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2const i=Se(e.shape.slice(1),n),a=0===r?0:e.size/r,o=(0,T.tidy)((()=>{const n=[];e=(0,T.reshape)(e,[1,r,a]);for(let r=0;r<t.length;++r){const o=[0,0===r?0:s[r-1],0],u=[1,t[r],a];n[r]=(0,T.reshape)((0,T.slice)(e,o,u),i)}return e.dispose(),n})),u=new Te([],n,e.dtype,t.length);for(let e=0;e<o.length;e++)u.setItem(e,o[e]);return u}(r,A("lengths",e,t,n),s);return n.addTensorList(i),[i.idTensor]}case"TensorListLength":{const r=A("tensorListId",e,t,n),s=n.getTensorList(r.id);return[(0,T.scalar)(s.size(),"int32")]}case"TensorListResize":{const r=A("tensorListId",e,t,n),s=A("size",e,t,n),i=n.getTensorList(r.id).resize(s);return n.addTensorList(i),[i.idTensor]}default:throw TypeError(`Node type ${e.op} is not implemented`)}})(e,t,n);case"convolution":return s((()=>((e,t,n,r=k)=>{switch(e.op){case"Conv1D":{const s=A("stride",e,t,n),i=A("pad",e,t,n),a=A("dataFormat",e,t,n).toUpperCase(),o=A("dilation",e,t,n);return[r.conv1d(A("x",e,t,n),A("filter",e,t,n),s,i,a,o)]}case"Conv2D":{const 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s=A("strides",e,t,n),i=A("pad",e,t,n),a=A("kernelSize",e,t,n),o=A("includeBatchInIndex",e,t,n),{result:u,indexes:l}=r.maxPoolWithArgmax(A("x",e,t,n),[a[1],a[2]],[s[1],s[2]],i,o);return[u,l]}case"AvgPool3D":{const s=A("strides",e,t,n),i=A("pad",e,t,n),a=A("kernelSize",e,t,n);return[r.avgPool3d(A("x",e,t,n),[a[1],a[2],a[3]],[s[1],s[2],s[3]],i)]}case"MaxPool3D":{const s=A("strides",e,t,n),i=A("pad",e,t,n),a=A("kernelSize",e,t,n);return[r.maxPool3d(A("x",e,t,n),[a[1],a[2],a[3]],[s[1],s[2],s[3]],i)]}case"Dilation2D":{const s=A("strides",e,t,n),i=A("pad",e,t,n),a=A("dilations",e,t,n),o=s[1],u=s[2],l=a[1],c=a[2];return[r.dilation2d(A("x",e,t,n),A("filter",e,t,n),[o,u],i,[l,c],"NHWC")]}default:throw TypeError(`Node type ${e.op} is not implemented`)}})(e,t,n)));case"creation":return s((()=>((e,t,n,r=k)=>{switch(e.op){case"Fill":{const s=A("shape",e,t,n),i=A("dtype",e,t,n),a=A("value",e,t,n);return[r.fill(s,a,i)]}case"LinSpace":{const s=A("start",e,t,n),i=A("stop",e,t,n),a=A("num",e,t,n);return[r.linspace(s,i,a)]}case"Multinomial":{const s=A("logits",e,t,n),i=A("numSamples",e,t,n),a=A("seed",e,t,n);return[r.multinomial(s,i,a)]}case"OneHot":{const s=A("indices",e,t,n),i=A("depth",e,t,n),a=A("onValue",e,t,n),o=A("offValue",e,t,n),u=A("dtype",e,t,n);return[r.oneHot(s,i,a,o,u)]}case"Ones":return[r.ones(A("shape",e,t,n),A("dtype",e,t,n))];case"OnesLike":return[r.onesLike(A("x",e,t,n))];case"RandomStandardNormal":return[r.randomStandardNormal(A("shape",e,t,n),A("dtype",e,t,n),A("seed",e,t,n))];case"RandomUniform":return[r.randomUniform(A("shape",e,t,n),A("minval",e,t,n),A("maxval",e,t,n),A("dtype",e,t,n))];case"RandomUniformInt":return[r.randomUniformInt(A("shape",e,t,n),A("minval",e,t,n),A("maxval",e,t,n),A("seed",e,t,n))];case"Range":{const s=A("start",e,t,n),i=A("stop",e,t,n),a=A("step",e,t,n);return[r.range(s,i,a,A("dtype",e,t,n))]}case"TruncatedNormal":{const 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s=A("images",e,t,n),i=A("size",e,t,n),a=A("alignCorners",e,t,n),o=A("halfPixelCenters",e,t,n);return[r.image.resizeBilinear(s,[i[0],i[1]],a,o)]}case"ResizeNearestNeighbor":{const s=A("images",e,t,n),i=A("size",e,t,n),a=A("alignCorners",e,t,n),o=A("halfPixelCenters",e,t,n);return[r.image.resizeNearestNeighbor(s,[i[0],i[1]],a,o)]}case"CropAndResize":{const s=A("image",e,t,n),i=A("boxes",e,t,n),a=A("boxInd",e,t,n),o=A("cropSize",e,t,n),u=A("method",e,t,n),l=A("extrapolationValue",e,t,n);return[r.image.cropAndResize(s,i,a,o,u,l)]}case"ImageProjectiveTransformV3":{const s=A("images",e,t,n),i=A("transforms",e,t,n),a=A("outputShape",e,t,n),o=A("fillValue",e,t,n),u=A("interpolation",e,t,n),l=A("fillMode",e,t,n);return[r.image.transform(s,i,u.toLowerCase(),l.toLowerCase(),o,a)]}default:throw TypeError(`Node type ${e.op} is not implemented`)}})(e,t,n)));case"graph":return s((()=>((e,t,n,r=k)=>{switch(e.op){case"Const":return t[e.name];case"PlaceholderWithDefault":const s=A("default",e,t,n);return[R(e.name,t,n)||s];case"Placeholder":return[R(e.name,t,n)];case"Identity":case"StopGradient":case"FakeQuantWithMinMaxVars":return[L(A("x",e,t,n))];case"IdentityN":return A("x",e,t,n).map((e=>L(e)));case"Snapshot":return[L(A("x",e,t,n))];case"Shape":return[r.tensor1d(A("x",e,t,n).shape,"int32")];case"ShapeN":return A("x",e,t,n).map((e=>r.tensor1d(e.shape)));case"Size":return[r.scalar(A("x",e,t,n).size,"int32")];case"Rank":return[r.scalar(A("x",e,t,n).rank,"int32")];case"NoOp":return[r.scalar(1)];case"Print":const i=A("x",e,t,n),a=A("data",e,t,n),o=A("message",e,t,n),u=A("summarize",e,t,n);console.warn("The graph has a tf.print() operation,usually used for debugging, which slows down performance."),console.log(o);for(let e=0;e<a.length;e++)console.log(Array.prototype.slice.call(a[e].dataSync()).slice(0,u));return[i];default:throw TypeError(`Node type ${e.op} is not implemented`)}})(e,t,n)));case"logical":return s((()=>((e,t,n,r=k)=>{switch(e.op){case"Equal":return[r.equal(A("a",e,t,n),A("b",e,t,n))];case"NotEqual":return[r.notEqual(A("a",e,t,n),A("b",e,t,n))];case"Greater":return[r.greater(A("a",e,t,n),A("b",e,t,n))];case"GreaterEqual":return[r.greaterEqual(A("a",e,t,n),A("b",e,t,n))];case"Less":return[r.less(A("a",e,t,n),A("b",e,t,n))];case"LessEqual":return[r.lessEqual(A("a",e,t,n),A("b",e,t,n))];case"LogicalAnd":return[r.logicalAnd(A("a",e,t,n),A("b",e,t,n))];case"LogicalNot":return[r.logicalNot(A("a",e,t,n))];case"LogicalOr":return[r.logicalOr(A("a",e,t,n),A("b",e,t,n))];case"Select":case"SelectV2":return[r.where(A("condition",e,t,n),A("a",e,t,n),A("b",e,t,n))];case"BitwiseAnd":return[r.bitwiseAnd(A("a",e,t,n),A("b",e,t,n))];default:throw TypeError(`Node type ${e.op} is not implemented`)}})(e,t,n)));case"matrices":return s((()=>((e,t,n,r=k)=>{switch(e.op){case"BatchMatMul":case"BatchMatMulV2":case"MatMul":return[r.matMul(A("a",e,t,n),A("b",e,t,n),A("transposeA",e,t,n),A("transposeB",e,t,n))];case"Einsum":return[r.einsum(A("equation",e,t,n),...A("tensors",e,t,n))];case"Transpose":return[r.transpose(A("x",e,t,n),A("perm",e,t,n))];case"_FusedMatMul":const[s,i]=A("fusedOps",e,t,n),a="biasadd"===s,o="prelu"===i,u=A("numArgs",e,t,n),l=A("leakyreluAlpha",e,t,n);if(a){if(o&&2!==u)throw new Error("Fused MatMul with BiasAdd and Prelu must have two extra arguments: bias and alpha.");if(!o&&1!==u)throw new Error("Fused MatMul with BiasAdd must have one extra argument: bias.")}const[c,d]=A("args",e,t,n);return[r.fused.matMul({a:A("a",e,t,n),b:A("b",e,t,n),transposeA:A("transposeA",e,t,n),transposeB:A("transposeB",e,t,n),bias:c,activation:i,preluActivationWeights:d,leakyreluAlpha:l})];case"MatrixBandPart":return[r.linalg.bandPart(A("a",e,t,n),A("numLower",e,t,n),A("numUpper",e,t,n))];default:throw TypeError(`Node type ${e.op} is not implemented`)}})(e,t,n)));case"normalization":return s((()=>((e,t,n,r=k)=>{switch(e.op){case"EuclideanNorm":return[r.euclideanNorm(A("x",e,t,n),A("axis",e,t,n),A("keepDims",e,t,n))];case"FusedBatchNorm":case"FusedBatchNormV2":case"FusedBatchNormV3":return[r.batchNorm(A("x",e,t,n),A("mean",e,t,n),A("variance",e,t,n),A("offset",e,t,n),A("scale",e,t,n),A("epsilon",e,t,n))];case"LRN":return[r.localResponseNormalization(A("x",e,t,n),A("radius",e,t,n),A("bias",e,t,n),A("alpha",e,t,n),A("beta",e,t,n))];case"Softmax":return[r.softmax(A("x",e,t,n))];case"LogSoftmax":return[r.logSoftmax(A("x",e,t,n))];default:throw TypeError(`Node type ${e.op} is not implemented`)}})(e,t,n)));case"ragged":return s((()=>((e,t,n,r=k)=>{switch(e.op){case"RaggedGather":{const{outputNestedSplits:s,outputDenseValues:i}=r.raggedGather(A("paramsNestedSplits",e,t,n),A("paramsDenseValues",e,t,n),A("indices",e,t,n),A("outputRaggedRank",e,t,n));return s.concat(i)}case"RaggedRange":{const{rtNestedSplits:s,rtDenseValues:i}=r.raggedRange(A("starts",e,t,n),A("limits",e,t,n),A("splits",e,t,n));return[s,i]}case"RaggedTensorToTensor":return[r.raggedTensorToTensor(A("shape",e,t,n),A("values",e,t,n),A("defaultValue",e,t,n),A("rowPartitionTensors",e,t,n),A("rowPartitionTypes",e,t,n))];default:throw TypeError(`Node type ${e.op} is not implemented`)}})(e,t,n)));case"reduction":return s((()=>((e,t,n,r=k)=>{switch(e.op){case"Max":{const s=A("axis",e,t,n),i=A("keepDims",e,t,n);return[r.max(A("x",e,t,n),s,i)]}case"Mean":{const s=A("axis",e,t,n),i=A("keepDims",e,t,n);return[r.mean(A("x",e,t,n),s,i)]}case"Min":{const s=A("axis",e,t,n),i=A("keepDims",e,t,n);return[r.min(A("x",e,t,n),s,i)]}case"Sum":{const s=A("axis",e,t,n),i=A("keepDims",e,t,n);return[r.sum(A("x",e,t,n),s,i)]}case"All":{const s=A("axis",e,t,n),i=A("keepDims",e,t,n);return[r.all(A("x",e,t,n),s,i)]}case"Any":{const s=A("axis",e,t,n),i=A("keepDims",e,t,n);return[r.any(A("x",e,t,n),s,i)]}case"ArgMax":{const s=A("axis",e,t,n);return[r.argMax(A("x",e,t,n),s)]}case"ArgMin":{const s=A("axis",e,t,n);return[r.argMin(A("x",e,t,n),s)]}case"Prod":{const s=A("axis",e,t,n),i=A("keepDims",e,t,n);return[r.prod(A("x",e,t,n),s,i)]}case"Cumprod":{const s=A("axis",e,t,n),i=A("exclusive",e,t,n),a=A("reverse",e,t,n);return[r.cumprod(A("x",e,t,n),s,i,a)]}case"Cumsum":{const s=A("axis",e,t,n),i=A("exclusive",e,t,n),a=A("reverse",e,t,n);return[r.cumsum(A("x",e,t,n),s,i,a)]}case"Bincount":const s=A("x",e,t,n),i=A("weights",e,t,n),a=A("size",e,t,n);return[r.bincount(s,i,a)];case"DenseBincount":{const s=A("x",e,t,n),i=A("weights",e,t,n),a=A("size",e,t,n),o=A("binaryOutput",e,t,n);return[r.denseBincount(s,i,a,o)]}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=A("n",e,t,n),i=A("axis",e,t,n);let a=A("tensors",e,t,n);return a=a.slice(0,s),[r.concat(a,i)]}case"Gather":{const s=A("x",e,t,n),i=A("indices",e,t,n);return[r.gather(s,r.cast(i,"int32"),0)]}case"GatherV2":{const s=A("axis",e,t,n),i=A("batchDims",e,t,n),a=A("x",e,t,n),o=A("indices",e,t,n);return[r.gather(a,r.cast(o,"int32"),s,i)]}case"Reverse":{const s=A("dims",e,t,n),i=[];for(let e=0;e<s.length;e++)s[e]&&i.push(e);const a=A("x",e,t,n);return[r.reverse(a,i)]}case"ReverseV2":{const s=A("axis",e,t,n),i=A("x",e,t,n);return[r.reverse(i,s)]}case"Slice":{const s=A("begin",e,t,n),i=A("size",e,t,n);return[r.slice(A("x",e,t,n),s,i)]}case"StridedSlice":{const s=A("begin",e,t,n),i=A("end",e,t,n),a=A("strides",e,t,n),o=A("beginMask",e,t,n),u=A("endMask",e,t,n),l=A("ellipsisMask",e,t,n),c=A("newAxisMask",e,t,n),d=A("shrinkAxisMask",e,t,n),h=A("x",e,t,n);return[r.stridedSlice(h,s,i,a,o,u,l,c,d)]}case"Pack":return(0,T.tidy)((()=>{const s=A("axis",e,t,n),i=A("tensors",e,t,n),a=i[0].shape,o=r.squeeze(i[0]).shape,u=i.map((e=>{const t=T.util.arraysEqual(e.shape,a);if(!t&&!T.util.arraysEqual(r.squeeze(e).shape,o))throw new Error("the input tensors shape does not match");return t?e:r.reshape(e,a)}));return[r.stack(u,s)]}));case"Unpack":{const s=A("axis",e,t,n),i=A("tensor",e,t,n);return r.unstack(i,s)}case"Tile":{const s=A("reps",e,t,n);return[r.tile(A("x",e,t,n),s)]}case"Split":case"SplitV":{const s=A("axis",e,t,n),i=A("numOrSizeSplits",e,t,n),a=A("x",e,t,n);return r.split(a,i,s)}case"ScatterNd":{const s=A("indices",e,t,n),i=A("values",e,t,n),a=A("shape",e,t,n);return[r.scatterND(s,i,a)]}case"GatherNd":{const s=A("x",e,t,n),i=A("indices",e,t,n);return[r.gatherND(s,i)]}case"SparseToDense":{const s=A("sparseIndices",e,t,n),i=A("outputShape",e,t,n),a=A("sparseValues",e,t,n),o=A("defaultValue",e,t,n);return[r.sparseToDense(s,a,i,a.dtype===o.dtype?o:r.cast(o,a.dtype))]}case"TensorScatterUpdate":{const s=A("indices",e,t,n),i=A("values",e,t,n),a=A("tensor",e,t,n);return[r.tensorScatterUpdate(a,s,i)]}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:i,emptyRowIndicator:a,reverseIndexMap:o}=r.sparse.sparseFillEmptyRows(A("indices",e,t,n),A("values",e,t,n),A("denseShape",e,t,n),A("defaultValue",e,t,n));return[s,i,a,o]}case"SparseReshape":{const{outputIndices:s,outputShape:i}=r.sparse.sparseReshape(A("inputIndices",e,t,n),A("inputShape",e,t,n),A("newShape",e,t,n));return[s,i]}case"SparseSegmentMean":return[r.sparse.sparseSegmentMean(A("data",e,t,n),A("indices",e,t,n),A("segmentIds",e,t,n))];case"SparseSegmentSum":return[r.sparse.sparseSegmentSum(A("data",e,t,n),A("indices",e,t,n),A("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(A("x",e,t,n))];case"IFFT":return[r.ifft(A("x",e,t,n))];case"RFFT":return[r.rfft(A("x",e,t,n))];case"IRFFT":return[r.irfft(A("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"StaticRegexReplace":return[r.string.staticRegexReplace(A("input",e,t,n),A("pattern",e,t,n),A("rewrite",e,t,n),A("replaceGlobal",e,t,n))];case"StringNGrams":{const{nGrams:s,nGramsSplits:i}=r.string.stringNGrams(A("data",e,t,n),A("dataSplits",e,t,n),A("separator",e,t,n),A("nGramWidths",e,t,n),A("leftPad",e,t,n),A("rightPad",e,t,n),A("padWidth",e,t,n),A("preserveShortSequences",e,t,n));return[s,i]}case"StringSplit":{const{indices:s,values:i,shape:a}=r.string.stringSplit(A("input",e,t,n),A("delimiter",e,t,n),A("skipEmpty",e,t,n));return[s,i,a]}case"StringToHashBucketFast":return[r.string.stringToHashBucketFast(A("input",e,t,n),A("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(A("x",e,t,n),A("dtype",e,t,n))];case"ExpandDims":{const s=A("axis",e,t,n);return[r.expandDims(A("x",e,t,n),s)]}case"Squeeze":{const s=A("axis",e,t,n);return[r.squeeze(A("x",e,t,n),s)]}case"Reshape":return[r.reshape(A("x",e,t,n),A("shape",e,t,n))];case"EnsureShape":return[r.ensureShape(A("x",e,t,n),A("shape",e,t,n))];case"MirrorPad":return[r.mirrorPad(A("x",e,t,n),A("padding",e,t,n),A("mode",e,t,n))];case"PadV2":case"Pad":return[r.pad(A("x",e,t,n),A("padding",e,t,n),A("constantValue",e,t,n))];case"SpaceToBatchND":{const s=A("blockShape",e,t,n),i=A("paddings",e,t,n);return[r.spaceToBatchND(A("x",e,t,n),s,i)]}case"BatchToSpaceND":{const s=A("blockShape",e,t,n),i=A("crops",e,t,n);return[r.batchToSpaceND(A("x",e,t,n),s,i)]}case"DepthToSpace":{const s=A("blockSize",e,t,n),i=A("dataFormat",e,t,n).toUpperCase();return[r.depthToSpace(A("x",e,t,n),s,i)]}case"BroadcastTo":return[r.broadcastTo(A("x",e,t,n),A("shape",e,t,n))];case"BroadcastArgs":return[r.broadcastArgs(A("s0",e,t,n),A("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=r.getHashTableHandleByName(e.name);if(null!=s)return[s];{const s=A("keyDType",e,t,n),i=A("valueDType",e,t,n),a=new _e(s,i);return r.addHashTable(e.name,a),[a.handle]}}case"InitializeTable":case"InitializeTableV2":case"LookupTableImport":case"LookupTableImportV2":{const s=A("tableHandle",e,t,n,r),i=A("keys",e,t,n),a=A("values",e,t,n),o=r.getHashTableById(s.id);return[await o.import(i,a)]}case"LookupTableFind":case"LookupTableFindV2":{const s=A("tableHandle",e,t,n,r),i=A("keys",e,t,n),a=A("defaultValue",e,t,n),o=r.getHashTableById(s.id);return[await o.find(i,a)]}case"LookupTableSize":case"LookupTableSizeV2":{const s=A("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);

2case"custom":const i=E(e.op);if(i&&i.customExecutor)return i.customExecutor(new be(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 T.util.isPromise(i)?i.then((e=>[].concat(e))):[].concat(i)}class Re{constructor(e={},t={},n={},r={},s){this.weightMap=e,this.tensorArrayMap=t,this.tensorListMap=n,this.functionMap=r,this.parseNodeNameCache=s,this.rootContext={id:0,frameName:"",iterationId:0},this.contexts=[this.rootContext],this.lastId=0,this.generateCurrentContextIds()}newFrame(e,t){return{id:e,frameName:t,iterationId:0}}set currentContext(e){this.contexts!==e&&(this.contexts=e,this.generateCurrentContextIds())}get currentContext(){return this.contexts}get currentContextId(){return this._currentContextIds[0]}get currentContextIds(){return this._currentContextIds}generateCurrentContextIds(){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 $e(e,t,n,r){const s=new Set,i=[];let a=null,o=null;const u=new Set,l=new Set(Object.keys(e).map((e=>D(e)[0])));r=r||[];const c=new Set(r.map((e=>D(e.name)[0]))),d=[...t];for(;d.length>0;){const e=d.pop();(Le(e)||ze(e)||Pe(e))&&null==a&&(a=e,o=a.children.map((e=>e.name)).filter((e=>s.has(e)))),s.add(e.name),null==n[e.name]&&(l.has(e.name)||c.has(e.name)||(0!==e.inputs.length?e.inputs.forEach((e=>{u.has(e.name)||(u.add(e.name),d.push(e))})):i.push(e.name)))}return{inputs:e,outputs:t,usedNodes:s,missingInputs:i,dynamicNode:a,syncInputs:o}}class Fe extends Error{constructor(e){super("NodesExecutionOrderError: "+e)}}const Oe=new Set(["Switch","Merge","Enter","Exit","NextIteration","StatelessIf","StatelessWhile","if","While"]),De=new Set(["NonMaxSuppressionV2","NonMaxSuppressionV3","NonMaxSuppressionV5","Where"]),Me=new Set(["HashTable","HashTableV2","LookupTableImport","LookupTableImportV2","LookupTableFind","LookupTableFindV2","LookupTableSize","LookupTableSizeV2"]);function Le(e){return Oe.has(e.op)}function ze(e){return De.has(e.op)}function Pe(e){return Me.has(e.op)}class Be{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}:${e.defaultOutput}`:t}))}get functions(){return Object.keys(this._functions).reduce(((e,t)=>(e[t]=this._functions[t].signature,e)),{})}constructor(e,t){this.graph=e,this.parent=t,this.compiledMap=new Map,this.parseNodeNameCache=new Map,this._weightMap={},this.SEPARATOR=",",this._functions={},this._functionExecutorMap={},this.keepIntermediateTensors=!1,this._outputs=e.outputs,this._inputs=e.inputs,this._initNodes=e.initNodes,this._signature=e.signature,this._functions=e.functions,null!=e.functions&&Object.keys(e.functions).forEach((t=>{this._functionExecutorMap[t]=new Be(e.functions[t],this)}))}getCompilationKey(e,t){const n=e.map((e=>e.name)).sort(),r=t.map((e=>e.name)).sort();return n.join(this.SEPARATOR)+"--"+r.join(this.SEPARATOR)}compile(e,t){const n=$e(e,t,this.weightMap,this._initNodes),{missingInputs:r,dynamicNode:s,syncInputs:i}=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 [${i}]`);if(r.length>0){const n=t.map((e=>e.name)),s=Object.keys(e);throw new Error(`Cannot compute the outputs [${n}] from the provided inputs [${s}]. Missing the following inputs: [${r}]`)}const a=function(e,t){const{usedNodes:n,inputs:r}=t,s=Object.keys(r).map((e=>D(e)[0])).map((t=>e.nodes[t])),i=e.initNodes||[],a=e=>n.has("string"==typeof e?e:e.name);function o(e){return[...new Map(e.map((e=>[e.name,e]))).values()]}const u=o([...s,...e.weights,...i]).filter(a),l=o([...u,...Object.values(e.nodes)]).filter(a),c=new Map(l.map((e=>[e.name,e]))),d={};for(const e of l){d[e.name]=d[e.name]||0;for(const t of e.children)a(t)||(d[t.name]=Number.POSITIVE_INFINITY),d[t.name]=(d[t.name]||0)+1}const h=Object.entries(d).filter((([,e])=>0===e)).map((([e])=>e)),p=[...h];for(;h.length>0;){const e=h.pop(),t=c.get(e);for(const e of t.children.filter(a))0==--d[e.name]&&(p.push(e.name),h.push(e.name))}const f=function(e,t){const n=new Map(e.map((e=>[e.name,e]))),r=t.map((e=>e.name)),s=new Set(r);for(;r.length>0;){const e=r.pop(),t=n.get(e);for(const e of t.children)n.has(e.name)&&!s.has(e.name)&&(s.add(e.name),r.push(e.name))}return e.filter((e=>s.has(e.name)))}(p.map((e=>c.get(e))),u);return function(e,t){const n=new Map(e.map(((e,t)=>[e.name,t]))),r=new Set(t.map((e=>e.name))),s=e=>r.has("string"==typeof e?e:e.name),i=new Set(e.map((e=>e.name))),a=e=>i.has("string"==typeof e?e:e.name);for(const t of e){for(const e of t.children.filter(a)){if(!n.has(e.name))throw new Fe(`Child ${e.name} of node ${t.name} is unreachable.`);if(n.get(t.name)>n.get(e.name))throw new Fe(`Node ${t.name} is scheduled to run after its child ${e.name}.`)}if(!s(t))for(const e of t.inputs){if(!n.has(e.name))throw new Fe(`Input ${e.name} of node ${t.name} is unreachable.`);if(n.get(e.name)>n.get(t.name))throw new Fe(`Node ${t.name} is scheduled to run before its input ${e.name}.`)}}}(f,u),f}(this.graph,n),o=function(e){const t=new Map(e.map(((e,t)=>[e.name,t]))),n=Number.MAX_SAFE_INTEGER,r=e.map(((e,t)=>Le(e)?n:t)),s=e=>{const n=r[t.get(e.name)];return null==n?-1:n},i=e.map(((e,t)=>e.children.map(s).reduce(((e,t)=>Math.max(e,t)),r[t]))),a=new Map;for(let t=0;t<e.length;++t){const r=i[t];if(r===n)continue;const s=e[t],o=e[r];a.has(o.name)||a.set(o.name,[]),a.get(o.name).push(s)}return a}(a);return{orderedNodes:a,nodeLiveUntilMap:o}}cloneAndKeepTensor(e){if(null==e)return null;const t=e.clone();return(0,T.keep)(t),t}cloneTensorList(e){return e?e.map((e=>this.cloneAndKeepTensor(e))):null}cloneTensorMap(e){return Object.fromEntries(Object.entries(e).map((([e,t])=>[e,this.cloneTensorList(t)])))}execute(e,t){this.disposeIntermediateTensors(),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[D(e)[0]])),s=t.map((e=>D(e)[0])),i=new Set(s);let a=s.map((e=>this.graph.nodes[e]));0===a.length&&(a=this._outputs);const o=this.getCompilationKey(r,a);let u=this.compiledMap.get(o);null==u&&(u=this.compile(e,a),this.compiledMap.set(o,u));try{this.keepIntermediateTensors=(0,T.env)().getBool("KEEP_INTERMEDIATE_TENSORS")}catch(e){this.keepIntermediateTensors=!1,console.warn(e.message)}const l={},c={};return(0,T.tidy)((()=>{const n=new Re(this.weightMap,l,c,this.functionExecutorMap,this.parseNodeNameCache),r=Object.assign({},this.weightMap);this.keepIntermediateTensors&&(this.clonedTensorsMap=this.cloneTensorMap(this.weightMap)),Object.keys(e).forEach((t=>{const[s,i]=D(t,n),a=[];a[i]=e[t],r[s]=a,this.keepIntermediateTensors&&(this.clonedTensorsMap[s]=this.cloneTensorList(a))}

2));const s=this.getFrozenTensorIds(r),{orderedNodes:a,nodeLiveUntilMap:o}=u;for(const e of a){if(r[e.name])continue;const t=Ae(e,r,n,this._resourceManager);if(T.util.isPromise(t))throw new Error(`The execution of the op '${e.op}' returned a promise. Please use model.executeAsync() instead.`);r[e.name]=t,this.keepIntermediateTensors&&(this.clonedTensorsMap[e.name]=this.cloneTensorList(t)),this.checkTensorForDisposalWithNodeLiveUntilInfo(e,r,n,s,i,o.get(e.name))}return null==this.parent&&n.dispose(s),t.map((e=>R(e,r,n)))}))}getFrozenTensorIds(e){const t=[].concat.apply([],Object.keys(e).map((t=>e[t])).map((e=>e.map((e=>e.id)))));return new Set(t)}checkTensorForDisposal(e,t,n,r,s,i,a){if(!Le(t)&&!i.has(e)){for(const r of n[e])null!=r&&(a[r.id]=(a[r.id]||0)+t.children.length);for(const e of t.inputs){if(Le(e))continue;const t=$(e.name,n,r);if(null!=t)for(const e of t){if(!e||e.kept||s.has(e.id))continue;const t=a[e.id];1===t?(e.dispose(),delete a[e.id]):null!=t&&a[e.id]--}}}}checkTensorForDisposalWithNodeLiveUntilInfo(e,t,n,r,s,i){function a(e){return Le(e)||s.has(e.name)}if(!Le(e)&&null!=i)for(const e of i){if(a(e))continue;const s=$(e.name,t,n);for(const e of s)!e||e.kept||r.has(e.id)||e.dispose()}}async executeAsync(e,t){return this._executeAsync(e,t)}disposeIntermediateTensors(){this.clonedTensorsMap&&(Object.values(this.clonedTensorsMap).forEach((e=>{for(const t of e)t&&!t.isDisposed&&t.dispose()})),this.clonedTensorsMap=null)}getIntermediateTensors(){return this.clonedTensorsMap}async _executeAsync(e,t,n=!1,r={},s={}){this.disposeIntermediateTensors(),n||(e=this.mapInputs(e),this.checkInputs(e),this.checkInputShapeAndType(e),t=this.mapOutputs(t),this.checkOutputs(t));try{this.keepIntermediateTensors=(0,T.env)().getBool("KEEP_INTERMEDIATE_TENSORS")}catch(e){this.keepIntermediateTensors=!1,console.warn(e.message)}const i=new Re(this.weightMap,r,s,this.functionExecutorMap,this.parseNodeNameCache);this.keepIntermediateTensors&&(this.clonedTensorsMap=this.cloneTensorMap(this.weightMap));const a=await this.executeWithControlFlow(e,i,t,n),o=t.map((e=>R(e,a,i))),u=o.map((e=>e.id)),l=Object.keys(e).map((t=>e[t].id)),c=new Set([...u,...l,...this.weightIds]);return Object.values(a).forEach((e=>{e.forEach((e=>{!e||e.isDisposed||c.has(e.id)||e.dispose()}))})),null==this.parent&&i.dispose(c),o}async executeFunctionAsync(e,t,n){const r=e.reduce(((e,t,n)=>(e[this.inputs[n].name]=t,e)),{});return this._executeAsync(r,this.outputNodes,!0,t,n)}async executeWithControlFlow(e,t,n,r){const s=Object.keys(e),i=s.map((e=>this.graph.nodes[D(e)[0]])),a=n.map((e=>D(e)[0])),o=new Set(a);let u=a.map((e=>this.graph.nodes[e]));0===u.length&&(u=this._outputs);const{usedNodes:l,missingInputs:c,dynamicNode:d,syncInputs:h}=$e(e,u,this.weightMap,this._initNodes),p=[...i,...this.graph.weights,...this._initNodes||[]].map((e=>({node:e,contexts:t.currentContext}))),f=Object.assign({},this.weightMap);Object.keys(e).forEach((t=>{const[n,r]=D(t),s=[];s[r]=e[t],f[n]=s}));const m={},g=this.getFrozenTensorIds(f),y={};for(;p.length>0;){const e=this.processStack(i,p,t,f,y,g,o,m,l);await Promise.all(e)}null!=d||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 b=u.filter((e=>!Le(e)&&!R(e.name,f,t))).map((e=>e.name));if(b.length>0){let e="";throw null!=d&&(e=`Alternatively, to avoid the dynamic ops, use model.execute() and specify the inputs [${h}]`),new Error(`Cannot compute the outputs [${b}] from the provided inputs [${s}]. Consider providing the following inputs: [${c}]. ${e}`)}return f}processStack(e,t,n,r,s,i,a,o,u){const l=[];for(;t.length>0;){const e=t.pop();n.currentContext=e.contexts;let c="";if("Enter"===e.node.op&&A("isConstant",e.node,r,n)&&([c]=F(e.node.name,n)),null==r[e.node.name]){const d=Ae(e.node,r,n,this._resourceManager);c||([c]=F(e.node.name,n));const h=n.currentContext;T.util.isPromise(d)?l.push(d.then((l=>(r[c]=l,this.keepIntermediateTensors&&(this.clonedTensorsMap[c]=this.cloneTensorList(l)),n.currentContext=h,this.checkTensorForDisposal(c,e.node,r,n,i,a,o),this.processChildNodes(e.node,t,n,r,s,u),l)))):(r[c]=d,this.keepIntermediateTensors&&(this.clonedTensorsMap[c]=this.cloneTensorList(d)),this.checkTensorForDisposal(c,e.node,r,n,i,a,o),this.processChildNodes(e.node,t,n,r,s,u))}else this.processChildNodes(e.node,t,n,r,s,u)}return l}processChildNodes(e,t,n,r,s,i){e.children.forEach((e=>{const[a]=F(e.name,n);!s[a]&&i.has(e.name)&&("Merge"===e.op?e.inputNames.some((e=>!!R(e,r,n)))&&(s[a]=!0,t.push({contexts:n.currentContext,node:e})):e.inputNames.every((e=>!!R(e,r,n)))&&(s[a]=!0,t.push({contexts:n.currentContext,node:e})))}))}dispose(){Object.keys(this.weightMap).forEach((e=>this.weightMap[e].forEach((e=>e.dispose()))))}checkInputShapeAndType(e){Object.keys(e).forEach((t=>{const n=e[t],[r]=D(t),s=this.graph.nodes[r];if(s.attrParams.shape&&s.attrParams.shape.value){const e=s.attrParams.shape.value,t=e.length===n.shape.length&&n.shape.every(((t,n)=>-1===e[n]||e[n]===t));T.util.assert(t,(()=>`The shape of dict['${s.name}'] provided in model.execute(dict) must be [${e}], but was [${n.shape}]`))}s.attrParams.dtype&&s.attrParams.dtype.value&&T.util.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 ${n.dtype}`))}))}mapInputs(e){var t,n;const r={};for(const s in e){const i=null===(n=null===(t=this._signature)||void 0===t?void 0:t.inputs)||void 0===n?void 0:n[s];null!=i?r[i.name]=e[s]:r[s]=e[s]}return r}checkInputs(e){const t=Object.keys(e).filter((e=>{const[t]=D(e);return null==this.graph.nodes[t]}));if(t.length>0)throw new Error(`The dict provided in model.execute(dict) has keys: [${t}] that are not part of graph`)}mapOutputs(e){return e.map((e=>{var t,n;const r=null===(n=null===(t=this._signature)||void 0===t?void 0:t.outputs)||void 0===n?void 0:n[e];return null!=r?r.name:e}),{})}checkOutputs(e){e.forEach((e=>{const[t]=D(e);if(!this.graph.nodes[t])throw new Error(`The output '${e}' is not found in the graph`)}))}}class Ve{constructor(e={},t={}){this.hashTableNameToHandle=e,this.hashTableMap=t}addHashTable(e,t){this.hashTableNameToHandle[e]=t.handle,this.hashTableMap[t.id]=t}getHashTableHandleByName(e){return this.hashTableNameToHandle[e]}getHashTableById(e){return this.hashTableMap[e]}dispose(){for(const e in this.hashTableMap)this.hashTableMap[e].clearAndClose(),delete this.hashTableMap[e];for(const e in this.hashTableNameToHandle)this.hashTableNameToHandle[e].dispose(),delete this.hashTableNameToHandle[e]}}class We{get modelVersion(){return this.version}get inputNodes(){return this.executor.inputNodes}get outputNodes(){return this.executor.outputNodes}get inputs(){return this.executor.inputs}get outputs(){return this.executor.outputs}get weights(){return this.executor.weightMap}get metadata(){return this.artifacts.userDefinedMetadata}get modelSignature(){return this.signature}get modelStructuredOutputKeys(){return this.structuredOutputKeys}constructor(e,t={},n=T.io){this.modelUrl=e,this.loadOptions=t,this.version="n/a",this.io=n,null==t&&(this.loadOptions={}),this.resourceManager=new Ve}findIOHandler(){const e=this.modelUrl;if(null!=e.load)this.handler=e;else if(null!=this.loadOptions.requestInit)this.handler=this.io.browserHTTPRequest(e,this.loadOptions);else{const t=this.io.getLoadHandlers(e,this.loadOptions);if(0===t.length)t.push(this.io.browserHTTPRequest(e,this.loadOptions));else if(t.length>1)throw new Error(`Found more than one (${t.length}) load handlers for URL '${[e]}'`);this.handler=t[0]}}load(){if(this.findIOHandler(),null==this.handler.load)throw new Error("Cannot proceed with model loading because the IOHandler provided does not have the `load` method implemented.");const e=this.handler.load();return T.util.isPromise(e)?e.then((e=>this.loadSync(e))):this.loadSync(e)}loadSync(e){this.artifacts=e;const t=this.artifacts.modelTopology;let n=this.artifacts.signature;if(null!=this.artifacts.userDefinedMetadata){const e=this.artifacts.userDefinedMetadata;null!=e.signature&&(n=e.signature),null!=e.structuredOutputKeys&&(this.structuredOutputKeys=e.structuredOutputKeys)}this.signature=n,this.version=`${t.versions.producer}.${t.versions.minConsumer}`;const r=this.io.decodeWeights(this.artifacts.weightData,this.artifacts.weightSpecs);if(this.executor=new Be(re.Instance.transformGraph(t,this.signature)),this.executor.weightMap=this.convertTensorMapToTensorsMap(r),this.executor.resourceManager=this.resourceManager,null!=e.modelInitializer&&null!=e.modelInitializer.node){const t=re.Instance.transformGraph(e.modelInitializer);this.initializer=new Be(t),this.initializer.weightMap=this.executor.weightMap,this.initializer.resourceManager=this.resourceManager,this.initializerSignature=e.initializerSignature}return!0}async save(e,t){if("string"==typeof e){const t=this.io.getSaveHandlers(e);if(0===t.length)throw new Error(`Cannot find any save handlers for URL '${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)}addStructuredOutputNames(e){if(this.structuredOutputKeys){const t=e instanceof T.Tensor?[e]:e,n={};return t.forEach(((e,t)=>n[this.structuredOutputKeys[t]]=e)),n}return e}predict(e,t){const n=this.execute(e,this.outputNodes);return this.addStructuredOutputNames(n)}async predictAsync(e,t){const n=await this.executeAsync(e,this.outputNodes);return this.addStructuredOutputNames(n)}normalizeInputs(e){var t;if(!(e instanceof T.Tensor||Array.isArray(e))){const n=null===(t=this.signature)||void 0===t?void 0:t.inputs;if(null!=n)for(const t in n){const r=n[t];null!=r.resourceId&&(e[t]=this.resourceIdToCapturedInput[r.resourceId])}return e}e=Array.isArray(e)?e:[e];const n=Object.keys(this.resourceIdToCapturedInput).length;if(e.length+n!==this.inputNodes.length)throw new Error(`Input tensor count mismatch, the graph model has ${this.inputNodes.length-n} non-resource placeholders, while there are ${e.length} input tensors provided.`);let r=0;return this.inputNodes.reduce(((t,n)=>{var s,i,a;const o=null===(a=null===(i=null===(s=this.signature)||void 0===s?void 0:s.inputs)||void 0===i?void 0:i[n])||void 0===a?void 0:a.resourceId;return t[n]=null!=o?this.resourceIdToCapturedInput[o]:e[r++],t}),{})}normalizeOutputs(e){return e=e||this.outputNodes,Array.isArray(e)?e:[e]}executeInitializerGraph(){return null==this.initializer?[]:null==this.initializerSignature?this.initializer.execute({},[]):this.initializer.execute({},Object.keys(this.initializerSignature.outputs))}async executeInitializerGraphAsync(){return null==this.initializer?[]:null==this.initializerSignature?this.initializer.executeAsync({},[]):this.initializer.executeAsync({},Object.keys(this.initializerSignature.outputs))}setResourceIdToCapturedInput(e){if(this.resourceIdToCapturedInput={},this.initializerSignature){const t=this.initializerSignature.outputs,n=Object.keys(t);for(let r=0;r<n.length;r++){const s=t[n[r]];this.resourceIdToCapturedInput[s.resourceId]=e[r]}}}execute(e,t){null==this.resourceIdToCapturedInput&&this.setResourceIdToCapturedInput(this.executeInitializerGraph()),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){null==this.resourceIdToCapturedInput&&this.setResourceIdToCapturedInput(await this.executeInitializerGraphAsync()),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.resourceIdToCapturedInput&&(0,T.dispose)(this.resourceIdToCapturedInput)),this.resourceManager.dispose()}}async function Ue(e,t={},n=T.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){return e.endsWith("/")||(e+="/"),e+"model.json?tfjs-format=file"}(e));const r=new We(e,t,n);return await r.load(),r}function Ge(e){if(null==e)throw new Error("modelUrl in loadGraphModelSync() cannot be null. Please provide model artifacts or an IOHandler that loads the model");let t;if(e instanceof Array){const[n,r]=e;if(!n)throw new Error("modelJSON must be the first element of the array");if(!(r&&r instanceof ArrayBuffer))throw new Error("An ArrayBuffer of weights must be the second element of the array");if(!("modelTopology"in n))throw new Error("Model JSON is missing 'modelTopology'");

2if(!("weightsManifest"in n))throw new Error("Model JSON is missing 'weightsManifest'");const s=T.io.getWeightSpecs(n.weightsManifest),i=T.io.getModelArtifactsForJSONSync(n,s,r);t=T.io.fromMemorySync(i)}else if("load"in e)t=e;else{if(!("modelTopology"in e&&"weightSpecs"in e&&"weightData"in e))throw new Error("Unknown model format");t=T.io.fromMemorySync(e)}const n=new We(t);return n.load(),n}const He="4.10.0"},68713:function(e,t,n){"use strict";n.d(t,{JL:function(){return r},Zu:function(){return s}});class 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 i("refCount")}incRef(e){return i("incRef")}timerAvailable(){return!0}time(e){return i("time")}read(e){return i("read")}readSync(e){return i("readSync")}readToGPU(e,t){return i("readToGPU")}numDataIds(){return i("numDataIds")}disposeData(e,t){return i("disposeData")}write(e,t,n){return i("write")}move(e,t,n,r,s){return i("move")}createTensorFromGPUData(e,t,n){return i("createTensorFromGPUData")}memory(){return i("memory")}floatPrecision(){return i("floatPrecision")}epsilon(){return 32===this.floatPrecision()?1e-7:1e-4}dispose(){return i("dispose")}}function i(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`)}},83337: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,i=0,a=!1;for(;r<s;){i=r+(s-r>>>1);const o=n(t,e[i]);o>0?r=i+1:(s=i,a=!o)}return a?r:-r-1}(e,t,n||s)}(e,t,n),i=r<0?-(r+1):r;e.splice(i,0,t)}function s(e,t){return e>t?1:e<t?-1:0}function i(e,t,n,r,s){return u(e,t,n,r,s,0)}function a(e,t,n,r,s,i){return u(e,t,n,r,s,0,!1,i,!0)}function o(e,t,n,r,s,i){return u(e,t,n,r,s,i,!0)}function u(e,t,n,s,i,a,o=!1,u=!1,h=!1){const p=[];for(let e=0;e<t.length;e++)t[e]>i&&p.push({score:t[e],boxIndex:e,suppressBeginIndex:0});p.sort(d);const f=a>0?-.5/a:0,m=[],g=[];for(;m.length<n&&p.length>0;){const t=p.pop(),{score:n,boxIndex:a,suppressBeginIndex:o}=t;if(n<i)break;let u=!1;for(let n=m.length-1;n>=o;--n){const r=l(e,a,m[n]);if(r>=s){u=!0;break}if(t.score=t.score*c(s,f,r),t.score<=i)break}t.suppressBeginIndex=m.length,u||(t.score===n?(m.push(a),g.push(t.score)):t.score>i&&r(p,t,d))}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 v={selectedIndices:m};return o&&(v.selectedScores=g),h&&(v.validOutputs=y),v}function l(e,t,n){const r=e.subarray(4*t,4*t+4),s=e.subarray(4*n,4*n+4),i=Math.min(r[0],r[2]),a=Math.min(r[1],r[3]),o=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]),d=Math.max(s[0],s[2]),h=Math.max(s[1],s[3]),p=(o-i)*(u-a),f=(d-l)*(h-c);if(p<=0||f<=0)return 0;const m=Math.max(i,l),g=Math.max(a,c),y=Math.min(o,d),b=Math.min(u,h),v=Math.max(y-m,0)*Math.max(b-g,0);return v/(p+f-v)}function c(e,t,n){const r=Math.exp(t*n*n);return n<=e?r:0}function d(e,t){return e.score-t.score||e.score===t.score&&t.boxIndex-e.boxIndex}n.d(t,{GP:function(){return i},qP:function(){return a},pA:function(){return o}})},48333:function(e,t,n){"use strict";n.d(t,{Z:function(){return s}});var r=n(72657);function s(e,t){const n=[];for(let e=0;e<t.length;e++)t[e]&&n.push(e);const s=(0,r.f)(e,"int32"),i=(0,r.f)([n.length,e.length],"int32");for(let t=0;t<n.length;t++){const r=s.indexToLoc(n[t]),a=t*e.length;i.values.set(r,a)}return i.toTensor()}},97097:function(e,t,n){"use strict";n.d(t,{BV:function(){return x},wv:function(){return v}});var r=n(68713),s=n(22885),i=n(55938),a=n(29121),o=n(26151),u=n(64706),l=n(79122),c=n(20569);class d{constructor(e,t){this.backendTimer=e,this.logger=t,null==t&&(this.logger=new p)}profileKernel(e,t,n){let r;const i=()=>{r=n()};let a;const o=l.now();if(this.backendTimer.timerAvailable())a=this.backendTimer.time(i);else{i();for(const e of r)e.dataSync();a=Promise.resolve({kernelMs:l.now()-o})}if((0,s.OB)().getBool("CHECK_COMPUTATION_FOR_ERRORS"))for(let t=0;t<r.length;t++){const n=r[t];n.data().then((t=>{h(t,n.dtype,e)}))}return{kernelName:e,outputs:r,inputs:t,timeMs:a.then((e=>e.kernelMs)),extraInfo:a.then((e=>null!=e.getExtraProfileInfo?e.getExtraProfileInfo():""))}}logKernelProfile(e){const{kernelName:t,outputs:n,timeMs:r,inputs:s,extraInfo:i}=e;n.forEach((e=>{Promise.all([e.data(),r,i]).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 t=0;t<e.length;t++){const r=e[t];if(isNaN(r)||!isFinite(r))return console.warn(`Found ${r} in the result of '${n}'`),!0}return!1}class p{logKernelProfile(e,t,n,r,s,i){const a="number"==typeof r?c.oj(r+"ms",9):r.error,o=c.oj(e,25),u=t.rank,l=t.size,d=c.oj(t.shape.toString(),14);let h="";for(const e in s){const n=s[e];if(null!=n){const r=n.shape||t.shape,s=r.length;h+=`${e}: ${s}D ${s>0?r:""} `}}console.log(`%c${o}\t%c${a}\t%c${u}D ${d}\t%c${l}\t%c${h}\t%c${i}`,"font-weight:bold","color:red","color:blue","color: orange","color: green","color: steelblue")}}var f=n(4077),m=n(80747);function g(e){return null!=e.kernelName}class y{constructor(){this.registeredVariables={},this.nextTapeNodeId=0,this.numBytes=0,this.numTensors=0,this.numStringTensors=0,this.numDataBuffers=0,this.gradientDepth=0,this.kernelDepth=0,this.scopeStack=[],this.numDataMovesStack=[],this.nextScopeId=0,this.tensorInfo=new WeakMap,this.profiling=!1,this.activeProfile={newBytes:0,newTensors:0,peakBytes:0,kernels:[],result:null,get kernelNames(){return Array.from(new Set(this.kernels.map((e=>e.name))))}}}dispose(){for(const e in this.registeredVariables)this.registeredVariables[e].dispose()}}class b{constructor(e){this.ENV=e,this.registry={},this.registryFactory={},this.pendingBackendInitId=0,this.state=new y}async ready(){if(null!=this.pendingBackendInit)return this.pendingBackendInit.then((()=>{}));if(null!=this.backendInstance)return;const e=this.getSortedBackends();for(let t=0;t<e.length;t++){const n=e[t];if(await this.initializeBackend(n).success)return void await this.setBackend(n)}throw new Error("Could not initialize any backends, all backend initializations failed.")}get backend(){if(null!=this.pendingBackendInit)throw new Error(`Backend '${this.backendName}' has not yet been initialized. Make sure to await tf.ready() or await tf.setBackend() before calling other methods`);if(null==this.backendInstance){const{name:e,asyncInit:t}=this.initializeBackendsAndReturnBest();if(t)throw new Error(`The highest priority backend '${e}' has not yet been initialized. Make sure to await tf.ready() or await tf.setBackend() before calling other methods`);this.setBackend(e)}return this.backendInstance}backendNames(){return Object.keys(this.registryFactory)}findBackend(e){if(!(e in this.registry)){if(!(e in this.registryFactory))return null;{const{asyncInit:t}=this.initializeBackend(e);if(t)return null}}return this.registry[e]}findBackendFactory(e){return e in this.registryFactory?this.registryFactory[e].factory:null}registerBackend(e,t,n=1){return e in this.registryFactory?(u.Z(e+" backend was already registered. Reusing existing backend factory."),!1):(this.registryFactory[e]={factory:t,priority:n},!0)}async setBackend(e){if(null==this.registryFactory[e])throw new Error(`Backend name '${e}' not found in registry`);

2if(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 d(this.backendInstance),!0}setupRegisteredKernels(){(0,o.tr)(this.backendName).forEach((e=>{null!=e.setupFunc&&e.setupFunc(this.backendInstance)}))}disposeRegisteredKernels(e){(0,o.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(t){return u.Z(`Initialization of backend ${e} failed`),u.Z(t.stack||t.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),i=r.refCount(t);r.disposeData(t,!0),n.backend=e,e.move(t,s,n.shape,n.dtype,i),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(e){throw t(),e}}nextTensorId(){return b.nextTensorId++}nextVariableId(){return b.nextVariableId++}clone(e){const t=x.runKernel(a.iJz,{x:e}),n={x:e};return this.addTapeNode(this.state.activeScope.name,n,[t],(e=>({x:()=>{const t={x:e};return x.runKernel(a.RFZ,t,{dtype:"float32"})}})),[],{}),t}runKernel(e,t,n){if(null==this.backendName&&this.backend,null==(0,o.pI)(e,this.backendName))throw new Error(`Kernel '${e}' not registered for backend '${this.backendName}'`);return this.runKernelFunc({kernelName:e,inputs:t,attrs:n})}shouldCheckForMemLeaks(){return this.ENV.getBool("IS_TEST")}checkKernelForMemLeak(e,t,n){const r=this.backend.numDataIds();let s=0;n.forEach((e=>{s+="complex64"===e.dtype?3:1}));const i=this.state.numDataMovesStack[this.state.numDataMovesStack.length-1],a=r-t-s-i;if(a>0)throw new Error(`Backend '${this.backendName}' has an internal memory leak (${a} data ids) after running '${e}'`)}runKernelFunc(e){let t,n=[];const r=this.isTapeOn(),s=this.state.numBytes,i=this.state.numTensors;let a,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:i}=e;null==this.backendName&&this.backend;const l=(0,o.pI)(t,this.backendName);c.hu(null!=l,(()=>`Cannot find registered kernel '${t}' for backend '${this.backendName}'`)),a=()=>{const e=this.backend.numDataIds();

2u=l.kernelFunc({inputs:s,attrs:i,backend:this.backend});const a=Array.isArray(u)?u:[u];this.shouldCheckForMemLeaks()&&this.checkKernelForMemLeak(t,e,a);const o=a.map((e=>null!=e.rank?e:this.makeTensorFromTensorInfo(e)));if(r){const e=this.getTensorsForGradient(t,s,o);n=this.saveTensorsForBackwardMode(e)}return o}}else{const{forwardFunc:t}=e,s=e=>{r&&(n=e.map((e=>this.keep(this.clone(e)))))};a=()=>{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:d,attrs:h}=e,p=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,d,(()=>a())),this.ENV.getBool("DEBUG")&&this.profiler.logKernelProfile(f),t=f.outputs):t=a()})),r&&this.addTapeNode(l,d,t,p,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-i,totalTensorsSnapshot:this.state.numTensors,inputShapes:Object.keys(d).map((e=>null!=d[e]?d[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,o.uk)(e);if(null!=r){const e=r.inputsToSave||[],s=r.outputsToSave||[];let i;r.saveAllInputs?(c.hu(Array.isArray(t),(()=>"saveAllInputs is true, expected inputs to be an array.")),i=Object.keys(t).map((e=>t[e]))):i=e.map((e=>t[e]));const a=n.filter(((e,t)=>s[t]));return i.concat(a)}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 i=r.write(s,t,n),a=new f.es(t,n,i,this.nextTensorId());if(this.trackTensor(a,r),"string"===n){const e=this.state.tensorInfo.get(i),t=(0,c.Ub)(s);this.state.numBytes+=t-e.bytes,e.bytes=t}return a}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,i=new f.es(r,s,n,this.nextTensorId());return this.trackTensor(i,t),i}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 e of this.state.activeProfile.kernels)e.kernelTimeMs=await e.kernelTimeMs,e.extraInfo=await e.extraInfo;return this.state.activeProfile}isTapeOn(){return this.state.gradientDepth>0&&0===this.state.kernelDepth}addTapeNode(e,t,n,r,s,i){const a={id:this.state.nextTapeNodeId++,kernelName:e,inputs:t,outputs:n,saved:s},u=(0,o.uk)(e);null!=u&&(r=u.gradFunc),null!=r&&(a.gradient=e=>(e=e.map(((e,t)=>{if(null==e){const e=n[t],r=c.wT(e.size,e.dtype);return this.makeTensor(r,e.shape,e.dtype)}return e})),r(e.length>1?e:e[0],s,i))),this.state.activeTape.push(a)}keep(e){return e.kept=!0,e}startTape(){0===this.state.gradientDepth&&(this.state.activeTape=[]),this.state.gradientDepth++}endTape(){this.state.gradientDepth--}startScope(e){const t={track:[],name:"unnamed scope",id:this.state.nextScopeId++};e&&(t.name=e),this.state.scopeStack.push(t),this.state.activeScope=t}endScope(e){const t=(0,m.getTensorsInContainer)(e),n=new Set(t.map((e=>e.id)));for(let e=0;e<this.state.activeScope.track.length;e++){const t=this.state.activeScope.track[e];t.kept||n.has(t.id)||t.dispose()}const r=this.state.scopeStack.pop();this.state.activeScope=0===this.state.scopeStack.length?null:this.state.scopeStack[this.state.scopeStack.length-1],t.forEach((e=>{e.kept||e.scopeId!==r.id||this.track(e)}))}gradients(e,t,n,r=!1){if(c.hu(t.length>0,(()=>"gradients() received an empty list of xs.")),null!=n&&"float32"!==n.dtype)throw new Error(`dy must have 'float32' dtype, but has '${n.dtype}'`);const s=this.scopedRun((()=>this.startTape()),(()=>this.endTape()),(()=>this.tidy("forward",e)));c.hu(s instanceof f.es,(()=>"The result y returned by f() must be a tensor."));const i=function(e,t,n){const r={},s={};for(let e=0;e<t.length;e++)r[t[e].id]=!0;for(let n=0;n<e.length;n++){const i=e[n],a=i.inputs;for(const e in a){const n=a[e];let o=!1;for(let e=0;e<t.length;e++)if(r[n.id]){i.outputs.forEach((e=>r[e.id]=!0)),o=!0,s[i.id]=!0;break}if(o)break}}const i={};i[n.id]=!0;const a={};for(let t=e.length-1;t>=0;t--){const n=e[t],r=n.inputs;for(let e=0;e<n.outputs.length;e++)if(i[n.outputs[e].id]){for(const e in r)i[r[e].id]=!0,a[n.id]=!0;break}}const o=[];for(let t=0;t<e.length;t++){const n=e[t];if(s[n.id]&&a[n.id]){const e={};for(const t in n.inputs){const s=n.inputs[t];r[s.id]&&(e[t]=s)}const t=Object.assign({},n);t.inputs=e,t.outputs=n.outputs,o.push(t)}}return o}(this.state.activeTape,t,s);if(!r&&0===i.length&&t.length>0)throw new Error("Cannot compute gradient of y=f(x) with respect to x. 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null!=e.signature&&(n.signature=e.signature),null!=e.userDefinedMetadata&&(n.userDefinedMetadata=e.userDefinedMetadata),null!=e.modelInitializer&&(n.modelInitializer=e.modelInitializer),null!=e.initializerSignature&&(n.initializerSignature=e.initializerSignature),null!=e.trainingConfig&&(n.trainingConfig=e.trainingConfig),n}function O(e,t,n){const r={modelTopology:e.modelTopology,format:e.format,generatedBy:e.generatedBy,convertedBy:e.convertedBy};if(null!=e.trainingConfig&&(r.trainingConfig=e.trainingConfig),null!=e.weightsManifest){if(!t)throw new Error("modelJSON has weightsManifest but weightSpecs is null");if(!n)throw new Error("modelJSON has weightsManifest but weightData is null");r.weightSpecs=t,r.weightData=n}return null!=e.signature&&(r.signature=e.signature),null!=e.userDefinedMetadata&&(r.userDefinedMetadata=e.userDefinedMetadata),null!=e.modelInitializer&&(r.modelInitializer=e.modelInitializer),null!=e.initializerSignature&&(r.initializerSignature=e.initializerSignature),r}async function D(e,t){let n,r;return null!=e.weightsManifest&&([n,r]=await t(e.weightsManifest)),O(e,n,r)}function M(e){if(e.modelTopology instanceof ArrayBuffer)throw new Error("Expected JSON model topology, received ArrayBuffer.");return{dateSaved:new Date,modelTopologyType:"JSON",modelTopologyBytes:null==e.modelTopology?0:A(JSON.stringify(e.modelTopology)),weightSpecsBytes:null==e.weightSpecs?0:A(JSON.stringify(e.weightSpecs)),weightDataBytes:null==e.weightData?0:new T(e.weightData).byteLength}}function L(e){const t=[];for(const n of e)t.push(...n.weights);return t}function z(){const e=function(){const e=e=>{let t=e<<13,n=0;for(;0==(8388608&t);)n-=8388608,t<<=1;return t&=-8388609,n+=947912704,t|n},t=new Uint32Array(2048);t[0]=0;for(let n=1;n<1024;n++)t[n]=e(n);for(let e=1024;e<2048;e++)t[e]=939524096+(e-1024<<13);return t}(),t=function(){const e=new Uint32Array(64);e[0]=0,e[31]=1199570944,e[32]=2147483648,e[63]=3347054592;for(let t=1;t<31;t++)e[t]=t<<23;for(let t=33;t<63;t++)e[t]=2147483648+(t-32<<23);return e}(),n=function(){const e=new Uint32Array(64);for(let t=0;t<64;t++)e[t]=1024;return e[0]=e[32]=0,e}();return r=>{const s=new ArrayBuffer(4*r.length),i=new Uint32Array(s);for(let s=0;s<r.length;s++){const a=r[s],o=e[n[a>>10]+(1023&a)]+t[a>>10];i[s]=o}return new Float32Array(s)}}class P{constructor(){this.saveRouters=[],this.loadRouters=[]}static getInstance(){return null==P.instance&&(P.instance=new P),P.instance}static registerSaveRouter(e){P.getInstance().saveRouters.push(e)}static registerLoadRouter(e){P.getInstance().loadRouters.push(e)}static getSaveHandlers(e){return P.getHandlers(e,"save")}static getLoadHandlers(e,t){return P.getHandlers(e,"load",t)}static 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save(e){if(e.modelTopology instanceof ArrayBuffer)throw new Error("BrowserLocalStorage.save() does not support saving model topology in binary formats yet.");return this.databaseAction(this.modelPath,e)}async load(){return this.databaseAction(this.modelPath)}databaseAction(e,t){return new Promise(((e,n)=>{const r=this.indexedDB.open(G,1);r.onupgradeneeded=()=>X(r),r.onsuccess=()=>{const s=r.result;if(null==t){const t=s.transaction(H,"readonly"),r=t.objectStore(H).get(this.modelPath);r.onsuccess=()=>{if(null==r.result)return s.close(),n(new Error(`Cannot find model with path '${this.modelPath}' in IndexedDB.`));e(r.result.modelArtifacts)},r.onerror=e=>(s.close(),n(r.error)),t.oncomplete=()=>s.close()}else{t.weightData=T.join(t.weightData);const r=M(t),i=s.transaction(j,"readwrite");let a,o,u=i.objectStore(j);try{a=u.put({modelPath:this.modelPath,modelArtifactsInfo:r})}catch(e){return n(e)}a.onsuccess=()=>{o=s.transaction(H,"readwrite");const a=o.objectStore(H);let 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i.result)t[e.modelPath]=e.modelArtifactsInfo;e(t)},i.onerror=e=>(r.close(),t(i.error)),s.oncomplete=()=>r.close()},n.onerror=e=>t(n.error)}))}async removeModel(e){var t;return e=(t=e).startsWith(K.URL_SCHEME)?t.slice(K.URL_SCHEME.length):t,new Promise(((t,n)=>{const r=this.indexedDB.open(G,1);r.onupgradeneeded=()=>X(r),r.onsuccess=()=>{const s=r.result,i=s.transaction(j,"readwrite"),a=i.objectStore(j),o=a.get(e);let u;o.onsuccess=()=>{if(null==o.result)return s.close(),n(new Error(`Cannot find model with path '${e}' in IndexedDB.`));{const r=a.delete(e),i=()=>{u=s.transaction(H,"readwrite");const r=u.objectStore(H).delete(e);r.onsuccess=()=>t(o.result.modelArtifactsInfo),r.onerror=e=>n(o.error)};r.onsuccess=i,r.onerror=e=>(i(),s.close(),n(o.error))}},o.onerror=e=>(s.close(),n(o.error)),i.oncomplete=()=>{null==u?s.close():u.oncomplete=()=>s.close()}},r.onerror=e=>n(r.error)}))}}const Z="/",J="tensorflowjs_models",ee="info",te="model_topology",ne="weight_specs",re="weight_data",se="model_metadata";function ie(e){return{info:[J,e,ee].join(Z),topology:[J,e,te].join(Z),weightSpecs:[J,e,ne].join(Z),weightData:[J,e,re].join(Z),modelMetadata:[J,e,se].join(Z)}}function ae(e){for(const t of Object.values(e))window.localStorage.removeItem(t)}function oe(e){const t=e.split(Z);if(t.length<3)throw new Error("Invalid key format: "+e);return t.slice(1,t.length-1).join(Z)}

