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vendor: 4,169 bytes, line 1
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vendor: 4,063 bytes, line 1
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1=!1,this.idTensor=(0,I.iD$)(0),(0,I.CnY)(this.idTensor)}get id(){return this.idTensor.id}get closed(){return this.closed_}clearAndClose(e){this.tensors.forEach((t=>{null!=e&&e.has(t.tensor.id)||t.tensor.dispose()})),this.tensors=[],this.closed_=!0,this.idTensor.dispose()}size(){return this.tensors.length}read(e){if(this.closed_)throw new Error(`TensorArray ${this.name} has already been closed.`);if(e<0||e>=this.size())throw new Error(`Tried to read from index ${e}, but array size is: ${this.size()}`);const t=this.tensors[e];if(t.cleared)throw new Error(`TensorArray ${this.name}: Could not read index ${e} twice because it was cleared after a previous read (perhaps try setting clear_after_read = false?).`);return this.clearAfterRead&&(t.cleared=!0),t.read=!0,t.tensor}readMany(e){return e.map((e=>this.read(e)))}write(e,t){if(this.closed_)throw new Error(`TensorArray ${this.name} has already been closed.`);if(e<0||!this.dynamicSize&&e>=this.maxSize)throw new Error(`Tried to write to index ${e}, but array is not resizeable and size is: ${this.maxSize}`);const n=this.tensors[e]||{};if(t.dtype!==this.dtype)throw new Error(`TensorArray ${this.name}: Could not write to TensorArray index ${e},\n because the value dtype is ${t.dtype}, but TensorArray dtype is ${this.dtype}.`);if(0!==this.size()||null!=this.elementShape&&0!==this.elementShape.length||(this.elementShape=t.shape),be(this.elementShape,t.shape,`TensorArray ${this.name}: Could not write to TensorArray index ${e}.`),n.read)throw new Error(`TensorArray ${this.name}: Could not write to TensorArray index ${e}, because it has already been read.`);if(n.written)throw new Error(`TensorArray ${this.name}: Could not write to TensorArray index ${e}, because it has already been written.`);n.tensor=t,(0,I.CnY)(t),n.written=!0,this.tensors[e]=n}writeMany(e,t){if(e.length!==t.length)throw new Error(`TensorArray ${this.name}: could not write multiple tensors,because the index size: ${e.length} is not the same as tensors size: ${t.length}.`);e.forEach(((e,n)=>this.write(e,t[n])))}gather(e,t){if(t&&t!==this.dtype)throw new Error(`TensorArray dtype is ${this.dtype} but gather requested dtype ${t}`);if(e)e=e.slice(0,this.size());else{e=[];for(let t=0;t<this.size();t++)e.push(t)}if(0===e.length)return(0,I.XeE)([],[0].concat(this.elementShape));const n=this.readMany(e);return be(this.elementShape,n[0].shape,"TensorArray shape mismatch: "),(0,I.knu)(n,0)}concat(e){if(e&&e!==this.dtype)throw new Error(`TensorArray dtype is ${this.dtype} but concat requested dtype ${e}`);if(0===this.size())return(0,I.XeE)([],[0].concat(this.elementShape));const t=[];for(let r=0;r<this.size();r++)t.push(r);const n=this.readMany(t);return be(this.elementShape,n[0].shape,`TensorArray shape mismatch: tensor array shape (${this.elementShape}) vs first tensor shape (${n[0].shape})`),(0,I.zoF)(n,0)}scatter(e,t){if(t.dtype!==this.dtype)throw new Error(`TensorArray dtype is ${this.dtype} but tensor has dtype ${t.dtype}`);if(e.length!==t.shape[0])throw new Error(`Expected len(indices) == tensor.shape[0], but saw: ${e.length} vs. ${t.shape[0]}`);const n=Math.max(...e);if(!this.dynamicSize&&n>=this.maxSize)throw new Error(`Max index must be < array size (${n} vs. ${this.maxSize})`);this.writeMany(e,(0,I.HHK)(t,0))}split(e,t){if(t.dtype!==this.dtype)throw new Error(`TensorArray dtype is ${this.dtype} but tensor has dtype ${t.dtype}`);let n=0;const r=e.map((e=>(n+=e,n)));if(n!==t.shape[0])throw new Error(`Expected sum of lengths to be equal to\n tensor.shape[0], but sum of lengths is\n ${n}, and tensor's shape is: ${t.shape}`);if(!this.dynamicSize&&e.length!==this.maxSize)throw new Error(`TensorArray's size is not equal to the size of lengths (${this.maxSize} vs. ${e.length}), and the TensorArray is not marked as dynamically resizeable`);const s=0===n?0:t.size/n,a=[];(0,I.lub)((()=>{t=(0,I.XLQ)(t,[1,n,s]);for(let n=0;n<e.length;++n){const o=[0,0===n?0:r[n-1],0],i=[1,e[n],s];a[n]=(0,I.XLQ)((0,I.tPi)(t,o,i),this.elementShape)}return a}));const o=[];for(let i=0;i<e.length;i++)o[i]=i;this.writeMany(o,a)}}class Ie{constructor(e,t,n,r=-1){this.tensors=e,this.elementShape=t,this.elementDtype=n,null!=e&&e.forEach((e=>{if(n!==e.dtype)throw new Error(`Invalid data types; op elements ${n}, but list elements ${e.dtype}`);be(t,e.shape,"TensorList shape mismatch: "),(0,I.CnY)(e)})),this.idTensor=(0,I.iD$)(0),this.maxNumElements=r,(0,I.CnY)(this.idTensor)}get id(){return this.idTensor.id}copy(){return new Ie([...this.tensors],this.elementShape,this.elementDtype)}
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1return n.enterFrame(r),[F(s)]}case"Exit":{const r=E("tensor",e,t,n);return n.exitFrame(),[F(r)]}case"NextIteration":{const r=E("tensor",e,t,n);return n.nextIteration(),[F(r)]}case"TensorArrayV3":{const r=E("size",e,t,n),s=E("dtype",e,t,n),a=E("elementShape",e,t,n),o=E("dynamicSize",e,t,n),i=E("clearAfterRead",e,t,n),u=E("identicalElementShapes",e,t,n),l=E("name",e,t,n),c=new ke(l,s,r,a,u,o,i);return n.addTensorArray(c),[c.idTensor,(0,I.iD$)(1)]}case"TensorArrayWriteV3":{const r=E("tensorArrayId",e,t,n),s=E("index",e,t,n),a=E("tensor",e,t,n),o=n.getTensorArray(r.id);return o.write(s,a),[o.idTensor]}case"TensorArrayReadV3":{const r=E("tensorArrayId",e,t,n),s=E("index",e,t,n);return[n.getTensorArray(r.id).read(s)]}case"TensorArrayGatherV3":{const r=E("tensorArrayId",e,t,n),s=E("indices",e,t,n),a=E("dtype",e,t,n);return[n.getTensorArray(r.id).gather(s,a)]}case"TensorArrayScatterV3":{const r=E("tensorArrayId",e,t,n),s=E("indices",e,t,n),a=E("tensor",e,t,n),o=n.getTensorArray(r.id);return o.scatter(s,a),[o.idTensor]}case"TensorArrayConcatV3":{const r=E("tensorArrayId",e,t,n),s=n.getTensorArray(r.id),a=E("dtype",e,t,n);return[s.concat(a)]}case"TensorArraySplitV3":{const r=E("tensorArrayId",e,t,n),s=E("tensor",e,t,n),a=E("lengths",e,t,n),o=n.getTensorArray(r.id);return o.split(a,s),[o.idTensor]}case"TensorArraySizeV3":{const r=E("tensorArrayId",e,t,n),s=n.getTensorArray(r.id);return[(0,I.iD$)(s.size(),"int32")]}case"TensorArrayCloseV3":{const r=E("tensorArrayId",e,t,n),s=n.getTensorArray(r.id);return s.clearAndClose(),[s.idTensor]}case"TensorListSetItem":{const r=E("tensorListId",e,t,n),s=E("index",e,t,n),a=E("tensor",e,t,n),o=n.getTensorList(r.id);return o.setItem(s,a),[o.idTensor]}case"TensorListGetItem":{const r=E("tensorListId",e,t,n),s=E("index",e,t,n),a=E("elementShape",e,t,n),o=E("elementDType",e,t,n);return[n.getTensorList(r.id).getItem(s,a,o)]}case"TensorListScatterV2":case"TensorListScatter":{const r=E("indices",e,t,n),s=function(e,t,n,r){if(t.length!==e.shape[0])throw new Error(`Expected len(indices) == tensor.shape[0], but saw: ${t.length} vs. ${e.shape[0]}`);const s=Math.max(...t);if(null!=r&&-1!==r&&s>=r)throw new Error(`Max index must be < array size (${s} vs. ${r})`);const a=new Ie([],n,e.dtype,r),o=(0,I.HHK)(e,0);return t.forEach(((e,t)=>{a.setItem(e,o[t])})),a}(E("tensor",e,t,n),r,E("elementShape",e,t,n),E("numElements",e,t,n));return n.addTensorList(s),[s.idTensor]}case"TensorListReserve":case"EmptyTensorList":{const r=E("elementShape",e,t,n),s=E("elementDType",e,t,n);let a;a="TensorListReserve"===e.op?"numElements":"maxNumElements";const o=E(a,e,t,n),i=function(e,t,n,r){return new Ie([],e,t,r)}(r,s,0,"TensorListReserve"===e.op?-1:o);return n.addTensorList(i),[i.idTensor]}case"TensorListGather":{const r=E("tensorListId",e,t,n),s=E("indices",e,t,n),a=E("elementShape",e,t,n),o=E("elementDType",e,t,n);return[n.getTensorList(r.id).gather(s,o,a)]}case"TensorListStack":{const r=E("tensorListId",e,t,n),s=E("elementShape",e,t,n),a=E("elementDType",e,t,n),o=E("numElements",e,t,n);return[n.getTensorList(r.id).stack(s,a,o)]}case"TensorListFromTensor":{const r=function(e,t,n){const r=e.dtype;if(e.shape.length<1)throw new Error(`Tensor must be at least a vector, but saw shape: ${e.shape}`);if(e.dtype!==n)throw new Error(`Invalid data types; op elements ${e.dtype}, but list elements ${n}`);be(e.shape.slice(1),t,"TensorList shape mismatch: ");const s=(0,I.HHK)(e);return new Ie(s,t,r)}(E("tensor",e,t,n),E("elementShape",e,t,n),E("elementDType",e,t,n));return n.addTensorList(r),[r.idTensor]}case"TensorListConcat":case"TensorListConcatV2":{const r=E("tensorListId",e,t,n),s=n.getTensorList(r.id),a=E("dtype",e,t,n),o=E("elementShape",e,t,n);return[s.concat(a,o)]}case"TensorListPushBack":{const r=E("tensorListId",e,t,n),s=E("tensor",e,t,n),a=n.getTensorList(r.id);return a.pushBack(s),[a.idTensor]}case"TensorListPopBack":{const r=E("tensorListId",e,t,n),s=E("elementShape",e,t,n),a=E("elementDType",e,t,n);return[n.getTensorList(r.id).popBack(s,a)]}case"TensorListSplit":{const r=E("tensor",e,t,n),s=E("elementShape",e,t,n),a=function(e,t,n){let r=0;const s=t.map((e=>(r+=e,r)));if(r!==e.shape[0])throw new Error(`Expected sum of lengths to be equal to\n tensor.shape[0], but sum of lengths is\n ${r}, and tensor's shape is: ${e.shape}`);const a=ve(e.shape.slice(1),n),o=0===r?0:e.size/r,i=(0,I.lub)((()=>{const n=[];
1e=(0,I.XLQ)(e,[1,r,o]);for(let r=0;r<t.length;++r){const i=[0,0===r?0:s[r-1],0],u=[1,t[r],o];n[r]=(0,I.XLQ)((0,I.tPi)(e,i,u),a)}return e.dispose(),n})),u=new Ie([],n,e.dtype,t.length);for(let l=0;l<i.length;l++)u.setItem(l,i[l]);return u}(r,E("lengths",e,t,n),s);return n.addTensorList(a),[a.idTensor]}case"TensorListLength":{const r=E("tensorListId",e,t,n),s=n.getTensorList(r.id);return[(0,I.iD$)(s.size(),"int32")]}case"TensorListResize":{const r=E("tensorListId",e,t,n),s=E("size",e,t,n),a=n.getTensorList(r.id).resize(s);return n.addTensorList(a),[a.idTensor]}default:throw TypeError(`Node type ${e.op} is not implemented`)}};function Se(e,t,n){const[r,s]=E("fusedOps",e,t,n),a="biasadd"===r,o=!a,i="prelu"===s,u="fusedbatchnorm"===r,l=E("numArgs",e,t,n);if(a){if(i&&2!==l)throw new Error("FusedConv2d and DepthwiseConv2d with BiasAdd and Prelu must have two extra arguments: bias and alpha.");if(!i&&a&&1!==l)throw new Error("FusedConv2d and DepthwiseConv2d with BiasAdd must have one extra argument: bias.")}if(u)throw new Error("FusedConv2d and DepthwiseConv2d with FusedBatchNorm is not supported");const c=E("strides",e,t,n),p=R(e,t,n),h=E("dataFormat",e,t,n).toUpperCase(),d=E("dilations",e,t,n);let[f,m]=E("args",e,t,n);o&&(m=f,f=void 0);return{stride:c,pad:p,dataFormat:h,dilations:d,biasArg:f,preluArg:m,activationFunc:s,leakyreluAlpha:E("leakyreluAlpha",e,t,n)}}function Te(e,t,n){return{boxes:E("boxes",e,t,n),scores:E("scores",e,t,n),maxOutputSize:E("maxOutputSize",e,t,n),iouThreshold:E("iouThreshold",e,t,n),scoreThreshold:E("scoreThreshold",e,t,n),softNmsSigma:E("softNmsSigma",e,t,n)}}var Ce=n(9494);class Ee{constructor(e,t){this.keyDType=e,this.valueDType=t,this.handle=(0,I.iD$)(0),this.tensorMap=new Map,(0,I.CnY)(this.handle)}get id(){return this.handle.id}clearAndClose(){this.tensorMap.forEach((e=>e.dispose())),this.tensorMap.clear(),this.handle.dispose()}size(){return this.tensorMap.size}tensorSize(){return Ce.i(this.size(),"int32")}async import(e,t){this.checkKeyAndValueTensor(e,t);const n=await e.data();return this.tensorMap.forEach((e=>e.dispose())),this.tensorMap.clear(),(0,I.lub)((()=>{const e=(0,I.HHK)(t),r=n.length,s=e.length;I.D5U.assert(r===s,(()=>`The number of elements doesn't match, keys has ${r} elements, the values has ${s} elements.`));for(let t=0;t<r;t++){const r=n[t],s=e[t];(0,I.CnY)(s),this.tensorMap.set(r,s)}return this.handle}))}async find(e,t){this.checkKeyAndValueTensor(e,t);const n=await e.data();return(0,I.lub)((()=>{const e=[];for(let r=0;r<n.length;r++){const s=n[r],a=this.findWithDefault(s,t);e.push(a)}return(0,I.knu)(e)}))}findWithDefault(e,t){const n=this.tensorMap.get(e);return null!=n?n:t}checkKeyAndValueTensor(e,t){if(e.dtype!==this.keyDType)throw new Error(`Expect key dtype ${this.keyDType}, but got ${e.dtype}`);if(t.dtype!==this.valueDType)throw new Error(`Expect value dtype ${this.valueDType}, but got ${t.dtype}`)}}function $e(e,t,n,r,s=I.lub){const a=((e,t,n)=>{switch(e.category){case"arithmetic":return s((()=>((e,t,n,r=k)=>{switch(e.op){case"BiasAdd":case"AddV2":case"Add":return[r.add(E("a",e,t,n),E("b",e,t,n))];case"AddN":return[r.addN(E("tensors",e,t,n))];case"FloorMod":case"Mod":return[r.mod(E("a",e,t,n),E("b",e,t,n))];case"Mul":return[r.mul(E("a",e,t,n),E("b",e,t,n))];case"RealDiv":case"Div":return[r.div(E("a",e,t,n),E("b",e,t,n))];case"DivNoNan":return[r.divNoNan(E("a",e,t,n),E("b",e,t,n))];case"FloorDiv":return[r.floorDiv(E("a",e,t,n),E("b",e,t,n))];case"Sub":return[r.sub(E("a",e,t,n),E("b",e,t,n))];case"Minimum":return[r.minimum(E("a",e,t,n),E("b",e,t,n))];case"Maximum":return[r.maximum(E("a",e,t,n),E("b",e,t,n))];case"Pow":return[r.pow(E("a",e,t,n),E("b",e,t,n))];case"SquaredDifference":return[r.squaredDifference(E("a",e,t,n),E("b",e,t,n))];default:throw TypeError(`Node type ${e.op} is not implemented`)}})(e,t,n)));case"basic_math":return 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vendor: 12,955 bytes, line 1
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s=E("x",e,t,n),a=E("weights",e,t,n),o=E("size",e,t,n);return[r.bincount(s,a,o)];case"DenseBincount":{const s=E("x",e,t,n),a=E("weights",e,t,n),o=E("size",e,t,n),i=E("binaryOutput",e,t,n);return[r.denseBincount(s,a,o,i)]}default:throw TypeError(`Node type ${e.op} is not implemented`)}})(e,t,n)));case"slice_join":return s((()=>((e,t,n,r=k)=>{switch(e.op){case"ConcatV2":case"Concat":{const s=E("n",e,t,n),a=E("axis",e,t,n);let o=E("tensors",e,t,n);return o=o.slice(0,s),[r.concat(o,a)]}case"Gather":{const s=E("x",e,t,n),a=E("indices",e,t,n);return[r.gather(s,r.cast(a,"int32"),0)]}case"GatherV2":{const s=E("axis",e,t,n),a=E("batchDims",e,t,n),o=E("x",e,t,n),i=E("indices",e,t,n);return[r.gather(o,r.cast(i,"int32"),s,a)]}case"Reverse":{const s=E("dims",e,t,n),a=[];for(let e=0;e<s.length;e++)s[e]&&a.push(e);const o=E("x",e,t,n);return[r.reverse(o,a)]}case"ReverseV2":{const s=E("axis",e,t,n),a=E("x",e,t,n);return[r.reverse(a,s)]}case"Slice":{const s=E("begin",e,t,n),a=E("size",e,t,n);return[r.slice(E("x",e,t,n),s,a)]}case"StridedSlice":{const s=E("begin",e,t,n),a=E("end",e,t,n),o=E("strides",e,t,n),i=E("beginMask",e,t,n),u=E("endMask",e,t,n),l=E("ellipsisMask",e,t,n),c=E("newAxisMask",e,t,n),p=E("shrinkAxisMask",e,t,n),h=E("x",e,t,n);return[r.stridedSlice(h,s,a,o,i,u,l,c,p)]}case"Pack":return(0,I.lub)((()=>{const s=E("axis",e,t,n),a=E("tensors",e,t,n),o=a[0].shape,i=r.squeeze(a[
vendor: 4,494 bytes, line 1
10]).shape,u=a.map((e=>{const t=I.D5U.arraysEqual(e.shape,o);if(!t&&!I.D5U.arraysEqual(r.squeeze(e).shape,i))throw new Error("the input tensors shape does not match");return t?e:r.reshape(e,o)}));return[r.stack(u,s)]}));case"Unpack":{const s=E("axis",e,t,n),a=E("tensor",e,t,n);return r.unstack(a,s)}case"Tile":{const s=E("reps",e,t,n);return[r.tile(E("x",e,t,n),s)]}case"Split":case"SplitV":{const s=E("axis",e,t,n),a=E("numOrSizeSplits",e,t,n),o=E("x",e,t,n);return r.split(o,a,s)}case"ScatterNd":{const s=E("indices",e,t,n),a=E("values",e,t,n),o=E("shape",e,t,n);return[r.scatterND(s,a,o)]}case"GatherNd":{const s=E("x",e,t,n),a=E("indices",e,t,n);return[r.gatherND(s,a)]}case"SparseToDense":{const s=E("sparseIndices",e,t,n),a=E("outputShape",e,t,n),o=E("sparseValues",e,t,n),i=E("defaultValue",e,t,n);return[r.sparseToDense(s,o,a,o.dtype===i.dtype?i:r.cast(i,o.dtype))]}default:throw TypeError(`Node type ${e.op} is not implemented`)}})(e,t,n)));case"sparse":return s((()=>((e,t,n,r=k)=>{switch(e.op){case"SparseFillEmptyRows":{const{outputIndices:s,outputValues:a,emptyRowIndicator:o,reverseIndexMap:i}=r.sparse.sparseFillEmptyRows(E("indices",e,t,n),E("values",e,t,n),E("denseShape",e,t,n),E("defaultValue",e,t,n));return[s,a,o,i]}case"SparseReshape":{const{outputIndices:s,outputShape:a}=r.sparse.sparseReshape(E("inputIndices",e,t,n),E("inputShape",e,t,n),E("newShape",e,t,n));return[s,a]}case"SparseSegmentMean":return[r.sparse.sparseSegmentMean(E("data",e,t,n),E("indices",e,t,n),E("segmentIds",e,t,n))];case"SparseSegmentSum":return[r.sparse.sparseSegmentSum(E("data",e,t,n),E("indices",e,t,n),E("segmentIds",e,t,n))];default:throw TypeError(`Node type ${e.op} is not implemented`)}})(e,t,n)));case"spectral":return s((()=>((e,t,n,r=k)=>{switch(e.op){case"FFT":return[r.fft(E("x",e,t,n))];case"IFFT":return[r.ifft(E("x",e,t,n))];case"RFFT":return[r.rfft(E("x",e,t,n))];case"IRFFT":return[r.irfft(E("x",e,t,n))];default:throw TypeError(`Node type ${e.op} is not implemented`)}})(e,t,n)));case"string":return s((()=>((e,t,n,r=k)=>{switch(e.op){case"StringNGrams":{const{nGrams:s,nGramsSplits:a}=r.string.stringNGrams(E("data",e,t,n),E("dataSplits",e,t,n),E("separator",e,t,n),E("nGramWidths",e,t,n),E("leftPad",e,t,n),E("rightPad",e,t,n),E("padWidth",e,t,n),E("preserveShortSequences",e,t,n));return[s,a]}case"StringSplit":{const{indices:s,values:a,shape:o}=r.string.stringSplit(E("input",e,t,n),E("delimiter",e,t,n),E("skipEmpty",e,t,n));return[s,a,o]}case"StringToHashBucketFast":return[r.string.stringToHashBucketFast(E("input",e,t,n),E("numBuckets",e,t,n))];default:throw TypeError(`Node type ${e.op} is not implemented`)}})(e,t,n)));case"transformation":return s((()=>((e,t,n,r=k)=>{switch(e.op){case"Cast":return[r.cast(E("x",e,t,n),E("dtype",e,t,n))];case"ExpandDims":{const s=E("axis",e,t,n);return[r.expandDims(E("x",e,t,n),s)]}case"Squeeze":{const s=E("axis",e,t,n);return[r.squeeze(E("x",e,t,n),s)]}case"Reshape":return[r.reshape(E("x",e,t,n),E("shape",e,t,n))];case"MirrorPad":return[r.mirrorPad(E("x",e,t,n),E("padding",e,t,n),E("mode",e,t,n))];case"PadV2":case"Pad":return[r.pad(E("x",e,t,n),E("padding",e,t,n),E("constantValue",e,t,n))];case"SpaceToBatchND":{const s=E("blockShape",e,t,n),a=E("paddings",e,t,n);return[r.spaceToBatchND(E("x",e,t,n),s,a)]}case"BatchToSpaceND":{const s=E("blockShape",e,t,n),a=E("crops",e,t,n);return[r.batchToSpaceND(E("x",e,t,n),s,a)]}case"DepthToSpace":{const s=E("blockSize",e,t,n),a=E("dataFormat",e,t,n).toUpperCase();return[r.depthToSpace(E("x",e,t,n),s,a)]}case"BroadcastTo":return[r.broadcastTo(E("x",e,t,n),E("shape",e,t,n))];case"BroadcastArgs":return[r.broadcastArgs(E("s0",e,t,n),E("s1",e,t,n))];default:throw TypeError(`Node type ${e.op} is not implemented`)}})(e,t,n)));case"hash_table":return(async(e,t,n,r)=>{switch(e.op){case"HashTable":case"HashTableV2":{const s=E("keyDType",e,t,n),a=E("valueDType",e,t,n),o=new Ee(s,a);return r.addHashTable(e.name,o),[o.handle]}case"LookupTableImport":case"LookupTableImportV2":{const s=E("tableHandle",e,t,n,r),a=E("keys",e,t,n),o=E("values",e,t,n),i=r.getHashTableById(s.id);return[await i.import(a,o)]}case"LookupTableFind":case"LookupTableFindV2":{const s=E("tableHandle",e,t,n,r),a=E("keys",e,t,n),o=E("defaultValue",e,t,n),i=r.getHashTableById(s.id);return[await i.find(a,o)]}case"LookupTableSize":case"LookupTableSizeV2":{const s=E("tableHandle",e,t,n,r);return[r.getHashTableById(s.id).tensorSize()]}default:throw TypeError(`Node type ${e.op} is not implemented`)}})(e,t,n,r);
1case"custom":const a=C(e.op);if(a&&a.customExecutor)return a.customExecutor(new ge(e,t,n));throw TypeError(`Custom op ${e.op} is not registered.`);default:throw TypeError(`Unknown op '${e.op}'. File an issue at https://github.com/tensorflow/tfjs/issues so we can add it, or register a custom execution with tf.registerOp()`)}})(e,t,n);return I.D5U.isPromise(a)?a.then((e=>[].concat(e))):[].concat(a)}class Ae{constructor(e={},t={},n={},r={}){this.weightMap=e,this.tensorArrayMap=t,this.tensorListMap=n,this.functionMap=r,this.rootContext={id:0,frameName:"",iterationId:0},this.contexts=[this.rootContext],this.lastId=0,this.generateCurrentContextIds()}newFrame(e,t){return{id:e,frameName:t,iterationId:0}}set currentContext(e){this.contexts!==e&&(this.contexts=e,this.generateCurrentContextIds())}get currentContext(){return this.contexts}get currentContextId(){return this._currentContextIds[0]}get currentContextIds(){return this._currentContextIds}generateCurrentContextIds(){const e=[];for(let t=0;t<this.contexts.length-1;t++){const n=this.contexts.slice(0,this.contexts.length-t);e.push(this.contextIdforContexts(n))}e.push(""),this._currentContextIds=e}contextIdforContexts(e){return e?e.map((e=>0===e.id&&0===e.iterationId?"":`${e.frameName}-${e.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))throw new Error("Cannot exit frame, the context is empty");this.contexts=this.contexts.slice(),this.contexts.splice(-1),this.currentContextIds.shift()}nextIteration(){if(!(this.contexts&&this.contexts.length>0))throw new Error("Cannot increase frame iteration, the context is empty");{this.contexts=this.contexts.slice(),this.lastId++;const 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))}}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(const t in this.tensorArrayMap)this.tensorArrayMap[t].clearAndClose(e);for(const t in this.tensorListMap)this.tensorListMap[t].clearAndClose(e)}}function De(e,t,n,r){const s=new Set,a=[];let o=null,i=null;const u=new Set,l=Object.keys(e).map((e=>_(e)[0]));let c=[];null!=r&&(c=r.map((e=>_(e.name)[0])));const p=[...t];for(;p.length>0;){const e=p.pop();(Oe(e)||Me(e)||Be(e))&&null==o&&(o=e,i=o.children.map((e=>e.name)).filter((e=>s.has(e)))),s.add(e.name),null==n[e.name]&&(-1===l.indexOf(e.name)&&-1===c.indexOf(e.name)&&(0!==e.inputs.length?e.inputs.forEach((e=>{u.has(e.name)||(u.add(e.name),p.push(e))})):a.push(e.name)))}return{inputs:e,outputs:t,usedNodes:s,missingInputs:a,dynamicNode:o,syncInputs:i}}const _e=["Switch","Merge","Enter","Exit","NextIteration","StatelessIf","StatelessWhile","if","While"],Re=["NonMaxSuppressionV2","NonMaxSuppressionV3","NonMaxSuppressionV5","Where"],Fe=["HashTable","HashTableV2","LookupTableImport","LookupTableImportV2","LookupTableFind","LookupTableFindV2","LookupTableSize","LookupTableSizeV2"];function Oe(e){return _e.indexOf(e.op)>=0}function Me(e){return Re.indexOf(e.op)>=0}function Be(e){return Fe.indexOf(e.op)>=0}class Le{constructor(e,t){this.graph=e,this.parent=t,this.compiledMap=new Map,this._weightMap={},this.SEPERATOR=",",this._functions={},this._functionExecutorMap={},this.intermediateTensors={},this.keepTensorForDebug=!1,this._outputs=e.outputs,this._inputs=e.inputs,this._initNodes=e.initNodes,this._signature=e.signature,this._functions=e.functions,null!=e.functions&&Object.keys(e.functions).forEach((t=>{this._functionExecutorMap[t]=new Le(e.functions[t],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){const t=Object.keys(e).map((t=>e[t].map((e=>e.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=>{const 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){const n=e.map((e=>e.name)).sort(),r=t.map((e=>e.name)).sort();return n.join(this.SEPERATOR)+"--"+r.join(this.SEPERATOR)}compile(e,t){const n=De(e,t,this.weightMap,this._initNodes),{missingInputs:r,dynamicNode:s,syncInputs:a}=n;if(null!=s)throw new Error(`This execution contains the node '${s.name}', which has the dynamic op '${s.op}'. Please use model.executeAsync() instead. Alternatively, to avoid the dynamic ops, specify the inputs [${a}]`);if(r.length>0){const n=t.map((e=>e.name)),s=Object.keys(e);throw new Error(`Cannot compute the outputs [${n}] from the provided inputs [${s}]. Missing the following inputs: [${r}]`)}return function(e,t,n){const{usedNodes:r,inputs:s}=n,a=[],o=Object.keys(s).map((e=>_(e)[0])).map((t=>e.nodes[t])),i=e.initNodes;o.forEach((e=>{r.has(e.name)&&a.push(e)}
1)),e.weights.forEach((e=>{r.has(e.name)&&a.push(e)})),null!=i&&i.forEach((e=>{r.has(e.name)&&a.push(e)}));const u=new Set,l=[];for(;a.length>0;){const e=a.pop();u.add(e.name),t[e.name]||l.push(e),e.children.forEach((e=>{!u.has(e.name)&&r.has(e.name)&&e.inputs.every((e=>u.has(e.name)))&&a.push(e)}))}return l}(this.graph,this.weightMap,n)}execute(e,t){e=this.mapInputs(e);const n=Object.keys(e).sort();this.checkInputs(e),this.checkInputShapeAndType(e),t=this.mapOutputs(t),this.checkOutputs(t);const r=n.map((e=>this.graph.nodes[_(e)[0]])),s=t.map((e=>_(e)[0]));let a=s.map((e=>this.graph.nodes[e]));this.resetIntermediateTensors(),0===a.length&&(a=this._outputs);const o=this.getCompilationKey(r,a);let i=this.compiledMap.get(o);null==i&&(i=this.compile(e,a),this.compiledMap.set(o,i));const u={},l={};return(0,I.lub)((()=>{const n=new Ae(this.weightMap,u,l,this.functionExecutorMap),r=Object.assign({},this.weightMap);Object.keys(e).forEach((t=>{const[n,s]=_(t),a=[];a[s]=e[t],r[n]=a}));const a=this.getFrozenTensorIds(r),o={};for(let e=0;e<i.length;e++){const t=i[e];if(!r[t.name]){const e=$e(t,r,n,this._resourceManager);if(I.D5U.isPromise(e))throw new Error(`The execution of the op '${t.op}' returned a promise. Please use model.executeAsync() instead.`);r[t.name]=e,this.checkTensorForDisposal(t.name,t,r,n,a,s,o)}}return null==this.parent&&n.dispose(a),t.map((e=>$(e,r,n)))}
1))}getFrozenTensorIds(e){const t=[].concat.apply([],Object.keys(e).map((t=>e[t])).map((e=>e.map((e=>e.id)))));return new Set(t)}checkTensorForDisposal(e,t,n,r,s,a,o){"control"!==t.category&&-1===a.indexOf(e)&&(n[e].forEach((e=>{null!=e&&(o[e.id]=(o[e.id]||0)+t.children.length)})),t.inputs.forEach((e=>{if("control"!==e.category){const a=function(e,t,n){return t[D(e,n.currentContextId)]}(e.name,n,r);null!=a&&a.forEach((e=>{if(e&&!e.kept&&!s.has(e.id)){const n=o[e.id];if(1===n){if(this.keepTensorForDebug){const[n,s]=A(t.name,r);this.intermediateTensors[n]||(this.intermediateTensors[n]=[]),this.intermediateTensors[n][s]=e}else e.dispose();delete o[e.id]}else null!=n&&o[e.id]--}}))}})))}async executeAsync(e,t){return this._executeAsync(e,t)}disposeIntermediateTensors(){this.intermediateTensors&&(Object.keys(this.intermediateTensors).forEach((e=>this.intermediateTensors[e].forEach((e=>e.dispose())))),this.disposeTensorsMap())}disposeTensorsMap(){this.tensorsMap&&Object.keys(this.tensorsMap).forEach((e=>{this.tensorsMap[e].forEach((e=>{!e||e.kept||e.isDisposed||this.keepIds.has(e.id)||e.dispose()}))}))}getIntermediateTensors(){return this.tensorsMap}resetIntermediateTensors(){for(const e in this.intermediateTensors)this.intermediateTensors[e].forEach((e=>e.dispose())),delete this.intermediateTensors[e]}async _executeAsync(e,t,n=!1,r={},s={}){n||(e=this.mapInputs(e),this.checkInputs(e),this.checkInputShapeAndType(e),t=this.mapOutputs(t),this.checkOutputs(t));try{this.keepTensorForDebug=(0,I.OBj)().getBool("KEEP_INTERMEDIATE_TENSORS")}catch(l){console.warn(l.message)}this.resetIntermediateTensors();const a=new Ae(this.weightMap,r,s,this.functionExecutorMap);this.tensorsMap=await this.executeWithControlFlow(e,a,t,n);const o=t.map((e=>$(e,this.tensorsMap,a))),i=o.map((e=>e.id)),u=Object.keys(e).map((t=>e[t].id));return this.keepIds=new Set([...i,...u,...this.weightIds]),this.keepTensorForDebug||this.disposeTensorsMap(),null==this.parent&&a.dispose(this.keepIds),o}async executeFunctionAsync(e,t,n){const r=e.reduce(((e,t,n)=>(e[this.inputs[n].name]=t,e)),{});return this._executeAsync(r,this.outputNodes,!0,t,n)}async executeWithControlFlow(e,t,n,r){const s=Object.keys(e),a=s.map((e=>this.graph.nodes[_(e)[0]])),o=n.map((e=>_(e)[0]));let i=o.map((e=>this.graph.nodes[e]));0===i.length&&(i=this._outputs);const{usedNodes:u,missingInputs:l,dynamicNode:c,syncInputs:p}=De(e,i,this.weightMap,this._initNodes),h=[...a,...this.graph.weights,...this._initNodes||[]].map((e=>({node:e,contexts:t.currentContext}))),d=Object.assign({},this.weightMap);Object.keys(e).forEach((t=>{const[n,r]=_(t),s=[];s[r]=e[t],d[n]=s}));const f={},m=this.getFrozenTensorIds(d),g={};for(;h.length>0;){const e=this.processStack(a,h,t,d,g,m,o,f,u);await Promise.all(e)}null!=c||r||console.warn("This model execution did not contain any nodes with control flow or dynamic output shapes. You can use model.execute() instead.");const y=i.filter((e=>!Oe(e)&&!$(e.name,d,t))).map((e=>e.name));if(y.length>0){let e="";throw null!=c&&(e=`Alternatively, to avoid the dynamic ops, use model.execute() and specify the inputs [${p}]`),new Error(`Cannot compute the outputs [${y}] from the provided inputs [${s}]. Consider providing the following inputs: [${l}]. ${e}`)}return d}processStack(e,t,n,r,s,a,o,i,u){const l=[];for(;t.length>0;){const e=t.pop();n.currentContext=e.contexts;let c="";if("Enter"===e.node.op&&E("isConstant",e.node,r,n)&&([c]=A(e.node.name,n)),null==r[e.node.name]){const p=$e(e.node,r,n,this._resourceManager);c||([c]=A(e.node.name,n));const h=n.currentContext;I.D5U.isPromise(p)?l.push(p.then((l=>(r[c]=l,n.currentContext=h,this.checkTensorForDisposal(c,e.node,r,n,a,o,i),this.processChildNodes(e.node,t,n,r,s,u),l)))):(r[c]=p,this.checkTensorForDisposal(c,e.node,r,n,a,o,i),this.processChildNodes(e.node,t,n,r,s,u))}else this.processChildNodes(e.node,t,n,r,s,u)}return l}processChildNodes(e,t,n,r,s,a){e.children.forEach((e=>{const[o]=A(e.name,n);!s[o]&&a.has(e.name)&&("Merge"===e.op?e.inputNames.some((e=>!!$(e,r,n)))&&(s[o]=!0,t.push({contexts:n.currentContext,node:e})):e.inputNames.every((e=>!!$(e,r,n)))&&(s[o]=!0,t.push({contexts:n.currentContext,node:e})))}))}
1dispose(){Object.keys(this.weightMap).forEach((e=>this.weightMap[e].forEach((e=>e.dispose()))))}checkInputShapeAndType(e){Object.keys(e).forEach((t=>{const n=e[t],[r]=_(t),s=this.graph.nodes[r];if(s.attrParams.shape&&s.attrParams.shape.value){const e=s.attrParams.shape.value,t=e.length===n.shape.length&&n.shape.every(((t,n)=>-1===e[n]||e[n]===t));I.D5U.assert(t,(()=>`The shape of dict['${s.name}'] provided in model.execute(dict) must be [${e}], but was [${n.shape}]`))}s.attrParams.dtype&&s.attrParams.dtype.value&&I.D5U.assert(n.dtype===s.attrParams.dtype.value,(()=>`The dtype of dict['${s.name}'] provided in model.execute(dict) must be ${s.attrParams.dtype.value}, but was ${n.dtype}`))}))}mapInputs(e){const t={};for(const n in e)if(null!=this._signature&&null!=this._signature.inputs&&null!=this._signature.inputs[n]){t[this._signature.inputs[n].name]=e[n]}else t[n]=e[n];return t}checkInputs(e){const t=Object.keys(e).filter((e=>{const[t]=_(e);return null==this.graph.nodes[t]}));if(t.length>0)throw new Error(`The dict provided in model.execute(dict) has keys: [${t}] that are not part of graph`)}mapOutputs(e){return e.map((e=>{if(null!=this._signature&&null!=this._signature.outputs&&null!=this._signature.outputs[e]){return this._signature.outputs[e].name}return e}),{})}checkOutputs(e){e.forEach((e=>{const[t]=_(e);if(!this.graph.nodes[t])throw new Error(`The output '${e}' is not found in the graph`)}))}}class We{constructor(e={},t={}){this.hashTableNameToHandle=e,this.hashTableMap=t}addHashTable(e,t){this.hashTableNameToHandle[e]=t.handle,this.hashTableMap[t.id]=t}getHashTableHandleByName(e){return this.hashTableNameToHandle[e]}getHashTableById(e){return this.hashTableMap[e]}dispose(){for(const e in this.hashTableMap)this.hashTableMap[e].clearAndClose(),delete this.hashTableMap[e];for(const e in this.hashTableNameToHandle)this.hashTableNameToHandle[e].dispose(),delete this.hashTableNameToHandle[e]}}class Pe{constructor(e,t={},n=I.io){this.modelUrl=e,this.loadOptions=t,this.version="n/a",this.io=n,null==t&&(this.loadOptions={}),this.resourceManager=new We}get modelVersion(){return this.version}get inputNodes(){return this.executor.inputNodes}get outputNodes(){return this.executor.outputNodes}get inputs(){return this.executor.inputs}get outputs(){return this.executor.outputs}get weights(){return this.executor.weightMap}get metadata(){return this.artifacts.userDefinedMetadata}get modelSignature(){return this.signature}get modelStructuredOutputKeys(){return this.structuredOutputKeys}findIOHandler(){const e=this.modelUrl;if(null!=e.load)this.handler=e;else if(null!=this.loadOptions.requestInit)this.handler=this.io.browserHTTPRequest(e,this.loadOptions);else{const t=this.io.getLoadHandlers(e,this.loadOptions);if(0===t.length)t.push(this.io.browserHTTPRequest(e,this.loadOptions));else if(t.length>1)throw new Error(`Found more than one (${t.length}) load handlers for URL '${[e]}'`);this.handler=t[0]}}load(){if(this.findIOHandler(),null==this.handler.load)throw new Error("Cannot proceed with model loading because the IOHandler provided does not have the `load` method implemented.");const e=this.handler.load();return I.D5U.isPromise(e)?e.then((e=>this.loadSync(e))):this.loadSync(e)}loadSync(e){this.artifacts=e;const t=this.artifacts.modelTopology;let n=this.artifacts.signature;if(null!=this.artifacts.userDefinedMetadata){const e=this.artifacts.userDefinedMetadata;null!=e.signature&&(n=e.signature),null!=e.structuredOutputKeys&&(this.structuredOutputKeys=e.structuredOutputKeys)}this.signature=n,this.version=`${t.versions.producer}.${t.versions.minConsumer}`;const r=this.io.decodeWeights(this.artifacts.weightData,this.artifacts.weightSpecs);if(this.executor=new Le(te.Instance.transformGraph(t,this.signature)),this.executor.weightMap=this.convertTensorMapToTensorsMap(r),this.executor.resourceManager=this.resourceManager,null!=e.modelInitializer&&null!=e.modelInitializer.node){const t=te.Instance.transformGraph(e.modelInitializer);this.initializer=new Le(t),this.initializer.weightMap=this.executor.weightMap,this.initializer.resourceManager=this.resourceManager,this.initializer.executeAsync({},[])}return!0}async save(e,t){if("string"===typeof e){const t=this.io.getSaveHandlers(e);if(0===t.length)throw new Error(`Cannot find any save handlers for URL '${e}'`);if(t.length>1)throw new Error(`Found more than one (${t.length}) save handlers for URL '${e}'`);e=t[0]}if(null==e.save)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){const n=this.execute(e,this.outputNodes);if(this.structuredOutputKeys){const e=n instanceof I.esB?[n]:n,t={};return e.forEach(((e,n)=>t[this.structuredOutputKeys[n]]=e)),t}return n}normalizeInputs(e){if(!(e instanceof I.esB)&&!Array.isArray(e))return e;if((e=Array.isArray(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 ${e.length} input tensors.`);return this.inputNodes.reduce(((t,n,r)=>(t[n]=e[r],t)),{})}normalizeOutputs(e){return e=e||this.outputNodes,Array.isArray(e)?e:[e]}execute(e,t){e=this.normalizeInputs(e),t=this.normalizeOutputs(t);const n=this.executor.execute(e,t);return n.length>1?n:n[0]}async executeAsync(e,t){e=this.normalizeInputs(e),t=this.normalizeOutputs(t);const n=await this.executor.executeAsync(e,t);return n.length>1?n:n[0]}getIntermediateTensors(){return this.executor.getIntermediateTensors()}disposeIntermediateTensors(){this.executor.disposeIntermediateTensors()}convertTensorMapToTensorsMap(e){return Object.keys(e).reduce(((t,n)=>(t[n]=[e[n]],t)),{})}dispose(){this.executor.dispose(),this.initializer&&this.initializer.dispose(),this.resourceManager.dispose()}}async function Ue(e,t={},n=I.io){if(null==e)throw new Error("modelUrl in loadGraphModel() cannot be null. Please provide a url or an IOHandler that loads the model");null==t&&(t={}),t.fromTFHub&&"string"===typeof e&&(e=function(e){e.endsWith("/")||(e+="/");return`${e}model.json?tfjs-format=file`}(e));const r=new Pe(e,t,n);return await r.load(),r}const ze="3.21.0"},8713:function(e,t,n){"use strict";n.d(t,{JL:function(){return r},Zu:function(){return s}});
1class r{constructor(e,t){this.backend=e,this.dataMover=t,this.data=new WeakMap,this.dataIdsCount=0}get(e){return this.data.has(e)||this.dataMover.moveData(this.backend,e),this.data.get(e)}set(e,t){this.dataIdsCount++,this.data.set(e,t)}has(e){return this.data.has(e)}delete(e){return this.dataIdsCount--,this.data.delete(e)}numDataIds(){return this.dataIdsCount}}class s{refCount(e){return a("refCount")}incRef(e){return a("incRef")}timerAvailable(){return!0}time(e){return a("time")}read(e){return a("read")}readSync(e){return a("readSync")}readToGPU(e,t){return a("readToGPU")}numDataIds(){return a("numDataIds")}disposeData(e,t){return a("disposeData")}write(e,t,n){return a("write")}move(e,t,n,r,s){return a("move")}memory(){return a("memory")}floatPrecision(){return a("floatPrecision")}epsilon(){return 32===this.floatPrecision()?1e-7:1e-4}dispose(){return a("dispose")}}function a(e){throw new Error(`'${e}' not yet implemented or not found in the registry. This kernel may not be supported by the tfjs backend you have chosen`)}},3337:function(e,t,n){"use strict";function r(e,t,n){const r=function(e,t,n){return function(e,t,n){let r=0,s=e.length,a=0,o=!1;for(;r<s;){a=r+(s-r>>>1);const i=n(t,e[a]);i>0?r=a+1:(s=a,o=!i)}return o?r:-r-1}(e,t,n||s)}(e,t,n),a=r<0?-(r+1):r;e.splice(a,0,t)}function s(e,t){return e>t?1:e<t?-1:0}function a(e,t,n,r,s){return u(e,t,n,r,s,0)}function o(e,t,n,r,s,a){return u(e,t,n,r,s,0,!1,a,!0)}function i(e,t,n,r,s,a){return u(e,t,n,r,s,a,!0)}function u(e,t,n,s,a,o,i=!1,u=!1,h=!1){const d=[];for(let r=0;r<t.length;r++)t[r]>a&&d.push({score:t[r],boxIndex:r,suppressBeginIndex:0});d.sort(p);const f=o>0?-.5/o:0,m=[],g=[];for(;m.length<n&&d.length>0;){const t=d.pop(),{score:n,boxIndex:o,suppressBeginIndex:i}=t;if(n<a)break;let u=!1;for(let r=m.length-1;r>=i;--r){const n=l(e,o,m[r]);if(n>=s){u=!0;break}if(t.score=t.score*c(s,f,n),t.score<=a)break}t.suppressBeginIndex=m.length,u||(t.score===n?(m.push(o),g.push(t.score)):t.score>a&&r(d,t,p))}const y=m.length,b=n-y;u&&b>0&&(m.push(...new Array(b).fill(0)),g.push(...new Array(b).fill(0)));const x={selectedIndices:m};return i&&(x.selectedScores=g),h&&(x.validOutputs=y),x}function l(e,t,n){const r=e.subarray(4*t,4*t+4),s=e.subarray(4*n,4*n+4),a=Math.min(r[0],r[2]),o=Math.min(r[1],r[3]),i=Math.max(r[0],r[2]),u=Math.max(r[1],r[3]),l=Math.min(s[0],s[2]),c=Math.min(s[1],s[3]),p=Math.max(s[0],s[2]),h=Math.max(s[1],s[3]),d=(i-a)*(u-o),f=(p-l)*(h-c);if(d<=0||f<=0)return 0;const m=Math.max(a,l),g=Math.max(o,c),y=Math.min(i,p),b=Math.min(u,h),x=Math.max(y-m,0)*Math.max(b-g,0);return x/(d+f-x)}function c(e,t,n){const r=Math.exp(t*n*n);return n<=e?r:0}function p(e,t){return e.score-t.score||e.score===t.score&&t.boxIndex-e.boxIndex}n.d(t,{GP:function(){return a},qP:function(){return o},pA:function(){return i}})},8333:function(e,t,n){"use strict";n.d(t,{Z:function(){return s}});var r=n(2657);function s(e,t){const n=[];for(let r=0;r<t.length;r++)t[r]&&n.push(r);const s=(0,r.f)(e,"int32"),a=(0,r.f)([n.length,e.length],"int32");for(let r=0;r<n.length;r++){const t=s.indexToLoc(n[r]),o=r*e.length;a.values.set(t,o)}return a.toTensor()}},7097:function(e,t,n){"use strict";n.d(t,{BV:function(){return w},wv:function(){return x}});var r=n(8713),s=n(2885),a=n(5938),o=n(9121),i=n(6151),u=n(4706),l=n(9122),c=n(569);class p{constructor(e,t){this.backendTimer=e,this.logger=t,null==t&&(this.logger=new d)}profileKernel(e,t,n){let r;const a=()=>{r=n()};let o;const i=l.now();if(this.backendTimer.timerAvailable())o=this.backendTimer.time(a);else{a();for(const e of r)e.dataSync();o=Promise.resolve({kernelMs:l.now()-i})}if((0,s.OB)().getBool("CHECK_COMPUTATION_FOR_ERRORS"))for(let s=0;s<r.length;s++){const t=r[s];t.data().then((n=>{h(n,t.dtype,e)}))}return{kernelName:e,outputs:r,inputs:t,timeMs:o.then((e=>e.kernelMs)),extraInfo:o.then((e=>null!=e.getExtraProfileInfo?e.getExtraProfileInfo():""))}}logKernelProfile(e){const{kernelName:t,outputs:n,timeMs:r,inputs:s,extraInfo:a}=e;n.forEach((e=>{Promise.all([e.data(),r,a]).then((n=>{this.logger.logKernelProfile(t,e,n[0],n[1],s,n[2])}))}))}}function h(e,t,n){if("float32"!==t)return!1;for(let r=0;r<e.length;r++){const t=e[r];if(isNaN(t)||!isFinite(t))return console.warn(`Found ${t} in the result of '${n}'`),!0}return!1}class d{logKernelProfile(e,t,n,r,s,a){const o="number"===typeof r?c.oj(`${r}ms`,9):r.error,i=c.oj(e,25),u=t.rank,l=t.size,p=c.oj(t.shape.toString(),14);let h="";for(const c in s){const e=s[c];if(null!=e){const n=e.shape||t.shape,r=n.length;h+=`${c}: ${r}D ${r>0?n:""} `}}console.log(`%c${i}\t%c${o}
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1if(this.backendName=e,null==this.registry[e]){this.backendInstance=null;const{success:t,asyncInit:n}=this.initializeBackend(e);if(!(n?await t:t))return!1}return this.backendInstance=this.registry[e],this.setupRegisteredKernels(),this.profiler=new p(this.backendInstance),!0}setupRegisteredKernels(){(0,i.tr)(this.backendName).forEach((e=>{null!=e.setupFunc&&e.setupFunc(this.backendInstance)}))}disposeRegisteredKernels(e){(0,i.tr)(e).forEach((t=>{null!=t.disposeFunc&&t.disposeFunc(this.registry[e])}))}initializeBackend(e){const t=this.registryFactory[e];if(null==t)throw new Error(`Cannot initialize backend ${e}, no registration found.`);try{const n=t.factory();if(!n||n instanceof r.Zu||"function"!==typeof n.then)return this.registry[e]=n,{success:!0,asyncInit:!1};{const t=++this.pendingBackendInitId,r=n.then((n=>!(t<this.pendingBackendInitId)&&(this.registry[e]=n,this.pendingBackendInit=null,!0))).catch((n=>(t<this.pendingBackendInitId||(this.pendingBackendInit=null,u.Z(`Initialization of backend ${e} failed`),u.Z(n.stack||n.message)),!1)));return this.pendingBackendInit=r,{success:r,asyncInit:!0}}}catch(n){return u.Z(`Initialization of backend ${e} failed`),u.Z(n.stack||n.message),{success:!1,asyncInit:!1}}}removeBackend(e){if(!(e in this.registryFactory))throw new Error(`${e} backend not found in registry`);this.backendName===e&&null!=this.pendingBackendInit&&this.pendingBackendInitId++,e in this.registry&&(this.disposeRegisteredKernels(e),this.registry[e].dispose(),delete this.registry[e]),delete this.registryFactory[e],this.backendName===e&&(this.pendingBackendInit=null,this.backendName=null,this.backendInstance=null)}getSortedBackends(){if(0===Object.keys(this.registryFactory).length)throw new Error("No backend found in registry.");return Object.keys(this.registryFactory).sort(((e,t)=>this.registryFactory[t].priority-this.registryFactory[e].priority))}initializeBackendsAndReturnBest(){const e=this.getSortedBackends();for(let t=0;t<e.length;t++){const n=e[t],{success:r,asyncInit:s}=this.initializeBackend(n);if(s||r)return{name:n,asyncInit:s}}throw new Error("Could not initialize any backends, all backend initializations failed.")}moveData(e,t){const n=this.state.tensorInfo.get(t),r=n.backend,s=this.readSync(t),a=r.refCount(t);r.disposeData(t,!0),n.backend=e,e.move(t,s,n.shape,n.dtype,a),this.shouldCheckForMemLeaks()&&this.state.numDataMovesStack[this.state.numDataMovesStack.length-1]++}tidy(e,t){let n,r=null;if(null==t){if("function"!==typeof e)throw new Error("Please provide a function to tidy()");t=e}else{if("string"!==typeof e&&!(e instanceof String))throw new Error("When calling with two arguments, the first argument to tidy() must be a string");if("function"!==typeof t)throw new Error("When calling with two arguments, the 2nd argument to tidy() must be a function");r=e}return this.scopedRun((()=>this.startScope(r)),(()=>this.endScope(n)),(()=>(n=t(),n instanceof Promise&&console.error("Cannot return a Promise inside of tidy."),n)))}scopedRun(e,t,n){e();try{const e=n();return t(),e}catch(r){throw t(),r}}nextTensorId(){return b.nextTensorId++}nextVariableId(){return b.nextVariableId++}clone(e){const t=w.runKernel(o.iJz,{x:e}),n={x:e};return this.addTapeNode(this.state.activeScope.name,n,[t],(e=>({x:()=>{const t={x:e},n={dtype:"float32"};return w.runKernel(o.RFZ,t,n)}})),[],{}),t}runKernel(e,t,n){null==this.backendName&&this.backend;if(!(null!=(0,i.pI)(e,this.backendName)))throw new Error(`Kernel '${e}' not registered for backend '${this.backendName}'`);return this.runKernelFunc({kernelName:e,inputs:t,attrs:n})}shouldCheckForMemLeaks(){return this.ENV.getBool("IS_TEST")}checkKernelForMemLeak(e,t,n){const r=this.backend.numDataIds();let s=0;n.forEach((e=>{s+="complex64"===e.dtype?3:1}));const a=this.state.numDataMovesStack[this.state.numDataMovesStack.length-1],o=r-t-s-a;if(o>0)throw new Error(`Backend '${this.backendName}' has an internal memory leak (${o} data ids) after running '${e}'`)}runKernelFunc(e){let t,n=[];const r=this.isTapeOn(),s=this.state.numBytes,a=this.state.numTensors;let o,u;this.shouldCheckForMemLeaks()&&this.state.numDataMovesStack.push(0),null==this.backendName&&this.backend;const l=g(e)?e.kernelName:null!=this.state.activeScope?this.state.activeScope.name:"";if(g(e)){const{kernelName:t,inputs:s,attrs:a}=e;null==this.backendName&&this.backend;const l=(0,i.pI)(t,this.backendName);c.hu(null!=l,(()=>`Cannot find registered kernel '${t}' for backend '${this.backendName}'`)),o=()=>{const e=this.backend.numDataIds();
vendor: 4,911 bytes, line 1
1u=l.kernelFunc({inputs:s,attrs:a,backend:this.backend});const o=Array.isArray(u)?u:[u];this.shouldCheckForMemLeaks()&&this.checkKernelForMemLeak(t,e,o);const i=o.map((e=>null!=e.rank?e:this.makeTensorFromTensorInfo(e)));if(r){const e=this.getTensorsForGradient(t,s,i);n=this.saveTensorsForBackwardMode(e)}return i}}else{const{forwardFunc:t}=e,s=e=>{r&&(n=e.map((e=>this.keep(this.clone(e)))))};o=()=>{const e=this.backend.numDataIds();u=this.tidy((()=>t(this.backend,s)));const n=Array.isArray(u)?u:[u];return this.shouldCheckForMemLeaks()&&this.checkKernelForMemLeak(l,e,n),n}}const{inputs:p,attrs:h}=e,d=g(e)?null:e.backwardsFunc;let f;return this.scopedRun((()=>this.state.kernelDepth++),(()=>this.state.kernelDepth--),(()=>{this.ENV.getBool("DEBUG")||this.state.profiling?(f=this.profiler.profileKernel(l,p,(()=>o())),this.ENV.getBool("DEBUG")&&this.profiler.logKernelProfile(f),t=f.outputs):t=o()})),r&&this.addTapeNode(l,p,t,d,n,h),this.state.profiling&&this.state.activeProfile.kernels.push({name:l,bytesAdded:this.state.numBytes-s,totalBytesSnapshot:this.state.numBytes,tensorsAdded:this.state.numTensors-a,totalTensorsSnapshot:this.state.numTensors,inputShapes:Object.keys(p).map((e=>null!=p[e]?p[e].shape:null)),outputShapes:t.map((e=>e.shape)),kernelTimeMs:f.timeMs,extraInfo:f.extraInfo}),Array.isArray(u)?t:t[0]}saveTensorsForBackwardMode(e){return e.map((e=>this.keep(this.clone(e))))}getTensorsForGradient(e,t,n){const r=(0,i.uk)(e);if(null!=r){const e=r.inputsToSave||[],s=r.outputsToSave||[];let a;r.saveAllInputs?(c.hu(Array.isArray(t),(()=>"saveAllInputs is true, expected inputs to be an array.")),a=Object.keys(t).map((e=>t[e]))):a=e.map((e=>t[e]));const o=n.filter(((e,t)=>s[t]));return a.concat(o)}return[]}makeTensor(e,t,n,r){if(null==e)throw new Error("Values passed to engine.makeTensor() are null");n=n||"float32",r=r||this.backend;let s=e;"string"===n&&c.HD(e[0])&&(s=e.map((e=>l.encodeString(e))));const a=r.write(s,t,n),o=new f.es(t,n,a,this.nextTensorId());if(this.trackTensor(o,r),"string"===n){const e=this.state.tensorInfo.get(a),t=(0,c.Ub)(s);this.state.numBytes+=t-e.bytes,e.bytes=t}return o}makeTensorFromDataId(e,t,n,r){const s={dataId:e,shape:t,dtype:n=n||"float32"};return this.makeTensorFromTensorInfo(s,r)}makeTensorFromTensorInfo(e,t){const{dataId:n,shape:r,dtype:s}=e,a=new f.es(r,s,n,this.nextTensorId());return this.trackTensor(a,t),a}makeVariable(e,t=!0,n,r){n=n||this.nextVariableId().toString(),null!=r&&r!==e.dtype&&(e=e.cast(r));const s=new f._w(e,t,n,this.nextTensorId());if(null!=this.state.registeredVariables[s.name])throw new Error(`Variable with name ${s.name} was already registered`);return this.state.registeredVariables[s.name]=s,this.incRef(s,this.backend),s}trackTensor(e,t){this.state.numTensors++,"string"===e.dtype&&this.state.numStringTensors++;let n=0;"complex64"!==e.dtype&&"string"!==e.dtype&&(n=e.size*c.bT(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 f._w||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;const t=this.state.tensorInfo.get(e.dataId);if(this.state.numTensors--,"string"===e.dtype&&(this.state.numStringTensors--,this.state.numBytes-=t.bytes),"complex64"!==e.dtype&&"string"!==e.dtype){const t=e.size*c.bT(e.dtype);this.state.numBytes-=t}t.backend.disposeData(e.dataId)&&this.removeDataId(e.dataId,t.backend)}disposeVariables(){for(const e in this.state.registeredVariables){const t=this.state.registeredVariables[e];this.disposeVariable(t)}}disposeVariable(e){this.disposeTensor(e),null!=this.state.registeredVariables[e.name]&&delete this.state.registeredVariables[e.name]}memory(){const 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,null==e.reasons&&(e.reasons=[]),e.reasons.push("Memory usage by string tensors is approximate (2 bytes per character)")),e}async profile(e){this.state.profiling=!0;const 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((e=>e.totalBytesSnapshot))),this.state.activeProfile.newBytes=this.state.numBytes-t,this.state.activeProfile.newTensors=this.state.numTensors-n;for(const r of this.state.activeProfile.kernels)r.kernelTimeMs=await r.kernelTimeMs,r.extraInfo=await r.extraInfo;return this.state.activeProfile}isTapeOn(){return this.state.gradientDepth>
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1M.registerSaveRouter(Be),M.registerLoadRouter(Be);class Pe{constructor(e){this.modelArtifacts=e}load(){return this.modelArtifacts}}class Ue{constructor(e){this.saveHandler=e}save(e){return this.saveHandler(e)}}class ze{constructor(e){e.load&&(this.load=()=>Promise.resolve(e.load())),e.save&&(this.save=t=>Promise.resolve(e.save(t)))}}function Ve(e,t,n,r){const s=arguments;return new ze(Ge(...s))}function Ge(e,t,n,r){if(1===arguments.length){return null!=e.modelTopology||null!=e.weightSpecs?new Pe(e):(console.warn("Please call tf.io.fromMemory() with only one argument. The argument should be of type ModelArtifacts. The multi-argument signature of tf.io.fromMemory() has been deprecated and will be removed in a future release."),new Pe({modelTopology:e}))}return console.warn("Please call tf.io.fromMemory() with only one argument. The argument should be of type ModelArtifacts. The multi-argument signature of tf.io.fromMemory() has been deprecated and will be removed in a future release."),new Pe({modelTopology:e,weightSpecs:t,weightData:n,trainingConfig:r})}function He(e){return new Ue(e)}function je(e){return new Ue(e)}var Xe=n(2200),qe=n(9121),Ke=n(6151),Qe=n(3740),Ye=n(2668),Ze=n(9906);let Je;function et(e,t=3){if(t>4)throw new Error("Cannot construct Tensor with more than 4 channels from pixels.");if(null==e)throw new Error("pixels passed to tf.browser.fromPixels() can not be null");let n=!1,r=!1,s=!1,a=!1,o=!1,i=!1;if(e.data instanceof Uint8Array)n=!0;else if("undefined"!==typeof ImageData&&e instanceof ImageData)r=!0;else if("undefined"!==typeof HTMLVideoElement&&e instanceof HTMLVideoElement)s=!0;else if("undefined"!==typeof HTMLImageElement&&e instanceof HTMLImageElement)a=!0;else if(null!=e.getContext)o=!0;else{if(!("undefined"!==typeof ImageBitmap&&e instanceof ImageBitmap))throw new Error(`pixels passed to tf.browser.fromPixels() must be either an HTMLVideoElement, HTMLImageElement, HTMLCanvasElement, ImageData in browser, or OffscreenCanvas, ImageData in webworker or {data: Uint32Array, width: number, height: number}, but was ${e.constructor.name}`);i=!0}if(null!=(0,Ke.pI)(qe.eBW,c.BV.backendName)){const n={pixels:e},r={numChannels:t};return c.BV.runKernel(qe.eBW,n,r)}const[u,l]=s?[e.videoWidth,e.videoHeight]:[e.width,e.height];let p,h;if(o)p=e.getContext("2d").getImageData(0,0,u,l).data;else if(r||n)p=e.data;else if(a||s||i){if(null==Je)if("undefined"===typeof document){if("undefined"===typeof OffscreenCanvas||"undefined"===typeof OffscreenCanvasRenderingContext2D)throw new Error("Cannot parse input in current context. Reason: OffscreenCanvas Context2D rendering is not supported.");Je=new OffscreenCanvas(1,1).getContext("2d")}else Je=document.createElement("canvas").getContext("2d",{willReadFrequently:!0});Je.canvas.width=u,Je.canvas.height=l,Je.drawImage(e,0,0,u,l),p=Je.getImageData(0,0,u,l).data}if(4===t)h=new Int32Array(p);else{const e=u*l;h=new Int32Array(e*t);for(let n=0;n<e;n++)for(let e=0;e<t;++e)h[n*t+e]=p[4*n+e]}const d=[l,u,t];return(0,Ze.w)(h,d,"int32")}function tt(e){return"undefined"!==typeof window&&"undefined"!==typeof ImageBitmap&&window.hasOwnProperty("createImageBitmap")&&!(e instanceof ImageBitmap)&&function(e){return null!=e&&0!==e.width&&0!==e.height}(e)&&!function(e){return null!=e&&e.data instanceof Uint8Array}(e)}async function nt(e,t=3){let n=null;if((0,m.OB)().getBool("WRAP_TO_IMAGEBITMAP")&&tt(e)){let t;try{t=await createImageBitmap(e,{premultiplyAlpha:"none"})}catch(r){t=null}n=null!=t&&t.width===e.width&&t.height===e.height?t:e}else n=e;return et(n,t)}async function rt(e,t){let n=(0,Qe._1)(e,"img","toPixels");if(!(e instanceof Se.es)){const e=n;n=(0,ke.p)(e,"int32"),e.dispose()}if(2!==n.rank&&3!==n.rank)throw new Error(`toPixels only supports rank 2 or 3 tensors, got rank ${n.rank}.`);const[r,s]=n.shape.slice(0,2),a=2===n.rank?1:n.shape[2];if(a>4||2===a)throw new Error(`toPixels only supports depth of size 1, 3 or 4 but got ${a}`);if("float32"!==n.dtype&&"int32"!==n.dtype)throw new Error(`Unsupported type for toPixels: ${n.dtype}. Please use float32 or int32 tensors.`);const o=await n.data(),i="float32"===n.dtype?255:1,u=new Uint8ClampedArray(s*r*4);for(let l=0;l<r*s;++l){const e=[0,0,0,255];for(let r=0;r<a;r++){const t=o[l*a+r];
1if("float32"===n.dtype){if(t<0||t>1)throw new Error(`Tensor values for a float32 Tensor must be in the range [0 - 1] but encountered ${t}.`)}else if("int32"===n.dtype&&(t<0||t>255))throw new Error(`Tensor values for a int32 Tensor must be in the range [0 - 255] but encountered ${t}.`);1===a?(e[0]=t*i,e[1]=t*i,e[2]=t*i):e[r]=t*i}const t=4*l;u[t+0]=Math.round(e[0]),u[t+1]=Math.round(e[1]),u[t+2]=Math.round(e[2]),u[t+3]=Math.round(e[3])}if(null!=t){t.width=s,t.height=r;const e=t.getContext("2d"),n=new ImageData(u,s,r);e.putImageData(n,0,0)}return n!==e&&n.dispose(),u}const st=(0,Ye.op)({fromPixels_:et});var at=n(7650);class ot{getClassName(){return this.constructor.className}static fromConfig(e,t){return new e(t)}}class it{constructor(){this.classNameMap={}}static getMap(){return null==it.instance&&(it.instance=new it),it.instance}static register(e){it.getMap().classNameMap[e.className]=[e,e.fromConfig]}}function ut(e){(0,w.hu)(null!=e.className,(()=>"Class being registered does not have the static className property defined.")),(0,w.hu)("string"===typeof e.className,(()=>"className is required to be a string, but got type "+typeof e.className)),(0,w.hu)(e.className.length>0,(()=>"Class being registered has an empty-string as its className, which is disallowed.")),it.register(e)}var lt=n(747),ct=n(9122);const pt="3.21.0";var ht=n(4368),dt=n(633),ft=n(9494);class mt extends ot{minimize(e,t=!1,n){const{value:r,grads:s}=this.computeGradients(e,n);if(null!=n){const e=n.map((e=>({name:e.name,tensor:s[e.name]})));this.applyGradients(e)}else this.applyGradients(s);return(0,ht.B9)(s),t?r:(r.dispose(),null)}get iterations(){return null==this.iterations_&&(this.iterations_=0),this.iterations_}incrementIterations(){this.iterations_=this.iterations+1}computeGradients(e,t){return(0,dt.pn)(e,t)}dispose(){null!=this.iterations_&&(0,ht.B9)(this.iterations_)}async saveIterations(){return null==this.iterations_&&(this.iterations_=0),{name:"iter",tensor:(0,ft.i)(this.iterations_,"int32")}}async getWeights(){throw new Error("getWeights() is not implemented for this optimizer yet.")}async setWeights(e){throw new Error(`setWeights() is not implemented for this optimizer class ${this.getClassName()}`)}async extractIterations(e){return this.iterations_=(await e[0].tensor.data())[0],e.slice(1)}}Object.defineProperty(mt,Symbol.hasInstance,{value:e=>null!=e.minimize&&null!=e.computeGradients&&null!=e.applyGradients});var gt=n(1221),yt=n(9370),bt=n(6407),xt=n(1274),wt=n(4841),vt=n(3261),kt=n(248),It=n(6577);class Nt extends mt{constructor(e,t,n=null){super(),this.learningRate=e,this.rho=t,this.epsilon=n,this.accumulatedGrads=[],this.accumulatedUpdates=[],null==n&&(this.epsilon=c.BV.backend.epsilon())}applyGradients(e){(Array.isArray(e)?e.map((e=>e.name)):Object.keys(e)).forEach(((t,n)=>{const r=c.BV.registeredVariables[t];null==this.accumulatedGrads[n]&&(this.accumulatedGrads[n]={originalName:`${t}/accum_grad`,variable:(0,ht.lu)((()=>(0,It.P)(r).variable(false)))}),null==this.accumulatedUpdates[n]&&(this.accumulatedUpdates[n]={originalName:`${t}/accum_var`,variable:(0,ht.lu)((()=>(0,It.P)(r).variable(false)))});const s=Array.isArray(e)?e[n].tensor:e[t];if(null==s)return;const a=this.accumulatedGrads[n].variable,o=this.accumulatedUpdates[n].variable;(0,ht.lu)((()=>{const e=(0,bt.I)((0,wt.d)(a,this.rho),(0,wt.d)((0,kt.h)(s),1-this.rho)),t=(0,wt.d)((0,xt.h)((0,vt._)((0,bt.I)(o,this.epsilon)),(0,vt._)((0,bt.I)(a,this.epsilon))),s),n=(0,bt.I)((0,wt.d)(o,this.rho),(0,wt.d)((0,kt.h)(t),1-this.rho));a.assign(e),o.assign(n);const i=(0,bt.I)((0,wt.d)(t,-this.learningRate),r);r.assign(i)}))})),this.incrementIterations()}dispose(){null!=this.accumulatedUpdates&&((0,ht.B9)(this.accumulatedGrads.map((e=>e.variable))),(0,ht.B9)(this.accumulatedUpdates.map((e=>e.variable))))}async getWeights(){const e=[...this.accumulatedGrads,...this.accumulatedUpdates];return[await this.saveIterations()].concat(e.map((e=>({name:e.originalName,tensor:e.variable}))))}async setWeights(e){const t=(e=await this.extractIterations(e)).length/2;this.accumulatedGrads=e.slice(0,t).map((e=>({originalName:e.name,variable:e.tensor.variable(false)}))),this.accumulatedUpdates=e.slice(t,2*t).map((e=>({originalName:e.name,variable:e.tensor.variable(false)})))}getConfig(){return{learningRate:this.learningRate,rho:this.rho,epsilon:this.epsilon}}static fromConfig(e,t){return new e(t.learningRate,t.rho,t.epsilon)}}Nt.className="Adadelta",ut(Nt);var St=n(4006);class Tt extends mt{constructor(e,t=.1){super(),this.learningRate=e,this.initialAccumulatorValue=t,this.accumulatedGrads=[]}applyGradients(e){(Array.isArray(e)?e.map((e=>
1e.name)):Object.keys(e)).forEach(((t,n)=>{const r=c.BV.registeredVariables[t];if(null==this.accumulatedGrads[n]){const e=!1;this.accumulatedGrads[n]={originalName:`${t}/accumulator`,variable:(0,ht.lu)((()=>(0,St.h)(r.shape,this.initialAccumulatorValue).variable(e)))}}const s=Array.isArray(e)?e[n].tensor:e[t];if(null==s)return;const a=this.accumulatedGrads[n].variable;(0,ht.lu)((()=>{const e=(0,bt.I)(a,(0,kt.h)(s));a.assign(e);const t=(0,bt.I)((0,wt.d)((0,xt.h)(s,(0,vt._)((0,bt.I)(e,c.BV.backend.epsilon()))),-this.learningRate),r);r.assign(t)}))})),this.incrementIterations()}dispose(){null!=this.accumulatedGrads&&(0,ht.B9)(this.accumulatedGrads.map((e=>e.variable)))}async getWeights(){return[await this.saveIterations()].concat(this.accumulatedGrads.map((e=>({name:e.originalName,tensor:e.variable}))))}async setWeights(e){e=await this.extractIterations(e);this.accumulatedGrads=e.map((e=>({originalName:e.name,variable:e.tensor.variable(false)})))}getConfig(){return{learningRate:this.learningRate,initialAccumulatorValue:this.initialAccumulatorValue}}static fromConfig(e,t){return new e(t.learningRate,t.initialAccumulatorValue)}}Tt.className="Adagrad",ut(Tt);var Ct=n(3453),Et=n(827);class $t extends mt{constructor(e,t,n,r=null){super(),this.learningRate=e,this.beta1=t,this.beta2=n,this.epsilon=r,this.accumulatedFirstMoment=[],this.accumulatedSecondMoment=[],(0,ht.lu)((()=>{this.accBeta1=(0,ft.i)(t).variable(),this.accBeta2=(0,ft.i)(n).variable()})),null==r&&(this.epsilon=c.BV.backend.epsilon())}applyGradients(e){const t=Array.isArray(e)?e.map((e=>e.name)):Object.keys(e);(0,ht.lu)((()=>{const n=(0,Et.l)(1,this.accBeta1),r=(0,Et.l)(1,this.accBeta2);t.forEach(((t,s)=>{const a=c.BV.registeredVariables[t];null==this.accumulatedFirstMoment[s]&&(this.accumulatedFirstMoment[s]={originalName:`${t}/m`,variable:(0,ht.lu)((()=>(0,It.P)(a).variable(false)))}),null==this.accumulatedSecondMoment[s]&&(this.accumulatedSecondMoment[s]={originalName:`${t}/v`,variable:(0,ht.lu)((()=>(0,It.P)(a).variable(false)))});const o=Array.isArray(e)?e[s].tensor:e[t];if(null==o)return;const i=this.accumulatedFirstMoment[s].variable,u=this.accumulatedSecondMoment[s].variable,l=(0,bt.I)((0,wt.d)(i,this.beta1),(0,wt.d)(o,1-this.beta1)),p=(0,bt.I)((0,wt.d)(u,this.beta2),(0,wt.d)((0,kt.h)(o),1-this.beta2)),h=(0,xt.h)(l,n),d=(0,xt.h)(p,r);i.assign(l),u.assign(p);const f=(0,bt.I)((0,wt.d)((0,xt.h)(h,(0,bt.I)((0,vt._)(d),this.epsilon)),-this.learningRate),a);a.assign(f)})),this.accBeta1.assign((0,wt.d)(this.accBeta1,this.beta1)),this.accBeta2.assign((0,wt.d)(this.accBeta2,this.beta2))})),this.incrementIterations()}dispose(){this.accBeta1.dispose(),this.accBeta2.dispose(),null!=this.accumulatedFirstMoment&&(0,ht.B9)(this.accumulatedFirstMoment.map((e=>e.variable))),null!=this.accumulatedSecondMoment&&(0,ht.B9)(this.accumulatedSecondMoment.map((e=>e.variable)))}async getWeights(){const e=[...this.accumulatedFirstMoment,...this.accumulatedSecondMoment];return[await this.saveIterations()].concat(e.map((e=>({name:e.originalName,tensor:e.variable}))))}async setWeights(e){e=await this.extractIterations(e),(0,ht.lu)((()=>{this.accBeta1.assign((0,Ct.s)(this.beta1,this.iterations_+1)),this.accBeta2.assign((0,Ct.s)(this.beta2,this.iterations_+1))}));const t=e.length/2;this.accumulatedFirstMoment=e.slice(0,t).map((e=>({originalName:e.name,variable:e.tensor.variable(false)}))),this.accumulatedSecondMoment=e.slice(t,2*t).map((e=>({originalName:e.name,variable:e.tensor.variable(false)})))}getConfig(){return{learningRate:this.learningRate,beta1:this.beta1,beta2:this.beta2,epsilon:this.epsilon}}static fromConfig(e,t){return new e(t.learningRate,t.beta1,t.beta2,t.epsilon)}}$t.className="Adam",ut($t);var At=n(6235),Dt=n(632);class _t extends mt{constructor(e,t,n,r=null,s=0){super(),this.learningRate=e,this.beta1=t,this.beta2=n,this.epsilon=r,this.decay=s,this.accumulatedFirstMoment=[],this.accumulatedWeightedInfNorm=[],(0,ht.lu)((()=>{this.iteration=(0,ft.i)(0).variable(),this.accBeta1=(0,ft.i)(t).variable()})),null==r&&(this.epsilon=c.BV.backend.epsilon())}applyGradients(e){const t=Array.isArray(e)?e.map((e=>e.name)):Object.keys(e);(0,ht.lu)((()=>{const n=(0,Et.l)(1,this.accBeta1),r=(0,xt.h)(-this.learningRate,(0,bt.I)((0,wt.d)(this.iteration,this.decay),1));t.forEach(((t,s)=>{const a=c.BV.registeredVariables[t];null==this.accumulatedFirstMoment[s]&&(this.accumulatedFirstMoment[s]={originalName:`${t}/m`,variable:(0,It.P)(a).variable(false)}),null==this.accumulatedWeightedInfNorm[s]&&(this.accumulatedWeightedInfNorm[s]={originalName:`${t}/v`,variable:(0,It.P)(a).variable(false)});const o=Array.isArray(e)?e[s].tensor:e[t];if(null==o)return;const i=this.accumulatedFirstMoment[s].variable,u=this.accumulatedWeightedInfNorm[s].variable,l=(0,bt.I)((0,wt.d)(i,this.beta1),(0,wt.d)(o,1-this.beta1)),p=(0,wt.d)(u,this.beta2),h=(0,At.W)(o),d=(0,Dt.g)(p,h);i.assign(l),u.assign(d);const f=(0,bt.I)((0,wt.d)((0,xt.h)(r,n),(0,xt.h)(l,(0,bt.I)(d,this.epsilon))),a);a.assign(f)})),this.iteration.assign((0,bt.I)(this.iteration,1)),this.accBeta1.assign((0,wt.d)(this.accBeta1,this.beta1))})),this.incrementIterations()}dispose(){this.accBeta1.dispose(),this.iteration.dispose(),null!=this.accumulatedFirstMoment&&(0,ht.B9)(this.accumulatedFirstMoment.map((e=>e.variable))),null!=this.accumulatedWeightedInfNorm&&(0,ht.B9)(this.accumulatedWeightedInfNorm.map((e=>e.variable)))}async getWeights(){throw new Error("getWeights() is not implemented for Adamax yet.")}async setWeights(e){throw new Error("setWeights() is not implemented for Adamax yet.")}getConfig(){return{learningRate:this.learningRate,beta1:this.beta1,beta2:this.beta2,epsilon:this.epsilon,decay:this.decay}}static fromConfig(e,t){return new e(t.learningRate,t.beta1,t.beta2,t.epsilon,t.decay)}}_t.className="Adamax",ut(_t);class Rt extends mt{constructor(e){super(),this.learningRate=e,this.setLearningRate(e)}
