/*!
 * ONNX Runtime Web v1.23.2
 * Copyright (c) Microsoft Corporation. All rights reserved.
 * Licensed under the MIT License.
 */
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Error(`tensorReshape: tensor location ${r.location} is not supported`)}}});var dt,ui=k(()=>{"use strict";pp();yp();wp();Ip();dt=class{constructor(e,n,t){vp();let o,i;if(typeof e=="object"&&"location"in e)switch(this.dataLocation=e.location,o=e.type,i=e.dims,e.location){case"cpu-pinned":{let s=Or.get(o);if(!s)throw new TypeError(`unsupported type "${o}" to create tensor from pinned buffer`);if(!(e.data instanceof s))throw new TypeError(`buffer should be of type ${s.name}`);this.cpuData=e.data;break}case"texture":{if(o!=="float32")throw new TypeError(`unsupported type "${o}" to create tensor from texture`);this.gpuTextureData=e.texture,this.downloader=e.download,this.disposer=e.dispose;break}case"gpu-buffer":{if(o!=="float32"&&o!=="float16"&&o!=="int32"&&o!=="int64"&&o!=="uint32"&&o!=="uint8"&&o!=="bool"&&o!=="uint4"&&o!=="int4")throw new TypeError(`unsupported type "${o}" to create tensor from gpu buffer`);this.gpuBufferData=e.gpuBuffer,this.downloader=e.download,this.disposer=e.dispose;break}case"ml-tensor":{if(o!=="float32"&&o!=="float16"&&o!=="int32"&&o!=="int64"&&o!=="uint32"&&o!=="uint64"&&o!=="int8"&&o!=="uint8"&&o!=="bool"&&o!=="uint4"&&o!=="int4")throw new TypeError(`unsupported type "${o}" to create tensor from MLTensor`);this.mlTensorData=e.mlTensor,this.downloader=e.download,this.disposer=e.dispose;break}default:throw new Error(`Tensor constructor: unsupported location '${this.dataLocation}'`)}else{let s,u;if(typeof e=="string")if(o=e,u=t,e==="string"){if(!Array.isArray(n))throw new TypeError("A string tensor's data must be a string array.");s=n}else{let l=Or.get(e);if(l===void 0)throw new TypeError(`Unsupported tensor type: ${e}.`);if(Array.isArray(n)){if(e==="float16"&&l===Uint16Array||e==="uint4"||e==="int4")throw new TypeError(`Creating a ${e} tensor from number array is not supported. 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14:o.type=14;break}if(t.f!=null&&(o.f=Number(t.f)),t.i!=null&&(P.Long?(o.i=P.Long.fromValue(t.i)).unsigned=!1:typeof t.i=="string"?o.i=parseInt(t.i,10):typeof t.i=="number"?o.i=t.i:typeof t.i=="object"&&(o.i=new P.LongBits(t.i.low>>>0,t.i.high>>>0).toNumber())),t.s!=null&&(typeof t.s=="string"?P.base64.decode(t.s,o.s=P.newBuffer(P.base64.length(t.s)),0):t.s.length>=0&&(o.s=t.s)),t.t!=null){if(typeof t.t!="object")throw TypeError(".onnx.AttributeProto.t: object expected");o.t=S.onnx.TensorProto.fromObject(t.t)}if(t.g!=null){if(typeof t.g!="object")throw TypeError(".onnx.AttributeProto.g: object expected");o.g=S.onnx.GraphProto.fromObject(t.g)}if(t.sparseTensor!=null){if(typeof t.sparseTensor!="object")throw TypeError(".onnx.AttributeProto.sparseTensor: object expected");o.sparseTensor=S.onnx.SparseTensorProto.fromObject(t.sparseTensor)}if(t.tp!=null){if(typeof t.tp!="object")throw TypeError(".onnx.AttributeProto.tp: object 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t.sparseTensors[i]!="object")throw TypeError(".onnx.AttributeProto.sparseTensors: object expected");o.sparseTensors[i]=S.onnx.SparseTensorProto.fromObject(t.sparseTensors[i])}}if(t.typeProtos){if(!Array.isArray(t.typeProtos))throw TypeError(".onnx.AttributeProto.typeProtos: array expected");o.typeProtos=[];for(var i=0;i<t.typeProtos.length;++i){if(typeof t.typeProtos[i]!="object")throw TypeError(".onnx.AttributeProto.typeProtos: object expected");o.typeProtos[i]=S.onnx.TypeProto.fromObject(t.typeProtos[i])}}return o},e.toObject=function(t,o){o||(o={});var i={};if((o.arrays||o.defaults)&&(i.floats=[],i.ints=[],i.strings=[],i.tensors=[],i.graphs=[],i.typeProtos=[],i.sparseTensors=[]),o.defaults){if(i.name="",i.f=0,P.Long){var a=new P.Long(0,0,!1);i.i=o.longs===String?a.toString():o.longs===Number?a.toNumber():a}else i.i=o.longs===String?"0":0;o.bytes===String?i.s="":(i.s=[],o.bytes!==Array&&(i.s=P.newBuffer(i.s))),i.t=null,i.g=null,i.docString="",i.tp=null,i.type=o.enums===String?"UNDEFINED":0,i.refAttrName="",i.sparseTensor=null}if(t.name!=null&&t.hasOwnProperty("name")&&(i.name=t.name),t.f!=null&&t.hasOwnProperty("f")&&(i.f=o.json&&!isFinite(t.f)?String(t.f):t.f),t.i!=null&&t.hasOwnProperty("i")&&(typeof t.i=="number"?i.i=o.longs===String?String(t.i):t.i:i.i=o.longs===String?P.Long.prototype.toString.call(t.i):o.longs===Number?new P.LongBits(t.i.low>>>0,t.i.high>>>0).toNumber():t.i),t.s!=null&&t.hasOwnProperty("s")&&(i.s=o.bytes===String?P.base64.encode(t.s,0,t.s.length):o.bytes===Array?Array.prototype.slice.call(t.s):t.s),t.t!=null&&t.hasOwnProperty("t")&&(i.t=S.onnx.TensorProto.toObject(t.t,o)),t.g!=null&&t.hasOwnProperty("g")&&(i.g=S.onnx.GraphProto.toObject(t.g,o)),t.floats&&t.floats.length){i.floats=[];for(var s=0;s<t.floats.length;++s)i.floats[s]=o.json&&!isFinite(t.floats[s])?String(t.floats[s]):t.floats[s]}if(t.ints&&t.ints.length){i.ints=[];for(var s=0;s<t.ints.length;++s)typeof t.ints[s]=="number"?i.ints[s]=o.longs===String?String(t.ints[s]):t.ints[s]:i.ints[s]=o.longs===String?P.Long.prototype.toString.call(t.ints[s]):o.longs===Number?new P.LongBits(t.ints[s].low>>>0,t.ints[s].high>>>0).toNumber():t.ints[s]}if(t.strings&&t.strings.length){i.strings=[];for(var s=0;s<t.strings.length;++s)i.strings[s]=o.bytes===String?P.base64.encode(t.strings[s],0,t.strings[s].length):o.bytes===Array?Array.prototype.slice.call(t.strings[s]):t.strings[s]}if(t.tensors&&t.tensors.length){i.tensors=[];for(var s=0;s<t.tensors.length;++s)i.tensors[s]=S.onnx.TensorProto.toObject(t.tensors[s],o)}if(t.graphs&&t.graphs.length){i.graphs=[];for(var s=0;s<t.graphs.length;++s)i.graphs[s]=S.onnx.GraphProto.toObject(t.graphs[s],o)}if(t.docString!=null&&t.hasOwnProperty("docString")&&(i.docString=t.docString),t.tp!=null&&t.hasOwnProperty("tp")&&(i.tp=S.onnx.TypeProto.toObject(t.tp,o)),t.typeProtos&&t.typeProtos.length){i.typeProtos=[];for(var s=0;s<t.typeProtos.length;++s)i.typeProtos[s]=S.onnx.TypeProto.toObject(t.typeProtos[s],o)}if(t.type!=null&&t.hasOwnProperty("type")&&(i.type=o.enums===String?S.onnx.AttributeProto.AttributeType[t.type]===void 0?t.type:S.onnx.AttributeProto.AttributeType[t.type]:t.type),t.refAttrName!=null&&t.hasOwnProperty("refAttrName")&&(i.refAttrName=t.refAttrName),t.sparseTensor!=null&&t.hasOwnProperty("sparseTensor")&&(i.sparseTensor=S.onnx.SparseTensorProto.toObject(t.sparseTensor,o)),t.sparseTensors&&t.sparseTensors.length){i.sparseTensors=[];for(var s=0;s<t.sparseTensors.length;++s)i.sparseTensors[s]=S.onnx.SparseTensorProto.toObject(t.sparseTensors[s],o)}return i},e.prototype.toJSON=function(){return this.constructor.toObject(this,Ue.util.toJSONOptions)},e.getTypeUrl=function(t){return t===void 0&&(t="type.googleapis.com"),t+"/onnx.AttributeProto"},e.AttributeType=function(){var n={},t=Object.create(n);return t[n[0]="UNDEFINED"]=0,t[n[1]="FLOAT"]=1,t[n[2]="INT"]=2,t[n[3]="STRING"]=3,t[n[4]="TENSOR"]=4,t[n[5]="GRAPH"]=5,t[n[11]="SPARSE_TENSOR"]=11,t[n[13]="TYPE_PROTO"]=13,t[n[6]="FLOATS"]=6,t[n[7]="INTS"]=7,t[n[8]="STRINGS"]=8,t[n[9]="TENSORS"]=9,t[n[10]="GRAPHS"]=10,t[n[12]="SPARSE_TENSORS"]=12,t[n[14]="TYPE_PROTOS"]=14,t}(),e}(),r.ValueInfoProto=function(){function e(n){if(n)for(var t=Object.keys(n),o=0;o<t.length;++o)n[t[o]]!=null&&(this[t[o]]=n[t[o]])}return e.prototype.name="",e.prototype.type=null,e.prototype.docString="",e.create=function(t){return new e(t)},e.encode=function(t,o){return o||(o=Qe.create()),t.name!=null&&Object.hasOwnProperty.call(t,"name")&&o.uint32(10).string(t.name),t.type!=null&&Object.hasOwnProperty.call(t,"type")&&S.onnx.TypeProto.encode(t.type,o.uint32(18).fork()).ldelim(),t.docString!=null&&Object.hasOwnProperty.call(t,"docString")&&o.uint32(26).string(t.docString),o},e.encodeDelimited=function(t,o){return this.encode(t,o).ldelim()},e.decode=function(t,o){t instanceof X||(t=X.create(t));for(var i=o===void 0?t.len:t.pos+o,a=new S.onnx.ValueInfoProto;t.pos<i;){var s=t.uint32();switch(s>>>3){case 1:{a.name=t.string();break}case 2:{a.type=S.onnx.TypeProto.decode(t,t.uint32());break}case 3:{a.docString=t.string();break}default:t.skipType(s&7);break}}return a},e.decodeDelimited=function(t){return t instanceof X||(t=new X(t)),this.decode(t,t.uint32())},e.verify=function(t){if(typeof t!="object"||t===null)return"object expected";if(t.name!=null&&t.hasOwnProperty("name")&&!P.isString(t.name))return"name: string 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this.constructor.toObject(this,Ue.util.toJSONOptions)},e.getTypeUrl=function(t){return t===void 0&&(t="type.googleapis.com"),t+"/onnx.ValueInfoProto"},e}(),r.NodeProto=function(){function e(n){if(this.input=[],this.output=[],this.attribute=[],n)for(var t=Object.keys(n),o=0;o<t.length;++o)n[t[o]]!=null&&(this[t[o]]=n[t[o]])}return e.prototype.input=P.emptyArray,e.prototype.output=P.emptyArray,e.prototype.name="",e.prototype.opType="",e.prototype.domain="",e.prototype.attribute=P.emptyArray,e.prototype.docString="",e.create=function(t){return new e(t)},e.encode=function(t,o){if(o||(o=Qe.create()),t.input!=null&&t.input.length)for(var i=0;i<t.input.length;++i)o.uint32(10).string(t.input[i]);if(t.output!=null&&t.output.length)for(var i=0;i<t.output.length;++i)o.uint32(18).string(t.output[i]);if(t.name!=null&&Object.hasOwnProperty.call(t,"name")&&o.uint32(26).string(t.name),t.opType!=null&&Object.hasOwnProperty.call(t,"opType")&&o.uint32(34).string(t.opType),t.attribute!=null&&t.attribute.length)for(var i=0;i<t.attribute.length;++i)S.onnx.AttributeProto.encode(t.attribute[i],o.uint32(42).fork()).ldelim();return t.docString!=null&&Object.hasOwnProperty.call(t,"docString")&&o.uint32(50).string(t.docString),t.domain!=null&&Object.hasOwnProperty.call(t,"domain")&&o.uint32(58).string(t.domain),o},e.encodeDelimited=function(t,o){return this.encode(t,o).ldelim()},e.decode=function(t,o){t instanceof X||(t=X.create(t));for(var i=o===void 0?t.len:t.pos+o,a=new S.onnx.NodeProto;t.pos<i;){var s=t.uint32();switch(s>>>3){case 1:{a.input&&a.input.length||(a.input=[]),a.input.push(t.string());break}case 2:{a.output&&a.output.length||(a.output=[]),a.output.push(t.string());break}case 3:{a.name=t.string();break}case 4:{a.opType=t.string();break}case 7:{a.domain=t.string();break}case 5:{a.attribute&&a.attribute.length||(a.attribute=[]),a.attribute.push(S.onnx.AttributeProto.decode(t,t.uint32()));break}case 6:{a.docString=t.string();break}default:t.skipType(s&7);break}}return a},e.decodeDelimited=function(t){return t instanceof X||(t=new X(t)),this.decode(t,t.uint32())},e.verify=function(t){if(typeof t!="object"||t===null)return"object expected";if(t.input!=null&&t.hasOwnProperty("input")){if(!Array.isArray(t.input))return"input: array expected";for(var o=0;o<t.input.length;++o)if(!P.isString(t.input[o]))return"input: string[] expected"}if(t.output!=null&&t.hasOwnProperty("output")){if(!Array.isArray(t.output))return"output: array expected";for(var o=0;o<t.output.length;++o)if(!P.isString(t.output[o]))return"output: string[] expected"}if(t.name!=null&&t.hasOwnProperty("name")&&!P.isString(t.name))return"name: string expected";if(t.opType!=null&&t.hasOwnProperty("opType")&&!P.isString(t.opType))return"opType: string expected";if(t.domain!=null&&t.hasOwnProperty("domain")&&!P.isString(t.domain))return"domain: string expected";if(t.attribute!=null&&t.hasOwnProperty("attribute")){if(!Array.isArray(t.attribute))return"attribute: array expected";for(var o=0;o<t.attribute.length;++o){var i=S.onnx.AttributeProto.verify(t.attribute[o]);if(i)return"attribute."+i}}return t.docString!=null&&t.hasOwnProperty("docString")&&!P.isString(t.docString)?"docString: string expected":null},e.fromObject=function(t){if(t instanceof S.onnx.NodeProto)return t;var o=new S.onnx.NodeProto;if(t.input){if(!Array.isArray(t.input))throw TypeError(".onnx.NodeProto.input: array expected");o.input=[];for(var i=0;i<t.input.length;++i)o.input[i]=String(t.input[i])}if(t.output){if(!Array.isArray(t.output))throw TypeError(".onnx.NodeProto.output: array expected");o.output=[];for(var i=0;i<t.output.length;++i)o.output[i]=String(t.output[i])}if(t.name!=null&&(o.name=String(t.name)),t.opType!=null&&(o.opType=String(t.opType)),t.domain!=null&&(o.domain=String(t.domain)),t.attribute){if(!Array.isArray(t.attribute))throw TypeError(".onnx.NodeProto.attribute: array expected");o.attribute=[];for(var i=0;i<t.attribute.length;++i){if(typeof t.attribute[i]!="object")throw TypeError(".onnx.NodeProto.attribute: object expected");o.attribute[i]=S.onnx.AttributeProto.fromObject(t.attribute[i])}}return t.docString!=null&&(o.docString=String(t.docString)),o},e.toObject=function(t,o){o||(o={});var i={};if((o.arrays||o.defaults)&&(i.input=[],i.output=[],i.attribute=[]),o.defaults&&(i.name="",i.opType="",i.docString="",i.domain=""),t.input&&t.input.length){i.input=[];for(var a=0;a<t.input.length;++a)i.input[a]=t.input[a]}if(t.output&&t.output.length){i.output=[];for(var a=0;a<t.output.length;++a)i.output[a]=t.output[a]}if(t.name!=null&&t.hasOwnProperty("name")&&(i.name=t.name),t.opType!=null&&t.hasOwnProperty("opType")&&(i.opType=t.opType),t.attribute&&t.attribute.length){i.attribute=[];for(var a=0;a<t.attribute.length;++a)i.attribute[a]=S.onnx.AttributeProto.toObject(t.attribute[a],o)}return t.docString!=null&&t.hasOwnProperty("docString")&&(i.docString=t.docString),t.domain!=null&&t.hasOwnProperty("domain")&&(i.domain=t.domain),i},e.prototype.toJSON=function(){return this.constructor.toObject(this,Ue.util.toJSONOptions)},e.getTypeUrl=function(t){return t===void 0&&(t="type.googleapis.com"),t+"/onnx.NodeProto"},e}(),r.TrainingInfoProto=function(){function e(n){if(this.initializationBinding=[],this.updateBinding=[],n)for(var t=Object.keys(n),o=0;o<t.length;++o)n[t[o]]!=null&&(this[t[o]]=n[t[o]])}return e.prototype.initialization=null,e.prototype.algorithm=null,e.prototype.initializationBinding=P.emptyArray,e.prototype.updateBinding=P.emptyArray,e.create=function(t){return new e(t)},e.encode=function(t,o){if(o||(o=Qe.create()),t.initialization!=null&&Object.hasOwnProperty.call(t,"initialization")&&S.onnx.GraphProto.encode(t.initialization,o.uint32(10).fork()).ldelim(),t.algorithm!=null&&Object.hasOwnProperty.call(t,"algorithm")&&S.onnx.GraphProto.encode(t.algorithm,o.uint32(18).fork()).ldelim(),t.initializationBinding!=null&&t.initializationBinding.length)for(var i=0;i<t.initializationBinding.length;++i)S.onnx.StringStringEntryProto.encode(t.initializationBinding[i],o.uint32(26).fork()).ldelim();if(t.updateBinding!=null&&t.updateBinding.length)for(var i=0;i<t.updateBinding.length;++i)S.onnx.StringStringEntryProto.encode(t.updateBinding[i],o.uint32(34).fork()).ldelim();return o},e.encodeDelimited=function(t,o){return this.encode(t,o).ldelim()},e.decode=function(t,o){t instanceof X||(t=X.create(t));for(var i=o===void 0?t.len:t.pos+o,a=new S.onnx.TrainingInfoProto;t.pos<i;){var s=t.uint32();switch(s>>>3){case 1:{a.initialization=S.onnx.GraphProto.decode(t,t.uint32());break}case 2:{a.algorithm=S.onnx.GraphProto.decode(t,t.uint32());break}case 3:{a.initializationBinding&&a.initializationBinding.length||(a.initializationBinding=[]),a.initializationBinding.push(S.onnx.StringStringEntryProto.decode(t,t.uint32()));break}case 4:{a.updateBinding&&a.updateBinding.length||(a.updateBinding=[]),a.updateBinding.push(S.onnx.StringStringEntryProto.decode(t,t.uint32()));break}default:t.skipType(s&7);break}}return a},e.decodeDelimited=function(t){return t instanceof X||(t=new X(t)),this.decode(t,t.uint32())},e.verify=function(t){if(typeof t!="object"||t===null)return"object expected";if(t.initialization!=null&&t.hasOwnProperty("initialization")){var o=S.onnx.GraphProto.verify(t.initialization);if(o)return"initialization."+o}if(t.algorithm!=null&&t.hasOwnProperty("algorithm")){var o=S.onnx.GraphProto.verify(t.algorithm);if(o)return"algorithm."+o}if(t.initializationBinding!=null&&t.hasOwnProperty("initializationBinding")){if(!Array.isArray(t.initializationBinding))return"initializationBinding: array expected";for(var i=0;i<t.initializationBinding.length;++i){var o=S.onnx.StringStringEntryProto.verify(t.initializationBinding[i]);if(o)return"initializationBinding."+o}}if(t.updateBinding!=null&&t.hasOwnProperty("updateBinding")){if(!Array.isArray(t.updateBinding))return"updateBinding: array expected";for(var i=0;i<t.updateBinding.length;++i){var o=S.onnx.StringStringEntryProto.verify(t.updateBinding[i]);if(o)return"updateBinding."+o}}return null},e.fromObject=function(t){if(t instanceof S.onnx.TrainingInfoProto)return t;var o=new S.onnx.TrainingInfoProto;if(t.initialization!=null){if(typeof t.initialization!="object")throw TypeError(".onnx.TrainingInfoProto.initialization: object expected");o.initialization=S.onnx.GraphProto.fromObject(t.initialization)}if(t.algorithm!=null){if(typeof t.algorithm!="object")throw TypeError(".onnx.TrainingInfoProto.algorithm: object expected");o.algorithm=S.onnx.GraphProto.fromObject(t.algorithm)}if(t.initializationBinding){if(!Array.isArray(t.initializationBinding))throw TypeError(".onnx.TrainingInfoProto.initializationBinding: array expected");o.initializationBinding=[];for(var i=0;i<t.initializationBinding.length;++i){if(typeof t.initializationBinding[i]!="object")throw TypeError(".onnx.TrainingInfoProto.initializationBinding: object expected");o.initializationBinding[i]=S.onnx.StringStringEntryProto.fromObject(t.initializationBinding[i])}}if(t.updateBinding){if(!Array.isArray(t.updateBinding))throw TypeError(".onnx.TrainingInfoProto.updateBinding: array expected");o.updateBinding=[];for(var i=0;i<t.updateBinding.length;++i){if(typeof t.updateBinding[i]!="object")throw TypeError(".onnx.TrainingInfoProto.updateBinding: object expected");o.updateBinding[i]=S.onnx.StringStringEntryProto.fromObject(t.updateBinding[i])}}return o},e.toObject=function(t,o){o||(o={});var i={};if((o.arrays||o.defaults)&&(i.initializationBinding=[],i.updateBinding=[]),o.defaults&&(i.initialization=null,i.algorithm=null),t.initialization!=null&&t.hasOwnProperty("initialization")&&(i.initialization=S.onnx.GraphProto.toObject(t.initialization,o)),t.algorithm!=null&&t.hasOwnProperty("algorithm")&&(i.algorithm=S.onnx.GraphProto.toObject(t.algorithm,o)),t.initializationBinding&&t.initializationBinding.length){i.initializationBinding=[];for(var a=0;a<t.initializationBinding.length;++a)i.initializationBinding[a]=S.onnx.StringStringEntryProto.toObject(t.initializationBinding[a],o)}if(t.updateBinding&&t.updateBinding.length){i.updateBinding=[];for(var a=0;a<t.updateBinding.length;++a)i.updateBinding[a]=S.onnx.StringStringEntryProto.toObject(t.updateBinding[a],o)}return i},e.prototype.toJSON=function(){return this.constructor.toObject(this,Ue.util.toJSONOptions)},e.getTypeUrl=function(t){return t===void 0&&(t="type.googleapis.com"),t+"/onnx.TrainingInfoProto"},e}(),r.ModelProto=function(){function e(n){if(this.opsetImport=[],this.metadataProps=[],this.trainingInfo=[],this.functions=[],n)for(var t=Object.keys(n),o=0;o<t.length;++o)n[t[o]]!=null&&(this[t[o]]=n[t[o]])}return e.prototype.irVersion=P.Long?P.Long.fromBits(0,0,!1):0,e.prototype.opsetImport=P.emptyArray,e.prototype.producerName="",e.prototype.producerVersion="",e.prototype.domain="",e.prototype.modelVersion=P.Long?P.Long.fromBits(0,0,!1):0,e.prototype.docString="",e.prototype.graph=null,e.prototype.metadataProps=P.emptyArray,e.prototype.trainingInfo=P.emptyArray,e.prototype.functions=P.emptyArray,e.create=function(t){return new e(t)},e.encode=function(t,o){if(o||(o=Qe.create()),t.irVersion!=null&&Object.hasOwnProperty.call(t,"irVersion")&&o.uint32(8).int64(t.irVersion),t.producerName!=null&&Object.hasOwnProperty.call(t,"producerName")&&o.uint32(18).string(t.producerName),t.producerVersion!=null&&Object.hasOwnProperty.call(t,"producerVersion")&&o.uint32(26).string(t.producerVersion),t.domain!=null&&Object.hasOwnProperty.call(t,"domain")&&o.uint32(34).string(t.domain),t.modelVersion!=null&&Object.hasOwnProperty.call(t,"modelVersion")&&o.uint32(40).int64(t.modelVersion),t.docString!=null&&Object.hasOwnProperty.call(t,"docString")&&o.uint32(50).string(t.docString),t.graph!=null&&Object.hasOwnProperty.call(t,"graph")&&S.onnx.GraphProto.encode(t.graph,o.uint32(58).fork()).ldelim(),t.opsetImport!=null&&t.opsetImport.length)for(var i=0;i<t.opsetImport.length;++i)S.onnx.OperatorSetIdProto.encode(t.opsetImport[i],o.uint32(66).fork()).ldelim();if(t.metadataProps!=null&&t.metadataProps.length)for(var i=0;i<t.metadataProps.length;++i)S.onnx.StringStringEntryProto.encode(t.metadataProps[i],o.uint32(114).fork()).ldelim();if(t.trainingInfo!=null&&t.trainingInfo.length)for(var i=0;i<t.trainingInfo.length;++i)S.onnx.TrainingInfoProto.encode(t.trainingInfo[i],o.uint32(162).fork()).ldelim();if(t.functions!=null&&t.functions.length)for(var i=0;i<t.functions.length;++i)S.onnx.FunctionProto.encode(t.functions[i],o.uint32(202).fork()).ldelim();return o},e.encodeDelimited=function(t,o){return this.encode(t,o).ldelim()},e.decode=function(t,o){t instanceof X||(t=X.create(t));for(var i=o===void 0?t.len:t.pos+o,a=new S.onnx.ModelProto;t.pos<i;){var s=t.uint32();switch(s>>>3){case 1:{a.irVersion=t.int64();break}case 8:{a.opsetImport&&a.opsetImport.length||(a.opsetImport=[]),a.opsetImport.push(S.onnx.OperatorSetIdProto.decode(t,t.uint32()));break}case 2:{a.producerName=t.string();break}case 3:{a.producerVersion=t.string();break}case 4:{a.domain=t.string();break}case 5:{a.modelVersion=t.int64();break}case 6:{a.docString=t.string();break}case 7:{a.graph=S.onnx.GraphProto.decode(t,t.uint32());break}case 14:{a.metadataProps&&a.metadataProps.length||(a.metadataProps=[]),a.metadataProps.push(S.onnx.StringStringEntryProto.decode(t,t.uint32()));break}case 20:{a.trainingInfo&&a.trainingInfo.length||(a.trainingInfo=[]),a.trainingInfo.push(S.onnx.TrainingInfoProto.decode(t,t.uint32()));break}case 25:{a.functions&&a.functions.length||(a.functions=[]),a.functions.push(S.onnx.FunctionProto.decode(t,t.uint32()));break}default:t.skipType(s&7);break}}return a},e.decodeDelimited=function(t){return t instanceof X||(t=new X(t)),this.decode(t,t.uint32())},e.verify=function(t){if(typeof t!="object"||t===null)return"object expected";if(t.irVersion!=null&&t.hasOwnProperty("irVersion")&&!P.isInteger(t.irVersion)&&!(t.irVersion&&P.isInteger(t.irVersion.low)&&P.isInteger(t.irVersion.high)))return"irVersion: integer|Long expected";if(t.opsetImport!=null&&t.hasOwnProperty("opsetImport")){if(!Array.isArray(t.opsetImport))return"opsetImport: array expected";for(var o=0;o<t.opsetImport.length;++o){var i=S.onnx.OperatorSetIdProto.verify(t.opsetImport[o]);if(i)return"opsetImport."+i}}if(t.producerName!=null&&t.hasOwnProperty("producerName")&&!P.isString(t.producerName))return"producerName: string expected";if(t.producerVersion!=null&&t.hasOwnProperty("producerVersion")&&!P.isString(t.producerVersion))return"producerVersion: string expected";if(t.domain!=null&&t.hasOwnProperty("domain")&&!P.isString(t.domain))return"domain: string expected";if(t.modelVersion!=null&&t.hasOwnProperty("modelVersion")&&!P.isInteger(t.modelVersion)&&!(t.modelVersion&&P.isInteger(t.modelVersion.low)&&P.isInteger(t.modelVersion.high)))return"modelVersion: integer|Long expected";if(t.docString!=null&&t.hasOwnProperty("docString")&&!P.isString(t.docString))return"docString: string expected";if(t.graph!=null&&t.hasOwnProperty("graph")){var i=S.onnx.GraphProto.verify(t.graph);if(i)return"graph."+i}if(t.metadataProps!=null&&t.hasOwnProperty("metadataProps")){if(!Array.isArray(t.metadataProps))return"metadataProps: array expected";for(var o=0;o<t.metadataProps.length;++o){var i=S.onnx.StringStringEntryProto.verify(t.metadataProps[o]);if(i)return"metadataProps."+i}}if(t.trainingInfo!=null&&t.hasOwnProperty("trainingInfo")){if(!Array.isArray(t.trainingInfo))return"trainingInfo: array expected";for(var o=0;o<t.trainingInfo.length;++o){var i=S.onnx.TrainingInfoProto.verify(t.trainingInfo[o]);if(i)return"trainingInfo."+i}}if(t.functions!=null&&t.hasOwnProperty("functions")){if(!Array.isArray(t.functions))return"functions: array expected";for(var o=0;o<t.functions.length;++o){var i=S.onnx.FunctionProto.verify(t.functions[o]);if(i)return"functions."+i}}return null},e.fromObject=function(t){if(t instanceof S.onnx.ModelProto)return t;var o=new S.onnx.ModelProto;if(t.irVersion!=null&&(P.Long?(o.irVersion=P.Long.fromValue(t.irVersion)).unsigned=!1:typeof t.irVersion=="string"?o.irVersion=parseInt(t.irVersion,10):typeof t.irVersion=="number"?o.irVersion=t.irVersion:typeof t.irVersion=="object"&&(o.irVersion=new P.LongBits(t.irVersion.low>>>0,t.irVersion.high>>>0).toNumber())),t.opsetImport){if(!Array.isArray(t.opsetImport))throw TypeError(".onnx.ModelProto.opsetImport: array expected");o.opsetImport=[];for(var i=0;i<t.opsetImport.length;++i){if(typeof t.opsetImport[i]!="object")throw TypeError(".onnx.ModelProto.opsetImport: object expected");o.opsetImport[i]=S.onnx.OperatorSetIdProto.fromObject(t.opsetImport[i])}}if(t.producerName!=null&&(o.producerName=String(t.producerName)),t.producerVersion!=null&&(o.producerVersion=String(t.producerVersion)),t.domain!=null&&(o.domain=String(t.domain)),t.modelVersion!=null&&(P.Long?(o.modelVersion=P.Long.fromValue(t.modelVersion)).unsigned=!1:typeof t.modelVersion=="string"?o.modelVersion=parseInt(t.modelVersion,10):typeof t.modelVersion=="number"?o.modelVersion=t.modelVersion:typeof t.modelVersion=="object"&&(o.modelVersion=new P.LongBits(t.modelVersion.low>>>0,t.modelVersion.high>>>0).toNumber())),t.docString!=null&&(o.docString=String(t.docString)),t.graph!=null){if(typeof t.graph!="object")throw TypeError(".onnx.ModelProto.graph: object expected");o.graph=S.onnx.GraphProto.fromObject(t.graph)}if(t.metadataProps){if(!Array.isArray(t.metadataProps))throw TypeError(".onnx.ModelProto.metadataProps: array expected");o.metadataProps=[];for(var i=0;i<t.metadataProps.length;++i){if(typeof t.metadataProps[i]!="object")throw TypeError(".onnx.ModelProto.metadataProps: object expected");o.metadataProps[i]=S.onnx.StringStringEntryProto.fromObject(t.metadataProps[i])}}if(t.trainingInfo){if(!Array.isArray(t.trainingInfo))throw TypeError(".onnx.ModelProto.trainingInfo: array expected");o.trainingInfo=[];for(var i=0;i<t.trainingInfo.length;++i){if(typeof t.trainingInfo[i]!="object")throw TypeError(".onnx.ModelProto.trainingInfo: object expected");o.trainingInfo[i]=S.onnx.TrainingInfoProto.fromObject(t.trainingInfo[i])}}if(t.functions){if(!Array.isArray(t.functions))throw TypeError(".onnx.ModelProto.functions: array expected");o.functions=[];for(var i=0;i<t.functions.length;++i){if(typeof t.functions[i]!="object")throw TypeError(".onnx.ModelProto.functions: object expected");o.functions[i]=S.onnx.FunctionProto.fromObject(t.functions[i])}}return o},e.toObject=function(t,o){o||(o={});var i={};if((o.arrays||o.defaults)&&(i.opsetImport=[],i.metadataProps=[],i.trainingInfo=[],i.functions=[]),o.defaults){if(P.Long){var a=new P.Long(0,0,!1);i.irVersion=o.longs===String?a.toString():o.longs===Number?a.toNumber():a}else i.irVersion=o.longs===String?"0":0;if(i.producerName="",i.producerVersion="",i.domain="",P.Long){var a=new P.Long(0,0,!1);i.modelVersion=o.longs===String?a.toString():o.longs===Number?a.toNumber():a}else i.modelVersion=o.longs===String?"0":0;i.docString="",i.graph=null}if(t.irVersion!=null&&t.hasOwnProperty("irVersion")&&(typeof t.irVersion=="number"?i.irVersion=o.longs===String?String(t.irVersion):t.irVersion:i.irVersion=o.longs===String?P.Long.prototype.toString.call(t.irVersion):o.longs===Number?new P.LongBits(t.irVersion.low>>>0,t.irVersion.high>>>0).toNumber():t.irVersion),t.producerName!=null&&t.hasOwnProperty("producerName")&&(i.producerName=t.producerName),t.producerVersion!=null&&t.hasOwnProperty("producerVersion")&&(i.producerVersion=t.producerVersion),t.domain!=null&&t.hasOwnProperty("domain")&&(i.domain=t.domain),t.modelVersion!=null&&t.hasOwnProperty("modelVersion")&&(typeof t.modelVersion=="number"?i.modelVersion=o.longs===String?String(t.modelVersion):t.modelVersion:i.modelVersion=o.longs===String?P.Long.prototype.toString.call(t.modelVersion):o.longs===Number?new P.LongBits(t.modelVersion.low>>>0,t.modelVersion.high>>>0).toNumber():t.modelVersion),t.docString!=null&&t.hasOwnProperty("docString")&&(i.docString=t.docString),t.graph!=null&&t.hasOwnProperty("graph")&&(i.graph=S.onnx.GraphProto.toObject(t.graph,o)),t.opsetImport&&t.opsetImport.length){i.opsetImport=[];for(var s=0;s<t.opsetImport.length;++s)i.opsetImport[s]=S.onnx.OperatorSetIdProto.toObject(t.opsetImport[s],o)}if(t.metadataProps&&t.metadataProps.length){i.metadataProps=[];for(var s=0;s<t.metadataProps.length;++s)i.metadataProps[s]=S.onnx.StringStringEntryProto.toObject(t.metadataProps[s],o)}if(t.trainingInfo&&t.trainingInfo.length){i.trainingInfo=[];for(var s=0;s<t.trainingInfo.length;++s)i.trainingInfo[s]=S.onnx.TrainingInfoProto.toObject(t.trainingInfo[s],o)}if(t.functions&&t.functions.length){i.functions=[];for(var s=0;s<t.functions.length;++s)i.functions[s]=S.onnx.FunctionProto.toObject(t.functions[s],o)}return i},e.prototype.toJSON=function(){return this.constructor.toObject(this,Ue.util.toJSONOptions)},e.getTypeUrl=function(t){return t===void 0&&(t="type.googleapis.com"),t+"/onnx.ModelProto"},e}(),r.StringStringEntryProto=function(){function e(n){if(n)for(var t=Object.keys(n),o=0;o<t.length;++o)n[t[o]]!=null&&(this[t[o]]=n[t[o]])}return e.prototype.key="",e.prototype.value="",e.create=function(t){return new e(t)},e.encode=function(t,o){return o||(o=Qe.create()),t.key!=null&&Object.hasOwnProperty.call(t,"key")&&o.uint32(10).string(t.key),t.value!=null&&Object.hasOwnProperty.call(t,"value")&&o.uint32(18).string(t.value),o},e.encodeDelimited=function(t,o){return this.encode(t,o).ldelim()},e.decode=function(t,o){t instanceof X||(t=X.create(t));for(var i=o===void 0?t.len:t.pos+o,a=new S.onnx.StringStringEntryProto;t.pos<i;){var s=t.uint32();switch(s>>>3){case 1:{a.key=t.string();break}case 2:{a.value=t.string();break}default:t.skipType(s&7);break}}return a},e.decodeDelimited=function(t){return t instanceof X||(t=new X(t)),this.decode(t,t.uint32())},e.verify=function(t){return typeof t!="object"||t===null?"object expected":t.key!=null&&t.hasOwnProperty("key")&&!P.isString(t.key)?"key: string expected":t.value!=null&&t.hasOwnProperty("value")&&!P.isString(t.value)?"value: string expected":null},e.fromObject=function(t){if(t instanceof S.onnx.StringStringEntryProto)return t;var o=new S.onnx.StringStringEntryProto;return t.key!=null&&(o.key=String(t.key)),t.value!=null&&(o.value=String(t.value)),o},e.toObject=function(t,o){o||(o={});var i={};return o.defaults&&(i.key="",i.value=""),t.key!=null&&t.hasOwnProperty("key")&&(i.key=t.key),t.value!=null&&t.hasOwnProperty("value")&&(i.value=t.value),i},e.prototype.toJSON=function(){return this.constructor.toObject(this,Ue.util.toJSONOptions)},e.getTypeUrl=function(t){return t===void 0&&(t="type.googleapis.com"),t+"/onnx.StringStringEntryProto"},e}(),r.TensorAnnotation=function(){function e(n){if(this.quantParameterTensorNames=[],n)for(var t=Object.keys(n),o=0;o<t.length;++o)n[t[o]]!=null&&(this[t[o]]=n[t[o]])}return e.prototype.tensorName="",e.prototype.quantParameterTensorNames=P.emptyArray,e.create=function(t){return new e(t)},e.encode=function(t,o){if(o||(o=Qe.create()),t.tensorName!=null&&Object.hasOwnProperty.call(t,"tensorName")&&o.uint32(10).string(t.tensorName),t.quantParameterTensorNames!=null&&t.quantParameterTensorNames.length)for(var i=0;i<t.quantParameterTensorNames.length;++i)S.onnx.StringStringEntryProto.encode(t.quantParameterTensorNames[i],o.uint32(18).fork()).ldelim();return o},e.encodeDelimited=function(t,o){return this.encode(t,o).ldelim()},e.decode=function(t,o){t instanceof X||(t=X.create(t));for(var i=o===void 0?t.len:t.pos+o,a=new S.onnx.TensorAnnotation;t.pos<i;){var s=t.uint32();switch(s>>>3){case 1:{a.tensorName=t.string();break}case 2:{a.quantParameterTensorNames&&a.quantParameterTensorNames.length||(a.quantParameterTensorNames=[]),a.quantParameterTensorNames.push(S.onnx.StringStringEntryProto.decode(t,t.uint32()));break}default:t.skipType(s&7);break}}return a},e.decodeDelimited=function(t){return t instanceof X||(t=new X(t)),this.decode(t,t.uint32())},e.verify=function(t){if(typeof t!="object"||t===null)return"object expected";if(t.tensorName!=null&&t.hasOwnProperty("tensorName")&&!P.isString(t.tensorName))return"tensorName: string expected";if(t.quantParameterTensorNames!=null&&t.hasOwnProperty("quantParameterTensorNames")){if(!Array.isArray(t.quantParameterTensorNames))return"quantParameterTensorNames: array expected";for(var o=0;o<t.quantParameterTensorNames.length;++o){var i=S.onnx.StringStringEntryProto.verify(t.quantParameterTensorNames[o]);if(i)return"quantParameterTensorNames."+i}}return null},e.fromObject=function(t){if(t instanceof S.onnx.TensorAnnotation)return t;var o=new S.onnx.TensorAnnotation;if(t.tensorName!=null&&(o.tensorName=String(t.tensorName)),t.quantParameterTensorNames){if(!Array.isArray(t.quantParameterTensorNames))throw TypeError(".onnx.TensorAnnotation.quantParameterTensorNames: array expected");o.quantParameterTensorNames=[];for(var i=0;i<t.quantParameterTensorNames.length;++i){if(typeof t.quantParameterTensorNames[i]!="object")throw TypeError(".onnx.TensorAnnotation.quantParameterTensorNames: object expected");o.quantParameterTensorNames[i]=S.onnx.StringStringEntryProto.fromObject(t.quantParameterTensorNames[i])}}return o},e.toObject=function(t,o){o||(o={});var i={};if((o.arrays||o.defaults)&&(i.quantParameterTensorNames=[]),o.defaults&&(i.tensorName=""),t.tensorName!=null&&t.hasOwnProperty("tensorName")&&(i.tensorName=t.tensorName),t.quantParameterTensorNames&&t.quantParameterTensorNames.length){i.quantParameterTensorNames=[];for(var a=0;a<t.quantParameterTensorNames.length;++a)i.quantParameterTensorNames[a]=S.onnx.StringStringEntryProto.toObject(t.quantParameterTensorNames[a],o)}return i},e.prototype.toJSON=function(){return this.constructor.toObject(this,Ue.util.toJSONOptions)},e.getTypeUrl=function(t){return t===void 0&&(t="type.googleapis.com"),t+"/onnx.TensorAnnotation"},e}(),r.GraphProto=function(){function e(n){if(this.node=[],this.initializer=[],this.sparseInitializer=[],this.input=[],this.output=[],this.valueInfo=[],this.quantizationAnnotation=[],n)for(var t=Object.keys(n),o=0;o<t.length;++o)n[t[o]]!=null&&(this[t[o]]=n[t[o]])}return e.prototype.node=P.emptyArray,e.prototype.name="",e.prototype.initializer=P.emptyArray,e.prototype.sparseInitializer=P.emptyArray,e.prototype.docString="",e.prototype.input=P.emptyArray,e.prototype.output=P.emptyArray,e.prototype.valueInfo=P.emptyArray,e.prototype.quantizationAnnotation=P.emptyArray,e.create=function(t){return new e(t)},e.encode=function(t,o){if(o||(o=Qe.create()),t.node!