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/*
Human
homepage: <https://github.com/vladmandic/human>
author: <https://github.com/vladmandic>'
*/
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this.scopedRun(()=>this.state.kernelDepth++,()=>this.state.kernelDepth--,()=>{!this.ENV.getBool("DEBUG")&&!this.state.profiling?t=o():(p=this.profiler.profileKernel(l,c,()=>o()),this.ENV.getBool("DEBUG")&&this.profiler.logKernelProfile(p),t=p.outputs)}),s&&this.addTapeNode(l,c,t,d,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-a,totalTensorsSnapshot:this.state.numTensors,inputShapes:Object.keys(c).map(h=>c[h]!=null?c[h].shape:null),outputShapes:t.map(h=>h.shape),kernelTimeMs:p.timeMs,extraInfo:p.extraInfo}),Array.isArray(i)?t:t[0]}saveTensorsForBackwardMode(e){return e.map(n=>this.keep(this.clone(n)))}getTensorsForGradient(e,t,n){let s=Gg(e);if(s!=null){let r=s.inputsToSave||[],a=s.outputsToSave||[],o;s.saveAllInputs?(O(Array.isArray(t),()=>"saveAllInputs is true, expected inputs to be an array."),o=Object.keys(t).map(l=>t[l])):o=r.map(l=>t[l]);let 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n=0;e.dtype!=="complex64"&&e.dtype!=="string"&&(n=e.size*zg(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 ad||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*zg(e.dtype);this.state.numBytes-=n}t.backend.disposeData(e.dataId)&&this.removeDataId(e.dataId,t.backend)}disposeVariables(){for(let e in this.state.registeredVariables){let t=this.state.registeredVariables[e];this.disposeVariable(t)}}disposeVariable(e){this.disposeTensor(e),this.state.registeredVariables[e.name]!=null&&delete this.state.registeredVariables[e.name]}memory(){let e=this.backend.memory();return e.numTensors=this.state.numTensors,e.numDataBuffers=this.state.numDataBuffers,e.numBytes=this.state.numBytes,this.state.numStringTensors>0&&(e.unreliable=!0,e.reasons==null&&(e.reasons=[]),e.reasons.push("Memory usage by string tensors is approximate (2 bytes per character)")),e}async profile(e){this.state.profiling=!0;let t=this.state.numBytes,n=this.state.numTensors;this.state.activeProfile.kernels=[],this.state.activeProfile.result=await e(),this.state.profiling=!1,this.state.activeProfile.peakBytes=Math.max(...this.state.activeProfile.kernels.map(s=>s.totalBytesSnapshot)),this.state.activeProfile.newBytes=this.state.numBytes-t,this.state.activeProfile.newTensors=this.state.numTensors-n;for(let s of this.state.activeProfile.kernels)s.kernelTimeMs=await s.kernelTimeMs,s.extraInfo=await s.extraInfo;return this.state.activeProfile}isTapeOn(){return this.state.gradientDepth>0&&this.state.kernelDepth===0}addTapeNode(e,t,n,s,r,a){let o={id:this.state.nextTapeNodeId++,kernelName:e,inputs:t,outputs:n,saved:r},i=Gg(e);i!=null&&(s=i.gradFunc),s!=null&&(o.gradient=l=>(l=l.map((c,u)=>{if(c==null){let d=n[u],p=th(d.size,d.dtype);return this.makeTensor(p,d.shape,d.dtype)}return c}),s(l.length>1?l:l[0],r,a))),this.state.activeTape.push(o)}keep(e){return e.kept=!0,e}startTape(){this.state.gradientDepth===0&&(this.state.activeTape=[]),this.state.gradientDepth++}endTape(){this.state.gradientDepth--}startScope(e){let t={track:[],name:"unnamed scope",id:this.state.nextScopeId++};e&&(t.name=e),this.state.scopeStack.push(t),this.state.activeScope=t}endScope(e){let t=Qg(e),n=new Set(t.map(r=>r.id));for(let r=0;r<this.state.activeScope.track.length;r++){let 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this.inboundLayers)t!=null?e.push(t.name):e.push(null);return{outboundLayer:this.outboundLayer?this.outboundLayer.name:null,inboundLayers:e,nodeIndices:this.nodeIndices,tensorIndices:this.tensorIndices}}},ML=0,st=class extends ue.Serializable{constructor(e={}){super();this._callHook=null,this._addedWeightNames=[],this._stateful=!1,this.id=ML++,this.activityRegularizer=null,this.inputSpec=null,this.supportsMasking=!1,this._trainableWeights=[],this._nonTrainableWeights=[],this._losses=[],this._updates=[],this._built=!1,this.inboundNodes=[],this.outboundNodes=[];let t=e.name;if(!t){let n=this.getClassName();t=Jr(n)+"_"+_f(n)}if(this.name=t,this.trainable_=e.trainable==null?!0:e.trainable,e.inputShape!=null||e.batchInputShape!=null){let n;if(e.batchInputShape!=null)n=e.batchInputShape;else if(e.inputShape!=null){let r=null;e.batchSize!=null&&(r=e.batchSize),n=[r].concat(e.inputShape)}this.batchInputShape=n;let s=e.dtype;s==null&&(s=e.inputDType),s==null&&(s="float32"),this.dtype=s}e.weights!=null?this.initialWeights=e.weights:this.initialWeights=null,this._refCount=null,this.fastWeightInitDuringBuild=!1}static nodeKey(e,t){return e.name+"_ib-"+t.toString()}getNodeAtIndex(e,t){if(this.inboundNodes.length===0)throw new dr(`The layer has never been called and thus has no defined ${t}.`);if(this.inboundNodes.length<=e)throw new H(`Asked to get ${t} at node ${e}, but the layer has only ${this.inboundNodes.length} inbound nodes.`);return this.inboundNodes[e]}getInputAt(e){return rs(this.getNodeAtIndex(e,"input").inputTensors)}getOutputAt(e){return rs(this.getNodeAtIndex(e,"output").outputTensors)}get input(){if(this.inboundNodes.length>1)throw new Yr(`Layer ${this.name} has multiple inbound nodes, hence the notion of "layer input" is ill-defined. 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Input received: ${e}`);for(let n=0;n<e.length;n++){let s=e[n],r=t[n];if(r==null)continue;let a=s.rank;if(r.ndim!=null&&a!==r.ndim)throw new H(`Input ${n} is incompatible with layer ${this.name}: expected ndim=${r.ndim}, found ndim=${a}`);if(r.maxNDim!=null&&a>r.maxNDim)throw new H(`Input ${n} is incompatible with layer ${this.name}: expected max_ndim=${r.maxNDim}, found ndim=${a}`);if(r.minNDim!=null&&a<r.minNDim)throw new H(`Input ${n} is incompatible with layer ${this.name}: expected min_ndim=${r.minNDim}, found ndim=${a}.`);if(r.dtype!=null&&s.dtype!==r.dtype)throw new H(`Input ${n} is incompatible with layer ${this.name} : expected dtype=${r.dtype}, found dtype=${s.dtype}.`);if(r.axes){let o=s.shape;for(let i in r.axes){let l=Number(i),c=r.axes[i],u=l>=0?o[l]:o[o.length+l];if(c!=null&&[c,null].indexOf(u)===-1)throw new H(`Input ${n} is incompatible with layer ${this.name}: expected axis ${l} of input shape to have value ${c} but got shape ${o}.`)}}if(r.shape!=null)for(let o=0;o<r.shape.length;++o){let i=r.shape[o],l=s.shape[o];if(i!=null&&l!=null&&i!==l)throw new H(`Input ${n} is incompatible with layer ${this.name}: expected shape=${r.shape}, found shape=${s.shape}.`)}}}call(e,t){return e}invokeCallHook(e,t){this._callHook!=null&&this._callHook(e,t)}setCallHook(e){this._callHook=e}clearCallHook(){this._callHook=null}apply(e,t){t=t||{},this.assertNotDisposed();let n=It(e),s=!0;for(let a of n)if(!(a instanceof fr)){s=!1;break}let r=!0;for(let a of n)if(a instanceof fr){r=!1;break}if(s===r)throw new H("Arguments to apply() must be all SymbolicTensors or all Tensors");return al(this.name,()=>{if(!this.built){this.assertInputCompatibility(e);let a=[];for(let o of It(e))a.push(o.shape);this.build(rs(a)),this.built=!0,this.initialWeights&&this.setWeights(this.initialWeights),this._refCount===null&&r&&(this._refCount=1)}if(this.assertInputCompatibility(e),r){let a=this.call(e,t),o=It(a),i=[];for(let l of o)n.indexOf(l)!==-1&&(l=l.clone()),i.push(l);if(a=rs(i),this.activityRegularizer!=null)throw new We("Layer invocation in the presence of activity regularizer(s) is not supported yet.");return a}else{let a=zL(e),o=this.computeOutputShape(a),i,l=LL(e);if(this.warnOnIncompatibleInputShape(Array.isArray(e)?a[0]:a),o!=null&&o.length>0&&Array.isArray(o[0])?i=o.map((c,u)=>new fr(l,c,this,It(e),t,this.name,u)):i=new fr(l,o,this,It(e),t,this.name),this.addInboundNode(e,i,null,null,a,o,t),this._refCount++,this.activityRegularizer!=null)throw new We("Layer invocation in the presence of activity regularizer(s) is not supported yet.");return i}})}warnOnIncompatibleInputShape(e){if(this.batchInputShape!=null)if(e.length!==this.batchInputShape.length)console.warn(`The rank of the input tensor provided (shape: ${JSON.stringify(e)}) does not match that of the batchInputShape (${JSON.stringify(this.batchInputShape)}) of the layer ${this.name}`);else{let t=!1;this.batchInputShape.forEach((n,s)=>{n!=null&&e[s]!=null&&e[s]!==n&&(t=!0)}),t&&console.warn(`The shape of the input tensor (${JSON.stringify(e)}) does not match the expectation of layer ${this.name}: ${JSON.stringify(this.batchInputShape)}`)}}get outputShape(){if(this.inboundNodes==null||this.inboundNodes.length===0)throw new Yr(`The layer ${this.name} has never been called and thus has no defined output shape.`);let e=[];for(let t of this.inboundNodes){let n=JSON.stringify(t.outputShapes);e.indexOf(n)===-1&&e.push(n)}if(e.length===1){let t=this.inboundNodes[0].outputShapes;return Array.isArray(t)&&Array.isArray(t[0])&&t.length===1?t[0]:t}else throw new Yr(`The layer ${this.name} has multiple inbound nodes with different output shapes. Hence the notion of "output shape" is ill-defined for the layer.`)}countParams(){if(!this.built)throw new dr(`You tried to call countParams() on ${this.name}, but the layer is not built yet. Build it first by calling build(batchInputShape).`);return Ff(this.weights)}build(e){this.built=!0}getWeights(e=!1){return P1(e?this.trainableWeights:this.weights)}setWeights(e){q(()=>{let t=this.weights;if(t.length!==e.length)throw new H(`You called setWeights(weights) on layer "${this.name}" with a weight list of length ${e.length}, but the layer was expecting ${t.length} weights. Provided weights: ${e}...`);if(t.length===0)return;let n=[],s=P1(t);for(let r=0;r<s.length;++r){let a=s[r],o=t[r],i=e[r];if(!v.arraysEqual(a.shape,i.shape))throw new H(`Layer weight shape ${a.shape} not compatible with provided weight shape ${i.shape}`);n.push([o,i])}F1(n)})}addWeight(e,t,n,s,r,a,o,i){if(this._addedWeightNames.indexOf(e)!==-1)throw new H(`Duplicate weight name ${e} for layer ${this.name}`);this._addedWeightNames.push(e),n==null&&(n="float32"),this.fastWeightInitDuringBuild&&(s=i!=null?i():Et("zeros"));let l=s.apply(t,n),c=new cw(l,n,e,a,o);return l.dispose(),r!=null&&this.addLoss(()=>r.apply(c.read())),a==null&&(a=!0),a?this._trainableWeights.push(c):this._nonTrainableWeights.push(c),c}setFastWeightInitDuringBuild(e){this.fastWeightInitDuringBuild=e}addLoss(e){e==null||Array.isArray(e)&&e.length===0||(e=It(e),this._losses!==void 0&&this._losses!==null&&this.losses.push(...e))}computeOutputShape(e){return e}computeMask(e,t){if(!this.supportsMasking){if(t!=null)if(Array.isArray(t))t.forEach(n=>{if(n!=null)throw new TypeError(`Layer ${this.name} does not support masking, but was passed an inputMask.`)});else throw new TypeError(`Layer ${this.name} does not support masking, but was passed an inputMask.`);return null}return t}addInboundNode(e,t,n,s,r,a,o=null){let i=It(e);t=It(t),n=It(n),s=It(s),r=Pf(r),a=Pf(a);let l=[],c=[],u=[];for(let d of i)l.push(d.sourceLayer),c.push(d.nodeIndex),u.push(d.tensorIndex);new Of({outboundLayer:this,inboundLayers:l,nodeIndices:c,tensorIndices:u,inputTensors:i,outputTensors:t,inputMasks:n,outputMasks:s,inputShapes:r,outputShapes:a},o);for(let d=0;d<t.length;d++)t[d].sourceLayer=this,t[d].nodeIndex=this.inboundNodes.length-1,t[d].tensorIndex=d}getConfig(){let e={name:this.name,trainable:this.trainable};return this.batchInputShape!=null&&(e.batchInputShape=this.batchInputShape),this.dtype!=null&&(e.dtype=this.dtype),e}disposeWeights(){return this.weights.forEach(e=>e.dispose()),this.weights.length}assertNotDisposed(){if(this._refCount===0)throw new Error(`Layer '${this.name}' is already disposed.`)}dispose(){if(!this.built)throw new Error(`Cannot dispose Layer ${this.name} because it has not been built yet.`);if(this._refCount===null)throw new Error(`Cannot dispose Layer ${this.name} because it has not been used yet.`);this.assertNotDisposed();let e=0;return--this._refCount==0&&(e=this.disposeWeights()),{refCountAfterDispose:this._refCount,numDisposedVariables:e}}};function zL(e){e=It(e);let t=[];for(let n of e)t.push(n.shape);return rs(t)}function LL(e){return"float32"}function dw(e,t,n){if((t==null||n!=null&&n>0)&&(t=e.sourceLayer,n=e.nodeIndex),t.inboundNodes.length===0)return[e];{let s=t.inboundNodes[n];if(s.inboundLayers.length===0)return s.inputTensors;{let r=[];for(let a=0;a<s.inboundLayers.length;a++){let o=s.inputTensors[a],i=s.inboundLayers[a],l=s.nodeIndices[a],c=dw(o,i,l);for(let u of c)r.indexOf(u)===-1&&r.push(u)}return r}}}var Pu=class extends st{constructor(e){super({dtype:e.dtype,name:e.name!=null?e.name:_f("input").toString()});if(e.batchSize==null&&(e.batchSize=null),e.sparse==null&&(e.sparse=!1),this.trainable=!1,this.built=!0,this.sparse=e.sparse,e.inputShape!=null&&e.batchInputShape!=null)throw new H("Only provide the inputShape OR batchInputShape argument to inputLayer, not both at the same time.");let t=e.batchInputShape;if(t==null){if(e.inputShape==null)throw new H("An InputLayer should be passed either a `batchInputShape` or an `inputShape`.");t=[e.batchSize].concat(e.inputShape)}else if(e.batchSize!=null)throw new H("Cannot specify batchSize if batchInputShape is specified when creating an InputLayer.");let n=e.dtype||"float32";this.batchInputShape=t,this.dtype=n,this.inputSpec=[{shape:t}];let s=new fr(this.dtype,this.batchInputShape,this,[],{},this.name);s.nodeIndex=0,s.tensorIndex=0,new Of({outboundLayer:this,inboundLayers:[],nodeIndices:[],tensorIndices:[],inputTensors:[s],outputTensors:[s],inputMasks:[null],outputMasks:[null],inputShapes:[t],outputShapes:[t]})}apply(e,t){throw new H(`Cannot pass any input to an InputLayer's apply() method. 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st{constructor(e){super({});if(this.containerNodes=new Set,this.name=e.name,this.name==null){let A=this.getClassName().toLowerCase();this.name=_f(A)}if(this.supportsMasking=!1,this.trainable_=!0,Array.isArray(e.inputs)?this.inputs=e.inputs.slice():this.inputs=[e.inputs],Array.isArray(e.outputs)?this.outputs=e.outputs.slice():this.outputs=[e.outputs],No(this.inputs).length!==this.inputs.length)throw new H(`The list of inputs passed to the model is redundant. All inputs should only appear once. Found: ${this.inputs.map(A=>A.name)}`);No(this.outputs).length!==this.outputs.length&&console.warn(`The list of outputs passed to the model is redundant. All outputs should only appear once. Found: ${this.outputs.map(A=>A.name)}`),this.inputLayers=[],this.inputLayersNodeIndices=[],this.inputLayersTensorIndices=[],this.outputLayers=[],this.outputLayersNodeIndices=[],this.outputLayersTensorIndices=[],this.layers=[],this.internalContainerRefs=[];for(let A of this.outputs){let x=A.sourceLayer,y=A.nodeIndex,b=A.tensorIndex;this.outputLayers.push(x),this.outputLayersNodeIndices.push(y),this.outputLayersTensorIndices.push(b)}for(let A of this.inputs){let x=A.sourceLayer,y=A.nodeIndex,b=A.tensorIndex;Er(y===0,"input layer has >1 nodes"),Er(b===0,"input layer has >1 tensors"),this.inputLayers.push(x),this.inputLayersNodeIndices.push(y),this.inputLayersTensorIndices.push(b)}this.inputNames=[],this.outputNames=[],this.feedInputShapes=[],this.feedInputNames=[],this.feedOutputNames=[];for(let A=0;A<this.inputLayers.length;A++){let x=this.inputLayers[A];if(!(x instanceof Pu))throw new TypeError(`Input layers to a LayersModel must be InputLayer objects. Received inputs: ${e.inputs}. Input ${A} (0-based) originates from layer type ${x.getClassName()}.`);this.inputNames.push(x.name),this.feedInputShapes.push(x.batchInputShape),this.feedInputNames.push(x.name)}for(let A of this.outputLayers)this.outputNames.push(A.name);this.internalInputShapes=this.inputs.map(A=>A.shape),this.internalOutputShapes=this.outputs.map(A=>A.shape);let t={},n={},s={},r={},a={},o=[],i=(A,x,y,b,w,k)=>{(b==null||w==null||k==null)&&(b=A.sourceLayer,w=A.nodeIndex,k=A.tensorIndex);let C=b.inboundNodes[w];if(y.indexOf(C)!==-1)throw new dr(`The tensor ${A.name} at layer "${b.name}" is part of a cycle.`);if(x.indexOf(C)!==-1)return;this.containerNodes.add(Dr.nodeKey(b,w)),b.id in a||(a[b.id]=Object.keys(a).length),y.indexOf(C)===-1&&y.push(C);let E=C.inboundLayers.length;for(let R=0;R<E;R++){let F=C.inputTensors[R],D=C.inboundLayers[R],P=C.nodeIndices[R],T=C.tensorIndices[R];i(F,x,y,D,P,T)}for(x.push(C);y.indexOf(C)>=0;)y.splice(y.indexOf(C),1);o.push(C)},l=[],c=[];for(let A of this.outputs)i(A,l,c);let u=o.slice().reverse();for(let A of u){n[A.id]=A,A.id in t||(t[A.id]=0);let x=t[A.id],y=s[A.outboundLayer.id]==null?0:s[A.outboundLayer.id];x=Math.max(x,y),s[A.outboundLayer.id]=x,r[A.outboundLayer.id]=A.outboundLayer,t[A.id]=x;for(let b=0;b<A.inboundLayers.length;b++){let w=A.inboundLayers[b],k=A.nodeIndices[b],C=w.inboundNodes[k],E=t[C.id]==null?0:t[C.id];t[C.id]=Math.max(x+1,E),n[C.id]=C}}let d={};for(let A in t){let x=t[A];x in d||(d[x]=[]),d[x].push(n[A])}let p={};for(let A in s){let x=s[A];x in p||(p[x]=[]),p[x].push(r[A])}let h=Object.keys(p).map(A=>parseInt(A,10)).sort(vf);this.layers=[];for(let A of h){let x=p[A];x.sort((y,b)=>{let w=a[y.id],k=a[b.id];return w<k?-1:w>k?1:0});for(let y of x)y instanceof Dr&&this.internalContainerRefs.push(y),this.layers.push(y)}this.layersByDepth=p,h=Object.keys(d).map(A=>parseInt(A,10)).sort(vf);let f=this.inputs.slice(),m=[];for(let A of h)for(let x of d[A]){let y=x.outboundLayer;if(y!=null){for(let b of x.inputTensors)if(f.indexOf(b)===-1)throw new dr(`Graph disconnected: cannot obtain value for tensor ${b} at layer "${y.name}". The following previous layers were accessed without issue: ${m}`);for(let b of x.outputTensors)f.push(b);m.push(y.name)}}this.nodesByDepth=d;let g=this.layers.map(A=>A.name);for(let A of g){let x=g.filter(y=>y===A).length;if(x!==1)throw new dr(`The name "${A}" is used ${x} times in the model. All layer names should be unique. Layer names: `+JSON.stringify(g))}this.outboundNodes=[],this.inboundNodes=[],new Of({outboundLayer:this,inboundLayers:[],nodeIndices:[],tensorIndices:[],inputTensors:this.inputs,outputTensors:this.outputs,inputMasks:this.inputs.map(A=>null),outputMasks:this.outputs.map(A=>null),inputShapes:this.inputs.map(A=>A.shape),outputShapes:this.outputs.map(A=>A.shape)}),this.built=!0,this._refCount=1}assertNotDisposed(){if(this._refCount===0)throw new Error(`Container '${this.name}' is already disposed.`)}dispose(){this.assertNotDisposed();let e={refCountAfterDispose:null,numDisposedVariables:0};if(--this._refCount==0){for(let t of this.layers)e.numDisposedVariables+=t.dispose().numDisposedVariables;for(let t of this.internalContainerRefs)e.numDisposedVariables+=t.dispose().numDisposedVariables}return e.refCountAfterDispose=this._refCount,e}get trainable(){return this.trainable_}set trainable(e){this.layers.forEach(t=>{t._trainableWeights.forEach(n=>n.trainable=e)}),this.trainable_=e}get trainableWeights(){if(this._trainableWeights.length>0)throw new H("Container instance unexpectedly contains _trainableWeights.The trainable weights of a Container are a union of the trainable weights of its consituent Layers. Its own _trainableWeights must remain an empty Array.");if(!this.trainable)return[];let e=[];for(let t of this.layers)e=e.concat(t.trainableWeights);return e}get nonTrainableWeights(){let e=[];for(let t of this.layers)e.push(...t.nonTrainableWeights);if(!this.trainable){let t=[];for(let n of this.layers)t.push(...n.trainableWeights);return t.concat(e)}return e}get weights(){return this.trainableWeights.concat(this.nonTrainableWeights)}loadWeights(e,t=!0){let n={},s=0;for(let a of this.layers)for(let o of a.weights){if(n[o.originalName]!=null)throw new H(`Duplicate weight name: ${o.originalName}`);n[o.originalName]=o,s++}let r=[];for(let a in e){let o=a;if(n[a]==null){let i=a.split("/");o=i.slice(0,-2).concat([i[i.length-1]]).join("/")}if(n[o]!=null)r.push([n[o],e[a]]);else if(t)throw new H(`Provided weight data has no target variable: ${a}`);delete n[o]}if(t){let a=[];for(let o in n)a.push(o);if(a.length>0)throw new H(`${a.length} of ${s} weights are not set: ${a}`)}F1(r)}updatedConfig(){let e=this.getConfig(),t={};return t.className=this.getClassName(),t.config=e,t.kerasVersion=`tfjs-layers ${G1}`,t.backend="TensorFlow.js",t}toJSON(e,t=!0){let n=U1(this.updatedConfig());return t?JSON.stringify(n):n}call(e,t){return q(()=>{e=It(e);let n=new ll;for(let s=0;s<this.inputs.length;++s)n.add(this.inputs[s],e[s]);return Rd(this.outputs,n,t)})}computeMask(e,t){return q(()=>{e=It(e);let n;return t==null?n=nl(null,e.length):n=It(t),this.runInternalGraph(e,n)[1]})}computeOutputShape(e){let t=Pf(e);if(t.length!==this.inputLayers.length)throw new H(`Invalid inputShape argument ${e}: model has ${this.inputLayers.length} tensor inputs.`);let n={};for(let o=0;o<t.length;o++){let i=this.inputLayers[o],l=t[o],c=i.name+"_0_0";n[c]=l}let s=Object.keys(this.nodesByDepth).map(o=>parseInt(o,10)).sort(vf);if(s.length>1)for(let o of s){let i=this.nodesByDepth[o];for(let l of i){let c=l.outboundLayer;if(this.inputLayers.map(f=>f.id).indexOf(c.id)!==-1)continue;let u=[];for(let f=0;f<l.inboundLayers.length;f++){let m=l.inboundLayers[f],g=l.nodeIndices[f],A=l.tensorIndices[f],x=`${m.name}_${g}_${A}`,y=n[x];u.push(y)}let d=c.computeOutputShape(rs(u)),p=Pf(d),h=c.inboundNodes.indexOf(l);for(let f=0;f<p.length;f++){let m=`${c.name}_${h}_${f}`;n[m]=p[f]}}}let r=[],a=[];for(let o=0;o<this.outputLayers.length;o++){let i=this.outputLayers[o],l=this.outputLayersNodeIndices[o],c=this.outputLayersTensorIndices[o],u=`${i.name}_${l}_${c}`;a.push(u)}for(let o=0;o<a.length;o++){let i=a[o];Er(i in n),r.push(n[i])}return rs(r)}runInternalGraph(e,t){t==null&&(t=nl(null,e.length));let n={};for(let i=0;i<this.inputs.length;++i){let l=this.inputs[i],c=e[i],u=t[i];n[l.id]=[c,u]}let s=Object.keys(this.nodesByDepth).map(i=>parseInt(i,10)).sort(vf);for(let i of s){let l=this.nodesByDepth[i];for(let c of l){let u=c.outboundLayer,d=c.inputTensors,p=c.outputTensors,h=new Array;for(let f of d)f.id in n&&h.push(n[f.id]);if(h.length===d.length){let f={},m,g,A,x;if(c.callArgs!=null&&(f=c.callArgs),h.length===1){let[y,b]=h[0];f.mask==null&&(f.mask=b),A=It(u.call(y,f)),x=It(u.computeMask(y,b)),m=[y],g=[b]}else m=h.map(y=>y[0]),g=h.map(y=>y[1]),f.mask==null&&(f.mask=g),A=It(u.call(m,f)),x=It(u.computeMask(m,g));if(u.activityRegularizer)throw new We("LayersModel invocation with concrete Tensor value(s) in the presence of activity regularizer(s) is not supported yet.");for(let y=0;y<p.length;++y){let b=p[y],w=A[y],k=x[y];n[b.id]=[w,k]}}}}let r=[],a=[],o=[];for(let i of this.outputs){Er(i.id in n,`Could not compute output ${i.name} : ${i.id}`);let[l,c]=n[i.id];o.push(l.shape),r.push(l),a.push(c)}return[r,a,o]}buildNodeConversionMap(e){let t={},n;for(let s of this.layers){n=s instanceof Dr?1:0;for(let r=0;r<s.inboundNodes.length;r++){let a=Dr.nodeKey(s,r);this.containerNodes.has(a)&&(t[a]=n,n+=1)}}return t}getLayer(e,t){if(t!=null){if(this.layers.length<=t)throw new H(`Was asked to retrieve layer at index ${t}, but model only has ${this.layers.length} layer(s).`);return this.layers[t]}else if(e==null)throw new H("Provide either a layer name or layer index");for(let n of this.layers)if(n.name===e)return n;throw new H(`No such layer: ${e}`)}calculateLosses(){return q(()=>{let e=[];for(let t of this.layers)for(let n=0;n<t.inboundNodes.length;++n){let s=Dr.nodeKey(t,n);this.containerNodes.has(s)&&e.push(...t.calculateLosses())}return e})}getConfig(){let e={name:this.name},t=this.buildNodeConversionMap(this.layers),n=[];for(let a of this.layers){let o=a.getClassName(),i=a.getConfig(),l=[];for(let u=0;u<a.inboundNodes.length;u++){let d=a.inboundNodes[u],p=Dr.nodeKey(a,u),h={};if(this.containerNodes.has(p)){if(d.callArgs)try{JSON.stringify(d.callArgs),h=d.callArgs}catch(f){console.warn(`Layer ${a.name} was passed non-serializable keyword arguments: ${d.callArgs}. They will not be included in the serialized model (and thus will be missing at deserialization time).`),h={}}if(d.inboundLayers.length>0){let f=[];for(let m=0;m<d.inboundLayers.length;m++){let g=d.inboundLayers[m],A=d.nodeIndices[m],x=d.tensorIndices[m],y=Dr.nodeKey(g,A),b=t[y];b==null&&(b=0),f.push([g.name,b,x,h])}l.push(f)}}}let c={};c.name=a.name,c.className=o,c.config=i,c.inboundNodes=l,n.push(c)}e.layers=n;let s=[];for(let a=0;a<this.inputLayers.length;a++){let o=this.inputLayers[a],i=this.inputLayersNodeIndices[a],l=Dr.nodeKey(o,i);if(!this.containerNodes.has(l))continue;let c=t[l];c==null&&(c=0);let u=this.inputLayersTensorIndices[a];s.push([o.name,c,u])}e.inputLayers=s;let r=[];for(let a=0;a<this.outputLayers.length;a++){let o=this.outputLayers[a],i=this.outputLayersNodeIndices[a],l=Dr.nodeKey(o,i);if(!this.containerNodes.has(l))continue;let c=t[l];c==null&&(c=0);let u=this.outputLayersTensorIndices[a];r.push([o.name,c,u])}return e.outputLayers=r,e}static fromConfig(e,t,n={},s=!1){let r={},a={};function o(m,g){m.name in a?a[m.name].push(g):a[m.name]=[g]}function i(m,g){let A=[],x;for(let y of g){let b=y[0],w=y[1],k=y[2];if(x=y[3]==null?{}:y[3],!(b in r)){o(m,g);return}let C=r[b];if(C.inboundNodes.length<=w){o(m,g);return}let E=C.inboundNodes[w];A.push(E.outputTensors[k])}A.length>0&&m.apply(rs(A),x)}function l(m){let g=m.name,A=mr(m,t.customObjects!=null?t.customObjects:{});A.setFastWeightInitDuringBuild(s),r[g]=A,m.inboundNodes.forEach(y=>{if(!(y instanceof Array))throw new H(`Corrupted configuration, expected array for nodeData: ${y}`);o(A,y)})}let c=t.name,u=t.layers;for(let m of u)l(m);for(;!qz(a);)for(let m of u){let g=r[m.name];if(g.name in a){let A=a[g.name];delete a[g.name];for(let x of A)i(g,x)}}let d=[],p=[],h=t.inputLayers;for(let m of h){let g=m[0],A=m[1],x=m[2];Er(g in r);let b=r[g].inboundNodes[A].outputTensors;d.push(b[x])}let f=t.outputLayers;for(let m of f){let g=m[0],A=m[1],x=m[2];Er(g in r);let b=r[g].inboundNodes[A].outputTensors;p.push(b[x])}return new e({inputs:d,outputs:p,name:c})}get stateful(){if(this._stateful)throw new H("Container instance unexpectedly has _stateful = true. The statefulness of a Container is determined by the Layers it contains. Its _stateful property must remain the default false.");for(let e of this.layers)if(e.stateful)return!0;return!1}resetStates(){q(()=>{this.layers.forEach(e=>{e.stateful&&e.resetStates()})})}};function AB(e,t,n){let s=t.length;if(e==null||Array.isArray(e)&&e.length===0)return t.map(r=>null);if(s===1)return Array.isArray(e)&&e.length===1?e:typeof e=="object"&&t[0]in e?[e[t[0]]]:[e];if(Array.isArray(e)){if(e.length!==s)throw new Error(`Provided ${n} is an array of ${e.length} element(s), but the model has ${s} outputs. Make sure a set of weights is provided for each model output.`);return e}else if(typeof e=="object"&&Object.keys(e).length>0&&typeof e[Object.keys(e)[0]]=="object"){let r=[];return t.forEach(a=>{a in e?r.push(e[a]):r.push(null)}),r}else throw new Error(`The model has multiple (${s}) outputs, so ${n} must be either an array with ${s} elements or an object with ${t} keys. Provided ${n} not understood: ${JSON.stringify(e)}`)}function Rw(e,t){return AB(e,t,"classWeight")}async function $w(e,t,n,s){if(t!=null||s!=null)throw new Error("Support sampleWeight is not implemented yet");if(n!=null){let r=q(()=>{if(e.shape.length===1)return or(e);if(e.shape.length===2){if(e.shape[1]>1)return Hs(e,1);if(e.shape[1]===1)return G(e,[e.shape[0]]);throw new Error(`Encountered unexpected last-dimension size (${e.shape[1]}) during handling of class weights. The size is expected to be >= 1.`)}else throw new Error(`Unexpected rank of target (y) tensor (${e.rank}) during handling of class weights. The rank is expected to be 1 or 2.`)}),a=Array.from(await r.data());ae(r);let o=[];return a.forEach(i=>{if(n[i]==null)throw new Error(`classWeight must contain all classes in the training data. The class ${i} exists in the data but not in classWeight`);o.push(n[i])}),Kt(o,"float32")}else return null}function yB(e,t){return W(e,t)}var xB=32;function Dw(e,t){let n,s,r=t;n=r.xs,s=r.ys,v.assert(n!=null&&s!=null,()=>`A Dataset iterator for fitDataset() is expected to generate objects of the form \`{xs: xVal, ys: yVal}\`, where the two values may be \`tf.Tensor\`, an array of Tensors, or a map of string to Tensor. The provided Dataset instead generates ${t}`);let a=_w("input",e.inputNames,n),o=_w("output",e.outputNames,s),i=a[0].shape[0];v.assert(a.length===e.inputs.length,()=>`LayersModel has ${e.inputs.length} inputs, but the dataset provides ${a.length} inputs. (Expected input keys: ${JSON.stringify(e.inputNames)})`),v.assert(o.length===e.outputs.length,()=>`LayersModel has ${e.outputs.length} outputs, but the dataset provides ${o.length} outputs. (Expected output keys: ${JSON.stringify(e.outputNames)})`);for(let l=0;l<a.length;l++)v.assert(a[l].shape[0]===i,()=>`Batch size mismatch: input ${e.inputNames[l]} has ${a[l].shape[0]}; expected ${i} based on input ${e.inputNames[0]}.`);for(let l=0;l<o.length;l++)v.assert(o[l].shape[0]===i,()=>`Batch size mismatch: output ${e.outputNames[l]} has ${o[l].shape[0]}; expected ${i} based on input ${e.inputNames[0]}.`);return{xs:a,ys:o}}function _w(e,t,n){if(n instanceof et)return[n];if(Array.isArray(n))return v.assert(n.length===t.length,()=>`Received an array of ${n.length} Tensors, but expected ${t.length} to match the ${e} keys ${t}.`),n;{let s=[];for(let r of t){if(n[r]==null)throw new H(`The feature data generated by the dataset lacks the required ${e} key '${r}'.`);s.push(n[r])}return s}}function bB(e){if(e.length===3)throw new We("Validation with sample weights is not implemented yet.");return{xs:e[0],ys:e[1]}}async function vB(e,t,n){let s=n.batchesPerEpoch!=null;if(v.assert(e.optimizer!=null,()=>"You must compile a model before training/testing. Use LayersModel.compile(modelCompileConfig)."),v.assert(n!=null,()=>"For fitDataset(), the 2nd argument (config) is required, but it is not provided in this call."),v.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}`),v.assert(!s||n.batchesPerEpoch>0&&Number.isInteger(n.batchesPerEpoch),()=>`For fitDataset(), config.batchesPerEpoch is expected to be a positive integer if specified, but got ${n.batchesPerEpoch}`),v.assert(n.validationSplit==null,()=>"`validationSplit` is not supported by `fitDataset()`. Use validationData instead."),e.isTraining)throw new Error("Cannot start training because another fit() call is ongoing.");e.isTraining=!0;try{let r=n.validationData!=null,a,o;if(r)if(Pw(n.validationData))v.assert(n.validationBatches==null||n.validationBatches>0&&Number.isInteger(n.validationBatches),()=>`For fitDataset() with dataset-based validation, config.validationBatches is expected not to be provided, or to be a positive integer, but got ${n.validationBatches}`);else{let g=bB(n.validationData);a=g.xs,o=g.ys}let i=e.makeTrainFunction(),l=e.getDedupedMetricsNames(),c;r?c=l.slice().concat(l.map(g=>"val_"+g)):c=l.slice();let u=yw(n.callbacks,n.yieldEvery),d=n.verbose==null?1:n.verbose,{callbackList:p,history:h}=xw(u,d,n.epochs,null,null,wB(t,n),null,r,c);p.setModel(e),e.history=h,await p.onTrainBegin(),e.stopTraining_=!1;let f=n.initialEpoch==null?0:n.initialEpoch,m=await t.iterator();for(;f<n.epochs;){let g={};await p.onEpochBegin(f);let A=0,x=0;for(s||(m=await t.iterator());s?A<n.batchesPerEpoch:!0;){let y=await m.next();if(s&&y.done){console.warn(`You provided \`batchesPerEpoch\` as ${n.batchesPerEpoch}, but your dataset iterator ran out of data after ${A} batches; interrupting training. 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Use LayersModel.compile(modelCompileArgs).");let r=[];for(let a=0;a<this.feedOutputShapes.length;++a){let o=this.feedOutputShapes[a];this.feedLossFns[a]===Lf?r.push(o.slice(0,o.length-1).concat([1])):r.push(o)}if(e=Mw(e,this.feedInputNames,this.feedInputShapes,!1,"input"),t=Mw(t,this.feedOutputNames,r,!1,"target"),NB(e,t,null),EB(t,this.feedLossFns,this.feedOutputShapes),this.stateful&&s!=null&&s>0&&e[0].shape[0]%s!=0)throw new H(`In a stateful network, you should only pass inputs with a number of samples that is divisible by the batch size ${s}. 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t}makeTrainFunction(){return e=>{let t=[],n=e.slice(0,this.inputs.length),s=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),a=[],o=()=>{let u=[];for(let f=0;f<this.inputs.length;++f)u.push({key:this.inputs[f],value:n[f]});let d=new ll(u),p=Rd(this.outputs,d,{training:!0}),h;for(let f=0;f<this.lossFunctions.length;++f){let g=this.lossFunctions[f](s[f],p[f]);r[f]!=null&&(g=yB(g,r[f]));let A=Wt(g);t.push(A),f===0?h=g:h=le(h,g)}for(let f=0;f<this.metricsTensors.length;++f){let m;if(this.outputs.length>1&&f<this.outputs.length)m=t[f];else{let g=this.metricsTensors[f][0],A=this.metricsTensors[f][1];m=Wt(g(s[A],p[A]))}fn(m),a.push(m)}return h=Wt(h),this.calculateLosses().forEach(f=>{h=le(h,f)}),h},i=this.collectedTrainableWeights.map(u=>u.read()),l=!0;return[this.optimizer_.minimize(o,l,i)].concat(a)}}makeTestFunction(){this.testFunction=e=>q(()=>{let t=[],n,s=e.slice(0,this.inputs.length),r=e.slice(this.inputs.length,this.inputs.length+this.outputs.length),a=[];for(let l=0;l<this.inputs.length;++l)a.push({key:this.inputs[l],value:s[l]});let o=new ll(a),i=Rd(this.outputs,o);for(let l=0;l<this.lossFunctions.length;++l){let c=this.lossFunctions[l],u=Wt(c(r[l],i[l]));l===0?n=u:n=le(n,u),t.push(n)}for(let l=0;l<this.metricsTensors.length;++l){let c=this.metricsTensors[l][0],u=this.metricsTensors[l][1],d=Wt(c(r[u],i[u]));t.push(d)}return t})}async fit(e,t,n={}){return CB(this,e,t,n)}async fitDataset(e,t){return vB(this,e,t)}async trainOnBatch(e,t){let n=await this.standardizeUserData(e,t),s=n[0],r=n[1],o=this.makeTrainFunction()(s.concat(r)),i=[];for(let l of o){let c=await l.data();i.push(c[0])}return ae(o),rs(i)}getNamedWeights(e){let t=[],n=e!=null&&e.trainableOnly,s=n?this.trainableWeights:this.weights,r=this.getWeights(n);for(let a=0;a<s.length;++a)n&&!s[a].trainable||t.push({name:s[a].originalName,tensor:r[a]});return t}set stopTraining(e){this.stopTraining_=e}get stopTraining(){return this.stopTraining_}get optimizer(){return this.optimizer_}set optimizer(e){this.optimizer_!==e&&(this.optimizer_=e,this.isOptimizerOwned=!1)}dispose(){let e=super.dispose();if(e.refCountAfterDispose===0&&this.optimizer!=null&&this.isOptimizerOwned){let t=Wh().numTensors;this.optimizer_.dispose(),e.numDisposedVariables+=t-Wh().numTensors}return e}getLossIdentifiers(){let e;if(typeof this.loss=="string")e=Jr(this.loss);else if(Array.isArray(this.loss)){for(let t of this.loss)if(typeof t!="string")throw new Error("Serialization of non-string loss is not supported.");e=this.loss.map(t=>Jr(t))}else{let t=Object.keys(this.loss);e={};let n=this.loss;for(let s of t)if(typeof n[s]=="string")e[s]=Jr(n[s]);else throw new Error("Serialization of non-string loss is not supported.")}return e}getMetricIdentifiers(){if(typeof this.metrics=="string"||typeof this.metrics=="function")return[Jr(Uf(this.metrics))];if(Array.isArray(this.metrics))return this.metrics.map(e=>Jr(Uf(e)));{let e={};for(let t in this.metrics)e[t]=Jr(Uf(this.metrics[t]));return e}}getTrainingConfig(){return{loss:this.getLossIdentifiers(),metrics:this.getMetricIdentifiers(),optimizer_config:{class_name:this.optimizer.getClassName(),config:this.optimizer.getConfig()}}}loadTrainingConfig(e){if(e.weighted_metrics!=null)throw new Error("Loading weight_metrics is not supported yet.");if(e.loss_weights!=null)throw new Error("Loading loss_weights is not supported yet.");if(e.sample_weight_mode!=null)throw new Error("Loading sample_weight_mode is not supported yet.");let t=Ed(e.optimizer_config),n=mr(t),s;if(typeof e.loss=="string")s=sl(e.loss);else if(Array.isArray(e.loss))s=e.loss.map(a=>sl(a));else if(e.loss!=null){s={};for(let a in e.loss)s[a]=sl(e.loss[a])}let r;if(Array.isArray(e.metrics))r=e.metrics.map(a=>sl(a));else if(e.metrics!=null){r={};for(let a in e.metrics)r[a]=sl(e.metrics[a])}this.compile({loss:s,metrics:r,optimizer:n})}async save(e,t){if(typeof e=="string"){let l=es.getSaveHandlers(e);if(l.length===0)throw new H(`Cannot find any save handlers for URL '${e}'`);if(l.length>1)throw new H(`Found more than one (${l.length}) save handlers for URL '${e}'`);e=l[0]}if(e.save==null)throw new H("LayersModel.save() cannot proceed because the IOHandler provided does not have the `save` attribute defined.");let n=await es.encodeWeights(this.getNamedWeights(t)),s=!1,r=null,o={modelTopology:this.toJSON(r,s),format:$B,generatedBy:`TensorFlow.js tfjs-layers v${G1}`,convertedBy:null};if((t==null?!1:t.includeOptimizer)&&this.optimizer!=null){o.trainingConfig=this.getTrainingConfig();let l="optimizer",{data:c,specs:u}=await es.encodeWeights(await this.optimizer.getWeights(),l);n.specs.push(...u),n.data=es.concatenateArrayBuffers([n.data,c])}if(this.userDefinedMetadata!=null){let l=!0;Cw(this.userDefinedMetadata,this.name,l),o.userDefinedMetadata=this.userDefinedMetadata}return o.weightData=n.data,o.weightSpecs=n.specs,e.save(o)}setUserDefinedMetadata(e){Cw(e,this.name),this.userDefinedMetadata=e}getUserDefinedMetadata(){return this.userDefinedMetadata}};Qr.className="Model";ue.registerClass(Qr);var Lw=class extends Qr{};Lw.className="Functional";ue.registerClass(Lw);async function DB(e,t){"modelTopology"in e||(e={modelTopology:e}),e=e;let n=e.modelTopology;n.model_config!=null&&(n=n.model_config);let s=Ed(n),r=mr(s,t);if(e.weightsManifest!=null){let a=await es.loadWeights(e.weightsManifest,e.pathPrefix,r.weights.map(i=>i.originalName)),o={};for(let i of r.weights)o[i.originalName]=a[i.originalName];r.loadWeights(o),ae(a)}return r}async function _B(e,t){if(t==null&&(t={}),typeof e=="string"){let n=es.getLoadHandlers(e,t);if(n.length===0)n.push(es.browserHTTPRequest(e,t));else if(n.length>1)throw new H(`Found more than one (${n.length}) load handlers for URL '${e}'`);e=n[0]}return PB(e,void 0,t)}async function PB(e,t,n){if(n==null&&(n={}),e.load==null)throw new H("Cannot proceed with model loading because the IOHandler provided does not have the `load` method implemented.");let s=await e.load(),r=s.modelTopology;r.model_config!=null&&(r=r.model_config);let a=n.strict==null?!0:n.strict,o=s.weightData!=null&&s.weightSpecs!=null&&a,i=mr(Ed(r),t,o),l=s.trainingConfig;if(l!=null&&i.loadTrainingConfig(l),s.userDefinedMetadata!=null&&i.setUserDefinedMetadata(s.userDefinedMetadata),s.weightData!=null){if(s.weightSpecs==null)throw new H("LayersModel artifacts contains weight data, but not weight specs. Therefore loading of weights cannot proceed.");let{modelWeights:c,optimizerWeights:u}=FB(s.weightData,s.weightSpecs);i.loadWeights(c,a),i.optimizer!=null&&u.length>0&&await i.optimizer.setWeights(u),ae(c),ae(u.map(d=>d.tensor))}return i}function FB(e,t){let n=es.decodeWeights(e,t),s={},r=[];return t.forEach(a=>{a.group==="optimizer"?r.push({name:a.name,tensor:n[a.name]}):s[a.name]=n[a.name]}),{modelWeights:s,optimizerWeights:r}}var Z1=class extends Qr{constructor(e){super({inputs:[],outputs:[]});if(e=e||{},this.trainable=!0,this.built=!1,this.name=e.name!=null?e.name:_f("sequential_"),e.layers!=null)for(let t of e.layers)this.add(t)}checkShape(e){if(e.inboundNodes[0].outputTensors[0].shape.some(n=>n<0))throw new H(`Negative dimension size caused by adding layer ${e.name} with input shape [${e.inboundNodes[0].inputTensors[0].shape}]`)}add(e){let t=e instanceof Z1||e instanceof Qr,n;if(t){if(n=e,n.outputs.length!==1)throw new H("All layers in a Sequential model should have a single output tensor. For multi-output layers, use the functional API.");if(n.inputs.length!==1)throw new H("All layers in a Sequential model should have a single input tensor. For multi-input layers, use the functional API.")}if(this.outputs.length===0){if(e.inboundNodes.length===0){if(e.batchInputShape==null)throw new H("The first layer in a Sequential model must get an `inputShape` or `batchInputShape` argument.");let s=pw({batchShape:e.batchInputShape,dtype:e.dtype,name:e.name+"_input"});e.apply(s)}if(t)this.outputs=n.outputs,this.inputs=n.inputs;else{if(e.inboundNodes.length!==1)throw new H(`A layer added to a Sequential model must not already be connected somewhere else. LayersModel received layer ${e.name} which has ${e.inboundNodes.length} pre-existing inbound connections.`);if(e.inboundNodes[0].outputTensors.length!==1)throw new H("All layers in a Sequential model should have a single output tensor. For multi-output layers, use the functional API.");this.checkShape(e),this.outputs=[e.inboundNodes[0].outputTensors[0]],this.inputs=dw(this.outputs[0])}this.inboundNodes=[],new Of({outboundLayer:this,inboundLayers:[],nodeIndices:[],tensorIndices:[],inputTensors:this.inputs,outputTensors:this.outputs,inputMasks:nl(null,this.inputs.length),outputMasks:[null],inputShapes:this.inputs.map(s=>s.shape),outputShapes:this.outputs[0].shape})}else{let s=e.apply(this.outputs[0]);if(Array.isArray(s))throw new TypeError("All layers in a Sequential model should have a single output tensor. For multi-output layers, use the functional API.");this.checkShape(e),this.outputs=[s],this.inboundNodes[0].outputTensors=this.outputs,this.inboundNodes[0].outputShapes=[this.outputs[0].shape]}this.layers.push(e),this.built=!1}pop(){if(this.layers.length===0)throw new TypeError("There are no layers in the model.");if(this.layers.pop(),this.layers.length===0)this.outputs=[],this.inboundNodes=[],this.outboundNodes=[];else{let e=this.layers.length-1;this.layers[e].outboundNodes=[],this.outputs=[this.layers[e].output],this.inboundNodes[0].outputTensors=this.outputs,this.inboundNodes[0].outputShapes=[this.outputs[0].shape]}}call(e,t){return this.model==null&&this.build(),this.model.call(e,t)}build(e){if(ft(e),this.inputs.length===0||this.outputs.length===0)throw new TypeError("Sequential model cannot be built: model is empty. Add some layers first.");this.model=new Qr({inputs:this.inputs,outputs:this.outputs[0],name:this.name+"_model"}),this.model.trainable=this.trainable,this.supportsMasking=this.model.supportsMasking,this.inputLayers=this.model.inputLayers,this.inputLayersNodeIndices=this.model.inputLayersNodeIndices,this.inputLayersTensorIndices=this.model.inputLayersTensorIndices,this.outputLayers=this.model.outputLayers,this.outputLayersNodeIndices=this.model.outputLayersNodeIndices,this.outputLayersTensorIndices=this.model.outputLayersTensorIndices,this.nodesByDepth=this.model.nodesByDepth,this.containerNodes=this.model.containerNodes,this.outputNames=this.model.outputNames,this.inputNames=this.model.inputNames,this.built=!0}countParams(){return this.built||this.build(),super.countParams()}summary(e,t,n=console.log){this.built||this.build(),super.summary(e,t,n)}setWeights(e){this.model==null&&this.build(),this.model.setWeights(e)}evaluate(e,t,n={}){if(!this.built)throw new dr("The model needs to be compiled before being used.");return this.model.evaluate(e,t,n)}async evaluateDataset(e,t){if(!this.built)throw new dr("The model needs to be compiled before being used.");return this.model.evaluateDataset(e,t)}predict(e,t={}){return this.model==null&&this.build(),this.model.predict(e,t)}predictOnBatch(e){return this.model==null&&this.build(),this.model.predictOnBatch(e)}compile(e){this.build(),this.model.compile(e),this.optimizer_=this.model.optimizer,this.isOptimizerOwned=this.model.isOptimizerOwned,this.loss=this.model.loss,this.metrics=this.model.metrics,this.metricsTensors=this.model.metricsTensors,this.metricsNames=this.model.metricsNames}get optimizer(){return this.model==null?void 0:this.model.optimizer}set optimizer(e){this.model.optimizer=e}async fit(e,t,n={}){if(!this.built)throw new dr("The model needs to be compiled before being used.");return this.model.fit(e,t,n)}async fitDataset(e,t){if(!this.built)throw new dr("The model needs to be compiled before being used.");return this.model.fitDataset(e,t)}async trainOnBatch(e,t){return this.model.trainOnBatch(e,t)}static fromConfig(e,t,n={},s=!1){let r,a={};if(t instanceof Array){if(t[0].className==null||t[0].className==="Merge")throw new H("Legacy serialization format not supported yet.");r=t}else v.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,a=t;let o=new e(a);if(!(o instanceof Z1))throw new We(`Sequential.fromConfig called on non-Sequential input: ${o}`);for(let i of r){let c=mr(i,void 0,s);s&&c.setFastWeightInitDuringBuild(!0),o.add(c)}return o}set stopTraining(e){if(this.model==null)throw new H("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 H("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}}},Hf=Z1;Hf.className="Sequential";ue.registerClass(Hf);function OB(e){return new Qr(e)}function MB(e){return new Hf(e)}function zB(e,t){return t==null&&(t={}),_B(e,t)}function Bw(e){return pw(e)}function LB(e,t){O1.registerCallbackConstructor(e,t)}var os=class extends ue.Serializable{getConfig(){return{}}},Ww=class extends os{apply(e,t=1){return dL(e,t)}};Ww.className="elu";ue.registerClass(Ww);var Vw=class extends os{apply(e){return J2(e)}};Vw.className="selu";ue.registerClass(Vw);var Uw=class extends os{apply(e){return Nr(e)}};Uw.className="relu";ue.registerClass(Uw);var Gw=class extends os{apply(e){return q(()=>md(6,Nr(e)))}};Gw.className="relu6";ue.registerClass(Gw);var Hw=class extends os{apply(e){return e}};Hw.className="linear";ue.registerClass(Hw);var jw=class extends os{apply(e){return ds(e)}};jw.className="sigmoid";ue.registerClass(jw);var qw=class extends os{apply(e){return hL(e)}};qw.className="hardSigmoid";ue.registerClass(qw);var Xw=class extends os{apply(e){return Iu(e)}};Xw.className="softplus";ue.registerClass(Xw);var Kw=class extends os{apply(e){return pL(e)}};Kw.className="softsign";ue.registerClass(Kw);var Zw=class extends os{apply(e){return bu(e)}};Zw.className="tanh";ue.registerClass(Zw);var Y1=class extends os{apply(e,t=-1){return Ru(e,t)}};Y1.className="softmax";ue.registerClass(Y1);var Yw=class extends os{apply(e,t=-1){return W2(e,t)}};Yw.className="logSoftmax";ue.registerClass(Yw);var Jw=class extends os{apply(e,t=1){return q(()=>W(ds(W(e,t)),e))}};Jw.className="swish";ue.registerClass(Jw);var Qw=class extends os{apply(e){return q(()=>W(e,bu(Iu(e))))}};Qw.className="mish";ue.registerClass(Qw);function Do(e){return e.getClassName()}function J1(e,t={}){return wd(e,ue.SerializationMap.getMap().classNameMap,t,"activation")}function _o(e){if(e==null){let t={};return t.className="linear",t.config={},J1(t)}if(typeof e=="string"){let t={};return t.className=e,t.config={},J1(t)}else return e instanceof os?e:J1(e)}function Q1(e){if(e!=null&&typeof e!="object")throw new Error(`Argument to L1L2 regularizer's constructor is expected to be an object, but received: ${e}`)}var ek=class extends ue.Serializable{},Dd=class extends ek{constructor(e){super();Q1(e),this.l1=e==null||e.l1==null?.01:e.l1,this.l2=e==null||e.l2==null?.01:e.l2,this.hasL1=this.l1!==0,this.hasL2=this.l2!==0}apply(e){return q(()=>{let t=Ht([1]);return this.hasL1&&(t=le(t,Se(W(this.l1,sn(e))))),this.hasL2&&(t=le(t,Se(W(this.l2,Cd(e))))),G(t,[])})}getConfig(){return{l1:this.l1,l2:this.l2}}static fromConfig(e,t){return new e({l1:t.l1,l2:t.l2})}};Dd.className="L1L2";ue.registerClass(Dd);function BB(e){return Q1(e),new Dd({l1:e!=null?e.l1:null,l2:0})}function WB(e){return Q1(e),new Dd({l2:e!=null?e.l2:null,l1:0})}var tk={l1l2:"L1L2"};function xt(e){return f1(e)}function 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st{constructor(e){super(e==null?{}:e);if(this.DEFAULT_ALPHA_INITIALIZER="zeros",e==null&&(e={}),this.supportsMasking=!0,this.alphaInitializer=Et(e.alphaInitializer||this.DEFAULT_ALPHA_INITIALIZER),this.alphaRegularizer=Rt(e.alphaRegularizer),this.alphaConstraint=on(e.alphaConstraint),e.sharedAxes==null)this.sharedAxes=null;else if(Array.isArray(e.sharedAxes))this.sharedAxes=e.sharedAxes;else if(typeof e.sharedAxes=="number")this.sharedAxes=[e.sharedAxes];else throw new H(`Expected sharedAxes to be a number or an array of numbers, but got ${e.sharedAxes}`)}build(e){e=ft(e);let t=e.slice(1);if(this.sharedAxes!=null)for(let s of this.sharedAxes)t[s-1]=1;this.alpha=this.addWeight("alpha",t,"float32",this.alphaInitializer,this.alphaRegularizer,!0,this.alphaConstraint);let n={};if(this.sharedAxes!=null)for(let s=1;s<e.length;++s)n[s]=e[s];this.inputSpec=[new Zt({ndim:e.length,axes:n})],this.built=!0}call(e,t){return e=Ve(e),rf(e,this.alpha.read())}getConfig(){let e={alphaInitializer:Mt(this.alphaInitializer),alphaRegularizer:xt(this.alphaRegularizer),alphaConstraint:an(this.alphaConstraint),sharedAxes:this.sharedAxes},t=super.getConfig();return Object.assign(e,t),e}};nA.className="PReLU";ue.registerClass(nA);var sA=class extends st{constructor(e){super(e==null?{}:e);if(this.DEFAULT_ALPHA=1,e==null&&(e={}),e.alpha!=null&&e.alpha!==this.DEFAULT_ALPHA)throw new We(`Non-default alpha value (${e.alpha}) is not supported by the ELU layer yet.`);this.alpha=e.alpha==null?this.DEFAULT_ALPHA:e.alpha}call(e,t){let n=Ve(e);return hd(n)}computeOutputShape(e){return e}getConfig(){let e={alpha:this.alpha},t=super.getConfig();return Object.assign(e,t),e}};sA.className="ELU";ue.registerClass(sA);var rA=class extends st{constructor(e){super(e==null?{}:e);this.DEFAULT_THETA=1,e==null&&(e={}),this.theta=e.theta==null?this.DEFAULT_THETA:e.theta}call(e,t){let n=Ve(e);return W(n,pe(hs(n,this.theta),"float32"))}computeOutputShape(e){return e}getConfig(){let e={theta:this.theta},t=super.getConfig();return Object.assign(e,t),e}};rA.className="ThresholdedReLU";ue.registerClass(rA);var aA=class extends st{constructor(e){super(e==null?{}:e);this.DEFAULT_AXIS=1,e==null&&(e={}),this.softmax=new Y1().apply,this.axis=e.axis==null?this.DEFAULT_AXIS:e.axis}call(e,t){let n=Ve(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}};aA.className="Softmax";ue.registerClass(aA);function Mu(e,t,n){if(typeof e=="number")return nl(e,t);if(e.length!==t)throw new H(`The ${n} argument must be an integer or tuple of ${t} integers. Received: ${e.length} elements.`);for(let s=0;s<t;++s){let r=e[s];if(!iL(r))throw new H(`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 gr(e,t,n,s,r=1){if(e==null)return e;let a=t+(t-1)*(r-1),o;return n==="same"?o=e:o=e-a+1,Math.floor((o+s-1)/s)}function _r(e,t,n,s){if(e==null)return null;if(s==="valid")e=e*t+Ro([n-t,0]);else if(s==="same")e=e*t;else throw new H(`Unsupport padding mode: ${s}.`);return e}function oA(e,t){return q(()=>(jt(t),t==="channelsFirst"?tt(e,[0,2,3,1]):e))}function sk(e,t){return q(()=>(jt(t),t==="channelsFirst"?tt(e,[0,2,3,4,1]):e))}function VB(e,t,n,s=1,r="valid",a,o=1){return q(()=>{if(a==null&&(a=cr()),jt(a),e.shape.length!==3)throw new H(`The input of a conv1dWithBias operation should be 3, but is ${e.shape.length} instead.`);if(t.shape.length!==3)throw new H(`The kernel for a conv1dWithBias operation should be 3, but is ${t.shape.length} instead`);if(n!=null&&n.shape.length!==1)throw new H(`The bias for a conv1dWithBias operation should be 1, but is ${t.shape.length} instead`);if(a==="channelsFirst"&&(e=tt(e,[0,2,1])),r==="causal")throw new We("The support for CAUSAL padding mode in conv1dWithBias is not implemented yet.");let i=_2(e,t,s,r==="same"?"same":"valid","NWC",o);return n!=null&&(i=hr(i,n)),i})}function rk(e,t,n,s=[1,1],r="valid",a,o,i=null){return q(()=>{if(a==null&&(a=cr()),jt(a),e.rank!==3&&e.rank!==4)throw new H(`conv2dWithBiasActivation expects input to be of rank 3 or 4, but received ${e.rank}.`);if(t.rank!==3&&t.rank!==4)throw new H(`conv2dWithBiasActivation expects kernel to be of rank 3 or 4, but received ${e.rank}.`);let l=oA(e,a);if(r==="causal")throw new We("The support for CAUSAL padding mode in conv1dWithBias is not implemented yet.");return l=Co.conv2d({x:l,filter:t,strides:s,pad:r==="same"?"same":"valid",dilations:o,dataFormat:"NHWC",bias:n,activation:i}),a==="channelsFirst"&&(l=tt(l,[0,3,1,2])),l})}function UB(e,t,n,s=[1,1,1],r="valid",a,o){return q(()=>{if(a==null&&(a=cr()),jt(a),e.rank!==4&&e.rank!==5)throw new H(`conv3dWithBias expects input to be of rank 4 or 5, but received ${e.rank}.`);if(t.rank!==4&&t.rank!==5)throw new H(`conv3dWithBias expects kernel to be of rank 4 or 5, but received ${e.rank}.`);let i=sk(e,a);if(r==="causal")throw new We("The support for CAUSAL padding mode in conv3dWithBias is not implemented yet.");return i=O2(i,t,s,r==="same"?"same":"valid","NDHWC",o),n!=null&&(i=hr(i,n)),a==="channelsFirst"&&(i=tt(i,[0,4,1,2,3])),i})}var iA=class extends st{constructor(e,t){super(t);if(this.bias=null,this.DEFAULT_KERNEL_INITIALIZER="glorotNormal",this.DEFAULT_BIAS_INITIALIZER="zeros",iA.verifyArgs(t),this.rank=e,gn(this.rank,"rank"),this.rank!==1&&this.rank!==2&&this.rank!==3)throw new We(`Convolution layer for rank other than 1, 2, or 3 (${this.rank}) is not implemented yet.`);if(this.kernelSize=Mu(t.kernelSize,e,"kernelSize"),this.strides=Mu(t.strides==null?1:t.strides,e,"strides"),this.padding=t.padding==null?"valid":t.padding,$s(this.padding),this.dataFormat=t.dataFormat==null?"channelsLast":t.dataFormat,jt(this.dataFormat),this.activation=_o(t.activation),this.useBias=t.useBias==null?!0:t.useBias,this.biasInitializer=Et(t.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.biasConstraint=on(t.biasConstraint),this.biasRegularizer=Rt(t.biasRegularizer),this.activityRegularizer=Rt(t.activityRegularizer),this.dilationRate=Mu(t.dilationRate==null?1:t.dilationRate,e,"dilationRate"),this.rank===1&&Array.isArray(this.dilationRate)&&this.dilationRate.length!==1)throw new H(`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 H(`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 H(`dilationRate must be a number or array of three numbers for 3D convolution, but received ${JSON.stringify(this.dilationRate)}`)}}static verifyArgs(e){if(Er("kernelSize"in e,"required key 'kernelSize' not in config"),typeof e.kernelSize!="number"&&!g1(e.kernelSize,"number",1,3))throw new H(`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:Do(this.activation),useBias:this.useBias,biasInitializer:Mt(this.biasInitializer),biasRegularizer:xt(this.biasRegularizer),activityRegularizer:xt(this.activityRegularizer),biasConstraint:an(this.biasConstraint)},t=super.getConfig();return Object.assign(e,t),e}},_d=class extends iA{constructor(e,t){super(e,t);this.kernel=null,_d.verifyArgs(t),this.filters=t.filters,gn(this.filters,"filters"),this.kernelInitializer=Et(t.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.kernelConstraint=on(t.kernelConstraint),this.kernelRegularizer=Rt(t.kernelRegularizer)}build(e){e=ft(e);let t=this.dataFormat==="channelsFirst"?1:e.length-1;if(e[t]==null)throw new H(`The channel dimension of the input should be defined. Found ${e[t]}`);let n=e[t],s=this.kernelSize.concat([n,this.filters]);this.kernel=this.addWeight("kernel",s,null,this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint),this.useBias&&(this.bias=this.addWeight("bias",[this.filters],null,this.biasInitializer,this.biasRegularizer,!0,this.biasConstraint)),this.inputSpec=[{ndim:this.rank+2,axes:{[t]:n}}],this.built=!0}call(e,t){return q(()=>{e=Ve(e);let n,s=this.bias==null?null:this.bias.read(),r=qv(this.activation.getClassName());if(r!=null&&this.rank===2)n=rk(e,this.kernel.read(),s,this.strides,this.padding,this.dataFormat,this.dilationRate,r);else{if(this.rank===1)n=VB(e,this.kernel.read(),s,this.strides[0],this.padding,this.dataFormat,this.dilationRate[0]);else if(this.rank===2)n=rk(e,this.kernel.read(),s,this.strides,this.padding,this.dataFormat,this.dilationRate);else if(this.rank===3)n=UB(e,this.kernel.read(),s,this.strides,this.padding,this.dataFormat,this.dilationRate);else throw new We("convolutions greater than 3D are not implemented yet.");this.activation!=null&&(n=this.activation.apply(n))}return n})}computeOutputShape(e){e=ft(e);let t=[],n=this.dataFormat==="channelsLast"?e.slice(1,e.length-1):e.slice(2);for(let r=0;r<n.length;++r){let a=gr(n[r],this.kernelSize[r],this.padding,this.strides[r],typeof this.dilationRate=="number"?this.dilationRate:this.dilationRate[r]);t.push(a)}let s=[e[0]];return this.dataFormat==="channelsLast"?(s=s.concat(t),s.push(this.filters)):(s.push(this.filters),s=s.concat(t)),s}getConfig(){let e={filters:this.filters,kernelInitializer:Mt(this.kernelInitializer),kernelRegularizer:xt(this.kernelRegularizer),kernelConstraint:an(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 H(`Convolution layer expected config.filters to be a 'number' > 0 but got ${JSON.stringify(e.filters)}`)}},ak=class extends _d{constructor(e){super(2,e);ak.verifyArgs(e)}getConfig(){let e=super.getConfig();return delete e.rank,e}static verifyArgs(e){if(typeof e.kernelSize!="number"&&!g1(e.kernelSize,"number",1,2))throw new H(`Conv2D expects config.kernelSize to be number or number[] with length 1 or 2, but received ${JSON.stringify(e.kernelSize)}.`)}},jf=ak;jf.className="Conv2D";ue.registerClass(jf);var ok=class extends _d{constructor(e){super(3,e);ok.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 H(`Conv3D expects config.kernelSize to be number or [number, number, number], but received ${JSON.stringify(e.kernelSize)}.`)}},qf=ok;qf.className="Conv3D";ue.registerClass(qf);var lA=class extends jf{constructor(e){super(e);if(this.inputSpec=[new Zt({ndim:4})],this.padding!=="same"&&this.padding!=="valid")throw new H(`Conv2DTranspose currently supports only padding modes 'same' and 'valid', but received padding mode ${this.padding}`)}build(e){if(e=ft(e),e.length!==4)throw new H("Input should have rank 4; Received input shape: "+JSON.stringify(e));let t=this.dataFormat==="channelsFirst"?1:e.length-1;if(e[t]==null)throw new H("The channel dimension of the inputs should be defined. Found `None`.");let n=e[t],s=this.kernelSize.concat([this.filters,n]);this.kernel=this.addWeight("kernel",s,"float32",this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint),this.useBias&&(this.bias=this.addWeight("bias",[this.filters],"float32",this.biasInitializer,this.biasRegularizer,!0,this.biasConstraint)),this.inputSpec=[new Zt({ndim:4,axes:{[t]:n}})],this.built=!0}call(e,t){return q(()=>{let n=Ve(e);if(n.shape.length!==4)throw new H(`Conv2DTranspose.call() expects input tensor to be rank-4, but received a tensor of rank-${n.shape.length}`);let s=n.shape,r=s[0],a,o;this.dataFormat==="channelsFirst"?(a=2,o=3):(a=1,o=2);let i=s[a],l=s[o],c=this.kernelSize[0],u=this.kernelSize[1],d=this.strides[0],p=this.strides[1],h=_r(i,d,c,this.padding),f=_r(l,p,u,this.padding),m=[r,h,f,this.filters];this.dataFormat!=="channelsLast"&&(n=tt(n,[0,2,3,1]));let g=F2(n,this.kernel.read(),m,this.strides,this.padding);return this.dataFormat!=="channelsLast"&&(g=tt(g,[0,3,1,2])),this.bias!=null&&(g=hr(g,this.bias.read(),this.dataFormat)),this.activation!=null&&(g=this.activation.apply(g)),g})}computeOutputShape(e){e=ft(e);let t=e.slice(),n,s,r;this.dataFormat==="channelsFirst"?(n=1,s=2,r=3):(n=3,s=1,r=2);let a=this.kernelSize[0],o=this.kernelSize[1],i=this.strides[0],l=this.strides[1];return t[n]=this.filters,t[s]=_r(t[s],i,a,this.padding),t[r]=_r(t[r],l,o,this.padding),t}getConfig(){let e=super.getConfig();return delete e.dilationRate,e}};lA.className="Conv2DTranspose";ue.registerClass(lA);var uA=class extends qf{constructor(e){super(e);if(this.inputSpec=[new Zt({ndim:5})],this.padding!=="same"&&this.padding!=="valid")throw new H(`Conv3DTranspose currently supports only padding modes 'same' and 'valid', but received padding mode ${this.padding}`)}build(e){if(e=ft(e),e.length!==5)throw new H("Input should have rank 5; Received input shape: "+JSON.stringify(e));let t=this.dataFormat==="channelsFirst"?1:e.length-1;if(e[t]==null)throw new H("The channel dimension of the inputs should be defined. Found `None`.");let n=e[t],s=this.kernelSize.concat([this.filters,n]);this.kernel=this.addWeight("kernel",s,"float32",this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint),this.useBias&&(this.bias=this.addWeight("bias",[this.filters],"float32",this.biasInitializer,this.biasRegularizer,!0,this.biasConstraint)),this.inputSpec=[new Zt({ndim:5,axes:{[t]:n}})],this.built=!0}call(e,t){return q(()=>{let n=Ve(e);if(n.shape.length!==5)throw new H(`Conv3DTranspose.call() expects input tensor to be rank-4, but received a tensor of rank-${n.shape.length}`);let s=n.shape,r=s[0],a,o,i;this.dataFormat==="channelsFirst"?(i=2,a=3,o=4):(i=1,a=2,o=3);let l=s[i],c=s[a],u=s[o],d=this.kernelSize[0],p=this.kernelSize[1],h=this.kernelSize[2],f=this.strides[0],m=this.strides[1],g=this.strides[2],A=_r(l,f,d,this.padding),x=_r(c,m,p,this.padding),y=_r(u,g,h,this.padding),b=[r,A,x,y,this.filters];this.dataFormat!=="channelsLast"&&(n=tt(n,[0,2,3,4,1]));let w=M3(n,this.kernel.read(),b,this.strides,this.padding);return this.dataFormat!=="channelsLast"&&(w=tt(w,[0,4,1,2,3])),this.bias!==null&&(w=hr(w,this.bias.read(),this.dataFormat)),this.activation!==null&&(w=this.activation.apply(w)),w})}computeOutputShape(e){e=ft(e);let t=e.slice(),n,s,r,a;this.dataFormat==="channelsFirst"?(n=1,s=2,r=3,a=4):(n=4,s=1,r=2,a=3);let o=this.kernelSize[0],i=this.kernelSize[1],l=this.kernelSize[2],c=this.strides[0],u=this.strides[1],d=this.strides[2];return t[n]=this.filters,t[s]=_r(t[s],c,o,this.padding),t[r]=_r(t[r],u,i,this.padding),t[a]=_r(t[a],d,l,this.padding),t}getConfig(){let e=super.getConfig();return delete e.dilationRate,e}};uA.className="Conv3DTranspose";ue.registerClass(uA);var ik=class extends _d{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 H("The `filters` configuration field is required by SeparableConv, but is unspecified.");if(t.kernelInitializer!=null||t.kernelRegularizer!=null||t.kernelConstraint!=null)throw new H("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 H(`SeparableConv${this.rank}D supports only padding modes: 'same' and 'valid', but received ${JSON.stringify(t.padding)}`);this.depthMultiplier=t.depthMultiplier==null?1:t.depthMultiplier,this.depthwiseInitializer=Et(t.depthwiseInitializer||this.DEFAULT_DEPTHWISE_INITIALIZER),this.depthwiseRegularizer=Rt(t.depthwiseRegularizer),this.depthwiseConstraint=on(t.depthwiseConstraint),this.pointwiseInitializer=Et(t.depthwiseInitializer||this.DEFAULT_POINTWISE_INITIALIZER),this.pointwiseRegularizer=Rt(t.pointwiseRegularizer),this.pointwiseConstraint=on(t.pointwiseConstraint)}build(e){if(e=ft(e),e.length<this.rank+2)throw new H(`Inputs to SeparableConv${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 H(`The channel dimension of the inputs should be defined, but found ${JSON.stringify(e[t])}`);let n=e[t],s=this.kernelSize.concat([n,this.depthMultiplier]),r=[];for(let o=0;o<this.rank;++o)r.push(1);r.push(n*this.depthMultiplier,this.filters);let a=!0;this.depthwiseKernel=this.addWeight("depthwise_kernel",s,"float32",this.depthwiseInitializer,this.depthwiseRegularizer,a,this.depthwiseConstraint),this.pointwiseKernel=this.addWeight("pointwise_kernel",r,"float32",this.pointwiseInitializer,this.pointwiseRegularizer,a,this.pointwiseConstraint),this.useBias?this.bias=this.addWeight("bias",[this.filters],"float32",this.biasInitializer,this.biasRegularizer,a,this.biasConstraint):this.bias=null,this.inputSpec=[new Zt({ndim:this.rank+2,axes:{[t]:n}})],this.built=!0}call(e,t){return q(()=>{e=Ve(e);let n;if(this.rank===1)throw new We("1D separable convolution is not implemented yet.");return this.rank===2&&(this.dataFormat==="channelsFirst"&&(e=tt(e,[0,2,3,1])),n=av(e,this.depthwiseKernel.read(),this.pointwiseKernel.read(),this.strides,this.padding,this.dilationRate,"NHWC")),this.useBias&&(n=hr(n,this.bias.read(),this.dataFormat)),this.activation!=null&&(n=this.activation.apply(n)),this.dataFormat==="channelsFirst"&&(n=tt(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=Mt(this.depthwiseInitializer),e.pointwiseInitializer=Mt(this.pointwiseInitializer),e.depthwiseRegularizer=xt(this.depthwiseRegularizer),e.pointwiseRegularizer=xt(this.pointwiseRegularizer),e.depthwiseConstraint=an(this.depthwiseConstraint),e.pointwiseConstraint=an(this.pointwiseConstraint),e}};ik.className="SeparableConv";var cA=class extends ik{constructor(e){super(2,e)}};cA.className="SeparableConv2D";ue.registerClass(cA);var lk=class extends _d{constructor(e){super(1,e);lk.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"&&!g1(e.kernelSize,"number",1,1))throw new H(`Conv1D expects config.kernelSize to be number or number[] with length 1, but received ${JSON.stringify(e.kernelSize)}.`)}},dA=lk;dA.className="Conv1D";ue.registerClass(dA);var pA=class extends st{constructor(e){super(e);typeof e.cropping=="number"?this.cropping=[[e.cropping,e.cropping],[e.cropping,e.cropping]]:typeof e.cropping[0]=="number"?this.cropping=[[e.cropping[0],e.cropping[0]],[e.cropping[1],e.cropping[1]]]:this.cropping=e.cropping,this.dataFormat=e.dataFormat===void 0?"channelsLast":e.dataFormat,this.inputSpec=[{ndim:4}]}computeOutputShape(e){return this.dataFormat==="channelsFirst"?[e[0],e[1],e[2]-this.cropping[0][0]-this.cropping[0][1],e[3]-this.cropping[1][0]-this.cropping[1][1]]:[e[0],e[1]-this.cropping[0][0]-this.cropping[0][1],e[2]-this.cropping[1][0]-this.cropping[1][1],e[3]]}call(e,t){return q(()=>{if(e=Ve(e),this.dataFormat==="channelsLast"){let n=kf(e,this.cropping[0][0],e.shape[1]-this.cropping[0][0]-this.cropping[0][1],2);return kf(n,this.cropping[1][0],e.shape[2]-this.cropping[1][1]-this.cropping[1][0],3)}else{let n=kf(e,this.cropping[0][0],e.shape[2]-this.cropping[0][0]-this.cropping[0][1],3);return kf(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}};pA.className="Cropping2D";ue.registerClass(pA);var hA=class extends st{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,jt(this.dataFormat),this.interpolation=e.interpolation==null?"nearest":e.interpolation,rL(this.interpolation)}computeOutputShape(e){if(this.dataFormat==="channelsFirst"){let t=e[2]==null?null:this.size[0]*e[2],n=e[3]==null?null:this.size[1]*e[3];return[e[0],e[1],t,n]}else{let t=e[1]==null?null:this.size[0]*e[1],n=e[2]==null?null:this.size[1]*e[2];return[e[0],t,n,e[3]]}}call(e,t){return q(()=>{let n=Ve(e),s=n.shape;if(this.dataFormat==="channelsFirst"){n=tt(n,[0,2,3,1]);let r=this.size[0]*s[2],a=this.size[1]*s[3],o=this.interpolation==="nearest"?$e.resizeNearestNeighbor(n,[r,a]):$e.resizeBilinear(n,[r,a]);return tt(o,[0,3,1,2])}else{let r=this.size[0]*s[1],a=this.size[1]*s[2];return this.interpolation==="nearest"?$e.resizeNearestNeighbor(n,[r,a]):$e.resizeBilinear(n,[r,a])}})}getConfig(){let e={size:this.size,dataFormat:this.dataFormat},t=super.getConfig();return Object.assign(e,t),e}};hA.className="UpSampling2D";ue.registerClass(hA);function GB(e,t,n=[1,1],s="valid",r,a){return q(()=>{r==null&&(r=cr()),jt(r);let o=oA(e,r);if(e.rank!==4)throw new H(`Input for depthwiseConv2d is required to be 4-D, but is instead ${e.rank}-D`);if(t.rank!==4)throw new H(`depthwiseKernel is required to be 4-D, but is instead ${t.rank}-D`);return o=pd(o,t,n,s==="same"?"same":"valid","NHWC",a),r==="channelsFirst"&&(o=tt(o,[0,3,1,2])),o})}var fA=class extends iA{constructor(e){super(2,e);this.depthwiseKernel=null,this.depthMultiplier=e.depthMultiplier==null?1:e.depthMultiplier,this.depthwiseInitializer=Et(e.depthwiseInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.depthwiseConstraint=on(e.depthwiseConstraint),this.depthwiseRegularizer=Rt(e.depthwiseRegularizer)}build(e){if(e=ft(e),e.length<4)throw new H(`Inputs to DepthwiseConv2D should have rank 4. Received input shape: ${JSON.stringify(e)}.`);let t=this.dataFormat==="channelsFirst"?1:3;if(e[t]==null||e[t]<0)throw new H(`The channel dimension of the inputs to DepthwiseConv2D should be defined, but is not (${e[t]}).`);let n=e[t],s=[this.kernelSize[0],this.kernelSize[1],n,this.depthMultiplier];this.depthwiseKernel=this.addWeight("depthwise_kernel",s,null,this.depthwiseInitializer,this.depthwiseRegularizer,!0,this.depthwiseConstraint),this.useBias?this.bias=this.addWeight("bias",[n*this.depthMultiplier],null,this.biasInitializer,this.biasRegularizer,!0,this.biasConstraint):this.bias=null,this.built=!0}call(e,t){return q(()=>{e=Ve(e);let n=GB(e,this.depthwiseKernel.read(),this.strides,this.padding,this.dataFormat,null);return this.useBias&&(n=hr(n,this.bias.read(),this.dataFormat)),this.activation!=null&&(n=this.activation.apply(n)),n})}computeOutputShape(e){e=ft(e);let t=this.dataFormat==="channelsFirst"?e[2]:e[1],n=this.dataFormat==="channelsFirst"?e[3]:e[2],s=this.dataFormat==="channelsFirst"?e[1]*this.depthMultiplier:e[3]*this.depthMultiplier,r=gr(t,this.kernelSize[0],this.padding,this.strides[0]),a=gr(n,this.kernelSize[1],this.padding,this.strides[1]);return this.dataFormat==="channelsFirst"?[e[0],s,r,a]:[e[0],r,a,s]}getConfig(){let e=super.getConfig();return e.depthMultiplier=this.depthMultiplier,e.depthwiseInitializer=Mt(this.depthwiseInitializer),e.depthwiseRegularizer=xt(this.depthwiseRegularizer),e.depthwiseConstraint=an(this.depthwiseRegularizer),e}};fA.className="DepthwiseConv2D";ue.registerClass(fA);function uk(e,t,n,s){if(Array.isArray(e)){if(t!=null||n!=null)throw new H("When inputs is an array, neither initialState or constants should be provided");s!=null&&(n=e.slice(e.length-s,e.length),e=e.slice(0,e.length-s)),e.length>1&&(t=e.slice(1,e.length)),e=e[0]}function r(a){return a==null||Array.isArray(a)?a:[a]}return t=r(t),n=r(n),{inputs:e,initialState:t,constants:n}}function ck(e,t,n,s=!1,r,a,o=!1,i=!1){return q(()=>{let l=t.shape.length;if(l<3)throw new H(`Input should be at least 3D, but is ${l}D.`);let c=[1,0].concat(pr(2,l));if(t=tt(t,c),a!=null)throw new We("The rnn() functoin of the deeplearn.js backend does not support constants yet.");o&&console.warn("Backend rnn(): the unroll = true option is not applicable to the imperative deeplearn.js backend."),r!=null&&(r=pe(pe(r,"bool"),"float32"),r.rank===l-1&&(r=mn(r,-1)),r=tt(r,c)),s&&(t=Rs(t,0),r!=null&&(r=Rs(r,0)));let u=[],d,p=n,h=t.shape[0],f=ss(t),m;r!=null&&(m=ss(r));for(let A=0;A<h;++A){let x=f[A],y=q(()=>e(x,p));if(r==null)d=y[0],p=y[1];else{let b=q(()=>{let w=m[A],k=xe(Es(w),w),C=le(W(y[0],w),W(p[0],k)),E=p.map((R,F)=>le(W(y[1][F],w),W(R,k)));return{output:C,newStates:E}});d=b.output,p=b.newStates}i&&u.push(d)}let g;return i&&(g=_n(u,1)),[d,g,p]})}var dk=class extends st{constructor(e){super(e);let t;if(e.cell==null)throw new H("cell property is missing for the constructor of RNN.");if(Array.isArray(e.cell)?t=new Zf({cells:e.cell}):t=e.cell,t.stateSize==null)throw new H("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 Zt({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 pr(0,e).map(t=>null)}else return this.states_}setStates(e){this.states_=e}computeOutputShape(e){_1(e)&&(e=e[0]),e=e;let t=this.cell.stateSize;Array.isArray(t)||(t=[t]);let n=t[0],s;if(this.returnSequences?s=[e[0],e[1],n]:s=[e[0],n],this.returnState){let r=[];for(let a of t)r.push([e[0],a]);return[s].concat(r)}else return s}computeMask(e,t){return q(()=>{Array.isArray(t)&&(t=t[0]);let n=this.returnSequences?t:null;if(this.returnState){let s=this.states.map(r=>null);return[n].concat(s)}else return n})}get states(){if(this.states_==null){let e=Array.isArray(this.cell.stateSize)?this.cell.stateSize.length:1,t=[];for(let n=0;n<e;++n)t.push(null);return t}else return this.states_}set states(e){this.states_=e}build(e){let t=null;if(this.numConstants!=null)throw new We("Constants support is not implemented in RNN yet.");_1(e)&&(e=e[0]),e=e;let n=this.stateful?e[0]:null,s=e.slice(2);this.inputSpec[0]=new Zt({shape:[n,null,...s]});let r=[e[0]].concat(e.slice(2));if(t!=null)throw new We("Constants support is not implemented in RNN yet.");this.cell.build(r);let a;if(Array.isArray(this.cell.stateSize)?a=this.cell.stateSize:a=[this.cell.stateSize],this.stateSpec!=null){if(!v.arraysEqual(this.stateSpec.map(o=>o.shape[o.shape.length-1]),a))throw new H(`An initialState was passed that is not compatible with cell.stateSize. Received stateSpec=${this.stateSpec}; However cell.stateSize is ${this.cell.stateSize}`)}else this.stateSpec=a.map(o=>new Zt({shape:[null,o]}));this.stateful&&this.resetStates()}resetStates(e,t=!1){q(()=>{if(!this.stateful)throw new Yr("Cannot call resetStates() on an RNN Layer that is not stateful.");let n=this.inputSpec[0].shape[0];if(n==null)throw new H("If an RNN is stateful, it needs to know its batch size. Specify the batch size of your input tensors: \n- If using a Sequential model, specify the batch size by passing a `batchInputShape` option to your first layer.\n- If using the functional API, specify the batch size by passing a `batchShape` option to your Input layer.");if(this.states_==null)Array.isArray(this.cell.stateSize)?this.states_=this.cell.stateSize.map(s=>Ht([n,s])):this.states_=[Ht([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(s=>Ht([n,s])):this.states_[0]=Ht([n,this.cell.stateSize]);else{if(Array.isArray(e)||(e=[e]),e.length!==this.states_.length)throw new H(`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 s=0;s<this.states_.length;++s){let r=e[s],a=Array.isArray(this.cell.stateSize)?this.cell.stateSize[s]:this.cell.stateSize,o=[n,a];if(!v.arraysEqual(r.shape,o))throw new H(`State ${s} is incompatible with layer ${this.name}: expected shape=${o}, received shape=${r.shape}`);this.states_[s]=r}}this.states_=this.states_.map(s=>fn(s.clone()))})}apply(e,t){let n=t==null?null:t.initialState,s=t==null?null:t.constants;t==null&&(t={});let r=uk(e,n,s,this.numConstants);e=r.inputs,n=r.initialState,s=r.constants;let a=[],o=[];if(n!=null){t.initialState=n,a=a.concat(n),this.stateSpec=[];for(let l of n)this.stateSpec.push(new Zt({shape:l.shape}));o=o.concat(this.stateSpec)}if(s!=null&&(t.constants=s,a=a.concat(s),this.numConstants=s.length),a[0]instanceof fr){let l=[e].concat(a),c=this.inputSpec.concat(o),u=this.inputSpec;this.inputSpec=c;let d=super.apply(l,t);return this.inputSpec=u,d}else return super.apply(e,t)}call(e,t){return q(()=>{let n=t==null?null:t.mask,s=t==null?null:t.training,r=t==null?null:t.initialState;e=Ve(e),r==null&&(this.stateful?r=this.states_:r=this.getInitialState(e));let a=Array.isArray(this.cell.stateSize)?this.cell.stateSize.length:1;if(r.length!==a)throw new H(`RNN Layer has ${a} state(s) but was passed ${r.length} initial state(s).`);this.unroll&&console.warn("Ignoring unroll = true for RNN layer, due to imperative backend.");let o={training:s},l=ck((h,f)=>{let m=this.cell.call([h].concat(f),o);return[m[0],m.slice(1)]},e,r,this.goBackwards,n,null,this.unroll,this.returnSequences),c=l[0],u=l[1],d=l[2];this.stateful&&this.resetStates(d,s);let p=this.returnSequences?u:c;return this.returnState?[p].concat(d):p})}getInitialState(e){return q(()=>{let t=Ht(e.shape);return t=Se(t,[1,2]),t=Id(t),Array.isArray(this.cell.stateSize)?this.cell.stateSize.map(n=>n>1?S1(t,[1,n]):t):this.cell.stateSize>1?[S1(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()===dk.className&&(t.cell={className:this.cell.getClassName(),config:n}),{...n,...e,...t}}static fromConfig(e,t,n={}){let s=t.cell,r=mr(s,n);return new e(Object.assign(t,{cell:r}))}},ea=dk;ea.className="RNN";ue.registerClass(ea);var Pd=class extends st{},Xf=class extends Pd{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,gn(this.units,"units"),this.activation=_o(e.activation==null?this.DEFAULT_ACTIVATION:e.activation),this.useBias=e.useBias==null?!0:e.useBias,this.kernelInitializer=Et(e.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.recurrentInitializer=Et(e.recurrentInitializer||this.DEFAULT_RECURRENT_INITIALIZER),this.biasInitializer=Et(e.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.kernelRegularizer=Rt(e.kernelRegularizer),this.recurrentRegularizer=Rt(e.recurrentRegularizer),this.biasRegularizer=Rt(e.biasRegularizer),this.kernelConstraint=on(e.kernelConstraint),this.recurrentConstraint=on(e.recurrentConstraint),this.biasConstraint=on(e.biasConstraint),this.dropout=_u([1,Ro([0,e.dropout==null?0:e.dropout])]),this.recurrentDropout=_u([1,Ro([0,e.recurrentDropout==null?0:e.recurrentDropout])]),this.dropoutFunc=e.dropoutFunc,this.stateSize=this.units,this.dropoutMask=null,this.recurrentDropoutMask=null}build(e){e=ft(e),this.kernel=this.addWeight("kernel",[e[e.length-1],this.units],null,this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint),this.recurrentKernel=this.addWeight("recurrent_kernel",[this.units,this.units],null,this.recurrentInitializer,this.recurrentRegularizer,!0,this.recurrentConstraint),this.useBias?this.bias=this.addWeight("bias",[this.units],null,this.biasInitializer,this.biasRegularizer,!0,this.biasConstraint):this.bias=null,this.built=!0}call(e,t){return q(()=>{if(e=e,e.length!==2)throw new H(`SimpleRNNCell expects 2 input Tensors, got ${e.length}.`);let n=e[1];e=e[0];let s=t.training==null?!1:t.training;0<this.dropout&&this.dropout<1&&this.dropoutMask==null&&(this.dropoutMask=Po({ones:()=>Es(e),rate:this.dropout,training:s,dropoutFunc:this.dropoutFunc})),0<this.recurrentDropout&&this.recurrentDropout<1&&this.recurrentDropoutMask==null&&(this.recurrentDropoutMask=Po({ones:()=>Es(n),rate:this.recurrentDropout,training:s,dropoutFunc:this.dropoutFunc}));let r,a=this.dropoutMask,o=this.recurrentDropoutMask;a!=null?r=Rr(W(e,a),this.kernel.read()):r=Rr(e,this.kernel.read()),this.bias!=null&&(r=hr(r,this.bias.read())),o!=null&&(n=W(n,o));let i=le(r,Rr(n,this.recurrentKernel.read()));return this.activation!=null&&(i=this.activation.apply(i)),[i,i]})}getConfig(){let e=super.getConfig(),t={units:this.units,activation:Do(this.activation),useBias:this.useBias,kernelInitializer:Mt(this.kernelInitializer),recurrentInitializer:Mt(this.recurrentInitializer),biasInitializer:Mt(this.biasInitializer),kernelRegularizer:xt(this.kernelRegularizer),recurrentRegularizer:xt(this.recurrentRegularizer),biasRegularizer:xt(this.biasRegularizer),activityRegularizer:xt(this.activityRegularizer),kernelConstraint:an(this.kernelConstraint),recurrentConstraint:an(this.recurrentConstraint),biasConstraint:an(this.biasConstraint),dropout:this.dropout,recurrentDropout:this.recurrentDropout};return{...e,...t}}};Xf.className="SimpleRNNCell";ue.registerClass(Xf);var mA=class extends ea{constructor(e){e.cell=new Xf(e);super(e)}call(e,t){return q(()=>{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,s=t==null?null:t.training,r=t==null?null:t.initialState;return super.call(e,{mask:n,training:s,initialState:r})})}static fromConfig(e,t){return new e(t)}};mA.className="SimpleRNN";ue.registerClass(mA);var Kf=class extends Pd{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 H("GRUCell does not support reset_after parameter set to true.");this.units=e.units,gn(this.units,"units"),this.activation=_o(e.activation===void 0?this.DEFAULT_ACTIVATION:e.activation),this.recurrentActivation=_o(e.recurrentActivation===void 0?this.DEFAULT_RECURRENT_ACTIVATION:e.recurrentActivation),this.useBias=e.useBias==null?!0:e.useBias,this.kernelInitializer=Et(e.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.recurrentInitializer=Et(e.recurrentInitializer||this.DEFAULT_RECURRENT_INITIALIZER),this.biasInitializer=Et(e.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.kernelRegularizer=Rt(e.kernelRegularizer),this.recurrentRegularizer=Rt(e.recurrentRegularizer),this.biasRegularizer=Rt(e.biasRegularizer),this.kernelConstraint=on(e.kernelConstraint),this.recurrentConstraint=on(e.recurrentConstraint),this.biasConstraint=on(e.biasConstraint),this.dropout=_u([1,Ro([0,e.dropout==null?0:e.dropout])]),this.recurrentDropout=_u([1,Ro([0,e.recurrentDropout==null?0:e.recurrentDropout])]),this.dropoutFunc=e.dropoutFunc,this.implementation=e.implementation,this.stateSize=this.units,this.dropoutMask=null,this.recurrentDropoutMask=null}build(e){e=ft(e);let t=e[e.length-1];this.kernel=this.addWeight("kernel",[t,this.units*3],null,this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint),this.recurrentKernel=this.addWeight("recurrent_kernel",[this.units,this.units*3],null,this.recurrentInitializer,this.recurrentRegularizer,!0,this.recurrentConstraint),this.useBias?this.bias=this.addWeight("bias",[this.units*3],null,this.biasInitializer,this.biasRegularizer,!0,this.biasConstraint):this.bias=null,this.built=!0}call(e,t){return q(()=>{if(e=e,e.length!==2)throw new H(`GRUCell expects 2 input Tensors (inputs, h, c), got ${e.length}.`);let n=t.training==null?!1:t.training,s=e[1];e=e[0],0<this.dropout&&this.dropout<1&&this.dropoutMask==null&&(this.dropoutMask=Po({ones:()=>Es(e),rate:this.dropout,training:n,count:3,dropoutFunc:this.dropoutFunc})),0<this.recurrentDropout&&this.recurrentDropout<1&&this.recurrentDropoutMask==null&&(this.recurrentDropoutMask=Po({ones:()=>Es(s),rate:this.recurrentDropout,training:n,count:3,dropoutFunc:this.dropoutFunc}));let r=this.dropoutMask,a=this.recurrentDropoutMask,o,i,l;0<this.dropout&&this.dropout<1&&(e=W(e,r[0]));let c=Rr(e,this.kernel.read());this.useBias&&(c=hr(c,this.bias.read())),0<this.recurrentDropout&&this.recurrentDropout<1&&(s=W(s,a[0]));let u=this.recurrentKernel.read(),[d,p]=kn(u,[2*this.units,this.units],u.rank-1),h=Rr(s,d),[f,m,g]=kn(c,3,c.rank-1),[A,x]=kn(h,2,h.rank-1);o=this.recurrentActivation.apply(le(f,A)),i=this.recurrentActivation.apply(le(m,x));let y=Rr(W(i,s),p);l=this.activation.apply(le(g,y));let b=le(W(o,s),W(le(1,Ot(o)),l));return[b,b]})}getConfig(){let e=super.getConfig(),t={units:this.units,activation:Do(this.activation),recurrentActivation:Do(this.recurrentActivation),useBias:this.useBias,kernelInitializer:Mt(this.kernelInitializer),recurrentInitializer:Mt(this.recurrentInitializer),biasInitializer:Mt(this.biasInitializer),kernelRegularizer:xt(this.kernelRegularizer),recurrentRegularizer:xt(this.recurrentRegularizer),biasRegularizer:xt(this.biasRegularizer),activityRegularizer:xt(this.activityRegularizer),kernelConstraint:an(this.kernelConstraint),recurrentConstraint:an(this.recurrentConstraint),biasConstraint:an(this.biasConstraint),dropout:this.dropout,recurrentDropout:this.recurrentDropout,implementation:this.implementation,resetAfter:!1};return{...e,...t}}};Kf.className="GRUCell";ue.registerClass(Kf);var gA=class extends ea{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 Kf(e);super(e)}call(e,t){return q(()=>{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,s=t==null?null:t.training,r=t==null?null:t.initialState;return super.call(e,{mask:n,training:s,initialState:r})})}static fromConfig(e,t){return t.implmentation===0&&(t.implementation=1),new e(t)}};gA.className="GRU";ue.registerClass(gA);var Fd=class extends Pd{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,gn(this.units,"units"),this.activation=_o(e.activation===void 0?this.DEFAULT_ACTIVATION:e.activation),this.recurrentActivation=_o(e.recurrentActivation===void 0?this.DEFAULT_RECURRENT_ACTIVATION:e.recurrentActivation),this.useBias=e.useBias==null?!0:e.useBias,this.kernelInitializer=Et(e.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.recurrentInitializer=Et(e.recurrentInitializer||this.DEFAULT_RECURRENT_INITIALIZER),this.biasInitializer=Et(e.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.unitForgetBias=e.unitForgetBias,this.kernelRegularizer=Rt(e.kernelRegularizer),this.recurrentRegularizer=Rt(e.recurrentRegularizer),this.biasRegularizer=Rt(e.biasRegularizer),this.kernelConstraint=on(e.kernelConstraint),this.recurrentConstraint=on(e.recurrentConstraint),this.biasConstraint=on(e.biasConstraint),this.dropout=_u([1,Ro([0,e.dropout==null?0:e.dropout])]),this.recurrentDropout=_u([1,Ro([0,e.recurrentDropout==null?0:e.recurrentDropout])]),this.dropoutFunc=e.dropoutFunc,this.implementation=e.implementation,this.stateSize=[this.units,this.units],this.dropoutMask=null,this.recurrentDropoutMask=null}build(e){var t;e=ft(e);let n=e[e.length-1];this.kernel=this.addWeight("kernel",[n,this.units*4],null,this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint),this.recurrentKernel=this.addWeight("recurrent_kernel",[this.units,this.units*4],null,this.recurrentInitializer,this.recurrentRegularizer,!0,this.recurrentConstraint);let s;if(this.useBias){if(this.unitForgetBias){let r=this.biasInitializer,a=this.units;s=new(t=class extends Zs{apply(o,i){let l=r.apply([a]),c=new If().apply([a]),u=r.apply([a*2]);return nw(nw(l,c),u)}},t.className="CustomInit",t)}else s=this.biasInitializer;this.bias=this.addWeight("bias",[this.units*4],null,s,this.biasRegularizer,!0,this.biasConstraint)}else this.bias=null;this.built=!0}call(e,t){return q(()=>{let n=t.training==null?!1:t.training;if(e=e,e.length!==3)throw new H(`LSTMCell expects 3 input Tensors (inputs, h, c), got ${e.length}.`);let s=e[1],r=e[2];e=e[0],0<this.dropout&&this.dropout<1&&this.dropoutMask==null&&(this.dropoutMask=Po({ones:()=>Es(e),rate:this.dropout,training:n,count:4,dropoutFunc:this.dropoutFunc})),0<this.recurrentDropout&&this.recurrentDropout<1&&this.recurrentDropoutMask==null&&(this.recurrentDropoutMask=Po({ones:()=>Es(s),rate:this.recurrentDropout,training:n,count:4,dropoutFunc:this.dropoutFunc}));let a=this.dropoutMask,o=this.recurrentDropoutMask,i,l,c,u;0<this.dropout&&this.dropout<1&&(e=W(e,a[0]));let d=Rr(e,this.kernel.read());0<this.recurrentDropout&&this.recurrentDropout<1&&(s=W(s,o[0])),d=le(d,Rr(s,this.recurrentKernel.read())),this.useBias&&(d=hr(d,this.bias.read()));let[p,h,f,m]=kn(d,4,d.rank-1);i=this.recurrentActivation.apply(p),l=this.recurrentActivation.apply(h),c=le(W(l,r),W(i,this.activation.apply(f))),u=this.recurrentActivation.apply(m);let g=W(u,this.activation.apply(c));return[g,g,c]})}getConfig(){let e=super.getConfig(),t={units:this.units,activation:Do(this.activation),recurrentActivation:Do(this.recurrentActivation),useBias:this.useBias,kernelInitializer:Mt(this.kernelInitializer),recurrentInitializer:Mt(this.recurrentInitializer),biasInitializer:Mt(this.biasInitializer),unitForgetBias:this.unitForgetBias,kernelRegularizer:xt(this.kernelRegularizer),recurrentRegularizer:xt(this.recurrentRegularizer),biasRegularizer:xt(this.biasRegularizer),activityRegularizer:xt(this.activityRegularizer),kernelConstraint:an(this.kernelConstraint),recurrentConstraint:an(this.recurrentConstraint),biasConstraint:an(this.biasConstraint),dropout:this.dropout,recurrentDropout:this.recurrentDropout,implementation:this.implementation};return{...e,...t}}};Fd.className="LSTMCell";ue.registerClass(Fd);var AA=class extends ea{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 Fd(e);super(e)}call(e,t){return q(()=>{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,s=t==null?null:t.training,r=t==null?null:t.initialState;return super.call(e,{mask:n,training:s,initialState:r})})}static fromConfig(e,t){return t.implmentation===0&&(t.implementation=1),new e(t)}};AA.className="LSTM";ue.registerClass(AA);var Zf=class extends Pd{constructor(e){super(e);this.cells=e.cells}get stateSize(){let e=[];for(let t of this.cells.slice().reverse())Array.isArray(t.stateSize)?e.push(...t.stateSize):e.push(t.stateSize);return e}call(e,t){return q(()=>{e=e;let n=e.slice(1),s=[];for(let o of this.cells.slice().reverse())Array.isArray(o.stateSize)?s.push(n.splice(0,o.stateSize.length)):s.push(n.splice(0,1));s.reverse();let r=[],a;for(let o=0;o<this.cells.length;++o){let i=this.cells[o];n=s[o],o===0?a=[e[0]].concat(n):a=[a[0]].concat(n),a=i.call(a,t),r.push(a.slice(1))}n=[];for(let o of r.slice().reverse())n.push(...o);return[a[0]].concat(n)})}build(e){_1(e)&&(e=e[0]),e=e;let t;this.cells.forEach((n,s)=>{al(`RNNCell_${s}`,()=>{n.build(e),Array.isArray(n.stateSize)?t=n.stateSize[0]:t=n.stateSize,e=[e[0],t]})}),this.built=!0}getConfig(){let e=super.getConfig(),t=r=>({className:r.getClassName(),config:r.getConfig()}),s={cells:this.cells.map(t)};return{...e,...s}}static fromConfig(e,t,n={}){let s=[];for(let r of t.cells)s.push(mr(r,n));return new e({cells:s})}get trainableWeights(){if(!this.trainable)return[];let e=[];for(let t of this.cells)e.push(...t.trainableWeights);return e}get nonTrainableWeights(){let e=[];for(let t of this.cells)e.push(...t.nonTrainableWeights);if(!this.trainable){let t=[];for(let n of this.cells)t.push(...n.trainableWeights);return t.concat(e)}return e}getWeights(){let e=[];for(let t of this.cells)e.push(...t.weights);return P1(e)}setWeights(e){let t=[];for(let n of this.cells){let s=n.weights.length,r=e.splice(s);for(let a=0;a<n.weights.length;++a)t.push([n.weights[a],r[a]])}F1(t)}};Zf.className="StackedRNNCells";ue.registerClass(Zf);function Po(e){let{ones:t,rate:n,training:s=!1,count:r=1,dropoutFunc:a}=e,o=()=>a!=null?a(t(),n):rw(t(),n),i=()=>Td(o,t,s);return!r||r<=1?fn(i().clone()):Array(r).fill(void 0).map(i).map(c=>fn(c.clone()))}var pk=class extends ea{constructor(e){if(e.unroll)throw new We("Unrolling is not possible with convolutional RNNs.");if(Array.isArray(e.cell))throw new We("It is not possible at the moment to stack convolutional cells.");super(e);this.inputSpec=[new Zt({ndim:5})]}call(e,t){return q(()=>{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 H("ConvRNN2D cell does not support constants");let n=t==null?null:t.mask,s=t==null?null:t.training,r=t==null?null:t.initialState;return super.call(e,{mask:n,training:s,initialState:r})})}computeOutputShape(e){let t=this.computeSingleOutputShape(e);return this.returnSequences||(t=[t[0],...t.slice(2)]),this.returnState&&(t=[t,...Array(2).fill([e[0],...t.slice(-3)])]),t}getInitialState(e){return q(()=>{let{stateSize:t}=this.cell,n=e.shape,s=this.computeSingleOutputShape(n),r=[s[0],...s.slice(2)],a=Ht(r);return Array.isArray(t)?Array(t.length).fill(a):[a]})}resetStates(e,t=!1){q(()=>{if(!this.stateful)throw new Yr("Cannot call resetStates() on an RNN Layer that is not stateful.");let n=this.inputSpec[0].shape,s=this.computeSingleOutputShape(n),r=[s[0],...s.slice(2)];if(n[0]==null)throw new H("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(()=>Ht(r)):this.states_=[Ht(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(()=>Ht(r)):this.states_[0]=Ht(r);else{if(Array.isArray(e)||(e=[e]),e.length!==this.states_.length)throw new H(`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 o=0;o<this.states_.length;++o){let i=e[o],l=r;if(!v.arraysEqual(i.shape,l))throw new H(`State ${o} is incompatible with layer ${this.name}: expected shape=${l}, received shape=${i.shape}`);this.states_[o]=i}}this.states_=this.states_.map(o=>fn(o.clone()))})}computeSingleOutputShape(e){let{dataFormat:t,filters:n,kernelSize:s,padding:r,strides:a,dilationRate:o}=this.cell,i=t==="channelsFirst",l=e[i?3:2],c=e[i?4:3],u=gr(l,s[0],r,a[0],o[0]),d=gr(c,s[1],r,a[1],o[1]);return[...e.slice(0,2),...i?[n,u,d]:[u,d,n]]}};pk.className="ConvRNN2D";var Yf=class extends Fd{constructor(e){let{filters:t,kernelSize:n,strides:s,padding:r,dataFormat:a,dilationRate:o}=e;super({...e,units:t});this.filters=t,gn(this.filters,"filters"),this.kernelSize=Mu(n,2,"kernelSize"),this.kernelSize.forEach(i=>gn(i,"kernelSize")),this.strides=Mu(s||1,2,"strides"),this.strides.forEach(i=>gn(i,"strides")),this.padding=r||"valid",$s(this.padding),this.dataFormat=a||"channelsLast",jt(this.dataFormat),this.dilationRate=Mu(o||1,2,"dilationRate"),this.dilationRate.forEach(i=>gn(i,"dilationRate"))}build(e){var t;e=ft(e);let n=this.dataFormat==="channelsFirst"?1:e.length-1;if(e[n]==null)throw new H(`The channel dimension of the input should be defined. Found ${e[n]}`);let s=e[n],r=4,a=this.kernelSize.concat([s,this.filters*r]);this.kernel=this.addWeight("kernel",a,null,this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint);let o=this.kernelSize.concat([this.filters,this.filters*r]);if(this.recurrentKernel=this.addWeight("recurrent_kernel",o,null,this.recurrentInitializer,this.recurrentRegularizer,!0,this.recurrentConstraint),this.useBias){let i;if(this.unitForgetBias){let l=this.biasInitializer,c=this.filters;i=new(t=class extends Zs{apply(u,d){let p=l.apply([c]),h=fs([c]),f=l.apply([c*2]);return k1([p,h,f])}},t.className="CustomInit",t)}else i=this.biasInitializer;this.bias=this.addWeight("bias",[this.filters*r],null,i,this.biasRegularizer,!0,this.biasConstraint)}this.built=!0}call(e,t){return q(()=>{if(e.length!==3)throw new H(`ConvLSTM2DCell expects 3 input Tensors (inputs, h, c), got ${e.length}.`);let n=t.training||!1,s=e[0],r=e[1],a=e[2],o=4;0<this.dropout&&this.dropout<1&&this.dropoutMask==null&&(this.dropoutMask=Po({ones:()=>Es(s),rate:this.dropout,training:n,count:o,dropoutFunc:this.dropoutFunc}));let i=this.dropoutMask,l=(Z,J,ee)=>!J||!J[ee]?Z:W(J[ee],Z),c=l(s,i,0),u=l(s,i,1),d=l(s,i,2),p=l(s,i,3);0<this.recurrentDropout&&this.recurrentDropout<1&&this.recurrentDropoutMask==null&&(this.recurrentDropoutMask=Po({ones:()=>Es(r),rate:this.recurrentDropout,training:n,count:o,dropoutFunc:this.dropoutFunc}));let h=this.recurrentDropoutMask,f=l(r,h,0),m=l(r,h,1),g=l(r,h,2),A=l(r,h,3),x=3,[y,b,w,k]=kn(this.kernel.read(),o,x),[C,E,R,F]=this.useBias?kn(this.bias.read(),o):[null,null,null,null];c=this.inputConv(c,y,C,this.padding),u=this.inputConv(u,b,E,this.padding),d=this.inputConv(d,w,R,this.padding),p=this.inputConv(p,k,F,this.padding);let[D,P,T,M]=kn(this.recurrentKernel.read(),o,x);f=this.recurrentConv(f,D),m=this.recurrentConv(m,P),g=this.recurrentConv(g,T),A=this.recurrentConv(A,M);let U=this.recurrentActivation.apply(le(c,f)),j=this.recurrentActivation.apply(le(u,m)),z=le(W(j,a),W(U,this.activation.apply(le(d,g)))),X=W(this.recurrentActivation.apply(le(p,A)),this.activation.apply(z));return[X,X,z]})}getConfig(){let{units:e,...t}=super.getConfig(),n={filters:this.filters,kernelSize:this.kernelSize,padding:this.padding,dataFormat:this.dataFormat,dilationRate:this.dilationRate,strides:this.strides};return{...t,...n}}inputConv(e,t,n,s){let r=So(e,t,this.strides,s||"valid",this.dataFormat==="channelsFirst"?"NCHW":"NHWC",this.dilationRate);return n?hr(r,n,this.dataFormat):r}recurrentConv(e,t){return So(e,t,1,"same",this.dataFormat==="channelsFirst"?"NCHW":"NHWC")}};Yf.className="ConvLSTM2DCell";ue.registerClass(Yf);var yA=class extends pk{constructor(e){let t=new Yf(e);super({...e,cell:t})}static fromConfig(e,t){return new e(t)}};yA.className="ConvLSTM2D";ue.registerClass(yA);var Jf=class extends st{constructor(e){super(e);this.rate=Math.max(Math.min(e.rate,1),0),this.noiseShape=e.noiseShape,this.seed=e.seed,this.supportsMasking=!0}getNoiseShape(e){if(this.noiseShape==null)return this.noiseShape;let t=e.shape,n=[];for(let s=0;s<this.noiseShape.length;++s)n.push(this.noiseShape[s]==null?t[s]:this.noiseShape[s]);return n}call(e,t){return q(()=>{this.invokeCallHook(e,t);let n=Ve(e);if(0<this.rate&&this.rate<1){let s=t.training==null?!1:t.training,r=this.getNoiseShape(n);return Td(()=>rw(n,this.rate,r,this.seed),()=>n,s)}return e})}getConfig(){let e={rate:this.rate,noiseShape:this.noiseShape,seed:this.seed},t=super.getConfig();return Object.assign(e,t),e}dispose(){return super.dispose()}};Jf.className="Dropout";ue.registerClass(Jf);var xA=class extends Jf{constructor(e){super(e);this.inputSpec=[{ndim:3}]}getNoiseShape(e){let t=e.shape;return[t[0],1,t[2]]}};xA.className="SpatialDropout1D";ue.registerClass(xA);var bA=class extends st{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,gn(this.units,"units"),this.activation=_o(e.activation),e.useBias!=null&&(this.useBias=e.useBias),this.kernelInitializer=Et(e.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.biasInitializer=Et(e.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.kernelConstraint=on(e.kernelConstraint),this.biasConstraint=on(e.biasConstraint),this.kernelRegularizer=Rt(e.kernelRegularizer),this.biasRegularizer=Rt(e.biasRegularizer),this.activityRegularizer=Rt(e.activityRegularizer),this.supportsMasking=!0,this.inputSpec=[{minNDim:2}]}build(e){e=ft(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=ft(e);let t=e.slice();return t[t.length-1]=this.units,t}call(e,t){return q(()=>{this.invokeCallHook(e,t);let n=Ve(e),s=qv(this.activation.getClassName()),r;return s!=null?r=Rr(n,this.kernel.read(),s,this.bias?this.bias.read():null):(r=Rr(n,this.kernel.read()),this.bias!=null&&(r=hr(r,this.bias.read())),this.activation!=null&&(r=this.activation.apply(r))),r})}getConfig(){let e={units:this.units,activation:Do(this.activation),useBias:this.useBias,kernelInitializer:Mt(this.kernelInitializer),biasInitializer:Mt(this.biasInitializer),kernelRegularizer:xt(this.kernelRegularizer),biasRegularizer:xt(this.biasRegularizer),activityRegularizer:xt(this.activityRegularizer),kernelConstraint:an(this.kernelConstraint),biasConstraint:an(this.biasConstraint)},t=super.getConfig();return Object.assign(e,t),e}};bA.className="Dense";ue.registerClass(bA);var vA=class extends st{constructor(e){e=e||{};super(e);this.inputSpec=[{minNDim:3}],this.dataFormat=e.dataFormat}computeOutputShape(e){e=ft(e);for(let t of e.slice(1))if(t==null)throw new H(`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],Eo(e,1)]}call(e,t){return q(()=>{this.invokeCallHook(e,t);let n=Ve(e);if(this.dataFormat==="channelsFirst"&&n.rank>1){let s=[0];for(let r=2;r<n.rank;++r)s.push(r);s.push(1),n=tt(n,s)}return cL(n)})}getConfig(){let e={};this.dataFormat!=null&&(e.dataFormat=this.dataFormat);let t=super.getConfig();return Object.assign(e,t),e}};vA.className="Flatten";ue.registerClass(vA);var wA=class extends st{constructor(e){super(e);this.supportsMasking=!0,this.activation=_o(e.activation)}call(e,t){return q(()=>{this.invokeCallHook(e,t);let n=Ve(e);return this.activation.apply(n)})}getConfig(){let e={activation:Do(this.activation)},t=super.getConfig();return Object.assign(e,t),e}};wA.className="Activation";ue.registerClass(wA);var kA=class extends st{constructor(e){super(e);this.n=e.n,this.inputSpec=[{ndim:2}]}computeOutputShape(e){return[e[0],this.n,e[1]]}call(e,t){return q(()=>(e=Ve(e),lL(e,this.n)))}getConfig(){let e={n:this.n},t=super.getConfig();return Object.assign(e,t),e}};kA.className="RepeatVector";ue.registerClass(kA);var SA=class extends st{constructor(e){super(e);this.targetShape=e.targetShape;for(let t=0;t<this.targetShape.length;++t)this.isUnknown(this.targetShape[t])&&(this.targetShape[t]=null)}isUnknown(e){return e<0||e==null}fixUnknownDimension(e,t){let n="Total size of new array must be unchanged.",s=t.slice(),r=1,a=null;for(let i=0;i<s.length;++i){let l=s[i];if(this.isUnknown(l))if(a===null)a=i;else throw new H("Can only specifiy one unknown dimension.");else r*=l}let o=Eo(e);if(a!==null){if(r===0||o%r!=0)throw new H(n);s[a]=o/r}else if(o!==r)throw new H(n);return s}computeOutputShape(e){let t=!1;for(let n=0;n<e.length;++n)if(this.isUnknown(e[n])){t=!0;break}return t?e.slice(0,1).concat(this.targetShape):e.slice(0,1).concat(this.fixUnknownDimension(e.slice(1),this.targetShape))}call(e,t){return q(()=>{this.invokeCallHook(e,t);let n=Ve(e),s=n.shape,r=s.slice(0,1).concat(this.fixUnknownDimension(s.slice(1),this.targetShape));return G(n,r)})}getConfig(){let e={targetShape:this.targetShape},t=super.getConfig();return Object.assign(e,t),e}};SA.className="Reshape";ue.registerClass(SA);var IA=class extends st{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=pr(1,e.dims.length+1);if(!v.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 Zt({ndim:this.dims.length+1})]}computeOutputShape(e){e=ft(e);let t=e.slice();return this.dims.forEach((n,s)=>{t[s+1]=e[n]}),t}call(e,t){return tt(Ve(e),this.dimsIncludingBatch)}getConfig(){let e={dims:this.dims},t=super.getConfig();return Object.assign(e,t),e}};IA.className="Permute";ue.registerClass(IA);var CA=class extends st{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=Ve(e),s=-1;return Gh(Cu(n,this.maskValue),s)}call(e,t){return q(()=>{this.invokeCallHook(e,t);let n=Ve(e),s=-1,r=!0,a=Gh(Cu(n,this.maskValue),s,r);return W(n,pe(a,n.dtype))})}};CA.className="Masking";ue.registerClass(CA);var TA=class extends st{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(It(e.inputLength))}this.inputDim=e.inputDim,gn(this.inputDim,"inputDim"),this.outputDim=e.outputDim,gn(this.outputDim,"outputDim"),this.embeddingsInitializer=Et(e.embeddingsInitializer||this.DEFAULT_EMBEDDINGS_INITIALIZER),this.embeddingsRegularizer=Rt(e.embeddingsRegularizer),this.activityRegularizer=Rt(e.activityRegularizer),this.embeddingsConstraint=on(e.embeddingsConstraint),this.maskZero=e.maskZero,this.supportsMasking=e.maskZero,this.inputLength=e.inputLength}build(e){this.embeddings=this.addWeight("embeddings",[this.inputDim,this.outputDim],this.dtype,this.embeddingsInitializer,this.embeddingsRegularizer,!0,this.embeddingsConstraint),this.built=!0}warnOnIncompatibleInputShape(e){}computeMask(e,t){return q(()=>this.maskZero?(e=Ve(e),Cu(e,nt(e))):null)}computeOutputShape(e){if(e=ft(e),this.inputLength==null)return[...e,this.outputDim];let t=It(this.inputLength);if(t.length!==e.length-1)throw new H(`"inputLength" is ${this.inputLength}, but received input shape has shape ${e}`);{let n=0;for(let s=0;s<t.length;++s){let r=t[s],a=e[s+1];if(r!=null&&a!=null&&r!==a)throw new H(`"inputLength" is ${this.inputLength}, but received input shape has shape ${e}`);r==null&&(t[n]=a),n++}}return[e[0],...t,this.outputDim]}call(e,t){return q(()=>{this.invokeCallHook(e,t);let n=Ve(e);n.dtype!