2class ue{constructor(e){if(!(0,b.OB)().getBool("IS_BROWSER")||"undefined"==typeof window||void 0===window.localStorage)throw new Error("The current environment does not support local storage.");if(this.LS=window.localStorage,null==e||!e)throw new Error("For local storage, modelPath must not be null, undefined or empty.");this.modelPath=e,this.keys=ie(this.modelPath)}async save(e){if(e.modelTopology instanceof ArrayBuffer)throw new Error("BrowserLocalStorage.save() does not support saving model topology in binary formats yet.");{const t=JSON.stringify(e.modelTopology),n=JSON.stringify(e.weightSpecs),r=M(e),s=T.join(e.weightData);try{this.LS.setItem(this.keys.info,JSON.stringify(r)),this.LS.setItem(this.keys.topology,t),this.LS.setItem(this.keys.weightSpecs,n),this.LS.setItem(this.keys.weightData,function(e){if(_)return Buffer.from(e).toString("base64");const t=new Uint8Array(e);let n="";for(let e=0,r=t.length;e<r;e++)n+=String.fromCharCode(t[e]);return btoa(n)}(s));const i={format:e.format,generatedBy:e.generatedBy,convertedBy:e.convertedBy,signature:null!=e.signature?e.signature:void 0,userDefinedMetadata:null!=e.userDefinedMetadata?e.userDefinedMetadata:void 0,modelInitializer:null!=e.modelInitializer?e.modelInitializer:void 0,initializerSignature:null!=e.initializerSignature?e.initializerSignature:void 0,trainingConfig:null!=e.trainingConfig?e.trainingConfig:void 0};return this.LS.setItem(this.keys.modelMetadata,JSON.stringify(i)),{modelArtifactsInfo:r}}catch(e){throw ae(this.keys),new Error(`Failed to save model '${this.modelPath}' to local storage: size quota being exceeded is a possible cause of this failure: modelTopologyBytes=${r.modelTopologyBytes}, weightSpecsBytes=${r.weightSpecsBytes}, weightDataBytes=${r.weightDataBytes}.`)}}}async load(){const e=JSON.parse(this.LS.getItem(this.keys.info));if(null==e)throw new Error(`In local storage, there is no model with name '${this.modelPath}'`);if("JSON"!==e.modelTopologyType)throw new Error("BrowserLocalStorage does not support loading non-JSON model topology yet.");const t={},n=JSON.parse(this.LS.getItem(this.keys.topology));if(null==n)throw new Error(`In local storage, the topology of model '${this.modelPath}' is missing.`);t.modelTopology=n;const r=JSON.parse(this.LS.getItem(this.keys.weightSpecs));if(null==r)throw new Error(`In local storage, the weight specs of model '${this.modelPath}' are missing.`);t.weightSpecs=r;const s=this.LS.getItem(this.keys.modelMetadata);if(null!=s){const e=JSON.parse(s);t.format=e.format,t.generatedBy=e.generatedBy,t.convertedBy=e.convertedBy,null!=e.signature&&(t.signature=e.signature),null!=e.userDefinedMetadata&&(t.userDefinedMetadata=e.userDefinedMetadata),null!=e.modelInitializer&&(t.modelInitializer=e.modelInitializer),null!=e.initializerSignature&&(t.initializerSignature=e.initializerSignature),null!=e.trainingConfig&&(t.trainingConfig=e.trainingConfig)}const i=this.LS.getItem(this.keys.weightData);if(null==i)throw new Error(`In local storage, the binary weight values of model '${this.modelPath}' are missing.`);return t.weightData=function(e){if(_){const t=Buffer.from(e,"base64");return t.buffer.slice(t.byteOffset,t.byteOffset+t.byteLength)}const t=atob(e),n=new Uint8Array(t.length);for(let e=0;e<t.length;++e)n.set([t.charCodeAt(e)],e);return n.buffer}(i),t}}ue.URL_SCHEME="localstorage://";const le=e=>{return(0,b.OB)().getBool("IS_BROWSER")&&!Array.isArray(e)&&e.startsWith(ue.URL_SCHEME)?(t=e.slice(ue.URL_SCHEME.length),new ue(t)):null;var t};P.registerSaveRouter(le),P.registerLoadRouter(le);class ce{constructor(){(0,k.hu)((0,b.OB)().getBool("IS_BROWSER"),(()=>"Current environment is not a web browser")),(0,k.hu)("undefined"==typeof window||void 0!==window.localStorage,(()=>"Current browser does not appear to support localStorage")),this.LS=window.localStorage}async listModels(){const e={},t=J+Z,n=Z+ee;for(let r=0;r<this.LS.length;++r){const s=this.LS.key(r);s.startsWith(t)&&s.endsWith(n)&&(e[oe(s)]=JSON.parse(this.LS.getItem(s)))}return e}async removeModel(e){var t;const n=ie(e=(t=e).startsWith(ue.URL_SCHEME)?t.slice(ue.URL_SCHEME.length):t);if(null==this.LS.getItem(n.info))throw new Error(`Cannot find model at path '${e}'`);const r=JSON.parse(this.LS.getItem(n.info));return ae(n),r}}const de="://";class he{constructor(){this.managers={}}static getInstance(){return null==he.instance&&(he.instance=new he),he.instance}static registerManager(e,t){(0,k.hu)(null!=e,(()=>"scheme must not be undefined or null.")),e.endsWith(de)&&(e=e.slice(0,e.indexOf(de))),(0,k.hu)(e.length>0,(()=>"scheme must not be an empty string."));const n=he.getInstance();(0,k.hu)(null==n.managers[e],(()=>`A model store manager is already registered for scheme '${e}'.`)),n.managers[e]=t}static getManager(e){const t=he.getInstance().managers[e];if(null==t)throw new Error(`Cannot find model manager for scheme '${e}'`);return t}static getSchemes(){return Object.keys(he.getInstance().managers)}}function pe(e){if(-1===e.indexOf(de))throw new Error("The url string provided does not contain a scheme. 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2wi.className="TruncatedNormal",p.serialization.registerClass(wi);class ki extends mi{constructor(e){super(),this.gain=null!=e.gain?e.gain:1}apply(e,t){return(0,p.tidy)((()=>{if(2!==e.length||e[0]!==e[1])throw new rs("Identity matrix initializer can only be used for 2D square matrices.");return(0,p.mul)(this.gain,(0,p.eye)(e[0]))}))}getConfig(){return{gain:this.gain}}}ki.className="Identity",p.serialization.registerClass(ki);class Si extends mi{constructor(e){if(super(),e.scale<0)throw new rs("scale must be a positive float. Got: "+e.scale);var t;this.scale=null==e.scale?1:e.scale,this.mode=null==e.mode?"fanIn":e.mode,t=this.mode,ws(pi,"FanMode",t),this.distribution=null==e.distribution?"normal":e.distribution,function(e){ws(fi,"Distribution",e)}(this.distribution),this.seed=e.seed}apply(e,t){const n=function(e,t="channelsLast"){let n,r;if(Ms(t),2===e.length)n=e[0],r=e[1];else if(-1!==[3,4,5].indexOf(e.length)){if("channelsFirst"===t){const t=Hs(e,2);n=e[1]*t,r=e[0]*t}else if("channelsLast"===t){const t=Hs(e,0,e.length-2);n=e[e.length-2]*t,r=e[e.length-1]*t}}else{const t=Hs(e);n=Math.sqrt(t),r=Math.sqrt(t)}return[n,r]}(e),r=n[0],s=n[1];let i=this.scale;if("fanIn"===this.mode?i/=Math.max(1,r):"fanOut"===this.mode?i/=Math.max(1,s):i/=Math.max(1,(r+s)/2),"normal"===this.distribution){const n=Math.sqrt(i);if("float32"!==(t=t||"float32")&&"int32"!==t)throw new ss(`${this.getClassName()} does not support dType ${t}.`);return(0,p.truncatedNormal)(e,0,n,t,this.seed)}{const n=Math.sqrt(3*i);return(0,p.randomUniform)(e,-n,n,t,this.seed)}}getConfig(){return{scale:this.scale,mode:this.mode,distribution:this.distribution,seed:this.seed}}}Si.className="VarianceScaling",p.serialization.registerClass(Si);class Ii extends Si{constructor(e){super({scale:1,mode:"fanAvg",distribution:"uniform",seed:null==e?null:e.seed})}getClassName(){return Si.className}}Ii.className="GlorotUniform",p.serialization.registerClass(Ii);class Ti extends Si{constructor(e){super({scale:1,mode:"fanAvg",distribution:"normal",seed:null==e?null:e.seed})}getClassName(){return Si.className}}Ti.className="GlorotNormal",p.serialization.registerClass(Ti);class Ni extends Si{constructor(e){super({scale:2,mode:"fanIn",distribution:"normal",seed:null==e?null:e.seed})}getClassName(){return Si.className}}Ni.className="HeNormal",p.serialization.registerClass(Ni);class Ci extends Si{constructor(e){super({scale:2,mode:"fanIn",distribution:"uniform",seed:null==e?null:e.seed})}getClassName(){return Si.className}}Ci.className="HeUniform",p.serialization.registerClass(Ci);class Ei extends Si{constructor(e){super({scale:1,mode:"fanIn",distribution:"normal",seed:null==e?null:e.seed})}getClassName(){return Si.className}}Ei.className="LeCunNormal",p.serialization.registerClass(Ei);class _i extends Si{constructor(e){super({scale:1,mode:"fanIn",distribution:"uniform",seed:null==e?null:e.seed})}getClassName(){return Si.className}}_i.className="LeCunUniform",p.serialization.registerClass(_i);class Ai extends mi{constructor(e){super(),this.DEFAULT_GAIN=1,this.ELEMENTS_WARN_SLOW=2e3,this.gain=null==e.gain?this.DEFAULT_GAIN:e.gain,this.seed=e.seed}apply(e,t){return(0,p.tidy)((()=>{if(e.length<2)throw new ss("Shape must be at least 2D.");if("int32"!==t&&"float32"!==t&&void 0!==t)throw new TypeError(`Unsupported data type ${t}.`);t=t;const n=p.util.sizeFromShape(e.slice(0,-1)),r=e[e.length-1],s=n*r;s>this.ELEMENTS_WARN_SLOW&&console.warn(`Orthogonal initializer is being called on a matrix with more than ${this.ELEMENTS_WARN_SLOW} (${s}) elements: Slowness may result.`);const i=ii([Math.max(r,n),Math.min(r,n)],0,1,t,this.seed),a=p.linalg.qr(i,!1);let o=a[0];const u=a[1].flatten().stridedSlice([0],[Math.min(r,n)*Math.min(r,n)],[Math.min(r,n)+1]);return o=(0,p.mul)(o,u.sign()),n<r&&(o=o.transpose()),(0,p.mul)((0,p.scalar)(this.gain),o.reshape(e))}))}getConfig(){return{gain:this.gain,seed:this.seed}}}Ai.className="Orthogonal",p.serialization.registerClass(Ai);const Ri={constant:"Constant",glorotNormal:"GlorotNormal",glorotUniform:"GlorotUniform",heNormal:"HeNormal",heUniform:"HeUniform",identity:"Identity",leCunNormal:"LeCunNormal",le

2CunUniform:"LeCunUniform",ones:"Ones",orthogonal:"Orthogonal",randomNormal:"RandomNormal",randomUniform:"RandomUniform",truncatedNormal:"TruncatedNormal",varianceScaling:"VarianceScaling",zeros:"Zeros"};function $i(e,t={}){return ys(e,p.serialization.SerializationMap.getMap().classNameMap,t,"initializer")}function Fi(e){return ms(e)}function Oi(e){if("string"==typeof e){const t=e in Ri?Ri[e]:e;if("GlorotNormal"===t)return new Ti;if("GlorotUniform"===t)return new Ii;if("HeNormal"===t)return new Ni;if("HeUniform"===t)return new Ci;if("LeCunNormal"===t)return new Ei;if("LeCunUniform"===t)return new _i;{const e={};return e.className=t,e.config={},$i(e)}}return e instanceof mi?e:$i(e)}function Di(e){return Array.isArray(e)&&Array.isArray(e[0])}function Mi(e){return 0===e.length?[]:Array.isArray(e[0])?e:[e]}function Li(e){let t;if(Array.isArray(e)){if(1!==e.length)throw new rs("Expected Tensor length to be 1; got "+e.length);t=e[0]}else t=e;return t}function zi(e){if(Array.isArray(e)&&Array.isArray(e[0])){if(1===e.length)return(e=e)[0];throw new rs("Expected exactly 1 Shape; got "+e.length)}return e}function Pi(e){let t=0;for(const n of e)0===n.shape.length?t+=1:t+=n.shape.reduce(((e,t)=>e*t));return t}class Bi{constructor(e,t="float32",n="Variable",r=!0,s=null){this.dtype=null==t?"float32":t,this.shape=e.shape,this.id=Cs(),n=null==n?"Variable":n,this.originalName=Vs(n),this.name=Ws(this.originalName),this.trainable_=r,this.constraint=s,this.val=p.variable(e,this.trainable_,this.name,this.dtype)}read(){return this.assertNotDisposed(),this.val}write(e){return this.assertNotDisposed(),function(e,t){if(e.shape.toString()!==t.shape.toString())throw new Error("Shape mismatch: "+JSON.stringify(e.shape)+" vs. "+JSON.stringify(t.shape))}(this.val,e),this.val.id!==e.id&&(this.val.assign(e),null!=this.constraint&&this.val.assign(this.constraint.apply(this.val))),this}dispose(){this.assertNotDisposed(),this.val.dispose()}assertNotDisposed(){if(this.val.isDisposed)throw new Error(`LayersVariable ${this.name} is already disposed.`)}get trainable(){return this.trainable_}set trainable(e){this.trainable_=e,this.val.trainable=e}}function Vi(e){return e.map((e=>e.read()))}function Wi(e){e.forEach((e=>{e[0].write(e[1])}))}class Ui{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 Gi{constructor(e,t,n,r,s,i,a){this.dtype=e,this.shape=t,this.sourceLayer=n,this.inputs=r,this.callArgs=s,this.outputTensorIndex=a,this.id=Cs(),null!=i&&(this.originalName=Vs(i),this.name=Ws(this.originalName)),this.rank=t.length}}let Hi=0;class ji{constructor(e,t){this.callArgs=t,this.id=Hi++,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 t of e.inboundLayers)null!=t&&t.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 qi=0;class Xi extends p.serialization.Serializable{constructor(e={}){super(),this._callHook=null,this._addedWeightNames=[],this._stateful=!1,this.id=qi++,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=hs(e)+"_"+_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}

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Use `getInputAt(nodeIndex)` instead.');if(0===this.inboundNodes.length)throw new ts("Layer "+this.name+" is not connected, no input to return.");return cs(this.getNodeAtIndex(0,"input").inputTensors)}get output(){if(0===this.inboundNodes.length)throw new ts("Layer "+this.name+" has no inbound nodes.");if(this.inboundNodes.length>1)throw new ts("Layer "+this.name+' has multiple inbound nodes, hence the notion of "layer output" is ill-defined. Use `getOutputAt(nodeIndex)` instead.');return cs(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=ds(e),null==this.inputSpec||0===this.inputSpec.length)return;const t=ds(this.inputSpec);if(e.length!==t.length)throw new rs(`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 i=r.rank;if(null!=s.ndim&&i!==s.ndim)throw new rs(`Input ${n} is incompatible with layer ${this.name}: expected ndim=${s.ndim}, found ndim=${i}`);if(null!=s.maxNDim&&i>s.maxNDim)throw new rs(`Input ${n} is incompatible with layer ${this.name}: expected max_ndim=${s.maxNDim}, found ndim=${i}`);if(null!=s.minNDim&&i<s.minNDim)throw new rs(`Input ${n} is incompatible with layer ${this.name}: expected min_ndim=${s.minNDim}, found ndim=${i}.`);if(null!=s.dtype&&r.dtype!==s.dtype)throw new rs(`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),i=s.axes[t],a=r>=0?e[r]:e[e.length+r];if(null!=i&&-1===[i,null].indexOf(a))throw new rs(`Input ${n} is incompatible with layer ${this.name}: expected axis ${r} of input shape to have value ${i} but got shape ${e}.`)}}if(null!=s.shape)for(let e=0;e<s.shape.length;++e){const t=s.shape[e],i=r.shape[e];if(null!=t&&null!=i&&t!==i)throw new rs(`Input ${n} is incompatible with layer ${this.name}: expected shape=${s.shape}, found shape=${r.shape}.`)}}}call(e,t){return e}invokeCallHook(e,t){null!=this._callHook&&this._callHook(e,t)}setCallHook(e){this._callHook=e}clearCallHook(){this._callHook=null}apply(e,t){t=t||{},this.assertNotDisposed();const n=ds(e);let r=!0;for(const e of n)if(!(e instanceof Gi)){r=!1;break}let s=!0;for(const e of n)if(e instanceof Gi){s=!1;break}if(r===s)throw new rs("Arguments to apply() must be all SymbolicTensors or all Tensors");return Bs(this.name,(()=>{if(!this.built){this.assertInputCompatibility(e);const t=[];for(const n of ds(e))t.push(n.shape);this.build(cs(t)),this.built=!0,this.initialWeights&&this.setWeights(this.initialWeights),null===this._refCount&&s&&(this._refCount=1)}if(this.assertInputCompatibility(e),s){let r=this.call(e,t);const s=ds(r),i=[];for(let e of s)-1!==n.indexOf(e)&&(e=e.clone()),i.push(e);if(r=cs(i),null!=this.activityRegularizer)throw new ss("Layer invocation in the presence of activity regularizer(s) is not supported yet.");return r}{const n=function(e){e=ds(e);const t=[];for(const n of e)t.push(n.shape);return cs(t)}(e),r=this.computeOutputShape(n);let s;const i="float32";if(this.warnOnIncompatibleInputShape(Array.isArray(e)?n[0]:n),s=null!=r&&r.length>0&&Array.isArray(r[0])?r.map(((n,r)=>new Gi(i,n,this,ds(e),t,this.name,r))):new Gi(i,r,this,ds(e),t,this.name),this.addInboundNode(e,s,null,null,n,r,t),this._refCount++,null!=this.activityRegularizer)throw new ss("Layer invocation in the presence of activity regularizer(s) is not supported yet.");return s}}))}warnOnIncompatibleInputShape(e){if(null!=this.batchInputShape)if(e.length!==this.batchInputShape.length)console.warn("The rank of the input tensor provided (shape: "+JSON.stringify(e)+") does not match that of the "+`batchInputShape (${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))}}get outputShape(){if(null==this.inboundNodes||0===this.inboundNodes.length)throw new ts(`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 ts(`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 ns(`You tried to call countParams() on ${this.name}, but the layer is not built yet. Build it first by calling build(batchInputShape).`);return Pi(this.weights)}build(e){this.built=!0}getWeights(e=!1){return Vi(e?this.trainableWeights:this.weights)}setWeights(e){(0,p.tidy)((()=>{const t=this.weights;if(t.length!==e.length)throw new rs(`You called setWeights(weights) on layer "${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=[],r=Vi(t);for(let s=0;s<r.length;++s){const i=r[s],a=t[s],o=e[s];if(!p.util.arraysEqual(i.shape,o.shape))throw new rs(`Layer weight shape ${i.shape} not compatible with provided weight shape `+o.shape);n.push([a,o])}Wi(n)}))}addWeight(e,t,n,r,s,i,a,o){if(-1!==this._addedWeightNames.indexOf(e))throw new rs(`Duplicate weight name ${e} for layer ${this.name}`);this._addedWeightNames.push(e),null==n&&(n="float32"),this.fastWeightInitDuringBuild&&(r=null!=o?o():Oi("zeros"));const u=r.apply(t,n),l=new Bi(u,n,e,i,a);return u.dispose(),null!=s&&this.addLoss((()=>s.apply(l.read()))),null==i&&(i=!0),i?this._trainableWeights.push(l):this._nonTrainableWeights.push(l),l}setFastWeightInitDuringBuild(e){this.fastWeightInitDuringBuild=e}addLoss(e){null==e||Array.isArray(e)&&0===e.length||(e=ds(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 ${this.name} does not support masking, but was passed an inputMask.`)}))}return null}return t}addInboundNode(e,t,n,r,s,i,a=null){const o=ds(e);t=ds(t),n=ds(n),r=ds(r),s=Mi(s),i=Mi(i);const u=[],l=[],c=[];for(const e of o)u.push(e.sourceLayer),l.push(e.nodeIndex),c.push(e.tensorIndex);new ji({outboundLayer:this,inboundLayers:u,nodeIndices:l,tensorIndices:c,inputTensors:o,outputTensors:t,inputMasks:n,outputMasks:r,inputShapes:s,outputShapes:i},a);for(let e=0;e<t.length;e++)t[e].sourceLayer=this,t[e].nodeIndex=this.inboundNodes.length-1,t[e].tensorIndex=e}getConfig(){const e={name:this.name,trainable:this.trainable};return null!=this.batchInputShape&&(e.batchInputShape=this.batchInputShape),null!=this.dtype&&(e.dtype=this.dtype),e}disposeWeights(){return this.weights.forEach((e=>e.dispose())),this.weights.length}assertNotDisposed(){if(0===this._refCount)throw new Error(`Layer '${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 Ki(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=Ki(e.inputTensors[n],e.inboundLayers[n],e.nodeIndices[n]);for(const e of r)-1===t.indexOf(e)&&t.push(e)}return t}}}class Yi extends Xi{constructor(e){if(super({dtype:e.dtype,name:null!=e.name?e.name:_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 rs("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 rs("An InputLayer should be passed either a `batchInputShape` or an `inputShape`.");t=[e.batchSize].concat(e.inputShape)}else if(null!=e.batchSize)throw new rs("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 Gi(this.dtype,this.batchInputShape,this,[],{},this.name);r.nodeIndex=0,r.tensorIndex=0,new ji({outboundLayer:this,inboundLayers:[],nodeIndices:[],tensorIndices:[],inputTensors:[r],outputTensors:[r],inputMasks:[null],outputMasks:[null],inputShapes:[t],outputShapes:[t]})}apply(e,t){throw new rs("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 Qi(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 rs("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;return null==n&&(n="float32"),new Yi({batchInputShape:t,name:e.name,dtype:n,sparse:e.sparse}).inboundNodes[0].outputTensors[0]}Yi.className="InputLayer",p.serialization.registerClass(Yi);class Zi{constructor(e){if(this.id2Value={},this.id2Mask={},this.name2Id={},e instanceof Zi)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 rs(`Duplicate key: name=${e.name}, id=${e.id}`);return this.id2Value[e.id]=function(e,t){if(null==e.dtype||e.dtype===t.dtype)return t;try{return(0,p.cast)(t,e.dtype)}catch(n){throw new rs(`The dtype of the feed (${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 Gi){if(null==this.id2Value[e.id])throw new rs("Nonexistent key: "+e.name);return this.id2Value[e.id]}{const t=this.name2Id[e];if(null==t)throw new rs("Feed dict has no SymbolicTensor name: "+e);return this.id2Value[t]}}getMask(e){if(e instanceof Gi){if(null==this.id2Value[e.id])throw new rs("Nonexistent key: "+e.name);return this.id2Mask[e.id]}{const t=this.name2Id[e];if(null==t)throw new rs("Feed dict has no SymbolicTensor name: "+e);return this.id2Mask[t]}}disposeMasks(){null!=this.id2Mask&&(0,p.dispose)(this.id2Mask)}}const Ji=new as,ea=new as;function ta(e,t,n,r){const s=null!=n&&n.training,i=Array.isArray(e),a=i?e:[e],o=a.map((e=>e.name)),u=[],l=t.names();for(const e of o)-1!==l.indexOf(e)?u.push(t.getValue(e)):u.push(null);null!=r&&(r.maxNumTensors=-1/0,r.minNumTensors=1/0);const c=o.join(",")+"|"+t.names().sort().join(",");let d,h=Ji.get(c);if(null==h){const e=function(e,t){p.util.assert(null!=e&&e.length>0,(()=>"Expected at least one fetch, got none"));let n=[],r={};if(1===e.length){const s=ra(e[0],t);n=s.sorted,r=s.recipientMap}else{const s=new Set;for(const i of e){const{sorted:e,recipientMap:a}=ra(i,t);for(const t of e)s.has(t.name)||(n.push(t),s.add(t.name));for(const e in a)null==r[e]&&(r[e]=new Set),a[e].forEach((t=>r[e].add(t)))}}return{sorted:n,recipientCounts:na(r)}}(a,t);h=e.sorted,d=e.recipientCounts,Ji.put(c,h),ea.put(c,d)}d={},s||Object.assign(d,ea.get(c));const f=new Zi(t);for(let e=0;e<h.length;++e){if(null!=r){const e=(0,p.memory)().numTensors;e>r.maxNumTensors&&(r.maxNumTensors=e),e<r.minNumTensors&&(r.minNumTensors=e)}const i=h[e],a=i.sourceLayer;if(a instanceof Yi)continue;const l=[],c=[],m=[];let g=!1;for(const e of i.inputs){const n=f.getValue(e),r=f.getMask(e);l.push(n),c.push(r),null!=r&&(g=!0),s||(d[e.name]--,0!==d[e.name]||t.hasKey(e)||-1!==o.indexOf(e.name)||n.isDisposed||!0===e.sourceLayer.stateful||m.push(n))}g&&((n=n||{}).mask=c[0]);const y=ds(a.apply(l,n));let b=null;a.supportsMasking&&(b=a.computeMask(l,c));const v=sa(i),x=Array.isArray(v)?v:[v];for(let e=0;e<x.length;++e){f.hasKey(x[e])||f.add(x[e],y[e],Array.isArray(b)?b[0]:b);const t=o.indexOf(x[e].name);-1!==t&&(u[t]=y[e])}s||(0,p.dispose)(m)}return f.disposeMasks(),i?u:u[0]}function na(e){const t={};for(const n in e)t[n]=e[n].size;return t}function ra(e,t){const n=new Set,r=[],s={};for(const e of t.names())n.add(e);const i=[],a=[];for(i.push(e);i.length>0;){const e=i[i.length-1];if(n.has(e.name)){i.pop();continue}const t=a[a.length-1]===i.length-1;if(0===e.inputs.length||t)i.pop(),r.push(e),n.add(e.name),t&&a.pop();else{a.push(i.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)||i.push(t)}}return{sorted:r,recipientMap:s}}function sa(e){let t;

2if("object"==typeof e){if(Object.getPrototypeOf(e)===Object.prototype){const t=Object.keys(e);for(const n of t){if("string"!=typeof n)return!1;if(!mo(e[n]))return!1}return!0}if(Array.isArray(e)){for(const t of e)if(!mo(t))return!1;return!0}return!1}{const t=typeof e;return"string"===t||"number"===t||"boolean"===t}}function go(e,t,n=console.log){let r="";for(let n=0;n<e.length;++n)n>0&&(r=r.slice(0,r.length-1)+" "),r+=e[n],r=r.slice(0,t[n]),r+=" ".repeat(t[n]-r.length);n(r)}function yo(e,t,n){let r,s;try{s=e.inboundNodes.map((e=>JSON.stringify(e.inputShapes))).join(",")}catch(e){s="multiple"}try{r=JSON.stringify(e.outputShape)}catch(e){r="multiple"}go([`${e.name} (${e.getClassName()})`,s,r,e.countParams().toString()],t,n)}function bo(e,t,n,r){let s,i;try{i=e.inboundNodes.map((e=>JSON.stringify(e.inputShapes))).join(",")}catch(e){i="multiple"}try{s=JSON.stringify(e.outputShape)}catch(e){s="multiple"}const a=[];for(const t of e.inboundNodes)if(!(null!=n&&n.length>0&&-1===n.indexOf(t)))for(let e=0;e<t.inboundLayers.length;++e){const n=t.inboundLayers[e].name,r=t.nodeIndices[e],s=t.tensorIndices[e];a.push(`${n}[${r}][${s}]`)}const o=e.name,u=e.getClassName(),l=0===a.length?"":a[0];go([`${o} (${u})`,i,s,e.countParams().toString(),l],t,r);for(let e=1;e<a.length;++e)go(["","","","",a[e]],t,r)}function vo(e,t,n){return("inboundNodes"===e||"outputLayers"===e||"inputLayers"===e)&&0===t&&"string"==typeof n}function xo(e,t){if(null===e)return null;if("string"==typeof e)return ps(e);if("number"==typeof e||"boolean"==typeof e)return e;if(e instanceof Array){const n=[],r=e.length;for(let s=0;s<r;++s){const r=e[s];vo(t,s,r)?n.push(r):n.push(xo(r,t))}return n}{const t={};for(const n of Object.keys(e)){const r=e[n];if("name"===n&&"string"==typeof r)t[n]=r;else{const e=ps(n);t[e]=xo(r,e)}}return t}}function wo(e,t){if(null==e)return null;if("string"==typeof e)return hs(e);if("number"==typeof e||"boolean"==typeof e)return e;if(e instanceof Array){const n=[],r=e.length;for(let s=0;s<r;++s){const r=e[s];vo(t,s,r)?n.push(r):n.push(wo(r,t))}return n}{const t={};for(const n of Object.keys(e)){const r=e[n];t[hs(n)]="name"!==n&&"className"!==n||"string"!=typeof r?wo(r,n):r}return t}}const ko="4.10.0";class So extends Xi{constructor(e){if(super({}),this.containerNodes=new Set,this.name=e.name,null==this.name){const e=this.getClassName().toLowerCase();this.name=_s(e)}if(this.supportsMasking=!1,this.trainable_=!0,Array.isArray(e.inputs)?this.inputs=e.inputs.slice():this.inputs=[e.inputs],Array.isArray(e.outputs)?this.outputs=e.outputs.slice():this.outputs=[e.outputs],vs(this.inputs).length!==this.inputs.length)throw new rs("The list of inputs passed to the model is redundant. All inputs should only appear once. Found: "+this.inputs.map((e=>e.name)));vs(this.outputs).length!==this.outputs.length&&console.warn("The list of outputs passed to the model is redundant. All outputs should only appear once. Found: "+this.outputs.map((e=>e.name))),this.inputLayers=[],this.inputLayersNodeIndices=[],this.inputLayersTensorIndices=[],this.outputLayers=[],this.outputLayersNodeIndices=[],this.outputLayersTensorIndices=[],this.layers=[],this.internalContainerRefs=[];for(const e of this.outputs){const t=e.sourceLayer,n=e.nodeIndex,r=e.tensorIndex;this.outputLayers.push(t),this.outputLayersNodeIndices.push(n),this.outputLayersTensorIndices.push(r)}for(const e of this.inputs){const t=e.sourceLayer,n=e.nodeIndex,r=e.tensorIndex;us(0===n,"input layer has >1 nodes"),us(0===r,"input layer has >1 tensors"),this.inputLayers.push(t),this.inputLayersNodeIndices.push(n),this.inputLayersTensorIndices.push(r)}this.inputNames=[],this.outputNames=[],this.feedInputShapes=[],this.feedInputNames=[],this.feedOutputNames=[];for(let t=0;t<this.inputLayers.length;t++){const n=this.inputLayers[t];if(!(n instanceof Yi))throw new TypeError(`Input layers to a LayersModel must be InputLayer objects. Received inputs: ${e.inputs}. Input ${t} (0-based) originates from layer type ${n.getClassName()}.`);this.inputNames.push(n.name),this.feedInputShapes.push(n.batchInputShape),this.feedInputNames.push(n.name)}for(const e of this.outputLayers)this.outputNames.push(e.name);this.internalInputShapes=this.inputs.map((e=>e.shape)),this.internalOutputShapes=this.outputs.map((e=>e.shape));const t={},n={},r={},s={},i={},a=[],o=(e,t,n,r,s,u)=>{null!=r&&null!=s&&null!=u||(r=e.sourceLayer,s=e.nodeIndex,u=e.tensorIndex);const l=r.inboundNodes[s];if(-1!==n.indexOf(l))throw new ns(`The tensor ${e.name} at layer "${r.name}" is part of a cycle.`);if(-1!==t.indexOf(l))return;this.containerNodes.add(So.nodeKey(r,s)),r.id in i||(i[r.id]=Object.keys(i).length),-1===n.indexOf(l)&&n.push(l);const c=l.inboundLayers.length;for(let e=0;e<c;e++){const r=l.inputTensors[e],s=l.inboundLayers[e],i=l.nodeIndices[e],a=l.tensorIndices[e];o(r,t,n,s,i,a)}for(t.push(l);n.indexOf(l)>=0;)n.splice(n.indexOf(l),1);a.push(l)},u=[],l=[];for(const e of this.outputs)o(e,u,l);const c=a.slice().reverse();for(const e of c){n[e.id]=e,e.id in t||(t[e.id]=0);let i=t[e.id];const a=null==r[e.outboundLayer.id]?0:r[e.outboundLayer.id];i=Math.max(i,a),r[e.outboundLayer.id]=i,s[e.outboundLayer.id]=e.outboundLayer,t[e.id]=i;for(let r=0;r<e.inboundLayers.length;r++){const s=e.inboundLayers[r],a=e.nodeIndices[r],o=s.inboundNodes[a],u=null==t[o.id]?0:t[o.id];t[o.id]=Math.max(i+1,u),n[o.id]=o}}const d={};for(const e in t){const r=t[e];r in d||(d[r]=[]),d[r].push(n[e])}const h={};for(const e in r){const t=r[e];t in h||(h[t]=[]),h[t].push(s[e])}let p=Object.keys(h).map((e=>parseInt(e,10))).sort(bs);this.layers=[];for(const e of p){const t=h[e];t.sort(((e,t)=>{const n=i[e.id],r=i[t.id];return n<r?-1:n>r?1:0}));for(const e of t)e instanceof So&&this.internalContainerRefs.push(e),this.layers.push(e)}this.layersByDepth=h,p=Object.keys(d).map((e=>parseInt(e,10))).sort(bs);const f=this.inputs.slice(),m=[];for(const e of p)for(const t of d[e]){const e=t.outboundLayer;if(null!=e){for(const n of t.inputTensors)if(-1===f.indexOf(n))throw new ns("Graph disconnected: cannot obtain value for tensor "+n+` at layer "${e.name}". The following previous layers were accessed without issue: `+m);for(const e of t.outputTensors)f.push(e);m.push(e.name)}}this.nodesByDepth=d;const g=this.layers.map((e=>e.name));for(const e of g){const t=g.filter((t=>t===e)).length;if(1!==t)throw new ns(`The name "${e}" is used ${t} times in the model. All layer names should be unique. Layer names: `+JSON.stringify(g))}this.outboundNodes=[],this.inboundNodes=[],new ji({outboundLayer:this,inboundLayers:[],nodeIndices:[],tensorIndices:[],inputTensors:this.inputs,outputTensors:this.outputs,inputMasks:this.inputs.map((e=>null)),outputMasks:this.outputs.map((e=>null)),inputShapes:this.inputs.map((e=>e.shape)),outputShapes:this.outputs.map((e=>e.shape))}),this.built=!0,this._refCount=1}assertNotDisposed(){if(0===this._refCount)throw new Error(`Container '${this.name}' is already disposed.`)}dispose(){this.assertNotDisposed();const e={refCountAfterDispose:null,numDisposedVariables:0};if(0==--this._refCount){for(const t of this.layers)e.numDisposedVariables+=t.dispose().numDisposedVariables;for(const t of this.internalContainerRefs)e.numDisposedVariables+=t.dispose().numDisposedVariables}return e.refCountAfterDispose=this._refCount,e}get trainable(){return this.trainable_}set trainable(e){this.layers.forEach((t=>{t._trainableWeights.forEach((t=>t.trainable=e))})),this.trainable_=e}get trainableWeights(){if(this._trainableWeights.length>0)throw new rs("Container instance unexpectedly contains _trainableWeights.The trainable weights of a Container are a union of the trainable weights of its consituent Layers. Its own _trainableWeights must remain an empty Array.");if(!this.trainable)return[];let e=[];for(const t of this.layers)e=e.concat(t.trainableWeights);return e}get nonTrainableWeights(){const e=[];for(const t of this.layers)e.push(...t.nonTrainableWeights);if(!this.trainable){const t=[];for(const e of this.layers)t.push(...e.trainableWeights);return t.concat(e)}return e}get weights(){return this.trainableWeights.concat(this.nonTrainableWeights)}loadWeights(e,t=!0){const n={};let r=0;const s=Object.keys(e)[0].split("/"),i=!isNaN(parseInt(s[s.length-1],10));for(const e of this.layers)for(const[t,s]of e.weights.entries()){const e=i?`${s.name.split("/").slice(0,-1).join("/")+"/"}${t}`:s.originalName;if(null!=n[e])throw new rs("Duplicate weight name: "+e);n[e]=s,r++}const a=[];for(const r in e){let s=r;if(null==n[r]){const e=r.split("/");s=e.slice(0,-2).concat([e[e.length-1]]).join("/")}if(null!=n[s])a.push([n[s],e[r]]);else if(t)throw new rs("Provided weight data has no target variable: "+r);delete n[s]}if(t){const e=[];for(const t in n)e.push(t);if(e.length>0)throw new rs(`${e.length} of ${r} weights are not set: `+e)}Wi(a)}updatedConfig(){const e=this.getConfig(),t={};return t.className=this.getClassName(),t.config=e,t.kerasVersion="tfjs-layers "+ko,t.backend="TensorFlow.js",t}toJSON(e,t=!0){const n=wo(this.updatedConfig());return t?JSON.stringify(n):n}call(e,t){return(0,p.tidy)((()=>{e=ds(e);const n=new Zi;for(let t=0;t<this.inputs.length;++t)n.add(this.inputs[t],e[t]);return ta(this.outputs,n,t)}))}computeMask(e,t){return(0,p.tidy)((()=>{let n;return e=ds(e),n=null==t?os(null,e.length):ds(t),this.runInternalGraph(e,n)[1]}))}computeOutputShape(e){const t=Mi(e);if(t.length!==this.inputLayers.length)throw new rs(`Invalid inputShape argument ${e}: model has ${this.inputLayers.length} tensor inputs.`);const n={};for(let e=0;e<t.length;e++){const r=this.inputLayers[e],s=t[e];n[r.name+"_0_0"]=s}const r=Object.keys(this.nodesByDepth).map((e=>parseInt(e,10))).sort(bs);if(r.length>1)for(const e of r){const t=this.nodesByDepth[e];for(const e of t){const t=e.outboundLayer;if(-1!==this.inputLayers.map((e=>e.id)).indexOf(t.id))continue;const r=[];for(let t=0;t<e.inboundLayers.length;t++){const s=e.inboundLayers[t],i=e.nodeIndices[t],a=e.tensorIndices[t],o=n[`${s.name}_${i}_${a}`];r.push(o)}const s=Mi(t.computeOutputShape(cs(r))),i=t.inboundNodes.indexOf(e);for(let e=0;e<s.length;e++)n[`${t.name}_${i}_${e}`]=s[e]}}const s=[],i=[];for(let e=0;e<this.outputLayers.length;e++){const t=this.outputLayers[e],n=this.outputLayersNodeIndices[e],r=this.outputLayersTensorIndices[e],s=`${t.name}_${n}_${r}`;i.push(s)}for(let e=0;e<i.length;e++){const t=i[e];us(t in n),s.push(n[t])}return cs(s)}runInternalGraph(e,t){null==t&&(t=os(null,e.length));const n={};for(let r=0;r<this.inputs.length;++r){const s=this.inputs[r],i=e[r],a=t[r];n[s.id]=[i,a]}const r=Object.keys(this.nodesByDepth).map((e=>parseInt(e,10))).sort(bs);for(const e of r){const t=this.nodesByDepth[e];for(const e of t){const t=e.outboundLayer,r=e.inputTensors,s=e.outputTensors,i=new Array;for(const e of r)e.id in n&&i.push(n[e.id]);if(i.length===r.length){let r,a,o,u,l={};if(null!=e.callArgs&&(l=e.callArgs),1===i.length){const[e,n]=i[0];null==l.mask&&(l.mask=n),o=ds(t.call(e,l)),u=ds(t.computeMask(e,n)),r=[e],a=[n]}else r=i.map((e=>e[0])),a=i.map((e=>e[1])),null==l.mask&&(l.mask=a),o=ds(t.call(r,l)),u=ds(t.computeMask(r,a));if(t.activityRegularizer)throw new ss("LayersModel invocation with concrete Tensor value(s) in the presence of activity regularizer(s) is not supported yet.");for(let e=0;e<s.length;++e){const t=s[e],r=o[e],i=u[e];n[t.id]=[r,i]}}}}const s=[],i=[],a=[];for(const e of this.outputs){us(e.id in n,`Could not compute output ${e.name} : ${e.id}`);const[t,r]=n[e.id];a.push(t.shape),s.push(t),i.push(r)}return[s,i,a]}buildNodeConversionMap(e){const t={};let n;for(const e of this.layers){n=e instanceof So?1:0;for(let r=0;r<e.inboundNodes.length;r++){const s=So.nodeKey(e,r);this.containerNodes.has(s)&&(t[s]=n,n+=1)}}return t}getLayer(e,t){if(null!=t)return this.findLayer(t);if(null==e)throw new rs("Provide either a layer name or layer index");if("number"==typeof e)return this.findLayer(e);for(const t of this.layers)if(t.name===e)return t;throw new rs("No such layer: "+e)}findLayer(e){if(this.layers.length<=e)throw new rs(`Was asked to retrieve layer at index ${e}, but model only has ${this.layers.length} layer(s).`);return this.layers[e]}calculateLosses(){return(0,p.tidy)((()=>{const e=[];for(const t of this.layers)for(let n=0;n<t.inboundNodes.length;++n){const r=So.nodeKey(t,n);this.containerNodes.has(r)&&e.push(...t.calculateLosses())}return e}))}getConfig(){const e={name:this.name},t=this.buildNodeConversionMap(this.layers),n=[];for(const e of this.layers){const r=e.getClassName(),s=e.getConfig(),i=[];for(let n=0;n<e.inboundNodes.length;n++){const r=e.inboundNodes[n],s=So.nodeKey(e,n);let a={};if(this.containerNodes.has(s)){if(r.callArgs)try{JSON.stringify(r.callArgs),a=r.callArgs}catch(t){console.warn(`Layer ${e.name} was passed non-serializable keyword arguments: `+r.callArgs+". They will not be included in the serialized model (and thus will be missing at deserialization time)."),a={}}if(r.inboundLayers.length>0){const e=[];for(let n=0;n<r.inboundLayers.length;n++){const s=r.inboundLayers[n],i=r.nodeIndices[n],o=r.tensorIndices[n];let u=t[So.nodeKey(s,i)];null==u&&(u=0),e.push([s.name,u,o,a])}i.push(e)}}}const a={};a.name=e.name,a.className=r,a.config=s,a.inboundNodes=i,n.push(a)}e.layers=n;const r=[];for(let e=0;e<this.inputLayers.length;e++){const n=this.inputLayers[e],s=this.inputLayersNodeIndices[e],i=So.nodeKey(n,s);if(!this.containerNodes.has(i))continue;let a=t[i];null==a&&(a=0);const o=this.inputLayersTensorIndices[e];r.push([n.name,a,o])}e.inputLayers=r;const s=[];for(let e=0;e<this.outputLayers.length;e++){const n=this.outputLayers[e],r=this.outputLayersNodeIndices[e],i=So.nodeKey(n,r);if(!this.containerNodes.has(i))continue;let a=t[i];null==a&&(a=0);const o=this.outputLayersTensorIndices[e];s.push([n.name,a,o])}return e.outputLayers=s,e}static fromConfig(e,t,n={},r=!1){const s={},i={};function a(e,t){e.name in i?i[e.name].push(t):i[e.name]=[t]}function o(e,t){const n=[];let r;for(const i of t){const o=i[0],u=i[1],l=i[2];if(r=null==i[3]?{}:i[3],!(o in s))return void a(e,t);const c=s[o];if(c.inboundNodes.length<=u)return void a(e,t);const d=c.inboundNodes[u];n.push(d.outputTensors[l])}n.length>0&&e.apply(cs(n),r)}function u(e){const n=e.name,i=Ha(e,null!=t.customObjects?t.customObjects:{});i.setFastWeightInitDuringBuild(r),s[n]=i,e.inboundNodes.forEach((e=>{if(!(e instanceof Array))throw new rs("Corrupted configuration, expected array for nodeData: "+e);a(i,e)}))}const l=t.name,c=t.layers;for(const e of c)u(e);for(;!xs(i);)for(const e of c){const t=s[e.name];if(t.name in i){const e=i[t.name];delete i[t.name];for(const n of e)o(t,n)}}const d=[],h=[],p=t.inputLayers;for(const e of p){const t=e[0],n=e[1],r=e[2];us(t in s);const i=s[t].inboundNodes[n].outputTensors;d.push(i[r])}const f=t.outputLayers;for(const e of f){const t=e[0],n=e[1],r=e[2];us(t in s);const i=s[t].inboundNodes[n].outputTensors;h.push(i[r])}return new e({inputs:d,outputs:h,name:l})}get stateful(){if(this._stateful)throw new rs("Container instance unexpectedly has _stateful = true. The statefulness of a Container is determined by the Layers it contains. Its _stateful property must remain the default false.");for(const e of this.layers)if(e.stateful)return!0;return!1}resetStates(){(0,p.tidy)((()=>{this.layers.forEach((e=>{e.stateful&&e.resetStates()}))}))}}function Io(e,t){return function(e,t,n){const r=t.length;if(null==e||Array.isArray(e)&&0===e.length)return t.map((e=>null));if(1===r)return Array.isArray(e)&&1===e.length?e:"object"==typeof e&&t[0]in e?[e[t[0]]]:[e];if(Array.isArray(e)){if(e.length!==r)throw new Error(`Provided ${n} is an array of ${e.length} element(s), but the model has ${r} outputs. Make sure a set of weights is provided for each model output.`);return e}if("object"==typeof e&&Object.keys(e).length>0&&"object"==typeof e[Object.keys(e)[0]]){const n=[];return t.forEach((t=>{t in e?n.push(e[t]):n.push(null)})),n}throw new Error(`The model has multiple (${r}) outputs, so ${n} must be either an array with ${r} elements or an object with ${t} keys. Provided ${n} not understood: ${JSON.stringify(e)}`)}(e,t,"classWeight")}async function To(e,t,n,r){if(null!=t||null!=r)throw new Error("Support sampleWeight is not implemented yet");if(null!=n){const t=(0,p.tidy)((()=>{if(1===e.shape.length)return(0,p.clone)(e);if(2===e.shape.length){if(e.shape[1]>1){const t=1;return(0,p.argMax)(e,t)}if(1===e.shape[1])return(0,p.reshape)(e,[e.shape[0]]);throw new Error(`Encountered unexpected last-dimension size (${e.shape[1]}) during handling of class weights. The size is expected to be >= 1.`)}throw new Error(`Unexpected rank of target (y) tensor (${e.rank}) during handling of class weights. The rank is expected to be 1 or 2.`)})),r=Array.from(await t.data());(0,p.dispose)(t);const s=[];return r.forEach((e=>{if(null==n[e])throw new Error(`classWeight must contain all classes in the training data. The class ${e} exists in the data but not in classWeight`);s.push(n[e])})),(0,p.tensor1d)(s,"float32")}return null}function No(e,t){return(0,p.mul)(e,t)}function Co(e,t){let n,r;const s=t;n=s.xs,r=s.ys,p.util.assert(null!=n&&null!=r,(()=>"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));

2const i=Eo("input",e.inputNames,n),a=Eo("output",e.outputNames,r),o=i[0].shape[0];p.util.assert(i.length===e.inputs.length,(()=>`LayersModel has ${e.inputs.length} inputs, but the dataset provides ${i.length} inputs.  (Expected input keys: `+JSON.stringify(e.inputNames)+")")),p.util.assert(a.length===e.outputs.length,(()=>`LayersModel has ${e.outputs.length} outputs, but the dataset provides ${a.length} outputs.  (Expected output keys: `+JSON.stringify(e.outputNames)+")"));for(let t=0;t<i.length;t++)p.util.assert(i[t].shape[0]===o,(()=>`Batch size mismatch: input ${e.inputNames[t]} has ${i[t].shape[0]}; expected  ${o} based on input ${e.inputNames[0]}.`));for(let t=0;t<a.length;t++)p.util.assert(a[t].shape[0]===o,(()=>`Batch size mismatch: output ${e.outputNames[t]} has ${a[t].shape[0]}; expected  ${o} based on input ${e.inputNames[0]}.`));return{xs:i,ys:a}}function Eo(e,t,n){if(n instanceof p.Tensor)return[n];if(Array.isArray(n))return p.util.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 rs(`The feature data generated by the dataset lacks the required ${e} key '${s}'.`);r.push(n[s])}return r}}function _o(e){return"function"==typeof e.iterator}function Ao(e){p.util.assert(e>0&&Number.isInteger(e),(()=>"batchSize is required to be a positive integer, but got "+e))}function Ro(e,t,n){return null==e?[null]:Array.isArray(e)?e.map((e=>Js(e,t,n-t))):Js(e,t,n-t)}function $o(e,t){return p.tidy((()=>null==e?null:Array.isArray(e)?e.map((e=>$o(e,t))):oi(e,"int32"===t.dtype?t:p.cast(t,"int32"))))}function Fo(e,t){const n=[];let r=0,s=null;for(;r<e;)s=r+t,s>=e&&(s=e),n.push([r,s]),r=s;return n}function Oo(e){const t=[];e instanceof p.Tensor&&(e=[e]);for(let n=0;n<e.length;++n){const r=e[n];if(1===r.rank)t.push(Zs(r,1));else{if(0===r.rank)throw new Error("Expected tensor to be at least 1D, but received a 0D tensor (scalar).");t.push(r)}}return t}function Do(e,t){if(null==e)return;const n=[];if(t instanceof p.Tensor)n.push(t.id);else if(Array.isArray(t))t.forEach((e=>n.push(e.id)));else if(null!=t)for(const e in t){const r=t[e];n.push(r.id)}const r=[];if(e instanceof p.Tensor)-1===n.indexOf(e.id)&&r.push(e);else if(Array.isArray(e))e.forEach((e=>{-1===n.indexOf(e.id)&&r.push(e)}));else if(null!=e)for(const t in e){const s=e[t];-1===n.indexOf(s.id)&&r.push(s)}r.forEach((e=>{e.isDisposed||e.dispose()}))}function Mo(e){return Array.isArray(e)}function Lo(e){return!function(e){return e instanceof p.Tensor}(e)&&!Mo(e)}function zo(e,t,n,r=!0,s=""){if(null==t||0===t.length){if(null!=e){let t=!1;if(Mo(e)&&e.length>0)t=!0;else if(Lo(e)){for(const n in e)if(e.hasOwnProperty(n)){t=!0;break}}else t=!0;if(t)throw new rs(`Error when checking model ${s} expected no data, but got `+e)}return[]}if(null==e)return t.map((e=>null));let i;if(Lo(e)){e=e,i=[];for(const n of t){if(null==e[n])throw new rs(`No data provided for "${n}". Need data for each key in: `+t);i.push(e[n])}}else if(Mo(e)){if((e=e).length!==t.length)throw new rs(`Error when checking model ${s}: the Array of Tensors that you are passing to your model is not the size the model expected. Expected to see ${t.length} Tensor(s), but instead got the following list of Tensor(s): `+e);i=e}else{if(e=e,t.length>1)throw new rs(`The model ${s} expects ${t.length} Tensor(s), but only received one Tensor. Found: Tensor with shape `+e.shape);i=[e]}if(i=Oo(i),null!=n)for(let e=0;e<t.length;++e){if(null==n[e])continue;const a=i[e];if(a.shape.length!==n[e].length)throw new rs(`Error when checking ${s}: expected ${t[e]} to have ${n[e].length} dimension(s). but got array with shape `+a.shape);for(let t=0;t<n[e].length;++t){if(0===t&&!r)continue;const i=a.shape[t],o=n[e][t];if(null!=o&&o>=0&&i!==o)throw new rs(s+" expected a batch of elements where each "+`example has shape [${n[e].slice(1,n[e].length)}] `+`(i.e.,tensor shape [*,${n[e].slice(1,n[e].length)}])`+` but the ${s} received an input with ${a.shape[0]}`+` examples, each with shape [${a.shape.slice(1,a.shape.length)}]`+` (tensor shape [${a.shape}])`)}}return i}function Po(e,t,n,r=!0,s=""){let i;if(Array.isArray(e)){if(e.length!==t.length)throw new rs(`Error when checking model ${s}: the Array of Tensors that you are passing to your model is not the size the the model expected. Expected to see ${t.length} Tensor(s), but instead got ${e.length} Tensors(s).`);i=e}else{if(t.length>1)throw new rs(`The model expects ${t.length} ${s} Tensors, but only received one Tensor. Found: array with shape `+JSON.stringify(e.shape)+".");i=[e]}if(null!=n)for(let e=0;e<t.length;++e){if(null==n[e])continue;const a=i[e];if(a.shape.length!==n[e].length)throw new rs(`Error when checking ${s}: expected ${t[e]} to have ${n[e].length} dimension(s), but got array with shape `+JSON.stringify(a.shape));for(let i=0;i<n[e].length;++i){if(0===i&&!r)continue;const o=a.shape[i],u=n[e][i];if(null!=u&&u!==o)throw new rs(`Error when checking ${s}: expected ${t[e]} to have shape ${JSON.stringify(n[e])} but got array with shape ${JSON.stringify(a.shape)}.`)}}}class Bo extends So{constructor(e){super(e),this.isTraining=!1}summary(e,t,n=console.log){if(!this.built)throw new rs("This model has never been called, thus its weights have not been created yet. So no summary can be displayed. Build the model first (e.g., by calling it on some test data).");!function(e,t,n,r=console.log){const s=function(e){let t=!0;const n=[],r=[];for(const t in e.nodesByDepth)n.push(e.nodesByDepth[t]);for(const e of n){if(e.length>1||1===e.length&&e[0].inboundLayers.length>1){t=!1;break}r.push(...e)}if(t)for(const n of e.layers){let e=!1;for(const s of n.inboundNodes)if(-1!==r.indexOf(s)){if(e){t=!1;break}e=!0}if(!t)break}return t}(e),i=["Layer (type)","Input Shape","Output shape","Param #"];let a;if(s?(t=t||90,n=n||[.32,.61,.89,1]):(t=t||115,n=n||[.24,.48,.7,.8,1]),n[n.length-1]<=1&&(n=n.map((e=>Math.floor(t*e)))),!s){i.push("Receives inputs"),a=[];for(const t in e.nodesByDepth)a.push(...e.nodesByDepth[t])}r("_".repeat(t)),go(i,n,r),r("=".repeat(t));const o=e.layers;for(let e=0;e<o.length;++e)s?yo(o[e],n,r):bo(o[e],n,a,r),r((e===o.length-1?"=":"_").repeat(t));e.checkTrainableWeightsConsistency();const u=function(e){let t;return t=null!=e.collectedTrainableWeights?Pi(e.collectedTrainableWeights):Pi(e.trainableWeights),t}(e),l=Pi(e.nonTrainableWeights);r("Total params: "+(u+l)),r("Trainable params: "+u),r("Non-trainable params: "+l),r("_".repeat(t))}(this,e,t,n)}compile(e){if(null==e.loss&&(e.loss=[]),this.loss=e.loss,"string"==typeof e.o

2ptimizer)this.optimizer_=function(e){const t={Adagrad:()=>p.train.adagrad(.01),Adadelta:()=>p.train.adadelta(1,.95,Ys()),Adam:()=>p.train.adam(.001,.9,.999,Ys()),Adamax:()=>p.train.adamax(.002,.9,.999,Ys(),0),RMSProp:()=>p.train.rmsprop(.001,.9,0,Ys()),SGD:()=>p.train.sgd(.01)};if(t.adagrad=t.Adagrad,t.adadelta=t.Adadelta,t.adam=t.Adam,t.adamax=t.Adamax,t.rmsprop=t.RMSProp,t.sgd=t.SGD,e in t)return t[e]();throw new rs("Unknown Optimizer "+e)}(e.optimizer),this.isOptimizerOwned=!0;else{if(!(e.optimizer instanceof p.Optimizer))throw new rs("User-defined optimizer must be an instance of tf.Optimizer.");this.optimizer_=e.optimizer,this.isOptimizerOwned=!1}let t=[];if(Array.isArray(e.loss)||"string"==typeof e.loss||"function"==typeof e.loss)if(Array.isArray(e.loss)){if(e.loss.length!==this.outputs.length)throw new rs(`When passing an Array as loss, it should have one entry per model output. The model has ${this.outputs.length} output(s), but you passed loss=${e.loss}.`);const n=e.loss;t=n.map((e=>to(e)))}else{const n=to(e.loss);this.outputs.forEach((e=>{t.push(n)}))}else{e.loss=e.loss;for(const t in e.loss)if(-1===this.outputNames.indexOf(t))throw new rs(`Unknown entry in loss dictionary: "${t}". Only expected the following keys: `+this.outputNames);for(const n of this.outputNames)null==e.loss[n]&&console.warn(`Output "${n}" is missing from loss dictionary. We assume this was done on purpose, and we will not be expecting data to be passed to ${n} during training`),t.push(to(e.loss[n]))}this.lossFunctions=t,this.feedOutputNames=[],this.feedOutputShapes=[],this.feedLossFns=[];for(let e=0;e<this.outputs.length;++e){const t=this.internalOutputShapes[e],n=this.outputNames[e];this.feedOutputNames.push(n),this.feedOutputShapes.push(t),this.feedLossFns.push(this.lossFunctions[e])}const n=[];this.metrics=e.metrics,this.metricsNames=["loss"],this.metricsTensors=[],Bs("loss",(()=>{for(let e=0;e<this.outputs.length;++e){if(-1!==n.indexOf(e))continue;const t=this.lossFunctions[e];this.outputs.length>1&&(this.metricsTensors.push([t,e]),this.metricsNames.push(this.outputNames[e]+"_loss"))}}));const r=function(e,t){if(null==e||Array.isArray(e)&&0===e.length)return t.map((e=>[]));let n;if("string"==typeof e||"function"==typeof e)n=[e];else{if(!Array.isArray(e)&&"object"!=typeof e)throw new TypeError("Type of metrics argument not understood. Expected an string,function, Array, or Object, found: "+e);n=e}if(Array.isArray(n))return t.map((e=>n));{const e=[];for(const r of t){let t=n.hasOwnProperty(r)?n[r]:[];Array.isArray(t)||(t=[t]),e.push(t)}return e}}(e.metrics,this.outputNames),s=(e,t,n)=>{this.outputNames.length>1&&(t=this.outputNames[e]+"_"+t),this.metricsNames.push(t),this.metricsTensors.push([n,e])};Bs("metric",(()=>{for(let e=0;e<this.outputs.length;++e)-1===n.indexOf(e)&&(t=>{let n,r,i;for(const a of t){if("string"==typeof a&&-1!==["accuracy","acc","crossentropy","ce"].indexOf(a)){const t=this.internalOutputShapes[e];let s;1===t[t.length-1]||this.lossFunctions[e]===Za?-1!==["accuracy","acc"].indexOf(a)?r=no:-1!==["crossentropy","ce"].indexOf(a)&&(r=ao):this.lossFunctions[e]===Qa?-1!==["accuracy","acc"].indexOf(a)?r=oo:-1!==["crossentropy","ce"].indexOf(a)&&(r=lo):-1!==["accuracy","acc"].indexOf(a)?r=ro:-1!==["crossentropy","ce"].indexOf(a)&&(r=uo),-1!==["accuracy","acc"].indexOf(a)?s="acc":-1!==["crossentropy","ce"].indexOf(a)&&(s="ce"),i=r,n=""+s}else{const e=ho(a);i=e,n=""+po(a)}let t;Bs(n,(()=>{t=i})),s(e,n,t)}})(r[e])})),this.collectedTrainableWeights=this.trainableWeights}checkTrainableWeightsConsistency(){null!=this.collectedTrainableWeights&&this.trainableWeights.length!==this.collectedTrainableWeights.length&&console.warn("Discrepancy between trainableweights and collected trainable weights. Did you set `model.trainable` without calling `model.compile()` afterwards?")}evaluate(e,t,n={}){const r=null==n.batchSize?32:n.batchSize;Ao(r);const s=this.standardizeUserDataXY(e,t,!0,r);try{const i=s[0].concat(s[1]);this.makeTestFunction();const a=this.testFunction;return cs(this.testLoop(a,i,r,n.verbose,n.steps))}finally{Do(s[0],e),Do(s[1],t)}}async evaluateDataset(e,t){return this.makeTestFunction(),async function(e,t,n){const r=null!=(n=n||{}).batches,s=e.testFunction;let i=[];if(n.verbose>0)throw new ss("Verbose mode is not implemented yet.");p.util.assert(!r||n.batches>0&&Number.isInteger(n.batches),(()=>"Test loop expects `batches` to be a positive integer, but received "+JSON.stringify(n.batches)));const a="function"==typeof t.next?t:await t.iterator();let o=0,u=0;for(;!r||u<n.batches;){const t=await a.next();if(i=p.tidy((()=>

2{if(t.value){const{xs:n,ys:r}=Co(e,t.value),a=n.concat(r),l=p.tidy((()=>s(a)));if(p.dispose(a),0===u)for(let e=0;e<l.length;++e)i.push((0,p.scalar)(0));const c=a[0].shape[0];for(let e=0;e<l.length;++e){const t=l[e],n=i[e];i[e]=p.tidy((()=>p.add(i[e],p.mul(c,t)))),u>0&&p.dispose(n)}p.dispose(l),o+=c,++u}return i})),t.done){r&&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). You may need to use the repeat() function when building your dataset.`);break}}for(let e=0;e<i.length;++e){const t=i[e];i[e]=p.div(i[e],o),p.dispose(t)}return cs(i)}(this,e,t)}checkNumSamples(e,t,n,r="steps"){let s;if(null!=n){if(s=null,null!=t)throw new rs(`If ${r} is set, batchSize must be null or undefined.Got batchSize = `+t)}else{if(null==e)throw new rs("Either the input data should have a defined shape, or "+r+" shoud be specified.");s=Array.isArray(e)?e[0].shape[0]:e.shape[0]}return s}execute(e,t){if(Array.isArray(t)&&0===t.length)throw new rs("`outputs` is an empty Array, which is not allowed.");const n=Array.isArray(t),r=n?t:[t],s=this.retrieveSymbolicTensors(r),i=new Zi;if(e instanceof p.Tensor&&(e=[e]),Array.isArray(e)){if(e.length!==this.inputs.length)throw new rs(`The number of inputs provided (${e.length}) does not match the number of inputs of this model (${this.inputs.length}).`);for(let t=0;t<this.inputs.length;++t)i.add(this.inputs[t],e[t])}else for(const t of this.inputs){const n=e[t.name];if(null==n)throw new rs("No value is provided for the model's input "+t.name);i.add(t,n)}const a=ta(s,i);return n?a:a[0]}retrieveSymbolicTensors(e){const t=os(null,e.length);let n=e.length;for(const r of this.layers){const s=Array.isArray(r.output)?r.output:[r.output],i=s.map((e=>e.name));for(let r=0;r<e.length;++r){const a=i.indexOf(e[r]);if(-1!==a&&(t[r]=s[a],n--),0===n)break}if(0===n)break}if(n>0){const n=[];throw t.forEach(((t,r)=>{null==t&&n.push(e[r])})),new rs("Cannot find SymbolicTensors for output name(s): "+JSON.stringify(n))}return t}predictLoop(e,t=32,n=!1){return p.tidy((()=>{const r=this.checkNumSamples(e);if(n)throw new ss("Verbose predictLoop() is not implemented yet.");const s=Fo(r,t),i=this.outputs.map((e=>[]));for(let t=0;t<s.length;++t)p.tidy((()=>{const n=s[t][0],r=s[t][1],i=Ro(e,n,r),a=[];if(Array.isArray(i))for(let e=0;e<i.length;++e)a.push({key:this.inputs[e],value:i[e]});else a.push({key:this.inputs[0],value:i});const o=new Zi(a);return ta(this.outputs,o)})).forEach(((e,t)=>i[t].push(e)));return cs(i.map((e=>p.concat(e,0))))}))}predict(e,t={}){const n=Oo(e);Po(n,this.inputNames,this.feedInputShapes,!1);try{const r=null==t.batchSize?32:t.batchSize;return Ao(r),this.predictLoop(n,r)}finally{Do(n,e)}}predictOnBatch(e){Po(e,this.inputNames,this.feedInputShapes,!0);const t=(Array.isArray(e)?e[0]:e).shape[0];return this.predictLoop(e,t)}standardizeUserDataXY(e,t,n=!0,r){if(null==this.optimizer_)throw new ns("You must compile a model before training/testing. Use LayersModel.compile(modelCompileArgs).");const s=[];for(let e=0;e<this.feedOutputShapes.length;++e){const t=this.feedOutputShapes[e];this.feedLossFns[e]===Qa?s.push(t.slice(0,t.length-1).concat([1])):s.push(t)}if(function(e,t,n){const r=vs(e.map((e=>e.shape[0])));r.sort();const s=vs(t.map((e=>e.shape[0])));if(s.sort(),r.length>1)throw new rs("All input Tensors (x) should have the same number of samples. Got array shapes: "+JSON.stringify(e.map((e=>e.shape))));if(s.length>1)throw new rs("All target Tensors (y) should have the same number of samples. Got array shapes: "+JSON.stringify(t.map((e=>e.shape))));if(r.length>0&&s.length>0&&!p.util.arraysEqual(r,s))throw new rs(`Input Tensors should have the same number of samples as target Tensors. Found ${r[0]} input sample(s) and ${s[0]} target sample(s).`)}(e=zo(e,this.feedInputNames,this.feedInputShapes,!1,"input"),t=zo(t,this.feedOutputNames,s,!1,"target")),function(e,t,n){const r=[qa,Za,Ya];for(let s=0;s<e.length;++s){const i=e[s],a=t[s],o=n[s];if(null!=a){if(a===Ya&&1===i.shape[i.shape.length-1])throw new rs(`You are passing a target array of shape ${i.shape} while using a loss 'categorical_crossentropy'. 'categorical_crossentropy'expects targets to be binary matrices (1s and 0s) of shape [samples, classes].`);if(-1!==r.indexOf(a)){const e=i.shape.slice(1),t=o.slice(1);for(let n=0;n<e.length;++n){const r=e[n],s=t[n];if(null!=s&&r!==s)throw new rs(`A target Tensor with shape ${i.shape} was passed for an output of shape ${o}, while using a loss function that expects targets to have the same shape as the output.`)}}}}}(t,this.feedLossFns,this.feedOutputShapes),this.stateful&&null!=r&&r>0&&e[0].shape[0]%r!=0)throw new rs(`In a stateful network, you should only pass inputs with a number of samples that is divisible by the batch size ${r}. Found: ${e[0].shape[0]} sample(s).`);return[e,t]}async standardizeUserData(e,t,n,r,s=!0,i){const[a,o]=this.standardizeUserDataXY(e,t,s,i);if(null!=n)throw new Error("sample weight is not supported yet.");let u=null;if(null!=r){const e=Io(r,this.outputNames);u=[];for(let t=0;t<e.length;++t)u.push(await To(o[t],null,e[t]))}return[a,o,u]}testLoop(e,t,n,r=0,s){return p.tidy((()=>{const i=this.checkNumSamples(t,n,s,"steps"),a=[];if(r>0)throw new ss("Verbose mode is not implemented yet.");if(null!=s)throw new ss("steps mode in testLoop() is not implemented yet");{const r=Fo(i,n),s=(0,p.tensor1d)(Xs(0,i));for(let n=0;n<r.length;++n){const i=r[n][0],o=r[n][1],u=Js(s,i,o-i),l=$o(t,u),c=e(l);if(0===n)for(let e=0;e<c.length;++e)a.push((0,p.scalar)(0));for(let e=0;e<c.length;++e){const t=c[e];a[e]=p.add(a[e],p.mul(o-i,t))}}for(let e=0;e<a.length;++e)a[e]=p.div(a[e],i)}return a}))}getDedupedMetricsNames(){const e=this.metricsNames,t=[];for(let n=0;n<e.length;++n){const r=e[n];let s=r;ls(e,r)>1&&(s+="_"+ls(e.slice(0,n),r)),t.push(s)}return t}makeTrainFunction(){return e=>{const t=[],n=e.slice(0,this.inputs.length),r=e.slice(this.inputs.length,this.inputs.length+this.outputs.length),s=e.slice(this.inputs.length+this.outputs.length,this.inputs.length+2*this.outputs.length),i=[],a=this.collectedTrainableWeights.map((e=>e.read()));return[this.optimizer_.minimize((()=>{const e=[];for(let t=0;t<this.inputs.length;++t)e.push({key:this.inputs[t],value:n[t]});const a=new Zi(e),o=ta(this.outputs,a,{training:!0});let u;for(let e=0;e<this.lossFunctions.length;++e){let n=(0,this.lossFunctions[e])(r[e],o[e]);null!=s[e]&&(n=No(n,s[e]));const i=p.mean(n);t.push(i),u=0===e?n:p.add(u,n)}for(let e=0;e<this.metricsTensors.length;++e){let n;if(this.outputs.length>1&&e<this.outputs.length)n=t[e];else{const t=this.metricsTensors[e][0],s=this.metricsTensors[e][1];n=p.mean(t(r[s],o[s]))}p.keep(n),i.push(n)}

