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face-api
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face-api/dist/face-api.js

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/*
Face-API
homepage: <https://github.com/vladmandic/face-api>
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 vF(this.backendInstance),!0}setupRegisteredKernels(){sh(this.backendName).forEach(e=>{e.setupFunc!=null&&e.setupFunc(this.backendInstance)})}disposeRegisteredKernels(e){sh(e).forEach(t=>{t.disposeFunc!=null&&t.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 Zu)&&typeof n.then=="function"){let a=++this.pendingBackendInitId,r=n.then(s=>a<this.pendingBackendInitId?!1:(this.registry[e]=s,this.pendingBackendInit=null,!0)).catch(s=>(a<this.pendingBackendInitId||(this.pendingBackendInit=null,console.warn(`Initialization of backend ${e} failed`),console.warn(s.stack||s.message)),!1));return this.pendingBackendInit=r,{success:r,asyncInit:!0}}else return this.registry[e]=n,{success:!0,asyncInit:!1}}catch(n){return console.warn(`Initialization of backend ${e} failed`),console.warn(n.stack||n.message),{success:!1,asyncInit:!1}}}removeBackend(e){if(!(e in this.registryFactory))throw new Error(`${e} backend not found in registry`);this.backendName===e&&this.pendingBackendInit!=null&&this.pendingBackendInitId++,e in this.registry&&(this.disposeRegisteredKernels(e),this.registry[e].dispose(),delete this.registry[e]),delete this.registryFactory[e],this.backendName===e&&(this.pendingBackendInit=null,this.backendName=null,this.backendInstance=null)}getSortedBackends(){if(Object.keys(this.registryFactory).length===0)throw new Error("No backend found in registry.");return Object.keys(this.registryFactory).sort((e,t)=>this.registryFactory[t].priority-this.registryFactory[e].priority)}initializeBackendsAndReturnBest(){let e=this.getSortedBackends();for(let t=0;t<e.length;t++){let n=e[t],{success:a,asyncInit:r}=this.initializeBackend(n);if(r||a)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),a=n.backend,r=this.readSync(t),s=a.refCount(t);a.disposeData(t,!0),n.backend=e,e.move(t,r,n.shape,n.dtype,s),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 a;return this.scopedRun(()=>this.startScope(n),()=>this.endScope(a),()=>(a=t(),a instanceof Promise&&console.error("Cannot return a Promise inside of tidy."),a))}scopedRun(e,t,n){e();try{let a=n();return t(),a}catch(a){throw t(),a}}nextTensorId(){return Sc.nextTensorId++}nextVariableId(){return Sc.nextVariableId++}clone(e){let t=M.runKernel(Xs,{x:e}),n={x:e},a=s=>({x:()=>{let i="float32",o={x:s},l={dtype:i};return M.runKernel(Ms,o,l)}}),r=[];return this.addTapeNode(this.state.activeScope.name,n,[t],a,r,{}),t}runKernel(e,t,n){if(rh(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 a=this.backend.numDataIds(),r=0;n.forEach(o=>{r+=o.dtype==="complex64"?3:1});let s=this.state.numDataMovesStack[this.state.numDataMovesStack.length-1],i=a-t-r-s;if(i>0)throw new Error(`Backend '${this.backendName}' has an internal memory leak (${i} data ids) after running '${e}'`)}runKernelFunc(e){let t,n=[],a=this.isTapeOn(),r=this.state.numBytes,s=this.state.numTensors;this.shouldCheckForMemLeaks()&&this.state.numDataMovesStack.push(0);let i;this.backendName==null&&this.backend;let o,l=my(e)?e.kernelName:this.state.activeScope!=null?this.state.activeScope.name:"";if(my(e)){let{kernelName:h,inputs:m,attrs:f}=e;this.backendName==null&&this.backend;let g=rh(h,this.backendName);$(g!=null,()=>`Cannot find registered kernel '${h}' for backend '${this.backendName}'`),i=()=>{let y=this.backend.numDataIds();o=g.kernelFunc({inputs:m,attrs:f,backend:this.backend});let b=Array.isArray(o)?o:[o];this.shouldCheckForMemLeaks()&&this.checkKernelForMemLeak(h,y,b);let x=b.map(v=>{if(v.rank!=null)return v;let{dataId:T,shape:k,dtype:S}=v;return this.makeTensorFromDataId(T,k,S)});if(a){let v=this.getTensorsForGradient(h,m,x);n=this.saveTensorsForBackwardMode(v)}return x}}else{let{forwardFunc:h}=e,m=f=>{!a||(n=f.map(g=>this.keep(this.clone(g))))};i=()=>{let f=this.backend.numDataIds();o=this.tidy(()=>h(this.backend,m));let g=Array.isArray(o)?o:[o];return this.shouldCheckForMemLeaks()&&this.checkKernelForMemLeak(l,f,g),g}}let{inputs:c,attrs:u}=e,p=my(e)?null:e.backwardsFunc,d;return this.scopedRun(()=>this.state.kernelDepth++,()=>this.state.kernelDepth--,()=>{!this.ENV.getBool("DEBUG")&&!this.state.profiling?t=i():(d=this.profiler.profileKernel(l,c,()=>i()),this.ENV.getBool("DEBUG")&&this.profiler.logKernelProfile(d),t=d.outputs)}),a&&this.addTapeNode(l,c,t,p,n,u),this.state.profiling&&this.state.activeProfile.kernels.push({name:l,bytesAdded:this.state.numBytes-r,totalBytesSnapshot:this.state.numBytes,tensorsAdded:this.state.numTensors-s,totalTensorsSnapshot:this.state.numTensors,inputShapes:Object.keys(c).map(h=>c[h]!=null?c[h].shape:null),outputShapes:t.map(h=>h.shape),kernelTimeMs:d.timeMs,extraInfo:d.extraInfo}),Array.isArray(o)?t:t[0]}saveTensorsForBackwardMode(e){return e.map(t=>this.keep(this.clone(t)))}getTensorsForGradient(e,t,n){let a=iy(e);if(a!=null){let r=a.inputsToSave||[],s=a.outputsToSave||[],i;a.saveAllInputs?($(Array.isArray(t),()=>"saveAllInputs is true, expected inputs to be an array."),i=Object.keys(t).map(l=>t[l])):i=r.map(l=>t[l]);let o=n.filter((l,c)=>s[c]);return i.concat(o)}return[]}makeTensor(e,t,n,a){if(e==null)throw new Error("Values passed to engine.makeTensor() are null");n=n||"float32",a=a||this.backend;let r=e;n==="string"&&Ur(e[0])&&(r=e.map(o=>kc(o)));let s=a.write(r,t,n),i=new Ee(t,n,s,this.nextTensorId());if(this.trackTensor(i,a),n==="string"){let o=this.state.tensorInfo.get(s),l=l0(r);this.state.numBytes+=l-o.bytes,o.bytes=l}return i}makeTensorFromDataId(e,t,n,a){n=n||"float32";let r=new Ee(t,n,e,this.nextTensorId());return this.trackTensor(r,a),r}makeVariable(e,t=!0,n,a){n=n||this.nextVariableId().toString(),a!=null&&a!==e.dtype&&(e=e.cast(a));let r=new Kr(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*Qg(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 Kr||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*Qg(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(a=>a.totalBytesSnapshot)),this.state.activeProfile.newBytes=this.state.numBytes-t,this.state.activeProfile.newTensors=this.state.numTensors-n;for(let a of 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s=this.state.activeScope.track[r];!s.kept&&!n.has(s.id)&&s.dispose()}let a=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===a.id&&this.track(r)})}gradients(e,t,n,a=!1){if($(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 '${n.dtype}'`);let r=this.scopedRun(()=>this.startTape(),()=>this.endTape(),()=>this.tidy("forward",e));$(r instanceof Ee,()=>"The result y returned by f() must be a tensor.");let s=wF(this.state.activeTape,t,r);if(!a&&s.length===0&&t.length>0)throw new Error("Cannot compute gradient of y=f(x) with respect to x. 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t=e.length/2,n=!1;this.accumulatedFirstMoment=e.slice(0,t).map(a=>({originalName:a.name,variable:a.tensor.variable(n)})),this.accumulatedSecondMoment=e.slice(t,t*2).map(a=>({originalName:a.name,variable:a.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)}};Kh.className="Adam";es(Kh);var Yh=class extends wr{constructor(e,t,n,a=null,r=0){super();this.learningRate=e,this.beta1=t,this.beta2=n,this.epsilon=a,this.decay=r,this.accumulatedFirstMoment=[],this.accumulatedWeightedInfNorm=[],D(()=>{this.iteration=ve(0).variable(),this.accBeta1=ve(t).variable()}),a==null&&(this.epsilon=M.backend.epsilon())}applyGradients(e){let t=Array.isArray(e)?e.map(n=>n.name):Object.keys(e);D(()=>{let n=he(1,this.accBeta1),a=ye(-this.learningRate,J(W(this.iteration,this.decay),1));t.forEach((r,s)=>{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)}};Yh.className="Adamax";es(Yh);var Gc=class extends wr{constructor(e){super();this.learningRate=e,this.setLearningRate(e)}applyGradients(e){(Array.isArray(e)?e.map(t=>t.name):Object.keys(e)).forEach((t,n)=>{let a=Array.isArray(e)?e[n].tensor:e[t];if(a==null)return;let r=M.registeredVariables[t];D(()=>{let s=J(W(this.c,a),r);r.assign(s)})}),this.incrementIterations()}setLearningRate(e){this.learningRate=e,this.c!=null&&this.c.dispose(),this.c=qt(ve(-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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They will not be included in the serialized model (and thus will be missing at deserialization time).`),h={}}if(p.inboundLayers.length>0){let m=[];for(let f=0;f<p.inboundLayers.length;f++){let g=p.inboundLayers[f],y=p.nodeIndices[f],b=p.tensorIndices[f],x=ar.nodeKey(g,y),v=t[x];v==null&&(v=0),m.push([g.name,v,b,h])}l.push(m)}}}let c={};c.name=s.name,c.className=i,c.config=o,c.inboundNodes=l,n.push(c)}e.layers=n;let a=[];for(let s=0;s<this.inputLayers.length;s++){let i=this.inputLayers[s],o=this.inputLayersNodeIndices[s],l=ar.nodeKey(i,o);if(!this.containerNodes.has(l))continue;let c=t[l];c==null&&(c=0);let u=this.inputLayersTensorIndices[s];a.push([i.name,c,u])}e.inputLayers=a;let r=[];for(let s=0;s<this.outputLayers.length;s++){let i=this.outputLayers[s],o=this.outputLayersNodeIndices[s],l=ar.nodeKey(i,o);if(!this.containerNodes.has(l))continue;let c=t[l];c==null&&(c=0);let u=this.outputLayersTensorIndices[s];r.push([i.name,c,u])}return e.outputLayers=r,e}static fromConfig(e,t,n={},a=!1){let r={},s={};function i(f,g){f.name in s?s[f.name].push(g):s[f.name]=[g]}function o(f,g){let y=[],b;for(let x of g){let v=x[0],T=x[1],k=x[2];if(b=x[3]==null?{}:x[3],!(v in r)){i(f,g);return}let S=r[v];if(S.inboundNodes.length<=T){i(f,g);return}let F=S.inboundNodes[T];y.push(F.outputTensors[k])}y.length>0&&f.apply(En(y),b)}function l(f){let g=f.name,y=Da(f,t.customObjects!=null?t.customObjects:{});y.setFastWeightInitDuringBuild(a),r[g]=y,f.inboundNodes.forEach(b=>{if(!(b instanceof Array))throw new B(`Corrupted configuration, expected array for nodeData: ${b}`);i(y,b)})}let c=t.name,u=t.layers;for(let f of u)l(f);for(;!oz(s);)for(let f of u){let g=r[f.name];if(g.name in s){let y=s[g.name];delete s[g.name];for(let b of y)o(g,b)}}let p=[],d=[],h=t.inputLayers;for(let f of h){let g=f[0],y=f[1],b=f[2];Za(g in r);let x=r[g].inboundNodes[y].outputTensors;p.push(x[b])}let m=t.outputLayers;for(let f of m){let g=f[0],y=f[1],b=f[2];Za(g in r);let x=r[g].inboundNodes[y].outputTensors;d.push(x[b])}return new e({inputs:p,outputs:d,name:c})}get stateful(){if(this._stateful)throw new B("Container instance unexpectedly has _stateful = true. The statefulness of a Container is determined by the Layers it contains. Its _stateful property must remain the default false.");for(let e of this.layers)if(e.stateful)return!0;return!1}resetStates(){D(()=>{this.layers.forEach(e=>{e.stateful&&e.resetStates()})})}};function PB(e,t,n){let a=t.length;if(e==null||Array.isArray(e)&&e.length===0)return t.map(r=>null);if(a===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!==a)throw new Error(`Provided ${n} is an array of ${e.length} element(s), but the model has ${a} outputs. Make sure a set of weights is provided for each model output.`);return e}else if(typeof e=="object"&&Object.keys(e).length>0&&typeof e[Object.keys(e)[0]]=="object"){let r=[];return t.forEach(s=>{s in e?r.push(e[s]):r.push(null)}),r}else throw new Error(`The model has multiple (${a}) outputs, so ${n} must be either an array with ${a} elements or an object with ${t} keys. Provided ${n} not understood: ${JSON.stringify(e)}`)}function uI(e,t){return PB(e,t,"classWeight")}async function cI(e,t,n,a){if(t!=null||a!=null)throw new Error("Support sampleWeight is not implemented yet");if(n!=null){let r=D(()=>{if(e.shape.length===1)return e.clone();if(e.shape.length===2)if(e.shape[1]>1){let o=1;return e.argMax(o)}else{if(e.shape[1]===1)return e.reshape([e.shape[0]]);throw new Error(`Encountered unexpected last-dimension size (${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.`)}),s=Array.from(await r.data());Ae(r);let i=[];return s.forEach(o=>{if(n[o]==null)throw new Error(`classWeight must contain all classes in the training data. The class ${o} exists in the data but not in classWeight`);i.push(n[o])}),Ze(i,"float32")}else return null}function OB(e,t){return W(e,t)}var LB=32;function dI(e,t){let n,a,r=t;n=r.xs,a=r.ys,w.assert(n!=null&&a!=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 s=pI("input",e.inputNames,n),i=pI("output",e.outputNames,a),o=s[0].shape[0];w.assert(s.length===e.inputs.length,()=>`LayersModel has ${e.inputs.length} inputs, but the dataset provides ${s.length} inputs. (Expected input keys: ${JSON.stringify(e.inputNames)})`),w.assert(i.length===e.outputs.length,()=>`LayersModel has ${e.outputs.length} outputs, but the dataset provides ${i.length} outputs. (Expected output keys: ${JSON.stringify(e.outputNames)})`);for(let l=0;l<s.length;l++)w.assert(s[l].shape[0]===o,()=>`Batch size mismatch: input ${e.inputNames[l]} has ${s[l].shape[0]}; expected ${o} based on input ${e.inputNames[0]}.`);for(let l=0;l<i.length;l++)w.assert(i[l].shape[0]===o,()=>`Batch size mismatch: output ${e.outputNames[l]} has ${i[l].shape[0]}; expected ${o} based on input ${e.inputNames[0]}.`);return{xs:s,ys:i}}function pI(e,t,n){if(n instanceof Ee)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 a=[];for(let r of t){if(n[r]==null)throw new B(`The feature data generated by the dataset lacks the required ${e} key '${r}'.`);a.push(n[r])}return a}}function zB(e){if(e.length===3)throw new $e("Validation with sample weights is not implemented yet.");return{xs:e[0],ys:e[1]}}async function BB(e,t,n){let a=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(!a||n.batchesPerEpoch>0&&Number.isInteger(n.batchesPerEpoch),()=>`For fitDataset(), config.batchesPerEpoch is expected to be a positive integer if specified, but got ${n.batchesPerEpoch}`),w.assert(n.validationSplit==null,()=>"`validationSplit` is not supported by `fitDataset()`. 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t}makeTrainFunction(){return e=>{let t=[],n=e.slice(0,this.inputs.length),a=e.slice(this.inputs.length,this.inputs.length+this.outputs.length),r=e.slice(this.inputs.length+this.outputs.length,this.inputs.length+this.outputs.length*2),s=[],i=()=>{let c=[];for(let h=0;h<this.inputs.length;++h)c.push({key:this.inputs[h],value:n[h]});let u=new Hi(c),p=tp(this.outputs,u,{training:!0}),d;for(let h=0;h<this.lossFunctions.length;++h){let m=this.lossFunctions[h](a[h],p[h]);r[h]!=null&&(m=OB(m,r[h]));let f=Ct(m);t.push(f),h===0?d=m:d=J(d,m)}for(let h=0;h<this.metricsTensors.length;++h){let m;if(this.outputs.length>1&&h<this.outputs.length)m=t[h];else{let f=this.metricsTensors[h][0],g=this.metricsTensors[h][1];m=Ct(f(a[g],p[g]))}qt(m),s.push(m)}return d=Ct(d),this.calculateLosses().forEach(h=>{d=J(d,h)}),d},o=this.collectedTrainableWeights.map(c=>c.read()),l=!0;return[this.optimizer_.minimize(i,l,o)].concat(s)}}makeTestFunction(){this.testFunction=e=>D(()=>{let 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e.metrics)r[s]=Wi(e.metrics[s])}this.compile({loss:a,metrics:r,optimizer:n})}async save(e,t){if(typeof e=="string"){let i=jt.getSaveHandlers(e);if(i.length===0)throw new B(`Cannot find any save handlers for URL '${e}'`);if(i.length>1)throw new B(`Found more than one (${i.length}) save handlers for URL '${e}'`);e=i[0]}if(e.save==null)throw new B("LayersModel.save() cannot proceed because the IOHandler provided does not have the `save` attribute defined.");let n=await jt.encodeWeights(this.getNamedWeights(t)),a=!1,r=null,s={modelTopology:this.toJSON(r,a),format:YB,generatedBy:`TensorFlow.js tfjs-layers v${Im}`,convertedBy:null};if((t==null?!1:t.includeOptimizer)&&this.optimizer!=null){s.trainingConfig=this.getTrainingConfig();let i="optimizer",{data:o,specs:l}=await jt.encodeWeights(await this.optimizer.getWeights(),i);n.specs.push(...l),n.data=jt.concatenateArrayBuffers([n.data,o])}if(this.userDefinedMetadata!=null){let 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this.model.fitDataset(e,t)}async trainOnBatch(e,t){return this.model.trainOnBatch(e,t)}static fromConfig(e,t,n={},a=!1){let r,s={};if(t instanceof Array){if(t[0].className==null||t[0].className==="Merge")throw new B("Legacy serialization format not supported yet.");r=t}else w.assert(t.layers!=null,()=>"When the config data for a Sequential model is not an Array, it must be an Object that contains the 'layers' field."),r=t.layers,delete t.layers,s=t;let i=new e(s);if(!(i instanceof su))throw new $e(`Sequential.fromConfig called on non-Sequential input: ${i}`);for(let o of r){let l=Da(o,void 0,a);a&&l.setFastWeightInitDuringBuild(!0),i.add(l)}return i}set stopTraining(e){if(this.model==null)throw new B("Cannot set the stopTraining property of a sequential model before it is compiled.");this.model.stopTraining=e}get stopTraining(){if(this.model==null)throw new B("Cannot get the stopTraining property of a sequential model before it is compiled.");return this.model.stopTraining}getConfig(){let e=[];for(let t of this.layers){let n={};n.className=t.getClassName(),n.config=t.getConfig(),e.push(n)}return{name:this.name,layers:e}}};su.className="Sequential";re.registerClass(su);function t4(e){return new Tr(e)}function n4(e){return new su(e)}function a4(e,t){return t==null&&(t={}),ZB(e,t)}function P1(e){return H1(e)}function r4(e,t){ya.registerCallbackConstructor(e,t)}var Wn=class extends re.Serializable{getConfig(){return{}}},xI=class extends Wn{apply(e,t=1){return Pz(e,t)}};xI.className="elu";re.registerClass(xI);var vI=class extends Wn{apply(e){return Rh(e)}};vI.className="selu";re.registerClass(vI);var wI=class extends Wn{apply(e){return qe(e)}};wI.className="relu";re.registerClass(wI);var kI=class extends Wn{apply(e){return D(()=>Xl(6,qe(e)))}};kI.className="relu6";re.registerClass(kI);var II=class extends Wn{apply(e){return e}};II.className="linear";re.registerClass(II);var TI=class extends Wn{apply(e){return 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e={theta:this.theta},t=super.getConfig();return Object.assign(e,t),e}};mx.className="ThresholdedReLU";re.registerClass(mx);var fx=class extends je{constructor(e){super(e==null?{}:e);this.DEFAULT_AXIS=1,e==null&&(e={}),this.softmax=new ox().apply,this.axis=e.axis==null?this.DEFAULT_AXIS:e.axis}call(e,t){let n=Pe(e);return this.softmax(n,this.axis)}computeOutputShape(e){return e}getConfig(){let e={axis:this.axis},t=super.getConfig();return Object.assign(e,t),e}};fx.className="Softmax";re.registerClass(fx);function iu(e,t,n){if(typeof e=="number")return zi(e,t);if(e.length!==t)throw new B(`The ${n} argument must be an integer or tuple of ${t} integers. Received: ${e.length} elements.`);for(let a=0;a<t;++a){let r=e[a];if(!$z(r))throw new B(`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 Ra(e,t,n,a,r=1){if(e==null)return e;let s=t+(t-1)*(r-1),i;return n==="same"?i=e:i=e-s+1,Math.floor((i+a-1)/a)}function Tm(e,t,n,a){if(e==null)return null;if(a==="valid")e=e*t+us([n-t,0]);else if(a==="same")e=e*t;else throw new B(`Unsupport padding mode: ${a}.`);return e}function gx(e,t){return D(()=>(Rt(t),t==="channelsFirst"?Ve(e,[0,2,3,1]):e))}function RI(e,t){return D(()=>(Rt(t),t==="channelsFirst"?Ve(e,[0,2,3,4,1]):e))}function o4(e,t,n,a=1,r="valid",s,i=1){return D(()=>{if(s==null&&(s=Ea()),Rt(s),e.shape.length!==3)throw new B(`The input of a conv1dWithBias operation should be 3, but is ${e.shape.length} instead.`);if(t.shape.length!==3)throw new B(`The kernel for a conv1dWithBias operation should be 3, but is ${t.shape.length} instead`);if(n!=null&&n.shape.length!==1)throw new B(`The bias for a conv1dWithBias operation should be 1, but is ${t.shape.length} instead`);if(s==="channelsFirst"&&(e=Ve(e,[0,2,1])),r==="causal")throw new $e("The support for CAUSAL padding mode in conv1dWithBias is not implemented yet.");let o=xh(e,t,a,r==="same"?"same":"valid","NWC",i);return n!=null&&(o=tr(o,n)),o})}function MI(e,t,n,a=[1,1],r="valid",s,i,o=null){return D(()=>{if(s==null&&(s=Ea()),Rt(s),e.rank!==3&&e.rank!==4)throw new B(`conv2dWithBiasActivation expects input to be of rank 3 or 4, but received ${e.rank}.`);if(t.rank!==3&&t.rank!==4)throw new B(`conv2dWithBiasActivation expects kernel to be of rank 3 or 4, but received ${e.rank}.`);let l=gx(e,s);if(r==="causal")throw new $e("The support for CAUSAL padding mode in conv1dWithBias is not implemented yet.");return l=is.conv2d({x:l,filter:t,strides:a,pad:r==="same"?"same":"valid",dilations:i,dataFormat:"NHWC",bias:n,activation:o}),s==="channelsFirst"&&(l=Ve(l,[0,3,1,2])),l})}function l4(e,t,n,a=[1,1,1],r="valid",s,i){return D(()=>{if(s==null&&(s=Ea()),Rt(s),e.rank!==4&&e.rank!==5)throw new B(`conv3dWithBias expects input to be of rank 4 or 5, but received ${e.rank}.`);if(t.rank!==4&&t.rank!==5)throw new B(`conv3dWithBias expects kernel to be of rank 4 or 5, but received ${e.rank}.`);let o=RI(e,s);if(r==="causal")throw new $e("The support for CAUSAL padding mode in conv3dWithBias is not implemented yet.");return o=qy(o,t,a,r==="same"?"same":"valid","NDHWC",i),n!=null&&(o=tr(o,n)),s==="channelsFirst"&&(o=Ve(o,[0,4,1,2,3])),o})}var yx=class extends je{constructor(e,t){super(t);if(this.bias=null,this.DEFAULT_KERNEL_INITIALIZER="glorotNormal",this.DEFAULT_BIAS_INITIALIZER="zeros",yx.verifyArgs(t),this.rank=e,Kt(this.rank,"rank"),this.rank!==1&&this.rank!==2&&this.rank!==3)throw new $e(`Convolution layer for rank other than 1, 2, or 3 (${this.rank}) is not implemented yet.`);if(this.kernelSize=iu(t.kernelSize,e,"kernelSize"),this.strides=iu(t.strides==null?1:t.strides,e,"strides"),this.padding=t.padding==null?"valid":t.padding,na(this.padding),this.dataFormat=t.dataFormat==null?"channelsLast":t.dataFormat,Rt(this.dataFormat),this.activation=ds(t.activation),this.useBias=t.useBias==null?!0:t.useBias,this.biasInitializer=vt(t.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.biasConstraint=Ut(t.biasConstraint),this.biasRegularizer=wt(t.biasRegularizer),this.activityRegularizer=wt(t.activityRegularizer),this.dilationRate=iu(t.dilationRate==null?1:t.dilationRate,e,"dilationRate"),this.rank===1&&Array.isArray(this.dilationRate)&&this.dilationRate.length!==1)throw new B(`dilationRate must be a number or an array of a single number for 1D convolution, but received ${JSON.stringify(this.dilationRate)}`);if(this.rank===2){if(typeof this.dilationRate=="number")this.dilationRate=[this.dilationRate,this.dilationRate];else if(this.dilationRate.length!==2)throw new B(`dilationRate must be a number or array of two numbers for 2D convolution, but received ${JSON.stringify(this.dilationRate)}`)}else if(this.rank===3){if(typeof this.dilationRate=="number")this.dilationRate=[this.dilationRate,this.dilationRate,this.dilationRate];else if(this.dilationRate.length!==3)throw new B(`dilationRate must be a number or array of three numbers for 3D convolution, but received ${JSON.stringify(this.dilationRate)}`)}}static verifyArgs(e){if(Za("kernelSize"in e,"required key 'kernelSize' not in config"),typeof e.kernelSize!="number"&&!Sb(e.kernelSize,"number",1,3))throw new B(`BaseConv expects config.kernelSize to be number or number[] with length 1, 2, or 3, but received ${JSON.stringify(e.kernelSize)}.`)}getConfig(){let e={kernelSize:this.kernelSize,strides:this.strides,padding:this.padding,dataFormat:this.dataFormat,dilationRate:this.dilationRate,activation:ps(this.activation),useBias:this.useBias,biasInitializer:_t(this.biasInitializer),biasRegularizer:pt(this.biasRegularizer),activityRegularizer:pt(this.activityRegularizer),biasConstraint:Vt(this.biasConstraint)},t=super.getConfig();return Object.assign(e,t),e}},rp=class extends yx{constructor(e,t){super(e,t);this.kernel=null,rp.verifyArgs(t),this.filters=t.filters,Kt(this.filters,"filters"),this.kernelInitializer=vt(t.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.kernelConstraint=Ut(t.kernelConstraint),this.kernelRegularizer=wt(t.kernelRegularizer)}build(e){e=ct(e);let t=this.dataFormat==="channelsFirst"?1:e.length-1;if(e[t]==null)throw new B(`The channel dimension of the input should be defined. Found ${e[t]}`);let n=e[t],a=this.kernelSize.concat([n,this.filters]);this.kernel=this.addWeight("kernel",a,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 D(()=>{e=Pe(e);let n,a=this.bias==null?null:this.bias.read(),r=v1(this.activation.getClassName());if(r!=null&&this.rank===2)n=MI(e,this.kernel.read(),a,this.strides,this.padding,this.dataFormat,this.dilationRate,r);else{if(this.rank===1)n=o4(e,this.kernel.read(),a,this.strides[0],this.padding,this.dataFormat,this.dilationRate[0]);else if(this.rank===2)n=MI(e,this.kernel.read(),a,this.strides,this.padding,this.dataFormat,this.dilationRate);else if(this.rank===3)n=l4(e,this.kernel.read(),a,this.strides,this.padding,this.dataFormat,this.dilationRate);else throw new $e("convolutions greater than 3D are not implemented yet.");this.activation!=null&&(n=this.activation.apply(n))}return n})}computeOutputShape(e){e=ct(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 s=Ra(n[r],this.kernelSize[r],this.padding,this.strides[r],typeof this.dilationRate=="number"?this.dilationRate:this.dilationRate[r]);t.push(s)}let a=[e[0]];return this.dataFormat==="channelsLast"?(a=a.concat(t),a.push(this.filters)):(a.push(this.filters),a=a.concat(t)),a}getConfig(){let e={filters:this.filters,kernelInitializer:_t(this.kernelInitializer),kernelRegularizer:pt(this.kernelRegularizer),kernelConstraint:Vt(this.kernelConstraint)},t=super.getConfig();return Object.assign(e,t),e}static verifyArgs(e){if(!("filters"in e)||typeof e.filters!="number"||e.filters<1)throw new B(`Convolution layer expected config.filters to be a 'number' > 0 but got ${JSON.stringify(e.filters)}`)}},sp=class extends rp{constructor(e){super(2,e);sp.verifyArgs(e)}getConfig(){let e=super.getConfig();return delete e.rank,e}static verifyArgs(e){if(typeof e.kernelSize!="number"&&!Sb(e.kernelSize,"number",1,2))throw new B(`Conv2D expects config.kernelSize to be number or number[] with length 1 or 2, but received ${JSON.stringify(e.kernelSize)}.`)}};sp.className="Conv2D";re.registerClass(sp);var Nm=class extends rp{constructor(e){super(3,e);Nm.verifyArgs(e)}getConfig(){let e=super.getConfig();return delete e.rank,e}static verifyArgs(e){if(typeof e.kernelSize!="number"&&!(Array.isArray(e.kernelSize)&&(e.kernelSize.length===1||e.kernelSize.length===3)))throw new B(`Conv3D expects config.kernelSize to be number or [number, number, number], but received ${JSON.stringify(e.kernelSize)}.`)}};Nm.className="Conv3D";re.registerClass(Nm);var bx=class extends sp{constructor(e){super(e);if(this.inputSpec=[new Yt({ndim:4})],this.padding!=="same"&&this.padding!=="valid")throw new B(`Conv2DTranspose currently supports only padding modes 'same' and 'valid', but received padding mode ${this.padding}`)}build(e){if(e=ct(e),e.length!==4)throw new B("Input should have rank 4; Received input shape: "+JSON.stringify(e));let t=this.dataFormat==="channelsFirst"?1:e.length-1;if(e[t]==null)throw new B("The channel dimension of the inputs should be defined. Found `None`.");let n=e[t],a=this.kernelSize.concat([this.filters,n]);this.kernel=this.addWeight("kernel",a,"float32",this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint),this.useBias&&(this.bias=this.addWeight("bias",[this.filters],"float32",this.biasInitializer,this.biasRegularizer,!0,this.biasConstraint)),this.inputSpec=[new Yt({ndim:4,axes:{[t]:n}})],this.built=!0}call(e,t){return D(()=>{let n=Pe(e);if(n.shape.length!==4)throw new B(`Conv2DTranspose.call() expects input tensor to be rank-4, but received a tensor of rank-${n.shape.length}`);let a=n.shape,r=a[0],s,i;this.dataFormat==="channelsFirst"?(s=2,i=3):(s=1,i=2);let o=a[s],l=a[i],c=this.kernelSize[0],u=this.kernelSize[1],p=this.strides[0],d=this.strides[1],h=Tm(o,p,c,this.padding),m=Tm(l,d,u,this.padding),f=[r,h,m,this.filters];this.dataFormat!=="channelsLast"&&(n=Ve(n,[0,2,3,1]));let g=vh(n,this.kernel.read(),f,this.strides,this.padding);return this.dataFormat!=="channelsLast"&&(g=Ve(g,[0,3,1,2])),this.bias!=null&&(g=tr(g,this.bias.read(),this.dataFormat)),this.activation!=null&&(g=this.activation.apply(g)),g})}computeOutputShape(e){e=ct(e);let t=e.slice(),n,a,r;this.dataFormat==="channelsFirst"?(n=1,a=2,r=3):(n=3,a=1,r=2);let s=this.kernelSize[0],i=this.kernelSize[1],o=this.strides[0],l=this.strides[1];return t[n]=this.filters,t[a]=Tm(t[a],o,s,this.padding),t[r]=Tm(t[r],l,i,this.padding),t}getConfig(){let e=super.getConfig();return delete e.dilationRate,e}};bx.className="Conv2DTranspose";re.registerClass(bx);var PI=class extends rp{constructor(e,t){super(e,t);if(this.DEFAULT_DEPTHWISE_INITIALIZER="glorotUniform",this.DEFAULT_POINTWISE_INITIALIZER="glorotUniform",this.depthwiseKernel=null,this.pointwiseKernel=null,t.filters==null)throw new B("The `filters` configuration field is required by SeparableConv, but is unspecified.");if(t.kernelInitializer!=null||t.kernelRegularizer!=null||t.kernelConstraint!=null)throw new B("Fields kernelInitializer, kernelRegularizer and kernelConstraint are invalid for SeparableConv2D. Use depthwiseInitializer, depthwiseRegularizer, depthwiseConstraint, pointwiseInitializer, pointwiseRegularizer and pointwiseConstraint instead.");if(t.padding!=null&&t.padding!=="same"&&t.padding!=="valid")throw new B(`SeparableConv${this.rank}D supports only padding modes: 'same' and 'valid', but received ${JSON.stringify(t.padding)}`);this.depthMultiplier=t.depthMultiplier==null?1:t.depthMultiplier,this.depthwiseInitializer=vt(t.depthwiseInitializer||this.DEFAULT_DEPTHWISE_INITIALIZER),this.depthwiseRegularizer=wt(t.depthwiseRegularizer),this.depthwiseConstraint=Ut(t.depthwiseConstraint),this.pointwiseInitializer=vt(t.depthwiseInitializer||this.DEFAULT_POINTWISE_INITIALIZER),this.pointwiseRegularizer=wt(t.pointwiseRegularizer),this.pointwiseConstraint=Ut(t.pointwiseConstraint)}build(e){if(e=ct(e),e.length<this.rank+2)throw new B(`Inputs to SeparableConv${this.rank}D should have rank ${this.rank+2}, but received input shape: ${JSON.stringify(e)}`);let t=this.dataFormat==="channelsFirst"?1:e.length-1;if(e[t]==null||e[t]<0)throw new B(`The channel dimension of the inputs should be defined, but found ${JSON.stringify(e[t])}`);let n=e[t],a=this.kernelSize.concat([n,this.depthMultiplier]),r=[];for(let i=0;i<this.rank;++i)r.push(1);r.push(n*this.depthMultiplier,this.filters);let s=!0;this.depthwiseKernel=this.addWeight("depthwise_kernel",a,"float32",this.depthwiseInitializer,this.depthwiseRegularizer,s,this.depthwiseConstraint),this.pointwiseKernel=this.addWeight("pointwise_kernel",r,"float32",this.pointwiseInitializer,this.pointwiseRegularizer,s,this.pointwiseConstraint),this.useBias?this.bias=this.addWeight("bias",[this.filters],"float32",this.biasInitializer,this.biasRegularizer,s,this.biasConstraint):this.bias=null,this.inputSpec=[new Yt({ndim:this.rank+2,axes:{[t]:n}})],this.built=!0}call(e,t){return D(()=>{e=Pe(e);let n;if(this.rank===1)throw new $e("1D separable convolution is not implemented yet.");return this.rank===2&&(this.dataFormat==="channelsFirst"&&(e=Ve(e,[0,2,3,1])),n=Pi(e,this.depthwiseKernel.read(),this.pointwiseKernel.read(),this.strides,this.padding,this.dilationRate,"NHWC")),this.useBias&&(n=tr(n,this.bias.read(),this.dataFormat)),this.activation!=null&&(n=this.activation.apply(n)),this.dataFormat==="channelsFirst"&&(n=Ve(n,[0,3,1,2])),n})}getConfig(){let e=super.getConfig();return delete e.rank,delete e.kernelInitializer,delete e.kernelRegularizer,delete e.kernelConstraint,e.depthwiseInitializer=_t(this.depthwiseInitializer),e.pointwiseInitializer=_t(this.pointwiseInitializer),e.depthwiseRegularizer=pt(this.depthwiseRegularizer),e.pointwiseRegularizer=pt(this.pointwiseRegularizer),e.depthwiseConstraint=Vt(this.depthwiseConstraint),e.pointwiseConstraint=Vt(this.pointwiseConstraint),e}};PI.className="SeparableConv";var xx=class extends PI{constructor(e){super(2,e)}};xx.className="SeparableConv2D";re.registerClass(xx);var Sm=class extends rp{constructor(e){super(1,e);Sm.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"&&!Sb(e.kernelSize,"number",1,1))throw new B(`Conv1D expects config.kernelSize to be number or number[] with length 1, but received ${JSON.stringify(e.kernelSize)}.`)}};Sm.className="Conv1D";re.registerClass(Sm);var vx=class extends je{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 D(()=>{if(e=Pe(e),this.dataFormat==="channelsLast"){let n=tm(e,this.cropping[0][0],e.shape[1]-this.cropping[0][0]-this.cropping[0][1],2);return tm(n,this.cropping[1][0],e.shape[2]-this.cropping[1][1]-this.cropping[1][0],3)}else{let n=tm(e,this.cropping[0][0],e.shape[2]-this.cropping[0][0]-this.cropping[0][1],3);return tm(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}};vx.className="Cropping2D";re.registerClass(vx);var wx=class extends je{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,Rt(this.dataFormat),this.interpolation=e.interpolation==null?"nearest":e.interpolation,Ez(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 D(()=>{let n=Pe(e),a=n.shape;if(this.dataFormat==="channelsFirst"){n=Ve(n,[0,2,3,1]);let r=this.size[0]*a[2],s=this.size[1]*a[3],i=this.interpolation==="nearest"?n.resizeNearestNeighbor([r,s]):n.resizeBilinear([r,s]);return Ve(i,[0,3,1,2])}else{let r=this.size[0]*a[1],s=this.size[1]*a[2];return this.interpolation==="nearest"?n.resizeNearestNeighbor([r,s]):n.resizeBilinear([r,s])}})}getConfig(){let e={size:this.size,dataFormat:this.dataFormat},t=super.getConfig();return Object.assign(e,t),e}};wx.className="UpSampling2D";re.registerClass(wx);function u4(e,t,n=[1,1],a="valid",r,s){return D(()=>{r==null&&(r=Ea()),Rt(r);let i=gx(e,r);if(e.rank!==4)throw new B(`Input for depthwiseConv2d is required to be 4-D, but is instead ${e.rank}-D`);if(t.rank!==4)throw new B(`depthwiseKernel is required to be 4-D, but is instead ${t.rank}-D`);return i=ns(i,t,n,a==="same"?"same":"valid","NHWC",s),r==="channelsFirst"&&(i=Ve(i,[0,3,1,2])),i})}var kx=class extends yx{constructor(e){super(2,e);this.depthwiseKernel=null,this.depthMultiplier=e.depthMultiplier==null?1:e.depthMultiplier,this.depthwiseInitializer=vt(e.depthwiseInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.depthwiseConstraint=Ut(e.depthwiseConstraint),this.depthwiseRegularizer=wt(e.depthwiseRegularizer)}build(e){if(e=ct(e),e.length<4)throw new B(`Inputs to DepthwiseConv2D should have rank 4. Received input shape: ${JSON.stringify(e)}.`);let t=this.dataFormat==="channelsFirst"?1:3;if(e[t]==null||e[t]<0)throw new B(`The channel dimension of the inputs to DepthwiseConv2D should be defined, but is not (${e[t]}).`);let n=e[t],a=[this.kernelSize[0],this.kernelSize[1],n,this.depthMultiplier];this.depthwiseKernel=this.addWeight("depthwise_kernel",a,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 D(()=>{e=Pe(e);let n=u4(e,this.depthwiseKernel.read(),this.strides,this.padding,this.dataFormat,null);return this.useBias&&(n=tr(n,this.bias.read(),this.dataFormat)),this.activation!=null&&(n=this.activation.apply(n)),n})}computeOutputShape(e){e=ct(e);let t=this.dataFormat==="channelsFirst"?e[2]:e[1],n=this.dataFormat==="channelsFirst"?e[3]:e[2],a=this.dataFormat==="channelsFirst"?e[1]*this.depthMultiplier:e[3]*this.depthMultiplier,r=Ra(t,this.kernelSize[0],this.padding,this.strides[0]),s=Ra(n,this.kernelSize[1],this.padding,this.strides[1]);return this.dataFormat==="channelsFirst"?[e[0],a,r,s]:[e[0],r,s,a]}getConfig(){let e=super.getConfig();return e.depthMultiplier=this.depthMultiplier,e.depthwiseInitializer=_t(this.depthwiseInitializer),e.depthwiseRegularizer=pt(this.depthwiseRegularizer),e.depthwiseConstraint=Vt(this.depthwiseRegularizer),e}};kx.className="DepthwiseConv2D";re.registerClass(kx);function OI(e,t,n,a){if(Array.isArray(e)){if(t!=null||n!=null)throw new B("When inputs is an array, neither initialState or constants should be provided");a!=null&&(n=e.slice(e.length-a,e.length),e=e.slice(0,e.length-a)),e.length>1&&(t=e.slice(1,e.length)),e=e[0]}function r(s){return s==null||Array.isArray(s)?s:[s]}return t=r(t),n=r(n),{inputs:e,initialState:t,constants:n}}function LI(e,t,n,a=!1,r,s,i=!1,o=!1){return D(()=>{let l=t.shape.length;if(l<3)throw new B(`Input should be at least 3D, but is ${l}D.`);let c=[1,0].concat(Aa(2,l));if(t=Ve(t,c),s!=null)throw new $e("The rnn() functoin of the deeplearn.js backend does not support constants yet.");i&&console.warn("Backend rnn(): the unroll = true option is not applicable to the imperative deeplearn.js backend."),r!=null&&(r=r.asType("bool").asType("float32"),r.rank===l-1&&(r=mn(r,-1)),r=Ve(r,c)),a&&(t=Ln(t,0),r!=null&&(r=Ln(r,0)));let u=[],p,d=n,h=t.shape[0],m=ut(t),f;r!=null&&(f=ut(r));for(let y=0;y<h;++y){let b=m[y],x=D(()=>e(b,d));if(r==null)p=x[0],d=x[1];else{let v=D(()=>{let T=f[y],k=On(T).sub(T),S=x[0].mul(T).add(d[0].mul(k)),F=d.map((A,R)=>x[1][R].mul(T).add(A.mul(k)));return{output:S,newStates:F}});p=v.output,d=v.newStates}o&&u.push(p)}let g;return o&&(g=Dt(u,1)),[p,g,d]})}var nr=class extends je{constructor(e){super(e);let t;if(e.cell==null)throw new B("cell property is missing for the constructor of RNN.");if(Array.isArray(e.cell)?t=new Cm({cells:e.cell}):t=e.cell,t.stateSize==null)throw new B("The RNN cell should have an attribute `stateSize` (tuple of integers, one integer per RNN state).");this.cell=t,this.returnSequences=e.returnSequences==null?!1:e.returnSequences,this.returnState=e.returnState==null?!1:e.returnState,this.goBackwards=e.goBackwards==null?!1:e.goBackwards,this._stateful=e.stateful==null?!1:e.stateful,this.unroll=e.unroll==null?!1:e.unroll,this.supportsMasking=!0,this.inputSpec=[new Yt({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 Aa(0,e).map(t=>null)}else return this.states_}setStates(e){this.states_=e}computeOutputShape(e){jb(e)&&(e=e[0]),e=e;let t=this.cell.stateSize;Array.isArray(t)||(t=[t]);let n=t[0],a;if(this.returnSequences?a=[e[0],e[1],n]:a=[e[0],n],this.returnState){let r=[];for(let s of t)r.push([e[0],s]);return[a].concat(r)}else return a}computeMask(e,t){return D(()=>{Array.isArray(t)&&(t=t[0]);let n=this.returnSequences?t:null;if(this.returnState){let a=this.states.map(r=>null);return[n].concat(a)}else return n})}get states(){if(this.states_==null){let e=Array.isArray(this.cell.stateSize)?this.cell.stateSize.length:1,t=[];for(let n=0;n<e;++n)t.push(null);return t}else return this.states_}set states(e){this.states_=e}build(e){let t=null;if(this.numConstants!=null)throw new $e("Constants support is not implemented in RNN yet.");jb(e)&&(e=e[0]),e=e;let n=this.stateful?e[0]:null,a=e.slice(2);this.inputSpec[0]=new Yt({shape:[n,null,...a]});let r=[e[0]].concat(e.slice(2));if(t!=null)throw new $e("Constants support is not implemented in RNN yet.");this.cell.build(r);let s;if(Array.isArray(this.cell.stateSize)?s=this.cell.stateSize:s=[this.cell.stateSize],this.stateSpec!=null){if(!w.arraysEqual(this.stateSpec.map(i=>i.shape[i.shape.length-1]),s))throw new B(`An initialState was passed that is not compatible with cell.stateSize. Received stateSpec=${this.stateSpec}; However cell.stateSize is ${this.cell.stateSize}`)}else this.stateSpec=s.map(i=>new Yt({shape:[null,i]}));this.stateful&&this.resetStates()}resetStates(e,t=!1){D(()=>{if(!this.stateful)throw new kr("Cannot call resetStates() on an RNN Layer that is not stateful.");let n=this.inputSpec[0].shape[0];if(n==null)throw new B("If an RNN is stateful, it needs to know its batch size. Specify the batch size of your input tensors: \n- If using a Sequential model, specify the batch size by passing a `batchInputShape` option to your first layer.\n- If using the functional API, specify the batch size by passing a `batchShape` option to your Input layer.");if(this.states_==null)Array.isArray(this.cell.stateSize)?this.states_=this.cell.stateSize.map(a=>xt([n,a])):this.states_=[xt([n,this.cell.stateSize])];else if(e==null)Ae(this.states_),this.keptStates!=null&&(Ae(this.keptStates),this.keptStates=[]),Array.isArray(this.cell.stateSize)?this.states_=this.cell.stateSize.map(a=>xt([n,a])):this.states_[0]=xt([n,this.cell.stateSize]);else{if(Array.isArray(e)||(e=[e]),e.length!==this.states_.length)throw new B(`Layer ${this.name} expects ${this.states_.length} state(s), but it received ${e.length} state value(s). Input received: ${e}`);t===!0?this.keptStates.push(this.states_.slice()):Ae(this.states_);for(let a=0;a<this.states_.length;++a){let r=e[a],s=Array.isArray(this.cell.stateSize)?this.cell.stateSize[a]:this.cell.stateSize,i=[n,s];if(!w.arraysEqual(r.shape,i))throw new B(`State ${a} is incompatible with layer ${this.name}: expected shape=${i}, received shape=${r.shape}`);this.states_[a]=r}}this.states_=this.states_.map(a=>qt(a.clone()))})}apply(e,t){let n=t==null?null:t.initialState,a=t==null?null:t.constants;t==null&&(t={});let r=OI(e,n,a,this.numConstants);e=r.inputs,n=r.initialState,a=r.constants;let s=[],i=[];if(n!=null){t.initialState=n,s=s.concat(n),this.stateSpec=[];for(let o of n)this.stateSpec.push(new Yt({shape:o.shape}));i=i.concat(this.stateSpec)}if(a!=null&&(t.constants=a,s=s.concat(a),this.numConstants=a.length),s[0]instanceof $a){let o=[e].concat(s),l=this.inputSpec.concat(i),c=this.inputSpec;this.inputSpec=l;let u=super.apply(o,t);return this.inputSpec=c,u}else return super.apply(e,t)}call(e,t){return D(()=>{let n=t==null?null:t.mask,a=t==null?null:t.training,r=t==null?null:t.initialState;e=Pe(e),r==null&&(this.stateful?r=this.states_:r=this.getInitialState(e));let s=Array.isArray(this.cell.stateSize)?this.cell.stateSize.length:1;if(r.length!==s)throw new B(`RNN Layer has ${s} state(s) but was passed ${r.length} initial state(s).`);this.unroll&&console.warn("Ignoring unroll = true for RNN layer, due to imperative backend.");let i={training:a},o=LI((d,h)=>{let m=this.cell.call([d].concat(h),i);return[m[0],m.slice(1)]},e,r,this.goBackwards,n,null,this.unroll,this.returnSequences),l=o[0],c=o[1],u=o[2];this.stateful&&this.resetStates(u,a);let p=this.returnSequences?c:l;return this.returnState?[p].concat(u):p})}getInitialState(e){return D(()=>{let t=xt(e.shape);return t=Se(t,[1,2]),t=Kc(t),Array.isArray(this.cell.stateSize)?this.cell.stateSize.map(n=>n>1?$b(t,[1,n]):t):this.cell.stateSize>1?[$b(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()===nr.className&&(t.cell={className:this.cell.getClassName(),config:n}),Object.assign({},n,e,t)}static fromConfig(e,t,n={}){let a=t.cell,r=Da(a,n);return new e(Object.assign(t,{cell:r}))}};nr.className="RNN";re.registerClass(nr);var Qc=class extends je{},_m=class extends Qc{constructor(e){super(e);this.DEFAULT_ACTIVATION="tanh",this.DEFAULT_KERNEL_INITIALIZER="glorotNormal",this.DEFAULT_RECURRENT_INITIALIZER="orthogonal",this.DEFAULT_BIAS_INITIALIZER="zeros",this.units=e.units,Kt(this.units,"units"),this.activation=ds(e.activation==null?this.DEFAULT_ACTIVATION:e.activation),this.useBias=e.useBias==null?!0:e.useBias,this.kernelInitializer=vt(e.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.recurrentInitializer=vt(e.recurrentInitializer||this.DEFAULT_RECURRENT_INITIALIZER),this.biasInitializer=vt(e.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.kernelRegularizer=wt(e.kernelRegularizer),this.recurrentRegularizer=wt(e.recurrentRegularizer),this.biasRegularizer=wt(e.biasRegularizer),this.kernelConstraint=Ut(e.kernelConstraint),this.recurrentConstraint=Ut(e.recurrentConstraint),this.biasConstraint=Ut(e.biasConstraint),this.dropout=tu([1,us([0,e.dropout==null?0:e.dropout])]),this.recurrentDropout=tu([1,us([0,e.recurrentDropout==null?0:e.recurrentDropout])]),this.stateSize=this.units,this.dropoutMask=null,this.recurrentDropoutMask=null}build(e){e=ct(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 D(()=>{if(e=e,e.length!==2)throw new B(`SimpleRNNCell expects 2 input Tensors, got ${e.length}.`);let n=e[1];e=e[0];let a=t.training==null?!1:t.training;0<this.dropout&&this.dropout<1&&this.dropoutMask==null&&(this.dropoutMask=hs({ones:()=>On(e),rate:this.dropout,training:a})),0<this.recurrentDropout&&this.recurrentDropout<1&&this.recurrentDropoutMask==null&&(this.recurrentDropoutMask=hs({ones:()=>On(n),rate:this.recurrentDropout,training:a}));let r,s=this.dropoutMask,i=this.recurrentDropoutMask;s!=null?r=er(W(e,s),this.kernel.read()):r=er(e,this.kernel.read()),this.bias!=null&&(r=tr(r,this.bias.read())),i!=null&&(n=W(n,i));let o=J(r,er(n,this.recurrentKernel.read()));return this.activation!=null&&(o=this.activation.apply(o)),[o,o]})}getConfig(){let e=super.getConfig(),t={units:this.units,activation:ps(this.activation),useBias:this.useBias,kernelInitializer:_t(this.kernelInitializer),recurrentInitializer:_t(this.recurrentInitializer),biasInitializer:_t(this.biasInitializer),kernelRegularizer:pt(this.kernelRegularizer),recurrentRegularizer:pt(this.recurrentRegularizer),biasRegularizer:pt(this.biasRegularizer),activityRegularizer:pt(this.activityRegularizer),kernelConstraint:Vt(this.kernelConstraint),recurrentConstraint:Vt(this.recurrentConstraint),biasConstraint:Vt(this.biasConstraint),dropout:this.dropout,recurrentDropout:this.recurrentDropout};return Object.assign({},e,t)}};_m.className="SimpleRNNCell";re.registerClass(_m);var Ix=class extends nr{constructor(e){e.cell=new _m(e),super(e)}call(e,t){return D(()=>{this.cell.dropoutMask!=null&&(Ae(this.cell.dropoutMask),this.cell.dropoutMask=null),this.cell.recurrentDropoutMask!=null&&(Ae(this.cell.recurrentDropoutMask),this.cell.recurrentDropoutMask=null);let n=t==null?null:t.mask,a=t==null?null:t.training,r=t==null?null:t.initialState;return super.call(e,{mask:n,training:a,initialState:r})})}static fromConfig(e,t){return new e(t)}};Ix.className="SimpleRNN";re.registerClass(Ix);var Em=class extends Qc{constructor(e){super(e);if(this.DEFAULT_ACTIVATION="tanh",this.DEFAULT_RECURRENT_ACTIVATION="hardSigmoid",this.DEFAULT_KERNEL_INITIALIZER="glorotNormal",this.DEFAULT_RECURRENT_INITIALIZER="orthogonal",this.DEFAULT_BIAS_INITIALIZER="zeros",e.resetAfter)throw new B("GRUCell does not support reset_after parameter set to true.");this.units=e.units,Kt(this.units,"units"),this.activation=ds(e.activation===void 0?this.DEFAULT_ACTIVATION:e.activation),this.recurrentActivation=ds(e.recurrentActivation===void 0?this.DEFAULT_RECURRENT_ACTIVATION:e.recurrentActivation),this.useBias=e.useBias==null?!0:e.useBias,this.kernelInitializer=vt(e.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.recurrentInitializer=vt(e.recurrentInitializer||this.DEFAULT_RECURRENT_INITIALIZER),this.biasInitializer=vt(e.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.kernelRegularizer=wt(e.kernelRegularizer),this.recurrentRegularizer=wt(e.recurrentRegularizer),this.biasRegularizer=wt(e.biasRegularizer),this.kernelConstraint=Ut(e.kernelConstraint),this.recurrentConstraint=Ut(e.recurrentConstraint),this.biasConstraint=Ut(e.biasConstraint),this.dropout=tu([1,us([0,e.dropout==null?0:e.dropout])]),this.recurrentDropout=tu([1,us([0,e.recurrentDropout==null?0:e.recurrentDropout])]),this.implementation=e.implementation,this.stateSize=this.units,this.dropoutMask=null,this.recurrentDropoutMask=null}build(e){e=ct(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 D(()=>{if(e=e,e.length!==2)throw new B(`GRUCell expects 2 input Tensors (inputs, h, c), got ${e.length}.`);let n=t.training==null?!1:t.training,a=e[1];e=e[0],0<this.dropout&&this.dropout<1&&this.dropoutMask==null&&(this.dropoutMask=hs({ones:()=>On(e),rate:this.dropout,training:n,count:3})),0<this.recurrentDropout&&this.recurrentDropout<1&&this.recurrentDropoutMask==null&&(this.recurrentDropoutMask=hs({ones:()=>On(a),rate:this.recurrentDropout,training:n,count:3}));let r=this.dropoutMask,s=this.recurrentDropoutMask,i,o,l;0<this.dropout&&this.dropout<1&&(e=W(e,r[0]));let c=er(e,this.kernel.read());this.useBias&&(c=tr(c,this.bias.read())),0<this.recurrentDropout&&this.recurrentDropout<1&&(a=W(a,s[0]));let u=this.recurrentKernel.read(),[p,d]=zn(u,[2*this.units,this.units],u.rank-1),h=er(a,p),[m,f,g]=zn(c,3,c.rank-1),[y,b]=zn(h,2,h.rank-1);i=this.recurrentActivation.apply(J(m,y)),o=this.recurrentActivation.apply(J(f,b));let x=er(W(o,a),d);l=this.activation.apply(J(g,x));let v=J(W(i,a),W(J(1,St(i)),l));return[v,v]})}getConfig(){let e=super.getConfig(),t={units:this.units,activation:ps(this.activation),recurrentActivation:ps(this.recurrentActivation),useBias:this.useBias,kernelInitializer:_t(this.kernelInitializer),recurrentInitializer:_t(this.recurrentInitializer),biasInitializer:_t(this.biasInitializer),kernelRegularizer:pt(this.kernelRegularizer),recurrentRegularizer:pt(this.recurrentRegularizer),biasRegularizer:pt(this.biasRegularizer),activityRegularizer:pt(this.activityRegularizer),kernelConstraint:Vt(this.kernelConstraint),recurrentConstraint:Vt(this.recurrentConstraint),biasConstraint:Vt(this.biasConstraint),dropout:this.dropout,recurrentDropout:this.recurrentDropout,implementation:this.implementation,resetAfter:!1};return Object.assign({},e,t)}};Em.className="GRUCell";re.registerClass(Em);var Tx=class extends nr{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 Em(e),super(e)}call(e,t){return D(()=>{this.cell.dropoutMask!=null&&(Ae(this.cell.dropoutMask),this.cell.dropoutMask=null),this.cell.recurrentDropoutMask!=null&&(Ae(this.cell.recurrentDropoutMask),this.cell.recurrentDropoutMask=null);let n=t==null?null:t.mask,a=t==null?null:t.training,r=t==null?null:t.initialState;return super.call(e,{mask:n,training:a,initialState:r})})}static fromConfig(e,t){return t.implmentation===0&&(t.implementation=1),new e(t)}};Tx.className="GRU";re.registerClass(Tx);var ip=class extends Qc{constructor(e){super(e);this.DEFAULT_ACTIVATION="tanh",this.DEFAULT_RECURRENT_ACTIVATION="hardSigmoid",this.DEFAULT_KERNEL_INITIALIZER="glorotNormal",this.DEFAULT_RECURRENT_INITIALIZER="orthogonal",this.DEFAULT_BIAS_INITIALIZER="zeros",this.units=e.units,Kt(this.units,"units"),this.activation=ds(e.activation===void 0?this.DEFAULT_ACTIVATION:e.activation),this.recurrentActivation=ds(e.recurrentActivation===void 0?this.DEFAULT_RECURRENT_ACTIVATION:e.recurrentActivation),this.useBias=e.useBias==null?!0:e.useBias,this.kernelInitializer=vt(e.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.recurrentInitializer=vt(e.recurrentInitializer||this.DEFAULT_RECURRENT_INITIALIZER),this.biasInitializer=vt(e.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.unitForgetBias=e.unitForgetBias,this.kernelRegularizer=wt(e.kernelRegularizer),this.recurrentRegularizer=wt(e.recurrentRegularizer),this.biasRegularizer=wt(e.biasRegularizer),this.kernelConstraint=Ut(e.kernelConstraint),this.recurrentConstraint=Ut(e.recurrentConstraint),this.biasConstraint=Ut(e.biasConstraint),this.dropout=tu([1,us([0,e.dropout==null?0:e.dropout])]),this.recurrentDropout=tu([1,us([0,e.recurrentDropout==null?0:e.recurrentDropout])]),this.implementation=e.implementation,this.stateSize=[this.units,this.units],this.dropoutMask=null,this.recurrentDropoutMask=null}build(e){var t;e=ct(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 a;if(this.useBias){if(this.unitForgetBias){let r=this.biasInitializer,s=this.units;a=new(t=class extends ga{apply(i,o){let l=r.apply([s]),c=new am().apply([s]),u=r.apply([s*2]);return F1(F1(l,c),u)}},t.className="CustomInit",t)}else a=this.biasInitializer;this.bias=this.addWeight("bias",[this.units*4],null,a,this.biasRegularizer,!0,this.biasConstraint)}else this.bias=null;this.built=!0}call(e,t){return D(()=>{let n=t.training==null?!1:t.training;if(e=e,e.length!==3)throw new B(`LSTMCell expects 3 input Tensors (inputs, h, c), got ${e.length}.`);let a=e[1],r=e[2];e=e[0],0<this.dropout&&this.dropout<1&&this.dropoutMask==null&&(this.dropoutMask=hs({ones:()=>On(e),rate:this.dropout,training:n,count:4})),0<this.recurrentDropout&&this.recurrentDropout<1&&this.recurrentDropoutMask==null&&(this.recurrentDropoutMask=hs({ones:()=>On(a),rate:this.recurrentDropout,training:n,count:4}));let s=this.dropoutMask,i=this.recurrentDropoutMask,o,l,c,u;0<this.dropout&&this.dropout<1&&(e=W(e,s[0]));let p=er(e,this.kernel.read());0<this.recurrentDropout&&this.recurrentDropout<1&&(a=W(a,i[0])),p=J(p,er(a,this.recurrentKernel.read())),this.useBias&&(p=tr(p,this.bias.read()));let[d,h,m,f]=zn(p,4,p.rank-1);o=this.recurrentActivation.apply(d),l=this.recurrentActivation.apply(h),c=J(W(l,r),W(o,this.activation.apply(m))),u=this.recurrentActivation.apply(f);let g=W(u,this.activation.apply(c));return[g,g,c]})}getConfig(){let e=super.getConfig(),t={units:this.units,activation:ps(this.activation),recurrentActivation:ps(this.recurrentActivation),useBias:this.useBias,kernelInitializer:_t(this.kernelInitializer),recurrentInitializer:_t(this.recurrentInitializer),biasInitializer:_t(this.biasInitializer),unitForgetBias:this.unitForgetBias,kernelRegularizer:pt(this.kernelRegularizer),recurrentRegularizer:pt(this.recurrentRegularizer),biasRegularizer:pt(this.biasRegularizer),activityRegularizer:pt(this.activityRegularizer),kernelConstraint:Vt(this.kernelConstraint),recurrentConstraint:Vt(this.recurrentConstraint),biasConstraint:Vt(this.biasConstraint),dropout:this.dropout,recurrentDropout:this.recurrentDropout,implementation:this.implementation};return Object.assign({},e,t)}};ip.className="LSTMCell";re.registerClass(ip);var Nx=class extends nr{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 ip(e),super(e)}call(e,t){return D(()=>{this.cell.dropoutMask!=null&&(Ae(this.cell.dropoutMask),this.cell.dropoutMask=null),this.cell.recurrentDropoutMask!=null&&(Ae(this.cell.recurrentDropoutMask),this.cell.recurrentDropoutMask=null);let n=t==null?null:t.mask,a=t==null?null:t.training,r=t==null?null:t.initialState;return super.call(e,{mask:n,training:a,initialState:r})})}static fromConfig(e,t){return t.implmentation===0&&(t.implementation=1),new e(t)}};Nx.className="LSTM";re.registerClass(Nx);var Cm=class extends Qc{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 D(()=>{e=e;let n=e.slice(1),a=[];for(let i of this.cells.slice().reverse())Array.isArray(i.stateSize)?a.push(n.splice(0,i.stateSize.length)):a.push(n.splice(0,1));a.reverse();let r=[],s;for(let i=0;i<this.cells.length;++i){let o=this.cells[i];n=a[i],i===0?s=[e[0]].concat(n):s=[s[0]].concat(n),s=o.call(s,t),r.push(s.slice(1))}n=[];for(let i of r.slice().reverse())n.push(...i);return[s[0]].concat(n)})}build(e){jb(e)&&(e=e[0]),e=e;let t;this.cells.forEach((n,a)=>{Vi(`RNNCell_${a}`,()=>{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=a=>({className:a.getClassName(),config:a.getConfig()}),n={cells:this.cells.map(t)};return Object.assign({},e,n)}static fromConfig(e,t,n={}){let a=[];for(let r of t.cells)a.push(Da(r,n));return new e({cells:a})}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 qb(e)}setWeights(e){let t=[];for(let n of this.cells){let a=n.weights.length,r=e.splice(a);for(let s=0;s<n.weights.length;++s)t.push([n.weights[s],r[s]])}Xb(t)}};Cm.className="StackedRNNCells";re.registerClass(Cm);function hs(e){let{ones:t,rate:n,training:a=!1,count:r=1}=e,s=()=>$1(t(),n),i=()=>Jc(s,t,a);return!r||r<=1?qt(i().clone()):Array(r).fill(void 0).map(i).map(o=>qt(o.clone()))}var c4=function(e,t){var n={};for(var a in e)Object.prototype.hasOwnProperty.call(e,a)&&t.indexOf(a)<0&&(n[a]=e[a]);if(e!=null&&typeof Object.getOwnPropertySymbols=="function")for(var r=0,a=Object.getOwnPropertySymbols(e);r<a.length;r++)t.indexOf(a[r])<0&&Object.prototype.propertyIsEnumerable.call(e,a[r])&&(n[a[r]]=e[a[r]]);return n},zI=class extends nr{constructor(e){if(e.unroll)throw new $e("Unrolling is not possible with convolutional RNNs.");if(Array.isArray(e.cell))throw new $e("It is not possible at the moment to stack convolutional cells.");super(e);this.inputSpec=[new Yt({ndim:5})]}call(e,t){return D(()=>{if(this.cell.dropoutMask!=null&&(Ae(this.cell.dropoutMask),this.cell.dropoutMask=null),this.cell.recurrentDropoutMask!=null&&(Ae(this.cell.recurrentDropoutMask),this.cell.recurrentDropoutMask=null),t&&t.constants)throw new B("ConvRNN2D cell does not support constants");let n=t==null?null:t.mask,a=t==null?null:t.training,r=t==null?null:t.initialState;return super.call(e,{mask:n,training:a,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 D(()=>{let{stateSize:t}=this.cell,n=e.shape,a=this.computeSingleOutputShape(n),r=[a[0],...a.slice(2)],s=xt(r);return Array.isArray(t)?Array(t.length).fill(s):[s]})}resetStates(e,t=!1){D(()=>{if(!this.stateful)throw new kr("Cannot call resetStates() on an RNN Layer that is not stateful.");let n=this.inputSpec[0].shape,a=this.computeSingleOutputShape(n),r=[a[0],...a.slice(2)];if(n[0]==null)throw new B("If an RNN is stateful, it needs to know its batch size. Specify the batch size of your input tensors: \n- If using a Sequential model, specify the batch size by passing a `batchInputShape` option to your first layer.\n- If using the functional API, specify the batch size by passing a `batchShape` option to your Input layer.");if(this.getStates()==null)Array.isArray(this.cell.stateSize)?this.states_=this.cell.stateSize.map(()=>xt(r)):this.states_=[xt(r)];else if(e==null)Ae(this.states_),this.keptStates!=null&&(Ae(this.keptStates),this.keptStates=[]),Array.isArray(this.cell.stateSize)?this.states_=this.cell.stateSize.map(()=>xt(r)):this.states_[0]=xt(r);else{if(Array.isArray(e)||(e=[e]),e.length!==this.states_.length)throw new B(`Layer ${this.name} expects ${this.states_.length} state(s), but it received ${e.length} state value(s). Input received: ${e}`);t?this.keptStates.push(this.states_.slice()):Ae(this.states_);for(let s=0;s<this.states_.length;++s){let i=e[s],o=r;if(!w.arraysEqual(i.shape,o))throw new B(`State ${s} is incompatible with layer ${this.name}: expected shape=${o}, received shape=${i.shape}`);this.states_[s]=i}}this.states_=this.states_.map(s=>qt(s.clone()))})}computeSingleOutputShape(e){let{dataFormat:t,filters:n,kernelSize:a,padding:r,strides:s,dilationRate:i}=this.cell,o=t==="channelsFirst",l=e[o?3:2],c=e[o?4:3],u=Ra(l,a[0],r,s[0],i[0]),p=Ra(c,a[1],r,s[1],i[1]);return[...e.slice(0,2),...o?[n,u,p]:[u,p,n]]}};zI.className="ConvRNN2D";var Fm=class extends ip{constructor(e){let{filters:t,kernelSize:n,strides:a,padding:r,dataFormat:s,dilationRate:i}=e;super(Object.assign({},e,{units:t}));this.filters=t,Kt(this.filters,"filters"),this.kernelSize=iu(n,2,"kernelSize"),this.kernelSize.forEach(o=>Kt(o,"kernelSize")),this.strides=iu(a||1,2,"strides"),this.strides.forEach(o=>Kt(o,"strides")),this.padding=r||"valid",na(this.padding),this.dataFormat=s||"channelsLast",Rt(this.dataFormat),this.dilationRate=iu(i||1,2,"dilationRate"),this.dilationRate.forEach(o=>Kt(o,"dilationRate"))}build(e){var t;e=ct(e);let n=this.dataFormat==="channelsFirst"?1:e.length-1;if(e[n]==null)throw new B(`The channel dimension of the input should be defined. Found ${e[n]}`);let a=e[n],r=4,s=this.kernelSize.concat([a,this.filters*r]);this.kernel=this.addWeight("kernel",s,null,this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint);let i=this.kernelSize.concat([this.filters,this.filters*r]);if(this.recurrentKernel=this.addWeight("recurrent_kernel",i,null,this.recurrentInitializer,this.recurrentRegularizer,!0,this.recurrentConstraint),this.useBias){let o;if(this.unitForgetBias){let l=this.biasInitializer,c=this.filters;o=new(t=class extends ga{apply(u,p){let d=l.apply([c]),h=Ya([c]),m=l.apply([c*2]);return Rb([d,h,m])}},t.className="CustomInit",t)}else o=this.biasInitializer;this.bias=this.addWeight("bias",[this.filters*r],null,o,this.biasRegularizer,!0,this.biasConstraint)}this.built=!0}call(e,t){return D(()=>{if(e.length!==3)throw new B(`ConvLSTM2DCell expects 3 input Tensors (inputs, h, c), got ${e.length}.`);let n=t.training||!1,a=e[0],r=e[1],s=e[2],i=4;0<this.dropout&&this.dropout<1&&this.dropoutMask==null&&(this.dropoutMask=hs({ones:()=>On(a),rate:this.dropout,training:n,count:i}));let o=this.dropoutMask,l=(Q,se,ne)=>!se||!se[ne]?Q:W(se[ne],Q),c=l(a,o,0),u=l(a,o,1),p=l(a,o,2),d=l(a,o,3);0<this.recurrentDropout&&this.recurrentDropout<1&&this.recurrentDropoutMask==null&&(this.recurrentDropoutMask=hs({ones:()=>On(r),rate:this.recurrentDropout,training:n,count:i}));let h=this.recurrentDropoutMask,m=l(r,h,0),f=l(r,h,1),g=l(r,h,2),y=l(r,h,3),b=3,[x,v,T,k]=zn(this.kernel.read(),i,b),[S,F,A,R]=this.useBias?zn(this.bias.read(),i):[null,null,null,null];c=this.inputConv(c,x,S,this.padding),u=this.inputConv(u,v,F,this.padding),p=this.inputConv(p,T,A,this.padding),d=this.inputConv(d,k,R,this.padding);let[P,z,V,G]=zn(this.recurrentKernel.read(),i,b);m=this.recurrentConv(m,P),f=this.recurrentConv(f,z),g=this.recurrentConv(g,V),y=this.recurrentConv(y,G);let H=this.recurrentActivation.apply(J(c,m)),X=this.recurrentActivation.apply(J(u,f)),j=J(W(X,s),W(H,this.activation.apply(J(p,g)))),te=W(this.recurrentActivation.apply(J(d,y)),this.activation.apply(j));return[te,te,j]})}getConfig(){let e=super.getConfig(),{units:t}=e,n=c4(e,["units"]),a={filters:this.filters,kernelSize:this.kernelSize,padding:this.padding,dataFormat:this.dataFormat,dilationRate:this.dilationRate,strides:this.strides};return Object.assign({},n,a)}inputConv(e,t,n,a){let r=At(e,t,this.strides,a||"valid",this.dataFormat==="channelsFirst"?"NCHW":"NHWC",this.dilationRate);return n?tr(r,n,this.dataFormat):r}recurrentConv(e,t){return At(e,t,1,"same",this.dataFormat==="channelsFirst"?"NCHW":"NHWC")}};Fm.className="ConvLSTM2DCell";re.registerClass(Fm);var Sx=class extends zI{constructor(e){let t=new Fm(e);super(Object.assign({},e,{cell:t}))}static fromConfig(e,t){return new e(t)}};Sx.className="ConvLSTM2D";re.registerClass(Sx);var Am=class extends je{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 a=0;a<this.noiseShape.length;++a)n.push(this.noiseShape[a]==null?t[a]:this.noiseShape[a]);return n}call(e,t){return D(()=>{this.invokeCallHook(e,t);let n=Pe(e);if(0<this.rate&&this.rate<1){let a=t.training==null?!1:t.training,r=this.getNoiseShape(n);return Jc(()=>$1(n,this.rate,r,this.seed),()=>n,a)}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()}};Am.className="Dropout";re.registerClass(Am);var Cx=class extends Am{constructor(e){super(e);this.inputSpec=[{ndim:3}]}getNoiseShape(e){let t=e.shape;return[t[0],1,t[2]]}};Cx.className="SpatialDropout1D";re.registerClass(Cx);var _x=class extends je{constructor(e){super(e);if(this.activation=null,this.useBias=!0,this.kernel=null,this.bias=null,this.DEFAULT_KERNEL_INITIALIZER="glorotNormal",this.DEFAULT_BIAS_INITIALIZER="zeros",e.batchInputShape==null&&e.inputShape==null&&e.inputDim!