=="int32"&&(n=wf(n,"int32"));let s=sw(this.embeddings.read(),G(n,[n.size]));return G(s,ft(this.computeOutputShape(n.shape)))})}getConfig(){let e={inputDim:this.inputDim,outputDim:this.outputDim,embeddingsInitializer:Mt(this.embeddingsInitializer),embeddingsRegularizer:xt(this.embeddingsRegularizer),activityRegularizer:xt(this.activityRegularizer),embeddingsConstraint:an(this.embeddingsConstraint),maskZero:this.maskZero,inputLength:this.inputLength},t=super.getConfig();return Object.assign(e,t),e}};TA.className="Embedding";ue.registerClass(TA);var cl=class extends st{constructor(e){super(e||{});this.supportsMasking=!0}mergeFunction(e){throw new We}computeElementwiseOpOutputShape(e,t){if(e==null||t==null)return null;if(e.length<t.length)return this.computeElementwiseOpOutputShape(t,e);if(t.length===0)return e;let n=e.slice(0,e.length-t.length);for(let s=0;s<t.length;++s){let r=e[e.length-t.length+s],a=t[s];if(r==null||a==null||r<0||a<0)n.push(null);else if(r===1)n.push(a);else if(a===1)n.push(r);else{if(r!==a)throw new H("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=[ft(e)]),e=e,e.length<2)throw new H(`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=No(t),t.length>1)throw new H(`Can not merge tensors with different batch sizes. Got tensors with shapes: ${JSON.stringify(e)}.`);let n=e[0]==null?null:e[0].slice(1);for(let r=1;r<e.length;++r){let a=e[r]==null?null:e[r].slice(1);n=this.computeElementwiseOpOutputShape(n,a)}let s=e.map(r=>r.length);e.indexOf(null)===-1&&No(s).length===1?this.reshapeRequired=!1:this.reshapeRequired=!0}call(e,t){return q(()=>{if(e=e,this.reshapeRequired){let n=[],s=e.map(r=>r.rank);if(s.indexOf(null)===-1){let r=Ro(s);for(let a of e){let o=a.rank;for(let i=0;i<r-o;++i)a=Id(a,1);n.push(a)}return this.mergeFunction(n)}else{let r=!1;for(let i of e){let l=i.rank;if(l==null){let c=i.shape,u=c[0],d=c.slice(1).concat([u]),p=G(i,[u].concat(Eo(c.slice(1))));p=tt(p,[1,0]),p=G(p,d),n.push(p),r=!0}else if(l>1){let c=pr(1,l).concat([0]);n.push(tt(i,c)),r=!0}else n.push(i)}let a=this.mergeFunction(n),o=a.rank;if(r){if(o==null){let i=a.shape,l=i.length,c=i[l-1],u=[c].concat(i.slice(0,i.length-1));a=G(tt(G(a,[-1,c]),[1,0]),u)}else if(o>1){let i=[o-1].concat(pr(0,o-1));a=tt(a,i)}}return a}}else return this.mergeFunction(e)})}computeOutputShape(e){e=e;let t;e[0]==null?t=null:t=e[0].slice(1);for(let s=1;s<e.length;++s){let r=e[s]==null?null:e[s].slice(1);t=this.computeElementwiseOpOutputShape(t,r)}let n=[];for(let s of e)s!=null&&s[0]!==null&&n.push(s[0]);return n=No(n),n.length===1?t=n.concat(t):t=[null].concat(t),t}computeMask(e,t){return q(()=>{if(t==null)return null;if(!Array.isArray(t))throw new H("`mask` should be an Array");if(!Array.isArray(e))throw new H("`inputs` should be an Array");if(t.length!==e.length)throw new H(`The Array 'inputs' and 'mask' are expected to have the same length, but have different lengths (${e.length} vs ${t.length})`);if(t.every(s=>s==null))return null;t=t.map(s=>s==null?s:mn(s,0));let n=t[0];for(let s=1;s<t.length-1;++s)n=lr(n,t[s]);return n})}},NA=class extends cl{constructor(e){super(e)}mergeFunction(e){return q(()=>{let t=e[0].clone();for(let n=1;n<e.length;++n)t=le(t,e[n]);return t})}};NA.className="Add";ue.registerClass(NA);var EA=class extends cl{constructor(e){super(e)}mergeFunction(e){return q(()=>{let t=e[0].clone();for(let n=1;n<e.length;++n)t=W(t,e[n]);return t})}};EA.className="Multiply";ue.registerClass(EA);var RA=class extends cl{constructor(e){super(e)}mergeFunction(e){return q(()=>{let t=e[0].clone();for(let n=1;n<e.length;++n)t=le(t,e[n]);return W(1/e.length,t)})}};RA.className="Average";ue.registerClass(RA);var $A=class extends cl{constructor(e){super(e)}mergeFunction(e){return q(()=>{let t=e[0];for(let n=1;n<e.length;++n)t=Xr(t,e[n]);return t})}};$A.className="Maximum";ue.registerClass($A);var DA=class extends cl{constructor(e){super(e)}mergeFunction(e){return q(()=>{let t=e[0];for(let n=1;n<e.length;++n)t=md(t,e[n]);return t})}};DA.className="Minimum";ue.registerClass(DA);var _A=class extends cl{constructor(e){super(e);this.DEFAULT_AXIS=-1,e==null&&(e={}),this.axis=e.axis==null?this.DEFAULT_AXIS:e.axis,this.supportsMasking=!0,this.reshapeRequired=!1}build(e){if(!(Array.isArray(e)&&Array.isArray(e[0]))||e.length===1)throw new H("A `Concatenate` layer should be called on a list of at least 2 inputs");e=e;let t=!0;for(let s of e)if(s!=null){t=!1;break}if(t)return;let n=[];for(let s=0;s<e.length;++s){let r=e[s].slice();r.splice(this.axis,1);let a=!1;for(let o of n)if(v.arraysEqual(o,r)){a=!0;break}a||n.push(r)}if(n.length>1)throw new H("A `Concatenate` layer requires inputs with matching shapes except for the concat axis. Got input shapes: "+JSON.stringify(e))}mergeFunction(e){return q(()=>k1(e,this.axis))}computeOutputShape(e){if(!(Array.isArray(e)&&Array.isArray(e[0])))throw new H("A `Concatenate` layer should be called on a list of inputs.");let t=e,n=t[0].slice(),s=this.axis<0?n.length+this.axis:this.axis;for(let r of t.slice(1)){if(n[s]==null||r[s]==null){n[s]=null;break}n[s]+=r[s]}return n}computeMask(e,t){if(t==null)return null;if(!Array.isArray(t))throw new H("`mask` should be an array for Concatenate");if(!Array.isArray(e))throw new H("`inputs` should be an array for Concatenate");if(t.length!==e.length)throw new H(`Mismatch in the length of mask (${t.length}) and the legnth of inputs (${e.length})`);return q(()=>{let n=!0;if(t.forEach(a=>{if(a!=null){n=!1;return}}),n)return null;let s=[];for(let a=0;a<e.length;++a)t[a]==null?s.push(pe(Es(e[a]),"bool")):t[a].rank<e[a].rank?s.push(mn(t[a],-1)):s.push(t[a]);let r=kt(s,this.axis);return N2(r,-1,!1)})}getConfig(){let e={axis:this.axis},t=super.getConfig();return Object.assign(e,t),e}};_A.className="Concatenate";ue.registerClass(_A);function Od(e,t){for(;e<0;)e+=t;return e}function HB(e,t,n){if(e.shape.length>3||t.shape.length>3)throw new We("batchDot is not implemented for tensors of 4D or higher rank yet");if(v.assert(e.shape.length>=2,()=>`batchDot requires the rank of x to be >= 2, but got ${e.shape.length}`),v.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 We("batchDot is not implemented for complex64-type Tensors yet.");let s=e.shape.length,r=t.shape.length;n==null&&(n=[s-1,r-2]);let a=n;return q(()=>{let o;if(s>r){o=s-r;let l=[];for(let c=0;c<o;++c)l.push(1);t=G(t,t.shape.concat(l))}else if(r>s){o=r-s;let l=[];for(let c=0;c<o;++c)l.push(1);e=G(e,e.shape.concat(l))}else o=0;let i;if(e.shape.length===2&&t.shape.length===2)a[0]===a[1]?i=Se(W(e,t),a[0]):i=Se(W(tt(e,[1,0]),t),a[1]);else{let l=a[0]!==e.shape.length-1,c=a[1]===t.shape.length-1;i=He(e,t,l,c)}if(o>0){let l;s>r?l=s+r-3:l=s-1;let c=[];for(let u=l;u<l+o;++u)c.push(u);i=ct(i,c)}return i.shape.length===1&&(i=mn(i,1)),i})}var PA=class extends cl{constructor(e){super(e);this.axes=e.axes,this.normalize=e.normalize==null?!1:e.normalize,this.supportsMasking=!0,this.reshapeRequired=!1}build(e){v.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 We("Dot layer does not support tensors of 4D or higher rank yet.");let s=this.interpretAxes(t,n);if(t[s[0]]!==n[s[1]])throw new H(`Dimension incompatibility: ${t[s[0]]} !== ${n[s[1]]}`)}mergeFunction(e){if(e.length!==2)throw new H(`A \`Dot\` layer must be called on exactly 2 inputs, but received ${e.length} input(s).`);let t=e[0],n=e[1],s;return Array.isArray(this.axes)?s=this.axes.map((r,a)=>Od(r,e[a].shape.length)):s=[Od(this.axes,t.shape.length),Od(this.axes,n.shape.length)],this.normalize&&(t=Mf(t,s[0]),n=Mf(n,s[1])),HB(t,n,s)}interpretAxes(e,t){let n;return Array.isArray(this.axes)?n=this.axes:n=[Od(this.axes,e.length),Od(this.axes,t.length)],n}computeOutputShape(e){v.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 We("Dot layer does not support tensors of 4D or higher rank yet.");let s=this.interpretAxes(t,n);t.splice(s[0],1),n.splice(s[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}};PA.className="Dot";ue.registerClass(PA);var FA=class extends st{constructor(e){super(e);this.supportsMasking=!0,this.stddev=e.stddev}computeOutputShape(e){return e}getConfig(){let e=super.getConfig(),t={stddev:this.stddev};return Object.assign(t,e),t}call(e,t){return q(()=>{this.invokeCallHook(e,t);let n=Ve(e);return Td(()=>le(Sf(n.shape,0,this.stddev),n),()=>n,t.training||!1)})}};FA.className="GaussianNoise";ue.registerClass(FA);var OA=class extends st{constructor(e){super(e);this.supportsMasking=!0,this.rate=e.rate}computeOutputShape(e){return e}getConfig(){let e=super.getConfig(),t={rate:this.rate};return Object.assign(t,e),t}call(e,t){return q(()=>{this.invokeCallHook(e,t);let n=Ve(e);return this.rate>0&&this.rate<1?Td(()=>{let r=Math.sqrt(this.rate/(1-this.rate));return W(n,Sf(n.shape,1,r))},()=>n,t.training||!1):n})}};OA.className="GaussianDropout";ue.registerClass(OA);var MA=class extends st{constructor(e){super(e);this.supportsMasking=!0,this.rate=e.rate,this.noiseShape=e.noiseShape}_getNoiseShape(e){return this.noiseShape||Ve(e).shape}computeOutputShape(e){return e}getConfig(){let e=super.getConfig(),t={rate:this.rate};return Object.assign(t,e),t}call(e,t){return q(()=>{if(this.rate<1&&this.rate>0){let n=this._getNoiseShape(e);return Td(()=>{let r=Ve(e),a=1.6732632423543772,o=1.0507009873554805,i=-a*o,l=Ji(Tu(n),this.rate);l=wf(l,"float32");let c=((1-this.rate)*(1+this.rate*i**2))**-.5,u=-c*i*this.rate,d=le(W(r,l),W(le(l,-1),i));return le(W(d,c),u)},()=>Ve(e),t.training||!1)}return e})}};MA.className="AlphaDropout";ue.registerClass(MA);function Md(e,t,n,s,r,a=.001){let o;if(e.rank===2)o=N3(e,t,n,s,r,a);else if(e.rank===3)o=E3(e,t,n,s,r,a);else if(e.rank===4)o=R3(e,t,n,s,r,a);else throw new We(`batchNormalization is not implemented for array of rank ${e.rank} yet`);return o}function jB(e,t,n,s,r=.001){return q(()=>{let a=nf(e,s),o=a.mean,i=a.variance;return[Md(e,o,i,n,t,r),o,i]})}function qB(e,t,n,s,r=.001){return q(()=>{let a=nf(e,s),o=a.mean,i=a.variance,l=[];for(let f of pr(0,e.rank))s.indexOf(f)!==-1?l.push(1):l.push(e.shape[f]);let c=G(o,l),u=G(i,l),d=t==null?null:G(t,l),p=n==null?null:G(n,l);return[Md(e,c,u,p,d,r),o,i]})}function XB(e,t,n,s,r=.001){return v.arraysEqual(s.slice().sort(),pr(0,e.rank-1))?jB(e,t,n,s,r):qB(e,t,n,s,r)}var zA=class extends st{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=Et(e.betaInitializer||"zeros"),this.gammaInitializer=Et(e.gammaInitializer||"ones"),this.movingMeanInitializer=Et(e.movingMeanInitializer||"zeros"),this.movingVarianceInitializer=Et(e.movingVarianceInitializer||"ones"),this.betaConstraint=on(e.betaConstraint),this.gammaConstraint=on(e.gammaConstraint),this.betaRegularizer=Rt(e.betaRegularizer),this.gammaRegularizer=Rt(e.gammaRegularizer)}build(e){e=ft(e);let t=this.axis>=0?this.axis:this.axis+e.length,n=e[t];if(n==null)throw new H(`Axis ${t} of input tensor should have a defined dimension but the layer received an input with shape ${JSON.stringify(e)}.`);this.inputSpec=[new Zt({ndim:e.length,axes:{[t]:n}})];let s=[n];this.scale&&(this.gamma=this.addWeight("gamma",s,null,this.gammaInitializer,this.gammaRegularizer,!0,this.gammaConstraint)),this.center&&(this.beta=this.addWeight("beta",s,null,this.betaInitializer,this.betaRegularizer,!0,this.betaConstraint)),this.movingMean=this.addWeight("moving_mean",s,null,this.movingMeanInitializer,null,!1),this.movingVariance=this.addWeight("moving_variance",s,null,this.movingVarianceInitializer,null,!1),this.built=!0}call(e,t){return q(()=>{let n=t.training==null?!1:t.training,s=Ve(e),r=s.shape,a=r.length,o=pr(0,a),i=this.axis>=0?this.axis:this.axis+a;o.splice(i,1);let l=nl(1,a);l[i]=r[i];let c=o.slice();c.sort();let u=!v.arraysEqual(c,pr(0,a).slice(0,a-1)),d=()=>{if(u){let A=G(this.movingMean.read(),l),x=G(this.movingVariance.read(),l),y=this.center?G(this.beta.read(),l):null,b=this.scale?G(this.gamma.read(),l):null;return Md(s,A,x,y,b,this.epsilon)}else return Md(s,this.movingMean.read(),this.movingVariance.read(),this.beta==null?null:this.beta.read(),this.gamma==null?null:this.gamma.read(),this.epsilon)};if(!n)return d();let[p,h,f]=XB(s,this.gamma.read(),this.beta.read(),o,this.epsilon),m=(A,x,y)=>{q(()=>{let b=1-y,w=A.read(),k=W(xe(w,x),b);A.write(xe(w,k))})};return(()=>{m(this.movingMean,h,this.momentum),m(this.movingVariance,f,this.momentum)})(),p})}getConfig(){let e={axis:this.axis,momentum:this.momentum,epsilon:this.epsilon,center:this.center,scale:this.scale,betaInitializer:Mt(this.betaInitializer),gammaInitializer:Mt(this.gammaInitializer),movingMeanInitializer:Mt(this.movingMeanInitializer),movingVarianceInitializer:Mt(this.movingVarianceInitializer),betaRegularizer:xt(this.betaRegularizer),gammaRegularizer:xt(this.gammaRegularizer),betaConstraint:an(this.betaConstraint),gammaConstraint:an(this.gammaConstraint)},t=super.getConfig();return Object.assign(e,t),e}};zA.className="BatchNormalization";ue.registerClass(zA);var LA=class extends st{constructor(e){e==null&&(e={});super(e);if(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=Et(e.betaInitializer||"zeros"),this.gammaInitializer=Et(e.gammaInitializer||"ones"),this.betaRegularizer=Rt(e.betaRegularizer),this.gammaRegularizer=Rt(e.gammaRegularizer),this.supportsMasking=!0}build(e){e=ft(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!==No(this.axis).length)throw new Error(`Found duplicate axes in: ${this.axis}`);let n=this.axis.map(r=>e[r]),s=!0;this.scale?this.gamma=this.addWeight("gamma",n,"float32",this.gammaInitializer,this.gammaRegularizer,s):this.gamma=null,this.center?this.beta=this.addWeight("beta",n,"float32",this.betaInitializer,this.betaRegularizer,s):this.beta=null,this.built=!0}call(e,t){let n=Ve(e),s=n.shape,r=s.length;return q(()=>{let a=!0,{mean:o,variance:i}=nf(n,this.axis,a),l=nl(1,r);for(let f of this.axis)l[f]=s[f];let c=f=>f!=null&&f.shape.length!==r?G(f,l):f,u=c(this.gamma.read()),d=c(this.beta.read()),p=[],h=[];for(let f=0;f<r;++f)this.axis.indexOf(f)!==-1?(p.push(s[f]),h.push(1)):(p.push(1),h.push(s[f]));return o=js(o,p),i=js(i,p),u=js(u,h),d=js(d,h),Md(n,o,i,d,u,this.epsilon)})}getConfig(){let e={axis:this.axis,epsilon:this.epsilon,center:this.center,scale:this.scale,betaInitializer:Mt(this.betaInitializer),gammaInitializer:Mt(this.gammaInitializer),betaRegularizer:xt(this.betaRegularizer),gammaRegularizer:xt(this.gammaRegularizer)},t=super.getConfig();return Object.assign(e,t),e}};LA.className="LayerNormalization";ue.registerClass(LA);function KB(e,t,n){return q(()=>{if(e.rank!==4)throw new H(`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 H("spatial2dPadding expects `padding` to be an Array of two Arrays, each of which is an Array of two integers.");if(n==null&&(n=cr()),n!=="channelsLast"&&n!=="channelsFirst")throw new H(`Unknown data format: ${n}. Supported data formats are 'channelsLast' and 'channelsFirst.`);let s;return n==="channelsFirst"?s=[[0,0],[0,0],t[0],t[1]]:s=[[0,0],t[0],t[1],[0,0]],qs(e,s)})}var BA=class extends st{constructor(e){e==null&&(e={});super(e);if(this.dataFormat=e.dataFormat==null?cr():e.dataFormat,e.padding==null)this.padding=[[1,1],[1,1]];else if(typeof e.padding=="number")this.padding=[[e.padding,e.padding],[e.padding,e.padding]];else{if(e.padding=e.padding,e.padding.length!==2)throw new H(`ZeroPadding2D expects padding to be a length-2 array, but received a length-${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 H(`ZeroPadding2D expects height padding to be a length-2 array, but received a length-${e.padding[0].length} array.`);if(t=e.padding[0],e.padding[1].length!==2)throw new H(`ZeroPadding2D expects width padding to be a length-2 array, but received a length-${e.padding[1].length} array.`);n=e.padding[1]}this.padding=[t,n]}this.inputSpec=[new Zt({ndim:4})]}computeOutputShape(e){e=ft(e);let t,n;return this.dataFormat==="channelsFirst"?(e[2]!=null&&e[2]>=0?t=e[2]+this.padding[0][0]+this.padding[0][1]:t=null,e[3]!=null&&e[3]>=0?n=e[3]+this.padding[1][0]+this.padding[1][1]:n=null,[e[0],e[1],t,n]):(e[1]!=null&&e[1]>=0?t=e[1]+this.padding[0][0]+this.padding[0][1]:t=null,e[2]!=null&&e[2]>=0?n=e[2]+this.padding[1][0]+this.padding[1][1]:n=null,[e[0],t,n,e[3]])}call(e,t){return q(()=>KB(Ve(e),this.padding,this.dataFormat))}getConfig(){let e={padding:this.padding,dataFormat:this.dataFormat},t=super.getConfig();return Object.assign(e,t),e}};BA.className="ZeroPadding2D";ue.registerClass(BA);function Qf(e,t,n,s,r,a){return q(()=>{jt(r),Yv(a),$s(s),n==null&&(n=[1,1]),s==null&&(s="valid"),r==null&&(r=cr()),a==null&&(a="max"),e=oA(e,r);let o,i=s==="same"?"same":"valid";return a==="max"?o=ef(e,t,n,i):o=jh(e,t,n,i),r==="channelsFirst"&&(o=tt(o,[0,3,1,2])),o})}function hk(e,t,n,s,r,a){return q(()=>{jt(r),Yv(a),$s(s),n==null&&(n=[1,1,1]),s==null&&(s="valid"),r==null&&(r=cr()),a==null&&(a="max"),e=sk(e,r);let o,i=s==="same"?"same":"valid";return a==="max"?o=H2(e,t,n,i):o=$2(e,t,n,i),r==="channelsFirst"&&(o=tt(o,[0,4,1,2,3])),o})}var fk=class extends st{constructor(e){e.poolSize==null&&(e.poolSize=2);super(e);if(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 H(`poolSize for 1D convolutional layer must be a number or an Array of a single number, but received ${JSON.stringify(e.poolSize)}`);if(gn(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 H(`strides for 1D convolutional layer must be a number or an Array of a single number, but received ${JSON.stringify(e.strides)}`);gn(this.strides,"strides"),this.padding=e.padding==null?"valid":e.padding,$s(this.padding),this.inputSpec=[new Zt({ndim:3})]}computeOutputShape(e){e=ft(e);let t=gr(e[1],this.poolSize[0],this.padding,this.strides[0]);return[e[0],t,e[2]]}call(e,t){return q(()=>{this.invokeCallHook(e,t),e=Id(Ve(e),2);let n=this.poolingFunction(Ve(e),[this.poolSize[0],1],[this.strides[0],1],this.padding,"channelsLast");return ct(n,[2])})}getConfig(){let e={poolSize:this.poolSize,padding:this.padding,strides:this.strides},t=super.getConfig();return Object.assign(e,t),e}},WA=class extends fk{constructor(e){super(e)}poolingFunction(e,t,n,s,r){return jt(r),$s(s),Qf(e,t,n,s,r,"max")}};WA.className="MaxPooling1D";ue.registerClass(WA);var VA=class extends fk{constructor(e){super(e)}poolingFunction(e,t,n,s,r){return jt(r),$s(s),Qf(e,t,n,s,r,"avg")}};VA.className="AveragePooling1D";ue.registerClass(VA);var mk=class extends st{constructor(e){e.poolSize==null&&(e.poolSize=[2,2]);super(e);if(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 H(`If the strides property of a 2D pooling layer is an Array, it is expected to have a length of 2, but received length ${e.strides.length}.`);this.strides=e.strides}else this.strides=[e.strides,e.strides];gn(this.poolSize,"poolSize"),gn(this.strides,"strides"),this.padding=e.padding==null?"valid":e.padding,this.dataFormat=e.dataFormat==null?"channelsLast":e.dataFormat,jt(this.dataFormat),$s(this.padding),this.inputSpec=[new Zt({ndim:4})]}computeOutputShape(e){e=ft(e);let t=this.dataFormat==="channelsFirst"?e[2]:e[1],n=this.dataFormat==="channelsFirst"?e[3]:e[2];return t=gr(t,this.poolSize[0],this.padding,this.strides[0]),n=gr(n,this.poolSize[1],this.padding,this.strides[1]),this.dataFormat==="channelsFirst"?[e[0],e[1],t,n]:[e[0],t,n,e[3]]}call(e,t){return q(()=>(this.invokeCallHook(e,t),this.poolingFunction(Ve(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}},UA=class extends mk{constructor(e){super(e)}poolingFunction(e,t,n,s,r){return jt(r),$s(s),Qf(e,t,n,s,r,"max")}};UA.className="MaxPooling2D";ue.registerClass(UA);var GA=class extends mk{constructor(e){super(e)}poolingFunction(e,t,n,s,r){return jt(r),$s(s),Qf(e,t,n,s,r,"avg")}};GA.className="AveragePooling2D";ue.registerClass(GA);var gk=class extends st{constructor(e){e.poolSize==null&&(e.poolSize=[2,2,2]);super(e);if(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 H(`If the strides property of a 3D pooling layer is an Array, it is expected to have a length of 3, but received length ${e.strides.length}.`);this.strides=e.strides}else this.strides=[e.strides,e.strides,e.strides];gn(this.poolSize,"poolSize"),gn(this.strides,"strides"),this.padding=e.padding==null?"valid":e.padding,this.dataFormat=e.dataFormat==null?"channelsLast":e.dataFormat,jt(this.dataFormat),$s(this.padding),this.inputSpec=[new Zt({ndim:5})]}computeOutputShape(e){e=ft(e);let t=this.dataFormat==="channelsFirst"?e[2]:e[1],n=this.dataFormat==="channelsFirst"?e[3]:e[2],s=this.dataFormat==="channelsFirst"?e[4]:e[3];return t=gr(t,this.poolSize[0],this.padding,this.strides[0]),n=gr(n,this.poolSize[1],this.padding,this.strides[1]),s=gr(s,this.poolSize[2],this.padding,this.strides[2]),this.dataFormat==="channelsFirst"?[e[0],e[1],t,n,s]:[e[0],t,n,s,e[4]]}call(e,t){return q(()=>(this.invokeCallHook(e,t),this.poolingFunction(Ve(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}},HA=class extends gk{constructor(e){super(e)}poolingFunction(e,t,n,s,r){return jt(r),$s(s),hk(e,t,n,s,r,"max")}};HA.className="MaxPooling3D";ue.registerClass(HA);var jA=class extends gk{constructor(e){super(e)}poolingFunction(e,t,n,s,r){return jt(r),$s(s),hk(e,t,n,s,r,"avg")}};jA.className="AveragePooling3D";ue.registerClass(jA);var Ak=class extends st{constructor(e){super(e);this.inputSpec=[new Zt({ndim:3})]}computeOutputShape(e){return[e[0],e[2]]}call(e,t){throw new We}},qA=class extends Ak{constructor(e){super(e||{})}call(e,t){return q(()=>{let n=Ve(e);return Wt(n,1)})}};qA.className="GlobalAveragePooling1D";ue.registerClass(qA);var XA=class extends Ak{constructor(e){super(e||{})}call(e,t){return q(()=>{let n=Ve(e);return ns(n,1)})}};XA.className="GlobalMaxPooling1D";ue.registerClass(XA);var yk=class extends st{constructor(e){super(e);this.dataFormat=e.dataFormat==null?"channelsLast":e.dataFormat,jt(this.dataFormat),this.inputSpec=[new Zt({ndim:4})]}computeOutputShape(e){return e=e,this.dataFormat==="channelsLast"?[e[0],e[3]]:[e[0],e[1]]}call(e,t){throw new We}getConfig(){let e={dataFormat:this.dataFormat},t=super.getConfig();return Object.assign(e,t),e}},KA=class extends yk{call(e,t){return q(()=>{let n=Ve(e);return this.dataFormat==="channelsLast"?Wt(n,[1,2]):Wt(n,[2,3])})}};KA.className="GlobalAveragePooling2D";ue.registerClass(KA);var ZA=class extends yk{call(e,t){return q(()=>{let n=Ve(e);return this.dataFormat==="channelsLast"?ns(n,[1,2]):ns(n,[2,3])})}};ZA.className="GlobalMaxPooling2D";ue.registerClass(ZA);var xk=class extends st{constructor(e){super(e);this.layer=e.layer}build(e){this.built=!0}get trainable(){return this.layer!=null?this.layer.trainable:!1}set trainable(e){this.layer!=null&&(this.layer.trainable=e)}get trainableWeights(){return this.layer.trainableWeights}get nonTrainableWeights(){return this.layer.nonTrainableWeights}get updates(){return this.layer._updates}get losses(){return this.layer.losses}getWeights(){return this.layer.getWeights()}setWeights(e){this.layer.setWeights(e)}getConfig(){let e={layer:{className:this.layer.getClassName(),config:this.layer.getConfig()}},t=super.getConfig();return Object.assign(e,t),e}setFastWeightInitDuringBuild(e){super.setFastWeightInitDuringBuild(e),this.layer!=null&&this.layer.setFastWeightInitDuringBuild(e)}static fromConfig(e,t,n={}){let s=t.layer,r=mr(s,n);delete t.layer;let a={layer:r};return Object.assign(a,t),new e(a)}},YA=class extends xk{constructor(e){super(e);this.supportsMasking=!0}build(e){if(e=ft(e),e.length<3)throw new H(`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=ft(e);let t=[e[0]].concat(e.slice(2)),n=this.layer.computeOutputShape(t),s=e[1];return[n[0],s].concat(n.slice(1))}call(e,t){return q(()=>(e=Ve(e),ck((a,o)=>[Ve(this.layer.call(a,t)),[]],e,[],!1,null,null,!1,!0)[1]))}};YA.className="TimeDistributed";ue.registerClass(YA);function ZB(e){rl(sL,"BidirectionalMergeMode",e)}var YB="concat",JA=class extends xk{constructor(e){super(e);let t=e.layer.getConfig(),n={};n.className=e.layer.getClassName(),n.config=t,this.forwardLayer=mr(n),t.goBackwards=t.goBackwards!==!0;let s={};if(s.className=e.layer.getClassName(),s.config=t,this.backwardLayer=mr(s),this.forwardLayer.name="forward_"+this.forwardLayer.name,this.backwardLayer.name="backward_"+this.backwardLayer.name,this.mergeMode=e.mergeMode===void 0?YB:e.mergeMode,ZB(this.mergeMode),e.weights)throw new We("weights support is not implemented for Bidirectional layer yet.");this._stateful=e.layer.stateful,this.returnSequences=e.layer.returnSequences,this.returnState=e.layer.returnState,this.supportsMasking=!0,this._trainable=!0,this.inputSpec=e.layer.inputSpec,this.numConstants=null}get trainable(){return this._trainable}set trainable(e){this._trainable=e,this.forwardLayer!=null&&(this.forwardLayer.trainable=e),this.backwardLayer!=null&&(this.backwardLayer.trainable=e)}getWeights(){return this.forwardLayer.getWeights().concat(this.backwardLayer.getWeights())}setWeights(e){let t=e.length,n=Math.floor(t/2);this.forwardLayer.setWeights(e.slice(0,n)),this.backwardLayer.setWeights(e.slice(n))}computeOutputShape(e){let t=this.forwardLayer.computeOutputShape(e);Array.isArray(t)&&Array.isArray(t[0])||(t=[t]),t=t;let n,s,r;return this.returnState&&(r=t.slice(1)),n=t[0],n=n,this.mergeMode==="concat"?(n[n.length-1]*=2,s=[n]):this.mergeMode==null?s=[n,n.slice()]:s=[n],this.returnState?this.mergeMode==null?s.concat(r).concat(r.slice()):[n].concat(r).concat(r.slice()):rs(s)}apply(e,t){let n=t==null?null:t.initialState,s=t==null?null:t.constants;t==null&&(t={});let r=uk(e,n,s,this.numConstants);if(e=r.inputs,n=r.initialState,s=r.constants,Array.isArray(e)&&(n=e.slice(1),e=e[0]),(n==null||n.length===0)&&s==null)return super.apply(e,t);let a=[],o=[];if(n!=null){let l=n.length;if(l%2>0)throw new H("When passing `initialState` to a Bidrectional RNN, the state should be an Array containing the states of the underlying RNNs.");t.initialState=n,a.push(...n);let c=n.map(u=>new Zt({shape:u.shape}));this.forwardLayer.stateSpec=c.slice(0,l/2),this.backwardLayer.stateSpec=c.slice(l/2),o.push(...c)}if(s!=null)throw new We("Support for constants in Bidirectional layers is not implemented yet.");let i=a[0]instanceof fr;for(let l of a)if(l instanceof fr!==i)throw new H("The initial state of a Bidirectional layer cannot be specified as a mix of symbolic and non-symbolic tensors");if(i){let l=[e].concat(a),c=this.inputSpec.concat(o),u=this.inputSpec;this.inputSpec=c;let d=super.apply(l,t);return this.inputSpec=u,d}else return super.apply(e,t)}call(e,t){return q(()=>{let n=t.initialState,s,r;if(n==null)s=this.forwardLayer.call(e,t),r=this.backwardLayer.call(e,t);else{let i=n.slice(0,n.length/2),l=n.slice(n.length/2);s=this.forwardLayer.call(e,Object.assign(t,{initialState:i})),r=this.backwardLayer.call(e,Object.assign(t,{initialState:l}))}let a;this.returnState&&(Array.isArray(s)&&(a=s.slice(1).concat(r.slice(1))),s=s[0],r=r[0]),this.returnSequences&&(r=Rs(r,1));let o;return this.mergeMode==="concat"?o=k1([s,r]):this.mergeMode==="sum"?o=le(s,r):this.mergeMode==="ave"?o=W(.5,le(s,r)):this.mergeMode==="mul"?o=W(s,r):this.mergeMode==null&&(o=[s,r]),this.returnState?this.mergeMode==null?o.concat(a):[o].concat(a):o})}resetStates(e){this.forwardLayer.resetStates(),this.backwardLayer.resetStates()}build(e){al(this.forwardLayer.name,()=>{this.forwardLayer.build(e)}),al(this.backwardLayer.name,()=>{this.backwardLayer.build(e)}),this.built=!0}computeMask(e,t){Array.isArray(t)&&(t=t[0]);let n;if(this.returnSequences?this.mergeMode==null?n=[t,t]:n=t:this.mergeMode==null?n=[null,null]:n=null,this.returnState){let r=this.forwardLayer.states.map(a=>null);return Array.isArray(n)?n.concat(r).concat(r):[n].concat(r).concat(r)}else return n}get trainableWeights(){return this.forwardLayer.trainableWeights.concat(this.backwardLayer.trainableWeights)}get nonTrainableWeights(){return this.forwardLayer.nonTrainableWeights.concat(this.backwardLayer.nonTrainableWeights)}setFastWeightInitDuringBuild(e){super.setFastWeightInitDuringBuild(e),this.forwardLayer!=null&&this.forwardLayer.setFastWeightInitDuringBuild(e),this.backwardLayer!=null&&this.backwardLayer.setFastWeightInitDuringBuild(e)}getConfig(){let e={mergeMode:this.mergeMode},t=super.getConfig();return Object.assign(e,t),e}static fromConfig(e,t){let n=mr(t.layer);if(delete t.layer,t.numConstants!=null)throw new We("Deserialization of a Bidirectional layer with numConstants present is not supported yet.");let s=t;return s.layer=n,new e(s)}};JA.className="Bidirectional";ue.registerClass(JA);function JB(e){return new Pu(e)}function QB(e){return new sA(e)}function eW(e){return new eA(e)}function tW(e){return new tA(e)}function nW(e){return new nA(e)}function sW(e){return new aA(e)}function rW(e){return new rA(e)}function aW(e){return new dA(e)}function oW(e){return new jf(e)}function iW(e){return new lA(e)}function 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q(()=>iU(a,o,i));case"dynamic":return lU(a,o,i);case"evaluation":return q(()=>uU(a,o,i));case"image":return q(()=>hU(a,o,i));case"graph":return q(()=>cU(a,o,i));case"logical":return q(()=>fU(a,o,i));case"matrices":return q(()=>mU(a,o,i));case"normalization":return q(()=>gU(a,o,i));case"reduction":return q(()=>AU(a,o,i));case"slice_join":return q(()=>yU(a,o,i));case"sparse":return q(()=>xU(a,o,i));case"spectral":return q(()=>bU(a,o,i));case"string":return q(()=>vU(a,o,i));case"transformation":return q(()=>wU(a,o,i));case"hash_table":return pU(a,o,i,s);case"custom":let l=$k(a.op);if(l&&l.customExecutor)return l.customExecutor(new YV(a,o,i));throw TypeError(`Custom op ${a.op} is not registered.`);default:throw TypeError(`Unknown op '${a.op}'. 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e?e.map(t=>t.id===0&&t.iterationId===0?"":`${t.frameName}-${t.iterationId}`).join("/"):""}enterFrame(e){this.contexts&&(this.lastId++,this.contexts=this.contexts.slice(),this.contexts.push(this.newFrame(this.lastId,e)),this._currentContextIds.unshift(this.contextIdforContexts(this.contexts)))}exitFrame(){if(this.contexts&&this.contexts.length>1)this.contexts=this.contexts.slice(),this.contexts.splice(-1),this.currentContextIds.shift();else throw new Error("Cannot exit frame, the context is empty")}nextIteration(){if(this.contexts&&this.contexts.length>0){this.contexts=this.contexts.slice(),this.lastId++;let e=Object.assign({},this.contexts[this.contexts.length-1]);e.iterationId+=1,e.id=this.lastId,this.contexts.splice(-1,1,e),this._currentContextIds.splice(0,1,this.contextIdforContexts(this.contexts))}else throw new Error("Cannot increase frame iteration, the context is empty")}getWeight(e){return this.weightMap[e]}addTensorArray(e){this.tensorArrayMap[e.id]=e}getTensorArray(e){return this.tensorArrayMap[e]}addTensorList(e){this.tensorListMap[e.id]=e}getTensorList(e){return this.tensorListMap[e]}dispose(e){for(let t in this.tensorArrayMap)this.tensorArrayMap[t].clearAndClose(e);for(let t in this.tensorListMap)this.tensorListMap[t].clearAndClose(e)}};function a7(e,t,n,s){let r=new Set,a=[],o=null,i=null,l=new Set,c=Object.keys(e).map(p=>ms(p)[0]),u=[];s!=null&&(u=s.map(p=>ms(p.name)[0]));let d=[...t];for(;d.length>0;){let p=d.pop();if((o7(p)||TU(p)||NU(p))&&o==null&&(o=p,i=o.children.map(h=>h.name).filter(h=>r.has(h))),r.add(p.name),n[p.name]==null&&c.indexOf(p.name)===-1&&u.indexOf(p.name)===-1){if(p.inputs.length===0){a.push(p.name);continue}p.inputs.forEach(h=>{l.has(h.name)||(l.add(h.name),d.push(h))})}}return{inputs:e,outputs:t,usedNodes:r,missingInputs:a,dynamicNode:o,syncInputs:i}}function kU(e,t,n){let{usedNodes:s,inputs:r}=n,a=[],o=Object.keys(r).map(u=>ms(u)[0]).map(u=>e.nodes[u]),i=e.initNodes;o.forEach(u=>{s.has(u.name)&&a.push(u)}),e.weights.forEach(u=>{s.has(u.name)&&a.push(u)}),i!=null&&i.forEach(u=>{s.has(u.name)&&a.push(u)});let l=new Set,c=[];for(;a.length>0;){let u=a.pop();l.add(u.name),t[u.name]||c.push(u),u.children.forEach(d=>{!l.has(d.name)&&s.has(d.name)&&d.inputs.every(p=>l.has(p.name))&&a.push(d)})}return c}var SU=["Switch","Merge","Enter","Exit","NextIteration","StatelessIf","StatelessWhile","if","While"],IU=["NonMaxSuppressionV2","NonMaxSuppressionV3","NonMaxSuppressionV5","Where"],CU=["HashTable","HashTableV2","LookupTableImport","LookupTableImportV2","LookupTableFind","LookupTableFindV2","LookupTableSize","LookupTableSizeV2"];function o7(e){return SU.indexOf(e.op)>=0}function TU(e){return IU.indexOf(e.op)>=0}function NU(e){return CU.indexOf(e.op)>=0}var gy=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 gy(e.functions[n],this)})}get weightIds(){return this.parent?this.parent.weightIds:this._weightIds}get functionExecutorMap(){return this.parent?this.parent.functionExecutorMap:this._functionExecutorMap}get weightMap(){return this.parent?this.parent.weightMap:this._weightMap}set weightMap(e){let t=Object.keys(e).map(n=>e[n].map(s=>s.id));this._weightIds=[].concat(...t),this._weightMap=e}set resourceManager(e){this._resourceManager=e}get inputs(){return this._inputs.map(e=>({name:e.name,shape:e.attrParams.shape?e.attrParams.shape.value:void 0,dtype:e.attrParams.dtype?e.attrParams.dtype.value:void 0}))}get outputs(){return this._outputs.map(e=>({name:e.name,shape:e.attrParams.shape?e.attrParams.shape.value:void 0,dtype:e.attrParams.dtype?e.attrParams.dtype.value:void 0}))}get inputNodes(){return this._inputs.map(e=>e.signatureKey||e.name)}get outputNodes(){return this._outputs.map(e=>{let t=e.signatureKey||e.name;return e.defaultOutput?`${t}:${e.defaultOutput}`:t})}get functions(){return Object.keys(this._functions).reduce((e,t)=>(e[t]=this._functions[t].signature,e),{})}getCompilationKey(e,t){let n=e.map(r=>r.name).sort(),s=t.map(r=>r.name).sort();return n.join(this.SEPERATOR)+"--"+s.join(this.SEPERATOR)}compile(e,t){let n=a7(e,t,this.weightMap,this._initNodes),{missingInputs:s,dynamicNode:r,syncInputs:a}=n;if(r!=null)throw new Error(`This execution contains the node '${r.name}', which has the dynamic op '${r.op}'. Please use model.executeAsync() instead. Alternatively, to avoid the dynamic ops, specify the inputs [${a}]`);if(s.length>0){let o=t.map(l=>l.name),i=Object.keys(e);throw new Error(`Cannot compute the outputs [${o}] from the provided inputs [${i}]. Missing the following inputs: [${s}]`)}return kU(this.graph,this.weightMap,n)}execute(e,t){e=this.mapInputs(e);let n=Object.keys(e).sort();this.checkInputs(e),this.checkInputShapeAndType(e),t=this.mapOutputs(t),this.checkOutputs(t);let s=n.map(u=>this.graph.nodes[ms(u)[0]]),r=t.map(u=>ms(u)[0]),a=r.map(u=>this.graph.nodes[u]);a.length===0&&(a=this._outputs);let o=this.getCompilationKey(s,a),i=this.compiledMap.get(o);i==null&&(i=this.compile(e,a),this.compiledMap.set(o,i));let l={},c={};return q(()=>{let u=new r7(this.weightMap,l,c,this.functionExecutorMap),d={...this.weightMap};Object.keys(e).forEach(f=>{let[m,g]=ms(f),A=[];A[g]=e[f],d[m]=A});let p=this.getFrozenTensorIds(d),h={};for(let f=0;f<i.length;f++){let m=i[f];if(!d[m.name]){let g=s7(m,d,u,this._resourceManager);if(v.isPromise(g))throw new Error(`The execution of the op '${m.op}' returned a promise. 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You can use model.execute() instead.");let A=i.filter(x=>!o7(x)&&!Vn(x.name,h,t)).map(x=>x.name);if(A.length>0){let x="";throw u!=null&&(x=`Alternatively, to avoid the dynamic ops, use model.execute() and specify the inputs [${d}]`),new Error(`Cannot compute the outputs [${A}] from the provided inputs [${r}]. 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c}processChildNodes(e,t,n,s,r,a){e.children.forEach(o=>{let[i]=ta(o.name,n);r[i]||!a.has(o.name)||(o.op==="Merge"?o.inputNames.some(l=>!!Vn(l,s,n))&&(r[i]=!0,t.push({contexts:n.currentContext,node:o})):o.inputNames.every(l=>!!Vn(l,s,n))&&(r[i]=!0,t.push({contexts:n.currentContext,node:o})))})}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],[s]=ms(t),r=this.graph.nodes[s];if(r.attrParams.shape&&r.attrParams.shape.value){let a=r.attrParams.shape.value,o=a.length===n.shape.length&&n.shape.every((i,l)=>a[l]===-1||a[l]===i);v.assert(o,()=>`The shape of dict['${r.name}'] provided in model.execute(dict) must be [${a}], but was [${n.shape}]`)}r.attrParams.dtype&&r.attrParams.dtype.value&&v.assert(n.dtype===r.attrParams.dtype.value,()=>`The dtype of dict['${r.name}'] provided in model.execute(dict) must be ${r.attrParams.dtype.value}, but was ${n.dtype}`)})}mapInputs(e){let t={};for(let n in e)if(this._signature!=null&&this._signature.inputs!=null&&this._signature.inputs[n]!=null){let s=this._signature.inputs[n];t[s.name]=e[n]}else t[n]=e[n];return t}checkInputs(e){let t=Object.keys(e).filter(n=>{let[s]=ms(n);return this.graph.nodes[s]==null});if(t.length>0)throw new Error(`The dict provided in model.execute(dict) has keys: [${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]=ms(t);if(!this.graph.nodes[n])throw new Error(`The output '${t}' is not found in the graph`)})}},EU=class{constructor(e={},t={}){this.hashTableNameToHandle=e,this.hashTableMap=t}addHashTable(e,t){this.hashTableNameToHandle[e]=t.handle,this.hashTableMap[t.id]=t}getHashTableHandleByName(e){return this.hashTableNameToHandle[e]}getHashTableById(e){return this.hashTableMap[e]}dispose(){for(let e in this.hashTableMap)this.hashTableMap[e].clearAndClose(),delete this.hashTableMap[e];for(let e in this.hashTableNameToHandle)this.hashTableNameToHandle[e].dispose(),delete this.hashTableNameToHandle[e]}},RU="?tfjs-format=file",$U="model.json",i7=class{constructor(e,t={}){this.modelUrl=e,this.loadOptions=t,this.version="n/a",t==null&&(this.loadOptions={}),this.resourceManager=new EU}get modelVersion(){return this.version}get inputNodes(){return this.executor.inputNodes}get outputNodes(){return this.executor.outputNodes}get inputs(){return this.executor.inputs}get outputs(){return this.executor.outputs}get weights(){return this.executor.weightMap}get metadata(){return this.artifacts.userDefinedMetadata}get modelSignature(){return this.signature}findIOHandler(){let e=this.modelUrl;if(e.load!=null)this.handler=e;else if(this.loadOptions.requestInit!=null)this.handler=es.browserHTTPRequest(e,this.loadOptions);else{let t=es.getLoadHandlers(e,this.loadOptions);if(t.length===0)t.push(es.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 s=es.decodeWeights(this.artifacts.weightData,this.artifacts.weightSpecs);if(this.executor=new gy(Yk.Instance.transformGraph(t,this.signature)),this.executor.weightMap=this.convertTensorMapToTensorsMap(s),this.executor.resourceManager=this.resourceManager,e.modelInitializer!=null&&e.modelInitializer.node!=null){let r=Yk.Instance.transformGraph(e.modelInitializer);this.initializer=new gy(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=es.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 et)&&!Array.isArray(e))return e;if(e=Array.isArray(e)?e:[e],e.length!==this.inputNodes.length)throw new Error(`Input tensor count mismatch,the graph model has ${this.inputNodes.length} placeholders, while there are ${e.length} input tensors.`);return this.inputNodes.reduce((t,n,s)=>(t[n]=e[s],t),{})}normalizeOutputs(e){return e=e||this.outputNodes,Array.isArray(e)?e:[e]}execute(e,t){e=this.normalizeInputs(e),t=this.normalizeOutputs(t);let n=this.executor.execute(e,t);return n.length>1?n:n[0]}async executeAsync(e,t){e=this.normalizeInputs(e),t=this.normalizeOutputs(t);let n=await this.executor.executeAsync(e,t);return n.length>1?n:n[0]}convertTensorMapToTensorsMap(e){return Object.keys(e).reduce((t,n)=>(t[n]=[e[n]],t),{})}dispose(){this.executor.dispose(),this.initializer&&this.initializer.dispose(),this.resourceManager.dispose()}};async function rt(e,t={}){if(e==null)throw new Error("modelUrl in loadGraphModel() cannot be null. 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next(){return this.lastRead=this.lastRead.then(()=>this.serialNext()),this.lastRead}async serialNext(){for(;;){let e=await this.upstream.next();if(e.done||this.predicate(e.value))return e;ae(e.value)}}},ZU=class extends An{constructor(e,t){super();this.upstream=e,this.transform=t}summary(){return`${this.upstream.summary()} -> Map`}async next(){let e=await this.upstream.next();if(e.done)return{value:null,done:!0};let t=rr.getTensorsInContainer(e.value),n=this.transform(e.value),s=rr.getTensorsInContainer(n);for(let r of t)rr.isTensorInList(r,s)||r.dispose();return{value:n,done:!1}}},YU=class extends An{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 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Mismatched at element ${this.count}.`);case 1:return{value:null,done:!0};case 2:default:}return this.count++,{value:r,done:!1}}async next(){return this.currentPromise=this.nextState(this.currentPromise),this.currentPromise}},y7=class extends An{constructor(e,t){super();this.upstream=e,this.bufferSize=t,this.buffer=new p7(t)}summary(){return`${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()}},eG=class extends y7{constructor(e,t,n){super(e,t);this.upstream=e,this.windowSize=t,this.upstreamExhausted=!1,this.random=PU.alea(n||v.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}}},Lu=class{constructor(){this.size=null}batch(e,t=!0){let n=this;v.assert(e>0,()=>`batchSize needs to be positive, but it is
${e}`);let s;return this.size===1/0||this.size==null?s=this.size:t?s=Math.ceil(this.size/e):s=Math.floor(this.size/e),gs(async()=>(await n.iterator()).columnMajorBatch(e,t,sG),s)}concatenate(e){let t=this,n;return this.size===1/0||e.size===1/0?n=1/0:this.size!=null&&e.size!=null?n=this.size+e.size:n=null,gs(async()=>(await t.iterator()).concatenate(await e.iterator()),n)}filter(e){let t=this,n;return this.size===1/0?n=1/0:n=null,gs(async()=>(await t.iterator()).filter(s=>q(()=>e(s))),n)}async forEachAsync(e){return(await this.iterator()).forEachAsync(e)}map(e){let t=this;return gs(async()=>(await t.iterator()).map(n=>q(()=>e(n))),this.size)}mapAsync(e){let t=this;return gs(async()=>(await t.iterator()).mapAsync(e),this.size)}prefetch(e){if(e==null)throw new RangeError("`Dataset.prefetch()` requires bufferSize to be specified.");let t=this;return gs(async()=>(await t.iterator()).prefetch(e),this.size)}repeat(e){let t=this,n;return this.size!=null&&e>0?n=this.size*e:e===0?n=0:this.size!=null&&(e===void 0||e<0)?n=1/0:n=null,gs(async()=>{let s=Ay(async()=>({value:await t.iterator(),done:!1}));return WU(s.take(e))},n)}skip(e){let t=this,n;return this.size!=null&&e>=0&&this.size>=e?n=this.size-e:this.size!=null&&(this.size<e||e===void 0||e<0)?n=0:n=null,gs(async()=>(await t.iterator()).skip(e),n)}shuffle(e,t,n=!0){if(e==null||e<0)throw this.size==null?new RangeError("`Dataset.shuffle()` requires bufferSize to be specified."):new RangeError(`\`Dataset.shuffle()\` requires bufferSize to be specified. If your data fits in main memory (for regular JS objects), and/or GPU memory (for \`tf.Tensor\`s), consider setting bufferSize to the dataset size (${this.size} elements)`);let s=this,r=_U.alea(t||v.now().toString());return gs(async()=>{let a=r.int32();return n&&(a+=r.int32()),(await s.iterator()).shuffle(e,a.toString())},this.size)}take(e){let t=this,n;return this.size!=null&&this.size>e?n=e:this.size!=null&&this.size<=e?n=this.size:n=null,gs(async()=>(await t.iterator()).take(e),n)}async toArray(){if(this.size===1/0)throw new Error("Can not convert infinite data stream to array.");return(await this.iterator()).toArray()}async toArrayForTest(){if(this.size===1/0)throw new Error("Can not convert infinite data stream to array.");return(await this.iterator()).toArrayForTest()}};Lu.MAX_BUFFER_SIZE=1e4;function gs(e,t=null){return new class extends Lu{constructor(){super(...arguments);this.size=t}async iterator(){return e()}}}function tG(e){return gs(async()=>m7(e),e.length)}function nG(e){if(!zu(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 gs(async()=>{let n=await d7(e,s=>{if(s instanceof Lu)return{value:s.iterator(),recurse:!1};if(zu(s))return{value:null,recurse:!0};throw new Error("Leaves of the structure passed to zip() must be Datasets, not primitives.")});return VU(n,rm.SHORTEST)},t)}function sG(e){if(e===null)return null;let t=e[0];return MU(t)?{value:rG(e),recurse:!1}:{value:null,recurse:!0}}function rG(e){if(e.length===0)throw new Error("Can't make a batch of zero elements.");return e[0]instanceof et?_n(e):Gt(e)}var x7=class extends Lu{constructor(e){super();this.input=e}async iterator(){return(await this.input.iterator()).decodeUTF8().split(`
`).map(s=>(s.endsWith("\r")&&(s=s.slice(0,-1)),s))}},am='"',Bd=Symbol("out"),b7=Symbol("field"),om=Symbol("quote"),xy=Symbol("quoteafterquote"),v7=Symbol("quoteinquote"),w7=class extends Lu{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 x7(e),t||(t={}),this.hasHeader=t.hasHeader!==!1,this.fullColumnNames=t.columnNames,this.columnConfigs=t.columnConfigs,this.configuredColumnsOnly=t.configuredColumnsOnly,t.delimWhitespace?(v.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&&v.assert(e.length===this.fullColumnNames.length,()=>"The length of provided columnNames ("+this.fullColumnNames.length.toString()+") does not match the length of the header line read from file ("+e.length.toString()+")."),this.fullColumnNames||(this.fullColumnNames=e);let t=this.fullColumnNames.reduce((s,r)=>(s[r]=s[r]+1||1,s),{}),n=Object.keys(t).filter(s=>t[s]>1);if(v.assert(n.length===0,()=>"Duplicate column names found: "+n.toString()),this.columnConfigs){for(let s of Object.keys(this.columnConfigs))if(this.fullColumnNames.indexOf(s)===-1)throw new Error('The key "'+s+'" provided in columnConfigs does not match any of the column names ('+this.fullColumnNames.toString()+").")}this.columnNamesValidated=!0}async maybeReadHeaderLine(){if(this.hasHeader){let t=await(await this.base.iterator()).next();if(t.done)throw new Error("No data was found for CSV parsing.");let n=t.value;return this.parseRow(n,!1)}else return null}async iterator(){this.columnNamesValidated||await this.setColumnNames();let e=await this.base.iterator();return this.hasHeader&&(e=e.skip(1)),e.map(t=>this.makeDataElement(t))}makeDataElement(e){let t=this.parseRow(e),n={},s={};for(let r=0;r<this.fullColumnNames.length;r++){let a=this.fullColumnNames[r],o=this.columnConfigs?this.columnConfigs[a]:null;if(!(this.configuredColumnsOnly&&!o)){let i=t[r],l=null;if(i==="")if(o&&o.default!==void 0)l=o.default;else{if(o&&(o.required||o.isLabel))throw new Error(`Required column ${a} is empty in this line: ${e}`);l=void 0}else{let c=Number(i);if(isNaN(c))o&&o.dtype==="bool"?l=this.getBoolean(i):l=i;else if(!o||!o.dtype)l=c;else switch(o.dtype){case"float32":l=c;break;case"int32":l=Math.floor(c);break;case"bool":l=this.getBoolean(i);break;default:l=c}}o&&o.isLabel?s[a]=l:n[a]=l}}return Object.keys(s).length===0?n:{xs:n,ys:s}}getBoolean(e){return e==="1"||e.toLowerCase()==="true"?1:0}parseRow(e,t=!0){let n=[],s=0,r=e.length,a=Bd;for(let o=0;o<r;o++)switch(a){case Bd:switch(e.charAt(o)){case am:s=o+1,a=om;break;case this.delimiter:if(s=o+1,this.delimiter===" "&&this.delimWhitespace)break;n.push(""),a=Bd;break;default:a=b7,s=o;break}break;case b7:switch(e.charAt(o)){case this.delimiter:n.push(e.substring(s,o)),a=Bd,s=o+1;break;default:}break;case om:switch(e.charAt(o)){case am:a=xy;break;default:}break;case xy:switch(e.charAt(o)){case this.delimiter:n.push(e.substring(s,o-1)),a=Bd,s=o+1;break;case am:a=om;break;default:a=v7;break}break;case v7:switch(e.charAt(o)){case am:a=om;break;default:}break;default:}if(a===xy?n.push(e.substring(s,r-1)):n.push(e.substring(s)),t&&n.length!==this.fullColumnNames.length)throw new Error(`Invalid row in csv file. Should have ${this.fullColumnNames.length} elements in a row, but got ${n}`);return n}},k7=class extends An{constructor(e){super();this.microphoneConfig=e,this.isClosed=!1,this.fftSize=e.fftSize||1024;let t=Math.log2(this.fftSize);if(this.fftSize<0||t<4||t>14||!Number.isInteger(t))throw new Error(`Invalid fftSize: it must be a power of 2 between 2 to 4 and 2 to 14, but got ${this.fftSize}`);if(this.numFrames=e.numFramesPerSpectrogram||43,this.sampleRateHz=e.sampleRateHz,this.columnTruncateLength=e.columnTruncateLength||this.fftSize,this.audioTrackConstraints=e.audioTrackConstraints,this.smoothingTimeConstant=e.smoothingTimeConstant||0,this.includeSpectrogram=e.includeSpectrogram!==!1,this.includeWaveform=e.includeWaveform===!0,!this.includeSpectrogram&&!this.includeWaveform)throw new Error("Both includeSpectrogram and includeWaveform are false. At least one type of data should be returned.")}summary(){return"microphone"}static async create(e={}){if(K().get("IS_NODE"))throw new Error("microphone API is only supported in browser environment.");let t=new k7(e);return await t.start(),t}async start(){try{this.stream=await navigator.mediaDevices.getUserMedia({audio:this.audioTrackConstraints==null?!0:this.audioTrackConstraints,video:!1})}catch(n){throw new Error(`Error thrown while initializing video stream: ${n.message}`)}if(!this.stream)throw new Error("Could not obtain audio from microphone.");let e=window.AudioContext||window.webkitAudioContext;if(this.audioContext=new e,!this.sampleRateHz)this.sampleRateHz=this.audioContext.sampleRate;else if(this.audioContext.sampleRate!==this.sampleRateHz)throw new Error(`Mismatch in sampling rate: Expected: ${this.sampleRateHz}; Actual: ${this.audioContext.sampleRate}`);let t=this.audioContext.createMediaStreamSource(this.stream);this.analyser=this.audioContext.createAnalyser(),this.analyser.fftSize=this.fftSize*2,this.analyser.smoothingTimeConstant=this.smoothingTimeConstant,t.connect(this.analyser),this.freqData=new Float32Array(this.fftSize),this.timeData=new Float32Array(this.fftSize)}async next(){if(this.isClosed)return{value:null,done:!0};let e,t,n=await this.getAudioData();if(this.includeSpectrogram){let s=this.flattenQueue(n.freqDataQueue);e=this.getTensorFromAudioDataArray(s,[this.numFrames,this.columnTruncateLength,1])}if(this.includeWaveform){let s=this.flattenQueue(n.timeDataQueue);t=this.getTensorFromAudioDataArray(s,[this.numFrames*this.fftSize,1])}return{value:{spectrogram:e,waveform:t},done:!1}}async capture(){return(await this.next()).value}async getAudioData(){let e=[],t=[],n=0;return new Promise(s=>{let r=setInterval(()=>{this.includeSpectrogram&&(this.analyser.getFloatFrequencyData(this.freqData),this.freqData[0]===-1/0&&s({freqDataQueue:e,timeDataQueue:t}),e.push(this.freqData.slice(0,this.columnTruncateLength))),this.includeWaveform&&(this.analyser.getFloatTimeDomainData(this.timeData),t.push(this.timeData.slice())),++n===this.numFrames&&(clearInterval(r),s({freqDataQueue:e,timeDataQueue:t}))},this.fftSize/this.sampleRateHz*1e3)})}stop(){this.isClosed||(this.isClosed=!0,this.analyser.disconnect(),this.audioContext.close(),this.stream!=null&&this.stream.getTracks().length>0&&this.stream.getTracks()[0].stop())}toArray(){throw new Error("Can not convert infinite audio stream to array.")}getSampleRate(){return this.sampleRateHz}flattenQueue(e){let t=e[0].length,n=new Float32Array(e.length*t);return e.forEach((s,r)=>n.set(s,r*t)),n}getTensorFromAudioDataArray(e,t){let n=new Float32Array(v.sizeFromShape(t));return n.set(e,n.length-e.length),Gt(n,t)}},S7=class extends An{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=Kt([0],"int32"),this.webcamConfig.centerCrop){let n=this.webcamConfig.resizeWidth*1/this.webcamVideoElement.width,s=this.webcamConfig.resizeHeight*1/this.webcamVideoElement.height,r=(1-n)/2,a=(1-s)/2,o=r+n,i=s+a;this.cropBox=ur([a,r,i,o],[1,4])}else this.cropBox=ur([0,0,1,1],[1,4])}summary(){return"webcam"}static async create(e,t={}){if(K().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 S7(e,t);return await n.start(),n}async start(){this.webcamConfig.facingMode&&v.assert(this.webcamConfig.facingMode==="user"||this.webcamConfig.facingMode==="environment",()=>`Invalid webcam facing mode: ${this.webcamConfig.facingMode}. Please provide 'user' or 'environment'`);try{this.stream=await navigator.mediaDevices.getUserMedia({video:{deviceId:this.webcamConfig.deviceId,facingMode:this.webcamConfig.facingMode?this.webcamConfig.facingMode:"user",width:this.webcamVideoElement.width,height:this.webcamVideoElement.height}})}catch(e){throw e.message=`Error thrown while initializing video stream: ${e.message}`,e}if(!this.stream)throw new Error("Could not obtain video from webcam.");try{this.webcamVideoElement.srcObject=this.stream}catch(e){console.log(e),this.webcamVideoElement.src=window.URL.createObjectURL(this.stream)}return this.webcamVideoElement.play(),this.isClosed=!1,new Promise(e=>{this.webcamVideoElement.onloadedmetadata=()=>{e()}})}async next(){if(this.isClosed)return{value:null,done:!0};let e;try{e=Gs.fromPixels(this.webcamVideoElement)}catch(t){throw new Error(`Error thrown converting video to pixels: ${JSON.stringify(t)}`)}if(this.resize)try{return{value:this.cropAndResizeFrame(e),done:!1}}catch(t){throw new Error(`Error thrown cropping the video: ${t.message}`)}finally{e.dispose()}else return{value:e,done:!1}}needToResize(){return!!(this.webcamConfig.resizeWidth&&this.webcamConfig.resizeHeight&&(this.webcamVideoElement.width!==this.webcamConfig.resizeWidth||this.webcamVideoElement.height!==this.webcamConfig.resizeHeight))}cropAndResizeFrame(e){return q(()=>{let t=mn(pe(e,"float32"),0),n;n=$e.cropAndResize(t,this.cropBox,this.cropBoxInd,this.cropSize,"bilinear");let s=n.shape;return G(n,s.slice(1))})}async capture(){return(await this.next()).value}stop(){this.stream.getTracks().forEach(t=>t.stop());try{this.webcamVideoElement.srcObject=null}catch(t){console.log(t),this.webcamVideoElement.src=null}this.isClosed=!0}toArray(){throw new Error("Can not convert infinite video stream to array.")}},I7=class{},C7=class extends An{split(e){return new aG(this,e)}},aG=class extends C7{constructor(e,t){super();this.upstream=e,this.impl=new oG(e,t)}summary(){return this.impl.summary()}async next(){return this.impl.next()}},oG=class extends yy{constructor(e,t){super();this.upstream=e,this.separator=t,this.carryover=""}summary(){return`${this.upstream.summary()} -> Split('${this.separator}')`}async pump(){let e=await this.upstream.next();if(e.done)return this.carryover===""?!1:(this.outputQueue.push(this.carryover),this.carryover="",!0);let t=e.value.split(this.separator);t[0]=this.carryover+t[0];for(let n of t.slice(0,-1))this.outputQueue.push(n);return this.carryover=t[t.length-1],!0}},iG=class extends An{decodeUTF8(){return new lG(this)}},lG=class extends C7{constructor(e){super();this.upstream=e,this.impl=new uG(e)}summary(){return this.impl.summary()}async next(){return this.impl.next()}},uG=class extends yy{constructor(e){super();if(this.upstream=e,K().get("IS_BROWSER"))this.decoder=new TextDecoder("utf-8");else{let{StringDecoder:t}=t5();this.decoder=new t("utf8")}}summary(){return`${this.upstream.summary()} -> Utf8`}async pump(){let e=await this.upstream.next(),t;if(e.done)return!1;t=e.value;let n;return K().get("IS_BROWSER")?n=this.decoder.decode(t,{stream:!0}):n=this.decoder.write(Buffer.from(t.buffer)),this.outputQueue.push(n),!0}},T7=class extends iG{constructor(e,t={}){super();this.file=e,this.options=t,v.assert(e instanceof Uint8Array||(K().get("IS_BROWSER")?e instanceof File||e instanceof Blob:!1),()=>"FileChunkIterator only supports File, Blob and Uint8Array right now."),this.offset=t.offset||0,this.chunkSize=t.chunkSize||1024*1024}summary(){return`FileChunks ${this.file}`}async next(){return this.offset>=(this.file instanceof Uint8Array?this.file.byteLength:this.file.size)?{value:null,done:!0}:{value:await new Promise((t,n)=>{let s=this.offset+this.chunkSize;if(this.file instanceof Uint8Array)t(new Uint8Array(this.file.slice(this.offset,s)));else{let r=new FileReader;r.onload=o=>{let i=r.result;if(i instanceof ArrayBuffer&&(i=new Uint8Array(i)),!(i instanceof Uint8Array))return n(new TypeError("FileReader returned unknown type."));t(i)},r.onabort=o=>n(new Error("Aborted")),r.onerror=o=>n(new Error(o.type));let a=this.file.slice(this.offset,s);r.readAsArrayBuffer(a)}this.offset=s}),done:!1}}};async function cG(e,t={},n){let s,r;typeof e=="string"?s=e:(s=e.url,r=dG(e));let a=await(n||v.fetch)(s,r);if(a.ok){let o=new Uint8Array(await a.arrayBuffer());return new T7(o,t)}else throw new Error(a.statusText)}var dG=e=>({method:e.method,headers:e.headers,body:e.body,mode:e.mode,credentials:e.credentials,cache:e.cache,redirect:e.redirect,referrer:e.referrer,integrity:e.integrity});function N7(e){return typeof e=="string"&&e.substr(0,7)==="file://"}var E7=class extends I7{constructor(e,t={}){super();this.input=e,this.options=t}async iterator(){if(N7(this.input)&&K().get("IS_NODE")){let e=Ws("fs");this.input=e.readFileSync(this.input.substr(7))}return new T7(this.input,this.options)}},R7=class extends I7{constructor(e,t={}){super();this.url=e,this.fileOptions=t}async iterator(){return N7(this.url)?new E7(this.url,this.fileOptions).iterator():cG(this.url,this.fileOptions)}};function pG(e,t={}){return new w7(new R7(e),t)}function hG(e){let t=Ay(e);return gs(async()=>t)}function fG(e){return gs(async()=>{let t=await e();return Ay(()=>t.next())})}async function mG(e,t){return S7.create(e,t)}async function gG(e){return k7.create(e)}var AG="0.0.0";function Ne(e,t){Array.isArray(e)||(e=[e]),e.forEach(n=>{n!=null&&v.assert(n.dtype!=="complex64",()=>`${t} does not support complex64 tensors in the CPU backend.`)})}var yG=Xs.whereImpl,$7=class extends Ll{constructor(){super();this.blockSize=48,this.firstUse=!0,this.data=new Pc(this,ts())}nextDataId(){return $7.nextDataId++}write(e,t,n){this.firstUse&&(this.firstUse=!1,K().get("IS_NODE")&&N.warn(`
============================
Hi there \u{1F44B}. Looks like you are running TensorFlow.js in Node.js. To speed things up dramatically, install our node backend, which binds to TensorFlow C++, by running npm i @tensorflow/tfjs-node, or npm i @tensorflow/tfjs-node-gpu if you have CUDA. Then call require('@tensorflow/tfjs-node'); (-gpu suffix for CUDA) at the start of your program. Visit https://github.com/tensorflow/tfjs-node for more details.
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n.makeTensorInfo(r.shape,r.dtype,m)}var cj={kernelName:za,backendName:"cpu",kernelFunc:uj};function dj(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{blockShape:a,crops:o}=s;Ne([r],"batchToSpaceND");let i=a.reduce((A,x)=>A*x),l=N.getReshaped(r.shape,a,i),c=N.getPermuted(l.length,a.length),u=N.getReshapedPermuted(r.shape,a,i),d=N.getSliceBeginCoords(o,a.length),p=N.getSliceSize(u,o,a.length),h=$t({inputs:{x:r},backend:n,attrs:{shape:l}}),f=Ds({inputs:{x:h},backend:n,attrs:{perm:c}}),m=$t({inputs:{x:f},backend:n,attrs:{shape:u}}),g=pl({inputs:{x:m},backend:n,attrs:{begin:d,size:p}});return n.disposeIntermediateTensorInfo(h),n.disposeIntermediateTensorInfo(f),n.disposeIntermediateTensorInfo(m),g}var pj={kernelName:ni,backendName:"cpu",kernelFunc:dj};function hj(e){let{inputs:t,backend:n,attrs:s}=e,{x:r,weights:a}=t,{size:o}=s,i=n.data.get(r.dataId).values,l=n.data.get(a.dataId).values,c=wy(i,l,a.dtype,a.shape,o);return n.makeTensorInfo([o],a.dtype,c)}var fj={kernelName:rh,backendName:"cpu",kernelFunc:hj};function mj(e){let{inputs:t,backend:n}=e,{s0:s,s1:r}=t,a=n.data.get(s.dataId).values,o=n.data.get(r.dataId).values,i=N.assertAndGetBroadcastShape(Array.from(a),Array.from(o));return n.makeTensorInfo([i.length],"int32",Int32Array.from(i))}var gj={kernelName:ah,backendName:"cpu",kernelFunc:mj},Aj=mt(Ur,(e,t)=>{let n=t;return e>n.clipValueMax?n.clipValueMax:e<n.clipValueMin?n.clipValueMin:e}),yj={kernelName:Ur,backendName:"cpu",kernelFunc:Aj},xj=e=>{let{x:t}=e.inputs,n=e.backend,s=new Float32Array(v.sizeFromShape(t.shape)),r=n.data.get(t.dataId),a=r.complexTensorInfos.real,o=r.complexTensorInfos.imag,i=n.data.get(a.dataId).values,l=n.data.get(o.dataId).values;for(let c=0;c<i.length;c++){let u=i[c],d=l[c];s[c]=Math.hypot(u,d)}return n.makeOutput(s,t.shape,"float32")},bj={kernelName:Lc,backendName:"cpu",kernelFunc:xj};function 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m.forEach(b=>n.disposeIntermediateTensorInfo(b)),g.forEach(b=>n.disposeIntermediateTensorInfo(b)),n.disposeIntermediateTensorInfo(A),n.disposeIntermediateTensorInfo(x),y}let c=i.map(m=>{let g=v.sizeFromShape(m.shape.slice(a));return $t({inputs:{x:m},backend:n,attrs:{shape:[-1,g]}})}),u=c.map(m=>({vals:n.data.get(m.dataId).values,shape:m.shape}));o=N.computeOutShape(c.map(m=>m.shape),1);let d=c[0].shape[0]===1,p=ky(u,o,t[0].dtype,d),h=N.computeOutShape(i.map(m=>m.shape),a),f=n.makeTensorInfo(h,t[0].dtype,p);return c.forEach(m=>n.disposeIntermediateTensorInfo(m)),f}var wj={kernelName:si,backendName:"cpu",kernelFunc:Vu};function SS(e){let{inputs:t,backend:n,attrs:s}=e,{x:r,filter:a}=t,{strides:o,pad:i,dataFormat:l,dilations:c,dimRoundingMode:u}=s;Ne([r,a],"conv2d");let d=N.convertConv2DDataFormat(l),p=N.computeConv2DInfo(r.shape,a.shape,o,c,i,u,!1,d),h=p.filterHeight,f=p.filterWidth,m=p.dilationHeight,g=p.dilationWidth,A=p.padInfo.left,x=p.padInfo.top,y=p.dataFormat==="channelsLast",b=new nn(p.outShape,r.dtype),w=v.computeStrides(r.shape),k=v.computeStrides(a.shape),C=w[0],E=y?w[1]:w[2],R=y?w[2]:1,F=y?1:w[1],D=b.strides[0],P=y?b.strides[1]:b.strides[2],T=y?b.strides[2]:1,M=y?1:b.strides[1],U=n.data.get(r.dataId).values,j=n.data.get(a.dataId).values,z=b.values;for(let X=0;X<p.batchSize;++X){let Z=X*C,J=X*D;for(let ee=0;ee<p.outHeight;++ee){let ne=J+ee*P,Q=ee*p.strideHeight-x;for(let te=0;te<h;++te){let oe=Q+te*m;if(oe<0||oe>=p.inHeight)continue;let he=te*k[0],be=Z+oe*E;for(let we=0;we<p.outWidth;++we){let Ce=ne+we*T,ze=we*p.strideWidth-A;for(let Ue=0;Ue<f;++Ue){let Xe=ze+Ue*g;if(Xe<0||Xe>=p.inWidth)continue;let Ye=he+Ue*k[1],pt=be+Xe*R,ht=Ye;for(let it=0;it<p.inChannels;++it){let St=U[pt+it*F];for(let gt=0;gt<p.outChannels;++gt)z[Ce+gt*M]+=St*j[ht+gt];ht+=p.outChannels}}}}}}return n.makeTensorInfo(b.shape,b.dtype,z)}var kj={kernelName:Na,backendName:"cpu",kernelFunc:SS};function Sj(e){let{inputs:t,backend:n,attrs:s}=e,{x:r,dy:a}=t,{strides:o,pad:i,dataFormat:l,dimRoundingMode:c,filterShape:u}=s;Ne([r,a],"conv2dBackpropFilter");let d=N.convertConv2DDataFormat(l),p=N.computeConv2DInfo(r.shape,u,o,1,i,c,!1,d),{strideHeight:h,strideWidth:f,filterHeight:m,filterWidth:g}=p,A=p.dataFormat==="channelsLast",x=new nn(p.filterShape,"float32"),y=p.padInfo.left,b=p.padInfo.top,w=n.data.get(r.dataId).values,k=n.data.get(a.dataId).values,C=new nn(r.shape,r.dtype,w),E=new nn(a.shape,a.dtype,k);for(let R=0;R<m;++R){let F=Math.max(0,Math.ceil((b-R)/h)),D=Math.min(p.outHeight,(p.inHeight+b-R)/h);for(let P=0;P<g;++P){let T=Math.max(0,Math.ceil((y-P)/f)),M=Math.min(p.outWidth,(p.inWidth+y-P)/f);for(let U=0;U<p.inChannels;++U)for(let j=0;j<p.outChannels;++j){let z=0;for(let X=0;X<p.batchSize;++X)for(let Z=F;Z<D;++Z){let J=R+Z*h-b;for(let ee=T;ee<M;++ee){let ne=P+ee*f-y;A?z+=C.get(X,J,ne,U)*E.get(X,Z,ee,j):z+=C.get(X,U,J,ne)*E.get(X,j,Z,ee)}}x.set(z,R,P,U,j)}}}return n.makeTensorInfo(x.shape,x.dtype,x.values)}var Ij={kernelName:oh,backendName:"cpu",kernelFunc:Sj};function Cj(e){let{inputs:t,backend:n,attrs:s}=e,{dy:r,filter:a}=t,{inputShape:o,strides:i,pad:l,dataFormat:c,dimRoundingMode:u}=s;Ne([r,a],"conv2dBackpropInput");let d=v.computeStrides(a.shape),p=v.computeStrides(r.shape),h=N.convertConv2DDataFormat(c),f=N.computeConv2DInfo(o,a.shape,i,1,l,u,!1,h),m=new nn(f.inShape,"float32"),g=m.values,A=n.data.get(r.dataId).values,x=n.data.get(a.dataId).values,[y,b,w]=d,{batchSize:k,filterHeight:C,filterWidth:E,inChannels:R,inHeight:F,inWidth:D,outChannels:P,outHeight:T,outWidth:M,strideHeight:U,strideWidth:j}=f;h=f.dataFormat;let z=C-1-f.padInfo.top,X=E-1-f.padInfo.left,Z=h==="channelsLast",J=m.strides[0],ee=Z?m.strides[1]:m.strides[2],ne=Z?m.strides[2]:1,Q=Z?1:m.strides[1],te=p[0],oe=Z?p[1]:p[2],he=Z?p[2]:1,be=Z?1:p[1];for(let we=0;we<k;++we)for(let Ce=0;Ce<R;++Ce)for(let ze=0;ze<F;++ze){let 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D=F*E[0],P=F*b.strides[0];for(let T=0;T<c.outDepth;++T){let M=P+T*b.strides[1],U=T*c.strideDepth-A;for(let j=0;j<u;++j){let z=U+j*h;if(z<0||z>=c.inDepth)continue;let X=j*R[0],Z=D+z*E[1];for(let J=0;J<c.outHeight;++J){let ee=M+J*b.strides[2],ne=J*c.strideHeight-y;for(let Q=0;Q<d;++Q){let te=ne+Q*f;if(te<0||te>=c.inHeight)continue;let oe=X+Q*R[1],he=Z+te*E[2];for(let be=0;be<c.outWidth;++be){let we=ee+be*c.outChannels,Ce=be*c.strideWidth-x;for(let ze=0;ze<p;++ze){let Ue=Ce+ze*m;if(Ue<0||Ue>=c.inWidth)continue;let Xe=oe+ze*R[2],Ye=he+Ue*c.inChannels,pt=Xe;for(let ht=0;ht<c.inChannels;++ht){let it=w[Ye+ht];for(let St=0;St<c.outChannels;++St)C[we+St]+=it*k[pt+St];pt+=c.outChannels}}}}}}}}return n.makeTensorInfo(b.shape,b.dtype,b.values)}var Ej={kernelName:Bc,backendName:"cpu",kernelFunc:Nj};function Rj(e){let{inputs:t,backend:n,attrs:s}=e,{x:r,dy:a}=t,{strides:o,pad:i,filterShape:l}=s;Ne([r,a],"conv3dBackpropFilterV2");let c=v.computeStrides(r.shape),u=v.computeStrides(a.shape),d=N.computeConv3DInfo(r.shape,l,o,1,i),p=d.strideDepth,h=d.strideHeight,f=d.strideWidth,m=d.filterDepth,g=d.filterHeight,A=d.filterWidth,x=new nn(d.filterShape,"float32"),y=x.values,[b,w,k,C]=x.strides,E=n.data.get(a.dataId).values,[R,F,D,P]=u,T=n.data.get(r.dataId).values,[M,U,j,z]=c,X=d.padInfo.front,Z=d.padInfo.left,J=d.padInfo.top;for(let ee=0;ee<m;++ee){let ne=Math.max(0,Math.ceil((X-ee)/p)),Q=Math.min(d.outDepth,(d.inDepth+X-ee)/p),te=ee*b;for(let oe=0;oe<g;++oe){let he=Math.max(0,Math.ceil((J-oe)/h)),be=Math.min(d.outHeight,(d.inHeight+J-oe)/h),we=oe*w+te;for(let Ce=0;Ce<A;++Ce){let ze=Math.max(0,Math.ceil((Z-Ce)/f)),Ue=Math.min(d.outWidth,(d.inWidth+Z-Ce)/f),Xe=Ce*k+we;for(let Ye=0;Ye<d.inChannels;++Ye){let pt=Ye*C+Xe;for(let ht=0;ht<d.outChannels;++ht){let it=0;for(let St=0;St<d.batchSize;++St){let gt=St*M,Tt=St*R;for(let _t=ne;_t<Q;++_t){let bn=(ee+_t*p-X)*U+gt,nr=_t*F+Tt;for(let Fn=he;Fn<be;++Fn){let Ls=(oe+Fn*h-J)*j+bn,Ss=Fn*D+nr;for(let vn=ze;vn<Ue;++vn){let En=(Ce+vn*f-Z)*z+Ls,br=vn*P+Ss;it+=T[En+Ye]*E[br+ht]}}}}y[pt+ht]=it}}}}}return n.makeTensorInfo(x.shape,x.dtype,x.values)}var $j={kernelName:ih,backendName:"cpu",kernelFunc:Rj};function Dj(e){let{inputs:t,backend:n,attrs:s}=e,{dy:r,filter:a}=t,{pad:o,strides:i,inputShape:l}=s;Ne([r],"conv3dBackpropInputV2");let c=v.computeStrides(r.shape),u=v.computeStrides(a.shape),d=N.computeConv3DInfo(l,a.shape,i,1,o),p=new nn(d.inShape,"float32"),h=p.values,[f,m,g,A]=p.strides,x=n.data.get(r.dataId).values,[y,b,w,k]=c,C=n.data.get(a.dataId).values,[E,R,F,D]=u,{batchSize:P,filterDepth:T,filterHeight:M,filterWidth:U,inChannels:j,inDepth:z,inHeight:X,inWidth:Z,outChannels:J,outDepth:ee,outHeight:ne,outWidth:Q,strideDepth:te,strideHeight:oe,strideWidth:he}=d,be=T-1-d.padInfo.front,we=M-1-d.padInfo.top,Ce=U-1-d.padInfo.left;for(let ze=0;ze<P;++ze)for(let Ue=0;Ue<j;++Ue)for(let Xe=0;Xe<z;++Xe){let 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zj(e){let{inputs:t,backend:n,attrs:s}=e,{image:r,boxes:a,boxInd:o}=t,{cropSize:i,method:l,extrapolationValue:c}=s,[u,d,p,h]=r.shape,f=a.shape[0],[m,g]=i,A=Be([f,m,g,h],"float32"),x=n.data.get(a.dataId).values,y=n.data.get(o.dataId).values,b=n.data.get(r.dataId).values,w=v.computeStrides(r.shape),k=v.computeStrides(A.shape);for(let C=0;C<f;C++){let E=C*4,R=x[E],F=x[E+1],D=x[E+2],P=x[E+3],T=y[C];if(T>=u)continue;let M=m>1?(D-R)*(d-1)/(m-1):0,U=g>1?(P-F)*(p-1)/(g-1):0;for(let j=0;j<m;j++){let z=m>1?R*(d-1)+j*M:.5*(R+D)*(d-1);if(z<0||z>d-1){for(let X=0;X<g;X++)for(let Z=0;Z<h;Z++){let J=Z+X*k[2]+j*k[1]+C*k[0];A.values[J]=c}continue}if(l==="bilinear"){let X=Math.floor(z),Z=Math.ceil(z),J=z-X;for(let ee=0;ee<g;ee++){let ne=g>1?F*(p-1)+ee*U:.5*(F+P)*(p-1);if(ne<0||ne>p-1){for(let he=0;he<h;he++){let be=he+ee*k[2]+j*k[1]+C*k[0];A.values[be]=c}continue}let Q=Math.floor(ne),te=Math.ceil(ne),oe=ne-Q;for(let he=0;he<h;he++){let be=he+Q*w[2]+X*w[1]+T*w[0],we=b[be];be=he+te*w[2]+X*w[1]+T*w[0];let Ce=b[be];be=he+Q*w[2]+Z*w[1]+T*w[0];let ze=b[be];be=he+te*w[2]+Z*w[1]+T*w[0];let Ue=b[be],Xe=we+(Ce-we)*oe,Ye=ze+(Ue-ze)*oe;be=he+ee*k[2]+j*k[1]+C*k[0],A.values[be]=Xe+(Ye-Xe)*J}}}else for(let X=0;X<g;++X){let Z=g>1?F*(p-1)+X*U:.5*(F+P)*(p-1);if(Z<0||Z>p-1){for(let ne=0;ne<h;ne++){let Q=ne+X*k[2]+j*k[1]+C*k[0];A.values[Q]=c}continue}let J=Math.round(Z),ee=Math.round(z);for(let ne=0;ne<h;ne++){let Q=ne+J*w[2]+ee*w[1]+T*w[0],te=ne+X*k[2]+j*k[1]+C*k[0];A.values[te]=b[Q]}}}}return n.makeTensorInfo(A.shape,A.dtype,A.values)}var Lj={kernelName:ai,backendName:"cpu",kernelFunc:zj};function Bj(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{axis:a,exclusive:o,reverse:i}=s;Ne(r,"cumsum");let l=N.getAxesPermutation([a],r.shape.length),c=r;l!=null&&(c=Ds({inputs:{x:r},backend:n,attrs:{perm:l}}));let u=N.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 d=Ln(c.dtype,"int32"),p=v.makeZerosTypedArray(v.sizeFromShape(c.shape),d),h=n.data.get(c.dataId).values,f=c.shape[c.shape.length-1],m=i?(A,x)=>A+f-x-1:(A,x)=>A+x;for(let A=0;A<h.length;A+=f)for(let x=0;x<f;x++){let y=m(A,x);if(x===0)p[y]=o?0:h[y];else{let b=m(A,x-1);p[y]=o?h[b]+p[b]:h[y]+p[b]}}let g=n.makeTensorInfo(c.shape,d,p);if(l!=null){let A=N.getUndoAxesPermutation(l),x=Ds({inputs:{x:g},backend:n,attrs:{perm:A}});return n.disposeIntermediateTensorInfo(g),n.disposeIntermediateTensorInfo(c),x}return g}var Wj={kernelName:ri,backendName:"cpu",kernelFunc:Bj};function Vj(e){let{inputs:t,backend:n,attrs:s}=e,{x:r,weights:a}=t,{size:o,binaryOutput:i}=s;if(r.shape.length===1){let l=n.data.get(r.dataId).values,c=n.data.get(a.dataId).values,u=wy(l,c,a.dtype,a.shape,o);return n.makeTensorInfo([o],a.dtype,u)}else if(r.shape.length===2){let l=n.bufferSync(r),c=n.bufferSync(a),u=P7(l,c,o,i);return n.makeTensorInfo(u.shape,a.dtype,u.values)}throw new Error(`Error in denseBincount: input must be 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qj(e){let{inputs:t,backend:n,attrs:s}=e,{x:r,dy:a}=t,{strides:o,dilations:i,pad:l,dimRoundingMode:c,filterShape:u}=s;Ne([r,a],"depthwiseConv2dNativeBackpropFilter");let d=N.computeConv2DInfo(r.shape,u,o,i,l,c,!0),{strideHeight:p,strideWidth:h,filterHeight:f,filterWidth:m}=d,g=new nn(d.filterShape,"float32"),A=d.padInfo.left,x=d.padInfo.top,y=d.outChannels/d.inChannels,b=n.data.get(r.dataId).values,w=new nn(r.shape,r.dtype,b),k=n.data.get(a.dataId).values,C=new nn(a.shape,a.dtype,k);for(let E=0;E<f;++E){let R=Math.max(0,Math.ceil((x-E)/p)),F=Math.min(d.outHeight,(d.inHeight+x-E)/p);for(let D=0;D<m;++D){let P=Math.max(0,Math.ceil((A-D)/h)),T=Math.min(d.outWidth,(d.inWidth+A-D)/h);for(let M=0;M<d.outChannels;++M){let U=Math.trunc(M/y),j=M%y,z=0;for(let X=0;X<d.batchSize;++X)for(let Z=R;Z<F;++Z){let J=E+Z*p-x;for(let ee=P;ee<T;++ee){let ne=D+ee*h-A;z+=w.get(X,J,ne,U)*C.get(X,Z,ee,M)}}g.set(z,E,D,U,j)}}}return n.makeTensorInfo(g.shape,g.dtype,g.values)}var Xj={kernelName:ch,backendName:"cpu",kernelFunc:qj};function Kj(e){let{inputs:t,backend:n,attrs:s}=e,{dy:r,filter:a}=t,{strides:o,dilations:i,pad:l,dimRoundingMode:c,inputShape:u}=s;Ne([r,a],"depthwiseConv2DNativeBackpropInput");let d=v.computeStrides(r.shape),p=v.computeStrides(a.shape),h=N.computeConv2DInfo(u,a.shape,o,i,l,c,!0),f=new nn(h.inShape,"float32"),m=f.values,[g,A,x]=f.strides,y=n.data.get(r.dataId).values,[b,w,k]=d,C=n.data.get(a.dataId).values,[E,R,F]=p,{batchSize:D,filterHeight:P,filterWidth:T,inChannels:M,inHeight:U,inWidth:j,outChannels:z,outHeight:X,outWidth:Z,strideHeight:J,strideWidth:ee}=h,ne=P-1-h.padInfo.top,Q=T-1-h.padInfo.left,te=z/M;for(let oe=0;oe<D;++oe)for(let he=0;he<M;++he)for(let be=0;be<U;++be){let we=be-ne,Ce=Math.max(0,Math.ceil(we/J)),ze=Math.min(X,(P+we)/J);for(let Ue=0;Ue<j;++Ue){let Xe=Ue-Q,Ye=Math.max(0,Math.ceil(Xe/ee)),pt=Math.min(Z,(T+Xe)/ee),ht=0;for(let it=Ce;it<ze;++it){let St=it*J-we;for(let gt=Ye;gt<pt;++gt){let 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tI(e){if(bm==null){let t=Fr(e);bm=t.getParameter(t.MAX_TEXTURE_IMAGE_UNITS)}return Math.min(16,bm)}function nI(e){if(e===0)return 0;let t,n=Fr(e);return Ps(n,"EXT_disjoint_timer_query_webgl2")&&e===2?t=2:Ps(n,"EXT_disjoint_timer_query")?t=1:t=0,t}function Ps(e,t){return e.getExtension(t)!=null}function Ly(e){try{if(Fr(e)!=null)return!0}catch(t){return console.log("Error when getting WebGL context: ",t),!1}return!1}function sI(e){if(e===0)return!1;let t=Fr(e);if(e===1){if(!Ps(t,"OES_texture_float"))return!1}else if(!Ps(t,"EXT_color_buffer_float"))return!1;return By(t)}function rI(e){if(e===0)return!1;let t=Fr(e);if(e===1){if(!Ps(t,"OES_texture_float")||!Ps(t,"WEBGL_color_buffer_float"))return!1}else{if(Ps(t,"EXT_color_buffer_float"))return By(t);let s="EXT_color_buffer_half_float";if(Ps(t,s)){let r=t.getExtension(s);return OZ(t,r)}return!1}return By(t)}function By(e){let t=Oy(e),n=e.createTexture();e.bindTexture(e.TEXTURE_2D,n);let s=1,r=1;e.texImage2D(e.TEXTURE_2D,0,t.internalFormatFloat,s,r,0,t.textureFormatFloat,t.textureTypeFloat,null);let a=e.createFramebuffer();e.bindFramebuffer(e.FRAMEBUFFER,a),e.framebufferTexture2D(e.FRAMEBUFFER,e.COLOR_ATTACHMENT0,e.TEXTURE_2D,n,0);let o=e.checkFramebufferStatus(e.FRAMEBUFFER)===e.FRAMEBUFFER_COMPLETE;return e.bindTexture(e.TEXTURE_2D,null),e.bindFramebuffer(e.FRAMEBUFFER,null),e.deleteTexture(n),e.deleteFramebuffer(a),o}function OZ(e,t){let n=Oy(e,t),s=e.createTexture();e.bindTexture(e.TEXTURE_2D,s);let r=1,a=1;e.texImage2D(e.TEXTURE_2D,0,n.internalFormatHalfFloat,r,a,0,n.textureFormatFloat,n.textureTypeHalfFloat,null);let o=e.createFramebuffer();e.bindFramebuffer(e.FRAMEBUFFER,o),e.framebufferTexture2D(e.FRAMEBUFFER,e.COLOR_ATTACHMENT0,e.TEXTURE_2D,s,0);let i=e.checkFramebufferStatus(e.FRAMEBUFFER)===e.FRAMEBUFFER_COMPLETE;return e.bindTexture(e.TEXTURE_2D,null),e.bindFramebuffer(e.FRAMEBUFFER,null),e.deleteTexture(s),e.deleteFramebuffer(o),i}function aI(e){return e!==2?!1:Fr(e).fenceSync!=null}function Gu(e,t){Array.isArray(e)||(e=[e]),e.forEach(n=>{n!=null&&v.assert(n.dtype!=="complex64",()=>`${t} does not support complex64 tensors in the WebGL backend.`)})}var De=K();De.registerFlag("HAS_WEBGL",()=>De.getNumber("WEBGL_VERSION")>0);De.registerFlag("WEBGL_VERSION",()=>Ly(2)?2:Ly(1)?1:0);De.registerFlag("WEBGL_CHECK_NUMERICAL_PROBLEMS",()=>!1);De.registerFlag("WEBGL_BUFFER_SUPPORTED",()=>De.get("WEBGL_VERSION")===2);De.registerFlag("WEBGL_CPU_FORWARD",()=>!0);De.registerFlag("WEBGL_FORCE_F16_TEXTURES",()=>!1);De.registerFlag("WEBGL_PACK",()=>De.getBool("HAS_WEBGL"));De.registerFlag("WEBGL_PACK_NORMALIZATION",()=>De.getBool("WEBGL_PACK"));De.registerFlag("WEBGL_PACK_CLIP",()=>De.getBool("WEBGL_PACK"));De.registerFlag("WEBGL_PACK_DEPTHWISECONV",()=>De.getBool("WEBGL_PACK"));De.registerFlag("WEBGL_PACK_BINARY_OPERATIONS",()=>De.getBool("WEBGL_PACK"));De.registerFlag("WEBGL_PACK_UNARY_OPERATIONS",()=>De.getBool("WEBGL_PACK"));De.registerFlag("WEBGL_PACK_ARRAY_OPERATIONS",()=>De.getBool("WEBGL_PACK"));De.registerFlag("WEBGL_PACK_IMAGE_OPERATIONS",()=>De.getBool("WEBGL_PACK"));De.registerFlag("WEBGL_PACK_REDUCE",()=>De.getBool("WEBGL_PACK"));De.registerFlag("WEBGL_LAZILY_UNPACK",()=>De.getBool("WEBGL_PACK"));De.registerFlag("WEBGL_CONV_IM2COL",()=>De.getBool("WEBGL_PACK"));De.registerFlag("WEBGL_MAX_TEXTURE_SIZE",()=>eI(De.getNumber("WEBGL_VERSION")));De.registerFlag("WEBGL_MAX_TEXTURES_IN_SHADER",()=>tI(De.getNumber("WEBGL_VERSION")));De.registerFlag("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION",()=>{let e=De.getNumber("WEBGL_VERSION");return e===0?0:nI(e)});De.registerFlag("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE",()=>De.getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION")>0&&!gu.isMobile());De.registerFlag("WEBGL_RENDER_FLOAT32_CAPABLE",()=>sI(De.getNumber("WEBGL_VERSION")));De.registerFlag("WEBGL_RENDER_FLOAT32_ENABLED",()=>De.getBool("WEBGL_FORCE_F16_TEXTURES")?!1:De.getBool("WEBGL_RENDER_FLOAT32_CAPABLE"));De.registerFlag("WEBGL_DOWNLOAD_FLOAT_ENABLED",()=>rI(De.getNumber("WEBGL_VERSION")));De.registerFlag("WEBGL_FENCE_API_ENABLED",()=>aI(De.getNumber("WEBGL_VERSION")));De.registerFlag("WEBGL_SIZE_UPLOAD_UNIFORM",()=>De.getBool("WEBGL_RENDER_FLOAT32_ENABLED")?4:0);De.registerFlag("WEBGL_DELETE_TEXTURE_THRESHOLD",()=>-1,e=>{if(e<0&&e!==-1)throw new Error(`WEBGL_DELETE_TEXTURE_THRESHOLD must be -1 (indicating never delete) or at least 0, but got ${e}.`)});De.registerFlag("WEBGL_FLUSH_THRESHOLD",()=>gu.isMobile()?1:-1,e=>{if(e<0&&e!==-1)throw new Error(`WEBGL_FLUSH_THRESHOLD must be -1 (indicating never manual flush) or at least 0, but got ${e}.`)});De.registerFlag("CPU_HANDOFF_SIZE_THRESHOLD",()=>128);De.registerFlag("WEBGL_USE_SHAPES_UNIFORMS",()=>!1);De.registerFlag("TOPK_LAST_DIM_CPU_HANDOFF_SIZE_THRESHOLD",()=>1e5);De.registerFlag("TOPK_K_CPU_HANDOFF_THRESHOLD",()=>128);function Un(){let e,t,n,s,r,a,o,i,l,c;return K().getNumber("WEBGL_VERSION")===2?(e="#version 300 es",t="in",n="out",s="in",r="texture",a="outputColor",o="out vec4 outputColor;",i=`
bool isnan_custom(float val) {
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",s="varying",r="texture2D",a="gl_FragColor",o="",i=`
#define isnan(value) isnan_custom(value)
bool isnan_custom(float val) {
return (val > 0. || val < 1. || val == 0.) ? false : true;
}
bvec4 isnan_custom(vec4 val) {
return bvec4(isnan(val.x), isnan(val.y), isnan(val.z), isnan(val.w));
}
`,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:s,texture2D:r,output:a,defineOutput:o,defineSpecialNaN:i,defineSpecialInf:l,defineRound:c}}function gl(e,t,n="index"){let s=v.computeStrides(t);return s.map((r,a)=>{let o=`int ${e[a]} = ${n} / ${r}`,i=a===s.length-1?`int ${e[a+1]} = ${n} - ${e[a]} * ${r}`:`index -= ${e[a]} * ${r}`;return`${o}; ${i};`}).join("")}function vm(e,t,n="index"){let s=v.computeStrides(t);return s.map((r,a)=>{let o=`int ${e[a]} = ${n} / outShapeStrides[${a}]`,i=a===s.length-1?`int ${e[a+1]} = ${n} - ${e[a]} * outShapeStrides[${a}]`:`index -= ${e[a]} * outShapeStrides[${a}]`;return`${o}; ${i};`}).join("")}function MZ(e,t){let n=e.length,s=e.map(a=>`${t}[${a}]`),r=new Array(n-1);r[n-2]=s[n-1];for(let a=n-3;a>=0;--a)r[a]=`(${r[a+1]} * ${s[a+1]})`;return r}function zZ(e,t,n="index"){let s=e.map((a,o)=>o),r=MZ(s,t);return r.map((a,o)=>{let i=`int ${e[o]} = ${n} / ${r[o]}`,l=o===r.length-1?`int ${e[o+1]} = ${n} - ${e[o]} * ${r[o]}`:`index -= ${e[o]} * ${r[o]}`;return`${i}; ${l};`}).join("")}function Wy(e){let t=v.computeStrides(e).map(n=>n.toString());return`
int getFlatIndex(ivec3 coords) {
return coords.x * ${t[0]} + coords.y * ${t[1]} + coords.z;
}
`}function Vy(){return`
int getFlatIndex(ivec3 coords) {
return coords.x * outShapeStrides[0] + coords.y * outShapeStrides[1] + coords.z;
}
`}var oI=`
const float FLOAT_MAX = 1.70141184e38;
const float FLOAT_MIN = 1.17549435e-38;
lowp vec4 encode_float(highp float v) {
if (isnan(v)) {
return vec4(255, 255, 255, 255);
}
highp float av = abs(v);
if(av < FLOAT_MIN) {
return vec4(0.0, 0.0, 0.0, 0.0);
} else if(v > FLOAT_MAX) {
return vec4(0.0, 0.0, 128.0, 127.0) / 255.0;
} else if(v < -FLOAT_MAX) {
return vec4(0.0, 0.0, 128.0, 255.0) / 255.0;
}
highp vec4 c = vec4(0,0,0,0);
highp float e = floor(log2(av));
highp float m = exp2(fract(log2(av))) - 1.0;
c[2] = floor(128.0 * m);
m -= c[2] / 128.0;
c[1] = floor(32768.0 * m);
m -= c[1] / 32768.0;
c[0] = floor(8388608.0 * m);
highp float ebias = e + 127.0;
c[3] = floor(ebias / 2.0);
ebias -= c[3] * 2.0;
c[2] += floor(ebias) * 128.0;
c[3] += 128.0 * step(0.0, -v);
return c / 255.0;
}
`,{getBroadcastDims:iI}=N;function LZ(e,t,n){let s=[];if(e.forEach(h=>{let f=v.sizeFromShape(h.shapeInfo.logicalShape);if(h.shapeInfo.isUniform?s.push(`uniform float ${h.name}${f>1?`[${f}]`:""};`):(s.push(`uniform sampler2D ${h.name};`),s.push(`uniform int offset${h.name};`)),n.enableShapeUniforms){let{uniformShape:m}=Uy(n.packedInputs,h.shapeInfo.logicalShape,h.shapeInfo.texShape);switch(m.length){case 1:s.push(`uniform int ${h.name}Shape;`);break;case 2:s.push(`uniform ivec2 ${h.name}Shape;`);break;case 3:s.push(`uniform ivec3 ${h.name}Shape;`);break;case 4:s.push(`uniform ivec4 ${h.name}Shape;`);break;default:break}s.push(`uniform ivec2 ${h.name}TexShape;`)}}),n.enableShapeUniforms){switch(t.logicalShape.length){case 1:s.push("uniform int outShape;");break;case 2:s.push("uniform ivec2 outShape;"),s.push("uniform int outShapeStrides;");break;case 3:s.push("uniform ivec3 outShape;"),s.push("uniform ivec2 outShapeStrides;");break;case 4:s.push("uniform ivec4 outShape;"),s.push("uniform ivec3 outShapeStrides;");break;default:break}s.push("uniform ivec2 outTexShape;")}n.customUniforms&&n.customUniforms.forEach(h=>{s.push(`uniform ${h.type} ${h.name}${h.arrayIndex?`[${h.arrayIndex}]`:""};`)});let r=s.join(`
`),a=e.map(h=>BZ(h,t,n.packedInputs,n.enableShapeUniforms)).join(`
`),o=t.texShape,i=Un(),l=UZ(i),c,u,d=jZ(i);return t.isPacked?(c=WZ(t.logicalShape,o,n.enableShapeUniforms),u=HZ(i)):(c=VZ(t.logicalShape,o,n.enableShapeUniforms),u=GZ(i)),n.packedInputs&&(d+=ZZ),[d,l,u,r,c,a,n.userCode].join(`
`)}function Hu(e,t=!1){let n=e.shapeInfo.logicalShape;switch(n.length){case 0:return lY(e,t);case 1:return cY(e,t);case 2:return pY(e,t);case 3:return fY(e,t);case 4:return gY(e,t);case 5:return AY(e);case 6:return yY(e);default:throw new Error(`${n.length}-D input sampling is not yet supported`)}}function lI(e,t){switch(e.shapeInfo.logicalShape.length){case 0:return iY(e);case 1:return uY(e,t);case 2:return dY(e,t);case 3:return hY(e,t);default:return mY(e,t)}}function BZ(e,t,n=!1,s){let r="";n?r+=lI(e,s):r+=Hu(e,s);let a=e.shapeInfo.logicalShape,o=t.logicalShape;return a.length<=o.length&&(n?r+=xY(e,t):r+=bY(e,t)),r}function WZ(e,t,n){switch(e.length){case 0:return uI();case 1:return YZ(e,t,n);case 2:return aY(e,t,n);case 3:return QZ(e,t,n);default:return tY(e,t,n)}}function VZ(e,t,n){switch(e.length){case 0:return uI();case 1:return JZ(e,t,n);case 2:return oY(e,t,n);case 3:return eY(e,t,n);case 4:return nY(e,t,n);case 5:return sY(e,t);case 6:return rY(e,t);default:throw new Error(`${e.length}-D output sampling is not yet supported`)}}function UZ(e){return`
float sampleTexture(sampler2D textureSampler, vec2 uv) {
return ${e.texture2D}(textureSampler, uv).r;
}
`}function GZ(e){return`
void setOutput(float val) {
${e.output} = vec4(val, 0, 0, 0);
}
`}function HZ(e){return`
void setOutput(vec4 val) {
${e.output} = val;
}
`}function jZ(e){return`${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);
}
${qZ}
${XZ}
${KZ}
`}var qZ=`
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);
}
`,XZ=`
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);
}
`,KZ=`
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);
}
`,ZZ=`
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 uI(){return`
int getOutputCoords() {
return 0;
}
`}function YZ(e,t,n){let s=[Math.ceil(t[0]/2),Math.ceil(t[1]/2)];return s[0]===1?n?`
int getOutputCoords() {
return 2 * int(resultUV.x * ceil(float(outTexShape[1]) / 2.0));
}
`:`
int getOutputCoords() {
return 2 * int(resultUV.x * ${s[1]}.0);
}
`:s[1]===1?n?`
int getOutputCoords() {
return 2 * int(resultUV.y * ceil(float(outTexShape[0]) / 2.0));
}
`:`
int getOutputCoords() {
return 2 * int(resultUV.y * ${s[0]}.0);
}
`:n?`
int getOutputCoords() {
ivec2 packedTexShape = ivec2(ceil(float(outTexShape[0]) / 2.0), ceil(float(outTexShape[1]) / 2.0));
ivec2 resTexRC = ivec2(resultUV.yx *
vec2(packedTexShape[0], packedTexShape[1]));
return 2 * (resTexRC.x * packedTexShape[1] + resTexRC.y);
}
`:`
int getOutputCoords() {
ivec2 resTexRC = ivec2(resultUV.yx *
vec2(${s[0]}, ${s[1]}));
return 2 * (resTexRC.x * ${s[1]} + resTexRC.y);
}
`}function JZ(e,t,n){return t[0]===1?n?`
int getOutputCoords() {
return int(resultUV.x * float(outTexShape[1]));
}
`:`
int getOutputCoords() {
return int(resultUV.x * ${t[1]}.0);
}
`:t[1]===1?n?`
int getOutputCoords() {
return int(resultUV.y * float(outTexShape[0]));
}
`:`
int getOutputCoords() {
return int(resultUV.y * ${t[0]}.0);
}
`:n?`
int getOutputCoords() {
ivec2 resTexRC = ivec2(resultUV.yx *
vec2(outTexShape[0], outTexShape[1]));
return resTexRC.x * outTexShape[1] + resTexRC.y;
}
`:`
int getOutputCoords() {
ivec2 resTexRC = ivec2(resultUV.yx *
vec2(${t[0]}, ${t[1]}));
return resTexRC.x * ${t[1]} + resTexRC.y;
}
`}function QZ(e,t,n){if(n)return`
ivec3 getOutputCoords() {
ivec2 packedTexShape = ivec2(ceil(float(outTexShape[0]) / 2.0), ceil(float(outTexShape[1]) / 2.0));
int texelsInLogicalRow = int(ceil(float(outShape[2]) / 2.0));
int texelsInBatch = texelsInLogicalRow * int(ceil(float(outShape[1]) / 2.0));
ivec2 resTexRC = ivec2(resultUV.yx *
vec2(packedTexShape[0], packedTexShape[1]));
int index = resTexRC.x * packedTexShape[1] + resTexRC.y;
int b = index / texelsInBatch;
index -= b * texelsInBatch;
int r = 2 * (index / texelsInLogicalRow);
int c = imod(index, texelsInLogicalRow) * 2;
return ivec3(b, r, c);
}
`;let s=[Math.ceil(t[0]/2),Math.ceil(t[1]/2)],r=Math.ceil(e[2]/2),a=r*Math.ceil(e[1]/2);return`
ivec3 getOutputCoords() {
ivec2 resTexRC = ivec2(resultUV.yx *
vec2(${s[0]}, ${s[1]}));
int index = resTexRC.x * ${s[1]} + resTexRC.y;
int b = index / ${a};
index -= b * ${a};
int r = 2 * (index / ${r});
int c = imod(index, ${r}) * 2;
return ivec3(b, r, c);
}
`}function eY(e,t,n){if(n)return`
ivec3 getOutputCoords() {
ivec2 resTexRC = ivec2(resultUV.yx *
vec2(outTexShape[0], outTexShape[1]));
int index = resTexRC.x * outTexShape[1] + resTexRC.y;
${vm(["r","c","d"],e)}
return ivec3(r, c, d);
}
`;let s=gl(["r","c","d"],e);return`
ivec3 getOutputCoords() {
ivec2 resTexRC = ivec2(resultUV.yx *
vec2(${t[0]}, ${t[1]}));
int index = resTexRC.x * ${t[1]} + resTexRC.y;
${s}
return ivec3(r, c, d);
}
`}function tY(e,t,n){if(n)return`
ivec4 getOutputCoords() {
ivec2 packedTexShape = ivec2(ceil(float(outTexShape[0]) / 2.0), ceil(float(outTexShape[1]) / 2.0));
ivec2 resTexRC = ivec2(resultUV.yx *
vec2(packedTexShape[0], packedTexShape[1]));
int index = resTexRC.x * packedTexShape[1] + resTexRC.y;
int texelsInLogicalRow = int(ceil(float(outShape[3]) / 2.0));
int texelsInBatch = texelsInLogicalRow * int(ceil(float(outShape[2]) / 2.0));
int texelsInBatchN = texelsInBatch * outShape[1];
int b2 = index / texelsInBatchN;
index -= b2 * texelsInBatchN;
int b = index / texelsInBatch;
index -= b * texelsInBatch;
int r = 2 * (index / texelsInLogicalRow);
int c = imod(index, texelsInLogicalRow) * 2;
return ivec4(b2, b, r, c);
}
`;let s=[Math.ceil(t[0]/2),Math.ceil(t[1]/2)],r=Math.ceil(e[e.length-1]/2),a=r*Math.ceil(e[e.length-2]/2),o=a,i="",l="b, r, c";for(let c=2;c<e.length-1;c++)o*=e[e.length-c-1],i=`
int b${c} = index / ${o};
index -= b${c} * ${o};
`+i,l=`b${c}, `+l;return`
ivec${e.length} getOutputCoords() {
ivec2 resTexRC = ivec2(resultUV.yx *
vec2(${s[0]}, ${s[1]}));
int index = resTexRC.x * ${s[1]} + resTexRC.y;
${i}
int b = index / ${a};
index -= b * ${a};
int r = 2 * (index / ${r});
int c = imod(index, ${r}) * 2;
return ivec${e.length}(${l});
}
`}function nY(e,t,n){if(n)return`
ivec4 getOutputCoords() {
ivec2 resTexRC = ivec2(resultUV.yx *
vec2(outTexShape[0], outTexShape[1]));
int index = resTexRC.x * outTexShape[1] + resTexRC.y;
${vm(["r","c","d","d2"],e)}
return ivec4(r, c, d, d2);
}
`;let s=gl(["r","c","d","d2"],e);return`
ivec4 getOutputCoords() {
ivec2 resTexRC = ivec2(resultUV.yx *
vec2(${t[0]}, ${t[1]}));
int index = resTexRC.x * ${t[1]} + resTexRC.y;
${s}
return ivec4(r, c, d, d2);
}
`}function sY(e,t){let n=gl(["r","c","d","d2","d3"],e);return`
ivec5 getOutputCoords() {
ivec2 resTexRC = ivec2(resultUV.yx * vec2(${t[0]},
${t[1]}));
int index = resTexRC.x * ${t[1]} + resTexRC.y;
${n}
ivec5 outShape = ivec5(r, c, d, d2, d3);
return outShape;
}
`}function rY(e,t){let n=gl(["r","c","d","d2","d3","d4"],e);return`
ivec6 getOutputCoords() {
ivec2 resTexRC = ivec2(resultUV.yx *
vec2(${t[0]}, ${t[1]}));
int index = resTexRC.x * ${t[1]} + resTexRC.y;
${n}
ivec6 result = ivec6(r, c, d, d2, d3, d4);
return result;
}
`}function aY(e,t,n){let s=[Math.ceil(t[0]/2),Math.ceil(t[1]/2)];if(v.arraysEqual(e,t))return n?`
ivec2 getOutputCoords() {
ivec2 packedTexShape = ivec2(ceil(float(outTexShape[0]) / 2.0), ceil(float(outTexShape[1]) / 2.0));
return 2 * ivec2(resultUV.yx * vec2(packedTexShape[0], packedTexShape[1]));
}
`:`
ivec2 getOutputCoords() {
return 2 * ivec2(resultUV.yx * vec2(${s[0]}, ${s[1]}));
}
`;let r=Math.ceil(e[1]/2);return n?`
ivec2 getOutputCoords() {
ivec2 packedTexShape = ivec2(ceil(float(outTexShape[0]) / 2.0), ceil(float(outTexShape[1]) / 2.0));
int texelsInLogicalRow = int(ceil(float(outShape[1]) / 2.0));
ivec2 resTexRC = ivec2(resultUV.yx *
vec2(packedTexShape[0], packedTexShape[1]));
int index = resTexRC.x * packedTexShape[1] + resTexRC.y;
int r = 2 * (index / texelsInLogicalRow);
int c = imod(index, texelsInLogicalRow) * 2;
return ivec2(r, c);
}
`:`
ivec2 getOutputCoords() {
ivec2 resTexRC = ivec2(resultUV.yx *
vec2(${s[0]}, ${s[1]}));
int index = resTexRC.x * ${s[1]} + resTexRC.y;
int r = 2 * (index / ${r});
int c = imod(index, ${r}) * 2;
return ivec2(r, c);
}
`}function oY(e,t,n){return v.arraysEqual(e,t)?n?`
ivec2 getOutputCoords() {
return ivec2(resultUV.yx * vec2(outTexShape[0], outTexShape[1]));
}
`:`
ivec2 getOutputCoords() {
return ivec2(resultUV.yx * vec2(${t[0]}, ${t[1]}));
}
`:e[1]===1?n?`
ivec2 getOutputCoords() {
ivec2 resTexRC = ivec2(resultUV.yx *
vec2(outTexShape[0], outTexShape[1]));
int index = resTexRC.x * outTexShape[1] + resTexRC.y;
return ivec2(index, 0);
}
`:`
ivec2 getOutputCoords() {
ivec2 resTexRC = ivec2(resultUV.yx *
vec2(${t[0]}, ${t[1]}));
int index = resTexRC.x * ${t[1]} + resTexRC.y;
return ivec2(index, 0);
}
`:e[0]===1?n?`
ivec2 getOutputCoords() {
ivec2 resTexRC = ivec2(resultUV.yx *
vec2(outTexShape[0], outTexShape[1]));
int index = resTexRC.x * outTexShape[1] + resTexRC.y;
return ivec2(0, index);
}
`:`
ivec2 getOutputCoords() {
ivec2 resTexRC = ivec2(resultUV.yx *
vec2(${t[0]}, ${t[1]}));
int index = resTexRC.x * ${t[1]} + resTexRC.y;
return ivec2(0, index);
}
`:n?`
ivec2 getOutputCoords() {
ivec2 resTexRC = ivec2(resultUV.yx *
vec2(outTexShape[0], outTexShape[1]));
int index = resTexRC.x * outTexShape[1] + resTexRC.y;
int r = index / outShape[1];
int c = index - r * outShape[1];
return ivec2(r, c);
}
`:`
ivec2 getOutputCoords() {
ivec2 resTexRC = ivec2(resultUV.yx *
vec2(${t[0]}, ${t[1]}));
int index = resTexRC.x * ${t[1]} + resTexRC.y;
int r = index / ${e[1]};
int c = index - r * ${e[1]};
return ivec2(r, c);
}
`}function Al(e){return`offset${e}`}function iY(e){let t=e.name,n="get"+t.charAt(0).toUpperCase()+t.slice(1),s=Un();return`
vec4 ${n}() {
return ${s.texture2D}(${t}, halfCR);
}
`}function lY(e,t){let n=e.name,s="get"+n.charAt(0).toUpperCase()+n.slice(1);if(e.shapeInfo.isUniform)return`float ${s}() {return ${n};}`;let[r,a]=e.shapeInfo.texShape;if(r===1&&a===1)return`
float ${s}() {
return sampleTexture(${n}, halfCR);
}
`;let o=Al(n);if(t)return`
float ${s}() {
vec2 uv = uvFromFlat(${n}TexShape[0], ${n}TexShape[1], ${o});
return sampleTexture(${n}, uv);
}
`;let[i,l]=e.shapeInfo.texShape;return`
float ${s}() {
vec2 uv = uvFromFlat(${i}, ${l}, ${o});
return sampleTexture(${n}, uv);
}
`}function uY(e,t){let n=e.name,s="get"+n.charAt(0).toUpperCase()+n.slice(1),r=e.shapeInfo.texShape,a=Un();if(t)return`
vec4 ${s}(int index) {
ivec2 packedTexShape = ivec2(ceil(float(${n}TexShape[0]) / 2.0), ceil(float(${n}TexShape[1]) / 2.0));
vec2 uv = packedUVfrom1D(
packedTexShape[0], packedTexShape[1], index);
return ${a.texture2D}(${n}, uv);
}
`;let o=[Math.ceil(r[0]/2),Math.ceil(r[1]/2)];return`
vec4 ${s}(int index) {
vec2 uv = packedUVfrom1D(
${o[0]}, ${o[1]}, index);
return ${a.texture2D}(${n}, uv);
}
`}function cY(e,t){let n=e.name,s="get"+n.charAt(0).toUpperCase()+n.slice(1);if(e.shapeInfo.isUniform)return`
float ${s}(int index) {
${ju(e)}
}
`;let r=e.shapeInfo.texShape,a=r[0],o=r[1];if(o===1&&a===1)return`
float ${s}(int index) {
return sampleTexture(${n}, halfCR);
}
`;let i=Al(n);return o===1?t?`
float ${s}(int index) {
vec2 uv = vec2(0.5, (float(index + ${i}) + 0.5) / float(${n}TexShape[0]));
return sampleTexture(${n}, uv);
}
`:`
float ${s}(int index) {
vec2 uv = vec2(0.5, (float(index + ${i}) + 0.5) / ${a}.0);
return sampleTexture(${n}, uv);
}
`:a===1?t?`
float ${s}(int index) {
vec2 uv = vec2((float(index + ${i}) + 0.5) / float(${n}TexShape[1]), 0.5);
return sampleTexture(${n}, uv);
}
`:`
float ${s}(int index) {
vec2 uv = vec2((float(index + ${i}) + 0.5) / ${o}.0, 0.5);
return sampleTexture(${n}, uv);
}
`:t?`
float ${s}(int index) {
vec2 uv = uvFromFlat(${n}TexShape[0], ${n}TexShape[1], index + ${i});
return sampleTexture(${n}, uv);
}
`:`
float ${s}(int index) {
vec2 uv = uvFromFlat(${a}, ${o}, index + ${i});
return sampleTexture(${n}, uv);
}
`}function dY(e,t){let n=e.shapeInfo.logicalShape,s=e.name,r="get"+s.charAt(0).toUpperCase()+s.slice(1),a=e.shapeInfo.texShape,o=a[0],i=a[1],l=Un();if(a!=null&&v.arraysEqual(n,a))return t?`
vec4 ${r}(int row, int col) {
vec2 uv = (vec2(col, row) + halfCR) / vec2(${s}TexShape[1], ${s}TexShape[0]);
return ${l.texture2D}(${s}, uv);
}
`:`
vec4 ${r}(int row, int col) {
vec2 uv = (vec2(col, row) + halfCR) / vec2(${i}.0, ${o}.0);
return ${l.texture2D}(${s}, uv);
}
`;if(t)return`
vec4 ${r}(int row, int col) {
ivec2 packedTexShape = ivec2(ceil(float(${s}TexShape[0]) / 2.0), ceil(float(${s}TexShape[1]) / 2.0));
int valuesPerRow = int(ceil(float(${s}Shape[1]) / 2.0));
vec2 uv = packedUVfrom2D(valuesPerRow, packedTexShape[0], packedTexShape[1], row, col);
return ${l.texture2D}(${s}, uv);
}
`;let c=[Math.ceil(a[0]/2),Math.ceil(a[1]/2)],u=Math.ceil(n[1]/2);return`
vec4 ${r}(int row, int col) {
vec2 uv = packedUVfrom2D(${u}, ${c[0]}, ${c[1]}, row, col);
return ${l.texture2D}(${s}, uv);
}
`}function pY(e,t){let n=e.shapeInfo.logicalShape,s=e.name,r="get"+s.charAt(0).toUpperCase()+s.slice(1),a=e.shapeInfo.texShape;if(a!=null&&v.arraysEqual(n,a)){if(t)return`
float ${r}(int row, int col) {
vec2 uv = (vec2(col, row) + halfCR) / vec2(${s}TexShape[1], ${s}TexShape[0]);
return sampleTexture(${s}, uv);
}
`;let p=a[0],h=a[1];return`
float ${r}(int row, int col) {
vec2 uv = (vec2(col, row) + halfCR) / vec2(${h}.0, ${p}.0);
return sampleTexture(${s}, uv);
}
`}let{newShape:o,keptDims:i}=v.squeezeShape(n),l=o;if(l.length<n.length){let p=qu(e,l),h=["row","col"];return`
${Hu(p,t)}
float ${r}(int row, int col) {
return ${r}(${Xu(h,i)});
}
`}if(e.shapeInfo.isUniform)return`
float ${r}(int row, int col) {
int index = round(dot(vec2(row, col), vec2(${n[1]}, 1)));
${ju(e)}
}
`;let c=a[0],u=a[1],d=Al(s);return u===1?t?`
float ${r}(int row, int col) {
float index = dot(vec3(row, col, ${d}), vec3(${s}Shape[1], 1, 1));
vec2 uv = vec2(0.5, (index + 0.5) / float(${s}TexShape[0]));
return sampleTexture(${s}, uv);
}
`:`
float ${r}(int row, int col) {
float index = dot(vec3(row, col, ${d}), vec3(${n[1]}, 1, 1));
vec2 uv = vec2(0.5, (index + 0.5) / ${c}.0);
return sampleTexture(${s}, uv);
}
`:c===1?t?`
float ${r}(int row, int col) {
float index = dot(vec3(row, col, ${d}), vec3(${s}Shape[1], 1, 1));
vec2 uv = vec2((index + 0.5) / float(${s}TexShape[1]), 0.5);
return sampleTexture(${s}, uv);
}
`:`
float ${r}(int row, int col) {
float index = dot(vec3(row, col, ${d}), vec3(${n[1]}, 1, 1));
vec2 uv = vec2((index + 0.5) / ${u}.0, 0.5);
return sampleTexture(${s}, uv);
}
`:t?`
float ${r}(int row, int col) {
// Explicitly use integer operations as dot() only works on floats.
int index = row * ${s}Shape[1] + col + ${d};
vec2 uv = uvFromFlat(${s}TexShape[0], ${s}TexShape[1], index);
return sampleTexture(${s}, uv);
}
`:`
float ${r}(int row, int col) {
// Explicitly use integer operations as dot() only works on floats.
int index = row * ${n[1]} + col + ${d};
vec2 uv = uvFromFlat(${c}, ${u}, index);
return sampleTexture(${s}, uv);
}
`}function hY(e,t){let n=e.shapeInfo.logicalShape,s=e.name,r="get"+s.charAt(0).toUpperCase()+s.slice(1),a=e.shapeInfo.texShape,o=[Math.ceil(a[0]/2),Math.ceil(a[1]/2)];if(n[0]===1){let p=n.slice(1),h=[1,2],f=qu(e,p),m=["b","row","col"];return`
${lI(f,t)}
vec4 ${r}(int b, int row, int col) {
return ${r}(${Xu(m,h)});
}
`}let i=Un();if(t)return`
vec4 ${r}(int b, int row, int col) {
ivec2 packedTexShape = ivec2(ceil(float(${s}TexShape[0]) / 2.0), ceil(float(${s}TexShape[1]) / 2.0));
int valuesPerRow = int(ceil(float(${s}Shape[2]) / 2.0));
int texelsInBatch = valuesPerRow * int(ceil(float(${s}Shape[1]) / 2.0));
vec2 uv = packedUVfrom3D(
packedTexShape[0], packedTexShape[1], texelsInBatch, valuesPerRow, b, row, col);
return ${i.texture2D}(${s}, uv);
}
`;let l=o[0],c=o[1],u=Math.ceil(n[2]/2),d=u*Math.ceil(n[1]/2);return`
vec4 ${r}(int b, int row, int col) {
vec2 uv = packedUVfrom3D(
${l}, ${c}, ${d}, ${u}, b, row, col);
return ${i.texture2D}(${s}, uv);
}
`}function fY(e,t){let n=e.shapeInfo.logicalShape,s=e.name,r="get"+s.charAt(0).toUpperCase()+s.slice(1),a=n[1]*n[2],o=n[2],{newShape:i,keptDims:l}=v.squeezeShape(n),c=i;if(c.length<n.length){let m=qu(e,c),g=["row","col","depth"];return`
${Hu(m,t)}
float ${r}(int row, int col, int depth) {
return ${r}(${Xu(g,l)});
}
`}if(e.shapeInfo.isUniform)return`
float ${r}(int row, int col, int depth) {
int index = round(dot(vec3(row, col, depth),
vec3(${a}, ${o}, 1)));
${ju(e)}
}
`;let u=e.shapeInfo.texShape,d=u[0],p=u[1],h=e.shapeInfo.flatOffset;if(p===a&&h==null)return t?`
float ${r}(int row, int col, int depth) {
int stride1 = ${s}Shape[2];
float texR = float(row);
float texC = dot(vec2(col, depth), vec2(stride1, 1));
vec2 uv = (vec2(texC, texR) + halfCR) /
vec2(${s}TexShape[1], ${s}TexShape[0]);
return sampleTexture(${s}, uv);
}
`:`
float ${r}(int row, int col, int depth) {
float texR = float(row);
float texC = dot(vec2(col, depth), vec2(${o}, 1));
vec2 uv = (vec2(texC, texR) + halfCR) /
vec2(${p}.0, ${d}.0);
return sampleTexture(${s}, uv);
}
`;if(p===o&&h==null)return t?`
float ${r}(int row, int col, int depth) {
float texR = dot(vec2(row, col), vec2(${s}Shape[1], 1));
float texC = float(depth);
vec2 uv = (vec2(texC, texR) + halfCR) / vec2(${s}TexShape[1], ${s}TexShape[0]);
return sampleTexture(${s}, uv);
}
`:`
float ${r}(int row, int col, int depth) {
float texR = dot(vec2(row, col), vec2(${n[1]}, 1));
float texC = float(depth);
vec2 uv = (vec2(texC, texR) + halfCR) / vec2(${p}.0, ${d}.0);
return sampleTexture(${s}, uv);
}
`;let f=Al(s);return t?`
float ${r}(int row, int col, int depth) {
// Explicitly use integer operations as dot() only works on floats.
int stride0 = ${s}Shape[1] * ${s}Shape[2];
int stride1 = ${s}Shape[2];
int index = row * ${a} + col * ${o} + depth + ${f};
vec2 uv = uvFromFlat(${s}TexShape[0], ${s}TexShape[1], index);
return sampleTexture(${s}, uv);
}
`:`
float ${r}(int row, int col, int depth) {
// Explicitly use integer operations as dot() only works on floats.
int index = row * ${a} + col * ${o} + depth + ${f};
vec2 uv = uvFromFlat(${d}, ${p}, index);
return sampleTexture(${s}, uv);
}
`}function mY(e,t){let n=e.name,s="get"+n.charAt(0).toUpperCase()+n.slice(1),r=Un();if(t)return`
vec4 ${s}(int b2, int b, int row, int col) {
int valuesPerRow = int(ceil(float(${n}Shape[3]) / 2.0));
int texelsInBatch = valuesPerRow * int(ceil(float(${n}Shape[2]) / 2.0));
int index = b * texelsInBatch + (row / 2) * valuesPerRow + (col / 2);
texelsInBatch *= ${n}Shape[1];
index = b2 * texelsInBatch + index;
ivec2 packedTexShape = ivec2(ceil(float(${n}TexShape[0]) / 2.0), ceil(float(${n}TexShape[1]) / 2.0));
int texR = index / packedTexShape[1];
int texC = index - texR * packedTexShape[1];
vec2 uv = (vec2(texC, texR) + halfCR) / vec2(packedTexShape[1], packedTexShape[0]); return ${r.texture2D}(${n}, uv);
}
`;let a=e.shapeInfo.logicalShape,o=a.length,i=e.shapeInfo.texShape,l=[Math.ceil(i[0]/2),Math.ceil(i[1]/2)],c=l[0],u=l[1],d=Math.ceil(a[o-1]/2),p=d*Math.ceil(a[o-2]/2),h="int b, int row, int col",f=`b * ${p} + (row / 2) * ${d} + (col / 2)`;for(let m=2;m<o-1;m++)h=`int b${m}, `+h,p*=a[o-m-1],f=`b${m} * ${p} + `+f;return`
vec4 ${s}(${h}) {
int index = ${f};
int texR = index / ${u};
int texC = index - texR * ${u};
vec2 uv = (vec2(texC, texR) + halfCR) / vec2(${u}, ${c});
return ${r.texture2D}(${n}, uv);
}
`}function gY(e,t){let n=e.shapeInfo.logicalShape,s=e.name,r="get"+s.charAt(0).toUpperCase()+s.slice(1),a=n[3],o=n[2]*a,i=n[1]*o,{newShape:l,keptDims:c}=v.squeezeShape(n);if(l.length<n.length){let x=qu(e,l),y=["row","col","depth","depth2"];return`
${Hu(x,t)}
float ${r}(int row, int col, int depth, int depth2) {
return ${r}(${Xu(y,c)});
}
`}if(e.shapeInfo.isUniform)return`
float ${r}(int row, int col, int depth, int depth2) {
int index = round(dot(vec4(row, col, depth, depth2),
vec4(${i}, ${o}, ${a}, 1)));
${ju(e)}
}
`;let u=e.shapeInfo.flatOffset,d=e.shapeInfo.texShape,p=d[0],h=d[1],f=`int stride2 = ${s}Shape[3];`,m=`int stride1 = ${s}Shape[2] * stride2;`,g=`int stride0 = ${s}Shape[1] * stride1;`;if(h===i&&u==null)return t?`
float ${r}(int row, int col, int depth, int depth2) {
${f}
${m}
float texR = float(row);
float texC =
dot(vec3(col, depth, depth2),
vec3(stride1, stride2, 1));
vec2 uv = (vec2(texC, texR) + halfCR) /
vec2(${s}TexShape[1], ${s}TexShape[0]);
return sampleTexture(${s}, uv);
}
`:`
float ${r}(int row, int col, int depth, int depth2) {
float texR = float(row);
float texC =
dot(vec3(col, depth, depth2),
vec3(${o}, ${a}, 1));
vec2 uv = (vec2(texC, texR) + halfCR) /
vec2(${h}.0, ${p}.0);
return sampleTexture(${s}, uv);
}
`;if(h===a&&u==null)return t?`
float ${r}(int row, int col, int depth, int depth2) {
float texR = dot(vec3(row, col, depth),
vec3(${s}Shape[1] * ${s}Shape[2], ${s}Shape[2], 1));
float texC = float(depth2);
vec2 uv = (vec2(texC, texR) + halfCR) /
vec2(${s}TexShape[1], ${s}TexShape[0]);
return sampleTexture(${s}, uv);
}
`:`
float ${r}(int row, int col, int depth, int depth2) {
float texR = dot(vec3(row, col, depth),
vec3(${n[1]*n[2]}, ${n[2]}, 1));
float texC = float(depth2);
vec2 uv = (vec2(texC, texR) + halfCR) /
vec2(${h}.0, ${p}.0);
return sampleTexture(${s}, uv);
}
`;let A=Al(s);return t?`
float ${r}(int row, int col, int depth, int depth2) {
// Explicitly use integer operations as dot() only works on floats.
