
let coords = getCoordsFromIndex(flatIndex);
let yR = coords.x;
let yC = coords.y;
${e.join(`
`)}
}
}
}
`}};function ih(e){let{inputs:t,backend:n}=e,{input:s}=t,r=n.tensorMap.get(s.dataId);return Un({inputs:{x:r.complexTensorInfos.imag},backend:n})}var Dre={kernelName:ip,backendName:"webgpu",kernelFunc:ih};function ig(e,t,n){let s=e[0].dtype;if(s==="complex64"){let h=e.map(y=>ic({inputs:{input:y},backend:n})),f=e.map(y=>ih({inputs:{input:y},backend:n})),m=ig(h,t,n),g=ig(f,t,n),b=hu({inputs:{real:m,imag:g},backend:n});return h.forEach(y=>n.disposeData(y.dataId)),f.forEach(y=>n.disposeData(y.dataId)),n.disposeData(m.dataId),n.disposeData(g.dataId),b}let r=n.shouldExecuteOnCPU(e);if(s==="string"&&(r=!0),r){let h=e.map(x=>{let k=w.sizeFromShape(x.shape.slice(t));return Le({inputs:{x},backend:n,attrs:{shape:[-1,k]}})}),f=h.map(x=>({vals:n.readSync(x.dataId),shape:x.shape})),m=C.computeOutShape(h.map(x=>x.shape),1),g=h[0].shape[0]===1,b=kse(f,m,s,g),y=C.computeOutShape(e.map(x=>x.shape),t),v=n.makeTensorInfo(y,s,b);return h.forEach(x=>n.disposeData(x.dataId)),v}let{tensors2D:a,outShape:o}=Fre(e,t,n),i=a.map(h=>h.shape),u=new Rre(i),l=[],c=new Array(i.length-1);if(c.length>0){c[0]=i[0][1],l.push({type:"int32",data:[c[0]]});for(let h=1;h<c.length;h++)c[h]=c[h-1]+i[h][1],l.push({type:"int32",data:[c[h]]})}let p=n.runWebGPUProgram(u,a,a[0].dtype,l);a.forEach(h=>n.disposeData(h.dataId));let d=Le({inputs:{x:p},backend:n,attrs:{shape:o}});return n.disposeData(p.dataId),d}function Fre(e,t,n){let s=C.computeOutShape(e.map(a=>a.shape),t);return{tensors2D:e.map(a=>Le({inputs:{x:a},backend:n,attrs:{shape:[w.sizeFromShape(a.shape.slice(0,t)),w.sizeFromShape(a.shape.slice(t))]}})),outShape:s}}function G2(e){let{inputs:t,backend:n,attrs:s}=e,{axis:r}=s,a=w.parseAxisParam(r,t[0].shape)[0],o=C.computeOutShape(t.map(l=>l.shape),a);if(w.sizeFromShape(o)===0)return n.makeTensorInfo(o,t[0].dtype,[]);let i=t.filter(l=>w.sizeFromShape(l.shape)>0);if(i.length===1)return Un({inputs:{x:i[0]},backend:n});let u=i.map(l=>l.shape);return C.assertParamsConsistent(u,a),ig(i,a,n)}var Ore={kernelName:hi,backendName:"webgpu",kernelFunc:G2},nr=e=>{switch(e){case 1:return"f32";case 2:return"vec2<f32>";case 3:return"vec3<f32>";case 4:return"vec4<f32>";default:throw new Error(`innerElementSize ${e} is not supported.`)}};function Pre(e,t,n,s,r=!1,a=null,o=!1,i=4,u=4,l=4){let c=D=>{switch(D){case 1:return"resData = x[xIndex];";case 3:return"resData = vec3<f32>(x[xIndex], x[xIndex + 1], x[xIndex + 2]);";case 4:return"resData = x[xIndex / 4];";default:throw new Error(`innerElementSize ${D} is not supported.`)}},p=D=>{switch(D){case 1:return"return W[row * uniforms.wShape[3] + colIn];";case 4:return"return W[row * uniforms.wShape[3] / 4 + colIn];";default:throw new Error(`innerElementSize ${D} is not supported.`)}},d=e?`
let coord = vec4<i32>(batch, xRow, xCol, xCh);
`:`
let coord = vec4<i32>(batch, xCh, xRow, xCol);
`,h=e?`
let outCoord = vec4<i32>(
batch,
row / outWidth,
row % outWidth,
col);
`:`
let outCoord = vec4<i32>(
batch,
row,
col / outWidth,
col % outWidth);
`,f=e?"uniforms.xShape[1]":"uniforms.xShape[2]",m=e?"uniforms.xShape[2]":"uniforms.xShape[3]",g=e?"row":"col",b=e?"col":"row",y=`
let inChannels = uniforms.wShape[2];
let outWidth = ${e?"uniforms.outShape[2]":"uniforms.outShape[3]"};
let outRow = ${g} / outWidth;
let outCol = ${g} % outWidth;
let WRow = ${b} / (uniforms.filterDims[1] * inChannels);
let WCol = ${b} / inChannels % uniforms.filterDims[1];
let xRow = outRow * uniforms.stride[0] + uniforms.dilation[0] * WRow - uniforms.pad[0];
let xCol = outCol * uniforms.stride[1] + uniforms.dilation[1] * WCol - uniforms.pad[1];
let xCh = ${b} % inChannels;
var resData = ${nr(i)}(0.0);
// The bounds checking is always needed since we use it to pad zero for
// the 'same' padding type.
if (xRow >= 0 && xRow < ${f} && xCol >= 0 && xCol < ${m}) {
${d}
let xIndex = getIndexFromCoords4D(coord, uniforms.xShape);
${c(i)}
}
return resData;`,v=e?t&&s?`
let col = colIn * ${i};
${y}`:`
let col = colIn * ${i};
if (row < uniforms.dimAOuter && col < uniforms.dimInner) {
${y}
}
return ${nr(i)}(0.0);`:s&&n?`
let col = colIn * ${i};
${y}`:`
let col = colIn * ${i};
if (row < uniforms.dimInner && col < uniforms.dimBOuter) {
${y}
}
return ${nr(i)}(0.0);`,x=`${p(u)}`,k=nr(l),I=nr(e?i:u),$=nr(e?u:i),R="",E="";if(a){let D=Io(a,l===4);o?R=`fn activation(a: ${k}, outCoord : vec4<i32>) -> ${k} {
let b = getPreluActivationWeightsByOutputCoords(outCoord);
${D}
}`:R=`
fn activation(a : ${k}, outCoord : vec4<i32>) -> ${k} {
${D}
}`,E="value = activation(value, outCoord);"}return`
${R}
fn mm_readA(row : i32, colIn : i32, globalId : vec3<u32>) -> ${I} {
var batch = i32(globalId.z);
${e?v:x}
}
fn mm_readB(row : i32, colIn : i32, globalId : vec3<u32>) -> ${$} {
var batch = i32(globalId.z);
${e?x:v}
}
fn mm_write(row : i32, colIn : i32, valueIn : ${k}, globalId : vec3<u32>) {
var col = colIn * ${l};
if (row < uniforms.dimAOuter && col < uniforms.dimBOuter)
{
var batch = i32(globalId.z);
var value = valueIn;
let outWidth = ${e?"uniforms.outShape[2]":"uniforms.outShape[3]"};
${h}
${r?"value = value + getBiasByOutputCoords(outCoord);":""}
${E}
setOutputAtCoords(outCoord[0], outCoord[1], outCoord[2], outCoord[3], value);
}
}`}var zre=class{constructor(e,t,n,s,r=!1,a=null,o=!1,i=!1){this.variableNames=["x","W"],this.uniforms="filterDims : vec2<i32>, pad : vec2<i32>, stride : vec2<i32>, dilation : vec2<i32>, dimAOuter : i32, dimBOuter : i32, dimInner : i32,",this.outputShape=e.outShape,this.isChannelsLast=e.dataFormat==="channelsLast",this.isVec4=i,this.dispatchLayout=this.isChannelsLast?{x:[3],y:[1,2],z:[0]}:{x:[2,3],y:[1],z:[0]},this.workGroupSize=Ov(this.dispatchLayout,this.outputShape,this.isVec4),this.elementsPerThread=Pv(this.dispatchLayout,this.outputShape,this.isVec4),this.dispatch=_e(this.dispatchLayout,this.outputShape,this.workGroupSize,this.elementsPerThread),this.innerElementSize=this.isVec4?e.inChannels%4===0?4:3:this.elementsPerThread[0],this.isVec4&&(this.variableTypes=this.innerElementSize===3?["f32","vec4<f32>"]:["vec4<f32>","vec4<f32>"]),r&&(this.variableNames.push("bias"),this.isVec4&&this.variableTypes.push("vec4<f32>")),o&&(this.variableNames.push("preluActivationWeights"),this.isVec4&&this.variableTypes.push("vec4<f32>")),this.addBias=r,this.activation=a,this.hasPreluActivationWeights=o,this.tileAOuter=this.workGroupSize[1]*this.elementsPerThread[1],this.tileBOuter=this.workGroupSize[0]*this.elementsPerThread[0],this.tileInner=Math.max(this.workGroupSize[0]*this.innerElementSize,this.workGroupSize[1]),this.fitAOuter=t%this.tileAOuter===0,this.fitBOuter=n%this.tileBOuter===0,this.fitInner=s%this.tileInner===0,this.shaderKey=`conv2DMM_${this.elementsPerThread}_${this.activation}}_${this.fitAOuter}_${this.fitBOuter}_${this.fitInner}_${this.isVec4}_${this.innerElementSize}_${this.isChannelsLast}`}getUserCode(){let e=this.isVec4?z2(this.elementsPerThread,this.tileAOuter,this.tileBOuter,this.tileInner,this.innerElementSize,!this.isChannelsLast):Mv(this.elementsPerThread,this.workGroupSize,!this.isChannelsLast,this.tileInner),t=this.isVec4?[this.isChannelsLast?this.innerElementSize:4,4,4]:[1,1,1];return`
${Pre(this.isChannelsLast,this.fitAOuter,this.fitBOuter,this.fitInner,this.addBias,this.activation,this.hasPreluActivationWeights,t[0],t[1],t[2])}
${e}
`}};function Gw(e,t){let n=e.length;return n>=3?t?[...e.slice(0,-3),e[n-3]*e[n-2],e[n-1]]:[...e.slice(0,-3),e[n-3],e[n-2]*e[n-1]]:!t&&n===1&&e[0]>1?[e[0],1]:null}function Lre({x:e,filter:t,convInfo:n,backend:s,bias:r=null,preluActivationWeights:a=null,leakyreluAlpha:o=0,activation:i=null}){let u=n.dataFormat==="channelsLast",l=!u,c=!1,p=u&&n.filterHeight===n.inHeight&&n.filterWidth===n.inWidth&&n.padInfo.type==="VALID",d=[],h,f;if(p){let b=n.inHeight*n.inWidth*n.inChannels;h=Le({inputs:{x:e},backend:s,attrs:{shape:[1,n.batchSize,b]}}),f=Le({inputs:{x:t},backend:s,attrs:{shape:[1,b,n.outChannels]}})}else h=Le({inputs:{x:e},backend:s,attrs:{shape:u?[n.batchSize,n.inHeight*n.inWidth,n.inChannels]:[n.batchSize,n.inChannels,n.inHeight*n.inWidth]}}),f=Le({inputs:{x:t},backend:s,attrs:{shape:[1,n.inChannels,n.outChannels]}});if(d.push(h),d.push(f),a!=null){let b=Gw(a.shape,u);b!=null&&(a=Le({inputs:{x:a},backend:s,attrs:{shape:b}}),d.push(a))}if(r!=null){let b=Gw(r.shape,u);b!=null&&(r=Le({inputs:{x:r},backend:s,attrs:{shape:b}}),d.push(r))}let m=Bv({a:u?h:f,b:u?f:h,transposeA:l,transposeB:c,backend:s,bias:r,activation:i,preluActivationWeights:a,leakyreluAlpha:o}),g=Le({inputs:{x:m},backend:s,attrs:{shape:n.outShape}});d.push(m);for(let b of d)s.disposeData(b.dataId);return g}function H2({x:e,filter:t,convInfo:n,backend:s,bias:r=null,preluActivationWeights:a=null,leakyreluAlpha:o=0,activation:i=null}){let u=r!=null,l=a!=null,c=n.dataFormat==="channelsLast";if(c&&n.filterHeight===n.inHeight&&n.filterWidth===n.inWidth&&n.padInfo.type==="VALID"||n.filterHeight===1&&n.filterWidth===1&&n.dilationHeight===1&&n.dilationWidth===1&&n.strideHeight===1&&n.strideWidth===1&&(n.padInfo.type==="SAME"||n.padInfo.type==="VALID"))return Lre({x:e,filter:t,convInfo:n,backend:s,bias:r,activation:i,preluActivationWeights:a,leakyreluAlpha:o});let d=((n.inChannels%4===0||n.inChannels%3===0)&&c||n.outWidth%4===0&&!c)&&n.outChannels%4===0,h=c?n.outHeight*n.outWidth:n.outChannels,f=c?n.outChannels:n.outHeight*n.outWidth,m=n.filterHeight*n.filterWidth*n.inChannels,g=[n.padInfo.top,n.padInfo.left],b=[{type:"int32",data:[n.filterHeight,n.filterWidth]},{type:"int32",data:[...g]},{type:"int32",data:[n.strideHeight,n.strideWidth]},{type:"int32",data:[n.dilationHeight,n.dilationWidth]},{type:"int32",data:[h]},{type:"int32",data:[f]},{type:"int32",data:[m]}],y=new zre(n,h,f,m,u,i,l,d),v=[],x=[e,t];u&&(!c&&r.shape.length===1&&(r=Le({inputs:{x:r},backend:s,attrs:{shape:[r.shape[0],1,1]}}),v.push(r)),x.push(r)),l&&(!c&&a.shape.length===1&&(a=Le({inputs:{x:a},backend:s,attrs:{shape:[a.shape[0],1,1]}}),v.push(a)),x.push(a)),i==="leakyrelu"&&(b.push({type:"float32",data:[o]}),y.uniforms+=" alpha : f32,");let k=s.runWebGPUProgram(y,x,e.dtype,b);for(let I of v)s.disposeData(I.dataId);return k}function Mre(e){let{inputs:t,attrs:n,backend:s}=e,{x:r,filter:a}=t,{strides:o,pad:i,dataFormat:u,dilations:l,dimRoundingMode:c}=n,p=C.convertConv2DDataFormat(u),d=C.computeConv2DInfo(r.shape,a.shape,o,l,i,c,!1,p);return H2({x:r,filter:a,convInfo:d,backend:s})}var Bre={kernelName:_a,backendName:"webgpu",kernelFunc:Mre},Vre=class{constructor(e){this.variableNames=["x","W"],this.uniforms="filterDims : vec2<i32>, pads : vec2<i32>, stride : vec2<i32>, outBackprop : vec4<i32>, dimAOuter : i32, dimBOuter : i32, dimInner : i32,",this.outputShape=e.inShape,w.assert(e.dataFormat==="channelsLast",()=>"TODO: NCHW is unimplemented"),this.dispatchLayout={x:[3],y:[1,2],z:[0]},this.workGroupSize=Ov(this.dispatchLayout,this.outputShape),this.elementsPerThread=Pv(this.dispatchLayout,this.outputShape),this.dispatch=_e(this.dispatchLayout,this.outputShape,this.workGroupSize,this.elementsPerThread),this.shaderKey=`conv2DDerInputMM_${this.elementsPerThread}`}getUserCode(){return`
fn mm_readA(row : i32, col : i32, globalId : vec3<u32>) -> f32 {
var batch = i32(globalId.z);
if (row < uniforms.dimAOuter && col < uniforms.dimInner) {
let outRow = row / uniforms.outShape[2];
let outCol = row % uniforms.outShape[2];
let WRow = col / (uniforms.filterDims[1] * uniforms.outBackprop[3]);
let WCol = col / uniforms.outBackprop[3] % uniforms.filterDims[1];
let xR = f32(outRow - uniforms.pads[0] + WRow) / f32(uniforms.stride[0]);
let xC = f32(outCol - uniforms.pads[1] + WCol) / f32(uniforms.stride[1]);
if (xR < 0.0 || xR >= f32(uniforms.outBackprop[1]) || fract(xR) > 0.0) {
return 0.0;
}
if (xC < 0.0 || xC >= f32(uniforms.outBackprop[2]) || fract(xC) > 0.0) {
return 0.0;
}
let coord = vec4<i32>(
batch,
i32(xR),
i32(xC),
col % uniforms.outBackprop[3]);
return x[getIndexFromCoords4D(coord, uniforms.xShape)];
}
return 0.0;
}
fn mm_readB(row : i32, col : i32, globalId : vec3<u32>) -> f32 {
let coordX = uniforms.filterDims.x - 1 -
row / (uniforms.filterDims[1] * uniforms.outBackprop[3]);
let coordY = uniforms.filterDims.y - 1 -
(row / uniforms.outBackprop[3]) % uniforms.filterDims[1];
if (row < uniforms.dimInner && col < uniforms.dimBOuter &&
coordX >= 0 && coordY >= 0) {
let coord = vec4<i32>(coordX, coordY, col,
row % uniforms.outBackprop[3]);
return W[getIndexFromCoords4D(coord, uniforms.wShape)];
}
return 0.0;
}
fn mm_write(row : i32, col : i32, valueInput : f32, globalId : vec3<u32>) {
if (row < uniforms.dimAOuter && col < uniforms.dimBOuter)
{
var batch = i32(globalId.z);
var value = valueInput;
let outCoord = vec4<i32>(
batch,
row / uniforms.outShape[2],
row % uniforms.outShape[2],
col);
result[getIndexFromCoords4D(outCoord, uniforms.outShape)] = value;
}
}
${Mv(this.elementsPerThread,this.workGroupSize)}
`}},Wre=class{constructor(e){this.variableNames=["dy","W"],this.uniforms="filterDims : vec2<i32>, pads : vec2<i32>, stride : vec2<i32>, outBackprop : vec4<i32>,",this.workGroupSize=[64,1,1],this.size=!0,this.outputShape=e.inShape,this.dispatchLayout=Ve(this.outputShape),this.dispatch=_e(this.dispatchLayout,this.outputShape,this.workGroupSize),this.isChannelsLast=e.dataFormat==="channelsLast",this.shaderKey=`conv2DDerInput_${this.isChannelsLast}`}getUserCode(){let e=this.isChannelsLast?1:2,t=this.isChannelsLast?2:3,n=this.isChannelsLast?3:1;return`
${Ue()} {
if(index < uniforms.size) {
let coords = getCoordsFromIndex(index);
let batch = coords[0];
let d1 = coords[${n}];
let dyCorner = vec2<i32>(coords[${e}]), coords[${t}]) - uniforms.pads;
let dyRCorner = dyCorner.x;
let dyCCorner = dyCorner.y;
// Convolve dy(?, ?, d2) with w(:, :, d1, d2) to compute dx(xR, xC, d1).
// ? = to be determined. : = across all values in that axis.
var dotProd = 0.0;
for (var wR = 0; wR < uniforms.filterDims.x; wR = wR + 1) {
let dyR = (f32(dyRCorner) + f32(wR)) / f32(uniforms.stride.x);
let wRPerm = uniforms.filterDims.x - 1 - wR;
if (dyR < 0.0 || dyR >= f32(uniforms.outBackprop[1]) || fract(dyR) > 0.0 ||
wRPerm < 0) {
continue;
}
let idyR = dyR;
for (var wC = 0; wC < uniforms.filterDims.y; wC = wC + 1) {
let dyC = (f32(dyCCorner) + f32(wC)) / f32(uniforms.stride.y);
let wCPerm = uniforms.filterDims.y - 1 - wC;
if (dyC < 0.0 || dyC >= f32(uniforms.outBackprop[2]) ||
fract(dyC) > 0.0 || wCPerm < 0) {
continue;
}
let idyC = dyC;
for (var d2 = 0; d2 < uniforms.outBackprop[3]; d2 = d2 + 1) {
if (${this.isChannelsLast}) {
let xValue = getDy(batch, idyR, idyC, d2);
let wValue = getW(wRPerm, wCPerm, d1, d2);
dotProd = dotProd + xValue * wValue;
} else {
let xValue = getDy(batch, d2, idyR, idyC);
let wValue = getW(wRPerm, wCPerm, d1, d2);
dotProd = dotProd + xValue * wValue;
}
}
}
}
setOutputAtIndex(index, dotProd);
}
}
`}};function Ure(e){let{inputs:t,backend:n,attrs:s}=e,{dy:r,filter:a}=t,{inputShape:o,strides:i,pad:u,dataFormat:l,dimRoundingMode:c}=s,p=C.convertConv2DDataFormat(l),d=C.computeConv2DInfo(o,a.shape,i,1,u,c,!1,p),h=[{type:"int32",data:[d.filterHeight,d.filterWidth]},{type:"int32",data:[d.filterHeight-1-d.padInfo.top,d.filterWidth-1-d.padInfo.left]},{type:"int32",data:[d.strideHeight,d.strideWidth]},{type:"int32",data:[d.batchSize,d.outHeight,d.outWidth,d.outChannels]}],f;if(K().getBool("WEBGPU_USE_NAIVE_CONV2D_TRANSPOSE"))f=new Wre(d);else{f=new Vre(d);let m=d.inShape[1]*d.inShape[2],g=d.inShape[3],b=d.filterHeight*d.filterWidth*d.outChannels;h.push({type:"uint32",data:[m]},{type:"uint32",data:[g]},{type:"uint32",data:[b]})}return n.runWebGPUProgram(f,[r,a],"float32",h)}var Gre={kernelName:Aa,backendName:"webgpu",kernelFunc:Ure},Hre=Kt({opType:2}),qre={kernelName:Ea,backendName:"webgpu",kernelFunc:Hre},jre=Kt({opType:3}),Kre={kernelName:Ra,backendName:"webgpu",kernelFunc:jre},Xre=class{constructor(e,t,n,s){this.variableNames=["Image","Boxes","BoxInd"],this.uniforms="extrapolationValue : f32,",this.workGroupSize=[64,1,1],this.size=!0;let[r]=t;this.outputShape=[r,n[0],n[1],e],this.dispatchLayout=Ve(this.outputShape),this.dispatch=_e(this.dispatchLayout,this.outputShape,this.workGroupSize),this.methodId=s==="bilinear"?1:0,this.cropHeightBiggerThan1=this.outputShape[1]>1,this.cropWidthBiggerThan1=this.outputShape[2]>1,this.shaderKey=`cropAndResize_${this.methodId}_${this.cropHeightBiggerThan1}_${this.cropWidthBiggerThan1}`}getUserCode(){let[e,t]=["f32(uniforms.imageShape[1] - 1)","f32(uniforms.imageShape[2] - 1)"],[n,s,r]=this.cropHeightBiggerThan1?[`(${e} / f32(uniforms.outShape[1] - 1))`,"(y2-y1) * height_ratio",`y1*${e} + f32(y)*(height_scale)`]:["0.0","0.0",`0.5 * (y1+y2) * ${e}`],[a,o,i]=this.cropWidthBiggerThan1?[`(${t} / f32(uniforms.outShape[2] - 1))`,"(x2-x1) * width_ratio",`x1*${t} + f32(x)*(width_scale)`]:["0.0","0.0",`0.5 * (x1+x2) * ${t}`];return`
${Ue()}
if (index < uniforms.size) {
let coords = getCoordsFromIndex(index);
let height_ratio = f32(${n});
let width_ratio = f32(${a});
let b = coords[0];
let y = coords[1];
let x = coords[2];
let d = coords[3];
// get box vals
let y1 = getBoxes(b, 0);
let x1 = getBoxes(b, 1);
let y2 = getBoxes(b, 2);
let x2 = getBoxes(b, 3);
// get image in batch index
let bInd = i32(round(getBoxInd(b)));
if(bInd < 0 || bInd >= uniforms.outShape[0]) {
return;
}
let height_scale = ${s};
let width_scale = ${o};
let in_y = ${r};
if( in_y < 0.0 || in_y > ${e} ) {
setOutputAtIndex(index, uniforms.extrapolationValue);
return;
}
let in_x = ${i};
if( in_x < 0.0 || in_x > ${t} ) {
setOutputAtIndex(index, uniforms.extrapolationValue);
return;
}
let sourceFracIndexCR = vec2<f32>(in_x,in_y);
if(${this.methodId} == 1) {
// Compute the four integer indices.
let sourceFloorCR = vec2<i32>(sourceFracIndexCR);
let sourceCeilCR = vec2<i32>(ceil(sourceFracIndexCR));
let topLeft = getImage(bInd, sourceFloorCR.y, sourceFloorCR.x, d);
let bottomLeft = getImage(bInd, sourceCeilCR.y, sourceFloorCR.x, d);
let topRight = getImage(bInd, sourceFloorCR.y, sourceCeilCR.x, d);
let bottomRight = getImage(bInd, sourceCeilCR.y, sourceCeilCR.x, d);
let fracCR = sourceFracIndexCR - vec2<f32>(sourceFloorCR);
let top = topLeft + (topRight - topLeft) * fracCR.x;
let bottom = bottomLeft + (bottomRight - bottomLeft) * fracCR.x;
let newValue = top + (bottom - top) * fracCR.y;
setOutputAtIndex(index, newValue);
} else {
// Compute the coordinators of nearest neighbor point.