=null){let t=null;e.batchSize!=null&&(t=e.batchSize),this.batchInputShape=[t,e.inputDim]}this.units=e.units,Kt(this.units,"units"),this.activation=ds(e.activation),e.useBias!=null&&(this.useBias=e.useBias),this.kernelInitializer=vt(e.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.biasInitializer=vt(e.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.kernelConstraint=Ut(e.kernelConstraint),this.biasConstraint=Ut(e.biasConstraint),this.kernelRegularizer=wt(e.kernelRegularizer),this.biasRegularizer=wt(e.biasRegularizer),this.activityRegularizer=wt(e.activityRegularizer),this.supportsMasking=!0,this.inputSpec=[{minNDim:2}]}build(e){e=ct(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=ct(e);let t=e.slice();return t[t.length-1]=this.units,t}call(e,t){return D(()=>{this.invokeCallHook(e,t);let n=Pe(e),a=v1(this.activation.getClassName()),r;return a!=null?r=er(n,this.kernel.read(),a,this.bias?this.bias.read():null):(r=er(n,this.kernel.read()),this.bias!=null&&(r=tr(r,this.bias.read())),this.activation!=null&&(r=this.activation.apply(r))),r})}getConfig(){let e={units:this.units,activation:ps(this.activation),useBias:this.useBias,kernelInitializer:_t(this.kernelInitializer),biasInitializer:_t(this.biasInitializer),kernelRegularizer:pt(this.kernelRegularizer),biasRegularizer:pt(this.biasRegularizer),activityRegularizer:pt(this.activityRegularizer),kernelConstraint:Vt(this.kernelConstraint),biasConstraint:Vt(this.biasConstraint)},t=super.getConfig();return Object.assign(e,t),e}};_x.className="Dense";re.registerClass(_x);var Ex=class extends je{constructor(e){e=e||{},super(e),this.inputSpec=[{minNDim:3}],this.dataFormat=e.dataFormat}computeOutputShape(e){e=ct(e);for(let t of e.slice(1))if(t==null)throw new B(`The shape of the input to "Flatten" is not fully defined (got ${e.slice(1)}). Make sure to pass a complete "input_shape" or "batch_input_shape" argument to the first layer in your model.`);return[e[0],ls(e,1)]}call(e,t){return D(()=>{this.invokeCallHook(e,t);let n=Pe(e);if(this.dataFormat==="channelsFirst"&&n.rank>1){let a=[0];for(let r=2;r<n.rank;++r)a.push(r);a.push(1),n=n.transpose(a)}return Mz(n)})}getConfig(){let e={};this.dataFormat!=null&&(e.dataFormat=this.dataFormat);let t=super.getConfig();return Object.assign(e,t),e}};Ex.className="Flatten";re.registerClass(Ex);var Fx=class extends je{constructor(e){super(e);this.supportsMasking=!0,this.activation=ds(e.activation)}call(e,t){return D(()=>{this.invokeCallHook(e,t);let n=Pe(e);return this.activation.apply(n)})}getConfig(){let e={activation:ps(this.activation)},t=super.getConfig();return Object.assign(e,t),e}};Fx.className="Activation";re.registerClass(Fx);var Ax=class extends je{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 D(()=>(e=Pe(e),Dz(e,this.n)))}getConfig(){let e={n:this.n},t=super.getConfig();return Object.assign(e,t),e}};Ax.className="RepeatVector";re.registerClass(Ax);var $x=class extends je{constructor(e){super(e);this.targetShape=e.targetShape;for(let t=0;t<this.targetShape.length;++t)this.isUnknown(this.targetShape[t])&&(this.targetShape[t]=null)}isUnknown(e){return e<0||e==null}fixUnknownDimension(e,t){let n="Total size of new array must be unchanged.",a=t.slice(),r=1,s=null;for(let o=0;o<a.length;++o){let l=a[o];if(this.isUnknown(l))if(s===null)s=o;else throw new B("Can only specifiy one unknown dimension.");else r*=l}let i=ls(e);if(s!==null){if(r===0||i%r!=0)throw new B(n);a[s]=i/r}else if(i!==r)throw new B(n);return a}computeOutputShape(e){let t=!1;for(let n=0;n<e.length;++n)if(this.isUnknown(e[n])){t=!0;break}return t?e.slice(0,1).concat(this.targetShape):e.slice(0,1).concat(this.fixUnknownDimension(e.slice(1),this.targetShape))}call(e,t){return D(()=>{this.invokeCallHook(e,t);let n=Pe(e),a=n.shape,r=a.slice(0,1).concat(this.fixUnknownDimension(a.slice(1),this.targetShape));return n.reshape(r)})}getConfig(){let e={targetShape:this.targetShape},t=super.getConfig();return Object.assign(e,t),e}};$x.className="Reshape";re.registerClass($x);var Dx=class extends je{constructor(e){super(e);if(e.dims==null)throw new Error("Required configuration field `dims` is missing during Permute constructor call.");if(!Array.isArray(e.dims))throw new Error(`Permute constructor requires \`dims\` to be an Array, but received ${e.dims} instead.`);let t=Aa(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 Yt({ndim:this.dims.length+1})]}computeOutputShape(e){e=ct(e);let t=e.slice();return this.dims.forEach((n,a)=>{t[a+1]=e[n]}),t}call(e,t){return Ve(Pe(e),this.dimsIncludingBatch)}getConfig(){let e={dims:this.dims},t=super.getConfig();return Object.assign(e,t),e}};Dx.className="Permute";re.registerClass(Dx);var Rx=class extends je{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=Pe(e),a=-1;return Fc(Mi(n,this.maskValue),a)}call(e,t){return D(()=>{this.invokeCallHook(e,t);let n=Pe(e),a=-1,r=!0,s=Fc(Mi(n,this.maskValue),a,r);return n.mul(s.asType(n.dtype))})}};Rx.className="Masking";re.registerClass(Rx);var Mx=class extends je{constructor(e){super(e);if(this.embeddings=null,this.DEFAULT_EMBEDDINGS_INITIALIZER="randomUniform",e.batchInputShape==null&&e.inputShape==null){let t=null;e.batchSize!=null&&(t=e.batchSize),e.inputLength==null?this.batchInputShape=[t,null]:this.batchInputShape=[t].concat(gt(e.inputLength))}this.inputDim=e.inputDim,Kt(this.inputDim,"inputDim"),this.outputDim=e.outputDim,Kt(this.outputDim,"outputDim"),this.embeddingsInitializer=vt(e.embeddingsInitializer||this.DEFAULT_EMBEDDINGS_INITIALIZER),this.embeddingsRegularizer=wt(e.embeddingsRegularizer),this.activityRegularizer=wt(e.activityRegularizer),this.embeddingsConstraint=Ut(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 D(()=>this.maskZero?(e=Pe(e),Mi(e,Ge(e))):null)}computeOutputShape(e){if(e=ct(e),this.inputLength==null)return[...e,this.outputDim];let t=gt(this.inputLength);if(t.length!==e.length-1)throw new B(`"inputLength" is ${this.inputLength}, but received input shape has shape ${e}`);{let n=0;for(let a=0;a<t.length;++a){let r=t[a],s=e[a+1];if(r!=null&&s!=null&&r!==s)throw new B(`"inputLength" is ${this.inputLength}, but received input shape has shape ${e}`);r==null&&(t[n]=s),n++}}return[e[0],...t,this.outputDim]}call(e,t){return D(()=>{this.invokeCallHook(e,t);let n=Pe(e);return n.dtype!=="int32"&&(n=Xc(n,"int32")),A1(this.embeddings.read(),n.as1D()).reshape(ct(this.computeOutputShape(n.shape)))})}getConfig(){let e={inputDim:this.inputDim,outputDim:this.outputDim,embeddingsInitializer:_t(this.embeddingsInitializer),embeddingsRegularizer:pt(this.embeddingsRegularizer),activityRegularizer:pt(this.activityRegularizer),embeddingsConstraint:Vt(this.embeddingsConstraint),maskZero:this.maskZero,inputLength:this.inputLength},t=super.getConfig();return Object.assign(e,t),e}};Mx.className="Embedding";re.registerClass(Mx);var qi=class extends je{constructor(e){super(e||{});this.supportsMasking=!0}mergeFunction(e){throw new $e}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 a=0;a<t.length;++a){let r=e[e.length-t.length+a],s=t[a];if(r==null||s==null||r<0||s<0)n.push(null);else if(r===1)n.push(s);else if(s===1)n.push(r);else{if(r!==s)throw new B("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=[ct(e)]),e=e,e.length<2)throw new B(`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=os(t),t.length>1)throw new B(`Can not merge tensors with different batch sizes. Got tensors with shapes: ${JSON.stringify(e)}.`);let n=e[0]==null?null:e[0].slice(1);for(let r=1;r<e.length;++r){let s=e[r]==null?null:e[r].slice(1);n=this.computeElementwiseOpOutputShape(n,s)}let a=e.map(r=>r.length);e.indexOf(null)===-1&&os(a).length===1?this.reshapeRequired=!1:this.reshapeRequired=!0}call(e,t){return D(()=>{if(e=e,this.reshapeRequired){let n=[],a=e.map(r=>r.rank);if(a.indexOf(null)===-1){let r=us(a);for(let s of e){let i=s.rank;for(let o=0;o<r-i;++o)s=Kc(s,1);n.push(s)}return this.mergeFunction(n)}else{let r=!1;for(let o of e){let l=o.rank;if(l==null){let c=o.shape,u=c[0],p=c.slice(1).concat([u]),d=o.reshape([u].concat(ls(c.slice(1))));d=Ve(d,[1,0]),d=d.reshape(p),n.push(d),r=!0}else if(l>1){let c=Aa(1,l).concat([0]);n.push(Ve(o,c)),r=!0}else n.push(o)}let s=this.mergeFunction(n),i=s.rank;if(r){if(i==null){let o=s.shape,l=o.length,c=o[l-1],u=[c].concat(o.slice(0,o.length-1));s=Ve(s.reshape([-1,c]),[1,0]).reshape(u)}else if(i>1){let o=[i-1].concat(Aa(0,i-1));s=Ve(s,o)}}return s}}else return this.mergeFunction(e)})}computeOutputShape(e){e=e;let t;e[0]==null?t=null:t=e[0].slice(1);for(let a=1;a<e.length;++a){let r=e[a]==null?null:e[a].slice(1);t=this.computeElementwiseOpOutputShape(t,r)}let n=[];for(let a of e)a!=null&&a[0]!==null&&n.push(a[0]);return n=os(n),n.length===1?t=n.concat(t):t=[null].concat(t),t}computeMask(e,t){return D(()=>{if(t==null)return null;if(!Array.isArray(t))throw new B("`mask` should be an Array");if(!Array.isArray(e))throw new B("`inputs` should be an Array");if(t.length!==e.length)throw new B(`The Array 'inputs' and 'mask' are expected to have the same length, but have different lengths (${e.length} vs ${t.length})`);if(t.every(a=>a==null))return null;t=t.map(a=>a==null?a:mn(a,0));let n=t[0];for(let a=1;a<t.length-1;++a)n=ma(n,t[a]);return n})}},Px=class extends qi{constructor(e){super(e)}mergeFunction(e){return D(()=>{let t=e[0].clone();for(let n=1;n<e.length;++n)t=J(t,e[n]);return t})}};Px.className="Add";re.registerClass(Px);var Ox=class extends qi{constructor(e){super(e)}mergeFunction(e){return D(()=>{let t=e[0].clone();for(let n=1;n<e.length;++n)t=W(t,e[n]);return t})}};Ox.className="Multiply";re.registerClass(Ox);var Lx=class extends qi{constructor(e){super(e)}mergeFunction(e){return D(()=>{let t=e[0].clone();for(let n=1;n<e.length;++n)t=J(t,e[n]);return W(1/e.length,t)})}};Lx.className="Average";re.registerClass(Lx);var zx=class extends qi{constructor(e){super(e)}mergeFunction(e){return D(()=>{let t=e[0];for(let n=1;n<e.length;++n)t=Ka(t,e[n]);return t})}};zx.className="Maximum";re.registerClass(zx);var Wx=class extends qi{constructor(e){super(e)}mergeFunction(e){return D(()=>{let t=e[0];for(let n=1;n<e.length;++n)t=Xl(t,e[n]);return t})}};Wx.className="Minimum";re.registerClass(Wx);var Bx=class extends qi{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 B("A `Concatenate` layer should be called on a list of at least 2 inputs");e=e;let t=!0;for(let a of e)if(a!=null){t=!1;break}if(t)return;let n=[];for(let a=0;a<e.length;++a){let r=e[a].slice();r.splice(this.axis,1);let s=!1;for(let i of n)if(w.arraysEqual(i,r)){s=!0;break}s||n.push(r)}if(n.length>1)throw new B("A `Concatenate` layer requires inputs with matching shapes except for the concat axis. Got input shapes: "+JSON.stringify(e))}mergeFunction(e){return D(()=>Rb(e,this.axis))}computeOutputShape(e){if(!(Array.isArray(e)&&Array.isArray(e[0])))throw new B("A `Concatenate` layer should be called on a list of inputs.");let t=e,n=t[0].slice(),a=this.axis<0?n.length+this.axis:this.axis;for(let r of t.slice(1)){if(n[a]==null||r[a]==null){n[a]=null;break}n[a]+=r[a]}return n}computeMask(e,t){if(t==null)return null;if(!Array.isArray(t))throw new B("`mask` should be an array for Concatenate");if(!Array.isArray(e))throw new B("`inputs` should be an array for Concatenate");if(t.length!==e.length)throw new B(`Mismatch in the length of mask (${t.length}) and the legnth of inputs (${e.length})`);return D(()=>{let n=!0;if(t.forEach(s=>{if(s!=null){n=!1;return}}),n)return null;let a=[];for(let s=0;s<e.length;++s)t[s]==null?a.push(On(e[s]).asType("bool")):t[s].rank<e[s].rank?a.push(mn(t[s],-1)):a.push(t[s]);let r=Je(a,this.axis);return yh(r,-1,!1)})}getConfig(){let e={axis:this.axis},t=super.getConfig();return Object.assign(e,t),e}};Bx.className="Concatenate";re.registerClass(Bx);function op(e,t){for(;e<0;)e+=t;return e}function p4(e,t,n){if(e.shape.length>3||t.shape.length>3)throw new $e("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 $e("batchDot is not implemented for complex64-type Tensors yet.");let a=e.shape.length,r=t.shape.length;n==null&&(n=[a-1,r-2]);let s=n;return D(()=>{let i;if(a>r){i=a-r;let l=[];for(let c=0;c<i;++c)l.push(1);t=t.reshape(t.shape.concat(l))}else if(r>a){i=r-a;let l=[];for(let c=0;c<i;++c)l.push(1);e=e.reshape(e.shape.concat(l))}else i=0;let o;if(e.shape.length===2&&t.shape.length===2)s[0]===s[1]?o=e.mul(t).sum(s[0]):o=e.transpose([1,0]).mul(t).sum(s[1]);else{let l=s[0]!==e.shape.length-1,c=s[1]===t.shape.length-1;o=e.matMul(t,l,c)}if(i>0){let l;a>r?l=a+r-3:l=a-1;let c=[];for(let u=l;u<l+i;++u)c.push(u);o=o.squeeze(c)}return o.shape.length===1&&(o=o.expandDims(1)),o})}var Vx=class extends qi{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 $e("Dot layer does not support tensors of 4D or higher rank yet.");let a=this.interpretAxes(t,n);if(t[a[0]]!==n[a[1]])throw new B(`Dimension incompatibility: ${t[a[0]]} !== ${n[a[1]]}`)}mergeFunction(e){if(e.length!==2)throw new B(`A \`Dot\` layer must be called on exactly 2 inputs, but received ${e.length} input(s).`);let t=e[0],n=e[1],a;return Array.isArray(this.axes)?a=this.axes.map((r,s)=>op(r,e[s].shape.length)):a=[op(this.axes,t.shape.length),op(this.axes,n.shape.length)],this.normalize&&(t=fm(t,a[0]),n=fm(n,a[1])),p4(t,n,a)}interpretAxes(e,t){let n;return Array.isArray(this.axes)?n=this.axes:n=[op(this.axes,e.length),op(this.axes,t.length)],n}computeOutputShape(e){w.assert(Array.isArray(e)&&e.length===2&&Array.isArray(e[0])&&Array.isArray(e[1]),()=>"A `Dot` layer should be called on a list of exactly 2 inputs.");let t=e[0].slice(),n=e[1].slice();if(t.length>3||n.length>3)throw new $e("Dot layer does not support tensors of 4D or higher rank yet.");let a=this.interpretAxes(t,n);t.splice(a[0],1),n.splice(a[1],1),n.splice(0,1);let r=t.concat(n);return r.length===1&&r.push(1),r}computeMask(e,t){return null}getConfig(){let e={axes:this.axes,normalize:this.normalize},t=super.getConfig();return Object.assign(e,t),e}};Vx.className="Dot";re.registerClass(Vx);var Ux=class extends je{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 D(()=>{this.invokeCallHook(e,t);let n=Pe(e);return Jc(()=>nm(n.shape,0,this.stddev).add(n),()=>n,t.training||!1)})}};Ux.className="GaussianNoise";re.registerClass(Ux);var Gx=class extends je{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 D(()=>{this.invokeCallHook(e,t);let n=Pe(e);return this.rate>0&&this.rate<1?Jc(()=>{let a=Math.sqrt(this.rate/(1-this.rate));return n.mul(nm(n.shape,1,a))},()=>n,t.training||!1):n})}};Gx.className="GaussianDropout";re.registerClass(Gx);var Hx=class extends je{constructor(e){super(e);this.supportsMasking=!0,this.rate=e.rate,this.noiseShape=e.noiseShape}_getNoiseShape(e){return this.noiseShape||Pe(e).shape}computeOutputShape(e){return e}getConfig(){let e=super.getConfig(),t={rate:this.rate};return Object.assign(t,e),t}call(e,t){return D(()=>{if(this.rate<1&&this.rate>0){let n=this._getNoiseShape(e);return Jc(()=>{let a=Pe(e),r=1.6732632423543772,s=1.0507009873554805,i=-r*s,o=rs(Kl(n),this.rate);o=Xc(o,"float32");let l=((1-this.rate)*(1+this.rate*i**2))**-.5,c=-l*i*this.rate;return a.mul(o).add(o.add(-1).mul(i)).mul(l).add(c)},()=>Pe(e),t.training||!1)}return e})}};Hx.className="AlphaDropout";re.registerClass(Hx);function lp(e,t,n,a,r,s=.001){let i;if(e.rank===2)i=mk(e,t,n,a,r,s);else if(e.rank===3)i=fk(e,t,n,a,r,s);else if(e.rank===4)i=gk(e,t,n,a,r,s);else throw new $e(`batchNormalization is not implemented for array of rank ${e.rank} yet`);return i}function d4(e,t,n,a,r=.001){return D(()=>{let s=Eh(e,a),i=s.mean,o=s.variance;return[lp(e,i,o,n,t,r),i,o]})}function h4(e,t,n,a,r=.001){return D(()=>{let s=Eh(e,a),i=s.mean,o=s.variance,l=[];for(let h of Aa(0,e.rank))a.indexOf(h)!==-1?l.push(1):l.push(e.shape[h]);let c=i.reshape(l),u=o.reshape(l),p=t==null?null:t.reshape(l),d=n==null?null:n.reshape(l);return[lp(e,c,u,d,p,r),i,o]})}function m4(e,t,n,a,r=.001){return w.arraysEqual(a.slice().sort(),Aa(0,e.rank-1))?d4(e,t,n,a,r):h4(e,t,n,a,r)}var jx=class extends je{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=vt(e.betaInitializer||"zeros"),this.gammaInitializer=vt(e.gammaInitializer||"ones"),this.movingMeanInitializer=vt(e.movingMeanInitializer||"zeros"),this.movingVarianceInitializer=vt(e.movingVarianceInitializer||"ones"),this.betaConstraint=Ut(e.betaConstraint),this.gammaConstraint=Ut(e.gammaConstraint),this.betaRegularizer=wt(e.betaRegularizer),this.gammaRegularizer=wt(e.gammaRegularizer)}build(e){e=ct(e);let t=this.axis>=0?this.axis:this.axis+e.length,n=e[t];if(n==null)throw new B(`Axis ${t} of input tensor should have a defined dimension but the layer received an input with shape ${JSON.stringify(e)}.`);this.inputSpec=[new Yt({ndim:e.length,axes:{[t]:n}})];let a=[n];this.scale&&(this.gamma=this.addWeight("gamma",a,null,this.gammaInitializer,this.gammaRegularizer,!0,this.gammaConstraint)),this.center&&(this.beta=this.addWeight("beta",a,null,this.betaInitializer,this.betaRegularizer,!0,this.betaConstraint)),this.movingMean=this.addWeight("moving_mean",a,null,this.movingMeanInitializer,null,!1),this.movingVariance=this.addWeight("moving_variance",a,null,this.movingVarianceInitializer,null,!1),this.built=!0}call(e,t){return D(()=>{let n=t.training==null?!1:t.training,a=Pe(e),r=a.shape,s=r.length,i=Aa(0,s),o=this.axis>=0?this.axis:this.axis+s;i.splice(o,1);let l=zi(1,s);l[o]=r[o];let c=i.slice();c.sort();let u=!w.arraysEqual(c,Aa(0,s).slice(0,s-1)),p=()=>{if(u){let g=this.movingMean.read().reshape(l),y=this.movingVariance.read().reshape(l),b=this.center?this.beta.read().reshape(l):null,x=this.scale?this.gamma.read().reshape(l):null;return lp(a,g,y,b,x,this.epsilon)}else return lp(a,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,m]=m4(a,this.gamma.read(),this.beta.read(),i,this.epsilon),f=(g,y,b)=>{D(()=>{let x=1-b,v=g.read(),T=v.sub(y).mul(x);g.write(v.sub(T))})};return(()=>{f(this.movingMean,h,this.momentum),f(this.movingVariance,m,this.momentum)})(),d})}getConfig(){let e={axis:this.axis,momentum:this.momentum,epsilon:this.epsilon,center:this.center,scale:this.scale,betaInitializer:_t(this.betaInitializer),gammaInitializer:_t(this.gammaInitializer),movingMeanInitializer:_t(this.movingMeanInitializer),movingVarianceInitializer:_t(this.movingVarianceInitializer),betaRegularizer:pt(this.betaRegularizer),gammaRegularizer:pt(this.gammaRegularizer),betaConstraint:Vt(this.betaConstraint),gammaConstraint:Vt(this.gammaConstraint)},t=super.getConfig();return Object.assign(e,t),e}};jx.className="BatchNormalization";re.registerClass(jx);var qx=class extends je{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=vt(e.betaInitializer||"zeros"),this.gammaInitializer=vt(e.gammaInitializer||"ones"),this.betaRegularizer=wt(e.betaRegularizer),this.gammaRegularizer=wt(e.gammaRegularizer),this.supportsMasking=!0}build(e){e=ct(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!==os(this.axis).length)throw new Error(`Found duplicate axes in: ${this.axis}`);let n=this.axis.map(r=>e[r]),a=!0;this.scale?this.gamma=this.addWeight("gamma",n,"float32",this.gammaInitializer,this.gammaRegularizer,a):this.gamma=null,this.center?this.beta=this.addWeight("beta",n,"float32",this.betaInitializer,this.betaRegularizer,a):this.beta=null,this.built=!0}call(e,t){let n=Pe(e),a=n.shape,r=a.length;return D(()=>{let s=!0,{mean:i,variance:o}=Eh(n,this.axis,s),l=zi(1,r);for(let m of this.axis)l[m]=a[m];let c=m=>m!=null&&m.shape.length!==r&&this.axis!==[r-1]?m.reshape(l):m,u=c(this.gamma.read()),p=c(this.beta.read()),d=[],h=[];for(let m=0;m<r;++m)this.axis.indexOf(m)!==-1?(d.push(a[m]),h.push(1)):(d.push(1),h.push(a[m]));return i=i.tile(d),o=o.tile(d),u=u.tile(h),p=p.tile(h),lp(n,i,o,p,u,this.epsilon)})}getConfig(){let e={axis:this.axis,epsilon:this.epsilon,center:this.center,scale:this.scale,betaInitializer:_t(this.betaInitializer),gammaInitializer:_t(this.gammaInitializer),betaRegularizer:pt(this.betaRegularizer),gammaRegularizer:pt(this.gammaRegularizer)},t=super.getConfig();return Object.assign(e,t),e}};qx.className="LayerNormalization";re.registerClass(qx);function f4(e,t,n){return D(()=>{if(e.rank!==4)throw new B(`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 B("spatial2dPadding expects `padding` to be an Array of two Arrays, each of which is an Array of two integers.");if(n==null&&(n=Ea()),n!=="channelsLast"&&n!=="channelsFirst")throw new B(`Unknown data format: ${n}. Supported data formats are 'channelsLast' and 'channelsFirst.`);let a;return n==="channelsFirst"?a=[[0,0],[0,0],t[0],t[1]]:a=[[0,0],t[0],t[1],[0,0]],ta(e,a)})}var Xx=class extends je{constructor(e){if(e==null&&(e={}),super(e),this.dataFormat=e.dataFormat==null?Ea():e.dataFormat,e.padding==null)this.padding=[[1,1],[1,1]];else if(typeof e.padding=="number")this.padding=[[e.padding,e.padding],[e.padding,e.padding]];else{if(e.padding=e.padding,e.padding.length!==2)throw new B(`ZeroPadding2D expects padding to be a length-2 array, but received a length-${e.padding.length} array.`);let t,n;if(typeof e.padding[0]=="number")t=[e.padding[0],e.padding[0]],n=[e.padding[1],e.padding[1]];else{if(e.padding=e.padding,e.padding[0].length!==2)throw new B(`ZeroPadding2D expects height padding to be a length-2 array, but received a length-${e.padding[0].length} array.`);if(t=e.padding[0],e.padding[1].length!==2)throw new B(`ZeroPadding2D expects width padding to be a length-2 array, but received a length-${e.padding[1].length} array.`);n=e.padding[1]}this.padding=[t,n]}this.inputSpec=[new Yt({ndim:4})]}computeOutputShape(e){e=ct(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 D(()=>f4(Pe(e),this.padding,this.dataFormat))}getConfig(){let e={padding:this.padding,dataFormat:this.dataFormat},t=super.getConfig();return Object.assign(e,t),e}};Xx.className="ZeroPadding2D";re.registerClass(Xx);function $m(e,t,n,a,r,s){return D(()=>{Rt(r),T1(s),na(a),n==null&&(n=[1,1]),a==null&&(a="valid"),r==null&&(r=Ea()),s==null&&(s="max"),e=gx(e,r);let i,o=a==="same"?"same":"valid";return s==="max"?i=$t(e,t,n,o):i=Zn(e,t,n,o),r==="channelsFirst"&&(i=Ve(i,[0,3,1,2])),i})}function WI(e,t,n,a,r,s){return D(()=>{Rt(r),T1(s),na(a),n==null&&(n=[1,1,1]),a==null&&(a="valid"),r==null&&(r=Ea()),s==null&&(s="max"),e=RI(e,r);let i,o=a==="same"?"same":"valid";return s==="max"?i=rb(e,t,n,o):i=Gy(e,t,n,o),r==="channelsFirst"&&(i=Ve(i,[0,4,1,2,3])),i})}var BI=class extends je{constructor(e){if(e.poolSize==null&&(e.poolSize=2),super(e),typeof e.poolSize=="number")this.poolSize=[e.poolSize];else if(Array.isArray(e.poolSize)&&e.poolSize.length===1&&typeof e.poolSize[0]=="number")this.poolSize=e.poolSize;else throw new B(`poolSize for 1D convolutional layer must be a number or an Array of a single number, but received ${JSON.stringify(e.poolSize)}`);if(Kt(this.poolSize,"poolSize"),e.strides==null)this.strides=this.poolSize;else if(typeof e.strides=="number")this.strides=[e.strides];else if(Array.isArray(e.strides)&&e.strides.length===1&&typeof e.strides[0]=="number")this.strides=e.strides;else throw new B(`strides for 1D convolutional layer must be a number or an Array of a single number, but received ${JSON.stringify(e.strides)}`);Kt(this.strides,"strides"),this.padding=e.padding==null?"valid":e.padding,na(this.padding),this.inputSpec=[new Yt({ndim:3})]}computeOutputShape(e){e=ct(e);let t=Ra(e[1],this.poolSize[0],this.padding,this.strides[0]);return[e[0],t,e[2]]}call(e,t){return D(()=>{this.invokeCallHook(e,t),e=Kc(Pe(e),2);let n=this.poolingFunction(Pe(e),[this.poolSize[0],1],[this.strides[0],1],this.padding,"channelsLast");return ss(n,[2])})}getConfig(){let e={poolSize:this.poolSize,padding:this.padding,strides:this.strides},t=super.getConfig();return Object.assign(e,t),e}},Kx=class extends BI{constructor(e){super(e)}poolingFunction(e,t,n,a,r){return Rt(r),na(a),$m(e,t,n,a,r,"max")}};Kx.className="MaxPooling1D";re.registerClass(Kx);var Yx=class extends BI{constructor(e){super(e)}poolingFunction(e,t,n,a,r){return Rt(r),na(a),$m(e,t,n,a,r,"avg")}};Yx.className="AveragePooling1D";re.registerClass(Yx);var VI=class extends je{constructor(e){if(e.poolSize==null&&(e.poolSize=[2,2]),super(e),this.poolSize=Array.isArray(e.poolSize)?e.poolSize:[e.poolSize,e.poolSize],e.strides==null)this.strides=this.poolSize;else if(Array.isArray(e.strides)){if(e.strides.length!==2)throw new B(`If the strides property of a 2D pooling layer is an Array, it is expected to have a length of 2, but received length ${e.strides.length}.`);this.strides=e.strides}else this.strides=[e.strides,e.strides];Kt(this.poolSize,"poolSize"),Kt(this.strides,"strides"),this.padding=e.padding==null?"valid":e.padding,this.dataFormat=e.dataFormat==null?"channelsLast":e.dataFormat,Rt(this.dataFormat),na(this.padding),this.inputSpec=[new Yt({ndim:4})]}computeOutputShape(e){e=ct(e);let t=this.dataFormat==="channelsFirst"?e[2]:e[1],n=this.dataFormat==="channelsFirst"?e[3]:e[2];return t=Ra(t,this.poolSize[0],this.padding,this.strides[0]),n=Ra(n,this.poolSize[1],this.padding,this.strides[1]),this.dataFormat==="channelsFirst"?[e[0],e[1],t,n]:[e[0],t,n,e[3]]}call(e,t){return D(()=>(this.invokeCallHook(e,t),this.poolingFunction(Pe(e),this.poolSize,this.strides,this.padding,this.dataFormat)))}getConfig(){let e={poolSize:this.poolSize,padding:this.padding,strides:this.strides,dataFormat:this.dataFormat},t=super.getConfig();return Object.assign(e,t),e}},Jx=class extends VI{constructor(e){super(e)}poolingFunction(e,t,n,a,r){return Rt(r),na(a),$m(e,t,n,a,r,"max")}};Jx.className="MaxPooling2D";re.registerClass(Jx);var Qx=class extends VI{constructor(e){super(e)}poolingFunction(e,t,n,a,r){return Rt(r),na(a),$m(e,t,n,a,r,"avg")}};Qx.className="AveragePooling2D";re.registerClass(Qx);var UI=class extends je{constructor(e){if(e.poolSize==null&&(e.poolSize=[2,2,2]),super(e),this.poolSize=Array.isArray(e.poolSize)?e.poolSize:[e.poolSize,e.poolSize,e.poolSize],e.strides==null)this.strides=this.poolSize;else if(Array.isArray(e.strides)){if(e.strides.length!==3)throw new B(`If the strides property of a 3D pooling layer is an Array, it is expected to have a length of 3, but received length ${e.strides.length}.`);this.strides=e.strides}else this.strides=[e.strides,e.strides,e.strides];Kt(this.poolSize,"poolSize"),Kt(this.strides,"strides"),this.padding=e.padding==null?"valid":e.padding,this.dataFormat=e.dataFormat==null?"channelsLast":e.dataFormat,Rt(this.dataFormat),na(this.padding),this.inputSpec=[new Yt({ndim:5})]}computeOutputShape(e){e=ct(e);let t=this.dataFormat==="channelsFirst"?e[2]:e[1],n=this.dataFormat==="channelsFirst"?e[3]:e[2],a=this.dataFormat==="channelsFirst"?e[4]:e[3];return t=Ra(t,this.poolSize[0],this.padding,this.strides[0]),n=Ra(n,this.poolSize[1],this.padding,this.strides[1]),a=Ra(a,this.poolSize[2],this.padding,this.strides[2]),this.dataFormat==="channelsFirst"?[e[0],e[1],t,n,a]:[e[0],t,n,a,e[4]]}call(e,t){return D(()=>(this.invokeCallHook(e,t),this.poolingFunction(Pe(e),this.poolSize,this.strides,this.padding,this.dataFormat)))}getConfig(){let e={poolSize:this.poolSize,padding:this.padding,strides:this.strides,dataFormat:this.dataFormat},t=super.getConfig();return Object.assign(e,t),e}},Zx=class extends UI{constructor(e){super(e)}poolingFunction(e,t,n,a,r){return Rt(r),na(a),WI(e,t,n,a,r,"max")}};Zx.className="MaxPooling3D";re.registerClass(Zx);var ev=class extends UI{constructor(e){super(e)}poolingFunction(e,t,n,a,r){return Rt(r),na(a),WI(e,t,n,a,r,"avg")}};ev.className="AveragePooling3D";re.registerClass(ev);var GI=class extends je{constructor(e){super(e);this.inputSpec=[new Yt({ndim:3})]}computeOutputShape(e){return[e[0],e[2]]}call(e,t){throw new $e}},tv=class extends GI{constructor(e){super(e||{})}call(e,t){return D(()=>{let n=Pe(e);return Ct(n,1)})}};tv.className="GlobalAveragePooling1D";re.registerClass(tv);var nv=class extends GI{constructor(e){super(e||{})}call(e,t){return D(()=>{let n=Pe(e);return ea(n,1)})}};nv.className="GlobalMaxPooling1D";re.registerClass(nv);var HI=class extends je{constructor(e){super(e);this.dataFormat=e.dataFormat==null?"channelsLast":e.dataFormat,Rt(this.dataFormat),this.inputSpec=[new Yt({ndim:4})]}computeOutputShape(e){return e=e,this.dataFormat==="channelsLast"?[e[0],e[3]]:[e[0],e[1]]}call(e,t){throw new $e}getConfig(){let e={dataFormat:this.dataFormat},t=super.getConfig();return Object.assign(e,t),e}},av=class extends HI{call(e,t){return D(()=>{let n=Pe(e);return this.dataFormat==="channelsLast"?Ct(n,[1,2]):Ct(n,[2,3])})}};av.className="GlobalAveragePooling2D";re.registerClass(av);var rv=class extends HI{call(e,t){return D(()=>{let n=Pe(e);return this.dataFormat==="channelsLast"?ea(n,[1,2]):ea(n,[2,3])})}};rv.className="GlobalMaxPooling2D";re.registerClass(rv);var jI=class extends je{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 a=t.layer,r=Da(a,n);delete t.layer;let s={layer:r};return Object.assign(s,t),new e(s)}},sv=class extends jI{constructor(e){super(e);this.supportsMasking=!0}build(e){if(e=ct(e),e.length<3)throw new B(`TimeDistributed layer expects an input shape >= 3D, but received input shape ${JSON.stringify(e)}`);this.inputSpec=[{shape:e}];let t=[e[0]].concat(e.slice(2));this.layer.built||(this.layer.build(t),this.layer.built=!0),super.build(e)}computeOutputShape(e){e=ct(e);let t=[e[0]].concat(e.slice(2)),n=this.layer.computeOutputShape(t),a=e[1];return[n[0],a].concat(n.slice(1))}call(e,t){return D(()=>(e=Pe(e),LI((n,a)=>[Pe(this.layer.call(n,t)),[]],e,[],!1,null,null,!1,!0)[1]))}};sv.className="TimeDistributed";re.registerClass(sv);function g4(e){Bi(_z,"BidirectionalMergeMode",e)}var y4="concat",iv=class extends jI{constructor(e){super(e);let t=e.layer.getConfig(),n={};n.className=e.layer.getClassName(),n.config=t,this.forwardLayer=Da(n),t.goBackwards=t.goBackwards!==!0;let a={};if(a.className=e.layer.getClassName(),a.config=t,this.backwardLayer=Da(a),this.forwardLayer.name="forward_"+this.forwardLayer.name,this.backwardLayer.name="backward_"+this.backwardLayer.name,this.mergeMode=e.mergeMode===void 0?y4:e.mergeMode,g4(this.mergeMode),e.weights)throw new $e("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,a,r;return this.returnState&&(r=t.slice(1)),n=t[0],n=n,this.mergeMode==="concat"?(n[n.length-1]*=2,a=[n]):this.mergeMode==null?a=[n,n.slice()]:a=[n],this.returnState?this.mergeMode==null?a.concat(r).concat(r.slice()):[n].concat(r).concat(r.slice()):En(a)}apply(e,t){let n=t==null?null:t.initialState,a=t==null?null:t.constants;t==null&&(t={});let r=OI(e,n,a,this.numConstants);if(e=r.inputs,n=r.initialState,a=r.constants,Array.isArray(e)&&(n=e.slice(1),e=e[0]),(n==null||n.length===0)&&a==null)return super.apply(e,t);let s=[],i=[];if(n!=null){let l=n.length;if(l%2>0)throw new B("When passing `initialState` to a Bidrectional RNN, the state should be an Array containing the states of the underlying RNNs.");t.initialState=n,s.push(...n);let c=n.map(u=>new Yt({shape:u.shape}));this.forwardLayer.stateSpec=c.slice(0,l/2),this.backwardLayer.stateSpec=c.slice(l/2),i.push(...c)}if(a!=null)throw new $e("Support for constants in Bidirectional layers is not implemented yet.");let o=s[0]instanceof $a;for(let l of s)if(l instanceof $a!==o)throw new B("The initial state of a Bidirectional layer cannot be specified as a mix of symbolic and non-symbolic tensors");if(o){let l=[e].concat(s),c=this.inputSpec.concat(i),u=this.inputSpec;this.inputSpec=c;let p=super.apply(l,t);return this.inputSpec=u,p}else return super.apply(e,t)}call(e,t){return D(()=>{let n=t.initialState,a,r;if(n==null)a=this.forwardLayer.call(e,t),r=this.backwardLayer.call(e,t);else{let o=n.slice(0,n.length/2),l=n.slice(n.length/2);a=this.forwardLayer.call(e,Object.assign(t,{initialState:o})),r=this.backwardLayer.call(e,Object.assign(t,{initialState:l}))}let s;this.returnState&&(Array.isArray(a)&&(s=a.slice(1).concat(r.slice(1))),a=a[0],r=r[0]),this.returnSequences&&(r=Ln(r,1));let i;return this.mergeMode==="concat"?i=Rb([a,r]):this.mergeMode==="sum"?i=J(a,r):this.mergeMode==="ave"?i=W(.5,J(a,r)):this.mergeMode==="mul"?i=W(a,r):this.mergeMode==null&&(i=[a,r]),this.returnState?this.mergeMode==null?i.concat(s):[i].concat(s):i})}resetStates(e){this.forwardLayer.resetStates(),this.backwardLayer.resetStates()}build(e){Vi(this.forwardLayer.name,()=>{this.forwardLayer.build(e)}),Vi(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 a=this.forwardLayer.states.map(r=>null);return Array.isArray(n)?n.concat(a).concat(a):[n].concat(a).concat(a)}else return n}get trainableWeights(){return this.forwardLayer.trainableWeights.concat(this.backwardLayer.trainableWeights)}get nonTrainableWeights(){return 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a=I("size",e,t,n),r=I("dtype",e,t,n),s=I("elementShape",e,t,n),i=I("dynamicSize",e,t,n),o=I("clearAfterRead",e,t,n),l=I("identicalElementShapes",e,t,n),c=I("name",e,t,n),u=new lV(c,r,a,s,l,i,o);return n.addTensorArray(u),[u.idTensor,ve(1)]}case"TensorArrayWriteV3":{let a=I("tensorArrayId",e,t,n),r=I("index",e,t,n),s=I("tensor",e,t,n),i=n.getTensorArray(a.id);return i.write(r,s),[i.idTensor]}case"TensorArrayReadV3":{let a=I("tensorArrayId",e,t,n),r=I("index",e,t,n);return[n.getTensorArray(a.id).read(r)]}case"TensorArrayGatherV3":{let a=I("tensorArrayId",e,t,n),r=I("indices",e,t,n),s=I("dtype",e,t,n);return[n.getTensorArray(a.id).gather(r,s)]}case"TensorArrayScatterV3":{let a=I("tensorArrayId",e,t,n),r=I("indices",e,t,n),s=I("tensor",e,t,n),i=n.getTensorArray(a.id);return i.scatter(r,s),[i.idTensor]}case"TensorArrayConcatV3":{let a=I("tensorArrayId",e,t,n),r=n.getTensorArray(a.id),s=I("dtype",e,t,n);return[r.concat(s)]}case"TensorArraySplitV3":{let 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a=I("elementShape",e,t,n),r=I("elementDType",e,t,n),s;e.op==="TensorListReserve"?s="numElements":s="maxNumElements";let i=I(s,e,t,n),o=cV(a,r,i);return n.addTensorList(o),[o.idTensor]}case"TensorListGather":{let a=I("tensorListId",e,t,n),r=I("indices",e,t,n),s=I("elementShape",e,t,n),i=I("elementDType",e,t,n);return[n.getTensorList(a.id).gather(r,i,s)]}case"TensorListStack":{let a=I("tensorListId",e,t,n),r=I("elementShape",e,t,n),s=I("elementDType",e,t,n),i=I("numElements",e,t,n);return[n.getTensorList(a.id).stack(r,s,i)]}case"TensorListFromTensor":{let a=I("tensor",e,t,n),r=I("elementShape",e,t,n),s=I("elementDType",e,t,n),i=uV(a,r,s);return n.addTensorList(i),[i.idTensor]}case"TensorListConcat":{let a=I("tensorListId",e,t,n),r=n.getTensorList(a.id),s=I("dtype",e,t,n),i=I("elementShape",e,t,n);return[r.concat(s,i)]}case"TensorListPushBack":{let a=I("tensorListId",e,t,n),r=I("tensor",e,t,n),s=n.getTensorList(a.id);return s.pushBack(r),[s.idTensor]}case"TensorListPopBack":{let a=I("tensorListId",e,t,n),r=I("elementShape",e,t,n),s=I("elementDType",e,t,n);return[n.getTensorList(a.id).popBack(r,s)]}case"TensorListSplit":{let a=I("tensor",e,t,n),r=I("elementShape",e,t,n),s=I("lengths",e,t,n),i=dV(a,s,r);return n.addTensorList(i),[i.idTensor]}default:throw TypeError(`Node type ${e.op} is not implemented`)}};function IT(e,t,n){let[a,r]=I("fusedOps",e,t,n),s=a==="biasadd",i=r==="prelu",o=a==="fusedbatchnorm",l=I("numArgs",e,t,n);if(s){if(i&&l!==2)throw new Error("FusedConv2d and DepthwiseConv2d with BiasAdd and Prelu must have two extra arguments: bias and alpha.");if(!i&&l!==1)throw new Error("FusedConv2d and DepthwiseConv2d with BiasAdd must have one extra argument: bias.")}if(o)throw new Error("FusedConv2d and DepthwiseConv2d with FusedBatchNorm is not supported.");let c=I("strides",e,t,n),u=Mm(e,t,n),p=I("dataFormat",e,t,n).toUpperCase(),d=I("dilations",e,t,n),[h,m]=I("args",e,t,n),f=I("leakyreluAlpha",e,t,n);return{stride:c,pad:u,dataFormat:p,dilations:d,biasArg:h,preluArg:m,activationFunc:r,leakyreluAlpha:f}}var mV=(e,t,n)=>{switch(e.op){case"Conv1D":{let a=I("stride",e,t,n),r=I("pad",e,t,n),s=I("dataFormat",e,t,n).toUpperCase(),i=I("dilation",e,t,n);return[xh(I("x",e,t,n),I("filter",e,t,n),a,r,s,i)]}case"Conv2D":{let a=I("strides",e,t,n),r=Mm(e,t,n),s=I("dataFormat",e,t,n).toUpperCase(),i=I("dilations",e,t,n);return[At(I("x",e,t,n),I("filter",e,t,n),[a[1],a[2]],r,s,[i[1],i[2]])]}case"_FusedConv2D":{let{stride:a,pad:r,dataFormat:s,dilations:i,biasArg:o,preluArg:l,activationFunc:c,leakyreluAlpha:u}=IT(e,t,n);return[is.conv2d({x:I("x",e,t,n),filter:I("filter",e,t,n),strides:[a[1],a[2]],pad:r,dataFormat:s,dilations:[i[1],i[2]],bias:o,activation:c,preluActivationWeights:l,leakyreluAlpha:u})]}case"FusedDepthwiseConv2dNative":{let{stride:a,pad:r,dataFormat:s,dilations:i,biasArg:o,preluArg:l,activationFunc:c,leakyreluAlpha:u}=IT(e,t,n);return[is.depthwiseConv2d({x:I("x",e,t,n),filter:I("filter",e,t,n),strides:[a[1],a[2]],pad:r,dataFormat:s,dilations:[i[1],i[2]],bias:o,activation:c,preluActivationWeights:l,leakyreluAlpha:u})]}case"Conv2DBackpropInput":case"Conv2dTranspose":{let 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AV(e,t,n){let{usedNodes:a,inputs:r}=n,s=[],i=Object.keys(r).map(u=>Bn(u)[0]).map(u=>e.nodes[u]),o=e.initNodes;i.forEach(u=>{a.has(u.name)&&s.push(u)}),e.weights.forEach(u=>{a.has(u.name)&&s.push(u)}),o!=null&&o.forEach(u=>{a.has(u.name)&&s.push(u)});let l=new Set,c=[];for(;s.length>0;){let u=s.pop();l.add(u.name),t[u.name]||c.push(u),u.children.forEach(p=>{!l.has(p.name)&&a.has(p.name)&&p.inputs.every(d=>l.has(d.name))&&s.push(p)})}return c}var $V=["Switch","Merge","Enter","Exit","NextIteration","StatelessIf","StatelessWhile","if","While"],DV=["NonMaxSuppressionV2","NonMaxSuppressionV3","NonMaxSuppressionV5","Where"],RV=["HashTable","HashTableV2","LookupTableImport","LookupTableImportV2","LookupTableFind","LookupTableFindV2","LookupTableSize","LookupTableSizeV2"];function ST(e){return $V.indexOf(e.op)>=0}function EV(e){return DV.indexOf(e.op)>=0}function FV(e){return RV.indexOf(e.op)>=0}var wv=class{constructor(e,t){this.graph=e,this.parent=t,this.compiledMap=new Map,this._weightMap={},this.SEPERATOR=",",this._functions={},this._functionExecutorMap={},this._outputs=e.outputs,this._inputs=e.inputs,this._initNodes=e.initNodes,this._signature=e.signature,this._functions=e.functions,e.functions!=null&&Object.keys(e.functions).forEach(n=>{this._functionExecutorMap[n]=new wv(e.functions[n],this)})}get weightIds(){return this.parent?this.parent.weightIds:this._weightIds}get functionExecutorMap(){return this.parent?this.parent.functionExecutorMap:this._functionExecutorMap}get weightMap(){return this.parent?this.parent.weightMap:this._weightMap}set weightMap(e){let t=Object.keys(e).map(n=>e[n].map(a=>a.id));this._weightIds=[].concat(...t),this._weightMap=e}set resourceManager(e){this._resourceManager=e}get inputs(){return this._inputs.map(e=>({name:e.name,shape:e.attrParams.shape?e.attrParams.shape.value:void 0,dtype:e.attrParams.dtype?e.attrParams.dtype.value:void 0}))}get outputs(){return this._outputs.map(e=>({name:e.name,shape:e.attrParams.shape?e.attrParams.shape.value:void 0,dtype:e.attrParams.dtype?e.attrParams.dtype.value:void 0}))}get inputNodes(){return this._inputs.map(e=>e.signatureKey||e.name)}get outputNodes(){return this._outputs.map(e=>{let t=e.signatureKey||e.name;return e.defaultOutput?`${t}:${e.defaultOutput}`:t})}get functions(){return Object.keys(this._functions).reduce((e,t)=>(e[t]=this._functions[t].signature,e),{})}getCompilationKey(e,t){let n=e.map(r=>r.name).sort(),a=t.map(r=>r.name).sort();return n.join(this.SEPERATOR)+"--"+a.join(this.SEPERATOR)}compile(e,t){let n=CT(e,t,this.weightMap,this._initNodes),{missingInputs:a,dynamicNode:r,syncInputs:s}=n;if(r!=null)throw new Error(`This execution contains the node '${r.name}', which has the dynamic op '${r.op}'. Please use model.executeAsync() instead. Alternatively, to avoid the dynamic ops, specify the inputs [${s}]`);if(a.length>0){let i=t.map(l=>l.name),o=Object.keys(e);throw new Error(`Cannot compute the outputs [${i}] from the provided inputs [${o}]. Missing the following inputs: [${a}]`)}return AV(this.graph,this.weightMap,n)}execute(e,t){e=this.mapInputs(e);let n=Object.keys(e).sort();this.checkInputs(e),this.checkInputShapeAndType(e),t=this.mapOutputs(t),this.checkOutputs(t);let a=n.map(u=>this.graph.nodes[Bn(u)[0]]),r=t.map(u=>Bn(u)[0]),s=r.map(u=>this.graph.nodes[u]);s.length===0&&(s=this._outputs);let i=this.getCompilationKey(a,s),o=this.compiledMap.get(i);o==null&&(o=this.compile(e,s),this.compiledMap.set(i,o));let l={},c={};return D(()=>{let u=new NT(this.weightMap,l,c,this.functionExecutorMap),p=Object.assign({},this.weightMap);Object.keys(e).forEach(m=>{let[f,g]=Bn(m),y=[];y[g]=e[m],p[f]=y});let d=this.getFrozenTensorIds(p),h={};for(let m=0;m<o.length;m++){let f=o[m];if(!p[f.name]){let g=TT(f,p,u,this._resourceManager);if(w.isPromise(g))throw new Error(`The execution of the op '${f.op}' returned a promise. 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c}processChildNodes(e,t,n,a,r,s){e.children.forEach(i=>{let[o]=Nr(i.name,n);r[o]||!s.has(i.name)||(i.op==="Merge"?i.inputNames.some(l=>!!An(l,a,n))&&(r[o]=!0,t.push({contexts:n.currentContext,node:i})):i.inputNames.every(l=>!!An(l,a,n))&&(r[o]=!0,t.push({contexts:n.currentContext,node:i})))})}dispose(){Object.keys(this.weightMap).forEach(e=>this.weightMap[e].forEach(t=>t.dispose()))}checkInputShapeAndType(e){Object.keys(e).forEach(t=>{let n=e[t],[a]=Bn(t),r=this.graph.nodes[a];if(r.attrParams.shape&&r.attrParams.shape.value){let s=r.attrParams.shape.value,i=s.length===n.shape.length&&n.shape.every((o,l)=>s[l]===-1||s[l]===o);w.assert(i,()=>`The shape of dict['${r.name}'] provided in model.execute(dict) must be [${s}], but was [${n.shape}]`)}r.attrParams.dtype&&r.attrParams.dtype.value&&w.assert(n.dtype===r.attrParams.dtype.value,()=>`The dtype of dict['${r.name}'] provided in model.execute(dict) must be ${r.attrParams.dtype.value}, but was ${n.dtype}`)})}mapInputs(e){let t={};for(let n in e)if(this._signature!=null&&this._signature.inputs!=null&&this._signature.inputs[n]!=null){let a=this._signature.inputs[n];t[a.name]=e[n]}else t[n]=e[n];return t}checkInputs(e){let t=Object.keys(e).filter(n=>{let[a]=Bn(n);return this.graph.nodes[a]==null});if(t.length>0)throw new Error(`The dict provided in model.execute(dict) has keys: [${t}] that are not part of graph`)}mapOutputs(e){return e.map(t=>this._signature!=null&&this._signature.outputs!=null&&this._signature.outputs[t]!=null?this._signature.outputs[t].name:t,{})}checkOutputs(e){e.forEach(t=>{let[n]=Bn(t);if(!this.graph.nodes[n])throw new Error(`The output '${t}' is not found in the graph`)})}},MV=class{constructor(e={},t={}){this.hashTableNameToHandle=e,this.hashTableMap=t}addHashTable(e,t){this.hashTableNameToHandle[e]=t.handle,this.hashTableMap[t.id]=t}getHashTableHandleByName(e){return this.hashTableNameToHandle[e]}getHashTableById(e){return this.hashTableMap[e]}dispose(){for(let e in 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t=jt.getLoadHandlers(e,this.loadOptions);if(t.length===0)t.push(jt.browserHTTPRequest(e,this.loadOptions));else if(t.length>1)throw new Error(`Found more than one (${t.length}) load handlers for URL '${[e]}'`);this.handler=t[0]}}async load(){if(this.findIOHandler(),this.handler.load==null)throw new Error("Cannot proceed with model loading because the IOHandler provided does not have the `load` method implemented.");let e=await this.handler.load();return this.loadSync(e)}loadSync(e){this.artifacts=e;let t=this.artifacts.modelTopology,n;this.artifacts.userDefinedMetadata!=null&&this.artifacts.userDefinedMetadata.signature!=null?n=this.artifacts.userDefinedMetadata.signature:n=this.artifacts.signature,this.signature=n,this.version=`${t.versions.producer}.${t.versions.minConsumer}`;let a=jt.decodeWeights(this.artifacts.weightData,this.artifacts.weightSpecs);if(this.executor=new wv(xT.Instance.transformGraph(t,this.signature)),this.executor.weightMap=this.convertTensorMapToTensorsMap(a),this.executor.resourceManager=this.resourceManager,e.modelInitializer!=null&&e.modelInitializer.node!=null){let r=xT.Instance.transformGraph(e.modelInitializer);this.initializer=new wv(r),this.initializer.weightMap=this.executor.weightMap,this.initializer.resourceManager=this.resourceManager,this.initializer.executeAsync({},[])}return!0}async save(e,t){if(typeof e=="string"){let n=jt.getSaveHandlers(e);if(n.length===0)throw new Error(`Cannot find any save handlers for URL '${e}'`);if(n.length>1)throw new Error(`Found more than one (${n.length}) save handlers for URL '${e}'`);e=n[0]}if(e.save==null)throw new Error("GraphModel.save() cannot proceed because the IOHandler provided does not have the `save` attribute defined.");return e.save(this.artifacts)}predict(e,t){return this.execute(e,this.outputNodes)}normalizeInputs(e){if(!(e instanceof Ee)&&!Array.isArray(e))return 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a=0;a<n;a++)t[a]=this.get(this.wrap(this.begin+a));this.data=t,this.capacity=e,this.doubledCapacity=2*this.capacity,this.begin=0,this.end=n}};kv.INITIAL_CAPACITY=32;function WT(e){return new eU(e)}function Iv(e){return new tU(e)}function nU(e,t){return new BT(e,t)}function rU(e,t=ms.FAIL){return new aU(e,t)}var Jt=class{async toArray(){let e=[],t=await this.next();for(;!t.done;)e.push(t.value),t=await this.next();return e}async toArrayForTest(){let e=this.prefetch(100),t=[],n=await e.next();for(;!n.done;)t.push(n.value),n=await e.next();return t}async resolveFully(){let e=await this.next();for(;!e.done;)e=await this.next()}async resolveWhile(e){let t=await this.next(),n=e(t.value);for(;!t.done&&n;)t=await this.next(),n=e(t.value)}handleErrors(e){return new pU(this,e)}filter(e){return new uU(this,e)}map(e){return new cU(this,e)}mapAsync(e){return new VT(this,e)}serialMapAsync(e){return new VT(this,e).serial()}flatmap(e){return new dU(this,e)}async forEachAsync(e){return this.map(e).resolveFully()}async serialForEach(e){return this.serialMapAsync(e).resolveWhile(t=>t===!0)}rowMajorBatch(e,t=!0){return new lU(this,e,t)}columnMajorBatch(e,t=!0,n=OT){return this.rowMajorBatch(e,t).map(a=>KV(a,n))}concatenate(e,t){return new BT(WT([this,e]),t)}take(e){return e<0||e==null?this:new oU(this,e)}skip(e){return e<0||e==null?this:new iU(this,e)}prefetch(e){return new UT(this,e)}shuffle(e,t){return new hU(this,e,t)}serial(){return new sU(this)}},eU=class extends Jt{constructor(e){super();this.items=e,this.trav=0}summary(){return`Array of ${this.items.length} items`}async next(){if(this.trav>=this.items.length)return{value:null,done:!0};let e=this.items[this.trav];return this.trav++,{value:ZV(e),done:!1}}},tU=class extends Jt{constructor(e){super();this.nextFn=e}summary(){return"Function call"}async next(){try{return this.nextFn()}catch(e){throw e.message=`Error thrown while iterating through a dataset: ${e.message}`,e}}},sU=class extends Jt{constructor(e){super();this.upstream=e,this.lastRead=Promise.resolve({value:null,done:!1})}summary(){return`${this.upstream.summary()} -> Serial`}async next(){return this.lastRead=this.lastRead.then(()=>this.serialNext()),this.lastRead}async serialNext(){return this.upstream.next()}},iU=class extends Jt{constructor(e,t){super();this.upstream=e,this.maxCount=t,this.count=0,this.lastRead=Promise.resolve({value:null,done:!1})}summary(){return`${this.upstream.summary()} -> Skip`}async next(){return this.lastRead=this.lastRead.then(()=>this.serialNext()),this.lastRead}async serialNext(){for(;this.count++<this.maxCount;){let e=await this.upstream.next();if(e.done)return e;Ae(e.value)}return this.upstream.next()}},oU=class extends Jt{constructor(e,t){super();this.upstream=e,this.maxCount=t,this.count=0}summary(){return`${this.upstream.summary()} -> Take`}async next(){return this.count++>=this.maxCount?{value:null,done:!0}:this.upstream.next()}},lU=class extends Jt{constructor(e,t,n=!0){super();this.upstream=e,this.batchSize=t,this.enableSmallLastBatch=n,this.lastRead=Promise.resolve({value:null,done:!1})}summary(){return`${this.upstream.summary()} -> RowMajorBatch`}async next(){return this.lastRead=this.lastRead.then(()=>this.serialNext()),this.lastRead}async serialNext(){let e=[];for(;e.length<this.batchSize;){let t=await this.upstream.next();if(t.done)return this.enableSmallLastBatch&&e.length>0?{value:e,done:!1}:{value:null,done:!0};e.push(t.value)}return{value:e,done:!1}}},uU=class extends Jt{constructor(e,t){super();this.upstream=e,this.predicate=t,this.lastRead=Promise.resolve({value:null,done:!1})}summary(){return`${this.upstream.summary()} -> Filter`}async next(){return this.lastRead=this.lastRead.then(()=>this.serialNext()),this.lastRead}async serialNext(){for(;;){let e=await this.upstream.next();if(e.done||this.predicate(e.value))return e;Ae(e.value)}}},cU=class extends Jt{constructor(e,t){super();this.upstream=e,this.transform=t}summary(){return`${this.upstream.summary()} -> Map`}async next(){let e=await this.upstream.next();if(e.done)return{value:null,done:!0};let t=Ta.getTensorsInContainer(e.value),n=this.transform(e.value),a=Ta.getTensorsInContainer(n);for(let r of t)Ta.isTensorInList(r,a)||r.dispose();return{value:n,done:!1}}},pU=class extends Jt{constructor(e,t){super();this.upstream=e,this.handler=t,this.count=0,this.lastRead=Promise.resolve({value:null,done:!1})}summary(){return`${this.upstream.summary()} -> handleErrors`}async next(){return this.lastRead=this.lastRead.then(()=>this.serialNext()),this.lastRead}async serialNext(){for(;;)try{return await this.upstream.next()}catch(e){if(!this.handler(e))return{value:null,done:!0}}}},VT=class extends Jt{constructor(e,t){super();this.upstream=e,this.transform=t}summary(){return`${this.upstream.summary()} -> AsyncMap`}async next(){let e=await this.upstream.next();if(e.done)return{value:null,done:!0};let t=Ta.getTensorsInContainer(e.value),n=await this.transform(e.value),a=Ta.getTensorsInContainer(n);for(let r of t)Ta.isTensorInList(r,a)||r.dispose();return{value:n,done:!1}}},Tv=class extends Jt{constructor(){super();this.outputQueue=new kv,this.lastRead=Promise.resolve({value:null,done:!1})}async next(){return this.lastRead=this.lastRead.then(()=>this.serialNext()),this.lastRead}async serialNext(){for(;this.outputQueue.length()===0;)if(!await this.pump())return{value:null,done:!0};return{value:this.outputQueue.shift(),done:!1}}},dU=class extends Tv{constructor(e,t){super();this.upstream=e,this.transform=t}summary(){return`${this.upstream.summary()} -> Flatmap`}async pump(){let e=await this.upstream.next();if(e.done)return!1;let t=Ta.getTensorsInContainer(e.value),n=this.transform(e.value),a=Ta.getTensorsInContainer(n);this.outputQueue.pushAll(n);for(let r of t)Ta.isTensorInList(r,a)||r.dispose();return!0}},BT=class extends Jt{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}},ms;(function(e){e[e.FAIL=0]="FAIL",e[e.SHORTEST=1]="SHORTEST",e[e.LONGEST=2]="LONGEST"})(ms||(ms={}));var aU=class extends Jt{constructor(e,t=ms.FAIL){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 a(s){return s instanceof Jt?{value:s.next().then(i=>(t++,i.done&&n++,i.value)),recurse:!1}:{value:null,recurse:!0}}let r=await LT(this.iterators,a);if(t===n)return{value:null,done:!0};if(n>0)switch(this.mismatchMode){case ms.FAIL:throw new Error(`Zipped streams should have the same length. Mismatched at element ${this.count}.`);case ms.SHORTEST:return{value:null,done:!0};case ms.LONGEST:default:}return this.count++,{value:r,done:!1}}async next(){return this.currentPromise=this.nextState(this.currentPromise),this.currentPromise}},UT=class extends Jt{constructor(e,t){super();this.upstream=e,this.bufferSize=t,this.buffer=new zT(t)}summary(){return`${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()}},hU=class extends UT{constructor(e,t,n){super(e,t);this.upstream=e,this.windowSize=t,this.upstreamExhausted=!1,this.random=qV.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}}},ou=class{constructor(){this.size=null}batch(e,t=!0){let n=this;w.assert(e>0,()=>`batchSize needs to be positive, but it is
${e}`);let a;return this.size===Infinity||this.size==null?a=this.size:t?a=Math.ceil(this.size/e):a=Math.floor(this.size/e),Vn(async()=>(await n.iterator()).columnMajorBatch(e,t,mU),a)}concatenate(e){let t=this,n;return this.size===Infinity||e.size===Infinity?n=Infinity:this.size!=null&&e.size!=null?n=this.size+e.size:n=null,Vn(async()=>(await t.iterator()).concatenate(await e.iterator()),n)}filter(e){let t=this,n;return this.size===Infinity?n=Infinity:n=null,Vn(async()=>(await t.iterator()).filter(a=>D(()=>e(a))),n)}async forEachAsync(e){return(await this.iterator()).forEachAsync(e)}map(e){let t=this;return Vn(async()=>(await t.iterator()).map(n=>D(()=>e(n))),this.size)}mapAsync(e){let t=this;return Vn(async()=>(await t.iterator()).mapAsync(e),this.size)}prefetch(e){if(e==null)throw new RangeError("`Dataset.prefetch()` requires bufferSize to be specified.");let t=this;return Vn(async()=>(await t.iterator()).prefetch(e),this.size)}repeat(e){let t=this,n;return this.size!=null&&e>0?n=this.size*e:e===0?n=0:this.size!=null&&(e===void 0||e<0)?n=Infinity:n=null,Vn(async()=>{let a=Iv(async()=>({value:await t.iterator(),done:!1}));return nU(a.take(e))},n)}skip(e){let t=this,n;return this.size!=null&&e>=0&&this.size>=e?n=this.size-e:this.size!=null&&(this.size<e||e===void 0||e<0)?n=0:n=null,Vn(async()=>(await t.iterator()).skip(e),n)}shuffle(e,t,n=!0){if(e==null||e<0)throw this.size==null?new RangeError("`Dataset.shuffle()` requires bufferSize to be specified."):new RangeError(`\`Dataset.shuffle()\` requires bufferSize to be specified. If your data fits in main memory (for regular JS objects), and/or GPU memory (for \`tf.Tensor\`s), consider setting bufferSize to the dataset size (${this.size} elements)`);let a=this,r=jV.alea(t||w.now().toString());return Vn(async()=>{let s=r.int32();return n&&(s+=r.int32()),(await a.iterator()).shuffle(e,s.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,Vn(async()=>(await t.iterator()).take(e),n)}async toArray(){if(this.size===Infinity)throw new Error("Can not convert infinite data stream to array.");return(await this.iterator()).toArray()}async toArrayForTest(){if(this.size===Infinity)throw new Error("Can not convert infinite data stream to array.");return(await this.iterator()).toArrayForTest()}};ou.MAX_BUFFER_SIZE=1e4;function Vn(e,t=null){return new class extends ou{constructor(){super(...arguments);this.size=t}async iterator(){return e()}}}function zV(e){return Vn(async()=>WT(e),e.length)}function WV(e){if(!lu(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 Vn(async()=>{let n=await LT(e,a=>{if(a instanceof ou)return{value:a.iterator(),recurse:!1};if(lu(a))return{value:null,recurse:!0};throw new Error("Leaves of the structure passed to zip() must be Datasets, not primitives.")});return rU(n,ms.SHORTEST)},t)}function mU(e){if(e===null)return null;let t=e[0];return JV(t)?{value:fU(e),recurse:!1}:{value:null,recurse:!0}}function fU(e){if(e.length===0)throw new Error("Can't make a batch of zero elements.");return e[0]instanceof Ee?Dt(e):Jn(e)}var AT=class extends ou{constructor(e){super();this.input=e}async iterator(){return(await this.input.iterator()).decodeUTF8().split(`
`).map(e=>(e.endsWith("\r")&&(e=e.slice(0,-1)),e))}},Om='"',pp=Symbol("out"),GT=Symbol("field"),Lm=Symbol("quote"),Nv=Symbol("quoteafterquote"),HT=Symbol("quoteinquote"),$T=class extends ou{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 AT(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((a,r)=>(a[r]=a[r]+1||1,a),{}),n=Object.keys(t).filter(a=>t[a]>1);if(w.assert(n.length===0,()=>"Duplicate column names found: "+n.toString()),this.columnConfigs){for(let a of Object.keys(this.columnConfigs))if(this.fullColumnNames.indexOf(a)===-1)throw new Error('The key "'+a+'" provided in columnConfigs does not match any of the column names ('+this.fullColumnNames.toString()+").")}this.columnNamesValidated=!0}async maybeReadHeaderLine(){if(this.hasHeader){let e=await(await this.base.iterator()).next();if(e.done)throw new Error("No data was found for CSV parsing.");let t=e.value;return this.parseRow(t,!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={},a={};for(let r=0;r<this.fullColumnNames.length;r++){let s=this.fullColumnNames[r],i=this.columnConfigs?this.columnConfigs[s]:null;if(!(this.configuredColumnsOnly&&!i)){let o=t[r],l=null;if(o==="")if(i&&i.default!==void 0)l=i.default;else{if(i&&(i.required||i.isLabel))throw new Error(`Required column ${s} is empty in this line: ${e}`);l=void 0}else{let c=Number(o);if(isNaN(c))i&&i.dtype==="bool"?l=this.getBoolean(o):l=o;else if(!i||!i.dtype)l=c;else switch(i.dtype){case"float32":l=c;break;case"int32":l=Math.floor(c);break;case"bool":l=this.getBoolean(o);break;default:l=c}}i&&i.isLabel?a[s]=l:n[s]=l}}return Object.keys(a).length===0?n:{xs:n,ys:a}}getBoolean(e){return e==="1"||e.toLowerCase()==="true"?1:0}parseRow(e,t=!0){let n=[],a=0,r=e.length,s=pp;for(let i=0;i<r;i++)switch(s){case pp:switch(e.charAt(i)){case Om:a=i+1,s=Lm;break;case this.delimiter:if(a=i+1,this.delimiter===" "&&this.delimWhitespace)break;n.push(""),s=pp;break;default:s=GT,a=i;break}break;case GT:switch(e.charAt(i)){case this.delimiter:n.push(e.substring(a,i)),s=pp,a=i+1;break;default:}break;case Lm:switch(e.charAt(i)){case Om:s=Nv;break;default:}break;case Nv:switch(e.charAt(i)){case this.delimiter:n.push(e.substring(a,i-1)),s=pp,a=i+1;break;case Om:s=Lm;break;default:s=HT;break}break;case HT:switch(e.charAt(i)){case Om:s=Lm;break;default:}break;default:}if(s===Nv?n.push(e.substring(a,r-1)):n.push(e.substring(a)),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}},jT=class extends Jt{constructor(e){super();this.microphoneConfig=e,this.isClosed=!1,this.fftSize=e.fftSize||1024;let t=Math.log2(this.fftSize);if(this.fftSize<0||t<4||t>14||!Number.isInteger(t))throw new Error(`Invalid fftSize: it must be a power of 2 between 2 to 4 and 2 to 14, but got ${this.fftSize}`);if(this.numFrames=e.numFramesPerSpectrogram||43,this.sampleRateHz=e.sampleRateHz,this.columnTruncateLength=e.columnTruncateLength||this.fftSize,this.audioTrackConstraints=e.audioTrackConstraints,this.smoothingTimeConstant=e.smoothingTimeConstant||0,this.includeSpectrogram=e.includeSpectrogram!==!1,this.includeWaveform=e.includeWaveform===!0,!this.includeSpectrogram&&!this.includeWaveform)throw new Error("Both includeSpectrogram and includeWaveform are false. At least one type of data should be returned.")}summary(){return"microphone"}static async create(e={}){if(Z().get("IS_NODE"))throw new Error("microphone API is only supported in browser environment.");let t=new jT(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 a=this.flattenQueue(n.freqDataQueue);e=this.getTensorFromAudioDataArray(a,[this.numFrames,this.columnTruncateLength,1])}if(this.includeWaveform){let a=this.flattenQueue(n.timeDataQueue);t=this.getTensorFromAudioDataArray(a,[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(a=>{let r=setInterval(()=>{this.includeSpectrogram&&(this.analyser.getFloatFrequencyData(this.freqData),this.freqData[0]===-Infinity&&a({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),a({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((a,r)=>n.set(a,r*t)),n}getTensorFromAudioDataArray(e,t){let n=new Float32Array(w.sizeFromShape(t));return n.set(e,n.length-e.length),Jn(n,t)}},qT=class extends Jt{constructor(e,t){super();if(this.webcamVideoElement=e,this.webcamConfig=t,this.isClosed=!0,this.resize=!1,this.needToResize())if(this.resize=!0,this.cropSize=[this.webcamConfig.resizeHeight,this.webcamConfig.resizeWidth],this.cropBoxInd=Ze([0],"int32"),this.webcamConfig.centerCrop){let n=this.webcamConfig.resizeWidth*1/this.webcamVideoElement.width,a=this.webcamConfig.resizeHeight*1/this.webcamVideoElement.height,r=(1-n)/2,s=(1-a)/2,i=r+n,o=a+s;this.cropBox=Ca([s,r,o,i],[1,4])}else this.cropBox=Ca([0,0,1,1],[1,4])}summary(){return"webcam"}static async create(e,t={}){if(Z().get("IS_NODE"))throw new Error("tf.data.webcam is only supported in browser environment.");if(!e){if(e=document.createElement("video"),!t.resizeWidth||!t.resizeHeight)throw new Error("Please provide webcam video element, or resizeWidth and resizeHeight to create a hidden video element.");e.width=t.resizeWidth,e.height=t.resizeHeight}let n=new qT(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}. 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============================
Hi there \u{1F44B}. Looks like you are running TensorFlow.js in Node.js. To speed things up dramatically, install our node backend, which binds to TensorFlow C++, by running npm i @tensorflow/tfjs-node, or npm i @tensorflow/tfjs-node-gpu if you have CUDA. Then call require('@tensorflow/tfjs-node'); (-gpu suffix for CUDA) at the start of your program. Visit https://github.com/tensorflow/tfjs-node for more details.