1applyGradients(e){(Array.isArray(e)?e.map((e=>e.name)):Object.keys(e)).forEach(((t,n)=>{const r=Array.isArray(e)?e[n].tensor:e[t];if(null==r)return;const s=c.BV.registeredVariables[t];(0,ht.lu)((()=>{const e=(0,bt.I)((0,wt.d)(this.c,r),s);s.assign(e)}))})),this.incrementIterations()}setLearningRate(e){this.learningRate=e,null!=this.c&&this.c.dispose(),this.c=(0,ht.Cn)((0,ft.i)(-e))}dispose(){this.c.dispose()}async getWeights(){return[await this.saveIterations()]}async setWeights(e){if(0!==(e=await this.extractIterations(e)).length)throw new Error("SGD optimizer does not have settable weights.")}getConfig(){return{learningRate:this.learningRate}}static fromConfig(e,t){return new e(t.learningRate)}}Rt.className="SGD",ut(Rt);class Ft extends Rt{constructor(e,t,n=!1){super(e),this.learningRate=e,this.momentum=t,this.useNesterov=n,this.accumulations=[],this.m=(0,ft.i)(this.momentum)}applyGradients(e){(Array.isArray(e)?e.map((e=>e.name)):Object.keys(e)).forEach(((t,n)=>{const r=c.BV.registeredVariables[t];if(null==this.accumulations[n]){const e=!1;this.accumulations[n]={originalName:`${t}/momentum`,variable:(0,ht.lu)((()=>(0,It.P)(r).variable(e)))}}const s=this.accumulations[n].variable,a=Array.isArray(e)?e[n].tensor:e[t];null!=a&&(0,ht.lu)((()=>{let e;const t=(0,bt.I)((0,wt.d)(this.m,s),a);e=this.useNesterov?(0,bt.I)((0,wt.d)(this.c,(0,bt.I)(a,(0,wt.d)(t,this.m))),r):(0,bt.I)((0,wt.d)(this.c,t),r),s.assign(t),r.assign(e)}))})),this.incrementIterations()}dispose(){this.m.dispose(),null!=this.accumulations&&(0,ht.B9)(this.accumulations.map((e=>e.variable)))}setMomentum(e){this.momentum=e}async getWeights(){return[await this.saveIterations()].concat(this.accumulations.map((e=>({name:e.originalName,tensor:e.variable}))))}async setWeights(e){e=await this.extractIterations(e);this.accumulations=e.map((e=>({originalName:e.name,variable:e.tensor.variable(false)})))}getConfig(){return{learningRate:this.learningRate,momentum:this.momentum,useNesterov:this.useNesterov}}static fromConfig(e,t){return new e(t.learningRate,t.momentum,t.useNesterov)}}Ft.className="Momentum",ut(Ft);class Ot extends mt{constructor(e,t=.9,n=0,r=null,s=!1){if(super(),this.learningRate=e,this.decay=t,this.momentum=n,this.epsilon=r,this.accumulatedMeanSquares=[],this.accumulatedMoments=[],this.accumulatedMeanGrads=[],this.centered=s,null==r&&(this.epsilon=c.BV.backend.epsilon()),null==e)throw new Error("learningRate for RMSPropOptimizer must be defined.")}applyGradients(e){(Array.isArray(e)?e.map((e=>e.name)):Object.keys(e)).forEach(((t,n)=>{const r=c.BV.registeredVariables[t],s=!1;null==this.accumulatedMeanSquares[n]&&(this.accumulatedMeanSquares[n]={originalName:`${t}/rms`,variable:(0,ht.lu)((()=>(0,It.P)(r).variable(s)))}),null==this.accumulatedMoments[n]&&(this.accumulatedMoments[n]={originalName:`${t}/momentum`,variable:(0,ht.lu)((()=>(0,It.P)(r).variable(s)))}),null==this.accumulatedMeanGrads[n]&&this.centered&&(this.accumulatedMeanGrads[n]={originalName:`${t}/mg`,variable:(0,ht.lu)((()=>(0,It.P)(r).variable(s)))});const a=Array.isArray(e)?e[n].tensor:e[t];if(null==a)return;const o=this.accumulatedMeanSquares[n].variable,i=this.accumulatedMoments[n].variable;(0,ht.lu)((()=>{const e=(0,bt.I)((0,wt.d)(o,this.decay),(0,wt.d)((0,kt.h)(a),1-this.decay));if(this.centered){const t=this.accumulatedMeanGrads[n].variable,s=(0,bt.I)((0,wt.d)(t,this.decay),(0,wt.d)(a,1-this.decay)),u=(0,xt.h)((0,wt.d)(a,this.learningRate),(0,vt._)((0,Et.l)(e,(0,bt.I)((0,kt.h)(s),this.epsilon)))),l=(0,bt.I)((0,wt.d)(i,this.momentum),u);o.assign(e),t.assign(s),i.assign(l);const c=(0,Et.l)(r,l);r.assign(c)}else{const e=(0,bt.I)((0,wt.d)(o,this.decay),(0,wt.d)((0,kt.h)(a),1-this.decay)),t=(0,bt.I)((0,wt.d)(i,this.momentum),(0,xt.h)((0,wt.d)(a,this.learningRate),(0,vt._)((0,bt.I)(e,this.epsilon))));o.assign(e),i.assign(t);const n=(0,Et.l)(r,t);r.assign(n)}}))})),this.incrementIterations()}dispose(){null!=this.accumulatedMeanSquares&&(0,ht.B9)(this.accumulatedMeanSquares.map((e=>e.variable))),null!=this.accumulatedMeanGrads&&this.centered&&(0,ht.B9)(this.accumulatedMeanGrads.map((e=>e.variable))),null!=this.accumulatedMoments&&(0,ht.B9)(this.accumulatedMoments.map((e=>e.variable)))}async getWeights(){const e=[...this.accumulatedMeanSquares,...this.accumulatedMoments];return this.centered&&e.push(...this.accumulatedMeanGrads),[await this.saveIterations()].concat(e.map((e=>({name:e.originalName,tensor:e.variable}
1))))}async setWeights(e){e=await this.extractIterations(e);const t=this.centered?e.length/3:e.length/2,n=!1;this.accumulatedMeanSquares=e.slice(0,t).map((e=>({originalName:e.name,variable:e.tensor.variable(n)}))),this.accumulatedMoments=e.slice(t,2*t).map((e=>({originalName:e.name,variable:e.tensor.variable(n)}))),this.centered&&(this.accumulatedMeanGrads=e.slice(2*t,3*t).map((e=>({originalName:e.name,variable:e.tensor.variable(n)}))))}getConfig(){return{learningRate:this.learningRate,decay:this.decay,momentum:this.momentum,epsilon:this.epsilon,centered:this.centered}}static fromConfig(e,t){return new e(t.learningRate,t.decay,t.momentum,t.epsilon,t.centered)}}Ot.className="RMSProp",ut(Ot);class Mt{static sgd(e){return new Rt(e)}static momentum(e,t,n=!1){return new Ft(e,t,n)}static rmsprop(e,t=.9,n=0,r=null,s=!1){return new Ot(e,t,n,r,s)}static adam(e=.001,t=.9,n=.999,r=null){return new $t(e,t,n,r)}static adadelta(e=.001,t=.95,n=null){return new Nt(e,t,n)}static adamax(e=.002,t=.9,n=.999,r=null,s=0){return new _t(e,t,n,r,s)}static adagrad(e,t=.1){return new Tt(e,t)}}const Bt={sgd:Mt.sgd,momentum:Mt.momentum,adadelta:Mt.adadelta,adagrad:Mt.adagrad,rmsprop:Mt.rmsprop,adamax:Mt.adamax,adam:Mt.adam},Lt="undefined"!==typeof requestAnimationFrame?requestAnimationFrame:"undefined"!==typeof setImmediate?setImmediate:e=>e();function Wt(){return new Promise((e=>Lt((()=>e()))))}var Pt=n(3591);function Ut(e,t){const n=e[0].length;e.forEach(((e,t)=>{w.hu(e.length===n,(()=>`Error in concat${n}D: rank of tensors[${t}] must be the same as the rank of the rest (${n})`))})),w.hu(t>=0&&t<n,(()=>`Error in concat${n}D: axis must be between 0 and ${n-1}.`));const r=e[0];e.forEach(((e,s)=>{for(let a=0;a<n;a++)w.hu(a===t||e[a]===r[a],(()=>`Error in concat${n}D: Shape of tensors[${s}] (${e}) does not match the shape of the rest (${r}) along the non-concatenated axis ${s}.`))}))}function zt(e,t){const n=e[0].slice();for(let r=1;r<e.length;r++)n[t]+=e[r][t];return n}var Vt,Gt=n(2582),Ht=n(9323);function jt(e,t,n){let r=new Array;if(null==n&&null==t)return r;if(null==t)for(;r.length<e+n.length;)r.push(-1);else r=t.slice();if(null==n)return r;if(e+n.length!==r.length)throw new Error(`rt input.shape and shape=${t} are incompatible: rt input.rank = ${e+n.length}, but shape.rank = ${r.length}`);for(let s=1;s<n.length;++s){const a=n[s],o=r[r.length-n.length+s],i=r[o];if(a>=0)if(i>=0){if(i!==a)throw new Error(`rt input.shape and shape=${t} are incompatible: rt input.shape[${s+e}] = ${a} but shape[${s+e}] = ${i}`)}else r[o]=a}return r}function Xt(e){const t={FIRST_DIM_SIZE:Vt.FIRST_DIM_SIZE,VALUE_ROWIDS:Vt.VALUE_ROWIDS,ROW_LENGTHS:Vt.ROW_LENGTHS,ROW_SPLITS:Vt.ROW_SPLITS,ROW_LIMITS:Vt.ROW_LIMITS,ROW_STARTS:Vt.ROW_STARTS},n=[];for(const r of e){if(!(r in t))break;n.push(t[r])}return n}function qt(e){return 0===e.length?0:e[0]===Vt.FIRST_DIM_SIZE?e.length-1:e.length}function Kt(e,t){if(null==e||null==t)return;const n=e.length,r=t.length;if(n>=r)throw new Error(`defaultValue.shape=${e} and ragged tensor flatValues.shape=${t}, are incompatible: defaultValue.rank = ${n} must be less than ragged tensor input flatValues.rank = ${r})`);for(let s=0;s<Math.min(n,r-1);++s){const n=e[s],r=t[s+1];if(n>=0&&r>=0&&1!==n&&n!==r)throw new Error(`defaultValue.shape=${e}, and ragged tensor input flatValues.shape=${t} are incompatible: defaultValue.shape[${s-e.length}] = ${n} but ragged tensor input.flatValues.shape[${s-e.length}] = ${r}`)}}!function(e){e[e.FIRST_DIM_SIZE=0]="FIRST_DIM_SIZE",e[e.VALUE_ROWIDS=1]="VALUE_ROWIDS",e[e.ROW_LENGTHS=2]="ROW_LENGTHS",e[e.ROW_SPLITS=3]="ROW_SPLITS",e[e.ROW_LIMITS=4]="ROW_LIMITS",e[e.ROW_STARTS=5]="ROW_STARTS"}(Vt||(Vt={}));const Qt=30;function Yt(e){return e<=Qt?e:(0,w.jP)(e,Math.floor(Math.sqrt(e)))}function Zt(e,t,n){return[n*("number"===typeof e?e:e[0]),t*("number"===typeof e?e:e[1])]}function Jt(e,t,n,r=!0){let s=[];if(r)s=s.concat(t.slice(0)),s.push(e[0]/n),s=s.concat(e.slice(1));else{s=s.concat(e[0]);const n=t.length;for(let r=0;r<n;++r)s=s.concat([e[r+1]/t[r],t[r]]);s=s.concat(e.slice(n+1))}return s}function en(e,t,n=!0){const r=[];if(n){r.push(t);
1for(let n=t+1;n<e;++n)n<=2*t?(r.push(n),r.push(n-(t+1))):r.push(n)}else{const n=[],s=[];for(let r=1;r<e;++r)r>=2*t+1||r%2===1?s.push(r):n.push(r);r.push(...n),r.push(0),r.push(...s)}return r}function tn(e,t,n,r=!0){const s=[];r?s.push(e[0]/n):s.push(e[0]*n);for(let a=1;a<e.length;++a)a<=t.length?r?s.push(t[a-1]*e[a]):s.push(e[a]/t[a-1]):s.push(e[a]);return s}function nn(e,t){const n=[0];for(let r=0;r<t;++r)n.push(e[r][0]);return n}function rn(e,t,n){const r=e.slice(0,1);for(let s=0;s<n;++s)r.push(e[s+1]-t[s][0]-t[s][1]);return r}function sn(e,t){const n=e.shape.length,r=t.shape.length;if(n<1)throw new Error(`tf.gatherND() expects the input to be rank 1 or higher, but the rank was ${n}.`);if(r<1)throw new Error(`tf.gatherND() expects the indices to be rank 1 or higher, but the rank was ${r}.`);if("int32"!==t.dtype)throw new Error(`tf.gatherND() expects the indices to be int32 type, but the dtype was ${t.dtype}.`);if(t.shape[r-1]>n)throw new Error(`index innermost dimension length must be <= tensor rank; saw: ${t.shape[r-1]} vs. ${n}`);if(0===(0,w.NA)(e.shape))throw new Error(`Requested more than 0 entries, but input is empty. Input shape: ${e.shape}.`);const s=t.shape,a=s[s.length-1];let o=1;for(let p=0;p<s.length-1;++p)o*=s[p];const i=e.shape,u=s.slice();u.pop();let l=1;for(let p=a;p<n;++p)l*=i[p],u.push(i[p]);const c=[...(0,w.e3)(e.shape).map((e=>e/l)),1].slice(0,a);return[u,o,l,c]}var an=n(3028),on=n(3179);const un=.3275911,ln=.254829592,cn=-.284496736,pn=1.421413741,hn=-1.453152027,dn=1.061405429;var fn=n(4706);function mn(e,t){if(e.length!==t.length)throw new Error(`Cannot merge real and imag arrays of different lengths. real:${e.length}, imag: ${t.length}.`);const n=new Float32Array(2*e.length);for(let r=0;r<n.length;r+=2)n[r]=e[r/2],n[r+1]=t[r/2];return n}function gn(e){const t=new Float32Array(e.length/2),n=new Float32Array(e.length/2);for(let r=0;r<e.length;r+=2)t[r/2]=e[r],n[r/2]=e[r+1];return{real:t,imag:n}}function yn(e){const t=Math.ceil(e.length/4),n=new Float32Array(t),r=new Float32Array(t);for(let s=0;s<e.length;s+=4)n[Math.floor(s/4)]=e[s],r[Math.floor(s/4)]=e[s+1];return{real:n,imag:r}}function bn(e){const t=Math.floor(e.length/4),n=new Float32Array(t),r=new Float32Array(t);for(let s=2;s<e.length;s+=4)n[Math.floor(s/4)]=e[s],r[Math.floor(s/4)]=e[s+1];return{real:n,imag:r}}function xn(e,t){return{real:e[2*t],imag:e[2*t+1]}}function wn(e,t,n,r){e[2*r]=t,e[2*r+1]=n}function vn(e,t){const n=new Float32Array(e/2),r=new Float32Array(e/2);for(let s=0;s<Math.ceil(e/2);s++){const a=(t?2:-2)*Math.PI*(s/e);n[s]=Math.cos(a),r[s]=Math.sin(a)}return{real:n,imag:r}}function kn(e,t,n){const r=(n?2:-2)*Math.PI*(e/t);return{real:Math.cos(r),imag:Math.sin(r)}}const In="->",Nn=/->/g;function Sn(e,t){const n=((e=e.replace(/\s/g,"")).length-e.replace(Nn,"").length)/In.length;if(n<1)throw new Error("Equations without an arrow are not supported.");if(n>1)throw new Error('Equation must contain exactly one arrow ("->").');const[r,s]=e.split(In);(0,w.hu)(-1===r.indexOf("..."),(()=>'The ellipsis notation ("...") is not supported yet.'));const a=r.split(","),o=a.length;if(t!==o)throw new Error(`Expected ${o} input tensors, received ${t}`);if(o>2)throw new Error("Support for more than 2 input tensors is not implemented yet.");const i=[];for(let p=0;p<s.length;++p){const e=s[p];if(!a.some((t=>-1!==t.indexOf(e))))throw new Error(`Output subscripts contain the label ${e} not present in the input subscripts.`);-1===i.indexOf(e)&&i.push(e)}for(let p=0;p<r.length;++p){const e=r[p];-1===i.indexOf(e)&&","!==e&&i.push(e)}const u=new Array(a.length);for(let p=0;p<o;++p){if(new Set(a[p].split("")).size!==a[p].length)throw new Error(`Found duplicate axes in input component ${a[p]}. 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instanceof i?e:f(e)}},7802:function(e,t,n){"use strict";n.d(t,{l2:function(){return l},ht:function(){return d},kS:function(){return h}});var r=n(2623),s=n(588);class a{constructor(e){this.maxEntries=e||100,this.cache=new Map}get(e){let t;return this.cache.has(e)&&(t=this.cache.get(e),this.cache.delete(e),this.cache.set(e,t)),t}put(e,t){if(this.cache.has(e))this.cache.delete(e);else if(this.cache.size>=this.maxEntries){const e=this.cache.keys().next().value;this.cache.delete(e)}this.cache.set(e,t)}getMaxEntries(){return this.maxEntries}setMaxEntries(e){if(e<0)throw new Error(`The maxEntries of LRU caches must be at least 0, but got ${e}.`);if(this.maxEntries>e)for(let t=0;t<this.maxEntries-e;t++){const e=this.cache.keys().next().value;this.cache.delete(e)}this.maxEntries=e}}var o=n(2931),i=n(4396),u=n(163);class l{constructor(e){if(this.id2Value={},this.id2Mask={},this.name2Id={},e instanceof l)for(const t in e.id2Value)this.id2Value[t]=e.id2Value[t],t in e.id2Mask&&(this.id2Mask[t]=e.id2Mask[t]);else{if(null==e)return;for(const t of e)this.add(t.key,t.value)}}add(e,t,n){if(null!=this.id2Value[e.id])throw new s.nu(`Duplicate key: name=${e.name}, id=${e.id}`);return this.id2Value[e.id]=function(e,t){if(null==e.dtype||e.dtype===t.dtype)return t;try{return(0,r.pju)(t,e.dtype)}catch(n){throw new s.nu(`The dtype of the feed (${t.dtype}) can not be cast to the dtype of the key '${e.name}' (${e.dtype}).`)}}(e,t),this.name2Id[e.name]=e.id,null!=n&&(this.id2Mask[e.id]=n),this}addFeed(e){this.add(e.key,e.value)}hasKey(e){return null!=this.id2Value[e.id]}names(){return Object.keys(this.name2Id)}getValue(e){if(e instanceof u.Iy){if(null==this.id2Value[e.id])throw new s.nu(`Nonexistent key: ${e.name}`);return this.id2Value[e.id]}{const t=this.name2Id[e];if(null==t)throw new s.nu(`Feed dict has no SymbolicTensor name: ${e}`);return this.id2Value[t]}}getMask(e){if(e instanceof u.Iy){if(null==this.id2Value[e.id])throw new s.nu(`Nonexistent key: ${e.name}`);return this.id2Mask[e.id]}{const t=this.name2Id[e];if(null==t)throw new s.nu(`Feed dict has no SymbolicTensor name: ${e}`);return this.id2Mask[t]}}disposeMasks(){null!=this.id2Mask&&(0,r.B90)(this.id2Mask)}}const c=new a,p=new a;function h(e){null!=c&&c.setMaxEntries(e),null!=p&&p.setMaxEntries(e)}function d(e,t,n,s){const a=null!=n&&n.training,u=Array.isArray(e),h=u?e:[e],d=h.map((e=>e.name)),y=[],b=t.names();for(const r of d)-1!==b.indexOf(r)?y.push(t.getValue(r)):y.push(null);null!=s&&(s.maxNumTensors=-1/0,s.minNumTensors=1/0);const x=d.join(",")+"|"+t.names().sort().join(",");let w,v=c.get(x);if(null==v){const e=function(e,t){r.D5U.assert(null!=e&&e.length>0,(()=>"Expected at least one fetch, got none"));let n=[],s={};if(1===e.length){const r=m(e[0],t);n=r.sorted,s=r.recipientMap}else{const r=new Set;for(const a of e){const{sorted:e,recipientMap:o}=m(a,t);for(const t of e)r.has(t.name)||(n.push(t),r.add(t.name));for(const t in o)null==s[t]&&(s[t]=new Set),o[t].forEach((e=>s[t].add(e)))}}return{sorted:n,recipientCounts:f(s)}}(h,t);v=e.sorted,w=e.recipientCounts,c.put(x,v),p.put(x,w)}w={},a||Object.assign(w,p.get(x));const k=new l(t);for(let l=0;l<v.length;++l){if(null!=s){const e=(0,r.sq6)().numTensors;e>s.maxNumTensors&&(s.maxNumTensors=e),e<s.minNumTensors&&(s.minNumTensors=e)}const e=v[l],u=e.sourceLayer;if(u instanceof i.l)continue;const c=[],p=[],h=[];let f=!1;for(const n of e.inputs){const e=k.getValue(n),r=k.getMask(n);c.push(e),p.push(r),null!=r&&(f=!0),a||(w[n.name]--,0!==w[n.name]||t.hasKey(n)||-1!==d.indexOf(n.name)||e.isDisposed||!0===n.sourceLayer.stateful||h.push(e))}f&&((n=n||{}).mask=p[0]);const m=(0,o.zZ)(u.apply(c,n));let b=null;u.supportsMasking&&(b=u.computeMask(c,p));const x=g(e),I=Array.isArray(x)?x:[x];for(let t=0;t<I.length;++t){k.hasKey(I[t])||k.add(I[t],m[t],Array.isArray(b)?b[0]:b);const e=d.indexOf(I[t].name);-1!==e&&(y[e]=m[t])}a||(0,r.B90)(h)}return k.disposeMasks(),u?y:y[0]}function f(e){const t={};for(const n in e)t[n]=e[n].size;return t}function m(e,t){const n=new Set,r=[],s={};for(const i of t.names())n.add(i);const a=[],o=[];for(a.push(e);a.length>0;){const e=a[a.length-1];if(n.has(e.name)){a.pop();continue}const t=o[o.length-1]===a.length-1;if(0===e.inputs.length||t)a.pop(),r.push(e),n.add(e.name),t&&o.pop();else{o.push(a.length-1);for(const t of e.inputs)null==s[t.name]&&(s[t.name]=new Set),s[t.name].add(e.name),n.has(t.name)||a.push(t)}}return{sorted:r,recipientMap:s}}function g(e){let t;
1if(1===e.sourceLayer.inboundNodes.length)t=e.sourceLayer.output;else{let n=null;for(let t=0;t<e.sourceLayer.inboundNodes.length;++t)for(const r of e.sourceLayer.inboundNodes[t].outputTensors)if(r.id===e.id){n=t;break}t=e.sourceLayer.getOutputAt(n)}return t}},4396:function(e,t,n){"use strict";n.d(t,{l:function(){return i},I:function(){return u}});var r=n(2623),s=n(1944),a=n(588),o=n(163);class i extends o.mh{constructor(e){if(super({dtype:e.dtype,name:null!=e.name?e.name:(0,s.s)("input").toString()}),null==e.batchSize&&(e.batchSize=null),null==e.sparse&&(e.sparse=!1),this.trainable=!1,this.built=!0,this.sparse=e.sparse,null!=e.inputShape&&null!=e.batchInputShape)throw new a.nu("Only provide the inputShape OR batchInputShape argument to inputLayer, not both at the same time.");let t=e.batchInputShape;if(null==t){if(null==e.inputShape)throw new a.nu("An InputLayer should be passed either a `batchInputShape` or an `inputShape`.");t=[e.batchSize].concat(e.inputShape)}else if(null!=e.batchSize)throw new a.nu("Cannot specify batchSize if batchInputShape is specified when creating an InputLayer.");const n=e.dtype||"float32";this.batchInputShape=t,this.dtype=n,this.inputSpec=[{shape:t}];const r=new o.Iy(this.dtype,this.batchInputShape,this,[],{},this.name);r.nodeIndex=0,r.tensorIndex=0,new o.NB({outboundLayer:this,inboundLayers:[],nodeIndices:[],tensorIndices:[],inputTensors:[r],outputTensors:[r],inputMasks:[null],outputMasks:[null],inputShapes:[t],outputShapes:[t]})}apply(e,t){throw new a.nu(`Cannot pass any input to an InputLayer's apply() method. InputLayer name: ${this.name}`)}dispose(){return{refCountAfterDispose:this._refCount,numDisposedVariables:0}}getConfig(){return{batchInputShape:this.batchInputShape,dtype:this.dtype,sparse:this.sparse,name:this.name}}}function u(e){if(null==e.batchShape&&null==e.shape)throw new Error("Please provide to Input either a `shape` or a `batchShape` argument. Note that `shape` does not include the batch dimension.");if(null!=e.batchShape&&null!=e.shape)throw new a.nu("Please provide either a `shape` or `batchShape` argument to Input, but not both.");let t=e.batchShape;null!=e.shape&&null==t&&(t=[null].concat(e.shape));let n=e.dtype;null==n&&(n="float32");return new i({batchInputShape:t,name:e.name,dtype:n,sparse:e.sparse}).inboundNodes[0].outputTensors[0]}i.className="InputLayer",r.m7h.registerClass(i)},163:function(e,t,n){"use strict";n.d(t,{Zg:function(){return h},Iy:function(){return d},NB:function(){return m},mh:function(){return y},hA:function(){return b}});var r=n(2623),s=n(1944),a=n(8090),o=n(588),i=n(6696),u=n(2931),l=n(7538),c=n(3013),p=n(1653);class h{constructor(e){this.dtype=e.dtype,this.shape=e.shape,null!=e.shape?this.ndim=e.shape.length:this.ndim=e.ndim,this.maxNDim=e.maxNDim,this.minNDim=e.minNDim,this.axes=e.axes||{}}}class d{constructor(e,t,n,r,o,i,u){this.dtype=e,this.shape=t,this.sourceLayer=n,this.inputs=r,this.callArgs=o,this.outputTensorIndex=u,this.id=(0,s.L)(),null!=i&&(this.originalName=(0,a.MU)(i),this.name=(0,a.w8)(this.originalName)),this.rank=t.length}}let f=0;class m{constructor(e,t){this.callArgs=t,this.id=f++,this.outboundLayer=e.outboundLayer,this.inboundLayers=e.inboundLayers,this.nodeIndices=e.nodeIndices,this.tensorIndices=e.tensorIndices,this.inputTensors=e.inputTensors,this.outputTensors=e.outputTensors,this.inputMasks=e.inputMasks,this.outputMasks=e.outputMasks,this.inputShapes=e.inputShapes,this.outputShapes=e.outputShapes;for(const n of e.inboundLayers)null!=n&&n.outboundNodes.push(this);e.outboundLayer.inboundNodes.push(this)}getConfig(){const e=[];for(const t of this.inboundLayers)null!=t?e.push(t.name):e.push(null);return{outboundLayer:this.outboundLayer?this.outboundLayer.name:null,inboundLayers:e,nodeIndices:this.nodeIndices,tensorIndices:this.tensorIndices}}}let g=0;class y extends r.m7h.Serializable{constructor(e={}){super(),this._callHook=null,this._addedWeightNames=[],this._stateful=!1,this.id=g++,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){const e=this.getClassName();t=u.D1(e)+"_"+(0,s.s)(e)}if(this.name=t,this.trainable_=null==e.trainable||e.trainable,null!=e.inputShape||null!=e.batchInputShape){let t;if(null!=e.batchInputShape)t=e.batchInputShape;else if(null!=e.inputShape){let n=null;null!=e.batchSize&&(n=e.batchSize),t=[n].concat(e.inputShape)}this.batchInputShape=t;let n=e.dtype;null==n&&(n=e.inputDType),null==n&&(n="float32"),this.dtype=n}null!=e.weights?this.initialWeights=e.weights:this.initialWeights=null,this._refCount=null,this.fastWeightInitDuringBuild=!1}
1static nodeKey(e,t){return e.name+"_ib-"+t.toString()}getNodeAtIndex(e,t){if(0===this.inboundNodes.length)throw new o.LH(`The layer has never been called and thus has no defined ${t}.`);if(this.inboundNodes.length<=e)throw new o.nu(`Asked to get ${t} at node ${e}, but the layer has only ${this.inboundNodes.length} inbound nodes.`);return this.inboundNodes[e]}getInputAt(e){return u.Bq(this.getNodeAtIndex(e,"input").inputTensors)}getOutputAt(e){return u.Bq(this.getNodeAtIndex(e,"output").outputTensors)}get input(){if(this.inboundNodes.length>1)throw new o.j1(`Layer ${this.name} has multiple inbound nodes, hence the notion of "layer input" is ill-defined. Use \`getInputAt(nodeIndex)\` instead.`);if(0===this.inboundNodes.length)throw new o.j1(`Layer ${this.name} is not connected, no input to return.`);return u.Bq(this.getNodeAtIndex(0,"input").inputTensors)}get output(){if(0===this.inboundNodes.length)throw new o.j1(`Layer ${this.name} has no inbound nodes.`);if(this.inboundNodes.length>1)throw new o.j1(`Layer ${this.name} has multiple inbound nodes, hence the notion of "layer output" is ill-defined. Use \`getOutputAt(nodeIndex)\` instead.`);return u.Bq(this.getNodeAtIndex(0,"output").outputTensors)}get losses(){return this._losses}calculateLosses(){return this.losses.map((e=>e()))}get updates(){return this._updates}get built(){return this._built}set built(e){this._built=e}get trainable(){return this.trainable_}set trainable(e){this._trainableWeights.forEach((t=>t.trainable=e)),this.trainable_=e}get trainableWeights(){return this.trainable_?this._trainableWeights.filter((e=>e.trainable)):[]}set trainableWeights(e){this._trainableWeights=e}get nonTrainableWeights(){return this.trainable?this._trainableWeights.filter((e=>!e.trainable)).concat(this._nonTrainableWeights):this._trainableWeights.concat(this._nonTrainableWeights)}set nonTrainableWeights(e){this._nonTrainableWeights=e}get weights(){return this.trainableWeights.concat(this.nonTrainableWeights)}get stateful(){return this._stateful}resetStates(){if(!this.stateful)throw new Error("Cannot call the resetStates() method of a non-stateful Layer object.")}assertInputCompatibility(e){if(e=u.zZ(e),null==this.inputSpec||0===this.inputSpec.length)return;const t=u.zZ(this.inputSpec);if(e.length!==t.length)throw new o.nu(`Layer ${this.name} expects ${t.length} inputs, but it received ${e.length} input tensors. Input received: ${e}`);for(let n=0;n<e.length;n++){const r=e[n],s=t[n];if(null==s)continue;const a=r.rank;if(null!=s.ndim&&a!==s.ndim)throw new o.nu(`Input ${n} is incompatible with layer ${this.name}: expected ndim=${s.ndim}, found ndim=${a}`);if(null!=s.maxNDim&&a>s.maxNDim)throw new o.nu(`Input ${n} is incompatible with layer ${this.name}: expected max_ndim=${s.maxNDim}, found ndim=${a}`);if(null!=s.minNDim&&a<s.minNDim)throw new o.nu(`Input ${n} is incompatible with layer ${this.name}: expected min_ndim=${s.minNDim}, found ndim=${a}.`);if(null!=s.dtype&&r.dtype!==s.dtype)throw new o.nu(`Input ${n} is incompatible with layer ${this.name} : expected dtype=${s.dtype}, found dtype=${r.dtype}.`);if(s.axes){const e=r.shape;for(const t in s.axes){const r=Number(t),a=s.axes[t],i=r>=0?e[r]:e[e.length+r];if(null!=a&&-1===[a,null].indexOf(i))throw new o.nu(`Input ${n} is incompatible with layer ${this.name}: expected axis ${r} of input shape to have value ${a} but got shape ${e}.`)}}if(null!=s.shape)for(let e=0;e<s.shape.length;++e){const t=s.shape[e],a=r.shape[e];if(null!=t&&null!=a&&t!==a)throw new o.nu(`Input ${n} is incompatible with layer ${this.name}: expected shape=${s.shape}, found shape=${r.shape}.`)}}}call(e,t){return e}invokeCallHook(e,t){null!=this._callHook&&this._callHook(e,t)}setCallHook(e){this._callHook=e}clearCallHook(){this._callHook=null}apply(e,t){t=t||{},this.assertNotDisposed();const n=u.zZ(e);let r=!0;for(const a of n)if(!(a instanceof d)){r=!1;break}let s=!0;for(const a of n)if(a instanceof d){s=!1;break}if(r===s)throw new o.nu("Arguments to apply() must be all SymbolicTensors or all Tensors");return(0,a.f4)(this.name,(()=>{if(!this.built){this.assertInputCompatibility(e);const t=[];for(const n of u.zZ(e))t.push(n.shape);this.build(u.Bq(t)),this.built=!0,this.initialWeights&&this.setWeights(this.initialWeights),null===this._refCount&&s&&(this._refCount=1)}if(this.assertInputCompatibility(e),s){let r=this.call(e,t);const s=u.zZ(r),a=[];for(let e of s)-1!==n.indexOf(e)&&(e=e.clone()),a.push(e);if(r=u.Bq(a),null!=this.activityRegularizer)throw new o.nj("Layer invocation in the presence of activity regularizer(s) is not supported yet.");return r}{const n=function(e){e=u.zZ(e);const t=[];for(const n of e)t.push(n.shape);return u.Bq(t)}(e),r=this.computeOutputShape(n);let s;const a="float32";if(this.warnOnIncompatibleInputShape(Array.isArray(e)?n[0]:n),s=null!=r&&r.length>0&&Array.isArray(r[0])?r.map(((n,r)=>new d(a,n,this,u.zZ(e),t,this.name,r))):new d(a,r,this,u.zZ(e),t,this.name),this.addInboundNode(e,s,null,null,n,r,t),this._refCount++,null!=this.activityRegularizer)throw new o.nj("Layer invocation in the presence of activity regularizer(s) is not supported yet.");return s}}))}warnOnIncompatibleInputShape(e){if(null!=this.batchInputShape)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 ${this.name}`);else{let t=!1;this.batchInputShape.forEach(((n,r)=>{null!=n&&null!=e[r]&&e[r]!==n&&(t=!0)})),t&&console.warn(`The shape of the input tensor (${JSON.stringify(e)}) does not match the expectation of layer ${this.name}: ${JSON.stringify(this.batchInputShape)}`)}}
1get outputShape(){if(null==this.inboundNodes||0===this.inboundNodes.length)throw new o.j1(`The layer ${this.name} has never been called and thus has no defined output shape.`);const e=[];for(const t of this.inboundNodes){const n=JSON.stringify(t.outputShapes);-1===e.indexOf(n)&&e.push(n)}if(1===e.length){const e=this.inboundNodes[0].outputShapes;return Array.isArray(e)&&Array.isArray(e[0])&&1===e.length?e[0]:e}throw new o.j1(`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 o.LH(`You tried to call countParams() on ${this.name}, but the layer is not built yet. Build it first by calling build(batchInputShape).`);return c.t(this.weights)}build(e){this.built=!0}getWeights(e=!1){return(0,p.FQ)(e?this.trainableWeights:this.weights)}setWeights(e){(0,r.lub)((()=>{const t=this.weights;if(t.length!==e.length)throw new o.nu(`You called setWeights(weights) on layer "${this.name}" with a weight list of length ${e.length}, but the layer was expecting ${t.length} weights. Provided weights: ${e}...`);if(0===t.length)return;const n=[],s=(0,p.FQ)(t);for(let a=0;a<s.length;++a){const i=s[a],u=t[a],l=e[a];if(!r.D5U.arraysEqual(i.shape,l.shape))throw new o.nu(`Layer weight shape ${i.shape} not compatible with provided weight shape ${l.shape}`);n.push([u,l])}(0,p.zb)(n)}))}addWeight(e,t,n,r,s,a,u,l){if(-1!==this._addedWeightNames.indexOf(e))throw new o.nu(`Duplicate weight name ${e} for layer ${this.name}`);this._addedWeightNames.push(e),null==n&&(n="float32"),this.fastWeightInitDuringBuild&&(r=null!=l?l():(0,i.L5)("zeros"));const c=r.apply(t,n),h=new p.fU(c,n,e,a,u);return c.dispose(),null!=s&&this.addLoss((()=>s.apply(h.read()))),null==a&&(a=!0),a?this._trainableWeights.push(h):this._nonTrainableWeights.push(h),h}setFastWeightInitDuringBuild(e){this.fastWeightInitDuringBuild=e}addLoss(e){null==e||Array.isArray(e)&&0===e.length||(e=u.zZ(e),void 0!==this._losses&&null!==this._losses&&this.losses.push(...e))}computeOutputShape(e){return e}computeMask(e,t){if(!this.supportsMasking){if(null!=t){if(!Array.isArray(t))throw new TypeError(`Layer ${this.name} does not support masking, but was passed an inputMask.`);t.forEach((e=>{if(null!=e)throw new TypeError(`Layer ${this.name} does not support masking, but was passed an inputMask.`)}))}return null}return t}addInboundNode(e,t,n,r,s,a,o=null){const i=u.zZ(e);t=u.zZ(t),n=u.zZ(n),r=u.zZ(r),s=l.x6(s),a=l.x6(a);const c=[],p=[],h=[];for(const u of i)c.push(u.sourceLayer),p.push(u.nodeIndex),h.push(u.tensorIndex);new m({outboundLayer:this,inboundLayers:c,nodeIndices:p,tensorIndices:h,inputTensors:i,outputTensors:t,inputMasks:n,outputMasks:r,inputShapes:s,outputShapes:a},o);for(let u=0;u<t.length;u++)t[u].sourceLayer=this,t[u].nodeIndex=this.inboundNodes.length-1,t[u].tensorIndex=u}getConfig(){const e={name:this.name,trainable:this.trainable};return null!=this.batchInputShape&&(e.batchInputShape=this.batchInputShape),null!=this.dtype&&(e.dtype=this.dtype),e}disposeWeights(){return this.weights.forEach((e=>e.dispose())),this.weights.length}assertNotDisposed(){if(0===this._refCount)throw new Error(`Layer '${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(null===this._refCount)throw new Error(`Cannot dispose Layer ${this.name} because it has not been used yet.`);this.assertNotDisposed();let e=0;return 0===--this._refCount&&(e=this.disposeWeights()),{refCountAfterDispose:this._refCount,numDisposedVariables:e}}}function b(e,t,n){if((null==t||null!=n&&n>0)&&(t=e.sourceLayer,n=e.nodeIndex),0===t.inboundNodes.length)return[e];{const e=t.inboundNodes[n];if(0===e.inboundLayers.length)return e.inputTensors;{const t=[];for(let n=0;n<e.inboundLayers.length;n++){const r=b(e.inputTensors[n],e.inboundLayers[n],e.nodeIndices[n]);for(const e of r)-1===t.indexOf(e)&&t.push(e)}return t}}}},8913:function(e,t,n){"use strict";n.d(t,{y:function(){return p},D:function(){return d}});var r=n(2623),s=n(8891),a=n(588),o=n(3146),i=n(2931),u=n(6529);function l(e,t){let n,s;const a=t;n=a.xs,s=a.ys,r.D5U.assert(null!=n&&null!=s,(()=>`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}`));
1const o=c("input",e.inputNames,n),i=c("output",e.outputNames,s),u=o[0].shape[0];r.D5U.assert(o.length===e.inputs.length,(()=>`LayersModel has ${e.inputs.length} inputs, but the dataset provides ${o.length} inputs. (Expected input keys: ${JSON.stringify(e.inputNames)})`)),r.D5U.assert(i.length===e.outputs.length,(()=>`LayersModel has ${e.outputs.length} outputs, but the dataset provides ${i.length} outputs. (Expected output keys: ${JSON.stringify(e.outputNames)})`));for(let l=0;l<o.length;l++)r.D5U.assert(o[l].shape[0]===u,(()=>`Batch size mismatch: input ${e.inputNames[l]} has ${o[l].shape[0]}; expected ${u} based on input ${e.inputNames[0]}.`));for(let l=0;l<i.length;l++)r.D5U.assert(i[l].shape[0]===u,(()=>`Batch size mismatch: output ${e.outputNames[l]} has ${i[l].shape[0]}; expected ${u} based on input ${e.inputNames[0]}.`));return{xs:o,ys:i}}function c(e,t,n){if(n instanceof r.esB)return[n];if(Array.isArray(n))return r.D5U.assert(n.length===t.length,(()=>`Received an array of ${n.length} Tensors, but expected ${t.length} to match the ${e} keys ${t}.`)),n;{const r=[];for(const s of t){if(null==n[s])throw new a.nu(`The feature data generated by the dataset lacks the required ${e} key '${s}'.`);r.push(n[s])}return r}}async function p(e,t,n){const c=null!=n.batchesPerEpoch;if(r.D5U.assert(null!=e.optimizer,(()=>"You must compile a model before training/testing. Use LayersModel.compile(modelCompileConfig).")),r.D5U.assert(null!=n,(()=>"For fitDataset(), the 2nd argument (config) is required, but it is not provided in this call.")),r.D5U.assert(null!=n.epochs&&n.epochs>0&&Number.isInteger(n.epochs),(()=>`For fitDataset(), config.epochs is expected to be a positive integer, but got ${n.epochs}`)),r.D5U.assert(!c||n.batchesPerEpoch>0&&Number.isInteger(n.batchesPerEpoch),(()=>`For fitDataset(), config.batchesPerEpoch is expected to be a positive integer if specified, but got ${n.batchesPerEpoch}`)),r.D5U.assert(null==n.validationSplit,(()=>"`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{const p=null!=n.validationData;let d,f;if(p)if(h(n.validationData))r.D5U.assert(null==n.validationBatches||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 ${n.validationBatches}`));else{const e=function(e){if(3===e.length)throw new a.nj("Validation with sample weights is not implemented yet.");return{xs:e[0],ys:e[1]}}(n.validationData);d=e.xs,f=e.ys}const m=e.makeTrainFunction(),g=e.getDedupedMetricsNames();let y;y=p?g.slice().concat(g.map((e=>"val_"+e))):g.slice();const b=(0,s.CZ)(n.callbacks,n.yieldEvery),x=null==n.verbose?1:n.verbose,{callbackList:w,history:v}=(0,s.m$)(b,x,n.epochs,null,null,function(e,t){let n=null;null!=t.batchesPerEpoch?n=t.batchesPerEpoch:Number.isFinite(e.size)&&(n=e.size);return n}(t,n),null,p,y);w.setModel(e),e.history=v,await w.onTrainBegin(),e.stopTraining_=!1;let k=null==n.initialEpoch?0:n.initialEpoch,I=await t.iterator();for(;k<n.epochs;){const s={};await w.onEpochBegin(k);let a=0,y=0;for(c||(I=await t.iterator());!c||a<n.batchesPerEpoch;){const t=await I.next();if(c&&t.done){console.warn(`You provided \`batchesPerEpoch\` as ${n.batchesPerEpoch}, but your dataset iterator ran out of data after ${a} batches; interrupting training. Make sure that your dataset can generate at least \`batchesPerEpoch * epochs\` batches (in this case, `+n.batchesPerEpoch*n.epochs+" batches). You may need to use the repeat() function when building your dataset.");break}if(null!=t.value){const{xs:s,ys:i}=l(e,t.value),c={};c.batch=y,c.size=s[0].shape[0],await w.onBatchBegin(y,c);const p=[];if(null!=n.classWeight){const t=(0,u.Vf)(n.classWeight,e.outputNames);for(let e=0;e<t.length;++e)p.push(await(0,u.tl)(i[e],null,t[e]))}const h=s.concat(i).concat(p),d=m(h);r.B90(h);for(let e=0;e<g.length;++e){const t=g[e],n=d[e];c[t]=n,r.CnY(n)}await w.onBatchEnd(y,c),(0,o.i)(c),y++,a++}if(c?a>=n.batchesPerEpoch:t.done){if(p){let t;t=h(n.validationData)?(0,i.zZ)(await e.evaluateDataset(n.validationData,{batches:n.validationBatches})):(0,i.zZ)(e.evaluate(d,f,{batchSize:null==n.validationBatchSize?32:n.validationBatchSize,verbose:0}));for(let n=0;n<e.metricsNames.length;++n)s[`val_${e.metricsNames[n]}`]=t[n]}break}if(e.stopTraining_)break}if(await w.onEpochEnd(k,s),k++,e.stopTraining_)break}return await w.onTrainEnd(),await e.history.syncData(),e.history}finally{e.isTraining=!1}}function h(e){return"function"===typeof e.iterator}async function d(e,t,n){const s=null!=(n=n||{}).batches,o=e.testFunction;let u=[];
1if(n.verbose>0)throw new a.nj("Verbose mode is not implemented yet.");r.D5U.assert(!s||n.batches>0&&Number.isInteger(n.batches),(()=>`Test loop expects \`batches\` to be a positive integer, but received ${JSON.stringify(n.batches)}`));const c="function"===typeof t.next?t:await t.iterator();let p=0,h=0;for(;!s||h<n.batches;){const t=await c.next();if(u=r.lub((()=>{if(t.value){const{xs:n,ys:s}=l(e,t.value),a=n.concat(s),i=r.lub((()=>o(a)));if(r.B90(a),0===h)for(let e=0;e<i.length;++e)u.push((0,r.iD$)(0));const c=a[0].shape[0];for(let e=0;e<i.length;++e){const t=i[e],n=u[e];u[e]=r.lub((()=>r.IHx(u[e],r.dC7(c,t)))),h>0&&r.B90(n)}r.B90(i),p+=c,++h}return u})),t.done){s&&console.warn(`Your dataset iterator ran out of data during evaluateDataset(). Interrupting evalution. Make sure that your dataset can generate at least \`batches\` batches (in this case, ${n.batches} batches). 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1const vn={kernelName:a.RuY,gradFunc:e=>({x:()=>(0,I.P)(e)})};var kn=n(6151);const In=[l,g,y,v,k,N,S,T,E,$,A,D,L,P,z,G,H,j,X,Z,J,te,oe,se,le,pe,de,ye,we,ve,Ct,ke,Ne,Se,Te,Ce,$e,Ee,_e,Fe,Be,Le,We,Pe,Ue,Ve,Ge,He,je,qe,Ye,Ye,Je,tt,rt,at,ot,it,lt,pt,ht,dt,mt,gt,bt,xt,xt,kt,It,Tt,Et,$t,At,Dt,_t,Rt,Ot,Mt,Bt,Wt,Ut,zt,Vt,Ht,Xt,Qt,Yt,Jt,tn,tn,rn,rn,sn,on,an,un,ln,cn,pn,hn,dn,fn,gn,wn,vn];for(const Nv of In)(0,kn.Li)(Nv);var Nn=n(6235),Sn=n(4077);(0,Sn.t3)().prototype.abs=function(){return this.throwIfDisposed(),(0,Nn.W)(this)};var Tn=n(7839);(0,Sn.t3)().prototype.acos=function(){return this.throwIfDisposed(),(0,Tn.K)(this)};var Cn=n(1470);(0,Sn.t3)().prototype.acosh=function(){return this.throwIfDisposed(),(0,Cn._)(this)},(0,Sn.t3)().prototype.add=function(e){return this.throwIfDisposed(),(0,C.I)(this,e)};var En=n(781);(0,Sn.t3)().prototype.all=function(e,t){return this.throwIfDisposed(),(0,En.$)(this,e,t)};var $n=n(2998);(0,Sn.t3)().prototype.any=function(e,t){return this.throwIfDisposed(),(0,$n.Y)(this,e,t)};var An=n(47);(0,Sn.t3)().prototype.argMax=function(e){return this.throwIfDisposed(),(0,An.N)(this,e)};var Dn=n(7394);(0,Sn.t3)().prototype.argMin=function(e){return this.throwIfDisposed(),(0,Dn.v)(this,e)},(0,Sn.t3)().prototype.asScalar=function(){return this.throwIfDisposed(),(0,F.hu)(1===this.size,(()=>"The array must have only 1 element.")),(0,x.X)(this,[])},(0,Sn.t3)().prototype.asType=function(e){return this.throwIfDisposed(),(0,o.p)(this,e)},(0,Sn.t3)().prototype.as1D=function(){return this.throwIfDisposed(),(0,x.X)(this,[this.size])},(0,Sn.t3)().prototype.as2D=function(e,t){return this.throwIfDisposed(),(0,x.X)(this,[e,t])},(0,Sn.t3)().prototype.as3D=function(e,t,n){return this.throwIfDisposed(),(0,x.X)(this,[e,t,n])},(0,Sn.t3)().prototype.as4D=function(e,t,n,r){return this.throwIfDisposed(),(0,x.X)(this,[e,t,n,r])},(0,Sn.t3)().prototype.as5D=function(e,t,n,r,s){return this.throwIfDisposed(),(0,x.X)(this,[e,t,n,r,s])};var _n=n(2421);(0,Sn.t3)().prototype.asin=function(){return this.throwIfDisposed(),(0,_n.Z)(this)};var Rn=n(1891);(0,Sn.t3)().prototype.asinh=function(){return this.throwIfDisposed(),(0,Rn.V)(this)};var Fn=n(7037);(0,Sn.t3)().prototype.atan=function(){return this.throwIfDisposed(),(0,Fn.z)(this)};var On=n(9812);(0,Sn.t3)().prototype.atan2=function(e){return this.throwIfDisposed(),(0,On.f)(this,e)};var Mn=n(369);(0,Sn.t3)().prototype.atanh=function(){return this.throwIfDisposed(),(0,Mn.C)(this)};var Bn=n(5176);(0,Sn.t3)().prototype.avgPool=function(e,t,n,r){return this.throwIfDisposed(),(0,Bn.w)(this,e,t,n,r)},(0,Sn.t3)().prototype.batchToSpaceND=function(e,t){return this.throwIfDisposed(),(0,en.E)(this,e,t)};var Ln=n(7505);(0,Sn.t3)().prototype.batchNorm=function(e,t,n,r,s){return this.throwIfDisposed(),(0,Ln.t)(this,e,t,n,r,s)};var Wn=n(8247);(0,Sn.t3)().prototype.broadcastTo=function(e){return this.throwIfDisposed(),(0,Wn.U)(this,e)},(0,Sn.t3)().prototype.cast=function(e){return this.throwIfDisposed(),(0,o.p)(this,e)};var Pn=n(6825);(0,Sn.t3)().prototype.ceil=function(){return this.throwIfDisposed(),(0,Pn.m)(this)};var Un=n(2279);(0,Sn.t3)().prototype.clipByValue=function(e,t){return this.throwIfDisposed(),(0,Un.i)(this,e,t)},(0,Sn.t3)().prototype.concat=function(e,t){return this.throwIfDisposed(),e instanceof Sn.es&&(e=[e]),(0,nn.z)([this,...e],t)};var zn=n(1355);(0,Sn.t3)().prototype.conv1d=function(e,t,n,r,s,a){return this.throwIfDisposed(),(0,zn.P)(this,e,t,n,r,s,a)};var Vn=n(1405);(0,Sn.t3)().prototype.conv2dTranspose=function(e,t,n,r,s){return this.throwIfDisposed(),(0,Vn.b)(this,e,t,n,r,s)},(0,Sn.t3)().prototype.conv2d=function(e,t,n,r,s,a){return this.throwIfDisposed(),(0,ae.T)(this,e,t,n,r,s,a)},(0,Sn.t3)().prototype.cos=function(){return this.throwIfDisposed(),(0,Gt.m)(this)},(0,Sn.t3)().prototype.cosh=function(){return this.throwIfDisposed(),(0,jt.f)(this)},(0,Sn.t3)().prototype.cumprod=function(e,t,n){return this.throwIfDisposed(),(0,Nt.$)(this,e,t,n)},(0,Sn.t3)().prototype.cumsum=function(e,t,n){return this.throwIfDisposed(),(0,me.z)(this,e,t,n)};var Gn=n(9112);(0,Sn.t3)().prototype.depthToSpace=function(e,t){return this.throwIfDisposed(),(0,Gn.n)(this,e,t)};var Hn=n(4718);(0,Sn.t3)().prototype.depthwiseConv2d=function(e,t,n,r,s,a){return this.throwIfDisposed(),(0,Hn.B)(this,e,t,n,r,s,a)};var jn=n(557);
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Zn=n(3426);(0,Sn.t3)().prototype.expm1=function(){return this.throwIfDisposed(),(0,Zn.t)(this)};var Jn=n(7020);(0,Sn.t3)().prototype.fft=function(){return this.throwIfDisposed(),(0,Jn.k)(this)},(0,Sn.t3)().prototype.flatten=function(){return this.throwIfDisposed(),(0,x.X)(this,[this.size])},(0,Sn.t3)().prototype.floor=function(){return this.throwIfDisposed(),(0,ct.G)(this)};var er=n(9165);(0,Sn.t3)().prototype.floorDiv=function(e){return this.throwIfDisposed(),(0,er.q)(this,e)},(0,Sn.t3)().prototype.gather=function(e,t){return this.throwIfDisposed(),(0,bn.I)(this,e,t)},(0,Sn.t3)().prototype.greaterEqual=function(e){return this.throwIfDisposed(),(0,q.b)(this,e)},(0,Sn.t3)().prototype.greater=function(e){return this.throwIfDisposed(),(0,ze.p)(this,e)};var tr=n(8447);(0,Sn.t3)().prototype.ifft=function(){return this.throwIfDisposed(),(0,tr.S)(this)};var nr=n(4415);(0,Sn.t3)().prototype.irfft=function(){return this.throwIfDisposed(),(0,nr.w)(this)};var rr=n(3963);(0,Sn.t3)().prototype.isFinite=function(){return this.throwIfDisposed(),(0,rr.x)(this)};var sr=n(5853);(0,Sn.t3)().prototype.isInf=function(){return this.throwIfDisposed(),(0,sr.U)(this)};var ar=n(6230);(0,Sn.t3)().prototype.isNaN=function(){return this.throwIfDisposed(),(0,ar.i)(this)};var or=n(9133);(0,Sn.t3)().prototype.leakyRelu=function(e){return this.throwIfDisposed(),(0,or.h)(this,e)},(0,Sn.t3)().prototype.lessEqual=function(e){return this.throwIfDisposed(),(0,K.z)(this,e)},(0,Sn.t3)().prototype.less=function(e){return this.throwIfDisposed(),(0,Ze.d)(this,e)};var ir=n(9648);(0,Sn.t3)().prototype.localResponseNormalization=function(e,t,n,r){return this.throwIfDisposed(),(0,ir.G)(this,e,t,n,r)};var ur=n(3888);(0,Sn.t3)().prototype.logSigmoid=function(){return this.throwIfDisposed(),(0,ur.e)(this)};var lr=n(1510);(0,Sn.t3)().prototype.logSoftmax=function(e){return this.throwIfDisposed(),(0,lr.C)(this,e)};var cr=n(1391);(0,Sn.t3)().prototype.logSumExp=function(e,t){return this.throwIfDisposed(),(0,cr.l)(this,e,t)},(0,Sn.t3)().prototype.log=function(){return this.throwIfDisposed(),(0,wt.c)(this)};var pr=n(7474);(0,Sn.t3)().prototype.log1p=function(){return this.throwIfDisposed(),(0,pr.K)(this)},(0,Sn.t3)().prototype.logicalAnd=function(e){return this.throwIfDisposed(),(0,Q.H)(this,e)},(0,Sn.t3)().prototype.logicalNot=function(){return this.throwIfDisposed(),(0,Lt.h)(this)};var hr=n(5750);(0,Sn.t3)().prototype.logicalOr=function(e){return this.throwIfDisposed(),(0,hr.K)(this,e)};var dr=n(596);(0,Sn.t3)().prototype.logicalXor=function(e){return this.throwIfDisposed(),(0,dr.e)(this,e)},(0,Sn.t3)().prototype.matMul=function(e,t,n){return this.throwIfDisposed(),(0,U.O)(this,e,t,n)};var fr=n(1174);(0,Sn.t3)().prototype.maxPool=function(e,t,n,r){return this.throwIfDisposed(),(0,fr._)(this,e,t,n,r)};var mr=n(3307);(0,Sn.t3)().prototype.max=function(e,t){return this.throwIfDisposed(),(0,mr.F)(this,e,t)},(0,Sn.t3)().prototype.maximum=function(e){return this.throwIfDisposed(),(0,xn.g)(this,e)};var gr=n(5130);(0,Sn.t3)().prototype.mean=function(e,t){return this.throwIfDisposed(),(0,gr.J)(this,e,t)};var yr=n(5735);(0,Sn.t3)().prototype.min=function(e,t){return this.throwIfDisposed(),(0,yr.V)(this,e,t)};var br=n(4513);(0,Sn.t3)().prototype.minimum=function(e){return this.throwIfDisposed(),(0,br.L)(this,e)};var xr=n(1483);(0,Sn.t3)().prototype.mirrorPad=function(e,t){return this.throwIfDisposed(),(0,xr.V)(this,e,t)};var wr=n(5228);(0,Sn.t3)().prototype.mod=function(e){return this.throwIfDisposed(),(0,wr.w)(this,e)},(0,Sn.t3)().prototype.mul=function(e){return this.throwIfDisposed(),(0,i.d)(this,e)},(0,Sn.t3)().prototype.neg=function(){return this.throwIfDisposed(),(0,p.W)(this)};var vr=n(3561);(0,Sn.t3)().prototype.norm=function(e,t,n){return this.throwIfDisposed(),(0,vr.K)(this,e,t,n)};var kr=n(6500);(0,Sn.t3)().prototype.notEqual=function(e){return this.throwIfDisposed(),(0,kr.Q)(this,e)};var Ir=n(6708);(0,Sn.t3)().prototype.oneHot=function(e,t=1,n=0){return this.throwIfDisposed(),(0,Ir.l)(this,e,t,n)};var Nr=n(7846);(0,Sn.t3)().prototype.onesLike=function(){return this.throwIfDisposed(),(0,Nr.J)(this)},(0,Sn.t3)().prototype.pad=function(e,t){return this.throwIfDisposed(),(0,qt.v)(this,e,t)};var Sr=n(5860);(0,Sn.t3)().prototype.pool=function(e,t,n,r,s,a){return this.throwIfDisposed(),(0,Sr.d)(this,e,t,n,r,s,a)},(0,Sn.t3)().prototype.pow=function(e){return this.throwIfDisposed(),(0,vt.s)(this,e)};var Tr=n(8151);(0,Sn.t3)().prototype.prelu=function(e){return this.throwIfDisposed(),(0,Tr.A)(this,e)};var Cr=n(9451);(0,Sn.t3)().prototype.prod=function(e,t){return this.throwIfDisposed(),(0,Cr.W)(this,e,t)};var Er=n(9036);(0,Sn.t3)().prototype.reciprocal=function(){return this.throwIfDisposed(),(0,Er.M)(this)};var $r=n(7409);(0,Sn.t3)().prototype.relu=function(){return this.throwIfDisposed(),(0,$r.U)(this)};var Ar=n(3582);(0,Sn.t3)().prototype.relu6=function(){return this.throwIfDisposed(),(0,Ar.b)(this)},(0,Sn.t3)().prototype.reshapeAs=function(e){return this.throwIfDisposed(),(0,x.X)(this,e.shape)},(0,Sn.t3)().prototype.reshape=function(e){return this.throwIfDisposed(),(0,x.X)(this,e)};var Dr=n(3305);(0,Sn.t3)().prototype.resizeBilinear=function(e,t,n){return this.throwIfDisposed(),(0,Dr.I)(this,e,t,n)};var _r=n(5098);(0,Sn.t3)().prototype.resizeNearestNeighbor=function(e,t,n){return this.throwIfDisposed(),(0,_r.j)(this,e,t,n)},(0,Sn.t3)().prototype.reverse=function(e){return this.throwIfDisposed(),(0,Ft.G)(this,e)};var Rr=n(3710);(0,Sn.t3)().prototype.rfft=function(){return this.throwIfDisposed(),(0,Rr.Q)(this)};var Fr=n(7809);(0,Sn.t3)().prototype.round=function(){return this.throwIfDisposed(),(0,Fr.N)(this)},(0,Sn.t3)().prototype.rsqrt=function(){return this.throwIfDisposed(),(0,Ae.b)(this)};var Or=n(5503);(0,Sn.t3)().prototype.selu=function(){return this.throwIfDisposed(),(0,Or.U)(this)};var Mr=n(8678);(0,Sn.t3)().prototype.separableConv2d=function(e,t,n,r,s,a){return this.throwIfDisposed(),(0,Mr.U)(this,e,t,n,r,s,a)},(0,Sn.t3)().prototype.sigmoid=function(){return this.throwIfDisposed(),(0,Zt.X)(this)};var Br=n(4434);(0,Sn.t3)().prototype.sign=function(){return this.throwIfDisposed(),(0,Br.X)(this)},(0,Sn.t3)().prototype.sin=function(){return this.throwIfDisposed(),(0,ce.O)(this)},(0,Sn.t3)().prototype.sinh=function(){return this.throwIfDisposed(),(0,he.R)(this)},(0,Sn.t3)().prototype.slice=function(e,t){return this.throwIfDisposed(),(0,ut.t)(this,e,t)};var Lr=n(2817);(0,Sn.t3)().prototype.softmax=function(e){return this.throwIfDisposed(),(0,Lr.X)(this,e)};var Wr=n(3694);(0,Sn.t3)().prototype.softplus=function(){return this.throwIfDisposed(),(0,Wr.W)(this)},(0,Sn.t3)().prototype.spaceToBatchND=function(e,t){return this.throwIfDisposed(),(0,V.f)(this,e,t)},(0,Sn.t3)().prototype.split=function(e,t){return this.throwIfDisposed(),(0,ee.V)(this,e,t)},(0,Sn.t3)().prototype.sqrt=function(){return this.throwIfDisposed(),(0,d._)(this)},(0,Sn.t3)().prototype.square=function(){return this.throwIfDisposed(),(0,f.h)(this)};var Pr=n(5265);(0,Sn.t3)().prototype.squaredDifference=function(e){return this.throwIfDisposed(),(0,Pr.$)(this,e)};var Ur=n(9590);(0,Sn.t3)().prototype.squeeze=function(e){return this.throwIfDisposed(),(0,Ur.L)(this,e)},(0,Sn.t3)().prototype.stack=function(e,t){this.throwIfDisposed();const n=e instanceof Sn.es?[this,e]:[this,...e];return(0,mn.k)(n,t)},(0,Sn.t3)().prototype.step=function(e){return this.throwIfDisposed(),(0,u.N)(this,e)};var zr=n(5158);(0,Sn.t3)().prototype.stridedSlice=function(e,t,n,r,s,a,o,i){return this.throwIfDisposed(),(0,zr.N)(this,e,t,n,r,s,a,o,i)},(0,Sn.t3)().prototype.sub=function(e){return this.throwIfDisposed(),(0,m.l)(this,e)},(0,Sn.t3)().prototype.sum=function(e,t){return this.throwIfDisposed(),(0,w.S)(this,e,t)};var Vr=n(1173);(0,Sn.t3)().prototype.tan=function(){return this.throwIfDisposed(),(0,Vr.O)(this)};var Gr=n(1869);(0,Sn.t3)().prototype.tanh=function(){return this.throwIfDisposed(),(0,Gr.A)(this)},(0,Sn.t3)().prototype.tile=function(e){return this.throwIfDisposed(),(0,De.G)(this,e)},(0,Sn.t3)().prototype.toBool=function(){return this.throwIfDisposed(),(0,o.p)(this,"bool")},(0,Sn.t3)().prototype.toFloat=function(){return this.throwIfDisposed(),(0,o.p)(this,"float32")},(0,Sn.t3)().prototype.toInt=function(){return this.throwIfDisposed(),(0,o.p)(this,"int32")};var Hr=n(3243);(0,Sn.t3)().prototype.topk=function(e,t){return this.throwIfDisposed(),(0,Hr.h)(this,e,t)},(0,Sn.t3)().prototype.transpose=function(e){return this.throwIfDisposed(),(0,ge.p)(this,e)};var jr=n(9608);(0,Sn.t3)().prototype.unique=function(e){return this.throwIfDisposed(),(0,jr.T)(this,e)},(0,Sn.t3)().prototype.unsortedSegmentSum=function(e,t){return this.throwIfDisposed(),(0,Re.p)(this,e,t)},(0,Sn.t3)().prototype.unstack=function(e){return this.throwIfDisposed(),(0,yt.H)(this,e)},(0,Sn.t3)().prototype.where=function(e,t){return this.throwIfDisposed(),(0,Y.a)(e,this,t)},(0,Sn.t3)().prototype.zerosLike=function(){return this.throwIfDisposed(),(0,I.P)(this)};var Xr=n(7802);(0,s.OBj)().registerFlag("TOPOLOGICAL_SORT_CACHE_MAX_ENTRIES",(()=>100),Xr.kS);var qr=n(4079);var Kr=n(6696);var Qr=n(4396),Yr=n(163),Zr=n(8891),Jr=n(5337),es=n(1944),ts=n(588),ns=n(9897),rs=n(2931),ss=(n(1977),n(7538));class as extends Jr.QV{constructor(e){if(super({inputs:[],outputs:[]}),e=e||{},this.trainable=!0,this.built=!1,this.name=null!=e.name?e.name:(0,es.s)("sequential_"),null!=e.layers
1)for(const t of e.layers)this.add(t)}checkShape(e){if(e.inboundNodes[0].outputTensors[0].shape.some((e=>e<0)))throw new ts.nu(`Negative dimension size caused by adding layer ${e.name} with input shape [${e.inboundNodes[0].inputTensors[0].shape}]`)}add(e){const t=e instanceof as||e instanceof Jr.QV;let n;if(t){if(n=e,1!==n.outputs.length)throw new ts.nu("All layers in a Sequential model should have a single output tensor. For multi-output layers, use the functional API.");if(1!==n.inputs.length)throw new ts.nu("All layers in a Sequential model should have a single input tensor. For multi-input layers, use the functional API.")}if(0===this.outputs.length){if(0===e.inboundNodes.length){if(null==e.batchInputShape)throw new ts.nu("The first layer in a Sequential model must get an `inputShape` or `batchInputShape` argument.");const t=(0,Qr.I)({batchShape:e.batchInputShape,dtype:e.dtype,name:e.name+"_input"});e.apply(t)}if(t)this.outputs=n.outputs,this.inputs=n.inputs;else{if(1!==e.inboundNodes.length)throw new ts.nu(`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(1!==e.inboundNodes[0].outputTensors.length)throw new ts.nu("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=(0,Yr.hA)(this.outputs[0])}this.inboundNodes=[],new Yr.NB({outboundLayer:this,inboundLayers:[],nodeIndices:[],tensorIndices:[],inputTensors:this.inputs,outputTensors:this.outputs,inputMasks:rs.JE(null,this.inputs.length),outputMasks:[null],inputShapes:this.inputs.map((e=>e.shape)),outputShapes:this.outputs[0].shape})}else{const t=e.apply(this.outputs[0]);if(Array.isArray(t))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=[t],this.inboundNodes[0].outputTensors=this.outputs,this.inboundNodes[0].outputShapes=[this.outputs[0].shape]}this.layers.push(e),this.built=!1}pop(){if(0===this.layers.length)throw new TypeError("There are no layers in the model.");if(this.layers.pop(),0===this.layers.length)this.outputs=[],this.inboundNodes=[],this.outboundNodes=[];else{const 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 null==this.model&&this.build(),this.model.call(e,t)}build(e){if((0,ss.Wf)(e),0===this.inputs.length||0===this.outputs.length)throw new TypeError("Sequential model cannot be built: model is empty. Add some layers first.");this.model=new Jr.QV({inputs:this.inputs,outputs:this.outputs[0],name:this.name+"_model"}),this.model.trainable=this.trainable,this.supportsMasking=this.model.supp
1ortsMasking,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){null==this.model&&this.build(),this.model.setWeights(e)}evaluate(e,t,n={}){if(!this.built)throw new ts.LH("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 ts.LH("The model needs to be compiled before being used.");return this.model.evaluateDataset(e,t)}predict(e,t={}){return null==this.model&&this.build(),this.model.predict(e,t)}predictOnBatch(e){return null==this.model&&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 null==this.model?void 0:this.model.optimizer}set optimizer(e){this.model.optimizer=e}async fit(e,t,n={}){if(!this.built)throw new ts.LH("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 ts.LH("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={},r=!1){let a,o={};if(t instanceof Array){if(null==t[0].className||"Merge"===t[0].className)throw new ts.nu("Legacy serialization format not supported yet.");a=t}else s.D5U.assert(null!=t.layers,(()=>"When the config data for a Sequential model is not an Array, it must be an Object that contains the 'layers' field.")),a=t.layers,delete t.layers,o=t;const i=new e(o);if(!(i instanceof as))throw new ts.nj(`Sequential.fromConfig called on non-Sequential input: ${i}`);for(const s of a){const e=void 0,t=(0,ns.v)(s,e,r);r&&t.setFastWeightInitDuringBuild(!0),i.add(t)}return i}set stopTraining(e){if(null==this.model)throw new ts.nu("Cannot set the stopTraining property of a sequential model before it is compiled.");this.model.stopTraining=e}get stopTraining(){if(null==this.model)throw new ts.nu("Cannot get the stopTraining property of a sequential model before it is compiled.");return this.model.stopTraining}getConfig(){const e=[];for(const t of this.layers){const n={};n.className=t.getClassName(),n.config=t.getConfig(),e.push(n)}return{name:this.name,layers:e}}}as.className="Sequential",s.m7h.registerClass(as);var os=n(8819),is=n(539);class us extends Yr.mh{constructor(e){super(null==e?{}:e),this.supportsMasking=!0,null!=e&&(this.maxValue=e.maxValue)}call(e,t){e=(0,ss.nQ)(e);let n=(0,s.UYe)(e);return null!=this.maxValue&&(n=(0,s.iUl)(n,0,this.maxValue)),n}computeOutputShape(e){return e}getConfig(){const e={maxValue:this.maxValue},t=super.getConfig();return Object.assign(e,t),e}}us.className="ReLU",s.m7h.registerClass(us);class ls extends Yr.mh{constructor(e){super(null==e?{}:e),this.DEFAULT_ALPHA=.3,null==e&&(e={}),this.alpha=null==e.alpha?this.DEFAULT_ALPHA:e.alpha}call(e,t){const n=(0,ss.nQ)(e);return(0,s.hi7)(n,this.alpha)}computeOutputShape(e){return e}getConfig(){const e={alpha:this.alpha},t=super.getConfig();return Object.assign(e,t),e}}ls.className="LeakyReLU",s.m7h.registerClass(ls);class cs extends Yr.mh{constructor(e){if(super(null==e?{}:e),this.DEFAULT_ALPHA_INITIALIZER="zeros",null==e&&(e={}),this.supportsMasking=!0,this.alphaInitializer=(0,Kr.L5)(e.alphaInitializer||this.DEFAULT_ALPHA_INITIALIZER),this.alphaRegularizer=(0,is.EC)(e.alphaRegularizer),this.alphaConstraint=(0,qr.Ad)(e.alphaConstraint),null==e.sharedAxes)this.sharedAxes=null;else if(Array.isArray(e.sharedAxes))this.sharedAxes=e.sharedAxes;else{if("number"!==typeof e.sharedAxes)throw new ts.nu(`Expected sharedAxes to be a number or an array of numbers, but got ${e.sharedAxes}`);this.sharedAxes=[e.sharedAxes]}}build(e){const t=(e=(0,ss.Wf)(e)).slice(1);if(null!=this.sharedAxes)for(const r of this.sharedAxes)t[r-1]=1;this.alpha=this.addWeight("alpha",t,"float32",this.alphaInitializer,this.alphaRegularizer,!0,this.alphaConstraint);const n={};if(null!=this.sharedAxes)for(let r=1;r<e.length;++r)n[r]=e[r];
1this.inputSpec=[new Yr.Zg({ndim:e.length,axes:n})],this.built=!0}call(e,t){return e=(0,ss.nQ)(e),(0,s.AL3)(e,this.alpha.read())}getConfig(){const e={alphaInitializer:(0,Kr.Cx)(this.alphaInitializer),alphaRegularizer:(0,is.SG)(this.alphaRegularizer),alphaConstraint:(0,qr.xF)(this.alphaConstraint),sharedAxes:this.sharedAxes},t=super.getConfig();return Object.assign(e,t),e}}cs.className="PReLU",s.m7h.registerClass(cs);class ps extends Yr.mh{constructor(e){if(super(null==e?{}:e),this.DEFAULT_ALPHA=1,null==e&&(e={}),null!=e.alpha&&e.alpha!==this.DEFAULT_ALPHA)throw new ts.nj(`Non-default alpha value (${e.alpha}) is not supported by the ELU layer yet.`);this.alpha=null==e.alpha?this.DEFAULT_ALPHA:e.alpha}call(e,t){const n=(0,ss.nQ)(e);return(0,s.pyx)(n)}computeOutputShape(e){return e}getConfig(){const e={alpha:this.alpha},t=super.getConfig();return Object.assign(e,t),e}}ps.className="ELU",s.m7h.registerClass(ps);class hs extends Yr.mh{constructor(e){super(null==e?{}:e),this.DEFAULT_THETA=1,null==e&&(e={}),this.theta=null==e.theta?this.DEFAULT_THETA:e.theta}call(e,t){const n=(0,ss.nQ)(e);return(0,s.dC7)(n,(0,s.pju)((0,s.pjt)(n,this.theta),"float32"))}computeOutputShape(e){return e}getConfig(){const e={theta:this.theta},t=super.getConfig();return Object.assign(e,t),e}}hs.className="ThresholdedReLU",s.m7h.registerClass(hs);class ds extends Yr.mh{constructor(e){super(null==e?{}:e),this.DEFAULT_AXIS=1,null==e&&(e={}),this.softmax=(new os.Gc).apply,this.axis=null==e.axis?this.DEFAULT_AXIS:e.axis}call(e,t){const n=(0,ss.nQ)(e);return this.softmax(n,this.axis)}computeOutputShape(e){return e}getConfig(){const e={axis:this.axis},t=super.getConfig();return Object.assign(e,t),e}}ds.className="Softmax",s.m7h.registerClass(ds);var fs=n(5650),ms=n(2012),gs=n(9840),ys=n(8090),bs=n(6517);class xs extends fs.nx{constructor(e){super(2,e),this.depthwiseKernel=null,this.depthMultiplier=null==e.depthMultiplier?1:e.depthMultiplier,this.depthwiseInitializer=(0,Kr.L5)(e.depthwiseInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.depthwiseConstraint=(0,qr.Ad)(e.depthwiseConstraint),this.depthwiseRegularizer=(0,is.EC)(e.depthwiseRegularizer)}build(e){if((e=(0,ss.Wf)(e)).length<4)throw new ts.nu(`Inputs to DepthwiseConv2D should have rank 4. Received input shape: ${JSON.stringify(e)}.`);const t="channelsFirst"===this.dataFormat?1:3;if(null==e[t]||e[t]<0)throw new ts.nu(`The channel dimension of the inputs to DepthwiseConv2D should be defined, but is not (${e[t]}).`);const n=e[t],r=[this.kernelSize[0],this.kernelSize[1],n,this.depthMultiplier];this.depthwiseKernel=this.addWeight("depthwise_kernel",r,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(0,s.lub)((()=>{let t=function(e,t,n=[1,1],r="valid",a,o){return(0,s.lub)((()=>{null==a&&(a=(0,ms.rf)()),(0,ys.cj)(a);let i=(0,fs.aP)(e,a);if(4!==e.rank)throw new ts.nu(`Input for depthwiseConv2d is required to be 4-D, but is instead ${e.rank}-D`);if(4!==t.rank)throw new ts.nu(`depthwiseKernel is required to be 4-D, but is instead ${t.rank}-D`);return i=s.B10(i,t,n,"same"===r?"same":"valid","NHWC",o),"channelsFirst"===a&&(i=s.p4s(i,[0,3,1,2])),i}))}(e=(0,ss.nQ)(e),this.depthwiseKernel.read(),this.strides,this.padding,this.dataFormat,null);return this.useBias&&(t=gs.a2(t,this.bias.read(),this.dataFormat)),null!=this.activation&&(t=this.activation.apply(t)),t}))}computeOutputShape(e){e=(0,ss.Wf)(e);const t="channelsFirst"===this.dataFormat?e[2]:e[1],n="channelsFirst"===this.dataFormat?e[3]:e[2],r="channelsFirst"===this.dataFormat?e[1]*this.depthMultiplier:e[3]*this.depthMultiplier,s=(0,bs.kt)(t,this.kernelSize[0],this.padding,this.strides[0]),a=(0,bs.kt)(n,this.kernelSize[1],this.padding,this.strides[1]);return"channelsFirst"===this.dataFormat?[e[0],r,s,a]:[e[0],s,a,r]}getConfig(){const e=super.getConfig();return e.depthMultiplier=this.depthMultiplier,e.depthwiseInitializer=(0,Kr.Cx)(this.depthwiseInitializer),e.depthwiseRegularizer=(0,is.SG)(this.depthwiseRegularizer),e.depthwiseConstraint=(0,qr.xF)(this.depthwiseRegularizer),e}}xs.className="DepthwiseConv2D",s.m7h.registerClass(xs);var ws=n(6014),vs=function(e,t){var n={};for(var r in e)Object.prototype.hasOwnProperty.call(e,r)&&t.indexOf(r)<0&&(n[r]=e[r]);if(null!=e&&"function"===typeof Object.getOwnPropertySymbols){var s=0;for(r=Object.getOwnPropertySymbols(e);s<r.length;s++)t.indexOf(r[s])<0&&Object.prototype.propertyIsEnumerable.call(e,r[s])&&(n[r[s]]=e[r[s]])}return n};class ks extends ws.$p{constructor(e){if(e.unroll)throw new ts.nj("Unrolling is not possible with convolutional RNNs.");if(Array.isArray(e.cell))throw new ts.nj("It is not possible at the moment to stack convolutional cells.");