let sourceNearestCR = vec2<i32>(floor(
sourceFracIndexCR + vec2<f32>(0.5,0.5)));
let newValue = getImage(
bInd, sourceNearestCR.y, sourceNearestCR.x, d);
setOutputAtIndex(index, newValue);
}
}
}
`}},Yre=e=>{let{inputs:t,backend:n,attrs:s}=e,{image:r,boxes:a,boxInd:o}=t,{cropSize:i,method:u,extrapolationValue:l}=s,c=new Xre(r.shape[3],a.shape,i,u),p=[{type:"float32",data:[l]}];return n.runWebGPUProgram(c,[r,a,o],"float32",p)},Qre={kernelName:mi,backendName:"webgpu",kernelFunc:Yre},Hw=class{constructor(e,t,n,s){this.variableNames=["x"],this.uniforms="index : f32,",this.size=!0;let r=128;this.workGroupSize=[r,1,1],this.outputShape=t,this.dispatchLayout=Ve(this.outputShape),this.dispatch=_e(this.dispatchLayout,this.outputShape,this.workGroupSize),this.exclusive=n,this.reverse=s,this.op=e,this.shaderKey=`cum_${this.op}_${this.exclusive}_${this.reverse}`}getUserCode(){let e=this.outputShape.length,t=this.op==="*"?"1.0":"0.0",n=this.exclusive?t:`getX(${qw(e,"coords",this.op)})`,s=this.outputShape[this.outputShape.length-1],r="",a="";return this.exclusive?(r=this.reverse?`end != ${s-1}`:"end != 0",a=this.reverse?"end + 1":"end - 1"):(r=this.reverse?`end + pow2 < ${s}`:"end >= pow2",a=this.reverse?"end + pow2":"end - pow2"),`
${Ue()}
if (index < uniforms.size) {
var coords = getCoordsFromIndex(index);
let end = ${jw(e,"coords",this.op)};
var val = ${n};
let pow2 = i32(pow(2.0, uniforms.index));
if (${r}) {
let idx = ${a};
${jw(e,"coords",this.op)} = idx;
val ${this.op}= getX(${qw(e,"coords",this.op)});
}
setOutputAtIndex(index, val);
}
}
`}};function qw(e,t,n){if(e===1)return`${t}`;if(e===2)return`${t}.x, ${t}.y`;if(e===3)return`${t}.x, ${t}.y, ${t}.z`;if(e===4)return`${t}.x, ${t}.y, ${t}.z, ${t}.w`;throw Error(`Cumulative ${n} for rank ${e} is not yet supported`)}function jw(e,t,n){if(e===1)return`${t}`;if(e===2)return`${t}.y`;if(e===3)return`${t}.z`;if(e===4)return`${t}.w`;throw Error(`Cumulative ${n} for rank ${e} is not yet supported`)}function q2(e,t,n,s,r,a){let o=t.shape.length,i=C.getAxesPermutation([s],o),u=t;i!=null&&(u=Ks({inputs:{x:t},backend:n,attrs:{perm:i}}));let l=C.getInnerMostAxes(1,o)[0];if(l!==o-1)throw new Error(`WebGPU cumprod shader expects an inner-most axis=${t.shape.length-1} but got axis=${s}`);let c=u.shape[l],p=Un({inputs:{x:u},backend:n});for(let d=0;d<=Math.ceil(Math.log2(c))-1;d++){let h=new Hw(e,u.shape,!1,a),f=p,m=[{type:"float32",data:[d]}];p=n.runWebGPUProgram(h,[p],p.dtype,m),n.disposeData(f.dataId)}if(r){let d=new Hw(e,u.shape,r,a),h=p,f=[{type:"float32",data:[0]}];p=n.runWebGPUProgram(d,[p],p.dtype,f),n.disposeData(h.dataId)}if(i!=null){let d=C.getUndoAxesPermutation(i),h=Ks({inputs:{x:p},backend:n,attrs:{perm:d}});return n.disposeData(p.dataId),n.disposeData(u.dataId),h}return p}function Zre(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{axis:a,exclusive:o,reverse:i}=s;return q2("*",r,n,a,o,i)}var Jre={kernelName:fi,backendName:"webgpu",kernelFunc:Zre};function eae(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{axis:a,exclusive:o,reverse:i}=s;return q2("+",r,n,a,o,i)}var tae={kernelName:Da,backendName:"webgpu",kernelFunc:eae},nae=class{constructor(e,t){this.variableNames=["x"],this.workGroupSize=[64,1,1],this.size=!0,this.uniforms="blockSize : i32,",this.outputShape=e,this.dispatchLayout=Ve(this.outputShape),this.dispatch=_e(this.dispatchLayout,this.outputShape,this.workGroupSize),this.shaderKey=`depthToSpace_${t}`,this.dataFormat=t}getUserCode(){return`
${Ue()}
if (index < uniforms.size) {
let coords = getCoordsFromIndex(index);
let b = coords[0];
let h = ${this.getHeightCoordString()};
let w = ${this.getWidthCoordString()};
let d = ${this.getDepthCoordString()};
let in_h = h / uniforms.blockSize;
let offset_h = h % uniforms.blockSize;
let in_w = w / uniforms.blockSize;
let offset_w = w % uniforms.blockSize;
let offset_d = (offset_h * uniforms.blockSize + offset_w) *
${this.getOutputDepthSize()};
let in_d = d + offset_d;
let rlt = ${this.getInputSamplingString()};
setOutputAtIndex(index, rlt);
}
}`}getHeightCoordString(){return this.dataFormat==="NHWC"?"coords[1]":"coords[2]"}getWidthCoordString(){return this.dataFormat==="NHWC"?"coords[2]":"coords[3]"}getDepthCoordString(){return this.dataFormat==="NHWC"?"coords[3]":"coords[1]"}getOutputDepthSize(){return this.dataFormat==="NHWC"?"uniforms.outShape[3]":"uniforms.outShape[1]"}getInputSamplingString(){return this.dataFormat==="NHWC"?"getX(b, in_h, in_w, in_d)":"getX(b, in_d, in_h, in_w)"}};function sae(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{blockSize:a,dataFormat:o}=s,i=r.shape[0],u=o==="NHWC"?r.shape[1]:r.shape[2],l=o==="NHWC"?r.shape[2]:r.shape[3],c=o==="NHWC"?r.shape[3]:r.shape[1],p=u*a,d=l*a,h=c/(a*a),f=o==="NHWC"?[i,p,d,h]:[i,h,p,d],m=[{type:"int32",data:[a]}],g=new nae(f,o);return n.runWebGPUProgram(g,[r],r.dtype,m)}var rae={kernelName:gi,backendName:"webgpu",kernelFunc:sae},j2=class{constructor(e,t=!1,n=null,s=!1){this.variableNames=["x","W"],this.uniforms="pad : vec2<i32>, stride : vec2<i32>, dilation : vec2<i32>, inDims : vec2<i32>,",this.workGroupSize=[4,4,4],this.isVec4=!0,this.outputShape=e.outShape,this.dispatchLayout={x:[0,1],y:[2],z:[3]},this.dispatch=_e(this.dispatchLayout,this.outputShape,this.workGroupSize,[1,4,4]),w.assert(e.dataFormat==="channelsLast",()=>"TODO: NCHW is unimplemented"),t&&this.variableNames.push("bias"),s&&this.variableNames.push("preluActivationWeights"),this.convInfo=e,this.addBias=t,this.activation=n,this.hasPreluActivation=s,this.shaderKey=`depthwise3x3_${n}`}getUserCode(){let e="",t="";if(this.activation){let r=Io(this.activation,this.isVec4);this.hasPreluActivation?e=`fn activation(a : vec4<f32>, outCoord : vec4<i32>) -> vec4<f32> {
let b = getPreluActivationWeightsByOutputCoords(outCoord);
${r}
}`:e=`
fn activation(a : vec4<f32>, outCoord : vec4<i32>) -> vec4<f32> {
${r}
}
`,t="dotProd[i] = activation(dotProd[i], coords);"}let n=this.addBias?"dotProd[i] = dotProd[i] + getBiasByOutputCoords(coords);":"";return`
${e}
${Lv()}
fn main(@builtin(global_invocation_id) globalId: vec3<u32>) {
let batch = 0;
let r = i32(globalId.x);
let c = i32(globalId.y) * 4;
let d2 = i32(globalId.z) * 4;
let xRCCorner = vec2<i32>(r, c) * uniforms.stride - uniforms.pad;
let d1 = d2;
let q = 0;
let xRCorner = xRCCorner.x;
let xCCorner = xRCCorner.y;
var wVals : array<vec4<f32>, 9>;
wVals[0] = getW(0, 0, d1, q);
wVals[1] = getW(0, 1, d1, q);
wVals[2] = getW(0, 2, d1, q);
wVals[3] = getW(1, 0, d1, q);
wVals[4] = getW(1, 1, d1, q);
wVals[5] = getW(1, 2, d1, q);
wVals[6] = getW(2, 0, d1, q);
wVals[7] = getW(2, 1, d1, q);
wVals[8] = getW(2, 2, d1, q);
var xVals : array<array<vec4<f32>, 6>, 3>;
for (var wR = 0; wR < 3; wR = wR + 1) {
let xR = xRCorner + wR * uniforms.dilation[0];
for (var wC = 0; wC < 6; wC = wC + 1) {
let xC = xCCorner + wC * uniforms.dilation[1];
if (xR < 0 || xR >= uniforms.inDims[0] || xC < 0 || xC >= uniforms.inDims[1]) {
xVals[wR][wC] = vec4<f32>(0.0);
} else {
xVals[wR][wC] = getX(batch, xR, xC, d1);
}
}
}
var dotProd : array<vec4<f32>, 4>;
dotProd[0] = vec4<f32>(0.0);
dotProd[1] = vec4<f32>(0.0);
dotProd[2] = vec4<f32>(0.0);
dotProd[3] = vec4<f32>(0.0);
for (var wR = 0; wR < 3; wR = wR + 1) {
for (var wC = 0; wC < 3; wC = wC + 1) {
let indexW = wR * 3 + wC;
dotProd[0] = dotProd[0] + xVals[wR][0 + wC] * wVals[indexW];
dotProd[1] = dotProd[1] + xVals[wR][1 + wC] * wVals[indexW];
dotProd[2] = dotProd[2] + xVals[wR][2 + wC] * wVals[indexW];
dotProd[3] = dotProd[3] + xVals[wR][3 + wC] * wVals[indexW];
}
}
for (var i = 0; i < 4; i = i + 1) {
let coords = vec4<i32>(batch, r, c + i, d2);
if (coordsInBounds4D(coords, uniforms.outShape)) {
${n}
${t}
setOutputAtCoords(coords[0], coords[1], coords[2], coords[3], dotProd[i]);
}
}
}
`}},K2=class{constructor(e,t=!1,n=null,s=!1){this.variableNames=["x","W"],this.uniforms=`pad : vec2<i32>, stride : vec2<i32>, dilation : vec2<i32>,
inDims : vec2<i32>, filterHeight : i32, filterWidth : i32,
channelMul : i32,`,this.workGroupSize=[256,1,1],this.outputShape=e.outShape,this.dispatchLayout=Ve(this.outputShape),this.dispatch=_e(this.dispatchLayout,this.outputShape,this.workGroupSize),w.assert(e.dataFormat==="channelsLast",()=>"TODO: NCHW is unimplemented"),t&&this.variableNames.push("bias"),s&&this.variableNames.push("preluActivationWeights"),this.convInfo=e,this.addBias=t,this.activation=n,this.hasPreluActivation=s,this.shaderKey=`depthwise_${this.activation}`}getUserCode(){let e="",t="";if(this.activation){let r=Io(this.activation,!1);this.hasPreluActivation?e=`fn activation(a : f32, outCoord : vec4<i32>) -> f32 {
let b = getPreluActivationWeightsByOutputCoords(outCoord);
${r}
}`:e=`
fn activation(a : f32, outCoord : vec4<i32>) -> f32 {
${r}
}
`,t="dotProd = activation(dotProd, coords);"}let n=this.addBias?"dotProd = dotProd + getBiasByOutputCoords(coords);":"";return`
${e}
fn writeResult(batch : i32, row : i32, col : i32, chan : i32,
value : f32) {
let coord = vec4<i32>(batch, row, col, chan);
if (coordsInBounds4D(coord, uniforms.outShape)) {
setOutputAtCoords(batch, row, col, chan, value);
}
}
${ac()}
let coords = getOutputCoords();
let batch = coords[0];
let xRCCorner = vec2<i32>(coords.yz) * uniforms.stride - uniforms.pad;
let d2 = coords[3];
let d1 = d2 / uniforms.channelMul;
let q = d2 - d1 * uniforms.channelMul;
let inputRowStart = xRCCorner.x;
let inputColStart = xRCCorner.y;
let inputRowEnd = inputRowStart + uniforms.filterHeight *
uniforms.dilation[0];
let inputColEnd = inputColStart + uniforms.filterWidth *
uniforms.dilation[1];
// Convolve x(?, ?, d1) with w(:, :, d1, q) to get y(yR, yC, d2).
// ? = to be determined. : = across all values in that axis.
var dotProd = 0.0;
// Extract if checking out of for loop for performance.
if (inputRowStart >= 0 && inputColStart >= 0 &&
inputRowEnd < uniforms.inDims[0] &&
inputColEnd < uniforms.inDims[1]) {
// Here using a constant value |this.convInfo.filterHeight| instead
// of uniform value is in order to loop unrolling.
for (var wR = 0; wR < uniforms.filterHeight; wR = wR + 1) {
let xR = inputRowStart + wR * uniforms.dilation[0];
for (var wC = 0; wC < uniforms.filterWidth; wC = wC + 1) {
let xC = inputColStart + wC * uniforms.dilation[1];
let xVal = getX(batch, xR, xC, d1);
let wVal = getW(wR, wC, d1, q);
dotProd = dotProd + xVal * wVal;
}
}
} else {
for (var wR = 0; wR < uniforms.filterHeight; wR = wR + 1) {
let xR = inputRowStart + wR * uniforms.dilation[0];
if (xR < 0 || xR >= uniforms.inDims[0]) {
continue;
}
for (var wC = 0; wC < uniforms.filterWidth; wC = wC + 1) {
let xC = inputColStart + wC * uniforms.dilation[1];
if (xC < 0 || xC >= uniforms.inDims[1]) {
continue;
}
let xVal = getX(batch, xR, xC, d1);
let wVal = getW(wR, wC, d1, q);
dotProd = dotProd + xVal * wVal;
}
}
}
${n}
${t}
writeResult(batch, coords[1], coords[2], d2, dotProd);
}
`}};function aae(e){let{inputs:t,backend:n,attrs:s}=e,{x:r,filter:a}=t,{strides:o,pad:i,dilations:u,dimRoundingMode:l}=s,c=u;c==null&&(c=[1,1]);let p=C.computeConv2DInfo(r.shape,a.shape,o,c,i,l,!0),d=[{type:"int32",data:[p.padInfo.top,p.padInfo.left]},{type:"int32",data:[p.strideHeight,p.strideWidth]},{type:"int32",data:[p.dilationHeight,p.dilationWidth]},{type:"int32",data:[p.inHeight,p.inWidth]}],h;return p.batchSize===1&&p.inHeight===p.outHeight&&p.inWidth===p.outWidth&&p.strideHeight===1&&p.strideWidth===1&&p.filterHeight===p.filterWidth&&p.inChannels===p.outChannels&&p.dilationHeight===1&&p.dilationWidth===1&&p.filterHeight===3&&p.inChannels%4===0?h=new j2(p):(h=new K2(p),d.push({type:"int32",data:[p.filterHeight]},{type:"int32",data:[p.filterWidth]},{type:"int32",data:[p.outChannels/p.inChannels]})),n.runWebGPUProgram(h,[r,a],r.dtype,d)}var oae={kernelName:Fa,backendName:"webgpu",kernelFunc:aae},X2=gn({opSnippet:0,cpuKernelImpl:zse,supportsComplex:!0}),iae={kernelName:Za,backendName:"webgpu",kernelFunc:X2},uae=class{constructor(e,t){this.workGroupSize=[64,1,1],this.variableNames=["x"],this.uniforms="reduceSize : i32,",this.size=!0,this.inputShape=[e.batchSize,e.inSize];let[n]=C.computeOutAndReduceShapes(this.inputShape,[1]);this.outputShape=n.length===0?[1]:n,this.dispatchLayout=Ve(this.outputShape),this.dispatch=_e(this.dispatchLayout,this.outputShape,[1,1,1]),this.reduceType=t,this.shaderKey=`reduce_${t}`}getUserCode(){let e="",t="0.0";this.reduceType==="min"||this.reduceType==="max"?(e=`
if (isnan(candidate)) {
bestValue = uniforms.NAN;
} else if (!isnan(bestValue) && candidate ${this.reduceType==="min"?"<":">"} bestValue)
{ bestValue = candidate; }`,t="f32(x[offset])"):this.reduceType==="sum"||this.reduceType==="mean"?e=" bestValue = bestValue + candidate; ":this.reduceType==="prod"&&(e=" bestValue = bestValue * candidate; ",t="1.0");let n=this.reduceType==="mean"?"setOutputAtIndex(outputIndex, bestValue / f32(uniforms.reduceSize));":"setOutputAtIndex(outputIndex, bestValue);";return`
fn DIV_CEIL(a : u32, b : u32) -> u32 {
return ((a - 1u) / b + 1u);
}
${`
var<workgroup> xBestValues : array<f32, ${this.workGroupSize[0]}>;
`}
fn getOffset(outputIndex : i32) -> i32 {
let outputCoords = getCoordsFromIndex(outputIndex);
let offset = ${this.outputShape.length===1?"outputCoords":"outputCoords[0]"} * uniforms.reduceSize;
return offset;
}
${Ue()}
let outputIndex = index / i32(workGroupSizeX);
let offset = getOffset(outputIndex);
var bestValue = ${t};
let Length = uniforms.reduceSize;
let WorkPerThread = DIV_CEIL(u32(Length), workGroupSizeX);
for (var k = i32(localId.x); k < Length && outputIndex < uniforms.size;
k = k + i32(workGroupSizeX)) {
let candidate = f32(x[offset + k]);
${e}
}
xBestValues[localId.x] = bestValue;
workgroupBarrier();
var reduceSize = min(u32(Length), workGroupSizeX);
for (var currentSize = reduceSize / 2u; reduceSize > 1u;
currentSize = reduceSize / 2u) {
let interval = DIV_CEIL(reduceSize, 2u);
if (localId.x < currentSize) {
let candidate = xBestValues[localId.x + interval];
${e}
xBestValues[localId.x] = bestValue;
}
reduceSize = interval;
workgroupBarrier();
}
if (localId.x == 0u && outputIndex < uniforms.size) {
${n}
}
}
`}};function uc(e,t,n,s,r){let a=e.shape.length,o=[],i=w.parseAxisParam(t,e.shape),u=i,l=C.getAxesPermutation(u,a),c=e;l!=null&&(c=Ks({inputs:{x:e},attrs:{perm:l},backend:r}),u=C.getInnerMostAxes(u.length,a),o.push(c)),C.assertAxesAreInnerMostDims(s,u,a);let[p,d]=C.computeOutAndReduceShapes(c.shape,u),h=p;n&&(h=C.expandShapeToKeepDim(p,i));let f;if((s==="max"||s==="prod")&&r.shouldExecuteOnCPU([c])){let m=r.tensorMap.get(c.dataId).values;switch(s){case"max":let g=Fse(m,w.sizeFromShape(d),h,e.dtype);f=r.makeTensorInfo(h,e.dtype,g);break;case"prod":let{outVals:b,outShape:y,outDtype:v}=Bse(c.shape,c.dtype,m,u);f=r.makeTensorInfo(y,v,b);break;default:throw new Error(`${s} CPU implementation is not yet supported.`)}}else{let m=w.sizeFromShape(d),b=w.sizeFromShape(c.shape)/m,y={windowSize:m,inSize:m,batchSize:b,outSize:1},v=s==="mean"?"float32":yp(e.dtype),x=[{type:"int32",data:[m]}],k=new uae(y,s),I=r.runWebGPUProgram(k,[c],v,x);o.push(I),f=Le({inputs:{x:I},attrs:{shape:h},backend:r})}return o.forEach(m=>r.disposeData(m.dataId)),f}function Vv(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{axis:a,keepDims:o}=s;return uc(r,a,o,"sum",n)}var lae={kernelName:co,backendName:"webgpu",kernelFunc:Vv};function cae(e){let{inputs:t,backend:n,attrs:s}=e,{equation:r}=s,a=t,{allDims:o,summedDims:i,idDims:u}=C.decodeEinsumEquation(r,a.length);C.checkEinsumDimSizes(o.length,u,a);let{path:l,steps:c}=C.getEinsumComputePath(i,u),p=c.length,d=null,h=o.length,f=[];for(let m=0;m<p;++m){for(let g of c[m]){let{permutationIndices:b,expandDims:y}=C.getEinsumPermutation(h,u[g]),v;C.isIdentityPermutation(b)?v=a[g]:(v=Ks({inputs:{x:a[g]},backend:n,attrs:{perm:b}}),f.push(v));let x=v.shape.slice();for(let k=0;k<y.length;++k)x.splice(y[k],0,1);w.arraysEqual(v.shape,x)||(v=Le({inputs:{x:v},backend:n,attrs:{shape:x}}),f.push(v)),d===null?d=v:(d=X2({inputs:{a:v,b:d},backend:n}),f.push(d))}m<p-1&&(l[m]>=0&&(d=Vv({inputs:{x:d},backend:n,attrs:{axis:l[m]-(o.length-h),keepDims:!1}}),f.push(d)),h--)}for(let m of f)m!==d&&n.disposeData(m.dataId);return d}var dae={kernelName:op,backendName:"webgpu",kernelFunc:cae},pae=Kt({opType:4}),hae={kernelName:Pa,backendName:"webgpu",kernelFunc:pae},fae=gn({opSnippet:4,dtype:"bool",cpuKernelImpl:Sse}),mae={kernelName:bi,backendName:"webgpu",kernelFunc:fae},Y2=Kt({opType:5,cpuKernelImpl:Ise,dtype:"float32"}),gae={kernelName:za,backendName:"webgpu",kernelFunc:Y2};function ug(e){let{inputs:t,attrs:n,backend:s}=e,{dim:r}=n,{input:a}=t,o=a.shape.length,i=a.shape.slice(),u=r;return r<0&&(w.assert(-(o+1)<=r,()=>`Axis must be in the interval [${-(o+1)}, ${o}]`),u=o+r+1),i.splice(u,0,1),Le({inputs:{x:a},backend:s,attrs:{shape:i}})}var bae={kernelName:yi,backendName:"webgpu",kernelFunc:ug},yae=Kt({opType:6,cpuKernelImpl:Cse}),vae={kernelName:vi,backendName:"webgpu",kernelFunc:yae},xae=class{constructor(e){this.variableNames=[],this.outputShape=[],this.uniforms="value : f32,",this.workGroupSize=[64,1,1],this.size=!0,this.outputShape=e,this.dispatchLayout=Ve(this.outputShape),this.dispatch=_e(this.dispatchLayout,this.outputShape,this.workGroupSize),this.shaderKey="fill"}getUserCode(){return`
${Ue()}
if (index < uniforms.size) {
setOutputAtIndex(index, uniforms.value);
}
}
`}};function mu(e){let{backend:t,attrs:n}=e,{shape:s,value:r}=n,{dtype:a}=n;if(a=a||w.inferDtype(r),a==="string"){let o=w.getArrayFromDType(a,w.sizeFromShape(s));return o.fill(r),t.makeTensorInfo(s,a,o)}else{let o=new xae(s),i=[{type:"float32",data:[r]}];return t.runWebGPUProgram(o,[],a,i)}}var wae={kernelName:vl,backendName:"webgpu",kernelFunc:mu},kae=class{constructor(e){this.outputShape=[],this.variableNames=["x"],this.workGroupSize=[64,1,1],this.size=!0,this.outputShape=e,this.dispatchLayout=Ve(this.outputShape),this.dispatch=_e(this.dispatchLayout,this.outputShape,this.workGroupSize),this.shaderKey="flipLeftRight"}getUserCode(){return`
${Ue()}
if (index < uniforms.size) {
let coords = getCoordsFromIndex(index);
let coordX = uniforms.xShape[2] - coords[2] - 1;
let outputValue = getX(coords[0], coords[1], coordX, coords[3]);
setOutputAtIndex(index, outputValue);
}
}
`}},Sae={kernelName:xi,backendName:"webgpu",kernelFunc:({inputs:e,backend:t})=>{let{image:n}=e,s=t,r=new kae(n.shape);return s.runWebGPUProgram(r,[n],n.dtype)}},Iae=Kt({opType:7,cpuKernelImpl:Nse}),Cae={kernelName:La,backendName:"webgpu",kernelFunc:Iae},Nae=gn({opSnippet:12,dtype:"int32"}),Tae={kernelName:Ma,backendName:"webgpu",kernelFunc:Nae},$ae=class{constructor(e,t=!1){this.outputShape=[0],this.variableNames=[],this.workGroupSize=[256,1,1],this.outputShape=e,this.dispatchLayout=Ve(this.outputShape),this.dispatch=_e(this.dispatchLayout,this.outputShape,this.workGroupSize),this.useImport=t,this.shaderKey=`fromPixels_${this.useImport}`}getUserCode(){let e=this.useImport?"textureLoad(src, vec2<i32>(coords.yx));":"textureLoad(src, vec2<i32>(coords.yx), 0)";return`