2return u=p.mean(u),this.calculateLosses().forEach((e=>{u=p.add(u,e)})),u}),!0,a)].concat(i)}}makeTestFunction(){this.testFunction=e=>p.tidy((()=>{const t=[];let n;const r=e.slice(0,this.inputs.length),s=e.slice(this.inputs.length,this.inputs.length+this.outputs.length),i=[];for(let e=0;e<this.inputs.length;++e)i.push({key:this.inputs[e],value:r[e]});const a=new Zi(i),o=ta(this.outputs,a);for(let e=0;e<this.lossFunctions.length;++e){const r=this.lossFunctions[e],i=p.mean(r(s[e],o[e]));n=0===e?i:p.add(n,i),t.push(n)}for(let e=0;e<this.metricsTensors.length;++e){const n=this.metricsTensors[e][0],r=this.metricsTensors[e][1],i=p.mean(n(s[r],o[r]));t.push(i)}return t}))}async fit(e,t,n={}){if(this.isTraining)throw new Error("Cannot start training because another fit() call is ongoing.");let r,s,i,a,o,u,l,c,d;this.isTraining=!0;try{const h=null==n.batchSize?32:n.batchSize;Ao(h);const f=!1,m=await this.standardizeUserData(e,t,n.sampleWeight,n.classWeight,f,h);r=m[0],s=m[1],d=m[2];let g,y=!1;if(null!=n.validationData&&n.validationData.length>0){if(y=!0,2!==n.validationData.length)throw 3===n.validationData.length?new ss("validationData including sample weights is not supported yet."):new rs("When passing validation data, it must contain 2 (valX, valY) or 3 (valX, valY, valSampleWeight) items; "+n.validationData+" is invalid.");o=n.validationData[0],u=n.validationData[1];const e=!0,t=await this.standardizeUserData(o,u,null,null,e,h);l=t[0],c=t[1],g=l.concat(c)}else if(null!=n.validationSplit&&n.validationSplit>0&&n.validationSplit<1){y=!0;const e=Math.floor(r[0].shape[0]*(1-n.validationSplit)),t=r[0].shape[0];l=Ro(r,e,t),i=r,r=Ro(r,0,e),c=Ro(s,e,t),a=s,s=Ro(s,0,e),g=l.concat(c)}else null!=n.validationSteps&&(y=!0);const b=r.concat(s).concat(d);this.checkTrainableWeightsConsistency();const v=this.makeTrainFunction(),x=this.getDedupedMetricsNames();let w,k;y?(this.makeTestFunction(),w=this.testFunction,k=x.slice().concat(x.map((e=>"val_"+e)))):(w=null,g=[],k=x.slice());const S=Wa(n.callbacks,n.yieldEvery);return await this.fitLoop(v,b,x,h,n.epochs,n.verbose,S,w,g,n.shuffle,k,n.initialEpoch,null,null)}finally{this.isTraining=!1,Do(r,e),Do(s,t),Do(i,e),Do(a,t),Do(l,o),Do(c,u),null!=d&&p.dispose(d)}}async fitLoop(e,t,n,r,s,i,a,o,u,l,c,d,h,f){null==r&&(r=32),null==s&&(s=1),null==l&&(l=!0),null==d&&(d=0);let m=!1;if(null!=o&&null!=u&&(m=!0),null!=f&&(m=!0,null==h))throw new rs("Can only use `validationSteps` when doing step-wise training, i.e., `stepsPerEpoch` must be set.");const g=this.checkNumSamples(t,r,h,"steps_per_epoch");let y;null!=g&&(y=Xs(0,g)),null==i&&(i=1);const{callbackList:b,history:v}=Ga(a,i,s,d,g,h,r,m,c);b.setModel(this),this.history=v,await b.onTrainBegin(),this.stopTraining_=!1;for(let i=d;i<s;++i){await b.onEpochBegin(i);const s={};if(null!=h)throw new ss("stepsPerEpoch mode is not implemented yet.");{if("batch"===l)throw new ss("batch shuffling is not implemneted yet");l&&p.util.shuffle(y);const i=(0,p.tensor1d)(y),a=Fo(g,r);for(let l=0;l<a.length;++l){const c={};if(await b.onBatchBegin(l,c),p.tidy((()=>{const d=a[l][0],h=a[l][1],f=Js(i,d,h-d);c.batch=l,c.size=h-d;const g=$o(t,f),y=e(g);for(let e=0;e<n.length;++e){const t=n[e],r=y[e];c[t]=r,p.keep(r)}if(l===a.length-1&&m){const e=this.testLoop(o,u,r);for(let t=0;t<n.length;++t){const r=n[t],i=e[t];p.keep(i),s["val_"+r]=i}}})),await b.onBatchEnd(l,c),Da(c),this.stopTraining_)break}i.dispose()}if(await b.onEpochEnd(i,s),this.stopTraining_)break}return await b.onTrainEnd(),await this.history.syncData(),this.history}async fitDataset(e,t){return async function(e,t,n){const r=null!=n.batchesPerEpoch;if(p.util.assert(null!=e.optimizer,(()=>"You must compile a model before training/testing. Use LayersModel.compile(modelCompileConfig).")),p.util.assert(null!=n,(()=>"For fitDataset(), the 2nd argument (config) is required, but it is not provided in this call.")),p.util.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)),p.util.assert(!r||n.batchesPerEpoch>0&&Number.isInteger(n.batchesPerEpoch),(()=>"For fitDataset(), config.batchesPerEpoch is expected to be a positive integer if specified, but got "+n.batchesPerEpoch)),p.util.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 s=null!=n.validationData;let i,a;if(s)if(_o(n.validationData))p.util.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 ss("Validation with sample weights is not implemented yet.");return{xs:e[0],ys:e[1]}}(n.validationData);

2i=e.xs,a=e.ys}const o=e.makeTrainFunction(),u=e.getDedupedMetricsNames();let l;l=s?u.slice().concat(u.map((e=>"val_"+e))):u.slice();const c=Wa(n.callbacks,n.yieldEvery),d=null==n.verbose?1:n.verbose,{callbackList:h,history:f}=Ga(c,d,n.epochs,null,null,function(e,t){let n=null;return null!=t.batchesPerEpoch?n=t.batchesPerEpoch:Number.isFinite(e.size)&&(n=e.size),n}(t,n),null,s,l);h.setModel(e),e.history=f,await h.onTrainBegin(),e.stopTraining_=!1;let m=null==n.initialEpoch?0:n.initialEpoch,g=await t.iterator();for(;m<n.epochs;){const l={};await h.onEpochBegin(m);let c=0,d=0;for(r||(g=await t.iterator());!r||c<n.batchesPerEpoch;){const t=await g.next();if(r&&t.done){console.warn("You provided `batchesPerEpoch` as "+n.batchesPerEpoch+", but your dataset iterator ran out of data after "+c+" 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:r,ys:s}=Co(e,t.value),i={};i.batch=d,i.size=r[0].shape[0],await h.onBatchBegin(d,i);const a=[];if(null!=n.classWeight){const t=Io(n.classWeight,e.outputNames);for(let e=0;e<t.length;++e)a.push(await To(s[e],null,t[e]))}const l=r.concat(s).concat(a),f=o(l);p.dispose(l);for(let e=0;e<u.length;++e){const t=u[e],n=f[e];i[t]=n,p.keep(n)}await h.onBatchEnd(d,i),Da(i),d++,c++}if(r?c>=n.batchesPerEpoch:t.done){if(s){let t;t=_o(n.validationData)?ds(await e.evaluateDataset(n.validationData,{batches:n.validationBatches})):ds(e.evaluate(i,a,{batchSize:null==n.validationBatchSize?32:n.validationBatchSize,verbose:0}));for(let n=0;n<e.metricsNames.length;++n)l["val_"+e.metricsNames[n]]=t[n]}break}if(e.stopTraining_)break}if(await h.onEpochEnd(m,l),m++,e.stopTraining_)break}return await h.onTrainEnd(),await e.history.syncData(),e.history}finally{e.isTraining=!1}}(this,e,t)}async trainOnBatch(e,t){const n=await this.standardizeUserData(e,t),r=n[0],s=n[1],i=this.makeTrainFunction()(r.concat(s)),a=[];for(const e of i){const t=await e.data();a.push(t[0])}return p.dispose(i),Do(n[0],e),Do(n[1],t),cs(a)}getNamedWeights(e){const t=[],n=null!=e&&e.trainableOnly,r=n?this.trainableWeights:this.weights,s=this.getWeights(n);for(let e=0;e<r.length;++e)n&&!r[e].trainable||t.push({name:r[e].originalName,tensor:s[e]});return t}set stopTraining(e){this.stopTraining_=e}get stopTraining(){return this.stopTraining_}get optimizer(){return this.optimizer_}set optimizer(e){this.optimizer_!==e&&(this.optimizer_=e,this.isOptimizerOwned=!1)}dispose(){const e=super.dispose();if(0===e.refCountAfterDispose&&null!=this.optimizer&&this.isOptimizerOwned){const t=p.memory().numTensors;this.optimizer_.dispose(),e.numDisposedVariables+=t-p.memory().numTensors}return e}getLossIdentifiers(){let e;if("string"==typeof this.loss)e=hs(this.loss);else if(Array.isArray(this.loss)){for(const e of this.loss)if("string"!=typeof e)throw new Error("Serialization of non-string loss is not supported.");e=this.loss.map((e=>hs(e)))}else{const t=Object.keys(this.loss);e={};const n=this.loss;for(const r of t){if("string"!=typeof n[r])throw new Error("Serialization of non-string loss is not supported.");e[r]=hs(n[r])}}return e}getMetricIdentifiers(){if("string"==typeof this.metrics||"function"==typeof this.metrics)return[hs(po(this.metrics))];if(Array.isArray(this.metrics))return this.metrics.map((e=>hs(po(e))));{const e={};for(const t in this.metrics)e[t]=hs(po(this.metrics[t]));return e}}getTrainingConfig(){return{loss:this.getLossIdentifiers(),metrics:this.getMetricIdentifiers(),optimizer_config:{class_name:this.optimizer.getClassName(),config:this.optimizer.getConfig()}}}loadTrainingConfig(e){if(null!=e.weighted_metrics)throw new Error("Loading weight_metrics is not supported yet.");if(null!=e.loss_weights)throw new Error("Loading loss_weights is not supported yet.");if(null!=e.sample_weight_mode)throw new Error("Loading sample_weight_mode is not supported yet.");const t=Ha(xo(e.optimizer_config));let n,r;if("string"==typeof e.loss)n=ps(e.loss);else if(Array.isArray(e.loss))n=e.loss.map((e=>ps(e)));else if(null!=e.loss){n={};for(const t in e.loss)n[t]=ps(e.loss[t])}if(Array.isArray(e.metrics))r=e.metrics.map((e=>ps(e)));else if(null!=e.metrics){r={};for(const t in e.metrics)r[t]=ps(e.metrics[t])}this.compile({loss:n,metrics:r,optimizer:t})}async save(e,t){if("string"==typeof e){const t=p.io.getSaveHandlers(e);if(0===t.length)throw new rs(`Cannot find any save handlers for URL '${e}'`);if(t.length>1)throw new rs(`Found more than one (${t.length}) save handlers for URL '${e}'`);e=t[0]}if(null==e.save)throw new rs("LayersModel.save() cannot proceed because the IOHandler provided does not have the `save` attribute defined.");const n=await p.io.encodeWeights(this.getNamedWeights(t)),r={modelTopology:this.toJSON(null,!1),format:"layers-model",generatedBy:"TensorFlow.js tfjs-layers v"+ko,convertedBy:null};if(null!=t&&t.includeOptimizer&&null!=this.optimizer){r.trainingConfig=this.getTrainingConfig();const e="optimizer",{data:t,specs:s}=await p.io.encodeWeights(await this.optimizer.getWeights(),e);n.specs.push(...s),n.data=p.io.concatenateArrayBuffers([n.data,t])}if(null!=this.userDefinedMetadata){const e=!0;fo(this.userDefinedMetadata,this.name,e),r.userDefinedMetadata=this.userDefinedMetadata}return r.weightData=n.data,r.weightSpecs=n.specs,e.save(r)}setUserDefinedMetadata(e){fo(e,this.name),this.userDefinedMetadata=e}getUserDefinedMetadata(){return this.userDefinedMetadata}}Bo.className="Model",p.serialization.registerClass(Bo);class Vo extends Bo{}async function Wo(e,t){"modelTopology"in e||(e={modelTopology:e});let n=(e=e).modelTopology;null!=n.model_config&&(n=n.model_config);const r=Ha(xo(n),t);

2if(null!=e.weightsManifest){const t=await p.io.loadWeights(e.weightsManifest,e.pathPrefix,r.weights.map((e=>e.originalName))),n={};for(const e of r.weights)n[e.originalName]=t[e.originalName];r.loadWeights(n),(0,p.dispose)(t)}return r}async function Uo(e,t){if(null==t&&(t={}),"string"==typeof e){const n=p.io.getLoadHandlers(e,t);if(0===n.length)n.push(p.io.browserHTTPRequest(e,t));else if(n.length>1)throw new rs(`Found more than one (${n.length}) load handlers for URL '${e}'`);e=n[0]}return async function(e,t,n){if(null==n&&(n={}),null==e.load)throw new rs("Cannot proceed with model loading because the IOHandler provided does not have the `load` method implemented.");const r=await e.load();let s=r.modelTopology;null!=s.model_config&&(s=s.model_config);const i=null==n.strict||n.strict,a=null!=r.weightData&&null!=r.weightSpecs&&i,o=Ha(xo(s),void 0,a),u=r.trainingConfig;if(null!=u&&o.loadTrainingConfig(u),null!=r.userDefinedMetadata&&o.setUserDefinedMetadata(r.userDefinedMetadata),null!=r.weightData){if(null==r.weightSpecs)throw new rs("LayersModel artifacts contains weight data, but not weight specs. Therefore loading of weights cannot proceed.");const{modelWeights:e,optimizerWeights:t}=function(e,t){const n=p.io.decodeWeights(e,t),r={},s=[];return t.forEach((e=>{"optimizer"===e.group?s.push({name:e.name,tensor:n[e.name]}):r[e.name]=n[e.name]})),{modelWeights:r,optimizerWeights:s}}(r.weightData,r.weightSpecs);o.loadWeights(e,i),null!=o.optimizer&&t.length>0&&await o.optimizer.setWeights(t),(0,p.dispose)(e),(0,p.dispose)(t.map((e=>e.tensor)))}return o}(e,0,t)}Vo.className="Functional",p.serialization.registerClass(Vo);class Go extends Bo{constructor(e){if(super({inputs:[],outputs:[]}),e=e||{},this.trainable=!0,this.built=!1,this.name=null!=e.name?e.name:_s("sequential_"),null!=e.layers)for(const t of e.layers)this.add(t)}checkShape(e){if(e.inboundNodes[0].outputTensors[0].shape.some((e=>e<0)))throw new rs("Negative dimension size caused by adding layer "+e.name+" with input shape ["+e.inboundNodes[0].inputTensors[0].shape+"]")}add(e){const t=e instanceof Go||e instanceof Bo;let n;if(t){if(n=e,1!==n.outputs.length)throw new rs("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 rs("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 rs("The first layer in a Sequential model must get an `inputShape` or `batchInputShape` argument.");const t=Qi({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 rs(`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 rs("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=Ki(this.outputs[0])}this.inboundNodes=[],new ji({outboundLayer:this,inboundLayers:[],nodeIndices:[],tensorIndices:[],inputTensors:this.inputs,outputTensors:this.outputs,inputMasks:os(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(zi(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 Bo({inputs:this.inputs,outputs:this.outputs[0],name:this.name+"_model"}),this.model.trainable=this.trainable,this.supportsMasking=this.model.supp

2ortsMasking,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 ns("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 ns("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 ns("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 ns("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 s,i={};if(t instanceof Array){if(null==t[0].className||"Merge"===t[0].className)throw new rs("Legacy serialization format not supported yet.");s=t}else p.util.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.")),s=t.layers,delete t.layers,i=t;const a=new e(i);if(!(a instanceof Go))throw new ss("Sequential.fromConfig called on non-Sequential input: "+a);for(const e of s){const t=Ha(e,void 0,r);r&&t.setFastWeightInitDuringBuild(!0),a.add(t)}return a}set stopTraining(e){if(null==this.model)throw new rs("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 rs("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}}}function Ho(e){return new Bo(e)}function jo(e){return new Go(e)}function qo(e){return Qi(e)}function Xo(e,t){Ua.registerCallbackConstructor(e,t)}Go.className="Sequential",p.serialization.registerClass(Go);class Ko extends p.serialization.Serializable{getConfig(){return{}}}class Yo extends Ko{apply(e,t=1){return function(e,t=1){if(1!==t)throw new ss(`Support for alpha values other than 1 (${t}) is not implemented yet.`);return p.elu(e)}(e,t)}}Yo.className="elu",p.serialization.registerClass(Yo);class Qo extends Ko{apply(e){return p.selu(e)}}Qo.className="selu",p.serialization.registerClass(Qo);class Zo extends Ko{apply(e){return p.relu(e)}}Zo.className="relu",p.serialization.registerClass(Zo);class Jo extends Ko{apply(e){return(0,p.tidy)((()=>p.minimum(6,p.relu(e))))}}Jo.className="relu6",p.serialization.registerClass(Jo);class eu extends Ko{apply(e){return e}}eu.className="linear",p.serialization.registerClass(eu);class tu extends Ko{apply(e){return p.sigmoid(e)}}tu.className="sigmoid",p.serialization.registerClass(tu);class nu extends Ko{apply(e){return function(e){return(0,p.tidy)((()=>{const t=p.add(.5,p.mul(.2,e));return p.clipByValue(t,0,1)}))}(e)}}

2ku.className="PReLU",p.serialization.registerClass(ku);class Su extends Xi{constructor(e){if(super(null==e?{}:e),this.DEFAULT_ALPHA=1,null==e&&(e={}),null!=e.alpha&&e.alpha!==this.DEFAULT_ALPHA)throw new ss(`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=Li(e);return(0,p.elu)(n)}computeOutputShape(e){return e}getConfig(){const e={alpha:this.alpha},t=super.getConfig();return Object.assign(e,t),e}}Su.className="ELU",p.serialization.registerClass(Su);class Iu extends Xi{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=Li(e);return(0,p.mul)(n,(0,p.cast)((0,p.greater)(n,this.theta),"float32"))}computeOutputShape(e){return e}getConfig(){const e={theta:this.theta},t=super.getConfig();return Object.assign(e,t),e}}Iu.className="ThresholdedReLU",p.serialization.registerClass(Iu);class Tu extends Xi{constructor(e){super(null==e?{}:e),this.DEFAULT_AXIS=1,null==e&&(e={}),this.softmax=(new au).apply,this.axis=null==e.axis?this.DEFAULT_AXIS:e.axis}call(e,t){const n=Li(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}}function Nu(e,t,n){if("number"==typeof e)return os(e,t);if(e.length!==t)throw new rs(`The ${n} argument must be an integer or tuple of ${t} integers. Received: ${e.length} elements.`);for(let s=0;s<t;++s){const i=e[s];if((r=i)!==parseInt(r.toString(),10))throw new rs(`The ${n} argument must be an integer or tuple of ${t} integers. Received: ${JSON.stringify(e)} including a non-integer number `+i)}return e;var r}function Cu(e,t,n,r,s=1){if(null==e)return e;let i;return i="same"===n?e:e-(t+(t-1)*(s-1))+1,Math.floor((i+r-1)/r)}function Eu(e,t,n,r){if(null==e)return null;if("valid"===r)e=e*t+qs([n-t,0]);else{if("same"!==r)throw new rs(`Unsupport padding mode: ${r}.`);e*=t}return e}function _u(e,t){return(0,p.tidy)((()=>(Ms(t),"channelsFirst"===t?p.transpose(e,[0,2,3,1]):e)))}function Au(e,t){return(0,p.tidy)((()=>(Ms(t),"channelsFirst"===t?p.transpose(e,[0,2,3,4,1]):e)))}function Ru(e,t,n,r=[1,1],s="valid",i,a,o=null){return(0,p.tidy)((()=>{if(null==i&&(i="channelsLast"),Ms(i),3!==e.rank&&4!==e.rank)throw new rs(`conv2dWithBiasActivation expects input to be of rank 3 or 4, but received ${e.rank}.`);if(3!==t.rank&&4!==t.rank)throw new rs(`conv2dWithBiasActivation expects kernel to be of rank 3 or 4, but received ${e.rank}.`);let u=_u(e,i);if("causal"===s)throw new ss("The support for CAUSAL padding mode in conv1dWithBias is not implemented yet.");return u=p.fused.conv2d({x:u,filter:t,strides:r,pad:"same"===s?"same":"valid",dilations:a,dataFormat:"NHWC",bias:n,activation:o}),"channelsFirst"===i&&(u=p.transpose(u,[0,3,1,2])),u}))}Tu.className="Softmax",p.serialization.registerClass(Tu);class $u extends Xi{constructor(e,t){if(super(t),this.bias=null,this.DEFAULT_KERNEL_INITIALIZER="glorotNormal",this.DEFAULT_BIAS_INITIALIZER="zeros",$u.verifyArgs(t),this.rank=e,Ss(this.rank,"rank"),1!==this.rank&&2!==this.rank&&3!==this.rank)throw new ss(`Convolution layer for rank other than 1, 2, or 3 (${this.rank}) is not implemented yet.`);if(this.kernelSize=Nu(t.kernelSize,e,"kernelSize"),this.strides=Nu(null==t.strides?1:t.strides,e,"strides"),this.padding=null==t.padding?"valid":t.padding,Ls(this.padding),this.dataFormat=null==t.dataFormat?"channelsLast":t.dataFormat,Ms(this.dataFormat),this.activation=hu(t.activation),this.useBias=null==t.useBias||t.useBias,this.biasInitializer=Oi(t.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.biasConstraint=fa(t.biasConstraint),this.biasRegularizer=vu(t.biasRegularizer),this.activityRegularizer=vu(t.activityRegularizer),this.dilationRate=Nu(null==t.dilationRate?1:t.dilationRate,e,"dilationRate"),1===this.rank&&Array.isArray(this.dilationRate)&&1!==this.dilationRate.length)throw new rs("dilationRate must be a number or an array of a single number for 1D convolution, but received "+JSON.stringify(this.dilationRate));if(2===this.rank){if("number"==typeof this.dilationRate)this.dilationRate=[this.dilationRate,this.dilationRate];else if(2!==this.dilationRate.length)throw new rs("dilationRate must be a number or array of two numbers for 2D convolution, but received "+JSON.stringify(this.dilationRate))}else if(3===this.rank)if("number"==typeof this.dilationRate)this.dilationRate=[this.dilationRate,this.dilationRate,this.dilationRate];else if(3!==this.dilationRate.length)throw new rs("dilationRate must be a number or array of three numbers for 3D convolution, but received "+JSON.stringify(this.dilationRate))}static verifyArgs(e){if(us("kernelSize"in e,"required key 'kernelSize' not in config"),"number"!=typeof e.kernelSize&&!ks(e.kernelSize,"number",1,3))throw new rs(`BaseConv expects config.kernelSize to be number or number[] with length 1, 2, or 3, but received ${JSON.stringify(e.kernelSize)}.`)}getConfig(){const e={kernelSize:this.kernelSize,strides:this.strides,padding:this.padding,dataFormat:this.dataFormat,dilationRate:this.dilationRate,activation:cu(this.activation),useBias:this.useBias,biasInitializer:Fi(this.biasInitializer),biasRegularizer:yu(this.biasRegularizer),activityRegularizer:yu(this.activityRegularizer),biasConstraint:ha(this.biasConstraint)},t=super.getConfig();return Object.assign(e,t),e}}class Fu extends $u{constructor(e,t){super(e,t),this.kernel=null,Fu.verifyArgs(t),this.filters=t.filters,Ss(this.filters,"filters"),this.kernelInitializer=Oi(t.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.kernelConstraint=fa(t.kernelConstraint),this.kernelRegularizer=vu(t.kernelRegularizer)}build(e){e=zi(e);const t="channelsFirst"===this.dataFormat?1:e.length-1;if(null==e[t])throw new rs("The channel dimension of the input should be defined. Found "+e[t]);const n=e[t],r=this.kernelSize.concat([n,this.filters]);this.kernel=this.addWeight("kernel",r,null,this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint),this.useBias&&(this.bias=this.addWeight("bias",[this.filters],null,this.biasInitializer,this.biasRegularizer,!0,this.biasConstraint)),this.inputSpec=[{ndim:this.rank+2,axes:{[t]:n}}],this.built=!0}call(e,t){return(0,p.tidy)((()=>{let t;e=Li(e);const n=null==this.bias?null:this.bias.read(),r=Ts(this.activation.getClassName());if(null!=r&&2===this.rank)t=Ru(e,this.kernel.read(),n,this.strides,this.padding,this.dataFormat,this.dilationRate,r);else{if(1===this.rank)t=function(e,t,n,r=1,s="valid",i,a=1){return(0,p.tidy)((()=>{if(null==i&&(i="channelsLast"),Ms(i),3!==e.shape.length)throw new rs("The input of a conv1dWithBias operation should be 3, but is "+e.shape.length+" instead.");if(3!==t.shape.length)throw new rs("The kernel for a conv1dWithBias operation should be 3, but is "+t.shape.length+" instead");if(null!=n&&1!==n.shape.length)throw new rs("The bias for a conv1dWithBias operation should be 1, but is "+t.shape.length+" instead");if("channelsFirst"===i&&(e=p.transpose(e,[0,2,1])),"causal"===s)throw new ss("The support for CAUSAL padding mode in conv1dWithBias is not implemented yet.");let o=p.conv1d(e,t,r,"same"===s?"same":"valid","NWC",a);return null!=n&&(o=ci(o,n)),o}))}(e,this.kernel.read(),n,this.strides[0],this.padding,this.dataFormat,this.dilationRate[0]);else if(2===this.rank)t=Ru(e,this.kernel.read(),n,this.strides,this.padding,this.dataFormat,this.dilationRate);else{if(3!==this.rank)throw new ss("convolutions greater than 3D are not implemented yet.");t=function(e,t,n,r=[1,1,1],s="valid",i,a){return(0,p.tidy)((()=>{if(null==i&&(i="channelsLast"),Ms(i),4!==e.rank&&5!==e.rank)throw new rs("conv3dWithBias expects input to be of rank 4 or 5, but received "+e.rank+".");if(4!==t.rank&&5!==t.rank)throw new rs("conv3dWithBias expects kernel to be of rank 4 or 5, but received "+e.rank+".");let o=Au(e,i);if("causal"===s)throw new ss("The support for CAUSAL padding mode in conv3dWithBias is not implemented yet.");return o=p.conv3d(o,t,r,"same"===s?"same":"valid","NDHWC",a),null!=n&&(o=ci(o,n)),"channelsFirst"===i&&(o=p.transpose(o,[0,4,1,2,3])),o}))}(e,this.kernel.read(),n,this.strides,this.padding,this.dataFormat,this.dilationRate)}null!=this.activation&&(t=this.activation.apply(t))}return t}))}computeOutputShape(e){e=zi(e);const t=[],n="channelsLast"===this.dataFormat?e.slice(1,e.length-1):e.slice(2);for(let e=0;e<n.length;++e){const r=Cu(n[e],this.kernelSize[e],this.padding,this.strides[e],"number"==typeof this.dilationRate?this.dilationRate:this.dilationRate[e]);t.push(r)}let r=[e[0]];return"channelsLast"===this.dataFormat?(r=r.concat(t),r.push(this.filters)):(r.push(this.filters),r=r.concat(t)),r}getConfig(){const e={filters:this.filters,kernelInitializer:Fi(this.kernelInitializer),kernelRegularizer:yu(this.kernelRegularizer),kernelConstraint:ha(this.kernelConstraint)},t=super.getConfig();return Object.assign(e,t),e}static verifyArgs(e){if(!("filters"in e)||"number"!=typeof e.filters||e.filters<1)throw new rs("Convolution layer expected config.filters to be a 'number' > 0 but got "+JSON.stringify(e.filters))}}class Ou extends Fu{constructor(e){super(2,e),Ou.verifyArgs(e)}getConfig(){const e=super.getConfig();return delete e.rank,e}static verifyArgs(e){if("number"!=typeof e.kernelSize&&!ks(e.kernelSize,"number",1,2))throw new rs(`Conv2D expects config.kernelSize to be number or number[] with length 1 or 2, but received ${JSON.stringify(e.kernelSize)}.`)}}Ou.className="Conv2D",p.serialization.registerClass(Ou);class Du extends Fu{constructor(e){super(3,e),Du.verifyArgs(e)}getConfig(){const e=super.getConfig();return delete e.rank,e}static verifyArgs(e){if("number"!=typeof e.kernelSize&&(!Array.isArray(e.kernelSize)||1!==e.kernelSize.length&&3!==e.kernelSize.length))throw new rs(`Conv3D expects config.kernelSize to be number or [number, number, number], but received ${JSON.stringify(e.kernelSize)}.`)}}Du.className="Conv3D",p.serialization.registerClass(Du);class Mu extends Ou{constructor(e){if(super(e),this.inputSpec=[new Ui({ndim:4})],"same"!==this.padding&&"valid"!==this.padding)throw new rs("Conv2DTranspose currently supports only padding modes 'same' and 'valid', but received padding mode "+this.padding)}build(e){if(4!==(e=zi(e)).length)throw new rs("Input should have rank 4; Received input shape: "+JSON.stringify(e));const t="channelsFirst"===this.dataFormat?1:e.length-1;if(null==e[t])throw new rs("The channel dimension of the inputs should be defined. Found `None`.");const n=e[t],r=this.kernelSize.concat([this.filters,n]);this.kernel=this.addWeight("kernel",r,"float32",this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint),this.useBias&&(this.bias=this.addWeight("bias",[this.filters],"float32",this.biasInitializer,this.biasRegularizer,!0,this.biasConstraint)),this.inputSpec=[new Ui({ndim:4,axes:{[t]:n}})],this.built=!0}call(e,t){return p.tidy((()=>{let t=Li(e);if(4!==t.shape.length)throw new rs("Conv2DTranspose.call() expects input tensor to be rank-4, but received a tensor of rank-"+t.shape.length);const n=t.shape,r=n[0];let s,i;"channelsFirst"===this.dataFormat?(s=2,i=3):(s=1,i=2);const a=n[s],o=n[i],u=this.kernelSize[0],l=this.kernelSize[1],c=this.strides[0],d=this.strides[1],h=[r,Eu(a,c,u,this.padding),Eu(o,d,l,this.padding),this.filters];"channelsLast"!==this.dataFormat&&(t=p.transpose(t,[0,2,3,1]));

2let f=p.conv2dTranspose(t,this.kernel.read(),h,this.strides,this.padding);return"channelsLast"!==this.dataFormat&&(f=p.transpose(f,[0,3,1,2])),null!=this.bias&&(f=ci(f,this.bias.read(),this.dataFormat)),null!=this.activation&&(f=this.activation.apply(f)),f}))}computeOutputShape(e){const t=(e=zi(e)).slice();let n,r,s;"channelsFirst"===this.dataFormat?(n=1,r=2,s=3):(n=3,r=1,s=2);const i=this.kernelSize[0],a=this.kernelSize[1],o=this.strides[0],u=this.strides[1];return t[n]=this.filters,t[r]=Eu(t[r],o,i,this.padding),t[s]=Eu(t[s],u,a,this.padding),t}getConfig(){const e=super.getConfig();return delete e.dilationRate,e}}Mu.className="Conv2DTranspose",p.serialization.registerClass(Mu);class Lu extends Du{constructor(e){if(super(e),this.inputSpec=[new Ui({ndim:5})],"same"!==this.padding&&"valid"!==this.padding)throw new rs("Conv3DTranspose currently supports only padding modes 'same' and 'valid', but received padding mode "+this.padding)}build(e){if(5!==(e=zi(e)).length)throw new rs("Input should have rank 5; Received input shape: "+JSON.stringify(e));const t="channelsFirst"===this.dataFormat?1:e.length-1;if(null==e[t])throw new rs("The channel dimension of the inputs should be defined. Found `None`.");const n=e[t],r=this.kernelSize.concat([this.filters,n]);this.kernel=this.addWeight("kernel",r,"float32",this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint),this.useBias&&(this.bias=this.addWeight("bias",[this.filters],"float32",this.biasInitializer,this.biasRegularizer,!0,this.biasConstraint)),this.inputSpec=[new Ui({ndim:5,axes:{[t]:n}})],this.built=!0}call(e,t){return p.tidy((()=>{let t=Li(e);if(5!==t.shape.length)throw new rs("Conv3DTranspose.call() expects input tensor to be rank-4, but received a tensor of rank-"+t.shape.length);const n=t.shape,r=n[0];let s,i,a;"channelsFirst"===this.dataFormat?(a=2,s=3,i=4):(a=1,s=2,i=3);const o=n[a],u=n[s],l=n[i],c=this.kernelSize[0],d=this.kernelSize[1],h=this.kernelSize[2],f=this.strides[0],m=this.strides[1],g=this.strides[2],y=[r,Eu(o,f,c,this.padding),Eu(u,m,d,this.padding),Eu(l,g,h,this.padding),this.filters];"channelsLast"!==this.dataFormat&&(t=p.transpose(t,[0,2,3,4,1]));let b=p.conv3dTranspose(t,this.kernel.read(),y,this.strides,this.padding);return"channelsLast"!==this.dataFormat&&(b=p.transpose(b,[0,4,1,2,3])),null!==this.bias&&(b=ci(b,this.bias.read(),this.dataFormat)),null!==this.activation&&(b=this.activation.apply(b)),b}))}computeOutputShape(e){const t=(e=zi(e)).slice();let n,r,s,i;"channelsFirst"===this.dataFormat?(n=1,r=2,s=3,i=4):(n=4,r=1,s=2,i=3);const a=this.kernelSize[0],o=this.kernelSize[1],u=this.kernelSize[2],l=this.strides[0],c=this.strides[1],d=this.strides[2];return t[n]=this.filters,t[r]=Eu(t[r],l,a,this.padding),t[s]=Eu(t[s],c,o,this.padding),t[i]=Eu(t[i],d,u,this.padding),t}getConfig(){const e=super.getConfig();return delete e.dilationRate,e}}Lu.className="Conv3DTranspose",p.serialization.registerClass(Lu);class zu extends Fu{constructor(e,t){if(super(e,t),this.DEFAULT_DEPTHWISE_INITIALIZER="glorotUniform",this.DEFAULT_POINTWISE_INITIALIZER="glorotUniform",this.depthwiseKernel=null,this.pointwiseKernel=null,null==t.filters)throw new rs("The `filters` configuration field is required by SeparableConv, but is unspecified.");if(null!=t.kernelInitializer||null!=t.kernelRegularizer||null!=t.kernelConstraint)throw new rs("Fields kernelInitializer, kernelRegularizer and kernelConstraint are invalid for SeparableConv2D. Use depthwiseInitializer, depthwiseRegularizer, depthwiseConstraint, pointwiseInitializer, pointwiseRegularizer and pointwiseConstraint instead.");if(null!=t.padding&&"same"!==t.padding&&"valid"!==t.padding)throw new rs(`SeparableConv${this.rank}D supports only padding modes: 'same' and 'valid', but received `+JSON.stringify(t.padding));this.depthMultiplier=null==t.depthMultiplier?1:t.depthMultiplier,this.depthwiseInitializer=Oi(t.depthwiseInitializer||this.DEFAULT_DEPTHWISE_INITIALIZER),this.depthwiseRegularizer=vu(t.depthwiseRegularizer),this.depthwiseConstraint=fa(t.depthwiseConstraint),this.pointwiseInitializer=Oi(t.depthwiseInitializer||this.DEFAULT_POINTWISE_INITIALIZER),this.pointwiseRegularizer=vu(t.pointwiseRegularizer),this.pointwiseConstraint=fa(t.pointwiseConstraint)}build(e){if((e=zi(e)).length<this.rank+2)throw new rs(`Inputs to SeparableConv${this.rank}D should have rank `+(this.rank+2)+", but received input shape: "+JSON.stringify(e));const t="channelsFirst"===this.dataFormat?1:e.length-1;if(null==e[t]||e[t]<0)throw new rs("The channel dimension of the inputs should be defined, but found "+JSON.stringify(e[t]));const n=e[t],r=this.kernelSize.concat([n,this.depthMultiplier]),s=[];for(let e=0;e<this.rank;++e)s.push(1);s.push(n*this.depthMultiplier,this.filters);const i=!0;this.depthwiseKernel=this.addWeight("depthwise_kernel",r,"float32",this.depthwiseInitializer,this.depthwiseRegularizer,i,this.depthwiseConstraint),this.pointwiseKernel=this.addWeight("pointwise_kernel",s,"float32",this.pointwiseInitializer,this.pointwiseRegularizer,i,this.pointwiseConstraint),this.useBias?this.bias=this.addWeight("bias",[this.filters],"float32",this.biasInitializer,this.biasRegularizer,i,this.biasConstraint):this.bias=null,this.inputSpec=[new Ui({ndim:this.rank+2,axes:{[t]:n}})],this.built=!0}call(e,t){return(0,p.tidy)((()=>{let t;if(e=Li(e),1===this.rank)throw new ss("1D separable convolution is not implemented yet.");return 2===this.rank&&("channelsFirst"===this.dataFormat&&(e=p.transpose(e,[0,2,3,1])),t=p.separableConv2d(e,this.depthwiseKernel.read(),this.pointwiseKernel.read(),this.strides,this.padding,this.dilationRate,"NHWC")),this.useBias&&(t=ci(t,this.bias.read(),this.dataFormat)),null!=this.activation&&(t=this.activation.apply(t)),"channelsFirst"===this.dataFormat&&(t=p.transpose(t,[0,3,1,2])),t}))}getConfig(){const e=super.getConfig();return delete e.rank,delete e.kernelInitializer,delete e.kernelRegularizer,delete e.kernelConstraint,e.depthwiseInitializer=Fi(this.depthwiseInitializer),e.pointwiseInitializer=Fi(this.pointwiseInitializer),e.depthwiseRegularizer=yu(this.depthwiseRegularizer),e.pointwiseRegularizer=yu(this.pointwiseRegularizer),e.depthwiseConstraint=ha(this.depthwiseConstraint),e.pointwiseConstraint=ha(this.pointwiseConstraint),e}}zu.className="SeparableConv";class Pu extends zu{constructor(e){super(2,e)}}Pu.className="SeparableConv2D",p.serialization.registerClass(Pu);class Bu extends Fu{constructor(e){super(1,e),Bu.verifyArgs(e),this.inputSpec=[{ndim:3}]}getConfig(){const e=super.getConfig();return delete e.rank,delete e.dataFormat,e}static verifyArgs(e){if("number"!=typeof e.kernelSize&&!ks(e.kernelSize,"number",1,1))throw new rs(`Conv1D expects config.kernelSize to be number or number[] with length 1, but received ${JSON.stringify(e.kernelSize)}.`)}}Bu.className="Conv1D",p.serialization.registerClass(Bu);class Vu extends Xi{constructor(e){super(e),"number"==typeof e.cropping?this.cropping=[[e.cropping,e.cropping],[e.cropping,e.cropping]]:"number"==typeof e.cropping[0]?this.cropping=[[e.cropping[0],e.cropping[0]],[e.cropping[1],e.cropping[1]]]:this.cropping=e.cropping,this.dataFormat=void 0===e.dataFormat?"channelsLast":e.dataFormat,this.inputSpec=[{ndim:4}]}computeOutputShape(e){return"channelsFirst"===this.dataFormat?[e[0],e[1],e[2]-this.cropping[0][0]-this.cropping[0][1],e[3]-this.cropping[1][0]-this.cropping[1][1]]:[e[0],e[1]-this.cropping[0][0]-this.cropping[0][1],e[2]-this.cropping[1][0]-this.cropping[1][1],e[3]]}call(e,t){return(0,p.tidy)((()=>{if(e=Li(e),"channelsLast"===this.dataFormat){const t=ti(e,this.cropping[0][0],e.shape[1]-this.cropping[0][0]-this.cropping[0][1],2);return ti(t,this.cropping[1][0],e.shape[2]-this.cropping[1][1]-this.cropping[1][0],3)}{const t=ti(e,this.cropping[0][0],e.shape[2]-this.cropping[0][0]-this.cropping[0][1],3);return ti(t,this.cropping[1][0],e.shape[3]-this.cropping[1][1]-this.cropping[1][0],4)}}))}getConfig(){const e={cropping:this.cropping,dataFormat:this.dataFormat},t=super.getConfig();return Object.assign(e,t),e}}Vu.className="Cropping2D",p.serialization.registerClass(Vu);class Wu extends Xi{constructor(e){var t;super(e),this.DEFAULT_SIZE=[2,2],this.inputSpec=[{ndim:4}],this.size=null==e.size?this.DEFAULT_SIZE:e.size,this.dataFormat=null==e.dataFormat?"channelsLast":e.dataFormat,Ms(this.dataFormat),this.interpolation=null==e.interpolation?"nearest":e.interpolation,t=this.interpolation,ws(Rs,"InterpolationFormat",t)}computeOutputShape(e){if("channelsFirst"===this.dataFormat){const t=null==e[2]?null:this.size[0]*e[2],n=null==e[3]?null:this.size[1]*e[3];return[e[0],e[1],t,n]}{const t=null==e[1]?null:this.size[0]*e[1],n=null==e[2]?null:this.size[1]*e[2];return[e[0],t,n,e[3]]}}call(e,t){return p.tidy((()=>{let t=Li(e);const n=t.shape;if("channelsFirst"===this.dataFormat){t=p.transpose(t,[0,2,3,1]);const e=this.size[0]*n[2],r=this.size[1]*n[3],s="nearest"===this.interpolation?p.image.resizeNearestNeighbor(t,[e,r]):p.image.resizeBilinear(t,[e,r]);return p.transpose(s,[0,3,1,2])}{const e=this.size[0]*n[1],r=this.size[1]*n[2];return"nearest"===this.interpolation?p.image.resizeNearestNeighbor(t,[e,r]):p.image.resizeBilinear(t,[e,r])}}))}getConfig(){const e={size:this.size,dataFormat:this.dataFormat,interpolation:this.interpolation},t=super.getConfig();return Object.assign(e,t),e}}Wu.className="UpSampling2D",p.serialization.registerClass(Wu);class Uu extends $u{constructor(e){super(2,e),this.depthwiseKernel=null,this.depthMultiplier=null==e.depthMultiplier?1:e.depthMultiplier,this.depthwiseInitializer=Oi(e.depthwiseInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.depthwiseConstraint=fa(e.depthwiseConstraint),this.depthwiseRegularizer=vu(e.depthwiseRegularizer)}build(e){if((e=zi(e)).length<4)throw new rs(`Inputs to DepthwiseConv2D should have rank 4. Received input shape: ${JSON.stringify(e)}.`);const t="channelsFirst"===this.dataFormat?1:3;if(null==e[t]||e[t]<0)throw new rs(`The channel dimension of the inputs to DepthwiseConv2D should be defined, but is not (${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,p.tidy)((()=>{let t=function(e,t,n=[1,1],r="valid",s,i){return(0,p.tidy)((()=>{null==s&&(s="channelsLast"),Ms(s);let a=_u(e,s);if(4!==e.rank)throw new rs("Input for depthwiseConv2d is required to be 4-D, but is instead "+e.rank+"-D");if(4!==t.rank)throw new rs("depthwiseKernel is required to be 4-D, but is instead "+t.rank+"-D");return a=p.depthwiseConv2d(a,t,n,"same"===r?"same":"valid","NHWC",i),"channelsFirst"===s&&(a=p.transpose(a,[0,3,1,2])),a}))}(e=Li(e),this.depthwiseKernel.read(),this.strides,this.padding,this.dataFormat,null);return this.useBias&&(t=ci(t,this.bias.read(),this.dataFormat)),null!=this.activation&&(t=this.activation.apply(t)),t}))}computeOutputShape(e){e=zi(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=Cu(t,this.kernelSize[0],this.padding,this.strides[0]),i=Cu(n,this.kernelSize[1],this.padding,this.strides[1]);return"channelsFirst"===this.dataFormat?[e[0],r,s,i]:[e[0],s,i,r]}getConfig(){const e=super.getConfig();return e.depthMultiplier=this.depthMultiplier,e.depthwiseInitializer=Fi(this.depthwiseInitializer),e.depthwiseRegularizer=yu(this.depthwiseRegularizer),e.depthwiseConstraint=ha(this.depthwiseRegularizer),e}}function Gu(e,t,n,r){if(Array.isArray(e)){if(null!=t||null!=n)throw new rs("When input

2))}apply(e,t){let n=null==t?null:t.initialState,r=null==t?null:t.constants;null==t&&(t={});const s=Gu(e,n,r,this.numConstants);e=s.inputs,n=s.initialState,r=s.constants;let i=[],a=[];if(null!=n){t.initialState=n,i=i.concat(n),this.stateSpec=[];for(const e of n)this.stateSpec.push(new Ui({shape:e.shape}));a=a.concat(this.stateSpec)}if(null!=r&&(t.constants=r,i=i.concat(r),this.numConstants=r.length),i[0]instanceof Gi){const n=[e].concat(i),r=this.inputSpec.concat(a),s=this.inputSpec;this.inputSpec=r;const o=super.apply(n,t);return this.inputSpec=s,o}return super.apply(e,t)}call(e,t){return(0,p.tidy)((()=>{const n=null==t?null:t.mask,r=null==t?null:t.training;let s=null==t?null:t.initialState;e=Li(e),null==s&&(s=this.stateful?this.states_:this.getInitialState(e));const i=Array.isArray(this.cell.stateSize)?this.cell.stateSize.length:1;if(s.length!==i)throw new rs(`RNN Layer has ${i} state(s) but was passed `+s.length+" initial state(s).");this.unroll&&console.warn("Ignoring unroll = true for RNN layer, due to imperative backend.");const a={training:r},o=Hu(((e,t)=>{const n=this.cell.call([e].concat(t),a);return[n[0],n.slice(1)]}),e,s,this.goBackwards,n,null,this.unroll,this.returnSequences),u=o[0],l=o[1],c=o[2];this.stateful&&this.resetStates(c,r);const d=this.returnSequences?l:u;return this.returnState?[d].concat(c):d}))}getInitialState(e){return(0,p.tidy)((()=>{let t=p.zeros(e.shape);return t=p.sum(t,[1,2]),t=Zs(t),Array.isArray(this.cell.stateSize)?this.cell.stateSize.map((e=>e>1?si(t,[1,e]):t)):this.cell.stateSize>1?[si(t,[1,this.cell.stateSize])]:[t]}))}get trainableWeights(){return this.trainable?this.cell.trainableWeights:[]}get nonTrainableWeights(){return this.trainable?this.cell.nonTrainableWeights:this.cell.weights}setFastWeightInitDuringBuild(e){super.setFastWeightInitDuringBuild(e),null!=this.cell&&this.cell.setFastWeightInitDuringBuild(e)}getConfig(){const e=super.getConfig(),t={returnSequences:this.returnSequences,returnState:this.returnState,goBackwards:this.goBackwards,stateful:this.stateful,unroll:this.unroll};null!=this.numConstants&&(t.numConstants=this.numConstants);const n=this.cell.getConfig();return this.getClassName()===ju.className&&(t.cell={className:this.cell.getClassName(),config:n}),Object.assign(Object.assign(Object.assign({},n),e),t)}static fromConfig(e,t,n={}){const r=Ha(t.cell,n);return new e(Object.assign(t,{cell:r}))}}ju.className="RNN",p.serialization.registerClass(ju);class qu extends Xi{}class Xu extends qu{constructor(e){super(e),this.DEFAULT_ACTIVATION="tanh",this.DEFAULT_KERNEL_INITIALIZER="glorotNormal",this.DEFAULT_RECURRENT_INITIALIZER="orthogonal",this.DEFAULT_BIAS_INITIALIZER="zeros",this.units=e.units,Ss(this.units,"units"),this.activation=hu(null==e.activation?this.DEFAULT_ACTIVATION:e.activation),this.useBias=null==e.useBias||e.useBias,this.kernelInitializer=Oi(e.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.recurrentInitializer=Oi(e.recurrentInitializer||this.DEFAULT_RECURRENT_INITIALIZER),this.biasInitializer=Oi(e.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.kernelRegularizer=vu(e.kernelRegularizer),this.recurrentRegularizer=vu(e.recurrentRegularizer),this.biasRegularizer=vu(e.biasRegularizer),this.kernelConstraint=fa(e.kernelConstraint),this.recurrentConstraint=fa(e.recurrentConstraint),this.biasConstraint=fa(e.biasConstraint),this.dropout=js([1,qs([0,null==e.dropout?0:e.dropout])]),this.recurrentDropout=js([1,qs([0,null==e.recurrentDropout?0:e.recurrentDropout])]),this.dropoutFunc=e.dropoutFunc,this.stateSize=this.units,this.dropoutMask=null,this.recurrentDropoutMask=null}build(e){e=zi(e),this.kernel=this.addWeight("kernel",[e[e.length-1],this.units],null,this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint),this.recurrentKernel=this.addWeight("recurrent_kernel",[this.units,this.units],null,this.recurrentInitializer,this.recurrentRegularizer,!0,this.recurrentConstraint),this.useBias?this.bias=this.addWeight("bias",[this.units],null,this.biasInitializer,this.biasRegularizer,!0,this.biasConstraint):this.bias=null,this.built=!0}call(e,t){return(0,p.tidy)((()=>

2{if(2!==(e=e).length)throw new rs(`SimpleRNNCell expects 2 input Tensors, got ${e.length}.`);let n=e[1];e=e[0];const r=null!=t.training&&t.training;let s;0<this.dropout&&this.dropout<1&&null==this.dropoutMask&&(this.dropoutMask=tl({ones:()=>p.onesLike(e),rate:this.dropout,training:r,dropoutFunc:this.dropoutFunc})),0<this.recurrentDropout&&this.recurrentDropout<1&&null==this.recurrentDropoutMask&&(this.recurrentDropoutMask=tl({ones:()=>p.onesLike(n),rate:this.recurrentDropout,training:r,dropoutFunc:this.dropoutFunc}));const i=this.dropoutMask,a=this.recurrentDropoutMask;s=ai(null!=i?p.mul(e,i):e,this.kernel.read()),null!=this.bias&&(s=ci(s,this.bias.read())),null!=a&&(n=p.mul(n,a));let o=p.add(s,ai(n,this.recurrentKernel.read()));return null!=this.activation&&(o=this.activation.apply(o)),[o,o]}))}getConfig(){const e=super.getConfig(),t={units:this.units,activation:cu(this.activation),useBias:this.useBias,kernelInitializer:Fi(this.kernelInitializer),recurrentInitializer:Fi(this.recurrentInitializer),biasInitializer:Fi(this.biasInitializer),kernelRegularizer:yu(this.kernelRegularizer),recurrentRegularizer:yu(this.recurrentRegularizer),biasRegularizer:yu(this.biasRegularizer),activityRegularizer:yu(this.activityRegularizer),kernelConstraint:ha(this.kernelConstraint),recurrentConstraint:ha(this.recurrentConstraint),biasConstraint:ha(this.biasConstraint),dropout:this.dropout,recurrentDropout:this.recurrentDropout};return Object.assign(Object.assign({},e),t)}}Xu.className="SimpleRNNCell",p.serialization.registerClass(Xu);class Ku extends ju{constructor(e){e.cell=new Xu(e),super(e)}call(e,t){return(0,p.tidy)((()=>{null!=this.cell.dropoutMask&&(p.dispose(this.cell.dropoutMask),this.cell.dropoutMask=null),null!=this.cell.recurrentDropoutMask&&(p.dispose(this.cell.recurrentDropoutMask),this.cell.recurrentDropoutMask=null);const n=null==t?null:t.mask,r=null==t?null:t.training,s=null==t?null:t.initialState;return super.call(e,{mask:n,training:r,initialState:s})}))}static fromConfig(e,t){return new e(t)}}Ku.className="SimpleRNN",p.serialization.registerClass(Ku);class Yu extends qu{constructor(e){if(super(e),this.DEFAULT_ACTIVATION="tanh",this.DEFAULT_RECURRENT_ACTIVATION="hardSigmoid",this.DEFAULT_KERNEL_INITIALIZER="glorotNormal",this.DEFAULT_RECURRENT_INITIALIZER="orthogonal",this.DEFAULT_BIAS_INITIALIZER="zeros",e.resetAfter)throw new rs("GRUCell does not support reset_after parameter set to true.");this.units=e.units,Ss(this.units,"units"),this.activation=hu(void 0===e.activation?this.DEFAULT_ACTIVATION:e.activation),this.recurrentActivation=hu(void 0===e.recurrentActivation?this.DEFAULT_RECURRENT_ACTIVATION:e.recurrentActivation),this.useBias=null==e.useBias||e.useBias,this.kernelInitializer=Oi(e.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.recurrentInitializer=Oi(e.recurrentInitializer||this.DEFAULT_RECURRENT_INITIALIZER),this.biasInitializer=Oi(e.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.kernelRegularizer=vu(e.kernelRegularizer),this.recurrentRegularizer=vu(e.recurrentRegularizer),this.biasRegularizer=vu(e.biasRegularizer),this.kernelConstraint=fa(e.kernelConstraint),this.recurrentConstraint=fa(e.recurrentConstraint),this.biasConstraint=fa(e.biasConstraint),this.dropout=js([1,qs([0,null==e.dropout?0:e.dropout])]),this.recurrentDropout=js([1,qs([0,null==e.recurrentDropout?0:e.recurrentDropout])]),this.dropoutFunc=e.dropoutFunc,this.implementation=e.implementation,this.stateSize=this.units,this.dropoutMask=null,this.recurrentDropoutMask=null}build(e){const t=(e=zi(e))[e.length-1];this.kernel=this.addWeight("kernel",[t,3*this.units],null,this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint),this.recurrentKernel=this.addWeight("recurrent_kernel",[this.units,3*this.units],null,this.recurrentInitializer,this.recurrentRegularizer,!0,this.recurrentConstraint),this.useBias?this.bias=this.addWeight("bias",[3*this.units],null,this.biasInitializer,this.biasRegularizer,!0,this.biasConstraint):this.bias=null,this.built=!0}call(e,t){return(0,p.tidy)((()=>{if(2!==(e=e).length)throw new rs("GRUCell expects 2 input Tensors (inputs, h, c), got "+e.length+".");const n=null!=t.training&&t.training;let r=e[1];e=e[0],0<this.dropout&&this.dropout<1&&null==this.dropoutMask&&(this.dropoutMask=tl({ones:()=>p.onesLike(e),rate:this.dropout,training:n,count:3,dropoutFunc:this.dropoutFunc})),0<this.recurrentDropout&&this.recurrentDropout<1&&null==this.recurrentDropoutMask&&(this.recurrentDropoutMask=tl({ones:()=>p.onesLike(r),rate:this.recurrentDropout,training:n,count:3,dropoutFunc:this.dropoutFunc}));const s=this.dropoutMask,i=this.recurrentDropoutMask;let a,o,u;0<this.dropout&&this.dropout<1&&(e=p.mul(e,s[0]));let l=ai(e,this.kernel.read());this.useBias&&(l=ci(l,this.bias.read())),0<this.recurrentDropout&&this.recurrentDropout<1&&(r=p.mul(r,i[0]));const c=this.recurrentKernel.read(),[d,h]=p.split(c,[2*this.units,this.units],c.rank-1),f=ai(r,d),[m,g,y]=p.split(l,3,l.rank-1),[b,v]=p.split(f,2,f.rank-1);a=this.recurrentActivation.apply(p.add(m,b)),o=this.recurrentActivation.apply(p.add(g,v));const x=ai(p.mul(o,r),h);u=this.activation.apply(p.add(y,x));const w=p.add(p.mul(a,r),p.mul(p.add(1,p.neg(a)),u));return[w,w]}))}getConfig(){const e=super.getConfig(),t={units:this.units,activation:cu(this.activation),recurrentActivation:cu(this.recurrentActivation),useBias:this.useBias,kernelInitializer:Fi(this.kernelInitializer),recurrentInitializer:Fi(this.recurrentInitializer),biasInitializer:Fi(this.biasInitializer),kernelRegularizer:yu(this.kernelRegularizer),recurrentRegularizer:yu(this.recurrentRegularizer),biasRegularizer:yu(this.biasRegularizer),activityRegularizer:yu(this.activityRegularizer),kernelConstraint:ha(this.kernelConstraint),recurrentConstraint:ha(this.recurrentConstraint),biasConstraint:ha(this.biasConstraint),dropout:this.dropout,recurrentDropout:this.recurrentDropout,implementation:this.implementation,resetAfter:!1};return Object.assign(Object.assign({},e),t)}}Yu.className="GRUCell",p.serialization.registerClass(Yu);class Qu extends ju{constructor(e){0===e.implementation&&console.warn("`implementation=0` has been deprecated, and now defaults to `implementation=1`. Please update your layer call."),e.cell=new Yu(e),super(e)}call(e,t){return(0,p.tidy)((()=>{null!=this.cell.dropoutMask&&(p.dispose(this.cell.dropoutMask),this.cell.dropoutMask=null),null!=this.cell.recurrentDropoutMask&&(p.dispose(this.cell.recurrentDropoutMask),this.cell.recurrentDropoutMask=null);const n=null==t?null:t.mask,r=null==t?null:t.training,s=null==t?null:t.initialState;return super.call(e,{mask:n,training:r,initialState:s})}))}static fromConfig(e,t){return 0===t.implmentation&&(t.implementation=1),new e(t)}}Qu.className="GRU",p.serialization.registerClass(Qu);class Zu extends qu{constructor(e){super(e),this.DEFAULT_ACTIVATION="tanh",this.DEFAULT_RECURRENT_ACTIVATION="hardSigmoid",this.DEFAULT_KERNEL_INITIALIZER="glorotNormal",this.DEFAULT_RECURRENT_INITIALIZER="orthogonal",this.DEFAULT_BIAS_INITIALIZER="zeros",this.units=e.units,Ss(this.units,"units"),this.activation=hu(void 0===e.activation?this.DEFAULT_ACTIVATION:e.activation),this.recurrentActivation=hu(void 0===e.recurrentActivation?this.DEFAULT_RECURRENT_ACTIVATION:e.recurrentActivation),this.useBias=null==e.useBias||e.useBias,this.kernelInitializer=Oi(e.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.recurrentInitializer=Oi(e.recurrentInitializer||this.DEFAULT_RECURRENT_INITIALIZER),this.biasInitializer=Oi(e.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.unitForgetBias=e.unitForgetBias,this.kernelRegularizer=vu(e.kernelRegularizer),this.recurrentRegularizer=vu(e.recurrentRegularizer),this.biasRegularizer=vu(e.biasRegularizer),this.kernelConstraint=fa(e.kernelConstraint),this.recurrentConstraint=fa(e.recurrentConstraint),this.biasConstraint=fa(e.biasConstraint),this.dropout=js([1,qs([0,null==e.dropout?0:e.dropout])]),this.recurrentDropout=js([1,qs([0,null==e.recurrentDropout?0:e.recurrentDropout])]),this.dropoutFunc=e.dropoutFunc,this.implementation=e.implementation,this.stateSize=[this.units,this.units],this.dropoutMask=null,this.recurrentDropoutMask=null}build(e){var t;const n=(e=zi(e))[e.length-1];let r;if(this.kernel=this.addWeight("kernel",[n,4*this.units],null,this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint),this.recurrentKernel=this.addWeight("recurrent_kernel",[this.units,4*this.units],null,this.recurrentInitializer,this.recurrentRegularizer,!0,this.recurrentConstraint),this.useBias){if(this.unitForgetBias){const e=this.biasInitializer,n=this.units;r=new((t=class extends mi{apply(t,r){const s=e.apply([n]),i=(new yi).apply([n]),a=e.apply([2*n]);return ri(ri(s,i),a)}}).className="CustomInit",t)}else r=this.biasInitializer;this.bias=this.addWeight("bias",[4*this.units],null,r,this.biasRegularizer,!0,this.biasConstraint)}else this.bias=null;this.built=!0}call(e,t){return(0,p.tidy)((()=>{const n=null!=t.training&&t.training;if(3!==(e=e).length)throw new rs("LSTMCell expects 3 input Tensors (inputs, h, c), got "+e.length+".");let r=e[1];const s=e[2];e=e[0],0<this.dropout&&this.dropout<1&&null==this.dropoutMask&&(this.dropoutMask=tl({ones:()=>p.onesLike(e),rate:this.dropout,training:n,count:4,dropoutFunc:this.dropoutFunc})),0<this.recurrentDropout&&this.recurrentDropout<1&&null==this.recurrentDropoutMask&&(this.recurrentDropoutMask=tl({ones:()=>p.onesLike(r),rate:this.recurrentDropout,training:n,count:4,dropoutFunc:this.dropoutFunc}));const i=this.dropoutMask,a=this.recurrentDropoutMask;let o,u,l,c;0<this.dropout&&this.dropout<1&&(e=p.mul(e,i[0]));let d=ai(e,this.kernel.read());0<this.recurrentDropout&&this.recurrentDropout<1&&(r=p.mul(r,a[0])),d=p.add(d,ai(r,this.recurrentKernel.read())),this.useBias&&(d=ci(d,this.bias.read()));const[h,f,m,g]=p.split(d,4,d.rank-1);o=this.recurrentActivation.apply(h),u=this.recurrentActivation.apply(f),l=p.add(p.mul(u,s),p.mul(o,this.activation.apply(m))),c=this.recurrentActivation.apply(g);const y=p.mul(c,this.activation.apply(l));return[y,y,l]}))}getConfig(){const e=super.getConfig(),t={units:this.units,activation:cu(this.activation),recurrentActivation:cu(this.recurrentActivation),useBias:this.useBias,kernelInitializer:Fi(this.kernelInitializer),recurrentInitializer:Fi(this.recurrentInitializer),biasInitializer:Fi(this.biasInitializer),unitForgetBias:this.unitForgetBias,kernelRegularizer:yu(this.kernelRegularizer),recurrentRegularizer:yu(this.recurrentRegularizer),biasRegularizer:yu(this.biasRegularizer),activityRegularizer:yu(this.activityRegularizer),kernelConstraint:ha(this.kernelConstraint),recurrentConstraint:ha(this.recurrentConstraint),biasConstraint:ha(this.biasConstraint),dropout:this.dropout,recurrentDropout:this.recurrentDropout,implementation:this.implementation};return Object.assign(Object.assign({},e),t)}}Zu.className="LSTMCell",p.serialization.registerClass(Zu);class Ju extends ju{constructor(e){0===e.implementation&&console.warn("`implementation=0` has been deprecated, and now defaults to `implementation=1`. Please update your layer call."),e.cell=new Zu(e),super(e)}call(e,t){return(0,p.tidy)((()=>{null!=this.cell.dropoutMask&&(p.dispose(this.cell.dropoutMask),this.cell.dropoutMask=null),null!=this.cell.recurrentDropoutMask&&(p.dispose(this.cell.recurrentDropoutMask),this.cell.recurrentDropoutMask=null);const n=null==t?null:t.mask,r=null==t?null:t.training,s=null==t?null:t.initialState;return super.call(e,{mask:n,training:r,initialState:s})}

2))}static fromConfig(e,t){return 0===t.implmentation&&(t.implementation=1),new e(t)}}Ju.className="LSTM",p.serialization.registerClass(Ju);class el extends qu{constructor(e){super(e),this.cells=e.cells}get stateSize(){const e=[];for(const t of this.cells.slice().reverse())Array.isArray(t.stateSize)?e.push(...t.stateSize):e.push(t.stateSize);return e}call(e,t){return(0,p.tidy)((()=>{let n=(e=e).slice(1);const r=[];for(const e of this.cells.slice().reverse())Array.isArray(e.stateSize)?r.push(n.splice(0,e.stateSize.length)):r.push(n.splice(0,1));r.reverse();const s=[];let i;for(let a=0;a<this.cells.length;++a){const o=this.cells[a];n=r[a],i=0===a?[e[0]].concat(n):[i[0]].concat(n),i=o.call(i,t),s.push(i.slice(1))}n=[];for(const e of s.slice().reverse())n.push(...e);return[i[0]].concat(n)}))}build(e){let t;Di(e)&&(e=e[0]),e=e,this.cells.forEach(((n,r)=>{Bs("RNNCell_"+r,(()=>{n.build(e),t=Array.isArray(n.stateSize)?n.stateSize[0]:n.stateSize,e=[e[0],t]}))})),this.built=!0}getConfig(){const e=super.getConfig(),t={cells:this.cells.map((e=>({className:e.getClassName(),config:e.getConfig()})))};return Object.assign(Object.assign({},e),t)}static fromConfig(e,t,n={}){const r=[];for(const e of t.cells)r.push(Ha(e,n));return new e({cells:r})}get trainableWeights(){if(!this.trainable)return[];const e=[];for(const t of this.cells)e.push(...t.trainableWeights);return e}get nonTrainableWeights(){const e=[];for(const t of this.cells)e.push(...t.nonTrainableWeights);if(!this.trainable){const t=[];for(const e of this.cells)t.push(...e.trainableWeights);return t.concat(e)}return e}getWeights(){const e=[];for(const t of this.cells)e.push(...t.weights);return Vi(e)}setWeights(e){const t=[];for(const n of this.cells){const r=n.weights.length,s=e.splice(r);for(let e=0;e<n.weights.length;++e)t.push([n.weights[e],s[e]])}Wi(t)}}function tl(e){const{ones:t,rate:n,training:r=!1,count:s=1,dropoutFunc:i}=e,a=()=>null!=i?i(t(),n):di(t(),n),o=()=>hi(a,t,r);return!s||s<=1?p.keep(o().clone()):Array(s).fill(void 0).map(o).map((e=>p.keep(e.clone())))}el.className="StackedRNNCells",p.serialization.registerClass(el);class nl extends ju{constructor(e){if(e.unroll)throw new ss("Unrolling is not possible with convolutional RNNs.");if(Array.isArray(e.cell))throw new ss("It is not possible at the moment to stack convolutional cells.");super(e),this.inputSpec=[new Ui({ndim:5})]}call(e,t){return p.tidy((()=>{if(null!=this.cell.dropoutMask&&(p.dispose(this.cell.dropoutMask),this.cell.dropoutMask=null),null!=this.cell.recurrentDropoutMask&&(p.dispose(this.cell.recurrentDropoutMask),this.cell.recurrentDropoutMask=null),t&&t.constants)throw new rs("ConvRNN2D cell does not support constants");const n=null==t?null:t.mask,r=null==t?null:t.training,s=null==t?null:t.initialState;return super.call(e,{mask:n,training:r,initialState:s})}))}computeOutputShape(e){let t=this.computeSingleOutputShape(e);return this.returnSequences||(t=[t[0],...t.slice(2)]),this.returnState&&(t=[t,...Array(2).fill([e[0],...t.slice(-3)])]),t}getInitialState(e){return p.tidy((()=>{const{stateSize:t}=this.cell,n=e.shape,r=this.computeSingleOutputShape(n),s=[r[0],...r.slice(2)],i=p.zeros(s);return Array.isArray(t)?Array(t.length).fill(i):[i]}))}resetStates(e,t=!1){p.tidy((()=>{if(!this.stateful)throw new ts("Cannot call resetStates() on an RNN Layer that is not stateful.");const n=this.inputSpec[0].shape,r=this.computeSingleOutputShape(n),s=[r[0],...r.slice(2)];if(null==n[0])throw new rs("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((()=>p.zeros(s))):this.states_=[p.zeros(s)];else if(null==e)p.dispose(this.states_),null!=this.keptStates&&(p.dispose(this.keptStates),this.keptStates=[]),Array.isArray(this.cell.stateSize)?this.states_=this.cell.stateSize.map((()=>p.zeros(s))):this.states_[0]=p.zeros(s);else{if(Array.isArray(e)||(e=[e]),e.length!==this.states_.length)throw new rs(`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()):p.dispose(this.states_);for(let t=0;t<this.states_.length;++t){const n=e[t],r=s;if(!p.util.arraysEqual(n.shape,r))throw new rs(`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=>p.keep(e.clone())))}