=null&&t.node.length)for(var i=0;i<t.node.length;++i)S.onnx.NodeProto.encode(t.node[i],o.uint32(10).fork()).ldelim();if(t.name!=null&&Object.hasOwnProperty.call(t,"name")&&o.uint32(18).string(t.name),t.initializer!=null&&t.initializer.length)for(var i=0;i<t.initializer.length;++i)S.onnx.TensorProto.encode(t.initializer[i],o.uint32(42).fork()).ldelim();if(t.docString!=null&&Object.hasOwnProperty.call(t,"docString")&&o.uint32(82).string(t.docString),t.input!=null&&t.input.length)for(var i=0;i<t.input.length;++i)S.onnx.ValueInfoProto.encode(t.input[i],o.uint32(90).fork()).ldelim();if(t.output!=null&&t.output.length)for(var i=0;i<t.output.length;++i)S.onnx.ValueInfoProto.encode(t.output[i],o.uint32(98).fork()).ldelim();if(t.valueInfo!=null&&t.valueInfo.length)for(var i=0;i<t.valueInfo.length;++i)S.onnx.ValueInfoProto.encode(t.valueInfo[i],o.uint32(106).fork()).ldelim();if(t.quantizationAnnotation!=null&&t.quantizationAnnotation.length)for(var i=0;i<t.quantizationAnnotation.length;++i)S.onnx.TensorAnnotation.encode(t.quantizationAnnotation[i],o.uint32(114).fork()).ldelim();if(t.sparseInitializer!=null&&t.sparseInitializer.length)for(var i=0;i<t.sparseInitializer.length;++i)S.onnx.SparseTensorProto.encode(t.sparseInitializer[i],o.uint32(122).fork()).ldelim();return o},e.encodeDelimited=function(t,o){return this.encode(t,o).ldelim()},e.decode=function(t,o){t instanceof X||(t=X.create(t));for(var i=o===void 0?t.len:t.pos+o,a=new S.onnx.GraphProto;t.pos<i;){var s=t.uint32();switch(s>>>3){case 1:{a.node&&a.node.length||(a.node=[]),a.node.push(S.onnx.NodeProto.decode(t,t.uint32()));break}case 2:{a.name=t.string();break}case 5:{a.initializer&&a.initializer.length||(a.initializer=[]),a.initializer.push(S.onnx.TensorProto.decode(t,t.uint32()));break}case 15:{a.sparseInitializer&&a.sparseInitializer.length||(a.sparseInitializer=[]),a.sparseInitializer.push(S.onnx.SparseTensorProto.decode(t,t.uint32()));break}case 10:{a.docString=t.string();break}case 11:{a.input&&a.input.length||(a.input=[]),a.input.push(S.onnx.ValueInfoProto.decode(t,t.uint32()));break}case 12:{a.output&&a.output.length||(a.output=[]),a.output.push(S.onnx.ValueInfoProto.decode(t,t.uint32()));break}case 13:{a.valueInfo&&a.valueInfo.length||(a.valueInfo=[]),a.valueInfo.push(S.onnx.ValueInfoProto.decode(t,t.uint32()));break}case 14:{a.quantizationAnnotation&&a.quantizationAnnotation.length||(a.quantizationAnnotation=[]),a.quantizationAnnotation.push(S.onnx.TensorAnnotation.decode(t,t.uint32()));break}default:t.skipType(s&7);break}}return a},e.decodeDelimited=function(t){return t instanceof X||(t=new X(t)),this.decode(t,t.uint32())},e.verify=function(t){if(typeof t!="object"||t===null)return"object expected";if(t.node!=null&&t.hasOwnProperty("node")){if(!Array.isArray(t.node))return"node: array expected";for(var o=0;o<t.node.length;++o){var i=S.onnx.NodeProto.verify(t.node[o]);if(i)return"node."+i}}if(t.name!=null&&t.hasOwnProperty("name")&&!P.isString(t.name))return"name: string expected";if(t.initializer!=null&&t.hasOwnProperty("initializer")){if(!Array.isArray(t.initializer))return"initializer: array expected";for(var o=0;o<t.initializer.length;++o){var i=S.onnx.TensorProto.verify(t.initializer[o]);if(i)return"initializer."+i}}if(t.sparseInitializer!=null&&t.hasOwnProperty("sparseInitializer")){if(!Array.isArray(t.sparseInitializer))return"sparseInitializer: array expected";for(var o=0;o<t.sparseInitializer.length;++o){var i=S.onnx.SparseTensorProto.verify(t.sparseInitializer[o]);if(i)return"sparseInitializer."+i}}if(t.docString!=null&&t.hasOwnProperty("docString")&&!P.isString(t.docString))return"docString: string expected";if(t.input!=null&&t.hasOwnProperty("input")){if(!Array.isArray(t.input))return"input: array expected";for(var o=0;o<t.input.length;++o){var i=S.onnx.ValueInfoProto.verify(t.input[o]);if(i)return"input."+i}}if(t.output!=null&&t.hasOwnProperty("output")){if(!Array.isArray(t.output))return"output: array expected";for(var o=0;o<t.output.length;++o){var i=S.onnx.ValueInfoProto.verify(t.output[o]);if(i)return"output."+i}}if(t.valueInfo!=null&&t.hasOwnProperty("valueInfo")){if(!Array.isArray(t.valueInfo))return"valueInfo: array expected";for(var o=0;o<t.valueInfo.length;++o){var i=S.onnx.ValueInfoProto.verify(t.valueInfo[o]);if(i)return"valueInfo."+i}}if(t.quantizationAnnotation!=null&&t.hasOwnProperty("quantizationAnnotation")){if(!Array.isArray(t.quantizationAnnotation))return"quantizationAnnotation: array expected";for(var o=0;o<t.quantizationAnnotation.length;++o){var i=S.onnx.TensorAnnotation.verify(t.quantizationAnnotation[o]);if(i)return"quantizationAnnotation."+i}}return null},e.fromObject=function(t){if(t instanceof S.onnx.GraphProto)return t;var o=new S.onnx.GraphProto;if(t.node){if(!Array.isArray(t.node))throw TypeError(".onnx.GraphProto.node: array expected");o.node=[];for(var i=0;i<t.node.length;++i){if(typeof t.node[i]!="object")throw TypeError(".onnx.GraphProto.node: object expected");o.node[i]=S.onnx.NodeProto.fromObject(t.node[i])}}if(t.name!=null&&(o.name=String(t.name)),t.initializer){if(!Array.isArray(t.initializer))throw TypeError(".onnx.GraphProto.initializer: array expected");o.initializer=[];for(var i=0;i<t.initializer.length;++i){if(typeof t.initializer[i]!="object")throw TypeError(".onnx.GraphProto.initializer: object expected");o.initializer[i]=S.onnx.TensorProto.fromObject(t.initializer[i])}}if(t.sparseInitializer){if(!Array.isArray(t.sparseInitializer))throw TypeError(".onnx.GraphProto.sparseInitializer: array expected");o.sparseInitializer=[];for(var i=0;i<t.sparseInitializer.length;++i){if(typeof t.sparseInitializer[i]!="object")throw TypeError(".onnx.GraphProto.sparseInitializer: object expected");o.sparseInitializer[i]=S.onnx.SparseTensorProto.fromObject(t.sparseInitializer[i])}}if(t.docString!=null&&(o.docString=String(t.docString)),t.input){if(!Array.isArray(t.input))throw TypeError(".onnx.GraphProto.input: array expected");o.input=[];for(var i=0;i<t.input.length;++i){if(typeof t.input[i]!="object")throw TypeError(".onnx.GraphProto.input: object expected");o.input[i]=S.onnx.ValueInfoProto.fromObject(t.input[i])}}if(t.output){if(!Array.isArray(t.output))throw TypeError(".onnx.GraphProto.output: array expected");o.output=[];for(var i=0;i<t.output.length;++i){if(typeof t.output[i]!="object")throw TypeError(".onnx.GraphProto.output: object expected");o.output[i]=S.onnx.ValueInfoProto.fromObject(t.output[i])}}if(t.valueInfo){if(!Array.isArray(t.valueInfo))throw TypeError(".onnx.GraphProto.valueInfo: array expected");o.valueInfo=[];for(var i=0;i<t.valueInfo.length;++i){if(typeof t.valueInfo[i]!="object")throw TypeError(".onnx.GraphProto.valueInfo: object expected");o.valueInfo[i]=S.onnx.ValueInfoProto.fromObject(t.valueInfo[i])}}if(t.quantizationAnnotation){if(!Array.isArray(t.quantizationAnnotation))throw TypeError(".onnx.GraphProto.quantizationAnnotation: array expected");o.quantizationAnnotation=[];for(var i=0;i<t.quantizationAnnotation.length;++i){if(typeof t.quantizationAnnotation[i]!="object")throw TypeError(".onnx.GraphProto.quantizationAnnotation: object expected");o.quantizationAnnotation[i]=S.onnx.TensorAnnotation.fromObject(t.quantizationAnnotation[i])}}return o},e.toObject=function(t,o){o||(o={});var i={};if((o.arrays||o.defaults)&&(i.node=[],i.initializer=[],i.input=[],i.output=[],i.valueInfo=[],i.quantizationAnnotation=[],i.sparseInitializer=[]),o.defaults&&(i.name="",i.docString=""),t.node&&t.node.length){i.node=[];for(var a=0;a<t.node.length;++a)i.node[a]=S.onnx.NodeProto.toObject(t.node[a],o)}if(t.name!=null&&t.hasOwnProperty("name")&&(i.name=t.name),t.initializer&&t.initializer.length){i.initializer=[];for(var a=0;a<t.initializer.length;++a)i.initializer[a]=S.onnx.TensorProto.toObject(t.initializer[a],o)}if(t.docString!=null&&t.hasOwnProperty("docString")&&(i.docString=t.docString),t.input&&t.input.length){i.input=[];for(var a=0;a<t.input.length;++a)i.input[a]=S.onnx.ValueInfoProto.toObject(t.input[a],o)}if(t.output&&t.output.length){i.output=[];for(var a=0;a<t.output.length;++a)i.output[a]=S.onnx.ValueInfoProto.toObject(t.output[a],o)}if(t.valueInfo&&t.valueInfo.length){i.valueInfo=[];for(var a=0;a<t.valueInfo.length;++a)i.valueInfo[a]=S.onnx.ValueInfoProto.toObject(t.valueInfo[a],o)}if(t.quantizationAnnotation&&t.quantizationAnnotation.length){i.quantizationAnnotation=[];for(var a=0;a<t.quantizationAnnotation.length;++a)i.quantizationAnnotation[a]=S.onnx.TensorAnnotation.toObject(t.quantizationAnnotation[a],o)}if(t.sparseInitializer&&t.sparseInitializer.length){i.sparseInitializer=[];for(var a=0;a<t.sparseInitializer.length;++a)i.sparseInitializer[a]=S.onnx.SparseTensorProto.toObject(t.sparseInitializer[a],o)}return i},e.prototype.toJSON=function(){return this.constructor.toObject(this,Ue.util.toJSONOptions)},e.getTypeUrl=function(t){return t===void 0&&(t="type.googleapis.com"),t+"/onnx.GraphProto"},e}(),r.TensorProto=function(){function e(n){if(this.dims=[],this.floatData=[],this.int32Data=[],this.stringData=[],this.int64Data=[],this.externalData=[],this.doubleData=[],this.uint64Data=[],n)for(var t=Object.keys(n),o=0;o<t.length;++o)n[t[o]]!=null&&(this[t[o]]=n[t[o]])}return e.prototype.dims=P.emptyArray,e.prototype.dataType=0,e.prototype.segment=null,e.prototype.floatData=P.emptyArray,e.prototype.int32Data=P.emptyArray,e.prototype.stringData=P.emptyArray,e.prototype.int64Data=P.emptyArray,e.prototype.name="",e.prototype.docString="",e.prototype.rawData=P.newBuffer([]),e.prototype.externalData=P.emptyArray,e.prototype.dataLocation=0,e.prototype.doubleData=P.emptyArray,e.prototype.uint64Data=P.emptyArray,e.create=function(t){return new e(t)},e.encode=function(t,o){if(o||(o=Qe.create()),t.dims!=null&&t.dims.length){o.uint32(10).fork();for(var i=0;i<t.dims.length;++i)o.int64(t.dims[i]);o.ldelim()}if(t.dataType!=null&&Object.hasOwnProperty.call(t,"dataType")&&o.uint32(16).int32(t.dataType),t.segment!=null&&Object.hasOwnProperty.call(t,"segment")&&S.onnx.TensorProto.Segment.encode(t.segment,o.uint32(26).fork()).ldelim(),t.floatData!=null&&t.floatData.length){o.uint32(34).fork();for(var i=0;i<t.floatData.length;++i)o.float(t.floatData[i]);o.ldelim()}if(t.int32Data!=null&&t.int32Data.length){o.uint32(42).fork();for(var i=0;i<t.int32Data.length;++i)o.int32(t.int32Data[i]);o.ldelim()}if(t.stringData!=null&&t.stringData.length)for(var i=0;i<t.stringData.length;++i)o.uint32(50).bytes(t.stringData[i]);if(t.int64Data!=null&&t.int64Data.length){o.uint32(58).fork();for(var i=0;i<t.int64Data.length;++i)o.int64(t.int64Data[i]);o.ldelim()}if(t.name!=null&&Object.hasOwnProperty.call(t,"name")&&o.uint32(66).string(t.name),t.rawData!=null&&Object.hasOwnProperty.call(t,"rawData")&&o.uint32(74).bytes(t.rawData),t.doubleData!=null&&t.doubleData.length){o.uint32(82).fork();for(var i=0;i<t.doubleData.length;++i)o.double(t.doubleData[i]);o.ldelim()}if(t.uint64Data!=null&&t.uint64Data.length){o.uint32(90).fork();for(var i=0;i<t.uint64Data.length;++i)o.uint64(t.uint64Data[i]);o.ldelim()}if(t.docString!=null&&Object.hasOwnProperty.call(t,"docString")&&o.uint32(98).string(t.docString),t.externalData!=null&&t.externalData.length)for(var i=0;i<t.externalData.length;++i)S.onnx.StringStringEntryProto.encode(t.externalData[i],o.uint32(106).fork()).ldelim();return t.dataLocation!=null&&Object.hasOwnProperty.call(t,"dataLocation")&&o.uint32(112).int32(t.dataLocation),o},e.encodeDelimited=function(t,o){return this.encode(t,o).ldelim()},e.decode=function(t,o){t instanceof X||(t=X.create(t));for(var i=o===void 0?t.len:t.pos+o,a=new S.onnx.TensorProto;t.pos<i;){var s=t.uint32();switch(s>>>3){case 1:{if(a.dims&&a.dims.length||(a.dims=[]),(s&7)===2)for(var u=t.uint32()+t.pos;t.pos<u;)a.dims.push(t.int64());else a.dims.push(t.int64());break}case 2:{a.dataType=t.int32();break}case 3:{a.segment=S.onnx.TensorProto.Segment.decode(t,t.uint32());break}case 4:{if(a.floatData&&a.floatData.length||(a.floatData=[]),(s&7)===2)for(var u=t.uint32()+t.pos;t.pos<u;)a.floatData.push(t.float());else a.floatData.push(t.float());break}case 5:{if(a.int32Data&&a.int32Data.length||(a.int32Data=[]),(s&7)===2)for(var u=t.uint32()+t.pos;t.pos<u;)a.int32Data.push(t.int32());else a.int32Data.push(t.int32());break}case 6:{a.stringData&&a.stringData.length||(a.stringData=[]),a.stringData.push(t.bytes());break}case 7:{if(a.int64Data&&a.int64Data.length||(a.int64Data=[]),(s&7)===2)for(var u=t.uint32()+t.pos;t.pos<u;)a.int64Data.push(t.int64());else a.int64Data.push(t.int64());break}case 8:{a.name=t.string();break}case 12:{a.docString=t.string();break}case 9:{a.rawData=t.bytes();break}case 13:{a.externalData&&a.externalData.length||(a.externalData=[]),a.externalData.push(S.onnx.StringStringEntryProto.decode(t,t.uint32()));break}case 14:{a.dataLocation=t.int32();break}case 10:{if(a.doubleData&&a.doubleData.length||(a.doubleData=[]),(s&7)===2)for(var u=t.uint32()+t.pos;t.pos<u;)a.doubleData.push(t.double());else a.doubleData.push(t.double());break}case 11:{if(a.uint64Data&&a.uint64Data.length||(a.uint64Data=[]),(s&7)===2)for(var u=t.uint32()+t.pos;t.pos<u;)a.uint64Data.push(t.uint64());else a.uint64Data.push(t.uint64());break}default:t.skipType(s&7);break}}return a},e.decodeDelimited=function(t){return t instanceof X||(t=new X(t)),this.decode(t,t.uint32())},e.verify=function(t){if(typeof t!="object"||t===null)return"object expected";if(t.dims!=null&&t.hasOwnProperty("dims")){if(!Array.isArray(t.dims))return"dims: array expected";for(var o=0;o<t.dims.length;++o)if(!P.isInteger(t.dims[o])&&!(t.dims[o]&&P.isInteger(t.dims[o].low)&&P.isInteger(t.dims[o].high)))return"dims: integer|Long[] expected"}if(t.dataType!=null&&t.hasOwnProperty("dataType")&&!P.isInteger(t.dataType))return"dataType: integer expected";if(t.segment!=null&&t.hasOwnProperty("segment")){var i=S.onnx.TensorProto.Segment.verify(t.segment);if(i)return"segment."+i}if(t.floatData!=null&&t.hasOwnProperty("floatData")){if(!Array.isArray(t.floatData))return"floatData: array expected";for(var o=0;o<t.floatData.length;++o)if(typeof t.floatData[o]!="number")return"floatData: number[] expected"}if(t.int32Data!=null&&t.hasOwnProperty("int32Data")){if(!Array.isArray(t.int32Data))return"int32Data: array expected";for(var o=0;o<t.int32Data.length;++o)if(!P.isInteger(t.int32Data[o]))return"int32Data: integer[] expected"}if(t.stringData!=null&&t.hasOwnProperty("stringData")){if(!Array.isArray(t.stringData))return"stringData: array expected";for(var o=0;o<t.stringData.length;++o)if(!(t.stringData[o]&&typeof t.stringData[o].length=="number"||P.isString(t.stringData[o])))return"stringData: buffer[] expected"}if(t.int64Data!=null&&t.hasOwnProperty("int64Data")){if(!Array.isArray(t.int64Data))return"int64Data: array expected";for(var o=0;o<t.int64Data.length;++o)if(!P.isInteger(t.int64Data[o])&&!(t.int64Data[o]&&P.isInteger(t.int64Data[o].low)&&P.isInteger(t.int64Data[o].high)))return"int64Data: integer|Long[] expected"}if(t.name!=null&&t.hasOwnProperty("name")&&!P.isString(t.name))return"name: string expected";if(t.docString!=null&&t.hasOwnProperty("docString")&&!P.isString(t.docString))return"docString: string expected";if(t.rawData!=null&&t.hasOwnProperty("rawData")&&!(t.rawData&&typeof t.rawData.length=="number"||P.isString(t.rawData)))return"rawData: buffer expected";if(t.externalData!=null&&t.hasOwnProperty("externalData")){if(!Array.isArray(t.externalData))return"externalData: array expected";for(var o=0;o<t.externalData.length;++o){var i=S.onnx.StringStringEntryProto.verify(t.externalData[o]);if(i)return"externalData."+i}}if(t.dataLocation!=null&&t.hasOwnProperty("dataLocation"))switch(t.dataLocation){default:return"dataLocation: enum value expected";case 0:case 1:break}if(t.doubleData!=null&&t.hasOwnProperty("doubleData")){if(!Array.isArray(t.doubleData))return"doubleData: array expected";for(var o=0;o<t.doubleData.length;++o)if(typeof t.doubleData[o]!="number")return"doubleData: number[] expected"}if(t.uint64Data!=null&&t.hasOwnProperty("uint64Data")){if(!Array.isArray(t.uint64Data))return"uint64Data: array expected";for(var o=0;o<t.uint64Data.length;++o)if(!P.isInteger(t.uint64Data[o])&&!(t.uint64Data[o]&&P.isInteger(t.uint64Data[o].low)&&P.isInteger(t.uint64Data[o].high)))return"uint64Data: integer|Long[] expected"}return null},e.fromObject=function(t){if(t instanceof S.onnx.TensorProto)return t;var o=new S.onnx.TensorProto;if(t.dims){if(!Array.isArray(t.dims))throw TypeError(".onnx.TensorProto.dims: array expected");o.dims=[];for(var i=0;i<t.dims.length;++i)P.Long?(o.dims[i]=P.Long.fromValue(t.dims[i])).unsigned=!1:typeof t.dims[i]=="string"?o.dims[i]=parseInt(t.dims[i],10):typeof t.dims[i]=="number"?o.dims[i]=t.dims[i]:typeof 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expected";if(t.input!=null&&t.hasOwnProperty("input")){if(!Array.isArray(t.input))return"input: array expected";for(var o=0;o<t.input.length;++o)if(!P.isString(t.input[o]))return"input: string[] expected"}if(t.output!=null&&t.hasOwnProperty("output")){if(!Array.isArray(t.output))return"output: array expected";for(var o=0;o<t.output.length;++o)if(!P.isString(t.output[o]))return"output: string[] expected"}if(t.attribute!=null&&t.hasOwnProperty("attribute")){if(!Array.isArray(t.attribute))return"attribute: array expected";for(var o=0;o<t.attribute.length;++o)if(!P.isString(t.attribute[o]))return"attribute: string[] expected"}if(t.attributeProto!=null&&t.hasOwnProperty("attributeProto")){if(!Array.isArray(t.attributeProto))return"attributeProto: array expected";for(var o=0;o<t.attributeProto.length;++o){var i=S.onnx.AttributeProto.verify(t.attributeProto[o]);if(i)return"attributeProto."+i}}if(t.node!=null&&t.hasOwnProperty("node")){if(!Array.isArray(t.node))return"node: array 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0&&(t="type.googleapis.com"),t+"/onnx.FunctionProto"},e}(),r}();yh.exports=S});function no(r,e){if(!r)throw new Error(typeof e=="string"?e:e())}function Co(r){return new TextDecoder().decode(r)}var We,kr,yl,gt,Li,ft,xt,ne,Eo,Nr,Lr,Rr,Me=k(()=>{"use strict";Fs();We=ve(to());zr();kr=class{static arraysEqual(e,n){if(e.length!==n.length)return!1;for(let t=0;t<e.length;t++)if(e[t]!==n[t])return!1;return!0}},yl=class{static preprocessInputShapes(e,n){let t=e.length===1?[1,e[0]]:e,o=n.length===1?[n[0],1]:n;return[t,o]}static postprocessOutputShape(e,n,t){n===1&&e.splice(e.length-2,1),t===1&&e.pop()}static calcMatMulShape(e,n){return e[1]!==n[0]?void 0:[e[0],n[1]]}},gt=class r{static calcShape(e,n,t=!1){let o=e.length,i=n.length;if(o===0)return n;if(i===0)return e;let a=Math.max(e.length,n.length),s=new Array(a);if(t){if(o<2||i<2)return;let u=yl.calcMatMulShape([e[o-2],e[o-1]],[n[i-2],n[i-1]]);if(u===void 0)return;[s[a-2],s[a-1]]=u}for(let u=t?3:1;u<=a;u++){let l=o-u<0?1:e[o-u],d=i-u<0?1:n[i-u];if(l!==d&&l>1&&d>1)return;s[a-u]=Math.max(l,d)}return s}static index(e,n){let t=new Array(n.length);return r.fillIndex(e,n,t),t}static fillIndex(e,n,t){let o=e.length-n.length;for(let i=0;i<n.length;i++)t[i]=e[o+i]%n[i]}static calc(e,n,t,o,i){let a=r.calcShape(e.dims,n.dims);if(a){if(o&&!ne.areEqual(a,e.dims))return;let s=ne.size(a),u=o?e:new nt(a,i||e.type);if(a.length===0)u.set([],t(e.get([]),n.get([])));else{let l=new Array(a.length),d=new Array(e.dims.length),f=new Array(n.dims.length),h=0,g=0,b=!1,_=!1;e.dims.length===0&&(h=e.get([]),b=!0),n.dims.length===0&&(g=n.get([]),_=!0);let T;for(let v=0;v<s;v++){T=v;for(let x=a.length-1;x>=0;x--)l[x]=T%a[x],T=Math.floor(T/a[x]);b||(r.fillIndex(l,e.dims,d),h=e.get(d)),_||(r.fillIndex(l,n.dims,f),g=n.get(f)),u.set(l,t(h,g))}}return u}}static isValidBroadcast(e,n){let t=e.length,o=n.length;if(t>o)return!1;for(let i=1;i<=t;i++)if(e[t-i]!==1&&e[t-i]!==n[o-i])return!1;return!0}static getBroadcastDims(e,n){let t=e.length,o=[];for(let i=0;i<t;i++){let a=t-1-i,s=e[a]||1;(n[n.length-1-i]||1)>1&&s===1&&o.unshift(a)}return o}},Li=class{static getShapeOfGemmResult(e,n,t,o,i){if(e.length!==2||t.length!==2)throw new Error("shape need to be of size 2");let a,s,u;n?(a=e[1],s=e[0]):(a=e[0],s=e[1]);let l=-1;if(o?(u=t[0],l=1):(u=t[1],l=0),t[l]!==s)throw new Error("dimension mismatch");if(a<=0||u<=0||s<=0)throw new Error("invalid shape specified");if(i&&!gt.isValidBroadcast(i,[a,u]))throw new Error("gemm: invalid bias shape for broadcast");return[a,u,s]}},ft=class r{static tensorDataTypeFromProto(e){switch(e){case We.onnx.TensorProto.DataType.INT8:return"int8";case We.onnx.TensorProto.DataType.UINT8:return"uint8";case We.onnx.TensorProto.DataType.BOOL:return"bool";case We.onnx.TensorProto.DataType.INT16:return"int16";case We.onnx.TensorProto.DataType.UINT16:return"uint16";case We.onnx.TensorProto.DataType.INT32:return"int32";case We.onnx.TensorProto.DataType.UINT32:return"uint32";case We.onnx.TensorProto.DataType.FLOAT:return"float32";case We.onnx.TensorProto.DataType.DOUBLE:return"float64";case We.onnx.TensorProto.DataType.STRING:return"string";case We.onnx.TensorProto.DataType.INT64:return"int32";case We.onnx.TensorProto.DataType.UINT64:return"uint32";default:throw new Error(`unsupported data type: ${We.onnx.TensorProto.DataType[e]}`)}}static tensorDataTypeStringToEnum(e){switch(e){case"int8":return We.onnx.TensorProto.DataType.INT8;case"uint8":return We.onnx.TensorProto.DataType.UINT8;case"bool":return We.onnx.TensorProto.DataType.BOOL;case"int16":return We.onnx.TensorProto.DataType.INT16;case"uint16":return We.onnx.TensorProto.DataType.UINT16;case"int32":return We.onnx.TensorProto.DataType.INT32;case"uint32":return We.onnx.TensorProto.DataType.UINT32;case"float32":return We.onnx.TensorProto.DataType.FLOAT;case"float64":return We.onnx.TensorProto.DataType.DOUBLE;case"string":return We.onnx.TensorProto.DataType.STRING;case"int64":return We.onnx.TensorProto.DataType.INT64;case"uint64":return We.onnx.TensorProto.DataType.UINT64;default:throw new Error(`unsupported data type: ${e}`)}}static tensorDimsFromProto(e){return e.map(n=>dr.isLong(n)?n.toNumber():n)}static tensorValueTypeFromProto(e){return{tensorType:r.tensorDataTypeFromProto(e.elemType),shape:{dims:r.tensorDimsFromProto(e.shape.dim.map(n=>n.dimValue))}}}static tensorDimsFromORTFormat(e){let n=[];for(let t=0;t<e.dimsLength();t++)n.push(xt.longToNumber(e.dims(t)));return n}static tensorAttributesFromORTFormat(e){let n=[];for(let t=0;t<e.attributesLength();t++)n.push(e.attributes(t));return n}},xt=class{static longToNumber(e){return dr.isLong(e)?e.toNumber():typeof e=="bigint"?Number(e):e}static isLong(e){return dr.isLong(e)||typeof e=="bigint"}},ne=class r{static size(e){return r.getSizeFromDimensionRange(e,0,e.length)}static sizeFromDimension(e,n){if(n<0||n>e.length)throw new Error(`invalid dimension of ${n} for sizeFromDimension as Tensor has ${e.length} dimensions.`);return r.getSizeFromDimensionRange(e,n,e.length)}static sizeToDimension(e,n){if(n<0||n>e.length)throw new Error(`invalid dimension of ${n} for sizeToDimension as Tensor has ${e.length} dimensions.`);return r.getSizeFromDimensionRange(e,0,n)}static getSizeFromDimensionRange(e,n,t){let o=1;for(let i=n;i<t;i++){if(e[i]<=0)throw new Error("cannot get valid size from specified dimension range. 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Input shape: [${e}] Output shape: [${n}]`);o[i]=s/a}else if(a!==s)throw new Error("reshapedDims and originalDims don't have matching sizes");return o}static sortBasedOnPerm(e,n){return n?n.map(t=>e[t]):e.slice().reverse()}static padShape(e,n){let t=e.length;return e.map((o,i)=>o+n[i]+n[i+t])}static areEqual(e,n){return e.length!==n.length?!1:e.every((t,o)=>t===n[o])}static validateDimsAndCalcSize(e){if(e.length>6)throw new TypeError("Only rank 0 to 6 is supported for tensor shape.");let n=1;for(let t of e){if(!Number.isInteger(t))throw new TypeError(`Invalid shape: ${t} is not an integer`);if(t<0||t>2147483647)throw new TypeError(`Invalid shape: length ${t} is not allowed`);n*=t}return n}static flattenShape(e,n){n<0&&(n+=e.length);let t=e.reduce((a,s)=>a*s,1),o=e.slice(n).reduce((a,s)=>a*s,1);return[t/o,o]}static squeezeShape(e,n){let t=new Array;n=r.normalizeAxes(n,e.length);for(let o=0;o<e.length;o++){let i=n.indexOf(o)>=0;if(i&&e[o]!==1)throw new Error("squeeze an axis of size different than 1");(n.length===0&&e[o]>1||n.length>0&&!i)&&t.push(e[o])}return t}static unsqueezeShape(e,n){let t=new Array(e.length+n.length);t.fill(0);for(let i=0;i<n.length;i++){let a=r.normalizeAxis(n[i],t.length);if(a>=t.length)throw new Error("'axes' has an out of range axis");if(t[a]!==0)throw new Error("'axes' has a duplicate axis");t[a]=1}let o=0;for(let i=0;i<t.length;i++)t[i]===0&&(t[i]=e[o++]);if(o!==e.length)throw new Error("the unsqueezed dimension could not be established");return t}},Eo=class r{static splitShape(e,n,t,o){if(t.length===0){if(!o)throw new Error("need to know number of outputs when the 'split' attribute is not specified");r.determineSplit(e[n],o,t)}let i=[],a=[0];for(let s=0;s<t.length;++s){s!==0&&a.push(a[s-1]+t[s-1]);let u=e.slice();u[n]=t[s],i.push(u)}return[i,a]}static determineSplit(e,n,t){if(e%n!==0)throw new Error("cannot split tensor to equal sized parts");for(let o=0;o<n;++o)t.push(e/n)}},Nr=class r{static adjustPoolAttributes(e,n,t,o,i,a){if(!e&&t.length!==n.length-2)throw new Error("length of specified kernel shapes should be 2 less than length of input dimensions");if(e)for(let s=0;s<n.length-2;s++)s>=t.length?t.push(n[s+2]):t[s]=n[s+2];for(let s=0;s<t.length;s++)if(s<o.length){if(o[s]<0)throw new Error("strides should be greater than or equal to 1")}else o.push(1);for(let s=0;s<t.length;s++)if(s<i.length){if(i[s]<0)throw new Error("dilations should be greater than or equal to 1")}else i.push(1);for(let s=0;s<t.length*2;s++)if(s<a.length){if(a[s]<0)throw new Error("pad should be greater than or equal to 1")}else a.push(0);for(let s=0;s<t.length;s++){if(t[s]<=0)throw new Error("kernel shapes need to be greater than 0");if(a[s]>=t[s]||a[s+t.length]>=t[s])throw new Error("pads should be smaller than kernel")}}static adjustPadsBasedOnAutoPad(e,n,t,o,i,a){if(a){if(i.length!==2*(e.length-2))throw new Error("length of pads should be twice the length of data dimensions");if(n.length!==e.length-2)throw new Error("length of strides should be the length of data dimensions");if(o.length!==e.length-2)throw new Error("length of kernel shapes should be the length of data dimensions");for(let s=0;s<e.length-2;s++)r.adjustPadAndReturnShape(e[s+2],n[s],t[s],o[s],i,s,s+e.length-2,a)}}static computePoolOutputShape(e,n,t,o,i,a,s){if(n.length<=0)throw new Error("input shape must be of size greater than 0");let u=[n[0],n[1]];return r.computeShapeHelper(e,n,u,t,o,i,a,s),u}static computeConvOutputShape(e,n,t,o,i,a,s){if(e.length<=0||n.length<=0)throw new Error("invalid input tensor dims or invalid filter tensor dims");let u=[e[0],n[0]];return r.computeShapeHelper(!1,e,u,t,o,i,a,s),u}static computeShapeHelper(e,n,t,o,i,a,s,u){if(e)for(let l=0;l<n.length-2;l++)t.push(1);else for(let l=0;l<n.length-2;l++)t.push(r.adjustPadAndReturnShape(n[l+2],o[l],i[l],a[l],s,l,l+n.length-2,u))}static adjustPadAndReturnShape(e,n,t,o,i,a,s,u){let l=t*(o-1)+1;if(u&&u!=="NOTSET")switch(u){case"VALID":return i[a]=0,i[s]=0,Math.floor((e-l)/n+1);case"SAME_LOWER":case"SAME_UPPER":if(t!==1)throw new Error("Dilation not supported for SAME_UPPER or SAME_LOWER");{let f=((e+n-1)/n-1)*n+o-e;return i[a]=Math.floor(u==="SAME_LOWER"?(f+1)/2:f/2),i[s]=f-i[a],Math.floor((e+f-o)/n+1)}default:throw new Error("Unsupported AutoPad type")}else return Math.floor((e+i[a]+i[s]-l)/n+1)}},Lr=-34028234663852886e22,Rr=34028234663852886e22});function vS(r){switch(r){case"bool":case"int8":case"uint8":return 1;case"int16":case"uint16":return 2;case"int32":case"uint32":case"float32":return 4;case"float64":return 8;default:throw new Error(`cannot calculate sizeof() on type ${r}`)}}function _h(r){switch(r){case Ie.onnx.TensorProto.DataType.UINT8:case Ie.onnx.TensorProto.DataType.INT8:case Ie.onnx.TensorProto.DataType.BOOL:return 1;case Ie.onnx.TensorProto.DataType.UINT16:case Ie.onnx.TensorProto.DataType.INT16:return 2;case Ie.onnx.TensorProto.DataType.FLOAT:case Ie.onnx.TensorProto.DataType.INT32:case Ie.onnx.TensorProto.DataType.UINT32:return 4;case Ie.onnx.TensorProto.DataType.INT64:case Ie.onnx.TensorProto.DataType.DOUBLE:case Ie.onnx.TensorProto.DataType.UINT64:return 8;default:throw new Error(`cannot calculate sizeof() on type ${Ie.onnx.TensorProto.DataType[r]}`)}}function wS(r,e){return new(xh(e))(r)}function xh(r){switch(r){case"bool":case"uint8":return Uint8Array;case"int8":return Int8Array;case"int16":return Int16Array;case"uint16":return Uint16Array;case"int32":return Int32Array;case"uint32":return Uint32Array;case"int64":return BigInt64Array;case"float32":return Float32Array;case"float64":return Float64Array;default:throw new Error("unspecified error")}}function _l(r,e){if(e===Ie.onnx.TensorProto.DataType.INT64||e===So.TensorDataType.INT64){if(r.greaterThanOrEqual(2147483648)||r.lessThan(-2147483648))throw new TypeError("int64 is not supported")}else if(e===Ie.onnx.TensorProto.DataType.UINT32||e===So.TensorDataType.UINT32||e===Ie.onnx.TensorProto.DataType.UINT64||e===So.TensorDataType.UINT64){if(r.greaterThanOrEqual(4294967296)||r.lessThan(0))throw new TypeError("uint64 is not supported")}else throw new TypeError(`not a LONG type: ${Ie.onnx.TensorProto.DataType[e]}`);return r.toNumber()}function vh(r,e,n){switch(e){case Ie.onnx.TensorProto.DataType.BOOL:case Ie.onnx.TensorProto.DataType.UINT8:return r.getUint8(n);case Ie.onnx.TensorProto.DataType.INT8:return r.getInt8(n);case Ie.onnx.TensorProto.DataType.UINT16:return r.getUint16(n,!0);case Ie.onnx.TensorProto.DataType.INT16:return r.getInt16(n,!0);case Ie.onnx.TensorProto.DataType.FLOAT:return r.getFloat32(n,!0);case Ie.onnx.TensorProto.DataType.INT32:return r.getInt32(n,!0);case Ie.onnx.TensorProto.DataType.UINT32:return r.getUint32(n,!0);case Ie.onnx.TensorProto.DataType.INT64:return _l(dr.fromBits(r.getUint32(n,!0),r.getUint32(n+4,!0),!1),e);case Ie.onnx.TensorProto.DataType.DOUBLE:return r.getFloat64(n,!0);case Ie.onnx.TensorProto.DataType.UINT64:return _l(dr.fromBits(r.getUint32(n,!0),r.getUint32(n+4,!0),!0),e);default:throw new Error(`cannot read from DataView for type ${Ie.onnx.TensorProto.DataType[e]}`)}}var wh,Ie,nt,zr=k(()=>{"use strict";wh=ve(zp());Fs();$o();Ie=ve(to());Me();nt=class r{constructor(e,n,t,o,i,a=wh.Guid.create()){this.dims=e;this.type=n;this.dataProvider=t;this.asyncDataProvider=o;this.cache=i;this.dataId=a;this.size=ne.validateDimsAndCalcSize(e);let s=this.size,u=t===void 0&&o===void 0&&i===void 0;if(i!==void 0&&i.length!==s)throw new RangeError("Input dims doesn't match data length.");if(n==="string"){if(i!==void 0&&(!Array.isArray(i)||!i.every(l=>typeof l=="string")))throw new TypeError("cache should be a string array");u&&(this.cache=new Array(s))}else{if(i!==void 0){let l=xh(n);if(!(i instanceof l))throw new TypeError(`cache should be type ${l.name}`)}if(u){let l=new ArrayBuffer(s*vS(n));this.cache=wS(l,n)}}}get data(){if(this.cache===void 0){let e=this.dataProvider(this.dataId);if(e.length!==this.size)throw new Error("Length of data provided by the Data Provider is inconsistent with the dims of this Tensor.");this.cache=e}return this.cache}get stringData(){if(this.type!=="string")throw new TypeError("data type is not string");return this.data}get integerData(){switch(this.type){case"uint8":case"int8":case"uint16":case"int16":case"int32":case"uint32":case"bool":return this.data;default:throw new TypeError("data type is not integer (uint8, int8, uint16, int16, int32, uint32, bool)")}}get floatData(){switch(this.type){case"float32":case"float64":return this.data;default:throw new TypeError("data type is not float (float32, float64)")}}get numberData(){if(this.type!=="string")return this.data;throw new TypeError("type cannot be non-number (string)")}get(e){return this.data[ne.indicesToOffset(e,this.strides)]}set(e,n){this.data[ne.indicesToOffset(e,this.strides)]=n}async getData(){return this.cache===void 0&&(this.cache=await this.asyncDataProvider(this.dataId)),this.cache}get strides(){return this._strides||(this._strides=ne.computeStrides(this.dims)),this._strides}static fromProto(e){if(!e)throw new Error("cannot construct Value from an empty tensor");let n=ft.tensorDataTypeFromProto(e.dataType),t=ft.tensorDimsFromProto(e.dims),o=new r(t,n);if(n==="string")e.stringData.forEach((i,a)=>{o.data[a]=Co(i)});else if(e.rawData&&typeof e.rawData.byteLength=="number"&&e.rawData.byteLength>0){let i=o.data,a=new DataView(e.rawData.buffer,e.rawData.byteOffset,e.rawData.byteLength),s=_h(e.dataType),u=e.rawData.byteLength/s;if(e.rawData.byteLength%s!==0)throw new Error("invalid buffer length");if(i.length!==u)throw new Error("buffer length mismatch");for(let l=0;l<u;l++){let d=vh(a,e.dataType,l*s);i[l]=d}}else{let i;switch(e.dataType){case Ie.onnx.TensorProto.DataType.FLOAT:i=e.floatData;break;case Ie.onnx.TensorProto.DataType.INT32:case Ie.onnx.TensorProto.DataType.INT16:case Ie.onnx.TensorProto.DataType.UINT16:case Ie.onnx.TensorProto.DataType.INT8:case Ie.onnx.TensorProto.DataType.UINT8:case Ie.onnx.TensorProto.DataType.BOOL:i=e.int32Data;break;case Ie.onnx.TensorProto.DataType.INT64:i=e.int64Data;break;case Ie.onnx.TensorProto.DataType.DOUBLE:i=e.doubleData;break;case Ie.onnx.TensorProto.DataType.UINT32:case Ie.onnx.TensorProto.DataType.UINT64:i=e.uint64Data;break;default:throw new Error("unspecific error")}if(i==null)throw new Error("failed to populate data from a tensorproto value");let a=o.data;if(a.length!==i.length)throw new Error("array length mismatch");for(let s=0;s<i.length;s++){let u=i[s];dr.isLong(u)?a[s]=_l(u,e.dataType):a[s]=u}}return o}static fromData(e,n,t){return new r(n,t,void 0,void 0,e)}static fromOrtTensor(e){if(!e)throw new Error("cannot construct Value from an empty tensor");let n=ft.tensorDimsFromORTFormat(e),t=ft.tensorDataTypeFromProto(e.dataType()),o=new r(n,t);if(t==="string")for(let i=0;i<e.stringDataLength();i++)o.data[i]=e.stringData(i);else if(e.rawDataArray()&&typeof e.rawDataLength()=="number"&&e.rawDataLength()>0){let i=o.data,a=new DataView(e.rawDataArray().buffer,e.rawDataArray().byteOffset,e.rawDataLength()),s=_h(e.dataType()),u=e.rawDataLength()/s;if(e.rawDataLength()%s!==0)throw new Error("invalid buffer length");if(i.length!==u)throw new Error("buffer length mismatch");for(let l=0;l<u;l++){let d=vh(a,e.dataType(),l*s);i[l]=d}}return o}}});function ue(r){return r===1?xS:TS}function Th(r){let e=ue(r);return`${e.version}
      precision highp float;
      ${e.attribute} vec3 position;
      ${e.attribute} vec2 textureCoord;