@binding(1) @group(0) var src: ${this.useImport?"texture_external":"texture_2d<f32>"};
${Ue()}
let flatIndexBase = index * uniforms.numChannels;
for (var i = 0; i < uniforms.numChannels; i = i + 1) {
let flatIndex = flatIndexBase + i;
if (flatIndex < uniforms.size) {
let coords = getCoordsFromIndex(flatIndexBase);
let values = ${e};
result[flatIndex] = i32(floor(255.0 * values[i]));
}
}
}
`}},_ae={kernelName:xd,backendName:"webgpu",kernelFunc:Aae},Uo;function Aae(e){let{inputs:t,backend:n,attrs:s}=e,{pixels:r}=t,{numChannels:a}=s;if(r==null)throw new Error("pixels passed to tf.browser.fromPixels() can not be null");let o=typeof HTMLVideoElement!="undefined"&&r instanceof HTMLVideoElement,i=typeof HTMLImageElement!="undefined"&&r instanceof HTMLImageElement,u=typeof HTMLCanvasElement!="undefined"&&r instanceof HTMLCanvasElement||typeof OffscreenCanvas!="undefined"&&r instanceof OffscreenCanvas,l=typeof ImageBitmap!="undefined"&&r instanceof ImageBitmap,[c,p]=o?[r.videoWidth,r.videoHeight]:[r.width,r.height],d=[p,c,a];if(K().getBool("WEBGPU_USE_IMPORT")&&o)return Kw({externalImage:r,backend:n,attrs:s,outShape:d,useImport:!0});if((o||i)&&(Uo==null&&(Uo=document.createElement("canvas").getContext("2d")),Uo.canvas.width=c,Uo.canvas.height=p,Uo.drawImage(r,0,0,c,p),r=Uo.canvas),l||u||o||i)return Kw({externalImage:r,backend:n,attrs:s,outShape:d,useImport:!1});let h=r.data,f=h;if(a!=null&&a!==4){f=new Uint8Array(r.width*r.height*a);let b=h.length,y=0;for(let v=0;v<b;v++)v%4<a&&(f[y++]=h[v])}let m=n.makeTensorInfo(d,"int32"),g=n.tensorMap.get(m.dataId);return g.values=new Int32Array(f),n.maybeReleaseBuffer(m.dataId),n.uploadToGPU(m.dataId),m}function Kw(e){let{externalImage:t,backend:n,attrs:s,outShape:r,useImport:a}=e,{numChannels:o}=s,i=w.sizeFromShape(r),u=w.computeStrides(r),l=new $ae(r,a),c=[{type:"uint32",data:[i]},{type:"uint32",data:[o]},{type:"uint32",data:[...u]},{type:"uint32",data:[...l.dispatch]}];return n.runFromPixelsProgram(l,r,c,a,t)}var Eae=class{constructor(e,t,n,s,r){this.uniforms="varianceEpsilon : f32,",this.workGroupSize=[128,1,1],this.size=!0,this.variableNames=["x","mean","variance"],C.assertAndGetBroadcastShape(e,t),C.assertAndGetBroadcastShape(e,n),this.outputShape=e,this.dispatchLayout=Ve(this.outputShape),this.dispatch=_e(this.dispatchLayout,this.outputShape,this.workGroupSize),s!=null&&(C.assertAndGetBroadcastShape(e,s),this.variableNames.push("offset")),r!=null&&(C.assertAndGetBroadcastShape(e,r),this.variableNames.push("scale")),this.offsetShape=s,this.scaleShape=r,this.shaderKey="batchNorm"}getUserCode(){let e="0.0";this.offsetShape!=null&&(e="getOffsetByOutputIndex(index)");let t="1.0";return this.scaleShape!=null&&(t="getScaleByOutputIndex(index)"),`
${Ue()}
if (index < uniforms.size)
{
let xValue = getXByOutputIndex(index);
let meanValue = getMeanByOutputIndex(index);
let varianValue = getVarianceByOutputIndex(index);
let offsetValue = ${e};
let scaleValue = ${t};
let inv = scaleValue * inverseSqrt(varianValue + f32(uniforms.varianceEpsilon));
setOutputAtIndex(index,dot(vec3<f32>(xValue, -meanValue, offsetValue), vec3<f32>(inv, inv, 1.0)));
}
}
`}},Rae={kernelName:Ba,backendName:"webgpu",kernelFunc:({inputs:e,attrs:t,backend:n})=>{let{x:s,scale:r,offset:a,mean:o,variance:i}=e,{varianceEpsilon:u}=t,l=n,c=[s,o,i],p=null;a!=null&&(p=a.shape,c.push(a));let d=null;r!=null&&(d=r.shape,c.push(r));let h=new Eae(s.shape,o.shape,i.shape,p,d),f=[{type:"float32",data:[u]}];return l.runWebGPUProgram(h,c,s.dtype,f)}};function Dae(e){let{inputs:t,backend:n,attrs:s}=e,{x:r,filter:a,bias:o,preluActivationWeights:i}=t,{strides:u,pad:l,dataFormat:c,dilations:p,dimRoundingMode:d,activation:h,leakyreluAlpha:f}=s,m=C.convertConv2DDataFormat(c),g=C.computeConv2DInfo(r.shape,a.shape,u,p,l,d,!1,m);return H2({x:r,filter:a,convInfo:g,backend:n,bias:o,preluActivationWeights:i,leakyreluAlpha:f,activation:h})}var Fae={kernelName:ia,backendName:"webgpu",kernelFunc:Dae};function Oae(e){let{inputs:t,backend:n,attrs:s}=e,{x:r,filter:a,bias:o,preluActivationWeights:i}=t,{strides:u,pad:l,dilations:c,dimRoundingMode:p,activation:d,leakyreluAlpha:h}=s,f=c;f==null&&(f=[1,1]),w.assert(C.eitherStridesOrDilationsAreOne(u,f),()=>`Error in depthwiseConv2d: Either strides or dilations must be 1. Got strides ${u} and dilations '${f}'`);let m=C.computeConv2DInfo(r.shape,a.shape,u,f,l,p,!0),g=[r,a],b=o!=null,y=i!=null;b&&g.push(o),y&&g.push(i);let v=[{type:"int32",data:[m.padInfo.top,m.padInfo.left]},{type:"int32",data:[m.strideHeight,m.strideWidth]},{type:"int32",data:[m.dilationHeight,m.dilationWidth]},{type:"int32",data:[m.inHeight,m.inWidth]}],x;return m.batchSize===1&&m.inHeight===m.outHeight&&m.inWidth===m.outWidth&&m.strideHeight===1&&m.strideWidth===1&&m.filterHeight===m.filterWidth&&m.inChannels===m.outChannels&&m.dilationHeight===1&&m.dilationWidth===1&&m.filterHeight===3&&m.inChannels%4===0?x=new j2(m,b,d,y):(x=new K2(m,b,d,y),v.push({type:"int32",data:[m.filterHeight]},{type:"int32",data:[m.filterWidth]},{type:"int32",data:[m.outChannels/m.inChannels]})),d==="leakyrelu"&&(v.push({type:"float32",data:[h]}),x.uniforms+=" alpha : f32,"),n.runWebGPUProgram(x,g,"float32",v)}var Pae={kernelName:ua,backendName:"webgpu",kernelFunc:Oae},zae=class{constructor(e,t){this.variableNames=["A","indices"],this.workGroupSize=[64,1,1],this.size=!0,this.outputShape=t,this.dispatchLayout=Ve(this.outputShape),this.dispatch=_e(this.dispatchLayout,this.outputShape,this.workGroupSize),this.shaderKey=`gathernd_${e}`,this.sliceDim=e,this.uniforms=`sliceDim : i32, strides : ${Wt(e)},`}getUserCode(){let e;return this.sliceDim>1?e="uniforms.strides[j]":e="uniforms.strides",`
${Ue()}
if (index < uniforms.size) {
let coords = getCoordsFromIndex(index);
var flattenIndex = 0;
for (var j = 0; j < uniforms.sliceDim; j = j + 1) {
let indexTemp = i32(round(getIndices(coords[0], j)));
let strideNum = ${e};
flattenIndex = flattenIndex + indexTemp * strideNum;
}
setOutputAtIndex(index, getA(flattenIndex, coords[1]));
}
}
`}};function Lae(e){let{inputs:t,backend:n}=e,{params:s,indices:r}=t,a=r.shape,o=a[a.length-1],i=w.sizeFromShape(s.shape),[u,l,c,p]=C.prepareAndValidate(s,r),d=Le({inputs:{x:r},backend:n,attrs:{shape:[l,o]}}),h=Le({inputs:{x:s},backend:n,attrs:{shape:[w.sizeFromShape(s.shape)/c,c]}});if(n.shouldExecuteOnCPU([s,r])||s.dtype==="string"){let y=n.readSync(r.dataId),v=n.bufferSync(s),x=Tse(y,v,s.dtype,l,o,c,p,s.shape,i);return n.makeTensorInfo(u,s.dtype,x.values)}let f=new zae(o,[l,c]),m=[{type:"int32",data:[o]},{type:"int32",data:p}],g=n.runWebGPUProgram(f,[h,d],h.dtype,m),b=Le({inputs:{x:g},backend:n,attrs:{shape:u}});return n.disposeData(d.dataId),n.disposeData(h.dataId),n.disposeData(g.dataId),b}var Mae={kernelName:ki,backendName:"webgpu",kernelFunc:Lae},Bae=class{constructor(e,t){this.variableNames=["A","indices"],this.workGroupSize=[64,1,1],this.size=!0,this.outputShape=e.slice(),this.aShape=e,this.outputShape=t,this.dispatchLayout=Ve(this.outputShape),this.dispatch=_e(this.dispatchLayout,this.outputShape,this.workGroupSize),this.shaderKey="gather"}getUserCode(){let e=Vae(this.aShape);return`
${Ue()}
if (index < uniforms.size) {
let resRC = getCoordsFromIndex(index);
let indexZ = i32(getIndices(resRC.x, resRC.z));
let inBounds = select(0.0, 1.0, indexZ >= 0 && indexZ < uniforms.aShape[2]);
setOutputAtIndex(index, inBounds * getA(${e}));
}
}
`}};function Vae(e){let t=["resRC.x","resRC.y","resRC.z","resRC.w"],n=[];for(let s=0;s<e.length;s++)s===2?n.push("indexZ"):n.push(`${t[s]}`);return n.join()}function Q2(e){let{inputs:t,backend:n,attrs:s}=e,{x:r,indices:a}=t,{axis:o,batchDims:i}=s,u=w.parseAxisParam(o,r.shape)[0],l=C.segment_util.collectGatherOpShapeInfo(r,a,u,i),c=w.sizeFromShape(a.shape),p=[],d=Le({inputs:{x:r},backend:n,attrs:{shape:[l.batchSize,l.outerSize,l.dimSize,l.sliceSize]}}),h=Le({inputs:{x:a},backend:n,attrs:{shape:[l.batchSize,c/l.batchSize]}});p.push(d),p.push(h);let f=[l.batchSize,l.outerSize,c/l.batchSize,l.sliceSize];if(n.shouldExecuteOnCPU([r,a])){let v=n.tensorMap.get(h.dataId).values,x=Ae(h.shape,h.dtype,v),I=n.tensorMap.get(d.dataId).values,$=Ae(d.shape,d.dtype,I),R=$se($,x,f);return p.forEach(E=>n.disposeData(E.dataId)),n.makeTensorInfo(l.outputShape,R.dtype,R.values)}let m=new Bae(d.shape,f),g=n.runWebGPUProgram(m,[d,h],d.dtype);p.push(g);let b=Le({inputs:{x:g},backend:n,attrs:{shape:l.outputShape}});return p.forEach(y=>n.disposeData(y.dataId)),b}var Wae={kernelName:wi,backendName:"webgpu",kernelFunc:Q2},Uae=gn({opSnippet:5,cpuKernelImpl:Ase,dtype:"bool"}),Gae={kernelName:Si,backendName:"webgpu",kernelFunc:Uae},Hae=gn({opSnippet:6,dtype:"bool",cpuKernelImpl:_se}),qae={kernelName:Va,backendName:"webgpu",kernelFunc:Hae};function jae(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{alpha:a}=s,o=[{type:"float32",data:[a]}],i=new oc(r.shape,14);return i.uniforms="alpha : f32,",n.runWebGPUProgram(i,[r],"float32",o)}var Kae={kernelName:Ua,backendName:"webgpu",kernelFunc:jae},Xae=gn({opSnippet:7,dtype:"bool",cpuKernelImpl:Rse}),Yae={kernelName:Ii,backendName:"webgpu",kernelFunc:Xae},Qae=gn({opSnippet:8,dtype:"bool",cpuKernelImpl:Ese}),Zae={kernelName:Ci,backendName:"webgpu",kernelFunc:Qae},Jae=Kt({opType:9,cpuKernelImpl:Dse}),eoe={kernelName:Ga,backendName:"webgpu",kernelFunc:Jae},toe=gn({opSnippet:9,dtype:"bool"}),noe={kernelName:Ni,backendName:"webgpu",kernelFunc:toe},soe=Kt({opType:10}),roe={kernelName:Ti,backendName:"webgpu",kernelFunc:soe};function Z2(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{reductionIndices:a,keepDims:o}=s;return uc(r,a,o,"max",n)}var aoe={kernelName:Ha,backendName:"webgpu",kernelFunc:Z2},ooe=gn({opSnippet:15,cpuKernelImpl:Ose}),ioe={kernelName:qa,backendName:"webgpu",kernelFunc:ooe};function uoe(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{filterSize:a,strides:o,pad:i,dimRoundingMode:u}=s,l=1,c=C.computePool2DInfo(r.shape,a,o,l,i,u),p,d=[];if(c.filterHeight===1&&c.filterWidth===1){if(w.arraysEqual(c.inShape,c.outShape))return Un({inputs:{x:r},backend:n});p=new W2(c),d.push({type:"int32",data:[c.strideHeight,c.strideWidth]})}else p=new V2(c,"max"),d.push({type:"int32",data:[c.strideHeight,c.strideWidth]},{type:"int32",data:[c.padInfo.top,c.padInfo.left]},{type:"int32",data:[c.dilationHeight,c.dilationWidth]},{type:"int32",data:[c.inHeight,c.inWidth]},{type:"int32",data:[c.effectiveFilterHeight,c.effectiveFilterWidth]});return n.runWebGPUProgram(p,[r],r.dtype,d)}var loe={kernelName:ja,backendName:"webgpu",kernelFunc:uoe};function coe(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{keepDims:a,axis:o}=s;return uc(r,o,a,"mean",n)}var doe={kernelName:Ka,backendName:"webgpu",kernelFunc:coe};function poe(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{axis:a,keepDims:o}=s;return uc(r,a,o,"min",n)}var hoe={kernelName:Xa,backendName:"webgpu",kernelFunc:poe},foe=gn({opSnippet:16,cpuKernelImpl:Pse}),moe={kernelName:Ya,backendName:"webgpu",kernelFunc:foe},goe=class{constructor(e,t,n){this.uniforms="",this.variableNames=["x"],this.workGroupSize=[64,1,1],this.size=!0,this.outputShape=t.map((s,r)=>s[0]+e[r]+s[1]),this.dispatchLayout=Ve(this.outputShape),this.dispatch=_e(this.dispatchLayout,this.outputShape,this.workGroupSize),this.xShape=e,t.map((s,r)=>{this.uniforms+=` pad${r} : vec2<i32>,`}),this.offset=n==="reflect"?0:1,this.shaderKey=`mirrorPad_${n}`}getUserCode(){let e=this.xShape.length,t=this.xShape.map((u,l)=>`uniforms.pad${l}[0]`).join(","),n=this.xShape.map((u,l)=>`uniforms.pad${l}[0] + uniforms.xShape${e>1?`[${l}]`:""}`).join(","),s=e===1?"start":"start[i]",r=e===1?"end":"end[i]",a=e===1?"outC":"outC[i]",o=Wt(e),i=e>1?["coords[0]","coords[1]","coords[2]","coords[3]"].slice(0,e):"coords";return`
${Ue()}
if (index < uniforms.size) {
let start = ${o}(${t});
let end = ${o}(${n});
var outC = getCoordsFromIndex(index);
for (var i = 0; i < ${e}; i = i + 1) {
if (${a} < ${s}) {
${a} = ${s} * 2 - ${a} - ${this.offset};
} else if(${a} >= ${r}) {
${a} = (${r} - 1) * 2 - ${a} + ${this.offset};
}
}
let coords = outC - start;
setOutputAtIndex(index, getX(${i}));
}
}
`}},boe={kernelName:Qa,backendName:"webgpu",kernelFunc:({inputs:e,attrs:t,backend:n})=>{let{x:s}=e,{paddings:r,mode:a}=t,o=n,i=r.map(c=>({type:"int32",data:[c[0],c[1]]})),u=new goe(s.shape,r,a);return o.runWebGPUProgram(u,[s],s.dtype,i)}};function yoe(e){let{inputs:t,backend:n}=e,{x:s}=t;if(n.shouldExecuteOnCPU([s])){let a=n.tensorMap.get(s.dataId),[o,i]=Lse(a.values,s.shape,s.dtype);return n.makeTensorInfo(i,s.dtype,o)}let r=new oc(s.shape,11);return n.runWebGPUProgram(r,[s],s.dtype)}var voe={kernelName:$i,backendName:"webgpu",kernelFunc:yoe};function xoe(e){console.warn("tf.nonMaxSuppression() in webgpu locks the UI thread. Call tf.nonMaxSuppressionAsync() instead");let{inputs:t,backend:n,attrs:s}=e,{boxes:r,scores:a}=t,{maxOutputSize:o,iouThreshold:i,scoreThreshold:u}=s,l=n.readSync(r.dataId),c=n.readSync(a.dataId),{selectedIndices:p}=ws.nonMaxSuppressionV3Impl(l,c,o,i,u);return n.makeTensorInfo([p.length],"int32",new Int32Array(p))}var woe={kernelName:Ai,backendName:"webgpu",kernelFunc:xoe};function koe(e){console.warn("tf.nonMaxSuppression() in webgpu locks the UI thread. Call tf.nonMaxSuppressionAsync() instead");let{inputs:t,backend:n,attrs:s}=e,{boxes:r,scores:a}=t,{maxOutputSize:o,iouThreshold:i,scoreThreshold:u,softNmsSigma:l}=s,c=n.readSync(r.dataId),p=n.readSync(a.dataId),d=o,h=i,f=u,m=l,{selectedIndices:g,selectedScores:b}=ws.nonMaxSuppressionV5Impl(c,p,d,h,f,m);return[n.makeTensorInfo([g.length],"int32",new Int32Array(g)),n.makeTensorInfo([b.length],"float32",new Float32Array(b))]}var Soe={kernelName:Ei,backendName:"webgpu",kernelFunc:koe};function Kd(e){let{inputs:t,backend:n}=e,{x:s}=t;if(s.dtype==="complex64"){let r=ic({inputs:{input:s},backend:n}),a=Kd({inputs:{x:r},backend:n}),o=ih({inputs:{input:s},backend:n}),i=Kd({inputs:{x:o},backend:n}),u=hu({inputs:{real:a,imag:i},backend:n});return n.disposeData(r.dataId),n.disposeData(a.dataId),n.disposeData(o.dataId),n.disposeData(i.dataId),u}else return mu({attrs:{shape:s.shape,dtype:s.dtype,value:s.dtype==="string"?"":0},backend:n})}var Ioe={kernelName:Xi,backendName:"webgpu",kernelFunc:Kd};function J2(e){let{inputs:t,backend:n}=e,{x:s}=t;if(s.dtype==="string")throw new Error("onesLike is not supported under string dtype");if(s.dtype==="complex64"){let r=ic({inputs:{input:s},backend:n}),a=J2({inputs:{x:r},backend:n}),o=ih({inputs:{input:s},backend:n}),i=Kd({inputs:{x:o},backend:n}),u=hu({inputs:{real:a,imag:i},backend:n});return n.disposeData(r.dataId),n.disposeData(a.dataId),n.disposeData(o.dataId),n.disposeData(i.dataId),u}else return mu({attrs:{shape:s.shape,dtype:s.dtype,value:1},backend:n})}var Coe={kernelName:Ri,backendName:"webgpu",kernelFunc:J2};function Noe(e){let{inputs:t,backend:n,attrs:s}=e,{axis:r}=s;if(t.length===1)return ug({inputs:{input:t[0]},backend:n,attrs:{dim:r}});let a=t[0].shape,o=t[0].dtype;t.forEach(c=>{w.assertShapesMatch(a,c.shape,"All tensors passed to stack must have matching shapes"),w.assert(o===c.dtype,()=>"All tensors passed to stack must have matching dtypes")});let i=[],u=t.map(c=>{let p=ug({inputs:{input:c},backend:n,attrs:{dim:r}});return i.push(p),p}),l=G2({inputs:u,backend:n,attrs:{axis:r}});return i.forEach(c=>n.disposeData(c.dataId)),l}var Toe={kernelName:Fi,backendName:"webgpu",kernelFunc:Noe},$oe=class{constructor(e,t){this.variableNames=["x"],this.uniforms="constantValue : f32,",this.workGroupSize=[64,1,1],this.size=!0,this.outputShape=t.map((n,s)=>n[0]+e[s]+n[1]),this.dispatchLayout=Ve(this.outputShape),this.dispatch=_e(this.dispatchLayout,this.outputShape,this.workGroupSize),t.map((n,s)=>{this.uniforms+=` pad${s} : vec2<i32>,`}),this.xShape=e,this.shaderKey="pad"}getUserCode(){let e=this.xShape.length,t=Wt(e),n=this.xShape.map((c,p)=>`uniforms.pad${p}[0]`).join(","),s=this.xShape.map((c,p)=>`uniforms.pad${p}[0] + uniforms.xShape${e>1?`[${p}]`:""}`).join(","),r=e>1?`${t}(${n})`:`${n}`,a=e>1?`${t}(${s})`:`${s}`,o=e>1?"any(outC < start)":"outC < start",i=e>1?"any(outC >= end)":"outC >= end",u=e>1?["coords[0]","coords[1]","coords[2]","coords[3]"].slice(0,e):"coords";return`
${Ue()}
if (index < uniforms.size) {
let start = ${r};
let end = ${a};
let outC = getCoordsFromIndex(index);
if (${o} || ${i}) {
setOutputAtIndex(index, uniforms.constantValue);
} else {
let coords = outC - start;
setOutputAtIndex(index, getX(${u}));
}
}
}
`}},eN=e=>{let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{paddings:a,constantValue:o}=s;if(a.every(l=>w.arraysEqual(l,[0,0])))return Un({inputs:{x:r},backend:n});if(w.sizeFromShape(r.shape)===0){let l=a.map((c,p)=>c[0]+r.shape[p]+c[1]);return mu({backend:n,attrs:{shape:l,value:o,dtype:r.dtype}})}let i=[{type:"float32",data:[o]}];a.map(l=>i.push({type:"int32",data:[l[0],l[1]]}));let u=new $oe(r.shape,a);return n.runWebGPUProgram(u,[r],r.dtype,i)},_oe={kernelName:Ja,backendName:"webgpu",kernelFunc:eN},Aoe=gn({opSnippet:13}),Eoe={kernelName:eo,backendName:"webgpu",kernelFunc:Aoe};function Roe(e){let{inputs:t,backend:n}=e,{x:s,alpha:r}=t,a=new M2(14,s.shape,r.shape);return n.runWebGPUProgram(a,[s,r],"float32")}var Doe={kernelName:to,backendName:"webgpu",kernelFunc:Roe};function Foe(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{axis:a,keepDims:o}=s;return uc(r,a,o,"prod",n)}var Ooe={kernelName:no,backendName:"webgpu",kernelFunc:Foe},Poe=e=>{let{backend:t,attrs:n}=e,{start:s,stop:r,step:a,dtype:o}=n,i=Vse(s,r,a,o);return t.makeTensorInfo([i.length],o,i)},zoe={kernelName:Tl,backendName:"webgpu",kernelFunc:Poe},tN=gn({opSnippet:3}),Loe={kernelName:Oa,backendName:"webgpu",kernelFunc:tN},Moe=Kt({opType:12}),Boe={kernelName:so,backendName:"webgpu",kernelFunc:Moe},Voe=Kt({opType:13}),Woe={kernelName:ao,backendName:"webgpu",kernelFunc:Voe},Uoe=class{constructor(e,t,n){this.variableNames=["x"],this.uniforms="adjustHeightWidth : vec2<f32>, halfPixelCenters : f32,",this.workGroupSize=[64,1,1],this.size=!0,this.outputShape=[e[0],t,n,e[3]],this.dispatchLayout=Ve(this.outputShape),this.dispatch=_e(this.dispatchLayout,this.outputShape,this.workGroupSize),this.shaderKey="resizeBilinear"}getUserCode(){return`
${Ue()}
if (index < uniforms.size) {
let coords = getCoordsFromIndex(index);
let b = coords[0];
let d = coords[3];
let rc = coords.yz;
let effectiveInSize = vec2<f32>(
f32(uniforms.xShape.y) - uniforms.adjustHeightWidth[0],
f32(uniforms.xShape.z) - uniforms.adjustHeightWidth[1]);
let effectiveOutSize = vec2<f32>(
f32(uniforms.outShape.y) - uniforms.adjustHeightWidth[0],
f32(uniforms.outShape.z) - uniforms.adjustHeightWidth[1]);
let effectiveInputOverOutputRatioRC =
effectiveInSize / effectiveOutSize;
// Fractional source index
let sourceFracIndexRC =
(vec2<f32>(rc) + vec2<f32>(uniforms.halfPixelCenters)) *
effectiveInputOverOutputRatioRC - vec2<f32>(uniforms.halfPixelCenters);
// Compute the four integer indices.