2))}computeSingleOutputShape(e){const{dataFormat:t,filters:n,kernelSize:r,padding:s,strides:i,dilationRate:a}=this.cell,o="channelsFirst"===t,u=e[o?3:2],l=e[o?4:3],c=Cu(u,r[0],s,i[0],a[0]),d=Cu(l,r[1],s,i[1],a[1]);return[...e.slice(0,2),...o?[n,c,d]:[c,d,n]]}}nl.className="ConvRNN2D";class rl extends Zu{constructor(e){const{filters:t,kernelSize:n,strides:r,padding:s,dataFormat:i,dilationRate:a}=e;super(Object.assign(Object.assign({},e),{units:t})),this.filters=t,Ss(this.filters,"filters"),this.kernelSize=Nu(n,2,"kernelSize"),this.kernelSize.forEach((e=>Ss(e,"kernelSize"))),this.strides=Nu(r||1,2,"strides"),this.strides.forEach((e=>Ss(e,"strides"))),this.padding=s||"valid",Ls(this.padding),this.dataFormat=i||"channelsLast",Ms(this.dataFormat),this.dilationRate=Nu(a||1,2,"dilationRate"),this.dilationRate.forEach((e=>Ss(e,"dilationRate")))}build(e){var t;e=zi(e);const n="channelsFirst"===this.dataFormat?1:e.length-1;if(null==e[n])throw new rs("The channel dimension of the input should be defined. Found "+e[n]);const r=e[n],s=this.kernelSize.concat([r,4*this.filters]);this.kernel=this.addWeight("kernel",s,null,this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint);const i=this.kernelSize.concat([this.filters,4*this.filters]);if(this.recurrentKernel=this.addWeight("recurrent_kernel",i,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 mi{apply(e,t){return ni([n.apply([r]),p.ones([r]),n.apply([2*r])])}}).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 p.tidy((()=>{if(3!==e.length)throw new rs("ConvLSTM2DCell expects 3 input Tensors (inputs, h, c), got "+e.length+".");const n=t.training||!1,r=e[0],s=e[1],i=e[2];0<this.dropout&&this.dropout<1&&null==this.dropoutMask&&(this.dropoutMask=tl({ones:()=>p.onesLike(r),rate:this.dropout,training:n,count:4,dropoutFunc:this.dropoutFunc}));const a=this.dropoutMask,o=(e,t,n)=>t&&t[n]?p.mul(t[n],e):e;let u=o(r,a,0),l=o(r,a,1),c=o(r,a,2),d=o(r,a,3);0<this.recurrentDropout&&this.recurrentDropout<1&&null==this.recurrentDropoutMask&&(this.recurrentDropoutMask=tl({ones:()=>p.onesLike(s),rate:this.recurrentDropout,training:n,count:4,dropoutFunc:this.dropoutFunc}));const h=this.recurrentDropoutMask;let f=o(s,h,0),m=o(s,h,1),g=o(s,h,2),y=o(s,h,3);const[b,v,x,w]=p.split(this.kernel.read(),4,3),[k,S,I,T]=this.useBias?p.split(this.bias.read(),4):[null,null,null,null];u=this.inputConv(u,b,k,this.padding),l=this.inputConv(l,v,S,this.padding),c=this.inputConv(c,x,I,this.padding),d=this.inputConv(d,w,T,this.padding);const[N,C,E,_]=p.split(this.recurrentKernel.read(),4,3);f=this.recurrentConv(f,N),m=this.recurrentConv(m,C),g=this.recurrentConv(g,E),y=this.recurrentConv(y,_);const A=this.recurrentActivation.apply(p.add(u,f)),R=this.recurrentActivation.apply(p.add(l,m)),$=p.add(p.mul(R,i),p.mul(A,this.activation.apply(p.add(c,g)))),F=p.mul(this.recurrentActivation.apply(p.add(d,y)),this.activation.apply($));return[F,F,$]}))}getConfig(){const e=super.getConfig(),{units:t}=e,n=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}(e,["units"]),r={filters:this.filters,kernelSize:this.kernelSize,padding:this.padding,dataFormat:this.dataFormat,dilationRate:this.dilationRate,strides:this.strides};return Object.assign(Object.assign({},n),r)}inputConv(e,t,n,r){const s=p.conv2d(e,t,this.strides,r||"valid","channelsFirst"===this.dataFormat?"NCHW":"NHWC",this.dilationRate);return n?ci(s,n,this.dataFormat):s}recurrentConv(e,t){return p.conv2d(e,t,1,"same","channelsFirst"===this.dataFormat?"NCHW":"NHWC")}}rl.className="ConvLSTM2DCell",p.serialization.registerClass(rl);class sl extends nl{constructor(e){const t=new rl(e);super(Object.assign(Object.assign({},e),{cell:t}))}static fromConfig(e,t){return new e(t)}}

2sl.className="ConvLSTM2D",p.serialization.registerClass(sl);class il extends Xi{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 e=0;e<this.noiseShape.length;++e)n.push(null==this.noiseShape[e]?t[e]:this.noiseShape[e]);return n}call(e,t){return(0,p.tidy)((()=>{this.invokeCallHook(e,t);const n=Li(e);if(0<this.rate&&this.rate<1){const e=null!=t.training&&t.training,r=this.getNoiseShape(n);return hi((()=>di(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()}}il.className="Dropout",p.serialization.registerClass(il);class al extends il{constructor(e){super(e),this.inputSpec=[{ndim:3}]}getNoiseShape(e){const t=e.shape;return[t[0],1,t[2]]}}al.className="SpatialDropout1D",p.serialization.registerClass(al);class ol extends Xi{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,Ss(this.units,"units"),this.activation=hu(e.activation),null!=e.useBias&&(this.useBias=e.useBias),this.kernelInitializer=Oi(e.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.biasInitializer=Oi(e.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.kernelConstraint=fa(e.kernelConstraint),this.biasConstraint=fa(e.biasConstraint),this.kernelRegularizer=vu(e.kernelRegularizer),this.biasRegularizer=vu(e.biasRegularizer),this.activityRegularizer=vu(e.activityRegularizer),this.supportsMasking=!0,this.inputSpec=[{minNDim:2}]}build(e){const t=(e=zi(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=zi(e)).slice();return t[t.length-1]=this.units,t}call(e,t){return(0,p.tidy)((()=>{this.invokeCallHook(e,t);const n=Li(e),r=Ts(this.activation.getClassName());let s;return null!=r?s=ai(n,this.kernel.read(),r,this.bias?this.bias.read():null):(s=ai(n,this.kernel.read()),null!=this.bias&&(s=ci(s,this.bias.read())),null!=this.activation&&(s=this.activation.apply(s))),s}))}getConfig(){const e={units:this.units,activation:cu(this.activation),useBias:this.useBias,kernelInitializer:Fi(this.kernelInitializer),biasInitializer:Fi(this.biasInitializer),kernelRegularizer:yu(this.kernelRegularizer),biasRegularizer:yu(this.biasRegularizer),activityRegularizer:yu(this.activityRegularizer),kernelConstraint:ha(this.kernelConstraint),biasConstraint:ha(this.biasConstraint)},t=super.getConfig();return Object.assign(e,t),e}}ol.className="Dense",p.serialization.registerClass(ol);class ul extends Xi{constructor(e){super(e=e||{}),this.inputSpec=[{minNDim:3}],this.dataFormat=e.dataFormat}computeOutputShape(e){e=zi(e);for(const t of e.slice(1))if(null==t)throw new rs(`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],Hs(e,1)]}call(e,t){return(0,p.tidy)((()=>{this.invokeCallHook(e,t);let n=Li(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,p.transpose)(n,e)}return function(e){if(e.rank<=1)throw new rs(`batchFlatten requires a minimum rank of 2. Got rank: ${e.rank}.`);const t=[e.shape[0],Hs(e.shape,1)];return p.reshape(e,t)}(n)}))}getConfig(){const e={};null!=this.dataFormat&&(e.dataFormat=this.dataFormat);const t=super.getConfig();return Object.assign(e,t),e}}ul.className="Flatten",p.serialization.registerClass(ul);class ll extends Xi{constructor(e){super(e),this.supportsMasking=!0,this.activation=hu(e.activation)}call(e,t){return(0,p.tidy)((()=>{this.invokeCallHook(e,t);const n=Li(e);return this.activation.apply(n)}))}getConfig(){const e={activation:cu(this.activation)},t=super.getConfig();return Object.assign(e,t),e}}ll.className="Activation",p.serialization.registerClass(ll);class cl extends Xi{constructor(e){super(e),this.n=e.n,this.inputSpec=[{ndim:2}]}computeOutputShape(e){return[e[0],this.n,e[1]]}call(e,t){return(0,p.tidy)((()=>{return e=Li(e),t=e,n=this.n,(0,p.tidy)((()=>{if(2!==t.shape.length)throw new rs(`repeat() expects a rank-2 tensor, but received a rank-${t.shape.length} tensor.`);return si(Zs(t,1),[1,n,1])}));var t,n}))}getConfig(){const e={n:this.n},t=super.getConfig();return Object.assign(e,t),e}}cl.className="RepeatVector",p.serialization.registerClass(cl);class dl extends Xi{constructor(e){super(e),this.targetShape=e.targetShape;for(let e=0;e<this.targetShape.length;++e)this.isUnknown(this.targetShape[e])&&(this.targetShape[e]=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,i=null;for(let e=0;e<r.length;++e){const t=r[e];if(this.isUnknown(t)){if(null!==i)throw new rs("Can only specifiy one unknown dimension.");i=e}else s*=t}const a=Hs(e);if(null!==i){if(0===s||a%s!=0)throw new rs(n);r[i]=a/s}else if(a!==s)throw new rs(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,p.tidy)((()=>{this.invokeCallHook(e,t);const n=Li(e),r=n.shape,s=r.slice(0,1).concat(this.fixUnknownDimension(r.slice(1),this.targetShape));return(0,p.reshape)(n,s)}))}getConfig(){const e={targetShape:this.targetShape},t=super.getConfig();return Object.assign(e,t),e}}dl.className="Reshape",p.serialization.registerClass(dl);class hl extends Xi{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=Xs(1,e.dims.length+1);if(!p.util.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 Ui({ndim:this.dims.length+1})]}computeOutputShape(e){const t=(e=zi(e)).slice();return this.dims.forEach(((n,r)=>{t[r+1]=e[n]})),t}call(e,t){return(0,p.transpose)(Li(e),this.dimsIncludingBatch)}getConfig(){const e={dims:this.dims},t=super.getConfig();return Object.assign(e,t),e}}hl.className="Permute",p.serialization.registerClass(hl);class pl extends Xi{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=Li(e);return(0,p.any)((0,p.notEqual)(n,this.maskValue),-1)}call(e,t){return(0,p.tidy)((()=>{this.invokeCallHook(e,t);const n=Li(e),r=(0,p.any)((0,p.notEqual)(n,this.maskValue),-1,!0);return(0,p.mul)(n,(0,p.cast)(r,n.dtype))}))}}pl.className="Masking",p.serialization.registerClass(pl);class fl extends Xi{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(ds(e.inputLength))}this.inputDim=e.inputDim,Ss(this.inputDim,"inputDim"),this.outputDim=e.outputDim,Ss(this.outputDim,"outputDim"),this.embeddingsInitializer=Oi(e.embeddingsInitializer||this.DEFAULT_EMBEDDINGS_INITIALIZER),this.embeddingsRegularizer=vu(e.embeddingsRegularizer),this.activityRegularizer=vu(e.activityRegularizer),this.embeddingsConstraint=fa(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,p.tidy)((()=>this.maskZero?(e=Li(e),(0,p.notEqual)(e,(0,p.zerosLike)(e))):null))}computeOutputShape(e){if(e=zi(e),null==this.inputLength)return[...e,this.outputDim];const t=ds(this.inputLength);if(t.length!==e.length-1)throw new rs(`"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],i=e[r+1];if(null!=s&&null!=i&&s!==i)throw new rs(`"inputLength" is ${this.inputLength}, but received input shape has shape `+e);null==s&&(t[n]=i),n++}}return[e[0],...t,this.outputDim]}call(e,t){return(0,p.tidy)((()=>{this.invokeCallHook(e,t);let n=Li(e);"int32"!==n.dtype&&(n=Qs(n,"int32"));const r=oi(this.embeddings.read(),(0,p.reshape)(n,[n.size]));return(0,p.reshape)(r,zi(this.computeOutputShape(n.shape)))}))}getConfig(){const e={inputDim:this.inputDim,outputDim:this.outputDim,embeddingsInitializer:Fi(this.embeddingsInitializer),embeddingsRegularizer:yu(this.embeddingsRegularizer),activityRegularizer:yu(this.activityRegularizer),embeddingsConstraint:ha(this.embeddingsConstraint),maskZero:this.maskZero,inputLength:this.inputLength},t=super.getConfig();return Object.assign(e,t),e}}fl.className="Embedding",p.serialization.registerClass(fl);class ml extends Xi{constructor(e){super(e||{}),this.supportsMasking=!0}mergeFunction(e){throw new ss}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],i=t[r];

2if(null==s||null==i||s<0||i<0)n.push(null);else if(1===s)n.push(i);else if(1===i)n.push(s);else{if(s!==i)throw new rs("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=[zi(e)]),(e=e).length<2)throw new rs(`A merge layer should be called on an Array of at least 2 inputs. Got ${e.length} input(s).`);let t=[];for(const n of e)null!=n&&null!==n[0]&&t.push(n[0]);if(t=vs(t),t.length>1)throw new rs(`Can not merge tensors with different batch sizes. Got tensors with shapes: ${JSON.stringify(e)}.`);let n=null==e[0]?null:e[0].slice(1);for(let t=1;t<e.length;++t){const r=null==e[t]?null:e[t].slice(1);n=this.computeElementwiseOpOutputShape(n,r)}const r=e.map((e=>e.length));-1===e.indexOf(null)&&1===vs(r).length?this.reshapeRequired=!1:this.reshapeRequired=!0}call(e,t){return(0,p.tidy)((()=>{if(e=e,this.reshapeRequired){const t=[],n=e.map((e=>e.rank));if(-1===n.indexOf(null)){const r=qs(n);for(let n of e){const e=n.rank;for(let t=0;t<r-e;++t)n=Zs(n,1);t.push(n)}return this.mergeFunction(t)}{let n=!1;for(const r of e){const e=r.rank;if(null==e){const e=r.shape,s=e[0],i=e.slice(1).concat([s]);let a=p.reshape(r,[s].concat(Hs(e.slice(1))));a=p.transpose(a,[1,0]),a=p.reshape(a,i),t.push(a),n=!0}else if(e>1){const s=Xs(1,e).concat([0]);t.push(p.transpose(r,s)),n=!0}else t.push(r)}let r=this.mergeFunction(t);const s=r.rank;if(n)if(null==s){const e=r.shape,t=e[e.length-1],n=[t].concat(e.slice(0,e.length-1));r=p.reshape(p.transpose(p.reshape(r,[-1,t]),[1,0]),n)}else if(s>1){const e=[s-1].concat(Xs(0,s-1));r=p.transpose(r,e)}return r}}return this.mergeFunction(e)}))}computeOutputShape(e){let t;t=null==(e=e)[0]?null:e[0].slice(1);for(let n=1;n<e.length;++n){const r=null==e[n]?null:e[n].slice(1);t=this.computeElementwiseOpOutputShape(t,r)}let n=[];for(const t of e)null!=t&&null!==t[0]&&n.push(t[0]);return n=vs(n),t=1===n.length?n.concat(t):[null].concat(t),t}computeMask(e,t){return p.tidy((()=>{if(null==t)return null;if(!Array.isArray(t))throw new rs("`mask` should be an Array");if(!Array.isArray(e))throw new rs("`inputs` should be an Array");if(t.length!==e.length)throw new rs(`The Array 'inputs' and 'mask' are expected to have the same length, but have different lengths (${e.length} vs ${t.length})`);if(t.every((e=>null==e)))return null;let n=(t=t.map((e=>null==e?e:p.expandDims(e,0))))[0];for(let e=1;e<t.length-1;++e)n=p.logicalAnd(n,t[e]);return n}))}}class gl extends ml{constructor(e){super(e)}mergeFunction(e){return(0,p.tidy)((()=>{let t=e[0].clone();for(let n=1;n<e.length;++n)t=p.add(t,e[n]);return t}))}}gl.className="Add",p.serialization.registerClass(gl);class yl extends ml{constructor(e){super(e)}mergeFunction(e){return(0,p.tidy)((()=>{let t=e[0].clone();for(let n=1;n<e.length;++n)t=p.mul(t,e[n]);return t}))}}yl.className="Multiply",p.serialization.registerClass(yl);class bl extends ml{constructor(e){super(e)}mergeFunction(e){return(0,p.tidy)((()=>{let t=e[0].clone();for(let n=1;n<e.length;++n)t=p.add(t,e[n]);return p.mul(1/e.length,t)}))}}bl.className="Average",p.serialization.registerClass(bl);class vl extends ml{constructor(e){super(e)}mergeFunction(e){return(0,p.tidy)((()=>{let t=e[0];for(let n=1;n<e.length;++n)t=p.maximum(t,e[n]);return t}))}}vl.className="Maximum",p.serialization.registerClass(vl);class xl extends ml{constructor(e){super(e)}mergeFunction(e){return(0,p.tidy)((()=>{let t=e[0];for(let n=1;n<e.length;++n)t=p.minimum(t,e[n]);return t}))}}xl.className="Minimum",p.serialization.registerClass(xl);class wl extends ml{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 rs("A `Concatenate` layer should be called on a list of at least 2 inputs");e=e;let t=!0;for(const n of e)if(null!=n){t=!1;break}if(t)return;const n=[];for(let t=0;t<e.length;++t){const r=e[t].slice();r.splice(this.axis,1);let s=!1;for(const e of n)if(p.util.arraysEqual(e,r)){s=!0;break}s||n.push(r)}if(n.length>1)throw new rs("A `Concatenate` layer requires inputs with matching shapes except for the concat axis. Got input shapes: "+JSON.stringify(e))}mergeFunction(e){return(0,p.tidy)((()=>

2ni(e,this.axis)))}computeOutputShape(e){if(!Array.isArray(e)||!Array.isArray(e[0]))throw new rs("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 e of t.slice(1)){if(null==n[r]||null==e[r]){n[r]=null;break}n[r]+=e[r]}return n}computeMask(e,t){if(null==t)return null;if(!Array.isArray(t))throw new rs("`mask` should be an array for Concatenate");if(!Array.isArray(e))throw new rs("`inputs` should be an array for Concatenate");if(t.length!==e.length)throw new rs(`Mismatch in the length of mask (${t.length}) and the legnth of inputs (${e.length})`);return p.tidy((()=>{let n=!0;if(t.forEach((e=>{null==e||(n=!1)})),n)return null;const r=[];for(let n=0;n<e.length;++n)null==t[n]?r.push(p.cast(p.onesLike(e[n]),"bool")):t[n].rank<e[n].rank?r.push(p.expandDims(t[n],-1)):r.push(t[n]);const s=p.concat(r,this.axis);return p.all(s,-1,!1)}))}getConfig(){const e={axis:this.axis},t=super.getConfig();return Object.assign(e,t),e}}function kl(e,t){for(;e<0;)e+=t;return e}wl.className="Concatenate",p.serialization.registerClass(wl);class Sl extends ml{constructor(e){super(e),this.axes=e.axes,this.normalize=null!=e.normalize&&e.normalize,this.supportsMasking=!0,this.reshapeRequired=!1}build(e){p.util.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 ss("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 rs(`Dimension incompatibility: ${t[r[0]]} !== ${n[r[1]]}`)}mergeFunction(e){if(2!==e.length)throw new rs(`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)=>kl(t,e[n].shape.length))):[kl(this.axes,n.shape.length),kl(this.axes,r.shape.length)],this.normalize&&(n=ja(n,t[0]),r=ja(r,t[1])),function(e,t,n){if(e.shape.length>3||t.shape.length>3)throw new ss("batchDot is not implemented for tensors of 4D or higher rank yet");if(p.util.assert(e.shape.length>=2,(()=>"batchDot requires the rank of x to be >= 2, but got "+e.shape.length)),p.util.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 ss("batchDot is not implemented for complex64-type Tensors yet.");const r=e.shape.length,s=t.shape.length;null==n&&(n=[r-1,s-2]);const i=n;return p.tidy((()=>{let n,a;if(r>s){n=r-s;const e=[];for(let t=0;t<n;++t)e.push(1);t=p.reshape(t,t.shape.concat(e))}else if(s>r){n=s-r;const t=[];for(let e=0;e<n;++e)t.push(1);e=p.reshape(e,e.shape.concat(t))}else n=0;if(2===e.shape.length&&2===t.shape.length)a=i[0]===i[1]?p.sum(p.mul(e,t),i[0]):p.sum(p.mul(p.transpose(e,[1,0]),t),i[1]);else{const n=i[0]!==e.shape.length-1,r=i[1]===t.shape.length-1;a=p.matMul(e,t,n,r)}if(n>0){let e;e=r>s?r+s-3:r-1;const t=[];for(let r=e;r<e+n;++r)t.push(r);a=p.squeeze(a,t)}return 1===a.shape.length&&(a=p.expandDims(a,1)),a}))}(n,r,t)}interpretAxes(e,t){let n;return n=Array.isArray(this.axes)?this.axes:[kl(this.axes,e.length),kl(this.axes,t.length)],n}computeOutputShape(e){p.util.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 ss("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 s=t.concat(n);return 1===s.length&&s.push(1),s}computeMask(e,t){return null}getConfig(){const e={axes:this.axes,normalize:this.normalize},t=super.getConfig();return Object.assign(e,t),e}}Sl.className="Dot",p.serialization.registerClass(Sl);class Il extends Xi{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,p.tidy)((()=>{this.invokeCallHook(e,t);const n=Li(e);return hi((()=>(0,p.add)(ii(n.shape,0,this.stddev),n)),(()=>n),t.training||!1)}))}}Il.className="GaussianNoise",p.serialization.registerClass(Il);class Tl extends Xi{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,p.tidy)((()=>{this.invokeCallHook(e,t);const n=Li(e);return this.rate>0&&this.rate<1?hi((()=>{const e=Math.sqrt(this.rate/(1-this.rate));return(0,p.mul)(n,ii(n.shape,1,e))}),(()=>n),t.training||!1):n}))}}Tl.className="GaussianDropout",p.serialization.registerClass(Tl);class Nl extends Xi{constructor(e){super(e),this.supportsMasking=!0,this.rate=e.rate,this.noiseShape=e.noiseShape}_getNoiseShape(e){return this.noiseShape||Li(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,p.tidy)((()=>

2_l.className="LayerNormalization",p.serialization.registerClass(_l);class Al extends Xi{constructor(e){if(null==e&&(e={}),super(e),this.dataFormat=null==e.dataFormat?"channelsLast":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 rs(`ZeroPadding2D expects padding to be a length-2 array, but received a length-${e.padding.length} array.`);let t,n;if("number"==typeof e.padding[0])t=[e.padding[0],e.padding[0]],n=[e.padding[1],e.padding[1]];else{if(e.padding=e.padding,2!==e.padding[0].length)throw new rs(`ZeroPadding2D expects height padding to be a length-2 array, but received a length-${e.padding[0].length} array.`);if(t=e.padding[0],2!==e.padding[1].length)throw new rs(`ZeroPadding2D expects width padding to be a length-2 array, but received a length-${e.padding[1].length} array.`);n=e.padding[1]}this.padding=[t,n]}this.inputSpec=[new Ui({ndim:4})]}computeOutputShape(e){let t,n;return e=zi(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,p.tidy)((()=>{return t=Li(e),n=this.padding,r=this.dataFormat,(0,p.tidy)((()=>{if(4!==t.rank)throw new rs("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 rs("spatial2dPadding expects `padding` to be an Array of two Arrays, each of which is an Array of two integers.");if(null==r&&(r="channelsLast"),"channelsLast"!==r&&"channelsFirst"!==r)throw new rs(`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]],p.pad(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 Rl(e,t,n,r,s,i){return(0,p.tidy)((()=>{let a;Ms(s),zs(i),Ls(r),null==n&&(n=[1,1]),null==r&&(r="valid"),null==s&&(s="channelsLast"),null==i&&(i="max"),e=_u(e,s);const o="same"===r?"same":"valid";return a="max"===i?p.maxPool(e,t,n,o):p.avgPool(e,t,n,o),"channelsFirst"===s&&(a=p.transpose(a,[0,3,1,2])),a}))}function $l(e,t,n,r,s,i){return(0,p.tidy)((()=>{let a;Ms(s),zs(i),Ls(r),null==n&&(n=[1,1,1]),null==r&&(r="valid"),null==s&&(s="channelsLast"),null==i&&(i="max"),e=Au(e,s);const o="same"===r?"same":"valid";return a="max"===i?p.maxPool3d(e,t,n,o):p.avgPool3d(e,t,n,o),"channelsFirst"===s&&(a=p.transpose(a,[0,4,1,2,3])),a}))}Al.className="ZeroPadding2D",p.serialization.registerClass(Al);class Fl extends Xi{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 rs("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(Ss(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 rs("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}Ss(this.strides,"strides"),this.padding=null==e.padding?"valid":e.padding,Ls(this.padding),this.inputSpec=[new Ui({ndim:3})]}computeOutputShape(e){const t=Cu((e=zi(e))[1],this.poolSize[0],this.padding,this.strides[0]);return[e[0],t,e[2]]}call(e,t){return(0,p.tidy)((()=>{this.invokeCallHook(e,t),e=Zs(Li(e),2);const n=this.poolingFunction(Li(e),[this.poolSize[0],1],[this.strides[0],1],this.padding,"channelsLast");return p.squeeze(n,[2])}))}getConfig(){const e={poolSize:this.poolSize,padding:this.padding,strides:this.strides},t=super.getConfig();return Object.assign(e,t),e}}class Ol extends Fl{constructor(e){super(e)}poolingFunction(e,t,n,r,s){return Ms(s),Ls(r),Rl(e,t,n,r,s,"max")}}Ol.className="MaxPooling1D",p.serialization.registerClass(Ol);class Dl extends Fl{constructor(e){super(e)}poolingFunction(e,t,n,r,s){return Ms(s),Ls(r),Rl(e,t,n,r,s,"avg")}}Dl.className="AveragePooling1D",p.serialization.registerClass(Dl);class Ml extends Xi{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 rs("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];Ss(this.poolSize,"poolSize"),Ss(this.strides,"strides"),this.padding=null==e.padding?"valid":e.padding,this.dataFormat=null==e.dataFormat?"channelsLast":e.dataFormat,Ms(this.dataFormat),Ls(this.padding),this.inputSpec=[new Ui({ndim:4})]}computeOutputShape(e){e=zi(e);let t="channelsFirst"===this.dataFormat?e[2]:e[1],n="channelsFirst"===this.dataFormat?e[3]:e[2];return t=Cu(t,this.poolSize[0],this.padding,this.strides[0]),n=Cu(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,p.tidy)((()=>(this.invokeCallHook(e,t),this.poolingFunction(Li(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 Ll extends Ml{constructor(e){super(e)}poolingFunction(e,t,n,r,s){return Ms(s),Ls(r),Rl(e,t,n,r,s,"max")}}Ll.className="MaxPooling2D",p.serialization.registerClass(Ll);class zl extends Ml{constructor(e){super(e)}poolingFunction(e,t,n,r,s){return Ms(s),Ls(r),Rl(e,t,n,r,s,"avg")}}zl.className="AveragePooling2D",p.serialization.registerClass(zl);class Pl extends Xi{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 rs("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];Ss(this.poolSize,"poolSize"),Ss(this.strides,"strides"),this.padding=null==e.padding?"valid":e.padding,this.dataFormat=null==e.dataFormat?"channelsLast":e.dataFormat,Ms(this.dataFormat),Ls(this.padding),this.inputSpec=[new Ui({ndim:5})]}computeOutputShape(e){e=zi(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=Cu(t,this.poolSize[0],this.padding,this.strides[0]),n=Cu(n,this.poolSize[1],this.padding,this.strides[1]),r=Cu(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,p.tidy)((()=>(this.invokeCallHook(e,t),this.poolingFunction(Li(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 Bl extends Pl{constructor(e){super(e)}poolingFunction(e,t,n,r,s){return Ms(s),Ls(r),$l(e,t,n,r,s,"max")}}Bl.className="MaxPooling3D",p.serialization.registerClass(Bl);class Vl extends Pl{constructor(e){super(e)}poolingFunction(e,t,n,r,s){return Ms(s),Ls(r),$l(e,t,n,r,s,"avg")}}Vl.className="AveragePooling3D",p.serialization.registerClass(Vl);class Wl extends Xi{constructor(e){super(e),this.inputSpec=[new Ui({ndim:3})]}computeOutputShape(e){return[e[0],e[2]]}call(e,t){throw new ss}}class Ul extends Wl{constructor(e){super(e||{})}call(e,t){return(0,p.tidy)((()=>{const t=Li(e);return p.mean(t,1)}))}}Ul.className="GlobalAveragePooling1D",p.serialization.registerClass(Ul);class Gl extends Wl{constructor(e){super(e||{})}call(e,t){return(0,p.tidy)((()=>{const t=Li(e);return p.max(t,1)}))}}Gl.className="GlobalMaxPooling1D",p.serialization.registerClass(Gl);class Hl extends Xi{constructor(e){super(e),this.dataFormat=null==e.dataFormat?"channelsLast":e.dataFormat,Ms(this.dataFormat),this.inputSpec=[new Ui({ndim:4})]}computeOutputShape(e){return e=e,"channelsLast"===this.dataFormat?[e[0],e[3]]:[e[0],e[1]]}call(e,t){throw new ss}getConfig(){const e={dataFormat:this.dataFormat},t=super.getConfig();return Object.assign(e,t),e}}class jl extends Hl{call(e,t){return(0,p.tidy)((()=>{const t=Li(e);return"channelsLast"===this.dataFormat?p.mean(t,[1,2]):p.mean(t,[2,3])}))}}jl.className="GlobalAveragePooling2D",p.serialization.registerClass(jl);class ql extends Hl{call(e,t){return(0,p.tidy)((()=>{const t=Li(e);return"channelsLast"===this.dataFormat?p.max(t,[1,2]):p.max(t,[2,3])}))}}ql.className="GlobalMaxPooling2D",p.serialization.registerClass(ql);class Xl extends Xi{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=Ha(t.layer,n);delete t.layer;const s={layer:r};return Object.assign(s,t),new e(s)}}class Kl extends Xl{constructor(e){super(e),this.supportsMasking=!0}build(e){if((e=zi(e)).length<3)throw new rs("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=zi(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,p.tidy)((()=>Hu(((e,n)=>[Li(this.layer.call(e,t)),[]]),e=Li(e),[],!1,null,null,!1,!0)[1]))}}

2Kl.className="TimeDistributed",p.serialization.registerClass(Kl);class Yl extends Xl{constructor(e){super(e);const t=e.layer.getConfig(),n={};n.className=e.layer.getClassName(),n.config=t,this.forwardLayer=Ha(n),t.goBackwards=!0!==t.goBackwards;const r={};var s;if(r.className=e.layer.getClassName(),r.config=t,this.backwardLayer=Ha(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,ws(Os,"BidirectionalMergeMode",s),e.weights)throw new ss("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()):cs(n)}apply(e,t){let n=null==t?null:t.initialState,r=null==t?null:t.constants;null==t&&(t={});const s=Gu(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 i=[],a=[];if(null!=n){const e=n.length;if(e%2>0)throw new rs("When passing `initialState` to a Bidrectional RNN, the state should be an Array containing the states of the underlying RNNs.");t.initialState=n,i.push(...n);const r=n.map((e=>new Ui({shape:e.shape})));this.forwardLayer.stateSpec=r.slice(0,e/2),this.backwardLayer.stateSpec=r.slice(e/2),a.push(...r)}if(null!=r)throw new ss("Support for constants in Bidirectional layers is not implemented yet.");const o=i[0]instanceof Gi;for(const e of i)if(e instanceof Gi!==o)throw new rs("The initial state of a Bidirectional layer cannot be specified as a mix of symbolic and non-symbolic tensors");if(o){const n=[e].concat(i),r=this.inputSpec.concat(a),s=this.inputSpec;this.inputSpec=r;const o=super.apply(n,t);return this.inputSpec=s,o}return super.apply(e,t)}call(e,t){return(0,p.tidy)((()=>{const n=t.initialState;let r,s,i,a;if(null==n)r=this.forwardLayer.call(e,t),s=this.backwardLayer.call(e,t);else{const i=n.slice(0,n.length/2),a=n.slice(n.length/2);r=this.forwardLayer.call(e,Object.assign(t,{initialState:i})),s=this.backwardLayer.call(e,Object.assign(t,{initialState:a}))}return this.returnState&&(Array.isArray(r)&&(i=r.slice(1).concat(s.slice(1))),r=r[0],s=s[0]),this.returnSequences&&(s=p.reverse(s,1)),"concat"===this.mergeMode?a=ni([r,s]):"sum"===this.mergeMode?a=p.add(r,s):"ave"===this.mergeMode?a=p.mul(.5,p.add(r,s)):"mul"===this.mergeMode?a=p.mul(r,s):null==this.mergeMode&&(a=[r,s]),this.returnState?null==this.mergeMode?a.concat(i):[a].concat(i):a}))}resetStates(e){this.forwardLayer.resetStates(),this.backwardLayer.resetStates()}build(e){Bs(this.forwardLayer.name,(()=>{this.forwardLayer.build(e)})),Bs(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=Ha(t.layer);if(delete t.layer,null!=t.numConstants)throw new ss("Deserialization of a Bidirectional layer with numConstants present is not supported yet.");const r=t;return r.layer=n,new e(r)}}Yl.className="Bidirectional",p.serialization.registerClass(Yl);class Ql extends Xi{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,p.tidy)((()=>("float32"!==(e=Li(e)).dtype&&(e=Qs(e,"float32")),(0,p.add)((0,p.mul)(e,this.scale),this.offset))))}}Ql.className="Rescaling",p.serialization.registerClass(Ql);const{resizeBilinear:Zl,cropAndResize:Jl}=p.image;class ec extends Xi{constructor(e){super(e),this.height=e.height,this.width=e.width}centerCrop(e,t,n,r,s,i,a,o){return(0,p.tidy)((()=>{let u,l=!1;const c=[t/i,n/a,(r+t)/i,(s+n)/a],d=[];3===e.rank?(l=!0,u=(0,p.stack)([e])):u=e;for(let e=0;e<u.shape[0];e++)d.push(c);const h=(0,p.tensor)(d,[d.length,4]),f=(0,p.range)(0,d.length,1,"int32"),m=Jl(u,h,f,[r,s],"nearest");return Qs(l?Li((0,p.unstack)(m)):m,o)}))}upsize(e,t,n,r){return(0,p.tidy)((()=>Qs(Zl(e,[t,n]),r)))}call(e,t){return(0,p.tidy)((()=>{const t=Li(e),n=t.dtype,r=t.shape,s=r[r.length-3],i=r[r.length-2];let a=0;s!==this.height&&(a=Math.floor((s-this.height)/2));let o=0;return i!==this.width&&(o=Math.floor((i-this.width)/2),0===o&&(o=1)),a>=0&&o>=0?this.centerCrop(t,a,o,this.height,this.width,s,i,n):this.upsize(e,this.height,this.width,n)}))}getConfig(){const e={height:this.height,width:this.width},t=super.getConfig();return Object.assign(e,t),e}computeOutputShape(e){const t=(e=zi(e)).length-3,n=e.length-2;return e[t]=this.height,e[n]=this.width,e}}

2function Jc(e){return new jl(e)}function ed(e){return new Gl(e)}function td(e){return new ql(e)}function nd(e){return new Ol(e)}function rd(e){return new Ll(e)}function sd(e){return new Bl(e)}function id(e){return new Qu(e)}function ad(e){return new Yu(e)}function od(e){return new Ju(e)}function ud(e){return new Zu(e)}function ld(e){return new Ku(e)}function cd(e){return new Xu(e)}function dd(e){return new sl(e)}function hd(e){return new rl(e)}function pd(e){return new ju(e)}function fd(e){return new el(e)}function md(e){return new Yl(e)}function gd(e){return new Kl(e)}oc.className="RandomWidth",p.serialization.registerClass(oc);const yd=ed,bd=td,vd=nd,xd=rd;function wd(e){return new Il(e)}function kd(e){return new Tl(e)}function Sd(e){return new Nl(e)}function Id(e){return new pl(e)}function Td(e){return new Ql(e)}function Nd(e){return new ec(e)}function Cd(e){return new rc(e)}function Ed(e){return new tc(e)}function _d(e){return new oc(e)}function Ad(e,t){return no(e,t)}function Rd(e,t){return ao(e,t)}function $d(e,t){return oo(e,t)}function Fd(e,t){return ro(e,t)}function Od(e,t){return uo(e,t)}function Dd(e,t){return io(e,t)}function Md(e,t){return function(e,t){return(0,p.tidy)((()=>{const n=so(e,t),r=function(e,t){return(0,p.tidy)((()=>p.cast(p.sum(p.logicalAnd(p.equal(e,1),p.equal(t,0))),"float32")))}(e,t),s=p.add(n,r);return p.cast(p.where(p.greater(s,0),p.div(n,s),0),"float32")}))}(e,t)}function Ld(e,t){return Ja(e,t)}function zd(e,t){return Xa(e,t)}function Pd(e,t){return Ka(e,t)}function Bd(e,t){return Ka(e,t)}function Vd(e,t){return Ka(e,t)}function Wd(e,t){return qa(e,t)}function Ud(e,t){return qa(e,t)}function Gd(e,t){return qa(e,t)}function Hd(e){return new mu(e)}function jd(e){return pu(t=e),new mu({l1:null!=t?t.l1:null,l2:0});var t}function qd(e){return pu(t=e),new mu({l2:null!=t?t.l2:null,l1:0});var t}class Xd extends La{constructor(){super(...arguments),this.model=null}setModel(e){if(!(e instanceof Bo))throw new Error("model must be a LayersModel, not some other Container");this.model=e}}function Kd(e,t){return e<t}function Yd(e,t){return e>t}class Qd extends Xd{constructor(e){if(super(),null==e&&(e={}),e.restoreBestWeights)throw new ss("restoreBestWeights = True is not implemented in EarlyStopping yet.");this.monitor=e.monitor||"val_loss",this.minDelta=Math.abs(e.minDelta||0),this.patience=e.patience||0,this.verbose=e.verbose||0,this.mode=e.mode||"auto",this.baseline=e.baseline,-1===["auto","min","max"].indexOf(this.mode)&&(console.warn(`EarlyStopping mode '${this.mode}' is invalid. Falling back to mode 'auto'.`),this.mode="auto"),"min"===this.mode?this.monitorFunc=Kd:"max"===this.mode||-1!==this.monitor.indexOf("acc")?this.monitorFunc=Yd:this.monitorFunc=Kd,this.monitorFunc===Kd&&(this.minDelta*=-1)}async onTrainBegin(e){this.wait=0,this.stoppedEpoch=0,null!=this.baseline?this.best=this.baseline:this.best=this.monitorFunc===Kd?1/0:-1/0}async onEpochEnd(e,t){await Oa(t);const n=this.getMonitorValue(t);null!=n&&(this.monitorFunc(n-this.minDelta,this.best)?(this.best=n,this.wait=0):(this.wait++,this.wait>=this.patience&&(this.stoppedEpoch=e,this.model.stopTraining=!0)))}async onTrainEnd(e){this.stoppedEpoch>0&&this.verbose&&console.log(`Epoch ${this.stoppedEpoch}: early stopping.`)}getMonitorValue(e){null==e&&(e={});const t=e[this.monitor];return null==t&&console.warn(`Metric for EarlyStopping ${this.monitor} is not available. Available metrics are: `+Object.keys(e)),t}}const Zd={earlyStopping:function(e){return new Qd(e)}};var Jd,eh=n(80013),th=n(36377);function nh(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.");if(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(oh(e)){const s=Array.isArray(e)?[]:{};r.add(e);for(const i in e){const a=nh(e[i],t,n,r);s[i]=a}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 rh(e,t=ih){return sh(e,t)}function sh(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(oh(r)){const s=Array.isArray(r)?[]:{};n.add(r);for(const i in r){const r=sh(e.map((e=>e[i])),t,n);s[i]=r}return n.delete(r),s}throw new Error("Can't recurse into non-iterable type: "+r)}return s.value}function ih(e){return null===e?null:oh(e[0])?{value:null,recurse:!0}:{value:e,recurse:!1}}async function ah(e,t){const n=new Map;nh(e,t,n);for(const e of Array.from(n.keys())){const t=n.get(e);if(p.util.isPromise(t)){const r=await t;n.set(e,r)}}return nh(e,t,n)}function oh(e){let t=!1;if(p.env().get("IS_BROWSER"))t=e instanceof TextDecoder;else{const{StringDecoder:r}=n(84589);t=e instanceof r}return null!=e&&!ArrayBuffer.isView(e)&&(Array.isArray(e)||"object"==typeof e&&!(e instanceof p.Tensor)&&!(e instanceof Promise)&&!t)}function uh(e){return e instanceof p.Tensor?{value:e.clone(),recurse:!1}:oh(e)?{value:null,recurse:!0}:{value:e,recurse:!1}}class lh{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 ch extends lh{constructor(){super(ch.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 e=0;e<n;e++)t[e]=this.get(this.wrap(this.begin+e));this.data=t,this.capacity=e,this.doubledCapacity=2*this.capacity,this.begin=0,this.end=n}}function dh(e){return new fh(e)}function hh(e){return new mh(e)}ch.INITIAL_CAPACITY=32;class ph{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 kh(this,e)}filter(e){return new xh(this,e)}map(e){return new wh(this,e)}mapAsync(e){return new Sh(this,e)}serialMapAsync(e){return new Sh(this,e).serial()}flatmap(e){return new Th(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 vh(this,e,t)}columnMajorBatch(e,t=!0,n=ih){return this.rowMajorBatch(e,t).map((e=>rh(e,n)))}concatenate(e,t){return new Nh(dh([this,e]),t)}take(e){return e<0||null==e?this:new bh(this,e)}skip(e){return e<0||null==e?this:new yh(this,e)}prefetch(e){return new Eh(this,e)}shuffle(e,t){return new _h(this,e,t)}serial(){return new gh(this)}}class fh extends ph{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:(t=e,function(e,t){return nh(e,t)}(t,uh)),done:!1};var t}}class mh extends ph{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 gh extends ph{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 yh extends ph{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;p.dispose(e.value)}return this.upstream.next()}}class bh extends ph{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 vh extends ph{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 xh extends ph{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;p.dispose(e.value)}}}class wh extends ph{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=p.tensor_util.getTensorsInContainer(e.value),n=this.transform(e.value),r=p.tensor_util.getTensorsInContainer(n);for(const e of t)p.tensor_util.isTensorInList(e,r)||e.dispose();return{value:n,done:!1}}}class kh extends ph{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 Sh extends ph{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=p.tensor_util.getTensorsInContainer(e.value),n=await this.transform(e.value),r=p.tensor_util.getTensorsInContainer(n);for(const e of t)p.tensor_util.isTensorInList(e,r)||e.dispose();return{value:n,done:!1}}}class Ih extends ph{constructor(){super(),this.outputQueue=new ch,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 Th extends Ih{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=p.tensor_util.getTensorsInContainer(e.value),n=this.transform(e.value),r=p.tensor_util.getTensorsInContainer(n);this.outputQueue.pushAll(n);for(const e of t)p.tensor_util.isTensorInList(e,r)||e.dispose();return!0}}class Nh extends ph{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}}

2!function(e){e[e.FAIL=0]="FAIL",e[e.SHORTEST=1]="SHORTEST",e[e.LONGEST=2]="LONGEST"}(Jd||(Jd={}));class Ch extends ph{constructor(e,t=Jd.FAIL){super(),this.iterators=e,this.mismatchMode=t,this.count=0,this.currentPromise=null}summary(){return"{TODO: fill in upstream of zip summaries} -> Zip"}async nextState(e){await e;let t=0,n=0;const r=await ah(this.iterators,(function(e){return e instanceof ph?{value:e.next().then((e=>(t++,e.done&&n++,e.value))),recurse:!1}:{value:null,recurse:!0}}));if(t===n)return{value:null,done:!0};if(n>0)switch(this.mismatchMode){case Jd.FAIL:throw new Error(`Zipped streams should have the same length. Mismatched at element ${this.count}.`);case Jd.SHORTEST:return{value:null,done:!0};case Jd.LONGEST:}return this.count++,{value:r,done:!1}}async next(){return this.currentPromise=this.nextState(this.currentPromise),this.currentPromise}}class Eh extends ph{constructor(e,t){super(),this.upstream=e,this.bufferSize=t,this.buffer=new lh(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 _h extends Eh{constructor(e,t,n){super(e,t),this.upstream=e,this.windowSize=t,this.upstreamExhausted=!1,this.random=th.alea(n||p.util.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 Ah{constructor(){this.size=null}batch(e,t=!0){const n=this;let r;return p.util.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),Rh((async()=>(await n.iterator()).columnMajorBatch(e,t,Oh)),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,Rh((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,Rh((async()=>(await t.iterator()).filter((t=>p.tidy((()=>e(t)))))),n)}async forEachAsync(e){return(await this.iterator()).forEachAsync(e)}map(e){const t=this;return Rh((async()=>(await t.iterator()).map((t=>p.tidy((()=>e(t)))))),this.size)}mapAsync(e){const t=this;return Rh((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 Rh((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,Rh((async()=>{return n=hh((async()=>({value:await t.iterator(),done:!1}))).take(e),new Nh(n,r);var n,r}),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,Rh((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,s=th.alea(t||p.util.now().toString());return Rh((async()=>{let t=s.int32();return n&&(t+=s.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,Rh((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 Rh(e,t=null){return new class extends Ah{constructor(){super(...arguments),this.size=t}async iterator(){return e()}}}function $h(e){return Rh((async()=>dh(e)),e.length)}function Fh(e){if(!oh(e))throw new Error("The argument to zip() must be an object or array.");let t;if(Array.isArray(e))for(let n=0;n<e.length;n++)t=null==t?e[n].size:Math.min(t,e[n].size);else if(e instanceof Object)for(const n in e)t=null==t?e[n].size:Math.min(t,e[n].size);return Rh((async()=>function(e,t=Jd.FAIL){return new Ch(e,t)}(await ah(e,(e=>{if(e instanceof Ah)return{value:e.iterator(),recurse:!1};if(oh(e))return{value:null,recurse:!0};throw new Error("Leaves of the structure passed to zip() must be Datasets, not primitives.")})),Jd.SHORTEST)),t)}function Oh(e){if(null===e)return null;return null==(t=e[0])||null===(n=t)||"object"!=typeof n&&"function"!=typeof n||Array.isArray(t)||"object"==typeof t&&t instanceof p.Tensor||p.util.isTypedArray(t)?{value:function(e){if(0===e.length)throw new Error("Can't make a batch of zero elements.");return e[0]instanceof p.Tensor?p.stack(e):p.tensor(e)}(e),recurse:!1}:{value:null,recurse:!0};var t,n}Ah.MAX_BUFFER_SIZE=1e4;class Dh extends Ah{constructor(e){super(),this.input=e}async iterator(){return(await this.input.iterator()).decodeUTF8().split("\n").map((e=>(e.endsWith("\r")&&(e=e.slice(0,-1)),e)))}}const Mh='"',Lh=Symbol("out"),zh=Symbol("field"),Ph=Symbol("quote"),Bh=Symbol("quoteafterquote"),Vh=Symbol("quoteinquote");class Wh extends Ah{async columnNames(){return this.columnNamesValidated||await this.setColumnNames(),this.configuredColumnsOnly?Object.keys(this.columnConfigs):this.fullColumnNames}async setColumnNames(){const e=await this.maybeReadHeaderLine();if(!this.fullColumnNames&&!e)throw new Error("Column names must be provided if there is no header line.");this.fullColumnNames&&e&&p.util.assert(e.length===this.fullColumnNames.length,(()=>"The length of provided columnNames ("+this.fullColumnNames.length.toString()+") does not match the length of the header line read from file ("+e.length.toString()+").")),this.fullColumnNames||(this.fullColumnNames=e);const t=this.fullColumnNames.reduce(((e,t)=>(e[t]=e[t]+1||1,e)),{}),n=Object.keys(t).filter((e=>t[e]>1));if(p.util.assert(0===n.length,(()=>"Duplicate column names found: "+n.toString())),this.columnConfigs)for(const e of Object.keys(this.columnConfigs))if(-1===this.fullColumnNames.indexOf(e))throw new Error('The key "'+e+'" provided in columnConfigs does not match any of the column names ('+this.fullColumnNames.toString()+").");this.columnNamesValidated=!0}async maybeReadHeaderLine(){if(this.hasHeader){const e=await this.base.iterator(),t=await e.next();if(t.done)throw new Error("No data was found for CSV parsing.");const n=t.value;return this.parseRow(n,!1)}return null}constructor(e,t){super(),this.input=e,this.hasHeader=!0,this.fullColumnNames=null,this.columnNamesValidated=!1,this.columnConfigs=null,this.configuredColumnsOnly=!1,this.delimiter=",",this.delimWhitespace=!1,this.base=new Dh(e),t||(t={}),this.hasHeader=!1!==t.hasHeader,this.fullColumnNames=t.columnNames,this.columnConfigs=t.columnConfigs,this.configuredColumnsOnly=t.configuredColumnsOnly,t.delimWhitespace?(p.util.assert(null==t.delimiter,(()=>"Delimiter should not be provided when delimWhitespace is true.")),this.delimWhitespace=!0,this.delimiter=" "):this.delimiter=t.delimiter?t.delimiter:","}async iterator(){this.columnNamesValidated||await this.setColumnNames();let e=await this.base.iterator();return this.hasHeader&&(e=e.skip(1)),e.map((e=>this.makeDataElement(e)))}makeDataElement(e){const t=this.parseRow(e),n={},r={};for(let s=0;s<this.fullColumnNames.length;s++){const i=this.fullColumnNames[s],a=this.columnConfigs?this.columnConfigs[i]:null;if(!this.configuredColumnsOnly||a){const o=t[s];let u=null;if(""===o)if(a&&void 0!==a.default)u=a.default;else{if(a&&(a.required||a.isLabel))throw new Error(`Required column ${i} is empty in this line: ${e}`);u=void 0}else{const e=Number(o);if(isNaN(e))u=a&&"bool"===a.dtype?this.getBoolean(o):o;else if(a&&a.dtype)switch(a.dtype){case"float32":u=e;break;case"int32":u=Math.floor(e);break;case"bool":u=this.getBoolean(o);break;default:u=e}else u=e}a&&a.isLabel?r[i]=u:n[i]=u}}return 0===Object.keys(r).length?n:{xs:n,ys:r}}getBoolean(e){return"1"===e||"true"===e.toLowerCase()?1:0}parseRow(e,t=!0){const n=[];let r=0;const s=e.length;let i=Lh;for(let t=0;t<s;t++)switch(i){case Lh:switch(e.charAt(t)){case Mh:r=t+1,i=Ph;break;case this.delimiter:if(r=t+1," "===this.delimiter&&this.delimWhitespace)break;n.push(""),i=Lh;break;default:i=zh,r=t}break;case zh:switch(e.charAt(t)){case this.delimiter:n.push(e.substring(r,t)),i=Lh,r=t+1}break;case Ph:switch(e.charAt(t)){case Mh:i=Bh}break;case Bh:switch(e.charAt(t)){case this.delimiter:n.push(e.substring(r,t-1)),i=Lh,r=t+1;break;case Mh:i=Ph;break;default:i=Vh}break;case Vh:switch(e.charAt(t)){case Mh:i=Ph}}if(i===Bh?n.push(e.substring(r,s-1)):n.push(e.substring(r)),t&&n.length!==this.fullColumnNames.length)throw new Error(`Invalid row in csv file. Should have ${this.fullColumnNames.length} elements in a row, but got ${n}`);return n}}class Uh extends ph{constructor(e){super(),this.microphoneConfig=e,this.isClosed=!1,this.fftSize=e.fftSize||1024;const t=Math.log2(this.fftSize);

2if(this.fftSize<0||t<4||t>14||!Number.isInteger(t))throw new Error("Invalid fftSize: it must be a power of 2 between 2 to 4 and 2 to 14, but got "+this.fftSize);if(this.numFrames=e.numFramesPerSpectrogram||43,this.sampleRateHz=e.sampleRateHz,this.columnTruncateLength=e.columnTruncateLength||this.fftSize,this.audioTrackConstraints=e.audioTrackConstraints,this.smoothingTimeConstant=e.smoothingTimeConstant||0,this.includeSpectrogram=!1!==e.includeSpectrogram,this.includeWaveform=!0===e.includeWaveform,!this.includeSpectrogram&&!this.includeWaveform)throw new Error("Both includeSpectrogram and includeWaveform are false. At least one type of data should be returned.")}summary(){return"microphone"}static async create(e={}){if(!(0,p.env)().get("IS_BROWSER"))throw new Error("microphone API is only supported in browser environment.");const t=new Uh(e);return await t.start(),t}async start(){try{this.stream=await navigator.mediaDevices.getUserMedia({audio:null==this.audioTrackConstraints||this.audioTrackConstraints,video:!1})}catch(e){throw new Error("Error thrown while initializing video stream: "+e.message)}if(!this.stream)throw new Error("Could not obtain audio from microphone.");const e=window.AudioContext||window.webkitAudioContext;if(this.audioContext=new e,this.sampleRateHz){if(this.audioContext.sampleRate!==this.sampleRateHz)throw new Error(`Mismatch in sampling rate: Expected: ${this.sampleRateHz}; Actual: `+this.audioContext.sampleRate)}else this.sampleRateHz=this.audioContext.sampleRate;const t=this.audioContext.createMediaStreamSource(this.stream);this.analyser=this.audioContext.createAnalyser(),this.analyser.fftSize=2*this.fftSize,this.analyser.smoothingTimeConstant=this.smoothingTimeConstant,t.connect(this.analyser),this.freqData=new Float32Array(this.fftSize),this.timeData=new Float32Array(this.fftSize)}async next(){if(this.isClosed)return{value:null,done:!0};let e,t;const n=await this.getAudioData();if(this.includeSpectrogram){const t=this.flattenQueue(n.freqDataQueue);e=this.getTensorFromAudioDataArray(t,[this.numFrames,this.columnTruncateLength,1])}if(this.includeWaveform){const e=this.flattenQueue(n.timeDataQueue);t=this.getTensorFromAudioDataArray(e,[this.numFrames*this.fftSize,1])}return{value:{spectrogram:e,waveform:t},done:!1}}async capture(){return(await this.next()).value}async getAudioData(){const e=[],t=[];let n=0;return new Promise((r=>{const s=setInterval((()=>{this.includeSpectrogram&&(this.analyser.getFloatFrequencyData(this.freqData),this.freqData[0]===-1/0&&r({freqDataQueue:e,timeDataQueue:t}),e.push(this.freqData.slice(0,this.columnTruncateLength))),this.includeWaveform&&(this.analyser.getFloatTimeDomainData(this.timeData),t.push(this.timeData.slice())),++n===this.numFrames&&(clearInterval(s),r({freqDataQueue:e,timeDataQueue:t}))}),this.fftSize/this.sampleRateHz*1e3)}))}stop(){this.isClosed||(this.isClosed=!0,this.analyser.disconnect(),this.audioContext.close(),null!=this.stream&&this.stream.getTracks().length>0&&this.stream.getTracks()[0].stop())}toArray(){throw new Error("Can not convert infinite audio stream to array.")}getSampleRate(){return this.sampleRateHz}flattenQueue(e){const t=e[0].length,n=new Float32Array(e.length*t);return e.forEach(((e,r)=>n.set(e,r*t))),n}getTensorFromAudioDataArray(e,t){const n=new Float32Array(p.util.sizeFromShape(t));return n.set(e,n.length-e.length),(0,p.tensor)(n,t)}}class Gh extends ph{constructor(e,t){if(super(),this.webcamVideoElement=e,this.webcamConfig=t,this.isClosed=!0,this.resize=!1,this.needToResize())if(this.resize=!0,this.cropSize=[this.webcamConfig.resizeHeight,this.webcamConfig.resizeWidth],this.cropBoxInd=(0,p.tensor1d)([0],"int32"),this.webcamConfig.centerCrop){const e=1*this.webcamConfig.resizeWidth/this.webcamVideoElement.width,t=1*this.webcamConfig.resizeHeight/this.webcamVideoElement.height,n=(1-e)/2,r=(1-t)/2,s=n+e,i=t+r;this.cropBox=(0,p.tensor2d)([r,n,i,s],[1,4])}else this.cropBox=(0,p.tensor2d)([0,0,1,1],[1,4])}summary(){return"webcam"}static async create(e,t={}){if(!(0,p.env)().get("IS_BROWSER"))throw new Error("tf.data.webcam is only supported in browser environment.");if(!e){if(e=document.createElement("video"),!t.resizeWidth||!t.resizeHeight)throw new Error("Please provide webcam video element, or resizeWidth and resizeHeight to create a hidden video element.");e.width=t.resizeWidth,e.height=t.resizeHeight}const n=new Gh(e,t);return await n.start(),n}async start(){this.webcamConfig.facingMode&&p.util.assert("user"===this.webcamConfig.facingMode||"environment"===this.webcamConfig.facingMode,(()=>`Invalid webcam facing mode: ${this.webcamConfig.facingMode}. Please provide 'user' or 'environment'`));try{this.stream=await navigator.mediaDevices.getUserMedia({video:{deviceId:this.webcamConfig.deviceId,facingMode:this.webcamConfig.facingMode?this.webcamConfig.facingMode:"user",width:this.webcamVideoElement.width,height:this.webcamVideoElement.height}})}catch(e){throw e.message="Error thrown while initializing video stream