${f}
${m}
${g}
int index = row * stride0 + col * stride1 +
depth * stride2 + depth2;
vec2 uv = uvFromFlat(${s}TexShape[0], ${s}TexShape[1], index + ${A});
return sampleTexture(${s}, uv);
}
`:`
float ${r}(int row, int col, int depth, int depth2) {
// Explicitly use integer operations as dot() only works on floats.
int index = row * ${i} + col * ${o} +
depth * ${a} + depth2;
vec2 uv = uvFromFlat(${p}, ${h}, index + ${A});
return sampleTexture(${s}, uv);
}
`}function AY(e){let t=e.shapeInfo.logicalShape,n=e.name,s="get"+n.charAt(0).toUpperCase()+n.slice(1),r=t[4],a=t[3]*r,o=t[2]*a,i=t[1]*o,{newShape:l,keptDims:c}=v.squeezeShape(t);if(l.length<t.length){let m=qu(e,l),g=["row","col","depth","depth2","depth3"];return`
${Hu(m)}
float ${s}(int row, int col, int depth, int depth2, int depth3) {
return ${s}(${Xu(g,c)});
}
`}if(e.shapeInfo.isUniform)return`
float ${s}(int row, int col, int depth, int depth2, int depth3) {
float index = dot(
vec4(row, col, depth, depth2),
vec4(${i}, ${o}, ${a}, ${r})) +
depth3;
${ju(e)}
}
`;let u=e.shapeInfo.flatOffset,d=e.shapeInfo.texShape,p=d[0],h=d[1];if(h===i&&u==null)return`
float ${s}(int row, int col, int depth, int depth2, int depth3) {
int texR = row;
float texC = dot(vec4(col, depth, depth2, depth3),
vec4(${o}, ${a}, ${r}, 1));
vec2 uv = (vec2(texC, texR) + halfCR) /
vec2(${h}.0, ${p}.0);
return sampleTexture(${n}, uv);
}
`;if(h===r&&u==null)return`
float ${s}(int row, int col, int depth, int depth2, int depth3) {
float texR = dot(
vec4(row, col, depth, depth2),
vec4(${t[1]*t[2]*t[3]},
${t[2]*t[3]}, ${t[3]}, 1));
int texC = depth3;
vec2 uv = (vec2(texC, texR) + halfCR) /
vec2(${h}.0, ${p}.0);
return sampleTexture(${n}, uv);
}
`;let f=Al(n);return`
float ${s}(int row, int col, int depth, int depth2, int depth3) {
// Explicitly use integer operations as dot() only works on floats.
int index = row * ${i} + col * ${o} + depth * ${a} +
depth2 * ${r} + depth3 + ${f};
vec2 uv = uvFromFlat(${p}, ${h}, index);
return sampleTexture(${n}, uv);
}
`}function yY(e){let t=e.shapeInfo.logicalShape,n=e.name,s="get"+n.charAt(0).toUpperCase()+n.slice(1),{newShape:r,keptDims:a}=v.squeezeShape(t);if(r.length<t.length){let g=qu(e,r),A=["row","col","depth","depth2","depth3","depth4"];return`
${Hu(g)}
float ${s}(int row, int col, int depth,
int depth2, int depth3, int depth4) {
return ${s}(${Xu(A,a)});
}
`}let o=t[5],i=t[4]*o,l=t[3]*i,c=t[2]*l,u=t[1]*c;if(e.shapeInfo.isUniform)return`
float ${s}(int row, int col, int depth,
int depth2, int depth3, int depth4) {
int index = round(dot(
vec4(row, col, depth, depth2),
vec4(${u}, ${c}, ${l}, ${i})) +
dot(
vec2(depth3, depth4),
vec2(${o}, 1)));
${ju(e)}
}
`;let d=e.shapeInfo.flatOffset,p=e.shapeInfo.texShape,h=p[0],f=p[1];if(f===u&&d==null)return`
float ${s}(int row, int col, int depth,
int depth2, int depth3, int depth4) {
int texR = row;
float texC = dot(vec4(col, depth, depth2, depth3),
vec4(${c}, ${l}, ${i}, ${o})) +
float(depth4);
vec2 uv = (vec2(texC, texR) + halfCR) /
vec2(${f}.0, ${h}.0);
return sampleTexture(${n}, uv);
}
`;if(f===o&&d==null)return`
float ${s}(int row, int col, int depth,
int depth2, int depth3, int depth4) {
float texR = dot(vec4(row, col, depth, depth2),
vec4(${t[1]*t[2]*t[3]*t[4]},
${t[2]*t[3]*t[4]},
${t[3]*t[4]},
${t[4]})) + float(depth3);
int texC = depth4;
vec2 uv = (vec2(texC, texR) + halfCR) /
vec2(${f}.0, ${h}.0);
return sampleTexture(${n}, uv);
}
`;let m=Al(n);return`
float ${s}(int row, int col, int depth,
int depth2, int depth3, int depth4) {
// Explicitly use integer operations as dot() only works on floats.
int index = row * ${u} + col * ${c} + depth * ${l} +
depth2 * ${i} + depth3 * ${o} + depth4 + ${m};
vec2 uv = uvFromFlat(${h}, ${f}, index);
return sampleTexture(${n}, uv);
}
`}function ju(e){let t=e.name,n=v.sizeFromShape(e.shapeInfo.logicalShape);return n<2?`return ${t};`:`
for (int i = 0; i < ${n}; i++) {
if (i == index) {
return ${t}[i];
}
}
`}function xY(e,t){let n=e.name,s=n.charAt(0).toUpperCase()+n.slice(1),r="get"+s+"AtOutCoords",a=e.shapeInfo.logicalShape.length,o=t.logicalShape.length,i=iI(e.shapeInfo.logicalShape,t.logicalShape),l=bt(o),c=o-a,u,d=["x","y","z","w","u","v"];a===0?u="":o<2&&i.length>=1?u="coords = 0;":u=i.map(x=>`coords.${d[x+c]} = 0;`).join(`
`);let p="";o<2&&a>0?p="coords":p=e.shapeInfo.logicalShape.map((x,y)=>`coords.${d[y+c]}`).join(", ");let h="return outputValue;",m=v.sizeFromShape(e.shapeInfo.logicalShape)===1,A=v.sizeFromShape(t.logicalShape)===1;if(a===1&&!m&&!A)h=`
return vec4(outputValue.xy, outputValue.xy);
`;else if(m&&!A)o===1?h=`
return vec4(outputValue.x, outputValue.x, 0., 0.);
`:h=`
return vec4(outputValue.x);
`;else if(i.length){let x=a-2,y=a-1;i.indexOf(x)>-1&&i.indexOf(y)>-1?h="return vec4(outputValue.x);":i.indexOf(x)>-1?h="return vec4(outputValue.x, outputValue.y, outputValue.x, outputValue.y);":i.indexOf(y)>-1&&(h="return vec4(outputValue.xx, outputValue.zz);")}return`
vec4 ${r}() {
${l} coords = getOutputCoords();
${u}
vec4 outputValue = get${s}(${p});
${h}
}
`}function bY(e,t){let n=e.name,s=n.charAt(0).toUpperCase()+n.slice(1),r="get"+s+"AtOutCoords",a=t.texShape,o=e.shapeInfo.texShape,i=e.shapeInfo.logicalShape.length,l=t.logicalShape.length;if(!e.shapeInfo.isUniform&&i===l&&e.shapeInfo.flatOffset==null&&v.arraysEqual(o,a))return`
float ${r}() {
return sampleTexture(${n}, resultUV);
}
`;let c=bt(l),u=iI(e.shapeInfo.logicalShape,t.logicalShape),d=l-i,p,h=["x","y","z","w","u","v"];i===0?p="":l<2&&u.length>=1?p="coords = 0;":p=u.map(m=>`coords.${h[m+d]} = 0;`).join(`
`);let f="";return l<2&&i>0?f="coords":f=e.shapeInfo.logicalShape.map((m,g)=>`coords.${h[g+d]}`).join(", "),`
float ${r}() {
${c} coords = getOutputCoords();
${p}
return get${s}(${f});
}
`}function bt(e){if(e<=1)return"int";if(e===2)return"ivec2";if(e===3)return"ivec3";if(e===4)return"ivec4";if(e===5)return"ivec5";if(e===6)return"ivec6";throw Error(`GPU for rank ${e} is not yet supported`)}function Uy(e,t,n){let{newShape:s,keptDims:r}=v.squeezeShape(t),a=t.length,o=e&&a===3&&t[0]===1,i=o?t.slice(1):s,l=!e&&a>1&&!v.arraysEqual(t,n)&&s.length<a||o;return{useSqueezeShape:l,uniformShape:l?i:t,keptDims:r}}function qu(e,t){let n=JSON.parse(JSON.stringify(e));return n.shapeInfo.logicalShape=t,n}function Xu(e,t){return t.map(n=>e[n]).join(", ")}function vY(e,t,n,s){let r=n.map((b,w)=>{let k={logicalShape:b.shape,texShape:b.isUniform?null:b.texData.texShape,isUniform:b.isUniform,isPacked:b.isUniform?!1:b.texData.isPacked,flatOffset:null};return b.texData!=null&&b.texData.slice!=null&&b.texData.slice.flatOffset>0&&(k.flatOffset=b.texData.slice.flatOffset),{name:t.variableNames[w],shapeInfo:k}}),a=r.map(b=>b.shapeInfo),o={logicalShape:s.shape,texShape:s.texData.texShape,isUniform:!1,isPacked:s.texData.isPacked,flatOffset:null},i=LZ(r,o,t),l=LS(e.gl,i),c=e.createProgram(l),u=null,d=e.getUniformLocation(c,"NAN",!1);K().getNumber("WEBGL_VERSION")===1&&(u=e.getUniformLocation(c,"INFINITY",!1));let p=!1,h={},f={},m={};for(let b=0;b<t.variableNames.length;b++){let w=t.variableNames[b];h[w]=e.getUniformLocation(c,w,p),h[`offset${w}`]=e.getUniformLocation(c,`offset${w}`,p),t.enableShapeUniforms&&(f[`${w}Shape`]=e.getUniformLocation(c,`${w}Shape`,p),m[`${w}TexShape`]=e.getUniformLocation(c,`${w}TexShape`,p))}let g,A,x;t.enableShapeUniforms&&(g=e.getUniformLocation(c,"outShape",p),x=e.getUniformLocation(c,"outShapeStrides",p),A=e.getUniformLocation(c,"outTexShape",p));let y=[];return t.customUniforms&&t.customUniforms.forEach((b,w)=>{y[w]=e.getUniformLocation(c,b.name,p)}),{program:t,fragmentShader:l,source:i,webGLProgram:c,uniformLocations:h,customUniformLocations:y,inShapeInfos:a,outShapeInfo:o,infLoc:u,nanLoc:d,inShapesLocations:f,inTexShapesLocations:m,outShapeLocation:g,outShapeStridesLocation:x,outTexShapeLocation:A}}function cI(e,t){if(e.length!==t.length)throw Error(`Binary was compiled with ${e.length} inputs, but was executed with ${t.length} inputs`);e.forEach((n,s)=>{let r=n.logicalShape,a=t[s],o=a.shape;if(!v.arraysEqual(r,o))throw Error(`Binary was compiled with different shapes than the current args. Shapes ${r} and ${o} must match`);if(n.isUniform&&a.isUniform)return;let i=n.texShape,l=a.isUniform?null:a.texData.texShape;if(!v.arraysEqual(i,l))throw Error(`Binary was compiled with different texture shapes than the current args. Shape ${i} and ${l} must match`)})}function wY(e,t,n,s,r){t.program.enableShapeUniforms||(cI(t.inShapeInfos,n),cI([t.outShapeInfo],[s]));let a=s.texData.texture,o=s.texData.texShape;s.texData.isPacked?e.setOutputPackedMatrixTexture(a,o[0],o[1]):e.setOutputMatrixTexture(a,o[0],o[1]),e.setProgram(t.webGLProgram),K().getNumber("WEBGL_VERSION")===1&&t.infLoc!==null&&e.gl.uniform1f(t.infLoc,1/0),t.nanLoc!==null&&e.gl.uniform1f(t.nanLoc,NaN),n.forEach((l,c)=>{let u=t.program.variableNames[c],d=t.uniformLocations[u],p=t.uniformLocations[`offset${u}`],h=t.inShapesLocations[`${u}Shape`],f=t.inTexShapesLocations[`${u}TexShape`];if(h){let{uniformShape:m}=Uy(t.program.packedInputs,l.shape,l.texData.texShape);switch(m.length){case 1:e.gl.uniform1iv(h,new Int32Array(m));break;case 2:e.gl.uniform2iv(h,new Int32Array(m));break;case 3:e.gl.uniform3iv(h,new Int32Array(m));break;case 4:e.gl.uniform4iv(h,new Int32Array(m));break;default:break}}if(f&&e.gl.uniform2i(f,l.texData.texShape[0],l.texData.texShape[1]),d!=null){if(l.isUniform){if(v.sizeFromShape(l.shape)<2)e.gl.uniform1f(d,l.uniformValues[0]);else{let m=l.uniformValues;m instanceof Float32Array||(m=new Float32Array(m)),e.gl.uniform1fv(d,m)}return}l.texData.slice!=null&&p!=null&&e.gl.uniform1i(p,l.texData.slice.flatOffset),e.setInputMatrixTexture(l.texData.texture,d,c)}});let i=t.outShapeLocation;if(i)switch(s.shape.length){case 1:e.gl.uniform1iv(i,new Int32Array(s.shape));break;case 2:e.gl.uniform2iv(i,new Int32Array(s.shape));break;case 3:e.gl.uniform3iv(i,new Int32Array(s.shape));break;case 4:e.gl.uniform4iv(i,new Int32Array(s.shape));break;default:break}if(t.outShapeStridesLocation){let l=v.computeStrides(s.shape);switch(s.shape.length){case 2:e.gl.uniform1iv(t.outShapeStridesLocation,new Int32Array(l));break;case 3:e.gl.uniform2iv(t.outShapeStridesLocation,new Int32Array(l));break;case 4:e.gl.uniform3iv(t.outShapeStridesLocation,new Int32Array(l));break;default:break}}t.outTexShapeLocation&&e.gl.uniform2i(t.outTexShapeLocation,s.texData.texShape[0],s.texData.texShape[1]),t.program.customUniforms&&r&&t.program.customUniforms.forEach((l,c)=>{let u=t.customUniformLocations[c],d=r[c];if(l.type==="float")e.gl.uniform1fv(u,d);else if(l.type==="vec2")e.gl.uniform2fv(u,d);else if(l.type==="vec3")e.gl.uniform3fv(u,d);else if(l.type==="vec4")e.gl.uniform4fv(u,d);else if(l.type==="int")e.gl.uniform1iv(u,d);else if(l.type==="ivec2")e.gl.uniform2iv(u,d);else if(l.type==="ivec3")e.gl.uniform3iv(u,d);else if(l.type==="ivec4")e.gl.uniform4iv(u,d);else throw Error(`uniform type ${l.type} is not supported yet.`)}),e.executeProgram()}function kY(e,t,n){let s="";t.concat(n).forEach(o=>{let i=o.texData!=null&&o.texData.slice!=null&&o.texData.slice.flatOffset>0;if(e.enableShapeUniforms&&!o.isUniform){let l=o.texData.texShape,{useSqueezeShape:c,uniformShape:u,keptDims:d}=Uy(e.packedInputs,o.shape,l),p="",h="",f="";if(u.length===1&&e.packedInputs){let w=[Math.ceil(l[0]/2),Math.ceil(l[1]/2)];p=`${w[0]>1}_${w[1]>1}`}else if(u.length===2&&!e.packedInputs)h=`${u[0]>1}_${u[1]>1}`;else if(u.length>2&&!e.packedInputs){let w=v.computeStrides(u);f=`${w[0]===l[1]}_${w[w.length-1]===l[1]}`}let m=o.shape.length,g=u.length===2&&v.arraysEqual(o.shape,l),A=v.sizeFromShape(o.shape)===1,x=N.getBroadcastDims(o.shape,n.shape),y=!e.packedInputs&&m===n.shape.length&&v.arraysEqual(l,n.texData.texShape),b=e.packedInputs||u.length>2?"":`${l[0]>1}_${l[1]>1}`;s+=`${m}_${y}_${c?d:""}_${u.length}_${A}_${x}_${g}_${p}_${h}_${f}_${b}_${i}`}else{let l=o.isUniform?"uniform":o.texData.texShape;s+=`${o.shape}_${l}_${i}`}});let r=e.userCode,a=e.constructor.name;return a+="_"+s+"_"+r+`${K().getNumber("WEBGL_VERSION")}`,a}function Fs(e){return K().getBool("WEBGL_USE_SHAPES_UNIFORMS")&&e<=4}var SY=class{constructor(e){this.variableNames=["A"],this.packedInputs=!1,this.packedOutput=!0,this.outPackingScheme=Hd.DENSE,this.customUniforms=[{name:"texShape",type:"ivec2"}];let t=Un();this.outputShape=e,this.enableShapeUniforms=Fs(this.outputShape.length),this.userCode=`
ivec3 outCoordsFromFlatIndex(int index) {
${this.enableShapeUniforms?vm(["r","c","d"],e):gl(["r","c","d"],e)}
return ivec3(r, c, d);
}
void main() {
ivec2 resTexRC = ivec2(resultUV.yx * vec2(texShape[0], texShape[1]));
int index = 4 * (resTexRC.x * texShape[1] + resTexRC.y);
vec4 result = vec4(0.);
for (int i=0; i<4; i++) {
int flatIndex = index + i;
ivec3 rc = outCoordsFromFlatIndex(flatIndex);
result[i] = getA(rc.x, rc.y, rc.z);
}
${t.output} = result;
}
`}},IY=class{constructor(e){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,this.outPackingScheme=Hd.DENSE,this.customUniforms=[{name:"texShape",type:"ivec2"}];let t=Un();this.outputShape=e,this.enableShapeUniforms=Fs(this.outputShape.length),this.userCode=`
ivec3 outCoordsFromFlatIndex(int index) {
${this.enableShapeUniforms?vm(["r","c","d"],e):gl(["r","c","d"],e)}
return ivec3(r, c, d);
}
void main() {
ivec2 resTexRC = ivec2(resultUV.yx * vec2(texShape[0], texShape[1]));
int index = 4 * (resTexRC.x * texShape[1] + resTexRC.y);
vec4 result = vec4(0.);
for (int i=0; i<4; i++) {
int flatIndex = index + i;
ivec3 rc = outCoordsFromFlatIndex(flatIndex);
result[i] = getChannel(getA(rc.x, rc.y, rc.z), vec2(rc.y, rc.z));
}
${t.output} = result;
}
`}},CY=class{constructor(e){this.variableNames=["A"],this.outTexUsage=_s.DOWNLOAD;let t=Un();this.outputShape=e,this.userCode=`
${oI}
void main() {
float x = getAAtOutCoords();
${t.output} = encode_float(x);
}
`}},TY=class{constructor(e){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!1,this.outTexUsage=_s.DOWNLOAD;let t=Un();this.outputShape=e,this.userCode=`
${oI}
void main() {
ivec3 coords = getOutputCoords();
float x = getChannel(getAAtOutCoords(), vec2(coords.y, coords.z));
${t.output} = encode_float(x);
}
`}},NY=class{constructor(e,t=!1){this.variableNames=["A"],this.customUniforms=[{name:"texShape",type:"ivec2"}];let n=Un();this.outputShape=e,this.enableShapeUniforms=Fs(this.outputShape.length);let s="result";t&&(s="floor(result * 255. + 0.5)"),this.userCode=`
${this.enableShapeUniforms?Vy():Wy(e)}
void main() {
ivec3 coords = getOutputCoords();
int flatIndex = getFlatIndex(coords);
int offset = imod(flatIndex, 4);
flatIndex = idiv(flatIndex, 4, 1.);
int r = flatIndex / texShape[1];
int c = imod(flatIndex, texShape[1]);
vec2 uv = (vec2(c, r) + halfCR) / vec2(texShape[1], texShape[0]);
vec4 values = ${n.texture2D}(A, uv);
float result;
if(offset == 0) {
result = values[0];
} else if(offset == 1) {
result = values[1];
} else if(offset == 2) {
result = values[2];
} else {
result = values[3];
}
${n.output} = vec4(${s}, 0., 0., 0.);
}
`}},EY=class{constructor(e,t=!1){this.variableNames=["A"],this.packedInputs=!1,this.packedOutput=!0,this.customUniforms=[{name:"texShape",type:"ivec2"}];let n=Un();this.outputShape=e,this.enableShapeUniforms=Fs(this.outputShape.length);let s="",r="result";t&&(r="floor(result * 255. + 0.5)");for(let a=0;a<=1;a++)for(let o=0;o<=1;o++){let i=a*2+o;s+=`
localCoords = coords;
if(localCoords[2] + ${o} < ${this.enableShapeUniforms?"outShape[2]":`${e[2]}`}) {
localCoords[2] += ${o};
if (localCoords[1] + ${a} < ${this.enableShapeUniforms?"outShape[1]":`${e[1]}`}) {
localCoords[1] += ${a};
flatIndex = getFlatIndex(localCoords);
offset = imod(flatIndex, 4);
flatIndex = idiv(flatIndex, 4, 1.);
int r = flatIndex / texShape[1];
int c = imod(flatIndex, texShape[1]);
vec2 uv = (vec2(c, r) + halfCR) / vec2(texShape[1], texShape[0]);
values = ${n.texture2D}(A, uv);
if (offset == 0) {
result[${i}] = values[0];
} else if (offset == 1) {
result[${i}] = values[1];
} else if (offset == 2) {
result[${i}] = values[2];
} else {
result[${i}] = values[3];
}
}
}
`}this.userCode=`
${this.enableShapeUniforms?Vy():Wy(e)}
void main() {
ivec3 coords = getOutputCoords();
vec4 result = vec4(0.);
int flatIndex, r, c, offset;
ivec3 localCoords;
vec2 uv;
vec4 values;
${s}
${n.output} = ${r};
}
`}},dI={};Me(dI,{bindVertexProgramAttributeStreams:()=>bI,createBufferFromOutputTexture:()=>kI,createFloat16MatrixTexture:()=>gI,createFloat16PackedMatrixTexture:()=>xI,createFloat32MatrixTexture:()=>mI,createIndexBuffer:()=>fI,createPackedMatrixTexture:()=>yI,createUnsignedBytesMatrixTexture:()=>AI,createVertexBuffer:()=>hI,createVertexShader:()=>pI,downloadByteEncodedFloatMatrixFromOutputTexture:()=>II,downloadFloat32MatrixFromBuffer:()=>SI,downloadMatrixFromPackedOutputTexture:()=>TI,downloadPackedMatrixFromBuffer:()=>CI,getInternalFormatForFloat16MatrixTexture:()=>Hy,getInternalFormatForFloat16PackedMatrixTexture:()=>Xy,getInternalFormatForFloat32MatrixTexture:()=>Gy,getInternalFormatForPackedMatrixTexture:()=>qy,getInternalFormatForUnsignedBytesMatrixTexture:()=>jy,uploadDenseMatrixToTexture:()=>vI,uploadPixelDataToTexture:()=>wI});function pI(e){let t=Un(),n=`${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 zS(e,n)}function hI(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 VS(e,t)}function fI(e){let t=new Uint16Array([0,1,2,2,1,3]);return US(e,t)}function Zd(e,t,n,s,r,a){HS(t,n);let o=GS(e),i=e.TEXTURE_2D;return Ie(e,()=>e.bindTexture(i,o)),Ie(e,()=>e.texParameteri(i,e.TEXTURE_WRAP_S,e.CLAMP_TO_EDGE)),Ie(e,()=>e.texParameteri(i,e.TEXTURE_WRAP_T,e.CLAMP_TO_EDGE)),Ie(e,()=>e.texParameteri(i,e.TEXTURE_MIN_FILTER,e.NEAREST)),Ie(e,()=>e.texParameteri(i,e.TEXTURE_MAG_FILTER,e.NEAREST)),Ie(e,()=>e.texImage2D(i,0,s,t,n,0,r,a,null)),Ie(e,()=>e.bindTexture(e.TEXTURE_2D,null)),o}function Gy(e){return e.internalFormatFloat}function mI(e,t,n,s){let[r,a]=jd(t,n);return Zd(e,r,a,Gy(s),s.textureFormatFloat,e.FLOAT)}function Hy(e){return e.internalFormatHalfFloat}function gI(e,t,n,s){let[r,a]=jd(t,n);return Zd(e,r,a,Hy(s),s.textureFormatFloat,s.textureTypeHalfFloat)}function jy(e){return e.downloadTextureFormat}function AI(e,t,n,s){let[r,a]=jd(t,n);return Zd(e,r,a,jy(s),e.RGBA,e.UNSIGNED_BYTE)}function qy(e){return e.internalFormatPackedFloat}function yI(e,t,n,s){let[r,a]=Uu(t,n);return Zd(e,r,a,qy(s),e.RGBA,e.FLOAT)}function Xy(e){return e.internalFormatPackedHalfFloat}function xI(e,t,n,s){let[r,a]=Uu(t,n);return Zd(e,r,a,Xy(s),e.RGBA,s.textureTypeHalfFloat)}function bI(e,t,n){let s=0,r=3*4,a=3*4+2*4;return Ie(e,()=>e.bindBuffer(e.ARRAY_BUFFER,n)),My(e,t,"clipSpacePos",n,3,a,s)&&My(e,t,"uv",n,2,a,r)}function vI(e,t,n,s,r,a){Ie(e,()=>e.bindTexture(e.TEXTURE_2D,t));let o,i,l;r instanceof Uint8Array?(o=new Uint8Array(n*s*4),i=e.UNSIGNED_BYTE,l=e.RGBA):(o=new Float32Array(n*s*4),i=e.FLOAT,l=a.internalFormatPackedFloat),o.set(r),Ie(e,()=>e.texImage2D(e.TEXTURE_2D,0,l,n,s,0,e.RGBA,i,o)),Ie(e,()=>e.bindTexture(e.TEXTURE_2D,null))}function wI(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 kI(e,t,n,s){let r=e.createBuffer();Ie(e,()=>e.bindBuffer(e.PIXEL_PACK_BUFFER,r));let i=4*4*t*n;return Ie(e,()=>e.bufferData(e.PIXEL_PACK_BUFFER,i,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 SI(e,t,n){let s=e,r=new Float32Array(n);return s.bindBuffer(s.PIXEL_PACK_BUFFER,t),s.getBufferSubData(s.PIXEL_PACK_BUFFER,0,r),s.bindBuffer(s.PIXEL_PACK_BUFFER,null),r}function II(e,t,n,s){let[r,a]=jd(t,n),o=4,i=new Uint8Array(SZ(t*n,o));return Ie(e,()=>e.readPixels(0,0,r,a,s.downloadTextureFormat,e.UNSIGNED_BYTE,i)),new Float32Array(i.buffer)}function CI(e,t,n,s,r,a,o,i){let l=e,c=new Float32Array(IZ(a,o));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 TI(e,t,n){let s=new Float32Array(t*n*4);return Ie(e,()=>e.readPixels(0,0,n,t,e.RGBA,e.FLOAT,s)),s}var wm=class{constructor(e){this.outputTexture=null,this.program=null,this.disposed=!1,this.vertexAttrsAreBound=!1,this.itemsToPoll=[];let t=K().getNumber("WEBGL_VERSION");e!=null?(this.gl=e,hm(t,e)):this.gl=Fr(t);let n="WEBGL_color_buffer_float",s="EXT_color_buffer_half_float";if(K().getNumber("WEBGL_VERSION")===1){let r="OES_texture_float",a="OES_texture_half_float";if(this.textureFloatExtension=qd(this.gl,r),Ps(this.gl,a))this.textureHalfFloatExtension=qd(this.gl,a);else if(K().get("WEBGL_FORCE_F16_TEXTURES"))throw new Error("GL context does not support half float textures, yet the environment flag WEBGL_FORCE_F16_TEXTURES is set to true.");if(this.colorBufferFloatExtension=this.gl.getExtension(n),Ps(this.gl,s))this.colorBufferHalfFloatExtension=qd(this.gl,s);else if(K().get("WEBGL_FORCE_F16_TEXTURES"))throw new Error("GL context does not support color renderable half floats, yet the environment flag WEBGL_FORCE_F16_TEXTURES is set to true.")}else if(n="EXT_color_buffer_float",Ps(this.gl,n))this.colorBufferFloatExtension=this.gl.getExtension(n);else if(Ps(this.gl,s))this.colorBufferHalfFloatExtension=this.gl.getExtension(s);else throw new Error("GL context does not support color renderable floats");this.vertexBuffer=hI(this.gl),this.indexBuffer=fI(this.gl),this.framebuffer=jS(this.gl),this.textureConfig=Oy(this.gl,this.textureHalfFloatExtension)}get debug(){return K().getBool("DEBUG")}dispose(){if(this.disposed)return;this.program!=null&&console.warn("Disposing a GPGPUContext that still has a bound WebGLProgram. This is probably a resource leak, delete the program with GPGPUContext.deleteProgram before disposing."),this.outputTexture!=null&&console.warn("Disposing a GPGPUContext that still has a bound output matrix texture. 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this.throwIfDisposed(),xI(this.gl,e,t,this.textureConfig)}createPackedMatrixTexture(e,t){return this.throwIfDisposed(),yI(this.gl,e,t,this.textureConfig)}deleteMatrixTexture(e){this.throwIfDisposed(),this.outputTexture===e&&(zy(this.gl,this.framebuffer),this.outputTexture=null),Ie(this.gl,()=>this.gl.deleteTexture(e))}downloadByteEncodedFloatMatrixFromOutputTexture(e,t,n){return this.downloadMatrixDriver(e,()=>II(this.gl,t,n,this.textureConfig))}downloadPackedMatrixFromBuffer(e,t,n,s,r,a){return CI(this.gl,e,t,n,s,r,a,this.textureConfig)}downloadFloat32MatrixFromBuffer(e,t){return SI(this.gl,e,t)}createBufferFromTexture(e,t,n){this.bindTextureToFrameBuffer(e);let s=kI(this.gl,t,n,this.textureConfig);return this.unbindTextureToFrameBuffer(),s}createAndWaitForFence(){let e=this.createFence(this.gl);return this.pollFence(e)}createFence(e){let t,n;if(K().getBool("WEBGL_FENCE_API_ENABLED")){let s=e,r=s.fenceSync(s.SYNC_GPU_COMMANDS_COMPLETE,0);e.flush(),n=()=>{let a=s.clientWaitSync(r,0,0);return a===s.ALREADY_SIGNALED||a===s.CONDITION_SATISFIED},t=r}else K().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION")>0?(t=this.beginQuery(),this.endQuery(),n=()=>this.isQueryAvailable(t,K().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION"))):n=()=>!0;return{query:t,isFencePassed:n}}downloadMatrixFromPackedTexture(e,t,n){return this.downloadMatrixDriver(e,()=>TI(this.gl,t,n))}createProgram(e){this.throwIfDisposed();let t=this.gl;this.vertexShader==null&&(this.vertexShader=pI(t));let n=BS(t);return Ie(t,()=>t.attachShader(n,this.vertexShader)),Ie(t,()=>t.attachShader(n,e)),WS(t,n),this.debug&&mm(t,n),this.vertexAttrsAreBound||(this.setProgram(n),this.vertexAttrsAreBound=bI(t,this.program,this.vertexBuffer)),n}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&&mm(this.gl,this.program),Ie(this.gl,()=>this.gl.useProgram(e))}getUniformLocation(e,t,n=!0){return this.throwIfDisposed(),n?XS(this.gl,e,t):KS(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(),ZS(this.gl,e,t,n)}setOutputMatrixTexture(e,t,n){this.setOutputMatrixTextureDriver(e,n,t)}setOutputPackedMatrixTexture(e,t,n){this.throwIfDisposed();let[s,r]=Uu(t,n);this.setOutputMatrixTextureDriver(e,s,r)}setOutputMatrixWriteRegion(e,t,n,s){this.setOutputMatrixWriteRegionDriver(n,e,s,t)}setOutputPackedMatrixWriteRegion(e,t,n,s){throw new Error("setOutputPackedMatrixWriteRegion not implemented.")}debugValidate(){this.program!=null&&mm(this.gl,this.program),Xd(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=qd(this.gl,K().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION")===2?"EXT_disjoint_timer_query_webgl2":"EXT_disjoint_timer_query")),this.disjointQueryTimerExtension}getQueryTimerExtensionWebGL2(){return this.getQueryTimerExtension()}getQueryTimerExtensionWebGL1(){return this.getQueryTimerExtension()}beginQuery(){if(K().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION")===2){let n=this.gl,s=this.getQueryTimerExtensionWebGL2(),r=n.createQuery();return n.beginQuery(s.TIME_ELAPSED_EXT,r),r}let e=this.getQueryTimerExtensionWebGL1(),t=e.createQueryEXT();return e.beginQueryEXT(e.TIME_ELAPSED_EXT,t),t}endQuery(){if(K().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION")===2){let t=this.gl,n=this.getQueryTimerExtensionWebGL2();t.endQuery(n.TIME_ELAPSED_EXT);return}let e=this.getQueryTimerExtensionWebGL1();e.endQueryEXT(e.TIME_ELAPSED_EXT)}async waitForQueryAndGetTime(e){return await 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e=RY(this.itemsToPoll.map(t=>t.isDoneFn));for(let t=0;t<=e;++t){let{resolveFn:n}=this.itemsToPoll[t];n()}this.itemsToPoll=this.itemsToPoll.slice(e+1)}addItemToPoll(e,t){this.itemsToPoll.push({isDoneFn:e,resolveFn:t}),!(this.itemsToPoll.length>1)&&v.repeatedTry(()=>(this.pollItems(),this.itemsToPoll.length===0))}bindTextureToFrameBuffer(e){this.throwIfDisposed(),gm(this.gl,e,this.framebuffer),this.debug&&Xd(this.gl)}unbindTextureToFrameBuffer(){this.outputTexture!=null?(gm(this.gl,this.outputTexture,this.framebuffer),this.debug&&Xd(this.gl)):zy(this.gl,this.framebuffer)}downloadMatrixDriver(e,t){this.bindTextureToFrameBuffer(e);let n=t();return this.unbindTextureToFrameBuffer(),n}setOutputMatrixTextureDriver(e,t,n){this.throwIfDisposed();let s=this.gl;gm(s,e,this.framebuffer),this.debug&&Xd(s),this.outputTexture=e,Ie(s,()=>s.viewport(0,0,t,n)),Ie(s,()=>s.scissor(0,0,t,n))}setOutputMatrixWriteRegionDriver(e,t,n,s){this.throwIfDisposed(),Ie(this.gl,()=>this.gl.scissor(e,t,n,s))}throwIfDisposed(){if(this.disposed)throw new Error("Attempted to use disposed GPGPUContext.")}throwIfNoProgram(){if(this.program==null)throw new Error("No GPU program is currently set.")}};function RY(e){let t=0;for(;t<e.length&&e[t]();++t);return t-1}var{addImpl:$Y,bincountImpl:NI,bincountReduceImpl:DY,ceilImpl:_Y,concatImpl:PY,equalImpl:FY,expImpl:OY,expm1Impl:MY,floorImpl:zY,gatherNdImpl:LY,gatherV2Impl:BY,greaterImpl:WY,greaterEqualImpl:VY,lessImpl:UY,lessEqualImpl:GY,linSpaceImpl:HY,logImpl:jY,maxImpl:qY,maximumImpl:XY,minimumImpl:KY,multiplyImpl:ZY,negImpl:YY,notEqualImpl:JY,prodImpl:QY,rangeImpl:eJ,rsqrtImpl:tJ,sigmoidImpl:nJ,simpleAbsImpl:EI,sliceImpl:sJ,sparseFillEmptyRowsImpl:rJ,sparseReshapeImpl:aJ,sparseSegmentReductionImpl:RI,sqrtImpl:oJ,stridedSliceImpl:iJ,stringNGramsImpl:lJ,stringSplitImpl:uJ,stringToHashBucketFastImpl:cJ,subImpl:dJ,tileImpl:pJ,topKImpl:hJ,transposeImpl:Ky,uniqueImpl:fJ}=im;function $I(e,t){return["x","y","z","w","u","v"].slice(0,t).map(n=>`${e}.${n}`)}function Gn(e,t){return t===1?[e]:$I(e,t)}function mJ(e,t){if(e===1)return"rc";let n="";for(let s=0;s<e;s++)n+=t[s],s<e-1&&(n+=",");return n}var gJ=class{constructor(e){this.variableNames=["A"],this.packedInputs=!1,this.packedOutput=!0,this.outputShape=e;let t=e.length;if(t===0)this.userCode=`
void main() {
setOutput(vec4(getA(), 0., 0., 0.));
}
`;else{let n=Gn("rc",t),s=bt(t),r=yJ(t,e,n),a=xJ(t,e[e.length-1],e[e.length-2],n),o=bJ(e,n);this.userCode=`
void main() {
${s} rc = getOutputCoords();
if(${r}) {
setOutput(vec4(0));
} else {
${a}
setOutput(vec4(${o}));
}
}
`}}};function AJ(e,t){let n=[];for(let s=0;s<=1;s++)for(let r=0;r<=1;r++){let a=`${s===0?"r":"rp1"}, ${r===0?"c":"cp1"}`;for(let o=2;o<e;o++)a=`${t[t.length-1-o]},`+a;n.push(a)}return n}function yJ(e,t,n){if(e===1)return`rc > ${t[0]}`;let s="";for(let r=e-2;r<e;r++)s+=`${n[r]} >= ${t[r]}`,r<e-1&&(s+="||");return s}function xJ(e,t,n,s){if(e===1)return"";let r=s.slice(-2);return`
int r = ${r[0]};
int c = ${r[1]};
int rp1 = r + 1;
int cp1 = c + 1;
bool cEdge = cp1 >= ${t};
bool rEdge = rp1 >= ${n};
`}function bJ(e,t){let n=e.length,s=AJ(n,t);return n===1?`getA(rc),
rc + 1 >= ${e[0]} ? 0. : getA(rc + 1),
0, 0`:`getA(${s[0]}),
cEdge ? 0. : getA(${s[1]}),
rEdge ? 0. : getA(${s[2]}),
rEdge || cEdge ? 0. : getA(${s[3]})`}var DI=class{constructor(e,t){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,this.customUniforms=[{name:"inputShape",type:"ivec3"}],this.outputShape=e,this.enableShapeUniforms=Fs(this.outputShape.length);let n="";for(let s=0;s<4;s++){let r="thisRC = rc;";s%2==1&&(r+="thisRC.z += 1;"),s>1&&(r+="thisRC.y += 1;"),n+=`
${r}
${s>0?"if(thisRC.y < rows && thisRC.z < cols){":""}
int flatIndex = getFlatIndex(thisRC);
ivec3 inputRC = inputCoordsFromReshapedOutCoords(flatIndex);
vec2 inputRCInnerDims = vec2(float(inputRC.y),float(inputRC.z));
result[${s}] =
getChannel(getA(inputRC.x, inputRC.y, inputRC.z), inputRCInnerDims);
${s>0?"}":""}
`}this.userCode=`
${vJ(t,this.enableShapeUniforms)}
${this.enableShapeUniforms?Vy():Wy(e)}
void main() {
ivec3 rc = getOutputCoords();
vec4 result = vec4(0.);
ivec3 thisRC;
int rows = ${this.enableShapeUniforms?"outShape[1]":e[1]};
int cols = ${this.enableShapeUniforms?"outShape[2]":e[2]};
${n}
setOutput(result);
}
`}};function vJ(e,t){return`
ivec3 inputCoordsFromReshapedOutCoords(int index) {
${t?zZ(["r","c","d"],"inputShape"):gl(["r","c","d"],e)}
return ivec3(r, c, d);
}
`}var wJ=class{constructor(e){this.gpgpu=e,this.numUsedTextures=0,this.numFreeTextures=0,this._numBytesAllocated=0,this._numBytesFree=0,this.freeTextures={},this.logEnabled=!1,this.usedTextures={}}acquireTexture(e,t,n){let s=PI(t,n),r=FI(e,s,n);r in this.freeTextures||(this.freeTextures[r]=[]),r in this.usedTextures||(this.usedTextures[r]=[]);let a=_I(e,s,this.gpgpu.gl,this.gpgpu.textureConfig,n);if(this.freeTextures[r].length>0){this.numFreeTextures--,this.numUsedTextures++,this._numBytesFree-=a,this.log();let i=this.freeTextures[r].shift();return this.usedTextures[r].push(i),i}let o;return s===Sn.PACKED_2X2_FLOAT32?o=this.gpgpu.createPackedMatrixTexture(e[0],e[1]):s===Sn.PACKED_2X2_FLOAT16?o=this.gpgpu.createFloat16PackedMatrixTexture(e[0],e[1]):s===Sn.UNPACKED_FLOAT32?o=this.gpgpu.createFloat32MatrixTexture(e[0],e[1]):s===Sn.UNPACKED_FLOAT16?o=this.gpgpu.createFloat16MatrixTexture(e[0],e[1]):s===Sn.PACKED_4X1_UNSIGNED_BYTE&&(o=this.gpgpu.createUnsignedBytesMatrixTexture(e[0],e[1])),this.usedTextures[r].push(o),this.numUsedTextures++,this._numBytesAllocated+=a,this.log(),o}releaseTexture(e,t,n,s){if(this.freeTextures==null)return;let r=PI(n,s),a=FI(t,r,s);a in this.freeTextures||(this.freeTextures[a]=[]);let o=_I(t,r,this.gpgpu.gl,this.gpgpu.textureConfig,s),i=K().get("WEBGL_DELETE_TEXTURE_THRESHOLD");i!==-1&&this._numBytesAllocated>i?(this.gpgpu.deleteMatrixTexture(e),this._numBytesAllocated-=o):(this.freeTextures[a].push(e),this.numFreeTextures++,this._numBytesFree+=o),this.numUsedTextures--;let l=this.usedTextures[a],c=l.indexOf(e);if(c<0)throw new Error("Cannot release a texture that was never provided by this texture manager");l.splice(c,1),this.log()}log(){if(!this.logEnabled)return;let e=this.numFreeTextures+this.numUsedTextures;console.log("Free/Used",`${this.numFreeTextures} / ${this.numUsedTextures}`,`(${e})`);let t=this._numBytesFree/this._numBytesAllocated;console.log(`Bytes allocated: ${this._numBytesAllocated}`),console.log(`Bytes unused: ${this._numBytesFree} (${Math.round(100*t)}%)`)}get numBytesAllocated(){return this._numBytesAllocated}get numBytesFree(){return this._numBytesFree}getNumUsedTextures(){return this.numUsedTextures}getNumFreeTextures(){return this.numFreeTextures}dispose(){if(this.freeTextures!=null){for(let e in this.freeTextures)this.freeTextures[e].forEach(t=>{this.gpgpu.deleteMatrixTexture(t)});for(let e in this.usedTextures)this.usedTextures[e].forEach(t=>{this.gpgpu.deleteMatrixTexture(t)});this.freeTextures=null,this.usedTextures=null,this.numUsedTextures=0,this.numFreeTextures=0,this._numBytesAllocated=0,this._numBytesFree=0}}};function kJ(e,t){let n=e;if(t===n.R32F)return 4;if(t===n.R16F)return 2;if(t===n.RGBA32F)return 16;if(t===e.RGBA)return 16;if(t===n.RGBA16F)return 8;throw new Error(`Unknown internal format ${t}`)}function _I(e,t,n,s,r){let a=SJ(t,s),o;if(r){let[l,c]=Uu(e[0],e[1]);o=l*c}else{let[l,c]=jd(e[0],e[1]);o=l*c}let i=kJ(n,a);return o*i}function SJ(e,t){switch(e){case Sn.PACKED_2X2_FLOAT32:return qy(t);case Sn.PACKED_2X2_FLOAT16:return Xy(t);case Sn.UNPACKED_FLOAT32:return Gy(t);case Sn.UNPACKED_FLOAT16:return Hy(t);case Sn.PACKED_4X1_UNSIGNED_BYTE:return jy(t);default:throw new Error(`Unknown physical texture type ${e}`)}}function IJ(e){return K().getBool("WEBGL_RENDER_FLOAT32_ENABLED")?e?Sn.PACKED_2X2_FLOAT32:Sn.UNPACKED_FLOAT32:e?Sn.PACKED_2X2_FLOAT16:Sn.UNPACKED_FLOAT16}function PI(e,t){if(e===_s.UPLOAD)return Sn.PACKED_2X2_FLOAT32;if(e===_s.RENDER||e==null)return IJ(t);if(e===_s.DOWNLOAD||e===_s.PIXELS)return Sn.PACKED_4X1_UNSIGNED_BYTE;throw new Error(`Unknown logical texture type ${e}`)}function FI(e,t,n){return`${e[0]}_${e[1]}_${t}_${n}`}var Mo=class{constructor(e,t){this.variableNames=["A"],this.outputShape=e,this.enableShapeUniforms=Fs(this.outputShape.length),this.userCode=`
float unaryOperation(float x) {
${t}
}
void main() {
float x = getAAtOutCoords();
float y = unaryOperation(x);
setOutput(y);
}
`}},yr="if (isnan(x)) return x;",CJ="return x;",OI="return abs(x);",TJ="return (x >= 0.0) ? x : (exp(x) - 1.0);",NJ=yr+`
return (x < 0.0) ? 0.0 : x;
`,EJ=yr+`
return (x < 0.0) ? 0.0 : min(6.0, x);
`,km="return x;",RJ="return 1.0 / (1.0 + exp(-1.0 * x));",$J="return x;",DJ=`
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;
`,_J=`
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;
`,PJ=`
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;
`,FJ="return 1.0 / (1.0 + exp(-1.0 * x));",Ku=class{constructor(e,t){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=e,this.enableShapeUniforms=Fs(this.outputShape.length),this.userCode=`
vec4 unaryOperation(vec4 x) {
${t}
}
void main() {
vec4 x = getAAtOutCoords();
vec4 y = unaryOperation(x);
setOutput(y);
}
`}},OJ=class{constructor(e){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!1,this.outputShape=e;let t=e.length,n=Gn("rc",t),s=bt(t),r=mJ(t,n),a=n.slice(-2),o=t<=1?"rc":`vec2(${a.join(",")})`;this.userCode=`
void main() {
${s} rc = getOutputCoords();
vec4 packedInput = getA(${r});
setOutput(getChannel(packedInput, ${o}));
}
`}},MJ=Xs.whereImpl,zJ=1e-7,LJ=1e-4,Sm={};function BJ(e){return e in Sm||(Sm[e]={}),Sm[e]}var WJ=K().getNumber("CPU_HANDOFF_SIZE_THRESHOLD"),VJ=600;function UJ(){return K().global.screen==null?1024:K().global.screen.height*K().global.screen.width*window.devicePixelRatio*VJ/1024/1024}var MI=class extends Ll{constructor(e){super();if(this.pendingRead=new WeakMap,this.pendingDisposal=new WeakSet,this.dataRefCount=new WeakMap,this.numBytesInGPU=0,this.uploadWaitMs=0,this.downloadWaitMs=0,this.lastGlFlushTime=0,this.warnedAboutMemory=!1,this.pendingDeletes=0,this.disposed=!1,!K().getBool("HAS_WEBGL"))throw new Error("WebGL is not supported on this device");if(e==null){let t=Fr(K().getNumber("WEBGL_VERSION"));this.binaryCache=BJ(K().getNumber("WEBGL_VERSION")),this.gpgpu=new wm(t),this.canvas=t.canvas,this.gpgpuCreatedLocally=!0}else this.gpgpu=e,this.binaryCache={},this.gpgpuCreatedLocally=!1,this.canvas=e.gl.canvas;this.textureManager=new wJ(this.gpgpu),this.numMBBeforeWarning=UJ(),this.texData=new Pc(this,ts())}nextDataId(){return MI.nextDataId++}numDataIds(){return this.texData.numDataIds()-this.pendingDeletes}write(e,t,n){if((K().getBool("WEBGL_CHECK_NUMERICAL_PROBLEMS")||K().getBool("DEBUG"))&&this.checkNumericalProblems(e),n==="complex64"&&e!=null)throw new Error("Cannot write to a complex64 dtype. Please use tf.complex(real, imag).");let s={id:this.nextDataId()};return this.texData.set(s,{shape:t,dtype:n,values:e,usage:_s.UPLOAD,refCount:1}),s}refCount(e){return this.texData.has(e)?this.texData.get(e).refCount:0}incRef(e){let t=this.texData.get(e);t.refCount++}decRef(e){if(this.texData.has(e)){let t=this.texData.get(e);t.refCount--}}move(e,t,n,s,r){if(K().getBool("DEBUG")&&this.checkNumericalProblems(t),s==="complex64")throw new Error("Cannot write to a complex64 dtype. Please use tf.complex(real, imag).");this.texData.set(e,{shape:n,dtype:s,values:t,usage:_s.UPLOAD,refCount:r})}disposeIntermediateTensorInfo(e){this.disposeData(e.dataId)}readSync(e){let t=this.texData.get(e),{values:n,dtype:s,complexTensorInfos:r,slice:a,shape:o,isPacked:i}=t;if(a!=null){let d;i?d=new Ku(o,km):d=new Mo(o,km);let p=this.runWebGLProgram(d,[{dataId:e,shape:o,dtype:s}],s),h=this.readSync(p.dataId);return this.disposeIntermediateTensorInfo(p),h}if(n!=null)return this.convertAndCacheOnCPU(e);if(s==="string")return n;let l=this.activeTimers!=null,c;l&&(c=v.now());let u;if(s==="complex64"){let d=this.readSync(r.real.dataId),p=this.readSync(r.imag.dataId);u=N.mergeRealAndImagArrays(d,p)}else u=this.getValuesFromTexture(e);return l&&(this.downloadWaitMs+=v.now()-c),this.convertAndCacheOnCPU(e,u)}async read(e){if(this.pendingRead.has(e)){let h=this.pendingRead.get(e);return new Promise(f=>h.push(f))}let t=this.texData.get(e),{values:n,shape:s,slice:r,dtype:a,complexTensorInfos:o,isPacked:i}=t;if(r!=null){let h;i?h=new Ku(s,km):h=new Mo(s,km);let f=this.runWebGLProgram(h,[{dataId:e,shape:s,dtype:a}],a),m=this.read(f.dataId);return this.disposeIntermediateTensorInfo(f),m}if(n!=null)return this.convertAndCacheOnCPU(e);if(K().getBool("DEBUG")&&!K().getBool("WEBGL_DOWNLOAD_FLOAT_ENABLED")&&K().getNumber("WEBGL_VERSION")===2)throw new Error("tensor.data() with WEBGL_DOWNLOAD_FLOAT_ENABLED=false and WEBGL_VERSION=2 not yet supported.");let l=null,c;if(a!=="complex64"&&K().get("WEBGL_BUFFER_SUPPORTED")){c=this.decode(e);let h=this.texData.get(c.dataId);l=this.gpgpu.createBufferFromTexture(h.texture,...fm(s))}this.pendingRead.set(e,[]),a!=="complex64"&&await this.gpgpu.createAndWaitForFence();let u;if(a==="complex64"){let h=await Promise.all([this.read(o.real.dataId),this.read(o.imag.dataId)]),f=h[0],m=h[1];u=N.mergeRealAndImagArrays(f,m)}else if(l==null)u=this.getValuesFromTexture(e);else{let h=v.sizeFromShape(s);u=this.gpgpu.downloadFloat32MatrixFromBuffer(l,h)}if(c!=null&&this.disposeIntermediateTensorInfo(c),l!=null){let h=this.gpgpu.gl;Ie(h,()=>h.deleteBuffer(l))}let d=this.convertAndCacheOnCPU(e,u),p=this.pendingRead.get(e);return this.pendingRead.delete(e),p.forEach(h=>h(d)),this.pendingDisposal.has(e)&&(this.pendingDisposal.delete(e),this.disposeData(e)&&ts().removeDataId(e,this),this.pendingDeletes--),d}bufferSync(e){let t=this.readSync(e.dataId),n=t;if(e.dtype==="string")try{n=t.map(s=>v.decodeString(s))}catch(s){throw new Error("Failed to decode encoded string bytes into utf-8")}return Be(e.shape,e.dtype,n)}checkNumericalProblems(e){if(e!=null)for(let t=0;t<e.length;t++){let n=e[t];if(!OS(n))throw K().getBool("WEBGL_RENDER_FLOAT32_CAPABLE")?Error(`The value ${n} cannot be represented with your current settings. Consider enabling float32 rendering: 'tf.env().set('WEBGL_RENDER_FLOAT32_ENABLED', true);'`):Error(`The value ${n} cannot be represented on this device.`)}}getValuesFromTexture(e){let{shape:t,dtype:n,isPacked:s}=this.texData.get(e),r=v.sizeFromShape(t);if(K().getBool("WEBGL_DOWNLOAD_FLOAT_ENABLED")){let d=this.decode(e),p=this.texData.get(d.dataId),h=this.gpgpu.downloadMatrixFromPackedTexture(p.texture,...fm(t)).subarray(0,r);return this.disposeIntermediateTensorInfo(d),h}let a=K().getBool("WEBGL_PACK")&&s===!0,o=a?Am(t):t,i=a?new TY(o):new CY(o),l=this.runWebGLProgram(i,[{shape:o,dtype:n,dataId:e}],"float32"),c=this.texData.get(l.dataId),u=this.gpgpu.downloadByteEncodedFloatMatrixFromOutputTexture(c.texture,c.texShape[0],c.texShape[1]).subarray(0,r);return this.disposeIntermediateTensorInfo(l),u}timerAvailable(){return K().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE")>0}async time(e){let t=this.activeTimers,n=[],s=!1;this.programTimersStack==null?(this.programTimersStack=n,s=!0):this.activeTimers.push(n),this.activeTimers=n,e();let r=v.flatten(this.activeTimers.map(i=>i.query)).filter(i=>i!=null),a=v.flatten(this.activeTimers.map(i=>i.name)).filter(i=>i!=null);this.activeTimers=t,s&&(this.programTimersStack=null);let o={uploadWaitMs:this.uploadWaitMs,downloadWaitMs:this.downloadWaitMs,kernelMs:null,wallMs:null};if(K().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE")>0){let i=await Promise.all(r);o.kernelMs=v.sum(i),o.getExtraProfileInfo=()=>i.map((l,c)=>({name:a[c],ms:l})).map(l=>`${l.name}: ${l.ms}`).join(", ")}else o.kernelMs={error:"WebGL query timers are not supported in this environment."};return this.uploadWaitMs=0,this.downloadWaitMs=0,o}memory(){return{unreliable:!1,numBytesInGPU:this.numBytesInGPU,numBytesInGPUAllocated:this.textureManager.numBytesAllocated,numBytesInGPUFree:this.textureManager.numBytesFree}}startTimer(){return K().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE")>0?this.gpgpu.beginQuery():{startMs:v.now(),endMs:null}}endTimer(e){return K().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE")>0?(this.gpgpu.endQuery(),e):(e.endMs=v.now(),e)}async getQueryTime(e){if(K().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE")>0)return this.gpgpu.waitForQueryAndGetTime(e);let t=e;return t.endMs-t.startMs}disposeData(e,t=!1){if(this.pendingDisposal.has(e))return!1;if(!this.texData.has(e))return!0;if(t?this.texData.get(e).refCount=0:this.texData.get(e).refCount--,!t&&this.texData.get(e).refCount>0)return!1;if(this.pendingRead.has(e))return this.pendingDisposal.add(e),this.pendingDeletes++,!1;this.releaseGPUData(e);let{complexTensorInfos:n}=this.texData.get(e);return n!=null&&(this.disposeData(n.real.dataId,t),this.disposeData(n.imag.dataId,t)),this.texData.delete(e),!0}releaseGPUData(e){let{texture:t,dtype:n,texShape:s,usage:r,isPacked:a,slice:o}=this.texData.get(e),i=o&&o.origDataId||e,l=this.dataRefCount.get(i);l>1?this.dataRefCount.set(i,l-1):(this.dataRefCount.delete(i),t!=null&&(this.numBytesInGPU-=this.computeBytes(s,n),this.textureManager.releaseTexture(t,s,r,a)));let c=this.texData.get(e);c.texture=null,c.texShape=null,c.isPacked=!1,c.slice=null}getTexture(e){return this.uploadToGPU(e),this.texData.get(e).texture}getDataInfo(e){return this.texData.get(e)}shouldExecuteOnCPU(e,t=WJ){return K().getBool("WEBGL_CPU_FORWARD")&&e.every(n=>this.texData.get(n.dataId).texture==null&&v.sizeFromShape(n.shape)<t)}getGPGPUContext(){return this.gpgpu}where(e){N.warn("tf.where() in webgl locks the UI thread. Call tf.whereAsync() instead");let t=e.dataSync();return MJ(e.shape,t)}packedUnaryOp(e,t,n){let s=new Ku(e.shape,t),r=this.compileAndRun(s,[e],n);return ts().makeTensorFromDataId(r.dataId,r.shape,r.dtype)}abs(e){if(this.shouldExecuteOnCPU([e])&&e.dtype!=="complex64"){let s=EI(this.texData.get(e.dataId).values);return this.makeOutput(e.shape,e.dtype,s)}if(K().getBool("WEBGL_PACK_UNARY_OPERATIONS"))return this.packedUnaryOp(e,OI,e.dtype);let t=new Mo(e.shape,OI),n=this.compileAndRun(t,[e]);return ts().makeTensorFromDataId(n.dataId,n.shape,n.dtype)}makeTensorInfo(e,t,n){let s;if(t==="string"&&n!=null&&n.length>0&&v.isString(n[0])){let r=n.map(a=>v.encodeString(a));s=this.write(r,e,t)}else s=this.write(n,e,t);return this.texData.get(s).usage=null,{dataId:s,shape:e,dtype:t}}makeOutput(e,t,n){let{dataId:s}=this.makeTensorInfo(e,t,n);return ts().makeTensorFromDataId(s,e,t,this)}unpackTensor(e){let t=new OJ(e.shape);return this.runWebGLProgram(t,[e],e.dtype)}packTensor(e){let t=new gJ(e.shape),n=!0;return this.runWebGLProgram(t,[e],e.dtype,null,n)}packedReshape(e,t){let n=[fl(e.shape),...ml(e.shape)],s={dtype:e.dtype,shape:n,dataId:e.dataId},r=[fl(t),...ml(t)],a=new DI(r,n),o=!0,i=[n],l=this.runWebGLProgram(a,[s],e.dtype,i,o);return{dataId:l.dataId,shape:t,dtype:l.dtype}}decode(e){let t=this.texData.get(e),{isPacked:n,shape:s,dtype:r}=t,a=Am(s),o,i=fm(a);n?o=new IY(a):o=new SY(a);let l=!0,c=[i],u=this.runWebGLProgram(o,[{shape:a,dtype:r,dataId:e}],r,c,l);return{dtype:r,shape:s,dataId:u.dataId}}runWebGLProgram(e,t,n,s,r=!1){let a=this.makeTensorInfo(e.outputShape,n),o=this.texData.get(a.dataId);if(e.packedOutput&&(o.isPacked=!0),e.outPackingScheme===Hd.DENSE){let m=fm(e.outputShape);o.texShape=m.map(g=>g*2)}if(e.outTexUsage!=null&&(o.usage=e.outTexUsage),v.sizeFromShape(a.shape)===0)return o.values=v.getTypedArrayFromDType(a.dtype,0),a;let i=[],l=t.map(m=>{if(m.dtype==="complex64")throw new Error("GPGPUProgram does not support complex64 input. For complex64 dtypes, please separate the program into real and imaginary parts.");let g=this.texData.get(m.dataId);if(g.texture==null){if(!e.packedInputs&&v.sizeFromShape(m.shape)<=K().getNumber("WEBGL_SIZE_UPLOAD_UNIFORM"))return{shape:m.shape,texData:null,isUniform:!0,uniformValues:g.values};e.packedInputs&&(g.isPacked=!0,g.shape=m.shape)}else if(!!g.isPacked!=!!e.packedInputs)m=g.isPacked?this.unpackTensor(m):this.packTensor(m),i.push(m),g=this.texData.get(m.dataId);else if(g.isPacked&&!Kd(g.shape,m.shape)){let A=m,x=m.shape;m.shape=g.shape,m=this.packedReshape(m,x),i.push(m),g=this.texData.get(m.dataId),A.shape=x}return this.uploadToGPU(m.dataId),{shape:m.shape,texData:g,isUniform:!1}});this.uploadToGPU(a.dataId);let c={shape:a.shape,texData:o,isUniform:!1},u=kY(e,l,c),d=this.getAndSaveBinary(u,()=>vY(this.gpgpu,e,l,c)),p=this.activeTimers!=null,h;p&&(h=this.startTimer()),wY(this.gpgpu,d,l,c,s),i.forEach(m=>this.disposeIntermediateTensorInfo(m)),p&&(h=this.endTimer(h),this.activeTimers.push({name:e.constructor.name,query:this.getQueryTime(h)}));let f=K().get("WEBGL_FLUSH_THRESHOLD");if(f>0){let m=v.now();m-this.lastGlFlushTime>f&&(this.gpgpu.gl.flush(),this.lastGlFlushTime=m)}if(!K().getBool("WEBGL_LAZILY_UNPACK")&&o.isPacked&&r===!1){let m=this.unpackTensor(a);return this.disposeIntermediateTensorInfo(a),m}return a}compileAndRun(e,t,n,s,r=!1){return n=n||t[0].dtype,this.runWebGLProgram(e,t,n,s,r)}getAndSaveBinary(e,t){return e in this.binaryCache||(this.binaryCache[e]=t()),this.binaryCache[e]}getTextureManager(){return this.textureManager}dispose(){this.disposed||(K().getBool("IS_TEST")||Object.keys(this.binaryCache).forEach(t=>{this.gpgpu.deleteProgram(this.binaryCache[t].webGLProgram),delete this.binaryCache[t]}),this.textureManager.dispose(),this.canvas!=null&&typeof HTMLCanvasElement!="undefined"&&this.canvas instanceof HTMLCanvasElement?this.canvas.remove():this.canvas=null,this.gpgpuCreatedLocally&&(this.gpgpu.program=null,this.gpgpu.dispose()),this.disposed=!0)}floatPrecision(){return this.floatPrecisionValue==null&&(this.floatPrecisionValue=q(()=>{if(!K().get("WEBGL_RENDER_FLOAT32_ENABLED")){let e=K().getBool("DEBUG");K().set("DEBUG",!1);let t=this.abs(Ee(1e-8)).dataSync()[0];if(K().set("DEBUG",e),t>0)return 32}return 16})),this.floatPrecisionValue}epsilon(){return this.floatPrecision()===32?zJ:LJ}uploadToGPU(e){let t=this.texData.get(e),{shape:n,dtype:s,values:r,texture:a,usage:o,isPacked:i}=t;if(a!=null)return;let l=this.activeTimers!=null,c;l&&(c=v.now());let u=t.texShape;if(u==null&&(u=QS(n,i),t.texShape=u),r!=null){let d=Am(n),p,h=u[1],f=u[0],m=r instanceof Uint8Array||r instanceof Uint8ClampedArray;i?([h,f]=Uu(u[0],u[1]),p=new EY(d,m)):p=new NY(d,m);let g=this.makeTensorInfo([f,h],s);m?this.texData.get(g.dataId).usage=_s.PIXELS:this.texData.get(g.dataId).usage=_s.UPLOAD,this.gpgpu.uploadDenseMatrixToTexture(this.getTexture(g.dataId),h,f,r);let A=[[f,h]],x=!0,y=this.runWebGLProgram(p,[g],s,A,x),b=this.texData.get(y.dataId);t.texture=b.texture,t.texShape=b.texShape,t.isPacked=b.isPacked,t.usage=b.usage,this.disposeIntermediateTensorInfo(g),this.texData.delete(y.dataId),t.values=null,l&&(this.uploadWaitMs+=v.now()-c)}else{let d=this.acquireTexture(u,o,s,i);t.texture=d}}convertAndCacheOnCPU(e,t){let n=this.texData.get(e),{dtype:s}=n;return this.releaseGPUData(e),t!=null&&(n.values=GJ(t,s)),n.values}acquireTexture(e,t,n,s){if(this.numBytesInGPU+=this.computeBytes(e,n),!this.warnedAboutMemory&&this.numBytesInGPU>this.numMBBeforeWarning*1024*1024){let r=(this.numBytesInGPU/1024/1024).toFixed(2);this.warnedAboutMemory=!0,console.warn(`High memory usage in GPU: ${r} MB, most likely due to a memory leak`)}return this.textureManager.acquireTexture(e,t,s)}computeBytes(e,t){return e[0]*e[1]*v.bytesPerElement(t)}},Yd=MI;Yd.nextDataId=0;function GJ(e,t){if(t==="float32"||t==="complex64")return e;if(t==="int32"||t==="bool"){let n=t==="int32"?new Int32Array(e.length):new Uint8Array(e.length);for(let s=0;s<n.length;++s)n[s]=Math.round(e[s]);return n}else throw new Error(`Unknown dtype ${t}`)}var HJ="0.0.0";function zI(){K().set("WEBGL_FORCE_F16_TEXTURES",!0)}gu.isBrowser()&&Zi("webgl",()=>new Yd,2);var jJ={forceHalfFloat:zI},LI=`
if (isnan(a)) return a;
if (isnan(b)) return b;
`,Zu=class{constructor(e,t,n){this.variableNames=["A","B"],this.outputShape=N.assertAndGetBroadcastShape(t,n),this.enableShapeUniforms=Fs(this.outputShape.length),this.userCode=`
float binaryOperation(float a, float b) {
${e}
}
void main() {
float a = getAAtOutCoords();
float b = getBAtOutCoords();
setOutput(binaryOperation(a, b));
}
`}},Im=`
result.r = isNaN.r > 0. ? NAN : result.r;
result.g = isNaN.g > 0. ? NAN : result.g;
result.b = isNaN.b > 0. ? NAN : result.b;
result.a = isNaN.a > 0. ? NAN : result.a;
`,Jd=class{constructor(e,t,n,s=!1){this.variableNames=["A","B"],this.supportsBroadcasting=!0,this.packedInputs=!0,this.packedOutput=!0,this.outputShape=N.assertAndGetBroadcastShape(t,n);let r=this.outputShape.length;this.enableShapeUniforms=Fs(r);let a="";if(s)if(r===0||v.sizeFromShape(this.outputShape)===1)a=`
result.y = 0.;
result.z = 0.;
result.w = 0.;
`;else if(a=`
${bt(r)} coords = getOutputCoords();
`,r===1)this.enableShapeUniforms?a+=`
result.y = (coords + 1) >= outShape ? 0. : result.y;
result.z = 0.;
result.w = 0.;
`:a+=`
result.y = (coords + 1) >= ${this.outputShape[0]} ? 0. : result.y;
result.z = 0.;
result.w = 0.;
`;else{let i=Gn("coords",r);this.enableShapeUniforms?a+=`
bool nextRowOutOfBounds =
(${i[r-2]} + 1) >= outShape[${r} - 2];
bool nextColOutOfBounds =
(${i[r-1]} + 1) >= outShape[${r} - 1];
result.y = nextColOutOfBounds ? 0. : result.y;
result.z = nextRowOutOfBounds ? 0. : result.z;
result.w = nextColOutOfBounds || nextRowOutOfBounds ? 0. : result.w;
`:a+=`
bool nextRowOutOfBounds =
(${i[r-2]} + 1) >= ${this.outputShape[r-2]};
bool nextColOutOfBounds =
(${i[r-1]} + 1) >= ${this.outputShape[r-1]};
result.y = nextColOutOfBounds ? 0. : result.y;
result.z = nextRowOutOfBounds ? 0. : result.z;
result.w = nextColOutOfBounds || nextRowOutOfBounds ? 0. : result.w;
`}this.userCode=`
vec4 binaryOperation(vec4 a, vec4 b) {
${e}
}
void main() {
vec4 a = getAAtOutCoords();
vec4 b = getBAtOutCoords();
vec4 result = binaryOperation(a, b);
${a}
setOutput(result);
}
`}};function ys(e){let{inputs:t,backend:n}=e,{x:s}=t;return n.incRef(s.dataId),{dataId:s.dataId,shape:s.shape,dtype:s.dtype}}var qJ={kernelName:Ba,backendName:"webgl",kernelFunc:ys};function zo(e){let{inputs:t,backend:n}=e,{real:s,imag:r}=t,a=n.makeTensorInfo(s.shape,"complex64"),o=n.texData.get(a.dataId),i=ys({inputs:{x:s},backend:n}),l=ys({inputs:{x:r},backend:n});return o.complexTensorInfos={real:i,imag:l},a}var XJ={kernelName:zc,backendName:"webgl",kernelFunc:zo},BI="return (a < 0.) ? b * a : a;",WI=`
vec4 aLessThanZero = vec4(lessThan(a, vec4(0.)));
return (aLessThanZero * (b * a)) + ((vec4(1.0) - aLessThanZero) * a);
`;function KJ(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{alpha:a}=s,o=n.makeTensorInfo([],"float32",v.createScalarValue(a,"float32")),i=K().getBool("WEBGL_PACK_BINARY_OPERATIONS")?new Jd(WI,r.shape,o.shape):new Zu(BI,r.shape,o.shape),l=n.runWebGLProgram(i,[r,o],"float32");return n.disposeIntermediateTensorInfo(o),l}var ZJ={kernelName:fi,backendName:"webgl",kernelFunc:KJ},VI="return (a < 0.) ? b * a : a;",UI=`
vec4 aLessThanZero = vec4(lessThan(a, vec4(0.)));
return (aLessThanZero * (b * a)) + ((vec4(1.0) - aLessThanZero) * a);
`;function YJ(e){let{inputs:t,backend:n}=e,{x:s,alpha:r}=t,a=K().getBool("WEBGL_PACK_BINARY_OPERATIONS")?new Jd(UI,s.shape,r.shape):new Zu(VI,s.shape,r.shape);return n.runWebGLProgram(a,[s,r],"float32")}var JJ={kernelName:Ja,backendName:"webgl",kernelFunc:YJ},GI="if (isnan(x)) return x;",QJ=`
if (isnan(a)) return a;
if (isnan(b)) return b;
`,eQ=`
result.r = isNaN.r > 0. ? NAN : result.r;
result.g = isNaN.g > 0. ? NAN : result.g;
result.b = isNaN.b > 0. ? NAN : result.b;
result.a = isNaN.a > 0. ? NAN : result.a;
`;function ot({opSnippet:e,packedOpSnippet:t,cpuKernelImpl:n,dtype:s}){return({inputs:r,backend:a})=>{let{x:o}=r,i=a,l=s||o.dtype;if(i.shouldExecuteOnCPU([o])&&n!=null){let d=i.texData.get(o.dataId),p=n(d.values,l);return i.makeTensorInfo(o.shape,l,p)}let c=K().getBool("WEBGL_PACK_UNARY_OPERATIONS")&&t!=null,u;return c?u=new Ku(o.shape,t):u=new Mo(o.shape,e),i.runWebGLProgram(u,[o],l)}}function In({opSnippet:e,packedOpSnippet:t,checkOutOfBounds:n=!1,supportsComplex:s=!1,cpuKernelImpl:r,dtype:a}){return({inputs:o,backend:i})=>{let{a:l,b:c}=o,u=i;if(s&&l.dtype==="complex64"){let f=u.texData.get(l.dataId),m=u.texData.get(c.dataId),[g,A]=[[f.complexTensorInfos.real,m.complexTensorInfos.real],[f.complexTensorInfos.imag,m.complexTensorInfos.imag]].map(y=>{let[b,w]=y,k={dataId:b.dataId,dtype:b.dtype,shape:l.shape},C={dataId:w.dataId,dtype:w.dtype,shape:c.shape},E=new Zu(e,l.shape,c.shape);return u.runWebGLProgram(E,[k,C],Ln(b.dtype,w.dtype))}),x=zo({inputs:{real:g,imag:A},backend:u});return u.disposeIntermediateTensorInfo(g),u.disposeIntermediateTensorInfo(A),x}let d=a||Ln(l.dtype,c.dtype);if((l.dtype==="string"||c.dtype==="string"||u.shouldExecuteOnCPU([l,c]))&&r!=null){let f=u.texData.get(l.dataId).values,m=u.texData.get(c.dataId).values,g=l.dtype==="string"?N.fromUint8ToStringArray(f):f,A=l.dtype==="string"?N.fromUint8ToStringArray(m):m,[x,y]=r(l.shape,c.shape,g,A,d),b=u.makeTensorInfo(y,d),w=u.texData.get(b.dataId);return w.values=x,b}let p=K().getBool("WEBGL_PACK_BINARY_OPERATIONS")&&t!=null,h;return p?h=new Jd(t,l.shape,c.shape,n):h=new Zu(e,l.shape,c.shape),u.runWebGLProgram(h,[l,c],d)}}function Cm(e,t=!1){if(e==="linear")return t?$J:CJ;if(e==="relu")return t?_J:NJ;if(e==="elu")return t?DJ:TJ;if(e==="relu6")return t?PJ:EJ;if(e==="prelu")return t?UI:VI;if(e==="leakyrelu")return t?WI:BI;if(e==="sigmoid")return t?FJ:RJ;throw new Error(`Activation ${e} has not been implemented for the WebGL backend.`)}var HI=class{constructor(e,t,n,s=!1,r=!1,a=!1,o=null,i=!1,l=!1){this.variableNames=["matrixA","matrixB"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=n,this.enableShapeUniforms=Fs(this.outputShape.length);let c=s?e[1]:e[2],u=Math.ceil(c/2),d=s?"i * 2, rc.y":"rc.y, i * 2",p=r?"rc.z, i * 2":"i * 2, rc.z",h=s?["a.xxyy","a.zzww"]:["a.xxzz","a.yyww"],f=r?["b.xzxz","b.ywyw"]:["b.xyxy","b.zwzw"],m="",g="";o&&(i?m=`vec4 activation(vec4 a) {
vec4 b = getPreluActivationWeightsAtOutCoords();
${o}
}`:l?m=`vec4 activation(vec4 a) {
vec4 b = getLeakyreluAlphaAtOutCoords();
${o}
}`:m=`vec4 activation(vec4 x) {
${o}
}`,g="result = activation(result);");let A=a?"result += getBiasAtOutCoords();":"";a&&this.variableNames.push("bias"),i&&this.variableNames.push("preluActivationWeights"),l&&this.variableNames.push("leakyreluAlpha");let x="rc.x",y="rc.x";e[0]<t[0]?x=`int(min(float(rc.x), ${e[0]-1}.))`:t[0]<e[0]&&(y=`int(min(float(rc.x), ${t[0]-1}.))`),this.userCode=`
${m}
// Don't use uniform for sharedDimensionPacked for performance.
const float sharedDimension = ${u}.0;
vec4 dot2x2ARowBCol(ivec3 rc) {
vec4 result = vec4(0);
for (int i = 0; i < ${u}; i++) {
int batchA = ${x};
int batchB = ${y};
vec4 a = getMatrixA(batchA, ${d});
vec4 b = getMatrixB(batchB, ${p});
// These swizzled products need to be separately added.
// See: https://github.com/tensorflow/tfjs/issues/1735
result += (${h[0]} * ${f[0]});
result += (${h[1]} * ${f[1]});
}
return result;
}
void main() {
ivec3 rc = getOutputCoords();
vec4 result = dot2x2ARowBCol(rc);
${A}
${g}
setOutput(result);
}
`}},jI={REAL:"return areal * breal - aimag * bimag;",IMAG:"return areal * bimag + aimag * breal;"},qI=class{constructor(e,t,n){this.variableNames=["AReal","AImag","BReal","BImag"],this.outputShape=N.assertAndGetBroadcastShape(t,n),this.userCode=`
float binaryOpComplex(
float areal, float aimag, float breal, float bimag) {
${e}
}
void main() {
float areal = getARealAtOutCoords();
float aimag = getAImagAtOutCoords();
float breal = getBRealAtOutCoords();
float bimag = getBImagAtOutCoords();
setOutput(binaryOpComplex(areal, aimag, breal, bimag));
}
`}},XI="return a * b;";function Zy(e){let{inputs:t,backend:n}=e,{a:s,b:r}=t,a=N.upcastType(s.dtype,r.dtype);if(s.dtype==="complex64"){let i=n.texData.get(s.dataId),l=n.texData.get(r.dataId),c=new qI(jI.REAL,s.shape,r.shape),u=new qI(jI.IMAG,s.shape,r.shape),d=[{dataId:i.complexTensorInfos.real.dataId,dtype:i.complexTensorInfos.real.dtype,shape:s.shape},{dataId:i.complexTensorInfos.imag.dataId,dtype:i.complexTensorInfos.imag.dtype,shape:s.shape},{dataId:l.complexTensorInfos.real.dataId,dtype:l.complexTensorInfos.real.dtype,shape:r.shape},{dataId:l.complexTensorInfos.imag.dataId,dtype:l.complexTensorInfos.imag.dtype,shape:r.shape}],p=n.runWebGLProgram(c,d,"float32"),h=n.runWebGLProgram(u,d,"float32"),f=zo({inputs:{real:p,imag:h},backend:n});return n.disposeIntermediateTensorInfo(p),n.disposeIntermediateTensorInfo(h),f}if(n.shouldExecuteOnCPU([s,r])){let i=n.texData.get(s.dataId),l=n.texData.get(r.dataId),[c,u]=ZY(s.shape,r.shape,i.values,l.values,a),d=n.makeTensorInfo(u,a),p=n.texData.get(d.dataId);return p.values=c,d}let o;return K().getBool("WEBGL_PACK_BINARY_OPERATIONS")?o=new Jd(XI,s.shape,r.shape):o=new Zu(XI,s.shape,r.shape),n.runWebGLProgram(o,[s,r],a)}var tQ={kernelName:Ka,backendName:"webgl",kernelFunc:Zy};function nQ(e,t,n){let s=[fl(e.shape),...ml(e.shape)],r={dtype:e.dtype,shape:s,dataId:e.dataId},a=[fl(t),...ml(t)],o=new DI(a,s),i=!0,l=[s],c=n.runWebGLProgram(o,[r],e.dtype,l,i);return{dataId:c.dataId,shape:t,dtype:c.dtype}}function ve(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{shape:a}=s,o=n,i=v.sizeFromShape(r.shape),l=v.inferFromImplicitShape(a,i),c=v.sizeFromShape(l);v.assert(i===c,()=>`The new shape (${l}) has ${c} elements and the old shape (${r.shape}) has ${i} elements. The new shape and old shape must have the same number of elements.`);let u=o.texData.get(r.dataId);return u.isPacked&&!Kd(r.shape,l)&&!(u.texture!==null&&Kd(u.shape,l))?nQ(r,l,o):(o.incRef(r.dataId),{dataId:r.dataId,shape:l,dtype:r.dtype})}var sQ={kernelName:Ci,backendName:"webgl",kernelFunc:ve},KI=class{constructor(e,t){this.variableNames=["x"];let{windowSize:n,batchSize:s,inSize:r,outSize:a}=e;this.outputShape=[s,a];let o=Math.floor(n/4)*4,i=n%4,l="sumValue += dot(values, ones);";if(t!=null){let u=1/t;l=`sumValue += dot(values * ${v.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 < ${o}; i += 4) {
int inIdx = inOffset + i;
vec4 values = vec4(
getValue(batch, inIdx),
getValue(batch, inIdx + 1),
getValue(batch, inIdx + 2),
getValue(batch, inIdx + 3)
);
${l}
}
int inIdx = inOffset + ${o};
if (${i===1}) {
vec4 values = vec4(getValue(batch, inIdx), 0.0, 0.0, 0.0);
${l}
} else if (${i===2}) {
vec4 values = vec4(
getValue(batch, inIdx),
getValue(batch, inIdx + 1), 0.0, 0.0);
${l}
} else if (${i===3}) {
vec4 values = vec4(
getValue(batch, inIdx),
getValue(batch, inIdx + 1),
getValue(batch, inIdx + 2), 0.0);
${l}
}
setOutput(sumValue);
}
`}},rQ=class{constructor(e,t){this.variableNames=["x"];let{windowSize:n,batchSize:s,inSize:r,outSize:a}=e;this.outputShape=[s,a];let o="0.0",i="";t==="prod"?o="1.0":t==="min"?(o="1.0 / 1e-20",i="min"):t==="max"&&(o="-1.0 / 1e-20",i="max");let l=`${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,d=`
if (${t==="sum"}) {
sumValue += dot(values, ones);
} else if (${t==="prod"}) {
vec2 tmp = vec2(values[0], values[1]) * vec2(values[2], values[3]);
prodValue *= tmp[0] * tmp[1];
} else {
minMaxValue = ${i}(values, minMaxValue);
if (${t==="min"} || ${t==="max"}) {
minMaxValue = ${i}(values, minMaxValue);
bvec4 isNaN = isnan(values);
if (isNaN.r || isNaN.g || isNaN.b || isNaN.a) {
minMaxValue = vec4(NAN);
}
}
}
`,p="vec4";t==="all"?(o="1.0",d=`
bool reducedAllValue = all(values);
float floatedReducedAllValue = float(reducedAllValue);
allValue = float(allValue >= 1.0 && floatedReducedAllValue >= 1.0);
`,p="bvec4"):t==="any"&&(o="0.0",d=`
bool reducedAnyValue = any(values);
float floatedReducedAnyValue = float(reducedAnyValue);
anyValue = float(anyValue >= 1.0 || floatedReducedAnyValue >= 1.0);
`,p="bvec4");let h="";r%n>0&&(h=`
if (inIdx < 0 || inIdx >= ${r}) {
return initializationValue;
}
`),this.userCode=`
const float initializationValue = ${o};
const vec4 ones = vec4(1.0, 1.0, 1.0, 1.0);
float getValue(int batch, int inIdx) {
${h}
return getX(batch, inIdx);
}
void main() {
ivec2 coords = getOutputCoords();
int batch = coords[0];
int outIdx = coords[1];
int inOffset = outIdx * ${n};
vec4 minMaxValue = vec4(${o});
float prodValue = 1.0;
float sumValue = 0.0;
float allValue = 1.0;
float anyValue = 0.0;
for (int i = 0; i < ${c}; i += 4) {
int inIdx = inOffset + i;
${p} values = ${p}(
getValue(batch, inIdx),
getValue(batch, inIdx + 1),
getValue(batch, inIdx + 2),
getValue(batch, inIdx + 3)
);
${d}
}
int inIdx = inOffset + ${c};
if (${u===1}) {
${p} values = ${p}(
getValue(batch, inIdx),
initializationValue,
initializationValue,
initializationValue
);
${d}
} else if (${u===2}) {
${p} values = ${p}(
getValue(batch, inIdx),
getValue(batch, inIdx + 1),
initializationValue,
initializationValue
);
${d}
} else if (${u===3}) {
${p} values = ${p}(
getValue(batch, inIdx),
getValue(batch, inIdx + 1),
getValue(batch, inIdx + 2),
initializationValue
);
${d}
}
setOutput(${l});
}
`}};function aQ(e){let t=[];for(;t.length===0||t[t.length-1].outSize!==1;){let n=t.length?t[t.length-1].outSize:e[1],s=N.computeOptimalWindowSize(n);t.push({inSize:n,windowSize:s,outSize:Math.ceil(n/s)})}return t}function yl(e,t,n,s){let r=aQ(e.shape),a=e;for(let o=0;o<r.length;o++){let{inSize:i,windowSize:l,outSize:c}=r[o],u,d;n==="mean"?u=o===0?new KI({windowSize:l,inSize:i,batchSize:e.shape[0],outSize:c},i):new KI({windowSize:l,inSize:i,batchSize:e.shape[0],outSize:c}):u=new rQ({windowSize:l,inSize:i,batchSize:e.shape[0],outSize:c},n),d=a,a=s.runWebGLProgram(u,[a],t),d.dataId!==e.dataId&&s.disposeIntermediateTensorInfo(d)}return a}var oQ=class{constructor(e,t){this.variableNames=["A"];let n=new Array(e.length);for(let a=0;a<n.length;a++)n[a]=e[t[a]];this.outputShape=n,this.rank=n.length;let s=bt(this.rank),r=iQ(t);this.userCode=`
void main() {
${s} resRC = getOutputCoords();
setOutput(getA(${r}));
}
`}};function iQ(e){let t=e.length;if(t>6)throw Error(`Transpose for rank ${t} is not yet supported`);let n=["resRC.x","resRC.y","resRC.z","resRC.w","resRC.u","resRC.v"],s=new Array(t);for(let r=0;r<e.length;r++)s[e[r]]=n[r];return s.join()}var lQ=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 ${this.rank} is not yet supported.`);let s=bt(this.rank),r=$I("rc",this.rank),a=new Array(this.rank);for(let c=0;c<t.length;c++)a[t[c]]=r[c];let o=`vec2(${a.slice(-2).join()})`,i=`++${r[this.rank-1]} < ${n[this.rank-1]}`,l=`getChannel(getA(${a.join()}), ${o})`;this.userCode=`
void main() {
${s} rc = getOutputCoords();
vec4 result = vec4(0.);
result[0] = ${l};
if(${i}) {
result[1] = ${l};
}
--${r[this.rank-1]};
if(++${r[this.rank-2]} < ${n[this.rank-2]}) {
result[2] = ${l};
if(${i}) {
result[3] = ${l};
}
}
setOutput(result);
}
`}};function Tm(e,t,n){let s=K().getBool("WEBGL_PACK_ARRAY_OPERATIONS")?new lQ(e.shape,t):new oQ(e.shape,t);return n.runWebGLProgram(s,[e],e.dtype)}function uQ(e,t,n,s){let r=t,a=e.shape.length,o=v.parseAxisParam(r,e.shape),i=o,l=N.getAxesPermutation(i,a),c=l!=null,u=e;c&&(u=Tm(e,l,s),i=N.getInnerMostAxes(i.length,a)),N.assertAxesAreInnerMostDims("sum",i,a);let[d,p]=N.computeOutAndReduceShapes(u.shape,i),h=d;n&&(h=N.expandShapeToKeepDim(d,o));let f=v.sizeFromShape(p),g=v.sizeFromShape(e.shape)/f,A=ve({inputs:{x:u},attrs:{shape:[g,f]},backend:s}),x=od(e.dtype),y=yl(A,x,"sum",s),b=ve({inputs:{x:y},attrs:{shape:h},backend:s});return s.disposeIntermediateTensorInfo(A),s.disposeIntermediateTensorInfo(y),c&&s.disposeIntermediateTensorInfo(u),b}function Nm(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{axis:a,keepDims:o}=s;return uQ(r,a,o,n)}var cQ={kernelName:oo,backendName:"webgl",kernelFunc:Nm};function Hn(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{perm:a}=s,o=n,i=r.shape.length,l=new Array(i);for(let u=0;u<l.length;u++)l[u]=r.shape[a[u]];let c;if(o.shouldExecuteOnCPU([r])){let d=o.texData.get(r.dataId).values,p=Ky(d,r.shape,r.dtype,a,l);c=o.makeTensorInfo(l,r.dtype);let h=o.texData.get(c.dataId);h.values=p}else c=Tm(r,a,o);return c}var dQ={kernelName:po,backendName:"webgl",kernelFunc:Hn},ZI=1e3;function Em({a:e,b:t,transposeA:n,transposeB:s,backend:r,bias:a=null,preluActivationWeights:o=null,leakyreluAlpha:i=0,activation:l=null}){let c=e.shape.length,u=t.shape.length,d=n?e.shape[c-2]:e.shape[c-1],p=s?t.shape[u-1]:t.shape[u-2],h=n?e.shape[c-1]:e.shape[c-2],f=s?t.shape[u-2]:t.shape[u-1],m=e.shape.slice(0,-2),g=t.shape.slice(0,-2),A=v.sizeFromShape(m),x=v.sizeFromShape(g),b=qi.assertAndGetBroadcastShape(e.shape.slice(0,-2),t.shape.slice(0,-2)).concat([h,f]);v.assert(d===p,()=>`Error in matMul: inner shapes (${d}) and (${p}) of Tensors with shapes ${e.shape} and ${t.shape} and transposeA=${n} and transposeB=${s} must match.`);let w=n?[A,d,h]:[A,h,d],k=s?[x,f,p]:[x,p,f],C=ve({inputs:{x:e},backend:r,attrs:{shape:w}}),E=ve({inputs:{x:t},backend:r,attrs:{shape:k}}),R=[C,E],F=Math.max(A,x),D=n?C.shape[1]:C.shape[2],P=a!=null,T=o!=null,M=l==="leakyrelu",U=l!=null?Cm(l,!0):null,j=P||T||M||U!=null,z;if((h===1||f===1)&&D>ZI&&j===!1){let Z=C,J=E;n&&(Z=Hn({inputs:{x:C},backend:r,attrs:{perm:[0,2,1]}}),R.push(Z)),s&&(J=Hn({inputs:{x:E},backend:r,attrs:{perm:[0,2,1]}}),R.push(J));let ee=f!==1,ne=f===1,Q=Z;ee&&(Q=ve({inputs:{x:Z},backend:r,attrs:{shape:[F,D,1]}}),R.push(Q));let te=f===1?2:1,oe=J;ne&&(oe=ve({inputs:{x:J},backend:r,attrs:{shape:[F,1,D]}}),R.push(oe));let he=Zy({inputs:{a:Q,b:oe},backend:r});z=Nm({inputs:{x:he},backend:r,attrs:{axis:te,keepDims:!0}}),R.push(he)}else{let Z=Ln(e.dtype,t.dtype),J=new HI(w,k,[F,h,f],n,s,P,U,T,M),ee=[C,E];if(a!=null&&ee.push(a),T&&ee.push(o),M){let ne=r.makeTensorInfo([],"float32",v.createScalarValue(i,"float32"));ee.push(ne),R.push(ne)}z=r.runWebGLProgram(J,ee,Z)}let X=ve({inputs:{x:z},backend:r,attrs:{shape:b}});R.push(z);for(let Z of R)r.disposeIntermediateTensorInfo(Z);return X}function pQ(e){let{inputs:t,backend:n,attrs:s}=e,{a:r,b:a,bias:o,preluActivationWeights:i}=t,{transposeA:l,transposeB:c,activation:u,leakyreluAlpha:d}=s;return Em({a:r,b:a,transposeA:l,transposeB:c,backend:n,bias:o,preluActivationWeights:i,leakyreluAlpha:d,activation:u})}var hQ={kernelName:fo,backendName:"webgl",kernelFunc:pQ},YI="return abs(x);";function fQ(e){let{inputs:t,backend:n}=e,{x:s}=t;if(n.shouldExecuteOnCPU([s])&&s.dtype!=="complex64"){let a=n.texData.get(s.dataId),o=EI(a.values);return n.makeTensorInfo(s.shape,s.dtype,o)}let r;return K().getBool("WEBGL_PACK_UNARY_OPERATIONS")?r=new Ku(s.shape,YI):r=new Mo(s.shape,YI),n.runWebGLProgram(r,[s],s.dtype)}var mQ={kernelName:ti,backendName:"webgl",kernelFunc:fQ},gQ=yr+`
if (abs(x) > 1.) {
return NAN;
}
return acos(x);
`,AQ=ot({opSnippet:gQ}),yQ={kernelName:Vl,backendName:"webgl",kernelFunc:AQ},xQ=yr+`
if (x < 1.0) return NAN;
return log(x + sqrt(x * x - 1.0));`,bQ=ot({opSnippet:xQ}),vQ={kernelName:Ul,backendName:"webgl",kernelFunc:bQ},JI="return a + b;",wQ=In({opSnippet:JI,packedOpSnippet:JI,supportsComplex:!0,cpuKernelImpl:$Y}),kQ={kernelName:Vr,backendName:"webgl",kernelFunc:wQ},SQ=class{constructor(e,t){this.outputShape=[],this.outputShape=e,this.variableNames=t.map((r,a)=>`T${a}`);let n=[];this.variableNames.forEach(r=>{n.push(`float v${r} = get${r}AtOutCoords();`)});let s=this.variableNames.map(r=>`v${r}`).join(" + ");this.userCode=`
void main() {
${n.join(`
`)}
float result = ${s};
setOutput(result);
}
`}},IQ=class{constructor(e,t){this.outputShape=[],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=e,this.variableNames=t.map((r,a)=>`T${a}`);let n=[];this.variableNames.forEach(r=>{n.push(`vec4 v${r} = get${r}AtOutCoords();`)});let s=this.variableNames.map(r=>`v${r}`).join(" + ");this.userCode=`
void main() {
${n.join(`
`)}
vec4 result = ${s};
setOutput(result);
}
`}};function Rm(e){let{inputs:t,backend:n}=e,s=t;if(s.length===1)return ys({inputs:{x:s[0]},backend:n});if(s.length>K().get("WEBGL_MAX_TEXTURES_IN_SHADER")){let l=Math.floor(s.length/2),c=Rm({inputs:s.slice(0,l),backend:n}),u=Rm({inputs:s.slice(l),backend:n});return Rm({inputs:[c,u],backend:n})}let r=s.map(l=>l.dtype).reduce((l,c)=>Ln(l,c)),a=s.map(l=>l.shape),i=K().getBool("WEBGL_PACK")?new IQ(s[0].shape,a):new SQ(s[0].shape,a);return n.runWebGLProgram(i,s,r)}var CQ={kernelName:wa,backendName:"webgl",kernelFunc:Rm};function TQ(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{axis:a,keepDims:o}=s,i=r.shape.length,l=v.parseAxisParam(a,r.shape),c=l,u=N.getAxesPermutation(c,i),d=r;u!=null&&(d=Hn({inputs:{x:r},backend:n,attrs:{perm:u}}),c=N.getInnerMostAxes(c.length,i)),N.assertAxesAreInnerMostDims("all",c,i);let[p,h]=N.computeOutAndReduceShapes(d.shape,c),f=v.sizeFromShape(h),m=ve({inputs:{x:d},backend:n,attrs:{shape:[-1,f]}}),g=yl(m,m.dtype,"all",n),A;if(o){let x=N.expandShapeToKeepDim(p,l);A=ve({inputs:{x:g},backend:n,attrs:{shape:x}})}else A=ve({inputs:{x:g},backend:n,attrs:{shape:p}});return n.disposeIntermediateTensorInfo(m),n.disposeIntermediateTensorInfo(g),u!=null&&n.disposeIntermediateTensorInfo(d),A}var NQ={kernelName:Gl,backendName:"webgl",kernelFunc:TQ};function EQ(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{axis:a,keepDims:o}=s,i=r.shape.length,l=v.parseAxisParam(a,r.shape),c=l,u=N.getAxesPermutation(c,i),d=r;u!=null&&(d=Hn({inputs:{x:r},backend:n,attrs:{perm:u}}),c=N.getInnerMostAxes(c.length,i)),N.assertAxesAreInnerMostDims("any",c,i);let[p,h]=N.computeOutAndReduceShapes(d.shape,c),f=v.sizeFromShape(h),m=ve({inputs:{x:d},backend:n,attrs:{shape:[-1,f]}}),g=yl(m,m.dtype,"any",n),A;if(o){let x=N.expandShapeToKeepDim(p,l);A=ve({inputs:{x:g},backend:n,attrs:{shape:x}})}else A=ve({inputs:{x:g},backend:n,attrs:{shape:p}});return n.disposeIntermediateTensorInfo(m),n.disposeIntermediateTensorInfo(g),u!=null&&n.disposeIntermediateTensorInfo(d),A}var RQ={kernelName:Hl,backendName:"webgl",kernelFunc:EQ},$Q=class{constructor(e,t,n){this.variableNames=["A"];let{windowSize:s,batchSize:r,outSize:a}=e;n||this.variableNames.push("bestIndicesA"),this.outputShape=[r,a];let o=t==="max"?">":"<",i=n?"inOffset + i;":"round(getBestIndicesA(batch, inOffset + i));";this.userCode=`
void main() {
ivec2 coords = getOutputCoords();
int batch = coords[0];
int outIdx = coords[1];
int inOffset = outIdx * ${s};
int bestIndex = inOffset;
float bestValue = getA(batch, bestIndex);
for (int i = 0; i < ${s}; i++) {
int inIdx = ${i};
float candidate = getA(batch, inIdx);
if (candidate ${o} bestValue) {
bestValue = candidate;
bestIndex = inIdx;
}
}
setOutput(float(bestIndex));
}
`}},DQ=class{constructor(e,t,n,s){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,v.assert(e.length>2,()=>`Packed arg${n.charAt(0).toUpperCase()+n.slice(1)} supports only inputs with rank above 2.`);let r=e[e.length-1],a=Math.ceil(r/t);this.outputShape=e.slice(0,-1),a>1&&this.outputShape.push(a),s||this.variableNames.push("bestIndicesA");let o=this.outputShape,i=o.length,l=bt(i),c=Gn("coords",i),u,d;if(a===1){d=i+1;let C=bt(d);u=`
${C} sourceLocR = ${C}(${c.join()}, 0);
++${c[i-1]};
${C} sourceLocG = ${C}(${c.join()}, 0);
++${c[i-2]};
${C} sourceLocA = ${C}(${c.join()}, 0);
--${c[i-1]};
${C} sourceLocB = ${C}(${c.join()}, 0);
--${c[i-2]};`}else d=i,u=`
${l} sourceLocR = coords;
++${c[i-1]};
${l} sourceLocG = coords;
++${c[i-2]};
${l} sourceLocA = coords;
--${c[i-1]};
${l} sourceLocB = coords;
--${c[i-2]};`;let p=["x","y","z","w","u","v"].slice(0,d),h="."+p[d-1],f=p.map(C=>"int "+C),m=Gn("sourceLocR",d-1).concat("inIdx.r"),g=Gn("sourceLocG",d-1).concat("inIdx.g"),A=Gn("sourceLocB",d-1).concat("inIdx.b"),x=Gn("sourceLocA",d-1).concat("inIdx.a"),y=n==="max"?"greaterThan":"lessThan",b=s?"":`
inIdx = round(vec4(getBestIndicesAChannel(${m.join()}),
getBestIndicesAChannel(${g.join()}),
getBestIndicesAChannel(${A.join()}),
getBestIndicesAChannel(${x.join()})));`,w=`vec4(
getAChannel(${m.join()}),
hasNextCol ? getAChannel(${g.join()}) : 0.,
hasNextRow ? getAChannel(${A.join()}) : 0.,
hasNextRow && hasNextCol ? getAChannel(${x.join()}) : 0.)`,k=s?"":`
float getBestIndicesAChannel(${f.join()}) {
return getChannel(getBestIndicesA(${p.join()}),
vec2(${p.slice(-2).join()}));
}`;this.userCode=`
float getAChannel(${f.join()}) {
return getChannel(getA(${p.join()}),
vec2(${p.slice(-2).join()}));
}
${k}
void main() {
${l} coords = getOutputCoords();
bool hasNextCol = ${c[i-1]} < ${o[i-1]-1};
bool hasNextRow = ${c[i-2]} < ${o[i-2]-1};
${u}
ivec4 srcIdx = ivec4(sourceLocR${h}, sourceLocG${h},
sourceLocB${h}, sourceLocA${h}) * ${t};
ivec4 inIdx = srcIdx;
vec4 bestIndex = vec4(inIdx);
vec4 bestValue = ${w};
for (int i = 0; i < ${t}; i++) {
inIdx = srcIdx;
${b}
vec4 candidate = ${w};
bvec4 nan = isnan(candidate);
bvec4 replace = bvec4(
vec4(${y}(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 QI(e,t,n,s=null){let r=t.shape[0],a=t.shape[1];s!=null&&(r=s.shape[0],a=s.shape[1]);let o=N.computeOptimalWindowSize(a),i={windowSize:o,inSize:a,batchSize:r,outSize:Math.ceil(a/o)},l=new $Q(i,n,s==null),c=[t];s!=null&&c.push(s);let u=e.runWebGLProgram(l,c,"int32");if(u.shape[1]===1)return u;let d=QI(e,t,n,u);return e.disposeIntermediateTensorInfo(u),d}function e4(e,t,n,s=null){let r=s!=null?s.shape:t.shape,a=r[r.length-1],o=N.computeOptimalWindowSize(a),i=new DQ(r,o,n,s==null),l=s==null?[t]:[t,s],c=e.runWebGLProgram(i,l,"int32");if(c.shape.length===t.shape.length){let u=e4(e,t,n,c);return e.disposeIntermediateTensorInfo(c),u}return c}function t4(e,t,n,s){let r=[n];if(N.assertAxesAreInnerMostDims("arg"+s.charAt(0).toUpperCase()+s.slice(1),r,t.shape.length),!K().getBool("WEBGL_PACK_REDUCE")||t.shape.length<=2){let a=[],o=e.texData.get(t.dataId),i=o!==null&&o.isPacked,l=t;i&&(l=e.unpackTensor(t),a.push(l));let[c,u]=N.computeOutAndReduceShapes(l.shape,r),d=v.sizeFromShape(u),p=ve({inputs:{x:l},backend:e,attrs:{shape:[-1,d]}});a.push(p);let h=QI(e,p,s);a.push(h);let f=ve({inputs:{x:h},backend:e,attrs:{shape:c}});return a.forEach(m=>e.disposeIntermediateTensorInfo(m)),f}return e4(e,t,s)}function _Q(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{axis:a}=s,o=v.parseAxisParam(a,r.shape),i=N.getAxesPermutation(o,r.shape.length),l=r,c=[];i!=null&&(l=Hn({inputs:{x:r},backend:n,attrs:{perm:i}}),c.push(l),o=N.getInnerMostAxes(o.length,l.shape.length)),N.assertAxesAreInnerMostDims("argMax",[o[0]],l.shape.length);let u=t4(n,l,o[0],"max");return c.forEach(d=>n.disposeIntermediateTensorInfo(d)),u}var PQ={kernelName:ka,backendName:"webgl",kernelFunc:_Q};function FQ(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{axis:a}=s,o=v.parseAxisParam(a,r.shape),i=N.getAxesPermutation(o,r.shape.length),l=r,c=[];i!=null&&(l=Hn({inputs:{x:r},backend:n,attrs:{perm:i}}),c.push(l),o=N.getInnerMostAxes(o.length,l.shape.length)),N.assertAxesAreInnerMostDims("argMin",[o[0]],l.shape.length);let u=t4(n,l,o[0],"min");return c.forEach(d=>n.disposeIntermediateTensorInfo(d)),u}var OQ={kernelName:jl,backendName:"webgl",kernelFunc:FQ},MQ=yr+`
if (abs(x) > 1.) {
return NAN;
}
return asin(x);
`,zQ=ot({opSnippet:MQ}),LQ={kernelName:ql,backendName:"webgl",kernelFunc:zQ},BQ=yr+"return log(x + sqrt(x * x + 1.0));",WQ=ot({opSnippet:BQ}),VQ={kernelName:Xl,backendName:"webgl",kernelFunc:WQ},UQ=yr+`
return atan(x);
`,GQ=ot({opSnippet:UQ}),HQ={kernelName:Kl,backendName:"webgl",kernelFunc:GQ},jQ=QJ+`
return atan(a, b);
`,qQ=`
vec4 result = atan(a, b);
vec4 isNaN = min(vec4(isnan(a)) + vec4(isnan(b)), vec4(1.0));
`+eQ+`
return result;
`,XQ=In({opSnippet:jQ,packedOpSnippet:qQ}),KQ={kernelName:Yl,backendName:"webgl",kernelFunc:XQ},ZQ=yr+`
if ((x < -1.0) || (x > 1.0)) return NAN;
return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,YQ=ot({opSnippet:ZQ}),JQ={kernelName:Zl,backendName:"webgl",kernelFunc:YQ},Qd=class{constructor(e,t,n,s=!1,r=!1){if(this.variableNames=["x"],t==="avg"&&n)throw new Error("Cannot compute positions for average pool.");let a=e.filterWidth,o=e.strideHeight,i=e.strideWidth,l=e.dilationHeight,c=e.dilationWidth,u=e.effectiveFilterHeight,d=e.effectiveFilterWidth,p=e.padInfo.top,h=e.padInfo.left;this.outputShape=e.outShape;let f=t==="avg",m=`((batch * ${e.inHeight} + xR) * ${e.inWidth} + xC) * ${e.inChannels} + d`,g=`(xR * ${e.inWidth} + xC) * ${e.inChannels} + d`,A="0.0";if(f||(A="-1.0 / 1e-20"),n){let C=">=";this.userCode=`
const ivec2 strides = ivec2(${o}, ${i});
const ivec2 pads = ivec2(${p}, ${h});
void main() {
ivec4 coords = getOutputCoords();
int batch = coords[0];
int d = coords[3];
ivec2 xRCCorner = coords.yz * strides - pads;
int xRCorner = xRCCorner.x;
int xCCorner = xRCCorner.y;
// max/min x(?, ?, d) to get y(yR, yC, d).
// ? = to be determined
float minMaxValue = 0.0;
float minMaxValueFound = 0.0;
int minMaxPosition = 0;
float avgValue = 0.0;
for (int wR = 0; wR < ${u};
wR += ${l}) {
int xR = xRCorner + wR;
if (xR < 0 || xR >= ${e.inHeight}) {
continue;
}
for (int wC = 0; wC < ${d};
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 ${C} currMinMaxValue) {
minMaxValue = value;
minMaxValueFound = 1.0;
minMaxPosition = ${s?r?m:g:`wR * ${d} + wC`};
}
}
}
setOutput(float(minMaxPosition));
}
`;return}let x="max",y=`${t}(${t}(${t}(minMaxValue[0], minMaxValue[1]), minMaxValue[2]), minMaxValue[3])`;t==="avg"&&(y="avgValue / count");let b=Math.floor(a/4)*4,w=a%4,k=`
if (${f}) {
avgValue += dot(values, ones);
} else {
minMaxValue = ${x}(values, minMaxValue);
}
`;this.userCode=`
const ivec2 strides = ivec2(${o}, ${i});
const ivec2 pads = ivec2(${p}, ${h});
const float initializationValue = ${A};
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 >= ${e.inWidth}) {
return initializationValue;
}
count += 1.0;
return getX(batch, xR, xC, d);
}
void main() {
ivec4 coords = getOutputCoords();
int batch = coords[0];
int d = coords[3];
ivec2 xRCCorner = coords.yz * strides - pads;
int xRCorner = xRCCorner.x;
int xCCorner = xRCCorner.y;
// max/min x(?, ?, d) to get y(yR, yC, d).
// ? = to be determined
vec4 minMaxValue = vec4(${A});
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 < ${b}; 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 + ${b};
if (${w===1}) {
vec4 values = vec4(
getValue(batch, xR, xC, d),
initializationValue,
initializationValue,
initializationValue
);
${k}
} else if (${w===2}) {
vec4 values = vec4(
getValue(batch, xR, xC, d),
getValue(batch, xR, xC + ${c}, d),
initializationValue,
initializationValue
);
${k}
} else if (${w===3}) {
vec4 values = vec4(
getValue(batch, xR, xC, d),
getValue(batch, xR, xC + ${c}, d),
getValue(batch, xR, xC + 2 * ${c}, d),
initializationValue
);
${k}
}
}
setOutput(${y});
}
`}},Yy=class{constructor(e,t,n,s=!1,r=!1){if(this.variableNames=["x"],t==="avg"&&n)throw new Error("Cannot compute positions for average pool.");let a=e.filterWidth,o=e.strideDepth,i=e.strideHeight,l=e.strideWidth,c=e.dilationDepth,u=e.dilationHeight,d=e.dilationWidth,p=e.effectiveFilterDepth,h=e.effectiveFilterHeight,f=e.effectiveFilterWidth,m=e.padInfo.front,g=e.padInfo.top,A=e.padInfo.left;this.outputShape=e.outShape;let x=t==="avg",y="0.0";if(x||(y="-1.0 / 1e-20"),n){let R=">=";this.userCode=`
const ivec3 strides =
ivec3(${o}, ${i}, ${l});
const ivec3 pads = ivec3(${m}, ${g}, ${A});
void main() {
ivec5 coords = getOutputCoords();
int batch = coords.x;
int ch = coords.u;
ivec3 xCorner = ivec3(coords.y, coords.z, coords.w) * strides - pads;
int xDCorner = xCorner.x;
int xRCorner = xCorner.y;
int xCCorner = xCorner.z;
// max/min x(?, ?, ?, ch) to get y(yD, yR, yC, ch).