let sourceFloorRC = vec2<i32>(sourceFracIndexRC);
let sourceCeilRC = vec2<i32>(
min(vec2<f32>(uniforms.xShape.yz) - vec2<f32>(1.0), ceil(sourceFracIndexRC)));
let topLeft = getX(b, sourceFloorRC.x, sourceFloorRC.y, d);
let bottomLeft = getX(b, sourceCeilRC.x, sourceFloorRC.y, d);
let topRight = getX(b, sourceFloorRC.x, sourceCeilRC.y, d);
let bottomRight = getX(b, sourceCeilRC.x, sourceCeilRC.y, d);
let fracRC = sourceFracIndexRC - vec2<f32>(sourceFloorRC);
let top = topLeft + (topRight - topLeft) * fracRC.y;
let bottom = bottomLeft + (bottomRight - bottomLeft) * fracRC.y;
let newValue = top + (bottom - top) * fracRC.x;
setOutputAtIndex(index, newValue);
}
}
`}};function Goe(e){let{inputs:t,backend:n,attrs:s}=e,{images:r}=t,{alignCorners:a,size:o,halfPixelCenters:i}=s,[u,l]=o,c=a&&u>1?1:0,p=a&&l>1?1:0,h=[{type:"float32",data:[c,p]},{type:"float32",data:[i?.5:0]}],f=new Uoe(r.shape,u,l);return n.runWebGPUProgram(f,[r],"float32",h)}var Hoe={kernelName:ro,backendName:"webgpu",kernelFunc:Goe},qoe=class{constructor(e,t,n,s){this.variableNames=["x"],this.uniforms="adjustHeightWidth : vec2<f32>, roundBase : f32,",this.workGroupSize=[64,1,1],this.size=!0,this.outputShape=[e[0],t,n,e[3]],this.dispatchLayout=Ve(this.outputShape),this.dispatch=_e(this.dispatchLayout,this.outputShape,this.workGroupSize),this.halfPixelCenters=s,this.shaderKey=`resizeNearest_${s}`}getUserCode(){let e;return this.halfPixelCenters?e="max((vec2<f32>(rc) + vec2<f32>(0.5)) * effectiveInputOverOutputRatioRC, vec2<f32>(0.0))":e="vec2<f32>(rc) * effectiveInputOverOutputRatioRC",`
${Ue()}
if (index < uniforms.size) {
let coords = getCoordsFromIndex(index);
let b = coords[0];
let d = coords[3];
let rc = coords.yz;
let effectiveInSize = vec2<f32>(
f32(uniforms.xShape.y) - uniforms.adjustHeightWidth[0],
f32(uniforms.xShape.z) - uniforms.adjustHeightWidth[1]);
let effectiveOutSize = vec2<f32>(
f32(uniforms.outShape.y) - uniforms.adjustHeightWidth[0],
f32(uniforms.outShape.z) - uniforms.adjustHeightWidth[1]);
let effectiveInputOverOutputRatioRC =
effectiveInSize / effectiveOutSize;
// Fractional source index
let sourceFracIndexRC = ${e};
// Compute the coordinators of nearest neighbor point.
let inputShapeRC = vec2<f32>(f32(uniforms.xShape.y), f32(uniforms.xShape.z));
let sourceNearestRC = vec2<i32>(
min(inputShapeRC - 1.0, floor(sourceFracIndexRC + uniforms.roundBase)));
let newValue = getX(b, sourceNearestRC.x, sourceNearestRC.y, d);
setOutputAtIndex(index, newValue);
}
}
`}};function joe(e){let{inputs:t,backend:n,attrs:s}=e,{images:r}=t,{alignCorners:a,halfPixelCenters:o,size:i}=s,[u,l]=i,c=a&&u>1?1:0,p=a&&l>1?1:0,h=[{type:"float32",data:[c,p]},{type:"float32",data:[a?.5:0]}],f=new qoe(r.shape,u,l,o);return n.runWebGPUProgram(f,[r],r.dtype,h)}var Koe={kernelName:_l,backendName:"webgpu",kernelFunc:joe},Xoe=class{constructor(e,t){this.outputShape=[],this.variableNames=["x"],this.workGroupSize=[64,1,1],this.size=!0,this.outputShape=e,this.dispatchLayout=Ve(this.outputShape),this.dispatch=_e(this.dispatchLayout,this.outputShape,this.workGroupSize),this.uniforms=`centerX : f32, centerY : f32, sinRadians : f32,
cosRadians : f32,`,this.shaderKey="rotate",this.outputShape=e,typeof t=="number"?(this.uniforms+=" fillValue : f32,",this.fillSnippet="var outputValue = uniforms.fillValue;",this.shaderKey+="_float"):(this.uniforms+=" fillValue : vec3<f32>,",this.fillSnippet="var outputValue = uniforms.fillValue[coords[3]];",this.shaderKey+="_vec3")}getUserCode(){return`
${Ue()}
if (index < uniforms.size) {
let coords = getCoordsFromIndex(index);
let coordXFloat = (f32(coords[2]) - uniforms.centerX) *
uniforms.cosRadians - (f32(coords[1]) - uniforms.centerY) *
uniforms.sinRadians;
let coordYFloat = (f32(coords[2]) - uniforms.centerX) *
uniforms.sinRadians + (f32(coords[1]) - uniforms.centerY) *
uniforms.cosRadians;
let coordX = i32(round(coordXFloat + uniforms.centerX));
let coordY = i32(round(coordYFloat + uniforms.centerY));
${this.fillSnippet}
if(coordX >= 0 && coordX < uniforms.xShape[2] && coordY >= 0 &&
coordY < uniforms.xShape[1]) {
outputValue = getX(coords[0], coordY, coordX, coords[3]);
}
setOutputAtIndex(index, outputValue);
}
}
`}},Yoe={kernelName:Yi,backendName:"webgpu",kernelFunc:({inputs:e,attrs:t,backend:n})=>{let{image:s}=e,{radians:r,fillValue:a,center:o}=t,i=n,u=new Xoe(s.shape,a),[l,c]=C.getImageCenter(o,s.shape[1],s.shape[2]),p=[{type:"float32",data:[l]},{type:"float32",data:[c]},{type:"float32",data:[Math.sin(r)]},{type:"float32",data:[Math.cos(r)]}];return typeof a=="number"?p.push({type:"float32",data:[Number.parseFloat(a.toFixed(2))]}):p.push({type:"float32",data:a}),i.runWebGPUProgram(u,[s],s.dtype,p)}},Qoe=Kt({opType:15,cpuKernelImpl:Wse}),Zoe={kernelName:oo,backendName:"webgpu",kernelFunc:Qoe},Joe=class{constructor(e,t,n,s,r,a,o){this.variableNames=["updates","indices"],this.workGroupSize=[64,1,1],this.atomic=!0,this.outputShape=a,this.type=o,this.dispatchLayout=Ve(e),this.dispatch=_e(this.dispatchLayout,e,this.workGroupSize),this.sliceDimGreaterThanOne=t>1,this.shaderKey=`scatter_${n}_${s}_${this.sliceDimGreaterThanOne}_${o}`;let i=Wt(r.length);this.uniforms=`sliceDim : i32, strides: ${i}, size: i32,`,this.updatesRank=s,this.indicesRank=n}getUserCode(){let e="";this.indicesRank===1?e="coords[0]":this.indicesRank===2&&(e="coords[0], j");let t=`getIndices(${e})`,n=this.sliceDimGreaterThanOne?"uniforms.strides[j]":"uniforms.strides",s="",r="",a="";this.updatesRank===1?(s="coords[0]",r="flattenedIndex",a=`
fn getUpdatesCoordsFromFlatIndex(index : i32) -> i32 {
return index;
}
`):this.updatesRank===2&&(s="coords[0], coords[1]",r="vec2<i32>(flattenedIndex, coords[1])",a=`
fn getUpdatesCoordsFromFlatIndex(index : i32) -> vec2<i32> {
let d0 = index / uniforms.updatesShape[1];
let d1 = index - d0 * uniforms.updatesShape[1];
return vec2<i32>(d0, d1);
}
`);let o=`getUpdates(${s})`,i=this.type==="int32"?"atomicAdd(&(result[flatIndex]), i32(updateValue));":`
var oldValue = atomicLoad(&(result[flatIndex]));
var exchanged = false;
for (; !exchanged;) {
let newValueF32 = bitcast<f32>(oldValue) + updateValue;
let newValue = bitcast<i32>(newValueF32);
let res = atomicCompareExchangeWeak(&(result[flatIndex]), oldValue, newValue);
oldValue = res.old_value;
exchanged = res.exchanged;
}
`;return`
${a}
${Ue()}
if (index < uniforms.size) {
let coords = getUpdatesCoordsFromFlatIndex(index);
var flattenedIndex = 0;
for (var j = 0; j < uniforms.sliceDim; j = j + 1) {
let indexInside = i32(round(${t}));
flattenedIndex = flattenedIndex + indexInside * ${n};
}
let updateValue = ${o};
let flatIndex = getOutputIndexFromCoords(${r});
${i}
}
}`}};function eie(e){let{inputs:t,backend:n,attrs:s}=e,{indices:r,updates:a}=t,{shape:o}=s,{sliceRank:i,numUpdates:u,sliceSize:l,strides:c,outputSize:p}=C.calculateShapes(a,r,o),d=[p/l,l];if(p===0)return n.makeTensorInfo(o,r.dtype);let h=Le({inputs:{x:r},backend:n,attrs:{shape:[u,i]}}),f=Le({inputs:{x:a},backend:n,attrs:{shape:[u,l]}}),m=f.dtype,g=mu({backend:n,attrs:{shape:d,value:0,dtype:m}}),b=w.sizeFromShape(f.shape),y=[{type:"int32",data:[i]},{type:"int32",data:c},{type:"int32",data:[b]}],v=new Joe(f.shape,i,h.shape.length,f.shape.length,c,d,m),x=n.runWebGPUProgram(v,[f,h],m,y,g),k=Le({inputs:{x},backend:n,attrs:{shape:o}});return n.disposeData(h.dataId),n.disposeData(f.dataId),n.disposeData(x.dataId),k}var tie={kernelName:Li,backendName:"webgpu",kernelFunc:eie},nie=class{constructor(e,t,n){this.variableNames=["c","a","b"],this.workGroupSize=[64,1,1],this.size=!0,this.outputShape=t,this.dispatchLayout=Ve(this.outputShape),this.dispatch=_e(this.dispatchLayout,this.outputShape,this.workGroupSize),this.cRank=e,this.rank=n,this.shaderKey="select"}getUserCode(){let e,t;if(this.rank>4)throw Error(`Where for rank ${this.rank} is not yet supported`);if(this.rank===1)t="resRC",e="resRC";else{let s=["resRC.x","resRC.y","resRC.z","resRC.w"],r=[],a=[];for(let o=0;o<this.outputShape.length;o++)a.push(`${s[o]}`),o<this.cRank&&r.push(`${s[o]}`);e=r.join(),t=a.join()}return`
${Ue()}
if (index < uniforms.size) {
let resRC = getCoordsFromIndex(index);
let cVal = getC(${e});
if (cVal >= 1.0) {
setOutputAtIndex(index, getA(${t}));
} else {
setOutputAtIndex(index, getB(${t}));
}
}
}
`}};function sie(e){let{inputs:t,backend:n}=e,{condition:s,t:r,e:a}=t,o=new nie(s.shape.length,r.shape,r.shape.length);return n.runWebGPUProgram(o,[s,r,a],cn(r.dtype,a.dtype))}var rie={kernelName:Mi,backendName:"webgpu",kernelFunc:sie},aie=Kt({opType:18}),oie={kernelName:uo,backendName:"webgpu",kernelFunc:aie},iie=Kt({opType:16}),uie={kernelName:io,backendName:"webgpu",kernelFunc:iie},lie=Kt({opType:17}),cie={kernelName:Vi,backendName:"webgpu",kernelFunc:lie},nN=gn({opSnippet:2,cpuKernelImpl:Kse,supportsComplex:!0}),die={kernelName:fo,backendName:"webgpu",kernelFunc:nN};function pie(e){let{inputs:t,backend:n,attrs:s}=e,{logits:r}=t,{dim:a}=s,o=w.parseAxisParam([a],r.shape),i=Z2({inputs:{x:r},backend:n,attrs:{reductionIndices:o,keepDims:!1}}),u=C.expandShapeToKeepDim(i.shape,o),l=Le({inputs:{x:i},backend:n,attrs:{shape:u}}),c=nN({inputs:{a:r,b:l},backend:n}),p=Y2({inputs:{x:c},backend:n}),d=Vv({inputs:{x:p},backend:n,attrs:{axis:o,keepDims:!1}}),h=Le({inputs:{x:d},backend:n,attrs:{shape:u}}),f=tN({inputs:{a:p,b:h},backend:n});return n.disposeData(i.dataId),n.disposeData(l.dataId),n.disposeData(c.dataId),n.disposeData(p.dataId),n.disposeData(d.dataId),n.disposeData(h.dataId),f}var hie={kernelName:po,backendName:"webgpu",kernelFunc:pie},fie=e=>{let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{blockShape:a,paddings:o}=s;w.assert(r.shape.length<=4,()=>"spaceToBatchND for rank > 4 with a WebGPU backend not implemented yet");let i=a.reduce((b,y)=>b*y),u=[[0,0]];u.push(...o);for(let b=1+a.length;b<r.shape.length;++b)u.push([0,0]);let l=[],c=eN({inputs:{x:r},backend:n,attrs:{paddings:u,constantValue:0}}),p=C.getReshaped(c.shape,a,i,!1),d=C.getPermuted(p.length,a.length,!1),h=C.getReshapedPermuted(c.shape,a,i,!1),f=Le({inputs:{x:c},backend:n,attrs:{shape:p}}),m=Ks({inputs:{x:f},backend:n,attrs:{perm:d}}),g=Le({inputs:{x:m},backend:n,attrs:{shape:h}});return l.push(c),l.push(f),l.push(m),l.forEach(b=>n.disposeData(b.dataId)),g},mie={kernelName:Wi,backendName:"webgpu",kernelFunc:fie},gie=class{constructor(e,t,n,s,r,a,o=!0){this.variableNames=["updates","indices","defaultValue"],this.workGroupSize=[64,1,1],this.workPerThread=4,this.size=!0,this.outputShape=a,this.dispatchLayout=Ve(this.outputShape),this.dispatch=_e(this.dispatchLayout,this.outputShape,this.workGroupSize,[this.workPerThread,1,1]);let i=t>1;this.shaderKey=`scatter_${n}_${s}_${i}`;let u=Wt(r.length);this.uniforms=`updateSize : i32, sliceDim : i32, strides: ${u},`;let l="";n===1?l="i":n===2&&(l="i, j"),this.indicesSnippet=`getIndices(${l})`;let c="";s===1?c="i":s===2&&(c="i, coords[1]"),this.updatesSnippet=`getUpdates(${c})`,this.strideString=i?"uniforms.strides[j]":"uniforms.strides"}getUserCode(){return`
${Ue()}
let globalIndex = index * ${this.workPerThread};
if (globalIndex < uniforms.size) {
var sum = vec4<f32>(0.0);
var found = vec4<bool>(false);
for (var i = 0; i < uniforms.updateSize; i = i + 1) {
var flattenedIndex = 0;
for (var j = 0; j < uniforms.sliceDim; j = j + 1) {
let indexInside = i32(round(${this.indicesSnippet}));
flattenedIndex = flattenedIndex + indexInside * ${this.strideString};
}
for (var innerIndex = 0; innerIndex < ${this.workPerThread}; innerIndex = innerIndex + 1) {
let curIndex = globalIndex + innerIndex;
let coords = getCoordsFromIndex(curIndex);
if (flattenedIndex == coords[0]) {
sum[innerIndex] = sum[innerIndex] + ${this.updatesSnippet};
found[innerIndex] = true;
}
}
}
for (var innerIndex = 0; innerIndex < ${this.workPerThread}; innerIndex = innerIndex + 1) {
let curIndex = globalIndex + innerIndex;
if (curIndex < uniforms.size)
{
setOutputAtIndex(curIndex, mix(getDefaultValue(), sum[innerIndex], f32(found[innerIndex])));
}
}
}
}`}};function bie(e){let{inputs:t,backend:n,attrs:s}=e,{sparseIndices:r,sparseValues:a,defaultValue:o}=t,{outputShape:i}=s,{sliceRank:u,numUpdates:l,sliceSize:c,strides:p,outputSize:d}=C.calculateShapes(a,r,i),h=!1;if(a.dtype==="string"){let y=n.bufferSync(r),v=n.bufferSync(a),x=w.decodeString(n.readSync(o.dataId)[0]),k=Use(y,v,i,d,c,l,u,p,x,h);return n.makeTensorInfo(i,k.dtype,k.values)}let f=[{type:"int32",data:[l]},{type:"int32",data:[u]},{type:"int32",data:p}],m=new gie(l,u,r.shape.length,a.shape.length,p,[d,1],h),g=n.runWebGPUProgram(m,[a,r,o],a.dtype,f),b=Le({inputs:{x:g},backend:n,attrs:{shape:i}});return n.disposeData(g.dataId),b}var yie={kernelName:fp,backendName:"webgpu",kernelFunc:bie};function vie(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{numOrSizeSplits:a,axis:o}=s,i=w.parseAxisParam(o,r.shape)[0],u=C.prepareSplitSize(r,a,i),l=r.shape.length,c=new Array(l).fill(0),p=r.shape.slice();return u.map(d=>{let h=[...p];h[i]=d;let f=fu({inputs:{x:r},backend:n,attrs:{begin:c,size:h}});return c[i]+=d,f})}var xie={kernelName:Ui,backendName:"webgpu",kernelFunc:vie},wie=Kt({opType:19}),kie={kernelName:lo,backendName:"webgpu",kernelFunc:wie},Sie={kernelName:Fl,backendName:"webgpu",kernelFunc:({inputs:e,backend:t})=>{let{x:n}=e,s=t,r=new oc(n.shape,20);return s.runWebGPUProgram(r,[n],n.dtype)}},Iie=gn({opSnippet:11}),Cie={kernelName:ho,backendName:"webgpu",kernelFunc:Iie},Nie=class{constructor(e){this.variableNames=["x"],this.workPerThread=1,this.workGroupSize=[64,1,1],this.size=!0,this.outputShape=e,this.dispatchLayout=Ve(this.outputShape),this.dispatch=_e(this.dispatchLayout,this.outputShape,this.workGroupSize,[this.workPerThread,1,1]);let t=Wt(this.outputShape.length);this.uniforms=`begin : ${t}, strides : ${t}, `,this.shaderKey="stridedSlice"}getUserCode(){let e=this.outputShape.length,t="";if(e===1)t="coords * uniforms.strides + uniforms.begin";else{let s=0;t=this.outputShape.map((r,a)=>(s++,this.outputShape.length===1?`coords * uniforms.strides[${a}] + uniforms.begin[${a}]`:`coords[${s-1}] * uniforms.strides[${a}] + uniforms.begin[${a}]`)).join(",")}return`
${Ue()}
if (index < uniforms.size) {
let coords = getCoordsFromIndex(index);
setOutputAtIndex(index, getX(${t}));
}
}
`}};function Tie(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{begin:a,end:o,strides:i,beginMask:u,endMask:l,ellipsisMask:c,newAxisMask:p,shrinkAxisMask:d}=s,{finalShapeSparse:h,finalShape:f,isIdentity:m,sliceDim0:g,isSimpleSlice:b,begin:y,end:v,strides:x}=kt.sliceInfo(r.shape,a,o,i,u,l,c,p,d),k;if(m)k=Le({inputs:{x:r},backend:n,attrs:{shape:f}});else if(g||b){w.assert(r.shape.length>=1,()=>`Input must have rank at least 1, got: ${r.shape.length}`);let I=kt.computeOutShape(y,v,x),$=fu({inputs:{x:r},backend:n,attrs:{begin:y,size:I}});k=Le({inputs:{x:$},backend:n,attrs:{shape:f}}),n.disposeData($.dataId)}else if(n.shouldExecuteOnCPU([r])){let $=n.readSync(r.dataId),R=Ae(r.shape,r.dtype,$),E=qse(h,R,x,y);k=n.makeTensorInfo(f,r.dtype,E.values)}else{let $=new Nie(h),R=[{type:"int32",data:y},{type:"int32",data:x}],E=n.runWebGPUProgram($,[r],r.dtype,R);k=Le({inputs:{x:E},backend:n,attrs:{shape:f}}),n.disposeData(E.dataId)}return k}var $ie={kernelName:Gi,backendName:"webgpu",kernelFunc:Tie};function _ie(e){let{inputs:t,backend:n,attrs:s}=e,{separator:r,nGramWidths:a,leftPad:o,rightPad:i,padWidth:u,preserveShortSequences:l}=s,{data:c,dataSplits:p}=t,d=n.readSync(c.dataId),h=n.readSync(p.dataId),[f,m]=jse(d,h,r,a,o,i,u,l);return[n.makeTensorInfo([f.length],"string",f),n.makeTensorInfo(p.shape,"int32",m)]}var Aie={kernelName:mp,backendName:"webgpu",kernelFunc:_ie},Eie=Kt({opType:21}),Rie={kernelName:mo,backendName:"webgpu",kernelFunc:Eie},Die=class{constructor(e,t){this.variableNames=["A"],this.workGroupSize=[64,1,1],this.size=!0;let n=new Array(e.length);for(let s=0;s<n.length;s++)n[s]=e[s]*t[s];this.outputShape=n,this.dispatchLayout=Ve(this.outputShape),this.dispatch=_e(this.dispatchLayout,this.outputShape,this.workGroupSize),this.rank=this.outputShape.length,this.shaderKey="tile"}getUserCode(){let e=Fie(this.rank,"uniforms.");return`
${Ue()}
if (index < uniforms.size) {
let resRC = getCoordsFromIndex(index);
setOutputAtIndex(index, getA(${e}));
}
}
`}};function Fie(e,t=""){if(e>=5)throw Error(`Tile for rank ${e} is not yet supported`);if(e===1)return`(resRC % ${t}aShape)`;let n=["resRC.x","resRC.y","resRC.z","resRC.w"],s=[];for(let r=0;r<e;r++)s.push(`(${n[r]} % ${t}aShape[${r}])`);return s.join()}function Oie(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{reps:a}=s;if(n.shouldExecuteOnCPU([r])||r.dtype==="string"||r.shape.length>=5){let u=n.readSync(r.dataId),l=r.dtype==="string"?u.map(d=>w.decodeString(d)):u,c=Ae(r.shape,r.dtype,l),p=Xse(c,a);return n.makeTensorInfo(p.shape,p.dtype,p.values)}let o=new Die(r.shape,a);return n.runWebGPUProgram(o,[r],r.dtype)}var Pie={kernelName:Tr,backendName:"webgpu",kernelFunc:Oie},zie=class{constructor(e){this.variableNames=["x","indices"],this.workGroupSize=[256,1,1],this.size=!0,this.outputShape=e,this.dispatchLayout=Ve(this.outputShape),this.dispatch=_e(this.dispatchLayout,this.outputShape,this.workGroupSize),this.uniforms=`inputSize : i32, firstPass : i32, negativeInf : f32,
dir : i32, inc : i32,`,this.shaderKey="swap"}getUserCode(){return`
${Ue()}
if (index < uniforms.size) {
let outC = getCoordsFromIndex(index);
let batch = outC[0];
let elemIdx = outC[1];
// We compare elements pair-wise within a group of size 2 * inc.