      ${e.varyingVertex} vec2 TexCoords;

      void main()
      {
          gl_Position = vec4(position, 1.0);
          TexCoords = textureCoord;
      }`}function Ih(r){let e=ue(r);return`${e.version}
    precision highp float;
    precision highp int;
    precision highp sampler2D;
    ${e.varyingFrag} vec2 TexCoords;
    ${e.outputDeclaration}
    const vec2 halfCR = vec2(0.5, 0.5);

    // Custom vector types to handle higher dimenalities.
    struct ivec5
    {
      int x;
      int y;
      int z;
      int w;
      int u;
    };

    struct ivec6
    {
      int x;
      int y;
      int z;
      int w;
      int u;
      int v;
    };

    int imod(int x, int y) {
      return x - y * (x / y);
    }

    `}function Sh(r,e){let n=ue(r);return`
  void main() {
    int indices[${e}];
    toVec(TexCoords, indices);
    vec4 result = vec4(process(indices));
    ${n.output} = result;
  }
  `}var xS,TS,Ke=k(()=>{"use strict";xS={version:"",attribute:"attribute",varyingVertex:"varying",varyingFrag:"varying",texture2D:"texture2D",output:"gl_FragColor",outputDeclaration:""},TS={version:"#version 300 es",attribute:"in",varyingVertex:"out",varyingFrag:"in",texture2D:"texture",output:"outputColor",outputDeclaration:"out vec4 outputColor;"}});var Ae=k(()=>{"use strict"});async function vl(r,e=t=>0,n){return new Promise((t,o)=>{let i=0,a=()=>{if(r()){t();return}i++;let s=e(i);if(n!=null&&i>=n){o();return}setTimeout(a,s)};a()})}function Ri(r){return no(typeof r<"u"&&r.length!==0,()=>"empty string found for sampler name"),"get"+r.charAt(0).toUpperCase()+r.slice(1)}function $h(r){return no(typeof r<"u"&&r.length!==0,()=>"empty string found for sampler name"),"get"+r.charAt(0).toUpperCase()+r.slice(1)+"AtOutCoords"}function ro(r,e){let n=JSON.parse(JSON.stringify(r));return n=e,n}function oo(r,e){return e.map(n=>r[n]).join(", ")}function bt(r){if(r<=1)return"int";if(r===2)return"ivec2";if(r===3)return"ivec3";if(r===4)return"ivec4";if(r===5)return"ivec5";if(r===6)return"ivec6";throw Error(`GPU for rank ${r} is not yet supported`)}function qt(r=6){return["x","y","z","w","u","v"].slice(0,r)}var Nn=k(()=>{"use strict";Me()});function IS(r,e){return qt(e).map(n=>`${r}.${n}`)}function io(r,e){return e===1?[r]:IS(r,e)}function Ln(){return`
    float getChannel(vec4 frag, int dim) {
      int modCoord = imod(dim, 2);
      return modCoord == 0 ? frag.r : frag.g;
    }

    float getChannel(vec4 frag, vec2 innerDims) {
      vec2 modCoord = mod(innerDims, 2.);
      return modCoord.x == 0. ?
        (modCoord.y == 0. ? frag.r : frag.g) :
        (modCoord.y == 0. ? frag.b : frag.a);
    }
  `}var Mr=k(()=>{"use strict";Nn()});function $S(r,e,n){if(r===0)return"false";if(r===1)return`rc > ${e[0]}`;let t="";for(let o=r-2;o<r;o++)t+=`${n[o]} >= ${e[o-r+2]}`,o<r-1&&(t+="||");return t}function AS(r,e){let n=r.length;if(n===0)return"getA(), 0, 0, 0";if(n===1)return`getA(rc),
            rc + 1 >= ${r[0]} ? 0. : getA(rc + 1),
            0, 0`;let t="r, c",o="r, cp1",i="rp1, c",a="rp1, cp1",s="";if(n>2)for(let u=0;u<n-2;++u)s=s+`${e[u]},`;return`getA(${s}${t}),
          rEdge ? 0. : getA(${s}${i}),
          cEdge ? 0. : getA(${s}${o}),
          rEdge || cEdge ? 0. : getA(${s}${a})`}function OS(r,e,n,t){return r===0||r===1?"":`
    int r = ${e[r-2]};
    int c = ${e[r-1]};
    int rp1 = ${e[r-2]} + 1;
    int cp1 = ${e[r-1]} + 1;
    bool rEdge = rp1 >= ${t};
    bool cEdge = cp1 >= ${n};
    `}var Ah,SS,Oh,Ph=k(()=>{"use strict";Ke();Ae();Nn();Mr();Ah={name:"pack",inputNames:["A"],inputTypes:[1]},SS=(r,e)=>{let n=ue(r.session.backend.glContext.version),t=e.dims,o=t.length,i=e.dims.length,a=bt(i),s=io("rc",i),u=OS(i,s,t[t.length-2],t[t.length-1]),l;o===0?l=[1,1]:o===1?l=[t[0],1]:l=[t[i-1],t[i-2]];let d=$S(i,l,s),f=AS(t,s),h=`
        void main() {
          ${a} rc = getOutputCoords();

          if(${d}) {
            ${n.output} = vec4(0);
          } else {
            ${u}

            ${n.output} = vec4(${f});
          }
        }
      `;return{...Ah,hasMain:!0,output:{dims:e.dims,type:e.type,textureType:2},shaderSource:h}},Oh=(r,e)=>({...Ah,get:()=>SS(r,e)})});function wl(r){if(r.length===0)return[1,1,1];let e=1;for(let n=0;n<r.length-2;++n)e*=r[n];return[e,r.length>1?r[r.length-2]:1,r[r.length-1]]}function Ch(r,e){let n=!1;return r.length===0||e.length===0?n=!0:r.length<2||e.length<2?n=r[r.length-1]===e[e.length-1]:n=r[r.length-1]===e[e.length-1]&&r[r.length-2]===e[e.length-2],n}function CS(r){let e=ne.computeStrides(r),n=["b","r","c"],t="index";return`
    ivec3 inputCoordsFromReshapedOutCoords(int index) {
      ${e.map((i,a)=>{let s=`int ${n[a]} = ${t} / ${i}`,u=a===e.length-1?`int ${n[a+1]} = ${t} - ${n[a]} * ${i}`:`index -= ${n[a]} * ${i}`;return`${s}; ${u};`}).join("")}
      return ivec3(b, r, c);
    }
  `}function DS(r){let e=ne.computeStrides(r);return`
  int getFlattenedIndex(ivec3 coords) {
    // reverse y, z order
    return coords.x * ${e[0]} + coords.z * ${e[1]} + coords.y;
  }
`}var PS,ES,Eh,Dh=k(()=>{"use strict";Me();Ke();Ae();Mr();PS=r=>({name:"Reshape (packed)",inputTypes:[2],inputNames:["A"],cacheHint:`${r}`}),ES=(r,e,n,t)=>{let o=e.dims,i=t,a="";for(let l=0;l<4;l++){let d="";switch(l){case 0:d="outputCoords = rc;";break;case 1:d="outputCoords = ivec3(rc.x, rc.y+1, rc.z);";break;case 2:d="outputCoords = ivec3(rc.x, rc.y, rc.z+1);";break;case 3:d="outputCoords = ivec3(rc.x, rc.y+1, rc.z+1);";break;default:throw new Error}a+=`
        ${d}
        ${l>0?"if(outputCoords.y < rows && outputCoords.z < cols){":""}
          int flattenedIndex = getFlattenedIndex(outputCoords);

          ivec3 inputRC = inputCoordsFromReshapedOutCoords(flattenedIndex);
          vec2 innerDims = vec2(float(inputRC.y),float(inputRC.z));

          result[${l}] = getChannel(getA(inputRC.x, inputRC.y, inputRC.z), innerDims);

        ${l>0?"}":""}
      `}let s=ue(r.session.backend.glContext.version),u=`
      ${CS(o)}
      ${DS(i)}
      ${Ln()}

      void main() {
        ivec3 rc = getOutputCoords();

        vec4 result = vec4(0.0);

        ivec3 outputCoords;
        int rows = ${i[2]};
        int cols = ${i[1]};

        ${a}
        ${s.output} = result;
      }
    `;return{...n,output:{dims:i,type:e.type,textureType:2},shaderSource:u,hasMain:!0}},Eh=(r,e,n)=>{let t=PS(n);return{...t,get:()=>ES(r,e,t,n)}}});var xl,kh=k(()=>{"use strict";Ke();Ae();xl=(r,e)=>{let n=e.shape,t=ue(r.session.backend.glContext.version),o=`
    const float FLOAT_MAX = 1.70141184e38;
    const float FLOAT_MIN = 1.17549435e-38;

    bool isNaN(float val) {
      return (val < 1.0 || 0.0 < val || val == 0.0) ? false : true;
    }

    highp vec4 encodeAsUint8(highp float v) {
      if (isNaN(v)) {
        return vec4(255, 255, 255, 255);
      }

      highp float av = abs(v);

      if(av < FLOAT_MIN) {
        return vec4(0.0, 0.0, 0.0, 0.0);
      } else if(v > FLOAT_MAX) {
        return vec4(0.0, 0.0, 128.0, 127.0) / 255.0;
      } else if(v < -FLOAT_MAX) {
        return vec4(0.0, 0.0,  128.0, 255.0) / 255.0;
      }

      highp vec4 c = vec4(0,0,0,0);

      highp float e = floor(log2(av));
      highp float m = exp2(fract(log2(av))) - 1.0;

      c[2] = floor(128.0 * m);
      m -= c[2] / 128.0;
      c[1] = floor(32768.0 * m);
      m -= c[1] / 32768.0;
      c[0] = floor(8388608.0 * m);

      highp float ebias = e + 127.0;
      c[3] = floor(ebias / 2.0);
      ebias -= c[3] * 2.0;
      c[2] += floor(ebias) * 128.0;

      c[3] += 128.0 * step(0.0, -v);

      return c / 255.0;
    }

    void main() {
      float value = ${t.texture2D}(X,TexCoords).r;
      ${t.output} = encodeAsUint8(value);
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          void main() {
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            coords.${f[a-1]} = coords.${f[a-2]};
            coords.${f[a-2]} = lastDim;

            vec4 result = vec4(getValue(${s}), 0., 0., 0.);

            ${s[a-1]} = ${s[a-1]} + 1;
            if (${s[a-1]} < ${i[a-1]}) {
              result.g = getValue(${s});
            }

            ${s[a-2]} = ${s[a-2]} + 1;
            if (${s[a-2]} < ${i[a-2]}) {
              result.a = getValue(${s});
            }

            ${s[a-1]} = ${s[a-1]} - 1;
            if (${s[a-2]} < ${i[a-2]} &&
                ${s[a-1]} < ${i[a-1]}) {
              result.b = getValue(${s});
            }
            ${I.output} = result;
          }
        `;return{...e,output:{dims:i,type:n[0].type,textureType:2},shaderSource:$,hasMain:!0}},sm=(r,e,n)=>{let t=n$(e.length,n.cacheKey);return{...t,get:()=>r$(r,t,e,n.axis)}},Gi=(r,e,n)=>{let t=r.indexOf(e);return r.map((i,a)=>a===t?`${i} - ${n}`:i).join()}});var lm,o$,i$,a$,cm,s$,u$,l$,dm,c$,pm=k(()=>{"use strict";lt();Ae();um();lm=(r,e,n)=>(c$(e),r.session.pack&&e[0].dims.length>1?[r.run(sm(r,e,n),e)]:[r.run(a$(r,e,n),e)]),o$=(r,e)=>({name:"Concat",inputNames:Array.from({length:r},(n,t)=>`X${t}`),inputTypes:Array(r).fill(0),cacheHint:e}),i$=(r,e,n,t)=>{let o=n[0].dims.slice();if(t>=o.length||t<-1*o.length)throw new Error("axis specified for concat doesn't match input dimensionality");t<0&&(t=o.length+t);let i=o.slice(0);for(let g=1;g<n.length;g++){let b=n[g].dims.slice();for(let _=0;_<o.length;_++)if(_===t)i[t]+=b[_];else if(o[_]!==b[_])throw new Error("non concat dimensions must match")}let a=i.length,s=new Array(n.length),u=0;for(let g=0;g<s.length;++g)u+=n[g].dims[t],s[g]=u;let l="";n.length<5?l=cm(s):l=s$(s);let d=u$(n.length,a),f=l$(s),h=`
        ${d}
        ${f}
        ${l}
        float process(int indices[${a}]) {
          int textureIndex = getTextureWhereDataResides (indices[${t}]);

          if(textureIndex != 0) {
            indices[${t}] = indices[${t}] - int(getSizeInConcatAxisValueFromIndex(textureIndex-int(1)));
          }

          return fetchDataFromCorrectTexture(textureIndex, indices);
        }`;return{...e,output:{dims:i,type:n[0].type,textureType:0},shaderSource:h}},a$=(r,e,n)=>{let t=o$(e.length,n.cacheKey);return{...t,get:()=>i$(r,t,e,n.axis)}},cm=r=>`int getTextureWhereDataResides(int index) {
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    }`,s$=r=>cm(r),u$=(r,e)=>{let n=[`float fetchDataFromCorrectTexture(int textureIndex, int indices[${e}]) {`];for(let t=0;t<r;++t)t===0?n.push(`	if (textureIndex == ${t}) { return _X${t}(indices); }`):t===r-1?n.push(`	else { return _X${t}(indices); }`):n.push(`	else if (textureIndex == ${t}) { return _X${t}(indices); }`);return n.push("	}"),n.join(`
`)},l$=r=>{let e=["int getSizeInConcatAxisValueFromIndex(int index) {"];for(let n=0;n<r.length;++n)n===0?e.push(`	if (index == ${n}) { return ${r[n]}; }`):n===r.length-1?e.push(`	else { return ${r[n]}; }`):e.push(`	else if (index == ${n}) { return ${r[n]}; }`);return e.push("	}"),e.join(`
`)},dm=r=>xe({axis:r.attributes.getInt("axis")}),c$=r=>{if(!r||r.length<1)throw new Error("too few inputs");let e=r[0].type,n=r[0].dims.length;if(e==="string")throw new Error("string tensor is not supported yet");for(let t of r){if(t.type!==e)throw new Error("input tensors should be one type");if(t.dims.length!==n)throw new Error("input tensors should have the same shape")}}});function d$(){return Bt("abs")}function p$(){return Bt("acos")}function f$(){return Bt("asin")}function h$(){return Bt("atan")}function m$(){return Bt("ceil")}function g$(){return Bt("cos")}function b$(r){let e="elu";return{body:`
  const float alpha = float(${r});

  float ${e}_(float a) {
    return a >= 0.0 ? a: (exp(a) - 1.0) * alpha;
  }
  vec4 ${e}_(vec4 v) {
    return vec4(${e}_(v.x), ${e}_(v.y), ${e}_(v.z), ${e}_(v.w));
  }
  `,name:e,type:0}}function y$(){return Bt("exp")}function _$(){return Bt("floor")}function Sl(r,e){let n="clip";return{body:`
  const float min = float(${r});
  const float max = float(${e});

  float ${n}_(float a) {
    return clamp(a, min, max);
  }
  vec4 ${n}_(vec4 v) {
    return clamp(v, min, max);
  }
  `,name:n,type:0}}function v$(){let r="indentity";return{body:`
  float ${r}_(float a) {
    return a;
  }
  vec4 ${r}_(vec4 v) {
    return v;
  }
  `,name:r,type:0}}function w$(r){let e="leakyRelu";return{body:`
  const float alpha = float(${r});

  float ${e}_(float a) {
    return a < 0.0 ? a * alpha : a;
  }
  vec4 ${e}_(vec4 v) {
    return vec4(${e}_(v.x), ${e}_(v.y), ${e}_(v.z), ${e}_(v.w));
  }
  `,name:e,type:0}}function x$(){return Bt("log")}function T$(){let r="neg";return{body:`
  float ${r}_(float a) {
    return -a;
  }
  vec4 ${r}_(vec4 v) {
    return -v;
  }
  `,name:r,type:0}}function I$(){let r="not";return{body:`
  float ${r}_(float a) {
    return float( ! bool(a) );
  }
  bool ${r}_(bool a) {
    return !a;
  }
  vec4 ${r}_(vec4 v) {
    return vec4(!bool(v.x), !bool(v.y), !bool(v.z), !bool(v.w));
  }
  bvec4 ${r}_(bvec4 v) {
    return bvec4(!v.x, !v.y, !v.z, !v.w);
  }
  `,name:r,type:0}}function S$(){return Bt("sin")}function $l(){let r="relu";return{body:`
  float ${r}_(float a) {
    return max( a, 0.0 );
  }
  vec4 ${r}_(vec4 v) {
    return max( v, 0.0 );
  }
  `,name:r,type:0}}function Al(){let r="sigmoid";return{body:`
  float ${r}_(float a) {
    return 1.0 / (1.0 + exp(-a));
  }
  vec4 ${r}_(vec4 v) {
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  }
  `,name:r,type:0}}function $$(){return Bt("sqrt")}function A$(){return Bt("tan")}function O$(){let r="tanh";return{body:`
  float ${r}_(float a) {
    a = clamp(a, -10., 10.);
    a = exp(2.*a);
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  }
  vec4 ${r}_(vec4 v) {
    v = clamp(v, -10., 10.);
    v = exp(2.*v);
    return (v - 1.) / (v + 1.);
  }
  `,name:r,type:0}}function Bt(r){return{body:`
  float ${r}_(float a) {
    return ${r}(a);
  }
  vec4 ${r}_(vec4 v) {
    return ${r}(v);
  }
  `,name:r,type:0}}var P$,et,fm,hm,mm,gm,Ol,bm,ym,E$,_m,vm,wm,xm,Tm,Im,Pl,Sm,$m,Am,Om,Pm,Em,Cm,Dm,km,Nm,Lm,El=k(()=>{"use strict";lt();Me();Jn();Ke();Ae();P$=(r,e,n,t)=>{let o=r.session.pack?2:0,i=ue(r.session.backend.glContext.version);return{...e,output:{dims:n.dims,type:n.type,textureType:o},shaderSource:`
     ${t.body}
     void main() {
       vec4 v = ${i.texture2D}(A, TexCoords);
       v = ${t.name}_(v);
       ${i.output} = v;
     }
     `,hasMain:!0}},et=(r,e,n,t)=>{let o=r.session.pack?2:0,i={name:n.name,inputTypes:[o],inputNames:["A"],cacheHint:t};return{...i,get:()=>P$(r,i,e,n)}},fm=(r,e)=>[r.run(et(r,e[0],d$()),e)],hm=(r,e)=>[r.run(et(r,e[0],p$()),e)],mm=(r,e)=>[r.run(et(r,e[0],f$()),e)],gm=(r,e)=>[r.run(et(r,e[0],h$()),e)],Ol=(r,e,n)=>[r.run(et(r,e[0],Sl(n.min,n.max),n.cacheKey),e)],bm=r=>xe({min:r.attributes.getFloat("min",Lr),max:r.attributes.getFloat("max",Rr)}),ym=(r,e)=>{let n=E$(r,e);return Ol(r,[e[0]],n)},E$=(r,e)=>{if(e.length>=3&&(!r.session.isInitializer(e[1].dataId)||!r.session.isInitializer(e[2].dataId)))throw new Error("dynamic clip attributes are not allowed");let n=e.length>=3?e[1].numberData[0]:Lr,t=e.length>=3?e[2].numberData[0]:Rr;return xe({min:n,max:t})},_m=(r,e)=>[r.run(et(r,e[0],m$()),e)],vm=(r,e)=>[r.run(et(r,e[0],g$()),e)],wm=(r,e,n)=>[r.run(et(r,e[0],b$(n.alpha),n.cacheKey),e)],xm=r=>xe({alpha:r.attributes.getFloat("alpha",1)}),Tm=(r,e)=>[r.run(et(r,e[0],y$()),e)],Im=(r,e)=>[r.run(et(r,e[0],_$()),e)],Pl=(r,e)=>[r.run(et(r,e[0],v$()),e)],Sm=(r,e,n)=>[r.run(et(r,e[0],w$(n.alpha),n.cacheKey),e)],$m=r=>xe({alpha:r.attributes.getFloat("alpha",.01)}),Am=(r,e)=>[r.run(et(r,e[0],x$()),e)],Om=(r,e)=>[r.run(et(r,e[0],T$()),e)],Pm=(r,e)=>[r.run(et(r,e[0],I$()),e)],Em=(r,e)=>[r.run(et(r,e[0],$l()),e)],Cm=(r,e)=>[r.run(et(r,e[0],Al()),e)],Dm=(r,e)=>[r.run(et(r,e[0],S$()),e)],km=(r,e)=>[r.run(et(r,e[0],$$()),e)],Nm=(r,e)=>[r.run(et(r,e[0],A$()),e)],Lm=(r,e)=>[r.run(et(r,e[0],O$()),e)]});function Rn(r){let e;switch(r.activation){case"Relu":e=$l();break;case"Sigmoid":e=Al();break;case"Clip":e=Sl(r.clipMin,r.clipMax);break;default:return{activationFunction:"",applyActivation:""}}let n=e.name,t=e.body,o=`value = ${n}_(value);`;return{activationFunction:t,applyActivation:o}}var ao,Br=k(()=>{"use strict";Me();El();ao=r=>{let e=r.getString("activation","");if(e==="Clip"){let[n,t]=r.getFloats("activation_params",[Lr,Rr]);return{activation:e,clipMax:t,clipMin:n,activationCacheKey:`${e}:${n},${t}`}}return{activation:e,activationCacheKey:e}}});var D$,k$,Rm,zm=k(()=>{"use strict";Ct();Ke();Ae();Ui();Br();D$=(r,e)=>({name:"GroupedConv",inputNames:r?["X","W","Bias"]:["X","W"],inputTypes:r?[0,0,0]:[0,0],cacheHint:e}),k$=(r,e,n,t)=>{let i=e.length>2?"value += getBias(output_channel);":"",a=e[0].dims.slice(),s=e[1].dims.slice(),u=s[0]/t.group;Fe.verbose("GroupedConv",`autpPad:${t.autoPad}, dilations:${t.dilations}, group:${t.group}, kernelShape:${t.kernelShape}, pads:${t.pads}, strides:${t.strides}`);let l=so(a,s,t.dilations,t.pads,t.strides),d=ue(r.session.backend.glContext.version),{activationFunction:f,applyActivation:h}=Rn(t),g=`
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  ${f}
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    int group_id = output_channel / ${u};

    float value = 0.0;
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      int input_channel = group_id * ${s[1]} + wInChannel;
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        if (xHeight < 0 || xHeight >= ${a[2]}) {
          continue;
        }

        for (int wWidth = 0; wWidth < ${s[3]}; wWidth++) {
          int xWidth = xRCCorner.y + wWidth * ${t.dilations[1]};
          if (xWidth < 0 || xWidth >= ${a[3]}) {
            continue;
          }

          float xVal = getX(batch, input_channel, xWidth, xHeight);
          float wVal = getW(output_channel, wInChannel, wWidth, wHeight);
          value += xVal*wVal;
        }
      }
    }
    ${i}
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  }
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            blockIndex = rc.x + ${x};
            pos = rc.y + ${v};

            if(blockIndex < ${f[1]} && pos < ${f[0]}) {
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                ${i.pads[0]};
              d0 = offsetY + ${i.dilations[0]} * (imod(pos, ${h}) / ${s[2]});

              if(d0 < ${a[u]} && d0 >= 0) {
                offsetX = imod(blockIndex, ${o[d-1]}) * ${i.strides[1]} -
                  ${i.pads[1]};
                d1 = offsetX + ${i.dilations[1]} * imod(imod(pos, ${h}), ${s[2]});

                if(d1 < ${a[l]} && d1 >= 0) {

                  ch = int(float(pos)/ ${h}.);
                    innerDims = vec2(d0, d1);
                    result[${v*2+x}] = getChannel(
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              }
            }

          `;let T=`
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          ${_}
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      }
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        float value;
        for (int k=0; k<${T}; ++k) {
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        }
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vec4 getAAtOutCoordsMatmul(int i) {
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  ${v}
  ${_}
  vec4 outputValue = getA(${o});
  return outputValue;
}

vec4 getBAtOutCoordsMatmul(int i) {
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  ${v}
  ${T}
  vec4 outputValue = getB(${i});
  return outputValue;
}`}function G$(r,e){let n="";for(let t=0;t<e-2;t++)n+=`rc.${r[t]}, `;return n+=`rc.${r[e-2]}, i*2`,n}function U$(r,e){let n="";for(let t=0;t<e-2;t++)n+=`rc.${r[t]}, `;return n+=`i*2, rc.${r[e-1]}`,n}var B$,F$,Hi,kl=k(()=>{"use strict";Me();Ke();Ae();Nn();Br();Wi();B$=(r,e)=>({name:"MatMul (packed)",inputNames:r?["A","B","Bias"]:["A","B"],inputTypes:r?[2,2,2]:[2,2],cacheHint:e}),F$=(r,e,n,t)=>{let o=n.length>2,i=o?"value += getBiasForMatmul();":"",a=n[0].dims,s=n[1].dims,u=gt.calcShape(a,s,!0),l=!ne.areEqual(n[0].dims,n[1].dims);if(!u)throw new Error("Can't use matmul on the given tensors");let d=a[a.length-1],f=Math.ceil(d/2),h=a.length,g=s.length,b=ue(r.session.backend.glContext.version),_=bt(u.length),T=u.length,v=qt(),{activationFunction:x,applyActivation:I}=Rn(t),$=o?`${Dl(_,v,n[2].dims,u,!0)}`:"",O=l?`${V$(_,v,n,u)}`:"",E=l?"getAAtOutCoordsMatmul(i)":`getA(${G$(v,h)})`,L=l?"getBAtOutCoordsMatmul(i)":`getB(${U$(v,g)})`,R=l?"":`${_} rc =
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              vec4 value = vec4(0);
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                vec4 b = ${L};