// ? = to be determined
float minMaxValue = 0.0;
float minMaxValueFound = 0.0;
int minMaxPosition = 0;
for (int wD = 0; wD < ${p};
wD += ${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 < ${f};
wC += ${d}) {
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 ${R} currMinMaxValue) {
minMaxValue = value;
minMaxValueFound = 1.0;
minMaxPosition = ${s?r?`(((batch * ${e.inDepth} + xD) * ${e.inHeight} + xR) * ${e.inWidth} + xC) * ${e.inChannels} + ch`:`((xD * ${e.inHeight} + xR) * ${e.inWidth} + xC) * ${e.inChannels} + ch`:`wD * ${h} * ${f} +
wR * ${f} + wC`};
}
}
}
}
setOutput(float(minMaxPosition));
}
`;return}let b="max",w=`${t}(${t}(${t}(minMaxValue[0], minMaxValue[1]), minMaxValue[2]), minMaxValue[3])`;t==="avg"&&(w="avgValue / count");let k=Math.floor(a/4)*4,C=a%4,E=`
if (${x}) {
avgValue += dot(values, ones);
} else {
minMaxValue = ${b}(values, minMaxValue);
}
`;this.userCode=`
const ivec3 strides =
ivec3(${o}, ${i}, ${l});
const ivec3 pads = ivec3(${m}, ${g}, ${A});
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 xD, int xR, int xC, int ch) {
if (xC < 0 || xC >= ${e.inWidth}) {
return initializationValue;
}
count += 1.0;
return getX(batch, xD, xR, xC, ch);
}
void main() {
ivec5 coords = getOutputCoords();
int batch = coords.x;
int ch = coords.u;
ivec3 xCorner = ivec3(coords.y, coords.z, coords.w) * strides - pads;
int xDCorner = xCorner.x;
int xRCorner = xCorner.y;
int xCCorner = xCorner.z;
// max/min x(?, ?, ?, d) to get y(yD, yR, yC, ch).
// ? = to be determined
vec4 minMaxValue = vec4(${y});
float avgValue = 0.0;
count = 0.0;
for (int wD = 0; wD < ${p};
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 * ${d};
vec4 values = vec4(
getValue(batch, xD, xR, xC, ch),
getValue(batch, xD, xR, xC + ${d}, ch),
getValue(batch, xD, xR, xC + 2 * ${d}, ch),
getValue(batch, xD, xR, xC + 3 * ${d}, ch)
);
${E}
}
int xC = xCCorner + ${k};
if (${C===1}) {
vec4 values = vec4(
getValue(batch, xD, xR, xC, ch),
initializationValue,
initializationValue,
initializationValue
);
${E}
} else if (${C===2}) {
vec4 values = vec4(
getValue(batch, xD, xR, xC, ch),
getValue(batch, xD, xR, xC + ${d}, ch),
initializationValue,
initializationValue
);
${E}
} else if (${C===3}) {
vec4 values = vec4(
getValue(batch, xD, xR, xC, ch),
getValue(batch, xD, xR, xC + ${d}, ch),
getValue(batch, xD, xR, xC + 2 * ${d}, ch),
initializationValue
);
${E}
}
}
setOutput(${w});
}
}
`}};function QQ(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t;Gu(r,"avgPool");let{filterSize:a,strides:o,pad:i,dimRoundingMode:l}=s,c=1;v.assert(N.eitherStridesOrDilationsAreOne(o,c),()=>`Error in avgPool: Either strides or dilations must be 1. Got strides ${o} and dilations '${c}'`);let u=N.computePool2DInfo(r.shape,a,o,c,i,l);if(u.filterWidth===1&&u.filterHeight===1&&v.arraysEqual(u.inShape,u.outShape))return ys({inputs:{x:r},backend:n});let d=new Qd(u,"avg",!1);return n.runWebGLProgram(d,[r],"float32")}var eee={kernelName:Sa,backendName:"webgl",kernelFunc:QQ};function tee(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{filterSize:a,strides:o,pad:i,dimRoundingMode:l,dataFormat:c}=s,u=[1,1,1],d=N.computePool3DInfo(r.shape,a,o,u,i,l,c),p=new Yy(d,"avg",!1);return n.runWebGLProgram(p,[r],"float32")}var nee={kernelName:Mc,backendName:"webgl",kernelFunc:tee},see=class{constructor(e){this.variableNames=["dy"],this.outputShape=e.inShape;let t=e.filterHeight,n=e.filterWidth,s=e.strideHeight,r=e.strideWidth,a=e.dilationHeight,o=e.dilationWidth,i=e.effectiveFilterHeight,l=e.effectiveFilterWidth,c=i-1-e.padInfo.top,u=l-1-e.padInfo.left,d=1/(t*n);this.userCode=`
const ivec2 pads = ivec2(${c}, ${u});
const float avgMultiplier = float(${d});
void main() {
ivec4 coords = getOutputCoords();
int b = coords[0];
int d = coords[3];
ivec2 dyRCCorner = coords.yz - pads;
int dyRCorner = dyRCCorner.x;
int dyCCorner = dyRCCorner.y;
// Convolve dy(?, ?, d) with pos mask(:, :, d) to get dx(xR, xC, d).
// ? = to be determined. : = across all values in that axis.
float dotProd = 0.0;
for (int wR = 0; wR < ${i};
wR += ${a}) {
float dyR = float(dyRCorner + wR) / ${s}.0;
if (dyR < 0.0 || dyR >= ${e.outHeight}.0 || fract(dyR) > 0.0) {
continue;
}
int idyR = int(dyR);
for (int wC = 0; wC < ${l};
wC+= ${o}) {
float dyC = float(dyCCorner + wC) / ${r}.0;
if (dyC < 0.0 || dyC >= ${e.outWidth}.0 ||
fract(dyC) > 0.0) {
continue;
}
int idyC = int(dyC);
float dyValue = getDy(b, idyR, idyC, d);
dotProd += dyValue * avgMultiplier;
}
}
setOutput(dotProd);
}
`}},ree=class{constructor(e){this.variableNames=["dy"],this.outputShape=e.inShape;let t=e.filterDepth,n=e.filterHeight,s=e.filterWidth,r=e.strideDepth,a=e.strideHeight,o=e.strideWidth,i=e.dilationDepth,l=e.dilationHeight,c=e.dilationWidth,u=e.effectiveFilterDepth,d=e.effectiveFilterHeight,p=e.effectiveFilterWidth,h=u-1-e.padInfo.front,f=d-1-e.padInfo.top,m=p-1-e.padInfo.left,g=1/(t*n*s);this.userCode=`
const ivec3 pads = ivec3(${h}, ${f}, ${m});
const float avgMultiplier = float(${g});
void main() {
ivec5 coords = getOutputCoords();
int batch = coords.x;
int ch = coords.u;
ivec3 dyCorner = ivec3(coords.y, coords.z, coords.w) - pads;
int dyDCorner = dyCorner.x;
int dyRCorner = dyCorner.y;
int dyCCorner = dyCorner.z;
// Convolve dy(?, ?, ?, d) with pos mask(:, :, :, ch) to get
// dx(xD, xR, xC, ch).
// ? = to be determined. : = across all values in that axis.
float dotProd = 0.0;
for (int wD = 0; wD < ${u};
wD += ${i}) {
float dyD = float(dyDCorner + wD) / ${r}.0;
if (dyD < 0.0 || dyD >= ${e.outDepth}.0 || fract(dyD) > 0.0) {
continue;
}
int idyD = int(dyD);
for (int wR = 0; wR < ${d};
wR += ${l}) {
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 < ${p};
wC += ${c}) {
float dyC = float(dyCCorner + wC) / ${o}.0;
if (dyC < 0.0 || dyC >= ${e.outWidth}.0 ||
fract(dyC) > 0.0) {
continue;
}
int idyC = int(dyC);
float dyValue = getDy(batch, idyD, idyR, idyC, ch);
dotProd += dyValue * avgMultiplier;
}
}
}
setOutput(dotProd);
}
`}};function aee(e){let{inputs:t,backend:n,attrs:s}=e,{dy:r,input:a}=t,o=a,{filterSize:i,strides:l,pad:c,dimRoundingMode:u}=s,d=[1,1,1],p=N.computePool3DInfo(o.shape,i,l,d,c,u),h=new ree(p);return n.runWebGLProgram(h,[r],o.dtype)}var oee={kernelName:sh,backendName:"webgl",kernelFunc:aee};function iee(e){let{inputs:t,backend:n,attrs:s}=e,{dy:r,input:a}=t,o=a;Gu([r,a],"avgPoolGrad");let{filterSize:i,strides:l,pad:c}=s,u=N.computePool2DInfo(o.shape,i,l,1,c),d=new see(u);return n.runWebGLProgram(d,[r],o.dtype)}var lee={kernelName:nh,backendName:"webgl",kernelFunc:iee};function uee(e){let{inputs:t,backend:n,attrs:s}=e,{a:r,b:a}=t,{transposeA:o,transposeB:i}=s;return Em({a:r,b:a,transposeA:o,transposeB:i,backend:n})}var cee={kernelName:Ia,backendName:"webgl",kernelFunc:uee},dee=class{constructor(e,t,n,s,r,a){this.outputShape=[],this.variableNames=["x","mean","variance"],N.assertAndGetBroadcastShape(e,t),N.assertAndGetBroadcastShape(e,n);let o="0.0";s!=null&&(N.assertAndGetBroadcastShape(e,s),this.variableNames.push("offset"),o="getOffsetAtOutCoords()");let i="1.0";r!=null&&(N.assertAndGetBroadcastShape(e,r),this.variableNames.push("scale"),i="getScaleAtOutCoords()"),this.outputShape=e,this.userCode=`
void main() {
float x = getXAtOutCoords();
float mean = getMeanAtOutCoords();
float variance = getVarianceAtOutCoords();
float offset = ${o};
float scale = ${i};
float inv = scale * inversesqrt(variance + float(${a}));
setOutput(dot(vec3(x, -mean, offset), vec3(inv, inv, 1)));
}
`}},pee=class{constructor(e,t,n,s,r,a){this.packedInputs=!0,this.packedOutput=!0,this.variableNames=["x","mean","variance"],N.assertAndGetBroadcastShape(e,t),N.assertAndGetBroadcastShape(e,n);let o="vec4(0.0)";s!=null&&(N.assertAndGetBroadcastShape(e,s),this.variableNames.push("offset"),o="getOffsetAtOutCoords()");let i="vec4(1.0)";r!=null&&(N.assertAndGetBroadcastShape(e,r),this.variableNames.push("scale"),i="getScaleAtOutCoords()"),this.outputShape=e,this.userCode=`
void main() {
vec4 offset = ${o};
vec4 scale = ${i};
vec4 x = getXAtOutCoords();
vec4 mean = getMeanAtOutCoords();
vec4 variance = getVarianceAtOutCoords();
vec4 inv = scale * inversesqrt(variance + vec4(${a}));
setOutput((x - mean) * inv + offset);
}
`}},hee=({inputs:e,backend:t,attrs:n})=>{let{x:s,mean:r,variance:a,offset:o,scale:i}=e;v.assert(r.shape.length===a.shape.length,()=>"Batch normalization gradient requires mean and variance to have equal ranks."),v.assert(o==null||r.shape.length===o.shape.length,()=>"Batch normalization gradient requires mean and offset to have equal ranks."),v.assert(i==null||r.shape.length===i.shape.length,()=>"Batch normalization gradient requires mean and scale to have equal ranks.");let{varianceEpsilon:l}=n;l==null&&(l=.001);let c=[s,r,a],u=null;o!=null&&(u=o.shape,c.push(o));let d=null;i!=null&&(d=i.shape,c.push(i));let p=K().getBool("WEBGL_PACK_NORMALIZATION")?new pee(s.shape,r.shape,a.shape,u,d,l):new dee(s.shape,r.shape,a.shape,u,d,l);return t.runWebGLProgram(p,c,c[0].dtype)},fee={kernelName:za,backendName:"webgl",kernelFunc:hee},mee=class{constructor(e){this.variableNames=["source"],this.outputShape=e,this.rank=e.length;let t=bt(this.rank);this.customUniforms=[{name:"start",arrayIndex:this.rank,type:"int"}];let n=gee(this.rank),s,r=e.map((a,o)=>`sourceLoc.${Jy[o]} = start[${o}] + coords.${Jy[o]};`);s=`
${t} sourceLoc;
${t} coords = getOutputCoords();
${r.join(`
`)}
`,this.userCode=`
void main() {
${s}
setOutput(getSource(${n}));
}
`}},Jy=["x","y","z","w","u","v"];function gee(e){if(e===1)return"sourceLoc";if(e<=6)return Jy.slice(0,e).map(t=>"sourceLoc."+t).join(",");throw Error(`Slicing for rank ${e} is not yet supported`)}var Aee=class{constructor(e){this.variableNames=["source"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=e,this.rank=e.length,this.customUniforms=[{name:"start",arrayIndex:this.rank,type:"int"}];let t=bt(this.rank),n=Gn("coords",this.rank),s=Gn("sourceLoc",this.rank),r=this.rank===1?"sourceLoc":`vec2(${s.slice(-2).join()})`,a=`getChannel(getSource(${s.join()}), ${r})`,o=`
result.x = ${a};
if (++${n[this.rank-1]} < ${e[this.rank-1]}) {
++${s[this.rank-1]};
result.y = ${a};
--${s[this.rank-1]};
}
`,i=this.rank===1?"":`
--${n[this.rank-1]};
if (++${n[this.rank-2]} < ${e[this.rank-2]}) {
++${s[this.rank-2]};
result.z = ${a};
if (++${n[this.rank-1]} < ${e[this.rank-1]}) {
++${s[this.rank-1]};
result.w = ${a};
}
}
`,l=this.rank<=4?`sourceLoc = coords +
${t}(${e.map((c,u)=>`start[${u}]`).join()});`:e.map((c,u)=>`${s[u]} = ${n[u]} + start[${u}];`).join(`
`);this.userCode=`
void main() {
${t} coords = getOutputCoords();
${t} sourceLoc;
${l}
vec4 result = vec4(0.);
${o}
${i}
setOutput(result);
}
`}};function yee(e,t,n,s){let r=s.texData.get(e.dataId),a=s.makeTensorInfo(n,e.dtype),o=s.texData.get(a.dataId);Object.assign(o,r),o.refCount=1,o.shape=n,o.dtype=e.dtype;let i=Ft.computeFlatOffset(t,v.computeStrides(e.shape));r.slice&&(i+=r.slice.flatOffset),o.slice={flatOffset:i,origDataId:r.slice&&r.slice.origDataId||e.dataId};let l=s.dataRefCount.get(o.slice.origDataId)||1;return s.dataRefCount.set(o.slice.origDataId,l+1),a}function Yu(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{begin:a,size:o}=s,[i,l]=Ft.parseSliceParams(r,a,o);if(Ft.assertParamsValid(r,i,l),v.sizeFromShape(l)===0)return n.makeTensorInfo(l,r.dtype,[]);if(n.shouldExecuteOnCPU([r])||r.dtype==="string"){let d=n.texData.get(r.dataId),p=sJ(d.values,i,l,r.shape,r.dtype);return n.makeTensorInfo(l,r.dtype,p)}let{isPacked:c}=n.texData.get(r.dataId),u=Ft.isSliceContinous(r.shape,i,l);if(c||!u){let d=K().getBool("WEBGL_PACK_ARRAY_OPERATIONS")?new Aee(l):new mee(l),p=[i];return n.runWebGLProgram(d,[r],r.dtype,p)}return n.uploadToGPU(r.dataId),yee(r,i,l,n)}var xee={kernelName:$i,backendName:"webgl",kernelFunc:Yu},bee=e=>{let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{blockShape:a,crops:o}=s;v.assert(r.shape.length<=4,()=>"batchToSpaceND for rank > 4 with a WebGL backend not implemented yet");let i=a.reduce((x,y)=>x*y),l=N.getReshaped(r.shape,a,i),c=N.getPermuted(l.length,a.length),u=N.getReshapedPermuted(r.shape,a,i),d=N.getSliceBeginCoords(o,a.length),p=N.getSliceSize(u,o,a.length),h=[],f=ve({inputs:{x:r},backend:n,attrs:{shape:l}}),m=Hn({inputs:{x:f},backend:n,attrs:{perm:c}}),g=ve({inputs:{x:m},backend:n,attrs:{shape:u}}),A=Yu({inputs:{x:g},backend:n,attrs:{begin:d,size:p}});return h.push(f),h.push(m),h.push(g),h.forEach(x=>n.disposeIntermediateTensorInfo(x)),A},vee={kernelName:ni,backendName:"webgl",kernelFunc:bee};function wee(e){let{inputs:t,backend:n,attrs:s}=e,{x:r,weights:a}=t,{size:o}=s,i=n.readSync(r.dataId),l=n.readSync(a.dataId),c=NI(i,l,a.dtype,a.shape,o);return n.makeTensorInfo([o],a.dtype,c)}var kee={kernelName:rh,backendName:"webgl",kernelFunc:wee};function See(e){let{inputs:t,backend:n}=e,{s0:s,s1:r}=t,a=n.readSync(s.dataId),o=n.readSync(r.dataId),i=N.assertAndGetBroadcastShape(Array.from(a),Array.from(o));return n.makeTensorInfo([i.length],"int32",Int32Array.from(i))}var Iee={kernelName:ah,backendName:"webgl",kernelFunc:See},Cee="return float(a != b);",n4=In({opSnippet:Cee,cpuKernelImpl:JY,dtype:"bool"}),Tee={kernelName:xi,backendName:"webgl",kernelFunc:n4};function ep(e){let{inputs:t,backend:n}=e,{input:s}=t,r=n.texData.get(s.dataId);return ys({inputs:{x:r.complexTensorInfos.real},backend:n})}var Nee={kernelName:qc,backendName:"webgl",kernelFunc:ep},Eee="return float(int(x));";function Ree(e,t){let n=new Mo(e.shape,Eee),s=t.runWebGLProgram(n,[e],"int32");return{dataId:s.dataId,shape:s.shape,dtype:s.dtype}}function Qy(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{dtype:a}=s;if(a==="complex64"){if(r.dtype==="complex64")return ys({inputs:{x:r},backend:n});let o=Ht(r.shape),i=Qy({inputs:{x:r},backend:n,attrs:{dtype:"float32"}}),l=zo({inputs:{real:i,imag:o},backend:n});return o.dispose(),n.disposeIntermediateTensorInfo(i),l}if(r.dtype==="complex64"){let o=ep({inputs:{input:r},backend:n}),i=Qy({inputs:{x:o},backend:n,attrs:{dtype:a}});return n.disposeIntermediateTensorInfo(o),i}if(!v.hasEncodingLoss(r.dtype,a)){let o=ys({inputs:{x:r},backend:n});return{dataId:o.dataId,shape:o.shape,dtype:a}}if(a==="int32")return Ree(r,n);if(a==="bool"){let o=n.makeTensorInfo([],"bool",v.getTypedArrayFromDType("bool",1)),l=n4({inputs:{a:r,b:o},backend:n});return n.disposeIntermediateTensorInfo(o),l}throw new Error(`Error in Cast: failed to cast ${r.dtype} to ${a}`)}var $ee={kernelName:Ca,backendName:"webgl",kernelFunc:Qy},s4="return ceil(x);",Dee=ot({opSnippet:s4,packedOpSnippet:s4,cpuKernelImpl:_Y}),_ee={kernelName:Ta,backendName:"webgl",kernelFunc:Dee},Pee=class{constructor(e){this.variableNames=["A"],this.customUniforms=[{name:"minVal",type:"float"},{name:"maxVal",type:"float"}],this.outputShape=e,this.userCode=`
void main() {
float value = getAAtOutCoords();
if (isnan(value)) {
setOutput(value);
return;
}
setOutput(clamp(value, minVal, maxVal));
}
`}},Fee=class{constructor(e){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,this.customUniforms=[{name:"minVal",type:"float"},{name:"maxVal",type:"float"}],this.outputShape=e,this.userCode=`
void main() {
vec4 value = getAAtOutCoords();
if (any(isnan(value))) {
setOutput(value);
return;
}
setOutput(clamp(value, vec4(minVal), vec4(maxVal)));
}
`}};function Oee(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{clipValueMin:a,clipValueMax:o}=s,i;K().getBool("WEBGL_PACK_CLIP")?i=new Fee(r.shape):i=new Pee(r.shape);let l=[[a],[o]];return n.runWebGLProgram(i,[r],r.dtype,l)}var Mee={kernelName:Ur,backendName:"webgl",kernelFunc:Oee},zee=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 r4(e,t){return{dataId:t.dataId,dtype:t.dtype,shape:e.shape}}function Lee(e){let{inputs:t,backend:n}=e,{x:s}=t,r=n.texData.get(s.dataId),a=new zee(s.shape),o=[r4(s,r.complexTensorInfos.real),r4(s,r.complexTensorInfos.imag)];return n.runWebGLProgram(a,o,o[0].dtype)}var Bee={kernelName:Lc,backendName:"webgl",kernelFunc:Lee},Wee=class{constructor(e){this.outputShape=[],this.outputShape=N.computeOutShape(e,1),this.variableNames=e.map((a,o)=>`T${o}`);let t=new Array(e.length-1);t[0]=e[0][1];for(let a=1;a<t.length;a++)t[a]=t[a-1]+e[a][1];let n=[`if (yC < ${t[0]}) setOutput(getT0(yR, yC));`];for(let a=1;a<t.length;a++){let o=t[a-1];n.push(`else if (yC < ${t[a]}) setOutput(getT${a}(yR, yC-${o}));`)}let s=t.length,r=t[t.length-1];n.push(`else setOutput(getT${s}(yR, yC-${r}));`),this.userCode=`
void main() {
ivec2 coords = getOutputCoords();
int yR = coords.x;
int yC = coords.y;
${n.join(`
`)}
}
`}},Vee=class{constructor(e,t){this.packedInputs=!0,this.packedOutput=!0,this.outputShape=[],this.outputShape=N.computeOutShape(e,t);let n=this.outputShape,s=n.length,r=bt(s),a=Gn("coords",s),o=["x","y","z","w","u","v"].slice(0,s);this.variableNames=e.map((f,m)=>`T${m}`);let i=new Array(e.length-1);i[0]=e[0][t];for(let f=1;f<i.length;f++)i[f]=i[f-1]+e[f][t];let l=o[t],c=o.slice(-2),u=o.join(),d=`if (${l} < ${i[0]}) {
return getChannel(
getT0(${u}), vec2(${c.join()}));
}`;for(let f=1;f<i.length;f++){let m=i[f-1];d+=`
if (${l} < ${i[f]} && ${l} >= ${i[f-1]}) {
return getChannel(
getT${f}(${$m(o,l,m)}),
vec2(${$m(c,l,m)}));
}`}let p=i.length,h=i[i.length-1];d+=`
return getChannel(
getT${p}(${$m(o,l,h)}),
vec2(${$m(c,l,h)}));`,this.userCode=`
float getValue(${o.map(f=>"int "+f)}) {
${d}
}
void main() {
${r} coords = getOutputCoords();
vec4 result = vec4(getValue(${a}), 0., 0., 0.);
${a[s-1]} = ${a[s-1]} + 1;
if (${a[s-1]} < ${n[s-1]}) {
result.g = getValue(${a});
}
${a[s-2]} = ${a[s-2]} + 1;
if (${a[s-2]} < ${n[s-2]}) {
result.a = getValue(${a});
}
${a[s-1]} = ${a[s-1]} - 1;
if (${a[s-2]} < ${n[s-2]} &&
${a[s-1]} < ${n[s-1]}) {
result.b = getValue(${a});
}
setOutput(result);
}
`}};function $m(e,t,n){let s=e.indexOf(t);return e.map((a,o)=>o===s?`${a} - ${n}`:a).join()}function Dm(e){let{inputs:t,backend:n}=e,{input:s}=t,r=n.texData.get(s.dataId);return ys({inputs:{x:r.complexTensorInfos.imag},backend:n})}var Uee={kernelName:Uc,backendName:"webgl",kernelFunc:Dm};function Ju(e,t,n){let s=e[0].dtype;if(s==="complex64"){let u=e.map(m=>ep({inputs:{input:m},backend:n})),d=e.map(m=>Dm({inputs:{input:m},backend:n})),p=Ju(u,t,n),h=Ju(d,t,n),f=zo({inputs:{real:p,imag:h},backend:n});return u.forEach(m=>n.disposeIntermediateTensorInfo(m)),d.forEach(m=>n.disposeIntermediateTensorInfo(m)),n.disposeIntermediateTensorInfo(p),n.disposeIntermediateTensorInfo(h),f}let r=n.shouldExecuteOnCPU(e);if(s==="string"&&(r=!0),r){let u=e.map(A=>{let x=v.sizeFromShape(A.shape.slice(t));return ve({inputs:{x:A},backend:n,attrs:{shape:[-1,x]}})}),d=u.map(A=>({vals:n.readSync(A.dataId),shape:A.shape})),p=N.computeOutShape(u.map(A=>A.shape),1),h=u[0].shape[0]===1,f=PY(d,p,s,h),m=N.computeOutShape(e.map(A=>A.shape),t),g=n.makeTensorInfo(m,s,f);return u.forEach(A=>n.disposeIntermediateTensorInfo(A)),g}if(e.length>K().getNumber("WEBGL_MAX_TEXTURES_IN_SHADER")){let u=Math.floor(e.length/2),d=Ju(e.slice(0,u),t,n),p=Ju(e.slice(u),t,n),h=Ju([d,p],t,n);return n.disposeIntermediateTensorInfo(d),n.disposeIntermediateTensorInfo(p),h}if(K().getBool("WEBGL_PACK_ARRAY_OPERATIONS")&&e[0].shape.length>1){let u=new Vee(e.map(d=>d.shape),t);return n.runWebGLProgram(u,e,s)}let{tensors2D:a,outShape:o}=Gee(e,t,n),i=new Wee(a.map(u=>u.shape)),l=n.runWebGLProgram(i,a,s);a.forEach(u=>n.disposeIntermediateTensorInfo(u));let c=ve({inputs:{x:l},attrs:{shape:o},backend:n});return n.disposeIntermediateTensorInfo(l),c}function Gee(e,t,n){let s=N.computeOutShape(e.map(a=>a.shape),t);return{tensors2D:e.map(a=>ve({inputs:{x:a},attrs:{shape:[-1,v.sizeFromShape(a.shape.slice(t))]},backend:n})),outShape:s}}function a4(e){let{inputs:t,backend:n,attrs:s}=e,{axis:r}=s,a=v.parseAxisParam(r,t[0].shape)[0],o=N.computeOutShape(t.map(c=>c.shape),a);if(v.sizeFromShape(o)===0)return n.makeTensorInfo(o,t[0].dtype,[]);let i=t.filter(c=>v.sizeFromShape(c.shape)>0);if(i.length===1)return ys({inputs:{x:i[0]},backend:n});let l=i.map(c=>c.shape);return N.assertParamsConsistent(l,a),Ju(i,a,n)}var Hee={kernelName:si,backendName:"webgl",kernelFunc:a4},o4=class{constructor(e,t=!1,n=null,s=!1,r=!1){this.variableNames=["x","W"],this.outputShape=e.outShape;let a=e.padInfo.top,o=e.padInfo.left,i=e.strideHeight,l=e.strideWidth,c=e.dilationHeight,u=e.dilationWidth,d=e.filterHeight,p=e.filterWidth,h=Math.floor(e.inChannels/4)*4,f=e.inChannels%4,m=e.dataFormat==="channelsLast",g=m?1:2,A=m?2:3,x=m?3:1,y="",b="";n&&(s?y=`float activation(float a) {
float b = getPreluActivationWeightsAtOutCoords();
${n}
}`:r?y=`float activation(float a) {
float b = getLeakyreluAlphaAtOutCoords();
${n}
}`:y=`
float activation(float x) {
${n}
}
`,b="result = activation(result);");let w=t?"result += getBiasAtOutCoords();":"";t&&this.variableNames.push("bias"),s&&this.variableNames.push("preluActivationWeights"),r&&this.variableNames.push("leakyreluAlpha"),this.userCode=`
${y}
const ivec2 strides = ivec2(${i}, ${l});
const ivec2 pads = ivec2(${a}, ${o});
void main() {
ivec4 coords = getOutputCoords();
int batch = coords[0];
int d2 = coords[${x}];
ivec2 xRCCorner =
ivec2(coords[${g}], coords[${A}]) * 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 < ${d}; wR++) {
int xR = xRCorner + wR * ${c};
if (xR < 0 || xR >= ${e.inHeight}) {
continue;
}
for (int wC = 0; wC < ${p}; wC++) {
int xC = xCCorner + wC * ${u};
if (xC < 0 || xC >= ${e.inWidth}) {
continue;
}
for (int d1 = 0; d1 < ${h}; d1 += 4) {
vec4 wValues = vec4(
getW(wR, wC, d1, d2),
getW(wR, wC, d1 + 1, d2),
getW(wR, wC, d1 + 2, d2),
getW(wR, wC, d1 + 3, d2)
);
if (${m}) {
vec4 xValues = vec4(
getX(batch, xR, xC, d1),
getX(batch, xR, xC, d1 + 1),
getX(batch, xR, xC, d1 + 2),
getX(batch, xR, xC, d1 + 3)
);
dotProd += dot(xValues, wValues);
} else {
vec4 xValues = vec4(
getX(batch, d1, xR, xC),
getX(batch, d1 + 1, xR, xC),
getX(batch, d1 + 2, xR, xC),
getX(batch, d1 + 3, xR, xC)
);
dotProd += dot(xValues, wValues);
}
}
if (${f===1}) {
if (${m}) {
dotProd +=
getX(batch, xR, xC, ${h}) *
getW(wR, wC, ${h}, d2);
} else {
dotProd +=
getX(batch, ${h}, xR, xC) *
getW(wR, wC, ${h}, d2);
}
} else if (${f===2}) {
vec2 wValues = vec2(
getW(wR, wC, ${h}, d2),
getW(wR, wC, ${h} + 1, d2)
);
if (${m}) {
vec2 xValues = vec2(
getX(batch, xR, xC, ${h}),
getX(batch, xR, xC, ${h} + 1)
);
dotProd += dot(xValues, wValues);
} else {
vec2 xValues = vec2(
getX(batch, ${h}, xR, xC),
getX(batch, ${h} + 1, xR, xC)
);
dotProd += dot(xValues, wValues);
}
} else if (${f===3}) {
vec3 wValues = vec3(
getW(wR, wC, ${h}, d2),
getW(wR, wC, ${h} + 1, d2),
getW(wR, wC, ${h} + 2, d2)
);
if (${m}) {
vec3 xValues = vec3(
getX(batch, xR, xC, ${h}),
getX(batch, xR, xC, ${h} + 1),
getX(batch, xR, xC, ${h} + 2)
);
dotProd += dot(xValues, wValues);
} else {
vec3 xValues = vec3(
getX(batch, ${h}, xR, xC),
getX(batch, ${h} + 1, xR, xC),
getX(batch, ${h} + 2, xR, xC)
);
dotProd += dot(xValues, wValues);
}
}
}
}
float result = dotProd;
${w}
${b}
setOutput(result);
}
`}},jee=class{constructor(e){this.variableNames=["x","W"],this.outputShape=e.outShape;let t=e.padInfo.front,n=e.padInfo.top,s=e.padInfo.left,r=e.strideDepth,a=e.strideHeight,o=e.strideWidth,i=e.dilationDepth,l=e.dilationHeight,c=e.dilationWidth,u=e.filterDepth,d=e.filterHeight,p=e.filterWidth,h=Math.floor(e.inChannels/4)*4,f=e.inChannels%4;this.userCode=`
const ivec3 strides = ivec3(${r}, ${a}, ${o});
const ivec3 pads = ivec3(${t}, ${n}, ${s});
void main() {
ivec5 coords = getOutputCoords();
int batch = coords.x;
int d2 = coords.u;
ivec3 xFRCCorner = ivec3(coords.y, coords.z, coords.w) * strides - pads;
int xFCorner = xFRCCorner.x;
int xRCorner = xFRCCorner.y;
int xCCorner = xFRCCorner.z;
// Convolve x(?, ?, ?, d1) with w(:, :, :, d1, d2) to get
// y(yF, yR, yC, d2). ? = to be determined. : = across all
// values in that axis.
float dotProd = 0.0;
for (int wF = 0; wF < ${u}; wF++) {
int xF = xFCorner + wF * ${i};
if (xF < 0 || xF >= ${e.inDepth}) {
continue;
}
for (int wR = 0; wR < ${d}; wR++) {
int xR = xRCorner + wR * ${l};
if (xR < 0 || xR >= ${e.inHeight}) {
continue;
}
for (int wC = 0; wC < ${p}; wC++) {
int xC = xCCorner + wC * ${c};
if (xC < 0 || xC >= ${e.inWidth}) {
continue;
}
for (int d1 = 0; d1 < ${h}; d1 += 4) {
vec4 xValues = vec4(
getX(batch, xF, xR, xC, d1),
getX(batch, xF, xR, xC, d1 + 1),
getX(batch, xF, xR, xC, d1 + 2),
getX(batch, xF, xR, xC, d1 + 3)
);
vec4 wValues = vec4(
getW(wF, wR, wC, d1, d2),
getW(wF, wR, wC, d1 + 1, d2),
getW(wF, wR, wC, d1 + 2, d2),
getW(wF, wR, wC, d1 + 3, d2)
);
dotProd += dot(xValues, wValues);
}
if (${f===1}) {
dotProd +=
getX(batch, xF, xR, xC, ${h}) *
getW(wF, wR, wC, ${h}, d2);
} else if (${f===2}) {
vec2 xValues = vec2(
getX(batch, xF, xR, xC, ${h}),
getX(batch, xF, xR, xC, ${h} + 1)
);
vec2 wValues = vec2(
getW(wF, wR, wC, ${h}, d2),
getW(wF, wR, wC, ${h} + 1, d2)
);
dotProd += dot(xValues, wValues);
} else if (${f===3}) {
vec3 xValues = vec3(
getX(batch, xF, xR, xC, ${h}),
getX(batch, xF, xR, xC, ${h} + 1),
getX(batch, xF, xR, xC, ${h} + 2)
);
vec3 wValues = vec3(
getW(wF, wR, wC, ${h}, d2),
getW(wF, wR, wC, ${h} + 1, d2),
getW(wF, wR, wC, ${h} + 2, d2)
);
dotProd += dot(xValues, wValues);
}
}
}
}
setOutput(dotProd);
}
`}},qee=class{constructor(e,t){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,this.customUniforms=[{name:"inputShape",type:"ivec3"},{name:"pad",type:"ivec2"},{name:"stride",type:"ivec2"},{name:"dilation",type:"ivec2"},{name:"inChannels",type:"int"},{name:"itemsPerBlockRow",type:"int"},{name:"outWidth",type:"int"}],this.outputShape=e,this.enableShapeUniforms=Fs(this.outputShape.length);let{dataFormat:n}=t,s=Un(),r=n==="channelsLast",a=r?0:1,o=r?1:2,i=this.enableShapeUniforms?"if(blockIndex < outShape[1] && pos < outShape[0]) {":`if(blockIndex < ${e[1]} && pos < ${e[0]}) {`,l="";for(let c=0;c<=1;c++)for(let u=0;u<=1;u++)l+=`
blockIndex = rc.y + ${u};
pos = rc.x + ${c};
${i}
offsetY = int(blockIndex / outWidth) * stride[0] - pad[0];
d0 = offsetY + dilation[0] * (pos / itemsPerBlockRow);
if(d0 < inputShape[${a}] && d0 >= 0) {
// Use custom imod instead mod. On Intel GPU, mod may generate
// unexpected value.
// https://github.com/tensorflow/tfjs/issues/5447
offsetX = imod(blockIndex, outWidth) * stride[1] - pad[1];
d1 = offsetX + dilation[1] * (imod(pos, itemsPerBlockRow) /
inChannels);
if(d1 < inputShape[${o}] && d1 >= 0) {
ch = imod(pos, inChannels);
if (${r}) {
innerDims = vec2(d1, ch);
result[${c*2+u}] = getChannel(
getA(d0, int(innerDims.x),
int(innerDims.y)), innerDims);
} else {
innerDims = vec2(d0, d1);
result[${c*2+u}] = getChannel(
getA(ch, int(innerDims.x),
int(innerDims.y)), innerDims);
}
}
}
}
`;this.userCode=`
void main() {
ivec2 rc = getOutputCoords();
vec4 result = vec4(0);
int blockIndex, pos, offsetY, d0, offsetX, d1, ch;
vec2 innerDims;
${l}
${s.output} = result;
}
`}};function i4({x:e,filter:t,convInfo:n,backend:s,bias:r=null,preluActivationWeights:a=null,leakyreluAlpha:o=0,activation:i=null}){let l=e.shape,c=s.texData.get(e.dataId),u=n.inChannels,d=l[0]*l[1]*l[2],p=n.outChannels,h=n.dataFormat==="channelsLast",f=!1,m=!1,g,A=[];if(!((d===1||p===1)&&u>ZI)&&c.isPacked&&h&&c.texture!=null&&l[2]%2!=0&&v.arraysEqual(c.shape.slice(-3),l.slice(-3))){let b=l[0]*l[1]*(l[2]+1),w={dataId:e.dataId,shape:[1,b,n.inChannels],dtype:e.dtype},k=c.shape;c.shape=c.shape.slice(),c.shape[c.shape.length-2]++,v.assert(Kd(c.shape,w.shape),()=>`packed reshape ${c.shape} to ${w.shape} isn't free`);let C=ve({inputs:{x:t},backend:s,attrs:{shape:[1,n.inChannels,n.outChannels]}});A.push(C);let E=Em({a:w,b:C,backend:s,transposeA:f,transposeB:m,bias:r,activation:i,preluActivationWeights:a,leakyreluAlpha:o}),R=s.texData.get(E.dataId);v.assert(R.isPacked,()=>"batchMatMul result is expected to be packed"),c.shape=k,R.shape=n.outShape,g=ys({inputs:{x:E},backend:s}),g.shape=n.outShape,A.push(E)}else{let b=h?l[0]*l[1]*l[2]:l[0]*l[2]*l[3],w=ve({inputs:{x:e},backend:s,attrs:{shape:[1,b,n.inChannels]}}),k=ve({inputs:{x:t},backend:s,attrs:{shape:[1,n.inChannels,n.outChannels]}}),C=Em({a:w,b:k,transposeA:f,transposeB:m,backend:s,bias:r,activation:i,preluActivationWeights:a,leakyreluAlpha:o});g=ve({inputs:{x:C},backend:s,attrs:{shape:n.outShape}}),A.push(w),A.push(k),A.push(C)}for(let b of A)s.disposeIntermediateTensorInfo(b);return g}function l4({x:e,filter:t,convInfo:n,backend:s,bias:r=null,preluActivationWeights:a=null,leakyreluAlpha:o=0,activation:i=null}){let{filterWidth:l,filterHeight:c,inChannels:u,outWidth:d,outHeight:p,dataFormat:h}=n,f=h==="channelsLast",m=l*c*u,g=p*d,A=[m,g],x=!0,y=!1,b=[],w=ve({inputs:{x:e},backend:s,attrs:{shape:e.shape.slice(1)}}),k=ve({inputs:{x:t},backend:s,attrs:{shape:[1,m,v.sizeFromShape(t.shape)/m]}});b.push(w),b.push(k);let C=new qee(A,n),E=[w.shape,[n.padInfo.top,n.padInfo.left],[n.strideHeight,n.strideWidth],[n.dilationHeight,n.dilationWidth],[n.inChannels],[n.filterWidth*n.inChannels],[n.outWidth]],R=s.runWebGLProgram(C,[w],"float32",E),F=ve({inputs:{x:R},backend:s,attrs:{shape:[1,A[0],A[1]]}});b.push(R),b.push(F);let D=r!=null,P=a!=null,T=i==="leakyrelu",M=i?Cm(i,!0):null,U=new HI(F.shape,k.shape,[1,g,n.outChannels],x,y,D,M,P,T),j=[F,k];if(r&&j.push(r),P&&j.push(a),T){let J=s.makeTensorInfo([],"float32",v.createScalarValue(o,"float32"));j.push(J),b.push(J)}let z=s.runWebGLProgram(U,j,"float32"),X=f?[1,p,d,n.outChannels]:[1,n.outChannels,p,d],Z=ve({inputs:{x:z},backend:s,attrs:{shape:X}});b.push(z);for(let J of b)s.disposeIntermediateTensorInfo(J);return Z}function Xee(e){let{inputs:t,backend:n,attrs:s}=e,{x:r,filter:a}=t,{strides:o,pad:i,dataFormat:l,dilations:c,dimRoundingMode:u}=s,d=N.convertConv2DDataFormat(l),p=N.computeConv2DInfo(r.shape,a.shape,o,c,i,u,!1,d),h;if(p.filterHeight===1&&p.filterWidth===1&&p.dilationHeight===1&&p.dilationWidth===1&&p.strideHeight===1&&p.strideWidth===1&&(p.padInfo.type==="SAME"||p.padInfo.type==="VALID"))h=i4({x:r,filter:a,convInfo:p,backend:n});else if(K().getBool("WEBGL_CONV_IM2COL")&&r.shape[0]===1)h=l4({x:r,filter:a,convInfo:p,backend:n});else{let m=new o4(p);h=n.runWebGLProgram(m,[r,a],"float32")}let f=ve({inputs:{x:h},backend:n,attrs:{shape:p.outShape}});return n.disposeIntermediateTensorInfo(h),f}var Kee={kernelName:Na,backendName:"webgl",kernelFunc:Xee},Zee=class{constructor(e){this.variableNames=["x","dy"],this.outputShape=e.filterShape;let t=e.strideHeight,n=e.strideWidth,s=e.padInfo.top,r=e.padInfo.left,a=e.dataFormat==="channelsLast";this.userCode=`
void main() {
ivec4 coords = getOutputCoords();
int wR = coords.x;
int wC = coords.y;
int d1 = coords.z;
int d2 = coords.w;
// Convolve x(?, ?, d1) with dy(:, :, d2) to get dw(wR, wC, d1, d2).
// ? = to be determined. : = across all values in that axis.
float dotProd = 0.0;
for (int b = 0; b < ${e.batchSize}; b++) {
for (int yR = 0; yR < ${e.outHeight}; yR++) {
int xR = wR + yR * ${t} - ${s};
if (xR < 0 || xR >= ${e.inHeight}) {
continue;
}
for (int yC = 0; yC < ${e.outWidth}; yC++) {
int xC = wC + yC * ${n} - ${r};
if (xC < 0 || xC >= ${e.inWidth}) {
continue;
}
if (${a}) {
float dyValue = getDy(b, yR, yC, d2);
float xValue = getX(b, xR, xC, d1);
dotProd += (xValue * dyValue);
} else {
float dyValue = getDy(b, d2, yR, yC);
float xValue = getX(b, d1, xR, xC);
dotProd += (xValue * dyValue);
}
}
}
}
setOutput(dotProd);
}
`}},Yee=class{constructor(e){this.variableNames=["dy","W"],this.outputShape=e.inShape;let t=e.filterHeight,n=e.filterWidth,s=e.strideHeight,r=e.strideWidth,a=e.dataFormat==="channelsLast",o=t-1-e.padInfo.top,i=n-1-e.padInfo.left,l=a?1:2,c=a?2:3,u=a?3:1;this.userCode=`
const ivec2 pads = ivec2(${o}, ${i});
void main() {
ivec4 coords = getOutputCoords();
int batch = coords[0];
int d1 = coords[${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) / ${s}.0;
if (dyR < 0.0 || dyR >= ${e.outHeight}.0 || fract(dyR) > 0.0) {
continue;
}
int idyR = int(dyR);
int wRPerm = ${t} - 1 - wR;
for (int wC = 0; wC < ${n}; wC++) {
float dyC = float(dyCCorner + wC) / ${r}.0;
if (dyC < 0.0 || dyC >= ${e.outWidth}.0 ||
fract(dyC) > 0.0) {
continue;
}
int idyC = int(dyC);
int wCPerm = ${n} - 1 - wC;
for (int d2 = 0; d2 < ${e.outChannels}; d2++) {
if (${a}) {
float xValue = getDy(batch, idyR, idyC, d2);
float wValue = getW(wRPerm, wCPerm, d1, d2);
dotProd += xValue * wValue;
} else {
float xValue = getDy(batch, d2, idyR, idyC);
float wValue = getW(wRPerm, wCPerm, d1, d2);
dotProd += xValue * wValue;
}
}
}
}
setOutput(dotProd);
}
`}},Jee=class{constructor(e){this.variableNames=["x","dy"],this.outputShape=e.filterShape;let t=e.strideDepth,n=e.strideHeight,s=e.strideWidth,r=e.padInfo.front,a=e.padInfo.top,o=e.padInfo.left;this.userCode=`
void main() {
ivec5 coords = getOutputCoords();
int wF = coords.x;
int wR = coords.y;
int wC = coords.z;
int d1 = coords.w;
int d2 = coords.u;
float dotProd = 0.0;
for (int b = 0; b < ${e.batchSize}; b++) {
for (int yF = 0; yF < ${e.outDepth}; yF++) {
int xF = wF + yF * ${t} - ${r};
if (xF < 0 || xF >= ${e.inDepth}) {
continue;
}
for (int yR = 0; yR < ${e.outHeight}; yR++) {
int xR = wR + yR * ${n} - ${a};
if (xR < 0 || xR >= ${e.inHeight}) {
continue;
}
for (int yC = 0; yC < ${e.outWidth}; yC++) {
int xC = wC + yC * ${s} - ${o};
if (xC < 0 || xC >= ${e.inWidth}) {
continue;
}
float dyValue = getDy(b, yF, yR, yC, d2);
float xValue = getX(b, xF, xR, xC, d1);
dotProd += (xValue * dyValue);
}
}
}
}
setOutput(dotProd);
}
`}},Qee=class{constructor(e){this.variableNames=["dy","W"],this.outputShape=e.inShape;let t=e.filterDepth,n=e.filterHeight,s=e.filterWidth,r=e.strideDepth,a=e.strideHeight,o=e.strideWidth,i=t-1-e.padInfo.front,l=n-1-e.padInfo.top,c=s-1-e.padInfo.left;this.userCode=`
const ivec3 pads = ivec3(${i}, ${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) / ${a}.0;
if (dyR < 0.0 || dyR >= ${e.outHeight}.0 ||
fract(dyR) > 0.0) {
continue;
}
int idyR = int(dyR);
int wRPerm = ${n} - 1 - wR;
for (int wC = 0; wC < ${s}; wC++) {
float dyC = float(dyCCorner + wC) / ${o}.0;
if (dyC < 0.0 || dyC >= ${e.outWidth}.0 ||
fract(dyC) > 0.0) {
continue;
}
int idyC = int(dyC);
int wCPerm = ${s} - 1 - wC;
for (int d2 = 0; d2 < ${e.outChannels}; d2++) {
float xValue = getDy(batch, idyF, idyR, idyC, d2);
float wValue = getW(wFPerm, wRPerm, wCPerm, d1, d2);
dotProd += xValue * wValue;
}
}
}
}
setOutput(dotProd);
}
`}};function ete(e){let{inputs:t,backend:n,attrs:s}=e,{x:r,dy:a}=t,{strides:o,pad:i,dataFormat:l,dimRoundingMode:c,filterShape:u}=s,d=N.convertConv2DDataFormat(l),p=N.computeConv2DInfo(r.shape,u,o,1,i,c,!1,d),h=new Zee(p);return n.runWebGLProgram(h,[r,a],"float32")}var tte={kernelName:oh,backendName:"webgl",kernelFunc:ete};function nte(e){let{inputs:t,backend:n,attrs:s}=e,{dy:r,filter:a}=t,{inputShape:o,strides:i,pad:l,dataFormat:c,dimRoundingMode:u}=s,d=N.convertConv2DDataFormat(c),p=N.computeConv2DInfo(o,a.shape,i,1,l,u,!1,d),h=new Yee(p);return n.runWebGLProgram(h,[r,a],"float32")}var ste={kernelName:Ea,backendName:"webgl",kernelFunc:nte};function rte(e){let{inputs:t,backend:n,attrs:s}=e,{x:r,filter:a}=t,{strides:o,pad:i,dilations:l}=s,c=N.computeConv3DInfo(r.shape,a.shape,o,l,i),u=new jee(c);return n.runWebGLProgram(u,[r,a],"float32")}var ate={kernelName:Bc,backendName:"webgl",kernelFunc:rte};function ote(e){let{inputs:t,backend:n,attrs:s}=e,{x:r,dy:a}=t,{strides:o,pad:i,filterShape:l}=s,c=N.computeConv3DInfo(r.shape,l,o,1,i),u=new Jee(c);return n.runWebGLProgram(u,[r,a],"float32")}var ite={kernelName:ih,backendName:"webgl",kernelFunc:ote};function lte(e){let{inputs:t,backend:n,attrs:s}=e,{dy:r,filter:a}=t,{pad:o,strides:i,inputShape:l}=s,c=N.computeConv3DInfo(l,a.shape,i,1,o),u=new Qee(c);return n.runWebGLProgram(u,[r,a],"float32")}var ute={kernelName:lh,backendName:"webgl",kernelFunc:lte},cte=GI+`
return cos(x);
`,dte=ot({opSnippet:cte}),pte={kernelName:Ra,backendName:"webgl",kernelFunc:dte},hte=`
float e2x = exp(-x);
return (e2x + 1.0 / e2x) / 2.0;
`,fte=ot({opSnippet:hte}),mte={kernelName:$a,backendName:"webgl",kernelFunc:fte},gte=class{constructor(e,t,n,s,r){this.variableNames=["Image","Boxes","BoxInd"],this.outputShape=[];let[a,o,i,l]=e,[c]=t,[u,d]=n;this.outputShape=[c,u,d,l];let p=s==="bilinear"?1:0,[h,f]=[`${o-1}.0`,`${i-1}.0`],[m,g,A]=u>1?[`${(o-1)/(u-1)}`,"(y2-y1) * height_ratio",`y1*${h} + float(y)*(height_scale)`]:["0.0","0.0",`0.5 * (y1+y2) * ${h}`],[x,y,b]=d>1?[`${(i-1)/(d-1)}`,"(x2-x1) * width_ratio",`x1*${f} + float(x)*(width_scale)`]:["0.0","0.0",`0.5 * (x1+x2) * ${f}`];this.userCode=`
const float height_ratio = float(${m});
const float width_ratio = float(${x});
void main() {
ivec4 coords = getOutputCoords();
int b = coords[0];
int y = coords[1];
int x = coords[2];
int d = coords[3];
// get box vals
float y1 = getBoxes(b,0);
float x1 = getBoxes(b,1);
float y2 = getBoxes(b,2);
float x2 = getBoxes(b,3);
// get image in batch index
int bInd = round(getBoxInd(b));
if(bInd < 0 || bInd >= ${a}) {
return;
}
float height_scale = ${g};
float width_scale = ${y};
float in_y = ${A};
if( in_y < 0.0 || in_y > ${h} ) {
setOutput(float(${r}));
return;
}
float in_x = ${b};
if( in_x < 0.0 || in_x > ${f} ) {
setOutput(float(${r}));
return;
}
vec2 sourceFracIndexCR = vec2(in_x,in_y);
if(${p} == 1) {
// Compute the four integer indices.
ivec2 sourceFloorCR = ivec2(sourceFracIndexCR);
ivec2 sourceCeilCR = ivec2(ceil(sourceFracIndexCR));
float topLeft = getImage(b, sourceFloorCR.y, sourceFloorCR.x, d);
float bottomLeft = getImage(b, sourceCeilCR.y, sourceFloorCR.x, d);
float topRight = getImage(b, sourceFloorCR.y, sourceCeilCR.x, d);
float bottomRight = getImage(b, sourceCeilCR.y, sourceCeilCR.x, d);
vec2 fracCR = sourceFracIndexCR - vec2(sourceFloorCR);
float top = topLeft + (topRight - topLeft) * fracCR.x;
float bottom = bottomLeft + (bottomRight - bottomLeft) * fracCR.x;
float newValue = top + (bottom - top) * fracCR.y;
setOutput(newValue);
} else {
// Compute the coordinators of nearest neighbor point.
ivec2 sourceNearestCR = ivec2(floor(
sourceFracIndexCR + vec2(0.5,0.5)));
float newValue = getImage(b, sourceNearestCR.y, sourceNearestCR.x, d);
setOutput(newValue);
}
}
`}},Ate=e=>{let{inputs:t,backend:n,attrs:s}=e,{image:r,boxes:a,boxInd:o}=t,{cropSize:i,method:l,extrapolationValue:c}=s,u=new gte(r.shape,a.shape,i,l,c);return n.runWebGLProgram(u,[r,a,o],"float32")},yte={kernelName:ai,backendName:"webgl",kernelFunc:Ate},u4=class{constructor(e,t,n){this.variableNames=["x"],this.customUniforms=[{name:"index",type:"float"}],this.outputShape=e;let s=e.length,r=t?"0.0":`getX(${c4(s,"coords")})`,a=e[e.length-1],o="",i="";t?(o=n?`end != ${a-1}`:"end != 0",i=n?"end + 1":"end - 1"):(o=n?`end + pow2 < ${a}`:"end >= pow2",i=n?"end + pow2":"end - pow2"),this.userCode=`
void main() {
${bt(s)} coords = getOutputCoords();
int end = ${d4(s,"coords")};
float val = ${r};
int pow2 = int(pow(2.0, index));
if (${o}) {
int idx = ${i};
${d4(s,"coords")} = idx;
val += getX(${c4(s,"coords")});
}
setOutput(val);
}
`}};function c4(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 ${e} is not yet supported`)}function d4(e,t){if(e===1)return`${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 ${e} is not yet supported`)}function xte(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{axis:a,exclusive:o,reverse:i}=s,l=r.shape.length,c=N.getAxesPermutation([a],l),u=r;c!=null&&(u=Hn({inputs:{x:r},backend:n,attrs:{perm:c}}));let d=N.getInnerMostAxes(1,l)[0];if(d!==l-1)throw new Error(`WebGL cumsum shader expects an inner-most axis=${r.shape.length-1} but got axis=${a}`);let p=u.shape[d],h=ys({inputs:{x:u},backend:n});for(let f=0;f<=Math.ceil(Math.log2(p))-1;f++){let m=new u4(u.shape,!1,i),g=[[f]],A=h;h=n.runWebGLProgram(m,[h],h.dtype,g),n.disposeIntermediateTensorInfo(A)}if(o){let f=new u4(u.shape,o,i),m=h;h=n.runWebGLProgram(f,[h],h.dtype),n.disposeIntermediateTensorInfo(m)}if(c!=null){let f=N.getUndoAxesPermutation(c),m=Hn({inputs:{x:h},backend:n,attrs:{perm:f}});return n.disposeIntermediateTensorInfo(h),n.disposeIntermediateTensorInfo(u),m}return h}var bte={kernelName:ri,backendName:"webgl",kernelFunc:xte};function vte(e){let{inputs:t,backend:n,attrs:s}=e,{x:r,weights:a}=t,{size:o,binaryOutput:i}=s;if(r.shape.length===1){let l=n.readSync(r.dataId),c=n.readSync(a.dataId),u=NI(l,c,a.dtype,a.shape,o);return n.makeTensorInfo([o],a.dtype,u)}else if(r.shape.length===2){let l=n.bufferSync(r),c=n.bufferSync(a),u=DY(l,c,o,i);return n.makeTensorInfo(u.shape,a.dtype,u.values)}throw new Error(`Error in denseBincount: input must be at most rank 2, but got rank${r.shape.length}.`)}var wte={kernelName:uh,backendName:"webgl",kernelFunc:vte},kte=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 Ste(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{blockSize:a,dataFormat:o}=s,i=r.shape[0],l=o==="NHWC"?r.shape[1]:r.shape[2],c=o==="NHWC"?r.shape[2]:r.shape[3],u=o==="NHWC"?r.shape[3]:r.shape[1],d=l*a,p=c*a,h=u/(a*a),f=o==="NHWC"?[i,d,p,h]:[i,h,d,p],m=new kte(f,a,o);return n.runWebGLProgram(m,[r],r.dtype)}var Ite={kernelName:oi,backendName:"webgl",kernelFunc:Ste},p4=class{constructor(e,t=!1,n=null,s=!1,r=!1){this.variableNames=["x","W"],this.customUniforms=[{name:"pads",type:"ivec2"},{name:"strides",type:"ivec2"},{name:"dilations",type:"ivec2"},{name:"inDims",type:"ivec2"}],this.outputShape=e.outShape,this.enableShapeUniforms=Fs(this.outputShape.length);let a=e.filterHeight,o=e.filterWidth,i=e.outChannels/e.inChannels,l="",c="";n&&(s?l=`float activation(float a) {
float b = getPreluActivationWeightsAtOutCoords();
${n}
}`:r?l=`float activation(float a) {
float b = getLeakyreluAlphaAtOutCoords();
${n}
}`:l=`
float activation(float x) {
${n}
}
`,c="result = activation(result);");let u=t?"result += getBiasAtOutCoords();":"";t&&this.variableNames.push("bias"),s&&this.variableNames.push("preluActivationWeights"),r&&this.variableNames.push("leakyreluAlpha"),this.userCode=`
${l}
void main() {
ivec4 coords = getOutputCoords();
int batch = coords.x;
ivec2 xRCCorner = coords.yz * strides - pads;
int d2 = coords.w;
int d1 = d2 / ${i};
int q = d2 - d1 * ${i};
int xRCorner = xRCCorner.x;
int xCCorner = xRCCorner.y;
// Convolve x(?, ?, d1) with w(:, :, d1, q) to get y(yR, yC, d2).
// ? = to be determined. : = across all values in that axis.
float dotProd = 0.0;
// TO DO(dsmilkov): Flatten the two for loops and vec4 the operations.
for (int wR = 0; wR < ${a}; wR++) {
int xR = xRCorner + wR * dilations[0];
if (xR < 0 || xR >= inDims[0]) {
continue;
}
for (int wC = 0; wC < ${o}; wC++) {
int xC = xCCorner + wC * dilations[1];
if (xC < 0 || xC >= inDims[1]) {
continue;
}
float xVal = getX(batch, xR, xC, d1);
float wVal = getW(wR, wC, d1, q);
dotProd += xVal * wVal;
}
}
float result = dotProd;
${u}
${c}
setOutput(result);
}
`}},h4=class{constructor(e,t=!1,n=null,s=!1,r=!1){this.variableNames=["x","W"],this.packedInputs=!0,this.packedOutput=!0,this.customUniforms=[{name:"pads",type:"ivec2"},{name:"strides",type:"ivec2"},{name:"dilations",type:"ivec2"},{name:"inDims",type:"ivec2"}],this.outputShape=e.outShape,this.enableShapeUniforms=Fs(this.outputShape.length);let a=e.outChannels/e.inChannels,o=e.padInfo.left,i=e.strideWidth,l=e.dilationWidth,c=e.filterHeight,u=e.filterWidth,d=u,p=`
int xR; int xC; int xCOffset;
vec4 wTexel; vec4 previous; vec4 final;`;for(let g=0;g<u;g++)p+=`
vec4 xTexelC${g*2};
int xTexelC${g*2}Ready;
vec4 xTexelC${g*2+1};
int xTexelC${g*2+1}Ready;
vec4 xC${g};`;p+=`
for (int r = 0; r < ${c}; r++) {
`;for(let g=0;g<u;g++)p+=`
xTexelC${g*2} = vec4(0.0);
xTexelC${g*2}Ready = 0;
xTexelC${g*2+1} = vec4(0.0);
xTexelC${g*2+1}Ready = 0;
xC${g} = vec4(0.0);`;p+=`
xR = xRCorner + r * dilations[0];
if (xR >=0 && xR < inDims[0]) {
`;for(let g=0;g<(d+1)/2;g++){let A=g*2;if(p+=`
xC = xCCorner + ${A*l};
`,i===1){if(A<u&&(o%2==1?(p+=`
xCOffset = xC + 1;
if (xCOffset >= 0 && xCOffset < inDims[1] && xTexelC${A}Ready == 0) {
xTexelC${A} = getX(batch, xR, xCOffset, d1);
// Need to manually clear unused channels in case
// we're reading from recycled texture.
if (xCOffset + 1 >= inDims[1]) {
xTexelC${A}.zw = vec2(0.0);
}
xTexelC${A}Ready = 1;
}
`,l===1&&A>0?p+=`
xC${A} = vec4(xTexelC${A-2}.zw, xTexelC${A}.xy);
`:p+=`
xCOffset = xC + 1 - 2;
if (xCOffset >= 0 && xCOffset < inDims[1]) {
previous = getX(batch, xR, xCOffset, d1);
// Need to manually clear unused channels in case
// we're reading from recycled texture.
if (xCOffset + 1 >= inDims[1]) {
previous.zw = vec2(0.0);
}
xC${A} = vec4(previous.zw, xTexelC${A}.xy);
} else {
xC${A} = vec4(0.0, 0.0, xTexelC${A}.xy);
}
`):p+=`
if (xC >= 0 && xC < inDims[1] && xTexelC${A}Ready == 0) {
xTexelC${A} = getX(batch, xR, xC, d1);
if (xC + 1 >= inDims[1]) {
xTexelC${A}.zw = vec2(0.0);
}
xTexelC${A}Ready = 1;
}
xC${A} = xTexelC${A};
`,A+1<u)){let x=o%2==0?v.nearestLargerEven(l):l;l%2==0&&o%2==1||l%2!=0&&o%2!=1?(p+=`
xCOffset = xC + imod(pads[1], 2) + ${x};
if (xCOffset >= 0 && xCOffset < inDims[1] && xTexelC${A+1}Ready == 0) {
xTexelC${A+1} = getX(batch, xR, xCOffset, d1);
// Need to manually clear unused channels in case
// we're reading from recycled texture.
if (xCOffset + 1 >= inDims[1]) {
xTexelC${A+1}.zw = vec2(0.0);
}
xTexelC${A+1}Ready = 1;
}
`,l>1&&(p+=`
xCOffset -= 2;
if (xCOffset >= 0 && xCOffset < inDims[1] && xTexelC${A}Ready == 0) {
xTexelC${A} = getX(batch, xR, xCOffset, d1);
xTexelC${A}Ready = 1;
}
`),p+=`
xC${A+1} = vec4(xTexelC${A}.zw, xTexelC${A+1}.xy);
`):x===1?p+=`
xC${A+1} = xTexelC${A};
`:p+=`
xCOffset = xC + ${x};
if (xCOffset >= 0 && xCOffset < inDims[1] && xTexelC${A+1}Ready == 0) {
xTexelC${A+1} = getX(batch, xR, xCOffset, d1);
if (xCOffset + 1 >= inDims[1]) {
xTexelC${A+1}.zw = vec2(0.0);
}
xTexelC${A+1}Ready = 1;
}
xC${A+1} = xTexelC${A+1};
`}}else A<u&&(o%2==1?(p+=`
xCOffset = xC + 1 - strides[1];
if(xCOffset >= 0 && xCOffset < inDims[1] && xTexelC${A}Ready == 0) {
xTexelC${A} = getX(batch, xR, xCOffset, d1);
// Need to manually clear unused channels in case
// we're reading from recycled texture.
if (xCOffset + 1 >= inDims[1]) {
xTexelC${A}.zw = vec2(0.0);
}
xTexelC${A}Ready = 1;
}
if(xC + 1 >= 0 && xC + 1 < inDims[1] && xTexelC${A+1}Ready == 0) {
xTexelC${A+1} = getX(batch, xR, xC + 1, d1);
// Need to manually clear unused channels in case
// we're reading from recycled texture.
if (xC + 2 >= inDims[1]) {
xTexelC${A+1}.zw = vec2(0.0);
}
xTexelC${A+1}Ready = 1;
}
xC${A} = vec4(xTexelC${A}.zw, xTexelC${A+1}.zw);
`,A+1<u&&(p+=`
final = vec4(0.0);
xCOffset = xC + 1 + strides[1];
if(xCOffset >= 0 && xCOffset < inDims[1]) {
final = getX(batch, xR, xCOffset, d1);
}
xC${A+1} = vec4(xTexelC${A+1}.xy, final.xy);
`)):(p+=`
if(xC >= 0 && xC < inDims[1] && xTexelC${A}Ready == 0) {
xTexelC${A} = getX(batch, xR, xC, d1);
if (xC + 1 >= inDims[1]) {
xTexelC${A}.zw = vec2(0.0);
}
xTexelC${A}Ready = 1;
}
xCOffset = xC + strides[1];
if(xCOffset >= 0 && xCOffset < inDims[1] && xTexelC${A+1}Ready == 0) {
xTexelC${A+1} = getX(batch, xR, xCOffset, d1);
if (xCOffset + 1 >= inDims[1]) {
xTexelC${A+1}.zw = vec2(0.);
}
xTexelC${A+1}Ready = 1;
}
xC${A} = vec4(
xTexelC${A}.xy, xTexelC${A+1}.xy);
`,A+1<u&&(p+=`
xC${A+1} = vec4(xTexelC${A}.zw, xTexelC${A+1}.zw);
`)));A<u&&(p+=`
wTexel = getW(r, ${A}, d1, q);
dotProd += xC${A} * vec4(wTexel.xz, wTexel.xz);
`,A+1<u&&(p+=`
wTexel = getW(r, ${A+1}, d1, q);
dotProd += xC${A+1} * vec4(wTexel.xz, wTexel.xz);
`))}p+=`
}
`,p+=`
}
`;let h="",f="";n&&(s?h=`vec4 activation(vec4 a) {
vec4 b = getPreluActivationWeightsAtOutCoords();
${n}
}`:r?h=`vec4 activation(vec4 a) {
vec4 b = getLeakyreluAlphaAtOutCoords();
${n}
}`:h=`vec4 activation(vec4 x) {
${n}
}`,f="result = activation(result);");let m=t?"result += getBiasAtOutCoords();":"";t&&this.variableNames.push("bias"),s&&this.variableNames.push("preluActivationWeights"),r&&this.variableNames.push("leakyreluAlpha"),this.userCode=`
${h}
void main() {
ivec4 coords = getOutputCoords();
int batch = coords.x;
ivec2 xRCCorner = coords.yz * strides - pads;
int d2 = coords.w;
int d1 = d2 / ${a};
int q = d2 - d1 * ${a};
int xRCorner = xRCCorner.x;
int xCCorner = xRCCorner.y;
//intialize dotProd with a small epsilon seems to reduce GPU accuracy loss.
vec4 dotProd = vec4(0.000000000000001);
${p}
vec4 result = dotProd - vec4(0.000000000000001);
${m}
${f}
setOutput(result);
}
`}};function Cte(e){let{inputs:t,backend:n,attrs:s}=e,{x:r,filter:a}=t,{strides:o,pad:i,dilations:l,dimRoundingMode:c}=s,u=l;u==null&&(u=[1,1]),v.assert(N.eitherStridesOrDilationsAreOne(o,u),()=>`Error in depthwiseConv2d: Either strides or dilations must be 1. Got strides ${o} and dilations '${u}'`);let d=N.computeConv2DInfo(r.shape,a.shape,o,u,i,c,!0),p;K().getBool("WEBGL_PACK_DEPTHWISECONV")&&d.strideWidth<=2&&d.outChannels/d.inChannels==1?p=new h4(d):p=new p4(d);let h=[[d.padInfo.top,d.padInfo.left],[d.strideHeight,d.strideWidth],[d.dilationHeight,d.dilationWidth],[d.inHeight,d.inWidth]];return n.runWebGLProgram(p,[r,a],"float32",h)}var Tte={kernelName:Da,backendName:"webgl",kernelFunc:Cte},Nte=class{constructor(e){this.variableNames=["x","dy"],this.outputShape=e.filterShape;let t=e.strideHeight,n=e.strideWidth,s=e.padInfo.top,r=e.padInfo.left,a=e.outChannels/e.inChannels;this.userCode=`
void main() {
ivec4 coords = getOutputCoords();
int wR = coords.x;
int wC = coords.y;
int d1 = coords.z;
int dm = coords.w;
int d2 = d1 * ${a} + dm;
float dotProd = 0.0;
// TO DO: Vec4 over the batch size
for (int b = 0; b < ${e.batchSize}; b++) {
for (int yR = 0; yR < ${e.outHeight}; yR++) {
int xR = wR + yR * ${t} - ${s};
if (xR < 0 || xR >= ${e.inHeight}) {
continue;
}
for (int yC = 0; yC < ${e.outWidth}; yC++) {
int xC = wC + yC * ${n} - ${r};
if (xC < 0 || xC >= ${e.inWidth}) {
continue;
}
float dyValue = getDy(b, yR, yC, d2);
float xValue = getX(b, xR, xC, d1);
dotProd += (xValue * dyValue);
}
}
}
setOutput(dotProd);
}
`}},Ete=class{constructor(e){this.variableNames=["dy","W"],this.outputShape=e.inShape;let t=e.filterHeight,n=e.filterWidth,s=e.strideHeight,r=e.strideWidth,a=t-1-e.padInfo.top,o=n-1-e.padInfo.left,i=e.outChannels/e.inChannels;this.userCode=`
const ivec2 pads = ivec2(${a}, ${o});
void main() {
ivec4 coords = getOutputCoords();
int batch = coords[0];
int d1 = coords[3];
ivec2 dyCorner = coords.yz - pads;
int dyRCorner = dyCorner.x;
int dyCCorner = dyCorner.y;
float dotProd = 0.0;
for (int wR = 0; wR < ${t}; wR++) {
float dyR = float(dyRCorner + wR) / ${s}.0;
if (dyR < 0.0 || dyR >= ${e.outHeight}.0 || fract(dyR) > 0.0) {
continue;
}
int idyR = int(dyR);
int wRPerm = ${t} - 1 - wR;
for (int wC = 0; wC < ${n}; wC++) {
float dyC = float(dyCCorner + wC) / ${r}.0;
if (dyC < 0.0 || dyC >= ${e.outWidth}.0 ||
fract(dyC) > 0.0) {
continue;
}
int idyC = int(dyC);
int wCPerm = ${n} - 1 - wC;
// TO DO: Vec4 over the channelMul
for (int dm = 0; dm < ${i}; dm++) {
int d2 = d1 * ${i} + dm;
float xValue = getDy(batch, idyR, idyC, d2);
float wValue = getW(wRPerm, wCPerm, d1, dm);
dotProd += xValue * wValue;
}
}
}
setOutput(dotProd);
}
`}};function Rte(e){let{inputs:t,backend:n,attrs:s}=e,{x:r,dy:a}=t,{strides:o,dilations:i,pad:l,dimRoundingMode:c,filterShape:u}=s,d=N.computeConv2DInfo(r.shape,u,o,i,l,c,!0),p=new Nte(d);return n.runWebGLProgram(p,[r,a],"float32")}var $te={kernelName:ch,backendName:"webgl",kernelFunc:Rte};function Dte(e){let{inputs:t,backend:n,attrs:s}=e,{dy:r,filter:a}=t,{strides:o,dilations:i,pad:l,dimRoundingMode:c,inputShape:u}=s,d=N.computeConv2DInfo(u,a.shape,o,i,l,c,!0),p=new Ete(d);return n.runWebGLProgram(p,[r,a],"float32")}var _te={kernelName:dh,backendName:"webgl",kernelFunc:Dte},Pte=class{constructor(e){this.variableNames=["X"],this.outputShape=[e,e],this.userCode=`
void main() {
ivec2 coords = getOutputCoords();
float val = coords[0] == coords[1] ? getX(coords[0]) : 0.0;
setOutput(val);
}
`}};function Fte(e){let{inputs:t,backend:n}=e,{x:s}=t,r=[...s.shape,...s.shape],a=v.sizeFromShape(s.shape),o=ve({inputs:{x:s},backend:n,attrs:{shape:[a]}}),i=new Pte(a),l=n.runWebGLProgram(i,[o],o.dtype),c=ve({inputs:{x:l},backend:n,attrs:{shape:r}});return n.disposeIntermediateTensorInfo(o),n.disposeIntermediateTensorInfo(l),c}var Ote={kernelName:ph,backendName:"webgl",kernelFunc:Fte},Mte=class{constructor(e){this.variableNames=["x","W"],this.outputShape=e.outShape;let{inHeight:t,inWidth:n,padInfo:s,strideHeight:r,strideWidth:a,filterHeight:o,filterWidth:i,dilationHeight:l,dilationWidth:c}=e,{top:u,left:d}=s;this.userCode=`
const ivec2 strides = ivec2(${r}, ${a});
const ivec2 pads = ivec2(${u}, ${d});
const float neg_infinity = -3.4e38;
void main() {
ivec4 coords = getOutputCoords();
int batch = coords.x;
int d1 = coords.w;
ivec2 outTopLeftCorner =
coords.yz * strides - pads;
int hBeg = outTopLeftCorner.x;
int wBeg = outTopLeftCorner.y;
float curVal = neg_infinity;
for (int h = 0; h < ${o}; h++) {
int hIn = hBeg + h * ${l};
if (hIn >= 0 && hIn < ${t}) {
for (int w = 0; w < ${i}; 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 zte(e){let{inputs:t,backend:n,attrs:s}=e,{x:r,filter:a}=t,{strides:o,pad:i,dilations:l}=s,c=N.computeDilation2DInfo(r.shape,a.shape,o,i,"NHWC",l),u,d=new Mte(c);u=n.runWebGLProgram(d,[r,a],"float32");let p=ve({inputs:{x:u},backend:n,attrs:{shape:c.outShape}});return n.disposeIntermediateTensorInfo(u),p}var Lte={kernelName:Wc,backendName:"webgl",kernelFunc:zte};function Bte(e){let{inputs:t,backend:n,attrs:s}=e,{equation:r}=s,a=t,{allDims:o,summedDims:i,idDims:l}=N.decodeEinsumEquation(r,a.length);N.checkEinsumDimSizes(o.length,l,a);let{path:c,steps:u}=N.getEinsumComputePath(i,l),d=u.length,p=null,h=o.length,f=[];for(let m=0;m<d;++m){for(let g of u[m]){let{permutationIndices:A,expandDims:x}=N.getEinsumPermutation(h,l[g]),y;N.isIdentityPermutation(A)?y=a[g]:(y=Hn({inputs:{x:a[g]},backend:n,attrs:{perm:A}}),f.push(y));let b=y.shape.slice();for(let w=0;w<x.length;++w)b.splice(x[w],0,1);v.arraysEqual(y.shape,b)||(y=ve({inputs:{x:y},backend:n,attrs:{shape:b}}),f.push(y)),p===null?p=y:(p=Zy({inputs:{a:y,b:p},backend:n}),f.push(p))}m<d-1&&(c[m]>=0&&(p=Nm({inputs:{x:p},backend:n,attrs:{axis:c[m]-(o.length-h),keepDims:!1}}),f.push(p)),h--)}for(let m of f)m!==p&&n.disposeIntermediateTensorInfo(m);return p}var Wte={kernelName:Vc,backendName:"webgl",kernelFunc:Bte},Vte="return (x >= 0.0) ? x : (exp(x) - 1.0);",Ute=`
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;
`,Gte=ot({opSnippet:Vte,packedOpSnippet:Ute}),Hte={kernelName:Pa,backendName:"webgl",kernelFunc:Gte},jte="return (b >= 1.0) ? a : a * (b + 1.0);",qte=`
vec4 bGTEZero = vec4(greaterThanEqual(b, vec4(0.)));
return (bGTEZero * a) + ((vec4(1.0) - bGTEZero) * (a * (b + vec4(1.0))));
`,Xte=e=>{let{inputs:t,backend:n}=e,{dy:s,y:r}=t,a=K().getBool("WEBGL_PACK_BINARY_OPERATIONS")?new Jd(qte,s.shape,r.shape):new Zu(jte,s.shape,r.shape);return n.runWebGLProgram(a,[s,r],s.dtype)},Kte={kernelName:mh,backendName:"webgl",kernelFunc:Xte},Zte=`
return vec4(equal(a, b));
`,Yte="return float(a == b);",Jte=In({opSnippet:Yte,packedOpSnippet:Zte,dtype:"bool",cpuKernelImpl:FY}),Qte={kernelName:ii,backendName:"webgl",kernelFunc:Jte},ene=`
// Error function is calculated approximately with elementary function.
// See "Handbook of Mathematical Functions with Formulas,
// Graphs, and Mathematical Tables", Abramowitz and Stegun.
float p = ${N.ERF_P};
float a1 = ${N.ERF_A1};
float a2 = ${N.ERF_A2};
float a3 = ${N.ERF_A3};
float a4 = ${N.ERF_A4};
float a5 = ${N.ERF_A5};
float sign = sign(x);
x = abs(x);
float t = 1.0 / (1.0 + p * x);
return sign * (1.0 - (((((a5*t + a4)*t) + a3)*t + a2)*t + a1)*t*exp(-x*x));
`,tne=ot({opSnippet:ene}),nne={kernelName:Jl,backendName:"webgl",kernelFunc:tne},f4="return exp(x);",m4=ot({opSnippet:f4,packedOpSnippet:f4,cpuKernelImpl:OY,dtype:"float32"}),sne={kernelName:Fa,backendName:"webgl",kernelFunc:m4};function ex(e){let{inputs:t,attrs:n,backend:s}=e,{dim:r}=n,{input:a}=t,o=a.shape.length,i=a.shape.slice(),l=r;return r<0&&(v.assert(-(o+1)<=r,()=>`Axis must be in the interval [${-(o+1)}, ${o}]`),l=o+r+1),i.splice(l,0,1),ve({inputs:{x:a},backend:s,attrs:{shape:i}})}var rne={kernelName:li,backendName:"webgl",kernelFunc:ex},g4="return exp(x) - 1.0;",ane=ot({opSnippet:g4,packedOpSnippet:g4,cpuKernelImpl:MY}),one={kernelName:ui,backendName:"webgl",kernelFunc:ane},A4=class{constructor(e,t,n){this.variableNames=["real","imag"];let s=t[1];this.outputShape=t;let r=n?`2.0 * ${Math.PI}`:`-2.0 * ${Math.PI}`,a=n?`${s}.0`:"1.0",o;if(e==="real")o="return real * expR - imag * expI;";else if(e==="imag")o="return real * expI + imag * expR;";else throw new Error(`FFT component must be either "real" or "imag", got ${e}.`);this.userCode=`
const float exponentMultiplier = ${r};
float unaryOpComplex(float real, float expR, float imag, float expI) {
${o}
}
float mulMatDFT(int batch, int index) {
float indexRatio = float(index) / float(${s});
float exponentMultiplierTimesIndexRatio =
exponentMultiplier * indexRatio;
float result = 0.0;
for (int i = 0; i < ${s}; i++) {
// x = (-2|2 * PI / N) * index * i;
float x = exponentMultiplierTimesIndexRatio * float(i);
float expR = cos(x);
float expI = sin(x);
float real = getReal(batch, i);
float imag = getImag(batch, i);
result +=
unaryOpComplex(real, expR, imag, expI) / ${a};
}
return result;
}
void main() {
ivec2 coords = getOutputCoords();
setOutput(mulMatDFT(coords[0], coords[1]));
}
`}};function y4(e,t,n){let s=n.texData.get(e.dataId),r=v.sizeFromShape(e.shape),a=e.shape[e.shape.length-1],o=r/a,i=ve({inputs:{x:e},backend:n,attrs:{shape:[o,a]}}),l=i.shape,c=new A4("real",l,t),u=new A4("imag",l,t),d=[{dataId:s.complexTensorInfos.real.dataId,dtype:s.complexTensorInfos.real.dtype,shape:l},{dataId:s.complexTensorInfos.imag.dataId,dtype:s.complexTensorInfos.imag.dtype,shape:l}],p=n.runWebGLProgram(c,d,"float32"),h=n.runWebGLProgram(u,d,"float32"),f=zo({inputs:{real:p,imag:h},backend:n});n.disposeIntermediateTensorInfo(p),n.disposeIntermediateTensorInfo(h);let m=ve({inputs:{x:f},backend:n,attrs:{shape:e.shape}});return n.disposeIntermediateTensorInfo(i),n.disposeIntermediateTensorInfo(f),m}function ine(e){let{inputs:t,backend:n}=e,{input:s}=t;return y4(s,!1,n)}var lne={kernelName:gh,backendName:"webgl",kernelFunc:ine},une=class{constructor(e,t){this.outputShape=[],this.customUniforms=[{name:"value",type:"float"}],this.variableNames=["x"],this.outputShape=e,this.userCode=`
void main() {
// Input can be obtained from uniform value.
setOutput(value);
}
`}};function tp(e){let{backend:t,attrs:n}=e,{shape:s,value:r}=n,{dtype:a}=n;if(a=a||v.inferDtype(r),a==="string"){let o=v.getArrayFromDType(a,v.sizeFromShape(s));return o.fill(r),t.makeTensorInfo(s,a,o)}else{let o=new une(s,r),i=[[r]];return t.runWebGLProgram(o,[],a,i)}}var cne={kernelName:Ql,backendName:"webgl",kernelFunc:tp},dne=class{constructor(e){this.variableNames=["Image"],this.outputShape=[];let t=e[2];this.outputShape=e,this.userCode=`
void main() {
ivec4 coords = getOutputCoords();
int x = coords[2];
int coordX = ${t} - x - 1;
float outputValue;
if(coordX >= 0 && coordX < ${t}) {
outputValue = getImage(coords[0], coords[1], coordX, coords[3]);
} else {
outputValue = getImage(coords[0], coords[1], coords[2], coords[3]);
}
setOutput(outputValue);
}
`}},pne={kernelName:ci,backendName:"webgl",kernelFunc:({inputs:e,backend:t})=>{let{image:n}=e,s=t,r=new dne(n.shape);return s.runWebGLProgram(r,[n],n.dtype)}},x4="return floor(x);",hne=ot({opSnippet:x4,packedOpSnippet:x4,cpuKernelImpl:zY}),fne={kernelName:Oa,backendName:"webgl",kernelFunc:hne},mne=`
float s = sign(a) * sign(b);
int ia = round(a);
int ib = round(b);
if (ib != 0) {
// Windows (D3D) wants guaranteed non-zero int division at compile-time.
return float(idiv(ia, ib, s));
} else {
return NAN;
}
`,gne=`
ivec4 ia = round(a);
ivec4 ib = round(b);
bvec4 cond = notEqual(ib, ivec4(0));
ivec4 result = ivec4(0);
vec4 s = sign(a) * sign(b);
// Windows (D3D) wants guaranteed non-zero int division at compile-time.
if (cond[0]) {
result[0] = idiv(ia[0], ib[0], s[0]);
}
if (cond[1]) {
result[1] = idiv(ia[1], ib[1], s[1]);
}
if (cond[2]) {
result[2] = idiv(ia[2], ib[2], s[2]);
}
if (cond[3]) {
result[3] = idiv(ia[3], ib[3], s[3]);
}
return vec4(result);
`,Ane=In({opSnippet:mne,packedOpSnippet:gne,dtype:"int32"}),yne={kernelName:Ma,backendName:"webgl",kernelFunc:Ane},xne=class{constructor(e){this.variableNames=["A"];let t=Un(),[n,s]=e;this.outputShape=e,this.userCode=`
void main() {
ivec3 coords = getOutputCoords();
int texR = coords[0];
int texC = coords[1];
int depth = coords[2];
vec2 uv = (vec2(texC, texR) + halfCR) / vec2(${s}.0, ${n}.0);
vec4 values = ${t.texture2D}(A, uv);
float value;
if (depth == 0) {
value = values.r;
} else if (depth == 1) {
value = values.g;
} else if (depth == 2) {
value = values.b;
} else if (depth == 3) {
value = values.a;
}
setOutput(floor(value * 255.0 + 0.5));
}
`}},bne=class{constructor(e){this.variableNames=["A"],this.packedInputs=!1,this.packedOutput=!0;let t=Un(),[n,s]=e;this.outputShape=e,this.userCode=`
void main() {
ivec3 coords = getOutputCoords();
int texR = coords[0];
int texC = coords[1];
int depth = coords[2];
vec4 result = vec4(0.);
for(int row=0; row<=1; row++) {
for(int col=0; col<=1; col++) {
texC = coords[1] + row;
depth = coords[2] + col;
vec2 uv = (vec2(texC, texR) + halfCR) /
vec2(${s}.0, ${n}.0);
vec4 values = ${t.texture2D}(A, uv);
float value;
if (depth == 0) {
value = values.r;
} else if (depth == 1) {
value = values.g;
} else if (depth == 2) {
value = values.b;
} else if (depth == 3) {
value = values.a;
}
result[row * 2 + col] = floor(value * 255.0 + 0.5);
}
}
${t.output} = result;
}
`}},vne={kernelName:Yc,backendName:"webgl",kernelFunc:wne},Qu;function wne(e){let{inputs:t,backend:n,attrs:s}=e,{pixels:r}=t,{numChannels:a}=s,o=typeof HTMLVideoElement!="undefined"&&r instanceof HTMLVideoElement,i=typeof HTMLImageElement!="undefined"&&r instanceof HTMLImageElement,[l,c]=o?[r.videoWidth,r.videoHeight]:[r.width,r.height],u=[c,l],d=[c,l,a];(i||o)&&(Qu==null&&(Qu=document.createElement("canvas").getContext("2d")),Qu.canvas.width=l,Qu.canvas.height=c,Qu.drawImage(r,0,0,l,c),r=Qu.canvas);let p=n.makeTensorInfo(u,"int32");n.texData.get(p.dataId).usage=_s.PIXELS,n.gpgpu.uploadPixelDataToTexture(n.getTexture(p.dataId),r);let h=K().getBool("WEBGL_PACK")?new bne(d):new xne(d),f=n.runWebGLProgram(h,[p],"int32");return n.disposeData(p.dataId),f}function kne(e){let{inputs:t,backend:n,attrs:s}=e,{x:r,filter:a,bias:o,preluActivationWeights:i}=t,{strides:l,pad:c,dataFormat:u,dilations:d,dimRoundingMode:p,activation:h,leakyreluAlpha:f}=s,m=N.convertConv2DDataFormat(u),g=N.computeConv2DInfo(r.shape,a.shape,l,d,c,p,!1,m),A,x=[];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"))A=i4({x:r,filter:a,convInfo:g,backend:n,bias:o,activation:h,preluActivationWeights:i,leakyreluAlpha:f});else if(K().getBool("WEBGL_CONV_IM2COL")&&r.shape[0]===1)A=l4({x:r,filter:a,convInfo:g,backend:n,bias:o,activation:h,preluActivationWeights:i,leakyreluAlpha:f});else{let b=o!=null,w=i!=null,k=h==="leakyrelu",C=h?Cm(h,!1):null,E=new o4(g,b,C,w,k),R=[r,a];if(o&&R.push(o),i&&R.push(i),k){let F=n.makeTensorInfo([],"float32",v.createScalarValue(f,"float32"));R.push(F),x.push(F)}A=n.runWebGLProgram(E,R,"float32")}let y=ve({inputs:{x:A},backend:n,attrs:{shape:g.outShape}});return x.push(A),x.forEach(b=>n.disposeIntermediateTensorInfo(b)),y}var Sne={kernelName:mo,backendName:"webgl",kernelFunc:kne};function Ine(e){let{inputs:t,backend:n,attrs:s}=e,{x:r,filter:a,bias:o,preluActivationWeights:i}=t,{strides:l,pad:c,dilations:u,dimRoundingMode:d,activation:p,leakyreluAlpha:h}=s,f=[],m=u;m==null&&(m=[1,1]),v.assert(N.eitherStridesOrDilationsAreOne(l,m),()=>`Error in depthwiseConv2d: Either strides or dilations must be 1. Got strides ${l} and dilations '${m}'`);let g=N.computeConv2DInfo(r.shape,a.shape,l,m,c,d,!0),A=K().getBool("WEBGL_PACK_DEPTHWISECONV")&&g.strideWidth<=2&&g.outChannels/g.inChannels==1,x=p?Cm(p,A):null,y=[r,a],b=o!=null,w=i!=null,k=p==="leakyrelu";if(b&&y.push(o),w&&y.push(i),k){let F=n.makeTensorInfo([],"float32",v.createScalarValue(h,"float32"));y.push(F),f.push(F)}let C;A?C=new h4(g,b,x,w,k):C=new p4(g,b,x,w,k);let E=[[g.padInfo.top,g.padInfo.left],[g.strideHeight,g.strideWidth],[g.dilationHeight,g.dilationWidth],[g.inHeight,g.inWidth]],R=n.runWebGLProgram(C,y,"float32",E);return f.forEach(F=>n.disposeIntermediateTensorInfo(F)),R}var Cne={kernelName:go,backendName:"webgl",kernelFunc:Ine},Tne=class{constructor(e,t,n){this.sliceDim=e,this.strides=t,this.variableNames=["x","indices"],this.outputShape=n;let s=bt(t.length),r=bt(n.length),a=this.sliceDim>1?"strides[j]":"strides";this.userCode=`
${s} strides = ${s}(${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 * ${a};
}
setOutput(getX(flattenIndex, coords[1]));
}
`}};function Nne(e){let{inputs:t,backend:n}=e,{params:s,indices:r}=t,a=r.shape,o=a[a.length-1],i=v.sizeFromShape(s.shape),[l,c,u,d]=N.prepareAndValidate(s,r),p=ve({inputs:{x:r},backend:n,attrs:{shape:[c,o]}}),h=ve({inputs:{x:s},backend:n,attrs:{shape:[v.sizeFromShape(s.shape)/u,u]}});if(n.shouldExecuteOnCPU([s,r])||s.dtype==="string"){let A=n.readSync(r.dataId),x=n.bufferSync(s),y=LY(A,x,s.dtype,c,o,u,d,s.shape,i);return n.makeTensorInfo(l,s.dtype,y.values)}let f=new Tne(o,d,[c,u]),m=n.runWebGLProgram(f,[h,p],h.dtype),g=ve({inputs:{x:m},backend:n,attrs:{shape:l}});return n.disposeIntermediateTensorInfo(p),n.disposeIntermediateTensorInfo(h),n.disposeIntermediateTensorInfo(m),g}var Ene={kernelName:pi,backendName:"webgl",kernelFunc:Nne},Rne=class{constructor(e,t){this.variableNames=["A","indices"],this.outputShape=t,this.rank=t.length;let n=bt(this.rank),s=$ne(e,2);this.userCode=`
void main() {
${n} resRC = getOutputCoords();
setOutput(getA(${s}));
}
`}};function $ne(e,t){let n=["resRC.x","resRC.y","resRC.z","resRC.w"],s=[];for(let r=0;r<e.length;r++)r===2?s.push("int(getIndices(resRC.x, resRC.z))"):s.push(`${n[r]}`);return s.join()}function b4(e){let{inputs:t,backend:n,attrs:s}=e,{x:r,indices:a}=t,{axis:o,batchDims:i}=s,l=v.parseAxisParam(o,r.shape)[0],c=n.readSync(a.dataId),u=r.shape[l];for(let b=0;b<c.length;++b){let w=c[b];v.assert(w<=u-1&&w>=0,()=>`GatherV2: the index value ${w} is not in [0, ${u-1}]`)}let d=N.segment_util.collectGatherOpShapeInfo(r,a,l,i),p=v.sizeFromShape(a.shape),h=[],f=ve({inputs:{x:r},backend:n,attrs:{shape:[d.batchSize,d.outerSize,d.dimSize,d.sliceSize]}}),m=ve({inputs:{x:a},backend:n,attrs:{shape:[d.batchSize,p/d.batchSize]}});h.push(f),h.push(m);let g=[d.batchSize,d.outerSize,p/d.batchSize,d.sliceSize];if(n.shouldExecuteOnCPU([r,a])||r.dtype==="string"){let b=n.bufferSync(m),w=n.bufferSync(f),k=BY(w,b,g);return h.forEach(C=>n.disposeIntermediateTensorInfo(C)),n.makeTensorInfo(d.outputShape,k.dtype,k.values)}let A=new Rne(f.shape,g),x=n.runWebGLProgram(A,[f,m],f.dtype);h.push(x);let y=ve({inputs:{x},backend:n,attrs:{shape:d.outputShape}});return h.forEach(b=>n.disposeIntermediateTensorInfo(b)),y}var Dne={kernelName:di,backendName:"webgl",kernelFunc:b4},_ne="return float(a > b);",Pne=`
return vec4(greaterThan(a, b));
`,Fne=In({opSnippet:_ne,packedOpSnippet:Pne,cpuKernelImpl:WY,dtype:"bool"}),One={kernelName:hi,backendName:"webgl",kernelFunc:Fne},Mne="return float(a >= b);",zne=`
return vec4(greaterThanEqual(a, b));
`,Lne=In({opSnippet:Mne,packedOpSnippet:zne,dtype:"bool",cpuKernelImpl:VY}),Bne={kernelName:La,backendName:"webgl",kernelFunc:Lne};function Wne(e){let{inputs:t,backend:n}=e,{input:s}=t;return y4(s,!0,n)}var Vne={kernelName:Ah,backendName:"webgl",kernelFunc:Wne},Une="return float(!isnan(x) && !isinf(x));",Gne=ot({opSnippet:Une,dtype:"bool"}),Hne={kernelName:eu,backendName:"webgl",kernelFunc:Gne},jne="return float(isinf(x));",qne=ot({opSnippet:jne,dtype:"bool"}),Xne={kernelName:tu,backendName:"webgl",kernelFunc:qne},Kne="return float(isnan(x));",Zne=ot({opSnippet:Kne,dtype:"bool"}),Yne={kernelName:nu,backendName:"webgl",kernelFunc:Zne},Jne="return float(a < b);",Qne=`
return vec4(lessThan(a, b));
`,ese=In({opSnippet:Jne,packedOpSnippet:Qne,cpuKernelImpl:UY,dtype:"bool"}),tse={kernelName:mi,backendName:"webgl",kernelFunc:ese},nse="return float(a <= b);",sse=`
return vec4(lessThanEqual(a, b));
`,rse=In({opSnippet:nse,packedOpSnippet:sse,cpuKernelImpl:GY,dtype:"bool"}),ase={kernelName:gi,backendName:"webgl",kernelFunc:rse};function ose(e){let{backend:t,attrs:n}=e,{start:s,stop:r,num:a}=n,o=HY(s,r,a);return t.makeTensorInfo([o.length],"float32",o)}var ise={kernelName:yh,backendName:"webgl",kernelFunc:ose},lse=`if (x < 0.0) return NAN;
return log(x);`,use=`
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;
`,cse=ot({opSnippet:lse,packedOpSnippet:use,cpuKernelImpl:jY}),dse={kernelName:Wa,backendName:"webgl",kernelFunc:cse},pse="return log(1.0 + x);",hse=ot({opSnippet:pse}),fse={kernelName:su,backendName:"webgl",kernelFunc:hse},mse="return float(a >= 1.0 && b >= 1.0);",gse=`
return vec4(
vec4(greaterThanEqual(a, vec4(1.0))) *
vec4(greaterThanEqual(b, vec4(1.0))));
`,Ase=In({opSnippet:mse,packedOpSnippet:gse,dtype:"bool"}),yse={kernelName:Ai,backendName:"webgl",kernelFunc:Ase},xse="return float(!(x >= 1.0));",bse=ot({opSnippet:xse}),vse={kernelName:ru,backendName:"webgl",kernelFunc:bse},wse="return float(a >= 1.0 || b >= 1.0);",kse=`
return min(
vec4(greaterThanEqual(a, vec4(1.0))) +
vec4(greaterThanEqual(b, vec4(1.0))),
vec4(1.0));
`,Sse=In({opSnippet:wse,packedOpSnippet:kse,dtype:"bool"}),Ise={kernelName:Gc,backendName:"webgl",kernelFunc:Sse},Cse=class{constructor(e,t,n,s,r){this.variableNames=["x"],this.outputShape=[];let a=t,o=e[3]-1;this.outputShape=e;let i,l=`float(${n}) + float(${s}) * sum`;r===.5?i=`inversesqrt(${l})`:r===1?i=`1.0/(${l})`:i=`exp(log(${l}) * float(-${r}));`,this.userCode=`
void main() {
ivec4 coords = getOutputCoords();
int b = coords[0];
int r = coords[1];
int c = coords[2];
int d = coords[3];
float x = getX(b, r, c, d);
float sum = 0.0;
for (int j = -${a}; j <= ${a}; j++) {
int idx = d + j;
if (idx >= 0 && idx <= ${o}) {
float z = getX(b, r, c, idx);
sum += z * z;
}
}
float val = x * ${i};
setOutput(val);
}
`}},Tse=class{constructor(e,t,n,s,r){this.variableNames=["x"],this.outputShape=[],this.packedInputs=!0,this.packedOutput=!0;let a=t,o=e[3]-1;this.outputShape=e;let i,l=`float(${n}) + float(${s}) * sum`;r===.5?i=`inversesqrt(${l})`:r===1?i=`1.0/(${l})`:i=`exp(log(${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 - ${a};
vec2 cache = vec2(0.);
if(firstChannel >= 0){
vec4 firstChannelFrag = getX(b, r, c, firstChannel);
cache.x = getChannel(firstChannelFrag, vec2(c, firstChannel));
if(hasNextRow){
cache.y = getChannel(firstChannelFrag, vec2(c + 1, firstChannel));
}
}
ivec2 depth = ivec2(d, d + 1);
for (int j = - ${a}; j <= ${a}; j++) {
ivec2 idx = depth + j;
bvec2 aboveLowerBound = greaterThanEqual(idx, ivec2(0));
bvec2 belowUpperBound = lessThanEqual(idx, ivec2(${o}));
bool depthInRange = aboveLowerBound.x && belowUpperBound.x;
bool depthPlusOneInRange = aboveLowerBound.y && belowUpperBound.y;
if(depthInRange || depthPlusOneInRange){
vec4 z = vec4(0.);
vec4 xFragAtCurrentDepth;
z.xz = cache.xy;
if(depthPlusOneInRange && hasNextCol){
xFragAtCurrentDepth = idx.y != d ?
getX(b, r, c, idx.y) : xFragAtOutputCoords;
z.y = getChannel(xFragAtCurrentDepth, vec2(c, idx.y));
if(hasNextRow){
z.w = getChannel(xFragAtCurrentDepth, vec2(c + 1, idx.y));
}
}
cache.xy = z.yw;
sum += z * z;
}
}
vec4 result = xAtOutputCoords * ${i};
setOutput(result);
}
`}},Nse=e=>{let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{depthRadius:a,bias:o,alpha:i,beta:l}=s,c=K().getBool("WEBGL_PACK_NORMALIZATION")?new Tse(r.shape,a,o,i,l):new Cse(r.shape,a,o,i,l);return n.runWebGLProgram(c,[r],r.dtype)},Ese={kernelName:Hc,backendName:"webgl",kernelFunc:Nse},Rse=class{constructor(e,t,n,s,r){this.variableNames=["inputImage","outputImage","dy"],this.outputShape=[],this.outputShape=e,this.depth=e[3],this.depthRadius=t,this.bias=n,this.alpha=s,this.beta=r,this.userCode=`
void main() {
ivec4 coords = getOutputCoords();
int b = coords[0];
int r = coords[1];
int c = coords[2];
float result = 0.0;
for (int d = 0; d < ${this.depth}; ++d) {
int depthBegin = int(max(0.0, float(d - ${t})));
int depthEnd = int(min(float(${this.depth}),
float(d + ${t} + 1)));
const int MIN_DEPTH_BEGIN = 0;
const int MAX_DEPTH_END = ${this.depth};
float norm = 0.0;
for (int k = MIN_DEPTH_BEGIN; k < MAX_DEPTH_END; ++k) {
if (k < depthBegin){
continue;
}
else if (k >= depthBegin && k < depthEnd) {
norm += getInputImage(b, r, c, k) * getInputImage(b, r, c, k);
}
else {
break;
}
}
norm = float(${s}) * norm + float(${n});
for(int k = MIN_DEPTH_BEGIN; k < MAX_DEPTH_END; ++k){
if (k < depthBegin){
continue;
}
else if (k >= depthBegin && k < depthEnd){
float dyi = -2.0 * float(${s})
* float(${r})
* getInputImage(b ,r ,c, k) * getOutputImage(b, r, c, d)
/ norm;
if (k == d) {
dyi += pow(norm, -1.0 * ${r});
}
if (k == coords[3]) {
dyi *= getDy(b, r, c, d);
result += dyi;
}
}
else {
break;
}
}
}
setOutput(result);
}
`}},$se=e=>{let{inputs:t,backend:n,attrs:s}=e,{x:r,y:a,dy:o}=t,{depthRadius:i,bias:l,alpha:c,beta:u}=s,d=new Rse(r.shape,i,l,c,u);return n.runWebGLProgram(d,[r,a,o],r.dtype)},Dse={kernelName:xh,backendName:"webgl",kernelFunc:$se};function _se(e,t,n,s){let r=v.sizeFromShape(t),o=v.sizeFromShape(e.shape)/r,i=ve({inputs:{x:e},attrs:{shape:[o,r]},backend:s}),l=yl(i,e.dtype,"max",s),c=ve({inputs:{x:l},attrs:{shape:n},backend:s});return s.disposeIntermediateTensorInfo(i),s.disposeIntermediateTensorInfo(l),c}function v4(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{reductionIndices:a,keepDims:o}=s,i=r.shape.length,l=v.parseAxisParam(a,r.shape),c=l,u=N.getAxesPermutation(c,i),d=u!=null,p=n.shouldExecuteOnCPU([r]),h=r;if(d){if(p){let y=n.texData.get(h.dataId).values,b=new Array(i);for(let C=0;C<b.length;C++)b[C]=r.shape[u[C]];let w=Ky(y,r.shape,r.dtype,u,b);h=n.makeTensorInfo(b,r.dtype);let k=n.texData.get(h.dataId);k.values=w}else h=Tm(r,u,n);c=N.getInnerMostAxes(c.length,i)}N.assertAxesAreInnerMostDims("max",c,i);let[f,m]=N.computeOutAndReduceShapes(h.shape,c),g=f;o&&(g=N.expandShapeToKeepDim(f,l));let A;if(p){let y=n.texData.get(h.dataId).values,b=qY(y,v.sizeFromShape(m),g,r.dtype);A=n.makeTensorInfo(g,r.dtype);let w=n.texData.get(A.dataId);w.values=b}else A=_se(h,m,g,n);return d&&n.disposeIntermediateTensorInfo(h),A}var Pse={kernelName:Va,backendName:"webgl",kernelFunc:v4},Fse=LI+`
return max(a, b);
`,Ose=`
vec4 result = vec4(max(a, b));
vec4 isNaN = min(vec4(isnan(a)) + vec4(isnan(b)), vec4(1.0));
`+Im+`
return result;
`,Mse=In({opSnippet:Fse,packedOpSnippet:Ose,cpuKernelImpl:XY}),zse={kernelName:Ua,backendName:"webgl",kernelFunc:Mse};function Lse(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t;Gu(r,"maxPool");let{filterSize:a,strides:o,pad:i,dimRoundingMode:l}=s,c=1;v.assert(N.eitherStridesOrDilationsAreOne(o,c),()=>`Error in maxPool: Either strides or dilations must be 1. Got strides ${o} and dilations '${c}'`);let u=N.computePool2DInfo(r.shape,a,o,c,i,l);if(u.filterWidth===1&&u.filterHeight===1&&v.arraysEqual(u.inShape,u.outShape))return ys({inputs:{x:r},backend:n});let d=new Qd(u,"max",!1);return n.runWebGLProgram(d,[r],r.dtype)}var Bse={kernelName:Ga,backendName:"webgl",kernelFunc:Lse};function Wse(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{filterSize:a,strides:o,pad:i,dataFormat:l,dimRoundingMode:c}=s,u=[1,1,1],d=N.computePool3DInfo(r.shape,a,o,u,i,c,l),p=new Yy(d,"max",!1);return n.runWebGLProgram(p,[r],r.dtype)}var Vse={kernelName:jc,backendName:"webgl",kernelFunc:Wse},Use=class{constructor(e){this.variableNames=["dy","maxPos"],this.outputShape=e.inShape;let t=e.strideHeight,n=e.strideWidth,s=e.dilationHeight,r=e.effectiveFilterHeight,a=e.effectiveFilterWidth,o=r-1-e.padInfo.top,i=a-1-e.padInfo.left,l=r*a-1;this.userCode=`
const ivec2 pads = ivec2(${o}, ${i});
void main() {
ivec4 coords = getOutputCoords();
int b = coords[0];
int d = coords[3];
ivec2 dyRCCorner = coords.yz - pads;
int dyRCorner = dyRCCorner.x;
int dyCCorner = dyRCCorner.y;
// Convolve dy(?, ?, d) with pos mask(:, :, d) to get dx(xR, xC, d).
// ? = to be determined. : = across all values in that axis.
float dotProd = 0.0;
for (int wR = 0; wR < ${r};
wR += ${s}) {
float dyR = float(dyRCorner + wR) / ${t}.0;
if (dyR < 0.0 || dyR >= ${e.outHeight}.0 || fract(dyR) > 0.0) {
continue;
}
int idyR = int(dyR);
for (int wC = 0; wC < ${a}; wC++) {
float dyC = float(dyCCorner + wC) / ${n}.0;
if (dyC < 0.0 || dyC >= ${e.outWidth}.0 ||
fract(dyC) > 0.0) {
continue;
}
int idyC = int(dyC);
float dyValue = getDy(b, idyR, idyC, d);
int maxPosValue = ${l} - int(getMaxPos(b, idyR, idyC, d));
// Get the current value, check it against the value from the
// position matrix.
int curPosValue = wR * ${a} + wC;
float mask = float(maxPosValue == curPosValue ? 1.0 : 0.0);
dotProd += dyValue * mask;
}
}
setOutput(dotProd);
}
`}},Gse=class{constructor(e){this.variableNames=["dy","maxPos"],this.outputShape=e.inShape;let t=e.strideDepth,n=e.strideHeight,s=e.strideWidth,r=e.dilationDepth,a=e.dilationHeight,o=e.dilationWidth,i=e.effectiveFilterDepth,l=e.effectiveFilterHeight,c=e.effectiveFilterWidth,u=i-1-e.padInfo.front,d=l-1-e.padInfo.top,p=c-1-e.padInfo.left,h=i*l*c-1;this.userCode=`
const ivec3 pads = ivec3(${u}, ${d}, ${p});
void main() {
ivec5 coords = getOutputCoords();
int batch = coords.x;
int ch = coords.u;
ivec3 dyCorner = ivec3(coords.y, coords.z, coords.w) - pads;
int dyDCorner = dyCorner.x;
int dyRCorner = dyCorner.y;
int dyCCorner = dyCorner.z;
// Convolve dy(?, ?, ?, ch) with pos mask(:, :, :, d) to get
// dx(xD, xR, xC, ch).