// The comparing rule for each group alternates between ascending
// and descending. Within each group, we compare each pair at
// positions i and i+inc. To decide whether an element at position i
// is x0 or x1, we mod it by 2 * inc, if the result is smaller than
// inc, it is in the first half of the group, we denote it as x0,
// otherwise we denote it as x1.
// For example, as shown in the Bitonic top K paper referenced
// above, Figure5(a) shows that element[1] is in the second half of
// the group when group size is 2, but it is in the first half of
// the group when group size is 4.
let isFirstInPair = elemIdx % (2 * uniforms.inc) < uniforms.inc;
var i = 0;
if (isFirstInPair) {
i = elemIdx;
} else {
i = elemIdx - uniforms.inc;
}
var i0 = 0;
if (uniforms.firstPass == 1) {
i0 = i;
} else {
i0 = i32(getIndices(batch, i));
}
var i1 = 0;
if (uniforms.firstPass == 1) {
i1 = i + uniforms.inc;
} else {
i1 = i32(getIndices(batch, i + uniforms.inc));
}
var x0 = f32(0.0);
var x1 = f32(0.0);
if (i0 < uniforms.inputSize) {
x0 = getX(batch, i0);
} else {
x0 = uniforms.negativeInf;
}
if (i1 < uniforms.inputSize) {
x1 = getX(batch, i1);
} else {
x1 = uniforms.negativeInf;
}
let reverse = elemIdx % (2 * uniforms.dir) >= uniforms.dir;
let isGreater = x0 > x1 || (x0 == x1 && i1 > i0);
if (reverse == isGreater) {
// Elements in opposite order of direction
let iTemp = i0;
i0 = i1;
i1 = iTemp;
}
if (isFirstInPair) {
setOutputAtIndex(index, f32(i0));
} else {
setOutputAtIndex(index, f32(i1));
}
}
}
`}},Lie=class{constructor(e){this.variableNames=["x","indices"],this.workGroupSize=[256,1,1],this.size=!0,this.outputShape=e,this.dispatchLayout=Ve(this.outputShape),this.dispatch=_e(this.dispatchLayout,this.outputShape,this.workGroupSize),this.uniforms="inputSize : i32, firstPass : i32, k : i32,",this.shaderKey="merge"}getUserCode(){return`
${Ue()}
if (index < uniforms.size) {
let outC = getCoordsFromIndex(index);
let batch = outC[0];
let elemIdx = outC[1];
// The output size is half of the previous size.
// If the previous sequence is | | | | _ _ _ _ | | | | _ _ _ _
// (k=4), we only need to output the indices at positions |, the
// indices at positions _ can be thrown away, see Figure5(b) After
// Phase 2 (Merge phase) in the Bitonic Top K paper referenced
// above.
// For example, the paper shows we only need to output the orange
// bars. The output sequence should look like this | | | | | | | |.
// Because the sequence is halved, to map the output index back to
// the previous sequence to find the corresponding value, we need
// to double the index. When we double the index, we basically
// interpolate a position, so 2i looks like
// | _ | _ | _ | _ | _ | _ | _. We move the | to the first k
// position of each 2k positions by - elemIdx % k. E.g. for output
// at index 4,5,6,7, we want to get the corresponding element at
// original index 8,9,10,11, for output at index 8,9,10,11,
// we want to get the corresponding element at original index
// 16,17,18,19, so on and so forth.
var i = 0;
if (elemIdx < uniforms.k) {
i = elemIdx;
} else {
i = elemIdx * 2 - elemIdx % uniforms.k;
}
var i0 = 0;
if (uniforms.firstPass == 1) {
i0 = i;
} else {
i0 = i32(getIndices(batch, i));
}
var i1 = 0;
if (uniforms.firstPass == 1) {
i1 = i + uniforms.k;
} else {
i1 = i32(getIndices(batch, i + uniforms.k));
}
let x0 = getX(batch, i0);
var x1 = f32(0.0);
if (i1 < uniforms.inputSize) {
x1 = getX(batch, i1);
} else {
x1 = x0;
}
if (x0 >= x1) {
setOutputAtIndex(index, f32(i0));
} else {
setOutputAtIndex(index, f32(i1));
}
}
}
`}};function Go(e,t){t!==null&&e.disposeData(t.dataId)}function Xw(e){let t=1;for(;t<e;)t*=2;return t}function Mie(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{k:a,sorted:o}=s,i=r.shape,u=i[i.length-1];if(n.shouldExecuteOnCPU([r])){let k=n.readSync(r.dataId),[I,$]=Yse(k,i,r.dtype,a,o);return[n.makeTensorInfo(I.shape,I.dtype,I.values),n.makeTensorInfo($.shape,$.dtype,$.values)]}if(a===0)return i[i.length-1]=0,[n.makeTensorInfo(i,r.dtype,[]),n.makeTensorInfo(i,"int32",[])];if(u===1)return[r,mu({attrs:{shape:i,dtype:"int32",value:0},backend:n})];let c=w.sizeFromShape(i)/u,p=Le({inputs:{x:r},attrs:{shape:[c,u]},backend:n}),d=Xw(a),h=Xw(u),f=null,m=()=>f===null?[p,p]:[p,f],g=(k,I,$)=>{let R=m(),E=new zie($),A=[{type:"int32",data:[u]},{type:"int32",data:[f===null?1:0]},{type:"float32",data:[Number.NEGATIVE_INFINITY]},{type:"int32",data:[k]},{type:"int32",data:[I]}],D=f;f=n.runWebGPUProgram(E,R,"int32",A),Go(n,D)};for(let k=1;k<d;k*=2){let I=k*2;for(let $=k;$>=1;$/=2)g(I,$,[c,h])}for(let k=h;k>d;k/=2){let I=m(),$=new Lie([c,k/2]),E=[{type:"int32",data:[u]},{type:"int32",data:[f===null?1:0]},{type:"int32",data:[d]}],P=f;f=n.runWebGPUProgram($,I,"int32",E),Go(n,P);let A=d/2,D=A*2;for(let T=A;T>=1;T/=2)g(D,T,f.shape)}let b=f;f=fu({inputs:{x:f},backend:n,attrs:{begin:0,size:[c,a]}}),Go(n,b);let y=Q2({inputs:{x:p,indices:f},backend:n,attrs:{axis:1,batchDims:1}});Go(n,p);let v=i.slice(0,-1);v.push(a),b=f,f=Le({inputs:{x:f},attrs:{shape:v},backend:n}),Go(n,b);let x=y;return y=Le({inputs:{x:y},attrs:{shape:v},backend:n}),Go(n,x),[y,f]}var Bie={kernelName:qi,backendName:"webgpu",kernelFunc:Mie},Vie=class{constructor(e){this.variableNames=["Image","Transforms"],this.uniforms="interpolationModeId : i32, fillModeId : i32, fillValue : f32,",this.workGroupSize=[64,1,1],this.size=!0,this.outputShape=e,this.dispatchLayout=Ve(this.outputShape),this.dispatch=_e(this.dispatchLayout,this.outputShape,this.workGroupSize),this.shaderKey="transform"}getUserCode(){return`
fn mapCoord(outCoord : f32, len : f32) -> f32{
var inCoord = outCoord;
if(uniforms.fillModeId == 2) {
if (inCoord < 0.0) {
if (len <= 1.0) {
inCoord = 0.0;
} else {
let sz2 = 2.0 * len;
if (inCoord < sz2) {
inCoord = sz2 * f32(i32(f32(-inCoord / sz2))) +
inCoord;
}
if (inCoord < -len) {
inCoord = inCoord + sz2;
} else {
inCoord = -inCoord - 1.0;
}
}
} else if (inCoord > len - 1.0) {
if (len <= 1.0) {
inCoord = 0.0;
} else {
let sz2 = 2.0 * len;
inCoord = inCoord - sz2 * f32(i32(f32(inCoord / sz2)));
if (inCoord >= len) {
inCoord = sz2 - inCoord - 1.0;
}
}
}
return clamp(inCoord, 0.0, len - 1.0);
} else if (uniforms.fillModeId == 3) {
if (inCoord < 0.0) {
if (len <= 1.0) {
inCoord = 0.0;
} else {
let sz = len - 1.0;
inCoord = inCoord + len * (f32(i32(f32(-inCoord / sz))) + 1.0);
}
} else if (inCoord > len - 1.0) {
if (len <= 1.0) {
inCoord = 0.0;
} else {
let sz = len - 1.0;
inCoord = inCoord - len * f32(i32(f32(inCoord / sz)));
}
}
return clamp(inCoord, 0.0, len - 1.0);
} else if (uniforms.fillModeId == 4) {
return clamp(outCoord, 0.0, len - 1.0);
}
return outCoord;
}
fn readWithFillValue(batch : i32, coordY : i32, coordX : i32,
channel : i32) -> f32 {
var outputValue : f32;
if (0 <= coordY && coordY < uniforms.imageShape[1] && 0 <= coordX && coordX < uniforms.imageShape[2]) {
outputValue = getImage(batch, coordY, coordX, channel);
} else {
outputValue = uniforms.fillValue;
}
return outputValue;
}
${Ue()}
if (index < uniforms.size) {
let coords = getCoordsFromIndex(index);
var outputValue : f32;
let batch = coords[0];
let x = coords[2];
let y = coords[1];
let channel = coords[3];
let xf = f32(x);
let yf = f32(y);
let a1 = getTransforms(batch, 0);
let a2 = getTransforms(batch, 1);
let a3 = getTransforms(batch, 2);
let b1 = getTransforms(batch, 3);
let b2 = getTransforms(batch, 4);
let b3 = getTransforms(batch, 5);
let c1 = getTransforms(batch, 6);
let c2 = getTransforms(batch, 7);
let projection = c1 * xf + c2 * yf + 1.0;
if (projection == 0.0) {
outputValue = uniforms.fillValue;
} else {
let inX = (a1 * xf + a2 * yf + a3) / projection;
let inY = (b1 * xf + b2 * yf + b3) / projection;
let mapX = mapCoord(inX, f32(uniforms.imageShape[2]));
let mapY = mapCoord(inY, f32(uniforms.imageShape[1]));
if (uniforms.interpolationModeId == 1) {
let coordY = i32(round(mapY));
let coordX = i32(round(mapX));
outputValue = readWithFillValue(batch, coordY, coordX,
channel);
} else {
let yFloor = floor(mapY);
let xFloor = floor(mapX);
let yCeil = yFloor + 1.0;
let xCeil = xFloor + 1.0;
let valueYFloor = (xCeil - mapX) *
readWithFillValue(batch, i32(yFloor), i32(xFloor), channel) +
(mapX - xFloor) *
readWithFillValue(batch, i32(yFloor), i32(xCeil), channel);
let valueYCeil = (xCeil - mapX) *
readWithFillValue(batch, i32(yCeil), i32(xFloor), channel) +
(mapX - xFloor) *
readWithFillValue(batch, i32(yCeil), i32(xCeil), channel);
outputValue = (yCeil - mapY) * valueYFloor +
(mapY - yFloor) * valueYCeil;
}
}
setOutputAtIndex(index, outputValue);
}
}
`}};function Wie(e){let{inputs:t,backend:n,attrs:s}=e,{image:r,transforms:a}=t,{interpolation:o,fillMode:i,fillValue:u,outputShape:l}=s,[c,p,d,h]=r.shape,[f,m]=l!=null?l:[p,d],g=[c,f,m,h],b=new Vie(g),y=o==="nearest"?1:2,v;switch(i){case"constant":v=1;break;case"reflect":v=2;break;case"wrap":v=3;break;case"nearest":v=4;break;default:v=1;break}let x=[{type:"int32",data:[y]},{type:"int32",data:[v]},{type:"float32",data:[u]}];return n.runWebGPUProgram(b,[r,a],"float32",x)}var Uie={kernelName:ji,backendName:"webgpu",kernelFunc:Wie};function Gie(e){let{inputs:t,backend:n,attrs:s}=e,{value:r}=t,{axis:a}=s;a<0&&(a+=r.shape.length);let o=r,i=o.shape.length,u=r.shape[a],l=new Array(i-1),c=0;for(let m=0;m<i;m++)m!==a&&(l[c++]=o.shape[m]);let p=[],d=new Array(i).fill(0),h=o.shape.slice();h[a]=1;let f=new Array(u);for(let m=0;m<f.length;m++){d[a]=m;let g=fu({inputs:{x:o},backend:n,attrs:{begin:d,size:h}}),b=Le({inputs:{x:g},backend:n,attrs:{shape:l}});f[m]=b,p.push(g)}return p.forEach(m=>n.disposeData(m.dataId)),f}var Hie={kernelName:Ki,backendName:"webgpu",kernelFunc:Gie},qie=[mse,Jse,tre,rre,cre,pre,fre,gre,wre,Cre,Tre,Ere,vse,Ore,Bre,Gre,qre,Kre,Qre,Jre,tae,rae,oae,dae,hae,mae,gae,bae,vae,wae,Sae,_ae,Cae,Tae,Rae,Fae,Pae,Mae,Wae,Gae,qae,yse,Dre,Kae,Yae,Zae,eoe,noe,roe,aoe,ioe,loe,doe,hoe,moe,boe,iae,voe,woe,Soe,kre,Coe,Toe,_oe,Eoe,Doe,Ooe,zoe,Sre,Loe,Boe,Woe,hse,Hoe,Koe,Yoe,Zoe,tie,rie,oie,uie,cie,vre,$ie,Aie,hie,mie,yie,xie,kie,Sie,Cie,die,lae,Rie,Pie,Bie,Uie,ure,Hie,Ioe];for(let e of qie)Ol(e);var jie=class{constructor(e){this.device=e,this.numUsedBuffers=0,this.numFreeBuffers=0,this.freeBuffers=new Map,this.usedBuffers=new Map,this.numBytesUsed=0,this.numBytesAllocated=0}acquireUploadBuffer(e,t){return this.acquireBuffer(e,t,!0)}acquireBuffer(e,t,n=!1){let s=Yw(e,t);if(this.freeBuffers.has(s)||this.freeBuffers.set(s,[]),this.usedBuffers.has(s)||this.usedBuffers.set(s,[]),this.numBytesUsed+=e,this.numUsedBuffers++,this.freeBuffers.get(s).length>0){this.numFreeBuffers--;let a=this.freeBuffers.get(s).shift();return this.usedBuffers.get(s).push(a),a}this.numBytesAllocated+=e;let r=this.device.createBuffer({mappedAtCreation:n,size:e,usage:t});return this.usedBuffers.get(s).push(r),r}releaseBuffer(e,t,n){if(this.freeBuffers.size===0)return;let s=Yw(t,n);this.freeBuffers.has(s)||this.freeBuffers.set(s,[]),this.freeBuffers.get(s).push(e),this.numFreeBuffers++,this.numUsedBuffers--;let r=this.usedBuffers.get(s),a=r.indexOf(e);if(a<0)throw new Error("Cannot release a buffer that was never provided by this buffer manager");r.splice(a,1),this.numBytesUsed-=t}releaseUploadBuffer(e,t,n){e.mapAsync(GPUMapMode.WRITE).then(()=>{this.releaseBuffer(e,t,n)},s=>{})}getNumUsedBuffers(){return this.numUsedBuffers}getNumFreeBuffers(){return this.numFreeBuffers}dispose(){this.freeBuffers.forEach((e,t)=>{e.forEach(n=>{n.destroy()})}),this.usedBuffers.forEach((e,t)=>{e.forEach(n=>{n.destroy()})}),this.freeBuffers=new Map,this.usedBuffers=new Map,this.numUsedBuffers=0,this.numFreeBuffers=0,this.numBytesUsed=0,this.numBytesAllocated=0}};function Yw(e,t){return`${e}_${t}`}var Kie=class{constructor(e){this.device=e,this.numUsedTextures=0,this.numFreeTextures=0,this.freeTextures=new Map,this.usedTextures=new Map,this.numBytesUsed=0,this.numBytesAllocated=0}acquireTexture(e,t,n,s){let r=Zw(n),a=e*t*r,o=Qw(e,t,n,s);if(this.freeTextures.has(o)||this.freeTextures.set(o,[]),this.usedTextures.has(o)||this.usedTextures.set(o,[]),this.numBytesUsed+=a,this.numUsedTextures++,this.freeTextures.get(o).length>0){this.numFreeTextures--;let u=this.freeTextures.get(o).shift();return this.usedTextures.get(o).push(u),u}this.numBytesAllocated+=a;let i=this.device.createTexture({size:[e,t],format:n,usage:s});return this.usedTextures.get(o).push(i),i}releaseTexture(e,t,n,s,r){if(this.freeTextures.size===0)return;let a=Qw(t,n,s,r);this.freeTextures.has(a)||this.freeTextures.set(a,[]),this.freeTextures.get(a).push(e),this.numFreeTextures++,this.numUsedTextures--;let o=this.usedTextures.get(a),i=o.indexOf(e);if(i<0)throw new Error("Cannot release a texture that was never provided by this texture manager");o.splice(i,1);let u=Zw(s),l=t*n*u;this.numBytesUsed-=l}getNumUsedTextures(){return this.numUsedTextures}getNumFreeTextures(){return this.numFreeTextures}dispose(){this.freeTextures.forEach((e,t)=>{e.forEach(n=>{n.destroy()})}),this.usedTextures.forEach((e,t)=>{e.forEach(n=>{n.destroy()})}),this.freeTextures=new Map,this.usedTextures=new Map,this.numUsedTextures=0,this.numFreeTextures=0,this.numBytesUsed=0,this.numBytesAllocated=0}};function Qw(e,t,n,s){return`${e}_${t}_${n}_${s}`}function Zw(e){if(e==="rgba8unorm")return 16;throw new Error(`${e} is not supported!`)}var Xie=K().getNumber("WEBGPU_CPU_HANDOFF_SIZE_THRESHOLD"),Jw=(e,t)=>{let n=e.limits.maxComputeWorkgroupsPerDimension,s=t.dispatchLayout,r=t.dispatch;if(r.every(o=>o<=n))return r;w.assert(r[0]>n&&s.y===void 0&&s.z===void 0,()=>"Dispatch size exceeds WebGPU limits in Y or Z dimension.");let a=Math.ceil(Math.sqrt(r[0]));return a>n?(a=Math.ceil(Math.cbrt(r[0])),w.assert(a<=n,()=>"Total dispatch size exceeds WebGPU maximum."),[a,a,a]):[a,a,1]},sN=class extends il{constructor(e,t=!1){if(super(),this.commandQueueOwnedIds=new WeakSet,this.tensorDisposalQueue=[],this.uniformDisposalQueue=[],this.stagingDisposalQueue=[],this.textureDisposalQueue=[],this.disposed=!1,this.uploadWaitMs=0,this.downloadWaitMs=0,this.dispatchNumberInEncoder=0,this.fromPixelTextureLayout=null,this.fromPixelImportTextureLayout=null,!zv())throw new Error("WebGPU is not supported on this device");this.layoutCache={},this.pipelineCache={},this.device=e,this.queue=e.queue,this.currentCommandEncoder=null,this.currentComputePass=null,this.supportTimeQuery=t,this.bufferManager=new jie(this.device),this.textureManager=new Kie(this.device),this.tensorMap=new Zd(this,ds()),this.supportTimeQuery&&(this.querySet=this.device.createQuerySet({type:"timestamp",count:2})),K().getBool("WEBGPU_USE_PROFILE_TOOL")&&(this.dummyCanvas=document.createElement("canvas"),this.dummyCanvas.width=1,this.dummyCanvas.height=1,this.dummyContext=this.dummyCanvas.getContext("webgpu"),this.dummyContext.configure({device:e,format:"bgra8unorm"}),document.body.appendChild(this.dummyCanvas))}nextDataId(){return sN.nextDataId++}floatPrecision(){return 32}defaultGpuBufferUsage(){return GPUBufferUsage.STORAGE|GPUBufferUsage.COPY_SRC|GPUBufferUsage.COPY_DST}flushDisposalQueue(){this.tensorDisposalQueue.forEach(e=>{this.maybeReleaseBuffer(e),this.tensorMap.delete(e)}),this.uniformDisposalQueue.forEach(e=>this.bufferManager.releaseBuffer(e.buffer,e.byteSize,e.usage)),this.stagingDisposalQueue.forEach(e=>this.bufferManager.releaseUploadBuffer(e.buffer,e.byteSize,e.usage)),this.textureDisposalQueue.forEach(e=>this.textureManager.releaseTexture(e.texture,e.width,e.height,e.format,e.usage)),this.tensorDisposalQueue=[],this.uniformDisposalQueue=[],this.stagingDisposalQueue=[],this.textureDisposalQueue=[]}disposeData(e,t=!1){if(this.tensorMap.has(e)){let n=this.tensorMap.get(e);if(n.refCount--,!t&&n.refCount>0)return!1;if(this.commandQueueOwnedIds.has(e))return this.tensorDisposalQueue.push(e),!1;this.maybeReleaseBuffer(e);let{complexTensorInfos:s}=this.tensorMap.get(e);s!=null&&(this.disposeData(s.real.dataId,!0),this.disposeData(s.imag.dataId,!0)),this.tensorMap.delete(e)}return!0}memory(){return{numBytesInGPU:this.bufferManager.numBytesUsed,numBytesAllocatedInGPU:this.bufferManager.numBytesAllocated,unreliable:!1}}getBufferManager(){return this.bufferManager}getTextureManager(){return this.textureManager}acquireBuffer(e,t=this.defaultGpuBufferUsage()){return this.bufferManager.acquireBuffer(e,t)}maybeReleaseBuffer(e){let t=this.tensorMap.get(e);t!=null&&t.bufferInfo.buffer!=null&&(this.bufferManager.releaseBuffer(t.bufferInfo.buffer,t.bufferInfo.byteSize,t.bufferInfo.usage),t.bufferInfo.buffer=null)}refCount(e){return this.tensorMap.has(e)?this.tensorMap.get(e).refCount:0}incRef(e){let t=this.tensorMap.get(e);t.refCount++}decRef(e){if(this.tensorMap.has(e)){let t=this.tensorMap.get(e);t.refCount--}}write(e,t,n){if(n==="complex64"&&e!=null)throw new Error("Cannot write to a complex64 dtype. Please use tf.complex(real, imag).");let s={id:this.nextDataId()},r=w.sizeFromShape(t)*md(n);return this.tensorMap.set(s,{dtype:n,shape:t,values:e,bufferInfo:{byteSize:r,usage:this.defaultGpuBufferUsage()},refCount:1}),s}move(e,t,n,s,r){if(s==="complex64")throw new Error("Cannot write to a complex64 dtype. Please use tf.complex(real, imag).");let a=w.sizeFromShape(n)*md(s);this.tensorMap.set(e,{dtype:s,shape:n,values:t,bufferInfo:{byteSize:a,usage:this.defaultGpuBufferUsage()},refCount:r})}submitQueue(){this.ensureComputePassEnded(),this.queue.submit([this.currentCommandEncoder.finish()]),this.currentCommandEncoder=null,this.dispatchNumberInEncoder=0,this.commandQueueOwnedIds=new WeakSet,this.flushDisposalQueue()}getBuffer(e){return this.uploadToGPU(e),this.tensorMap.get(e).bufferInfo.buffer}ensureCommandEncoderReady(){this.currentCommandEncoder||(this.currentCommandEncoder=this.device.createCommandEncoder())}ensureComputePassEnded(){this.currentComputePass&&(this.currentComputePass.end(),this.currentComputePass=null)}getComputePass(){return this.currentComputePass||(this.currentComputePass=this.currentCommandEncoder.beginComputePass()),this.currentComputePass}async getBufferData(e,t){let n=this.acquireBuffer(t,GPUBufferUsage.COPY_DST|GPUBufferUsage.MAP_READ);this.ensureCommandEncoderReady(),this.ensureComputePassEnded(),this.currentCommandEncoder.copyBufferToBuffer(e,0,n,0,t),this.submitQueue(),await n.mapAsync(GPUMapMode.READ);let s=n.getMappedRange().slice(0);return n.unmap(),n!=null&&this.bufferManager.releaseBuffer(n,t,GPUBufferUsage.COPY_DST|GPUBufferUsage.MAP_READ),K().getBool("WEBGPU_USE_PROFILE_TOOL")&&(w.assert(this.dummyContext!