1super(e),this.inputSpec=[new Yr.Zg({ndim:5})]}call(e,t){return s.lub((()=>{if(null!=this.cell.dropoutMask&&(s.B90(this.cell.dropoutMask),this.cell.dropoutMask=null),null!=this.cell.recurrentDropoutMask&&(s.B90(this.cell.recurrentDropoutMask),this.cell.recurrentDropoutMask=null),t&&t.constants)throw new ts.nu("ConvRNN2D cell does not support constants");const n=null==t?null:t.mask,r=null==t?null:t.training,a=null==t?null:t.initialState;return super.call(e,{mask:n,training:r,initialState:a})}))}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 s.lub((()=>{const{stateSize:t}=this.cell,n=e.shape,r=this.computeSingleOutputShape(n),a=[r[0],...r.slice(2)],o=s.lls(a);return Array.isArray(t)?Array(t.length).fill(o):[o]}))}resetStates(e,t=!1){s.lub((()=>{if(!this.stateful)throw new ts.j1("Cannot call resetStates() on an RNN Layer that is not stateful.");const n=this.inputSpec[0].shape,r=this.computeSingleOutputShape(n),a=[r[0],...r.slice(2)];if(null==n[0])throw new ts.nu("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(null==this.getStates())Array.isArray(this.cell.stateSize)?this.states_=this.cell.stateSize.map((()=>s.lls(a))):this.states_=[s.lls(a)];else if(null==e)s.B90(this.states_),null!=this.keptStates&&(s.B90(this.keptStates),this.keptStates=[]),Array.isArray(this.cell.stateSize)?this.states_=this.cell.stateSize.map((()=>s.lls(a))):this.states_[0]=s.lls(a);else{if(Array.isArray(e)||(e=[e]),e.length!==this.states_.length)throw new ts.nu(`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()):s.B90(this.states_);for(let t=0;t<this.states_.length;++t){const n=e[t],r=a;if(!s.D5U.arraysEqual(n.shape,r))throw new ts.nu(`State ${t} is incompatible with layer ${this.name}: expected shape=${r}, received shape=${n.shape}`);this.states_[t]=n}}this.states_=this.states_.map((e=>s.CnY(e.clone())))}))}computeSingleOutputShape(e){const{dataFormat:t,filters:n,kernelSize:r,padding:s,strides:a,dilationRate:o}=this.cell,i="channelsFirst"===t,u=e[i?3:2],l=e[i?4:3],c=(0,bs.kt)(u,r[0],s,a[0],o[0]),p=(0,bs.kt)(l,r[1],s,a[1],o[1]);return[...e.slice(0,2),...i?[n,c,p]:[c,p,n]]}}ks.className="ConvRNN2D";class Is extends ws.U7{constructor(e){const{filters:t,kernelSize:n,strides:r,padding:s,dataFormat:a,dilationRate:o}=e;super(Object.assign({},e,{units:t})),this.filters=t,(0,rs.iQ)(this.filters,"filters"),this.kernelSize=(0,bs.AF)(n,2,"kernelSize"),this.kernelSize.forEach((e=>(0,rs.iQ)(e,"kernelSize"))),this.strides=(0,bs.AF)(r||1,2,"strides"),this.strides.forEach((e=>(0,rs.iQ)(e,"strides"))),this.padding=s||"valid",(0,ys.zb)(this.padding),this.dataFormat=a||"channelsLast",(0,ys.cj)(this.dataFormat),this.dilationRate=(0,bs.AF)(o||1,2,"dilationRate"),this.dilationRate.forEach((e=>(0,rs.iQ)(e,"dilationRate")))}build(e){var t;e=(0,ss.Wf)(e);const n="channelsFirst"===this.dataFormat?1:e.length-1;if(null==e[n])throw new ts.nu(`The channel dimension of the input should be defined. Found ${e[n]}`);const r=e[n],a=this.kernelSize.concat([r,4*this.filters]);this.kernel=this.addWeight("kernel",a,null,this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint);const o=this.kernelSize.concat([this.filters,4*this.filters]);if(this.recurrentKernel=this.addWeight("recurrent_kernel",o,null,this.recurrentInitializer,this.recurrentRegularizer,!0,this.recurrentConstraint),this.useBias){let e;if(this.unitForgetBias){const n=this.biasInitializer,r=this.filters;e=new((t=class extends Kr.m7{apply(e,t){const a=n.apply([r]),o=s.iUs([r]),i=n.apply([2*r]);return gs.mV([a,o,i])}}).className="CustomInit",t)}else e=this.biasInitializer;this.bias=this.addWeight("bias",[4*this.filters],null,e,this.biasRegularizer,!0,this.biasConstraint)}this.built=!0}call(e,t){return s.lub((()=>
1{if(3!==e.length)throw new ts.nu(`ConvLSTM2DCell expects 3 input Tensors (inputs, h, c), got ${e.length}.`);const n=t.training||!1,r=e[0],a=e[1],o=e[2];0<this.dropout&&this.dropout<1&&null==this.dropoutMask&&(this.dropoutMask=(0,ws._0)({ones:()=>s.JpU(r),rate:this.dropout,training:n,count:4,dropoutFunc:this.dropoutFunc}));const i=this.dropoutMask,u=(e,t,n)=>t&&t[n]?s.dC7(t[n],e):e;let l=u(r,i,0),c=u(r,i,1),p=u(r,i,2),h=u(r,i,3);0<this.recurrentDropout&&this.recurrentDropout<1&&null==this.recurrentDropoutMask&&(this.recurrentDropoutMask=(0,ws._0)({ones:()=>s.JpU(a),rate:this.recurrentDropout,training:n,count:4,dropoutFunc:this.dropoutFunc}));const d=this.recurrentDropoutMask;let f=u(a,d,0),m=u(a,d,1),g=u(a,d,2),y=u(a,d,3);const[b,x,w,v]=s.Vl2(this.kernel.read(),4,3),[k,I,N,S]=this.useBias?s.Vl2(this.bias.read(),4):[null,null,null,null];l=this.inputConv(l,b,k,this.padding),c=this.inputConv(c,x,I,this.padding),p=this.inputConv(p,w,N,this.padding),h=this.inputConv(h,v,S,this.padding);const[T,C,E,$]=s.Vl2(this.recurrentKernel.read(),4,3);f=this.recurrentConv(f,T),m=this.recurrentConv(m,C),g=this.recurrentConv(g,E),y=this.recurrentConv(y,$);const A=this.recurrentActivation.apply(s.IHx(l,f)),D=this.recurrentActivation.apply(s.IHx(c,m)),_=s.IHx(s.dC7(D,o),s.dC7(A,this.activation.apply(s.IHx(p,g)))),R=s.dC7(this.recurrentActivation.apply(s.IHx(h,y)),this.activation.apply(_));return[R,R,_]}))}getConfig(){const e=super.getConfig(),{units:t}=e,n=vs(e,["units"]),r={filters:this.filters,kernelSize:this.kernelSize,padding:this.padding,dataFormat:this.dataFormat,dilationRate:this.dilationRate,strides:this.strides};return Object.assign({},n,r)}inputConv(e,t,n,r){const a=s.Tek(e,t,this.strides,r||"valid","channelsFirst"===this.dataFormat?"NCHW":"NHWC",this.dilationRate);return n?gs.a2(a,n,this.dataFormat):a}recurrentConv(e,t){return s.Tek(e,t,1,"same","channelsFirst"===this.dataFormat?"NCHW":"NHWC")}}Is.className="ConvLSTM2DCell",s.m7h.registerClass(Is);class Ns extends ks{constructor(e){const t=new Is(e);super(Object.assign({},e,{cell:t}))}static fromConfig(e,t){return new e(t)}}Ns.className="ConvLSTM2D",s.m7h.registerClass(Ns);var Ss=n(6040);class Ts extends Yr.mh{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(null==this.noiseShape)return this.noiseShape;const t=e.shape,n=[];for(let r=0;r<this.noiseShape.length;++r)n.push(null==this.noiseShape[r]?t[r]:this.noiseShape[r]);return n}call(e,t){return(0,s.lub)((()=>{this.invokeCallHook(e,t);const n=(0,ss.nQ)(e);if(0<this.rate&&this.rate<1){const e=null!=t.training&&t.training,r=this.getNoiseShape(n);return gs.KC((()=>gs.rv(n,this.rate,r,this.seed)),(()=>n),e)}return e}))}getConfig(){const e={rate:this.rate,noiseShape:this.noiseShape,seed:this.seed},t=super.getConfig();return Object.assign(e,t),e}dispose(){return super.dispose()}}Ts.className="Dropout",s.m7h.registerClass(Ts);class Cs extends Ts{constructor(e){super(e),this.inputSpec=[{ndim:3}]}getNoiseShape(e){const t=e.shape;return[t[0],1,t[2]]}}Cs.className="SpatialDropout1D",s.m7h.registerClass(Cs);class Es extends Yr.mh{constructor(e){if(super(e),this.activation=null,this.useBias=!0,this.kernel=null,this.bias=null,this.DEFAULT_KERNEL_INITIALIZER="glorotNormal",this.DEFAULT_BIAS_INITIALIZER="zeros",null==e.batchInputShape&&null==e.inputShape&&null!=e.inputDim){let t=null;null!=e.batchSize&&(t=e.batchSize),this.batchInputShape=[t,e.inputDim]}this.units=e.units,(0,rs.iQ)(this.units,"units"),this.activation=(0,os.aI)(e.activation),null!=e.useBias&&(this.useBias=e.useBias),this.kernelInitializer=(0,Kr.L5)(e.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.biasInitializer=(0,Kr.L5)(e.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.kernelConstraint=(0,qr.Ad)(e.kernelConstraint),this.biasConstraint=(0,qr.Ad)(e.biasConstraint),this.kernelRegularizer=(0,is.EC)(e.kernelRegularizer),this.biasRegularizer=(0,is.EC)(e.biasRegularizer),this.activityRegularizer=(0,is.EC)(e.activityRegularizer),this.supportsMasking=!0,this.inputSpec=[{minNDim:2}]}build(e){const t=(e=(0,ss.Wf)(e))[e.length-1];null==this.kernel&&(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){const t=(e=(0,ss.Wf)(e)).slice();return t[t.length-1]=this.units,t}call(e,t){return(0,s.lub)((()=>{this.invokeCallHook(e,t);const n=(0,ss.nQ)(e),r=(0,rs.WT)(this.activation.getClassName());let s;return null!=r?s=gs.AK(n,this.kernel.read(),r,this.bias?this.bias.read():null):(s=gs.AK(n,this.kernel.read()),null!=this.bias&&(s=gs.a2(s,this.bias.read())),null!=this.activation&&(s=this.activation.apply(s))),s}))}getConfig(){const e={units:this.units,activation:(0,os.GD)(this.activation),useBias:this.useBias,kernelInitializer:(0,Kr.Cx)(this.kernelInitializer),biasInitializer:(0,Kr.Cx)(this.biasInitializer),kernelRegularizer:(0,is.SG)(this.kernelRegularizer),biasRegularizer:(0,is.SG)(this.biasRegularizer),activityRegularizer:(0,is.SG)(this.activityRegularizer),kernelConstraint:(0,qr.xF)(this.kernelConstraint),biasConstraint:(0,qr.xF)(this.biasConstraint)},t=super.getConfig();return Object.assign(e,t),e}}
1Es.className="Dense",s.m7h.registerClass(Es);class $s extends Yr.mh{constructor(e){super(e=e||{}),this.inputSpec=[{minNDim:3}],this.dataFormat=e.dataFormat}computeOutputShape(e){e=(0,ss.Wf)(e);for(const t of e.slice(1))if(null==t)throw new ts.nu(`The shape of the input to "Flatten" is not fully defined (got ${e.slice(1)}). Make sure to pass a complete "input_shape" or "batch_input_shape" argument to the first layer in your model.`);return[e[0],(0,Ss.NS)(e,1)]}call(e,t){return(0,s.lub)((()=>{this.invokeCallHook(e,t);let n=(0,ss.nQ)(e);if("channelsFirst"===this.dataFormat&&n.rank>1){const e=[0];for(let t=2;t<n.rank;++t)e.push(t);e.push(1),n=(0,s.p4s)(n,e)}return gs.Uz(n)}))}getConfig(){const e={};null!=this.dataFormat&&(e.dataFormat=this.dataFormat);const t=super.getConfig();return Object.assign(e,t),e}}$s.className="Flatten",s.m7h.registerClass($s);class As extends Yr.mh{constructor(e){super(e),this.supportsMasking=!0,this.activation=(0,os.aI)(e.activation)}call(e,t){return(0,s.lub)((()=>{this.invokeCallHook(e,t);const n=(0,ss.nQ)(e);return this.activation.apply(n)}))}getConfig(){const e={activation:(0,os.GD)(this.activation)},t=super.getConfig();return Object.assign(e,t),e}}As.className="Activation",s.m7h.registerClass(As);class Ds extends Yr.mh{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(0,s.lub)((()=>(e=(0,ss.nQ)(e),gs.rx(e,this.n))))}getConfig(){const e={n:this.n},t=super.getConfig();return Object.assign(e,t),e}}Ds.className="RepeatVector",s.m7h.registerClass(Ds);class _s extends Yr.mh{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||null==e}fixUnknownDimension(e,t){const n="Total size of new array must be unchanged.",r=t.slice();let s=1,a=null;for(let i=0;i<r.length;++i){const e=r[i];if(this.isUnknown(e)){if(null!==a)throw new ts.nu("Can only specifiy one unknown dimension.");a=i}else s*=e}const o=(0,Ss.NS)(e);if(null!==a){if(0===s||o%s!==0)throw new ts.nu(n);r[a]=o/s}else if(o!==s)throw new ts.nu(n);return r}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(0,s.lub)((()=>{this.invokeCallHook(e,t);const n=(0,ss.nQ)(e),r=n.shape,a=r.slice(0,1).concat(this.fixUnknownDimension(r.slice(1),this.targetShape));return(0,s.XLQ)(n,a)}))}getConfig(){const e={targetShape:this.targetShape},t=super.getConfig();return Object.assign(e,t),e}}_s.className="Reshape",s.m7h.registerClass(_s);class Rs extends Yr.mh{constructor(e){if(super(e),null==e.dims)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.`);const t=(0,Ss.w6)(1,e.dims.length+1);if(!s.D5U.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 Yr.Zg({ndim:this.dims.length+1})]}computeOutputShape(e){const t=(e=(0,ss.Wf)(e)).slice();return this.dims.forEach(((n,r)=>{t[r+1]=e[n]})),t}call(e,t){return(0,s.p4s)((0,ss.nQ)(e),this.dimsIncludingBatch)}getConfig(){const e={dims:this.dims},t=super.getConfig();return Object.assign(e,t),e}}Rs.className="Permute",s.m7h.registerClass(Rs);class Fs extends Yr.mh{constructor(e){super(null==e?{}:e),this.supportsMasking=!0,this.maskValue=null!=e?null==e.maskValue?0:e.maskValue:0}computeOutputShape(e){return e}getConfig(){const e=super.getConfig(),t={maskValue:this.maskValue};return Object.assign(t,e),t}computeMask(e,t){const n=(0,ss.nQ)(e);return(0,s.YjB)((0,s.Quu)(n,this.maskValue),-1)}call(e,t){return(0,s.lub)((()=>{this.invokeCallHook(e,t);const n=(0,ss.nQ)(e),r=(0,s.YjB)((0,s.Quu)(n,this.maskValue),-1,!0);return(0,s.dC7)(n,(0,s.pju)(r,n.dtype))}))}}Fs.className="Masking",s.m7h.registerClass(Fs);class Os extends Yr.mh{constructor(e){if(super(e),this.embeddings=null,this.DEFAULT_EMBEDDINGS_INITIALIZER="randomUniform",null==e.batchInputShape&&null==e.inputShape){let t=null;null!=e.batchSize&&(t=e.batchSize),null==e.inputLength?this.batchInputShape=[t,null]:this.batchInputShape=[t].concat(rs.zZ(e.inputLength))}this.inputDim=e.inputDim,rs.iQ(this.inputDim,"inputDim"),this.outputDim=e.outputDim,rs.iQ(this.outputDim,"outputDim"),this.embeddingsInitializer=(0,Kr.L5)(e.embeddingsInitializer||this.DEFAULT_EMBEDDINGS_INITIALIZER),this.embeddingsRegularizer=(0,is.EC)(e.embeddingsRegularizer),this.activityRegularizer=(0,is.EC)(e.activityRegularizer),this.embeddingsConstraint=(0,qr.Ad)(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(0,s.lub)((()=>this.maskZero?(e=(0,ss.nQ)(e),(0,s.Quu)(e,(0,s.P84)(e))):null))}computeOutputShape(e){if(e=(0,ss.Wf)(e),null==this.inputLength)return[...e,this.outputDim];const t=rs.zZ(this.inputLength);
1if(t.length!==e.length-1)throw new ts.nu(`"inputLength" is ${this.inputLength}, but received input shape has shape ${e}`);{let n=0;for(let r=0;r<t.length;++r){const s=t[r],a=e[r+1];if(null!=s&&null!=a&&s!==a)throw new ts.nu(`"inputLength" is ${this.inputLength}, but received input shape has shape ${e}`);null==s&&(t[n]=a),n++}}return[e[0],...t,this.outputDim]}call(e,t){return(0,s.lub)((()=>{this.invokeCallHook(e,t);let n=(0,ss.nQ)(e);"int32"!==n.dtype&&(n=gs.pj(n,"int32"));const r=gs.Iq(this.embeddings.read(),(0,s.XLQ)(n,[n.size]));return(0,s.XLQ)(r,(0,ss.Wf)(this.computeOutputShape(n.shape)))}))}getConfig(){const e={inputDim:this.inputDim,outputDim:this.outputDim,embeddingsInitializer:(0,Kr.Cx)(this.embeddingsInitializer),embeddingsRegularizer:(0,is.SG)(this.embeddingsRegularizer),activityRegularizer:(0,is.SG)(this.activityRegularizer),embeddingsConstraint:(0,qr.xF)(this.embeddingsConstraint),maskZero:this.maskZero,inputLength:this.inputLength},t=super.getConfig();return Object.assign(e,t),e}}Os.className="Embedding",s.m7h.registerClass(Os);var Ms=n(6275);class Bs extends Yr.mh{constructor(e){super(e||{}),this.supportsMasking=!0}mergeFunction(e){throw new ts.nj}computeElementwiseOpOutputShape(e,t){if(null==e||null==t)return null;if(e.length<t.length)return this.computeElementwiseOpOutputShape(t,e);if(0===t.length)return e;const n=e.slice(0,e.length-t.length);for(let r=0;r<t.length;++r){const s=e[e.length-t.length+r],a=t[r];if(null==s||null==a||s<0||a<0)n.push(null);else if(1===s)n.push(a);else if(1===a)n.push(s);else{if(s!==a)throw new ts.nu("Operands could not be broadcast together with shapes "+JSON.stringify(e)+" "+JSON.stringify(t));n.push(s)}}return n}build(e){if(Array.isArray(e)&&!Array.isArray(e[0])&&(e=[(0,ss.Wf)(e)]),(e=e).length<2)throw new ts.nu(`A merge layer should be called on an Array of at least 2 inputs. Got ${e.length} input(s).`);let t=[];for(const s of e)null!=s&&null!==s[0]&&t.push(s[0]);if(t=rs.Tw(t),t.length>1)throw new ts.nu(`Can not merge tensors with different batch sizes. Got tensors with shapes: ${JSON.stringify(e)}.`);let n=null==e[0]?null:e[0].slice(1);for(let s=1;s<e.length;++s){const t=null==e[s]?null:e[s].slice(1);n=this.computeElementwiseOpOutputShape(n,t)}const r=e.map((e=>e.length));-1===e.indexOf(null)&&1===rs.Tw(r).length?this.reshapeRequired=!1:this.reshapeRequired=!0}call(e,t){return(0,s.lub)((()=>{if(e=e,this.reshapeRequired){const t=[],n=e.map((e=>e.rank));if(-1===n.indexOf(null)){const r=Ss.Fp(n);for(let n of e){const e=n.rank;for(let t=0;t<r-e;++t)n=gs.dt(n,1);t.push(n)}return this.mergeFunction(t)}{let n=!1;for(const o of e){const e=o.rank;if(null==e){const e=o.shape,r=e[0],a=e.slice(1).concat([r]);let i=s.XLQ(o,[r].concat(Ss.NS(e.slice(1))));i=s.p4s(i,[1,0]),i=s.XLQ(i,a),t.push(i),n=!0}else if(e>1){const r=Ss.w6(1,e).concat([0]);t.push(s.p4s(o,r)),n=!0}else t.push(o)}let r=this.mergeFunction(t);const a=r.rank;if(n)if(null==a){const e=r.shape,t=e[e.length-1],n=[t].concat(e.slice(0,e.length-1));r=s.XLQ(s.p4s(s.XLQ(r,[-1,t]),[1,0]),n)}else if(a>1){const e=[a-1].concat(Ss.w6(0,a-1));r=s.p4s(r,e)}return r}}return this.mergeFunction(e)}))}computeOutputShape(e){let t;t=null==(e=e)[0]?null:e[0].slice(1);for(let r=1;r<e.length;++r){const n=null==e[r]?null:e[r].slice(1);t=this.computeElementwiseOpOutputShape(t,n)}let n=[];for(const r of e)null!=r&&null!==r[0]&&n.push(r[0]);return n=rs.Tw(n),t=1===n.length?n.concat(t):[null].concat(t),t}computeMask(e,t){return s.lub((()=>{if(null==t)return null;if(!Array.isArray(t))throw new ts.nu("`mask` should be an Array");if(!Array.isArray(e))throw new ts.nu("`inputs` should be an Array");if(t.length!==e.length)throw new ts.nu(`The Array 'inputs' and 'mask' are expected to have the same length, but have different lengths (${e.length} vs ${t.length})`);if(t.every((e=>null==e)))return null;let n=(t=t.map((e=>null==e?e:s.dt4(e,0))))[0];for(let e=1;e<t.length-1;++e)n=s.HvI(n,t[e]);return n}))}}class Ls extends Bs{constructor(e){super(e)}mergeFunction(e){return(0,s.lub)((()=>{let t=e[0].clone();for(let n=1;n<e.length;++n)t=s.IHx(t,e[n]);return t}))}}Ls.className="Add",s.m7h.registerClass(Ls);class Ws extends Bs{constructor(e){super(e)}mergeFunction(e){return(0,s.lub)((()=>{let t=e[0].clone();for(let n=1;n<e.length;++n)t=s.dC7(t,e[n]);return t}))}}Ws.className="Multiply",s.m7h.registerClass(Ws);class Ps extends Bs{constructor(e){super(e)}mergeFunction(e){return(0,s.lub)((()=>{let t=e[0].clone();for(let n=1;n<e.length;++n)t=s.IHx(t,e[n]);return s.dC7(1/e.length,t)}))}}Ps.className="Average",s.m7h.registerClass(Ps);class Us extends Bs{constructor(e){super(e)}mergeFunction(e){return(0,s.lub)((()=>{let t=e[0];for(let n=1;n<e.length;++n)t=s.gWQ(t,e[n]);return t}))}}Us.className="Maximum",s.m7h.registerClass(Us);class zs extends Bs{constructor(e){super(e)}mergeFunction(e){return(0,s.lub)((()=>{let t=e[0];for(let n=1;n<e.length;++n)t=s.LTh(t,e[n]);return t}))}}zs.className="Minimum",s.m7h.registerClass(zs);class Vs extends Bs{constructor(e){super(e),this.DEFAULT_AXIS=-1,null==e&&(e={}),this.axis=null==e.axis?this.DEFAULT_AXIS:e.axis,this.supportsMasking=!0,this.reshapeRequired=!1}build(e){if(!Array.isArray(e)||!Array.isArray(e[0])||1===e.length)throw new ts.nu("A `Concatenate` layer should be called on a list of at least 2 inputs");e=e;let t=!0;for(const r of e)if(null!=r){t=!1;break}if(t)return;const n=[];for(let r=0;r<e.length;++r){const t=e[r].slice();t.splice(this.axis,1);let a=!1;for(const e of n)if(s.D5U.arraysEqual(e,t)){a=!0;break}a||n.push(t)}if(n.length>1)throw new ts.nu("A `Concatenate` layer requires inputs with matching shapes except for the concat axis. Got input shapes: "+JSON.stringify(e))}mergeFunction(e){return(0,s.lub)((()=>
1gs.mV(e,this.axis)))}computeOutputShape(e){if(!Array.isArray(e)||!Array.isArray(e[0]))throw new ts.nu("A `Concatenate` layer should be called on a list of inputs.");const t=e,n=t[0].slice(),r=this.axis<0?n.length+this.axis:this.axis;for(const s of t.slice(1)){if(null==n[r]||null==s[r]){n[r]=null;break}n[r]+=s[r]}return n}computeMask(e,t){if(null==t)return null;if(!Array.isArray(t))throw new ts.nu("`mask` should be an array for Concatenate");if(!Array.isArray(e))throw new ts.nu("`inputs` should be an array for Concatenate");if(t.length!==e.length)throw new ts.nu(`Mismatch in the length of mask (${t.length}) and the legnth of inputs (${e.length})`);return s.lub((()=>{let n=!0;if(t.forEach((e=>{null==e||(n=!1)})),n)return null;const r=[];for(let o=0;o<e.length;++o)null==t[o]?r.push(s.pju(s.JpU(e[o]),"bool")):t[o].rank<e[o].rank?r.push(s.dt4(t[o],-1)):r.push(t[o]);const a=s.zoF(r,this.axis);return s.$6P(a,-1,!1)}))}getConfig(){const e={axis:this.axis},t=super.getConfig();return Object.assign(e,t),e}}function Gs(e,t){for(;e<0;)e+=t;return e}Vs.className="Concatenate",s.m7h.registerClass(Vs);class Hs extends Bs{constructor(e){super(e),this.axes=e.axes,this.normalize=null!=e.normalize&&e.normalize,this.supportsMasking=!0,this.reshapeRequired=!1}build(e){s.D5U.assert(Array.isArray(e)&&2===e.length&&Array.isArray(e[0])&&Array.isArray(e[1]),(()=>"A `Dot` layer should be called on a list of exactly 2 inputs."));const t=e[0],n=e[1];if(t.length>3||n.length>3)throw new ts.nj("Dot layer does not support tensors of 4D or higher rank yet.");const r=this.interpretAxes(t,n);if(t[r[0]]!==n[r[1]])throw new ts.nu(`Dimension incompatibility: ${t[r[0]]} !== ${n[r[1]]}`)}mergeFunction(e){if(2!==e.length)throw new ts.nu(`A \`Dot\` layer must be called on exactly 2 inputs, but received ${e.length} input(s).`);let t,n=e[0],r=e[1];return t=Array.isArray(this.axes)?this.axes.map(((t,n)=>Gs(t,e[n].shape.length))):[Gs(this.axes,n.shape.length),Gs(this.axes,r.shape.length)],this.normalize&&(n=(0,Ms.Eq)(n,t[0]),r=(0,Ms.Eq)(r,t[1])),function(e,t,n){if(e.shape.length>3||t.shape.length>3)throw new ts.nj("batchDot is not implemented for tensors of 4D or higher rank yet");if(s.D5U.assert(e.shape.length>=2,(()=>`batchDot requires the rank of x to be >= 2, but got ${e.shape.length}`)),s.D5U.assert(e.shape.length>=2,(()=>`batchDot requires the rank of y to be >= 2, but got ${t.shape.length}`)),"number"===typeof n&&(n=[n,n]),"complex64"===e.dtype||"complex64"===t.dtype)throw new ts.nj("batchDot is not implemented for complex64-type Tensors yet.");const r=e.shape.length,a=t.shape.length;null==n&&(n=[r-1,a-2]);const o=n;return s.lub((()=>{let n,i;if(r>a){n=r-a;const e=[];for(let t=0;t<n;++t)e.push(1);t=s.XLQ(t,t.shape.concat(e))}else if(a>r){n=a-r;const t=[];for(let e=0;e<n;++e)t.push(1);e=s.XLQ(e,e.shape.concat(t))}else n=0;if(2===e.shape.length&&2===t.shape.length)i=o[0]===o[1]?s.Smz(s.dC7(e,t),o[0]):s.Smz(s.dC7(s.p4s(e,[1,0]),t),o[1]);else{const n=o[0]!==e.shape.length-1,r=o[1]===t.shape.length-1;i=s.OI3(e,t,n,r)}if(n>0){let e;e=r>a?r+a-3:r-1;const t=[];for(let r=e;r<e+n;++r)t.push(r);i=s.L9e(i,t)}return 1===i.shape.length&&(i=s.dt4(i,1)),i}))}(n,r,t)}interpretAxes(e,t){let n;return n=Array.isArray(this.axes)?this.axes:[Gs(this.axes,e.length),Gs(this.axes,t.length)],n}computeOutputShape(e){s.D5U.assert(Array.isArray(e)&&2===e.length&&Array.isArray(e[0])&&Array.isArray(e[1]),(()=>"A `Dot` layer should be called on a list of exactly 2 inputs."));const t=e[0].slice(),n=e[1].slice();if(t.length>3||n.length>3)throw new ts.nj("Dot layer does not support tensors of 4D or higher rank yet.");const r=this.interpretAxes(t,n);t.splice(r[0],1),n.splice(r[1],1),n.splice(0,1);const a=t.concat(n);return 1===a.length&&a.push(1),a}computeMask(e,t){return null}getConfig(){const e={axes:this.axes,normalize:this.normalize},t=super.getConfig();return Object.assign(e,t),e}}Hs.className="Dot",s.m7h.registerClass(Hs);class js extends Yr.mh{constructor(e){super(e),this.supportsMasking=!0,this.stddev=e.stddev}computeOutputShape(e){return e}getConfig(){const e=super.getConfig(),t={stddev:this.stddev};return Object.assign(t,e),t}call(e,t){return(0,s.lub)((()=>{this.invokeCallHook(e,t);const n=(0,ss.nQ)(e);return gs.KC((()=>(0,s.IHx)(gs.nG(n.shape,0,this.stddev),n)),(()=>n),t.training||!1)}))}}js.className="GaussianNoise",s.m7h.registerClass(js);class Xs extends Yr.mh{constructor(e){super(e),this.supportsMasking=!0,this.rate=e.rate}computeOutputShape(e){return e}getConfig(){const e=super.getConfig(),t={rate:this.rate};return Object.assign(t,e),t}call(e,t){return(0,s.lub)((()=>{this.invokeCallHook(e,t);const n=(0,ss.nQ)(e);if(this.rate>0&&this.rate<1){const e=()=>{const e=Math.sqrt(this.rate/(1-this.rate));return(0,s.dC7)(n,gs.nG(n.shape,1,e))};return gs.KC(e,(()=>n),t.training||!1)}return n}))}}Xs.className="GaussianDropout",s.m7h.registerClass(Xs);class qs extends Yr.mh{constructor(e){super(e),this.supportsMasking=!0,this.rate=e.rate,this.noiseShape=e.noiseShape}_getNoiseShape(e){return this.noiseShape||(0,ss.nQ)(e).shape}computeOutputShape(e){return e}getConfig(){const e=super.getConfig(),t={rate:this.rate};return Object.assign(t,e),t}call(e,t){return(0,s.lub)((()=>
1{if(this.rate<1&&this.rate>0){const n=this._getNoiseShape(e),r=()=>{const t=(0,ss.nQ)(e),r=-1.7580993408473766;let a=(0,s.brS)((0,s.LGj)(n),this.rate);a=gs.pj(a,"float32");const o=((1-this.rate)*(1+this.rate*r**2))**-.5,i=-o*r*this.rate,u=(0,s.IHx)((0,s.dC7)(t,a),(0,s.dC7)((0,s.IHx)(a,-1),r));return(0,s.IHx)((0,s.dC7)(u,o),i)};return gs.KC(r,(()=>(0,ss.nQ)(e)),t.training||!1)}return e}))}}function Ks(e,t,n,r,a,o=.001){let i;if(2===e.rank)i=s.Dxk(e,t,n,r,a,o);else if(3===e.rank)i=s.JY5(e,t,n,r,a,o);else{if(4!==e.rank)throw new ts.nj(`batchNormalization is not implemented for array of rank ${e.rank} yet`);i=s.p3b(e,t,n,r,a,o)}return i}function Qs(e,t,n,r,a=.001){return s.D5U.arraysEqual(r.slice().sort(),Ss.w6(0,e.rank-1))?function(e,t,n,r,a=.001){return(0,s.lub)((()=>{const o=s.Gi7(e,r),i=o.mean,u=o.variance;return[Ks(e,i,u,n,t,a),i,u]}))}(e,t,n,r,a):function(e,t,n,r,a=.001){return(0,s.lub)((()=>{const o=s.Gi7(e,r),i=o.mean,u=o.variance,l=[];for(const t of Ss.w6(0,e.rank))-1!==r.indexOf(t)?l.push(1):l.push(e.shape[t]);const c=(0,s.XLQ)(i,l),p=(0,s.XLQ)(u,l),h=null==t?null:(0,s.XLQ)(t,l),d=null==n?null:(0,s.XLQ)(n,l);return[Ks(e,c,p,d,h,a),i,u]}))}(e,t,n,r,a)}qs.className="AlphaDropout",s.m7h.registerClass(qs);class Ys extends Yr.mh{constructor(e){null==e&&(e={}),super(e),this.supportsMasking=!0,this.axis=null==e.axis?-1:e.axis,this.momentum=null==e.momentum?.99:e.momentum,this.epsilon=null==e.epsilon?.001:e.epsilon,this.center=null==e.center||e.center,this.scale=null==e.scale||e.scale,this.betaInitializer=(0,Kr.L5)(e.betaInitializer||"zeros"),this.gammaInitializer=(0,Kr.L5)(e.gammaInitializer||"ones"),this.movingMeanInitializer=(0,Kr.L5)(e.movingMeanInitializer||"zeros"),this.movingVarianceInitializer=(0,Kr.L5)(e.movingVarianceInitializer||"ones"),this.betaConstraint=(0,qr.Ad)(e.betaConstraint),this.gammaConstraint=(0,qr.Ad)(e.gammaConstraint),this.betaRegularizer=(0,is.EC)(e.betaRegularizer),this.gammaRegularizer=(0,is.EC)(e.gammaRegularizer)}build(e){e=(0,ss.Wf)(e);const t=this.axis>=0?this.axis:this.axis+e.length,n=e[t];if(null==n)throw new ts.nu(`Axis ${t} of input tensor should have a defined dimension but the layer received an input with shape ${JSON.stringify(e)}.`);this.inputSpec=[new Yr.Zg({ndim:e.length,axes:{[t]:n}})];const r=[n];this.scale&&(this.gamma=this.addWeight("gamma",r,null,this.gammaInitializer,this.gammaRegularizer,!0,this.gammaConstraint)),this.center&&(this.beta=this.addWeight("beta",r,null,this.betaInitializer,this.betaRegularizer,!0,this.betaConstraint)),this.movingMean=this.addWeight("moving_mean",r,null,this.movingMeanInitializer,null,!1),this.movingVariance=this.addWeight("moving_variance",r,null,this.movingVarianceInitializer,null,!1),this.built=!0}call(e,t){return(0,s.lub)((()=>{const n=null!=t.training&&t.training,r=(0,ss.nQ)(e),a=r.shape,o=a.length,i=Ss.w6(0,o),u=this.axis>=0?this.axis:this.axis+o;i.splice(u,1);const l=rs.JE(1,o);l[u]=a[u];const c=i.slice();c.sort();const p=!s.D5U.arraysEqual(c,Ss.w6(0,o).slice(0,o-1));if(!n)return(()=>{if(p){const e=(0,s.XLQ)(this.movingMean.read(),l),t=(0,s.XLQ)(this.movingVariance.read(),l),n=this.center?(0,s.XLQ)(this.beta.read(),l):null,a=this.scale?(0,s.XLQ)(this.gamma.read(),l):null;return Ks(r,e,t,n,a,this.epsilon)}return Ks(r,this.movingMean.read(),this.movingVariance.read(),null==this.beta?null:this.beta.read(),null==this.gamma?null:this.gamma.read(),this.epsilon)})();const[h,d,f]=Qs(r,this.gamma.read(),this.beta.read(),i,this.epsilon),m=(e,t,n)=>{s.lub((()=>{const r=1-n,a=e.read(),o=s.dC7(s.luU(a,t),r);e.write(s.luU(a,o))}))};return(()=>{m(this.movingMean,d,this.momentum),m(this.movingVariance,f,this.momentum)})(),h}))}getConfig(){const e={axis:this.axis,momentum:this.momentum,epsilon:this.epsilon,center:this.center,scale:this.scale,betaInitializer:(0,Kr.Cx)(this.betaInitializer),gammaInitializer:(0,Kr.Cx)(this.gammaInitializer),movingMeanInitializer:(0,Kr.Cx)(this.movingMeanInitializer),movingVarianceInitializer:(0,Kr.Cx)(this.movingVarianceInitializer),betaRegularizer:(0,is.SG)(this.betaRegularizer),gammaRegularizer:(0,is.SG)(this.gammaRegularizer),betaConstraint:(0,qr.xF)(this.betaConstraint),gammaConstraint:(0,qr.xF)(this.gammaConstraint)},t=super.getConfig();return Object.assign(e,t),e}}Ys.className="BatchNormalization",s.m7h.registerClass(Ys);class Zs extends Yr.mh{constructor(e){if(null==e&&(e={}),super(e),this.axis=null==e.axis?-1:e.axis,"number"===typeof this.axis){if(!Number.isInteger(this.axis))throw new Error(`Expected axis to be an integer, but received ${this.axis}`)}else{if(!Array.isArray(this.axis))throw new Error(`Expected axis to be an integer or an array of integers, but received ${JSON.stringify(this.axis)}`);for(const e of this.axis)if(!Number.isInteger(e))throw new Error(`Expected axis to be an array of integers, but received ${JSON.stringify(this.axis)}`)}this.epsilon=null==e.epsilon?.001:e.epsilon,this.center=null==e.center||e.center,this.scale=null==e.scale||e.scale,this.betaInitializer=(0,Kr.L5)(e.betaInitializer||"zeros"),this.gammaInitializer=(0,Kr.L5)(e.gammaInitializer||"ones"),this.betaRegularizer=(0,is.EC)(e.betaRegularizer),this.gammaRegularizer=(0,is.EC)(e.gammaRegularizer),this.supportsMasking=!0}build(e){const t=(e=(0,ss.Wf)(e)).length;"number"===typeof this.axis&&(this.axis=[this.axis]);for(let r=0;r<this.axis.length;++r)this.axis[r]<0&&(this.axis[r]+=t);for(const r of this.axis)if(r<0||r>=t)throw new Error(`Invalid axis: ${r}`);if(this.axis.length!==rs.Tw(this.axis).length)throw new Error(`Found duplicate axes in: ${this.axis}`);const n=this.axis.map((t=>e[t]));this.scale?this.gamma=this.addWeight("gamma",n,"float32",this.gammaInitializer,this.gammaRegularizer,true):this.gamma=null,this.center?this.beta=this.addWeight("beta",n,"float32",this.betaInitializer,this.betaRegularizer,true):this.beta=null,this.built=!0}call(e,t){const n=(0,ss.nQ)(e),r=n.shape,a=r.length;return(0,s.lub)((()=>{let{mean:e,variance:t}=(0,s.Gi7)(n,this.axis,!0);const o=rs.JE(1,a);for(const n of this.axis)o[n]=r[n];const i=e=>null!=e&&e.shape.length!==a?s.XLQ(e,o):e;let u=this.scale?i(this.gamma.read()):null,l=this.center?i(this.beta.read()):null;const c=[],p=[];for(let n=0;n<a;++n)-1!==this.axis.indexOf(n)?(c.push(r[n]),p.push(1)):(c.push(1),p.push(r[n]));return e=s.Gg6(e,c),t=s.Gg6(t,c),null!=u&&(u=s.Gg6(u,p)),null!=l&&(l=s.Gg6(l,p)),Ks(n,e,t,l,u,this.epsilon)}))}getConfig(){const e={axis:this.axis,epsilon:this.epsilon,center:this.center,scale:this.scale,betaInitializer:(0,Kr.Cx)(this.betaInitializer),gammaInitializer:(0,Kr.Cx)(this.gammaInitializer),betaRegularizer:(0,is.SG)(this.betaRegularizer),gammaRegularizer:(0,is.SG)(this.gammaRegularizer)},t=super.getConfig();return Object.assign(e,t),e}}Zs.className="LayerNormalization",s.m7h.registerClass(Zs);class Js extends Yr.mh{constructor(e){if(null==e&&(e={}),super(e),this.dataFormat=null==e.dataFormat?(0,ms.rf)():e.dataFormat,null==e.padding)this.padding=[[1,1],[1,1]];else if("number"===typeof e.padding)this.padding=[[e.padding,e.padding],[e.padding,e.padding]];else{if(e.padding=e.padding,2!==e.padding.length)throw new ts.nu(`ZeroPadding2D expects padding to be a length-2 array, but received a length-${e.padding.length} array.`);let t,n;if("number"===typeof e.padding[0])t=[e.padding[0],e.padding[0]],n=[e.padding[1],e.padding[1]];else{if(e.padding=e.padding,2!==e.padding[0].length)throw new ts.nu(`ZeroPadding2D expects height padding to be a length-2 array, but received a length-${e.padding[0].length} array.`);if(t=e.padding[0],2!==e.padding[1].length)throw new ts.nu(`ZeroPadding2D expects width padding to be a length-2 array, but received a length-${e.padding[1].length} array.`);n=e.padding[1]}this.padding=[t,n]}
1this.inputSpec=[new Yr.Zg({ndim:4})]}computeOutputShape(e){let t,n;return e=(0,ss.Wf)(e),"channelsFirst"===this.dataFormat?(t=null!=e[2]&&e[2]>=0?e[2]+this.padding[0][0]+this.padding[0][1]:null,n=null!=e[3]&&e[3]>=0?e[3]+this.padding[1][0]+this.padding[1][1]:null,[e[0],e[1],t,n]):(t=null!=e[1]&&e[1]>=0?e[1]+this.padding[0][0]+this.padding[0][1]:null,n=null!=e[2]&&e[2]>=0?e[2]+this.padding[1][0]+this.padding[1][1]:null,[e[0],t,n,e[3]])}call(e,t){return(0,s.lub)((()=>{return t=(0,ss.nQ)(e),n=this.padding,r=this.dataFormat,(0,s.lub)((()=>{if(4!==t.rank)throw new ts.nu(`temporalPadding expects input tensor to be 4-D, but received a ${t.rank}-D tensor.`);if(null==n&&(n=[[1,1],[1,1]]),2!==n.length||2!==n[0].length||2!==n[1].length)throw new ts.nu("spatial2dPadding expects `padding` to be an Array of two Arrays, each of which is an Array of two integers.");if(null==r&&(r=(0,ms.rf)()),"channelsLast"!==r&&"channelsFirst"!==r)throw new ts.nu(`Unknown data format: ${r}. Supported data formats are 'channelsLast' and 'channelsFirst.`);let e;return e="channelsFirst"===r?[[0,0],[0,0],n[0],n[1]]:[[0,0],n[0],n[1],[0,0]],s.vku(t,e)}));var t,n,r}))}getConfig(){const e={padding:this.padding,dataFormat:this.dataFormat},t=super.getConfig();return Object.assign(e,t),e}}function ea(e,t,n,r,a,o){return(0,s.lub)((()=>{let i;(0,ys.cj)(a),(0,ys.Lp)(o),(0,ys.zb)(r),null==n&&(n=[1,1]),null==r&&(r="valid"),null==a&&(a=(0,ms.rf)()),null==o&&(o="max"),e=(0,fs.aP)(e,a);const u="same"===r?"same":"valid";return i="max"===o?s._sB(e,t,n,u):s.wS1(e,t,n,u),"channelsFirst"===a&&(i=s.p4s(i,[0,3,1,2])),i}))}function ta(e,t,n,r,a,o){return(0,s.lub)((()=>{let i;(0,ys.cj)(a),(0,ys.Lp)(o),(0,ys.zb)(r),null==n&&(n=[1,1,1]),null==r&&(r="valid"),null==a&&(a=(0,ms.rf)()),null==o&&(o="max"),e=(0,fs.fN)(e,a);const u="same"===r?"same":"valid";return i="max"===o?s.YQQ(e,t,n,u):s.uR5(e,t,n,u),"channelsFirst"===a&&(i=s.p4s(i,[0,4,1,2,3])),i}))}Js.className="ZeroPadding2D",s.m7h.registerClass(Js);class na extends Yr.mh{constructor(e){if(null==e.poolSize&&(e.poolSize=2),super(e),"number"===typeof e.poolSize)this.poolSize=[e.poolSize];else{if(!Array.isArray(e.poolSize)||1!==e.poolSize.length||"number"!==typeof e.poolSize[0])throw new ts.nu(`poolSize for 1D convolutional layer must be a number or an Array of a single number, but received ${JSON.stringify(e.poolSize)}`);this.poolSize=e.poolSize}if((0,rs.iQ)(this.poolSize,"poolSize"),null==e.strides)this.strides=this.poolSize;else if("number"===typeof e.strides)this.strides=[e.strides];else{if(!Array.isArray(e.strides)||1!==e.strides.length||"number"!==typeof e.strides[0])throw new ts.nu(`strides for 1D convolutional layer must be a number or an Array of a single number, but received ${JSON.stringify(e.strides)}`);this.strides=e.strides}(0,rs.iQ)(this.strides,"strides"),this.padding=null==e.padding?"valid":e.padding,(0,ys.zb)(this.padding),this.inputSpec=[new Yr.Zg({ndim:3})]}computeOutputShape(e){e=(0,ss.Wf)(e);const t=(0,bs.kt)(e[1],this.poolSize[0],this.padding,this.strides[0]);return[e[0],t,e[2]]}call(e,t){return(0,s.lub)((()=>{this.invokeCallHook(e,t),e=gs.dt((0,ss.nQ)(e),2);const n=this.poolingFunction((0,ss.nQ)(e),[this.poolSize[0],1],[this.strides[0],1],this.padding,"channelsLast");return s.L9e(n,[2])}))}getConfig(){const e={poolSize:this.poolSize,padding:this.padding,strides:this.strides},t=super.getConfig();return Object.assign(e,t),e}}class ra extends na{constructor(e){super(e)}poolingFunction(e,t,n,r,s){return(0,ys.cj)(s),(0,ys.zb)(r),ea(e,t,n,r,s,"max")}}ra.className="MaxPooling1D",s.m7h.registerClass(ra);class sa extends na{constructor(e){super(e)}poolingFunction(e,t,n,r,s){return(0,ys.cj)(s),(0,ys.zb)(r),ea(e,t,n,r,s,"avg")}}sa.className="AveragePooling1D",s.m7h.registerClass(sa);class aa extends Yr.mh{constructor(e){if(null==e.poolSize&&(e.poolSize=[2,2]),super(e),this.poolSize=Array.isArray(e.poolSize)?e.poolSize:[e.poolSize,e.poolSize],null==e.strides)this.strides=this.poolSize;else if(Array.isArray(e.strides)){if(2!==e.strides.length)throw new ts.nu(`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];(0,rs.iQ)(this.poolSize,"poolSize"),(0,rs.iQ)(this.strides,"strides"),this.padding=null==e.padding?"valid":e.padding,this.dataFormat=null==e.dataFormat?"channelsLast":e.dataFormat,(0,ys.cj)(this.dataFormat),(0,ys.zb)(this.padding),this.inputSpec=[new Yr.Zg({ndim
1:4})]}computeOutputShape(e){e=(0,ss.Wf)(e);let t="channelsFirst"===this.dataFormat?e[2]:e[1],n="channelsFirst"===this.dataFormat?e[3]:e[2];return t=(0,bs.kt)(t,this.poolSize[0],this.padding,this.strides[0]),n=(0,bs.kt)(n,this.poolSize[1],this.padding,this.strides[1]),"channelsFirst"===this.dataFormat?[e[0],e[1],t,n]:[e[0],t,n,e[3]]}call(e,t){return(0,s.lub)((()=>(this.invokeCallHook(e,t),this.poolingFunction((0,ss.nQ)(e),this.poolSize,this.strides,this.padding,this.dataFormat))))}getConfig(){const e={poolSize:this.poolSize,padding:this.padding,strides:this.strides,dataFormat:this.dataFormat},t=super.getConfig();return Object.assign(e,t),e}}class oa extends aa{constructor(e){super(e)}poolingFunction(e,t,n,r,s){return(0,ys.cj)(s),(0,ys.zb)(r),ea(e,t,n,r,s,"max")}}oa.className="MaxPooling2D",s.m7h.registerClass(oa);class ia extends aa{constructor(e){super(e)}poolingFunction(e,t,n,r,s){return(0,ys.cj)(s),(0,ys.zb)(r),ea(e,t,n,r,s,"avg")}}ia.className="AveragePooling2D",s.m7h.registerClass(ia);class ua extends Yr.mh{constructor(e){if(null==e.poolSize&&(e.poolSize=[2,2,2]),super(e),this.poolSize=Array.isArray(e.poolSize)?e.poolSize:[e.poolSize,e.poolSize,e.poolSize],null==e.strides)this.strides=this.poolSize;else if(Array.isArray(e.strides)){if(3!==e.strides.length)throw new ts.nu(`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];(0,rs.iQ)(this.poolSize,"poolSize"),(0,rs.iQ)(this.strides,"strides"),this.padding=null==e.padding?"valid":e.padding,this.dataFormat=null==e.dataFormat?"channelsLast":e.dataFormat,(0,ys.cj)(this.dataFormat),(0,ys.zb)(this.padding),this.inputSpec=[new Yr.Zg({ndim:5})]}computeOutputShape(e){e=(0,ss.Wf)(e);let t="channelsFirst"===this.dataFormat?e[2]:e[1],n="channelsFirst"===this.dataFormat?e[3]:e[2],r="channelsFirst"===this.dataFormat?e[4]:e[3];return t=(0,bs.kt)(t,this.poolSize[0],this.padding,this.strides[0]),n=(0,bs.kt)(n,this.poolSize[1],this.padding,this.strides[1]),r
1=(0,bs.kt)(r,this.poolSize[2],this.padding,this.strides[2]),"channelsFirst"===this.dataFormat?[e[0],e[1],t,n,r]:[e[0],t,n,r,e[4]]}call(e,t){return(0,s.lub)((()=>(this.invokeCallHook(e,t),this.poolingFunction((0,ss.nQ)(e),this.poolSize,this.strides,this.padding,this.dataFormat))))}getConfig(){const e={poolSize:this.poolSize,padding:this.padding,strides:this.strides,dataFormat:this.dataFormat},t=super.getConfig();return Object.assign(e,t),e}}class la extends ua{constructor(e){super(e)}poolingFunction(e,t,n,r,s){return(0,ys.cj)(s),(0,ys.zb)(r),ta(e,t,n,r,s,"max")}}la.className="MaxPooling3D",s.m7h.registerClass(la);class ca extends ua{constructor(e){super(e)}poolingFunction(e,t,n,r,s){return(0,ys.cj)(s),(0,ys.zb)(r),ta(e,t,n,r,s,"avg")}}ca.className="AveragePooling3D",s.m7h.registerClass(ca);class pa extends Yr.mh{constructor(e){super(e),this.inputSpec=[new Yr.Zg({ndim:3})]}computeOutputShape(e){return[e[0],e[2]]}call(e,t){throw new ts.nj}}class ha extends pa{constructor(e){super(e||{})}call(e,t){return(0,s.lub)((()=>{const t=(0,ss.nQ)(e);return s.J69(t,1)}))}}ha.className="GlobalAveragePooling1D",s.m7h.registerClass(ha);class da extends pa{constructor(e){super(e||{})}call(e,t){return(0,s.lub)((()=>{const t=(0,ss.nQ)(e);return s.Fp7(t,1)}))}}da.className="GlobalMaxPooling1D",s.m7h.registerClass(da);class fa extends Yr.mh{constructor(e){super(e),this.dataFormat=null==e.dataFormat?"channelsLast":e.dataFormat,(0,ys.cj)(this.dataFormat),this.inputSpec=[new Yr.Zg({ndim:4})]}computeOutputShape(e){return e=e,"channelsLast"===this.dataFormat?[e[0],e[3]]:[e[0],e[1]]}call(e,t){throw new ts.nj}getConfig(){const e={dataFormat:this.dataFormat},t=super.getConfig();return Object.assign(e,t),e}}class ma extends fa{call(e,t){return(0,s.lub)((()=>{const t=(0,ss.nQ)(e);return"channelsLast"===this.dataFormat?s.J69(t,[1,2]):s.J69(t,[2,3])}))}}ma.className="GlobalAveragePooling2D",s.m7h.registerClass(ma);class ga extends fa{call(e,t){return(0,s.lub)((()=>{const t=(0,ss.nQ)(e);return"channelsLast"===this.dataFormat?s.Fp7(t,[1,2]):s.Fp7(t,[2,3])}))}}ga.className="GlobalMaxPooling2D",s.m7h.registerClass(ga);var ya=n(4685);class ba extends Yr.mh{constructor(e){super(e),this.layer=e.layer}build(e){this.built=!0}get trainable(){return null!=this.layer&&this.layer.trainable}set trainable(e){null!=this.layer&&(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(){const e={layer:{className:this.layer.getClassName(),config:this.layer.getConfig()}},t=super.getConfig();return Object.assign(e,t),e}setFastWeightInitDuringBuild(e){super.setFastWeightInitDuringBuild(e),null!=this.layer&&this.layer.setFastWeightInitDuringBuild(e)}static fromConfig(e,t,n={}){const r=t.layer,s=(0,ns.v)(r,n);delete t.layer;const a={layer:s};return Object.assign(a,t),new e(a)}}class xa extends ba{constructor(e){super(e),this.supportsMasking=!0}build(e){if((e=(0,ss.Wf)(e)).length<3)throw new ts.nu(`TimeDistributed layer expects an input shape >= 3D, but received input shape ${JSON.stringify(e)}`);this.inputSpec=[{shape:e}];const t=[e[0]].concat(e.slice(2));this.layer.built||(this.layer.build(t),this.layer.built=!0),super.build(e)}computeOutputShape(e){const t=[(e=(0,ss.Wf)(e))[0]].concat(e.slice(2)),n=this.layer.computeOutputShape(t),r=e[1];return[n[0],r].concat(n.slice(1))}call(e,t){return(0,s.lub)((()=>{e=(0,ss.nQ)(e);return(0,ws.nd)(((e,n)=>[(0,ss.nQ)(this.layer.call(e,t)),[]]),e,[],!1,null,null,!1,!0)[1]}))}}xa.className="TimeDistributed",s.m7h.registerClass(xa);class wa extends ba{constructor(e){super(e);const t=e.layer.getConfig(),n={};n.className=e.layer.getClassName(),n.config=t,this.forwardLayer=(0,ns.v)(n),t.goBackwards=!0!==t.goBackwards;const r={};var s;if(r.className=e.layer.getClassName(),r.config=t,this.backwardLayer=(0,ns.v)(r),this.forwardLayer.name="forward_"+this.forwardLayer.name,this.backwardLayer.name="backward_"+this.backwardLayer.name,this.mergeMode=void 0===e.mergeMode?"concat":e.mergeMode,s=this.mergeMode,rs.xn(ya.eY,"BidirectionalMergeMode",s),e.weights)throw new ts.nj("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,null!=this.forwardLayer&&(this.forwardLayer.trainable=e),null!=this.backwardLayer&&(this.backwardLayer.trainable=e)}getWeights(){return this.forwardLayer.getWeights().concat(this.backwardLayer.getWeights())}setWeights(e){const 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,n,r,s=this.forwardLayer.computeOutputShape(e);return Array.isArray(s)&&Array.isArray(s[0])||(s=[s]),s=s,this.returnState?(r=s.slice(1),t=s[0]):t=s[0],t=t,"concat"===this.mergeMode?(t[t.length-1]*=2,n=[t]):n=null==this.mergeMode?[t,t.slice()]:[t],this.returnState?null==this.mergeMode?n.concat(r).concat(r.slice()):[t].concat(r).concat(r.slice()):rs.Bq(n)}apply(e,t){let n=null==t?null:t.initialState,r=null==t?null:t.constants;null==t&&(t={});const s=(0,ws.lx)(e,n,r,this.numConstants);if(e=s.inputs,n=s.initialState,r=s.constants,Array.isArray(e)&&(n=e.slice(1),e=e[0]),(null==n||0===n.length)&&null==r)return super.apply(e,t);const a=[],o=[];if(null!=n){const e=n.length;if(e%2>0)throw new ts.nu("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);const r=n.map((e=>new Yr.Zg({shape:e.shape})));this.forwardLayer.stateSpec=r.slice(0,e/2),this.backwardLayer.stateSpec=r.slice(e/2),o.push(...r)}if(null!=r)throw new ts.nj("Support for constants in Bidirectional layers is not implemented yet.");const i=a[0]instanceof Yr.Iy;for(const u of a)if(u instanceof Yr.Iy!==i)throw new ts.nu("The initial state of a Bidirectional layer cannot be specified as a mix of symbolic and non-symbolic tensors");if(i){const n=[e].concat(a),r=this.inputSpec.concat(o),s=this.inputSpec;this.inputSpec=r;const i=super.apply(n,t);return this.inputSpec=s,i}return super.apply(e,t)}call(e,t){return(0,s.lub)((()=>{const n=t.initialState;let r,a,o,i;if(null==n)r=this.forwardLayer.call(e,t),a=this.backwardLayer.call(e,t);else{const s=n.slice(0,n.length/2),o=n.slice(n.length/2);r=this.forwardLayer.call(e,Object.assign(t,{initialState:s})),a=this.backwardLayer.call(e,Object.assign(t,{initialState:o}))}return this.returnState&&(Array.isArray(r)&&(o=r.slice(1).concat(a.slice(1))),r=r[0],a=a[0]),this.returnSequences&&(a=s.GYS(a,1)),"concat"===this.mergeMode?i=gs.mV([r,a]):"sum"===this.mergeMode?i=s.IHx(r,a):"ave"===this.mergeMode?i=s.dC7(.5,s.IHx(r,a)):"mul"===this.mergeMode?i=s.dC7(r,a):null==this.mergeMode&&(i=[r,a]),this.returnState?null==this.mergeMode?i.concat(o):[i].concat(o):i}))}resetStates(e){this.forwardLayer.resetStates(),this.backwardLayer.resetStates()}build(e){(0,ys.f4)(this.forwardLayer.name,(()=>{this.forwardLayer.build(e)})),(0,ys.f4)(this.backwardLayer.name,(()=>{this.backwardLayer.build(e)})),this.built=!0}computeMask(e,t){let n;if(Array.isArray(t)&&(t=t[0]),n=this.returnSequences?null==this.mergeMode?[t,t]:t:null==this.mergeMode?[null,null]:null,this.returnState){const e=this.forwardLayer.states.map((e=>null));return Array.isArray(n)?n.concat(e).concat(e):[n].concat(e).concat(e)}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),null!=this.forwardLayer&&this.forwardLayer.setFastWeightInitDuringBuild(e),null!=this.backwardLayer&&this.backwardLayer.setFastWeightInitDuringBuild(e)}getConfig(){const e={mergeMode:this.mergeMode},t=super.getConfig();return Object.assign(e,t),e}static fromConfig(e,t){const n=(0,ns.v)(t.layer);if(delete t.layer,null!=t.numConstants)throw new ts.nj("Deserialization of a Bidirectional layer with numConstants present is not supported yet.");const r=t;return r.layer=n,new e(r)}}wa.className="Bidirectional",s.m7h.registerClass(wa);class va extends Yr.mh{constructor(e){super(e),this.scale=e.scale,e.offset?this.offset=e.offset:this.offset=0}getConfig(){const e={scale:this.scale,offset:this.offset},t=super.getConfig();return Object.assign(e,t),e}call(e,t){return(0,s.lub)((()=>("float32"!==(e=(0,ss.nQ)(e)).dtype&&(e=gs.pj(e,"float32")),(0,s.IHx)((0,s.dC7)(e,this.scale),this.offset))))}}va.className="Rescaling",s.m7h.registerClass(va);n(7629);n(3146);Zr.ex;n(1653);var ka,Ia=n(7385),Na=n(4618),Sa=n(6377);function Ta(e,t,n=new Map,r=new Set){if(null==e)return null;if("function"===typeof Blob&&e instanceof Blob)return e.slice();if(r.has(e))throw new Error("Circular references are not supported.");
vendor: 9,630 bytes, line 1
1if(n.has(e))return n.get(e);const s=t(e);if(s.recurse&&null!==s.value)throw new Error("A deep map function may not return both a value and recurse=true.");if(s.recurse){if(Aa(e)){const s=Array.isArray(e)?[]:{};r.add(e);for(const a in e){const o=Ta(e[a],t,n,r);s[a]=o}return r.delete(e),e.__proto__&&(s.__proto__=e.__proto__),s}throw new Error(`Can't recurse into non-iterable type: ${e}`)}return n.set(e,s.value),s.value}function Ca(e,t=$a){return Ea(e,t)}function Ea(e,t,n=new Set){const r=e[0];if(n.has(r))throw new Error("Circular references are not supported.");const s=t(e);if(s.recurse&&null!==s.value)throw new Error("A deep zip function may not return both a value and recurse=true.");if(s.recurse){if(Aa(r)){const s=Array.isArray(r)?[]:{};n.add(r);for(const a in r){const r=Ea(e.map((e=>e[a])),t,n);s[a]=r}return n.delete(r),s}throw new Error(`Can't recurse into non-iterable type: ${r}`)}return s.value}function $a(e){return null===e?null:Aa(e[0])?{value:null,recurse:!0}:{value:e,recurse:!1}}function Aa(e){let t=!1;if(s.OBj().get("IS_BROWSER"))t=e instanceof TextDecoder;else{const{StringDecoder:r}=n(8963);t=e instanceof r}return null!=e&&!ArrayBuffer.isView(e)&&(Array.isArray(e)||"object"===typeof e&&!(e instanceof s.esB)&&!(e instanceof Promise)&&!t)}function Da(e){return function(e,t){return Ta(e,t)}(e,_a)}function _a(e){return e instanceof s.esB?{value:e.clone(),recurse:!1}:Aa(e)?{value:null,recurse:!0}:{value:e,recurse:!1}}class Ra{constructor(e){if(this.capacity=e,this.begin=0,this.end=0,null==e)throw new RangeError("Can't create a ring buffer of unknown capacity.");if(e<1)throw new RangeError("Can't create ring buffer of capacity < 1.");this.data=new Array(e),this.doubledCapacity=2*e}wrap(e){for(;e<0;)e+=this.doubledCapacity;return e%this.doubledCapacity}get(e){if(e<0)throw new RangeError("Can't get item at a negative index.");return this.data[e%this.capacity]}set(e,t){if(e<0)throw new RangeError("Can't set item at a negative index.");this.data[e%this.capacity]=t}length(){let e=this.end-this.begin;return e<0&&(e=this.doubledCapacity+e),e}isFull(){return this.length()===this.capacity}isEmpty(){return 0===this.length()}push(e){if(this.isFull())throw new RangeError("Ring buffer is full.");this.set(this.end,e),this.end=this.wrap(this.end+1)}pushAll(e){for(const t of e)this.push(t)}pop(){if(this.isEmpty())throw new RangeError("Ring buffer is empty.");this.end=this.wrap(this.end-1);const e=this.get(this.end);return this.set(this.end,void 0),e}unshift(e){if(this.isFull())throw new RangeError("Ring buffer is full.");this.begin=this.wrap(this.begin-1),this.set(this.begin,e)}shift(){if(this.isEmpty())throw new RangeError("Ring buffer is empty.");const e=this.get(this.begin);return this.set(this.begin,void 0),this.begin=this.wrap(this.begin+1),e}shuffleExcise(e){if(this.isEmpty())throw new RangeError("Ring buffer is empty.");const t=this.wrap(this.begin+e),n=this.get(t);return this.set(t,this.pop()),n}}class Fa extends Ra{constructor(){super(Fa.INITIAL_CAPACITY)}isFull(){return!1}push(e){super.isFull()&&this.expand(),super.push(e)}unshift(e){super.isFull()&&this.expand(),super.unshift(e)}expand(){const e=2*this.capacity,t=new Array(e),n=this.length();for(let r=0;r<n;r++)t[r]=this.get(this.wrap(this.begin+r));this.data=t,this.capacity=e,this.doubledCapacity=2*this.capacity,this.begin=0,this.end=n}}function Oa(e){return new Wa(e)}function Ma(e,t){return new Qa(e,t)}Fa.INITIAL_CAPACITY=32;class Ba{async toArray(){const e=[];let t=await this.next();for(;!t.done;)e.push(t.value),t=await this.next();return e}async toArrayForTest(){const e=this.prefetch(100),t=[];let n=await e.next();for(;!n.done;)t.push(n.value),n=await e.next();return t}async resolveFully(){let e=await this.next();for(;!e.done;)e=await this.next()}async resolveWhile(e){let t=await this.next(),n=e(t.value);for(;!t.done&&n;)t=await this.next(),n=e(t.value)}handleErrors(e){return new ja(this,e)}filter(e){return new Ga(this,e)}map(e){return new Ha(this,e)}mapAsync(e){return new Xa(this,e)}serialMapAsync(e){return new Xa(this,e).serial()}flatmap(e){return new Ka(this,e)}async forEachAsync(e){return this.map(e).resolveFully()}async serialForEach(e){return this.serialMapAsync(e).resolveWhile((e=>!0===e))}rowMajorBatch(e,t=!0){return new Va(this,e,t)}columnMajorBatch(e,t=!0,n=$a){return this.rowMajorBatch(e,t).map((e=>Ca(e,n)))}concatenate(e,t){return new Qa(new La([this,e]),t)}take(e){return e<0||null==e?this:new za(this,e)}skip(e){return e<0||null==e?this:new Ua(this,e)}prefetch(e){return new Ya(this,e)}shuffle(e,t){return new Za(this,e,t)}serial(){return new Pa(this)}}class La extends Ba{constructor(e){super(),this.items=e,this.trav=0}summary(){return`Array of ${this.items.length} items`}async next(){if(this.trav>=this.items.length)return{value:null,done:!0};const e=this.items[this.trav];return this.trav++,{value:Da(e),done:!1}}}class Wa extends Ba{constructor(e){super(),this.nextFn=e}summary(){return"Function call"}async next(){try{return this.nextFn()}catch(e){throw e.message=`Error thrown while iterating through a dataset: ${e.message}`,e}}}class Pa extends Ba{constructor(e){super(),this.upstream=e,this.lastRead=Promise.resolve({value:null,done:!1})}summary(){return`${this.upstream.summary()} -> Serial`}async next(){return this.lastRead=this.lastRead.then((()=>this.serialNext())),this.lastRead}async serialNext(){return this.upstream.next()}}class Ua extends Ba{constructor(e,t){super(),this.upstream=e,this.maxCount=t,this.count=0,this.lastRead=Promise.resolve({value:null,done:!1})}summary(){return`${this.upstream.summary()} -> Skip`}async next(){return this.lastRead=this.lastRead.then((()=>this.serialNext())),this.lastRead}async serialNext(){for(;this.count++<this.maxCount;){const e=await this.upstream.next();if(e.done)return e;s.B90(e.value)}return this.upstream.next()}}class za extends Ba{constructor(e,t){super(),this.upstream=e,this.maxCount=t,this.count=0}summary(){return`${this.upstream.summary()} -> Take`}async next(){return this.count++>=this.maxCount?{value:null,done:!0}:this.upstream.next()}}class Va extends Ba{constructor(e,t,n=!0){super(),this.upstream=e,this.batchSize=t,this.enableSmallLastBatch=n,this.lastRead=Promise.resolve({value:null,done:!1})}summary(){return`${this.upstream.summary()} -> RowMajorBatch`}async next(){return this.lastRead=this.lastRead.then((()=>this.serialNext())),this.lastRead}async serialNext(){const e=[];for(;e.length<this.batchSize;){const t=await this.upstream.next();if(t.done)return this.enableSmallLastBatch&&e.length>0?{value:e,done:!1}:{value:null,done:!0};e.push(t.value)}return{value:e,done:!1}}}class Ga extends Ba{constructor(e,t){super(),this.upstream=e,this.predicate=t,this.lastRead=Promise.resolve({value:null,done:!1})}summary(){return`${this.upstream.summary()} -> Filter`}async next(){return this.lastRead=this.lastRead.then((()=>this.serialNext())),this.lastRead}async serialNext(){for(;;){const e=await this.upstream.next();if(e.done||this.predicate(e.value))return e;s.B90(e.value)}}}class Ha extends Ba{constructor(e,t){super(),this.upstream=e,this.transform=t}summary(){return`${this.upstream.summary()} -> Map`}async next(){const e=await this.upstream.next();if(e.done)return{value:null,done:!0};const t=s.piX.getTensorsInContainer(e.value),n=this.transform(e.value),r=s.piX.getTensorsInContainer(n);for(const a of t)s.piX.isTensorInList(a,r)||a.dispose();return{value:n,done:!1}}}class ja extends Ba{constructor(e,t){super(),this.upstream=e,this.handler=t,this.count=0,this.lastRead=Promise.resolve({value:null,done:!1})}summary(){return`${this.upstream.summary()} -> handleErrors`}async next(){return this.lastRead=this.lastRead.then((()=>this.serialNext())),this.lastRead}async serialNext(){for(;;)try{return await this.upstream.next()}catch(e){if(!this.handler(e))return{value:null,done:!0}}}}class Xa extends Ba{constructor(e,t){super(),this.upstream=e,this.transform=t}summary(){return`${this.upstream.summary()} -> AsyncMap`}async next(){const e=await this.upstream.next();if(e.done)return{value:null,done:!0};const t=s.piX.getTensorsInContainer(e.value),n=await this.transform(e.value),r=s.piX.getTensorsInContainer(n);for(const a of t)s.piX.isTensorInList(a,r)||a.dispose();return{value:n,done:!1}}}class qa extends Ba{constructor(){super(),this.outputQueue=new Fa,this.lastRead=Promise.resolve({value:null,done:!1})}async next(){return this.lastRead=this.lastRead.then((()=>this.serialNext())),this.lastRead}async serialNext(){for(;0===this.outputQueue.length();)if(!(await this.pump()))return{value:null,done:!0};return{value:this.outputQueue.shift(),done:!1}}}class Ka extends qa{constructor(e,t){super(),this.upstream=e,this.transform=t}summary(){return`${this.upstream.summary()} -> Flatmap`}async pump(){const e=await this.upstream.next();if(e.done)return!1;const t=s.piX.getTensorsInContainer(e.value),n=this.transform(e.value),r=s.piX.getTensorsInContainer(n);this.outputQueue.pushAll(n);for(const a of t)s.piX.isTensorInList(a,r)||a.dispose();return!0}}class Qa extends Ba{constructor(e,t){super(),this.baseErrorHandler=t,this.lastRead=null,this.iterator=null,this.moreIterators=e}summary(){return"TODO: fill in upstream of chained summaries -> Chained"}async next(){return this.lastRead=this.readFromChain(this.lastRead),this.lastRead}async readFromChain(e){if(await e,null==this.iterator){const e=await this.moreIterators.next();if(e.done)return{value:null,done:!0};this.iterator=e.value,null!=this.baseErrorHandler&&(this.iterator=this.iterator.handleErrors(this.baseErrorHandler))}const t=await this.iterator.next();return t.done?(this.iterator=null,this.readFromChain(e)):t}}
1!function(e){e[e.FAIL=0]="FAIL",e[e.SHORTEST=1]="SHORTEST",e[e.LONGEST=2]="LONGEST"}(ka||(ka={}));class Ya extends Ba{constructor(e,t){super(),this.upstream=e,this.bufferSize=t,this.buffer=new Ra(t)}summary(){return`${this.upstream.summary()} -> Prefetch`}refill(){for(;!this.buffer.isFull();){const e=this.upstream.next();this.buffer.push(e)}}next(){return this.refill(),this.buffer.shift()}}class Za extends Ya{constructor(e,t,n){super(e,t),this.upstream=e,this.windowSize=t,this.upstreamExhausted=!1,this.random=Sa.alea(n||s.D5U.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();){const e=this.chooseIndex(),t=await this.buffer.shuffleExcise(e);if(!t.done)return this.refill(),t;this.upstreamExhausted=!0}return{value:null,done:!0}}}class Ja{constructor(){this.size=null}batch(e,t=!0){const n=this;let r;return s.D5U.assert(e>0,(()=>`batchSize needs to be positive, but it is\n ${e}`)),r=this.size===1/0||null==this.size?this.size:t?Math.ceil(this.size/e):Math.floor(this.size/e),eo((async()=>(await n.iterator()).columnMajorBatch(e,t,to)),r)}concatenate(e){const t=this;let n;return n=this.size===1/0||e.size===1/0?1/0:null!=this.size&&null!=e.size?this.size+e.size:null,eo((async()=>(await t.iterator()).concatenate(await e.iterator())),n)}filter(e){const t=this;let n;return n=this.size===1/0?1/0:null,eo((async()=>(await t.iterator()).filter((t=>s.lub((()=>e(t)))))),n)}async forEachAsync(e){return(await this.iterator()).forEachAsync(e)}map(e){const t=this;return eo((async()=>(await t.iterator()).map((t=>s.lub((()=>e(t)))))),this.size)}mapAsync(e){const t=this;return eo((async()=>(await t.iterator()).mapAsync(e)),this.size)}prefetch(e){if(null==e)throw new RangeError("`Dataset.prefetch()` requires bufferSize to be specified.");const t=this;return eo((async()=>(await t.iterator()).prefetch(e)),this.size)}repeat(e){const t=this;let n;return n=null!=this.size&&e>0?this.size*e:0===e?0:null!=this.size&&(void 0===e||e<0)?1/0:null,eo((async()=>Ma(Oa((async()=>({value:await t.iterator(),done:!1}))).take(e))),n)}skip(e){const t=this;let n;return n=null!=this.size&&e>=0&&this.size>=e?this.size-e:null!=this.size&&(this.size<e||void 0===e||e<0)?0:null,eo((async()=>(await t.iterator()).skip(e)),n)}shuffle(e,t,n=!0){if(null==e||e<0)throw null==this.size?new RangeError("`Dataset.shuffle()` requires bufferSize to be specified."):new RangeError(`\`Dataset.shuffle()\` requires bufferSize to be specified. If your data fits in main memory (for regular JS objects), and/or GPU memory (for \`tf.Tensor\`s), consider setting bufferSize to the dataset size (${this.size} elements)`);const r=this,a=Sa.alea(t||s.D5U.now().toString());return eo((async()=>{let t=a.int32();return n&&(t+=a.int32()),(await r.iterator()).shuffle(e,t.toString())}),this.size)}take(e){const t=this;let n;return n=null!=this.size&&this.size>e?e:null!=this.size&&this.size<=e?this.size:null,eo((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()}}function eo(e,t=null){return new class extends Ja{constructor(){super(...arguments),this.size=t}async iterator(){return e()}}}function to(e){if(null===e)return null;if(function(e){return null==e||null===(t=e)||"object"!==typeof t&&"function"!==typeof t||Array.isArray(e)||"object"===typeof e&&e instanceof s.esB||s.D5U.isTypedArray(e);var t}(e[0])){return{value:function(e){if(0===e.length)throw new Error("Can't make a batch of zero elements.");return e[0]instanceof s.esB?s.knu(e):s.XeE(e)}(e),recurse:!1}}return{value:null,recurse:!0}}Ja.MAX_BUFFER_SIZE=1e4;Symbol("out"),Symbol("field"),Symbol("quote"),Symbol("quoteafterquote"),Symbol("quoteinquote");n(8764).Buffer;function no(e,t){Array.isArray(e)||(e=[e]),e.forEach((e=>{null!=e&&s.D5U.assert("complex64"!==e.dtype,(()=>`${t} does not support complex64 tensors in the CPU backend.`))}))}const ro=s.GDt.ZA;class so extends s.Zuw{constructor(){super(),this.blockSize=48,this.firstUse=!0,this.data=new s.JLz(this,(0,s.SRH)())}nextDataId(){return so.nextDataId++}write(e,t,n){this.firstUse&&(this.firstUse=!1,(0,s.OBj)().get("IS_NODE")&&s.Wap.warn("\n============================\nHi, looks like you are running TensorFlow.js in Node.js. To speed things up dramatically, install our node backend, visit https://github.com/tensorflow/tfjs-node for more details. \n============================"));const r={id:this.nextDataId()};return this.data.set(r,{values:e,dtype:n,refCount:1}),r}makeTensorInfo(e,t,n){let r;if("string"===t&&null!=n&&n.length>0&&s.D5U.isString(n[0])){const a=n.map((e=>s.D5U.encodeString(e)));r=this.write(a,e,t)}else r=this.write(n,e,t);return{dataId:r,shape:e,dtype:t}}refCount(e){if(this.data.has(e)){return this.data.get(e).refCount}return 0}incRef(e){this.data.get(e).refCount++}decRef(e){if(this.data.has(e)){this.data.get(e).refCount--}}