// ? = to be determined. : = across all values in that axis.
float dotProd = 0.0;
for (int wD = 0; wD < ${i};
wD += ${r}) {
float dyD = float(dyDCorner + wD) / ${t}.0;
if (dyD < 0.0 || dyD >= ${e.outDepth}.0 || fract(dyD) > 0.0) {
continue;
}
int idyD = int(dyD);
for (int wR = 0; wR < ${l};
wR += ${a}) {
float dyR = float(dyRCorner + wR) / ${n}.0;
if (dyR < 0.0 || dyR >= ${e.outHeight}.0 ||
fract(dyR) > 0.0) {
continue;
}
int idyR = int(dyR);
for (int wC = 0; wC < ${c};
wC += ${o}) {
float dyC = float(dyCCorner + wC) / ${s}.0;
if (dyC < 0.0 || dyC >= ${e.outWidth}.0 ||
fract(dyC) > 0.0) {
continue;
}
int idyC = int(dyC);
float dyValue = getDy(batch, idyD, idyR, idyC, ch);
int maxPosValue = ${h} -
int(getMaxPos(batch, idyD, idyR, idyC, ch));
// Get the current value, check it against the value from the
// position matrix.
int curPosValue =
wD * ${l} * ${c} +
wR * ${c} + wC;
float mask = float(maxPosValue == curPosValue ? 1.0 : 0.0);
dotProd += dyValue * mask;
}
}
}
setOutput(dotProd);
}
`}};function Hse(e){let{inputs:t,backend:n,attrs:s}=e,{dy:r,input:a}=t,o=a,{filterSize:i,strides:l,pad:c,dimRoundingMode:u}=s,d=[1,1,1],p=N.computePool3DInfo(o.shape,i,l,d,c,u),h=new Yy(p,"max",!0),f=n.runWebGLProgram(h,[o],o.dtype),m=new Gse(p),g=n.runWebGLProgram(m,[r,f],o.dtype);return n.disposeIntermediateTensorInfo(f),g}var jse={kernelName:vh,backendName:"webgl",kernelFunc:Hse};function qse(e){let{inputs:t,backend:n,attrs:s}=e,{dy:r,input:a,output:o}=t,i=a;Gu([a,o],"maxPoolGrad");let{filterSize:l,strides:c,pad:u,dimRoundingMode:d}=s,p=N.computePool2DInfo(i.shape,l,c,1,u,d),h=!0,f=new Qd(p,"max",h),m=n.runWebGLProgram(f,[i],i.dtype),g=new Use(p),A=n.runWebGLProgram(g,[r,m],i.dtype);return n.disposeIntermediateTensorInfo(m),A}var Xse={kernelName:bh,backendName:"webgl",kernelFunc:qse};function Kse(e,t,n,s){let r=new Qd(n,"max",!1),a=s.runWebGLProgram(r,[e],"float32");r=new Qd(n,"max",!0,!0,t);let o=s.runWebGLProgram(r,[e],"float32");return[a,o]}var Zse={kernelName:wh,backendName:"webgl",kernelFunc:({inputs:e,attrs:t,backend:n})=>{let{x:s}=e,{filterSize:r,strides:a,pad:o,includeBatchInIndex:i}=t,l=n;v.assert(s.shape.length===4,()=>`Error in maxPool: input must be rank 4 but got rank ${s.shape.length}.`);let c=[1,1];v.assert(N.eitherStridesOrDilationsAreOne(a,c),()=>`Error in maxPool: Either strides or dilations must be 1. Got strides ${a} and dilations '${c}'`);let u=N.computePool2DInfo(s.shape,r,a,c,o),[d,p]=Kse(s,i,u,l);return[d,p]}};function Yse(e,t,n,s){let r=v.sizeFromShape(t),o=v.sizeFromShape(e.shape)/r,i=ve({inputs:{x:e},attrs:{shape:[o,r]},backend:s}),l=yl(i,"float32","mean",s),c=ve({inputs:{x:l},attrs:{shape:n},backend:s});return s.disposeIntermediateTensorInfo(i),s.disposeIntermediateTensorInfo(l),c}var Jse={kernelName:Ha,backendName:"webgl",kernelFunc:({inputs:e,attrs:t,backend:n})=>{let{x:s}=e,{keepDims:r,axis:a}=t,o=n,i=s.shape.length,l=v.parseAxisParam(a,s.shape),c=l,u=N.getAxesPermutation(c,i),d=u!=null,p=o.shouldExecuteOnCPU([s]),h=[],f=s;if(d){if(p){let b=o.texData.get(f.dataId).values,w=new Array(i);for(let E=0;E<w.length;E++)w[E]=s.shape[u[E]];let k=Ky(b,s.shape,s.dtype,u,w);f=o.makeTensorInfo(w,s.dtype);let C=o.texData.get(f.dataId);C.values=k}else f=Tm(s,u,o);h.push(f),c=N.getInnerMostAxes(c.length,i)}N.assertAxesAreInnerMostDims("sum",c,i);let[m,g]=N.computeOutAndReduceShapes(f.shape,c),A=m;r&&(A=N.expandShapeToKeepDim(m,l));let x=Yse(f,g,A,o);for(let y of h)o.disposeIntermediateTensorInfo(y);return x}};function Qse(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{axis:a,keepDims:o}=s,i=r.shape.length,l=v.parseAxisParam(a,r.shape),c=l,u=N.getAxesPermutation(c,i),d=r;u!=null&&(d=Hn({inputs:{x:r},backend:n,attrs:{perm:u}}),c=N.getInnerMostAxes(c.length,r.shape.length)),N.assertAxesAreInnerMostDims("min",c,i);let[p,h]=N.computeOutAndReduceShapes(d.shape,c),f=v.sizeFromShape(h),m=ve({inputs:{x:d},backend:n,attrs:{shape:[-1,f]}}),g=yl(m,m.dtype,"min",n),A;if(o){let x=N.expandShapeToKeepDim(p,l);A=ve({inputs:{x:g},backend:n,attrs:{shape:x}})}else A=ve({inputs:{x:g},backend:n,attrs:{shape:p}});return n.disposeIntermediateTensorInfo(m),n.disposeIntermediateTensorInfo(g),u!=null&&n.disposeIntermediateTensorInfo(d),A}var ere={kernelName:ja,backendName:"webgl",kernelFunc:Qse},tre=LI+`
return min(a, b);
`,nre=`
vec4 result = vec4(min(a, b));
vec4 isNaN = min(vec4(isnan(a)) + vec4(isnan(b)), vec4(1.0));
`+Im+`
return result;
`,sre=In({opSnippet:tre,packedOpSnippet:nre,cpuKernelImpl:KY}),rre={kernelName:qa,backendName:"webgl",kernelFunc:sre},are=class{constructor(e,t,n){this.variableNames=["x"],this.outputShape=t.map((c,u)=>c[0]+e[u]+c[1]);let s=e.length,r=bt(s),a=t.map(c=>c[0]).join(","),o=t.map((c,u)=>c[0]+e[u]).join(","),i=["coords[0]","coords[1]","coords[2]","coords[3]"].slice(0,s),l=n==="reflect"?0:1;if(s===1){this.userCode=`
int start = ${a};
int end = ${o};
void main() {
int outC = getOutputCoords();
if (outC < start) {
outC = start * 2 - outC - ${l};
} else if(outC >= end) {
outC = (end - 1) * 2 - outC + ${l};
}
setOutput(getX(outC - start));
}
`;return}this.userCode=`
${r} start = ${r}(${a});
${r} end = ${r}(${o});
void main() {
${r} outC = getOutputCoords();
for (int i = 0; i < ${s}; i++) {
if (outC[i] < start[i]) {
outC[i] = start[i] * 2 - outC[i] - ${l};
} else if(outC[i] >= end[i]) {
outC[i] = (end[i] - 1) * 2 - outC[i] + ${l};
}
}
${r} coords = outC - start;
setOutput(getX(${i}));
}
`}},ore=class{constructor(e,t,n){this.variableNames=["x"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=t.map((h,f)=>h[0]+e[f]+h[1]);let s=e.length,r=bt(s),a=t.map(h=>h[0]).join(","),o=t.map((h,f)=>h[0]+e[f]).join(","),i=Gn("rc",s),l=Gn("source",s),c=`${i[s-1]} < ${this.outputShape[s-1]}`,u=s===1?"source":`vec2(${l.slice(-2).join()})`,d=n==="reflect"?0:1,p="";if(s===1){let h=`
${r} source = rc;
if (source < start) {
source = start * 2 - source - ${d};
} else if (source >= end) {
source = (end - 1) * 2 - source + ${d};
}
source -= start;
`;p=`
${r} rc = outputLoc;
${h}
result[0] = getChannel(getX(${l.join()}), ${u});
${i[s-1]} += 1;
if(${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 - ${d}) +
gte * ((end - 1) * 2 - source + ${d});
source -= start;
`;p=`
${r} rc = outputLoc;
${h}
result[0] = getChannel(getX(${l.join()}), ${u});
${i[s-1]} += 1;
if(${c}) {
${h}
result[1] = getChannel(getX(${l.join()}), ${u});
}
rc = outputLoc;
${i[s-2]} += 1;
if(${i[s-2]} < ${this.outputShape[s-2]}) {
${h}
result[2] = getChannel(getX(${l.join()}), ${u});
${i[s-1]} += 1;
if(${c}) {
${h}
result[3] = getChannel(getX(${l.join()}), ${u});
}
}
`}this.userCode=`
const ${r} start = ${r}(${a});
const ${r} end = ${r}(${o});
void main() {
${r} outputLoc = getOutputCoords();
vec4 result = vec4(0.);
${p}
setOutput(result);
}
`}},ire=({inputs:e,backend:t,attrs:n})=>{let{x:s}=e,{paddings:r,mode:a}=n,o=K().getBool("WEBGL_PACK_ARRAY_OPERATIONS")?new ore(s.shape,r,a):new are(s.shape,r,a);return t.runWebGLProgram(o,[s],s.dtype)},lre={kernelName:Xa,backendName:"webgl",kernelFunc:ire},ure=`if (b == 0.0) return NAN;
return mod(a, b);`,cre=`
vec4 result = mod(a, b);
vec4 isNaN = vec4(equal(b, vec4(0.0)));
`+Im+`
return result;
`,dre=In({opSnippet:ure,packedOpSnippet:cre}),pre={kernelName:au,backendName:"webgl",kernelFunc:dre},hre=class{constructor(e,t,n){this.variableNames=["probs"],this.customUniforms=[{name:"seed",type:"float"}],this.outputShape=[e,n],this.userCode=`
void main() {
ivec2 coords = getOutputCoords();
int batch = coords[0];
float r = random(seed);
float cdf = 0.0;
for (int i = 0; i < ${t-1}; i++) {
cdf += getProbs(batch, i);
if (r < cdf) {
setOutput(float(i));
return;
}
}
// If no other event happened, last event happened.
setOutput(float(${t-1}));
}
`}},fre=`
if (a == b) {
return 1.0;
};
return a / b;`,mre=`
// 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;
`,w4=In({opSnippet:fre,packedOpSnippet:mre,checkOutOfBounds:!0}),gre={kernelName:_a,backendName:"webgl",kernelFunc:w4},k4="return a - b;",S4=In({opSnippet:k4,packedOpSnippet:k4,supportsComplex:!0,cpuKernelImpl:dJ}),Are={kernelName:uo,backendName:"webgl",kernelFunc:S4};function I4(e){let{inputs:t,backend:n,attrs:s}=e,{logits:r}=t,{dim:a}=s,o=v.parseAxisParam([a],r.shape),i=v4({inputs:{x:r},backend:n,attrs:{reductionIndices:o,keepDims:!1}}),l=N.expandShapeToKeepDim(i.shape,o),c=ve({inputs:{x:i},backend:n,attrs:{shape:l}}),u=S4({inputs:{a:r,b:c},backend:n}),d=m4({inputs:{x:u},backend:n}),p=Nm({inputs:{x:d},backend:n,attrs:{axis:o,keepDims:!1}}),h=ve({inputs:{x:p},backend:n,attrs:{shape:l}}),f=w4({inputs:{a:d,b:h},backend:n});return n.disposeIntermediateTensorInfo(i),n.disposeIntermediateTensorInfo(c),n.disposeIntermediateTensorInfo(u),n.disposeIntermediateTensorInfo(d),n.disposeIntermediateTensorInfo(p),n.disposeIntermediateTensorInfo(h),f}var yre={kernelName:io,backendName:"webgl",kernelFunc:I4};function xre(e){let{inputs:t,backend:n,attrs:s}=e,{logits:r}=t,{numSamples:a,seed:o,normalized:i}=s,l=i?r:I4({inputs:{logits:r},backend:n,attrs:{dim:r.shape.length-1}}),c=l.shape[0],u=l.shape[1],d=new hre(c,u,a),p=[[o]],h=n.runWebGLProgram(d,[l],"int32",p);return i||n.disposeIntermediateTensorInfo(l),h}var bre={kernelName:kh,backendName:"webgl",kernelFunc:xre},C4="return -x;";function vre(e){let{inputs:t,backend:n}=e,{x:s}=t;if(n.shouldExecuteOnCPU([s])){let a=n.texData.get(s.dataId),[o,i]=YY(a.values,s.shape,s.dtype);return n.makeTensorInfo(i,s.dtype,o)}let r;return K().getBool("WEBGL_PACK_UNARY_OPERATIONS")?r=new Ku(s.shape,C4):r=new Mo(s.shape,C4),n.runWebGLProgram(r,[s],s.dtype)}var wre={kernelName:yi,backendName:"webgl",kernelFunc:vre},kre=Xs.nonMaxSuppressionV3Impl;function Sre(e){N.warn("tf.nonMaxSuppression() in webgl locks the UI thread. Call tf.nonMaxSuppressionAsync() instead");let{inputs:t,backend:n,attrs:s}=e,{boxes:r,scores:a}=t,{maxOutputSize:o,iouThreshold:i,scoreThreshold:l}=s,c=n.readSync(r.dataId),u=n.readSync(a.dataId),{selectedIndices:d}=kre(c,u,o,i,l);return n.makeTensorInfo([d.length],"int32",new Int32Array(d))}var Ire={kernelName:bi,backendName:"webgl",kernelFunc:Sre},Cre=Xs.nonMaxSuppressionV4Impl;function Tre(e){N.warn("tf.nonMaxSuppression() in webgl locks the UI thread. Call tf.nonMaxSuppressionAsync() instead");let{inputs:t,backend:n,attrs:s}=e,{boxes:r,scores:a}=t,{maxOutputSize:o,iouThreshold:i,scoreThreshold:l,padToMaxOutputSize:c}=s,u=n.readSync(r.dataId),d=n.readSync(a.dataId),{selectedIndices:p,validOutputs:h}=Cre(u,d,o,i,l,c);return[n.makeTensorInfo([p.length],"int32",new Int32Array(p)),n.makeTensorInfo([],"int32",new Int32Array([h]))]}var Nre={kernelName:ou,backendName:"webgl",kernelFunc:Tre},Ere=Xs.nonMaxSuppressionV5Impl;function Rre(e){N.warn("tf.nonMaxSuppression() in webgl locks the UI thread. Call tf.nonMaxSuppressionAsync() instead");let{inputs:t,backend:n,attrs:s}=e,{boxes:r,scores:a}=t,{maxOutputSize:o,iouThreshold:i,scoreThreshold:l,softNmsSigma:c}=s,u=n.readSync(r.dataId),d=n.readSync(a.dataId),p=o,h=i,f=l,m=c,{selectedIndices:g,selectedScores:A}=Ere(u,d,p,h,f,m);return[n.makeTensorInfo([g.length],"int32",new Int32Array(g)),n.makeTensorInfo([A.length],"float32",new Float32Array(A))]}var $re={kernelName:vi,backendName:"webgl",kernelFunc:Rre},Dre=class{constructor(e,t,n,s){this.variableNames=["indices"],this.outputShape=[e,t],this.userCode=`
void main() {
ivec2 coords = getOutputCoords();
int index = round(getIndices(coords.x));
setOutput(mix(float(${s}), float(${n}),
float(index == coords.y)));
}
`}},_re=e=>{let{inputs:t,backend:n,attrs:s}=e,{indices:r}=t,{depth:a,onValue:o,offValue:i}=s,l=v.sizeFromShape(r.shape),c=new Dre(l,a,o,i),u=ve({inputs:{x:r},backend:n,attrs:{shape:[l]}}),d=n.runWebGLProgram(c,[u],r.dtype);n.disposeIntermediateTensorInfo(u);let p=[...r.shape,a],h=ve({inputs:{x:d},backend:n,attrs:{shape:p}});return n.disposeIntermediateTensorInfo(d),h},Pre={kernelName:ki,backendName:"webgl",kernelFunc:_re};function _m(e){let{inputs:t,backend:n}=e,{x:s}=t;if(s.dtype==="complex64"){let r=ep({inputs:{input:s},backend:n}),a=_m({inputs:{x:r},backend:n}),o=Dm({inputs:{input:s},backend:n}),i=_m({inputs:{x:o},backend:n}),l=zo({inputs:{real:a,imag:i},backend:n});return n.disposeIntermediateTensorInfo(r),n.disposeIntermediateTensorInfo(a),n.disposeIntermediateTensorInfo(o),n.disposeIntermediateTensorInfo(i),l}else return tp({attrs:{shape:s.shape,dtype:s.dtype,value:s.dtype==="string"?"":0},backend:n})}var Fre={kernelName:Bi,backendName:"webgl",kernelFunc:_m};function T4(e){let{inputs:t,backend:n}=e,{x:s}=t;if(s.dtype==="string")throw new Error("onesLike is not supported under string dtype");if(s.dtype==="complex64"){let r=ep({inputs:{input:s},backend:n}),a=T4({inputs:{x:r},backend:n}),o=Dm({inputs:{input:s},backend:n}),i=_m({inputs:{x:o},backend:n}),l=zo({inputs:{real:a,imag:i},backend:n});return n.disposeIntermediateTensorInfo(r),n.disposeIntermediateTensorInfo(a),n.disposeIntermediateTensorInfo(o),n.disposeIntermediateTensorInfo(i),l}else return tp({attrs:{shape:s.shape,dtype:s.dtype,value:1},backend:n})}var Ore={kernelName:wi,backendName:"webgl",kernelFunc:T4};function Mre(e){let{inputs:t,backend:n,attrs:s}=e,{axis:r}=s;if(t.length===1)return ex({inputs:{input:t[0]},backend:n,attrs:{dim:r}});let a=t[0].shape,o=t[0].dtype;t.forEach(u=>{v.assertShapesMatch(a,u.shape,"All tensors passed to stack must have matching shapes"),v.assert(o===u.dtype,()=>"All tensors passed to stack must have matching dtypes")});let i=[],l=t.map(u=>{let d=ex({inputs:{input:u},backend:n,attrs:{dim:r}});return i.push(d),d}),c=a4({inputs:l,backend:n,attrs:{axis:r}});return i.forEach(u=>n.disposeIntermediateTensorInfo(u)),c}var zre={kernelName:Si,backendName:"webgl",kernelFunc:Mre},Lre=class{constructor(e,t,n){this.variableNames=["x"],this.customUniforms=[{name:"value",type:"float"}],this.outputShape=t.map((l,c)=>l[0]+e[c]+l[1]);let s=e.length,r=bt(s),a=t.map(l=>l[0]).join(","),o=t.map((l,c)=>l[0]+e[c]).join(","),i=["coords[0]","coords[1]","coords[2]","coords[3]"].slice(0,s);if(s===1){this.userCode=`
int start = ${a};
int end = ${o};
void main() {
int outC = getOutputCoords();
if (outC < start || outC >= end) {
setOutput(value);
} else {
setOutput(getX(outC - start));
}
}
`;return}this.userCode=`
${r} start = ${r}(${a});
${r} end = ${r}(${o});
void main() {
${r} outC = getOutputCoords();
if (any(lessThan(outC, start)) || any(greaterThanEqual(outC, end))) {
setOutput(value);
} else {
${r} coords = outC - start;
setOutput(getX(${i}));
}
}
`}},Bre=class{constructor(e,t,n){this.variableNames=["x"],this.packedInputs=!0,this.packedOutput=!0,this.customUniforms=[{name:"value",type:"float"}],this.outputShape=t.map((f,m)=>f[0]+e[m]+f[1]);let s=e.length,r=bt(s),a=t.map(f=>f[0]).join(","),o=t.map((f,m)=>f[0]+e[m]).join(","),i=Gn("rc",s),l=Gn("source",s),c=`${i[s-1]} < ${this.outputShape[s-1]}`,u=s===1?"source":`vec2(${l.slice(-2).join()})`,d=[`${r} rc = outputLoc;`,`${i[s-1]} += 1;
if(${c}) {
`,s===1?"":`}
rc = outputLoc;
${i[s-2]} += 1;
if(${i[s-2]} < ${this.outputShape[s-2]}) {`,s===1?"":` ${i[s-1]} += 1;
if(${c}) {`],p=s===1?"rc < start || rc >= end":"any(lessThan(rc, start)) || any(greaterThanEqual(rc, end))",h="";for(let f=0,m=s===1?2:4;f<m;f++)h+=`
${d[f]}
if (${p}) {
result[${f}] = float(value);
} else {
${r} source = rc - start;
result[${f}] = getChannel(getX(${l.join()}), ${u});
}
`;h+=s===1?"} ":"}}",this.userCode=`
const ${r} start = ${r}(${a});
const ${r} end = ${r}(${o});
void main() {
${r} outputLoc = getOutputCoords();
vec4 result = vec4(0.);
${h}
setOutput(result);
}
`}},N4=e=>{let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{paddings:a,constantValue:o}=s;if(v.sizeFromShape(r.shape)===0){let c=a.map((u,d)=>u[0]+r.shape[d]+u[1]);return tp({backend:n,attrs:{shape:c,value:o,dtype:r.dtype}})}let i=K().getBool("WEBGL_PACK_ARRAY_OPERATIONS")?new Bre(r.shape,a,o):new Lre(r.shape,a,o),l=[[o]];return n.runWebGLProgram(i,[r],r.dtype,l)},Wre={kernelName:Za,backendName:"webgl",kernelFunc:N4},Vre=`
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);
`,Ure=`
// 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));
`+Im+`
return result;
`,Gre=In({opSnippet:Vre,packedOpSnippet:Ure}),Hre={kernelName:Ya,backendName:"webgl",kernelFunc:Gre};function jre(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{axis:a,keepDims:o}=s,i=r.shape.length,l=[],c=v.parseAxisParam(a,r.shape),u=c,d=N.getAxesPermutation(u,i),p=r;d!=null&&(p=Hn({inputs:{x:r},backend:n,attrs:{perm:d}}),u=N.getInnerMostAxes(u.length,i),l.push(p)),N.assertAxesAreInnerMostDims("prod",u,i);let h;if(n.shouldExecuteOnCPU([p])){let f=n.texData.get(p.dataId).values,{outVals:m,outShape:g,outDtype:A}=QY(p.shape,p.dtype,f,u);h=n.makeTensorInfo(g,A,m)}else{let[f,m]=N.computeOutAndReduceShapes(p.shape,u),g=v.sizeFromShape(m),A=ve({inputs:{x:p},backend:n,attrs:{shape:[-1,g]}}),x=od(r.dtype),y=yl(A,x,"prod",n);h=ve({inputs:{x:y},backend:n,attrs:{shape:f}}),l.push(A),l.push(y)}if(o){l.push(h);let f=N.expandShapeToKeepDim(h.shape,c);h=ve({inputs:{x:h},backend:n,attrs:{shape:f}})}return l.forEach(f=>n.disposeIntermediateTensorInfo(f)),h}var qre={kernelName:Ii,backendName:"webgl",kernelFunc:jre},E4=e=>{let{backend:t,attrs:n}=e,{start:s,stop:r,step:a,dtype:o}=n,i=eJ(s,r,a,o);return t.makeTensorInfo([i.length],o,i)},Xre={kernelName:iu,backendName:"webgl",kernelFunc:E4},Kre="return 1.0 / x;",Zre=ot({opSnippet:Kre}),Yre={kernelName:lu,backendName:"webgl",kernelFunc:Zre},Jre=yr+`
return (x < 0.0) ? 0.0 : x;
`,Qre=`
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;
`,eae=ot({opSnippet:Jre,packedOpSnippet:Qre}),tae={kernelName:Qa,backendName:"webgl",kernelFunc:eae},nae=yr+`
return (x < 0.0) ? 0.0 : min(6.0, x);
`,sae=`
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;
`,rae=ot({opSnippet:nae,packedOpSnippet:sae}),aae={kernelName:to,backendName:"webgl",kernelFunc:rae},oae=class{constructor(e,t,n,s,r){this.variableNames=["A"],this.outputShape=[];let[a,o,i,l]=e;this.outputShape=[a,t,n,l];let c=[s&&t>1?o-1:o,s&&n>1?i-1:i],u=[s&&t>1?t-1:t,s&&n>1?n-1:n],d;r?d="(vec2(yRC) + vec2(0.5)) * effectiveInputOverOutputRatioRC - vec2(0.5)":d="vec2(yRC) * effectiveInputOverOutputRatioRC",this.userCode=`
const vec2 effectiveInputOverOutputRatioRC = vec2(
${c[0]/u[0]},
${c[1]/u[1]});
const vec2 inputShapeRC = vec2(${o}.0, ${i}.0);
void main() {
ivec4 coords = getOutputCoords();
int b = coords[0];
int d = coords[3];
ivec2 yRC = coords.yz;
// Fractional source index.
vec2 sourceFracIndexRC = ${d};
// Compute the 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);
}
`}},iae=class{constructor(e,t,n,s,r){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=[];let[a,o,i,l]=e;this.outputShape=[a,t,n,l];let c=[s&&t>1?o-1:o,s&&n>1?i-1:i],u=[s&&t>1?t-1:t,s&&n>1?n-1:n],d;r?d="(vec3(yRC) + vec3(0.5)) * effectiveInputOverOutputRatioRC - vec3(0.5)":d="vec3(yRC) * effectiveInputOverOutputRatioRC",this.userCode=`
const vec3 effectiveInputOverOutputRatioRC = vec3(
${c[0]/u[0]},
${c[1]/u[1]},
${c[1]/u[1]});
const vec3 inputShapeRC = vec3(${o}.0, ${i}.0,
${i}.0);
float getAValue(int b, int r, int c, int d) {
return getChannel(getA(b, r, c, d), vec2(c, d));
}
void main() {
ivec4 coords = getOutputCoords();
int b = coords[0];
int d = coords[3];
// Calculate values for next column in yRC.z.
ivec3 yRC = coords.yzz + ivec3(0, 0, 1);
// Fractional source index.
vec3 sourceFracIndexRC = ${d};
// Compute the 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 lae(e){let{inputs:t,backend:n,attrs:s}=e,{images:r}=t,{alignCorners:a,halfPixelCenters:o,size:i}=s,[l,c]=i,u=K().getBool("WEBGL_PACK_IMAGE_OPERATIONS")?new iae(r.shape,l,c,a,o):new oae(r.shape,l,c,a,o);return n.runWebGLProgram(u,[r],"float32")}var uae={kernelName:eo,backendName:"webgl",kernelFunc:lae},cae=class{constructor(e,t,n){this.variableNames=["dy"],this.outputShape=[],this.outputShape=t;let[,s,r]=t,[,a,o]=e,i=[n&&a>1?s-1:s,n&&o>1?r-1:r],l=[n&&a>1?a-1:a,n&&o>1?o-1:o],c=i[0]/l[0],u=i[1]/l[1],d=1/c,p=1/u,h=Math.ceil(d)*2+2,f=Math.ceil(p)*2+2;this.userCode=`
void main() {
ivec4 coords = getOutputCoords();
int b = coords[0];
int d = coords[3];
int r = coords[1];
int c = coords[2];
float accumulator = 0.0;
const float heightScale = float(${c});
const float widthScale = float(${u});
const float invHeightScale = float(${d});
const float invWidthScale = float(${p});
const int winHeight = int(${h});
const int winWidth = int(${f});
// Compute bounds for where in dy we will look
float startRLerp = floor(float(r) * invHeightScale);
int startDyR = int(startRLerp - float(winHeight / 2));
float startCLerp = floor(float(c) * invWidthScale);
int startDyC = int(startCLerp - float(winWidth / 2));
// Loop over dy
for (int dyROffset = 0; dyROffset < winHeight; dyROffset++) {
int dyR = dyROffset + startDyR;
// Guard against the window exceeding the bounds of dy
if (dyR < 0 || dyR >= ${a}) {
continue;
}
for (int dyCOffset = 0; dyCOffset < winWidth; dyCOffset++) {
int dyC = dyCOffset + startDyC;
// Guard against the window exceeding the bounds of dy
if (dyC < 0 || dyC >= ${o}) {
continue;
}
float dxR = float(dyR) * heightScale;
int topDxRIndex = int(floor(dxR));
int bottomDxRIndex = int(min(ceil(dxR), ${s-1}.0));
float dxRLerp = dxR - float(topDxRIndex);
float inverseDxRLerp = 1.0 - dxRLerp;
float dxC = float(dyC) * widthScale;
int leftDxCIndex = int(floor(dxC));
int rightDxCIndex = int(min(ceil(dxC), ${r-1}.0));
float dxCLerp = dxC - float(leftDxCIndex);
float inverseDxCLerp = 1.0 - dxCLerp;
if (r == topDxRIndex && c == leftDxCIndex) {
// topLeft
accumulator +=
getDy(b, dyR, dyC, d) * inverseDxRLerp * inverseDxCLerp;
}
if (r == topDxRIndex && c == rightDxCIndex) {
// topRight
accumulator += getDy(b, dyR, dyC, d) * inverseDxRLerp * dxCLerp;
}
if (r == bottomDxRIndex && c == leftDxCIndex) {
// bottomLeft
accumulator += getDy(b, dyR, dyC, d) * dxRLerp * inverseDxCLerp;
}
if (r == bottomDxRIndex && c == rightDxCIndex) {
// bottomRight
accumulator += getDy(b, dyR, dyC, d) * dxRLerp * dxCLerp;
}
}
}
// End loop over dy
setOutput(accumulator);
}
`}};function dae(e){let{inputs:t,backend:n,attrs:s}=e,{images:r,dy:a}=t,{alignCorners:o}=s,i=new cae(a.shape,r.shape,o);return n.runWebGLProgram(i,[a],a.dtype)}var pae={kernelName:Ih,backendName:"webgl",kernelFunc:dae},hae=class{constructor(e,t,n,s,r){this.variableNames=["A"],this.outputShape=[];let[a,o,i,l]=e;this.outputShape=[a,t,n,l];let c=[s&&t>1?o-1:o,s&&n>1?i-1:i],u=[s&&t>1?t-1:t,s&&n>1?n-1:n],d=s?"0.5":"0.0",p;r?p="max((vec2(yRC) + vec2(0.5)) * effectiveInputOverOutputRatioRC, vec2(0.0))":p="vec2(yRC) * effectiveInputOverOutputRatioRC",this.userCode=`
const vec2 effectiveInputOverOutputRatioRC = vec2(
${c[0]/u[0]},
${c[1]/u[1]});
const vec2 inputShapeRC = vec2(${o}.0, ${i}.0);
void main() {
ivec4 coords = getOutputCoords();
int b = coords[0];
int d = coords[3];
ivec2 yRC = coords.yz;
// Fractional source index.
vec2 sourceFracIndexRC = ${p};
// Compute the coordinators of nearest neighbor point.
ivec2 sourceNearestRC = ivec2(
min(inputShapeRC - 1.0, floor(sourceFracIndexRC + ${d})));
float newValue = getA(b, sourceNearestRC.x, sourceNearestRC.y, d);
setOutput(newValue);
}
`}},fae=class{constructor(e,t,n,s,r){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=[];let[a,o,i,l]=e;this.outputShape=[a,t,n,l];let c=[s&&t>1?o-1:o,s&&n>1?i-1:i],u=[s&&t>1?t-1:t,s&&n>1?n-1:n],d=s?"0.5":"0.0",p;r?p="max((vec3(yRC) + vec3(0.5)) * effectiveInputOverOutputRatioRC, vec3(0.0))":p="vec3(yRC) * effectiveInputOverOutputRatioRC",this.userCode=`
const vec3 effectiveInputOverOutputRatioRC = vec3(
${c[0]/u[0]},
${c[1]/u[1]},
${c[1]/u[1]});
const vec3 inputShapeRC = vec3(${o}.0, ${i}.0,
${i}.0);
float getAValue(int b, int r, int c, int d) {
return getChannel(getA(b, r, c, d), vec2(c, d));
}
void main() {
ivec4 coords = getOutputCoords();
int b = coords[0];
int d = coords[3];
// Calculate values for next column in yRC.z.
ivec3 yRC = coords.yzz + ivec3(0, 0, 1);
// Fractional source index.
vec3 sourceFracIndexRC = ${p};
// Compute the coordinators of nearest neighbor point.
ivec3 sourceNearestRC = ivec3(
min(inputShapeRC - 1.0, floor(sourceFracIndexRC + ${d})));
// Should we calculate next column and row elements in 2x2 packed cell.
bool hasNextCol = d < ${l-1};
bool hasNextRow = coords.z < ${n-1};
vec4 newValue = vec4(
getAValue(b, sourceNearestRC.x, sourceNearestRC.y, d),
hasNextCol ? getAValue(b, sourceNearestRC.x, sourceNearestRC.y, d + 1)
: 0.0,
hasNextRow ? getAValue(b, sourceNearestRC.x, sourceNearestRC.z, d)
: 0.0,
(hasNextRow && hasNextCol) ?
getAValue(b, sourceNearestRC.x, sourceNearestRC.z, d + 1) : 0.0);
setOutput(newValue);
}
`}};function mae(e){let{inputs:t,backend:n,attrs:s}=e,{images:r}=t,{alignCorners:a,halfPixelCenters:o,size:i}=s,[l,c]=i,u=K().getBool("WEBGL_PACK_IMAGE_OPERATIONS")?new fae(r.shape,l,c,a,o):new hae(r.shape,l,c,a,o);return n.runWebGLProgram(u,[r],r.dtype)}var gae={kernelName:uu,backendName:"webgl",kernelFunc:mae},Aae=class{constructor(e,t,n){this.variableNames=["dy"],this.outputShape=[],this.outputShape=t;let[,s,r]=t,[,a,o]=e,i=[n&&a>1?s-1:s,n&&o>1?r-1:r],l=[n&&a>1?a-1:a,n&&o>1?o-1:o],c=i[0]/l[0],u=i[1]/l[1],d=1/c,p=1/u,h=Math.ceil(d)*2+2,f=Math.ceil(p)*2+2;this.userCode=`
void main() {
ivec4 coords = getOutputCoords();
int b = coords[0];
int d = coords[3];
int r = coords[1];
int c = coords[2];
float accumulator = 0.0;
const float heightScale = float(${c});
const float widthScale = float(${u});
const float invHeightScale = float(${d});
const float invWidthScale = float(${p});
const int winHeight = int(${h});
const int winWidth = int(${f});
// Compute bounds for where in dy we will look
float startRLerp = floor(float(r) * invHeightScale);
int startDyR = int(floor(startRLerp - float(winHeight / 2)));
float startCLerp = floor(float(c) * invWidthScale);
int startDyC = int(floor(startCLerp - float(winWidth / 2)));
// Loop over dy
for (int dyROffset = 0; dyROffset < winHeight; dyROffset++) {
int dyR = dyROffset + startDyR;
// Guard against the window exceeding the bounds of dy
if (dyR < 0 || dyR >= ${a}) {
continue;
}
for (int dyCOffset = 0; dyCOffset < winWidth; dyCOffset++) {
int dyC = dyCOffset + startDyC;
// Guard against the window exceeding the bounds of dy
if (dyC < 0 || dyC >= ${o}) {
continue;
}
float sourceFracRow =
float(${i[0]}) *
(float(dyR) / float(${l[0]}));
float sourceFracCol =
float(${i[1]}) *
(float(dyC) / float(${l[1]}));
int sourceNearestRow = int(min(
float(int(${s}) - 1),
${n} ? float(round(sourceFracRow)) :
float(floor(sourceFracRow))));
int sourceNearestCol = int(min(
float(int(${r}) - 1),
${n} ? float(round(sourceFracCol)) :
float(floor(sourceFracCol))));
if (r == sourceNearestRow && c == sourceNearestCol) {
accumulator += getDy(b, dyR, dyC, d);
}
}
}
// End loop over dy
setOutput(accumulator);
}
`}};function yae(e){let{inputs:t,backend:n,attrs:s}=e,{images:r,dy:a}=t,{alignCorners:o}=s,i=new Aae(a.shape,r.shape,o);return n.runWebGLProgram(i,[a],a.dtype)}var xae={kernelName:Sh,backendName:"webgl",kernelFunc:yae},bae=class{constructor(e,t){this.variableNames=["x"];let n=e.length;if(n>4)throw new Error(`WebGL backend: Reverse of rank-${n} tensor is not yet supported`);if(this.outputShape=e,n===1){this.userCode=`
void main() {
int coord = getOutputCoords();
setOutput(getX(${e[0]} - coord - 1));
}
`;return}let s=o=>t.indexOf(o)!==-1&&e[o]!==1?`${e[o]} - coords[${o}] - 1`:`coords[${o}]`,r=e.map((o,i)=>s(i)).join(","),a=bt(n);this.userCode=`
void main() {
${a} coords = getOutputCoords();
setOutput(getX(${r}));
}
`}},vae=class{constructor(e,t){this.variableNames=["x"],this.packedInputs=!0,this.packedOutput=!0;let n=e.length;if(n>4)throw new Error(`WebGL backend: Reverse of rank-${n} tensor is not yet supported`);this.outputShape=e;let s=Gn("rc",n),r=`${s[n-1]} + 1 < ${this.outputShape[n-1]}`,a=`${s[n-2]} + 1 < ${this.outputShape[n-2]}`,o=bt(n);n===1?this.userCode=`
void main(){
int rc = getOutputCoords();
vec4 result = vec4(0.);
result.r = getChannel(getX(${e[0]} - rc - 1),
${e[0]} - rc - 1);
if(${r}){
result.g = getChannel(getX(${e[0]} - (rc + 1) - 1),
${e[0]} - (rc + 1) - 1);
}
setOutput(result);
}
`:this.userCode=`
void main() {
${o} rc = getOutputCoords();
vec4 result = vec4(0.);
result.r = ${i(s.slice())};
if(${r}){
result.g = ${l(s.slice())};
}
if(${a}) {
result.b = ${c(s.slice())};
if(${r}) {
result.a = ${u(s.slice())};
}
}
setOutput(result);
}
`;function i(h){return d(h)}function l(h){return h[n-1]="("+h[n-1]+" + 1)",d(h)}function c(h){return h[n-2]="("+h[n-2]+" + 1)",d(h)}function u(h){return h[n-1]="("+h[n-1]+" + 1)",h[n-2]="("+h[n-2]+" + 1)",d(h)}function d(h){let f=e.map((A,x)=>p(x,h)),m=f.join(","),g=f.slice(-2).join(",");return`getChannel(getX(${m}), vec2(${g}))`}function p(h,f){return t.indexOf(h)!==-1&&e[h]!==1?`${e[h]} - ${f[h]} - 1`:`${f[h]}`}}};function wae(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{dims:a}=s,o=r.shape.length,i=v.parseAxisParam(a,r.shape);if(o===0)return ys({inputs:{x:r},backend:n});let l=K().getBool("WEBGL_PACK_ARRAY_OPERATIONS")?new vae(r.shape,i):new bae(r.shape,i);return n.runWebGLProgram(l,[r],r.dtype)}var kae={kernelName:Ti,backendName:"webgl",kernelFunc:wae},Sae=class{constructor(e,t){this.variableNames=["Image"],this.outputShape=[],this.customUniforms=[{name:"params",type:"vec4"}];let n=e[1],s=e[2];this.outputShape=e;let r="";typeof t=="number"?r=`float outputValue = ${t.toFixed(2)};`:r=`
vec3 fill = vec3(${t.join(",")});
float outputValue = fill[coords[3]];`,this.userCode=`
void main() {
ivec4 coords = getOutputCoords();
int x = coords[2];
int y = coords[1];
float coordXFloat = (float(x) - params[0]) * params[3] -
(float(y) - params[1]) * params[2];
float coordYFloat = (float(x) - params[0]) * params[2] +
(float(y) - params[1]) * params[3];
int coordX = int(round(coordXFloat + params[0]));
int coordY = int(round(coordYFloat + params[1]));
${r}
if(coordX >= 0 && coordX < ${s} && coordY >= 0 && coordY < ${n}) {
outputValue = getImage(coords[0], coordY, coordX, coords[3]);
}
setOutput(outputValue);
}
`}},Iae={kernelName:Wi,backendName:"webgl",kernelFunc:({inputs:e,attrs:t,backend:n})=>{let{image:s}=e,{radians:r,fillValue:a,center:o}=t,i=n,l=new Sae(s.shape,a),[c,u]=N.getImageCenter(o,s.shape[1],s.shape[2]),d=[[c,u,Math.sin(r),Math.cos(r)]];return i.runWebGLProgram(l,[s],s.dtype,d)}},Cae=`
// OpenGL ES does not support round function.
// The algorithm is based on banker's rounding.
float base = floor(x);
if ((x - base) < 0.5) {
return floor(x);
} else if ((x - base) > 0.5) {
return ceil(x);
} else {
if (mod(base, 2.0) == 0.0) {
return base;
} else {
return base + 1.0;
}
}
`,Tae=ot({opSnippet:Cae}),Nae={kernelName:Ni,backendName:"webgl",kernelFunc:Tae},Eae="return inversesqrt(x);",Rae=ot({opSnippet:Eae,cpuKernelImpl:tJ}),$ae={kernelName:no,backendName:"webgl",kernelFunc:Rae},R4=class{constructor(e,t,n,s,r,a,o=!0){this.variableNames=["updates","indices","defaultValue"],this.outputShape=a;let i=bt(r.length),l=bt(a.length),c="";n===1?c="i":n===2&&(c="i, j");let u=`getIndices(${c})`,d="";s===1?d="i":s===2&&(d="i, coords[1]");let p=`getUpdates(${d})`,h=t>1?"strides[j]":"strides";this.userCode=`
${i} strides = ${i}(${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 += ${p};
found = true;
}
}
setOutput(mix(getDefaultValue(), sum, float(found)));
}
`}};function Dae(e){let{inputs:t,backend:n,attrs:s}=e,{indices:r,updates:a}=t,{shape:o}=s,{sliceRank:i,numUpdates:l,sliceSize:c,strides:u,outputSize:d}=N.calculateShapes(a,r,o),p=[d/c,c];if(d===0)return n.makeTensorInfo(o,r.dtype);let h=ve({inputs:{x:r},backend:n,attrs:{shape:[l,i]}}),f=ve({inputs:{x:a},backend:n,attrs:{shape:[l,c]}}),m=n.makeTensorInfo([],"float32",new Float32Array([0])),g=new R4(l,i,h.shape.length,f.shape.length,u,p),A=n.runWebGLProgram(g,[f,h,m],f.dtype),x=ve({inputs:{x:A},backend:n,attrs:{shape:o}});return n.disposeIntermediateTensorInfo(h),n.disposeIntermediateTensorInfo(f),n.disposeIntermediateTensorInfo(A),n.disposeIntermediateTensorInfo(m),x}var _ae={kernelName:Ei,backendName:"webgl",kernelFunc:Dae},Pae=class{constructor(e,t,n){this.variableNames=["c","a","b"],this.outputShape=t;let s,r;if(n>4)throw Error(`Where for rank ${n} is not yet supported`);if(n===1)r="resRC",s="resRC";else{let o=["resRC.x","resRC.y","resRC.z","resRC.w"],i=[],l=[];for(let c=0;c<t.length;c++)l.push(`${o[c]}`),c<e&&i.push(`${o[c]}`);s=i.join(),r=l.join()}let a=bt(n);this.userCode=`
void main() {
${a} resRC = getOutputCoords();
float cVal = getC(${s});
if (cVal >= 1.0) {
setOutput(getA(${r}));
} else {
setOutput(getB(${r}));
}
}
`}};function Fae(e){let{inputs:t,backend:n}=e,{condition:s,t:r,e:a}=t,o=new Pae(s.shape.length,r.shape,r.shape.length);return n.runWebGLProgram(o,[s,r,a],Ln(r.dtype,a.dtype))}var Oae={kernelName:Ri,backendName:"webgl",kernelFunc:Fae},Mae=`
// Stable and Attracting Fixed Point (0, 1) for Normalized Weights.
// see: https://arxiv.org/abs/1706.02515
float scaleAlpha = ${N.SELU_SCALEALPHA};
float scale = ${N.SELU_SCALE};
return (x >= 0.0) ? scale * x : scaleAlpha * (exp(x) - 1.0);
`,zae=ot({opSnippet:Mae}),Lae={kernelName:cu,backendName:"webgl",kernelFunc:zae},$4="return 1.0 / (1.0 + exp(-1.0 * x));",Bae=ot({opSnippet:$4,packedOpSnippet:$4,cpuKernelImpl:nJ}),Wae={kernelName:ro,backendName:"webgl",kernelFunc:Bae},Vae=`
if (isnan(x)) { return 0.0; }
return sign(x);
`,Uae=ot({opSnippet:Vae}),Gae={kernelName:du,backendName:"webgl",kernelFunc:Uae},Hae=GI+`
return sin(x);
`,jae=ot({opSnippet:Hae}),qae={kernelName:so,backendName:"webgl",kernelFunc:jae},Xae=`
float e2x = exp(x);
return (e2x - 1.0 / e2x) / 2.0;
`,Kae=ot({opSnippet:Xae}),Zae={kernelName:Di,backendName:"webgl",kernelFunc:Kae},Yae=`
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;
`,Jae=ot({opSnippet:Yae}),Qae={kernelName:pu,backendName:"webgl",kernelFunc:Jae},eoe=e=>{let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{blockShape:a,paddings:o}=s;v.assert(r.shape.length<=4,()=>"spaceToBatchND for rank > 4 with a WebGL backend not implemented yet");let i=a.reduce((A,x)=>A*x),l=[[0,0]];l.push(...o);for(let A=1+a.length;A<r.shape.length;++A)l.push([0,0]);let c=[],u=N4({inputs:{x:r},backend:n,attrs:{paddings:l,constantValue:0}}),d=N.getReshaped(u.shape,a,i,!1),p=N.getPermuted(d.length,a.length,!1),h=N.getReshapedPermuted(u.shape,a,i,!1),f=ve({inputs:{x:u},backend:n,attrs:{shape:d}}),m=Hn({inputs:{x:f},backend:n,attrs:{perm:p}}),g=ve({inputs:{x:m},backend:n,attrs:{shape:h}});return c.push(u),c.push(f),c.push(m),c.forEach(A=>n.disposeIntermediateTensorInfo(A)),g},toe={kernelName:_i,backendName:"webgl",kernelFunc:eoe};function noe(e){let{inputs:t,backend:n}=e,{indices:s,values:r,denseShape:a,defaultValue:o}=t;if(a.shape.length!==1)throw new Error(`Dense shape must be a vector, saw:
${a.shape}`);if(s.shape.length!==2)throw new Error(`Indices must be a matrix, saw:
${s.shape}`);if(r.shape.length!==1)throw new Error(`Values must be a vector, saw:
${r.shape}`);if(o.shape.length!==0)throw new Error(`Default value must be a scalar, saw:
${o.shape}`);let i=n.readSync(s.dataId),l=n.readSync(r.dataId),c=n.readSync(a.dataId),u=n.readSync(o.dataId)[0],[d,p,h,f,m]=rJ(i,s.shape,s.dtype,l,r.dtype,c,u);return[n.makeTensorInfo(p,s.dtype,d),n.makeTensorInfo([p[0]],r.dtype,h),n.makeTensorInfo([f.length],"bool",new Uint8Array(f.map(g=>Number(g)))),n.makeTensorInfo([m.length],s.dtype,new Int32Array(m))]}var soe={kernelName:Ch,backendName:"webgl",kernelFunc:noe};function roe(e){let{inputs:t,backend:n}=e,{inputIndices:s,inputShape:r,newShape:a}=t;if(s.shape.length!==2)throw new Error(`Input indices should be a matrix but received shape ${s.shape}`);if(r.shape.length!==1)throw new Error(`Input shape should be a vector but received shape ${r.shape}`);if(a.shape.length!==1)throw new Error(`Target shape should be a vector but received shape ${a.shape}`);let o=Array.from(n.readSync(r.dataId)),i=n.readSync(s.dataId),l=Array.from(n.readSync(a.dataId)),[c,u,d]=aJ(i,s.shape,s.dtype,o,l);return[n.makeTensorInfo(u,s.dtype,c),n.makeTensorInfo([d.length],a.dtype,new Int32Array(d))]}var aoe={kernelName:Th,backendName:"webgl",kernelFunc:roe};function ooe(e){let{inputs:t,backend:n}=e,{data:s,indices:r,segmentIds:a}=t;if(s.shape.length<1)throw new Error("Data should be at least 1 dimensional but received scalar");if(r.shape.length!==1)throw new Error(`Indices should be a vector but received shape
${r.shape}`);if(a.shape.length!==1)throw new Error(`Segment ids should be a vector but received shape
${a.shape}`);let o=n.readSync(s.dataId),i=n.readSync(r.dataId),l=n.readSync(a.dataId),[c,u]=RI(o,s.shape,s.dtype,i,l,!0);return n.makeTensorInfo(u,s.dtype,c)}var ioe={kernelName:Nh,backendName:"webgl",kernelFunc:ooe};function loe(e){let{inputs:t,backend:n}=e,{data:s,indices:r,segmentIds:a}=t;if(s.shape.length<1)throw new Error("Data should be at least 1 dimensional but received scalar");if(r.shape.length!==1)throw new Error(`Indices should be a vector but received shape
${r.shape}`);if(a.shape.length!==1)throw new Error(`Segment ids should be a vector but received shape
${a.shape}`);let o=n.readSync(s.dataId),i=n.readSync(r.dataId),l=n.readSync(a.dataId),[c,u]=RI(o,s.shape,s.dtype,i,l);return n.makeTensorInfo(u,s.dtype,c)}var uoe={kernelName:Eh,backendName:"webgl",kernelFunc:loe};function coe(e){let{inputs:t,backend:n,attrs:s}=e,{sparseIndices:r,sparseValues:a,defaultValue:o}=t,{outputShape:i}=s,{sliceRank:l,numUpdates:c,strides:u,outputSize:d}=N.calculateShapes(a,r,i),p=!1,h=new R4(c,l,r.shape.length,a.shape.length,u,[d,1],p),f=n.runWebGLProgram(h,[a,r,o],a.dtype),m=ve({inputs:{x:f},backend:n,attrs:{shape:i}});return n.disposeIntermediateTensorInfo(f),m}var doe={kernelName:Xc,backendName:"webgl",kernelFunc:coe};function poe(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{numOrSizeSplits:a,axis:o}=s,i=v.parseAxisParam(o,r.shape)[0],l=N.prepareSplitSize(r,a,i),c=r.shape.length,u=new Array(c).fill(0),d=r.shape.slice();return l.map(p=>{let h=[...d];h[i]=p;let f=Yu({inputs:{x:r},backend:n,attrs:{begin:u,size:h}});return u[i]+=p,f})}var hoe={kernelName:Pi,backendName:"webgl",kernelFunc:poe},D4="return sqrt(x);",foe=ot({opSnippet:D4,packedOpSnippet:D4,cpuKernelImpl:oJ}),moe={kernelName:ao,backendName:"webgl",kernelFunc:foe},goe="return x * x;",Aoe=ot({opSnippet:goe}),yoe={kernelName:hu,backendName:"webgl",kernelFunc:Aoe},_4="return (a - b) * (a - b);",xoe=In({opSnippet:_4,packedOpSnippet:_4}),boe={kernelName:lo,backendName:"webgl",kernelFunc:xoe};function voe({inputs:e,attrs:t,backend:n}){let{x:s}=e,r=yr+`
return x > 0.0 ? 1.0 : float(${t.alpha});
`,a=new Mo(s.shape,r);return n.runWebGLProgram(a,[s],s.dtype)}var woe={kernelName:ho,backendName:"webgl",kernelFunc:voe},koe=class{constructor(e,t,n){this.variableNames=["x"],this.outputShape=n;let s=n.length,r=bt(n.length),a=bt(n.length),o="";if(s===1)o="coords * strides + begin";else{let i=0;o=n.map((l,c)=>(i++,n.length===1?`coords * strides[${c}] + begin[${c}]`:`coords[${i-1}] * strides[${c}] + begin[${c}]`)).join(",")}this.userCode=`
${r} begin = ${r}(${e});
${r} strides = ${r}(${t});
void main() {
${a} coords = getOutputCoords();
setOutput(getX(${o}));
}
`}};function Soe(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{begin:a,end:o,strides:i,beginMask:l,endMask:c,ellipsisMask:u,newAxisMask:d,shrinkAxisMask:p}=s,{finalShapeSparse:h,finalShape:f,isIdentity:m,sliceDim0:g,isSimpleSlice:A,begin:x,end:y,strides:b}=Ft.sliceInfo(r.shape,a,o,i,l,c,u,d,p),w;if(m)w=ve({inputs:{x:r},backend:n,attrs:{shape:f}});else if(g||A){v.assert(r.shape.length>=1,()=>`Input must have rank at least 1, got: ${r.shape.length}`);let C=Ft.computeOutShape(x,y,b),E=Yu({inputs:{x:r},backend:n,attrs:{begin:x,size:C}});w=ve({inputs:{x:E},backend:n,attrs:{shape:f}}),n.disposeIntermediateTensorInfo(E)}else if(n.shouldExecuteOnCPU([r])){let E=n.readSync(r.dataId),R=Be(r.shape,r.dtype,E),F=iJ(h,R,b,x);w=n.makeTensorInfo(f,r.dtype,F.values)}else{let E=new koe(x,b,h);w=n.runWebGLProgram(E,[r],r.dtype)}let k=ve({inputs:{x:w},backend:n,attrs:{shape:f}});return n.disposeIntermediateTensorInfo(w),k}var Ioe={kernelName:Fi,backendName:"webgl",kernelFunc:Soe};function Coe(e){let{inputs:t,backend:n,attrs:s}=e,{separator:r,nGramWidths:a,leftPad:o,rightPad:i,padWidth:l,preserveShortSequences:c}=s,{data:u,dataSplits:d}=t,p=n.readSync(u.dataId),h=n.readSync(d.dataId),[f,m]=lJ(p,h,r,a,o,i,l,c);return[n.makeTensorInfo([f.length],"string",f),n.makeTensorInfo(d.shape,"int32",m)]}var Toe={kernelName:Kc,backendName:"webgl",kernelFunc:Coe};function Noe(e){let{inputs:t,backend:n,attrs:s}=e,{skipEmpty:r}=s,{input:a,delimiter:o}=t;if(a.dtype!=="string")throw new Error("Input must be of datatype string");if(a.shape.length!==1)throw new Error(`Input must be a vector, got shape: ${a.shape}`);if(o.shape.length!==0)throw new Error(`Delimiter must be a scalar, got shape: ${o.shape}`);let i=n.readSync(a.dataId),l=n.readSync(o.dataId)[0],[c,u,d]=uJ(i,l,r),p=u.length;return[n.makeTensorInfo([p,2],"int32",c),n.makeTensorInfo([p],"string",u),n.makeTensorInfo([2],"int32",new Int32Array(d))]}var Eoe={kernelName:Rh,backendName:"webgl",kernelFunc:Noe};function Roe(e){let{inputs:t,backend:n,attrs:s}=e,{numBuckets:r}=s,{input:a}=t;if(a.dtype!=="string")throw new Error("Input must be of datatype string");if(r<=0)throw new Error("Number of buckets must be at least 1");let o=n.readSync(a.dataId),i=cJ(o,r);return n.makeTensorInfo(a.shape,"int32",i)}var $oe={kernelName:$h,backendName:"webgl",kernelFunc:Roe},Doe="return tan(x);",_oe=ot({opSnippet:Doe}),Poe={kernelName:Oi,backendName:"webgl",kernelFunc:_oe},Foe=`
float e2x = exp(-2.0 * abs(x));
return sign(x) * (1.0 - e2x) / (1.0 + e2x);
`,Ooe=ot({opSnippet:Foe}),Moe={kernelName:co,backendName:"webgl",kernelFunc:Ooe},zoe=class{constructor(e,t){this.variableNames=["A"];let n=new Array(e.length);for(let a=0;a<n.length;a++)n[a]=e[a]*t[a];this.outputShape=n,this.rank=n.length;let s=bt(this.rank),r=Loe(e);this.userCode=`
void main() {
${s} resRC = getOutputCoords();
setOutput(getA(${r}));
}
`}};function Loe(e){let t=e.length;if(t>5)throw Error(`Tile for rank ${t} is not yet supported`);if(t===1)return`imod(resRC, ${e[0]})`;let n=["resRC.x","resRC.y","resRC.z","resRC.w","resRC.u"],s=[];for(let r=0;r<e.length;r++)s.push(`imod(${n[r]}, ${e[r]})`);return s.join()}function P4(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{reps:a}=s;if(r.dtype==="string"||r.shape.length>5){let l=n.readSync(r.dataId),c=r.dtype==="string"?l.map(p=>v.decodeString(p)):l,u=Be(r.shape,r.dtype,c),d=pJ(u,a);return n.makeTensorInfo(d.shape,d.dtype,d.values)}let o=new zoe(r.shape,a);return n.runWebGLProgram(o,[r],r.dtype)}var Boe={kernelName:Gr,backendName:"webgl",kernelFunc:P4},Woe=class{constructor(e){this.variableNames=["x","indices"],this.customUniforms=[{name:"n",type:"int"},{name:"firstPass",type:"int"},{name:"negativeInf",type:"float"},{name:"dir",type:"int"},{name:"inc",type:"int"}],this.outputShape=e,this.userCode=`
void main() {
ivec2 coords = getOutputCoords();
int batch = coords[0];
int elemIdx = coords[1];
// We compare elements pair-wise within a group of size 2 * inc.
// The comparing rule for each group alternates between ascending
// and descending. Within each group, we compare each pair at
// positions i and i+inc. To decide whether an element at position i
// is x0 or x1, we mod it by 2 * inc, if the result is smaller than
// inc, it is in the first half of the group, we denote it as x0,
// otherwise we denote it as x1.
// For example, as shown in the Bitonic top K paper referenced above,
// Figure5(a) shows that element[1] is in the
// second half of the group when group size is 2, but it is in the
// first half of the group when group size is 4.
bool isFirstInPair = imod(elemIdx, 2 * inc) < inc;
int i = isFirstInPair ? elemIdx : elemIdx - inc;
int i0 = firstPass == 1 ? i : int(getIndices(batch, i));
int i1 = firstPass == 1 ? i + inc : int(getIndices(batch, i + inc));
float x0 = i0 < n ? getX(batch, i0) : negativeInf;
float x1 = i1 < n ? getX(batch, i1) : negativeInf;
// Denotes which direction indices are in (ascending or descending).
bool reverse = imod(elemIdx, 2 * dir) >= dir;
bool isGreater = x0 > x1 || (x0 == x1 && i1 > i0);
if (reverse == isGreater) { // Elements in opposite order of direction
int iTemp = i0;
i0 = i1;
i1 = iTemp;
}
if (isFirstInPair) {
setOutput(float(i0));
} else {
setOutput(float(i1));
}
}
`}},Voe=class{constructor(e){this.variableNames=["x","indices"],this.customUniforms=[{name:"n",type:"int"},{name:"firstPass",type:"int"},{name:"k",type:"int"}],this.outputShape=e,this.userCode=`
void main() {
// Takes max of indices (0, k), (1, k + 1), (2, k + 2) ...
ivec2 coords = getOutputCoords();
int batch = coords[0];
int elemIdx = coords[1];
// The output size is half of the previous size.
// If the previous sequence is | | | | _ _ _ _ | | | | _ _ _ _ (k=4),
// we only need to output the indices at positions |, the indices at
// positions _ can be thrown away, see Figure5(b) After Phase 2
// (Merge phase) in the Bitonic Top K paper referenced above.
// For example, the paper shows we only need to output the orange bars.
// The output sequence should look like this | | | | | | | |.
// Because the sequence is halved, to map the output index back
// to the previous sequence to find the corresponding value,
// we need to double the index. When we double the index,
// we basically interpolate a position, so 2i looks like
// | _ | _ | _ | _ | _ | _ | _. We move the | to the first k position
// of each 2k positions by - elemIdx % k. E.g. for output at
// index 4,5,6,7, we want to get the corresponding element at
// original index 8,9,10,11, for output at index 8,9,10,11,
// we want to get the corresponding element at original index
// 16,17,18,19, so on and so forth.
int i = elemIdx < k ? elemIdx : (elemIdx * 2 - imod(elemIdx, k));
int i0 = firstPass == 1 ? i : int(getIndices(batch, i));
int i1 = firstPass == 1 ? i + k : int(getIndices(batch, i + k));
float x0 = getX(batch, i0);
float x1 = i1 < n ? getX(batch, i1) : x0;
setOutput(x0 >= x1 ? float(i0) : float(i1));
}
`}};function xl(e,t){t!==null&&e.disposeIntermediateTensorInfo(t)}function F4(e){let t=1;for(;t<e;)t*=2;return t}function Uoe(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{k:a,sorted:o}=s,i=K().getNumber("TOPK_LAST_DIM_CPU_HANDOFF_SIZE_THRESHOLD"),l=K().getNumber("TOPK_K_CPU_HANDOFF_THRESHOLD"),c=r.shape,u=c[c.length-1];if(n.shouldExecuteOnCPU([r])||u<i||a>l){let F=n.readSync(r.dataId),[D,P]=hJ(F,c,r.dtype,a,o);return[n.makeTensorInfo(D.shape,D.dtype,D.values),n.makeTensorInfo(P.shape,P.dtype,P.values)]}if(a===0)return c[c.length-1]=0,[n.makeTensorInfo(c,r.dtype,[]),n.makeTensorInfo(c,"int32",[])];if(u===1)return[r,tp({attrs:{shape:c,dtype:"int32",value:0},backend:n})];let d=n.texData.get(r.dataId),p=d!==null&&d.isPacked,h=p?n.unpackTensor(r):r,m=v.sizeFromShape(c)/u,g=ve({inputs:{x:h},attrs:{shape:[m,u]},backend:n});p&&xl(n,h);let A=F4(a),x=F4(u),y=null,b=()=>y===null?[g,g]:[g,y],w=(F,D,P)=>{let T=b(),M=new Woe(P),j=[[u],[y===null?1:0],[Number.NEGATIVE_INFINITY],[F],[D]],z=y;y=n.runWebGLProgram(M,T,"int32",j),xl(n,z)};for(let F=1;F<A;F*=2){let D=F*2;for(let P=F;P>=1;P/=2)w(D,P,[m,x])}for(let F=x;F>A;F/=2){let D=b(),P=new Voe([m,F/2]),M=[[u],[y===null?1:0],[A]],U=y;y=n.runWebGLProgram(P,D,"int32",M),xl(n,U);let j=A/2,z=j*2;for(let X=j;X>=1;X/=2)w(z,X,y.shape)}let k=y;y=Yu({inputs:{x:y},backend:n,attrs:{begin:0,size:[m,a]}}),xl(n,k);let C=b4({inputs:{x:g,indices:y},backend:n,attrs:{axis:1,batchDims:1}});xl(n,g);let E=c.slice(0,-1);E.push(a),k=y,y=ve({inputs:{x:y},attrs:{shape:E},backend:n}),xl(n,k);let R=C;return C=ve({inputs:{x:C},attrs:{shape:E},backend:n}),xl(n,R),[C,y]}var Goe={kernelName:Mi,backendName:"webgl",kernelFunc:Uoe},Hoe=class{constructor(e,t,n,s,r,a){this.variableNames=["Image","Transforms"],this.outputShape=a;let o=n==="nearest"?1:2,i;switch(s){case"constant":i=1;break;case"reflect":i=2;break;case"wrap":i=3;break;case"nearest":i=4;break;default:i=1;break}this.userCode=`
float mapCoord(float outCoord, float len) {
float inCoord = outCoord;
if(${i} == 2) {
if (inCoord < 0.0) {
if (len <= 1.0) {
inCoord = 0.0;
} else {
float sz2 = 2.0 * len;
if (inCoord < sz2) {
inCoord = sz2 * float(int(float(-inCoord / sz2))) +
inCoord;
}
inCoord = inCoord < -len ? inCoord + sz2 : -inCoord - 1.0;
}
} else if (inCoord > len - 1.0) {
if (len <= 1.0) {
inCoord = 0.0;
} else {
float sz2 = 2.0 * len;
inCoord -= sz2 * float(int(float(inCoord / sz2)));
if (inCoord >= len) {
inCoord = sz2 - inCoord - 1.0;
}
}
}
return clamp(inCoord, 0.0, len - 1.0);
} else if (${i} == 3) {
if (inCoord < 0.0) {
if (len <= 1.0) {
inCoord = 0.0;
} else {
float sz = len - 1.0;
inCoord += len * (float(int(float(-inCoord / sz))) + 1.0);
}
} else if (inCoord > len - 1.0) {
if (len <= 1.0) {
inCoord = 0.0;
} else {
float sz = len - 1.0;
inCoord -= len * float(int(float(inCoord / sz)));
}
}
return clamp(inCoord, 0.0, len - 1.0);
} else if (${i} == 4) {
return clamp(outCoord, 0.0, len - 1.0);
} else {
return outCoord;
}
}
float readWithFillValue(int batch, int coordY, int coordX,
int channel) {
float outputValue;
if (0 <= coordY && coordY < ${e} && 0 <= coordX && coordX < ${t}) {
outputValue = getImage(batch, coordY, coordX, channel);
} else {
outputValue = float(${r});
}
return outputValue;
}
void main() {
ivec4 coords = getOutputCoords();
float outputValue;
int batch = coords[0];
int x = coords[2];
int y = coords[1];
int channel = coords[3];
float xf = float(x);
float yf = float(y);
float a1 = getTransforms(batch, 0);
float a2 = getTransforms(batch, 1);
float a3 = getTransforms(batch, 2);
float b1 = getTransforms(batch, 3);
float b2 = getTransforms(batch, 4);
float b3 = getTransforms(batch, 5);
float c1 = getTransforms(batch, 6);
float c2 = getTransforms(batch, 7);
float projection = c1 * xf + c2 * yf + 1.0;
if (projection == 0.0) {
outputValue = float(${r});
} else {
float inX = (a1 * xf + a2 * yf + a3) / projection;
float inY = (b1 * xf + b2 * yf + b3) / projection;
float mapX = mapCoord(inX, float(${t}));
float mapY = mapCoord(inY, float(${e}));
if (${o} == 1) {
int coordY = int(round(mapY));
int coordX = int(round(mapX));
outputValue = readWithFillValue(batch, coordY, coordX,
channel);
} else {
float yFloor = floor(mapY);
float xFloor = floor(mapX);
float yCeil = yFloor + 1.0;
float xCeil = xFloor + 1.0;
float valueYFloor = (xCeil - mapX) *
readWithFillValue(batch, int(yFloor), int(xFloor), channel) +
(mapX - xFloor) *
readWithFillValue(batch, int(yFloor), int(xCeil), channel);
float valueYCeil = (xCeil - mapX) *
readWithFillValue(batch, int(yCeil), int(xFloor), channel) +
(mapX - xFloor) *
readWithFillValue(batch, int(yCeil), int(xCeil), channel);
outputValue = (yCeil - mapY) * valueYFloor +
(mapY - yFloor) * valueYCeil;
}
}
setOutput(outputValue);
}
`}};function joe(e){let{inputs:t,backend:n,attrs:s}=e,{image:r,transforms:a}=t,{interpolation:o,fillMode:i,fillValue:l,outputShape:c}=s,[u,d,p,h]=r.shape,[f,m]=c!=null?c:[d,p],g=[u,f,m,h],A=new Hoe(d,p,o,i,l,g);return n.runWebGLProgram(A,[r,a],"float32")}var qoe={kernelName:zi,backendName:"webgl",kernelFunc:joe};function Xoe(e){let{inputs:t,attrs:n,backend:s}=e,{axis:r}=n,{x:a}=t;Gu(a,"unique"),console.warn("WARNING: ","UI might be locked temporarily as data is being downloaded");let o=s.readSync(a.dataId),{outputValues:i,outputShape:l,indices:c}=fJ(o,r,a.shape,a.dtype);return[s.makeTensorInfo(l,a.dtype,i),s.makeTensorInfo([c.length],"int32",c)]}var Koe={kernelName:Dh,backendName:"webgl",kernelFunc:Xoe};function Zoe(e){let{inputs:t,backend:n,attrs:s}=e,{value:r}=t,{axis:a}=s;a<0&&(a+=r.shape.length);let o=r,i=o.shape.length,l=r.shape[a],c=new Array(i-1),u=0;for(let m=0;m<i;m++)m!==a&&(c[u++]=o.shape[m]);let d=[],p=new Array(i).fill(0),h=o.shape.slice();h[a]=1;let f=new Array(l);for(let m=0;m<f.length;m++){p[a]=m;let g=Yu({inputs:{x:o},backend:n,attrs:{begin:p,size:h}}),A=ve({inputs:{x:g},backend:n,attrs:{shape:c}});f[m]=A,d.push(g)}return d.forEach(m=>n.disposeIntermediateTensorInfo(m)),f}var Yoe={kernelName:Li,backendName:"webgl",kernelFunc:Zoe},Joe=class{constructor(e,t){this.variableNames=["x","segmentIds"];let n=e.windowSize,s=e.batchSize,r=e.inSize,a=e.numSegments,o=a*Math.ceil(r/n);this.outputShape=[s,o];let i="0.0",l="sumValue",c=Math.floor(n/4)*4,u=n%4,d=`
sumValue += dot(values, segFilter);
`,p="";r%n>0&&(p=`
if (inIdx < 0 || inIdx >= ${r}) {
return initializationValue;
}
`);let h="";r%n>0&&(h=`
if (inIdx < 0 || inIdx >= ${r}) {
return -1.0;
}
`),this.userCode=`
const float initializationValue = ${i};
float getValue(int batch, int inIdx) {
${p}
return getX(batch, inIdx);
}
float getSegmentIdAtIndex(int inIdx) {
${h}
return getSegmentIds(inIdx);
}
void main() {
ivec2 coords = getOutputCoords();
int batch = coords[0];
int outIdx = coords[1];
int inOffset = int(floor(float(outIdx) / float(
${a})) * float(${n}));
int currentSeg = int(mod(float(outIdx), float(${a})));
float sumValue = 0.0;
for (int i = 0; i < ${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
);
${d}
}
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
);
${d}
} else if (${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
);
${d}
} else if (${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
);
${d}
}
setOutput(${l});
}
`}};function Qoe(e){let{inputs:t,backend:n,attrs:s}=e,{x:r,segmentIds:a}=t,{numSegments:o}=s,i=r.shape.length,l=[],c=0,u=N.getAxesPermutation([c],i),d=r;u!=null&&(d=Hn({inputs:{x:r},backend:n,attrs:{perm:u}}),l.push(d),c=N.getInnerMostAxes(1,i)[0]);let p=N.segment_util.computeOutShape(d.shape,c,o),h=v.sizeFromShape([d.shape[c]]),f=ve({inputs:{x:d},backend:n,attrs:{shape:[-1,h]}});l.push(f);let m=od(r.dtype),g=(b,w,k,C,E)=>{let R=b.shape[0],F=b.shape[1],D=N.segment_util.segOpComputeOptimalWindowSize(F,E),P={windowSize:D,inSize:F,batchSize:R,numSegments:E},T=new Joe(P,w),M=n.compileAndRun(T,[b,k],C);if(l.push(M),M.shape[1]===E)return M;let U=E4({backend:n,attrs:{start:0,stop:E,step:1,dtype:"float32"}}),j=P4({inputs:{x:U},backend:n,attrs:{reps:[F/D]}});return l.push(U),l.push(j),g(M,w,j,C,E)},A=g(f,"unsortedSegmentSum",a,m,o),x=ve({inputs:{x:A},backend:n,attrs:{shape:p}}),y=x;if(u!=null){l.push(x);let b=N.getUndoAxesPermutation(u);y=Hn({inputs:{x:y},backend:n,attrs:{perm:b}})}return l.forEach(b=>n.disposeIntermediateTensorInfo(b)),y}var eie={kernelName:Zc,backendName:"webgl",kernelFunc:Qoe},tie=[Ese,Dse,hQ,mQ,yQ,vQ,kQ,CQ,NQ,RQ,PQ,OQ,LQ,VQ,KQ,HQ,JQ,nee,eee,oee,lee,cee,fee,vee,kee,Iee,$ee,_ee,Mee,Bee,XJ,Hee,tte,ste,Kee,ite,ute,ate,pte,mte,yte,bte,wte,Ite,$te,_te,Tte,Ote,Lte,Wte,Hte,Kte,Qte,nne,sne,rne,one,lne,cne,pne,fne,yne,vne,Sne,Cne,Ene,Dne,One,Bne,qJ,Vne,Uee,Hne,Xne,Yne,ZJ,tse,ase,ise,fse,dse,yse,vse,Ise,Pse,Vse,Bse,jse,Xse,Zse,zse,Jse,ere,rre,lre,pre,bre,tQ,wre,Ire,Nre,$re,Tee,Pre,Ore,zre,Wre,Hre,JJ,qre,Xre,Nee,gre,Yre,aae,tae,sQ,uae,pae,gae,xae,kae,Iae,Nae,$ae,_ae,Oae,Lae,Wae,Gae,qae,Zae,xee,yre,Qae,toe,soe,aoe,ioe,uoe,doe,hoe,moe,yoe,boe,woe,Ioe,Toe,Eoe,$oe,Are,cQ,Poe,Moe,Boe,Goe,qoe,dQ,Koe,Yoe,eie,Fre];for(let e of tie)jr(e);var Or=K();Or.registerFlag("WEBGPU_DEFERRED_SUBMIT_BATCH_SIZE",()=>15);Or.registerFlag("WEBGPU_CPU_FORWARD",()=>!0);Or.registerFlag("WEBGPU_MATMUL_WORK_PER_THREAD",()=>4);Or.registerFlag("WEBGPU_USE_NAIVE_CONV2D",()=>!1);Or.registerFlag("WEBGPU_USE_NAIVE_CONV2D_TRANSPOSE",()=>!1);Or.registerFlag("WEBGPU_CONV_SEPARATE_IM2COL_SHADER",()=>!1);Or.registerFlag("WEBGPU_USE_LOW_POWER_GPU",()=>!1);Or.registerFlag("WEBGPU_CPU_HANDOFF_SIZE_THRESHOLD",()=>1e3);Or.registerFlag("WEBGPU_USE_PROFILE_TOOL",()=>!1);Or.registerFlag("WEBGPU_USE_IMPORT",()=>!1);function nie(e,t){if(Math.max(...e)>3)throw new Error("Cannot symbolically compute strides for rank > 4 tensor.");let n=e.length,s=e.map(a=>`${t}[${a}]`),r=new Array(n-1);r[n-2]=s[n-1];for(let a=n-3;a>=0;--a)r[a]=`(${r[a+1]} * ${s[a+1]})`;return r}function Jt(e){if(e<=1)return"i32";if(e===2)return"vec2<i32>";if(e===3)return"vec3<i32>";if(e===4)return"vec4<i32>";throw Error(`GPU for rank ${e} is not yet supported`)}function Pm(e,t){return e==="float32"?t?"vec4<f32>":"f32":e==="int32"||e==="bool"?t?"vec4<i32>":"i32":e}function Le(){return`
let index = getGlobalIndex(globalId, localId);
`}function Fe(){return`
[[stage(compute), workgroup_size(workGroupSizeX, workGroupSizeY, workGroupSizeZ)]]
fn main([[builtin(local_invocation_id)]] localId : vec3<u32>, [[builtin(global_invocation_id)]] globalId : vec3<u32>)
`}function sie(e,t,n,s=!1){let r=`
let workGroupSizeX = ${n.workGroupSize[0]}u;
let workGroupSizeY = ${n.workGroupSize[1]}u;
let workGroupSizeZ = ${n.workGroupSize[2]}u;`;if(s===!0){let h=z4(t.shape),f=`
[[block]] struct Matrix0 {
numbers: array<${Pm(t.dtype,n.isVec4)}>;
};
[[block]] struct Uniform {
size : i32;
numChannels : i32;
outShapeStrides : vec2<i32>;
dispatchSize : vec3<u32>;
};
[[group(0), binding(0)]] var<storage, write> result : Matrix0;
[[group(0), binding(2)]] var<uniform> uniforms: Uniform;
`;return[O4,f,r,M4,h,n.getUserCode()].join(`
`)}let a=[],o="[[block]] struct Uniforms { NAN : f32; ";n.variableNames.forEach((h,f)=>{o+=`${h.charAt(0).toLowerCase()+h.slice(1)}Shape : ${Jt(e[f].shape.length)}; `}),o+=`outShape : ${Jt(t.shape.length)} ; `;let i=t.shape.length-1;o+=`
outShapeStrides: ${Jt(i)}; `,n.size!=null&&(o+="size : i32; "),o+="dispatchSize : vec3<u32>; ",n.uniforms&&(o+=n.uniforms),o+="};",a.push(o),n.atomic?a.push(`
[[block]] struct Matrix0 {
numbers: array<atomic<i32>>;
};
[[group(0), binding(0)]] var<storage, read_write> result : Matrix0;
`):a.push(`
[[block]] struct Matrix0 {
numbers: array<${Pm(t.dtype,n.isVec4)}>;
};
[[group(0), binding(0)]] var<storage, write> result : Matrix0;
`),n.variableNames.forEach((h,f)=>{a.push(`
[[block]] struct Matrix${1+f} {
numbers: array<${Pm(e[f].dtype,n.isVec4)}>;
};
[[group(0), binding(${1+f})]] var<storage, read> ${h} : Matrix${1+f};
`)}),o!==""&&a.push(`
[[group(0), binding(${1+n.variableNames.length})]] var<uniform> uniforms : Uniforms;
`),a.push(r);let[l,c]=uie(t.shape,n.dispatchLayout),u=z4(t.shape),d=[O4,a.join(`
`),M4,u,l,rie(t.shape.length)];if(n.atomic||d.push(aie(t.shape,t.dtype,n.isVec4)),c===t.shape.length){let h=e.map(f=>oie(f,t.shape,n.isVec4,n.dispatchLayout.x.length===t.shape.length)).join(`
`);d.push(h)}return d.push(n.getUserCode()),d.join(`
`)}var O4=`
fn idiv(a: i32, b: i32, sign: f32) -> i32 {
var res: i32 = a / b;
let mod: i32 = a % b;
if (sign < 0. && mod != 0) {
res = res - 1;
}
return res;
}
fn isNanCustom(val : f32) -> bool {
if (val > 0.0) {
return false;
}
if (val < 0.0) {
return false;
}
if (val == 0.0) {
return false;
}
return true;
}
fn isNanCustomVec4F32(val : vec4<f32>) -> vec4<f32> {
var res = vec4<f32> (0.0);
for (var i = 0u; i < 4u; i = i + 1u) {
if (isNanCustom(val[i])) {
res[i] = 1.0;
} else {
res[i] = 0.0;
}
}
return res;
}
// Checks whether coordinates lie within the bounds of the shape.
fn coordsInBounds4D(coord : vec4<i32>, shape : vec4<i32>) -> bool {
return all(coord >= vec4<i32>(0)) &&
all(coord < shape);
}
fn coordsInBounds3D(coord : vec3<i32>, shape : vec3<i32>) -> bool {
return all(coord >= vec3<i32>(0)) &&
all(coord < shape);
}
fn coordsInBounds2D(coord : vec2<i32>, shape : vec2<i32>) -> bool {
return all(coord >= vec2<i32>(0)) &&
all(coord < shape);
}
`,M4=`
fn getFlatIndex1D(coord : i32, shape : i32) -> i32 {
return coord;
}
fn getFlatIndex2D(coords : vec2<i32>, shape : vec2<i32>) -> i32 {
return i32(dot(vec2<f32>(coords), vec2<f32>(f32(shape.y), 1.0)));
}
fn getFlatIndex3D(coords : vec3<i32>, shape : vec3<i32>) -> i32 {
return i32(dot(vec3<f32>(coords), vec3<f32>(f32(shape.y) * f32(shape.z), f32(shape.z), 1.0)));
}
fn getFlatIndex4D(coords : vec4<i32>, shape : vec4<i32>) -> i32 {
return i32(dot(vec4<f32>(coords), vec4<f32>(
f32(shape.y) * f32(shape.z) * f32(shape.w), f32(shape.z) * f32(shape.w), f32(shape.w), 1.0)));
}
// Only used when the y/z dimension of workgroup size is 1.
fn getGlobalIndex(globalId : vec3<u32>, localId : vec3<u32>) -> i32 {
if (uniforms.dispatchSize.y == 1u && uniforms.dispatchSize.z == 1u) {
return i32(globalId.x);
}
let localInvocationIndex = localId.z * workGroupSizeX * workGroupSizeY +
localId.y * workGroupSizeX + localId.x;
let workGroupID = (globalId - localId)/vec3<u32>(
workGroupSizeX, workGroupSizeY, workGroupSizeZ);
return i32((workGroupID.z * uniforms.dispatchSize.x * uniforms.dispatchSize.y +
workGroupID.y * uniforms.dispatchSize.x + workGroupID.x) *
(workGroupSizeX * workGroupSizeY * workGroupSizeZ) +
localInvocationIndex);
}
`;function rie(e){let t="";switch(e){case 0:case 1:t+=`
fn getOutputFlatIndex(coords : i32) -> i32 {
return coords;
}
`;break;case 2:t+=`
fn getOutputFlatIndex(coords : vec2<i32>) -> i32 {
return i32(dot(vec2<f32>(coords), vec2<f32>(f32(uniforms.outShapeStrides), 1.0)));
}
`;break;case 3:t+=`
fn getOutputFlatIndex(coords : vec3<i32>) -> i32 {
return i32(dot(vec3<f32>(coords), vec3<f32>(f32(uniforms.outShapeStrides.x), f32(uniforms.outShapeStrides.y), 1.0)));
}
`;break;case 4:t+=`
fn getOutputFlatIndex(coords : vec4<i32>) -> i32 {
return i32(dot(vec4<f32>(coords), vec4<f32>(
f32(uniforms.outShapeStrides.x), f32(uniforms.outShapeStrides.y), f32(uniforms.outShapeStrides.z), 1.0)));
}
`;break;default:v.assert(!1,()=>`Unsupported ${e}D shape`);break}return t}function aie(e,t,n){let s=e.length,r=Pm(t,n),a;if(n?a=`fn setOutputFlat(flatIndex : i32, value : vec4<f32>) {
result.numbers[flatIndex] = ${r}(value);
}
fn setOutputFlatI32(flatIndex : i32, value : vec4<i32>) {
result.numbers[flatIndex] = ${r}(value);
}`:a=`fn setOutputFlat(flatIndex : i32, value : f32) {
result.numbers[flatIndex] = ${r}(value);
}
fn setOutputFlatI32(flatIndex : i32, value : i32) {
result.numbers[flatIndex] = ${r}(value);
}`,s>=2){let o=["d0","d1","d2","d3"].slice(0,s),i=Jt(s);n?a+=`
fn setOutput(${o.map(l=>`${l} : i32`).join(", ")}, value : vec4<f32>) {
let flatIndex = getOutputFlatIndex(${i}(${o.join(", ")}));
setOutputFlat(flatIndex / 4, value);
}
fn setOutputI32(${o.map(l=>`${l} : i32`).join(", ")}, value : vec4<i32>) {
let flatIndex = getOutputFlatIndex(${i}(${o.join(", ")}));
setOutputFlatI32(flatIndex / 4, value);
}
`:a+=`
fn setOutput(${o.map(l=>`${l} : i32`).join(", ")}, value : f32) {
let flatIndex = getOutputFlatIndex(${i}(${o.join(", ")}));
setOutputFlat(flatIndex, value);
}
fn setOutputI32(${o.map(l=>`${l} : i32`).join(", ")}, value : i32) {
let flatIndex = getOutputFlatIndex(${i}(${o.join(", ")}));
setOutputFlatI32(flatIndex, value);
}
`}return a}function oie(e,t,n,s){let r=iie(e,n);return e.shape.length<=t.length&&(r+=lie(e,t,n,s)),r}function iie(e,t){let n=e.name,s=e.shape.length,r=Jt(s),a="get"+n.charAt(0).toUpperCase()+n.slice(1),o=["d0","d1","d2","d3"].slice(0,s),i=o.map(u=>`${u} : i32`).join(", ");if(s<1)return t?`
fn ${a}() -> vec4<f32> {
return vec4<f32>(${n}.numbers[0]);
}
`:`
fn ${a}() ->f32 {
return f32(${n}.numbers[0]);
}
`;let l=`uniforms.${n.charAt(0).toLowerCase()+n.slice(1)}Shape`,c=`${s}D`;return s===0&&(c="1D"),t?`
fn ${a}(${i}) -> vec4<f32> {
return vec4<f32>(${n}.numbers[getFlatIndex${c}(${r}(${o.join(",")}),
${l}) / 4]);
}
`:`
fn ${a}(${i}) -> f32 {
return f32(${n}.numbers[getFlatIndex${c}(${r}(${o.join(",")}),
${l})]);
}
`}function lie(e,t,n,s){let r=e.name,a=r.charAt(0).toUpperCase()+r.slice(1),o="get"+a+"AtOutCoords",i=e.shape.length,l=t.length,c=Jt(l);if(v.arraysEqual(e.shape,t)&&s)return n?`
fn ${o}ByGlobalId(globalId : vec3<u32>, globalIndex : i32) -> vec4<f32> {
return vec4<f32>(${r}.numbers[globalIndex]);
}
fn ${o}ByCoords(coords : ${c}) -> vec4<f32> {
return vec4<f32>(${r}.numbers[${l>1?"getOutputFlatIndex(coords)":"coords"} / 4]);
}
`:`
fn ${o}ByGlobalId(globalId : vec3<u32>, globalIndex : i32) -> f32 {
return f32(${r}.numbers[globalIndex]);
}
fn ${o}ByCoords(coords : ${c}) -> f32 {
return f32(${r}.numbers[${l>1?"getOutputFlatIndex(coords)":"coords"}]);
}
`;let u=N.getBroadcastDims(e.shape,t),d=l-i,p="";if(i===0)return n?`
fn ${o}ByGlobalId(globalId : vec3<u32>, globalIndex : i32) -> vec4<f32> {
return get${a}();
}
fn ${o}ByCoords(coords : ${c}) -> vec4<f32> {
return get${a}();
}
`:`
fn ${o}ByGlobalId(globalId : vec3<u32>, globalIndex : i32) -> f32{
return get${a}();
}
fn ${o}ByCoords(coords : ${c}) -> f32{
return get${a}();
}
`;l<2&&u.length>=1?p="coords = 0;":p=u.map(g=>`coords[${g+d}] = 0;`).join(`
`);let h="";if(l<2&&i>0)h="coords";else if(l>1){let g=Jt(i),A=e.shape.map((x,y)=>`coords[${y+d}]`).join(", ");h=`${g}(${A})`}else h="coords";let f=`uniforms.${r.charAt(0).toLowerCase()+r.slice(1)}Shape`,m=`${i}D`;return n?`
fn ${o}ByGlobalId(globalId : vec3<u32>, globalIndex : i32) -> vec4<f32> {
var coords = getOutputCoords(globalId, globalIndex);
${p}
return ${r}.numbers[getFlatIndex${m}(${h}, ${f}) / 4];
}
fn ${o}ByCoords(coordsIn : ${c}) -> vec4<f32> {
var coords = coordsIn;
${p}
return ${r}.numbers[getFlatIndex${m}(${h}, ${f}) / 4];
}
`:`
fn ${o}ByGlobalId(globalId : vec3<u32>, globalIndex : i32) -> f32 {
var coords = getOutputCoords(globalId, globalIndex);
${p}
return f32(${r}.numbers[getFlatIndex${m}(${h}, ${f})]);
}
fn ${o}ByCoords(coordsIn : ${c}) -> f32 {
var coords = coordsIn;
${p}
return f32(${r}.numbers[getFlatIndex${m}(${h}, ${f})]);
}
`}function uie(e,t){let{x:n,y:s=[],z:r=[]}=t,a=e.length;if(n.length===a)return[`fn getOutputCoords(globalId : vec3<u32>, globalIndex : i32) -> ${Jt(a)}{
return getCoordsFromFlatIndex(i32(globalIndex));
}
`,a];let o="",i=[n,s,r],l=0;for(let p=0;p<i.length;p++){let h=i[p];if(h.length!==0)if(l+=h.length,h.length===1)o+=`let d${h[0]} = i32(globalId[${p}]);`;else{let f=nie(h,"uniforms.outShape");o+=`var index${p} = i32(globalId[${p}]);`;for(let m=0;m<f.length;m++)o+=`let d${h[m]} = index${p} / ${f[m]};`,m===f.length-1?o+=`let d${h[m+1]} = index${p} - d${h[m]} * ${f[m]};`:o+=`index${p} = index${p} - d${h[m]} * ${f[m]};`}}let c=[];for(let p=0;p<l;p++)c.push(`d${p}`);let u=Jt(l),d=`fn getOutputCoords(globalId : vec3<u32>, globalIndex : i32) -> ${u} {
${o}
`;return c.length===0?d+=`return ${u}(0); }`:d+=`return ${u}(${c.join(",")}); }`,[d,l]}function z4(e){let t=e.length;if(t<=1)return"fn getCoordsFromFlatIndex(index : i32) -> i32 { return index; }";let n=v.computeStrides(e),s=Jt(t),r=[];for(let o=0;o<t;o++)r.push(`d${o}`);if(n.length===1)return` fn getCoordsFromFlatIndex(index : i32) -> vec2<i32> {
let d0 = index / uniforms.outShapeStrides; let d1 = index - d0 * uniforms.outShapeStrides;
return vec2<i32>(d0, d1);
}`;let a="var index2 = index;"+n.map((o,i)=>{let l=`let ${r[i]} = index2 / uniforms.outShapeStrides[${i}]`,c=i===n.length-1?`let ${r[i+1]} = index2 - ${r[i]} * uniforms.outShapeStrides[${i}]`:`index2 = index2 - ${r[i]} * uniforms.outShapeStrides[${i}]`;return`${l}; ${c};`}).join("");return`
fn getCoordsFromFlatIndex(index : i32) -> ${s} {
${a}
return ${s}(${r.join(",")});
}
`}var L4={};Me(L4,{ArrayBufferToTypedArray:()=>B4,GPUBytesPerElement:()=>rx,computeDispatch:()=>Oe,computeWorkGroupSizeForConv2d:()=>tx,computeWorkGroupSizeForMatMul:()=>nx,computeWorkPerThreadForConv2d:()=>sx,flatDispatchLayout:()=>Ke,isWebGPUSupported:()=>ax,tilesFitEvenlyIntoShape:()=>ra});var ec=65535,bl=e=>{let t=1;for(let n=0;n<e.length;n++)t*=e[n];return t};function ra(e,t){if(e.length!==t.length)throw new Error(`Cannot compute whether rank ${e.length} tiles fit evenly into rank ${t.length} shape - ranks must match.`);return t.every((n,s)=>n%e[s]==0)}function Oe(e,t,n=[1,1,1],s=[1,1,1]){let[r,a,o]=[Math.ceil(bl(e.x.map(l=>t[l]))/(n[0]*s[0])),e.y?Math.ceil(bl(e.y.map(l=>t[l]))/(n[1]*s[1])):1,e.z?Math.ceil(bl(e.z.map(l=>t[l]))/(n[2]*s[2])):1];if(r<=ec&&a<=ec&&o<=ec)return[r,a,o];v.assert(r>ec&&e.y===void 0&&e.z===void 0,()=>"Dispatch size exceeds WebGPU limits in Y or Z dimension.");let i=Math.ceil(Math.sqrt(r));return i>ec?(i=Math.ceil(Math.cbrt(r)),v.assert(i<=ec,()=>"Total dispatch size exceeds WebGPU maximum."),[i,i,i]):[i,i,1]}function tx(e,t){let n=bl(e.x.map(r=>t[r])),s=bl(e.y.map(r=>t[r]));return n<=4?[4,16,1]:s<=4?[16,4,1]:[16,16,1]}function nx(e,t,n){return e===1?[32,1,1]:n===1?[1,32,1]:[8,8,1]}function sx(e,t){let n=bl(e.x.map(r=>t[r])),s=bl(e.y.map(r=>t[r]));return n<=4?[1,2,1]:s<=4?[2,1,1]:[2,2,1]}function Ke(e){return{x:e.map((t,n)=>n)}}function rx(e){if(e==="float32"||e==="int32"||e==="bool"||e==="string")return 4;if(e==="complex64")return 8;throw new Error(`Unknown dtype ${e}`)}function B4(e,t){if(t==="float32")return new Float32Array(e);if(t==="int32")return new Int32Array(e);if(t==="bool"||t==="string"){let n=new Int32Array(e),s=new ArrayBuffer(n.length),r=new Uint8Array(s);for(let a=0;a<n.length;a++)r[a]=n[a];return r}else throw new Error(`Unknown dtype ${t}`)}function ax(){return!!navigator.gpu}var Vt;(function(e){e[e.MUL=0]="MUL",e[e.ADD=1]="ADD",e[e.SUB=2]="SUB",e[e.DIV=3]="DIV",e[e.EQUAL=4]="EQUAL",e[e.GREATER=5]="GREATER",e[e.GREATER_EQUAL=6]="GREATER_EQUAL",e[e.LESS=7]="LESS",e[e.LESS_EQUAL=8]="LESS_EQUAL",e[e.LOGICAL_AND=9]="LOGICAL_AND",e[e.NOT_EQUAL=10]="NOT_EQUAL",e[e.SQUARED_DIFFERENCE=11]="SQUARED_DIFFERENCE",e[e.INT_DIV=12]="INT_DIV",e[e.POW=13]="POW",e[e.PRELU=14]="PRELU",e[e.MAX=15]="MAX",e[e.MIN=16]="MIN",e[e.COMPLEX_MULTIPLY_REAL=17]="COMPLEX_MULTIPLY_REAL",e[e.COMPLEX_MULTIPLY_IMAG=18]="COMPLEX_MULTIPLY_IMAG"})(Vt||(Vt={}));var cie="return a + b;",die="return areal * breal - aimag * bimag;",pie="return areal * bimag + aimag * breal;",hie="return a / b;",fie="return a * b;",mie="return (a - b) * (a - b);",gie="return a - b;",Aie="return f32(a == b);",yie="return vec4<f32>(a == b);",xie="return f32(a > b);",bie="return vec4<f32>(a > b);",vie="return f32(a >= b);",wie="return vec4<f32>(a >= b);",kie="return f32(a < b);",Sie="return vec4<f32>(a < b);",Iie="return f32(a <= b);",Cie="return vec4<f32>(a <= b);",Tie="return f32(f32(a) >= 1.0 && f32(b) >= 1.0);",Nie=`return (vec4<f32>(a >= vec4<f32>(1.0)) *
vec4<f32>(b >= vec4<f32>(1.0)));`,Eie=`
if (isNanCustom(a)) { return a; }
if (isNanCustom(b)) { return b; }
`,W4=`
if (isNaN.r > 0.) {
resultTemp.r = uniforms.NAN;
}
if (isNaN.g > 0.) {
resultTemp.g = uniforms.NAN;
}
if (isNaN.b > 0.) {
resultTemp.b = uniforms.NAN;
}
if (isNaN.a > 0.) {
resultTemp.a = uniforms.NAN;
}
`,Rie=`
let s = sign(a) * sign(b);
let ia = i32(round(a));
let ib = i32(round(b));
return f32(idiv(ia, ib, s));
`,$ie=`
let ia = vec4<i32>(round(a));
let ib = vec4<i32>(round(b));
let cond = ib != vec4<i32>(0);
var resultTemp = vec4<i32>(0);
let s = sign(a) * sign(b);
// Windows (D3D) wants guaranteed non-zero int division at compile-time.
if (cond[0]) {
resultTemp[0] = idiv(ia[0], ib[0], s[0]);
}
if (cond[1]) {
resultTemp[1] = idiv(ia[1], ib[1], s[1]);
}
if (cond[2]) {
resultTemp[2] = idiv(ia[2], ib[2], s[2]);
}
if (cond[3]) {
resultTemp[3] = idiv(ia[3], ib[3], s[3]);
}
return vec4<f32>(resultTemp);
`,Die="return f32(a != b);",_ie="return vec4<f32>(a != b);",Pie=`
if(a < 0.0 && floor(b) < b) {
return uniforms.NAN;
}
if (b == 0.0) {
return 1.0;
}
if (round(abs(b) % 2.0) != 1.0) {
return pow(abs(a), b);
}
return sign(a) * pow(abs(a), b);
`,Fie=`
let isModRound1Bool = vec4<i32>(round(abs(b) % vec4<f32>(2.0))) == vec4<i32>(1);
let isModRound1 = vec4<f32>(isModRound1Bool);
let multiplier = sign(a) * isModRound1 + (vec4<f32>(1.0) - isModRound1);
var resultTemp = multiplier * pow(abs(a), b);
// Ensure that a^0 = 1, including 0^0 = 1 as this correspond to TF and JS
let isExpZero = b == vec4<f32>(0.0);
if (isExpZero.r) {
resultTemp.r = 1.0;
}
if (isExpZero.g) {
resultTemp.g = 1.0;
}
if (isExpZero.b) {
resultTemp.b = 1.0;
}
if (isExpZero.a) {
resultTemp.a = 1.0;
}
let isNaN = vec4<f32>(a < vec4<f32>(0.0)) * vec4<f32>(floor(b) < b);
${W4}
return resultTemp;
`,Oie="if (a < 0.0) { return b * a; } return a;",Mie=`
let aLessThanZero = vec4<f32>(a < vec4<f32>(0.0));
return (aLessThanZero * (b * a)) + ((vec4<f32>(1.0) - aLessThanZero) * a);
`;function V4(e,t){let n=t?W4:Eie;return t?`
var resultTemp = vec4<f32>(${e}(a, b));
let isNaN = min(vec4<f32>(isNanCustomVec4F32(a)) + vec4<f32>(isNanCustomVec4F32(b)), vec4<f32>(1.0));
`+n+`
return resultTemp;
`:n+`
return ${e}(a, b);
`}function np(e,t){switch(e){case 0:return fie;case 1:return cie;case 2:return gie;case 3:return hie;case 4:return t?yie:Aie;case 5:return t?bie:xie;case 6:return t?wie:vie;case 7:return t?Sie:kie;case 8:return t?Cie:Iie;case 9:return t?Nie:Tie;case 10:return t?_ie:Die;case 11:return mie;case 12:return t?$ie:Rie;case 14:return t?Mie:Oie;case 15:return V4("max",t);case 16:return V4("min",t);case 13:return t?Fie:Pie;case 17:return die;case 18:return pie;default:throw new Error(`BinaryType ${e} is not implemented!`)}}var vt;(function(e){e[e.ABS=0]="ABS",e[e.CEIL=1]="CEIL",e[e.COS=2]="COS",e[e.COSH=3]="COSH",e[e.ELU=4]="ELU",e[e.EXP=5]="EXP",e[e.EXPM1=6]="EXPM1",e[e.FLOOR=7]="FLOOR",e[e.LINEAR=8]="LINEAR",e[e.LOG=9]="LOG",e[e.LOGICAL_NOT=10]="LOGICAL_NOT",e[e.NEG=11]="NEG",e[e.PRELU=12]="PRELU",e[e.RELU=13]="RELU",e[e.RELU6=14]="RELU6",e[e.RSQRT=15]="RSQRT",e[e.SIN=16]="SIN",e[e.SINH=17]="SINH",e[e.SIGMOID=18]="SIGMOID",e[e.SQRT=19]="SQRT",e[e.SQUARE=20]="SQUARE",e[e.TANH=21]="TANH",e[e.TO_INT=22]="TO_INT"})(vt||(vt={}));var zie="return abs(a);",Lie="return ceil(a);",Bie="return cos(a);",Wie=`
let e2x = exp(-a);
return (e2x + 1.0 / e2x) / 2.0;
`,Vie="return exp(a) - 1.0;",Uie="if (a >= 0.0) { return a; } return (exp(a) - 1.0);",Gie=`
var resFloat = exp(a) - vec4<f32>(1.0);
if (a.r >= 0.0) {
resFloat.r = a.r;
}
if (a.g >= 0.0) {
resFloat.g = a.g;
}
if (a.b >= 0.0) {
resFloat.b = a.b;
}
if (a.a >= 0.0) {
resFloat.a = a.a;
}
return resFloat;
`,Hie="return exp(a);",jie="return floor(a);",qie="return a;",Xie=`if (a < 0.0) { return 1.0/0.0; }
return log(a);`,Kie="return f32(!(a >= 1.0));",Zie="return -a;",Yie="return (a < 0.0) ? b * a : a;",Jie="return max(a, 0.0);",Qie="return clamp(a, 0.0, 6.0);",ele="return clamp(a, vec4<f32>(0.0, 0.0, 0.0, 0.0), vec4<f32>(6.0, 6.0, 6.0, 6.0));",tle=`
var resFloat = a * vec4<f32>(a >= vec4<f32>(0.0));
let isNaN = isNan(a);
if (isNaN.r) {
resFloat.r = a.r;
}
if (isNaN.g) {
resFloat.g = a.g;
}
if (isNaN.b) {
resFloat.b = a.b;
}
if (isNaN.a) {
resFloat.a = a.a;
}
return resFloat;
`,nle="return 1.0/sqrt(a);",sle="return 1.0 / (1.0 + exp(-1.0 * a));",rle="return sin(a);",ale=`
let e2x = exp(a);
return (e2x - 1.0 / e2x) / 2.0;
`,ole="return sqrt(a);",ile="return a * a;",lle=`
let e2x = exp(-2.0 * abs(a));
return sign(a) * (1.0 - e2x) / (1.0 + e2x);
`,ule="return f32(i32((a)));";function tc(e,t){switch(e){case 0:return zie;case 2:return Bie;case 3:return Wie;case 1:return Lie;case 4:return t?Gie:Uie;case 5:return Hie;case 6:return Vie;case 7:return jie;case 8:return qie;case 9:return Xie;case 10:return Kie;case 11:return Zie;case 12:return Yie;case 13:return t?tle:Jie;case 14:return t?ele:Qie;case 15:return nle;case 18:return sle;case 16:return rle;case 17:return ale;case 19:return ole;case 20:return ile;case 21:return lle;case 22:return ule;default:throw new Error(`BinaryType ${e} is not implemented!`)}}function aa(e,t=!1){if(e===null)return null;if(e==="linear")return tc(vt.LINEAR);if(e==="relu")return tc(vt.RELU,t);if(e==="elu")return tc(vt.ELU,t);if(e==="relu6")return tc(vt.RELU6,t);if(e==="prelu")return np(Vt.PRELU,t);if(e==="sigmoid")return tc(vt.SIGMOID);throw new Error(`Activation ${e} has not been implemented for the WebGPU backend.`)}function U4(e,t){let n={RowPerThread:e[1],ColPerThread:e[0],TileAOuter:t[1]*e[1],TileBOuter:t[0]*e[0],TileInner:t[0]*e[0]};return`
var<workgroup> mm_Asub : array<array<vec4<f32>, ${n.TileInner/n.ColPerThread}>, ${n.TileAOuter}>;
var<workgroup> mm_Bsub : array<array<vec4<f32>, ${n.TileBOuter/n.ColPerThread}>, ${n.TileInner}>;
let RowPerThread = ${n.RowPerThread};
let ColPerThread = ${n.ColPerThread}; // only support ColPerThread = 4
let TileAOuter = ${n.TileAOuter};
let TileBOuter = ${n.TileBOuter};
let TileInner = ${n.TileInner};
${Fe()} {
let tileRow = i32(localId.y) * RowPerThread;
let tileCol = i32(localId.x);
let globalRow = i32(globalId.y) * RowPerThread;
let globalCol = i32(globalId.x);
let numTiles = (uniforms.dimInner - 1) / TileInner + 1;
var acc: array<vec4<f32>, ${n.RowPerThread}>;
var ACached : vec4<f32>;
var BCached : array<vec4<f32>, 4>;
// Loop over shared dimension.
var globalColA = tileCol;
let RowPerThreadB = TileInner / ${t[1]};
let tileRowB = i32(localId.y) * RowPerThreadB;
for (var t = 0; t < numTiles; t = t + 1) {
// Load one tile of A into local memory.
for (var innerRow = 0; innerRow < RowPerThread; innerRow = innerRow + 1) {
let inputRow = tileRow + innerRow;
let inputCol = tileCol;
mm_Asub[inputRow][inputCol] = mm_readA(globalRow + innerRow, globalColA, globalId);
}
globalColA = globalColA + TileInner / ColPerThread;
// Load one tile of B into local memory.
for (var innerRow = 0; innerRow < RowPerThreadB; innerRow = innerRow + 1) {
let inputRow = tileRowB + innerRow;
let inputCol = tileCol;
mm_Bsub[inputRow][inputCol] = mm_readB(t * TileInner + inputRow, globalCol, globalId);
}
workgroupBarrier();
// Compute acc values for a single thread.
for (var k = 0; k < TileInner / ColPerThread; k = k + 1) {
BCached[0] = mm_Bsub[k * ColPerThread][tileCol];
BCached[1] = mm_Bsub[k * ColPerThread + 1][tileCol];
BCached[2] = mm_Bsub[k * ColPerThread + 2][tileCol];
BCached[3] = mm_Bsub[k * ColPerThread + 3][tileCol];
for (var i = 0; i < RowPerThread; i = i + 1) {
ACached = mm_Asub[tileRow + i][k];
acc[i] = BCached[0] * ACached.x + acc[i];
acc[i] = BCached[1] * ACached.y + acc[i];
acc[i] = BCached[2] * ACached.z + acc[i];
acc[i] = BCached[3] * ACached.w + acc[i];
}
}
workgroupBarrier();
}
for (var innerRow = 0; innerRow < RowPerThread; innerRow = innerRow + 1) {
mm_write(globalRow + innerRow,
globalCol,
acc[innerRow], globalId);
}
}`}function cle(e){return`
var<workgroup> mm_Asub : array<vec4<f32>, ${e[0]}>;
let tileSize = ${e[0]*4};
${Fe()} {
let tileCol = i32(localId.x);
let globalCol = i32(globalId.x);
let globalRow = i32(globalId.y);
let numTiles = (uniforms.dimInner - 1) / tileSize + 1;
// Without this initialization strange values show up in acc.
var acc = vec4<f32>(0.0);
// Loop over shared dimension.
for (var t = 0; t < numTiles; t = t + 1) {
// Load one tile of A into local memory.
let colA = t * tileSize / 4 + tileCol;
mm_Asub[tileCol] = mm_readA(globalRow, colA, globalId);
workgroupBarrier();
// Compute acc values for a single thread.
for (var k = 0; k < tileSize / 4; k = k + 1) {
let rowB = t * tileSize + k * 4;
let BCached0 = mm_readB(rowB, globalCol, globalId);
let BCached1 = mm_readB(rowB + 1, globalCol, globalId);
let BCached2 = mm_readB(rowB + 2, globalCol, globalId);
let BCached3 = mm_readB(rowB + 3, globalCol, globalId);
let ACached = mm_Asub[k];
acc = acc + BCached0 * ACached.x;
acc = acc + BCached1 * ACached.y;
acc = acc + BCached2 * ACached.z;
acc = acc + BCached3 * ACached.w;
}
workgroupBarrier();
}
if (globalRow < uniforms.dimAOuter && globalCol < uniforms.dimBOuter) {
mm_write(globalRow, globalCol, acc, globalId);
}
}
`}var dle=class{constructor(e,t,n,s=null,r=null,a=null){this.variableNames=["A","B"],this.uniforms="dimAOuter : i32; dimBOuter : i32; dimInner : i32;",this.workGroupSize=[16,16,1],this.isVec4=!0,this.vecSize=4,this.outputShape=t,this.workGroupSize=nx(t[1],e[2],t[2]),this.dispatchLayout={x:[2],y:[1],z:[0]},t[1]===1&&(n=1),this.dispatch=Oe(this.dispatchLayout,this.outputShape,this.workGroupSize,[this.vecSize,n,1]);let o=s!=null,i=a!=null;o&&this.variableNames.push("bias"),i&&this.variableNames.push("preluActivationWeights"),this.workPerThread=n,this.aShape=e,this.addBias=o,this.activation=r,this.hasPreluActivationWeights=i,[this.fitA,this.fitB]=this.getShapeFit(),this.shaderKey=`matMulPackedVec4_${n}_${this.activation}_${this.fitA}_${this.fitB}_${this.outputShape[1]>1}`}getShapeFit(){let e=this.aShape[2],t=this.outputShape[2],n=[this.outputShape[0],e,t],s=this.workGroupSize[1]*this.workPerThread,r=this.workGroupSize[0]*this.vecSize,a=r,o=[s,a],i=[a,r];return[ra(o,this.aShape.slice(1)),ra(i,n.slice(1))]}getUserCode(){let e=this.fitA?"return A.numbers[batch * batchASize + row * uniforms.dimInner / 4 + col]":`if (coordsInBounds2D(vec2<i32>(row, col * 4), vec2<i32>(uniforms.dimAOuter, uniforms.dimInner))) {
return A.numbers[batch * batchASize + row * uniforms.dimInner / 4 + col];
}
return vec4<f32>(0.0)`,t=this.fitB?"return B.numbers[batch * batchBSize + row * uniforms.dimBOuter / 4 + col]":`if(coordsInBounds2D(vec2<i32>(row, col * 4), vec2<i32>(uniforms.dimInner, uniforms.dimBOuter))) {
return B.numbers[batch * batchBSize + row * uniforms.dimBOuter / 4 + col];
}
return vec4<f32>(0.0)`,n="",s="";if(this.activation){let o=aa(this.activation,this.isVec4);this.hasPreluActivationWeights?n=`fn activation(a : vec4<f32>, outCoord : vec3<i32>) -> vec4<f32> {
let b = getPreluActivationWeightsAtOutCoordsByCoords(outCoord);
${o}
}`:n=`
fn activation(a : vec4<f32>, outCoord : vec3<i32>) -> vec4<f32> {
${o}
}`,s="value = activation(value, outCoord);"}let r=this.addBias?"value = value + getBiasAtOutCoordsByCoords(outCoord);":"";return`
${n}
fn mm_readA(row : i32, col : i32, globalId : vec3<u32>) -> vec4<f32> {
let batchASize = uniforms.aShape[1] * uniforms.aShape[2] / ${this.vecSize};
let batch = i32(globalId.z);
${e};
}
fn mm_readB(row : i32, col : i32, globalId : vec3<u32>) -> vec4<f32> {
let batchBSize = uniforms.bShape[1] * uniforms.bShape[2] / ${this.vecSize};
let batch = i32(globalId.z);
${t};
}
fn mm_write(row : i32, col : i32, valueIn : vec4<f32>, globalId : vec3<u32>) {
if (row < uniforms.aShape[1] && col * 4 < uniforms.bShape[2])
{
var value = valueIn;
let batch = i32(globalId.z);
let outCoord = vec3<i32>(batch, row, col * 4);
${r}
${s}
setOutput(outCoord[0], outCoord[1], outCoord[2], value);
}
}
${this.outputShape[1]>1?U4([this.vecSize,this.workPerThread,1],this.workGroupSize):cle(this.workGroupSize)}
`}};function ox(e,t){let n=t[1]*e[1],s=t[0]*e[0],r=n>s?n:s;return`
var<workgroup> mm_Asub : array<array<f32, ${r}>, ${n}>;
var<workgroup> mm_Bsub : array<array<f32, ${s}>, ${r}>;
${Fe()} {
let tileRow = i32(localId.y) * ${e[1]};
let tileCol = i32(localId.x) * ${e[0]};
let globalRow = i32(globalId.y) * ${e[1]};
let globalCol = i32(globalId.x) * ${e[0]};
let numTiles = (uniforms.dimInner - 1) / ${r} + 1;
var acc : array<array<f32, ${e[0]}>, ${e[1]}>;
var ACached : f32;
var BCached : array<f32, ${e[0]}>;
// Without this initialization strange values show up in acc.
for (var innerRow = 0; innerRow < ${e[1]}; innerRow = innerRow + 1) {
for (var innerCol = 0; innerCol < ${e[0]}; innerCol = innerCol + 1) {
acc[innerRow][innerCol] = 0.0;
}
}
let ColPerThreadA = ${r} / ${t[0]};
let tileColA = i32(localId.x) * ColPerThreadA;
let RowPerThreadB = ${r} / ${t[1]};
let tileRowB = i32(localId.y) * RowPerThreadB;
// Loop over shared dimension.
for (var t = 0; t < numTiles; t = t + 1) {
// Load one tile of A into local memory.
for (var innerRow = 0; innerRow < ${e[1]}; innerRow = innerRow + 1) {
for (var innerCol = 0; innerCol < ColPerThreadA; innerCol = innerCol + 1) {
let inputRow = tileRow + innerRow;
let inputCol = tileColA + innerCol;
mm_Asub[inputRow][inputCol] = mm_readA(
globalRow + innerRow,
t * ${r} + inputCol, globalId);
}
}
// Load one tile of B into local memory.
for (var innerRow = 0; innerRow < RowPerThreadB; innerRow = innerRow + 1) {
for (var innerCol = 0; innerCol < ${e[0]}; innerCol = innerCol + 1) {
let inputRow = tileRowB + innerRow;
let inputCol = tileCol + innerCol;
mm_Bsub[inputRow][inputCol] = mm_readB(
t * ${r} + inputRow,
globalCol + innerCol, globalId);
}
}
workgroupBarrier();
// Compute acc values for a single thread.
for (var k = 0; k < ${r}; k = k + 1) {
for (var inner = 0; inner < ${e[0]}; inner = inner + 1) {
BCached[inner] = mm_Bsub[k][tileCol + inner];
}
for (var innerRow = 0; innerRow < ${e[1]}; innerRow = innerRow + 1) {
ACached = mm_Asub[tileRow + innerRow][k];
for (var innerCol = 0; innerCol < ${e[0]}; innerCol = innerCol + 1) {
acc[innerRow][innerCol] = acc[innerRow][innerCol] + ACached * BCached[innerCol];
}
}
}
workgroupBarrier();
}
for (var innerRow = 0; innerRow < ${e[1]}; innerRow = innerRow + 1) {
for (var innerCol = 0; innerCol < ${e[0]}; innerCol = innerCol + 1) {
if ((globalCol + innerCol) < uniforms.dimBOuter &&
(globalRow + innerRow) < uniforms.dimAOuter) {
mm_write(globalRow + innerRow,
globalCol + innerCol,
acc[innerRow][innerCol], globalId);
}
}
}
}
`}function ple(e){return`
let TileSize = ${e[0]*4};
var<workgroup> mm_Asub : array<vec4<f32>, ${e[0]}>;
${Fe()} {
let tileCol = i32(localId.x);
let globalCol = i32(globalId.x);
let globalRow = i32(globalId.y);
let numTiles = (uniforms.dimInner - 1) / TileSize + 1;
// Without this initialization strange values show up in acc.
var acc = 0.0;
// Loop over shared dimension.
for (var t = 0; t < numTiles; t = t + 1) {
// Load one tile of A into local memory.
let colA = t * TileSize + tileCol * 4;
mm_Asub[tileCol] = vec4<f32>(mm_readA(globalRow, colA, globalId),
mm_readA(globalRow, colA + 1, globalId),
mm_readA(globalRow, colA + 2, globalId),
mm_readA(globalRow, colA + 3, globalId));
workgroupBarrier();
// Compute acc values for a single thread.
for (var k = 0; k < TileSize / 4; k = k + 1) {
let rowB = t * TileSize + k * 4;
let BCached = vec4<f32>(mm_readB(rowB, globalCol, globalId),
mm_readB(rowB + 1, globalCol, globalId),
mm_readB(rowB + 2, globalCol, globalId),
mm_readB(rowB + 3, globalCol, globalId));
let ACached = mm_Asub[k];
acc = acc + dot(ACached, BCached);
}
workgroupBarrier();
}
if (globalRow < uniforms.dimAOuter && globalCol < uniforms.dimBOuter) {
mm_write(globalRow, globalCol, acc, globalId);
}
}
`}var G4=class{constructor(e,t,n,s=!1,r=!1,a=null,o=null,i=null){this.variableNames=["A","B"],this.uniforms="dimAOuter : i32; dimBOuter : i32; dimInner : i32;",this.workGroupSize=[16,16,1],this.outputShape=t,this.dispatchLayout={x:[2],y:[1],z:[0]};let l=s?e[1]:e[2];this.workGroupSize=nx(t[1],l,t[2]),(t[1]===1||t[2]===1)&&(n=1),this.dispatch=Oe(this.dispatchLayout,this.outputShape,this.workGroupSize,[n,n,1]),v.arraysEqual(this.dispatch,[1,1,1])&&(n=1,this.dispatch=Oe(this.dispatchLayout,this.outputShape,this.workGroupSize,[n,n,1]));let c=a!=null,u=i!=null;c&&this.variableNames.push("bias"),u&&this.variableNames.push("preluActivationWeights"),this.workPerThread=n,this.aShape=e,this.transposeA=s,this.transposeB=r,this.addBias=c,this.activation=o,this.hasPreluActivationWeights=u;let d=this.outputShape[2],p=this.transposeB?[this.outputShape[0],d,l]:[this.outputShape[0],l,d];[this.fitA,this.fitB]=this.getShapeFit(p),this.shaderKey=`matMulPacked_${this.workPerThread}_${s}_${r}_${this.activation}_${this.fitA}_${this.fitB}_${this.outputShape[1]>1}`}getShapeFit(e){let t=this.workGroupSize[1]*this.workPerThread,n=this.workGroupSize[0]*this.workPerThread,s=t>n?t:n;this.outputShape[1]===1&&(s*=4),v.assert(s%this.workGroupSize[0]==0&&s%this.workGroupSize[1]==0,()=>"tileInner must be multiple of workgroupsize.x and workgroupsize.y");let r=[t,s],a=[s,n];return[ra(r,this.aShape.slice(1)),ra(a,e.slice(1))]}getUserCode(){let e;this.transposeA===!1?e=this.fitA?"return A.numbers[batch * batchASize + row * uniforms.dimInner + col];":`if(coordsInBounds2D(vec2<i32>(row, col), vec2<i32>(uniforms.dimAOuter, uniforms.dimInner))) {
return A.numbers[batch * batchASize + row * uniforms.dimInner + col];
}
return 0.0;`:e=this.fitA?"return A.numbers[batch * batchASize + col * uniforms.dimAOuter + row];":`if(coordsInBounds2D(vec2<i32>(row, col), vec2<i32>(uniforms.dimAOuter, uniforms.dimInner))) {
return A.numbers[batch* batchASize + col * uniforms.dimAOuter + row];
}
return 0.0;`;let t;this.transposeB===!1?t=this.fitB?"return B.numbers[batch * batchBSize + row * uniforms.dimBOuter + col];":`if(coordsInBounds2D(vec2<i32>(row, col), vec2<i32>(uniforms.dimInner, uniforms.dimBOuter))) {
return B.numbers[batch * batchBSize + row * uniforms.dimBOuter + col];
}
return 0.0;`:t=this.fitB?"return B.numbers[batch * batchBSize + col * uniforms.dimInner + row];":`if(coordsInBounds2D(vec2<i32>(row, col), vec2<i32>(uniforms.dimInner, uniforms.dimBOuter))) {
return B.numbers[batch * batchBSize + col * uniforms.dimInner + row];
}
return 0.0;`;let n="",s="";if(this.activation){let o=aa(this.activation,!1);this.hasPreluActivationWeights?n=`fn activation(a : f32, outCoord : vec3<i32>) -> f32 {
let b = getPreluActivationWeightsAtOutCoordsByCoords(outCoord);
${o}
}`:n=`
fn activation(a : f32, outCoord : vec3<i32>) -> f32 {
${o}
}
`,s="value = activation(value, outCoord);"}let r=this.addBias?"value = value + getBiasAtOutCoordsByCoords(outCoord);":"";return`
${n}
fn mm_readA(row : i32, col : i32, globalId : vec3<u32>) -> f32 {
let batchASize = uniforms.aShape[1] * uniforms.aShape[2];
let batch = i32(globalId.z);
${e}
}
fn mm_readB(row : i32, col : i32, globalId : vec3<u32>) -> f32 {
let batch = i32(globalId.z);
let batchBSize = uniforms.bShape[1] * uniforms.bShape[2];
${t}
}
fn mm_write(row : i32, col : i32, valueIn : f32, globalId : vec3<u32>) {
var value = valueIn;
let batch = i32(globalId.z);
let outCoord = vec3<i32>(batch, row, col);
${r}
${s}
setOutput(batch, row, col, value);
}
${this.outputShape[1]>1?ox([this.workPerThread,this.workPerThread,1],this.workGroupSize):ple(this.workGroupSize)}
`}};function hle(){return`
var<workgroup> sumValues : array<f32, workGroupSizeX>;
${Fe()} {
${Le()}
let coords = getOutputCoords(globalId, index);
let batch = coords[0];
let row = coords[1];
let col = coords[2];
var sum = 0.0;
let Length = uniforms.dimInner;
for (var k = i32(localId.x); k < Length; k = k + i32(workGroupSizeX)) {
let dataA = mm_readA(batch, row, k);
let dataB = mm_readB(batch, k, col);
sum = sum + dataA * dataB;
}
sumValues[localId.x] = sum;
workgroupBarrier();
for(var currentSize = workGroupSizeX / 2u; currentSize > 1u;
currentSize = currentSize / 2u) {
if (localId.x < currentSize)
{
sumValues[localId.x] = sumValues[localId.x] + sumValues[localId.x + currentSize];
}
workgroupBarrier();
}
if (localId.x == 0u) {
sum = sumValues[0] + sumValues[1];
mm_write(batch, row, col, sum);
}
}
`}var fle=class{constructor(e,t=!1,n=!1,s=null,r=null,a=null){this.variableNames=["A","B"],this.uniforms="dimAOuter : i32; dimBOuter : i32; dimInner : i32;",this.workGroupSize=[256,1,1],this.outputShape=e,this.dispatchLayout={x:[],y:[1,2],z:[0]},this.dispatch=Oe(this.dispatchLayout,this.outputShape,this.workGroupSize);let o=s!=null,i=a!=null;o&&this.variableNames.push("bias"),i&&this.variableNames.push("preluActivationWeights"),this.transposeA=t,this.transposeB=n,this.addBias=o,this.activation=r,this.hasPreluActivationWeights=i,this.shaderKey=`matMulReduce_${this.activation}_${t}_${n}`}getUserCode(){let e;this.transposeA===!1?e="return A.numbers[batch * batchASize + row * uniforms.dimInner + col];":e="return A.numbers[batch * batchASize + col * uniforms.dimAOuter + row];";let t;this.transposeB===!1?t="return B.numbers[batch * batchBSize + row * uniforms.dimBOuter + col];":t="return B.numbers[batch * batchBSize + col * uniforms.dimInner + row];";let n="",s="";if(this.activation){let o=aa(this.activation,!1);this.hasPreluActivationWeights?n=`fn activation(a : f32, outCoord : vec3<i32>) -> f32 {
let b = getPreluActivationWeightsAtOutCoordsByCoords(outCoord);
${o}
}`:n=`
fn activation(a : f32, outCoord : vec3<i32>) -> f32 {
${o}
}
`,s="value = activation(value, outCoord);"}let r=this.addBias?"value = value + getBiasAtOutCoordsByCoords(outCoord);":"";return`
${n}
fn mm_readA(batch: i32, row : i32, col : i32) -> f32 {
let batchASize = uniforms.aShape[1] * uniforms.aShape[2];
${e}
}
fn mm_readB(batch: i32, row : i32, col : i32) -> f32 {
let batchBSize = uniforms.bShape[1] * uniforms.bShape[2];
${t}
}
fn mm_write(batch: i32, row : i32, col : i32, valueIn : f32) {
var value = valueIn;
let outCoord = vec3<i32>(batch, row, col);
${r}
${s}
setOutput(batch, row, col, value);
}
${hle()}
`}};function mle(e){let t=e[1]/2,n=e[0],s=t>n?t:n;return`
var<workgroup> mm_Asub1 : array<array<f32, ${s}>, ${t}>;
var<workgroup> mm_Bsub1 : array<array<f32, ${n}>, ${s}>;
var<workgroup> mm_Asub2 : array<array<f32, ${s}>, ${t}>;
var<workgroup> mm_Bsub2 : array<array<f32, ${n}>, ${s}>;
// If the output size is small for matrix multiplication, avoid to use vec4
// and handle some elements per thread to optimally utilize the ALU.
// Introduces two shared memory buffers, some logical threads could handle
// arithmetic operations and others handle IO operations between barrier api,
// makes ALUs and load/store units work simultaneously, could improves
// the performance.
${Fe()} {
let tileRow = i32(localId.y);
let tileCol = i32(localId.x);
let globalRow = i32(globalId.y);
let globalCol = i32(globalId.x);
// uniforms.dimInner should be greater than 0.
let numTiles = (uniforms.dimInner - 1) / ${s} + 1;
var acc = 0.0;
var globalColA = tileCol;
var globalRowB = tileRow;
for (var t = 0; t < numTiles; t = t + 1) {
if (t == 0) {
if (tileRow < ${t}) {
// Load one tile of A and B into local memory.
// globalRow is always greater than or equal tileRow.
mm_Asub1[tileRow][tileCol] =
mm_readA((globalRow - tileRow) / 2 + tileRow, globalColA, globalId);
globalColA = globalColA + ${s};
mm_Bsub1[tileRow][tileCol] = mm_readB(globalRowB, globalCol, globalId);
globalRowB = globalRowB + ${s};
}
} else {
if (tileRow < ${t}) {
// Load one tile of A and B into local memory.
// globalRow is always greater than or equal tileRow.
mm_Asub1[tileRow][tileCol] =
mm_readA((globalRow - tileRow) / 2 + tileRow, globalColA, globalId);
globalColA = globalColA + ${s};
mm_Bsub1[tileRow][tileCol] = mm_readB(globalRowB, globalCol, globalId);
globalRowB = globalRowB + ${s};
} else {
// Compute acc values for a single thread.
for (var k = 0; k < ${s}; k = k + 1) {
let subRow = tileRow - ${t};
if (subRow < 0) {
continue;
}
acc = acc + mm_Asub2[subRow][k] * mm_Bsub2[k][tileCol];
}
}
}
workgroupBarrier();
if (t != 0) {
t = t + 1;
}
if (t < numTiles) {
if (tileRow < ${t}) {
// Load one tile of A and B into local memory.