==void 0,()=>"Fail to get context for profiling tool"),this.dummyContext.getCurrentTexture()),s}convertAndCacheOnCPU(e,t){let n=this.tensorMap.get(e);return this.maybeReleaseBuffer(e),n.values=t,n.values}readSync(e){let t=this.tensorMap.get(e),{values:n}=t;if(n==null)throw new Error("WebGPU readSync is only available for CPU-resident tensors.");return n}async read(e){if(!this.tensorMap.has(e))throw new Error(`Tensor ${e} was not registered!`);let t=this.tensorMap.get(e),{values:n}=t;if(n!=null)return this.convertAndCacheOnCPU(e,n);let s;if(t.dtype==="complex64"){let r=await Promise.all([this.read(t.complexTensorInfos.real.dataId),this.read(t.complexTensorInfos.imag.dataId)]),a=r[0],o=r[1];s=C.mergeRealAndImagArrays(a,o)}else{let r=t.values!=null?t.values:await this.getBufferData(t.bufferInfo.buffer,t.bufferInfo.byteSize);s=P2(r,t.dtype)}return this.convertAndCacheOnCPU(e,s),s}readToGPU(e){let t=this.tensorMap.get(e),{values:n,dtype:s,shape:r,bufferInfo:a}=t;if(s==="complex64")throw new Error("Does not support reading buffer for complex64 dtype.");if(a.buffer==null)throw n!=null?new Error("Data is not on GPU but on CPU."):new Error("There is no data on GPU or CPU.");let o=w.sizeFromShape(r)*md(s),i=this.acquireBuffer(o);this.ensureCommandEncoderReady(),this.ensureComputePassEnded(),this.currentCommandEncoder.copyBufferToBuffer(a.buffer,0,i,0,o),this.submitQueue();let u=this.makeTensorInfo(r,s),l=ds().makeTensorFromTensorInfo(u),c=this.tensorMap.get(u.dataId);return c.bufferInfo.buffer=i,{tensorRef:l,buffer:i,bufSize:o}}bufferSync(e){let t=this.readSync(e.dataId);if(e.dtype==="string")try{let n=t.map(s=>w.decodeString(s));return Ae(e.shape,e.dtype,n)}catch(n){throw new Error("Failed to decode encoded string bytes into utf-8")}return Ae(e.shape,e.dtype,t)}async time(e){let t=this.activeTimers,n=[],s=!1;this.programTimersStack==null?(this.programTimersStack=n,s=!0):this.activeTimers.push(n),this.activeTimers=n,e();let r=w.flatten(this.activeTimers.map(u=>u.query)).filter(u=>u!=null),a=w.flatten(this.activeTimers.map(u=>u.name)).filter(u=>u!=null);this.activeTimers=t,s&&(this.programTimersStack=null);let o={uploadWaitMs:this.uploadWaitMs,downloadWaitMs:this.downloadWaitMs,kernelMs:null,wallMs:null},i=await Promise.all(r);return o.kernelMs=w.sum(i),o.getExtraProfileInfo=()=>i.map((u,l)=>({name:a[l],ms:u})).map(u=>`${u.name}: ${u.ms}`).join(", "),this.uploadWaitMs=0,this.downloadWaitMs=0,o}getAndSavePipeline(e,t){return e in this.pipelineCache||(this.pipelineCache[e]=t()),this.pipelineCache[e]}makeTensorInfo(e,t,n){let s;if(t==="string"&&n!=null&&n.length>0&&w.isString(n[0])){let r=n.map(a=>w.encodeString(a));s=this.write(r,e,t)}else s=this.write(n,e,t);return{dataId:s,shape:e,dtype:t}}tensorToBinding(e){if(!e)return null;let t=this.tensorMap.get(e.dataId);return{offset:0,size:t.bufferInfo.byteSize,buffer:t.bufferInfo.buffer}}async getQueryTime(e){return this.supportTimeQuery?this.getTimeFromQuerySet(e):0}uploadToGPU(e){let t=this.tensorMap.get(e);if(t.bufferInfo.buffer==null&&(t.bufferInfo.buffer=this.acquireBuffer(t.bufferInfo.byteSize),t.values)){let n=this.bufferManager.acquireUploadBuffer(t.bufferInfo.byteSize,GPUBufferUsage.MAP_WRITE|GPUBufferUsage.COPY_SRC),s=n.getMappedRange();t.dtype==="int32"||t.dtype==="bool"?new Int32Array(s).set(t.values):new Float32Array(s).set(t.values),n.unmap(),this.ensureCommandEncoderReady(),this.ensureComputePassEnded(),this.currentCommandEncoder.copyBufferToBuffer(n,0,t.bufferInfo.buffer,0,t.bufferInfo.byteSize);let r={byteSize:t.bufferInfo.byteSize,usage:GPUBufferUsage.MAP_WRITE|GPUBufferUsage.COPY_SRC,buffer:n};this.stagingDisposalQueue.push(r)}}makeUniforms(e){let t=0,n=0,s=[];e.forEach(i=>{i.data.length===0&&(i.data=[1]);let u;switch(i.data.length){case 1:u=4;break;case 2:u=8;break;case 3:u=16;break;case 4:u=16;break;case 5:u=16;break;case 6:u=16;break;default:w.assert(!1,()=>`Unsupported ${i.data.length}D shape`)}(n===5||n===6)&&(u=16),t=Math.ceil(t/u)*u,n=i.data.length,s.push(t),t+=i.data.length*4});let r=new ArrayBuffer(t);e.forEach((i,u)=>{let l=s[u];i.type==="int32"?new Int32Array(r,l,i.data.length).set(i.data):i.type==="uint32"?new Uint32Array(r,l,i.data.length).set(i.data):new Float32Array(r,l,i.data.length).set(i.data)});let a=this.acquireBuffer(t,GPUBufferUsage.COPY_DST|GPUBufferUsage.UNIFORM);this.queue.writeBuffer(a,0,r,0,t);let o={byteSize:t,usage:GPUBufferUsage.COPY_DST|GPUBufferUsage.UNIFORM,buffer:a};return this.uniformDisposalQueue.push(o),{offset:0,size:t,buffer:a}}createLayout(e){let t=[];t.push({binding:0,visibility:GPUShaderStage.COMPUTE,buffer:{type:"storage"}});for(let r=0;r<e;r++)t.push({binding:r+1,visibility:GPUShaderStage.COMPUTE,buffer:{type:"read-only-storage"}});t.push({binding:e+1,visibility:GPUShaderStage.COMPUTE,buffer:{type:"uniform"}});let n=this.device.createBindGroupLayout({entries:t}),s=this.device.createPipelineLayout({bindGroupLayouts:[n]});return{bindGroupLayout:n,pipelineLayout:s}}getCachedOrCreateLayout(e){return e in this.layoutCache||(this.layoutCache[e]=this.createLayout(e)),this.layoutCache[e]}makeBindGroup(e,t,n,s,r){let a=[s,...n];return r&&a.push(r),e.createBindGroup({layout:t,entries:a.map((o,i)=>({binding:i,resource:o}))})}runWebGPUProgram(e,t,n,s,r){if(!r){if(r=this.makeTensorInfo(e.outputShape,n),w.sizeFromShape(r.shape)===0){let I=this.tensorMap.get(r.dataId);return I.values=w.getTypedArrayFromDType(r.dtype,0),r}this.uploadToGPU(r.dataId)}e.dispatch=Jw(this.device,e);let a=[{type:"float32",data:[NaN]}],o=t.concat(r).map(I=>I.shape),i="int32";o.map(I=>{a.push({type:i,data:I})});let u=w.computeStrides(r.shape);if(a.push({type:i,data:u}),e.size){let I=w.sizeFromShape(e.outputShape);a.push({type:i,data:[e.isVec4?I/4:I]})}s&&(a=[...a,...s]);let l=this.makeUniforms(a),c=t.map((I,$)=>{if(I.dtype==="complex64")throw new Error("GPGPUProgram does not support complex64 input. For complex64 dtypes, please separate the program into real and imaginary parts.");return this.uploadToGPU(I.dataId),{dtype:this.tensorMap.get(I.dataId).dtype,shape:I.shape,name:e.variableNames[$]}}),p=c.map(I=>I.dtype).concat(r.dtype),d=c.map(I=>C.getBroadcastDims(I.shape,r.shape)),h=c.map(I=>w.arraysEqual(I.shape,r.shape)).join("_"),f=d.map(I=>I.join("_")).join(";"),m=Mw(e,o,p,f,h),{bindGroupLayout:g,pipelineLayout:b}=this.getCachedOrCreateLayout(e.variableNames.length),y=this.getAndSavePipeline(m,()=>Lw(this.device,e,b,c,r)),v=this.activeTimers!=null,x=this.makeBindGroup(this.device,g,t.map(I=>this.tensorToBinding(I)),this.tensorToBinding(r),l);this.ensureCommandEncoderReady();let k=this.getComputePass();return v&&this.supportTimeQuery&&k.writeTimestamp(this.querySet,0),k.setPipeline(y),k.setBindGroup(0,x),k.dispatchWorkgroups(e.dispatch[0],e.dispatch[1],e.dispatch[2]),v&&this.supportTimeQuery&&k.writeTimestamp(this.querySet,1),this.dispatchNumberInEncoder++,t.forEach(I=>{this.commandQueueOwnedIds.add(I.dataId)}),this.commandQueueOwnedIds.add(r.dataId),K().get("WEBGPU_DEFERRED_SUBMIT_BATCH_SIZE")<=this.dispatchNumberInEncoder&&this.submitQueue(),v&&this.activeTimers.push({name:e.constructor.name,query:this.getQueryTime(this.querySet)}),r}getFromPixelTextureLayout(e){return e?(this.fromPixelImportTextureLayout===null&&(this.fromPixelImportTextureLayout=this.createFromPixelTextureLayout(!0)),this.fromPixelImportTextureLayout):(this.fromPixelTextureLayout===null&&(this.fromPixelTextureLayout=this.createFromPixelTextureLayout(!1)),this.fromPixelTextureLayout)}createFromPixelTextureLayout(e){let t=[];t.push({binding:0,visibility:GPUShaderStage.COMPUTE,buffer:{type:"storage"}}),e?t.push({binding:1,visibility:GPUShaderStage.COMPUTE,externalTexture:{}}):t.push({binding:1,visibility:GPUShaderStage.COMPUTE,texture:{}}),t.push({binding:2,visibility:GPUShaderStage.COMPUTE,buffer:{}});let n=this.device.createBindGroupLayout({entries:t}),s=this.device.createPipelineLayout({bindGroupLayouts:[n]});return{bindGroupLayout:n,pipelineLayout:s}}copyExternalImageToTexture(e,t){let n=GPUTextureUsage.COPY_DST|GPUTextureUsage.RENDER_ATTACHMENT|GPUTextureUsage.TEXTURE_BINDING,s="rgba8unorm",r=this.textureManager.acquireTexture(t[1],t[0],s,n),a=r.createView();this.queue.copyExternalImageToTexture({source:e},{texture:r},[t[1],t[0]]);let o={width:t[1],height:t[0],format:s,usage:n,texture:r};return this.textureDisposalQueue.push(o),a}runFromPixelsProgram(e,t,n,s,r){e.dispatch=Jw(this.device,e);let a=this.makeTensorInfo(t,"int32");if(w.sizeFromShape(a.shape)===0){let m=this.tensorMap.get(a.dataId);return m.values=w.getTypedArrayFromDType(a.dtype,0),a}this.uploadToGPU(a.dataId);let o=Mw(e,[a.shape]),i=this.getFromPixelTextureLayout(s),u=this.getAndSavePipeline(o,()=>Lw(this.device,e,i.pipelineLayout,[],a,!0)),l;if(s){let m={source:r};l=this.device.importExternalTexture(m)}else l=this.copyExternalImageToTexture(r,a.shape);let c=this.tensorToBinding(a),p=this.makeUniforms(n),d=this.device.createBindGroup({layout:i.bindGroupLayout,entries:[{binding:0,resource:{buffer:c.buffer}},{binding:1,resource:l},{binding:2,resource:{buffer:p.buffer}}]});this.ensureCommandEncoderReady();let h=this.getComputePass(),f=this.activeTimers!=null;return f&&this.supportTimeQuery&&h.writeTimestamp(this.querySet,0),h.setPipeline(u),h.setBindGroup(0,d),h.dispatchWorkgroups(e.dispatch[0],e.dispatch[1],e.dispatch[2]),f&&this.supportTimeQuery&&h.writeTimestamp(this.querySet,1),this.commandQueueOwnedIds.add(a.dataId),this.dispatchNumberInEncoder++,K().get("WEBGPU_DEFERRED_SUBMIT_BATCH_SIZE")<=this.dispatchNumberInEncoder&&this.submitQueue(),f&&this.activeTimers.push({name:e.constructor.name,query:this.getQueryTime(this.querySet)}),a}async getTimeFromQuerySet(e){let t=this.acquireBuffer(16,GPUBufferUsage.COPY_SRC|GPUBufferUsage.QUERY_RESOLVE),n=this.acquireBuffer(16,GPUBufferUsage.MAP_READ|GPUBufferUsage.COPY_DST);this.ensureCommandEncoderReady(),this.ensureComputePassEnded(),this.currentCommandEncoder.resolveQuerySet(e,0,2,t,0),this.currentCommandEncoder.copyBufferToBuffer(t,0,n,0,16),this.submitQueue(),await n.mapAsync(GPUMapMode.READ);let s=new BigUint64Array(n.getMappedRange()),r=Number(s[1]-s[0]);return n.unmap(),this.bufferManager.releaseBuffer(n,16,GPUBufferUsage.MAP_READ|GPUBufferUsage.COPY_DST),this.bufferManager.releaseBuffer(t,16,GPUBufferUsage.COPY_SRC|GPUBufferUsage.QUERY_RESOLVE),r/1e6}shouldExecuteOnCPU(e,t=Xie){return K().getBool("WEBGPU_CPU_FORWARD")&&e.every(n=>this.tensorMap.get(n.dataId).bufferInfo.buffer==null&&w.sizeFromShape(n.shape)<t)}numDataIds(){return this.tensorMap.numDataIds()-this.tensorDisposalQueue.length}dispose(){this.disposed||(this.bufferManager.dispose(),this.textureManager.dispose(),this.disposed=!0)}},Wv=sN;Wv.nextDataId=0;var Yie={};Ee(Yie,{WebGPUBackend:()=>Wv,webgpu_util:()=>F2});zv()&&xp("webgpu",async()=>{K().set("CHECK_COMPUTATION_FOR_ERRORS",!1);let e={powerPreference:K().get("WEBGPU_USE_LOW_POWER_GPU")?"low-power":"high-performance"},t=await navigator.gpu.requestAdapter(e),n=t.limits,s={},r=t.features.has("timestamp-query");s.requiredLimits={maxComputeWorkgroupStorageSize:n.maxComputeWorkgroupStorageSize,maxComputeWorkgroupsPerDimension:n.maxComputeWorkgroupsPerDimension},r?s.requiredFeatures=["timestamp-query"]:console.warn("This device doesn't support timestamp-query extension. Start Chrome browser with flag --disable-dawn-features=disallow_unsafe_apis then try again. Or zero will shown for the kernel time when profiling mode isenabled. Using performance.now is not workable for webgpu sinceit doesn't support synchronously to read data from GPU.");let a=await t.requestDevice(s);return new Wv(a,r)},3);var St=(e=>(e[e.float32=0]="float32",e[e.int32=1]="int32",e[e.bool=2]="bool",e[e.string=3]="string",e[e.complex64=4]="complex64",e))(St||{}),uh=(e=>(e[e.linear=0]="linear",e[e.relu=1]="relu",e[e.relu6=2]="relu6",e[e.prelu=3]="prelu",e[e.leakyrelu=4]="leakyrelu",e[e.sigmoid=5]="sigmoid",e[e.elu=6]="elu",e))(uh||{}),rN;function Qie(e){rN=e.wasm.cwrap(oa,null,["number","array","number","number","array","number","number","number","number","number","number","number","number"])}function Zie(e){let{inputs:t,backend:n,attrs:s}=e,{a:r,b:a,bias:o,preluActivationWeights:i}=t;if(r.dtype!=="float32"||a.dtype!=="float32")throw new Error("_FusedMatMul for non non-float32 tensors not yet supported.");let{transposeA:u,transposeB:l,activation:c,leakyreluAlpha:p}=s,d=n.dataIdMap.get(r.dataId).id,h=n.dataIdMap.get(a.dataId).id,f=0;if(o!=null){let R=n.dataIdMap.get(o.dataId);if(R.shape.length!==1)throw new Error(`_FusedMatMul only supports rank-1 bias but got rank ${R.shape.length}.`);f=R.id}let m=i==null?0:n.dataIdMap.get(i.dataId).id,g=uh[c];if(g==null)throw new Error(`${c} activation not yet supported for FusedConv2D in the wasm backend.`);let b=u?r.shape[2]:r.shape[1],y=l?a.shape[1]:a.shape[2],v=Qi.assertAndGetBroadcastShape(r.shape.slice(0,-2),a.shape.slice(0,-2)),x=n.makeOutput([...v,b,y],r.dtype),k=n.dataIdMap.get(x.dataId).id,I=new Uint8Array(new Int32Array(r.shape).buffer),$=new Uint8Array(new Int32Array(a.shape).buffer);return rN(d,I,r.shape.length,h,$,a.shape.length,u,l,g,f,m,p||0,k),x}var Jie={kernelName:oa,backendName:"wasm",setupFunc:Qie,kernelFunc:Zie};function Ht(e,t){let n;function s(a){n=a.wasm.cwrap(e,null,["number","number","number"])}function r(a){let{backend:o,inputs:{x:i}}=a,u=o.dataIdMap.get(i.dataId).id,l=o.makeOutput(i.shape,t||i.dtype),c=o.dataIdMap.get(l.dataId).id;return w.sizeFromShape(l.shape)===0||n(u,St[i.dtype],c),l}return{kernelName:e,backendName:"wasm",setupFunc:s,kernelFunc:r}}var eue=Ht(di);function Xt(e,t,n){let s;function r(o){s=o.wasm.cwrap(e,null,["number","array","number","number","array","number","number","number"])}function a(o){let{backend:i,inputs:u}=o,{a:l,b:c}=u,p=i.dataIdMap.get(l.dataId).id,d=i.dataIdMap.get(c.dataId).id,h=n!=null?n:l.dtype,f=C.assertAndGetBroadcastShape(l.shape,c.shape),m=i.makeOutput(f,h);if(w.sizeFromShape(f)===0)return m;let g=new Uint8Array(new Int32Array(l.shape).buffer),b=new Uint8Array(new Int32Array(c.shape).buffer),y=i.dataIdMap.get(m.dataId).id;return(()=>s(p,g,l.shape.length,d,b,c.shape.length,St[l.dtype],y))(),m}return{kernelName:e,backendName:"wasm",setupFunc:r,kernelFunc:a}}var tue=!0,nue=Xt(Cr,tue),aN;function sue(e){aN=e.wasm.cwrap(Sa,null,["array","number","number","number"])}function rue(e){let{inputs:t,backend:n}=e,s=n.makeOutput(t[0].shape,t[0].dtype);if(w.sizeFromShape(s.shape)===0)return s;let r=t.map(i=>n.dataIdMap.get(i.dataId).id),a=new Uint8Array(new Int32Array(r).buffer),o=n.dataIdMap.get(s.dataId).id;return aN(a,r.length,St[s.dtype],o),s}var aue={kernelName:Sa,backendName:"wasm",setupFunc:sue,kernelFunc:rue};function lh(e){let{inputs:{x:t},backend:n}=e,s=n.makeOutput(t.shape,t.dtype),r=n.typedArrayFromHeap(t);return n.typedArrayFromHeap(s).set(r),s}var oue={kernelName:Wa,backendName:"wasm",kernelFunc:lh},oN;function iue(e){oN=e.wasm.cwrap(Hs,null,["number","array","number","number","number","array","number"])}function Sr(e){let{inputs:t,backend:n,attrs:s}=e,[r,a]=lue(t.x.shape,s.perm),o=!0;for(let f=0;f<a.length;f++)a[f]!==f&&(o=!1);let i=uue(t.x.shape,s.perm),u={dataId:t.x.dataId,shape:r,dtype:t.x.dtype};if(o){let f=lh({inputs:t,backend:n});return f.shape=i,f}let l=n.makeOutput(i,u.dtype),c=n.dataIdMap.get(u.dataId).id,p=n.dataIdMap.get(l.dataId).id,d=new Uint8Array(new Int32Array(a).buffer),h=new Uint8Array(new Int32Array(u.shape).buffer);return oN(c,h,u.shape.length,St[u.dtype],p,d,a.length),l}function uue(e,t){let n=new Array(e.length);for(let s=0;s<n.length;s++)n[s]=e[t[s]];return n}function lue(e,t){let n=[],s=[];for(let r=0;r<e.length;++r)e[r]!==1&&n.push(e[r]),e[t[r]]!==1&&s.push(t[r]);for(let r=0;r<s.length;++r){let a=-1;for(let o=0;o<s.length;++o)s[o]>=r&&(a===-1||s[a]>s[o])&&(a=o);s[a]=r}return[n,s]}var cue={kernelName:Hs,backendName:"wasm",kernelFunc:Sr,setupFunc:iue};function Pr(e,t,n){let s=e.shape,r=e.shape.length,a=w.parseAxisParam(t,s),o=a,i=C.getAxesPermutation(o,r),u=null,l=!1;if(i!=null){let c=new Array(r);for(let h=0;h<c.length;h++)c[h]=s[i[h]];o=C.getInnerMostAxes(o.length,r),u=Sr({inputs:{x:e},attrs:{perm:i},backend:n});let p=n.dataIdMap.get(e.dataId).id;n.dataIdMap.get(u.dataId).id!==p&&(l=!0)}return{transposed:u,originalAxes:a,axes:o,inputWasTransposed:l}}var iN;function due(e){iN=e.wasm.cwrap(cl,null,["number, number, number"])}function pue(e){let{backend:t,inputs:n,attrs:s}=e,{axis:r,keepDims:a}=s,{x:o}=n,u=t.dataIdMap.get(o.dataId).id,l=o,{transposed:c,axes:p,originalAxes:d,inputWasTransposed:h}=Pr(o,r,t);if(h){let v=t.dataIdMap.get(c.dataId).id;l=c,u=v}let f=l.shape.length;C.assertAxesAreInnerMostDims("all",p,f);let[m,g]=C.computeOutAndReduceShapes(l.shape,p),b=w.sizeFromShape(g),y=t.makeOutput(m,o.dtype);if(w.sizeFromShape(l.shape)!==0){let v=t.dataIdMap.get(y.dataId).id;iN(u,b,v)}if(h&&t.disposeData(c.dataId),a){let v=C.expandShapeToKeepDim(y.shape,d);y.shape=v}return y}var hue={kernelName:cl,backendName:"wasm",setupFunc:due,kernelFunc:pue},uN;function fue(e){uN=e.wasm.cwrap(dl,null,["number, number, number"])}function mue(e){let{backend:t,inputs:n,attrs:s}=e,{axis:r,keepDims:a}=s,{x:o}=n,u=t.dataIdMap.get(o.dataId).id,l=o,{transposed:c,axes:p,originalAxes:d,inputWasTransposed:h}=Pr(o,r,t);if(h){let v=t.dataIdMap.get(c.dataId).id;l=c,u=v}let f=l.shape.length;C.assertAxesAreInnerMostDims("any",p,f);let[m,g]=C.computeOutAndReduceShapes(l.shape,p),b=w.sizeFromShape(g),y=t.makeOutput(m,o.dtype);if(w.sizeFromShape(l.shape)!==0){let v=t.dataIdMap.get(y.dataId).id;uN(u,b,v)}if(h&&t.disposeData(c.dataId),a){let v=C.expandShapeToKeepDim(y.shape,d);y.shape=v}return y}var gue={kernelName:dl,backendName:"wasm",setupFunc:fue,kernelFunc:mue},lN;function bue(e){lN=e.wasm.cwrap(Ia,null,["number","number","number","number","number"])}function yue(e){let{backend:t,inputs:n,attrs:s}=e,{axis:r}=s,{x:a}=n,o=t.dataIdMap.get(a.dataId).id,i=o,u=a,{transposed:l,axes:c,inputWasTransposed:p}=Pr(a,r,t);if(p){let b=t.dataIdMap.get(l.dataId).id;b!==o&&(u=l,i=b)}let d=u.shape.slice(0,-1),h=t.makeOutput(d,"int32"),f=t.dataIdMap.get(h.dataId).id,m=w.sizeFromShape(h.shape),g=u.shape[c[0]];return lN(i,St[u.dtype],m,g,f),p&&t.disposeData(l.dataId),h}var vue={kernelName:Ia,backendName:"wasm",kernelFunc:yue,setupFunc:bue},cN;function xue(e){cN=e.wasm.cwrap(Ca,null,["number","number","number","number","number","number","number","number","number","number","number","number","number","number"])}function wue(e){let{inputs:t,attrs:n,backend:s}=e,r=t.x,a=s.dataIdMap.get(r.dataId).id,{filterSize:o,strides:i,pad:u,dimRoundingMode:l}=n,c=C.computePool2DInfo(r.shape,o,i,1,u,l),p=c.filterHeight,d=c.filterWidth,h=c.padInfo.top,f=c.padInfo.right,m=c.padInfo.bottom,g=c.padInfo.left,b=c.strideHeight,y=c.strideWidth,v=c.inChannels;if(c.dataFormat!