                value += (a.rrbb * b.rgrg);
                value += (a.ggaa * b.baba);
              }
              ${i}
              ${I}
              ${b.output} = value;
            }`;return{...e,output:{dims:u,type:n[0].type,textureType:2},shaderSource:F,hasMain:!0}},Hi=(r,e,n)=>{let t=B$(e.length>2,n.activationCacheKey);return{...t,get:()=>F$(r,t,e,n)}}});var Gm,Um=k(()=>{"use strict";Ui();Bm();kl();Gm=(r,e,n)=>{let t=e[0].dims,o=e[1].dims,i=so(t,o,n.dilations,n.pads,n.strides),a=r.run(Mm(r,e[0],e[1],i,n),[e[0]]),s=r.reshapePacked(e[1],[o[0],o[1]*o[2]*o[3]]),u=e.length===3?[s,a,e[2]]:[s,a],l=r.run(Hi(r,u,n),u);return r.reshapePacked(l,i)}});var W$,H$,Wm,Nl,Ll=k(()=>{"use strict";Ae();W$=r=>({name:"Im2Col",inputNames:["X"],inputTypes:[0],cacheHint:r}),H$=(r,e,n,t,o,i)=>{let a=n.dims,s=t.dims,u=o.length,l=Nl(a,s,o,4),d=`
        const int XC = ${a[1]};
        const int XH = ${a[2]};
        const int XW = ${a[3]};
        const int KH = ${i.kernelShape[0]};
        const int KW = ${i.kernelShape[1]};
        const int dilationH = ${i.dilations[0]};
        const int dilationW = ${i.dilations[1]};
        const int strideH = ${i.strides[0]};
        const int strideW = ${i.strides[1]};
        const int padH = ${i.pads[0]};
        const int padW = ${i.pads[1]};
        const int KHKW = KH*KW;
        const int XCKHKW = XC * KHKW;
        const int outputChannels = 4;
        vec4 process(int indices[${u}]) {
          int b  = indices[0]; // batch size
          int oh = indices[1] * strideH - padH; //output height
          int ow = indices[2] * strideW - padW; //output width
          int p = indices[3] * outputChannels; //patch
          vec4 value = vec4(0.0);
          for(int i=0; i < outputChannels; ++i) {
            if(p < XCKHKW) {
              int patchC = p / KHKW;
              int patchH = (p - patchC*KHKW) / KW;
              int patchW = (p - patchC*KHKW) - patchH * KW;
              int xh2 = oh + patchH * dilationH;
              int xw2 = ow + patchW * dilationW;
              int x[${a.length}];
              x[0] = b;
              x[1] = patchC;
              x[2] = xh2;
              x[3] = xw2;
              if(xh2 >= 0 &&
                  xh2 < XH &&
                  xw2 >= 0 &&
                  xw2 < XW) {
                value[i] = _X(x);
              }
            }
            ++p;
          }
          return value;
        }
        `;return{...e,output:{dims:l,type:n.type,textureType:4},shaderSource:d}},Wm=(r,e,n,t,o)=>{let i=W$(o.cacheKey);return{...i,get:()=>H$(r,i,e,n,t,o)}},Nl=(r,e,n,t=4)=>[n[0],n[2],n[3],Math.ceil(r[1]*e[2]*e[3]/t)]});var j$,q$,Hm,jm=k(()=>{"use strict";Me();Ke();Ae();Br();Ll();j$=(r,e)=>({name:"ConvDotProduct",inputNames:r?["Im2Col","K","B"]:["Im2Col","K"],inputTypes:r?[0,4,0]:[0,4],cacheKey:e.activationCacheKey}),q$=(r,e,n,t,o)=>{let i=n[0].dims,a=n[1].dims,s=[a[0],Math.ceil(i[1]*a[2]*a[3]/4)],u=Nl(i,a,t),[l,d]=r.calculateTextureWidthAndHeight(s,4),f=ne.computeStrides(u),[h,g]=r.calculateTextureWidthAndHeight(u,4),b=t.length,_=n.length<3?"0.0":"_B(b)",T=Math.ceil(i[1]*a[2]*a[3]/4),{activationFunction:v,applyActivation:x}=Rn(o),I=ue(r.session.backend.glContext.version),$=`
${v}
float process(int indices[${b}]) {
  int b[1];
  b[0] = indices[1];
  int im2col[4];
  im2col[0] = indices[0];
  im2col[1] = indices[2];
  im2col[2] = indices[3];
  int im2colOffset = im2col[0] * ${f[0]} + im2col[1] * ${f[1]} + im2col[2] * ${f[2]};
  int kernelOffset = indices[1] * ${s[1]};
  float value = ${_};
  for (int i = 0; i < ${T}; ++i) {
    vec2 im2colCoords = offsetToCoords(im2colOffset, ${h}, ${g});
    vec2 kernelCoords = offsetToCoords(kernelOffset, ${l}, ${d});
    value += dot(${I.texture2D}(Im2Col, im2colCoords), ${I.texture2D}(K, kernelCoords));
    ++im2colOffset;
    ++kernelOffset;
  }
  ${x}
  return value;
}`;return{...e,output:{dims:t,type:n[0].type,textureType:0},shaderSource:$}},Hm=(r,e,n,t)=>{let o=j$(e.length>2,t);return{...o,get:()=>q$(r,o,e,n,t)}}});var so,Rl,K$,X$,Z$,J$,zl,Y$,Ui=k(()=>{"use strict";lt();Me();zm();Um();jm();Br();Ll();Wi();so=(r,e,n,t,o)=>{let i=r[0],a=r.slice(2),s=a.length,u=e[0],d=e.slice(2).map((b,_)=>b+(b-1)*(n[_]-1)),h=a.map((b,_)=>b+t[_]+t[_+s]).map((b,_)=>Math.floor((b-d[_]+o[_])/o[_]));return[i,u].concat(...h)},Rl=(r,e,n)=>(Y$(e,n),K$(r,e,n)),K$=(r,e,n)=>{let t=J$(n,e),o=r.session.pack,i=t.kernelShape[0]===1&&t.kernelShape[1]===1;return t.group>1?[r.run(Rm(r,e,t),e)]:i&&o?[X$(r,e,t)]:o&&e[0].dims.length===4&&e[0].dims[0]===1&&!i?[Gm(r,e,t)]:[Z$(r,e,t)]},X$=(r,e,n)=>{let t=e[0].dims,o=e[1].dims,i=so(t,o,n.dilations,n.pads,n.strides),a=r.reshapeUnpacked(e[0],[t[1],t[2]*t[3]]),s=r.reshapeUnpacked(e[1],[o[0],o[1]]),u=e.length>2?[s,a,e[2]]:[s,a],l=r.run(Cl(u,n),u);return r.reshapeUnpacked(l,i)},Z$=(r,e,n)=>{let t=e[0].dims,o=e[1].dims,i=so(t,o,n.dilations,n.pads,n.strides),a=r.run(Wm(r,e[0],e[1],i,n),[e[0]]),s=e.length===3?[a,e[1],e[2]]:[a,e[1]];return r.run(Hm(r,e,i,n),s)},J$=(r,e)=>{let n=r.kernelShape.slice();if(r.kernelShape.length===0)for(let i=2;i<e[1].dims.length;++i)n.push(e[1].dims[i]);let t=r.pads.slice();Nr.adjustPadsBasedOnAutoPad(e[0].dims,r.strides,r.dilations,n,t,r.autoPad);let o=Object.assign({},r);return Object.assign(o,{kernelShape:n,pads:t,cacheKey:r.cacheKey}),o},zl=r=>{let e=r.attributes,n=ao(e),t=e.getString("auto_pad","NOTSET"),o=e.getInts("dilations",[1,1]),i=e.getInt("group",1),a=e.getInts("kernel_shape",[]),s=e.getInts("pads",[0,0,0,0]),u=e.getInts("strides",[1,1]);return xe({autoPad:t,dilations:o,group:i,kernelShape:a,pads:s,strides:u,...n})},Y$=(r,e)=>{if(!r||r.length!==2&&r.length!==3)throw new Error("Conv requires 2 or 3 inputs");if(r[0].dims.length!==4||r[1].dims.length!==4)throw new Error("currently only support 2-dimensional conv");let n=r[0].dims[1],t=r[1].dims[1]*e.group;if(n!==t)throw new Error("FILTER_IN_CHANNEL should be equal to DATA_CHANNEL");if(r.length===3&&(r[2].dims.length!==1||r[1].dims[0]!==r[2].dims[0]))throw new Error("invalid bias");let o=r[0].dims.length-2;if(e.dilations.length!==o)throw new Error(`dilations should be ${o}D`);if(e.strides.length!==o)throw new Error(`strides should be ${o}D`);if(e.pads.length!==o*2)throw new Error(`pads should be ${o*2}D`);if(e.kernelShape.length!==0&&e.kernelShape.length!==r[1].dims.length-2)throw new Error("invalid kernel shape");if(r[0].type!=="float32"||r[1].type!=="float32")throw new Error("Conv input(X,W) should be float tensor");if(r.length===3&&r[2].type!=="float32")throw new Error("Conv input(bias) should be float tensor")}});var Q$,eA,tA,qm,nA,rA,oA,iA,aA,sA,Km,uA,Xm=k(()=>{"use strict";lt();Ke();Ae();Br();Q$=(r,e,n,t,o,i)=>(r-1)*e+n+(t-1)*o+1-i,eA=(r,e,n,t,o)=>{let i=Math.floor(r/2);e==="SAME_UPPER"?(n[t]=i,n[o]=r-i):e==="SAME_LOWER"&&(n[t]=r-i,n[o]=i)},tA=(r,e,n,t,o,i,a,s)=>{let u=r.length-2,l=s.length===0;for(let d=0;d<u;++d){let f=l?r[d+2]*i[d]:s[d],h=Q$(r[d+2],i[d],o[d],e[d],n[d],f);eA(h,t,o,d,d+u),l&&s.push(i[d]*(r[d+2]-1)+a[d]+(e[d]-1)*n[d]+1-o[d]-o[d+u])}},qm=(r,e,n)=>(uA(e,n),nA(r,e,n)),nA=(r,e,n)=>{let t=sA(n,e);return[aA(r,e,t)]},rA=(r,e)=>({name:"ConvTranspose",inputNames:r?["X","W","B"]:["X","W"],inputTypes:r?[0,0,0]:[0,0],cacheHint:e}),oA=(r,e,n,t)=>{let i=e.length>2?"getB(output_channel)":"0.0",a=e[0].dims,s=e[1].dims,u=s[1],l=s[0]/t.group,d=[e[0].dims[0],e[1].dims[1]*t.group,...t.outputShape],f=ue(r.session.backend.glContext.version),{activationFunction:h,applyActivation:g}=Rn(t),b=`
  const ivec2 strides = ivec2(${t.strides[0]}, ${t.strides[1]});
  const ivec2 pads = ivec2(${t.pads[0]}, ${t.pads[1]});
  ${h}
  void main() {
    ivec4 coords = getOutputCoords();
    int batch = coords.x;
    int output_channel = coords.y;

    ivec2 loc = coords.zw + pads;

    int group_id = output_channel / ${u};
    int wOutChannel = output_channel - group_id * ${u};

    float value = ${i};
    for (int inChannelOffset = 0; inChannelOffset < ${l}; inChannelOffset++) {
      int input_channel = group_id * ${l} + inChannelOffset;
      for (int wWOff = 0; wWOff < ${s[2]}; wWOff++) {
        for (int wHOff = 0; wHOff < ${s[3]}; wHOff++) {
          ivec2 wOff = ivec2(wWOff * ${t.dilations[0]}, wHOff * ${t.dilations[1]});
          ivec2 wLoc = loc - wOff;
          ivec2 wLocIn = wLoc / strides;
          if (
            wLocIn * strides == wLoc &&
            wLocIn.x >= 0 && wLocIn.x < ${a[2]} &&
            wLocIn.y >= 0 && wLocIn.y < ${a[3]}
          ) {
            float xVal = getX(batch, input_channel, wLocIn.y, wLocIn.x);
            float wVal = getW(input_channel, wOutChannel, wHOff, wWOff);
            value += xVal * wVal;
          }
        }
      }
    }
    ${g}
    ${f.output} = vec4(value, .0, .0, .0);
  }
`;return{...n,output:{dims:d,type:e[0].type,textureType:0},shaderSource:b,hasMain:!0}},iA=(r,e,n)=>{let t=rA(e.length>2,n.cacheKey);return{...t,get:()=>oA(r,e,t,n)}},aA=(r,e,n)=>r.run(iA(r,e,n),e),sA=(r,e)=>{let n=r.kernelShape.slice();if(r.kernelShape.length===0)for(let s=2;s<e[1].dims.length;++s)n.push(e[1].dims[s]);let t=r.pads.slice(),o=r.outputShape.slice(),i=e[0].dims;tA(i,n,r.dilations,r.autoPad,t,r.strides,r.outputPadding,o);let a=Object.assign({},r);return Object.assign(a,{kernelShape:n,pads:t,outputShape:o,cacheKey:r.cacheKey}),a},Km=r=>{let e=r.attributes,n=ao(e),t=e.getString("auto_pad","NOTSET"),o=e.getInts("dilations",[1,1]),i=e.getInt("group",1),a=e.getInts("kernel_shape",[]),s=e.getInts("output_padding",[0,0]),u=e.getInts("output_shape",[]),l=e.getInts("pads",[0,0,0,0]),d=e.getInts("strides",[1,1]);return xe({autoPad:t,dilations:o,group:i,kernelShape:a,outputPadding:s,outputShape:u,pads:l,strides:d,...n})},uA=(r,e)=>{if(!r||r.length!==2&&r.length!==3)throw new Error("Conv requires 2 or 3 inputs");if(r[0].dims.length!==4||r[1].dims.length!==4)throw new Error("currently only support 2-dimensional conv");let n=r[0].dims[1],t=r[1].dims[0];if(n!==t)throw new Error("FILTER_IN_CHANNEL should be equal to DATA_CHANNEL");let o=r[1].dims[1]*e.group;if(r.length===3&&(r[2].dims.length!==1||r[2].dims[0]!==o))throw new Error("invalid bias");let i=r[0].dims.length-2;if(e.dilations.length!==i)throw new Error(`dilations should be ${i}D`);if(e.strides.length!==i)throw new Error(`strides should be ${i}D`);if(e.pads.length!==i*2)throw new Error(`pads should be ${i*2}D`);if(e.outputPadding.length!==i)throw new Error(`output_padding should be ${i}D`);if(e.kernelShape.length!==0&&e.kernelShape.length!==r[1].dims.length-2)throw new Error("invalid kernel shape");if(e.outputShape.length!==0&&e.outputShape.length!==r[0].dims.length-2)throw new Error("invalid output shape");if(r[0].type!=="float32"||r[1].type!=="float32")throw new Error("ConvTranspose input(X,W) should be float tensor");if(r.length===3&&r[2].type!=="float32")throw new Error("ConvTranspose input(bias) should be float tensor")}});var Zm,Fr,Jm,lA,Ym,cA,dA,pA,ji=k(()=>{"use strict";lt();Me();Ae();Zm={name:"Transpose",inputNames:["A"],inputTypes:[0]},Fr=(r,e,n)=>(pA(e),[r.run({...Zm,cacheHint:n.cacheKey,get:()=>lA(r,e[0],n.perm)},e)]),Jm=r=>xe({perm:r.attributes.getInts("perm",[])}),lA=(r,e,n)=>{let t=e.dims;n=Ym(t,n);let o=cA(t,n),i=t.length,a=`
      ${dA("perm",n,i)}
      float process(int indices[${i}]) {
        int a[${i}];
        perm(a, indices);
        return _A(a);
      }`;return{...Zm,output:{dims:o,type:e.type,textureType:0},shaderSource:a}},Ym=(r,e)=>(e&&e.length!==r.length&&(e=[...r.keys()].reverse()),e),cA=(r,e)=>(e=Ym(r,e),ne.sortBasedOnPerm(r,e)),dA=(r,e,n)=>{let t=[];t.push(`void ${r}(out int a[${n}], int src[${n}]) {`);for(let o=0;o<n;++o)t.push(`	a[${e[o]}]=src[${o}];`);return t.push("	}"),t.join(`
`)},pA=r=>{if(!r||r.length!==1)throw new Error("Transpose requires 1 input.");if(r[0].type!=="float32"&&r[0].type!=="float64")throw new Error("input should be float tensor")}});var Qm,eg,fA,tg=k(()=>{"use strict";ji();Qm=(r,e,n)=>{fA(e);let t=n.blocksize,o=t*t,i=n.mode==="DCR"?[0,3,4,1,5,2]:[0,1,4,2,5,3],a=n.mode==="DCR"?[e[0].dims[0],t,t,e[0].dims[1]/o,e[0].dims[2],e[0].dims[3]]:[e[0].dims[0],e[0].dims[1]/o,t,t,e[0].dims[2],e[0].dims[3]],s=r.reshapeUnpacked(e[0],a),u={perm:i,cacheKey:`${i}`},[l]=Fr(r,[s],u),d=[e[0].dims[0],e[0].dims[1]/o,e[0].dims[2]*t,e[0].dims[3]*t];return[r.reshapeUnpacked(l,d)]},eg=r=>{let e=r.attributes.getInt("blocksize");if(e<1)throw new Error(`blocksize must be >= 1, but got : ${e} for DepthToSpace`);let n=r.attributes.getString("mode","DCR");if(n!=="DCR"&&n!=="CRD")throw new Error(`unrecognized mode: ${n} for DepthToSpace`);return{mode:n,blocksize:e}},fA=r=>{if(r.length!==1)throw new Error(`DepthToSpace expect 1 inputs, but got ${r.length}`);if(r[0].type==="string"||r[0].dims.length!==4)throw new TypeError("DepthToSpace input should be a 4-D numeric tensor")}});var ng,rg,hA,og=k(()=>{"use strict";Me();ng=(r,e,n)=>{hA(e,n);let t=ne.flattenShape(e[0].dims,n);return[r.reshapeUnpacked(e[0],t)]},rg=r=>r.attributes.getInt("axis",1),hA=(r,e)=>{if(!r||r.length!==1)throw new Error("Flatten requires 1 input.");let n=r[0].dims.length;if(n===0)throw new Error("scalar tensor is not supported.");if(e<-n||e>n)throw new Error("Invalid axis");if(r[0].type==="string")throw new Error("string tensor is not supported.")}});var gr,Ro=k(()=>{"use strict";gr=["float32","float64","int32","int16","int8","uint16","uint32","uint8"]});var ig,ag,mA,gA,bA,yA,sg=k(()=>{"use strict";lt();Ro();Me();Ae();ig=(r,e,n)=>(yA(e,n.axis),[r.run(bA(r,e,n),e)]),ag=r=>xe({axis:r.attributes.getInt("axis",0)}),mA={name:"Gather",inputNames:["A","B"],inputTypes:[0,0]},gA=(r,e,n,t)=>{let o=n[0].dims.slice(),i=n[1].dims.slice(),a=new Array(o.length+i.length-1);t=ne.normalizeAxis(t,o.length);let s=[];for(let h=0;h<a.length;h++)h<t?(a[h]=o[h],s.push(`inputIdx[${h}] = outputIdx[${h}];`)):h<t+i.length?(a[h]=i[h-t],s.push(`indexDataIdx[${h-t}] = outputIdx[${h}];`)):(a[h]=o[h-i.length+1],s.push(`inputIdx[${h-i.length+1}] = outputIdx[${h}];`));let u=a.length||1,l=o.length,d=i.length||1,f=`
      float process(int outputIdx[${u}]) {
        int inputIdx[${l}];
        int indexDataIdx[${d}];
        indexDataIdx[0] = 0;
        ${s.join(`
        `)}
        int idx = int(_B(indexDataIdx));
        inputIdx[${t}] = idx < 0 ? idx + ${o[t]} : idx;
        return _A(inputIdx);
      }`;return{...e,output:{dims:a,type:n[0].type,textureType:0},shaderSource:f}},bA=(r,e,n)=>{let t={...mA,cacheHint:n.cacheKey};return{...t,get:()=>gA(r,t,e,n.axis)}},yA=(r,e)=>{if(!r||r.length!==2)throw new Error("Gather requires 2 inputs.");let n=r[0].dims.length;if(n<1)throw new Error("Invalid input shape.");if(e<-n||e>n-1)throw new Error("Invalid axis.");if(gr.indexOf(r[0].type)===-1)throw new Error("Invaid input type.");if(r[1].type!=="int32"&&r[1].type!=="int16")throw new Error("Invaid input type.")}});var Ml,ug,lg,cg,_A,vA,wA,dg=k(()=>{"use strict";lt();Me();Ae();Ml=(r,e,n)=>(wA(e,n),[r.run(_A(e,n),e)]),ug=(r,e)=>{let n=r.attributes.getInt("transA",0)!==0,t=r.attributes.getInt("transB",0)!==0,o=r.attributes.getFloat("alpha",1),i=r.attributes.getFloat("beta",1);return xe({transA:n,transB:t,alpha:o,beta:i,isOptionalC:e})},lg=r=>ug(r,!1),cg=r=>ug(r,!0),_A=(r,e)=>{let n={name:"Gemm",inputNames:r.length===3?["A","B","C"]:["A","B"],inputTypes:r.length===3?[0,0,0]:[0,0],key:e.cacheKey};return{...n,get:()=>vA(n,r,e)}},vA=(r,e,n)=>{let t=e[0].dims.slice(),o=e[1].dims.slice(),[i,a]=Li.getShapeOfGemmResult(t,n.transA,o,n.transB,e.length===3?e[2].dims:void 0),s=[i,a];if(!s)throw new Error("Can't use gemm on the given tensors");let u=t[t.length-1],l="";n.transA&&(u=t[0]),n.transA&&n.transB?l="value += _A_T(a) * _B_T(b);":n.transA&&!n.transB?l="value += _A_T(a) * _B(b);":!n.transA&&n.transB?l="value += _A(a) * _B_T(b);":!n.transA&&!n.transB&&(l="value += _A(a) * _B(b);");let d=s.length,f=e.length===3?`int c[${e[2].dims.length}];`:"",h=e.length===3?"bcastIndices_C(indices, c);":"",g=e.length===3?"value += beta * _C(c);":"",b=`
      float process(int indices[${d}]) {
          int a[${d}];
          int b[${d}];
          ${f}

          copyVec(indices, a);
          copyVec(indices, b);
          ${h}

          float value = 0.0;
          for (int k=0; k<${u}; ++k) {
              a[${d-1}] = k;
              b[${d-2}] = k;
              ${l}
          }

          value = value * alpha;
          ${g}
          return value;
      }`;return{...r,output:{dims:s,type:e[0].type,textureType:0},variables:[{name:"alpha",type:"float",data:n.alpha},{name:"beta",type:"float",data:n.beta}],shaderSource:b}},wA=(r,e)=>{if(!r)throw new Error("Input is missing");if(e.isOptionalC&&(r.length<2||r.length>3))throw new Error("Invaid input shape.");if(!e.isOptionalC&&r.length!==3)throw new Error("Gemm requires 3 inputs");if(r.length===3&&r[2].dims.length!==1&&r[2].dims.length!==2)throw new Error("Invalid input shape of C");if(r[0].type!=="float32"&&r[0].type!=="float64"||r[1].type!=="float32"&&r[1].type!=="float64"||r.length===3&&r[2].type!=="float32"&&r[2].type!=="float64")throw new Error("Invalid input type.");if(r[0].type!==r[1].type||r.length===3&&r[0].type!==r[2].type)throw new Error("Input types are mismatched")}});var pg,fg,xA,TA,IA,SA,$A,hg=k(()=>{"use strict";lt();Ae();pg=(r,e,n)=>($A(e),[r.run(IA(r,e,n),e)]),fg=r=>{let e=r.attributes.getFloat("scale"),n=r.attributes.getFloats("bias");return xe({scale:e,bias:n})},xA={name:"ImageScaler",inputNames:["X"],inputTypes:[0]},TA=(r,e,n,t)=>{let o=n[0].dims.slice(),i=o.length,s=`
      ${SA(t.bias.length)}
      float process(int indices[${i}]) {
        return _X(indices) * scale + getBias(bias, indices[1]);
      }`;return{...e,output:{dims:o,type:n[0].type,textureType:0},variables:[{name:"bias",type:"float",arrayLength:t.bias.length,data:t.bias},{name:"scale",type:"float",data:t.scale}],shaderSource:s}},IA=(r,e,n)=>{let t={...xA,cacheHint:n.cacheKey};return{...t,get:()=>TA(r,t,e,n)}},SA=r=>{let e=[`float getBias(float bias[${r}], int channel) {`];for(let n=0;n<r;++n)n===0?e.push(`	if (channel == ${n}) { return bias[${n}]; }`):n===r-1?e.push(`	else { return bias[${n}]; }`):e.push(`	else if (channel == ${n}) { return bias[${n}]; }`);return e.push("	}"),e.join(`
`)},$A=r=>{if(!r||r.length!==1)throw new Error("ImageScaler requires 1 input.");if(r[0].dims.length!==4)throw new Error("Invalid input shape.");if(r[0].type!=="float32"&&r[0].type!=="float64")throw new Error("Invalid input type.")}});var gg,bg,mg,AA,OA,PA,EA,CA,DA,yg=k(()=>{"use strict";Ke();Ae();gg=(r,e,n)=>{DA(e);let t=r.run(OA(e[0]),e);return[r.run(CA(r,e[0],n,t.dims),[e[0],t,e[1],e[2]])]},bg=r=>r.attributes.getFloat("epsilon",1e-5),mg={name:"InstanceNormalization_MeanAndVariance",inputNames:["X"],inputTypes:[0]},AA=(r,e)=>{let n=e.dims.slice(),t=n[1],o=n[2]*n[3],i=[n[0],t],a=`
      vec4 process(int[2] indices) {
        vec4 v = vec4(0.0);
        int a[4];
        a[0] = indices[0];
        a[1] = indices[1];
        float temp = 0.0;
        for(int a2=0; a2<${n[2]}; a2++) {
          a[2] = a2;
          for(int a3=0; a3<${n[3]}; a3++) {
            a[3] = a3;
            float x = _X(a);
            temp += x;
          }
        }
        float mean = temp / float(${o});
        temp = 0.0;
        for(int a2=0; a2<${n[2]}; a2++) {
          a[2] = a2;
          for(int a3=0; a3<${n[3]}; a3++) {
            a[3] = a3;
            float x = _X(a);
            temp += (x - mean) * (x - mean);
          }
        }
        v.r = mean;
        v.g = temp / float(${o});

        return v;
      }`;return{...r,output:{dims:i,type:e.type,textureType:4},shaderSource:a}},OA=r=>({...mg,get:()=>AA(mg,r)}),PA={name:"InstanceNormalization_ComputeOutput",inputNames:["X","MeanAndVariance","Scale","B"],inputTypes:[0,4,0,0]},EA=(r,e,n,t,o)=>{let i=ue(r.session.backend.glContext.version),[a,s]=r.calculateTextureWidthAndHeight(o,4),[u,l]=[a/4,s],d=`
      vec4 get_MeanAndVariance(int[2] mv) {
        int offset = indicesToOffset_MeanAndVariance(mv);
        vec2 coords = offsetToCoords(offset, ${u}, ${l});
        return ${i.texture2D}(MeanAndVariance, coords);
      }

      float process(int[4] indices) {
        int mv[2];
        mv[0] = indices[0];
        mv[1] = indices[1];
        vec4 mean_and_variance = get_MeanAndVariance(mv);
        float mean = mean_and_variance.r;
        float variance = mean_and_variance.g;

        int sb[1];
        sb[0] = indices[1];
        float scale = _Scale(sb);
        float b = _B(sb);

        return scale * (_X(indices) - mean) / sqrt(variance + epsilon) + b;
      }`;return{...e,output:{dims:n.dims,type:n.type,textureType:0},variables:[{name:"epsilon",type:"float",data:t}],shaderSource:d}},CA=(r,e,n,t)=>{let o={...PA,cacheHint:`${n}`};return{...o,get:()=>EA(r,o,e,n,t)}},DA=r=>{if(!r||r.length!==3)throw new Error("InstanceNormalization requires 3 inputs.");let e=r[0],n=r[1],t=r[2];if(e.dims.length<3||n.dims.length!==1||t.dims.length!==1)throw new Error("Invalid input shape.");if(n.dims[0]!==e.dims[1]||t.dims[0]!==e.dims[1])throw new Error("Input shapes are mismatched.");if(e.type!=="float32"&&e.type!=="float64"||n.type!=="float32"&&n.type!=="float64"||t.type!=="float32"&&t.type!=="float64")throw new Error("Invalid input type.");if(r[0].dims.length!==4)throw new Error("Only support 4-D input shape.")}});function kA(r,e){let n=r[0].dims[1],t=r[0].dims.length,o=-Math.floor((e.size-1)/2),i=Math.ceil((e.size-1)/2),a=`float(${e.alpha}) / float(${e.size})`,s=`float(${e.bias})`,u=`float(${e.beta})`,l=`
    float process(int indices[${t}]) {
        int c = indices[1];
        float x = _X(indices);
        float square_sum = 0.0;

        for (int i = ${o}; i <= ${i}; i++) {
          int idx = c + i;
          if (c >= 0 && c < ${n}) {
            indices[1] = idx;
            float j = _X(indices);
            square_sum += j * j;
          }
        }
        return x / pow(${s} + ${a} * square_sum, ${u});
    }`;return{...wg,cacheHint:e.cacheKey,output:{dims:r[0].dims,type:r[0].type,textureType:0},shaderSource:l}}function NA(r,e){return{...wg,cacheHint:e.cacheKey,get:()=>kA(r,e)}}var _g,vg,wg,LA,xg=k(()=>{"use strict";lt();Ae();_g=(r,e,n)=>(LA(e),[r.run(NA(e,n),e)]),vg=r=>{let e=r.attributes.getFloat("alpha",1e-4),n=r.attributes.getFloat("beta",.75),t=r.attributes.getFloat("bias",1),o=r.attributes.getInt("size");return xe({alpha:e,beta:n,bias:t,size:o})},wg={name:"LRN",inputNames:["X"],inputTypes:[0]};LA=r=>{if(!r||r.length!==1)throw new Error("LRN requires 1 input.");if(r[0].dims.length!==4)throw new Error('currently only support LRN for input with "NCHW" format');if(r[0].type!=="float32")throw new Error("input should be float type")}});var RA,Bl,Tg,Ig,Sg,zA,MA,BA,FA,VA,GA,UA,WA,$g=k(()=>{"use strict";lt();Me();Ke();Ae();RA={name:"Pad",inputNames:["A"],inputTypes:[0]},Bl=(r,e,n)=>(BA(e),[r.run({...RA,cacheHint:n.cacheKey,get:()=>MA(r,e[0],n)},e)]),Tg=r=>{let e=r.attributes.getString("mode","constant"),n=r.attributes.getFloat("value",0),t=r.attributes.getInts("pads");return xe({mode:e,value:n,pads:t})},Ig=(r,e,n)=>{FA(e);let t=zA(r,e,n);return Bl(r,[e[0]],t)},Sg=r=>r.attributes.getString("mode","constant"),zA=(r,e,n)=>{if(!r.session.isInitializer(e[1].dataId)||e.length>=3&&!r.session.isInitializer(e[2].dataId))throw new Error("dynamic pad attributes are not allowed");let t=Array.from(e[1].integerData),o=e.length>=3?e[2].floatData[0]:0;return xe({mode:n,pads:t,value:o})},MA=(r,e,n)=>{let t=ne.padShape(e.dims.slice(),n.pads),o=t.length,a=`
      ${VA(r,e,n)}
      float process(int[${o}] indices) {
          return padA(indices);
      }`;return{name:"Pad",inputNames:["A"],inputTypes:[0],output:{dims:t,type:e.type,textureType:0},shaderSource:a}},BA=r=>{if(!r||r.length!==1)throw new Error("Pad requires 1 input");if(r[0].type!=="float32"&&r[0].type!=="float64")throw new Error("Invalid input type.")},FA=r=>{if(!r||r.length!==2&&r.length!==3)throw new Error("Pad requires 2 or 3 inputs");if(r[1].type!=="int32")throw new Error("Invalid input type.");if(r.length>=3&&r[2].type==="string")throw new Error("Invalid input type.")},VA=(r,e,n)=>{let t=ue(r.session.backend.glContext.version),[o,i]=r.calculateTextureWidthAndHeight(e.dims,0),a=ne.computeStrides(e.dims);switch(n.mode){case"constant":return GA(t,e.dims,a,o,i,n.pads,n.value);case"reflect":return UA(t,e.dims,a,o,i,n.pads);case"edge":return WA(t,e.dims,a,o,i,n.pads);default:throw new Error("Invalid mode")}},GA=(r,e,n,t,o,i,a)=>{let s=e.length,u="";for(let l=s-1;l>=0;--l)u+=`
        k = m[${l}] - ${i[l]};
        if (k < 0)  return constant;
        if (k >= ${e[l]}) return constant;
        offset += k * ${n[l]};
        `;return`
      float padA(int m[${s}]) {
        const float constant = float(${a});
        int offset = 0;
        int k = 0;
        ${u}
        vec2 coords = offsetToCoords(offset, ${t}, ${o});
        float value = getColorAsFloat(${r.texture2D}(A, coords));
        return value;
      }
      `},UA=(r,e,n,t,o,i)=>{let a=e.length,s="";for(let u=a-1;u>=0;--u)s+=`
        k = m[${u}] - ${i[u]};
        if (k < 0) { k = -k; }
        {
          const int _2n_1 = ${2*(e[u]-1)};
          k = int( mod( float(k), float(_2n_1) ) ) ;
          if(k >= ${e[u]}) { k = _2n_1 - k; }
        }
        offset += k * ${n[u]};
        `;return`
      float padA(int m[${a}]) {
        int offset = 0;
        int k = 0;
        ${s}
        vec2 coords = offsetToCoords(offset, ${t}, ${o});
        float value = getColorAsFloat(${r.texture2D}(A, coords));
        return value;
      }
      `},WA=(r,e,n,t,o,i)=>{let a=e.length,s="";for(let u=a-1;u>=0;--u)s+=`
        k = m[${u}] - ${i[u]};
        if (k < 0)  k = 0;
        if (k >= ${e[u]}) k = ${e[u]-1};
        offset += k * ${n[u]};
      `;return`
      float padA(int m[${a}]) {
        int offset = 0;
        int k = 0;
        ${s}
        vec2 coords = offsetToCoords(offset, ${t}, ${o});
        float value = getColorAsFloat(${r.texture2D}(A, coords));
        return value;
      }
      `}});var Og,Pg,Eg,Cg,Dg,kg,Ng,Lg,Rg,HA,Ag,zg,Ki,Mg,qi,jA,Bg=k(()=>{"use strict";lt();Me();Ae();Og=(r,e,n)=>{Ki(e);let t={name:"AveragePool",inputNames:["X"],inputTypes:[0],cacheHint:n.cacheKey};return[r.run({...t,get:()=>Eg(e,t,!1,n)},e)]},Pg=r=>{let e=r.attributes.getString("auto_pad","NOTSET"),n=r.attributes.getInt("ceil_mode",0),t=r.attributes.getInt("count_include_pad",0)!==0,o=r.attributes.getInts("kernel_shape"),i=r.attributes.getInts("strides",[]),a=r.attributes.getInts("pads",[]);if(n!==0)throw new Error("using ceil() in shape computation is not yet supported for AveragePool");return xe({autoPad:e,ceilMode:n,countIncludePad:t,kernelShape:o,strides:i,pads:a})},Eg=(r,e,n,t)=>{let[o,i]=Rg(r,t,n),a=ne.size(o.kernelShape),s="value += _X(x);",u="";o.countIncludePad?u+=`value /= float(${a});`:u+=`value /= float(${a} - pad);`;let d=`
        ${Mg(r[0].dims,o,s,u,"0.0")}
      `;return{...e,output:{dims:i,type:r[0].type,textureType:0},shaderSource:d}},Cg=(r,e,n)=>{Ki(e);let t={name:"GlobalAveragePool",inputNames:["X"],inputTypes:[0],cacheHint:`${n.countIncludePad}`};return[r.run({...t,get:()=>Eg(e,t,!0,n)},e)]},Dg=r=>{let e=r.attributes.getInt("count_include_pad",0)!==0;return xe({autoPad:"",ceilMode:0,countIncludePad:e,kernelShape:[],strides:[],pads:[]})},kg=(r,e,n)=>{Ki(e);let t={name:"MaxPool",inputNames:["X"],inputTypes:[0],cacheHint:n.cacheKey};return[r.run({...t,get:()=>Lg(e,t,!1,n)},e)]},Ng=r=>{let e=r.attributes.getString("auto_pad","NOTSET"),n=r.attributes.getInt("ceil_mode",0),t=r.attributes.getInts("kernel_shape"),o=r.attributes.getInts("strides",[]),i=r.attributes.getInts("pads",[]),a=r.attributes.getInt("storage_order",0),s=r.attributes.getInts("dilations",[]);if(a!==0)throw new Error("column major storage order is not yet supported for MaxPool");if(n!==0)throw new Error("using ceil() in shape computation is not yet supported for MaxPool");return xe({autoPad:e,ceilMode:n,countIncludePad:!1,kernelShape:t,strides:o,pads:i,storageOrder:a,dilations:s})},Lg=(r,e,n,t)=>{let[o,i]=Rg(r,t,n),l=`
      ${Mg(r[0].dims,o,`
      value = max(_X(x), value);
    `,"","-1e5")}
    `;return{...e,output:{dims:i,type:r[0].type,textureType:0},shaderSource:l}},Rg=(r,e,n)=>{let t=r[0].dims.slice(),o=Object.hasOwnProperty.call(e,"dilations"),i=e.kernelShape.slice(),a=e.strides.slice(),s=o?e.dilations.slice():[],u=e.pads.slice();Nr.adjustPoolAttributes(n,t,i,a,s,u);let l=Nr.computePoolOutputShape(n,t,a,s,i,u,e.autoPad),d=Object.assign({},e);return o?Object.assign(d,{kernelShape:i,strides:a,pads:u,dilations:s,cacheKey:e.cacheKey}):Object.assign(d,{kernelShape:i,strides:a,pads:u,cacheKey:e.cacheKey}),[d,l]},HA={autoPad:"",ceilMode:0,countIncludePad:!1,kernelShape:[],strides:[],pads:[],storageOrder:0,dilations:[],cacheKey:""},Ag={name:"GlobalMaxPool",inputNames:["X"],inputTypes:[0]},zg=(r,e)=>(Ki(e),[r.run({...Ag,get:()=>Lg(e,Ag,!0,HA)},e)]),Ki=r=>{if(!r||r.length!==1)throw new Error("Pool ops requires 1 input.");if(r[0].type!=="float32"&&r[0].type!=="float64")throw new Error("Invalid input type.")},Mg=(r,e,n,t,o)=>{let i=r.length;if(e.kernelShape.length<=2){let a=e.kernelShape[e.kernelShape.length-1],s=e.strides[e.strides.length-1],u=e.pads[e.pads.length/2-1],l=e.pads[e.pads.length-1],d=r[i-1],f="",h="",g="";if(u+l!==0?f=`
          for (int i = 0; i < ${a}; i++) {
            x[${i} - 1] = indices[${i} - 1] * ${s} - ${u} + i;
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              pad++;
              continue;
            }
            ${n}
          }`:f=`
          for (int i = 0; i < ${a}; i++) {
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            ${n}
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            for (int j = 0; j < ${_}; j++) {
              x[${i} - 2] = indices[${i} - 2] * ${T} - ${v} + j;
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                pad+= ${a};
                continue;
              }
          `:h=`
            for (int j = 0; j < ${_}; j++) {
              x[${i} - 2] = indices[${i} - 2] * ${T} - ${v} + j;
            `,g=`
          }
        `}return`
        float process(int indices[${i}]) {
          int x[${i}];
          copyVec(indices, x);

          float value = ${o};
          int pad = 0;
          ${h}
          ${f}
          ${g}
          ${t}
          return value;
        }
      `}else{let a=ne.size(e.kernelShape),s=ne.computeStrides(e.kernelShape),u=s.length,l=e.pads.length,d=jA(u),f=qi(r,"inputDims"),h=qi(e.pads,"pads"),g=qi(s,"kernelStrides"),b=qi(e.strides,"strides"),_=e.pads.reduce((x,I)=>x+I),T="";return _?T=`
            if (x[j] >= inputDims[j] || x[j] < 0) {
              pad++;
              isPad = true;
              break;
            }
          }
          if (!isPad) {
            ${n}
          }`:T=`
          }
          ${n}
        `,`
        ${d}
        float process(int indices[${i}]) {
          int x[${i}];
          copyVec(indices, x);
          int offset[${u}];
          int pads[${l}];
          int inputDims[${i}];
          int kernelStrides[${u}];
          int strides[${u}];
          ${h}
          ${f}
          ${b}
          ${g}

          float value = ${o};
          int pad = 0;
          bool isPad = false;
          for (int i = 0; i < ${a}; i++) {
            offsetToIndices(i, kernelStrides, offset);
            isPad = false;
            for (int j = ${i} - ${u}; j < ${i}; j++) {
              x[j] = indices[j] * strides[j - ${i} + ${u}]
                + offset[j - ${i} + ${u}] - pads[j - 2];
              ${T}
          }
          ${t}

          return value;
        }
      `}},qi=(r,e)=>{let n="";for(let t=0;t<r.length;t++)n+=`
      ${e}[${t}] = ${r[t]};
    `;return n},jA=r=>`
  void offsetToIndices(int offset, int[${r}] strides, out int[${r}] indices) {
    if (${r} == 0) {
      return;
    }
    for (int i = 0; i < ${r} - 1; ++i) {
      indices[i] = offset / strides[i];
      offset -= indices[i] * strides[i];
    }
    indices[${r} - 1] = offset;
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          for(int j${b} = 0; j${b} < ${e[0].dims[b]}; j${b}++) {
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            ${f}
          }`):(u.push(`inputIdx[${b}] = outputIdx[${a.length}];`),a.push(e[0].dims[b]));let g=`
      float process(int outputIdx[${a.length||1}]) {
        float value;                 // final result
        int inputIdx[${s}];      // addressing input data
        ${u.join(`
`)}
        ${d[0]}       // init ops for reduce max/min
        ${f}
        ${d[2]}       // final computation for reduce mean
        return value;
      }`;return{...i,output:{dims:a,type:e[0].type,textureType:0},shaderSource:g}},KA=r=>{if(!r||r.length!==1)throw new Error("Reduce op requires 1 input.");if(gr.indexOf(r[0].type)===-1)throw new Error("Invalid input type.")},Fg=(r,e,n)=>Vr(r,e,n,"ReduceSum",()=>["value = 0.0;","value += _A(inputIdx);",""]),Vg=(r,e,n)=>Vr(r,e,n,"ReduceMean",(o,i)=>{let a=1;for(let s=0;s<o[0].dims.length;s++)(i.indexOf(s)>=0||i.length===0)&&(a*=o[0].dims[s]);return["value = 0.0;","value += _A(inputIdx);",`value /= ${a}.;`]}),Gg=(r,e,n)=>Vr(r,e,n,"ReduceMax",(o,i)=>{let a=[];for(let s=0;s<o[0].dims.length;s++)(i.indexOf(s)>=0||i.length===0)&&a.push(`inputIdx[${s}] = 0;`);return[`${a.join(`
`)}
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`)}
value = _A(inputIdx);`,"value = min(value, _A(inputIdx));",""]}),Wg=(r,e,n)=>Vr(r,e,n,"ReduceProd",()=>["value = 1.0;","value *= _A(inputIdx);",""]),Hg=(r,e,n)=>Vr(r,e,n,"ReduceLogSum",()=>["value = 0.0;","value += _A(inputIdx);","value = log(value);"]),jg=(r,e,n)=>Vr(r,e,n,"ReduceLogSumSquare",()=>["float t; value = 0.0;","t = _A(inputIdx); value += t * t;",""])});var Kg,Xg=k(()=>{"use strict";Me();Kg=(r,e)=>{let n=ne.calculateReshapedDims(e[0].dims,e[1].integerData);return r.session.pack?[r.reshapePacked(e[0],n)]:[r.reshapeUnpacked(e[0],n)]}});var Zg,Fl,Jg,Yg,zo,XA,Vl,Xi,Gl=k(()=>{"use strict";lt();Ke();Ae();Zg={name:"Upsample",inputNames:["X"],inputTypes:[0]},Fl=(r,e,n)=>(Vl(e,n),[r.run({...Zg,cacheHint:n.cacheKey,get:()=>XA(r,e,n)},e)]),Jg=r=>zo(r,7),Yg=r=>zo(r,9),zo=(r,e)=>{let n=e>=10,t=r.attributes.getString("mode","nearest");if(t!=="nearest"&&t!=="linear"&&(e<11||t!=="cubic"))throw new Error(`unrecognized mode: ${t}`);let o=[];e<9&&(o=r.attributes.getFloats("scales"),Xi(o,t,n));let i=r.attributes.getFloat("extrapolation_value",0),a=e>10?r.attributes.getString("coordinate_transformation_mode","half_pixel"):"asymmetric";if(["asymmetric","pytorch_half_pixel","tf_half_pixel_for_nn","align_corners","tf_crop_and_resize","half_pixel"].indexOf(a)===-1)throw new Error(`coordinate_transform_mode '${a}' is not supported`);let s=a==="tf_crop_and_resize",u=s,l=t==="nearest"&&e>=11?r.attributes.getString("nearest_mode","round_prefer_floor"):"";if(["round_prefer_floor","round_prefer_ceil","floor","ceil",""].indexOf(l)===-1)throw new Error(`nearest_mode '${l}' is not supported`);let d=r.attributes.getFloat("cubic_coeff_a",-.75),f=r.attributes.getInt("exclude_outside",0)!==0;if(f&&t!=="cubic")throw new Error("exclude_outside can be set to 1 only when mode is CUBIC.");let h=e<11?!0:t==="nearest"&&a==="asymmetric"&&l==="floor",g=0,b=0,_=0;return e>10?r.inputs.length>2?(g=1,b=2,_=3):(b=1,_=2):e===9&&(b=1),xe({opset:e,isResize:n,mode:t,scales:o,extrapolationValue:i,coordinateTransformMode:a,useExtrapolation:u,needRoiInput:s,nearestMode:l,cubicCoefficientA:d,excludeOutside:f,useNearest2xOptimization:h,roiInputIdx:g,scalesInputIdx:b,sizesInputIdx:_})},XA=(r,e,n)=>{let t=ue(r.session.backend.glContext.version),[o,i]=r.calculateTextureWidthAndHeight(e[0].dims,0),a=e[0].dims.map((_,T)=>Math.floor(_*n.scales[T])),[s,u]=r.calculateTextureWidthAndHeight(a,0),l=a.length,d=new Array(l),f=new Array(l),h=`
      int output_pitches[${l}];
      int input_pitches[${l}];
      `;for(let _=l-1;_>=0;_--)d[_]=_===l-1?1:d[_+1]*a[_+1],f[_]=_===l-1?1:f[_+1]*e[0].dims[_+1],h+=`
        output_pitches[${_}] = ${d[_]};
        input_pitches[${_}] = ${f[_]};
        `;let g=`
      float getInputFloat(int index) {
        vec2 coords = offsetToCoords(index, ${o}, ${i});
        float value = getColorAsFloat(${t.texture2D}(X, coords));
        return value;
      }
      `,b=n.mode==="nearest"?`
    ${g}
    float process(int indices[${l}]) {
      int input_index = 0;
      int output_index = coordsToOffset(TexCoords, ${s}, ${u});