1move(e,t,n,r,s){this.data.set(e,{values:t,dtype:r,refCount:s})}numDataIds(){return this.data.numDataIds()}async read(e){return this.readSync(e)}readSync(e){const{dtype:t,complexTensorInfos:n}=this.data.get(e);if("complex64"===t){const e=this.readSync(n.real.dataId),t=this.readSync(n.imag.dataId);return s.Wap.mergeRealAndImagArrays(e,t)}return this.data.get(e).values}bufferSync(e){const t=this.readSync(e.dataId);if("string"===e.dtype)try{const n=t.map((e=>s.D5U.decodeString(e)));return(0,s.f3b)(e.shape,e.dtype,n)}catch(n){throw new Error("Failed to decode encoded string bytes into utf-8")}return(0,s.f3b)(e.shape,e.dtype,t)}makeOutput(e,t,n){return(0,s.SRH)().makeTensorFromTensorInfo(this.makeTensorInfo(t,n,e),this)}disposeData(e,t=!1){if(this.data.has(e)){if(this.data.get(e).refCount--,!t&&this.data.get(e).refCount>0)return!1;const{complexTensorInfos:n}=this.data.get(e);null!=n&&(this.disposeData(n.real.dataId,!0),this.disposeData(n.imag.dataId,!0)),this.data.delete(e)}return!0}disposeIntermediateTensorInfo(e){this.disposeData(e.dataId)}async time(e){const t=s.D5U.now();e();return{kernelMs:s.D5U.now()-t}}memory(){return{unreliable:!0,reasons:["The reported memory is an upper bound. Due to automatic garbage collection, the true allocated memory may be less."]}}where(e){no([e],"where");const t=this.readSync(e.dataId);return ro(e.shape,t)}dispose(){}floatPrecision(){return 32}epsilon(){return super.epsilon()}}so.nextDataId=0;function ao(e,t,n){return({inputs:r,attrs:a,backend:o})=>{const{x:i}=r;if(no(i,e),"string"===i.dtype||"string"===n)throw new Error("unaryKernelFunc does not support string input/output");const u=o,l=u.data.get(i.dataId).values,c=s.D5U.sizeFromShape(i.shape),p=n||i.dtype,h=s.D5U.getArrayFromDType(p,c);for(let e=0;e<c;++e)h[e]=t(l[e],a);return u.makeTensorInfo(i.shape,p,h)}}function oo(e,t,n){return({inputs:r,attrs:s,backend:a})=>{const{x:o}=r;if(no(o,e),"string"===o.dtype||"string"===n)throw new Error("unaryKernelFunc does not support string input/output");const i=a,u=i.data.get(o.dataId).values,l=n||o.dtype,c=t(u,l,s);return i.makeTensorInfo(o.shape,l,c)}}(0,s.jqO)("cpu",(()=>new so),1);const io=ao(s.SX0,(e=>e>=0?e:Math.exp(e)-1)),uo={kernelName:s.SX0,backendName:"cpu",kernelFunc:io};function lo(e){const{inputs:t,backend:n}=e,{x:r}=t;return n.incRef(r.dataId),{dataId:r.dataId,shape:r.shape,dtype:r.dtype}}const co={kernelName:s.iJz,backendName:"cpu",kernelFunc:lo};function po(e){const{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{alpha:o}=r;no([a],"leakyRelu");const i=s.D5U.sizeFromShape(a.shape),u=n.data.get(a.dataId).values,l=s.D5U.getTypedArrayFromDType("float32",i);for(let s=0;s<u.length;s++)l[s]=u[s]<0?o*u[s]:u[s];return n.makeTensorInfo(a.shape,"float32",l)}const ho={kernelName:s.J$2,backendName:"cpu",kernelFunc:po};function fo(e){return(t,n,r,a,o)=>{const i=s.Wap.assertAndGetBroadcastShape(t,n),u=i.length,l=s.D5U.computeStrides(i),c=s.D5U.sizeFromShape(i),p=s.D5U.getTypedArrayFromDType(o,c),h=t.length,d=n.length,f=s.D5U.computeStrides(t),m=s.D5U.computeStrides(n),g=s.Wap.getBroadcastDims(t,i),y=s.Wap.getBroadcastDims(n,i);if(g.length+y.length===0)for(let s=0;s<p.length;++s)p[s]=e(r[s%r.length],a[s%a.length]);else for(let b=0;b<p.length;++b){const t=s.D5U.indexToLoc(b,u,l),n=t.slice(-h);g.forEach((e=>n[e]=0));const o=s.D5U.locToIndex(n,h,f),i=t.slice(-d);y.forEach((e=>i[e]=0));const c=s.D5U.locToIndex(i,d,m);p[b]=e(r[o],a[c])}return[p,i]}}const mo=fo(((e,t)=>e<0?t*e:e));function go(e){const{inputs:t,backend:n}=e,{x:r,alpha:s}=t;no([r,s],"prelu");const a=n.data.get(r.dataId).values,o=n.data.get(s.dataId).values,[i,u]=mo(r.shape,s.shape,a,o,"float32");return n.makeTensorInfo(u,"float32",i)}const yo={kernelName:s.o0g,backendName:"cpu",kernelFunc:go},bo=ao(s.qkr,(e=>Math.max(0,e))),xo={kernelName:s.qkr,backendName:"cpu",kernelFunc:bo},wo=ao(s.SbG,(e=>Math.min(Math.max(0,e),6))),vo={kernelName:s.SbG,backendName:"cpu",kernelFunc:wo};function ko(e){return(t,n,r)=>{const a=s.D5U.getTypedArrayFromDType(n,t.length);for(let s=0;s<t.length;++s)a[s]=e(t[s],r);return a}}const Io=ko((e=>1/(1+Math.exp(-e)))),No=ao(s.a5O,(e=>1/(1+Math.exp(-e)))),So={kernelName:s.a5O,backendName:"cpu",kernelFunc:No};
vendor: 112,026 bytes, line 1
1function To(e,t,n,r,s){if("linear"===n)return lo({inputs:{x:t},backend:e});if("relu"===n)return bo({inputs:{x:t},backend:e});if("elu"===n)return io({inputs:{x:t},backend:e});if("relu6"===n)return wo({inputs:{x:t},backend:e});if("prelu"===n)return go({inputs:{x:t,alpha:r},backend:e});if("leakyrelu"===n)return po({inputs:{x:t},backend:e,attrs:{alpha:s}});if("sigmoid"===n)return No({inputs:{x:t},backend:e});throw new Error(`Activation ${n} has not been implemented for the CPU backend.`)}function Co(e){const{inputs:t,backend:n}=e,{real:r,imag:s}=t,a=n.data.get(r.dataId).values,o=n.data.get(s.dataId).values,i=n.makeTensorInfo(r.shape,"complex64");return n.data.get(i.dataId).complexTensorInfos={real:n.makeTensorInfo(r.shape,"float32",a),imag:n.makeTensorInfo(s.shape,"float32",o)},i}const Eo={kernelName:s.Zz9,backendName:"cpu",kernelFunc:Co};function $o(e,t,n="float32"){if("complex64"===n){return Co({inputs:{real:$o(e,t,"float32"),imag:$o(e,t,"float32")},backend:e})}const r=s.D5U.makeZerosTypedArray(s.D5U.sizeFromShape(t),n);return e.makeTensorInfo(t,n,r)}function Ao(e){const{inputs:t,backend:n}=e,{input:r}=t,s=n.data.get(r.dataId).complexTensorInfos.real,a=n.data.get(s.dataId).values;return n.makeTensorInfo(s.shape,s.dtype,a)}const Do={kernelName:s.xJR,backendName:"cpu",kernelFunc:Ao};function _o(e,t,n,r){if("int32"===r){return[t,"int32",Int32Array.from(e)]}if("bool"===r){const r=s.D5U.toTypedArray([0],n),[a,o]=fo(((e,t)=>e!==t?1:0))(t,[],e,r,"bool");return[o,"bool",a]}throw new Error(`Error in Cast: failed to cast ${n} to ${r}`)}function Ro(e){const{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{dtype:o}=r;if("complex64"===o){if("complex64"===a.dtype)return lo({inputs:{x:a},backend:n});const e=$o(n,a.shape,a.dtype),t=Ro({inputs:{x:a},backend:n,attrs:{dtype:"float32"}}),r=Co({inputs:{real:t,imag:e},backend:n});return n.disposeIntermediateTensorInfo(e),n.disposeIntermediateTensorInfo(t),r}if("complex64"===a.dtype){const e=Ao({inputs:{input:a},backend:n}),t=Ro({inputs:{x:e},backend:n,attrs:{dtype:o}});return n.disposeIntermediateTensorInfo(e),t}if(!s.D5U.hasEncodingLoss(a.dtype,o)){const e=lo({inputs:{x:a},backend:n});return{dataId:e.dataId,shape:e.shape,dtype:o}}const i=n.data.get(a.dataId).values,[u,l,c]=_o(i,a.shape,a.dtype,o);return n.makeTensorInfo(u,l,c)}const Fo={kernelName:s.RFZ,backendName:"cpu",kernelFunc:Ro};function Oo(e,t,n,r){return null==n?({inputs:n,backend:a})=>{const{a:o,b:i}=n,u=a;no([o,i],e);const l=u.data.get(o.dataId).values,c=u.data.get(i.dataId).values,p="string"===o.dtype?s.Wap.fromUint8ToStringArray(l):l,h="string"===o.dtype?s.Wap.fromUint8ToStringArray(c):c,d=r||o.dtype,[f,m]=t(o.shape,i.shape,p,h,d);return u.makeTensorInfo(m,d,f)}:({inputs:e,backend:s})=>{const{a:a,b:o}=e,i=s;if("complex64"===a.dtype||"complex64"===o.dtype){const e=Ro({inputs:{x:a},backend:i,attrs:{dtype:"complex64"}}),t=i.data.get(e.dataId),r=t.complexTensorInfos.real,s=t.complexTensorInfos.imag,u=i.data.get(r.dataId).values,l=i.data.get(s.dataId).values,c=Ro({inputs:{x:o},backend:i,attrs:{dtype:"complex64"}}),p=i.data.get(c.dataId),h=p.complexTensorInfos.real,d=p.complexTensorInfos.imag,f=i.data.get(h.dataId).values,m=i.data.get(d.dataId).values,[g,y,b]=n(a.shape,o.shape,u,l,f,m),x=i.makeTensorInfo(b,"float32",g),w=i.makeTensorInfo(b,"float32",y),v=Co({inputs:{real:x,imag:w},backend:i});return i.disposeIntermediateTensorInfo(e),i.disposeIntermediateTensorInfo(c),i.disposeIntermediateTensorInfo(x),i.disposeIntermediateTensorInfo(w),v}{const e=i.data.get(a.dataId).values,n=i.data.get(o.dataId).values,s=r||a.dtype,[u,l]=t(a.shape,o.shape,e,n,s);return i.makeTensorInfo(l,s,u)}}}function Mo(e){return(t,n,r,a,o,i)=>{const u=s.Wap.assertAndGetBroadcastShape(t,n),l=s.D5U.sizeFromShape(u),c=u.length,p=s.D5U.computeStrides(u),h=s.D5U.getTypedArrayFromDType("float32",l),d=s.D5U.getTypedArrayFromDType("float32",l),f=s.Wap.getBroadcastDims(t,u),m=s.Wap.getBroadcastDims(n,u),g=s.Wap.mergeRealAndImagArrays(r,a),y=s.Wap.mergeRealAndImagArrays(o,i),b=t.length,x=s.D5U.computeStrides(t),w=n.length,v=s.D5U.computeStrides(n);if(f.length+m.length===0)for(let s=0;s<h.length;s++){const t=s%g.length,n=s%y.length,r=e(g[2*t],g[2*t+1],y[2*n],y[2*n+1]);h[s]=r.real,d[s]=r.imag}else for(let k=0;k<h.length;k++){const t=s.D5U.indexToLoc(k,c,p),n=t.slice(-b);f.forEach((e=>n[e]=0));const r=s.D5U.locToIndex(n,b,x),a=t.slice(-w);m.forEach((e=>a[e]=0));const o=s.D5U.locToIndex(a,w,v),i=e(g[2*r],g[2*r+1],y[2*o],y[2*o+1]);h[k]=i.real,d[k]=i.imag}return[h,d,u]}}const Bo=fo(((e,t)=>e+t)),Lo=Mo(((e,t,n,r)=>({real:e+n,imag:t+r}))),Wo=Oo(s.mm_,Bo,Lo),Po={kernelName:s.mm_,backendName:"cpu",kernelFunc:Wo};function Uo(e){const{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{shape:o}=r,i=s.D5U.sizeFromShape(a.shape),u=s.D5U.inferFromImplicitShape(o,i),l=s.D5U.sizeFromShape(u);s.D5U.assert(i===l,(()=>`The new shape (${u}) has ${l} elements and the old shape (${a.shape}) has ${i} elements. 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o=0;o<d.inChannels;++o)for(let i=0;i<d.outChannels;++i){let u=0;for(let l=0;l<d.batchSize;++l)for(let c=e;c<t;++c){const e=s+c*f-v;for(let t=r;t<a;++t){const r=n+t*m-w;u+=b?N.get(l,e,r,o)*S.get(l,c,t,i):N.get(l,o,e,r)*S.get(l,i,c,t)}}x.set(u,s,n,o,i)}}}return n.makeTensorInfo(x.shape,x.dtype,x.values)}};const Xi={kernelName:s.wm,backendName:"cpu",kernelFunc:function(e){const{inputs:t,backend:n,attrs:r}=e,{dy:a,filter:o}=t,{inputShape:i,strides:u,pad:l,dataFormat:c,dimRoundingMode:p}=r;no([a,o],"conv2dBackpropInput");const h=s.D5U.computeStrides(o.shape),d=s.D5U.computeStrides(a.shape);let f=s.Wap.convertConv2DDataFormat(c);const m=s.Wap.computeConv2DInfo(i,o.shape,u,1,l,p,!1,f),g=new s.YDk(m.inShape,"float32"),y=g.values,b=n.data.get(a.dataId).values,x=n.data.get(o.dataId).values,[w,v,k]=h,{batchSize:I,filterHeight:N,filterWidth:S,inChannels:T,inHeight:C,inWidth:E,outChannels:$,outHeight:A,outWidth:D,strideHeight:_,strideWidth:R}=m;f=m.dataFormat;const 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l=s.Wap.getAxesPermutation([o],a.shape.length);let c=a;null!=l&&(c=ei({inputs:{x:a},backend:n,attrs:{perm:l}}));const p=s.Wap.getInnerMostAxes(1,a.shape.length)[0];if(p!==c.shape.length-1)throw new Error(`backend.cumprod in CPU expects an inner-most axis=${c.shape.length-1} but got axis=${p}`);const h=(0,s.x8V)(c.dtype,"int32"),d=s.D5U.makeOnesTypedArray(s.D5U.sizeFromShape(c.shape),h),f=n.data.get(c.dataId).values,m=c.shape[c.shape.length-1],g=u?(e,t)=>e+m-t-1:(e,t)=>e+t;for(let s=0;s<f.length;s+=m)for(let e=0;e<m;e++){const t=g(s,e);if(0===e)d[t]=i?1:f[t];else{const n=g(s,e-1);d[t]=i?f[n]*d[n]:f[t]*d[n]}}const y=n.makeTensorInfo(c.shape,h,d);if(null!=l){const e=ei({inputs:{x:y},backend:n,attrs:{perm:s.Wap.getUndoAxesPermutation(l)}});return n.disposeIntermediateTensorInfo(y),n.disposeIntermediateTensorInfo(c),e}return y}};const ru={kernelName:s.iHb,backendName:"cpu",kernelFunc:function(e){const{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{axis:o,exclusive:i,reverse:u}=r;no(a,"cumsum");const l=s.Wap.getAxesPermutation([o],a.shape.length);let c=a;null!=l&&(c=ei({inputs:{x:a},backend:n,attrs:{perm:l}}));const p=s.Wap.getInnerMostAxes(1,a.shape.length)[0];if(p!==c.shape.length-1)throw new Error(`backend.cumsum in CPU expects an inner-most axis=${c.shape.length-1} but got axis=${p}`);const h=(0,s.x8V)(c.dtype,"int32"),d=s.D5U.makeZerosTypedArray(s.D5U.sizeFromShape(c.shape),h),f=n.data.get(c.dataId).values,m=c.shape[c.shape.length-1],g=u?(e,t)=>e+m-t-1:(e,t)=>e+t;for(let s=0;s<f.length;s+=m)for(let e=0;e<m;e++){const t=g(s,e);if(0===e)d[t]=i?0:f[t];else{const n=g(s,e-1);d[t]=i?f[n]+d[n]:f[t]+d[n]}}const y=n.makeTensorInfo(c.shape,h,d);if(null!=l){const e=ei({inputs:{x:y},backend:n,attrs:{perm:s.Wap.getUndoAxesPermutation(l)}});return n.disposeIntermediateTensorInfo(y),n.disposeIntermediateTensorInfo(c),e}return y}};const su={kernelName:s.QRR,backendName:"cpu",kernelFunc:function(e){const{inputs:t,backend:n,attrs:r}=e,{x:s,weights:a}=t,{size:o,binaryOutput:i}=r;if(1===s.shape.length){const e=$i(n.data.get(s.dataId).values,n.data.get(a.dataId).values,a.dtype,a.shape,o);return n.makeTensorInfo([o],a.dtype,e)}if(2===s.shape.length){const e=Ai(n.bufferSync(s),n.bufferSync(a),o,i);return n.makeTensorInfo(e.shape,a.dtype,e.values)}throw new Error(`Error in denseBincount: input must be at most rank 2, but got rank${s.shape.length}.`)}};const au={kernelName:s.T0n,backendName:"cpu",kernelFunc:function(e){const{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{blockSize:o,dataFormat:i}=r;s.D5U.assert("NHWC"===i,(()=>`Only NHWC dataFormat supported on CPU for depthToSpace. Got ${i}`));const u=a.shape[0],l=a.shape[1],c=a.shape[2],p=a.shape[3],h=l*o,d=c*o,f=p/(o*o),m=n.data.get(a.dataId).values,g=new Float32Array(u*h*d*f);let y=0;for(let s=0;s<u;++s)for(let e=0;e<h;++e){const t=Math.floor(e/o),n=e%o;for(let e=0;e<d;++e){const r=Math.floor(e/o),a=(n*o+e%o)*f;for(let e=0;e<f;++e){const n=e+a+p*(r+c*(t+l*s));g[y++]=m[n]}}}return n.makeTensorInfo([u,h,d,f],a.dtype,g)}};function ou(e){const{inputs:t,backend:n,attrs:r}=e,{x:a,filter:o}=t,{strides:i,pad:u,dilations:l,dimRoundingMode:c}=r;no([a,o],"depthwiseConv2DNative");const p=s.D5U.computeStrides(a.shape),h=s.D5U.computeStrides(o.shape);let d=l;null==d&&(d=[1,1]),s.D5U.assert(s.Wap.eitherStridesOrDilationsAreOne(i,d),(()=>`Error in depthwiseConv2d: Either strides or dilations must be 1. 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Yu={kernelName:s.vwp,backendName:"cpu",kernelFunc:function(e){const{inputs:t,backend:n}=e,{input:r}=t,a=s.D5U.sizeFromShape(r.shape),o=r.shape[r.shape.length-1],i=Uo({inputs:{x:r},backend:n,attrs:{shape:[a/o,o]}}),u=qu(i,!1,n),l=Uo({inputs:{x:u},backend:n,attrs:{shape:r.shape}});return n.disposeIntermediateTensorInfo(i),n.disposeIntermediateTensorInfo(u),l}};function Zu(e){const{backend:t,attrs:n}=e,{shape:r,value:a,dtype:o}=n,i=o||s.D5U.inferDtype(a),u=s.D5U.getArrayFromDType(i,s.D5U.sizeFromShape(r));return function(e,t,n){e.fill(t)}(u,a),t.makeTensorInfo(r,i,u)}const Ju={kernelName:s.deh,backendName:"cpu",kernelFunc:Zu};const el={kernelName:s.Uyb,backendName:"cpu",kernelFunc:({inputs:e,attrs:t,backend:n})=>{const{image:r}=e,a=n,o=s.D5U.getTypedArrayFromDType(r.dtype,s.D5U.sizeFromShape(r.shape)),[i,u,l,c]=r.shape,p=a.data.get(r.dataId).values;for(let s=0;s<i;s++){const e=s*l*u*c;for(let t=0;t<u;t++){const n=t*(l*c);for(let t=0;t<l;t++){const r=t*c;for(let s=0;s<c;s++){const a=Math.round(l-t-1),i=e+n+r+s;let u=p[i];if(a>=0&&a<l){u=p[e+n+a*c+s]}o[i]=u}}}}return{dataId:a.write(o,r.shape,r.dtype),shape:r.shape,dtype:r.dtype}}},tl=ko((e=>Math.floor(e))),nl=oo(s.OR,tl),rl={kernelName:s.OR,backendName:"cpu",kernelFunc:nl},sl=fo(((e,t)=>Math.floor(e/t))),al=Oo(s.jeX,sl,null,"int32"),ol={kernelName:s.jeX,backendName:"cpu",kernelFunc:al};const il={kernelName:s._V0,backendName:"cpu",kernelFunc:function(e){const{inputs:t,backend:n,attrs:r}=e,{x:s,filter:a,bias:o,preluActivationWeights:i}=t,{strides:u,pad:l,dataFormat:c,dilations:p,dimRoundingMode:h,activation:d,leakyreluAlpha:f}=r;let m=Gi({inputs:{x:s,filter:a},backend:n,attrs:{strides:u,pad:l,dataFormat:c,dilations:p,dimRoundingMode:h}});if(o){const e=m;if("NCHW"===c&&1===o.shape.length&&1!==o.shape[0]){const e=Uo({inputs:{x:o},backend:n,attrs:{shape:[o.shape[0],1,1]}});m=Wo({inputs:{a:m,b:e},backend:n}),n.disposeIntermediateTensorInfo(e)}else m=Wo({inputs:{a:m,b:o},backend:n});n.disposeIntermediateTensorInfo(e)}if(d){const e=m;if("NCHW"===c&&"prelu"===d&&1===i.shape.length&&1!==i.shape[0]){const e=Uo({inputs:{x:i},backend:n,attrs:{shape:[i.shape[0],1,1]}});m=To(n,m,d,e,f),n.disposeIntermediateTensorInfo(e)}else m=To(n,m,d,i,f);n.disposeIntermediateTensorInfo(e)}return m}};const ul={kernelName:s.luS,backendName:"cpu",kernelFunc:function(e){const{inputs:t,backend:n,attrs:r}=e,{x:s,filter:a,bias:o,preluActivationWeights:i}=t,{strides:u,pad:l,dataFormat:c,dilations:p,dimRoundingMode:h,activation:d,leakyreluAlpha:f}=r;let m=ou({inputs:{x:s,filter:a},backend:n,attrs:{strides:u,pad:l,dataFormat:c,dilations:p,dimRoundingMode:h}});if(o){const e=m;m=Wo({inputs:{a:m,b:o},backend:n}),n.disposeIntermediateTensorInfo(e)}if(d){const e=m;m=To(n,m,d,i,f),n.disposeIntermediateTensorInfo(e)}return m}};function ll(e,t,n,r,a,o,i,u,l){const c=(0,s.f3b)([r,o],n);for(let s=0;s<r;s++){const n=[];let r=0;for(let t=0;t<a;t++){const o=e[s*a+t];r+=o*i[t],n.push(o)}if(r<0||r>=l/o)throw new Error(`Invalid indices: ${n} does not index into ${u}`);for(let e=0;e<o;e++)c.values[s*o+e]=t.get(...t.indexToLoc(r*o+e))}return c}const cl={kernelName:s.q1x,backendName:"cpu",kernelFunc:function(e){const{inputs:t,backend:n}=e,{params:r,indices:a}=t,o=s.D5U.sizeFromShape(r.shape),i=a.shape,u=i[i.length-1],[l,c,p,h]=s.Wap.prepareAndValidate(r,a);if(0===c)return n.makeTensorInfo(l,r.dtype,[]);const d=ll(n.data.get(a.dataId).values,n.bufferSync(r),r.dtype,c,u,p,h,r.shape,o);return n.makeTensorInfo(l,r.dtype,d.values)}};function pl(e,t,n){const r=(0,s.f3b)(n,e.dtype);for(let s=0;s<r.size;++s){const n=r.indexToLoc(s).slice(),a=n[0],o=n[2],i=t.locToIndex([a,o]);n[2]=t.values[i];const u=e.locToIndex(n);0<=u&&u<e.values.length&&(r.values[s]=e.values[u])}return r}const hl={kernelName:s.qi_,backendName:"cpu",kernelFunc:function(e){const{inputs:t,backend:n,attrs:r}=e,{x:a,indices:o}=t,{axis:i,batchDims:u}=r;no([a,o],"gatherV2");const l=s.D5U.parseAxisParam(i,a.shape)[0],c=n.data.get(o.dataId).values,p=a.shape[l];for(let w=0;w<c.length;++w){const e=c[w];s.D5U.assert(e<=p-1&&e>=0,(()=>`GatherV2: the index value ${e} is not in [0, ${p-1}]`))}let h=u;null==u&&(h=0);const d=s.D5U.sizeFromShape(o.shape),f=s.Wap.segment_util.collectGatherOpShapeInfo(a,o,l,h),m=Uo({inputs:{x:a},backend:n,attrs:{shape:[f.batchSize,f.outerSize,f.dimSize,f.sliceSize]}}),g=Uo({inputs:{x:o},backend:n,attrs:{shape:[f.batchSize,d/f.batchSize]}}),y=[f.batchSize,f.outerSize,d/f.batchSize,f.sliceSize],b=n.bufferSync(g),x=pl(n.bufferSync(m),b,y);return n.disposeIntermediateTensorInfo(m),n.disposeIntermediateTensorInfo(g),n.makeTensorInfo(f.outputShape,x.dtype,x.values)}},dl=fo(((e,t)=>e>t?1:0)),fl=Oo(s.iZT,dl,null,"bool"),ml={kernelName:s.iZT,backendName:"cpu",kernelFunc:fl},gl=fo(((e,t)=>e>=t?1:0)),yl=Oo(s.Acj,gl,null,"bool"),bl={kernelName:s.Acj,backendName:"cpu",kernelFunc:yl};const xl={kernelName:s.Qg5,backendName:"cpu",kernelFunc:function(e){const{inputs:t,backend:n}=e,{input:r}=t,a=s.D5U.sizeFromShape(r.shape),o=r.shape[r.shape.length-1],i=Uo({inputs:{x:r},backend:n,attrs:{shape:[a/o,o]}}),u=qu(i,!0,n),l=Uo({inputs:{x:u},backend:n,attrs:{shape:r.shape}});return n.disposeIntermediateTensorInfo(i),n.disposeIntermediateTensorInfo(u),l}},wl=ao(s.avt,(e=>Number.isFinite(e)?1:0),"bool"),vl={kernelName:s.avt,backendName:"cpu",kernelFunc:wl},kl=ao(s.iWB,(e=>Math.abs(e)===1/0?1:0),"bool"),Il={kernelName:s.iWB,backendName:"cpu",kernelFunc:kl},Nl=ao(s.r7n,(e=>Number.isNaN(e)?1:0),"bool"),Sl={kernelName:s.r7n,backendName:"cpu",kernelFunc:Nl},Tl=fo(((e,t)=>e<t?1:0)),Cl=Oo(s.vtC,Tl,null,"bool"),El={kernelName:s.vtC,backendName:"cpu",kernelFunc:Cl},$l=fo(((e,t)=>e<=t?1:0)),Al=Oo(s.CAk,$l,null,"bool"),Dl={kernelName:s.CAk,backendName:"cpu",kernelFunc:Al};function _l(e,t,n){const r=(t-e)/(n-1),a=s.D5U.makeZerosTypedArray(n,"float32");a[0]=e;for(let s=1;s<a.length;s++)a[s]=a[s-1]+r;return a}const Rl={kernelName:s.e7N,backendName:"cpu",kernelFunc:function(e){const{backend:t,attrs:n}=e,{start:r,stop:s,num:a}=n,o=_l(r,s,a);return t.makeTensorInfo([o.length],"float32",o)}},Fl=ko((e=>Math.log(e))),Ol=oo(s.ZbH,Fl),Ml={kernelName:s.ZbH,backendName:"cpu",kernelFunc:Ol},Bl=ao(s.kU,(e=>Math.log1p(e))),Ll={kernelName:s.kU,backendName:"cpu",kernelFunc:Bl},Wl=fo(((e,t)=>e&&t)),Pl=Oo(s.PYm,Wl,null,"bool"),Ul={kernelName:s.PYm,backendName:"cpu",kernelFunc:Pl},zl=ao(s.VfG,(e=>e?0:1),"bool"),Vl={kernelName:s.VfG,backendName:"cpu",kernelFunc:zl},Gl=fo(((e,t)=>e||t)),Hl=Oo(s.MZg,Gl,null,"bool"),jl={kernelName:s.MZg,backendName:"cpu",kernelFunc:Hl};const Xl={kernelName:s.eZ0,backendName:"cpu",kernelFunc:function(e){const{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{depthRadius:o,bias:i,alpha:u,beta:l}=r;no(a,"LRN");const c=a.shape[3],p=c-1,h=n.data.get(a.dataId).values,d=s.D5U.sizeFromShape(a.shape),f=new Float32Array(d);function m(e){const t=e%c;let n=e-t+Math.max(0,t-o);const r=e-t+Math.min(t+o,p);let s=0;for(;n<=r;n++){const e=h[n];s+=e*e}return s}for(let s=0;s<d;s++){const e=m(s),t=h[s]*Math.pow(i+u*e,-l);f[s]=t}return n.makeTensorInfo(a.shape,a.dtype,f)}};const ql={kernelName:s.Hhh,backendName:"cpu",kernelFunc:function(e){const{inputs:t,backend:n,attrs:r}=e,{x:a,y:o,dy:i}=t,{depthRadius:u,bias:l,alpha:c,beta:p}=r;no(i,"LRNGrad");const h=s.D5U.sizeFromShape(i.shape),d=i.shape[3],f=n.data.get(i.dataId).values,m=n.data.get(a.dataId).values,g=n.data.get(o.dataId).values,y=new Float32Array(h),b=h;for(let s=0;s<b;s++){const e=s%d,t=s-e+Math.max(0,e-u),n=s-e+Math.min(d,e+u+1);let r=0;for(let s=t;s<n;s++)r+=Math.pow(m[s],2);r=c*r+l;for(let a=t;a<n;a++){let e=-2*c*p*m[a]*g[s]/r;s===a&&(e+=Math.pow(r,-p)),e*=f[s],y[a]+=e}}return n.makeTensorInfo(i.shape,a.dtype,y)}};function Kl(e,t,n,r){const a=s.D5U.getTypedArrayFromDType(r,s.D5U.sizeFromShape(n));for(let s=0;s<a.length;++s){const n=s*t;let r=e[n];for(let s=0;s<t;++s){const 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tc={kernelName:s.mTV,backendName:"cpu",kernelFunc:function(e){const{inputs:t,backend:n,attrs:r}=e,{x:a}=t;no(a,"maxPool");const{filterSize:o,strides:i,pad:u,dimRoundingMode:l}=r;s.D5U.assert(s.Wap.eitherStridesOrDilationsAreOne(i,1),(()=>`Error in maxPool: Either strides or dilations must be 1. 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p=s.Wap.computePool3DInfo(o.shape,i,u,1,l,c),h=function(e,t){const n=(0,s.f3b)(t.outShape,"int32"),r=t.strideDepth,a=t.strideHeight,o=t.strideWidth,i=t.dilationDepth,u=t.dilationHeight,l=t.dilationWidth,c=t.effectiveFilterDepth,p=t.effectiveFilterHeight,h=t.effectiveFilterWidth,d=t.padInfo.front,f=t.padInfo.top,m=t.padInfo.left;for(let s=0;s<t.batchSize;++s)for(let g=0;g<t.inChannels;++g)for(let y=0;y<t.outDepth;++y){const b=y*r-d;let x=b;for(;x<0;)x+=i;const w=Math.min(t.inDepth,c+b);for(let r=0;r<t.outHeight;++r){const c=r*a-f;let d=c;for(;d<0;)d+=u;const v=Math.min(t.inHeight,p+c);for(let a=0;a<t.outWidth;++a){const f=a*o-m;let k=f;for(;k<0;)k+=l;const I=Math.min(t.inWidth,h+f);let N=Number.NEGATIVE_INFINITY,S=-1;for(let t=x;t<w;t+=i){const n=t-b;for(let r=d;r<v;r+=u){const a=r-c;for(let o=k;o<I;o+=l){const i=o-f,u=e.get(s,t,r,o,g);u>=N&&(N=u,S=n*p*h+a*p+i)}}}n.set(S,s,y,r,a,g)}}}return n}(n.bufferSync(o),p),d=p.strideDepth,f=p.strideHeight,m=p.strideWidth,g=p.dilationDepth,y=p.dilationHeight,b=p.dilationWidth,x=p.effectiveFilterDepth,w=p.effectiveFilterHeight,v=p.effectiveFilterWidth,k=x-1-p.padInfo.front,I=v-1-p.padInfo.left,N=w-1-p.padInfo.top,S=(0,s.f3b)(o.shape,"float32"),T=n.bufferSync(a);for(let s=0;s<p.batchSize;++s)for(let e=0;e<p.inChannels;++e)for(let t=0;t<p.inDepth;++t)for(let n=0;n<p.inHeight;++n)for(let r=0;r<p.inWidth;++r){const a=t-k,o=n-N,i=r-I;let u=0;for(let t=0;t<x;t+=g){const n=(a+t)/d;if(!(n<0||n>=p.outDepth||Math.floor(n)!==n))for(let r=0;r<w;r+=y){const a=(o+r)/f;if(!(a<0||a>=p.outHeight||Math.floor(a)!==a))for(let o=0;o<v;o+=b){const l=(i+o)/m;if(l<0||l>=p.outWidth||Math.floor(l)!==l)continue;const c=x*w*v-1-h.get(s,n,a,l,e)===t*w*v+r*v+o?1:0;if(0===c)continue;u+=T.get(s,n,a,l,e)*c}}}S.set(u,s,t,n,r,e)}return n.makeTensorInfo(S.shape,S.dtype,S.values)}};const sc={kernelName:s.OV7,backendName:"cpu",kernelFunc:function(e){const{inputs:t,backend:n,attrs:r}=e,{dy:a,input:o,output:i}=t,u=o;no([o,i],"maxPoolGrad");const{filterSize:l,strides:c,pad:p,dimRoundingMode:h}=r,d=s.Wap.computePool2DInfo(u.shape,l,c,1,p,h),f=n.data.get(u.dataId).values,m=(0,s.f3b)(d.outShape,u.dtype,bi(f,u.shape,u.dtype,d).values),g=d.strideHeight,y=d.strideWidth,b=d.dilationHeight,x=d.dilationWidth,w=d.effectiveFilterHeight,v=d.effectiveFilterWidth,k=v-1-d.padInfo.left,I=w-1-d.padInfo.top,N=(0,s.f3b)(u.shape,"float32"),S=n.data.get(a.dataId).values,T=(0,s.f3b)(a.shape,"float32",S);for(let s=0;s<d.batchSize;++s)for(let e=0;e<d.inChannels;++e)for(let t=0;t<d.inHeight;++t)for(let n=0;n<d.inWidth;++n){const r=t-I,a=n-k;let o=0;for(let t=0;t<w;t+=b){const n=(r+t)/g;if(!(n<0||n>=d.outHeight||Math.floor(n)!==n))for(let r=0;r<v;r+=x){const i=(a+r)/y;if(i<0||i>=d.outWidth||Math.floor(i)!==i)continue;const 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Float32Array([c]));p.push(h);const d=Ro({inputs:{x:a},backend:n,attrs:{dtype:"float32"}});p.push(d);const f=zu({inputs:{a:d,b:h},backend:n});p.push(f);const m=bu({inputs:{x:f},backend:n,attrs:{axis:o,keepDims:i}});return p.forEach((e=>n.disposeIntermediateTensorInfo(e))),m}};const ic={kernelName:s.c17,backendName:"cpu",kernelFunc:function(e){const{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{axis:o,keepDims:i}=r;no(a,"min");const u=s.D5U.parseAxisParam(o,a.shape);let l=u;const c=s.Wap.getAxesPermutation(l,a.shape.length);let p=a;null!=c&&(p=ei({inputs:{x:a},backend:n,attrs:{perm:c}}),l=s.Wap.getInnerMostAxes(l.length,a.shape.length)),s.Wap.assertAxesAreInnerMostDims("min",l,p.shape.length);const[h,d]=s.Wap.computeOutAndReduceShapes(p.shape,l),f=s.D5U.sizeFromShape(d),m=s.D5U.makeZerosTypedArray(s.D5U.sizeFromShape(h),p.dtype),g=n.data.get(p.dataId).values;for(let s=0;s<m.length;++s){const e=s*f;let t=g[e];for(let n=0;n<f;++n){const r=g[e+n];(Number.isNaN(r)||r<t)&&(t=r)}m[s]=t}null!=c&&n.disposeIntermediateTensorInfo(p);const y=n.makeTensorInfo(h,p.dtype,m);if(i){const e=Uo({inputs:{x:y},backend:n,attrs:{shape:s.Wap.expandShapeToKeepDim(h,u)}});return n.disposeIntermediateTensorInfo(y),e}return y}},uc=fo(((e,t)=>Math.min(e,t))),lc=Oo(s.q8u,uc),cc={kernelName:s.q8u,backendName:"cpu",kernelFunc:lc};const pc={kernelName:s.jQs,backendName:"cpu",kernelFunc:function(e){const{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{paddings:o,mode:i}=r;no(a,"mirrorPad");const u=o.map(((e,t)=>e[0]+a.shape[t]+e[1])),l=o.map((e=>e[0])),c=o.map(((e,t)=>e[0]+a.shape[t])),p="reflect"===i?0:1,h=n.data.get(a.dataId).values,d=a.shape.length,f=s.D5U.computeStrides(a.shape),m=s.D5U.sizeFromShape(u),g=u.length,y=s.D5U.computeStrides(u),b=s.D5U.getTypedArrayFromDType(a.dtype,m);for(let x=0;x<m;x++){let e=s.D5U.indexToLoc(x,g,y);for(let n=0;n<g;n++)e[n]<l[n]?e[n]=2*l[n]-e[n]-p:e[n]>=c[n]&&(e[n]=2*(c[n]-1)-e[n]+p);e=e.map(((e,t)=>e-l[t]));const t=s.D5U.locToIndex(e,d,f);b[x]=h[t]}return{dataId:n.write(b,u,a.dtype),shape:u,dtype:a.dtype}}},hc=fo(((e,t)=>{const n=e%t;return e<0&&t<0||e>=0&&t>=0?n:(n+t)%t})),dc=Oo(s.Vbg,hc),fc={kernelName:s.Vbg,backendName:"cpu",kernelFunc:dc};function mc(e){const{inputs:t,backend:n,attrs:r}=e,{logits:a}=t,{dim:o}=r,i=a.shape.length;let u=o;if(-1===u&&(u=i-1),u!==i-1)throw Error(`Softmax along a non-last dimension is not yet supported. Logits was rank ${i} and dim was ${u}`);const l=s.D5U.parseAxisParam([u],a.shape),c=Ql({inputs:{x:a},backend:n,attrs:{reductionIndices:l,keepDims:!1}}),p=s.Wap.expandShapeToKeepDim(c.shape,l),h=Uo({inputs:{x:c},backend:n,attrs:{shape:p}}),d=ju({inputs:{a:a,b:h},backend:n}),f=Fu({inputs:{x:d},backend:n}),m=bu({inputs:{x:f},backend:n,attrs:{axis:l,keepDims:!1}}),g=Uo({inputs:{x:m},backend:n,attrs:{shape:p}}),y=zu({inputs:{a:f,b:g},backend:n});return n.disposeIntermediateTensorInfo(c),n.disposeIntermediateTensorInfo(h),n.disposeIntermediateTensorInfo(d),n.disposeIntermediateTensorInfo(f),n.disposeIntermediateTensorInfo(m),n.disposeIntermediateTensorInfo(g),y}const gc={kernelName:s.Gcp,backendName:"cpu",kernelFunc:mc};const yc={kernelName:s.NZg,backendName:"cpu",kernelFunc:function(e){const{inputs:t,backend:n,attrs:r}=e,{logits:a}=t,{numSamples:o,seed:i,normalized:u}=r;no(a,"multinomial");const l=u?a:mc({inputs:{logits:a},backend:n,attrs:{dim:-1}}),c=l.shape[0],p=l.shape[1],h=n.data.get(l.dataId).values,d=[c,o],f=s.D5U.makeZerosTypedArray(s.D5U.sizeFromShape(d),"int32");for(let s=0;s<c;++s){const e=s*p,t=new Float32Array(p-1);t[0]=h[e];for(let s=1;s<t.length;++s)t[s]=t[s-1]+h[e+s];const n=Sa.alea(i.toString()),r=s*o;for(let s=0;s<o;++s){const e=n();f[r+s]=t.length;for(let n=0;n<t.length;n++)if(e<t[n]){f[r+s]=n;break}}}return u||n.disposeIntermediateTensorInfo(l),n.makeTensorInfo(d,"int32",f)}};function bc(e,t,n){const r=s.D5U.createScalarValue(-1,n);return fu([],t,r,e,n)}const xc={kernelName:s.kuV,backendName:"cpu",kernelFunc:function(e){const{inputs:t,backend:n}=e,{x:r}=t;no(r,"neg");const s=n.data.get(r.dataId).values,[a,o]=bc(s,r.shape,r.dtype);return n.makeTensorInfo(o,r.dtype,a)}},wc=s.GDt.GP;const vc={kernelName:s.uv1,backendName:"cpu",kernelFunc:function(e){const{inputs:t,backend:n,attrs:r}=e,{boxes:s,scores:a}=t,{maxOutputSize:o,iouThreshold:i,scoreThreshold:u}=r;no(s,"NonMaxSuppression");const l=n.data.get(s.dataId).values,c=n.data.get(a.dataId).values,{selectedIndices:p}=wc(l,c,o,i,u);return n.makeTensorInfo([p.length],"int32",new Int32Array(p))}},kc=s.GDt.qP;const Ic={kernelName:s.cye,backendName:"cpu",kernelFunc:function(e){const{inputs:t,backend:n,attrs:r}=e,{boxes:s,scores:a}=t,{maxOutputSize:o,iouThreshold:i,scoreThreshold:u,padToMaxOutputSize:l}=r;no(s,"NonMaxSuppressionPadded");const c=n.data.get(s.dataId).values,p=n.data.get(a.dataId).values,{selectedIndices:h,validOutputs:d}=kc(c,p,o,i,u,l);return[n.makeTensorInfo([h.length],"int32",new Int32Array(h)),n.makeTensorInfo([],"int32",new Int32Array([d]))]}},Nc=s.GDt.pA;const Sc={kernelName:s.W0H,backendName:"cpu",kernelFunc:function(e){const{inputs:t,backend:n,attrs:r}=e,{boxes:s,scores:a}=t,{maxOutputSize:o,iouThreshold:i,scoreThreshold:u,softNmsSigma:l}=r;no(s,"NonMaxSuppressionWithScore");const c=n.data.get(s.dataId).values,p=n.data.get(a.dataId).values,h=o,d=i,f=u,m=l,{selectedIndices:g,selectedScores:y}=Nc(c,p,h,d,f,m);return[n.makeTensorInfo([g.length],"int32",new Int32Array(g)),n.makeTensorInfo([y.length],"float32",new Float32Array(y))]}},Tc=fo(((e,t)=>e!==t?1:0)),Cc=Oo(s.yQU,Tc,null,"bool"),Ec={kernelName:s.yQU,backendName:"cpu",kernelFunc:Cc};const $c={kernelName:s.we_,backendName:"cpu",kernelFunc:function(e){const{inputs:t,backend:n,attrs:r}=e,{indices:a}=t,{dtype:o,depth:i,onValue:u,offValue:l}=r;no(a,"oneHot");const c=s.D5U.sizeFromShape(a.shape),p=new Float32Array(c*i);p.fill(l);const h=n.data.get(a.dataId).values;for(let s=0;s<c;++s)h[s]>=0&&h[s]<i&&(p[s*i+h[s]]=u);return n.makeTensorInfo([...a.shape,i],o,p)}};function Ac(e){const{inputs:t,backend:n}=e,{x:r}=t;if("string"===r.dtype)throw new Error("zerosLike is not supported for string tensors");if("complex64"===r.dtype){const e=Ao({inputs:{input:r},backend:n}),t=Ac({inputs:{x:e},backend:n}),s=Pi({inputs:{input:r},backend:n}),a=Ac({inputs:{x:s},backend:n}),o=Co({inputs:{real:t,imag:a},backend:n});return n.disposeIntermediateTensorInfo(e),n.disposeIntermediateTensorInfo(t),n.disposeIntermediateTensorInfo(s),n.disposeIntermediateTensorInfo(a),o}return Zu({backend:n,attrs:{shape:r.shape,value:0,dtype:r.dtype}})}const Dc={kernelName:s.RuY,backendName:"cpu",kernelFunc:Ac};const _c={kernelName:s.qWM,backendName:"cpu",kernelFunc:function e(t){const{inputs:n,backend:r}=t,{x:s}=n;if("string"===s.dtype)throw new Error("onesLike is not supported for string tensors");if("complex64"===s.dtype){const t=Ao({inputs:{input:s},backend:r}),n=e({inputs:{x:t},backend:r}),a=Pi({inputs:{input:s},backend:r}),o=Ac({inputs:{x:a},backend:r}),i=Co({inputs:{real:n,imag:o},backend:r});return r.disposeIntermediateTensorInfo(t),r.disposeIntermediateTensorInfo(n),r.disposeIntermediateTensorInfo(a),r.disposeIntermediateTensorInfo(o),i}return Zu({backend:r,attrs:{shape:s.shape,value:1,dtype:s.dtype}})}};function Rc(e){const{inputs:t,backend:n,attrs:r}=e,{axis:a}=r;if(1===t.length)return Mu({inputs:{input:t[0]},backend:n,attrs:{dim:a}});const o=t[0].shape,i=t[0].dtype;t.forEach((e=>{s.D5U.assertShapesMatch(o,e.shape,"All tensors passed to stack must have matching shapes"),s.D5U.assert(i===e.dtype,(()=>"All tensors passed to stack must have matching dtypes"))}));const u=[],l=zi({inputs:t.map((e=>{const t=Mu({inputs:{input:e},backend:n,attrs:{dim:a}});return u.push(t),t})),backend:n,attrs:{axis:a}});return u.forEach((e=>n.disposeIntermediateTensorInfo(e))),l}const Fc={kernelName:s.QiL,backendName:"cpu",kernelFunc:Rc};const Oc={kernelName:s.lyA,backendName:"cpu",kernelFunc:function(e){const{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{paddings:o,constantValue:i}=r;no(a,"pad");const u=o.map(((e,t)=>e[0]+a.shape[t]+e[1])),l=o.map((e=>e[0])),c=n.data.get(a.dataId).values,p=s.D5U.sizeFromShape(a.shape),h=a.shape.length,d=s.D5U.computeStrides(a.shape),f=s.D5U.sizeFromShape(u),m=u.length,g=s.D5U.computeStrides(u),y=s.D5U.getTypedArrayFromDType(a.dtype,f);0!==i&&y.fill(i);for(let b=0;b<p;b++){const e=s.D5U.indexToLoc(b,h,d).map(((e,t)=>e+l[t]));y[s.D5U.locToIndex(e,m,g)]=c[b]}return{dataId:n.write(y,u,a.dtype),shape:u,dtype:a.dtype}}},Mc=fo(((e,t)=>Math.pow(e,t))),Bc=Oo(s.pe_,Mc),Lc={kernelName:s.pe_,backendName:"cpu",kernelFunc:Bc};function Wc(e,t,n,r){const[a,o]=s.Wap.computeOutAndReduceShapes(e,r),i=(0,s.x8V)(t,"int32"),u=s.D5U.makeZerosTypedArray(s.D5U.sizeFromShape(a),i),l=s.D5U.sizeFromShape(o);for(let s=0;s<u.length;++s){const e=s*l;let t=1;for(let r=0;r<l;++r)t*=n[e+r];u[s]=t}return{outVals:u,outShape:a,outDtype:i}}const Pc={kernelName:s.DlI,backendName:"cpu",kernelFunc:function(e){const{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{axis:o,keepDims:i}=r;no(a,"prod");const u=a.shape.length,l=s.D5U.parseAxisParam(o,a.shape),c=s.Wap.getAxesPermutation(l,u);let p=l,h=a;const d=[];null!=c&&(h=ei({inputs:{x:a},backend:n,attrs:{perm:c}}),d.push(h),p=s.Wap.getInnerMostAxes(p.length,u));const f=n.data.get(h.dataId).values,{outVals:m,outShape:g,outDtype:y}=Wc(h.shape,h.dtype,f,p);let b=g;return i&&(b=s.Wap.expandShapeToKeepDim(g,l)),d.forEach((e=>n.disposeIntermediateTensorInfo(e))),n.makeTensorInfo(b,y,m)}};function Uc(e,t,n,r){const s=[];let a=0;const o=t.length-1+n.length,i=new Array(o).fill(null).map((()=>[0]));!function(e,t){for(let n=0;n<e.length;++n){const r=e[n],s=n===e.length-1?t:e[n+1].length;if(0===r.length)throw new Error("Ragged splits may not be empty");if(r[0]<0)throw new Error("Ragged splits must be non-negative");if(r[r.length-1]>s)throw new Error("Ragged splits must not point past values");for(let e=1;e<r.length;++e)if(r[e-1]>r[e])throw new Error("Ragged splits must be sorted in ascending order")}}(n,r);let u=1;for(let l=0;l<t.length-1;++l){u*=t[l];const e=t[l+1];for(let t=1;t<u+1;++t)i[l].push(t*e)}for(let l=0;l<e.length;++l){let r=e[l],o=e[l]+1;for(let e=0;e<n.length;++e){const s=n[e],a=e+t.length-1;if(a>=0){const e=i[a],t=e[e.length-1]-s[r];for(let n=r;n<o;++n)i[a].push(s[n+1]+t)}r=s[r],o=s[o]}o!==r&&(s.push([r,o]),a+=o-r)}return{outSplits:i,valueSlices:s,numValues:a}}function zc(e,t){const n=e.slice(0,t);for(;n.length<t;)n.push(1);for(let r=t;r<e.length;r++)n[t-1]*=e[r];return n}function Vc(e,t,n,r,a){const o=t.slice();o[0]=a;const i=s.D5U.getArrayFromDType(n,s.D5U.sizeFromShape(o)),u=e.length;return function(e,t,n,r,s,a){const o=zc(t,2)[1],i=zc(a,2)[1];let u=0;for(const l of n)for(let t=l[0];t<l[1];++t){for(let n=0;n<r;++n)s[u*i+n]=e[t*o+n];++u}}(e,t,r,0===u?0:u/t[0],i,o),[i,o]}function Gc(e,t,n,r,a,o,i,u){if(0===e.length)throw new Error("paramsNestedSplits must be non empty");if(0===t[0].length)throw new Error("Split tensors must not be scalars");if(function(e,t,n){e.forEach(((e,r)=>{if(e<0||e>=n){const a=s.D5U.indexToLoc(r,t.length,s.D5U.computeStrides(t)).join(",");throw new Error(`indices[${a}] = ${e} is not in [0, ${n})`)}}))}(o,i,t[0][0]-1),0===r.length)throw new Error("params.rank must be nonzero");const l=r[0],{outSplits:c,valueSlices:p,numValues:h}=Uc(o,i,e,l),d=function(e){const t=[];for(let n=0;n<e.length;++n){const r=e[n].length,a=s.D5U.getArrayFromDType("int32",r);t.push(a),e[n].forEach(((e,t)=>a[t]=e))}return t}(c),f=Vc(n,r,a,p,h);return[d,f[0],f[1]]}const Hc={kernelName:s.dDz,backendName:"cpu",kernelFunc:function(e){const{inputs:t,backend:n,attrs:r}=e,{paramsNestedSplits:s,paramsDenseValues:a,indices:o}=t,{outputRaggedRank:i}=r,u=s.map((e=>n.data.get(e.dataId).values)),l=s.map((e=>e.shape)),c=n.data.get(a.dataId).values,p=n.data.get(o.dataId).values,[h,d,f]=Gc(u,l,c,a.shape,a.dtype,p,o.shape),m=h.map((e=>n.makeTensorInfo([e.length],"int32",e))),g=n.makeTensorInfo(f,a.dtype,d);return m.concat([g])}};var jc=s.Wap.RowPartitionType;class Xc{constructor(e,t,n,r,a,o,i,u,l,c){this.shape=e,this.shapeShape=t,this.values=n,this.valuesShape=r,this.valuesDType=a,this.defaultValue=o,this.defaultValueShape=i,this.rowPartitionValues=u,this.rowPartitionValuesShapes=l,this.rowPartitionTypes=s.Wap.getRowPartitionTypesHelper(c),this.raggedRank=s.Wap.getRaggedRank(this.rowPartitionTypes)}getRowPartitionTypeByDimension(e){return this.rowPartitionTypes[0]===jc.FIRST_DIM_SIZE?this.rowPartitionTypes[e+1]:this.rowPartitionTypes[e]}getRowPartitionTensor(e){return this.rowPartitionTypes[0]===jc.FIRST_DIM_SIZE?this.rowPartitionValues[e+1]:this.rowPartitionValues[e]}getMaxWidth(e){const t=this.getRowPartitionTensor(e-1);switch(this.getRowPartitionTypeByDimension(e-1)){case jc.VALUE_ROWIDS:return Xc.getMaxWidthValueRowID(t);case jc.ROW_SPLITS:return Xc.getMaxWidthRowSplit(t);default:throw new Error(`Cannot handle partition type ${jc[this.getRowPartitionTypeByDimension(e-1)]}`)}}static getMaxWidthRowSplit(e){const t=e.length;if(0===t||1===t)return 0;let n=0;for(let r=0;r<t-1;++r){const t=e[r+1]-e[r];t>n&&(n=t)}return n}static getMaxWidthValueRowID(e){const t=e.length;if(0===t)return 0;let n=0,r=e[0],s=0;for(let a=1;a<t;++a){const t=e[a];t!==r&&(r=t,s=Math.max(a-n,s),n=a)}return Math.max(t-n,s)}tensorShapeFromTensor(e,t,n=!0){if(0===t.length){if(-1===e[0])return[];throw new Error("The only valid scalar shape tensor is the fully unknown shape specified as -1.")}return Kc(e,n)}calculateOutputSize(e){const t=this.valuesShape,n=this.defaultValueShape;s.Wap.validateDefaultValueShape(n,t);const r=this.tensorShapeFromTensor(this.shape,this.shapeShape),a=s.Wap.combineRaggedTensorToTensorShapes(this.raggedRank,r,t);a[0]<0&&(a[0]=e);for(let s=1;s<=this.raggedRank;++s)a[s]<0&&(a[s]=this.getMaxWidth(s));return a}calculateFirstParentOutputIndex(e,t,n){const r=Math.min(e,n),a=[];let o=0;for(let s=0;s<r;++s,o+=t)a.push(o);for(let s=r;s<e;++s)a.push(-1);return s.D5U.assert(a.length===e,(()=>"Final length of result must be equal to firstDimension.")),a}calculateOutputIndexRowSplit(e,t,n,r){const s=e.length,a=[];for(let o=0;o<s-1;++o){const s=e[o+1]-e[o];let i=Math.min(r,s),u=t[o];-1===u&&(i=0);for(let e=0;e<i;++e)a.push(u),u+=n;for(let e=0;e<s-i;++e)a.push(-1)}if(s>0&&a.length!==e[s-1])throw new Error("Invalid row split size.");return a}calculateOutputIndexValueRowID(e,t,n,r){const s=e.length,a=[];if(0===s)return[];let o=0,i=e[0];if(i>=t.length)throw new Error(`Got currentValueRowId=${i}, which is not less than ${t.length}`);let u=t[i];a.push(u);for(let l=1;l<s;++l){const s=e[l];if(s===i)u>=0&&(++o,o<r?u+=n:u=-1);else{if(o=0,i=s,s>=t.length)throw new Error(`Got nextValueRowId=${s} which is not less than ${t.length}`);u=t[s]}a.push(u)}if(a.length!==e.length)throw new Error("Invalid row ids.");return a}calculateOutputIndex(e,t,n,r){const s=this.getRowPartitionTensor(e),a=this.getRowPartitionTypeByDimension(e);switch(a){case jc.VALUE_ROWIDS:return this.calculateOutputIndexValueRowID(s,t,n,r);case jc.ROW_SPLITS:if(s.length-1>t.length)throw new Error(`Row partition size is greater than output size: ${s.length-1} > ${t.length}`);return this.calculateOutputIndexRowSplit(s,t,n,r);default:throw new Error(`Unsupported partition type: ${jc[a]}`)}}getFirstDimensionSize(){const e=this.rowPartitionValues[0];if(0===this.rowPartitionTypes.length)throw new Error("No row_partition_types given.");const t=this.rowPartitionTypes[0];switch(t){case jc.FIRST_DIM_SIZE:return e[0];case jc.VALUE_ROWIDS:throw new Error("Cannot handle VALUE_ROWIDS in first dimension.");case jc.ROW_SPLITS:return this.rowPartitionValuesShapes[0][0]-1;default:throw new Error(`Cannot handle type ${jc[t]}`)}}compute(){if(this.rowPartitionValues[0].length<=0)throw new Error("Invalid first partition input. Tensor requires at least one element.");const e=this.getFirstDimensionSize(),t=this.calculateOutputSize(e),n=new Array(this.raggedRank+1);n[n.length-1]=1;for(let s=n.length-2;s>=0;--s)n[s]=n[s+1]*t[s+1];const r=Kc(t,!1),a=s.D5U.getArrayFromDType(this.valuesDType,s.D5U.sizeFromShape(r));if(n[0]*t[0]>0){let s=this.calculateFirstParentOutputIndex(e,n[0],t[0]);for(let e=1;e<=this.raggedRank;++e){s=this.calculateOutputIndex(e-1,s,n[e],t[e])}this.setOutput(this.raggedRank,s,a,r)}return[r,a]}setOutput(e,t,n,r){if(0===n.length)return;const a=this.values,o=n;let i=r.slice();i=i.slice(e+1);const u=s.D5U.sizeFromShape(i),l=t.length;let c=this.defaultValue;if(c.length!==u&&1!==c.length){const e=this.defaultValueShape;(0,s.lub)((()=>{const t=(0,s.XLQ)(c,e),n=(0,s.UFq)(t,i);c=n.dataSync()}))}let p=0,h=0,d=0;for(let s=0;s<=l;++s){let e=s<l?t[s]:-1;if(e!==d){if(h<d){const e=a.subarray(p*u);qc(o.subarray(h*u),e,(d-h)*u)}if(s>=l){const t=n.length;e=Math.floor(t/u)}if(e>d)if(1===this.defaultValue.length)o.subarray(d*u,e*u).fill(this.defaultValue[0]),d=e;else for(;e>d;){qc(o.slice(d*u),c,u),++d}e<0?(p=s+1,h=d):(p=s,h=d,d=h+1)}else++d}}}function qc(e,t,n){for(let r=0;r<n;r++)e[r]=t[r]}function Kc(e,t){const n=[];for(let r of e){if(r<0){if(!t)throw new Error(`Dimension ${r} must be >= 0`);if(r<-1)throw new Error(`Dimension ${r} must be >= -1`);r=-1}n.push(r)}return n}function Qc(e,t,n,r,s,a,o,i,u,l){return new Xc(e,t,n,r,s,a,o,i,u,l).compute()}const Yc={kernelName:s.BiW,backendName:"cpu",kernelFunc:function(e){const{inputs:t,backend:n,attrs:r}=e,{shape:s,values:a,defaultValue:o,rowPartitionTensors:i}=t,{rowPartitionTypes:u}=r,l=n.data.get(s.dataId).values,c=n.data.get(a.dataId).values,p=n.data.get(o.dataId).values,h=i.map((e=>n.data.get(e.dataId).values)),d=i.map((e=>e.shape)),[f,m]=Qc(l,s.shape,c,a.shape,a.dtype,p,o.shape,h,d,u);return n.makeTensorInfo(f,a.dtype,m)}};function 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n=t*(h*d);for(let r=0;r<h;r++){const s=r*d;for(let a=0;a<d;a++){const i=[c,t,r,a],u=i[2],x=i[1];let w=(u-f)*y-(x-m)*g,v=(u-f)*g+(x-m)*y;w=Math.round(w+f),v=Math.round(v+m);let k=o;if("number"!==typeof o&&(k=3===a?255:o[a]),w>=0&&w<h&&v>=0&&v<p){k=b[e+v*(h*d)+w*d+a]}l[e+n+s+a]=k}}}}return{dataId:u.write(l,r.shape,r.dtype),shape:r.shape,dtype:r.dtype}}},up=ao(s.e07,(e=>{const t=Math.floor(e);return e-t<.5?Math.floor(e):e-t>.5?Math.ceil(e):t%2===0?t:t+1})),lp={kernelName:s.e07,backendName:"cpu",kernelFunc:up},cp=ko((e=>1/Math.sqrt(e))),pp=oo(s.bV0,cp),hp={kernelName:s.bV0,backendName:"cpu",kernelFunc:pp};function dp(e,t,n,r,a,o,i,u,l,c){const p=[r/a,a],h=e.values,d=t.values;if(0===r)return(0,s.f3b)(n,t.dtype);const f=(0,s.f3b)(p,t.dtype);"string"===typeof l||"number"===typeof l?f.values.fill(l):"boolean"===typeof l&&f.values.fill(+l);for(let s=0;s<o;s++){const e=[];let o=0;for(let t=0;t<i;t++){const n=h[s*i+t];e.push(n),o+=n*u[t]}if(o<0||o>=r/a)throw new Error(`Invalid indices: ${e} does 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c=Oc.kernelFunc({inputs:{x:a},backend:n,attrs:{paddings:l,constantValue:0}}),p=s.Wap.getReshaped(c.shape,o,u,!1),h=s.Wap.getPermuted(p.length,o.length,!1),d=s.Wap.getReshapedPermuted(c.shape,o,u,!1),f=Uo({inputs:{x:c},backend:n,attrs:{shape:p}}),m=ei({inputs:{x:f},backend:n,attrs:{perm:h}}),g=Uo({inputs:{x:m},backend:n,attrs:{shape:d}});return n.disposeIntermediateTensorInfo(c),n.disposeIntermediateTensorInfo(f),n.disposeIntermediateTensorInfo(m),g}};function Rp(e,t,n,r,a,o,i){const u=t[0],l=o[0],c=new Array(l),p=new Array(u),h=t[1];if(0===l){if(0!==u)throw new Error(s.Wap.getSparseFillEmptyRowsIndicesDenseShapeMismatch(u));return[s.D5U.getArrayFromDType(n,0),[0,h],s.D5U.getArrayFromDType(a,0),c,p]}let d=!0,f=0;const m=new Array(l).fill(0);for(let y=0;y<u;++y){const t=e[y*h];if(t<0)throw new Error(s.Wap.getSparseFillEmptyRowsNegativeIndexErrorMessage(y,t));if(t>=l)throw new Error(s.Wap.getSparseFillEmptyRowsOutOfRangeIndexErrorMessage(y,t,l));++m[t],d=d&&t>=f,f=t}let g=!0;for(let s=0;s<l;++s){const e=0===m[s];c[s]=e,g=g&&!e,m[s]=Math.max(m[s],1),s>0&&(m[s]+=m[s-1])}if(g&&d){const t=e,n=r;for(let e=0;e<u;++e)p[e]=e;return[t,[u,h],n,c,p]}{const t=m[l-1],o=s.D5U.getArrayFromDType(n,t*h),d=s.D5U.getArrayFromDType(a,t),f=new Array(l).fill(0);for(let n=0;n<u;++n){const t=e[n*h],s=f[t],a=(0===t?0:m[t-1])+s;f[t]++;for(let r=0;r<h;++r)o[a*h+r]=e[n*h+r];d[a]=r[n],p[n]=a}for(let e=0;e<l;++e){if(0===f[e]){const t=0===e?0:m[e-1];o[t*h+0]=e;for(let e=1;e<h;++e)o[t*h+e]=0;d[t]=i}}return[o,[t,h],d,c,p]}}const Fp={kernelName:s.O3z,backendName:"cpu",kernelFunc:function(e){const{inputs:t,backend:n}=e,{indices:r,values:s,denseShape:a,defaultValue:o}=t;if(1!==a.shape.length)throw new Error(`Dense shape must be a vector, saw:\n ${a.shape}`);if(2!==r.shape.length)throw new Error(`Indices must be a matrix, saw:\n ${r.shape}`);if(1!==s.shape.length)throw new Error(`Values must be a vector, saw:\n ${s.shape}`);if(0!==o.shape.length)throw new Error(`Default value must be a scalar, saw:\n 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o=Array.from(n.data.get(s.dataId).values),i=n.data.get(r.dataId).values,u=Array.from(n.data.get(a.dataId).values),[l,c,p]=Op(i,r.shape,r.dtype,o,u);return[n.makeTensorInfo(c,r.dtype,l),n.makeTensorInfo([p.length],a.dtype,new Int32Array(p))]}};function Bp(e,t,n,r,a,o=!1,i=0){const u=r.length,l=[t[0],e.length/t[0]],c=l[1],p=u>0?a[u-1]+1:0;if(p<0)throw new Error(s.Wap.getSparseSegmentReductionNegativeSegmentIdsErrorMessage());const h=t.slice();h[0]=p;const d=h.reduce(((e,t)=>e*t),1),f=s.D5U.getArrayFromDType(n,d);if(0===u)return p>0&&f.fill(i),[f,h];if(p<=0)throw new Error(s.Wap.getSparseSegmentReductionNegativeSegmentIdsErrorMessage());let m=0,g=1,y=0,b=a[m];for(;;){let t=0;if(g<u){if(t=a[g],b===t){++g;continue}if(b>=t)throw new Error(s.Wap.getSparseSegmentReductionNonIncreasingSegmentIdsErrorMessage())}if(b<0||b>=p)throw new Error(s.Wap.getSparseSegmentReductionSegmentIdOutOfRangeErrorMessage(b,p));b>y&&f.fill(i,y*c,b*c);for(let n=m;n<g;++n){const t=r[n];if(t<0||t>=l[0])throw new 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Wp={kernelName:s.ZjV,backendName:"cpu",kernelFunc:function(e){const{inputs:t,backend:n}=e,{data:r,indices:s,segmentIds:a}=t;if(r.shape.length<1)throw new Error("Data should be at least 1 dimensional but received scalar");if(1!==s.shape.length)throw new Error(`Indices should be a vector but received shape\n ${s.shape}`);if(1!==a.shape.length)throw new Error(`Segment ids should be a vector but received shape\n ${a.shape}`);if(s.shape[0]!==a.shape[0])throw new Error("segmentIds and indices should have same size.");const o=n.data.get(r.dataId).values,i=n.data.get(s.dataId).values,u=n.data.get(a.dataId).values,[l,c]=Bp(o,r.shape,r.dtype,i,u);return n.makeTensorInfo(c,r.dtype,l)}};const Pp={kernelName:s.D2d,backendName:"cpu",kernelFunc:function(e){const{inputs:t,backend:n,attrs:r}=e,{sparseIndices:a,sparseValues:o,defaultValue:i}=t,{outputShape:u}=r,{sliceRank:l,numUpdates:c,sliceSize:p,strides:h,outputSize:d}=s.Wap.calculateShapes(o,a,u),f=!1,m=n.bufferSync(a);let g;switch(o.dtype){case"bool":g=dp(m,n.bufferSync(o),u,d,p,c,l,h,Boolean(n.data.get(i.dataId).values[0]),f);break;case"float32":g=dp(m,n.bufferSync(o),u,d,p,c,l,h,n.data.get(i.dataId).values[0],f);break;case"int32":g=dp(m,n.bufferSync(o),u,d,p,c,l,h,n.data.get(i.dataId).values[0],f);break;case"string":g=dp(m,n.bufferSync(o),u,d,p,c,l,h,s.D5U.decodeString(n.data.get(i.dataId).values[0]),f);break;default:throw new Error(`Unsupported type ${o.dtype}`)}return n.makeTensorInfo(u,g.dtype,g.values)}};const Up={kernelName:s.L8s,backendName:"cpu",kernelFunc:function(e){const{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{numOrSizeSplits:o,axis:i}=r,u=s.D5U.parseAxisParam(i,a.shape)[0],l=s.Wap.prepareSplitSize(a,o,u),c=new Array(a.shape.length).fill(0),p=a.shape.slice();return l.map((e=>{const t=[...p];t[u]=e;const r=Ti({inputs:{x:a},backend:n,attrs:{begin:c,size:t}});return 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Zp={kernelName:s.jQk,backendName:"cpu",kernelFunc:function(e){const{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{begin:o,end:i,strides:u,beginMask:l,endMask:c,ellipsisMask:p,newAxisMask:h,shrinkAxisMask:d}=r;no(a,"stridedSlice");const{finalShapeSparse:f,finalShape:m,isIdentity:g,sliceDim0:y,isSimpleSlice:b,begin:x,end:w,strides:v}=s.kuN.sliceInfo(a.shape,o,i,u,l,c,p,h,d);let k;if(g)k=Uo({inputs:{x:a},backend:n,attrs:{shape:m}});else if(y||b){s.D5U.assert(a.shape.length>=1,(()=>`Input must have rank at least 1, got: ${a.shape.length}`));const e=s.kuN.computeOutShape(x,w,v),t=Ti({inputs:{x:a},backend:n,attrs:{begin:x,size:e}});k=Uo({inputs:{x:t},backend:n,attrs:{shape:m}}),n.disposeIntermediateTensorInfo(t)}else{const e=Yp(f,n.bufferSync(a),v,x);k=n.makeTensorInfo(m,e.dtype,e.values)}return k}};class Jp{constructor(e,t,n,r,a,o){this.separator=s.D5U.encodeString(e),this.nGramWidths=t,this.leftPad=s.D5U.encodeString(n),this.rightPad=s.D5U.encodeString(r),this.padWidth=a,this.preserveShort=o}getPadWidth(e){return Math.min(this.padWidth<0?e-1:this.padWidth,e-1)}getNumNGrams(e,t){const n=this.getPadWidth(t);return Math.max(0,e+2*n-t+1)}createNGrams(e,t,n,r,s,a){for(let o=0;o<s;++o){const i=this.getPadWidth(a),u=Math.max(0,i-o),l=Math.max(0,i-(s-(o+1))),c=a-(u+l),p=t+(u>0?0:o-i);let h=0;h+=u*this.leftPad.length;for(let t=0;t<c;++t)h+=e[p+t].length;h+=l*this.rightPad.length;h+=(u+l+c-1)*this.separator.length,n[r+o]=new Uint8Array(h);const d=n[r+o];let f=0;const m=e=>e.forEach((e=>d[f++]=e));for(let e=0;e<u;++e)m(this.leftPad),m(this.separator);for(let t=0;t<c-1;++t)m(e[p+t]),m(this.separator);if(c>0){m(e[p+c-1]);for(let e=0;e<l;++e)m(this.separator),m(this.rightPad)}else{for(let e=0;e<l-1;++e)m(this.rightPad),m(this.separator);m(this.rightPad)}}}compute(e,t){const n=e.length,r=t.length;if(r>0){let e=t[0];if(0!==e)throw new Error(`First split value must be 0, got ${e}`);for(let s=1;s<r;++s){let r=t[s]>=e;if(r=r&&t[s]<=n,!r)throw new Error(`Invalid split value ${t[s]}, must be in [${e}, ${n}]`);e=t[s]}if(e!==n)throw new Error(`Last split value must be data size. 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th={kernelName:s._JP,backendName:"cpu",kernelFunc:function(e){const{inputs:t,backend:n,attrs:r}=e,{separator:s,nGramWidths:a,leftPad:o,rightPad:i,padWidth:u,preserveShortSequences:l}=r,{data:c,dataSplits:p}=t,h=n.data.get(c.dataId).values,d=n.data.get(p.dataId).values,[f,m]=eh(h,d,s,a,o,i,u,l);return[n.makeTensorInfo([f.length],"string",f),n.makeTensorInfo(p.shape,"int32",m)]}};function nh(e,t,n,r){if(!e.length)return;if(0===t.length){for(let t=0;t<e.length;++t)r.push(e.subarray(t,t+1));return}if(1===t.length){const s=t[0];let a=e.indexOf(s);for(;-1!==a;){const t=e.subarray(0,a);n&&0===t.length||r.push(t),a=(e=e.subarray(a+1)).indexOf(s)}return void(n&&0===e.length||r.push(e))}let s=0;for(let a=0;a<e.length+1;a++)if(a===e.length||-1!==t.indexOf(e[a])){const t=e.subarray(s,a);n&&0===t.length||r.push(t),s=a+1}}function rh(e,t,n){const r=e.length,a=[];let o=0,i=0;const u=new Array(r);for(let s=0;s<r;++s){const r=a.length;nh(e[s],t,n,a);const l=a.length-r;u[s]=l,o+=l,i=Math.max(i,l)}const l=s.D5U.getArrayFromDType("int32",2*o),c=new Array(o),p=[r,i];let h=0;for(let s=0;s<r;++s)for(let e=0;e<u[s];++e)l[2*h]=s,l[2*h+1]=e,c[h]=a[h],++h;return[l,c,p]}const sh={kernelName:s.s1s,backendName:"cpu",kernelFunc:function(e){const{inputs:t,backend:n,attrs:r}=e,{skipEmpty:s}=r,{input:a,delimiter:o}=t;if("string"!==a.dtype)throw new Error("Input must be of datatype string");if(1!==a.shape.length)throw new Error(`Input must be a vector, got shape: ${a.shape}`);if(0!==o.shape.length)throw new Error(`Delimiter must be a scalar, got shape: ${o.shape}`);const i=n.data.get(a.dataId).values,u=n.data.get(o.dataId).values[0],[l,c,p]=rh(i,u,s),h=c.length;return[n.makeTensorInfo([h,2],"int32",l),n.makeTensorInfo([h],"string",c),n.makeTensorInfo([2],"int32",new Int32Array(p))]}};function ah(e,t){const n=s.D5U.getArrayFromDType("int32",e.length);for(let r=0;r<e.length;++r)n[r]=s.D5U.fingerPrint64(e[r]).modulo(t).getLowBitsUnsigned();return n}const oh={kernelName:s.XkS,backendName:"cpu",kernelFunc:function(e){const{inputs:t,backend:n,attrs:r}=e,{numBuckets:s}=r,{input:a}=t;if("string"!==a.dtype)throw new Error("Input must be of datatype string");if(s<=0)throw new Error("Number of buckets must be at least 1");const o=ah(n.data.get(a.dataId).values,s);return n.makeTensorInfo(a.shape,"int32",o)}},ih=ao(s.sEM,(e=>Math.tan(e))),uh={kernelName:s.sEM,backendName:"cpu",kernelFunc:ih},lh=ao(s.MIZ,(e=>Math.tanh(e))),ch={kernelName:s.MIZ,backendName:"cpu",kernelFunc:lh};function ph(e,t){const n=new Array(e.rank);for(let s=0;s<n.length;s++)n[s]=e.shape[s]*t[s];const r=(0,s.f3b)(n,e.dtype);for(let s=0;s<r.values.length;++s){const t=r.indexToLoc(s),n=new Array(e.rank);for(let r=0;r<n.length;r++)n[r]=t[r]%e.shape[r];const a=e.locToIndex(n);r.values[s]=e.values[a]}return r}const hh={kernelName:s.n9L,backendName:"cpu",kernelFunc:function(e){const{inputs:t,backend:n,attrs:r}=e,{x:s}=t,{reps:a}=r;no(s,"tile");const o=ph(n.bufferSync(s),a);return n.makeTensorInfo(o.shape,o.dtype,o.values)}},dh=(e,t)=>{const n=t.value-e.value;return 0===n?e.index-t.index:n};function fh(e,t,n=0,r=e.length-1){for(;r>n;){if(r-n>600){const s=r-n+1,a=t-n+1,o=Math.log(s),i=.5*Math.exp(2*o/3),u=.5*Math.sqrt(o*i*(s-i)/s)*Math.sign(a-s/2);fh(e,t,Math.max(n,Math.floor(t-a*i/s+u)),Math.min(r,Math.floor(t+(s-a)*i/s+u)))}const a=e[t];let o=n,i=r;for(s.D5U.swap(e,n,t),dh(e[r],a)>0&&s.D5U.swap(e,n,r);o<i;){for(s.D5U.swap(e,o,i),o++,i--;dh(e[o],a)<0;)o+=1;for(;dh(e[i],a)>0;)i-=1}0===dh(e[n],a)?s.D5U.swap(e,n,i):(i+=1,s.D5U.swap(e,i,r)),i<=t&&(n=i+1),t<=i&&(r=i-1)}}function mh(e,t,n,r,a){const o=t[t.length-1],[i,u]=[e.length/o,o],l=s.D5U.getTypedArrayFromDType(n,i*r),c=s.D5U.getTypedArrayFromDType("int32",i*r);for(let s=0;s<i;s++){const t=s*u,n=e.subarray(t,t+u);let o=new Array(n.length);n.forEach(((e,t)=>o[t]={value:e,index:t})),r<o.length&&(fh(o,r),o=o.slice(0,r)),a&&o.sort(dh);const i=s*r,p=l.subarray(i,i+r),h=c.subarray(i,i+r);for(let e=0;e<r;e++)p[e]=o[e].value,h[e]=o[e].index}const p=t.slice();return p[p.length-1]=r,[(0,s.f3b)(p,n,l),(0,s.f3b)(p,"int32",c)]}const gh={kernelName:s.cWu,backendName:"cpu",kernelFunc:function(e){const{inputs:t,backend:n,attrs:r}=e,{x:s}=t,{k:a,sorted:o}=r;no(s,"topk");const i=n.data.get(s.dataId).values,[u,l]=mh(i,s.shape,s.dtype,a,o);return[n.makeTensorInfo(u.shape,u.dtype,u.values),n.makeTensorInfo(l.shape,l.dtype,l.values)]}};const yh={kernelName:s.wx7,backendName:"cpu",kernelFunc:function(e){const{inputs:t,attrs:n,backend:r}=e,{image:a,transforms:o}=t,{interpolation:i,fillMode:u,fillValue:l,outputShape:c}=n,[p,h,d,f]=a.shape,[m,g]=null!=c?c:[h,d],y=[p,m,g,f],b=s.D5U.computeStrides(a.shape),x=b[0],w=b[1],v=b[2],k=s.D5U.computeStrides(y),I=k[0],N=k[1],S=k[2],T=s.D5U.getTypedArrayFromDType(a.dtype,s.D5U.sizeFromShape(y));T.fill(l);const C=r.data.get(a.dataId).values,E=r.data.get(o.dataId).values;for(let s=0;s<p;++s){const e=1===o.shape[0]?E:E.subarray(8*s,8*s+8);for(let t=0;t<m;++t)for(let n=0;n<g;++n)for(let r=0;r<f;++r){let a;const o=e[6]*n+e[7]*t+1;if(0===o)continue;const c=(e[0]*n+e[1]*t+e[2])/o,p=(e[3]*n+e[4]*t+e[5])/o,f=bh(c,d,u),m=bh(p,h,u);switch(i){case"nearest":a=wh(C,h,d,x,w,v,s,m,f,r,l);break;case"bilinear":a=vh(C,h,d,x,w,v,s,m,f,r,l);break;default:throw new Error(`Error in Transform: Expect 'nearest' or 'bilinear', but got ${i}`)}T[s*I+t*N+n*S+r]=a}return r.makeTensorInfo(y,a.dtype,T)}return{dataId:r.write(T,y,a.dtype),shape:a.shape,dtype:a.dtype}}};function bh(e,t,n){switch(n){case"reflect":return function(e,t){let n=e;if(n<0)if(t<=1)n=0;else{const e=2*t;n<e&&(n=e*Math.trunc(-n/e)+n),n=n<-t?n+e:-n-1}else if(n>t-1)if(t<=1)n=0;else{const e=2*t;n-=e*Math.trunc(n/e),n>=t&&(n=e-n-1)}return s.D5U.clamp(0,n,t-1)}(e,t);case"wrap":return function(e,t){let n=e;if(n<0)if(t<=1)n=0;else{const e=t-1;n+=t*(Math.trunc(-n/e)+1)}else if(n>t-1)if(t<=1)n=0;else{const e=t-1;n-=t*Math.trunc(n/e)}return s.D5U.clamp(0,n,t-1)}(e,t);case"nearest":return 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1h(((t,r)=>{i[r]=e.getUniformLocation(n,t.name,d)})),{uniformLocations:r,customUniformLocations:i,infLoc:p,nanLoc:h,inShapesLocations:a,inTexShapesLocations:o,outShapeLocation:u,outShapeStridesLocation:c,outTexShapeLocation:l}}function dd(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(((e,n)=>{const r=e.logicalShape,a=t[n],o=a.shape;if(!s.D5U.arraysEqual(r,o))throw Error(`Binary was compiled with different shapes than the current args. Shapes ${r} and ${o} must match`);if(e.isUniform&&a.isUniform)return;const i=e.texShape,u=a.isUniform?null:a.texData.texShape;if(!s.D5U.arraysEqual(i,u))throw Error(`Binary was compiled with different texture shapes than the current args. Shape ${i} and ${u} must match`)}))}function fd(e){return(0,s.OBj)().getBool("WEBGL_USE_SHAPES_UNIFORMS")&&e<=4}var md=n(8110);class gd{constructor(e){this.variableNames=["A"],this.packedInputs=!1,this.packedOutput=!0,this.outPackingScheme=Ah.DENSE,this.customUniforms=[{name:"texShape",type:"ivec2"}];const t=(0,ld.A)();this.outputShape=e,this.enableShapeUniforms=fd(this.outputShape.length),this.userCode=`\n ivec3 outCoordsFromFlatIndex(int index) {\n ${this.enableShapeUniforms?md.Kn(["r","c","d"],e):md.RW(["r","c","d"],e)}\n return ivec3(r, c, d);\n }\n\n void main() {\n ivec2 resTexRC = ivec2(resultUV.yx * vec2(texShape[0], texShape[1]));\n int index = 4 * (resTexRC.x * texShape[1] + resTexRC.y);\n\n vec4 result = vec4(0.);\n\n for (int i=0; i<4; i++) {\n int flatIndex = index + i;\n ivec3 rc = outCoordsFromFlatIndex(flatIndex);\n result[i] = getA(rc.x, rc.y, rc.z);\n }\n\n ${t.output} = result;\n }\n `}}class yd{constructor(e){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,this.outPackingScheme=Ah.DENSE,this.customUniforms=[{name:"texShape",type:"ivec2"}];const t=(0,ld.A)();this.outputShape=e,this.enableShapeUniforms=fd(this.outputShape.length),this.userCode=`\n ivec3 outCoordsFromFlatIndex(int index) {\n ${this.enableShapeUniforms?md.Kn(["r","c","d"],e):md.RW(["r","c","d"],e)}\n return ivec3(r, c, d);\n }\n\n void main() {\n ivec2 resTexRC = ivec2(resultUV.yx * vec2(texShape[0], texShape[1]));\n int index = 4 * (resTexRC.x * texShape[1] + resTexRC.y);\n\n vec4 result = vec4(0.);\n\n for (int i=0; i<4; i++) {\n int flatIndex = index + i;\n ivec3 rc = outCoordsFromFlatIndex(flatIndex);\n result[i] = getChannel(getA(rc.x, rc.y, rc.z), vec2(rc.y, rc.z));\n }\n\n ${t.output} = result;\n }\n `}}class bd{constructor(e){this.variableNames=["A"],this.outTexUsage=Dh.DOWNLOAD;const t=(0,ld.A)();this.outputShape=e,this.userCode=`\n ${md.ye}\n\n void main() {\n float x = getAAtOutCoords();\n ${t.output} = encode_float(x);\n }\n `}}class xd{constructor(e){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!1,this.outTexUsage=Dh.DOWNLOAD;const t=(0,ld.A)();this.outputShape=e,this.userCode=`\n ${md.ye}\n\n void main() {\n ivec3 coords = getOutputCoords();\n float x = getChannel(getAAtOutCoords(), vec2(coords.y, coords.z));\n ${t.output} = encode_float(x);\n }\n `}}class wd{constructor(e,t=!1){this.variableNames=["A"],this.customUniforms=[{name:"texShape",type:"ivec2"}];const n=(0,ld.A)();this.outputShape=e,this.enableShapeUniforms=fd(this.outputShape.length);let r="result";t&&(r="floor(result * 255. + 0.5)"),this.userCode=`\n ${this.enableShapeUniforms?md.nc():md.ku(e)}\n\n void main() {\n ivec3 coords = getOutputCoords();\n\n int flatIndex = getFlatIndex(coords);\n int offset = imod(flatIndex, 4);\n\n flatIndex = idiv(flatIndex, 4, 1.);\n\n int r = flatIndex / texShape[1];\n int c = imod(flatIndex, texShape[1]);\n vec2 uv = (vec2(c, r) + halfCR) / vec2(texShape[1], texShape[0]);\n vec4 values = ${n.texture2D}(A, uv);\n\n float result;\n\n if(offset == 0) {\n result = values[0];\n } else if(offset == 1) {\n result = values[1];\n } else if(offset == 2) {\n result = values[2];\n } else {\n result = values[3];\n }\n\n ${n.output} = vec4(${r}, 0., 0., 0.);\n }\n `}}class vd{constructor(e,t=!1){this.variableNames=["A"],this.packedInputs=!1,this.packedOutput=!0,this.customUniforms=[{name:"texShape",type:"ivec2"}];const n=(0,ld.A)();this.outputShape=e,this.enableShapeUniforms=fd(this.outputShape.length);let r="",s="result";t&&(s="floor(result * 255. + 0.5)");for(let a=0;a<=1;a++)for(let t=0;t<=1;t++){const s=2*a+t;r+=`\n localCoords = coords;\n if(localCoords[2] + ${t} < ${this.enableShapeUniforms?"outShape[2]":`${e[2]}`}