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pe=ee*l-y,oe=pe;for(;oe<0;)oe+=p;let fe=Math.min(r.inWidth,m+pe),me=ie+ee*F,we=b,Te=0,_e=0;for(let Fe=H;Fe<X;Fe+=c){let nt=P+Fe*a[1];for(let at=se;at<ne;at+=u){let ot=nt+at*a[2];for(let Ke=oe;Ke<fe;Ke+=p){let ft=ot+Ke*a[3],We=e[ft+z];if(s==="max"&&We>we?we=We:s==="avg"&&(Te+=We,_e++),isNaN(we))break}if(isNaN(we))break}if(isNaN(we))break}let Re=me+z;v[Re]=s==="avg"?Te/_e:we}}}}return x}function qG(e,t){let n=Me(t.outShape,"int32"),a=t.strideDepth,r=t.strideHeight,s=t.strideWidth,i=t.dilationDepth,o=t.dilationHeight,l=t.dilationWidth,c=t.effectiveFilterDepth,u=t.effectiveFilterHeight,p=t.effectiveFilterWidth,d=t.padInfo.front,h=t.padInfo.top,m=t.padInfo.left;for(let f=0;f<t.batchSize;++f)for(let g=0;g<t.inChannels;++g)for(let y=0;y<t.outDepth;++y){let b=y*a-d,x=b;for(;x<0;)x+=i;let v=Math.min(t.inDepth,c+b);for(let T=0;T<t.outHeight;++T){let k=T*r-h,S=k;for(;S<0;)S+=o;let F=Math.min(t.inHeight,u+k);for(let A=0;A<t.outWidth;++A){let R=A*s-m,P=R;for(;P<0;)P+=l;let 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n.makeTensorInfo(r.shape,r.dtype,f)}var aH={kernelName:js,backendName:"cpu",kernelFunc:nH};function rH(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{blockShape:s,crops:i}=a;xe([r],"batchToSpaceND");let o=s.reduce((y,b)=>y*b),l=_.getReshaped(r.shape,s,o),c=_.getPermuted(l.length,s.length),u=_.getReshapedPermuted(r.shape,s,o),p=_.getSliceBeginCoords(i,s.length),d=_.getSliceSize(u,i,s.length),h=kt({inputs:{x:r},backend:n,attrs:{shape:l}}),m=xa({inputs:{x:h},backend:n,attrs:{perm:c}}),f=kt({inputs:{x:m},backend:n,attrs:{shape:u}}),g=Ki({inputs:{x:f},backend:n,attrs:{begin:p,size:d}});return n.disposeIntermediateTensorInfo(h),n.disposeIntermediateTensorInfo(m),n.disposeIntermediateTensorInfo(f),g}var sH={kernelName:rc,backendName:"cpu",kernelFunc:rH};function iH(e){let{inputs:t,backend:n,attrs:a}=e,{x:r,weights:s}=t,{size:i}=a,o=n.data.get(r.dataId).values,l=n.data.get(s.dataId).values,c=Cv(o,l,s.dtype,s.shape,i);return n.makeTensorInfo([i],s.dtype,c)}var oH={kernelName:Fd,backendName:"cpu",kernelFunc:iH},lH=st(jr,(e,t)=>{let n=t;return e>n.clipValueMax?n.clipValueMax:e<n.clipValueMin?n.clipValueMin:e}),uH={kernelName:jr,backendName:"cpu",kernelFunc:lH},cH=e=>{let{x:t}=e.inputs,n=e.backend,a=new Float32Array(w.sizeFromShape(t.shape)),r=n.data.get(t.dataId),s=r.complexTensorInfos.real,i=r.complexTensorInfos.imag,o=n.data.get(s.dataId).values,l=n.data.get(i.dataId).values;for(let c=0;c<o.length;c++){let u=o[c],p=l[c];a[c]=Math.hypot(u,p)}return n.makeOutput(a,t.shape,"float32")},pH={kernelName:sc,backendName:"cpu",kernelFunc:cH};function pu(e){let{inputs:t,backend:n}=e,{input:a}=t,r=n.data.get(a.dataId).complexTensorInfos.imag,s=n.data.get(r.dataId).values;return n.makeTensorInfo(r.shape,r.dtype,s)}var dH={kernelName:Gd,backendName:"cpu",kernelFunc:pu};function du(e){let{inputs:t,backend:n,attrs:a}=e,{axis:r}=a,s=w.parseAxisParam(r,t[0].shape)[0],i=_.computeOutShape(t.map(f=>f.shape),s);if(w.sizeFromShape(i)===0)return 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c.forEach(f=>n.disposeIntermediateTensorInfo(f)),m}var hH={kernelName:Go,backendName:"cpu",kernelFunc:du};function P2(e){let{inputs:t,backend:n,attrs:a}=e,{x:r,filter:s}=t,{strides:i,pad:o,dataFormat:l,dilations:c,dimRoundingMode:u}=a;xe([r,s],"conv2d");let p=_.convertConv2DDataFormat(l),d=_.computeConv2DInfo(r.shape,s.shape,i,c,o,u,!1,p),h=d.filterHeight,m=d.filterWidth,f=d.dilationHeight,g=d.dilationWidth,y=d.padInfo.left,b=d.padInfo.top,x=d.dataFormat==="channelsLast",v=new Lt(d.outShape,r.dtype),T=w.computeStrides(r.shape),k=w.computeStrides(s.shape),S=T[0],F=x?T[1]:T[2],A=x?T[2]:1,R=x?1:T[1],P=v.strides[0],z=x?v.strides[1]:v.strides[2],V=x?v.strides[2]:1,G=x?1:v.strides[1],H=n.data.get(r.dataId).values,X=n.data.get(s.dataId).values,j=v.values;for(let te=0;te<d.batchSize;++te){let Q=te*S,se=te*P;for(let ne=0;ne<d.outHeight;++ne){let ie=se+ne*z,ee=ne*d.strideHeight-b;for(let pe=0;pe<h;++pe){let oe=ee+pe*f;if(oe<0||oe>=d.inHeight)continue;let fe=pe*k[0],me=Q+oe*F;for(let 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bH={kernelName:Ls,backendName:"cpu",kernelFunc:yH};function xH(e){let{inputs:t,backend:n,attrs:a}=e,{x:r,filter:s}=t,{strides:i,pad:o,dilations:l}=a;xe([r,s],"conv3d");let c=_.computeConv3DInfo(r.shape,s.shape,i,l,o),{filterDepth:u,filterHeight:p,filterWidth:d,dilationDepth:h,dilationHeight:m,dilationWidth:f,padInfo:g}=c,y=g.front,b=g.left,x=g.top,v=new Lt(c.outShape,r.dtype),T=n.data.get(r.dataId).values,k=n.data.get(s.dataId).values,S=v.values,F=w.computeStrides(r.shape),A=w.computeStrides(s.shape);for(let R=0;R<c.batchSize;++R){let P=R*F[0],z=R*v.strides[0];for(let V=0;V<c.outDepth;++V){let G=z+V*v.strides[1],H=V*c.strideDepth-y;for(let X=0;X<u;++X){let j=H+X*h;if(j<0||j>=c.inDepth)continue;let te=X*A[0],Q=P+j*F[1];for(let se=0;se<c.outHeight;++se){let ne=G+se*v.strides[2],ie=se*c.strideHeight-x;for(let ee=0;ee<p;++ee){let pe=ie+ee*m;if(pe<0||pe>=c.inHeight)continue;let oe=te+ee*A[1],fe=Q+pe*F[2];for(let me=0;me<c.outWidth;++me){let 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ie=Math.max(0,Math.ceil((te-ne)/d)),ee=Math.min(p.outDepth,(p.inDepth+te-ne)/d),pe=ne*v;for(let oe=0;oe<g;++oe){let fe=Math.max(0,Math.ceil((se-oe)/h)),me=Math.min(p.outHeight,(p.inHeight+se-oe)/h),we=oe*T+pe;for(let Te=0;Te<y;++Te){let _e=Math.max(0,Math.ceil((Q-Te)/m)),Re=Math.min(p.outWidth,(p.inWidth+Q-Te)/m),Fe=Te*k+we;for(let nt=0;nt<p.inChannels;++nt){let at=nt*S+Fe;for(let ot=0;ot<p.outChannels;++ot){let Ke=0;for(let ft=0;ft<p.batchSize;++ft){let We=ft*G,kn=ft*A;for(let It=ie;It<ee;++It){let Xn=(ne+It*d-te)*H+We,tn=It*R+kn;for(let In=fe;In<me;++In){let Kn=(oe+In*h-se)*X+Xn,Mn=In*P+tn;for(let dn=_e;dn<Re;++dn){let nn=(Te+dn*m-Q)*j+Kn,Va=dn*z+Mn;Ke+=V[nn+nt]*F[Va+ot]}}}}x[at+ot]=Ke}}}}}return n.makeTensorInfo(b.shape,b.dtype,b.values)}var kH={kernelName:Dd,backendName:"cpu",kernelFunc:wH};function IH(e){let{inputs:t,backend:n,attrs:a}=e,{dy:r,filter:s}=t,{pad:i,strides:o,inputShape:l}=a;xe([r],"conv3dBackpropInputV2");let c=w.computeStrides(r.shape),u=w.computeStrides(s.shape),p=_.computeConv3DInfo(l,s.shape,o,1,i),d=new Lt(p.inShape,"float32"),h=d.values,[m,f,g,y]=d.strides,b=n.data.get(r.dataId).values,[x,v,T,k]=c,S=n.data.get(s.dataId).values,[F,A,R,P]=u,{batchSize:z,filterDepth:V,filterHeight:G,filterWidth:H,inChannels:X,inDepth:j,inHeight:te,inWidth:Q,outChannels:se,outDepth:ne,outHeight:ie,outWidth:ee,strideDepth:pe,strideHeight:oe,strideWidth:fe}=p,me=V-1-p.padInfo.front,we=G-1-p.padInfo.top,Te=H-1-p.padInfo.left;for(let _e=0;_e<z;++_e)for(let Re=0;Re<X;++Re)for(let Fe=0;Fe<j;++Fe){let nt=Fe-me,at=Math.max(0,Math.ceil(nt/pe)),ot=Math.min(ne,(V+nt)/pe);for(let Ke=0;Ke<te;++Ke){let ft=Ke-we,We=Math.max(0,Math.ceil(ft/oe)),kn=Math.min(ie,(G+ft)/oe);for(let It=0;It<Q;++It){let Xn=It-Te,tn=Math.max(0,Math.ceil(Xn/fe)),In=Math.min(ee,(H+Xn)/fe),Kn=0;for(let Mn=at;Mn<ot;++Mn){let dn=Mn*pe-nt;for(let nn=We;nn<kn;++nn){let Va=nn*oe-ft;for(let oa=tn;oa<In;++oa){let la=oa*fe-Xn,Pr=x*_e+v*Mn+T*nn+k*oa,pr=F*(V-1-dn)+A*(G-1-Va)+R*(H-1-la)+P*Re;for(let Or=0;Or<se;++Or){let wo=b[Pr+Or],Ia=S[pr+Or];Kn+=wo*Ia}}}}h[m*_e+f*Fe+g*Ke+y*It+Re]=Kn}}}return n.makeTensorInfo(d.shape,d.dtype,d.values)}var TH={kernelName:Rd,backendName:"cpu",kernelFunc:IH},NH=st(zs,e=>Math.cos(e)),SH={kernelName:zs,backendName:"cpu",kernelFunc:NH},CH=st(Ho,e=>Math.cosh(e)),_H={kernelName:Ho,backendName:"cpu",kernelFunc:CH};function EH(e){let{inputs:t,backend:n,attrs:a}=e,{image:r,boxes:s,boxInd:i}=t,{cropSize:o,method:l,extrapolationValue:c}=a,[u,p,d,h]=r.shape,m=s.shape[0],[f,g]=o,y=Me([m,f,g,h],"float32"),b=n.data.get(s.dataId).values,x=n.data.get(i.dataId).values,v=n.data.get(r.dataId).values,T=w.computeStrides(r.shape),k=w.computeStrides(y.shape);for(let S=0;S<m;S++){let F=S*4,A=b[F],R=b[F+1],P=b[F+2],z=b[F+3],V=x[S];if(V>=u)continue;let G=f>1?(P-A)*(p-1)/(f-1):0,H=g>1?(z-R)*(d-1)/(g-1):0;for(let X=0;X<f;X++){let j=f>1?A*(p-1)+X*G:.5*(A+P)*(p-1);if(j<0||j>p-1){for(let te=0;te<g;te++)for(let Q=0;Q<h;Q++){let se=Q+te*k[2]+X*k[1]+S*k[0];y.values[se]=c}continue}if(l==="bilinear"){let te=Math.floor(j),Q=Math.ceil(j),se=j-te;for(let ne=0;ne<g;ne++){let ie=g>1?R*(d-1)+ne*H:.5*(R+z)*(d-1);if(ie<0||ie>d-1){for(let fe=0;fe<h;fe++){let me=fe+ne*k[2]+X*k[1]+S*k[0];y.values[me]=c}continue}let ee=Math.floor(ie),pe=Math.ceil(ie),oe=ie-ee;for(let fe=0;fe<h;fe++){let me=fe+ee*T[2]+te*T[1]+V*T[0],we=v[me];me=fe+pe*T[2]+te*T[1]+V*T[0];let Te=v[me];me=fe+ee*T[2]+Q*T[1]+V*T[0];let _e=v[me];me=fe+pe*T[2]+Q*T[1]+V*T[0];let Re=v[me],Fe=we+(Te-we)*oe,nt=_e+(Re-_e)*oe;me=fe+ne*k[2]+X*k[1]+S*k[0],y.values[me]=Fe+(nt-Fe)*se}}}else for(let te=0;te<g;++te){let Q=g>1?R*(d-1)+te*H:.5*(R+z)*(d-1);if(Q<0||Q>d-1){for(let ie=0;ie<h;ie++){let ee=ie+te*k[2]+X*k[1]+S*k[0];y.values[ee]=c}continue}let se=Math.round(Q),ne=Math.round(j);for(let ie=0;ie<h;ie++){let ee=ie+se*T[2]+ne*T[1]+V*T[0],pe=ie+te*k[2]+X*k[1]+S*k[0];y.values[pe]=v[ee]}}}}return n.makeTensorInfo(y.shape,y.dtype,y.values)}var FH={kernelName:jo,backendName:"cpu",kernelFunc:EH};function AH(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{axis:s,exclusive:i,reverse:o}=a;xe(r,"cumsum");let l=_.getAxesPermutation([s],r.shape.length),c=r;l!=null&&(c=xa({inputs:{x:r},backend:n,attrs:{perm:l}}));let u=_.getInnerMostAxes(1,r.shape.length)[0];if(u!==c.shape.length-1)throw new Error(`backend.cumsum in CPU expects an inner-most axis=${c.shape.length-1} but got axis=${u}`);let p=pa(c.dtype,"int32"),d=w.makeZerosTypedArray(w.sizeFromShape(c.shape),p),h=n.data.get(c.dataId).values,m=c.shape[c.shape.length-1],f=o?(y,b)=>y+m-b-1:(y,b)=>y+b;for(let y=0;y<h.length;y+=m)for(let b=0;b<m;b++){let x=f(y,b);if(b===0)d[x]=i?0:h[x];else{let v=f(y,b-1);d[x]=i?h[v]+d[v]:h[x]+d[v]}}let g=n.makeTensorInfo(c.shape,p,d);if(l!=null){let y=_.getUndoAxesPermutation(l),b=xa({inputs:{x:g},backend:n,attrs:{perm:y}});return n.disposeIntermediateTensorInfo(g),n.disposeIntermediateTensorInfo(c),b}return g}var $H={kernelName:Ws,backendName:"cpu",kernelFunc:AH};function DH(e){let{inputs:t,backend:n,attrs:a}=e,{x:r,weights:s}=t,{size:i,binaryOutput:o}=a;if(r.shape.length===1){let l=n.data.get(r.dataId).values,c=n.data.get(s.dataId).values,u=Cv(l,c,s.dtype,s.shape,i);return n.makeTensorInfo([i],s.dtype,u)}else if(r.shape.length===2){let l=n.bufferSync(r),c=n.bufferSync(s),u=t2(l,c,i,o);return n.makeTensorInfo(u.shape,s.dtype,u.values)}throw new Error(`Error in denseBincount: input must be at most rank 2, but got rank${r.shape.length}.`)}var RH={kernelName:Md,backendName:"cpu",kernelFunc:DH};function MH(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{blockSize:s,dataFormat:i}=a;w.assert(i==="NHWC",()=>`Only NHWC dataFormat supported on CPU for depthToSpace. Got ${i}`),w.assert(s>1,()=>`blockSize should be > 1 for depthToSpace, but was: ${s}`);let o=r.shape[0],l=r.shape[1],c=r.shape[2],u=r.shape[3],p=l*s,d=c*s,h=u/(s*s),m=n.data.get(r.dataId).values,f=new Float32Array(o*p*d*h),g=0;for(let y=0;y<o;++y)for(let b=0;b<p;++b){let x=Math.floor(b/s),v=b%s;for(let T=0;T<d;++T){let k=Math.floor(T/s),S=T%s,F=(v*s+S)*h;for(let A=0;A<h;++A){let R=A+F+u*(k+c*(x+l*y));f[g++]=m[R]}}}return n.makeTensorInfo([o,p,d,h],r.dtype,f)}var PH={kernelName:qo,backendName:"cpu",kernelFunc:MH};function O2(e){let{inputs:t,backend:n,attrs:a}=e,{x:r,filter:s}=t,{strides:i,pad:o,dilations:l,dimRoundingMode:c}=a;xe([r,s],"depthwiseConv2DNative");let u=w.computeStrides(r.shape),p=w.computeStrides(s.shape),d=l;d==null&&(d=[1,1]),w.assert(_.eitherStridesOrDilationsAreOne(i,d),()=>`Error in depthwiseConv2d: Either strides or dilations must be 1. Got strides ${i} and dilations '${d}'`);let h=_.computeConv2DInfo(r.shape,s.shape,i,d,o,c,!0),{filterHeight:m,filterWidth:f,dilationHeight:g,dilationWidth:y,padInfo:b}=h,x=b.left,v=b.top,T=h.outChannels/h.inChannels,k=new Lt(h.outShape,r.dtype),S=n.data.get(r.dataId).values,F=n.data.get(s.dataId).values,A=k.values;for(let R=0;R<h.batchSize;++R){let P=R*u[0],z=R*k.strides[0];for(let V=0;V<h.outHeight;++V){let G=z+V*k.strides[1],H=V*h.strideHeight-x;for(let X=0;X<m;++X){let j=H+X*g;if(j<0||j>=h.inHeight)continue;let te=X*p[0],Q=P+j*u[1];for(let se=0;se<h.outWidth;++se){let ne=G+se*k.strides[2],ie=se*h.strideWidth-v;for(let ee=0;ee<f;++ee){let pe=ie+ee*y;if(pe<0||pe>=h.inWidth)continue;let oe=te+ee*p[1],fe=Q+pe*h.inChannels,me=ne,we=oe;for(let Te=0;Te<h.inChannels;++Te){let _e=S[fe+Te];for(let Re=0;Re<T;++Re)A[me+Re]+=_e*F[we+Re];me+=T,we+=T}}}}}}return n.makeTensorInfo(k.shape,k.dtype,k.values)}var OH={kernelName:Bs,backendName:"cpu",kernelFunc:O2};function LH(e){let{inputs:t,backend:n,attrs:a}=e,{x:r,dy:s}=t,{strides:i,dilations:o,pad:l,dimRoundingMode:c,filterShape:u}=a;xe([r,s],"depthwiseConv2dNativeBackpropFilter");let p=_.computeConv2DInfo(r.shape,u,i,o,l,c,!0),{strideHeight:d,strideWidth:h,filterHeight:m,filterWidth:f}=p,g=new Lt(p.filterShape,"float32"),y=p.padInfo.left,b=p.padInfo.top,x=p.outChannels/p.inChannels,v=n.data.get(r.dataId).values,T=new Lt(r.shape,r.dtype,v),k=n.data.get(s.dataId).values,S=new Lt(s.shape,s.dtype,k);for(let F=0;F<m;++F){let A=Math.max(0,Math.ceil((b-F)/d)),R=Math.min(p.outHeight,(p.inHeight+b-F)/d);for(let P=0;P<f;++P){let z=Math.max(0,Math.ceil((y-P)/h)),V=Math.min(p.outWidth,(p.inWidth+y-P)/h);for(let G=0;G<p.outChannels;++G){let H=Math.trunc(G/x),X=G%x,j=0;for(let te=0;te<p.batchSize;++te)for(let Q=A;Q<R;++Q){let se=F+Q*d-b;for(let ne=z;ne<V;++ne){let ie=P+ne*h-y;j+=T.get(te,se,ie,H)*S.get(te,Q,ne,G)}}g.set(j,F,P,H,X)}}}return n.makeTensorInfo(g.shape,g.dtype,g.values)}var zH={kernelName:Pd,backendName:"cpu",kernelFunc:LH};function WH(e){let{inputs:t,backend:n,attrs:a}=e,{dy:r,filter:s}=t,{strides:i,dilations:o,pad:l,dimRoundingMode:c,inputShape:u}=a;xe([r,s],"depthwiseConv2DNativeBackpropInput");let p=w.computeStrides(r.shape),d=w.computeStrides(s.shape),h=_.computeConv2DInfo(u,s.shape,i,o,l,c,!0),m=new Lt(h.inShape,"float32"),f=m.values,[g,y,b]=m.strides,x=n.data.get(r.dataId).values,[v,T,k]=p,S=n.data.get(s.dataId).values,[F,A,R]=d,{batchSize:P,filterHeight:z,filterWidth:V,inChannels:G,inHeight:H,inWidth:X,outChannels:j,outHeight:te,outWidth:Q,strideHeight:se,strideWidth:ne}=h,ie=z-1-h.padInfo.top,ee=V-1-h.padInfo.left,pe=j/G;for(let oe=0;oe<P;++oe)for(let fe=0;fe<G;++fe)for(let me=0;me<H;++me){let we=me-ie,Te=Math.max(0,Math.ceil(we/se)),_e=Math.min(te,(z+we)/se);for(let Re=0;Re<X;++Re){let Fe=Re-ee,nt=Math.max(0,Math.ceil(Fe/ne)),at=Math.min(Q,(V+Fe)/ne),ot=0;for(let Ke=Te;Ke<_e;++Ke){let ft=Ke*se-we;for(let We=nt;We<at;++We){let kn=We*ne-Fe,It=v*oe+T*Ke+k*We,Xn=F*(z-1-ft)+A*(V-1-kn)+R*fe;for(let tn=0;tn<pe;++tn){let In=fe*pe+tn,Kn=x[It+In],Mn=S[Xn+tn];ot+=Kn*Mn}}}f[g*oe+y*me+b*Re+fe]=ot}}return n.makeTensorInfo(m.shape,m.dtype,m.values)}var BH={kernelName:Od,backendName:"cpu",kernelFunc:WH};function VH(e){let{inputs:t,backend:n}=e,{x:a}=t,r=w.sizeFromShape(a.shape),s=n.data.get(a.dataId).values,i=Me([r,r],a.dtype),o=i.values;for(let c=0;c<s.length;c++)o[c*r+c]=s[c];let l=[...a.shape,...a.shape];return n.makeTensorInfo(l,i.dtype,i.values)}var UH={kernelName:Ld,backendName:"cpu",kernelFunc:VH},GH={kernelName:oc,backendName:"cpu",kernelFunc:({inputs:e,backend:t,attrs:n})=>{let{x:a,filter:r}=e,{strides:s,pad:i,dilations:o}=n,l=t,c=l.data.get(a.dataId).values,u=a.shape.length,p=l.data.get(r.dataId).values,d=r.shape.length,{batchSize:h,inHeight:m,inWidth:f,inChannels:g,outHeight:y,outWidth:b,padInfo:x,strideHeight:v,strideWidth:T,filterHeight:k,filterWidth:S,dilationHeight:F,dilationWidth:A,outShape:R}=_.computeDilation2DInfo(a.shape,r.shape,s,i,"NHWC",o),P=w.sizeFromShape(R),z=R.length,V=w.getArrayFromDType(a.dtype,P);for(let G=0;G<h;++G)for(let H=0;H<y;++H){let X=H*v-x.top;for(let j=0;j<b;++j){let te=j*T-x.left;for(let Q=0;Q<g;++Q){let se=Number.MIN_SAFE_INTEGER;for(let ie=0;ie<k;++ie){let ee=X+ie*F;if(ee>=0&&ee<m)for(let pe=0;pe<S;++pe){let oe=te+pe*A;if(oe>=0&&oe<f){let fe=w.locToIndex([G,ee,oe,Q],u,w.computeStrides(a.shape)),me=w.locToIndex([ie,pe,Q],d,w.computeStrides(r.shape)),we=c[fe]+p[me];we>se&&(se=we)}}}let ne=w.locToIndex([G,H,j,Q],z,w.computeStrides(R));V[ne]=se}}}return{dataId:l.write(w.toTypedArray(V,a.dtype),R,a.dtype),shape:R,dtype:a.dtype}}},HH={kernelName:Wd,backendName:"cpu",kernelFunc:({inputs:e,backend:t,attrs:n})=>{let{x:a,filter:r,dy:s}=e,{strides:i,pad:o,dilations:l}=n,c=t,u=w.toNestedArray(a.shape,c.data.get(a.dataId).values),p=w.toNestedArray(r.shape,c.data.get(r.dataId).values),{batchSize:d,inHeight:h,inWidth:m,inChannels:f,outHeight:g,outWidth:y,padInfo:b,strideHeight:x,strideWidth:v,filterHeight:T,filterWidth:k,dilationHeight:S,dilationWidth:F,outShape:A}=_.computeDilation2DInfo(a.shape,r.shape,i,o,"NHWC",l);w.assert(s.rank===A.length,()=>`Error in ${Wd}, dy must have the same rank as output ${A.length}, but got ${s.rank}`);let R=w.toNestedArray(A,c.data.get(s.dataId).values),P=w.makeZerosNestedTypedArray(r.shape,r.dtype);for(let z=0;z<d;++z)for(let V=0;V<g;++V){let G=V*x-b.top;for(let H=0;H<y;++H){let X=H*v-b.left;for(let j=0;j<f;++j){let 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R=w.toNestedArray(A,c.data.get(s.dataId).values),P=w.makeZerosNestedTypedArray(a.shape,a.dtype);for(let z=0;z<d;++z)for(let V=0;V<g;++V){let G=V*x-b.top;for(let H=0;H<y;++H){let X=H*v-b.left;for(let j=0;j<f;++j){let te=Number.MIN_SAFE_INTEGER,Q=G<0?0:G,se=X<0?0:X;for(let ne=0;ne<T;++ne){let ie=G+ne*S;if(ie>=0&&ie<h)for(let ee=0;ee<k;++ee){let pe=X+ee*F;if(pe>=0&&pe<m){let oe=u[z][ie][pe][j]+p[ne][ee][j];oe>te&&(te=oe,Q=ie,se=pe)}}}P[z][Q][se][j]+=R[z][V][H][j]}}}return{dataId:c.write(w.toTypedArray(P,a.dtype),a.shape,a.dtype),shape:a.shape,dtype:a.dtype}}};function qH(e){let{inputs:t,backend:n}=e,{dy:a,y:r}=t;xe([a,r],"eluGrad");let s=new Float32Array(w.sizeFromShape(r.shape)),i=n.data.get(r.dataId).values,o=n.data.get(a.dataId).values;for(let l=0;l<i.length;++l){let c=i[l];c>=1?s[l]=o[l]:s[l]=o[l]*(c+1)}return n.makeTensorInfo(r.shape,"float32",s)}var 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bool isnan_custom(float val) {
return (val > 0.0 || val < 0.0) ? false : val != 0.0;
}
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)
`,l="",c=`
#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",a="varying",r="texture2D",s="gl_FragColor",i="",o=`
#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));
}
`,l=`
uniform float INFINITY;
bool isinf(float val) {
return abs(val) == INFINITY;
}
bvec4 isinf(vec4 val) {
return equal(abs(val), vec4(INFINITY));
}
`,c=`
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:a,texture2D:r,output:s,defineOutput:i,defineSpecialNaN:o,defineSpecialInf:l,defineRound:c}}function Ji(e,t,n="index"){let a=w.computeStrides(t);return a.map((r,s)=>{let i=`int ${e[s]} = ${n} / ${r}`,o=s===a.length-1?`int ${e[s+1]} = ${n} - ${e[s]} * ${r}`:`index -= ${e[s]} * ${r}`;return`${i}; ${o};`}).join("")}function qv(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;
}
`}var J2=`
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;
}
`,G5=class{constructor(e){this.variableNames=["A"],this.packedInputs=!1,this.packedOutput=!0,this.outPackingScheme=mp.DENSE;let t=gp(e),n=gn();this.outputShape=e,this.userCode=`
ivec3 outCoordsFromFlatIndex(int index) {
${Ji(["r","c","d"],e)}
return ivec3(r, c, d);
}
void main() {
ivec2 resTexRC = ivec2(resultUV.yx *
vec2(${t[0]}, ${t[1]}));
int index = 4 * (resTexRC.x * ${t[1]} + resTexRC.y);
vec4 result = vec4(0.);
for (int i=0; i<4; i++) {
int flatIndex = index + i;
ivec3 rc = outCoordsFromFlatIndex(flatIndex);
result[i] = getA(rc.x, rc.y, rc.z);
}
${n.output} = result;
}
`}},H5=class{constructor(e){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,this.outPackingScheme=mp.DENSE;let t=gp(e),n=gn();this.outputShape=e,this.userCode=`
ivec3 outCoordsFromFlatIndex(int index) {
${Ji(["r","c","d"],e)}
return ivec3(r, c, d);
}
void main() {
ivec2 resTexRC = ivec2(resultUV.yx *
vec2(${t[0]}, ${t[1]}));
int index = 4 * (resTexRC.x * ${t[1]} + resTexRC.y);
vec4 result = vec4(0.);
for (int i=0; i<4; i++) {
int flatIndex = index + i;
ivec3 rc = outCoordsFromFlatIndex(flatIndex);
result[i] = getChannel(getA(rc.x, rc.y, rc.z), vec2(rc.y, rc.z));
}
${n.output} = result;
}
`}},j5=class{constructor(e){this.variableNames=["A"],this.outTexUsage=aa.DOWNLOAD;let t=gn();this.outputShape=e,this.userCode=`
${J2}
void main() {
float x = getAAtOutCoords();
${t.output} = encode_float(x);
}
`}},q5=class{constructor(e){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!1,this.outTexUsage=aa.DOWNLOAD;let t=gn();this.outputShape=e,this.userCode=`
${J2}
void main() {
ivec3 coords = getOutputCoords();
float x = getChannel(getAAtOutCoords(), vec2(coords.y, coords.z));
${t.output} = encode_float(x);
}
`}},X5=class{constructor(e,t,n=!1){this.variableNames=["A"];let a=gn(),[r,s]=t;this.outputShape=e;let i="result";n&&(i="floor(result * 255. + 0.5)"),this.userCode=`
${qv(e)}
void main() {
ivec3 coords = getOutputCoords();
int flatIndex = getFlatIndex(coords);
int offset = imod(flatIndex, 4);
flatIndex = idiv(flatIndex, 4, 1.);
int r = flatIndex / ${s};
int c = imod(flatIndex, ${s});
vec2 uv = (vec2(c, r) + halfCR) / vec2(${s}.0, ${r}.0);
vec4 values = ${a.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];
}
${a.output} = vec4(${i}, 0., 0., 0.);
}
`}},K5=class{constructor(e,t,n=!1){this.variableNames=["A"],this.packedInputs=!1,this.packedOutput=!0;let a=gn(),[r,s]=t;this.outputShape=e;let i="",o="result";n&&(o="floor(result * 255. + 0.5)");for(let l=0;l<=1;l++)for(let c=0;c<=1;c++){let u=l*2+c;i+=`
localCoords = coords;
if(localCoords[2] + ${c} < ${e[2]}) {
localCoords[2] += ${c};
if(localCoords[1] + ${l} < ${e[1]}) {
localCoords[1] += ${l};
flatIndex = getFlatIndex(localCoords);
offset = imod(flatIndex, 4);
flatIndex = idiv(flatIndex, 4, 1.);
r = flatIndex / ${s};
c = imod(flatIndex, ${s});
uv = (vec2(c, r) + halfCR) / vec2(${s}.0, ${r}.0);
values = ${a.texture2D}(A, uv);
if(offset == 0) {
result[${u}] = values[0];
} else if(offset == 1) {
result[${u}] = values[1];
} else if(offset == 2) {
result[${u}] = values[2];
} else {
result[${u}] = values[3];
}
}
}
`}this.userCode=`
${qv(e)}
void main() {
ivec3 coords = getOutputCoords();
vec4 result = vec4(0.);
int flatIndex, r, c, offset;
ivec3 localCoords;
vec2 uv;
vec4 values;
${i}
${a.output} = ${o};
}
`}};function Y5(e){let t=gn(),n=`${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 x5(e,n)}function J5(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 N5(e,t)}function Q5(e){let t=new Uint16Array([0,1,2,2,1,3]);return S5(e,t)}function bp(e,t,n,a,r,s){_5(t,n);let i=C5(e),o=e.TEXTURE_2D;return Ie(e,()=>e.bindTexture(o,i)),Ie(e,()=>e.texParameteri(o,e.TEXTURE_WRAP_S,e.CLAMP_TO_EDGE)),Ie(e,()=>e.texParameteri(o,e.TEXTURE_WRAP_T,e.CLAMP_TO_EDGE)),Ie(e,()=>e.texParameteri(o,e.TEXTURE_MIN_FILTER,e.NEAREST)),Ie(e,()=>e.texParameteri(o,e.TEXTURE_MAG_FILTER,e.NEAREST)),Ie(e,()=>e.texImage2D(o,0,a,t,n,0,r,s,null)),Ie(e,()=>e.bindTexture(e.TEXTURE_2D,null)),i}function Q2(e){return e.internalFormatFloat}function Z5(e,t,n,a){let[r,s]=fp(t,n);return bp(e,r,s,Q2(a),a.textureFormatFloat,e.FLOAT)}function Z2(e){return e.internalFormatHalfFloat}function e8(e,t,n,a){let[r,s]=fp(t,n);return bp(e,r,s,Z2(a),a.textureFormatFloat,a.textureTypeHalfFloat)}function eN(e){return e.downloadTextureFormat}function t8(e,t,n,a){let[r,s]=fp(t,n);return bp(e,r,s,eN(a),e.RGBA,e.UNSIGNED_BYTE)}function tN(e){return e.internalFormatPackedFloat}function n8(e,t,n,a){let[r,s]=hu(t,n);return bp(e,r,s,tN(a),e.RGBA,e.FLOAT)}function nN(e){return e.internalFormatPackedHalfFloat}function a8(e,t,n,a){let[r,s]=hu(t,n);return bp(e,r,s,nN(a),e.RGBA,a.textureTypeHalfFloat)}function r8(e,t,n){let a=0,r=3*4,s=3*4+2*4;return Ie(e,()=>e.bindBuffer(e.ARRAY_BUFFER,n)),X2(e,t,"clipSpacePos",n,3,s,a)&&X2(e,t,"uv",n,2,s,r)}function s8(e,t,n,a,r,s){Ie(e,()=>e.bindTexture(e.TEXTURE_2D,t));let i,o,l;r instanceof Uint8Array?(i=new Uint8Array(n*a*4),o=e.UNSIGNED_BYTE,l=e.RGBA):(i=new Float32Array(n*a*4),o=e.FLOAT,l=s.internalFormatPackedFloat),i.set(r),Ie(e,()=>e.texImage2D(e.TEXTURE_2D,0,l,n,a,0,e.RGBA,o,i)),Ie(e,()=>e.bindTexture(e.TEXTURE_2D,null))}function i8(e,t,n){Ie(e,()=>e.bindTexture(e.TEXTURE_2D,t)),n.data instanceof Uint8Array?Ie(e,()=>e.texImage2D(e.TEXTURE_2D,0,e.RGBA,n.width,n.height,0,e.RGBA,e.UNSIGNED_BYTE,n.data)):Ie(e,()=>e.texImage2D(e.TEXTURE_2D,0,e.RGBA,e.RGBA,e.UNSIGNED_BYTE,n)),Ie(e,()=>e.bindTexture(e.TEXTURE_2D,null))}function o8(e,t,n,a){let r=e.createBuffer();Ie(e,()=>e.bindBuffer(e.PIXEL_PACK_BUFFER,r));let s=4*4*t*n;return Ie(e,()=>e.bufferData(e.PIXEL_PACK_BUFFER,s,e.STREAM_READ)),Ie(e,()=>e.readPixels(0,0,n,t,e.RGBA,e.FLOAT,0)),Ie(e,()=>e.bindBuffer(e.PIXEL_PACK_BUFFER,null)),r}function l8(e,t,n){let a=e,r=new Float32Array(n);return a.bindBuffer(a.PIXEL_PACK_BUFFER,t),a.getBufferSubData(a.PIXEL_PACK_BUFFER,0,r),a.bindBuffer(a.PIXEL_PACK_BUFFER,null),r}function u8(e,t,n,a){let[r,s]=fp(t,n),i=4,o=new Uint8Array(d5(t*n,i));return Ie(e,()=>e.readPixels(0,0,r,s,a.downloadTextureFormat,e.UNSIGNED_BYTE,o)),new Float32Array(o.buffer)}function c8(e,t,n,a,r,s,i,o){let l=e,c=new Float32Array(h5(s,i));return l.bindBuffer(l.PIXEL_PACK_BUFFER,t),l.getBufferSubData(l.PIXEL_PACK_BUFFER,0,c),l.bindBuffer(l.PIXEL_PACK_BUFFER,null),c}function p8(e,t,n){let a=new Float32Array(t*n*4);return Ie(e,()=>e.readPixels(0,0,n,t,e.RGBA,e.FLOAT,a)),a}var h8=class{constructor(e){this.outputTexture=null,this.program=null,this.disposed=!1,this.vertexAttrsAreBound=!1,this.itemsToPoll=[];let t=Z().getNumber("WEBGL_VERSION");e!=null?(this.gl=e,u5(t,e)):this.gl=sr(t);let n="WEBGL_color_buffer_float",a="EXT_color_buffer_half_float";if(Z().getNumber("WEBGL_VERSION")===1){let r="OES_texture_float",s="OES_texture_half_float";if(this.textureFloatExtension=Um(this.gl,r),va(this.gl,s))this.textureHalfFloatExtension=Um(this.gl,s);else if(Z().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),va(this.gl,a))this.colorBufferHalfFloatExtension=Um(this.gl,a);else if(Z().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",va(this.gl,n))this.colorBufferFloatExtension=this.gl.getExtension(n);else if(va(this.gl,a))this.colorBufferHalfFloatExtension=this.gl.getExtension(a);else throw new Error("GL context does not support color renderable floats");this.vertexBuffer=J5(this.gl),this.indexBuffer=Q5(this.gl),this.framebuffer=E5(this.gl),this.textureConfig=Wv(this.gl,this.textureHalfFloatExtension)}get debug(){return Z().getBool("DEBUG")}dispose(){if(this.disposed)return;this.program!=null&&console.warn("Disposing a GPGPUContext that still has a bound WebGLProgram. 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this.throwIfDisposed(),a8(this.gl,e,t,this.textureConfig)}createPackedMatrixTexture(e,t){return this.throwIfDisposed(),n8(this.gl,e,t,this.textureConfig)}deleteMatrixTexture(e){this.throwIfDisposed(),this.outputTexture===e&&(K2(this.gl,this.framebuffer),this.outputTexture=null),Ie(this.gl,()=>this.gl.deleteTexture(e))}downloadByteEncodedFloatMatrixFromOutputTexture(e,t,n){return this.downloadMatrixDriver(e,()=>u8(this.gl,t,n,this.textureConfig))}downloadPackedMatrixFromBuffer(e,t,n,a,r,s){return c8(this.gl,e,t,n,a,r,s,this.textureConfig)}downloadFloat32MatrixFromBuffer(e,t){return l8(this.gl,e,t)}createBufferFromTexture(e,t,n){this.bindTextureToFrameBuffer(e);let a=o8(this.gl,t,n,this.textureConfig);return this.unbindTextureToFrameBuffer(),a}createAndWaitForFence(){let e=this.createFence(this.gl);return this.pollFence(e)}createFence(e){let t,n;if(Z().getBool("WEBGL_FENCE_API_ENABLED")){let a=e,r=a.fenceSync(a.SYNC_GPU_COMMANDS_COMPLETE,0);e.flush(),n=()=>{let 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Ie(t,()=>t.attachShader(r,a)),Ie(t,()=>t.attachShader(r,n)),T5(t,r),this.debug&&Bv(t,r),this.vertexAttrsAreBound||(this.setProgram(r),this.vertexAttrsAreBound=r8(t,this.program,this.vertexBuffer)),r}deleteProgram(e){this.throwIfDisposed(),e===this.program&&(this.program=null),e!=null&&Ie(this.gl,()=>this.gl.deleteProgram(e))}setProgram(e){this.throwIfDisposed(),this.program=e,this.program!=null&&this.debug&&Bv(this.gl,this.program),Ie(this.gl,()=>this.gl.useProgram(e))}getUniformLocation(e,t,n=!0){return this.throwIfDisposed(),n?$5(this.gl,e,t):D5(this.gl,e,t)}getAttributeLocation(e,t){return this.throwIfDisposed(),Ie(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(),R5(this.gl,e,t,n)}setOutputMatrixTexture(e,t,n){this.setOutputMatrixTextureDriver(e,n,t)}setOutputPackedMatrixTexture(e,t,n){this.throwIfDisposed();let[a,r]=hu(t,n);this.setOutputMatrixTextureDriver(e,a,r)}setOutputMatrixWriteRegion(e,t,n,a){this.setOutputMatrixWriteRegionDriver(n,e,a,t)}setOutputPackedMatrixWriteRegion(e,t,n,a){throw new Error("setOutputPackedMatrixWriteRegion not implemented.")}debugValidate(){this.program!=null&&Bv(this.gl,this.program),Gm(this.gl)}executeProgram(){this.throwIfDisposed(),this.throwIfNoProgram();let e=this.gl;this.debug&&this.debugValidate(),Ie(e,()=>e.drawElements(e.TRIANGLES,6,e.UNSIGNED_SHORT,0))}blockUntilAllProgramsCompleted(){this.throwIfDisposed(),Ie(this.gl,()=>this.gl.finish())}getQueryTimerExtension(){return this.disjointQueryTimerExtension==null&&(this.disjointQueryTimerExtension=Um(this.gl,Z().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(Z().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION")===2){let n=this.gl,a=this.getQueryTimerExtensionWebGL2(),r=n.createQuery();return n.beginQuery(a.TIME_ELAPSED_EXT,r),r}let e=this.getQueryTimerExtensionWebGL1(),t=e.createQueryEXT();return e.beginQueryEXT(e.TIME_ELAPSED_EXT,t),t}endQuery(){if(Z().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 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a=this.gl;Vv(a,e,this.framebuffer),this.debug&&Gm(a),this.outputTexture=e,Ie(a,()=>a.viewport(0,0,t,n)),Ie(a,()=>a.scissor(0,0,t,n))}setOutputMatrixWriteRegionDriver(e,t,n,a){this.throwIfDisposed(),Ie(this.gl,()=>this.gl.scissor(e,t,n,a))}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 d8(e){let t=0;for(;t<e.length&&e[t]();++t);return t-1}var{getBroadcastDims:aN}=_;function k8(e,t,n,a){let r=[];e.forEach(h=>{let m=w.sizeFromShape(h.shapeInfo.logicalShape);h.shapeInfo.isUniform?r.push(`uniform float ${h.name}${m>1?`[${m}]`:""};`):(r.push(`uniform sampler2D ${h.name};`),r.push(`uniform int offset${h.name};`))});let s=r.join(`
`),i=e.map(h=>m8(h,t,a)).join(`
`),o=t.texShape,l=gn(),c=y8(l),u,p,d=v8(l);return t.isPacked?(u=f8(t.logicalShape,o),p=x8(l)):(u=g8(t.logicalShape,o),p=b8(l)),a&&(d+=w8),[d,c,p,s,u,i,n].join(`
`)}function gu(e){let t=e.shapeInfo.logicalShape;switch(t.length){case 0:return I8(e);case 1:return T8(e);case 2:return N8(e);case 3:return S8(e);case 4:return C8(e);case 5:return _8(e);case 6:return E8(e);default:throw new Error(`${t.length}-D input sampling is not yet supported`)}}function rN(e){switch(e.shapeInfo.logicalShape.length){case 0:return F8(e);case 1:return A8(e);case 2:return $8(e);case 3:return D8(e);default:return R8(e)}}function m8(e,t,n=!1){let a="";n?a+=rN(e):a+=gu(e);let r=e.shapeInfo.logicalShape,s=t.logicalShape;return r.length<=s.length&&(n?a+=M8(e,t):a+=P8(e,t)),a}function f8(e,t){switch(e.length){case 0:return sN();case 1:return O8(e,t);case 2:return W8(e,t);case 3:return L8(e,t);default:return z8(e,t)}}function g8(e,t){switch(e.length){case 0:return sN();case 1:return B8(e,t);case 2:return j8(e,t);case 3:return V8(e,t);case 4:return U8(e,t);case 5:return G8(e,t);case 6:return H8(e,t);default:throw new Error(`${e.length}-D output sampling is not yet supported`)}}function y8(e){return`
float sampleTexture(sampler2D textureSampler, vec2 uv) {
return ${e.texture2D}(textureSampler, uv).r;
}
`}function b8(e){return`
void setOutput(float val) {
${e.output} = vec4(val, 0, 0, 0);
}
`}function x8(e){return`
void setOutput(vec4 val) {
${e.output} = val;
}
`}function v8(e){return`${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);
}
${q8}
${X8}
${K8}
`}var q8=`
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);
}
`,X8=`
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);
}
`,K8=`
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);
}
`,w8=`
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 sN(){return`
int getOutputCoords() {
return 0;
}
`}function O8(e,t){let n=[Math.ceil(t[0]/2),Math.ceil(t[1]/2)];return n[0]===1?`
int getOutputCoords() {
return 2 * int(resultUV.x * ${n[1]}.0);
}
`:n[1]===1?`
int getOutputCoords() {
return 2 * int(resultUV.y * ${n[0]}.0);
}
`:`
int getOutputCoords() {
ivec2 resTexRC = ivec2(resultUV.yx *
vec2(${n[0]}, ${n[1]}));
return 2 * (resTexRC.x * ${n[1]} + resTexRC.y);
}
`}function B8(e,t){return t[0]===1?`
int getOutputCoords() {
return int(resultUV.x * ${t[1]}.0);
}
`:t[1]===1?`
int getOutputCoords() {
return int(resultUV.y * ${t[0]}.0);
}
`:`
int getOutputCoords() {
ivec2 resTexRC = ivec2(resultUV.yx *
vec2(${t[0]}, ${t[1]}));
return resTexRC.x * ${t[1]} + resTexRC.y;
}
`}function L8(e,t){let n=[Math.ceil(t[0]/2),Math.ceil(t[1]/2)],a=Math.ceil(e[2]/2),r=a*Math.ceil(e[1]/2);return`
ivec3 getOutputCoords() {
ivec2 resTexRC = ivec2(resultUV.yx *
vec2(${n[0]}, ${n[1]}));
int index = resTexRC.x * ${n[1]} + resTexRC.y;
int b = index / ${r};
index -= b * ${r};
int r = 2 * (index / ${a});
int c = imod(index, ${a}) * 2;
return ivec3(b, r, c);
}
`}function V8(e,t){let n=Ji(["r","c","d"],e);return`
ivec3 getOutputCoords() {
ivec2 resTexRC = ivec2(resultUV.yx *
vec2(${t[0]}, ${t[1]}));
int index = resTexRC.x * ${t[1]} + resTexRC.y;
${n}
return ivec3(r, c, d);
}
`}function z8(e,t){let n=[Math.ceil(t[0]/2),Math.ceil(t[1]/2)],a=Math.ceil(e[e.length-1]/2),r=a*Math.ceil(e[e.length-2]/2),s=r,i="",o="b, r, c";for(let l=2;l<e.length-1;l++)s*=e[e.length-l-1],i=`
int b${l} = index / ${s};
index -= b${l} * ${s};
`+i,o=`b${l}, `+o;return`
ivec${e.length} getOutputCoords() {
ivec2 resTexRC = ivec2(resultUV.yx *
vec2(${n[0]}, ${n[1]}));
int index = resTexRC.x * ${n[1]} + resTexRC.y;
${i}
int b = index / ${r};
index -= b * ${r};
int r = 2 * (index / ${a});
int c = imod(index, ${a}) * 2;
return ivec${e.length}(${o});
}
`}function U8(e,t){let n=Ji(["r","c","d","d2"],e);return`
ivec4 getOutputCoords() {
ivec2 resTexRC = ivec2(resultUV.yx *
vec2(${t[0]}, ${t[1]}));
int index = resTexRC.x * ${t[1]} + resTexRC.y;
${n}
return ivec4(r, c, d, d2);
}
`}function G8(e,t){let n=Ji(["r","c","d","d2","d3"],e);return`
ivec5 getOutputCoords() {
ivec2 resTexRC = ivec2(resultUV.yx * vec2(${t[0]},
${t[1]}));
int index = resTexRC.x * ${t[1]} + resTexRC.y;
${n}
ivec5 outShape = ivec5(r, c, d, d2, d3);
return outShape;
}
`}function H8(e,t){let n=Ji(["r","c","d","d2","d3","d4"],e);return`
ivec6 getOutputCoords() {
ivec2 resTexRC = ivec2(resultUV.yx *
vec2(${t[0]}, ${t[1]}));
int index = resTexRC.x * ${t[1]} + resTexRC.y;
${n}
ivec6 result = ivec6(r, c, d, d2, d3, d4);
return result;
}
`}function W8(e,t){let n=[Math.ceil(t[0]/2),Math.ceil(t[1]/2)];if(w.arraysEqual(e,t))return`
ivec2 getOutputCoords() {
return 2 * ivec2(resultUV.yx * vec2(${n[0]}, ${n[1]}));
}
`;let a=Math.ceil(e[1]/2);return`
ivec2 getOutputCoords() {
ivec2 resTexRC = ivec2(resultUV.yx *
vec2(${n[0]}, ${n[1]}));
int index = resTexRC.x * ${n[1]} + resTexRC.y;
int r = 2 * (index / ${a});
int c = imod(index, ${a}) * 2;
return ivec2(r, c);
}
`}function j8(e,t){return w.arraysEqual(e,t)?`
ivec2 getOutputCoords() {
return ivec2(resultUV.yx * vec2(${t[0]}, ${t[1]}));
}
`:e[1]===1?`
ivec2 getOutputCoords() {
ivec2 resTexRC = ivec2(resultUV.yx *
vec2(${t[0]}, ${t[1]}));
int index = resTexRC.x * ${t[1]} + resTexRC.y;
return ivec2(index, 0);
}
`:e[0]===1?`
ivec2 getOutputCoords() {
ivec2 resTexRC = ivec2(resultUV.yx *
vec2(${t[0]}, ${t[1]}));
int index = resTexRC.x * ${t[1]} + resTexRC.y;
return ivec2(0, index);
}
`:`
ivec2 getOutputCoords() {
ivec2 resTexRC = ivec2(resultUV.yx *
vec2(${t[0]}, ${t[1]}));
int index = resTexRC.x * ${t[1]} + resTexRC.y;
int r = index / ${e[1]};
int c = index - r * ${e[1]};
return ivec2(r, c);
}
`}function Qi(e){return`offset${e}`}function F8(e){let t=e.name,n="get"+t.charAt(0).toUpperCase()+t.slice(1),a=gn();return`
vec4 ${n}() {
return ${a.texture2D}(${t}, halfCR);
}
`}function I8(e){let t=e.name,n="get"+t.charAt(0).toUpperCase()+t.slice(1);if(e.shapeInfo.isUniform)return`float ${n}() {return ${t};}`;let[a,r]=e.shapeInfo.texShape;if(a===1&&r===1)return`
float ${n}() {
return sampleTexture(${t}, halfCR);
}
`;let[s,i]=e.shapeInfo.texShape,o=Qi(t);return`
float ${n}() {
vec2 uv = uvFromFlat(${s}, ${i}, ${o});
return sampleTexture(${t}, uv);
}
`}function A8(e){let t=e.name,n="get"+t.charAt(0).toUpperCase()+t.slice(1),a=e.shapeInfo.texShape,r=[Math.ceil(a[0]/2),Math.ceil(a[1]/2)],s=gn();return`
vec4 ${n}(int index) {
vec2 uv = packedUVfrom1D(
${r[0]}, ${r[1]}, index);
return ${s.texture2D}(${t}, uv);
}
`}function T8(e){let t=e.name,n="get"+t.charAt(0).toUpperCase()+t.slice(1);if(e.shapeInfo.isUniform)return`
float ${n}(int index) {
${yu(e)}
}
`;let a=e.shapeInfo.texShape,r=a[0],s=a[1];if(s===1&&r===1)return`
float ${n}(int index) {
return sampleTexture(${t}, halfCR);
}
`;let i=Qi(t);return s===1?`
float ${n}(int index) {
vec2 uv = vec2(0.5, (float(index + ${i}) + 0.5) / ${r}.0);
return sampleTexture(${t}, uv);
}
`:r===1?`
float ${n}(int index) {
vec2 uv = vec2((float(index + ${i}) + 0.5) / ${s}.0, 0.5);
return sampleTexture(${t}, uv);
}
`:`
float ${n}(int index) {
vec2 uv = uvFromFlat(${r}, ${s}, index + ${i});
return sampleTexture(${t}, uv);
}
`}function $8(e){let t=e.shapeInfo.logicalShape,n=e.name,a="get"+n.charAt(0).toUpperCase()+n.slice(1),r=e.shapeInfo.texShape,s=r[0],i=r[1],o=gn();if(r!=null&&w.arraysEqual(t,r))return`
vec4 ${a}(int row, int col) {
vec2 uv = (vec2(col, row) + halfCR) / vec2(${i}.0, ${s}.0);
return ${o.texture2D}(${n}, uv);
}
`;let l=[Math.ceil(r[0]/2),Math.ceil(r[1]/2)],c=Math.ceil(t[1]/2);return`
vec4 ${a}(int row, int col) {
vec2 uv = packedUVfrom2D(${c}, ${l[0]}, ${l[1]}, row, col);
return ${o.texture2D}(${n}, uv);
}
`}function N8(e){let t=e.shapeInfo.logicalShape,n=e.name,a="get"+n.charAt(0).toUpperCase()+n.slice(1),r=e.shapeInfo.texShape;if(r!=null&&w.arraysEqual(t,r)){let p=r[0],d=r[1];return`
float ${a}(int row, int col) {
vec2 uv = (vec2(col, row) + halfCR) / vec2(${d}.0, ${p}.0);
return sampleTexture(${n}, uv);
}
`}let{newShape:s,keptDims:i}=w.squeezeShape(t),o=s;if(o.length<t.length){let p=bu(e,o),d=["row","col"];return`
${gu(p)}
float ${a}(int row, int col) {
return ${a}(${xu(d,i)});
}
`}if(e.shapeInfo.isUniform)return`
float ${a}(int row, int col) {
int index = round(dot(vec2(row, col), vec2(${t[1]}, 1)));
${yu(e)}
}
`;let l=r[0],c=r[1],u=Qi(n);return c===1?`
float ${a}(int row, int col) {
float index = dot(vec3(row, col, ${u}), vec3(${t[1]}, 1, 1));
vec2 uv = vec2(0.5, (index + 0.5) / ${l}.0);
return sampleTexture(${n}, uv);
}
`:l===1?`
float ${a}(int row, int col) {
float index = dot(vec3(row, col, ${u}), vec3(${t[1]}, 1, 1));
vec2 uv = vec2((index + 0.5) / ${c}.0, 0.5);
return sampleTexture(${n}, uv);
}
`:`
float ${a}(int row, int col) {
// Explicitly use integer operations as dot() only works on floats.