      ${h}

      int d, m;
      for (int dim = 0; dim < ${l}; ++dim) {
        d = output_index / output_pitches[dim];
        m = output_index - d * output_pitches[dim];
        output_index = m;

        if (scales[dim] != 1 && d > 0) {
          int d2 = d / scales[dim];
          m = d - d2 * scales[dim];
          d = d2;
        }
        input_index += input_pitches[dim] * d;
      }

      return getInputFloat(input_index);
    }`:l===4?`
    ${g}
    float process(int indices[4]) {
      int input_index = 0;
      int output_index = coordsToOffset(TexCoords, ${s}, ${u});

      ${h}

      int m;
      int index_of_dim0, index_of_dim1, index_of_dim2, index_of_dim3;
      index_of_dim0 = output_index / output_pitches[0];
      m = output_index - index_of_dim0 * output_pitches[0];
      index_of_dim1 = m / output_pitches[1];
      m = m - index_of_dim1 * output_pitches[1];
      index_of_dim2 = m / output_pitches[2];
      m = m - index_of_dim2 * output_pitches[2];
      index_of_dim3 = m;

      int index_of_input_dim2, index_of_input_dim3, x_offset, y_offset;
      index_of_input_dim2 = index_of_dim2 / scales[2];
      y_offset = index_of_dim2 - index_of_input_dim2 * scales[2];
      index_of_input_dim3 = index_of_dim3 / scales[3];
      x_offset = index_of_dim3 - index_of_input_dim3 * scales[3];

      input_index = index_of_dim0 * input_pitches[0] +
            index_of_dim1 * input_pitches[1] +
            index_of_input_dim2 * input_pitches[2] +
            index_of_input_dim3;

      float x00 = getInputFloat(input_index);
      float x10, x01, x11;

      bool end_of_dim2 = false;
      if (index_of_input_dim2 == (${e[0].dims[2]} - 1)) {
        // It's the end in dimension 2
        x01 = x00;
        end_of_dim2 = true;
      } else {
        x01 = getInputFloat(input_index + input_pitches[2]);
      }

      if (index_of_input_dim3 == (input_pitches[2] - 1)) {
        // It's the end in dimension 3
        x10 = x00;
        x11 = x01;
      }
      else {
        x10 = getInputFloat(input_index + 1);
        x11 = end_of_dim2 ? x10 : getInputFloat(input_index + input_pitches[2] + 1);
      }

      float y0 = x00 + float(y_offset) * (x01 - x00) / float(scales[2]);
      float y1 = x10 + float(y_offset) * (x11 - x10) / float(scales[2]);
      return y0 + float(x_offset) * (y1 - y0) / float(scales[3]);
    }`:`
    ${g}
    float process(int indices[2]) {
      int input_index = 0;
      int output_index = coordsToOffset(TexCoords, ${s}, ${u});

      ${h}

      int m;
      int index_of_dim0, index_of_dim1;
      index_of_dim0 = output_index / output_pitches[0];
      m = output_index - index_of_dim0 * output_pitches[0];
      index_of_dim1 = m;

      int index_of_input_dim0, index_of_input_dim1, x_offset, y_offset;
      index_of_input_dim0 = index_of_dim0 / scales[0];
      y_offset = index_of_dim0 - index_of_input_dim0 * scales[0];
      index_of_input_dim1 = index_of_dim1 / scales[1];
      x_offset = index_of_dim1 - index_of_input_dim1 * scales[1];

      input_index = index_of_input_dim0 * input_pitches[0] + index_of_input_dim1;

      float x00 = getInputFloat(input_index);
      float x10, x01, x11;

      bool end_of_dim0 = false;
      if (index_of_input_dim0 == (${e[0].dims[0]} - 1)) {
        // It's the end in dimension 0
        x01 = x00;
        end_of_dim0 = true;
      } else {
        x01 = getInputFloat(input_index + input_pitches[0]);
      }

      if (index_of_input_dim1 == (input_pitches[0] - 1)) {
        // It's the end in dimension 1
        x10 = x00;
        x11 = x01;
      }
      else {
        x10 = getInputFloat(input_index + 1);
        x11 = end_of_dim0 ? x10 : getInputFloat(input_index + input_pitches[0] + 1);
      }

      float y0 = x00 + float(y_offset) * (x01 - x00) / float(scales[0]);
      float y1 = x10 + float(y_offset) * (x11 - x10) / float(scales[0]);
      return y0 + float(x_offset) * (y1 - y0) / float(scales[1]);
    }`;return{...Zg,output:{dims:a,type:e[0].type,textureType:0},shaderSource:b,variables:[{name:"scales",type:"int",arrayLength:n.scales.length,data:n.scales.map(_=>Math.ceil(_))}]}},Vl=(r,e)=>{if(!r||e.opset<9&&r.length!==1||e.opset>=9&&e.opset<11&&r.length!==2||e.opset>=11&&r.length<2)throw new Error("invalid inputs.");if(e.scales.length>0&&r[0].dims.length!==e.scales.length)throw new Error("Invalid input shape.");if(r[0].type==="string")throw new Error("Invalid input tensor types.")},Xi=(r,e,n)=>{if(n){for(let t of r)if(t<=0)throw new Error("Scale value should be greater than 0.")}else for(let t of r)if(t<1)throw new Error("Scale value should be greater than or equal to 1.");if((e==="linear"||e==="cubic")&&r.length!==2&&(r.length!==4||r[0]!==1||r[1]!==1))throw new Error(`'Linear' mode and 'Cubic' mode only support 2-D inputs ('Bilinear', 'Bicubic')         or 4-D inputs with the corresponding outermost 2 scale values being 1         in the ${n?"Resize":"Upsample"} opeartor.`)}});var Ul,Wl,Qg,eb,ZA,JA,YA,QA,tb=k(()=>{"use strict";Ke();Ae();Nn();Mr();Gl();Ul={name:"Resize",inputNames:["A"],inputTypes:[2]},Wl=(r,e,n)=>(Vl(e,n),[r.run({...Ul,cacheHint:n.cacheKey,get:()=>ZA(r,e,n)},e)]),Qg=r=>zo(r,10),eb=r=>zo(r,11),ZA=(r,e,n)=>{let t=ue(r.session.backend.glContext.version),[o,i]=JA(e,n);if(o.every(I=>I===1)&&n.coordinateTransformMode!=="tf_crop_and_resize")return{...Ul,output:{dims:i,type:e[0].type,textureType:2},hasMain:!0,shaderSource:`void main() {
                    vec4 v = ${t.texture2D}(X, TexCoords);
                    ${t.output} = v;
                }`};let s=i.length;if(s<2)throw new Error(`output dimension should be at least 2, but got ${s}`);let u=i[s-2],l=i[s-1],d=e[0].dims;if(s!==d.length)throw new Error(`output dimension should match input ${d.length}, but got ${s}`);let f=d[s-2],h=d[s-1],g=o[s-2],b=o[s-1],_="";if(n.mode!=="linear")throw new Error(`resize (packed) does not support mode: '${n.mode}'`);switch(n.coordinateTransformMode){case"asymmetric":_=`
                    vec4 getSourceFracIndex(ivec4 coords) {
                        return vec4(coords) / scaleWHWH;
                    }
                `;break;case"half_pixel":_=`
                    vec4 getSourceFracIndex(ivec4 coords) {
                        return (vec4(coords) + 0.5) / scaleWHWH - 0.5;
                    }
                `;break;case"pytorch_half_pixel":_=`
                    vec4 getSourceFracIndex(ivec4 coords) {
                        vec4 fcoords = vec4(coords);
                        return vec4(
                            ${l}.0 > 1.0 ? (fcoords.x + 0.5) / scaleWHWH.x - 0.5 : 0.0,
                            ${u}.0 > 1.0 ? (fcoords.y + 0.5) / scaleWHWH.y - 0.5 : 0.0,
                            ${l}.0 > 1.0 ? (fcoords.z + 0.5) / scaleWHWH.z - 0.5 : 0.0,
                            ${u}.0 > 1.0 ? (fcoords.w + 0.5) / scaleWHWH.w - 0.5 : 0.0
                          );
                    }
                `;break;case"align_corners":_=`
                    vec4 getSourceFracIndex(ivec4 coords) {
                        vec4 resized = vec4(${l}.0 - 1.0, ${u}.0 - 1.0, ${l}.0 - 1.0,
                            ${u}.0 - 1.0);
                        vec4 original = vec4(${h}.0 - 1.0, ${f}.0 - 1.0, ${h}.0 - 1.0,
                            ${f}.0 - 1.0);
                        vec4 new_scale = original / resized;
                        return vec4(coords) * new_scale;
                    }
                `;break;default:throw new Error(`resize (packed) does not support coordinateTransformMode:                                 '${n.coordinateTransformMode}'`)}let T=bt(s),v=Ln(),x=`
            const vec2 inputWH = vec2(${f}.0, ${h}.0);
            const vec4 scaleWHWH = vec4(float(${g}), float(${b}), float(${g}), float(${b}));
            ${v}
            ${_}
            float getAValue(int x10, int r, int c, int d) {
                return getChannel(getA(x10, r, c, d), vec2(c, d));
            }
            void main() {
                ${T} rc = getOutputCoords();

                int batch = rc[0];
                int depth = rc[1];

                // retrieve the 4 coordinates that is used in the 4 packed output values.
                ivec4 coords = ivec4(rc.wz, rc.w + 1, rc.z + 1);

                // calculate the source index in fraction
                vec4 sourceFrac = getSourceFracIndex(coords);

                // get the lower and upper bound of the 4 values that will be packed into one texel.
                ivec4 x00 = ivec4(max(sourceFrac.xy, vec2(0.0)), min(inputWH - 1.0, ceil(sourceFrac.xy)));
                ivec4 x01 = ivec4(max(sourceFrac.xw, vec2(0.0)), min(inputWH - 1.0, ceil(sourceFrac.xw)));
                ivec4 x10 = ivec4(max(sourceFrac.zy, vec2(0.0)), min(inputWH - 1.0, ceil(sourceFrac.zy)));
                ivec4 x11 = ivec4(max(sourceFrac.zw, vec2(0.0)), min(inputWH - 1.0, ceil(sourceFrac.zw)));

                bool hasNextRow = rc.w < ${u-1};
                bool hasNextCol = rc.z < ${l-1};

                // pack x00, x01, x10, x11's top-left corner into one vec4 structure
                vec4 topLeft = vec4(
                    getAValue(batch, depth, x00.x, x00.y),
                    hasNextCol ? getAValue(batch, depth, x01.x, x01.y) : 0.0,
                    hasNextRow ? getAValue(batch, depth, x10.x, x10.y) : 0.0,
                    (hasNextRow && hasNextCol) ? getAValue(batch, depth, x11.x, x11.y) : 0.0);

                // pack x00, x01, x10, x11's top-right corner into one vec4 structure
                vec4 topRight = vec4(
                    getAValue(batch, depth, x00.x, x00.w),
                    hasNextCol ? getAValue(batch, depth, x01.x, x01.w) : 0.0,
                    hasNextRow ? getAValue(batch, depth, x10.x, x10.w) : 0.0,
                    (hasNextRow && hasNextCol) ? getAValue(batch, depth, x11.x, x11.w) : 0.0);

                // pack x00, x01, x10, x11's bottom-left corner into one vec4 structure
                vec4 bottomLeft = vec4(
                    getAValue(batch, depth, x00.z, x00.y),
                    hasNextCol ? getAValue(batch, depth, x01.z, x01.y) : 0.0,
                    hasNextRow ? getAValue(batch, depth, x10.z, x10.y) : 0.0,
                    (hasNextRow && hasNextCol) ? getAValue(batch, depth, x11.z, x11.y) : 0.0);

                // pack x00, x01, x10, x11's bottom-right corner into one vec4 structure
                vec4 bottomRight = vec4(
                    getAValue(batch, depth, x00.z, x00.w),
                    hasNextCol ? getAValue(batch, depth, x01.z, x01.w) : 0.0,
                    hasNextRow ? getAValue(batch, depth, x10.z, x10.w) : 0.0,
                    (hasNextRow && hasNextCol) ? getAValue(batch, depth, x11.z, x11.w) : 0.0);

                // calculate the interpolation fraction on u and v direction
                vec4 frac = vec4(sourceFrac) - floor(sourceFrac);
                vec4 clampFrac = clamp(frac, vec4(0.0), vec4(1.0));

                vec4 top = mix(topLeft, topRight, clampFrac.ywyw);
                vec4 bottom = mix(bottomLeft, bottomRight, clampFrac.ywyw);
                vec4 newValue = mix(top, bottom, clampFrac.xxzz);

                ${t.output} = vec4(newValue);
            }
        `;return{...Ul,output:{dims:i,type:e[0].type,textureType:2},hasMain:!0,shaderSource:x}},JA=(r,e)=>{let t=r[0].dims,o=e.scales,i;if(o.length===0){let s=r[e.scalesInputIdx];if(s&&s.size!==0){if(r[e.sizesInputIdx])throw new Error("Only one of scales or sizes must be provided as input.");o=YA(s,e.mode,e.isResize)}else{let u=r[e.sizesInputIdx];if(!u||u.size===0)throw new Error("Either scales or sizes MUST be provided as input.");i=Array.from(u.integerData),o=QA(i,t,e.mode,e.isResize)}}else if(r[e.sizesInputIdx])throw new Error("Only one of scales or sizes must be provided as input.");let a=i||t.map((s,u)=>Math.floor(s*o[u]));return[o,a]},YA=(r,e,n)=>{let t=Array.from(r.floatData);return Xi(t,e,n),t},QA=(r,e,n,t)=>{let o=e.length,i=new Array(o);for(let a=0,s=o;a<s;a++)if(e[a]===0){if(r[a]!==0)throw new Error("Input dim is zero but required output dim is non-zero.");i[a]=1}else i[a]=r[a]/e[a];return Xi(i,n,t),i}});var nb,eO,rb=k(()=>{"use strict";zr();nb=(r,e)=>(eO(e),[new nt([e[0].dims.length],"int32",void 0,void 0,new Int32Array(e[0].dims))]),eO=r=>{if(!r||r.length!==1)throw new Error("Shape requires 1 input.")}});var Hl,ob,ib,ab,tO,sb,nO,rO,ub=k(()=>{"use strict";lt();Ro();Me();Ae();Hl={name:"Slice",inputNames:["A"],inputTypes:[0]},ob=(r,e,n)=>(tO(e),[r.run({...Hl,cacheHint:n.cacheKey,get:()=>ab(r,e[0],n)},e)]),ib=r=>{let e=r.attributes.getInts("starts"),n=r.attributes.getInts("ends"),t=r.attributes.getInts("axes",[]);return xe({starts:e,ends:n,axes:t})},ab=(r,e,n)=>{let t=n.axes.length===0?e.dims.slice(0).map((f,h)=>h):n.axes,o=ne.normalizeAxes(t,e.dims.length),i=n.starts.map((f,h)=>f>e.dims[o[h]]-1?e.dims[o[h]]:ne.normalizeAxis(f,e.dims[o[h]])),a=n.ends.map((f,h)=>f>e.dims[o[h]]-1?e.dims[o[h]]:ne.normalizeAxis(f,e.dims[o[h]])),s=e.dims.slice(),u=[];for(let f=0;f<o.length;f++)s[o[f]]=a[f]-i[f],i[f]>0&&u.push(`outputIdx[${o[f]}] += ${i[f]};`);let d=`
      float process(int outputIdx[${s.length}]) {
        ${u.join(`
      `)}
        return _A(outputIdx);
      }`;return{...Hl,output:{dims:s,type:e.type,textureType:0},shaderSource:d}},tO=r=>{if(!r||r.length!==1)throw new Error("Slice requires 1 input.");if(gr.indexOf(r[0].type)===-1)throw new Error("Invalid input type.")},sb=(r,e)=>{rO(e);let n=nO(r,e);return[r.run({...Hl,cacheHint:n.cacheKey,get:()=>ab(r,e[0],n)},[e[0]])]},nO=(r,e)=>{if(!r.session.isInitializer(e[1].dataId)||!r.session.isInitializer(e[2].dataId)||e.length>=4&&!r.session.isInitializer(e[3].dataId)||e.length>=5&&!r.session.isInitializer(e[4].dataId))throw new Error("dynamic slice attributes are not allowed");if(e.length>=5&&e[4].integerData.some(a=>a!==1))throw new Error("currently non-1 steps is not supported for Slice");let n=Array.from(e[1].integerData),t=Array.from(e[2].integerData),o=e.length>=4?Array.from(e[3].integerData):[],i=`${o};${n};${t}`;return{starts:n,ends:t,axes:o,cacheKey:i}},rO=r=>{if(!r||r.length<3||r.length>5)throw new Error("Invalid input number.");if(r[1].type!=="int32"||r[1].dims.length!==1)throw new Error("Invalid input type.");if(r[2].type!=="int32"||r[2].dims.length!==1)throw new Error("Invalid input type.");if(r.length>=4&&(r[3].type!=="int32"||r[3].dims.length!==1))throw new Error("Invalid input type.");if(r.length>=5&&(r[4].type!=="int32"||r[4].dims.length!==1))throw new Error("Invalid input type.")}});var lb,cb,db,pb,fb,hb,mb,gb,oO,iO,aO,bb,yb=k(()=>{"use strict";lt();Me();Ke();Ae();ji();lb={name:"SoftmaxComputeMax",inputNames:["A"],inputTypes:[0]},cb={name:"SoftmaxComputeScale",inputNames:["A","Max"],inputTypes:[0,0]},db={name:"SoftMax",inputNames:["A","Max","Norm"],inputTypes:[0,0,0]},pb=(r,e,n)=>{bb(e);let t=e[0].dims.slice(),o=ne.normalizeAxis(n.axis,t.length),i=ne.sizeToDimension(t,o),a=ne.sizeFromDimension(t,o);return gb(r,e,n,i,a)},fb=r=>xe({axis:r.attributes.getInt("axis",1)}),hb=r=>xe({axis:r.attributes.getInt("axis",-1)}),mb=(r,e,n)=>{bb(e);let t=e[0].dims.slice(),o=ne.normalizeAxis(n.axis,t.length),i=t.length,a=o!==i-1,s=[],u=[],l=[],d;a&&(u=Array.from({length:i}).map((b,_)=>_),u[o]=i-1,u[i-1]=o,u.map(b=>s.push(t[b])),d=xe({perm:u}),l=Fr(r,e,d));let f=a?ne.sizeToDimension(s,i-1):ne.sizeToDimension(t,i-1),h=a?ne.sizeFromDimension(s,i-1):ne.sizeFromDimension(t,i-1),g=gb(r,a?l:e,n,f,h);return a?Fr(r,g,d):g},gb=(r,e,n,t,o)=>{let i=oO(r,e[0],t,o,[t]),a=r.run({...lb,cacheHint:n.cacheKey,get:()=>i},e),s=iO(r,e[0],t,o,i.output.dims,[t]),u=r.run({...cb,cacheHint:n.cacheKey,get:()=>s},[e[0],a]),l=aO(r,e[0],t,o,i.output.dims,s.output.dims);return[r.run({...db,cacheHint:n.cacheKey,get:()=>l},[e[0],a,u])]},oO=(r,e,n,t,o)=>{let[i,a]=r.calculateTextureWidthAndHeight(e.dims,0),s=o.length;if(n<1||t<1)throw new Error("Logical row count N and feature count D must be greater than or equal to 1");if(o.length!==1)throw new Error("Dimensionality of the output should be 1");if(o[0]!==n)throw new Error("Shape of the output should be equal to logical row count");let u=ue(r.session.backend.glContext.version),l=`
      float process(int[${s}] indices) {
        int logical_row_start_offset = indices[0] * ${t};

        float max = getColorAsFloat(${u.texture2D}(A, offsetToCoords(logical_row_start_offset, ${i},
        ${a} )));
        for(int i=1; i<${t}; ++i)
        {
          float current = getColorAsFloat(${u.texture2D}(A, offsetToCoords(logical_row_start_offset + i,
            ${i}, ${a})));
          if(current > max)
          max = current;
        }

        return max;
      }`;return{...lb,output:{dims:o,type:e.type,textureType:0},shaderSource:l}},iO=(r,e,n,t,o,i)=>{let[a,s]=r.calculateTextureWidthAndHeight(e.dims,0),u=i.length;if(n<1||t<1)throw new Error("Logical row count N and feature count D must be greater than or equal to 1");if(i.length!==1)throw new Error("Dimensionality of the output should be 1");if(i[0]!==n)throw new Error("Shape of the output should be equal to logical row count");if(o.length!==1)throw new Error("Dimensionality of the intermediate results should be 1");if(o[0]!==n)throw new Error("Shape of the intermediate results should be equal to logical row count");let l=ue(r.session.backend.glContext.version),d=`
      float process(int[${u}] indices) {
        int logical_row_start_offset = indices[0] * ${t};

        float norm_factor = 0.0;
        float max = _Max(indices);
        for(int i=0; i<${t}; ++i)
        {
          norm_factor += exp(getColorAsFloat(${l.texture2D}(A, offsetToCoords(logical_row_start_offset + i,
            ${a}, ${s}))) - max);
        }

        return norm_factor;
      }`;return{...cb,output:{dims:i,type:e.type,textureType:0},shaderSource:d}},aO=(r,e,n,t,o,i)=>{let[a,s]=r.calculateTextureWidthAndHeight(e.dims,0),u=e.dims.length;if(n<1||t<1)throw new Error("Logical row count N and feature count D must be greater than or equal to 1");if(o.length!==1||i.length!==1)throw new Error("Dimensionality of the intermediate results should be 1");if(o[0]!==n||i[0]!==n)throw new Error("Shape of the intermediate results should be equal to logical row count");let l=`
      float process(int[${u}] indices) {

      // get offset of current logical tensor index from the 2-D texture coordinates (TexCoords)
      int offset = coordsToOffset(TexCoords, ${a}, ${s});

      //determine the logical row for this index
      int logical_row_index[1];
      logical_row_index[0] = offset / ${t};

      float norm_factor = _Norm(logical_row_index);

      // avoid possible division by 0
      // if norm_facor is 0, all elements are zero
      // if so, return 0
      if(norm_factor == 0.0)
        return 0.0;