1) {\n localCoords[2] += ${t};\n if (localCoords[1] + ${a} < ${this.enableShapeUniforms?"outShape[1]":`${e[1]}`}) {\n localCoords[1] += ${a};\n\n flatIndex = getFlatIndex(localCoords);\n offset = imod(flatIndex, 4);\n\n flatIndex = idiv(flatIndex, 4, 1.);\n\n int r = flatIndex / texShape[1];\n int c = imod(flatIndex, texShape[1]);\n vec2 uv = (vec2(c, r) + halfCR) / vec2(texShape[1], texShape[0]);\n values = ${n.texture2D}(A, uv);\n\n if (offset == 0) {\n result[${s}] = values[0];\n } else if (offset == 1) {\n result[${s}] = values[1];\n } else if (offset == 2) {\n result[${s}] = values[2];\n } else {\n result[${s}] = values[3];\n }\n }\n }\n `}this.userCode=`\n ${this.enableShapeUniforms?md.nc():md.ku(e)}\n\n void main() {\n ivec3 coords = getOutputCoords();\n\n vec4 result = vec4(0.);\n int flatIndex, r, c, offset;\n ivec3 localCoords;\n vec2 uv;\n vec4 values;\n\n ${r}\n\n ${n.output} = ${s};\n }\n `}}function kd(e){const t=(0,ld.A)();return function(e,t){const n=qh(e,(()=>e.createShader(e.VERTEX_SHADER)),"Unable to create vertex WebGLShader.");if(Bh(e,(()=>e.shaderSource(n,t))),Bh(e,(()=>e.compileShader(n))),!1===e.getShaderParameter(n,e.COMPILE_STATUS))throw console.log(e.getShaderInfoLog(n)),new Error("Failed to compile vertex shader.");return n}(e,`${t.version}\n precision highp float;\n ${t.attribute} vec3 clipSpacePos;\n ${t.attribute} vec2 uv;\n ${t.varyingVs} vec2 resultUV;\n\n void main() {\n gl_Position = vec4(clipSpacePos, 1);\n resultUV = uv;\n }`)}function Id(e){return function(e,t){const n=qh(e,(()=>e.createBuffer()),"Unable to create WebGLBuffer");return Bh(e,(()=>e.bindBuffer(e.ARRAY_BUFFER,n))),Bh(e,(()=>e.bufferData(e.ARRAY_BUFFER,t,e.STATIC_DRAW))),n}(e,new Float32Array([-1,1,0,0,1,-1,-1,0,0,0,1,1,0,1,1,1,-1,0,1,0]))}function Nd(e){return function(e,t){const n=qh(e,(()=>e.createBuffer()),"Unable to create WebGLBuffer");return Bh(e,(()=>e.bindBuffer(e.ELEMENT_ARRAY_BUFFER,n))),Bh(e,(()=>e.bufferData(e.ELEMENT_ARRAY_BUFFER,t,e.STATIC_DRAW))),n}(e,new Uint16Array([0,1,2,2,1,3]))}function Sd(e,t,n,r,a,o){!function(e,t){const n=(0,s.OBj)().getNumber("WEBGL_MAX_TEXTURE_SIZE");if(e<=0||t<=0)throw new Error(`Requested texture size [${e}x${t}] is invalid.`);if(e>n||t>n)throw new Error(`Requested texture size [${e}x${t}] greater than WebGL maximum on this browser / GPU [${n}x${n}].`)}(t,n);const i=function(e){return qh(e,(()=>e.createTexture()),"Unable to create WebGLTexture.")}(e),u=e.TEXTURE_2D;return Bh(e,(()=>e.bindTexture(u,i))),Bh(e,(()=>e.texParameteri(u,e.TEXTURE_WRAP_S,e.CLAMP_TO_EDGE))),Bh(e,(()=>e.texParameteri(u,e.TEXTURE_WRAP_T,e.CLAMP_TO_EDGE))),Bh(e,(()=>e.texParameteri(u,e.TEXTURE_MIN_FILTER,e.NEAREST))),Bh(e,(()=>e.texParameteri(u,e.TEXTURE_MAG_FILTER,e.NEAREST))),1===(0,s.OBj)().getNumber("WEBGL_VERSION")?Bh(e,(()=>e.texImage2D(u,0,r,t,n,0,a,o,null))):Bh(e,(()=>e.texStorage2D(u,1,r,t,n))),Bh(e,(()=>e.bindTexture(e.TEXTURE_2D,null))),{texture:i,texShape:[n,t]}}function Td(e){return e.internalFormatFloat}function Cd(e){return e.internalFormatHalfFloat}function Ed(e){return e.downloadTextureFormat}function $d(e){return e.internalFormatPackedFloat}function Ad(e){return e.internalFormatPackedHalfFloat}function Dd(e,t,n,r,s,a,o,i){const u=e,l=new Float32Array(function(e,t){const[n,r]=Oh(e,t);return n*r*4}(a,o));return u.bindBuffer(u.PIXEL_PACK_BUFFER,t),u.getBufferSubData(u.PIXEL_PACK_BUFFER,0,l),u.bindBuffer(u.PIXEL_PACK_BUFFER,null),l}class _d{constructor(e){this.outputTexture=null,this.program=null,this.disposed=!1,this.vertexAttrsAreBound=!1,this.itemsToPoll=[];const t=(0,s.OBj)().getNumber("WEBGL_VERSION");null!=e?(this.gl=e,function(e,t){Ch[e]=t}(t,e)):this.gl=$h(t);let n="WEBGL_color_buffer_float";const r="EXT_color_buffer_half_float";if(this.parallelCompilationExtension=this.gl.getExtension("KHR_parallel_shader_compile"),1===(0,s.OBj)().getNumber("WEBGL_VERSION")){const e="OES_texture_float",t="OES_texture_half_float";if(this.textureFloatExtension=Wh(this.gl,e),rd(this.gl,t))this.textureHalfFloatExtension=Wh(this.gl,t);else if((0,s.OBj)().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),rd(this.gl,r))this.colorBufferHalfFloatExtension=Wh(this.gl,r);else if((0,s.OBj)().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",rd(this.gl,n))this.colorBufferFloatExtension=this.gl.getExtension(n);else{if(!rd(this.gl,r))throw new Error("GL context does not support color renderable floats");this.colorBufferHalfFloatExtension=this.gl.getExtension(r)}this.vertexBuffer=Id(this.gl),this.indexBuffer=Nd(this.gl),this.framebuffer=function(e){return qh(e,(()=>e.createFramebuffer()),"Unable to create WebGLFramebuffer.")}(this.gl),this.textureConfig=Mh(this.gl,this.textureHalfFloatExtension)}get debug(){return(0,s.OBj)().getBool("DEBUG")}dispose(){if(this.disposed)return;null!=this.program&&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."),null!=this.outputTexture&&console.warn("Disposing a GPGPUContext that still has a bound output matrix texture. This is probably a resource leak, delete the output matrix texture with GPGPUContext.deleteMatrixTexture before disposing.");const e=this.gl;Bh(e,(()=>e.finish())),Bh(e,(()=>e.bindFramebuffer(e.FRAMEBUFFER,null))),Bh(e,(()=>e.deleteFramebuffer(this.framebuffer))),Bh(e,(()=>e.bindBuffer(e.ARRAY_BUFFER,null))),Bh(e,(()=>e.bindBuffer(e.ELEMENT_ARRAY_BUFFER,null))),Bh(e,(()=>e.deleteBuffer(this.indexBuffer))),this.disposed=!0}createFloat32MatrixTexture(e,t){return this.throwIfDisposed(),function(e,t,n,r){const[s,a]=Rh(t,n);return Sd(e,s,a,Td(r),r.textureFormatFloat,e.FLOAT)}(this.gl,e,t,this.textureConfig)}createFloat16MatrixTexture(e,t){return this.throwIfDisposed(),function(e,t,n,r){const[s,a]=Rh(t,n);return Sd(e,s,a,Cd(r),r.textureFormatFloat,r.textureTypeHalfFloat)}(this.gl,e,t,this.textureConfig)}createUnsignedBytesMatrixTexture(e,t){return this.throwIfDisposed(),function(e,t,n,r){const[s,a]=Rh(t,n);return Sd(e,s,a,Ed(r),e.RGBA,e.UNSIGNED_BYTE)}(this.gl,e,t,this.textureConfig)}uploadPixelDataToTexture(e,t){this.throwIfDisposed(),function(e,t,n){Bh(e,(()=>e.bindTexture(e.TEXTURE_2D,t))),n.data instanceof Uint8Array?2===(0,s.OBj)().getNumber("WEBGL_VERSION")?Bh(e,(()=>e.texSubImage2D(e.TEXTURE_2D,0,0,0,n.width,n.height,e.RGBA,e.UNSIGNED_BYTE,n.data))):Bh(e,(()=>e.texImage2D(e.TEXTURE_2D,0,e.RGBA,n.width,n.height,0,e.RGBA,e.UNSIGNED_BYTE,n.data))):2===(0,s.OBj)().getNumber("WEBGL_VERSION")?Bh(e,(()=>e.texSubImage2D(e.TEXTURE_2D,0,0,0,e.RGBA,e.UNSIGNED_BYTE,n))):Bh(e,(()=>e.texImage2D(e.TEXTURE_2D,0,e.RGBA,e.RGBA,e.UNSIGNED_BYTE,n))),Bh(e,(()=>e.bindTexture(e.TEXTURE_2D,null)))}(this.gl,e,t)}uploadDenseMatrixToTexture(e,t,n,r){this.throwIfDisposed(),function(e,t,n,r,a,o){let i,u,l;Bh(e,(()=>e.bindTexture(e.TEXTURE_2D,t))),a instanceof Uint8Array?(i=new Uint8Array(n*r*4),u=e.UNSIGNED_BYTE,l=e.RGBA):(i=new Float32Array(n*r*4),u=e.FLOAT,l=o.internalFormatPackedFloat),i.set(a),2===(0,s.OBj)().getNumber("WEBGL_VERSION")?Bh(e,(()=>e.texSubImage2D(e.TEXTURE_2D,0,0,0,n,r,e.RGBA,u,i))):Bh(e,(()=>e.texImage2D(e.TEXTURE_2D,0,l,n,r,0,e.RGBA,u,i))),Bh(e,(()=>e.bindTexture(e.TEXTURE_2D,null)))}(this.gl,e,t,n,r,this.textureConfig)}createFloat16PackedMatrixTexture(e,t){return this.throwIfDisposed(),function(e,t,n,r){const[s,a]=Oh(t,n);return Sd(e,s,a,Ad(r),e.RGBA,r.textureTypeHalfFloat)}(this.gl,e,t,this.textureConfig)}createPackedMatrixTexture(e,t){return this.throwIfDisposed(),function(e,t,n,r){const[s,a]=Oh(t,n);return Sd(e,s,a,$d(r),e.RGBA,e.FLOAT)}(this.gl,e,t,this.textureConfig)}deleteMatrixTexture(e){this.throwIfDisposed(),this.outputTexture===e&&(jh(this.gl,this.framebuffer),this.outputTexture=null),Bh(this.gl,(()=>this.gl.deleteTexture(e)))}downloadByteEncodedFloatMatrixFromOutputTexture(e,t,n){return this.downloadMatrixDriver(e,(()=>function(e,t,n,r){const[s,a]=Rh(t,n),o=new Uint8Array(t*n*4);return Bh(e,(()=>e.readPixels(0,0,s,a,r.downloadTextureFormat,e.UNSIGNED_BYTE,o))),new Float32Array(o.buffer)}(this.gl,t,n,this.textureConfig)))}downloadPackedMatrixFromBuffer(e,t,n,r,s,a){return Dd(this.gl,e,0,0,0,s,a,this.textureConfig)}downloadFloat32MatrixFromBuffer(e,t){return function(e,t,n){const r=e,s=new Float32Array(n);return r.bindBuffer(r.PIXEL_PACK_BUFFER,t),r.getBufferSubData(r.PIXEL_PACK_BUFFER,0,s),r.bindBuffer(r.PIXEL_PACK_BUFFER,null),s}(this.gl,e,t)}createBufferFromTexture(e,t,n){this.bindTextureToFrameBuffer(e);const r=function(e,t,n,r){const s=e.createBuffer();Bh(e,(()=>e.bindBuffer(e.PIXEL_PACK_BUFFER,s)));const a=16*t*n;return Bh(e,(()=>e.bufferData(e.PIXEL_PACK_BUFFER,a,e.STREAM_READ))),Bh(e,(()=>e.readPixels(0,0,n,t,e.RGBA,e.FLOAT,0))),Bh(e,(()=>e.bindBuffer(e.PIXEL_PACK_BUFFER,null))),s}(this.gl,t,n,this.textureConfig);return this.unbindTextureToFrameBuffer(),r}createAndWaitForFence(){const e=this.createFence(this.gl);
1return this.pollFence(e)}createFence(e){let t,n;if((0,s.OBj)().getBool("WEBGL_FENCE_API_ENABLED")){const r=e,s=r.fenceSync(r.SYNC_GPU_COMMANDS_COMPLETE,0);e.flush(),n=()=>{const e=r.clientWaitSync(s,0,0);return e===r.ALREADY_SIGNALED||e===r.CONDITION_SATISFIED},t=s}else(0,s.OBj)().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION")>0?(t=this.beginQuery(),this.endQuery(),n=()=>this.isQueryAvailable(t,(0,s.OBj)().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION"))):n=()=>!0;return{query:t,isFencePassed:n}}downloadMatrixFromPackedTexture(e,t,n){return this.downloadMatrixDriver(e,(()=>function(e,t,n){const r=new Float32Array(t*n*4);return Bh(e,(()=>e.readPixels(0,0,n,t,e.RGBA,e.FLOAT,r))),r}(this.gl,t,n)))}createProgram(e){this.throwIfDisposed();const t=this.gl;null==this.vertexShader&&(this.vertexShader=kd(t));const n=function(e){return qh(e,(()=>e.createProgram()),"Unable to create WebGLProgram.")}(t);return Bh(t,(()=>t.attachShader(n,this.vertexShader))),Bh(t,(()=>t.attachShader(n,e))),function(e,t){if(Bh(e,(()=>e.linkProgram(t))),!(0,s.OBj)().get("ENGINE_COMPILE_ONLY")&&!1===e.getProgramParameter(t,e.LINK_STATUS))throw console.log(e.getProgramInfoLog(t)),new Error("Failed to link vertex and fragment shaders.")}(t,n),this.debug&&zh(t,n),this.vertexAttrsAreBound||(this.setProgram(n),this.vertexAttrsAreBound=function(e,t,n){return Bh(e,(()=>e.bindBuffer(e.ARRAY_BUFFER,n))),Vh(e,t,"clipSpacePos",n,3,20,0)&&Vh(e,t,"uv",n,2,20,12)}(t,this.program,this.vertexBuffer)),n}deleteProgram(e){this.throwIfDisposed(),e===this.program&&(this.program=null),null!=e&&Bh(this.gl,(()=>this.gl.deleteProgram(e)))}setProgram(e){this.throwIfDisposed(),this.program=e,null!=this.program&&this.debug&&zh(this.gl,this.program),Bh(this.gl,(()=>this.gl.useProgram(e)))}getUniformLocation(e,t,n=!0){return this.throwIfDisposed(),n?function(e,t,n){return qh(e,(()=>e.getUniformLocation(t,n)),'uniform "'+n+'" not present in program.')}(this.gl,e,t):function(e,t,n){return e.getUniformLocation(t,n)}(this.gl,e,t)}getAttributeLocation(e,t){return this.throwIfDisposed(),Bh(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(),Gh(this.gl,e,t,n)}setOutputMatrixTexture(e,t,n){this.setOutputMatrixTextureDriver(e,n,t)}setOutputPackedMatrixTexture(e,t,n){this.throwIfDisposed();const[r,s]=Oh(t,n);this.setOutputMatrixTextureDriver(e,r,s)}setOutputMatrixWriteRegion(e,t,n,r){this.setOutputMatrixWriteRegionDriver(n,e,r,t)}setOutputPackedMatrixWriteRegion(e,t,n,r){throw new Error("setOutputPackedMatrixWriteRegion not implemented.")}debugValidate(){null!=this.program&&zh(this.gl,this.program),Xh(this.gl)}executeProgram(){this.throwIfDisposed(),this.throwIfNoProgram();const e=this.gl;this.debug&&this.debugValidate(),Bh(e,(()=>e.drawElements(e.TRIANGLES,6,e.UNSIGNED_SHORT,0)))}blockUntilAllProgramsCompleted(){this.throwIfDisposed(),Bh(this.gl,(()=>this.gl.finish()))}getQueryTimerExtension(){return null==this.disjointQueryTimerExtension&&(this.disjointQueryTimerExtension=Wh(this.gl,2===(0,s.OBj)().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION")?"EXT_disjoint_timer_query_webgl2":"EXT_disjoint_timer_query")),this.disjointQueryTimerExtension}getQueryTimerExtensionWebGL2(){return this.getQueryTimerExtension()}getQueryTimerExtensionWebGL1(){return this.getQueryTimerExtension()}beginQuery(){if(2===(0,s.OBj)().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION")){const e=this.gl,t=this.getQueryTimerExtensionWebGL2(),n=e.createQuery();return e.beginQuery(t.TIME_ELAPSED_EXT,n),n}const e=this.getQueryTimerExtensionWebGL1(),t=e.createQueryEXT();return e.beginQueryEXT(e.TIME_ELAPSED_EXT,t),t}endQuery(){if(2===(0,s.OBj)().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION")){const e=this.gl,t=this.getQueryTimerExtensionWebGL2();return void e.endQuery(t.TIME_ELAPSED_EXT)}const e=this.getQueryTimerExtensionWebGL1();e.endQueryEXT(e.TIME_ELAPSED_EXT)}async waitForQueryAndGetTime(e){return await s.D5U.repeatedTry((()=>this.disposed||this.isQueryAvailable(e,(0,s.OBj)().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION")))),this.getQueryTime(e,(0,s.OBj)().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION"))}getQueryTime(e,t){if(0===t)return null;if(2===t){const t=this.gl;return t.getQueryParameter(e,t.QUERY_RESULT)/1e6}{const t=this.getQueryTimerExtensionWebGL1();return t.getQueryObjectEXT(e,t.QUERY_RESULT_EXT)/1e6}}isQueryAvailable(e,t){if(0===t)return!0;
vendor: 4,607 bytes, line 1
1if(2===t){const t=this.gl,n=this.getQueryTimerExtensionWebGL2(),r=t.getQueryParameter(e,t.QUERY_RESULT_AVAILABLE);return null==this.disjoint&&(this.disjoint=this.gl.getParameter(n.GPU_DISJOINT_EXT)),r&&!this.disjoint}{const t=this.getQueryTimerExtensionWebGL1(),n=t.getQueryObjectEXT(e,t.QUERY_RESULT_AVAILABLE_EXT);return null==this.disjoint&&(this.disjoint=this.gl.getParameter(t.GPU_DISJOINT_EXT)),n&&!this.disjoint}}pollFence(e){return new Promise((t=>{this.addItemToPoll((()=>e.isFencePassed()),(()=>t()))}))}pollItems(){const e=function(e){let t=0;for(;t<e.length;++t){if(!e[t]())break}return t-1}(this.itemsToPoll.map((e=>e.isDoneFn)));for(let t=0;t<=e;++t){const{resolveFn:e}=this.itemsToPoll[t];e()}this.itemsToPoll=this.itemsToPoll.slice(e+1)}addItemToPoll(e,t){if(this.itemsToPoll.push({isDoneFn:e,resolveFn:t}),this.itemsToPoll.length>1)return;let n;"setTimeoutCustom"in(0,s.OBj)().platform&&(n=(0,s.OBj)().platform.setTimeoutCustom.bind((0,s.OBj)().platform)),s.D5U.repeatedTry((()=>(this.pollItems(),0===this.itemsToPoll.length)),(()=>0),null,n)}bindTextureToFrameBuffer(e){this.throwIfDisposed(),Hh(this.gl,e,this.framebuffer),this.debug&&Xh(this.gl)}unbindTextureToFrameBuffer(){null!=this.outputTexture?(Hh(this.gl,this.outputTexture,this.framebuffer),this.debug&&Xh(this.gl)):jh(this.gl,this.framebuffer)}downloadMatrixDriver(e,t){this.bindTextureToFrameBuffer(e);const n=t();return this.unbindTextureToFrameBuffer(),n}setOutputMatrixTextureDriver(e,t,n){this.throwIfDisposed();const r=this.gl;Hh(r,e,this.framebuffer),this.debug&&Xh(r),this.outputTexture=e,Bh(r,(()=>r.viewport(0,0,t,n))),Bh(r,(()=>r.scissor(0,0,t,n)))}setOutputMatrixWriteRegionDriver(e,t,n,r){this.throwIfDisposed(),Bh(this.gl,(()=>this.gl.scissor(e,t,n,r)))}throwIfDisposed(){if(this.disposed)throw new Error("Attempted to use disposed GPGPUContext.")}throwIfNoProgram(){if(null==this.program)throw new Error("No GPU program is currently set.")}}const{addImpl:Rd,bincountImpl:Fd,bincountReduceImpl:Od,castImpl:Md,ceilImpl:Bd,concatImpl:Ld,equalImpl:Wd,expImpl:Pd,expm1Impl:Ud,floorImpl:zd,gatherNdImpl:Vd,gatherV2Impl:Gd,greaterImpl:Hd,greaterEqualImpl:jd,lessImpl:Xd,lessEqualImpl:qd,linSpaceImpl:Kd,logImpl:Qd,maxImpl:Yd,maximumImpl:Zd,minimumImpl:Jd,multiplyImpl:ef,negImpl:tf,notEqualImpl:nf,prodImpl:rf,raggedGatherImpl:sf,raggedTensorToTensorImpl:af,rangeImpl:of,rsqrtImpl:uf,scatterImpl:lf,sigmoidImpl:cf,simpleAbsImpl:pf,sliceImpl:hf,sparseFillEmptyRowsImpl:df,sparseReshapeImpl:ff,sparseSegmentReductionImpl:mf,sqrtImpl:gf,stridedSliceImpl:yf,stringNGramsImpl:bf,stringSplitImpl:xf,stringToHashBucketFastImpl:wf,subImpl:vf,tileImpl:kf,topKImpl:If,transposeImpl:Nf,uniqueImpl:Sf}=r;function Tf(e,t){return["x","y","z","w","u","v"].slice(0,t).map((t=>`${e}.${t}`))}function Cf(e,t){return 1===t?[e]:Tf(e,t)}class Ef{constructor(e){if(this.variableNames=["A"],this.packedInputs=!1,this.packedOutput=!0,this.outputShape=e,this.rank=e.length,this.enableShapeUniforms=fd(this.outputShape.length),0===this.rank)this.userCode="\n void main() {\n setOutput(vec4(getA(), 0., 0., 0.));\n }\n ";else{const e=Cf("rc",this.rank),t=(0,cd.kW)(this.rank),n=this.getOutOfBoundsCondition(e),r=this.getSetup(e),s=this.getOutput(e);this.userCode=`\n void main() {\n ${t} rc = getOutputCoords();\n\n if(${n}) {\n setOutput(vec4(0));\n } else {\n ${r}\n\n setOutput(vec4(${s}));\n }\n }\n `}}getSourceCoordsArr(e){const t=[];for(let n=0;n<=1;n++)for(let r=0;r<=1;r++){let s=`${0===n?"r":"rp1"}, ${0===r?"c":"cp1"}`;for(let t=2;t<this.rank;t++)s=`${e[e.length-1-t]},`+s;t.push(s)}return t}getOutOfBoundsCondition(e){if(1===this.rank)return`rc > ${this.enableShapeUniforms?"outShape":this.outputShape[0]}`;let t="";for(let n=this.rank-2;n<this.rank;n++)t+=`${e[n]} >= ${this.enableShapeUniforms?`outShape[${n}]`:this.outputShape[n]}`,n<this.rank-1&&(t+="||");return t}getSetup(e){if(1===this.rank)return"";const t=e.slice(-2),n=this.enableShapeUniforms?`outShape[${this.rank} - 1]`:this.outputShape[this.rank-1],r=this.enableShapeUniforms?`outShape[${this.rank} - 2]`:this.outputShape[this.rank-2];return`\n int r = ${t[0]};\n int c = ${t[1]};\n int rp1 = r + 1;\n int cp1 = c + 1;\n\n bool cEdge = cp1 >= ${n};\n bool rEdge = rp1 >= ${r};\n `}getOutput(e){const t=this.getSourceCoordsArr(e);if(1===this.rank){return`getA(rc), (rc + 1 >= ${this.enableShapeUniforms?"outShape":this.outputShape[0]} ? 0. : getA(rc + 1)), 0, 0`}return`getA(${t[0]}),\n cEdge ? 0. : getA(${t[1]}
1),\n rEdge ? 0. : getA(${t[2]}),\n rEdge || cEdge ? 0. : getA(${t[3]})`}}class $f{constructor(e,t){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,this.customUniforms=[{name:"inputShape",type:"ivec3"}],this.outputShape=e,this.enableShapeUniforms=fd(this.outputShape.length);let n="";for(let a=0;a<4;a++){let e="thisRC = rc;";a%2===1&&(e+="thisRC.z += 1;"),a>1&&(e+="thisRC.y += 1;"),n+=`\n ${e}\n ${a>0?"if(thisRC.y < rows && thisRC.z < cols){":""}\n int flatIndex = getFlatIndex(thisRC);\n\n ivec3 inputRC = inputCoordsFromReshapedOutCoords(flatIndex);\n vec2 inputRCInnerDims = vec2(float(inputRC.y),float(inputRC.z));\n\n result[${a}] =\n getChannel(getA(inputRC.x, inputRC.y, inputRC.z), inputRCInnerDims);\n ${a>0?"}":""}\n `}var r,s;this.userCode=`\n ${r=t,s=this.enableShapeUniforms,`\n ivec3 inputCoordsFromReshapedOutCoords(int index) {\n ${s?md.al(["r","c","d"],"inputShape"):md.RW(["r","c","d"],r)}\n return ivec3(r, c, d);\n }\n `}\n ${this.enableShapeUniforms?md.nc():md.ku(e)}\n\n void main() {\n ivec3 rc = getOutputCoords();\n\n vec4 result = vec4(0.);\n\n ivec3 thisRC;\n int rows = ${this.enableShapeUniforms?"outShape[1]":e[1]};\n int cols = ${this.enableShapeUniforms?"outShape[2]":e[2]};\n\n ${n}\n\n setOutput(result);\n }\n `}}class Af{constructor(e){this.gpgpu=e,this.numUsedTextures=0,this.numFreeTextures=0,this._numBytesAllocated=0,this._numBytesFree=0,this.freeTextures={},this.logEnabled=!1,this.usedTextures={}}acquireTexture(e,t,n){const r=_f(t,n),s=Rf(e,r,n);s in this.freeTextures||(this.freeTextures[s]=[]),s in this.usedTextures||(this.usedTextures[s]=[]);const a=Df(e,r,this.gpgpu.gl,this.gpgpu.textureConfig,n);if(this.freeTextures[s].length>0){this.numFreeTextures--,this.numUsedTextures++,this._numBytesFree-=a,this.log();const e=this.freeTextures[s].shift();return this.usedTextures[s].push(e),e}let o;return r===_h.PACKED_2X2_FLOAT32?o=this.gpgpu.createPackedMatrixTexture(e[0],e[1]):r===_h.PACKED_2X2_FLOAT16?o=this.gpgpu.createFloat16PackedMatrixTexture(e[0],e[1]):r===_h.UNPACKED_FLOAT32?o=this.gpgpu.createFloat32MatrixTexture(e[0],e[1]):r===_h.UNPACKED_FLOAT16?o=this.gpgpu.createFloat16MatrixTexture(e[0],e[1]):r===_h.PACKED_4X1_UNSIGNED_BYTE&&(o=this.gpgpu.createUnsignedBytesMatrixTexture(e[0],e[1])),this.usedTextures[s].push(o),this.numUsedTextures++,this._numBytesAllocated+=a,this.log(),o}releaseTexture(e,t,n,r){if(null==this.freeTextures)return;const a=_f(n,r),o=Rf(t,a,r);o in this.freeTextures||(this.freeTextures[o]=[]);const i=Df(t,a,this.gpgpu.gl,this.gpgpu.textureConfig,r),u=(0,s.OBj)().get("WEBGL_DELETE_TEXTURE_THRESHOLD");-1!==u&&this._numBytesAllocated>u?(this.gpgpu.deleteMatrixTexture(e.texture),this._numBytesAllocated-=i):(this.freeTextures[o].push(e),this.numFreeTextures++,this._numBytesFree+=i),this.numUsedTextures--;const l=this.usedTextures[o],c=l.indexOf(e);if(c<0)throw new Error("Cannot release a texture that was never provided by this texture manager");l.splice(c,1),this.log()}log(){if(!this.logEnabled)return;const e=this.numFreeTextures+this.numUsedTextures;console.log("Free/Used",`${this.numFreeTextures} / ${this.numUsedTextures}`,`(${e})`);const 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(null!=this.freeTextures){for(const e in this.freeTextures)this.freeTextures[e].forEach((e=>{this.gpgpu.deleteMatrixTexture(e.texture)}));for(const e in this.usedTextures)this.usedTextures[e].forEach((e=>{this.gpgpu.deleteMatrixTexture(e.texture)}));this.freeTextures=null,this.usedTextures=null,this.numUsedTextures=0,this.numFreeTextures=0,this._numBytesAllocated=0,this._numBytesFree=0}}}
1function Df(e,t,n,r,s){const a=function(e,t){switch(e){case _h.PACKED_2X2_FLOAT32:return $d(t);case _h.PACKED_2X2_FLOAT16:return Ad(t);case _h.UNPACKED_FLOAT32:return Td(t);case _h.UNPACKED_FLOAT16:return Cd(t);case _h.PACKED_4X1_UNSIGNED_BYTE:return Ed(t);default:throw new Error(`Unknown physical texture type ${e}`)}}(t,r);let o;if(s){const[t,n]=Oh(e[0],e[1]);o=t*n}else{const[t,n]=Rh(e[0],e[1]);o=t*n}return o*function(e,t){const n=e;if(t===n.R32F)return 4;if(t===n.R16F)return 2;if(t===n.RGBA32F)return 16;if(t===e.RGBA)return 16;if(t===n.RGBA16F)return 8;if(t===n.RGBA8)return 4;throw new Error(`Unknown internal format ${t}`)}(n,a)}function _f(e,t){if(e===Dh.UPLOAD)return _h.PACKED_2X2_FLOAT32;if(e===Dh.RENDER||null==e)return function(e){return(0,s.OBj)().getBool("WEBGL_RENDER_FLOAT32_ENABLED")?e?_h.PACKED_2X2_FLOAT32:_h.UNPACKED_FLOAT32:e?_h.PACKED_2X2_FLOAT16:_h.UNPACKED_FLOAT16}(t);if(e===Dh.DOWNLOAD||e===Dh.PIXELS)return _h.PACKED_4X1_UNSIGNED_BYTE;throw new Error(`Unknown logical texture type ${e}`)}function Rf(e,t,n){return`${e[0]}_${e[1]}_${t}_${n}`}class Ff{constructor(e,t){this.variableNames=["A"],this.outputShape=e,this.enableShapeUniforms=fd(this.outputShape.length),this.userCode=`\n float unaryOperation(float x) {\n ${t}\n }\n\n void main() {\n float x = getAAtOutCoords();\n float y = unaryOperation(x);\n\n setOutput(y);\n }\n `}}const Of="return abs(x);";const Mf="return x;";class Bf{constructor(e,t){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=e,this.enableShapeUniforms=fd(this.outputShape.length),this.userCode=`\n vec4 unaryOperation(vec4 x) {\n ${t}\n }\n\n void main() {\n vec4 x = getAAtOutCoords();\n vec4 y = unaryOperation(x);\n\n setOutput(y);\n }\n `}}class Lf{constructor(e){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!1,this.outputShape=e,this.enableShapeUniforms=fd(this.outputShape.length);const t=e.length,n=Cf("rc",t),r=(0,cd.kW)(t),s=function(e,t){if(1===e)return"rc";let n="";for(let r=0;r<e;r++)n+=t[r],r<e-1&&(n+=",");return n}(t,n),a=n.slice(-2),o=t<=1?"rc":`vec2(${a.join(",")})`;this.userCode=`\n void main() {\n ${r} rc = getOutputCoords();\n vec4 packedInput = getA(${s});\n\n setOutput(getChannel(packedInput, ${o}));\n }\n `}}const Wf=s.GDt.ZA,Pf={};const Uf=(0,s.OBj)().getNumber("CPU_HANDOFF_SIZE_THRESHOLD");class zf extends s.Zuw{constructor(e){if(super(),this.pendingRead=new WeakMap,this.pendingDisposal=new WeakSet,this.dataRefCount=new WeakMap,this.numBytesInGPU=0,this.uploadWaitMs=0,this.downloadWaitMs=0,this.lastGlFlushTime=0,this.warnedAboutMemory=!1,this.pendingDeletes=0,this.disposed=!1,!(0,s.OBj)().getBool("HAS_WEBGL"))throw new Error("WebGL is not supported on this device");let t;if(null!=e){if(e instanceof _d)t=e;else{const n=$h((0,s.OBj)().getNumber("WEBGL_VERSION"),e);t=new _d(n)}this.binaryCache={},this.gpgpuCreatedLocally=!1}else{const e=$h((0,s.OBj)().getNumber("WEBGL_VERSION"));t=new _d(e),this.binaryCache=((n=(0,s.OBj)().getNumber("WEBGL_VERSION"))in Pf||(Pf[n]={}),Pf[n]),this.gpgpuCreatedLocally=!0}var n;this.gpgpu=t,this.canvas=this.gpgpu.gl.canvas,this.textureManager=new Af(this.gpgpu),this.numMBBeforeWarning=null==(0,s.OBj)().global.screen?1024:(0,s.OBj)().global.screen.height*(0,s.OBj)().global.screen.width*window.devicePixelRatio*600/1024/1024,this.texData=new s.JLz(this,(0,s.SRH)())}nextDataId(){return zf.nextDataId++}numDataIds(){return this.texData.numDataIds()-this.pendingDeletes}write(e,t,n){if(((0,s.OBj)().getBool("WEBGL_CHECK_NUMERICAL_PROBLEMS")||(0,s.OBj)().getBool("DEBUG"))&&this.checkNumericalProblems(e),"complex64"===n&&null!=e)throw new Error("Cannot write to a complex64 dtype. Please use tf.complex(real, imag).");const r={id:this.nextDataId()};return this.texData.set(r,{shape:t,dtype:n,values:e,usage:Dh.UPLOAD,refCount:1}),r}refCount(e){if(this.texData.has(e)){return this.texData.get(e).refCount}return 0}incRef(e){this.texData.get(e).refCount++}decRef(e){if(this.texData.has(e)){this.texData.get(e).refCount--}}move(e,t,n,r,a){if((0,s.OBj)().getBool("DEBUG")&&this.checkNumericalProblems(t),"complex64"===r)throw new Error("Cannot write to a complex64 dtype. Please use tf.complex(real, imag).");this.texData.set(e,{shape:n,dtype:r,values:t,usage:Dh.UPLOAD,refCount:a})}disposeIntermediateTensorInfo(e){this.disposeData(e.dataId)}readSync(e){const t=this.texData.get(e),{values:n,dtype:r,complexTensorInfos:a,slice:o,shape:i,isPacked:u}=t;if(null!=o){let t;t=u?new Bf(i,Mf):new Ff(i,Mf);const n=this.runWebGLProgram(t,[{dataId:e,shape:i,dtype:r}],r),s=this.readSync(n.dataId);return this.disposeIntermediateTensorInfo(n),s}if(null!=n)return this.convertAndCacheOnCPU(e);if("string"===r)return n;const l=null!=this.activeTimers;let c,p;if(l&&(c=s.D5U.now()),"complex64"===r){const e=this.readSync(a.real.dataId),t=this.readSync(a.imag.dataId);p=s.Wap.mergeRealAndImagArrays(e,t)}else p=this.getValuesFromTexture(e);return l&&(this.downloadWaitMs+=s.D5U.now()-c),this.convertAndCacheOnCPU(e,p)}async read(e){if(this.pendingRead.has(e)){const t=this.pendingRead.get(e);return new Promise((e=>t.push(e)))}const t=this.texData.get(e),{values:n,shape:r,slice:a,dtype:o,complexTensorInfos:i,isPacked:u}=t;if(null!=a){let t;t=u?new Bf(r,Mf):new Ff(r,Mf);const n=this.runWebGLProgram(t,[{dataId:e,shape:r,dtype:o}],o),s=this.read(n.dataId);return this.disposeIntermediateTensorInfo(n),s}if(null!=n)return this.convertAndCacheOnCPU(e);if((0,s.OBj)().getBool("DEBUG")&&!(0,s.OBj)().getBool("WEBGL_DOWNLOAD_FLOAT_ENABLED")&&2===(0,s.OBj)().getNumber("WEBGL_VERSION"))throw new Error("tensor.data() with WEBGL_DOWNLOAD_FLOAT_ENABLED=false and WEBGL_VERSION=2 not yet supported.");let l,c,p=null;if("complex64"!==o&&(0,s.OBj)().get("WEBGL_BUFFER_SUPPORTED")){l=this.decode(e);const t=this.texData.get(l.dataId);p=this.gpgpu.createBufferFromTexture(t.texture.texture,...Fh(r))}if(this.pendingRead.set(e,[]),"complex64"!==o&&await this.gpgpu.createAndWaitForFence(),"complex64"===o){const e=await Promise.all([this.read(i.real.dataId),this.read(i.imag.dataId)]),t=e[0],n=e[1];c=s.Wap.mergeRealAndImagArrays(t,n)}else if(null==p)c=this.getValuesFromTexture(e);else{const e=s.D5U.sizeFromShape(r);c=this.gpgpu.downloadFloat32MatrixFromBuffer(p,e)}if(null!=l&&this.disposeIntermediateTensorInfo(l),null!=p){const e=this.gpgpu.gl;Bh(e,(()=>e.deleteBuffer(p)))}const h=this.convertAndCacheOnCPU(e,c),d=this.pendingRead.get(e);return this.pendingRead.delete(e),d.forEach((e=>e(h))),this.pendingDisposal.has(e)&&(this.pendingDisposal.delete(e),this.disposeData(e)&&(0,s.SRH)().removeDataId(e,this),this.pendingDeletes--),h}readToGPU(e,t={}){const n=this.texData.get(e),{values:r,shape:a,slice:o,dtype:i,isPacked:u,texture:l}=n;if("complex64"===i)throw new Error("Does not support reading texture for complex64 dtype.");if(null!=o){let n;n=u?new Bf(a,Mf):new Ff(a,Mf);const r=this.runWebGLProgram(n,[{dataId:e,shape:a,dtype:i}],i),s=this.readToGPU(r,t);return this.disposeIntermediateTensorInfo(r),s}if(null==l)throw null!=r?new Error("Data is not on GPU but on CPU."):new Error("There is no data on GPU or CPU.");const c=this.decode(e,t.customTexShape),p=(0,s.SRH)().makeTensorFromTensorInfo(c),h=this.texData.get(c.dataId);return Object.assign({tensorRef:p},h.texture)}bufferSync(e){const t=this.readSync(e.dataId);if("string"===e.dtype)try{const n=t.map((e=>s.D5U.decodeString(e)));return(0,s.f3b)(e.shape,e.dtype,n)}catch(n){throw new Error("Failed to decode encoded string bytes into utf-8")}return(0,s.f3b)(e.shape,e.dtype,t)}
1checkNumericalProblems(e){if(null!=e)for(let t=0;t<e.length;t++){const n=e[t];if(!Lh(n)){if((0,s.OBj)().getBool("WEBGL_RENDER_FLOAT32_CAPABLE"))throw Error(`The value ${n} cannot be represented with your current settings. Consider enabling float32 rendering: 'tf.env().set('WEBGL_RENDER_FLOAT32_ENABLED', true);'`);throw Error(`The value ${n} cannot be represented on this device.`)}}}getValuesFromTexture(e){const{shape:t,dtype:n,isPacked:r}=this.texData.get(e),a=s.D5U.sizeFromShape(t);if((0,s.OBj)().getBool("WEBGL_DOWNLOAD_FLOAT_ENABLED")){const n=this.decode(e),r=this.texData.get(n.dataId),s=this.gpgpu.downloadMatrixFromPackedTexture(r.texture.texture,...Fh(t)).subarray(0,a);return this.disposeIntermediateTensorInfo(n),s}const o=(0,s.OBj)().getBool("WEBGL_PACK")&&!0===r,i=o?Zh(t):t,u=o?new xd(i):new bd(i),l=this.runWebGLProgram(u,[{shape:i,dtype:n,dataId:e}],"float32"),c=this.texData.get(l.dataId),p=this.gpgpu.downloadByteEncodedFloatMatrixFromOutputTexture(c.texture.texture,c.texShape[0],c.texShape[1]).subarray(0,a);return this.disposeIntermediateTensorInfo(l),p}timerAvailable(){return(0,s.OBj)().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE")>0}time(e){const t=this.activeTimers,n=[];let r=!1;null==this.programTimersStack?(this.programTimersStack=n,r=!0):this.activeTimers.push(n),this.activeTimers=n,e();const a=s.D5U.flatten(this.activeTimers.map((e=>e.query))).filter((e=>null!=e)),o=s.D5U.flatten(this.activeTimers.map((e=>e.name))).filter((e=>null!=e));this.activeTimers=t,r&&(this.programTimersStack=null);const i={uploadWaitMs:this.uploadWaitMs,downloadWaitMs:this.downloadWaitMs,kernelMs:null,wallMs:null};return(async()=>{if((0,s.OBj)().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE")>0){const e=await Promise.all(a);i.kernelMs=s.D5U.sum(e),i.getExtraProfileInfo=()=>e.map(((e,t)=>({name:o[t],ms:e}))).map((e=>`${e.name}: ${e.ms}`)).join(", ")}else i.kernelMs={error:"WebGL query timers are not supported in this environment."};return this.uploadWaitMs=0,this.downloadWaitMs=0,i})()}memory(){return{unreliable:!1,numBytesInGPU:this.numBytesInGPU,numBytesInGPUAllocated:this.textureManager.numBytesAllocated,numBytesInGPUFree:this.textureManager.numBytesFree}}startTimer(){return(0,s.OBj)().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE")>0?this.gpgpu.beginQuery():{startMs:s.D5U.now(),endMs:null}}endTimer(e){return(0,s.OBj)().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE")>0?(this.gpgpu.endQuery(),e):(e.endMs=s.D5U.now(),e)}async getQueryTime(e){if((0,s.OBj)().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE")>0)return this.gpgpu.waitForQueryAndGetTime(e);const 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);const{complexTensorInfos:n}=this.texData.get(e);return null!=n&&(this.disposeData(n.real.dataId,t),this.disposeData(n.imag.dataId,t)),this.texData.delete(e),!0}releaseGPUData(e){const{texture:t,dtype:n,texShape:r,usage:s,isPacked:a,slice:o}=this.texData.get(e),i=o&&o.origDataId||e,u=this.dataRefCount.get(i);u>1?this.dataRefCount.set(i,u-1):(this.dataRefCount.delete(i),null!=t&&(this.numBytesInGPU-=this.computeBytes(r,n),this.textureManager.releaseTexture(t,r,s,a)));const l=this.texData.get(e);l.texture=null,l.texShape=null,l.isPacked=!1,l.slice=null}getTexture(e){return this.uploadToGPU(e),this.texData.get(e).texture.texture}getDataInfo(e){return this.texData.get(e)}shouldExecuteOnCPU(e,t=Uf){return(0,s.OBj)().getBool("WEBGL_CPU_FORWARD")&&e.every((e=>null==this.texData.get(e.dataId).texture&&s.D5U.sizeFromShape(e.shape)<t))}getGPGPUContext(){return this.gpgpu}where(e){s.Wap.warn("tf.where() in webgl locks the UI thread. Call tf.whereAsync() instead");const t=e.dataSync();return Wf(e.shape,t)}packedUnaryOp(e,t,n){const r=new Bf(e.shape,t),a=this.compileAndRun(r,[e],n);return(0,s.SRH)().makeTensorFromTensorInfo(a)}abs(e){if(this.shouldExecuteOnCPU([e])&&"complex64"!==e.dtype){const t=pf(this.texData.get(e.dataId).values);return this.makeOutput(e.shape,e.dtype,t)}if((0,s.OBj)().getBool("WEBGL_PACK_UNARY_OPERATIONS"))return this.packedUnaryOp(e,Of,e.dtype);const t=new Ff(e.shape,Of),n=this.compileAndRun(t,[e]);return(0,s.SRH)().makeTensorFromTensorInfo(n)}makeTensorInfo(e,t,n){let r;if("string"===t&&null!=n&&n.length>0&&s.D5U.isString(n[0])){const a=n.map((e=>s.D5U.encodeString(e)));r=this.write(a,e,t)}else r=this.write(n,e,t);return this.texData.get(r).usage=null,{dataId:r,shape:e,dtype:t}}makeOutput(e,t,n){return(0,s.SRH)().makeTensorFromTensorInfo(this.makeTensorInfo(e,t,n),this)}unpackTensor(e){const t=new Lf(e.shape);return this.runWebGLProgram(t,[e],e.dtype)}packTensor(e){const t=new Ef(e.shape);return this.runWebGLProgram(t,[e],e.dtype,null,!0)}packedReshape(e,t){const n=[Qh(e.shape),...Yh(e.shape)],r={dtype:e.dtype,shape:n,dataId:e.dataId},s=[Qh(t),...Yh(t)],a=new $f(s,n),o=[n],i=this.runWebGLProgram(a,[r],e.dtype,o,!0);return{dataId:i.dataId,shape:t,dtype:i.dtype}}decode(e,t){const n=this.texData.get(e),{isPacked:r,shape:a,dtype:o}=n;
1if(null!=t){const e=s.D5U.sizeFromShape(a),n=t[0]*t[1]*4;s.D5U.assert(e<=n,(()=>"customTexShape is too small. Row * Column * 4 should be equal or larger than the size of the tensor data."))}const i=Zh(a);let u;u=r?new yd(i):new gd(i);const l=[null!=t?t:Fh(i)];return{dtype:o,shape:a,dataId:this.runWebGLProgram(u,[{shape:i,dtype:o,dataId:e}],o,l,!0,t).dataId}}runWebGLProgram(e,t,n,r,a=!1,o){const i=this.makeTensorInfo(e.outputShape,n),u=this.texData.get(i.dataId);if(e.packedOutput&&(u.isPacked=!0),e.outPackingScheme===Ah.DENSE){const t=null!=o?o:Fh(e.outputShape);u.texShape=t.map((e=>2*e))}if(null!=e.outTexUsage&&(u.usage=e.outTexUsage),0===s.D5U.sizeFromShape(i.shape))return u.values=s.D5U.getTypedArrayFromDType(i.dtype,0),i;const l=[],c=t.map((t=>{if("complex64"===t.dtype)throw new Error("GPGPUProgram does not support complex64 input. For complex64 dtypes, please separate the program into real and imaginary parts.");let n=this.texData.get(t.dataId);if(null==n.texture){if(!e.packedInputs&&s.D5U.sizeFromShape(t.shape)<=(0,s.OBj)().getNumber("WEBGL_SIZE_UPLOAD_UNIFORM"))return{shape:t.shape,texData:null,isUniform:!0,uniformValues:n.values};e.packedInputs&&(n.isPacked=!0,n.shape=t.shape)}if(this.uploadToGPU(t.dataId),!!n.isPacked!==!!e.packedInputs)t=n.isPacked?this.unpackTensor(t):this.packTensor(t),l.push(t),n=this.texData.get(t.dataId);else if(n.isPacked&&!ed(n.shape,t.shape)){const e=t,r=t.shape;t.shape=n.shape,t=this.packedReshape(t,r),l.push(t),n=this.texData.get(t.dataId),e.shape=r}return{shape:t.shape,texData:n,isUniform:!1}}));this.uploadToGPU(i.dataId);const p={shape:i.shape,texData:u,isUniform:!1},h=function(e,t,n){let r="";t.concat(n).forEach((t=>{const a=null!=t.texData&&null!=t.texData.slice&&t.texData.slice.flatOffset>0;if(e.enableShapeUniforms&&!t.isUniform){const o=t.texData.texShape,{useSqueezeShape:i,uniformShape:u,keptDims:l}=cd.Tt(e.packedInputs,t.shape,o);let c="",p="",h="";if(1===u.length&&e.packedInputs){const e=[Math.ceil(o[0]/2),Math.ceil(o[1]/2)];c=`${e[0]>1}_${e[1]>1}`}else if(2!==u.length||e.packedInputs){if(u.length>2&&!e.packedInputs){const e=s.D5U.computeStrides(u);h=`${e[0]===o[1]}_${e[e.length-1]===o[1]}`}}else p=`${u[0]>1}_${u[1]>1}`;const d=t.shape.length,f=2===u.length&&s.D5U.arraysEqual(t.shape,o),m=1===s.D5U.sizeFromShape(t.shape),g=s.Wap.getBroadcastDims(t.shape,n.shape),y=!e.packedInputs&&d===n.shape.length&&s.D5U.arraysEqual(o,n.texData.texShape),b=e.packedInputs||u.length>2?"":`${o[0]>1}_${o[1]>1}`;r+=`${d}_${y}_${i?l:""}_${u.length}_${m}_${g}_${f}_${c}_${p}_${h}_${b}_${a}`}else{const e=t.isUniform?"uniform":t.texData.texShape;r+=`${t.shape}_${e}_${a}`}}));const a=e.userCode;let o=e.constructor.name;return o+="_"+r+"_"+a+`${(0,s.OBj)().getNumber("WEBGL_VERSION")}`,o}(e,c,p),d=this.getAndSaveBinary(h,(()=>pd(this.gpgpu,e,c,p))),f=null!=this.activeTimers;let m;f&&(m=this.startTimer()),(0,s.OBj)().get("ENGINE_COMPILE_ONLY")||function(e,t,n,r,a){t.program.enableShapeUniforms||(dd(t.inShapeInfos,n),dd([t.outShapeInfo],[r]));const o=r.texData.texture,i=r.texData.texShape;r.texData.isPacked?e.setOutputPackedMatrixTexture(o.texture,i[0],i[1]):e.setOutputMatrixTexture(o.texture,i[0],i[1]),e.setProgram(t.webGLProgram),1===(0,s.OBj)().getNumber("WEBGL_VERSION")&&null!==t.infLoc&&e.gl.uniform1f(t.infLoc,1/0),null!==t.nanLoc&&e.gl.uniform1f(t.nanLoc,NaN),n.forEach(((n,r)=>{const a=t.program.variableNames[r],o=t.uniformLocations[a],i=t.uniformLocations[`offset${a}`],u=t.inShapesLocations[`${a}Shape`],l=t.inTexShapesLocations[`${a}TexShape`];if(u){const{uniformShape:r}=cd.Tt(t.program.packedInputs,n.shape,n.texData.texShape);switch(r.length){case 1:e.gl.uniform1iv(u,new Int32Array(r));break;case 2:e.gl.uniform2iv(u,new Int32Array(r));break;case 3:e.gl.uniform3iv(u,new Int32Array(r));break;case 4:e.gl.uniform4iv(u,new Int32Array(r))}}if(l&&e.gl.uniform2i(l,n.texData.texShape[0],n.texData.texShape[1]),null!=o)if(n.isUniform)if(s.D5U.sizeFromShape(n.shape)<2)e.gl.uniform1f(o,n.uniformValues[0]);else{let t=n.uniformValues;t instanceof Float32Array||(t=new Float32Array(t)),e.gl.uniform1fv(o,t)}else null!=n.texData.slice&&null!=i&&e.gl.uniform1i(i,n.texData.slice.flatOffset),e.setInputMatrixTexture(n.texData.texture.texture,o,r)}));const u=t.outShapeLocation;if(u)switch(r.shape.length){case 1:e.gl.uniform1iv(u,new Int32Array(r.shape));break;case 2:e.gl.uniform2iv(u,new Int32Array(r.shape));break;case 3:e.gl.uniform3iv(u,new Int32Array(r.shape));break;case 4:e.gl.uniform4iv(u,new Int32Array(r.shape))}if(t.outShapeStridesLocation){const n=s.D5U.computeStrides(r.shape);switch(r.shape.length){case 2:e.gl.uniform1iv(t.outShapeStridesLocation,new Int32Array(n));break;case 3:e.gl.uniform2iv(t.outShapeStridesLocation,new Int32Array(n));break;case 4:e.gl.uniform3iv(t.outShapeStridesLocation,new Int32Array(n))}}t.outTexShapeLocation&&e.gl.uniform2i(t.outTexShapeLocation,r.texData.texShape[0],r.texData.texShape[1]),t.program.customUniforms&&a&&t.program.customUniforms.forEac
1h(((n,r)=>{const s=t.customUniformLocations[r],o=a[r];if("float"===n.type)e.gl.uniform1fv(s,o);else if("vec2"===n.type)e.gl.uniform2fv(s,o);else if("vec3"===n.type)e.gl.uniform3fv(s,o);else if("vec4"===n.type)e.gl.uniform4fv(s,o);else if("int"===n.type)e.gl.uniform1iv(s,o);else if("ivec2"===n.type)e.gl.uniform2iv(s,o);else if("ivec3"===n.type)e.gl.uniform3iv(s,o);else{if("ivec4"!==n.type)throw Error(`uniform type ${n.type} is not supported yet.`);e.gl.uniform4iv(s,o)}})),e.executeProgram()}(this.gpgpu,d,c,p,r),l.forEach((e=>this.disposeIntermediateTensorInfo(e))),f&&(m=this.endTimer(m),this.activeTimers.push({name:e.constructor.name,query:this.getQueryTime(m)}));const g=(0,s.OBj)().get("WEBGL_FLUSH_THRESHOLD");if(g>0){const e=s.D5U.now();e-this.lastGlFlushTime>g&&(this.gpgpu.gl.flush(),this.lastGlFlushTime=e)}if(!(0,s.OBj)().getBool("WEBGL_LAZILY_UNPACK")&&u.isPacked&&!1===a){const e=this.unpackTensor(i);return this.disposeIntermediateTensorInfo(i),e}return i}compileAndRun(e,t,n,r,s=!1){n=n||t[0].dtype;return this.runWebGLProgram(e,t,n,r,s)}getAndSaveBinary(e,t){return e in this.binaryCache||(this.binaryCache[e]=t()),this.binaryCache[e]}getTextureManager(){return this.textureManager}dispose(){if(!this.disposed){if(!(0,s.OBj)().getBool("IS_TEST")){Object.keys(this.binaryCache).forEach((e=>{this.gpgpu.deleteProgram(this.binaryCache[e].webGLProgram),delete this.binaryCache[e]}))}this.textureManager.dispose(),null!=this.canvas&&"undefined"!==typeof HTMLCanvasElement&&this.canvas instanceof HTMLCanvasElement?this.canvas.remove():this.canvas=null,this.gpgpuCreatedLocally&&(this.gpgpu.program=null,this.gpgpu.dispose()),this.disposed=!0}}floatPrecision(){return null==this.floatPrecisionValue&&(this.floatPrecisionValue=(0,s.lub)((()=>{if(!(0,s.OBj)().get("WEBGL_RENDER_FLOAT32_ENABLED")){const e=(0,s.OBj)().getBool("DEBUG");(0,s.OBj)().set("DEBUG",!1);const t=this.abs((0,s.iD$)(1e-8)).dataSync()[0];if((0,s.OBj)().set("DEBUG",e),t>0)return 32}return 16}))),this.floatPrecisionValue}epsilon(){return 32===this.floatPrecision()?1e-7:1e-4}uploadToGPU(e){const t=this.texData.get(e),{shape:n,dtype:r,values:a,texture:o,usage:i,isPacked:u}=t;if(null!=o)return;const l=null!=this.activeTimers;let c;l&&(c=s.D5U.now());let p=t.texShape;if(null==p&&(p=function(e,t=!1){let n=(0,s.OBj)().getNumber("WEBGL_MAX_TEXTURE_SIZE"),r=(0,s.OBj)().getNumber("WEBGL_MAX_SIZE_FOR_NARROW_TEXTURE");if(r===1/0&&(0,s.OBj)().getBool("WEBGL_AUTO_SQUARIFY_NARROW_TEXTURE_SHAPE")&&(r=n/2),t&&(n*=2,r*=2,1===(e=e.map(((t,n)=>n>=e.length-2?s.D5U.nearestLargerEven(e[n]):e[n]))).length&&(e=[2,e[0]])),2!==e.length){const t=s.D5U.squeezeShape(e);e=t.newShape}let a=s.D5U.sizeFromShape(e),o=null;e.length<=1&&a<=n?o=[1,a]:2===e.length&&e[0]<=n&&e[1]<=n?o=e:3===e.length&&e[0]*e[1]<=n&&e[2]<=n?o=[e[0]*e[1],e[2]]:3===e.length&&e[0]<=n&&e[1]*e[2]<=n?o=[e[0],e[1]*e[2]]:4===e.length&&e[0]*e[1]*e[2]<=n&&e[3]<=n?o=[e[0]*e[1]*e[2],e[3]]:4===e.length&&e[0]<=n&&e[1]*e[2]*e[3]<=n&&(o=[e[0],e[1]*e[2]*e[3]]);const i=null!=o&&Math.max(...o)>r&&Math.min(...o)<=(t?2:1)&&Math.min(...o)>0;if(null==o||i)if(t){const t=Qh(e);let n=2,r=2;e.length&&([n,r]=Yh(e)),a=t*(n/2)*(r/2),o=s.D5U.sizeToSquarishShape(a).map((e=>2*e))}else o=s.D5U.sizeToSquarishShape(a);return o}(n,u),t.texShape=p),null!=a){const e=Zh(n);let o,i=p[1],h=p[0];const d=a instanceof Uint8Array||a instanceof Uint8ClampedArray;!u&&d||([i,h]=Oh(p[0],p[1])),o=u?new vd(e,d):new wd(e,d);const f=d?[h,i]:p,m=this.makeTensorInfo(f,r),g=this.texData.get(m.dataId);g.usage=d?Dh.PIXELS:Dh.UPLOAD,g.texShape=f,this.gpgpu.uploadDenseMatrixToTexture(this.getTexture(m.dataId),i,h,a);const y=[[h,i]],b=!0,x=this.runWebGLProgram(o,[m],r,y,b),w=this.texData.get(x.dataId);t.texShape=w.texShape,t.isPacked=w.isPacked,t.usage=w.usage,(0,s.OBj)().get("ENGINE_COMPILE_ONLY")?this.disposeData(x.dataId):(t.texture=w.texture,t.values=null,this.texData.delete(x.dataId)),this.disposeIntermediateTensorInfo(m),l&&(this.uploadWaitMs+=s.D5U.now()-c)}else{const e=this.acquireTexture(p,i,r,u);t.texture=e}}convertAndCacheOnCPU(e,t){const n=this.texData.get(e),{dtype:r}=n;return this.releaseGPUData(e),null!=t&&(n.values=function(e,t){if("float32"===t||"complex64"===t)return e;if("int32"===t||"bool"===t){const n="int32"===t?new Int32Array(e.length):new Uint8Array(e.length);for(let t=0;t<n.length;++t)n[t]=Math.round(e[t]);return n}throw new Error(`Unknown dtype ${t}`)}(t,r)),n.values}acquireTexture(e,t,n,r){if(this.numBytesInGPU+=this.computeBytes(e,n),!this.warnedAboutMemory&&this.numBytesInGPU>1024*this.numMBBeforeWarning*1024){const e=(this.numBytesInGPU/1024/1024).toFixed(2);this.warnedAboutMemory=!0,console.warn(`High memory usage in GPU: ${e} MB, most likely due to a memory leak`)}return this.textureManager.acquireTexture(e,t,r)}computeBytes(e,t){return e[0]*e[1]*s.D5U.bytesPerElement(t)}checkCompileCompletion(){for(const[,e]of Object.entries(this.binaryCache))this.checkCompletion_(e)}async checkCompileCompletionAsync(){const e=[];
1if(this.gpgpu.parallelCompilationExtension){for(const[,t]of Object.entries(this.binaryCache))e.push(this.checkCompletionAsync_(t));return Promise.all(e)}for(const[,t]of Object.entries(this.binaryCache)){const n=new Promise((e=>{try{this.checkCompletion_(t),e(!0)}catch(n){throw n}}));e.push(n)}return Promise.all(e)}async checkCompletionAsync_(e){return this.gpgpu.gl.getProgramParameter(e.webGLProgram,this.gpgpu.parallelCompilationExtension.COMPLETION_STATUS_KHR)?this.checkCompletion_(e):(await(0,s.glt)(),this.checkCompletionAsync_(e))}checkCompletion_(e){if(!1===this.gpgpu.gl.getProgramParameter(e.webGLProgram,this.gpgpu.gl.LINK_STATUS)){if(console.log(this.gpgpu.gl.getProgramInfoLog(e.webGLProgram)),!1===this.gpgpu.gl.getShaderParameter(e.fragmentShader,this.gpgpu.gl.COMPILE_STATUS))throw Uh(e.source,this.gpgpu.gl.getShaderInfoLog(e.fragmentShader)),new Error("Failed to compile fragment shader.");throw new Error("Failed to link vertex and fragment shaders.")}return!0}getUniformLocations(){for(const[,e]of Object.entries(this.binaryCache)){const{uniformLocations:t,customUniformLocations:n,infLoc:r,nanLoc:s,inShapesLocations:a,inTexShapesLocations:o,outShapeLocation:i,outShapeStridesLocation:u,outTexShapeLocation:l}=hd(this.gpgpu,e.program,e.webGLProgram);e.uniformLocations=t,e.customUniformLocations=n,e.infLoc=r,e.nanLoc=s,e.inShapesLocations=a,e.inTexS
1hapesLocations=o,e.outShapeLocation=i,e.outShapeStridesLocation=u,e.outTexShapeLocation=l}}}zf.nextDataId=0;s.C2$.isBrowser()&&(0,s.jqO)("webgl",(()=>new zf),2);class Vf{constructor(e,t,n){this.variableNames=["A","B"],this.outputShape=s.Wap.assertAndGetBroadcastShape(t,n),this.enableShapeUniforms=fd(this.outputShape.length),this.userCode=`\n float binaryOperation(float a, float b) {\n ${e}\n }\n\n void main() {\n float a = getAAtOutCoords();\n float b = getBAtOutCoords();\n setOutput(binaryOperation(a, b));\n }\n `}}class Gf{constructor(e,t,n,r=!1){this.variableNames=["A","B"],this.supportsBroadcasting=!0,this.packedInputs=!0,this.packedOutput=!0,this.outputShape=s.Wap.assertAndGetBroadcastShape(t,n);const a=this.outputShape.length;this.enableShapeUniforms=fd(a);let o="";if(r)if(0===a||1===s.D5U.sizeFromShape(this.outputShape))o="\n result.y = 0.;\n result.z = 0.;\n result.w = 0.;\n ";else{if(o=`\n ${(0,cd.kW)(a)} coords = getOutputCoords();\n `,1===a)this.enableShapeUniforms?o+="\n result.y = (coords + 1) >= outShape ? 0. : result.y;\n result.z = 0.;\n result.w = 0.;\n ":o+=`\n result.y = (coords + 1) >= ${this.outputShape[0]} ? 0. : result.y;\n result.z = 0.;\n result.w = 0.;\n `;else{const e=Cf("coords",a);this.enableShapeUniforms?o+=`\n bool nextRowOutOfBounds =\n (${e[a-2]} + 1) >= outShape[${a} - 2];\n bool nextColOutOfBounds =\n (${e[a-1]} + 1) >= outShape[${a} - 1];\n result.y = nextColOutOfBounds ? 0. : result.y;\n result.z = nextRowOutOfBounds ? 0. : result.z;\n result.w = nextColOutOfBounds || nextRowOutOfBounds ? 0. : result.w;\n `:o+=`\n bool nextRowOutOfBounds =\n (${e[a-2]} + 1) >= ${this.outputShape[a-2]};\n bool nextColOutOfBounds =\n (${e[a-1]} + 1) >= ${this.outputShape[a-1]};\n result.y = nextColOutOfBounds ? 0. : result.y;\n result.z = nextRowOutOfBounds ? 0. : result.z;\n result.w = nextColOutOfBounds || nextRowOutOfBounds ? 0. : result.w;\n `}}this.userCode=`\n vec4 binaryOperation(vec4 a, vec4 b) {\n ${e}\n }\n\n void main() {\n vec4 a = getAAtOutCoords();\n vec4 b = getBAtOutCoords();\n\n vec4 result = binaryOperation(a, b);\n ${o}\n\n setOutput(result);\n }\n `}}function Hf(e){const{inputs:t,backend:n}=e,{x:r}=t;return n.incRef(r.dataId),{dataId:r.dataId,shape:r.shape,dtype:r.dtype}}const jf={kernelName:s.iJz,backendName:"webgl",kernelFunc:Hf};function Xf(e){const{inputs:t,backend:n}=e,{real:r,imag:s}=t,a=n.makeTensorInfo(r.shape,"complex64"),o=n.texData.get(a.dataId),i=Hf({inputs:{x:r},backend:n}),u=Hf({inputs:{x:s},backend:n});return o.complexTensorInfos={real:i,imag:u},a}const qf={kernelName:s.Zz9,backendName:"webgl",kernelFunc:Xf},Kf="return (a < 0.) ? b * a : a;",Qf="\n vec4 aLessThanZero = vec4(lessThan(a, vec4(0.)));
1\n return (aLessThanZero * (b * a)) + ((vec4(1.0) - aLessThanZero) * a);\n";const Yf={kernelName:s.J$2,backendName:"webgl",kernelFunc:function(e){const{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{alpha:o}=r,i=n.makeTensorInfo([],"float32",s.D5U.createScalarValue(o,"float32")),u=(0,s.OBj)().getBool("WEBGL_PACK_BINARY_OPERATIONS")?new Gf(Qf,a.shape,i.shape):new Vf(Kf,a.shape,i.shape),l=n.runWebGLProgram(u,[a,i],"float32");return n.disposeIntermediateTensorInfo(i),l}},Zf="return (a < 0.) ? b * a : a;",Jf="\n vec4 aLessThanZero = vec4(lessThan(a, vec4(0.)));\n return (aLessThanZero * (b * a)) + ((vec4(1.0) - aLessThanZero) * a);\n";const em={kernelName:s.o0g,backendName:"webgl",kernelFunc:function(e){const{inputs:t,backend:n}=e,{x:r,alpha:a}=t,o=(0,s.OBj)().getBool("WEBGL_PACK_BINARY_OPERATIONS")?new Gf(Jf,r.shape,a.shape):new Vf(Zf,r.shape,a.shape);return n.runWebGLProgram(o,[r,a],"float32")}};function tm({opSnippet:e,packedOpSnippet:t,cpuKernelImpl:n,dtype:r}){return({inputs:a,backend:o})=>{const{x:i}=a,u=o,l=r||i.dtype;if(u.shouldExecuteOnCPU([i])&&null!=n){const e=u.texData.get(i.dataId),t=n(e.values,l);return u.makeTensorInfo(i.shape,l,t)}let c;return c=(0,s.OBj)().getBool("WEBGL_PACK_UNARY_OPERATIONS")&&null!=t?new Bf(i.shape,t):new Ff(i.shape,e),u.runWebGLProgram(c,[i],l)}}function nm({opSnippet:e,packedOpSnippet:t,checkOutOfBounds:n=!1,supportsComplex:r=!1,cpuKernelImpl:a,dtype:o}){return({inputs:i,backend:u})=>{const{a:l,b:c}=i,p=u;if(r&&"complex64"===l.dtype){const t=p.texData.get(l.dataId),n=p.texData.get(c.dataId),[r,a]=[[t.complexTensorInfos.real,n.complexTensorInfos.real],[t.complexTensorInfos.imag,n.complexTensorInfos.imag]].map((t=>{const[n,r]=t,a={dataId:n.dataId,dtype:n.dtype,shape:l.shape},o={dataId:r.dataId,dtype:r.dtype,shape:c.shape},i=new Vf(e,l.shape,c.shape);return p.runWebGLProgram(i,[a,o],(0,s.x8V)(n.dtype,r.dtype))})),o=Xf({inputs:{real:r,imag:a},backend:p});return p.disposeIntermediateTensorInfo(r),p.disposeIntermediateTensorInfo(a),o}const h=o||(0,s.x8V)(l.dtype,c.dtype);if(("string"===l.dtype||"string"===c.dtype||p.shouldExecuteOnCPU([l,c]))&&null!=a){const e=p.texData.get(l.dataId).values,t=p.texData.get(c.dataId).values,n="string"===l.dtype?s.Wap.fromUint8ToStringArray(e):e,r="string"===l.dtype?s.Wap.fromUint8ToStringArray(t):t,[o,i]=a(l.shape,c.shape,n,r,h),u=p.makeTensorInfo(i,h);return p.texData.get(u.dataId).values=o,u}let d;return d=(0,s.OBj)().getBool("WEBGL_PACK_BINARY_OPERATIONS")&&null!=t?new Gf(t,l.shape,c.shape,n):new Vf(e,l.shape,c.shape),p.runWebGLProgram(d,[l,c],h)}}
vendor: 23,835 bytes, line 1
1function rm(e,t=!1){if("linear"===e)return"return x;";if("relu"===e)return t?"\n vec4 result = x * vec4(greaterThanEqual(x, vec4(0.0)));\n bvec4 isNaN = isnan(x);\n\n result.r = isNaN.r ? x.r : result.r;\n result.g = isNaN.g ? x.g : result.g;\n result.b = isNaN.b ? x.b : result.b;\n result.a = isNaN.a ? x.a : result.a;\n\n return result;\n":"if (isnan(x)) return x;\n return (x < 0.0) ? 0.0 : x;\n";if("elu"===e)return t?"\n vec4 result;\n\n result.r = (x.r >= 0.0) ? x.r : (exp(x.r) - 1.0);\n result.g = (x.g >= 0.0) ? x.g : (exp(x.g) - 1.0);\n result.b = (x.b >= 0.0) ? x.b : (exp(x.b) - 1.0);\n result.a = (x.a >= 0.0) ? x.a : (exp(x.a) - 1.0);\n\n return result;\n":"return (x >= 0.0) ? x : (exp(x) - 1.0);";if("relu6"===e)return t?"\n vec4 result = min(x, vec4(6.)) * vec4(greaterThanEqual(x, vec4(0.0)));\n bvec4 isNaN = isnan(x);\n\n result.r = isNaN.r ? x.r : result.r;\n result.g = isNaN.g ? x.g : result.g;\n result.b = isNaN.b ? x.b : result.b;\n result.a = isNaN.a ? x.a : result.a;\n\n return result;\n":"if (isnan(x)) return x;\n return (x < 0.0) ? 0.0 : min(6.0, x);\n";if("prelu"===e)return t?Jf:Zf;if("leakyrelu"===e)return t?Qf:Kf;if("sigmoid"===e)return"return 1.0 / (1.0 + exp(-1.0 * x));";throw new Error(`Activation ${e} has not been implemented for the WebGL backend.`)}class sm{constructor(e,t,n,r=!1,s=!1,a=!1,o=null,i=!1,u=!1){this.variableNames=["matrixA","matrixB"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=n,this.enableShapeUniforms=fd(this.outputShape.length);const l=r?e[1]:e[2],c=Math.ceil(l/2),p=r?"i * 2, rc.y":"rc.y, i * 2",h=s?"rc.z, i * 2":"i * 2, rc.z",d=r?["a.xxyy","a.zzww"]:["a.xxzz","a.yyww"],f=s?["b.xzxz","b.ywyw"]:["b.xyxy","b.zwzw"];let m="",g="";o&&(m=i?`vec4 activation(vec4 a) {\n vec4 b = getPreluActivationWeightsAtOutCoords();\n ${o}\n }`:u?`vec4 activation(vec4 a) {\n vec4 b = getLeakyreluAlphaAtOutCoords();\n ${o}\n }`:`vec4 activation(vec4 x) {\n ${o}\n }`,g="result = activation(result);");const y=a?"result += getBiasAtOutCoords();":"";a&&this.variableNames.push("bias"),i&&this.variableNames.push("preluActivationWeights"),u&&this.variableNames.push("leakyreluAlpha");let b="rc.x",x="rc.x";e[0]<t[0]?b=`int(min(float(rc.x), ${e[0]-1}.))`:t[0]<e[0]&&(x=`int(min(float(rc.x), ${t[0]-1}.))`),this.userCode=`\n ${m}\n // Don't use uniform for sharedDimensionPacked for performance.\n const float sharedDimension = ${c}.0;\n\n vec4 dot2x2ARowBCol(ivec3 rc) {\n vec4 result = vec4(0);\n for (int i = 0; i < ${c}; i++) {\n int batchA = ${b};\n int batchB = ${x};\n vec4 a = getMatrixA(batchA, ${p});\n vec4 b = getMatrixB(batchB, ${h});\n\n // These swizzled products need to be separately added.\n // See: https://github.com/tensorflow/tfjs/issues/1735\n result += (${d[0]} * ${f[0]});\n result += (${d[1]} * ${f[1]});\n }\n return result;\n }\n\n void main() {\n ivec3 rc = getOutputCoords();\n vec4 result = dot2x2ARowBCol(rc);\n\n ${y}\n\n ${g}\n\n setOutput(result);\n }\n `}}const am="return areal * breal - aimag * bimag;",om="return areal * bimag + aimag * breal;";class im{constructor(e,t,n){this.variableNames=["AReal","AImag","BReal","BImag"],this.outputShape=s.Wap.assertAndGetBroadcastShape(t,n),this.userCode=`\n float binaryOpComplex(\n float areal, float aimag, float breal, float bimag) {\n ${e}\n }\n\n void main() {\n float areal = getARealAtOutCoords();\n float aimag = getAImagAtOutCoords();\n float breal = getBRealAtOutCoords();\n float bimag = getBImagAtOutCoords();\n setOutput(binaryOpComplex(areal, aimag, breal, bimag));\n }\n `}}const um="return a * b;";function lm(e){const{inputs:t,backend:n}=e,{a:r,b:a}=t,o=s.Wap.upcastType(r.dtype,a.dtype);if("complex64"===r.dtype){const e=n.texData.get(r.dataId),t=n.texData.get(a.dataId),s=new im(am,r.shape,a.shape),o=new im(om,r.shape,a.shape),i=[{dataId:e.complexTensorInfos.real.dataId,dtype:e.complexTensorInfos.real.dtype,shape:r.shape},{dataId:e.complexTensorInfos.imag.dataId,dtype:e.complexTensorInfos.imag.dtype,shape:r.shape},{dataId:t.complexTensorInfos.real.dataId,dtype:t.complexTensorInfos.real.dtype,shape:a.shape},{dataId:t.complexTensorInfos.imag.dataId,dtype:t.complexTensorInfos.imag.dtype,shape:a.shape}],u=n.runWebGLProgram(s,i,"float32"),l=n.runWebGLProgram(o,i,"float32"),c=Xf({inputs:{real:u,imag:l},backend:n});return n.disposeIntermediateTensorInfo(u),n.disposeIntermediateTensorInfo(l),c}if(n.shouldExecuteOnCPU([r,a])){const e=n.texData.get(r.dataId),t=n.texData.get(a.dataId),[s,i]=ef(r.shape,a.shape,e.values,t.values,o),u=n.makeTensorInfo(i,o);return n.texData.get(u.dataId).values=s,u}let i;return i=(0,s.OBj)().getBool("WEBGL_PACK_BINARY_OPERATIONS")?new Gf(um,r.shape,a.shape):new Vf(um,r.shape,a.shape),n.runWebGLProgram(i,[r,a],o)}const cm={kernelName:s.wYn,backendName:"webgl",kernelFunc:lm};function pm(e){const{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{shape:o}=r,i=n,u=s.D5U.sizeFromShape(a.shape),l=s.D5U.inferFromImplicitShape(o,u),c=s.D5U.sizeFromShape(l);s.D5U.assert(u===c,(()=>`The new shape (${l}) has ${c} elements and the old shape (${a.shape}) has ${u} elements. The new shape and old shape must have the same number of elements.`));const p=i.texData.get(a.dataId);return!p.isPacked||ed(a.shape,l)||null!==p.texture&&ed(p.shape,l)?(i.incRef(a.dataId),{dataId:a.dataId,shape:l,dtype:a.dtype}):function(e,t,n){const r=[Qh(e.shape),...Yh(e.shape)],s={dtype:e.dtype,shape:r,dataId:e.dataId},a=[Qh(t),...Yh(t)],o=new $f(a,r),i=[r],u=n.runWebGLProgram(o,[s],e.dtype,i,!0);return{dataId:u.dataId,shape:t,dtype:u.dtype}}(a,l,i)}const hm={kernelName:s.HZH,backendName:"webgl",kernelFunc:pm};class dm{constructor(e,t){this.variableNames=["x"];const{windowSize:n,batchSize:r,inSize:a,outSize:o}=e;this.outputShape=[r,o];const i=4*Math.floor(n/4),u=n%4;let l="sumValue += dot(values, ones);";if(null!=t){const e=1/t;l=`sumValue += dot(values * ${s.D5U.isInt(e)?e.toPrecision(2):e}, ones);`}let c="";a%n>0&&(c=`\n if (inIdx < 0 || inIdx >= ${a}) {\n return 0.0;\n }\n `),this.userCode=`\n const vec4 ones = vec4(1.0, 1.0, 1.0, 1.0);\n\n float getValue(int batch, int inIdx) {\n ${c}\n return getX(batch, inIdx);\n }\n\n void main() {\n ivec2 coords = getOutputCoords();\n int batch = coords[0];\n int outIdx = coords[1];\n int inOffset = outIdx * ${n};\n\n float sumValue = 0.0;\n\n for (int i = 0; i < ${i}; i += 4) {\n int inIdx = inOffset + i;\n vec4 values = vec4(\n getValue(batch, inIdx),\n getValue(batch, inIdx + 1),\n getValue(batch, inIdx + 2),\n getValue(batch, inIdx + 3)\n );\n\n ${l}\n }\n\n int inIdx = inOffset + ${i};\n if (${1===u}) {\n vec4 values = vec4(getValue(batch, inIdx), 0.0, 0.0, 0.0);\n\n ${l}\n } else if (${2===u}) {\n vec4 values = vec4(\n getValue(batch, inIdx),\n getValue(batch, inIdx + 1), 0.0, 0.0);\n\n ${l}\n } else if (${3===u}) {\n vec4 values = vec4(\n getValue(batch, inIdx),\n getValue(batch, inIdx + 1),\n getValue(batch, inIdx + 2), 0.0);\n\n ${l}\n }\n setOutput(sumValue);\n }\n `}}class fm{constructor(e,t){this.variableNames=["x"];const{windowSize:n,batchSize:r,inSize:s,outSize:a}=e;this.outputShape=[r,a];let o="0.0",i="";"prod"===t?o="1.0":"min"===t?(o="1.0 / 1e-20",i="min"):"max"===t&&(o="-1.0 / 1e-20",i="max");let u=`${t}(${t}(${t}(minMaxValue[0], minMaxValue[1]), minMaxValue[2]), minMaxValue[3])`;"sum"===t?u="sumValue":"prod"===t?u="prodValue":"all"===t?u="allValue":"any"===t&&(u="anyValue");const l=4*Math.floor(n/4),c=n%4;let p=`\n if (${"sum"===t}) {\n sumValue += dot(values, ones);\n } else if (${"prod"===t}) {\n vec2 tmp = vec2(values[0], values[1]) * vec2(values[2], values[3]);\n prodValue *= tmp[0] * tmp[1];\n } else {\n minMaxValue = ${i}(values, minMaxValue);\n if (${"min"===t} || ${"max"===t}) {\n minMaxValue = ${i}(values, minMaxValue);\n bvec4 isNaN = isnan(values);\n if (isNaN.r || isNaN.g || isNaN.b || isNaN.a) {\n minMaxValue = vec4(NAN);\n }\n }\n }\n `,h="vec4";"all"===t?(o="1.0",p="\n bool reducedAllValue = all(values);\n float floatedReducedAllValue = float(reducedAllValue);\n allValue = float(allValue >= 1.0 && floatedReducedAllValue >= 1.0);\n ",h="bvec4"):"any"===t&&(o="0.0",p="\n bool reducedAnyValue = any(values);\n float floatedReducedAnyValue = float(reducedAnyValue);\n anyValue = float(anyValue >= 1.0 || floatedReducedAnyValue >= 1.0);\n ",h="bvec4");let d="";s%n>0&&(d=`\n if (inIdx < 0 || inIdx >= ${s}) {\n return initializationValue;\n }\n `),this.userCode=`\n const float initializationValue = ${o};\n const vec4 ones = vec4(1.0, 1.0, 1.0, 1.0);\n\n float getValue(int batch, int inIdx) {\n ${d}\n return getX(batch, inIdx);\n }\n\n void main() {\n ivec2 coords = getOutputCoords();\n int batch = coords[0];\n int outIdx = coords[1];\n int inOffset = outIdx * ${n};\n\n vec4 minMaxValue = vec4(${o});\n float prodValue = 1.0;\n float sumValue = 0.0;\n float allValue = 1.0;\n float anyValue = 0.0;\n\n for (int i = 0; i < ${l}; i += 4) {\n int inIdx = inOffset + i;\n ${h} values = ${h}(\n getValue(batch, inIdx),\n getValue(batch, inIdx + 1),\n getValue(batch, inIdx + 2),\n getValue(batch, inIdx + 3)\n );\n\n ${p}\n }\n\n int inIdx = inOffset + ${l};\n if (${1===c}) {\n ${h} values = ${h}(\n getValue(batch, inIdx),\n initializationValue,\n initializationValue,\n initializationValue\n );\n\n ${p}\n } else if (${2===c}) {\n ${h} values = ${h}(\n getValue(batch, inIdx),\n getValue(batch, inIdx + 1),\n initializationValue,\n initializationValue\n );\n\n ${p}\n } else if (${3===c}) {\n ${h} values = ${h}(\n getValue(batch, inIdx),\n getValue(batch, inIdx + 1),\n getValue(batch, inIdx + 2),\n initializationValue\n );\n\n ${p}\n }\n setOutput(${u});\n }\n `}}function mm(e,t,n,r){const a=function(e){const t=[];for(;0===t.length||1!==t[t.length-1].outSize;){const n=t.length?t[t.length-1].outSize:e[1],r=s.Wap.computeOptimalWindowSize(n);t.push({inSize:n,windowSize:r,outSize:Math.ceil(n/r)})}return t}(e.shape);let o=e;for(let s=0;s<a.length;s++){const{inSize:i,windowSize:u,outSize:l}=a[s];let c,p;c="mean"===n?0===s?new dm({windowSize:u,inSize:i,batchSize:e.shape[0],outSize:l},i):new dm({windowSize:u,inSize:i,batchSize:e.shape[0],outSize:l}):new fm({windowSize:u,inSize:i,batchSize:e.shape[0],outSize:l},n),p=o,o=r.runWebGLProgram(c,[o],t),p.dataId!==e.dataId&&r.disposeIntermediateTensorInfo(p)}return o}class gm{constructor(e,t){this.variableNames=["A"];const 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;const r=(0,cd.kW)(this.rank),s=function(e){const t=e.length;if(t>6)throw Error(`Transpose for rank ${t} is not yet supported`);const n=["resRC.x","resRC.y","resRC.z","resRC.w","resRC.u","resRC.v"],r=new Array(t);for(let s=0;s<e.length;s++)r[e[s]]=n[s];return r.join()}(t);this.userCode=`\n void main() {\n ${r} resRC = getOutputCoords();\n setOutput(getA(${s}));\n }\n `}}class ym{constructor(e,t){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0;const n=new Array(e.length);for(let l=0;l<n.length;l++)n[l]=e[t[l]];if(this.outputShape=n,this.rank=n.length,this.rank>6)throw Error(`Packed transpose for rank ${this.rank} is not yet supported.`);const r=(0,cd.kW)(this.rank),s=Tf("rc",this.rank),a=new Array(this.rank);for(let l=0;l<t.length;l++)a[t[l]]=s[l];const o=`vec2(${a.slice(-2).join()})`,i=`++${s[this.rank-1]} < ${n[this.rank-1]}`,u=`getChannel(getA(${a.join()}), ${o})`;this.userCode=`\n void main() {\n ${r} rc = getOutputCoords();\n vec4 result = vec4(0.);\n result[0] = ${u};\n if(${i}) {\n result[1] = ${u};\n }\n --${s[this.rank-1]};\n if(++${s[this.rank-2]} < ${n[this.rank-2]}) {\n result[2] = ${u};\n if(${i}) {\n result[3] = ${u};\n }\n }\n setOutput(result);\n }\n `}}function bm(e,t,n){const r=(0,s.OBj)().getBool("WEBGL_PACK_ARRAY_OPERATIONS")?new ym(e.shape,t):new gm(e.shape,t);return n.runWebGLProgram(r,[e],e.dtype)}function xm(e){const{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{axis:o,keepDims:i}=r;return function(e,t,n,r){const a=t,o=e.shape.length,i=s.D5U.parseAxisParam(a,e.shape);let u=i;const l=s.Wap.getAxesPermutation(u,o),c=null!=l;let p=e;c&&(p=bm(e,l,r),u=s.Wap.getInnerMostAxes(u.length,o)),s.Wap.assertAxesAreInnerMostDims("sum",u,o);const[h,d]=s.Wap.computeOutAndReduceShapes(p.shape,u);let f=h;n&&(f=s.Wap.expandShapeToKeepDim(h,i));const m=s.D5U.sizeFromShape(d),g=pm({inputs:{x:p},attrs:{shape:[s.D5U.sizeFromShape(e.shape)/m,m]},backend:r}),y=mm(g,(0,s.z4k)(e.dtype),"sum",r),b=pm({inputs:{x:y},attrs:{shape:f},backend:r});return r.disposeIntermediateTensorInfo(g),r.disposeIntermediateTensorInfo(y),c&&r.disposeIntermediateTensorInfo(p),b}(a,o,i,n)}const wm={kernelName:s.GBy,backendName:"webgl",kernelFunc:xm};function vm(e){const{inputs:t,backend:n,attrs:r}=e,{x:s}=t,{perm:a}=r,o=n,i=s.shape.length,u=new Array(i);for(let c=0;c<u.length;c++)u[c]=s.shape[a[c]];let l;if(o.shouldExecuteOnCPU([s])){const e=o.texData.get(s.dataId).values,t=Nf(e,s.shape,s.dtype,a,u);l=o.makeTensorInfo(u,s.dtype);o.texData.get(l.dataId).values=t}else l=bm(s,a,o);return l}const km={kernelName:s.G3Y,backendName:"webgl",kernelFunc:vm};function Im({a:e,b:t,transposeA:n,transposeB:r,backend:a,bias:o=null,preluActivationWeights:i=null,leakyreluAlpha:u=0,activation:l=null}){const c=e.shape.length,p=t.shape.length,h=n?e.shape[c-2]:e.shape[c-1],d=r?t.shape[p-1]:t.shape[p-2],f=n?e.shape[c-1]:e.shape[c-2],m=r?t.shape[p-2]:t.shape[p-1],g=e.shape.slice(0,-2),y=t.shape.slice(0,-2),b=s.D5U.sizeFromShape(g),x=s.D5U.sizeFromShape(y),w=s.Jyw.assertAndGetBroadcastShape(e.shape.slice(0,-2),t.shape.slice(0,-2)).concat([f,m]);s.D5U.assert(h===d,(()=>`Error in matMul: inner shapes (${h}) and (${d}) of Tensors with shapes ${e.shape} and ${t.shape} and transposeA=${n} and transposeB=${r} must match.`));const v=n?[b,h,f]:[b,f,h],k=r?[x,m,d]:[x,d,m],I=pm({inputs:{x:e},backend:a,attrs:{shape:v}}),N=pm({inputs:{x:t},backend:a,attrs:{shape:k}}),S=[I,N],T=Math.max(b,x),C=n?I.shape[1]:I.shape[2],E=null!=o,$=null!=i,A="leakyrelu"===l,D=null!=l?rm(l,!0):null;let _;if((1===f||1===m)&&C>1e3&&!1===(E||$||A||null!=D)){let e=I,t=N;n&&(e=vm({inputs:{x:I},backend:a,attrs:{perm:[0,2,1]}}),S.push(e)),r&&(t=vm({inputs:{x:N},backend:a,attrs:{perm:[0,2,1]}}),S.push(t));const s=1===m;let o=e;1!==m&&(o=pm({inputs:{x:e},backend:a,attrs:{shape:[T,C,1]}}),S.push(o));const i=1===m?2:1;let u=t;s&&(u=pm({inputs:{x:t},backend:a,attrs:{shape:[T,1,C]}}),S.push(u));const l=lm({inputs:{a:o,b:u},backend:a});_=xm({inputs:{x:l},backend:a,attrs:{axis:i,keepDims:!0}}),S.push(l)}else{const l=(0,s.x8V)(e.dtype,t.dtype),c=new sm(v,k,[T,f,m],n,r,E,D,$,A),p=[I,N];if(null!=o&&p.push(o),$&&p.push(i),A){const e=a.makeTensorInfo([],"float32",s.D5U.createScalarValue(u,"float32"));p.push(e),S.push(e)}_=a.runWebGLProgram(c,p,l)}const R=pm({inputs:{x:_},backend:a,attrs:{shape:w}});S.push(_);for(const s of S)a.disposeIntermediateTensorInfo(s);return R}const Nm={kernelName:s.usg,backendName:"webgl",kernelFunc:function(e){const{inputs:t,backend:n,attrs:r}=e,{a:s,b:a,bias:o,preluActivationWeights:i}=t,{transposeA:u,transposeB:l,activation:c,leakyreluAlpha:p}=r;return Im({a:s,b:a,transposeA:u,transposeB:l,backend:n,bias:o,preluActivationWeights:i,leakyreluAlpha:p,activation:c})}},Sm="return abs(x);";const Tm={kernelName:s.SYM,backendName:"webgl",kernelFunc:function(e){const{inputs:t,backend:n}=e,{x:r}=t;if(n.shouldExecuteOnCPU([r])&&"complex64"!==r.dtype){const e=n.texData.get(r.dataId),t=pf(e.values);return n.makeTensorInfo(r.shape,r.dtype,t)}let a;return a=(0,s.OBj)().getBool("WEBGL_PACK_UNARY_OPERATIONS")?new Bf(r.shape,Sm):new Ff(r.shape,Sm),n.runWebGLProgram(a,[r],r.dtype)}},Cm=tm({opSnippet:"if (isnan(x)) return x;\n if (abs(x) > 1.) {\n return NAN;\n }\n return acos(x);\n"}),Em={kernelName:s.VGw,backendName:"webgl",kernelFunc:Cm},$m=tm({opSnippet:"if (isnan(x)) return x;\n if (x < 1.0) return NAN;\nreturn log(x + sqrt(x * x - 1.0));"}),Am={kernelName:s.SpW,backendName:"webgl",kernelFunc:$m},Dm="return a + b;",_m=nm({opSnippet:Dm,packedOpSnippet:Dm,supportsComplex:!0,cpuKernelImpl:Rd}),Rm={kernelName:s.mm_,backendName:"webgl",kernelFunc:_m};class Fm{constructor(e,t){this.outputShape=[],this.outputShape=e,this.variableNames=t.map(((e,t)=>`T${t}`));const n=[];this.variableNames.forEach((e=>{n.push(`float v${e} = get${e}AtOutCoords();`)}));const r=this.variableNames.map((e=>`v${e}`)).join(" + ");this.userCode=`\n void main() {\n ${n.join("\n ")}\n\n float result = ${r};\n setOutput(result);\n }\n `}}class Om{constructor(e,t){this.outputShape=[],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=e,this.variableNames=t.map(((e,t)=>`T${t}`));const n=[];this.variableNames.forEach((e=>{n.push(`vec4 v${e} = get${e}AtOutCoords();`)}));const r=this.variableNames.map((e=>`v${e}`)).join(" + ");this.userCode=`\n void main() {\n ${n.join("\n ")}\n\n vec4 result = ${r};\n setOutput(result);\n }\n `}}const Mm={kernelName:s.Xze,backendName:"webgl",kernelFunc:function e(t){const{inputs:n,backend:r}=t,a=n;if(1===a.length)return Hf({inputs:{x:a[0]},backend:r});if(a.length>(0,s.OBj)().get("WEBGL_MAX_TEXTURES_IN_SHADER")){const t=Math.floor(a.length/2),n=e({inputs:a.slice(0,t),backend:r}),s=e({inputs:a.slice(t),backend:r});return e({inputs:[n,s],backend:r})}const o=a.map((e=>e.dtype)).reduce(((e,t)=>(0,s.x8V)(e,t))),i=a.map((e=>e.shape)),u=(0,s.OBj)().getBool("WEBGL_PACK")?new Om(a[0].shape,i):new Fm(a[0].shape,i);return r.runWebGLProgram(u,a,o)}};const Bm={kernelName:s.oT6,backendName:"webgl",kernelFunc:function(e){const{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{axis:o,keepDims:i}=r,u=a.shape.length,l=s.D5U.parseAxisParam(o,a.shape);let c=l;const p=s.Wap.getAxesPermutation(c,u);let h=a;null!=p&&(h=vm({inputs:{x:a},backend:n,attrs:{perm:p}}),c=s.Wap.getInnerMostAxes(c.length,u)),s.Wap.assertAxesAreInnerMostDims("all",c,u);const[d,f]=s.Wap.computeOutAndReduceShapes(h.shape,c),m=pm({inputs:{x:h},backend:n,attrs:{shape:[-1,s.D5U.sizeFromShape(f)]}}),g=mm(m,m.dtype,"all",n);let y;if(i){y=pm({inputs:{x:g},backend:n,attrs:{shape:s.Wap.expandShapeToKeepDim(d,l)}})}else y=pm({inputs:{x:g},backend:n,attrs:{shape:d}});return n.disposeIntermediateTensorInfo(m),n.disposeIntermediateTensorInfo(g),null!=p&&n.disposeIntermediateTensorInfo(h),y}};const Lm={kernelName:s.IKK,backendName:"webgl",kernelFunc:function(e){const{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{axis:o,keepDims:i}=r,u=a.shape.length,l=s.D5U.parseAxisParam(o,a.shape);let c=l;const p=s.Wap.getAxesPermutation(c,u);let h=a;null!=p&&(h=vm({inputs:{x:a},backend:n,attrs:{perm:p}}),c=s.Wap.getInnerMostAxes(c.length,u)),s.Wap.assertAxesAreInnerMostDims("any",c,u);const[d,f]=s.Wap.computeOutAndReduceShapes(h.shape,c),m=pm({inputs:{x:h},backend:n,attrs:{shape:[-1,s.D5U.sizeFromShape(f)]}}),g=mm(m,m.dtype,"any",n);let y;if(i){y=pm({inputs:{x:g},backend:n,attrs:{shape:s.Wap.expandShapeToKeepDim(d,l)}})}else y=pm({inputs:{x:g},backend:n,attrs:{shape:d}});return n.disposeIntermediateTensorInfo(m),n.disposeIntermediateTensorInfo(g),null!=p&&n.disposeIntermediateTensorInfo(h),y}};class Wm{constructor(e,t,n){this.variableNames=["A"];const{windowSize:r,batchSize:s,outSize:a}=e;n||this.variableNames.push("bestIndicesA"),this.outputShape=[s,a];const o="max"===t?">":"<",i=n?"inOffset + i;":"round(getBestIndicesA(batch, inOffset + i));";this.userCode=`\n void main() {\n ivec2 coords = getOutputCoords();\n int batch = coords[0];\n int outIdx = coords[1];\n int inOffset = outIdx * ${r};\n\n int bestIndex = inOffset;\n float bestValue = getA(batch, bestIndex);\n\n for (int i = 0; i < ${r}; i++) {\n int inIdx = ${i};\n float candidate = getA(batch, inIdx);\n if (candidate ${o} bestValue) {\n bestValue = candidate;\n bestIndex = inIdx;\n }\n }\n setOutput(float(bestIndex));\n }\n `}}class Pm{constructor(e,t,n,r){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,s.D5U.assert(e.length>2,(()=>`Packed arg${n.charAt(0).toUpperCase()+n.slice(1)} supports only inputs with rank above 2.`));const a=e[e.length-1],o=Math.ceil(a/t);this.outputShape=e.slice(0,-1),o>1&&this.outputShape.push(o),r||this.variableNames.push("bestIndicesA");const i=this.outputShape,u=i.length,l=(0,cd.kW)(u),c=Cf("coords",u);let p,h;if(1===o){h=u+1;const e=(0,cd.kW)(h);p=`\n ${e} sourceLocR = ${e}(${c.join()}, 0);\n ++${c[u-1]};\n ${e} sourceLocG = ${e}(${c.join()}, 0);\n ++${c[u-2]};\n ${e} sourceLocA = ${e}(${c.join()}, 0);\n --${c[u-1]};\n ${e} sourceLocB = ${e}(${c.join()}, 0);\n --${c[u-2]};`}else h=u,p=`\n ${l} sourceLocR = coords;\n ++${c[u-1]};\n ${l} sourceLocG = coords;\n ++${c[u-2]};\n ${l} sourceLocA = coords;\n --${c[u-1]};\n ${l} sourceLocB = coords;\n --${c[u-2]};`;const d=["x","y","z","w","u","v"].slice(0,h),f="."+d[h-1],m=d.map((e=>"int "+e)),g=Cf("sourceLocR",h-1).concat("inIdx.r"),y=Cf("sourceLocG",h-1).concat("inIdx.g"),b=Cf("sourceLocB",h-1).concat("inIdx.b"),x=Cf("sourceLocA",h-1).concat("inIdx.a"),w="max"===n?"greaterThan":"lessThan",v=r?"":`\n inIdx = round(vec4(getBestIndicesAChannel(${g.join()}),\n getBestIndicesAChannel(${y.join()}),\n getBestIndicesAChannel(${b.join()}),\n getBestIndicesAChannel(${x.join()})));`,k=`vec4(\n getAChannel(${g.join()}),\n hasNextCol ? getAChannel(${y.join()}) : 0.,\n hasNextRow ? getAChannel(${b.join()}) : 0.,\n hasNextRow && hasNextCol ? getAChannel(${x.join()}) : 0.)`,I=r?"":`\n float getBestIndicesAChannel(${m.join()}) {\n return getChannel(getBestIndicesA(${d.join()}),\n vec2(${d.slice(-2).join()}));\n }`;this.userCode=`\n float getAChannel(${m.join()}) {\n return getChannel(getA(${d.join()}),\n vec2(${d.slice(-2).join()}));\n }\n ${I}\n void main() {\n ${l} coords = getOutputCoords();\n bool hasNextCol = ${c[u-1]} < ${i[u-1]-1};\n bool hasNextRow = ${c[u-2]} < ${i[u-2]-1};\n ${p}\n ivec4 srcIdx = ivec4(sourceLocR${f}, sourceLocG${f},\n sourceLocB${f}, sourceLocA${f}) * ${t};\n ivec4 inIdx = srcIdx;\n vec4 bestIndex = vec4(inIdx);\n vec4 bestValue = ${k};\n\n for (int i = 0; i < ${t};