int index = row * ${t[1]} + col + ${u};
vec2 uv = uvFromFlat(${l}, ${c}, index);
return sampleTexture(${n}, uv);
}
`}function D8(e){let t=e.shapeInfo.logicalShape,n=e.name,a="get"+n.charAt(0).toUpperCase()+n.slice(1),r=e.shapeInfo.texShape,s=[Math.ceil(r[0]/2),Math.ceil(r[1]/2)];if(t[0]===1){let p=t.slice(1),d=[1,2],h=bu(e,p),m=["b","row","col"];return`
${rN(h)}
vec4 ${a}(int b, int row, int col) {
return ${a}(${xu(m,d)});
}
`}let i=s[0],o=s[1],l=Math.ceil(t[2]/2),c=l*Math.ceil(t[1]/2),u=gn();return`
vec4 ${a}(int b, int row, int col) {
vec2 uv = packedUVfrom3D(
${i}, ${o}, ${c}, ${l}, b, row, col);
return ${u.texture2D}(${n}, uv);
}
`}function S8(e){let t=e.shapeInfo.logicalShape,n=e.name,a="get"+n.charAt(0).toUpperCase()+n.slice(1),r=t[1]*t[2],s=t[2],{newShape:i,keptDims:o}=w.squeezeShape(t),l=i;if(l.length<t.length){let m=bu(e,l),f=["row","col","depth"];return`
${gu(m)}
float ${a}(int row, int col, int depth) {
return ${a}(${xu(f,o)});
}
`}if(e.shapeInfo.isUniform)return`
float ${a}(int row, int col, int depth) {
int index = round(dot(vec3(row, col, depth),
vec3(${r}, ${s}, 1)));
${yu(e)}
}
`;let c=e.shapeInfo.texShape,u=c[0],p=c[1],d=e.shapeInfo.flatOffset;if(p===r&&d==null)return`
float ${a}(int row, int col, int depth) {
float texR = float(row);
float texC = dot(vec2(col, depth), vec2(${s}, 1));
vec2 uv = (vec2(texC, texR) + halfCR) /
vec2(${p}.0, ${u}.0);
return sampleTexture(${n}, uv);
}
`;if(p===s&&d==null)return`
float ${a}(int row, int col, int depth) {
float texR = dot(vec2(row, col), vec2(${t[1]}, 1));
float texC = float(depth);
vec2 uv = (vec2(texC, texR) + halfCR) / vec2(${p}.0, ${u}.0);
return sampleTexture(${n}, uv);
}
`;let h=Qi(n);return`
float ${a}(int row, int col, int depth) {
// Explicitly use integer operations as dot() only works on floats.
int index = row * ${r} + col * ${s} + depth + ${h};
vec2 uv = uvFromFlat(${u}, ${p}, index);
return sampleTexture(${n}, uv);
}
`}function R8(e){let t=e.shapeInfo.logicalShape,n=t.length,a=e.name,r="get"+a.charAt(0).toUpperCase()+a.slice(1),s=e.shapeInfo.texShape,i=[Math.ceil(s[0]/2),Math.ceil(s[1]/2)],o=i[0],l=i[1],c=Math.ceil(t[n-1]/2),u=c*Math.ceil(t[n-2]/2),p="int b, int row, int col",d=`b * ${u} + (row / 2) * ${c} + (col / 2)`;for(let m=2;m<n-1;m++)p=`int b${m}, `+p,u*=t[n-m-1],d=`b${m} * ${u} + `+d;let h=gn();return`
vec4 ${r}(${p}) {
int index = ${d};
int texR = index / ${l};
int texC = index - texR * ${l};
vec2 uv = (vec2(texC, texR) + halfCR) / vec2(${l}, ${o});
return ${h.texture2D}(${a}, uv);
}
`}function C8(e){let t=e.shapeInfo.logicalShape,n=e.name,a="get"+n.charAt(0).toUpperCase()+n.slice(1),r=t[3],s=t[2]*r,i=t[1]*s,{newShape:o,keptDims:l}=w.squeezeShape(t);if(o.length<t.length){let m=bu(e,o),f=["row","col","depth","depth2"];return`
${gu(m)}
float ${a}(int row, int col, int depth, int depth2) {
return ${a}(${xu(f,l)});
}
`}if(e.shapeInfo.isUniform)return`
float ${a}(int row, int col, int depth, int depth2) {
int index = round(dot(vec4(row, col, depth, depth2),
vec4(${i}, ${s}, ${r}, 1)));
${yu(e)}
}
`;let c=e.shapeInfo.flatOffset,u=e.shapeInfo.texShape,p=u[0],d=u[1];if(d===i&&c==null)return`
float ${a}(int row, int col, int depth, int depth2) {
float texR = float(row);
float texC =
dot(vec3(col, depth, depth2),
vec3(${s}, ${r}, 1));
vec2 uv = (vec2(texC, texR) + halfCR) /
vec2(${d}.0, ${p}.0);
return sampleTexture(${n}, uv);
}
`;if(d===r&&c==null)return`
float ${a}(int row, int col, int depth, int depth2) {
float texR = dot(vec3(row, col, depth),
vec3(${t[1]*t[2]}, ${t[2]}, 1));
float texC = float(depth2);
vec2 uv = (vec2(texC, texR) + halfCR) /
vec2(${d}.0, ${p}.0);
return sampleTexture(${n}, uv);
}
`;let h=Qi(n);return`
float ${a}(int row, int col, int depth, int depth2) {
// Explicitly use integer operations as dot() only works on floats.
int index = row * ${i} + col * ${s} +
depth * ${r} + depth2;
vec2 uv = uvFromFlat(${p}, ${d}, index + ${h});
return sampleTexture(${n}, uv);
}
`}function _8(e){let t=e.shapeInfo.logicalShape,n=e.name,a="get"+n.charAt(0).toUpperCase()+n.slice(1),r=t[4],s=t[3]*r,i=t[2]*s,o=t[1]*i,{newShape:l,keptDims:c}=w.squeezeShape(t);if(l.length<t.length){let f=bu(e,l),g=["row","col","depth","depth2","depth3"];return`
${gu(f)}
float ${a}(int row, int col, int depth, int depth2, int depth3) {
return ${a}(${xu(g,c)});
}
`}if(e.shapeInfo.isUniform)return`
float ${a}(int row, int col, int depth, int depth2, int depth3) {
float index = dot(
vec4(row, col, depth, depth2),
vec4(${o}, ${i}, ${s}, ${r})) +
depth3;
${yu(e)}
}
`;let u=e.shapeInfo.flatOffset,p=e.shapeInfo.texShape,d=p[0],h=p[1];if(h===o&&u==null)return`
float ${a}(int row, int col, int depth, int depth2, int depth3) {
int texR = row;
float texC = dot(vec4(col, depth, depth2, depth3),
vec4(${i}, ${s}, ${r}, 1));
vec2 uv = (vec2(texC, texR) + halfCR) /
vec2(${h}.0, ${d}.0);
return sampleTexture(${n}, uv);
}
`;if(h===r&&u==null)return`
float ${a}(int row, int col, int depth, int depth2, int depth3) {
float texR = dot(
vec4(row, col, depth, depth2),
vec4(${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 m=Qi(n);return`
float ${a}(int row, int col, int depth, int depth2, int depth3) {
// Explicitly use integer operations as dot() only works on floats.
int index = row * ${o} + col * ${i} + depth * ${s} +
depth2 * ${r} + depth3 + ${m};
vec2 uv = uvFromFlat(${d}, ${h}, index);
return sampleTexture(${n}, uv);
}
`}function E8(e){let t=e.shapeInfo.logicalShape,n=e.name,a="get"+n.charAt(0).toUpperCase()+n.slice(1),{newShape:r,keptDims:s}=w.squeezeShape(t);if(r.length<t.length){let g=bu(e,r),y=["row","col","depth","depth2","depth3","depth4"];return`
${gu(g)}
float ${a}(int row, int col, int depth,
int depth2, int depth3, int depth4) {
return ${a}(${xu(y,s)});
}
`}let i=t[5],o=t[4]*i,l=t[3]*o,c=t[2]*l,u=t[1]*c;if(e.shapeInfo.isUniform)return`
float ${a}(int row, int col, int depth,
int depth2, int depth3, int depth4) {
int index = round(dot(
vec4(row, col, depth, depth2),
vec4(${u}, ${c}, ${l}, ${o})) +
dot(
vec2(depth3, depth4),
vec2(${i}, 1)));
${yu(e)}
}
`;let p=e.shapeInfo.flatOffset,d=e.shapeInfo.texShape,h=d[0],m=d[1];if(m===u&&p==null)return`
float ${a}(int row, int col, int depth,
int depth2, int depth3, int depth4) {
int texR = row;
float texC = dot(vec4(col, depth, depth2, depth3),
vec4(${c}, ${l}, ${o}, ${i})) +
float(depth4);
vec2 uv = (vec2(texC, texR) + halfCR) /
vec2(${m}.0, ${h}.0);
return sampleTexture(${n}, uv);
}
`;if(m===i&&p==null)return`
float ${a}(int row, int col, int depth,
int depth2, int depth3, int depth4) {
float texR = dot(vec4(row, col, depth, depth2),
vec4(${t[1]*t[2]*t[3]*t[4]},
${t[2]*t[3]*t[4]},
${t[3]*t[4]},
${t[4]})) + float(depth3);
int texC = depth4;
vec2 uv = (vec2(texC, texR) + halfCR) /
vec2(${m}.0, ${h}.0);
return sampleTexture(${n}, uv);
}
`;let f=Qi(n);return`
float ${a}(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 * ${u} + col * ${c} + depth * ${l} +
depth2 * ${o} + depth3 * ${i} + depth4 + ${f};
vec2 uv = uvFromFlat(${h}, ${m}, index);
return sampleTexture(${n}, uv);
}
`}function yu(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 ${t}[i];
}
}
`}function M8(e,t){let n=e.name,a=n.charAt(0).toUpperCase()+n.slice(1),r="get"+a+"AtOutCoords",s=e.shapeInfo.logicalShape.length,i=t.logicalShape.length,o=aN(e.shapeInfo.logicalShape,t.logicalShape),l=dt(i),c=i-s,u,p=["x","y","z","w","u","v"];s===0?u="":i<2&&o.length>=1?u="coords = 0;":u=o.map(g=>`coords.${p[g+c]} = 0;`).join(`
`);let d="";i<2&&s>0?d="coords":d=e.shapeInfo.logicalShape.map((g,y)=>`coords.${p[y+c]}`).join(", ");let h="return outputValue;",m=w.sizeFromShape(e.shapeInfo.logicalShape)===1,f=w.sizeFromShape(t.logicalShape)===1;if(s===1&&!m&&!f)h=`
return vec4(outputValue.xy, outputValue.xy);
`;else if(m&&!f)i===1?h=`
return vec4(outputValue.x, outputValue.x, 0., 0.);
`:h=`
return vec4(outputValue.x);
`;else if(o.length){let g=s-2,y=s-1;o.indexOf(g)>-1&&o.indexOf(y)>-1?h="return vec4(outputValue.x);":o.indexOf(g)>-1?h="return vec4(outputValue.x, outputValue.y, outputValue.x, outputValue.y);":o.indexOf(y)>-1&&(h="return vec4(outputValue.xx, outputValue.zz);")}return`
vec4 ${r}() {
${l} coords = getOutputCoords();
${u}
vec4 outputValue = get${a}(${d});
${h}
}
`}function P8(e,t){let n=e.name,a=n.charAt(0).toUpperCase()+n.slice(1),r="get"+a+"AtOutCoords",s=t.texShape,i=e.shapeInfo.texShape,o=e.shapeInfo.logicalShape.length,l=t.logicalShape.length;if(!e.shapeInfo.isUniform&&o===l&&e.shapeInfo.flatOffset==null&&w.arraysEqual(i,s))return`
float ${r}() {
return sampleTexture(${n}, resultUV);
}
`;let c=dt(l),u=aN(e.shapeInfo.logicalShape,t.logicalShape),p=l-o,d,h=["x","y","z","w","u","v"];o===0?d="":l<2&&u.length>=1?d="coords = 0;":d=u.map(f=>`coords.${h[f+p]} = 0;`).join(`
`);let m="";return l<2&&o>0?m="coords":m=e.shapeInfo.logicalShape.map((f,g)=>`coords.${h[g+p]}`).join(", "),`
float ${r}() {
${c} coords = getOutputCoords();
${d}
return get${a}(${m});
}
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}`:f=`vec4 activation(vec4 x) {
${i}
}`,g="result = activation(result);");let y=s?"result += getBiasAtOutCoords();":"";s&&this.variableNames.push("bias"),o&&this.variableNames.push("preluActivationWeights"),l&&this.variableNames.push("leakyreluAlpha");let b="rc.x",x="rc.x";e[0]<t[0]?b=`int(min(float(rc.x), ${e[0]-1}.))`:t[0]<e[0]&&(x=`int(min(float(rc.x), ${t[0]-1}.))`),this.userCode=`
${f}
const float sharedDimension = ${u}.0;
vec4 dot2x2ARowBCol(ivec3 rc) {
vec4 result = vec4(0);
for (int i = 0; i < ${u}; i++) {
int batchA = ${b};
int batchB = ${x};
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]} * ${m[0]});
result += (${h[1]} * ${m[1]});
}
return result;
}
void main() {
ivec3 rc = getOutputCoords();
vec4 result = dot2x2ARowBCol(rc);
${y}
${g}
setOutput(result);
}
`}},kN={REAL:"return areal * breal - aimag * bimag;",IMAG:"return areal * bimag + aimag * breal;"},IN=class{constructor(e,t,n){this.variableNames=["AReal","AImag","BReal","BImag"],this.outputShape=_.assertAndGetBroadcastShape(t,n),this.userCode=`
float binaryOpComplex(
float areal, float aimag, float breal, float bimag) {
${e}
}
void main() {
float areal = getARealAtOutCoords();
float aimag = getAImagAtOutCoords();
float breal = getBRealAtOutCoords();
float bimag = getBImagAtOutCoords();
setOutput(binaryOpComplex(areal, aimag, breal, bimag));
}
`}},TN="return a * b;";function NN(e){let{inputs:t,backend:n}=e,{a,b:r}=t,s=_.upcastType(a.dtype,r.dtype);if(a.dtype==="complex64"){let o=n.texData.get(a.dataId),l=n.texData.get(r.dataId),c=new IN(kN.REAL,a.shape,r.shape),u=new IN(kN.IMAG,a.shape,r.shape),p=[{dataId:o.complexTensorInfos.real.dataId,dtype:o.complexTensorInfos.real.dtype,shape:a.shape},{dataId:o.complexTensorInfos.imag.dataId,dtype:o.complexTensorInfos.imag.dtype,shape:a.shape},{dataId:l.complexTensorInfos.real.dataId,dtype:l.complexTensorInfos.real.dtype,shape:r.shape},{dataId:l.complexTensorInfos.imag.dataId,dtype:l.complexTensorInfos.imag.dtype,shape:r.shape}],d=n.runWebGLProgram(c,p,"float32"),h=n.runWebGLProgram(u,p,"float32"),m=ys({inputs:{real:d,imag:h},backend:n});return n.disposeIntermediateTensorInfo(d),n.disposeIntermediateTensorInfo(h),m}if(n.shouldExecuteOnCPU([a,r])){let o=n.texData.get(a.dataId),l=n.texData.get(r.dataId),[c,u]=mX(a.shape,r.shape,o.values,l.values,s),p=n.makeTensorInfo(u,s),d=n.texData.get(p.dataId);return d.values=c,p}let i;return Z().getBool("WEBGL_PACK_BINARY_OPERATIONS")?i=new xp(TN,a.shape,r.shape):i=new wu(TN,a.shape,r.shape),n.runWebGLProgram(i,[a,r],s)}var lK={kernelName:ai,backendName:"webgl",kernelFunc:NN};function uK(e,t,n){let a=[mu(e.shape),...fu(e.shape)],r={dtype:e.dtype,shape:a,dataId:e.dataId},s=[mu(t),...fu(t)],i=new cN(s,a),o=!0,l=n.runWebGLProgram(i,[r],e.dtype,null,o);return{dataId:l.dataId,shape:t,dtype:l.dtype}}function ge(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{shape:s}=a,i=n,o=w.sizeFromShape(r.shape),l=w.inferFromImplicitShape(s,o),c=w.sizeFromShape(l);w.assert(o===c,()=>`The new shape (${l}) has ${c} elements and the old shape (${r.shape}) has ${o} elements. The new shape and old shape must have the same number of elements.`);let u=i.texData.get(r.dataId);return u.isPacked&&!jm(r.shape,l)&&!(u.texture!==null&&jm(u.shape,l))?uK(r,l,i):(i.incRef(r.dataId),{dataId:r.dataId,shape:l,dtype:r.dtype})}var cK={kernelName:vl,backendName:"webgl",kernelFunc:ge},SN=class{constructor(e,t){this.variableNames=["x"];let{windowSize:n,batchSize:a,inSize:r,outSize:s}=e;this.outputShape=[a,s];let i=Math.floor(n/4)*4,o=n%4,l="sumValue += dot(values, ones);";if(t!=null){let u=1/t;l=`sumValue += dot(values * ${w.isInt(u)?u.toPrecision(2):u}, ones);`}let c="";r%n>0&&(c=`
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) {
${c}
return getX(batch, inIdx);
}
void main() {
ivec2 coords = getOutputCoords();
int batch = coords[0];
int outIdx = coords[1];
int inOffset = outIdx * ${n};
float sumValue = 0.0;
for (int i = 0; i < ${i}; i += 4) {
int inIdx = inOffset + i;
vec4 values = vec4(
getValue(batch, inIdx),
getValue(batch, inIdx + 1),
getValue(batch, inIdx + 2),
getValue(batch, inIdx + 3)
);
${l}
}
int inIdx = inOffset + ${i};
if (${o===1}) {
vec4 values = vec4(getValue(batch, inIdx), 0.0, 0.0, 0.0);
${l}
} else if (${o===2}) {
vec4 values = vec4(
getValue(batch, inIdx),
getValue(batch, inIdx + 1), 0.0, 0.0);
${l}
} else if (${o===3}) {
vec4 values = vec4(
getValue(batch, inIdx),
getValue(batch, inIdx + 1),
getValue(batch, inIdx + 2), 0.0);
${l}
}
setOutput(sumValue);
}
`}},pK=class{constructor(e,t){this.variableNames=["x"];let{windowSize:n,batchSize:a,inSize:r,outSize:s}=e;this.outputShape=[a,s];let i="0.0",o="";t==="prod"?i="1.0":t==="min"?(i="1.0 / 1e-20",o="min"):t==="max"&&(i="-1.0 / 1e-20",o="max");let l=`${t}(${t}(${t}(minMaxValue[0], minMaxValue[1]), minMaxValue[2]), minMaxValue[3])`;t==="sum"?l="sumValue":t==="prod"?l="prodValue":t==="all"?l="allValue":t==="any"&&(l="anyValue");let c=Math.floor(n/4)*4,u=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 = ${o}(values, minMaxValue);
}
`,d="vec4";t==="all"?(i="1.0",p=`
bool reducedAllValue = all(values);
float floatedReducedAllValue = float(reducedAllValue);
allValue = float(allValue >= 1.0 && floatedReducedAllValue >= 1.0);
`,d="bvec4"):t==="any"&&(i="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 = ${i};
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 * ${n};
vec4 minMaxValue = vec4(${i});
float prodValue = 1.0;
float sumValue = 0.0;
float allValue = 1.0;
float anyValue = 0.0;
for (int i = 0; i < ${c}; i += 4) {
int inIdx = inOffset + i;
${d} values = ${d}(
getValue(batch, inIdx),
getValue(batch, inIdx + 1),
getValue(batch, inIdx + 2),
getValue(batch, inIdx + 3)
);
${p}
}
int inIdx = inOffset + ${c};
if (${u===1}) {
${d} values = ${d}(
getValue(batch, inIdx),
initializationValue,
initializationValue,
initializationValue
);
${p}
} else if (${u===2}) {
${d} values = ${d}(
getValue(batch, inIdx),
getValue(batch, inIdx + 1),
initializationValue,
initializationValue
);
${p}
} else if (${u===3}) {
${d} values = ${d}(
getValue(batch, inIdx),
getValue(batch, inIdx + 1),
getValue(batch, inIdx + 2),
initializationValue
);
${p}
}
setOutput(${l});
}
`}};function dK(e){let t=[];for(;t.length===0||t[t.length-1].outSize!==1;){let n=t.length?t[t.length-1].outSize:e[1],a=_.computeOptimalWindowSize(n);t.push({inSize:n,windowSize:a,outSize:Math.ceil(n/a)})}return t}function Zi(e,t,n,a){let r=dK(e.shape),s=e;for(let i=0;i<r.length;i++){let{inSize:o,windowSize:l,outSize:c}=r[i],u,p;n==="mean"?u=i===0?new SN({windowSize:l,inSize:o,batchSize:e.shape[0],outSize:c},o):new SN({windowSize:l,inSize:o,batchSize:e.shape[0],outSize:c}):u=new pK({windowSize:l,inSize:o,batchSize:e.shape[0],outSize:c},n),p=s,s=a.runWebGLProgram(u,[s],t),p.dataId!==e.dataId&&a.disposeIntermediateTensorInfo(p)}return s}var mK=class{constructor(e,t){this.variableNames=["A"];let n=new Array(e.length);for(let s=0;s<n.length;s++)n[s]=e[t[s]];this.outputShape=n,this.rank=n.length;let a=dt(this.rank),r=hK(t);this.userCode=`
void main() {
${a} resRC = getOutputCoords();
setOutput(getA(${r}));
}
`}};function hK(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"],a=new Array(t);for(let r=0;r<e.length;r++)a[e[r]]=n[r];return a.join()}var fK=class{constructor(e,t){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0;let n=new Array(e.length);for(let c=0;c<n.length;c++)n[c]=e[t[c]];if(this.outputShape=n,this.rank=n.length,this.rank>6)throw Error(`Packed transpose for rank ${this.rank} is not yet supported.`);let a=dt(this.rank),r=uN("rc",this.rank),s=new Array(this.rank);for(let c=0;c<t.length;c++)s[t[c]]=r[c];let i=`vec2(${s.slice(-2).join()})`,o=`++${r[this.rank-1]} < ${n[this.rank-1]}`,l=`getChannel(getA(${s.join()}), ${i})`;this.userCode=`
void main() {
${a} rc = getOutputCoords();
vec4 result = vec4(0.);
result[0] = ${l};
if(${o}) {
result[1] = ${l};
}
--${r[this.rank-1]};
if(++${r[this.rank-2]} < ${n[this.rank-2]}) {
result[2] = ${l};
if(${o}) {
result[3] = ${l};
}
}
setOutput(result);
}
`}};function Ym(e,t,n){let a=Z().getBool("WEBGL_PACK_ARRAY_OPERATIONS")?new fK(e.shape,t):new mK(e.shape,t);return n.runWebGLProgram(a,[e],e.dtype)}function gK(e,t,n,a){let r=t,s=e.shape.length,i=w.parseAxisParam(r,e.shape),o=i,l=_.getAxesPermutation(o,s),c=l!=null,u=e;c&&(u=Ym(e,l,a),o=_.getInnerMostAxes(o.length,s)),_.assertAxesAreInnerMostDims("sum",o,s);let[p,d]=_.computeOutAndReduceShapes(u.shape,o),h=p;n&&(h=_.expandShapeToKeepDim(p,i));let m=w.sizeFromShape(d),f=w.sizeFromShape(e.shape)/m,g=ge({inputs:{x:u},attrs:{shape:[f,m]},backend:a}),y=uh(e.dtype),b=Zi(g,y,"sum",a),x=ge({inputs:{x:b},attrs:{shape:h},backend:a});return a.disposeIntermediateTensorInfo(g),a.disposeIntermediateTensorInfo(b),c&&a.disposeIntermediateTensorInfo(u),x}function Jv(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{axis:s,keepDims:i}=a;return gK(r,s,i,n)}var yK={kernelName:yi,backendName:"webgl",kernelFunc:Jv};function $n(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{perm:s}=a,i=n,o=r.shape.length,l=new Array(o);for(let u=0;u<l.length;u++)l[u]=r.shape[s[u]];let c;if(i.shouldExecuteOnCPU([r])){let u=i.texData.get(r.dataId).values,p=Xv(u,r.shape,r.dtype,s,l);c=i.makeTensorInfo(l,r.dtype);let d=i.texData.get(c.dataId);d.values=p}else c=Ym(r,s,i);return c}var bK={kernelName:ki,backendName:"webgl",kernelFunc:$n},CN=1e3;function Jm({a:e,b:t,transposeA:n,transposeB:a,backend:r,bias:s=null,preluActivationWeights:i=null,leakyreluAlpha:o=0,activation:l=null}){let c=e.shape.length,u=t.shape.length,p=n?e.shape[c-2]:e.shape[c-1],d=a?t.shape[u-1]:t.shape[u-2],h=n?e.shape[c-1]:e.shape[c-2],m=a?t.shape[u-2]:t.shape[u-1],f=e.shape.slice(0,-2),g=t.shape.slice(0,-2),y=w.sizeFromShape(f),b=w.sizeFromShape(g),x=y===b||y===1||b===1;w.assert(c>=2&&u>=2&&x,()=>`Error in matMul: the input batch dimensions must either be the same or at least one input batch dimension must be 1. Got input batch dimensions of (${f}) and (${g}).`);let v=(y>b?e.shape.slice(0,-2):t.shape.slice(0,-2)).concat([h,m]);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=${a} must match.`);let T=n?[y,p,h]:[y,h,p],k=a?[b,m,d]:[b,d,m],S=ge({inputs:{x:e},backend:r,attrs:{shape:T}}),F=ge({inputs:{x:t},backend:r,attrs:{shape:k}}),A=[S,F],R=Math.max(y,b),P=n?S.shape[1]:S.shape[2],z=s!=null,V=i!=null,G=l==="leakyrelu",H=l!=null?Km(l,!0):null,X=z||V||G||H!=null,j;if((h===1||m===1)&&P>CN&&X===!1){let Q=S,se=F;n&&(Q=$n({inputs:{x:S},backend:r,attrs:{perm:[0,2,1]}}),A.push(Q)),a&&(se=$n({inputs:{x:F},backend:r,attrs:{perm:[0,2,1]}}),A.push(se));let ne=m!==1,ie=m===1,ee=Q;ne&&(ee=ge({inputs:{x:Q},backend:r,attrs:{shape:[R,P,1]}}),A.push(ee));let pe=m===1?2:1,oe=se;ie&&(oe=ge({inputs:{x:se},backend:r,attrs:{shape:[R,1,P]}}),A.push(oe));let fe=NN({inputs:{a:ee,b:oe},backend:r});j=Jv({inputs:{x:fe},backend:r,attrs:{axis:pe,keepDims:!0}}),A.push(fe)}else{let Q=pa(e.dtype,t.dtype),se=new wN(T,k,[R,h,m],n,a,z,H,V,G),ne=[S,F];if(s!=null&&ne.push(s),V&&ne.push(i),G){let ie=r.makeTensorInfo([],"float32",w.createScalarValue(o,"float32"));ne.push(ie),A.push(ie)}j=r.runWebGLProgram(se,ne,Q)}let te=ge({inputs:{x:j},backend:r,attrs:{shape:v}});A.push(j);for(let Q of A)r.disposeIntermediateTensorInfo(Q);return te}function xK(e){let{inputs:t,backend:n,attrs:a}=e,{a:r,b:s,bias:i,preluActivationWeights:o}=t,{transposeA:l,transposeB:c,activation:u,leakyreluAlpha:p}=a;return Jm({a:r,b:s,transposeA:l,transposeB:c,backend:n,bias:i,preluActivationWeights:o,leakyreluAlpha:p,activation:u})}var vK={kernelName:Ii,backendName:"webgl",kernelFunc:xK},_N="return abs(x);";function wK(e){let{inputs:t,backend:n}=e,{x:a}=t;if(n.shouldExecuteOnCPU([a])&&a.dtype!=="complex64"){let s=n.texData.get(a.dataId),i=lN(s.values);return n.makeTensorInfo(a.shape,a.dtype,i)}let r;return Z().getBool("WEBGL_PACK_UNARY_OPERATIONS")?r=new vu(a.shape,_N):r=new gs(a.shape,_N),n.runWebGLProgram(r,[a],a.dtype)}var kK={kernelName:Po,backendName:"webgl",kernelFunc:wK},IK=Pa+`
if (abs(x) > 1.) {
return NAN;
}
return acos(x);
`,TK=Xe({opSnippet:IK}),NK={kernelName:Oo,backendName:"webgl",kernelFunc:TK},SK=Pa+`
if (x < 1.0) return NAN;
return log(x + sqrt(x * x - 1.0));`,CK=Xe({opSnippet:SK}),_K={kernelName:Lo,backendName:"webgl",kernelFunc:CK},EN="return a + b;",EK=ln({opSnippet:EN,packedOpSnippet:EN,supportsComplex:!0,cpuKernelImpl:Z8}),FK={kernelName:Hr,backendName:"webgl",kernelFunc:EK},AK=class{constructor(e,t){this.outputShape=[],this.outputShape=e,this.variableNames=t.map((r,s)=>`T${s}`);let n=[];this.variableNames.forEach(r=>{n.push(`float v${r} = get${r}AtOutCoords();`)});let a=this.variableNames.map(r=>`v${r}`).join(" + ");this.userCode=`
void main() {
${n.join(`
`)}
float result = ${a};
setOutput(result);
}
`}},$K=class{constructor(e,t){this.outputShape=[],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=e,this.variableNames=t.map((r,s)=>`T${s}`);let n=[];this.variableNames.forEach(r=>{n.push(`vec4 v${r} = get${r}AtOutCoords();`)});let a=this.variableNames.map(r=>`v${r}`).join(" + ");this.userCode=`
void main() {
${n.join(`
`)}
vec4 result = ${a};
setOutput(result);
}
`}};function Qm(e){let{inputs:t,backend:n}=e,a=t;if(a.length===1)return Gn({inputs:{x:a[0]},backend:n});if(a.length>Z().get("WEBGL_MAX_TEXTURES_IN_SHADER")){let o=Math.floor(a.length/2),l=Qm({inputs:a.slice(0,o),backend:n}),c=Qm({inputs:a.slice(o),backend:n});return Qm({inputs:[l,c],backend:n})}let r=a.map(o=>o.dtype).reduce((o,l)=>pa(o,l)),s=a.map(o=>o.shape),i=Z().getBool("WEBGL_PACK")?new $K(a[0].shape,s):new AK(a[0].shape,s);return n.runWebGLProgram(i,a,r)}var DK={kernelName:As,backendName:"webgl",kernelFunc:Qm};function RK(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{axis:s,keepDims:i}=a,o=r.shape.length,l=w.parseAxisParam(s,r.shape),c=l,u=_.getAxesPermutation(c,o),p=r;u!=null&&(p=$n({inputs:{x:r},backend:n,attrs:{perm:u}}),c=_.getInnerMostAxes(c.length,o)),_.assertAxesAreInnerMostDims("all",c,o);let[d,h]=_.computeOutAndReduceShapes(p.shape,c),m=w.sizeFromShape(h),f=ge({inputs:{x:p},backend:n,attrs:{shape:[-1,m]}}),g=Zi(f,f.dtype,"all",n),y;if(i){let b=_.expandShapeToKeepDim(d,l);y=ge({inputs:{x:g},backend:n,attrs:{shape:b}})}else y=ge({inputs:{x:g},backend:n,attrs:{shape:d}});return n.disposeIntermediateTensorInfo(f),n.disposeIntermediateTensorInfo(g),u!=null&&n.disposeIntermediateTensorInfo(p),y}var MK={kernelName:Sd,backendName:"webgl",kernelFunc:RK};function PK(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{axis:s,keepDims:i}=a,o=r.shape.length,l=w.parseAxisParam(s,r.shape),c=l,u=_.getAxesPermutation(c,o),p=r;u!=null&&(p=$n({inputs:{x:r},backend:n,attrs:{perm:u}}),c=_.getInnerMostAxes(c.length,o)),_.assertAxesAreInnerMostDims("any",c,o);let[d,h]=_.computeOutAndReduceShapes(p.shape,c),m=w.sizeFromShape(h),f=ge({inputs:{x:p},backend:n,attrs:{shape:[-1,m]}}),g=Zi(f,f.dtype,"any",n),y;if(i){let b=_.expandShapeToKeepDim(d,l);y=ge({inputs:{x:g},backend:n,attrs:{shape:b}})}else y=ge({inputs:{x:g},backend:n,attrs:{shape:d}});return n.disposeIntermediateTensorInfo(f),n.disposeIntermediateTensorInfo(g),u!=null&&n.disposeIntermediateTensorInfo(p),y}var OK={kernelName:Cd,backendName:"webgl",kernelFunc:PK},LK=class{constructor(e,t,n){this.variableNames=["A"];let{windowSize:a,batchSize:r,outSize:s}=e;n||this.variableNames.push("bestIndicesA"),this.outputShape=[r,s];let i=t==="max"?">":"<",o=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 * ${a};
int bestIndex = inOffset;
float bestValue = getA(batch, bestIndex);
for (int i = 0; i < ${a}; i++) {
int inIdx = ${o};
float candidate = getA(batch, inIdx);
if (candidate ${i} bestValue) {
bestValue = candidate;
bestIndex = inIdx;
}
}
setOutput(float(bestIndex));
}
`}},zK=class{constructor(e,t,n,a){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,w.assert(e.length>2,()=>`Packed arg${n.charAt(0).toUpperCase()+n.slice(1)} supports only inputs with rank above 2.`);let r=e[e.length-1],s=Math.ceil(r/t);this.outputShape=e.slice(0,-1),s>1&&this.outputShape.push(s),a||this.variableNames.push("bestIndicesA");let i=this.outputShape,o=i.length,l=dt(o),c=yn("coords",o),u,p;if(s===1){p=o+1;let S=dt(p);u=`
${S} sourceLocR = ${S}(${c.join()}, 0);
++${c[o-1]};
${S} sourceLocG = ${S}(${c.join()}, 0);
++${c[o-2]};
${S} sourceLocA = ${S}(${c.join()}, 0);
--${c[o-1]};
${S} sourceLocB = ${S}(${c.join()}, 0);
--${c[o-2]};`}else p=o,u=`
${l} sourceLocR = coords;
++${c[o-1]};
${l} sourceLocG = coords;
++${c[o-2]};
${l} sourceLocA = coords;
--${c[o-1]};
${l} sourceLocB = coords;
--${c[o-2]};`;let d=["x","y","z","w","u","v"].slice(0,p),h="."+d[p-1],m=d.map(S=>"int "+S),f=yn("sourceLocR",p-1).concat("inIdx.r"),g=yn("sourceLocG",p-1).concat("inIdx.g"),y=yn("sourceLocB",p-1).concat("inIdx.b"),b=yn("sourceLocA",p-1).concat("inIdx.a"),x=n==="max"?"greaterThan":"lessThan",v=a?"":`
inIdx = round(vec4(getBestIndicesAChannel(${f.join()}),
getBestIndicesAChannel(${g.join()}),
getBestIndicesAChannel(${y.join()}),
getBestIndicesAChannel(${b.join()})));`,T=`vec4(
getAChannel(${f.join()}),
hasNextCol ? getAChannel(${g.join()}) : 0.,
hasNextRow ? getAChannel(${y.join()}) : 0.,
hasNextRow && hasNextCol ? getAChannel(${b.join()}) : 0.)`,k=a?"":`
float getBestIndicesAChannel(${m.join()}) {
return getChannel(getBestIndicesA(${d.join()}),
vec2(${d.slice(-2).join()}));
}`;this.userCode=`
float getAChannel(${m.join()}) {
return getChannel(getA(${d.join()}),
vec2(${d.slice(-2).join()}));
}
${k}
void main() {
${l} coords = getOutputCoords();
bool hasNextCol = ${c[o-1]} < ${i[o-1]-1};
bool hasNextRow = ${c[o-2]} < ${i[o-2]-1};
${u}
ivec4 srcIdx = ivec4(sourceLocR${h}, sourceLocG${h},
sourceLocB${h}, sourceLocA${h}) * ${t};
ivec4 inIdx = srcIdx;
vec4 bestIndex = vec4(inIdx);
vec4 bestValue = ${T};
for (int i = 0; i < ${t}; i++) {
inIdx = srcIdx;
${v}
vec4 candidate = ${T};
bvec4 nan = isnan(candidate);
bvec4 replace = bvec4(
vec4(${x}(candidate, bestValue)) * (vec4(1.0) - vec4(nan)));
bestValue = vec4(replace.x ? candidate.x : bestValue.x,
replace.y ? candidate.y : bestValue.y,
replace.z ? candidate.z : bestValue.z,
replace.w ? candidate.w : bestValue.w);
bestIndex = mix(bestIndex, vec4(inIdx), vec4(replace));
srcIdx++;
}
setOutput(bestIndex);
}
`}};function FN(e,t,n,a=null){let r=t.shape[0],s=t.shape[1];a!=null&&(r=a.shape[0],s=a.shape[1]);let i=_.computeOptimalWindowSize(s),o={windowSize:i,inSize:s,batchSize:r,outSize:Math.ceil(s/i)},l=new LK(o,n,a==null),c=[t];a!=null&&c.push(a);let u=e.runWebGLProgram(l,c,"int32");if(u.shape[1]===1)return u;let p=FN(e,t,n,u);return e.disposeIntermediateTensorInfo(u),p}function AN(e,t,n,a=null){let r=a!=null?a.shape:t.shape,s=r[r.length-1],i=_.computeOptimalWindowSize(s),o=new zK(r,i,n,a==null),l=a==null?[t]:[t,a],c=e.runWebGLProgram(o,l,"int32");if(c.shape.length===t.shape.length){let u=AN(e,t,n,c);return e.disposeIntermediateTensorInfo(c),u}return c}function $N(e,t,n,a){let r=[n];if(_.assertAxesAreInnerMostDims("arg"+a.charAt(0).toUpperCase()+a.slice(1),r,t.shape.length),!Z().getBool("WEBGL_PACK_REDUCE")||t.shape.length<=2){let s=[],[i,o]=_.computeOutAndReduceShapes(t.shape,r),l=w.sizeFromShape(o),c=ge({inputs:{x:t},backend:e,attrs:{shape:[-1,l]}});s.push(c);let u=FN(e,c,a);s.push(u);let p=ge({inputs:{x:u},backend:e,attrs:{shape:i}});return s.forEach(d=>e.disposeIntermediateTensorInfo(d)),p}return AN(e,t,a)}function WK(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{axis:s}=a,i=w.parseAxisParam(s,r.shape),o=_.getAxesPermutation(i,r.shape.length),l=r,c=[];o!=null&&(l=$n({inputs:{x:r},backend:n,attrs:{perm:o}}),c.push(l),i=_.getInnerMostAxes(i.length,l.shape.length)),_.assertAxesAreInnerMostDims("argMax",[i[0]],l.shape.length);let u=$N(n,l,i[0],"max");return c.forEach(p=>n.disposeIntermediateTensorInfo(p)),u}var BK={kernelName:$s,backendName:"webgl",kernelFunc:WK};function VK(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{axis:s}=a,i=w.parseAxisParam(s,r.shape),o=_.getAxesPermutation(i,r.shape.length),l=r,c=[];o!=null&&(l=$n({inputs:{x:r},backend:n,attrs:{perm:o}}),c.push(l),i=_.getInnerMostAxes(i.length,l.shape.length)),_.assertAxesAreInnerMostDims("argMin",[i[0]],l.shape.length);let u=$N(n,l,i[0],"min");return c.forEach(p=>n.disposeIntermediateTensorInfo(p)),u}var UK={kernelName:nc,backendName:"webgl",kernelFunc:VK},GK=Pa+`
if (abs(x) > 1.) {
return NAN;
}
return asin(x);
`,HK=Xe({opSnippet:GK}),jK={kernelName:zo,backendName:"webgl",kernelFunc:HK},qK=Pa+"return log(x + sqrt(x * x + 1.0));",XK=Xe({opSnippet:qK}),KK={kernelName:Wo,backendName:"webgl",kernelFunc:XK},YK=Pa+`
return atan(x);
`,JK=Xe({opSnippet:YK}),QK={kernelName:Bo,backendName:"webgl",kernelFunc:JK},ZK=iK+`
return atan(a, b);
`,eY=`
vec4 result = atan(a, b);
vec4 isNaN = min(vec4(isnan(a)) + vec4(isnan(b)), vec4(1.0));
`+oK+`
return result;
`,tY=ln({opSnippet:ZK,packedOpSnippet:eY}),nY={kernelName:Uo,backendName:"webgl",kernelFunc:tY},aY=Pa+`
if ((x < -1.0) || (x > 1.0)) return NAN;
return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,rY=Xe({opSnippet:aY}),sY={kernelName:Vo,backendName:"webgl",kernelFunc:rY},vp=class{constructor(e,t,n,a=!1,r=!1){if(this.variableNames=["x"],t==="avg"&&n)throw new Error("Cannot compute positions for average pool.");let s=e.filterWidth,i=e.strideHeight,o=e.strideWidth,l=e.dilationHeight,c=e.dilationWidth,u=e.effectiveFilterHeight,p=e.effectiveFilterWidth,d=e.padInfo.top,h=e.padInfo.left;this.outputShape=e.outShape;let m=t==="avg",f=`((batch * ${e.inHeight} + xR) * ${e.inWidth} + xC) * ${e.inChannels} + d`,g=`(xR * ${e.inWidth} + xC) * ${e.inChannels} + d`,y="0.0";if(m||(y="-1.0 / 1e-20"),n){let S=">=";this.userCode=`
const ivec2 strides = ivec2(${i}, ${o});
const ivec2 pads = ivec2(${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 < ${u};
wR += ${l}) {
int xR = xRCorner + wR;
if (xR < 0 || xR >= ${e.inHeight}) {
continue;
}
for (int wC = 0; wC < ${p};
wC += ${c}) {
int xC = xCCorner + wC;
if (xC < 0 || xC >= ${e.inWidth}) {
continue;
}
float value = getX(batch, xR, xC, d);
// If a min / max value has already been found, use it. If not,
// use the current value.
float currMinMaxValue = mix(
value, minMaxValue, minMaxValueFound);
if (value ${S} currMinMaxValue) {
minMaxValue = value;
minMaxValueFound = 1.0;
minMaxPosition = ${a?r?f:g:`wR * ${p} + wC`};
}
}
}
setOutput(float(minMaxPosition));
}
`;return}let b="max",x=`${t}(${t}(${t}(minMaxValue[0], minMaxValue[1]), minMaxValue[2]), minMaxValue[3])`;t==="avg"&&(x="avgValue / count");let v=Math.floor(s/4)*4,T=s%4,k=`
if (${m}) {
avgValue += dot(values, ones);
} else {
minMaxValue = ${b}(values, minMaxValue);
}
`;this.userCode=`
const ivec2 strides = ivec2(${i}, ${o});
const ivec2 pads = ivec2(${d}, ${h});
const float initializationValue = ${y};
const vec4 ones = vec4(1.0, 1.0, 1.0, 1.0);
float count = 0.0;
float getValue(int batch, int xR, int xC, int d) {
if (xC < 0 || xC >= ${e.inWidth}) {
return initializationValue;
}
count += 1.0;
return getX(batch, xR, xC, d);
}
void main() {
ivec4 coords = getOutputCoords();
int batch = coords[0];
int d = coords[3];
ivec2 xRCCorner = coords.yz * strides - pads;
int xRCorner = xRCCorner.x;
int xCCorner = xRCCorner.y;
// max/min x(?, ?, d) to get y(yR, yC, d).
// ? = to be determined
vec4 minMaxValue = vec4(${y});
float avgValue = 0.0;
count = 0.0;
for (int wR = 0; wR < ${u};
wR += ${l}) {
int xR = xRCorner + wR;
if (xR < 0 || xR >= ${e.inHeight}) {
continue;
}
for (int wC = 0; wC < ${v}; wC += 4) {
int xC = xCCorner + wC * ${c};
vec4 values = vec4(
getValue(batch, xR, xC, d),
getValue(batch, xR, xC + ${c}, d),
getValue(batch, xR, xC + 2 * ${c}, d),
getValue(batch, xR, xC + 3 * ${c}, d)
);
${k}
}
int xC = xCCorner + ${v};
if (${T===1}) {
vec4 values = vec4(
getValue(batch, xR, xC, d),
initializationValue,
initializationValue,
initializationValue
);
${k}
} else if (${T===2}) {
vec4 values = vec4(
getValue(batch, xR, xC, d),
getValue(batch, xR, xC + ${c}, d),
initializationValue,
initializationValue
);
${k}
} else if (${T===3}) {
vec4 values = vec4(
getValue(batch, xR, xC, d),
getValue(batch, xR, xC + ${c}, d),
getValue(batch, xR, xC + 2 * ${c}, d),
initializationValue
);
${k}
}
}
setOutput(${x});
}
`}},Qv=class{constructor(e,t,n,a=!1,r=!1){if(this.variableNames=["x"],t==="avg"&&n)throw new Error("Cannot compute positions for average pool.");let s=e.filterWidth,i=e.strideDepth,o=e.strideHeight,l=e.strideWidth,c=e.dilationDepth,u=e.dilationHeight,p=e.dilationWidth,d=e.effectiveFilterDepth,h=e.effectiveFilterHeight,m=e.effectiveFilterWidth,f=e.padInfo.front,g=e.padInfo.top,y=e.padInfo.left;this.outputShape=e.outShape;let b=t==="avg",x="0.0";if(b||(x="-1.0 / 1e-20"),n){let A=">=";this.userCode=`
const ivec3 strides =
ivec3(${i}, ${o}, ${l});
const ivec3 pads = ivec3(${f}, ${g}, ${y});
void main() {
ivec5 coords = getOutputCoords();
int batch = coords.x;
int ch = coords.u;
ivec3 xCorner = ivec3(coords.y, coords.z, coords.w) * strides - pads;
int xDCorner = xCorner.x;
int xRCorner = xCorner.y;
int xCCorner = xCorner.z;
// max/min x(?, ?, ?, ch) to get y(yD, yR, yC, ch).
// ? = to be determined
float minMaxValue = 0.0;
float minMaxValueFound = 0.0;
int minMaxPosition = 0;
for (int wD = 0; wD < ${d};
wD += ${c}) {
int xD = xDCorner + wD;
if (xD < 0 || xD >= ${e.inDepth}) {
continue;
}
for (int wR = 0; wR < ${h};
wR += ${u}) {
int xR = xRCorner + wR;
if (xR < 0 || xR >= ${e.inHeight}) {
continue;
}
for (int wC = 0; wC < ${m};
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 ${A} currMinMaxValue) {
minMaxValue = value;
minMaxValueFound = 1.0;
minMaxPosition = ${a?r?`(((batch * ${e.inDepth} + xD) * ${e.inHeight} + xR) * ${e.inWidth} + xC) * ${e.inChannels} + ch`:`((xD * ${e.inHeight} + xR) * ${e.inWidth} + xC) * ${e.inChannels} + ch`:`wD * ${h} * ${m} +
wR * ${m} + wC`};
}
}
}
}
setOutput(float(minMaxPosition));
}
`;return}let v="max",T=`${t}(${t}(${t}(minMaxValue[0], minMaxValue[1]), minMaxValue[2]), minMaxValue[3])`;t==="avg"&&(T="avgValue / count");let k=Math.floor(s/4)*4,S=s%4,F=`
if (${b}) {
avgValue += dot(values, ones);
} else {
minMaxValue = ${v}(values, minMaxValue);
}
`;this.userCode=`
const ivec3 strides =
ivec3(${i}, ${o}, ${l});
const ivec3 pads = ivec3(${f}, ${g}, ${y});
const float initializationValue = ${x};
const vec4 ones = vec4(1.0, 1.0, 1.0, 1.0);
float count = 0.0;
float getValue(int batch, int xD, int xR, int xC, int ch) {
if (xC < 0 || xC >= ${e.inWidth}) {
return initializationValue;
}
count += 1.0;
return getX(batch, xD, xR, xC, ch);
}
void main() {
ivec5 coords = getOutputCoords();
int batch = coords.x;
int ch = coords.u;
ivec3 xCorner = ivec3(coords.y, coords.z, coords.w) * strides - pads;
int xDCorner = xCorner.x;
int xRCorner = xCorner.y;
int xCCorner = xCorner.z;
// max/min x(?, ?, ?, d) to get y(yD, yR, yC, ch).
// ? = to be determined
vec4 minMaxValue = vec4(${x});
float avgValue = 0.0;
count = 0.0;
for (int wD = 0; wD < ${d};
wD += ${c}) {
int xD = xDCorner + wD;
if (xD < 0 || xD >= ${e.inDepth}) {
continue;
}
for (int wR = 0; wR < ${h};
wR += ${u}) {
int xR = xRCorner + wR;
if (xR < 0 || xR >= ${e.inHeight}) {
continue;
}
for (int wC = 0; wC < ${k}; wC += 4) {
int xC = xCCorner + wC * ${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)
);
${F}
}
int xC = xCCorner + ${k};
if (${S===1}) {
vec4 values = vec4(
getValue(batch, xD, xR, xC, ch),
initializationValue,
initializationValue,
initializationValue
);
${F}
} else if (${S===2}) {
vec4 values = vec4(
getValue(batch, xD, xR, xC, ch),
getValue(batch, xD, xR, xC + ${p}, ch),
initializationValue,
initializationValue
);
${F}
} else if (${S===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
);
${F}
}
}
setOutput(${T});
}
}
`}};function iY(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t;yp(r,"avgPool");let{filterSize:s,strides:i,pad:o,dimRoundingMode:l}=a,c=1;w.assert(_.eitherStridesOrDilationsAreOne(i,c),()=>`Error in avgPool: Either strides or dilations must be 1. Got strides ${i} and dilations '${c}'`);let u=_.computePool2DInfo(r.shape,s,i,c,o,l);if(u.filterWidth===1&&u.filterHeight===1&&w.arraysEqual(u.inShape,u.outShape))return Gn({inputs:{x:r},backend:n});let p=new vp(u,"avg",!1);return n.runWebGLProgram(p,[r],"float32")}var oY={kernelName:Ds,backendName:"webgl",kernelFunc:iY};function lY(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{filterSize:s,strides:i,pad:o,dimRoundingMode:l,dataFormat:c}=a,u=[1,1,1],p=_.computePool3DInfo(r.shape,s,i,u,o,l,c),d=new Qv(p,"avg",!1);return n.runWebGLProgram(d,[r],"float32")}var uY={kernelName:ac,backendName:"webgl",kernelFunc:lY},cY=class{constructor(e){this.variableNames=["dy"],this.outputShape=e.inShape;let t=e.filterHeight,n=e.filterWidth,a=e.strideHeight,r=e.strideWidth,s=e.dilationHeight,i=e.dilationWidth,o=e.effectiveFilterHeight,l=e.effectiveFilterWidth,c=o-1-e.padInfo.top,u=l-1-e.padInfo.left,p=1/(t*n);this.userCode=`
const ivec2 pads = ivec2(${c}, ${u});
const float avgMultiplier = float(${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 < ${o};
wR += ${s}) {
float dyR = float(dyRCorner + wR) / ${a}.0;
if (dyR < 0.0 || dyR >= ${e.outHeight}.0 || fract(dyR) > 0.0) {
continue;
}
int idyR = int(dyR);
for (int wC = 0; wC < ${l};
wC+= ${i}) {
float dyC = float(dyCCorner + wC) / ${r}.0;
if (dyC < 0.0 || dyC >= ${e.outWidth}.0 ||
fract(dyC) > 0.0) {
continue;
}
int idyC = int(dyC);
float dyValue = getDy(b, idyR, idyC, d);
dotProd += dyValue * avgMultiplier;
}
}
setOutput(dotProd);
}
`}},pY=class{constructor(e){this.variableNames=["dy"],this.outputShape=e.inShape;let t=e.filterDepth,n=e.filterHeight,a=e.filterWidth,r=e.strideDepth,s=e.strideHeight,i=e.strideWidth,o=e.dilationDepth,l=e.dilationHeight,c=e.dilationWidth,u=e.effectiveFilterDepth,p=e.effectiveFilterHeight,d=e.effectiveFilterWidth,h=u-1-e.padInfo.front,m=p-1-e.padInfo.top,f=d-1-e.padInfo.left,g=1/(t*n*a);this.userCode=`
const ivec3 pads = ivec3(${h}, ${m}, ${f});
const float avgMultiplier = float(${g});
void main() {
ivec5 coords = getOutputCoords();
int batch = coords.x;
int ch = coords.u;
ivec3 dyCorner = ivec3(coords.y, coords.z, coords.w) - pads;
int dyDCorner = dyCorner.x;
int dyRCorner = dyCorner.y;
int dyCCorner = dyCorner.z;
// Convolve dy(?, ?, ?, d) with pos mask(:, :, :, ch) to get
// dx(xD, xR, xC, ch).