      return exp(_A(indices) - _Max(logical_row_index)) / norm_factor;
    }`;return{...db,output:{dims:e.dims,type:e.type,textureType:0},shaderSource:l}},bb=r=>{if(!r||r.length!==1)throw new Error("Softmax requires 1 input.");if(r[0].type!=="float32"&&r[0].type!=="float64")throw new Error("Invalid input type")}});var _b,vb,wb,sO,uO,lO,xb=k(()=>{"use strict";lt();Me();Ae();_b={name:"Split",inputNames:["A"],inputTypes:[0]},vb=(r,e,n)=>{lO(e);let t=ne.normalizeAxis(n.axis,e[0].dims.length),o=sO(r,e,t,n),i=[];for(let a=0;a<o;++a)i.push(r.run({..._b,cacheHint:`${n.cacheKey};${a}`,get:()=>uO(r,e[0],n,t,a)},e));return i},wb=r=>{let e=r.attributes.getInt("axis",0),n=r.attributes.getInts("split",[]),t=r.outputs.length;return xe({axis:e,split:n,numOutputs:t})},sO=(r,e,n,t)=>{let[,o]=Eo.splitShape(e[0].dims,n,t.split,t.numOutputs);return o.length},uO=(r,e,n,t,o)=>{let[i,a]=Eo.splitShape(e.dims,t,n.split,n.numOutputs),s=a[o],u=i[o],d=`
      float process(int indices[${u.length}]) {
        indices[${t}] += ${s};
        return _A(indices);
      }
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      ivec4 getOutputCoords() {
          ivec2 resTexRC = ivec2(TexCoords.xy *
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          int index = resTexRC.y * ${n[0]} + resTexRC.x;
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        }
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      ivec5 getOutputCoords() {
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          int index = resTexRC.y * ${n[0]} + resTexRC.x;
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        }
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     ivec6 getOutputCoords() {
         ivec2 resTexRC = ivec2(TexCoords.xy *
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         int index = resTexRC.y * ${n[0]} + resTexRC.x;
         ${s}
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       }
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    }
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        int texelIndex = index / 2;
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        int texC = texelIndex - texR * texNumC;
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      }
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        int texelIndex = (row / 2) * texelsInLogicalRow + (col / 2);
        int texR = texelIndex / texNumC;
        int texC = texelIndex - texR * texNumC;
        return (vec2(texC, texR) + halfCR) / vec2(texNumC, texNumR);
      }
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        int index = b * texelsInBatch + (row / 2) * texelsInLogicalRow + (col / 2);
        int texR = index / texNumC;
        int texC = index - texR * texNumC;
        return (vec2(texC, texR) + halfCR) / vec2(texNumC, texNumR);
      }
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              break;
            }
            indices[i] = 0;
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o=this.gl;o.bindTexture(o.TEXTURE_2D,e),o.bindFramebuffer(o.FRAMEBUFFER,this.framebuffer),o.framebufferTexture2D(o.FRAMEBUFFER,o.COLOR_ATTACHMENT0,o.TEXTURE_2D,e,0),this.checkError(),o.viewport(0,0,n,t),o.scissor(0,0,n,t)}readTexture(e,n,t,o,i,a){let s=this.gl;a||(a=1),this.frameBufferBound||this.attachFramebuffer(e,n,t);let u=this.getEncoder(i,a),l=u.allocate(n*t);return s.bindTexture(s.TEXTURE_2D,e),s.framebufferTexture2D(s.FRAMEBUFFER,s.COLOR_ATTACHMENT0,s.TEXTURE_2D,e,0),s.readPixels(0,0,n,t,s.RGBA,u.textureType,l),this.checkError(),u.decode(l,o)}isFramebufferReady(){return!0}getActiveTexture(){let e=this.gl;return`TEXTURE${e.getParameter(this.gl.ACTIVE_TEXTURE)-e.TEXTURE0}`}getTextureBinding(){return this.gl.getParameter(this.gl.TEXTURE_BINDING_2D)}getFramebufferBinding(){return this.gl.getParameter(this.gl.FRAMEBUFFER_BINDING)}setVertexAttributes(e,n){let t=this.gl;t.vertexAttribPointer(e,3,t.FLOAT,!1,20,0),t.enableVertexAttribArray(e),n!==-1&&(t.vertexAttribPointer(n,2,t.FLOAT,!1,20,12),t.enableVertexAttribArray(n)),this.checkError()}createProgram(e,n){let t=this.gl,o=t.createProgram();return t.attachShader(o,e),t.attachShader(o,n),t.linkProgram(o),o}compileShader(e,n){let t=this.gl,o=t.createShader(n);if(!o)throw new Error(`createShader() returned null with type ${n}`);if(t.shaderSource(o,e),t.compileShader(o),t.getShaderParameter(o,t.COMPILE_STATUS)===!1)throw new Error(`Failed to compile shader: ${t.getShaderInfoLog(o)}
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t===1||this.isRenderFloat32Supported?new Do(this.gl,n):new Do(this.gl,n,this.textureHalfFloatExtension.HALF_FLOAT_OES);case"int":throw new Error("not implemented");case"byte":return new Mi(this.gl,n);default:throw new Error(`Invalid dataType: ${e}`)}}clearActiveTextures(){let e=this.gl;for(let n=0;n<this.maxTextureImageUnits;++n)e.activeTexture(e.TEXTURE0+n),e.bindTexture(e.TEXTURE_2D,null)}dispose(){if(this.disposed)return;let e=this.gl;e.bindFramebuffer(e.FRAMEBUFFER,null),e.deleteFramebuffer(this.framebuffer),e.bindBuffer(e.ARRAY_BUFFER,null),e.deleteBuffer(this.vertexbuffer),e.bindBuffer(e.ELEMENT_ARRAY_BUFFER,null),e.finish(),this.disposed=!0}createDefaultGeometry(){return new Float32Array([-1,1,0,0,1,-1,-1,0,0,0,1,1,0,1,1,1,-1,0,1,0])}createVertexbuffer(){let e=this.gl,n=e.createBuffer();if(!n)throw new Error("createBuffer() returned null");let t=this.createDefaultGeometry();return e.bindBuffer(e.ARRAY_BUFFER,n),e.bufferData(e.ARRAY_BUFFER,t,e.STATIC_DRAW),this.checkError(),n}createFramebuffer(){let e=this.gl.createFramebuffer();if(!e)throw new Error("createFramebuffer returned null");return e}queryVitalParameters(){let e=this.gl;if(this.isFloatTextureAttachableToFrameBuffer=this.checkFloatTextureAttachableToFrameBuffer(),this.isRenderFloat32Supported=this.checkRenderFloat32(),this.isFloat32DownloadSupported=this.checkFloat32Download(),this.version===1&&!this.textureHalfFloatExtension&&!this.isRenderFloat32Supported)throw new Error("both float32 and float16 TextureType are not 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i=e.checkFramebufferStatus(e.FRAMEBUFFER)===e.FRAMEBUFFER_COMPLETE;return e.bindTexture(e.TEXTURE_2D,null),e.bindFramebuffer(e.FRAMEBUFFER,null),e.deleteTexture(n),e.deleteFramebuffer(o),i}checkRenderFloat32(){if(this.version===2){if(!this.colorBufferFloatExtension)return!1}else if(!this.textureFloatExtension)return!1;return this.isFloatTextureAttachableToFrameBuffer}checkFloat32Download(){if(this.version===2){if(!this.colorBufferFloatExtension)return!1}else if(!this.textureFloatExtension||!this.gl.getExtension("WEBGL_color_buffer_float"))return!1;return this.isFloatTextureAttachableToFrameBuffer}checkFloat32Blend(){let e=this.gl,n,t,o,i,a;try{n=e.createTexture(),t=e.createFramebuffer(),e.bindTexture(e.TEXTURE_2D,n);let s=this.version===2?e.RGBA32F:e.RGBA;return e.texImage2D(e.TEXTURE_2D,0,s,1,1,0,e.RGBA,e.FLOAT,null),e.bindFramebuffer(e.FRAMEBUFFER,t),e.framebufferTexture2D(e.FRAMEBUFFER,e.COLOR_ATTACHMENT0,e.TEXTURE_2D,n,0),e.enable(e.BLEND),o=e.createShader(e.VERTEX_SHADER),!o||(e.shaderSource(o,"void main(){}"),e.compileShader(o),i=e.createShader(e.FRAGMENT_SHADER),!i)||(e.shaderSource(i,"precision highp float;void main(){gl_FragColor=vec4(0.5);}"),e.compileShader(i),a=e.createProgram(),!a)?!1:(e.attachShader(a,o),e.attachShader(a,i),e.linkProgram(a),e.useProgram(a),e.drawArrays(e.POINTS,0,1),e.getError()===e.NO_ERROR)}finally{e.disable(e.BLEND),a&&e.deleteProgram(a),o&&e.deleteShader(o),i&&e.deleteShader(i),t&&(e.bindFramebuffer(e.FRAMEBUFFER,null),e.deleteFramebuffer(t)),n&&(e.bindTexture(e.TEXTURE_2D,null),e.deleteTexture(n))}}beginTimer(){if(this.version===2&&this.disjointTimerQueryWebgl2Extension){let e=this.gl,n=this.disjointTimerQueryWebgl2Extension,t=e.createQuery();return e.beginQuery(n.TIME_ELAPSED_EXT,t),t}else throw new 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this.profiler.event("node",f.node.name,async()=>f.op.impl(t,g,f.op.context));if(b.length!==f.node.outputs.length)throw new Error("the size of output does not match model definition.");b.forEach((T,v)=>{let x=f.node.outputs[v];if(this._values[x])throw new Error(`output [${x}] already has value: op:${f.node.name}`);this._values[x]=T});let _=new Set;b.forEach((T,v)=>{let x=f.node.outputs[v];for(let I of a[x].to){let $=s[I],O=!0;for(let E of $.inputs)if(!this._values[E]){O=!1;break}O&&_.add(I)}}),i.push(..._)}let l=[];for(let d=0;d<this.graph.getOutputIndices().length;d++){let f=this.graph.getOutputIndices()[d],h=this._values[f];if(h===void 0)throw new Error(`required output [${f}] does not have value`);f===0?await h.getData():h.data,l.push(h)}return Fe.verbose("ExecPlan","disposing of inferenceHandler"),t.dispose(),l})}}});var $e,Bo,ny=k(()=>{"use strict";$o();$e=ve(to());zr();Me();Bo=class r{constructor(e){if(this._attributes=new Map,e!=null){for(let n of e)n instanceof 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$e.onnx.AttributeProto.AttributeType.FLOAT:return"float";case $e.onnx.AttributeProto.AttributeType.INT:return"int";case $e.onnx.AttributeProto.AttributeType.STRING:return"string";case $e.onnx.AttributeProto.AttributeType.TENSOR:return"tensor";case $e.onnx.AttributeProto.AttributeType.FLOATS:return"floats";case $e.onnx.AttributeProto.AttributeType.INTS:return"ints";case $e.onnx.AttributeProto.AttributeType.STRINGS:return"strings";case $e.onnx.AttributeProto.AttributeType.TENSORS:return"tensors";default:throw new Error(`attribute type is not supported yet: ${$e.onnx.AttributeProto.AttributeType[n]}`)}}static getValue(e){let n=e instanceof $e.onnx.AttributeProto?e.type:e.type();if(n===$e.onnx.AttributeProto.AttributeType.GRAPH||n===$e.onnx.AttributeProto.AttributeType.GRAPHS)throw new Error("graph attribute is not supported yet");let t=this.getValueNoCheck(e);if(n===$e.onnx.AttributeProto.AttributeType.INT&&xt.isLong(t))return xt.longToNumber(t);if(n===$e.onnx.AttributeProto.AttributeType.INTS){let o=t,i=new Array(o.length);for(let a=0;a<o.length;a++){let s=o[a];i[a]=xt.longToNumber(s)}return i}if(n===$e.onnx.AttributeProto.AttributeType.TENSOR)return e instanceof $e.onnx.AttributeProto?nt.fromProto(t):nt.fromOrtTensor(t);if(n===$e.onnx.AttributeProto.AttributeType.TENSORS){if(e instanceof $e.onnx.AttributeProto)return t.map(i=>nt.fromProto(i));if(e instanceof Pi.Attribute)return t.map(i=>nt.fromOrtTensor(i))}return n===$e.onnx.AttributeProto.AttributeType.STRING&&e instanceof $e.onnx.AttributeProto?Co(t):n===$e.onnx.AttributeProto.AttributeType.STRINGS&&e instanceof $e.onnx.AttributeProto?t.map(Co):t}static getValueNoCheck(e){return e instanceof $e.onnx.AttributeProto?this.getValueNoCheckFromOnnxFormat(e):this.getValueNoCheckFromOrtFormat(e)}static getValueNoCheckFromOnnxFormat(e){switch(e.type){case $e.onnx.AttributeProto.AttributeType.FLOAT:return e.f;case $e.onnx.AttributeProto.AttributeType.INT:return e.i;case $e.onnx.AttributeProto.AttributeType.STRING:return e.s;case $e.onnx.AttributeProto.AttributeType.TENSOR:return e.t;case $e.onnx.AttributeProto.AttributeType.GRAPH:return e.g;case $e.onnx.AttributeProto.AttributeType.FLOATS:return e.floats;case $e.onnx.AttributeProto.AttributeType.INTS:return e.ints;case $e.onnx.AttributeProto.AttributeType.STRINGS:return e.strings;case $e.onnx.AttributeProto.AttributeType.TENSORS:return e.tensors;case $e.onnx.AttributeProto.AttributeType.GRAPHS:return e.graphs;default:throw new Error(`unsupported attribute type: ${$e.onnx.AttributeProto.AttributeType[e.type]}`)}}static getValueNoCheckFromOrtFormat(e){switch(e.type()){case Lt.AttributeType.FLOAT:return e.f();case Lt.AttributeType.INT:return e.i();case Lt.AttributeType.STRING:return e.s();case Lt.AttributeType.TENSOR:return e.t();case Lt.AttributeType.GRAPH:return e.g();case Lt.AttributeType.FLOATS:return e.floatsArray();case Lt.AttributeType.INTS:{let n=[];for(let t=0;t<e.intsLength();t++)n.push(e.ints(t));return n}case Lt.AttributeType.STRINGS:{let n=[];for(let t=0;t<e.stringsLength();t++)n.push(e.strings(t));return n}case Lt.AttributeType.TENSORS:{let n=[];for(let t=0;t<e.tensorsLength();t++)n.push(e.tensors(t));return n}default:throw new Error(`unsupported attribute type: ${Lt.AttributeType[e.type()]}`)}}}});var ec,tc,zn,ua,Ql,ry=k(()=>{"use strict";ny();$o();ec=ve(to());zr();Me();tc={from:(r,e)=>new Ql(r,e)},zn=class{constructor(e){this._from=void 0,this._to=[],this.tensor=void 0,this.type=void 0,e&&(this.type=ft.tensorValueTypeFromProto(e.type.tensorType))}get from(){return this._from}get to(){return this._to}},ua=class{constructor(e,n){e instanceof ec.onnx.NodeProto?(this.name=e.name,this.opType=e.opType,this.attributes=new Bo(e.attribute)):e instanceof nl.Node&&(this.name=n??e.name(),this.opType=e.opType(),this.attributes=new Bo(ft.tensorAttributesFromORTFormat(e))),this.inputs=[],this.outputs=[],this.executeNode=!0}},Ql=class{constructor(e,n){if(!e)throw new TypeError("graph is empty");this.buildGraph(e),this.transformGraph(n),this.checkIsAcyclic()}getInputIndices(){return this._allInputIndices}getInputNames(){return this._allInputNames}getOutputIndices(){return this._allOutputIndices}getOutputNames(){return this._allOutputNames}getValues(){return this._allData}getNodes(){return this._nodes}buildGraph(e){if(e instanceof ec.onnx.GraphProto)this.buildGraphFromOnnxFormat(e);else if(e instanceof el.Graph)this.buildGraphFromOrtFormat(e);else throw new TypeError("Graph type is not supported.")}buildGraphFromOnnxFormat(e){let n=new Map;this._allData=[],this._allInputIndices=[],this._allInputNames=[],this._allOutputIndices=[],this._allOutputNames=[],this._nodes=[];let t=new Map;if(!e.input)throw new Error("missing information in graph: input");let o=[];for(let i of e.input){if(n.has(i.name))throw new Error(`duplicated input name: ${i.name}`);let a=this._allData.push(new zn(i))-1;n.set(i.name,a),o.push(i.name)}if(!e.initializer)throw new Error("missing information in graph: initializer");for(let i of e.initializer){let a=n.get(i.name);if(a===void 0){let s=new zn;s.type={shape:{dims:ft.tensorDimsFromProto(i.dims)},tensorType:ft.tensorDataTypeFromProto(i.dataType)},a=this._allData.push(s)-1,n.set(i.name,a)}this._allData[a]._from=-1,this._allData[a].tensor=nt.fromProto(i)}for(let i=0;i<this._allData.length;i++)this._allData[i].tensor||(this._allInputIndices.push(i),this._allInputNames.push(o[i]));if(!e.output)throw new Error("missing information in graph: output");for(let i of e.output){if(n.has(i.name))throw new Error(`duplicated output name: ${i.name}`);let a=this._allData.push(new zn(i))-1;n.set(i.name,a),this._allOutputIndices.push(a),this._allOutputNames.push(i.name)}if(!e.node)throw new Error("missing information in graph: node");for(let i of e.node){if(!i.name)for(let s=0;;s++){let u=`unnamed_${i.opType}_${s}`;if(!t.has(u)){i.name=u;break}}if(t.has(i.name))throw new Error(`duplicated node name: ${i.name}`);let a=this._nodes.push(new ua(i))-1;t.set(i.name,a)}for(let i=0;i<this._nodes.length;i++){let a=this._nodes[i],s=e.node[i];if(!s.output)throw new Error(`missing output for node: ${s.name}`);for(let u of s.output){let l=n.get(u);if(typeof l>"u"&&(l=this._allData.push(new zn)-1,n.set(u,l)),a.outputs.push(l),this._allData[l]._from!==void 0)throw new Error(`multiple nodes output to one data value: ${l}`);if(this._allData[l]._from=i,s.opType==="Constant"){if(!s.attribute||s.attribute.length!==1||!s.attribute[0].t)throw new Error("missing attributes or missing tensor value in attributes for this Constant operator");if(!s.output||s.output.length!==1)throw new Error("missing output or incorrect number of outputs for this Constant operator");a.outputs.pop(),a.executeNode=!1,this._allData[l]._from=-1,this._allData[l].tensor=nt.fromProto(s.attribute[0].t)}}}for(let i=0;i<this._nodes.length;i++){let a=this._nodes[i],s=e.node[i];if(!s.input)throw new Error(`missing input for node: ${s.name}`);for(let u of s.input){let l=n.get(u);if(typeof l>"u"){if(u===""&&(s.input.length===3||s.input.length===4)&&s.opType==="Resize")continue;throw new Error(`unrecognized input '${u}' for node: ${s.name}`)}a.inputs.push(l),this._allData[l]._to.push(i)}}return!0}buildGraphFromOrtFormat(e){let n=new Map;this._allData=[],this._allInputIndices=[],this._allInputNames=[],this._allOutputIndices=[],this._allOutputNames=[],this._nodes=[];let t=new Map,o=[];for(let i=0;i<e.inputsLength();i++){let a=e.inputs(i);if(n.has(a))throw new Error(`duplicated input name: ${a}`);for(let s=0;s<e.nodeArgsLength();s++)if(e.nodeArgs(s)?.name()===a){let u=new zn;if(e.nodeArgs(s)?.type()?.valueType()!==ol.TypeInfoValue.tensor_type)throw new Error("Unexpected value type for the nodeArg.");let d=e.nodeArgs(s).type().value(new rl.TensorTypeAndShape),f=ft.tensorDataTypeFromProto(d.elemType()),h=d.shape(),g=[];for(let _=0;_<h.dimLength();_++)g.push(xt.longToNumber(h.dim(_).value().dimValue()));u.type={shape:{dims:g},tensorType:f};let b=this._allData.push(u)-1;n.set(a,b),o.push(a)}}for(let i=0;i<e.initializersLength();i++){let a=e.initializers(i),s=n.get(a.name());if(s===void 0){let u=new zn,l=ft.tensorDimsFromORTFormat(a),d=ft.tensorDataTypeFromProto(a.dataType());u.type={shape:{dims:l},tensorType:d},s=this._allData.push(u)-1,n.set(a.name(),s)}this._allData[s]._from=-1,this._allData[s].tensor=nt.fromOrtTensor(a)}for(let i=0;i<this._allData.length;i++)this._allData[i].tensor||(this._allInputIndices.push(i),this._allInputNames.push(o[i]));for(let i=0;i<e.outputsLength();i++){let a=e.outputs(i);if(n.has(a))throw new Error(`duplicated output name: ${a}`);let s=this._allData.push(new zn)-1;n.set(a,s),this._allOutputIndices.push(s),this._allOutputNames.push(a)}if(!e.nodes)throw new Error("missing information in graph: node");for(let i=0;i<e.nodesLength();i++){let a=e.nodes(i),s=a.name();if(!s)for(let l=0;s=`unnamed_${a.opType()}_${l}`,!!t.has(s);l++);if(t.has(s))throw new Error(`duplicated node name: ${s}`);let u=this._nodes.push(new ua(a,s))-1;t.set(s,u)}for(let i=0;i<this._nodes.length;i++){let a=this._nodes[i],s=e.nodes(i);if(s==null)throw new Error(`No node exists at index ${i}`);if(s?.outputsLength()===0)throw new Error(`missing output for node: ${s.name}`);for(let u=0;u<s?.outputsLength();u++){let l=s?.outputs(u),d=n.get(l);if(typeof d>"u"&&(d=this._allData.push(new zn)-1,n.set(l,d)),a.outputs.push(d),this._allData[d]._from!==void 0)throw new Error(`multiple nodes output to one data value: ${d}`);if(this._allData[d]._from=i,s.opType()==="Constant"){if(s.attributesLength()!==1||!s.attributes(0).t())throw new Error("missing attributes or missing tensor value in attributes for this Constant operator");if(s.outputsLength()!==1)throw new Error("missing output or incorrect number of outputs for this Constant operator");a.outputs.pop(),a.executeNode=!1,this._allData[d]._from=-1,this._allData[d].tensor=nt.fromOrtTensor(s.attributes(0).t())}}}for(let i=0;i<this._nodes.length;i++){let a=this._nodes[i],s=e.nodes(i);if(s.inputsLength()===0)throw new Error(`missing input for node: ${s.name}`);for(let u=0;u<s.inputsLength();u++){let l=s.inputs(u),d=n.get(l);if(typeof d>"u")throw new Error(`unrecognized input '${l}' for node: ${s.name()}`);a.inputs.push(d),this._allData[d]._to.push(i)}}}checkIsAcyclic(){let e=new Set;this._allInputIndices.forEach(o=>{this._allData[o]._to.forEach(a=>{e.add(a)})});let n=Array.from(e),t=new Array(this._nodes.length).fill("white");for(;n.length>0;){let o=n.pop();t[o]==="gray"?t[o]="black":(n.push(o),t[o]="gray",this._nodes[o].outputs.forEach(i=>{let a=this._allData[i];if(typeof a.tensor<"u")throw new Error("node outputs 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Releasing tensor."),this.releaseTensor()}this.activeUpload?this.activeUpload.set(n):this.activeUpload=new Uint8Array(n)}async download(e){if(this.activeUpload){let n=this.wrapper?.isDataConverted?Ry(this.activeUpload,this.wrapper?.type):this.activeUpload;if(e){e instanceof ArrayBuffer?new Uint8Array(e).set(n):new Uint8Array(e.buffer,e.byteOffset,e.byteLength).set(n);return}else return n.buffer}if(!this.wrapper)throw new Error("Tensor has not been created.");return e?this.wrapper.read(e):this.wrapper.read()}},pc=class{constructor(e){this.backend=e;this.tensorTrackersById=new Map;this.freeTensors=[];this.externalTensors=new Set}getMLContext(e){let n=this.backend.getMLContext(e);if(!n)throw new Error("MLContext not found for session.");return n}reserveTensorId(){let e=Ny();return this.tensorTrackersById.set(e,new Ca(this)),e}releaseTensorId(e){let n=this.tensorTrackersById.get(e);n&&(this.tensorTrackersById.delete(e),n.tensorWrapper&&this.releaseTensor(n.tensorWrapper))}async ensureTensor(e,n,t,o,i){_e("verbose",()=>`[WebNN] TensorManager.ensureTensor {tensorId: ${n}, dataType: ${t}, shape: ${o}, copyOld: ${i}}`);let a=this.tensorTrackersById.get(n);if(!a)throw new Error("Tensor not found.");return a.ensureTensor(e,t,o,i)}upload(e,n){let t=this.tensorTrackersById.get(e);if(!t)throw new Error("Tensor not found.");t.upload(n)}async download(e,n){_e("verbose",()=>`[WebNN] TensorManager.download {tensorId: ${e}, dstBuffer: ${n?.byteLength}}`);let t=this.tensorTrackersById.get(e);if(!t)throw new Error("Tensor not found.");return t.download(n)}releaseTensorsForSession(e){for(let n of this.freeTensors)n.sessionId===e&&n.destroy();this.freeTensors=this.freeTensors.filter(n=>n.sessionId!==e)}registerTensor(e,n,t,o){let i=this.getMLContext(e),a=Ny(),s=new Ea({sessionId:e,context:i,tensor:n,dataType:t,shape:o});return this.tensorTrackersById.set(a,new Ca(this,s)),this.externalTensors.add(s),a}async getCachedTensor(e,n,t,o,i,a,s){let u=this.getMLContext(e);for(let[d,f]of this.freeTensors.entries())if(f.canReuseTensor(u,n,t)){_e("verbose",()=>`[WebNN] Reusing tensor {dataType: ${n}, ${s?`fallbackDataType: ${s},`:""} shape: ${t}`);let h=this.freeTensors.splice(d,1)[0];return h.sessionId=e,h}_e("verbose",()=>`[WebNN] MLContext.createTensor {dataType: ${n}, ${s?`fallbackDataType: ${s},`:""} shape: ${t}}`);let l=await u.createTensor({dataType:s??n,shape:t,dimensions:t,usage:o,writable:i,readable:a});return new Ea({sessionId:e,context:u,tensor:l,dataType:n,shape:t,fallbackDataType:s})}releaseTensor(e){this.externalTensors.has(e)&&this.externalTensors.delete(e),this.freeTensors.push(e)}},My=(...r)=>new pc(...r)});var Da,KO,ka,Fy=k(()=>{"use strict";le();yr();dc();By();Bn();Da=new Map([[1,"float32"],[10,"float16"],[6,"int32"],[12,"uint32"],[7,"int64"],[13,"uint64"],[22,"int4"],[21,"uint4"],[3,"int8"],[2,"uint8"],[9,"uint8"]]),KO=(r,e)=>{if(r===e)return!0;if(r===void 0||e===void 0)return!1;let n=Object.keys(r).sort(),t=Object.keys(e).sort();return n.length===t.length&&n.every((o,i)=>o===t[i]&&r[o]===e[o])},ka=class{constructor(e){this.tensorManager=My(this);this.mlContextBySessionId=new Map;this.sessionIdsByMLContext=new Map;this.mlContextCache=[];this.sessionGraphInputs=new Map;this.sessionGraphOutputs=new Map;this.temporaryGraphInputs=[];this.temporaryGraphOutputs=[];this.temporarySessionTensorIds=new Map;Aa(e.logLevel,!!e.debug)}get currentSessionId(){if(this.activeSessionId===void 0)throw new Error("No active session");return this.activeSessionId}onRunStart(e){_e("verbose",()=>`[WebNN] onRunStart {sessionId: ${e}}`),this.activeSessionId=e}onRunEnd(e){_e("verbose",()=>`[WebNN] onRunEnd {sessionId: ${e}}`);let n=this.temporarySessionTensorIds.get(e);if(n){for(let t of n)_e("verbose",()=>`[WebNN] releasing temporary tensor {tensorId: ${t}}`),this.tensorManager.releaseTensorId(t);this.temporarySessionTensorIds.delete(e),this.activeSessionId=void 0}}async createMLContext(e){if(e instanceof GPUDevice){let t=this.mlContextCache.findIndex(o=>o.gpuDevice===e);if(t!==-1)return this.mlContextCache[t].mlContext;{let o=await navigator.ml.createContext(e);return this.mlContextCache.push({gpuDevice:e,mlContext:o}),o}}else if(e===void 0){let t=this.mlContextCache.findIndex(o=>o.options===void 0&&o.gpuDevice===void 0);if(t!==-1)return this.mlContextCache[t].mlContext;{let o=await navigator.ml.createContext();return this.mlContextCache.push({mlContext:o}),o}}let n=this.mlContextCache.findIndex(t=>KO(t.options,e));if(n!==-1)return this.mlContextCache[n].mlContext;{let t=await navigator.ml.createContext(e);return this.mlContextCache.push({options:e,mlContext:t}),t}}registerMLContext(e,n){this.mlContextBySessionId.set(e,n);let t=this.sessionIdsByMLContext.get(n);t||(t=new Set,this.sessionIdsByMLContext.set(n,t)),t.add(e),this.temporaryGraphInputs.length>0&&(this.sessionGraphInputs.set(e,this.temporaryGraphInputs),this.temporaryGraphInputs=[]),this.temporaryGraphOutputs.length>0&&(this.sessionGraphOutputs.set(e,this.temporaryGraphOutputs),this.temporaryGraphOutputs=[])}onReleaseSession(e){this.sessionGraphInputs.delete(e),this.sessionGraphOutputs.delete(e);let n=this.mlContextBySessionId.get(e);if(!n)return;this.tensorManager.releaseTensorsForSession(e),this.mlContextBySessionId.delete(e);let t=this.sessionIdsByMLContext.get(n);if(t.delete(e),t.size===0){this.sessionIdsByMLContext.delete(n);let o=this.mlContextCache.findIndex(i=>i.mlContext===n);o!==-1&&this.mlContextCache.splice(o,1)}}getMLContext(e){return this.mlContextBySessionId.get(e)}reserveTensorId(){return this.tensorManager.reserveTensorId()}releaseTensorId(e){_e("verbose",()=>`[WebNN] releaseTensorId {tensorId: ${e}}`),this.tensorManager.releaseTensorId(e)}async ensureTensor(e,n,t,o,i){let a=Da.get(t);if(!a)throw new Error(`Unsupported ONNX data type: ${t}`);return this.tensorManager.ensureTensor(e??this.currentSessionId,n,a,o,i)}async createTemporaryTensor(e,n,t){_e("verbose",()=>`[WebNN] createTemporaryTensor {onnxDataType: ${n}, shape: ${t}}`);let o=Da.get(n);if(!o)throw new Error(`Unsupported ONNX data type: ${n}`);let i=this.tensorManager.reserveTensorId();await this.tensorManager.ensureTensor(e,i,o,t,!1);let a=this.temporarySessionTensorIds.get(e);return a?a.push(i):this.temporarySessionTensorIds.set(e,[i]),i}uploadTensor(e,n){if(!Be().shouldTransferToMLTensor)throw new Error("Trying to upload to a MLTensor while shouldTransferToMLTensor is false");_e("verbose",()=>`[WebNN] uploadTensor {tensorId: ${e}, data: ${n.byteLength}}`),this.tensorManager.upload(e,n)}async downloadTensor(e,n){return this.tensorManager.download(e,n)}createMLTensorDownloader(e,n){return async()=>{let t=await this.tensorManager.download(e);return Pa(t,n)}}registerMLTensor(e,n,t,o){let i=Da.get(t);if(!i)throw new Error(`Unsupported ONNX data type: ${t}`);let a=this.tensorManager.registerTensor(e,n,i,o);return _e("verbose",()=>`[WebNN] registerMLTensor {tensor: ${n}, dataType: ${i}, dimensions: ${o}} -> {tensorId: ${a}}`),a}registerMLConstant(e,n,t,o,i,a,s=!1){if(!a)throw new Error("External mounted files are not available.");let u=e;e.startsWith("./")&&(u=e.substring(2));let l=a.get(u);if(!l)throw new Error(`File with name ${u} not found in preloaded files.`);if(n+t>l.byteLength)throw new Error("Out of bounds: data offset and length exceed the external file data size.");let d=l.slice(n,n+t).buffer,f;switch(i.dataType){case"float32":f=new Float32Array(d);break;case"float16":f=typeof Float16Array<"u"&&Float16Array.from?new Float16Array(d):new Uint16Array(d);break;case"int32":f=new Int32Array(d);break;case"uint32":f=new Uint32Array(d);break;case"int64":if(s){let h=fc(new Uint8Array(d),"int64");f=new Int32Array(h.buffer),i.dataType="int32"}else f=new 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Na=k(()=>{"use strict"});var Vy,hc,mc,XO,ZO,Gy,bc,gc,Wy,Hy=k(()=>{"use strict";Bn();Na();Vy=new Map([[64,250],[128,200],[256,200],[512,200],[2048,230],[4096,200],[8192,50],[16384,50],[32768,50],[65536,50],[131072,50],[262144,50],[524288,50],[1048576,50],[2097152,30],[4194304,20],[8388608,10],[12582912,10],[16777216,10],[26214400,15],[33554432,22],[44236800,2],[58982400,6],[67108864,6],[134217728,6],[167772160,6]]),hc=[],mc=r=>Math.ceil(Number(r)/16)*16,XO=r=>{for(let e=0;e<hc.length;e++){let n=hc[e];if(r<=n)return n}return Math.ceil(r/16)*16},ZO=1,Gy=()=>ZO++,bc=async(r,e,n,t)=>{let o=mc(n),i=r.device.createBuffer({size:o,usage:GPUBufferUsage.COPY_DST|GPUBufferUsage.MAP_READ});try{let a=r.getCommandEncoder();r.endComputePass(),a.copyBufferToBuffer(e,0,i,0,o),r.flush(),await i.mapAsync(GPUMapMode.READ);let s=i.getMappedRange();if(t){let u=t();return u.set(new Uint8Array(s,0,n)),u}else return new Uint8Array(s.slice(0,n))}finally{i.destroy()}},gc=class{constructor(e){this.backend=e;this.storageCache=new Map,this.freeBuffers=new Map,this.freeUniformBuffers=new Map,this.buffersPending=[],this.capturedPendingBuffers=new Map;for(let[n]of Vy)hc.push(n),this.freeBuffers.set(n,[]),this.freeUniformBuffers.set(n,[]);this.sessionCount=0}upload(e,n){let t=n.buffer,o=n.byteOffset,i=n.byteLength,a=mc(i),s=this.storageCache.get(e);if(!s)throw new Error("gpu data for uploading does not exist");if(Number(s.originalSize)!==i)throw new Error(`inconsistent data size. gpu data size=${s.originalSize}, data size=${i}`);let u=this.backend.device.createBuffer({mappedAtCreation:!0,size:a,usage:GPUBufferUsage.MAP_WRITE|GPUBufferUsage.COPY_SRC}),l=u.getMappedRange();new Uint8Array(l).set(new Uint8Array(t,o,i)),u.unmap();let d=this.backend.device.createCommandEncoder();d.copyBufferToBuffer(u,0,s.gpuData.buffer,0,a),this.backend.device.queue.submit([d.finish()]),u.destroy(),_e("verbose",()=>`[WebGPU] 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    let dim${U} = current / ${Q(v,U,a)};
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    indices[${U}] = dim${U};
    current = rest${U};
    `;x+=`indices[${a-1}] = current;`;let I=a<2?"":`
  fn o2i_${r}(offset: u32) -> ${h.indices} {
    var indices: ${h.indices};
    var current = offset;
    ${x}
    return indices;
  }`,$=U=>(b.offsetToIndices=!0,a<2?U:`o2i_${r}(${U})`),O=[];if(a>=2)for(let U=a-1;U>=0;U--)O.push(`${Q(v,U,a)} * (indices[${U}])`);let E=a<2?"":`
  fn i2o_${r}(indices: ${h.indices}) -> u32 {
    return ${O.join("+")};
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             return ${ct.length>0?ct.join("+"):"0u"};
           }`,`${ie}(${U})`},pe=(U,z)=>(()=>{if(h.storage===h.value)return`${r}[${U}]=${z};`;if(h.storage==="vec2<u32>"&&h.value==="i32")return`${r}[${U}]=vec2<u32>(u32(${z}), select(0u, 0xFFFFFFFFu, ${z} < 0));`;if(h.storage==="vec2<u32>"&&h.value==="u32")return`${r}[${U}]=vec2<u32>(u32(${z}), 0u);`;if(h.storage==="u32"&&h.value==="vec4<bool>")return`${r}[${U}]=dot(vec4<u32>(0x1, 0x100, 0x10000, 0x1000000), vec4<u32>(${z}));`;throw new Error(`not supported combination of storage type ${h.storage} and value type ${h.value} yet`)})(),K=U=>(()=>{if(h.storage===h.value)return`${r}[${U}]`;if(h.storage==="vec2<u32>"&&h.value==="i32")return`i32(${r}[${U}].x)`;if(h.storage==="vec2<u32>"&&h.value==="u32")return`u32(${r}[${U}].x)`;if(h.storage==="u32"&&h.value==="vec4<bool>")return`vec4<bool>(bool(${r}[${U}] & 0xFFu), bool(${r}[${U}] & 0xFF00u), bool(${r}[${U}] & 0xFF0000u), bool(${r}[${U}] & 0xFF000000u))`;throw new Error(`not supported combination of storage type ${h.storage} and value type ${h.value} yet`)})(),Oe=a<2?"":`
  fn get_${r}ByIndices(indices: ${h.indices}) -> ${d} {
    return ${K(`i2o_${r}(indices)`)};
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  fn get_${r}(${U}) -> ${d} {
    return get_${r}ByIndices(${R(z)});
  }`})(),J=(...U)=>{if(U.length!==a)throw new Error(`indices length must be ${a}`);let z=U.map(g).join(",");return a===0?K("0u"):a===1?K(z[0]):(b.get=!0,b.getByIndices=!0,b.indicesToOffset=!0,`get_${r}(${z})`)},he=U=>a<2?K(U):(b.getByIndices=!0,b.indicesToOffset=!0,`get_${r}ByIndices(${U})`),ee=a<2?"":`
  fn set_${r}ByIndices(indices: ${h.indices}, value: ${d}) {
    ${pe(`i2o_${r}(indices)`,"value")}
  }`,Te=a<2?"":(()=>{let U=s.map(ie=>`d${ie}: u32`).join(", "),z=s.map(ie=>`d${ie}`).join(", ");return`
  fn set_${r}(${U}, value: ${d}) {
    set_${r}ByIndices(${R(z)}, value);
  }`})();return{impl:()=>{let U=[],z=!1;return b.offsetToIndices&&(U.push(I),z=!0),b.indicesToOffset&&(U.push(E),z=!0),b.broadcastedIndicesToOffset&&(Object.values(Z).forEach(ie=>U.push(ie)),z=!0),b.set&&(U.push(Te),z=!0),b.setByIndices&&(U.push(ee),z=!0),b.get&&(U.push(fe),z=!0),b.getByIndices&&(U.push(Oe),z=!0),!i&&z&&U.unshift(`const ${T} = ${h.indices}(${n.join(",")});`,`const ${v} = ${h.indices}(${C.computeStrides(n).join(",")});`),U.join(`
`)},type:h,offsetToIndices:$,indicesToOffset:L,broadcastedIndicesToOffset:re,indices:R,indicesGet:F,indicesSet:q,set:(...U)=>{if(U.length!==a+1)throw new Error(`indices length must be ${a}`);let z=U[a];if(typeof z!="string")throw new Error("value must be string");let ie=U.slice(0,a).map(g).join(",");return a===0?pe("0u",z):a===1?pe(ie[0],z):(b.set=!0,b.setByIndices=!0,b.indicesToOffset=!0,`set_${r}(${ie}, ${z})`)},setByOffset:pe,setByIndices:(U,z)=>a<2?pe(U,z):(b.setByIndices=!0,b.indicesToOffset=!0,`set_${r}ByIndices(${U}, ${z});`),get:J,getByOffset:K,getByIndices:he,usage:t,name:r,strides:v,shape:T,rank:a}},N=(r,e,n,t=1)=>La(r,e,n,"input",t),G=(r,e,n,t=1)=>La(r,e,n,"output",t),jy=(r,e,n)=>La(r,e,n,"atomicOutput",1),Ra=(r,e,n,t=1)=>La(r,e,n,"internal",t),_c=class{constructor(e,n){this.normalizedDispatchGroup=e;this.limits=n;this.internalVariables=[];this.variables=[];this.uniforms=[];this.variableIndex=0}guardAgainstOutOfBoundsWorkgroupSizes(e){return`if (global_idx >= ${typeof e=="number"?`${e}u`:e}) { return; }`}mainStart(e=Wr){let n=typeof e=="number"?e:e[0],t=typeof e=="number"?1:e[1],o=typeof e=="number"?1:e[2];if(n>this.limits.maxComputeWorkgroupSizeX||t>this.limits.maxComputeWorkgroupSizeY||o>this.limits.maxComputeWorkgroupSizeZ)throw new Error(`workgroup size [${n}, ${t}, ${o}] exceeds the maximum workgroup size [${this.limits.maxComputeWorkgroupSizeX}, ${this.limits.maxComputeWorkgroupSizeY}, ${this.limits.maxComputeWorkgroupSizeZ}].`);if(n*t*o>this.limits.maxComputeInvocationsPerWorkgroup)throw new Error(`workgroup size [${n}, ${t}, ${o}] exceeds the maximum workgroup invocations ${this.limits.maxComputeInvocationsPerWorkgroup}.`);let i=this.normalizedDispatchGroup[1]===1&&this.normalizedDispatchGroup[2]===1,a=i?`@builtin(global_invocation_id) global_id : vec3<u32>,
    @builtin(workgroup_id) workgroup_id : vec3<u32>,
    @builtin(local_invocation_index) local_idx : u32,
    @builtin(local_invocation_id) local_id : vec3<u32>`:`@builtin(global_invocation_id) global_id : vec3<u32>,
                                             @builtin(local_invocation_id) local_id : vec3<u32>,
    @builtin(local_invocation_index) local_idx : u32,
    @builtin(workgroup_id) workgroup_id : vec3<u32>,
    @builtin(num_workgroups) num_workgroups : vec3<u32>`,s=i?`let global_idx = global_id.x;
         let workgroup_index = workgroup_id.x;`:`let workgroup_index = workgroup_id.z * num_workgroups[0] * num_workgroups[1] +
             workgroup_id.y * num_workgroups[0] + workgroup_id.x;
         let global_idx = workgroup_index * ${n*t*o}u + local_idx;`;return`@compute @workgroup_size(${n}, ${t}, ${o})
  fn main(${a}) {
    ${s}
  `}appendVariableUniforms(e){e.rank!==0&&(e.shape.startsWith("uniforms.")&&this.uniforms.push({name:e.shape.replace("uniforms.",""),type:"u32",length:e.rank}),e.strides.startsWith("uniforms.")&&this.uniforms.push({name:e.strides.replace("uniforms.",""),type:"u32",length:e.rank}))}declareVariable(e,n){if(e.usage==="internal")throw new Error("cannot use internal variable with declareVariable(). use registerInternalVariables() instead.");this.variables.push(e),this.appendVariableUniforms(e);let t=e.usage==="input"?"read":"read_write",o=e.usage==="atomicOutput"?"atomic<i32>":e.type.storage;return`@group(0) @binding(${n}) var<storage, ${t}> ${e.name}: array<${o}>;`}declareVariables(...e){return e.map(n=>this.declareVariable(n,this.variableIndex++)).join(`
`)}registerInternalVariable(e){if(e.usage!=="internal")throw new Error("cannot use input or output variable with registerInternalVariable(). use declareVariables() instead.");this.internalVariables.push(e),this.appendVariableUniforms(e)}registerInternalVariables(...e){return e.forEach(n=>this.registerInternalVariable(n)),this}registerUniform(e,n,t=1){return this.uniforms.push({name:e,type:n,length:t}),this}registerUniforms(e){return this.uniforms=this.uniforms.concat(e),this}uniformDeclaration(){if(this.uniforms.length===0)return"";let e=[];for(let{name:n,type:t,length:o}of this.uniforms)if(o&&o>4)t==="f16"?e.push(`@align(16) ${n}:array<mat2x4<${t}>, ${Math.ceil(o/8)}>`):e.push(`${n}:array<vec4<${t}>, ${Math.ceil(o/4)}>`);else{let i=o==null||o===1?t:`vec${o}<${t}>`;e.push(`${n}:${i}`)}return`
      struct Uniforms { ${e.join(", ")} };
      @group(0) @binding(${this.variableIndex}) var<uniform> uniforms: Uniforms;`}get additionalImplementations(){return this.uniformDeclaration()+this.variables.map(e=>e.impl()).join(`
`)+this.internalVariables.map(e=>e.impl()).join(`
`)}get variablesInfo(){if(this.uniforms.length===0)return;let e=n=>[12,10,1,6][["u32","f16","f32","i32"].indexOf(n)];return this.uniforms.map(n=>[e(n.type),n.length??1])}},qy=(r,e)=>new _c(r,e)});var JO,Ky,YO,QO,eP,tP,at,Xy,Zy,Yn=k(()=>{"use strict";le();ge();Xe();ye();JO=(r,e)=>{if(!r||r.length!==1)throw new Error("Transpose requires 1 input.");if(e.length!==0&&e.length!==r[0].dims.length)throw new Error(`perm size ${e.length} does not match input rank ${r[0].dims.length}`)},Ky=(r,e)=>e.length!==0?e:[...new Array(r).keys()].reverse(),YO=(r,e)=>C.sortBasedOnPerm(r,Ky(r.length,e)),QO=(r,e,n,t)=>{let o=`fn perm(i: ${t.type.indices}) -> ${n.type.indices} {
    var a: ${n.type.indices};`;for(let i=0;i<e;++i)o+=`a[${r[i]}]=i[${i}];`;return o+="return a;}"},eP=(r,e)=>{let n=[],t=[];for(let o=0;o<r.length;++o)r[o]!==1&&n.push(r[o]),r[e[o]]!==1&&t.push(e[o]);return{newShape:n,newPerm:t}},tP=(r,e)=>{let n=0;for(let t=0;t<r.length;++t)if(e[r[t]]!==1){if(r[t]<n)return!1;n=r[t]}return!0},at=(r,e)=>{let n=r.dataType,t=r.dims.length,o=Ky(t,e),i=YO(r.dims,o),a=r.dims,s=i,u=t<2||tP(o,r.dims),l;if(u)return l=_=>{let T=N("input",n,a,4),v=G("output",n,s,4);return`
  ${_.registerUniform("output_size","u32").declareVariables(T,v)}
  ${_.mainStart()}
    ${_.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.output_size")}
    output[global_idx] = input[global_idx];
  }`},{name:"TransposeCopy",shaderCache:{inputDependencies:["type"]},getRunData:()=>{let _=C.size(i);return{outputs:[{dims:i,dataType:r.dataType}],dispatchGroup:{x:Math.ceil(_/64/4)},programUniforms:[{type:12,data:Math.ceil(_/4)}]}},getShaderSource:l};let{newShape:d,newPerm:f}=eP(r.dims,o),h=C.areEqual(f,[2,3,1]),g=C.areEqual(f,[3,1,2]);if(d.length===2||h||g){a=h?[d[0],d[1]*d[2]]:g?[d[0]*d[1],d[2]]:d,s=[a[1],a[0]];let _=16;return l=T=>{let v=N("a",n,a.length),x=G("output",n,s.length);return`
  ${T.registerUniform("output_size","u32").declareVariables(v,x)}
  var<workgroup> tile : array<array<${x.type.value}, ${_+1}>, ${_}>;
  ${T.mainStart([_,_,1])}
    let stride = (uniforms.output_shape[1] - 1) / ${_} + 1;
    let workgroup_id_x = workgroup_index % stride;
    let workgroup_id_y = workgroup_index / stride;
    let input_col = workgroup_id_y * ${_}u + local_id.x;
    let input_row = workgroup_id_x * ${_}u + local_id.y;
    if (input_row < uniforms.a_shape[0] && input_col < uniforms.a_shape[1]) {
      tile[local_id.y][local_id.x] = ${v.getByIndices(`${v.type.indices}(input_row, input_col)`)};
    }
    workgroupBarrier();

    let output_col = workgroup_id_x * ${_}u + local_id.x;
    let output_row = workgroup_id_y * ${_}u + local_id.y;
    if (output_row < uniforms.output_shape[0] && output_col < uniforms.output_shape[1]) {
      ${x.setByIndices(`${x.type.indices}(output_row, output_col)`,"tile[local_id.x][local_id.y]")}
    }
  }`},{name:"TransposeShared",shaderCache:{inputDependencies:["type"]},getRunData:()=>{let T=C.size(i);return{outputs:[{dims:i,dataType:r.dataType}],dispatchGroup:{x:Math.ceil(s[1]/_),y:Math.ceil(s[0]/_)},programUniforms:[{type:12,data:T},...W(a,s)]}},getShaderSource:l}}return l=_=>{let T=N("a",n,a.length),v=G("output",n,s.length);return`
  ${_.registerUniform("output_size","u32").declareVariables(T,v)}

  ${QO(o,t,T,v)}

  ${_.mainStart()}
    ${_.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.output_size")}

    let indices = ${v.offsetToIndices("global_idx")};
    let aIndices = perm(indices);