1 i++) {\n inIdx = srcIdx;\n ${v}\n vec4 candidate = ${k};\n bvec4 nan = isnan(candidate);\n bvec4 replace = bvec4(\n vec4(${w}(candidate, bestValue)) * (vec4(1.0) - vec4(nan)));\n\n bestValue = vec4(replace.x ? candidate.x : bestValue.x,\n replace.y ? candidate.y : bestValue.y,\n replace.z ? candidate.z : bestValue.z,\n replace.w ? candidate.w : bestValue.w);\n bestIndex = mix(bestIndex, vec4(inIdx), vec4(replace));\n srcIdx++;\n }\n setOutput(bestIndex);\n }\n `}}function Um(e,t,n,r=null){let a=t.shape[0],o=t.shape[1];null!=r&&(a=r.shape[0],o=r.shape[1]);const i=s.Wap.computeOptimalWindowSize(o),u={windowSize:i,inSize:o,batchSize:a,outSize:Math.ceil(o/i)},l=new Wm(u,n,null==r),c=[t];null!=r&&c.push(r);const p=e.runWebGLProgram(l,c,"int32");if(1===p.shape[1])return p;const h=Um(e,t,n,p);return e.disposeIntermediateTensorInfo(p),h}function zm(e,t,n,r=null){const a=null!=r?r.shape:t.shape,o=a[a.length-1],i=s.Wap.computeOptimalWindowSize(o),u=new Pm(a,i,n,null==r),l=null==r?[t]:[t,r],c=e.runWebGLProgram(u,l,"int32");if(c.shape.length===t.shape.length){const r=zm(e,t,n,c);return e.disposeIntermediateTensorInfo(c),r}return c}function Vm(e,t,n,r){const a=[n];if(s.Wap.assertAxesAreInnerMostDims("arg"+r.charAt(0).toUpperCase()+r.slice(1),a,t.shape.length),!(0,s.OBj)().getBool("WEBGL_PACK_REDUCE")||t.shape.length<=2){const n=[],o=e.texData.get(t.dataId);let i=t;null!==o&&o.isPacked&&(i=e.unpackTensor(t),n.push(i));const[u,l]=s.Wap.computeOutAndReduceShapes(i.shape,a),c=s.D5U.sizeFromShape(l),p=pm({inputs:{x:i},backend:e,attrs:{shape:[-1,c]}});n.push(p);const h=Um(e,p,r);n.push(h);const d=pm({inputs:{x:h},backend:e,attrs:{shape:u}});return n.forEach((t=>e.disposeIntermediateTensorInfo(t))),d}return zm(e,t,r)}const Gm={kernelName:s.sJF,backendName:"webgl",kernelFunc:function(e){const{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{axis:o}=r;let i=s.D5U.parseAxisParam(o,a.shape);const u=s.Wap.getAxesPermutation(i,a.shape.length);let l=a;const c=[];null!=u&&(l=vm({inputs:{x:a},backend:n,attrs:{perm:u}}),c.push(l),i=s.Wap.getInnerMostAxes(i.length,l.shape.length)),s.Wap.assertAxesAreInnerMostDims("argMax",[i[0]],l.shape.length);const p=Vm(n,l,i[0],"max");return c.forEach((e=>n.disposeIntermediateTensorInfo(e))),p}};const Hm={kernelName:s.aJk,backendName:"webgl",kernelFunc:function(e){const{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{axis:o}=r;let i=s.D5U.parseAxisParam(o,a.shape);const u=s.Wap.getAxesPermutation(i,a.shape.length);let l=a;const c=[];null!=u&&(l=vm({inputs:{x:a},backend:n,attrs:{perm:u}}),c.push(l),i=s.Wap.getInnerMostAxes(i.length,l.shape.length)),s.Wap.assertAxesAreInnerMostDims("argMin",[i[0]],l.shape.length);const p=Vm(n,l,i[0],"min");return c.forEach((e=>n.disposeIntermediateTensorInfo(e))),p}},jm=tm({opSnippet:"if (isnan(x)) return x;\n if (abs(x) > 1.) {\n return NAN;\n }\n return asin(x);\n"}),Xm={kernelName:s.M2y,backendName:"webgl",kernelFunc:jm},qm=tm({opSnippet:"if (isnan(x)) return x;return log(x + sqrt(x * x + 1.0));"}),Km={kernelName:s.qw7,backendName:"webgl",kernelFunc:qm},Qm=tm({opSnippet:"if (isnan(x)) return x;\n return atan(x);\n"}),Ym={kernelName:s.jMg,backendName:"webgl",kernelFunc:Qm},Zm=nm({opSnippet:"\n if (isnan(a)) return a;\n if (isnan(b)) return b;\n\n return atan(a, b);\n",packedOpSnippet:"\n vec4 result = atan(a, b);\n bvec4 isNaNA = isnan(a);\n bvec4 isNaNB = isnan(b);\n bvec4 isNaN = bvec4(isNaNA.x || isNaNB.x, isNaNA.y || isNaNB.y, isNaNA.z || isNaNB.z, isNaNA.w || isNaNB.w);\n \n result.r = isNaN.r ? NAN : result.r;\n result.g = isNaN.g ? NAN : result.g;\n result.b = isNaN.b ? NAN : result.b;\n result.a = isNaN.a ? NAN : result.a;\n\n return result;\n"}),Jm={kernelName:s.QCc,backendName:"webgl",kernelFunc:Zm},eg=tm({opSnippet:"if (isnan(x)) return x;\n if ((x < -1.0) || (x > 1.0)) return NAN;\nreturn (log(1.0 + x) - log(1.0 - x)) / 2.0;"}),tg={kernelName:s.Oyi,backendName:"webgl",kernelFunc:eg};class ng{constructor(e,t,n,r=!1,s=!1){if(this.variableNames=["x"],"avg"===t&&n)throw new Error("Cannot compute positions for average pool.");const a=e.filterWidth,o=e.strideHeight,i=e.strideWidth,u=e.dilationHeight,l=e.dilationWidth,c=e.effectiveFilterHeight,p=e.effectiveFilterWidth,h=e.padInfo.top,d=e.padInfo.left;this.outputShape=e.outShape;const f="avg"===t,m=`((batch * ${e.inHeight} + xR) * ${e.inWidth} + xC) * ${e.inChannels} + d`,g=`(xR * ${e.inWidth} + xC) * ${e.inChannels} + d`;let y="0.0";if(f||(y="-1.0 / 1e-20"),n){const t=">=";return void(this.userCode=`\n const ivec2 strides = ivec2(${o}, ${i});\n const ivec2 pads = ivec2(${h}, ${d});\n\n void main() {\n ivec4 coords = getOutputCoords();\n int batch = coords[0];\n int d = coords[3];\n\n ivec2 xRCCorner = coords.yz * strides - pads;\n int xRCorner = xRCCorner.x;\n int xCCorner = xRCCorner.y;\n\n // max/min x(?, ?, d) to get y(yR, yC, d).\n // ? = to be determined\n float minMaxValue = 0.0;\n float minMaxValueFound = 0.0;\n int minMaxPosition = 0;\n float avgValue = 0.0;\n\n for (int wR = 0; wR < ${c};\n wR += ${u}) {\n int xR = xRCorner + wR;\n\n if (xR < 0 || xR >= ${e.inHeight}) {\n continue;\n }\n\n for (int wC = 0; wC < ${p};\n wC += ${l}) {\n int xC = xCCorner + wC;\n\n if (xC < 0 || xC >= ${e.inWidth}) {\n continue;\n }\n\n float value = getX(batch, xR, xC, d);\n\n // If a min / max value has already been found, use it. If not,\n // use the current value.\n float currMinMaxValue = mix(\n value, minMaxValue, minMaxValueFound);\n if (value ${t} currMinMaxValue) {\n minMaxValue = value;\n minMaxValueFound = 1.0;\n minMaxPosition = ${r?s?m:g:`wR * ${p} + wC`};\n }\n }\n }\n setOutput(float(minMaxPosition));\n }\n `)}let b=`${t}(${t}(${t}(minMaxValue[0], minMaxValue[1]), minMaxValue[2]), minMaxValue[3])`;"avg"===t&&(b="avgValue / count");const x=4*Math.floor(a/4),w=a%4,v=`\n if (${f}
1) {\n avgValue += dot(values, ones);\n } else {\n minMaxValue = max(values, minMaxValue);\n }\n `;this.userCode=`\n const ivec2 strides = ivec2(${o}, ${i});\n const ivec2 pads = ivec2(${h}, ${d});\n const float initializationValue = ${y};\n const vec4 ones = vec4(1.0, 1.0, 1.0, 1.0);\n\n float count = 0.0;\n\n float getValue(int batch, int xR, int xC, int d) {\n if (xC < 0 || xC >= ${e.inWidth}) {\n return initializationValue;\n }\n count += 1.0;\n return getX(batch, xR, xC, d);\n }\n\n void main() {\n ivec4 coords = getOutputCoords();\n int batch = coords[0];\n int d = coords[3];\n\n ivec2 xRCCorner = coords.yz * strides - pads;\n int xRCorner = xRCCorner.x;\n int xCCorner = xRCCorner.y;\n\n // max/min x(?, ?, d) to get y(yR, yC, d).\n // ? = to be determined\n vec4 minMaxValue = vec4(${y});\n float avgValue = 0.0;\n count = 0.0;\n\n for (int wR = 0; wR < ${c};\n wR += ${u}) {\n int xR = xRCorner + wR;\n\n if (xR < 0 || xR >= ${e.inHeight}) {\n continue;\n }\n\n for (int wC = 0; wC < ${x}; wC += 4) {\n int xC = xCCorner + wC * ${l};\n\n vec4 values = vec4(\n getValue(batch, xR, xC, d),\n getValue(batch, xR, xC + ${l}, d),\n getValue(batch, xR, xC + 2 * ${l}, d),\n getValue(batch, xR, xC + 3 * ${l}, d)\n );\n\n ${v}\n }\n\n int xC = xCCorner + ${x};\n if (${1===w}) {\n vec4 values = vec4(\n getValue(batch, xR, xC, d),\n initializationValue,\n initializationValue,\n initializationValue\n );\n\n ${v}\n } else if (${2===w}) {\n vec4 values = vec4(\n getValue(batch, xR, xC, d),\n getValue(batch, xR, xC + ${l}, d),\n initializationValue,\n initializationValue\n );\n\n ${v}\n } else if (${3===w}) {\n vec4 values = vec4(\n getValue(batch, xR, xC, d),\n getValue(batch, xR, xC + ${l}, d),\n getValue(batch, xR, xC + 2 * ${l}, d),\n initializationValue\n );\n\n ${v}\n }\n }\n setOutput(${b});\n }\n `}}class rg{constructor(e,t,n,r=!1,s=!1){if(this.variableNames=["x"],"avg"===t&&n)throw new Error("Cannot compute positions for average pool.");const a=e.filterWidth,o=e.strideDepth,i=e.strideHeight,u=e.strideWidth,l=e.dilationDepth,c=e.dilationHeight,p=e.dilationWidth,h=e.effectiveFilterDepth,d=e.effectiveFilterHeight,f=e.effectiveFilterWidth,m=e.padInfo.front,g=e.padInfo.top,y=e.padInfo.left;this.outputShape=e.outShape;const b="avg"===t;let x="0.0";if(b||(x="-1.0 / 1e-20"),n){const t=">=";return void(this.userCode=`\n const ivec3 strides =\n ivec3(${o}, ${i}, ${u});\n const ivec3 pads = ivec3(${m}, ${g}, ${y});\n\n void main() {\n ivec5 coords = getOutputCoords();\n int batch = coords.x;\n int ch = coords.u;\n\n ivec3 xCorner = ivec3(coords.y, coords.z, coords.w) * strides - pads;\n int xDCorner = xCorner.x;\n int xRCorner = xCorner.y;\n int xCCorner = xCorner.z;\n\n // max/min x(?, ?, ?, ch) to get y(yD, yR, yC, ch).\n // ? = to be determined\n float minMaxValue = 0.0;\n float minMaxValueFound = 0.0;\n int minMaxPosition = 0;\n\n for (int wD = 0; wD < ${h};\n wD += ${l}) {\n int xD = xDCorner + wD;\n\n if (xD < 0 || xD >= ${e.inDepth}) {\n continue;\n }\n\n for (int wR = 0; wR < ${d};\n wR += ${c}) {\n int xR = xRCorner + wR;\n\n if (xR < 0 || xR >= ${e.inHeight}) {\n continue;\n }\n\n for (int wC = 0; wC < ${f};\n wC += ${p}) {\n int xC = xCCorner + wC;\n\n if (xC < 0 || xC >= ${e.inWidth}) {\n continue;\n }\n\n float value = getX(batch, xD, xR, xC, ch);\n\n // If a min / max value has already been found, use it. If not,\n // use the current value.\n float currMinMaxValue = mix(\n value, minMaxValue, minMaxValueFound);\n if (value ${t} currMinMaxValue) {\n minMaxValue = value;\n minMaxValueFound = 1.0;\n minMaxPosition = ${r?s?`(((batch * ${e.inDepth} + xD) * ${e.inHeight} + xR) * ${e.inWidth} + xC) * ${e.inChannels} + ch`:`((xD * ${e.inHeight} + xR) * ${e.inWidth} + xC) * ${e.inChannels} + ch`:`wD * ${d} * ${f} +\n wR * ${f} + wC`};\n }\n }\n }\n }\n setOutput(float(minMaxPosition));\n }\n `)}let w=`${t}(${t}(${t}(minMaxValue[0], minMaxValue[1]), minMaxValue[2]), minMaxValue[3])`;"avg"===t&&(w="avgValue / count");const v=4*Math.floor(a/4),k=a%4,I=`\n if (${b}
1) {\n avgValue += dot(values, ones);\n } else {\n minMaxValue = max(values, minMaxValue);\n }\n `;this.userCode=`\n const ivec3 strides =\n ivec3(${o}, ${i}, ${u});\n const ivec3 pads = ivec3(${m}, ${g}, ${y});\n const float initializationValue = ${x};\n const vec4 ones = vec4(1.0, 1.0, 1.0, 1.0);\n\n float count = 0.0;\n\n float getValue(int batch, int xD, int xR, int xC, int ch) {\n if (xC < 0 || xC >= ${e.inWidth}) {\n return initializationValue;\n }\n count += 1.0;\n return getX(batch, xD, xR, xC, ch);\n }\n\n void main() {\n ivec5 coords = getOutputCoords();\n int batch = coords.x;\n int ch = coords.u;\n\n ivec3 xCorner = ivec3(coords.y, coords.z, coords.w) * strides - pads;\n int xDCorner = xCorner.x;\n int xRCorner = xCorner.y;\n int xCCorner = xCorner.z;\n\n // max/min x(?, ?, ?, d) to get y(yD, yR, yC, ch).\n // ? = to be determined\n vec4 minMaxValue = vec4(${x});\n float avgValue = 0.0;\n count = 0.0;\n\n for (int wD = 0; wD < ${h};\n wD += ${l}) {\n int xD = xDCorner + wD;\n\n if (xD < 0 || xD >= ${e.inDepth}) {\n continue;\n }\n\n for (int wR = 0; wR < ${d};\n wR += ${c}) {\n int xR = xRCorner + wR;\n\n if (xR < 0 || xR >= ${e.inHeight}) {\n continue;\n }\n\n for (int wC = 0; wC < ${v}; wC += 4) {\n int xC = xCCorner + wC * ${p};\n\n vec4 values = vec4(\n getValue(batch, xD, xR, xC, ch),\n getValue(batch, xD, xR, xC + ${p}, ch),\n getValue(batch, xD, xR, xC + 2 * ${p}, ch),\n getValue(batch, xD, xR, xC + 3 * ${p}, ch)\n );\n\n ${I}\n }\n\n int xC = xCCorner + ${v};\n if (${1===k}) {\n vec4 values = vec4(\n getValue(batch, xD, xR, xC, ch),\n initializationValue,\n initializationValue,\n initializationValue\n );\n\n ${I}\n } else if (${2===k}) {\n vec4 values = vec4(\n getValue(batch, xD, xR, xC, ch),\n getValue(batch, xD, xR, xC + ${p}, ch),\n initializationValue,\n initializationValue\n );\n\n ${I}\n } else if (${3===k}) {\n vec4 values = vec4(\n getValue(batch, xD, xR, xC, ch),\n getValue(batch, xD, xR, xC + ${p}, ch),\n getValue(batch, xD, xR, xC + 2 * ${p}, ch),\n initializationValue\n );\n\n ${I}\n }\n }\n setOutput(${w});\n }\n }\n `}}const sg={kernelName:s.JhU,backendName:"webgl",kernelFunc:function(e){const{inputs:t,backend:n,attrs:r}=e,{x:a}=t;id(a,"avgPool");const{filterSize:o,strides:i,pad:u,dimRoundingMode:l}=r;s.D5U.assert(s.Wap.eitherStridesOrDilationsAreOne(i,1),(()=>`Error in avgPool: Either strides or dilations must be 1. Got strides ${i} and dilations '1'`));const c=s.Wap.computePool2DInfo(a.shape,o,i,1,u,l);if(1===c.filterWidth&&1===c.filterHeight&&s.D5U.arraysEqual(c.inShape,c.outShape))return Hf({inputs:{x:a},backend:n});const p=new ng(c,"avg",!1);return n.runWebGLProgram(p,[a],"float32")}};const ag={kernelName:s._k9,backendName:"webgl",kernelFunc:function(e){const{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{filterSize:o,strides:i,pad:u,dimRoundingMode:l,dataFormat:c}=r,p=s.Wap.computePool3DInfo(a.shape,o,i,[1,1,1],u,l,c),h=new rg(p,"avg",!1);return n.runWebGLProgram(h,[a],"float32")}};class og{constructor(e){this.variableNames=["dy"],this.outputShape=e.inShape;const t=e.filterHeight,n=e.filterWidth,r=e.strideHeight,s=e.strideWidth,a=e.dilationHeight,o=e.dilationWidth,i=e.effectiveFilterHeight,u=e.effectiveFilterWidth,l=i-1-e.padInfo.top,c=u-1-e.padInfo.left,p=1/(t*n);this.userCode=`\n const ivec2 pads = ivec2(${l}, ${c});\n const float avgMultiplier = float(${p});\n\n void main() {\n ivec4 coords = getOutputCoords();\n int b = coords[0];\n int d = coords[3];\n\n ivec2 dyRCCorner = coords.yz - pads;\n int dyRCorner = dyRCCorner.x;\n int dyCCorner = dyRCCorner.y;\n\n // Convolve dy(?, ?, d) with pos mask(:, :, d) to get dx(xR, xC, d).\n // ? = to be determined. : = across all values in that axis.\n float dotProd = 0.0;\n for (int wR = 0; wR < ${i};\n wR += ${a}) {\n float dyR = float(dyRCorner + wR) / ${r}.0;\n\n if (dyR < 0.0 || dyR >= ${e.outHeight}.0 || fract(dyR) > 0.0) {\n continue;\n }\n int idyR = int(dyR);\n\n for (int wC = 0; wC < ${u};\n wC+= ${o}) {\n float dyC = float(dyCCorner + wC) / ${s}.0;\n\n if (dyC < 0.0 || dyC >= ${e.outWidth}.0 ||\n fract(dyC) > 0.0) {\n continue;\n }\n int idyC = int(dyC);\n\n float dyValue = getDy(b, idyR, idyC, d);\n\n dotProd += dyValue * avgMultiplier;\n }\n }\n setOutput(dotProd);\n }\n `}}class ig{constructor(e){this.variableNames=["dy"],this.outputShape=e.inShape;const t=e.filterDepth,n=e.filterHeight,r=e.filterWidth,s=e.strideDepth,a=e.strideHeight,o=e.strideWidth,i=e.dilationDepth,u=e.dilationHeight,l=e.dilationWidth,c=e.effectiveFilterDepth,p=e.effectiveFilterHeight,h=e.effectiveFilterWidth,d=c-1-e.padInfo.front,f=p-1-e.padInfo.top,m=h-1-e.padInfo.left,g=1/(t*n*r);this.userCode=`\n const ivec3 pads = ivec3(${d}, ${f}, ${m});\n const float avgMultiplier = float(${g});\n\n void main() {\n ivec5 coords = getOutputCoords();\n int batch = coords.x;\n int ch = coords.u;\n\n ivec3 dyCorner = ivec3(coords.y, coords.z, coords.w) - pads;\n int dyDCorner = dyCorner.x;\n int dyRCorner = dyCorner.y;\n int dyCCorner = dyCorner.z;\n\n // Convolve dy(?, ?, ?, d) with pos mask(:, :, :, ch) to get\n // dx(x
1D, xR, xC, ch).\n // ? = to be determined. : = across all values in that axis.\n float dotProd = 0.0;\n\n for (int wD = 0; wD < ${c};\n wD += ${i}) {\n float dyD = float(dyDCorner + wD) / ${s}.0;\n\n if (dyD < 0.0 || dyD >= ${e.outDepth}.0 || fract(dyD) > 0.0) {\n continue;\n }\n int idyD = int(dyD);\n\n for (int wR = 0; wR < ${p};\n wR += ${u}) {\n float dyR = float(dyRCorner + wR) / ${a}.0;\n\n if (dyR < 0.0 || dyR >= ${e.outHeight}.0 ||\n fract(dyR) > 0.0) {\n continue;\n }\n int idyR = int(dyR);\n\n for (int wC = 0; wC < ${h};\n wC += ${l}) {\n float dyC = float(dyCCorner + wC) / ${o}.0;\n\n if (dyC < 0.0 || dyC >= ${e.outWidth}.0 ||\n fract(dyC) > 0.0) {\n continue;\n }\n int idyC = int(dyC);\n\n float dyValue = getDy(batch, idyD, idyR, idyC, ch);\n\n dotProd += dyValue * avgMultiplier;\n }\n }\n }\n setOutput(dotProd);\n }\n `}}const ug={kernelName:s.IMb,backendName:"webgl",kernelFunc:function(e){const{inputs:t,backend:n,attrs:r}=e,{dy:a,input:o}=t,i=o,{filterSize:u,strides:l,pad:c,dimRoundingMode:p}=r,h=s.Wap.computePool3DInfo(i.shape,u,l,[1,1,1],c,p),d=new ig(h);return n.runWebGLProgram(d,[a],i.dtype)}};const lg={kernelName:s.ROF,backendName:"webgl",kernelFunc:function(e){const{inputs:t,backend:n,attrs:r}=e,{dy:a,input:o}=t,i=o;id([a,o],"avgPoolGrad");const{filterSize:u,strides:l,pad:c}=r,p=s.Wap.computePool2DInfo(i.shape,u,l,1,c),h=new og(p);return n.runWebGLProgram(h,[a],i.dtype)}};const cg={kernelName:s.XLW,backendName:"webgl",kernelFunc:function(e){const{inputs:t,backend:n,attrs:r}=e,{a:s,b:a}=t,{transposeA:o,transposeB:i}=r;return Im({a:s,b:a,transposeA:o,transposeB:i,backend:n})}};class pg{constructor(e,t,n,r,a,o){this.outputShape=[],this.variableNames=["x","mean","variance"],s.Wap.assertAndGetBroadcastShape(e,t),s.Wap.assertAndGetBroadcastShape(e,n);let i="0.0";null!=r&&(s.Wap.assertAndGetBroadcastShape(e,r),this.variableNames.push("offset"),i="getOffsetAtOutCoords()");let u="1.0";null!=a&&(s.Wap.assertAndGetBroadcastShape(e,a),this.variableNames.push("scale"),u="getScaleAtOutCoords()"),this.outputShape=e,this.userCode=`\n void main() {\n float x = getXAtOutCoords();\n float mean = getMeanAtOutCoords();\n float variance = getVarianceAtOutCoords();\n float offset = ${i};\n float scale = ${u};\n float inv = scale * inversesqrt(variance + float(${o}));\n setOutput(dot(vec3(x, -mean, offset), vec3(inv, inv, 1)));\n }\n `}}class hg{constructor(e,t,n,r,a,o){this.packedInputs=!0,this.packedOutput=!0,this.variableNames=["x","mean","variance"],s.Wap.assertAndGetBroadcastShape(e,t),s.Wap.assertAndGetBroadcastShape(e,n);let i="vec4(0.0)";null!=r&&(s.Wap.assertAndGetBroadcastShape(e,r),this.variableNames.push("offset"),i="getOffsetAtOutCoords()");let u="vec4(1.0)";null!=a&&(s.Wap.assertAndGetBroadcastShape(e,a),this.variableNames.push("scale"),u="getScaleAtOutCoords()"),this.outputShape=e,this.userCode=`\n void main() {\n vec4 offset = ${i};\n vec4 scale = ${u};\n\n vec4 x = getXAtOutCoords();\n vec4 mean = getMeanAtOutCoords();\n vec4 variance = getVarianceAtOutCoords();\n\n vec4 inv = scale * inversesqrt(variance + vec4(${o}));\n\n setOutput((x - mean) * inv + offset);\n }\n `}}const dg={kernelName:s.sHE,backendName:"webgl",kernelFunc:({inputs:e,backend:t,attrs:n})=>{const{x:r,mean:a,variance:o,offset:i,scale:u}=e;s.D5U.assert(a.shape.length===o.shape.length,(()=>"Batch normalization gradient requires mean and variance to have equal ranks.")),s.D5U.assert(null==i||a.shape.length===i.shape.length,(()=>"Batch normalization gradient requires mean and offset to have equal ranks.")),s.D5U.assert(null==u||a.shape.length===u.shape.length,(()=>"Batch normalization gradient requires mean and scale to have equal ranks."));let{varianceEpsilon:l}=n;null==l&&(l=.001);const c=[r,a,o];let p=null;null!=i&&(p=i.shape,c.push(i));let h=null;null!=u&&(h=u.shape,c.push(u));const d=(0,s.OBj)().getBool("WEBGL_PACK_NORMALIZATION")?new hg(r.shape,a.shape,o.shape,p,h,l):new pg(r.shape,a.shape,o.shape,p,h,l);return t.runWebGLProgram(d,c,c[0].dtype)}};class fg{constructor(e){this.variableNames=["source"],this.outputShape=e,this.rank=e.length;const t=(0,cd.kW)(this.rank);this.customUniforms=[{name:"start",arrayIndex:this.rank,type:"int"}];const n=function(e){if(1===e)return"sourceLoc";if(e<=6)return mg.slice(0,e).map((e=>"sourceLoc."+e)).join(",");throw Error(`Slicing for rank ${e} is not yet supported`)}(this.rank);let r;r=`\n ${t} sourceLoc;\n ${t} coords = getOutputCoords();\n ${e.map(((e,t)=>`sourceLoc.${mg[t]} = start[${t}] + coords.${mg[t]};`)).join("\n")}\n `,this.userCode=`\n void main() {\n ${r}\n setOutput(getSource(${n}));\n }\n `}}const mg=["x","y","z","w","u","v"];class gg{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"}];const t=(0,cd.kW)(this.rank),n=Cf("coords",this.rank),r=Cf("sourceLoc",this.rank),s=1===this.rank?"sourceLoc":`vec2(${r.slice(-2).join()})`,a=`getChannel(getSource(${r.join()}), ${s})`,o=`\n result.x = ${a};\n if (++${n[this.rank-1]} < ${e[this.rank-1]}) {\n ++${r[this.rank-1]};\n result.y = ${a};\n --${r[this.rank-1]};\n }\n `,i=1===this.rank?"":`\n --${n[this.rank-1]};\n if (++${n[this.rank-2]} < ${e[this.rank-2]}) {\n ++${r[this.rank-2]};\n result.z = ${a};\n if (++${n[this.rank-1]} < ${e[this.rank-1]}) {\n ++${r[this.rank-1]};\n result.w = ${a};\n }\n }
1\n `,u=this.rank<=4?`sourceLoc = coords +\n ${t}(${e.map(((e,t)=>`start[${t}]`)).join()});`:e.map(((e,t)=>`${r[t]} = ${n[t]} + start[${t}];`)).join("\n");this.userCode=`\n void main() {\n ${t} coords = getOutputCoords();\n ${t} sourceLoc;\n ${u}\n vec4 result = vec4(0.);\n ${o}\n ${i}\n setOutput(result);\n }\n `}}function yg(e){const{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{begin:o,size:i}=r,[u,l]=s.kuN.parseSliceParams(a,o,i);if(s.kuN.assertParamsValid(a,u,l),0===s.D5U.sizeFromShape(l))return n.makeTensorInfo(l,a.dtype,[]);if(n.shouldExecuteOnCPU([a])||"string"===a.dtype){const e=n.texData.get(a.dataId),t=hf(e.values,u,l,a.shape,a.dtype);return n.makeTensorInfo(l,a.dtype,t)}const{isPacked:c}=n.texData.get(a.dataId),p=s.kuN.isSliceContinous(a.shape,u,l);if(c||!p){const e=(0,s.OBj)().getBool("WEBGL_PACK_ARRAY_OPERATIONS")?new gg(l):new fg(l),t=[u];return n.runWebGLProgram(e,[a],a.dtype,t)}return n.uploadToGPU(a.dataId),function(e,t,n,r){const a=r.texData.get(e.dataId),o=r.makeTensorInfo(n,e.dtype),i=r.texData.get(o.dataId);Object.assign(i,a),i.refCount=1,i.shape=n,i.dtype=e.dtype;let u=s.kuN.computeFlatOffset(t,s.D5U.computeStrides(e.shape));a.slice&&(u+=a.slice.flatOffset),i.slice={flatOffset:u,origDataId:a.slice&&a.slice.origDataId||e.dataId};const l=r.dataRefCount.get(i.slice.origDataId)||1;return r.dataRefCount.set(i.slice.origDataId,l+1),o}(a,u,l,n)}const bg={kernelName:s.p2w,backendName:"webgl",kernelFunc:yg},xg={kernelName:s.zws,backendName:"webgl",kernelFunc:e=>{const{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{blockShape:o,crops:i}=r;s.D5U.assert(a.shape.length<=4,(()=>"batchToSpaceND for rank > 4 with a WebGL backend not implemented yet"));const u=o.reduce(((e,t)=>e*t)),l=s.Wap.getReshaped(a.shape,o,u),c=s.Wap.getPermuted(l.length,o.length),p=s.Wap.getReshapedPermuted(a.shape,o,u),h=s.Wap.getSliceBeginCoords(i,o.length),d=s.Wap.getSliceSize(p,i,o.length),f=[],m=pm({inputs:{x:a},backend:n,attrs:{shape:l}}),g=vm({inputs:{x:m},backend:n,attrs:{perm:c}}),y=pm({inputs:{x:g},backend:n,attrs:{shape:p}}),b=yg({inputs:{x:y},backend:n,attrs:{begin:h,size:d}});return f.push(m),f.push(g),f.push(y),f.forEach((e=>n.disposeIntermediateTensorInfo(e))),b}};const wg={kernelName:s.zvY,backendName:"webgl",kernelFunc:function(e){const{inputs:t,backend:n,attrs:r}=e,{x:s,weights:a}=t,{size:o}=r,i=n.readSync(s.dataId),u=n.readSync(a.dataId),l=Fd(i,u,a.dtype,a.shape,o);return n.makeTensorInfo([o],a.dtype,l)}};const vg={kernelName:s.eEB,backendName:"webgl",kernelFunc:function(e){const{inputs:t,backend:n}=e,{s0:r,s1:a}=t,o=n.readSync(r.dataId),i=n.readSync(a.dataId),u=s.Wap.assertAndGetBroadcastShape(Array.from(o),Array.from(i));return n.makeTensorInfo([u.length],"int32",Int32Array.from(u))}},kg=nm({opSnippet:"return float(a != b);",cpuKernelImpl:nf,dtype:"bool"}),Ig={kernelName:s.yQU,backendName:"webgl",kernelFunc:kg};function Ng(e){const{inputs:t,backend:n}=e,{input:r}=t;return Hf({inputs:{x:n.texData.get(r.dataId).complexTensorInfos.real},backend:n})}const Sg={kernelName:s.xJR,backendName:"webgl",kernelFunc:Ng};const Tg={kernelName:s.RFZ,backendName:"webgl",kernelFunc:function e(t){const{inputs:n,backend:r,attrs:a}=t,{x:o}=n,{dtype:i}=a;if("complex64"===i){if("complex64"===o.dtype)return Hf({inputs:{x:o},backend:r});const t=s.lls(o.shape),n=e({inputs:{x:o},backend:r,attrs:{dtype:"float32"}}),a=Xf({inputs:{real:n,imag:t},backend:r});return t.dispose(),r.disposeIntermediateTensorInfo(n),a}if("complex64"===o.dtype){const t=Ng({inputs:{input:o},backend:r}),n=e({inputs:{x:t},backend:r,attrs:{dtype:i}});return r.disposeIntermediateTensorInfo(t),n}if(!s.D5U.hasEncodingLoss(o.dtype,i)){const e=Hf({inputs:{x:o},backend:r});return{dataId:e.dataId,shape:e.shape,dtype:i}}if(r.shouldExecuteOnCPU([o])){const e=r.texData.get(o.dataId).values,[t,n,s]=Md(e,o.shape,o.dtype,i);return r.makeTensorInfo(t,n,s)}if("int32"===i)return function(e,t){const n=new Ff(e.shape,"return float(int(x));"),r=t.runWebGLProgram(n,[e],"int32");return{dataId:r.dataId,shape:r.shape,dtype:r.dtype}}(o,r);if("bool"===i){const e=r.makeTensorInfo([],"bool",s.D5U.getTypedArrayFromDType("bool",1)),t=kg({inputs:{a:o,b:e},backend:r});return r.disposeIntermediateTensorInfo(e),t}throw new Error(`Error in Cast: failed to cast ${o.dtype} to ${i}`)}},Cg="return ceil(x);",Eg=tm({opSnippet:Cg,packedOpSnippet:Cg,cpuKernelImpl:Bd}),$g={kernelName:s.gJX,backendName:"webgl",kernelFunc:Eg};class Ag{constructor(e){this.variableNames=["A"],this.customUniforms=[{name:"minVal",type:"float"},{name:"maxVal",type:"float"}],this.outputShape=e,this.userCode="\n\n void main() {\n float value = getAAtOutCoords();\n if (isnan(value)) {\n setOutput(value);\n return;\n }\n\n setOutput(clamp(value, minVal, maxVal));\n }\n "}}class Dg{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="\n void main() {\n vec4 value = getAAtOutCoords();\n\n if (any(isnan(value))) {\n setOutput(value);\n return;\n }\n\n setOutput(clamp(value, vec4(minVal), vec4(maxVal)));\n }\n "}}const _g={kernelName:s.xnO,backendName:"webgl",kernelFunc:function(e){const{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{clipValueMin:o,clipValueMax:i}=r;let u;u=(0,s.OBj)().getBool("WEBGL_PACK_CLIP")?new Dg(a.shape):new Ag(a.shape);const l=[[o],[i]];return n.runWebGLProgram(u,[a],a.dtype,l)}};class Rg{constructor(e){this.variableNames=["real","imag"],this.outputShape=e,this.userCode="\n void main() {\n float re = abs(getRealAtOutCoords());\n float im = abs(getImagAtOutCoords());\n float mx = max(re, im);\n\n // sadly the length function in glsl is not underflow-safe\n // (at least not on Intel GPUs). So the safe solution is\n // to ensure underflow-safety in all cases.\n setOutput(\n mx == 0.0 ? 0.0 : mx * length(vec2(1, min(re, im)/mx))\n );\n }\n "}}function Fg(e,t){return{dataId:t.dataId,dtype:t.dtype,shape:e.shape}}const Og={kernelName:s.yj2,backendName:"webgl",kernelFunc:function(e){const{inputs:t,backend:n}=e,{x:r}=t,s=n.texData.get(r.dataId),a=new Rg(r.shape),o=[Fg(r,s.complexTensorInfos.real),Fg(r,s.complexTensorInfos.imag)];return n.runWebGLProgram(a,o,o[0].dtype)}};class Mg{constructor(e){this.outputShape=[],this.outputShape=s.Wap.computeOutShape(e,1),this.variableNames=e.map(((e,t)=>`T${t}`));const t=new Array(e.length-1);t[0]=e[0][1];for(let s=1;s<t.length;s++)t[s]=t[s-1]+e[s][1];const n=[`if (yC < ${t[0]}) setOutput(getT0(yR, yC));`];for(let s=1;s<t.length;s++){const e=t[s-1];n.push(`else if (yC < ${t[s]}) setOutput(getT${s}(yR, yC-${e}));`)}const r=t.length,a=t[t.length-1];n.push(`else setOutput(getT${r}(yR, yC-${a}));`),this.userCode=`\n void main() {\n ivec2 coords = getOutputCoords();\n int yR = coords.x;\n int yC = coords.y;\n\n ${n.join("\n ")}\n }\n `}}class Bg{constructor(e,t){this.packedInputs=!0,this.packedOutput=!0,this.outputShape=[],this.outputShape=s.Wap.computeOutShape(e,t);const n=this.outputShape,r=n.length,a=(0,cd.kW)(r),o=Cf("coords",r),i=["x","y","z","w","u","v"].slice(0,r);this.variableNames=e.map(((e,t)=>`T${t}`));const u=new Array(e.length-1);u[0]=e[0][t];for(let s=1;s<u.length;s++)u[s]=u[s-1]+e[s][t];const l=i[t],c=i.slice(-2),p=i.join();let h=`if (${l} < ${u[0]}) {\n return getChannel(\n getT0(${p}), vec2(${c.join()}));\n }`;for(let s=1;s<u.length;s++){const e=u[s-1];h+=`\n if (${l} < ${u[s]} && ${l} >= ${u[s-1]}) {\n return getChannel(\n getT${s}(${Lg(i,l,e)}),\n vec2(${Lg(c,l,e)}));\n }`}const d=u.length,f=u[u.length-1];h+=`\n return getChannel(\n getT${d}(${Lg(i,l,f)}),\n vec2(${Lg(c,l,f)}));`,this.userCode=`\n float getValue(${i.map((e=>"int "+e))}) {\n ${h}\n }\n\n void main() {\n ${a} coords = getOutputCoords();\n vec4 result = vec4(getValue(${o}), 0., 0., 0.);\n\n ${o[r-1]} = ${o[r-1]} + 1;\n if (${o[r-1]} < ${n[r-1]}) {\n result.g = getValue(${o});\n }\n\n ${o[r-2]} = ${o[r-2]} + 1;\n if (${o[r-2]} < ${n[r-2]}) {\n result.a = getValue(${o});\n }\n\n ${o[r-1]} = ${o[r-1]} - 1;\n if (${o[r-2]} < ${n[r-2]} &&\n ${o[r-1]} < ${n[r-1]}) {\n result.b = getValue(${o});\n }\n setOutput(result);\n }\n `}}function Lg(e,t,n){const r=e.indexOf(t);return e.map(((e,t)=>t===r?`${e} - ${n}`:e)).join()}function Wg(e){const{inputs:t,backend:n}
vendor: 7,054 bytes, line 1
1=e,{input:r}=t;return Hf({inputs:{x:n.texData.get(r.dataId).complexTensorInfos.imag},backend:n})}const Pg={kernelName:s.J_u,backendName:"webgl",kernelFunc:Wg};function Ug(e,t,n){const r=e[0].dtype;if("complex64"===r){const r=e.map((e=>Ng({inputs:{input:e},backend:n}))),s=e.map((e=>Wg({inputs:{input:e},backend:n}))),a=Ug(r,t,n),o=Ug(s,t,n),i=Xf({inputs:{real:a,imag:o},backend:n});return r.forEach((e=>n.disposeIntermediateTensorInfo(e))),s.forEach((e=>n.disposeIntermediateTensorInfo(e))),n.disposeIntermediateTensorInfo(a),n.disposeIntermediateTensorInfo(o),i}let a=n.shouldExecuteOnCPU(e);if("string"===r&&(a=!0),a){const a=e.map((e=>{const r=s.D5U.sizeFromShape(e.shape.slice(t));return pm({inputs:{x:e},backend:n,attrs:{shape:[-1,r]}})})),o=a.map((e=>({vals:n.readSync(e.dataId),shape:e.shape}))),i=s.Wap.computeOutShape(a.map((e=>e.shape)),1),u=1===a[0].shape[0],l=Ld(o,i,r,u),c=s.Wap.computeOutShape(e.map((e=>e.shape)),t),p=n.makeTensorInfo(c,r,l);return a.forEach((e=>n.disposeIntermediateTensorInfo(e))),p}const o=(0,s.OBj)().getNumber("WEBGL_MAX_TEXTURES_IN_SHADER");if(e.length>o){const r=[];for(let a=0;a<e.length;a+=o){const s=e.slice(a,a+o);r.push(Ug(s,t,n))}const s=Ug(r,t,n);for(const e of r)n.disposeIntermediateTensorInfo(e);return s}if((0,s.OBj)().getBool("WEBGL_PACK_ARRAY_OPERATIONS")&&e[0].shape.length>1){const s=new Bg(e.map((e=>e.shape)),t);return n.runWebGLProgram(s,e,r)}const{tensors2D:i,outShape:u}=function(e,t,n){const r=s.Wap.computeOutShape(e.map((e=>e.shape)),t);return{tensors2D:e.map((e=>pm({inputs:{x:e},attrs:{shape:[-1,s.D5U.sizeFromShape(e.shape.slice(t))]},backend:n}))),outShape:r}}(e,t,n),l=new Mg(i.map((e=>e.shape))),c=n.runWebGLProgram(l,i,r);i.forEach((e=>n.disposeIntermediateTensorInfo(e)));const p=pm({inputs:{x:c},attrs:{shape:u},backend:n});return n.disposeIntermediateTensorInfo(c),p}function zg(e){const{inputs:t,backend:n,attrs:r}=e,{axis:a}=r,o=s.D5U.parseAxisParam(a,t[0].shape)[0],i=t.map((e=>e.shape));s.Wap.assertParamsConsistent(i,o);const u=s.Wap.computeOutShape(t.map((e=>e.shape)),o);if(0===s.D5U.sizeFromShape(u))return n.makeTensorInfo(u,t[0].dtype,[]);const l=t.filter((e=>s.D5U.sizeFromShape(e.shape)>0));return 1===l.length?Hf({inputs:{x:l[0]},backend:n}):Ug(l,o,n)}const Vg={kernelName:s.Eh3,backendName:"webgl",kernelFunc:zg};class Gg{constructor(e,t=!1,n=null,r=!1,s=!1){this.variableNames=["x","W"],this.outputShape=e.outShape;const a=e.padInfo.top,o=e.padInfo.left,i=e.strideHeight,u=e.strideWidth,l=e.dilationHeight,c=e.dilationWidth,p=e.filterHeight,h=e.filterWidth,d=4*Math.floor(e.inChannels/4),f=e.inChannels%4,m="channelsLast"===e.dataFormat,g=m?1:2,y=m?2:3,b=m?3:1;let x="",w="";n&&(x=r?`float activation(float a) {\n float b = getPreluActivationWeightsAtOutCoords();\n ${n}\n }`:s?`float activation(float a) {\n float b = getLeakyreluAlphaAtOutCoords();\n ${n}\n }`:`\n float activation(float x) {\n ${n}\n }\n `,w="result = activation(result);");const v=t?"result += getBiasAtOutCoords();":"";t&&this.variableNames.push("bias"),r&&this.variableNames.push("preluActivationWeights"),s&&this.variableNames.push("leakyreluAlpha"),this.userCode=`\n ${x}\n\n const ivec2 strides = ivec2(${i}, ${u});\n const ivec2 pads = ivec2(${a}, ${o});\n\n void main() {\n ivec4 coords = getOutputCoords();\n int batch = coords[0];\n int d2 = coords[${b}];\n\n ivec2 xRCCorner =\n ivec2(coords[${g}], coords[${y}]) * strides - pads;\n int xRCorner = xRCCorner.x;\n int xCCorner = xRCCorner.y;\n\n // Convolve x(?, ?, d1) with w(:, :, d1, d2) to get y(yR, yC, d2).\n // ? = to be determined. : = across all values in that axis.\n float dotProd = 0.0;\n for (int wR = 0; wR < ${p}; wR++) {\n int xR = xRCorner + wR * ${l};\n\n if (xR < 0 || xR >= ${e.inHeight}) {\n continue;\n }\n\n for (int wC = 0; wC < ${h}; wC++) {\n int xC = xCCorner + wC * ${c};\n\n if (xC < 0 || xC >= ${e.inWidth}) {\n continue;\n }\n\n for (int d1 = 0; d1 < ${d}; d1 += 4) {\n vec4 wValues = vec4(\n getW(wR, wC, d1, d2),\n getW(wR, wC, d1 + 1, d2),\n getW(wR, wC, d1 + 2, d2),\n getW(wR, wC, d1 + 3, d2)\n );\n\n if (${m}) {\n vec4 xValues = vec4(\n getX(batch, xR, xC, d1),\n getX(batch, xR, xC, d1 + 1),\n getX(batch, xR, xC, d1 + 2),\n getX(batch, xR, xC, d1 + 3)\n );\n dotProd += dot(xValues, wValues);\n } else {\n vec4 xValues = vec4(\n getX(batch, d1, xR, xC),\n getX(batch, d1 + 1, xR, xC),\n getX(batch, d1 + 2, xR, xC),\n getX(batch, d1 + 3, xR, xC)\n );\n dotProd += dot(xValues, wValues);\n }\n }\n\n if (${1===f}) {\n\n if (${m}) {\n dotProd +=\n getX(batch, xR, xC, ${d}) *\n getW(wR, wC, ${d}, d2);\n } else {\n dotProd +=\n getX(batch, ${d}, xR, xC) *\n getW(wR, wC, ${d}, d2);\n }\n\n } else if (${2===f}) {\n vec2 wValues = vec2(\n getW(wR, wC, ${d}, d2),\n getW(wR, wC, ${d} + 1, d2)\n );\n\n if (${m}) {\n vec2 xValues = vec2(\n getX(batch, xR, xC, ${d}),\n getX(batch, xR, xC, ${d} + 1)\n );\n dotProd += dot(xValues, wValues);\n } else {\n vec2 xValues = vec2(\n getX(batch, ${d}, xR, xC),\n getX(batch, ${d} + 1, xR, xC)\n );\n dotProd += dot(xValues, wValues);\n }\n\n } else if (${3===f}) {\n vec3 wValues = vec3(\n getW(wR, wC, ${d}, d2),\n getW(wR, wC, ${d} + 1, d2),\n getW(wR, wC, ${d} + 2, d2)\n );\n\n if (${m}) {\n vec3 xValues = vec3(\n getX(batch, xR, xC, ${d}),\n getX(batch, xR, xC, ${d} + 1),\n getX(batch, xR, xC, ${d} + 2)\n );\n dotProd += dot(xValues, wValues);\n } else {\n vec3 xValues = vec3(\n getX(batch, ${d}, xR, xC),\n getX(batch, ${d} + 1, xR, xC),\n getX(batch, ${d} + 2, xR, xC)\n );\n dotProd += dot(xValues, wValues);\n }\n\n }\n }\n }\n\n float result = dotProd;\n ${v}\n ${w}\n setOutput(result);\n }\n `}}
vendor: 4,175 bytes, line 1
1class Hg{constructor(e){this.variableNames=["x","W"],this.outputShape=e.outShape;const t=e.padInfo.front,n=e.padInfo.top,r=e.padInfo.left,s=e.strideDepth,a=e.strideHeight,o=e.strideWidth,i=e.dilationDepth,u=e.dilationHeight,l=e.dilationWidth,c=e.filterDepth,p=e.filterHeight,h=e.filterWidth,d=4*Math.floor(e.inChannels/4),f=e.inChannels%4;this.userCode=`\n const ivec3 strides = ivec3(${s}, ${a}, ${o});\n const ivec3 pads = ivec3(${t}, ${n}, ${r});\n\n void main() {\n ivec5 coords = getOutputCoords();\n int batch = coords.x;\n int d2 = coords.u;\n\n ivec3 xFRCCorner = ivec3(coords.y, coords.z, coords.w) * strides - pads;\n int xFCorner = xFRCCorner.x;\n int xRCorner = xFRCCorner.y;\n int xCCorner = xFRCCorner.z;\n\n // Convolve x(?, ?, ?, d1) with w(:, :, :, d1, d2) to get\n // y(yF, yR, yC, d2). ? = to be determined. : = across all\n // values in that axis.\n float dotProd = 0.0;\n for (int wF = 0; wF < ${c}; wF++) {\n int xF = xFCorner + wF * ${i};\n\n if (xF < 0 || xF >= ${e.inDepth}) {\n continue;\n }\n\n for (int wR = 0; wR < ${p}; wR++) {\n int xR = xRCorner + wR * ${u};\n\n if (xR < 0 || xR >= ${e.inHeight}) {\n continue;\n }\n\n for (int wC = 0; wC < ${h}; wC++) {\n int xC = xCCorner + wC * ${l};\n\n if (xC < 0 || xC >= ${e.inWidth}) {\n continue;\n }\n\n for (int d1 = 0; d1 < ${d}; d1 += 4) {\n vec4 xValues = vec4(\n getX(batch, xF, xR, xC, d1),\n getX(batch, xF, xR, xC, d1 + 1),\n getX(batch, xF, xR, xC, d1 + 2),\n getX(batch, xF, xR, xC, d1 + 3)\n );\n vec4 wValues = vec4(\n getW(wF, wR, wC, d1, d2),\n getW(wF, wR, wC, d1 + 1, d2),\n getW(wF, wR, wC, d1 + 2, d2),\n getW(wF, wR, wC, d1 + 3, d2)\n );\n\n dotProd += dot(xValues, wValues);\n }\n\n if (${1===f}) {\n dotProd +=\n getX(batch, xF, xR, xC, ${d}) *\n getW(wF, wR, wC, ${d}, d2);\n } else if (${2===f}) {\n vec2 xValues = vec2(\n getX(batch, xF, xR, xC, ${d}),\n getX(batch, xF, xR, xC, ${d} + 1)\n );\n vec2 wValues = vec2(\n getW(wF, wR, wC, ${d}, d2),\n getW(wF, wR, wC, ${d} + 1, d2)\n );\n dotProd += dot(xValues, wValues);\n } else if (${3===f}) {\n vec3 xValues = vec3(\n getX(batch, xF, xR, xC, ${d}),\n getX(batch, xF, xR, xC, ${d} + 1),\n getX(batch, xF, xR, xC, ${d} + 2)\n );\n vec3 wValues = vec3(\n getW(wF, wR, wC, ${d}, d2),\n getW(wF, wR, wC, ${d} + 1, d2),\n getW(wF, wR, wC, ${d} + 2, d2)\n );\n dotProd += dot(xValues, wValues);\n }\n }\n }\n }\n setOutput(dotProd);\n }\n `}}class jg{constructor(e,t=!1,n=null,r=!1,a=!1){this.variableNames=["x","W"],this.packedInputs=!0,this.packedOutput=!0,this.customUniforms=[{name:"pads",type:"ivec2"},{name:"strides",type:"ivec2"},{name:"dilations",type:"ivec2"},{name:"inDims",type:"ivec2"}],this.outputShape=e.outShape,this.enableShapeUniforms=fd(this.outputShape.length);const o=e.padInfo.left,i=e.strideWidth,u=e.dilationWidth,l=e.filterHeight,c=e.filterWidth,p=c;let h="\n int xR; int xC; int xCOffset;\n vec4 wTexel; vec4 previous; vec4 final;";for(let s=0;s<c;s++)h+=`\n vec4 xTexelC${2*s};\n int xTexelC${2*s}Ready;\n vec4 xTexelC${2*s+1};\n int xTexelC${2*s+1}Ready;\n vec4 xC${s};`;h+=`\n for (int r = 0; r < ${l}; r++) {\n for (int d1 = 0; d1 < ${e.inChannels}; d1 += 2) {\n `;
1for(let s=0;s<c;s++)h+=`\n xTexelC${2*s} = vec4(0.0);\n xTexelC${2*s}Ready = 0;\n xTexelC${2*s+1} = vec4(0.0);\n xTexelC${2*s+1}Ready = 0;\n xC${s} = vec4(0.0);`;h+="\n xR = xRCorner + r * dilations[0];\n if (xR >=0 && xR < inDims[0]) {\n ";for(let g=0;g<(p+1)/2;g++){const t=2*g;if(h+=`\n xC = xCCorner + ${t*u};\n `,1===i){if(t<c&&(o%2===1?(h+=`\n xCOffset = xC + 1;\n if (xCOffset >= 0 && xCOffset < inDims[1] && xTexelC${t}Ready == 0) {\n xTexelC${t} = getX(batch, xR, xCOffset, d1);\n\n // Need to manually clear unused channels in case\n // we're reading from recycled texture.\n if (xCOffset + 1 >= inDims[1]) {\n xTexelC${t}.zw = vec2(0.0);\n }\n xTexelC${t}Ready = 1;\n }\n `,h+=1===u&&t>0?`\n xC${t} = vec4(xTexelC${t-2}.zw, xTexelC${t}.xy);\n `:`\n xCOffset = xC + 1 - 2;\n\n if (xCOffset >= 0 && xCOffset < inDims[1]) {\n previous = getX(batch, xR, xCOffset, d1);\n\n // Need to manually clear unused channels in case\n // we're reading from recycled texture.\n if (xCOffset + 1 >= inDims[1]) {\n previous.zw = vec2(0.0);\n }\n\n xC${t} = vec4(previous.zw, xTexelC${t}.xy);\n } else {\n xC${t} = vec4(0.0, 0.0, xTexelC${t}.xy);\n }\n `):h+=`\n if (xC >= 0 && xC < inDims[1] && xTexelC${t}Ready == 0) {\n xTexelC${t} = getX(batch, xR, xC, d1);\n if (xC + 1 >= inDims[1]) {\n xTexelC${t}.zw = vec2(0.0);\n }\n xTexelC${t}Ready = 1;\n }\n\n xC${t} = xTexelC${t};\n `,t+1<c)){const e=o%2===0?s.D5U.nearestLargerEven(u):u;u%2===0&&o%2===1||u%2!==0&&o%2!==1?(h+=`\n xCOffset = xC + imod(pads[1], 2) + ${e};\n\n if (xCOffset >= 0 && xCOffset < inDims[1] && xTexelC${t+1}Ready == 0) {\n xTexelC${t+1} = getX(batch, xR, xCOffset, d1);\n\n // Need to manually clear unused channels in case\n // we're reading from recycled texture.\n if (xCOffset + 1 >= inDims[1]) {\n xTexelC${t+1}.zw = vec2(0.0);\n }\n xTexelC${t+1}Ready = 1;\n }\n `,h+=u>1?`\n xCOffset -= 2;\n if (xCOffset >= 0 && xCOffset < inDims[1]) {\n previous = getX(batch, xR, xCOffset, d1);\n xC${t+1} = vec4(previous.zw, xTexelC${t+1}.xy);\n } else {\n xC${t+1} = vec4(0.0, 0.0, xTexelC${t+1}.xy);\n }\n `:`\n xC${t+1} = vec4(xTexelC${t}.zw, xTexelC${t+1}.xy);\n `):h+=1===e?`\n xC${t+1} = xTexelC${t};\n `:`\n xCOffset = xC + ${e};\n\n if (xCOffset >= 0 && xCOffset < inDims[1] && xTexelC${t+1}Ready == 0) {\n xTexelC${t+1} = getX(batch, xR, xCOffset, d1);\n if (xCOffset + 1 >= inDims[1]) {\n xTexelC${t+1}.zw = vec2(0.0);\n }\n xTexelC${t+1}Ready = 1;\n }\n\n xC${t+1} = xTexelC${t+1};\n `}}else t<c&&(o%2===1?(h+=`\n xCOffset = xC + 1 - strides[1];\n if(xCOffset >= 0 && xCOffset < inDims[1] && xTexelC${t}Ready == 0) {\n xTexelC${t} = getX(batch, xR, xCOffset, d1);\n // Need to manually clear unused channels in case\n // we're reading from recycled texture.\n if (xCOffset + 1 >= inDims[1]) {\n xTexelC${t}.zw = vec2(0.0);\n }\n xTexelC${t}Ready = 1;\n }\n\n if(xC + 1 >= 0 && xC + 1 < inDims[1] && xTexelC${t+1}Ready == 0) {\n xTexelC${t+1} = getX(batch, xR, xC + 1, d1);\n // Need to manually clear unused channels in case\n // we're reading from recycled texture.\n if (xC + 2 >= inDims[1]) {\n xTexelC${t+1}.zw = vec2(0.0);\n }\n xTexelC${t+1}Ready = 1;\n }\n\n xC${t} = vec4(xTexelC${t}.zw, xTexelC${t+1}.zw);\n `,t+1<c&&(h+=`\n final = vec4(0.0);\n xCOffset = xC + 1 + strides[1];\n if(xCOffset >= 0 && xCOffset < inDims[1]) {\n final = getX(batch, xR, xCOffset, d1);\n }\n xC${t+1} = vec4(xTexelC${t+1}.xy, final.xy);\n `)):(h+=`\n if(xC >= 0 && xC < inDims[1] && xTexelC${t}Ready == 0) {\n xTexelC${t} = getX(batch, xR, xC, d1);\n if (xC + 1 >= inDims[1]) {\n xTexelC${t}.zw = vec2(0.0);\n }\n xTexelC${t}Ready = 1;\n }\n\n xCOffset = xC + strides[1];\n if(xCOffset >= 0 && xCOffset < inDims[1] && xTexelC${t+1}Ready == 0) {\n xTexelC${t+1} = getX(batch, xR, xCOffset, d1);\n if (xCOffset + 1 >= inDims[1]) {\n xTexelC${t+1}.zw = vec2(0.);\n }\n xTexelC${t+1}Ready = 1;\n }\n\n xC${t} = vec4(\n xTexelC${t}.xy, xTexelC${t+1}.xy);\n `,t+1<c&&(h+=`\n xC${t+1} = vec4(xTexelC${t}.zw, xTexelC${t+1}.zw);\n `)));t<c&&(h+=`\n wTexel = getW(r, ${t}, d1, d2);\n dotProd += xC${t}.xxzz * vec4(wTexel.xy, wTexel.xy);\n if(d1 + 1 < ${e.inChannels}) {\n dotProd += xC${t}.yyww * vec4(wTexel.zw, wTexel.zw);\n }\n `,t+1<c&&(h+=`\n wTexel = getW(r, ${t+1}, d1, d2);\n dotProd += xC${t+1}.xxzz * vec4(wTexel.xy, wTexel.xy);\n if(d1 + 1 < ${e.inChannels}) {\n dotProd += xC${t+1}.yyww * vec4(wTexel.zw, wTexel.zw);\n }\n `))}h+="\n }\n ",h+="\n }\n ",h+="\n }\n ";let d="",f="";n&&(d=r?`vec4 activation(vec4 a) {\n vec4 b = getPreluActivationWeightsAtOutCoords();\n ${n}\n }`:a?`vec4 activation(vec4 a) {\n vec4 b = getLeakyreluAlphaAtOutCoords();\n ${n}\n }
1`:`vec4 activation(vec4 x) {\n ${n}\n }`,f="result = activation(result);");const m=t?"result += getBiasAtOutCoords();":"";t&&this.variableNames.push("bias"),r&&this.variableNames.push("preluActivationWeights"),a&&this.variableNames.push("leakyreluAlpha"),this.userCode=`\n ${d}\n\n void main() {\n ivec4 coords = getOutputCoords();\n int batch = coords.x;\n ivec2 xRCCorner = coords.yz * strides - pads;\n int d2 = coords.w;\n int xRCorner = xRCCorner.x;\n int xCCorner = xRCCorner.y;\n\n //intialize dotProd with a small epsilon seems to reduce GPU accuracy loss.\n vec4 dotProd = vec4(0.000000000000001);\n\n ${h}\n\n vec4 result = dotProd - vec4(0.000000000000001);\n ${m}\n ${f}\n setOutput(result);\n }\n `}}class Xg{constructor(e,t){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,this.customUniforms=[{name:"inputShape",type:"ivec4"},{name:"pad",type:"ivec2"},{name:"stride",type:"ivec2"},{name:"dilation",type:"ivec2"},{name:"inChannels",type:"int"},{name:"itemsPerBlockRow",type:"int"},{name:"outWidth",type:"int"}],this.outputShape=e,this.enableShapeUniforms=fd(this.outputShape.length);const{dataFormat:n}=t,r=(0,ld.A)(),s="channelsLast"===n,a=s?1:2,o=s?2:3,i=this.enableShapeUniforms?"if(blockIndex < outShape[2] && pos < outShape[1]) {":`if(blockIndex < ${e[2]} && pos < ${e[1]}) {`;let u="";for(let l=0;l<=1;l++)for(let e=0;e<=1;e++)u+=`\n blockIndex = rc.z + ${e};\n pos = rc.y + ${l};\n\n ${i}\n offsetY = int(blockIndex / outWidth) * stride[0] - pad[0];\n d0 = offsetY + dilation[0] * (pos / itemsPerBlockRow);\n\n if(d0 < inputShape[${a}] && d0 >= 0) {\n // Use custom imod instead mod. On Intel GPU, mod may generate\n // unexpected value.\n // https://github.com/tensorflow/tfjs/issues/5447\n offsetX = imod(blockIndex, outWidth) * stride[1] - pad[1];\n d1 = offsetX + dilation[1] * (imod(pos, itemsPerBlockRow) /\n inChannels);\n\n if(d1 < inputShape[${o}] && d1 >= 0) {\n\n ch = imod(pos, inChannels);\n\n if (${s}) {\n innerDims = vec2(d1, ch);\n result[${2*l+e}] = getChannel(\n getA(rc.x, d0, int(innerDims.x),\n int(innerDims.y)), innerDims);\n } else {\n innerDims = vec2(d0, d1);\n result[${2*l+e}] = getChannel(\n getA(rc.x, ch, int(innerDims.x),\n int(innerDims.y)), innerDims);\n }\n }\n }\n }\n `;this.userCode=`\n void main() {\n ivec3 rc = getOutputCoords();\n\n vec4 result = vec4(0);\n\n int blockIndex, pos, offsetY, d0, offsetX, d1, ch;\n vec2 innerDims;\n\n ${u}\n\n ${r.output} = result;\n }\n `}}function qg(e,t){const n=e.length;return n>=3?t?[...e.slice(0,-3),e[n-3]*e[n-2],e[n-1]]:[...e.slice(0,-3),e[n-3],e[n-2]*e[n-1]]:!t&&1===n&&e[0]>1?[e[0],1]:null}function Kg({x:e,filter:t,convInfo:n,backend:r,bias:a=null,preluActivationWeights:o=null,leakyreluAlpha:i=0,activation:u=null}){const l=e.shape,c=r.texData.get(e.dataId),p=n.inChannels,h=l[0]*l[1]*l[2],d=n.outChannels,f="channelsLast"===n.dataFormat;let m;const g=[];if(null!=o){const e=qg(o.shape,f);null!=e&&(o=pm({inputs:{x:o},backend:r,attrs:{shape:e}}),g.push(o))}if(null!=a){const e=qg(a.shape,f);null!=e&&(a=pm({inputs:{x:a},backend:r,attrs:{shape:e}}),g.push(a))}if(!((1===h||1===d)&&p>1e3)&&c.isPacked&&f&&null!=c.texture&&l[2]%2!==0&&s.D5U.arraysEqual(c.shape.slice(-3),l.slice(-3))){const p=l[0]*l[1]*(l[2]+1),h={dataId:e.dataId,shape:[1,p,n.inChannels],dtype:e.dtype},d=c.shape;c.shape=c.shape.slice(),c.shape[c.shape.length-2]++,s.D5U.assert(ed(c.shape,h.shape),(()=>`packed reshape ${c.shape} to ${h.shape} isn't free`));const f=pm({inputs:{x:t},backend:r,attrs:{shape:[1,n.inChannels,n.outChannels]}});g.push(f);const y=Im({a:h,b:f,backend:r,transposeA:false,transposeB:false,bias:a,activation:u,preluActivationWeights:o,leakyreluAlpha:i}),b=r.texData.get(y.dataId);s.D5U.assert(b.isPacked,(()=>"batchMatMul result is expected to be packed")),c.shape=d,b.shape=n.outShape,m=Hf({inputs:{x:y},backend:r}),m.shape=n.outShape,g.push(y)}else{const s=n.outHeight*n.outWidth,l=pm({inputs:{x:e},backend:r,attrs:{shape:f?[n.batchSize,s,n.inChannels]:[n.batchSize,n.inChannels,s]}}),c=pm({inputs:{x:t},backend:r,attrs:{shape:[1,n.inChannels,n.outChannels]}}),p=Im({a:f?l:c,b:f?c:l,transposeA:!f,transposeB:false,backend:r,bias:a,activation:u,preluActivationWeights:o,leakyreluAlpha:i});m=pm({inputs:{x:p},backend:r,attrs:{shape:n.outShape}}),g.push(l),g.push(c),g.push(p)}for(const s of g)r.disposeIntermediateTensorInfo(s);return m}function Qg({x:e,filter:t,convInfo:n,backend:r,bias:a=null,preluActivationWeights:o=null,leakyreluAlpha:i=0,activation:u=null}){const{filterWidth:l,filterHeight:c,inChannels:p,outWidth:h,outHeight:d,dataFormat:f}
vendor: 6,138 bytes, line 1
1=n,m="channelsLast"===f,g=l*c*p,y=d*h,b=[n.batchSize,g,y],x=[];if(null!=o){const e=qg(o.shape,m);null!=e&&(o=pm({inputs:{x:o},backend:r,attrs:{shape:e}}),x.push(o))}if(null!=a){const e=qg(a.shape,m);null!=e&&(a=pm({inputs:{x:a},backend:r,attrs:{shape:e}}),x.push(a))}const w=pm({inputs:{x:t},backend:r,attrs:{shape:[1,g,s.D5U.sizeFromShape(t.shape)/g]}});x.push(w);const v=new Xg(b,n),k=[e.shape,[n.padInfo.top,n.padInfo.left],[n.strideHeight,n.strideWidth],[n.dilationHeight,n.dilationWidth],[n.inChannels],[n.filterWidth*n.inChannels],[n.outWidth]],I=r.runWebGLProgram(v,[e],"float32",k),N=pm({inputs:{x:I},backend:r,attrs:{shape:b}});x.push(I),x.push(N);const S=null!=a,T=null!=o,C="leakyrelu"===u,E=u?rm(u,!0):null,$=new sm(m?N.shape:w.shape,m?w.shape:N.shape,m?[n.batchSize,y,n.outChannels]:[n.batchSize,n.outChannels,y],!0,!1,S,E,T,C),A=m?[N,w]:[w,N];if(a&&A.push(a),T&&A.push(o),C){const e=r.makeTensorInfo([],"float32",s.D5U.createScalarValue(i,"float32"));A.push(e),x.push(e)}const D=r.runWebGLProgram($,A,"float32"),_=pm({inputs:{x:D},backend:r,attrs:{shape:n.outShape}});x.push(D);for(const s of x)r.disposeIntermediateTensorInfo(s);return _}const Yg={kernelName:s.mhS,backendName:"webgl",kernelFunc:function(e){const{inputs:t,backend:n,attrs:r}=e,{x:a,filter:o}=t,{strides:i,pad:u,dataFormat:l,dilations:c,dimRoundingMode:p}=r,h=s.Wap.convertConv2DDataFormat(l),d=s.Wap.computeConv2DInfo(a.shape,o.shape,i,c,u,p,!1,h);let f;if(1!==d.filterHeight||1!==d.filterWidth||1!==d.dilationHeight||1!==d.dilationWidth||1!==d.strideHeight||1!==d.strideWidth||"SAME"!==d.padInfo.type&&"VALID"!==d.padInfo.type)if(d.strideWidth<=2&&"channelsLast"===h&&(0,s.OBj)().getBool("WEBGL_EXP_CONV")){const e=new jg(d),t=[[d.padInfo.top,d.padInfo.left],[d.strideHeight,d.strideWidth],[d.dilationHeight,d.dilationWidth],[d.inHeight,d.inWidth]];f=n.runWebGLProgram(e,[a,o],"float32",t)}else if((0,s.OBj)().getBool("WEBGL_CONV_IM2COL"))f=Qg({x:a,filter:o,convInfo:d,backend:n});else{const e=new Gg(d);f=n.runWebGLProgram(e,[a,o],"float32")}else f=Kg({x:a,filter:o,convInfo:d,backend:n});const m=pm({inputs:{x:f},backend:n,attrs:{shape:d.outShape}});return n.disposeIntermediateTensorInfo(f),m}};class Zg{constructor(e){this.variableNames=["x","dy"],this.outputShape=e.filterShape;const t=e.strideHeight,n=e.strideWidth,r=e.padInfo.top,s=e.padInfo.left,a="channelsLast"===e.dataFormat;this.userCode=`\n void main() {\n ivec4 coords = getOutputCoords();\n int wR = coords.x;\n int wC = coords.y;\n int d1 = coords.z;\n int d2 = coords.w;\n\n // Convolve x(?, ?, d1) with dy(:, :, d2) to get dw(wR, wC, d1, d2).\n // ? = to be determined. : = across all values in that axis.\n float dotProd = 0.0;\n\n for (int b = 0; b < ${e.batchSize}; b++) {\n for (int yR = 0; yR < ${e.outHeight}; yR++) {\n int xR = wR + yR * ${t} - ${r};\n\n if (xR < 0 || xR >= ${e.inHeight}) {\n continue;\n }\n\n for (int yC = 0; yC < ${e.outWidth}; yC++) {\n int xC = wC + yC * ${n} - ${s};\n\n if (xC < 0 || xC >= ${e.inWidth}) {\n continue;\n }\n\n if (${a}) {\n float dyValue = getDy(b, yR, yC, d2);\n float xValue = getX(b, xR, xC, d1);\n dotProd += (xValue * dyValue);\n } else {\n float dyValue = getDy(b, d2, yR, yC);\n float xValue = getX(b, d1, xR, xC);\n dotProd += (xValue * dyValue);\n }\n\n }\n }\n }\n setOutput(dotProd);\n }\n `}}class Jg{constructor(e){this.variableNames=["dy","W"],this.outputShape=e.inShape;const t=e.filterHeight,n=e.filterWidth,r=e.strideHeight,s=e.strideWidth,a="channelsLast"===e.dataFormat,o=t-1-e.padInfo.top,i=n-1-e.padInfo.left,u=a?1:2,l=a?2:3,c=a?3:1;this.userCode=`\n const ivec2 pads = ivec2(${o}, ${i});\n\n void main() {\n ivec4 coords = getOutputCoords();\n int batch = coords[0];\n int d1 = coords[${c}];\n\n ivec2 dyCorner = ivec2(coords[${u}], coords[${l}]) - pads;\n int dyRCorner = dyCorner.x;\n int dyCCorner = dyCorner.y;\n\n // Convolve dy(?, ?, d2) with w(:, :, d1, d2) to compute dx(xR, xC, d1).\n // ? = to be determined. : = across all values in that axis.\n float dotProd = 0.0;\n for (int wR = 0; wR < ${t}; wR++) {\n float dyR = float(dyRCorner + wR) / ${r}.0;\n\n if (dyR < 0.0 || dyR >= ${e.outHeight}.0 || fract(dyR) > 0.0) {\n continue;\n }\n int idyR = int(dyR);\n\n int wRPerm = ${t} - 1 - wR;\n\n for (int wC = 0; wC < ${n}; wC++) {\n float dyC = float(dyCCorner + wC) / ${s}.0;\n\n if (dyC < 0.0 || dyC >= ${e.outWidth}.0 ||\n fract(dyC) > 0.0) {\n continue;\n }\n int idyC = int(dyC);\n\n int wCPerm = ${n} - 1 - wC;\n\n for (int d2 = 0; d2 < ${e.outChannels}; d2++) {\n\n if (${a}) {\n float xValue = getDy(batch, idyR, idyC, d2);\n float wValue = getW(wRPerm, wCPerm, d1, d2);\n dotProd += xValue * wValue;\n } else {\n float xValue = getDy(batch, d2, idyR, idyC);\n float wValue = getW(wRPerm, wCPerm, d1, d2);\n dotProd += xValue * wValue;\n }\n\n }\n }\n }\n setOutput(dotProd);\n }\n `}}class ey{constructor(e){this.variableNames=["x","dy"],this.outputShape=e.filterShape;const t=e.strideDepth,n=e.strideHeight,r=e.strideWidth,s=e.padInfo.front,a=e.padInfo.top,o=e.padInfo.left;this.userCode=`\n void main() {\n ivec5 coords = getOutputCoords();\n int wF = coords.x;\n int wR = coords.y;\n int wC = coords.z;\n int d1 = coords.w;\n int d2 = coords.u;\n\n float dotProd = 0.0;\n\n for (int b = 0; b < ${e.batchSize}; b++) {\n for (int yF = 0; yF < ${e.outDepth};
1 yF++) {\n int xF = wF + yF * ${t} - ${s};\n\n if (xF < 0 || xF >= ${e.inDepth}) {\n continue;\n }\n\n for (int yR = 0; yR < ${e.outHeight}; yR++) {\n int xR = wR + yR * ${n} - ${a};\n\n if (xR < 0 || xR >= ${e.inHeight}) {\n continue;\n }\n\n for (int yC = 0; yC < ${e.outWidth}; yC++) {\n int xC = wC + yC * ${r} - ${o};\n\n if (xC < 0 || xC >= ${e.inWidth}) {\n continue;\n }\n\n float dyValue = getDy(b, yF, yR, yC, d2);\n float xValue = getX(b, xF, xR, xC, d1);\n dotProd += (xValue * dyValue);\n }\n }\n }\n }\n setOutput(dotProd);\n }\n `}}class ty{constructor(e){this.variableNames=["dy","W"],this.outputShape=e.inShape;const t=e.filterDepth,n=e.filterHeight,r=e.filterWidth,s=e.strideDepth,a=e.strideHeight,o=e.strideWidth,i=t-1-e.padInfo.front,u=n-1-e.padInfo.top,l=r-1-e.padInfo.left;this.userCode=`\n const ivec3 pads = ivec3(${i}, ${u}, ${l});\n\n void main() {\n ivec5 coords = getOutputCoords();\n int batch = coords.x;\n int d1 = coords.u;\n\n\n ivec3 dyCorner = ivec3(coords.y, coords.z, coords.w) - pads;\n int dyFCorner = dyCorner.x;\n int dyRCorner = dyCorner.y;\n int dyCCorner = dyCorner.z;\n\n float dotProd = 0.0;\n for (int wF = 0; wF < ${t}; wF++) {\n float dyF = float(dyFCorner + wF) / ${s}.0;\n\n if (dyF < 0.0 || dyF >= ${e.outDepth}.0 || fract(dyF) > 0.0) {\n continue;\n }\n int idyF = int(dyF);\n\n int wFPerm = ${t} - 1 - wF;\n\n for (int wR = 0; wR < ${n}; wR++) {\n float dyR = float(dyRCorner + wR) / ${a}.0;\n\n if (dyR < 0.0 || dyR >= ${e.outHeight}.0 ||\n fract(dyR) > 0.0) {\n continue;\n }\n int idyR = int(dyR);\n\n int wRPerm = ${n} - 1 - wR;\n\n for (int wC = 0; wC < ${r}; wC++) {\n float dyC = float(dyCCorner + wC) / ${o}.0;\n\n if (dyC < 0.0 || dyC >= ${e.outWidth}.0 ||\n fract(dyC) > 0.0) {\n continue;\n }\n int idyC = int(dyC);\n\n int wCPerm = ${r} - 1 - wC;\n\n for (int d2 = 0; d2 < ${e.outChannels}; d2++) {\n float xValue = getDy(batch, idyF, idyR, idyC, d2);\n float wValue = getW(wFPerm, wRPerm, wCPerm, d1, d2);\n dotProd += xValue * wValue;\n }\n }\n }\n }\n setOutput(dotProd);\n }\n `}}const ny={kernelName:s.wUP,backendName:"webgl",kernelFunc:function(e){const{inputs:t,backend:n,attrs:r}=e,{x:a,dy:o}=t,{strides:i,pad:u,dataFormat:l,dimRoundingMode:c,filterShape:p}=r,h=s.Wap.convertConv2DDataFormat(l),d=s.Wap.computeConv2DInfo(a.shape,p,i,1,u,c,!1,h),f=new Zg(d);return n.runWebGLProgram(f,[a,o],"float32")}};const ry={kernelName:s.wm,backendName:"webgl",kernelFunc:function(e){const{inputs:t,backend:n,attrs:r}=e,{dy:a,filter:o}=t,{inputShape:i,strides:u,pad:l,dataFormat:c,dimRoundingMode:p}=r,h=s.Wap.convertConv2DDataFormat(c),d=s.Wap.computeConv2DInfo(i,o.shape,u,1,l,p,!1,h),f=new Jg(d);return n.runWebGLProgram(f,[a,o],"float32")}};const sy={kernelName:s.x12,backendName:"webgl",kernelFunc:function(e){const{inputs:t,backend:n,attrs:r}=e,{x:a,filter:o}=t,{strides:i,pad:u,dilations:l}=r,c=s.Wap.computeConv3DInfo(a.shape,o.shape,i,l,u),p=new Hg(c);return n.runWebGLProgram(p,[a,o],"float32")}};const ay={kernelName:s.o2y,backendName:"webgl",kernelFunc:function(e){const{inputs:t,backend:n,attrs:r}=e,{x:a,dy:o}=t,{strides:i,pad:u,filterShape:l}=r,c=s.Wap.computeConv3DInfo(a.shape,l,i,1,u),p=new ey(c);return n.runWebGLProgram(p,[a,o],"float32")}};const oy={kernelName:s.ik2,backendName:"webgl",kernelFunc:function(e){const{inputs:t,backend:n,attrs:r}=e,{dy:a,filter:o}=t,{pad:i,strides:u,inputShape:l}=r,c=s.Wap.computeConv3DInfo(l,o.shape,u,1,i),p=new ty(c);return n.runWebGLProgram(p,[a,o],"float32")}},iy=tm({opSnippet:"if (isnan(x)) return x;\n return cos(x);\n"}),uy={kernelName:s.mc4,backendName:"webgl",kernelFunc:iy},ly=tm({opSnippet:"\n float e2x = exp(-x);\n return (e2x + 1.0 / e2x) / 2.0;\n"}),cy={kernelName:s.TR1,backendName:"webgl",kernelFunc:ly};
vendor: 16,962 bytes, line 1