// ? = to be determined. : = across all values in that axis.
float dotProd = 0.0;
for (int wD = 0; wD < ${u};
wD += ${o}) {
float dyD = float(dyDCorner + wD) / ${r}.0;
if (dyD < 0.0 || dyD >= ${e.outDepth}.0 || fract(dyD) > 0.0) {
continue;
}
int idyD = int(dyD);
for (int wR = 0; wR < ${p};
wR += ${l}) {
float dyR = float(dyRCorner + wR) / ${s}.0;
if (dyR < 0.0 || dyR >= ${e.outHeight}.0 ||
fract(dyR) > 0.0) {
continue;
}
int idyR = int(dyR);
for (int wC = 0; wC < ${d};
wC += ${c}) {
float dyC = float(dyCCorner + wC) / ${i}.0;
if (dyC < 0.0 || dyC >= ${e.outWidth}.0 ||
fract(dyC) > 0.0) {
continue;
}
int idyC = int(dyC);
float dyValue = getDy(batch, idyD, idyR, idyC, ch);
dotProd += dyValue * avgMultiplier;
}
}
}
setOutput(dotProd);
}
`}};function dY(e){let{inputs:t,backend:n,attrs:a}=e,{dy:r,input:s}=t,i=s,{filterSize:o,strides:l,pad:c,dimRoundingMode:u}=a,p=[1,1,1],d=_.computePool3DInfo(i.shape,o,l,p,c,u),h=new pY(d);return n.runWebGLProgram(h,[r],i.dtype)}var hY={kernelName:Ed,backendName:"webgl",kernelFunc:dY};function mY(e){let{inputs:t,backend:n,attrs:a}=e,{dy:r,input:s}=t,i=s;yp([r,s],"avgPoolGrad");let{filterSize:o,strides:l,pad:c}=a,u=_.computePool2DInfo(i.shape,o,l,1,c),p=new cY(u);return n.runWebGLProgram(p,[r],i.dtype)}var fY={kernelName:_d,backendName:"webgl",kernelFunc:mY};function gY(e){let{inputs:t,backend:n,attrs:a}=e,{a:r,b:s}=t,{transposeA:i,transposeB:o}=a;return Jm({a:r,b:s,transposeA:i,transposeB:o,backend:n})}var yY={kernelName:Rs,backendName:"webgl",kernelFunc:gY},bY=class{constructor(e,t,n,a,r,s){this.outputShape=[],this.variableNames=["x","mean","variance"],_.assertAndGetBroadcastShape(e,t),_.assertAndGetBroadcastShape(e,n);let i="0.0";a!=null&&(_.assertAndGetBroadcastShape(e,a),this.variableNames.push("offset"),i="getOffsetAtOutCoords()");let o="1.0";r!=null&&(_.assertAndGetBroadcastShape(e,r),this.variableNames.push("scale"),o="getScaleAtOutCoords()"),this.outputShape=e,this.userCode=`
void main() {
float x = getXAtOutCoords();
float mean = getMeanAtOutCoords();
float variance = getVarianceAtOutCoords();
float offset = ${i};
float scale = ${o};
float inv = scale * inversesqrt(variance + float(${s}));
setOutput(dot(vec3(x, -mean, offset), vec3(inv, inv, 1)));
}
`}},xY=class{constructor(e,t,n,a,r,s){this.packedInputs=!0,this.packedOutput=!0,this.variableNames=["x","mean","variance"],_.assertAndGetBroadcastShape(e,t),_.assertAndGetBroadcastShape(e,n);let i="vec4(0.0)";a!=null&&(_.assertAndGetBroadcastShape(e,a),this.variableNames.push("offset"),i="getOffsetAtOutCoords()");let o="vec4(1.0)";r!=null&&(_.assertAndGetBroadcastShape(e,r),this.variableNames.push("scale"),o="getScaleAtOutCoords()"),this.outputShape=e,this.userCode=`
void main() {
vec4 offset = ${i};
vec4 scale = ${o};
vec4 x = getXAtOutCoords();
vec4 mean = getMeanAtOutCoords();
vec4 variance = getVarianceAtOutCoords();
vec4 inv = scale * inversesqrt(variance + vec4(${s}));
setOutput((x - mean) * inv + offset);
}
`}},vY=({inputs:e,backend:t,attrs:n})=>{let{x:a,mean:r,variance:s,offset:i,scale:o}=e;w.assert(r.shape.length===s.shape.length,()=>"Batch normalization gradient requires mean and variance to have equal ranks."),w.assert(i==null||r.shape.length===i.shape.length,()=>"Batch normalization gradient requires mean and offset to have equal ranks."),w.assert(o==null||r.shape.length===o.shape.length,()=>"Batch normalization gradient requires mean and scale to have equal ranks.");let{varianceEpsilon:l}=n;l==null&&(l=.001);let c=[a,r,s],u=null;i!=null&&(u=i.shape,c.push(i));let p=null;o!=null&&(p=o.shape,c.push(o));let d=Z().getBool("WEBGL_PACK_NORMALIZATION")?new xY(a.shape,r.shape,s.shape,u,p,l):new bY(a.shape,r.shape,s.shape,u,p,l);return t.runWebGLProgram(d,c,c[0].dtype)},wY={kernelName:js,backendName:"webgl",kernelFunc:vY},IY=class{constructor(e){this.variableNames=["source"],this.outputShape=e,this.rank=e.length;let t=dt(this.rank),n=`uniform int start[${this.rank}];`,a=kY(this.rank),r,s=e.map((i,o)=>`sourceLoc.${Zv[o]} = start[${o}] + coords.${Zv[o]};`);r=`
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${t} coords = getOutputCoords();
${s.join(`
`)}
`,this.userCode=`
${n}
void main() {
${r}
setOutput(getSource(${a}));
}
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result.x = ${s};
if (++${n[this.rank-1]} < ${e[this.rank-1]}) {
++${a[this.rank-1]};
result.y = ${s};
--${a[this.rank-1]};
}
`,o=this.rank===1?"":`
--${n[this.rank-1]};
if (++${n[this.rank-2]} < ${e[this.rank-2]}) {
++${a[this.rank-2]};
result.z = ${s};
if (++${n[this.rank-1]} < ${e[this.rank-1]}) {
++${a[this.rank-1]};
result.w = ${s};
}
}
`,l=this.rank<=4?`sourceLoc = coords +
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`);this.userCode=`
uniform int start[${this.rank}];
void main() {
${t} coords = getOutputCoords();
${t} sourceLoc;
${l}
vec4 result = vec4(0.);
${i}
${o}
setOutput(result);
}
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uniform float minVal;
uniform float maxVal;
void main() {
float value = getAAtOutCoords();
if (isnan(value)) {
setOutput(value);
return;
}
setOutput(clamp(value, minVal, maxVal));
}
`}getCustomSetupFunc(e,t){return(n,a)=>{this.minLoc==null&&(this.minLoc=n.getUniformLocationNoThrow(a,"minVal"),this.maxLoc=n.getUniformLocationNoThrow(a,"maxVal")),n.gl.uniform1f(this.minLoc,e),n.gl.uniform1f(this.maxLoc,t)}}},WY=class{constructor(e){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=e,this.userCode=`
uniform float minVal;
uniform float maxVal;
void main() {
vec4 value = getAAtOutCoords();
if (any(isnan(value))) {
setOutput(value);
return;
}
setOutput(clamp(value, vec4(minVal), vec4(maxVal)));
}
`}getCustomSetupFunc(e,t){return(n,a)=>{this.minLoc==null&&(this.minLoc=n.getUniformLocationNoThrow(a,"minVal"),this.maxLoc=n.getUniformLocationNoThrow(a,"maxVal")),n.gl.uniform1f(this.minLoc,e),n.gl.uniform1f(this.maxLoc,t)}}};function BY(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{clipValueMin:s,clipValueMax:i}=a,o;Z().getBool("WEBGL_PACK_CLIP")?o=new WY(r.shape):o=new zY(r.shape);let l=o.getCustomSetupFunc(s,i);return n.runWebGLProgram(o,[r],r.dtype,l)}var VY={kernelName:jr,backendName:"webgl",kernelFunc:BY},UY=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 MN(e,t){return{dataId:t.dataId,dtype:t.dtype,shape:e.shape}}function GY(e){let{inputs:t,backend:n}=e,{x:a}=t,r=n.texData.get(a.dataId),s=new UY(a.shape),i=[MN(a,r.complexTensorInfos.real),MN(a,r.complexTensorInfos.imag)];return n.runWebGLProgram(s,i,i[0].dtype)}var HY={kernelName:sc,backendName:"webgl",kernelFunc:GY},jY=class{constructor(e){this.outputShape=[],this.outputShape=_.computeOutShape(e,1),this.variableNames=e.map((s,i)=>`T${i}`);let t=new Array(e.length-1);t[0]=e[0][1];for(let s=1;s<t.length;s++)t[s]=t[s-1]+e[s][1];let n=[`if (yC < ${t[0]}) setOutput(getT0(yR, yC));`];for(let s=1;s<t.length;s++){let i=t[s-1];n.push(`else if (yC < ${t[s]}) setOutput(getT${s}(yR, yC-${i}));`)}let a=t.length,r=t[t.length-1];n.push(`else setOutput(getT${a}(yR, yC-${r}));`),this.userCode=`
void main() {
ivec2 coords = getOutputCoords();
int yR = coords.x;
int yC = coords.y;
${n.join(`
`)}
}
`}},qY=class{constructor(e,t){this.packedInputs=!0,this.packedOutput=!0,this.outputShape=[],this.outputShape=_.computeOutShape(e,t);let n=this.outputShape,a=n.length,r=dt(a),s=yn("coords",a),i=["x","y","z","w","u","v"].slice(0,a);this.variableNames=e.map((m,f)=>`T${f}`);let o=new Array(e.length-1);o[0]=e[0][t];for(let m=1;m<o.length;m++)o[m]=o[m-1]+e[m][t];let l=i[t],c=i.slice(-2),u=i.join(),p=`if (${l} < ${o[0]}) {
return getChannel(
getT0(${u}), vec2(${c.join()}));
}`;for(let m=1;m<o.length;m++){let f=o[m-1];p+=`
if (${l} < ${o[m]} && ${l} >= ${o[m-1]}) {
return getChannel(
getT${m}(${Zm(i,l,f)}),
vec2(${Zm(c,l,f)}));
}`}let d=o.length,h=o[o.length-1];p+=`
return getChannel(
getT${d}(${Zm(i,l,h)}),
vec2(${Zm(c,l,h)}));`,this.userCode=`
float getValue(${i.map(m=>"int "+m)}) {
${p}
}
void main() {
${r} coords = getOutputCoords();
vec4 result = vec4(getValue(${s}), 0., 0., 0.);
${s[a-1]} = ${s[a-1]} + 1;
if (${s[a-1]} < ${n[a-1]}) {
result.g = getValue(${s});
}
${s[a-2]} = ${s[a-2]} + 1;
if (${s[a-2]} < ${n[a-2]}) {
result.a = getValue(${s});
}
${s[a-1]} = ${s[a-1]} - 1;
if (${s[a-2]} < ${n[a-2]} &&
${s[a-1]} < ${n[a-1]}) {
result.b = getValue(${s});
}
setOutput(result);
}
`}};function Zm(e,t,n){let a=e.indexOf(t);return e.map((r,s)=>s===a?`${r} - ${n}`:r).join()}function ef(e){let{inputs:t,backend:n}=e,{input:a}=t,r=n.texData.get(a.dataId);return Gn({inputs:{x:r.complexTensorInfos.imag},backend:n})}var XY={kernelName:Gd,backendName:"webgl",kernelFunc:ef};function ku(e,t,n){let a=e[0].dtype;if(a==="complex64"){let c=e.map(m=>kp({inputs:{input:m},backend:n})),u=e.map(m=>ef({inputs:{input:m},backend:n})),p=ku(c,t,n),d=ku(u,t,n),h=ys({inputs:{real:p,imag:d},backend:n});return c.forEach(m=>n.disposeIntermediateTensorInfo(m)),u.forEach(m=>n.disposeIntermediateTensorInfo(m)),n.disposeIntermediateTensorInfo(p),n.disposeIntermediateTensorInfo(d),h}if(a==="string"){let{tensors2D:c,outShape:u}=PN(e,t,n),p=c.map(g=>({vals:n.readSync(g.dataId),shape:g.shape})),d=c[0].shape[0]===1,h=nX(p,u,a,d),m=_.computeOutShape(e.map(g=>g.shape),t),f=n.makeTensorInfo(m,a,h);return c.forEach(g=>n.disposeIntermediateTensorInfo(g)),f}if(e.length>Z().getNumber("WEBGL_MAX_TEXTURES_IN_SHADER")){let c=Math.floor(e.length/2),u=ku(e.slice(0,c),t,n),p=ku(e.slice(c),t,n),d=ku([u,p],t,n);return n.disposeIntermediateTensorInfo(u),n.disposeIntermediateTensorInfo(p),d}if(Z().getBool("WEBGL_PACK_ARRAY_OPERATIONS")&&e[0].shape.length>1){let c=new qY(e.map(u=>u.shape),t);return n.runWebGLProgram(c,e,a)}let{tensors2D:r,outShape:s}=PN(e,t,n),i=new jY(r.map(c=>c.shape)),o=n.runWebGLProgram(i,r,a);r.forEach(c=>n.disposeIntermediateTensorInfo(c));let l=ge({inputs:{x:o},attrs:{shape:s},backend:n});return n.disposeIntermediateTensorInfo(o),l}function PN(e,t,n){let a=_.computeOutShape(e.map(r=>r.shape),t);return{tensors2D:e.map(r=>ge({inputs:{x:r},attrs:{shape:[-1,w.sizeFromShape(r.shape.slice(t))]},backend:n})),outShape:a}}function ON(e){let{inputs:t,backend:n,attrs:a}=e,{axis:r}=a,s=w.parseAxisParam(r,t[0].shape)[0],i=_.computeOutShape(t.map(c=>c.shape),s);if(w.sizeFromShape(i)===0)return n.makeTensorInfo(i,t[0].dtype,[]);let o=t.filter(c=>w.sizeFromShape(c.shape)>0);if(o.length===1)return Gn({inputs:{x:o[0]},backend:n});let l=o.map(c=>c.shape);return _.assertParamsConsistent(l,s),ku(o,s,n)}var KY={kernelName:Go,backendName:"webgl",kernelFunc:ON},LN=class{constructor(e,t=!1,n=null,a=!1,r=!1){this.variableNames=["x","W"],this.outputShape=e.outShape;let s=e.padInfo.top,i=e.padInfo.left,o=e.strideHeight,l=e.strideWidth,c=e.dilationHeight,u=e.dilationWidth,p=e.filterHeight,d=e.filterWidth,h=Math.floor(e.inChannels/4)*4,m=e.inChannels%4,f=e.dataFormat==="channelsLast",g=f?1:2,y=f?2:3,b=f?3:1,x="",v="";n&&(a?x=`float activation(float a) {
float b = getPreluActivationWeightsAtOutCoords();
${n}
}`:r?x=`float activation(float a) {
float b = getLeakyreluAlphaAtOutCoords();
${n}
}`:x=`
float activation(float x) {
${n}
}
`,v="result = activation(result);");let T=t?"result += getBiasAtOutCoords();":"";t&&this.variableNames.push("bias"),a&&this.variableNames.push("preluActivationWeights"),r&&this.variableNames.push("leakyreluAlpha"),this.userCode=`
${x}
const ivec2 strides = ivec2(${o}, ${l});
const ivec2 pads = ivec2(${s}, ${i});
void main() {
ivec4 coords = getOutputCoords();
int batch = coords[0];
int d2 = coords[${b}];
ivec2 xRCCorner =
ivec2(coords[${g}], coords[${y}]) * 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 * ${c};
if (xR < 0 || xR >= ${e.inHeight}) {
continue;
}
for (int wC = 0; wC < ${d}; wC++) {
int xC = xCCorner + wC * ${u};
if (xC < 0 || xC >= ${e.inWidth}) {
continue;
}
for (int d1 = 0; d1 < ${h}; d1 += 4) {
vec4 wValues = vec4(
getW(wR, wC, d1, d2),
getW(wR, wC, d1 + 1, d2),
getW(wR, wC, d1 + 2, d2),
getW(wR, wC, d1 + 3, d2)
);
if (${f}) {
vec4 xValues = vec4(
getX(batch, xR, xC, d1),
getX(batch, xR, xC, d1 + 1),
getX(batch, xR, xC, d1 + 2),
getX(batch, xR, xC, d1 + 3)
);
dotProd += dot(xValues, wValues);
} else {
vec4 xValues = vec4(
getX(batch, d1, xR, xC),
getX(batch, d1 + 1, xR, xC),
getX(batch, d1 + 2, xR, xC),
getX(batch, d1 + 3, xR, xC)
);
dotProd += dot(xValues, wValues);
}
}
if (${m===1}) {
if (${f}) {
dotProd +=
getX(batch, xR, xC, ${h}) *
getW(wR, wC, ${h}, d2);
} else {
dotProd +=
getX(batch, ${h}, xR, xC) *
getW(wR, wC, ${h}, d2);
}
} else if (${m===2}) {
vec2 wValues = vec2(
getW(wR, wC, ${h}, d2),
getW(wR, wC, ${h} + 1, d2)
);
if (${f}) {
vec2 xValues = vec2(
getX(batch, xR, xC, ${h}),
getX(batch, xR, xC, ${h} + 1)
);
dotProd += dot(xValues, wValues);
} else {
vec2 xValues = vec2(
getX(batch, ${h}, xR, xC),
getX(batch, ${h} + 1, xR, xC)
);
dotProd += dot(xValues, wValues);
}
} else if (${m===3}) {
vec3 wValues = vec3(
getW(wR, wC, ${h}, d2),
getW(wR, wC, ${h} + 1, d2),
getW(wR, wC, ${h} + 2, d2)
);
if (${f}) {
vec3 xValues = vec3(
getX(batch, xR, xC, ${h}),
getX(batch, xR, xC, ${h} + 1),
getX(batch, xR, xC, ${h} + 2)
);
dotProd += dot(xValues, wValues);
} else {
vec3 xValues = vec3(
getX(batch, ${h}, xR, xC),
getX(batch, ${h} + 1, xR, xC),
getX(batch, ${h} + 2, xR, xC)
);
dotProd += dot(xValues, wValues);
}
}
}
}
float result = dotProd;
${T}
${v}
setOutput(result);
}
`}},YY=class{constructor(e){this.variableNames=["x","W"],this.outputShape=e.outShape;let t=e.padInfo.front,n=e.padInfo.top,a=e.padInfo.left,r=e.strideDepth,s=e.strideHeight,i=e.strideWidth,o=e.dilationDepth,l=e.dilationHeight,c=e.dilationWidth,u=e.filterDepth,p=e.filterHeight,d=e.filterWidth,h=Math.floor(e.inChannels/4)*4,m=e.inChannels%4;this.userCode=`
const ivec3 strides = ivec3(${r}, ${s}, ${i});
const ivec3 pads = ivec3(${t}, ${n}, ${a});
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 < ${u}; wF++) {
int xF = xFCorner + wF * ${o};
if (xF < 0 || xF >= ${e.inDepth}) {
continue;
}
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 < ${h}; d1 += 4) {
vec4 xValues = vec4(
getX(batch, xF, xR, xC, d1),
getX(batch, xF, xR, xC, d1 + 1),
getX(batch, xF, xR, xC, d1 + 2),
getX(batch, xF, xR, xC, d1 + 3)
);
vec4 wValues = vec4(
getW(wF, wR, wC, d1, d2),
getW(wF, wR, wC, d1 + 1, d2),
getW(wF, wR, wC, d1 + 2, d2),
getW(wF, wR, wC, d1 + 3, d2)
);
dotProd += dot(xValues, wValues);
}
if (${m===1}) {
dotProd +=
getX(batch, xF, xR, xC, ${h}) *
getW(wF, wR, wC, ${h}, d2);
} else if (${m===2}) {
vec2 xValues = vec2(
getX(batch, xF, xR, xC, ${h}),
getX(batch, xF, xR, xC, ${h} + 1)
);
vec2 wValues = vec2(
getW(wF, wR, wC, ${h}, d2),
getW(wF, wR, wC, ${h} + 1, d2)
);
dotProd += dot(xValues, wValues);
} else if (${m===3}) {
vec3 xValues = vec3(
getX(batch, xF, xR, xC, ${h}),
getX(batch, xF, xR, xC, ${h} + 1),
getX(batch, xF, xR, xC, ${h} + 2)
);
vec3 wValues = vec3(
getW(wF, wR, wC, ${h}, d2),
getW(wF, wR, wC, ${h} + 1, d2),
getW(wF, wR, wC, ${h} + 2, d2)
);
dotProd += dot(xValues, wValues);
}
}
}
}
setOutput(dotProd);
}
`}},JY=class{constructor(e,t,n){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=e;let{filterWidth:a,inChannels:r,strideWidth:s,strideHeight:i,padInfo:o,outWidth:l,dilationWidth:c,dilationHeight:u,dataFormat:p}=n,{left:d,top:h}=o,m=r*a,f=gn(),g=p==="channelsLast",y=g?0:1,b=g?1:2,x="";for(let v=0;v<=1;v++)for(let T=0;T<=1;T++)x+=`
blockIndex = rc.y + ${T};
pos = rc.x + ${v};
if(blockIndex < ${e[1]} && pos < ${e[0]}) {
offsetY = int(blockIndex / (${l})) * ${i} - ${h};
d0 = offsetY + ${u} * (pos / ${m});
if(d0 < ${t[y]} && d0 >= 0) {
offsetX = int(mod(float(blockIndex), ${l}.) * ${s}. - ${d}.);
d1 = offsetX + ${c} * (int(mod(float(pos), ${m}.) / ${r}.));
if(d1 < ${t[b]} && d1 >= 0) {
ch = int(mod(float(pos), ${r}.));
if (${g}) {
innerDims = vec2(d1, ch);
result[${v*2+T}] = getChannel(
getA(d0, int(innerDims.x),
int(innerDims.y)), innerDims);
} else {
innerDims = vec2(d0, d1);
result[${v*2+T}] = getChannel(
getA(ch, int(innerDims.x),
int(innerDims.y)), innerDims);
}
}
}
}
`;this.userCode=`
void main() {
ivec2 rc = getOutputCoords();
vec4 result = vec4(0);
int blockIndex, pos, offsetY, d0, offsetX, d1, ch;
vec2 innerDims;
${x}
${f.output} = result;
}
`}};function zN({x:e,filter:t,convInfo:n,backend:a,bias:r=null,preluActivationWeights:s=null,leakyreluAlpha:i=0,activation:o=null}){let l=e.shape,c=a.texData.get(e.dataId),u=n.inChannels,p=l[0]*l[1]*l[2],d=n.outChannels,h=n.dataFormat==="channelsLast",m=!1,f=!1,g,y=[],b=(p===1||d===1)&&u>CN,x=l[2]%2!=0&&!!c.isPacked;if(b||!Z().getBool("WEBGL_LAZILY_UNPACK")||!Z().getBool("WEBGL_PACK_BINARY_OPERATIONS")||!x){let v=h?l[0]*l[1]*l[2]:l[0]*l[2]*l[3],T=ge({inputs:{x:e},backend:a,attrs:{shape:[1,v,n.inChannels]}}),k=ge({inputs:{x:t},backend:a,attrs:{shape:[1,n.inChannels,n.outChannels]}}),S=Jm({a:T,b:k,transposeA:m,transposeB:f,backend:a,bias:r,activation:o,preluActivationWeights:s,leakyreluAlpha:i});g=ge({inputs:{x:S},backend:a,attrs:{shape:n.outShape}}),y.push(T),y.push(k),y.push(S)}else{let v=h?l[0]*l[1]*(l[2]+1):l[0]*l[2]*(l[3]+1),T={dataId:e.dataId,shape:[1,v,n.inChannels],dtype:e.dtype},k=c.shape;c.shape=c.shape.slice(),c.shape[c.shape.length-2]++,w.assert(jm(c.shape,T.shape),()=>`packed reshape ${c.shape} to ${T.shape} isn't free`);let S=ge({inputs:{x:t},backend:a,attrs:{shape:[1,n.inChannels,n.outChannels]}});y.push(S);let F=Jm({a:T,b:S,backend:a,transposeA:m,transposeB:f,bias:r,activation:o,preluActivationWeights:s,leakyreluAlpha:i}),A=a.texData.get(F.dataId);w.assert(A.isPacked,()=>"batchMatMul result is expected to be packed"),c.shape=k,A.shape=n.outShape,g=Gn({inputs:{x:F},backend:a}),g.shape=n.outShape,y.push(F)}for(let v of y)a.disposeIntermediateTensorInfo(v);return g}function WN({x:e,filter:t,convInfo:n,backend:a,bias:r=null,preluActivationWeights:s=null,leakyreluAlpha:i=0,activation:o=null}){let{filterWidth:l,filterHeight:c,inChannels:u,outWidth:p,outHeight:d,dataFormat:h}=n,m=h==="channelsLast",f=l*c*u,g=d*p,y=[f,g],b=!0,x=!1,v=[],T=ge({inputs:{x:e},backend:a,attrs:{shape:e.shape.slice(1)}}),k=ge({inputs:{x:t},backend:a,attrs:{shape:[1,f,w.sizeFromShape(t.shape)/f]}});v.push(T),v.push(k);let S=new JY(y,T.shape,n),F=a.runWebGLProgram(S,[T],"float32"),A=ge({inputs:{x:F},backend:a,attrs:{shape:[1,y[0],y[1]]}});v.push(F),v.push(A);let R=r!=null,P=s!=null,z=o==="leakyrelu",V=o?Km(o,!0):null,G=new wN(A.shape,k.shape,[1,g,n.outChannels],b,x,R,V,P,z),H=[A,k];if(r&&H.push(r),P&&H.push(s),z){let Q=a.makeTensorInfo([],"float32",w.createScalarValue(i,"float32"));H.push(Q),v.push(Q)}let X=a.runWebGLProgram(G,H,"float32"),j=m?[1,d,p,n.outChannels]:[1,n.outChannels,d,p],te=ge({inputs:{x:X},backend:a,attrs:{shape:j}});v.push(X);for(let Q of v)a.disposeIntermediateTensorInfo(Q);return te}function QY(e){let{inputs:t,backend:n,attrs:a}=e,{x:r,filter:s}=t,{strides:i,pad:o,dataFormat:l,dilations:c,dimRoundingMode:u}=a,p=_.convertConv2DDataFormat(l),d=_.computeConv2DInfo(r.shape,s.shape,i,c,o,u,!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=zN({x:r,filter:s,convInfo:d,backend:n});else if(Z().getBool("WEBGL_CONV_IM2COL")&&r.shape[0]===1)h=WN({x:r,filter:s,convInfo:d,backend:n});else{let f=new LN(d);h=n.runWebGLProgram(f,[r,s],"float32")}let m=ge({inputs:{x:h},backend:n,attrs:{shape:d.outShape}});return n.disposeIntermediateTensorInfo(h),m}var ZY={kernelName:Os,backendName:"webgl",kernelFunc:QY},e7=class{constructor(e){this.variableNames=["x","dy"],this.outputShape=e.filterShape;let t=e.strideHeight,n=e.strideWidth,a=e.padInfo.top,r=e.padInfo.left,s=e.dataFormat==="channelsLast";this.userCode=`
void main() {
ivec4 coords = getOutputCoords();
int wR = coords.x;
int wC = coords.y;
int d1 = coords.z;
int d2 = coords.w;
// Convolve x(?, ?, d1) with dy(:, :, d2) to get dw(wR, wC, d1, d2).
// ? = to be determined. : = across all values in that axis.
float dotProd = 0.0;
for (int b = 0; b < ${e.batchSize}; b++) {
for (int yR = 0; yR < ${e.outHeight}; yR++) {
int xR = wR + yR * ${t} - ${a};
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 >= ${e.inWidth}) {
continue;
}
if (${s}) {
float dyValue = getDy(b, yR, yC, d2);
float xValue = getX(b, xR, xC, d1);
dotProd += (xValue * dyValue);
} else {
float dyValue = getDy(b, d2, yR, yC);
float xValue = getX(b, d1, xR, xC);
dotProd += (xValue * dyValue);
}
}
}
}
setOutput(dotProd);
}
`}},t7=class{constructor(e){this.variableNames=["dy","W"],this.outputShape=e.inShape;let t=e.filterHeight,n=e.filterWidth,a=e.strideHeight,r=e.strideWidth,s=e.dataFormat==="channelsLast",i=t-1-e.padInfo.top,o=n-1-e.padInfo.left,l=s?1:2,c=s?2:3,u=s?3:1;this.userCode=`
const ivec2 pads = ivec2(${i}, ${o});
void main() {
ivec4 coords = getOutputCoords();
int batch = coords[0];
int d1 = coords[${u}];
ivec2 dyCorner = ivec2(coords[${l}], coords[${c}]) - pads;
int dyRCorner = dyCorner.x;
int dyCCorner = dyCorner.y;
// Convolve dy(?, ?, d2) with w(:, :, d1, d2) to compute dx(xR, xC, d1).
// ? = to be determined. : = across all values in that axis.
float dotProd = 0.0;
for (int wR = 0; wR < ${t}; 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 = ${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 < ${e.outChannels}; d2++) {
if (${s}) {
float xValue = getDy(batch, idyR, idyC, d2);
float wValue = getW(wRPerm, wCPerm, d1, d2);
dotProd += xValue * wValue;
} else {
float xValue = getDy(batch, d2, idyR, idyC);
float wValue = getW(wRPerm, wCPerm, d1, d2);
dotProd += xValue * wValue;
}
}
}
}
setOutput(dotProd);
}
`}},n7=class{constructor(e){this.variableNames=["x","dy"],this.outputShape=e.filterShape;let t=e.strideDepth,n=e.strideHeight,a=e.strideWidth,r=e.padInfo.front,s=e.padInfo.top,i=e.padInfo.left;this.userCode=`
void main() {
ivec5 coords = getOutputCoords();
int wF = coords.x;
int wR = coords.y;
int wC = coords.z;
int d1 = coords.w;
int d2 = coords.u;
float dotProd = 0.0;
for (int b = 0; b < ${e.batchSize}; b++) {
for (int yF = 0; yF < ${e.outDepth}; yF++) {
int xF = wF + yF * ${t} - ${r};
if (xF < 0 || xF >= ${e.inDepth}) {
continue;
}
for (int yR = 0; yR < ${e.outHeight}; yR++) {
int xR = wR + yR * ${n} - ${s};
if (xR < 0 || xR >= ${e.inHeight}) {
continue;
}
for (int yC = 0; yC < ${e.outWidth}; yC++) {
int xC = wC + yC * ${a} - ${i};
if (xC < 0 || xC >= ${e.inWidth}) {
continue;
}
float dyValue = getDy(b, yF, yR, yC, d2);
float xValue = getX(b, xF, xR, xC, d1);
dotProd += (xValue * dyValue);
}
}
}
}
setOutput(dotProd);
}
`}},a7=class{constructor(e){this.variableNames=["dy","W"],this.outputShape=e.inShape;let t=e.filterDepth,n=e.filterHeight,a=e.filterWidth,r=e.strideDepth,s=e.strideHeight,i=e.strideWidth,o=t-1-e.padInfo.front,l=n-1-e.padInfo.top,c=a-1-e.padInfo.left;this.userCode=`
const ivec3 pads = ivec3(${o}, ${l}, ${c});
void main() {
ivec5 coords = getOutputCoords();
int batch = coords.x;
int d1 = coords.u;
ivec3 dyCorner = ivec3(coords.y, coords.z, coords.w) - pads;
int dyFCorner = dyCorner.x;
int dyRCorner = dyCorner.y;
int dyCCorner = dyCorner.z;
float dotProd = 0.0;
for (int wF = 0; wF < ${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) / ${s}.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 < ${a}; wC++) {
float dyC = float(dyCCorner + wC) / ${i}.0;
if (dyC < 0.0 || dyC >= ${e.outWidth}.0 ||
fract(dyC) > 0.0) {
continue;
}
int idyC = int(dyC);
int wCPerm = ${a} - 1 - wC;
for (int d2 = 0; d2 < ${e.outChannels}; d2++) {
float xValue = getDy(batch, idyF, idyR, idyC, d2);
float wValue = getW(wFPerm, wRPerm, wCPerm, d1, d2);
dotProd += xValue * wValue;
}
}
}
}
setOutput(dotProd);
}
`}};function r7(e){let{inputs:t,backend:n,attrs:a}=e,{x:r,dy:s}=t,{strides:i,pad:o,dataFormat:l,dimRoundingMode:c,filterShape:u}=a,p=_.convertConv2DDataFormat(l),d=_.computeConv2DInfo(r.shape,u,i,1,o,c,!1,p),h=new e7(d);return n.runWebGLProgram(h,[r,s],"float32")}var s7={kernelName:$d,backendName:"webgl",kernelFunc:r7};function i7(e){let{inputs:t,backend:n,attrs:a}=e,{dy:r,filter:s}=t,{inputShape:i,strides:o,pad:l,dataFormat:c,dimRoundingMode:u}=a,p=_.convertConv2DDataFormat(c),d=_.computeConv2DInfo(i,s.shape,o,1,l,u,!1,p),h=new t7(d);return n.runWebGLProgram(h,[r,s],"float32")}var o7={kernelName:Ls,backendName:"webgl",kernelFunc:i7};function l7(e){let{inputs:t,backend:n,attrs:a}=e,{x:r,filter:s}=t,{strides:i,pad:o,dilations:l}=a,c=_.computeConv3DInfo(r.shape,s.shape,i,l,o),u=new YY(c);return n.runWebGLProgram(u,[r,s],"float32")}var u7={kernelName:ic,backendName:"webgl",kernelFunc:l7};function c7(e){let{inputs:t,backend:n,attrs:a}=e,{x:r,dy:s}=t,{strides:i,pad:o,filterShape:l}=a,c=_.computeConv3DInfo(r.shape,l,i,1,o),u=new n7(c);return n.runWebGLProgram(u,[r,s],"float32")}var p7={kernelName:Dd,backendName:"webgl",kernelFunc:c7};function d7(e){let{inputs:t,backend:n,attrs:a}=e,{dy:r,filter:s}=t,{pad:i,strides:o,inputShape:l}=a,c=_.computeConv3DInfo(l,s.shape,o,1,i),u=new a7(c);return n.runWebGLProgram(u,[r,s],"float32")}var h7={kernelName:Rd,backendName:"webgl",kernelFunc:d7},m7=vN+`
return cos(x);
`,f7=Xe({opSnippet:m7}),g7={kernelName:zs,backendName:"webgl",kernelFunc:f7},y7=`
float e2x = exp(-x);
return (e2x + 1.0 / e2x) / 2.0;
`,b7=Xe({opSnippet:y7}),x7={kernelName:Ho,backendName:"webgl",kernelFunc:b7},v7=class{constructor(e,t,n,a,r){this.variableNames=["Image","Boxes","BoxInd"],this.outputShape=[];let[s,i,o,l]=e,[c]=t,[u,p]=n;this.outputShape=[c,u,p,l];let d=a==="bilinear"?1:0,[h,m]=[`${i-1}.0`,`${o-1}.0`],[f,g,y]=u>1?[`${(i-1)/(u-1)}`,"(y2-y1) * height_ratio",`y1*${h} + float(y)*(height_scale)`]:["0.0","0.0",`0.5 * (y1+y2) * ${h}`],[b,x,v]=p>1?[`${(o-1)/(p-1)}`,"(x2-x1) * width_ratio",`x1*${m} + float(x)*(width_scale)`]:["0.0","0.0",`0.5 * (x1+x2) * ${m}`];this.userCode=`
const float height_ratio = float(${f});
const float width_ratio = float(${b});
void main() {
ivec4 coords = getOutputCoords();
int b = coords[0];
int y = coords[1];
int x = coords[2];
int d = coords[3];
// get box vals
float y1 = getBoxes(b,0);
float x1 = getBoxes(b,1);
float y2 = getBoxes(b,2);
float x2 = getBoxes(b,3);
// get image in batch index
int bInd = round(getBoxInd(b));
if(bInd < 0 || bInd >= ${s}) {
return;
}
float height_scale = ${g};
float width_scale = ${x};
float in_y = ${y};
if( in_y < 0.0 || in_y > ${h} ) {
setOutput(float(${r}));
return;
}
float in_x = ${v};
if( in_x < 0.0 || in_x > ${m} ) {
setOutput(float(${r}));
return;
}
vec2 sourceFracIndexCR = vec2(in_x,in_y);
if(${d} == 1) {
// Compute the four integer indices.
ivec2 sourceFloorCR = ivec2(sourceFracIndexCR);
ivec2 sourceCeilCR = ivec2(ceil(sourceFracIndexCR));
float topLeft = getImage(b, sourceFloorCR.y, sourceFloorCR.x, d);
float bottomLeft = getImage(b, sourceCeilCR.y, sourceFloorCR.x, d);
float topRight = getImage(b, sourceFloorCR.y, sourceCeilCR.x, d);
float bottomRight = getImage(b, sourceCeilCR.y, sourceCeilCR.x, d);
vec2 fracCR = sourceFracIndexCR - vec2(sourceFloorCR);
float top = topLeft + (topRight - topLeft) * fracCR.x;
float bottom = bottomLeft + (bottomRight - bottomLeft) * fracCR.x;
float newValue = top + (bottom - top) * fracCR.y;
setOutput(newValue);
} else {
// Compute the coordinators of nearest neighbor point.
ivec2 sourceNearestCR = ivec2(floor(
sourceFracIndexCR + vec2(0.5,0.5)));
float newValue = getImage(b, sourceNearestCR.y, sourceNearestCR.x, d);
setOutput(newValue);
}
}
`}},w7=e=>{let{inputs:t,backend:n,attrs:a}=e,{image:r,boxes:s,boxInd:i}=t,{cropSize:o,method:l,extrapolationValue:c}=a,u=new v7(r.shape,s.shape,o,l,c);return n.runWebGLProgram(u,[r,s,i],"float32")},k7={kernelName:jo,backendName:"webgl",kernelFunc:w7},UN=class{constructor(e,t,n){this.variableNames=["x"],this.outputShape=e;let a=e.length,r=t?"0.0":`getX(${BN(a,"coords")})`,s=e[e.length-1],i="",o="";t?(i=n?`end != ${s-1}`:"end != 0",o=n?"end + 1":"end - 1"):(i=n?`end + pow2 < ${s}`:"end >= pow2",o=n?"end + pow2":"end - pow2"),this.userCode=`
uniform float index;
void main() {
${dt(a)} coords = getOutputCoords();
int end = ${VN(a,"coords")};
float val = ${r};
int pow2 = int(pow(2.0, index));
if (${i}) {
int idx = ${o};
${VN(a,"coords")} = idx;
val += getX(${BN(a,"coords")});
}
setOutput(val);
}
`}getCustomSetupFunc(e){return(t,n)=>{this.index==null&&(this.index=t.getUniformLocation(n,"index")),t.gl.uniform1f(this.index,e)}}};function BN(e,t){if(e===1)return`${t}`;if(e===2)return`${t}.x, ${t}.y`;if(e===3)return`${t}.x, ${t}.y, ${t}.z`;if(e===4)return`${t}.x, ${t}.y, ${t}.z, ${t}.w`;throw Error(`Cumulative sum for rank ${e} is not yet supported`)}function VN(e,t){if(e===1)return`${t}`;if(e===2)return`${t}.y`;if(e===3)return`${t}.z`;if(e===4)return`${t}.w`;throw Error(`Cumulative sum for rank ${e} is not yet supported`)}function I7(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{axis:s,exclusive:i,reverse:o}=a,l=r.shape.length,c=_.getAxesPermutation([s],l),u=r;c!=null&&(u=$n({inputs:{x:r},backend:n,attrs:{perm:c}}));let p=_.getInnerMostAxes(1,l)[0];if(p!==l-1)throw new Error(`WebGL cumsum shader expects an inner-most axis=${r.shape.length-1} but got axis=${s}`);let d=u.shape[p],h=Gn({inputs:{x:u},backend:n});for(let m=0;m<=Math.ceil(Math.log2(d))-1;m++){let f=new UN(u.shape,!1,o),g=f.getCustomSetupFunc(m),y=h;h=n.runWebGLProgram(f,[h],h.dtype,g),n.disposeIntermediateTensorInfo(y)}if(i){let m=new UN(u.shape,i,o),f=h;h=n.runWebGLProgram(m,[h],h.dtype),n.disposeIntermediateTensorInfo(f)}if(c!=null){let m=_.getUndoAxesPermutation(c),f=$n({inputs:{x:h},backend:n,attrs:{perm:m}});return n.disposeIntermediateTensorInfo(h),n.disposeIntermediateTensorInfo(u),f}return h}var T7={kernelName:Ws,backendName:"webgl",kernelFunc:I7};function N7(e){let{inputs:t,backend:n,attrs:a}=e,{x:r,weights:s}=t,{size:i,binaryOutput:o}=a;if(r.shape.length===1){let l=n.readSync(r.dataId),c=n.readSync(s.dataId),u=oN(l,c,s.dtype,s.shape,i);return n.makeTensorInfo([i],s.dtype,u)}else if(r.shape.length===2){let l=n.bufferSync(r),c=n.bufferSync(s),u=eX(l,c,i,o);return n.makeTensorInfo(u.shape,s.dtype,u.values)}throw new Error(`Error in denseBincount: input must be at most rank 2, but got rank${r.shape.length}.`)}var S7={kernelName:Md,backendName:"webgl",kernelFunc:N7},C7=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 _7(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{blockSize:s,dataFormat:i}=a;w.assert(s>1,()=>`blockSize should be > 1 for depthToSpace, but was: ${s}`);let o=r.shape[0],l=i==="NHWC"?r.shape[1]:r.shape[2],c=i==="NHWC"?r.shape[2]:r.shape[3],u=i==="NHWC"?r.shape[3]:r.shape[1],p=l*s,d=c*s,h=u/(s*s),m=i==="NHWC"?[o,p,d,h]:[o,h,p,d],f=new C7(m,s,i);return n.runWebGLProgram(f,[r],r.dtype)}var E7={kernelName:qo,backendName:"webgl",kernelFunc:_7},GN=class{constructor(e,t=!1,n=null,a=!1,r=!1){this.variableNames=["x","W"],this.outputShape=e.outShape;let s=e.inHeight,i=e.inWidth,o=e.padInfo.top,l=e.padInfo.left,c=e.strideHeight,u=e.strideWidth,p=e.dilationHeight,d=e.dilationWidth,h=e.filterHeight,m=e.filterWidth,f=e.outChannels/e.inChannels,g="",y="";n&&(a?g=`float activation(float a) {
float b = getPreluActivationWeightsAtOutCoords();
${n}
}`:r?g=`float activation(float a) {
float b = getLeakyreluAlphaAtOutCoords();
${n}
}`:g=`
float activation(float x) {
${n}
}
`,y="result = activation(result);");let b=t?"result += getBiasAtOutCoords();":"";t&&this.variableNames.push("bias"),a&&this.variableNames.push("preluActivationWeights"),r&&this.variableNames.push("leakyreluAlpha"),this.userCode=`
${g}
const ivec2 strides = ivec2(${c}, ${u});
const ivec2 pads = ivec2(${o}, ${l});
void main() {
ivec4 coords = getOutputCoords();
int batch = coords.x;
ivec2 xRCCorner = coords.yz * strides - pads;
int d2 = coords.w;
int d1 = d2 / ${f};
int q = d2 - d1 * ${f};
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 < ${h}; wR++) {
int xR = xRCorner + wR * ${p};
if (xR < 0 || xR >= ${s}) {
continue;
}
for (int wC = 0; wC < ${m}; wC++) {
int xC = xCCorner + wC * ${d};
if (xC < 0 || xC >= ${i}) {
continue;
}
float xVal = getX(batch, xR, xC, d1);
float wVal = getW(wR, wC, d1, q);
dotProd += xVal * wVal;
}
}
float result = dotProd;
${b}
${y}
setOutput(result);
}
`}},HN=class{constructor(e,t=!1,n=null,a=!1,r=!1){this.variableNames=["x","W"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=e.outShape;let s=e.inHeight,i=e.inWidth,o=e.padInfo.top,l=e.padInfo.left,c=e.strideHeight,u=e.strideWidth,p=e.dilationHeight,d=e.dilationWidth,h=e.filterHeight,m=e.filterWidth,f=m,g="int xR; int xC; int xCOffset;";for(let v=0;v<h;v++)for(let T=0;T<m;T++)g+=`
vec4 xTexelR${v}C${T*2} = vec4(0.);
vec4 wR${v}C${T} = vec4(0.);
vec4 xR${v}C${T} = vec4(0.);`;for(let v=0;v<h;v++)for(let T=0;T<f;T++){let k=T*2;if(g+=`
xR = xRCorner + ${v*p};
xC = xCCorner + ${k*d};
`,u===1){if(k<m&&(l%2==1?g+=`
xCOffset = xC + 1;
if(xR >= 0 && xR < ${s} && xCOffset >= 0 && xCOffset < ${i}) {
xTexelR${v}C${k} = getX(batch, xR, xCOffset, d1);
// Need to manually clear unused channels in case
// we're reading from recycled texture.
if(xCOffset + 1 >= ${i}) {
xTexelR${v}C${k}.zw = vec2(0.);
}
} else {
xTexelR${v}C${k} = vec4(0.);
}
xCOffset = xC + 1 - 2;
if(xR >= 0 && xR < ${s} && xCOffset >= 0 && xCOffset < ${i}) {
vec4 previous = getX(batch, xR, xCOffset, d1);
// Need to manually clear unused channels in case
// we're reading from recycled texture.
if(xCOffset + 1 >= ${i}) {
previous.zw = vec2(0.);
}
xR${v}C${k} = vec4(previous.zw, xTexelR${v}C${k}.xy);
} else {
xR${v}C${k} = vec4(0, 0, xTexelR${v}C${k}.xy);
}
`:g+=`
if(xR >= 0 && xR < ${s} && xC >= 0 && xC < ${i}) {
xTexelR${v}C${k} = getX(batch, xR, xC, d1);
} else {
xTexelR${v}C${k} = vec4(0.);
}
xR${v}C${k} = xTexelR${v}C${k};
`,k+1<m)){let S=l%2==0?w.nearestLargerEven(d):d;d%2==0&&l%2==1||d%2!=0&&l%2!=1?(g+=`
xCOffset = xC + ${l%2} + ${S};
if(xR >= 0 && xR < ${s} &&
xCOffset >= 0 && xCOffset < ${i}) {
xTexelR${v}C${k+2} = getX(batch, xR, xCOffset, d1);
}
`,d>1&&(g+=`
xCOffset -= 2;
if(xR >= 0 && xR < ${s} &&
xCOffset >= 0 && xCOffset < ${i}) {
xTexelR${v}C${k} = getX(batch, xR, xCOffset, d1);
} else {
xTexelR${v}C${k} = vec4(0.);
}
`),g+=`
xR${v}C${k+1} = vec4(
xTexelR${v}C${k}.zw, xTexelR${v}C${k+2}.xy);
`):g+=`
xCOffset = xC + ${S};
if(xR >= 0 && xR < ${s} &&
xCOffset >= 0 && xCOffset < ${i}) {
xTexelR${v}C${k+2} = getX(batch, xR, xCOffset, d1);
}
xR${v}C${k+1} = xTexelR${v}C${k+2};
`}}else k<m&&(g+=`
if(xR >= 0 && xR < ${s}) {
`,l%2==1?(g+=`
xCOffset = xC + 1 - ${u};
if(xCOffset >= 0 && xCOffset < ${i}) {
xTexelR${v}C${k} = getX(batch, xR, xCOffset, d1);
} else {
xTexelR${v}C${k} = vec4(0.);
}
if(xC + 1 >= 0 && xC + 1 < ${i}) {
xTexelR${v}C${k+2} = getX(batch, xR, xC + 1, d1);
} else {
xTexelR${v}C${k+2} = vec4(0.);
}
xR${v}C${k} = vec4(
xTexelR${v}C${k}.zw, xTexelR${v}C${k+2}.zw);
`,k+1<m&&(g+=`
vec4 final = vec4(0.);
xCOffset = xC + 1 + ${u};
if(xCOffset >= 0 && xCOffset < ${i}) {
final = getX(batch, xR, xCOffset, d1);
}
xR${v}C${k+1} = vec4(xTexelR${v}C${k+2}.xy, final.xy);
`)):(g+=`
if(xC >= 0 && xC < ${i}) {
xTexelR${v}C${k} = getX(batch, xR, xC, d1);
} else {
xTexelR${v}C${k} = vec4(0.);
}
xCOffset = xC + ${u};
if(xCOffset >= 0 && xCOffset < ${i}) {
xTexelR${v}C${k+2} = getX(batch, xR, xCOffset, d1);
} else {
xTexelR${v}C${k+2} = vec4(0.);
}
xR${v}C${k} = vec4(
xTexelR${v}C${k}.xy, xTexelR${v}C${k+2}.xy);
`,k+1<m&&(g+=`
xR${v}C${k+1} = vec4(
xTexelR${v}C${k}.zw, xTexelR${v}C${k+2}.zw);
`)),g+="}");k<m&&(g+=`
vec4 wTexelR${v}C${k} = getW(${v}, ${k}, d1, q);
wR${v}C${k} = vec4(wTexelR${v}C${k}.xz, wTexelR${v}C${k}.xz);
`,k+1<m&&(g+=`
vec4 wTexelR${v}C${k+1} = getW(${v}, ${k+1}, d1, q);
wR${v}C${k+1} =
vec4(wTexelR${v}C${k+1}.xz, wTexelR${v}C${k+1}.xz);`))}for(let v=0;v<h;v++)for(let T=0;T<m;T++)g+=`dotProd += xR${v}C${T} * wR${v}C${T};`;let y="",b="";n&&(a?y=`vec4 activation(vec4 a) {
vec4 b = getPreluActivationWeightsAtOutCoords();
${n}
}`:r?y=`vec4 activation(vec4 a) {
vec4 b = getLeakyreluAlphaAtOutCoords();
${n}
}`:y=`vec4 activation(vec4 x) {
${n}
}`,b="result = activation(result);");let x=t?"result += getBiasAtOutCoords();":"";t&&this.variableNames.push("bias"),a&&this.variableNames.push("preluActivationWeights"),r&&this.variableNames.push("leakyreluAlpha"),this.userCode=`
${y}
const ivec2 strides = ivec2(${c}, ${u});
const ivec2 pads = ivec2(${o}, ${l});
void main() {
ivec4 coords = getOutputCoords();
int batch = coords.x;
ivec2 xRCCorner = coords.yz * strides - pads;
int d2 = coords.w;
int d1 = d2;
int q = 0;
int xRCorner = xRCCorner.x;
int xCCorner = xRCCorner.y;
vec4 dotProd = vec4(0.);
${g}
vec4 result = dotProd;
${x}
${b}
setOutput(result);
}
`}};function F7(e){let{inputs:t,backend:n,attrs:a}=e,{x:r,filter:s}=t,{strides:i,pad:o,dilations:l,dimRoundingMode:c}=a,u=l;u==null&&(u=[1,1]),w.assert(_.eitherStridesOrDilationsAreOne(i,u),()=>`Error in depthwiseConv2d: Either strides or dilations must be 1. Got strides ${i} and dilations '${u}'`);let p=_.computeConv2DInfo(r.shape,s.shape,i,u,o,c,!0),d;return Z().getBool("WEBGL_PACK_DEPTHWISECONV")&&p.strideWidth<=2&&p.outChannels/p.inChannels==1?d=new HN(p):d=new GN(p),n.runWebGLProgram(d,[r,s],"float32")}var A7={kernelName:Bs,backendName:"webgl",kernelFunc:F7},$7=class{constructor(e){this.variableNames=["x","dy"],this.outputShape=e.filterShape;let t=e.strideHeight,n=e.strideWidth,a=e.padInfo.top,r=e.padInfo.left,s=e.outChannels/e.inChannels;this.userCode=`
void main() {
ivec4 coords = getOutputCoords();
int wR = coords.x;
int wC = coords.y;
int d1 = coords.z;
int dm = coords.w;
int d2 = d1 * ${s} + dm;
float dotProd = 0.0;
// TO DO: Vec4 over the batch size
for (int b = 0; b < ${e.batchSize}; b++) {
for (int yR = 0; yR < ${e.outHeight}; yR++) {
int xR = wR + yR * ${t} - ${a};
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 >= ${e.inWidth}) {
continue;
}
float dyValue = getDy(b, yR, yC, d2);
float xValue = getX(b, xR, xC, d1);
dotProd += (xValue * dyValue);
}
}
}
setOutput(dotProd);
}
`}},D7=class{constructor(e){this.variableNames=["dy","W"],this.outputShape=e.inShape;let t=e.filterHeight,n=e.filterWidth,a=e.strideHeight,r=e.strideWidth,s=t-1-e.padInfo.top,i=n-1-e.padInfo.left,o=e.outChannels/e.inChannels;this.userCode=`
const ivec2 pads = ivec2(${s}, ${i});
void main() {
ivec4 coords = getOutputCoords();
int batch = coords[0];
int d1 = coords[3];
ivec2 dyCorner = coords.yz - pads;
int dyRCorner = dyCorner.x;
int dyCCorner = dyCorner.y;
float dotProd = 0.0;
for (int wR = 0; wR < ${t}; wR++) {
float dyR = float(dyRCorner + wR) / ${a}.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 < ${o}; dm++) {
int d2 = d1 * ${o} + dm;
float xValue = getDy(batch, idyR, idyC, d2);
float wValue = getW(wRPerm, wCPerm, d1, dm);
dotProd += xValue * wValue;
}
}
}
setOutput(dotProd);
}
`}};function R7(e){let{inputs:t,backend:n,attrs:a}=e,{x:r,dy:s}=t,{strides:i,dilations:o,pad:l,dimRoundingMode:c,filterShape:u}=a,p=_.computeConv2DInfo(r.shape,u,i,o,l,c,!0),d=new $7(p);return n.runWebGLProgram(d,[r,s],"float32")}var M7={kernelName:Pd,backendName:"webgl",kernelFunc:R7};function P7(e){let{inputs:t,backend:n,attrs:a}=e,{dy:r,filter:s}=t,{strides:i,dilations:o,pad:l,dimRoundingMode:c,inputShape:u}=a,p=_.computeConv2DInfo(u,s.shape,i,o,l,c,!0),d=new D7(p);return n.runWebGLProgram(d,[r,s],"float32")}var O7={kernelName:Od,backendName:"webgl",kernelFunc:P7},L7=class{constructor(e){this.variableNames=["X"],this.outputShape=[e,e],this.userCode=`
void main() {
ivec2 coords = getOutputCoords();
float val = coords[0] == coords[1] ? getX(coords[0]) : 0.0;
setOutput(val);
}
`}};function z7(e){let{inputs:t,backend:n}=e,{x:a}=t,r=[...a.shape,...a.shape],s=w.sizeFromShape(a.shape),i=ge({inputs:{x:a},backend:n,attrs:{shape:[s]}}),o=new L7(s),l=n.runWebGLProgram(o,[i],i.dtype),c=ge({inputs:{x:l},backend:n,attrs:{shape:r}});return n.disposeIntermediateTensorInfo(i),n.disposeIntermediateTensorInfo(l),c}var W7={kernelName:Ld,backendName:"webgl",kernelFunc:z7},B7=class{constructor(e){this.variableNames=["x","W"],this.outputShape=e.outShape;let{inHeight:t,inWidth:n,padInfo:a,strideHeight:r,strideWidth:s,filterHeight:i,filterWidth:o,dilationHeight:l,dilationWidth:c}=e,{top:u,left:p}=a;this.userCode=`
const ivec2 strides = ivec2(${r}, ${s});
const ivec2 pads = ivec2(${u}, ${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 < ${i}; h++) {
int hIn = hBeg + h * ${l};
if (hIn >= 0 && hIn < ${t}) {
for (int w = 0; w < ${o}; w++) {
int wIn = wBeg + w * ${c};
if (wIn >= 0 && wIn < ${n}) {
float xVal = getX(batch, hIn, wIn, d1);
float wVal = getW(h, w, d1);
float val = xVal + wVal;
if (val > curVal) {
curVal = val;
}
}
}
}
}
float result = curVal;
setOutput(result);
}
`}};function V7(e){let{inputs:t,backend:n,attrs:a}=e,{x:r,filter:s}=t,{strides:i,pad:o,dilations:l}=a,c=_.computeDilation2DInfo(r.shape,s.shape,i,o,"NHWC",l),u,p=new B7(c);u=n.runWebGLProgram(p,[r,s],"float32");let d=ge({inputs:{x:u},backend:n,attrs:{shape:c.outShape}});return n.disposeIntermediateTensorInfo(u),d}var U7={kernelName:oc,backendName:"webgl",kernelFunc:V7},G7="return (x >= 0.0) ? x : (exp(x) - 1.0);",H7=`
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;
`,j7=Xe({opSnippet:G7,packedOpSnippet:H7}),q7={kernelName:Xo,backendName:"webgl",kernelFunc:j7},X7="return (b >= 1.0) ? a : a * (b + 1.0);",K7=`
vec4 bGTEZero = vec4(greaterThanEqual(b, vec4(0.)));
return (bGTEZero * a) + ((vec4(1.0) - bGTEZero) * (a * (b + vec4(1.0))));
`,Y7=e=>{let{inputs:t,backend:n}=e,{dy:a,y:r}=t,s=Z().getBool("WEBGL_PACK_BINARY_OPERATIONS")?new xp(K7,a.shape,r.shape):new wu(X7,a.shape,r.shape);return n.runWebGLProgram(s,[a,r],a.dtype)},J7={kernelName:Bd,backendName:"webgl",kernelFunc:Y7},Q7=`
return vec4(equal(a, b));
`,Z7="return float(a == b);",e9=ln({opSnippet:Z7,packedOpSnippet:Q7,dtype:"bool"}),t9={kernelName:Yo,backendName:"webgl",kernelFunc:e9},n9=`
// Error function is calculated approximately with elementary function.