// globalRow is always greater than or equal tileRow.
mm_Asub2[tileRow][tileCol] =
mm_readA((globalRow - tileRow) / 2 + tileRow, globalColA, globalId);
globalColA = globalColA + ${s};
mm_Bsub2[tileRow][tileCol] = mm_readB(globalRowB, globalCol, globalId);
globalRowB = globalRowB + ${s};
} else {
// Compute acc values for a single thread.
for (var k = 0; k < ${s}; k = k + 1) {
let subRow = tileRow - ${t};
if (subRow < 0) {
continue;
}
acc = acc + mm_Asub1[subRow][k] * mm_Bsub1[k][tileCol];
}
}
}
workgroupBarrier();
}
let writeCol = (globalRow - tileRow) / 2 + tileRow - ${t};
if (tileRow >= ${t} && writeCol >= 0) {
mm_write(writeCol, globalCol, acc, globalId);
}
}
`}var gle=class{constructor(e,t,n,s=null,r=null,a=null){this.variableNames=["A","B"],this.uniforms="dimAOuter : i32; dimBOuter : i32; dimInner : i32;",this.workGroupSize=[8,16,1],v.assert(e[1]<=16||t[2]<=16,()=>"This program can be only used when A width or B Height are small"),this.outputShape=n,this.dispatchLayout={x:[2],y:[1],z:[0]},this.dispatch=[Math.ceil(n[2]/this.workGroupSize[0]),Math.ceil(n[1]*2/this.workGroupSize[1]),n[0]];let o=s!=null;o&&this.variableNames.push("bias");let i=a!=null;i&&this.variableNames.push("preluActivationWeights"),this.addBias=o,this.activation=r,this.hasPreluActivationWeights=i,this.shaderKey=`matMulSmallOutputSize_${this.activation}`}getUserCode(){let e=`if (coordsInBounds2D(vec2<i32>(row, col), vec2<i32>(uniforms.dimAOuter, uniforms.dimInner))) {
return A.numbers[batch * batchASize + row * uniforms.dimInner + col];
}
return 0.0;`,t=`if (coordsInBounds2D(vec2<i32>(row, col), vec2<i32>(uniforms.dimInner, uniforms.dimBOuter))) {
return B.numbers[batch * batchBSize + row * uniforms.dimBOuter + col];
}
return 0.0;`,n="",s="";if(this.activation){let o=aa(this.activation,!1);this.hasPreluActivationWeights?n=`fn activation(a : f32, outCoord : vec3<i32>) -> f32 {
let b = getPreluActivationWeightsAtOutCoordsByCoords(outCoord);
${o}
}`:n=`fn activation(a : f32, outCoord : vec3<i32>) -> f32 {
${o}
}`,s="value = activation(value, outCoord);"}let r=this.addBias?"value = value + getBiasAtOutCoordsByCoords(outCoord);":"";return`
${n}
fn mm_readA(row : i32, col : i32, globalId : vec3<u32>) -> f32 {
let batchASize = uniforms.aShape[1] * uniforms.aShape[2];
let batch = i32(globalId.z);
${e}
}
fn mm_readB(row : i32, col : i32, globalId : vec3<u32>) -> f32 {
let batch = i32(globalId.z);
let batchBSize = uniforms.bShape[1] * uniforms.bShape[2];
${t}
}
fn mm_write(row : i32, col : i32, valueIn : f32, globalId : vec3<u32>) {
if (coordsInBounds2D(vec2<i32>(row, col), vec2<i32>(uniforms.dimAOuter, uniforms.dimBOuter))) {
let batch = i32(globalId.z);
let outCoord = vec3<i32>(batch, row, col);
var value = valueIn;
${r}
${s}
setOutput(batch, row, col, value);
}
}
${mle(this.workGroupSize)}
`}};function je(e){let{inputs:t,attrs:n}=e,{x:s}=t,{shape:r}=n,a=v.sizeFromShape(s.shape),o=v.inferFromImplicitShape(r,a),i=v.sizeFromShape(o);return v.assert(a===i,()=>`The new shape (${o}) has ${i} elements and the old shape (${s.shape}) has ${a} elements. The new shape and old shape must have the same number of elements.`),e.backend.incRef(s.dataId),{dataId:s.dataId,shape:o,dtype:s.dtype}}var Ale={kernelName:Ci,backendName:"webgpu",kernelFunc:je};function ix({a:e,b:t,transposeA:n,transposeB:s,backend:r,bias:a=null,preluActivationWeights:o=null,leakyreluAlpha:i=0,activation:l=null}){let c=e.shape.length,u=t.shape.length,d=n?e.shape[c-2]:e.shape[c-1],p=s?t.shape[u-1]:t.shape[u-2],h=n?e.shape[c-1]:e.shape[c-2],f=s?t.shape[u-2]:t.shape[u-1],m=e.shape.slice(0,-2),g=t.shape.slice(0,-2),A=v.sizeFromShape(m),x=v.sizeFromShape(g),b=qi.assertAndGetBroadcastShape(e.shape.slice(0,-2),t.shape.slice(0,-2)).concat([h,f]);v.assert(d===p,()=>`Error in matMul: inner shapes (${d}) and (${p}) of Tensors with shapes ${e.shape} and ${t.shape} and transposeA=${n} and transposeB=${s} must match.`);let w=n?[A,d,h]:[A,h,d],k=s?[x,f,p]:[x,p,f],C=je({inputs:{x:e},backend:r,attrs:{shape:w}}),E=je({inputs:{x:t},backend:r,attrs:{shape:k}}),R=[C,E],F=Math.max(A,x),D=d%4==0&&f%4==0&&!n&&!s&&f>=32,P;h*f<=32?P=new fle([F,h,f],n,s,a,l,o):!n&&!s&&(h<=16&&(f<=512||p>=2*f)||f<=16&&(h<=512||d>=2*h))?P=new gle(w,k,[F,h,f],a,l,o):D?P=new dle(w,[F,h,f],K().get("WEBGPU_MATMUL_WORK_PER_THREAD"),a,l,o):P=new G4(w,[F,h,f],K().get("WEBGPU_MATMUL_WORK_PER_THREAD"),n,s,a,l,o);let T=[C,E];a&&T.push(a),o&&T.push(o);let M=[{type:"int32",data:[h]},{type:"int32",data:[f]},{type:"int32",data:[d]}],U=r.runWebGPUProgram(P,T,e.dtype,M),j=je({inputs:{x:U},backend:r,attrs:{shape:b}});R.push(U);for(let z of R)r.disposeData(z.dataId);return j}function yle(e){let{inputs:t,backend:n,attrs:s}=e,{a:r,b:a,bias:o,preluActivationWeights:i}=t,{transposeA:l,transposeB:c,activation:u,leakyreluAlpha:d}=s;return ix({a:r,b:a,transposeA:l,transposeB:c,backend:n,bias:o,preluActivationWeights:i,leakyreluAlpha:d,activation:u})}var xle={kernelName:fo,backendName:"webgpu",kernelFunc:yle},H4=class{constructor(e,t,n){this.variableNames=["AReal","AImag","BReal","BImag"],this.workGroupSize=[128,1,1],this.outputShape=N.assertAndGetBroadcastShape(t,n),this.dispatchLayout=Ke(this.outputShape),this.dispatch=Oe(this.dispatchLayout,this.outputShape,this.workGroupSize),this.shaderKey=`binaryOpComplex_${e}`,this.op=e,this.size=v.sizeFromShape(this.outputShape)}getUserCode(){return`
fn binaryOpComplex(
areal : f32, aimag : f32, breal : f32, bimag : f32) -> f32 {
${np(this.op,!1)}
}
${Fe()} {
${Le()}
if(index < uniforms.size) {
let areal = getARealAtOutCoordsByGlobalId(globalId, index);
let aimag = getAImagAtOutCoordsByGlobalId(globalId, index);
let breal = getBRealAtOutCoordsByGlobalId(globalId, index);
let bimag = getBImagAtOutCoordsByGlobalId(globalId, index);
setOutputFlat(index, binaryOpComplex(areal, aimag, breal, bimag));
}
}
`}},ble=class{constructor(e,t,n,s){this.variableNames=["A","B"];let r=256;this.workGroupSize=[r,1,1],this.outputShape=N.assertAndGetBroadcastShape(t,n),this.dispatchLayout=Ke(this.outputShape),this.lastDimensionSize=s?n[0]:t[0],this.lastDimensionSize<256?this.workPerThread=1:this.lastDimensionSize<512?this.workPerThread=2:this.workPerThread=4,this.dispatch=Oe(this.dispatchLayout,this.outputShape,this.workGroupSize,[this.workPerThread,1,1]),this.useSharedMemoryWithB=s,this.op=e,this.size=v.sizeFromShape(this.outputShape),this.sizeFit=this.size%(this.workGroupSize[0]*this.workPerThread)==0,this.shaderKey=`binaryShared_${e}_${this.lastDimensionSize}_${this.useSharedMemoryWithB}_${this.sizeFit}`}getUserCode(){let e=this.lastDimensionSize>1?`coords[${this.outputShape.length-1}]`:"0",t=this.useSharedMemoryWithB?`let a = getAAtOutCoordsByCoords(coords);
let b = sharedBuf[${e}];`:`let a = sharedBuf[${e}];
let b = getBAtOutCoordsByCoords(coords);`,n=this.sizeFit?`let coords = getCoordsFromFlatIndex(flatIndex);
${t}
setOutputFlat(flatIndex, binaryOperation(a, b));`:`if(flatIndex < uniforms.size) {
let coords = getCoordsFromFlatIndex(flatIndex);
${t}
setOutputFlat(flatIndex, binaryOperation(a, b));
}`;return`
fn binaryOperation(a : f32, b : f32) -> f32 {
${np(this.op,!1)}
}
var<workgroup> sharedBuf : array<f32, ${this.lastDimensionSize}>;
${Fe()} {
${Le()}
// Fill in the shared memory buffer. Here we need a loop to make sure
// that all data in A|B are uploaded when |sharedMemorySize| is larger
// than work group size.
for(var localIndex = i32(localId.x); localIndex < ${this.lastDimensionSize}; localIndex = localIndex + ${this.workGroupSize[0]}) {
sharedBuf[localIndex] = f32(${this.useSharedMemoryWithB?"B":"A"}.numbers[localIndex]);
}
workgroupBarrier();
for(var i = 0; i < ${this.workPerThread}; i = i + 1) {
let flatIndex = index * ${this.workPerThread} + i;
${n}
}
}
`}},vle=class{constructor(e,t,n){this.variableNames=["A","B"],this.workPerThread=4,this.isVec4=!0;let s=128;this.workGroupSize=[s,1,1],this.outputShape=N.assertAndGetBroadcastShape(t,n),this.dispatchLayout=Ke(this.outputShape),this.dispatch=Oe(this.dispatchLayout,this.outputShape,this.workGroupSize,[this.workPerThread,1,1]),this.op=e,this.fitShape=this.size%this.workGroupSize[0]==0,this.shaderKey=`binaryVec4_${e}_${this.fitShape}`,this.size=v.sizeFromShape(this.outputShape)/this.workPerThread}getUserCode(){let e,n=`fn binaryOperation(a : vec4<f32>, b : vec4<f32>) -> vec4<f32> {
${np(this.op,this.isVec4)}
}`;return this.fitShape?e=`
${n}
${Fe()} {
${Le()}
let a = vec4<f32>(A.numbers[index]);
let b = vec4<f32>(B.numbers[index]);
setOutputFlat(index, binaryOperation(a, b));
}
`:e=`
${n}
${Fe()} {
${Le()}
if (index < uniforms.size) {
let a = vec4<f32>(A.numbers[index]);
let b = vec4<f32>(B.numbers[index]);
setOutputFlat(index, binaryOperation(a, b));
}
}
`,e}},j4=class{constructor(e,t,n){this.variableNames=["A","B"];let s=128;this.workGroupSize=[s,1,1],this.outputShape=N.assertAndGetBroadcastShape(t,n),this.dispatchLayout=Ke(this.outputShape),this.size=v.sizeFromShape(this.outputShape),this.sizeFit=this.size%s==0,this.shapesFit=v.arraysEqual(t,n)&&this.sizeFit,this.workPerThread=this.sizeFit||this.shapesFit?1:2,this.dispatch=Oe(this.dispatchLayout,this.outputShape,this.workGroupSize,[this.workPerThread,1,1]),this.shaderKey=`binary_${e}_${this.sizeFit}_${this.shapesFit}`,this.op=e}getUserCode(){let e,n=` fn binaryOperation(a : f32, b : f32) -> f32 {
${np(this.op,!1)}
}`;return this.shapesFit?e=`
${n}
${Fe()} {
${Le()}
let a = f32(A[index]);
let b = f32(B[index]);
setOutputFlat(index, binaryOperation(a, b));
}
`:this.sizeFit?e=`
${n}
${Fe()} {
${Le()}
let coords = getCoordsFromFlatIndex(index);
let a = getAAtOutCoordsByCoords(coords);
let b = getBAtOutCoordsByCoords(coords);
setOutputFlat(index, binaryOperation(a, b));
}
`:e=`
${n}
${Fe()} {
${Le()}
for (var i = 0; i < ${this.workPerThread}; i = i + 1 ) {
let flatIndex = index * ${this.workPerThread} + i;
if(flatIndex < uniforms.size) {
let coords = getCoordsFromFlatIndex(flatIndex);
let a = getAAtOutCoordsByCoords(coords);
let b = getBAtOutCoordsByCoords(coords);
setOutputFlat(flatIndex, binaryOperation(a, b));
}
}
}
`,e}};function q4(e,t,n){if(v.arraysEqual(t,n)&&v.sizeFromShape(t)%4==0)return new vle(e,t,n);let r=t.length===1&&n.length>1&&t[0]<1024,a=n.length===1&&t.length>1&&n[0]<1024;return r||a?new ble(e,t,n,a):new j4(e,t,n)}function Js(e){let{inputs:t}=e,{x:n}=t;return e.backend.incRef(n.dataId),{dataId:n.dataId,shape:n.shape,dtype:n.dtype}}var wle={kernelName:Ba,backendName:"webgpu",kernelFunc:Js};function nc(e){let{inputs:t,backend:n}=e,{real:s,imag:r}=t,a=n.makeTensorInfo(s.shape,"complex64"),o=n.tensorMap.get(a.dataId),i=Js({inputs:{x:s},backend:n}),l=Js({inputs:{x:r},backend:n});return o.complexTensorInfos={real:i,imag:l},a}var kle={kernelName:zc,backendName:"webgpu",kernelFunc:nc},Fm=class{constructor(e,t){this.variableNames=["A"];let n=128;this.workGroupSize=[n,1,1],this.outputShape=e,this.size=v.sizeFromShape(this.outputShape),this.dispatchLayout=Ke(this.outputShape),this.dispatch=Oe(this.dispatchLayout,this.outputShape,this.workGroupSize),this.op=t,this.shaderKey=`unary_${t}`}getUserCode(){return`
fn unaryOperation(a : f32) -> f32 {
${tc(this.op,!1)}
}
${Fe()} {
${Le()}
if (index < uniforms.size) {
let a = getAAtOutCoordsByGlobalId(globalId, index);
setOutputFlat(index, unaryOperation(a));
}
}
`}};function Cn({opType:e,cpuKernelImpl:t,dtype:n}){return({inputs:s,backend:r})=>{let{x:a}=s,o=r,i=n||a.dtype;if(o.shouldExecuteOnCPU([a])&&t!=null){let c=o.tensorMap.get(a.dataId),u=t(c.values,i);return o.makeTensorInfo(a.shape,i,u)}let l=new Fm(a.shape,e);return o.runWebGPUProgram(l,[a],i)}}function jn({opSnippet:e,cpuKernelImpl:t,supportsComplex:n=!1,dtype:s}){return({inputs:r,backend:a})=>{let{a:o,b:i}=r,l=a;if(n&&o.dtype==="complex64"){let d=l.tensorMap.get(o.dataId),p=l.tensorMap.get(i.dataId),h,f;if(e!==Vt.MUL)[h,f]=[[d.complexTensorInfos.real,p.complexTensorInfos.real],[d.complexTensorInfos.imag,p.complexTensorInfos.imag]].map(g=>{let[A,x]=g,y={dataId:A.dataId,dtype:A.dtype,shape:o.shape},b={dataId:x.dataId,dtype:x.dtype,shape:i.shape},w=q4(e,o.shape,i.shape);return l.runWebGPUProgram(w,[y,b],Ln(A.dtype,x.dtype))});else{let g=new H4(Vt.COMPLEX_MULTIPLY_REAL,o.shape,i.shape),A=new H4(Vt.COMPLEX_MULTIPLY_IMAG,o.shape,i.shape),x=[{dataId:d.complexTensorInfos.real.dataId,dtype:d.complexTensorInfos.real.dtype,shape:o.shape},{dataId:d.complexTensorInfos.imag.dataId,dtype:d.complexTensorInfos.imag.dtype,shape:o.shape},{dataId:p.complexTensorInfos.real.dataId,dtype:p.complexTensorInfos.real.dtype,shape:i.shape},{dataId:p.complexTensorInfos.imag.dataId,dtype:p.complexTensorInfos.imag.dtype,shape:i.shape}];h=l.runWebGPUProgram(g,x,"float32"),f=l.runWebGPUProgram(A,x,"float32")}let m=nc({inputs:{real:h,imag:f},backend:l});return l.disposeData(h.dataId),l.disposeData(f.dataId),m}let c=s||Ln(o.dtype,i.dtype);if((o.dtype==="string"||i.dtype==="string"||l.shouldExecuteOnCPU([o,i]))&&t!=null){let d=l.tensorMap.get(o.dataId).values,p=l.tensorMap.get(i.dataId).values,h=o.dtype==="string"?N.fromUint8ToStringArray(d):d,f=o.dtype==="string"?N.fromUint8ToStringArray(p):p,[m,g]=t(o.shape,i.shape,h,f,c);return l.makeTensorInfo(g,c,m)}let u=q4(e,o.shape,i.shape);return l.runWebGPUProgram(u,[o,i],c)}}var{addImpl:Sle,ceilImpl:Ile,concatImpl:Cle,equalImpl:Tle,expImpl:Nle,expm1Impl:Ele,floorImpl:Rle,gatherNdImpl:$le,gatherV2Impl:Dle,greaterEqualImpl:_le,greaterImpl:Ple,lessEqualImpl:Fle,lessImpl:Ole,logImpl:Mle,maxImpl:zle,maximumImpl:Lle,minimumImpl:Ble,multiplyImpl:Wle,negImpl:Vle,notEqualImpl:Ule,prodImpl:Gle,rangeImpl:Hle,rsqrtImpl:jle,simpleAbsImpl:qle,sliceImpl:Xle,stridedSliceImpl:Kle,stringNGramsImpl:Zle,subImpl:Yle,tileImpl:Jle,topKImpl:Qle,transposeImpl:eue,uniqueImpl:i2e}=im,tue=Cn({opType:vt.ABS,cpuKernelImpl:qle}),nue={kernelName:ti,backendName:"webgpu",kernelFunc:tue},sue=jn({opSnippet:Vt.ADD,cpuKernelImpl:Sle,supportsComplex:!0}),rue={kernelName:Vr,backendName:"webgpu",kernelFunc:sue},aue=class{constructor(e){this.workPerThread=4,this.workGroupSize=[64,1,1],this.outputShape=e[0],this.variableNames=e.map((t,n)=>`T${n}`),this.dispatchLayout=Ke(this.outputShape),this.dispatch=Oe(this.dispatchLayout,this.outputShape,this.workGroupSize,[this.workPerThread,1,1]),this.shaderKey="addN",this.size=v.sizeFromShape(this.outputShape)}getUserCode(){let e=[];this.variableNames.forEach(s=>{e.push(`let v${s} = get${s}AtOutCoordsByCoords(coords);`)});let t=this.variableNames.map(s=>`v${s}`).join(" + ");return`
${Fe()} {
${Le()}
for (var i = 0; i < ${this.workPerThread}; i = i + 1) {
let flatIndex = index * ${this.workPerThread} + i;
if (flatIndex < uniforms.size) {
let coords = getCoordsFromFlatIndex(flatIndex);
${e.join(`
`)}
setOutputFlat(flatIndex, ${t});
}
}
}
`}};function oue(e){let{inputs:t,backend:n}=e,s=t;if(s.length===1)return Js({inputs:{x:s[0]},backend:n});let r=s.map(i=>i.dtype).reduce((i,l)=>Ln(i,l)),a=s.map(i=>i.shape),o=new aue(a);return n.runWebGPUProgram(o,s,r)}var iue={kernelName:wa,backendName:"webgpu",kernelFunc:oue},X4=class{constructor(e,t,n){this.variableNames=["x"],this.uniforms="axis : i32;";let s=[t];N.assertAxesAreInnerMostDims("arg"+n.charAt(0).toUpperCase()+n.slice(1),s,e.length),this.op=n==="min"?"<":">";let[r,a]=N.computeOutAndReduceShapes(e,s);this.outputShape=r.length===0?[1]:r;let o=v.sizeFromShape(a);this.reductionFactor=2;let i=256,l=Math.min(Math.ceil(o/this.reductionFactor),i);this.workGroupSize=[l,1,1],this.dispatchLayout={x:[],y:this.outputShape.map((c,u)=>u)},this.dispatch=Oe(this.dispatchLayout,this.outputShape,this.workGroupSize),this.inputShape=e,this.shaderKey=`argMinMax${this.op}`}getUserCode(){let e=this.workGroupSize[0]>1,t=`
var<workgroup> xBestIndices : array<i32, ${this.workGroupSize[0]}>;
var<workgroup> xBestValues : array<f32, ${this.workGroupSize[0]}>;
`,n=`
xBestIndices[localId.x] = bestIndex;
xBestValues[localId.x] = bestValue;
for(var currentSize = WorkGroupSize; currentSize > 1; currentSize = DIV_CEIL(currentSize, ${this.reductionFactor})) {
workgroupBarrier();
for (var w = 0; w < ${this.reductionFactor}; w = w + 1) {
let i = i32(localId.x) * ${this.reductionFactor} + w;
if (i < currentSize) {
let candidateIndex = xBestIndices[i];
let candidate = xBestValues[i];
if(candidate ${this.op} bestValue && !isNanCustom(candidate)) {
bestValue = candidate;
bestIndex = candidateIndex;
}
}
}
xBestIndices[localId.x] = bestIndex;
xBestValues[localId.x] = bestValue;
}
if (localId.x == 0u) {
setOutputFlatI32(flatOutputIndex, i32(bestIndex));
}
`,s=Jt(this.outputShape.length),r=(i,l)=>this.outputShape.length===1?i:`${i}[${l}]`,a=i=>this.inputShape.length===1?"uniforms.xShape":`uniforms.xShape[${i}]`;return`
fn DIV_CEIL(a : i32, b : i32) -> i32 {
return ((a - 1) / b + 1);
}
let WorkGroupSize = ${this.workGroupSize[0]};
${e?t:""}
// In order to get a flattened index into the input tensor, we need to
// add back the index along the reduced dimension to |outputCoords|.
// This function outputs the offset to the first value along
// |axis| and the stride to get the next value of the input along |axis|.
fn getInputCoordInfo(globalId : vec3<u32>, globalIndex : i32) -> vec2<i32>{
let outputCoords : ${s} = getOutputCoords(globalId, globalIndex);
var i = ${this.outputShape.length-1};
var stride = 1;
var inputStride = 1;
var offset = 0;
for (var r = 1; r <= ${this.inputShape.length}; r = r + 1) {
let length = ${a(`${this.inputShape.length} - r`)};
if (${this.inputShape.length} - r == uniforms.axis) {
inputStride = stride;
} else {
offset = offset + ${r("outputCoords","i")} * stride;
i = i - 1;
}
stride = stride * length;
}
return vec2<i32>(offset, inputStride);
}
fn getInputIndex(coordInfo : vec2<i32>, index : i32) -> i32{
return coordInfo[0] + coordInfo[1] * index;
}
${Fe()} {
${Le()}
let coordInfo = getInputCoordInfo(globalId, index);
var bestIndex = 0;
var bestValue = f32(x.numbers[getInputIndex(coordInfo, bestIndex)]);
let Length = ${a("uniforms.axis")};
let WorkPerThread = DIV_CEIL(Length, WorkGroupSize);
for (var w = 0; w < WorkPerThread; w = w + 1) {
let i = i32(globalId.x) * WorkPerThread + w;
if (i < Length) {
let candidate = f32(x.numbers[getInputIndex(coordInfo, i)]);
if (candidate ${this.op} bestValue && !isNanCustom(f32(candidate))) {
bestValue = candidate;
bestIndex = i;
}
}
}
let flatOutputIndex = i32(globalId.y);
${e?n:"setOutputFlatI32(flatOutputIndex, bestIndex);"}
}
`}},lue=class{constructor(e,t){this.variableNames=["A"],this.workGroupSize=[16,16,1];let n=new Array(e.length);for(let s=0;s<n.length;s++)n[s]=e[t[s]];this.outputShape=n,this.dispatchLayout={x:[0],y:[1]},this.dispatch=Oe(this.dispatchLayout,this.outputShape,this.workGroupSize,[1,1,1]),this.shaderKey="transposeShared"}getUserCode(){return`
let TILE_DIM = ${this.workGroupSize[0]};
var<workgroup> tile : array<array<f32, ${this.workGroupSize[0]+1}>, ${this.workGroupSize[0]}>;
${Fe()} {
${Le()}
let workGroupID = (globalId - localId)/vec3<u32>(${this.workGroupSize[0]}u, ${this.workGroupSize[1]}u, ${this.workGroupSize[2]}u);
var x = i32(workGroupID.x) * TILE_DIM + i32(localId.x);
var y = i32(workGroupID.y) * TILE_DIM + i32(localId.y);
let width = uniforms.outShape[0];
let height = uniforms.outShape[1];
if (x < width && y < height) {
tile[localId.y][localId.x] =
A.numbers[y * width + x];
}
workgroupBarrier();
x = i32(workGroupID.y) * TILE_DIM + i32(localId.x);
y = i32(workGroupID.x) * TILE_DIM + i32(localId.y);
if (x < height && y < width) {
setOutputFlat((y * height + x), tile[localId.x]
[localId.y]);
}
}
`}},uue=class{constructor(e,t){this.variableNames=["A"],this.workPerThread=4,this.workGroupSize=[64,1,1];let n=new Array(e.length);for(let s=0;s<n.length;s++)n[s]=e[t[s]];this.outputShape=n,this.dispatchLayout=Ke(this.outputShape),this.dispatch=Oe(this.dispatchLayout,this.outputShape,this.workGroupSize,[this.workPerThread,1,1]),this.newDim=t,this.shaderKey=`transpose_${t}`,this.size=v.sizeFromShape(this.outputShape)}getUserCode(){let e=Jt(this.outputShape.length),t=cue(this.newDim);return`
${Fe()} {
${Le()}
for(var i = 0; i < ${this.workPerThread}; i = i + 1) {
let flatIndex = index * ${this.workPerThread} + i;
if(flatIndex < uniforms.size) {
let resRC = getCoordsFromFlatIndex(flatIndex);
setOutputFlat(flatIndex, A.numbers[getFlatIndex${this.outputShape.length}D(
${e}(${t}), uniforms.aShape)]);
}
}
}
`}};function cue(e){let t=e.length;if(t>4)throw Error(`Transpose for rank ${t} is not yet supported`);let n=new Array(t);for(let s=0;s<e.length;s++)n[e[s]]=`resRC[${s}]`;return n.join()}function vl(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{perm:a}=s,o=n,i=r.shape.length,l=new Array(i);for(let u=0;u<l.length;u++)l[u]=r.shape[a[u]];if(n.shouldExecuteOnCPU([r])){let d=o.tensorMap.get(r.dataId).values,p=eue(d,r.shape,r.dtype,a,l);return n.makeTensorInfo(l,r.dtype,p)}if(r.shape.length===2&&v.arraysEqual(a,[1,0])){let u=new lue(r.shape,a);return o.runWebGPUProgram(u,[r],r.dtype)}let c=new uue(r.shape,a);return o.runWebGPUProgram(c,[r],r.dtype)}var due={kernelName:po,backendName:"webgpu",kernelFunc:vl};function pue(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{axis:a}=s,o=v.parseAxisParam(a,r.shape),i=N.getAxesPermutation(o,r.shape.length),l=r,c=[];i!=null&&(l=vl({inputs:{x:r},backend:n,attrs:{perm:i}}),c.push(l),o=N.getInnerMostAxes(o.length,l.shape.length)),N.assertAxesAreInnerMostDims("argMax",[o[0]],l.shape.length);let u=new X4(l.shape,o[0],"max"),d=[{type:"int32",data:[o[0]]}],p=n.runWebGPUProgram(u,[l],"int32",d);return c.forEach(h=>n.disposeData(h.dataId)),p}var hue={kernelName:ka,backendName:"webgpu",kernelFunc:pue};function fue(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{axis:a}=s,o=v.parseAxisParam(a,r.shape),i=N.getAxesPermutation(o,r.shape.length),l=r,c=[];i!=null&&(l=vl({inputs:{x:r},backend:n,attrs:{perm:i}}),c.push(l),o=N.getInnerMostAxes(o.length,l.shape.length)),N.assertAxesAreInnerMostDims("argMin",[o[0]],l.shape.length);let u=new X4(l.shape,o[0],"min"),d=[{type:"int32",data:[o[0]]}],p=n.runWebGPUProgram(u,[l],"int32",d);return c.forEach(h=>n.disposeData(h.dataId)),p}var mue={kernelName:jl,backendName:"webgpu",kernelFunc:fue},K4=class{constructor(e,t){this.variableNames=["x"],this.uniforms="stride : vec2<i32>; pad : vec2<i32>; dilation : vec2<i32>; convDims : vec2<i32>; filterDims : vec2<i32>;",this.workGroupSize=[128,1,1],this.outputShape=e.outShape,this.dispatchLayout=Ke(this.outputShape),this.dispatch=Oe(this.dispatchLayout,this.outputShape,this.workGroupSize),this.shaderKey=`pool2D_${t}`,this.poolType=t}getUserCode(){let e="resultValue = max(value, resultValue);";this.poolType==="avg"&&(e="resultValue = resultValue + value; count = count + 1.0;");let t="resultValue";return this.poolType==="avg"&&(t="resultValue / count"),`
${Fe()} {
${Le()}
let coords = getOutputCoords(globalId, index);
if (coordsInBounds4D(coords, uniforms.outShape)) {
let batch = coords[0];
let xRCCorner = vec2<i32>(coords.yz) * uniforms.stride - uniforms.pad;
let xRCorner = xRCCorner.x;
let xCCorner = xRCCorner.y;
var resultValue = ${this.poolType==="avg"?"0.0":"-1.0 / pow(10.0, -20.0)"};
var count = 0.0;
for (var wR = 0; wR < uniforms.filterDims.x; wR = wR + uniforms.dilation.x) {
let xR = xRCorner + wR;
if (xR < 0 || xR >= uniforms.convDims.x) {
continue;
}
for (var wC = 0; wC < uniforms.filterDims.y; wC = wC + uniforms.dilation.y) {
let xC = xCCorner + wC;
if (xC < 0 || xC >= uniforms.convDims.y) {
continue;
}
let value = getX(batch, xR, xC, coords[3]);
${e}
}
}
setOutput(batch, coords[1], coords[2], coords[3], ${t});
}
}
`}},Z4=class{constructor(e){this.variableNames=["x"],this.uniforms="stride : vec2<i32>;",this.workGroupSize=[256,1,1],this.outputShape=e.outShape,this.dispatchLayout=Ke(this.outputShape),this.dispatch=Oe(this.dispatchLayout,this.outputShape,this.workGroupSize),this.shaderKey="poolWithFilterSizeEqualsOne"}getUserCode(){return`
${Fe()} {
${Le()}
let coords = getOutputCoords(globalId, index);
let batch = coords[0];
let d = coords[3];
if (all(coords < uniforms.outShape)) {
let xRCCorner = coords.yz * uniforms.stride;
let xRCorner = xRCCorner.x;
let xCCorner = xRCCorner.y;
let value = getX(batch, xRCorner, xCCorner, d);
setOutput(batch, coords[1], coords[2], d, value);
}
}
`}};function gue(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{filterSize:a,strides:o,pad:i,dimRoundingMode:l}=s,c=1,u=N.computePool2DInfo(r.shape,a,o,c,i,l);if(u.filterWidth===1&&u.filterHeight===1&&v.arraysEqual(u.inShape,u.outShape))return Js({inputs:{x:r},backend:n});let d,p=[{type:"int32",data:[u.strideHeight,u.strideWidth]}];return u.filterHeight===1&&u.filterWidth===1?d=new Z4(u):(d=new K4(u,"avg"),p.push({type:"int32",data:[u.padInfo.top,u.padInfo.left]},{type:"int32",data:[u.dilationHeight,u.dilationWidth]},{type:"int32",data:[u.inHeight,u.inWidth]},{type:"int32",data:[u.effectiveFilterHeight,u.effectiveFilterWidth]})),n.runWebGPUProgram(d,[r],r.dtype,p)}var Aue={kernelName:Sa,backendName:"webgpu",kernelFunc:gue};function yue(e){let{inputs:t,backend:n,attrs:s}=e,{a:r,b:a}=t,{transposeA:o,transposeB:i}=s;return ix({a:r,b:a,transposeA:o,transposeB:i,backend:n})}var xue={kernelName:Ia,backendName:"webgpu",kernelFunc:yue},bue=class{constructor(e,t){this.variableNames=["source"],this.workPerThread=1,this.workGroupSize=[64,1,1],this.outputShape=t,this.rank=t.length,this.dispatchLayout=Ke(this.outputShape),this.dispatch=Oe(this.dispatchLayout,this.outputShape,this.workGroupSize,[this.workPerThread,1,1]),this.start=e,this.uniforms=`start : ${Jt(e.length)}; `,this.shaderKey="slice",this.size=v.sizeFromShape(this.outputShape)}getUserCode(){let e=Jt(this.rank),t=vue(this.rank),n;return this.start.length===1?n=this.outputShape.map((r,a)=>"sourceLoc = uniforms.start + coords;"):n=this.outputShape.map((r,a)=>`sourceLoc.${lx[a]} = uniforms.start[${a}] + coords.${lx[a]};`),`
${Fe()} {
${Le()}
if (index < uniforms.size) {
var sourceLoc : ${e};
let coords = getOutputCoords(globalId, index);
${n.join(`
`)}
setOutputFlat(index, getSource(${t}));
}
}
`}},lx=["x","y","z","w","u","v"];function vue(e){if(e===1)return"sourceLoc";if(e<=6)return lx.slice(0,e).map(t=>`sourceLoc.${t}`).join(",");throw Error(`Slicing for rank ${e} is not yet supported`)}function sc(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{begin:a,size:o}=s,[i,l]=Ft.parseSliceParams(r,a,o);if(Ft.assertParamsValid(r,i,l),n.shouldExecuteOnCPU([r])||r.dtype==="string"){let d=n.tensorMap.get(r.dataId),p=Xle(d.values,i,l,r.shape,r.dtype);return n.makeTensorInfo(l,r.dtype,p)}if(v.sizeFromShape(l)===0)return n.makeTensorInfo(l,r.dtype,[]);let c=new bue(i,l),u=[{type:"int32",data:i}];return n.runWebGPUProgram(c,[r],r.dtype,u)}var wue={kernelName:$i,backendName:"webgpu",kernelFunc:sc},kue=e=>{let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{blockShape:a,crops:o}=s;v.assert(r.shape.length<=4,()=>"batchToSpaceND for rank > 4 with a WebGPU backend not implemented yet");let i=a.reduce((x,y)=>x*y),l=N.getReshaped(r.shape,a,i),c=N.getPermuted(l.length,a.length),u=N.getReshapedPermuted(r.shape,a,i),d=N.getSliceBeginCoords(o,a.length),p=N.getSliceSize(u,o,a.length),h=[],f=je({inputs:{x:r},backend:n,attrs:{shape:l}}),m=vl({inputs:{x:f},backend:n,attrs:{perm:c}}),g=je({inputs:{x:m},backend:n,attrs:{shape:u}}),A=sc({inputs:{x:g},backend:n,attrs:{begin:d,size:p}});return h.push(f),h.push(m),h.push(g),h.forEach(x=>n.disposeData(x.dataId)),A},Sue={kernelName:ni,backendName:"webgpu",kernelFunc:kue},Y4=jn({opSnippet:Vt.NOT_EQUAL,dtype:"bool",cpuKernelImpl:Ule}),Iue={kernelName:xi,backendName:"webgpu",kernelFunc:Y4};function sp(e){let{inputs:t,backend:n}=e,{input:s}=t,r=n.tensorMap.get(s.dataId);return Js({inputs:{x:r.complexTensorInfos.real},backend:n})}var Cue={kernelName:qc,backendName:"webgpu",kernelFunc:sp};function Tue(e,t){let n=new Fm(e.shape,vt.TO_INT),s=t.runWebGPUProgram(n,[e],"int32");return{dataId:s.dataId,shape:s.shape,dtype:s.dtype}}function ux(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{dtype:a}=s;if(a==="complex64"){if(r.dtype==="complex64")return Js({inputs:{x:r},backend:n});let o=Ht(r.shape),i=ux({inputs:{x:r},backend:n,attrs:{dtype:"float32"}}),l=nc({inputs:{real:i,imag:o},backend:n});return o.dispose(),n.disposeData(i.dataId),l}if(r.dtype==="complex64"){let o=sp({inputs:{input:r},backend:n}),i=ux({inputs:{x:o},backend:n,attrs:{dtype:a}});return n.disposeData(o.dataId),i}if(!v.hasEncodingLoss(r.dtype,a)){let o=Js({inputs:{x:r},backend:n});return{dataId:o.dataId,shape:o.shape,dtype:a}}if(a==="int32")return Tue(r,n);if(a==="bool"){let o=n.makeTensorInfo([],"bool",v.getTypedArrayFromDType("bool",1)),l=Y4({inputs:{a:r,b:o},backend:n});return n.disposeData(o.dataId),l}throw new Error(`Error in Cast: failed to cast ${r.dtype} to ${a}`)}var Nue={kernelName:Ca,backendName:"webgpu",kernelFunc:ux},Eue=Cn({opType:vt.CEIL,cpuKernelImpl:Ile}),Rue={kernelName:Ta,backendName:"webgpu",kernelFunc:Eue},$ue=class{constructor(e){this.variableNames=["A"],this.uniforms="minVal : f32; maxVal : f32;",this.workPerThread=4,this.workGroupSize=[64,1,1],this.isVec4=!0,this.outputShape=e,this.dispatchLayout=Ke(this.outputShape),this.dispatch=Oe(this.dispatchLayout,this.outputShape,this.workGroupSize,[this.workPerThread,1,1]),this.shaderKey="clipVec4",this.size=v.sizeFromShape(this.outputShape)/4}getUserCode(){return`
${Fe()} {
${Le()}
if(index < uniforms.size) {
let value = getAAtOutCoordsByGlobalId(globalId, index);
var clampedValue : vec4<f32>;
for (var i = 0; i < 4; i = i + 1) {
if (isNanCustom(value[i])) {
clampedValue[i] = value[i];
} else {
clampedValue[i] = clamp(value[i], uniforms.minVal, uniforms.maxVal);
}
}
setOutputFlat(index, clampedValue);
}
}
`}},Due=class{constructor(e){this.variableNames=["A"],this.uniforms="minVal : f32; maxVal : f32;",this.workGroupSize=[64,1,1],this.outputShape=e,this.dispatchLayout=Ke(this.outputShape),this.dispatch=Oe(this.dispatchLayout,this.outputShape,this.workGroupSize),this.shaderKey="clip",this.size=v.sizeFromShape(this.outputShape)}getUserCode(){return`
${Fe()} {
${Le()}
if(index < uniforms.size) {
let value = getAAtOutCoordsByGlobalId(globalId, index);
if (isNanCustom(value)) {
setOutputFlat(index, value);
return;
}
setOutputFlat(index, clamp(value, uniforms.minVal, uniforms.maxVal));
}
}
`}};function _ue(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{clipValueMin:a,clipValueMax:o}=s,i,l=[{type:"float32",data:[a]},{type:"float32",data:[o]}];return v.sizeFromShape(r.shape)%4==0?i=new $ue(r.shape):i=new Due(r.shape),n.runWebGPUProgram(i,[r],r.dtype,l)}var Pue={kernelName:Ur,backendName:"webgpu",kernelFunc:_ue},Fue=class{constructor(e){this.workPerThread=4,this.workGroupSize=[64,1,1],this.outputShape=N.computeOutShape(e,1),this.variableNames=e.map((t,n)=>`T${n}`),this.dispatchLayout=Ke(this.outputShape),this.dispatch=Oe(this.dispatchLayout,this.outputShape,this.workGroupSize,[this.workPerThread,1,1]),this.shapes=e,this.shaderKey=`concat${e}`,this.size=v.sizeFromShape(this.outputShape)}getUserCode(){let e=new Array(this.shapes.length-1),t=[];if(e.length>0){e[0]=this.shapes[0][1];for(let a=1;a<e.length;a++)e[a]=e[a-1]+this.shapes[a][1];t.push(`if (yC < ${e[0]}){ setOutput(coords.x, coords.y, getT0(yR, yC)); }`);for(let a=1;a<e.length;a++){let o=e[a-1];t.push(`elseif (yC < ${e[a]}){ setOutput(coords.x, coords.y, getT${a}(yR, yC - ${o})); }`)}let s=e.length,r=e[e.length-1];t.push(`else { setOutput(coords.x, coords.y, getT${s}(yR, yC - ${r})); }`)}else t.push("setOutput(coords.x, coords.y, getT0(yR, yC));");return`
${Fe()} {
${Le()}
for(var i = 0; i < ${this.workPerThread}; i = i + 1) {
let flatIndex = index * ${this.workPerThread} + i;
if(flatIndex < uniforms.size) {
let coords = getCoordsFromFlatIndex(flatIndex);
let yR = coords.x;
let yC = coords.y;
${t.join(`
`)}
}
}
}
`}};function Om(e){let{inputs:t,backend:n}=e,{input:s}=t,r=n.tensorMap.get(s.dataId);return Js({inputs:{x:r.complexTensorInfos.imag},backend:n})}var Oue={kernelName:Uc,backendName:"webgpu",kernelFunc:Om};function cx(e,t,n){let s=e[0].dtype;if(s==="complex64"){let u=e.map(m=>sp({inputs:{input:m},backend:n})),d=e.map(m=>Om({inputs:{input:m},backend:n})),p=cx(u,t,n),h=cx(d,t,n),f=nc({inputs:{real:p,imag:h},backend:n});return u.forEach(m=>n.disposeData(m.dataId)),d.forEach(m=>n.disposeData(m.dataId)),n.disposeData(p.dataId),n.disposeData(h.dataId),f}let r=n.shouldExecuteOnCPU(e);if(s==="string"&&(r=!0),r){let u=e.map(A=>{let x=v.sizeFromShape(A.shape.slice(t));return je({inputs:{x:A},backend:n,attrs:{shape:[-1,x]}})}),d=u.map(A=>({vals:n.readSync(A.dataId),shape:A.shape})),p=N.computeOutShape(u.map(A=>A.shape),1),h=u[0].shape[0]===1,f=Cle(d,p,s,h),m=N.computeOutShape(e.map(A=>A.shape),t),g=n.makeTensorInfo(m,s,f);return u.forEach(A=>n.disposeData(A.dataId)),g}let{tensors2D:a,outShape:o}=Mue(e,t,n),i=new Fue(a.map(u=>u.shape)),l=n.runWebGPUProgram(i,a,a[0].dtype);a.forEach(u=>n.disposeData(u.dataId));let c=je({inputs:{x:l},backend:n,attrs:{shape:o}});return n.disposeData(l.dataId),c}function Mue(e,t,n){let s=N.computeOutShape(e.map(a=>a.shape),t);return{tensors2D:e.map(a=>je({inputs:{x:a},backend:n,attrs:{shape:[v.sizeFromShape(a.shape.slice(0,t)),v.sizeFromShape(a.shape.slice(t))]}})),outShape:s}}function J4(e){let{inputs:t,backend:n,attrs:s}=e,{axis:r}=s,a=v.parseAxisParam(r,t[0].shape)[0],o=N.computeOutShape(t.map(c=>c.shape),a);if(v.sizeFromShape(o)===0)return n.makeTensorInfo(o,t[0].dtype,[]);let i=t.filter(c=>v.sizeFromShape(c.shape)>0);if(i.length===1)return Js({inputs:{x:i[0]},backend:n});let l=i.map(c=>c.shape);return N.assertParamsConsistent(l,a),cx(i,a,n)}var zue={kernelName:si,backendName:"webgpu",kernelFunc:J4},Lue=class{constructor(e,t){this.variableNames=["A"],this.uniforms=`pad : vec2<i32>; stride : vec2<i32>; dilation : vec2<i32>; outWidth : i32; itemsPerBlockRow : i32;
inChannels : i32;`,this.workPerThread=4,this.workGroupSize=[64,1,1],this.outputShape=e,this.dispatchLayout=Ke(this.outputShape),this.dispatch=Oe(this.dispatchLayout,this.outputShape,this.workGroupSize,[this.workPerThread,1,1]),this.isChannelsLast=t,this.shaderKey=`im2col_${this.isChannelsLast}`,this.size=v.sizeFromShape(this.outputShape)}getUserCode(){let e=this.isChannelsLast?0:1,t=this.isChannelsLast?1:2;return`
${Fe()} {
${Le()}
for(var i = 0; i<${this.workPerThread}; i = i + 1) {
let flatIndex = index * ${this.workPerThread} + i;
let rc = getCoordsFromFlatIndex(flatIndex);
if(flatIndex < uniforms.size) {
let blockIndex = rc[0];
let pos = rc[1];
let offsetY = blockIndex / uniforms.outWidth * uniforms.stride[1] - uniforms.pad[1];
let d0 = offsetY + uniforms.dilation[1] * pos / uniforms.itemsPerBlockRow;
var value = 0.0;
if(d0 < uniforms.aShape[${e}] && d0 >= 0) {
let offsetX = (blockIndex % uniforms.outWidth) * uniforms.stride[0] -
uniforms.pad[0];
let d1 = offsetX + uniforms.dilation[0] * ((pos %
uniforms.itemsPerBlockRow) / uniforms.inChannels);
let ch = pos % uniforms.inChannels;
if(d1 < uniforms.aShape[${t}] && d1 >= 0) {
value = getA(d0, d1, ch);
}
}
setOutputFlat(flatIndex, value);
}
}
}
`}};function Q4({x:e,filter:t,convInfo:n,backend:s,bias:r=null,preluActivationWeights:a=null,leakyreluAlpha:o=0,activation:i=null}){let l=e.shape,c=n.dataFormat==="channelsLast",u=!1,d=!1,p=c?l[0]*l[1]*l[2]:l[0]*l[2]*l[3],h=je({inputs:{x:e},backend:s,attrs:{shape:[1,p,n.inChannels]}}),f=je({inputs:{x:t},backend:s,attrs:{shape:[1,n.inChannels,n.outChannels]}}),m=ix({a:h,b:f,transposeA:u,transposeB:d,backend:s,bias:r,activation:i,preluActivationWeights:a,leakyreluAlpha:o}),g=je({inputs:{x:m},backend:s,attrs:{shape:n.outShape}});return s.disposeData(h.dataId),s.disposeData(f.dataId),s.disposeData(m.dataId),g}function Bue({x:e,filter:t,convInfo:n,backend:s,bias:r=null,preluActivationWeights:a=null,leakyreluAlpha:o=0,activation:i=null}){let{filterWidth:l,filterHeight:c,inChannels:u,strideWidth:d,strideHeight:p,padInfo:h,outWidth:f,outHeight:m,dilationWidth:g,dilationHeight:A,dataFormat:x}=n,y=x==="channelsLast",b=l*c*u,w=m*f,k=[w,b],C=!1,E=!1,R=[],F=je({inputs:{x:e},backend:s,attrs:{shape:e.shape.slice(1)}}),D=je({inputs:{x:t},backend:s,attrs:{shape:[1,b,-1]}});R.push(F),R.push(D);let P=new Lue(k,y),T=[{type:"int32",data:[h.left,h.top]},{type:"int32",data:[d,p]},{type:"int32",data:[g,A]},{type:"int32",data:[f]},{type:"int32",data:[u*l]},{type:"int32",data:[u]}],M=s.runWebGPUProgram(P,[F],F.dtype,T),U=je({inputs:{x:M},backend:s,attrs:{shape:[1,k[0],k[1]]}});R.push(M),R.push(U);let j=[1,k[0],k[1]],z=new G4(j,[1,w,n.outChannels],K().get("WEBGPU_MATMUL_WORK_PER_THREAD"),C,E),X=j[1],Z=j[2],J=n.outChannels,ee=[{type:"int32",data:[X]},{type:"int32",data:[J]},{type:"int32",data:[Z]}],ne=s.runWebGPUProgram(z,[U,D],U.dtype,ee),Q=y?[1,m,f,n.outChannels]:[1,n.outChannels,m,f],te=je({inputs:{x:ne},backend:s,attrs:{shape:Q}});R.push(ne);for(let oe of R)s.disposeData(oe.dataId);return te}var eC=class{constructor(e,t=!1,n=null,s=!1,r=!1){this.variableNames=["x","W"],this.uniforms=`filterDims : vec2<i32>; pad : vec2<i32>; stride : vec2<i32>; dilation : vec2<i32>;
dimAOuter : i32; dimBOuter : i32; dimInner : i32;`,this.isVec4=!0,this.outputShape=e.outShape,v.assert(e.dataFormat==="channelsLast",()=>"TODO: NCHW is unimplemented"),this.dispatchLayout={x:[3],y:[1,2],z:[0]},this.workGroupSize=[8,8,1];let a=[4,4,1];this.dispatch=Oe(this.dispatchLayout,this.outputShape,this.workGroupSize,a),this.convInfo=e,this.addBias=t,this.activation=n,this.hasPreluActivationWeights=s,this.hasLeakyreluAlpha=r,this.addBias&&this.variableNames.push("bias"),this.hasPreluActivationWeights&&this.variableNames.push("preluActivationWeights"),this.hasLeakyreluAlpha&&this.variableNames.push("leakyreluAlpha"),[this.fitA,this.fitB]=this.getShapeFit(a),this.shaderKey=`conv2DMMVec4_${this.activation}_${this.fitA}_${this.fitB}`}getShapeFit(e){let t=this.workGroupSize[1]*e[1],n=this.workGroupSize[0]*e[0],s=n,r=[t,s],a=[s,n],o=this.outputShape[1]*this.outputShape[2],i=this.outputShape[3],l=this.convInfo.filterHeight*this.convInfo.filterWidth*this.convInfo.inChannels;return[ra(r,[o,l]),ra(a,[l,i])]}getSampleAWithRemainder(e){return`let flatIndex${e} = getFlatIndex4D(coord, uniforms.xShape);
let divBy4Remainder${e} = flatIndex${e} % 4;
let divBy4Index${e} = flatIndex${e} / 4;
let curData${e} = x.numbers[divBy4Index${e}];
if (divBy4Remainder${e} == 0) {
temp = curData${e};
} else {
// TODO: This could end up being a redundant load with another one in
// the same shader invocation. Perhaps there's an opportunity for
// optimization
let nextData${e} = x.numbers[divBy4Index${e} + 1];
if (divBy4Remainder${e} == 1) {
temp = vec4<f32>(curData${e}.yzw, nextData${e}.x);
} elseif (divBy4Remainder${e} == 2) {
temp = vec4<f32>(curData${e}.zw, nextData${e}.xy);
} elseif (divBy4Remainder${e} == 3) {
temp = vec4<f32>(curData${e}.w, nextData${e}.xyz);
}
}
`}getUserCode(){let t=U4([4,4,1],this.workGroupSize),r=`let outRow = r / uniforms.outShape[2];
let outCol = r % uniforms.outShape[2];
let WRow = c / (uniforms.filterDims[1] * uniforms.xShape[3]);
let WCol = c / uniforms.xShape[3] % uniforms.filterDims[1];
let inChCoord = c % uniforms.xShape[3];
var coord = vec4<i32>(
batch,
outRow * uniforms.stride[0] + uniforms.dilation[0] * WRow - uniforms.pad[0],
outCol * uniforms.stride[1] + uniforms.dilation[1] * WCol - uniforms.pad[1],
inChCoord);
var resData = vec4<f32>(0.0);
${this.convInfo.inChannels%4===0?`// The bounds checking is always needed since we use it to pad zero for
// the 'same' padding type.
if (coordsInBounds4D(coord, uniforms.xShape)) {
resData = x.numbers[getFlatIndex4D(coord, uniforms.xShape) / 4];
} else {
resData = vec4<f32>(0.0); }`:`var temp = vec4<f32>(0.0);
${this.getSampleAWithRemainder(1)}
resData = temp;
if (WCol == (uniforms.filterDims[1] - 1)) {
coord = vec4<i32>(
coord.x, coord.y + 1, coord.z + 1 - uniforms.filterDims[1], 0);
${this.getSampleAWithRemainder(2)}
if (inChCoord == 0) {
resData = vec4<f32>(resData.xyz, temp.x);
} elseif (inChCoord == 1) {
resData = vec4<f32>(resData.xy, temp.xy);
} else {
resData = vec4<f32>(resData.x, temp.xyz);
}
}
`}
return resData;`,a=this.fitA?`${r}`:`if (r < uniforms.dimAOuter && c < uniforms.dimInner) {
${r}
}
return vec4<f32>(0.0);
`,o=this.fitB?"return W.numbers[row * uniforms.dimBOuter / 4 + col];":`if(coordsInBounds2D(vec2<i32>(row, col * 4), vec2<i32>(uniforms.dimInner, uniforms.dimBOuter))) {
return W.numbers[row * uniforms.dimBOuter / 4 + col];
}
return vec4<f32>(0.0);
`,i="",l="";if(this.activation){let d=aa(this.activation,this.isVec4);if(this.hasPreluActivationWeights)i=`fn activation(a : vec4<f32>, outCoord : vec4<i32>) -> vec4<f32> {
let b = getPreluActivationWeightsAtOutCoordsByCoords(outCoord);
${d}
}`;else{if(this.hasLeakyreluAlpha)throw i=`fn activation(a: vec4<f32>) -> vec4<f32> {
let b = getLeakyreluAlphaAtOutCoords();
${d}
}`,new Error("Leakyrelu is not supported.");i=`
fn activation(a : vec4<f32>, outCoord : vec4<i32>) -> vec4<f32> {
${d}
}`}l="value = activation(value, outCoord);"}let c=this.addBias?"value = value + getBiasAtOutCoordsByCoords(outCoord);":"";return`
${i}
fn mm_readA(row : i32, col : i32, globalId : vec3<u32>) -> vec4<f32> {
let r = row;
let c = col * 4;
var batch = i32(globalId.z);
${a}
}
fn mm_readB(row : i32, col : i32, globalId : vec3<u32>) -> vec4<f32> {
${o}
}
fn mm_write(row : i32, col : i32, valueInput : vec4<f32>, globalId : vec3<u32>) {
var batch = i32(globalId.z);
var value = valueInput;
if (row < uniforms.dimAOuter && col * 4 < uniforms.dimBOuter)
{
let outCoord = vec4<i32>(
batch,
row / uniforms.outShape[2],
row % uniforms.outShape[2],
col * 4);
${c}
${l}
setOutput(outCoord[0], outCoord[1], outCoord[2], outCoord[3],
value);
}
}
${t}
`}},tC=class{constructor(e,t=!1,n=null,s=!1){this.variableNames=["x","W"],this.uniforms="filterDims : vec2<i32>; pad : vec2<i32>; stride : vec2<i32>; dilation : vec2<i32>; dimAOuter : i32; dimBOuter : i32; dimInner : i32;",this.outputShape=e.outShape,v.assert(e.dataFormat==="channelsLast",()=>"TODO: NCHW is unimplemented"),this.dispatchLayout={x:[3],y:[1,2],z:[0]},this.workGroupSize=tx(this.dispatchLayout,this.outputShape),this.elementsPerThread=sx(this.dispatchLayout,this.outputShape),this.dispatch=Oe(this.dispatchLayout,this.outputShape,this.workGroupSize,this.elementsPerThread),t&&this.variableNames.push("bias"),s&&this.variableNames.push("preluActivationWeights"),this.convInfo=e,this.addBias=t,this.activation=n,this.hasPreluActivationWeights=s,[this.fitA,this.fitB]=this.getShapeFit(),this.shaderKey=`conv2DMM_${this.elementsPerThread}_${this.activation}_${this.fitA}_${this.fitB}`}getShapeFit(){let e=this.workGroupSize[1]*this.elementsPerThread[1],t=this.workGroupSize[0]*this.elementsPerThread[0],n=e>t?e:t;v.assert(n%this.workGroupSize[0]==0&&n%this.workGroupSize[1]==0,()=>"tileInner must be multiple of workgroupsize.x and workgroupsize.y");let s=[e,n],r=[n,t],a=this.outputShape[1]*this.outputShape[2],o=this.outputShape[3],i=this.convInfo.filterHeight*this.convInfo.filterWidth*this.convInfo.inChannels;return[ra(s,[a,i]),ra(r,[i,o])]}getUserCode(){let e=ox(this.elementsPerThread,this.workGroupSize),t=`
let outRow = row / uniforms.outShape[2];
let outCol = row % uniforms.outShape[2];
let WRow = col / (uniforms.filterDims[1] * uniforms.xShape[3]);
let WCol = col / uniforms.xShape[3] % uniforms.filterDims[1];
let coord = vec4<i32>(
batch,
outRow * uniforms.stride[0] + uniforms.dilation[0] * WRow - uniforms.pad[0],
outCol * uniforms.stride[1] + uniforms.dilation[1] * WCol - uniforms.pad[1],
col % uniforms.xShape[3]);
// The bounds checking is always needed since we use it to pad zero for the
// 'same' padding type.
if(coordsInBounds4D(coord, uniforms.xShape)) {
return x.numbers[getFlatIndex4D(coord, uniforms.xShape)];
}
return 0.0;`,n=this.fitA?`${t}`:`if (row < uniforms.dimAOuter && col < uniforms.dimInner) {
${t}
}
return 0.0;
`,s=this.fitB?"return W.numbers[row * uniforms.dimBOuter + col];":`if(coordsInBounds2D(vec2<i32>(row, col), vec2<i32>(uniforms.dimInner, uniforms.dimBOuter))) {
return W.numbers[row * uniforms.dimBOuter + col];
}
return 0.0;
`,r="",a="";if(this.activation){let l=aa(this.activation,!1);this.hasPreluActivationWeights?r=`fn activation(a: f32, outCoord : vec4<i32>) -> f32 {
let b = getPreluActivationWeightsAtOutCoordsByCoords(outCoord);
${l}
}`:r=`
fn activation(a : f32, outCoord : vec4<i32>) -> f32 {
${l}
}
`,a="value = activation(value, outCoord);"}let o=this.addBias?"value = value + getBiasAtOutCoordsByCoords(outCoord);":"";return`
${r}
fn mm_readA(row : i32, col : i32, globalId : vec3<u32>) -> f32 {
var batch = i32(globalId.z);
${n}
}
fn mm_readB(row : i32, col : i32, globalId : vec3<u32>) -> f32 {
${s}
}
fn mm_write(row : i32, col : i32, valueInput : f32, globalId : vec3<u32>) {
var batch = i32(globalId.z);
var value = valueInput;
let outCoord = vec4<i32>(
batch,
row / uniforms.outShape[2],
row % uniforms.outShape[2],
col);
${o}
${a}
result.numbers[getFlatIndex4D(outCoord, uniforms.outShape)] = value;
}
${e}
`}},nC=class{constructor(e,t=!1,n=null,s=!1){this.variableNames=["x","W"],this.uniforms="filterDims : vec2<i32>; pad : vec2<i32>; stride : vec2<i32>; dilation : vec2<i32>;",this.workGroupSize=[128,1,1],this.outputShape=e.outShape,this.dispatchLayout=Ke(this.outputShape),this.dispatch=Oe(this.dispatchLayout,this.outputShape,this.workGroupSize),v.assert(e.dataFormat==="channelsLast",()=>"TODO: NCHW is unimplemented"),t&&this.variableNames.push("bias"),s&&this.variableNames.push("preluActivationWeights"),this.convInfo=e,this.addBias=t,this.activation=n,this.hasPreluActivationWeights=s,this.shaderKey=`conv2DNaive_${this.activation}`}getUserCode(){let e="",t="";if(this.activation){let r=aa(this.activation);this.hasPreluActivationWeights?e=`fn activation(a : f32, outCoord : vec4<i32>) -> f32{
let b = getPreluActivationWeightsAtOutCoordsByCoords(outCoord);
${r}
}`:e=`
fn activation(a : f32, outCoord : vec4<i32>) -> f32{
${r}
}
`,t="value = activation(value, outCoord);"}let n=this.addBias?"value = value + getBiasAtOutCoordsByCoords(outCoord);":"";return`
${e}
fn readInp(batch : i32, row : i32, col : i32, chan : i32) -> f32 {
let coord = vec4<i32>(batch, row, col, chan);
if(coordsInBounds4D(coord, uniforms.xShape)) {
return getX(batch, row, col, chan);
}
return 0.0;
}
fn readFilt(row : i32, col : i32, xChannel : i32, outChannel : i32) -> f32{
let coord = vec4<i32>(row, col, xChannel, outChannel);
if(coordsInBounds4D(coord, uniforms.wShape)) {
return getW(row, col, xChannel, outChannel);
}
return 0.0;
}
fn writeResult(batch : i32, row : i32, col : i32, chan : i32, value : f32) {
let coord = vec4<i32>(batch, row, col, chan);
if (coordsInBounds4D(coord, uniforms.outShape)) {
${n}
${t}
setOutput(batch, row, col, chan, value);
}
}
${Fe()} {
${Le()}
let coords = getOutputCoords(globalId, index);
let batch = coords[0];
let outChannel = coords[3];
var acc = 0.0;
for (var row = 0; row < uniforms.filterDims[0]; row = row + 1) {
for (var col = 0; col < uniforms.filterDims[1]; col = col + 1) {
for (var xChannel = 0; xChannel < uniforms.xShape[3]; xChannel = xChannel + 1) {
let coordRow = coords[1] * uniforms.stride[0] + uniforms.dilation[0] * row - uniforms.pad[0];
let coordCol = coords[2] * uniforms.stride[1] + uniforms.dilation[1] * col - uniforms.pad[1];
let v = readInp(batch, coordRow, coordCol, xChannel);
let f = readFilt(row, col, xChannel, outChannel);
acc = acc + v * f;
}
}
}
writeResult(batch, coords[1], coords[2], outChannel, acc);
}
`}};function Wue(e){let{inputs:t,attrs:n,backend:s}=e,{x:r,filter:a}=t,{strides:o,pad:i,dataFormat:l,dilations:c,dimRoundingMode:u}=n,d=N.convertConv2DDataFormat(l),p=N.computeConv2DInfo(r.shape,a.shape,o,c,i,u,!1,d);if(p.filterHeight===1&&p.filterWidth===1&&p.dilationHeight===1&&p.dilationWidth===1&&p.strideHeight===1&&p.strideWidth===1&&(p.padInfo.type==="SAME"||p.padInfo.type==="VALID"))return Q4({x:r,filter:a,convInfo:p,backend:s});if(K().getBool("WEBGPU_CONV_SEPARATE_IM2COL_SHADER")&&r.shape[0]===1)return Bue({x:r,filter:a,convInfo:p,backend:s});let h,f=[p.padInfo.top,p.padInfo.left],m=[{type:"int32",data:[p.filterHeight,p.filterWidth]},{type:"int32",data:[...f]},{type:"int32",data:[p.strideHeight,p.strideWidth]},{type:"int32",data:[p.dilationHeight,p.dilationWidth]}],g=K().getBool("WEBGPU_USE_NAIVE_CONV2D");if(g?h=new nC(p):(p.inChannels%4==0||p.inChannels===3&&p.padInfo.type==="VALID")&&p.outChannels%4==0&&p.outChannels>=64?h=new eC(p):h=new tC(p),!g){let A=p.outShape[1]*p.outShape[2],x=p.outShape[3],y=p.filterHeight*p.filterWidth*p.inShape[3];m.push({type:"int32",data:[A]},{type:"int32",data:[x]},{type:"int32",data:[y]})}return s.runWebGPUProgram(h,[r,a],r.dtype,m)}var Vue={kernelName:Na,backendName:"webgpu",kernelFunc:Wue},Uue=class{constructor(e){this.variableNames=["x","W"],this.uniforms="filterDims : vec2<i32>; pads : vec2<i32>; stride : vec2<i32>; outBackprop : vec4<i32>; dimAOuter : i32; dimBOuter : i32; dimInner : i32;",this.outputShape=e.inShape,v.assert(e.dataFormat==="channelsLast",()=>"TODO: NCHW is unimplemented"),this.dispatchLayout={x:[3],y:[1,2],z:[0]},this.workGroupSize=tx(this.dispatchLayout,this.outputShape),this.elementsPerThread=sx(this.dispatchLayout,this.outputShape),this.dispatch=Oe(this.dispatchLayout,this.outputShape,this.workGroupSize,this.elementsPerThread),this.shaderKey=`conv2DDerInputMM_${this.elementsPerThread}`}getUserCode(){return`
fn mm_readA(row : i32, col : i32, globalId : vec3<u32>) -> f32 {
var batch = i32(globalId.z);
if (row < uniforms.dimAOuter && col < uniforms.dimInner) {
let outRow = row / uniforms.outShape[2];
let outCol = row % uniforms.outShape[2];
let WRow = col / (uniforms.filterDims[1] * uniforms.outBackprop[3]);
let WCol = col / uniforms.outBackprop[3] % uniforms.filterDims[1];
let xR = f32(outRow - uniforms.pads[0] + WRow) / f32(uniforms.stride[0]);
let xC = f32(outCol - uniforms.pads[1] + WCol) / f32(uniforms.stride[1]);
if (xR < 0.0 || xR >= f32(uniforms.outBackprop[1]) || fract(xR) > 0.0) {
return 0.0;
}
if (xC < 0.0 || xC >= f32(uniforms.outBackprop[2]) || fract(xC) > 0.0) {
return 0.0;
}
let coord = vec4<i32>(
batch,
i32(xR),
i32(xC),
col % uniforms.outBackprop[3]);
return x.numbers[getFlatIndex4D(coord, uniforms.xShape)];
}
return 0.0;
}
fn mm_readB(row : i32, col : i32, globalId : vec3<u32>) -> f32 {
let coordX = uniforms.filterDims.x - 1 -
row / (uniforms.filterDims[1] * uniforms.outBackprop[3]);
let coordY = uniforms.filterDims.y - 1 -
(row / uniforms.outBackprop[3]) % uniforms.filterDims[1];
if (row < uniforms.dimInner && col < uniforms.dimBOuter &&
coordX >= 0 && coordY >= 0) {
let coord = vec4<i32>(coordX, coordY, col,
row % uniforms.outBackprop[3]);
return W.numbers[getFlatIndex4D(coord, uniforms.wShape)];
}
return 0.0;
}
fn mm_write(row : i32, col : i32, valueInput : f32, globalId : vec3<u32>) {
var batch = i32(globalId.z);
var value = valueInput;
let outCoord = vec4<i32>(
batch,
row / uniforms.outShape[2],
row % uniforms.outShape[2],
col);
result.numbers[getFlatIndex4D(outCoord, uniforms.outShape)] = value;
}
${ox(this.elementsPerThread,this.workGroupSize)}
`}},Gue=class{constructor(e){this.variableNames=["dy","W"],this.uniforms="filterDims : vec2<i32>; pads : vec2<i32>; stride : vec2<i32>; outBackprop : vec4<i32>;",this.workGroupSize=[64,1,1],this.outputShape=e.inShape,this.dispatchLayout=Ke(this.outputShape),this.dispatch=Oe(this.dispatchLayout,this.outputShape,this.workGroupSize),this.isChannelsLast=e.dataFormat==="channelsLast",this.shaderKey=`conv2DDerInput_${this.isChannelsLast}`}getUserCode(){let e=this.isChannelsLast?1:2,t=this.isChannelsLast?2:3,n=this.isChannelsLast?3:1;return`
${Fe()} {
${Le()}
let coords = getOutputCoords(globalId, index);
if (coordsInBounds4D(coords, uniforms.outShape)) {
let batch = coords[0];
let d1 = coords[${n}];
let dyCorner = vec2<i32>(coords[${e}]), coords[${t}]) - uniforms.pads;
let dyRCorner = dyCorner.x;
let dyCCorner = dyCorner.y;
// Convolve dy(?, ?, d2) with w(:, :, d1, d2) to compute dx(xR, xC, d1).
// ? = to be determined. : = across all values in that axis.
var dotProd = 0.0;
for (var wR = 0; wR < uniforms.filterDims.x; wR = wR + 1) {
let dyR = (f32(dyRCorner) + f32(wR)) / f32(uniforms.stride.x);
let wRPerm = uniforms.filterDims.x - 1 - wR;
if (dyR < 0.0 || dyR >= f32(uniforms.outBackprop[1]) || fract(dyR) > 0.0 ||
wRPerm < 0) {
continue;
}
let idyR = dyR;
for (var wC = 0; wC < uniforms.filterDims.y; wC = wC + 1) {
let dyC = (f32(dyCCorner) + f32(wC)) / f32(uniforms.stride.y);
let wCPerm = uniforms.filterDims.y - 1 - wC;
if (dyC < 0.0 || dyC >= f32(uniforms.outBackprop[2]) ||
fract(dyC) > 0.0 || wCPerm < 0) {
continue;
}
let idyC = dyC;
for (var d2 = 0; d2 < uniforms.outBackprop[3]; d2 = d2 + 1) {
if (${this.isChannelsLast}) {
let xValue = getDy(batch, idyR, idyC, d2);
let wValue = getW(wRPerm, wCPerm, d1, d2);
dotProd = dotProd + xValue * wValue;
} else {
let xValue = getDy(batch, d2, idyR, idyC);
let wValue = getW(wRPerm, wCPerm, d1, d2);
dotProd = dotProd + xValue * wValue;
}
}
}
}
setOutput(coords[0], coords[1], coords[2], coords[3], dotProd);
}
}
`}};function Hue(e){let{inputs:t,backend:n,attrs:s}=e,{dy:r,filter:a}=t,{inputShape:o,strides:i,pad:l,dataFormat:c,dimRoundingMode:u}=s,d=N.convertConv2DDataFormat(c),p=N.computeConv2DInfo(o,a.shape,i,1,l,u,!1,d),h=[{type:"int32",data:[p.filterHeight,p.filterWidth]},{type:"int32",data:[p.filterHeight-1-p.padInfo.top,p.filterWidth-1-p.padInfo.left]},{type:"int32",data:[p.strideHeight,p.strideWidth]},{type:"int32",data:[p.batchSize,p.outHeight,p.outWidth,p.outChannels]}],f;if(K().getBool("WEBGPU_USE_NAIVE_CONV2D_TRANSPOSE"))f=new Gue(p);else{f=new Uue(p);let m=p.inShape[1]*p.inShape[2],g=p.inShape[3],A=p.filterHeight*p.filterWidth*p.outChannels;h.push({type:"uint32",data:[m]},{type:"uint32",data:[g]},{type:"uint32",data:[A]})}return n.runWebGPUProgram(f,[r,a],"float32",h)}var jue={kernelName:Ea,backendName:"webgpu",kernelFunc:Hue},que=Cn({opType:vt.COS}),Xue={kernelName:Ra,backendName:"webgpu",kernelFunc:que},Kue=Cn({opType:vt.COSH}),Zue={kernelName:$a,backendName:"webgpu",kernelFunc:Kue},Yue=class{constructor(e,t,n,s){this.variableNames=["Image","Boxes","BoxInd"],this.uniforms="extrapolationValue : f32;",this.workGroupSize=[64,1,1];let[r]=t;this.outputShape=[r,n[0],n[1],e],this.dispatchLayout=Ke(this.outputShape),this.dispatch=Oe(this.dispatchLayout,this.outputShape,this.workGroupSize),this.methodId=s==="bilinear"?1:0,this.cropHeightBiggerThan1=this.outputShape[1]>1,this.cropWidthBiggerThan1=this.outputShape[2]>1,this.shaderKey=`cropAndResize_${this.methodId}_${this.cropHeightBiggerThan1}_${this.cropWidthBiggerThan1}`}getUserCode(){let[e,t]=["f32(uniforms.imageShape[1] - 1)","f32(uniforms.imageShape[2] - 1)"],[n,s,r]=this.cropHeightBiggerThan1?[`(${e} / f32(uniforms.outShape[1] - 1))`,"(y2-y1) * height_ratio",`y1*${e} + f32(y)*(height_scale)`]:["0.0","0.0",`0.5 * (y1+y2) * ${e}`],[a,o,i]=this.cropWidthBiggerThan1?[`(${t} / f32(uniforms.outShape[2] - 1))`,"(x2-x1) * width_ratio",`x1*${t} + f32(x)*(width_scale)`]:["0.0","0.0",`0.5 * (x1+x2) * ${t}`];return`
fn writeResult(coords : vec4<i32>, value : f32) {
if (coordsInBounds4D(coords, uniforms.outShape)) {
setOutput(coords[0], coords[1], coords[2], coords[3], value);
}
}
${Fe()} {
${Le()}
let height_ratio = f32(${n});
let width_ratio = f32(${a});
let coords = getOutputCoords(globalId, index);
let b = coords[0];
let y = coords[1];
let x = coords[2];
let d = coords[3];
// get box vals
let y1 = getBoxes(b, 0);
let x1 = getBoxes(b, 1);
let y2 = getBoxes(b, 2);
let x2 = getBoxes(b, 3);
// get image in batch index
let bInd = i32(round(getBoxInd(b)));
if(bInd < 0 || bInd >= uniforms.outShape[0]) {
return;
}
let height_scale = ${s};
let width_scale = ${o};
let in_y = ${r};
if( in_y < 0.0 || in_y > ${e} ) {
writeResult(coords, uniforms.extrapolationValue);
return;
}
let in_x = ${i};
if( in_x < 0.0 || in_x > ${t} ) {
writeResult(coords, uniforms.extrapolationValue);
return;
}
let sourceFracIndexCR = vec2<f32>(in_x,in_y);
if(${this.methodId} == 1) {
// Compute the four integer indices.
let sourceFloorCR = vec2<i32>(sourceFracIndexCR);
let sourceCeilCR = vec2<i32>(ceil(sourceFracIndexCR));
let topLeft = getImage(bInd, sourceFloorCR.y, sourceFloorCR.x, d);
let bottomLeft = getImage(bInd, sourceCeilCR.y, sourceFloorCR.x, d);
let topRight = getImage(bInd, sourceFloorCR.y, sourceCeilCR.x, d);
let bottomRight = getImage(bInd, sourceCeilCR.y, sourceCeilCR.x, d);
let fracCR = sourceFracIndexCR - vec2<f32>(sourceFloorCR);
let top = topLeft + (topRight - topLeft) * fracCR.x;
let bottom = bottomLeft + (bottomRight - bottomLeft) * fracCR.x;
let newValue = top + (bottom - top) * fracCR.y;
writeResult(coords, newValue);
} else {
// Compute the coordinators of nearest neighbor point.
let sourceNearestCR = vec2<i32>(floor(
sourceFracIndexCR + vec2<f32>(0.5,0.5)));
let newValue = getImage(
bInd, sourceNearestCR.y, sourceNearestCR.x, d);
writeResult(coords,newValue);
}
}
`}},Jue=e=>{let{inputs:t,backend:n,attrs:s}=e,{image:r,boxes:a,boxInd:o}=t,{cropSize:i,method:l,extrapolationValue:c}=s,u=new Yue(r.shape[3],a.shape,i,l),d=[{type:"float32",data:[c]}];return n.runWebGPUProgram(u,[r,a,o],"float32",d)},Que={kernelName:ai,backendName:"webgpu",kernelFunc:Jue},ece=class{constructor(e,t){this.variableNames=["x"],this.workGroupSize=[64,1,1],this.uniforms="blockSize : i32;",this.outputShape=e,this.dispatchLayout=Ke(this.outputShape),this.dispatch=Oe(this.dispatchLayout,this.outputShape,this.workGroupSize),this.shaderKey=`depthToSpace_${t}`,this.size=v.sizeFromShape(this.outputShape),this.dataFormat=t}getUserCode(){return`
${Fe()} {
${Le()}
if (index < uniforms.size) {
let coords = getOutputCoords(globalId, index);
let b = coords[0];
let h = ${this.getHeightCoordString()};
let w = ${this.getWidthCoordString()};
let d = ${this.getDepthCoordString()};
let in_h = h / uniforms.blockSize;
let offset_h = h % uniforms.blockSize;
let in_w = w / uniforms.blockSize;
let offset_w = w % uniforms.blockSize;
let offset_d = (offset_h * uniforms.blockSize + offset_w) *
${this.getOutputDepthSize()};
let in_d = d + offset_d;
let rlt = ${this.getInputSamplingString()};
setOutputFlat(index, rlt);
}
}`}getHeightCoordString(){return this.dataFormat==="NHWC"?"coords[1]":"coords[2]"}getWidthCoordString(){return this.dataFormat==="NHWC"?"coords[2]":"coords[3]"}getDepthCoordString(){return this.dataFormat==="NHWC"?"coords[3]":"coords[1]"}getOutputDepthSize(){return this.dataFormat==="NHWC"?"uniforms.outShape[3]":"uniforms.outShape[1]"}getInputSamplingString(){return this.dataFormat==="NHWC"?"getX(b, in_h, in_w, in_d)":"getX(b, in_d, in_h, in_w)"}};function tce(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{blockSize:a,dataFormat:o}=s,i=r.shape[0],l=o==="NHWC"?r.shape[1]:r.shape[2],c=o==="NHWC"?r.shape[2]:r.shape[3],u=o==="NHWC"?r.shape[3]:r.shape[1],d=l*a,p=c*a,h=u/(a*a),f=o==="NHWC"?[i,d,p,h]:[i,h,d,p],m=[{type:"int32",data:[a]}],g=new ece(f,o);return n.runWebGPUProgram(g,[r],r.dtype,m)}var nce={kernelName:oi,backendName:"webgpu",kernelFunc:tce},sC=class{constructor(e,t=!1,n=null,s=!1){this.variableNames=["x","W"],this.uniforms="pad : vec2<i32>; stride : vec2<i32>; dilation : vec2<i32>; inDims : vec2<i32>;",this.workGroupSize=[4,4,4],this.isVec4=!0,this.outputShape=e.outShape,this.dispatchLayout={x:[0,1],y:[2],z:[3]},this.dispatch=Oe(this.dispatchLayout,this.outputShape,this.workGroupSize,[1,4,4]),v.assert(e.dataFormat==="channelsLast",()=>"TODO: NCHW is unimplemented"),t&&this.variableNames.push("bias"),s&&this.variableNames.push("preluActivationWeights"),this.convInfo=e,this.addBias=t,this.activation=n,this.hasPreluActivation=s,this.shaderKey=`depthwise3x3_${n}`}getUserCode(){let e="",t="";if(this.activation){let r=aa(this.activation,this.isVec4);this.hasPreluActivation?e=`fn activation(a : vec4<f32>, globalId : vec3<u32>, globalIndex : i32) -> vec4<f32> {
let b = getPreluActivationWeightsAtOutCoordsByGlobalId(globalId, globalIndex);
${r}
}`:e=`
fn activation(a : vec4<f32>, globalId : vec3<u32>, globalIndex : i32) -> vec4<f32> {
${r}
}
`,t="dotProd[i] = activation(dotProd[i], globalId, index);"}let n=this.addBias?"dotProd[i] = dotProd[i] + getBiasAtOutCoordsByCoords(coords);":"";return`
${e}
${Fe()} {
${Le()}
let batch = 0;
let r = i32(globalId.x);
let c = i32(globalId.y) * 4;
let d2 = i32(globalId.z) * 4;
let xRCCorner = vec2<i32>(r, c) * uniforms.stride - uniforms.pad;
let d1 = d2;
let q = 0;
let xRCorner = xRCCorner.x;
let xCCorner = xRCCorner.y;
var wVals : array<vec4<f32>, 9>;
wVals[0] = getW(0, 0, d1, q);
wVals[1] = getW(0, 1, d1, q);
wVals[2] = getW(0, 2, d1, q);
wVals[3] = getW(1, 0, d1, q);
wVals[4] = getW(1, 1, d1, q);
wVals[5] = getW(1, 2, d1, q);
wVals[6] = getW(2, 0, d1, q);
wVals[7] = getW(2, 1, d1, q);
wVals[8] = getW(2, 2, d1, q);
var xVals : array<array<vec4<f32>, 6>, 3>;
for (var wR = 0; wR < 3; wR = wR + 1) {
let xR = xRCorner + wR * uniforms.dilation[0];
for (var wC = 0; wC < 6; wC = wC + 1) {
let xC = xCCorner + wC * uniforms.dilation[1];
if (xR < 0 || xR >= uniforms.inDims[0] || xC < 0 || xC >= uniforms.inDims[1]) {
xVals[wR][wC] = vec4<f32>(0.0);
} else {
xVals[wR][wC] = getX(batch, xR, xC, d1);
}
}
}
var dotProd : array<vec4<f32>, 4>;
dotProd[0] = vec4<f32>(0.0);
dotProd[1] = vec4<f32>(0.0);
dotProd[2] = vec4<f32>(0.0);
dotProd[3] = vec4<f32>(0.0);
for (var wR = 0; wR < 3; wR = wR + 1) {
for (var wC = 0; wC < 3; wC = wC + 1) {
let indexW = wR * 3 + wC;
dotProd[0] = dotProd[0] + xVals[wR][0 + wC] * wVals[indexW];
dotProd[1] = dotProd[1] + xVals[wR][1 + wC] * wVals[indexW];
dotProd[2] = dotProd[2] + xVals[wR][2 + wC] * wVals[indexW];
dotProd[3] = dotProd[3] + xVals[wR][3 + wC] * wVals[indexW];
}
}
for (var i = 0; i < 4; i = i + 1) {
let coords = vec4<i32>(batch, r, c + i, d2);
if (coordsInBounds4D(coords, uniforms.outShape)) {
${n}
${t}
setOutput(coords[0], coords[1], coords[2], coords[3], dotProd[i]);
}
}
}
`}},rC=class{constructor(e,t=!1,n=null,s=!1){this.variableNames=["x","W"],this.uniforms="pad : vec2<i32>; stride : vec2<i32>; dilation : vec2<i32>; inDims : vec2<i32>;",this.workGroupSize=[256,1,1],this.outputShape=e.outShape,this.dispatchLayout=Ke(this.outputShape),this.dispatch=Oe(this.dispatchLayout,this.outputShape,this.workGroupSize),v.assert(e.dataFormat==="channelsLast",()=>"TODO: NCHW is unimplemented"),t&&this.variableNames.push("bias"),s&&this.variableNames.push("preluActivationWeights"),this.convInfo=e,this.addBias=t,this.activation=n,this.hasPreluActivation=s,this.shaderKey=`depthwise_${this.convInfo.filterHeight}_${this.convInfo.filterWidth}_${this.activation}_${this.convInfo.outChannels/this.convInfo.inChannels}`}getUserCode(){let e=this.convInfo.outChannels/this.convInfo.inChannels,t="",n="";if(this.activation){let a=aa(this.activation,!1);this.hasPreluActivation?t=`fn activation(a : f32, globalId : vec3<u32>, index : i32) -> f32 {
let b = getPreluActivationWeightsAtOutCoordsByGlobalId(globalId, index);
${a}
}`:t=`
fn activation(a : f32, globalId : vec3<u32>, index : i32) -> f32 {
${a}
}
`,n="dotProd = activation(dotProd, globalId, index);"}let s=this.addBias?"dotProd = dotProd + getBiasAtOutCoordsByGlobalId(globalId, index);":"";return`
${t}
fn writeResult(batch : i32, row : i32, col : i32, chan : i32, value : f32) {
let coord = vec4<i32>(batch, row, col, chan);
if (coordsInBounds4D(coord, uniforms.outShape)) {
setOutput(batch, row, col, chan, value);
}
}
${Fe()} {
${Le()}
let coords = getOutputCoords(globalId, index);
let batch = coords[0];
let xRCCorner = vec2<i32>(coords.yz) * uniforms.stride - uniforms.pad;
let d2 = coords[3];
let d1 = d2 / ${e};
let q = d2 - d1 * ${e};
let inputRowStart = xRCCorner.x;
let inputColStart = xRCCorner.y;
let inputRowEnd = inputRowStart + ${this.convInfo.filterHeight} * uniforms.dilation[0];
let inputColEnd = inputColStart + ${this.convInfo.filterWidth} * uniforms.dilation[1];
// Convolve x(?, ?, d1) with w(:, :, d1, q) to get y(yR, yC, d2).
// ? = to be determined. : = across all values in that axis.
var dotProd = 0.0;
// Extract if checking out of for loop for performance.
if (inputRowStart >= 0 && inputColStart >= 0 &&
inputRowEnd < uniforms.inDims[0] && inputColEnd < uniforms.inDims[1]) {
// Here using a constant value |this.convInfo.filterHeight| instead
// of uniform value is in order to loop unrolling.
for (var wR = 0; wR < ${this.convInfo.filterHeight}; wR = wR + 1) {
let xR = inputRowStart + wR * uniforms.dilation[0];
for (var wC = 0; wC < ${this.convInfo.filterWidth}; wC = wC + 1) {
let xC = inputColStart + wC * uniforms.dilation[1];
let xVal = getX(batch, xR, xC, d1);
let wVal = getW(wR, wC, d1, q);
dotProd = dotProd + xVal * wVal;
}
}
} else {
for (var wR = 0; wR < ${this.convInfo.filterHeight}; wR = wR + 1) {
let xR = inputRowStart + wR * uniforms.dilation[0];
if (xR < 0 || xR >= uniforms.inDims[0]) {
continue;
}
for (var wC = 0; wC < ${this.convInfo.filterWidth}; wC = wC + 1) {
let xC = inputColStart + wC * uniforms.dilation[1];
if (xC < 0 || xC >= uniforms.inDims[1]) {
continue;
}
let xVal = getX(batch, xR, xC, d1);
let wVal = getW(wR, wC, d1, q);
dotProd = dotProd + xVal * wVal;
}
}
}
${s}
${n}
writeResult(batch, coords[1], coords[2], d2, dotProd);
}
`}};function sce(e){let{inputs:t,backend:n,attrs:s}=e,{x:r,filter:a}=t,{strides:o,pad:i,dilations:l,dimRoundingMode:c}=s,u=l;u==null&&(u=[1,1]);let d=N.computeConv2DInfo(r.shape,a.shape,o,u,i,c,!0),p;d.batchSize===1&&d.inHeight===d.outHeight&&d.inWidth===d.outWidth&&d.strideHeight===1&&d.strideWidth===1&&d.filterHeight===d.filterWidth&&d.inChannels===d.outChannels&&d.filterHeight===3&&d.inChannels%4==0?p=new sC(d):p=new rC(d);let h=[{type:"int32",data:[d.padInfo.top,d.padInfo.left]},{type:"int32",data:[d.strideHeight,d.strideWidth]},{type:"int32",data:[d.dilationHeight,d.dilationWidth]},{type:"int32",data:[d.inHeight,d.inWidth]}];return n.runWebGPUProgram(p,[r,a],r.dtype,h)}var rce={kernelName:Da,backendName:"webgpu",kernelFunc:sce},aC=jn({opSnippet:Vt.MUL,cpuKernelImpl:Wle,supportsComplex:!0}),ace={kernelName:Ka,backendName:"webgpu",kernelFunc:aC},oce=class{constructor(e,t,n){this.variableNames=["x"],this.uniforms="reduceSize : i32;",this.inputShape=[e.batchSize,e.inSize];let[s]=N.computeOutAndReduceShapes(this.inputShape,[1]);this.outputShape=s.length===0?[1]:s,this.reductionFactor=2;let r=256,a=Math.min(Math.ceil(e.inSize/this.reductionFactor),r);this.workGroupSize=[a,1,1],this.dispatchLayout={x:[],y:this.outputShape.map((o,i)=>i)},this.dispatch=Oe(this.dispatchLayout,this.outputShape,this.workGroupSize),this.reduceType=t,this.shaderKey=`reduce_${t}_${n}`}getUserCode(){let e=this.workGroupSize[0]>1,t="",n="0.0";this.reduceType==="min"||this.reduceType==="max"?(t=`
if (isNanCustom(candidate)) {
bestValue = uniforms.NAN;
} elseif (candidate ${this.reduceType==="min"?"<":">"}
bestValue)
{ bestValue = candidate; }`,n="f32(x.numbers[offset])"):this.reduceType==="sum"||this.reduceType==="mean"?t=" bestValue = bestValue + candidate; ":this.reduceType==="prod"&&(t=" bestValue = bestValue * candidate; ",n="1.0");let s=this.reduceType==="mean"?"setOutputFlat(flatOutputIndex, bestValue / f32(uniforms.reduceSize));":"setOutputFlat(flatOutputIndex, bestValue);",r=`
var<workgroup> xBestValues : array<f32, ${this.workGroupSize[0]}>;
`,a=`
xBestValues[localId.x] = bestValue;
${this.reduceType==="sum"||this.reduceType==="mean"||this.reduceType==="prod"?`bestValue = ${n};`:" "}
var currentSize = WorkGroupSize;
for(; currentSize > 1;) {
workgroupBarrier();
for (var w = 0; w < ${this.reductionFactor}; w = w + 1) {
let i = i32(localId.x) * ${this.reductionFactor} + w;
if (i < currentSize) {
let candidate = xBestValues[i];
${t}
}
}
workgroupBarrier();
xBestValues[localId.x] = bestValue;
currentSize = DIV_CEIL(currentSize, ${this.reductionFactor});
${this.reduceType==="sum"||this.reduceType==="mean"||this.reduceType==="prod"?`if(currentSize > 1) { bestValue = ${n}; }`:""}
}
if (localId.x == 0u) {
${s}
}
`;return`
fn DIV_CEIL(a : i32, b : i32) -> i32 {
return ((a - 1) / b + 1);
}
let WorkGroupSize = ${this.workGroupSize[0]};
${e?r:""}
fn getOffset(globalId : vec3<u32>, index : i32) -> i32 {
let outputCoords = getOutputCoords(globalId, index);
let offset = ${this.outputShape.length===1?"outputCoords":"outputCoords[0]"} * uniforms.reduceSize;
return offset;
}
${Fe()} {
${Le()}
let offset= getOffset(globalId, index);
var bestValue = ${n};
let Length = uniforms.reduceSize;
let WorkPerThread = DIV_CEIL(Length, WorkGroupSize);
for (var w = 0; w < WorkPerThread; w = w + 1) {
let i = i32(globalId.x) * WorkPerThread + w;
if (i < Length) {
let candidate = f32(x.numbers[offset + i]);
${t}
}
}
let flatOutputIndex = i32(globalId.y);
${e?a:s}
}
`}};function rp(e,t,n,s,r){let a=e.shape.length,o=[],i=v.parseAxisParam(t,e.shape),l=i,c=N.getAxesPermutation(l,a),u=e;c!=null&&(u=vl({inputs:{x:e},attrs:{perm:c},backend:r}),l=N.getInnerMostAxes(l.length,a),o.push(u)),N.assertAxesAreInnerMostDims(s,l,a);let[d,p]=N.computeOutAndReduceShapes(u.shape,l),h=d;n&&(h=N.expandShapeToKeepDim(d,i));let f;if((s==="max"||s==="prod")&&r.shouldExecuteOnCPU([u])){let m=r.tensorMap.get(u.dataId).values;switch(s){case"max":let g=zle(m,v.sizeFromShape(p),h,e.dtype);f=r.makeTensorInfo(h,e.dtype,g);break;case"prod":let{outVals:A,outShape:x,outDtype:y}=Gle(u.shape,u.dtype,m,l);f=r.makeTensorInfo(x,y,A);break;default:throw new Error(`${s} CPU implementation is not yet supported.`)}}else{let m=v.sizeFromShape(p),A=v.sizeFromShape(u.shape)/m,x={windowSize:m,inSize:m,batchSize:A,outSize:1},y=s==="mean"?"float32":od(e.dtype),b=[{type:"int32",data:[m]}],w=new oce(x,s,y),k=r.runWebGPUProgram(w,[u],y,b);o.push(k),f=je({inputs:{x:k},attrs:{shape:h},backend:r})}return o.forEach(m=>r.disposeData(m.dataId)),f}function dx(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{axis:a,keepDims:o}=s;return rp(r,a,o,"sum",n)}var ice={kernelName:oo,backendName:"webgpu",kernelFunc:dx};function lce(e){let{inputs:t,backend:n,attrs:s}=e,{equation:r}=s,a=t,{allDims:o,summedDims:i,idDims:l}=N.decodeEinsumEquation(r,a.length);N.checkEinsumDimSizes(o.length,l,a);let{path:c,steps:u}=N.getEinsumComputePath(i,l),d=u.length,p=null,h=o.length,f=[];for(let m=0;m<d;++m){for(let g of u[m]){let{permutationIndices:A,expandDims:x}=N.getEinsumPermutation(h,l[g]),y;N.isIdentityPermutation(A)?y=a[g]:(y=vl({inputs:{x:a[g]},backend:n,attrs:{perm:A}}),f.push(y));let b=y.shape.slice();for(let w=0;w<x.length;++w)b.splice(x[w],0,1);v.arraysEqual(y.shape,b)||(y=je({inputs:{x:y},backend:n,attrs:{shape:b}}),f.push(y)),p===null?p=y:(p=aC({inputs:{a:y,b:p},backend:n}),f.push(p))}m<d-1&&(c[m]>=0&&(p=dx({inputs:{x:p},backend:n,attrs:{axis:c[m]-(o.length-h),keepDims:!1}}),f.push(p)),h--)}for(let m of f)m!==p&&n.disposeData(m.dataId);return p}var uce={kernelName:Vc,backendName:"webgpu",kernelFunc:lce},cce=Cn({opType:vt.ELU}),dce={kernelName:Pa,backendName:"webgpu",kernelFunc:cce},pce=jn({opSnippet:Vt.EQUAL,dtype:"bool",cpuKernelImpl:Tle}),hce={kernelName:ii,backendName:"webgpu",kernelFunc:pce},oC=Cn({opType:vt.EXP,cpuKernelImpl:Nle,dtype:"float32"}),fce={kernelName:Fa,backendName:"webgpu",kernelFunc:oC};function px(e){let{inputs:t,attrs:n,backend:s}=e,{dim:r}=n,{input:a}=t,o=a.shape.length,i=a.shape.slice(),l=r;return r<0&&(v.assert(-(o+1)<=r,()=>`Axis must be in the interval [${-(o+1)}, ${o}]`),l=o+r+1),i.splice(l,0,1),je({inputs:{x:a},backend:s,attrs:{shape:i}})}var mce={kernelName:li,backendName:"webgpu",kernelFunc:px},gce=Cn({opType:vt.EXPM1,cpuKernelImpl:Ele}),Ace={kernelName:ui,backendName:"webgpu",kernelFunc:gce},yce=class{constructor(e){this.variableNames=[],this.outputShape=[],this.uniforms="value : f32;",this.workGroupSize=[64,1,1],this.outputShape=e,this.dispatchLayout=Ke(this.outputShape),this.dispatch=Oe(this.dispatchLayout,this.outputShape,this.workGroupSize),this.shaderKey="fill",this.size=v.sizeFromShape(this.outputShape)}getUserCode(){return`
${Fe()} {
${Le()}
if (index < uniforms.size) {
setOutputFlat(index, uniforms.value);
}
}
`}};function rc(e){let{backend:t,attrs:n}=e,{shape:s,value:r}=n,{dtype:a}=n;if(a=a||v.inferDtype(r),a==="string"){let o=v.getArrayFromDType(a,v.sizeFromShape(s));return o.fill(r),t.makeTensorInfo(s,a,o)}else{let o=new yce(s),i=[{type:"float32",data:[r]}];return t.runWebGPUProgram(o,[],a,i)}}var xce={kernelName:Ql,backendName:"webgpu",kernelFunc:rc},bce=class{constructor(e){this.outputShape=[],this.variableNames=["x"],this.workGroupSize=[64,1,1],this.outputShape=e,this.dispatchLayout=Ke(this.outputShape),this.dispatch=Oe(this.dispatchLayout,this.outputShape,this.workGroupSize),this.shaderKey="flipLeftRight",this.size=v.sizeFromShape(this.outputShape)}getUserCode(){return`
${Fe()} {
${Le()}
if (index < uniforms.size) {
let coords = getOutputCoords(globalId, index);
let coordX = uniforms.xShape[2] - coords[2] - 1;
let outputValue = getX(coords[0], coords[1], coordX, coords[3]);
setOutputFlat(index, outputValue);
}
}
`}},vce={kernelName:ci,backendName:"webgpu",kernelFunc:({inputs:e,backend:t})=>{let{image:n}=e,s=t,r=new bce(n.shape);return s.runWebGPUProgram(r,[n],n.dtype)}},wce=Cn({opType:vt.FLOOR,cpuKernelImpl:Rle}),kce={kernelName:Oa,backendName:"webgpu",kernelFunc:wce},Sce=jn({opSnippet:Vt.INT_DIV,dtype:"int32"}),Ice={kernelName:Ma,backendName:"webgpu",kernelFunc:Sce},Cce=(e,t,n,s,r)=>{let a=[s,...n];return r&&a.push(r),e.createBindGroup({layout:t,entries:a.map((o,i)=>({binding:i,resource:o}))})},iC=(e,t,n,s,r,a=!1)=>{let o={dtype:r.dtype,shape:r.shape},i=sie(s,o,t,a),l=e.createShaderModule({code:i});return e.createComputePipeline({layout:n,compute:{module:l,entryPoint:"main"}})};function lC(e,t,n,s="",r=""){return(e.workGroupSize?e.workGroupSize.join(","):"")+t.map(o=>o.length).join(",")+n.join(",")+e.variableNames.join(",")+s+r+e.shaderKey}function uC(e){let{externalImage:t,backend:n,attrs:s,outShape:r,useImport:a}=e,{numChannels:o}=s,i=v.sizeFromShape(r),l=v.computeStrides(r),c=n.makeTensorInfo(r,"int32"),u=n.getFromPixelsProgram(a?"import":"copyExternal");u.updateOutputShape(r);let d=[c.shape],p=[c.dtype,a?"import":"copyExternal"],h=lC(u,d,p),f=u.getLayout(n.device),m=n.getAndSavePipeline(h,()=>iC(n.device,u,f.pipelineLayout,[],c,!0));u.setPipeline(m),a||n.queue.copyExternalImageToTexture({source:t,origin:{x:0,y:0}},{texture:u.makeInputTexture(n.device,r[1],r[0])},[r[1],r[0]]);let g=n.tensorMap.get(c.dataId);g.bufferInfo.buffer=n.acquireBuffer(g.bufferInfo.byteSize);let A=[i,o,...l,...u.dispatch];u.setUniform(n.device,A);let x;if(a){let y={source:t};x=n.device.importExternalTexture(y)}else x=u.inputTexture.createView();return n.runFromPixelsProgram(u,g.bufferInfo.buffer,f,x,c.dataId),c}var Tce={kernelName:Yc,backendName:"webgpu",kernelFunc:Nce},ac;function Nce(e){let{inputs:t,backend:n,attrs:s}=e,{pixels:r}=t,{numChannels:a}=s;if(r==null)throw new Error("pixels passed to tf.browser.fromPixels() can not be null");let o=typeof HTMLVideoElement!="undefined"&&r instanceof HTMLVideoElement,i=typeof HTMLImageElement!="undefined"&&r instanceof HTMLImageElement,l=typeof HTMLCanvasElement!="undefined"&&r instanceof HTMLCanvasElement||typeof OffscreenCanvas!="undefined"&&r instanceof OffscreenCanvas,c=typeof ImageBitmap!="undefined"&&r instanceof ImageBitmap,[u,d]=o?[r.videoWidth,r.videoHeight]:[r.width,r.height],p=[d,u,a];if(K().getBool("WEBGPU_USE_IMPORT")&&o)return uC({externalImage:r,backend:n,attrs:s,outShape:p,useImport:!0});if((o||i)&&(ac==null&&(ac=document.createElement("canvas").getContext("2d")),ac.canvas.width=u,ac.canvas.height=d,ac.drawImage(r,0,0,u,d),r=ac.canvas),c||l||o||i)return uC({externalImage:r,backend:n,attrs:s,outShape:p,useImport:!1});let h=r.data,f=h;if(a!=null&&a!==4){f=new Uint8Array(r.width*r.height*a);let A=h.length,x=0;for(let y=0;y<A;y++)y%4<a&&(f[x++]=h[y])}let m=n.makeTensorInfo(p,"int32"),g=n.tensorMap.get(m.dataId);return g.values=new Int32Array(f),n.maybeReleaseBuffer(m.dataId),n.uploadToGPU(m.dataId),m}var Ece=class{constructor(e,t,n,s,r){this.uniforms="varianceEpsilon : f32;",this.workGroupSize=[128,1,1],this.variableNames=["x","mean","variance"],N.assertAndGetBroadcastShape(e,t),N.assertAndGetBroadcastShape(e,n),this.outputShape=e,this.dispatchLayout=Ke(this.outputShape),this.dispatch=Oe(this.dispatchLayout,this.outputShape,this.workGroupSize),s!=null&&(N.assertAndGetBroadcastShape(e,s),this.variableNames.push("offset")),r!=null&&(N.assertAndGetBroadcastShape(e,r),this.variableNames.push("scale")),this.offsetShape=s,this.scaleShape=r,this.shaderKey="batchNorm"}getUserCode(){let e="0.0";this.offsetShape!=null&&(e="getOffsetAtOutCoordsByGlobalId(globalId, index)");let t="1.0";this.scaleShape!=null&&(t="getScaleAtOutCoordsByGlobalId(globalId, index)");let n=this.outputShape.length,s=Jt(n),r="setOutput(coords[0], coords[1], coords[2], coords[3], value);";return n===2&&(r="setOutput(coords[0], coords[1], value);"),n===3&&(r="setOutput(coords[0], coords[1], coords[2], value);"),`
fn writeResult(coords : ${s}, value : f32) {
if (coordsInBounds${n}D(coords, uniforms.outShape)) {
${r}
}
}
${Fe()} {
${Le()}
let coords = getOutputCoords(globalId, index);
let xValue = getXAtOutCoordsByGlobalId(globalId, index);
let meanValue = getMeanAtOutCoordsByGlobalId(globalId, index);
let varianValue = getVarianceAtOutCoordsByGlobalId(globalId, index);
let offsetValue = ${e};
let scaleValue = ${t};
let inv = scaleValue * inverseSqrt(varianValue + f32(uniforms.varianceEpsilon));
writeResult(coords,dot(vec3<f32>(xValue, -meanValue, offsetValue), vec3<f32>(inv, inv, 1.0)));
}
`}},Rce={kernelName:za,backendName:"webgpu",kernelFunc:({inputs:e,attrs:t,backend:n})=>{let{x:s,scale:r,offset:a,mean:o,variance:i}=e,{varianceEpsilon:l}=t,c=n,u=[s,o,i],d=null;a!=null&&(d=a.shape,u.push(a));let p=null;r!=null&&(p=r.shape,u.push(r));let h=new Ece(s.shape,o.shape,i.shape,d,p),f=[{type:"float32",data:[l]}];return c.runWebGPUProgram(h,u,s.dtype,f)}};function $ce(e){let{inputs:t,backend:n,attrs:s}=e,{x:r,filter:a,bias:o,preluActivationWeights:i}=t,{strides:l,pad:c,dataFormat:u,dilations:d,dimRoundingMode:p,activation:h,leakyreluAlpha:f}=s,m=N.convertConv2DDataFormat(u),g=N.computeConv2DInfo(r.shape,a.shape,l,d,c,p,!1,m),A=o!=null,x=i!=null,y;if(g.filterHeight===1&&g.filterWidth===1&&g.dilationHeight===1&&g.dilationWidth===1&&g.strideHeight===1&&g.strideWidth===1&&(g.padInfo.type==="SAME"||g.padInfo.type==="VALID"))return Q4({x:r,filter:a,convInfo:g,backend:n,bias:o,activation:h,preluActivationWeights:i,leakyreluAlpha:f});let b=K().getBool("WEBGPU_USE_NAIVE_CONV2D"),w=g.inChannels%4==0&&g.outChannels%4==0,k=[g.padInfo.top,g.padInfo.left],C=[{type:"int32",data:[g.filterHeight,g.filterWidth]},{type:"int32",data:[...k]},{type:"int32",data:[g.strideHeight,g.strideWidth]},{type:"int32",data:[g.dilationHeight,g.dilationWidth]}];if(b)y=new nC(g,A,h,x);else{w?y=new eC(g,A,h,x):y=new tC(g,A,h,x);let R=g.outShape[1]*g.outShape[2],F=g.outShape[3],D=g.filterHeight*g.filterWidth*g.inShape[3];C.push({type:"int32",data:[R]},{type:"int32",data:[F]},{type:"int32",data:[D]})}let E=[r,a];return A&&E.push(o),x&&E.push(i),n.runWebGPUProgram(y,E,r.dtype,C)}var Dce={kernelName:mo,backendName:"webgpu",kernelFunc:$ce};function _ce(e){let{inputs:t,backend:n,attrs:s}=e,{x:r,filter:a,bias:o,preluActivationWeights:i}=t,{strides:l,pad:c,dilations:u,dimRoundingMode:d,activation:p}=s,h=u;h==null&&(h=[1,1]),v.assert(N.eitherStridesOrDilationsAreOne(l,h),()=>`Error in depthwiseConv2d: Either strides or dilations must be 1. Got strides ${l} and dilations '${h}'`);let f=N.computeConv2DInfo(r.shape,a.shape,l,h,c,d,!0),m=[r,a],g=o!=null,A=i!=null;g&&m.push(o),A&&m.push(i);let x;f.batchSize===1&&f.inHeight===f.outHeight&&f.inWidth===f.outWidth&&f.strideHeight===1&&f.strideWidth===1&&f.filterHeight===f.filterWidth&&f.inChannels===f.outChannels&&f.filterHeight===3&&f.inChannels%4==0?x=new sC(f,g,p,A):x=new rC(f,g,p,A);let y=[{type:"int32",data:[f.padInfo.top,f.padInfo.left]},{type:"int32",data:[f.strideHeight,f.strideWidth]},{type:"int32",data:[f.dilationHeight,f.dilationWidth]},{type:"int32",data:[f.inHeight,f.inWidth]}];return n.runWebGPUProgram(x,m,"float32",y)}var Pce={kernelName:go,backendName:"webgpu",kernelFunc:_ce},Fce=class{constructor(e,t){this.variableNames=["A","indices"],this.workGroupSize=[64,1,1],this.outputShape=t,this.dispatchLayout=Ke(this.outputShape),this.dispatch=Oe(this.dispatchLayout,this.outputShape,this.workGroupSize),this.shaderKey=`gathernd_${e}`,this.size=v.sizeFromShape(this.outputShape),this.sliceDim=e,this.uniforms=`sliceDim : i32; strides : ${Jt(e)};`}getUserCode(){let e;return this.sliceDim>1?e="uniforms.strides[j]":e="uniforms.strides",`
${Fe()} {
${Le()}
let coords = getOutputCoords(globalId, index);
var flattenIndex = 0;
for (var j = 0; j < uniforms.sliceDim; j = j + 1) {
let indexTemp = i32(round(getIndices(coords[0], j)));
let strideNum = ${e};
flattenIndex = flattenIndex + indexTemp * strideNum;
}
if (index < uniforms.size) {
setOutputFlat(index, getA(flattenIndex, coords[1]));
}
}
`}};function Oce(e){let{inputs:t,backend:n}=e,{params:s,indices:r}=t,a=r.shape,o=a[a.length-1],i=v.sizeFromShape(s.shape),[l,c,u,d]=N.prepareAndValidate(s,r),p=je({inputs:{x:r},backend:n,attrs:{shape:[c,o]}}),h=je({inputs:{x:s},backend:n,attrs:{shape:[v.sizeFromShape(s.shape)/u,u]}});if(n.shouldExecuteOnCPU([s,r])||s.dtype==="string"){let x=n.readSync(r.dataId),y=n.bufferSync(s),b=$le(x,y,s.dtype,c,o,u,d,s.shape,i);return n.makeTensorInfo(l,s.dtype,b.values)}let f=new Fce(o,[c,u]),m=[{type:"int32",data:[o]},{type:"int32",data:d}],g=n.runWebGPUProgram(f,[h,p],h.dtype,m),A=je({inputs:{x:g},backend:n,attrs:{shape:l}});return n.disposeData(p.dataId),n.disposeData(h.dataId),n.disposeData(g.dataId),A}var Mce={kernelName:pi,backendName:"webgpu",kernelFunc:Oce},zce=class{constructor(e,t){this.variableNames=["A","indices"],this.workGroupSize=[64,1,1],this.outputShape=e.slice(),this.aShape=e,this.outputShape=t,this.dispatchLayout=Ke(this.outputShape),this.dispatch=Oe(this.dispatchLayout,this.outputShape,this.workGroupSize),this.shaderKey="gather",this.size=v.sizeFromShape(this.outputShape)}getUserCode(){let e=Lce(this.aShape,"i32");return`
${Fe()} {
${Le()}
let resRC = getOutputCoords(globalId, index);
if (index < uniforms.size) {
setOutputFlat(index, getA(${e}));
}
}
`}};function Lce(e,t="int"){let n=["resRC.x","resRC.y","resRC.z","resRC.w"],s=[];for(let r=0;r<e.length;r++)r===2?s.push(`${t}(getIndices(resRC.x, resRC.z))`):s.push(`${n[r]}`);return s.join()}function cC(e){let{inputs:t,backend:n,attrs:s}=e,{x:r,indices:a}=t,{axis:o,batchDims:i}=s,l=v.parseAxisParam(o,r.shape)[0],c=N.segment_util.collectGatherOpShapeInfo(r,a,l,i),u=v.sizeFromShape(a.shape),d=[],p=je({inputs:{x:r},backend:n,attrs:{shape:[c.batchSize,c.outerSize,c.dimSize,c.sliceSize]}}),h=je({inputs:{x:a},backend:n,attrs:{shape:[c.batchSize,u/c.batchSize]}});d.push(p),d.push(h);let f=[c.batchSize,c.outerSize,u/c.batchSize,c.sliceSize];if(n.shouldExecuteOnCPU([r,a])){let y=n.tensorMap.get(h.dataId).values,b=Be(h.shape,h.dtype,y),k=n.tensorMap.get(p.dataId).values,C=Be(p.shape,p.dtype,k),E=Dle(C,b,f);return d.forEach(R=>n.disposeData(R.dataId)),n.makeTensorInfo(c.outputShape,E.dtype,E.values)}let m=new zce(p.shape,f),g=n.runWebGPUProgram(m,[p,h],p.dtype);d.push(g);let A=je({inputs:{x:g},backend:n,attrs:{shape:c.outputShape}});return d.forEach(x=>n.disposeData(x.dataId)),A}var Bce={kernelName:di,backendName:"webgpu",kernelFunc:cC},Wce=jn({opSnippet:Vt.GREATER,cpuKernelImpl:Ple,dtype:"bool"}),Vce={kernelName:hi,backendName:"webgpu",kernelFunc:Wce},Uce=jn({opSnippet:Vt.GREATER_EQUAL,dtype:"bool",cpuKernelImpl:_le}),Gce={kernelName:La,backendName:"webgpu",kernelFunc:Uce},Hce=jn({opSnippet:Vt.LESS,dtype:"bool",cpuKernelImpl:Ole}),jce={kernelName:mi,backendName:"webgpu",kernelFunc:Hce},qce=jn({opSnippet:Vt.LESS_EQUAL,dtype:"bool",cpuKernelImpl:Fle}),Xce={kernelName:gi,backendName:"webgpu",kernelFunc:qce},Kce=Cn({opType:vt.LOG,cpuKernelImpl:Mle}),Zce={kernelName:Wa,backendName:"webgpu",kernelFunc:Kce},Yce=jn({opSnippet:Vt.LOGICAL_AND,dtype:"bool"}),Jce={kernelName:Ai,backendName:"webgpu",kernelFunc:Yce},Qce=Cn({opType:vt.LOGICAL_NOT}),ede={kernelName:ru,backendName:"webgpu",kernelFunc:Qce};function dC(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{reductionIndices:a,keepDims:o}=s;return rp(r,a,o,"max",n)}var tde={kernelName:Va,backendName:"webgpu",kernelFunc:dC},nde=jn({opSnippet:Vt.MAX,cpuKernelImpl:Lle}),sde={kernelName:Ua,backendName:"webgpu",kernelFunc:nde};function rde(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{filterSize:a,strides:o,pad:i,dimRoundingMode:l}=s,c=1,u=N.computePool2DInfo(r.shape,a,o,c,i,l),d,p=[];if(u.filterHeight===1&&u.filterWidth===1){if(v.arraysEqual(u.inShape,u.outShape))return Js({inputs:{x:r},backend:n});d=new Z4(u),p.push({type:"int32",data:[u.strideHeight,u.strideWidth]})}else d=new K4(u,"max"),p.push({type:"int32",data:[u.strideHeight,u.strideWidth]},{type:"int32",data:[u.padInfo.top,u.padInfo.left]},{type:"int32",data:[u.dilationHeight,u.dilationWidth]},{type:"int32",data:[u.inHeight,u.inWidth]},{type:"int32",data:[u.effectiveFilterHeight,u.effectiveFilterWidth]});return n.runWebGPUProgram(d,[r],r.dtype,p)}var ade={kernelName:Ga,backendName:"webgpu",kernelFunc:rde};function ode(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{keepDims:a,axis:o}=s;return rp(r,o,a,"mean",n)}var ide={kernelName:Ha,backendName:"webgpu",kernelFunc:ode};function lde(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{axis:a,keepDims:o}=s;return rp(r,a,o,"min",n)}var ude={kernelName:ja,backendName:"webgpu",kernelFunc:lde},cde=jn({opSnippet:Vt.MIN,cpuKernelImpl:Ble}),dde={kernelName:qa,backendName:"webgpu",kernelFunc:cde},pde=class{constructor(e,t,n){this.uniforms="",this.variableNames=["x"],this.workGroupSize=[64,1,1],this.outputShape=t.map((s,r)=>s[0]+e[r]+s[1]),this.dispatchLayout=Ke(this.outputShape),this.dispatch=Oe(this.dispatchLayout,this.outputShape,this.workGroupSize),this.xShape=e,t.map((s,r)=>{this.uniforms+=` pad${r} : vec2<i32>;`}),this.offset=n==="reflect"?0:1,this.shaderKey=`mirrorPad_${n}`,this.size=v.sizeFromShape(this.outputShape)}getUserCode(){let e=this.xShape.length,t=this.xShape.map((l,c)=>`uniforms.pad${c}[0]`).join(","),n=this.xShape.map((l,c)=>`uniforms.pad${c}[0] + uniforms.xShape${e>1?`[${c}]`:""}`).join(","),s=e===1?"start":"start[i]",r=e===1?"end":"end[i]",a=e===1?"outC":"outC[i]",o=Jt(e),i=e>1?["coords[0]","coords[1]","coords[2]","coords[3]"].slice(0,e):"coords";return`
${Fe()} {
${Le()}
let start = ${o}(${t});
let end = ${o}(${n});
var outC = getOutputCoords(globalId, index);
if (index < uniforms.size) {
for (var i = 0; i < ${e}; i = i + 1) {
if (${a} < ${s}) {
${a} = ${s} * 2 - ${a} - ${this.offset};
} elseif(${a} >= ${r}) {
${a} = (${r} - 1) * 2 - ${a} + ${this.offset};
}
}
let coords = outC - start;
setOutputFlat(index, getX(${i}));
}
}
`}},hde={kernelName:Xa,backendName:"webgpu",kernelFunc:({inputs:e,attrs:t,backend:n})=>{let{x:s}=e,{paddings:r,mode:a}=t,o=n,i=r.map(u=>({type:"int32",data:[u[0],u[1]]})),l=new pde(s.shape,r,a);return o.runWebGPUProgram(l,[s],s.dtype,i)}};function fde(e){let{inputs:t,backend:n}=e,{x:s}=t;if(n.shouldExecuteOnCPU([s])){let a=n.tensorMap.get(s.dataId),[o,i]=Vle(a.values,s.shape,s.dtype);return n.makeTensorInfo(i,s.dtype,o)}let r=new Fm(s.shape,vt.NEG);return n.runWebGPUProgram(r,[s],s.dtype)}var mde={kernelName:yi,backendName:"webgpu",kernelFunc:fde};function gde(e){console.warn("tf.nonMaxSuppression() in webgpu locks the UI thread. Call tf.nonMaxSuppressionAsync() instead");let{inputs:t,backend:n,attrs:s}=e,{boxes:r,scores:a}=t,{maxOutputSize:o,iouThreshold:i,scoreThreshold:l}=s,c=n.readSync(r.dataId),u=n.readSync(a.dataId),{selectedIndices:d}=Xs.nonMaxSuppressionV3Impl(c,u,o,i,l);return n.makeTensorInfo([d.length],"int32",new Int32Array(d))}var Ade={kernelName:bi,backendName:"webgpu",kernelFunc:gde};function yde(e){console.warn("tf.nonMaxSuppression() in webgpu locks the UI thread. Call tf.nonMaxSuppressionAsync() instead");let{inputs:t,backend:n,attrs:s}=e,{boxes:r,scores:a}=t,{maxOutputSize:o,iouThreshold:i,scoreThreshold:l,softNmsSigma:c}=s,u=n.readSync(r.dataId),d=n.readSync(a.dataId),p=o,h=i,f=l,m=c,{selectedIndices:g,selectedScores:A}=Xs.nonMaxSuppressionV5Impl(u,d,p,h,f,m);return[n.makeTensorInfo([g.length],"int32",new Int32Array(g)),n.makeTensorInfo([A.length],"float32",new Float32Array(A))]}var xde={kernelName:vi,backendName:"webgpu",kernelFunc:yde};function Mm(e){let{inputs:t,backend:n}=e,{x:s}=t;if(s.dtype==="complex64"){let r=sp({inputs:{input:s},backend:n}),a=Mm({inputs:{x:r},backend:n}),o=Om({inputs:{input:s},backend:n}),i=Mm({inputs:{x:o},backend:n}),l=nc({inputs:{real:a,imag:i},backend:n});return n.disposeData(r.dataId),n.disposeData(a.dataId),n.disposeData(o.dataId),n.disposeData(i.dataId),l}else return rc({attrs:{shape:s.shape,dtype:s.dtype,value:s.dtype==="string"?"":0},backend:n})}var bde={kernelName:Bi,backendName:"webgpu",kernelFunc:Mm};function pC(e){let{inputs:t,backend:n}=e,{x:s}=t;if(s.dtype==="string")throw new Error("onesLike is not supported under string dtype");if(s.dtype==="complex64"){let r=sp({inputs:{input:s},backend:n}),a=pC({inputs:{x:r},backend:n}),o=Om({inputs:{input:s},backend:n}),i=Mm({inputs:{x:o},backend:n}),l=nc({inputs:{real:a,imag:i},backend:n});return n.disposeData(r.dataId),n.disposeData(a.dataId),n.disposeData(o.dataId),n.disposeData(i.dataId),l}else return rc({attrs:{shape:s.shape,dtype:s.dtype,value:1},backend:n})}var vde={kernelName:wi,backendName:"webgpu",kernelFunc:pC};function wde(e){let{inputs:t,backend:n,attrs:s}=e,{axis:r}=s;if(t.length===1)return px({inputs:{input:t[0]},backend:n,attrs:{dim:r}});let a=t[0].shape,o=t[0].dtype;t.forEach(u=>{v.assertShapesMatch(a,u.shape,"All tensors passed to stack must have matching shapes"),v.assert(o===u.dtype,()=>"All tensors passed to stack must have matching dtypes")});let i=[],l=t.map(u=>{let d=px({inputs:{input:u},backend:n,attrs:{dim:r}});return i.push(d),d}),c=J4({inputs:l,backend:n,attrs:{axis:r}});return i.forEach(u=>n.disposeData(u.dataId)),c}var kde={kernelName:Si,backendName:"webgpu",kernelFunc:wde},Sde=class{constructor(e,t){this.variableNames=["x"],this.uniforms="constantValue : f32;",this.workGroupSize=[64,1,1],this.outputShape=t.map((n,s)=>n[0]+e[s]+n[1]),this.dispatchLayout=Ke(this.outputShape),this.dispatch=Oe(this.dispatchLayout,this.outputShape,this.workGroupSize),t.map((n,s)=>{this.uniforms+=` pad${s} : vec2<i32>;`}),this.xShape=e,this.shaderKey="pad",this.size=v.sizeFromShape(this.outputShape)}getUserCode(){let e=this.xShape.length,t=Jt(e),n=this.xShape.map((u,d)=>`uniforms.pad${d}[0]`).join(","),s=this.xShape.map((u,d)=>`uniforms.pad${d}[0] + uniforms.xShape${e>1?`[${d}]`:""}`).join(","),r=e>1?`${t}(${n})`:`${n}`,a=e>1?`${t}(${s})`:`${s}`,o=e>1?"any(outC < start)":"outC < start",i=e>1?"any(outC >= end)":"outC >= end",l=e>1?["coords[0]","coords[1]","coords[2]","coords[3]"].slice(0,e):"coords";return`
${Fe()} {
${Le()}
let start = ${r};
let end = ${a};
if (index < uniforms.size) {
let outC = getOutputCoords(globalId, index);
if (${o} || ${i}) {
setOutputFlat(index, uniforms.constantValue);
} else {
let coords = outC - start;
setOutputFlat(index, getX(${l}));
}
}
}
`}},hC=e=>{let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{paddings:a,constantValue:o}=s;if(a.every(c=>v.arraysEqual(c,[0,0])))return Js({inputs:{x:r},backend:n});if(v.sizeFromShape(r.shape)===0){let c=a.map((u,d)=>u[0]+r.shape[d]+u[1]);return rc({backend:n,attrs:{shape:c,value:o,dtype:r.dtype}})}let i=[{type:"float32",data:[o]}];a.map(c=>i.push({type:"int32",data:[c[0],c[1]]}));let l=new Sde(r.shape,a);return n.runWebGPUProgram(l,[r],r.dtype,i)},Ide={kernelName:Za,backendName:"webgpu",kernelFunc:hC},Cde=jn({opSnippet:Vt.POW}),Tde={kernelName:Ya,backendName:"webgpu",kernelFunc:Cde};function Nde(e){let{inputs:t,backend:n}=e,{x:s,alpha:r}=t,a=new j4(Vt.PRELU,s.shape,r.shape);return n.runWebGPUProgram(a,[s,r],"float32")}var Ede={kernelName:Ja,backendName:"webgpu",kernelFunc:Nde};function Rde(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{axis:a,keepDims:o}=s;return rp(r,a,o,"prod",n)}var $de={kernelName:Ii,backendName:"webgpu",kernelFunc:Rde},Dde=e=>{let{backend:t,attrs:n}=e,{start:s,stop:r,step:a,dtype:o}=n,i=Hle(s,r,a,o);return t.makeTensorInfo([i.length],o,i)},_de={kernelName:iu,backendName:"webgpu",kernelFunc:Dde},fC=jn({opSnippet:Vt.DIV}),Pde={kernelName:_a,backendName:"webgpu",kernelFunc:fC},Fde=Cn({opType:vt.RELU}),Ode={kernelName:Qa,backendName:"webgpu",kernelFunc:Fde},Mde=Cn({opType:vt.RELU6}),zde={kernelName:to,backendName:"webgpu",kernelFunc:Mde},Lde=class{constructor(e,t,n,s,r){this.variableNames=["x"],this.workGroupSize=[64,1,1],this.outputShape=[e[0],t,n,e[3]],this.dispatchLayout=Ke(this.outputShape),this.dispatch=Oe(this.dispatchLayout,this.outputShape,this.workGroupSize),this.alignCorners=s,this.halfPixelCenters=r,this.shaderKey=`resizeBilinear_${s}_${r}_${this.outputShape[1]>1}_${this.outputShape[2]>1}`}getUserCode(){let e=this.alignCorners&&this.outputShape[1]>1,t=this.alignCorners&&this.outputShape[2]>1;return`
${Fe()} {
${Le()}
let coords = getOutputCoords(globalId, index);
if (all(coords < uniforms.outShape)) {
let b = coords[0];
let d = coords[3];
let rc = coords.yz;
let effectiveInSize = vec2<f32>(
${e?"f32(uniforms.xShape.y) - 1.0":"f32(uniforms.xShape.y)"},
${t?"f32(uniforms.xShape.z) - 1.0":"f32(uniforms.xShape.z)"});
let effectiveOutSize = vec2<f32>(
${e?"f32(uniforms.outShape.y) - 1.0":"f32(uniforms.outShape.y)"},
${t?"f32(uniforms.outShape.z) - 1.0":"f32(uniforms.outShape.z)"});
let effectiveInputOverOutputRatioRC =
effectiveInSize / effectiveOutSize;
// Fractional source index
let sourceFracIndexRC = ${this.halfPixelCenters?"(vec2<f32>(rc) + vec2<f32>(0.5)) * effectiveInputOverOutputRatioRC - vec2<f32>(0.5)":"vec2<f32>(rc) * effectiveInputOverOutputRatioRC"};
// Compute the four integer indices.
let sourceFloorRC = vec2<i32>(sourceFracIndexRC);
let sourceCeilRC = vec2<i32>(
min(vec2<f32>(uniforms.xShape.yz) - vec2<f32>(1.0), ceil(sourceFracIndexRC)));
let topLeft = getX(b, sourceFloorRC.x, sourceFloorRC.y, d);
let bottomLeft = getX(b, sourceCeilRC.x, sourceFloorRC.y, d);
let topRight = getX(b, sourceFloorRC.x, sourceCeilRC.y, d);
let bottomRight = getX(b, sourceCeilRC.x, sourceCeilRC.y, d);
let fracRC = sourceFracIndexRC - vec2<f32>(sourceFloorRC);
let top = topLeft + (topRight - topLeft) * fracRC.y;
let bottom = bottomLeft + (bottomRight - bottomLeft) * fracRC.y;
let newValue = top + (bottom - top) * fracRC.x;
setOutput(b, coords[1], coords[2], d, newValue);
}
}
`}};function Bde(e){let{inputs:t,backend:n,attrs:s}=e,{images:r}=t,{alignCorners:a,size:o,halfPixelCenters:i}=s,[l,c]=o,u=new Lde(r.shape,l,c,a,i);return n.runWebGPUProgram(u,[r],"float32")}var Wde={kernelName:eo,backendName:"webgpu",kernelFunc:Bde},Vde=class{constructor(e,t,n,s,r){this.variableNames=["x"],this.workGroupSize=[64,1,1],this.outputShape=[e[0],t,n,e[3]],this.dispatchLayout=Ke(this.outputShape),this.dispatch=Oe(this.dispatchLayout,this.outputShape,this.workGroupSize),this.alignCorners=s,this.halfPixelCenters=r,this.shaderKey=`resizeNearest_${s}_${this.outputShape[1]>1}_${this.outputShape[2]>1}_${r}`}getUserCode(){let e=this.alignCorners?"0.5":"0.0",t;this.halfPixelCenters?t="max((vec2<f32>(rc) + vec2<f32>(0.5)) * effectiveInputOverOutputRatioRC, vec2<f32>(0.0))":t="vec2<f32>(rc) * effectiveInputOverOutputRatioRC";let n=this.alignCorners&&this.outputShape[1]>1,s=this.alignCorners&&this.outputShape[2]>1;return`
${Fe()} {
${Le()}
let coords = getOutputCoords(globalId, index);
if (all(coords < uniforms.outShape)) {
let b = coords[0];
let d = coords[3];
let rc = coords.yz;
let effectiveInSize = vec2<f32>(
${n?"f32(uniforms.xShape.y) - 1.0":"f32(uniforms.xShape.y)"},
${s?"f32(uniforms.xShape.z) - 1.0":"f32(uniforms.xShape.z)"});
let effectiveOutSize = vec2<f32>(
${n?"f32(uniforms.outShape.y) - 1.0":"f32(uniforms.outShape.y)"},
${s?"f32(uniforms.outShape.z) - 1.0":"f32(uniforms.outShape.z)"});
let effectiveInputOverOutputRatioRC =
effectiveInSize / effectiveOutSize;
// Fractional source index
let sourceFracIndexRC = ${t};
// Compute the coordinators of nearest neighbor point.