2))}async function ip(e,t){return Gh.create(e,t)}async function ap(e){return Uh.create(e)}const op="4.10.0";function up(e,t){Array.isArray(e)||(e=[e]),e.forEach((e=>{null!=e&&p.util.assert("complex64"!==e.dtype,(()=>t+" does not support complex64 tensors in the CPU backend."))}))}const lp=p.kernel_impls.whereImpl;class cp extends p.KernelBackend{nextDataId(){return cp.nextDataId++}constructor(){super(),this.blockSize=48,this.firstUse=!0,this.data=new p.DataStorage(this,(0,p.engine)())}write(e,t,n){this.firstUse&&(this.firstUse=!1,(0,p.env)().get("IS_NODE")&&p.backend_util.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&&p.util.isString(n[0])){const s=n.map((e=>p.util.encodeString(e)));r=this.write(s,e,t)}else r=this.write(n,e,t);return{dataId:r,shape:e,dtype:t}}refCount(e){return this.data.has(e)?this.data.get(e).refCount:0}incRef(e){this.data.get(e).refCount++}decRef(e){this.data.has(e)&&this.data.get(e).refCount--}move(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 p.backend_util.mergeRealAndImagArrays(e,t)}return p.util.convertBackendValuesAndArrayBuffer(this.data.get(e).values,t)}bufferSync(e){const t=this.readSync(e.dataId);if("string"===e.dtype)try{const n=t.map((e=>p.util.decodeString(e)));return(0,p.buffer)(e.shape,e.dtype,n)}catch(e){throw new Error("Failed to decode encoded string bytes into utf-8")}return(0,p.buffer)(e.shape,e.dtype,t)}makeOutput(e,t,n){return(0,p.engine)().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=p.util.now();return e(),{kernelMs:p.util.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){up([e],"where");const t=this.readSync(e.dataId);return lp(e.shape,t)}dispose(){}floatPrecision(){return 32}epsilon(){return super.epsilon()}}function dp(e){const t=new Float32Array(e.length);for(let n=0;n<e.length;++n)t[n]=Math.abs(e[n]);return t}cp.nextDataId=0;const hp={kernelName:p.Abs,backendName:"cpu",kernelFunc:e=>{const{x:t}=e.inputs,n=e.backend;up(t,"abs");let r=new Float32Array(p.util.sizeFromShape(t.shape));return r=dp(n.data.get(t.dataId).values),n.makeOutput(r,t.shape,t.dtype)}};function pp(e){return(t,n,r,s,i)=>{const a=p.backend_util.assertAndGetBroadcastShape(t,n),o=a.length,u=p.util.computeStrides(a),l=p.util.sizeFromShape(a),c=p.util.getTypedArrayFromDType(i,l),d=t.length,h=n.length,f=p.util.computeStrides(t),m=p.util.computeStrides(n),g=p.backend_util.getBroadcastDims(t,a),y=p.backend_util.getBroadcastDims(n,a);if(g.length+y.length===0)for(let t=0;t<c.length;++t)c[t]=e(r[t%r.length],s[t%s.length]);else for(let t=0;t<c.length;++t){const n=p.util.indexToLoc(t,o,u),i=n.slice(-d);g.forEach((e=>i[e]=0));const a=p.util.locToIndex(i,d,f),l=n.slice(-h);y.forEach((e=>l[e]=0));const b=p.util.locToIndex(l,h,m);c[t]=e(r[a],s[b])}return[c,a]}}function fp(e){const{inputs:t,backend:n}=e,{real:r,imag:s}=t,i=n.data.get(r.dataId).values,a=n.data.get(s.dataId).values,o=n.makeTensorInfo(r.shape,"complex64");return n.data.get(o.dataId).complexTensorInfos={real:n.makeTensorInfo(r.shape,"float32",i),imag:n.makeTensorInfo(s.shape,"float32",a)},o}const mp={kernelName:p.Complex,backendName:"cpu",kernelFunc:fp};function gp(e,t,n="float32"){if("complex64"===n)return fp({inputs:{real:gp(e,t,"float32"),imag:gp(e,t,"float32")},backend:e});const r=p.util.makeZerosTypedArray(p.util.sizeFromShape(t),n);return e.makeTensorInfo(t,n,r)}function yp(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 bp={kernelName:p.Identity,backendName:"cpu",kernelFunc:yp};function vp(e){const{inputs:t,backend:n}=e,{input:r}=t,s=n.data.get(r.dataId).complexTensorInfos.real,i=n.data.get(s.dataId).values;return n.makeTensorInfo(s.shape,s.dtype,i)}const xp={kernelName:p.Real,backendName:"cpu",kernelFunc:vp};function wp(e,t,n,r){if("int32"===r)return[t,"int32",Int32Array.from(e)];if("bool"===r){const r=p.util.toTypedArray([0],n),[s,i]=pp(((e,t)=>e!==t?1:0))(t,[],e,r,"bool");return[i,"bool",s]}throw new Error(`Error in Cast: failed to cast ${n} to ${r}`)}function kp(e){const{inputs:t,backend:n,attrs:r}=e,{x:s}=t,{dtype:i}=r;if("complex64"===i){if("complex64"===s.dtype)return yp({inputs:{x:s},backend:n});const e=gp(n,s.shape,s.dtype),t=kp({inputs:{x:s},backend:n,attrs:{dtype:"float32"}}),r=fp({inputs:{real:t,imag:e},backend:n});return n.disposeIntermediateTensorInfo(e),n.disposeIntermediateTensorInfo(t),r}if("complex64"===s.dtype){const e=vp({inputs:{input:s},backend:n}),t=kp({inputs:{x:e},backend:n,attrs:{dtype:i}});return n.disposeIntermediateTensorInfo(e),t}if(!p.util.hasEncodingLoss(s.dtype,i)){const e=yp({inputs:{x:s},backend:n});return{dataId:e.dataId,shape:e.shape,dtype:i}}const a=n.data.get(s.dataId).values,[o,u,l]=wp(a,s.shape,s.dtype,i);return n.makeTensorInfo(o,u,l)}const Sp={kernelName:p.Cast,backendName:"cpu",kernelFunc:kp};function Ip(e,t,n,r){return null==n?({inputs:n,backend:s})=>{const{a:i,b:a}=n,o=s;up([i,a],e);const u=o.data.get(i.dataId).values,l=o.data.get(a.dataId).values,c="string"===i.dtype?p.backend_util.fromUint8ToStringArray(u):u,d="string"===i.dtype?p.backend_util.fromUint8ToStringArray(l):l,h=r||i.dtype,[f,m]=t(i.shape,a.shape,c,d,h);return o.makeTensorInfo(m,h,f)}:({inputs:e,backend:s})=>{const{a:i,b:a}=e,o=s;if("complex64"===i.dtype||"complex64"===a.dtype){const e=kp({inputs:{x:i},backend:o,attrs:{dtype:"complex64"}}),t=o.data.get(e.dataId),r=t.complexTensorInfos.real,s=t.complexTensorInfos.imag,u=o.data.get(r.dataId).values,l=o.data.get(s.dataId).values,c=kp({inputs:{x:a},backend:o,attrs:{dtype:"complex64"}}),d=o.data.get(c.dataId),h=d.complexTensorInfos.real,p=d.complexTensorInfos.imag,f=o.data.get(h.dataId).values,m=o.data.get(p.dataId).values,[g,y,b]=n(i.shape,a.shape,u,l,f,m),v=o.makeTensorInfo(b,"float32",g),x=o.makeTensorInfo(b,"float32",y),w=fp({inputs:{real:v,imag:x},backend:o});return o.disposeIntermediateTensorInfo(e),o.disposeIntermediateTensorInfo(c),o.disposeIntermediateTensorInfo(v),o.disposeIntermediateTensorInfo(x),w}{const e=o.data.get(i.dataId).values,n=o.data.get(a.dataId).values,s=r||i.dtype,[u,l]=t(i.shape,a.shape,e,n,s);return o.makeTensorInfo(l,s,u)}}}function Tp(e){return(t,n,r,s,i,a)=>{const o=p.backend_util.assertAndGetBroadcastShape(t,n),u=p.util.sizeFromShape(o),l=o.length,c=p.util.computeStrides(o),d=p.util.getTypedArrayFromDType("float32",u),h=p.util.getTypedArrayFromDType("float32",u),f=p.backend_util.getBroadcastDims(t,o),m=p.backend_util.getBroadcastDims(n,o),g=p.backend_util.mergeRealAndImagArrays(r,s),y=p.backend_util.mergeRealAndImagArrays(i,a),b=t.length,v=p.util.computeStrides(t),x=n.length,w=p.util.computeStrides(n);if(f.length+m.length===0)for(let t=0;t<d.length;t++){const n=t%g.length,r=t%y.length,s=e(g[2*n],g[2*n+1],y[2*r],y[2*r+1]);d[t]=s.real,h[t]=s.imag}else for(let t=0;t<d.length;t++){const n=p.util.indexToLoc(t,l,c),r=n.slice(-b);f.forEach((e=>r[e]=0));const s=p.util.locToIndex(r,b,v),i=n.slice(-x);m.forEach((e=>i[e]=0));const a=p.util.locToIndex(i,x,w),o=e(g[2*s],g[2*s+1],y[2*a],y[2*a+1]);d[t]=o.real,h[t]=o.imag}return[d,h,o]}}const Np=pp(((e,t)=>e+t)),Cp=Tp(((e,t,n,r)=>({real:e+n,imag:t+r}))),Ep=Ip(p.Add,Np,Cp),_p={kernelName:p.Add,backendName:"cpu",kernelFunc:Ep};function Ap(e,t,n,r,s){const i=p.util.sizeFromShape(r),a=p.util.makeZerosTypedArray(s,n);for(let n=0;n<e.length;n++){const r=e[n];if(r<0)throw new Error("Input x must be non-negative!");r>=s||(a[r]+=i>0?t[n]:1)}return a}function Rp(e,t,n,r=!1){const s=e.shape[0],i=e.shape[1],a=(0,p.buffer)([s,n],t.dtype);for(let o=0;o<s;o++)for(let s=0;s<i;s++){const i=e.get(o,s);if(i<0)throw new Error("Input x must be non-negative!");i>=n||(r?a.set(1,o,i):t.size>0?a.set(a.get(o,i)+t.get(o,s),o,i):a.set(a.get(o,i)+1,o,i))}return a}const $p=pp(((e,t)=>e&t)),Fp=Ip(p.BitwiseAnd,$p),Op={kernelName:p.BitwiseAnd,backendName:"cpu",kernelFunc:Fp};function Dp(e){return(t,n,r)=>{const s=p.util.getArrayFromDType(n,t.length);for(let n=0;n<t.length;++n)s[n]=e(t[n],r);return s}}function Mp(e,t,n){return Lp(e,Dp(t),n)}function Lp(e,t,n){return({inputs:r,attrs:s,backend:i})=>{const{x:a}=r;up(a,e);const o=i,u=o.data.get(a.dataId).values;let l;if("string"===a.dtype){if(!Array.isArray(u))throw new Error("String tensor's value was not an instance of Array");l=p.backend_util.fromUint8ToStringArray(u)}else l=u;const c=n||a.dtype,d=t(l,c,s);return o.makeTensorInfo(a.shape,c,d)}}const zp=Dp((e=>Math.ceil(e))),Pp=Lp(p.Ceil,zp),Bp={kernelName:p.Ceil,backendName:"cpu",kernelFunc:Pp};function Vp(e,t,n,r){const s=p.util.getArrayFromDType(n,p.util.sizeFromShape(t));if(r&&"string"!==n){let t=0;e.forEach((e=>{const n=p.util.sizeFromShape(e.shape);s.set(e.vals,t),t+=n}))}else{let r=0;e.forEach((e=>{const i="string"===n?p.backend_util.fromUint8ToStringArray(e.vals):e.vals;let a=0;for(let n=0;n<e.shape[0];++n){const o=n*t[1]+r;for(let t=0;t<e.shape[1];++t)s[o+t]=i[a++]}r+=e.shape[1]}))}return s}const Wp=pp(((e,t)=>e===t?1:0)),Up=Ip(p.Equal,Wp,null,"bool"),Gp={kernelName:p.Equal,backendName:"cpu",kernelFunc:Up},Hp=Dp((e=>Math.exp(e))),jp=Lp(p.Exp,Hp,"float32"),qp={kernelName:p.Exp,backendName:"cpu",kernelFunc:jp},Xp=Dp((e=>Math.expm1(e))),Kp=Lp(p.Expm1,Xp),Yp={kernelName:p.Expm1,backendName:"cpu",kernelFunc:Kp},Qp=Dp((e=>Math.floor(e))),Zp=Lp(p.Floor,Qp),Jp={kernelName:p.Floor,backendName:"cpu",kernelFunc:Zp},ef=pp(((e,t)=>Math.floor(e/t))),tf=Ip(p.FloorDiv,ef,null,"int32"),nf={kernelName:p.FloorDiv,backendName:"cpu",kernelFunc:tf};function rf(e,t,n,r,s,i,a,o,u){const l=(0,p.buffer)([r,i],n);for(let n=0;n<r;n++){const r=[];let c=0;for(let t=0;t<s;t++){const i=e[n*s+t];c+=i*a[t],r.push(i)}if(c<0||c>=u/i)throw new Error(`Invalid indices: ${r} does not index into ${o}`);for(let e=0;e<i;e++)l.values[n*i+e]=t.get(...t.indexToLoc(c*i+e))}return l}function sf(e,t,n){const r=(0,p.buffer)(n,e.dtype);for(let n=0;n<r.size;++n){const s=r.indexToLoc(n).slice(),i=s[0],a=s[2],o=t.locToIndex([i,a]);s[2]=t.values[o];const u=e.locToIndex(s);0<=u&&u<e.values.length&&(r.values[n]=e.values[u])}return r}const af=pp(((e,t)=>e>t?1:0)),of=Ip(p.Greater,af,null,"bool"),uf={kernelName:p.Greater,backendName:"cpu",kernelFunc:of},lf=pp(((e,t)=>e>=t?1:0)),cf=Ip(p.GreaterEqual,lf,null,"bool"),df={kernelName:p.GreaterEqual,backendName:"cpu",kernelFunc:cf},hf=pp(((e,t)=>e<t?1:0)),pf=Ip(p.Less,hf,null,"bool"),ff={kernelName:p.Less,backendName:"cpu",kernelFunc:pf},mf=pp(((e,t)=>e<=t?1:0)),gf=Ip(p.LessEqual,mf,null,"bool"),yf={kernelName:p.LessEqual,backendName:"cpu",kernelFunc:gf};function bf(e,t,n){const r=(t-e)/(n-1),s=p.util.makeZerosTypedArray(n,"float32");s[0]=e;for(let e=1;e<s.length;e++)s[e]=s[e-1]+r;return s}const vf=Dp((e=>Math.log(e))),xf=Lp(p.Log,vf),wf={kernelName:p.Log,backendName:"cpu",kernelFunc:xf};function kf(e,t,n,r){const s=p.util.getTypedArrayFromDType(r,p.util.sizeFromShape(n));for(let n=0;n<s.length;++n){const r=n*t;let i=e[r];for(let n=0;n<t;++n){const t=e[r+n];(Number.isNaN(t)||t>i)&&(i=t)}s[n]=i}return s}const Sf=pp(((e,t)=>Math.max(e,t))),If=Ip(p.Maximum,Sf),Tf={kernelName:p.Maximum,backendName:"cpu",kernelFunc:If},Nf=pp(((e,t)=>Math.min(e,t))),Cf=Ip(p.Minimum,Nf),Ef={kernelName:p.Minimum,backendName:"cpu",kernelFunc:Cf},_f=pp(((e,t)=>e*t)),Af=Tp(((e,t,n,r)=>({real:e*n-t*r,imag:e*r+t*n}))),Rf=Ip(p.Multiply,_f,Af),$f={kernelName:p.Multiply,backendName:"cpu",kernelFunc:Rf};function Ff(e,t,n){const r=p.util.createScalarValue(-1,n);return _f([],t,r,e,n)}const Of={kernelName:p.Neg,backendName:"cpu",kernelFunc:function(e){const{inputs:t,backend:n}=e,{x:r}=t;up(r,"neg");const s=n.data.get(r.dataId).values,[i,a]=Ff(s,r.shape,r.dtype);return n.makeTensorInfo(a,r.dtype,i)}},Df=pp(((e,t)=>e!==t?1:0)),Mf=Ip(p.NotEqual,Df,null,"bool"),Lf={kernelName:p.NotEqual,backendName:"cpu",kernelFunc:Mf};function zf(e,t,n,r,s){const i=t.length,a=p.util.sizeFromShape(t),o=p.util.computeStrides(t),u=p.util.computeStrides(s),l=p.util.getTypedArrayFromDType(n,p.util.sizeFromShape(s));for(let t=0;t<a;++t){const n=p.util.indexToLoc(t,i,o),s=new Array(n.length);for(let e=0;e<s.length;e++)s[e]=n[r[e]];l[p.util.locToIndex(s,i,u)]=e[t]}return l}function Pf(e){const{inputs:t,attrs:n,backend:r}=e,{x:s}=t,{perm:i}=n;up(s,"transpose");const a=s.shape.length,o=new Array(a);for(let e=0;e<o.length;e++)o[e]=s.shape[i[e]];const u=zf(r.data.get(s.dataId).values,s.shape,s.dtype,i,o);return{dataId:r.write(u,o,s.dtype),shape:o,dtype:s.dtype}}const Bf={kernelName:p.Transpose,backendName:"cpu",kernelFunc:Pf};function Vf(e,t,n,r){const[s,i]=p.backend_util.computeOutAndReduceShapes(e,r),a=(0,p.upcastType)(t,"int32"),o=p.util.makeZerosTypedArray(p.util.sizeFromShape(s),a),u=p.util.sizeFromShape(i);for(let e=0;e<o.length;++e){const t=e*u;let r=1;for(let e=0;e<u;++e)r*=n[t+e];o[e]=r}return{outVals:o,outShape:s,outDtype:a}}const Wf={kernelName:p.Prod,backendName:"cpu",kernelFunc:function(e){const{inputs:t,backend:n,attrs:r}=e,{x:s}=t,{axis:i,keepDims:a}=r;up(s,"prod");const o=s.shape.length,u=p.util.parseAxisParam(i,s.shape),l=p.backend_util.getAxesPermutation(u,o);let c=u,d=s;const h=[];null!=l&&(d=Pf({inputs:{x:s},backend:n,attrs:{perm:l}}),h.push(d),c=p.backend_util.getInnerMostAxes(c.length,o));const f=n.data.get(d.dataId).values,{outVals:m,outShape:g,outDtype:y}=Vf(d.shape,d.dtype,f,c);let b=g;return a&&(b=p.backend_util.expandShapeToKeepDim(g,u)),h.forEach((e=>n.disposeIntermediateTensorInfo(e))),n.makeTensorInfo(b,y,m)}};function Uf(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 Gf(e,t,n,r,s,i,a,o){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 s=p.util.indexToLoc(r,t.length,p.util.computeStrides(t)).join(",");throw new Error(`indices[${s}] = ${e} is not in [0, ${n})`)}}))}(i,a,t[0][0]-1),0===r.length)throw new Error("params.rank must be nonzero");const u=r[0],{outSplits:l,valueSlices:c,numValues:d}=function(e,t,n,r){const s=[];let i=0;const a=t.length-1+n.length,o=new Array(a).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 e=0;e<t.length-1;++e){u*=t[e];const n=t[e+1];for(let t=1;t<u+1;++t)o[e].push(t*n)}for(let r=0;r<e.length;++r){let a=e[r],u=e[r]+1;for(let e=0;e<n.length;++e){const r=n[e],s=e+t.length-1;if(s>=0){const e=o[s],t=e[e.length-1]-r[a];for(let e=a;e<u;++e)o[s].push(r[e+1]+t)}a=r[a],u=r[u]}u!==a&&(s.push([a,u]),i+=u-a)}return{outSplits:o,valueSlices:s,numValues:i}}(i,a,e,u),h=function(e){const t=[];for(let n=0;n<e.length;++n){const r=e[n].length,s=p.util.getArrayFromDType("int32",r);t.push(s),e[n].forEach(((e,t)=>s[t]=e))}return t}(l),f=function(e,t,n,r,s){const i=t.slice();i[0]=s;const a=p.util.getArrayFromDType(n,p.util.sizeFromShape(i)),o=e.length;return function(e,t,n,r,s,i){const a=Uf(t,2)[1],o=Uf(i,2)[1];let u=0;for(const t of n)for(let n=t[0];n<t[1];++n){for(let t=0;t<r;++t)s[u*o+t]=e[n*a+t];++u}}(e,t,r,0===o?0:o/t[0],a,i),[a,i]}(n,r,s,c,d);return[h,f[0],f[1]]}function Hf(e,t,n,r,s,i,a){if(t.length>1)throw new Error("starts must be a scalar or vector");if(s.length>1)throw new Error("limits must be a scalar or vector");if(a.length>1)throw new Error("deltas must be a scalar or vector");const o=0===t.length,u=0===s.length,l=0===a.length,c=[];o||c.push(t[0]),u||c.push(s[0]),l||c.push(a[0]);for(let e=1;e<c.length;++e)if(c[e]!==c[e-1])throw new Error("starts, limits, and deltas must have the same shape");const d=0===c.length?1:c[0],h=p.util.getArrayFromDType("int32",d+1);h[0]=0;for(let t=0;t<d;++t){const n=o?e[0]:e[t],s=u?r[0]:r[t],a=l?i[0]:i[t];if(0===a)throw new Error("Requires delta != 0");let c;if(a>0&&s<n||a<0&&s>n)c=0;else if(c=Math.ceil(Math.abs((s-n)/a)),c>2147483647)throw new Error("Requires ((limit - start) / delta) <= 2147483647");h[t+1]=h[t]+c}const f=h[d],m=p.util.getArrayFromDType(n,f);let g=0;for(let t=0;t<d;++t){const n=h[t+1]-h[t];let r=o?e[0]:e[t];const s=l?i[0]:i[t];for(let e=0;e<n;++e)m[g++]=r,r+=s}return[h,m]}var jf=p.backend_util.RowPartitionType;class qf{constructor(e,t,n,r,s,i,a,o,u,l){this.shape=e,this.shapeShape=t,this.values=n,this.valuesShape=r,this.valuesDType=s,this.defaultValue=i,this.defaultValueShape=a,this.rowPartitionValues=o,this.rowPartitionValuesShapes=u,this.rowPartitionTypes=p.backend_util.getRowPartitionTypesHelper(l),this.raggedRank=p.backend_util.getRaggedRank(this.rowPartitionTypes)}getRowPartitionTypeByDimension(e){return this.rowPartitionTypes[0]===jf.FIRST_DIM_SIZE?this.rowPartitionTypes[e+1]:this.rowPartitionTypes[e]}getRowPartitionTensor(e){return this.rowPartitionTypes[0]===jf.FIRST_DIM_SIZE?this.rowPartitionValues[e+1]:this.rowPartitionValues[e]}getMaxWidth(e){const t=this.getRowPartitionTensor(e-1);switch(this.getRowPartitionTypeByDimension(e-1)){case jf.VALUE_ROWIDS:return qf.getMaxWidthValueRowID(t);case jf.ROW_SPLITS:return qf.getMaxWidthRowSplit(t);default:throw new Error("Cannot handle partition type "+jf[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 i=1;i<t;++i){const t=e[i];t!==r&&(r=t,s=Math.max(i-n,s),n=i)}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 Kf(e,n)}calculateOutputSize(e){const t=this.valuesShape,n=this.defaultValueShape;p.backend_util.validateDefaultValueShape(n,t);const r=this.tensorShapeFromTensor(this.shape,this.shapeShape),s=p.backend_util.combineRaggedTensorToTensorShapes(this.raggedRank,r,t);s[0]<0&&(s[0]=e);for(let e=1;e<=this.raggedRank;++e)s[e]<0&&(s[e]=this.getMaxWidth(e));return s}calculateFirstParentOutputIndex(e,t,n){const r=Math.min(e,n),s=[];let i=0;for(let e=0;e<r;++e,i+=t)s.push(i);for(let t=r;t<e;++t)s.push(-1);return p.util.assert(s.length===e,(()=>"Final length of result must be equal to firstDimension.")),s}calculateOutputIndexRowSplit(e,t,n,r){const s=e.length,i=[];for(let a=0;a<s-1;++a){const s=e[a+1]-e[a];let o=Math.min(r,s),u=t[a];-1===u&&(o=0);for(let e=0;e<o;++e)i.push(u),u+=n;for(let e=0;e<s-o;++e)i.push(-1)}if(s>0&&i.length!==e[s-1])throw new Error("Invalid row split size.");return i}calculateOutputIndexValueRowID(e,t,n,r){const s=e.length,i=[];if(0===s)return[];let a=0,o=e[0];if(o>=t.length)throw new Error(`Got currentValueRowId=${o}, which is not less than ${t.length}`);let u=t[o];i.push(u);for(let l=1;l<s;++l){const s=e[l];if(s===o)u>=0&&(++a,a<r?u+=n:u=-1);else{if(a=0,o=s,s>=t.length)throw new Error(`Got nextValueRowId=${s} which is not less than ${t.length}`);u=t[s]}i.push(u)}if(i.length!==e.length)throw new Error("Invalid row ids.");return i}calculateOutputIndex(e,t,n,r){const s=this.getRowPartitionTensor(e),i=this.getRowPartitionTypeByDimension(e);switch(i){case jf.VALUE_ROWIDS:return this.calculateOutputIndexValueRowID(s,t,n,r);case jf.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: "+jf[i])}}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 jf.FIRST_DIM_SIZE:return e[0];case jf.VALUE_ROWIDS:throw new Error("Cannot handle VALUE_ROWIDS in first dimension.");case jf.ROW_SPLITS:return this.rowPartitionValuesShapes[0][0]-1;default:throw new Error("Cannot handle type "+jf[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 e=n.length-2;e>=0;--e)n[e]=n[e+1]*t[e+1];const r=Kf(t,!1),s=p.util.getArrayFromDType(this.valuesDType,p.util.sizeFromShape(r));if(n[0]*t[0]>0){let i=this.calculateFirstParentOutputIndex(e,n[0],t[0]);for(let e=1;e<=this.raggedRank;++e)i=this.calculateOutputIndex(e-1,i,n[e],t[e]);this.setOutput(this.raggedRank,i,s,r)}return[r,s]}setOutput(e,t,n,r){if(0===n.length)return;const s=this.values,i=n;let a=r.slice();a=a.slice(e+1);const o=p.util.sizeFromShape(a),u=t.length;let l=this.defaultValue;if(l.length!==o&&1!==l.length){const e=this.defaultValueShape;(0,p.tidy)((()=>{const t=(0,p.reshape)(l,e),n=(0,p.broadcastTo)(t,a);l=n.dataSync()}))}let c=0,d=0,h=0;for(let e=0;e<=u;++e){let r=e<u?t[e]:-1;if(r!==h){if(d<h){const e=s.subarray(c*o);Xf(i.subarray(d*o),e,(h-d)*o)}if(e>=u){const e=n.length;r=Math.floor(e/o)}if(r>h)if(1===this.defaultValue.length)i.subarray(h*o,r*o).fill(this.defaultValue[0]),h=r;else for(;r>h;)Xf(i.slice(h*o),l,o),++h;r<0?(c=e+1,d=h):(c=e,d=h,h=d+1)}else++h}}}function Xf(e,t,n){for(let r=0;r<n;r++)e[r]=t[r]}function Kf(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 Yf(e,t,n,r,s,i,a,o,u,l){return new qf(e,t,n,r,s,i,a,o,u,l).compute()}function Qf(e,t,n,r){if(e===t||e<t&&n<0||t<e&&n>1)return p.util.makeZerosTypedArray(0,r);const s=Math.abs(Math.ceil((t-e)/n)),i=p.util.makeZerosTypedArray(s,r);t<e&&1===n&&(n=-1),i[0]=e;for(let e=1;e<i.length;e++)i[e]=i[e-1]+n;return i}const Zf=Dp((e=>1/Math.sqrt(e))),Jf=Lp(p.Rsqrt,Zf),em={kernelName:p.Rsqrt,backendName:"cpu",kernelFunc:Jf};function tm(e,t,n,r,s,i,a,o,u,l){const c=[r/s,s],d=e.values,h=t.values;if(0===r)return(0,p.buffer)(n,t.dtype);const f=u instanceof p.TensorBuffer?u:(0,p.buffer)(c,t.dtype);"string"==typeof u||"number"==typeof u?f.values.fill(u):"boolean"==typeof u&&f.values.fill(+u);for(let e=0;e<i;e++){const i=[];let u=0;for(let t=0;t<a;t++){const n=d[e*a+t];i.push(n),u+=n*o[t]}if(u<0||u>=r/s)throw new Error(`Invalid indices: ${i} does not index into ${n}`);for(let n=0;n<s;n++)l?f.values[u*s+n]+=h[e*s+n]:f.values[u*s+n]=0===t.rank?h[0]:h[e*s+n]}return f}const nm=Dp((e=>1/(1+Math.exp(-e)))),rm=Mp(p.Sigmoid,(e=>1/(1+Math.exp(-e)))),sm={kernelName:p.Sigmoid,backendName:"cpu",kernelFunc:rm};function im(e,t,n,r,s){const i=p.slice_util.isSliceContinous(r,t,n),a=p.util.sizeFromShape(n),o=p.util.computeStrides(r);if(i){const n=p.slice_util.computeFlatOffset(t,o);return"string"===s?e.slice(n,n+a):e.subarray(n,n+a)}const u="string"===s?p.backend_util.fromUint8ToStringArray(e):e,l=(0,p.buffer)(r,s,u),c=(0,p.buffer)(n,s);for(let e=0;e<c.size;++e){const n=c.indexToLoc(e),r=n.map(((e,n)=>e+t[n]));c.set(l.get(...r),...n)}return"string"===s?p.backend_util.fromStringArrayToUint8(c.values):c.values}function am(e){const{inputs:t,backend:n,attrs:r}=e,{x:s}=t,{begin:i,size:a}=r;up(s,"slice");const[o,u]=p.slice_util.parseSliceParams(s,i,a);p.slice_util.assertParamsValid(s,o,u);const l=im(n.data.get(s.dataId).values,o,u,s.shape,s.dtype);return n.makeTensorInfo(u,s.dtype,l)}const om={kernelName:p.Slice,backendName:"cpu",kernelFunc:am};function um(e,t,n,r,s,i,a){const o=t[0],u=i[0],l=new Array(u),c=new Array(o),d=t[1];if(0===u){if(0!==o)throw new Error(p.backend_util.getSparseFillEmptyRowsIndicesDenseShapeMismatch(o));return[p.util.getArrayFromDType(n,0),[0,d],p.util.getArrayFromDType(s,0),l,c]}let h=!0,f=0;const m=new Array(u).fill(0);for(let t=0;t<o;++t){const n=e[t*d];if(n<0)throw new Error(p.backend_util.getSparseFillEmptyRowsNegativeIndexErrorMessage(t,n));if(n>=u)throw new Error(p.backend_util.getSparseFillEmptyRowsOutOfRangeIndexErrorMessage(t,n,u));++m[n],h=h&&n>=f,f=n}let g=!0;for(let e=0;e<u;++e){const t=0===m[e];l[e]=t,g=g&&!t,m[e]=Math.max(m[e],1),e>0&&(m[e]+=m[e-1])}if(g&&h){const t=e,n=r;for(let e=0;e<o;++e)c[e]=e;return[t,[o,d],n,l,c]}{const t=m[u-1],i=p.util.getArrayFromDType(n,t*d),h=p.util.getArrayFromDType(s,t),f=new Array(u).fill(0);for(let t=0;t<o;++t){const n=e[t*d],s=f[n],a=(0===n?0:m[n-1])+s;f[n]++;for(let n=0;n<d;++n)i[a*d+n]=e[t*d+n];h[a]=r[t],c[t]=a}for(let e=0;e<u;++e)if(0===f[e]){const t=0===e?0:m[e-1];i[t*d+0]=e;for(let e=1;e<d;++e)i[t*d+e]=0;h[t]=a}return[i,[t,d],h,l,c]}}function lm(e,t,n,r,s){const i=p.util.sizeFromShape(r),a=t[0],o=s.length,u=[];let l=1,c=-1;for(let e=0;e<o;++e){const t=s[e];if(-1===t){if(-1!==c)throw new Error(p.backend_util.getSparseReshapeMultipleNegativeOneOutputDimErrorMessage(c,e));c=e,u.push(1)}else{if(t<0)throw new Error(p.backend_util.getSparseReshapeNegativeOutputDimErrorMessage(e,t));l*=t,u.push(t)}}if(-1!==c){if(l<=0)throw new Error(p.backend_util.getSparseReshapeEmptyTensorZeroOutputDimErrorMessage());const e=Math.trunc(i/l);if(l*e!==i)throw new Error(p.backend_util.getSparseReshapeInputOutputMultipleErrorMessage(r,u));u[c]=e}if(p.util.sizeFromShape(u)!==i)throw new Error(p.backend_util.getSparseReshapeInputOutputMismatchErrorMessage(r,u));const d=r.length,h=[];if(d>0){h[d-1]=1;for(let e=d-2;e>=0;--e)h[e]=h[e+1]*r[e+1]}const f=[];if(o>0){f[o-1]=1;for(let e=o-2;e>=0;--e)f[e]=f[e+1]*u[e+1]}const m=p.util.getArrayFromDType(n,a*o);for(let t=0;t<a;++t){let n=0;for(let r=0;r<d;++r)n+=e[t*d+r]*h[r];for(let e=0;e<o;++e)m[t*o+e]=Math.trunc(n/f[e]),n%=f[e]}return[m,[a,o],u]}function cm(e,t,n,r,s,i=!1,a=0){const o=r.length,u=[t[0],e.length/t[0]],l=u[1],c=o>0?s[o-1]+1:0;if(c<0)throw new Error(p.backend_util.getSparseSegmentReductionNegativeSegmentIdsErrorMessage());const d=t.slice();d[0]=c;const h=d.reduce(((e,t)=>e*t),1),f=p.util.getArrayFromDType(n,h);if(0===o)return c>0&&f.fill(a),[f,d];if(c<=0)throw new Error(p.backend_util.getSparseSegmentReductionNegativeSegmentIdsErrorMessage());let m=0,g=1,y=0,b=s[m];for(;;){let t=0;if(g<o){if(t=s[g],b===t){++g;continue}if(b>=t)throw new Error(p.backend_util.getSparseSegmentReductionNonIncreasingSegmentIdsErrorMessage())}if(b<0||b>=c)throw new Error(p.backend_util.getSparseSegmentReductionSegmentIdOutOfRangeErrorMessage(b,c));b>y&&f.fill(a,y*l,b*l);for(let t=m;t<g;++t){const n=r[t];if(n<0||n>=u[0])throw new Error(p.backend_util.getSparseSegmentReductionIndicesOutOfRangeErrorMessage(t,r[t],u[0]));for(let t=0;t<l;t++)f[b*l+t]+=e[n*l+t]}if(i)for(let e=0;e<l;e++)f[b*l+e]/=g-m;if(m=g,++g,y=b+1,b=t,g>o)break}return y<c&&f.fill(a,y*l,c*l),[f,d]}const dm=Dp((e=>Math.sqrt(e))),hm=Mp(p.Sqrt,(e=>Math.sqrt(e))),pm={kernelName:p.Sqrt,backendName:"cpu",kernelFunc:hm},fm=pp(((e,t)=>{const n=e-t;return n*n})),mm=Ip(p.SquaredDifference,fm),gm={kernelName:p.SquaredDifference,backendName:"cpu",kernelFunc:mm},ym=Dp(((e,t)=>{const{pattern:n,replaceGlobal:r,rewrite:s}=t;return e.replace(new RegExp(n,r?"g":""),s)})),bm=Lp(p.StaticRegexReplace,ym),vm={kernelName:p.StaticRegexReplace,backendName:"cpu",kernelFunc:bm};function xm(e,t,n,r){const s=(0,p.buffer)(e,t.dtype);for(let e=0;e<s.size;e++){const i=s.indexToLoc(e),a=new Array(i.length);for(let e=0;e<a.length;e++)a[e]=i[e]*n[e]+r[e];s.set(t.get(...a),...i)}return s}class wm{constructor(e,t,n,r,s,i){this.separator=p.util.encodeString(e),this.nGramWidths=t,this.leftPad=p.util.encodeString(n),this.rightPad=p.util.encodeString(r),this.padWidth=s,this.preserveShort=i}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,i){for(let a=0;a<s;++a){const o=this.getPadWidth(i),u=Math.max(0,o-a),l=Math.max(0,o-(s-(a+1))),c=i-(u+l),d=t+(u>0?0:a-o);let h=0;h+=u*this.leftPad.length;for(let t=0;t<c;++t)h+=e[d+t].length;h+=l*this.rightPad.length,h+=(u+l+c-1)*this.separator.length,n[r+a]=new Uint8Array(h);const p=n[r+a];let f=0;const m=e=>e.forEach((e=>p[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[d+t]),m(this.separator);if(c>0){m(e[d+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. Expected ${n}, got ${e}`)}const s=r-1,i=p.util.getArrayFromDType("int32",r);if(0===n||0===r){const e=new Array(n);for(let e=0;e<=s;++e)i[e]=0;return[e,i]}i[0]=0;for(let e=1;e<=s;++e){const n=t[e]-t[e-1];let r=0;this.nGramWidths.forEach((e=>{r+=this.getNumNGrams(n,e)})),this.preserveShort&&n>0&&0===r&&(r=1),i[e]=i[e-1]+r}const a=new Array(i[s]);for(let n=0;n<s;++n){const r=t[n];let s=i[n];if(this.nGramWidths.forEach((i=>{const o=t[n+1]-t[n],u=this.getNumNGrams(o,i);this.createNGrams(e,r,a,s,u,i),s+=u})),this.preserveShort&&s===i[n]){const i=t[n+1]-t[n];if(0===i)continue;const o=i+2*this.padWidth,u=1;this.createNGrams(e,r,a,s,u,o)}}return[a,i]}}function km(e,t,n,r,s,i,a,o){return new wm(n,r,s,i,a,o).compute(e,t)}function Sm(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 i=e.indexOf(s);for(;-1!==i;){const t=e.subarray(0,i);n&&0===t.length||r.push(t),i=(e=e.subarray(i+1)).indexOf(s)}return void(n&&0===e.length||r.push(e))}let s=0;for(let i=0;i<e.length+1;i++)if(i===e.length||-1!==t.indexOf(e[i])){const t=e.subarray(s,i);n&&0===t.length||r.push(t),s=i+1}}function Im(e,t,n){const r=e.length,s=[];let i=0,a=0;const o=new Array(r);for(let u=0;u<r;++u){const r=s.length;Sm(e[u],t,n,s);const l=s.length-r;o[u]=l,i+=l,a=Math.max(a,l)}const u=p.util.getArrayFromDType("int32",2*i),l=new Array(i),c=[r,a];let d=0;for(let e=0;e<r;++e)for(let t=0;t<o[e];++t)u[2*d]=e,u[2*d+1]=t,l[d]=s[d],++d;return[u,l,c]}function Tm(e,t){const n=p.util.getArrayFromDType("int32",e.length);for(let r=0;r<e.length;++r)n[r]=p.util.fingerPrint64(e[r]).modulo(t).getLowBitsUnsigned();return n}const Nm=pp(((e,t)=>e-t)),Cm=Tp(((e,t,n,r)=>({real:e-n,imag:t-r}))),Em=Ip(p.Sub,Nm,Cm),_m={kernelName:p.Sub,backendName:"cpu",kernelFunc:Em};function Am(e,t){const n=new Array(e.rank);for(let r=0;r<n.length;r++)n[r]=e.shape[r]*t[r];const r=(0,p.buffer)(n,e.dtype);for(let t=0;t<r.values.length;++t){const n=r.indexToLoc(t),s=new Array(e.rank);for(let t=0;t<s.length;t++)s[t]=n[t]%e.shape[t];const i=e.locToIndex(s);r.values[t]=e.values[i]}return r}const Rm=(e,t)=>{const n=t.value-e.value;return 0===n?e.index-t.index:n};function $m(e,t,n=0,r=e.length-1){for(;r>n;){if(r-n>600){const s=r-n+1,i=t-n+1,a=Math.log(s),o=.5*Math.exp(2*a/3),u=.5*Math.sqrt(a*o*(s-o)/s)*Math.sign(i-s/2);$m(e,t,Math.max(n,Math.floor(t-i*o/s+u)),Math.min(r,Math.floor(t+(s-i)*o/s+u)))}const s=e[t];let i=n,a=r;for(p.util.swap(e,n,t),Rm(e[r],s)>0&&p.util.swap(e,n,r);i<a;){for(p.util.swap(e,i,a),i++,a--;Rm(e[i],s)<0;)i+=1;for(;Rm(e[a],s)>0;)a-=1}0===Rm(e[n],s)?p.util.swap(e,n,a):(a+=1,p.util.swap(e,a,r)),a<=t&&(n=a+1),t<=a&&(r=a-1)}}function Fm(e,t,n,r,s){const i=t[t.length-1],[a,o]=[e.length/i,i],u=p.util.getTypedArrayFromDType(n,a*r),l=p.util.getTypedArrayFromDType("int32",a*r);for(let t=0;t<a;t++){const n=t*o,i=e.subarray(n,n+o);let a=new Array(i.length);i.forEach(((e,t)=>a[t]={value:e,index:t})),r<a.length&&($m(a,r),a=a.slice(0,r)),s&&a.sort(Rm);const c=t*r,d=u.subarray(c,c+r),h=l.subarray(c,c+r);for(let e=0;e<r;e++)d[e]=a[e].value,h[e]=a[e].index}const c=t.slice();return c[c.length-1]=r,[(0,p.buffer)(c,n,u),(0,p.buffer)(c,"int32",l)]}function Om(e,t,n,r){const s=p.util.parseAxisParam(t,n)[0],i=[1,n[0],1];for(let e=0;e<s;e++)i[0]*=n[e];i[1]=n[s];for(let e=s+1;e<n.length;e++)i[2]*=n[e];const a=new Map,o=new Int32Array(n[s]),u=new p.TensorBuffer(i,r,e),l=[],c=1===i[0]&&1===i[2];for(let t=0;t<n[s];t++){let n;if(c)n=e[t].toString();else{const e=[];for(let n=0;n<i[0];n++)for(let r=0;r<i[2];r++)e.push(u.get(n,t,r));n=e.join(",")}const r=a.get(n);if(null!=r)o[t]=r;else{const e=a.size;a.set(n,e),o[t]=e,l.push(t)}}const d=i.slice();d[1]=a.size;const h=new p.TensorBuffer(d,r);l.forEach(((e,t)=>{for(let n=0;n<i[0];n++)for(let r=0;r<i[2];r++)h.set(u.get(n,e,r),n,t,r)}));const f=n.slice();return f[s]=d[1],{outputValues:h.values,outputShape:f,indices:o}}const Dm="4.10.0";(0,p.registerBackend)("cpu",(()=>new cp),1);const Mm=Mp(p.Elu,(e=>e>=0?e:Math.exp(e)-1)),Lm={kernelName:p.Elu,backendName:"cpu",kernelFunc:Mm};function zm(e){const{inputs:t,backend:n,attrs:r}=e,{x:s}=t,{alpha:i}=r;up([s],"leakyRelu");const a=p.util.sizeFromShape(s.shape),o=n.data.get(s.dataId).values,u=p.util.getTypedArrayFromDType("float32",a);for(let e=0;e<o.length;e++)u[e]=o[e]<0?i*o[e]:o[e];return n.makeTensorInfo(s.shape,"float32",u)}const Pm={kernelName:p.LeakyRelu,backendName:"cpu",kernelFunc:zm},Bm=pp(((e,t)=>e<0?t*e:e));function Vm(e){const{inputs:t,backend:n}=e,{x:r,alpha:s}=t;up([r,s],"prelu");const i=n.data.get(r.dataId).values,a=n.data.get(s.dataId).values,[o,u]=Bm(r.shape,s.shape,i,a,"float32");return n.makeTensorInfo(u,"float32",o)}const Wm={kernelName:p.Prelu,backendName:"cpu",kernelFunc:Vm},Um=Mp(p.Relu,(e=>Math.max(0,e))),Gm={kernelName:p.Relu,backendName:"cpu",kernelFunc:Um},Hm=Mp(p.Relu6,(e=>Math.min(Math.max(0,e),6))),jm={kernelName:p.Relu6,backendName:"cpu",kernelFunc:Hm};function qm(e,t,n,r,s){if("linear"===n)return yp({inputs:{x:t},backend:e});if("relu"===n)return Um({inputs:{x:t},backend:e});if("elu"===n)return Mm({inputs:{x:t},backend:e});if("relu6"===n)return Hm({inputs:{x:t},backend:e});if("prelu"===n)return Vm({inputs:{x:t,alpha:r},backend:e});if("leakyrelu"===n)return zm({inputs:{x:t},backend:e,attrs:{alpha:s}});if("sigmoid"===n)return rm({inputs:{x:t},backend:e});throw new Error(`Activation ${n} has not been implemented for the CPU backend.`)}function Xm(e){const{inputs:t,backend:n,attrs:r}=e,{x:s}=t,{shape:i}=r,a=p.util.sizeFromShape(s.shape),o=p.util.inferFromImplicitShape(i,a),u=p.util.sizeFromShape(o);p.util.assert(a===u,(()=>`The new shape (${o}) has ${u} elements and the old shape (${s.shape}) has ${a} elements. The new shape and old shape must have the same number of elements.`)),n.incRef(s.dataId);const l=n.data.get(s.dataId);if(null!=l.complexTensorInfos){const e=l.complexTensorInfos.real,t=l.complexTensorInfos.imag;e.shape=o,t.shape=o}return{dataId:s.dataId,shape:o,dtype:s.dtype}}const Km={kernelName:p.Reshape,backendName:"cpu",kernelFunc:Xm};function Ym(e){const{inputs:t,backend:n,attrs:r}=e,{a:s,b:i}=t,{transposeA:a,transposeB:o}=r;up([s,i],"matMul");const u=s.shape.length,l=i.shape.length,c=a?s.shape[u-2]:s.shape[u-1],d=o?i.shape[l-1]:i.shape[l-2],h=a?s.shape[u-1]:s.shape[u-2],f=o?i.shape[l-2]:i.shape[l-1],m=s.shape.slice(0,-2),g=i.shape.slice(0,-2),y=p.util.sizeFromShape(m),b=p.util.sizeFromShape(g),v=p.broadcast_util.assertAndGetBroadcastShape(s.shape.slice(0,-2),i.shape.slice(0,-2)).concat([h,f]);p.util.assert(c===d,(()=>`Error in matMul: inner shapes (${c}) and (${d}) of Tensors with shapes ${s.shape} and ${i.shape} and transposeA=${a} and transposeB=${o} must match.`));const x=o?[b,f,d]:[b,d,f],w=Xm({inputs:{x:s},backend:n,attrs:{shape:a?[y,c,h]:[y,h,c]}}),k=Xm({inputs:{x:i},backend:n,attrs:{shape:x}}),S=a?w.shape[1]:w.shape[2],I=a?w.shape[2]:w.shape[1],T=o?k.shape[1]:k.shape[2],N=Math.max(y,b),C=n.data.get(w.dataId).values,E=n.data.get(k.dataId).values,_=p.util.computeStrides(w.shape),A=p.util.computeStrides(k.shape),[R,$,F]=a?[_[0],1,_[1]]:[_[0],_[1],1],[O,D,M]=o?[1,A[1],A[0]]:[A[1],1,A[0]],L=I*T,z=(0,p.buffer)([N,I,T],w.dtype),P=z.values,B=n.blockSize;for(let e=0;e<N;e++){const t=e%y,n=e%b;for(let r=0;r<I;r+=B){const s=Math.min(r+B,I);for(let i=0;i<T;i+=B){const a=Math.min(i+B,T);for(let o=0;o<S;o+=B){const u=Math.min(o+B,S);for(let l=r;l<s;l++)for(let r=i;r<a;r++){let s=0;for(let e=o;e<u;e++)s+=C[t*R+l*$+e*F]*E[e*O+r*D+n*M];P[e*L+(l*T+r)]+=s}}}}}return n.disposeIntermediateTensorInfo(w),n.disposeIntermediateTensorInfo(k),n.makeTensorInfo(v,z.dtype,z.values)}const Qm={kernelName:p.BatchMatMul,backendName:"cpu",kernelFunc:Ym},Zm={kernelName:p._FusedMatMul,backendName:"cpu",kernelFunc:function(e){const{inputs:t,backend:n,attrs:r}=e,{a:s,b:i,bias:a,preluActivationWeights:o}=t,{transposeA:u,transposeB:l,activation:c,leakyreluAlpha:d}=r;let h,p,f;const m=[];h=Ym({inputs:{a:s,b:i},attrs:{transposeA:u,transposeB:l},backend:n}),a&&(p=Ep({inputs:{a:h,b:a},backend:n}),m.push(h),h=p),c&&(f=qm(n,h,c,o,d),m.push(h),h=f);for(const e of m)n.disposeIntermediateTensorInfo(e);return h}},Jm=Mp(p.Acos,(e=>Math.acos(e))),eg={kernelName:p.Acos,backendName:"cpu",kernelFunc:Jm},tg=Mp(p.Acosh,(e=>Math.acosh(e))),ng={kernelName:p.Acosh,backendName:"cpu",kernelFunc:tg},rg={kernelName:p.AddN,backendName:"cpu",kernelFunc:function(e){const{inputs:t,backend:n}=e,r=t;up(t,"addN");const s=r.map((e=>n.data.get(e.dataId).values)),i=(0,p.buffer)(r[0].shape,r[0].dtype),a=i.values;for(let e=0;e<r.length;e++){const t=s[e];for(let e=0;e<a.length;e++)a[e]+=t[e]}return n.makeTensorInfo(i.shape,i.dtype,i.values)}},sg={kernelName:p.All,backendName:"cpu",kernelFunc:function(e){const{inputs:t,backend:n,attrs:r}=e,{x:s}=t,{axis:i,keepDims:a}=r;up(s,"all");const o=p.util.parseAxisParam(i,s.shape);let u=o;const l=p.backend_util.getAxesPermutation(u,s.shape.length);let c=s;null!=l&&(c=Pf({inputs:{x:s},backend:n,attrs:{perm:l}}),u=p.backend_util.getInnerMostAxes(u.length,s.shape.length)),p.backend_util.assertAxesAreInnerMostDims("all",u,c.shape.length);const[d,h]=p.backend_util.computeOutAndReduceShapes(c.shape,u),f=p.util.sizeFromShape(h),m=p.util.makeZerosTypedArray(p.util.sizeFromShape(d),c.dtype),g=n.data.get(c.dataId).values;for(let e=0;e<m.length;++e){const t=e*f;let n=g[t];for(let e=0;e<f;++e){const r=g[t+e];n=n&&r}m[e]=n}null!=l&&n.disposeIntermediateTensorInfo(c);const y=n.makeTensorInfo(d,c.dtype,m);if(a){const e=Xm({inputs:{x:y},backend:n,attrs:{shape:p.backend_util.expandShapeToKeepDim(d,o)}});return n.disposeIntermediateTensorInfo(y),e}return y}},ig={kernelName:p.Any,backendName:"cpu",kernelFunc:function(e){const{inputs:t,backend:n,attrs:r}=e,{x:s}=t,{axis:i,keepDims:a}=r;up(s,"any");const o=p.util.parseAxisParam(i,s.shape);let u=o;const l=p.backend_util.getAxesPermutation(u,s.shape.length);let c=s;null!=l&&(c=Pf({inputs:{x:s},backend:n,attrs:{perm:l}}),u=p.backend_util.getInnerMostAxes(u.length,s.shape.length)),p.backend_util.assertAxesAreInnerMostDims("any",u,c.shape.length);const[d,h]=p.backend_util.computeOutAndReduceShapes(c.shape,u),f=p.util.sizeFromShape(h),m=p.util.makeZerosTypedArray(p.util.sizeFromShape(d),c.dtype),g=n.data.get(c.dataId).values;for(let e=0;e<m.length;++e){const t=e*f;let n=g[t];for(let e=0;e<f;++e){const r=g[t+e];n=n||r}m[e]=n}null!=l&&n.disposeIntermediateTensorInfo(c);const y=n.makeTensorInfo(d,c.dtype,m);if(a){const e=Xm({inputs:{x:y},backend:n,attrs:{shape:p.backend_util.expandShapeToKeepDim(d,o)}});return n.disposeIntermediateTensorInfo(y),e}return y}},ag={kernelName:p.ArgMax,backendName:"cpu",kernelFunc:function(e){const{inputs:t,backend:n,attrs:r}=e,{x:s}=t,{axis:i}=r;up(s,"argMax");let a=p.util.parseAxisParam(i,s.shape);const o=p.backend_util.getAxesPermutation(a,s.shape.length);let u=s;const l=[];null!=o&&(u=Pf({inputs:{x:s},backend:n,attrs:{perm:o}}),l.push(u),a=p.backend_util.getInnerMostAxes(a.length,u.shape.length)),a=[a[0]],p.backend_util.assertAxesAreInnerMostDims("argMax",a,u.shape.length);const[c,d]=p.backend_util.computeOutAndReduceShapes(u.shape,a),h=p.util.sizeFromShape(c),f=p.util.makeZerosTypedArray(h,"int32"),m=p.util.sizeFromShape(d),g=n.data.get(u.dataId).values;for(let e=0;e<f.length;++e){const t=e*m;let n=g[t],r=0;for(let e=0;e<m;++e){const s=g[t+e];s>n&&(n=s,r=e)}f[e]=r}return l.forEach((e=>n.disposeIntermediateTensorInfo(e))),n.makeTensorInfo(c,"int32",f)}},og={kernelName:p.ArgMin,backendName:"cpu",kernelFunc:function(e){const{inputs:t,backend:n,attrs:r}=e,{x:s}=t,{axis:i}=r;up(s,"argMin");let a=p.util.parseAxisParam(i,s.shape);const o=p.backend_util.getAxesPermutation(a,s.shape.length);let u=s;const l=[];null!=o&&(u=Pf({inputs:{x:s},backend:n,attrs:{perm:o}}),l.push(u),a=p.backend_util.getInnerMostAxes(a.length,u.shape.length)),a=[a[0]],p.backend_util.assertAxesAreInnerMostDims("argMin",a,u.shape.length);const[c,d]=p.backend_util.computeOutAndReduceShapes(u.shape,a),h=p.util.sizeFromShape(c),f=p.util.makeZerosTypedArray(h,"int32"),m=p.util.sizeFromShape(d),g=n.data.get(u.dataId).values;for(let e=0;e<f.length;++e){const t=e*m;let n=g[t],r=0;for(let e=0;e<m;++e){const s=g[t+e];s<n&&(n=s,r=e)}f[e]=r}return l.forEach((e=>n.disposeIntermediateTensorInfo(e))),n.makeTensorInfo(c,"int32",f)}},ug=Mp(p.Asin,(e=>Math.asin(e))),lg={kernelName:p.Asin,backendName:"cpu",kernelFunc:ug},cg=Mp(p.Asinh,(e=>Math.asinh(e))),dg={kernelName:p.Asinh,backendName:"cpu",kernelFunc:cg},hg=Mp(p.Atan,(e=>Math.atan(e))),pg={kernelName:p.Atan,backendName:"cpu",kernelFunc:hg},fg=pp(((e,t)=>Math.atan2(e,t))),mg=Ip(p.Atan2,fg),gg={kernelName:p.Atan2,backendName:"cpu",kernelFunc:mg},yg=Mp(p.Atanh,(e=>Math.atanh(e))),bg={kernelName:p.Atanh,backendName:"cpu",kernelFunc:yg};function vg(e,t,n,r,s,i){const a=s.strideHeight,o=s.strideWidth,u=s.dilationHeight,l=s.dilationWidth,c=s.effectiveFilterHeight,d=s.effectiveFilterWidth,h=s.padInfo.top,f=s.padInfo.left,m="max"===i?Number.NEGATIVE_INFINITY:Number.POSITIVE_INFINITY,g=(0,p.buffer)(s.outShape,n),y=g.values,b=s.outShape[1]*s.outShape[2]*s.outShape[3],v=s.outShape[2]*s.outShape[3],x=s.outShape[3];for(let t=0;t<s.batchSize;++t){const n=t*b,p=t*r[0];for(let t=0;t<s.inChannels;++t)for(let g=0;g<s.outHeight;++g){const b=g*a-h,w=Math.max(0,b),k=Math.min(s.inHeight,c+b),S=n+g*v;for(let n=0;n<s.outWidth;++n){const a=n*o-f,c=Math.max(0,a),h=Math.min(s.inWidth,d+a);let g=m,b=0,v=0;for(let n=w;n<k;n+=u){const s=p+n*r[1];for(let n=c;n<h;n+=l){const a=e[s+n*r[2]+t];"max"===i&&a>g?g=a:"avg"===i&&(b+=a,v++)}if(isNaN(g))break}y[S+n*x+t]="avg"===i?b/v:g}}}return g}function xg(e,t,n,r,s=!1,i=!1){const a=(0,p.buffer)(r.outShape,"int32"),o=r.strideHeight,u=r.strideWidth,l=r.dilationHeight,c=r.dilationWidth,d=r.effectiveFilterHeight,h=r.effectiveFilterWidth,f=r.padInfo.top,m=r.padInfo.left,g=(0,p.buffer)(t,n,e);for(let e=0;e<r.batchSize;++e)for(let t=0;t<r.inChannels;++t)for(let n=0;n<r.outHeight;++n){const p=n*o-f;let y=p;for(;y<0;)y+=l;const b=Math.min(r.inHeight,d+p);for(let o=0;o<r.outWidth;++o){const d=o*u-m;let f=d;for(;f<0;)f+=c;const v=Math.min(r.inWidth,h+d);let x=Number.NEGATIVE_INFINITY,w=-1;for(let n=y;n<b;n+=l){const a=n-p;for(let o=f;o<v;o+=c){const u=o-d,l=g.get(e,n,o,t);l>x&&(x=l,w=s?i?((e*r.inHeight+n)*r.inWidth+o)*r.inChannels+t:(n*r.inWidth+o)*r.inChannels+t:a*h+u)}}a.set(w,e,n,o,t)}}return a}function wg(e,t,n,r,s,i){const a=s.strideDepth,o=s.strideHeight,u=s.strideWidth,l=s.dilationDepth,c=s.dilationHeight,d=s.dilationWidth,h=s.effectiveFilterDepth,f=s.effectiveFilterHeight,m=s.effectiveFilterWidth,g=s.padInfo.front,y=s.padInfo.top,b=s.padInfo.left,v="max"===i?Number.NEGATIVE_INFINITY:Number.POSITIVE_INFINITY,x=(0,p.buffer)(s.outShape,n),w=x.values,k=s.outShape[1]*s.outShape[2]*s.outShape[3]*s.outShape[4],S=s.outShape[2]*s.outShape[3]*s.outShape[4],I=s.outShape[3]*s.outShape[4],T=s.outShape[4];for(let t=0;t<s.batchSize;++t){const n=t*k,p=t*r[0];for(let t=0;t<s.inChannels;++t)for(let x=0;x<s.outDepth;++x){const k=x*a-g;let N=k;for(;N<0;)N+=l;const C=Math.min(s.inDepth,h+k),E=n+x*S;for(let n=0;n<s.outHeight;++n){const a=n*o-y;let h=a;for(;h<0;)h+=c;const g=Math.min(s.inHeight,f+a),x=E+n*I;for(let n=0;n<s.outWidth;++n){const a=n*u-b;let o=a;for(;o<0;)o+=d;const f=Math.min(s.inWidth,m+a),y=x+n*T;let k=v,S=0,I=0;for(let n=N;n<C;n+=l){const s=p+n*r[1];for(let n=h;n<g;n+=c){const a=s+n*r[2];for(let n=o;n<f;n+=d){const s=e[a+n*r[3]+t];if("max"===i&&s>k?k=s:"avg"===i&&(S+=s,I++),isNaN(k))break}if(isNaN(k))break}if(isNaN(k))break}w[y+t]="avg"===i?S/Math.max(I,1):k}}}}return x}const kg={kernelName:p.AvgPool,backendName:"cpu",kernelFunc:function(e){const{inputs:t,backend:n,attrs:r}=e,{x:s}=t;up(s,"avgPool");const{filterSize:i,strides:a,pad:o,dimRoundingMode:u}=r;p.util.assert(p.backend_util.eitherStridesOrDilationsAreOne(a,1),(()=>`Error in avgPool: Either strides or dilations must be 1. Got strides ${a} and dilations '1'`));const l=p.backend_util.computePool2DInfo(s.shape,i,a,1,o,u);let c;if(1===l.filterWidth&&1===l.filterHeight&&p.util.arraysEqual(l.inShape,l.outShape))c=yp({inputs:{x:s},backend:n});else{const e=n.data.get(s.dataId).values,t=p.util.computeStrides(s.shape),r=vg(e,s.shape,s.dtype,t,l,"avg");c=n.makeTensorInfo(l.outShape,s.dtype,r.values)}return c}},Sg={kernelName:p.AvgPool3D,backendName:"cpu",kernelFunc:function(e){const{inputs:t,backend:n,attrs:r}=e,{x:s}=t,{filterSize:i,strides:a,pad:o,dimRoundingMode:u,dataFormat:l}=r;up(s,"avgPool3d");const c=p.backend_util.computePool3DInfo(s.shape,i,a,1,o,u,l),d=wg(n.data.get(s.dataId).values,s.shape,s.dtype,p.util.computeStrides(s.shape),c,"avg");return n.makeTensorInfo(d.shape,"float32",d.values)}},Ig={kernelName:p.AvgPool3DGrad,backendName:"cpu",kernelFunc:function(e){const{inputs:t,backend:n,attrs:r}=e,{dy:s,input:i}=t,{filterSize:a,strides:o,pad:u,dimRoundingMode:l}=r;up([s,i],"avgPool3DGrad");const c=p.backend_util.computePool3DInfo(i.shape,a,o,1,u,l),d=c.strideDepth,h=c.strideHeight,f=c.strideWidth,m=c.filterDepth,g=c.filterHeight,y=c.filterWidth,b=c.dilationDepth,v=c.dilationHeight,x=c.dilationWidth,w=c.effectiveFilterDepth,k=c.effectiveFilterHeight,S=c.effectiveFilterWidth,I=w-1-c.padInfo.front,T=S-1-c.padInfo.left,N=k-1-c.padInfo.top,C=(0,p.buffer)(i.shape,"float32"),E=1/(m*g*y),_=n.bufferSync(s);for(let e=0;e<c.batchSize;++e)for(let t=0;t<c.inChannels;++t)for(let n=0;n<c.inDepth;++n)for(let r=0;r<c.inHeight;++r)for(let s=0;s<c.inWidth;++s){const i=n-I,a=r-N,o=s-T;let u=0;for(let n=0;n<w;n+=b){const r=(i+n)/d;if(!(r<0||r>=c.outDepth||Math.floor(r)!==r))for(let n=0;n<k;n+=v){const s=(a+n)/h;if(!(s<0||s>=c.outHeight||Math.floor(s)!==s))for(let n=0;n<S;n+=x){const i=(o+n)/f;i<0||i>=c.outWidth||Math.floor(i)!==i||(u+=_.get(e,r,s,i,t))}}}C.set(u*E,e,n,r,s,t)}return n.makeTensorInfo(C.shape,C.dtype,C.values)}},Tg={kernelName:p.AvgPoolGrad,backendName:"cpu",kernelFunc:function(e){const{inputs:t,backend:n,attrs:r}=e,{dy:s,input:i}=t,a=i;up([s,i],"avgPoolGrad");const{filterSize:o,strides:u,pad:l}=r,c=p.backend_util.computePool2DInfo(a.shape,o,u,1,l),d=c.strideHeight,h=c.strideWidth,f=c.filterHeight,m=c.filterWidth,g=c.dilationHeight,y=c.dilationWidth,b=c.effectiveFilterHeight,v=c.effectiveFilterWidth,x=v-1-c.padInfo.left,w=b-1-c.padInfo.top,k=(0,p.buffer)(a.shape,"float32"),S=1/(f*m),I=n.data.get(s.dataId).values,T=(0,p.buffer)(s.shape,"float32",I);for(let e=0;e<c.batchSize;++e)for(let t=0;t<c.inChannels;++t)for(let n=0;n<c.inHeight;++n)for(let r=0;r<c.inWidth;++r){const s=n-w,i=r-x;let a=0;for(let n=0;n<b;n+=g){const r=(s+n)/d;if(!(r<0||r>=c.outHeight||Math.floor(r)!==r))for(let n=0;n<v;n+=y){const s=(i+n)/h;s<0||s>=c.outWidth||Math.floor(s)!==s||(a+=T.get(e,r,s,t))}}k.set(a*S,e,n,r,t)}return n.makeTensorInfo(k.shape,k.dtype,k.values)}},Ng={kernelName:p.FusedBatchNorm,backendName:"cpu",kernelFunc:function(e){const{inputs:t,backend:n,attrs:r}=e,{x:s,scale:i,offset:a,mean:o,variance:u}=t;p.util.assert(o.shape.length===u.shape.length,(()=>"Batch normalization gradient requires mean and variance to have equal ranks.")),p.util.assert(null==a||o.shape.length===a.shape.length,(()=>"Batch normalization gradient requires mean and offset to have equal ranks.")),p.util.assert(null==i||o.shape.length===i.shape.length,(()=>"Batch normalization gradient requires mean and scale to have equal ranks.")),up([s,o,u,i,a],"batchNorm");let{varianceEpsilon:l}=r;null==l&&(l=.001);const c=n.data.get(s.dataId).values,d=n.data.get(o.dataId).values,h=n.data.get(u.dataId).values,f=i?n.data.get(i.dataId).values:new Float32Array([1]),m=a?n.data.get(a.dataId).values:new Float32Array([0]),g=new Float32Array(c.length),y=m.length,b=f.length,v=h.length,x=d.length;let w=0,k=0,S=0,I=0;for(let e=0;e<c.length;++e)g[e]=m[w++]+(c[e]-d[k++])*f[S++]/Math.sqrt(h[I++]+l),w>=y&&(w=0),k>=x&&(k=0),S>=b&&(S=0),I>=v&&(I=0);return n.makeTensorInfo(s.shape,s.dtype,g)}},Cg={kernelName:p.BatchToSpaceND,backendName:"cpu",kernelFunc:function(e){const{inputs:t,backend:n,attrs:r}=e,{x:s}=t,{blockShape:i,crops:a}=r;up([s],"batchToSpaceND");const o=i.reduce(((e,t)=>e*t)),u=p.backend_util.getReshaped(s.shape,i,o),l=p.backend_util.getPermuted(u.length,i.length),c=p.backend_util.getReshapedPermuted(s.shape,i,o),d=p.backend_util.getSliceBeginCoords(a,i.length),h=p.backend_util.getSliceSize(c,a,i.length),f=Xm({inputs:{x:s},backend:n,attrs:{shape:u}}),m=Pf({inputs:{x:f},backend:n,attrs:{perm:l}}),g=Xm({inputs:{x:m},backend:n,attrs:{shape:c}}),y=am({inputs:{x:g},backend:n,attrs:{begin:d,size:h}});return n.disposeIntermediateTensorInfo(f),n.disposeIntermediateTensorInfo(m),n.disposeIntermediateTensorInfo(g),y}},Eg={kernelName:p.Bincount,backendName:"cpu",kernelFunc:function(e){const{inputs:t,backend:n,attrs:r}=e,{x:s,weights:i}=t,{size:a}=r,o=Ap(n.data.get(s.dataId).values,n.data.get(i.dataId).values,i.dtype,i.shape,a);return n.makeTensorInfo([a],i.dtype,o)}},_g={kernelName:p.BroadcastArgs,backendName:"cpu",kernelFunc:function(e){const{inputs:t,backend:n}=e,{s0:r,s1:s}=t,i=n.data.get(r.dataId).values,a=n.data.get(s.dataId).values,o=p.backend_util.assertAndGetBroadcastShape(Array.from(i),Array.from(a));return n.makeTensorInfo([o.length],"int32",Int32Array.from(o))}},Ag=Mp(p.ClipByValue,((e,t)=>{const n=t;return e>n.clipValueMax?n.clipValueMax:e<n.clipValueMin?n.clipValueMin:e})),Rg={kernelName:p.ClipByValue,backendName:"cpu",kernelFunc:Ag},$g={kernelName:p.ComplexAbs,backendName:"cpu",kernelFunc:e=>{const{x:t}=e.inputs,n=e.backend,r=new Float32Array(p.util.sizeFromShape(t.shape)),s=n.data.get(t.dataId),i=s.complexTensorInfos.real,a=s.complexTensorInfos.imag,o=n.data.get(i.dataId).values,u=n.data.get(a.dataId).values;for(let e=0;e<o.length;e++){const t=o[e],n=u[e];r[e]=Math.hypot(t,n)}return n.makeOutput(r,t.shape,"float32")}};function Fg(e){const{inputs:t,backend:n}=e,{input:r}=t,s=n.data.get(r.dataId).complexTensorInfos.imag,i=n.data.get(s.dataId).values;return n.makeTensorInfo(s.shape,s.dtype,i)}const Og={kernelName:p.Imag,backendName:"cpu",kernelFunc:Fg};function Dg(e){const{inputs:t,backend:n,attrs:r}=e,{axis:s}=r,i=p.util.parseAxisParam(s,t[0].shape)[0],a=t.map((e=>e.shape));p.backend_util.assertParamsConsistent(a,i);let o=p.backend_util.computeOutShape(t.map((e=>e.shape)),i);if(0===p.util.sizeFromShape(o))return n.makeTensorInfo(o,t[0].dtype,[]);const u=t.filter((e=>p.util.sizeFromShape(e.shape)>0));if(1===u.length)return yp({inputs:{x:u[0]},backend:n});if("complex64"===u[0].dtype){const e=u.map((e=>vp({inputs:{input:e},backend:n}))),t=u.map((e=>Fg({inputs:{input:e},backend:n}))),r=Dg({inputs:e,backend:n,attrs:{axis:i}}),s=Dg({inputs:t,backend:n,attrs:{axis:i}}),a=fp({inputs:{real:r,imag:s},backend:n});return e.forEach((e=>n.disposeIntermediateTensorInfo(e))),t.forEach((e=>n.disposeIntermediateTensorInfo(e))),n.disposeIntermediateTensorInfo(r),n.disposeIntermediateTensorInfo(s),a}const l=u.map((e=>{const t=p.util.sizeFromShape(e.shape.slice(i));return Xm({inputs:{x:e},backend:n,attrs:{shape:[-1,t]}})})),c=l.map((e=>({vals:n.data.get(e.dataId).values,shape:e.shape})));o=p.backend_util.computeOutShape(l.map((e=>e.shape)),1);const d=1===l[0].shape[0],h=Vp(c,o,t[0].dtype,d),f=p.backend_util.computeOutShape(u.map((e=>e.shape)),i),m=n.makeTensorInfo(f,t[0].dtype,h);return l.forEach((e=>n.disposeIntermediateTensorInfo(e))),m}const Mg={kernelName:p.Concat,backendName:"cpu",kernelFunc:Dg};function Lg(e){const{inputs:t,backend:n,attrs:r}=e,{x:s,filter:i}=t,{strides:a,pad:o,dataFormat:u,dilations:l,dimRoundingMode:c}=r;up([s,i],"conv2d");const d=p.backend_util.convertConv2DDataFormat(u),h=p.backend_util.computeConv2DInfo(s.shape,i.shape,a,l,o,c,!1,d),f=h.filterHeight,m=h.filterWidth,g=h.dilationHeight,y=h.dilationWidth,b=h.padInfo.left,v=h.padInfo.top,x="channelsLast"===h.dataFormat,w=new p.TensorBuffer(h.outShape,s.dtype),k=p.util.computeStrides(s.shape),S=p.util.computeStrides(i.shape),I=k[0],T=x?k[1]:k[2],N=x?k[2]:1,C=x?1:k[1],E=w.strides[0],_=x?w.strides[1]:w.strides[2],A=x?w.strides[2]:1,R=x?1:w.strides[1],$=n.data.get(s.dataId).values,F=n.data.get(i.dataId).values,O=w.values;for(let e=0;e<h.batchSize;++e){const t=e*I,n=e*E;for(let e=0;e<h.outHeight;++e){const r=n+e*_,s=e*h.strideHeight-v;for(let e=0;e<f;++e){const n=s+e*g;if(n<0||n>=h.inHeight)continue;const i=e*S[0],a=t+n*T;for(let e=0;e<h.outWidth;++e){const t=r+e*A,n=e*h.strideWidth-b;for(let e=0;e<m;++e){const r=n+e*y;if(r<0||r>=h.inWidth)continue;const s=a+r*N;let o=i+e*S[1];for(let e=0;e<h.inChannels;++e){const n=$[s+e*C];for(let e=0;e<h.outChannels;++e)O[t+e*R]+=n*F[o+e];o+=h.outChannels}}}}}}return n.makeTensorInfo(w.shape,w.dtype,O)}const zg={kernelName:p.Conv2D,backendName:"cpu",kernelFunc:Lg},Pg={kernelName:p.Conv2DBackpropFilter,backendName:"cpu",kernelFunc:function(e){const{inputs:t,backend:n,attrs:r}=e,{x:s,dy:i}=t,{strides:a,pad:o,dataFormat:u,dimRoundingMode:l,filterShape:c}=r;up([s,i],"conv2dBackpropFilter");const d=p.backend_util.convertConv2DDataFormat(u),h=p.backend_util.computeConv2DInfo(s.shape,c,a,1,o,l,!1,d),{strideHeight:f,strideWidth:m,filterHeight:g,filterWidth:y}=h,b="channelsLast"===h.dataFormat,v=new p.TensorBuffer(h.filterShape,"float32"),x=h.padInfo.left,w=h.padInfo.top,k=n.data.get(s.dataId).values,S=n.data.get(i.dataId).values,I=new p.TensorBuffer(s.shape,s.dtype,k),T=new p.TensorBuffer(i.shape,i.dtype,S);for(let e=0;e<g;++e){const t=Math.max(0,Math.ceil((w-e)/f)),n=Math.min(h.outHeight,(h.inHeight+w-e)/f);for(let r=0;r<y;++r){const s=Math.max(0,Math.ceil((x-r)/m)),i=Math.min(h.outWidth,(h.inWidth+x-r)/m);for(let a=0;a<h.inChannels;++a)for(let o=0;o<h.outChannels;++o){let u=0;for(let l=0;l<h.batchSize;++l)for(let c=t;c<n;++c){const t=e+c*f-w;for(let e=s;e<i;++e){const n=r+e*m-x;u+=b?I.get(l,t,n,a)*T.get(l,c,e,o):I.get(l,a,t,n)*T.get(l,o,c,e)}}v.set(u,e,r,a,o)}}}return n.makeTensorInfo(v.shape,v.dtype,v.values)}},Bg={kernelName:p.Conv2DBackpropInput,backendName:"cpu",kernelFunc:function(e){const{inputs:t,backend:n,attrs:r}=e,{dy:s,filter:i}=t,{inputShape:a,strides:o,pad:u,dataFormat:l,dimRoundingMode:c}=r;up([s,i],"conv2dBackpropInput");const d=p.util.computeStrides(i.shape),h=p.util.computeStrides(s.shape);let f=p.backend_util.convertConv2DDataFormat(l);const m=p.backend_util.computeConv2DInfo(a,i.shape,o,1,u,c,!1,f),g=new p.TensorBuffer(m.inShape,"float32"),y=g.values,b=n.data.get(s.dataId).values,v=n.data.get(i.dataId).values,[x,w,k]=d,{batchSize:S,filterHeight:I,filterWidth:T,inChannels:N,inHeight:C,inWidth:E,outChannels:_,outHeight:A,outWidth:R,strideHeight:$,strideWidth:F}=m;f=m.dataFormat;const O=I-1-m.padInfo.top,D=T-1-m.padInfo.left,M="channelsLast"===f,L=g.strides[0],z=M?g.strides[1]:g.strides[2],P=M?g.strides[2]:1,B=M?1:g.strides[1],V=h[0],W=M?h[1]:h[2],U=M?h[2]:1,G=M?1:h[1];for(let e=0;e<S;++e)for(let t=0;t<N;++t)for(let n=0;n<C;++n){const r=n-O,s=Math.max(0,Math.ceil(r/$)),i=Math.min(A,(I+r)/$);for(let a=0;a<E;++a){const o=a-D,u=Math.max(0,Math.ceil(o/F)),l=Math.min(R,(T+o)/F);let c=0;for(let n=s;n<i;++n){const s=n*$-r;for(let r=u;r<l;++r){const i=V*e+W*n+U*r,a=x*(I-1-s)+w*(T-1-(r*F-o))+k*t;for(let e=0;e<_;++e)c+=b[i+G*e]*v[a+e]}}y[L*e+z*n+P*a+B*t]=c}}return n.makeTensorInfo(g.shape,g.dtype,g.values)}},Vg={kernelName:p.Conv3D,backendName:"cpu",kernelFunc:function(e){const{inputs:t,backend:n,attrs:r}=e,{x:s,filter:i}=t,{strides:a,pad:o,dilations:u}=r;up([s,i],"conv3d");const l=p.backend_util.computeConv3DInfo(s.shape,i.shape,a,u,o),{filterDepth:c,filterHeight:d,filterWidth:h,dilationDepth:f,dilationHeight:m,dilationWidth:g,padInfo:y}=l,b=y.front,v=y.left,x=y.top,w=new p.TensorBuffer(l.outShape,s.dtype),k=n.data.get(s.dataId).values,S=n.data.get(i.dataId).values,I=w.values,T=p.util.computeStrides(s.shape),N=p.util.computeStrides(i.shape);for(let e=0;e<l.batchSize;++e){const t=e*T[0],n=e*w.strides[0];for(let e=0;e<l.outDepth;++e){const r=n+e*w.strides[1],s=e*l.strideDepth-b;for(let e=0;e<c;++e){const n=s+e*f;if(n<0||n>=l.inDepth)continue;const i=e*N[0],a=t+n*T[1];for(let e=0;e<l.outHeight;++e){const t=r+e*w.strides[2],n=e*l.strideHeight-x;for(let e=0;e<d;++e){const r=n+e*m;if(r<0||r>=l.inHeight)continue;const s=i+e*N[1],o=a+r*T[2];for(let e=0;e<l.outWidth;++e){const n=t+e*l.outChannels,r=e*l.strideWidth-v;for(let e=0;e<h;++e){const t=r+e*g;if(t<0||t>=l.inWidth)continue;const i=s+e*N[2],a=o+t*l.inChannels;let u=i;for(let e=0;e<l.inChannels;++e){const t=k[a+e];for(let e=0;e<l.outChannels;++e)I[n+e]+=t*S[u+e];u+=l.outChannels}}}}}}}}return n.makeTensorInfo(w.shape,w.dtype,w.values)}},Wg={kernelName:p.Conv3DBackpropFilterV2,backendName:"cpu",kernelFunc:function(e){const{inputs:t,backend:n,attrs:r}=e,{x:s,dy:i}=t,{strides:a,pad:o,filterShape:u}=r;up([s,i],"conv3dBackpropFilterV2");const l=p.util.computeStrides(s.shape),c=p.util.computeStrides(i.shape),d=p.backend_util.computeConv3DInfo(s.shape,u,a,1,o),h=d.strideDepth,f=d.strideHeight,m=d.strideWidth,g=d.filterDepth,y=d.filterHeight,b=d.filterWidth,v=new p.TensorBuffer(d.filterShape,"float32"),x=v.values,[w,k,S,I]=v.strides,T=n.data.get(i.dataId).values,[N,C,E,_]=c,A=n.data.get(s.dataId).values,[R,$,F,O]=l,D=d.padInfo.front,M=d.padInfo.left,L=d.padInfo.top;for(let 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l=p.util.computeStrides(s.shape),c=p.util.computeStrides(i.shape),d=p.backend_util.computeConv3DInfo(u,i.shape,o,1,a),h=new p.TensorBuffer(d.inShape,"float32"),f=h.values,[m,g,y,b]=h.strides,v=n.data.get(s.dataId).values,[x,w,k,S]=l,I=n.data.get(i.dataId).values,[T,N,C,E]=c,{batchSize:_,filterDepth:A,filterHeight:R,filterWidth:$,inChannels:F,inDepth:O,inHeight:D,inWidth:M,outChannels:L,outDepth:z,outHeight:P,outWidth:B,strideDepth:V,strideHeight:W,strideWidth:U}=d,G=A-1-d.padInfo.front,H=R-1-d.padInfo.top,j=$-1-d.padInfo.left;for(let e=0;e<_;++e)for(let t=0;t<F;++t)for(let n=0;n<O;++n){const r=n-G,s=Math.max(0,Math.ceil(r/V)),i=Math.min(z,(A+r)/V);for(let a=0;a<D;++a){const o=a-H,u=Math.max(0,Math.ceil(o/W)),l=Math.min(P,(R+o)/W);for(let c=0;c<M;++c){const d=c-j,h=Math.max(0,Math.ceil(d/U)),p=Math.min(B,($+d)/U);let _=0;for(let n=s;n<i;++n){const s=n*V-r;for(let r=u;r<l;++r){const i=r*W-o;for(let a=h;a<p;++a){const o=x*e+w*n+k*r+S*a,u=T*(A-1-s)+N*(R-1-i)+C*($-1-(a*U-d))+E*t;for(let e=0;e<L;++e)_+=v[o+e]*I[u+e]}}}f[m*e+g*n+y*a+b*c+t]=_}}}return n.makeTensorInfo(h.shape,h.dtype,h.values)}},Gg=Mp(p.Cos,(e=>Math.cos(e))),Hg={kernelName:p.Cos,backendName:"cpu",kernelFunc:Gg},jg=Mp(p.Cosh,(e=>Math.cosh(e))),qg={kernelName:p.Cosh,backendName:"cpu",kernelFunc:jg},Xg={kernelName:p.CropAndResize,backendName:"cpu",kernelFunc:function(e){const{inputs:t,backend:n,attrs:r}=e,{image:s,boxes:i,boxInd:a}=t,{cropSize:o,method:u,extrapolationValue:l}=r,[c,d,h,f]=s.shape,m=i.shape[0],[g,y]=o,b=(0,p.buffer)([m,g,y,f],"float32"),v=n.data.get(i.dataId).values,x=n.data.get(a.dataId).values,w=n.data.get(s.dataId).values,k=p.util.computeStrides(s.shape),S=p.util.computeStrides(b.shape);for(let e=0;e<m;e++){const t=4*e,n=v[t],r=v[t+1],s=v[t+2],i=v[t+3],a=x[e];if(a>=c)continue;const o=g>1?(s-n)*(d-1)/(g-1):0,p=y>1?(i-r)*(h-1)/(y-1):0;for(let t=0;t<g;t++){const c=g>1?n*(d-1)+t*o:.5*(n+s)*(d-1);if(c<0||c>d-1)for(let n=0;n<y;n++)for(let r=0;r<f;r++){const s=r+n*S[2]+t*S[1]+e*S[0];b.values[s]=l}else if("bilinear"===u){const n=Math.floor(c),s=Math.ceil(c),o=c-n;for(let u=0;u<y;u++){const c=y>1?r*(h-1)+u*p:.5*(r+i)*(h-1);if(c<0||c>h-1){for(let n=0;n<f;n++){const r=n+u*S[2]+t*S[1]+e*S[0];b.values[r]=l}continue}const d=Math.floor(c),m=Math.ceil(c),g=c-d;for(let r=0;r<f;r++){let i=r+d*k[2]+n*k[1]+a*k[0];const l=w[i];i=r+m*k[2]+n*k[1]+a*k[0];const c=w[i];i=r+d*k[2]+s*k[1]+a*k[0];const h=w[i];i=r+m*k[2]+s*k[1]+a*k[0];const p=l+(c-l)*g,f=h+(w[i]-h)*g;i=r+u*S[2]+t*S[1]+e*S[0],b.values[i]=p+(f-p)*o}}}else for(let n=0;n<y;++n){const s=y>1?r*(h-1)+n*p:.5*(r+i)*(h-1);if(s<0||s>h-1){for(let r=0;r<f;r++){const s=r+n*S[2]+t*S[1]+e*S[0];b.values[s]=l}continue}const o=Math.round(s),u=Math.round(c);for(let r=0;r<f;r++){const s=r+o*k[2]+u*k[1]+a*k[0],i=r+n*S[2]+t*S[1]+e*S[0];b.values[i]=w[s]}}}}return n.makeTensorInfo(b.shape,b.dtype,b.values)}},Kg={kernelName:p.Cumprod,backendName:"cpu",kernelFunc:function(e){const{inputs:t,backend:n,attrs:r}=e,{x:s}=t,{axis:i,exclusive:a,reverse:o}=r;up(s,"cumprod");const u=p.backend_util.getAxesPermutation([i],s.shape.length);let l=s;null!=u&&(l=Pf({inputs:{x:s},backend:n,attrs:{perm:u}}));const c=p.backend_util.getInnerMostAxes(1,s.shape.length)[0];if(c!==l.shape.length-1)throw new Error(`backend.cumprod in CPU expects an inner-most axis=${l.shape.length-1} but got axis=${c}`);const d=(0,p.upcastType)(l.dtype,"int32"),h=p.util.makeOnesTypedArray(p.util.sizeFromShape(l.shape),d),f=n.data.get(l.dataId).values,m=l.shape[l.shape.length-1],g=o?(e,t)=>e+m-t-1:(e,t)=>e+t;for(let e=0;e<f.length;e+=m)for(let t=0;t<m;t++){const n=g(e,t);if(0===t)h[n]=a?1:f[n];else{const r=g(e,t-1);h[n]=a?f[r]*h[r]:f[n]*h[r]}}const y=n.makeTensorInfo(l.shape,d,h);if(null!=u){const e=Pf({inputs:{x:y},backend:n,attrs:{perm:p.backend_util.getUndoAxesPermutation(u)}});return n.disposeIntermediateTensorInfo(y),n.disposeIntermediateTensorInfo(l),e}return y}},Yg={kernelName:p.Cumsum,backendName:"cpu",kernelFunc:function(e){const{inputs:t,backend:n,attrs:r}=e,{x:s}=t,{axis:i,exclusive:a,reverse:o}=r;up(s,"cumsum");const u=p.backend_util.getAxesPermutation([i],s.shape.length);let l=s;null!=u&&(l=Pf({inputs:{x:s},backend:n,attrs:{perm:u}}));const c=p.backend_util.getInnerMostAxes(1,s.shape.length)[0];if(c!==l.shape.length-1)throw new Error(`backend.cumsum in CPU expects an inner-most axis=${l.shape.length-1} but got axis=${c}`);const d=(0,p.upcastType)(l.dtype,"int32"),h=p.util.makeZerosTypedArray(p.util.sizeFromShape(l.shape),d),f=n.data.get(l.dataId).values,m=l.shape[l.shape.length-1],g=o?(e,t)=>e+m-t-1:(e,t)=>e+t;for(let e=0;e<f.length;e+=m)for(let t=0;t<m;t++){const n=g(e,t);if(0===t)h[n]=a?0:f[n];else{const r=g(e,t-1);h[n]=a?f[r]+h[r]:f[n]+h[r]}}const y=n.makeTensorInfo(l.shape,d,h);if(null!=u){const e=Pf({inputs:{x:y},backend:n,attrs:{perm:p.backend_util.getUndoAxesPermutation(u)}});return n.disposeIntermediateTensorInfo(y),n.disposeIntermediateTensorInfo(l),e}return y}},Qg={kernelName:p.DenseBincount,backendName:"cpu",kernelFunc:function(e){const{inputs:t,backend:n,attrs:r}=e,{x:s,weights:i}=t,{size:a,binaryOutput:o}=r;if(1===s.shape.length){const e=Ap(n.data.get(s.dataId).values,n.data.get(i.dataId).values,i.dtype,i.shape,a);return n.makeTensorInfo([a],i.dtype,e)}if(2===s.shape.length){const e=Rp(n.bufferSync(s),n.bufferSync(i),a,o);return n.makeTensorInfo(e.shape,i.dtype,e.values)}throw new Error("Error in denseBincount: input must be at most rank 2, but got rank"+s.shape.length+".")}},Zg={kernelName:p.DepthToSpace,backendName:"cpu",kernelFunc:function(e){const{inputs:t,backend:n,attrs:r}=e,{x:s}=t,{blockSize:i,dataFormat:a}=r;p.util.assert("NHWC"===a,(()=>"Only NHWC dataFormat supported on CPU for depthToSpace. Got "+a));const o=s.shape[0],u=s.shape[1],l=s.shape[2],c=s.shape[3],d=u*i,h=l*i,f=c/(i*i),m=n.data.get(s.dataId).values,g=new Float32Array(o*d*h*f);let y=0;for(let e=0;e<o;++e)for(let t=0;t<d;++t){const n=Math.floor(t/i),r=t%i;for(let t=0;t<h;++t){const s=Math.floor(t/i),a=(r*i+t%i)*f;for(let t=0;t<f;++t){const r=t+a+c*(s+l*(n+u*e));g[y++]=m[r]}}}return n.makeTensorInfo([o,d,h,f],s.dtype,g)}};function Jg(e){const{inputs:t,backend:n,attrs:r}=e,{x:s,filter:i}=t,{strides:a,pad:o,dilations:u,dimRoundingMode:l}=r;up([s,i],"depthwiseConv2DNative");const c=p.util.computeStrides(s.shape),d=p.util.computeStrides(i.shape);let h=u;null==h&&(h=[1,1]),p.util.assert(p.backend_util.eitherStridesOrDilationsAreOne(a,h),(()=>`Error in depthwiseConv2d: Either strides or dilations must be 1. Got strides ${a} and dilations '${h}'`));const f=p.backend_util.computeConv2DInfo(s.shape,i.shape,a,h,o,l,!0),{filterHeight:m,filterWidth:g,dilationHeight:y,dilationWidth:b,padInfo:v}=f,x=v.left,w=v.top,k=f.outChannels/f.inChannels,S=new p.TensorBuffer(f.outShape,s.dtype),I=n.data.get(s.dataId).values,T=n.data.get(i.dataId).values,N=S.values;for(let e=0;e<f.batchSize;++e){const t=e*c[0],n=e*S.strides[0];for(let e=0;e<f.outHeight;++e){const r=n+e*S.strides[1],s=e*f.strideHeight-w;for(let e=0;e<m;++e){const n=s+e*y;if(n<0||n>=f.inHeight)continue;const i=e*d[0],a=t+n*c[1];for(let e=0;e<f.outWidth;++e){const t=r+e*S.strides[2],n=e*f.strideWidth-x;for(let e=0;e<g;++e){const r=n+e*b;if(r<0||r>=f.inWidth)continue;const s=i+e*d[1],o=a+r*f.inChannels;let u=t,l=s;for(let e=0;e<f.inChannels;++e){const t=I[o+e];for(let e=0;e<k;++e)N[u+e]+=t*T[l+e];u+=k,l+=k}}}}}}return n.makeTensorInfo(S.shape,S.dtype,S.values)}const ey={kernelName:p.DepthwiseConv2dNative,backendName:"cpu",kernelFunc:Jg},ty={kernelName:p.DepthwiseConv2dNativeBackpropFilter,backendName:"cpu",kernelFunc:function(e){const{inputs:t,backend:n,attrs:r}=e,{x:s,dy:i}=t,{strides:a,dilations:o,pad:u,dimRoundingMode:l,filterShape:c}=r;up([s,i],"depthwiseConv2dNativeBackpropFilter");const d=p.backend_util.computeConv2DInfo(s.shape,c,a,o,u,l,!0),{strideHeight:h,strideWidth:f,filterHeight:m,filterWidth:g}=d,y=new p.TensorBuffer(d.filterShape,"float32"),b=d.padInfo.left,v=d.padInfo.top,x=d.outChannels/d.inChannels,w=n.data.get(s.dataId).values,k=new p.TensorBuffer(s.shape,s.dtype,w),S=n.data.get(i.dataId).values,I=new p.TensorBuffer(i.shape,i.dtype,S);for(let e=0;e<m;++e){const t=Math.max(0,Math.ceil((v-e)/h)),n=Math.min(d.outHeight,(d.inHeight+v-e)/h);for(let r=0;r<g;++r){const s=Math.max(0,Math.ceil((b-r)/f)),i=Math.min(d.outWidth,(d.inWidth+b-r)/f);for(let a=0;a<d.outChannels;++a){const o=Math.trunc(a/x),u=a%x;let l=0;for(let u=0;u<d.batchSize;++u)for(let c=t;c<n;++c){const t=e+c*h-v;for(let e=s;e<i;++e){const n=r+e*f-b;l+=k.get(u,t,n,o)*I.get(u,c,e,a)}}y.set(l,e,r,o,u)}}}return n.makeTensorInfo(y.shape,y.dtype,y.values)}},ny={kernelName:p.DepthwiseConv2dNativeBackpropInput,backendName:"cpu",kernelFunc:function(e){const{inputs:t,backend:n,attrs:r}=e,{dy:s,filter:i}=t,{strides:a,dilations:o,pad:u,dimRoundingMode:l,inputShape:c}=r;up([s,i],"depthwiseConv2DNativeBackpropInput");const d=p.util.computeStrides(s.shape),h=p.util.computeStrides(i.shape),f=p.backend_util.computeConv2DInfo(c,i.shape,a,o,u,l,!0),m=new p.TensorBuffer(f.inShape,"float32"),g=m.values,[y,b,v]=m.strides,x=n.data.get(s.dataId).values,[w,k,S]=d,I=n.data.get(i.dataId).values,[T,N,C]=h,{batchSize:E,filterHeight:_,filterWidth:A,inChannels:R,inHeight:$,inWidth:F,outChannels:O,outHeight:D,outWidth:M,strideHeight:L,strideWidth:z}=f,P=_-1-f.padInfo.top,B=A-1-f.padInfo.left,V=O/R;for(let e=0;e<E;++e)for(let t=0;t<R;++t)for(let n=0;n<$;++n){const r=n-P,s=Math.max(0,Math.ceil(r/L)),i=Math.min(D,(_+r)/L);for(let a=0;a<F;++a){const o=a-B,u=Math.max(0,Math.ceil(o/z)),l=Math.min(M,(A+o)/z);let c=0;for(let n=s;n<i;++n){const s=n*L-r;for(let r=u;r<l;++r){const i=w*e+k*n+S*r,a=T*(_-1-s)+N*(A-1-(r*z-o))+C*t;for(let e=0;e<V;++e)c+=x[i+(t*V+e)]*I[a+e]}}g[y*e+b*n+v*a+t]=c}}return n.makeTensorInfo(m.shape,m.dtype,m.values)}},ry={kernelName:p.Diag,backendName:"cpu",kernelFunc:function(e){const{inputs:t,backend:n}=e,{x:r}=t,s=p.util.sizeFromShape(r.shape),i=n.data.get(r.dataId).values,a=(0,p.buffer)([s,s],r.dtype),o=a.values;for(let e=0;e<i.length;e++)o[e*s+e]=i[e];const u=[...r.shape,...r.shape];return n.makeTensorInfo(u,a.dtype,a.values)}},sy={kernelName:p.Dilation2D,backendName:"cpu",kernelFunc:({inputs:e,backend:t,attrs:n})=>{const{x:r,filter:s}=e,{strides:i,pad:a,dilations:o}=n,u=t,l=u.data.get(r.dataId).values,c=r.shape.length,d=u.data.get(s.dataId).values,h=s.shape.length,{batchSize:f,inHeight:m,inWidth:g,inChannels:y,outHeight:b,outWidth:v,padInfo:x,strideHeight:w,strideWidth:k,filterHeight:S,filterWidth:I,dilationHeight:T,dilationWidth:N,outShape:C}=p.backend_util.computeDilation2DInfo(r.shape,s.shape,i,a,"NHWC",o),E=p.util.sizeFromShape(C),_=C.length,A=p.util.getArrayFromDType(r.dtype,E);for(let e=0;e<f;++e)for(let t=0;t<b;++t){const n=t*w-x.top;for(let i=0;i<v;++i){const a=i*k-x.left;for(let o=0;o<y;++o){let u=Number.MIN_SAFE_INTEGER;for(let t=0;t<S;++t){const i=n+t*T;if(i>=0&&i<m)for(let n=0;n<I;++n){const f=a+n*N;if(f>=0&&f<g){const a=p.util.locToIndex([e,i,f,o],c,p.util.computeStrides(r.shape)),m=p.util.locToIndex([t,n,o],h,p.util.computeStrides(s.shape)),g=l[a]+d[m];g>u&&(u=g)}}}A[p.util.locToIndex([e,t,i,o],_,p.util.computeStrides(C))]=u}}}return{dataId:u.write(p.util.toTypedArray(A,r.dtype),C,r.dtype),shape:C,dtype:r.dtype}}},iy={kernelName:p.Dilation2DBackpropFilter,backendName:"cpu",kernelFunc:({inputs:e,backend:t,attrs:n})=>{const{x:r,filter:s,dy:i}=e,{strides:a,pad:o,dilations:u}=n,l=t,c=p.util.toNestedArray(r.shape,l.data.get(r.dataId).values),d=p.util.toNestedArray(s.shape,l.data.get(s.dataId).values),{batchSize:h,inHeight:f,inWidth:m,inChannels:g,outHeight:y,outWidth:b,padInfo:v,strideHeight:x,strideWidth:w,filterHeight:k,filterWidth:S,dilationHeight:I,dilationWidth:T,outShape:N}=p.backend_util.computeDilation2DInfo(r.shape,s.shape,a,o,"NHWC",u);p.util.assert(i.rank===N.length,(()=>`Error in ${p.Dilation2DBackpropFilter}, dy must have the same rank as output ${N.length}, but got `+i.rank));const 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n.disposeIntermediateTensorInfo(e),n.disposeIntermediateTensorInfo(t),n.disposeIntermediateTensorInfo(i),n.disposeIntermediateTensorInfo(a),n.disposeIntermediateTensorInfo(u),n.disposeIntermediateTensorInfo(l),{real:c,imag:d}}return s}{const e=function(e,t,n){const r=new Float32Array(2*t);for(let s=0;s<t;s++){let i=0,a=0;for(let r=0;r<t;r++){const o=p.backend_util.exponent(s*r,t,n),u=p.backend_util.getComplexWithIndex(e,r);i+=u.real*o.real-u.imag*o.imag,a+=u.real*o.imag+u.imag*o.real}n&&(i/=t,a/=t),p.backend_util.assignToTypedArray(r,i,a,s)}return r}(p.backend_util.mergeRealAndImagArrays(i,a),r,t);return p.backend_util.splitRealAndImagArrays(e)}var o}function Cy(e,t,n,r,s){if(1===n)return{real:e,imag:t};const 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n.disposeIntermediateTensorInfo(m),n.disposeIntermediateTensorInfo(g),n.makeTensorInfo(f.outputShape,v.dtype,v.values)}},My={kernelName:p.IFFT,backendName:"cpu",kernelFunc:function(e){const{inputs:t,backend:n}=e,{input:r}=t,s=p.util.sizeFromShape(r.shape),i=r.shape[r.shape.length-1],a=Xm({inputs:{x:r},backend:n,attrs:{shape:[s/i,i]}}),o=Ty(a,!0,n),u=Xm({inputs:{x:o},backend:n,attrs:{shape:r.shape}});return n.disposeIntermediateTensorInfo(a),n.disposeIntermediateTensorInfo(o),u}},Ly=Mp(p.IsFinite,(e=>Number.isFinite(e)?1:0),"bool"),zy={kernelName:p.IsFinite,backendName:"cpu",kernelFunc:Ly},Py=Mp(p.IsInf,(e=>Math.abs(e)===1/0?1:0),"bool"),By={kernelName:p.IsInf,backendName:"cpu",kernelFunc:Py},Vy=Mp(p.IsNan,(e=>Number.isNaN(e)?1:0),"bool"),Wy={kernelName:p.IsNan,backendName:"cpu",kernelFunc:Vy},Uy={kernelName:p.LinSpace,backendName:"cpu",kernelFunc:function(e){const{backend:t,attrs:n}=e,{start:r,stop:s,num:i}=n,a=bf(r,s,i);return t.makeTensorInfo([a.length],"float32",a)}},Gy=Mp(p.Log1p,(e=>Math.log1p(e))),Hy={kernelName:p.Log1p,backendName:"cpu",kernelFunc:Gy},jy=pp(((e,t)=>e&&t)),qy=Ip(p.LogicalAnd,jy,null,"bool"),Xy={kernelName:p.LogicalAnd,backendName:"cpu",kernelFunc:qy},Ky=Mp(p.LogicalNot,(e=>e?0:1),"bool"),Yy={kernelName:p.LogicalNot,backendName:"cpu",kernelFunc:Ky},Qy=pp(((e,t)=>e||t)),Zy=Ip(p.LogicalOr,Qy,null,"bool"),Jy={kernelName:p.LogicalOr,backendName:"cpu",kernelFunc:Zy},eb={kernelName:p.LRN,backendName:"cpu",kernelFunc:function(e){const{inputs:t,backend:n,attrs:r}=e,{x:s}=t,{depthRadius:i,bias:a,alpha:o,beta:u}=r;up(s,"LRN");const l=s.shape[3],c=l-1,d=n.data.get(s.dataId).values,h=p.util.sizeFromShape(s.shape),f=new Float32Array(h);function m(e){const t=e%l;let n=e-t+Math.max(0,t-i);const r=e-t+Math.min(t+i,c);let s=0;for(;n<=r;n++){const e=d[n];s+=e*e}return s}for(let e=0;e<h;e++){const t=m(e),n=d[e]*Math.pow(a+o*t,-u);f[e]=n}return n.makeTensorInfo(s.shape,s.dtype,f)}},tb={kernelName:p.LRNGrad,backendName:"cpu",kernelFunc:function(e){const{inputs:t,backend:n,attrs:r}=e,{x:s,y:i,dy:a}=t,{depthRadius:o,bias:u,alpha:l,beta:c}=r;up(a,"LRNGrad");const d=p.util.sizeFromShape(a.shape),h=a.shape[3],f=n.data.get(a.dataId).values,m=n.data.get(s.dataId).values,g=n.data.get(i.dataId).values,y=new Float32Array(d),b=d;for(let e=0;e<b;e++){const t=e%h,n=e-t+Math.max(0,t-o),r=e-t+Math.min(h,t+o+1);let s=0;for(let e=n;e<r;e++)s+=Math.pow(m[e],2);s=l*s+u;for(let t=n;t<r;t++){let n=-2*l*c*m[t]*g[e]/s;e===t&&(n+=Math.pow(s,-c)),n*=f[e],y[t]+=n}}return n.makeTensorInfo(a.shape,s.dtype,y)}};function nb(e){const{inputs:t,backend:n,attrs:r}=e,{x:s}=t,{reductionIndices:i,keepDims:a}=r,o=n;let u=s.shape;const l=u.length,c=p.util.parseAxisParam(i,u);let d=c;const h=p.backend_util.getAxesPermutation(d,l);let f=o.data.get(s.dataId).values;if(null!=h){const e=new Array(l);for(let t=0;t<e.length;t++)e[t]=u[h[t]];f=zf(f,u,s.dtype,h,e),d=p.backend_util.getInnerMostAxes(d.length,l),u=e}up(s,"max"),p.backend_util.assertAxesAreInnerMostDims("max",d,l);const[m,g]=p.backend_util.computeOutAndReduceShapes(u,d),y=kf(f,p.util.sizeFromShape(g),m,s.dtype),b=o.write(y,m,s.dtype);let v=m;return a&&(v=p.backend_util.expandShapeToKeepDim(m,c)),{dataId:b,shape:v,dtype:s.dtype}}const rb={kernelName:p.Max,backendName:"cpu",kernelFunc:nb},sb={kernelName:p.MaxPool,backendName:"cpu",kernelFunc:function(e){const{inputs:t,backend:n,attrs:r}=e,{x:s}=t;up(s,"maxPool");const{filterSize:i,strides:a,pad:o,dimRoundingMode:u}=r;p.util.assert(p.backend_util.eitherStridesOrDilationsAreOne(a,1),(()=>`Error in maxPool: Either strides or dilations must be 1. 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c=p.backend_util.computePool3DInfo(i.shape,a,o,1,u,l),d=function(e,t){const n=(0,p.buffer)(t.outShape,"int32"),r=t.strideDepth,s=t.strideHeight,i=t.strideWidth,a=t.dilationDepth,o=t.dilationHeight,u=t.dilationWidth,l=t.effectiveFilterDepth,c=t.effectiveFilterHeight,d=t.effectiveFilterWidth,h=t.padInfo.front,f=t.padInfo.top,m=t.padInfo.left;for(let p=0;p<t.batchSize;++p)for(let g=0;g<t.inChannels;++g)for(let y=0;y<t.outDepth;++y){const b=y*r-h;let v=b;for(;v<0;)v+=a;const x=Math.min(t.inDepth,l+b);for(let r=0;r<t.outHeight;++r){const l=r*s-f;let h=l;for(;h<0;)h+=o;const w=Math.min(t.inHeight,c+l);for(let s=0;s<t.outWidth;++s){const f=s*i-m;let k=f;for(;k<0;)k+=u;const S=Math.min(t.inWidth,d+f);let I=Number.NEGATIVE_INFINITY,T=-1;for(let t=v;t<x;t+=a){const n=t-b;for(let r=h;r<w;r+=o){const s=r-l;for(let i=k;i<S;i+=u){const a=i-f,o=e.get(p,t,r,i,g);o>=I&&(I=o,T=n*c*d+s*c+a)}}}n.set(T,p,y,r,s,g)}}}return n}(n.bufferSync(i),c),h=c.strideDepth,f=c.strideHeight,m=c.strideWidth,g=c.dilationDepth,y=c.dilationHeight,b=c.dilationWidth,v=c.effectiveFilterDepth,x=c.effectiveFilterHeight,w=c.effectiveFilterWidth,k=v-1-c.padInfo.front,S=w-1-c.padInfo.left,I=x-1-c.padInfo.top,T=(0,p.buffer)(i.shape,"float32"),N=n.bufferSync(s);for(let e=0;e<c.batchSize;++e)for(let t=0;t<c.inChannels;++t)for(let n=0;n<c.inDepth;++n)for(let r=0;r<c.inHeight;++r)for(let s=0;s<c.inWidth;++s){const i=n-k,a=r-I,o=s-S;let u=0;for(let n=0;n<v;n+=g){const r=(i+n)/h;if(!(r<0||r>=c.outDepth||Math.floor(r)!==r))for(let s=0;s<x;s+=y){const i=(a+s)/f;if(!(i<0||i>=c.outHeight||Math.floor(i)!==i))for(let a=0;a<w;a+=b){const l=(o+a)/m;if(l<0||l>=c.outWidth||Math.floor(l)!==l)continue;const h=v*x*w-1-d.get(e,r,i,l,t)===n*x*w+s*w+a?1:0;0!==h&&(u+=N.get(e,r,i,l,t)*h)}}}T.set(u,e,n,r,s,t)}return n.makeTensorInfo(T.shape,T.dtype,T.values)}},ob={kernelName:p.MaxPoolGrad,backendName:"cpu",kernelFunc:function(e){const{inputs:t,backend:n,attrs:r}=e,{dy:s,input:i,output:a}=t,o=i;up([i,a],"maxPoolGrad");const{filterSize:u,strides:l,pad:c,dimRoundingMode:d}=r,h=p.backend_util.computePool2DInfo(o.shape,u,l,1,c,d),f=n.data.get(o.dataId).values,m=(0,p.buffer)(h.outShape,o.dtype,xg(f,o.shape,o.dtype,h).values),g=h.strideHeight,y=h.strideWidth,b=h.dilationHeight,v=h.dilationWidth,x=h.effectiveFilterHeight,w=h.effectiveFilterWidth,k=w-1-h.padInfo.left,S=x-1-h.padInfo.top,I=(0,p.buffer)(o.shape,"float32"),T=n.data.get(s.dataId).values,N=(0,p.buffer)(s.shape,"float32",T);for(let e=0;e<h.batchSize;++e)for(let t=0;t<h.inChannels;++t)for(let n=0;n<h.inHeight;++n)for(let r=0;r<h.inWidth;++r){const s=n-S,i=r-k;let a=0;for(let n=0;n<x;n+=b){const r=(s+n)/g;if(!(r<0||r>=h.outHeight||Math.floor(r)!==r))for(let s=0;s<w;s+=v){const o=(i+s)/y;if(o<0||o>=h.outWidth||Math.floor(o)!==o)continue;const u=x*w-1-m.get(e,r,o,t)===n*w+s?1:0;0!==u&&(a+=N.get(e,r,o,t)*u)}}I.set(a,e,n,r,t)}return n.makeTensorInfo(I.shape,I.dtype,I.values)}},ub={kernelName:p.MaxPoolWithArgmax,backendName:"cpu",kernelFunc:({inputs:e,attrs:t,backend:n})=>{const{x:r}=e,{filterSize:s,strides:i,pad:a,includeBatchInIndex:o}=t,u=n;up(r,"MaxPoolWithArgmax");const l=u.data.get(r.dataId).values,c=p.backend_util.computePool2DInfo(r.shape,s,i,[1,1],a),[d,h]=function(e,t,n,r,s){const i=vg(e,0,n,p.util.computeStrides(t),s,"max"),a=xg(e,t,n,s,!0,r);return[i.values,a.values]}(l,r.shape,r.dtype,o,c),f=u.write(d,c.outShape,r.dtype),m=u.write(h,c.outShape,r.dtype);return[{dataId:f,shape:c.outShape,dtype:r.dtype},{dataId:m,shape:c.outShape,dtype:"int32"}]}},lb={kernelName:p.Mean,backendName:"cpu",kernelFunc:function(e){const{inputs:t,backend:n,attrs:r}=e,{x:s}=t,{axis:i,keepDims:a}=r,o=p.util.parseAxisParam(i,s.shape),u=p.backend_util.computeOutAndReduceShapes(s.shape,o)[1],l=p.util.sizeFromShape(u),c=[],d=n.makeTensorInfo([],"float32",new Float32Array([l]));c.push(d);const h=kp({inputs:{x:s},backend:n,attrs:{dtype:"float32"}});c.push(h);const f=Sy({inputs:{a:h,b:d},backend:n});c.push(f);const m=uy({inputs:{x:f},backend:n,attrs:{axis:i,keepDims:a}});return c.forEach((e=>n.disposeIntermediateTensorInfo(e))),m}},cb={kernelName:p.Min,backendName:"cpu",kernelFunc:function(e){const{inputs:t,backend:n,attrs:r}=e,{x:s}=t,{axis:i,keepDims:a}=r;up(s,"min");const o=p.util.parseAxisParam(i,s.shape);let u=o;const l=p.backend_util.getAxesPermutation(u,s.shape.length);let c=s;null!=l&&(c=Pf({inputs:{x:s},backend:n,attrs:{perm:l}}),u=p.backend_util.getInnerMostAxes(u.length,s.shape.length)),p.backend_util.assertAxesAreInnerMostDims("min",u,c.shape.length);const[d,h]=p.backend_util.computeOutAndReduceShapes(c.shape,u),f=p.util.sizeFromShape(h),m=p.util.makeZerosTypedArray(p.util.sizeFromShape(d),c.dtype),g=n.data.get(c.dataId).values;for(let e=0;e<m.length;++e){const t=e*f;let n=g[t];for(let e=0;e<f;++e){const r=g[t+e];(Number.isNaN(r)||r<n)&&(n=r)}m[e]=n}null!=l&&n.disposeIntermediateTensorInfo(c);const y=n.makeTensorInfo(d,c.dtype,m);if(a){const e=Xm({inputs:{x:y},backend:n,attrs:{shape:p.backend_util.expandShapeToKeepDim(d,o)}});return n.disposeIntermediateTensorInfo(y),e}return y}},db={kernelName:p.MirrorPad,backendName:"cpu",kernelFunc:function(e){const{inputs:t,backend:n,attrs:r}=e,{x:s}=t,{paddings:i,mode:a}=r;up(s,"mirrorPad");const o=i.map(((e,t)=>e[0]+s.shape[t]+e[1])),u=i.map((e=>e[0])),l=i.map(((e,t)=>e[0]+s.shape[t])),c="reflect"===a?0:1,d=n.data.get(s.dataId).values,h=s.shape.length,f=p.util.computeStrides(s.shape),m=p.util.sizeFromShape(o),g=o.length,y=p.util.computeStrides(o),b=p.util.getTypedArrayFromDType(s.dtype,m);for(let e=0;e<m;e++){let t=p.util.indexToLoc(e,g,y);for(let e=0;e<g;e++)t[e]<u[e]?t[e]=2*u[e]-t[e]-c:t[e]>=l[e]&&(t[e]=2*(l[e]-1)-t[e]+c);t=t.map(((e,t)=>e-u[t]));const n=p.util.locToIndex(t,h,f);b[e]=d[n]}return{dataId:n.write(b,o,s.dtype),shape:o,dtype:s.dtype}}},hb=pp(((e,t)=>{const n=e%t;return e<0&&t<0||e>=0&&t>=0?n:(n+t)%t})),pb=Ip(p.Mod,hb),fb={kernelName:p.Mod,backendName:"cpu",kernelFunc:pb};function mb(e){const{inputs:t,backend:n,attrs:r}=e,{logits:s}=t,{dim:i}=r,a=s.shape.length;let o=i;if(-1===o&&(o=a-1),o!==a-1)throw Error(`Softmax along a non-last dimension is not yet supported. Logits was rank ${a} and dim was ${o}`);const u=p.util.parseAxisParam([o],s.shape),l=nb({inputs:{x:s},backend:n,attrs:{reductionIndices:u,keepDims:!1}}),c=p.backend_util.expandShapeToKeepDim(l.shape,u),d=Xm({inputs:{x:l},backend:n,attrs:{shape:c}}),h=Em({inputs:{a:s,b:d},backend:n}),f=jp({inputs:{x:h},backend:n}),m=uy({inputs:{x:f},backend:n,attrs:{axis:u,keepDims:!1}}),g=Xm({inputs:{x:m},backend:n,attrs:{shape:c}}),y=Sy({inputs:{a:f,b:g},backend:n});return n.disposeIntermediateTensorInfo(l),n.disposeIntermediateTensorInfo(d),n.disposeIntermediateTensorInfo(h),n.disposeIntermediateTensorInfo(f),n.disposeIntermediateTensorInfo(m),n.disposeIntermediateTensorInfo(g),y}const gb={kernelName:p.Softmax,backendName:"cpu",kernelFunc:mb},yb={kernelName:p.Multinomial,backendName:"cpu",kernelFunc:function(e){const{inputs:t,backend:n,attrs:r}=e,{logits:s}=t,{numSamples:i,seed:a,normalized:o}=r;up(s,"multinomial");const u=o?s:mb({inputs:{logits:s},backend:n,attrs:{dim:-1}}),l=u.shape[0],c=u.shape[1],d=n.data.get(u.dataId).values,h=[l,i],f=p.util.makeZerosTypedArray(p.util.sizeFromShape(h),"int32");for(let e=0;e<l;++e){const t=e*c,n=new Float32Array(c-1);n[0]=d[t];for(let e=1;e<n.length;++e)n[e]=n[e-1]+d[t+e];const r=th.alea(a.toString()),s=e*i;for(let e=0;e<i;++e){const t=r();f[s+e]=n.length;for(let r=0;r<n.length;r++)if(t<n[r]){f[s+e]=r;break}}}return o||n.disposeIntermediateTensorInfo(u),n.makeTensorInfo(h,"int32",f)}},bb=p.kernel_impls.nonMaxSuppressionV3Impl,vb={kernelName:p.NonMaxSuppressionV3,backendName:"cpu",kernelFunc:function(e){const{inputs:t,backend:n,attrs:r}=e,{boxes:s,scores:i}=t,{maxOutputSize:a,iouThreshold:o,scoreThreshold:u}=r;up(s,"NonMaxSuppression");const l=n.data.get(s.dataId).values,c=n.data.get(i.dataId).values,{selectedIndices:d}=bb(l,c,a,o,u);return n.makeTensorInfo([d.length],"int32",new Int32Array(d))}},xb=p.kernel_impls.nonMaxSuppressionV4Impl,wb={kernelName:p.NonMaxSuppressionV4,backendName:"cpu",kernelFunc:function(e){const{inputs:t,backend:n,attrs:r}=e,{boxes:s,scores:i}=t,{maxOutputSize:a,iouThreshold:o,scoreThreshold:u,padToMaxOutputSize:l}=r;up(s,"NonMaxSuppressionPadded");const c=n.data.get(s.dataId).values,d=n.data.get(i.dataId).values,{selectedIndices:h,validOutputs:p}=xb(c,d,a,o,u,l);return[n.makeTensorInfo([h.length],"int32",new Int32Array(h)),n.makeTensorInfo([],"int32",new Int32Array([p]))]}},kb=p.kernel_impls.nonMaxSuppressionV5Impl,Sb={kernelName:p.NonMaxSuppressionV5,backendName:"cpu",kernelFunc:function(e){const{inputs:t,backend:n,attrs:r}=e,{boxes:s,scores:i}=t,{maxOutputSize:a,iouThreshold:o,scoreThreshold:u,softNmsSigma:l}=r;up(s,"NonMaxSuppressionWithScore");const c=n.data.get(s.dataId).values,d=n.data.get(i.dataId).values,h=a,p=o,f=u,m=l,{selectedIndices:g,selectedScores:y}=kb(c,d,h,p,f,m);return[n.makeTensorInfo([g.length],"int32",new Int32Array(g)),n.makeTensorInfo([y.length],"float32",new Float32Array(y))]}},Ib={kernelName:p.OneHot,backendName:"cpu",kernelFunc:function(e){const{inputs:t,backend:n,attrs:r}=e,{indices:s}=t,{dtype:i,depth:a,onValue:o,offValue:u}=r;up(s,"oneHot");const l=p.util.sizeFromShape(s.shape),c=new Float32Array(l*a);c.fill(u);const d=n.data.get(s.dataId).values;for(let e=0;e<l;++e)d[e]>=0&&d[e]<a&&(c[e*a+d[e]]=o);return n.makeTensorInfo([...s.shape,a],i,c)}};function Tb(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=vp({inputs:{input:r},backend:n}),t=Tb({inputs:{x:e},backend:n}),s=Fg({inputs:{input:r},backend:n}),i=Tb({inputs:{x:s},backend:n}),a=fp({inputs:{real:t,imag:i},backend:n});return n.disposeIntermediateTensorInfo(e),n.disposeIntermediateTensorInfo(t),n.disposeIntermediateTensorInfo(s),n.disposeIntermediateTensorInfo(i),a}return _y({backend:n,attrs:{shape:r.shape,value:0,dtype:r.dtype}})}const Nb={kernelName:p.ZerosLike,backendName:"cpu",kernelFunc:Tb},Cb={kernelName:p.OnesLike,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=vp({inputs:{input:s},backend:r}),n=e({inputs:{x:t},backend:r}),i=Fg({inputs:{input:s},backend:r}),a=Tb({inputs:{x:i},backend:r}),o=fp({inputs:{real:n,imag:a},backend:r});return r.disposeIntermediateTensorInfo(t),r.disposeIntermediateTensorInfo(n),r.disposeIntermediateTensorInfo(i),r.disposeIntermediateTensorInfo(a),o}return _y({backend:r,attrs:{shape:s.shape,value:1,dtype:s.dtype}})}};function Eb(e){const{inputs:t,backend:n,attrs:r}=e,{axis:s}=r;if(1===t.length)return xy({inputs:{input:t[0]},backend:n,attrs:{dim:s}});const i=t[0].shape,a=t[0].dtype;t.forEach((e=>{p.util.assertShapesMatch(i,e.shape,"All tensors passed to stack must have matching shapes"),p.util.assert(a===e.dtype,(()=>"All tensors passed to stack must have matching dtypes"))}));const o=[],u=Dg({inputs:t.map((e=>{const t=xy({inputs:{input:e},backend:n,attrs:{dim:s}});return o.push(t),t})),backend:n,attrs:{axis:s}});return o.forEach((e=>n.disposeIntermediateTensorInfo(e))),u}const _b={kernelName:p.Pack,backendName:"cpu",kernelFunc:Eb},Ab={kernelName:p.PadV2,backendName:"cpu",kernelFunc:function(e){const{inputs:t,backend:n,attrs:r}=e,{x:s}=t,{paddings:i,constantValue:a}=r;up(s,"pad");const o=i.map(((e,t)=>e[0]+s.shape[t]+e[1])),u=i.map((e=>e[0])),l=n.data.get(s.dataId).values,c=p.util.sizeFromShape(s.shape),d=s.shape.length,h=p.util.computeStrides(s.shape),f=p.util.sizeFromShape(o),m=o.length,g=p.util.computeStrides(o),y=p.util.getTypedArrayFromDType(s.dtype,f);0!==a&&y.fill(a);for(let e=0;e<c;e++){const 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o=p.util.sizeFromShape(i),u=[[0,0]];u.push(...a);for(let e=1+i.length;e<s.shape.length;++e)u.push([0,0]);const l=Ab.kernelFunc({inputs:{x:s},backend:n,attrs:{paddings:u,constantValue:0}}),c=p.backend_util.getReshaped(l.shape,i,o,!1),d=p.backend_util.getPermuted(c.length,i.length,!1),h=p.backend_util.getReshapedPermuted(l.shape,i,o,!1),f=Xm({inputs:{x:l},backend:n,attrs:{shape:c}}),m=Pf({inputs:{x:f},backend:n,attrs:{perm:d}}),g=Xm({inputs:{x:m},backend:n,attrs:{shape:h}});return n.disposeIntermediateTensorInfo(l),n.disposeIntermediateTensorInfo(f),n.disposeIntermediateTensorInfo(m),g}},pv={kernelName:p.SparseFillEmptyRows,backendName:"cpu",kernelFunc:function(e){const{inputs:t,backend:n}=e,{indices:r,values:s,denseShape:i,defaultValue:a}=t;if(1!==i.shape.length)throw new Error("Dense shape must be a vector, saw:\n        "+i.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, 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n.makeTensorInfo(c,r.dtype,l)}},gv={kernelName:p.SparseSegmentSum,backendName:"cpu",kernelFunc:function(e){const{inputs:t,backend:n}=e,{data:r,indices:s,segmentIds:i}=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!==i.shape.length)throw new Error("Segment ids should be a vector but received shape\n         "+i.shape);if(s.shape[0]!==i.shape[0])throw new Error("segmentIds and indices should have same size.");const a=n.data.get(r.dataId).values,o=n.data.get(s.dataId).values,u=n.data.get(i.dataId).values,[l,c]=cm(a,r.shape,r.dtype,o,u);return n.makeTensorInfo(c,r.dtype,l)}},yv={kernelName:p.SparseToDense,backendName:"cpu",kernelFunc:function(e){const{inputs:t,backend:n,attrs:r}=e,{sparseIndices:s,sparseValues:i,defaultValue:a}=t,{outputShape:o}=r,{sliceRank:u,numUpdates:l,sliceSize:c,strides:d,outputSize:h}=p.backend_util.calculateShapes(i,s,o),f=!1,m=n.bufferSync(s);let g;switch(i.dtype){case"bool":g=tm(m,n.bufferSync(i),o,h,c,l,u,d,Boolean(n.data.get(a.dataId).values[0]),f);break;case"float32":case"int32":g=tm(m,n.bufferSync(i),o,h,c,l,u,d,n.data.get(a.dataId).values[0],f);break;case"string":g=tm(m,n.bufferSync(i),o,h,c,l,u,d,p.util.decodeString(n.data.get(a.dataId).values[0]),f);break;default:throw new Error("Unsupported type "+i.dtype)}return n.makeTensorInfo(o,g.dtype,g.values)}},bv={kernelName:p.SplitV,backendName:"cpu",kernelFunc:function(e){const{inputs:t,backend:n,attrs:r}=e,{x:s}=t,{numOrSizeSplits:i,axis:a}=r,o=p.util.parseAxisParam(a,s.shape)[0],u=p.backend_util.prepareSplitSize(s,i,o),l=new Array(s.shape.length).fill(0),c=s.shape.slice();return u.map((e=>{const t=[...c];t[o]=e;const r=am({inputs:{x:s},backend:n,attrs:{begin:l,size:t}});return l[o]+=e,r}))}},vv={kernelName:p.Square,backendName:"cpu",kernelFunc:({inputs:e,backend:t})=>{const{x:n}=e,r=t;up(n,"square");const s=r.data.get(n.dataId).values,i=new Float32Array(s.length);for(let e=0;e<s.length;++e){const t=s[e];i[e]=t*t}return{dataId:r.write(i,n.shape,n.dtype),shape:n.shape,dtype:n.dtype}}},xv=Mp(p.Step,((e,t)=>{const n=t;return isNaN(e)?NaN:e>0?1:n.alpha})),wv={kernelName:p.Step,backendName:"cpu",kernelFunc:xv},kv={kernelName:p.StridedSlice,backendName:"cpu",kernelFunc:function(e){const{inputs:t,backend:n,attrs:r}=e,{x:s}=t,{begin:i,end:a,strides:o,beginMask:u,endMask:l,ellipsisMask:c,newAxisMask:d,shrinkAxisMask:h}=r;up(s,"stridedSlice");const{finalShapeSparse:f,finalShape:m,isIdentity:g,sliceDim0:y,isSimpleSlice:b,begin:v,end:x,strides:w}=p.slice_util.sliceInfo(s.shape,i,a,o,u,l,c,d,h);let k;if(g)k=Xm({inputs:{x:s},backend:n,attrs:{shape:m}});else if(y||b){p.util.assert(s.shape.length>=1,(()=>"Input must have rank at least 1, got: "+s.shape.length));const e=p.slice_util.computeOutShape(v,x,w),t=am({inputs:{x:s},backend:n,attrs:{begin:v,size:e}});k=Xm({inputs:{x:t},backend:n,attrs:{shape:m}}),n.disposeIntermediateTensorInfo(t)}else{const e=xm(f,n.bufferSync(s),w,v);k=n.makeTensorInfo(m,e.dtype,e.values)}return k}},Sv={kernelName:p.StringNGrams,backendName:"cpu",kernelFunc:function(e){const{inputs:t,backend:n,attrs:r}=e,{separator:s,nGramWidths:i,leftPad:a,rightPad:o,padWidth:u,preserveShortSequences:l}=r,{data:c,dataSplits:d}=t,h=n.data.get(c.dataId).values,p=n.data.get(d.dataId).values,[f,m]=km(h,p,s,i,a,o,u,l);return[n.makeTensorInfo([f.length],"string",f),n.makeTensorInfo(d.shape,"int32",m)]}},Iv={kernelName:p.StringSplit,backendName:"cpu",kernelFunc:function(e){const{inputs:t,backend:n,attrs:r}=e,{skipEmpty:s}=r,{input:i,delimiter:a}=t;if("string"!==i.dtype)throw new Error("Input must be of datatype string");if(1!==i.shape.length)throw new Error("Input must be a vector, got shape: "+i.shape);if(0!==a.shape.length)throw new Error("Delimiter must be a scalar, got shape: "+a.shape);const o=n.data.get(i.dataId).values,u=n.data.get(a.dataId).values[0],[l,c,d]=Im(o,u,s),h=c.length;return[n.makeTensorInfo([h,2],"int32",l),n.makeTensorInfo([h],"string",c),n.makeTensorInfo([2],"int32",new Int32Array(d))]}},Tv={kernelName:p.StringToHashBucketFast,backendName:"cpu",kernelFunc:function(e){const{inputs:t,backend:n,attrs:r}=e,{numBuckets:s}=r,{input:i}=t;if("string"!==i.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 a=Tm(n.data.get(i.dataId).values,s);return n.makeTensorInfo(i.shape,"int32",a)}},Nv=Mp(p.Tan,(e=>Math.tan(e))),Cv={kernelName:p.Tan,backendName:"cpu",kernelFunc:Nv},Ev=Mp(p.Tanh,(e=>Math.tanh(e))),_v={kernelName:p.Tanh,backendName:"cpu",kernelFunc:Ev},Av={kernelName:p.TensorScatterUpdate,backendName:"cpu",kernelFunc:function(e){const{inputs:t,backend:n}=e,{tensor:r,indices:s,updates:i}=t,{sliceRank:a,numUpdates:o,sliceSize:u,strides:l,outputSize:c}=p.backend_util.calculateShapes(i,s,r.shape),d=n.bufferSync(s),h=n.bufferSync(i),f=n.bufferSync(r),m=tm(d,h,r.shape,c,u,o,a,l,f,!1);return n.makeTensorInfo(r.shape,m.dtype,m.values)}},Rv={kernelName:p.Tile,backendName:"cpu",kernelFunc:function(e){const{inputs:t,backend:n,attrs:r}=e,{x:s}=t,{reps:i}=r;up(s,"tile");const a=Am(n.bufferSync(s),i);return n.makeTensorInfo(a.shape,a.dtype,a.values)}},$v={kernelName:p.TopK,backendName:"cpu",kernelFunc:function(e){const{inputs:t,backend:n,attrs:r}=e,{x:s}=t,{k:i,sorted:a}=r;up(s,"topk");const o=n.data.get(s.dataId).values,[u,l]=Fm(o,s.shape,s.dtype,i,a);return[n.makeTensorInfo(u.shape,u.dtype,u.values),n.makeTensorInfo(l.shape,l.dtype,l.values)]}},Fv={kernelName:p.Transform,backendName:"cpu",kernelFunc:function(e){const{inputs:t,attrs:n,backend:r}=e,{image:s,transforms:i}=t,{interpolation:a,fillMode:o,fillValue:u,outputShape:l}=n,[c,d,h,f]=s.shape,[m,g]=null!=l?l:[d,h],y=[c,m,g,f],b=p.util.computeStrides(s.shape),v=b[0],x=b[1],w=b[2],k=p.util.computeStrides(y),S=k[0],I=k[1],T=k[2],N=p.util.getTypedArrayFromDType(s.dtype,p.util.sizeFromShape(y));N.fill(u);const C=r.data.get(s.dataId).values,E=r.data.get(i.dataId).values;for(let e=0;e<c;++e){const t=1===i.shape[0]?E:E.subarray(8*e,8*e+8);for(let n=0;n<m;++n)for(let r=0;r<g;++r)for(let s=0;s<f;++s){let i;const l=t[6]*r+t[7]*n+1;if(0===l)continue;const c=(t[0]*r+t[1]*n+t[2])/l,p=(t[3]*r+t[4]*n+t[5])/l,f=Ov(c,h,o),m=Ov(p,d,o);switch(a){case"nearest":i=Mv(C,d,h,v,x,w,e,m,f,s,u);break;case"bilinear":i=Lv(C,d,h,v,x,w,e,m,f,s,u);break;default:throw new Error("Error in Transform: Expect 'nearest' or 'bilinear', but got "+a)}N[e*S+n*I+r*T+s]=i}return r.makeTensorInfo(y,s.dtype,N)}return{dataId:r.write(N,y,s.dtype),shape:s.shape,dtype:s.dtype}}};function Ov(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 p.util.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 p.util.clamp(0,n,t-1)}(e,t);case"nearest":return function(e,t){return p.util.clamp(0,e,t-1)}(e,t);case"constant":default:return function(e,t){return e}(e)}}function Dv(e,t,n,r,s,i,a,o,u,l,c){return 0<=o&&o<t&&0<=u&&u<n?e[a*r+o*s+u*i+l]:c}function Mv(e,t,n,r,s,i,a,o,u,l,c){return Dv(e,t,n,r,s,i,a,Math.round(o),Math.round(u),l,c)}function Lv(e,t,n,r,s,i,a,o,u,l,c){const d=Math.floor(o),h=Math.floor(u),p=d+1,f=h+1;return(p-o)*((f-u)*Dv(e,t,n,r,s,i,a,d,h,l,c)+(u-h)*Dv(e,t,n,r,s,i,a,d,f,l,c))+(o-d)*((f-u)*Dv(e,t,n,r,s,i,a,p,h,l,c)+(u-h)*Dv(e,t,n,r,s,i,a,p,f,l,c))}const zv={kernelName:p.Unique,backendName:"cpu",kernelFunc:function(e){const{inputs:t,attrs:n,backend:r}=e,{axis:s}=n,{x:i}=t;up(i,"unique");const a=r.data.get(i.dataId).values,{outputValues:o,outputShape:u,indices:l}=Om(a,s,i.shape,i.dtype);return[r.makeTensorInfo(u,i.dtype,o),r.makeTensorInfo([l.length],"int32",l)]}},Pv={kernelName:p.Unpack,backendName:"cpu",kernelFunc:function(e){const{inputs:t,backend:n,attrs:r}=e,{value:s}=t;let{axis:i}=r;i<0&&(i+=s.shape.length);const a=s.shape.length,o=s.shape[i],u=new Array(a-1);let l=0;for(let e=0;e<a;e++)e!==i&&(u[l++]=s.shape[e]);const c=new Array(a).fill(0),d=s.shape.slice();d[i]=1;const h=new Array(o);for(let e=0;e<h.length;e++){c[i]=e;const t=am({inputs:{x:s},backend:n,attrs:{begin:c,size:d}});h[e]=Xm({inputs:{x:t},backend:n,attrs:{shape:u}}),n.disposeIntermediateTensorInfo(t)}return h}},Bv={kernelName:p.UnsortedSegmentSum,backendName:"cpu",kernelFunc:function(e){const{inputs:t,backend:n,attrs:r}=e,{x:s,segmentIds:i}=t,{numSegments:a}=r;up(s,"unsortedSegmentSum");const o=[],u=[],l=s.shape.length-i.shape.length;let c=i;for(let e=0;e<l;