    ${v.setByOffset("global_idx",T.getByIndices("aIndices"))}
  }`},{name:"Transpose",shaderCache:{hint:`${e}`,inputDependencies:["rank"]},getRunData:()=>{let _=C.size(i);return{outputs:[{dims:i,dataType:r.dataType}],dispatchGroup:{x:Math.ceil(_/64)},programUniforms:[{type:12,data:_},...W(a,s)]}},getShaderSource:l}},Xy=(r,e)=>{JO(r.inputs,e.perm),r.compute(at(r.inputs[0],e.perm))},Zy=r=>ce({perm:r.perm})});var nP,rP,oP,iP,aP,sP,uP,lP,cP,dP,Vn,Jy,Yy,Qy,e_,t_,n_,r_,o_,i_,a_,s_=k(()=>{"use strict";le();ge();ye();za();Yn();nP={max:"select(bestValue, candidate, candidate > bestValue)",min:"select(bestValue, candidate, candidate < bestValue)",mean:"bestValue + candidate",sum:"bestValue + candidate",prod:"bestValue * candidate",sumSquare:"bestValue + candidate * candidate",logSumExp:"bestValue + exp(candidate)",l1:"bestValue + abs(candidate)",l2:"bestValue + candidate * candidate",logSum:"bestValue + candidate"},rP={max:"select(bestValue, candidate, candidate > bestValue)",min:"select(bestValue, candidate, candidate < bestValue)",mean:"bestValue + candidate",sum:"bestValue + candidate",prod:"bestValue * candidate",sumSquare:"bestValue + candidate",logSumExp:"bestValue + candidate",l1:"bestValue + candidate",l2:"bestValue + candidate",logSum:"bestValue + candidate"},oP={max:"_A[offset]",min:"_A[offset]",mean:"0",sum:"0",prod:"1",sumSquare:"0",logSumExp:"0",l1:"0",l2:"0",logSum:"0"},iP={max:"bestValue",min:"bestValue",sum:"bestValue",prod:"bestValue",sumSquare:"bestValue",logSumExp:"log(bestValue)",l1:"bestValue",l2:"sqrt(bestValue)",logSum:"log(bestValue)"},aP=(r,e)=>{let n=[];for(let t=e-r;t<e;++t)n.push(t);return n},sP=(r,e)=>{let n=[],t=r.length;for(let i=0;i<t;i++)e.indexOf(i)===-1&&n.push(r[i]);let o=e.map(i=>r[i]);return[n,o]},uP=(r,e)=>{let n=r.length+e.length,t=[],o=0;for(let i=0;i<n;i++)e.indexOf(i)===-1?t.push(r[o++]):t.push(1);return t},lP=(r,e)=>{for(let n=0;n<r.length;++n)if(r[r.length-n-1]!==e-1-n)return!1;return!0},cP=(r,e)=>{let n=[];if(!lP(r,e)){for(let t=0;t<e;++t)r.indexOf(t)===-1&&n.push(t);r.forEach(t=>n.push(t))}return n},dP=(r,e,n,t,o,i,a)=>{let s=n[0].dims,u=C.size(i),l=C.size(a),d=N("_A",n[0].dataType,s),f=G("output",o,i),h=64;u===1&&(h=256);let g=`
          var<workgroup> aBestValues : array<f32, ${h}>;
       `,b=_=>`
        ${_.registerUniform("reduceSize","u32").declareVariables(d,f)}
        ${g}
        fn DIV_CEIL(a : u32, b : u32) -> u32 {
          return ((a - 1u) / b + 1u);
         }
         ${_.mainStart(h)}

          let outputIndex = global_idx / ${h};
          let offset = outputIndex * uniforms.reduceSize;

          var bestValue = f32(${oP[t]});
          let Length = uniforms.reduceSize;
          for (var k = local_idx; k < Length; k = k + ${h}) {
           let candidate = f32(${d.getByOffset("offset + k")});
           bestValue = ${nP[t]};
          }
          aBestValues[local_idx] = bestValue;
          workgroupBarrier();

         var reduceSize = min(Length, ${h}u);
         for (var currentSize = reduceSize / 2u; reduceSize > 1u;
             currentSize = reduceSize / 2u) {
           let interval = DIV_CEIL(reduceSize, 2u);
           if (local_idx < currentSize) {
            let candidate = aBestValues[local_idx + interval];
            bestValue = ${rP[t]};
            aBestValues[local_idx] = bestValue;
           }
           reduceSize = interval;
           workgroupBarrier();
         }

         if (local_idx == 0u) {
          ${f.setByOffset("outputIndex",`${t==="mean"?`${f.type.storage}(bestValue / f32(uniforms.reduceSize))`:`${f.type.storage}(${iP[t]})`}`)};
         }
        }`;return{name:r,shaderCache:{hint:`${e};${h}`,inputDependencies:["type"]},getShaderSource:b,getRunData:()=>({outputs:[{dims:i,dataType:o}],dispatchGroup:{x:u},programUniforms:[{type:12,data:l}]})}},Vn=(r,e,n,t)=>{let o=r.inputs.length===1?n:xc(r.inputs,n),i=o.axes;i.length===0&&!o.noopWithEmptyAxes&&(i=r.inputs[0].dims.map((g,b)=>b));let a=C.normalizeAxes(i,r.inputs[0].dims.length),s=a,u=r.inputs[0],l=cP(s,r.inputs[0].dims.length);l.length>0&&(u=r.compute(at(r.inputs[0],l),{inputs:[0],outputs:[-1]})[0],s=aP(s.length,u.dims.length));let[d,f]=sP(u.dims,s),h=d;o.keepDims&&(h=uP(d,a)),r.compute(dP(e,o.cacheKey,[u],t,r.inputs[0].dataType,h,f),{inputs:[u]})},Jy=(r,e)=>{Vn(r,"ReduceMeanShared",e,"mean")},Yy=(r,e)=>{Vn(r,"ReduceL1Shared",e,"l1")},Qy=(r,e)=>{Vn(r,"ReduceL2Shared",e,"l2")},e_=(r,e)=>{Vn(r,"ReduceLogSumExpShared",e,"logSumExp")},t_=(r,e)=>{Vn(r,"ReduceMaxShared",e,"max")},n_=(r,e)=>{Vn(r,"ReduceMinShared",e,"min")},r_=(r,e)=>{Vn(r,"ReduceProdShared",e,"prod")},o_=(r,e)=>{Vn(r,"ReduceSumShared",e,"sum")},i_=(r,e)=>{Vn(r,"ReduceSumSquareShared",e,"sumSquare")},a_=(r,e)=>{Vn(r,"ReduceLogSumShared",e,"logSum")}});var Gn,pP,Ma,xc,Un,fP,hP,mP,gP,bP,yP,_P,vP,wP,xP,Wn,u_,l_,c_,d_,p_,f_,h_,m_,g_,b_,za=k(()=>{"use strict";le();ge();Xe();ye();s_();Gn=r=>{if(!r||r.length===0||r.length>2)throw new Error("Reduce op requires 1 or 2 inputs.");if(r.length===2&&r[1].dims.length!==1)throw new Error("Invalid axes input dims.")},pP=r=>["","",`var value = ${r.getByIndices("input_indices")};`,""],Ma=(r,e,n,t,o,i,a=!1,s=!1)=>{let u=[],l=n[0].dims,d=l.length,f=C.normalizeAxes(o,d),h=!s&&f.length===0;l.forEach((T,v)=>{h||f.indexOf(v)>=0?a&&u.push(1):u.push(T)});let g=u.length,b=C.size(u);return{name:r,shaderCache:e,getShaderSource:T=>{let v=[],x=N("_A",n[0].dataType,d),I=G("output",i,g),$=t(x,I,f),O=$[2];for(let E=0,L=0;E<d;E++)h||f.indexOf(E)>=0?(a&&L++,O=`for(var j${E}: u32 = 0; j${E} < ${l[E]}; j${E}++) {
                  ${$[2].includes("last_index")?`let last_index = j${E};`:""}
                  ${x.indicesSet("input_indices",E,`j${E}`)}
                  ${O}
                }`):(v.push(`${x.indicesSet("input_indices",E,I.indicesGet("output_indices",L))};`),L++);return`

        ${T.registerUniform("output_size","u32").declareVariables(x,I)}

        ${T.mainStart()}
          ${T.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.output_size")}
          var input_indices: ${x.type.indices};
          let output_indices = ${I.offsetToIndices("global_idx")};

          ${v.join(`
`)}
          ${$[0]}       // init ops for reduce max/min
          ${$[1]}
          ${O}
          ${$[3]}
          ${$.length===4?I.setByOffset("global_idx","value"):$.slice(4).join(`
`)}
        }`},getRunData:()=>({outputs:[{dims:u,dataType:i}],dispatchGroup:{x:Math.ceil(b/64)},programUniforms:[{type:12,data:b},...W(l,u)]})}},xc=(r,e)=>{let n=[];return r[1].dims[0]>0&&r[1].getBigInt64Array().forEach(t=>n.push(Number(t))),ce({axes:n,keepDims:e.keepDims,noopWithEmptyAxes:e.noopWithEmptyAxes})},Un=(r,e,n,t)=>{let o=r.inputs,i=o.length===1?n:xc(o,n);r.compute(Ma(e,{hint:i.cacheKey,inputDependencies:["rank"]},[o[0]],i.noopWithEmptyAxes&&i.axes.length===0?pP:t,i.axes,o[0].dataType,i.keepDims,i.noopWithEmptyAxes),{inputs:[0]})},fP=(r,e)=>{Gn(r.inputs),Un(r,"ReduceLogSum",e,(t,o)=>[`var value = ${o.type.storage}(0);`,"",`value += ${t.getByIndices("input_indices")};`,"value = log(value);"])},hP=(r,e)=>{Gn(r.inputs),Un(r,"ReduceL1",e,(t,o)=>[`var value = ${o.type.storage}(0);`,"",`value += abs(${t.getByIndices("input_indices")});`,""])},mP=(r,e)=>{Gn(r.inputs),Un(r,"ReduceL2",e,(t,o)=>[`var t = ${o.type.value}(0); var value = ${o.type.value}(0);`,"",`t = ${t.getByIndices("input_indices")}; value += (t * t);`,"value = sqrt(value);"])},gP=(r,e)=>{Gn(r.inputs),Un(r,"ReduceLogSumExp",e,(t,o)=>[`var value = ${o.type.storage}(0);`,"",`value += exp(${t.getByIndices("input_indices")});`,"value = log(value);"])},bP=(r,e)=>{Gn(r.inputs),Un(r,"ReduceMax",e,(t,o,i)=>{let a=[];for(let s=0;s<t.rank;s++)(i.indexOf(s)>=0||i.length===0)&&a.push(t.indicesSet("input_indices",s,0));return[`${a.join(`
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`)}`,`var value = ${t.getByIndices("input_indices")};
var best_index : i32 = 0;`,`if (${t.getByIndices("input_indices")} ${e.selectLastIndex>0?"<=":"<"} value) {
         value = ${t.getByIndices("input_indices")};
         best_index = i32(last_index);
       }`,"",o.setByOffset("global_idx","best_index")]};r.compute(Ma("ArgMin",{hint:e.cacheKey,inputDependencies:["rank"]},[r.inputs[0]],n,[e.axis],7,e.keepDims),{inputs:[0]})},v_=(r,e)=>{y_(r.inputs);let n=(t,o,i)=>{let a=[];for(let s=0;s<t.rank;s++)(i.indexOf(s)>=0||i.length===0)&&a.push(`input_indices[${s}] = 0;`);return[`${a.join(`
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var best_index : i32 = 0;`,`if (${t.getByIndices("input_indices")} ${e.selectLastIndex>0?">=":">"} value) {
         value = ${t.getByIndices("input_indices")};
         best_index = i32(last_index);
       }`,"",o.setByOffset("global_idx","best_index")]};r.compute(Ma("argMax",{hint:e.cacheKey,inputDependencies:["rank"]},[r.inputs[0]],n,[e.axis],7,e.keepDims),{inputs:[0]})},Tc=r=>ce(r)});var TP,Ic,IP,SP,$P,po,AP,x_,Ba=k(()=>{"use strict";le();ge();Na();ye();TP=(r,e)=>{let n=r[0],t=r[1],o=r[2],i=r[3],a=r[4],s=r[5];if(a&&s)throw new Error("Attention cannot have both past and attention_bias");if(n.dims.length!==3)throw new Error('Input "input" must have 3 dimensions');let u=n.dims[0],l=n.dims[1],d=n.dims[2];if(o.dims.length!==1)throw new Error('Input "bias" is expected to have 1 dimensions');if(t.dims.length!==2)throw new Error('Input "weights" is expected to have 2 dimensions');if(t.dims[0]!==d)throw new Error("Input 1 dimension 0 should have same length as dimension 2 of input 0");if(o.dims[0]!==t.dims[1])throw new Error('Input "bias" dimension 0 should have same length as dimension 1 of input "weights"');let f=o.dims[0]/3,h=f,g=h;if(e.qkvHiddenSizes.length>0){if(e.qkvHiddenSizes.length!==3)throw new Error("qkv_hidden_sizes attribute should have 3 elements");for(let I of e.qkvHiddenSizes)if(I%e.numHeads!==0)throw new Error("qkv_hidden_sizes should be divisible by num_heads");f=e.qkvHiddenSizes[0],h=e.qkvHiddenSizes[1],g=e.qkvHiddenSizes[2]}let b=l;if(f!==h)throw new Error("qkv_hidden_sizes first element should be same as the second");if(o.dims[0]!==f+h+g)throw new Error('Input "bias" dimension 0 should have same length as sum of Q/K/V hidden sizes');let _=0;if(a){if(h!==g)throw new Error('Input "past" expect k_hidden_size == v_hidden_size');if(a.dims.length!==5)throw new Error('Input "past" must have 5 dimensions');if(a.dims[0]!==2)throw new Error('Input "past" first dimension must be 2');if(a.dims[1]!==u)throw new Error('Input "past" second dimension must be batch_size');if(a.dims[2]!==e.numHeads)throw new Error('Input "past" third dimension must be num_heads');if(a.dims[4]!==h/e.numHeads)throw new Error('Input "past" fifth dimension must be k_hidden_size / num_heads');e.pastPresentShareBuffer||(_=a.dims[3])}let T=b+_,v=-1,x=0;if(i)throw new Error("Mask not supported");if(a)throw new Error("past is not supported");if(s){if(s.dims.length!==4)throw new Error('Input "attention_bias" must have 4 dimensions');if(s.dims[0]!==u||s.dims[1]!==e.numHeads||s.dims[2]!==l||s.dims[3]!==T)throw new Error('Expect "attention_bias" shape (batch_size, num_heads, sequence_length, total_sequence_length)')}return{batchSize:u,sequenceLength:l,pastSequenceLength:_,kvSequenceLength:b,totalSequenceLength:T,maxSequenceLength:v,inputHiddenSize:d,hiddenSize:f,vHiddenSize:g,headSize:Math.floor(f/e.numHeads),vHeadSize:Math.floor(g/e.numHeads),numHeads:e.numHeads,isUnidirectional:!1,pastPresentShareBuffer:!1,maskFilterValue:e.maskFilterValue,maskType:x,scale:e.scale,broadcastResPosBias:!1,passPastInKv:!1,qkvFormat:1}},Ic=(r,e,n)=>e&&r?`
      let total_sequence_length_input = u32(${e.getByOffset("0")});
      let present_sequence_length = max(total_sequence_length_input, uniforms.past_sequence_length);
      let is_subsequent_prompt: bool = sequence_length > 1 && sequence_length != total_sequence_length_input;
      let is_first_prompt: bool = is_subsequent_prompt == false && sequence_length == total_sequence_length_input;
      total_sequence_length = u32(${r?.getByOffset("batchIdx")}) + 1;
      var past_sequence_length: u32 = 0;
      if (is_first_prompt == false) {
        past_sequence_length = total_sequence_length - sequence_length;
      }
       `:`
    ${n?"let past_sequence_length = uniforms.past_sequence_length":""};
    let present_sequence_length = total_sequence_length;
    `,IP=(r,e,n,t,o,i,a,s)=>{let u=Ee(a?1:i),l=64,d=i/u;d<l&&(l=32);let f=Math.ceil(i/u/l),h=[{type:12,data:e},{type:12,data:n},{type:12,data:t},{type:12,data:o},{type:12,data:d},{type:12,data:f}],g=Ve(r.dataType,u),b=it(1,u),_=["type"];a&&_.push("type"),s&&_.push("type");let T=v=>{let x=G("x",r.dataType,r.dims,u),I=[x],$=a?N("seq_lens",a.dataType,a.dims):void 0;$&&I.push($);let O=s?N("total_sequence_length_input",s.dataType,s.dims):void 0;O&&I.push(O);let E=it(r.dataType),L=[{name:"batch_size",type:"u32"},{name:"num_heads",type:"u32"},{name:"past_sequence_length",type:"u32"},{name:"sequence_length",type:"u32"},{name:"total_sequence_length",type:"u32"},{name:"elements_per_thread",type:"u32"}];return`
  var<workgroup> thread_max: array<f32, ${l}>;
  var<workgroup> thread_sum: array<f32, ${l}>;
  ${v.registerUniforms(L).declareVariables(...I)}
  ${v.mainStart([l,1,1])}
    let batchIdx = workgroup_id.z / uniforms.num_heads;
    let headIdx = workgroup_id.z % uniforms.num_heads;
    let sequence_length = uniforms.sequence_length;
    var total_sequence_length = uniforms.total_sequence_length;
    ${Ic($,O,!1)}
    let local_offset = local_idx * uniforms.elements_per_thread;
    let offset = (global_idx / ${l}) * uniforms.total_sequence_length + local_offset;
    let seq_causal_length = ${a?"u32(past_sequence_length + workgroup_id.y + 1)":"total_sequence_length"};
    var thread_max_vector = ${b}(-3.402823e+38f);
    for (var i: u32 = 0; i < uniforms.elements_per_thread && i + local_offset < seq_causal_length; i++) {
      thread_max_vector = max(${b}(x[offset + i]), thread_max_vector);
    }
    thread_max[local_idx] = ${(()=>{switch(u){case 1:return"thread_max_vector";case 2:return"max(thread_max_vector.x, thread_max_vector.y)";case 4:return"max(max(thread_max_vector.x, thread_max_vector.y), max(thread_max_vector.z, thread_max_vector.w))";default:throw new Error(`Unsupported components: ${u}`)}})()};
    workgroupBarrier();

    var max_value =  f32(-3.402823e+38f);
    for (var i = 0u; i < ${l}; i++) {
      max_value = max(thread_max[i], max_value);
    }

    var sum_vector = ${b}(0);
    for (var i: u32 = 0; i < uniforms.elements_per_thread && i + local_offset < seq_causal_length; i++) {
      sum_vector += exp(${b}(x[offset + i]) - max_value);
    }
    thread_sum[local_idx] = ${(()=>{switch(u){case 1:return"sum_vector";case 2:return"sum_vector.x + sum_vector.y";case 4:return"sum_vector.x + sum_vector.y + sum_vector.z + sum_vector.w";default:throw new Error(`Unsupported components: ${u}`)}})()};
    workgroupBarrier();

    var sum: f32 = 0;
    for (var i = 0u; i < ${l}; i++) {
      sum += thread_sum[i];
    }

    if (sum == 0) {
      for (var i: u32 = 0; i < uniforms.elements_per_thread && i + local_offset < seq_causal_length; i++) {
        x[offset + i] = ${x.type.value}(${E}(1.0) / ${E}(seq_causal_length));
      }
    } else {
      for (var i: u32 = 0; i < uniforms.elements_per_thread && i + local_offset < seq_causal_length; i++) {
        var f32input = ${b}(x[offset + i]);
        x[offset + i] = ${x.type.value}(exp(f32input - max_value) / sum);
      }
    }
      ${a?`
        for (var total_seq_id: u32 = seq_causal_length; total_seq_id + local_offset < uniforms.total_sequence_length; total_seq_id++) {
          x[offset + total_seq_id] = ${x.type.value}(${E}(0));
        }`:""};
  }`};return{name:"AttentionProbsSoftmax",shaderCache:{hint:`${l};${g};${u}`,inputDependencies:_},getShaderSource:T,getRunData:()=>({outputs:[],dispatchGroup:{x:1,y:o,z:e*n},programUniforms:h})}},SP=(r,e,n,t,o,i,a,s,u)=>{let l=a+i.kvSequenceLength,d=[i.batchSize,i.numHeads,i.sequenceLength,l],f=r>1&&t,h=i.kvNumHeads?i.kvNumHeads:i.numHeads,g=f?[i.batchSize,h,l,i.headSize]:void 0,b=i.nReps?i.nReps:1,_=i.scale===0?1/Math.sqrt(i.headSize):i.scale,T=Ee(i.headSize),v=i.headSize/T,x=12,I={x:Math.ceil(l/x),y:Math.ceil(i.sequenceLength/x),z:i.batchSize*i.numHeads},$=[{type:12,data:i.sequenceLength},{type:12,data:v},{type:12,data:l},{type:12,data:i.numHeads},{type:12,data:i.headSize},{type:1,data:_},{type:12,data:a},{type:12,data:i.kvSequenceLength},{type:12,data:b}],O=f&&t&&C.size(t.dims)>0,E=["type","type"];O&&E.push("type"),o&&E.push("type"),s&&E.push("type"),u&&E.push("type");let L=[{dims:d,dataType:e.dataType,gpuDataType:0}];f&&L.push({dims:g,dataType:e.dataType,gpuDataType:0});let R=F=>{let q=N("q",e.dataType,e.dims,T),Z=N("key",n.dataType,n.dims,T),re=[q,Z];if(O){let ee=N("past_key",t.dataType,t.dims,T);re.push(ee)}o&&re.push(N("attention_bias",o.dataType,o.dims));let pe=s?N("seq_lens",s.dataType,s.dims):void 0;pe&&re.push(pe);let K=u?N("total_sequence_length_input",u.dataType,u.dims):void 0;K&&re.push(K);let Oe=G("output",e.dataType,d),fe=[Oe];f&&fe.push(G("present_key",e.dataType,g,T));let J=it(1,T),he=[{name:"M",type:"u32"},{name:"K",type:"u32"},{name:"N",type:"u32"},{name:"num_heads",type:"u32"},{name:"head_size",type:"u32"},{name:"alpha",type:"f32"},{name:"past_sequence_length",type:"u32"},{name:"kv_sequence_length",type:"u32"},{name:"n_reps",type:"u32"}];return`
  const TILE_SIZE = ${x}u;

  var<workgroup> tileQ: array<${q.type.storage}, ${x*x}>;
  var<workgroup> tileK: array<${q.type.storage}, ${x*x}>;
  ${F.registerUniforms(he).declareVariables(...re,...fe)}
  ${F.mainStart([x,x,1])}
    // x holds the N and y holds the M
    let headIdx = workgroup_id.z % uniforms.num_heads;
    let kvHeadIdx = ${b===1?"headIdx":"headIdx / uniforms.n_reps"};
    let kv_num_heads = ${b===1?"uniforms.num_heads":"uniforms.num_heads / uniforms.n_reps"};
    let batchIdx = workgroup_id.z / uniforms.num_heads;
    let m = workgroup_id.y * TILE_SIZE;
    let n = workgroup_id.x * TILE_SIZE;
    let sequence_length = uniforms.M;
    var total_sequence_length = uniforms.N;
    ${Ic(pe,K,!0)}
    let absKvHeadIdx = batchIdx * kv_num_heads + kvHeadIdx;
    let qOffset = workgroup_id.z * uniforms.M * uniforms.K + m * uniforms.K;
    ${O&&f?"let pastKeyOffset = absKvHeadIdx * uniforms.past_sequence_length * uniforms.K;":""};
    let kOffset = absKvHeadIdx * uniforms.kv_sequence_length * uniforms.K;
    ${f?"let presentKeyOffset = absKvHeadIdx * uniforms.N * uniforms.K;":""}
    var value = ${J}(0);
    for (var w: u32 = 0u; w < uniforms.K; w += TILE_SIZE) {
      if (global_id.y < uniforms.M && w + local_id.x < uniforms.K) {
        tileQ[TILE_SIZE * local_id.y + local_id.x] = q[qOffset + local_id.y * uniforms.K + w + local_id.x];
      }
      if (n + local_id.y < uniforms.N && w + local_id.x < uniforms.K) {
        var idx = TILE_SIZE * local_id.y + local_id.x;
      ${O&&f?`
              if (n + local_id.y < past_sequence_length) {
                tileK[idx] = past_key[pastKeyOffset + (n + local_id.y) * uniforms.K + w + local_id.x];
              } else if (n + local_id.y - past_sequence_length < uniforms.kv_sequence_length) {
                tileK[idx] = key[kOffset + (n + local_id.y - past_sequence_length) * uniforms.K + w + local_id.x];
              }`:`
          if (n + local_id.y < uniforms.kv_sequence_length) {
            tileK[idx] = key[kOffset + (n + local_id.y) * uniforms.K + w + local_id.x];
          }`}
      ${f?`if (n + local_id.y < present_sequence_length) {
        present_key[presentKeyOffset + (n + local_id.y) * uniforms.K + w + local_id.x] = tileK[idx];
      }`:""}
      }
      workgroupBarrier();

      for (var k: u32 = 0u; k < TILE_SIZE && w+k < uniforms.K; k++) {
          value += ${J}(tileQ[TILE_SIZE * local_id.y + k] * tileK[TILE_SIZE * local_id.x + k]);
      }

      workgroupBarrier();
    }

    if (global_id.y < uniforms.M && global_id.x < total_sequence_length) {
      let headOffset = workgroup_id.z * uniforms.M * uniforms.N;
      let outputIdx = headOffset + global_id.y * uniforms.N + global_id.x;
      var sum: f32 = ${(()=>{switch(T){case 1:return"value";case 2:return"value.x + value.y";case 4:return"value.x + value.y + value.z + value.w";default:throw new Error(`Unsupported components: ${T}`)}})()};
        output[outputIdx] = ${Oe.type.value} (sum * uniforms.alpha) + ${o?"attention_bias[outputIdx]":"0.0"};
    }
  }`};return{name:"AttentionProbs",shaderCache:{hint:`${T};${o!==void 0};${t!==void 0};${r}`,inputDependencies:E},getRunData:()=>({outputs:L,dispatchGroup:I,programUniforms:$}),getShaderSource:R}},$P=(r,e,n,t,o,i,a=void 0,s=void 0)=>{let u=i+o.kvSequenceLength,l=o.nReps?o.nReps:1,d=o.vHiddenSize*l,f=r>1&&t,h=o.kvNumHeads?o.kvNumHeads:o.numHeads,g=f?[o.batchSize,h,u,o.headSize]:void 0,b=[o.batchSize,o.sequenceLength,d],_=12,T={x:Math.ceil(o.vHeadSize/_),y:Math.ceil(o.sequenceLength/_),z:o.batchSize*o.numHeads},v=[{type:12,data:o.sequenceLength},{type:12,data:u},{type:12,data:o.vHeadSize},{type:12,data:o.numHeads},{type:12,data:o.headSize},{type:12,data:d},{type:12,data:i},{type:12,data:o.kvSequenceLength},{type:12,data:l}],x=f&&t&&C.size(t.dims)>0,I=["type","type"];x&&I.push("type"),a&&I.push("type"),s&&I.push("type");let $=[{dims:b,dataType:e.dataType,gpuDataType:0}];f&&$.push({dims:g,dataType:e.dataType,gpuDataType:0});let O=E=>{let L=N("probs",e.dataType,e.dims),R=N("v",n.dataType,n.dims),F=[L,R];x&&F.push(N("past_value",t.dataType,t.dims));let q=a?N("seq_lens",a.dataType,a.dims):void 0;a&&F.push(q);let Z=s?N("total_sequence_length_input",s.dataType,s.dims):void 0;s&&F.push(Z);let pe=[G("output",e.dataType,b)];f&&pe.push(G("present_value",e.dataType,g));let K=[{name:"M",type:"u32"},{name:"K",type:"u32"},{name:"N",type:"u32"},{name:"num_heads",type:"u32"},{name:"head_size",type:"u32"},{name:"v_hidden_size",type:"u32"},{name:"past_sequence_length",type:"u32"},{name:"kv_sequence_length",type:"u32"},{name:"n_reps",type:"u32"}];return`
  const TILE_SIZE = ${_}u;
  var<workgroup> tileQ: array<${L.type.value}, ${_*_}>;
  var<workgroup> tileV: array<${L.type.value}, ${_*_}>;
  ${E.registerUniforms(K).declareVariables(...F,...pe)}
  ${E.mainStart([_,_,1])}
   let headIdx = workgroup_id.z % uniforms.num_heads;
   let batchIdx = workgroup_id.z / uniforms.num_heads;
   let kvHeadIdx = ${l===1?"headIdx":"headIdx / uniforms.n_reps"};
   let kv_num_heads = ${l===1?"uniforms.num_heads":"uniforms.num_heads / uniforms.n_reps"};
   let m = global_id.y;
   let n = global_id.x;
   let sequence_length = uniforms.M;
   var total_sequence_length = uniforms.K;
   ${Ic(q,Z,!0)}
   let offsetA = workgroup_id.z * uniforms.M * uniforms.K + m * uniforms.K;
   let absKvHeadIdx = batchIdx * kv_num_heads + kvHeadIdx; // kvHeadIdx is relative to the batch
   ${x&&f?"let pastValueOffset = absKvHeadIdx * uniforms.N * uniforms.past_sequence_length + n;":""};
   let vOffset = absKvHeadIdx * uniforms.N * uniforms.kv_sequence_length + n;
   ${f?"let presentValueOffset = absKvHeadIdx * uniforms.N * uniforms.K + n;":""}
   var value = ${L.type.storage}(0);
   for (var w: u32 = 0u; w < uniforms.K; w += TILE_SIZE) {
      if (m < uniforms.M && w + local_id.x < uniforms.K) {
        tileQ[TILE_SIZE * local_id.y + local_id.x] = probs[offsetA + w + local_id.x];
      }
      if (n < uniforms.N && w + local_id.y < uniforms.K) {
        var idx = TILE_SIZE * local_id.y + local_id.x;
        ${x&&f?`
        if (w + local_id.y < past_sequence_length) {
          tileV[idx] = past_value[pastValueOffset + (w + local_id.y) * uniforms.N];
        } else if (w + local_id.y - past_sequence_length < uniforms.kv_sequence_length) {
          tileV[idx] = v[vOffset + (w + local_id.y - past_sequence_length) * uniforms.N];
        }
      `:`
            if (w + local_id.y < uniforms.kv_sequence_length) {
              tileV[idx] = v[vOffset + (w + local_id.y) * uniforms.N];
            }`}
        ${f?`
            if (w + local_id.y < present_sequence_length) {
          present_value[presentValueOffset + (w + local_id.y) * uniforms.N] = tileV[idx];
        }`:""}
      }
     workgroupBarrier();
     for (var k: u32 = 0u; k < TILE_SIZE && w+k < total_sequence_length; k++) {
       value += tileQ[TILE_SIZE * local_id.y + k] * tileV[TILE_SIZE * k + local_id.x];
     }
     workgroupBarrier();
   }

   // we need to transpose output from BNSH_v to BSND_v
   if (m < uniforms.M && n < uniforms.N) {
     let outputIdx = batchIdx * uniforms.M * uniforms.v_hidden_size + m * uniforms.v_hidden_size
       + headIdx * uniforms.N + n;
     output[outputIdx] = value;
   }
  }`};return{name:"AttentionScore",shaderCache:{hint:`${t!==void 0};${r}`,inputDependencies:I},getRunData:()=>({outputs:$,dispatchGroup:T,programUniforms:v}),getShaderSource:O}},po=(r,e,n,t,o,i,a,s,u,l,d=void 0,f=void 0)=>{let h=Math.min(r.outputCount,1+(a?1:0)+(s?1:0)),g=h>1?l.pastSequenceLength:0,b=g+l.kvSequenceLength,_=u&&C.size(u.dims)>0?u:void 0,T=[e,n];h>1&&a&&C.size(a.dims)>0&&T.push(a),_&&T.push(_),d&&T.push(d),f&&T.push(f);let v=r.compute(SP(h,e,n,a,_,l,g,d,f),{inputs:T,outputs:h>1?[-1,1]:[-1]})[0];r.compute(IP(v,l.batchSize,l.numHeads,g,l.sequenceLength,b,d,f),{inputs:d&&f?[v,d,f]:[v],outputs:[]});let x=[v,t];h>1&&s&&C.size(s.dims)>0&&x.push(s),d&&x.push(d),f&&x.push(f),r.compute($P(h,v,t,s,l,g,d,f),{inputs:x,outputs:h>1?[0,2]:[0]})},AP=(r,e)=>{let n=[e.batchSize,e.numHeads,e.sequenceLength,e.headSize],t=e.sequenceLength,o=e.inputHiddenSize,i=e.headSize,a=12,s={x:Math.ceil(e.headSize/a),y:Math.ceil(e.sequenceLength/a),z:e.batchSize*e.numHeads},u=[r.inputs[0],r.inputs[1],r.inputs[2]],l=[{type:12,data:t},{type:12,data:o},{type:12,data:i},{type:12,data:e.numHeads},{type:12,data:e.headSize},{type:12,data:e.hiddenSize},{type:12,data:e.hiddenSize+e.hiddenSize+e.vHiddenSize}],d=f=>{let h=G("output_q",u[0].dataType,n),g=G("output_k",u[0].dataType,n),b=G("output_v",u[0].dataType,n),_=N("input",u[0].dataType,u[0].dims),T=N("weight",u[1].dataType,u[1].dims),v=N("bias",u[2].dataType,u[2].dims),x=_.type.storage,I=[{name:"M",type:"u32"},{name:"K",type:"u32"},{name:"N",type:"u32"},{name:"num_heads",type:"u32"},{name:"head_size",type:"u32"},{name:"hidden_size",type:"u32"},{name:"ldb",type:"u32"}];return`
  const TILE_SIZE = ${a}u;
  var<workgroup> tileInput: array<${x}, ${a*a}>;
  var<workgroup> tileWeightQ: array<${x}, ${a*a}>;
  var<workgroup> tileWeightK: array<${x}, ${a*a}>;
  var<workgroup> tileWeightV: array<${x}, ${a*a}>;
  ${f.registerUniforms(I).declareVariables(_,T,v,h,g,b)}
  ${f.mainStart([a,a,1])}
    let batchIndex = workgroup_id.z / uniforms.num_heads;
    let headNumber = workgroup_id.z % uniforms.num_heads;
    let m = global_id.y;
    let n = global_id.x;

    let inputOffset = batchIndex * (uniforms.M * uniforms.K) + m * uniforms.K;
    let biasOffsetQ = headNumber * uniforms.head_size;
    let biasOffsetK = uniforms.hidden_size + biasOffsetQ;
    let biasOffsetV = uniforms.hidden_size + biasOffsetK;

    var valueQ = ${x}(0);
    var valueK = ${x}(0);
    var valueV = ${x}(0);
    for (var w: u32 = 0u; w < uniforms.K; w += TILE_SIZE) {
      if (m < uniforms.M && w + local_id.x < uniforms.K) {
        tileInput[TILE_SIZE * local_id.y + local_id.x] = input[inputOffset + w + local_id.x];
      }
      if (n < uniforms.N && w + local_id.y < uniforms.K) {
        let offset = n + (w + local_id.y) * uniforms.ldb;
        tileWeightQ[TILE_SIZE * local_id.y + local_id.x] = weight[biasOffsetQ + offset];
        tileWeightK[TILE_SIZE * local_id.y + local_id.x] = weight[biasOffsetK + offset];
        tileWeightV[TILE_SIZE * local_id.y + local_id.x] = weight[biasOffsetV + offset];
      }
      workgroupBarrier();
      for (var k: u32 = 0u; k<TILE_SIZE && w+k < uniforms.K; k++) {
        let inputTileOffset = TILE_SIZE * local_id.y + k;
        let weightTileOffset = TILE_SIZE * k + local_id.x;
        valueQ += tileInput[inputTileOffset] * tileWeightQ[weightTileOffset];
        valueK += tileInput[inputTileOffset] * tileWeightK[weightTileOffset];
        valueV += tileInput[inputTileOffset] * tileWeightV[weightTileOffset];
      }

      workgroupBarrier();
    }

    let headOffset = (m * uniforms.N + n) % uniforms.head_size;
    valueQ += bias[headOffset + biasOffsetQ];
    valueK += bias[headOffset + biasOffsetK];
    valueV += bias[headOffset + biasOffsetV];

    let offset = workgroup_id.z * uniforms.M * uniforms.N;
    if (m < uniforms.M && n < uniforms.N) {
      let outputIdx = offset + m * uniforms.N + n;
      output_q[outputIdx] = valueQ;
      output_k[outputIdx] = valueK;
      output_v[outputIdx] = valueV;
    }
  }`};return r.compute({name:"AttentionPrepare",shaderCache:{inputDependencies:["type","type","type"]},getRunData:()=>({outputs:[{dims:n,dataType:r.inputs[0].dataType,gpuDataType:0},{dims:n,dataType:r.inputs[0].dataType,gpuDataType:0},{dims:n,dataType:r.inputs[0].dataType,gpuDataType:0}],dispatchGroup:s,programUniforms:l}),getShaderSource:d},{inputs:u,outputs:[-1,-1,-1]})},x_=(r,e)=>{let n=TP(r.inputs,e),[t,o,i]=AP(r,n);return po(r,t,o,i,r.inputs[4],void 0,void 0,void 0,r.inputs[5],n)}});var OP,PP,EP,T_,I_=k(()=>{"use strict";pt();le();ge();Xe();ye();OP=(r,e)=>{if(!r||r.length!==5)throw new Error("BatchNormalization requires 5 inputs");let n=(t,o,i)=>{let a=o.length;if(a!==t.length)throw new Error(`${i}: num dimensions != ${a}`);o.forEach((s,u)=>{if(s!==t[u])throw new Error(`${i}: dim[${u}] do not match`)})};if(r[0].dims.length>1){let t=e.format==="NHWC"?e.spatial?r[0].dims.slice(-1):r[0].dims.slice(-1).concat(r[0].dims.slice(1,r[0].dims.length-1)):r[0].dims.slice(1,e.spatial?2:void 0);n(r[1].dims,t,"Invalid input scale"),n(r[2].dims,t,"Invalid input B"),n(r[3].dims,t,"Invalid input mean"),n(r[4].dims,t,"Invalid input var")}else n(r[1].dims,[1],"Invalid input scale"),n(r[2].dims,[1],"Invalid input B"),n(r[3].dims,[1],"Invalid input mean"),n(r[4].dims,[1],"Invalid input var")},PP=(r,e)=>{let{epsilon:n,spatial:t,format:o}=e,i=r[0].dims,a=t?Ee(i[i.length-1]):1,s=o==="NHWC"&&i.length>1?a:1,u=C.size(i)/a,l=t,d=l?i.length:i,f=N("x",r[0].dataType,r[0].dims,a),h=N("scale",r[1].dataType,r[1].dims,s),g=N("bias",r[2].dataType,r[2].dims,s),b=N("inputMean",r[3].dataType,r[3].dims,s),_=N("inputVar",r[4].dataType,r[4].dims,s),T=G("y",r[0].dataType,d,a),v=()=>{let I="";if(t)I=`let cOffset = ${i.length===1?"0u":o==="NHWC"?`outputIndices[${i.length-1}] / ${a}`:"outputIndices[1]"};`;else if(o==="NCHW")I=`
            ${T.indicesSet("outputIndices","0","0")}
            let cOffset = ${T.indicesToOffset("outputIndices")};`;else{I=`var cIndices = ${h.type.indices}(0);
                       cIndices[0] = outputIndices[${i.length-1}];`;for(let $=1;$<h.rank;$++)I+=`cIndices[${$}] = outputIndices[${$}];`;I+=`let cOffset = ${h.indicesToOffset("cIndices")};`}return I},x=I=>`
  const epsilon = ${n};
  ${I.registerUniform("outputSize","u32").declareVariables(f,h,g,b,_,T)}
  ${I.mainStart()}
  ${I.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.outputSize")}
    var outputIndices = ${T.offsetToIndices(`global_idx * ${a}`)};
    ${v()}
    let scale = ${h.getByOffset("cOffset")};
    let bias = ${g.getByOffset("cOffset")};
    let inputMean = ${b.getByOffset("cOffset")};
    let inputVar = ${_.getByOffset("cOffset")};
    let x = ${f.getByOffset("global_idx")};
    let value = (x - inputMean) * inverseSqrt(inputVar + epsilon) * scale + bias;
    ${T.setByOffset("global_idx","value")}
  }`;return{name:"BatchNormalization",shaderCache:{hint:`${e.epsilon}_${e.format}_${t}_${a}`,inputDependencies:l?["rank","type","type","type","type"]:void 0},getShaderSource:x,getRunData:()=>({outputs:[{dims:r[0].dims,dataType:r[0].dataType}],dispatchGroup:{x:Math.ceil(u/64)},programUniforms:l?[{type:12,data:u},...W(i)]:[{type:12,data:u}]})}},EP=r=>ce(r),T_=(r,e)=>{let{inputs:n,outputCount:t}=r,o=EP({...e,outputCount:t});if(me.webgpu.validateInputContent&&OP(n,o),e.trainingMode)throw new Error("BatchNormalization trainingMode is not supported yet.");r.compute(PP(n,o))}});var CP,DP,S_,$_=k(()=>{"use strict";ge();ye();CP=r=>{if(r[0].dims.length!==3)throw new Error("input should have 3 dimensions");if(![320,640,1280].includes(r[0].dims[2]))throw new Error("number of channels should be 320, 640 or 1280");if(r[1].dims.length!==1)throw new Error("bias is expected to have 1 dimensions");if(r[0].dims[2]!==r[1].dims[0])throw new Error("last dimension of input and bias are not the same")},DP=r=>{let e=r[0].dims,n=r[0].dims[2],t=C.size(e)/4,o=r[0].dataType,i=N("input",o,e,4),a=N("bias",o,[n],4),s=N("residual",o,e,4),u=G("output",o,e,4);return{name:"BiasAdd",getRunData:()=>({outputs:[{dims:e,dataType:r[0].dataType}],dispatchGroup:{x:Math.ceil(t/64)}}),getShaderSource:d=>`
  const channels = ${n}u / 4;
  ${d.declareVariables(i,a,s,u)}