let inputShapeRC = vec2<f32>(f32(uniforms.xShape.y), f32(uniforms.xShape.z));
let sourceNearestRC = vec2<i32>(
min(inputShapeRC - 1.0, floor(sourceFracIndexRC + ${e})));
let newValue = getX(b, sourceNearestRC.x, sourceNearestRC.y, d);
setOutput(b, coords[1], coords[2], d, newValue);
}
}
`}};function Ude(e){let{inputs:t,backend:n,attrs:s}=e,{images:r}=t,{alignCorners:a,halfPixelCenters:o,size:i}=s,[l,c]=i,u=new Vde(r.shape,l,c,a,o);return n.runWebGPUProgram(u,[r],r.dtype)}var Gde={kernelName:uu,backendName:"webgpu",kernelFunc:Ude},Hde=class{constructor(e,t){this.outputShape=[],this.variableNames=["x"],this.workGroupSize=[64,1,1],this.outputShape=e,this.dispatchLayout=Ke(this.outputShape),this.dispatch=Oe(this.dispatchLayout,this.outputShape,this.workGroupSize),this.uniforms=`centerX : f32; centerY : f32; sinRadians : f32;
cosRadians : f32;`,this.shaderKey="rotate",this.size=v.sizeFromShape(this.outputShape),this.outputShape=e,typeof t=="number"?(this.uniforms+=" fillValue : f32;",this.fillSnippet="var outputValue = uniforms.fillValue;",this.shaderKey+="_float"):(this.uniforms+=" fillValue : vec3<f32>;",this.fillSnippet="var outputValue = uniforms.fillValue[coords[3]];",this.shaderKey+="_vec3")}getUserCode(){return`
${Fe()} {
${Le()}
if (index < uniforms.size) {
let coords = getOutputCoords(globalId, index);
let coordXFloat = (f32(coords[2]) - uniforms.centerX) *
uniforms.cosRadians - (f32(coords[1]) - uniforms.centerY) *
uniforms.sinRadians;
let coordYFloat = (f32(coords[2]) - uniforms.centerX) *
uniforms.sinRadians + (f32(coords[1]) - uniforms.centerY) *
uniforms.cosRadians;
let coordX = i32(round(coordXFloat + uniforms.centerX));
let coordY = i32(round(coordYFloat + uniforms.centerY));
${this.fillSnippet}
if(coordX >= 0 && coordX < uniforms.xShape[2] && coordY >= 0 &&
coordY < uniforms.xShape[1]) {
outputValue = getX(coords[0], coordY, coordX, coords[3]);
}
setOutputFlat(index, outputValue);
}
}
`}},jde={kernelName:Wi,backendName:"webgpu",kernelFunc:({inputs:e,attrs:t,backend:n})=>{let{image:s}=e,{radians:r,fillValue:a,center:o}=t,i=n,l=new Hde(s.shape,a),[c,u]=N.getImageCenter(o,s.shape[1],s.shape[2]),d=[{type:"float32",data:[c]},{type:"float32",data:[u]},{type:"float32",data:[Math.sin(r)]},{type:"float32",data:[Math.cos(r)]}];return typeof a=="number"?d.push({type:"float32",data:[Number.parseFloat(a.toFixed(2))]}):d.push({type:"float32",data:a}),i.runWebGPUProgram(l,[s],s.dtype,d)}},qde=Cn({opType:vt.RSQRT,cpuKernelImpl:jle}),Xde={kernelName:no,backendName:"webgpu",kernelFunc:qde},Kde=class{constructor(e,t,n,s,r,a,o){this.variableNames=["updates","indices"],this.workGroupSize=[64,1,1],this.atomic=!0,this.outputShape=a,this.type=o,this.dispatchLayout=Ke(e),this.dispatch=Oe(this.dispatchLayout,e,this.workGroupSize),this.sliceDimGreaterThanOne=t>1,this.shaderKey=`scatter_${n}_${s}_${this.sliceDimGreaterThanOne}_${o}`,this.size=v.sizeFromShape(e);let i=Jt(r.length);this.uniforms=`sliceDim : i32; strides: ${i};`,this.updatesRank=s,this.indicesRank=n}getUserCode(){let e="";this.indicesRank===1?e="coords[0]":this.indicesRank===2&&(e="coords[0], j");let t=`getIndices(${e})`,n=this.sliceDimGreaterThanOne?"uniforms.strides[j]":"uniforms.strides",s="",r="",a="";this.updatesRank===1?(s="coords[0]",r="flattenedIndex",a=`
fn getUpdatesCoordsFromFlatIndex(index : i32) -> i32 {
return index;
}
`):this.updatesRank===2&&(s="coords[0], coords[1]",r="vec2<i32>(flattenedIndex, coords[1])",a=`
fn getUpdatesCoordsFromFlatIndex(index : i32) -> vec2<i32> {
let d0 = index / uniforms.updatesShape[1];
let d1 = index - d0 * uniforms.updatesShape[1];
return vec2<i32>(d0, d1);
}
`);let o=`getUpdates(${s})`,i=this.type==="int32"?"ignore(atomicAdd(&(result.numbers[flatIndex]), i32(updateValue)));":`
var assumed = atomicLoad(&(result.numbers[flatIndex]));
var success = 0;
for (; success == 0;) {
let new = bitcast<f32>(assumed) + updateValue;
let newI32 = bitcast<i32>(new);
let resValue = atomicCompareExchangeWeak(&(result.numbers[flatIndex]), assumed, newI32);
assumed = resValue[0];
success = resValue[1];
}
`;return`
${a}
${Fe()} {
${Le()}
if (index < uniforms.size) {
let coords = getUpdatesCoordsFromFlatIndex(index);
var flattenedIndex = 0;
for (var j = 0; j < uniforms.sliceDim; j = j + 1) {
let indexInside = i32(round(${t}));
flattenedIndex = flattenedIndex + indexInside * ${n};
}
let updateValue = ${o};
let flatIndex = getOutputFlatIndex(${r});
${i}
}
}`}};function Zde(e){let{inputs:t,backend:n,attrs:s}=e,{indices:r,updates:a}=t,{shape:o}=s,{sliceRank:i,numUpdates:l,sliceSize:c,strides:u,outputSize:d}=N.calculateShapes(a,r,o),p=[d/c,c];if(d===0)return n.makeTensorInfo(o,r.dtype);let h=je({inputs:{x:r},backend:n,attrs:{shape:[l,i]}}),f=je({inputs:{x:a},backend:n,attrs:{shape:[l,c]}}),m=f.dtype,g=rc({backend:n,attrs:{shape:p,value:0,dtype:m}}),A=[{type:"int32",data:[i]},{type:"int32",data:u}],x=new Kde(f.shape,i,h.shape.length,f.shape.length,u,p,m),y=n.runWebGPUProgram(x,[f,h],m,A,g),b=je({inputs:{x:y},backend:n,attrs:{shape:o}});return n.disposeData(h.dataId),n.disposeData(f.dataId),n.disposeData(y.dataId),b}var Yde={kernelName:Ei,backendName:"webgpu",kernelFunc:Zde},Jde=class{constructor(e,t,n){this.variableNames=["c","a","b"],this.workGroupSize=[64,1,1],this.outputShape=t,this.dispatchLayout=Ke(this.outputShape),this.dispatch=Oe(this.dispatchLayout,this.outputShape,this.workGroupSize),this.cRank=e,this.rank=n,this.shaderKey="select",this.size=v.sizeFromShape(this.outputShape)}getUserCode(){let e,t;if(this.rank>4)throw Error(`Where for rank ${this.rank} is not yet supported`);if(this.rank===1)t="resRC",e="resRC";else{let s=["resRC.x","resRC.y","resRC.z","resRC.w"],r=[],a=[];for(let o=0;o<this.outputShape.length;o++)a.push(`${s[o]}`),o<this.cRank&&r.push(`${s[o]}`);e=r.join(),t=a.join()}return`
${Fe()} {
${Le()}
if (index < uniforms.size) {
let resRC = getOutputCoords(globalId, index);
let cVal = getC(${e});
if (cVal >= 1.0) {
setOutputFlat(index, getA(${t}));
} else {
setOutputFlat(index, getB(${t}));
}
}
}
`}};function Qde(e){let{inputs:t,backend:n}=e,{condition:s,t:r,e:a}=t,o=new Jde(s.shape.length,r.shape,r.shape.length);return n.runWebGPUProgram(o,[s,r,a],Ln(r.dtype,a.dtype))}var epe={kernelName:Ri,backendName:"webgpu",kernelFunc:Qde},tpe=Cn({opType:vt.SIGMOID}),npe={kernelName:ro,backendName:"webgpu",kernelFunc:tpe},spe=Cn({opType:vt.SIN}),rpe={kernelName:so,backendName:"webgpu",kernelFunc:spe},ape=Cn({opType:vt.SINH}),ope={kernelName:Di,backendName:"webgpu",kernelFunc:ape},mC=jn({opSnippet:Vt.SUB,cpuKernelImpl:Yle,supportsComplex:!0}),ipe={kernelName:uo,backendName:"webgpu",kernelFunc:mC};function lpe(e){let{inputs:t,backend:n,attrs:s}=e,{logits:r}=t,{dim:a}=s,o=v.parseAxisParam([a],r.shape),i=dC({inputs:{x:r},backend:n,attrs:{reductionIndices:o,keepDims:!1}}),l=N.expandShapeToKeepDim(i.shape,o),c=je({inputs:{x:i},backend:n,attrs:{shape:l}}),u=mC({inputs:{a:r,b:c},backend:n}),d=oC({inputs:{x:u},backend:n}),p=dx({inputs:{x:d},backend:n,attrs:{axis:o,keepDims:!1}}),h=je({inputs:{x:p},backend:n,attrs:{shape:l}}),f=fC({inputs:{a:d,b:h},backend:n});return n.disposeData(i.dataId),n.disposeData(c.dataId),n.disposeData(u.dataId),n.disposeData(d.dataId),n.disposeData(p.dataId),n.disposeData(h.dataId),f}var upe={kernelName:io,backendName:"webgpu",kernelFunc:lpe},cpe=e=>{let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{blockShape:a,paddings:o}=s;v.assert(r.shape.length<=4,()=>"spaceToBatchND for rank > 4 with a WebGPU backend not implemented yet");let i=a.reduce((A,x)=>A*x),l=[[0,0]];l.push(...o);for(let A=1+a.length;A<r.shape.length;++A)l.push([0,0]);let c=[],u=hC({inputs:{x:r},backend:n,attrs:{paddings:l,constantValue:0}}),d=N.getReshaped(u.shape,a,i,!1),p=N.getPermuted(d.length,a.length,!1),h=N.getReshapedPermuted(u.shape,a,i,!1),f=je({inputs:{x:u},backend:n,attrs:{shape:d}}),m=vl({inputs:{x:f},backend:n,attrs:{perm:p}}),g=je({inputs:{x:m},backend:n,attrs:{shape:h}});return c.push(u),c.push(f),c.push(m),c.forEach(A=>n.disposeData(A.dataId)),g},dpe={kernelName:_i,backendName:"webgpu",kernelFunc:cpe},ppe=class{constructor(e,t,n,s,r,a,o=!0){this.variableNames=["updates","indices","defaultValue"],this.workGroupSize=[64,1,1],this.workPerThread=4,this.outputShape=a,this.dispatchLayout=Ke(this.outputShape),this.dispatch=Oe(this.dispatchLayout,this.outputShape,this.workGroupSize,[this.workPerThread,1,1]);let i=t>1;this.shaderKey=`scatter_${n}_${s}_${i}`,this.size=v.sizeFromShape(this.outputShape);let l=Jt(r.length);this.uniforms=`updateSize : i32; sliceDim : i32; strides: ${l};`;let c="";n===1?c="i":n===2&&(c="i, j"),this.indicesSnippet=`getIndices(${c})`;let u="";s===1?u="i":s===2&&(u="i, coords[1]"),this.updatesSnippet=`getUpdates(${u})`,this.strideString=i?"uniforms.strides[j]":"uniforms.strides"}getUserCode(){return`
${Fe()} {
${Le()}
let globalIndex = index * ${this.workPerThread};
if (globalIndex < uniforms.size) {
var sum = vec4<f32>(0.0);
var found = vec4<bool>(false);
for (var i = 0; i < uniforms.updateSize; i = i + 1) {
var flattenedIndex = 0;
for (var j = 0; j < uniforms.sliceDim; j = j + 1) {
let indexInside = i32(round(${this.indicesSnippet}));
flattenedIndex = flattenedIndex + indexInside * ${this.strideString};
}
for (var innerIndex = 0; innerIndex < ${this.workPerThread}; innerIndex = innerIndex + 1) {
let curIndex = globalIndex + innerIndex;
let coords = getCoordsFromFlatIndex(curIndex);
if (flattenedIndex == coords[0]) {
sum[innerIndex] = sum[innerIndex] + ${this.updatesSnippet};
found[innerIndex] = true;
}
}
}
for (var innerIndex = 0; innerIndex < ${this.workPerThread}; innerIndex = innerIndex + 1) {
let curIndex = globalIndex + innerIndex;
if (curIndex < uniforms.size)
{
setOutputFlat(curIndex, mix(getDefaultValue(), sum[innerIndex], f32(found[innerIndex])));
}
}
}
}`}};function hpe(e){let{inputs:t,backend:n,attrs:s}=e,{sparseIndices:r,sparseValues:a,defaultValue:o}=t,{outputShape:i}=s,{sliceRank:l,numUpdates:c,strides:u,outputSize:d}=N.calculateShapes(a,r,i),p=!1,h=[{type:"int32",data:[c]},{type:"int32",data:[l]},{type:"int32",data:u}],f=new ppe(c,l,r.shape.length,a.shape.length,u,[d,1],p),m=n.runWebGPUProgram(f,[a,r,o],a.dtype,h),g=je({inputs:{x:m},backend:n,attrs:{shape:i}});return n.disposeData(m.dataId),g}var fpe={kernelName:Xc,backendName:"webgpu",kernelFunc:hpe};function mpe(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{numOrSizeSplits:a,axis:o}=s,i=v.parseAxisParam(o,r.shape)[0],l=N.prepareSplitSize(r,a,i),c=r.shape.length,u=new Array(c).fill(0),d=r.shape.slice();return l.map(p=>{let h=[...d];h[i]=p;let f=sc({inputs:{x:r},backend:n,attrs:{begin:u,size:h}});return u[i]+=p,f})}var gpe={kernelName:Pi,backendName:"webgpu",kernelFunc:mpe},Ape=Cn({opType:vt.SQRT}),ype={kernelName:ao,backendName:"webgpu",kernelFunc:Ape},xpe={kernelName:hu,backendName:"webgpu",kernelFunc:({inputs:e,backend:t})=>{let{x:n}=e,s=t,r=new Fm(n.shape,vt.SQUARE);return s.runWebGPUProgram(r,[n],n.dtype)}},bpe=jn({opSnippet:Vt.SQUARED_DIFFERENCE}),vpe={kernelName:lo,backendName:"webgpu",kernelFunc:bpe},wpe=class{constructor(e){this.variableNames=["x"],this.workPerThread=1,this.workGroupSize=[64,1,1],this.outputShape=e,this.dispatchLayout=Ke(this.outputShape),this.dispatch=Oe(this.dispatchLayout,this.outputShape,this.workGroupSize,[this.workPerThread,1,1]);let t=Jt(this.outputShape.length);this.uniforms=`begin : ${t}; strides : ${t}; `,this.shaderKey="stridedSlice",this.size=v.sizeFromShape(this.outputShape)}getUserCode(){let e=this.outputShape.length,t="";if(e===1)t="coords * uniforms.strides + uniforms.begin";else{let s=0;t=this.outputShape.map((r,a)=>(s++,this.outputShape.length===1?`coords * uniforms.strides[${a}] + uniforms.begin[${a}]`:`coords[${s-1}] * uniforms.strides[${a}] + uniforms.begin[${a}]`)).join(",")}return`
${Fe()} {
${Le()}
if (index < uniforms.size) {
let coords = getOutputCoords(globalId, index);
setOutputFlat(index, getX(${t}));
}
}
`}};function kpe(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{begin:a,end:o,strides:i,beginMask:l,endMask:c,ellipsisMask:u,newAxisMask:d,shrinkAxisMask:p}=s,{finalShapeSparse:h,finalShape:f,isIdentity:m,sliceDim0:g,isSimpleSlice:A,begin:x,end:y,strides:b}=Ft.sliceInfo(r.shape,a,o,i,l,c,u,d,p),w;if(m)w=je({inputs:{x:r},backend:n,attrs:{shape:f}});else if(g||A){v.assert(r.shape.length>=1,()=>`Input must have rank at least 1, got: ${r.shape.length}`);let k=Ft.computeOutShape(x,y,b),C=sc({inputs:{x:r},backend:n,attrs:{begin:x,size:k}});w=je({inputs:{x:C},backend:n,attrs:{shape:f}}),n.disposeData(C.dataId)}else if(n.shouldExecuteOnCPU([r])){let C=n.readSync(r.dataId),E=Be(r.shape,r.dtype,C),R=Kle(h,E,b,x);w=n.makeTensorInfo(f,r.dtype,R.values)}else{let C=new wpe(h),E=[{type:"int32",data:x},{type:"int32",data:b}],R=n.runWebGPUProgram(C,[r],r.dtype,E);w=je({inputs:{x:R},backend:n,attrs:{shape:f}}),n.disposeData(R.dataId)}return w}var Spe={kernelName:Fi,backendName:"webgpu",kernelFunc:kpe};function Ipe(e){let{inputs:t,backend:n,attrs:s}=e,{separator:r,nGramWidths:a,leftPad:o,rightPad:i,padWidth:l,preserveShortSequences:c}=s,{data:u,dataSplits:d}=t,p=n.readSync(u.dataId),h=n.readSync(d.dataId),[f,m]=Zle(p,h,r,a,o,i,l,c);return[n.makeTensorInfo([f.length],"string",f),n.makeTensorInfo(d.shape,"int32",m)]}var Cpe={kernelName:Kc,backendName:"webgpu",kernelFunc:Ipe},Tpe=Cn({opType:vt.TANH}),Npe={kernelName:co,backendName:"webgpu",kernelFunc:Tpe},Epe=class{constructor(e,t){this.variableNames=["A"],this.workGroupSize=[64,1,1];let n=new Array(e.length);for(let s=0;s<n.length;s++)n[s]=e[s]*t[s];this.outputShape=n,this.dispatchLayout=Ke(this.outputShape),this.dispatch=Oe(this.dispatchLayout,this.outputShape,this.workGroupSize),this.rank=this.outputShape.length,this.size=v.sizeFromShape(this.outputShape),this.shaderKey="tile"}getUserCode(){let e=Rpe(this.rank,"uniforms.");return`
${Fe()} {
${Le()}
if (index < uniforms.size) {
let resRC = getOutputCoords(globalId, index);
setOutputFlat(index, getA(${e}));
}
}
`}};function Rpe(e,t=""){if(e>=5)throw Error(`Tile for rank ${e} is not yet supported`);if(e===1)return`(resRC % ${t}aShape)`;let n=["resRC.x","resRC.y","resRC.z","resRC.w"],s=[];for(let r=0;r<e;r++)s.push(`(${n[r]} % ${t}aShape[${r}])`);return s.join()}function $pe(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{reps:a}=s;if(n.shouldExecuteOnCPU([r])||r.dtype==="string"||r.shape.length>=5){let l=n.readSync(r.dataId),c=r.dtype==="string"?l.map(p=>v.decodeString(p)):l,u=Be(r.shape,r.dtype,c),d=Jle(u,a);return n.makeTensorInfo(d.shape,d.dtype,d.values)}let o=new Epe(r.shape,a);return n.runWebGPUProgram(o,[r],r.dtype)}var Dpe={kernelName:Gr,backendName:"webgpu",kernelFunc:$pe},_pe=class{constructor(e){this.variableNames=["x","indices"],this.workGroupSize=[256,1,1],this.outputShape=e,this.dispatchLayout=Ke(this.outputShape),this.dispatch=Oe(this.dispatchLayout,this.outputShape,this.workGroupSize),this.uniforms=`inputSize : i32; firstPass : i32; negativeInf : f32;
dir : i32; inc : i32;`,this.shaderKey="swap",this.size=v.sizeFromShape(this.outputShape)}getUserCode(){return`
${Fe()} {
${Le()}
if (index < uniforms.size) {
let outC = getOutputCoords(globalId, index);
let batch = outC[0];
let elemIdx = outC[1];
// We compare elements pair-wise within a group of size 2 * inc.
// The comparing rule for each group alternates between ascending
// and descending. Within each group, we compare each pair at
// positions i and i+inc. To decide whether an element at position i
// is x0 or x1, we mod it by 2 * inc, if the result is smaller than
// inc, it is in the first half of the group, we denote it as x0,
// otherwise we denote it as x1.
// For example, as shown in the Bitonic top K paper referenced
// above, Figure5(a) shows that element[1] is in the second half of
// the group when group size is 2, but it is in the first half of
// the group when group size is 4.
let isFirstInPair = elemIdx % (2 * uniforms.inc) < uniforms.inc;
var i = 0;
if (isFirstInPair) {
i = elemIdx;
} else {
i = elemIdx - uniforms.inc;
}
var i0 = 0;
if (uniforms.firstPass == 1) {
i0 = i;
} else {
i0 = i32(getIndices(batch, i));
}
var i1 = 0;
if (uniforms.firstPass == 1) {
i1 = i + uniforms.inc;
} else {
i1 = i32(getIndices(batch, i + uniforms.inc));
}
var x0 = f32(0.0);
var x1 = f32(0.0);
if (i0 < uniforms.inputSize) {
x0 = getX(batch, i0);
} else {
x0 = uniforms.negativeInf;
}
if (i1 < uniforms.inputSize) {
x1 = getX(batch, i1);
} else {
x1 = uniforms.negativeInf;
}
let reverse = elemIdx % (2 * uniforms.dir) >= uniforms.dir;
let isGreater = x0 > x1 || (x0 == x1 && i1 > i0);
if (reverse == isGreater) {
// Elements in opposite order of direction
let iTemp = i0;
i0 = i1;
i1 = iTemp;
}
if (isFirstInPair) {
setOutputFlat(index, f32(i0));
} else {
setOutputFlat(index, f32(i1));
}
}
}
`}},Ppe=class{constructor(e){this.variableNames=["x","indices"],this.workGroupSize=[256,1,1],this.outputShape=e,this.dispatchLayout=Ke(this.outputShape),this.dispatch=Oe(this.dispatchLayout,this.outputShape,this.workGroupSize),this.uniforms="inputSize : i32; firstPass : i32; k : i32;",this.shaderKey="merge",this.size=v.sizeFromShape(this.outputShape)}getUserCode(){return`
${Fe()} {
${Le()}
if (index < uniforms.size) {
let outC = getOutputCoords(globalId, index);
let batch = outC[0];
let elemIdx = outC[1];
// The output size is half of the previous size.
// If the previous sequence is | | | | _ _ _ _ | | | | _ _ _ _
// (k=4), we only need to output the indices at positions |, the
// indices at positions _ can be thrown away, see Figure5(b) After
// Phase 2 (Merge phase) in the Bitonic Top K paper referenced
// above.
// For example, the paper shows we only need to output the orange
// bars. The output sequence should look like this | | | | | | | |.
// Because the sequence is halved, to map the output index back to
// the previous sequence to find the corresponding value, we need
// to double the index. When we double the index, we basically
// interpolate a position, so 2i looks like
// | _ | _ | _ | _ | _ | _ | _. We move the | to the first k
// position of each 2k positions by - elemIdx % k. E.g. for output
// at index 4,5,6,7, we want to get the corresponding element at
// original index 8,9,10,11, for output at index 8,9,10,11,
// we want to get the corresponding element at original index
// 16,17,18,19, so on and so forth.
var i = 0;
if (elemIdx < uniforms.k) {
i = elemIdx;
} else {
i = elemIdx * 2 - elemIdx % uniforms.k;
}
var i0 = 0;
if (uniforms.firstPass == 1) {
i0 = i;
} else {
i0 = i32(getIndices(batch, i));
}
var i1 = 0;
if (uniforms.firstPass == 1) {
i1 = i + uniforms.k;
} else {
i1 = i32(getIndices(batch, i + uniforms.k));
}
let x0 = getX(batch, i0);
var x1 = f32(0.0);
if (i1 < uniforms.inputSize) {
x1 = getX(batch, i1);
} else {
x1 = x0;
}
if (x0 >= x1) {
setOutputFlat(index, f32(i0));
} else {
setOutputFlat(index, f32(i1));
}
}
}
`}};function oc(e,t){t!==null&&e.disposeData(t.dataId)}function gC(e){let t=1;for(;t<e;)t*=2;return t}function Fpe(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{k:a,sorted:o}=s,i=r.shape,l=i[i.length-1];if(n.shouldExecuteOnCPU([r])){let w=n.readSync(r.dataId),[k,C]=Qle(w,i,r.dtype,a,o);return[n.makeTensorInfo(k.shape,k.dtype,k.values),n.makeTensorInfo(C.shape,C.dtype,C.values)]}if(a===0)return i[i.length-1]=0,[n.makeTensorInfo(i,r.dtype,[]),n.makeTensorInfo(i,"int32",[])];if(l===1)return[r,rc({attrs:{shape:i,dtype:"int32",value:0},backend:n})];let u=v.sizeFromShape(i)/l,d=je({inputs:{x:r},attrs:{shape:[u,l]},backend:n}),p=gC(a),h=gC(l),f=null,m=()=>f===null?[d,d]:[d,f],g=(w,k,C)=>{let E=m(),R=new _pe(C),D=[{type:"int32",data:[l]},{type:"int32",data:[f===null?1:0]},{type:"float32",data:[Number.NEGATIVE_INFINITY]},{type:"int32",data:[w]},{type:"int32",data:[k]}],P=f;f=n.runWebGPUProgram(R,E,"int32",D),oc(n,P)};for(let w=1;w<p;w*=2){let k=w*2;for(let C=w;C>=1;C/=2)g(k,C,[u,h])}for(let w=h;w>p;w/=2){let k=m(),C=new Ppe([u,w/2]),R=[{type:"int32",data:[l]},{type:"int32",data:[f===null?1:0]},{type:"int32",data:[p]}],F=f;f=n.runWebGPUProgram(C,k,"int32",R),oc(n,F);let D=p/2,P=D*2;for(let T=D;T>=1;T/=2)g(P,T,f.shape)}let A=f;f=sc({inputs:{x:f},backend:n,attrs:{begin:0,size:[u,a]}}),oc(n,A);let x=cC({inputs:{x:d,indices:f},backend:n,attrs:{axis:1,batchDims:1}});oc(n,d);let y=i.slice(0,-1);y.push(a),A=f,f=je({inputs:{x:f},attrs:{shape:y},backend:n}),oc(n,A);let b=x;return x=je({inputs:{x},attrs:{shape:y},backend:n}),oc(n,b),[x,f]}var Ope={kernelName:Mi,backendName:"webgpu",kernelFunc:Fpe},Mpe=class{constructor(e){this.variableNames=["Image","Transforms"],this.uniforms="interpolationModeId : i32; fillModeId : i32; fillValue : f32;",this.workGroupSize=[64,1,1],this.outputShape=e,this.dispatchLayout=Ke(this.outputShape),this.dispatch=Oe(this.dispatchLayout,this.outputShape,this.workGroupSize),this.shaderKey="transform"}getUserCode(){return`
fn mapCoord(outCoord : f32, len : f32) -> f32{
var inCoord = outCoord;
if(uniforms.fillModeId == 2) {
if (inCoord < 0.0) {
if (len <= 1.0) {
inCoord = 0.0;
} else {
let sz2 = 2.0 * len;
if (inCoord < sz2) {
inCoord = sz2 * f32(i32(f32(-inCoord / sz2))) +
inCoord;
}
if (inCoord < -len) {
inCoord = inCoord + sz2;
} else {
inCoord = -inCoord - 1.0;
}
}
} elseif (inCoord > len - 1.0) {
if (len <= 1.0) {
inCoord = 0.0;
} else {
let sz2 = 2.0 * len;
inCoord = inCoord - sz2 * f32(i32(f32(inCoord / sz2)));
if (inCoord >= len) {
inCoord = sz2 - inCoord - 1.0;
}
}
}
return clamp(inCoord, 0.0, len - 1.0);
} elseif (uniforms.fillModeId == 3) {
if (inCoord < 0.0) {
if (len <= 1.0) {
inCoord = 0.0;
} else {
let sz = len - 1.0;
inCoord = inCoord + len * (f32(i32(f32(-inCoord / sz))) + 1.0);
}
} elseif (inCoord > len - 1.0) {
if (len <= 1.0) {
inCoord = 0.0;
} else {
let sz = len - 1.0;
inCoord = inCoord - len * f32(i32(f32(inCoord / sz)));
}
}
return clamp(inCoord, 0.0, len - 1.0);
} elseif (uniforms.fillModeId == 4) {
return clamp(outCoord, 0.0, len - 1.0);
}
return outCoord;
}
fn readWithFillValue(batch : i32, coordY : i32, coordX : i32,
channel : i32) -> f32 {
var outputValue : f32;
if (0 <= coordY && coordY < uniforms.imageShape[1] && 0 <= coordX && coordX < uniforms.imageShape[2]) {
outputValue = getImage(batch, coordY, coordX, channel);
} else {
outputValue = uniforms.fillValue;
}
return outputValue;
}
${Fe()} {
${Le()}
let coords = getOutputCoords(globalId, index);
if (coordsInBounds4D(coords, uniforms.outShape)) {
var outputValue : f32;
let batch = coords[0];
let x = coords[2];
let y = coords[1];
let channel = coords[3];
let xf = f32(x);
let yf = f32(y);
let a1 = getTransforms(batch, 0);
let a2 = getTransforms(batch, 1);
let a3 = getTransforms(batch, 2);
let b1 = getTransforms(batch, 3);
let b2 = getTransforms(batch, 4);
let b3 = getTransforms(batch, 5);
let c1 = getTransforms(batch, 6);
let c2 = getTransforms(batch, 7);
let projection = c1 * xf + c2 * yf + 1.0;
if (projection == 0.0) {
outputValue = uniforms.fillValue;
} else {
let inX = (a1 * xf + a2 * yf + a3) / projection;
let inY = (b1 * xf + b2 * yf + b3) / projection;
let mapX = mapCoord(inX, f32(uniforms.imageShape[2]));
let mapY = mapCoord(inY, f32(uniforms.imageShape[1]));
if (uniforms.interpolationModeId == 1) {
let coordY = i32(round(mapY));
let coordX = i32(round(mapX));
outputValue = readWithFillValue(batch, coordY, coordX,
channel);
} else {
let yFloor = floor(mapY);
let xFloor = floor(mapX);
let yCeil = yFloor + 1.0;
let xCeil = xFloor + 1.0;
let valueYFloor = (xCeil - mapX) *
readWithFillValue(batch, i32(yFloor), i32(xFloor), channel) +
(mapX - xFloor) *
readWithFillValue(batch, i32(yFloor), i32(xCeil), channel);
let valueYCeil = (xCeil - mapX) *
readWithFillValue(batch, i32(yCeil), i32(xFloor), channel) +
(mapX - xFloor) *
readWithFillValue(batch, i32(yCeil), i32(xCeil), channel);
outputValue = (yCeil - mapY) * valueYFloor +
(mapY - yFloor) * valueYCeil;
}
}
setOutput(coords[0], coords[1], coords[2], coords[3], outputValue);
}
}
`}};function zpe(e){let{inputs:t,backend:n,attrs:s}=e,{image:r,transforms:a}=t,{interpolation:o,fillMode:i,fillValue:l,outputShape:c}=s,[u,d,p,h]=r.shape,[f,m]=c!=null?c:[d,p],g=[u,f,m,h],A=new Mpe(g),x=o==="nearest"?1:2,y;switch(i){case"constant":y=1;break;case"reflect":y=2;break;case"wrap":y=3;break;case"nearest":y=4;break;default:y=1;break}let b=[{type:"int32",data:[x]},{type:"int32",data:[y]},{type:"float32",data:[l]}];return n.runWebGPUProgram(A,[r,a],"float32",b)}var Lpe={kernelName:zi,backendName:"webgpu",kernelFunc:zpe};function Bpe(e){let{inputs:t,backend:n,attrs:s}=e,{value:r}=t,{axis:a}=s;a<0&&(a+=r.shape.length);let o=r,i=o.shape.length,l=r.shape[a],c=new Array(i-1),u=0;for(let m=0;m<i;m++)m!==a&&(c[u++]=o.shape[m]);let d=[],p=new Array(i).fill(0),h=o.shape.slice();h[a]=1;let f=new Array(l);for(let m=0;m<f.length;m++){p[a]=m;let g=sc({inputs:{x:o},backend:n,attrs:{begin:p,size:h}}),A=je({inputs:{x:g},backend:n,attrs:{shape:c}});f[m]=A,d.push(g)}return d.forEach(m=>n.disposeData(m.dataId)),f}var Wpe={kernelName:Li,backendName:"webgpu",kernelFunc:Bpe},Vpe=[xle,nue,rue,iue,hue,mue,Aue,xue,Sue,Nue,Rue,Pue,kle,zue,Vue,jue,Xue,Zue,Que,nce,rce,uce,dce,hce,mce,fce,Ace,xce,vce,Tce,kce,Ice,Rce,Dce,Pce,Mce,Bce,Vce,Gce,wle,Oue,jce,Xce,Zce,Jce,ede,tde,sde,ade,ide,ude,dde,hde,ace,mde,Ade,xde,Iue,vde,kde,Ide,Ede,$de,Tde,_de,Cue,Pde,Ode,zde,Ale,Wde,Gde,jde,Xde,Yde,epe,npe,rpe,ope,wue,Spe,Cpe,upe,dpe,gpe,fpe,ype,xpe,vpe,ipe,ice,Npe,Dpe,Ope,Lpe,due,Wpe,bde];for(let e of Vpe)jr(e);var Upe=class{constructor(e){this.device=e,this.numUsedBuffers=0,this.numFreeBuffers=0,this.freeBuffers=new Map,this.usedBuffers=new Map,this.numBytesUsed=0,this.numBytesAllocated=0}acquireBuffer(e,t){let n=AC(e,t);if(this.freeBuffers.has(n)||this.freeBuffers.set(n,[]),this.usedBuffers.has(n)||this.usedBuffers.set(n,[]),this.numBytesUsed+=e,this.numUsedBuffers++,this.freeBuffers.get(n).length>0){this.numFreeBuffers--;let r=this.freeBuffers.get(n).shift();return this.usedBuffers.get(n).push(r),r}this.numBytesAllocated+=e;let s=this.device.createBuffer({size:e,usage:t});return this.usedBuffers.get(n).push(s),s}releaseBuffer(e,t,n){if(this.freeBuffers==null)return;let s=AC(t,n);this.freeBuffers.has(s)||this.freeBuffers.set(s,[]),this.freeBuffers.get(s).push(e),this.numFreeBuffers++,this.numUsedBuffers--;let r=this.usedBuffers.get(s),a=r.indexOf(e);if(a<0)throw new Error("Cannot release a buffer that was never provided by this buffer manager");r.splice(a,1),this.numBytesUsed-=t}getNumUsedBuffers(){return this.numUsedBuffers}getNumFreeBuffers(){return this.numFreeBuffers}reset(){this.freeBuffers=new Map,this.usedBuffers=new Map,this.numUsedBuffers=0,this.numFreeBuffers=0,this.numBytesUsed=0,this.numBytesAllocated=0}dispose(){this.freeBuffers==null&&this.usedBuffers==null||(this.freeBuffers.forEach((e,t)=>{e.forEach(n=>{n.destroy()})}),this.usedBuffers.forEach((e,t)=>{e.forEach(n=>{n.destroy()})}),this.freeBuffers=null,this.usedBuffers=null,this.numUsedBuffers=0,this.numFreeBuffers=0,this.numBytesUsed=0,this.numBytesAllocated=0)}};function AC(e,t){return`${e}_${t}`}var yC=class{constructor(){this.outputShape=[0],this.variableNames=[],this.workGroupSize=[256,1,1],this.lastUniformData=[],this.inputTexture=null,this.layout=null,this.lastPixelSize={width:0,height:0},this.disposed=!1,this.shaderKey="fromPixels",this.useImport=!1}updateOutputShape(e){v.arraysEqual(this.outputShape,e)||(this.outputShape=e,this.workPerThread=e[2],this.dispatchLayout=Ke(this.outputShape),this.dispatch=Oe(this.dispatchLayout,this.outputShape,this.workGroupSize,[this.workPerThread,1,1]))}makeFromPixelsSource(){let e=this.useImport?"textureLoad(src, vec2<i32>(coords.yx));":"textureLoad(src, vec2<i32>(coords.yx), 0)";return`
[[binding(1), group(0)]] var src: ${this.useImport?"texture_external":"texture_2d<f32>"};
${Fe()} {
${Le()}
let flatIndexBase = index * uniforms.numChannels;
let coords = getCoordsFromFlatIndex(flatIndexBase);
let values = ${e};
for (var i = 0; i < uniforms.numChannels; i = i + 1) {
let flatIndex = flatIndexBase + i;
if (flatIndex < uniforms.size) {
result.numbers[flatIndex] = i32(floor(255.0 * values[i]));
}
}
}
`}getUserCode(){return this.makeFromPixelsSource()}setPipeline(e){this.pipeline=e}setUniform(e,t){if(!this.uniform){let n=e.createBuffer({size:t.length*4,usage:GPUBufferUsage.UNIFORM|GPUBufferUsage.COPY_DST});this.uniform=n}!t||t.length===this.lastUniformData.length&&t.every((n,s)=>n===this.lastUniformData[s])||(e.queue.writeBuffer(this.uniform,0,new Uint32Array(t)),this.lastUniformData=t)}makeInputTexture(e,t,n){return(!this.inputTexture||this.lastPixelSize.width!==t||this.lastPixelSize.height!==n)&&(this.inputTexture&&this.inputTexture.destroy(),this.inputTexture=e.createTexture({size:[t,n],format:"rgba8unorm",usage:GPUTextureUsage.COPY_DST|GPUTextureUsage.RENDER_ATTACHMENT|GPUTextureUsage.TEXTURE_BINDING}),this.lastPixelSize.width=t,this.lastPixelSize.height=n),this.inputTexture}dispose(){this.disposed||(this.uniform&&this.uniform.destroy(),this.inputTexture&&this.inputTexture.destroy(),this.disposed=!0)}getLayout(e){return this.layout===null&&(this.layout=this.createTextureLayout(e)),this.layout}createTextureLayout(e){let t=[];t.push({binding:0,visibility:GPUShaderStage.COMPUTE,buffer:{type:"storage"}}),t.push({binding:1,visibility:GPUShaderStage.COMPUTE,texture:{}}),t.push({binding:2,visibility:GPUShaderStage.COMPUTE,buffer:{}});let n=e.createBindGroupLayout({entries:t}),s=e.createPipelineLayout({bindGroupLayouts:[n]});return{bindGroupLayout:n,pipelineLayout:s}}},Gpe=class extends yC{constructor(){super(...arguments);this.layout=null,this.useImport=!0}getUserCode(){return this.makeFromPixelsSource()}getLayout(e){return this.layout===null&&(this.layout=this.createTextureImportLayout(e)),this.layout}createTextureImportLayout(e){let t=[];t.push({binding:0,visibility:GPUShaderStage.COMPUTE,buffer:{type:"storage"}}),t.push({binding:1,visibility:GPUShaderStage.COMPUTE,externalTexture:{}}),t.push({binding:2,visibility:GPUShaderStage.COMPUTE,buffer:{}});let n=e.createBindGroupLayout({entries:t}),s=e.createPipelineLayout({bindGroupLayouts:[n]});return{bindGroupLayout:n,pipelineLayout:s}}},Hpe=K().getNumber("WEBGPU_CPU_HANDOFF_SIZE_THRESHOLD"),xC=class extends Ll{constructor(e,t=!1){super();if(this.commandQueueOwnedIds=new WeakSet,this.tensorDisposalQueue=[],this.uniformDisposalQueue=[],this.disposed=!1,this.uploadWaitMs=0,this.downloadWaitMs=0,this.dispatchNumberInEncoder=0,!ax())throw new Error("WebGPU is not supported on this device");this.layoutCache={},this.pipelineCache={},this.device=e,this.queue=e.queue,this.currentCommandEncoder=null,this.currentComputePass=null,this.supportTimeQuery=t,this.bufferManager=new Upe(this.device),this.tensorMap=new Pc(this,ts()),this.supportTimeQuery&&(this.querySet=this.device.createQuerySet({type:"timestamp",count:2})),K().getBool("WEBGPU_USE_PROFILE_TOOL")&&(this.dummyCanvas=document.createElement("canvas"),this.dummyCanvas.width=1,this.dummyCanvas.height=1,this.dummyContext=this.dummyCanvas.getContext("webgpu"),this.dummyContext.configure({device:e,format:"bgra8unorm"}),document.body.appendChild(this.dummyCanvas))}nextDataId(){return xC.nextDataId++}floatPrecision(){return 32}defaultGpuBufferUsage(){return GPUBufferUsage.STORAGE|GPUBufferUsage.COPY_SRC|GPUBufferUsage.COPY_DST}flushDisposalQueue(){this.tensorDisposalQueue.forEach(e=>{this.maybeReleaseBuffer(e),this.tensorMap.delete(e)}),this.uniformDisposalQueue.forEach(e=>this.bufferManager.releaseBuffer(e.buffer,e.byteSize,e.usage)),this.tensorDisposalQueue=[],this.uniformDisposalQueue=[]}disposeData(e,t=!1){if(this.tensorMap.has(e)){let n=this.tensorMap.get(e);if(n.refCount--,!t&&n.refCount>0)return!1;if(this.commandQueueOwnedIds.has(e))return this.tensorDisposalQueue.push(e),!1;this.maybeReleaseBuffer(e);let{complexTensorInfos:s}=this.tensorMap.get(e);s!=null&&(this.disposeData(s.real.dataId,!0),this.disposeData(s.imag.dataId,!0)),this.tensorMap.delete(e)}return!0}memory(){return{numBytesInGPU:this.bufferManager.numBytesUsed,numBytesAllocatedInGPU:this.bufferManager.numBytesAllocated,unreliable:!1}}getBufferManager(){return this.bufferManager}acquireBuffer(e,t=this.defaultGpuBufferUsage()){return this.bufferManager.acquireBuffer(e,t)}maybeReleaseBuffer(e){let t=this.tensorMap.get(e);t!=null&&t.bufferInfo.buffer!=null&&(this.bufferManager.releaseBuffer(t.bufferInfo.buffer,t.bufferInfo.byteSize,t.bufferInfo.usage),t.bufferInfo.buffer=null)}refCount(e){return this.tensorMap.has(e)?this.tensorMap.get(e).refCount:0}incRef(e){let t=this.tensorMap.get(e);t.refCount++}decRef(e){if(this.tensorMap.has(e)){let t=this.tensorMap.get(e);t.refCount--}}write(e,t,n){if(n==="complex64"&&e!=null)throw new Error("Cannot write to a complex64 dtype. Please use tf.complex(real, imag).");let s={id:this.nextDataId()},r=v.sizeFromShape(t)*rx(n);return n==="bool"&&e instanceof Uint8Array&&(e=Int32Array.from(e)),this.tensorMap.set(s,{dtype:n,values:e,bufferInfo:{byteSize:r,usage:this.defaultGpuBufferUsage()},refCount:1}),s}move(e,t,n,s,r){if(s==="complex64")throw new Error("Cannot write to a complex64 dtype. Please use tf.complex(real, imag).");let a=v.sizeFromShape(n)*rx(s);this.tensorMap.set(e,{dtype:s,values:t,bufferInfo:{byteSize:a,usage:this.defaultGpuBufferUsage()},refCount:r})}submitQueue(){this.ensureComputePassEnded(),this.queue.submit([this.currentCommandEncoder.finish()]),this.currentCommandEncoder=null,this.dispatchNumberInEncoder=0,this.commandQueueOwnedIds=new WeakSet,this.flushDisposalQueue()}getBuffer(e){return this.uploadToGPU(e),this.tensorMap.get(e).bufferInfo.buffer}getFromPixelsProgram(e){switch(e){case"copyExternal":return this.fromPixelProgram||(this.fromPixelProgram=new yC),this.fromPixelProgram;case"import":return this.fromPixelImportProgram||(this.fromPixelImportProgram=new Gpe),this.fromPixelImportProgram;default:v.assert(!1,()=>"Unsupported fromPixels shape");return}}ensureCommandEncoderReady(){this.currentCommandEncoder||(this.currentCommandEncoder=this.device.createCommandEncoder())}ensureComputePassEnded(){this.currentComputePass&&(this.currentComputePass.endPass(),this.currentComputePass=null)}getComputePass(){return this.currentComputePass||(this.currentComputePass=this.currentCommandEncoder.beginComputePass()),this.currentComputePass}async getBufferData(e){if(e.values!=null)return e.values;let t=this.acquireBuffer(e.bufferInfo.byteSize,GPUBufferUsage.COPY_DST|GPUBufferUsage.MAP_READ);this.ensureCommandEncoderReady(),this.ensureComputePassEnded(),this.currentCommandEncoder.copyBufferToBuffer(e.bufferInfo.buffer,0,t,0,e.bufferInfo.byteSize),this.submitQueue(),await t.mapAsync(GPUMapMode.READ);let n=t.getMappedRange().slice(0);return t.unmap(),t!=null&&this.bufferManager.releaseBuffer(t,e.bufferInfo.byteSize,GPUBufferUsage.COPY_DST|GPUBufferUsage.MAP_READ),K().getBool("WEBGPU_USE_PROFILE_TOOL")&&(v.assert(this.dummyContext!==void 0,()=>"Fail to get context for profiling tool"),this.dummyContext.getCurrentTexture()),n}convertAndCacheOnCPU(e,t){let n=this.tensorMap.get(e);return this.maybeReleaseBuffer(e),n.values=t,n.values}readSync(e){let t=this.tensorMap.get(e),{values:n}=t;if(n==null)throw new Error("WebGPU readSync is only available for CPU-resident tensors.");return n}async read(e){if(!this.tensorMap.has(e))throw new Error(`Tensor ${e} was not registered!`);let t=this.tensorMap.get(e),{values:n}=t;if(n!=null)return this.convertAndCacheOnCPU(e,n);let s;if(t.dtype==="complex64"){let r=await Promise.all([this.read(t.complexTensorInfos.real.dataId),this.read(t.complexTensorInfos.imag.dataId)]),a=r[0],o=r[1];s=N.mergeRealAndImagArrays(a,o)}else{let r=await this.getBufferData(t);s=B4(r,t.dtype)}return this.convertAndCacheOnCPU(e,s),s}bufferSync(e){let t=this.readSync(e.dataId),n=t;if(e.dtype==="string")try{n=t.map(s=>v.decodeString(s))}catch(s){throw new Error("Failed to decode encoded string bytes into utf-8")}return Be(e.shape,e.dtype,n)}async time(e){let t=this.activeTimers,n=[],s=!1;this.programTimersStack==null?(this.programTimersStack=n,s=!0):this.activeTimers.push(n),this.activeTimers=n,e();let r=v.flatten(this.activeTimers.map(l=>l.query)).filter(l=>l!=null),a=v.flatten(this.activeTimers.map(l=>l.name)).filter(l=>l!=null);this.activeTimers=t,s&&(this.programTimersStack=null);let o={uploadWaitMs:this.uploadWaitMs,downloadWaitMs:this.downloadWaitMs,kernelMs:null,wallMs:null},i=await Promise.all(r);return o.kernelMs=v.sum(i),o.getExtraProfileInfo=()=>i.map((l,c)=>({name:a[c],ms:l})).map(l=>`${l.name}: ${l.ms}`).join(", "),this.uploadWaitMs=0,this.downloadWaitMs=0,o}getAndSavePipeline(e,t){return e in this.pipelineCache||(this.pipelineCache[e]=t()),this.pipelineCache[e]}makeTensorInfo(e,t,n){let s;if(t==="string"&&n!=null&&n.length>0&&v.isString(n[0])){let r=n.map(a=>v.encodeString(a));s=this.write(r,e,t)}else s=this.write(n,e,t);return{dataId:s,shape:e,dtype:t}}tensorToBinding(e){if(!e)return null;let t=this.tensorMap.get(e.dataId);return{offset:0,size:t.bufferInfo.byteSize,buffer:t.bufferInfo.buffer}}async getQueryTime(e){return this.supportTimeQuery?this.getTimeFromQuerySet(e):0}uploadToGPU(e){let t=this.tensorMap.get(e);t.bufferInfo.buffer==null&&(t.bufferInfo.buffer=this.acquireBuffer(t.bufferInfo.byteSize),t.values&&this.queue.writeBuffer(t.bufferInfo.buffer,0,t.values))}makeUniformsDataView(e){let t=this.acquireBuffer(e.byteLength,GPUBufferUsage.COPY_DST|GPUBufferUsage.UNIFORM);return this.queue.writeBuffer(t,0,e),{offset:0,size:e.byteLength,buffer:t}}arrayToDataView(e,t){let n=4,s=new DataView(new ArrayBuffer(t*n)),r=0;return e.forEach(a=>{let o=a.data;if(a.type!=="int32"&&a.type!=="float32"&&a.type!=="uint32")throw new Error(`${a.type} not supported!`);a.type==="int32"?o.forEach(i=>{s.setInt32(r*n,i,!0),r++}):a.type==="uint32"?o.forEach(i=>{s.setUint32(r*n,i,!0),r++}):o.forEach(i=>{s.setFloat32(r*n,i,!0),r++})}),s}computePadding(e){let t=0,n=0,s=0,r=[];return e.forEach((a,o)=>{a.data.length===0&&(a.data=[1]);let i;switch(a.data.length){case 0:i=1;break;case 1:i=1;break;case 2:i=2;break;case 3:i=4;break;case 4:i=4;break;default:v.assert(!1,()=>`Unsupported ${a.data.length}D shape`)}n=Math.ceil(t/i)*i-t;for(let l=0;l<n;++l)r.push({type:a.type,data:[0]}),s++;r.push({type:a.type,data:a.data}),s=s+a.data.length,t+=a.data.length+n}),this.arrayToDataView(r,s)}createLayout(e){let t=[];t.push({binding:0,visibility:GPUShaderStage.COMPUTE,buffer:{type:"storage"}});for(let r=0;r<e;r++)t.push({binding:r+1,visibility:GPUShaderStage.COMPUTE,buffer:{type:"read-only-storage"}});t.push({binding:e+1,visibility:GPUShaderStage.COMPUTE,buffer:{type:"uniform"}});let n=this.device.createBindGroupLayout({entries:t}),s=this.device.createPipelineLayout({bindGroupLayouts:[n]});return{bindGroupLayout:n,pipelineLayout:s}}getCachedOrCreateLayout(e){return e in this.layoutCache||(this.layoutCache[e]=this.createLayout(e)),this.layoutCache[e]}runWebGPUProgram(e,t,n,s,r){if(!r){if(r=this.makeTensorInfo(e.outputShape,n),v.sizeFromShape(r.shape)===0){let E=this.tensorMap.get(r.dataId);return E.values=v.getTypedArrayFromDType(r.dtype,0),r}this.uploadToGPU(r.dataId)}let a=[{type:"float32",data:[NaN]}],o=t.concat(r).map(E=>E.shape),i="int32";o.map(E=>{a.push({type:i,data:E})});let l=v.computeStrides(r.shape);a.push({type:i,data:l}),e.size!=null&&a.push({type:i,data:[e.size]}),a.push({type:"uint32",data:e.dispatch}),s&&(a=[...a,...s]);let c=null,u=this.computePadding(a),d=u.byteLength;c=this.makeUniformsDataView(u);let p=t.map((E,R)=>{if(E.dtype==="complex64")throw new Error("GPGPUProgram does not support complex64 input. For complex64 dtypes, please separate the program into real and imaginary parts.");return this.uploadToGPU(E.dataId),{dtype:this.tensorMap.get(E.dataId).dtype,shape:E.shape,name:e.variableNames[R]}}),h=p.map(E=>E.dtype).concat(r.dtype),f=p.map(E=>N.getBroadcastDims(E.shape,r.shape)),m=p.map(E=>v.arraysEqual(E.shape,r.shape)).join("_"),g=f.map(E=>E.join("_")).join(";"),A=lC(e,o,h,g,m),{bindGroupLayout:x,pipelineLayout:y}=this.getCachedOrCreateLayout(e.variableNames.length),b=this.getAndSavePipeline(A,()=>iC(this.device,e,y,p,r)),w=this.activeTimers!=null,k=Cce(this.device,x,t.map(E=>this.tensorToBinding(E)),this.tensorToBinding(r),c);this.ensureCommandEncoderReady();let C=this.getComputePass();if(w&&this.supportTimeQuery&&C.writeTimestamp(this.querySet,0),C.setPipeline(b),C.setBindGroup(0,k),C.dispatch(e.dispatch[0],e.dispatch[1],e.dispatch[2]),w&&this.supportTimeQuery&&C.writeTimestamp(this.querySet,1),this.dispatchNumberInEncoder++,t.forEach(E=>{this.commandQueueOwnedIds.add(E.dataId)}),this.commandQueueOwnedIds.add(r.dataId),c){let E={byteSize:d,usage:GPUBufferUsage.COPY_DST|GPUBufferUsage.UNIFORM,buffer:c.buffer};this.uniformDisposalQueue.push(E)}return K().get("WEBGPU_DEFERRED_SUBMIT_BATCH_SIZE")<=this.dispatchNumberInEncoder&&this.submitQueue(),w&&this.activeTimers.push({name:e.constructor.name,query:this.getQueryTime(this.querySet)}),r}runFromPixelsProgram(e,t,n,s,r){let a=this.device.createBindGroup({layout:n.bindGroupLayout,entries:[{binding:0,resource:{buffer:t}},{binding:1,resource:s},{binding:2,resource:{buffer:e.uniform}}]});this.ensureCommandEncoderReady();let o=this.getComputePass(),i=this.activeTimers!=null;i&&this.supportTimeQuery&&o.writeTimestamp(this.querySet,0),o.setPipeline(e.pipeline),o.setBindGroup(0,a),o.dispatch(e.dispatch[0],e.dispatch[1],e.dispatch[2]),i&&this.supportTimeQuery&&o.writeTimestamp(this.querySet,1),this.commandQueueOwnedIds.add(r),this.submitQueue(),i&&this.activeTimers.push({name:e.constructor.name,query:this.getQueryTime(this.querySet)})}async getTimeFromQuerySet(e){let t=this.acquireBuffer(16,GPUBufferUsage.COPY_SRC|GPUBufferUsage.QUERY_RESOLVE),n=this.acquireBuffer(16,GPUBufferUsage.MAP_READ|GPUBufferUsage.COPY_DST);this.ensureCommandEncoderReady(),this.ensureComputePassEnded(),this.currentCommandEncoder.resolveQuerySet(e,0,2,t,0),this.currentCommandEncoder.copyBufferToBuffer(t,0,n,0,16),this.submitQueue(),await n.mapAsync(GPUMapMode.READ);let s=new BigUint64Array(n.getMappedRange()),r=Number(s[1]-s[0]);return n.unmap(),this.bufferManager.releaseBuffer(n,16,GPUBufferUsage.MAP_READ|GPUBufferUsage.COPY_DST),this.bufferManager.releaseBuffer(t,16,GPUBufferUsage.COPY_SRC|GPUBufferUsage.QUERY_RESOLVE),r/1e6}shouldExecuteOnCPU(e,t=Hpe){return K().getBool("WEBGPU_CPU_FORWARD")&&e.every(n=>this.tensorMap.get(n.dataId).bufferInfo.buffer==null&&v.sizeFromShape(n.shape)<t)}numDataIds(){return this.tensorMap.numDataIds()-this.tensorDisposalQueue.length}dispose(){this.disposed||(this.bufferManager.dispose(),this.fromPixelProgram&&this.fromPixelProgram.dispose(),this.fromPixelImportProgram&&this.fromPixelImportProgram.dispose(),this.disposed=!0)}},hx=xC;hx.nextDataId=0;var bC={};Me(bC,{WebGPUBackend:()=>hx,webgpu_util:()=>L4});gu.isBrowser()&&ax()&&Zi("webgpu",async()=>{K().set("CHECK_COMPUTATION_FOR_ERRORS",!1);let e={powerPreference:K().get("WEBGPU_USE_LOW_POWER_GPU")?"low-power":"high-performance"},t=await navigator.gpu.requestAdapter(e),n={},s=t.features.has("timestamp-query");s?n={requiredFeatures:["timestamp-query"]}:console.warn("This device doesn't support timestamp-query extension. Start Chrome browser with flag --disable-dawn-features=disallow_unsafe_apis then try again. Or zero will shown for the kernel time when profiling mode isenabled. Using performance.now is not workable for webgpu sinceit doesn't support synchronously to read data from GPU.");let r=await t.requestDevice(n);return new hx(r,s)},3);var Qt;(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"})(Qt||(Qt={}));var ap;(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",e[e.sigmoid=5]="sigmoid",e[e.elu=6]="elu"})(ap||(ap={}));var vC;function jpe(e){vC=e.wasm.cwrap(fo,null,["number","array","number","number","array","number","number","number","number","number","number","number","number"])}function qpe(e){let{inputs:t,backend:n,attrs:s}=e,{a:r,b:a,bias:o,preluActivationWeights:i}=t;if(r.dtype!=="float32"||a.dtype!=="float32")throw new Error("_FusedMatMul for non non-float32 tensors not yet supported.");let{transposeA:l,transposeB:c,activation:u,leakyreluAlpha:d}=s,p=n.dataIdMap.get(r.dataId).id,h=n.dataIdMap.get(a.dataId).id,f=0;if(o!=null){let E=n.dataIdMap.get(o.dataId);if(E.shape.length!==1)throw new Error(`_FusedMatMul only supports rank-1 bias but got rank ${E.shape.length}.`);f=E.id}let m=i==null?0:n.dataIdMap.get(i.dataId).id,g=ap[u];if(g==null)throw new Error(`${u} activation not yet supported for FusedConv2D in the wasm backend.`);let A=l?r.shape[2]:r.shape[1],x=c?a.shape[1]:a.shape[2],y=qi.assertAndGetBroadcastShape(r.shape.slice(0,-2),a.shape.slice(0,-2)),b=n.makeOutput([...y,A,x],r.dtype),w=n.dataIdMap.get(b.dataId).id,k=new Uint8Array(new Int32Array(r.shape).buffer),C=new Uint8Array(new Int32Array(a.shape).buffer);return vC(p,k,r.shape.length,h,C,a.shape.length,l,c,g,f,m,d||0,w),b}var Xpe={kernelName:fo,backendName:"wasm",setupFunc:jpe,kernelFunc:qpe};function Tn(e,t){let n;function s(a){n=a.wasm.cwrap(e,null,["number","number","number"])}function r(a){let{backend:o,inputs:{x:i}}=a,l=o.dataIdMap.get(i.dataId).id,c=o.makeOutput(i.shape,t||i.dtype),u=o.dataIdMap.get(c.dataId).id;return v.sizeFromShape(c.shape)===0||n(l,Qt[i.dtype],u),c}return{kernelName:e,backendName:"wasm",setupFunc:s,kernelFunc:r}}var Kpe=Tn(ti);function qn(e,t,n){let s;function r(o){s=o.wasm.cwrap(e,null,["number","array","number","number","array","number","number","number"])}function a(o){let{backend:i,inputs:l}=o,{a:c,b:u}=l,d=i.dataIdMap.get(c.dataId).id,p=i.dataIdMap.get(u.dataId).id,h=n!=null?n:c.dtype,f=N.assertAndGetBroadcastShape(c.shape,u.shape),m=i.makeOutput(f,h);if(v.sizeFromShape(f)===0)return m;let g=new Uint8Array(new Int32Array(c.shape).buffer),A=new Uint8Array(new Int32Array(u.shape).buffer),x=i.dataIdMap.get(m.dataId).id,y=()=>s(d,g,c.shape.length,p,A,u.shape.length,Qt[c.dtype],x);if(t&&c.dtype==="float32")return y(),m;let b=N.getBroadcastDims(c.shape,f),w=N.getBroadcastDims(u.shape,f),k=b.every((E,R)=>E===R),C=w.every((E,R)=>E===R);if(k&&C)return y(),m;throw new Error(`Broadcasting along outer dims is not yet supported for ${c.dtype} ${e}.`)}return{kernelName:e,backendName:"wasm",setupFunc:r,kernelFunc:a}}var Zpe=!0,Ype=qn(Vr,Zpe),wC;function Jpe(e){wC=e.wasm.cwrap(wa,null,["array","number","number","number"])}function Qpe(e){let{inputs:t,backend:n}=e,s=n.makeOutput(t[0].shape,t[0].dtype);if(v.sizeFromShape(s.shape)===0)return s;let r=t.map(i=>n.dataIdMap.get(i.dataId).id),a=new Uint8Array(new Int32Array(r).buffer),o=n.dataIdMap.get(s.dataId).id;return wC(a,r.length,Qt[s.dtype],o),s}var ehe={kernelName:wa,backendName:"wasm",setupFunc:Jpe,kernelFunc:Qpe};function zm(e){let{inputs:{x:t},backend:n}=e,s=n.makeOutput(t.shape,t.dtype),r=n.typedArrayFromHeap(t);return n.typedArrayFromHeap(s).set(r),s}var the={kernelName:Ba,backendName:"wasm",kernelFunc:zm},kC;function nhe(e){kC=e.wasm.cwrap(po,null,["number","array","number","number","number","array","number"])}function ic(e){let{inputs:t,backend:n,attrs:s}=e,[r,a]=rhe(t.x.shape,s.perm),o=!0;for(let f=0;f<a.length;f++)a[f]!==f&&(o=!1);let i=she(t.x.shape,s.perm),l={dataId:t.x.dataId,shape:r,dtype:t.x.dtype};if(o){let f=zm({inputs:t,backend:n});return f.shape=i,f}let c=n.makeOutput(i,l.dtype),u=n.dataIdMap.get(l.dataId).id,d=n.dataIdMap.get(c.dataId).id,p=new Uint8Array(new Int32Array(a).buffer),h=new Uint8Array(new Int32Array(l.shape).buffer);return kC(u,h,l.shape.length,Qt[l.dtype],d,p,a.length),c}function she(e,t){let n=new Array(e.length);for(let s=0;s<n.length;s++)n[s]=e[t[s]];return n}function rhe(e,t){let n=[],s=[];for(let r=0;r<e.length;++r)e[r]!==1&&n.push(e[r]),e[t[r]]!==1&&s.push(t[r]);for(let r=0;r<s.length;++r){let a=-1;for(let o=0;o<s.length;++o)s[o]>=r&&(a===-1||s[a]>s[o])&&(a=o);s[a]=r}return[n,s]}var ahe={kernelName:po,backendName:"wasm",kernelFunc:ic,setupFunc:nhe};function Lo(e,t,n){let s=e.shape,r=e.shape.length,a=v.parseAxisParam(t,s),o=a,i=N.getAxesPermutation(o,r),l=null,c=!1;if(i!=null){let u=new Array(r);for(let h=0;h<u.length;h++)u[h]=s[i[h]];o=N.getInnerMostAxes(o.length,r),l=ic({inputs:{x:e},attrs:{perm:i},backend:n});let d=n.dataIdMap.get(e.dataId).id;n.dataIdMap.get(l.dataId).id!==d&&(c=!0)}return{transposed:l,originalAxes:a,axes:o,inputWasTransposed:c}}var SC;function ohe(e){SC=e.wasm.cwrap(Gl,null,["number, number, number"])}function ihe(e){let{backend:t,inputs:n,attrs:s}=e,{axis:r,keepDims:a}=s,{x:o}=n,l=t.dataIdMap.get(o.dataId).id,c=o,{transposed:u,axes:d,originalAxes:p,inputWasTransposed:h}=Lo(o,r,t);if(h){let y=t.dataIdMap.get(u.dataId).id;c=u,l=y}let f=c.shape.length;N.assertAxesAreInnerMostDims("all",d,f);let[m,g]=N.computeOutAndReduceShapes(c.shape,d),A=v.sizeFromShape(g),x=t.makeOutput(m,o.dtype);if(v.sizeFromShape(c.shape)!==0){let y=t.dataIdMap.get(x.dataId).id;SC(l,A,y)}if(h&&t.disposeData(u.dataId),a){let 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hhe(e){let{backend:t,inputs:n,attrs:s}=e,{axis:r}=s,{x:a}=n,o=t.dataIdMap.get(a.dataId).id,i=o,l=a,{transposed:c,axes:u,inputWasTransposed:d}=Lo(a,r,t);if(d){let A=t.dataIdMap.get(c.dataId).id;A!==o&&(l=c,i=A)}let p=l.shape.slice(0,-1),h=t.makeOutput(p,"int32"),f=t.dataIdMap.get(h.dataId).id,m=v.sizeFromShape(h.shape),g=l.shape[u[0]];return CC(i,Qt[l.dtype],m,g,f),d&&t.disposeData(c.dataId),h}var fhe={kernelName:ka,backendName:"wasm",kernelFunc:hhe,setupFunc:phe},TC;function mhe(e){TC=e.wasm.cwrap(Sa,null,["number","number","number","number","number","number","number","number","number","number","number","number","number","number"])}function ghe(e){let{inputs:t,attrs:n,backend:s}=e,r=t.x,a=s.dataIdMap.get(r.dataId).id,{filterSize:o,strides:i,pad:l,dimRoundingMode:c}=n,u=N.computePool2DInfo(r.shape,o,i,1,l,c),d=u.filterHeight,p=u.filterWidth,h=u.padInfo.top,f=u.padInfo.right,m=u.padInfo.bottom,g=u.padInfo.left,A=u.strideHeight,x=u.strideWidth,y=u.inChannels;if(u.dataFormat!=="channelsLast")throw new Error(`wasm backend does not support dataFormat:'${u.dataFormat}'. 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Che(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{blockShape:a,crops:o}=s,i=a.reduce((A,x)=>A*x),l=N.getReshaped(r.shape,a,i),c=N.getPermuted(l.length,a.length),u=N.getReshapedPermuted(r.shape,a,i),d=N.getSliceBeginCoords(o,a.length),p=N.getSliceSize(u,o,a.length),h=is({inputs:{x:r},backend:n,attrs:{shape:l}}),f=ic({inputs:{x:h},backend:n,attrs:{perm:c}}),m=is({inputs:{x:f},backend:n,attrs:{shape:u}}),g=op({inputs:{x:m},backend:n,attrs:{begin:d,size:p}});return n.disposeData(h.dataId),n.disposeData(f.dataId),n.disposeData(h.dataId),g}var The={kernelName:ni,backendName:"wasm",kernelFunc:Che};function ip(e){let{inputs:{x:t},attrs:{dtype:n},backend:s}=e,r=s.makeOutput(t.shape,n),a=s.typedArrayFromHeap(t);return s.typedArrayFromHeap(r).set(a),r}var Nhe={kernelName:Ca,backendName:"wasm",kernelFunc:ip},Ehe=Tn(Ta),EC;function Rhe(e){EC=e.wasm.cwrap(Ur,null,["number","number","number","number"])}function $he(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{clipValueMin:a,clipValueMax:o}=s,i=n.dataIdMap.get(r.dataId).id,l=n.makeOutput(r.shape,r.dtype),c=n.dataIdMap.get(l.dataId).id;return EC(i,a,o,c),l}var Dhe={kernelName:Ur,backendName:"wasm",setupFunc:Rhe,kernelFunc:$he};function RC(e){let{inputs:t,backend:n}=e,s=v.parseAxisParam(e.attrs.axis,t[0].shape)[0],r=N.computeOutShape(t.map(h=>h.shape),s),a=t.filter(h=>v.sizeFromShape(h.shape)>0);if(a.length===1)return zm({inputs:{x:a[0]},backend:n});let o=n.makeOutput(r,t[0].dtype);if(v.sizeFromShape(r)===0)return o;let i=a.map(h=>h.shape);if(N.assertParamsConsistent(i,s),a[0].dtype==="string"){let h=a.map(y=>{let b=v.sizeFromShape(y.shape.slice(s));return is({inputs:{x:y},backend:n,attrs:{shape:[-1,b]}})}),f=h.map(y=>({vals:n.readSync(y.dataId),shape:y.shape}));r=N.computeOutShape(h.map(y=>y.shape),1);let m=h[0].shape[0]===1,g=ky(f,r,t[0].dtype,m),A=N.computeOutShape(a.map(y=>y.shape),s);o.shape=A;let x=n.dataIdMap.get(o.dataId);return x.stringBytes=N.fromStringArrayToUint8(g),h.forEach(y=>n.disposeData(y.dataId)),o}let l=v.sizeFromShape(a[0].shape.slice(0,s)),c=0,u=a.map(h=>{let f=v.sizeFromShape(h.shape.slice(s));return c+=f,f}),d=a.map(h=>n.typedArrayFromHeap(h)),p=n.typedArrayFromHeap(o);for(let h=0;h<l;h++){let f=h*c;for(let m=0;m<d.length;m++){let g=u[m],A=h*g,x=d[m].subarray(A,A+g);p.set(x,f),f+=g}}return o}var _he={kernelName:si,backendName:"wasm",kernelFunc:RC},$C;function Phe(e){$C=e.wasm.cwrap(Na,null,["number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number"])}function Fhe(e){let{inputs:t,attrs:n,backend:s}=e,{x:r,filter:a}=t,o=s.dataIdMap.get(r.dataId).id,i=s.dataIdMap.get(a.dataId).id,{strides:l,dilations:c,pad:u,dimRoundingMode:d,dataFormat:p}=n,h=N.convertConv2DDataFormat(p),f=N.computeConv2DInfo(r.shape,a.shape,l,c,u,d,!1,h),m=f.filterHeight,g=f.filterWidth,A=f.padInfo.top,x=f.padInfo.right,y=f.padInfo.bottom,b=f.padInfo.left,w=f.dilationHeight,k=f.dilationWidth,C=f.strideHeight,E=f.strideWidth,R=f.inChannels,F=f.outChannels,D=f.padInfo.type==="SAME"?1:0;if(f.dataFormat!=="channelsLast")throw new Error(`wasm backend Conv2D does not support dataFormat:'${f.dataFormat}'. Please use 'channelsLast'.`);let P=s.makeOutput(f.outShape,"float32"),T=s.dataIdMap.get(P.dataId).id;return $C(o,r.shape[0],r.shape[1],r.shape[2],i,m,g,A,x,y,b,D,w,k,C,E,R,F,T),P}var Ohe={kernelName:Na,backendName:"wasm",setupFunc:Phe,kernelFunc:Fhe},DC;function Mhe(e){DC=e.wasm.cwrap(Ea,null,["number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number"])}function zhe(e){let{backend:t,inputs:n,attrs:s}=e,{dy:r,filter:a}=n,{strides:o,pad:i,dataFormat:l,dimRoundingMode:c,inputShape:u}=s,d=1,p=N.convertConv2DDataFormat(l),h=N.computeConv2DInfo(u,a.shape,o,d,i,c,!1,p),{batchSize:f,filterHeight:m,filterWidth:g,inChannels:A,inHeight:x,inWidth:y,outChannels:b,outHeight:w,outWidth:k,strideHeight:C,strideWidth:E}=h,R=m-1-h.padInfo.top,F=g-1-h.padInfo.left,D=h.dataFormat==="channelsLast",P=v.computeStrides(h.inShape),T=v.computeStrides(r.shape),[M,U,j]=v.computeStrides(a.shape),z=P[0],X=D?P[1]:P[2],Z=D?P[2]:1,J=D?1:P[1],ee=T[0],ne=D?T[1]:T[2],Q=D?T[2]:1,te=D?1:T[1],oe=t.makeOutput(h.inShape,"float32"),he=t.dataIdMap.get(oe.dataId).id,be=t.dataIdMap.get(r.dataId).id,we=t.dataIdMap.get(a.dataId).id;return DC(be,we,f,m,g,x,y,A,w,k,b,C,E,R,F,M,U,j,z,X,Z,J,ee,ne,Q,te,he),oe}var Lhe={kernelName:Ea,backendName:"wasm",setupFunc:Mhe,kernelFunc:zhe},Bhe=Tn(Ra),Whe=Tn($a),fx;(function(e){e[e.bilinear=0]="bilinear",e[e.nearest=1]="nearest"})(fx||(fx={}));var _C;function Vhe(e){_C=e.wasm.cwrap(ai,null,["number","number","number","number","array","number","number","number","number","number"])}function Uhe(e){let{backend:t,inputs:n,attrs:s}=e,{method:r,extrapolationValue:a,cropSize:o}=s,{image:i,boxes:l,boxInd:c}=n,u=l.shape[0],[d,p]=o,h=[u,d,p,i.shape[3]],f=t.dataIdMap.get(i.dataId),m;i.dtype!=="float32"&&(m=ip({backend:t,inputs:{x:i},attrs:{dtype:"float32"}}),f=t.dataIdMap.get(m.dataId));let g=f.id,A=t.dataIdMap.get(l.dataId).id,x=t.dataIdMap.get(c.dataId).id,y=t.makeOutput(h,"float32"),b=t.dataIdMap.get(y.dataId).id,w=new Uint8Array(new Int32Array(i.shape).buffer);return _C(g,A,x,u,w,d,p,fx[r],a,b),m!=null&&t.disposeData(m.dataId),y}var Ghe={kernelName:ai,backendName:"wasm",setupFunc:Vhe,kernelFunc:Uhe},PC;function Hhe(e){PC=e.wasm.cwrap(ri,null,["number","number","number","number","number","number"])}function jhe(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{axis:a,exclusive:o,reverse:i}=s,l=r.shape.length;v.assert(r.dtype==="float32"||r.dtype==="int32",()=>`cumsum does not support ${r.dtype} tensors in the WASM backend`);let c=N.getAxesPermutation([a],l),u=r;c!==null&&(u=ic({inputs:{x:r},attrs:{perm:c},backend:n}));let d=N.getInnerMostAxes(1,l)[0];N.assertAxesAreInnerMostDims("cumsum",[d],l);let p=n.makeOutput(u.shape,u.dtype),h=u.shape[d],f=n.dataIdMap.get(u.dataId).id,m=n.dataIdMap.get(p.dataId).id;PC(f,o?1:0,i?1:0,h,m,Qt[r.dtype]);let g=p;if(c!==null){let A=N.getUndoAxesPermutation(c);g=ic({inputs:{x:p},attrs:{perm:A},backend:n}),n.disposeData(u.dataId),n.disposeData(p.dataId)}return g}var qhe={kernelName:ri,backendName:"wasm",setupFunc:Hhe,kernelFunc:jhe},FC;function Xhe(e){FC=e.wasm.cwrap(oi,null,["number","number","number","array","number","array","array","number","number"])}function Khe(e){let{backend:t,inputs:n,attrs:s}=e,{x:r}=n,{blockSize:a,dataFormat:o}=s,i=r.shape[0],l=o==="NHWC"?r.shape[1]:r.shape[2],c=o==="NHWC"?r.shape[2]:r.shape[3],u=o==="NHWC"?r.shape[3]:r.shape[1],d=l*a,p=c*a,h=u/(a*a),f=o==="NHWC"?[i,d,p,h]:[i,h,d,p],m=t.makeOutput(f,"float32"),A=t.dataIdMap.get(r.dataId).id,x=new Uint8Array(new Int32Array(v.computeStrides(r.shape)).buffer),y=new Uint8Array(new Int32Array(f).buffer),b=new Uint8Array(new Int32Array(v.computeStrides(f)).buffer),w=t.dataIdMap.get(m.dataId).id;return FC(A,a,o==="NHWC"?1:0,x,r.shape.length-1,y,b,f.length,w),m}var Zhe={kernelName:oi,backendName:"wasm",setupFunc:Xhe,kernelFunc:Khe},OC;function Yhe(e){OC=e.wasm.cwrap(Da,null,["number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number"])}function Jhe(e){let{inputs:t,attrs:n,backend:s}=e,{x:r,filter:a}=t,o=s.dataIdMap.get(r.dataId).id,i=s.dataIdMap.get(a.dataId).id,{strides:l,dilations:c,pad:u,dimRoundingMode:d}=n,p=c==null?[1,1]:c,h=N.computeConv2DInfo(r.shape,a.shape,l,p,u,d,!0),f=h.filterHeight,m=h.filterWidth,g=h.padInfo.top,A=h.padInfo.right,x=h.padInfo.bottom,y=h.padInfo.left,b=h.dilationHeight,w=h.dilationWidth,k=h.strideHeight,C=h.strideWidth,E=h.inChannels,R=h.outChannels,F=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 D=s.makeOutput(h.outShape,"float32"),P=s.dataIdMap.get(D.dataId).id;return OC(o,r.shape[0],r.shape[1],r.shape[2],i,f,m,g,A,x,y,F,b,w,k,C,E,R,P),D}var Qhe={kernelName:Da,backendName:"wasm",setupFunc:Yhe,kernelFunc:Jhe},efe=Tn(Pa),tfe=!1,nfe=qn(ii,tfe,"bool"),sfe=Tn(Fa,"float32");function mx(e){let{inputs:t,attrs:n,backend:s}=e,{input:r}=t,{dim:a}=n,o=r.shape.length,i=r.shape.slice(),l=a;return a<0&&(v.assert(-(o+1)<=a,()=>`Axis must be in the interval [${-(o+1)}, ${o}]`),l=o+a+1),i.splice(l,0,1),is({inputs:{x:r},backend:s,attrs:{shape:i}})}var rfe={kernelName:li,backendName:"wasm",kernelFunc:mx};function MC(e){let{attrs:{shape:t,value:n,dtype:s},backend:r}=e,a=r.makeOutput(t,s);return r.typedArrayFromHeap(a).fill(n),a}var afe={kernelName:Ql,backendName:"wasm",kernelFunc:MC},zC;function ofe(e){zC=e.wasm.cwrap(ci,null,["number","number","number","number","number","number"])}function ife(e){let{inputs:t,backend:n}=e,{image:s}=t,r=n.makeOutput(s.shape,s.dtype),a=n.dataIdMap.get(s.dataId).id,o=n.dataIdMap.get(r.dataId).id,[i,l,c,u]=s.shape;return zC(a,i,l,c,u,o),r}var lfe={kernelName:ci,backendName:"wasm",kernelFunc:ife,setupFunc:ofe},ufe=Tn(Oa),cfe=!1,dfe=qn(Ma,cfe),LC;function pfe(e){LC=e.wasm.cwrap(za,null,["number","number","number","number","number","number","number"])}function hfe(e){let{backend:t,inputs:n,attrs:s}=e,{varianceEpsilon:r}=s,{x:a,mean:o,variance:i,offset:l,scale:c}=n,u=t.dataIdMap.get(a.dataId).id,d=t.dataIdMap.get(o.dataId).id,p=t.dataIdMap.get(i.dataId).id,h=l!=null?t.dataIdMap.get(l.dataId).id:0,f=c!=null?t.dataIdMap.get(c.dataId).id:0,m=t.makeOutput(a.shape,a.dtype);if(v.sizeFromShape(a.shape)===0)return m;let g=t.dataIdMap.get(m.dataId).id;return LC(u,d,p,h,f,r,g),m}var ffe={kernelName:za,backendName:"wasm",setupFunc:pfe,kernelFunc:hfe},BC;function mfe(e){BC=e.wasm.cwrap(mo,null,["number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number"])}function gfe(e){let{inputs:t,attrs:n,backend:s}=e,{x:r,filter:a,bias:o,preluActivationWeights:i}=t,{strides:l,pad:c,dilations:u,dataFormat:d,dimRoundingMode:p,activation:h,leakyreluAlpha:f}=n,m=N.computeConv2DInfo(r.shape,a.shape,l,u,c,p),g=ap[h];if(g==null)throw new Error(`${h} activation not yet supported for FusedConv2D in the wasm backend.`);let A=s.dataIdMap.get(r.dataId).id,x=s.dataIdMap.get(a.dataId).id,y=m.outChannels,b=0;if(o!=null){let Q=s.dataIdMap.get(o.dataId);if(Q.shape.length!==1)throw new Error(`FusedConv2D only supports rank-1 bias but got rank ${Q.shape.length}.`);if(Q.shape[0]!==y)throw new Error(`FusedConv2D bias shape (${Q.shape}) does not match the number of output channels (${y})`);b=Q.id}let w=m.filterHeight,k=m.filterWidth,C=m.padInfo.top,E=m.padInfo.right,R=m.padInfo.bottom,F=m.padInfo.left,D=m.dilationHeight,P=m.dilationWidth,T=m.strideHeight,M=m.strideWidth,U=m.inChannels,j=m.padInfo.type==="SAME"?1:0,z=m.batchSize,X=m.inHeight,Z=m.inWidth;if(d!=="NHWC")throw new Error(`wasm backend FusedConv2D does not support dataFormat:'${d}'. Please use 'NHWC'.`);let J=s.makeOutput(m.outShape,"float32"),ee=s.dataIdMap.get(J.dataId).id,ne=i==null?0:s.dataIdMap.get(i.dataId).id;return BC(A,z,X,Z,x,w,k,b,C,E,R,F,j,D,P,T,M,U,y,g,ne,f||0,ee),J}var Afe={kernelName:mo,backendName:"wasm",setupFunc:mfe,kernelFunc:gfe},WC;function yfe(e){WC=e.wasm.cwrap(go,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 xfe(e){let{inputs:t,attrs:n,backend:s}=e,{x:r,filter:a,bias:o,preluActivationWeights:i}=t,{strides:l,pad:c,dilations:u,dataFormat:d,dimRoundingMode:p,activation:h,leakyreluAlpha:f}=n,m=N.computeConv2DInfo(r.shape,a.shape,l,u,c,p,!0),g=ap[h];if(g==null)throw new Error(`${h} activation not yet supported for FusedDepthwiseConv2D in the wasm backend.`);let A=s.dataIdMap.get(r.dataId).id,x=s.dataIdMap.get(a.dataId).id,y=m.outChannels,b=0;if(o!=null){let Q=s.dataIdMap.get(o.dataId);if(Q.shape.length!==1)throw new Error(`FusedDepthwiseConv2D only supports rank-1 bias but got rank ${Q.shape.length}.`);if(Q.shape[0]!==y)throw new Error(`FusedDepthwiseConv2D bias shape (${Q.shape}) does not match the number of output channels (${y})`);b=Q.id}let w=m.filterHeight,k=m.filterWidth,C=m.padInfo.top,E=m.padInfo.right,R=m.padInfo.bottom,F=m.padInfo.left,D=m.dilationHeight,P=m.dilationWidth,T=m.strideHeight,M=m.strideWidth,U=m.inChannels,j=m.padInfo.type==="SAME"?1:0,z=m.batchSize,X=m.inHeight,Z=m.inWidth;if(d!=="NHWC")throw new Error(`wasm backend FusedDepthwiseConv2D does not support dataFormat:'${d}'. Please use 'NHWC'.`);let J=s.makeOutput(m.outShape,"float32"),ee=s.dataIdMap.get(J.dataId).id,ne=i==null?0:s.dataIdMap.get(i.dataId).id;return WC(A,z,X,Z,x,w,k,b,C,E,R,F,j,D,P,T,M,U,y,g,ne,f||0,ee),J}var bfe={kernelName:go,backendName:"wasm",setupFunc:yfe,kernelFunc:xfe},VC;function vfe(e){VC=e.wasm.cwrap(pi,null,["number","number","number","number","number","number","array","number"])}function wfe(e){let{backend:t,inputs:n}=e,{params:s,indices:r}=n,[a,o,i,l]=A2.prepareAndValidate(s,r),c=t.makeOutput(a,s.dtype);if(o===0)return c;let u=r.shape,d=u[u.length-1],h=t.dataIdMap.get(s.dataId).id,m=t.dataIdMap.get(r.dataId).id,g=new Uint8Array(new Int32Array(l).buffer),A=t.dataIdMap.get(c.dataId).id;return VC(h,Qt[s.dtype],m,o,d,i,g,A),c}var kfe={kernelName:pi,backendName:"wasm",setupFunc:vfe,kernelFunc:wfe},UC;function Sfe(e){UC=e.wasm.cwrap("Gather",null,["number","number","array","number","number","number","array","number"])}function Ife(e){let{backend:t,inputs:n,attrs:s}=e,{x:r,indices:a}=n,{axis:o,batchDims:i}=s,l=v.parseAxisParam(o,r.shape)[0],c=t.readSync(a.dataId),u=r.shape[l];for(let R=0;R<c.length;++R){let F=c[R];v.assert(F<=u-1&&F>=0,()=>`GatherV2: the index value ${F} is not in [0, ${u-1}]`)}let d=N.segment_util.collectGatherOpShapeInfo(r,a,l,i),p=is({inputs:{x:r},attrs:{shape:[d.batchSize,d.outerSize,d.dimSize,d.sliceSize]},backend:t}),h=v.sizeFromShape(a.shape),f=is({inputs:{x:a},attrs:{shape:[d.batchSize,h/d.batchSize]},backend:t}),m=[d.batchSize,d.outerSize,h/d.batchSize,d.sliceSize],g=t.makeOutput(m,r.dtype);if(v.sizeFromShape(r.shape)===0)return g;let A=p.shape.length-1,y=t.dataIdMap.get(p.dataId).id,w=t.dataIdMap.get(f.dataId).id,k=t.dataIdMap.get(g.dataId).id,C=new Uint8Array(new Int32Array(v.computeStrides(p.shape)).buffer),E=new Uint8Array(new Int32Array(v.computeStrides(m)).buffer);return UC(y,Qt[r.dtype],C,A,w,d.batchSize,E,k),t.disposeData(p.dataId),t.disposeData(f.dataId),g.shape=d.outputShape,g}var Cfe={kernelName:di,backendName:"wasm",setupFunc:Sfe,kernelFunc:Ife},Tfe=!1,Nfe=qn(hi,Tfe,"bool"),Efe=!1,Rfe=qn(La,Efe,"bool"),GC;function $fe(e){GC=e.wasm.cwrap(fi,null,["number","number","number","number"])}function Dfe(e){let{inputs:{x:t},attrs:{alpha:n},backend:s}=e,r=s.dataIdMap.get(t.dataId).id,a=s.makeOutput(t.shape,"float32");if(v.sizeFromShape(t.shape)!==0){let o=s.dataIdMap.get(a.dataId).id;GC(r,Qt[t.dtype],n,o)}return a}var _fe={kernelName:fi,backendName:"wasm",setupFunc:$fe,kernelFunc:Dfe},Pfe=!1,Ffe=qn(mi,Pfe,"bool"),Ofe=!1,Mfe=qn(gi,Ofe,"bool"),zfe=Tn(Wa),Lfe=!1,Bfe=qn(Ai,Lfe,"bool"),HC;function Wfe(e){HC=e.wasm.cwrap(Va,null,["number","number","number","number"])}function Vfe(e){let{backend:t,inputs:n,attrs:s}=e,{reductionIndices:r,keepDims:a}=s,{x:o}=n,l=t.dataIdMap.get(o.dataId).id,c=o,{transposed:u,axes:d,originalAxes:p,inputWasTransposed:h}=Lo(o,r,t);if(h){let y=t.dataIdMap.get(u.dataId).id;c=u,l=y}let f=c.shape.length;N.assertAxesAreInnerMostDims("max",d,f);let[m,g]=N.computeOutAndReduceShapes(c.shape,d),A=v.sizeFromShape(g),x=t.makeOutput(m,o.dtype);if(v.sizeFromShape(c.shape)!==0){let y=t.dataIdMap.get(x.dataId).id;HC(l,Qt[o.dtype],A,y)}if(h&&t.disposeData(u.dataId),a){let y=N.expandShapeToKeepDim(x.shape,p);x.shape=y}return x}var Ufe={kernelName:Va,backendName:"wasm",setupFunc:Wfe,kernelFunc:Vfe},Gfe=!1,Hfe=qn(Ua,Gfe),jC;function jfe(e){jC=e.wasm.cwrap(Ga,null,["number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number"])}function qfe(e){let{inputs:t,attrs:n,backend:s}=e,r=t.x,a=s.dataIdMap.get(r.dataId).id;v.assert(r.dtype==="float32",()=>`Error in MaxPool: only float32 input is supported. Got ${r.dtype}.`);let{filterSize:o,strides:i,pad:l,dimRoundingMode:c}=n,u=N.computePool2DInfo(r.shape,o,i,1,l,c),d=u.filterHeight,p=u.filterWidth,h=u.padInfo.top,f=u.padInfo.right,m=u.padInfo.bottom,g=u.padInfo.left,A=u.dilationHeight,x=u.dilationWidth,y=u.strideHeight,b=u.strideWidth,w=u.inChannels,k=u.outChannels;if(u.dataFormat!=="channelsLast")throw new Error(`wasm backend does not support dataFormat:'${u.dataFormat}'. Please use 'channelsLast'.`);let C=s.makeOutput(u.outShape,"float32"),E=s.dataIdMap.get(C.dataId).id;return jC(a,r.shape[0],r.shape[1],r.shape[2],d,p,h,f,m,g,A,x,y,b,w,k,E),C}var Xfe={kernelName:Ga,backendName:"wasm",setupFunc:jfe,kernelFunc:qfe},qC;function Kfe(e){qC=e.wasm.cwrap(Ha,null,["number, number, number"])}function Zfe(e){let{backend:t,inputs:n,attrs:s}=e,{axis:r,keepDims:a}=s,{x:o}=n,i=t.dataIdMap.get(o.dataId).id,l=i,c=o,{transposed:u,axes:d,originalAxes:p,inputWasTransposed:h}=Lo(o,r,t),f=d;if(h){let b=t.dataIdMap.get(u.dataId).id;b!==i&&(c=u,l=b,f=N.getInnerMostAxes(f.length,c.shape.length))}N.assertAxesAreInnerMostDims("mean",f,c.shape.length);let[m,g]=N.computeOutAndReduceShapes(c.shape,f),A=v.sizeFromShape(g),x=c;c.dtype!=="float32"&&(x=ip({backend:t,inputs:{x:c},attrs:{dtype:"float32"}}),l=t.dataIdMap.get(x.dataId).id);let y=t.makeOutput(m,"float32");if(v.sizeFromShape(c.shape)!==0){let b=t.dataIdMap.get(y.dataId).id;qC(l,A,b)}if(h&&t.disposeData(u.dataId),a){let b=N.expandShapeToKeepDim(y.shape,p);y.shape=b}return c.dtype!=="float32"&&t.disposeData(x.dataId),y}var Yfe={kernelName:Ha,backendName:"wasm",setupFunc:Kfe,kernelFunc:Zfe},XC;function Jfe(e){XC=e.wasm.cwrap(ja,null,["number","number","number","number"])}function Qfe(e){let{backend:t,inputs:n,attrs:s}=e,{axis:r,keepDims:a}=s,{x:o}=n,i=t.dataIdMap.get(o.dataId).id,l=i,c=o,{transposed:u,axes:d,originalAxes:p,inputWasTransposed:h}=Lo(o,r,t);if(h){let y=t.dataIdMap.get(u.dataId).id;y!==i&&(c=u,l=y)}let f=c.shape.length;N.assertAxesAreInnerMostDims("min",d,f);let[m,g]=N.computeOutAndReduceShapes(c.shape,d),A=v.sizeFromShape(g),x=t.makeOutput(m,c.dtype);if(v.sizeFromShape(c.shape)!==0){let y=t.dataIdMap.get(x.dataId).id;XC(l,Qt[o.dtype],A,y)}if(h&&t.disposeData(u.dataId),a){let y=N.expandShapeToKeepDim(x.shape,p);x.shape=y}return x}var eme={kernelName:ja,backendName:"wasm",setupFunc:Jfe,kernelFunc:Qfe},tme=!1,nme=qn(qa,tme),gx;(function(e){e[e.reflect=0]="reflect",e[e.symmetric=1]="symmetric"})(gx||(gx={}));var KC;function sme(e){KC=e.wasm.cwrap(Xa,null,["number","array","number","number","array","array","number","number"])}function rme(e){let{inputs:{x:t},backend:n,attrs:{paddings:s,mode:r}}=e,a=s.map((f,m)=>f[0]+t.shape[m]+f[1]),o=n.dataIdMap.get(t.dataId).id,i=n.makeOutput(a,t.dtype),l=n.dataIdMap.get(i.dataId).id,c=new Uint8Array(new Int32Array(t.shape).buffer),u=s.map(f=>f[0]),d=s.map(f=>f[1]),p=new Uint8Array(new Int32Array(u).buffer),h=new Uint8Array(new Int32Array(d).buffer);return KC(o,c,t.shape.length,Qt[t.dtype],p,h,gx[r],l),i}var ame={kernelName:Xa,backendName:"wasm",kernelFunc:rme,setupFunc:sme},ome=!0,ime=qn(Ka,ome),lme=Tn(yi);function Ax(e,t){let n=new Int32Array(e.wasm.HEAPU8.buffer,t,4),s=n[0],r=n[1],a=n[2],o=n[3];return e.wasm._free(t),{pSelectedIndices:s,selectedSize:r,pSelectedScores:a,pValidOutputs:o}}var ZC;function ume(e){ZC=e.wasm.cwrap(bi,"number",["number","number","number","number","number"])}function cme(e){let{backend:t,inputs:n,attrs:s}=e,{iouThreshold:r,maxOutputSize:a,scoreThreshold:o}=s,{boxes:i,scores:l}=n,c=t.dataIdMap.get(i.dataId).id,u=t.dataIdMap.get(l.dataId).id,d=ZC(c,u,a,r,o),{pSelectedIndices:p,selectedSize:h,pSelectedScores:f,pValidOutputs:m}=Ax(t,d);return t.wasm._free(f),t.wasm._free(m),t.makeOutput([h],"int32",p)}var dme={kernelName:bi,backendName:"wasm",setupFunc:ume,kernelFunc:cme},YC;function pme(e){YC=e.wasm.cwrap(ou,"number",["number","number","number","number","number","bool"])}function 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Uint8Array([0,97,115,109,1,0,0,0,1,4,1,96,0,0,3,2,1,0,10,9,1,7,0,65,0,253,15,26,11])));yx.registerFlag("WASM_HAS_MULTITHREAD_SUPPORT",async()=>{if(yx.get("IS_NODE"))return!1;try{return new MessageChannel().port1.postMessage(new SharedArrayBuffer(1)),WebAssembly.validate(new Uint8Array([0,97,115,109,1,0,0,0,1,4,1,96,0,0,3,2,1,0,5,4,1,3,1,1,10,11,1,9,0,65,0,254,16,2,0,26,11]))}catch(e){return!1}});var A6=Jo(UN()),X0e='var Module={};function threadPrintErr(){var text=Array.prototype.slice.call(arguments).join(" ");console.error(text)}function threadAlert(){var text=Array.prototype.slice.call(arguments).join(" ");postMessage({cmd:"alert",text:text,threadId:Module["_pthread_self"]()})}var err=threadPrintErr;this.alert=threadAlert;Module["instantiateWasm"]=function(info,receiveInstance){var instance=new WebAssembly.Instance(Module["wasmModule"],info);Module["wasmModule"]=null;receiveInstance(instance);return instance.exports};function 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Ll{constructor(e){super();this.wasm=e,this.dataIdNextNumber=1,this.wasm.tfjs.initWithThreadsCount(v6),bx=this.wasm.tfjs.getThreadsCount(),this.dataIdMap=new Pc(this,ts())}write(e,t,n){let s={id:this.dataIdNextNumber++};return this.move(s,e,t,n,1),s}numDataIds(){return this.dataIdMap.numDataIds()}async time(e){let t=v.now();return e(),{kernelMs:v.now()-t}}move(e,t,n,s,r){let a=this.dataIdNextNumber++;if(s==="string"){let c=t;this.dataIdMap.set(e,{id:a,stringBytes:c,shape:n,dtype:s,memoryOffset:null,refCount:r});return}let o=v.sizeFromShape(n),i=o*v.bytesPerElement(s),l=this.wasm._malloc(i);this.dataIdMap.set(e,{id:a,memoryOffset:l,shape:n,dtype:s,refCount:r}),this.wasm.tfjs.registerTensor(a,o,l),t!=null&&this.wasm.HEAPU8.set(new Uint8Array(t.buffer,t.byteOffset,i),l)}async read(e){return this.readSync(e)}readSync(e){let{memoryOffset:t,dtype:n,shape:s,stringBytes:r}=this.dataIdMap.get(e);if(n==="string")return r;let 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precision highp float;
attribute vec2 pos;
attribute vec2 uv;
varying vec2 vUv;
uniform float flipY;
void main(void) {
vUv = uv;
gl_Position = vec4(pos.x, pos.y*flipY, 0.0, 1.);
}
`;var S6=`
precision highp float;
varying vec2 vUv;
uniform sampler2D texture;
uniform float m[20];
void main(void) {
vec4 c = texture2D(texture, vUv);
gl_FragColor.r = m[0] * c.r + m[1] * c.g + m[2] * c.b + m[3] * c.a + m[4];
gl_FragColor.g = m[5] * c.r + m[6] * c.g + m[7] * c.b + m[8] * c.a + m[9];
gl_FragColor.b = m[10] * c.r + m[11] * c.g + m[12] * c.b + m[13] * c.a + m[14];
gl_FragColor.a = m[15] * c.r + m[16] * c.g + m[17] * c.b + m[18] * c.a + m[19];
}
`,I6=`
precision highp float;
varying vec2 vUv;
uniform sampler2D texture;
uniform float m[20];
void main(void) {
vec4 c = texture2D(texture, vUv);
gl_FragColor.r = m[0] * c.r + m[1] * c.g + m[2] * c.b + m[4];
gl_FragColor.g = m[5] * c.r + m[6] * c.g + m[7] * c.b + m[9];
gl_FragColor.b = m[10] * c.r + m[11] * c.g + m[12] * c.b + m[14];
gl_FragColor.a = c.a;
}
`,C6=`
precision highp float;
varying vec2 vUv;
uniform vec2 size;
uniform sampler2D texture;
vec2 pixelate(vec2 coord, vec2 size) {
return floor( coord / size ) * size;
}
void main(void) {
gl_FragColor = vec4(0.0);
vec2 coord = pixelate(vUv, size);
gl_FragColor += texture2D(texture, coord);
}
`,T6=`
precision highp float;
varying vec2 vUv;
uniform sampler2D texture;
uniform vec2 px;
void main(void) {
gl_FragColor = vec4(0.0);
gl_FragColor += texture2D(texture, vUv + vec2(-7.0*px.x, -7.0*px.y))*0.0044299121055113265;
gl_FragColor += texture2D(texture, vUv + vec2(-6.0*px.x, -6.0*px.y))*0.00895781211794;
gl_FragColor += texture2D(texture, vUv + vec2(-5.0*px.x, -5.0*px.y))*0.0215963866053;
gl_FragColor += texture2D(texture, vUv + vec2(-4.0*px.x, -4.0*px.y))*0.0443683338718;
gl_FragColor += texture2D(texture, vUv + vec2(-3.0*px.x, -3.0*px.y))*0.0776744219933;
gl_FragColor += texture2D(texture, vUv + vec2(-2.0*px.x, -2.0*px.y))*0.115876621105;
gl_FragColor += texture2D(texture, vUv + vec2(-1.0*px.x, -1.0*px.y))*0.147308056121;
gl_FragColor += texture2D(texture, vUv )*0.159576912161;
gl_FragColor += texture2D(texture, vUv + vec2( 1.0*px.x, 1.0*px.y))*0.147308056121;
gl_FragColor += texture2D(texture, vUv + vec2( 2.0*px.x, 2.0*px.y))*0.115876621105;
gl_FragColor += texture2D(texture, vUv + vec2( 3.0*px.x, 3.0*px.y))*0.0776744219933;
gl_FragColor += texture2D(texture, vUv + vec2( 4.0*px.x, 4.0*px.y))*0.0443683338718;
gl_FragColor += texture2D(texture, vUv + vec2( 5.0*px.x, 5.0*px.y))*0.0215963866053;
gl_FragColor += texture2D(texture, vUv + vec2( 6.0*px.x, 6.0*px.y))*0.00895781211794;
gl_FragColor += texture2D(texture, vUv + vec2( 7.0*px.x, 7.0*px.y))*0.0044299121055113265;
}
`,N6=`
precision highp float;
varying vec2 vUv;
uniform sampler2D texture;
uniform vec2 px;
uniform float m[9];
void main(void) {
vec4 c11 = texture2D(texture, vUv - px); // top left
vec4 c12 = texture2D(texture, vec2(vUv.x, vUv.y - px.y)); // top center
vec4 c13 = texture2D(texture, vec2(vUv.x + px.x, vUv.y - px.y)); // top right
vec4 c21 = texture2D(texture, vec2(vUv.x - px.x, vUv.y) ); // mid left
vec4 c22 = texture2D(texture, vUv); // mid center
vec4 c23 = texture2D(texture, vec2(vUv.x + px.x, vUv.y) ); // mid right
vec4 c31 = texture2D(texture, vec2(vUv.x - px.x, vUv.y + px.y) ); // bottom left
vec4 c32 = texture2D(texture, vec2(vUv.x, vUv.y + px.y) ); // bottom center
vec4 c33 = texture2D(texture, vUv + px ); // bottom right
gl_FragColor =
c11 * m[0] + c12 * m[1] + c22 * m[2] +
c21 * m[3] + c22 * m[4] + c23 * m[5] +
c31 * m[6] + c32 * m[7] + c33 * m[8];
gl_FragColor.a = c22.a;
}
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d.enableVertexAttribArray(i.attribute.pos),d.vertexAttribPointer(i.attribute.pos,2,d.FLOAT,!1,b,0*y),d.enableVertexAttribArray(i.attribute.uv),d.vertexAttribPointer(i.attribute.uv,2,d.FLOAT,!1,b,2*y),c[x]=i,i}let A={colorMatrix:x=>{let y=new Float32Array(x);y[4]/=255,y[9]/=255,y[14]/=255,y[19]/=255;let b=y[18]===1&&y[3]===0&&y[8]===0&&y[13]===0&&y[15]===0&&y[16]===0&&y[17]===0&&y[19]===0?I6:S6,w=g(b);d.uniform1fv(w.uniform.m,y),m()},brightness:x=>{let y=(x||0)+1;A.colorMatrix([y,0,0,0,0,0,y,0,0,0,0,0,y,0,0,0,0,0,1,0])},saturation:x=>{let y=(x||0)*2/3+1,b=(y-1)*-.5;A.colorMatrix([y,b,b,0,0,b,y,b,0,0,b,b,y,0,0,0,0,0,1,0])},desaturate:()=>{A.saturation(-1)},contrast:x=>{let y=(x||0)+1,b=-128*(y-1);A.colorMatrix([y,0,0,0,b,0,y,0,0,b,0,0,y,0,b,0,0,0,1,0])},negative:()=>{A.contrast(-2)},hue:x=>{x=(x||0)/180*Math.PI;let y=Math.cos(x),b=Math.sin(x),w=.213,k=.715,C=.072;A.colorMatrix([w+y*(1-w)+b*-w,k+y*-k+b*-k,C+y*-C+b*(1-C),0,0,w+y*-w+b*.143,k+y*(1-k)+b*.14,C+y*-C+b*-.283,0,0,w+y*-w+b*-(1-w),k+y*-k+b*k,C+y*(1-C)+b*C,0,0,0,0,0,1,0])},desaturateLuminance:()=>{A.colorMatrix([.2764723,.929708,.0938197,0,-37.1,.2764723,.929708,.0938197,0,-37.1,.2764723,.929708,.0938197,0,-37.1,0,0,0,1,0])},sepia:()=>{A.colorMatrix([.393,.7689999,.18899999,0,0,.349,.6859999,.16799999,0,0,.272,.5339999,.13099999,0,0,0,0,0,1,0])},brownie:()=>{A.colorMatrix([.5997023498159715,.34553243048391263,-.2708298674538042,0,47.43192855600873,-.037703249837783157,.8609577587992641,.15059552388459913,0,-36.96841498319127,.24113635128153335,-.07441037908422492,.44972182064877153,0,-7.562075277591283,0,0,0,1,0])},vintagePinhole:()=>{A.colorMatrix([.6279345635605994,.3202183420819367,-.03965408211312453,0,9.651285835294123,.02578397704808868,.6441188644374771,.03259127616149294,0,7.462829176470591,.0466055556782719,-.0851232987247891,.5241648018700465,0,5.159190588235296,0,0,0,1,0])},kodachrome:()=>{A.colorMatrix([1.1285582396593525,-.3967382283601348,-.03992559172921793,0,63.72958762196502,-.16404339962244616,1.0835251566291304,-.05498805115633132,0,24.732407896706203,-.16786010706155763,-.5603416277695248,1.6014850761964943,0,35.62982807460946,0,0,0,1,0])},technicolor:()=>{A.colorMatrix([1.9125277891456083,-.8545344976951645,-.09155508482755585,0,11.793603434377337,-.3087833385928097,1.7658908555458428,-.10601743074722245,0,-70.35205161461398,-.231103377548616,-.7501899197440212,1.847597816108189,0,30.950940869491138,0,0,0,1,0])},polaroid:()=>{A.colorMatrix([1.438,-.062,-.062,0,0,-.122,1.378,-.122,0,0,-.016,-.016,1.483,0,0,0,0,0,1,0])},shiftToBGR:()=>{A.colorMatrix([0,0,1,0,0,0,1,0,0,0,1,0,0,0,0,0,0,0,1,0])},convolution:x=>{let y=new Float32Array(x),b=1/l.width,w=1/l.height,k=g(N6);d.uniform1fv(k.uniform.m,y),d.uniform2f(k.uniform.px,b,w),m()},detectEdges:()=>{A.convolution.call(this,[0,1,0,1,-4,1,0,1,0])},sobelX:()=>{A.convolution.call(this,[-1,0,1,-2,0,2,-1,0,1])},sobelY:()=>{A.convolution.call(this,[-1,-2,-1,0,0,0,1,2,1])},sharpen:x=>{let y=x||1;A.convolution.call(this,[0,-1*y,0,-1*y,1+4*y,-1*y,0,-1*y,0])},emboss:x=>{let y=x||1;A.convolution.call(this,[-2*y,-1*y,0,-1*y,1,1*y,0,1*y,2*y])},blur:x=>{let y=x/7/l.width,b=x/7/l.height,w=g(T6);d.uniform2f(w.uniform.px,0,b),m(u.INTERMEDIATE),d.uniform2f(w.uniform.px,y,0),m()},pixelate:x=>{let y=x/l.width,b=x/l.height,w=g(C6);d.uniform2f(w.uniform.size,y,b),m()}};this.add=function(x){let y=Array.prototype.slice.call(arguments,1),b=A[x];a.push({func:b,args:y})},this.reset=function(){a=[]},this.get=function(){return 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globalThis.Canvas(e,t));return n}function wx(e,t){let n=t||Xn(e.width,e.height);return n.getContext("2d").drawImage(e,0,0),n}function uc(e,t,n=!0){if(!e)return t.debug&&re("input is missing"),{tensor:null,canvas:null};if(!(e instanceof et)&&!(typeof Image!="undefined"&&e instanceof Image)&&!(typeof ye.Canvas!="undefined"&&e instanceof ye.Canvas)&&!(typeof globalThis.Canvas!="undefined"&&e instanceof globalThis.Canvas)&&!(typeof ImageData!="undefined"&&e instanceof ImageData)&&!(typeof ImageBitmap!="undefined"&&e instanceof ImageBitmap)&&!(typeof HTMLImageElement!="undefined"&&e instanceof HTMLImageElement)&&!(typeof HTMLMediaElement!="undefined"&&e instanceof HTMLMediaElement)&&!(typeof HTMLVideoElement!="undefined"&&e instanceof HTMLVideoElement)&&!(typeof HTMLCanvasElement!="undefined"&&e instanceof HTMLCanvasElement)&&!(typeof OffscreenCanvas!="undefined"&&e instanceof OffscreenCanvas))throw new Error("input type is not recognized");if(e instanceof et){if(e.isDisposedInternal)throw new Error("input tensor is disposed");if(!e.shape||e.shape.length!==4||e.shape[0]!==1||e.shape[3]!==3)throw new Error(`input tensor shape must be [1, height, width, 3] and instead was ${e.shape}`);return{tensor:or(e),canvas:t.filter.return?Nn:null}}else{if(typeof e.readyState!="undefined"&&e.readyState<=2)return t.debug&&re("input stream is not ready"),{tensor:null,canvas:xn};let s=e.naturalWidth||e.videoWidth||e.width||e.shape&&e.shape[1]>0,r=e.naturalHeight||e.videoHeight||e.height||e.shape&&e.shape[2]>0;if(!s||!r)return t.debug&&re("cannot determine input dimensions"),{tensor:null,canvas:xn};let a=s,o=r;if(a>Bm&&(a=Bm,o=Math.trunc(a*r/s)),o>Bm&&(o=Bm,a=Math.trunc(o*s/r)),(t.filter.width||0)>0?a=t.filter.width:(t.filter.height||0)>0&&(a=s*((t.filter.height||0)/r)),(t.filter.height||0)>0?o=t.filter.height:(t.filter.width||0)>0&&(o=r*((t.filter.width||0)/s)),!a||!o)throw new Error("input cannot determine dimension");(!xn||xn.width!==a||xn.height!==o)&&(xn=Xn(a,o));let i=xn.getContext("2d");if(typeof ImageData!="undefined"&&e instanceof ImageData?i.putImageData(e,0,0):t.filter.flip&&typeof i.translate!="undefined"?(i.translate(s,0),i.scale(-1,1),i.drawImage(e,0,0,s,r,0,0,xn.width,xn.height),i.setTransform(1,0,0,1,0,0)):i.drawImage(e,0,0,s,r,0,0,xn.width,xn.height),(!Nn||xn.width!==Nn.width||xn.height!==Nn.height)&&(Nn=Xn(xn.width,xn.height)),t.filter.enabled&&ye.webgl.supported){if(Dt||(Dt=ye.browser?new R6:null),ye.filter=!!Dt,!Dt)return{tensor:null,canvas:xn};Dt.reset(),t.filter.brightness!==0&&Dt.add("brightness",t.filter.brightness),t.filter.contrast!==0&&Dt.add("contrast",t.filter.contrast),t.filter.sharpness!==0&&Dt.add("sharpen",t.filter.sharpness),t.filter.blur!==0&&Dt.add("blur",t.filter.blur),t.filter.saturation!==0&&Dt.add("saturation",t.filter.saturation),t.filter.hue!==0&&Dt.add("hue",t.filter.hue),t.filter.negative&&Dt.add("negative"),t.filter.sepia&&Dt.add("sepia"),t.filter.vintage&&Dt.add("brownie"),t.filter.sepia&&Dt.add("sepia"),t.filter.kodachrome&&Dt.add("kodachrome"),t.filter.technicolor&&Dt.add("technicolor"),t.filter.polaroid&&Dt.add("polaroid"),t.filter.pixelate!==0&&Dt.add("pixelate",t.filter.pixelate),Dt.get()>0?Nn=Dt.apply(xn):Nn=Dt.draw(xn)}else wx(xn,Nn),Dt&&(Dt=null),ye.filter=!!Dt;if(!n)return{tensor:null,canvas:Nn};if(!Nn)throw new Error("cannot create output canvas");let l,c=3;if(typeof ImageData!="undefined"&&e instanceof ImageData||e.data&&e.width&&e.height)if(ye.browser&&Gs)l=Gs?Gs.fromPixels(e):null;else{c=e.data.length/e.height/e.width;let p=new Uint8Array(e.data.buffer);l=Gt(p,[e.height,e.width,c],"int32")}else if((!lc||Nn.width!==lc.width||Nn.height!==lc.height)&&(lc=Xn(Nn.width,Nn.height)),Gs&&ye.browser)t.backend==="webgl"||t.backend==="humangl"||t.backend==="webgpu"?l=Gs.fromPixels(Nn):(lc=wx(Nn),l=Gs.fromPixels(lc));else{let f=wx(Nn).getContext("2d").getImageData(0,0,a,o);c=f.data.length/a/o;let m=new Uint8Array(f.data.buffer);l=Gt(m,[a,o,c])}if(c===4){let p=Eu(l,[0,0,0],[-1,-1,3]);ae(l),l=p}if(!l)throw new Error("cannot create tensor from input");let u=pe(l,"float32"),d=mn(u,0);return ae([l,u]),{tensor:d,canvas:t.filter.return?Nn:null}}}var kx=0,Sx=1,Ix=0,age=async e=>{let t=48,n=$e.resizeBilinear(e,[Math.trunc((e.shape[1]||1)/t),Math.trunc((e.shape[2]||1)/t)]),s=async()=>{let o=Se(n),i=await o.data();return ae(o),i[0]},r=async()=>{let o=await n.data(),i=0;for(let l=0;l<o.length/3;l++)i+=o[3*l+2];return i};if(Ix===0){let o=ce();await r();let i=ce();await s();let l=ce();Ix=i-o<l-i?1:2}let a=Ix===1?await r():await s();return ae(n),a};async function $6(e,t){if(e.cacheSensitivity===0)return!1;let n=await age(t),s=100*(Math.max(n,kx)/Math.min(n,kx)-1);kx=n;let r=s<Math.max(e.cacheSensitivity,Sx);return Sx=s>10*e.cacheSensitivity?0:s,r=r&&Sx>0,r}var D6=class{constructor(){de(this,"browser");de(this,"node");de(this,"worker");de(this,"platform","");de(this,"agent","");de(this,"backends",[]);de(this,"initial");de(this,"filter");de(this,"tfjs");de(this,"offscreen");de(this,"perfadd",!1);de(this,"wasm",{supported:void 0,backend:void 0,simd:void 0,multithread:void 0});de(this,"webgl",{supported:void 0,backend:void 0,version:void 0,renderer:void 0});de(this,"webgpu",{supported:void 0,backend:void 0,adapter:void 0});de(this,"cpu",{model:void 0,flags:[]});de(this,"kernels",[]);de(this,"Canvas");de(this,"Image");de(this,"ImageData");if(this.browser=typeof navigator!="undefined",this.node=typeof process!="undefined",this.tfjs={version:dp},this.offscreen=typeof OffscreenCanvas!="undefined",this.initial=!0,this.worker=this.browser&&this.offscreen?typeof WorkerGlobalScope!="undefined":void 0,typeof navigator!="undefined"){let t=navigator.userAgent.match(/\(([^()]+)\)/g);if(t&&t[0]){let n=t[0].match(/\(([^()]+)\)/g);this.platform=n&&n[0]?n[0].replace(/\(|\)/g,""):"",this.agent=navigator.userAgent.replace(t[0],""),this.platform[1]&&(this.agent=this.agent.replace(t[1],"")),this.agent=this.agent.replace(/ /g," ")}}else typeof process!="undefined"&&(this.platform=`${process.platform} ${process.arch}`,this.agent=`NodeJS ${process.version}`)}async updateBackend(){this.backends=Object.keys(ts().registryFactory),this.wasm.supported=typeof WebAssembly!="undefined",this.wasm.backend=this.backends.includes("wasm"),this.wasm.supported&&this.wasm.backend&&ir()==="wasm"&&(this.wasm.simd=await K().getAsync("WASM_HAS_SIMD_SUPPORT"),this.wasm.multithread=await K().getAsync("WASM_HAS_MULTITHREAD_SUPPORT"));let t=Xn(100,100),n=t?t.getContext("webgl2"):void 0;if(this.webgl.supported=typeof n!="undefined",this.webgl.backend=this.backends.includes("webgl"),this.webgl.supported&&this.webgl.backend&&(ir()==="webgl"||ir()==="humangl")){let s=Ir().gpgpu!=="undefined"?await Ir().getGPGPUContext().gl:null;s&&(this.webgl.version=s.getParameter(s.VERSION),this.webgl.renderer=s.getParameter(s.RENDERER))}this.webgpu.supported=this.browser&&typeof navigator.gpu!="undefined",this.webgpu.backend=this.backends.includes("webgpu"),this.webgpu.supported&&(this.webgpu.adapter=(await navigator.gpu.requestAdapter()).name),this.kernels=Hr(ir()).map(s=>s.kernelName.toLowerCase())}async updateCPU(){let t={model:"",flags:[]};if(this.node&&this.platform.startsWith("linux")){let n=Aa("fs");try{let s=n.readFileSync("/proc/cpuinfo").toString();for(let r of s.split(`