2Kx.registerFlag("HAS_WEBGL",(()=>Kx.getNumber("WEBGL_VERSION")>0)),Kx.registerFlag("WEBGL_VERSION",(()=>Ux(2)?2:Ux(1)?1:0)),Kx.registerFlag("WEBGL_CHECK_NUMERICAL_PROBLEMS",(()=>!1)),Kx.registerFlag("WEBGL_BUFFER_SUPPORTED",(()=>2===Kx.get("WEBGL_VERSION"))),Kx.registerFlag("WEBGL_CPU_FORWARD",(()=>!0)),Kx.registerFlag("WEBGL_FORCE_F16_TEXTURES",(()=>!1)),Kx.registerFlag("WEBGL_PACK",(()=>Kx.getBool("HAS_WEBGL"))),Kx.registerFlag("WEBGL_PACK_NORMALIZATION",(()=>Kx.getBool("WEBGL_PACK"))),Kx.registerFlag("WEBGL_PACK_CLIP",(()=>Kx.getBool("WEBGL_PACK"))),Kx.registerFlag("WEBGL_PACK_DEPTHWISECONV",(()=>Kx.getBool("WEBGL_PACK"))),Kx.registerFlag("WEBGL_PACK_BINARY_OPERATIONS",(()=>Kx.getBool("WEBGL_PACK"))),Kx.registerFlag("WEBGL_PACK_UNARY_OPERATIONS",(()=>Kx.getBool("WEBGL_PACK"))),Kx.registerFlag("WEBGL_PACK_ARRAY_OPERATIONS",(()=>Kx.getBool("WEBGL_PACK"))),Kx.registerFlag("WEBGL_PACK_IMAGE_OPERATIONS",(()=>Kx.getBool("WEBGL_PACK"))),Kx.registerFlag("WEBGL_PACK_REDUCE",(()=>Kx.getBool("WEBGL_PACK"))),Kx.registerFlag("WEBGL_LAZILY_UNPACK",(()=>Kx.getBool("WEBGL_PACK"))),Kx.registerFlag("WEBGL_CONV_IM2COL",(()=>Kx.getBool("WEBGL_PACK"))),Kx.registerFlag("WEBGL_MAX_TEXTURE_SIZE",(()=>Lx(Kx.getNumber("WEBGL_VERSION")))),Kx.registerFlag("WEBGL_MAX_TEXTURES_IN_SHADER",(()=>Bx(Kx.getNumber("WEBGL_VERSION")))),Kx.registerFlag("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION",(()=>{const e=Kx.getNumber("WEBGL_VERSION");return 0===e?0:Vx(e)})),Kx.registerFlag("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE",(()=>Kx.getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION")>0&&!p.device_util.isMobile())),Kx.registerFlag("WEBGL_RENDER_FLOAT32_CAPABLE",(()=>Gx(Kx.getNumber("WEBGL_VERSION")))),Kx.registerFlag("WEBGL_RENDER_FLOAT32_ENABLED",(()=>!Kx.getBool("WEBGL_FORCE_F16_TEXTURES")&&Kx.getBool("WEBGL_RENDER_FLOAT32_CAPABLE"))),Kx.registerFlag("WEBGL_DOWNLOAD_FLOAT_ENABLED",(()=>Hx(Kx.getNumber("WEBGL_VERSION")))),Kx.registerFlag("WEBGL_FENCE_API_ENABLED",(()=>qx(Kx.getNumber("WEBGL_VERSION")))),Kx.registerFlag("WEBGL_SIZE_UPLOAD_UNIFORM",(()=>Kx.getBool("WEBGL_RENDER_FLOAT32_ENABLED")?4:0)),Kx.registerFlag("WEBGL_DELETE_TEXTURE_THRESHOLD",(()=>-1),(e=>{if("number"!=typeof e)throw new Error(`WEBGL_DELETE_TEXTURE_THRESHOLD must be a number but got ${e}.`);if(e<0&&-1!==e)throw new Error(`WEBGL_DELETE_TEXTURE_THRESHOLD must be -1 (indicating never delete) or at least 0, but got ${e}.`)})),Kx.registerFlag("WEBGL_FLUSH_THRESHOLD",(()=>p.device_util.isMobile()?1:-1),(e=>{if("number"!=typeof e)throw new Error("WEBGL_FLUSH_THRESHOLD must be a number but got "+e+".");if(e<0&&-1!==e)throw new Error(`WEBGL_FLUSH_THRESHOLD must be -1 (indicating never manual flush) or at least 0, but got ${e}.`)})),Kx.registerFlag("CPU_HANDOFF_SIZE_THRESHOLD",(()=>128)),Kx.registerFlag("WEBGL_USE_SHAPES_UNIFORMS",(()=>!1)),Kx.registerFlag("TOPK_LAST_DIM_CPU_HANDOFF_SIZE_THRESHOLD",(()=>1e5)),Kx.registerFlag("TOPK_K_CPU_HANDOFF_THRESHOLD",(()=>128)),Kx.registerFlag("WEBGL_EXP_CONV",(()=>!1)),Kx.registerFlag("SOFTWARE_WEBGL_ENABLED",(()=>Kx.getBool("IS_TEST"))),Kx.registerFlag("WEBGL_MAX_SIZE_FOR_NARROW_TEXTURE",(()=>1/0)),Kx.registerFlag("WEBGL_AUTO_SQUARIFY_NARROW_TEXTURE_SHAPE",(()=>!1)),Kx.registerFlag("WEBGL2_ISNAN_CUSTOM",(()=>!1)),Kx.registerFlag("ENGINE_COMPILE_ONLY",(()=>!1));const ew="\n  const float FLOAT_MAX = 1.70141184e38;\n  const float FLOAT_MIN = 1.17549435e-38;\n\n  lowp vec4 encode_float(highp float v) {\n    if (isnan(v)) {\n      return vec4(255, 255, 255, 255);\n    }\n\n    highp float av = abs(v);\n\n    if(av < FLOAT_MIN) {\n      return vec4(0.0, 0.0, 0.0, 0.0);\n    } else if(v > FLOAT_MAX) {\n      return vec4(0.0, 0.0, 128.0, 127.0) / 255.0;\n    } else if(v < -FLOAT_MAX) {\n      return vec4(0.0, 0.0,  128.0, 255.0) / 255.0;\n    }\n\n    highp vec4 c = vec4(0,0,0,0);\n\n    highp float e = floor(log2(av));\n    highp float m = exp2(fract(log2(av))) - 1.0;\n\n    c[2] = floor(128.0 * m);\n    m -= c[2] / 128.0;\n    c[1] = floor(32768.0 * m);\n    m -= c[1] / 32768.0;\n    c[0] = floor(8388608.0 * m);\n\n    highp float ebias = e + 127.0;\n    c[3] = floor(ebias / 2.0);\n    ebias -= c[3] * 2.0;\n    c[2] += floor(ebias) * 128.0;\n\n    c[3] += 128.0 * step(0.0, -v);\n\n    return c / 255.0;\n  }\n",{getBroadcastDims:tw}=p.backend_util;function nw(e,t,n){const r=[];if(e.forEach((e=>{const t=p.util.sizeFromShape(e.shapeInfo.logicalShape);if(e.shapeInfo.isUniform?r.push(`uniform float ${e.name}${t>1?`[${t}]`:""};`):(r.push(`uniform sampler2D ${e.name};`),r.push(`uniform int offset${e.name};`)),n.enableShapeUniforms){const{uniformShape:t}=hw(n.packedInputs,e.shapeInfo.logicalShape,e.shapeInfo.texShape);switch(t.length){case 1:r.push(`uniform int ${e.name}Shape;`);break;case 2:r.push(`uniform ivec2 ${e.name}Shape;`);break;case 3:r.push(`uniform ivec3 ${e.name}Shape;`);break;case 4:r.push(`uniform ivec4 ${e.name}Shape;`)}r.push(`uniform ivec2 ${e.name}TexShape;`)}})),n.enableShapeUniforms){switch(t.logicalShape.length){case 1:r.push("uniform int outShape;");break;case 2:r.push("uniform ivec2 outShape;"),r.push("uniform int outShapeStrides;");break;case 3:r.push("uniform ivec3 outShape;"),r.push("uniform ivec2 outShapeStrides;");break;case 4:r.push("uniform ivec4 outShape;"),r.push("uniform ivec3 outShapeStrides;")}r.push("uniform ivec2 outTexShape;")}