// See "Handbook of Mathematical Functions with Formulas,
// Graphs, and Mathematical Tables", Abramowitz and Stegun.
float p = ${_.ERF_P};
float a1 = ${_.ERF_A1};
float a2 = ${_.ERF_A2};
float a3 = ${_.ERF_A3};
float a4 = ${_.ERF_A4};
float a5 = ${_.ERF_A5};
float sign = sign(x);
x = abs(x);
float t = 1.0 / (1.0 + p * x);
return sign * (1.0 - (((((a5*t + a4)*t) + a3)*t + a2)*t + a1)*t*exp(-x*x));
`,a9=Xe({opSnippet:n9}),r9={kernelName:Ko,backendName:"webgl",kernelFunc:a9},jN="return exp(x);",qN=Xe({opSnippet:jN,packedOpSnippet:jN,cpuKernelImpl:aX}),s9={kernelName:Us,backendName:"webgl",kernelFunc:qN};function tw(e){let{inputs:t,attrs:n,backend:a}=e,{dim:r}=n,{input:s}=t,i=s.shape.length,o=s.shape.slice(),l=r;return r<0&&(w.assert(-(i+1)<=r,()=>`Axis must be in the interval [${-(i+1)}, ${i}]`),l=i+r+1),o.splice(l,0,1),ge({inputs:{x:s},backend:a,attrs:{shape:o}})}var i9={kernelName:Jo,backendName:"webgl",kernelFunc:tw},XN="return exp(x) - 1.0;",o9=Xe({opSnippet:XN,packedOpSnippet:XN,cpuKernelImpl:rX}),l9={kernelName:Qo,backendName:"webgl",kernelFunc:o9},KN=class{constructor(e,t,n){this.variableNames=["real","imag"];let a=t[1];this.outputShape=t;let r=n?`2.0 * ${Math.PI}`:`-2.0 * ${Math.PI}`,s=n?`${a}.0`:"1.0",i;if(e==="real")i="return real * expR - imag * expI;";else if(e==="imag")i="return real * expI + imag * expR;";else throw new Error(`FFT component must be either "real" or "imag", got ${e}.`);this.userCode=`
const float exponentMultiplier = ${r};
float unaryOpComplex(float real, float expR, float imag, float expI) {
${i}
}
float mulMatDFT(int batch, int index) {
float indexRatio = float(index) / float(${a});
float exponentMultiplierTimesIndexRatio =
exponentMultiplier * indexRatio;
float result = 0.0;
for (int i = 0; i < ${a}; i++) {
// x = (-2|2 * PI / N) * index * i;
float x = exponentMultiplierTimesIndexRatio * float(i);
float expR = cos(x);
float expI = sin(x);
float real = getReal(batch, i);
float imag = getImag(batch, i);
result +=
unaryOpComplex(real, expR, imag, expI) / ${s};
}
return result;
}
void main() {
ivec2 coords = getOutputCoords();
setOutput(mulMatDFT(coords[0], coords[1]));
}
`}};function YN(e,t,n){let a=n.texData.get(e.dataId),r=w.sizeFromShape(e.shape),s=e.shape[e.shape.length-1],i=r/s,o=ge({inputs:{x:e},backend:n,attrs:{shape:[i,s]}}),l=o.shape,c=new KN("real",l,t),u=new KN("imag",l,t),p=[{dataId:a.complexTensorInfos.real.dataId,dtype:a.complexTensorInfos.real.dtype,shape:l},{dataId:a.complexTensorInfos.imag.dataId,dtype:a.complexTensorInfos.imag.dtype,shape:l}],d=n.runWebGLProgram(c,p,"float32"),h=n.runWebGLProgram(u,p,"float32"),m=ys({inputs:{real:d,imag:h},backend:n});n.disposeIntermediateTensorInfo(d),n.disposeIntermediateTensorInfo(h);let f=ge({inputs:{x:m},backend:n,attrs:{shape:e.shape}});return n.disposeIntermediateTensorInfo(o),n.disposeIntermediateTensorInfo(m),f}function u9(e){let{inputs:t,backend:n}=e,{input:a}=t;return YN(a,!1,n)}var c9={kernelName:Vd,backendName:"webgl",kernelFunc:u9},p9=class{constructor(e,t){this.outputShape=[],this.variableNames=["x"],this.outputShape=e,this.userCode=`
uniform float value;
void main() {
// Input can be obtained from uniform value.
setOutput(value);
}
`}getCustomSetupFunc(e){return(t,n)=>{this.valueLoc==null&&(this.valueLoc=t.getUniformLocationNoThrow(n,"value")),t.gl.uniform1f(this.valueLoc,e)}}};function nw(e){let{backend:t,attrs:n}=e,{shape:a,value:r}=n,{dtype:s}=n;if(s=s||w.inferDtype(r),s==="string"){let i=w.getArrayFromDType(s,w.sizeFromShape(a));return i.fill(r),t.makeTensorInfo(a,s,i)}else{let i=new p9(a,r),o=i.getCustomSetupFunc(r);return t.runWebGLProgram(i,[],s,o)}}var d9={kernelName:lc,backendName:"webgl",kernelFunc:nw},h9=class{constructor(e){this.variableNames=["Image"],this.outputShape=[];let t=e[2];this.outputShape=e,this.userCode=`
void main() {
ivec4 coords = getOutputCoords();
int x = coords[2];
int coordX = ${t} - x;
float outputValue;
if(coordX >= 0 && coordX < ${t}) {
outputValue = getImage(coords[0], coords[1], coordX, coords[3]);
} else {
outputValue = getImage(coords[0], coords[1], coords[2], coords[3]);
}
setOutput(outputValue);
}
`}},m9={kernelName:Zo,backendName:"webgl",kernelFunc:({inputs:e,backend:t})=>{let{image:n}=e,a=t,r=new h9(n.shape);return a.runWebGLProgram(r,[n],n.dtype)}},JN="return floor(x);",f9=Xe({opSnippet:JN,packedOpSnippet:JN,cpuKernelImpl:sX}),g9={kernelName:Gs,backendName:"webgl",kernelFunc:f9},y9=`
float s = sign(a) * sign(b);
int ia = round(a);
int ib = round(b);
if (ib != 0) {
// Windows (D3D) wants guaranteed non-zero int division at compile-time.
return float(idiv(ia, ib, s));
} else {
return NAN;
}
`,b9=`
ivec4 ia = round(a);
ivec4 ib = round(b);
bvec4 cond = notEqual(ib, ivec4(0));
ivec4 result = ivec4(0);
vec4 s = sign(a) * sign(b);
// Windows (D3D) wants guaranteed non-zero int division at compile-time.
if (cond[0]) {
result[0] = idiv(ia[0], ib[0], s[0]);
}
if (cond[1]) {
result[1] = idiv(ia[1], ib[1], s[1]);
}
if (cond[2]) {
result[2] = idiv(ia[2], ib[2], s[2]);
}
if (cond[3]) {
result[3] = idiv(ia[3], ib[3], s[3]);
}
return vec4(result);
`,x9=ln({opSnippet:y9,packedOpSnippet:b9,dtype:"int32"}),v9={kernelName:Hs,backendName:"webgl",kernelFunc:x9},w9=class{constructor(e){this.variableNames=["A"];let t=gn(),[n,a]=e;this.outputShape=e,this.userCode=`
void main() {
ivec3 coords = getOutputCoords();
int texR = coords[0];
int texC = coords[1];
int depth = coords[2];
vec2 uv = (vec2(texC, texR) + halfCR) / vec2(${a}.0, ${n}.0);
vec4 values = ${t.texture2D}(A, uv);
float value;
if (depth == 0) {
value = values.r;
} else if (depth == 1) {
value = values.g;
} else if (depth == 2) {
value = values.b;
} else if (depth == 3) {
value = values.a;
}
setOutput(floor(value * 255.0 + 0.5));
}
`}},k9=class{constructor(e){this.variableNames=["A"],this.packedInputs=!1,this.packedOutput=!0;let t=gn(),[n,a]=e;this.outputShape=e,this.userCode=`
void main() {
ivec3 coords = getOutputCoords();
int texR = coords[0];
int texC = coords[1];
int depth = coords[2];
vec4 result = vec4(0.);
for(int row=0; row<=1; row++) {
for(int col=0; col<=1; col++) {
texC = coords[1] + row;
depth = coords[2] + col;
vec2 uv = (vec2(texC, texR) + halfCR) /
vec2(${a}.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;
}
`}},T9={kernelName:ah,backendName:"webgl",kernelFunc:I9},Iu;function I9(e){let{inputs:t,backend:n,attrs:a}=e,{pixels:r}=t,{numChannels:s}=a,i=typeof HTMLVideoElement!="undefined"&&r instanceof HTMLVideoElement,o=typeof HTMLImageElement!="undefined"&&r instanceof HTMLImageElement,[l,c]=i?[r.videoWidth,r.videoHeight]:[r.width,r.height],u=[c,l],p=[c,l,s];(o||i)&&(Iu==null&&(Iu=document.createElement("canvas").getContext("2d")),Iu.canvas.width=l,Iu.canvas.height=c,Iu.drawImage(r,0,0,l,c),r=Iu.canvas);let d=n.makeTensorInfo(u,"int32");n.texData.get(d.dataId).usage=aa.PIXELS,n.gpgpu.uploadPixelDataToTexture(n.getTexture(d.dataId),r);let h=Z().getBool("WEBGL_PACK")?new k9(p):new w9(p),m=n.runWebGLProgram(h,[d],"int32");return n.disposeData(d.dataId),m}function N9(e){let{inputs:t,backend:n,attrs:a}=e,{x:r,filter:s,bias:i,preluActivationWeights:o}=t,{strides:l,pad:c,dataFormat:u,dilations:p,dimRoundingMode:d,activation:h,leakyreluAlpha:m}=a,f=_.convertConv2DDataFormat(u),g=_.computeConv2DInfo(r.shape,s.shape,l,p,c,d,!1,f),y,b=[];if(g.filterHeight===1&&g.filterWidth===1&&g.dilationHeight===1&&g.dilationWidth===1&&g.strideHeight===1&&g.strideWidth===1&&(g.padInfo.type==="SAME"||g.padInfo.type==="VALID"))y=zN({x:r,filter:s,convInfo:g,backend:n,bias:i,activation:h,preluActivationWeights:o,leakyreluAlpha:m});else if(Z().getBool("WEBGL_CONV_IM2COL")&&r.shape[0]===1)y=WN({x:r,filter:s,convInfo:g,backend:n,bias:i,activation:h,preluActivationWeights:o,leakyreluAlpha:m});else{let v=i!=null,T=o!=null,k=h==="leakyrelu",S=h?Km(h,!1):null,F=new LN(g,v,S,T,k),A=[r,s];if(i&&A.push(i),o&&A.push(o),k){let R=n.makeTensorInfo([],"float32",w.createScalarValue(m,"float32"));A.push(R),b.push(R)}y=n.runWebGLProgram(F,A,"float32")}let x=ge({inputs:{x:y},backend:n,attrs:{shape:g.outShape}});return b.push(y),b.forEach(v=>n.disposeIntermediateTensorInfo(v)),x}var S9={kernelName:Ti,backendName:"webgl",kernelFunc:N9};function C9(e){let{inputs:t,backend:n,attrs:a}=e,{x:r,filter:s,bias:i,preluActivationWeights:o}=t,{strides:l,pad:c,dilations:u,dimRoundingMode:p,activation:d,leakyreluAlpha:h}=a,m=[],f=u;f==null&&(f=[1,1]),w.assert(_.eitherStridesOrDilationsAreOne(l,f),()=>`Error in depthwiseConv2d: Either strides or dilations must be 1. Got strides ${l} and dilations '${f}'`);let g=_.computeConv2DInfo(r.shape,s.shape,l,f,c,p,!0),y=Z().getBool("WEBGL_PACK_DEPTHWISECONV")&&g.strideWidth<=2&&g.outChannels/g.inChannels==1,b=d?Km(d,y):null,x=[r,s],v=i!=null,T=o!=null,k=d==="leakyrelu";if(v&&x.push(i),T&&x.push(o),k){let A=n.makeTensorInfo([],"float32",w.createScalarValue(h,"float32"));x.push(A),m.push(A)}let S;y?S=new HN(g,v,b,T,k):S=new GN(g,v,b,T,k);let F=n.runWebGLProgram(S,x,"float32");return m.forEach(A=>n.disposeIntermediateTensorInfo(A)),F}var _9={kernelName:Ni,backendName:"webgl",kernelFunc:C9},E9=class{constructor(e,t,n){this.sliceDim=e,this.strides=t,this.variableNames=["x","indices"],this.outputShape=n;let a=dt(t.length),r=dt(n.length),s=this.sliceDim>1?"strides[j]":"strides";this.userCode=`
${a} strides = ${a}(${this.strides});
void main() {
${r} coords = getOutputCoords();
int flattenIndex = 0;
for (int j = 0; j < ${this.sliceDim}; j++) {
int index = round(getIndices(coords[0], j));
flattenIndex += index * ${s};
}
setOutput(getX(flattenIndex, coords[1]));
}
`}};function F9(e){let{inputs:t,backend:n}=e,{params:a,indices:r}=t,s=r.shape,i=s[s.length-1],[o,l,c,u]=_.prepareAndValidate(a,r),p=ge({inputs:{x:r},backend:n,attrs:{shape:[l,i]}}),d=ge({inputs:{x:a},backend:n,attrs:{shape:[w.sizeFromShape(a.shape)/c,c]}}),h=new E9(i,u,[l,c]),m=n.runWebGLProgram(h,[d,p],d.dtype),f=ge({inputs:{x:m},backend:n,attrs:{shape:o}});return n.disposeIntermediateTensorInfo(p),n.disposeIntermediateTensorInfo(d),n.disposeIntermediateTensorInfo(m),f}var A9={kernelName:tl,backendName:"webgl",kernelFunc:F9},D9=class{constructor(e,t){this.variableNames=["A","indices"],this.outputShape=t,this.rank=t.length;let n=dt(this.rank),a=$9(e,2);this.userCode=`
void main() {
${n} resRC = getOutputCoords();
setOutput(getA(${a}));
}
`}};function $9(e,t){let n=["resRC.x","resRC.y","resRC.z","resRC.w"],a=[];for(let r=0;r<e.length;r++)r===2?a.push("int(getIndices(resRC.x, resRC.z))"):a.push(`${n[r]}`);return a.join()}function R9(e){let{inputs:t,backend:n,attrs:a}=e,{x:r,indices:s}=t,{axis:i,batchDims:o}=a,l=w.parseAxisParam(i,r.shape)[0],c=_.segment_util.collectGatherOpShapeInfo(r,s,l,o),u=w.sizeFromShape(s.shape),p=[],d=ge({inputs:{x:r},backend:n,attrs:{shape:[c.batchSize,c.outerSize,c.dimSize,c.sliceSize]}}),h=ge({inputs:{x:s},backend:n,attrs:{shape:[c.batchSize,u/c.batchSize]}});p.push(d),p.push(h);let m=[c.batchSize,c.outerSize,u/c.batchSize,c.sliceSize];if(n.shouldExecuteOnCPU([r,s])||r.dtype==="string"){let b=n.bufferSync(h),x=n.bufferSync(d),v=iX(x,b,m);return p.forEach(T=>n.disposeIntermediateTensorInfo(T)),n.makeTensorInfo(c.outputShape,v.dtype,v.values)}let f=new D9(d.shape,m),g=n.runWebGLProgram(f,[d,h],d.dtype);p.push(g);let y=ge({inputs:{x:g},backend:n,attrs:{shape:c.outputShape}});return p.forEach(b=>n.disposeIntermediateTensorInfo(b)),y}var M9={kernelName:el,backendName:"webgl",kernelFunc:R9},P9="return float(a > b);",O9=`
return vec4(greaterThan(a, b));
`,L9=ln({opSnippet:P9,packedOpSnippet:O9,cpuKernelImpl:oX,dtype:"bool"}),z9={kernelName:nl,backendName:"webgl",kernelFunc:L9},W9="return float(a >= b);",B9=`
return vec4(greaterThanEqual(a, b));
`,V9=ln({opSnippet:W9,packedOpSnippet:B9,dtype:"bool"}),U9={kernelName:qs,backendName:"webgl",kernelFunc:V9};function G9(e){let{inputs:t,backend:n}=e,{input:a}=t;return YN(a,!0,n)}var H9={kernelName:Ud,backendName:"webgl",kernelFunc:G9},j9="return float(!isnan(x) && !isinf(x));",q9=Xe({opSnippet:j9,dtype:"bool"}),X9={kernelName:al,backendName:"webgl",kernelFunc:q9},K9="return float(isinf(x));",Y9=Xe({opSnippet:K9,dtype:"bool"}),J9={kernelName:rl,backendName:"webgl",kernelFunc:Y9},Q9="return float(isnan(x));",Z9=Xe({opSnippet:Q9,dtype:"bool"}),eJ={kernelName:sl,backendName:"webgl",kernelFunc:Z9},tJ="return float(a < b);",nJ=`
return vec4(lessThan(a, b));
`,aJ=ln({opSnippet:tJ,packedOpSnippet:nJ,cpuKernelImpl:lX,dtype:"bool"}),rJ={kernelName:il,backendName:"webgl",kernelFunc:aJ},sJ="return float(a <= b);",iJ=`
return vec4(lessThanEqual(a, b));
`,oJ=ln({opSnippet:sJ,packedOpSnippet:iJ,dtype:"bool"}),lJ={kernelName:ol,backendName:"webgl",kernelFunc:oJ};function uJ(e){let{backend:t,attrs:n}=e,{start:a,stop:r,num:s}=n,i=uX(a,r,s);return t.makeTensorInfo([i.length],"float32",i)}var cJ={kernelName:Hd,backendName:"webgl",kernelFunc:uJ},pJ=`if (x < 0.0) return NAN;
return log(x);`,dJ=`
vec4 result = log(x);
vec4 isNaN = vec4(lessThan(x, vec4(0.0)));
result.r = isNaN.r == 1.0 ? NAN : result.r;
result.g = isNaN.g == 1.0 ? NAN : result.g;
result.b = isNaN.b == 1.0 ? NAN : result.b;
result.a = isNaN.a == 1.0 ? NAN : result.a;
return result;
`,hJ=Xe({opSnippet:pJ,packedOpSnippet:dJ,cpuKernelImpl:cX}),mJ={kernelName:Ys,backendName:"webgl",kernelFunc:hJ},fJ="return log(1.0 + x);",gJ=Xe({opSnippet:fJ}),yJ={kernelName:ll,backendName:"webgl",kernelFunc:gJ},bJ="return float(a >= 1.0 && b >= 1.0);",xJ=`
return vec4(
vec4(greaterThanEqual(a, vec4(1.0))) *
vec4(greaterThanEqual(b, vec4(1.0))));
`,vJ=ln({opSnippet:bJ,packedOpSnippet:xJ,dtype:"bool"}),wJ={kernelName:ul,backendName:"webgl",kernelFunc:vJ},kJ="return float(!(x >= 1.0));",IJ=Xe({opSnippet:kJ}),TJ={kernelName:uc,backendName:"webgl",kernelFunc:IJ},NJ="return float(a >= 1.0 || b >= 1.0);",SJ=`
return min(
vec4(greaterThanEqual(a, vec4(1.0))) +
vec4(greaterThanEqual(b, vec4(1.0))),
vec4(1.0));
`,CJ=ln({opSnippet:NJ,packedOpSnippet:SJ,dtype:"bool"}),_J={kernelName:cc,backendName:"webgl",kernelFunc:CJ},EJ=class{constructor(e,t,n,a,r){this.variableNames=["x"],this.outputShape=[];let s=t,i=e[3]-1;this.outputShape=e;let o,l=`float(${n}) + float(${a}) * sum`;r===.5?o=`inversesqrt(${l})`:r===1?o=`1.0/(${l})`:o=`exp(log(${l}) * float(-${r}));`,this.userCode=`
void main() {
ivec4 coords = getOutputCoords();
int b = coords[0];
int r = coords[1];
int c = coords[2];
int d = coords[3];
float x = getX(b, r, c, d);
float sum = 0.0;
for (int j = -${s}; j <= ${s}; j++) {
int idx = d + j;
if (idx >= 0 && idx <= ${i}) {
float z = getX(b, r, c, idx);
sum += z * z;
}
}
float val = x * ${o};
setOutput(val);
}
`}},FJ=class{constructor(e,t,n,a,r){this.variableNames=["x"],this.outputShape=[],this.packedInputs=!0,this.packedOutput=!0;let s=t,i=e[3]-1;this.outputShape=e;let o,l=`float(${n}) + float(${a}) * sum`;r===.5?o=`inversesqrt(${l})`:r===1?o=`1.0/(${l})`:o=`exp(log(${l}) * float(-${r}));`,this.userCode=`
void main() {
ivec4 coords = getOutputCoords();
int b = coords.x;
int r = coords.y;
int c = coords.z;
int d = coords.w;
bool hasNextCol = d < ${this.outputShape[3]};
bool hasNextRow = c < ${this.outputShape[2]};
vec4 sum = vec4(0.);
vec4 xFragAtOutputCoords = getX(b, r, c, d);
vec4 xAtOutputCoords = vec4(
getChannel(xFragAtOutputCoords, vec2(c, d)),
hasNextCol ?
getChannel(xFragAtOutputCoords, vec2(c, d + 1)) : 0.0,
hasNextRow ?
getChannel(xFragAtOutputCoords , vec2(c + 1, d)) : 0.0,
(hasNextRow && hasNextCol) ?
getChannel(xFragAtOutputCoords, vec2(c + 1, d + 1)) : 0.0
);
int firstChannel = d - ${s};
vec2 cache = vec2(0.);
if(firstChannel >= 0){
vec4 firstChannelFrag = getX(b, r, c, firstChannel);
cache.x = getChannel(firstChannelFrag, vec2(c, firstChannel));
if(hasNextRow){
cache.y = getChannel(firstChannelFrag, vec2(c + 1, firstChannel));
}
}
ivec2 depth = ivec2(d, d + 1);
for (int j = - ${s}; j <= ${s}; j++) {
ivec2 idx = depth + j;
bvec2 aboveLowerBound = greaterThanEqual(idx, ivec2(0));
bvec2 belowUpperBound = lessThanEqual(idx, ivec2(${i}));
bool depthInRange = aboveLowerBound.x && belowUpperBound.x;
bool depthPlusOneInRange = aboveLowerBound.y && belowUpperBound.y;
if(depthInRange || depthPlusOneInRange){
vec4 z = vec4(0.);
vec4 xFragAtCurrentDepth;
z.xz = cache.xy;
if(depthPlusOneInRange && hasNextCol){
xFragAtCurrentDepth = idx.y != d ?
getX(b, r, c, idx.y) : xFragAtOutputCoords;
z.y = getChannel(xFragAtCurrentDepth, vec2(c, idx.y));
if(hasNextRow){
z.w = getChannel(xFragAtCurrentDepth, vec2(c + 1, idx.y));
}
}
cache.xy = z.yw;
sum += z * z;
}
}
vec4 result = xAtOutputCoords * ${o};
setOutput(result);
}
`}},AJ=e=>{let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{depthRadius:s,bias:i,alpha:o,beta:l}=a,c=Z().getBool("WEBGL_PACK_NORMALIZATION")?new FJ(r.shape,s,i,o,l):new EJ(r.shape,s,i,o,l);return n.runWebGLProgram(c,[r],r.dtype)},$J={kernelName:pc,backendName:"webgl",kernelFunc:AJ},DJ=class{constructor(e,t,n,a,r){this.variableNames=["inputImage","outputImage","dy"],this.outputShape=[],this.outputShape=e,this.depth=e[3],this.depthRadius=t,this.bias=n,this.alpha=a,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 = ${this.depth};
float norm = 0.0;
for (int k = MIN_DEPTH_BEGIN; k < MAX_DEPTH_END; ++k) {
if (k < depthBegin){
continue;
}
else if (k >= depthBegin && k < depthEnd) {
norm += getInputImage(b, r, c, k) * getInputImage(b, r, c, k);
}
else {
break;
}
}
norm = float(${a}) * norm + float(${n});
for(int k = MIN_DEPTH_BEGIN; k < MAX_DEPTH_END; ++k){
if (k < depthBegin){
continue;
}
else if (k >= depthBegin && k < depthEnd){
float dyi = -2.0 * float(${a})
* 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);
}
`}},RJ=e=>{let{inputs:t,backend:n,attrs:a}=e,{x:r,y:s,dy:i}=t,{depthRadius:o,bias:l,alpha:c,beta:u}=a,p=new DJ(r.shape,o,l,c,u);return n.runWebGLProgram(p,[r,s,i],r.dtype)},MJ={kernelName:jd,backendName:"webgl",kernelFunc:RJ};function PJ(e,t,n,a){let r=w.sizeFromShape(t),s=w.sizeFromShape(e.shape)/r,i=ge({inputs:{x:e},attrs:{shape:[s,r]},backend:a}),o=Zi(i,e.dtype,"max",a),l=ge({inputs:{x:o},attrs:{shape:n},backend:a});return a.disposeIntermediateTensorInfo(i),a.disposeIntermediateTensorInfo(o),l}function QN(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{reductionIndices:s,keepDims:i}=a,o=r.shape.length,l=w.parseAxisParam(s,r.shape),c=l,u=_.getAxesPermutation(c,o),p=u!=null,d=n.shouldExecuteOnCPU([r]),h=r;if(p){if(d){let b=n.texData.get(h.dataId).values,x=new Array(o);for(let k=0;k<x.length;k++)x[k]=r.shape[u[k]];let v=Xv(b,r.shape,r.dtype,u,x);h=n.makeTensorInfo(x,r.dtype);let T=n.texData.get(h.dataId);T.values=v}else h=Ym(r,u,n);c=_.getInnerMostAxes(c.length,o)}_.assertAxesAreInnerMostDims("max",c,o);let[m,f]=_.computeOutAndReduceShapes(h.shape,c),g=m;i&&(g=_.expandShapeToKeepDim(m,l));let y;if(d){let b=n.texData.get(h.dataId).values,x=pX(b,w.sizeFromShape(f),g,r.dtype);y=n.makeTensorInfo(g,r.dtype);let v=n.texData.get(y.dataId);v.values=x}else y=PJ(h,f,g,n);return p&&n.disposeIntermediateTensorInfo(h),y}var OJ={kernelName:Js,backendName:"webgl",kernelFunc:QN},LJ=fN+`
return max(a, b);
`,zJ=`
vec4 result = vec4(max(a, b));
vec4 isNaN = min(vec4(isnan(a)) + vec4(isnan(b)), vec4(1.0));
`+Xm+`
return result;
`,WJ=ln({opSnippet:LJ,packedOpSnippet:zJ,cpuKernelImpl:dX}),BJ={kernelName:Qs,backendName:"webgl",kernelFunc:WJ};function VJ(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t;yp(r,"maxPool");let{filterSize:s,strides:i,pad:o,dimRoundingMode:l}=a,c=1;w.assert(_.eitherStridesOrDilationsAreOne(i,c),()=>`Error in maxPool: Either strides or dilations must be 1. Got strides ${i} and dilations '${c}'`);let u=_.computePool2DInfo(r.shape,s,i,c,o,l);if(u.filterWidth===1&&u.filterHeight===1&&w.arraysEqual(u.inShape,u.outShape))return Gn({inputs:{x:r},backend:n});let p=new vp(u,"max",!1);return n.runWebGLProgram(p,[r],r.dtype)}var UJ={kernelName:Zs,backendName:"webgl",kernelFunc:VJ};function GJ(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{filterSize:s,strides:i,pad:o,dataFormat:l,dimRoundingMode:c}=a,u=[1,1,1],p=_.computePool3DInfo(r.shape,s,i,u,o,c,l),d=new Qv(p,"max",!1);return n.runWebGLProgram(d,[r],r.dtype)}var HJ={kernelName:dc,backendName:"webgl",kernelFunc:GJ},jJ=class{constructor(e){this.variableNames=["dy","maxPos"],this.outputShape=e.inShape;let t=e.strideHeight,n=e.strideWidth,a=e.dilationHeight,r=e.effectiveFilterHeight,s=e.effectiveFilterWidth,i=r-1-e.padInfo.top,o=s-1-e.padInfo.left,l=r*s-1;this.userCode=`
const ivec2 pads = ivec2(${i}, ${o});
void main() {
ivec4 coords = getOutputCoords();
int b = coords[0];
int d = coords[3];
ivec2 dyRCCorner = coords.yz - pads;
int dyRCorner = dyRCCorner.x;
int dyCCorner = dyRCCorner.y;
// Convolve dy(?, ?, d) with pos mask(:, :, d) to get dx(xR, xC, d).
// ? = to be determined. : = across all values in that axis.
float dotProd = 0.0;
for (int wR = 0; wR < ${r};
wR += ${a}) {
float dyR = float(dyRCorner + wR) / ${t}.0;
if (dyR < 0.0 || dyR >= ${e.outHeight}.0 || fract(dyR) > 0.0) {
continue;
}
int idyR = int(dyR);
for (int wC = 0; wC < ${s}; wC++) {
float dyC = float(dyCCorner + wC) / ${n}.0;
if (dyC < 0.0 || dyC >= ${e.outWidth}.0 ||
fract(dyC) > 0.0) {
continue;
}
int idyC = int(dyC);
float dyValue = getDy(b, idyR, idyC, d);
int maxPosValue = ${l} - int(getMaxPos(b, idyR, idyC, d));
// Get the current value, check it against the value from the
// position matrix.
int curPosValue = wR * ${s} + wC;
float mask = float(maxPosValue == curPosValue ? 1.0 : 0.0);
dotProd += dyValue * mask;
}
}
setOutput(dotProd);
}
`}},qJ=class{constructor(e){this.variableNames=["dy","maxPos"],this.outputShape=e.inShape;let t=e.strideDepth,n=e.strideHeight,a=e.strideWidth,r=e.dilationDepth,s=e.dilationHeight,i=e.dilationWidth,o=e.effectiveFilterDepth,l=e.effectiveFilterHeight,c=e.effectiveFilterWidth,u=o-1-e.padInfo.front,p=l-1-e.padInfo.top,d=c-1-e.padInfo.left,h=o*l*c-1;this.userCode=`
const ivec3 pads = ivec3(${u}, ${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 < ${o};
wD += ${r}) {
float dyD = float(dyDCorner + wD) / ${t}.0;
if (dyD < 0.0 || dyD >= ${e.outDepth}.0 || fract(dyD) > 0.0) {
continue;
}
int idyD = int(dyD);
for (int wR = 0; wR < ${l};
wR += ${s}) {
float dyR = float(dyRCorner + wR) / ${n}.0;
if (dyR < 0.0 || dyR >= ${e.outHeight}.0 ||
fract(dyR) > 0.0) {
continue;
}
int idyR = int(dyR);
for (int wC = 0; wC < ${c};
wC += ${i}) {
float dyC = float(dyCCorner + wC) / ${a}.0;
if (dyC < 0.0 || dyC >= ${e.outWidth}.0 ||
fract(dyC) > 0.0) {
continue;
}
int idyC = int(dyC);
float dyValue = getDy(batch, idyD, idyR, idyC, ch);
int maxPosValue = ${h} -
int(getMaxPos(batch, idyD, idyR, idyC, ch));
// Get the current value, check it against the value from the
// position matrix.
int curPosValue =
wD * ${l} * ${c} +
wR * ${c} + wC;
float mask = float(maxPosValue == curPosValue ? 1.0 : 0.0);
dotProd += dyValue * mask;
}
}
}
setOutput(dotProd);
}
`}};function XJ(e){let{inputs:t,backend:n,attrs:a}=e,{dy:r,input:s}=t,i=s,{filterSize:o,strides:l,pad:c,dimRoundingMode:u}=a,p=[1,1,1],d=_.computePool3DInfo(i.shape,o,l,p,c,u),h=new Qv(d,"max",!0),m=n.runWebGLProgram(h,[i],i.dtype),f=new qJ(d),g=n.runWebGLProgram(f,[r,m],i.dtype);return n.disposeIntermediateTensorInfo(m),g}var KJ={kernelName:Xd,backendName:"webgl",kernelFunc:XJ};function YJ(e){let{inputs:t,backend:n,attrs:a}=e,{dy:r,input:s,output:i}=t,o=s;yp([s,i],"maxPoolGrad");let{filterSize:l,strides:c,pad:u,dimRoundingMode:p}=a,d=_.computePool2DInfo(o.shape,l,c,1,u,p),h=!0,m=new vp(d,"max",h),f=n.runWebGLProgram(m,[o],o.dtype),g=new jJ(d),y=n.runWebGLProgram(g,[r,f],o.dtype);return n.disposeIntermediateTensorInfo(f),y}var JJ={kernelName:qd,backendName:"webgl",kernelFunc:YJ};function QJ(e,t,n,a){let r=new vp(n,"max",!1),s=a.runWebGLProgram(r,[e],"float32");r=new vp(n,"max",!0,!0,t);let i=a.runWebGLProgram(r,[e],"float32");return[s,i]}var ZJ={kernelName:Kd,backendName:"webgl",kernelFunc:({inputs:e,attrs:t,backend:n})=>{let{x:a}=e,{filterSize:r,strides:s,pad:i,includeBatchInIndex:o}=t,l=n;w.assert(a.shape.length===4,()=>`Error in maxPool: input must be rank 4 but got rank ${a.shape.length}.`);let c=[1,1];w.assert(_.eitherStridesOrDilationsAreOne(s,c),()=>`Error in maxPool: Either strides or dilations must be 1. Got strides ${s} and dilations '${c}'`);let u=_.computePool2DInfo(a.shape,r,s,c,i),[p,d]=QJ(a,o,u,l);return[p,d]}};function eQ(e,t,n,a){let r=w.sizeFromShape(t),s=w.sizeFromShape(e.shape)/r,i=ge({inputs:{x:e},attrs:{shape:[s,r]},backend:a}),o=Zi(i,"float32","mean",a),l=ge({inputs:{x:o},attrs:{shape:n},backend:a});return a.disposeIntermediateTensorInfo(i),a.disposeIntermediateTensorInfo(o),l}var tQ={kernelName:ei,backendName:"webgl",kernelFunc:({inputs:e,attrs:t,backend:n})=>{let{x:a}=e,{keepDims:r,axis:s}=t,i=n,o=a.shape.length,l=w.parseAxisParam(s,a.shape),c=l,u=_.getAxesPermutation(c,o),p=u!=null,d=i.shouldExecuteOnCPU([a]),h=[],m=a;if(p){if(d){let x=i.texData.get(m.dataId).values,v=new Array(o);for(let S=0;S<v.length;S++)v[S]=a.shape[u[S]];let T=Xv(x,a.shape,a.dtype,u,v);m=i.makeTensorInfo(v,a.dtype);let k=i.texData.get(m.dataId);k.values=T}else m=Ym(a,u,i);h.push(m),c=_.getInnerMostAxes(c.length,o)}_.assertAxesAreInnerMostDims("sum",c,o);let[f,g]=_.computeOutAndReduceShapes(m.shape,c),y=f;r&&(y=_.expandShapeToKeepDim(f,l));let b=eQ(m,g,y,i);for(let x of h)i.disposeIntermediateTensorInfo(x);return b}};function nQ(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{axis:s,keepDims:i}=a,o=r.shape.length,l=w.parseAxisParam(s,r.shape),c=l,u=_.getAxesPermutation(c,o),p=r;u!=null&&(p=$n({inputs:{x:r},backend:n,attrs:{perm:u}}),c=_.getInnerMostAxes(c.length,r.shape.length)),_.assertAxesAreInnerMostDims("min",c,o);let[d,h]=_.computeOutAndReduceShapes(p.shape,c),m=w.sizeFromShape(h),f=ge({inputs:{x:p},backend:n,attrs:{shape:[-1,m]}}),g=Zi(f,f.dtype,"min",n),y;if(i){let b=_.expandShapeToKeepDim(d,l);y=ge({inputs:{x:g},backend:n,attrs:{shape:b}})}else y=ge({inputs:{x:g},backend:n,attrs:{shape:d}});return n.disposeIntermediateTensorInfo(f),n.disposeIntermediateTensorInfo(g),u!=null&&n.disposeIntermediateTensorInfo(p),y}var aQ={kernelName:ti,backendName:"webgl",kernelFunc:nQ},rQ=fN+`
return min(a, b);
`,sQ=`
vec4 result = vec4(min(a, b));
vec4 isNaN = min(vec4(isnan(a)) + vec4(isnan(b)), vec4(1.0));
`+Xm+`
return result;
`,iQ=ln({opSnippet:rQ,packedOpSnippet:sQ,cpuKernelImpl:hX}),oQ={kernelName:ni,backendName:"webgl",kernelFunc:iQ},lQ=class{constructor(e,t,n){this.variableNames=["x"],this.outputShape=t.map((c,u)=>c[0]+e[u]+c[1]);let a=e.length,r=dt(a),s=t.map(c=>c[0]).join(","),i=t.map((c,u)=>c[0]+e[u]).join(","),o=["coords[0]","coords[1]","coords[2]","coords[3]"].slice(0,a),l=n==="reflect"?0:1;if(a===1){this.userCode=`
int start = ${s};
int end = ${i};
void main() {
int outC = getOutputCoords();
if (outC < start) {
outC = start * 2 - outC - ${l};
} else if(outC >= end) {
outC = (end - 1) * 2 - outC + ${l};
}
setOutput(getX(outC - start));
}
`;return}this.userCode=`
${r} start = ${r}(${s});
${r} end = ${r}(${i});
void main() {
${r} outC = getOutputCoords();
for (int i = 0; i < ${a}; i++) {
if (outC[i] < start[i]) {
outC[i] = start[i] * 2 - outC[i] - ${l};
} else if(outC[i] >= end[i]) {
outC[i] = (end[i] - 1) * 2 - outC[i] + ${l};
}
}
${r} coords = outC - start;
setOutput(getX(${o}));
}
`}},uQ=class{constructor(e,t,n){this.variableNames=["x"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=t.map((h,m)=>h[0]+e[m]+h[1]);let a=e.length,r=dt(a),s=t.map(h=>h[0]).join(","),i=t.map((h,m)=>h[0]+e[m]).join(","),o=yn("rc",a),l=yn("source",a),c=`${o[a-1]} < ${this.outputShape[a-1]}`,u=a===1?"source":`vec2(${l.slice(-2).join()})`,p=n==="reflect"?0:1,d="";if(a===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;
${h}
result[0] = getChannel(getX(${l.join()}), ${u});
${o[a-1]} += 1;
if(${c}) {
${h}
result[1] = getChannel(getX(${l.join()}), ${u});
}
`}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;
${h}
result[0] = getChannel(getX(${l.join()}), ${u});
${o[a-1]} += 1;
if(${c}) {
${h}
result[1] = getChannel(getX(${l.join()}), ${u});
}
rc = outputLoc;
${o[a-2]} += 1;
if(${o[a-2]} < ${this.outputShape[a-2]}) {
${h}
result[2] = getChannel(getX(${l.join()}), ${u});
${o[a-1]} += 1;
if(${c}) {
${h}
result[3] = getChannel(getX(${l.join()}), ${u});
}
}
`}this.userCode=`
const ${r} start = ${r}(${s});
const ${r} end = ${r}(${i});
void main() {
${r} outputLoc = getOutputCoords();
vec4 result = vec4(0.);
${d}
setOutput(result);
}
`}},cQ=({inputs:e,backend:t,attrs:n})=>{let{x:a}=e,{paddings:r,mode:s}=n,i=Z().getBool("WEBGL_PACK_ARRAY_OPERATIONS")?new uQ(a.shape,r,s):new lQ(a.shape,r,s);return t.runWebGLProgram(i,[a],a.dtype)},pQ={kernelName:hc,backendName:"webgl",kernelFunc:cQ},dQ=`if (b == 0.0) return NAN;
return mod(a, b);`,hQ=`
vec4 result = mod(a, b);
vec4 isNaN = vec4(equal(b, vec4(0.0)));
`+Xm+`
return result;
`,mQ=ln({opSnippet:dQ,packedOpSnippet:hQ}),fQ={kernelName:cl,backendName:"webgl",kernelFunc:mQ},gQ=class{constructor(e,t,n){this.variableNames=["probs"],this.outputShape=[e,n],this.userCode=`
uniform float seed;
void main() {
ivec2 coords = getOutputCoords();
int batch = coords[0];
float r = random(seed);
float cdf = 0.0;
for (int i = 0; i < ${t-1}; i++) {
cdf += getProbs(batch, i);
if (r < cdf) {
setOutput(float(i));
return;
}
}
// If no other event happened, last event happened.
setOutput(float(${t-1}));
}
`}getCustomSetupFunc(e){return(t,n)=>{this.seedLoc==null&&(this.seedLoc=t.getUniformLocation(n,"seed")),t.gl.uniform1f(this.seedLoc,e)}}},yQ=`
if (a == b) {
return 1.0;
};
return a / b;`,bQ=`
// 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;
`,ZN=ln({opSnippet:yQ,packedOpSnippet:bQ,checkOutOfBounds:!0}),xQ={kernelName:Vs,backendName:"webgl",kernelFunc:ZN},eS="return a - b;",tS=ln({opSnippet:eS,packedOpSnippet:eS,supportsComplex:!0,cpuKernelImpl:wX}),vQ={kernelName:vi,backendName:"webgl",kernelFunc:tS};function nS(e){let{inputs:t,backend:n,attrs:a}=e,{logits:r}=t,{dim:s}=a,i=w.parseAxisParam([s],r.shape),o=QN({inputs:{x:r},backend:n,attrs:{reductionIndices:i,keepDims:!1}}),l=_.expandShapeToKeepDim(o.shape,i),c=ge({inputs:{x:o},backend:n,attrs:{shape:l}}),u=tS({inputs:{a:r,b:c},backend:n}),p=qN({inputs:{x:u},backend:n}),d=Jv({inputs:{x:p},backend:n,attrs:{axis:i,keepDims:!1}}),h=ge({inputs:{x:d},backend:n,attrs:{shape:l}}),m=ZN({inputs:{a:p,b:h},backend:n});return n.disposeIntermediateTensorInfo(o),n.disposeIntermediateTensorInfo(c),n.disposeIntermediateTensorInfo(u),n.disposeIntermediateTensorInfo(p),n.disposeIntermediateTensorInfo(d),n.disposeIntermediateTensorInfo(h),m}var wQ={kernelName:bi,backendName:"webgl",kernelFunc:nS};function kQ(e){let{inputs:t,backend:n,attrs:a}=e,{logits:r}=t,{numSamples:s,seed:i,normalized:o}=a,l=o?r:nS({inputs:{logits:r},backend:n,attrs:{dim:r.shape.length-1}}),c=l.shape[0],u=l.shape[1],p=new gQ(c,u,s),d=p.getCustomSetupFunc(i),h=n.runWebGLProgram(p,[l],"int32",d);return o||n.disposeIntermediateTensorInfo(l),h}var IQ={kernelName:Yd,backendName:"webgl",kernelFunc:kQ},aS="return -x;";function TQ(e){let{inputs:t,backend:n}=e,{x:a}=t;if(n.shouldExecuteOnCPU([a])){let s=n.texData.get(a.dataId),[i,o]=fX(s.values,a.shape,a.dtype);return n.makeTensorInfo(o,a.dtype,i)}let r;return Z().getBool("WEBGL_PACK_UNARY_OPERATIONS")?r=new vu(a.shape,aS):r=new gs(a.shape,aS),n.runWebGLProgram(r,[a],a.dtype)}var NQ={kernelName:pl,backendName:"webgl",kernelFunc:TQ},SQ=Qa.nonMaxSuppressionV3Impl;function CQ(e){_.warn("tf.nonMaxSuppression() in webgl locks the UI thread. Call tf.nonMaxSuppressionAsync() instead");let{inputs:t,backend:n,attrs:a}=e,{boxes:r,scores:s}=t,{maxOutputSize:i,iouThreshold:o,scoreThreshold:l}=a,c=n.readSync(r.dataId),u=n.readSync(s.dataId),{selectedIndices:p}=SQ(c,u,i,o,l);return n.makeTensorInfo([p.length],"int32",new Int32Array(p))}var _Q={kernelName:hl,backendName:"webgl",kernelFunc:CQ},EQ=Qa.nonMaxSuppressionV4Impl;function FQ(e){_.warn("tf.nonMaxSuppression() in webgl locks the UI thread. Call tf.nonMaxSuppressionAsync() instead");let{inputs:t,backend:n,attrs:a}=e,{boxes:r,scores:s}=t,{maxOutputSize:i,iouThreshold:o,scoreThreshold:l,padToMaxOutputSize:c}=a,u=n.readSync(r.dataId),p=n.readSync(s.dataId),{selectedIndices:d,validOutputs:h}=EQ(u,p,i,o,l,c);return[n.makeTensorInfo([d.length],"int32",new Int32Array(d)),n.makeTensorInfo([],"int32",new Int32Array([h]))]}var AQ={kernelName:ml,backendName:"webgl",kernelFunc:FQ},$Q=Qa.nonMaxSuppressionV5Impl;function DQ(e){_.warn("tf.nonMaxSuppression() in webgl locks the UI thread. Call tf.nonMaxSuppressionAsync() instead");let{inputs:t,backend:n,attrs:a}=e,{boxes:r,scores:s}=t,{maxOutputSize:i,iouThreshold:o,scoreThreshold:l,softNmsSigma:c}=a,u=n.readSync(r.dataId),p=n.readSync(s.dataId),d=i,h=o,m=l,f=c,{selectedIndices:g,selectedScores:y}=$Q(u,p,d,h,m,f);return[n.makeTensorInfo([g.length],"int32",new Int32Array(g)),n.makeTensorInfo([y.length],"float32",new Float32Array(y))]}var RQ={kernelName:fl,backendName:"webgl",kernelFunc:DQ},MQ=class{constructor(e,t,n,a){this.variableNames=["indices"],this.outputShape=[e,t],this.userCode=`
void main() {
ivec2 coords = getOutputCoords();
int index = round(getIndices(coords.x));
setOutput(mix(float(${a}), float(${n}),
float(index == coords.y)));
}
`}},PQ=e=>{let{inputs:t,backend:n,attrs:a}=e,{indices:r}=t,{depth:s,onValue:i,offValue:o}=a,l=w.sizeFromShape(r.shape),c=new MQ(l,s,i,o),u=ge({inputs:{x:r},backend:n,attrs:{shape:[l]}}),p=n.runWebGLProgram(c,[u],r.dtype);n.disposeIntermediateTensorInfo(u);let d=[...r.shape,s],h=ge({inputs:{x:p},backend:n,attrs:{shape:d}});return n.disposeIntermediateTensorInfo(p),h},OQ={kernelName:ri,backendName:"webgl",kernelFunc:PQ};function tf(e){let{inputs:t,backend:n}=e,{x:a}=t;if(a.dtype==="complex64"){let r=kp({inputs:{input:a},backend:n}),s=tf({inputs:{x:r},backend:n}),i=ef({inputs:{input:a},backend:n}),o=tf({inputs:{x:i},backend:n}),l=ys({inputs:{real:s,imag:o},backend:n});return n.disposeIntermediateTensorInfo(r),n.disposeIntermediateTensorInfo(s),n.disposeIntermediateTensorInfo(i),n.disposeIntermediateTensorInfo(o),l}else return nw({attrs:{shape:a.shape,dtype:a.dtype,value:a.dtype==="string"?"":0},backend:n})}var LQ={kernelName:Dl,backendName:"webgl",kernelFunc:tf};function rS(e){let{inputs:t,backend:n}=e,{x:a}=t;if(a.dtype==="string")throw new Error("onesLike is not supported under string dtype");if(a.dtype==="complex64"){let r=kp({inputs:{input:a},backend:n}),s=rS({inputs:{x:r},backend:n}),i=ef({inputs:{input:a},backend:n}),o=tf({inputs:{x:i},backend:n}),l=ys({inputs:{real:s,imag:o},backend:n});return n.disposeIntermediateTensorInfo(r),n.disposeIntermediateTensorInfo(s),n.disposeIntermediateTensorInfo(i),n.disposeIntermediateTensorInfo(o),l}else return nw({attrs:{shape:a.shape,dtype:a.dtype,value:1},backend:n})}var zQ={kernelName:gl,backendName:"webgl",kernelFunc:rS};function WQ(e){let{inputs:t,backend:n,attrs:a}=e,{axis:r}=a;if(t.length===1)return tw({inputs:{input:t[0]},backend:n,attrs:{dim:r}});let s=t[0].shape,i=t[0].dtype;t.forEach(u=>{w.assertShapesMatch(s,u.shape,"All tensors passed to stack must have matching shapes"),w.assert(i===u.dtype,()=>"All tensors passed to stack must have matching dtypes")});let o=[],l=t.map(u=>{let p=tw({inputs:{input:u},backend:n,attrs:{dim:r}});return o.push(p),p}),c=ON({inputs:l,backend:n,attrs:{axis:r}});return o.forEach(u=>n.disposeIntermediateTensorInfo(u)),c}var BQ={kernelName:yl,backendName:"webgl",kernelFunc:WQ},VQ=class{constructor(e,t,n){this.variableNames=["x"],this.outputShape=t.map((l,c)=>l[0]+e[c]+l[1]);let a=e.length,r=dt(a),s=t.map(l=>l[0]).join(","),i=t.map((l,c)=>l[0]+e[c]).join(","),o=["coords[0]","coords[1]","coords[2]","coords[3]"].slice(0,a);if(a===1){this.userCode=`
int start = ${s};
int end = ${i};
uniform float value;
void main() {
int outC = getOutputCoords();
if (outC < start || outC >= end) {
setOutput(value);
} else {
setOutput(getX(outC - start));
}
}
`;return}this.userCode=`
${r} start = ${r}(${s});
${r} end = ${r}(${i});
uniform float value;
void main() {
${r} outC = getOutputCoords();
if (any(lessThan(outC, start)) || any(greaterThanEqual(outC, end))) {
setOutput(value);
} else {
${r} coords = outC - start;
setOutput(getX(${o}));
}
}
`}getCustomSetupFunc(e){return(t,n)=>{this.valueLoc==null&&(this.valueLoc=t.getUniformLocationNoThrow(n,"value")),t.gl.uniform1f(this.valueLoc,e)}}},UQ=class{constructor(e,t,n){this.variableNames=["x"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=t.map((m,f)=>m[0]+e[f]+m[1]);let a=e.length,r=dt(a),s=t.map(m=>m[0]).join(","),i=t.map((m,f)=>m[0]+e[f]).join(","),o=yn("rc",a),l=yn("source",a),c=`${o[a-1]} < ${this.outputShape[a-1]}`,u=a===1?"source":`vec2(${l.slice(-2).join()})`,p=[`${r} rc = outputLoc;`,`${o[a-1]} += 1;
if(${c}) {
`,a===1?"":`}
rc = outputLoc;
${o[a-2]} += 1;
if(${o[a-2]} < ${this.outputShape[a-2]}) {`,a===1?"":` ${o[a-1]} += 1;
if(${c}) {`],d=a===1?"rc < start || rc >= end":"any(lessThan(rc, start)) || any(greaterThanEqual(rc, end))",h="";for(let m=0,f=a===1?2:4;m<f;m++)h+=`
${p[m]}
if (${d}) {
result[${m}] = float(value);
} else {
${r} source = rc - start;
result[${m}] = getChannel(getX(${l.join()}), ${u});
}
`;h+=a===1?"} ":"}}",this.userCode=`
const ${r} start = ${r}(${s});
const ${r} end = ${r}(${i});
uniform float value;
void main() {
${r} outputLoc = getOutputCoords();
vec4 result = vec4(0.);
${h}
setOutput(result);
}
`}getCustomSetupFunc(e){return(t,n)=>{this.valueLoc==null&&(this.valueLoc=t.getUniformLocationNoThrow(n,"value")),t.gl.uniform1f(this.valueLoc,e)}}},sS=e=>{let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{paddings:s,constantValue:i}=a,o=Z().getBool("WEBGL_PACK_ARRAY_OPERATIONS")?new UQ(r.shape,s,i):new VQ(r.shape,s,i),l=o.getCustomSetupFunc(i);return n.runWebGLProgram(o,[r],r.dtype,l)},GQ={kernelName:si,backendName:"webgl",kernelFunc:sS},HQ=`
if(a < 0.0 && floor(b) < b){
return NAN;
}
if (b == 0.0) {
return 1.0;
}
return (round(mod(b, 2.0)) != 1) ?
pow(abs(a), b) : sign(a) * pow(abs(a), b);
`,jQ=`
// isModRound1 has 1 for components with round(mod(b, 2.0)) == 1, 0 otherwise.
vec4 isModRound1 = vec4(equal(round(mod(b, 2.0)), ivec4(1)));
vec4 multiplier = sign(a) * isModRound1 + (vec4(1.0) - isModRound1);
vec4 result = multiplier * pow(abs(a), b);
// Ensure that a^0 = 1, including 0^0 = 1 as this correspond to TF and JS
bvec4 isExpZero = equal(b, vec4(0.0));
result.r = isExpZero.r ? 1.0 : result.r;
result.g = isExpZero.g ? 1.0 : result.g;
result.b = isExpZero.b ? 1.0 : result.b;
result.a = isExpZero.a ? 1.0 : result.a;
vec4 isNaN = vec4(lessThan(a, vec4(0.0))) * vec4(lessThan(floor(b), b));
`+Xm+`
return result;
`,qQ=ln({opSnippet:HQ,packedOpSnippet:jQ}),XQ={kernelName:ii,backendName:"webgl",kernelFunc:qQ};function KQ(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{axis:s,keepDims:i}=a,o=r.shape.length,l=[],c=w.parseAxisParam(s,r.shape),u=c,p=_.getAxesPermutation(u,o),d=r;p!=null&&(d=$n({inputs:{x:r},backend:n,attrs:{perm:p}}),u=_.getInnerMostAxes(u.length,o),l.push(d)),_.assertAxesAreInnerMostDims("prod",u,o);let h;if(n.shouldExecuteOnCPU([d])){let m=n.texData.get(d.dataId).values,{outVals:f,outShape:g,outDtype:y}=gX(d.shape,d.dtype,m,u);h=n.makeTensorInfo(g,y,f)}else{let[m,f]=_.computeOutAndReduceShapes(d.shape,u),g=w.sizeFromShape(f),y=ge({inputs:{x:d},backend:n,attrs:{shape:[-1,g]}}),b=uh(r.dtype),x=Zi(y,b,"prod",n);h=ge({inputs:{x},backend:n,attrs:{shape:m}}),l.push(y),l.push(x)}if(i){l.push(h);let m=_.expandShapeToKeepDim(h.shape,c);h=ge({inputs:{x:h},backend:n,attrs:{shape:m}})}return l.forEach(m=>n.disposeIntermediateTensorInfo(m)),h}var YQ={kernelName:bl,backendName:"webgl",kernelFunc:KQ},iS=e=>{let{backend:t,attrs:n}=e,{start:a,stop:r,step:s,dtype:i}=n,o=yX(a,r,s,i);return t.makeTensorInfo([o.length],i,o)},JQ={kernelName:mc,backendName:"webgl",kernelFunc:iS},QQ="return 1.0 / x;",ZQ=Xe({opSnippet:QQ}),eZ={kernelName:xl,backendName:"webgl",kernelFunc:ZQ},tZ=Pa+`
return (x < 0.0) ? 0.0 : x;
`,nZ=`
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;
`,aZ=Xe({opSnippet:tZ,packedOpSnippet:nZ}),rZ={kernelName:li,backendName:"webgl",kernelFunc:aZ},sZ=Pa+`
return (x < 0.0) ? 0.0 : min(6.0, x);
`,iZ=`
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;
`,oZ=Xe({opSnippet:sZ,packedOpSnippet:iZ}),lZ={kernelName:ci,backendName:"webgl",kernelFunc:oZ},uZ=class{constructor(e,t,n,a,r){this.variableNames=["A"],this.outputShape=[];let[s,i,o,l]=e;this.outputShape=[s,t,n,l];let c=[a&&t>1?i-1:i,a&&n>1?o-1:o],u=[a&&t>1?t-1:t,a&&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(
${c[0]/u[0]},
${c[1]/u[1]});
const vec2 inputShapeRC = vec2(${i}.0, ${o}.0);
void main() {
ivec4 coords = getOutputCoords();
int b = coords[0];
int d = coords[3];
ivec2 yRC = coords.yz;
// Fractional source index.
vec2 sourceFracIndexRC = ${p};
// Compute the 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);
}
`}},cZ=class{constructor(e,t,n,a,r){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=[];let[s,i,o,l]=e;this.outputShape=[s,t,n,l];let c=[a&&t>1?i-1:i,a&&n>1?o-1:o],u=[a&&t>1?t-1:t,a&&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(
${c[0]/u[0]},
${c[1]/u[1]},
${c[1]/u[1]});
const vec3 inputShapeRC = vec3(${i}.0, ${o}.0,
${o}.0);
float getAValue(int b, int r, int c, int d) {
return getChannel(getA(b, r, c, d), vec2(c, d));
}
void main() {
ivec4 coords = getOutputCoords();
int b = coords[0];
int d = coords[3];
// Calculate values for next column in yRC.z.
ivec3 yRC = coords.yzz + ivec3(0, 0, 1);
// Fractional source index.
vec3 sourceFracIndexRC = ${p};
// Compute the four integer indices.
ivec3 sourceFloorRC = ivec3(max(sourceFracIndexRC, vec3(0.0)));
ivec3 sourceCeilRC = ivec3(
min(inputShapeRC - 1.0, ceil(sourceFracIndexRC)));
// Should we calculate next column and row elements in 2x2 packed cell.
bool hasNextCol = d < ${l-1};
bool hasNextRow = coords.z < ${n-1};
// In parallel, construct four corners for all four components in
// packed 2x2 cell.
vec4 topLeft = vec4(
getAValue(b, sourceFloorRC.x, sourceFloorRC.y, d),
hasNextCol ? getAValue(b, sourceFloorRC.x, sourceFloorRC.y, d + 1)
: 0.0,
hasNextRow ? getAValue(b, sourceFloorRC.x, sourceFloorRC.z, d)
: 0.0,
(hasNextRow && hasNextCol) ?
getAValue(b, sourceFloorRC.x, sourceFloorRC.z, d + 1) : 0.0);
vec4 bottomLeft = vec4(
getAValue(b, sourceCeilRC.x, sourceFloorRC.y, d),
hasNextCol ? getAValue(b, sourceCeilRC.x, sourceFloorRC.y, d + 1)
: 0.0,
hasNextRow ? getAValue(b, sourceCeilRC.x, sourceFloorRC.z, d)
: 0.0,
(hasNextRow && hasNextCol) ?
getAValue(b, sourceCeilRC.x, sourceFloorRC.z, d + 1) : 0.0);
vec4 topRight = vec4(
getAValue(b, sourceFloorRC.x, sourceCeilRC.y, d),
hasNextCol ? getAValue(b, sourceFloorRC.x, sourceCeilRC.y, d + 1)
: 0.0,
hasNextRow ? getAValue(b, sourceFloorRC.x, sourceCeilRC.z, d)
: 0.0,
(hasNextRow && hasNextCol) ?
getAValue(b, sourceFloorRC.x, sourceCeilRC.z, d + 1) : 0.0);
vec4 bottomRight = vec4(
getAValue(b, sourceCeilRC.x, sourceCeilRC.y, d),
hasNextCol ? getAValue(b, sourceCeilRC.x, sourceCeilRC.y, d + 1)
: 0.0,
hasNextRow ? getAValue(b, sourceCeilRC.x, sourceCeilRC.z, d)
: 0.0,
(hasNextRow && hasNextCol) ?
getAValue(b, sourceCeilRC.x, sourceCeilRC.z, d + 1) : 0.0);
vec3 fracRC = sourceFracIndexRC - vec3(sourceFloorRC);
vec4 top = mix(topLeft, topRight, fracRC.yyzz);
vec4 bottom = mix(bottomLeft, bottomRight, fracRC.yyzz);
vec4 newValue = mix(top, bottom, fracRC.x);
setOutput(newValue);
}
`}};function pZ(e){let{inputs:t,backend:n,attrs:a}=e,{images:r}=t,{alignCorners:s,halfPixelCenters:i,size:o}=a,[l,c]=o,u=Z().getBool("WEBGL_PACK_IMAGE_OPERATIONS")?new cZ(r.shape,l,c,s,i):new uZ(r.shape,l,c,s,i);return n.runWebGLProgram(u,[r],"float32")}var dZ={kernelName:ui,backendName:"webgl",kernelFunc:pZ},hZ=class{constructor(e,t,n){this.variableNames=["dy"],this.outputShape=[],this.outputShape=t;let[,a,r]=t,[,s,i]=e,o=[n&&s>1?a-1:a,n&&i>1?r-1:r],l=[n&&s>1?s-1:s,n&&i>1?i-1:i],c=o[0]/l[0],u=o[1]/l[1],p=1/c,d=1/u,h=Math.ceil(p)*2+2,m=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(${c});
const float widthScale = float(${u});
const float invHeightScale = float(${p});
const float invWidthScale = float(${d});
const int winHeight = int(${h});
const int winWidth = int(${m});
// Compute bounds for where in dy we will look
float startRLerp = floor(float(r) * invHeightScale);
int startDyR = int(startRLerp - float(winHeight / 2));
float startCLerp = floor(float(c) * invWidthScale);
int startDyC = int(startCLerp - float(winWidth / 2));
// Loop over dy
for (int dyROffset = 0; dyROffset < winHeight; dyROffset++) {
int dyR = dyROffset + startDyR;
// Guard against the window exceeding the bounds of dy
if (dyR < 0 || dyR >= ${s}) {
continue;
}
for (int dyCOffset = 0; dyCOffset < winWidth; dyCOffset++) {
int dyC = dyCOffset + startDyC;
// Guard against the window exceeding the bounds of dy
if (dyC < 0 || dyC >= ${i}) {
continue;
}
float dxR = float(dyR) * heightScale;
int topDxRIndex = int(floor(dxR));
int bottomDxRIndex = int(min(ceil(dxR), ${a-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 mZ(e){let{inputs:t,backend:n,attrs:a}=e,{images:r,dy:s}=t,{alignCorners:i}=a,o=new hZ(s.shape,r.shape,i);return n.runWebGLProgram(o,[s],s.dtype)}var fZ={kernelName:Zd,backendName:"webgl",kernelFunc:mZ},gZ=class{constructor(e,t,n,a,r){this.variableNames=["A"],this.outputShape=[];let[s,i,o,l]=e;this.outputShape=[s,t,n,l];let c=[a&&t>1?i-1:i,a&&n>1?o-1:o],u=[a&&t>1?t-1:t,a&&n>1?n-1:n],p=a?"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(
${c[0]/u[0]},
${c[1]/u[1]});
const vec2 inputShapeRC = vec2(${i}.0, ${o}.0);
void main() {
ivec4 coords = getOutputCoords();
int b = coords[0];
int d = coords[3];
ivec2 yRC = coords.yz;
// Fractional source index.
vec2 sourceFracIndexRC = ${d};
// Compute the coordinators of nearest neighbor point.
ivec2 sourceNearestRC = ivec2(
min(inputShapeRC - 1.0, floor(sourceFracIndexRC + ${p})));
float newValue = getA(b, sourceNearestRC.x, sourceNearestRC.y, d);
setOutput(newValue);
}
`}};function yZ(e){let{inputs:t,backend:n,attrs:a}=e,{images:r}=t,{alignCorners:s,halfPixelCenters:i,size:o}=a,[l,c]=o,u=new gZ(r.shape,l,c,s,i);return n.runWebGLProgram(u,[r],r.dtype)}var bZ={kernelName:fc,backendName:"webgl",kernelFunc:yZ},xZ=class{constructor(e,t,n){this.variableNames=["dy"],this.outputShape=[],this.outputShape=t;let[,a,r]=t,[,s,i]=e,o=[n&&s>1?a-1:a,n&&i>1?r-1:r],l=[n&&s>1?s-1:s,n&&i>1?i-1:i],c=o[0]/l[0],u=o[1]/l[1],p=1/c,d=1/u,h=Math.ceil(p)*2+2,m=Math.ceil(d)*2+2;this.userCode=`
void main() {
ivec4 coords = getOutputCoords();
int b = coords[0];
int d = coords[3];
int r = coords[1];
int c = coords[2];
float accumulator = 0.0;
const float heightScale = float(${c});
const float widthScale = float(${u});
const float invHeightScale = float(${p});
const float invWidthScale = float(${d});
const int winHeight = int(${h});
const int winWidth = int(${m});
// Compute bounds for where in dy we will look
float startRLerp = floor(float(r) * invHeightScale);
int startDyR = int(floor(startRLerp - float(winHeight / 2)));
float startCLerp = floor(float(c) * invWidthScale);
int startDyC = int(floor(startCLerp - float(winWidth / 2)));
// Loop over dy
for (int dyROffset = 0; dyROffset < winHeight; dyROffset++) {
int dyR = dyROffset + startDyR;
// Guard against the window exceeding the bounds of dy
if (dyR < 0 || dyR >= ${s}) {
continue;
}
for (int dyCOffset = 0; dyCOffset < winWidth; dyCOffset++) {
int dyC = dyCOffset + startDyC;
// Guard against the window exceeding the bounds of dy
if (dyC < 0 || dyC >= ${i}) {
continue;
}
float sourceFracRow =
float(${o[0]}) *
(float(dyR) / float(${l[0]}));
float sourceFracCol =
float(${o[1]}) *
(float(dyC) / float(${l[1]}));
int sourceNearestRow = int(min(
float(int(${a}) - 1),
${n} ? float(round(sourceFracRow)) :
float(floor(sourceFracRow))));
int sourceNearestCol = int(min(
float(int(${r}) - 1),
${n} ? float(round(sourceFracCol)) :
float(floor(sourceFracCol))));
if (r == sourceNearestRow && c == sourceNearestCol) {
accumulator += getDy(b, dyR, dyC, d);
}
}
}
// End loop over dy
setOutput(accumulator);
}
`}};function vZ(e){let{inputs:t,backend:n,attrs:a}=e,{images:r,dy:s}=t,{alignCorners:i}=a,o=new xZ(s.shape,r.shape,i);return n.runWebGLProgram(o,[s],s.dtype)}var wZ={kernelName:Qd,backendName:"webgl",kernelFunc:vZ},kZ=class{constructor(e,t){this.variableNames=["x"];let n=e.length;if(n>4)throw new Error(`WebGL backend: Reverse of rank-${n} tensor is not yet supported`);if(this.outputShape=e,n===1){this.userCode=`
void main() {
int coord = getOutputCoords();
setOutput(getX(${e[0]} - coord - 1));
}
`;return}let a=i=>t.indexOf(i)!==-1&&e[i]!==1?`${e[i]} - coords[${i}] - 1`:`coords[${i}]`,r=e.map((i,o)=>a(o)).join(","),s=dt(n);this.userCode=`
void main() {
${s} coords = getOutputCoords();
setOutput(getX(${r}));
}
`}},IZ=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-${n} tensor is not yet supported`);this.outputShape=e;let a=yn("rc",n),r=`${a[n-1]} + 1 < ${this.outputShape[n-1]}`,s=`${a[n-2]} + 1 < ${this.outputShape[n-2]}`,i=dt(n);n===1?this.userCode=`
void main(){
int rc = getOutputCoords();
vec4 result = vec4(0.);
result.r = getChannel(getX(${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() {
${i} rc = getOutputCoords();
vec4 result = vec4(0.);
result.r = ${o(a.slice())};
if(${r}){
result.g = ${l(a.slice())};
}
if(${s}) {
result.b = ${c(a.slice())};
if(${r}) {
result.a = ${u(a.slice())};
}
}
setOutput(result);
}
`;function o(h){return p(h)}function l(h){return h[n-1]="("+h[n-1]+" + 1)",p(h)}function c(h){return h[n-2]="("+h[n-2]+" + 1)",p(h)}function u(h){return h[n-1]="("+h[n-1]+" + 1)",h[n-2]="("+h[n-2]+" + 1)",p(h)}function p(h){let m=e.map((y,b)=>d(b,h)),f=m.join(","),g=m.slice(-2).join(",");return`getChannel(getX(${f}), vec2(${g}))`}function d(h,m){return t.indexOf(h)!==-1&&e[h]!==1?`${e[h]} - ${m[h]} - 1`:`${m[h]}`}}};function TZ(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{dims:s}=a,i=r.shape.length,o=w.parseAxisParam(s,r.shape);if(i===0)return Gn({inputs:{x:r},backend:n});let l=Z().getBool("WEBGL_PACK_ARRAY_OPERATIONS")?new IZ(r.shape,o):new kZ(r.shape,o);return n.runWebGLProgram(l,[r],r.dtype)}var NZ={kernelName:pi,backendName:"webgl",kernelFunc:TZ},SZ=class{constructor(e,t){this.variableNames=["Image"],this.outputShape=[];let n=e[1],a=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=`
uniform vec4 params;
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 < ${a} && coordY >= 0 && coordY < ${n}) {
outputValue = getImage(coords[0], coordY, coordX, coords[3]);
}
setOutput(outputValue);
}
`}getCustomSetupFunc(e,t,n,a){return(r,s)=>{this.paramsLoc==null&&(this.paramsLoc=r.getUniformLocationNoThrow(s,"params")),r.gl.uniform4f(this.paramsLoc,e,t,n,a)}}},CZ={kernelName:Rl,backendName:"webgl",kernelFunc:({inputs:e,attrs:t,backend:n})=>{let{image:a}=e,{radians:r,fillValue:s,center:i}=t,o=n,l=new SZ(a.shape,s),[c,u]=_.getImageCenter(i,a.shape[1],a.shape[2]),p=l.getCustomSetupFunc(c,u,Math.sin(r),Math.cos(r));return o.runWebGLProgram(l,[a],a.dtype,p)}},_Z=`
// OpenGL ES does not support round function.