=="channelsLast")throw new Error(`wasm backend does not support dataFormat:'${c.dataFormat}'. 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t.dtype==="string"?p.stringBytes=u.slice(f,f+w.sizeFromShape(o)):r.typedArrayFromHeap(l).set(u.subarray(f,f+w.sizeFromShape(o))),l}if(t.dtype==="string"){let f=Vd(u,a,o,t.shape,t.dtype);return p.stringBytes=f,l}let d=r.typedArrayFromHeap(l),h=t.shape.length;if(h===2)Tue(u,c[0],d,a,o);else if(h===3)$ue(u,c[0],c[1],d,a,o);else if(h===4)_ue(u,c[0],c[1],c[2],d,a,o);else{let f=Vd(u,a,o,t.shape,t.dtype);d.set(f)}return l}function Tue(e,t,n,s,r){let a=0,o=s[0],i=s[1],u=o+r[0];for(let l=o;l<u;l++){let c=l*t+i;n.set(e.subarray(c,c+r[1]),a),a+=r[1]}}function $ue(e,t,n,s,r,a){let o=0,i=r[0],u=r[1],l=r[2],c=i+a[0],p=u+a[1];for(let d=i;d<c;d++)for(let h=u;h<p;h++){let f=d*t+h*n+l;s.set(e.subarray(f,f+a[2]),o),o+=a[2]}}function _ue(e,t,n,s,r,a,o){let i=0,u=a[0],l=a[1],c=a[2],p=u+o[0],d=l+o[1],h=c+o[2],f=a[3];for(let m=u;m<p;m++)for(let g=l;g<d;g++)for(let b=c;b<h;b++){let y=m*t+g*n+b*s+f;r.set(e.subarray(y,y+o[3]),i),i+=o[3]}}var Aue={kernelName:Bi,backendName:"wasm",kernelFunc:xa};function Eue(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{blockShape:a,crops:o}=s,i=a.reduce((b,y)=>b*y),u=C.getReshaped(r.shape,a,i),l=C.getPermuted(u.length,a.length),c=C.getReshapedPermuted(r.shape,a,i),p=C.getSliceBeginCoords(o,a.length),d=C.getSliceSize(c,o,a.length),h=yn({inputs:{x:r},backend:n,attrs:{shape:u}}),f=Sr({inputs:{x:h},backend:n,attrs:{perm:l}}),m=yn({inputs:{x:f},backend:n,attrs:{shape:c}}),g=xa({inputs:{x:m},backend:n,attrs:{begin:p,size:d}});return n.disposeData(h.dataId),n.disposeData(f.dataId),n.disposeData(h.dataId),g}var Rue={kernelName:pi,backendName:"wasm",kernelFunc:Eue};function lc(e){let{inputs:{x:t},attrs:{dtype:n},backend:s}=e,r=s.makeOutput(t.shape,n),a=s.typedArrayFromHeap(t);return s.typedArrayFromHeap(r).set(a),r}var Due={kernelName:Ta,backendName:"wasm",kernelFunc:lc},Fue=Ht($a),pN;function Oue(e){pN=e.wasm.cwrap(Nr,null,["number","number","number","number"])}function Pue(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{clipValueMin:a,clipValueMax:o}=s,i=n.dataIdMap.get(r.dataId).id,u=n.makeOutput(r.shape,r.dtype),l=n.dataIdMap.get(u.dataId).id;return pN(i,a,o,l),u}var zue={kernelName:Nr,backendName:"wasm",setupFunc:Oue,kernelFunc:Pue};function hN(e){let{inputs:t,backend:n}=e,s=w.parseAxisParam(e.attrs.axis,t[0].shape)[0],r=C.computeOutShape(t.map(h=>h.shape),s),a=t.filter(h=>w.sizeFromShape(h.shape)>0);if(a.length===1)return lh({inputs:{x:a[0]},backend:n});let o=n.makeOutput(r,t[0].dtype);if(w.sizeFromShape(r)===0)return o;let i=a.map(h=>h.shape);if(C.assertParamsConsistent(i,s),a[0].dtype==="string"){let h=a.map(v=>{let x=w.sizeFromShape(v.shape.slice(s));return yn({inputs:{x:v},backend:n,attrs:{shape:[-1,x]}})}),f=h.map(v=>({vals:n.readSync(v.dataId),shape:v.shape}));r=C.computeOutShape(h.map(v=>v.shape),1);let m=h[0].shape[0]===1,g=hv(f,r,t[0].dtype,m),b=C.computeOutShape(a.map(v=>v.shape),s);o.shape=b;let y=n.dataIdMap.get(o.dataId);return y.stringBytes=C.fromStringArrayToUint8(g),h.forEach(v=>n.disposeData(v.dataId)),o}let u=w.sizeFromShape(a[0].shape.slice(0,s)),l=0,c=a.map(h=>{let f=w.sizeFromShape(h.shape.slice(s));return l+=f,f}),p=a.map(h=>n.typedArrayFromHeap(h)),d=n.typedArrayFromHeap(o);for(let h=0;h<u;h++){let f=h*l;for(let m=0;m<p.length;m++){let g=c[m],b=h*g,y=p[m].subarray(b,b+g);d.set(y,f),f+=g}}return o}var Lue={kernelName:hi,backendName:"wasm",kernelFunc:hN},fN;function Mue(e){fN=e.wasm.cwrap(_a,null,["number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number"])}function Bue(e){let{inputs:t,attrs:n,backend:s}=e,{x:r,filter:a}=t,o=s.dataIdMap.get(r.dataId).id,i=s.dataIdMap.get(a.dataId).id,{strides:u,dilations:l,pad:c,dimRoundingMode:p,dataFormat:d}=n,h=C.convertConv2DDataFormat(d),f=C.computeConv2DInfo(r.shape,a.shape,u,l,c,p,!1,h),m=f.filterHeight,g=f.filterWidth,b=f.padInfo.top,y=f.padInfo.right,v=f.padInfo.bottom,x=f.padInfo.left,k=f.dilationHeight,I=f.dilationWidth,$=f.strideHeight,R=f.strideWidth,E=f.inChannels,P=f.outChannels,A=f.padInfo.type==="SAME"?1:0;if(f.dataFormat!=="channelsLast")throw new Error(`wasm backend Conv2D does not support dataFormat:'${f.dataFormat}'. 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Uue(e){let{backend:t,inputs:n,attrs:s}=e,{dy:r,filter:a}=n,{strides:o,pad:i,dataFormat:u,dimRoundingMode:l,inputShape:c}=s,p=1,d=C.convertConv2DDataFormat(u),h=C.computeConv2DInfo(c,a.shape,o,p,i,l,!1,d),{batchSize:f,filterHeight:m,filterWidth:g,inChannels:b,inHeight:y,inWidth:v,outChannels:x,outHeight:k,outWidth:I,strideHeight:$,strideWidth:R}=h,E=m-1-h.padInfo.top,P=g-1-h.padInfo.left,A=h.dataFormat==="channelsLast",D=w.computeStrides(h.inShape),T=w.computeStrides(r.shape),[L,W,j]=w.computeStrides(a.shape),Y=D[0],X=A?D[1]:D[2],Z=A?D[2]:1,ne=A?1:D[1],ee=T[0],se=A?T[1]:T[2],te=A?T[2]:1,ie=A?1:T[1],ae=t.makeOutput(h.inShape,"float32"),de=t.dataIdMap.get(ae.dataId).id,me=t.dataIdMap.get(r.dataId).id,ke=t.dataIdMap.get(a.dataId).id;return mN(me,ke,f,m,g,y,v,b,k,I,x,$,R,E,P,L,W,j,Y,X,Z,ne,ee,se,te,ie,de),ae}var Gue={kernelName:Aa,backendName:"wasm",setupFunc:Wue,kernelFunc:Uue},Hue=Ht(Ea),que=Ht(Ra),gN=(e=>(e[e.bilinear=0]="bilinear",e[e.nearest=1]="nearest",e))(gN||{}),bN;function jue(e){bN=e.wasm.cwrap(mi,null,["number","number","number","number","array","number","number","number","number","number"])}function Kue(e){let{backend:t,inputs:n,attrs:s}=e,{method:r,extrapolationValue:a,cropSize:o}=s,{image:i,boxes:u,boxInd:l}=n,c=u.shape[0],[p,d]=o,h=[c,p,d,i.shape[3]],f=t.dataIdMap.get(i.dataId),m;i.dtype!=="float32"&&(m=lc({backend:t,inputs:{x:i},attrs:{dtype:"float32"}}),f=t.dataIdMap.get(m.dataId));let g=f.id,b=t.dataIdMap.get(u.dataId).id,y=t.dataIdMap.get(l.dataId).id,v=t.makeOutput(h,"float32"),x=t.dataIdMap.get(v.dataId).id,k=new Uint8Array(new Int32Array(i.shape).buffer);return bN(g,b,y,c,k,p,d,gN[r],a,x),m!=null&&t.disposeData(m.dataId),v}var Xue={kernelName:mi,backendName:"wasm",setupFunc:jue,kernelFunc:Kue},yN;function Yue(e){yN=e.wasm.cwrap(fi,null,["number","number","number","number","number","number"])}function Que(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{axis:a,exclusive:o,reverse:i}=s,u=r.shape.length;w.assert(r.dtype==="float32"||r.dtype==="int32",()=>`cumprod does not support ${r.dtype} tensors in the WASM backend`);let l=C.getAxesPermutation([a],u),c=r;l!==null&&(c=Sr({inputs:{x:r},attrs:{perm:l},backend:n}));let p=C.getInnerMostAxes(1,u)[0];C.assertAxesAreInnerMostDims("cumprod",[p],u);let d=n.makeOutput(c.shape,c.dtype),h=c.shape[p],f=n.dataIdMap.get(c.dataId).id,m=n.dataIdMap.get(d.dataId).id;yN(f,o?1:0,i?1:0,h,m,St[r.dtype]);let g=d;if(l!==null){let b=C.getUndoAxesPermutation(l);g=Sr({inputs:{x:d},attrs:{perm:b},backend:n}),n.disposeData(c.dataId),n.disposeData(d.dataId)}return g}var Zue={kernelName:fi,backendName:"wasm",setupFunc:Yue,kernelFunc:Que},vN;function Jue(e){vN=e.wasm.cwrap(Da,null,["number","number","number","number","number","number"])}function ele(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{axis:a,exclusive:o,reverse:i}=s,u=r.shape.length;w.assert(r.dtype==="float32"||r.dtype==="int32",()=>`cumsum does not support ${r.dtype} tensors in the WASM backend`);let l=C.getAxesPermutation([a],u),c=r;l!==null&&(c=Sr({inputs:{x:r},attrs:{perm:l},backend:n}));let p=C.getInnerMostAxes(1,u)[0];C.assertAxesAreInnerMostDims("cumsum",[p],u);let d=n.makeOutput(c.shape,c.dtype),h=c.shape[p],f=n.dataIdMap.get(c.dataId).id,m=n.dataIdMap.get(d.dataId).id;vN(f,o?1:0,i?1:0,h,m,St[r.dtype]);let g=d;if(l!==null){let b=C.getUndoAxesPermutation(l);g=Sr({inputs:{x:d},attrs:{perm:b},backend:n}),n.disposeData(c.dataId),n.disposeData(d.dataId)}return g}var tle={kernelName:Da,backendName:"wasm",setupFunc:Jue,kernelFunc:ele},xN;function nle(e){xN=e.wasm.cwrap(gi,null,["number","number","number","array","number","array","array","number","number"])}function sle(e){let{backend:t,inputs:n,attrs:s}=e,{x:r}=n,{blockSize:a,dataFormat:o}=s,i=r.shape[0],u=o==="NHWC"?r.shape[1]:r.shape[2],l=o==="NHWC"?r.shape[2]:r.shape[3],c=o==="NHWC"?r.shape[3]:r.shape[1],p=u*a,d=l*a,h=c/(a*a),f=o==="NHWC"?[i,p,d,h]:[i,h,p,d],m=t.makeOutput(f,"float32"),b=t.dataIdMap.get(r.dataId).id,y=new Uint8Array(new Int32Array(w.computeStrides(r.shape)).buffer),v=new Uint8Array(new Int32Array(f).buffer),x=new Uint8Array(new Int32Array(w.computeStrides(f)).buffer),k=t.dataIdMap.get(m.dataId).id;return xN(b,a,o==="NHWC"?1:0,y,r.shape.length-1,v,x,f.length,k),m}var rle={kernelName:gi,backendName:"wasm",setupFunc:nle,kernelFunc:sle},wN;function ale(e){wN=e.wasm.cwrap(Fa,null,["number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number"])}function ole(e){let{inputs:t,attrs:n,backend:s}=e,{x:r,filter:a}=t,o=s.dataIdMap.get(r.dataId).id,i=s.dataIdMap.get(a.dataId).id,{strides:u,dilations:l,pad:c,dimRoundingMode:p}=n,d=l==null?[1,1]:l,h=C.computeConv2DInfo(r.shape,a.shape,u,d,c,p,!0),f=h.filterHeight,m=h.filterWidth,g=h.padInfo.top,b=h.padInfo.right,y=h.padInfo.bottom,v=h.padInfo.left,x=h.dilationHeight,k=h.dilationWidth,I=h.strideHeight,$=h.strideWidth,R=h.inChannels,E=h.outChannels,P=h.padInfo.type==="SAME"?1:0;if(h.dataFormat!=="channelsLast")throw new Error(`wasm backend DepthwiseConv2dNative does not support dataFormat:'${h.dataFormat}'. Please use 'channelsLast'.`);let A=s.makeOutput(h.outShape,"float32"),D=s.dataIdMap.get(A.dataId).id;return wN(o,r.shape[0],r.shape[1],r.shape[2],i,f,m,g,b,y,v,P,x,k,I,$,R,E,D),A}var ile={kernelName:Fa,backendName:"wasm",setupFunc:ale,kernelFunc:ole},ule=Ht(Pa),lle=!1,cle=Xt(bi,lle,"bool"),dle=Ht(za,"float32");function lg(e){let{inputs:t,attrs:n,backend:s}=e,{input:r}=t,{dim:a}=n,o=r.shape.length,i=r.shape.slice(),u=a;return a<0&&(w.assert(-(o+1)<=a,()=>`Axis must be in the interval [${-(o+1)}, ${o}]`),u=o+a+1),i.splice(u,0,1),yn({inputs:{x:r},backend:s,attrs:{shape:i}})}var ple={kernelName:yi,backendName:"wasm",kernelFunc:lg};function kN(e){let{attrs:{shape:t,value:n,dtype:s},backend:r}=e,a=r.makeOutput(t,s);return r.typedArrayFromHeap(a).fill(n),a}var hle={kernelName:vl,backendName:"wasm",kernelFunc:kN},SN;function fle(e){SN=e.wasm.cwrap(xi,null,["number","number","number","number","number","number"])}function mle(e){let{inputs:t,backend:n}=e,{image:s}=t,r=n.makeOutput(s.shape,s.dtype),a=n.dataIdMap.get(s.dataId).id,o=n.dataIdMap.get(r.dataId).id,[i,u,l,c]=s.shape;return SN(a,i,u,l,c,o),r}var gle={kernelName:xi,backendName:"wasm",kernelFunc:mle,setupFunc:fle},ble=Ht(La),yle=!1,vle=Xt(Ma,yle),IN;function xle(e){IN=e.wasm.cwrap(Ba,null,["number","number","number","number","number","number","number"])}function wle(e){let{backend:t,inputs:n,attrs:s}=e,{varianceEpsilon:r}=s,{x:a,mean:o,variance:i,offset:u,scale:l}=n,c=t.dataIdMap.get(a.dataId).id,p=t.dataIdMap.get(o.dataId).id,d=t.dataIdMap.get(i.dataId).id,h=u!=null?t.dataIdMap.get(u.dataId).id:0,f=l!=null?t.dataIdMap.get(l.dataId).id:0,m=t.makeOutput(a.shape,a.dtype);if(w.sizeFromShape(a.shape)===0)return m;let g=t.dataIdMap.get(m.dataId).id;return IN(c,p,d,h,f,r,g),m}var kle={kernelName:Ba,backendName:"wasm",setupFunc:xle,kernelFunc:wle},CN;function Sle(e){CN=e.wasm.cwrap(ia,null,["number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number"])}function Ile(e){let{inputs:t,attrs:n,backend:s}=e,{x:r,filter:a,bias:o,preluActivationWeights:i}=t,{strides:u,pad:l,dilations:c,dataFormat:p,dimRoundingMode:d,activation:h,leakyreluAlpha:f}=n,m=C.computeConv2DInfo(r.shape,a.shape,u,c,l,d),g=uh[h];if(g==null)throw new Error(`${h} activation not yet supported for FusedConv2D in the wasm backend.`);let b=s.dataIdMap.get(r.dataId).id,y=s.dataIdMap.get(a.dataId).id,v=m.outChannels,x=0;if(o!=null){let te=s.dataIdMap.get(o.dataId);if(te.shape.length!==1)throw new Error(`FusedConv2D only supports rank-1 bias but got rank ${te.shape.length}.`);if(te.shape[0]!==v)throw new Error(`FusedConv2D bias shape (${te.shape}) does not match the number of output channels (${v})`);x=te.id}let k=m.filterHeight,I=m.filterWidth,$=m.padInfo.top,R=m.padInfo.right,E=m.padInfo.bottom,P=m.padInfo.left,A=m.dilationHeight,D=m.dilationWidth,T=m.strideHeight,L=m.strideWidth,W=m.inChannels,j=m.padInfo.type==="SAME"?1:0,Y=m.batchSize,X=m.inHeight,Z=m.inWidth;if(p!=="NHWC")throw new Error(`wasm backend FusedConv2D does not support dataFormat:'${p}'. Please use 'NHWC'.`);let ne=s.makeOutput(m.outShape,"float32"),ee=s.dataIdMap.get(ne.dataId).id,se=i==null?0:s.dataIdMap.get(i.dataId).id;return CN(b,Y,X,Z,y,k,I,x,$,R,E,P,j,A,D,T,L,W,v,g,se,f||0,ee),ne}var Cle={kernelName:ia,backendName:"wasm",setupFunc:Sle,kernelFunc:Ile},NN;function Nle(e){NN=e.wasm.cwrap(ua,null,["number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number"])}function Tle(e){let{inputs:t,attrs:n,backend:s}=e,{x:r,filter:a,bias:o,preluActivationWeights:i}=t,{strides:u,pad:l,dilations:c,dataFormat:p,dimRoundingMode:d,activation:h,leakyreluAlpha:f}=n,m=C.computeConv2DInfo(r.shape,a.shape,u,c,l,d,!0),g=uh[h];if(g==null)throw new Error(`${h} activation not yet supported for FusedDepthwiseConv2D in the wasm backend.`);let b=s.dataIdMap.get(r.dataId).id,y=s.dataIdMap.get(a.dataId).id,v=m.outChannels,x=0;if(o!=null){let te=s.dataIdMap.get(o.dataId);if(te.shape.length!==1)throw new Error(`FusedDepthwiseConv2D only supports rank-1 bias but got rank ${te.shape.length}.`);if(te.shape[0]!==v)throw new Error(`FusedDepthwiseConv2D bias shape (${te.shape}) does not match the number of output channels (${v})`);x=te.id}let k=m.filterHeight,I=m.filterWidth,$=m.padInfo.top,R=m.padInfo.right,E=m.padInfo.bottom,P=m.padInfo.left,A=m.dilationHeight,D=m.dilationWidth,T=m.strideHeight,L=m.strideWidth,W=m.inChannels,j=m.padInfo.type==="SAME"?1:0,Y=m.batchSize,X=m.inHeight,Z=m.inWidth;if(p!=="NHWC")throw new Error(`wasm backend FusedDepthwiseConv2D does not support dataFormat:'${p}'. Please use 'NHWC'.`);let ne=s.makeOutput(m.outShape,"float32"),ee=s.dataIdMap.get(ne.dataId).id,se=i==null?0:s.dataIdMap.get(i.dataId).id;return NN(b,Y,X,Z,y,k,I,x,$,R,E,P,j,A,D,T,L,W,v,g,se,f||0,ee),ne}var $le={kernelName:ua,backendName:"wasm",setupFunc:Nle,kernelFunc:Tle},TN;function _le(e){TN=e.wasm.cwrap(ki,null,["number","number","number","number","number","number","array","number"])}function Ale(e){let{backend:t,inputs:n}=e,{params:s,indices:r}=n,[a,o,i,u]=qk.prepareAndValidate(s,r),l=t.makeOutput(a,s.dtype);if(o===0)return l;let c=r.shape,p=c[c.length-1],h=t.dataIdMap.get(s.dataId).id,m=t.dataIdMap.get(r.dataId).id,g=new Uint8Array(new Int32Array(u).buffer),b=t.dataIdMap.get(l.dataId).id;return TN(h,St[s.dtype],m,o,p,i,g,b),l}var Ele={kernelName:ki,backendName:"wasm",setupFunc:_le,kernelFunc:Ale},$N;function Rle(e){$N=e.wasm.cwrap("Gather",null,["number","number","array","number","number","number","array","number"])}function Dle(e){let{backend:t,inputs:n,attrs:s}=e,{x:r,indices:a}=n,{axis:o,batchDims:i}=s,u=w.parseAxisParam(o,r.shape)[0],l=t.readSync(a.dataId),c=r.shape[u];for(let E=0;E<l.length;++E){let P=l[E];w.assert(P<=c-1&&P>=0,()=>`GatherV2: the index value ${P} is not in [0, ${c-1}]`)}let p=C.segment_util.collectGatherOpShapeInfo(r,a,u,i),d=yn({inputs:{x:r},attrs:{shape:[p.batchSize,p.outerSize,p.dimSize,p.sliceSize]},backend:t}),h=w.sizeFromShape(a.shape),f=yn({inputs:{x:a},attrs:{shape:[p.batchSize,h/p.batchSize]},backend:t}),m=[p.batchSize,p.outerSize,h/p.batchSize,p.sliceSize],g=t.makeOutput(m,r.dtype);if(w.sizeFromShape(r.shape)===0)return g;let b=d.shape.length-1,v=t.dataIdMap.get(d.dataId).id,k=t.dataIdMap.get(f.dataId).id,I=t.dataIdMap.get(g.dataId).id,$=new Uint8Array(new Int32Array(w.computeStrides(d.shape)).buffer),R=new Uint8Array(new Int32Array(w.computeStrides(m)).buffer);return $N(v,St[r.dtype],$,b,k,p.batchSize,R,I),t.disposeData(d.dataId),t.disposeData(f.dataId),g.shape=p.outputShape,g}var Fle={kernelName:wi,backendName:"wasm",setupFunc:Rle,kernelFunc:Dle},Ole=!1,Ple=Xt(Si,Ole,"bool"),zle=!1,Lle=Xt(Va,zle,"bool"),_N;function Mle(e){_N=e.wasm.cwrap(Ua,null,["number","number","number","number"])}function Ble(e){let{inputs:{x:t},attrs:{alpha:n},backend:s}=e,r=s.dataIdMap.get(t.dataId).id,a=s.makeOutput(t.shape,"float32");if(w.sizeFromShape(t.shape)!==0){let o=s.dataIdMap.get(a.dataId).id;_N(r,St[t.dtype],n,o)}return a}var Vle={kernelName:Ua,backendName:"wasm",setupFunc:Mle,kernelFunc:Ble},Wle=!1,Ule=Xt(Ii,Wle,"bool"),Gle=!1,Hle=Xt(Ci,Gle,"bool"),qle=Ht(Ga),jle=!1,Kle=Xt(Ni,jle,"bool"),Xle=Ht(Ti),Yle=!1,Qle=Xt(Il,Yle,"bool"),Zle=!1,Jle=Xt(K$,Zle,"bool"),AN;function ece(e){AN=e.wasm.cwrap(Ha,null,["number","number","number","number"])}function tce(e){let{backend:t,inputs:n,attrs:s}=e,{reductionIndices:r,keepDims:a}=s,{x:o}=n,u=t.dataIdMap.get(o.dataId).id,l=o,{transposed:c,axes:p,originalAxes:d,inputWasTransposed:h}=Pr(o,r,t);if(h){let v=t.dataIdMap.get(c.dataId).id;l=c,u=v}let f=l.shape.length;C.assertAxesAreInnerMostDims("max",p,f);let[m,g]=C.computeOutAndReduceShapes(l.shape,p),b=w.sizeFromShape(g),y=t.makeOutput(m,o.dtype);if(w.sizeFromShape(l.shape)!==0){let v=t.dataIdMap.get(y.dataId).id;AN(u,St[o.dtype],b,v)}if(h&&t.disposeData(c.dataId),a){let v=C.expandShapeToKeepDim(y.shape,d);y.shape=v}return y}var nce={kernelName:Ha,backendName:"wasm",setupFunc:ece,kernelFunc:tce},sce=!1,rce=Xt(qa,sce),EN;function ace(e){EN=e.wasm.cwrap(ja,null,["number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number"])}function oce(e){let{inputs:t,attrs:n,backend:s}=e,r=t.x,a=s.dataIdMap.get(r.dataId).id;w.assert(r.dtype==="float32",()=>`Error in MaxPool: only float32 input is supported. Got ${r.dtype}.`);let{filterSize:o,strides:i,pad:u,dimRoundingMode:l}=n,c=C.computePool2DInfo(r.shape,o,i,1,u,l),p=c.filterHeight,d=c.filterWidth,h=c.padInfo.top,f=c.padInfo.right,m=c.padInfo.bottom,g=c.padInfo.left,b=c.dilationHeight,y=c.dilationWidth,v=c.strideHeight,x=c.strideWidth,k=c.inChannels,I=c.outChannels;if(c.dataFormat!=="channelsLast")throw new Error(`wasm backend does not support dataFormat:'${c.dataFormat}'. Please use 'channelsLast'.