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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;Jv(e,(()=>e.finish())),Jv(e,(()=>e.bindFramebuffer(e.FRAMEBUFFER,null))),Jv(e,(()=>e.deleteFramebuffer(this.framebuffer))),Jv(e,(()=>e.bindBuffer(e.ARRAY_BUFFER,null))),Jv(e,(()=>e.bindBuffer(e.ELEMENT_ARRAY_BUFFER,null))),Jv(e,(()=>e.deleteBuffer(this.indexBuffer))),this.disposed=!0}createFloat32MatrixTexture(e,t){return this.throwIfDisposed(),Aw(this.gl,e,t,this.textureConfig)}createFloat16MatrixTexture(e,t){return this.throwIfDisposed(),$w(this.gl,e,t,this.textureConfig)}createUnsignedBytesMatrixTexture(e,t){return this.throwIfDisposed(),Ow(this.gl,e,t,this.textureConfig)}uploadPixelDataToTexture(e,t){this.throwIfDisposed(),Vw(this.gl,e,t)}uploadDenseMatrixToTexture(e,t,n,r){this.throwIfDisposed(),Bw(this.gl,e,t,n,r,this.textureConfig)}createFloat16PackedMatrixTexture(e,t){return this.throwIfDisposed(),zw(this.gl,e,t,this.textureConfig)}createPackedMatrixTexture(e,t){return this.throwIfDisposed(),Mw(this.gl,e,t,this.textureConfig)}deleteMatrixTexture(e){this.throwIfDisposed(),this.outputTexture===e&&(Ix(this.gl,this.framebuffer),this.outputTexture=null),Jv(this.gl,(()=>this.gl.deleteTexture(e)))}downloadByteEncodedFloatMatrixFromOutputTexture(e,t,n){return this.downloadMatrixDriver(e,(()=>Gw(this.gl,t,n,this.textureConfig)))}downloadPackedMatrixFromBuffer(e,t,n,r,s,i){return Hw(this.gl,e,0,0,0,s,i,this.textureConfig)}downloadFloat32MatrixFromBuffer(e,t){return Uw(this.gl,e,t)}createBufferFromTexture(e,t,n){this.bindTextureToFrameBuffer(e);const r=Ww(this.gl,t,n,this.textureConfig);return this.unbindTextureToFrameBuffer(),r}createAndWaitForFence(){const e=this.createFence(this.gl);return this.pollFence(e)}createFence(e){let t,n;if((0,p.env)().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,p.env)().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION")>0?(t=this.beginQuery(),this.endQuery(),n=()=>this.isQueryAvailable(t,(0,p.env)().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION"))):n=()=>!0;return{query:t,isFencePassed:n}}downloadMatrixFromPackedTexture(e,t,n){return this.downloadMatrixDriver(e,(()=>jw(this.gl,t,n)))}createProgram(e){this.throwIfDisposed();const t=this.gl;null==this.vertexShader&&(this.vertexShader=Tw(t));const n=ox(t);Jv(t,(()=>t.attachShader(n,this.vertexShader))),Jv(t,(()=>t.attachShader(n,e))),ux(t,n);const r=Object.assign(n,{vao:this.createVertexArray()});return this.debug&&lx(t,r),r}buildVao(e){this.setProgram(e),this.bindVertexArray(e.vao);const t=this.gl;Jv(t,(()=>t.bindBuffer(t.ELEMENT_ARRAY_BUFFER,this.indexBuffer))),Pw(t,e,this.vertexBuffer)}deleteProgram(e){this.throwIfDisposed(),e===this.program&&(this.program=null),null!=e&&(Jv(this.gl,(()=>this.gl.deleteProgram(e))),this.deleteVertexArray(e.vao))}setProgram(e){this.throwIfDisposed(),this.program=e,null!=this.program&&this.debug&&lx(this.gl,this.program),Jv(this.gl,(()=>this.gl.useProgram(e)))}getUniformLocation(e,t,n=!0){return this.throwIfDisposed(),n?vx(this.gl,e,t):xx(this.gl,e,t)}getAttributeLocation(e,t){return this.throwIfDisposed(),Jv(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(),wx(this.gl,e,t,n)}setOutputMatrixTexture(e,t,n){this.setOutputMatrixTextureDriver(e,n,t)}setOutputPackedMatrixTexture(e,t,n){this.throwIfDisposed();const[r,s]=Qv(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&&lx(this.gl,this.program),Tx(this.gl)}executeProgram(){this.throwIfDisposed(),this.throwIfNoProgram();const e=this.gl;if(this.debug){const e=this.getVertexArray();console.assert(e===this.program.vao,"VAO changed between setProgram and executeProgram!"),this.debugValidate()}Jv(e,(()=>e.drawElements(e.TRIANGLES,6,e.UNSIGNED_SHORT,0)))}blockUntilAllProgramsCompleted(){this.throwIfDisposed(),Jv(this.gl,(()=>this.gl.finish()))}getQueryTimerExtension(){return null==this.disjointQueryTimerExtension&&(this.disjointQueryTimerExtension=nx(this.gl,2===(0,p.env)().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,p.env)().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,p.env)().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 p.util.repeatedTry((()=>this.disposed||this.isQueryAvailable(e,(0,p.env)().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION")))),this.getQueryTime(e,(0,p.env)().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;if(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&&e[t]();++t);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=void 0;"setTimeoutCustom"in(0,p.env)().platform&&(n=(0,p.env)().platform.setTimeoutCustom.bind((0,p.env)().platform)),p.util.repeatedTry((()=>(this.pollItems(),0===this.itemsToPoll.length)),(()=>0),null,n)}bindTextureToFrameBuffer(e){this.throwIfDisposed(),Sx(this.gl,e,this.framebuffer),this.debug&&Tx(this.gl)}unbindTextureToFrameBuffer(){null!=this.outputTexture?(Sx(this.gl,this.outputTexture,this.framebuffer),this.debug&&Tx(this.gl)):Ix(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;Sx(r,e,this.framebuffer),this.debug&&Tx(r),this.outputTexture=e,Jv(r,(()=>r.viewport(0,0,t,n))),Jv(r,(()=>r.scissor(0,0,t,n)))}setOutputMatrixWriteRegionDriver(e,t,n,r){this.throwIfDisposed(),Jv(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:Xw,bincountImpl:Kw,bincountReduceImpl:Yw,bitwiseAndImpl:Qw,castImpl:Zw,ceilImpl:Jw,concatImpl:ek,equalImpl:tk,expImpl:nk,expm1Impl:rk,floorImpl:sk,gatherNdImpl:ik,gatherV2Impl:ak,greaterImpl:ok,greaterEqualImpl:uk,lessImpl:lk,lessEqualImpl:ck,linSpaceImpl:dk,logImpl:hk,maxImpl:pk,maximumImpl:fk,minimumImpl:mk,multiplyImpl:gk,negImpl:yk,notEqualImpl:bk,prodImpl:vk,raggedGatherImpl:xk,raggedRangeImpl:wk,raggedTensorToTensorImpl:kk,rangeImpl:Sk,rsqrtImpl:Ik,scatterImpl:Tk,sigmoidImpl:Nk,simpleAbsImpl:Ck,sliceImpl:Ek,sparseFillEmptyRowsImpl:_k,sparseReshapeImpl:Ak,sparseSegmentReductionImpl:Rk,sqrtImpl:$k,staticRegexReplaceImpl:Fk,stridedSliceImpl:Ok,stringNGramsImpl:Dk,stringSplitImpl:Mk,stringToHashBucketFastImpl:Lk,subImpl:zk,tileImpl:Pk,topKImpl:Bk,transposeImpl:Vk,uniqueImpl:Wk}=c;function Uk(e,t){return["x","y","z","w","u","v"].slice(0,t).map((t=>`${e}.${t}`))}function Gk(e,t){return 1===t?[e]:Uk(e,t)}class Hk{constructor(e){if(this.variableNames=["A"],this.packedInputs=!1,this.packedOutput=!0,this.outputShape=e,this.rank=e.length,this.enableShapeUniforms=yw(this.outputShape.length),0===this.rank)this.userCode="\n        void main() {\n          setOutput(vec4(getA(), 0., 0., 0.));\n        }\n      ";else{const e=Gk("rc",this.rank),t=dw(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);return 1===this.rank?`getA(rc), (rc + 1 >= ${this.enableShapeUniforms?"outShape":this.outputShape[0]} ? 0. : getA(rc + 1)), 0, 0`:`getA(${t[0]}),\n            cEdge ? 0. : getA(${t[1]}

2move(e,t,n,r,s){if((0,p.env)().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:qv.UPLOAD,refCount:s})}disposeIntermediateTensorInfo(e){this.disposeData(e.dataId)}readSync(e){const t=this.texData.get(e),{values:n,dtype:r,complexTensorInfos:s,slice:i,shape:a,isPacked:o}=t;if(null!=i){let t;t=o?new eS(a,Jk):new Qk(a,Jk);const n=this.runWebGLProgram(t,[{dataId:e,shape:a,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 u=null!=this.activeTimers;let l,c;if(u&&(l=p.util.now()),"complex64"===r){const e=this.readSync(s.real.dataId),t=this.readSync(s.imag.dataId);c=p.backend_util.mergeRealAndImagArrays(e,t)}else c=this.getValuesFromTexture(e);return u&&(this.downloadWaitMs+=p.util.now()-l),this.convertAndCacheOnCPU(e,c)}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:s,dtype:i,complexTensorInfos:a,isPacked:o}=t;if(null!=s){let t;t=o?new eS(r,Jk):new Qk(r,Jk);const n=this.runWebGLProgram(t,[{dataId:e,shape:r,dtype:i}],i),s=this.read(n.dataId);return this.disposeIntermediateTensorInfo(n),s}if(null!=n)return this.convertAndCacheOnCPU(e);if((0,p.env)().getBool("DEBUG")&&!(0,p.env)().getBool("WEBGL_DOWNLOAD_FLOAT_ENABLED")&&2===(0,p.env)().getNumber("WEBGL_VERSION"))throw new Error("tensor.data() with WEBGL_DOWNLOAD_FLOAT_ENABLED=false and WEBGL_VERSION=2 not yet supported.");let u,l,c=null;if("complex64"!==i&&(0,p.env)().get("WEBGL_BUFFER_SUPPORTED")){u=this.decode(e);const t=this.texData.get(u.dataId);c=this.gpgpu.createBufferFromTexture(t.texture.texture,...Yv(r))}if(this.pendingRead.set(e,[]),"complex64"!==i&&await this.gpgpu.createAndWaitForFence(),"complex64"===i){const e=await Promise.all([this.read(a.real.dataId),this.read(a.imag.dataId)]),t=e[0],n=e[1];l=p.backend_util.mergeRealAndImagArrays(t,n)}else if(null==c)l=this.getValuesFromTexture(e);else{const e=p.util.sizeFromShape(r);l=this.gpgpu.downloadFloat32MatrixFromBuffer(c,e)}if(null!=u&&this.disposeIntermediateTensorInfo(u),null!=c){const e=this.gpgpu.gl;Jv(e,(()=>e.deleteBuffer(c)))}const d=this.convertAndCacheOnCPU(e,l),h=this.pendingRead.get(e);return this.pendingRead.delete(e),h.forEach((e=>e(d))),this.pendingDisposal.has(e)&&(this.pendingDisposal.delete(e),this.disposeData(e)&&(0,p.engine)().removeDataId(e,this),this.pendingDeletes--),d}readToGPU(e,t={}){const n=this.texData.get(e),{values:r,shape:s,slice:i,dtype:a,isPacked:o,texture:u}=n;if("complex64"===a)throw new Error("Does not support reading texture for complex64 dtype.");if(null!=i){let n;n=o?new eS(s,Jk):new Qk(s,Jk);const r=this.runWebGLProgram(n,[{dataId:e,shape:s,dtype:a}],a),i=this.readToGPU(r,t);return this.disposeIntermediateTensorInfo(r),i}if(null==u)throw null!=r?new Error("Data is not on GPU but on CPU."):new Error("There is no data on GPU or CPU.");const l=this.decode(e,t.customTexShape),c=(0,p.engine)().makeTensorFromTensorInfo(l),d=this.texData.get(l.dataId);return Object.assign({tensorRef:c},d.texture)}bufferSync(e){const t=this.readSync(e.dataId);if("string"===e.dtype)try{const n=t.map((e=>p.util.decodeString(e)));return(0,p.buffer)(e.shape,e.dtype,n)}catch(e){throw new Error("Failed to decode encoded string bytes into utf-8")}return(0,p.buffer)(e.shape,e.dtype,t)}checkNumericalProblems(e){if(null!=e)for(let t=0;t<e.length;t++){const n=e[t];if(!ex(n)){if((0,p.env)().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 ${n} cannot be represented on this device.`)}}}getValuesFromTexture(e){const{shape:t,dtype:n,isPacked:r}=this.texData.get(e),s=p.util.sizeFromShape(t);if((0,p.env)().getBool("WEBGL_DOWNLOAD_FLOAT_ENABLED")){const n=this.decode(e),r=this.texData.get(n.dataId),i=this.gpgpu.downloadMatrixFromPackedTexture(r.texture.texture,...Yv(t)).subarray(0,s);return this.disposeIntermediateTensorInfo(n),i}const i=(0,p.env)().getBool("WEBGL_PACK")&&!0===r,a=i?Rx(t):t,o=i?new ww(a):new xw(a),u=this.runWebGLProgram(o,[{shape:a,dtype:n,dataId:e}],"float32"),l=this.texData.get(u.dataId),c=this.gpgpu.downloadByteEncodedFloatMatrixFromOutputTexture(l.texture.texture,l.texShape[0],l.texShape[1]).subarray(0,s);return this.disposeIntermediateTensorInfo(u),c}timerAvailable(){return(0,p.env)().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE")>0}time(e){const t=this.activeTimers,n=[];let r=!1;null==this.programTimersStack?(this.programTimersStack=n,r=!0):this.activeTimers.push(n),this.activeTimers=n,e();const s=p.util.flatten(this.activeTimers.map((e=>e.query))).filter((e=>null!=e)),i=p.util.flatten(this.activeTimers.map((e=>e.name))).filter((e=>null!=e));this.activeTimers=t,r&&(this.programTimersStack=null);const a={uploadWaitMs:this.uploadWaitMs,downloadWaitMs:this.downloadWaitMs,kernelMs:null,wallMs:null};return(async()=>{if((0,p.env)().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE")>0){const e=await Promise.all(s);a.kernelMs=p.util.sum(e),a.getExtraProfileInfo=()=>e.map(((e,t)=>({name:i[t],ms:e}))).map((e=>`${e.name}: ${e.ms}`)).join(", ")}else a.kernelMs={error:"WebGL query timers are not supported in this environment."};return this.uploadWaitMs=0,this.downloadWaitMs=0,a})()}memory(){return{unreliable:!1,numBytesInGPU:this.numBytesInGPU,numBytesInGPUAllocated:this.textureManager.numBytesAllocated,numBytesInGPUFree:this.textureManager.numBytesFree}}startTimer(){return(0,p.env)().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE")>0?this.gpgpu.beginQuery():{startMs:p.util.now(),endMs:null}}endTimer(e){return(0,p.env)().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE")>0?(this.gpgpu.endQuery(),e):(e.endMs=p.util.now(),e)}async getQueryTime(e){if((0,p.env)().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:i,slice:a}=this.texData.get(e),o=a&&a.origDataId||e,u=this.dataRefCount.get(o);u>1?this.dataRefCount.set(o,u-1):(this.dataRefCount.delete(o),null!=t&&(this.numBytesInGPU-=this.computeBytes(r,n),this.textureManager.releaseTexture(t,r,s,i)));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=sS){return(0,p.env)().getBool("WEBGL_CPU_FORWARD")&&e.every((e=>null==this.texData.get(e.dataId).texture&&p.util.sizeFromShape(e.shape)<t))}getGPGPUContext(){return this.gpgpu}where(e){p.backend_util.warn("tf.where() in webgl locks the UI thread. Call tf.whereAsync() instead");const t=e.dataSync();return nS(e.shape,t)}packedUnaryOp(e,t,n){const r=new eS(e.shape,t),s=this.compileAndRun(r,[e],n);return(0,p.engine)().makeTensorFromTensorInfo(s)}abs(e){if(this.shouldExecuteOnCPU([e])&&"complex64"!==e.dtype){const t=Ck(this.texData.get(e.dataId).values);return this.makeOutput(e.shape,e.dtype,t)}if((0,p.env)().getBool("WEBGL_PACK_UNARY_OPERATIONS"))return this.packedUnaryOp(e,Zk,e.dtype);const t=new Qk(e.shape,Zk),n=this.compileAndRun(t,[e]);return(0,p.engine)().makeTensorFromTensorInfo(n)}makeTensorInfo(e,t,n){let r;if("string"===t&&null!=n&&n.length>0&&p.util.isString(n[0])){const s=n.map((e=>p.util.encodeString(e)));r=this.write(s,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,p.engine)().makeTensorFromTensorInfo(this.makeTensorInfo(e,t,n),this)}unpackTensor(e){const t=new tS(e.shape);return this.runWebGLProgram(t,[e],e.dtype)}packTensor(e){const t=new Hk(e.shape);return this.runWebGLProgram(t,[e],e.dtype,null,!0)}packedReshape(e,t){const n=[_x(e.shape),...Ax(e.shape)],r={dtype:e.dtype,shape:n,dataId:e.dataId},s=[_x(t),...Ax(t)],i=new jk(s,n),a=[n],o=this.runWebGLProgram(i,[r],e.dtype,a,!0);return{dataId:o.dataId,shape:t,dtype:o.dtype}}decode(e,t){const n=this.texData.get(e),{isPacked:r,shape:s,dtype:i}=n;if(null!=t){const e=p.util.sizeFromShape(s),n=t[0]*t[1]*4;p.util.assert(e<=n,(()=>"customTexShape is too small. Row * Column * 4 should be equal or larger than the size of the tensor data."))}const a=Rx(s);let o;o=r?new vw(a):new bw(a);const u=[null!=t?t:Yv(a)];return{dtype:i,shape:s,dataId:this.runWebGLProgram(o,[{shape:a,dtype:i,dataId:e}],i,u,!0,t).dataId}}runWebGLProgram(e,t,n,r,s=!1,i){const a=this.makeTensorInfo(e.outputShape,n),o=this.texData.get(a.dataId);if(e.packedOutput&&(o.isPacked=!0),e.outPackingScheme===jv.DENSE){const t=null!=i?i:Yv(e.outputShape);o.texShape=t.map((e=>2*e))}if(null!=e.outTexUsage&&(o.usage=e.outTexUsage),0===p.util.sizeFromShape(a.shape))return o.values=p.util.getTypedArrayFromDType(a.dtype,0),a;const u=[],l=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&&p.util.sizeFromShape(t.shape)<=(0,p.env)().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),u.push(t),n=this.texData.get(t.dataId);else if(n.isPacked&&!Ox(n.shape,t.shape)){const e=t,r=t.shape;t.shape=n.shape,t=this.packedReshape(t,r),u.push(t),n=this.texData.get(t.dataId),e.shape=r}return{shape:t.shape,texData:n,isUniform:!1}}));this.uploadToGPU(a.dataId);const c={shape:a.shape,texData:o,isUniform:!1},d=function(e,t,n){let r="";t.concat(n).forEach((t=>{const s=null!=t.texData&&null!=t.texData.slice&&t.texData.slice.flatOffset>0;if(e.enableShapeUniforms&&!t.isUniform){const i=t.texData.texShape,{useSqueezeShape:a,uniformShape:o,keptDims:u}=hw(e.packedInputs,t.shape,i);let l="",c="",d="";if(1===o.length&&e.packedInputs){const e=[Math.ceil(i[0]/2),Math.ceil(i[1]/2)];l=`${e[0]>1}_${e[1]>1}`}else if(2!==o.length||e.packedInputs){if(o.length>2&&!e.packedInputs){const e=p.util.computeStrides(o);d=`${e[0]===i[1]}_${e[e.length-1]===i[1]}`}}else c=`${o[0]>1}_${o[1]>1}`;const h=t.shape.length,f=2===o.length&&p.util.arraysEqual(t.shape,i),m=1===p.util.sizeFromShape(t.shape),g=p.backend_util.getBroadcastDims(t.shape,n.shape),y=!e.packedInputs&&h===n.shape.length&&p.util.arraysEqual(i,n.texData.texShape),b=e.packedInputs||o.length>2?"":`${i[0]>1}_${i[1]>1}`;r+=`${h}_${y}_${a?u:""}_${o.length}_${m}_${g}_${f}_${l}_${c}_${d}_${b}_${s}`}else{const e=t.isUniform?"uniform":t.texData.texShape;r+=`${t.shape}_${e}_${s}`}}));const s=e.userCode;let i=e.constructor.name;return i+="_"+r+"_"+s+(0,p.env)().getNumber("WEBGL_VERSION"),i}(e,l,c),h=this.getAndSaveBinary(d,(()=>function(e,t,n,r){const s=n.map(((e,n)=>{const r={logicalShape:e.shape,texShape:e.isUniform?null:e.texData.texShape,isUniform:e.isUniform,isPacked:!e.isUniform&&e.texData.isPacked,flatOffset:null};return null!=e.texData&&null!=e.texData.slice&&e.texData.slice.flatOffset>0&&(r.flatOffset=e.texData.slice.flatOffset),{name:t.variableNames[n],shapeInfo:r}})),i=s.map((e=>e.shapeInfo)),a={logicalShape:r.shape,texShape:r.texData.texShape,isUniform:!1,isPacked:r.texData.isPacked,flatOffset:null},o=nw(s,a,t),u=sx(e.gl,o),l=e.createProgram(u);return(0,p.env)().get("ENGINE_COMPILE_ONLY")?{program:t,fragmentShader:u,source:o,webGLProgram:l,inShapeInfos:i,outShapeInfo:a,variablesLocations:null,customUniformLocations:null,infLoc:null,nanLoc:null,outShapeLocation:null,outShapeStridesLocation:null,outTexShapeLocation:null}:(e.buildVao(l),Object.assign({program:t,fragmentShader:u,source:o,webGLProgram:l,inShapeInfos:i,outShapeInfo:a},mw(e,t,l)))}(this.gpgpu,e,l,c))),f=null!=this.activeTimers;let m;f&&(m=this.startTimer()),(0,p.env)().get("ENGINE_COMPILE_ONLY")||function(e,t,n,r,s){t.program.enableShapeUniforms||(gw(t.inShapeInfos,n),gw([t.outShapeInfo],[r]));const i=r.texData.texture,a=r.texData.texShape;r.texData.isPacked?e.setOutputPackedMatrixTexture(i.texture,a[0],a[1]):e.setOutputMatrixTexture(i.texture,a[0],a[1]),e.setProgram(t.webGLProgram),e.bindVertexArray(t.webGLProgram.vao),1===(0,p.env)().getNumber("WEBGL_VERSION")&&null!==t.infLoc&&e.gl.uniform1f(t.infLoc,1/0),null!==t.nanLoc&&e.gl.uniform1f(t.nanLoc,NaN);for(let r=0;r<n.length;++r){const s=n[r],{uniform:i,offset:a,shape:o,texShape:u}=t.variablesLocations[r];if(o){const{uniformShape:n}=hw(t.program.packedInputs,s.shape,s.texData.texShape);switch(n.length){case 1:e.gl.uniform1iv(o,new Int32Array(n));break;case 2:e.gl.uniform2iv(o,new Int32Array(n));break;case 3:e.gl.uniform3iv(o,new Int32Array(n));break;case 4:e.gl.uniform4iv(o,new Int32Array(n))}}if(u&&e.gl.uniform2i(u,s.texData.texShape[0],s.texData.texShape[1]),null!=i)if(s.isUniform)if(p.util.sizeFromShape(s.shape)<2)e.gl.uniform1f(i,s.uniformValues[0]);else{let t=s.uniformValues;t instanceof Float32Array||(t=new Float32Array(t)),e.gl.uniform1fv(i,t)}else null!=s.texData.slice&&null!=a&&e.gl.uniform1i(a,s.texData.slice.flatOffset),e.setInputMatrixTexture(s.texData.texture.texture,i,r)}const o=t.outShapeLocation;if(o)switch(r.shape.length){case 1:e.gl.uniform1iv(o,new Int32Array(r.shape));break;case 2:e.gl.uniform2iv(o,new Int32Array(r.shape));break;case 3:e.gl.uniform3iv(o,new Int32Array(r.shape));break;case 4:e.gl.uniform4iv(o,new Int32Array(r.shape))}if(t.outShapeStridesLocation){const n=p.util.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))}}if(t.outTexShapeLocation&&e.gl.uniform2i(t.outTexShapeLocation,r.texData.texShape[0],r.texData.texShape[1]),t.program.customUniforms&&s)for(let n=0;n<t.program.customUniforms.length;++n){const r=t.program.customUniforms[n],i=t.customUniformLocations[n],a=s[n];if("float"===r.type)e.gl.uniform1fv(i,a);else if("vec2"===r.type)e.gl.uniform2fv(i,a);else if("vec3"===r.type)e.gl.uniform3fv(i,a);else if("vec4"===r.type)e.gl.uniform4fv(i,a);else if("int"===r.type)e.gl.uniform1iv(i,a);else if("ivec2"===r.type)e.gl.uniform2iv(i,a);else if("ivec3"===r.type)e.gl.uniform3iv(i,a);else{if("ivec4"!==r.type)throw Error(`uniform type ${r.type} is not supported yet.`);e.gl.uniform4iv(i,a)}}e.executeProgram()}(this.gpgpu,h,l,c,r),u.forEach((e=>this.disposeIntermediateTensorInfo(e))),f&&(m=this.endTimer(m),this.activeTimers.push({name:e.constructor.name,query:this.getQueryTime(m)}));const g=(0,p.env)().getNumber("WEBGL_FLUSH_THRESHOLD");if(g>0){const e=p.util.now();e-this.lastGlFlushTime>g&&(this.gpgpu.gl.flush(),this.lastGlFlushTime=e)}if(!(0,p.env)().getBool("WEBGL_LAZILY_UNPACK")&&o.isPacked&&!1===s){const e=this.unpackTensor(a);return this.disposeIntermediateTensorInfo(a),e}return a}compileAndRun(e,t,n,r,s=!1){return n=n||t[0].dtype,this.runWebGLProgram(e,t,n,r,s)}getAndSaveBinary(e,t){return e in this.binaryCache||(this.binaryCache[e]=t()),this.binaryCache[e]}getTextureManager(){return this.textureManager}dispose(){this.disposed||((0,p.env)().getBool("IS_TEST")||Object.keys(this.binaryCache).forEach((e=>{this.gpgpu.deleteProgram(this.binaryCache[e].webGLProgram),delete this.binaryCache[e]})),this.textureManager.dispose(),null!=this.canvas&&"undefined"!=typeof HTMLCanvasElement&&this.canvas instanceof HTMLCanvasElement?this.canvas.remove():this.canvas=null,this.gpgpuCreatedLocally&&(this.gpgpu.program=null,this.gpgpu.dispose()),this.disposed=!0)}floatPrecision(){return null==this.floatPrecisionValue&&(this.floatPrecisionValue=(0,p.tidy)((()=>{if(!(0,p.env)().get("WEBGL_RENDER_FLOAT32_ENABLED")){const e=(0,p.env)().getBool("DEBUG");(0,p.env)().set("DEBUG",!1);const t=this.abs((0,p.scalar)(1e-8)).dataSync()[0];if((0,p.env)().set("DEBUG",e),t>0)return 32}return 16}))),this.floatPrecisionValue}epsilon(){return 32===this.floatPrecision()?1e-7:1e-4}uploadToGPU(e){const t=this.texData.get(e),{shape:n,dtype:r,values:s,texture:i,usage:a,isPacked:o}=t;if(null!=i)return;const u=null!=this.activeTimers;let l;u&&(l=p.util.now());let c=t.texShape;if(null==c&&(c=$x(n,o),t.texShape=c),null!=s){const e=Rx(n);let i,a=c[1],d=c[0];const h=s instanceof Uint8Array||s instanceof Uint8ClampedArray;!o&&h||([a,d]=Qv(c[0],c[1])),i=o?new Iw(e,h):new Sw(e,h);const f=h?[d,a]:c,m=this.makeTensorInfo(f,r),g=this.texData.get(m.dataId);g.usage=h?qv.PIXELS:qv.UPLOAD,g.texShape=f,this.gpgpu.uploadDenseMatrixToTexture(this.getTexture(m.dataId),a,d,s);const y=[[d,a]],b=!0,v=this.runWebGLProgram(i,[m],r,y,b),x=this.texData.get(v.dataId);t.texShape=x.texShape,t.isPacked=x.isPacked,t.usage=x.usage,(0,p.env)().get("ENGINE_COMPILE_ONLY")?this.disposeData(v.dataId):(t.texture=x.texture,t.values=null,this.texData.delete(v.dataId)),this.disposeIntermediateTensorInfo(m),u&&(this.uploadWaitMs+=p.util.now()-l)}else{const e=this.acquireTexture(c,a,r,o);t.texture=e}}convertAndCacheOnCPU(e,t){const n=this.texData.get(e),{dtype:r}=n;return 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]*p.util.bytesPerElement(t)}checkCompileCompletion(){for(const[,e]of Object.entries(this.binaryCache))this.checkCompletion_(e)}async checkCompileCompletionAsync(){const e=[];if(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(e){throw e}}));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,p.nextFrame)(),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 ax(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.values(this.binaryCache)){this.gpgpu.buildVao(e.webGLProgram);const{variablesLocations:t,customUniformLocations:n,infLoc:r,nanLoc:s,outShapeLocation:i,outShapeStridesLocation:a,outTexShapeLocation:o}=mw(this.gpgpu,e.program,e.webGLProgram);e.variablesLocations=t,e.customUniformLocations=n,e.infLoc=r,e.nanLoc=s,e.outShapeLocation=i,e.outShapeStridesLocation=a,e.outTexShapeLocation=o}}createTensorFromGPUData(e,t,n){e.channels=e.channels||"RGBA";const{texture:r,height:s,width:i,channels:a}=e,o=(0,p.engine)().backend;if(!o.gpgpu.gl.isTexture(r))throw new Error("The texture is invali

2a="-1.0 / 1e-20",o="max");let u=`${t}(${t}(${t}(minMaxValue[0], minMaxValue[1]), minMaxValue[2]), minMaxValue[3])`;"sum"===t?u="sumValue":"prod"===t?u="prodValue":"all"===t?u="allValue":"any"===t&&(u="anyValue");const l=4*Math.floor(n/4),c=n%4;let d=`\n      if (${"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 = ${o}(values, minMaxValue);\n        if (${"min"===t} || ${"max"===t}) {\n          minMaxValue = ${o}(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?(a="1.0",d="\n        bool reducedAllValue = all(values);\n        float floatedReducedAllValue = float(reducedAllValue);\n        allValue = float(allValue >= 1.0 && floatedReducedAllValue >= 1.0);\n      ",h="bvec4"):"any"===t&&(a="0.0",d="\n        bool reducedAnyValue = any(values);\n        float floatedReducedAnyValue = float(reducedAnyValue);\n        anyValue = float(anyValue >= 1.0 || floatedReducedAnyValue >= 1.0);\n      ",h="bvec4");let p="";s%n>0&&(p=`\n        if (inIdx < 0 || inIdx >= ${s}) {\n          return initializationValue;\n        }\n      `),this.userCode=`\n      const float initializationValue = ${a};\n      const vec4 ones = vec4(1.0, 1.0, 1.0, 1.0);\n\n      float getValue(int batch, int inIdx) {\n        ${p}\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 * ${n};\n\n        vec4 minMaxValue = vec4(${a});\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          ${d}\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          ${d}\n        } else if (${2===c}) {\n          ${h} values = ${h}(\n            getValue(batch, inIdx),\n            getValue(batch, inIdx + 1),\n            initializationValue,\n            initializationValue\n          );\n\n          ${d}\n        } else if (${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          ${d}\n        }\n        setOutput(${u});\n      }\n    `}}function OS(e,t,n,r){const s=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=p.backend_util.computeOptimalWindowSize(n);t.push({inSize:n,windowSize:r,outSize:Math.ceil(n/r)})}return t}(e.shape);let i=e;for(let a=0;a<s.length;a++){const{inSize:o,windowSize:u,outSize:l}=s[a];let c,d;c="mean"===n?0===a?new $S({windowSize:u,inSize:o,batchSize:e.shape[0],outSize:l},o):new $S({windowSize:u,inSize:o,batchSize:e.shape[0],outSize:l}):new FS({windowSize:u,inSize:o,batchSize:e.shape[0],outSize:l},n),d=i,i=r.runWebGLProgram(c,[i],t),d.dataId!==e.dataId&&r.disposeIntermediateTensorInfo(d)}return i}class DS{constructor(e,t){this.variableNames=["A"];const n=new Array(e.length);for(let r=0;r<n.length;r++)n[r]=e[t[r]];this.outputShape=n,this.rank=n.length;const r=dw(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 t=0;t<e.length;t++)r[e[t]]=n[t];return r.join()}(t);this.userCode=`\n    void main() {\n      ${r} resRC = getOutputCoords();\n      setOutput(getA(${s}));\n    }\n    `}}class MS{constructor(e,t){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0;const n=new Array(e.length);for(let r=0;r<n.length;r++)n[r]=e[t[r]];if(this.outputShape=n,this.rank=n.length,this.rank>6)throw Error(`Packed transpose for rank ${this.rank} is not yet supported.`);const r=dw(this.rank),s=Uk("rc",this.rank),i=new Array(this.rank);for(let e=0;e<t.length;e++)i[t[e]]=s[e];const a=`vec2(${i.slice(-2).join()})`,o=`++${s[this.rank-1]} < ${n[this.rank-1]}`,u=`getChannel(getA(${i.join()}), ${a})`;this.userCode=`\n    void main() {\n      ${r} rc = getOutputCoords();\n      vec4 result = vec4(0.);\n      result[0] = ${u};\n      if(${o}) {\n        result[1] = ${u};\n      }\n      --${s[this.rank-1]};\n      if(++${s[this.rank-2]} < ${n[this.rank-2]}) {\n        result[2] = ${u};\n        if(${o}) {\n          result[3] = ${u};\n        }\n      }\n      setOutput(result);\n    }\n    `}}function LS(e,t,n){const r=(0,p.env)().getBool("WEBGL_PACK_ARRAY_OPERATIONS")?new MS(e.shape,t):new DS(e.shape,t);return n.runWebGLProgram(r,[e],e.dtype)}function zS(e){const{inputs:t,backend:n,attrs:r}=e,{x:s}=t,{axis:i,keepDims:a}=r;return function(e,t,n,r){const s=t,i=e.shape.length,a=p.util.parseAxisParam(s,e.shape);let o=a;const u=p.backend_util.getAxesPermutation(o,i),l=null!=u;let c=e;l&&(c=LS(e,u,r),o=p.backend_util.getInnerMostAxes(o.length,i)),p.backend_util.assertAxesAreInnerMostDims("sum",o,i);const[d,h]=p.backend_util.computeOutAndReduceShapes(c.shape,o);let f=d;n&&(f=p.backend_util.expandShapeToKeepDim(d,a));const m=p.util.sizeFromShape(h),g=AS({inputs:{x:c},attrs:{shape:[p.util.sizeFromShape(e.shape)/m,m]},backend:r}),y=OS(g,(0,p.sumOutType)(e.dtype),"sum",r),b=AS({inputs:{x:y},attrs:{shape:f},backend:r});return r.disposeIntermediateTensorInfo(g),r.disposeIntermediateTensorInfo(y),l&&r.disposeIntermediateTensorInfo(c),b}(s,i,a,n)}const PS={kernelName:p.Sum,backendName:"webgl",kernelFunc:zS};function BS(e){const{inputs:t,backend:n,attrs:r}=e,{x:s}=t,{perm:i}=r,a=n,o=s.shape.length,u=new Array(o);for(let e=0;e<u.length;e++)u[e]=s.shape[i[e]];let l;if(a.shouldExecuteOnCPU([s])){const e=a.texData.get(s.dataId).values,t=Vk(e,s.shape,s.dtype,i,u);l=a.makeTensorInfo(u,s.dtype),a.texData.get(l.dataId).values=t}else l=LS(s,i,a);return l}const VS={kernelName:p.Transpose,backendName:"webgl",kernelFunc:BS};function WS({a:e,b:t,transposeA:n,transposeB:r,backend:s,bias:i=null,preluActivationWeights:a=null,leakyreluAlpha:o=0,activation:u=null}){const l=e.shape.length,c=t.shape.length,d=n?e.shape[l-2]:e.shape[l-1],h=r?t.shape[c-1]:t.shape[c-2],f=n?e.shape[l-1]:e.shape[l-2],m=r?t.shape[c-2]:t.shape[c-1],g=e.shape.slice(0,-2),y=t.shape.slice(0,-2),b=p.util.sizeFromShape(g),v=p.util.sizeFromShape(y),x=p.broadcast_util.assertAndGetBroadcastShape(e.shape.slice(0,-2),t.shape.slice(0,-2)).concat([f,m]);p.util.assert(d===h,(()=>`Error in matMul: inner shapes (${d}) and (${h}) of Tensors with shapes ${e.shape} and ${t.shape} and transposeA=${n} and transposeB=${r} must match.`));const w=n?[b,d,f]:[b,f,d],k=r?[v,m,h]:[v,h,m],S=AS({inputs:{x:e},backend:s,attrs:{shape:w}}),I=AS({inputs:{x:t},backend:s,attrs:{shape:k}}),T=[S,I],N=Math.max(b,v),C=n?S.shape[1]:S.shape[2],E=null!=i,_=null!=a,A="leakyrelu"===u,R=null!=u?IS(u,!0):null;let $;if((1===f||1===m)&&C>1e3&&!1===(E||_||A||null!=R)){let e=S,t=I;n&&(e=BS({inputs:{x:S},backend:s,attrs:{perm:[0,2,1]}}),T.push(e)),r&&(t=BS({inputs:{x:I},backend:s,attrs:{perm:[0,2,1]}}),T.push(t));const i=1===m;let a=e;1!==m&&(a=AS({inputs:{x:e},backend:s,attrs:{shape:[N,C,1]}}),T.push(a));const o=1===m?2:1;let u=t;i&&(u=AS({inputs:{x:t},backend:s,attrs:{shape:[N,1,C]}}),T.push(u));const l=ES({inputs:{a:a,b:u},backend:s});$=zS({inputs:{x:l},backend:s,attrs:{axis:o,keepDims:!0}}),T.push(l)}else{const u=(0,p.upcastType)(e.dtype,t.dtype),l=new TS(w,k,[N,f,m],n,r,E,R,_,A),c=[S,I];if(null!=i&&c.push(i),_&&c.push(a),A){const e=s.makeTensorInfo([],"float32",p.util.createScalarValue(o,"float32"));c.push(e),T.push(e)}$=s.runWebGLProgram(l,c,u)}const F=AS({inputs:{x:$},backend:s,attrs:{shape:x}});T.push($);for(const e of T)s.disposeIntermediateTensorInfo(e);return F}const US={kernelName:p._FusedMatMul,backendName:"webgl",kernelFunc:function(e){const{inputs:t,backend:n,attrs:r}=e,{a:s,b:i,bias:a,preluActivationWeights:o}=t,{transposeA:u,transposeB:l,activation:c,leakyreluAlpha:d}=r;return WS({a:s,b:i,transposeA:u,transposeB:l,backend:n,bias:a,preluActivationWeights:o,leakyreluAlpha:d,activation:c})}},GS="return abs(x);",HS={kernelName:p.Abs,backendName:"webgl",kernelFunc:function(e){const{inputs:t,backend:n}=e,{x:r}=t;if(n.shouldExecuteOnCPU([r])&&"complex64"!==r.dtype){const e=n.texData.get(r.dataId),t=Ck(e.values);return n.makeTensorInfo(r.shape,r.dtype,t)}let s;return s=(0,p.env)().getBool("WEBGL_PACK_UNARY_OPERATIONS")?new eS(r.shape,GS):new Qk(r.shape,GS),n.runWebGLProgram(s,[r],r.dtype)}},jS=kS({opSnippet:"if (isnan(x)) return x;\n  if (abs(x) > 1.) {\n    return NAN;\n  }\n  return acos(x);\n"}),qS={kernelName:p.Acos,backendName:"webgl",kernelFunc:jS},XS=kS({opSnippet:"if (isnan(x)) return x;\n  if (x < 1.0) return NAN;\nreturn log(x + sqrt(x * x - 1.0));"}),KS={kernelName:p.Acosh,backendName:"webgl",kernelFunc:XS},YS="return a + b;",QS=SS({opSnippet:YS,packedOpSnippet:YS,supportsComplex:!0,cpuKernelImpl:Xw}),ZS={kernelName:p.Add,backendName:"webgl",kernelFunc:QS};class JS{constructor(e,t){this.outputShape=[],this.outputShape=e,this.variableNames=t.map(((e,t)=>"T"+t));const n=[];this.variableNames.forEach((e=>{n.push(`float v${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 = ${r};\n        setOutput(result);\n      }\n    `}}class eI{constructor(e,t){this.outputShape=[],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=e,this.variableNames=t.map(((e,t)=>"T"+t));const n=[];this.variableNames.forEach((e=>{n.push(`vec4 v${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 tI={kernelName:p.AddN,backendName:"webgl",kernelFunc:function e(t){const{inputs:n,backend:r}=t,s=n;if(1===s.length)return hS({inputs:{x:s[0]},backend:r});if(s.length>(0,p.env)().getNumber("WEBGL_MAX_TEXTURES_IN_SHADER")){const t=Math.floor(s.length/2),n=e({inputs:s.slice(0,t),backend:r}),i=e({inputs:s.slice(t),backend:r});return e({inputs:[n,i],backend:r})}const i=s.map((e=>e.dtype)).reduce(((e,t)=>(0,p.upcastType)(e,t))),a=s.map((e=>e.shape)),o=(0,p.env)().getBool("WEBGL_PACK")?new eI(s[0].shape,a):new JS(s[0].shape,a);return r.runWebGLProgram(o,s,i)}},nI={kernelName:p.All,backendName:"webgl",kernelFunc:function(e){const{inputs:t,backend:n,attrs:r}=e,{x:s}=t,{axis:i,keepDims:a}=r,o=s.shape.length,u=p.util.parseAxisParam(i,s.shape);let l=u;const c=p.backend_util.getAxesPermutation(l,o);let d=s;null!=c&&(d=BS({inputs:{x:s},backend:n,attrs:{perm:c}}),l=p.backend_util.getInnerMostAxes(l.length,o)),p.backend_util.assertAxesAreInnerMostDims("all",l,o);const[h,f]=p.backend_util.computeOutAndReduceShapes(d.shape,l),m=AS({inputs:{x:d},backend:n,attrs:{shape:[-1,p.util.sizeFromShape(f)]}}),g=OS(m,m.dtype,"all",n);let y;return y=AS(a?{inputs:{x:g},backend:n,attrs:{shape:p.backend_util.expandShapeToKeepDim(h,u)}}:{inputs:{x:g},backend:n,attrs:{shape:h}}),n.disposeIntermediateTensorInfo(m),n.disposeIntermediateTensorInfo(g),null!=c&&n.disposeIntermediateTensorInfo(d),y}},rI={kernelName:p.Any,backendName:"webgl",kernelFunc:function(e){const{inputs:t,backend:n,attrs:r}=e,{x:s}=t,{axis:i,keepDims:a}=r,o=s.shape.length,u=p.util.parseAxisParam(i,s.shape);let l=u;const c=p.backend_util.getAxesPermutation(l,o);let d=s;null!=c&&(d=BS({inputs:{x:s},backend:n,attrs:{perm:c}}),l=p.backend_util.getInnerMostAxes(l.length,o)),p.backend_util.assertAxesAreInnerMostDims("any",l,o);const[h,f]=p.backend_util.computeOutAndReduceShapes(d.shape,l),m=AS({inputs:{x:d},backend:n,attrs:{shape:[-1,p.util.sizeFromShape(f)]}}),g=OS(m,m.dtype,"any",n);let y;return y=AS(a?{inputs:{x:g},backend:n,attrs:{shape:p.backend_util.expandShapeToKeepDim(h,u)}}:{inputs:{x:g},backend:n,attrs:{shape:h}}),n.disposeIntermediateTensorInfo(m),n.disposeIntermediateTensorInfo(g),null!=c&&n.disposeIntermediateTensorInfo(d),y}};class sI{constructor(e,t,n){this.variableNames=["A"];const{windowSize:r,batchSize:s,outSize:i}=e;n||this.variableNames.push("bestIndicesA"),this.outputShape=[s,i];const a="max"===t?">":"<",o=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 = ${o};\n          float candidate = getA(batch, inIdx);\n          if (candidate ${a} bestValue) {\n            bestValue = candidate;\n            bestIndex = inIdx;\n          }\n        }\n        setOutput(float(bestIndex));\n      }\n    `}}class iI{constructor(e,t,n,r){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,p.util.assert(e.length>2,(()=>`Packed arg${n.charAt(0).toUpperCase()+n.slice(1)} supports only inputs with rank above 2.`));const s=e[e.length-1],i=Math.ceil(s/t);this.outputShape=e.slice(0,-1),i>1&&this.outputShape.push(i),r||this.variableNames.push("bestIndicesA");const a=this.outputShape,o=a.length,u=dw(o),l=Gk("coords",o);let c,d;if(1===i){d=o+1;const e=dw(d);c=`\n        ${e} sourceLocR = ${e}(${l.join()}, 0);\n        ++${l[o-1]};\n        ${e} sourceLocG = ${e}(${l.join()}, 0);\n        ++${l[o-2]};\n        ${e} sourceLocA = ${e}(${l.join()}, 0);\n        --${l[o-1]};\n        ${e} sourceLocB = ${e}(${l.join()}, 0);\n        --${l[o-2]};`}else d=o,c=`\n        ${u} sourceLocR = coords;\n        ++${l[o-1]};\n        ${u} sourceLocG = coords;\n        ++${l[o-2]};\n        ${u} sourceLocA = coords;\n        --${l[o-1]};\n        ${u} sourceLocB = coords;\n        --${l[o-2]};`;const h=["x","y","z","w","u","v"].slice(0,d),f="."+h[d-1],m=h.map((e=>"int "+e)),g=Gk("sourceLocR",d-1).concat("inIdx.r"),y=Gk("sourceLocG",d-1).concat("inIdx.g"),b=Gk("sourceLocB",d-1).concat("inIdx.b"),v=Gk("sourceLocA",d-1).concat("inIdx.a"),x="max"===n?"greaterThan":"lessThan",w=r?"":`\n          inIdx = round(vec4(getBestIndicesAChannel(${g.join()}),\n                             getBestIndicesAChannel(${y.join()}),\n                             getBestIndicesAChannel(${b.join()}),\n                             getBestIndicesAChannel(${v.join()})));`,k=`vec4(\n            getAChannel(${g.join()}),\n            hasNextCol ? getAChannel(${y.join()}) : 0.,\n            hasNextRow ? getAChannel(${b.join()}) : 0.,\n            hasNextRow && hasNextCol ? getAChannel(${v.join()}) : 0.)`,S=r?"":`\n      float getBestIndicesAChannel(${m.join()}) {\n        return getChannel(getBestIndicesA(${h.join()}),\n                                          vec2(${h.slice(-2).join()}));\n      }`;this.userCode=`\n      float getAChannel(${m.join()}) {\n        return getChannel(getA(${h.join()}),\n                               vec2(${h.slice(-2).join()}));\n      }\n      ${S}\n      void main() {\n        ${u} coords = getOutputCoords();\n        bool hasNextCol = ${l[o-1]} < ${a[o-1]-1};\n        bool hasNextRow = ${l[o-2]} < ${a[o-2]-1};\n        ${c}\n        ivec4 srcIdx = ivec4(sourceLocR${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};

2if("complex64"===a){if("complex64"===i.dtype)return hS({inputs:{x:i},backend:r});const t=p.zeros(i.shape),n=e({inputs:{x:i},backend:r,attrs:{dtype:"float32"}}),s=fS({inputs:{real:n,imag:t},backend:r});return t.dispose(),r.disposeIntermediateTensorInfo(n),s}if("complex64"===i.dtype){const t=GI({inputs:{input:i},backend:r}),n=e({inputs:{x:t},backend:r,attrs:{dtype:a}});return r.disposeIntermediateTensorInfo(t),n}if(!p.util.hasEncodingLoss(i.dtype,a)){const e=hS({inputs:{x:i},backend:r});return{dataId:e.dataId,shape:e.shape,dtype:a}}if(r.shouldExecuteOnCPU([i])){const e=r.texData.get(i.dataId).values,[t,n,s]=Zw(e,i.shape,i.dtype,a);return r.makeTensorInfo(t,n,s)}if("int32"===a)return function(e,t){const n=new Qk(e.shape,"return float(int(x));"),r=t.runWebGLProgram(n,[e],"int32");return{dataId:r.dataId,shape:r.shape,dtype:r.dtype}}(i,r);if("bool"===a){const e=r.makeTensorInfo([],"bool",p.util.getTypedArrayFromDType("bool",1)),t=WI({inputs:{a:i,b:e},backend:r});return r.disposeIntermediateTensorInfo(e),t}throw new Error(`Error in Cast: failed to cast ${i.dtype} to ${a}`)}},qI="return ceil(x);",XI=kS({opSnippet:qI,packedOpSnippet:qI,cpuKernelImpl:Jw}),KI={kernelName:p.Ceil,backendName:"webgl",kernelFunc:XI};class YI{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 QI{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 ZI={kernelName:p.ClipByValue,backendName:"webgl",kernelFunc:function(e){const{inputs:t,backend:n,attrs:r}=e,{x:s}=t,{clipValueMin:i,clipValueMax:a}=r;let o;o=(0,p.env)().getBool("WEBGL_PACK_CLIP")?new QI(s.shape):new YI(s.shape);const u=[[i],[a]];return n.runWebGLProgram(o,[s],s.dtype,u)}};class JI{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 eT(e,t){return{dataId:t.dataId,dtype:t.dtype,shape:e.shape}}const tT={kernelName:p.ComplexAbs,backendName:"webgl",kernelFunc:function(e){const{inputs:t,backend:n}=e,{x:r}=t,s=n.texData.get(r.dataId),i=new JI(r.shape),a=[eT(r,s.complexTensorInfos.real),eT(r,s.complexTensorInfos.imag)];return n.runWebGLProgram(i,a,a[0].dtype)}};class nT{constructor(e){this.outputShape=[],this.outputShape=p.backend_util.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 n=1;n<t.length;n++)t[n]=t[n-1]+e[n][1];const n=[`if (yC < ${t[0]}) setOutput(getT0(yR, yC));`];for(let e=1;e<t.length;e++){const r=t[e-1];n.push(`else if (yC < ${t[e]}) setOutput(getT${e}(yR, yC-${r}));`)}const r=t.length,s=t[t.length-1];n.push(`else setOutput(getT${r}(yR, yC-${s}));`),this.userCode=`\n      void main() {\n        ivec2 coords = getOutputCoords();\n        int yR = coords.x;\n        int yC = coords.y;\n\n        ${n.join("\n        ")}\n      }\n    `}}class rT{constructor(e,t){this.packedInputs=!0,this.packedOutput=!0,this.outputShape=[],this.outputShape=p.backend_util.computeOutShape(e,t);const n=this.outputShape,r=n.length,s=dw(r),i=Gk("coords",r),a=["x","y","z","w","u","v"].slice(0,r);this.variableNames=e.map(((e,t)=>"T"+t));const o=new Array(e.length-1);o[0]=e[0][t];for(let n=1;n<o.length;n++)o[n]=o[n-1]+e[n][t];const u=a[t],l=a.slice(-2),c=a.join();let d=`if (${u} < ${o[0]}) {\n        return getChannel(\n            getT0(${c}), vec2(${l.join()}));\n        }`;for(let e=1;e<o.length;e++){const t=o[e-1];d+=`\n        if (${u} < ${o[e]}  && ${u} >= ${o[e-1]}) {\n          return getChannel(\n            getT${e}(${sT(a,u,t)}),\n            vec2(${sT(l,u,t)}));\n        }`}const h=o.length,f=o[o.length-1];d+=`\n        return getChannel(\n          getT${h}(${sT(a,u,f)}),\n          vec2(${sT(l,u,f)}));`,this.userCode=`\n      float getValue(${a.map((e=>"int "+e))}) {\n        ${d}\n      }\n\n      void main() {\n        ${s} coords = getOutputCoords();\n        vec4 result = vec4(getValue(${i}), 0., 0., 0.);\n\n        ${i[r-1]} = ${i[r-1]} + 1;\n        if (${i[r-1]} < ${n[r-1]}) {\n          result.g = getValue(${i});\n        }\n\n        ${i[r-2]} = ${i[r-2]} + 1;\n        if (${i[r-2]} < ${n[r-2]}) {\n          result.a = getValue(${i});\n        }\n\n        ${i[r-1]} = ${i[r-1]} - 1;\n        if (${i[r-2]} < ${n[r-2]} &&\n            ${i[r-1]} < ${n[r-1]}) {\n          result.b = getValue(${i});\n        }\n        setOutput(result);\n      }\n    `}}function sT(e,t,n){const r=e.indexOf(t);return e.map(((e,t)=>t===r?`${e} - ${n}`:e)).join()}function iT(e){const{inputs:t,backend:n}=e,{input:r}=t;return hS({inputs:{x:n.texData.get(r.dataId).complexTensorInfos.imag},backend:n})}const aT={kernelName:p.Imag,backendName:"webgl",kernelFunc:iT};function oT(e,t,n){const r=e[0].dtype;if("complex64"===r){const r=e.map((e=>GI({inputs:{input:e},backend:n}))),s=e.map((e=>iT({inputs:{input:e},backend:n}))),i=oT(r,t,n),a=oT(s,t,n),o=fS({inputs:{real:i,imag:a},backend:n});return r.forEach((e=>n.disposeIntermediateTensorInfo(e))),s.forEach((e=>n.disposeIntermediateTensorInfo(e))),n.disposeIntermediateTensorInfo(i),n.disposeIntermediateTensorInfo(a),o}let s=n.shouldExecuteOnCPU(e);if("string"===r&&(s=!0),s){const s=e.map((e=>{const r=p.util.sizeFromShape(e.shape.slice(t));return AS({inputs:{x:e},backend:n,attrs:{shape:[-1,r]}})})),i=s.map((e=>({vals:n.readSync(e.dataId),shape:e.shape}))),a=p.backend_util.computeOutShape(s.map((e=>e.shape)),1),o=1===s[0].shape[0],u=ek(i,a,r,o),l=p.backend_util.computeOutShape(e.map((e=>e.shape)),t),c=n.makeTensorInfo(l,r,u);return s.forEach((e=>n.disposeIntermediateTensorInfo(e))),c}const i=e.filter((e=>p.util.sizeFromShape(e.shape)>0)),a=(0,p.env)().getBool("WEBGL_PACK_ARRAY_OPERATIONS")&&i[0].shape.length>1;if(1===i.length){const t=a?new Qk(e[0].shape,Jk):new eS(e[0].shape,Jk);return n.runWebGLProgram(t,e,r)}const o=(0,p.env)().getNumber("WEBGL_MAX_TEXTURES_IN_SHADER");if(i.length>o){const e=[];for(let r=0;r<i.length;r+=o){const s=i.slice(r,r+o);e.push(oT(s,t,n))}const r=oT(e,t,n);for(const t of e)n.disposeIntermediateTensorInfo(t);return r}if(a){const e=new rT(i.map((e=>e.shape)),t);return n.runWebGLProgram(e,i,r)}const{tensors2D:u,outShape:l}=function(e,t,n){const r=p.backend_util.computeOutShape(e.map((e=>e.shape)),t);return{tensors2D:e.map((e=>AS({inputs:{x:e},attrs:{shape:[-1,p.util.sizeFromShape(e.shape.slice(t))]},backend:n}))),outShape:r}}(i,t,n),c=new nT(u.map((e=>e.shape))),d=n.runWebGLProgram(c,u,r);u.forEach((e=>n.disposeIntermediateTensorInfo(e)));const h=AS({inputs:{x:d},attrs:{shape:l},backend:n});return n.disposeIntermediateTensorInfo(d),h}function uT(e){const{inputs:t,backend:n,attrs:r}=e,{axis:s}=r,i=p.util.parseAxisParam(s,t[0].shape)[0],a=t.map((e=>e.shape));p.backend_util.assertParamsConsistent(a,i);const o=p.backend_util.computeOutShape(t.map((e=>e.shape)),i);if(0===p.util.sizeFromShape(o))return n.makeTensorInfo(o,t[0].dtype,[]);const u=t.filter((e=>p.util.sizeFromShape(e.shape)>0));return 1===u.length?hS({inputs:{x:u[0]},backend:n}):oT(u,i,n)}const lT={kernelName:p.Concat,backendName:"webgl",kernelFunc:uT};class cT{constructor(e,t=!1,n=null,r=!1,s=!1){this.variableNames=["x","W"],this.outputShape=e.outShape;const i=e.padInfo.top,a=e.padInfo.left,o=e.strideHeight,u=e.strideWidth,l=e.dilationHeight,c=e.dilationWidth,d=e.filterHeight,h=e.filterWidth,p=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 v="",x="";n&&(v=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        `,x="result = activation(result);");const w=t?"result += getBiasAtOutCoords();":"";t&&this.variableNames.push("bias"),r&&this.variableNames.push("preluActivationWeights"),s&&this.variableNames.push("leakyreluAlpha"),this.userCode=`\n      ${v}\n\n      const ivec2 strides = ivec2(${o}, ${u});\n      const ivec2 pads = ivec2(${i}, ${a});\n\n      void main() {\n        ivec4 coords = getOutputCoords();\n        int batch = coords[0];\n        int d2 = coords[${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 < ${d}; 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 < ${p}; d1 += 4) {\n              vec4 wValues = vec4(\n                getW(wR, wC, d1, d2),\n                getW(wR, wC, d1 + 1, d2),\n                getW(wR, wC, d1 + 2, d2),\n                getW(wR, wC, d1 + 3, d2)\n              );\n\n              if (${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, ${p}) *\n                    getW(wR, wC, ${p}, d2);\n              } else {\n                dotProd +=\n                    getX(batch, ${p}, xR, xC) *\n                    getW(wR, wC, ${p}, d2);\n              }\n\n            } else if (${2===f}) {\n              vec2 wValues = vec2(\n                getW(wR, wC, ${p}, d2),\n                getW(wR, wC, ${p} + 1, d2)\n              );\n\n              if (${m}) {\n                vec2 xValues = vec2(\n                  getX(batch, xR, xC, ${p}),\n                  getX(batch, xR, xC, ${p} + 1)\n                );\n                dotProd += dot(xValues, wValues);\n              } else {\n                vec2 xValues = vec2(\n                  getX(batch, ${p}, xR, xC),\n                  getX(batch, ${p} + 1, xR, xC)\n                );\n                dotProd += dot(xValues, wValues);\n              }\n\n            } else if (${3===f}) {\n              vec3 wValues = vec3(\n                getW(wR, wC, ${p}, d2),\n                getW(wR, wC, ${p} + 1, d2),\n                getW(wR, wC, ${p} + 2, d2)\n              );\n\n              if (${m}) {\n                vec3 xValues = vec3(\n                  getX(batch, xR, xC, ${p}),\n                  getX(batch, xR, xC, ${p} + 1),\n                  getX(batch, xR, xC, ${p} + 2)\n                );\n                dotProd += dot(xValues, wValues);\n              } else {\n                vec3 xValues = vec3(\n                  getX(batch, ${p}, xR, xC),\n                  getX(batch, ${p} + 1, xR, xC),\n                  getX(batch, ${p} + 2, xR, xC)\n                );\n                dotProd += dot(xValues, wValues);\n              }\n\n            }\n          }\n        }\n\n        float result = dotProd;\n        ${w}\n        ${x}\n        setOutput(result);\n      }\n    `}}class dT{constructor(e){this.variableNames=["x","W"],this.outputShape=e.outShape;const t=e.padInfo.front,n=e.padInfo.top,r=e.padInfo.left,s=e.strideDepth,i=e.strideHeight,a=e.strideWidth,o=e.dilationDepth,u=e.dilationHeight,l=e.dilationWidth,c=e.filterDepth,d=e.filterHeight,h=e.filterWidth,p=4*Math.floor(e.inChannels/4),f=e.inChannels%4;this.userCode=`\n      const ivec3 strides = ivec3(${s}, ${i}, ${a});\n      const ivec3 pads = ivec3(${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};

2=r,d=p.backend_util.convertConv2DDataFormat(u),h=p.backend_util.computeConv2DInfo(s.shape,i.shape,a,l,o,c,!1,d);let f;if(1!==h.filterHeight||1!==h.filterWidth||1!==h.dilationHeight||1!==h.dilationWidth||1!==h.strideHeight||1!==h.strideWidth||"SAME"!==h.padInfo.type&&"VALID"!==h.padInfo.type)if(h.strideWidth<=2&&"channelsLast"===d&&(0,p.env)().getBool("WEBGL_EXP_CONV")){const e=new hT(h),t=[[h.padInfo.top,h.padInfo.left],[h.strideHeight,h.strideWidth],[h.dilationHeight,h.dilationWidth],[h.inHeight,h.inWidth]];f=n.runWebGLProgram(e,[s,i],"float32",t)}else if((0,p.env)().getBool("WEBGL_CONV_IM2COL"))f=gT({x:s,filter:i,convInfo:h,backend:n});else{const e=new cT(h);f=n.runWebGLProgram(e,[s,i],"float32")}else f=mT({x:s,filter:i,convInfo:h,backend:n});const m=AS({inputs:{x:f},backend:n,attrs:{shape:h.outShape}});return n.disposeIntermediateTensorInfo(f),m}};class bT{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,i="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              ${i?"float dyValue = getDy(b, yR, yC, d2);\n              float xValue = getX(b, xR, xC, d1);\n              dotProd += (xValue * dyValue);":"float dyValue = getDy(b, d2, yR, yC);\n              float xValue = getX(b, d1, xR, xC);\n              dotProd += (xValue * dyValue);"}\n            }\n          }\n        }\n        setOutput(dotProd);\n      }\n    `}}class vT{constructor(e){this.variableNames=["dy","W"],this.outputShape=e.inShape;const t=e.filterHeight,n=e.filterWidth,r=e.strideHeight,s=e.strideWidth,i="channelsLast"===e.dataFormat,a=t-1-e.padInfo.top,o=n-1-e.padInfo.left,u=i?1:2,l=i?2:3,c=i?3:1;this.userCode=`\n      const ivec2 pads = ivec2(${a}, ${o});\n\n      void main() {\n        ivec4 coords = getOutputCoords();\n        int batch = coords[0];\n        int d1 = coords[${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 < ${e.outChannels}; d2++) {\n\n              if (${i}) {\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 xT{constructor(e){this.variableNames=["x","dy"],this.outputShape=e.filterShape;const t=e.strideDepth,n=e.strideHeight,r=e.strideWidth,s=e.padInfo.front,i=e.padInfo.top,a=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};

2=r,d=p.backend_util.convertConv2DDataFormat(l),h=p.backend_util.computeConv2DInfo(a,i.shape,o,1,u,c,!1,d);if((0,p.env)().getBool("WEBGL_PACK")&&"channelsLast"===d){const e=[[h.strideHeight,h.strideWidth]],t=new ST(h);return n.runWebGLProgram(t,[s,i],"float32",e)}{const e=new vT(h);return n.runWebGLProgram(e,[s,i],"float32")}}},TT={kernelName:p.Conv3D,backendName:"webgl",kernelFunc:function(e){const{inputs:t,backend:n,attrs:r}=e,{x:s,filter:i}=t,{strides:a,pad:o,dilations:u}=r,l=p.backend_util.computeConv3DInfo(s.shape,i.shape,a,u,o),c=new dT(l);return n.runWebGLProgram(c,[s,i],"float32")}},NT={kernelName:p.Conv3DBackpropFilterV2,backendName:"webgl",kernelFunc:function(e){const{inputs:t,backend:n,attrs:r}=e,{x:s,dy:i}=t,{strides:a,pad:o,filterShape:u}=r,l=p.backend_util.computeConv3DInfo(s.shape,u,a,1,o),c=new xT(l);return n.runWebGLProgram(c,[s,i],"float32")}},CT={kernelName:p.Conv3DBackpropInputV2,backendName:"webgl",kernelFunc:function(e){const{inputs:t,backend:n,attrs:r}=e,{dy:s,filter:i}=t,{pad:a,strides:o,inputShape:u}=r,l=p.backend_util.computeConv3DInfo(u,i.shape,o,1,a),c=new wT(l);return n.runWebGLProgram(c,[s,i],"float32")}},ET=kS({opSnippet:"if (isnan(x)) return x;\n  return cos(x);\n",packedOpSnippet:`\n  vec4 result = cos(x);\n  bvec4 isNaN = isnan(x);\n  ${cS}\n  return result;\n`}),_T={kernelName:p.Cos,backendName:"webgl",kernelFunc:ET},AT=kS({opSnippet:"\n  float e2x = exp(-x);\n  return (e2x + 1.0 / e2x) / 2.0;\n"}),RT={kernelName:p.Cosh,backendName:"webgl",kernelFunc:AT};class $T{constructor(e,t,n,r,s){this.variableNames=["Image","Boxes","BoxInd"],this.outputShape=[];const[i,a,o,u]=e,[l]=t,[c,d]=n;this.outputShape=[l,c,d,u];const h="bilinear"===r?1:0,[p,f]=[a-1+".0",o-1+".0"],[m,g,y]=c>1?[""+(a-1)/(c-1),"(y2-y1) * height_ratio",`y1*${p} + float(y)*(height_scale)`]:["0.0","0.0","0.5 * (y1+y2) * "+p],[b,v,x]=d>1?[""+(o-1)/(d-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 >= ${i}) {\n          return;\n        }\n\n        float height_scale = ${g};\n        float width_scale = ${v};\n\n        float in_y = ${y};\n        if( in_y < 0.0 || in_y > ${p} ) {\n          setOutput(float(${s}));\n          return;\n        }\n        float in_x = ${x};\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 FT={kernelName:p.CropAndResize,backendName:"webgl",kernelFunc:e=>{const{inputs:t,backend:n,attrs:r}=e,{image:s,boxes:i,boxInd:a}=t,{cropSize:o,method:u,extrapolationValue:l}=r,c=new $T(s.shape,i.shape,o,u,l);return n.runWebGLProgram(c,[s,i,a],"float32")}};var OT;!function(e){e.Prod="*",e.Sum="+"}(OT||(OT={}));class DT{constructor(e,t,n,r){this.op=e,this.outputShape=t,this.variableNames=["x"],this.customUniforms=[{name:"index",type:"float"}];const s=this.outputShape.length,i=this.op===OT.Prod?"1.0":"0.0",a=n?i:`getX(${MT(s,"coords",this.op)})`,o=this.outputShape[this.outputShape.length-1];let u="",l="";n?(u=r?"end != "+(o-1):"end != 0",l=r?"end + 1":"end - 1"):(u=r?"end + pow2 < "+o:"end >= pow2",l=r?"end + pow2":"end - pow2"),this.userCode=`\n      void main() {\n        ${dw(s)} coords = getOutputCoords();