// The algorithm is based on banker's rounding.
float base = floor(x);
if ((x - base) < 0.5) {
return floor(x);
} else if ((x - base) > 0.5) {
return ceil(x);
} else {
if (mod(base, 2.0) == 0.0) {
return base;
} else {
return base + 1.0;
}
}
`,EZ=Xe({opSnippet:_Z}),FZ={kernelName:di,backendName:"webgl",kernelFunc:EZ},AZ="return inversesqrt(x);",$Z=Xe({opSnippet:AZ,cpuKernelImpl:bX}),DZ={kernelName:hi,backendName:"webgl",kernelFunc:$Z},oS=class{constructor(e,t,n,a,r,s,i=!0){this.variableNames=["updates","indices","defaultValue"],this.outputShape=s;let o=dt(r.length),l=dt(s.length),c="";n===1?c="i":n===2&&(c="i, j");let u=`getIndices(${c})`,p="";a===1?p="i":a===2&&(p="i, coords[1]");let d=`getUpdates(${p})`,h=t>1?"strides[j]":"strides";this.userCode=`
${o} strides = ${o}(${r});
void main() {
${l} coords = getOutputCoords();
float sum = 0.0;
bool found = false;
for (int i = 0; i < ${e}; i++) {
int flattenedIndex = 0;
for (int j = 0; j < ${t}; j++) {
int index = round(${u});
flattenedIndex += index * ${h};
}
if (flattenedIndex == coords[0]) {
sum += ${d};
found = true;
}
}
setOutput(mix(getDefaultValue(), sum, float(found)));
}
`}};function RZ(e){let{inputs:t,backend:n,attrs:a}=e,{indices:r,updates:s}=t,{shape:i}=a,{sliceRank:o,numUpdates:l,sliceSize:c,strides:u,outputSize:p}=_.calculateShapes(s,r,i),d=[p/c,c];if(p===0)return n.makeTensorInfo(i,r.dtype);let h=ge({inputs:{x:r},backend:n,attrs:{shape:[l,o]}}),m=ge({inputs:{x:s},backend:n,attrs:{shape:[l,c]}}),f=n.makeTensorInfo([],"float32",new Float32Array([0])),g=new oS(l,o,h.shape.length,m.shape.length,u,d),y=n.runWebGLProgram(g,[m,h,f],m.dtype),b=ge({inputs:{x:y},backend:n,attrs:{shape:i}});return n.disposeIntermediateTensorInfo(h),n.disposeIntermediateTensorInfo(m),n.disposeIntermediateTensorInfo(y),n.disposeIntermediateTensorInfo(f),b}var MZ={kernelName:wl,backendName:"webgl",kernelFunc:RZ},PZ=class{constructor(e,t,n){this.variableNames=["c","a","b"],this.outputShape=t;let a,r;if(n>4)throw Error(`Where for rank ${n} is not yet supported`);if(n===1)r="resRC",a="resRC";else{let i=["resRC.x","resRC.y","resRC.z","resRC.w"],o=[],l=[];for(let c=0;c<t.length;c++)l.push(`${i[c]}`),c<e&&o.push(`${i[c]}`);a=o.join(),r=l.join()}let s=dt(n);this.userCode=`
void main() {
${s} resRC = getOutputCoords();
float cVal = getC(${a});
if (cVal >= 1.0) {
setOutput(getA(${r}));
} else {
setOutput(getB(${r}));
}
}
`}};function OZ(e){let{inputs:t,backend:n}=e,{condition:a,t:r,e:s}=t,i=new PZ(a.shape.length,r.shape,r.shape.length);return n.runWebGLProgram(i,[a,r,s],pa(r.dtype,s.dtype))}var LZ={kernelName:kl,backendName:"webgl",kernelFunc:OZ},zZ=`
// Stable and Attracting Fixed Point (0, 1) for Normalized Weights.
// see: https://arxiv.org/abs/1706.02515
float scaleAlpha = ${_.SELU_SCALEALPHA};
float scale = ${_.SELU_SCALE};
return (x >= 0.0) ? scale * x : scaleAlpha * (exp(x) - 1.0);
`,WZ=Xe({opSnippet:zZ}),BZ={kernelName:Il,backendName:"webgl",kernelFunc:WZ},VZ="return 1.0 / (1.0 + exp(-1.0 * x));",UZ=Xe({opSnippet:VZ}),GZ={kernelName:fi,backendName:"webgl",kernelFunc:UZ},HZ=`
if (isnan(x)) { return 0.0; }
return sign(x);
`,jZ=Xe({opSnippet:HZ}),qZ={kernelName:Sl,backendName:"webgl",kernelFunc:jZ},XZ=vN+`
return sin(x);
`,KZ=Xe({opSnippet:XZ}),YZ={kernelName:mi,backendName:"webgl",kernelFunc:KZ},JZ=`
float e2x = exp(x);
return (e2x - 1.0 / e2x) / 2.0;
`,QZ=Xe({opSnippet:JZ}),ZZ={kernelName:Nl,backendName:"webgl",kernelFunc:QZ},eee=`
float epsilon = 1.1920928955078125e-7;
float threshold = log(epsilon) + 2.0;
bool too_large = x > -threshold;
bool too_small = x < threshold;
float result;
float exp_x = exp(x);
if (too_large){
result = x;
}
else if (too_small){
result = exp_x;
}
else{
result = log(exp_x + 1.0);
}
return result;
`,tee=Xe({opSnippet:eee}),nee={kernelName:Cl,backendName:"webgl",kernelFunc:tee},aee=e=>{let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{blockShape:s,paddings:i}=a;w.assert(r.shape.length<=4,()=>"spaceToBatchND for rank > 4 with a WebGL backend not implemented yet");let o=s.reduce((y,b)=>y*b),l=[[0,0]];l.push(...i);for(let y=1+s.length;y<r.shape.length;++y)l.push([0,0]);let c=[],u=sS({inputs:{x:r},backend:n,attrs:{paddings:l,constantValue:0}}),p=_.getReshaped(u.shape,s,o,!1),d=_.getPermuted(p.length,s.length,!1),h=_.getReshapedPermuted(u.shape,s,o,!1),m=ge({inputs:{x:u},backend:n,attrs:{shape:p}}),f=$n({inputs:{x:m},backend:n,attrs:{perm:d}}),g=ge({inputs:{x:f},backend:n,attrs:{shape:h}});return c.push(u),c.push(m),c.push(f),c.forEach(y=>n.disposeIntermediateTensorInfo(y)),g},ree={kernelName:gc,backendName:"webgl",kernelFunc:aee};function see(e){let{inputs:t,backend:n,attrs:a}=e,{sparseIndices:r,sparseValues:s,defaultValue:i}=t,{outputShape:o}=a,{sliceRank:l,numUpdates:c,strides:u,outputSize:p}=_.calculateShapes(s,r,o),d=!1,h=new oS(c,l,r.shape.length,s.shape.length,u,[p,1],d),m=n.runWebGLProgram(h,[s,r,i],s.dtype),f=ge({inputs:{x:m},backend:n,attrs:{shape:o}});return n.disposeIntermediateTensorInfo(m),f}var iee={kernelName:eh,backendName:"webgl",kernelFunc:see};function oee(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{numOrSizeSplits:s,axis:i}=a,o=w.parseAxisParam(i,r.shape)[0],l=_.prepareSplitSize(r,s,o),c=r.shape.length,u=new Array(c).fill(0),p=r.shape.slice();return l.map(d=>{let h=[...p];h[o]=d;let m=wp({inputs:{x:r},backend:n,attrs:{begin:u,size:h}});return u[o]+=d,m})}var lee={kernelName:_l,backendName:"webgl",kernelFunc:oee},uee="return sqrt(x);",cee=Xe({opSnippet:uee}),pee={kernelName:gi,backendName:"webgl",kernelFunc:cee},dee="return x * x;",hee=Xe({opSnippet:dee}),mee={kernelName:yc,backendName:"webgl",kernelFunc:hee},lS="return (a - b) * (a - b);",fee=ln({opSnippet:lS,packedOpSnippet:lS}),gee={kernelName:xi,backendName:"webgl",kernelFunc:fee};function yee({inputs:e,attrs:t,backend:n}){let{x:a}=e,r=Pa+`
return x > 0.0 ? 1.0 : float(${t.alpha});
`,s=new gs(a.shape,r);return n.runWebGLProgram(s,[a],a.dtype)}var bee={kernelName:Xr,backendName:"webgl",kernelFunc:yee},xee=class{constructor(e,t,n){this.variableNames=["x"],this.outputShape=n;let a=n.length,r=dt(n.length),s=dt(n.length),i="";if(a===1)i="coords * strides + begin";else{let o=0;i=n.map((l,c)=>(o++,n.length===1?`coords * strides[${c}] + begin[${c}]`:`coords[${o-1}] * strides[${c}] + begin[${c}]`)).join(",")}this.userCode=`
${r} begin = ${r}(${e});
${r} strides = ${r}(${t});
void main() {
${s} coords = getOutputCoords();
setOutput(getX(${i}));
}
`}};function vee(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{begin:s,end:i,strides:o,beginMask:l,endMask:c,ellipsisMask:u,newAxisMask:p,shrinkAxisMask:d}=a,{nonStrided:h,$begin:m,$strides:f,size:g,newShape:y,outShape:b}=rn.sliceInfo(r.shape,s,i,o,l,c,u,p,d),x=ge({inputs:{x:r},backend:n,attrs:{shape:y}}),v;if(h){let k=wp({inputs:{x},backend:n,attrs:{begin:m,size:g}});v=ge({inputs:{x:k},backend:n,attrs:{shape:b}}),n.disposeIntermediateTensorInfo(k)}else if(b.some(k=>k===0))v=n.makeTensorInfo(b,r.dtype,[]);else if(n.shouldExecuteOnCPU([x])){let k=n.texData.get(x.dataId).values,S=Me(x.shape,x.dtype,k),F=vX(b,S,f,m);v=n.makeTensorInfo(b,x.dtype,F.values)}else{let k=new xee(m,f,b);v=n.runWebGLProgram(k,[x],x.dtype)}let T=ge({inputs:{x:v},backend:n,attrs:{shape:b}});return n.disposeIntermediateTensorInfo(x),n.disposeIntermediateTensorInfo(v),T}var wee={kernelName:El,backendName:"webgl",kernelFunc:vee},kee="return tan(x);",Iee=Xe({opSnippet:kee}),Tee={kernelName:Fl,backendName:"webgl",kernelFunc:Iee},Nee=`
float e2x = exp(-2.0 * abs(x));
return sign(x) * (1.0 - e2x) / (1.0 + e2x);
`,See=Xe({opSnippet:Nee}),Cee={kernelName:wi,backendName:"webgl",kernelFunc:See},Eee=class{constructor(e,t){this.variableNames=["A"];let n=new Array(e.length);for(let s=0;s<n.length;s++)n[s]=e[s]*t[s];this.outputShape=n,this.rank=n.length;let a=dt(this.rank),r=_ee(e);this.userCode=`
void main() {
${a} resRC = getOutputCoords();
setOutput(getA(${r}));
}
`}};function _ee(e){let t=e.length;if(t>5)throw Error(`Tile for rank ${t} is not yet supported`);if(t===1)return`imod(resRC, ${e[0]})`;let n=["resRC.x","resRC.y","resRC.z","resRC.w","resRC.u"],a=[];for(let r=0;r<e.length;r++)a.push(`imod(${n[r]}, ${e[r]})`);return a.join()}function uS(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{reps:s}=a;if(r.dtype==="string"){let o=n.readSync(r.dataId).map(u=>w.decodeString(u)),l=Me(r.shape,r.dtype,o),c=kX(l,s);return n.makeTensorInfo(c.shape,c.dtype,c.values)}let i=new Eee(r.shape,s);return n.runWebGLProgram(i,[r],r.dtype)}var Fee={kernelName:qr,backendName:"webgl",kernelFunc:uS};function Aee(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{k:s,sorted:i}=a,o=n.readSync(r.dataId),[l,c]=IX(o,r.shape,r.dtype,s,i);return[n.makeTensorInfo(l.shape,l.dtype,l.values),n.makeTensorInfo(c.shape,c.dtype,c.values)]}var $ee={kernelName:Al,backendName:"webgl",kernelFunc:Aee},Dee=class{constructor(e,t,n,a,r,s){this.variableNames=["Image","Transforms"],this.outputShape=s;let i=n==="nearest"?1:2,o;switch(a){case"constant":o=1;break;case"reflect":o=2;break;case"wrap":o=3;break;case"nearest":o=4;break;default:o=1;break}this.userCode=`
float mapCoord(float outCoord, float len) {
float inCoord = outCoord;
if(${o} == 2) {
if (inCoord < 0.0) {
if (len <= 1.0) {
inCoord = 0.0;
} else {
float sz2 = 2.0 * len;
if (inCoord < sz2) {
inCoord = sz2 * float(int(float(-inCoord / sz2))) +
inCoord;
}
inCoord = inCoord < -len ? inCoord + sz2 : -inCoord - 1.0;
}
} else if (inCoord > len - 1.0) {
if (len <= 1.0) {
inCoord = 0.0;
} else {
float sz2 = 2.0 * len;
inCoord -= sz2 * float(int(float(inCoord / sz2)));
if (inCoord >= len) {
inCoord = sz2 - inCoord - 1.0;
}
}
}
return clamp(inCoord, 0.0, len - 1.0);
} else if (${o} == 3) {
if (inCoord < 0.0) {
if (len <= 1.0) {
inCoord = 0.0;
} else {
float sz = len - 1.0;
inCoord += len * (float(int(float(-inCoord / sz))) + 1.0);
}
} else if (inCoord > len - 1.0) {
if (len <= 1.0) {
inCoord = 0.0;
} else {
float sz = len - 1.0;
inCoord -= len * float(int(float(inCoord / sz)));
}
}
return clamp(inCoord, 0.0, len - 1.0);
} else if (${o} == 4) {
return clamp(outCoord, 0.0, len - 1.0);
} else {
return outCoord;
}
}
float readWithFillValue(int batch, int coordY, int coordX,
int channel) {
float outputValue;
if (0 <= coordY && coordY < ${e} && 0 <= coordX && coordX < ${t}) {
outputValue = getImage(batch, coordY, coordX, channel);
} else {
outputValue = float(${r});
}
return outputValue;
}
void main() {
ivec4 coords = getOutputCoords();
float outputValue;
int batch = coords[0];
int x = coords[2];
int y = coords[1];
int channel = coords[3];
float xf = float(x);
float yf = float(y);
float a1 = getTransforms(batch, 0);
float a2 = getTransforms(batch, 1);
float a3 = getTransforms(batch, 2);
float b1 = getTransforms(batch, 3);
float b2 = getTransforms(batch, 4);
float b3 = getTransforms(batch, 5);
float c1 = getTransforms(batch, 6);
float c2 = getTransforms(batch, 7);
float projection = c1 * xf + c2 * yf + 1.0;
if (projection == 0.0) {
outputValue = float(${r});
} else {
float inX = (a1 * xf + a2 * yf + a3) / projection;
float inY = (b1 * xf + b2 * yf + b3) / projection;
float mapX = mapCoord(inX, float(${t}));
float mapY = mapCoord(inY, float(${e}));
if (${i} == 1) {
int coordY = int(round(mapY));
int coordX = int(round(mapX));
outputValue = readWithFillValue(batch, coordY, coordX,
channel);
} else {
float yFloor = floor(mapY);
float xFloor = floor(mapX);
float yCeil = yFloor + 1.0;
float xCeil = xFloor + 1.0;
float valueYFloor = (xCeil - mapX) *
readWithFillValue(batch, int(yFloor), int(xFloor), channel) +
(mapX - xFloor) *
readWithFillValue(batch, int(yFloor), int(xCeil), channel);
float valueYCeil = (xCeil - mapX) *
readWithFillValue(batch, int(yCeil), int(xFloor), channel) +
(mapX - xFloor) *
readWithFillValue(batch, int(yCeil), int(xCeil), channel);
outputValue = (yCeil - mapY) * valueYFloor +
(mapY - yFloor) * valueYCeil;
}
}
setOutput(outputValue);
}
`}};function Ree(e){let{inputs:t,backend:n,attrs:a}=e,{image:r,transforms:s}=t,{interpolation:i,fillMode:o,fillValue:l,outputShape:c}=a,[u,p,d,h]=r.shape,[m,f]=c!=null?c:[p,d],g=[u,m,f,h],y=new Dee(p,d,i,o,l,g);return n.runWebGLProgram(y,[r,s],"float32")}var Mee={kernelName:th,backendName:"webgl",kernelFunc:Ree};function Pee(e){let{inputs:t,attrs:n,backend:a}=e,{axis:r}=n,{x:s}=t;yp(s,"unique"),console.warn("WARNING: ","UI might be locked temporarily as data is being downloaded");let i=a.readSync(s.dataId),{outputValues:o,outputShape:l,indices:c}=TX(i,r,s.shape,s.dtype);return[a.makeTensorInfo(l,s.dtype,o),a.makeTensorInfo([c.length],"int32",c)]}var Oee={kernelName:nh,backendName:"webgl",kernelFunc:Pee};function Lee(e){let{inputs:t,backend:n,attrs:a}=e,{value:r}=t,{axis:s}=a;s<0&&(s+=r.shape.length);let i=r,o=i.shape.length,l=r.shape[s],c=new Array(o-1),u=0;for(let f=0;f<o;f++)f!==s&&(c[u++]=i.shape[f]);let p=[],d=new Array(o).fill(0),h=i.shape.slice();h[s]=1;let m=new Array(l);for(let f=0;f<m.length;f++){d[s]=f;let g=wp({inputs:{x:i},backend:n,attrs:{begin:d,size:h}}),y=ge({inputs:{x:g},backend:n,attrs:{shape:c}});m[f]=y,p.push(g)}return p.forEach(f=>n.disposeIntermediateTensorInfo(f)),m}var zee={kernelName:$l,backendName:"webgl",kernelFunc:Lee},Wee=class{constructor(e,t){this.variableNames=["x","segmentIds"];let n=e.windowSize,a=e.batchSize,r=e.inSize,s=e.numSegments,i=s*Math.ceil(r/n);this.outputShape=[a,i];let o="0.0",l="sumValue",c=Math.floor(n/4)*4,u=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 = ${o};
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(
${s})) * float(${n}));
int currentSeg = int(mod(float(outIdx), float(${s})));
float sumValue = 0.0;
for (int i = 0; i < ${c}; i += 4) {
int inIdx = inOffset + i;
vec4 values = vec4(
getValue(batch, inIdx),
getValue(batch, inIdx + 1),
getValue(batch, inIdx + 2),
getValue(batch, inIdx + 3)
);
vec4 segFilter = vec4(
int(getSegmentIdAtIndex(inIdx)) == currentSeg ? 1 : 0,
int(getSegmentIdAtIndex(inIdx + 1)) == currentSeg ? 1 : 0,
int(getSegmentIdAtIndex(inIdx + 2)) == currentSeg ? 1 : 0,
int(getSegmentIdAtIndex(inIdx + 3)) == currentSeg ? 1 : 0
);
${p}
}
int inIdx = inOffset + ${c};
if (${u===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
);
${p}
} else if (${u===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
);
${p}
} else if (${u===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
);
${p}
}
setOutput(${l});
}
`}};function Bee(e){let{inputs:t,backend:n,attrs:a}=e,{x:r,segmentIds:s}=t,{numSegments:i}=a,o=r.shape.length,l=[],c=0,u=_.getAxesPermutation([c],o),p=r;u!=null&&(p=$n({inputs:{x:r},backend:n,attrs:{perm:u}}),l.push(p),c=_.getInnerMostAxes(1,o)[0]);let d=_.segment_util.computeOutShape(p.shape,c,i),h=w.sizeFromShape([p.shape[c]]),m=ge({inputs:{x:p},backend:n,attrs:{shape:[-1,h]}});l.push(m);let f=uh(r.dtype),g=(v,T,k,S,F)=>{let A=v.shape[0],R=v.shape[1],P=_.segment_util.segOpComputeOptimalWindowSize(R,F),z={windowSize:P,inSize:R,batchSize:A,numSegments:F},V=new Wee(z,T),G=n.compileAndRun(V,[v,k],S);if(l.push(G),G.shape[1]===F)return G;let H=iS({backend:n,attrs:{start:0,stop:F,step:1,dtype:"float32"}}),X=uS({inputs:{x:H},backend:n,attrs:{reps:[R/P]}});return l.push(H),l.push(X),g(G,T,X,S,F)},y=g(m,"unsortedSegmentSum",s,f,i),b=ge({inputs:{x:y},backend:n,attrs:{shape:d}}),x=b;if(u!=null){l.push(b);let v=_.getUndoAxesPermutation(u);x=$n({inputs:{x},backend:n,attrs:{perm:v}})}return l.forEach(v=>n.disposeIntermediateTensorInfo(v)),x}var Vee={kernelName:bc,backendName:"webgl",kernelFunc:Bee},Uee=[$J,MJ,vK,kK,NK,_K,FK,DK,MK,OK,BK,UK,jK,KK,nY,QK,sY,uY,oY,hY,fY,yY,wY,_Y,FY,PY,LY,VY,HY,tK,KY,s7,o7,ZY,p7,h7,u7,g7,x7,k7,T7,S7,E7,M7,O7,A7,W7,U7,q7,J7,t9,r9,s9,i9,l9,c9,d9,m9,g9,v9,T9,S9,_9,A9,M9,z9,U9,eK,H9,XY,X9,J9,eJ,aK,rJ,lJ,cJ,yJ,mJ,wJ,TJ,_J,OJ,HJ,UJ,KJ,JJ,ZJ,BJ,tQ,aQ,oQ,pQ,fQ,IQ,lK,NQ,_Q,AQ,RQ,$Y,OQ,zQ,BQ,GQ,XQ,sK,YQ,JQ,DY,xQ,eZ,lZ,rZ,cK,dZ,fZ,bZ,wZ,NZ,CZ,FZ,DZ,MZ,LZ,BZ,GZ,qZ,YZ,ZZ,SY,wQ,nee,ree,iee,lee,pee,mee,gee,bee,wee,vQ,yK,Tee,Cee,Fee,$ee,Mee,bK,Oee,zee,Vee,LQ];for(let e of Uee)vc(e);var Gee="3.3.0",Hee={"tfjs-core":lk,"tfjs-backend-cpu":pG,"tfjs-backend-webgl":ZX,"tfjs-data":MT,"tfjs-layers":Im,"tfjs-converter":ET,tfjs:Gee},Hn;(function(e){e[e.float32=0]="float32",e[e.int32=1]="int32",e[e.bool=2]="bool",e[e.string=3]="string",e[e.complex64=4]="complex64"})(Hn||(Hn={}));var Ip;(function(e){e[e.linear=0]="linear",e[e.relu=1]="relu",e[e.relu6=2]="relu6",e[e.prelu=3]="prelu",e[e.leakyrelu=4]="leakyrelu"})(Ip||(Ip={}));var cS;function jee(e){cS=e.wasm.cwrap(Ii,null,["number","array","number","number","array","number","number","number","number","number","number","number","number"])}function qee(e){let{inputs:t,backend:n,attrs:a}=e,{a:r,b:s,bias:i,preluActivationWeights:o}=t;if(r.dtype!=="float32"||s.dtype!=="float32")throw new Error("_FusedMatMul for non non-float32 tensors not yet supported.");let{transposeA:l,transposeB:c,activation:u,leakyreluAlpha:p}=a,d=n.dataIdMap.get(r.dataId).id,h=n.dataIdMap.get(s.dataId).id,m=0;if(i!=null){let F=n.dataIdMap.get(i.dataId);if(F.shape.length!==1)throw new Error(`_FusedMatMul only supports rank-1 bias but got rank ${F.shape.length}.`);m=F.id}let f=o==null?0:n.dataIdMap.get(o.dataId).id,g=Ip[u];if(g==null)throw new Error(`${u} activation not yet supported for FusedConv2D in the wasm backend.`);let y=l?r.shape[2]:r.shape[1],b=c?s.shape[1]:s.shape[2],x=r.shape[0],v=n.makeOutput([x,y,b],r.dtype),T=n.dataIdMap.get(v.dataId).id,k=new Uint8Array(new Int32Array(r.shape).buffer),S=new Uint8Array(new Int32Array(s.shape).buffer);return cS(d,k,r.shape.length,h,S,s.shape.length,l,c,g,m,f,p||0,T),v}var Xee={kernelName:Ii,backendName:"wasm",setupFunc:jee,kernelFunc:qee};function Dn(e){let t;function n(r){t=r.wasm.cwrap(e,null,["number","number"])}function a(r){let{backend:s,inputs:{x:i}}=r,o=s.dataIdMap.get(i.dataId).id,l=s.makeOutput(i.shape,i.dtype),c=s.dataIdMap.get(l.dataId).id;return w.sizeFromShape(l.shape)===0||t(o,c),l}return{kernelName:e,backendName:"wasm",setupFunc:n,kernelFunc:a}}var Kee=Dn(Po);function bn(e,t,n){let a;function r(i){a=i.wasm.cwrap(e,null,["number","array","number","number","array","number","number","number"])}function s(i){let{backend:o,inputs:l}=i,{a:c,b:u}=l,p=o.dataIdMap.get(c.dataId).id,d=o.dataIdMap.get(u.dataId).id,h=n!=null?n:c.dtype,m=_.assertAndGetBroadcastShape(c.shape,u.shape),f=o.makeOutput(m,h);if(w.sizeFromShape(m)===0)return f;let g=new Uint8Array(new Int32Array(c.shape).buffer),y=new Uint8Array(new Int32Array(u.shape).buffer),b=o.dataIdMap.get(f.dataId).id,x=()=>a(p,g,c.shape.length,d,y,u.shape.length,Hn[c.dtype],b);if(t&&c.dtype==="float32")return x(),f;let v=_.getBroadcastDims(c.shape,m),T=_.getBroadcastDims(u.shape,m),k=v.every((F,A)=>F===A),S=T.every((F,A)=>F===A);if(k&&S)return x(),f;throw new Error(`Broadcasting along outer dims is not yet supported for ${c.dtype} ${e}.`)}return{kernelName:e,backendName:"wasm",setupFunc:r,kernelFunc:s}}var Yee=!0,Jee=bn(Hr,Yee),pS;function Qee(e){pS=e.wasm.cwrap(As,null,["array","number","number","number"])}function Zee(e){let{inputs:t,backend:n}=e,a=n.makeOutput(t[0].shape,t[0].dtype);if(w.sizeFromShape(a.shape)===0)return a;let r=t.map(o=>n.dataIdMap.get(o.dataId).id),s=new Uint8Array(new Int32Array(r).buffer),i=n.dataIdMap.get(a.dataId).id;return pS(s,r.length,Hn[a.dtype],i),a}var ete={kernelName:As,backendName:"wasm",setupFunc:Qee,kernelFunc:Zee};function nf(e){let{inputs:{x:t},backend:n}=e,a=n.makeOutput(t.shape,t.dtype),r=n.typedArrayFromHeap(t);return n.typedArrayFromHeap(a).set(r),a}var tte={kernelName:Xs,backendName:"wasm",kernelFunc:nf},dS;function nte(e){dS=e.wasm.cwrap(ki,null,["number","array","number","number","number","array","number"])}function af(e){let{inputs:t,backend:n,attrs:a}=e,[r,s]=rte(t.x.shape,a.perm),i=!0;for(let m=0;m<s.length;m++)s[m]!==m&&(i=!1);let o=ate(t.x.shape,a.perm),l={dataId:t.x.dataId,shape:r,dtype:t.x.dtype};if(i){let m=nf({inputs:t,backend:n});return m.shape=o,m}let c=n.makeOutput(o,l.dtype),u=n.dataIdMap.get(l.dataId).id,p=n.dataIdMap.get(c.dataId).id,d=new Uint8Array(new Int32Array(s).buffer),h=new Uint8Array(new Int32Array(l.shape).buffer);return dS(u,h,l.shape.length,Hn[l.dtype],p,d,s.length),c}function ate(e,t){let n=new Array(e.length);for(let a=0;a<n.length;a++)n[a]=e[t[a]];return n}function rte(e,t){let n=[],a=[];for(let r=0;r<e.length;++r)e[r]!==1&&n.push(e[r]),e[t[r]]!==1&&a.push(t[r]);for(let r=0;r<a.length;++r){let s=-1;for(let i=0;i<a.length;++i)a[i]>=r&&(s===-1||a[s]>a[i])&&(s=i);a[s]=r}return[n,a]}var ste={kernelName:ki,backendName:"wasm",kernelFunc:af,setupFunc:nte};function Tu(e,t,n){let a=e.shape,r=e.shape.length,s=w.parseAxisParam(t,a),i=s,o=_.getAxesPermutation(i,r),l=null,c=!1;if(o!=null){let u=new Array(r);for(let d=0;d<u.length;d++)u[d]=a[o[d]];i=_.getInnerMostAxes(i.length,r),l=af({inputs:{x:e},attrs:{perm:o},backend:n});let p=n.dataIdMap.get(e.dataId).id;n.dataIdMap.get(l.dataId).id!==p&&(c=!0)}return{transposed:l,originalAxes:s,axes:i,inputWasTransposed:c}}var hS;function ite(e){hS=e.wasm.cwrap($s,null,["number","number","number","number","number"])}function ote(e){let{backend:t,inputs:n,attrs:a}=e,{axis:r}=a,{x:s}=n,i=t.dataIdMap.get(s.dataId).id,o=i,l=s,{transposed:c,axes:u,inputWasTransposed:p}=Tu(s,r,t);if(p){let y=t.dataIdMap.get(c.dataId).id;y!==i&&(l=c,o=y)}let d=l.shape.slice(0,-1),h=t.makeOutput(d,"int32"),m=t.dataIdMap.get(h.dataId).id,f=w.sizeFromShape(h.shape),g=l.shape[u[0]];return hS(o,Hn[l.dtype],f,g,m),p&&t.disposeData(c.dataId),h}var lte={kernelName:$s,backendName:"wasm",kernelFunc:ote,setupFunc:ite},mS;function ute(e){mS=e.wasm.cwrap(Ds,null,["number","number","number","number","number","number","number","number","number","number","number","number","number","number"])}function cte(e){let{inputs:t,attrs:n,backend:a}=e,r=t.x,s=a.dataIdMap.get(r.dataId).id,{filterSize:i,strides:o,pad:l,dimRoundingMode:c}=n,u=_.computePool2DInfo(r.shape,i,o,1,l,c),p=u.filterHeight,d=u.filterWidth,h=u.padInfo.top,m=u.padInfo.right,f=u.padInfo.bottom,g=u.padInfo.left,y=u.strideHeight,b=u.strideWidth,x=u.inChannels;if(u.dataFormat!=="channelsLast")throw new Error(`wasm backend does not support dataFormat:'${u.dataFormat}'. Please use 'channelsLast'.`);if(u.dilationWidth!==1||u.dilationHeight!==1)throw new Error(`was backend only supports average pooling with dilation = [1, 1], got [${u.dilationHeight}, ${u.dilationWidth}].`);let v=a.makeOutput(u.outShape,"float32"),T=a.dataIdMap.get(v.dataId).id;return mS(s,r.shape[0],r.shape[1],r.shape[2],p,d,h,m,f,g,y,b,x,T),v}var pte={kernelName:Ds,backendName:"wasm",setupFunc:ute,kernelFunc:cte};function Oa(e){let{inputs:t,attrs:n}=e,{x:a}=t,{shape:r}=n,s=w.sizeFromShape(a.shape),i=w.inferFromImplicitShape(r,s);return w.assert(s===w.sizeFromShape(i),()=>`new shape: ${i}, old shape: ${a.shape}. New shape and old shape must have the same number of elements.`),e.backend.incRef(a.dataId),{dataId:a.dataId,shape:i,dtype:a.dtype}}var dte={kernelName:vl,backendName:"wasm",kernelFunc:Oa},fS;function hte(e){fS=e.wasm.cwrap(Rs,null,["number","array","number","number","array","number","number","number","number"])}function mte(e){let{inputs:t,backend:n,attrs:a}=e,{a:r,b:s}=t,{transposeA:i,transposeB:o}=a;if(r.dtype!=="float32"||s.dtype!=="float32")throw new Error("BatchMatMul for non non-float32 tensors not yet supported.");let l=r.shape.length,c=s.shape.length,u=i?r.shape[l-2]:r.shape[l-1],p=o?s.shape[c-1]:s.shape[c-2],d=i?r.shape[l-1]:r.shape[l-2],h=o?s.shape[c-2]:s.shape[c-1],m=r.shape.slice(0,-2),f=s.shape.slice(0,-2),g=w.sizeFromShape(m),y=w.sizeFromShape(f),b=g===y||g===1||y===1;w.assert(l>=2&&c>=2&&b,()=>`Error in matMul: the input batch dimensions must either be the same or at least one input batch dimension must be 1. Got input batch dimensions of (${m}) and (${f}).`);let x=(g>y?r.shape.slice(0,-2):s.shape.slice(0,-2)).concat([d,h]);w.assert(u===p,()=>`Error in matMul: inner shapes (${u}) and (${p}) of Tensors with shapes ${r.shape} and ${s.shape} and transposeA=${i} and transposeB=${o} must match.`);let v=i?[g,u,d]:[g,d,u],T=o?[y,h,p]:[y,p,h],k=Oa({inputs:{x:r},backend:n,attrs:{shape:v}}),S=Oa({inputs:{x:s},backend:n,attrs:{shape:T}}),F=n.dataIdMap.get(k.dataId).id,A=n.dataIdMap.get(S.dataId).id,R=i?k.shape[2]:k.shape[1],P=o?S.shape[1]:S.shape[2],z=Math.max(g,y),V=n.makeOutput([z,R,P],k.dtype),G=n.dataIdMap.get(V.dataId).id,H=new Uint8Array(new Int32Array(k.shape).buffer),X=new Uint8Array(new Int32Array(S.shape).buffer);return fS(F,H,k.shape.length,A,X,S.shape.length,i,o,G),n.disposeData(k.dataId),n.disposeData(S.dataId),V.shape=x,V}var fte={kernelName:Rs,backendName:"wasm",setupFunc:hte,kernelFunc:mte};function rf(e){let{inputs:{x:t},attrs:{dtype:n},backend:a}=e,r=a.makeOutput(t.shape,n),s=a.typedArrayFromHeap(t);return a.typedArrayFromHeap(r).set(s),r}var gte={kernelName:Ms,backendName:"wasm",kernelFunc:rf},yte=Dn(Ps),gS;function bte(e){gS=e.wasm.cwrap(jr,null,["number","number","number","number"])}function xte(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{clipValueMin:s,clipValueMax:i}=a,o=n.dataIdMap.get(r.dataId).id,l=n.makeOutput(r.shape,r.dtype),c=n.dataIdMap.get(l.dataId).id;return gS(o,s,i,c),l}var vte={kernelName:jr,backendName:"wasm",setupFunc:bte,kernelFunc:xte};function wte(e,t,n,a){let r=w.getArrayFromDType(n,w.sizeFromShape(t));if(a&&n!=="string"){let s=0;e.forEach(i=>{let o=w.sizeFromShape(i.shape);r.set(i.vals,s),s+=o})}else{let s=0;e.forEach(i=>{let o=n==="string"?_.fromUint8ToStringArray(i.vals):i.vals,l=0;for(let c=0;c<i.shape[0];++c){let u=c*t[1]+s;for(let p=0;p<i.shape[1];++p)r[u+p]=o[l++]}s+=i.shape[1]})}return r}function kte(e,t,n,a){let r=e===t,s=e<t&&n<0,i=t<e&&n>1;if(r||s||i)return w.makeZerosTypedArray(0,a);let o=Math.abs(Math.ceil((t-e)/n)),l=w.makeZerosTypedArray(o,a);t<e&&n===1&&(n=-1),l[0]=e;for(let c=1;c<l.length;c++)l[c]=l[c-1]+n;return l}function yS(e,t,n,a,r){let s=rn.isSliceContinous(a,t,n),i=w.sizeFromShape(n),o=w.computeStrides(a);if(s){let p=rn.computeFlatOffset(t,o);return r==="string"?e.slice(p,p+i):e.subarray(p,p+i)}let l=r==="string"?_.fromUint8ToStringArray(e):e,c=Me(a,r,l),u=Me(n,r);for(let p=0;p<u.size;++p){let d=u.indexToLoc(p),h=d.map((m,f)=>m+t[f]);u.set(c.get(...h),...d)}return r==="string"?_.fromStringArrayToUint8(u.values):u.values}function bS(e){let{inputs:t,backend:n}=e,a=w.parseAxisParam(e.attrs.axis,t[0].shape)[0],r=_.computeOutShape(t.map(h=>h.shape),a),s=t.filter(h=>w.sizeFromShape(h.shape)>0);if(s.length===1)return nf({inputs:{x:s[0]},backend:n});let i=n.makeOutput(r,t[0].dtype);if(w.sizeFromShape(r)===0)return i;let o=s.map(h=>h.shape);if(_.assertParamsConsistent(o,a),s[0].dtype==="string"){let h=s.map(x=>{let v=w.sizeFromShape(x.shape.slice(a));return Oa({inputs:{x},backend:n,attrs:{shape:[-1,v]}})}),m=h.map(x=>({vals:n.readSync(x.dataId),shape:x.shape}));r=_.computeOutShape(h.map(x=>x.shape),1);let f=h[0].shape[0]===1,g=wte(m,r,t[0].dtype,f),y=_.computeOutShape(s.map(x=>x.shape),a);i.shape=y;let b=n.dataIdMap.get(i.dataId);return b.stringBytes=_.fromStringArrayToUint8(g),h.forEach(x=>n.disposeData(x.dataId)),i}let l=w.sizeFromShape(s[0].shape.slice(0,a)),c=0,u=s.map(h=>{let m=w.sizeFromShape(h.shape.slice(a));return c+=m,m}),p=s.map(h=>n.typedArrayFromHeap(h)),d=n.typedArrayFromHeap(i);for(let h=0;h<l;h++){let m=h*c;for(let f=0;f<p.length;f++){let g=u[f],y=h*g,b=p[f].subarray(y,y+g);d.set(b,m),m+=g}}return i}var Ite={kernelName:Go,backendName:"wasm",kernelFunc:bS},xS;function Tte(e){xS=e.wasm.cwrap(Os,null,["number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number"])}function Nte(e){let{inputs:t,attrs:n,backend:a}=e,{x:r,filter:s}=t,i=a.dataIdMap.get(r.dataId).id,o=a.dataIdMap.get(s.dataId).id,{strides:l,dilations:c,pad:u,dimRoundingMode:p,dataFormat:d}=n,h=_.convertConv2DDataFormat(d),m=_.computeConv2DInfo(r.shape,s.shape,l,c,u,p,!1,h),f=m.filterHeight,g=m.filterWidth,y=m.padInfo.top,b=m.padInfo.right,x=m.padInfo.bottom,v=m.padInfo.left,T=m.dilationHeight,k=m.dilationWidth,S=m.strideHeight,F=m.strideWidth,A=m.inChannels,R=m.outChannels,P=m.padInfo.type==="SAME"?1:0;if(m.dataFormat!=="channelsLast")throw new Error(`wasm backend Conv2D does not support dataFormat:'${m.dataFormat}'. Please use 'channelsLast'.`);let z=a.makeOutput(m.outShape,"float32"),V=a.dataIdMap.get(z.dataId).id;return xS(i,r.shape[0],r.shape[1],r.shape[2],o,f,g,y,b,x,v,P,T,k,S,F,A,R,V),z}var Ste={kernelName:Os,backendName:"wasm",setupFunc:Tte,kernelFunc:Nte},vS;function Cte(e){vS=e.wasm.cwrap(Ls,null,["number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number"])}function _te(e){let{backend:t,inputs:n,attrs:a}=e,{dy:r,filter:s}=n,{strides:i,pad:o,dataFormat:l,dimRoundingMode:c,inputShape:u}=a,p=1,d=_.convertConv2DDataFormat(l),h=_.computeConv2DInfo(u,s.shape,i,p,o,c,!1,d),{batchSize:m,filterHeight:f,filterWidth:g,inChannels:y,inHeight:b,inWidth:x,outChannels:v,outHeight:T,outWidth:k,strideHeight:S,strideWidth:F}=h,A=f-1-h.padInfo.top,R=g-1-h.padInfo.left,P=h.dataFormat==="channelsLast",z=w.computeStrides(h.inShape),V=w.computeStrides(r.shape),[G,H,X]=w.computeStrides(s.shape),j=z[0],te=P?z[1]:z[2],Q=P?z[2]:1,se=P?1:z[1],ne=V[0],ie=P?V[1]:V[2],ee=P?V[2]:1,pe=P?1:V[1],oe=t.makeOutput(h.inShape,"float32"),fe=t.dataIdMap.get(oe.dataId).id,me=t.dataIdMap.get(r.dataId).id,we=t.dataIdMap.get(s.dataId).id;return vS(me,we,m,f,g,b,x,y,T,k,v,S,F,A,R,G,H,X,j,te,Q,se,ne,ie,ee,pe,fe),oe}var Ete={kernelName:Ls,backendName:"wasm",setupFunc:Cte,kernelFunc:_te},Fte=Dn(zs),aw;(function(e){e[e.bilinear=0]="bilinear",e[e.nearest=1]="nearest"})(aw||(aw={}));var wS;function Ate(e){wS=e.wasm.cwrap(jo,null,["number","number","number","number","array","number","number","number","number","number"])}function $te(e){let{backend:t,inputs:n,attrs:a}=e,{method:r,extrapolationValue:s,cropSize:i}=a,{image:o,boxes:l,boxInd:c}=n,u=l.shape[0],[p,d]=i,h=[u,p,d,o.shape[3]],m=t.dataIdMap.get(o.dataId),f;o.dtype!=="float32"&&(f=rf({backend:t,inputs:{x:o},attrs:{dtype:"float32"}}),m=t.dataIdMap.get(f.dataId));let g=m.id,y=t.dataIdMap.get(l.dataId).id,b=t.dataIdMap.get(c.dataId).id,x=t.makeOutput(h,"float32"),v=t.dataIdMap.get(x.dataId).id,T=new Uint8Array(new Int32Array(o.shape).buffer);return wS(g,y,b,u,T,p,d,aw[r],s,v),f!=null&&t.disposeData(f.dataId),x}var Dte={kernelName:jo,backendName:"wasm",setupFunc:Ate,kernelFunc:$te},kS;function Rte(e){kS=e.wasm.cwrap(Ws,null,["number","number","number","number","number","number"])}function Mte(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{axis:s,exclusive:i,reverse:o}=a,l=r.shape.length;w.assert(r.dtype==="float32"||r.dtype==="int32",()=>`cumsum does not support ${r.dtype} tensors in the WASM backend`);let c=_.getAxesPermutation([s],l),u=r;c!==null&&(u=af({inputs:{x:r},attrs:{perm:c},backend:n}));let p=_.getInnerMostAxes(1,l)[0];_.assertAxesAreInnerMostDims("cumsum",[p],l);let d=n.makeOutput(u.shape,u.dtype),h=u.shape[p],m=n.dataIdMap.get(u.dataId).id,f=n.dataIdMap.get(d.dataId).id;kS(m,i?1:0,o?1:0,h,f,Hn[r.dtype]);let g=d;if(c!==null){let y=_.getUndoAxesPermutation(c);g=af({inputs:{x:d},attrs:{perm:y},backend:n}),n.disposeData(u.dataId),n.disposeData(d.dataId)}return g}var Pte={kernelName:Ws,backendName:"wasm",setupFunc:Rte,kernelFunc:Mte},IS;function Ote(e){IS=e.wasm.cwrap(qo,null,["number","number","number","array","number","array","array","number","number"])}function Lte(e){let{backend:t,inputs:n,attrs:a}=e,{x:r}=n,{blockSize:s,dataFormat:i}=a;w.assert(s>1,()=>`blockSize should be > 1 for depthToSpace, but was: ${s}`);let o=r.shape[0],l=i==="NHWC"?r.shape[1]:r.shape[2],c=i==="NHWC"?r.shape[2]:r.shape[3],u=i==="NHWC"?r.shape[3]:r.shape[1],p=l*s,d=c*s,h=u/(s*s),m=i==="NHWC"?[o,p,d,h]:[o,h,p,d],f=t.makeOutput(m,"float32"),g=t.dataIdMap.get(r.dataId).id,y=new Uint8Array(new Int32Array(w.computeStrides(r.shape)).buffer),b=new Uint8Array(new Int32Array(m).buffer),x=new Uint8Array(new Int32Array(w.computeStrides(m)).buffer),v=t.dataIdMap.get(f.dataId).id;return IS(g,s,i==="NHWC"?1:0,y,r.shape.length-1,b,x,m.length,v),f}var zte={kernelName:qo,backendName:"wasm",setupFunc:Ote,kernelFunc:Lte},TS;function Wte(e){TS=e.wasm.cwrap(Bs,null,["number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number"])}function Bte(e){let{inputs:t,attrs:n,backend:a}=e,{x:r,filter:s}=t,i=a.dataIdMap.get(r.dataId).id,o=a.dataIdMap.get(s.dataId).id,{strides:l,dilations:c,pad:u,dimRoundingMode:p}=n,d=c==null?[1,1]:c,h=_.computeConv2DInfo(r.shape,s.shape,l,d,u,p,!0),m=h.filterHeight,f=h.filterWidth,g=h.padInfo.top,y=h.padInfo.right,b=h.padInfo.bottom,x=h.padInfo.left,v=h.dilationHeight,T=h.dilationWidth,k=h.strideHeight,S=h.strideWidth,F=h.inChannels,A=h.outChannels,R=h.padInfo.type==="SAME"?1:0;if(h.dataFormat!=="channelsLast")throw new Error(`wasm backend DepthwiseConv2dNative does not support dataFormat:'${h.dataFormat}'. Please use 'channelsLast'.`);let P=a.makeOutput(h.outShape,"float32"),z=a.dataIdMap.get(P.dataId).id;return TS(i,r.shape[0],r.shape[1],r.shape[2],o,m,f,g,y,b,x,R,v,T,k,S,F,A,z),P}var Vte={kernelName:Bs,backendName:"wasm",setupFunc:Wte,kernelFunc:Bte},Ute=!1,Gte=bn(Yo,Ute,"bool"),Hte=Dn(Us);function rw(e){let{inputs:t,attrs:n,backend:a}=e,{input:r}=t,{dim:s}=n,i=r.shape.length,o=r.shape.slice(),l=s;return s<0&&(w.assert(-(i+1)<=s,()=>`Axis must be in the interval [${-(i+1)}, ${i}]`),l=i+s+1),o.splice(l,0,1),Oa({inputs:{x:r},backend:a,attrs:{shape:o}})}var jte={kernelName:Jo,backendName:"wasm",kernelFunc:rw};function qte(e){let{attrs:{shape:t,value:n,dtype:a},backend:r}=e,s=r.makeOutput(t,a);return r.typedArrayFromHeap(s).fill(n),s}var Xte={kernelName:lc,backendName:"wasm",kernelFunc:qte},NS;function Kte(e){NS=e.wasm.cwrap(Zo,null,["number","number","number","number","number","number"])}function Yte(e){let{inputs:t,backend:n}=e,{image:a}=t,r=n.makeOutput(a.shape,a.dtype),s=n.dataIdMap.get(a.dataId).id,i=n.dataIdMap.get(r.dataId).id,[o,l,c,u]=a.shape;return NS(s,o,l,c,u,i),r}var Jte={kernelName:Zo,backendName:"wasm",kernelFunc:Yte,setupFunc:Kte},Qte=Dn(Gs),Zte=!1,ene=bn(Hs,Zte),SS;function tne(e){SS=e.wasm.cwrap(js,null,["number","number","number","number","number","number","number"])}function nne(e){let{backend:t,inputs:n,attrs:a}=e,{varianceEpsilon:r}=a,{x:s,mean:i,variance:o,offset:l,scale:c}=n,u=t.dataIdMap.get(s.dataId).id,p=t.dataIdMap.get(i.dataId).id,d=t.dataIdMap.get(o.dataId).id,h=l!=null?t.dataIdMap.get(l.dataId).id:0,m=c!=null?t.dataIdMap.get(c.dataId).id:0,f=t.makeOutput(s.shape,s.dtype);if(w.sizeFromShape(s.shape)===0)return f;let g=t.dataIdMap.get(f.dataId).id;return SS(u,p,d,h,m,r,g),f}var ane={kernelName:js,backendName:"wasm",setupFunc:tne,kernelFunc:nne},CS;function rne(e){CS=e.wasm.cwrap(Ti,null,["number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number"])}function sne(e){let{inputs:t,attrs:n,backend:a}=e,{x:r,filter:s,bias:i,preluActivationWeights:o}=t,{strides:l,pad:c,dilations:u,dataFormat:p,dimRoundingMode:d,activation:h,leakyreluAlpha:m}=n,f=_.computeConv2DInfo(r.shape,s.shape,l,u,c,d),g=Ip[h];if(g==null)throw new Error(`${h} activation not yet supported for FusedConv2D in the wasm backend.`);let y=a.dataIdMap.get(r.dataId).id,b=a.dataIdMap.get(s.dataId).id,x=f.outChannels,v=0;if(i!=null){let ee=a.dataIdMap.get(i.dataId);if(ee.shape.length!==1)throw new Error(`FusedConv2D only supports rank-1 bias but got rank ${ee.shape.length}.`);if(ee.shape[0]!==x)throw new Error(`FusedConv2D bias shape (${ee.shape}) does not match the number of output channels (${x})`);v=ee.id}let T=f.filterHeight,k=f.filterWidth,S=f.padInfo.top,F=f.padInfo.right,A=f.padInfo.bottom,R=f.padInfo.left,P=f.dilationHeight,z=f.dilationWidth,V=f.strideHeight,G=f.strideWidth,H=f.inChannels,X=f.padInfo.type==="SAME"?1:0,j=f.batchSize,te=f.inHeight,Q=f.inWidth;if(p!=="NHWC")throw new Error(`wasm backend FusedConv2D does not support dataFormat:'${p}'. Please use 'NHWC'.`);let se=a.makeOutput(f.outShape,"float32"),ne=a.dataIdMap.get(se.dataId).id,ie=o==null?0:a.dataIdMap.get(o.dataId).id;return CS(y,j,te,Q,b,T,k,v,S,F,A,R,X,P,z,V,G,H,x,g,ie,m||0,ne),se}var ine={kernelName:Ti,backendName:"wasm",setupFunc:rne,kernelFunc:sne},_S;function one(e){_S=e.wasm.cwrap(Ni,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 lne(e){let{inputs:t,attrs:n,backend:a}=e,{x:r,filter:s,bias:i,preluActivationWeights:o}=t,{strides:l,pad:c,dilations:u,dataFormat:p,dimRoundingMode:d,activation:h,leakyreluAlpha:m}=n,f=_.computeConv2DInfo(r.shape,s.shape,l,u,c,d,!0),g=Ip[h];if(g==null)throw new Error(`${h} activation not yet supported for FusedDepthwiseConv2D in the wasm backend.`);let y=a.dataIdMap.get(r.dataId).id,b=a.dataIdMap.get(s.dataId).id,x=f.outChannels,v=0;if(i!=null){let ee=a.dataIdMap.get(i.dataId);if(ee.shape.length!==1)throw new Error(`FusedDepthwiseConv2D only supports rank-1 bias but got rank ${ee.shape.length}.`);if(ee.shape[0]!==x)throw new Error(`FusedDepthwiseConv2D bias shape (${ee.shape}) does not match the number of output channels (${x})`);v=ee.id}let T=f.filterHeight,k=f.filterWidth,S=f.padInfo.top,F=f.padInfo.right,A=f.padInfo.bottom,R=f.padInfo.left,P=f.dilationHeight,z=f.dilationWidth,V=f.strideHeight,G=f.strideWidth,H=f.inChannels,X=f.padInfo.type==="SAME"?1:0,j=f.batchSize,te=f.inHeight,Q=f.inWidth;if(p!=="NHWC")throw new Error(`wasm backend FusedDepthwiseConv2D does not support dataFormat:'${p}'. Please use 'NHWC'.`);let se=a.makeOutput(f.outShape,"float32"),ne=a.dataIdMap.get(se.dataId).id,ie=o==null?0:a.dataIdMap.get(o.dataId).id;return _S(y,j,te,Q,b,T,k,v,S,F,A,R,X,P,z,V,G,H,x,g,ie,m||0,ne),se}var une={kernelName:Ni,backendName:"wasm",setupFunc:one,kernelFunc:lne},ES;function cne(e){ES=e.wasm.cwrap(tl,null,["number","number","number","number","number","number","array","number"])}function pne(e){let{backend:t,inputs:n}=e,{params:a,indices:r}=n,[s,i,o,l]=Sy.prepareAndValidate(a,r),c=t.makeOutput(s,a.dtype);if(i===0)return c;let u=r.shape,p=u[u.length-1],d=t.dataIdMap.get(a.dataId).id,h=t.dataIdMap.get(r.dataId).id,m=new Uint8Array(new Int32Array(l).buffer),f=t.dataIdMap.get(c.dataId).id;return ES(d,Hn[a.dtype],h,i,p,o,m,f),c}var dne={kernelName:tl,backendName:"wasm",setupFunc:cne,kernelFunc:pne},FS;function hne(e){FS=e.wasm.cwrap("Gather",null,["number","number","array","number","number","number","array","number"])}function mne(e){let{backend:t,inputs:n,attrs:a}=e,{x:r,indices:s}=n,{axis:i,batchDims:o}=a,l=w.parseAxisParam(i,r.shape)[0],c=_.segment_util.collectGatherOpShapeInfo(r,s,l,o),u=Oa({inputs:{x:r},attrs:{shape:[c.batchSize,c.outerSize,c.dimSize,c.sliceSize]},backend:t}),p=w.sizeFromShape(s.shape),d=Oa({inputs:{x:s},attrs:{shape:[c.batchSize,p/c.batchSize]},backend:t}),h=[c.batchSize,c.outerSize,p/c.batchSize,c.sliceSize],m=t.makeOutput(h,r.dtype);if(w.sizeFromShape(r.shape)===0)return m;let f=u.shape.length-1,g=t.dataIdMap.get(u.dataId).id,y=t.dataIdMap.get(d.dataId).id,b=t.dataIdMap.get(m.dataId).id,x=new Uint8Array(new Int32Array(w.computeStrides(u.shape)).buffer),v=new Uint8Array(new Int32Array(w.computeStrides(h)).buffer);return FS(g,Hn[r.dtype],x,f,y,c.batchSize,v,b),t.disposeData(u.dataId),t.disposeData(d.dataId),m.shape=c.outputShape,m}var fne={kernelName:el,backendName:"wasm",setupFunc:hne,kernelFunc:mne},gne=!1,yne=bn(nl,gne,"bool"),bne=!1,xne=bn(qs,bne,"bool"),AS;function vne(e){AS=e.wasm.cwrap(Ks,null,["number","number","number"])}function wne(e){let{inputs:{x:t},attrs:{alpha:n},backend:a}=e,r=a.dataIdMap.get(t.dataId).id,s=a.makeOutput(t.shape,t.dtype);if(w.sizeFromShape(t.shape)!==0){let i=a.dataIdMap.get(s.dataId).id;AS(r,n,i)}return s}var kne={kernelName:Ks,backendName:"wasm",setupFunc:vne,kernelFunc:wne},Ine=!1,Tne=bn(il,Ine,"bool"),Nne=!1,Sne=bn(ol,Nne,"bool"),Cne=Dn(Ys),_ne=!1,Ene=bn(ul,_ne,"bool"),$S;function Fne(e){$S=e.wasm.cwrap(Js,null,["number, number, number"])}function Ane(e){let{backend:t,inputs:n,attrs:a}=e,{reductionIndices:r,keepDims:s}=a,{x:i}=n,o=t.dataIdMap.get(i.dataId).id,l=i,{transposed:c,axes:u,originalAxes:p,inputWasTransposed:d}=Tu(i,r,t);if(d){let b=t.dataIdMap.get(c.dataId).id;l=c,o=b}let h=l.shape.length;_.assertAxesAreInnerMostDims("max",u,h);let[m,f]=_.computeOutAndReduceShapes(l.shape,u),g=w.sizeFromShape(f),y=t.makeOutput(m,i.dtype);if(w.sizeFromShape(l.shape)!==0){let b=t.dataIdMap.get(y.dataId).id;$S(o,g,b)}if(d&&t.disposeData(c.dataId),s){let b=_.expandShapeToKeepDim(y.shape,p);y.shape=b}return y}var $ne={kernelName:Js,backendName:"wasm",setupFunc:Fne,kernelFunc:Ane},Dne=!1,Rne=bn(Qs,Dne),DS;function Mne(e){DS=e.wasm.cwrap(Zs,null,["number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number"])}function Pne(e){let{inputs:t,attrs:n,backend:a}=e,r=t.x,s=a.dataIdMap.get(r.dataId).id,{filterSize:i,strides:o,pad:l,dimRoundingMode:c}=n,u=_.computePool2DInfo(r.shape,i,o,1,l,c),p=u.filterHeight,d=u.filterWidth,h=u.padInfo.top,m=u.padInfo.right,f=u.padInfo.bottom,g=u.padInfo.left,y=u.dilationHeight,b=u.dilationWidth,x=u.strideHeight,v=u.strideWidth,T=u.inChannels,k=u.outChannels;if(u.dataFormat!=="channelsLast")throw new Error(`wasm backend does not support dataFormat:'${u.dataFormat}'. Please use 'channelsLast'.`);let S=a.makeOutput(u.outShape,"float32"),F=a.dataIdMap.get(S.dataId).id;return DS(s,r.shape[0],r.shape[1],r.shape[2],p,d,h,m,f,g,y,b,x,v,T,k,F),S}var One={kernelName:Zs,backendName:"wasm",setupFunc:Mne,kernelFunc:Pne},RS;function Lne(e){RS=e.wasm.cwrap(ei,null,["number, number, number"])}function zne(e){let{backend:t,inputs:n,attrs:a}=e,{axis:r,keepDims:s}=a,{x:i}=n,o=t.dataIdMap.get(i.dataId).id,l=o,c=i,{transposed:u,axes:p,originalAxes:d,inputWasTransposed:h}=Tu(i,r,t),m=p;if(h){let v=t.dataIdMap.get(u.dataId).id;v!==o&&(c=u,l=v,m=_.getInnerMostAxes(m.length,c.shape.length))}_.assertAxesAreInnerMostDims("mean",m,c.shape.length);let[f,g]=_.computeOutAndReduceShapes(c.shape,m),y=w.sizeFromShape(g),b=c;c.dtype!=="float32"&&(b=rf({backend:t,inputs:{x:c},attrs:{dtype:"float32"}}),l=t.dataIdMap.get(b.dataId).id);let x=t.makeOutput(f,"float32");if(w.sizeFromShape(c.shape)!==0){let v=t.dataIdMap.get(x.dataId).id;RS(l,y,v)}if(h&&t.disposeData(u.dataId),s){let v=_.expandShapeToKeepDim(x.shape,d);x.shape=v}return c.dtype!=="float32"&&t.disposeData(b.dataId),x}var Wne={kernelName:ei,backendName:"wasm",setupFunc:Lne,kernelFunc:zne},MS;function Bne(e){MS=e.wasm.cwrap(ti,null,["number, number, number"])}function Vne(e){let{backend:t,inputs:n,attrs:a}=e,{axis:r,keepDims:s}=a,{x:i}=n,o=t.dataIdMap.get(i.dataId).id,l=o,c=i,{transposed:u,axes:p,originalAxes:d,inputWasTransposed:h}=Tu(i,r,t);if(h){let x=t.dataIdMap.get(u.dataId).id;x!==o&&(c=u,l=x)}let m=c.shape.length;_.assertAxesAreInnerMostDims("min",p,m);let[f,g]=_.computeOutAndReduceShapes(c.shape,p),y=w.sizeFromShape(g),b=t.makeOutput(f,c.dtype);if(w.sizeFromShape(c.shape)!==0){let x=t.dataIdMap.get(b.dataId).id;MS(l,y,x)}if(h&&t.disposeData(u.dataId),s){let x=_.expandShapeToKeepDim(b.shape,d);b.shape=x}return b}var Une={kernelName:ti,backendName:"wasm",setupFunc:Bne,kernelFunc:Vne},Gne=!1,Hne=bn(ni,Gne),jne=!0,qne=bn(ai,jne),Xne=Dn(pl);function sw(e,t){let n=new Int32Array(e.wasm.HEAPU8.buffer,t,4),a=n[0],r=n[1],s=n[2],i=n[3];return e.wasm._free(t),{pSelectedIndices:a,selectedSize:r,pSelectedScores:s,pValidOutputs:i}}var PS;function Kne(e){PS=e.wasm.cwrap(hl,"number",["number","number","number","number","number"])}function Yne(e){let{backend:t,inputs:n,attrs:a}=e,{iouThreshold:r,maxOutputSize:s,scoreThreshold:i}=a,{boxes:o,scores:l}=n,c=t.dataIdMap.get(o.dataId).id,u=t.dataIdMap.get(l.dataId).id,p=PS(c,u,s,r,i),{pSelectedIndices:d,selectedSize:h,pSelectedScores:m,pValidOutputs:f}=sw(t,p);return t.wasm._free(m),t.wasm._free(f),t.makeOutput([h],"int32",d)}var Jne={kernelName:hl,backendName:"wasm",setupFunc:Kne,kernelFunc:Yne},OS;function Qne(e){OS=e.wasm.cwrap(ml,"number",["number","number","number","number","number","bool"])}function Zne(e){let{backend:t,inputs:n,attrs:a}=e,{iouThreshold:r,maxOutputSize:s,scoreThreshold:i,padToMaxOutputSize:o}=a,{boxes:l,scores:c}=n,u=t.dataIdMap.get(l.dataId).id,p=t.dataIdMap.get(c.dataId).id,d=OS(u,p,s,r,i,o),{pSelectedIndices:h,selectedSize:m,pSelectedScores:f,pValidOutputs:g}=sw(t,d);t.wasm._free(f);let y=t.makeOutput([m],"int32",h),b=t.makeOutput([],"int32",g);return[y,b]}var eae={kernelName:ml,backendName:"wasm",setupFunc:Qne,kernelFunc:Zne},LS;function tae(e){LS=e.wasm.cwrap(fl,"number",["number","number","number","number","number","number"])}function nae(e){let{backend:t,inputs:n,attrs:a}=e,{iouThreshold:r,maxOutputSize:s,scoreThreshold:i,softNmsSigma:o}=a,{boxes:l,scores:c}=n,u=t.dataIdMap.get(l.dataId).id,p=t.dataIdMap.get(c.dataId).id,d=LS(u,p,s,r,i,o),{pSelectedIndices:h,selectedSize:m,pSelectedScores:f,pValidOutputs:g}=sw(t,d);t.wasm._free(g);let y=t.makeOutput([m],"int32",h),b=t.makeOutput([m],"float32",f);return[y,b]}var aae={kernelName:fl,backendName:"wasm",setupFunc:tae,kernelFunc:nae},rae=!1,sae=bn(dl,rae,"bool"),zS;function iae(e){zS=e.wasm.cwrap(ri,null,["number","number","number","number","number"])}function oae(e){let{inputs:t,backend:n,attrs:a}=e,{indices:r}=t,{depth:s,onValue:i,offValue:o}=a,l=n.makeOutput([...r.shape,s],"int32"),c=n.dataIdMap.get(l.dataId).id,u=n.dataIdMap.get(r.dataId).id;return zS(u,s,i,o,c),l}var lae={kernelName:ri,backendName:"wasm",setupFunc:iae,kernelFunc:oae};function uae(e){let{inputs:{x:t},backend:n}=e,a=n.makeOutput(t.shape,t.dtype);return n.typedArrayFromHeap(a).fill(1),a}var cae={kernelName:gl,backendName:"wasm",kernelFunc:uae};function pae(e){let{inputs:t,backend:n,attrs:a}=e,{axis:r}=a;if(t.length===1)return rw({inputs:{input:t[0]},backend:n,attrs:{dim:r}});let s=t[0].shape,i=t[0].dtype;t.forEach(u=>{w.assertShapesMatch(s,u.shape,"All tensors passed to stack must have matching shapes"),w.assert(i===u.dtype,()=>"All tensors passed to stack must have matching dtypes")});let o=[],l=t.map(u=>{let p=rw({inputs:{input:u},backend:n,attrs:{dim:r}});return o.push(p),p}),c=bS({inputs:l,backend:n,attrs:{axis:r}});return o.forEach(u=>n.disposeData(u.dataId)),c}var dae={kernelName:yl,backendName:"wasm",kernelFunc:pae},WS;function hae(e){WS=e.wasm.cwrap(si,null,["number","array","number","number","array","array","number","number"])}function mae(e){let{inputs:{x:t},backend:n,attrs:{paddings:a,constantValue:r}}=e,s=a.map((m,f)=>m[0]+t.shape[f]+m[1]),i=n.dataIdMap.get(t.dataId).id,o=n.makeOutput(s,t.dtype),l=n.dataIdMap.get(o.dataId).id,c=new Uint8Array(new Int32Array(t.shape).buffer),u=a.map(m=>m[0]),p=a.map(m=>m[1]),d=new Uint8Array(new Int32Array(u).buffer),h=new Uint8Array(new Int32Array(p).buffer);return WS(i,c,t.shape.length,Hn[t.dtype],d,h,r,l),o}var fae={kernelName:si,backendName:"wasm",kernelFunc:mae,setupFunc:hae},gae=!1,yae=bn(ii,gae),BS;function bae(e){BS=e.wasm.cwrap(oi,null,["number","number","number"])}function xae(e){let{inputs:t,backend:n}=e,{x:a,alpha:r}=t,s=n.dataIdMap.get(a.dataId).id,i=n.dataIdMap.get(r.dataId).id,o=n.makeOutput(a.shape,"float32"),l=n.dataIdMap.get(o.dataId).id;return BS(s,i,l),o}var vae={kernelName:oi,backendName:"wasm",setupFunc:bae,kernelFunc:xae},VS;function wae(e){VS=e.wasm.cwrap(bl,null,["number","number","number","number"])}function kae(e){let{backend:t,inputs:n,attrs:a}=e,{axis:r,keepDims:s}=a,{x:i}=n,o=t.dataIdMap.get(i.dataId).id,l=o,c=i,{transposed:u,axes:p,originalAxes:d,inputWasTransposed:h}=Tu(i,r,t),m=p;if(h){let x=t.dataIdMap.get(u.dataId).id;x!