1class py{constructor(e,t,n,r,s){this.variableNames=["Image","Boxes","BoxInd"],this.outputShape=[];const[a,o,i,u]=e,[l]=t,[c,p]=n;this.outputShape=[l,c,p,u];const h="bilinear"===r?1:0,[d,f]=[o-1+".0",i-1+".0"],[m,g,y]=c>1?[""+(o-1)/(c-1),"(y2-y1) * height_ratio",`y1*${d} + float(y)*(height_scale)`]:["0.0","0.0",`0.5 * (y1+y2) * ${d}`],[b,x,w]=p>1?[""+(i-1)/(p-1),"(x2-x1) * width_ratio",`x1*${f} + float(x)*(width_scale)`]:["0.0","0.0",`0.5 * (x1+x2) * ${f}`];this.userCode=`\n const float height_ratio = float(${m});\n const float width_ratio = float(${b});\n void main() {\n ivec4 coords = getOutputCoords();\n int b = coords[0];\n int y = coords[1];\n int x = coords[2];\n int d = coords[3];\n\n // get box vals\n float y1 = getBoxes(b,0);\n float x1 = getBoxes(b,1);\n float y2 = getBoxes(b,2);\n float x2 = getBoxes(b,3);\n\n // get image in batch index\n int bInd = round(getBoxInd(b));\n if(bInd < 0 || bInd >= ${a}) {\n return;\n }\n\n float height_scale = ${g};\n float width_scale = ${x};\n\n float in_y = ${y};\n if( in_y < 0.0 || in_y > ${d} ) {\n setOutput(float(${s}));\n return;\n }\n float in_x = ${w};\n if( in_x < 0.0 || in_x > ${f} ) {\n setOutput(float(${s}));\n return;\n }\n\n vec2 sourceFracIndexCR = vec2(in_x,in_y);\n if(${h} == 1) {\n // Compute the four integer indices.\n ivec2 sourceFloorCR = ivec2(sourceFracIndexCR);\n ivec2 sourceCeilCR = ivec2(ceil(sourceFracIndexCR));\n\n float topLeft = getImage(b, sourceFloorCR.y, sourceFloorCR.x, d);\n float bottomLeft = getImage(b, sourceCeilCR.y, sourceFloorCR.x, d);\n float topRight = getImage(b, sourceFloorCR.y, sourceCeilCR.x, d);\n float bottomRight = getImage(b, sourceCeilCR.y, sourceCeilCR.x, d);\n\n vec2 fracCR = sourceFracIndexCR - vec2(sourceFloorCR);\n\n float top = topLeft + (topRight - topLeft) * fracCR.x;\n float bottom = bottomLeft + (bottomRight - bottomLeft) * fracCR.x;\n float newValue = top + (bottom - top) * fracCR.y;\n setOutput(newValue);\n } else {\n // Compute the coordinators of nearest neighbor point.\n ivec2 sourceNearestCR = ivec2(floor(\n sourceFracIndexCR + vec2(0.5,0.5)));\n float newValue = getImage(b, sourceNearestCR.y, sourceNearestCR.x, d);\n setOutput(newValue);\n }\n }\n `}}const hy={kernelName:s.VcC,backendName:"webgl",kernelFunc:e=>{const{inputs:t,backend:n,attrs:r}=e,{image:s,boxes:a,boxInd:o}=t,{cropSize:i,method:u,extrapolationValue:l}=r,c=new py(s.shape,a.shape,i,u,l);return n.runWebGLProgram(c,[s,a,o],"float32")}};var dy;!function(e){e.Prod="*",e.Sum="+"}(dy||(dy={}));class fy{constructor(e,t,n,r){this.op=e,this.outputShape=t,this.variableNames=["x"],this.customUniforms=[{name:"index",type:"float"}];const s=this.outputShape.length,a=this.op===dy.Prod?"1.0":"0.0",o=n?a:`getX(${my(s,"coords",this.op)})`,i=this.outputShape[this.outputShape.length-1];let u="",l="";n?(u=r?"end != "+(i-1):"end != 0",l=r?"end + 1":"end - 1"):(u=r?`end + pow2 < ${i}`:"end >= pow2",l=r?"end + pow2":"end - pow2"),this.userCode=`\n void main() {\n ${(0,cd.kW)(s)} coords = getOutputCoords();\n int end = ${gy(s,"coords",this.op)};\n float val = ${o};\n int pow2 = int(pow(2.0, index));\n if (${u}) {\n int idx = ${l};\n ${gy(s,"coords",this.op)} = idx;\n val ${this.op}= getX(${my(s,"coords",this.op)});\n }\n setOutput(val);\n }\n `}}function my(e,t,n){if(1===e)return`${t}`;if(2===e)return`${t}.x, ${t}.y`;if(3===e)return`${t}.x, ${t}.y, ${t}.z`;if(4===e)return`${t}.x, ${t}.y, ${t}.z, ${t}.w`;throw new Error(`Cumulative ${n} for rank ${e} is not yet supported`)}function gy(e,t,n){if(1===e)return`${t}`;if(2===e)return`${t}.y`;if(3===e)return`${t}.z`;if(4===e)return`${t}.w`;throw new Error(`Cumulative ${n} for rank ${e} is not yet supported`)}function yy(e,t,n,r,a,o){const i=t.shape.length,u=s.Wap.getAxesPermutation([r],i);let l=t;null!=u&&(l=vm({inputs:{x:t},backend:n,attrs:{perm:u}}));const c=s.Wap.getInnerMostAxes(1,i)[0];if(c!==i-1)throw new Error(`WebGL cumprod shader expects an inner-most axis=${t.shape.length-1} but got axis=${r}`);const p=l.shape[c];let h=Hf({inputs:{x:l},backend:n});for(let s=0;s<=Math.ceil(Math.log2(p))-1;s++){const t=new fy(e,l.shape,!1,o),r=[[s]],a=h;h=n.runWebGLProgram(t,[h],h.dtype,r),n.disposeIntermediateTensorInfo(a)}if(a){const t=new fy(e,l.shape,a,o),r=h;h=n.runWebGLProgram(t,[h],h.dtype),n.disposeIntermediateTensorInfo(r)}if(null!=u){const e=vm({inputs:{x:h},backend:n,attrs:{perm:s.Wap.getUndoAxesPermutation(u)}});return n.disposeIntermediateTensorInfo(h),n.disposeIntermediateTensorInfo(l),e}return h}const by={kernelName:s.Byc,backendName:"webgl",kernelFunc:function(e){const{inputs:t,backend:n,attrs:r}=e,{x:s}=t,{axis:a,exclusive:o,reverse:i}=r;return yy(dy.Prod,s,n,a,o,i)}};const xy={kernelName:s.iHb,backendName:"webgl",kernelFunc:function(e){const{inputs:t,backend:n,attrs:r}=e,{x:s}=t,{axis:a,exclusive:o,reverse:i}=r;return yy(dy.Sum,s,n,a,o,i)}};const wy={kernelName:s.QRR,backendName:"webgl",kernelFunc:function(e){const{inputs:t,backend:n,attrs:r}=e,{x:s,weights:a}=t,{size:o,binaryOutput:i}=r;if(1===s.shape.length){const e=n.readSync(s.dataId),t=n.readSync(a.dataId),r=Fd(e,t,a.dtype,a.shape,o);return n.makeTensorInfo([o],a.dtype,r)}if(2===s.shape.length){const e=n.bufferSync(s),t=n.bufferSync(a),r=Od(e,t,o,i);return n.makeTensorInfo(r.shape,a.dtype,r.values)}throw new Error(`Error in denseBincount: input must be at most rank 2, but got rank${s.shape.length}.`)}};class vy{constructor(e,t,n){this.variableNames=["x"],this.outputShape=[],this.outputShape=e,this.blockSize=t,this.dataFormat=n,this.userCode=`\n void main() {\n ivec4 coords = getOutputCoords();\n int b = coords[0];\n int h = ${this.getHeightCoordString()};\n int w = ${this.getWidthCoordString()};\n int d = ${this.getDepthCoordString()};\n\n int in_h = h / ${t};\n int offset_h = imod(h, ${t});\n int in_w = w / ${t};\n int offset_w = imod(w, ${t});\n int offset_d = (offset_h * ${t} + offset_w) *\n ${this.getOutputDepthSize()};\n int in_d = d + offset_d;\n\n float result = ${this.getInputSamplingString()};\n setOutput(result);\n }\n `}getHeightCoordString(){return"NHWC"===this.dataFormat?"coords[1]":"coords[2]"}getWidthCoordString(){return"NHWC"===this.dataFormat?"coords[2]":"coords[3]"}getDepthCoordString(){return"NHWC"===this.dataFormat?"coords[3]":"coords[1]"}getOutputDepthSize(){return"NHWC"===this.dataFormat?this.outputShape[3]:this.outputShape[1]}getInputSamplingString(){return"NHWC"===this.dataFormat?"getX(b, in_h, in_w, in_d)":"getX(b, in_d, in_h, in_w)"}}const ky={kernelName:s.T0n,backendName:"webgl",kernelFunc:function(e){const{inputs:t,backend:n,attrs:r}=e,{x:s}=t,{blockSize:a,dataFormat:o}=r,i=s.shape[0],u=("NHWC"===o?s.shape[1]:s.shape[2])*a,l=("NHWC"===o?s.shape[2]:s.shape[3])*a,c=("NHWC"===o?s.shape[3]:s.shape[1])/(a*a),p=new vy("NHWC"===o?[i,u,l,c]:[i,c,u,l],a,o);return n.runWebGLProgram(p,[s],s.dtype)}};class Iy{constructor(e,t=!1,n=null,r=!1,s=!1){this.variableNames=["x","W"],this.customUniforms=[{name:"pads",type:"ivec2"},{name:"strides",type:"ivec2"},{name:"dilations",type:"ivec2"},{name:"inDims",type:"ivec2"}],this.outputShape=e.outShape,this.enableShapeUniforms=fd(this.outputShape.length);const a=e.filterHeight,o=e.filterWidth,i=e.outChannels/e.inChannels;let u="",l="";n&&(u=r?`float activation(float a) {\n float b = getPreluActivationWeightsAtOutCoords();\n ${n}\n }`:s?`float activation(float a) {\n float b = getLeakyreluAlphaAtOutCoords();\n ${n}\n }`:`\n float activation(float x) {\n ${n}\n }\n `,l="result = activation(result);");const c=t?"result += getBiasAtOutCoords();":"";t&&this.variableNames.push("bias"),r&&this.variableNames.push("preluActivationWeights"),s&&this.variableNames.push("leakyreluAlpha"),this.userCode=`\n ${u}\n\n void main() {\n ivec4 coords = getOutputCoords();\n int batch = coords.x;\n ivec2 xRCCorner = coords.yz * strides - pads;\n int d2 = coords.w;\n int d1 = d2 / ${i};\n int q = d2 - d1 * ${i};\n\n int xRCorner = xRCCorner.x;\n int xCCorner = xRCCorner.y;\n\n // Convolve x(?, ?, d1) with w(:, :, d1, q) to get y(yR, yC, d2).\n // ? = to be determined. : = across all values in that axis.\n float dotProd = 0.0;\n // TO DO(dsmilkov): Flatten the two for loops and vec4 the operations.\n for (int wR = 0; wR < ${a}; wR++) {\n int xR = xRCorner + wR * dilations[0];\n\n if (xR < 0 || xR >= inDims[0]) {\n continue;\n }\n\n for (int wC = 0; wC < ${o}; wC++) {\n int xC = xCCorner + wC * dilations[1];\n\n if (xC < 0 || xC >= inDims[1]) {\n continue;\n }\n\n float xVal = getX(batch, xR, xC, d1);\n float wVal = getW(wR, wC, d1, q);\n dotProd += xVal * wVal;\n }\n }\n\n float result = dotProd;\n ${c}\n ${l}\n setOutput(result);\n }\n `}}class Ny{constructor(e,t=!1,n=null,r=!1,a=!1){this.variableNames=["x","W"],this.packedInputs=!0,this.packedOutput=!0,this.customUniforms=[{name:"pads",type:"ivec2"},{name:"strides",type:"ivec2"},{name:"dilations",type:"ivec2"},{name:"inDims",type:"ivec2"}],this.outputShape=e.outShape,this.enableShapeUniforms=fd(this.outputShape.length);const o=e.outChannels/e.inChannels,i=e.padInfo.left,u=e.strideWidth,l=e.dilationWidth,c=e.filterHeight,p=e.filterWidth,h=p;let d="\n int xR; int xC; int xCOffset;\n vec4 wTexel; vec4 previous; vec4 final;";for(let s=0;s<p;s++)d+=`\n vec4 xTexelC${2*s};\n int xTexelC${2*s}Ready;\n vec4 xTexelC${2*s+1};\n int xTexelC${2*s+1}Ready;\n vec4 xC${s};`;d+=`\n for (int r = 0; r < ${c}; r++) {\n `;for(let s=0;s<p;s++)d+=`\n xTexelC${2*s} = vec4(0.0);\n xTexelC${2*s}Ready = 0;\n xTexelC${2*s+1} = vec4(0.0);\n xTexelC${2*s+1}Ready = 0;\n xC${s} = vec4(0.0);`;d+="\n xR = xRCorner + r * dilations[0];\n if (xR >=0 && xR < inDims[0]) {\n ";for(let y=0;y<(h+1)/2;y++){const e=2*y;if(d+=`\n xC = xCCorner + ${e*l};\n `,1===u){if(e<p&&(i%2===1?(d+=`\n xCOffset = xC + 1;\n if (xCOffset >= 0 && xCOffset < inDims[1] && xTexelC${e}Ready == 0) {\n xTexelC${e} = getX(batch, xR, xCOffset, d1);\n\n // Need to manually clear unused channels in case\n // we're reading from recycled texture.\n if (xCOffset + 1 >= inDims[1]) {\n xTexelC${e}.zw = vec2(0.0);\n }\n xTexelC${e}Ready = 1;\n }\n `,d+=1===l&&e>0?`\n xC${e} = vec4(xTexelC${e-2}.zw, xTexelC${e}.xy);\n `:`\n xCOffset = xC + 1 - 2;\n\n if (xCOffset >= 0 && xCOffset < inDims[1]) {\n previous = getX(batch, xR, xCOffset, d1);\n\n // Need to manually clear unused channels in case\n // we're reading from recycled texture.\n if (xCOffset + 1 >= inDims[1]) {\n previous.zw = vec2(0.0);\n }\n\n xC${e} = vec4(previous.zw, xTexelC${e}.xy);\n } else {\n xC${e} = vec4(0.0, 0.0, xTexelC${e}.xy);\n }\n `):d+=`\n if (xC >= 0 && xC < inDims[1] && xTexelC${e}Ready == 0) {\n xTexelC${e} = getX(batch, xR, xC, d1);\n if (xC + 1 >= inDims[1]) {\n xTexelC${e}.zw = vec2(0.0);\n }\n xTexelC${e}Ready = 1;\n }\n\n xC${e} = xTexelC${e};\n `,e+1<p)){const t=i%2===0?s.D5U.nearestLargerEven(l):l;l%2===0&&i%2===1||l%2!==0&&i%2!==1?(d+=`\n xCOffset = xC + imod(pads[1], 2) + ${t};\n\n if (xCOffset >= 0 && xCOffset < inDims[1] && xTexelC${e+1}Ready == 0) {\n xTexelC${e+1} = getX(batch, xR, xCOffset, d1);\n\n // Need to manually clear unused channels in case\n // we're reading from recycled texture.\n if (xCOffset + 1 >= inDims[1]) {\n xTexelC${e+1}.zw = vec2(0.0);\n }\n xTexelC${e+1}Ready = 1;\n }\n `,d+=l>1?`\n xCOffset -= 2;\n if (xCOffset >= 0 && xCOffset < inDims[1]) {\n previous = getX(batch, xR, xCOffset, d1);\n xC${e+1} = vec4(previous.zw, xTexelC${e+1}.xy);\n } else {\n xC${e+1} = vec4(0.0, 0.0, xTexelC${e+1}.xy);\n }\n `:`\n xC${e+1} = vec4(xTexelC${e}.zw, xTexelC${e+1}.xy);\n `):d+=1===t?`\n xC${e+1} = xTexelC${e};\n `:`\n xCOffset = xC + ${t};\n\n if (xCOffset >= 0 && xCOffset < inDims[1] && xTexelC${e+1}Ready == 0) {\n xTexelC${e+1} = getX(batch, xR, xCOffset, d1);\n if (xCOffset + 1 >= inDims[1]) {\n xTexelC${e+1}.zw = vec2(0.0);\n }\n xTexelC${e+1}Ready = 1;\n }\n\n xC${e+1} = xTexelC${e+1};\n `}}else e<p&&(i%2===1?(d+=`\n xCOffset = xC + 1 - strides[1];\n if(xCOffset >= 0 && xCOffset < inDims[1] && xTexelC${e}Ready == 0) {\n xTexelC${e} = getX(batch, xR, xCOffset, d1);\n // Need to manually clear unused channels in case\n // we're reading from recycled texture.\n if (xCOffset + 1 >= inDims[1]) {\n xTexelC${e}.zw = vec2(0.0);\n }\n xTexelC${e}Ready = 1;\n }\n\n if(xC + 1 >= 0 && xC + 1 < inDims[1] && xTexelC${e+1}Ready == 0) {\n xTexelC${e+1} = getX(batch, xR, xC + 1, d1);\n // Need to manually clear unused channels in case\n // we're reading from recycled texture.\n if (xC + 2 >= inDims[1]) {\n xTexelC${e+1}.zw = vec2(0.0);\n }\n xTexelC${e+1}Ready = 1;\n }\n\n xC${e} = vec4(xTexelC${e}.zw, xTexelC${e+1}.zw);\n `,e+1<p&&(d+=`\n final = vec4(0.0);\n xCOffset = xC + 1 + strides[1];\n if(xCOffset >= 0 && xCOffset < inDims[1]) {\n final = getX(batch, xR, xCOffset, d1);\n }\n xC${e+1} = vec4(xTexelC${e+1}.xy, final.xy);\n `)):(d+=`\n if(xC >= 0 && xC < inDims[1] && xTexelC${e}Ready == 0) {\n xTexelC${e} = getX(batch, xR, xC, d1);\n if (xC + 1 >= inDims[1]) {\n xTexelC${e}.zw = vec2(0.0);\n }\n xTexelC${e}Ready = 1;\n }\n\n xCOffset = xC + strides[1];\n if(xCOffset >= 0 && xCOffset < inDims[1] && xTexelC${e+1}Ready == 0) {\n xTexelC${e+1} = getX(batch, xR, xCOffset, d1);\n if (xCOffset + 1 >= inDims[1]) {\n xTexelC${e+1}.zw = vec2(0.);\n }\n xTexelC${e+1}Ready = 1;\n }\n\n xC${e} = vec4(\n xTexelC${e}.xy, xTexelC${e+1}.xy);\n `,e+1<p&&(d+=`\n xC${e+1} = vec4(xTexelC${e}.zw, xTexelC${e+1}.zw);\n `)));e<p&&(d+=`\n wTexel = getW(r, ${e}, d1, q);\n dotProd += xC${e} * vec4(wTexel.xz, wTexel.xz);\n `,e+1<p&&(d+=`\n wTexel = getW(r, ${e+1}, d1, q);\n dotProd += xC${e+1} * vec4(wTexel.xz, wTexel.xz);\n `))}d+="\n }\n ",d+="\n }\n ";let f="",m="";n&&(f=r?`vec4 activation(vec4 a) {\n vec4 b = getPreluActivationWeightsAtOutCoords();\n ${n}\n }`:a?`vec4 activation(vec4 a) {\n vec4 b = getLeakyreluAlphaAtOutCoords();\n ${n}\n }
1`:`vec4 activation(vec4 x) {\n ${n}\n }`,m="result = activation(result);");const g=t?"result += getBiasAtOutCoords();":"";t&&this.variableNames.push("bias"),r&&this.variableNames.push("preluActivationWeights"),a&&this.variableNames.push("leakyreluAlpha"),this.userCode=`\n ${f}\n\n void main() {\n ivec4 coords = getOutputCoords();\n int batch = coords.x;\n ivec2 xRCCorner = coords.yz * strides - pads;\n int d2 = coords.w;\n int d1 = d2 / ${o};\n int q = d2 - d1 * ${o};\n int xRCorner = xRCCorner.x;\n int xCCorner = xRCCorner.y;\n\n //intialize dotProd with a small epsilon seems to reduce GPU accuracy loss.\n vec4 dotProd = vec4(0.000000000000001);\n\n ${d}\n\n vec4 result = dotProd - vec4(0.000000000000001);\n ${g}\n ${m}\n setOutput(result);\n }\n `}}const Sy={kernelName:s.cie,backendName:"webgl",kernelFunc:function(e){const{inputs:t,backend:n,attrs:r}=e,{x:a,filter:o}=t,{strides:i,pad:u,dilations:l,dimRoundingMode:c}=r;let p=l;null==p&&(p=[1,1]),s.D5U.assert(s.Wap.eitherStridesOrDilationsAreOne(i,p),(()=>`Error in depthwiseConv2d: Either strides or dilations must be 1. Got strides ${i} and dilations '${p}'`));const h=s.Wap.computeConv2DInfo(a.shape,o.shape,i,p,u,c,!0);let d;d=(0,s.OBj)().getBool("WEBGL_PACK_DEPTHWISECONV")&&h.strideWidth<=2&&h.outChannels/h.inChannels===1?new Ny(h):new Iy(h);const f=[[h.padInfo.top,h.padInfo.left],[h.strideHeight,h.strideWidth],[h.dilationHeight,h.dilationWidth],[h.inHeight,h.inWidth]];return n.runWebGLProgram(d,[a,o],"float32",f)}};class Ty{constructor(e){this.variableNames=["x","dy"],this.outputShape=e.filterShape;const t=e.strideHeight,n=e.strideWidth,r=e.padInfo.top,s=e.padInfo.left,a=e.outChannels/e.inChannels;this.userCode=`\n void main() {\n ivec4 coords = getOutputCoords();\n int wR = coords.x;\n int wC = coords.y;\n int d1 = coords.z;\n int dm = coords.w;\n int d2 = d1 * ${a} + dm;\n\n float dotProd = 0.0;\n\n // TO DO: Vec4 over the batch size\n for (int b = 0; b < ${e.batchSize}; b++) {\n for (int yR = 0; yR < ${e.outHeight}; yR++) {\n int xR = wR + yR * ${t} - ${r};\n\n if (xR < 0 || xR >= ${e.inHeight}) {\n continue;\n }\n\n for (int yC = 0; yC < ${e.outWidth}; yC++) {\n int xC = wC + yC * ${n} - ${s};\n\n if (xC < 0 || xC >= ${e.inWidth}) {\n continue;\n }\n\n float dyValue = getDy(b, yR, yC, d2);\n float xValue = getX(b, xR, xC, d1);\n dotProd += (xValue * dyValue);\n }\n }\n }\n setOutput(dotProd);\n }\n `}}class Cy{constructor(e){this.variableNames=["dy","W"],this.outputShape=e.inShape;const t=e.filterHeight,n=e.filterWidth,r=e.strideHeight,s=e.strideWidth,a=t-1-e.padInfo.top,o=n-1-e.padInfo.left,i=e.outChannels/e.inChannels;this.userCode=`\n const ivec2 pads = ivec2(${a}, ${o});\n\n void main() {\n ivec4 coords = getOutputCoords();\n int batch = coords[0];\n int d1 = coords[3];\n ivec2 dyCorner = coords.yz - pads;\n int dyRCorner = dyCorner.x;\n int dyCCorner = dyCorner.y;\n\n float dotProd = 0.0;\n\n for (int wR = 0; wR < ${t}; wR++) {\n float dyR = float(dyRCorner + wR) / ${r}.0;\n\n if (dyR < 0.0 || dyR >= ${e.outHeight}.0 || fract(dyR) > 0.0) {\n continue;\n }\n int idyR = int(dyR);\n\n int wRPerm = ${t} - 1 - wR;\n\n for (int wC = 0; wC < ${n}; wC++) {\n float dyC = float(dyCCorner + wC) / ${s}.0;\n\n if (dyC < 0.0 || dyC >= ${e.outWidth}.0 ||\n fract(dyC) > 0.0) {\n continue;\n }\n int idyC = int(dyC);\n\n int wCPerm = ${n} - 1 - wC;\n\n // TO DO: Vec4 over the channelMul\n for (int dm = 0; dm < ${i}; dm++) {\n int d2 = d1 * ${i} + dm;\n float xValue = getDy(batch, idyR, idyC, d2);\n float wValue = getW(wRPerm, wCPerm, d1, dm);\n dotProd += xValue * wValue;\n }\n }\n }\n setOutput(dotProd);\n }\n `}}const Ey={kernelName:s.sL$,backendName:"webgl",kernelFunc:function(e){const{inputs:t,backend:n,attrs:r}=e,{x:a,dy:o}=t,{strides:i,dilations:u,pad:l,dimRoundingMode:c,filterShape:p}=r,h=s.Wap.computeConv2DInfo(a.shape,p,i,u,l,c,!0),d=new Ty(h);return n.runWebGLProgram(d,[a,o],"float32")}};const $y={kernelName:s.y7R,backendName:"webgl",kernelFunc:function(e){const{inputs:t,backend:n,attrs:r}=e,{dy:a,filter:o}=t,{strides:i,dilations:u,pad:l,dimRoundingMode:c,inputShape:p}=r,h=s.Wap.computeConv2DInfo(p,o.shape,i,u,l,c,!0),d=new Cy(h);return n.runWebGLProgram(d,[a,o],"float32")}};class Ay{constructor(e){this.variableNames=["X"],this.outputShape=[e,e],this.userCode="\n void main() {\n ivec2 coords = getOutputCoords();\n float val = coords[0] == coords[1] ? getX(coords[0]) : 0.0;\n setOutput(val);\n }\n "}}const Dy={kernelName:s.$w,backendName:"webgl",kernelFunc:function(e){const{inputs:t,backend:n}=e,{x:r}=t,a=[...r.shape,...r.shape],o=s.D5U.sizeFromShape(r.shape),i=pm({inputs:{x:r},backend:n,attrs:{shape:[o]}}),u=new Ay(o),l=n.runWebGLProgram(u,[i],i.dtype),c=pm({inputs:{x:l},backend:n,attrs:{shape:a}});return n.disposeIntermediateTensorInfo(i),n.disposeIntermediateTensorInfo(l),c}};class _y{constructor(e){this.variableNames=["x","W"],this.outputShape=e.outShape;const{inHeight:t,inWidth:n,padInfo:r,strideHeight:s,strideWidth:a,filterHeight:o,filterWidth:i,dilationHeight:u,dilationWidth:l}=e,{top:c,left:p}=r;this.userCode=`\n const ivec2 strides = ivec2(${s}, ${a});\n const ivec2 pads = ivec2(${c}, ${p});\n const float neg_infinity = -3.4e38;\n\n void main() {\n ivec4 coords = getOutputCoords();\n int batch = coords.x;\n int d1 = coords.w;\n ivec2 outTopLeftCorner =\n coords.yz * strides - pads;\n int hBeg = outTopLeftCorner.x;\n int wBeg = outTopLeftCorner.y;\n\n float curVal = neg_infinity;\n for (int h = 0; h < ${o}; h++) {\n int hIn = hBeg + h * ${u};\n\n if (hIn >= 0 && hIn < ${t}
1) {\n for (int w = 0; w < ${i}; w++) {\n int wIn = wBeg + w * ${l};\n\n if (wIn >= 0 && wIn < ${n}) {\n float xVal = getX(batch, hIn, wIn, d1);\n float wVal = getW(h, w, d1);\n\n float val = xVal + wVal;\n if (val > curVal) {\n curVal = val;\n }\n }\n }\n }\n }\n\n float result = curVal;\n setOutput(result);\n }\n `}}const Ry={kernelName:s.p4S,backendName:"webgl",kernelFunc:function(e){const{inputs:t,backend:n,attrs:r}=e,{x:a,filter:o}=t,{strides:i,pad:u,dilations:l}=r,c=s.Wap.computeDilation2DInfo(a.shape,o.shape,i,u,"NHWC",l);let p;const h=new _y(c);p=n.runWebGLProgram(h,[a,o],"float32");const d=pm({inputs:{x:p},backend:n,attrs:{shape:c.outShape}});return n.disposeIntermediateTensorInfo(p),d}};const Fy={kernelName:s.$g6,backendName:"webgl",kernelFunc:function(e){const{inputs:t,backend:n,attrs:r}=e,{equation:a}=r,o=t,{allDims:i,summedDims:u,idDims:l}=s.Wap.decodeEinsumEquation(a,o.length);s.Wap.checkEinsumDimSizes(i.length,l,o);const{path:c,steps:p}=s.Wap.getEinsumComputePath(u,l),h=p.length;let d=null,f=i.length;const m=[];for(let g=0;g<h;++g){for(const e of p[g]){const{permutationIndices:t,expandDims:r}=s.Wap.getEinsumPermutation(f,l[e]);let a;s.Wap.isIdentityPermutation(t)?a=o[e]:(a=vm({inputs:{x:o[e]},backend:n,attrs:{perm:t}}),m.push(a));const i=a.shape.slice();for(let e=0;e<r.length;++e)i.splice(r[e],0,1);s.D5U.arraysEqual(a.shape,i)||(a=pm({inputs:{x:a},backend:n,attrs:{shape:i}}),m.push(a)),null===d?d=a:(d=lm({inputs:{a:a,b:d},backend:n}),m.push(d))}g<h-1&&(c[g]>=0&&(d=xm({inputs:{x:d},backend:n,attrs:{axis:c[g]-(i.length-f),keepDims:!1}}),m.push(d)),f--)}for(const s of m)s!==d&&n.disposeIntermediateTensorInfo(s);return d}},Oy=tm({opSnippet:"return (x >= 0.0) ? x : (exp(x) - 1.0);",packedOpSnippet:"\n vec4 result;\n\n result.r = (x.r >= 0.0) ? x.r : (exp(x.r) - 1.0);\n result.g = (x.g >= 0.0) ? x.g : (exp(x.g) - 1.0);\n result.b = (x.b >= 0.0) ? x.b : (exp(x.b) - 1.0);\n result.a = (x.a >= 0.0) ? x.a : (exp(x.a) - 1.0);\n\n return result;\n"}),My={kernelName:s.SX0,backendName:"webgl",kernelFunc:Oy},By={kernelName:s.HEU,backendName:"webgl",kernelFunc:e=>{const{inputs:t,backend:n}=e,{dy:r,y:a}=t,o=(0,s.OBj)().getBool("WEBGL_PACK_BINARY_OPERATIONS")?new Gf("\n vec4 bGTEZero = vec4(greaterThanEqual(b, vec4(0.)));
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1put can be obtained from uniform value.\n setOutput(value);\n }\n "}}function Jy(e){const{backend:t,attrs:n}=e,{shape:r,value:a}=n;let{dtype:o}=n;if(o=o||s.D5U.inferDtype(a),"string"===o){const e=s.D5U.getArrayFromDType(o,s.D5U.sizeFromShape(r));return e.fill(a),t.makeTensorInfo(r,o,e)}{const e=new Zy(r,a),n=[[a]];return t.runWebGLProgram(e,[],o,n)}}const eb={kernelName:s.deh,backendName:"webgl",kernelFunc:Jy};class tb{constructor(e){this.variableNames=["Image"],this.outputShape=[];const t=e[2];this.outputShape=e,this.userCode=`\n void main() {\n ivec4 coords = getOutputCoords();\n int x = coords[2];\n\n int coordX = ${t} - x - 1;\n float outputValue;\n if(coordX >= 0 && coordX < ${t}) {\n outputValue = getImage(coords[0], coords[1], coordX, coords[3]);\n } else {\n outputValue = getImage(coords[0], coords[1], coords[2], coords[3]);\n }\n setOutput(outputValue);\n }\n `}}const nb={kernelName:s.Uyb,backendName:"webgl",kernelFunc:({inputs:e,backend:t})=>{const{image:n}=e,r=t,s=new tb(n.shape);return r.runWebGLProgram(s,[n],n.dtype)}},rb="return floor(x);",sb=tm({opSnippet:rb,packedOpSnippet:rb,cpuKernelImpl:zd}),ab={kernelName:s.OR,backendName:"webgl",kernelFunc:sb},ob=nm({opSnippet:"\n float s = sign(a) * sign(b);\n int ia = round(a);\n int ib = round(b);\n if (ib != 0) {\n // Windows (D3D) wants guaranteed non-zero int division at compile-time.\n return float(idiv(ia, ib, s));\n } else {\n return NAN;\n }\n",packedOpSnippet:"\n ivec4 ia = round(a);\n ivec4 ib = round(b);\n bvec4 cond = notEqual(ib, ivec4(0));\n ivec4 result = ivec4(0);\n vec4 s = sign(a) * sign(b);\n\n // Windows (D3D) wants guaranteed non-zero int division at compile-time.\n if (cond[0]) {\n result[0] = idiv(ia[0], ib[0], s[0]);\n }\n if (cond[1]) {\n result[1] = idiv(ia[1], ib[1], s[1]);\n }\n if (cond[2]) {\n result[2] = idiv(ia[2], ib[2], s[2]);\n }\n if (cond[3]) {\n result[3] = idiv(ia[3], ib[3], s[3]);\n }\n return vec4(result);\n",dtype:"int32"}),ib={kernelName:s.jeX,backendName:"webgl",kernelFunc:ob};class ub{constructor(e){this.variableNames=["A"];const t=(0,ld.A)(),[n,r]=e;this.outputShape=e,this.userCode=`\n void main() {\n ivec3 coords = getOutputCoords();\n int texR = coords[0];\n int texC = coords[1];\n int depth = coords[2];\n vec2 uv = (vec2(texC, texR) + halfCR) / vec2(${r}.0, ${n}.0);\n\n vec4 values = ${t.texture2D}(A, uv);\n float value;\n if (depth == 0) {\n value = values.r;\n } else if (depth == 1) {\n value = values.g;\n } else if (depth == 2) {\n value = values.b;\n } else if (depth == 3) {\n value = values.a;\n }\n\n setOutput(floor(value * 255.0 + 0.5));\n }\n `}}class lb{constructor(e){this.variableNames=["A"],this.packedInputs=!1,this.packedOutput=!0;const t=(0,ld.A)(),[n,r]=e;this.outputShape=e,this.userCode=`\n void main() {\n ivec3 coords = getOutputCoords();\n int texR = coords[0];\n int texC = coords[1];\n int depth = coords[2];\n\n vec4 result = vec4(0.);\n\n for(int row=0; row<=1; row++) {\n for(int col=0; col<=1; col++) {\n texC = coords[1] + row;\n depth = coords[2] + col;\n\n vec2 uv = (vec2(texC, texR) + halfCR) /\n vec2(${r}.0, ${n}.0);\n vec4 values = ${t.texture2D}(A, uv);\n float value;\n if (depth == 0) {\n value = values.r;\n } else if (depth == 1) {\n value = values.g;\n } else if (depth == 2) {\n value = values.b;\n } else if (depth == 3) {\n value = values.a;\n }\n\n result[row * 2 + col] = floor(value * 255.0 + 0.5);\n }\n }\n\n ${t.output} = result;\n }\n `}}const cb={kernelName:s.eBW,backendName:"webgl",kernelFunc:function(e){const{inputs:t,backend:n,attrs:r}=e;let{pixels:a}=t;const{numChannels:o}=r,i="undefined"!==typeof HTMLVideoElement&&a instanceof HTMLVideoElement,u="undefined"!==typeof HTMLImageElement&&a instanceof HTMLImageElement,[l,c]=i?[a.videoWidth,a.videoHeight]:[a.width,a.height],p=[c,l],h=[c,l,o];if(u||i){const e=(0,s.OBj)().getBool("CANVAS2D_WILL_READ_FREQUENTLY_FOR_GPU");null!=pb&&e===hb||(hb=e,pb=document.createElement("canvas").getContext("2d",{willReadFrequently:hb})),pb.canvas.width=l,pb.canvas.height=c,pb.drawImage(a,0,0,l,c),a=pb.canvas}const d=n.makeTensorInfo(p,"int32");n.texData.get(d.dataId).usage=Dh.PIXELS,n.gpgpu.uploadPixelDataToTexture(n.getTexture(d.dataId),a);const f=(0,s.OBj)().getBool("WEBGL_PACK")?new lb(h):new ub(h),m=n.runWebGLProgram(f,[d],"int32");return n.disposeData(d.dataId),m}};let pb,hb=(0,s.OBj)().getBool("CANVAS2D_WILL_READ_FREQUENTLY_FOR_GPU");const db={kernelName:s._V0,backendName:"webgl",kernelFunc:function(e){const{inputs:t,backend:n,attrs:r}
vendor: 10,721 bytes, line 1
1=e,{x:a,filter:o,bias:i,preluActivationWeights:u}=t,{strides:l,pad:c,dataFormat:p,dilations:h,dimRoundingMode:d,activation:f,leakyreluAlpha:m}=r,g=s.Wap.convertConv2DDataFormat(p),y=s.Wap.computeConv2DInfo(a.shape,o.shape,l,h,c,d,!1,g);let b;const x=[],w=null!=i,v=null!=u,k="leakyrelu"===f,I=()=>{const e=[a,o],t=(e,t)=>{if("NCHW"===t&&1===e.shape.length&&1!==e.shape[0]){const t=pm({inputs:{x:e},backend:n,attrs:{shape:[e.shape[0],1,1]}});return x.push(t),t}return e};if(w&&e.push(t(i,p)),v&&e.push(t(u,p)),k){const t=n.makeTensorInfo([],"float32",s.D5U.createScalarValue(m,"float32"));e.push(t),x.push(t)}return e};if(1!==y.filterHeight||1!==y.filterWidth||1!==y.dilationHeight||1!==y.dilationWidth||1!==y.strideHeight||1!==y.strideWidth||"SAME"!==y.padInfo.type&&"VALID"!==y.padInfo.type)if(y.strideWidth<=2&&"channelsLast"===g&&(0,s.OBj)().getBool("WEBGL_EXP_CONV")){const e=f?rm(f,!0):null,t=new jg(y,w,e,v,k),r=[[y.padInfo.top,y.padInfo.left],[y.strideHeight,y.strideWidth],[y.dilationHeight,y.dilationWidth],[y.inHeight,y.inWidth]],s=I();b=n.runWebGLProgram(t,s,"float32",r)}else if((0,s.OBj)().getBool("WEBGL_CONV_IM2COL"))b=Qg({x:a,filter:o,convInfo:y,backend:n,bias:i,activation:f,preluActivationWeights:u,leakyreluAlpha:m});else{const e=f?rm(f,!1):null,t=new Gg(y,w,e,v,k),r=I();b=n.runWebGLProgram(t,r,"float32")}else b=Kg({x:a,filter:o,convInfo:y,backend:n,bias:i,activation:f,preluActivationWeights:u,leakyreluAlpha:m});const N=pm({inputs:{x:b},backend:n,attrs:{shape:y.outShape}});return x.push(b),x.forEach((e=>n.disposeIntermediateTensorInfo(e))),N}};const fb={kernelName:s.luS,backendName:"webgl",kernelFunc:function(e){const{inputs:t,backend:n,attrs:r}=e,{x:a,filter:o,bias:i,preluActivationWeights:u}=t,{strides:l,pad:c,dilations:p,dimRoundingMode:h,activation:d,leakyreluAlpha:f}=r,m=[];let g=p;null==g&&(g=[1,1]),s.D5U.assert(s.Wap.eitherStridesOrDilationsAreOne(l,g),(()=>`Error in depthwiseConv2d: Either strides or dilations must be 1. Got strides ${l} and dilations '${g}'`));const y=s.Wap.computeConv2DInfo(a.shape,o.shape,l,g,c,h,!0),b=(0,s.OBj)().getBool("WEBGL_PACK_DEPTHWISECONV")&&y.strideWidth<=2&&y.outChannels/y.inChannels===1,x=d?rm(d,b):null,w=[a,o],v=null!=i,k=null!=u,I="leakyrelu"===d;if(v&&w.push(i),k&&w.push(u),I){const e=n.makeTensorInfo([],"float32",s.D5U.createScalarValue(f,"float32"));w.push(e),m.push(e)}let N;N=b?new Ny(y,v,x,k,I):new Iy(y,v,x,k,I);const S=[[y.padInfo.top,y.padInfo.left],[y.strideHeight,y.strideWidth],[y.dilationHeight,y.dilationWidth],[y.inHeight,y.inWidth]],T=n.runWebGLProgram(N,w,"float32",S);return m.forEach((e=>n.disposeIntermediateTensorInfo(e))),T}};class mb{constructor(e,t,n,r){this.sliceDim=e,this.strides=t,this.paramsShape=r,this.variableNames=["x","indices"],this.outputShape=n;const s=(0,cd.kW)(n.length);let a="\n int index;";for(let o=0;o<this.sliceDim;o++)a+=`\n index = round(getIndices(coords[0], ${o}));\n out_of_bounds = out_of_bounds || index < 0;\n out_of_bounds = out_of_bounds || index >= ${this.paramsShape[o]};\n flattenIndex += index * ${this.strides[o]};`;this.userCode=`\n void main() {\n ${s} coords = getOutputCoords();\n int flattenIndex = 0;\n bool out_of_bounds = false;\n\n ${a}\n\n setOutput(out_of_bounds ? 0.0 : getX(flattenIndex, coords[1]));\n }\n `}}const gb={kernelName:s.q1x,backendName:"webgl",kernelFunc:function(e){const{inputs:t,backend:n}=e,{params:r,indices:a}=t,o=a.shape,i=o[o.length-1],u=s.D5U.sizeFromShape(r.shape),[l,c,p,h]=s.Wap.prepareAndValidate(r,a),d=pm({inputs:{x:a},backend:n,attrs:{shape:[c,i]}}),f=pm({inputs:{x:r},backend:n,attrs:{shape:[s.D5U.sizeFromShape(r.shape)/p,p]}});if(n.shouldExecuteOnCPU([r,a])||"string"===r.dtype){const e=n.readSync(a.dataId),t=n.bufferSync(r),s=Vd(e,t,r.dtype,c,i,p,h,r.shape,u);return n.makeTensorInfo(l,r.dtype,s.values)}const m=new mb(i,h,[c,p],r.shape),g=n.runWebGLProgram(m,[f,d],f.dtype),y=pm({inputs:{x:g},backend:n,attrs:{shape:l}});return n.disposeIntermediateTensorInfo(d),n.disposeIntermediateTensorInfo(f),n.disposeIntermediateTensorInfo(g),y}};class yb{constructor(e,t){this.variableNames=["A","indices"],this.outputShape=t,this.rank=t.length;const n=(0,cd.kW)(this.rank),r=function(e,t){const n=["resRC.x","resRC.y","resRC.z","resRC.w"],r=[];for(let s=0;s<e.length;s++)2===s?r.push("index"):r.push(`${n[s]}`);return r.join()}(e);this.userCode=`\n void main() {\n ${n} resRC = getOutputCoords();\n int index = int(getIndices(resRC.x, resRC.z));\n float inBounds = (index >= 0) && (index < ${e[2]}) ? 1.0 : 0.0;\n setOutput(inBounds * getA(${r}));\n }\n `}}function bb(e){const{inputs:t,backend:n,attrs:r}=e,{x:a,indices:o}=t,{axis:i,batchDims:u}=r,l=s.D5U.parseAxisParam(i,a.shape)[0];if((0,s.OBj)().get("DEBUG")){const e=n.readSync(o.dataId),t=a.shape[l];for(let n=0;n<e.length;++n){const r=e[n];s.D5U.assert(r<=t-1&&r>=0,(()=>`GatherV2: the index value ${r} is not in [0, ${t-1}]`))}}const c=s.Wap.segment_util.collectGatherOpShapeInfo(a,o,l,u),p=s.D5U.sizeFromShape(o.shape),h=[],d=pm({inputs:{x:a},backend:n,attrs:{shape:[c.batchSize,c.outerSize,c.dimSize,c.sliceSize]}}),f=pm({inputs:{x:o},backend:n,attrs:{shape:[c.batchSize,p/c.batchSize]}});h.push(d),h.push(f);const m=[c.batchSize,c.outerSize,p/c.batchSize,c.sliceSize];if(n.shouldExecuteOnCPU([a,o])||"string"===a.dtype){const e=n.bufferSync(f),t=n.bufferSync(d),r=Gd(t,e,m);return h.forEach((e=>n.disposeIntermediateTensorInfo(e))),n.makeTensorInfo(c.outputShape,r.dtype,r.values)}const g=new yb(d.shape,m),y=n.runWebGLProgram(g,[d,f],d.dtype);h.push(y);const b=pm({inputs:{x:y},backend:n,attrs:{shape:c.outputShape}});return h.forEach((e=>n.disposeIntermediateTensorInfo(e))),b}const xb={kernelName:s.qi_,backendName:"webgl",kernelFunc:bb},wb=nm({opSnippet:"return float(a > b);",packedOpSnippet:"\n return vec4(greaterThan(a, b));\n",cpuKernelImpl:Hd,dtype:"bool"}),vb={kernelName:s.iZT,backendName:"webgl",kernelFunc:wb},kb=nm({opSnippet:"return float(a >= b);",packedOpSnippet:"\n return vec4(greaterThanEqual(a, b));\n",dtype:"bool",cpuKernelImpl:jd}),Ib={kernelName:s.Acj,backendName:"webgl",kernelFunc:kb};const Nb={kernelName:s.Qg5,backendName:"webgl",kernelFunc:function(e){const{inputs:t,backend:n}=e,{input:r}=t;return Qy(r,!0,n)}},Sb=tm({opSnippet:"return float(!isnan(x) && !isinf(x));",dtype:"bool"}),Tb={kernelName:s.avt,backendName:"webgl",kernelFunc:Sb},Cb=tm({opSnippet:"return float(isinf(x));",dtype:"bool"}),Eb={kernelName:s.iWB,backendName:"webgl",kernelFunc:Cb},$b=tm({opSnippet:"return float(isnan(x));",dtype:"bool"}),Ab={kernelName:s.r7n,backendName:"webgl",kernelFunc:$b},Db=nm({opSnippet:"return float(a < b);",packedOpSnippet:"\n return vec4(lessThan(a, b));\n",cpuKernelImpl:Xd,dtype:"bool"}),_b={kernelName:s.vtC,backendName:"webgl",kernelFunc:Db},Rb=nm({opSnippet:"return float(a <= b);",packedOpSnippet:"\n return vec4(lessThanEqual(a, b));\n",cpuKernelImpl:qd,dtype:"bool"}),Fb={kernelName:s.CAk,backendName:"webgl",kernelFunc:Rb};const Ob={kernelName:s.e7N,backendName:"webgl",kernelFunc:function(e){const{backend:t,attrs:n}=e,{start:r,stop:s,num:a}=n,o=Kd(r,s,a);return t.makeTensorInfo([o.length],"float32",o)}},Mb=tm({opSnippet:"if (isnan(x)) return x;\n return x < 0.0 ? 0./0. : log(x);\n",packedOpSnippet:"\n vec4 result = log(x);\n bvec4 isNaN = isnan(x);\n result.r = isNaN.r ? x.r : (x.r < 0.0 ? 0./0. : result.r);\n result.g = isNaN.g ? x.g : (x.g < 0.0 ? 0./0. : result.g);\n result.b = isNaN.b ? x.b : (x.b < 0.0 ? 0./0. : result.b);\n result.a = isNaN.a ? x.a : (x.a < 0.0 ? 0./0. : result.a);\n return result;\n",cpuKernelImpl:Qd}),Bb={kernelName:s.ZbH,backendName:"webgl",kernelFunc:Mb},Lb=tm({opSnippet:"if (isnan(x)) return x;\n return log(1.0 + x);\n"}),Wb={kernelName:s.kU,backendName:"webgl",kernelFunc:Lb},Pb=nm({opSnippet:"return float(a >= 1.0 && b >= 1.0);",packedOpSnippet:"\n return vec4(\n vec4(greaterThanEqual(a, vec4(1.0))) *\n vec4(greaterThanEqual(b, vec4(1.0))));\n",dtype:"bool"}),Ub={kernelName:s.PYm,backendName:"webgl",kernelFunc:Pb},zb=tm({opSnippet:"return float(!(x >= 1.0));"}),Vb={kernelName:s.VfG,backendName:"webgl",kernelFunc:zb},Gb=nm({opSnippet:"return float(a >= 1.0 || b >= 1.0);",packedOpSnippet:"\n return min(\n vec4(greaterThanEqual(a, vec4(1.0))) +\n vec4(greaterThanEqual(b, vec4(1.0))),\n vec4(1.0));\n",dtype:"bool"}),Hb={kernelName:s.MZg,backendName:"webgl",kernelFunc:Gb};class jb{constructor(e,t,n,r,s){this.variableNames=["x"],this.outputShape=[];const a=t,o=e[3]-1;let i;this.outputShape=e;const u=`float(${n}) + float(${r}) * sum`;i=.5===s?`inversesqrt(${u})`:1===s?`1.0/(${u})`:`exp(log(${u}) * float(-${s}));`,this.userCode=`\n void main() {\n ivec4 coords = getOutputCoords();\n int b = coords[0];\n int r = coords[1];\n int c = coords[2];\n int d = coords[3];\n float x = getX(b, r, c, d);\n float sum = 0.0;\n for (int j = -${a}; j <= ${a}; j++) {\n int idx = d + j;\n if (idx >= 0 && idx <= ${o}) {\n float z = getX(b, r, c, idx);\n sum += z * z;\n }\n }\n float val = x * ${i};\n setOutput(val);\n }\n `}}class Xb{constructor(e,t,n,r,s){this.variableNames=["x"],this.outputShape=[],this.packedInputs=!0,this.packedOutput=!0;const a=t,o=e[3]-1;let i;this.outputShape=e;const u=`float(${n}) + float(${r}) * sum`;i=.5===s?`inversesqrt(${u})`:1===s?`1.0/(${u})`:`exp(log(${u}) * float(-${s}));`,this.userCode=`\n void main() {\n ivec4 coords = getOutputCoords();\n int b = coords.x;\n int r = coords.y;\n int c = coords.z;\n int d = coords.w;\n\n bool hasNextCol = d < ${this.outputShape[3]};\n bool hasNextRow = c < ${this.outputShape[2]};\n\n vec4 sum = vec4(0.);\n vec4 xFragAtOutputCoords = getX(b, r, c, d);\n\n vec4 xAtOutputCoords = vec4(\n getChannel(xFragAtOutputCoords, vec2(c, d)),\n hasNextCol ?\n getChannel(xFragAtOutputCoords, vec2(c, d + 1)) : 0.0,\n hasNextRow ?\n getChannel(xFragAtOutputCoords , vec2(c + 1, d)) : 0.0,\n (hasNextRow && hasNextCol) ?\n getChannel(xFragAtOutputCoords, vec2(c + 1, d + 1)) : 0.0\n );\n\n int firstChannel = d - ${a};\n vec2 cache = vec2(0.);\n if(firstChannel >= 0){\n vec4 firstChannelFrag = getX(b, r, c, firstChannel);\n cache.x = getChannel(firstChannelFrag, vec2(c, firstChannel));\n if(hasNextRow){\n cache.y = getChannel(firstChannelFrag, vec2(c + 1, firstChannel));\n }\n }
1\n\n ivec2 depth = ivec2(d, d + 1);\n for (int j = - ${a}; j <= ${a}; j++) {\n ivec2 idx = depth + j;\n bvec2 aboveLowerBound = greaterThanEqual(idx, ivec2(0));\n bvec2 belowUpperBound = lessThanEqual(idx, ivec2(${o}));\n\n bool depthInRange = aboveLowerBound.x && belowUpperBound.x;\n bool depthPlusOneInRange = aboveLowerBound.y && belowUpperBound.y;\n\n if(depthInRange || depthPlusOneInRange){\n vec4 z = vec4(0.);\n vec4 xFragAtCurrentDepth;\n z.xz = cache.xy;\n if(depthPlusOneInRange && hasNextCol){\n xFragAtCurrentDepth = idx.y != d ?\n getX(b, r, c, idx.y) : xFragAtOutputCoords;\n z.y = getChannel(xFragAtCurrentDepth, vec2(c, idx.y));\n if(hasNextRow){\n z.w = getChannel(xFragAtCurrentDepth, vec2(c + 1, idx.y));\n }\n }\n cache.xy = z.yw;\n sum += z * z;\n }\n }\n vec4 result = xAtOutputCoords * ${i};\n setOutput(result);\n }\n `}}const qb={kernelName:s.eZ0,backendName:"webgl",kernelFunc:e=>{const{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{depthRadius:o,bias:i,alpha:u,beta:l}=r,c=(0,s.OBj)().getBool("WEBGL_PACK_NORMALIZATION")?new Xb(a.shape,o,i,u,l):new jb(a.shape,o,i,u,l);return n.runWebGLProgram(c,[a],a.dtype)}};class Kb{constructor(e,t,n,r,s){this.variableNames=["inputImage","outputImage","dy"],this.outputShape=[],this.outputShape=e,this.depth=e[3],this.depthRadius=t,this.bias=n,this.alpha=r,this.beta=s,this.userCode=`\n void main() {\n ivec4 coords = getOutputCoords();\n int b = coords[0];\n int r = coords[1];\n int c = coords[2];\n\n float result = 0.0;\n for (int d = 0; d < ${this.depth}; ++d) {\n int depthBegin = int(max(0.0, float(d - ${t})));\n int depthEnd = int(min(float(${this.depth}),\n float(d + ${t} + 1)));\n\n const int MIN_DEPTH_BEGIN = 0;\n const int MAX_DEPTH_END = ${this.depth};\n\n float norm = 0.0;\n for (int k = MIN_DEPTH_BEGIN; k < MAX_DEPTH_END; ++k) {\n if (k < depthBegin){\n continue;\n }\n else if (k >= depthBegin && k < depthEnd) {\n norm += getInputImage(b, r, c, k) * getInputImage(b, r, c, k);\n }\n else {\n break;\n }\n }\n\n norm = float(${r}) * norm + float(${n});\n\n for(int k = MIN_DEPTH_BEGIN; k < MAX_DEPTH_END; ++k){\n if (k < depthBegin){\n continue;\n }\n else if (k >= depthBegin && k < depthEnd){\n float dyi = -2.0 * float(${r})\n * float(${s})\n * getInputImage(b ,r ,c, k) * getOutputImage(b, r, c, d)\n / norm;\n if (k == d) {\n dyi += pow(norm, -1.0 * ${s});\n }\n if (k == coords[3]) {\n dyi *= getDy(b, r, c, d);\n result += dyi;\n }\n }\n else {\n break;\n }\n }\n }\n setOutput(result);\n }\n `}}const Qb={kernelName:s.Hhh,backendName:"webgl",kernelFunc:e=>{const{inputs:t,backend:n,attrs:r}=e,{x:s,y:a,dy:o}=t,{depthRadius:i,bias:u,alpha:l,beta:c}=r,p=new Kb(s.shape,i,u,l,c);return n.runWebGLProgram(p,[s,a,o],s.dtype)}};function Yb(e){const{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{reductionIndices:o,keepDims:i}=r,u=a.shape.length,l=s.D5U.parseAxisParam(o,a.shape);let c=l;const p=s.Wap.getAxesPermutation(c,u),h=null!=p,d=n.shouldExecuteOnCPU([a]);let f=a;if(h){if(d){const e=n.texData.get(f.dataId).values,t=new Array(u);for(let n=0;n<t.length;n++)t[n]=a.shape[p[n]];const r=Nf(e,a.shape,a.dtype,p,t);f=n.makeTensorInfo(t,a.dtype);n.texData.get(f.dataId).values=r}else f=bm(a,p,n);c=s.Wap.getInnerMostAxes(c.length,u)}s.Wap.assertAxesAreInnerMostDims("max",c,u);const[m,g]=s.Wap.computeOutAndReduceShapes(f.shape,c);let y,b=m;if(i&&(b=s.Wap.expandShapeToKeepDim(m,l)),d){const e=n.texData.get(f.dataId).values,t=Yd(e,s.D5U.sizeFromShape(g),b,a.dtype);y=n.makeTensorInfo(b,a.dtype);n.texData.get(y.dataId).values=t}else y=function(e,t,n,r){const a=s.D5U.sizeFromShape(t),o=pm({inputs:{x:e},attrs:{shape:[s.D5U.sizeFromShape(e.shape)/a,a]},backend:r}),i=mm(o,e.dtype,"max",r),u=pm({inputs:{x:i},attrs:{shape:n},backend:r});return r.disposeIntermediateTensorInfo(o),r.disposeIntermediateTensorInfo(i),u}(f,g,b,n);return h&&n.disposeIntermediateTensorInfo(f),y}const Zb={kernelName:s.YoZ,backendName:"webgl",kernelFunc:Yb},Jb=nm({opSnippet:"\n if (isnan(a)) return a;\n if (isnan(b)) return b;\n\n return max(a, b);\n",packedOpSnippet:"\n vec4 result = vec4(max(a, b));\n bvec4 isNaNA = isnan(a);\n bvec4 isNaNB = isnan(b);\n bvec4 isNaN = bvec4(isNaNA.x || isNaNB.x, isNaNA.y || isNaNB.y, isNaNA.z || isNaNB.z, isNaNA.w || isNaNB.w);
1\n \n result.r = isNaN.r ? NAN : result.r;\n result.g = isNaN.g ? NAN : result.g;\n result.b = isNaN.b ? NAN : result.b;\n result.a = isNaN.a ? NAN : result.a;\n\n return result;\n",cpuKernelImpl:Zd}),ex={kernelName:s.BMI,backendName:"webgl",kernelFunc:Jb};const tx={kernelName:s.mTV,backendName:"webgl",kernelFunc:function(e){const{inputs:t,backend:n,attrs:r}=e,{x:a}=t;id(a,"maxPool");const{filterSize:o,strides:i,pad:u,dimRoundingMode:l}=r;s.D5U.assert(s.Wap.eitherStridesOrDilationsAreOne(i,1),(()=>`Error in maxPool: Either strides or dilations must be 1. Got strides ${i} and dilations '1'`));const c=s.Wap.computePool2DInfo(a.shape,o,i,1,u,l);if(1===c.filterWidth&&1===c.filterHeight&&s.D5U.arraysEqual(c.inShape,c.outShape))return Hf({inputs:{x:a},backend:n});const p=new ng(c,"max",!1);return n.runWebGLProgram(p,[a],a.dtype)}};const nx={kernelName:s.OAf,backendName:"webgl",kernelFunc:function(e){const{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{filterSize:o,strides:i,pad:u,dataFormat:l,dimRoundingMode:c}=r,p=s.Wap.computePool3DInfo(a.shape,o,i,[1,1,1],u,c,l),h=new rg(p,"max",!1);return n.runWebGLProgram(h,[a],a.dtype)}};class rx{constructor(e){this.variableNames=["dy","maxPos"],this.outputShape=e.inShape;const t=e.strideHeight,n=e.strideWidth,r=e.dilationHeight,s=e.effectiveFilterHeight,a=e.effectiveFilterWidth,o=s-1-e.padInfo.top,i=a-1-e.padInfo.left,u=s*a-1;this.userCode=`\n const ivec2 pads = ivec2(${o}, ${i});\n\n void main() {\n ivec4 coords = getOutputCoords();\n int b = coords[0];\n int d = coords[3];\n\n ivec2 dyRCCorner = coords.yz - pads;\n int dyRCorner = dyRCCorner.x;\n int dyCCorner = dyRCCorner.y;\n\n // Convolve dy(?, ?, d) with pos mask(:, :, d) to get dx(xR, xC, d).\n // ? = to be determined. : = across all values in that axis.\n float dotProd = 0.0;\n for (int wR = 0; wR < ${s};\n wR += ${r}) {\n float dyR = float(dyRCorner + wR) / ${t}.0;\n\n if (dyR < 0.0 || dyR >= ${e.outHeight}.0 || fract(dyR) > 0.0) {\n continue;\n }\n int idyR = int(dyR);\n\n for (int wC = 0; wC < ${a}; wC++) {\n float dyC = float(dyCCorner + wC) / ${n}.0;\n\n if (dyC < 0.0 || dyC >= ${e.outWidth}.0 ||\n fract(dyC) > 0.0) {\n continue;\n }\n int idyC = int(dyC);\n\n float dyValue = getDy(b, idyR, idyC, d);\n int maxPosValue = ${u} - int(getMaxPos(b, idyR, idyC, d));\n\n // Get the current value, check it against the value from the\n // position matrix.\n int curPosValue = wR * ${a} + wC;\n float mask = float(maxPosValue == curPosValue ? 1.0 : 0.0);\n\n dotProd += dyValue * mask;\n }\n }\n setOutput(dotProd);\n }\n `}}class sx{constructor(e){this.variableNames=["dy","maxPos"],this.outputShape=e.inShape;const t=e.strideDepth,n=e.strideHeight,r=e.strideWidth,s=e.dilationDepth,a=e.dilationHeight,o=e.dilationWidth,i=e.effectiveFilterDepth,u=e.effectiveFilterHeight,l=e.effectiveFilterWidth,c=i-1-e.padInfo.front,p=u-1-e.padInfo.top,h=l-1-e.padInfo.left,d=i*u*l-1;this.userCode=`\n const ivec3 pads = ivec3(${c}, ${p}, ${h});\n\n void main() {\n ivec5 coords = getOutputCoords();\n int batch = coords.x;\n int ch = coords.u;\n\n ivec3 dyCorner = ivec3(coords.y, coords.z, coords.w) - pads;\n int dyDCorner = dyCorner.x;\n int dyRCorner = dyCorner.y;\n int dyCCorner = dyCorner.z;\n\n // Convolve dy(?, ?, ?, ch) with pos mask(:, :, :, d) to get\n // dx(xD, xR, xC, ch).\n // ? = to be determined. : = across all values in that axis.\n float dotProd = 0.0;\n\n for (int wD = 0; wD < ${i};\n wD += ${s}) {\n float dyD = float(dyDCorner + wD) / ${t}.0;\n\n if (dyD < 0.0 || dyD >= ${e.outDepth}.0 || fract(dyD) > 0.0) {\n continue;\n }\n int idyD = int(dyD);\n\n for (int wR = 0; wR < ${u};\n wR += ${a}) {\n float dyR = float(dyRCorner + wR) / ${n}.0;\n\n if (dyR < 0.0 || dyR >= ${e.outHeight}.0 ||\n fract(dyR) > 0.0) {\n continue;\n }\n int idyR = int(dyR);\n\n for (int wC = 0; wC < ${l};\n wC += ${o}) {\n float dyC = float(dyCCorner + wC) / ${r}.0;\n\n if (dyC < 0.0 || dyC >= ${e.outWidth}.0 ||\n fract(dyC) > 0.0) {\n continue;\n }\n int idyC = int(dyC);\n\n float dyValue = getDy(batch, idyD, idyR, idyC, ch);\n int maxPosValue = ${d} -\n int(getMaxPos(batch, idyD, idyR, idyC, ch));\n\n // Get the current value, check it against the value from the\n // position matrix.\n int curPosValue =\n wD * ${u} * ${l} +\n wR * ${l} + wC;\n float mask = float(maxPosValue == curPosValue ? 1.0 : 0.0);\n\n dotProd += dyValue * mask;\n }\n }\n }\n setOutput(dotProd);\n }\n `}}const ax={kernelName:s.OU7,backendName:"webgl",kernelFunc:function(e){const{inputs:t,backend:n,attrs:r}=e,{dy:a,input:o}=t,i=o,{filterSize:u,strides:l,pad:c,dimRoundingMode:p}=r,h=s.Wap.computePool3DInfo(i.shape,u,l,[1,1,1],c,p),d=new rg(h,"max",!0),f=n.runWebGLProgram(d,[i],i.dtype),m=new sx(h),g=n.runWebGLProgram(m,[a,f],i.dtype);return n.disposeIntermediateTensorInfo(f),g}};const ox={kernelName:s.OV7,backendName:"webgl",kernelFunc:function(e){const{inputs:t,backend:n,attrs:r}=e,{dy:a,input:o,output:i}=t,u=o;id([o,i],"maxPoolGrad");const{filterSize:l,strides:c,pad:p,dimRoundingMode:h}=r,d=s.Wap.computePool2DInfo(u.shape,l,c,1,p,h),f=new ng(d,"max",!0),m=n.runWebGLProgram(f,[u],u.dtype),g=new rx(d),y=n.runWebGLProgram(g,[a,m],u.dtype);return n.disposeIntermediateTensorInfo(m),y}};const ix={kernelName:s.vFR,backendName:"webgl",kernelFunc:({input
vendor: 4,740 bytes, line 1
1s:e,attrs:t,backend:n})=>{const{x:r}=e,{filterSize:a,strides:o,pad:i,includeBatchInIndex:u}=t,l=n;s.D5U.assert(4===r.shape.length,(()=>`Error in maxPool: input must be rank 4 but got rank ${r.shape.length}.`));const c=[1,1];s.D5U.assert(s.Wap.eitherStridesOrDilationsAreOne(o,c),(()=>`Error in maxPool: Either strides or dilations must be 1. Got strides ${o} and dilations '${c}'`));const p=s.Wap.computePool2DInfo(r.shape,a,o,c,i),[h,d]=function(e,t,n,r){let s=new ng(n,"max",!1);const a=r.runWebGLProgram(s,[e],"float32");return s=new ng(n,"max",!0,!0,t),[a,r.runWebGLProgram(s,[e],"float32")]}(r,u,p,l);return[h,d]}};const ux={kernelName:s.q2K,backendName:"webgl",kernelFunc:({inputs:e,attrs:t,backend:n})=>{const{x:r}=e,{keepDims:a,axis:o}=t,i=n,u=r.shape.length,l=s.D5U.parseAxisParam(o,r.shape);let c=l;const p=s.Wap.getAxesPermutation(c,u),h=null!=p,d=i.shouldExecuteOnCPU([r]),f=[];let m=r;if(h){if(d){const e=i.texData.get(m.dataId).values,t=new Array(u);for(let s=0;s<t.length;s++)t[s]=r.shape[p[s]];const n=Nf(e,r.shape,r.dtype,p,t);m=i.makeTensorInfo(t,r.dtype);i.texData.get(m.dataId).values=n}else m=bm(r,p,i);f.push(m),c=s.Wap.getInnerMostAxes(c.length,u)}s.Wap.assertAxesAreInnerMostDims("sum",c,u);const[g,y]=s.Wap.computeOutAndReduceShapes(m.shape,c);let b=g;a&&(b=s.Wap.expandShapeToKeepDim(g,l));const x=function(e,t,n,r){const a=s.D5U.sizeFromShape(t),o=pm({inputs:{x:e},attrs:{shape:[s.D5U.sizeFromShape(e.shape)/a,a]},backend:r}),i=mm(o,"float32","mean",r),u=pm({inputs:{x:i},attrs:{shape:n},backend:r});return r.disposeIntermediateTensorInfo(o),r.disposeIntermediateTensorInfo(i),u}(m,y,b,i);for(const s of f)i.disposeIntermediateTensorInfo(s);return x}};const lx={kernelName:s.c17,backendName:"webgl",kernelFunc:function(e){const{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{axis:o,keepDims:i}=r,u=a.shape.length,l=s.D5U.parseAxisParam(o,a.shape);let c=l;const p=s.Wap.getAxesPermutation(c,u);let h=a;null!=p&&(h=vm({inputs:{x:a},backend:n,attrs:{perm:p}}),c=s.Wap.getInnerMostAxes(c.length,a.shape.length)),s.Wap.assertAxesAreInnerMostDims("min",c,u);const[d,f]=s.Wap.computeOutAndReduceShapes(h.shape,c),m=pm({inputs:{x:h},backend:n,attrs:{shape:[-1,s.D5U.sizeFromShape(f)]}}),g=mm(m,m.dtype,"min",n);let y;if(i){y=pm({inputs:{x:g},backend:n,attrs:{shape:s.Wap.expandShapeToKeepDim(d,l)}})}else y=pm({inputs:{x:g},backend:n,attrs:{shape:d}});return n.disposeIntermediateTensorInfo(m),n.disposeIntermediateTensorInfo(g),null!=p&&n.disposeIntermediateTensorInfo(h),y}},cx=nm({opSnippet:"\n if (isnan(a)) return a;\n if (isnan(b)) return b;\n\n return min(a, b);\n",packedOpSnippet:"\n vec4 result = vec4(min(a, b));\n bvec4 isNaNA = isnan(a);\n bvec4 isNaNB = isnan(b);\n bvec4 isNaN = bvec4(isNaNA.x || isNaNB.x, isNaNA.y || isNaNB.y, isNaNA.z || isNaNB.z, isNaNA.w || isNaNB.w);\n \n result.r = isNaN.r ? NAN : result.r;\n result.g = isNaN.g ? NAN : result.g;\n result.b = isNaN.b ? NAN : result.b;\n result.a = isNaN.a ? NAN : result.a;\n\n return result;\n",cpuKernelImpl:Jd}),px={kernelName:s.q8u,backendName:"webgl",kernelFunc:cx};class hx{constructor(e,t,n){this.variableNames=["x"],this.outputShape=t.map(((t,n)=>t[0]+e[n]+t[1]));const r=e.length,s=(0,cd.kW)(r),a=t.map((e=>e[0])).join(","),o=t.map(((t,n)=>t[0]+e[n])).join(","),i=["coords[0]","coords[1]","coords[2]","coords[3]"].slice(0,r),u="reflect"===n?0:1;this.userCode=1!==r?`\n ${s} start = ${s}(${a});\n ${s} end = ${s}(${o});\n\n void main() {\n ${s} outC = getOutputCoords();\n for (int i = 0; i < ${r}; i++) {\n if (outC[i] < start[i]) {\n outC[i] = start[i] * 2 - outC[i] - ${u};\n } else if(outC[i] >= end[i]) {\n outC[i] = (end[i] - 1) * 2 - outC[i] + ${u};\n }\n }\n ${s} coords = outC - start;\n setOutput(getX(${i}));\n }\n `:`\n int start = ${a};\n int end = ${o};\n\n void main() {\n int outC = getOutputCoords();\n if (outC < start) {\n outC = start * 2 - outC - ${u};\n } else if(outC >= end) {\n outC = (end - 1) * 2 - outC + ${u};\n }\n setOutput(getX(outC - start));\n }\n `}}class dx{constructor(e,t,n){this.variableNames=["x"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=t.map(((t,n)=>t[0]+e[n]+t[1]));const r=e.length,s=(0,cd.kW)(r),a=t.map((e=>e[0])).join(","),o=t.map(((t,n)=>t[0]+e[n])).join(","),i=Cf("rc",r),u=Cf("source",r),l=`${i[r-1]} < ${this.outputShape[r-1]}`,c=1===r?"source":`vec2(${u.slice(-2).join()})`,p="reflect"===n?0:1;let h="";if(1===r){const e=`\n ${s} source = rc;\n if (source < start) {\n source = start * 2 - source - ${p};\n } else if (source >
1= end) {\n source = (end - 1) * 2 - source + ${p};\n }\n source -= start;\n `;h=`\n ${s} rc = outputLoc;\n ${e}\n result[0] = getChannel(getX(${u.join()}), ${c});\n ${i[r-1]} += 1;\n if(${l}) {\n ${e}\n result[1] = getChannel(getX(${u.join()}), ${c});\n }\n `}else{const e=`\n ${s} source = rc;\n ${s} lt = ${s}(lessThan(source, start));\n ${s} gte = ${s}(greaterThanEqual(source, end));\n ${s} orig = 1 - (lt + gte);\n source = orig * source +\n lt * (start * 2 - source - ${p}) +\n gte * ((end - 1) * 2 - source + ${p});\n source -= start;\n `;h=`\n ${s} rc = outputLoc;\n ${e}\n result[0] = getChannel(getX(${u.join()}), ${c});\n ${i[r-1]} += 1;\n if(${l}) {\n ${e}\n result[1] = getChannel(getX(${u.join()}), ${c});\n }\n rc = outputLoc;\n ${i[r-2]} += 1;\n if(${i[r-2]} < ${this.outputShape[r-2]}) {\n ${e}\n result[2] = getChannel(getX(${u.join()}), ${c});\n ${i[r-1]} += 1;\n if(${l}) {\n ${e}\n result[3] = getChannel(getX(${u.join()}), ${c});\n }\n }\n `}this.userCode=`\n const ${s} start = ${s}(${a});\n const ${s} end = ${s}(${o});\n\n void main() {\n ${s} outputLoc = getOutputCoords();\n vec4 result = vec4(0.);\n ${h}\n setOutput(result);\n }\n `}}const fx={kernelName:s.jQs,backendName:"webgl",kernelFunc:({inputs:e,backend:t,attrs:n})=>{const{x:r}=e,{paddings:a,mode:o}=n,i=(0,s.OBj)().getBool("WEBGL_PACK_ARRAY_OPERATIONS")?new dx(r.shape,a,o):new hx(r.shape,a,o);return t.runWebGLProgram(i,[r],r.dtype)}},mx=nm({opSnippet:"if (b == 0.0) return NAN;\n return mod(a, b);",packedOpSnippet:"\n vec4 result = mod(a, b);\n bvec4 isNaN = equal(b, vec4(0.0));\n \n result.r = isNaN.r ? NAN : result.r;\n result.g = isNaN.g ? NAN : result.g;\n result.b = isNaN.b ? NAN : result.b;\n result.a = isNaN.a ? NAN : result.a;\n\n return result;\n"}),gx={kernelName:s.Vbg,backendName:"webgl",kernelFunc:mx};class yx{constructor(e,t,n){this.variableNames=["probs"],this.customUniforms=[{name:"seed",type:"float"}],this.outputShape=[e,n],this.userCode=`\n void main() {\n ivec2 coords = getOutputCoords();\n int batch = coords[0];\n\n float r = random(seed);\n float cdf = 0.0;\n\n for (int i = 0; i < ${t-1}; i++) {\n cdf += getProbs(batch, i);\n\n if (r < cdf) {\n setOutput(float(i));\n return;\n }\n }\n\n // If no other event happened, last event happened.\n setOutput(float(${t-1}));\n }\n `}}const bx=nm({opSnippet:"\nif (a == b) {\n return 1.0;\n};\nreturn a / b;",packedOpSnippet:"\n // vec4 one = vec4(equal(a, b));\n // return one + (vec4(1.0) - one) * a / b;\n vec4 result = a / b;\n if(a.x == b.x) {\n result.x = 1.;\n }\n if(a.y == b.y) {\n result.y = 1.;\n }\n if(a.z == b.z) {\n result.z = 1.;\n }\n if(a.w == b.w) {\n result.w = 1.;\n }\n\n return result;\n",checkOutOfBounds:!0}),xx={kernelName:s.oHH,backendName:"webgl",kernelFunc:bx},wx="return a - b;",vx=nm({opSnippet:wx,packedOpSnippet:wx,supportsComplex:!0,cpuKernelImpl:vf}),kx={kernelName:s.Tr8,backendName:"webgl",kernelFunc:vx};function Ix(e){const{inputs:t,backend:n,attrs:r}=e,{logits:a}=t,{dim:o}=r,i=s.D5U.parseAxisParam([o],a.shape),u=Yb({inputs:{x:a},backend:n,attrs:{reductionIndices:i,keepDims:!1}}),l=s.Wap.expandShapeToKeepDim(u.shape,i),c=pm({inputs:{x:u},backend:n,attrs:{shape:l}}),p=vx({inputs:{a:a,b:c},backend:n}),h=zy({inputs:{x:p},backend:n}),d=xm({inputs:{x:h},backend:n,attrs:{axis:i,keepDims:!1}}),f=pm({inputs:{x:d},backend:n,attrs:{shape:l}}),m=bx({inputs:{a:h,b:f},backend:n});return n.disposeIntermediateTensorInfo(u),n.disposeIntermediateTensorInfo(c),n.disposeIntermediateTensorInfo(p),n.disposeIntermediateTensorInfo(h),n.disposeIntermediateTensorInfo(d),n.disposeIntermediateTensorInfo(f),m}const Nx={kernelName:s.Gcp,backendName:"webgl",kernelFunc:Ix};const Sx={kernelName:s.NZg,backendName:"webgl",kernelFunc:function(e){const{inputs:t,backend:n,attrs:r}=e,{logits:s}=t,{numSamples:a,seed:o,normalized:i}=r,u=i?s:Ix({inputs:{logits:s},backend:n,attrs:{dim:s.shape.length-1}}),l=u.shape[0],c=u.shape[1],p=new yx(l,c,a),h=[[o]],d=n.runWebGLProgram(p,[u],"int32",h);return i||n.disposeIntermediateTensorInfo(u),d}};const Tx={kernelName:s.kuV,backendName:"webgl",kernelFunc:function(e){const{inputs:t,backend:n}=e,{x:r}=t;if(n.shouldExecuteOnCPU([r])){const e=n.texData.get(r.dataId),[t,s]=tf(e.values,r.shape,r.dtype);return n.makeTensorInfo(s,r.dtype,t)}let a;return a=(0,s.OBj)().getBool("WEBGL_PACK_UNARY_OPERATIONS")?new Bf(r.shape,"\n vec4 result = -x;\n bvec4 isNaN = isnan(x);\n\n result.r = isNaN.r ? x.r : result.r;\n result.g = isNaN.g ? x.g : result.g;\n result.b = isNaN.b ? x.b : result.b;\n result.a = isNaN.a ? x.a : result.a;\n\n return result;\n"):new Ff(r.shape,"if (isnan(x)) return x;\n return -x;\n"),n.runWebGLProgram(a,[r],r.dtype)}},Cx=s.GDt.GP;const Ex={kernelName:s.uv1,backendName:"webgl",kernelFunc:function(e){s.Wap.warn("tf.nonMaxSuppression() in webgl locks the UI thread. Call tf.nonMaxSuppressionAsync() instead");const{inputs:t,backend:n,attrs:r}=e,{boxes:a,scores:o}=t,{maxOutputSize:i,iouThreshold:u,scoreThreshold:l}=r,c=n.readSync(a.dataId),p=n.readSync(o.dataId),{selectedIndices:h}=Cx(c,p,i,u,l);return n.makeTensorInfo([h.length],"int32",new Int32Array(h))}},$x=s.GDt.qP;const Ax={kernelName:s.cye,backendName:"webgl",kernelFunc:function(e){s.Wap.warn("tf.nonMaxSuppression() in webgl locks the UI thread. Call tf.nonMaxSuppressionAsync() instead");const{inputs:t,backend:n,attrs:r}=e,{boxes:a,scores:o}=t,{maxOutputSize:i,iouThreshold:u,scoreThreshold:l,padToMaxOutputSize:c}=r,p=n.readSync(a.dataId),h=n.readSync(o.dataId),{selectedIndices:d,validOutputs:f}=$x(p,h,i,u,l,c);return[n.makeTensorInfo([d.length],"int32",new Int32Array(d)),n.makeTensorInfo([],"int32",new Int32Array([f]))]}},Dx=s.GDt.pA;const _x={kernelName:s.W0H,backendName:"webgl",kernelFunc:function(e){s.Wap.warn("tf.nonMaxSuppression() in webgl locks the UI thread. Call tf.nonMaxSuppressionAsync() instead");const{inputs:t,backend:n,attrs:r}=e,{boxes:a,scores:o}=t,{maxOutputSize:i,iouThreshold:u,scoreThreshold:l,softNmsSigma:c}=r,p=n.readSync(a.dataId),h=n.readSync(o.dataId),d=i,f=u,m=l,g=c,{selectedIndices:y,selectedScores:b}=Dx(p,h,d,f,m,g);return[n.makeTensorInfo([y.length],"int32",new Int32Array(y)),n.makeTensorInfo([b.length],"float32",new Float32Array(b))]}};class Rx{constructor(e,t,n,r){this.variableNames=["indices"],this.outputShape=[e,t],this.userCode=`\n void main() {\n ivec2 coords = getOutputCoords();\n int index = round(getIndices(coords.x));\n setOutput(mix(float(${r}), float(${n}),\n float(index == coords.y)));\n }\n `}}const Fx={kernelName:s.we_,backendName:"webgl",kernelFunc:e=>{const{inputs:t,backend:n,attrs:r}=e,{indices:a}=t,{dtype:o,depth:i,onValue:u,offValue:l}=r,c=s.D5U.sizeFromShape(a.shape),p=new Rx(c,i,u,l),h=pm({inputs:{x:a},backend:n,attrs:{shape:[c]}}),d=n.runWebGLProgram(p,[h],o);n.disposeIntermediateTensorInfo(h);const f=pm({inputs:{x:d},backend:n,attrs:{shape:[...a.shape,i]}});return n.disposeIntermediateTensorInfo(d),f}};function Ox(e){const{inputs:t,backend:n}=e,{x:r}=t;if("complex64"===r.dtype){const e=Ng({inputs:{input:r},backend:n}
vendor: 5,332 bytes, line 1
1),t=Ox({inputs:{x:e},backend:n}),s=Wg({inputs:{input:r},backend:n}),a=Ox({inputs:{x:s},backend:n}),o=Xf({inputs:{real:t,imag:a},backend:n});return n.disposeIntermediateTensorInfo(e),n.disposeIntermediateTensorInfo(t),n.disposeIntermediateTensorInfo(s),n.disposeIntermediateTensorInfo(a),o}return Jy({attrs:{shape:r.shape,dtype:r.dtype,value:"string"===r.dtype?"":0},backend:n})}const Mx={kernelName:s.RuY,backendName:"webgl",kernelFunc:Ox};const Bx={kernelName:s.qWM,backendName:"webgl",kernelFunc:function e(t){const{inputs:n,backend:r}=t,{x:s}=n;if("string"===s.dtype)throw new Error("onesLike is not supported under string dtype");if("complex64"===s.dtype){const t=Ng({inputs:{input:s},backend:r}),n=e({inputs:{x:t},backend:r}),a=Wg({inputs:{input:s},backend:r}),o=Ox({inputs:{x:a},backend:r}),i=Xf({inputs:{real:n,imag:o},backend:r});return r.disposeIntermediateTensorInfo(t),r.disposeIntermediateTensorInfo(n),r.disposeIntermediateTensorInfo(a),r.disposeIntermediateTensorInfo(o),i}return Jy({attrs:{shape:s.shape,dtype:s.dtype,value:1},backend:r})}};const Lx={kernelName:s.QiL,backendName:"webgl",kernelFunc:function(e){const{inputs:t,backend:n,attrs:r}=e,{axis:a}=r;if(1===t.length)return Gy({inputs:{input:t[0]},backend:n,attrs:{dim:a}});const o=t[0].shape,i=t[0].dtype;t.forEach((e=>{s.D5U.assertShapesMatch(o,e.shape,"All tensors passed to stack must have matching shapes"),s.D5U.assert(i===e.dtype,(()=>"All tensors passed to stack must have matching dtypes"))}));const u=[],l=zg({inputs:t.map((e=>{const t=Gy({inputs:{input:e},backend:n,attrs:{dim:a}});return u.push(t),t})),backend:n,attrs:{axis:a}});return u.forEach((e=>n.disposeIntermediateTensorInfo(e))),l}};class Wx{constructor(e,t,n){this.variableNames=["x"],this.customUniforms=[{name:"value",type:"float"}],this.outputShape=t.map(((t,n)=>t[0]+e[n]+t[1]));const r=e.length,s=(0,cd.kW)(r),a=t.map((e=>e[0])).join(","),o=t.map(((t,n)=>t[0]+e[n])).join(","),i=["coords[0]","coords[1]","coords[2]","coords[3]"].slice(0,r);this.userCode=1!==r?`\n ${s} start = ${s}(${a});\n ${s} end = ${s}(${o});\n\n void main() {\n ${s} outC = getOutputCoords();\n if (any(lessThan(outC, start)) || any(greaterThanEqual(outC, end))) {\n setOutput(value);\n } else {\n ${s} coords = outC - start;\n setOutput(getX(${i}));\n }\n }\n `:`\n int start = ${a};\n int end = ${o};\n\n void main() {\n int outC = getOutputCoords();\n if (outC < start || outC >= end) {\n setOutput(value);\n } else {\n setOutput(getX(outC - start));\n }\n }\n `}}class Px{constructor(e,t,n){this.variableNames=["x"],this.packedInputs=!0,this.packedOutput=!0,this.customUniforms=[{name:"value",type:"float"}],this.outputShape=t.map(((t,n)=>t[0]+e[n]+t[1]));const r=e.length,s=(0,cd.kW)(r),a=t.map((e=>e[0])).join(","),o=t.map(((t,n)=>t[0]+e[n])).join(","),i=Cf("rc",r),u=Cf("source",r),l=`${i[r-1]} < ${this.outputShape[r-1]}`,c=1===r?"source":`vec2(${u.slice(-2).join()})`,p=[`${s} rc = outputLoc;`,`${i[r-1]} += 1;\n if(${l}) {\n `,1===r?"":`}\n rc = outputLoc;\n ${i[r-2]} += 1;\n if(${i[r-2]} < ${this.outputShape[r-2]}) {`,1===r?"":` ${i[r-1]} += 1;\n if(${l}) {`],h=1===r?"rc < start || rc >= end":"any(lessThan(rc, start)) || any(greaterThanEqual(rc, end))";let d="";for(let f=0,m=1===r?2:4;f<m;f++)d+=`\n ${p[f]}\n if (${h}) {\n result[${f}] = float(value);\n } else {\n ${s} source = rc - start;\n result[${f}] = getChannel(getX(${u.join()}), ${c});\n }\n `;d+=1===r?"} ":"}}",this.userCode=`\n const ${s} start = ${s}(${a});\n const ${s} end = ${s}(${o});\n\n void main() {\n ${s} outputLoc = getOutputCoords();\n vec4 result = vec4(0.);\n ${d}\n setOutput(result);\n }\n `}}const Ux=e=>{const{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{paddings:o,constantValue:i}=r;if(0===s.D5U.sizeFromShape(a.shape)){return Jy({backend:n,attrs:{shape:o.map(((e,t)=>e[0]+a.shape[t]+e[1])),value:i,dtype:a.dtype}})}const u=(0,s.OBj)().getBool("WEBGL_PACK_ARRAY_OPERATIONS")?new Px(a.shape,o,i):new Wx(a.shape,o,i),l=[[i]];return n.runWebGLProgram(u,[a],a.dtype,l)},zx={kernelName:s.lyA,backendName:"webgl",kernelFunc:Ux},Vx=nm({opSnippet:"\n if(a < 0.0 && floor(b) < b){\n return NAN;\n }\n if (b == 0.0) {\n return 1.0;\n }\n return (round(mod(b, 2.0)) != 1) ?\n pow(abs(a), b) : sign(a) * pow(abs(a), b);\n",packedOpSnippet:"\n // isModRound1 has 1 for components with round(mod(b, 2.0)) == 1, 0 otherwise.\n vec4 isModRound1 = vec4(equal(round(mod(b, 2.0)), ivec4(1)));\n vec4 multiplier = sign(a) * isModRound1 + (vec4(1.0) - isModRound1);\n vec4 result = multiplier * pow(abs(a), b);\n\n // Ensure that a^0 = 1, including 0^0 = 1 as this correspond to TF and JS\n bvec4 isExpZero = equal(b, vec4(0.0));\n result.r = isExpZero.r ? 1.0 : result.r;\n result.g = isExpZero.g ? 1.0 : result.g;\n result.b = isExpZero.b ? 1.0 : result.b;\n result.a = isExpZero.a ? 1.0 : result.a;\n\n bvec4 isNaN1 = lessThan(a, vec4(0.0));\n bvec4 isNaN2 = lessThan(floor(b), b);\n bvec4 isNaN = bvec4(isNaN1.x && isNaN2.x, isNaN1.y && isNaN2.y, isNaN1.z && isNaN2.z, isNaN1.w && isNaN2.