`);let $=s.makeOutput(c.outShape,"float32"),R=s.dataIdMap.get($.dataId).id;return EN(a,r.shape[0],r.shape[1],r.shape[2],p,d,h,f,m,g,b,y,v,x,k,I,R),$}var ice={kernelName:ja,backendName:"wasm",setupFunc:ace,kernelFunc:oce},RN;function uce(e){RN=e.wasm.cwrap(Ka,null,["number, number, number"])}function lce(e){let{backend:t,inputs:n,attrs:s}=e,{axis:r,keepDims:a}=s,{x:o}=n,i=t.dataIdMap.get(o.dataId).id,u=i,l=o,{transposed:c,axes:p,originalAxes:d,inputWasTransposed:h}=Pr(o,r,t),f=p;if(h){let x=t.dataIdMap.get(c.dataId).id;x!==i&&(l=c,u=x,f=C.getInnerMostAxes(f.length,l.shape.length))}C.assertAxesAreInnerMostDims("mean",f,l.shape.length);let[m,g]=C.computeOutAndReduceShapes(l.shape,f),b=w.sizeFromShape(g),y=l;l.dtype!=="float32"&&(y=lc({backend:t,inputs:{x:l},attrs:{dtype:"float32"}}),u=t.dataIdMap.get(y.dataId).id);let v=t.makeOutput(m,"float32");if(w.sizeFromShape(l.shape)!==0){let x=t.dataIdMap.get(v.dataId).id;RN(u,b,x)}if(h&&t.disposeData(c.dataId),a){let x=C.expandShapeToKeepDim(v.shape,d);v.shape=x}return l.dtype!=="float32"&&t.disposeData(y.dataId),v}var cce={kernelName:Ka,backendName:"wasm",setupFunc:uce,kernelFunc:lce},DN;function dce(e){DN=e.wasm.cwrap(Xa,null,["number","number","number","number"])}function pce(e){let{backend:t,inputs:n,attrs:s}=e,{axis:r,keepDims:a}=s,{x:o}=n,i=t.dataIdMap.get(o.dataId).id,u=i,l=o,{transposed:c,axes:p,originalAxes:d,inputWasTransposed:h}=Pr(o,r,t);if(h){let v=t.dataIdMap.get(c.dataId).id;v!==i&&(l=c,u=v)}let f=l.shape.length;C.assertAxesAreInnerMostDims("min",p,f);let[m,g]=C.computeOutAndReduceShapes(l.shape,p),b=w.sizeFromShape(g),y=t.makeOutput(m,l.dtype);if(w.sizeFromShape(l.shape)!==0){let v=t.dataIdMap.get(y.dataId).id;DN(u,St[o.dtype],b,v)}if(h&&t.disposeData(c.dataId),a){let v=C.expandShapeToKeepDim(y.shape,d);y.shape=v}return y}var hce={kernelName:Xa,backendName:"wasm",setupFunc:dce,kernelFunc:pce},fce=!1,mce=Xt(Ya,fce),FN=(e=>(e[e.reflect=0]="reflect",e[e.symmetric=1]="symmetric",e))(FN||{}),ON;function gce(e){ON=e.wasm.cwrap(Qa,null,["number","array","number","number","array","array","number","number"])}function bce(e){let{inputs:{x:t},backend:n,attrs:{paddings:s,mode:r}}=e,a=s.map((f,m)=>f[0]+t.shape[m]+f[1]),o=n.dataIdMap.get(t.dataId).id,i=n.makeOutput(a,t.dtype),u=n.dataIdMap.get(i.dataId).id,l=new Uint8Array(new Int32Array(t.shape).buffer),c=s.map(f=>f[0]),p=s.map(f=>f[1]),d=new Uint8Array(new Int32Array(c).buffer),h=new Uint8Array(new Int32Array(p).buffer);return ON(o,l,t.shape.length,St[t.dtype],d,h,FN[r],u),i}var yce={kernelName:Qa,backendName:"wasm",kernelFunc:bce,setupFunc:gce},vce=!0,xce=Xt(Za,vce),wce=Ht($i);function Uv(e,t){let n=new Int32Array(e.wasm.HEAPU8.buffer,t,4),s=n[0],r=n[1],a=n[2],o=n[3];return e.wasm._free(t),{pSelectedIndices:s,selectedSize:r,pSelectedScores:a,pValidOutputs:o}}var PN;function kce(e){PN=e.wasm.cwrap(Ai,"number",["number","number","number","number","number"])}function Sce(e){let{backend:t,inputs:n,attrs:s}=e,{iouThreshold:r,maxOutputSize:a,scoreThreshold:o}=s,{boxes:i,scores:u}=n,l=t.dataIdMap.get(i.dataId).id,c=t.dataIdMap.get(u.dataId).id,p=PN(l,c,a,r,o),{pSelectedIndices:d,selectedSize:h,pSelectedScores:f,pValidOutputs:m}=Uv(t,p);return t.wasm._free(f),t.wasm._free(m),t.makeOutput([h],"int32",d)}var Ice={kernelName:Ai,backendName:"wasm",setupFunc:kce,kernelFunc:Sce},zN;function Cce(e){zN=e.wasm.cwrap(Nl,"number",["number","number","number","number","number","bool"])}function Nce(e){let{backend:t,inputs:n,attrs:s}=e,{iouThreshold:r,maxOutputSize:a,scoreThreshold:o,padToMaxOutputSize:i}=s,{boxes:u,scores:l}=n,c=t.dataIdMap.get(u.dataId).id,p=t.dataIdMap.get(l.dataId).id,d=zN(c,p,a,r,o,i),{pSelectedIndices:h,selectedSize:f,pSelectedScores:m,pValidOutputs:g}=Uv(t,d);t.wasm._free(m);let b=t.makeOutput([f],"int32",h),y=t.makeOutput([],"int32",g);return[b,y]}var Tce={kernelName:Nl,backendName:"wasm",setupFunc:Cce,kernelFunc:Nce},LN;function $ce(e){LN=e.wasm.cwrap(Ei,"number",["number","number","number","number","number","number"])}function _ce(e){let{backend:t,inputs:n,attrs:s}=e,{iouThreshold:r,maxOutputSize:a,scoreThreshold:o,softNmsSigma:i}=s,{boxes:u,scores:l}=n,c=t.dataIdMap.get(u.dataId).id,p=t.dataIdMap.get(l.dataId).id,d=LN(c,p,a,r,o,i),{pSelectedIndices:h,selectedSize:f,pSelectedScores:m,pValidOutputs:g}=Uv(t,d);t.wasm._free(g);let b=t.makeOutput([f],"int32",h),y=t.makeOutput([f],"float32",m);return[b,y]}var Ace={kernelName:Ei,backendName:"wasm",setupFunc:$ce,kernelFunc:_ce},Ece=!1,Rce=Xt(_i,Ece,"bool"),MN;function Dce(e){MN=e.wasm.cwrap(Di,null,["number","number","number","number","number"])}function Fce(e){let{inputs:t,backend:n,attrs:s}=e,{indices:r}=t,{depth:a,onValue:o,offValue:i}=s,u=n.makeOutput([...r.shape,a],"int32"),l=n.dataIdMap.get(u.dataId).id,p=n.dataIdMap.get(r.dataId).id;return MN(p,a,o,i,l),u}var Oce={kernelName:Di,backendName:"wasm",setupFunc:Dce,kernelFunc:Fce};function Pce(e){let{inputs:{x:t},backend:n}=e,s=n.makeOutput(t.shape,t.dtype);return n.typedArrayFromHeap(s).fill(1),s}var zce={kernelName:Ri,backendName:"wasm",kernelFunc:Pce};function Lce(e){let{inputs:t,backend:n,attrs:s}=e,{axis:r}=s;if(t.length===1)return lg({inputs:{input:t[0]},backend:n,attrs:{dim:r}});let a=t[0].shape,o=t[0].dtype;t.forEach(c=>{w.assertShapesMatch(a,c.shape,"All tensors passed to stack must have matching shapes"),w.assert(o===c.dtype,()=>"All tensors passed to stack must have matching dtypes")});let i=[],u=t.map(c=>{let p=lg({inputs:{input:c},backend:n,attrs:{dim:r}});return i.push(p),p}),l=hN({inputs:u,backend:n,attrs:{axis:r}});return i.forEach(c=>n.disposeData(c.dataId)),l}var Mce={kernelName:Fi,backendName:"wasm",kernelFunc:Lce},BN;function Bce(e){BN=e.wasm.cwrap(Ja,null,["number","array","number","number","array","array","number","number"])}function Vce(e){let{inputs:{x:t},backend:n,attrs:{paddings:s,constantValue:r}}=e,a=s.map((m,g)=>m[0]+t.shape[g]+m[1]);if(w.sizeFromShape(t.shape)===0)return kN({backend:n,attrs:{shape:a,value:r,dtype:t.dtype}});let o=n.dataIdMap.get(t.dataId).id,i=n.makeOutput(a,t.dtype),l=n.dataIdMap.get(i.dataId).id,c=new Uint8Array(new Int32Array(t.shape).buffer),p=s.map(m=>m[0]),d=s.map(m=>m[1]),h=new Uint8Array(new Int32Array(p).buffer),f=new Uint8Array(new Int32Array(d).buffer);return BN(o,c,t.shape.length,St[t.dtype],h,f,r,l),i}var VN={kernelName:Ja,backendName:"wasm",kernelFunc:Vce,setupFunc:Bce},Wce=!1,Uce=Xt(eo,Wce),WN;function Gce(e){WN=e.wasm.cwrap(to,null,["number","number","number"])}function 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v=t.dataIdMap.get(y.dataId).id;UN(u,b,St[y.dtype],v)}if(h&&t.disposeData(c.dataId),a){let v=C.expandShapeToKeepDim(y.shape,d);y.shape=v}return y}var Xce={kernelName:no,backendName:"wasm",setupFunc:jce,kernelFunc:Kce},Yce=e=>{let{backend:t,attrs:n}=e,{start:s,stop:r,step:a,dtype:o}=n,i=gv(s,r,a,o),u=t.makeOutput([i.length],o);return t.typedArrayFromHeap(u).set(i),u},Qce={kernelName:Tl,backendName:"wasm",kernelFunc:Yce},Zce=!0,Jce=Xt(Oa,Zce),ede=Ht(so),tde=Ht(ao),GN;function nde(e){GN=e.wasm.cwrap(ro,null,["number","number","number","number","number","number","number","number","number","number"])}function sde(e){let{backend:t,inputs:n,attrs:s}=e,{images:r}=n,{alignCorners:a,halfPixelCenters:o,size:i}=s,[u,l]=i,[c,p,d,h]=r.shape,f=[c,u,l,h],m=t.dataIdMap.get(r.dataId),g;m.dtype!=="float32"&&(g=lc({backend:t,inputs:{x:r},attrs:{dtype:"float32"}}),m=t.dataIdMap.get(g.dataId));let b=m.id,y=t.makeOutput(f,"float32");if(w.sizeFromShape(r.shape)===0)return y;let 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Sde(e){YN=e.wasm.cwrap(po,null,["number","number","number","number"])}function Ide(e){let{backend:t,inputs:{logits:n},attrs:{dim:s}}=e,r=t.dataIdMap.get(n.dataId).id,a=t.makeOutput(n.shape,n.dtype),o=t.dataIdMap.get(a.dataId).id,i=n.shape[s],u=w.sizeFromShape(n.shape)/i;return w.sizeFromShape(a.shape)===0||YN(r,o,i,u),a}var Cde={kernelName:po,backendName:"wasm",setupFunc:Sde,kernelFunc:Ide};function Nde(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{blockShape:a,paddings:o}=s,i=w.sizeFromShape(a),u=[[0,0]];u.push(...o);for(let I=1+a.length;I<r.shape.length;++I)u.push([0,0]);let l=VN.kernelFunc({inputs:{x:r},backend:n,attrs:{paddings:u,constantValue:0}}),c=C.getReshaped(l.shape,a,i,!1),p=C.getPermuted(c.length,a.length,!1),d=C.getReshapedPermuted(l.shape,a,i,!1),m=yn({inputs:{x:l},backend:n,attrs:{shape:c}}),y=Sr({inputs:{x:m},backend:n,attrs:{perm:p}}),k=yn({inputs:{x:y},backend:n,attrs:{shape:d}});return n.disposeData(l.dataId),n.disposeData(m.dataId),n.disposeData(y.dataId),k}var Tde={kernelName:Wi,backendName:"wasm",kernelFunc:Nde},QN;function $de(e){QN=e.wasm.cwrap("SparseFillEmptyRows","number",["number","number","number","number","number","number","number","number","number","number","number","number"])}function _de(e){let{backend:t,inputs:n}=e,{indices:s,values:r,denseShape:a,defaultValue:o}=n,i=s.shape[0],u=s.shape[1],l=t.readSync(a.dataId)[0],c=[i+l,u],p=t.dataIdMap.get(s.dataId).id,d=t.dataIdMap.get(r.dataId).id,h=t.dataIdMap.get(o.dataId).id,f=t.makeOutput(c,s.dtype),m=t.dataIdMap.get(f.dataId).id,g=t.makeOutput(c.slice(0,1),r.dtype),b=t.dataIdMap.get(g.dataId).id,y=t.makeOutput([l],"bool"),v=t.dataIdMap.get(y.dataId).id,x=t.makeOutput([i],s.dtype),k=t.dataIdMap.get(x.dataId).id,I=t.makeOutput([4],"int32"),$=t.dataIdMap.get(I.dataId).id,R=QN(p,d,St[r.dtype],i,l,u,h,m,b,v,k,$),E=t.readSync(I.dataId),P;switch(E[0]){case 1:{P=C.getSparseFillEmptyRowsIndicesDenseShapeMismatch(E[1]);break}case 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v=n.readSync(b.dataId),x;switch(v[0]){case 0:{x=C.getSparseSegmentReductionNegativeSegmentIdsErrorMessage();break}case 1:{x=C.getSparseSegmentReductionNonIncreasingSegmentIdsErrorMessage();break}case 2:x=C.getSparseSegmentReductionSegmentIdOutOfRangeErrorMessage(v[1],v[2]);break;case 3:x=C.getSparseSegmentReductionIndicesOutOfRangeErrorMessage(v[1],v[2],v[3]);break;default:x=""}if(n.disposeData(b.dataId),x)throw n.disposeData(m.dataId),new Error(x);return m}function Fde(e){return tT(e,!0)}var Ode={kernelName:pp,backendName:"wasm",setupFunc:eT,kernelFunc:Fde};function Pde(e){return tT(e,!1)}var zde={kernelName:hp,backendName:"wasm",setupFunc:eT,kernelFunc:Pde};function Lde(e){let{inputs:t,attrs:n,backend:s}=e,{x:r}=t,{numOrSizeSplits:a,axis:o}=n,i=w.parseAxisParam(o,r.shape)[0],u=C.prepareSplitSize(r,a,i),l=new Array(r.shape.length).fill(0),c=r.shape.slice();return u.map(p=>{let d=[...c];d[i]=p;let h=xa({inputs:{x:r},attrs:{begin:l,size:d},backend:s});return l[i]+=p,h})}var Mde={kernelName:Ui,backendName:"wasm",kernelFunc:Lde},Bde=Ht(lo),Vde=Ht(Fl),Wde=!0,Ude=Xt(ho,Wde),nT;function Gde(e){nT=e.wasm.cwrap(go,null,["number","number","number","number"])}function Hde(e){let{backend:t,inputs:n,attrs:s}=e,{alpha:r}=s,{x:a}=n,o=t.dataIdMap.get(a.dataId).id,i=t.makeOutput(a.shape,a.dtype),u=t.dataIdMap.get(i.dataId).id;return nT(o,r,St[a.dtype],u),i}var qde={kernelName:go,backendName:"wasm",setupFunc:Gde,kernelFunc:Hde},sT;function jde(e){sT=e.wasm.cwrap(Gi,null,["number","array","number","array","array","array","array","array","number","number"])}function Kde(e){let{backend:t,inputs:n,attrs:s}=e,{x:r}=n,{begin:a,end:o,strides:i,beginMask:u,endMask:l,ellipsisMask:c,newAxisMask:p,shrinkAxisMask:d}=s,{finalShapeSparse:h,finalShape:f,isIdentity:m,sliceDim0:g,isSimpleSlice:b,begin:y,end:v,strides:x}=kt.sliceInfo(r.shape,a,o,i,u,l,c,p,d),k;if(m)k=yn({inputs:{x:r},backend:t,attrs:{shape:f}});else if(g||b){w.assert(r.shape.length>=1,()=>`Input must have rank at least 1, 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as addN,lS as all,Sm as any,Yu as argMax,wE as argMin,SE as asin,CE as asinh,TE as atan,_E as atan2,EE as atanh,nb as avgPool,hS as avgPool3d,$A as backend,C as backend_util,Hpe as basicLSTMCell,Zu as batchNorm,YE as batchNorm2d,ZE as batchNorm3d,eR as batchNorm4d,sb as batchToSpaceND,fS as bincount,vhe as booleanMaskAsync,sR as broadcastArgs,id as broadcastTo,Qi as broadcast_util,Gk as browser,Ae as buffer,_he as callbacks,le as cast,oR as ceil,Wn as clipByValue,lr as clone,mr as complex,Ft as concat,lR as concat1d,dR as concat2d,hR as concat3d,mR as concat4d,qM as constraints,mS as conv1d,da as conv2d,gS as conv2dTranspose,bS as conv3d,SR as conv3dTranspose,Ape as copyRegisteredKernels,ab as cos,vS as cosh,GS as cosineWindow,Cm as cumprod,xS as cumsum,js as customGrad,oU as data,_R as denseBincount,Wk as deprecationWarn,ER as depthToSpace,kp as depthwiseConv2d,Ehe as deregisterOp,vp as device_util,qpe as diag,OR as dilation2d,Dpe as disableDeprecationWarnings,De as dispose,Fpe as disposeVariables,xe as div,BR as divNoNan,jpe as dot,CF as dropout,UR as einsum,Sp as elu,Rpe as enableDebugMode,Epe as enableProdMode,NF as enclosingPowerOfTwo,ds as engine,K as env,Xn as equal,qR as erf,sD as euclideanNorm,Yn as exp,Pn as expandDims,iD as expm1,CS as eye,wb as fft,Bl as fill,Vpe as findBackend,Wpe as findBackendFactory,Ip as floor,uS as floorDiv,I8 as forceHalfFloat,fa as fused,Ju as gather,kF as gatherND,qk as gather_util,Mpe as getBackend,hx as getGradient,lm as getKernel,cm as getKernelsForBackend,Whe as getThreadsCount,dX as gpgpu_util,Ype as grad,Qpe as grads,Gn as greater,Zi as greaterEqual,$d as ifft,wp as imag,Kn as image,whe as inTopKAsync,QM as initializers,lV as input,An as io,MS as irfft,Kpe as isFinite,Xpe as isInf,bD as isNaN,qt as keep,ws as kernel_impls,hB as layers,lb as leakyRelu,NS as less,Ji as lessEqual,cP as linalg,wD as linspace,Rhe as loadGraphModel,Dhe as loadGraphModelSync,The as loadLayersModel,SD as localResponseNormalization,Qn as log,cb as log1p,ehe as logSigmoid,TS as logSoftmax,RD as logSumExp,Ds as logicalAnd,db as logicalNot,$S as logicalOr,the as logicalXor,Ihe as losses,LD as lowerBound,We as matMul,CA as math,As as max,pb as maxPool,AS as maxPool3d,WD as maxPoolWithArgmax,Ar as maximum,It as mean,xm as memory,nhe as meshgrid,RW as metrics,Nm as min,Np as minimum,jD as mirrorPad,XD as mod,Che as model,KW as models,hb as moments,xhe as movingAverage,V as mul,she as multiRNNCell,JD as multinomial,vt as neg,ZS as nextFrame,ub as norm,el as notEqual,Cd as oneHot,Ln as ones,Zn as onesLike,M as op,rhe as outerProduct,bo as pad,ahe as pad1d,ohe as pad2d,ihe as pad3d,uhe as pad4d,lhe as pool,ha as pow,mb as prelu,iA as print,ES as prod,Ope as profile,che as rand,dhe as randomGamma,v3 as randomNormal,Wl as randomUniform,tl as range,Lpe as ready,Xu as real,k3 as reciprocal,xp as registerBackend,$he as registerCallbackConstructor,Q$ as registerGradient,Ol as registerKernel,Ahe as registerOp,XW as regularizers,Xs as relu,RS as relu6,Bpe as removeBackend,U as reshape,Jn as reverse,phe as reverse1d,hhe as reverse2d,fhe as reverse3d,mhe as reverse4d,kb as rfft,DS as round,FS as rsqrt,we as scalar,yF as scatterND,Kk as scatter_util,_S as searchSorted,OS as selu,F3 as separableConv2d,Nhe as sequential,re as serialization,zpe as setBackend,Upe as setPlatform,Vhe as setThreadsCount,Mhe as setWasmPath,Bhe as setWasmPaths,sK as setWebGLContext,P3 as setdiff1dAsync,cv as shared,qs as sigmoid,L3 as sign,She as signal,PS as sin,zS as sinh,qe as slice,yb as slice1d,LS as slice2d,vb as slice3d,Td as slice4d,kt as slice_util,xb as softmax,Vl as softplus,fb as spaceToBatchND,jc as sparse,US as sparseToDense,khe as spectral,Bn as split,dn as sqrt,ct as square,BS as squaredDifference,br as squeeze,es as stack,Tp as step,nF as stridedSlice,Yf as string,ge as sub,ve as sum,yp as sumOutType,rF as tan,Qu as tanh,ms as tensor,Zt as tensor1d,Zo as tensor2d,OA as tensor3d,ghe as tensor4d,bhe as tensor5d,yhe as tensor6d,_s as tensor_util,ZA as test_util,q as tidy,hs as tile,Ppe as time,oF as topk,Mo as train,Ge as transpose,Sb as truncatedNormal,Ix as unique,_pe as unregisterGradient,$pe as unregisterKernel,cF as unsortedSegmentSum,Fs as unstack,cn as upcastType,pF as upperBound,w as util,Zpe as valueAndGrad,Jpe as valueAndGrads,hF as variable,ND as variableGrads,Ghe as version,Fhe as version_converter,Gpe as version_core,Ohe as version_cpu,NI as version_layers,Uhe as version_wasm,Phe as version_webgl,zhe as webgl,nK as webgl_util,Yie as webgpu,vn as where,WS as whereAsync,$t as zeros,je 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 backend 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 2020 Google LLC. All Rights Reserved.
* Licensed under the Apache License, Version 2.0 (the License);
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an AS IS BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
* =============================================================================
*/
/**
* @license
* Copyright 2021 Google LLC. All Rights Reserved.
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
* =============================================================================
*/
/**
* @license
* Copyright 2021 Google LLC. All Rights Reserved.
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* https://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
* =============================================================================
*/
/**
* @license
* Copyright 2021 Google LLC. All Rights Reserved.
* Licensed under the Apache License, Version 2.0 (the License);
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an AS IS BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
* =============================================================================
*/
/**
* @license
* Copyright 2022 Google Inc. All Rights Reserved.
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
* =============================================================================
*/
/**
* @license
* Copyright 2022 Google LLC
*
* Use of this source code is governed by an MIT-style
* license that can be found in the LICENSE file or at
* https://opensource.org/licenses/MIT.
* =============================================================================
*/
/**
* @license
* Copyright 2022 Google LLC. All Rights Reserved.
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
* =============================================================================
*/
/**
* @license
* Copyright 2022 Google LLC. All Rights Reserved.
* Licensed under the Apache License, Version 2.0 (the 'License');
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an 'AS IS' BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
* =============================================================================
*/
/**
* @license
* Copyright 2018 Google LLC. All Rights Reserved.
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
* =============================================================================
*/
/** @license See the LICENSE file. */
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