==o&&(c=u,l=x,m=_.getInnerMostAxes(m.length,c.shape.length))}_.assertAxesAreInnerMostDims("prod",m,c.shape.length);let[f,g]=_.computeOutAndReduceShapes(c.shape,m),y=w.sizeFromShape(g),b=t.makeOutput(f,c.dtype);if(w.sizeFromShape(c.shape)!==0){let x=t.dataIdMap.get(b.dataId).id;VS(l,y,Hn[b.dtype],x)}if(h&&t.disposeData(u.dataId),s){let x=_.expandShapeToKeepDim(b.shape,d);b.shape=x}return b}var Iae={kernelName:bl,backendName:"wasm",setupFunc:wae,kernelFunc:kae},Tae=e=>{let{backend:t,attrs:n}=e,{start:a,stop:r,step:s,dtype:i}=n,o=kte(a,r,s,i),l=t.makeOutput([o.length],i);return t.typedArrayFromHeap(l).set(o),l},Nae={kernelName:mc,backendName:"wasm",kernelFunc:Tae},Sae=!0,Cae=bn(Vs,Sae),_ae=Dn(li),Eae=Dn(ci),US;function Fae(e){US=e.wasm.cwrap(ui,null,["number","number","number","number","number","number","number","number","number","number"])}function Aae(e){let{backend:t,inputs:n,attrs:a}=e,{images:r}=n,{alignCorners:s,halfPixelCenters:i,size:o}=a,[l,c]=o,[u,p,d,h]=r.shape,m=[u,l,c,h],f=t.dataIdMap.get(r.dataId),g;f.dtype!=="float32"&&(g=rf({backend:t,inputs:{x:r},attrs:{dtype:"float32"}}),f=t.dataIdMap.get(g.dataId));let y=f.id,b=t.makeOutput(m,"float32");if(w.sizeFromShape(r.shape)===0)return b;let x=t.dataIdMap.get(b.dataId).id;return US(y,u,p,d,h,l,c,s?1:0,i?1:0,x),g!=null&&t.disposeData(g.dataId),b}var $ae={kernelName:ui,backendName:"wasm",setupFunc:Fae,kernelFunc:Aae},GS;function Dae(e){GS=e.wasm.cwrap(pi,null,["number","array","number","array","number","number"])}function Rae(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{dims:s}=a,i=w.parseAxisParam(s,r.shape);if(r.shape.length===0)return nf({inputs:{x:r},backend:n});let o=n.makeOutput(r.shape,r.dtype),l=n.dataIdMap.get(r.dataId).id,c=n.dataIdMap.get(o.dataId).id,u=new Uint8Array(new Int32Array(i).buffer),p=new Uint8Array(new Int32Array(r.shape).buffer);GS(l,u,i.length,p,r.shape.length,c);let d=Oa({inputs:{x:o},attrs:{shape:r.shape},backend:n});return n.disposeData(o.dataId),d}var Mae={kernelName:pi,backendName:"wasm",kernelFunc:Rae,setupFunc:Dae},HS;function Pae(e){HS=e.wasm.cwrap(Rl,null,["number","number","number","number","number","number","number","number","array","number","number"])}function Oae(e){let{inputs:t,backend:n,attrs:a}=e,{image:r}=t,{radians:s,fillValue:i,center:o}=a,l=n.makeOutput(r.shape,r.dtype),c=n.dataIdMap.get(r.dataId).id,u=n.dataIdMap.get(l.dataId).id,[p,d,h,m]=r.shape,[f,g]=_.getImageCenter(o,d,h),y=i===0,b=255,x=typeof i=="number"?[i,i,i,y?0:b]:[...i,b],v=new Uint8Array(new Int32Array(x).buffer);return HS(c,p,d,h,m,s,f,g,v,x.length,u),l}var Lae={kernelName:Rl,backendName:"wasm",kernelFunc:Oae,setupFunc:Pae},zae=Dn(di),Wae=Dn(hi),jS;function Bae(e){jS=e.wasm.cwrap(wl,null,["number","number","number","number","number","number","array","number","number"])}function Vae(e){let{backend:t,inputs:n,attrs:a}=e,{indices:r,updates:s}=n,{shape:i}=a,o=t.makeOutput(i,s.dtype);if(w.sizeFromShape(i)===0)return o;let{sliceRank:l,numUpdates:c,sliceSize:u,strides:p,outputSize:d}=Cy.calculateShapes(s,r,i),h=t.dataIdMap.get(r.dataId).id,m=t.dataIdMap.get(s.dataId).id,f=new Uint8Array(new Int32Array(p).buffer),g=t.dataIdMap.get(o.dataId).id;return 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c=l?a(`${o}/conv0`):r(`${o}/conv0`),u=r(`${o}/conv1`),p=r(`${o}/conv2`),d=r(`${o}/conv3`);return{conv0:c,conv1:u,conv2:p,conv3:d}}return{extractDenseBlock3Params:s,extractDenseBlock4Params:i}}function dC(e){let t=[],{extractDenseBlock4Params:n}=_f(e,t),a={dense0:n("dense0",!0),dense1:n("dense1"),dense2:n("dense2"),dense3:n("dense3")};return vn(e,t),{params:a,paramMappings:t}}var Fp=class extends en{constructor(){super("FaceFeatureExtractor")}forwardInput(t){let{params:n}=this;if(!n)throw new Error("FaceFeatureExtractor - load model before inference");return D(()=>{let a=ue(t.toBatchTensor(112,!0),"float32"),s=wa(a,[122.782,117.001,104.298]).div(255),i=Ep(s,n.dense0,!0);return i=Ep(i,n.dense1),i=Ep(i,n.dense2),i=Ep(i,n.dense3),i=Zn(i,[7,7],[2,2],"valid"),i})}async forward(t){return this.forwardInput(await ht(t))}getDefaultModelName(){return"face_feature_extractor_model"}extractParamsFromWeightMap(t){return dC(t)}extractParams(t){return pC(t)}};function Ap(e,t){return D(()=>J(ze(e,t.weights),t.bias))}function hC(e,t,n){let a=[],{extractWeights:r,getRemainingWeights:s}=wn(e),o=Tf(r,a)(t,n,"fc");if(s().length!==0)throw new Error(`weights remaing after extract: ${s().length}`);return{paramMappings:a,params:{fc:o}}}function mC(e){let t=[],n=qn(e,t);function a(s){let i=n(`${s}/weights`,2),o=n(`${s}/bias`,1);return{weights:i,bias:o}}let r={fc:a("fc")};return vn(e,t),{params:r,paramMappings:t}}function Ef(e){let t={},n={};return Object.keys(e).forEach(a=>{let r=a.startsWith("fc")?n:t;r[a]=e[a]}),{featureExtractorMap:t,classifierMap:n}}var $p=class extends en{constructor(t,n){super(t);this._faceFeatureExtractor=n}get faceFeatureExtractor(){return this._faceFeatureExtractor}runNet(t){let{params:n}=this;if(!n)throw new Error(`${this._name} - load model before inference`);return D(()=>{let a=t instanceof ur?this.faceFeatureExtractor.forwardInput(t):t;return Ap(a.as2D(a.shape[0],-1),n.fc)})}dispose(t=!0){this.faceFeatureExtractor.dispose(t),super.dispose(t)}loadClassifierParams(t){let{params:n,paramMappings:a}=this.extractClassifierParams(t);this._params=n,this._paramMappings=a}extractClassifierParams(t){return hC(t,this.getClassifierChannelsIn(),this.getClassifierChannelsOut())}extractParamsFromWeightMap(t){let{featureExtractorMap:n,classifierMap:a}=Ef(t);return this.faceFeatureExtractor.loadFromWeightMap(n),mC(a)}extractParams(t){let n=this.getClassifierChannelsIn(),a=this.getClassifierChannelsOut(),r=a*n+a,s=t.slice(0,t.length-r),i=t.slice(t.length-r);return this.faceFeatureExtractor.extractWeights(s),this.extractClassifierParams(i)}};var Ff=["neutral","happy","sad","angry","fearful","disgusted","surprised"],Ar=class{constructor(t){if(t.length!==7)throw new Error(`FaceExpressions.constructor - expected probabilities.length to be 7, have: ${t.length}`);Ff.forEach((n,a)=>{this[n]=t[a]})}asSortedArray(){return Ff.map(t=>({expression:t,probability:this[t]})).sort((t,n)=>n.probability-t.probability)}};var Dp=class extends $p{constructor(t=new Fp){super("FaceExpressionNet",t)}forwardInput(t){return D(()=>Sa(this.runNet(t)))}async forward(t){return this.forwardInput(await ht(t))}async predictExpressions(t){let n=await ht(t),a=await this.forwardInput(n),r=await Promise.all(ut(a).map(async i=>{let o=i.dataSync();return i.dispose(),o}));a.dispose();let s=r.map(i=>new Ar(i));return n.isBatchInput?s:s[0]}getDefaultModelName(){return"face_expression_model"}getClassifierChannelsIn(){return 256}getClassifierChannelsOut(){return 7}};function Af(e){return e.expressions instanceof Ar}function Rp(e,t){return{...e,...{expressions:t}}}function Qre(e,t,n=.1,a){(Array.isArray(t)?t:[t]).forEach(s=>{let i=s instanceof Ar?s:Af(s)?s.expressions:void 0;if(!i)throw new Error("drawFaceExpressions - expected faceExpressions to be FaceExpressions | WithFaceExpressions<{}> or array thereof");let l=i.asSortedArray().filter(p=>p.probability>n),c=za(s)?s.detection.box.bottomLeft:a||new De(0,0);new vs(l.map(p=>`${p.expression} (${to(p.probability)})`),c).draw(e)})}function Ts(e){return za(e)&&e.landmarks instanceof jn&&e.unshiftedLandmarks instanceof jn&&e.alignedRect instanceof mt}function Zre(e){let t=(o,l,c,u)=>Math.atan2(u-l,c-o)%Math.PI,n=o=>o*180/Math.PI,a={roll:void 0,pitch:void 0,yaw:void 0};if(!e||!e._positions||e._positions.length!==68)return a;let r=e._positions;a.roll=-t(r[36]._x,r[36]._y,r[45]._x,r[45]._y),a.pitch=t(0,Math.abs(r[0]._x-r[30]._x)/r[30]._x,Math.PI,Math.abs(r[16]._x-r[30]._x)/r[30]._x);let s=r.reduce((o,l)=>o<l._y?o:l._y,Infinity),i=r.reduce((o,l)=>o>l._y?o:l._y,-Infinity);return a.yaw=Math.PI*(e._imgDims._height/(i-s)/1.4-1),a}function uo(e,t){let{box:n}=e.detection,a=t.shiftBy(n.x,n.y),r=a.align(),{imageDims:s}=e.detection,i=new mt(e.detection.score,r.rescale(s.reverse()),s),o=Zre(t);return{...e,...{landmarks:a,unshiftedLandmarks:t,alignedRect:i,angle:o}}}var ww=class{constructor(t={}){let{drawLines:n=!0,drawPoints:a=!0,lineWidth:r,lineColor:s,pointSize:i,pointColor:o}=t;this.drawLines=n,this.drawPoints=a,this.lineWidth=r||1,this.pointSize=i||2,this.lineColor=s||"rgba(0, 255, 255, 1)",this.pointColor=o||"rgba(255, 0, 255, 1)"}},kw=class{constructor(t,n={}){this.faceLandmarks=t,this.options=new ww(n)}draw(t){let n=xn(t),{drawLines:a,drawPoints:r,lineWidth:s,lineColor:i,pointSize:o,pointColor:l}=this.options;if(a&&this.faceLandmarks instanceof so&&(n.strokeStyle=i,n.lineWidth=s,_r(n,this.faceLandmarks.getJawOutline()),_r(n,this.faceLandmarks.getLeftEyeBrow()),_r(n,this.faceLandmarks.getRightEyeBrow()),_r(n,this.faceLandmarks.getNose()),_r(n,this.faceLandmarks.getLeftEye(),!0),_r(n,this.faceLandmarks.getRightEye(),!0),_r(n,this.faceLandmarks.getMouth(),!0)),r){n.strokeStyle=l,n.fillStyle=l;let c=u=>{n.beginPath(),n.arc(u.x,u.y,o,0,2*Math.PI),n.fill()};this.faceLandmarks.positions.forEach(c)}}};function ese(e,t){(Array.isArray(t)?t:[t]).forEach(a=>{let r=a instanceof jn?a:Ts(a)?a.landmarks:void 0;if(!r)throw new Error("drawFaceLandmarks - expected faceExpressions to be FaceLandmarks | WithFaceLandmarks<WithFaceDetection<{}>> or array thereof");new kw(r).draw(e)})}var fC="1.1.4";function tse(e,t){let n=Au(e,t),a=$u(e,t);function r(i,o,l){let c=a(i,o,`${l}/separable_conv0`),u=a(o,o,`${l}/separable_conv1`),p=n(i,o,1,`${l}/expansion_conv`);return{separable_conv0:c,separable_conv1:u,expansion_conv:p}}function s(i,o){let l=a(i,i,`${o}/separable_conv0`),c=a(i,i,`${o}/separable_conv1`),u=a(i,i,`${o}/separable_conv2`);return{separable_conv0:l,separable_conv1:c,separable_conv2:u}}return{extractConvParams:n,extractSeparableConvParams:a,extractReductionBlockParams:r,extractMainBlockParams:s}}function gC(e,t){let 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a.age.dispose(),a.gender.dispose(),n.isBatchInput?o:o[0]}getDefaultModelName(){return"age_gender_model"}dispose(t=!0){this.faceFeatureExtractor.dispose(t),super.dispose(t)}loadClassifierParams(t){let{params:n,paramMappings:a}=this.extractClassifierParams(t);this._params=n,this._paramMappings=a}extractClassifierParams(t){return xC(t)}extractParamsFromWeightMap(t){let{featureExtractorMap:n,classifierMap:a}=Ef(t);return this.faceFeatureExtractor.loadFromWeightMap(n),vC(a)}extractParams(t){let n=512*1+1+(512*2+2),a=t.slice(0,t.length-n),r=t.slice(t.length-n);return this.faceFeatureExtractor.extractWeights(a),this.extractClassifierParams(r)}};var Pp=class extends $p{postProcess(t,n,a){let r=a.map(({width:i,height:o})=>{let l=n/Math.max(o,i);return{width:i*l,height:o*l}}),s=r.length;return D(()=>{let i=(p,d)=>Dt([_n([68],p,"float32"),_n([68],d,"float32")],1).as2D(1,136).as1D(),o=(p,d)=>{let{width:h,height:m}=r[p];return 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256}};function wC(e){let t=[],{extractDenseBlock3Params:n}=_f(e,t),a={dense0:n("dense0",!0),dense1:n("dense1"),dense2:n("dense2")};return vn(e,t),{params:a,paramMappings:t}}function kC(e){let t=[],{extractWeights:n,getRemainingWeights:a}=wn(e),{extractDenseBlock3Params:r}=Sf(n,t),s=r(3,32,"dense0",!0),i=r(32,64,"dense1"),o=r(64,128,"dense2");if(a().length!==0)throw new Error(`weights remaing after extract: ${a().length}`);return{paramMappings:t,params:{dense0:s,dense1:i,dense2:o}}}var Sw=class extends en{constructor(){super("TinyFaceFeatureExtractor")}forwardInput(t){let{params:n}=this;if(!n)throw new Error("TinyFaceFeatureExtractor - load model before inference");return D(()=>{let a=ue(t.toBatchTensor(112,!0),"float32"),s=wa(a,[122.782,117.001,104.298]).div(255),i=If(s,n.dense0,!0);return i=If(i,n.dense1),i=If(i,n.dense2),i=Zn(i,[14,14],[2,2],"valid"),i})}async forward(t){return this.forwardInput(await 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a(i){let o=n(`${i}/scale/weights`,1),l=n(`${i}/scale/biases`,1);return{weights:o,biases:l}}function r(i){let o=n(`${i}/conv/filters`,4),l=n(`${i}/conv/bias`,1),c=a(i);return{conv:{filters:o,bias:l},scale:c}}function s(i){return{conv1:r(`${i}/conv1`),conv2:r(`${i}/conv2`)}}return{extractConvLayerParams:r,extractResidualLayerParams:s}}function SC(e){let t=[],{extractConvLayerParams:n,extractResidualLayerParams:a}=sse(e,t),r=n("conv32_down"),s=a("conv32_1"),i=a("conv32_2"),o=a("conv32_3"),l=a("conv64_down"),c=a("conv64_1"),u=a("conv64_2"),p=a("conv64_3"),d=a("conv128_down"),h=a("conv128_1"),m=a("conv128_2"),f=a("conv256_down"),g=a("conv256_1"),y=a("conv256_2"),b=a("conv256_down_out"),{fc:x}=e;if(t.push({originalPath:"fc",paramPath:"fc"}),!uw(x))throw new Error(`expected weightMap[fc] to be a Tensor2D, instead have ${x}`);let v={conv32_down:r,conv32_1:s,conv32_2:i,conv32_3:o,conv64_down:l,conv64_1:c,conv64_2:u,conv64_3:p,conv128_down:d,conv128_1:h,conv128_2:m,conv256_down:f,conv256_1:g,conv256_2:y,conv256_down_out:b,fc:x};return vn(e,t),{params:v,paramMappings:t}}function Wa(e,t){let n=TC(e,t.conv1);return n=Ew(n,t.conv2),n=J(n,e),n=qe(n),n}function Lp(e,t){let n=$f(e,t.conv1);n=Ew(n,t.conv2);let a=Zn(e,2,2,"valid"),r=xt(a.shape),s=a.shape[3]!==n.shape[3];if(a.shape[1]!==n.shape[1]||a.shape[2]!==n.shape[2]){let o=[...n.shape];o[1]=1;let l=xt(o);n=Je([n,l],1);let c=[...n.shape];c[2]=1;let u=xt(c);n=Je([n,u],2)}return a=s?Je([a,r],3):a,n=J(a,n),n=qe(n),n}var po=class extends en{constructor(){super("FaceRecognitionNet")}forwardInput(t){let{params:n}=this;if(!n)throw new Error("FaceRecognitionNet - load model before inference");return D(()=>{let a=ue(t.toBatchTensor(150,!0),"float32"),s=wa(a,[122.782,117.001,104.298]).div(255),i=$f(s,n.conv32_down);i=$t(i,3,2,"valid"),i=Wa(i,n.conv32_1),i=Wa(i,n.conv32_2),i=Wa(i,n.conv32_3),i=Lp(i,n.conv64_down),i=Wa(i,n.conv64_1),i=Wa(i,n.conv64_2),i=Wa(i,n.conv64_3),i=Lp(i,n.conv128_down),i=Wa(i,n.conv128_1),i=Wa(i,n.conv128_2),i=Lp(i,n.conv256_down),i=Wa(i,n.conv256_1),i=Wa(i,n.conv256_2),i=Lp(i,n.conv256_down_out);let o=i.mean([1,2]);return ze(o,n.fc)})}async forward(t){return this.forwardInput(await ht(t))}async computeFaceDescriptor(t){var s;if((s=t==null?void 0:t.shape)==null?void 0:s.some(i=>i<=0))return new Float32Array(128);let n=await ht(t),a=D(()=>ut(this.forwardInput(n))),r=await Promise.all(a.map(i=>i.data()));return a.forEach(i=>i.dispose()),n.isBatchInput?r:r[0]}getDefaultModelName(){return"face_recognition_model"}extractParamsFromWeightMap(t){return SC(t)}extractParams(t){return NC(t)}};function CC(e){let t=new po;return t.extractWeights(e),t}function zp(e,t){return{...e,...{descriptor:t}}}function _C(e){return typeof e.age=="number"}function Wp(e,t){return{...e,...{age:t}}}function EC(e){return(e.gender===cr.MALE||e.gender===cr.FEMALE)&&Nu(e.genderProbability)}function Bp(e,t,n){return{...e,...{gender:t,genderProbability:n}}}function ise(e,t){function n(l,c){let u=_a(e(3*3*l),[3,3,l,1]),p=Ze(e(l)),d=Ze(e(l)),h=Ze(e(l)),m=Ze(e(l));return t.push({paramPath:`${c}/filters`},{paramPath:`${c}/batch_norm_scale`},{paramPath:`${c}/batch_norm_offset`},{paramPath:`${c}/batch_norm_mean`},{paramPath:`${c}/batch_norm_variance`}),{filters:u,batch_norm_scale:p,batch_norm_offset:d,batch_norm_mean:h,batch_norm_variance:m}}function a(l,c,u,p,d){let h=_a(e(l*c*u*u),[u,u,l,c]),m=Ze(e(c));return t.push({paramPath:`${p}/filters`},{paramPath:`${p}/${d?"batch_norm_offset":"bias"}`}),{filters:h,bias:m}}function r(l,c,u,p){let{filters:d,bias:h}=a(l,c,u,p,!0);return{filters:d,batch_norm_offset:h}}function s(l,c,u){let p=n(l,`${u}/depthwise_conv`),d=r(l,c,1,`${u}/pointwise_conv`);return{depthwise_conv:p,pointwise_conv:d}}function i(){let l=r(3,32,3,"mobilenetv1/conv_0"),c=s(32,64,"mobilenetv1/conv_1"),u=s(64,128,"mobilenetv1/conv_2"),p=s(128,128,"mobilenetv1/conv_3"),d=s(128,256,"mobilenetv1/conv_4"),h=s(256,256,"mobilenetv1/conv_5"),m=s(256,512,"mobilenetv1/conv_6"),f=s(512,512,"mobilenetv1/conv_7"),g=s(512,512,"mobilenetv1/conv_8"),y=s(512,512,"mobilenetv1/conv_9"),b=s(512,512,"mobilenetv1/conv_10"),x=s(512,512,"mobilenetv1/conv_11"),v=s(512,1024,"mobilenetv1/conv_12"),T=s(1024,1024,"mobilenetv1/conv_13");return{conv_0:l,conv_1:c,conv_2:u,conv_3:p,conv_4:d,conv_5:h,conv_6:m,conv_7:f,conv_8:g,conv_9:y,conv_10:b,conv_11:x,conv_12:v,conv_13:T}}function o(){let l=r(1024,256,1,"prediction_layer/conv_0"),c=r(256,512,3,"prediction_layer/conv_1"),u=r(512,128,1,"prediction_layer/conv_2"),p=r(128,256,3,"prediction_layer/conv_3"),d=r(256,128,1,"prediction_layer/conv_4"),h=r(128,256,3,"prediction_layer/conv_5"),m=r(256,64,1,"prediction_layer/conv_6"),f=r(64,128,3,"prediction_layer/conv_7"),g=a(512,12,1,"prediction_layer/box_predictor_0/box_encoding_predictor"),y=a(512,9,1,"prediction_layer/box_predictor_0/class_predictor"),b=a(1024,24,1,"prediction_layer/box_predictor_1/box_encoding_predictor"),x=a(1024,18,1,"prediction_layer/box_predictor_1/class_predictor"),v=a(512,24,1,"prediction_layer/box_predictor_2/box_encoding_predictor"),T=a(512,18,1,"prediction_layer/box_predictor_2/class_predictor"),k=a(256,24,1,"prediction_layer/box_predictor_3/box_encoding_predictor"),S=a(256,18,1,"prediction_layer/box_predictor_3/class_predictor"),F=a(256,24,1,"prediction_layer/box_predictor_4/box_encoding_predictor"),A=a(256,18,1,"prediction_layer/box_predictor_4/class_predictor"),R=a(128,24,1,"prediction_layer/box_predictor_5/box_encoding_predictor"),P=a(128,18,1,"prediction_layer/box_predictor_5/class_predictor");return{conv_0:l,conv_1:c,conv_2:u,conv_3:p,conv_4:d,conv_5:h,conv_6:m,conv_7:f,box_predictor_0:{box_encoding_predictor:g,class_predictor:y},box_predictor_1:{box_encoding_predictor:b,class_predictor:x},box_predictor_2:{box_encoding_predictor:v,class_predictor:T},box_predictor_3:{box_encoding_predictor:k,class_predictor:S},box_predictor_4:{box_encoding_predictor:F,class_predictor:A},box_predictor_5:{box_encoding_predictor:R,class_predictor:P}}}return{extractMobilenetV1Params:i,extractPredictionLayerParams:o}}function FC(e){let t=[],{extractWeights:n,getRemainingWeights:a}=wn(e),{extractMobilenetV1Params:r,extractPredictionLayerParams:s}=ise(n,t),i=r(),o=s(),c={extra_dim:dh(n(5118*4),[1,5118,4])};if(t.push({paramPath:"output_layer/extra_dim"}),a().length!==0)throw new Error(`weights remaing after extract: ${a().length}`);return{params:{mobilenetv1:i,prediction_layer:o,output_layer:c},paramMappings:t}}function ose(e,t){let n=qn(e,t);function a(c,u,p){let d=n(`${c}/Conv2d_${u}_pointwise/weights`,4,`${p}/filters`),h=n(`${c}/Conv2d_${u}_pointwise/convolution_bn_offset`,1,`${p}/batch_norm_offset`);return{filters:d,batch_norm_offset:h}}function r(c){let u=`mobilenetv1/conv_${c}`,p=`MobilenetV1/Conv2d_${c}_depthwise`,d=`${u}/depthwise_conv`,h=`${u}/pointwise_conv`,m=n(`${p}/depthwise_weights`,4,`${d}/filters`),f=n(`${p}/BatchNorm/gamma`,1,`${d}/batch_norm_scale`),g=n(`${p}/BatchNorm/beta`,1,`${d}/batch_norm_offset`),y=n(`${p}/BatchNorm/moving_mean`,1,`${d}/batch_norm_mean`),b=n(`${p}/BatchNorm/moving_variance`,1,`${d}/batch_norm_variance`);return{depthwise_conv:{filters:m,batch_norm_scale:f,batch_norm_offset:g,batch_norm_mean:y,batch_norm_variance:b},pointwise_conv:a("MobilenetV1",c,h)}}function s(){return{conv_0:a("MobilenetV1",0,"mobilenetv1/conv_0"),conv_1:r(1),conv_2:r(2),conv_3:r(3),conv_4:r(4),conv_5:r(5),conv_6:r(6),conv_7:r(7),conv_8:r(8),conv_9:r(9),conv_10:r(10),conv_11:r(11),conv_12:r(12),conv_13:r(13)}}function i(c,u){let p=n(`${c}/weights`,4,`${u}/filters`),d=n(`${c}/biases`,1,`${u}/bias`);return{filters:p,bias:d}}function o(c){let u=i(`Prediction/BoxPredictor_${c}/BoxEncodingPredictor`,`prediction_layer/box_predictor_${c}/box_encoding_predictor`),p=i(`Prediction/BoxPredictor_${c}/ClassPredictor`,`prediction_layer/box_predictor_${c}/class_predictor`);return{box_encoding_predictor:u,class_predictor:p}}function l(){return{conv_0:a("Prediction",0,"prediction_layer/conv_0"),conv_1:a("Prediction",1,"prediction_layer/conv_1"),conv_2:a("Prediction",2,"prediction_layer/conv_2"),conv_3:a("Prediction",3,"prediction_layer/conv_3"),conv_4:a("Prediction",4,"prediction_layer/conv_4"),conv_5:a("Prediction",5,"prediction_layer/conv_5"),conv_6:a("Prediction",6,"prediction_layer/conv_6"),conv_7:a("Prediction",7,"prediction_layer/conv_7"),box_predictor_0:o(0),box_predictor_1:o(1),box_predictor_2:o(2),box_predictor_3:o(3),box_predictor_4:o(4),box_predictor_5:o(5)}}return{extractMobilenetV1Params:s,extractPredictionLayerParams:l}}function AC(e){let t=[],{extractMobilenetV1Params:n,extractPredictionLayerParams:a}=ose(e,t),r=e["Output/extra_dim"];if(t.push({originalPath:"Output/extra_dim",paramPath:"output_layer/extra_dim"}),!Er(r))throw new Error(`expected weightMap['Output/extra_dim'] to be a Tensor3D, instead have ${r}`);let s={mobilenetv1:n(),prediction_layer:a(),output_layer:{extra_dim:r}};return vn(e,t),{params:s,paramMappings:t}}function ka(e,t,n){return D(()=>{let a=At(e,t.filters,n,"same");return a=J(a,t.batch_norm_offset),Xt(a,0,6)})}var lse=.0010000000474974513;function use(e,t,n){return D(()=>{let a=ns(e,t.filters,n,"same");return a=br(a,t.batch_norm_mean,t.batch_norm_variance,t.batch_norm_offset,t.batch_norm_scale,lse),Xt(a,0,6)})}function cse(e){return[2,4,6,12].some(t=>t===e)?[2,2]:[1,1]}function $C(e,t){return D(()=>{let n,a=ka(e,t.conv_0,[2,2]);if([t.conv_1,t.conv_2,t.conv_3,t.conv_4,t.conv_5,t.conv_6,t.conv_7,t.conv_8,t.conv_9,t.conv_10,t.conv_11,t.conv_12,t.conv_13].forEach((s,i)=>{let o=i+1,l=cse(o);a=use(a,s.depthwise_conv,l),a=ka(a,s.pointwise_conv,[1,1]),o===11&&(n=a)}),n===null)throw new Error("mobileNetV1 - output of conv layer 11 is null");return{out:a,conv11:n}})}function pse(e,t,n){let a=e.arraySync(),r=Math.min(a[t][0],a[t][2]),s=Math.min(a[t][1],a[t][3]),i=Math.max(a[t][0],a[t][2]),o=Math.max(a[t][1],a[t][3]),l=Math.min(a[n][0],a[n][2]),c=Math.min(a[n][1],a[n][3]),u=Math.max(a[n][0],a[n][2]),p=Math.max(a[n][1],a[n][3]),d=(i-r)*(o-s),h=(u-l)*(p-c);if(d<=0||h<=0)return 0;let m=Math.max(r,l),f=Math.max(s,c),g=Math.min(i,u),y=Math.min(o,p),b=Math.max(g-m,0)*Math.max(y-f,0);return b/(d+h-b)}function DC(e,t,n,a,r){let s=e.shape[0],i=Math.min(n,s),o=t.map((u,p)=>({score:u,boxIndex:p})).filter(u=>u.score>r).sort((u,p)=>p.score-u.score),l=u=>u<=a?1:0,c=[];return o.forEach(u=>{if(c.length>=i)return;let p=u.score;for(let d=c.length-1;d>=0;--d){let h=pse(e,u.boxIndex,c[d]);if(h!==0&&(u.score*=l(h),u.score<=r))break}p===u.score&&c.push(u.boxIndex)}),c}function dse(e){let t=ut(Ve(e,[1,0])),n=[he(t[2],t[0]),he(t[3],t[1])],a=[J(t[0],ye(n[0],2)),J(t[1],ye(n[1],2))];return{sizes:n,centers:a}}function hse(e,t){let{sizes:n,centers:a}=dse(e),r=ut(Ve(t,[1,0])),s=ye(W(hn(ye(r[2],5)),n[0]),2),i=J(W(ye(r[0],10),n[0]),a[0]),o=ye(W(hn(ye(r[3],5)),n[1]),2),l=J(W(ye(r[1],10),n[1]),a[1]);return Ve(Dt([he(i,s),he(l,o),J(i,s),J(l,o)]),[1,0])}function RC(e,t,n){return D(()=>{let a=e.shape[0],r=hse(U(qa(n.extra_dim,[a,1,1]),[-1,4]),U(e,[-1,4]));r=U(r,[a,r.shape[0]/a,4]);let s=da(Be(t,[0,0,1],[-1,-1,-1])),i=Be(s,[0,0,0],[-1,-1,1]);i=U(i,[a,i.shape[1]]);let o=ut(r),l=ut(i);return{boxes:o,scores:l}})}function ho(e,t){return D(()=>{let n=e.shape[0],a=U(lo(e,t.box_encoding_predictor),[n,-1,1,4]),r=U(lo(e,t.class_predictor),[n,-1,3]);return{boxPredictionEncoding:a,classPrediction:r}})}function MC(e,t,n){return D(()=>{let a=ka(e,n.conv_0,[1,1]),r=ka(a,n.conv_1,[2,2]),s=ka(r,n.conv_2,[1,1]),i=ka(s,n.conv_3,[2,2]),o=ka(i,n.conv_4,[1,1]),l=ka(o,n.conv_5,[2,2]),c=ka(l,n.conv_6,[1,1]),u=ka(c,n.conv_7,[2,2]),p=ho(t,n.box_predictor_0),d=ho(e,n.box_predictor_1),h=ho(r,n.box_predictor_2),m=ho(i,n.box_predictor_3),f=ho(l,n.box_predictor_4),g=ho(u,n.box_predictor_5),y=Je([p.boxPredictionEncoding,d.boxPredictionEncoding,h.boxPredictionEncoding,m.boxPredictionEncoding,f.boxPredictionEncoding,g.boxPredictionEncoding],1),b=Je([p.classPrediction,d.classPrediction,h.classPrediction,m.classPrediction,f.classPrediction,g.classPrediction],1);return{boxPredictions:y,classPredictions:b}})}var sa=class{constructor({minConfidence:t,maxResults:n}={}){this._name="SsdMobilenetv1Options";if(this._minConfidence=t||.5,this._maxResults=n||100,typeof this._minConfidence!="number"||this._minConfidence<=0||this._minConfidence>=1)throw new Error(`${this._name} - expected minConfidence to be a number between 0 and 1`);if(typeof this._maxResults!="number")throw new Error(`${this._name} - expected maxResults to be a number`)}get minConfidence(){return this._minConfidence}get maxResults(){return this._maxResults}};var Ns=class extends en{constructor(){super("SsdMobilenetv1")}forwardInput(t){let{params:n}=this;if(!n)throw new Error("SsdMobilenetv1 - load model before inference");return D(()=>{let a=ue(t.toBatchTensor(512,!1),"float32"),r=he(ye(a,127.5),1),s=$C(r,n.mobilenetv1),{boxPredictions:i,classPredictions:o}=MC(s.out,s.conv11,n.prediction_layer);return RC(i,o,n.output_layer)})}async forward(t){return this.forwardInput(await ht(t))}async locateFaces(t,n={}){let{maxResults:a,minConfidence:r}=new sa(n),s=await ht(t),{boxes:i,scores:o}=this.forwardInput(s),l=i[0],c=o[0];for(let x=1;x<i.length;x++)i[x].dispose(),o[x].dispose();let u=Array.from(c.dataSync()),d=DC(l,u,a,.5,r),h=s.getReshapedInputDimensions(0),m=s.inputSize,f=m/h.width,g=m/h.height,y=l.arraySync(),b=d.map(x=>{let[v,T]=[Math.max(0,y[x][0]),Math.min(1,y[x][2])].map(F=>F*g),[k,S]=[Math.max(0,y[x][1]),Math.min(1,y[x][3])].map(F=>F*f);return new mt(u[x],new ro(k,v,S-k,T-v),{height:s.getInputHeight(0),width:s.getInputWidth(0)})});return l.dispose(),c.dispose(),b}getDefaultModelName(){return"ssd_mobilenetv1_model"}extractParamsFromWeightMap(t){return AC(t)}extractParams(t){return FC(t)}};function Fw(e){let t=new Ns;return t.extractWeights(e),t}function PC(e){return Fw(e)}var Aw=class extends Ns{};var OC=.4,LC=[new De(.738768,.874946),new De(2.42204,2.65704),new De(4.30971,7.04493),new De(10.246,4.59428),new De(12.6868,11.8741)],zC=[new De(1.603231,2.094468),new De(6.041143,7.080126),new De(2.882459,3.518061),new De(4.266906,5.178857),new De(9.041765,10.66308)],WC=[117.001,114.697,97.404],BC="tiny_yolov2_model",VC="tiny_yolov2_separable_conv_model";var Df=e=>typeof e=="number";function Rf(e){if(!e)throw new Error(`invalid config: ${e}`);if(typeof e.withSeparableConvs!="boolean")throw new Error(`config.withSeparableConvs has to be a boolean, have: ${e.withSeparableConvs}`);if(!Df(e.iouThreshold)||e.iouThreshold<0||e.iouThreshold>1)throw new Error(`config.iouThreshold has to be a number between [0, 1], have: ${e.iouThreshold}`);if(!Array.isArray(e.classes)||!e.classes.length||!e.classes.every(t=>typeof t=="string"))throw new Error(`config.classes has to be an array class names: string[], have: ${JSON.stringify(e.classes)}`);if(!Array.isArray(e.anchors)||!e.anchors.length||!e.anchors.map(t=>t||{}).every(t=>Df(t.x)&&Df(t.y)))throw new Error(`config.anchors has to be an array of { x: number, y: number }, have: ${JSON.stringify(e.anchors)}`);if(e.meanRgb&&(!Array.isArray(e.meanRgb)||e.meanRgb.length!==3||!e.meanRgb.every(Df)))throw new Error(`config.meanRgb has to be an array of shape [number, number, number], have: ${JSON.stringify(e.meanRgb)}`)}function Ru(e){return D(()=>{let t=W(e,ve(.10000000149011612));return J(qe(he(e,t)),t)})}function $r(e,t){return D(()=>{let n=ta(e,[[0,0],[1,1],[1,1],[0,0]]);return n=At(n,t.conv.filters,[1,1],"valid"),n=he(n,t.bn.sub),n=W(n,t.bn.truediv),n=J(n,t.conv.bias),Ru(n)})}function Dr(e,t){return D(()=>{let n=ta(e,[[0,0],[1,1],[1,1],[0,0]]);return n=Pi(n,t.depthwise_filter,t.pointwise_filter,[1,1],"valid"),n=J(n,t.bias),Ru(n)})}function mse(e,t){let n=Au(e,t);function a(i,o){let l=Ze(e(i)),c=Ze(e(i));return t.push({paramPath:`${o}/sub`},{paramPath:`${o}/truediv`}),{sub:l,truediv:c}}function r(i,o,l){let c=n(i,o,3,`${l}/conv`),u=a(o,`${l}/bn`);return{conv:c,bn:u}}let s=$u(e,t);return{extractConvParams:n,extractConvWithBatchNormParams:r,extractSeparableConvParams:s}}function UC(e,t,n,a){let{extractWeights:r,getRemainingWeights:s}=wn(e),i=[],{extractConvParams:o,extractConvWithBatchNormParams:l,extractSeparableConvParams:c}=mse(r,i),u;if(t.withSeparableConvs){let[p,d,h,m,f,g,y,b,x]=a,v=t.isFirstLayerConv2d?o(p,d,3,"conv0"):c(p,d,"conv0"),T=c(d,h,"conv1"),k=c(h,m,"conv2"),S=c(m,f,"conv3"),F=c(f,g,"conv4"),A=c(g,y,"conv5"),R=b?c(y,b,"conv6"):void 0,P=x?c(b,x,"conv7"):void 0,z=o(x||b||y,5*n,1,"conv8");u={conv0:v,conv1:T,conv2:k,conv3:S,conv4:F,conv5:A,conv6:R,conv7:P,conv8:z}}else{let[p,d,h,m,f,g,y,b,x]=a,v=l(p,d,"conv0"),T=l(d,h,"conv1"),k=l(h,m,"conv2"),S=l(m,f,"conv3"),F=l(f,g,"conv4"),A=l(g,y,"conv5"),R=l(y,b,"conv6"),P=l(b,x,"conv7"),z=o(x,5*n,1,"conv8");u={conv0:v,conv1:T,conv2:k,conv3:S,conv4:F,conv5:A,conv6:R,conv7:P,conv8:z}}if(s().length!==0)throw new Error(`weights remaing after extract: ${s().length}`);return{params:u,paramMappings:i}}function fse(e,t){let n=qn(e,t);function a(o){let l=n(`${o}/sub`,1),c=n(`${o}/truediv`,1);return{sub:l,truediv:c}}function r(o){let l=n(`${o}/filters`,4),c=n(`${o}/bias`,1);return{filters:l,bias:c}}function s(o){let l=r(`${o}/conv`),c=a(`${o}/bn`);return{conv:l,bn:c}}let i=Du(n);return{extractConvParams:r,extractConvWithBatchNormParams:s,extractSeparableConvParams:i}}function GC(e,t){let n=[],{extractConvParams:a,extractConvWithBatchNormParams:r,extractSeparableConvParams:s}=fse(e,n),i;if(t.withSeparableConvs){let o=t.filterSizes&&t.filterSizes.length||9;i={conv0:t.isFirstLayerConv2d?a("conv0"):s("conv0"),conv1:s("conv1"),conv2:s("conv2"),conv3:s("conv3"),conv4:s("conv4"),conv5:s("conv5"),conv6:o>7?s("conv6"):void 0,conv7:o>8?s("conv7"):void 0,conv8:a("conv8")}}else i={conv0:r("conv0"),conv1:r("conv1"),conv2:r("conv2"),conv3:r("conv3"),conv4:r("conv4"),conv5:r("conv5"),conv6:r("conv6"),conv7:r("conv7"),conv8:a("conv8")};return vn(e,n),{params:i,paramMappings:n}}var Ba=class{constructor({inputSize:t,scoreThreshold:n}={}){this._name="TinyYolov2Options";if(this._inputSize=t||416,this._scoreThreshold=n||.5,typeof this._inputSize!="number"||this._inputSize%32!=0)throw new Error(`${this._name} - expected inputSize to be a number divisible by 32`);if(typeof this._scoreThreshold!="number"||this._scoreThreshold<=0||this._scoreThreshold>=1)throw new Error(`${this._name} - expected scoreThreshold to be a number between 0 and 1`)}get inputSize(){return this._inputSize}get scoreThreshold(){return this._scoreThreshold}};var $w=class extends en{constructor(t){super("TinyYolov2");Rf(t),this._config=t}get config(){return this._config}get withClassScores(){return this.config.withClassScores||this.config.classes.length>1}get boxEncodingSize(){return 5+(this.withClassScores?this.config.classes.length:0)}runTinyYolov2(t,n){let a=$r(t,n.conv0);return a=$t(a,[2,2],[2,2],"same"),a=$r(a,n.conv1),a=$t(a,[2,2],[2,2],"same"),a=$r(a,n.conv2),a=$t(a,[2,2],[2,2],"same"),a=$r(a,n.conv3),a=$t(a,[2,2],[2,2],"same"),a=$r(a,n.conv4),a=$t(a,[2,2],[2,2],"same"),a=$r(a,n.conv5),a=$t(a,[2,2],[1,1],"same"),a=$r(a,n.conv6),a=$r(a,n.conv7),lo(a,n.conv8,"valid",!1)}runMobilenet(t,n){let a=this.config.isFirstLayerConv2d?Ru(lo(t,n.conv0,"valid",!1)):Dr(t,n.conv0);return a=$t(a,[2,2],[2,2],"same"),a=Dr(a,n.conv1),a=$t(a,[2,2],[2,2],"same"),a=Dr(a,n.conv2),a=$t(a,[2,2],[2,2],"same"),a=Dr(a,n.conv3),a=$t(a,[2,2],[2,2],"same"),a=Dr(a,n.conv4),a=$t(a,[2,2],[2,2],"same"),a=Dr(a,n.conv5),a=$t(a,[2,2],[1,1],"same"),a=n.conv6?Dr(a,n.conv6):a,a=n.conv7?Dr(a,n.conv7):a,lo(a,n.conv8,"valid",!1)}forwardInput(t,n){let{params:a}=this;if(!a)throw new Error("TinyYolov2 - load model before inference");return D(()=>{let r=ue(t.toBatchTensor(n,!1),"float32");return r=this.config.meanRgb?wa(r,this.config.meanRgb):r,r=r.div(255),this.config.withSeparableConvs?this.runMobilenet(r,a):this.runTinyYolov2(r,a)})}async forward(t,n){return this.forwardInput(await ht(t),n)}async detect(t,n={}){let{inputSize:a,scoreThreshold:r}=new Ba(n),s=await ht(t),i=await this.forwardInput(s,a),o=D(()=>ut(i)[0].expandDims()),l={width:s.getInputWidth(0),height:s.getInputHeight(0)},c=await this.extractBoxes(o,s.getReshapedInputDimensions(0),r);i.dispose(),o.dispose();let u=c.map(g=>g.box),p=c.map(g=>g.score),d=c.map(g=>g.classScore),h=c.map(g=>this.config.classes[g.label]);return df(u.map(g=>g.rescale(a)),p,this.config.iouThreshold,!0).map(g=>new Fr(p[g],d[g],h[g],u[g],l))}getDefaultModelName(){return""}extractParamsFromWeightMap(t){return GC(t,this.config)}extractParams(t){let n=this.config.filterSizes||$w.DEFAULT_FILTER_SIZES,a=n?n.length:void 0;if(a!==7&&a!==8&&a!==9)throw new Error(`TinyYolov2 - expected 7 | 8 | 9 convolutional filters, but found ${a} filterSizes in config`);return UC(t,this.config,this.boxEncodingSize,n)}async extractBoxes(t,n,a){let{width:r,height:s}=n,i=Math.max(r,s),o=i/r,l=i/s,c=t.shape[1],u=this.config.anchors.length,[p,d,h]=D(()=>{let y=t.reshape([c,c,u,this.boxEncodingSize]),b=y.slice([0,0,0,0],[c,c,u,4]),x=y.slice([0,0,0,4],[c,c,u,1]),v=this.withClassScores?Sa(y.slice([0,0,0,5],[c,c,u,this.config.classes.length]),3):ve(0);return[b,x,v]}),m=[],f=await d.array(),g=await p.array();for(let y=0;y<c;y++)for(let b=0;b<c;b++)for(let x=0;x<u;x++){let v=Su(f[y][b][x][0]);if(!a||v>a){let T=(b+Su(g[y][b][x][0]))/c*o,k=(y+Su(g[y][b][x][1]))/c*l,S=Math.exp(g[y][b][x][2])*this.config.anchors[x].x/c*o,F=Math.exp(g[y][b][x][3])*this.config.anchors[x].y/c*l,A=T-S/2,R=k-F/2,P={row:y,col:b,anchor:x},{classScore:z,label:V}=this.withClassScores?await this.extractPredictedClass(h,P):{classScore:1,label:0};m.push({box:new ao(A,R,A+S,R+F),score:v,classScore:v*z,label:V,...P})}}return p.dispose(),d.dispose(),h.dispose(),m}async extractPredictedClass(t,n){let{row:a,col:r,anchor:s}=n,i=await t.array();return Array(this.config.classes.length).fill(0).map((o,l)=>i[a][r][s][l]).map((o,l)=>({classScore:o,label:l})).reduce((o,l)=>o.classScore>l.classScore?o:l)}},Mu=$w;Mu.DEFAULT_FILTER_SIZES=[3,16,32,64,128,256,512,1024,1024];var mo=class extends Mu{constructor(t=!0){let n={withSeparableConvs:t,iouThreshold:OC,classes:["face"],...t?{anchors:zC,meanRgb:WC}:{anchors:LC,withClassScores:!0}};super(n)}get withSeparableConvs(){return this.config.withSeparableConvs}get anchors(){return this.config.anchors}async locateFaces(t,n){return(await this.detect(t,n)).map(r=>new mt(r.score,r.relativeBox,{width:r.imageWidth,height:r.imageHeight}))}getDefaultModelName(){return this.withSeparableConvs?VC:BC}extractParamsFromWeightMap(t){return super.extractParamsFromWeightMap(t)}};function HC(e,t=!0){let n=new mo(t);return n.extractWeights(e),n}var Vp=class extends Ba{constructor(){super(...arguments);this._name="TinyFaceDetectorOptions"}};var ia=class{async then(t){return t(await this.run())}async run(){throw new Error("ComposableTask - run is not implemented")}};async function fo(e,t,n,a,r=({alignedRect:s})=>s){let s=e.map(l=>Ts(l)?r(l):l.detection),i=a||(t instanceof Ee?await oo(t,s):await io(t,s)),o=await n(i);return i.forEach(l=>l instanceof Ee&&l.dispose()),o}async function Pu(e,t,n,a,r){return fo([e],t,async s=>n(s[0]),a,r)}var jC=.4,qC=[new De(1.603231,2.094468),new De(6.041143,7.080126),new De(2.882459,3.518061),new De(4.266906,5.178857),new De(9.041765,10.66308)],XC=[117.001,114.697,97.404];var go=class extends Mu{constructor(){let t={withSeparableConvs:!0,iouThreshold:jC,classes:["face"],anchors:qC,meanRgb:XC,isFirstLayerConv2d:!0,filterSizes:[3,16,32,64,128,256,512]};super(t)}get anchors(){return this.config.anchors}async locateFaces(t,n){return(await this.detect(t,n)).map(r=>new mt(r.score,r.relativeBox,{width:r.imageWidth,height:r.imageHeight}))}getDefaultModelName(){return"tiny_face_detector_model"}extractParamsFromWeightMap(t){return super.extractParamsFromWeightMap(t)}};var Qe={ssdMobilenetv1:new Ns,tinyFaceDetector:new go,tinyYolov2:new mo,faceLandmark68Net:new co,faceLandmark68TinyNet:new Op,faceRecognitionNet:new po,faceExpressionNet:new Dp,ageGenderNet:new Mp},Dw=(e,t)=>Qe.ssdMobilenetv1.locateFaces(e,t),KC=(e,t)=>Qe.tinyFaceDetector.locateFaces(e,t),YC=(e,t)=>Qe.tinyYolov2.locateFaces(e,t),Rw=e=>Qe.faceLandmark68Net.detectLandmarks(e),JC=e=>Qe.faceLandmark68TinyNet.detectLandmarks(e),QC=e=>Qe.faceRecognitionNet.computeFaceDescriptor(e),ZC=e=>Qe.faceExpressionNet.predictExpressions(e),e_=e=>Qe.ageGenderNet.predictAgeAndGender(e),Mw=e=>Qe.ssdMobilenetv1.load(e),t_=e=>Qe.tinyFaceDetector.load(e),n_=e=>Qe.tinyYolov2.load(e),a_=e=>Qe.faceLandmark68Net.load(e),r_=e=>Qe.faceLandmark68TinyNet.load(e),s_=e=>Qe.faceRecognitionNet.load(e),i_=e=>Qe.faceExpressionNet.load(e),o_=e=>Qe.ageGenderNet.load(e),l_=Mw,u_=Dw,c_=Rw;var Pw=class extends ia{constructor(t,n,a){super();this.parentTask=t;this.input=n;this.extractedFaces=a}},zu=class extends Pw{async run(){let t=await this.parentTask,n=await fo(t,this.input,async a=>Promise.all(a.map(r=>Qe.faceExpressionNet.predictExpressions(r))),this.extractedFaces);return t.map((a,r)=>Rp(a,n[r]))}withAgeAndGender(){return new Ou(this,this.input)}},Wu=class extends Pw{async run(){let t=await this.parentTask;if(!t)return;let n=await Pu(t,this.input,a=>Qe.faceExpressionNet.predictExpressions(a),this.extractedFaces);return Rp(t,n)}withAgeAndGender(){return new Lu(this,this.input)}},xo=class extends zu{withAgeAndGender(){return new yo(this,this.input)}withFaceDescriptors(){return new Rr(this,this.input)}},vo=class extends Wu{withAgeAndGender(){return new bo(this,this.input)}withFaceDescriptor(){return new Mr(this,this.input)}};var Ow=class extends ia{constructor(t,n,a){super();this.parentTask=t;this.input=n;this.extractedFaces=a}},Ou=class extends Ow{async run(){let t=await this.parentTask,n=await fo(t,this.input,async a=>Promise.all(a.map(r=>Qe.ageGenderNet.predictAgeAndGender(r))),this.extractedFaces);return t.map((a,r)=>{let{age:s,gender:i,genderProbability:o}=n[r];return Wp(Bp(a,i,o),s)})}withFaceExpressions(){return new zu(this,this.input)}},Lu=class extends Ow{async run(){let t=await this.parentTask;if(!t)return;let{age:n,gender:a,genderProbability:r}=await Pu(t,this.input,s=>Qe.ageGenderNet.predictAgeAndGender(s),this.extractedFaces);return Wp(Bp(t,a,r),n)}withFaceExpressions(){return new Wu(this,this.input)}},yo=class extends Ou{withFaceExpressions(){return new xo(this,this.input)}withFaceDescriptors(){return new Rr(this,this.input)}},bo=class extends Lu{withFaceExpressions(){return new vo(this,this.input)}withFaceDescriptor(){return new Mr(this,this.input)}};var Up=class extends ia{constructor(t,n){super();this.parentTask=t;this.input=n}},Rr=class extends Up{async run(){let t=await this.parentTask;return(await fo(t,this.input,a=>Promise.all(a.map(r=>Qe.faceRecognitionNet.computeFaceDescriptor(r))),null,a=>a.landmarks.align(null,{useDlibAlignment:!0}))).map((a,r)=>zp(t[r],a))}withFaceExpressions(){return new xo(this,this.input)}withAgeAndGender(){return new yo(this,this.input)}},Mr=class extends Up{async run(){let t=await this.parentTask;if(!t)return;let n=await Pu(t,this.input,a=>Qe.faceRecognitionNet.computeFaceDescriptor(a),null,a=>a.landmarks.align(null,{useDlibAlignment:!0}));return zp(t,n)}withFaceExpressions(){return new vo(this,this.input)}withAgeAndGender(){return new bo(this,this.input)}};var Gp=class extends ia{constructor(t,n,a){super();this.parentTask=t;this.input=n;this.useTinyLandmarkNet=a}get landmarkNet(){return this.useTinyLandmarkNet?Qe.faceLandmark68TinyNet:Qe.faceLandmark68Net}},Hp=class extends Gp{async run(){let t=await this.parentTask,n=t.map(s=>s.detection),a=this.input instanceof Ee?await oo(this.input,n):await io(this.input,n),r=await Promise.all(a.map(s=>this.landmarkNet.detectLandmarks(s)));return a.forEach(s=>s instanceof Ee&&s.dispose()),t.map((s,i)=>uo(s,r[i]))}withFaceExpressions(){return new xo(this,this.input)}withAgeAndGender(){return new yo(this,this.input)}withFaceDescriptors(){return new Rr(this,this.input)}},jp=class extends Gp{async run(){let t=await this.parentTask;if(!t)return;let{detection:n}=t,a=this.input instanceof Ee?await oo(this.input,[n]):await io(this.input,[n]),r=await this.landmarkNet.detectLandmarks(a[0]);return a.forEach(s=>s instanceof Ee&&s.dispose()),uo(t,r)}withFaceExpressions(){return new vo(this,this.input)}withAgeAndGender(){return new bo(this,this.input)}withFaceDescriptor(){return new Mr(this,this.input)}};var qp=class extends ia{constructor(t,n=new sa){super();this.input=t;this.options=n}},Bu=class extends qp{async run(){let{input:t,options:n}=this,a;if(n instanceof Vp)a=Qe.tinyFaceDetector.locateFaces(t,n);else if(n instanceof sa)a=Qe.ssdMobilenetv1.locateFaces(t,n);else if(n instanceof Ba)a=Qe.tinyYolov2.locateFaces(t,n);else throw new Error("detectFaces - expected options to be instance of TinyFaceDetectorOptions | SsdMobilenetv1Options | TinyYolov2Options");return a}runAndExtendWithFaceDetections(){return new Promise(async t=>{let n=await this.run();t(n.map(a=>bs({},a)))})}withFaceLandmarks(t=!1){return new Hp(this.runAndExtendWithFaceDetections(),this.input,t)}withFaceExpressions(){return new zu(this.runAndExtendWithFaceDetections(),this.input)}withAgeAndGender(){return new Ou(this.runAndExtendWithFaceDetections(),this.input)}},Xp=class extends qp{async run(){let t=await new Bu(this.input,this.options),n=t[0];return t.forEach(a=>{a.score>n.score&&(n=a)}),n}runAndExtendWithFaceDetection(){return new Promise(async t=>{let n=await this.run();t(n?bs({},n):void 0)})}withFaceLandmarks(t=!1){return new jp(this.runAndExtendWithFaceDetection(),this.input,t)}withFaceExpressions(){return new Wu(this.runAndExtendWithFaceDetection(),this.input)}withAgeAndGender(){return new Lu(this.runAndExtendWithFaceDetection(),this.input)}};function p_(e,t=new sa){return new Xp(e,t)}function Kp(e,t=new sa){return new Bu(e,t)}async function Lw(e,t){return Kp(e,new sa(t?{minConfidence:t}:{})).withFaceLandmarks().withFaceDescriptors()}async function d_(e,t={}){return Kp(e,new Ba(t)).withFaceLandmarks().withFaceDescriptors()}var h_=Lw;function Mf(e,t){if(e.length!==t.length)throw new Error("euclideanDistance: arr1.length !== arr2.length");let n=Array.from(e),a=Array.from(t);return Math.sqrt(n.map((r,s)=>r-a[s]).reduce((r,s)=>r+s**2,0))}var Yp=class{constructor(t,n=.6){this._distanceThreshold=n;let a=Array.isArray(t)?t:[t];if(!a.length)throw new Error("FaceRecognizer.constructor - expected atleast one input");let r=1,s=()=>`person ${r++}`;this._labeledDescriptors=a.map(i=>{if(i instanceof or)return i;if(i instanceof Float32Array)return new or(s(),[i]);if(i.descriptor&&i.descriptor instanceof Float32Array)return new or(s(),[i.descriptor]);throw new Error("FaceRecognizer.constructor - expected inputs to be of type LabeledFaceDescriptors | WithFaceDescriptor<any> | Float32Array | Array<LabeledFaceDescriptors | WithFaceDescriptor<any> | Float32Array>")})}get labeledDescriptors(){return this._labeledDescriptors}get distanceThreshold(){return this._distanceThreshold}computeMeanDistance(t,n){return n.map(a=>Mf(a,t)).reduce((a,r)=>a+r,0)/(n.length||1)}matchDescriptor(t){return this.labeledDescriptors.map(({descriptors:n,label:a})=>new Cu(a,this.computeMeanDistance(t,n))).reduce((n,a)=>n.distance<a.distance?n:a)}findBestMatch(t){let n=this.matchDescriptor(t);return n.distance<this.distanceThreshold?n:new Cu("unknown",n.distance)}toJSON(){return{distanceThreshold:this.distanceThreshold,labeledDescriptors:this.labeledDescriptors.map(t=>t.toJSON())}}static fromJSON(t){let n=t.labeledDescriptors.map(a=>or.fromJSON(a));return new Yp(n,t.distanceThreshold)}};function m_(e){let t=new go;return t.extractWeights(e),t}function zw(e,t){let{width:n,height:a}=new pn(t.width,t.height);if(n<=0||a<=0)throw new Error(`resizeResults - invalid dimensions: ${JSON.stringify({width:n,height:a})}`);if(Array.isArray(e))return e.map(r=>zw(r,{width:n,height:a}));if(Ts(e)){let r=e.detection.forSize(n,a),s=e.unshiftedLandmarks.forSize(r.box.width,r.box.height);return uo(bs(e,r),s)}return za(e)?bs(e,e.detection.forSize(n,a)):e instanceof jn||e instanceof mt?e.forSize(n,a):e}var yse=typeof process!="undefined",bse=typeof navigator!="undefined"&&typeof navigator.userAgent!="undefined",f_={faceapi:fC,node:yse,browser:bse};return gse;})();
/**
* @license
* Copyright 2017 Google LLC. All Rights Reserved.
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
* =============================================================================
*/
/**
* @license
* Copyright 2018 Google LLC
*
* Use of this source code is governed by an MIT-style
* license that can be found in the LICENSE file or at
* https://opensource.org/licenses/MIT.
* =============================================================================
*/
/**
* @license
* Copyright 2018 Google LLC. All Rights Reserved.
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
*
* =============================================================================
*/
/**
* @license
* Copyright 2018 Google LLC. All Rights Reserved.
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
* =============================================================================
*/
/**
* @license
* Copyright 2019 Google LLC
*
* Use of this source code is governed by an MIT-style
* license that can be found in the LICENSE file or at
* https://opensource.org/licenses/MIT.
* =============================================================================
*/
/**
* @license
* Copyright 2019 Google LLC. All Rights Reserved.
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
*
* =============================================================================
*/
/**
* @license
* Copyright 2019 Google LLC. All Rights Reserved.
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
* =============================================================================
*/
/**
* @license
* Copyright 2020 Google Inc. All Rights Reserved.
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
* =============================================================================
*/
/**
* @license
* Copyright 2020 Google LLC
*
* Use of this source code is governed by an MIT-style
* license that can be found in the LICENSE file or at
* https://opensource.org/licenses/MIT.
* =============================================================================
*/
/**
* @license
* Copyright 2020 Google LLC. All Rights Reserved.
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
* =============================================================================
*/
/**
* @license
* Copyright 2020 Google LLC. All Rights Reserved.
* Licensed under the Apache License, Version 2.0 (the License);
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an AS IS BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
* =============================================================================
*/
/**
* @license
* Copyright 2021 Google LLC. All Rights Reserved.
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
* =============================================================================
*/
/** @license See the LICENSE file. */
//# sourceMappingURL=face-api.js.map
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