1w);\n \n result.r = isNaN.r ? NAN : result.r;\n result.g = isNaN.g ? NAN : result.g;\n result.b = isNaN.b ? NAN : result.b;\n result.a = isNaN.a ? NAN : result.a;\n\n return result;\n"}),Gx={kernelName:s.pe_,backendName:"webgl",kernelFunc:Vx};const Hx={kernelName:s.DlI,backendName:"webgl",kernelFunc:function(e){const{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{axis:o,keepDims:i}=r,u=a.shape.length,l=[],c=s.D5U.parseAxisParam(o,a.shape);let p=c;const h=s.Wap.getAxesPermutation(p,u);let d,f=a;if(null!=h&&(f=vm({inputs:{x:a},backend:n,attrs:{perm:h}}),p=s.Wap.getInnerMostAxes(p.length,u),l.push(f)),s.Wap.assertAxesAreInnerMostDims("prod",p,u),n.shouldExecuteOnCPU([f])){const e=n.texData.get(f.dataId).values,{outVals:t,outShape:r,outDtype:s}=rf(f.shape,f.dtype,e,p);d=n.makeTensorInfo(r,s,t)}else{const[e,t]=s.Wap.computeOutAndReduceShapes(f.shape,p),r=s.D5U.sizeFromShape(t),o=pm({inputs:{x:f},backend:n,attrs:{shape:[-1,r]}}),i=mm(o,(0,s.z4k)(a.dtype),"prod",n);d=pm({inputs:{x:i},backend:n,attrs:{shape:e}}),l.push(o),l.push(i)}if(i){l.push(d);const e=s.Wap.expandShapeToKeepDim(d.shape,c);d=pm({inputs:{x:d},backend:n,attrs:{shape:e}})}return l.forEach((e=>n.disposeIntermediateTensorInfo(e))),d}};const jx={kernelName:s.dDz,backendName:"webgl",kernelFunc:function(e){const{inputs:t,backend:n,attrs:r}=e,{paramsNestedSplits:s,paramsDenseValues:a,indices:o}=t,{outputRaggedRank:i}=r,u=s.map((e=>n.readSync(e.dataId))),l=s.map((e=>e.shape)),c=n.readSync(a.dataId),p=n.readSync(o.dataId),[h,d,f]=sf(u,l,c,a.shape,a.dtype,p,o.shape,i),m=h.map((e=>n.makeTensorInfo([e.length],"int32",e))),g=n.makeTensorInfo(f,a.dtype,d);return m.concat([g])}};const Xx={kernelName:s.BiW,backendName:"webgl",kernelFunc:function(e){const{inputs:t,backend:n,attrs:r}=e,{shape:s,values:a,defaultValue:o,rowPartitionTensors:i}=t,{rowPartitionTypes:u}=r,l=n.readSync(s.dataId),c=n.readSync(a.dataId),p=n.readSync(o.dataId),h=i.map((e=>n.readSync(e.dataId))),d=i.map((e=>e.shape)),[f,m]=af(l,s.shape,c,a.shape,a.dtype,p,o.shape,h,d,u);return n.makeTensorInfo(f,a.dtype,m)}},qx=e=>{const{backend:t,attrs:n}=e,{start:r,stop:s,step:a,dtype:o}=n,i=of(r,s,a,o);return t.makeTensorInfo([i.length],o,i)},Kx={kernelName:s.e6w,backendName:"webgl",kernelFunc:qx},Qx=tm({opSnippet:"return 1.0 / x;"}),Yx={kernelName:s.$HU,backendName:"webgl",kernelFunc:Qx},Zx=tm({opSnippet:"if (isnan(x)) return x;\n return (x < 0.0) ? 0.0 : x;\n",packedOpSnippet:"\n vec4 result = x * vec4(greaterThanEqual(x, vec4(0.0)));\n bvec4 isNaN = isnan(x);\n\n result.r = isNaN.r ? x.r : result.r;\n result.g = isNaN.g ? x.g : result.g;\n result.b = isNaN.b ? x.b : result.b;\n result.a = isNaN.a ? x.a : result.a;\n\n return result;\n"}),Jx={kernelName:s.qkr,backendName:"webgl",kernelFunc:Zx},ew=tm({opSnippet:"if (isnan(x)) return x;\n return (x < 0.0) ? 0.0 : min(6.0, x);\n",packedOpSnippet:"\n vec4 result = min(x, vec4(6.)) * vec4(greaterThanEqual(x, vec4(0.0)));\n bvec4 isNaN = isnan(x);\n\n result.r = isNaN.r ? x.r : result.r;\n result.g = isNaN.g ? x.g : result.g;\n result.b = isNaN.b ? x.b : result.b;\n result.a = isNaN.a ? x.a : result.a;\n\n return result;\n"}),tw={kernelName:s.SbG,backendName:"webgl",kernelFunc:ew};class nw{constructor(e,t,n,r,s){this.variableNames=["A"],this.outputShape=[];const[a,o,i,u]=e;this.outputShape=[a,t,n,u];const l=[r&&t>1?o-1:o,r&&n>1?i-1:i],c=[r&&t>1?t-1:t,r&&n>1?n-1:n];let p;p=s?"(vec2(yRC) + vec2(0.5)) * effectiveInputOverOutputRatioRC - vec2(0.5)":"vec2(yRC) * effectiveInputOverOutputRatioRC",this.userCode=`\n const vec2 effectiveInputOverOutputRatioRC = vec2(\n ${l[0]/c[0]},\n ${l[1]/c[1]});\n const vec2 inputShapeRC = vec2(${o}.0, ${i}.0);\n\n void main() {\n ivec4 coords = getOutputCoords();\n int b = coords[0];\n int d = coords[3];\n ivec2 yRC = coords.yz;\n\n // Fractional source index.\n vec2 sourceFracIndexRC = ${p};\n\n // Compute the four integer indices.\n ivec2 sourceFloorRC = ivec2(max(sourceFracIndexRC, vec2(0.0)));\n ivec2 sourceCeilRC = ivec2(\n min(inputShapeRC - 1.0, ceil(sourceFracIndexRC)));\n\n float topLeft = getA(b, sourceFloorRC.x, sourceFloorRC.y, d);\n float bottomLeft = getA(b, sourceCeilRC.x, sourceFloorRC.y, d);\n float topRight = getA(b, sourceFloorRC.x, sourceCeilRC.y, d);\n float bottomRight = getA(b, sourceCeilRC.x, sourceCeilRC.y, d);\n\n vec2 fracRC = sourceFracIndexRC - vec2(sourceFloorRC);\n\n float top = topLeft + (topRight - topLeft) * fracRC.y;\n float bottom = bottomLeft + (bottomRight - bottomLeft) * fracRC.y;\n float newValue = top + (bottom - top) * fracRC.x;\n\n setOutput(newValue);\n }\n `}}
vendor: 3,261 bytes, line 1
1class rw{constructor(e,t,n,r,s){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=[];const[a,o,i,u]=e;this.outputShape=[a,t,n,u];const l=[r&&t>1?o-1:o,r&&n>1?i-1:i],c=[r&&t>1?t-1:t,r&&n>1?n-1:n];let p;p=s?"(vec3(yRC) + vec3(0.5)) * effectiveInputOverOutputRatioRC - vec3(0.5)":"vec3(yRC) * effectiveInputOverOutputRatioRC",this.userCode=`\n const vec3 effectiveInputOverOutputRatioRC = vec3(\n ${l[0]/c[0]},\n ${l[1]/c[1]},\n ${l[1]/c[1]});\n const vec3 inputShapeRC = vec3(${o}.0, ${i}.0,\n ${i}.0);\n\n float getAValue(int b, int r, int c, int d) {\n return getChannel(getA(b, r, c, d), vec2(c, d));\n }\n\n void main() {\n ivec4 coords = getOutputCoords();\n int b = coords[0];\n int d = coords[3];\n // Calculate values for next column in yRC.z.\n ivec3 yRC = coords.yzz + ivec3(0, 0, 1);\n\n // Fractional source index.\n vec3 sourceFracIndexRC = ${p};\n\n // Compute the four integer indices.\n ivec3 sourceFloorRC = ivec3(max(sourceFracIndexRC, vec3(0.0)));\n ivec3 sourceCeilRC = ivec3(\n min(inputShapeRC - 1.0, ceil(sourceFracIndexRC)));\n\n // Should we calculate next column and row elements in 2x2 packed cell.\n bool hasNextCol = d < ${u-1};\n bool hasNextRow = coords.z < ${n-1};\n\n // In parallel, construct four corners for all four components in\n // packed 2x2 cell.\n vec4 topLeft = vec4(\n getAValue(b, sourceFloorRC.x, sourceFloorRC.y, d),\n hasNextCol ? getAValue(b, sourceFloorRC.x, sourceFloorRC.y, d + 1)\n : 0.0,\n hasNextRow ? getAValue(b, sourceFloorRC.x, sourceFloorRC.z, d)\n : 0.0,\n (hasNextRow && hasNextCol) ?\n getAValue(b, sourceFloorRC.x, sourceFloorRC.z, d + 1) : 0.0);\n\n vec4 bottomLeft = vec4(\n getAValue(b, sourceCeilRC.x, sourceFloorRC.y, d),\n hasNextCol ? getAValue(b, sourceCeilRC.x, sourceFloorRC.y, d + 1)\n : 0.0,\n hasNextRow ? getAValue(b, sourceCeilRC.x, sourceFloorRC.z, d)\n : 0.0,\n (hasNextRow && hasNextCol) ?\n getAValue(b, sourceCeilRC.x, sourceFloorRC.z, d + 1) : 0.0);\n\n vec4 topRight = vec4(\n getAValue(b, sourceFloorRC.x, sourceCeilRC.y, d),\n hasNextCol ? getAValue(b, sourceFloorRC.x, sourceCeilRC.y, d + 1)\n : 0.0,\n hasNextRow ? getAValue(b, sourceFloorRC.x, sourceCeilRC.z, d)\n : 0.0,\n (hasNextRow && hasNextCol) ?\n getAValue(b, sourceFloorRC.x, sourceCeilRC.z, d + 1) : 0.0);\n\n vec4 bottomRight = vec4(\n getAValue(b, sourceCeilRC.x, sourceCeilRC.y, d),\n hasNextCol ? getAValue(b, sourceCeilRC.x, sourceCeilRC.y, d + 1)\n : 0.0,\n hasNextRow ? getAValue(b, sourceCeilRC.x, sourceCeilRC.z, d)\n : 0.0,\n (hasNextRow && hasNextCol) ?\n getAValue(b, sourceCeilRC.x, sourceCeilRC.z, d + 1) : 0.0);\n\n vec3 fracRC = sourceFracIndexRC - vec3(sourceFloorRC);
1\n\n vec4 top = mix(topLeft, topRight, fracRC.yyzz);\n vec4 bottom = mix(bottomLeft, bottomRight, fracRC.yyzz);\n vec4 newValue = mix(top, bottom, fracRC.x);\n\n setOutput(newValue);\n }\n `}}const sw={kernelName:s._Yw,backendName:"webgl",kernelFunc:function(e){const{inputs:t,backend:n,attrs:r}=e,{images:a}=t,{alignCorners:o,halfPixelCenters:i,size:u}=r,[l,c]=u,p=(0,s.OBj)().getBool("WEBGL_PACK_IMAGE_OPERATIONS")?new rw(a.shape,l,c,o,i):new nw(a.shape,l,c,o,i);return n.runWebGLProgram(p,[a],"float32")}};class aw{constructor(e,t,n){this.variableNames=["dy"],this.outputShape=[],this.outputShape=t;const[,r,s]=t,[,a,o]=e,i=[n&&a>1?r-1:r,n&&o>1?s-1:s],u=[n&&a>1?a-1:a,n&&o>1?o-1:o],l=i[0]/u[0],c=i[1]/u[1],p=1/l,h=1/c,d=2*Math.ceil(p)+2,f=2*Math.ceil(h)+2;this.userCode=`\n void main() {\n ivec4 coords = getOutputCoords();\n int b = coords[0];\n int d = coords[3];\n int r = coords[1];\n int c = coords[2];\n\n float accumulator = 0.0;\n\n const float heightScale = float(${l});\n const float widthScale = float(${c});\n\n const float invHeightScale = float(${p});\n const float invWidthScale = float(${h});\n\n const int winHeight = int(${d});\n const int winWidth = int(${f});\n\n // Compute bounds for where in dy we will look\n float startRLerp = floor(float(r) * invHeightScale);\n int startDyR = int(startRLerp - float(winHeight / 2));\n\n float startCLerp = floor(float(c) * invWidthScale);\n int startDyC = int(startCLerp - float(winWidth / 2));\n\n // Loop over dy\n for (int dyROffset = 0; dyROffset < winHeight; dyROffset++) {\n int dyR = dyROffset + startDyR;\n\n // Guard against the window exceeding the bounds of dy\n if (dyR < 0 || dyR >= ${a}) {\n continue;\n }\n\n for (int dyCOffset = 0; dyCOffset < winWidth; dyCOffset++) {\n int dyC = dyCOffset + startDyC;\n\n // Guard against the window exceeding the bounds of dy\n if (dyC < 0 || dyC >= ${o}) {\n continue;\n }\n\n float dxR = float(dyR) * heightScale;\n int topDxRIndex = int(floor(dxR));\n int bottomDxRIndex = int(min(ceil(dxR), ${r-1}.0));\n float dxRLerp = dxR - float(topDxRIndex);\n float inverseDxRLerp = 1.0 - dxRLerp;\n\n float dxC = float(dyC) * widthScale;\n int leftDxCIndex = int(floor(dxC));\n int rightDxCIndex = int(min(ceil(dxC), ${s-1}.0));\n float dxCLerp = dxC - float(leftDxCIndex);\n float inverseDxCLerp = 1.0 - dxCLerp;\n\n if (r == topDxRIndex && c == leftDxCIndex) {\n // topLeft\n accumulator +=\n getDy(b, dyR, dyC, d) * inverseDxRLerp * inverseDxCLerp;\n }\n\n if (r == topDxRIndex && c == rightDxCIndex) {\n // topRight\n accumulator += getDy(b, dyR, dyC, d) * inverseDxRLerp * dxCLerp;\n }\n\n if (r == bottomDxRIndex && c == leftDxCIndex) {\n // bottomLeft\n accumulator += getDy(b, dyR, dyC, d) * dxRLerp * inverseDxCLerp;\n }\n\n if (r == bottomDxRIndex && c == rightDxCIndex) {\n // bottomRight\n accumulator += getDy(b, dyR, dyC, d) * dxRLerp * dxCLerp;\n }\n }\n }\n // End loop over dy\n\n setOutput(accumulator);\n }\n `}}const ow={kernelName:s.zbQ,backendName:"webgl",kernelFunc:function(e){const{inputs:t,backend:n,attrs:r}=e,{images:s,dy:a}=t,{alignCorners:o}=r,i=new aw(a.shape,s.shape,o);return n.runWebGLProgram(i,[a],a.dtype)}};class iw{constructor(e,t,n,r,s){this.variableNames=["A"],this.outputShape=[];const[a,o,i,u]=e;this.outputShape=[a,t,n,u];const l=[r&&t>1?o-1:o,r&&n>1?i-1:i],c=[r&&t>1?t-1:t,r&&n>1?n-1:n],p=r?"0.5":"0.0";let h;h=s?"max((vec2(yRC) + vec2(0.5)) * effectiveInputOverOutputRatioRC, vec2(0.0))":"vec2(yRC) * effectiveInputOverOutputRatioRC",this.userCode=`\n const vec2 effectiveInputOverOutputRatioRC = vec2(\n ${l[0]/c[0]},\n ${l[1]/c[1]});\n const vec2 inputShapeRC = vec2(${o}.0, ${i}.0);\n\n void main() {\n ivec4 coords = getOutputCoords();\n int b = coords[0];\n int d = coords[3];\n ivec2 yRC = coords.yz;\n\n // Fractional source index.\n vec2 sourceFracIndexRC = ${h};\n\n // Compute the coordinators of nearest neighbor point.\n ivec2 sourceNearestRC = ivec2(\n min(inputShapeRC - 1.0, floor(sourceFracIndexRC + ${p})));\n float newValue = getA(b, sourceNearestRC.x, sourceNearestRC.y, d);\n\n setOutput(newValue);\n }\n `}}
vendor: 8,178 bytes, line 1
1class uw{constructor(e,t,n,r,s){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=[];const[a,o,i,u]=e;this.outputShape=[a,t,n,u];const l=[r&&t>1?o-1:o,r&&n>1?i-1:i],c=[r&&t>1?t-1:t,r&&n>1?n-1:n],p=r?"0.5":"0.0";let h;h=s?"max((vec3(yRC) + vec3(0.5)) * effectiveInputOverOutputRatioRC, vec3(0.0))":"vec3(yRC) * effectiveInputOverOutputRatioRC",this.userCode=`\n const vec3 effectiveInputOverOutputRatioRC = vec3(\n ${l[0]/c[0]},\n ${l[1]/c[1]},\n ${l[1]/c[1]});\n const vec3 inputShapeRC = vec3(${o}.0, ${i}.0,\n ${i}.0);\n\n float getAValue(int b, int r, int c, int d) {\n return getChannel(getA(b, r, c, d), vec2(c, d));\n }\n\n void main() {\n ivec4 coords = getOutputCoords();\n int b = coords[0];\n int d = coords[3];\n // Calculate values for next column in yRC.z.\n ivec3 yRC = coords.yzz + ivec3(0, 0, 1);\n\n // Fractional source index.\n vec3 sourceFracIndexRC = ${h};\n\n // Compute the coordinators of nearest neighbor point.\n ivec3 sourceNearestRC = ivec3(\n min(inputShapeRC - 1.0, floor(sourceFracIndexRC + ${p})));\n\n // Should we calculate next column and row elements in 2x2 packed cell.\n bool hasNextCol = d < ${u-1};\n bool hasNextRow = coords.z < ${n-1};\n\n vec4 newValue = vec4(\n getAValue(b, sourceNearestRC.x, sourceNearestRC.y, d),\n hasNextCol ? getAValue(b, sourceNearestRC.x, sourceNearestRC.y, d + 1)\n : 0.0,\n hasNextRow ? getAValue(b, sourceNearestRC.x, sourceNearestRC.z, d)\n : 0.0,\n (hasNextRow && hasNextCol) ?\n getAValue(b, sourceNearestRC.x, sourceNearestRC.z, d + 1) : 0.0);\n\n setOutput(newValue);\n }\n `}}const lw={kernelName:s.dpD,backendName:"webgl",kernelFunc:function(e){const{inputs:t,backend:n,attrs:r}=e,{images:a}=t,{alignCorners:o,halfPixelCenters:i,size:u}=r,[l,c]=u,p=(0,s.OBj)().getBool("WEBGL_PACK_IMAGE_OPERATIONS")?new uw(a.shape,l,c,o,i):new iw(a.shape,l,c,o,i);return n.runWebGLProgram(p,[a],a.dtype)}};class cw{constructor(e,t,n){this.variableNames=["dy"],this.outputShape=[],this.outputShape=t;const[,r,s]=t,[,a,o]=e,i=[n&&a>1?r-1:r,n&&o>1?s-1:s],u=[n&&a>1?a-1:a,n&&o>1?o-1:o],l=i[0]/u[0],c=i[1]/u[1],p=1/l,h=1/c,d=2*Math.ceil(p)+2,f=2*Math.ceil(h)+2;this.userCode=`\n void main() {\n ivec4 coords = getOutputCoords();\n int b = coords[0];\n int d = coords[3];\n int r = coords[1];\n int c = coords[2];\n\n float accumulator = 0.0;\n\n const float heightScale = float(${l});\n const float widthScale = float(${c});\n\n const float invHeightScale = float(${p});\n const float invWidthScale = float(${h});\n\n const int winHeight = int(${d});\n const int winWidth = int(${f});\n\n // Compute bounds for where in dy we will look\n float startRLerp = floor(float(r) * invHeightScale);\n int startDyR = int(floor(startRLerp - float(winHeight / 2)));\n\n float startCLerp = floor(float(c) * invWidthScale);\n int startDyC = int(floor(startCLerp - float(winWidth / 2)));\n\n // Loop over dy\n for (int dyROffset = 0; dyROffset < winHeight; dyROffset++) {\n int dyR = dyROffset + startDyR;\n\n // Guard against the window exceeding the bounds of dy\n if (dyR < 0 || dyR >= ${a}) {\n continue;\n }\n\n for (int dyCOffset = 0; dyCOffset < winWidth; dyCOffset++) {\n int dyC = dyCOffset + startDyC;\n\n // Guard against the window exceeding the bounds of dy\n if (dyC < 0 || dyC >= ${o}) {\n continue;\n }\n\n float sourceFracRow =\n float(${i[0]}) *\n (float(dyR) / float(${u[0]}));\n\n float sourceFracCol =\n float(${i[1]}) *\n (float(dyC) / float(${u[1]}));\n\n int sourceNearestRow = int(min(\n float(int(${r}) - 1),\n ${n} ? float(round(sourceFracRow)) :\n float(floor(sourceFracRow))));\n\n int sourceNearestCol = int(min(\n float(int(${s}) - 1),\n ${n} ? float(round(sourceFracCol)) :\n float(floor(sourceFracCol))));\n\n if (r == sourceNearestRow && c == sourceNearestCol) {\n accumulator += getDy(b, dyR, dyC, d);\n }\n }\n }\n // End loop over dy\n\n setOutput(accumulator);\n }\n `}}const pw={kernelName:s.Hmb,backendName:"webgl",kernelFunc:function(e){const{inputs:t,backend:n,attrs:r}=e,{images:s,dy:a}=t,{alignCorners:o}=r,i=new cw(a.shape,s.shape,o);return n.runWebGLProgram(i,[a],a.dtype)}};class hw{constructor(e,t){this.variableNames=["x"];const n=e.length;if(n>4)throw new Error(`WebGL backend: Reverse of rank-${n} tensor is not yet supported`);if(this.outputShape=e,1===n)return void(this.userCode=`\n void main() {\n int coord = getOutputCoords();\n setOutput(getX(${e[0]} - coord - 1));\n }\n `);const r=e.map(((n,r)=>(n=>-1!==t.indexOf(n)&&1!==e[n]?`${e[n]} - coords[${n}] - 1`:`coords[${n}]`)(r))).join(","),s=(0,cd.kW)(n);this.userCode=`\n void main() {\n ${s} coords = getOutputCoords();\n setOutput(getX(${r}));\n }\n `}}class dw{constructor(e,t){this.variableNames=["x"],this.packedInputs=!0,this.packedOutput=!0;const n=e.length;if(n>4)throw new Error(`WebGL backend: Reverse of rank-${n} tensor is not yet supported`);this.outputShape=e;const r=Cf("rc",n),s=`${r[n-1]} + 1 < ${this.outputShape[n-1]}`,a=`${r[n-2]} + 1 < ${this.outputShape[n-2]}`,o=(0,cd.kW)(n);function i(n){const r=e.map(((r,s)=>function(n,r){return-1!==t.indexOf(n)&&1!==e[n]?`${e[n]} - ${r[n]} - 1`:`${r[n]}`}(s,n)));return`getChannel(getX(${r.join(",")}), vec2(${r.slice(-2).join(",")}))`}this.userCode=1===n?`\n void main(){\n int rc = getOutputCoords();\n vec4 result = vec4(0.);\n result.r = getChannel(getX(${e[0]} - rc - 1),\n ${e[0]} - rc - 1);\n if(${s}){\n result.g = getChannel(getX(${e[0]} - (rc + 1) - 1),\n ${e[0]} - (rc + 1) - 1);\n }\n setOutput(result);\n }\n `:`\n void main() {\n ${o} rc = getOutputCoords();\n vec4 result = vec4(0.);\n result.r = ${function(e){return i(e)}(r.slice())};\n if(${s}){\n result.g = ${function(e){return e[n-1]="("+e[n-1]+" + 1)",i(e)}(r.slice())};\n }\n if(${a}) {\n result.b = ${function(e){return e[n-2]="("+e[n-2]+" + 1)",i(e)}(r.slice())};\n if(${s}) {\n result.a = ${function(e){return e[n-1]="("+e[n-1]+" + 1)",e[n-2]="("+e[n-2]+" + 1)",i(e)}(r.slice())};\n }\n }\n setOutput(result);\n }\n `}}const fw={kernelName:s.mKl,backendName:"webgl",kernelFunc:function(e){const{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{dims:o}=r,i=a.shape.length,u=s.D5U.parseAxisParam(o,a.shape);if(0===i)return Hf({inputs:{x:a},backend:n});const l=(0,s.OBj)().getBool("WEBGL_PACK_ARRAY_OPERATIONS")?new dw(a.shape,u):new hw(a.shape,u);return n.runWebGLProgram(l,[a],a.dtype)}};class mw{constructor(e,t){this.variableNames=["Image"],this.outputShape=[],this.customUniforms=[{name:"params",type:"vec4"}];const n=e[1],r=e[2];this.outputShape=e;let s="";s="number"===typeof t?`float outputValue = ${t.toFixed(2)};`:`\n vec3 fill = vec3(${t.join(",")});\n float outputValue = fill[coords[3]];`,this.userCode=`\n void main() {\n ivec4 coords = getOutputCoords();\n int x = coords[2];\n int y = coords[1];\n float coordXFloat = (float(x) - params[0]) * params[3] -\n (float(y) - params[1]) * params[2];\n float coordYFloat = (float(x) - params[0]) * params[2] +\n (float(y) - params[1]) * params[3];
1\n int coordX = int(round(coordXFloat + params[0]));\n int coordY = int(round(coordYFloat + params[1]));\n ${s}\n if(coordX >= 0 && coordX < ${r} && coordY >= 0 && coordY < ${n}) {\n outputValue = getImage(coords[0], coordY, coordX, coords[3]);\n }\n setOutput(outputValue);\n }\n `}}const gw={kernelName:s.b9H,backendName:"webgl",kernelFunc:({inputs:e,attrs:t,backend:n})=>{const{image:r}=e,{radians:a,fillValue:o,center:i}=t,u=n,l=new mw(r.shape,o),[c,p]=s.Wap.getImageCenter(i,r.shape[1],r.shape[2]),h=[[c,p,Math.sin(a),Math.cos(a)]];return u.runWebGLProgram(l,[r],r.dtype,h)}},yw=tm({opSnippet:"\n // OpenGL ES does not support round function.\n // The algorithm is based on banker's rounding.\n float base = floor(x);\n if ((x - base) < 0.5) {\n return floor(x);\n } else if ((x - base) > 0.5) {\n return ceil(x);\n } else {\n if (mod(base, 2.0) == 0.0) {\n return base;\n } else {\n return base + 1.0;\n }\n }\n"}),bw={kernelName:s.e07,backendName:"webgl",kernelFunc:yw},xw=tm({opSnippet:"return inversesqrt(x);",cpuKernelImpl:uf}),ww={kernelName:s.bV0,backendName:"webgl",kernelFunc:xw};class vw{constructor(e,t,n,r,s,a,o=!0){this.variableNames=["updates","indices","defaultValue"],this.outputShape=a;const i=(0,cd.kW)(s.length),u=(0,cd.kW)(a.length);let l="";1===n?l="i":2===n&&(l="i, j");const c=`getIndices(${l})`;let p="";1===r?p="i":2===r&&(p="i, coords[1]");const h=`getUpdates(${p})`,d=t>1?"strides[j]":"strides";this.userCode=`\n ${i} strides = ${i}(${s});\n\n void main() {\n ${u} coords = getOutputCoords();\n float sum = 0.0;\n bool found = false;\n for (int i = 0; i < ${e}; i++) {\n int flattenedIndex = 0;\n for (int j = 0; j < ${t}; j++) {\n int index = round(${c});\n flattenedIndex += index * ${d};\n }\n if (flattenedIndex == coords[0]) {\n sum += ${h};\n found = true;\n }\n }\n setOutput(mix(getDefaultValue(), sum, float(found)));\n }\n `}}const kw={kernelName:s.xQA,backendName:"webgl",kernelFunc:function(e){const{inputs:t,backend:n,attrs:r}=e,{indices:a,updates:o}=t,{shape:i}=r,{sliceRank:u,numUpdates:l,sliceSize:c,strides:p,outputSize:h}=s.Wap.calculateShapes(o,a,i),d=[h/c,c];if(0===h)return n.makeTensorInfo(i,a.dtype);const f=pm({inputs:{x:a},backend:n,attrs:{shape:[l,u]}}),m=pm({inputs:{x:o},backend:n,attrs:{shape:[l,c]}}),g=n.makeTensorInfo([],"float32",new Float32Array([0])),y=new vw(l,u,f.shape.length,m.shape.length,p,d),b=n.runWebGLProgram(y,[m,f,g],m.dtype),x=pm({inputs:{x:b},backend:n,attrs:{shape:i}});return n.disposeIntermediateTensorInfo(f),n.disposeIntermediateTensorInfo(m),n.disposeIntermediateTensorInfo(b),n.disposeIntermediateTensorInfo(g),x}};class Iw{constructor(e,t,n,r){this.variableNames=["sortedSequence","values"],this.customUniforms=[{name:"numInputs",type:"int"}],this.outputShape=[e,n];const a=`for (int i = 0; i < ${Math.ceil(Math.log2(t+1))}; ++i) { if (left >= right) break;`,o=2===(0,s.OBj)().getNumber("WEBGL_VERSION")?"while (left < right) {":a,i="left"===r?"<":"<=";this.userCode=`\n int findBound(int batch, float value) {\n int left = 0;\n int right = numInputs;\n int mid;\n ${o}\n mid = (left + right) / 2;\n if (getSortedSequence(batch, mid) ${i} value) {\n left = mid + 1;\n } else {\n right = mid;\n }\n }\n return right;\n }\n\n void main() {\n ivec2 coords = getOutputCoords();\n int batch = coords[0];\n int valueIndex = coords[1];\n\n float value = getValues(batch, valueIndex);\n\n setOutput(float(findBound(batch, value)));\n }\n `}}const Nw={kernelName:s.nr8,backendName:"webgl",kernelFunc:function(e){const{inputs:t,backend:n,attrs:r}=e,{sortedSequence:s,values:a}=t,{side:o}=r,i=new Iw(s.shape[0],s.shape[1],a.shape[1],o),u=[[s.shape[1]]];return n.runWebGLProgram(i,[s,a],"int32",u)}};class Sw{constructor(e,t,n){let r,s;if(this.variableNames=["c","a","b"],this.outputShape=t,n>4)throw Error(`Where for rank ${n} is not yet supported`);if(1===n)s="resRC",r="resRC";else{const n=["resRC.x","resRC.y","resRC.z","resRC.w"],a=[],o=[];for(let r=0;r<t.length;r++)o.push(`${n[r]}`),r<e&&a.push(`${n[r]}`);r=a.join(),s=o.join()}const a=(0,cd.kW)(n);this.userCode=`\n void main() {\n ${a} resRC = getOutputCoords();\n float cVal = getC(${r});\n if (cVal >= 1.0) {\n setOutput(getA(${s}));\n } else {\n setOutput(getB(${s}));\n }\n }\n `}}const Tw={kernelName:s.PhF,backendName:"webgl",kernelFunc:function(e){const{inputs:t,backend:n}=e,{condition:r,t:a,e:o}=t,i=new Sw(r.shape.length,a.shape,a.shape.length);return n.runWebGLProgram(i,[r,a,o],(0,s.x8V)(a.dtype,o.dtype))}},Cw=tm({opSnippet:`\n // Stable and Attracting Fixed Point (0, 1) for Normalized Weights.\
1n // see: https://arxiv.org/abs/1706.02515\n float scaleAlpha = ${s.Wap.SELU_SCALEALPHA};\n float scale = ${s.Wap.SELU_SCALE};\n return (x >= 0.0) ? scale * x : scaleAlpha * (exp(x) - 1.0);\n`}),Ew={kernelName:s.oFR,backendName:"webgl",kernelFunc:Cw},$w=tm({opSnippet:"if (isnan(x)) return x;\n return 1.0 / (1.0 + exp(-1.0 * x));\n",packedOpSnippet:"\n vec4 result = 1.0 / (1.0 + exp(-1.0 * x));\n bvec4 isNaN = isnan(x);\n\n result.r = isNaN.r ? x.r : result.r;\n result.g = isNaN.g ? x.g : result.g;\n result.b = isNaN.b ? x.b : result.b;\n result.a = isNaN.a ? x.a : result.a;\n\n return result;\n",cpuKernelImpl:cf}),Aw={kernelName:s.a5O,backendName:"webgl",kernelFunc:$w},Dw=tm({opSnippet:"\n if (isnan(x)) { return 0.0; }\n return sign(x);\n"}),_w={kernelName:s.i5y,backendName:"webgl",kernelFunc:Dw},Rw=tm({opSnippet:"if (isnan(x)) return x;\n return sin(x);\n"}),Fw={kernelName:s.RQH,backendName:"webgl",kernelFunc:Rw},Ow=tm({opSnippet:"\n float e2x = exp(x);\n return (e2x - 1.0 / e2x) / 2.0;\n"}),Mw={kernelName:s.wYB,backendName:"webgl",kernelFunc:Ow},Bw=tm({opSnippet:"\n float epsilon = 1.1920928955078125e-7;\n float threshold = log(epsilon) + 2.0;\n\n bool too_large = x > -threshold;\n bool too_small = x < threshold;\n\n float result;\n float exp_x = exp(x);\n\n if (too_large){\n result = x;\n }\n else if (too_small){\n result = exp_x;\n }\n else{\n result = log(exp_x + 1.0);\n }\n return result;\n"}),Lw={kernelName:s.MRv,backendName:"webgl",kernelFunc:Bw},Ww={kernelName:s.TQc,backendName:"webgl",kernelFunc:e=>{const{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{blockShape:o,paddings:i}=r;s.D5U.assert(a.shape.length<=4,(()=>"spaceToBatchND for rank > 4 with a WebGL backend not implemented yet"));const u=o.reduce(((e,t)=>e*t)),l=[[0,0]];l.push(...i);for(let s=1+o.length;s<a.shape.length;++s)l.push([0,0]);const c=[],p=Ux({inputs:{x:a},backend:n,attrs:{paddings:l,constantValue:0}}),h=s.Wap.getReshaped(p.shape,o,u,!1),d=s.Wap.getPermuted(h.length,o.length,!1),f=s.Wap.getReshapedPermuted(p.shape,o,u,!1),m=pm({inputs:{x:p},backend:n,attrs:{shape:h}}),g=vm({inputs:{x:m},backend:n,attrs:{perm:d}}),y=pm({inputs:{x:g},backend:n,attrs:{shape:f}});return c.push(p),c.push(m),c.push(g),c.forEach((e=>n.disposeIntermediateTensorInfo(e))),y}};const Pw={kernelName:s.O3z,backendName:"webgl",kernelFunc:function(e){const{inputs:t,backend:n}=e,{indices:r,values:s,denseShape:a,defaultValue:o}=t;if(1!==a.shape.length)throw new Error(`Dense shape must be a vector, saw:\n ${a.shape}`);if(2!==r.shape.length)throw new Error(`Indices must be a matrix, saw:\n ${r.shape}`);if(1!==s.shape.length)throw new Error(`Values must be a vector, saw:\n ${s.shape}`);if(0!==o.shape.length)throw new Error(`Default value must be a scalar, saw:\n ${o.shape}`);const i=n.readSync(r.dataId),u=n.readSync(s.dataId),l=n.readSync(a.dataId),c=n.readSync(o.dataId)[0],[p,h,d,f,m]=df(i,r.shape,r.dtype,u,s.dtype,l,c);return[n.makeTensorInfo(h,r.dtype,p),n.makeTensorInfo([h[0]],s.dtype,d),n.makeTensorInfo([f.length],"bool",new Uint8Array(f.map((e=>Number(e))))),n.makeTensorInfo([m.length],r.dtype,new Int32Array(m))]}};const Uw={kernelName:s.nhH,backendName:"webgl",kernelFunc:function(e){const{inputs:t,backend:n}=e,{inputIndices:r,inputShape:s,newShape:a}=t;if(2!==r.shape.length)throw new Error(`Input indices should be a matrix but received shape ${r.shape}`);if(1!==s.shape.length)throw new Error(`Input shape should be a vector but received shape ${s.shape}`);if(1!==a.shape.length)throw new Error(`Target shape should be a vector but received shape ${a.shape}`);const o=Array.from(n.readSync(s.dataId)),i=n.readSync(r.dataId),u=Array.from(n.readSync(a.dataId)),[l,c,p]=ff(i,r.shape,r.dtype,o,u);return[n.makeTensorInfo(c,r.dtype,l),n.makeTensorInfo([p.length],a.dtype,new Int32Array(p))]}};const zw={kernelName:s.w3H,backendName:"webgl",kernelFunc:function(e){const{inputs:t,backend:n}=e,{data:r,indices:s,segmentIds:a}=t;if(r.shape.length<1)throw new Error("Data should be at least 1 dimensional but received scalar");if(1!==s.shape.length)throw new Error(`Indices should be a vector but received shape\n ${s.shape}`);if(1!==a.shape.length)throw new Error(`Segment ids should be a vector but received shape\n ${a.shape}`);const o=n.readSync(r.dataId),i=n.readSync(s.dataId),u=n.readSync(a.dataId),[l,c]=mf(o,r.shape,r.dtype,i,u,!0);return n.makeTensorInfo(c,r.dtype,l)}};const Vw={kernelName:s.ZjV,backendName:"webgl",kernelFunc:function(e){const{inputs:t,backend:n}=e,{data:r,indices:s,segmentIds:a}=t;if(r.shape.length<1)throw new Error("Data should be at least 1 dimensional but received scalar");
vendor: 6,699 bytes, line 1
1if(1!==s.shape.length)throw new Error(`Indices should be a vector but received shape\n ${s.shape}`);if(1!==a.shape.length)throw new Error(`Segment ids should be a vector but received shape\n ${a.shape}`);const o=n.readSync(r.dataId),i=n.readSync(s.dataId),u=n.readSync(a.dataId),[l,c]=mf(o,r.shape,r.dtype,i,u);return n.makeTensorInfo(c,r.dtype,l)}};const Gw={kernelName:s.D2d,backendName:"webgl",kernelFunc:function(e){const{inputs:t,backend:n,attrs:r}=e,{sparseIndices:a,sparseValues:o,defaultValue:i}=t,{outputShape:u}=r,{sliceRank:l,numUpdates:c,sliceSize:p,strides:h,outputSize:d}=s.Wap.calculateShapes(o,a,u);if("string"===o.dtype){const e=n.bufferSync(a),t=n.bufferSync(o),r=s.D5U.decodeString(n.readSync(i.dataId)[0]),f=lf(e,t,u,d,p,c,l,h,r,false);return n.makeTensorInfo(u,f.dtype,f.values)}const f=new vw(c,l,a.shape.length,o.shape.length,h,[d,1],false),m=n.runWebGLProgram(f,[o,a,i],o.dtype),g=pm({inputs:{x:m},backend:n,attrs:{shape:u}});return n.disposeIntermediateTensorInfo(m),g}};const Hw={kernelName:s.L8s,backendName:"webgl",kernelFunc:function(e){const{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{numOrSizeSplits:o,axis:i}=r,u=s.D5U.parseAxisParam(i,a.shape)[0],l=s.Wap.prepareSplitSize(a,o,u),c=a.shape.length,p=new Array(c).fill(0),h=a.shape.slice();return l.map((e=>{const t=[...h];t[u]=e;const r=yg({inputs:{x:a},backend:n,attrs:{begin:p,size:t}});return p[u]+=e,r}))}},jw="return sqrt(x);",Xw=tm({opSnippet:jw,packedOpSnippet:jw,cpuKernelImpl:gf}),qw={kernelName:s.FKq,backendName:"webgl",kernelFunc:Xw},Kw=tm({opSnippet:"return x * x;"}),Qw={kernelName:s.bK0,backendName:"webgl",kernelFunc:Kw},Yw="return (a - b) * (a - b);",Zw=nm({opSnippet:Yw,packedOpSnippet:Yw}),Jw={kernelName:s._tC,backendName:"webgl",kernelFunc:Zw};const ev={kernelName:s.h8e,backendName:"webgl",kernelFunc:function({inputs:e,attrs:t,backend:n}){const{x:r}=e,s=`if (isnan(x)) return x;\n return x > 0.0 ? 1.0 : float(${t.alpha});\n `,a=new Ff(r.shape,s);return n.runWebGLProgram(a,[r],r.dtype)}};class tv{constructor(e,t,n){this.variableNames=["x"],this.outputShape=n;const r=n.length,s=(0,cd.kW)(n.length),a=(0,cd.kW)(n.length);let o="";if(1===r)o="coords * strides + begin";else{let e=0;o=n.map(((t,r)=>(e++,1===n.length?`coords * strides[${r}] + begin[${r}]`:`coords[${e-1}] * strides[${r}] + begin[${r}]`))).join(",")}this.userCode=`\n ${s} begin = ${s}(${e});\n ${s} strides = ${s}(${t});\n\n void main() {\n ${a} coords = getOutputCoords();\n setOutput(getX(${o}));\n }\n `}}const nv={kernelName:s.jQk,backendName:"webgl",kernelFunc:function(e){const{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{begin:o,end:i,strides:u,beginMask:l,endMask:c,ellipsisMask:p,newAxisMask:h,shrinkAxisMask:d}=r,{finalShapeSparse:f,finalShape:m,isIdentity:g,sliceDim0:y,isSimpleSlice:b,begin:x,end:w,strides:v}=s.kuN.sliceInfo(a.shape,o,i,u,l,c,p,h,d);let k;if(g)k=pm({inputs:{x:a},backend:n,attrs:{shape:m}});else if(y||b){s.D5U.assert(a.shape.length>=1,(()=>`Input must have rank at least 1, got: ${a.shape.length}`));const e=s.kuN.computeOutShape(x,w,v),t=yg({inputs:{x:a},backend:n,attrs:{begin:x,size:e}});k=pm({inputs:{x:t},backend:n,attrs:{shape:m}}),n.disposeIntermediateTensorInfo(t)}else{if(n.shouldExecuteOnCPU([a])){const e=n.readSync(a.dataId),t=(0,s.f3b)(a.shape,a.dtype,e),r=yf(f,t,v,x);k=n.makeTensorInfo(m,a.dtype,r.values)}else{const e=new tv(x,v,f);k=n.runWebGLProgram(e,[a],a.dtype)}}const I=pm({inputs:{x:k},backend:n,attrs:{shape:m}});return n.disposeIntermediateTensorInfo(k),I}};const rv={kernelName:s._JP,backendName:"webgl",kernelFunc:function(e){const{inputs:t,backend:n,attrs:r}=e,{separator:s,nGramWidths:a,leftPad:o,rightPad:i,padWidth:u,preserveShortSequences:l}=r,{data:c,dataSplits:p}=t,h=n.readSync(c.dataId),d=n.readSync(p.dataId),[f,m]=bf(h,d,s,a,o,i,u,l);return[n.makeTensorInfo([f.length],"string",f),n.makeTensorInfo(p.shape,"int32",m)]}};const sv={kernelName:s.s1s,backendName:"webgl",kernelFunc:function(e){const{inputs:t,backend:n,attrs:r}=e,{skipEmpty:s}=r,{input:a,delimiter:o}=t;if("string"!==a.dtype)throw new Error("Input must be of datatype string");if(1!==a.shape.length)throw new Error(`Input must be a vector, got shape: ${a.shape}`);if(0!==o.shape.length)throw new Error(`Delimiter must be a scalar, got shape: ${o.shape}`);const i=n.readSync(a.dataId),u=n.readSync(o.dataId)[0],[l,c,p]=xf(i,u,s),h=c.length;return[n.makeTensorInfo([h,2],"int32",l),n.makeTensorInfo([h],"string",c),n.makeTensorInfo([2],"int32",new Int32Array(p))]}};const av={kernelName:s.XkS,backendName:"webgl",kernelFunc:function(e){const{inputs:t,backend:n,attrs:r}=e,{numBuckets:s}=r,{input:a}=t;if("string"!==a.dtype)throw new Error("Input must be of datatype string");if(s<=0)throw new Error("Number of buckets must be at least 1");const o=n.readSync(a.dataId),i=wf(o,s);return n.makeTensorInfo(a.shape,"int32",i)}},ov=tm({opSnippet:"return tan(x);"}),iv={kernelName:s.sEM,backendName:"webgl",kernelFunc:ov},uv=tm({opSnippet:"\n float e2x = exp(-2.0 * abs(x));\n return sign(x) * (1.0 - e2x) / (1.0 + e2x);\n"}),lv={kernelName:s.MIZ,backendName:"webgl",kernelFunc:uv};class cv{constructor(e,t){this.variableNames=["A"];const 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;const r=(0,cd.kW)(this.rank),s=function(e){const t=e.length;if(t>5)throw Error(`Tile for rank ${t} is not yet supported`);if(1===t)return`imod(resRC, ${e[0]})`;const n=["resRC.x","resRC.y","resRC.z","resRC.w","resRC.u"],r=[];for(let s=0;s<e.length;s++)r.push(`imod(${n[s]}, ${e[s]})`);return r.join()}(e);this.userCode=`\n void main() {\n ${r} resRC = getOutputCoords();\n setOutput(getA(${s}));\n }\n `}}function pv(e){const{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{reps:o}=r;if("string"===a.dtype||a.shape.length>5){const e=n.readSync(a.dataId),t="string"===a.dtype?e.map((e=>s.D5U.decodeString(e))):e,r=(0,s.f3b)(a.shape,a.dtype,t),i=kf(r,o);return n.makeTensorInfo(i.shape,i.dtype,i.values)}const i=new cv(a.shape,o);return n.runWebGLProgram(i,[a],a.dtype)}const hv={kernelName:s.n9L,backendName:"webgl",kernelFunc:pv};class dv{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="\n void main() {\n ivec2 coords = getOutputCoords();\n int batch = coords[0];\n int elemIdx = coords[1];\n\n // We compare elements pair-wise within a group of size 2 * inc.\n // The comparing rule for each group alternates between ascending\n // and desce
1nding. Within each group, we compare each pair at\n // positions i and i+inc. To decide whether an element at position i\n // is x0 or x1, we mod it by 2 * inc, if the result is smaller than\n // inc, it is in the first half of the group, we denote it as x0,\n // otherwise we denote it as x1.\n // For example, as shown in the Bitonic top K paper referenced above,\n // Figure5(a) shows that element[1] is in the\n // second half of the group when group size is 2, but it is in the\n // first half of the group when group size is 4.\n\n bool isFirstInPair = imod(elemIdx, 2 * inc) < inc;\n int i = isFirstInPair ? elemIdx : elemIdx - inc;\n\n int i0 = firstPass == 1 ? i : int(getIndices(batch, i));\n int i1 = firstPass == 1 ? i + inc : int(getIndices(batch, i + inc));\n float x0 = i0 < n ? getX(batch, i0) : negativeInf;\n float x1 = i1 < n ? getX(batch, i1) : negativeInf;\n\n // Denotes which direction indices are in (ascending or descending).\n bool reverse = imod(elemIdx, 2 * dir) >= dir;\n bool isGreater = x0 > x1 || (x0 == x1 && i1 > i0);\n if (reverse == isGreater) { // Elements in opposite order of direction\n int iTemp = i0;\n i0 = i1;\n i1 = iTemp;\n }\n if (isFirstInPair) {\n setOutput(float(i0));\n } else {\n setOutput(float(i1));\n }\n }\n "}}class fv{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="\n void main() {\n // Takes max of indices (0, k), (1, k + 1), (2, k + 2) ...\n ivec2 coords = getOutputCoords();\n int batch = coords[0];\n int elemIdx = coords[1];\n\n // The output size is half of the previous size.\n // If the previous sequence is | | | | _ _ _ _ | | | | _ _ _ _ (k=4),\n // we only need to output the indices at positions |, the indices at\n // positions _ can be thrown away, see Figure5(b) After Phase 2\n // (Merge phase) in the Bitonic Top K paper referenced above.\n // For example, the paper shows we only need to output the orange bars.\n // The output sequence should look like this | | | | | | | |.\n // Because the sequence is halved, to map the output index back\n // to the previous sequence to find the corresponding value,\n // we need to double the index. When we double the index,\n // we basically interpolate a position, so 2i looks like\n // | _ | _ | _ | _ | _ | _ | _. We move the | to the first k position\n // of each 2k positions by - elemIdx % k. E.g. for output at\n // index 4,5,6,7, we want to get the corresponding element at\n // original index 8,9,10,11, for output at index 8,9,10,11,\n // we want to get the corresponding element at original index\n // 16,17,18,19, so on and so forth.\n\n int i = elemIdx < k ? elemIdx : (elemIdx * 2 - imod(elemIdx, k));\n int i0 = firstPass == 1 ? i : int(getIndices(batch, i));\n int i1 = firstPass == 1 ? i + k : int(getIndices(batch, i + k));\n\n float x0 = getX(batch, i0);\n float x1 = i1 < n ? getX(batch, i1) : x0;\n\n setOutput(x0 >= x1 ? float(i0) : float(i1));\n }\n "}}function mv(e,t){null!==t&&e.disposeIntermediateTensorInfo(t)}function gv(e){let t=1;for(;t<e;)t*=2;return t}const yv={kernelName:s.cWu,backendName:"webgl",kernelFunc:function(e){const{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{k:o,sorted:i}=r,u=(0,s.OBj)().getNumber("TOPK_LAST_DIM_CPU_HANDOFF_SIZE_THRESHOLD"),l=(0,s.OBj)().getNumber("TOPK_K_CPU_HANDOFF_THRESHOLD"),c=a.shape,p=c[c.length-1];if(n.shouldExecuteOnCPU([a])||p<u||o>l){const e=n.readSync(a.dataId),[t,r]=If(e,c,a.dtype,o,i);return[n.makeTensorInfo(t.shape,t.dtype,t.values),n.makeTensorInfo(r.shape,r.dtype,r.values)]}if(0===o)return c[c.length-1]=0,[n.makeTensorInfo(c,a.dtype,[]),n.makeTensorInfo(c,"int32",[])];if(1===p)return[a,Jy({attrs:{shape:c,dtype:"int32",value:0},backend:n})];const h=n.texData.get(a.dataId),d=null!==h&&h.isPacked,f=d?n.unpackTensor(a):a,m=s.D5U.sizeFromShape(c)/p,g=pm({inputs:{x:f},attrs:{shape:[m,p]},backend:n});d&&mv(n,f);const y=gv(o),b=gv(p);let x=null;const w=()=>null===x?[g,g]:[g,x],v=(e,t,r)=>{const s=w(),a=new dv(r),o=[[p],[null===x?1:0],[Number.NEGATIVE_INFINITY],[e],[t]],i=x;x=n.runWebGLProgram(a,s,"int32",o),mv(n,i)};for(let s=1;s<y;s*=2){const e=2*s;for(let t=s;t>=1;t/=2)v(e,t,[m,b])}for(let s=b;s>y;s/=2){const e=w(),t=new fv([m,s/2]),r=[[p],[null===x?1:0],[y]],a=x;x=n.runWebGLProgram(t,e,"int32",r),mv(n,a);const o=y/2,i=2*o;for(let n=o;n>=1;n/=2)v(i,n,x.shape)}let k=x;x=yg({inputs:{x:x},backend:n,attrs:{begin:0,size:[m,o]}}),mv(n,k);let I=bb({inputs:{x:g,indices:x},backend:n,attrs:{axis:1,batchDims:1}});mv(n,g);const N=c.slice(0,-1);N.push(o),k=x,x=pm({inputs:{x:x},attrs:{shape:N},backend:n}),mv(n,k);const S=I;return I=pm({inputs:{x:I},attrs:{shape:N},backend:n}),mv(n,S),[I,x]}};class bv{constructor(e,t,n,r,s,a){this.variableNames=["Image","Transforms"],this.outputShape=a;const o="nearest"===n?1:2;let i;switch(r){case"constant":i=1;break;case"reflect":i=2;break;case"wrap":i=3;break;case"nearest":i=4;break;default:i=1}this.userCode=`\n float mapCoord(float outCoord, float len) {\n float inCoord = outCoord;\n if(${i} == 2) {\n if (inCoord < 0.0) {\n if (len <= 1.0) {\n inCoord = 0.0;\n } else {\n float sz2 = 2.0 * len;\n if (inCoord < sz2) {\n inCoord = sz2 * float(int(float(-inCoord / sz2))) +\n inCoord;\n }\n inCoord = inCoord < -len ? inCoord + sz2 : -inCoord - 1.0;\n }\n } else if (inCoord > len - 1.0) {\n if (len <= 1.0) {\n inCoord = 0.0;\n } else {\n float sz2 = 2.0 * len;\n inCoord -= sz2 * float(int(float(inCoord / sz2)));\n if (inCoord >= len) {\n inCoord = sz2 - inCoord - 1.0;\n }\n }\n }\n return clamp(inCoord, 0.0, len - 1.0);\n } else if (${i} == 3) {\n if (inCoord < 0.0) {\n if (len <= 1.0) {\n inCoord = 0.0;\n } else {\n float sz = len - 1.0;\n inCoord += len * (float(int(float(-inCoord / sz))) + 1.0);\n }\n } else if (inCoord > len - 1.0) {\n if (len <= 1.0) {\n inCoord = 0.0;\n } else {\n float sz = len - 1.0;\n inCoord -= len * float(int(float(inCoord / sz)));\n }\n }\n return clamp(inCoord, 0.0, len - 1.0);\n } else if (${i} == 4) {\n return clamp(outCoord, 0.0, len - 1.0);\n } else {\n return outCoord;\n }\n }\n\n float readWithFillValue(int batch, int coordY, int coordX,\n int channel) {\n float outputValue;\n if (0 <= coordY && coordY < ${e} && 0 <= coordX && coordX < ${t}
1) {\n outputValue = getImage(batch, coordY, coordX, channel);\n } else {\n outputValue = float(${s});\n }\n return outputValue;\n }\n\n void main() {\n ivec4 coords = getOutputCoords();\n float outputValue;\n int batch = coords[0];\n int x = coords[2];\n int y = coords[1];\n int channel = coords[3];\n float xf = float(x);\n float yf = float(y);\n float a1 = getTransforms(batch, 0);\n float a2 = getTransforms(batch, 1);\n float a3 = getTransforms(batch, 2);\n float b1 = getTransforms(batch, 3);\n float b2 = getTransforms(batch, 4);\n float b3 = getTransforms(batch, 5);\n float c1 = getTransforms(batch, 6);\n float c2 = getTransforms(batch, 7);\n float projection = c1 * xf + c2 * yf + 1.0;\n if (projection == 0.0) {\n outputValue = float(${s});\n } else {\n float inX = (a1 * xf + a2 * yf + a3) / projection;\n float inY = (b1 * xf + b2 * yf + b3) / projection;\n float mapX = mapCoord(inX, float(${t}));\n float mapY = mapCoord(inY, float(${e}));\n\n if (${o} == 1) {\n int coordY = int(round(mapY));
1\n int coordX = int(round(mapX));\n outputValue = readWithFillValue(batch, coordY, coordX,\n channel);\n } else {\n float yFloor = floor(mapY);\n float xFloor = floor(mapX);\n float yCeil = yFloor + 1.0;\n float xCeil = xFloor + 1.0;\n float valueYFloor = (xCeil - mapX) *\n readWithFillValue(batch, int(yFloor), int(xFloor), channel) +\n (mapX - xFloor) *\n readWithFillValue(batch, int(yFloor), int(xCeil), channel);\n float valueYCeil = (xCeil - mapX) *\n readWithFillValue(batch, int(yCeil), int(xFloor), channel) +\n (mapX - xFloor) *\n readWithFillValue(batch, int(yCeil), int(xCeil), channel);\n outputValue = (yCeil - mapY) * valueYFloor +\n (mapY - yFloor) * valueYCeil;\n }\n }\n setOutput(outputValue);\n }\n `}}const xv={kernelName:s.wx7,backendName:"webgl",kernelFunc:function(e){const{inputs:t,backend:n,attrs:r}=e,{image:s,transforms:a}=t,{interpolation:o,fillMode:i,fillValue:u,outputShape:l}=r,[c,p,h,d]=s.shape,[f,m]=null!=l?l:[p,h],g=new bv(p,h,o,i,u,[c,f,m,d]);return n.runWebGLProgram(g,[s,a],"float32")}};const wv={kernelName:s.kpP,backendName:"webgl",kernelFunc:function(e){const{inputs:t,attrs:n,backend:r}=e,{axis:s}=n,{x:a}=t;id(a,"unique"),console.warn("WARNING: ","UI might be locked temporarily as data is being downloaded");const o=r.readSync(a.dataId),{outputValues:i,outputShape:u,indices:l}=Sf(o,s,a.shape,a.dtype);return[r.makeTensorInfo(u,a.dtype,i),r.makeTensorInfo([l.length],"int32",l)]}};const vv={kernelName:s.ToN,backendName:"webgl",kernelFunc:function(e){const{inputs:t,backend:n,attrs:r}=e,{value:s}=t;let{axis:a}=r;a<0&&(a+=s.shape.length);const o=s,i=o.shape.length,u=s.shape[a],l=new Array(i-1);let c=0;for(let m=0;m<i;m++)m!==a&&(l[c++]=o.shape[m]);const p=[],h=new Array(i).fill(0),d=o.shape.slice();d[a]=1;const f=new Array(u);for(let m=0;m<f.length;m++){h[a]=m;const e=yg({inputs:{x:o},backend:n,attrs:{begin:h,size:d}}),t=pm({inputs:{x:e},backend:n,attrs:{shape:l}});f[m]=t,p.push(e)}return p.forEach((e=>n.disposeIntermediateTensorInfo(e))),f}};class kv{constructor(e,t){this.variableNames=["x","segmentIds"];const n=e.windowSize,r=e.batchSize,s=e.inSize,a=e.numSegments,o=a*Math.ceil(s/n);this.outputShape=[r,o];const i=4*Math.floor(n/4),u=n%4,l="\n sumValue += dot(values, segFilter);\n ";let c="";s%n>0&&(c=`\n if (inIdx < 0 || inIdx >= ${s}) {\n return initializationValue;\n }\n `);let p="";s%n>0&&(p=`\n if (inIdx < 0 || inIdx >= ${s}) {\n return -1.0;\n }\n `),this.userCode=`\n const float initializationValue = 0.0;\n\n float getValue(int batch, int inIdx) {\n ${c}\n return getX(batch, inIdx);\n }\n\n float getSegmentIdAtIndex(int inIdx) {\n ${p}\n return getSegmentIds(inIdx);\n }\n\n void main() {\n ivec2 coords = getOutputCoords();\n int batch = coords[0];\n int outIdx = coords[1];\n int inOffset = int(floor(float(outIdx) / float(\n ${a})) * float(${n}));\n int currentSeg = int(mod(float(outIdx), float(${a})));\n\n float sumValue = 0.0;\n\n for (int i = 0; i < ${i}; i += 4) {\n int inIdx = inOffset + i;\n vec4 values = vec4(\n getValue(batch, inIdx),\n getValue(batch, inIdx + 1),\n getValue(batch, inIdx + 2),\n getValue(batch, inIdx + 3)\n );\n\n vec4 segFilter = vec4(\n int(getSegmentIdAtIndex(inIdx)) == currentSeg ? 1 : 0,\n int(getSegmentIdAtIndex(inIdx + 1)) == currentSeg ? 1 : 0,\n int(getSegmentIdAtIndex(inIdx + 2)) == currentSeg ? 1 : 0,\n int(getSegmentIdAtIndex(inIdx + 3)) == currentSeg ? 1 : 0\n );\n\n ${l}\n }\n\n int inIdx = inOffset + ${i};\n if (${1===u}) {\n vec4 values = vec4(\n getValue(batch, inIdx),\n initializationValue,\n initializationValue,\n initializationValue\n );\n\n int inIdxSeg = int(getSegmentIdAtIndex(inIdx));\n\n vec4 segFilter = vec4(\n int(getSegmentIdAtIndex(inIdx)) == currentSeg ? 1 : 0,\n 0,\n 0,\n 0\n );\n\n ${l}\n } else if (${2===u}) {\n vec4 values = vec4(\n getValue(batch, inIdx),\n getValue(batch, inIdx + 1),\n initializationValue,\n initializationValue\n );\n\n vec4 segFilter = vec4(\n int(getSegmentIdAtIndex(inIdx)) == currentSeg ? 1 : 0,\n int(getSegmentIdAtIndex(inIdx + 1)) == currentSeg ? 1 : 0,\n 0,\n 0\n );\n\n ${l}\n } else if (${3===u}) {\n vec4 values = vec4(\n getValue(batch, inIdx),\n getValue(batch, inIdx + 1),\n getValue(batch, inIdx + 2),\n initializationValue\n );\n\n vec4 segFilter = vec4(\n int(getSegmentIdAtIndex(inIdx)) == currentSeg ? 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Line numbers count LF bytes from the start of the resource, as the search results do. Vendor segments are library code the classifier recognised; they are stored but not indexed. Bytes are shown as Latin1 characters, one per byte.