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ige=[127,234,132,58,172,150,149,148,152,377,378,379,397,288,361,454,356,70,63,105,66,107,336,296,334,293,300,168,6,195,4,98,97,2,326,327,33,160,158,133,153,144,362,385,387,263,373,380,57,40,37,0,267,270,287,321,314,17,84,91,78,81,13,311,308,402,14,178],lge=[33,133,362,263,1,62,308,159,145,386,374,6,102,331,2,13,14,70,105,107,336,334,300,54,10,284,50,280,234,454,58,288,152],uge=[33,133,362,263,1,78,308],R2e=ige.map(e=>hp[e]),$2e=lge.map(e=>hp[e]),D2e=uge.map(e=>hp[e]);var fp=e=>[Math.abs(e.endPoint[0]-e.startPoint[0]),Math.abs(e.endPoint[1]-e.startPoint[1])],Vm=e=>[e.startPoint[0]+(e.endPoint[0]-e.startPoint[0])/2,e.startPoint[1]+(e.endPoint[1]-e.startPoint[1])/2],$x=(e,t)=>e?[Math.trunc(Math.max(0,e.startPoint[0])),Math.trunc(Math.max(0,e.startPoint[1])),Math.trunc(Math.min(t.shape[2]||0,e.endPoint[0])-Math.max(0,e.startPoint[0])),Math.trunc(Math.min(t.shape[1]||0,e.endPoint[1])-Math.max(0,e.startPoint[1]))]:[0,0,0,0],Dx=(e,t)=>e?[e.startPoint[0]/(t.shape[2]||0),e.startPoint[1]/(t.shape[1]||0),(e.endPoint[0]-e.startPoint[0])/(t.shape[2]||0),(e.endPoint[1]-e.startPoint[1])/(t.shape[1]||0)]:[0,0,0,0],M6=(e,t)=>{let n=[e.startPoint[0]*t[0],e.startPoint[1]*t[1]],s=[e.endPoint[0]*t[0],e.endPoint[1]*t[1]];return{startPoint:n,endPoint:s,landmarks:e.landmarks,confidence:e.confidence}},Um=(e,t,n)=>{let s=t.shape[1],r=t.shape[2],a=$e.cropAndResize(t,[[e.startPoint[1]/s,e.startPoint[0]/r,e.endPoint[1]/s,e.endPoint[0]/r]],[0],n),o=fe(a,255);return ae(a),o},mp=(e,t)=>{let n=Vm(e),s=fp(e),r=[t*s[0]/2,t*s[1]/2];return{startPoint:[n[0]-r[0],n[1]-r[1]],endPoint:[n[0]+r[0],n[1]+r[1]],landmarks:e.landmarks,confidence:e.confidence}},gp=e=>{let t=Vm(e),n=fp(e),s=Math.max(...n)/2;return{startPoint:[Math.round(t[0]-s),Math.round(t[1]-s)],endPoint:[Math.round(t[0]+s),Math.round(t[1]+s)],landmarks:e.landmarks,confidence:e.confidence}},Gm=e=>{let t=e.map(s=>s[0]),n=e.map(s=>s[1]);return{startPoint:[Math.min(...t),Math.min(...n)],endPoint:[Math.max(...t),Math.max(...n)],landmarks:e}},Ap=[[1,0,0],[0,1,0],[0,0,1]],cge=e=>e-2*Math.PI*Math.floor((e+Math.PI)/(2*Math.PI)),dge=(e,t)=>cge(Math.PI/2-Math.atan2(-(t[1]-e[1]),t[0]-e[0]));var z6=(e,t)=>[[1,0,e],[0,1,t],[0,0,1]],Sl=(e,t)=>{let n=0;for(let s=0;s<e.length;s++)n+=e[s]*t[s];return n},pge=(e,t)=>{let n=[];for(let s=0;s<e.length;s++)n.push(e[s][t]);return n},L6=(e,t)=>{let n=[],s=e.length;for(let r=0;r<s;r++){n.push([]);for(let a=0;a<s;a++)n[r].push(Sl(e[r],pge(t,a)))}return n},B6=(e,t)=>{let n=Math.cos(e),s=Math.sin(e),r=[[n,-s,0],[s,n,0],[0,0,1]],a=z6(t[0],t[1]),o=L6(a,r),i=z6(-t[0],-t[1]);return L6(o,i)},hge=e=>{let t=[[e[0][0],e[1][0]],[e[0][1],e[1][1]]],n=[e[0][2],e[1][2]],s=[-Sl(t[0],n),-Sl(t[1],n)];return[t[0].concat(s[0]),t[1].concat(s[1]),[0,0,1]]},fge=(e,t)=>[Sl(e,t[0]),Sl(e,t[1])];function W6(e){let t={strides:[e/16,e/8],anchors:[2,6]},n=[];for(let s=0;s<t.strides.length;s++){let r=t.strides[s],a=Math.floor((e+r-1)/r),o=Math.floor((e+r-1)/r),i=t.anchors[s];for(let l=0;l<a;l++){let c=r*(l+.5);for(let u=0;u<o;u++){let d=r*(u+.5);for(let p=0;p<i;p++)n.push([d,c])}}}return n}function V6(e,t,n,s,r){let a=fp(t),o=e.map(p=>[a[0]/r*(p[0]-r/2),a[1]/r*(p[1]-r/2),p[2]||0]),i=n&&n!==0&&Math.abs(n)>.2,l=i?B6(n,[0,0]):Ap,c=i?o.map(p=>[...fge(p,l),p[2]]):o,u=i?hge(s):Ap,d=[...Vm({startPoint:t.startPoint,endPoint:t.endPoint}),1];return c.map(p=>[Math.round(p[0]+Sl(d,u[0])),Math.round(p[1]+Sl(d,u[1])),Math.round(p[2]||0)])}function U6(e,t,n){let s=e.landmarks.length>=Ex.count?Ex.symmetryLine:pp.symmetryLine,r=dge(e.landmarks[s[0]],e.landmarks[s[1]]),a=r&&r!==0&&Math.abs(r)>.2,o,i;if(a){let l=Vm({startPoint:e.startPoint,endPoint:e.endPoint}),c=[l[0]/t.shape[2],l[1]/t.shape[1]],u=$e.rotateWithOffset(t,r,0,c);o=B6(-r,l),i=Um(e,u,[n,n]),ae(u)}else o=Ap,i=Um(e,t,[n,n]);return[r,o,i]}var G6=6,Os,H6=[],j6=null,Ms=0,yp=()=>Ms;async function q6(e){var t,n;return ye.initial&&(Os=null),Os?e.debug&&re("cached model:",Os.modelUrl):(Os=await rt(at(e.modelBasePath,((t=e.face.detector)==null?void 0:t.modelPath)||"")),!Os||!Os.modelUrl?re("load model failed:",(n=e.face.detector)==null?void 0:n.modelPath):e.debug&&re("load model:",Os.modelUrl)),Ms=Os.inputs[0].shape?Os.inputs[0].shape[2]:0,Ms===-1&&(Ms=64),H6=W6(Ms),j6=ur(H6),Os}function mge(e){let t={};t.boxStarts=_e(e,[0,1],[-1,2]),t.centers=le(t.boxStarts,j6),t.boxSizes=_e(e,[0,3],[-1,2]),t.boxSizesNormalized=fe(t.boxSizes,Ms),t.centersNormalized=fe(t.centers,Ms),t.halfBoxSize=fe(t.boxSizesNormalized,2),t.starts=xe(t.centersNormalized,t.halfBoxSize),t.ends=le(t.centersNormalized,t.halfBoxSize),t.startNormalized=W(t.starts,Ms),t.endNormalized=W(t.ends,Ms);let n=wu([t.startNormalized,t.endNormalized],1);return Object.keys(t).forEach(s=>ae(t[s])),n}async function X6(e,t){var i,l,c,u;if(!e||e.isDisposedInternal||e.shape.length!==4||e.shape[1]<1||e.shape[2]<1)return{boxes:[]};let n={};n.resized=$e.resizeBilinear(e,[Ms,Ms]),n.div=fe(n.resized,127.5),n.normalized=xe(n.div,.5);let s=Os==null?void 0:Os.execute(n.normalized);if(Array.isArray(s)){let d=s.sort((p,h)=>p.size-h.size);n.concat384=kt([d[0],d[2]],2),n.concat512=kt([d[1],d[3]],2),n.concat=kt([n.concat512,n.concat384],1),n.batch=ct(n.concat,0)}else n.batch=ct(s);ae(s),n.boxes=mge(n.batch),n.logits=_e(n.batch,[0,0],[-1,1]),n.sigmoid=ds(n.logits),n.scores=ct(n.sigmoid),n.nms=await $e.nonMaxSuppressionAsync(n.boxes,n.scores,((i=t.face.detector)==null?void 0:i.maxDetected)||0,((l=t.face.detector)==null?void 0:l.iouThreshold)||0,((c=t.face.detector)==null?void 0:c.minConfidence)||0);let r=await n.nms.array(),a=[],o=await n.scores.data();for(let d=0;d<r.length;d++){let p=o[r[d]];if(p>(((u=t.face.detector)==null?void 0:u.minConfidence)||0)){let h={};h.bbox=_e(n.boxes,[r[d],0],[1,-1]),h.slice=_e(n.batch,[r[d],G6-1],[1,-1]),h.squeeze=ct(h.slice),h.landmarks=G(h.squeeze,[G6,-1]),h.startPoint=_e(h.bbox,[0,0],[-1,2]),h.endPoint=_e(h.bbox,[0,2],[-1,2]),a.push({box:{startPoint:await h.startPoint.data(),endPoint:await h.endPoint.data()},landmarks:await h.landmarks.array(),confidence:p}),Object.keys(h).forEach(f=>ae(h[f]))}}return Object.keys(n).forEach(d=>ae(n[d])),{boxes:a,scaleFactor:[e.shape[2]/Ms,e.shape[1]/Ms]}}var Fx={};Rc(Fx,{connected:()=>Px,kpt:()=>_x});var _x=["nose","leftEyeInside","leftEye","leftEyeOutside","rightEyeInside","rightEye","rightEyeOutside","leftEar","rightEar","leftMouth","rightMouth","leftShoulder","rightShoulder","leftElbow","rightElbow","leftWrist","rightWrist","leftPalm","rightPalm","leftIndex","rightIndex","leftPinky","rightPinky","leftHip","rightHip","leftKnee","rightKnee","leftAnkle","rightAnkle","leftHeel","rightHeel","leftFoot","rightFoot","bodyCenter","bodyTop","leftThumb","leftHand","rightThumb","rightHand"],Px={leftLeg:["leftHip","leftKnee","leftAnkle","leftHeel","leftFoot"],rightLeg:["rightHip","rightKnee","rightAnkle","rightHeel","rightFoot"],torso:["leftShoulder","rightShoulder","rightHip","leftHip","leftShoulder"],leftArm:["leftShoulder","leftElbow","leftWrist","leftPalm"],rightArm:["rightShoulder","rightElbow","rightWrist","rightPalm"],leftHand:[],rightHand:[],head:[]};var K6={initial:!0},un=[null,null],Vo=[[0,0],[0,0]],Ox=Number.MAX_SAFE_INTEGER,Mx,Hm=null,Uo=[[0,0],[0,0],[0,0],[0,0]],Z6=0;async function Y6(e){var t,n,s;if(K6.initial&&(un[0]=null),!un[0]&&((t=e.body.detector)==null?void 0:t.modelPath)){un[0]=await rt(at(e.modelBasePath,((n=e.body.detector)==null?void 0:n.modelPath)||""));let r=Object.values(un[0].modelSignature.inputs);Vo[0][0]=Array.isArray(r)?parseInt(r[0].tensorShape.dim[1].size):0,Vo[0][1]=Array.isArray(r)?parseInt(r[0].tensorShape.dim[2].size):0,!un[0]||!un[0].modelUrl?re("load model failed:",(s=e.body.detector)==null?void 0:s.modelPath):e.debug&&re("load model:",un[0].modelUrl)}else e.debug&&un[0]&&re("cached model:",un[0].modelUrl);return un[0]}async function J6(e){var t;if(K6.initial&&(un[1]=null),un[1])e.debug&&re("cached model:",un[1].modelUrl);else{un[1]=await rt(at(e.modelBasePath,e.body.modelPath||""));let n=Object.values(un[1].modelSignature.inputs);Vo[1][0]=Array.isArray(n)?parseInt(n[0].tensorShape.dim[1].size):0,Vo[1][1]=Array.isArray(n)?parseInt(n[0].tensorShape.dim[2].size):0,((t=e.body.modelPath)==null?void 0:t.includes("lite"))?Mx=["ld_3d","output_segmentation","output_heatmap","world_3d","output_poseflag"]:Mx=["Identity","Identity_2","Identity_3","Identity_4","Identity_1"],!un[1]||!un[1].modelUrl?re("load model failed:",e.body.modelPath):e.debug&&re("load model:",un[1].modelUrl)}return un[1]}function gge(e,t){let n=e.map(o=>o.position[0]),s=e.map(o=>o.position[1]),r=[Math.min(...n),Math.min(...s),Math.max(...n)-Math.min(...n),Math.max(...s)-Math.min(...s)],a=[r[0]/t[0],r[1]/t[1],r[2]/t[0],r[3]/t[1]];return{keypointsBox:r,keypointsBoxRaw:a}}async function Age(e){let t={};if(!e.shape||!e.shape[1]||!e.shape[2])return e;Uo=[[0,0],[e.shape[2]>e.shape[1]?Math.trunc((e.shape[2]-e.shape[1])/2):0,e.shape[2]>e.shape[1]?Math.trunc((e.shape[2]-e.shape[1])/2):0],[e.shape[1]>e.shape[2]?Math.trunc((e.shape[1]-e.shape[2])/2):0,e.shape[1]>e.shape[2]?Math.trunc((e.shape[1]-e.shape[2])/2):0],[0,0]],t.pad=qs(e,Uo),t.resize=$e.resizeBilinear(t.pad,[Vo[1][0],Vo[1][1]]);let n=fe(t.resize,255);return Object.keys(t).forEach(s=>ae(t[s])),n}function yge(e,t){for(let n of e)n.position=[n.position[0]*(t[0]+Uo[2][0]+Uo[2][1])/t[0]-Uo[2][0],n.position[1]*(t[1]+Uo[1][0]+Uo[1][1])/t[1]-Uo[1][0],n.position[2]],n.positionRaw=[n.position[0]/t[0],n.position[1]/t[1],n.position[2]];return e}var Q6=e=>1-1/(1+Math.exp(e));async function xge(e,t,n){var h;let s={};s.input=await Age(e),[s.ld,s.segmentation,s.heatmap,s.world,s.poseflag]=(h=un[1])==null?void 0:h.execute(s.input,Mx);let r=(await s.poseflag.data())[0],a=Math.max(0,(r-.8)/(1-.8)),o=await s.ld.data(),i=[],l=5;for(let f=0;f<o.length/l;f++){let m=Q6(o[l*f+3]),g=Q6(o[l*f+4]),A=Math.trunc(100*m*g*a)/100,x=[o[l*f+0]/Vo[1][0],o[l*f+1]/Vo[1][1],o[l*f+2]+0],y=[Math.trunc(n[0]*x[0]),Math.trunc(n[1]*x[1]),x[2]];i.push({part:_x[f],positionRaw:x,position:y,score:A})}if(a<(t.body.minConfidence||0))return null;let c=yge(i,n),u=gge(c,[n[0],n[1]]);Object.keys(s).forEach(f=>ae(s[f]));let d={};for(let[f,m]of Object.entries(Px)){let g=[];for(let A=0;A<m.length-1;A++){let x=c.find(b=>b.part===m[A]),y=c.find(b=>b.part===m[A+1]);x&&y&&x.score>(t.body.minConfidence||0)&&y.score>(t.body.minConfidence||0)&&g.push([x.position,y.position])}d[f]=g}return{id:0,score:Math.trunc(100*a)/100,box:u.keypointsBox,boxRaw:u.keypointsBoxRaw,keypoints:c,annotations:d}}async function zx(e,t){let n=[e.shape[2]||0,e.shape[1]||0],s=(t.body.skipTime||0)>ce()-Z6,r=Ox<(t.body.skipFrames||0);return t.skipAllowed&&s&&r&&Hm!==null?Ox++:(Hm=await xge(e,t,n),Z6=ce(),Ox=0),Hm?[Hm]:[]}var cc=[{class:1,label:"person"},{class:2,label:"bicycle"},{class:3,label:"car"},{class:4,label:"motorcycle"},{class:5,label:"airplane"},{class:6,label:"bus"},{class:7,label:"train"},{class:8,label:"truck"},{class:9,label:"boat"},{class:10,label:"traffic light"},{class:11,label:"fire hydrant"},{class:12,label:"stop sign"},{class:13,label:"parking meter"},{class:14,label:"bench"},{class:15,label:"bird"},{class:16,label:"cat"},{class:17,label:"dog"},{class:18,label:"horse"},{class:19,label:"sheep"},{class:20,label:"cow"},{class:21,label:"elephant"},{class:22,label:"bear"},{class:23,label:"zebra"},{class:24,label:"giraffe"},{class:25,label:"backpack"},{class:26,label:"umbrella"},{class:27,label:"handbag"},{class:28,label:"tie"},{class:29,label:"suitcase"},{class:30,label:"frisbee"},{class:31,label:"skis"},{class:32,label:"snowboard"},{class:33,label:"sports ball"},{class:34,label:"kite"},{class:35,label:"baseball bat"},{class:36,label:"baseball glove"},{class:37,label:"skateboard"},{class:38,label:"surfboard"},{class:39,label:"tennis racket"},{class:40,label:"bottle"},{class:41,label:"wine glass"},{class:42,label:"cup"},{class:43,label:"fork"},{class:44,label:"knife"},{class:45,label:"spoon"},{class:46,label:"bowl"},{class:47,label:"banana"},{class:48,label:"apple"},{class:49,label:"sandwich"},{class:50,label:"orange"},{class:51,label:"broccoli"},{class:52,label:"carrot"},{class:53,label:"hot dog"},{class:54,label:"pizza"},{class:55,label:"donut"},{class:56,label:"cake"},{class:57,label:"chair"},{class:58,label:"couch"},{class:59,label:"potted plant"},{class:60,label:"bed"},{class:61,label:"dining table"},{class:62,label:"toilet"},{class:63,label:"tv"},{class:64,label:"laptop"},{class:65,label:"mouse"},{class:66,label:"remote"},{class:67,label:"keyboard"},{class:68,label:"cell phone"},{class:69,label:"microwave"},{class:70,label:"oven"},{class:71,label:"toaster"},{class:72,label:"sink"},{class:73,label:"refrigerator"},{class:74,label:"book"},{class:75,label:"clock"},{class:76,label:"vase"},{class:77,label:"scissors"},{class:78,label:"teddy bear"},{class:79,label:"hair drier"},{class:80,label:"toothbrush"}];var Qs,Il=0,jm=[],e8=0,Lx=Number.MAX_SAFE_INTEGER;async function t8(e){if(ye.initial&&(Qs=null),Qs)e.debug&&re("cached model:",Qs.modelUrl);else{dc(["floormod"],e),Qs=await rt(at(e.modelBasePath,e.object.modelPath||""));let t=Object.values(Qs.modelSignature.inputs);Il=Array.isArray(t)?parseInt(t[0].tensorShape.dim[2].size):0,!Qs||!Qs.modelUrl?re("load model failed:",e.object.modelPath):e.debug&&re("load model:",Qs.modelUrl)}return Qs}async function bge(e,t,n){if(!e)return[];let s=[],r=await e.array(),a=ct(e);ae(e);let o=kn(a,6,1);ae(a);let i=_n([o[1],o[0],o[3],o[2]],1),l=ct(i);ae(i);let c=ct(o[4]),u=ct(o[5]);o.forEach(f=>ae(f));let d=await $e.nonMaxSuppressionAsync(l,c,n.object.maxDetected,n.object.iouThreshold,n.object.minConfidence);ae(l),ae(c),ae(u);let p=await d.data();ae(d);let h=0;for(let f of p){let m=Math.trunc(100*r[0][f][4])/100,g=r[0][f][5],A=cc[g].label,[x,y]=[r[0][f][0]/Il,r[0][f][1]/Il],b=[x,y,r[0][f][2]/Il-x,r[0][f][3]/Il-y],w=[Math.trunc(b[0]*t[0]),Math.trunc(b[1]*t[1]),Math.trunc(b[2]*t[0]),Math.trunc(b[3]*t[1])];s.push({id:h++,score:m,class:g,label:A,box:w,boxRaw:b})}return s}async function Bx(e,t){let n=(t.object.skipTime||0)>ce()-e8,s=Lx<(t.object.skipFrames||0);return t.skipAllowed&&n&&s&&jm.length>0?(Lx++,jm):(Lx=0,!ye.kernels.includes("mod")||!ye.kernels.includes("sparsetodense")?jm:new Promise(async r=>{let a=[e.shape[2],e.shape[1]],o=$e.resizeBilinear(e,[Il,Il]),i=t.object.enabled?Qs==null?void 0:Qs.execute(o,["tower_0/detections"]):null;e8=ce(),ae(o);let l=await bge(i,a,t);jm=l,r(l)}))}var Ux={};Rc(Ux,{connected:()=>Vx,kpt:()=>Wx});var Wx=["head","neck","rightShoulder","rightElbow","rightWrist","chest","leftShoulder","leftElbow","leftWrist","bodyCenter","rightHip","rightKnee","rightAnkle","leftHip","leftKnee","leftAnkle"],Vx={leftLeg:["leftHip","leftKnee","leftAnkle"],rightLeg:["rightHip","rightKnee","rightAnkle"],torso:["leftShoulder","rightShoulder","rightHip","leftHip","leftShoulder"],leftArm:["leftShoulder","leftElbow","leftWrist"],rightArm:["rightShoulder","rightElbow","rightWrist"],head:[]};var cn,n8=0,Kn={id:0,keypoints:[],box:[0,0,0,0],boxRaw:[0,0,0,0],score:0,annotations:{}},Gx=Number.MAX_SAFE_INTEGER;async function Hx(e){return ye.initial&&(cn=null),cn?e.debug&&re("cached model:",cn.modelUrl):(cn=await rt(at(e.modelBasePath,e.body.modelPath||"")),!cn||!cn.modelUrl?re("load model failed:",e.body.modelPath):e.debug&&re("load model:",cn.modelUrl)),cn}function vge(e,t){let[n,s]=e.shape;return q(()=>{let r=(i,l)=>xe(i,W(fe(i,Ee(l,"int32")),Ee(l,"int32"))),a=G(e,[s*n]),o=ns(a,0).dataSync()[0];if(o>t){let i=Hs(a,0),l=r(i,n).dataSync()[0],c=fe(i,Ee(n,"int32")).dataSync()[0];return[l,c,o]}return[0,0,o]})}async function jx(e,t){let n=(t.body.skipTime||0)>ce()-n8,s=Gx<(t.body.skipFrames||0);return t.skipAllowed&&n&&s&&Object.keys(Kn.keypoints).length>0?(Gx++,[Kn]):(Gx=0,new Promise(async r=>{var d;let a=q(()=>{if(!(cn==null?void 0:cn.inputs[0].shape))return null;let p=$e.resizeBilinear(e,[cn.inputs[0].shape[2],cn.inputs[0].shape[1]],!1);return W(p,2).sub(1)}),o;if(t.body.enabled&&(o=cn==null?void 0:cn.execute(a)),n8=ce(),ae(a),o){Kn.keypoints.length=0;let p=o.squeeze();ae(o);let h=p.unstack(2);ae(p);for(let f=0;f<h.length;f++){let[m,g,A]=vge(h[f],t.body.minConfidence);A>(((d=t.body)==null?void 0:d.minConfidence)||0)&&Kn.keypoints.push({score:Math.round(100*A)/100,part:Wx[f],positionRaw:[m/cn.inputs[0].shape[2],g/cn.inputs[0].shape[1]],position:[Math.round(e.shape[2]*m/cn.inputs[0].shape[2]),Math.round(e.shape[1]*g/cn.inputs[0].shape[1])]})}h.forEach(f=>ae(f))}Kn.score=Kn.keypoints.reduce((p,h)=>h.score>p?h.score:p,0);let i=Kn.keypoints.map(p=>p.position[0]),l=Kn.keypoints.map(p=>p.position[1]);Kn.box=[Math.min(...i),Math.min(...l),Math.max(...i)-Math.min(...i),Math.max(...l)-Math.min(...l)];let c=Kn.keypoints.map(p=>p.positionRaw[0]),u=Kn.keypoints.map(p=>p.positionRaw[1]);Kn.boxRaw=[Math.min(...c),Math.min(...u),Math.max(...c)-Math.min(...c),Math.max(...u)-Math.min(...u)];for(let[p,h]of Object.entries(Vx)){let f=[];for(let m=0;m<h.length-1;m++){let g=Kn.keypoints.find(x=>x.part===h[m]),A=Kn.keypoints.find(x=>x.part===h[m+1]);g&&A&&g.score>(t.body.minConfidence||0)&&A.score>(t.body.minConfidence||0)&&f.push([g.position,A.position])}Kn.annotations[p]=f}r([Kn])}))}var wge=["angry","disgust","fear","happy","sad","surprise","neutral"],Zn,qm=[],s8=0,r8=0,qx=Number.MAX_SAFE_INTEGER,Xx=[.2989,.587,.114];async function a8(e){var t,n;return ye.initial&&(Zn=null),Zn?e.debug&&re("cached model:",Zn.modelUrl):(Zn=await rt(at(e.modelBasePath,((t=e.face.emotion)==null?void 0:t.modelPath)||"")),!Zn||!Zn.modelUrl?re("load model failed:",(n=e.face.emotion)==null?void 0:n.modelPath):e.debug&&re("load model:",Zn.modelUrl)),Zn}async function Kx(e,t,n,s){var o,i;if(!Zn)return null;let r=qx<(((o=t.face.emotion)==null?void 0:o.skipFrames)||0),a=(((i=t.face.emotion)==null?void 0:i.skipTime)||0)>ce()-r8;return t.skipAllowed&&a&&r&&s8===s&&qm[n]&&qm[n].length>0?(qx++,qm[n]):(qx=0,new Promise(async l=>{var u,d;let c=[];if((u=t.face.emotion)==null?void 0:u.enabled){let p={},h=(Zn==null?void 0:Zn.inputs[0].shape)?Zn.inputs[0].shape[2]:0;p.resize=$e.resizeBilinear(e,[h,h],!1),[p.red,p.green,p.blue]=kn(p.resize,3,3),p.redNorm=W(p.red,Xx[0]),p.greenNorm=W(p.green,Xx[1]),p.blueNorm=W(p.blue,Xx[2]),p.grayscale=Uh([p.redNorm,p.greenNorm,p.blueNorm]),p.grayscaleSub=xe(p.grayscale,.5),p.grayscaleMul=W(p.grayscaleSub,2),p.emotion=Zn==null?void 0:Zn.execute(p.grayscaleMul),r8=ce();let f=await p.emotion.data();for(let m=0;m<f.length;m++)f[m]>(((d=t.face.emotion)==null?void 0:d.minConfidence)||0)&&c.push({score:Math.min(.99,Math.trunc(100*f[m])/100),emotion:wge[m]});c.sort((m,g)=>g.score-m.score),Object.keys(p).forEach(m=>ae(p[m]))}qm[n]=c,s8=s,l(c)}))}var er,Go=0,kge=2.3,Zx=Mr.leftEyeLower0,Yx=Mr.rightEyeLower0,pc={leftBounds:[Zx[0],Zx[Zx.length-1]],rightBounds:[Yx[0],Yx[Yx.length-1]]},hc={upperCenter:3,lowerCenter:4,index:71,numCoordinates:76};async function o8(e){var t,n;return ye.initial&&(er=null),er?e.debug&&re("cached model:",er.modelUrl):(er=await rt(at(e.modelBasePath,((t=e.face.iris)==null?void 0:t.modelPath)||"")),!er||!er.modelUrl?re("load model failed:",(n=e.face.iris)==null?void 0:n.modelPath):e.debug&&re("load model:",er.modelUrl)),Go=er.inputs[0].shape?er.inputs[0].shape[2]:0,Go===-1&&(Go=64),er}function Xm(e,t,n,s){for(let r=0;r<Rx.length;r++){let{key:a,indices:o}=Rx[r],i=Mr[`${n}${a}`];if(!s||s.includes(a))for(let l=0;l<o.length;l++){let c=o[l];e[i[l]]=[t[c][0],t[c][1],(t[c][2]+e[i[l]][2])/2]}}}var Sge=e=>{let t=e[pc.leftBounds[0]][2],n=e[pc.rightBounds[0]][2];return t-n},i8=(e,t,n,s,r=!1,a)=>{let o=gp(mp(Gm([e[n],e[s]]),kge)),i=fp(o),l=$e.cropAndResize(t,[[o.startPoint[1]/a,o.startPoint[0]/a,o.endPoint[1]/a,o.endPoint[0]/a]],[0],[Go,Go]);if(r&&ye.kernels.includes("flipleftright")){let c=$e.flipLeftRight(l);ae(l),l=c}return{box:o,boxSize:i,crop:l}},l8=(e,t,n,s=!1)=>{let r=[];for(let a=0;a<hc.numCoordinates;a++){let o=e[a*3],i=e[a*3+1],l=e[a*3+2];r.push([(s?1-o/Go:o/Go)*n[0]+t.startPoint[0],i/Go*n[1]+t.startPoint[1],l])}return{rawCoords:r,iris:r.slice(hc.index)}},u8=(e,t,n)=>{let s=e[Mr[`${n}EyeUpper0`][hc.upperCenter]][2],r=e[Mr[`${n}EyeLower0`][hc.lowerCenter]][2],a=(s+r)/2;return t.map((o,i)=>{let l=a;return i===2?l=s:i===4&&(l=r),[o[0],o[1],l]})};async function c8(e,t,n,s){if(!er)return n.debug&&re("face mesh iris detection requested, but model is not loaded"),e;let{box:r,boxSize:a,crop:o}=i8(e,t,pc.leftBounds[0],pc.leftBounds[1],!0,s),{box:i,boxSize:l,crop:c}=i8(e,t,pc.rightBounds[0],pc.rightBounds[1],!0,s),u=kt([o,c]);ae(o),ae(c);let d=er.execute(u);ae(u);let p=await d.data();ae(d);let h=p.slice(0,hc.numCoordinates*3),{rawCoords:f,iris:m}=l8(h,r,a,!0),g=p.slice(hc.numCoordinates*3),{rawCoords:A,iris:x}=l8(g,i,l),y=Sge(e);Math.abs(y)<30?(Xm(e,f,"left",null),Xm(e,A,"right",null)):y<1?Xm(e,f,"left",["EyeUpper0","EyeLower0"]):Xm(e,A,"right",["EyeUpper0","EyeLower0"]);let b=u8(e,m,"left"),w=u8(e,x,"right");return e.concat(b).concat(w)}var fc=[],tr=null,zr=0,Jx=Number.MAX_SAFE_INTEGER,d8=0,p8=1.6;async function h8(e,t){var i,l,c,u,d,p,h,f;let n=(((i=t.face.detector)==null?void 0:i.skipTime)||0)>ce()-d8,s=Jx<(((l=t.face.detector)==null?void 0:l.skipFrames)||0);if(!t.skipAllowed||!n||!s||fc.length===0){let m=await X6(e,t);d8=ce(),fc=[];for(let g of m.boxes){let A={startPoint:g.box.startPoint,endPoint:g.box.endPoint,landmarks:g.landmarks,confidence:g.confidence};fc.push(gp(mp(M6(A,m.scaleFactor),Math.sqrt(p8))))}Jx=0}else Jx++;let r=[],a=[],o=0;for(let m=0;m<fc.length;m++){let g=fc[m],A=0,x,y={id:o++,mesh:[],meshRaw:[],box:[0,0,0,0],boxRaw:[0,0,0,0],score:0,boxScore:0,faceScore:0,annotations:{}};if(((c=t.face.detector)==null?void 0:c.rotation)&&((u=t.face.mesh)==null?void 0:u.enabled)&&ye.kernels.includes("rotatewithoffset")?[A,x,y.tensor]=U6(g,e,zr):(x=Ap,y.tensor=Um(g,e,((d=t.face.mesh)==null?void 0:d.enabled)?[zr,zr]:[yp(),yp()])),y.boxScore=Math.round(100*g.confidence)/100,(p=t.face.mesh)==null?void 0:p.enabled)if(!tr)t.debug&&re("face mesh detection requested, but model is not loaded");else{let[b,w,k]=tr.execute(y.tensor),C=await w.data();y.faceScore=Math.round(100*C[0])/100;let E=G(k,[-1,3]),R=await E.array();if(ae([k,E,w,b]),y.faceScore<(((h=t.face.detector)==null?void 0:h.minConfidence)||1))g.confidence=y.faceScore;else{((f=t.face.iris)==null?void 0:f.enabled)&&(R=await c8(R,y.tensor,t,zr)),y.mesh=V6(R,g,A,x,zr),y.meshRaw=y.mesh.map(F=>[F[0]/(e.shape[2]||0),F[1]/(e.shape[1]||0),(F[2]||0)/zr]);for(let F of Object.keys(Mr))y.annotations[F]=Mr[F].map(D=>y.mesh[D]);g=gp(mp(Gm(y.mesh),p8)),y.box=$x(g,e),y.boxRaw=Dx(g,e),y.score=y.faceScore,a.push(g)}}else{y.box=$x(g,e),y.boxRaw=Dx(g,e),y.boxScore=Math.round(100*g.confidence||0)/100,y.score=y.boxScore,y.mesh=g.landmarks.map(b=>[(g.startPoint[0]+g.endPoint[0])/2+(g.endPoint[0]+g.startPoint[0])*b[0]/yp(),(g.startPoint[1]+g.endPoint[1])/2+(g.endPoint[1]+g.startPoint[1])*b[1]/yp()]),y.meshRaw=y.mesh.map(b=>[b[0]/(e.shape[2]||0),b[1]/(e.shape[1]||0),(b[2]||0)/zr]);for(let b of Object.keys(pp))y.annotations[b]=[y.mesh[pp[b]]]}r.push(y)}return fc=[...a],r}async function f8(e){var t,n;return ye.initial&&(tr=null),tr?e.debug&&re("cached model:",tr.modelUrl):(tr=await rt(at(e.modelBasePath,((t=e.face.mesh)==null?void 0:t.modelPath)||"")),!tr||!tr.modelUrl?re("load model failed:",(n=e.face.mesh)==null?void 0:n.modelPath):e.debug&&re("load model:",tr.modelUrl)),zr=tr.inputs[0].shape?tr.inputs[0].shape[2]:0,zr===-1&&(zr=64),tr}var m8=kl,g8=hp;var xs,Km=[],A8=0,y8=0,Qx=Number.MAX_SAFE_INTEGER;async function x8(e){var n,s;let t=at(e.modelBasePath,((n=e.face.description)==null?void 0:n.modelPath)||"");return ye.initial&&(xs=null),xs?e.debug&&re("cached model:",t):(xs=await rt(t),xs?e.debug&&re("load model:",t):re("load model failed:",((s=e.face.description)==null?void 0:s.modelPath)||"")),xs}function eb(e){let t=e.image||e.tensor||e;if(!(xs==null?void 0:xs.inputs[0].shape))return t;let n=$e.resizeBilinear(t,[xs.inputs[0].shape[2],xs.inputs[0].shape[1]],!1),s=W(n,255);return ae(n),s}async function tb(e,t,n,s){var o,i,l,c;if(!xs)return null;let r=Qx<(((o=t.face.description)==null?void 0:o.skipFrames)||0),a=(((i=t.face.description)==null?void 0:i.skipTime)||0)>ce()-A8;return t.skipAllowed&&r&&a&&y8===s&&((l=Km[n])==null?void 0:l.age)&&((c=Km[n])==null?void 0:c.age)>0?(Qx++,Km[n]):(Qx=0,new Promise(async u=>{var p,h;let d={age:0,gender:"unknown",genderScore:0,descriptor:[]};if((p=t.face.description)==null?void 0:p.enabled){let f=eb(e),m=xs==null?void 0:xs.execute(f);A8=ce(),ae(f);let A=await(await m.find(R=>R.shape[1]===1)).data(),x=Math.trunc(200*Math.abs(A[0]-.5))/100;x>(((h=t.face.description)==null?void 0:h.minConfidence)||0)&&(d.gender=A[0]<=.5?"female":"male",d.genderScore=Math.min(.99,x));let y=Hs(m.find(R=>R.shape[1]===100),1),b=(await y.data())[0];ae(y);let k=await m.find(R=>R.shape[1]===100).data();d.age=Math.round(k[b-1]>k[b+1]?10*b-100*k[b-1]:10*b+100*k[b+1])/10;let C=m.find(R=>R.shape[1]===1024),E=C?await C.data():[];d.descriptor=Array.from(E),m.forEach(R=>ae(R))}Km[n]=d,y8=s,u(d)}))}function Zm(e){return[Math.abs(e.endPoint[0]-e.startPoint[0]),Math.abs(e.endPoint[1]-e.startPoint[1])]}function xp(e){return[e.startPoint[0]+(e.endPoint[0]-e.startPoint[0])/2,e.startPoint[1]+(e.endPoint[1]-e.startPoint[1])/2]}function b8(e,t,n){let s=t.shape[1],r=t.shape[2],a=[[e.startPoint[1]/s,e.startPoint[0]/r,e.endPoint[1]/s,e.endPoint[0]/r]];return $e.cropAndResize(t,a,[0],n)}function v8(e,t){let n=[e.startPoint[0]*t[0],e.startPoint[1]*t[1]],s=[e.endPoint[0]*t[0],e.endPoint[1]*t[1]],r=e.palmLandmarks.map(a=>[a[0]*t[0],a[1]*t[1]]);return{startPoint:n,endPoint:s,palmLandmarks:r,confidence:e.confidence}}function Ym(e,t=1.5){let n=xp(e),s=Zm(e),r=[t*s[0]/2,t*s[1]/2],a=[n[0]-r[0],n[1]-r[1]],o=[n[0]+r[0],n[1]+r[1]];return{startPoint:a,endPoint:o,palmLandmarks:e.palmLandmarks}}function Jm(e){let t=xp(e),n=Zm(e),r=Math.max(...n)/2,a=[t[0]-r,t[1]-r],o=[t[0]+r,t[1]+r];return{startPoint:a,endPoint:o,palmLandmarks:e.palmLandmarks}}function Ige(e){return e-2*Math.PI*Math.floor((e+Math.PI)/(2*Math.PI))}function w8(e,t){let n=Math.PI/2-Math.atan2(-(t[1]-e[1]),t[0]-e[0]);return Ige(n)}var k8=(e,t)=>[[1,0,e],[0,1,t],[0,0,1]];function Ho(e,t){let n=0;for(let s=0;s<e.length;s++)n+=e[s]*t[s];return n}function Cge(e,t){let n=[];for(let s=0;s<e.length;s++)n.push(e[s][t]);return n}function S8(e,t){let n=[],s=e.length;for(let r=0;r<s;r++){n.push([]);for(let a=0;a<s;a++)n[r].push(Ho(e[r],Cge(t,a)))}return n}function nb(e,t){let n=Math.cos(e),s=Math.sin(e),r=[[n,-s,0],[s,n,0],[0,0,1]],a=k8(t[0],t[1]),o=S8(a,r),i=k8(-t[0],-t[1]);return S8(o,i)}function I8(e){let t=[[e[0][0],e[1][0]],[e[0][1],e[1][1]]],n=[e[0][2],e[1][2]],s=[-Ho(t[0],n),-Ho(t[1],n)];return[t[0].concat(s[0]),t[1].concat(s[1]),[0,0,1]]}function sb(e,t){return[Ho(e,t[0]),Ho(e,t[1])]}var 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s={};s.reshape=G(t,[-1,7,2]),s.div=fe(s.reshape,this.inputSizeTensor),s.landmarks=le(s.div,this.anchors[n]);let r=W(s.landmarks,this.inputSizeTensor);return Object.keys(s).forEach(a=>ae(s[a])),r}async predict(t,n){let s={};s.resize=$e.resizeBilinear(t,[this.inputSize,this.inputSize]),s.div=fe(s.resize,127.5),s.image=xe(s.div,1),s.batched=this.model.execute(s.image),s.predictions=ct(s.batched),s.slice=_e(s.predictions,[0,0],[-1,1]),s.sigmoid=ds(s.slice),s.scores=ct(s.sigmoid);let r=await s.scores.data();s.boxes=_e(s.predictions,[0,1],[-1,4]),s.norm=this.normalizeBoxes(s.boxes),s.nms=await $e.nonMaxSuppressionAsync(s.norm,s.scores,3*n.hand.maxDetected,n.hand.iouThreshold,n.hand.minConfidence);let a=await s.nms.array(),o=[];for(let i of a){let l={};l.box=_e(s.norm,[i,0],[1,-1]),l.slice=_e(s.predictions,[i,5],[1,14]),l.norm=this.normalizeLandmarks(l.slice,i),l.palmLandmarks=G(l.norm,[-1,2]);let c=await l.box.data(),u=c.slice(0,2),d=c.slice(2,4),p=await l.palmLandmarks.array(),h={startPoint:u,endPoint:d,palmLandmarks:p,confidence:r[i]},f=v8(h,[t.shape[2]/this.inputSize,t.shape[1]/this.inputSize]);o.push(f),Object.keys(l).forEach(m=>ae(l[m]))}return Object.keys(s).forEach(i=>ae(s[i])),o}};var Tge=5,T8=1.65,N8=[0,5,9,13,17,1,2],Nge=0,Ege=2,E8=0,ab=class{constructor(t,n){de(this,"handDetector");de(this,"handPoseModel");de(this,"inputSize");de(this,"storedBoxes");de(this,"skipped");de(this,"detectedHands");this.handDetector=t,this.handPoseModel=n,this.inputSize=this.handPoseModel&&this.handPoseModel.inputs[0].shape?this.handPoseModel.inputs[0].shape[2]:0,this.storedBoxes=[],this.skipped=Number.MAX_SAFE_INTEGER,this.detectedHands=0}calculateLandmarksBoundingBox(t){let n=t.map(o=>o[0]),s=t.map(o=>o[1]),r=[Math.min(...n),Math.min(...s)],a=[Math.max(...n),Math.max(...s)];return{startPoint:r,endPoint:a}}getBoxForPalmLandmarks(t,n){let s=t.map(a=>sb([...a,1],n)),r=this.calculateLandmarksBoundingBox(s);return Ym(Jm(r),Tge)}getBoxForHandLandmarks(t){let n=this.calculateLandmarksBoundingBox(t),s=Ym(Jm(n),T8);s.palmLandmarks=[];for(let r=0;r<N8.length;r++)s.palmLandmarks.push(t[N8[r]].slice(0,2));return s}transformRawCoords(t,n,s,r){let a=Zm(n),o=[a[0]/this.inputSize,a[1]/this.inputSize,(a[0]+a[1])/this.inputSize/2],i=t.map(h=>[o[0]*(h[0]-this.inputSize/2),o[1]*(h[1]-this.inputSize/2),o[2]*h[2]]),l=nb(s,[0,0]),c=i.map(h=>[...sb(h,l),h[2]]),u=I8(r),d=[...xp(n),1],p=[Ho(d,u[0]),Ho(d,u[1])];return c.map(h=>[Math.trunc(h[0]+p[0]),Math.trunc(h[1]+p[1]),Math.trunc(h[2])])}async estimateHands(t,n){let s=!1,r,a=(n.hand.skipTime||0)>ce()-E8,o=this.skipped<(n.hand.skipFrames||0);n.skipAllowed&&a&&o&&(r=await this.handDetector.predict(t,n),this.skipped=0),n.skipAllowed&&this.skipped++,r&&r.length>0&&(r.length!==this.detectedHands&&this.detectedHands!==n.hand.maxDetected||!n.hand.landmarks)&&(this.detectedHands=0,this.storedBoxes=[...r],this.storedBoxes.length>0&&(s=!0));let i=[];for(let l=0;l<this.storedBoxes.length;l++){let c=this.storedBoxes[l];if(!!c)if(n.hand.landmarks){let u=n.hand.rotation?w8(c.palmLandmarks[Nge],c.palmLandmarks[Ege]):0,d=xp(c),p=[d[0]/t.shape[2],d[1]/t.shape[1]],h=n.hand.rotation&&ye.kernels.includes("rotatewithoffset")?$e.rotateWithOffset(t,u,0,p):t.clone(),f=nb(-u,d),m=s?this.getBoxForPalmLandmarks(c.palmLandmarks,f):c,g=b8(m,h,[this.inputSize,this.inputSize]),A=fe(g,255);ae(g),ae(h);let[x,y]=this.handPoseModel.execute(A);E8=ce(),ae(A);let b=(await x.data())[0];if(ae(x),b>=n.hand.minConfidence/4){let w=G(y,[-1,3]),k=await w.array();ae(y),ae(w);let C=this.transformRawCoords(k,m,u,f),E=this.getBoxForHandLandmarks(C);this.storedBoxes[l]={...E,confidence:b};let R={landmarks:C,confidence:b,boxConfidence:c.confidence,fingerConfidence:b,box:{topLeft:E.startPoint,bottomRight:E.endPoint}};i.push(R)}else this.storedBoxes[l]=null;ae(y)}else{let u=Ym(Jm(c),T8),d={confidence:c.confidence,boxConfidence:c.confidence,fingerConfidence:0,box:{topLeft:u.startPoint,bottomRight:u.endPoint},landmarks:[]};i.push(d)}}return this.storedBoxes=this.storedBoxes.filter(l=>l!==null),this.detectedHands=i.length,i.length>n.hand.maxDetected&&(i.length=n.hand.maxDetected),i}};var Ze={thumb:0,index:1,middle:2,ring:3,pinky:4,all:[0,1,2,3,4],nameMapping:{0:"thumb",1:"index",2:"middle",3:"ring",4:"pinky"},pointsMapping:{0:[[0,1],[1,2],[2,3],[3,4]],1:[[0,5],[5,6],[6,7],[7,8]],2:[[0,9],[9,10],[10,11],[11,12]],3:[[0,13],[13,14],[14,15],[15,16]],4:[[0,17],[17,18],[18,19],[19,20]]},getName:e=>Ze.nameMapping[e],getPoints:e=>Ze.pointsMapping[e]},ls={none:0,half:1,full:2,nameMapping:{0:"none",1:"half",2:"full"},getName:e=>ls.nameMapping[e]},qe={verticalUp:0,verticalDown:1,horizontalLeft:2,horizontalRight:3,diagonalUpRight:4,diagonalUpLeft:5,diagonalDownRight:6,diagonalDownLeft:7,nameMapping:{0:"verticalUp",1:"verticalDown",2:"horizontalLeft",3:"horizontalRight",4:"diagonalUpRight",5:"diagonalUpLeft",6:"diagonalDownRight",7:"diagonalDownLeft"},getName:e=>qe.nameMapping[e]},Qm=class{constructor(t){de(this,"name");de(this,"curls");de(this,"directions");de(this,"weights");de(this,"weightsRelative");this.name=t,this.curls={},this.directions={},this.weights=[1,1,1,1,1],this.weightsRelative=[1,1,1,1,1]}addCurl(t,n,s){typeof this.curls[t]=="undefined"&&(this.curls[t]=[]),this.curls[t].push([n,s])}addDirection(t,n,s){this.directions[t]||(this.directions[t]=[]),this.directions[t].push([n,s])}setWeight(t,n){this.weights[t]=n;let s=this.weights.reduce((r,a)=>r+a,0);this.weightsRelative=this.weights.map(r=>r*5/s)}matchAgainst(t,n){let s=0;for(let r in t){let a=t[r],o=this.curls[r];if(typeof o=="undefined"){s+=this.weightsRelative[r];continue}for(let[i,l]of o)if(a===i){s+=l*this.weightsRelative[r];break}}for(let r in n){let a=n[r],o=this.directions[r];if(typeof o=="undefined"){s+=this.weightsRelative[r];continue}for(let[i,l]of o)if(a===i){s+=l*this.weightsRelative[r];break}}return s/10}};var jo=new Qm("thumbs up");jo.addCurl(Ze.thumb,ls.none,1);jo.addDirection(Ze.thumb,qe.verticalUp,1);jo.addDirection(Ze.thumb,qe.diagonalUpLeft,.25);jo.addDirection(Ze.thumb,qe.diagonalUpRight,.25);for(let e of[Ze.index,Ze.middle,Ze.ring,Ze.pinky])jo.addCurl(e,ls.full,1),jo.addDirection(e,qe.horizontalLeft,1),jo.addDirection(e,qe.horizontalRight,1);var en=new Qm("victory");en.addCurl(Ze.thumb,ls.half,.5);en.addCurl(Ze.thumb,ls.none,.5);en.addDirection(Ze.thumb,qe.verticalUp,1);en.addDirection(Ze.thumb,qe.diagonalUpLeft,1);en.addCurl(Ze.index,ls.none,1);en.addDirection(Ze.index,qe.verticalUp,.75);en.addDirection(Ze.index,qe.diagonalUpLeft,1);en.addCurl(Ze.middle,ls.none,1);en.addDirection(Ze.middle,qe.verticalUp,1);en.addDirection(Ze.middle,qe.diagonalUpLeft,.75);en.addCurl(Ze.ring,ls.full,1);en.addDirection(Ze.ring,qe.verticalUp,.2);en.addDirection(Ze.ring,qe.diagonalUpLeft,1);en.addDirection(Ze.ring,qe.horizontalLeft,.2);en.addCurl(Ze.pinky,ls.full,1);en.addDirection(Ze.pinky,qe.verticalUp,.2);en.addDirection(Ze.pinky,qe.diagonalUpLeft,1);en.addDirection(Ze.pinky,qe.horizontalLeft,.2);en.setWeight(Ze.index,2);en.setWeight(Ze.middle,2);var R8=[jo,en];var Rge=.7,Cl={HALF_CURL_START_LIMIT:60,NO_CURL_START_LIMIT:130,DISTANCE_VOTE_POWER:1.1,SINGLE_ANGLE_VOTE_POWER:.9,TOTAL_ANGLE_VOTE_POWER:1.6};function $8(e,t,n,s){let r=(t-s)/(e-n),a=Math.atan(r)*180/Math.PI;return a<=0?a=-a:a>0&&(a=180-a),a}function D8(e,t){if(!e||!t)return[0,0];let n=$8(e[0],e[1],t[0],t[1]);if(e.length===2)return n;let s=$8(e[1],e[2],t[1],t[2]);return[n,s]}function _8(e,t=1){let n=0,s=0,r=0;return e>=75&&e<=105?n=1*t:e>=25&&e<=155?s=1*t:r=1*t,[n,s,r]}function $ge(e,t,n){let s=e[0]-t[0],r=e[0]-n[0],a=t[0]-n[0],o=e[1]-t[1],i=e[1]-n[1],l=t[1]-n[1],c=e[2]-t[2],u=e[2]-n[2],d=t[2]-n[2],p=Math.sqrt(s*s+o*o+c*c),h=Math.sqrt(r*r+i*i+u*u),f=Math.sqrt(a*a+l*l+d*d),m=(f*f+p*p-h*h)/(2*f*p);m>1?m=1:m<-1&&(m=-1);let g=Math.acos(m);g=57.2958*g%180;let A;return g>Cl.NO_CURL_START_LIMIT?A=ls.none:g>Cl.HALF_CURL_START_LIMIT?A=ls.half:A=ls.full,A}function P8(e,t,n,s){let r;return s===Math.abs(e)?e>0?r=qe.horizontalLeft:r=qe.horizontalRight:s===Math.abs(t)?t>0?r=qe.horizontalLeft:r=qe.horizontalRight:n>0?r=qe.horizontalLeft:r=qe.horizontalRight,r}function F8(e,t,n,s){let r;return s===Math.abs(e)?e<0?r=qe.verticalDown:r=qe.verticalUp:s===Math.abs(t)?t<0?r=qe.verticalDown:r=qe.verticalUp:n<0?r=qe.verticalDown:r=qe.verticalUp,r}function Dge(e,t,n,s,r,a,o,i){let l,c=F8(e,t,n,s),u=P8(r,a,o,i);return c===qe.verticalUp?u===qe.horizontalLeft?l=qe.diagonalUpLeft:l=qe.diagonalUpRight:u===qe.horizontalLeft?l=qe.diagonalDownLeft:l=qe.diagonalDownRight,l}function _ge(e,t,n,s){let r=e[0]-t[0],a=e[0]-n[0],o=t[0]-n[0],i=e[1]-t[1],l=e[1]-n[1],c=t[1]-n[1],u=Math.max(Math.abs(r),Math.abs(a),Math.abs(o)),d=Math.max(Math.abs(i),Math.abs(l),Math.abs(c)),p=0,h=0,f=0,m=d/(u+1e-5);m>1.5?p+=Cl.DISTANCE_VOTE_POWER:m>.66?h+=Cl.DISTANCE_VOTE_POWER:f+=Cl.DISTANCE_VOTE_POWER;let g=Math.sqrt(r*r+i*i),A=Math.sqrt(a*a+l*l),x=Math.sqrt(o*o+c*c),y=Math.max(g,A,x),b=e[0],w=e[1],k=n[0],C=n[1];y===g?(k=n[0],C=n[1]):y===x&&(b=t[0],w=t[1]);let F=D8([b,w],[k,C]),D=_8(F,Cl.TOTAL_ANGLE_VOTE_POWER);p+=D[0],h+=D[1],f+=D[2];for(let T of s){let M=_8(T,Cl.SINGLE_ANGLE_VOTE_POWER);p+=M[0],h+=M[1],f+=M[2]}let P;return p===Math.max(p,h,f)?P=F8(l,i,c,d):f===Math.max(h,f)?P=P8(a,r,o,u):P=Dge(l,i,c,d,a,r,o,u),P}function O8(e){let t=[],n=[],s=[],r=[];if(!e)return{curls:s,directions:r};for(let a of Ze.all){let o=Ze.getPoints(a),i=[],l=[];for(let c of o){let u=e[c[0]],d=e[c[1]],p=D8(u,d),h=p[0],f=p[1];i.push(h),l.push(f)}t.push(i),n.push(l)}for(let a of Ze.all){let o=a===Ze.thumb?1:0,i=Ze.getPoints(a),l=e[i[o][0]],c=e[i[o+1][1]],u=e[i[3][1]],d=$ge(l,c,u),p=_ge(l,c,u,t[a].slice(o));s[a]=d,r[a]=p}return{curls:s,directions:r}}function e0(e){if(!e||e.length===0)return null;let t=O8(e),n={};for(let s of Ze.all)n[Ze.getName(s)]={curl:ls.getName(t.curls[s]),direction:qe.getName(t.directions[s])};return n}function M8(e){let t=[];if(!e||e.length===0)return t;let n=O8(e);for(let s of R8){let r=s.matchAgainst(n.curls,n.directions);r>=Rge&&t.push({name:s.name,confidence:r})}return t}var z8={thumb:[1,2,3,4],index:[5,6,7,8],middle:[9,10,11,12],ring:[13,14,15,16],pinky:[17,18,19,20],palm:[0]},oa,ia,L8;async function ob(e,t){let n=await L8.estimateHands(e,t);if(!n)return[];let s=[];for(let r=0;r<n.length;r++){let a={};if(n[r].landmarks)for(let u of Object.keys(z8))a[u]=z8[u].map(d=>n[r].landmarks[d]);let o=n[r].landmarks,i=[Number.MAX_SAFE_INTEGER,Number.MAX_SAFE_INTEGER,0,0],l=[0,0,0,0];if(o&&o.length>0){for(let u of o)u[0]<i[0]&&(i[0]=u[0]),u[1]<i[1]&&(i[1]=u[1]),u[0]>i[2]&&(i[2]=u[0]),u[1]>i[3]&&(i[3]=u[1]);i[2]-=i[0],i[3]-=i[1],l=[i[0]/(e.shape[2]||0),i[1]/(e.shape[1]||0),i[2]/(e.shape[2]||0),i[3]/(e.shape[1]||0)]}else i=n[r].box?[Math.trunc(Math.max(0,n[r].box.topLeft[0])),Math.trunc(Math.max(0,n[r].box.topLeft[1])),Math.trunc(Math.min(e.shape[2]||0,n[r].box.bottomRight[0])-Math.max(0,n[r].box.topLeft[0])),Math.trunc(Math.min(e.shape[1]||0,n[r].box.bottomRight[1])-Math.max(0,n[r].box.topLeft[1]))]:[0,0,0,0],l=[n[r].box.topLeft[0]/(e.shape[2]||0),n[r].box.topLeft[1]/(e.shape[1]||0),(n[r].box.bottomRight[0]-n[r].box.topLeft[0])/(e.shape[2]||0),(n[r].box.bottomRight[1]-n[r].box.topLeft[1])/(e.shape[1]||0)];let c=e0(o);s.push({id:r,score:Math.round(100*n[r].confidence)/100,boxScore:Math.round(100*n[r].boxConfidence)/100,fingerScore:Math.round(100*n[r].fingerConfidence)/100,label:"hand",box:i,boxRaw:l,keypoints:o,annotations:a,landmarks:c})}return s}async function ib(e){var n,s,r,a,o,i;ye.initial&&(oa=null,ia=null),!oa||!ia?([oa,ia]=await Promise.all([e.hand.enabled?rt(at(e.modelBasePath,((n=e.hand.detector)==null?void 0:n.modelPath)||""),{fromTFHub:(((s=e.hand.detector)==null?void 0:s.modelPath)||"").includes("tfhub.dev")}):null,e.hand.landmarks?rt(at(e.modelBasePath,((r=e.hand.skeleton)==null?void 0:r.modelPath)||""),{fromTFHub:(((a=e.hand.skeleton)==null?void 0:a.modelPath)||"").includes("tfhub.dev")}):null]),e.hand.enabled&&(!oa||!oa.modelUrl?re("load model failed:",((o=e.hand.detector)==null?void 0:o.modelPath)||""):e.debug&&re("load model:",oa.modelUrl),!ia||!ia.modelUrl?re("load model failed:",((i=e.hand.skeleton)==null?void 0:i.modelPath)||""):e.debug&&re("load model:",ia.modelUrl))):(e.debug&&re("cached model:",oa.modelUrl),e.debug&&re("cached model:",ia.modelUrl));let t=new rb(oa);return L8=new ab(t,ia),[oa,ia]}function Tl(e,t=[1,1]){let n=[e.map(i=>i[0]),e.map(i=>i[1])],s=[Math.min(...n[0]),Math.min(...n[1])],r=[Math.max(...n[0]),Math.max(...n[1])],a=[s[0],s[1],r[0]-s[0],r[1]-s[1]],o=[a[0]/t[0],a[1]/t[1],a[2]/t[0],a[3]/t[1]];return{box:a,boxRaw:o}}function B8(e,t=[1,1]){let n=[e.map(c=>c[0]),e.map(c=>c[1])],s=[Math.min(...n[0]),Math.min(...n[1])],r=[Math.max(...n[0]),Math.max(...n[1])],a=[(s[0]+r[0])/2,(s[1]+r[1])/2],o=Math.max(a[0]-s[0],a[1]-s[1],-a[0]+r[0],-a[1]+r[1]),i=[Math.trunc(a[0]-o),Math.trunc(a[1]-o),Math.trunc(2*o),Math.trunc(2*o)],l=[i[0]/t[0],i[1]/t[1],i[2]/t[0],i[3]/t[1]];return{box:i,boxRaw:l}}function t0(e,t){let n=[e[2]*t,e[3]*t];return[e[0]-(n[0]-e[2])/2,e[1]-(n[1]-e[3])/2,n[0],n[1]]}function lb(e){return[Math.max(0,e[1]),Math.max(0,e[0]),Math.min(1,e[3]+e[1]),Math.min(1,e[2]+e[0])]}var Ct=[null,null],Pge=["StatefulPartitionedCall/Postprocessor/Slice","StatefulPartitionedCall/Postprocessor/ExpandDims_1"],qo=[[0,0],[0,0]],Fge=["hand","fist","pinch","point","face","tip","pinchtip"],W8=4,V8=1.7,Oge=512,Mge=1.4,n0=Number.MAX_SAFE_INTEGER,ub=0,la=[0,0],qt={boxes:[],hands:[]},U8={thumb:[1,2,3,4],index:[5,6,7,8],middle:[9,10,11,12],ring:[13,14,15,16],pinky:[17,18,19,20],palm:[0]};async function G8(e){var t,n;if(ye.initial&&(Ct[0]=null),Ct[0])e.debug&&re("cached model:",Ct[0].modelUrl);else{dc(["tensorlistreserve","enter","tensorlistfromtensor","merge","loopcond","switch","exit","tensorliststack","nextiteration","tensorlistsetitem","tensorlistgetitem","reciprocal","shape","split","where"],e),Ct[0]=await rt(at(e.modelBasePath,((t=e.hand.detector)==null?void 0:t.modelPath)||""));let s=Object.values(Ct[0].modelSignature.inputs);qo[0][0]=Array.isArray(s)?parseInt(s[0].tensorShape.dim[1].size):0,qo[0][1]=Array.isArray(s)?parseInt(s[0].tensorShape.dim[2].size):0,!Ct[0]||!Ct[0].modelUrl?re("load model failed:",(n=e.hand.detector)==null?void 0:n.modelPath):e.debug&&re("load model:",Ct[0].modelUrl)}return Ct[0]}async function H8(e){var t,n;if(ye.initial&&(Ct[1]=null),Ct[1])e.debug&&re("cached model:",Ct[1].modelUrl);else{Ct[1]=await rt(at(e.modelBasePath,((t=e.hand.skeleton)==null?void 0:t.modelPath)||""));let s=Object.values(Ct[1].modelSignature.inputs);qo[1][0]=Array.isArray(s)?parseInt(s[0].tensorShape.dim[1].size):0,qo[1][1]=Array.isArray(s)?parseInt(s[0].tensorShape.dim[2].size):0,!Ct[1]||!Ct[1].modelUrl?re("load model failed:",(n=e.hand.skeleton)==null?void 0:n.modelPath):e.debug&&re("load model:",Ct[1].modelUrl)}return Ct[1]}async function zge(e,t){let n=[];if(!e||!Ct[0])return n;let s={},r=(e.shape[2]||1)/(e.shape[1]||1),a=Math.min(Math.round((e.shape[1]||0)/8)*8,Oge),o=Math.round(a*r/8)*8;s.resize=$e.resizeBilinear(e,[a,o]),s.cast=pe(s.resize,"int32"),[s.rawScores,s.rawBoxes]=await Ct[0].executeAsync(s.cast,Pge),s.boxes=ct(s.rawBoxes,[0,2]),s.scores=ct(s.rawScores,[0]);let i=ss(s.scores,1);ae(i[W8]),i.splice(W8,1),s.filtered=_n(i,1),ae(i),s.max=ns(s.filtered,1),s.argmax=Hs(s.filtered,1);let l=0;s.nms=await $e.nonMaxSuppressionAsync(s.boxes,s.max,t.hand.maxDetected,t.hand.iouThreshold,t.hand.minConfidence);let c=await s.nms.data(),u=await s.max.data(),d=await s.argmax.data();for(let p of Array.from(c)){let h=_e(s.boxes,p,1),f=await h.data();ae(h);let m=[f[1],f[0],f[3]-f[1],f[2]-f[0]],g=t0(m,Mge),A=lb(g),x=[Math.trunc(m[0]*la[0]),Math.trunc(m[1]*la[1]),Math.trunc(m[2]*la[0]),Math.trunc(m[3]*la[1])],y=u[p],b=Fge[d[p]],w={id:l++,score:y,box:x,boxRaw:g,boxCrop:A,label:b};n.push(w)}return Object.keys(s).forEach(p=>ae(s[p])),n.sort((p,h)=>h.score-p.score),n.length>(t.hand.maxDetected||1)&&(n.length=t.hand.maxDetected||1),n}async function cb(e,t,n){let s={id:t.id,score:Math.round(100*t.score)/100,boxScore:Math.round(100*t.score)/100,fingerScore:0,box:t.box,boxRaw:t.boxRaw,label:t.label,keypoints:[],landmarks:{},annotations:{}};if(e&&Ct[1]&&n.hand.landmarks&&t.score>(n.hand.minConfidence||0)){let r={};r.crop=$e.cropAndResize(e,[t.boxCrop],[0],[qo[1][0],qo[1][1]],"bilinear"),r.cast=pe(r.crop,"float32"),r.div=fe(r.cast,255),[r.score,r.keypoints]=Ct[1].execute(r.div,["Identity_1","Identity"]);let a=(await r.score.data())[0],o=(100-Math.trunc(100/(1+Math.exp(a))))/100;if(o>=(n.hand.minConfidence||0)){s.fingerScore=o,r.reshaped=G(r.keypoints,[-1,3]);let c=(await r.reshaped.array()).map(u=>[u[0]/qo[1][1],u[1]/qo[1][0],u[2]||0]).map(u=>[u[0]*t.boxRaw[2],u[1]*t.boxRaw[3],u[2]||0]);s.keypoints=c.map(u=>[la[0]*(u[0]+t.boxRaw[0]),la[1]*(u[1]+t.boxRaw[1]),u[2]||0]),s.landmarks=e0(s.keypoints);for(let u of Object.keys(U8))s.annotations[u]=U8[u].map(d=>s.landmarks&&s.keypoints[d]?s.keypoints[d]:null)}Object.keys(r).forEach(i=>ae(r[i]))}return s}async function db(e,t){var r,a;if(!Ct[0]||!Ct[1]||!((r=Ct[0])==null?void 0:r.inputs[0].shape)||!((a=Ct[1])==null?void 0:a.inputs[0].shape))return[];la=[e.shape[2]||0,e.shape[1]||0],n0++;let n=(t.hand.skipTime||0)>ce()-ub,s=n0<(t.hand.skipFrames||0);return t.skipAllowed&&n&&s?qt.hands:new Promise(async o=>{let i=3*(t.hand.skipTime||0)>ce()-ub,l=n0<3*(t.hand.skipFrames||0);t.skipAllowed&&qt.hands.length===t.hand.maxDetected?qt.hands=await Promise.all(qt.boxes.map(u=>cb(e,u,t))):t.skipAllowed&&i&&l&&qt.hands.length>0?qt.hands=await Promise.all(qt.boxes.map(u=>cb(e,u,t))):(qt.boxes=await zge(e,t),ub=ce(),qt.hands=await Promise.all(qt.boxes.map(u=>cb(e,u,t))),n0=0);let c=[...qt.boxes];if(qt.boxes.length=0,t.cacheSensitivity>0)for(let u=0;u<qt.hands.length;u++){let d=B8(qt.hands[u].keypoints,la);if(d.box[2]/(e.shape[2]||1)>.05&&d.box[3]/(e.shape[1]||1)>.05&&qt.hands[u].fingerScore&&qt.hands[u].fingerScore>(t.hand.minConfidence||0)){let p=t0(d.box,V8),h=t0(d.boxRaw,V8),f=lb(h);qt.boxes.push({...c[u],box:p,boxRaw:h,boxCrop:f})}}for(let u=0;u<qt.hands.length;u++){let d=Tl(qt.hands[u].keypoints,la);qt.hands[u].box=d.box,qt.hands[u].boxRaw=d.boxRaw}o(qt.hands)})}var mb={};Rc(mb,{connected:()=>r0,horizontal:()=>pb,kpt:()=>s0,relative:()=>fb,vertical:()=>hb});var s0=["nose","leftEye","rightEye","leftEar","rightEar","leftShoulder","rightShoulder","leftElbow","rightElbow","leftWrist","rightWrist","leftHip","rightHip","leftKnee","rightKnee","leftAnkle","rightAnkle"],pb=[["leftEye","rightEye"],["leftEar","rightEar"],["leftShoulder","rightShoulder"],["leftElbow","rightElbow"],["leftWrist","rightWrist"],["leftHip","rightHip"],["leftKnee","rightKnee"],["leftAnkle","rightAnkle"]],hb=[["leftKnee","leftShoulder"],["rightKnee","rightShoulder"],["leftAnkle","leftKnee"],["rightAnkle","rightKnee"]],fb=[[["leftHip","rightHip"],["leftShoulder","rightShoulder"]],[["leftElbow","rightElbow"],["leftShoulder","rightShoulder"]]],r0={leftLeg:["leftHip","leftKnee","leftAnkle"],rightLeg:["rightHip","rightKnee","rightAnkle"],torso:["leftShoulder","rightShoulder","rightHip","leftHip","leftShoulder"],leftArm:["leftShoulder","leftElbow","leftWrist"],rightArm:["rightShoulder","rightElbow","rightWrist"],head:[]};var j8=.005,bs={keypoints:[],padding:[[0,0],[0,0],[0,0],[0,0]]};function gb(e){for(let t of pb){let n=e.keypoints.findIndex(r=>r.part===t[0]),s=e.keypoints.findIndex(r=>r.part===t[1]);if(e.keypoints[n]&&e.keypoints[s]&&e.keypoints[n].position[0]<e.keypoints[s].position[0]){let r=e.keypoints[n];e.keypoints[n]=e.keypoints[s],e.keypoints[s]=r}}for(let t of hb){let n=e.keypoints.findIndex(r=>r&&r.part===t[0]),s=e.keypoints.findIndex(r=>r&&r.part===t[1]);e.keypoints[n]&&e.keypoints[s]&&e.keypoints[n].position[1]<e.keypoints[s].position[1]&&e.keypoints.splice(n,1)}for(let[t,n]of fb){let s=e.keypoints.findIndex(c=>c&&c.part===t[0]),r=e.keypoints.findIndex(c=>c&&c.part===t[1]),a=e.keypoints.findIndex(c=>c&&c.part===n[0]),o=e.keypoints.findIndex(c=>c&&c.part===n[1]);if(!e.keypoints[a]||!e.keypoints[o])continue;let i=e.keypoints[s]?[Math.abs(e.keypoints[a].position[0]-e.keypoints[s].position[0]),Math.abs(e.keypoints[o].position[0]-e.keypoints[s].position[0])]:[0,0],l=e.keypoints[r]?[Math.abs(e.keypoints[o].position[0]-e.keypoints[r].position[0]),Math.abs(e.keypoints[a].position[0]-e.keypoints[r].position[0])]:[0,0];if(i[0]>i[1]||l[0]>l[1]){let c=e.keypoints[s];e.keypoints[s]=e.keypoints[r],e.keypoints[r]=c}}}function q8(e){for(let t=0;t<e.length;t++)if(e[t]&&bs.keypoints[t]){let n=[Math.abs(e[t].positionRaw[0]-bs.keypoints[t].positionRaw[0]),Math.abs(e[t].positionRaw[1]-bs.keypoints[t].positionRaw[1])];n[0]<j8&&n[1]<j8?e[t]=bs.keypoints[t]:bs.keypoints[t]=e[t]}else bs.keypoints[t]=e[t];return e}function X8(e,t){let n={};if(!e.shape||!e.shape[1]||!e.shape[2])return e;bs.padding=[[0,0],[e.shape[2]>e.shape[1]?Math.trunc((e.shape[2]-e.shape[1])/2):0,e.shape[2]>e.shape[1]?Math.trunc((e.shape[2]-e.shape[1])/2):0],[e.shape[1]>e.shape[2]?Math.trunc((e.shape[1]-e.shape[2])/2):0,e.shape[1]>e.shape[2]?Math.trunc((e.shape[1]-e.shape[2])/2):0],[0,0]],n.pad=qs(e,bs.padding),n.resize=$e.resizeBilinear(n.pad,[t,t]);let s=pe(n.resize,"int32");return Object.keys(n).forEach(r=>ae(n[r])),s}function K8(e,t){e.keypoints=e.keypoints.filter(s=>s&&s.position);for(let s of e.keypoints)s.position=[s.position[0]*(t[0]+bs.padding[2][0]+bs.padding[2][1])/t[0]-bs.padding[2][0],s.position[1]*(t[1]+bs.padding[1][0]+bs.padding[1][1])/t[1]-bs.padding[1][0]],s.positionRaw=[s.position[0]/t[0],s.position[1]/t[1]];let n=Tl(e.keypoints.map(s=>s.position),t);return e.box=n.box,e.boxRaw=n.boxRaw,e}var Pn,a0=0,Ab=Number.MAX_SAFE_INTEGER,Nl={boxes:[],bodies:[],last:0};async function Z8(e){return ye.initial&&(Pn=null),Pn?e.debug&&re("cached model:",Pn.modelUrl):(dc(["size"],e),Pn=await rt(at(e.modelBasePath,e.body.modelPath||"")),!Pn||!Pn.modelUrl?re("load model failed:",e.body.modelPath):e.debug&&re("load model:",Pn.modelUrl)),a0=Pn.inputs[0].shape?Pn.inputs[0].shape[2]:0,a0===-1&&(a0=256),Pn}async function Lge(e,t,n,s){let r=e[0][0],a=[],o=0;for(let d=0;d<r.length;d++)if(o=r[d][2],o>t.body.minConfidence){let p=[(s[3]-s[1])*r[d][1]+s[1],(s[2]-s[0])*r[d][0]+s[0]];a.push({score:Math.round(100*o)/100,part:s0[d],positionRaw:p,position:[Math.round((n.shape[2]||0)*p[0]),Math.round((n.shape[1]||0)*p[1])]})}o=a.reduce((d,p)=>p.score>d?p.score:d,0);let i=[],l=Tl(a.map(d=>d.position),[n.shape[2],n.shape[1]]),c={};for(let[d,p]of Object.entries(r0)){let h=[];for(let f=0;f<p.length-1;f++){let m=a.find(A=>A.part===p[f]),g=a.find(A=>A.part===p[f+1]);m&&g&&m.score>(t.body.minConfidence||0)&&g.score>(t.body.minConfidence||0)&&h.push([m.position,g.position])}c[d]=h}let u={id:0,score:o,box:l.box,boxRaw:l.boxRaw,keypoints:a,annotations:c};return gb(u),i.push(u),i}async function Bge(e,t,n,s){let r=[];for(let a=0;a<e[0].length;a++){let o=e[0][a],i=Math.round(100*o[51+4])/100;if(i>t.body.minConfidence){let l=[];for(let p=0;p<17;p++){let h=o[3*p+2];if(h>t.body.minConfidence){let f=[(s[3]-s[1])*o[3*p+1]+s[1],(s[2]-s[0])*o[3*p+0]+s[0]];l.push({part:s0[p],score:Math.round(100*h)/100,positionRaw:f,position:[Math.round((n.shape[2]||0)*f[0]),Math.round((n.shape[1]||0)*f[1])]})}}let c=Tl(l.map(p=>p.position),[n.shape[2],n.shape[1]]),u={};for(let[p,h]of Object.entries(r0)){let f=[];for(let m=0;m<h.length-1;m++){let g=l.find(x=>x.part===h[m]),A=l.find(x=>x.part===h[m+1]);g&&A&&g.score>(t.body.minConfidence||0)&&A.score>(t.body.minConfidence||0)&&f.push([g.position,A.position])}u[p]=f}let d={id:a,score:i,box:c.box,boxRaw:c.boxRaw,keypoints:[...l],annotations:u};gb(d),r.push(d)}}return r.sort((a,o)=>o.score-a.score),r.length>t.body.maxDetected&&(r.length=t.body.maxDetected),r}async function yb(e,t){if(!Pn||!(Pn==null?void 0:Pn.inputs[0].shape))return[];t.skipAllowed||(Nl.boxes.length=0),Ab++;let n=(t.body.skipTime||0)>ce()-Nl.last,s=Ab<(t.body.skipFrames||0);return t.skipAllowed&&n&&s?Nl.bodies:new Promise(async r=>{let a={};Ab=0,a.input=X8(e,a0),a.res=Pn==null?void 0:Pn.execute(a.input),Nl.last=ce();let o=await a.res.array();Nl.bodies=a.res.shape[2]===17?await Lge(o,t,e,[0,0,1,1]):await Bge(o,t,e,[0,0,1,1]);for(let i of Nl.bodies)K8(i,[e.shape[2]||1,e.shape[1]||1]),q8(i.keypoints);Object.keys(a).forEach(i=>ae(a[i])),r(Nl.bodies)})}var vs,o0=[],Y8=0,xb=Number.MAX_SAFE_INTEGER,i0=2.5;async function J8(e){if(!vs||ye.initial){vs=await rt(at(e.modelBasePath,e.object.modelPath||""));let t=Object.values(vs.modelSignature.inputs);if(vs.inputSize=Array.isArray(t)?parseInt(t[0].tensorShape.dim[2].size):null,!vs.inputSize)throw new Error(`cannot determine model inputSize: ${e.object.modelPath}`);!vs||!vs.modelUrl?re("load model failed:",e.object.modelPath):e.debug&&re("load model:",vs.modelUrl)}else e.debug&&re("cached model:",vs.modelUrl);return vs}async function Wge(e,t,n,s){let r=0,a=[];for(let c of[1,2,4])q(async()=>{var g,A;let u=c*13,d=(g=e.find(x=>x.shape[1]===u**2&&x.shape[2]===cc.length))==null?void 0:g.squeeze(),p=(A=e.find(x=>x.shape[1]===u**2&&x.shape[2]<cc.length))==null?void 0:A.squeeze(),f=await p.reshape([-1,4,p.shape[1]/4]).argMax(2).array(),m=await d.array();for(let x=0;x<d.shape[0];x++)for(let y=0;y<d.shape[1];y++){let b=m[x][y];if(b>s.object.minConfidence&&y!==61){let w=(.5+Math.trunc(x%u))/u,k=(.5+Math.trunc(x/u))/u,C=f[x].map(U=>U*(u/c/t)),[E,R]=[w-i0/c*C[0],k-i0/c*C[1]],[F,D]=[w+i0/c*C[2]-E,k+i0/c*C[3]-R],P=[E,R,F,D];P=P.map(U=>Math.max(0,Math.min(U,1)));let T=[P[0]*n[0],P[1]*n[1],P[2]*n[0],P[3]*n[1]],M={id:r++,score:Math.round(100*b)/100,class:y+1,label:cc[y].label,box:T.map(U=>Math.trunc(U)),boxRaw:P};a.push(M)}}});e.forEach(c=>ae(c));let o=a.map(c=>[c.boxRaw[1],c.boxRaw[0],c.boxRaw[3],c.boxRaw[2]]),i=a.map(c=>c.score),l=[];if(o&&o.length>0){let c=await $e.nonMaxSuppressionAsync(o,i,s.object.maxDetected,s.object.iouThreshold,s.object.minConfidence);l=await c.data(),ae(c)}return a=a.filter((c,u)=>l.includes(u)).sort((c,u)=>u.score-c.score),a}async function bb(e,t){let n=(t.object.skipTime||0)>ce()-Y8,s=xb<(t.object.skipFrames||0);return t.skipAllowed&&n&&s&&o0.length>0?(xb++,o0):(xb=0,!ye.kernels.includes("mod")||!ye.kernels.includes("sparsetodense")?o0:new Promise(async r=>{let a=[e.shape[2],e.shape[1]],o=$e.resizeBilinear(e,[vs.inputSize,vs.inputSize],!1),i=fe(o,255),l=i.transpose([0,3,1,2]);ae(i),ae(o);let c;t.object.enabled&&(c=vs.execute(l)),Y8=ce(),ae(l);let u=await Wge(c,vs.inputSize,a,t);o0=u,r(u)}))}var bp=["nose","leftEye","rightEye","leftEar","rightEar","leftShoulder","rightShoulder","leftElbow","rightElbow","leftWrist","rightWrist","leftHip","rightHip","leftKnee","rightKnee","leftAnkle","rightAnkle"],Vge=bp.length,vp=bp.reduce((e,t,n)=>(e[t]=n,e),{}),Uge=[["leftHip","leftShoulder"],["leftElbow","leftShoulder"],["leftElbow","leftWrist"],["leftHip","leftKnee"],["leftKnee","leftAnkle"],["rightHip","rightShoulder"],["rightElbow","rightShoulder"],["rightElbow","rightWrist"],["rightHip","rightKnee"],["rightKnee","rightAnkle"],["leftShoulder","rightShoulder"],["leftHip","rightHip"]],wAe=Uge.map(([e,t])=>[vp[e],vp[t]]),Q8=[["nose","leftEye"],["leftEye","leftEar"],["nose","rightEye"],["rightEye","rightEar"],["nose","leftShoulder"],["leftShoulder","leftElbow"],["leftElbow","leftWrist"],["leftShoulder","leftHip"],["leftHip","leftKnee"],["leftKnee","leftAnkle"],["nose","rightShoulder"],["rightShoulder","rightElbow"],["rightElbow","rightWrist"],["rightShoulder","rightHip"],["rightHip","rightKnee"],["rightKnee","rightAnkle"]];function eT(e){let t=e.reduce(({maxX:n,maxY:s,minX:r,minY:a},{position:{x:o,y:i}})=>({maxX:Math.max(n,o),maxY:Math.max(s,i),minX:Math.min(r,o),minY:Math.min(a,i)}),{maxX:Number.NEGATIVE_INFINITY,maxY:Number.NEGATIVE_INFINITY,minX:Number.POSITIVE_INFINITY,minY:Number.POSITIVE_INFINITY});return[t.minX,t.minY,t.maxX-t.minX,t.maxY-t.minY]}function tT(e,[t,n],[s,r]){let a=t/s,o=n/r,i=(c,u)=>({id:u,score:c.score,boxRaw:[c.box[0]/r,c.box[1]/s,c.box[2]/r,c.box[3]/s],box:[Math.trunc(c.box[0]*o),Math.trunc(c.box[1]*a),Math.trunc(c.box[2]*o),Math.trunc(c.box[3]*a)],keypoints:c.keypoints.map(({score:d,part:p,position:h})=>({score:d,part:p,position:[Math.trunc(h.x*o),Math.trunc(h.y*a)],positionRaw:[h.x/s,h.y/s]}))});return e.map((c,u)=>i(c,u))}var vb=class{constructor(t,n){de(this,"priorityQueue");de(this,"numberOfElements");de(this,"getElementValue");this.priorityQueue=new Array(t),this.numberOfElements=-1,this.getElementValue=n}enqueue(t){this.priorityQueue[++this.numberOfElements]=t,this.swim(this.numberOfElements)}dequeue(){let t=this.priorityQueue[0];return this.exchange(0,this.numberOfElements--),this.sink(0),this.priorityQueue[this.numberOfElements+1]=null,t}empty(){return this.numberOfElements===-1}size(){return this.numberOfElements+1}all(){return this.priorityQueue.slice(0,this.numberOfElements+1)}max(){return this.priorityQueue[0]}swim(t){for(;t>0&&this.less(Math.floor(t/2),t);)this.exchange(t,Math.floor(t/2)),t=Math.floor(t/2)}sink(t){for(;2*t<=this.numberOfElements;){let n=2*t;if(n<this.numberOfElements&&this.less(n,n+1)&&n++,!this.less(t,n))break;this.exchange(t,n),t=n}}getValueAt(t){return this.getElementValue(this.priorityQueue[t])}less(t,n){return this.getValueAt(t)<this.getValueAt(n)}exchange(t,n){let s=this.priorityQueue[t];this.priorityQueue[t]=this.priorityQueue[n],this.priorityQueue[n]=s}};function 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d=Math.abs(u.annotations.leftEyeIris[3][0]-u.annotations.leftEyeIris[1][0])/2,p=Math.abs(u.annotations.leftEyeIris[4][1]-u.annotations.leftEyeIris[2][1])/2;r.ellipse(u.annotations.leftEyeIris[0][0],u.annotations.leftEyeIris[0][1],d,p,0,0,2*Math.PI),r.stroke(),s.fillPolygons&&(r.fillStyle=s.useDepth?"rgba(255, 255, 200, 0.3)":s.color,r.fill())}if(u.annotations&&u.annotations.rightEyeIris&&u.annotations.rightEyeIris[0]){r.strokeStyle=s.useDepth?"rgba(255, 200, 255, 0.3)":s.color,r.beginPath();let d=Math.abs(u.annotations.rightEyeIris[3][0]-u.annotations.rightEyeIris[1][0])/2,p=Math.abs(u.annotations.rightEyeIris[4][1]-u.annotations.rightEyeIris[2][1])/2;r.ellipse(u.annotations.rightEyeIris[0][0],u.annotations.rightEyeIris[0][1],d,p,0,0,2*Math.PI),r.stroke(),s.fillPolygons&&(r.fillStyle=s.useDepth?"rgba(255, 255, 200, 0.3)":s.color,r.fill())}if(s.drawGaze&&((a=u.rotation)==null?void 0:a.angle)){r.strokeStyle="pink";let d=u.box[0]+u.box[2]/2-u.box[3]*gc(u.rotation.angle.yaw)/90,p=u.box[1]+u.box[3]/2+u.box[2]*gc(u.rotation.angle.pitch)/90,h=new Path2D(`
M ${u.box[0]+u.box[2]/2} ${u.box[1]}
C
${d} ${u.box[1]},
${d} ${u.box[1]+u.box[3]},
${u.box[0]+u.box[2]/2} ${u.box[1]+u.box[3]}
`),f=new Path2D(`
M ${u.box[0]} ${u.box[1]+u.box[3]/2}
C
${u.box[0]} ${p},
${u.box[0]+u.box[2]} ${p},
${u.box[0]+u.box[2]} ${u.box[1]+u.box[3]/2}
`);r.stroke(f),r.stroke(h)}if(s.drawGaze&&((i=(o=u.rotation)==null?void 0:o.gaze)==null?void 0:i.strength)&&((c=(l=u.rotation)==null?void 0:l.gaze)==null?void 0:c.bearing)&&u.annotations.leftEyeIris&&u.annotations.rightEyeIris&&u.annotations.leftEyeIris[0]&&u.annotations.rightEyeIris[0]){r.strokeStyle="pink",r.fillStyle="pink";let d=[u.annotations.leftEyeIris[0][0]+Math.sin(u.rotation.gaze.bearing)*u.rotation.gaze.strength*u.box[3],u.annotations.leftEyeIris[0][1]+Math.cos(u.rotation.gaze.bearing)*u.rotation.gaze.strength*u.box[2]];dT(r,[u.annotations.leftEyeIris[0][0],u.annotations.leftEyeIris[0][1]],[d[0],d[1]],4);let p=[u.annotations.rightEyeIris[0][0]+Math.sin(u.rotation.gaze.bearing)*u.rotation.gaze.strength*u.box[3],u.annotations.rightEyeIris[0][1]+Math.cos(u.rotation.gaze.bearing)*u.rotation.gaze.strength*u.box[2]];dT(r,[u.annotations.rightEyeIris[0][0],u.annotations.rightEyeIris[0][1]],[p[0],p[1]],4)}}}}}async function Fb(e,t,n){var a;let s=Rn(ua,n);if(!t||!e)return;let r=El(e);r.lineJoin="round";for(let o=0;o<t.length;o++){if(r.strokeStyle=s.color,r.fillStyle=s.color,r.lineWidth=s.lineWidth,r.font=s.font,s.drawBoxes&&t[o].box&&((a=t[o].box)==null?void 0:a.length)===4&&(wp(r,t[o].box[0],t[o].box[1],t[o].box[2],t[o].box[3],s),s.drawLabels&&(s.shadowColor&&s.shadowColor!==""&&(r.fillStyle=s.shadowColor,r.fillText(`body ${100*t[o].score}%`,t[o].box[0]+3,1+t[o].box[1]+s.lineHeight,t[o].box[2])),r.fillStyle=s.labelColor,r.fillText(`body ${100*t[o].score}%`,t[o].box[0]+2,0+t[o].box[1]+s.lineHeight,t[o].box[2]))),s.drawPoints&&t[o].keypoints)for(let i=0;i<t[o].keypoints.length;i++)r.fillStyle=s.useDepth&&t[o].keypoints[i].position[2]?`rgba(${127.5+2*(t[o].keypoints[i].position[2]||0)}, ${127.5-2*(t[o].keypoints[i].position[2]||0)}, 255, 0.5)`:s.color,Db(r,t[o].keypoints[i].position[0],t[o].keypoints[i].position[1],0,s);if(s.drawLabels&&t[o].keypoints){r.font=s.font;for(let i of t[o].keypoints)r.fillStyle=s.useDepth&&i.position[2]?`rgba(${127.5+2*i.position[2]}, ${127.5-2*i.position[2]}, 255, 0.5)`:s.color,r.fillText(`${i.part} ${Math.trunc(100*i.score)}%`,i.position[0]+4,i.position[1]+4)}if(s.drawPolygons&&t[o].keypoints&&t[o].annotations)for(let i of Object.values(t[o].annotations))for(let l of i)Jge(r,l,s)}}async function Ob(e,t,n){let s=Rn(ua,n);if(!t||!e)return;let r=El(e);r.lineJoin="round",r.font=s.font;for(let a of t){if(s.drawBoxes&&(r.strokeStyle=s.color,r.fillStyle=s.color,wp(r,a.box[0],a.box[1],a.box[2],a.box[3],s),s.drawLabels&&(s.shadowColor&&s.shadowColor!==""&&(r.fillStyle=s.shadowColor,r.fillText(`hand:${Math.trunc(100*a.score)}%`,a.box[0]+3,1+a.box[1]+s.lineHeight,a.box[2])),r.fillStyle=s.labelColor,r.fillText(`hand:${Math.trunc(100*a.score)}%`,a.box[0]+2,0+a.box[1]+s.lineHeight,a.box[2])),r.stroke()),s.drawPoints&&a.keypoints&&a.keypoints.length>0)for(let o of a.keypoints)r.fillStyle=s.useDepth?`rgba(${127.5+2*(o[2]||0)}, ${127.5-2*(o[2]||0)}, 255, 0.5)`:s.color,Db(r,o[0],o[1],0,s);if(s.drawLabels&&a.annotations){let o=(i,l)=>{!i||i.length===0||!i[0]||(r.fillStyle=s.useDepth?`rgba(${127.5+2*i[i.length-1][2]}, ${127.5-2*i[i.length-1][2]}, 255, 0.5)`:s.color,r.fillText(l,i[i.length-1][0]+4,i[i.length-1][1]+4))};r.font=s.font,o(a.annotations.index,"index"),o(a.annotations.middle,"middle"),o(a.annotations.ring,"ring"),o(a.annotations.pinky,"pinky"),o(a.annotations.thumb,"thumb"),o(a.annotations.palm,"palm")}if(s.drawPolygons&&a.annotations){let o=i=>{if(!(!i||i.length===0||!i[0]))for(let l=0;l<i.length;l++)r.beginPath(),r.strokeStyle=s.useDepth?`rgba(${127.5+l*i[l][2]}, ${127.5-l*i[l][2]}, 255, 0.5)`:s.color,r.moveTo(i[l>0?l-1:0][0],i[l>0?l-1:0][1]),r.lineTo(i[l][0],i[l][1]),r.stroke()};r.lineWidth=s.lineWidth,o(a.annotations.index),o(a.annotations.middle),o(a.annotations.ring),o(a.annotations.pinky),o(a.annotations.thumb)}}}async function Mb(e,t,n){let s=Rn(ua,n);if(!t||!e)return;let r=El(e);r.lineJoin="round",r.font=s.font;for(let a of t)if(s.drawBoxes){if(r.strokeStyle=s.color,r.fillStyle=s.color,wp(r,a.box[0],a.box[1],a.box[2],a.box[3],s),s.drawLabels){let o=`${a.label} ${Math.round(100*a.score)}%`;s.shadowColor&&s.shadowColor!==""&&(r.fillStyle=s.shadowColor,r.fillText(o,a.box[0]+3,1+a.box[1]+s.lineHeight,a.box[2])),r.fillStyle=s.labelColor,r.fillText(o,a.box[0]+2,0+a.box[1]+s.lineHeight,a.box[2])}r.stroke()}}async function pT(e,t,n){let s=Rn(ua,n);if(!t||!e)return;let r=El(e);r.lineJoin="round",r.font=s.font;for(let a=0;a<t.length;a++)if(s.drawBoxes){if(r.strokeStyle=s.color,r.fillStyle=s.color,wp(r,t[a].box[0],t[a].box[1],t[a].box[2],t[a].box[3],s),s.drawLabels){let o=`person #${a}`;s.shadowColor&&s.shadowColor!==""&&(r.fillStyle=s.shadowColor,r.fillText(o,t[a].box[0]+3,1+t[a].box[1]+s.lineHeight,t[a].box[2])),r.fillStyle=s.labelColor,r.fillText(o,t[a].box[0]+2,0+t[a].box[1]+s.lineHeight,t[a].box[2])}r.stroke()}}async function hT(e,t){if(!e||!t)return;El(t).drawImage(e,0,0)}async function fT(e,t,n){if(!t||!t.performance||!t||!e)return null;let s=ce(),r=Rn(ua,n),a=Promise.all([Pb(e,t.face,r),Fb(e,t.body,r),Ob(e,t.hand,r),Mb(e,t.object,r),_b(e,t.gesture,r)]);return $b=ye.perfadd?$b+Math.round(ce()-s):Math.round(ce()-s),t.performance.draw=$b,a}var Qge=e=>{let t=(d,p)=>Math.atan2(d[1]-p[1],d[0]-p[0]);if(!e.annotations.rightEyeIris||!e.annotations.leftEyeIris)return{bearing:0,strength:0};let n=[0,-.1],s=1,r=e.mesh[33][2]>e.mesh[263][2],a=r?e.mesh[473]:e.mesh[468],o=r?[(e.mesh[133][0]+e.mesh[33][0])/2,(e.mesh[133][1]+e.mesh[33][1])/2]:[(e.mesh[263][0]+e.mesh[362][0])/2,(e.mesh[263][1]+e.mesh[362][1])/2],i=r?[e.mesh[133][0]-e.mesh[33][0],e.mesh[23][1]-e.mesh[27][1]]:[e.mesh[263][0]-e.mesh[362][0],e.mesh[253][1]-e.mesh[257][1]],l=[(o[0]-a[0])/i[0]-n[0],s*(a[1]-o[1])/i[1]-n[1]],c=Math.sqrt(l[0]**2+l[1]**2);return c=Math.min(c,e.boxRaw[2]/2,e.boxRaw[3]/2),{bearing:(t([0,0],l)+Math.PI/2)%Math.PI,strength:c}},mT=(e,t)=>{let n=g=>{let A=Math.sqrt(g[0]*g[0]+g[1]*g[1]+g[2]*g[2]);return g[0]/=A,g[1]/=A,g[2]/=A,g},s=(g,A)=>{let x=g[0]-A[0],y=g[1]-A[1],b=g[2]-A[2];return[x,y,b]},r=(g,A)=>{let x=g[1]*A[2]-g[2]*A[1],y=g[2]*A[0]-g[0]*A[2],b=g[0]*A[1]-g[1]*A[0];return[x,y,b]},a=g=>{let[A,x,y,b,w,k,C,E,R]=g,F,D,P;return b<1?b>-1?(P=Math.asin(b),D=Math.atan2(-C,A),F=Math.atan2(-k,w)):(P=-Math.PI/2,D=-Math.atan2(E,R),F=0):(P=Math.PI/2,D=Math.atan2(E,R),F=0),isNaN(F)&&(F=0),isNaN(D)&&(D=0),isNaN(P)&&(P=0),{pitch:2*-F,yaw:2*-D,roll:2*-P}},o=g=>{let A=(y,b,w,k)=>Math.atan2(k-b,w-y);return{pitch:A(g[10][1],g[10][2],g[152][1],g[152][2]),yaw:A(g[33][0],g[33][2],g[263][0],g[263][2]),roll:A(g[33][0],g[33][1],g[263][0],g[263][1])}},i=e.meshRaw;if(!i||i.length<300)return{angle:{pitch:0,yaw:0,roll:0},matrix:[1,0,0,0,1,0,0,0,1],gaze:{bearing:0,strength:0}};let l=Math.max(e.boxRaw[2]*t[0],e.boxRaw[3]*t[1])/1.5,c=[i[10],i[152],i[234],i[454]].map(g=>[g[0]*t[0]/l,g[1]*t[1]/l,g[2]]),u=n(s(c[1],c[0])),d=n(s(c[3],c[2])),p=n(r(d,u));d=r(u,p);let h=[d[0],d[1],d[2],u[0],u[1],u[2],p[0],p[1],p[2]],f=a(h),m=i.length===478?Qge(e):{bearing:0,strength:0};return{angle:f,matrix:h,gaze:m}};var zb=async(e,t)=>{var p,h,f,m;let n,s,r,a,o,i,l,c,u=[];e.state="run:face",n=ce();let d=await h8(t,e.config);if(e.performance.face=ye.perfadd?(e.performance.face||0)+Math.trunc(ce()-n):Math.trunc(ce()-n),!t.shape||t.shape.length!==4)return[];if(!d)return[];for(let g=0;g<d.length;g++){if(e.analyze("Get Face"),!d[g].tensor||d[g].tensor.isDisposedInternal){re("Face object is disposed:",d[g].tensor);continue}let A=mT(d[g],[t.shape[2],t.shape[1]]);e.analyze("Start Emotion:"),e.config.async?o=e.config.face.emotion.enabled?Kx(d[g].tensor||Gt([]),e.config,g,d.length):null:(e.state="run:emotion",n=ce(),o=e.config.face.emotion.enabled?await Kx(d[g].tensor||Gt([]),e.config,g,d.length):null,e.performance.emotion=ye.perfadd?(e.performance.emotion||0)+Math.trunc(ce()-n):Math.trunc(ce()-n)),e.analyze("End Emotion:"),e.analyze("Start AntiSpoof:"),e.config.async?l=e.config.face.antispoof.enabled?Nx(d[g].tensor||Gt([]),e.config,g,d.length):null:(e.state="run:antispoof",n=ce(),l=e.config.face.antispoof.enabled?await Nx(d[g].tensor||Gt([]),e.config,g,d.length):null,e.performance.antispoof=ye.perfadd?(e.performance.antispoof||0)+Math.trunc(ce()-n):Math.trunc(ce()-n)),e.analyze("End AntiSpoof:"),e.analyze("Start Description:"),e.config.async?c=e.config.face.description.enabled?tb(d[g].tensor||Gt([]),e.config,g,d.length):null:(e.state="run:description",n=ce(),c=e.config.face.description.enabled?await tb(d[g].tensor||Gt([]),e.config,g,d.length):null,e.performance.description=ye.perfadd?(e.performance.description||0)+Math.trunc(ce()-n):Math.trunc(ce()-n)),e.analyze("End Description:"),e.config.async&&([s,a,o,i,c,r,l]=await Promise.all([s,a,o,i,c,r,l])),e.analyze("Finish Face:"),!e.config.face.iris.enabled&&((h=(p=d[g])==null?void 0:p.annotations)==null?void 0:h.leftEyeIris)&&((m=(f=d[g])==null?void 0:f.annotations)==null?void 0:m.rightEyeIris)&&(delete d[g].annotations.leftEyeIris,delete d[g].annotations.rightEyeIris);let x=d[g].annotations&&d[g].annotations.leftEyeIris&&d[g].annotations.leftEyeIris[0]&&d[g].annotations.rightEyeIris&&d[g].annotations.rightEyeIris[0]&&d[g].annotations.leftEyeIris.length>0&&d[g].annotations.rightEyeIris.length>0&&d[g].annotations.leftEyeIris[0]!==null&&d[g].annotations.rightEyeIris[0]!==null?Math.max(Math.abs(d[g].annotations.leftEyeIris[3][0]-d[g].annotations.leftEyeIris[1][0]),Math.abs(d[g].annotations.rightEyeIris[4][1]-d[g].annotations.rightEyeIris[2][1]))/t.shape[2]:0,y=e.config.face.detector.return?ct(d[g].tensor):null;ae(d[g].tensor),d[g].tensor&&delete d[g].tensor,u.push({...d[g],id:g,age:c==null?void 0:c.age,gender:c==null?void 0:c.gender,genderScore:c==null?void 0:c.genderScore,embedding:c==null?void 0:c.descriptor,emotion:o,real:l,iris:x!==0?Math.trunc(500/x/11.7)/100:0,rotation:A,tensor:y}),e.analyze("End Face")}return e.analyze("End FaceMesh:"),e.config.async&&(e.performance.face&&delete e.performance.face,e.performance.age&&delete e.performance.age,e.performance.gender&&delete e.performance.gender,e.performance.emotion&&delete e.performance.emotion),u};var gT=e=>{if(!e)return[];let t=[];for(let n=0;n<e.length;n++){let s=e[n].keypoints.find(l=>l.part==="leftWrist"),r=e[n].keypoints.find(l=>l.part==="rightWrist"),a=e[n].keypoints.find(l=>l.part==="nose");a&&s&&r&&s.position[1]<a.position[1]&&r.position[1]<a.position[1]?t.push({body:n,gesture:"i give up"}):a&&s&&s.position[1]<a.position[1]?t.push({body:n,gesture:"raise left hand"}):a&&r&&r.position[1]<a.position[1]&&t.push({body:n,gesture:"raise right hand"});let o=e[n].keypoints.find(l=>l.part==="leftShoulder"),i=e[n].keypoints.find(l=>l.part==="rightShoulder");o&&i&&t.push({body:n,gesture:`leaning ${o.position[1]>i.position[1]?"left":"right"}`})}return t},AT=e=>{if(!e)return[];let t=[];for(let n=0;n<e.length;n++)if(e[n].mesh&&e[n].mesh.length>450){let s=e[n].mesh[33][2]-e[n].mesh[263][2];Math.abs(s)<10?t.push({face:n,gesture:"facing center"}):t.push({face:n,gesture:`facing ${s<0?"left":"right"}`}),Math.abs(e[n].mesh[374][1]-e[n].mesh[386][1])/Math.abs(e[n].mesh[443][1]-e[n].mesh[450][1])<.2&&t.push({face:n,gesture:"blink left eye"}),Math.abs(e[n].mesh[145][1]-e[n].mesh[159][1])/Math.abs(e[n].mesh[223][1]-e[n].mesh[230][1])<.2&&t.push({face:n,gesture:"blink right eye"});let o=Math.min(100,500*Math.abs(e[n].mesh[13][1]-e[n].mesh[14][1])/Math.abs(e[n].mesh[10][1]-e[n].mesh[152][1]));o>10&&t.push({face:n,gesture:`mouth ${Math.trunc(o)}% open`});let i=e[n].mesh[152][2];Math.abs(i)>10&&t.push({face:n,gesture:`head ${i<0?"up":"down"}`})}return t},yT=e=>{if(!e)return[];let t=[];for(let n=0;n<e.length;n++){if(!e[n].annotations||!e[n].annotations.leftEyeIris||!e[n].annotations.leftEyeIris[0]||!e[n].annotations.rightEyeIris||!e[n].annotations.rightEyeIris[0])continue;let s=e[n].annotations.leftEyeIris[3][0]-e[n].annotations.leftEyeIris[1][0],r=e[n].annotations.leftEyeIris[4][1]-e[n].annotations.leftEyeIris[2][1],a=Math.abs(s*r),o=e[n].annotations.rightEyeIris[3][0]-e[n].annotations.rightEyeIris[1][0],i=e[n].annotations.rightEyeIris[4][1]-e[n].annotations.rightEyeIris[2][1],l=Math.abs(o*i),c=!1;Math.abs(a-l)/Math.max(a,l)<.25&&(c=!0,t.push({iris:n,gesture:"facing center"}));let d=Math.abs(e[n].mesh[263][0]-e[n].annotations.leftEyeIris[0][0])/e[n].box[2],p=Math.abs(e[n].mesh[33][0]-e[n].annotations.rightEyeIris[0][0])/e[n].box[2];(d>.06||p>.06)&&(c=!1),d>p?d>.05&&t.push({iris:n,gesture:"looking right"}):p>.05&&t.push({iris:n,gesture:"looking left"});let h=Math.abs(e[n].mesh[145][1]-e[n].annotations.rightEyeIris[0][1])/e[n].box[3],f=Math.abs(e[n].mesh[374][1]-e[n].annotations.leftEyeIris[0][1])/e[n].box[3];(f<.01||h<.01||f>.022||h>.022)&&(c=!1),(f<.01||h<.01)&&t.push({iris:n,gesture:"looking down"}),(f>.022||h>.022)&&t.push({iris:n,gesture:"looking up"}),c&&t.push({iris:n,gesture:"looking center"})}return t},xT=e=>{if(!e)return[];let t=[];for(let n=0;n<e.length;n++){let s=[];if(e[n].annotations)for(let[r,a]of Object.entries(e[n].annotations))r!=="palmBase"&&Array.isArray(a)&&a[0]&&s.push({name:r.toLowerCase(),position:a[0]});if(s&&s.length>0){let r=s.reduce((o,i)=>o.position[2]<i.position[2]?o:i);t.push({hand:n,gesture:`${r.name} forward`});let a=s.reduce((o,i)=>o.position[1]<i.position[1]?o:i);t.push({hand:n,gesture:`${a.name} up`})}if(e[n].keypoints){let r=M8(e[n].keypoints);for(let a of r)t.push({hand:n,gesture:a.name})}}return t};var Pe={face:[],body:[],hand:[],gesture:[],object:[],persons:[],performance:{},timestamp:0},Lb=0;function bT(e,t){var o,i,l,c,u,d,p,h,f,m,g,A,x,y,b,w,k,C,E,R,F,D,P,T,M,U,j;let n=ce();if(!e)return{face:[],body:[],hand:[],gesture:[],object:[],persons:[],performance:{},timestamp:0};let s=Date.now()-e.timestamp,r=s<1e3?8-Math.log(s+1):1;if(Pe.canvas=e.canvas,!Pe.body||e.body.length!==Pe.body.length)Pe.body=JSON.parse(JSON.stringify(e.body));else for(let z=0;z<e.body.length;z++){let 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if(e.hand[z].annotations)for(let ne of Object.keys(e.hand[z].annotations))ee[ne]=e.hand[z].annotations[ne]&&e.hand[z].annotations[ne][0]?e.hand[z].annotations[ne].map((Q,te)=>Q.map((oe,he)=>((r-1)*Pe.hand[z].annotations[ne][te][he]+oe)/r)):null;Pe.hand[z]={...e.hand[z],box:X,boxRaw:Z,keypoints:J,annotations:ee}}if(!Pe.face||e.face.length!==Pe.face.length)Pe.face=JSON.parse(JSON.stringify(e.face));else for(let z=0;z<e.face.length;z++){let X=e.face[z].box.map((ee,ne)=>((r-1)*Pe.face[z].box[ne]+ee)/r),Z=e.face[z].boxRaw.map((ee,ne)=>((r-1)*Pe.face[z].boxRaw[ne]+ee)/r),J={matrix:[0,0,0,0,0,0,0,0,0],angle:{roll:0,yaw:0,pitch:0},gaze:{bearing:0,strength:0}};J.matrix=(p=e.face[z].rotation)==null?void 0:p.matrix,J.angle={roll:((r-1)*(((f=(h=Pe.face[z].rotation)==null?void 0:h.angle)==null?void 0:f.roll)||0)+(((g=(m=e.face[z].rotation)==null?void 0:m.angle)==null?void 0:g.roll)||0))/r,yaw:((r-1)*(((x=(A=Pe.face[z].rotation)==null?void 0:A.angle)==null?void 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2Q==`;async function e2e(e){let t=(r,a="application/octet-stream")=>fetch(`data:${a};base64,${r}`).then(o=>o.blob()),n,s;switch(e.config.warmup){case"face":n=await t(d0);break;case"body":case"full":n=await t(p0);break;default:n=null}if(n){let r=await createImageBitmap(n);s=await e.detect(r,e.config),r.close()}return s}async function t2e(e){return new Promise(t=>{let n;switch(e.config.warmup){case"face":n="data:image/jpeg;base64,"+d0;break;case"full":case"body":n="data:image/jpeg;base64,"+p0;break;default:n=null}let s;typeof Image!="undefined"?s=new Image:ye.Image&&(s=new ye.Image),s.onload=async()=>{let r=Xn(s.naturalWidth,s.naturalHeight);if(!r)re("Warmup: Canvas not found"),t({});else{let a=r.getContext("2d");a&&a.drawImage(s,0,0);let o=await e.image(r),i=await e.detect(o.tensor,e.config);t(i)}},n?s.src=n:t(null)})}async function n2e(e){let t=r=>Buffer.from(r,"base64"),n;if(e.config.warmup==="face"&&(n=t(d0)),(e.config.warmup==="body"||e.config.warmup==="full")&&(n=t(p0)),!n)return null;let s;if(typeof void 0!="undefined"){let r=(void 0).decodeJpeg(n),a=r.expandDims(0);e.tf.dispose(r),s=await e.detect(a,e.config),e.tf.dispose(a)}else e.config.debug&&re("Warmup tfjs-node not loaded");return s}async function ST(e,t){let n=ce();if(e.state="warmup",t&&(e.config=Rn(e.config,t)),!e.config.warmup||e.config.warmup==="none")return{error:"null"};let s;return new Promise(async r=>{typeof createImageBitmap=="function"?s=await e2e(e):typeof Image!="undefined"||ye.Canvas!==void 0?s=await t2e(e):s=await n2e(e);let a=ce();e.config.debug&&re("Warmup",e.config.warmup,Math.round(a-n),"ms"),e.emit("warmup"),r(s)})}var Ac,kp,Sp,h0,CT=class{constructor(t){de(this,"version");de(this,"config");de(this,"result");de(this,"state");de(this,"process");de(this,"tf");de(this,"env");de(this,"draw");de(this,"models");de(this,"events");de(this,"faceTriangulation");de(this,"faceUVMap");de(this,"performance");Dc(this,Ac,void 0);Dc(this,kp,void 0);Dc(this,Sp,void 0);de(this,"gl");de(this,"analyze",(...t)=>{if(!$c(this,kp))return;let n=this.tf.engine().state.numTensors,s=$c(this,Ac);_c(this,Ac,n);let r=n-s;r!==0&&re(...t,r)});Dc(this,h0,t=>{if(!$c(this,Sp))return null;if(!t)return"input is not defined";if(this.env.node&&!(t instanceof et))return"input must be a tensor";try{this.tf.getBackend()}catch(n){return"backend not loaded"}return null});de(this,"similarity",vT);de(this,"distance",c0);de(this,"match",wT);de(this,"emit",t=>{var n;this.events&&this.events.dispatchEvent&&((n=this.events)==null||n.dispatchEvent(new Event(t)))});this.env=ye,ya.wasmPath=dp.includes("-")?"https://vladmandic.github.io/tfjs/dist/":`https://cdn.jsdelivr.net/npm/@tensorflow/tfjs-backend-wasm@${dp}/dist/`,ya.modelBasePath=ye.browser?"../models/":"file://models/",ya.backend=ye.browser?"humangl":"tensorflow",this.version=Cx,Object.defineProperty(this,"version",{value:Cx}),this.config=JSON.parse(JSON.stringify(ya)),Object.seal(this.config),t&&(this.config=Rn(this.config,t)),this.tf=wl,this.state="idle",_c(this,Ac,0),_c(this,kp,!1),_c(this,Sp,!1),this.performance={},this.events=typeof EventTarget!="undefined"?new EventTarget:void 0,this.models=new Eb,this.draw={options:ua,canvas:(n,s)=>hT(n,s),face:(n,s,r)=>Pb(n,s,r),body:(n,s,r)=>Fb(n,s,r),hand:(n,s,r)=>Ob(n,s,r),gesture:(n,s,r)=>_b(n,s,r),object:(n,s,r)=>Mb(n,s,r),person:(n,s,r)=>pT(n,s,r),all:(n,s,r)=>fT(n,s,r)},this.result={face:[],body:[],hand:[],gesture:[],object:[],performance:{},timestamp:0,persons:[]},this.process={tensor:null,canvas:null},this.faceTriangulation=m8,this.faceUVMap=g8,this.gl=zt,this.emit("create")}reset(){let t=this.config.backend;this.config=JSON.parse(JSON.stringify(ya)),this.config.backend=t}validate(t){return Mg(ya,t||this.config)}now(){return ce()}image(t,n=!0){return uc(t,this.config,n)}async segmentation(t,n){return oT(t,n,this.config)}enhance(t){return eb(t)}async init(){await u0(this,!0),await this.tf.ready()}async load(t){this.state="load";let n=ce(),s=Object.values(this.models).filter(o=>o).length;t&&(this.config=Rn(this.config,t)),this.env.initial&&(this.config.debug&&re(`version: ${this.version}`),this.config.debug&&re(`tfjs version: ${this.tf.version_core}`),await u0(this)||re("error: backend check failed"),await Vh(),this.env.browser&&(this.config.debug&&re("configuration:",this.config),this.config.debug&&re("environment:",this.env),this.config.debug&&re("tf flags:",this.tf.ENV.flags))),await iT(this),this.env.initial&&this.config.debug&&re("tf engine state:",this.tf.engine().state.numBytes,"bytes",this.tf.engine().state.numTensors,"tensors"),this.env.initial=!1,Object.values(this.models).filter(o=>o).length!==s&&(await lT(this),this.emit("load"));let a=Math.trunc(ce()-n);a>(this.performance.loadModels||0)&&(this.performance.loadModels=this.env.perfadd?(this.performance.loadModels||0)+a:a)}next(t=this.result){return bT(t,this.config)}async warmup(t){let n=ce(),s=await ST(this,t),r=ce();return this.performance.warmup=Math.trunc(r-n),s}async profile(t,n){let s=await this.tf.profile(()=>this.detect(t,n)),r={};for(let i of s.kernels)r[i.name]?r[i.name]+=i.kernelTimeMs:r[i.name]=i.kernelTimeMs;let a=[];Object.entries(r).forEach(i=>a.push({name:i[0],ms:i[1]})),a.sort((i,l)=>l.ms-i.ms),a.length=20;let o={};for(let i of a)o[i.name]=i.ms;return o}async detect(t,n){return this.state="detect",new Promise(async s=>{var g,A,x,y,b,w,k,C,E,R,F,D,P,T,M,U,j,z,X,Z,J,ee;this.state="config";let r;this.config=Rn(this.config,n),this.state="check";let a=$c(this,h0).call(this,t);a&&(re(a,t),s({error:a}));let o=ce();await u0(this),await this.load(),r=ce(),this.state="image";let i=uc(t,this.config);if(this.process=i,this.performance.inputProcess=this.env.perfadd?(this.performance.inputProcess||0)+Math.trunc(ce()-r):Math.trunc(ce()-r),this.analyze("Get Image:"),!i.tensor){this.config.debug&&re("could not convert input to tensor"),s({error:"could not convert input to tensor"});return}this.emit("image"),r=ce(),this.config.skipAllowed=await $6(this.config,i.tensor),this.performance.totalFrames||(this.performance.totalFrames=0),this.performance.cachedFrames||(this.performance.cachedFrames=0),this.performance.totalFrames++,this.config.skipAllowed&&this.performance.cachedFrames++,this.performance.inputCheck=this.env.perfadd?(this.performance.inputCheck||0)+Math.trunc(ce()-r):Math.trunc(ce()-r),this.analyze("Check Changed:");let l=[],c=[],u=[],d=[];this.state="detect:face",this.config.async?(l=this.config.face.enabled?zb(this,i.tensor):[],this.performance.face&&delete this.performance.face):(r=ce(),l=this.config.face.enabled?await zb(this,i.tensor):[],this.performance.face=this.env.perfadd?(this.performance.face||0)+Math.trunc(ce()-r):Math.trunc(ce()-r)),this.config.async&&(this.config.body.maxDetected===-1||this.config.hand.maxDetected===-1)&&(l=await l),this.analyze("Start Body:"),this.state="detect:body";let p=this.config.body.maxDetected===-1?Rn(this.config,{body:{maxDetected:this.config.face.enabled?1*l.length:1}}):this.config;this.config.async?(((g=this.config.body.modelPath)==null?void 0:g.includes("posenet"))?c=this.config.body.enabled?Cb(i.tensor,p):[]:((A=this.config.body.modelPath)==null?void 0:A.includes("blazepose"))?c=this.config.body.enabled?zx(i.tensor,p):[]:((x=this.config.body.modelPath)==null?void 0:x.includes("efficientpose"))?c=this.config.body.enabled?jx(i.tensor,p):[]:((y=this.config.body.modelPath)==null?void 0:y.includes("movenet"))&&(c=this.config.body.enabled?yb(i.tensor,p):[]),this.performance.body&&delete this.performance.body):(r=ce(),((b=this.config.body.modelPath)==null?void 0:b.includes("posenet"))?c=this.config.body.enabled?await Cb(i.tensor,p):[]:((w=this.config.body.modelPath)==null?void 0:w.includes("blazepose"))?c=this.config.body.enabled?await zx(i.tensor,p):[]:((k=this.config.body.modelPath)==null?void 0:k.includes("efficientpose"))?c=this.config.body.enabled?await jx(i.tensor,p):[]:((C=this.config.body.modelPath)==null?void 0:C.includes("movenet"))&&(c=this.config.body.enabled?await yb(i.tensor,p):[]),this.performance.body=this.env.perfadd?(this.performance.body||0)+Math.trunc(ce()-r):Math.trunc(ce()-r)),this.analyze("End Body:"),this.analyze("Start Hand:"),this.state="detect:hand";let h=this.config.hand.maxDetected===-1?Rn(this.config,{hand:{maxDetected:this.config.face.enabled?2*l.length:1}}):this.config;this.config.async?(((R=(E=this.config.hand.detector)==null?void 0:E.modelPath)==null?void 0:R.includes("handdetect"))?u=this.config.hand.enabled?ob(i.tensor,h):[]:((D=(F=this.config.hand.detector)==null?void 0:F.modelPath)==null?void 0:D.includes("handtrack"))&&(u=this.config.hand.enabled?db(i.tensor,h):[]),this.performance.hand&&delete this.performance.hand):(r=ce(),((T=(P=this.config.hand.detector)==null?void 0:P.modelPath)==null?void 0:T.includes("handdetect"))?u=this.config.hand.enabled?await ob(i.tensor,h):[]:((U=(M=this.config.hand.detector)==null?void 0:M.modelPath)==null?void 0:U.includes("handtrack"))&&(u=this.config.hand.enabled?await db(i.tensor,h):[]),this.performance.hand=this.env.perfadd?(this.performance.hand||0)+Math.trunc(ce()-r):Math.trunc(ce()-r)),this.analyze("End Hand:"),this.analyze("Start Object:"),this.state="detect:object",this.config.async?(((j=this.config.object.modelPath)==null?void 0:j.includes("nanodet"))?d=this.config.object.enabled?bb(i.tensor,this.config):[]:((z=this.config.object.modelPath)==null?void 0:z.includes("centernet"))&&(d=this.config.object.enabled?Bx(i.tensor,this.config):[]),this.performance.object&&delete this.performance.object):(r=ce(),((X=this.config.object.modelPath)==null?void 0:X.includes("nanodet"))?d=this.config.object.enabled?await bb(i.tensor,this.config):[]:((Z=this.config.object.modelPath)==null?void 0:Z.includes("centernet"))&&(d=this.config.object.enabled?await Bx(i.tensor,this.config):[]),this.performance.object=this.env.perfadd?(this.performance.object||0)+Math.trunc(ce()-r):Math.trunc(ce()-r)),this.analyze("End Object:"),this.state="detect:await",this.config.async&&([l,c,u,d]=await Promise.all([l,c,u,d])),this.state="detect:gesture";let f=[];this.config.gesture.enabled&&(r=ce(),f=[...AT(l),...gT(c),...xT(u),...yT(l)],this.config.async?this.performance.gesture&&delete this.performance.gesture:this.performance.gesture=this.env.perfadd?(this.performance.gesture||0)+Math.trunc(ce()-r):Math.trunc(ce()-r)),this.performance.total=this.env.perfadd?(this.performance.total||0)+Math.trunc(ce()-o):Math.trunc(ce()-o);let m=((ee=(J=this.process)==null?void 0:J.tensor)==null?void 0:ee.shape)||[];this.result={face:l,body:c,hand:u,gesture:f,object:d,performance:this.performance,canvas:this.process.canvas,timestamp:Date.now(),get persons(){return kT(l,c,u,f,m)}},ae(i.tensor),this.emit("detect"),this.state="idle",s(this.result)})}};Ac=new WeakMap,kp=new WeakMap,Sp=new WeakMap,h0=new WeakMap;return s2e;})();
/**
* @license
* Copyright 2017 Google LLC. All Rights Reserved.
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
* =============================================================================
*/
/**
* @license
* Copyright 2018 Google LLC
*
* Use of this source code is governed by an MIT-style
* license that can be found in the LICENSE file or at
* https://opensource.org/licenses/MIT.
* =============================================================================
*/
/**
* @license
* Copyright 2018 Google LLC. All Rights Reserved.
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
*
* =============================================================================
*/
/**
* @license
* Copyright 2018 Google LLC. All Rights Reserved.
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
* =============================================================================
*/
/**
* @license
* Copyright 2019 Google LLC
*
* Use of this source code is governed by an MIT-style
* license that can be found in the LICENSE file or at
* https://opensource.org/licenses/MIT.
* =============================================================================
*/
/**
* @license
* Copyright 2019 Google LLC. All Rights Reserved.
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
*
* =============================================================================
*/
/**
* @license
* Copyright 2019 Google LLC. All Rights Reserved.
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
* =============================================================================
*/
/**
* @license
* Copyright 2020 Google Inc. All Rights Reserved.
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
* =============================================================================
*/
/**
* @license
* Copyright 2020 Google LLC
*
* Use of this source code is governed by an MIT-style
* license that can be found in the LICENSE file or at
* https://opensource.org/licenses/MIT.
* =============================================================================
*/
/**
* @license
* Copyright 2020 Google LLC. All Rights Reserved.
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use backend file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
* =============================================================================
*/
/**
* @license
* Copyright 2020 Google LLC. All Rights Reserved.
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
* =============================================================================
*/
/**
* @license
* Copyright 2020 Google LLC. All Rights Reserved.
* Licensed under the Apache License, Version 2.0 (the License);
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an AS IS BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
* =============================================================================
*/
/**
* @license
* Copyright 2021 Google LLC. All Rights Reserved.
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
* =============================================================================
*/
/**
* @license
* Copyright 2021 Google LLC. All Rights Reserved.
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* https://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
* =============================================================================
*/
/**
* @license
* Copyright 2021 Google LLC. All Rights Reserved.
* Licensed under the Apache License, Version 2.0 (the License);
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an AS IS BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
* =============================================================================
*/
/**
* Human main module
* @default Human Library
* @summary <https://github.com/vladmandic/human>
* @author <https://github.com/vladmandic>
* @copyright <https://github.com/vladmandic>
* @license 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 See the LICENSE file. */
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