2getHeightCoordString(){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 UT={kernelName:p.DepthToSpace,backendName:"webgl",kernelFunc:function(e){const{inputs:t,backend:n,attrs:r}=e,{x:s}=t,{blockSize:i,dataFormat:a}=r,o=s.shape[0],u=("NHWC"===a?s.shape[1]:s.shape[2])*i,l=("NHWC"===a?s.shape[2]:s.shape[3])*i,c=("NHWC"===a?s.shape[3]:s.shape[1])/(i*i),d=new WT("NHWC"===a?[o,u,l,c]:[o,c,u,l],i,a);return n.runWebGLProgram(d,[s],s.dtype)}};class GT{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=yw(this.outputShape.length);const i=e.filterHeight,a=e.filterWidth,o=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 / ${o};\n        int q = d2 - d1 * ${o};\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 < ${i}; 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 < ${a}; 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        ${l}\n        setOutput(result);\n      }\n    `}}class HT{constructor(e,t=!1,n=null,r=!1,s=!1){this.variableNames=["x","W"],this.packedInputs=!0,this.packedOutput=!0,this.customUniforms=[{name:"pads",type:"ivec2"},{name:"strides",type:"ivec2"},{name:"dilations",type:"ivec2"},{name:"inDims",type:"ivec2"}],this.outputShape=e.outShape,this.enableShapeUniforms=yw(this.outputShape.length);const i=e.outChannels/e.inChannels,a=e.padInfo.left,o=e.strideWidth,u=e.dilationWidth,l=e.filterHeight,c=e.filterWidth,d=c;let h="\n      int xR; int xC; int xCOffset;\n      vec4 wTexel; vec4 previous; vec4 final;";for(let e=0;e<c;e++)h+=`\n          vec4 xTexelC${2*e};\n          int xTexelC${2*e}Ready;\n          vec4 xTexelC${2*e+1};\n          int xTexelC${2*e+1}Ready;\n          vec4 xC${e};`;h+=`\n    for (int r = 0; r < ${l}; r++) {\n      `;for(let e=0;e<c;e++)h+=`\n          xTexelC${2*e} = vec4(0.0);\n          xTexelC${2*e}Ready = 0;\n          xTexelC${2*e+1} = vec4(0.0);\n          xTexelC${2*e+1}Ready = 0;\n          xC${e} = vec4(0.0);`;h+="\n        xR = xRCorner + r * dilations[0];\n        if (xR >=0 && xR < inDims[0]) {\n      ";for(let e=0;e<(d+1)/2;e++){const t=2*e;if(h+=`\n          xC = xCCorner + ${t*u};\n          `,1===o){if(t<c&&(a%2==1?(h+=`\n                xCOffset = xC + 1;\n                if (xCOffset >= 0 && xCOffset < inDims[1] && xTexelC${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=a%2==0?p.util.nearestLargerEven(u):u;u%2==0&&a%2==1||u%2!=0&&a%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);

2]];return n.runWebGLProgram(h,[s,i],"float32",f)}};class qT{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,i=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 * ${i} + 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 >= ${e.inWidth}) {\n                continue;\n              }\n\n              float dyValue = getDy(b, yR, yC, d2);\n              float xValue = getX(b, xR, xC, d1);\n              dotProd += (xValue * dyValue);\n            }\n          }\n        }\n        setOutput(dotProd);\n      }\n    `}}class XT{constructor(e){this.variableNames=["dy","W"],this.outputShape=e.inShape;const t=e.filterHeight,n=e.filterWidth,r=e.strideHeight,s=e.strideWidth,i=t-1-e.padInfo.top,a=n-1-e.padInfo.left,o=e.outChannels/e.inChannels;this.userCode=`\n      const ivec2 pads = ivec2(${i}, ${a});\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 < ${o}; dm++) {\n              int d2 = d1 * ${o} + dm;\n              float xValue = getDy(batch, idyR, idyC, d2);\n              float wValue = getW(wRPerm, wCPerm, d1, dm);\n              dotProd += xValue * wValue;\n            }\n          }\n        }\n        setOutput(dotProd);\n      }\n    `}}const KT={kernelName:p.DepthwiseConv2dNativeBackpropFilter,backendName:"webgl",kernelFunc:function(e){const{inputs:t,backend:n,attrs:r}=e,{x:s,dy:i}=t,{strides:a,dilations:o,pad:u,dimRoundingMode:l,filterShape:c}=r,d=p.backend_util.computeConv2DInfo(s.shape,c,a,o,u,l,!0),h=new qT(d);return n.runWebGLProgram(h,[s,i],"float32")}},YT={kernelName:p.DepthwiseConv2dNativeBackpropInput,backendName:"webgl",kernelFunc:function(e){const{inputs:t,backend:n,attrs:r}=e,{dy:s,filter:i}=t,{strides:a,dilations:o,pad:u,dimRoundingMode:l,inputShape:c}=r,d=p.backend_util.computeConv2DInfo(c,i.shape,a,o,u,l,!0),h=new XT(d);return n.runWebGLProgram(h,[s,i],"float32")}};class QT{constructor(e){this.variableNames=["X"],this.outputShape=[e,e],this.userCode="\n      void main() {\n          ivec2 coords = getOutputCoords();\n          float val = coords[0] == coords[1] ? getX(coords[0]) : 0.0;\n          setOutput(val);\n      }\n    "}}const ZT={kernelName:p.Diag,backendName:"webgl",kernelFunc:function(e){const{inputs:t,backend:n}=e,{x:r}=t,s=[...r.shape,...r.shape],i=p.util.sizeFromShape(r.shape),a=AS({inputs:{x:r},backend:n,attrs:{shape:[i]}}),o=new QT(i),u=n.runWebGLProgram(o,[a],a.dtype),l=AS({inputs:{x:u},backend:n,attrs:{shape:s}});return n.disposeIntermediateTensorInfo(a),n.disposeIntermediateTensorInfo(u),l}};class JT{constructor(e){this.variableNames=["x","W"],this.outputShape=e.outShape;const{inHeight:t,inWidth:n,padInfo:r,strideHeight:s,strideWidth:i,filterHeight:a,filterWidth:o,dilationHeight:u,dilationWidth:l}=e,{top:c,left:d}=r;this.userCode=`\n      const ivec2 strides = ivec2(${s}, ${i});\n      const ivec2 pads = ivec2(${c}, ${d});\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 < ${a}; h++) {\n          int hIn = hBeg + h * ${u};\n\n          if (hIn >= 0 && hIn < ${t}

2=e,{input:r}=t;return yN(r,!1,n)}};class vN{constructor(e,t){this.outputShape=[],this.customUniforms=[{name:"value",type:"float"}],this.variableNames=["x"],this.outputShape=e,this.userCode="\n      void main() {\n        // Input can be obtained from uniform value.\n        setOutput(value);\n      }\n    "}}function xN(e){const{backend:t,attrs:n}=e,{shape:r,value:s}=n;let{dtype:i}=n;if(i=i||p.util.inferDtype(s),"string"===i){const e=p.util.getArrayFromDType(i,p.util.sizeFromShape(r));return e.fill(s),t.makeTensorInfo(r,i,e)}{const e=new vN(r,s),n=[[s]];return t.runWebGLProgram(e,[],i,n)}}const wN={kernelName:p.Fill,backendName:"webgl",kernelFunc:xN};class kN{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 < ${t}) {\n            outputValue = getImage(coords[0], coords[1], coordX, coords[3]);\n          } else {\n            outputValue = getImage(coords[0], coords[1], coords[2], coords[3]);\n          }\n          setOutput(outputValue);\n        }\n    `}}const SN={kernelName:p.FlipLeftRight,backendName:"webgl",kernelFunc:({inputs:e,backend:t})=>{const{image:n}=e,r=t,s=new kN(n.shape);return r.runWebGLProgram(s,[n],n.dtype)}},IN="return floor(x);",TN=kS({opSnippet:IN,packedOpSnippet:IN,cpuKernelImpl:sk}),NN={kernelName:p.Floor,backendName:"webgl",kernelFunc:TN},CN=SS({opSnippet:"\n  float s = sign(a) * sign(b);\n  int ia = round(a);\n  int ib = round(b);\n  if (ib != 0) {\n    // Windows (D3D) wants guaranteed non-zero int division at compile-time.\n    return float(idiv(ia, ib, s));\n  } else {\n    return NAN;\n  }\n",packedOpSnippet:"\n  ivec4 ia = round(a);\n  ivec4 ib = round(b);\n  bvec4 cond = notEqual(ib, ivec4(0));\n  ivec4 result = ivec4(0);\n  vec4 s = sign(a) * sign(b);\n\n  // Windows (D3D) wants guaranteed non-zero int division at compile-time.\n  if (cond[0]) {\n    result[0] = idiv(ia[0], ib[0], s[0]);\n  }\n  if (cond[1]) {\n    result[1] = idiv(ia[1], ib[1], s[1]);\n  }\n  if (cond[2]) {\n    result[2] = idiv(ia[2], ib[2], s[2]);\n  }\n  if (cond[3]) {\n    result[3] = idiv(ia[3], ib[3], s[3]);\n  }\n  return vec4(result);\n",dtype:"int32"}),EN={kernelName:p.FloorDiv,backendName:"webgl",kernelFunc:CN};class _N{constructor(e){this.variableNames=["A"];const t=Yx(),[n,r]=e;this.outputShape=e,this.userCode=`\n      void main() {\n        ivec3 coords = getOutputCoords();\n        int texR = coords[0];\n        int texC = coords[1];\n        int depth = coords[2];\n        vec2 uv = (vec2(texC, texR) + halfCR) / vec2(${r}.0, ${n}.0);\n\n        vec4 values = ${t.texture2D}(A, uv);\n        float value;\n        if (depth == 0) {\n          value = values.r;\n        } else if (depth == 1) {\n          value = values.g;\n        } else if (depth == 2) {\n          value = values.b;\n        } else if (depth == 3) {\n          value = values.a;\n        }\n\n        setOutput(floor(value * 255.0 + 0.5));\n      }\n    `}}class AN{constructor(e){this.variableNames=["A"],this.packedInputs=!1,this.packedOutput=!0;const t=Yx(),[n,r]=e;this.outputShape=e,this.userCode=`\n      void main() {\n        ivec3 coords = getOutputCoords();\n        int texR = coords[0];\n        int texC = coords[1];\n        int depth = coords[2];\n\n        vec4 result = vec4(0.);\n\n        for(int row=0; row<=1; row++) {\n          for(int col=0; col<=1; col++) {\n            texC = coords[1] + row;\n            depth = coords[2] + col;\n\n            vec2 uv = (vec2(texC, texR) + halfCR) /\n                       vec2(${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        ${t.output} = result;\n      }\n    `}}const RN={kernelName:p.FromPixels,backendName:"webgl",kernelFunc:function(e){const{inputs:t,backend:n,attrs:r}=e;let{pixels:s}=t;const{numChannels:i}=r,a="undefined"!=typeof HTMLVideoElement&&s instanceof HTMLVideoElement,o="undefined"!=typeof HTMLImageElement&&s instanceof HTMLImageElement,[u,l]=a?[s.videoWidth,s.videoHeight]:[s.width,s.height],c=[l,u],d=[l,u,i];if(o||a){const e=(0,p.env)().getBool("CANVAS2D_WILL_READ_FREQUENTLY_FOR_GPU");null!=$N&&e===FN||(FN=e,$N=document.createElement("canvas").getContext("2d",{willReadFrequently:FN})),$N.canvas.width=u,$N.canvas.height=l,$N.drawImage(s,0,0,u,l),s=$N.canvas}const h=n.makeTensorInfo(c,"int32");n.texData.get(h.dataId).usage=qv.PIXELS,n.gpgpu.uploadPixelDataToTexture(n.getTexture(h.dataId),s);const f=(0,p.env)().getBool("WEBGL_PACK")?new AN(d):new _N(d),m=n.runWebGLProgram(f,[h],"int32");return n.disposeData(h.dataId),m}};let $N,FN=(0,p.env)().getBool("CANVAS2D_WILL_READ_FREQUENTLY_FOR_GPU");const ON={kernelName:p.FusedConv2D,backendName:"webgl",kernelFunc:function(e){const{inputs:t,backend:n,attrs:r}=e,{x:s,filter:i,bias:a,preluActivationWeights:o}=t,{strides:u,pad:l,dataFormat:c,dilations:d,dimRoundingMode:h,activation:f,leakyreluAlpha:m}=r,g=p.backend_util.convertConv2DDataFormat(c),y=p.backend_util.computeConv2DInfo(s.shape,i.shape,u,d,l,h,!1,g);let b;const v=[],x=null!=a,w=null!=o,k="leakyrelu"===f,S=()=>{const e=[s,i],t=(e,t)=>{if("NCHW"===t&&1===e.shape.length&&1!==e.shape[0]){const t=AS({inputs:{x:e},backend:n,attrs:{shape:[e.shape[0],1,1]}});return v.push(t),t}return e};if(x&&e.push(t(a,c)),w&&e.push(t(o,c)),k){const t=n.makeTensorInfo([],"float32",p.util.createScalarValue(m,"float32"));e.push(t),v.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,p.env)().getBool("WEBGL_EXP_CONV")){const e=f?IS(f,!0):null,t=new hT(y,x,e,w,k),r=[[y.padInfo.top,y.padInfo.left],[y.strideHeight,y.strideWidth],[y.dilationHeight,y.dilationWidth],[y.inHeight,y.inWidth]],s=S();b=n.runWebGLProgram(t,s,"float32",r)}else if((0,p.env)().getBool("WEBGL_CONV_IM2COL"))b=gT({x:s,filter:i,convInfo:y,backend:n,bias:a,activation:f,preluActivationWeights:o,leakyreluAlpha:m});else{const e=f?IS(f,!1):null,t=new cT(y,x,e,w,k),r=S();b=n.runWebGLProgram(t,r,"float32")}else b=mT({x:s,filter:i,convInfo:y,backend:n,bias:a,activation:f,preluActivationWeights:o,leakyreluAlpha:m});const I=AS({inputs:{x:b},backend:n,attrs:{shape:y.outShape}});return v.push(b),v.forEach((e=>n.disposeIntermediateTensorInfo(e))),I}},DN={kernelName:p.FusedDepthwiseConv2D,backendName:"webgl",kernelFunc:function(e){const{inputs:t,backend:n,attrs:r}=e,{x:s,filter:i,bias:a,preluActivationWeights:o}=t,{strides:u,pad:l,dilations:c,dimRoundingMode:d,activation:h,leakyreluAlpha:f}=r,m=[];let g=c;null==g&&(g=[1,1]),p.util.assert(p.backend_util.eitherStridesOrDilationsAreOne(u,g),(()=>`Error in depthwiseConv2d: Either strides or dilations must be 1. Got strides ${u} and dilations '${g}'`));const y=p.backend_util.computeConv2DInfo(s.shape,i.shape,u,g,l,d,!0),b=(0,p.env)().getBool("WEBGL_PACK_DEPTHWISECONV")&&y.strideWidth<=2&&y.outChannels/y.inChannels==1,v=h?IS(h,b):null,x=[s,i],w=null!=a,k=null!=o,S="leakyrelu"===h;if(w&&x.push(a),k&&x.push(o),S){const e=n.makeTensorInfo([],"float32",p.util.createScalarValue(f,"float32"));x.push(e),m.push(e)}let I;I=b?new HT(y,w,v,k,S):new GT(y,w,v,k,S);const T=[[y.padInfo.top,y.padInfo.left],[y.strideHeight,y.strideWidth],[y.dilationHeight,y.dilationWidth],[y.inHeight,y.inWidth]],N=n.runWebGLProgram(I,x,"float32",T);return m.forEach((e=>n.disposeIntermediateTensorInfo(e))),N}};class MN{constructor(e,t,n,r){this.sliceDim=e,this.strides=t,this.paramsShape=r,this.variableNames=["x","indices"],this.outputShape=n;const s=dw(n.length);let i="\n    int index;";for(let e=0;e<this.sliceDim;e++)i+=`\n          index = round(getIndices(coords[0], ${e}));\n          out_of_bounds = out_of_bounds || index < 0;\n          out_of_bounds = out_of_bounds || index >= ${this.paramsShape[e]};\n          flattenIndex += index * ${this.strides[e]};`;this.userCode=`\n         void main() {\n          ${s} coords = getOutputCoords();\n          int flattenIndex = 0;\n          bool out_of_bounds = false;\n\n          ${i}\n\n          setOutput(out_of_bounds ? 0.0 : getX(flattenIndex, coords[1]));\n        }\n      `}}const LN={kernelName:p.GatherNd,backendName:"webgl",kernelFunc:function(e){const{inputs:t,backend:n}=e,{params:r,indices:s}=t,i=s.shape,a=i[i.length-1],o=p.util.sizeFromShape(r.shape),[u,l,c,d]=p.backend_util.prepareAndValidate(r,s),h=AS({inputs:{x:s},backend:n,attrs:{shape:[l,a]}}),f=AS({inputs:{x:r},backend:n,attrs:{shape:[p.util.sizeFromShape(r.shape)/c,c]}});if(n.shouldExecuteOnCPU([r,s])||"string"===r.dtype){const e=n.readSync(s.dataId),t=n.bufferSync(r),i=ik(e,t,r.dtype,l,a,c,d,r.shape,o);return n.makeTensorInfo(u,r.dtype,i.values)}const m=new MN(a,d,[l,c],r.shape),g=n.runWebGLProgram(m,[f,h],f.dtype),y=AS({inputs:{x:g},backend:n,attrs:{shape:u}});return n.disposeIntermediateTensorInfo(h),n.disposeIntermediateTensorInfo(f),n.disposeIntermediateTensorInfo(g),y}};class zN{constructor(e,t){this.variableNames=["A","indices"],this.outputShape=t,this.rank=t.length;const n=dw(this.rank),r=function(e,t){const n=["resRC.x","resRC.y","resRC.z","resRC.w"],r=[];for(let t=0;t<e.length;t++)2===t?r.push("index"):r.push(""+n[t]);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 PN(e){const{inputs:t,backend:n,attrs:r}=e,{x:s,indices:i}=t,{axis:a,batchDims:o}=r,u=p.util.parseAxisParam(a,s.shape)[0];if((0,p.env)().get("DEBUG")){const e=n.readSync(i.dataId),t=s.shape[u];for(let n=0;n<e.length;++n){const r=e[n];p.util.assert(r<=t-1&&r>=0,(()=>`GatherV2: the index value ${r} is not in [0, ${t-1}]`))}}const l=p.backend_util.segment_util.collectGatherOpShapeInfo(s,i,u,o),c=p.util.sizeFromShape(i.shape),d=[],h=AS({inputs:{x:s},backend:n,attrs:{shape:[l.batchSize,l.outerSize,l.dimSize,l.sliceSize]}}),f=AS({inputs:{x:i},backend:n,attrs:{shape:[l.batchSize,c/l.batchSize]}});d.push(h),d.push(f);const m=[l.batchSize,l.outerSize,c/l.batchSize,l.sliceSize];if(n.shouldExecuteOnCPU([s,i])||"string"===s.dtype){const e=n.bufferSync(f),t=n.bufferSync(h),r=ak(t,e,m);return d.forEach((e=>n.disposeIntermediateTensorInfo(e))),n.makeTensorInfo(l.outputShape,r.dtype,r.values)}const g=new zN(h.shape,m),y=n.runWebGLProgram(g,[h,f],h.dtype);d.push(y);const b=AS({inputs:{x:y},backend:n,attrs:{shape:l.outputShape}});return d.forEach((e=>n.disposeIntermediateTensorInfo(e))),b}const BN={kernelName:p.GatherV2,backendName:"webgl",kernelFunc:PN},VN=SS({opSnippet:"return float(a > b);",packedOpSnippet:"\n  return vec4(greaterThan(a, b));\n",cpuKernelImpl:ok,dtype:"bool"}),WN={kernelName:p.Greater,backendName:"webgl",kernelFunc:VN},UN=SS({opSnippet:"return float(a >= b);",packedOpSnippet:"\n  return vec4(greaterThanEqual(a, b));\n",dtype:"bool",cpuKernelImpl:uk}),GN={kernelName:p.GreaterEqual,backendName:"webgl",kernelFunc:UN},HN={kernelName:p.IFFT,backendName:"webgl",kernelFunc:function(e){const{inputs:t,backend:n}=e,{input:r}=t;return yN(r,!0,n)}},jN=kS({opSnippet:"return float(!isnan(x) && !isinf(x));",dtype:"bool"}),qN={kernelName:p.IsFinite,backendName:"webgl",kernelFunc:jN},XN=kS({opSnippet:"return float(isinf(x));",dtype:"bool"}),KN={kernelName:p.IsInf,backendName:"webgl",kernelFunc:XN},YN=kS({opSnippet:"return float(isnan(x));",dtype:"bool"}),QN={kernelName:p.IsNan,backendName:"webgl",kernelFunc:YN},ZN=SS({opSnippet:"return float(a < b);",packedOpSnippet:"\n  return vec4(lessThan(a, b));\n",cpuKernelImpl:lk,dtype:"bool"}),JN={kernelName:p.Less,backendName:"webgl",kernelFunc:ZN},eC=SS({opSnippet:"return float(a <= b);",packedOpSnippet:"\n  return vec4(lessThanEqual(a, b));\n",cpuKernelImpl:ck,dtype:"bool"}),tC={kernelName:p.LessEqual,backendName:"webgl",kernelFunc:eC},nC={kernelName:p.LinSpace,backendName:"webgl",kernelFunc:function(e){const{backend:t,attrs:n}=e,{start:r,stop:s,num:i}=n,a=dk(r,s,i);return t.makeTensorInfo([a.length],"float32",a)}},rC=kS({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:hk}),sC={kernelName:p.Log,backendName:"webgl",kernelFunc:rC},iC=kS({opSnippet:"if (isnan(x)) return x;\n  return log(1.0 + x);\n"}),aC={kernelName:p.Log1p,backendName:"webgl",kernelFunc:iC},oC=SS({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"}),uC={kernelName:p.LogicalAnd,backendName:"webgl",kernelFunc:oC},lC=kS({opSnippet:"return float(!(x >= 1.0));"}),cC={kernelName:p.LogicalNot,backendName:"webgl",kernelFunc:lC},dC=SS({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"}),hC={kernelName:p.LogicalOr,backendName:"webgl",kernelFunc:dC};class pC{constructor(e,t,n,r,s){this.variableNames=["x"],this.outputShape=[];const i=t,a=e[3]-1;let o;this.outputShape=e;const u=`float(${n}) + float(${r}) * sum`;o=.5===s?`inversesqrt(${u})`:1===s?`1.0/(${u})`:`exp(log(${u}) * float(-${s}));`,this.userCode=`\n      void main() {\n        ivec4 coords = getOutputCoords();\n        int b = coords[0];\n        int r = coords[1];\n        int c = coords[2];\n        int d = coords[3];\n        float x = getX(b, r, c, d);\n        float sum = 0.0;\n        for (int j = -${i}; j <= ${i}; j++) {\n          int idx = d + j;\n          if (idx >= 0 && idx <=  ${a}) {\n            float z = getX(b, r, c, idx);\n            sum += z * z;\n          }\n        }\n        float val = x * ${o};\n        setOutput(val);\n      }\n    `}}class fC{constructor(e,t,n,r,s){this.variableNames=["x"],this.outputShape=[],this.packedInputs=!0,this.packedOutput=!0;const i=t,a=e[3]-1;let o;this.outputShape=e;const u=`float(${n}) + float(${r}) * sum`;o=.5===s?`inversesqrt(${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 - ${i};\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        }

2I(d,"max",!0),f=n.runWebGLProgram(h,[a],a.dtype),m=new TC(d),g=n.runWebGLProgram(m,[s,f],a.dtype);return n.disposeIntermediateTensorInfo(f),g}},CC={kernelName:p.MaxPoolGrad,backendName:"webgl",kernelFunc:function(e){const{inputs:t,backend:n,attrs:r}=e,{dy:s,input:i,output:a}=t,o=i;Xx([i,a],"maxPoolGrad");const{filterSize:u,strides:l,pad:c,dimRoundingMode:d}=r,h=p.backend_util.computePool2DInfo(o.shape,u,l,1,c,d),f=new wI(h,"max",!0),m=n.runWebGLProgram(f,[o],o.dtype),g=new IC(h),y=n.runWebGLProgram(g,[s,m],o.dtype);return n.disposeIntermediateTensorInfo(m),y}},EC={kernelName:p.MaxPoolWithArgmax,backendName:"webgl",kernelFunc:({inputs:e,attrs:t,backend:n})=>{const{x:r}=e,{filterSize:s,strides:i,pad:a,includeBatchInIndex:o}=t,u=n;p.util.assert(4===r.shape.length,(()=>`Error in maxPool: input must be rank 4 but got rank ${r.shape.length}.`));const l=[1,1];p.util.assert(p.backend_util.eitherStridesOrDilationsAreOne(i,l),(()=>`Error in maxPool: Either strides or dilations must be 1. Got strides ${i} and dilations '${l}'`));const c=p.backend_util.computePool2DInfo(r.shape,s,i,l,a),[d,h]=function(e,t,n,r){let s=new wI(n,"max",!1);const i=r.runWebGLProgram(s,[e],"float32");return s=new wI(n,"max",!0,!0,t),[i,r.runWebGLProgram(s,[e],"float32")]}(r,o,c,u);return[d,h]}},_C={kernelName:p.Mean,backendName:"webgl",kernelFunc:({inputs:e,attrs:t,backend:n})=>{const{x:r}=e,{keepDims:s,axis:i}=t,a=n,o=r.shape.length,u=p.util.parseAxisParam(i,r.shape);let l=u;const c=p.backend_util.getAxesPermutation(l,o),d=null!=c,h=a.shouldExecuteOnCPU([r]),f=[];let m=r;if(d){if(h){const e=a.texData.get(m.dataId).values,t=new Array(o);for(let e=0;e<t.length;e++)t[e]=r.shape[c[e]];const n=Vk(e,r.shape,r.dtype,c,t);m=a.makeTensorInfo(t,r.dtype),a.texData.get(m.dataId).values=n}else m=LS(r,c,a);f.push(m),l=p.backend_util.getInnerMostAxes(l.length,o)}p.backend_util.assertAxesAreInnerMostDims("sum",l,o);const[g,y]=p.backend_util.computeOutAndReduceShapes(m.shape,l);let b=g;s&&(b=p.backend_util.expandShapeToKeepDim(g,u));const v=function(e,t,n,r){const s=p.util.sizeFromShape(t),i=AS({inputs:{x:e},attrs:{shape:[p.util.sizeFromShape(e.shape)/s,s]},backend:r}),a=OS(i,"float32","mean",r),o=AS({inputs:{x:a},attrs:{shape:n},backend:r});return r.disposeIntermediateTensorInfo(i),r.disposeIntermediateTensorInfo(a),o}(m,y,b,a);for(const e of f)a.disposeIntermediateTensorInfo(e);return v}},AC={kernelName:p.Min,backendName:"webgl",kernelFunc:function(e){const{inputs:t,backend:n,attrs:r}=e,{x:s}=t,{axis:i,keepDims:a}=r,o=s.shape.length,u=p.util.parseAxisParam(i,s.shape);let l=u;const c=p.backend_util.getAxesPermutation(l,o);let d=s;null!=c&&(d=BS({inputs:{x:s},backend:n,attrs:{perm:c}}),l=p.backend_util.getInnerMostAxes(l.length,s.shape.length)),p.backend_util.assertAxesAreInnerMostDims("min",l,o);const[h,f]=p.backend_util.computeOutAndReduceShapes(d.shape,l),m=AS({inputs:{x:d},backend:n,attrs:{shape:[-1,p.util.sizeFromShape(f)]}}),g=OS(m,m.dtype,"min",n);let y;return y=AS(a?{inputs:{x:g},backend:n,attrs:{shape:p.backend_util.expandShapeToKeepDim(h,u)}}:{inputs:{x:g},backend:n,attrs:{shape:h}}),n.disposeIntermediateTensorInfo(m),n.disposeIntermediateTensorInfo(g),null!=c&&n.disposeIntermediateTensorInfo(d),y}},RC=SS({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:mk}),$C={kernelName:p.Minimum,backendName:"webgl",kernelFunc:RC};class FC{constructor(e,t,n){this.variableNames=["x"],this.outputShape=t.map(((t,n)=>t[0]+e[n]+t[1]));const r=e.length,s=dw(r),i=t.map((e=>e[0])).join(","),a=t.map(((t,n)=>t[0]+e[n])).join(","),o=["coords[0]","coords[1]","coords[2]","coords[3]"].slice(0,r),u="reflect"===n?0:1;this.userCode=1!==r?`\n      ${s} start = ${s}(${i});\n      ${s} end = ${s}(${a});\n\n      void main() {\n        ${s} outC = getOutputCoords();\n        for (int i = 0; i < ${r};

2if("complex64"===r.dtype){const e=GI({inputs:{input:r},backend:n}),t=nE({inputs:{x:e},backend:n}),s=iT({inputs:{input:r},backend:n}),i=nE({inputs:{x:s},backend:n}),a=fS({inputs:{real:t,imag:i},backend:n});return n.disposeIntermediateTensorInfo(e),n.disposeIntermediateTensorInfo(t),n.disposeIntermediateTensorInfo(s),n.disposeIntermediateTensorInfo(i),a}return xN({attrs:{shape:r.shape,dtype:r.dtype,value:"string"===r.dtype?"":0},backend:n})}const rE={kernelName:p.ZerosLike,backendName:"webgl",kernelFunc:nE},sE={kernelName:p.OnesLike,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=GI({inputs:{input:s},backend:r}),n=e({inputs:{x:t},backend:r}),i=iT({inputs:{input:s},backend:r}),a=nE({inputs:{x:i},backend:r}),o=fS({inputs:{real:n,imag:a},backend:r});return r.disposeIntermediateTensorInfo(t),r.disposeIntermediateTensorInfo(n),r.disposeIntermediateTensorInfo(i),r.disposeIntermediateTensorInfo(a),o}return xN({attrs:{shape:s.shape,dtype:s.dtype,value:1},backend:r})}},iE={kernelName:p.Pack,backendName:"webgl",kernelFunc:function(e){const{inputs:t,backend:n,attrs:r}=e,{axis:s}=r;if(1===t.length)return dN({inputs:{input:t[0]},backend:n,attrs:{dim:s}});const i=t[0].shape,a=t[0].dtype;t.forEach((e=>{p.util.assertShapesMatch(i,e.shape,"All tensors passed to stack must have matching shapes"),p.util.assert(a===e.dtype,(()=>"All tensors passed to stack must have matching dtypes"))}));const o=[],u=uT({inputs:t.map((e=>{const t=dN({inputs:{input:e},backend:n,attrs:{dim:s}});return o.push(t),t})),backend:n,attrs:{axis:s}});return o.forEach((e=>n.disposeIntermediateTensorInfo(e))),u}};class aE{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=dw(r),i=t.map((e=>e[0])).join(","),a=t.map(((t,n)=>t[0]+e[n])).join(","),o=["coords[0]","coords[1]","coords[2]","coords[3]"].slice(0,r);this.userCode=1!==r?`\n      ${s} start = ${s}(${i});\n      ${s} end = ${s}(${a});\n\n      void main() {\n        ${s} outC = getOutputCoords();\n        if (any(lessThan(outC, start)) || any(greaterThanEqual(outC, end))) {\n          setOutput(value);\n        } else {\n          ${s} coords = outC - start;\n          setOutput(getX(${o}));\n        }\n      }\n    `:`\n        int start = ${i};\n        int end = ${a};\n\n        void main() {\n          int outC = getOutputCoords();\n          if (outC < start || outC >= end) {\n            setOutput(value);\n          } else {\n            setOutput(getX(outC - start));\n          }\n        }\n      `}}class oE{constructor(e,t,n){this.variableNames=["x"],this.packedInputs=!0,this.packedOutput=!0,this.customUniforms=[{name:"value",type:"float"}],this.outputShape=t.map(((t,n)=>t[0]+e[n]+t[1]));const r=e.length,s=dw(r),i=t.map((e=>e[0])).join(","),a=t.map(((t,n)=>t[0]+e[n])).join(","),o=Gk("rc",r),u=Gk("source",r),l=`${o[r-1]} < ${this.outputShape[r-1]}`,c=1===r?"source":`vec2(${u.slice(-2).join()})`,d=[s+" rc = outputLoc;",`${o[r-1]} += 1;\n       if(${l}) {\n      `,1===r?"":`}\n       rc = outputLoc;\n       ${o[r-2]} += 1;\n       if(${o[r-2]} < ${this.outputShape[r-2]}) {`,1===r?"":`  ${o[r-1]} += 1;\n         if(${l}) {`],h=1===r?"rc < start || rc >= end":"any(lessThan(rc, start)) || any(greaterThanEqual(rc, end))";let p="";for(let e=0,t=1===r?2:4;e<t;e++)p+=`\n        ${d[e]}\n        if (${h}) {\n          result[${e}] = float(value);\n        } else {\n          ${s} source = rc - start;\n          result[${e}] = getChannel(getX(${u.join()}), ${c});\n        }\n      `;p+=1===r?"} ":"}}",this.userCode=`\n      const ${s} start = ${s}(${i});\n      const ${s} end = ${s}(${a});\n\n      void main() {\n        ${s} outputLoc = getOutputCoords();\n        vec4 result = vec4(0.);\n        ${p}\n        setOutput(result);\n      }\n    `}}const uE=e=>{const{inputs:t,backend:n,attrs:r}=e,{x:s}=t,{paddings:i,constantValue:a}=r;if(0===p.util.sizeFromShape(s.shape))return xN({backend:n,attrs:{shape:i.map(((e,t)=>e[0]+s.shape[t]+e[1])),value:a,dtype:s.dtype}});const o=(0,p.env)().getBool("WEBGL_PACK_ARRAY_OPERATIONS")?new oE(s.shape,i,a):new aE(s.shape,i,a),u=[[a]];return n.runWebGLProgram(o,[s],s.dtype,u)},lE={kernelName:p.PadV2,backendName:"webgl",kernelFunc:uE},cE=SS({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.

2class TE{constructor(e,t,n,r,s){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=[];const[i,a,o,u]=e;this.outputShape=[i,t,n,u];const l=[r&&t>1?a-1:a,r&&n>1?o-1:o],c=[r&&t>1?t-1:t,r&&n>1?n-1:n];let d;d=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          ${l[1]/c[1]});\n      const vec3 inputShapeRC = vec3(${a}.0, ${o}.0,\n                                     ${o}.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 = ${d};\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);

2class AE{constructor(e,t,n,r,s){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=[];const[i,a,o,u]=e;this.outputShape=[i,t,n,u];const l=[r&&t>1?a-1:a,r&&n>1?o-1:o],c=[r&&t>1?t-1:t,r&&n>1?n-1:n],d=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          ${l[1]/c[1]});\n      const vec3 inputShapeRC = vec3(${a}.0, ${o}.0,\n                                     ${o}.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 + ${d})));\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 RE={kernelName:p.ResizeNearestNeighbor,backendName:"webgl",kernelFunc:function(e){const{inputs:t,backend:n,attrs:r}=e,{images:s}=t,{alignCorners:i,halfPixelCenters:a,size:o}=r,[u,l]=o,c=(0,p.env)().getBool("WEBGL_PACK_IMAGE_OPERATIONS")?new AE(s.shape,u,l,i,a):new _E(s.shape,u,l,i,a);return n.runWebGLProgram(c,[s],s.dtype)}};class $E{constructor(e,t,n){this.variableNames=["dy"],this.outputShape=[],this.outputShape=t;const[,r,s]=t,[,i,a]=e,o=[n&&i>1?r-1:r,n&&a>1?s-1:s],u=[n&&i>1?i-1:i,n&&a>1?a-1:a],l=o[0]/u[0],c=o[1]/u[1],d=1/l,h=1/c,p=2*Math.ceil(d)+2,f=2*Math.ceil(h)+2;this.userCode=`\n      void main() {\n        ivec4 coords = getOutputCoords();\n        int b = coords[0];\n        int d = coords[3];\n        int r = coords[1];\n        int c = coords[2];\n\n        float accumulator = 0.0;\n\n        const float heightScale = float(${l});\n        const float widthScale = float(${c});\n\n        const float invHeightScale = float(${d});\n        const float invWidthScale = float(${h});\n\n        const int winHeight = int(${p});\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 >= ${i}) {\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 >= ${a}) {\n              continue;\n            }\n\n            float sourceFracRow =\n              float(${o[0]}) *\n                (float(dyR) / float(${u[0]}));\n\n            float sourceFracCol =\n                float(${o[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 FE={kernelName:p.ResizeNearestNeighborGrad,backendName:"webgl",kernelFunc:function(e){const{inputs:t,backend:n,attrs:r}=e,{images:s,dy:i}=t,{alignCorners:a}=r,o=new $E(i.shape,s.shape,a);return n.runWebGLProgram(o,[i],i.dtype)}};class OE{constructor(e,t){this.variableNames=["x"];const n=e.length;if(n>4)throw new Error(`WebGL backend: Reverse of rank-${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=dw(n);this.userCode=`\n      void main() {\n        ${s} coords = getOutputCoords();\n        setOutput(getX(${r}));\n      }\n    `}}class DE{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-${n} tensor is not yet supported`);this.outputShape=e;const r=Gk("rc",n),s=`${r[n-1]} + 1 < ${this.outputShape[n-1]}`,i=`${r[n-2]} + 1 < ${this.outputShape[n-2]}`,a=dw(n);function o(n){const r=e.map(((r,s)=>function(n,r){return-1!==t.indexOf(n)&&1!==e[n]?`${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          ${a} rc = getOutputCoords();\n          vec4 result = vec4(0.);\n          result.r = ${function(e){return o(e)}(r.slice())};\n          if(${s}){\n            result.g = ${function(e){return e[n-1]="("+e[n-1]+" + 1)",o(e)}(r.slice())};\n          }\n          if(${i}) {\n            result.b = ${function(e){return e[n-2]="("+e[n-2]+" + 1)",o(e)}(r.slice())};\n            if(${s}) {\n              result.a = ${function(e){return e[n-1]="("+e[n-1]+" + 1)",e[n-2]="("+e[n-2]+" + 1)",o(e)}(r.slice())};\n            }\n          }\n          setOutput(result);\n        }\n    `}}const ME={kernelName:p.Reverse,backendName:"webgl",kernelFunc:function(e){const{inputs:t,backend:n,attrs:r}=e,{x:s}=t,{dims:i}=r,a=s.shape.length,o=p.util.parseAxisParam(i,s.shape);if(0===a)return hS({inputs:{x:s},backend:n});const u=(0,p.env)().getBool("WEBGL_PACK_ARRAY_OPERATIONS")?new DE(s.shape,o):new OE(s.shape,o);return n.runWebGLProgram(u,[s],s.dtype)}};class LE{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];

2if(1!==s.shape.length)throw new Error("Indices should be a vector but received shape\n              "+s.shape);if(1!==i.shape.length)throw new Error("Segment ids should be a vector but received shape\n              "+i.shape);const a=n.readSync(r.dataId),o=n.readSync(s.dataId),u=n.readSync(i.dataId),[l,c]=Rk(a,r.shape,r.dtype,o,u,!0);return n.makeTensorInfo(c,r.dtype,l)}},h_={kernelName:p.SparseSegmentSum,backendName:"webgl",kernelFunc:function(e){const{inputs:t,backend:n}=e,{data:r,indices:s,segmentIds:i}=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!==i.shape.length)throw new Error("Segment ids should be a vector but received shape\n             "+i.shape);const a=n.readSync(r.dataId),o=n.readSync(s.dataId),u=n.readSync(i.dataId),[l,c]=Rk(a,r.shape,r.dtype,o,u);return n.makeTensorInfo(c,r.dtype,l)}},p_={kernelName:p.SparseToDense,backendName:"webgl",kernelFunc:function(e){const{inputs:t,backend:n,attrs:r}=e,{sparseIndices:s,sparseValues:i,defaultValue:a}=t,{outputShape:o}=r,{sliceRank:u,numUpdates:l,sliceSize:c,strides:d,outputSize:h}=p.backend_util.calculateShapes(i,s,o);if("string"===i.dtype){const e=n.bufferSync(s),t=n.bufferSync(i),r=p.util.decodeString(n.readSync(a.dataId)[0]),f=Tk(e,t,o,h,c,l,u,d,r,!1);return n.makeTensorInfo(o,f.dtype,f.values)}const f=new UE(l,u,s.shape.length,i.shape.length,d,[h,1],!1),m=n.runWebGLProgram(f,[i,s,a],i.dtype),g=AS({inputs:{x:m},backend:n,attrs:{shape:o}});return n.disposeIntermediateTensorInfo(m),g}},f_={kernelName:p.SplitV,backendName:"webgl",kernelFunc:function(e){const{inputs:t,backend:n,attrs:r}=e,{x:s}=t,{numOrSizeSplits:i,axis:a}=r,o=p.util.parseAxisParam(a,s.shape)[0],u=p.backend_util.prepareSplitSize(s,i,o),l=s.shape.length,c=new Array(l).fill(0),d=s.shape.slice();return u.map((e=>{const t=[...d];t[o]=e;const r=MI({inputs:{x:s},backend:n,attrs:{begin:c,size:t}});return c[o]+=e,r}))}},m_="return sqrt(x);",g_=kS({opSnippet:m_,packedOpSnippet:m_,cpuKernelImpl:$k}),y_={kernelName:p.Sqrt,backendName:"webgl",kernelFunc:g_},b_=kS({opSnippet:"return x * x;"}),v_={kernelName:p.Square,backendName:"webgl",kernelFunc:b_},x_="return (a - b) * (a - b);",w_=SS({opSnippet:x_,packedOpSnippet:x_}),k_={kernelName:p.SquaredDifference,backendName:"webgl",kernelFunc:w_},S_={kernelName:p.StaticRegexReplace,backendName:"webgl",kernelFunc:function(e){const{inputs:t,backend:n,attrs:r}=e,{x:s}=t;if("string"!==s.dtype)throw new Error("Input must be of datatype string");const i=n.readSync(s.dataId),a=p.backend_util.fromUint8ToStringArray(i),o=Fk(a,"string",r);return n.makeTensorInfo(s.shape,"string",o)}},I_={kernelName:p.Step,backendName:"webgl",kernelFunc:function({inputs:e,attrs:t,backend:n}){const{x:r}=e,s=`if (isnan(x)) return x;\n    return x > 0.0 ? 1.0 : float(${t.alpha});\n  `,i=new Qk(r.shape,s);return n.runWebGLProgram(i,[r],r.dtype)}};class T_{constructor(e,t,n){this.variableNames=["x"],this.outputShape=n;const r=n.length,s=dw(n.length),i=dw(n.length);let a="";if(1===r)a="coords * strides + begin";else{let e=0;a=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        ${i} coords = getOutputCoords();\n        setOutput(getX(${a}));\n      }\n    `}}const N_={kernelName:p.StridedSlice,backendName:"webgl",kernelFunc:function(e){const{inputs:t,backend:n,attrs:r}=e,{x:s}=t,{begin:i,end:a,strides:o,beginMask:u,endMask:l,ellipsisMask:c,newAxisMask:d,shrinkAxisMask:h}=r,{finalShapeSparse:f,finalShape:m,isIdentity:g,sliceDim0:y,isSimpleSlice:b,begin:v,end:x,strides:w}=p.slice_util.sliceInfo(s.shape,i,a,o,u,l,c,d,h);let k;if(g)k=AS({inputs:{x:s},backend:n,attrs:{shape:m}});else if(y||b){p.util.assert(s.shape.length>=1,(()=>"Input must have rank at least 1, got: "+s.shape.length));const e=p.slice_util.computeOutShape(v,x,w),t=MI({inputs:{x:s},backend:n,attrs:{begin:v,size:e}});k=AS({inputs:{x:t},backend:n,attrs:{shape:m}}),n.disposeIntermediateTensorInfo(t)}else if(n.shouldExecuteOnCPU([s])){const e=n.readSync(s.dataId),t=(0,p.buffer)(s.shape,s.dtype,e),r=Ok(f,t,w,v);k=n.makeTensorInfo(m,s.dtype,r.values)}else{const e=new T_(v,w,f);k=n.runWebGLProgram(e,[s],s.dtype)}const S=AS({inputs:{x:k},backend:n,attrs:{shape:m}});return n.disposeIntermediateTensorInfo(k),S}},C_={kernelName:p.StringNGrams,backendName:"webgl",kernelFunc:function(e){const{inputs:t,backend:n,attrs:r}=e,{separator:s,nGramWidths:i,leftPad:a,rightPad:o,padWidth:u,preserveShortSequences:l}=r,{data:c,dataSplits:d}=t,h=n.readSync(c.dataId),p=n.readSync(d.dataId),[f,m]=Dk(h,p,s,i,a,o,u,l);return[n.makeTensorInfo([f.length],"string",f),n.makeTensorInfo(d.shape,"int32",m)]}},E_={kernelName:p.StringSplit,backendName:"webgl",kernelFunc:function(e){const{inputs:t,backend:n,attrs:r}=e,{skipEmpty:s}=r,{input:i,delimiter:a}=t;if("string"!==i.dtype)throw new Error("Input must be of datatype string");if(1!==i.shape.length)throw new Error("Input must be a vector, got shape: "+i.shape);if(0!==a.shape.length)throw new Error("Delimiter must be a scalar, got shape: "+a.shape);const o=n.readSync(i.dataId),u=n.readSync(a.dataId)[0],[l,c,d]=Mk(o,u,s),h=c.length;return[n.makeTensorInfo([h,2],"int32",l),n.makeTensorInfo([h],"string",c),n.makeTensorInfo([2],"int32",new Int32Array(d))]}},__={kernelName:p.StringToHashBucketFast,backendName:"webgl",kernelFunc:function(e){const{inputs:t,backend:n,attrs:r}=e,{numBuckets:s}=r,{input:i}=t;if("string"!==i.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 a=n.readSync(i.dataId),o=Lk(a,s);return n.makeTensorInfo(i.shape,"int32",o)}},A_=kS({opSnippet:"return tan(x);"}),R_={kernelName:p.Tan,backendName:"webgl",kernelFunc:A_},$_=kS({opSnippet:"\n  float e2x = exp(-2.0 * abs(x));\n  return sign(x) * (1.0 - e2x) / (1.0 + e2x);\n"}),F_={kernelName:p.Tanh,backendName:"webgl",kernelFunc:$_},O_={kernelName:p.TensorScatterUpdate,backendName:"webgl",kernelFunc:function(e){const{inputs:t,backend:n,attrs:r}=e,{tensor:s,indices:i,updates:a}=t,{}=r,{sliceRank:o,numUpdates:u,sliceSize:l,strides:c,outputSize:d}=p.backend_util.calculateShapes(a,i,s.shape),h=[d/l,l];if(0===d)return n.makeTensorInfo(s.shape,i.dtype);const f=AS({inputs:{x:i},backend:n,attrs:{shape:[u,o]}}),m=AS({inputs:{x:a},backend:n,attrs:{shape:[u,l]}}),g=AS({inputs:{x:s},backend:n,attrs:{shape:h}}),y=new UE(u,o,f.shape.length,m.shape.length,c,h,!1,!0),b=n.runWebGLProgram(y,[m,f,g],g.dtype),v=AS({inputs:{x:b},backend:n,attrs:{shape:s.shape}});return n.disposeIntermediateTensorInfo(f),n.disposeIntermediateTensorInfo(m),n.disposeIntermediateTensorInfo(g),n.disposeIntermediateTensorInfo(b),v}};class D_{constructor(e,t){this.variableNames=["A"];const n=new Array(e.length);for(let r=0;r<n.length;r++)n[r]=e[r]*t[r];this.outputShape=n,this.rank=n.length;const r=dw(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, ${e[0]})`;const n=["resRC.x","resRC.y","resRC.z","resRC.w","resRC.u"],r=[];for(let t=0;t<e.length;t++)r.push(`imod(${n[t]}, ${e[t]})`);return r.join()}(e);this.userCode=`\n      void main() {\n        ${r} resRC = getOutputCoords();\n        setOutput(getA(${s}));\n      }\n    `}}function M_(e){const{inputs:t,backend:n,attrs:r}=e,{x:s}=t,{reps:i}=r;if("string"===s.dtype||s.shape.length>5){const e=n.readSync(s.dataId),t="string"===s.dtype?e.map((e=>p.util.decodeString(e))):e,r=(0,p.buffer)(s.shape,s.dtype,t),a=Pk(r,i);return n.makeTensorInfo(a.shape,a.dtype,a.values)}const a=new D_(s.shape,i);return n.runWebGLProgram(a,[s],s.dtype)}const L_={kernelName:p.Tile,backendName:"webgl",kernelFunc:M_};class z_{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 descending. 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;

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r=e&&t.allowTouchMove&&i.touchStartPreventDefault;!i.touchStartForcePreventDefault&&!r||d[0].isContentEditable||c.preventDefault()}t.emit("touchStart",c)}function A(e){const t=a(),n=this,r=n.touchEventsData,{params:s,touches:i,rtlTranslate:o,enabled:u}=n;if(!u)return;let l=e;if(l.originalEvent&&(l=l.originalEvent),!r.isTouched)return void(r.startMoving&&r.isScrolling&&n.emit("touchMoveOpposite",l));if(r.isTouchEvent&&"touchmove"!==l.type)return;const c="touchmove"===l.type&&l.targetTouches&&(l.targetTouches[0]||l.changedTouches[0]),d="touchmove"===l.type?c.pageX:l.pageX,h="touchmove"===l.type?c.pageY:l.pageY;if(l.preventedByNestedSwiper)return i.startX=d,void(i.startY=h);if(!n.allowTouchMove)return n.allowClick=!1,void(r.isTouched&&(Object.assign(i,{startX:d,startY:h,currentX:d,currentY:h}),r.touchStartTime=b()));if(r.isTouchEvent&&s.touchReleaseOnEdges&&!s.loop)if(n.isVertical()){if(h<i.startY&&n.translate<=n.maxTranslate()||h>i.startY&&n.translate>=n.minTranslate())return 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void(r.isTouched=!1);if(!r.startMoving)return;n.allowClick=!1,!s.cssMode&&l.cancelable&&l.preventDefault(),s.touchMoveStopPropagation&&!s.nested&&l.stopPropagation(),r.isMoved||(s.loop&&!s.cssMode&&n.loopFix(),r.startTranslate=n.getTranslate(),n.setTransition(0),n.animating&&n.$wrapperEl.trigger("webkitTransitionEnd transitionend"),r.allowMomentumBounce=!1,!s.grabCursor||!0!==n.allowSlideNext&&!0!==n.allowSlidePrev||n.setGrabCursor(!0),n.emit("sliderFirstMove",l)),n.emit("sliderMove",l),r.isMoved=!0;let m=n.isHorizontal()?p:f;i.diff=m,m*=s.touchRatio,o&&(m=-m),n.swipeDirection=m>0?"prev":"next",r.currentTranslate=m+r.startTranslate;let y=!0,v=s.resistanceRatio;if(s.touchReleaseOnEdges&&(v=0),m>0&&r.currentTranslate>n.minTranslate()?(y=!1,s.resistance&&(r.currentTranslate=n.minTranslate()-1+(-n.minTranslate()+r.startTranslate+m)**v)):m<0&&r.currentTranslate<n.maxTranslate()&&(y=!1,s.resistance&&(r.currentTranslate=n.maxTranslate()+1-(n.maxTranslate()-r.startTranslate-m)**v)),y&&(l.preventedByNestedSwiper=!0),!n.allowSlideNext&&"next"===n.swipeDirection&&r.currentTranslate<r.startTranslate&&(r.currentTranslate=r.startTranslate),!n.allowSlidePrev&&"prev"===n.swipeDirection&&r.currentTranslate>r.startTranslate&&(r.currentTranslate=r.startTranslate),n.allowSlidePrev||n.allowSlideNext||(r.currentTranslate=r.startTranslate),s.threshold>0){if(!(Math.abs(m)>s.threshold||r.allowThresholdMove))return void(r.currentTranslate=r.startTranslate);if(!r.allowThresholdMove)return r.allowThresholdMove=!0,i.startX=i.currentX,i.startY=i.currentY,r.currentTranslate=r.startTranslate,void(i.diff=n.isHorizontal()?i.currentX-i.startX:i.currentY-i.startY)}s.followFinger&&!s.cssMode&&((s.freeMode&&s.freeMode.enabled&&n.freeMode||s.watchSlidesProgress)&&(n.updateActiveIndex(),n.updateSlidesClasses()),n.params.freeMode&&s.freeMode.enabled&&n.freeMode&&n.freeMode.onTouchMove(),n.updateProgress(r.currentTranslate),n.setTranslate(r.currentTranslate))}function R(e){const t=this,n=t.touchEventsData,{params:r,touches:s,rtlTranslate:i,slidesGrid:a,enabled:o}=t;if(!o)return;let u=e;if(u.originalEvent&&(u=u.originalEvent),n.allowTouchCallbacks&&t.emit("touchEnd",u),n.allowTouchCallbacks=!1,!n.isTouched)return n.isMoved&&r.grabCursor&&t.setGrabCursor(!1),n.isMoved=!1,void(n.startMoving=!1);r.grabCursor&&n.isMoved&&n.isTouched&&(!0===t.allowSlideNext||!0===t.allowSlidePrev)&&t.setGrabCursor(!1);const l=b(),c=l-n.touchStartTime;if(t.allowClick){const e=u.path||u.composedPath&&u.composedPath();t.updateClickedSlide(e&&e[0]||u.target),t.emit("tap click",u),c<300&&l-n.lastClickTime<300&&t.emit("doubleTap doubleClick",u)}if(n.lastClickTime=b(),y((()=>{t.destroyed||(t.allowClick=!0)})),!n.isTouched||!n.isMoved||!t.swipeDirection||0===s.diff||n.currentTranslate===n.startTranslate)return n.isTouched=!1,n.isMoved=!1,void(n.startMoving=!1);let d;if(n.isTouched=!1,n.isMoved=!1,n.startMoving=!1,d=r.followFinger?i?t.translate:-t.translate:-n.currentTranslate,r.cssMode)return;if(t.params.freeMode&&r.freeMode.enabled)return void t.freeMode.onTouchEnd({currentPos:d});let h=0,p=t.slidesSizesGrid[0];for(let e=0;e<a.length;e+=e<r.slidesPerGroupSkip?1:r.slidesPerGroup){const t=e<r.slidesPerGroupSkip-1?1:r.slidesPerGroup;void 0!==a[e+t]?d>=a[e]&&d<a[e+t]&&(h=e,p=a[e+t]-a[e]):d>=a[e]&&(h=e,p=a[a.length-1]-a[a.length-2])}const f=(d-a[h])/p,m=h<r.slidesPerGroupSkip-1?1:r.slidesPerGroup;if(c>r.longSwipesMs){if(!r.longSwipes)return void t.slideTo(t.activeIndex);"next"===t.swipeDirection&&(f>=r.longSwipesRatio?t.slideTo(h+m):t.slideTo(h)),"prev"===t.swipeDirection&&(f>1-r.longSwipesRatio?t.slideTo(h+m):t.slideTo(h))}else{if(!r.shortSwipes)return void t.slideTo(t.activeIndex);!t.navigation||u.target!==t.navigation.nextEl&&u.target!==t.navigation.prevEl?("next"===t.swipeDirection&&t.slideTo(h+m),"prev"===t.swipeDirection&&t.slideTo(h)):u.target===t.navigation.nextEl?t.slideTo(h+m):t.slideTo(h)}}function $(){const e=this,{params:t,el:n}=e;if(n&&0===n.offsetWidth)return;t.breakpoints&&e.setBreakpoint();const{allowSlideNext:r,allowSlidePrev:s,snapGrid:i}=e;e.allowSlideNext=!0,e.allowSlidePrev=!0,e.updateSize(),e.updateSlides(),e.updateSlidesClasses(),("auto"===t.slidesPerView||t.slidesPerView>1)&&e.isEnd&&!e.isBeginning&&!e.params.centeredSlides?e.slideTo(e.slides.length-1,0,!1,!0):e.slideTo(e.activeIndex,0,!1,!0),e.autoplay&&e.autoplay.running&&e.autoplay.paused&&e.autoplay.run(),e.allowSlidePrev=s,e.allowSlideNext=r,e.params.watchOverflow&&i!==e.snapGrid&&e.checkOverflow()}function F(e){const t=this;t.enabled&&(t.allowClick||(t.params.preventClicks&&e.preventDefault(),t.params.preventClicksPropagation&&t.animating&&(e.stopPropagation(),e.stopImmediatePropagation())))}function O(){const e=this,{wrapperEl:t,rtlTranslate:n,enabled:r}=e;if(!r)return;let 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0,breakpointsBase:"window",spaceBetween:0,slidesPerView:1,slidesPerGroup:1,slidesPerGroupSkip:0,slidesPerGroupAuto:!1,centeredSlides:!1,centeredSlidesBounds:!1,slidesOffsetBefore:0,slidesOffsetAfter:0,normalizeSlideIndex:!0,centerInsufficientSlides:!1,watchOverflow:!0,roundLengths:!1,touchRatio:1,touchAngle:45,simulateTouch:!0,shortSwipes:!0,longSwipes:!0,longSwipesRatio:.5,longSwipesMs:300,followFinger:!0,allowTouchMove:!0,threshold:0,touchMoveStopPropagation:!1,touchStartPreventDefault:!0,touchStartForcePreventDefault:!1,touchReleaseOnEdges:!1,uniqueNavElements:!0,resistance:!0,resistanceRatio:.85,watchSlidesProgress:!1,grabCursor:!1,preventClicks:!0,preventClicksPropagation:!0,slideToClickedSlide:!1,preloadImages:!0,updateOnImagesReady:!0,loop:!1,loopAdditionalSlides:0,loopedSlides:null,loopFillGroupWithBlank:!1,loopPreventsSlide:!0,rewind:!1,allowSlidePrev:!0,allowSlideNext:!0,swipeHandler:null,noSwiping:!0,noSwipingClass:"swiper-no-swiping",noSwipingSelector:null,passiveListeners:!0,containerModifierClass:"swiper-",slideClass:"swiper-slide",slideBlankClass:"swiper-slide-invisible-blank",slideActiveClass:"swiper-slide-active",slideDuplicateActiveClass:"swiper-slide-duplicate-active",slideVisibleClass:"swiper-slide-visible",slideDuplicateClass:"swiper-slide-duplicate",slideNextClass:"swiper-slide-next",slideDuplicateNextClass:"swiper-slide-duplicate-next",slidePrevClass:"swiper-slide-prev",slideDuplicatePrevClass:"swiper-slide-duplicate-prev",wrapperClass:"swiper-wrapper",runCallbacksOnInit:!0,_emitClasses:!1};function B(e,t){return function(n={}){const r=Object.keys(n)[0],s=n[r];"object"==typeof s&&null!==s?(["navigation","pagination","scrollbar"].indexOf(r)>=0&&!0===e[r]&&(e[r]={auto:!0}),r in e&&"enabled"in s?(!0===e[r]&&(e[r]={enabled:!0}),"object"!=typeof e[r]||"enabled"in e[r]||(e[r].enabled=!0),e[r]||(e[r]={enabled:!1}),x(t,n)):x(t,n)):x(t,n)}}const V={eventsEmitter:C,update:{updateSize:function(){const e=this;let t,n;const r=e.$el;t=void 0!==e.params.width&&null!==e.params.width?e.params.width:r[0].clientWidth,n=void 0!==e.params.height&&null!==e.params.height?e.params.height:r[0].clientHeight,0===t&&e.isHorizontal()||0===n&&e.isVertical()||(t=t-parseInt(r.css("padding-left")||0,10)-parseInt(r.css("padding-right")||0,10),n=n-parseInt(r.css("padding-top")||0,10)-parseInt(r.css("padding-bottom")||0,10),Number.isNaN(t)&&(t=0),Number.isNaN(n)&&(n=0),Object.assign(e,{width:t,height:n,size:e.isHorizontal()?t:n}))},updateSlides:function(){const e=this;function t(t){return e.isHorizontal()?t:{width:"height","margin-top":"margin-left","margin-bottom ":"margin-right","margin-left":"margin-top","margin-right":"margin-bottom","padding-left":"padding-top","padding-right":"padding-bottom",marginRight:"marginBottom"}[t]}function n(e,n){return parseFloat(e.getPropertyValue(t(n))||0)}const r=e.params,{$wrapperEl:s,size:i,rtlTranslate:a,wrongRTL:o}=e,u=e.virtual&&r.virtual.enabled,l=u?e.virtual.slides.length:e.slides.length,c=s.children("."+e.params.slideClass),d=u?e.virtual.slides.length:c.length;let h=[];const p=[],f=[];let m=r.slidesOffsetBefore;"function"==typeof m&&(m=r.slidesOffsetBefore.call(e));let g=r.slidesOffsetAfter;"function"==typeof g&&(g=r.slidesOffsetAfter.call(e));const y=e.snapGrid.length,b=e.slidesGrid.length;let v=r.spaceBetween,x=-m,k=0,S=0;if(void 0===i)return;"string"==typeof v&&v.indexOf("%")>=0&&(v=parseFloat(v.replace("%",""))/100*i),e.virtualSize=-v,a?c.css({marginLeft:"",marginBottom:"",marginTop:""}):c.css({marginRight:"",marginBottom:"",marginTop:""}),r.centeredSlides&&r.cssMode&&(w(e.wrapperEl,"--swiper-centered-offset-before",""),w(e.wrapperEl,"--swiper-centered-offset-after",""));const I=r.grid&&r.grid.rows>1&&e.grid;let T;I&&e.grid.initSlides(d);const N="auto"===r.slidesPerView&&r.breakpoints&&Object.keys(r.breakpoints).filter((e=>void 0!==r.breakpoints[e].slidesPerView)).length>0;for(let s=0;s<d;s+=1){T=0;const a=c.eq(s);if(I&&e.grid.updateSlide(s,a,d,t),"none"!==a.css("display")){if("auto"===r.slidesPerView){N&&(c[s].style[t("width")]="");const i=getComputedStyle(a[0]),o=a[0].style.transform,u=a[0].style.webkitTransform;if(o&&(a[0].style.transform="none"),u&&(a[0].style.webkitTransform="none"),r.roundLengths)T=e.isHorizontal()?a.outerWidth(!0):a.outerHeight(!0);else{const e=n(i,"width"),t=n(i,"padding-left"),r=n(i,"padding-right"),s=n(i,"margin-left"),o=n(i,"margin-right"),u=i.getPropertyValue("box-sizing");if(u&&"border-box"===u)T=e+s+o;else{const{clientWidth:n,offsetWidth:i}=a[0];T=e+t+r+s+o+(i-n)}}o&&(a[0].style.transform=o),u&&(a[0].style.webkitTransform=u),r.roundLengths&&(T=Math.floor(T))}else T=(i-(r.slidesPerView-1)*v)/r.slidesPerView,r.roundLengths&&(T=Math.floor(T)),c[s]&&(c[s].style[t("width")]=T+"px");c[s]&&(c[s].swiperSlideSize=T),f.push(T),r.centeredSlides?(x=x+T/2+k/2+v,0===k&&0!==s&&(x=x-i/2-v),0===s&&(x=x-i/2-v),Math.abs(x)<.001&&(x=0),r.roundLengths&&(x=Math.floor(x)),S%r.slidesPerGroup==0&&h.push(x),p.push(x)):(r.roundLengths&&(x=Math.floor(x)),(S-Math.min(e.params.slidesPerGroupSkip,S))%e.params.slidesPerGroup==0&&h.push(x),p.push(x),x=x+T+v),e.virtualSize+=T+v,k=T,S+=1}}if(e.virtualSize=Math.max(e.virtualSize,i)+g,a&&o&&("slide"===r.effect||"coverflow"===r.effect)&&s.css({width:e.virtualSize+r.spaceBetween+"px"}),r.setWrapperSize&&s.css({[t("width")]:e.virtualSize+r.spaceBetween+"px"}),I&&e.grid.updateWrapperSize(T,h,t),!r.centeredSlides){const t=[];for(let n=0;n<h.length;n+=1){let s=h[n];r.roundLengths&&(s=Math.floor(s)),h[n]<=e.virtualSize-i&&t.push(s)}h=t,Math.floor(e.virtualSize-i)-Math.floor(h[h.length-1])>1&&h.push(e.virtualSize-i)}if(0===h.length&&(h=[0]),0!==r.spaceBetween){const n=e.isHorizontal()&&a?"marginLeft":t("marginRight");c.filter(((e,t)=>!r.cssMode||t!==c.length-1)).css({[n]:v+"px"})}if(r.centeredSlides&&r.centeredSlidesBounds){let e=0;f.forEach((t=>{e+=t+(r.spaceBetween?r.spaceBetween:0)})),e-=r.spaceBetween;const t=e-i;h=h.map((e=>e<0?-m:e>t?t+g:e))}if(r.centerInsufficientSlides){let e=0;if(f.forEach((t=>{e+=t+(r.spaceBetween?r.spaceBetween:0)})),e-=r.spaceBetween,e<i){const t=(i-e)/2;h.forEach(((e,n)=>{h[n]=e-t})),p.forEach(((e,n)=>{p[n]=e+t}))}}if(Object.assign(e,{slides:c,snapGrid:h,slidesGrid:p,slidesSizesGrid:f}),r.centeredSlides&&r.cssMode&&!r.centeredSlidesBounds){w(e.wrapperEl,"--swiper-centered-offset-before",-h[0]+"px"),w(e.wrapperEl,"--swiper-centered-offset-after",e.size/2-f[f.length-1]/2+"px");const t=-e.snapGrid[0],n=-e.slidesGrid[0];e.snapGrid=e.snapGrid.map((e=>e+t)),e.slidesGrid=e.slidesGrid.map((e=>e+n))}d!==l&&e.emit("slidesLengthChange"),h.length!==y&&(e.params.watchOverflow&&e.checkOverflow(),e.emit("snapGridLengthChange")),p.length!==b&&e.emit("slidesGridLengthChange"),r.watchSlidesProgress&&e.updateSlidesOffset()},updateAutoHeight:function(e){const t=this,n=[],r=t.virtual&&t.params.virtual.enabled;let s,i=0;"number"==typeof e?t.setTransition(e):!0===e&&t.setTransition(t.params.speed);const a=e=>r?t.slides.filter((t=>parseInt(t.getAttribute("data-swiper-slide-index"),10)===e))[0]:t.slides.eq(e)[0];if("auto"!==t.params.slidesPerView&&t.params.slidesPerView>1)if(t.params.centeredSlides)t.visibleSlides.each((e=>{n.push(e)}));else for(s=0;s<Math.ceil(t.params.slidesPerView);s+=1){const e=t.activeIndex+s;if(e>t.slides.length&&!r)break;n.push(a(e))}else n.push(a(t.activeIndex));for(s=0;s<n.length;s+=1)if(void 0!==n[s]){const 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3/* Wed May 22 2024 17:13:42 GMT+0700 (Indochina Time) */
4/* v2.1.3 */