  ${d.mainStart()}
    ${d.guardAgainstOutOfBoundsWorkgroupSizes(t)}
    let value = ${i.getByOffset("global_idx")}
      + ${a.getByOffset("global_idx % channels")} + ${s.getByOffset("global_idx")};
    ${u.setByOffset("global_idx","value")}
  }`}},S_=r=>{CP(r.inputs),r.compute(DP(r.inputs))}});var kP,Le,A_,O_,P_,E_,C_,D_,k_,N_,L_,NP,R_,z_,M_,B_,Wo,F_,Fa,V_,G_,U_,W_,H_,j_,q_,K_,X_,Z_,J_,Y_,Q_,ev,tv,nv,rv,ov,Sc,$c,iv,av,sv,LP,RP,uv,Va=k(()=>{"use strict";le();ge();Xe();ye();kP=(r,e,n,t,o,i,a)=>{let s=Math.ceil(e/4),u="";typeof o=="string"?u=`${o}(a)`:u=o("a");let l=N("inputData",n,[s],4),d=G("outputData",t,[s],4),f=[{name:"vec_size",type:"u32"}];return a&&f.push(...a),`
      ${r.registerUniforms(f).declareVariables(l,d)}

  ${i??""}

  ${r.mainStart()}
    ${r.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.vec_size")}

    let a = ${l.getByOffset("global_idx")};
    ${d.setByOffset("global_idx",u)}
  }`},Le=(r,e,n,t,o,i=r.dataType,a,s)=>{let u=[{type:12,data:Math.ceil(C.size(r.dims)/4)}];return a&&u.push(...a),{name:e,shaderCache:{hint:o,inputDependencies:["type"]},getShaderSource:l=>kP(l,C.size(r.dims),r.dataType,i,n,t,s),getRunData:l=>({outputs:[{dims:r.dims,dataType:i}],dispatchGroup:{x:Math.ceil(C.size(l[0].dims)/64/4)},programUniforms:u})}},A_=r=>{r.compute(Le(r.inputs[0],"Abs","abs"))},O_=r=>{r.compute(Le(r.inputs[0],"Acos","acos"))},P_=r=>{r.compute(Le(r.inputs[0],"Acosh","acosh"))},E_=r=>{r.compute(Le(r.inputs[0],"Asin","asin"))},C_=r=>{r.compute(Le(r.inputs[0],"Asinh","asinh"))},D_=r=>{r.compute(Le(r.inputs[0],"Atan","atan"))},k_=r=>{r.compute(Le(r.inputs[0],"Atanh","atanh"))},N_=r=>ce(r),L_=(r,e)=>{let n;switch(e.to){case 10:n="vec4<f16>";break;case 1:n="vec4<f32>";break;case 12:n="vec4<u32>";break;case 6:n="vec4<i32>";break;case 9:n="vec4<bool>";break;default:throw new RangeError(`not supported type (specified in attribute 'to' from 'Cast' operator): ${e.to}`)}r.compute(Le(r.inputs[0],"Cast",n,void 0,e.cacheKey,e.to))},NP=r=>{let e,n,t=r.length>=2&&r[1].data!==0,o=r.length>=3&&r[2].data!==0;switch(r[0].dataType){case 1:e=t?r[1].getFloat32Array()[0]:-34028234663852886e22,n=o?r[2].getFloat32Array()[0]:34028234663852886e22;break;case 10:e=t?r[1].getUint16Array()[0]:64511,n=o?r[2].getUint16Array()[0]:31743;break;default:throw new Error("Unsupport data type")}return ce({min:e,max:n})},R_=(r,e)=>{let n=e||NP(r.inputs),t=it(r.inputs[0].dataType);r.compute(Le(r.inputs[0],"Clip",o=>`clamp(${o}, vec4<${t}>(uniforms.min), vec4<${t}>(uniforms.max))`,void 0,n.cacheKey,void 0,[{type:r.inputs[0].dataType,data:n.min},{type:r.inputs[0].dataType,data:n.max}],[{name:"min",type:t},{name:"max",type:t}]),{inputs:[0]})},z_=r=>{r.compute(Le(r.inputs[0],"Ceil","ceil"))},M_=r=>{r.compute(Le(r.inputs[0],"Cos","cos"))},B_=r=>{r.compute(Le(r.inputs[0],"Cosh","cosh"))},Wo=r=>ce(r),F_=(r,e)=>{let n=it(r.inputs[0].dataType);r.compute(Le(r.inputs[0],"Elu",t=>`elu_vf32(${t})`,`
  const elu_alpha_ = ${n}(${e.alpha});

  fn elu_f32(a: ${n}) -> ${n} {
  return select((exp(a) - 1.0) * elu_alpha_, a, a >= 0.0);
  }

  fn elu_vf32(v: vec4<${n}>) -> vec4<${n}> {
  return vec4(elu_f32(v.x), elu_f32(v.y), elu_f32(v.z), elu_f32(v.w));
  }`,e.cacheKey))},Fa=(r="f32")=>`
const r0: ${r} = 0.3275911;
const r1: ${r} = 0.254829592;
const r2: ${r} = -0.284496736;
const r3: ${r} = 1.421413741;
const r4: ${r} = -1.453152027;
const r5: ${r} = 1.061405429;

fn erf_vf32(v: vec4<${r}>) -> vec4<${r}> {
  let absv = abs(v);
  let x = 1.0 / (1.0 + r0 * absv);
  return sign(v) * (1.0 - ((((r5 * x + r4) * x + r3) * x + r2) * x + r1) * x * exp(-absv * absv));
}`,V_=r=>{let e=it(r.inputs[0].dataType);r.compute(Le(r.inputs[0],"Erf",n=>`erf_vf32(${n})`,Fa(e)))},G_=r=>{r.compute(Le(r.inputs[0],"Exp","exp"))},U_=r=>{r.compute(Le(r.inputs[0],"Floor","floor"))},W_=r=>{let e=it(r.inputs[0].dataType);r.compute(Le(r.inputs[0],"Gelu",n=>`0.5 * ${n} * (1.0 + erf_vf32(${n} * 0.7071067811865475))`,Fa(e)))},H_=(r,e)=>{let n=it(r.inputs[0].dataType);r.compute(Le(r.inputs[0],"LeakyRelu",t=>`select(leaky_relu_alpha_ * ${t}, ${t}, ${t} >= vec4<${n}>(0.0))`,`const leaky_relu_alpha_ = ${n}(${e.alpha});`,e.cacheKey))},j_=r=>{r.compute(Le(r.inputs[0],"Not",e=>`!${e}`))},q_=r=>{r.compute(Le(r.inputs[0],"Neg",e=>`-${e}`))},K_=r=>{r.compute(Le(r.inputs[0],"Reciprocal",e=>`1.0/${e}`))},X_=r=>{let e=it(r.inputs[0].dataType);r.compute(Le(r.inputs[0],"Relu",n=>`select(vec4<${e}>(0.0), ${n}, ${n} > vec4<${e}>(0.0))`))},Z_=r=>{r.compute(Le(r.inputs[0],"Sigmoid",e=>`(1.0 / (1.0 + exp(-${e})))`))},J_=r=>ce(r),Y_=(r,e)=>{let n=it(r.inputs[0].dataType);r.compute(Le(r.inputs[0],"HardSigmoid",t=>`max(vec4<${n}>(0.0), min(vec4<${n}>(1.0), ${e.alpha} * ${t} + vec4<${n}>(${e.beta})))`,void 0,e.cacheKey))},Q_=r=>{r.compute(Le(r.inputs[0],"Sin","sin"))},ev=r=>{r.compute(Le(r.inputs[0],"Sinh","sinh"))},tv=r=>{r.compute(Le(r.inputs[0],"Sqrt","sqrt"))},nv=r=>{r.compute(Le(r.inputs[0],"Tan","tan"))},rv=r=>`sign(${r}) * (1 - exp(-2 * abs(${r}))) / (1 + exp(-2 * abs(${r})))`,ov=r=>{r.compute(Le(r.inputs[0],"Tanh",rv))},Sc=(r="f32")=>`
const fast_gelu_a: ${r} = 0.5;
const fast_gelu_b: ${r} = 0.7978845608028654;
const fast_gelu_c: ${r} = 0.035677408136300125;

fn tanh_v(v: vec4<${r}>) -> vec4<${r}> {
  return ${rv("v")};
}
`,$c=r=>`(fast_gelu_a + fast_gelu_a * tanh_v(${r} * (fast_gelu_c * ${r} * ${r} + fast_gelu_b))) * ${r}`,iv=r=>{let e=it(r.inputs[0].dataType);r.compute(Le(r.inputs[0],"FastGelu",$c,Sc(e),void 0,r.inputs[0].dataType))},av=(r,e)=>{let n=it(r.inputs[0].dataType);return r.compute(Le(r.inputs[0],"ThresholdedRelu",t=>`select(vec4<${n}>(0.0), ${t}, ${t} > thresholded_relu_alpha_)`,`const thresholded_relu_alpha_ = vec4<${n}>(${e.alpha});`,e.cacheKey)),0},sv=r=>{r.compute(Le(r.inputs[0],"Log","log"))},LP=(r,e)=>`
const alpha = vec4<${r}>(${e});
const one = ${r}(1.0);
const zero = ${r}(0.0);

fn quick_gelu_impl(x: vec4<${r}>) -> vec4<${r}> {
  let v = x *alpha;
  var x1 : vec4<${r}>;
  for (var i = 0; i < 4; i = i + 1) {
    if (v[i] >= zero) {
      x1[i] = one / (one + exp(-v[i]));
    } else {
      x1[i] = one - one / (one + exp(v[i]));
    }
  }
  return x * x1;
}
`,RP=r=>`quick_gelu_impl(${r})`,uv=(r,e)=>{let n=it(r.inputs[0].dataType);r.compute(Le(r.inputs[0],"QuickGelu",RP,LP(n,e.alpha),e.cacheKey,r.inputs[0].dataType))}});var zP,MP,cv,dv=k(()=>{"use strict";ge();ye();Va();zP=r=>{if(r[0].dims.length!==3)throw new Error("input should have 3 dimensions");if(![2560,5120,10240].includes(r[0].dims[2]))throw new Error("hidden state should be 2560, 5120 or 10240");if(r[1].dims.length!==1)throw new Error("bias is expected to have 1 dimensions");if(r[0].dims[2]!==r[1].dims[0])throw new Error("last dimension of input and bias are not the same")},MP=r=>{let e=r[0].dims.slice();e[2]=e[2]/2;let n=N("input",r[0].dataType,r[0].dims,4),t=N("bias",r[0].dataType,[r[0].dims[2]],4),o=G("output",r[0].dataType,e,4),i=C.size(e)/4,a=Ve(r[0].dataType);return{name:"BiasSplitGelu",getRunData:()=>({outputs:[{dims:e,dataType:r[0].dataType}],dispatchGroup:{x:Math.ceil(i/64)}}),getShaderSource:u=>`
  const M_SQRT2 = sqrt(2.0);
  const halfChannels = ${r[0].dims[2]/4/2}u;

  ${u.declareVariables(n,t,o)}

  ${Fa(a)}

  ${u.mainStart()}
    ${u.guardAgainstOutOfBoundsWorkgroupSizes(i)}
    let biasIdx = global_idx % halfChannels;
    let batchIndex = global_idx / halfChannels;
    let inputOffset = biasIdx + batchIndex * halfChannels * 2;
    let valueLeft = input[inputOffset] + bias[biasIdx];
    let valueRight = input[inputOffset + halfChannels] + bias[biasIdx + halfChannels];
    let geluRight = valueRight * 0.5 * (erf_vf32(valueRight / M_SQRT2) + 1);

    ${o.setByOffset("global_idx","valueLeft * geluRight")}
  }`}},cv=r=>{zP(r.inputs),r.compute(MP(r.inputs))}});var BP,FP,Hn,pv,fv,hv,mv,gv,bv,yv,_v,vv,wv,xv=k(()=>{"use strict";le();ge();ye();BP=(r,e,n,t,o,i,a,s,u,l,d,f)=>{let h,g;typeof s=="string"?h=g=(x,I)=>`${s}((${x}),(${I}))`:typeof s=="function"?h=g=s:(h=s.scalar,g=s.vector);let b=G("outputData",d,t.length,4),_=N("aData",u,e.length,4),T=N("bData",l,n.length,4),v;if(o)if(i){let x=C.size(e)===1,I=C.size(n)===1,$=e.length>0&&e[e.length-1]%4===0,O=n.length>0&&n[n.length-1]%4===0;x||I?v=b.setByOffset("global_idx",g(x?`${_.type.value}(${_.getByOffset("0")}.x)`:_.getByOffset("global_idx"),I?`${T.type.value}(${T.getByOffset("0")}.x)`:T.getByOffset("global_idx"))):v=`
            let outputIndices = ${b.offsetToIndices("global_idx * 4u")};
            let offsetA = ${_.broadcastedIndicesToOffset("outputIndices",b)};
            let offsetB = ${T.broadcastedIndicesToOffset("outputIndices",b)};
            ${b.setByOffset("global_idx",g(a||$?_.getByOffset("offsetA / 4u"):`${_.type.value}(${_.getByOffset("offsetA / 4u")}[offsetA % 4u])`,a||O?T.getByOffset("offsetB / 4u"):`${T.type.value}(${T.getByOffset("offsetB / 4u")}[offsetB % 4u])`))}
          `}else v=b.setByOffset("global_idx",g(_.getByOffset("global_idx"),T.getByOffset("global_idx")));else{if(!i)throw new Error("no necessary to use scalar implementation for element-wise binary op implementation.");let x=(I,$,O="")=>{let E=`aData[indexA${$}][componentA${$}]`,L=`bData[indexB${$}][componentB${$}]`;return`
            let outputIndices${$} = ${b.offsetToIndices(`global_idx * 4u + ${$}u`)};
            let offsetA${$} = ${_.broadcastedIndicesToOffset(`outputIndices${$}`,b)};
            let offsetB${$} = ${T.broadcastedIndicesToOffset(`outputIndices${$}`,b)};
            let indexA${$} = offsetA${$} / 4u;
            let indexB${$} = offsetB${$} / 4u;
            let componentA${$} = offsetA${$} % 4u;
            let componentB${$} = offsetB${$} % 4u;
            ${I}[${$}] = ${O}(${h(E,L)});
          `};d===9?v=`
            var data = vec4<u32>(0);
            ${x("data",0,"u32")}
            ${x("data",1,"u32")}
            ${x("data",2,"u32")}
            ${x("data",3,"u32")}
            outputData[global_idx] = dot(vec4<u32>(0x1, 0x100, 0x10000, 0x1000000), vec4<u32>(data));`:v=`
            ${x("outputData[global_idx]",0)}
            ${x("outputData[global_idx]",1)}
            ${x("outputData[global_idx]",2)}
            ${x("outputData[global_idx]",3)}
          `}return`
        ${r.registerUniform("vec_size","u32").declareVariables(_,T,b)}

        ${f??""}

        ${r.mainStart()}
        ${r.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.vec_size")}
        ${v}
      }`},FP=(r,e,n,t,o,i,a=n.dataType)=>{let s=n.dims.map(_=>Number(_)??1),u=t.dims.map(_=>Number(_)??1),l=!C.areEqual(s,u),d=s,f=C.size(s),h=!1,g=!1,b=[l];if(l){let _=Fn.calcShape(s,u,!1);if(!_)throw new Error("Can't perform binary op on the given tensors");d=_.slice(),f=C.size(d);let T=C.size(s)===1,v=C.size(u)===1,x=s.length>0&&s[s.length-1]%4===0,I=u.length>0&&u[u.length-1]%4===0;b.push(T),b.push(v),b.push(x),b.push(I);let $=1;for(let O=1;O<d.length;O++){let E=s[s.length-O],L=u[u.length-O];if(E===L)$*=E;else break}$%4===0?(g=!0,h=!0):(T||v||x||I)&&(h=!0)}else h=!0;return b.push(h),{name:r,shaderCache:{hint:e+b.map(_=>_.toString()).join("_"),inputDependencies:["rank","rank"]},getShaderSource:_=>BP(_,s,u,d,h,l,g,o,n.dataType,t.dataType,a,i),getRunData:()=>({outputs:[{dims:d,dataType:a}],dispatchGroup:{x:Math.ceil(f/64/4)},programUniforms:[{type:12,data:Math.ceil(C.size(d)/4)},...W(s,u,d)]})}},Hn=(r,e,n,t,o,i)=>{r.compute(FP(e,o??"",r.inputs[0],r.inputs[1],n,t,i))},pv=r=>{Hn(r,"Add",(e,n)=>`${e}+${n}`)},fv=r=>{Hn(r,"Div",(e,n)=>`${e}/${n}`)},hv=r=>{Hn(r,"Equal",{scalar:(e,n)=>`u32(${e}==${n})`,vector:(e,n)=>`vec4<u32>(${e}==${n})`},void 0,void 0,9)},mv=r=>{Hn(r,"Mul",(e,n)=>`${e}*${n}`)},gv=r=>{let e=N("input",r.inputs[0].dataType,r.inputs[0].dims).type.value;Hn(r,"Pow",{scalar:(t,o)=>`pow_custom(${t},${o})`,vector:(t,o)=>`pow_vector_custom(${t},${o})`},`
    fn pow_custom(a : ${e}, b : ${e}) -> ${e} {
      if (b == ${e}(0.0)) {
        return ${e}(1.0);
      } else if (a < ${e}(0.0) && f32(b) != floor(f32(b))) {
        return ${e}(pow(f32(a), f32(b))); // NaN
      }
      return select(sign(a), ${e}(1.0), round(f32(abs(b) % ${e}(2.0))) != 1.0) * ${e}(${e==="i32"?"round":""}(pow(f32(abs(a)), f32(b))));
    }
    fn pow_vector_custom(a : vec4<${e}>, b : vec4<${e}>) -> vec4<${e}> {
      // TODO: implement vectorized pow
      return vec4<${e}>(pow_custom(a.x, b.x), pow_custom(a.y, b.y), pow_custom(a.z, b.z), pow_custom(a.w, b.w));
    }
      `)},bv=r=>{Hn(r,"Sub",(e,n)=>`${e}-${n}`)},yv=r=>{Hn(r,"Greater",{scalar:(e,n)=>`u32(${e}>${n})`,vector:(e,n)=>`vec4<u32>(${e}>${n})`},void 0,void 0,9)},_v=r=>{Hn(r,"Less",{scalar:(e,n)=>`u32(${e}<${n})`,vector:(e,n)=>`vec4<u32>(${e}<${n})`},void 0,void 0,9)},vv=r=>{Hn(r,"GreaterOrEqual",{scalar:(e,n)=>`u32(${e}>=${n})`,vector:(e,n)=>`vec4<u32>(${e}>=${n})`},void 0,void 0,9)},wv=r=>{Hn(r,"LessOrEqual",{scalar:(e,n)=>`u32(${e}<=${n})`,vector:(e,n)=>`vec4<u32>(${e}<=${n})`},void 0,void 0,9)}});var GP,UP,WP,HP,Tv,Iv,Sv=k(()=>{"use strict";le();ge();Xe();ye();GP=(r,e)=>{if(!r||r.length<1)throw new Error("too few inputs");let n=0,t=r[n],o=t.dataType,i=t.dims.length;r.forEach((a,s)=>{if(s!==n){if(a.dataType!==o)throw new Error("input tensors should be one type");if(a.dims.length!==i)throw new Error("input tensors should have the same shape");a.dims.forEach((u,l)=>{if(l!==e&&u!==t.dims[l])throw new Error("non concat dimensions must match")})}})},UP=(r,e)=>`
  fn calculateInputIndex(index: u32) -> u32 {
    let sizeInConcatAxis = array<u32, ${r}u>(${e});
    for (var i: u32 = 0u; i < ${r}; i += 1u ) {
      if (index < sizeInConcatAxis[i]) {
        return i;
      }
    }
    return ${r}u;
  }`,WP=(r,e)=>{let n=r.length,t=[];for(let o=0;o<n;++o){let i=e.setByOffset("global_idx",r[o].getByIndices("indices"));n===1?t.push(i):o===0?t.push(`if (inputIndex == ${o}u) { ${i} }`):o===n-1?t.push(`else { ${i} }`):t.push(`else if (inputIndex == ${o}) { ${i} }`)}return t.join(`
`)},HP=(r,e,n,t)=>{let o=C.size(n),i=new Array(r.length),a=new Array(r.length),s=0,u=[],l=[],d=[{type:12,data:o}];for(let _=0;_<r.length;++_)s+=r[_].dims[e],i[_]=s,l.push(r[_].dims.length),a[_]=N(`input${_}`,t,l[_]),u.push("rank"),d.push({type:12,data:i[_]});for(let _=0;_<r.length;++_)d.push(...W(r[_].dims));d.push(...W(n));let f=G("output",t,n.length),h=f.indicesGet("indices",e),g=Array.from(Array(i.length).keys()).map(_=>`uniforms.sizeInConcatAxis${_}`).join(","),b=_=>`

  ${(()=>{_.registerUniform("outputSize","u32");for(let T=0;T<r.length;T++)_.registerUniform(`sizeInConcatAxis${T}`,"u32");return _.declareVariables(...a,f)})()}

  ${UP(i.length,g)}

  ${_.mainStart()}
    ${_.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.outputSize")}

    var indices = ${f.offsetToIndices("global_idx")};

    let inputIndex = calculateInputIndex(${h});
    if (inputIndex != 0u) {
      let sizeInConcatAxis = array<u32, ${i.length}u>(${g});
      ${h} -= sizeInConcatAxis[inputIndex - 1u];
    }

    ${WP(a,f)}
  }`;return{name:"Concat",shaderCache:{hint:`${e}`,inputDependencies:u},getRunData:()=>({outputs:[{dims:n,dataType:t}],dispatchGroup:{x:Math.ceil(o/64)},programUniforms:d}),getShaderSource:b}},Tv=(r,e)=>{let n=r.inputs,t=n[0].dims,o=C.normalizeAxis(e.axis,t.length);GP(n,o);let i=t.slice();i[o]=n.reduce((s,u)=>s+(u.dims.length>o?u.dims[o]:0),0);let a=n.filter(s=>C.size(s.dims)>0);r.compute(HP(a,o,i,n[0].dataType),{inputs:a})},Iv=r=>ce({axis:r.axis})});var Xt,Zt,Jt,Ga,wr=k(()=>{"use strict";le();ge();Xt=(r,e,n="f32")=>{switch(r.activation){case"Relu":return`value = max(value, ${e}(0.0));`;case"Sigmoid":return`value = (${e}(1.0) / (${e}(1.0) + exp(-value)));`;case"Clip":return`value = clamp(value, ${e}(${n}(uniforms.clip_min)), ${e}(${n}(uniforms.clip_max)));`;case"HardSigmoid":return`value = max(${e}(0.0), min(${e}(1.0), ${n}(uniforms.alpha) * value + ${n}(uniforms.beta)));`;case"LeakyRelu":return`value = select(${n}(uniforms.alpha) * value, value, value >= ${e}(0.0));`;case"Tanh":return`let e2x = exp(-2.0 * abs(value));
              value = sign(value) * (1.0 - e2x) / (1.0 + e2x);
        `;case"":return"";default:throw new Error(`Unsupported activation ${r.activation}`)}},Zt=(r,e)=>{r.activation==="Clip"?e.push({type:1,data:r.clipMax},{type:1,data:r.clipMin}):r.activation==="HardSigmoid"?e.push({type:1,data:r.alpha},{type:1,data:r.beta}):r.activation==="LeakyRelu"&&e.push({type:1,data:r.alpha})},Jt=(r,e)=>{r.activation==="Clip"?e.push({name:"clip_max",type:"f32"},{name:"clip_min",type:"f32"}):r.activation==="HardSigmoid"?e.push({name:"alpha",type:"f32"},{name:"beta",type:"f32"}):r.activation==="LeakyRelu"&&e.push({name:"alpha",type:"f32"})},Ga=r=>{let e=r?.activation||"";if(e==="HardSigmoid"){let[n,t]=r?.activation_params||[.2,.5];return{activation:e,alpha:n,beta:t}}else if(e==="Clip"){let[n,t]=r?.activation_params||[Dy,ky];return{activation:e,clipMax:t,clipMin:n}}else if(e==="LeakyRelu"){let[n]=r?.activation_params||[.01];return{activation:e,alpha:n}}return{activation:e}}});var rt,$v,Ua=k(()=>{"use strict";rt=(r,e)=>{switch(r){case 1:return e;case 2:return`vec2<${e}>`;case 3:return`vec3<${e}>`;case 4:return`vec4<${e}>`;default:throw new Error(`${r}-component is not supported.`)}},$v=r=>`
      ${r?"value = value + getBiasByOutputCoords(coords);":""}
      `});var Av,Ov=k(()=>{"use strict";Av=r=>`
fn getIndexFromCoords4D(coords : vec4<i32>, shape : vec4<i32>) -> i32 {
  return dot(coords, vec4<i32>(
      shape.y * shape.z * shape.w, shape.z * shape.w, shape.w, 1));
}
fn getOutputIndexFromCoords(coords : vec4<i32>) -> i32 {
  return dot(coords, vec4<i32>(
    i32(${r}.x), i32(${r}.y), i32(${r}.z), 1));
}
`});var Ho,Wa,Ha=k(()=>{"use strict";le();ge();ye();wr();Ho=(r,e,n,t,o)=>{let i=t-n;return`
      ${Array.from({length:n}).map((a,s)=>`
      if (${Q(e.shape,s,e.rank)} != 1) {
        ${e.indicesSet(r,s,Q(o,s+i,t))}
      } else {
        ${e.indicesSet(r,s,0)}
      }`).join("")}
`},Wa=(r,e,n,t,o=!1,i)=>{let a=r[0].dims,s=r[1].dims,u=a[a.length-2],l=s[s.length-1],d=a[a.length-1],f=Ee(l),h=Ee(d),g=Ee(u),b=C.size(n)/f/g,_=r.length>2,T=t?t.slice(0,-2):n.slice(0,-2),x=[C.size(T),u,l],I=[{type:12,data:b},{type:12,data:u},{type:12,data:l},{type:12,data:d}];Zt(e,I),I.push(...W(T,a,s)),_&&I.push(...W(r[2].dims)),I.push(...W(x));let $=O=>{let E=Ra("batch_dims",r[0].dataType,T.length),L=N("a",r[0].dataType,a.length,h),R=N("b",r[1].dataType,s.length,f),F=G("output",r[0].dataType,x.length,f),q=Ve(F.type.tensor),Z=Xt(e,F.type.value,q),re=[L,R],pe="";if(_){let fe=o?f:1;re.push(N("bias",r[2].dataType,r[2].dims.length,fe)),pe=`${o?`value += bias[col / ${fe}];`:`value += ${F.type.value}(bias[row + i]);`}`}let K=[{name:"output_size",type:"u32"},{name:"M",type:"u32"},{name:"N",type:"u32"},{name:"K",type:"u32"}];Jt(e,K);let Oe=()=>{let fe=`var a_data: ${L.type.value};`;for(let J=0;J<h;J++)fe+=`
              let b_data${J} = b[(b_offset + (k + ${J}) * uniforms.N + col) / ${f}];`;for(let J=0;J<g;J++){fe+=`a_data = a[(a_offset + (row + ${J}) * uniforms.K + k) / ${h}];`;for(let he=0;he<h;he++)fe+=`
            values[${J}] = fma(${R.type.value}(a_data${h===1?"":`[${he}]`}), b_data${he}, values[${J}]);
`}return fe};return`
  ${O.registerUniforms(K).registerInternalVariables(E).declareVariables(...re,F)}
  ${O.mainStart()}
    ${O.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.output_size")}
    let col = (global_idx % (uniforms.N / ${f})) * ${f};
    var index1 = global_idx / (uniforms.N / ${f});
    let stride1 = uniforms.M / ${g};
    let row = (index1 % stride1) * ${g};
    let batch = index1 / stride1;

    ${n.length===2?"":`let batch_indices = ${E.offsetToIndices("batch")};`}

    var a_indices: ${L.type.indices};
    ${Ho("a_indices",L,L.rank-2,E.rank,"batch_indices")}
    ${L.indicesSet("a_indices",L.rank-2,0)}
    ${L.indicesSet("a_indices",L.rank-1,0)}
    let a_offset = ${L.indicesToOffset("a_indices")};

    var b_indices: ${R.type.indices};
    ${Ho("b_indices",R,R.rank-2,E.rank,"batch_indices")}
    ${R.indicesSet("b_indices",R.rank-2,0)}
    ${R.indicesSet("b_indices",R.rank-1,0)}
    let b_offset = ${R.indicesToOffset("b_indices")};
    var values: array<${F.type.value}, ${g}>;
    for (var k: u32 = 0u; k < uniforms.K; k = k + ${h}) {
      ${Oe()}
    }
    for (var i = 0u; i < ${g}u; i++) {
      var value = values[i];
      ${pe}
      ${Z}
      let cur_indices = ${F.type.indices}(batch, row + i, col);
      let offset = ${F.indicesToOffset("cur_indices")};
      ${F.setByOffset(`offset / ${f}`,"value")};
    }
  }
  `};return{name:"MatMulNaive",shaderCache:{hint:`${e.activation};${f};${h};${g};${o}`,inputDependencies:_?["rank","rank","rank"]:["rank","rank"]},getRunData:()=>({outputs:[{dims:i?i(n):n,dataType:r[0].dataType}],dispatchGroup:{x:Math.ceil(b/64)},programUniforms:I}),getShaderSource:$}}});var jP,qP,Ac,Pv,KP,Oc,XP,jo,ja=k(()=>{"use strict";le();ge();ye();wr();Ha();Ua();jP=(r,e)=>r?`
        mm_Asub[inputRow][inputCol] = mm_readA(batch,
          kStart + inputRow,
          globalRowStart / innerElementSize + inputCol${e?", batchIndices":""});
        `:`
        mm_Asub[inputRow][inputCol] = mm_readA(batch,
          globalRow + innerRow,
          kStart / innerElementSize + inputCol${e?", batchIndices":""});
        `,qP=(r,e)=>r?`
        let ACached0 = mm_Asub[k * innerElementSize][localRow];
        let ACached1 = mm_Asub[k * innerElementSize + 1][localRow];
        let ACached2 = mm_Asub[k * innerElementSize + 2][localRow];
        ${e===3?"":"let ACached3 = mm_Asub[k * innerElementSize + 3][localRow];"}
        for (var i = 0; i < rowPerThread; i = i + 1) {
          acc[i] = BCached0 * ACached0[i] + acc[i];
          acc[i] = BCached1 * ACached1[i] + acc[i];
          acc[i] = BCached2 * ACached2[i] + acc[i];
          ${e===3?"":"acc[i] = BCached3 * ACached3[i] + acc[i];"}
        }`:`
        for (var i = 0; i < rowPerThread; i = i + 1) {
          let ACached = mm_Asub[tileRow + i][k];
          acc[i] = BCached0 * ACached.x + acc[i];
          acc[i] = BCached1 * ACached.y + acc[i];
          acc[i] = BCached2 * ACached.z + acc[i];
          ${e===3?"":"acc[i] = BCached3 * ACached.w + acc[i];"}
        }`,Ac=(r,e,n="f32",t,o=!1,i=32,a=!1,s=32)=>{let u=e[1]*r[1],l=e[0]*r[0],d=o?u:i,f=o?i:u,h=d/e[0],g=i/e[1];if(!((o&&h===4&&r[1]===4||!o&&(h===3||h===4))&&d%e[0]===0&&i%e[1]===0&&r[0]===4))throw new Error(`If transposeA ${o} is true, innerElementSize ${h} and workPerThread[1] ${r[1]} must be 4.
      Otherwise, innerElementSize ${h} must be 3 or 4.
  tileAWidth ${d} must be divisible by workgroupSize[0]${e[0]}. tileInner ${i} must be divisible by workgroupSize[1] ${e[1]}. colPerThread ${r[0]} must be 4.`);return`
var<workgroup> mm_Asub: array<array<vec${h}<${n}>, ${d/h}>, ${f}>;
var<workgroup> mm_Bsub: array<array<vec4<${n}>, ${l/r[0]}>, ${i}>;

const rowPerThread = ${r[1]};
const colPerThread = ${r[0]};
const innerElementSize = ${h};
const tileInner = ${i};

@compute @workgroup_size(${e[0]}, ${e[1]}, ${e[2]})
fn main(@builtin(local_invocation_id) localId : vec3<u32>,
        @builtin(global_invocation_id) globalId : vec3<u32>,
        @builtin(workgroup_id) workgroupId : vec3<u32>) {
  let localRow = i32(localId.y);
  let tileRow = localRow * rowPerThread;
  let tileCol = i32(localId.x);

  let globalRow =i32(globalId.y) * rowPerThread;
  let globalCol = i32(globalId.x);
  let batch = ${a?"0":"i32(globalId.z)"};
  ${t?`let batchIndices = ${t.offsetToIndices("u32(batch)")};`:""}
  let globalRowStart = i32(workgroupId.y) * ${u};

  let num_tiles = ${a?`${Math.ceil(s/i)}`:"(uniforms.dim_inner - 1) / tileInner + 1"};
  var kStart = ${a?`i32(globalId.z) * ${s}`:"0"};

  var acc: array<vec4<${n}>, rowPerThread>;

  // Loop over shared dimension.
  let tileRowB = localRow * ${g};
  for (var t = 0; t < num_tiles; t = t + 1) {
      // Load one tile of A into local memory.
      for (var innerRow = 0; innerRow < rowPerThread; innerRow = innerRow + 1) {
          let inputRow = tileRow + innerRow;
          let inputCol = tileCol;
          ${jP(o,t)}
      }

      // Load one tile of B into local memory.
      for (var innerRow = 0; innerRow < ${g}; innerRow = innerRow + 1) {
          let inputRow = tileRowB + innerRow;
          let inputCol = tileCol;
          mm_Bsub[inputRow][inputCol] = mm_readB(batch, kStart + inputRow, globalCol${t?", batchIndices":""});
      }
      kStart = kStart + tileInner;
      workgroupBarrier();

      // Compute acc values for a single thread.
      for (var k = 0; k < tileInner / innerElementSize; k = k + 1) {
          let BCached0 = mm_Bsub[k * innerElementSize][tileCol];
          let BCached1 = mm_Bsub[k * innerElementSize + 1][tileCol];
          let BCached2 = mm_Bsub[k * innerElementSize + 2][tileCol];
          ${h===3?"":"let BCached3 = mm_Bsub[k * innerElementSize + 3][tileCol];"}

          ${qP(o,h)}
      }

      workgroupBarrier();
  }

  for (var innerRow = 0; innerRow < rowPerThread; innerRow = innerRow + 1) {
      mm_write(batch, globalRow + innerRow, globalCol, acc[innerRow]);
  }
}`},Pv=(r,e)=>r?`
            mm_Asub[inputRow][inputCol] = mm_readA(batch,
              kStart + inputRow,
              globalRowStart + inputCol${e?", batchIndices":""});
            `:`
            mm_Asub[inputRow][inputCol] = mm_readA(batch,
              globalRowStart + inputRow,
              kStart + inputCol${e?", batchIndices":""});
            `,KP=r=>r?"let ACached = mm_Asub[k][tileRow + innerRow];":"let ACached = mm_Asub[tileRow + innerRow][k];",Oc=(r,e,n="f32",t,o=!1,i=32,a=!1,s=32,u=!1)=>{let l=r[1]*e[1],d=r[0]*e[0],f=o?l:i,h=o?i:l;if(!(h%e[1]===0&&f%e[0]===0&&i%e[1]===0))throw new Error(`tileAHight ${h} must be divisible by workgroupSize[1]${e[1]}, tileAWidth ${f} must be divisible by workgroupSize[0]${e[0]}, tileInner ${i} must be divisible by workgroupSize[1]${e[1]}`);let g=h/e[1],b=f/e[0],_=i/e[1],T=u?`
    let localRow = i32(localId.y);
    let localCol = i32(localId.x);
    let globalRowStart = i32(workgroupId.y) * ${l};
    let globalColStart = i32(workgroupId.x) * ${d};

    // Loop over shared dimension.
    for (var t = 0; t < num_tiles; t = t + 1) {
      // Load one tile of A into local memory.
      for (var inputRow = localRow; inputRow < ${h}; inputRow = inputRow + ${e[1]}) {
        for (var inputCol = localCol; inputCol < ${f}; inputCol = inputCol + ${e[0]}) {
          ${Pv(o,t)}
        }
      }
      // Load one tile of B into local memory.
      for (var inputRow = localRow; inputRow < ${i}; inputRow = inputRow + ${e[1]}) {
            for (var inputCol = localCol; inputCol < ${d}; inputCol = inputCol + ${e[0]}) {
          mm_Bsub[inputRow][inputCol] = mm_readB(batch,
            kStart + inputRow,
            globalColStart + inputCol${t?", batchIndices":""});
        }
      }
      kStart = kStart + tileInner;
      workgroupBarrier();

      // Compute acc values for a single thread.
      var BCached : array<${n}, colPerThread>;
      for (var k = 0; k < tileInner; k = k + 1) {
        for (var inner = 0; inner < colPerThread; inner = inner + 1) {
          BCached[inner] = mm_Bsub[k][localCol + inner * ${e[0]}];
        }
        for (var innerRow = 0; innerRow < rowPerThread; innerRow = innerRow + 1) {
          let ACached = ${o?`mm_Asub[k][localRow + innerRow * ${e[1]}];`:`mm_Asub[localRow + innerRow * ${e[1]}][k];`}
          for (var innerCol = 0; innerCol < colPerThread; innerCol = innerCol + 1) {
            acc[innerRow][innerCol] = acc[innerRow][innerCol] +
                ACached * BCached[innerCol];
          }
        }
      }
      workgroupBarrier();
    }
    for (var innerRow = 0; innerRow < rowPerThread; innerRow = innerRow + 1) {
      let gRow = globalRowStart + localRow + innerRow * ${e[1]};
      for (var innerCol = 0; innerCol < colPerThread; innerCol = innerCol + 1) {
        let gCol = globalColStart + localCol + innerCol * ${e[0]};
        mm_write(batch, gRow, gCol, acc[innerRow][innerCol]);
      }
    }
    `:`
let tileRow = i32(localId.y) * rowPerThread;
let tileCol = i32(localId.x) * colPerThread;

let globalRow = i32(globalId.y) * rowPerThread;
let globalCol = i32(globalId.x) * colPerThread;
let globalRowStart = i32(workgroupId.y) * ${l};

let tileRowA = i32(localId.y) * ${g};
let tileColA = i32(localId.x) * ${b};
let tileRowB = i32(localId.y) * ${_};
// Loop over shared dimension.
for (var t = 0; t < num_tiles; t = t + 1) {
  // Load one tile of A into local memory.
  for (var innerRow = 0; innerRow < ${g}; innerRow = innerRow + 1) {
    for (var innerCol = 0; innerCol < ${b}; innerCol = innerCol + 1) {
      let inputRow = tileRowA + innerRow;
      let inputCol = tileColA + innerCol;
      ${Pv(o,t)}
    }
  }

  // Load one tile of B into local memory.
  for (var innerRow = 0; innerRow < ${_}; innerRow = innerRow + 1) {
    for (var innerCol = 0; innerCol < colPerThread; innerCol = innerCol + 1) {
      let inputRow = tileRowB + innerRow;
      let inputCol = tileCol + innerCol;
      mm_Bsub[inputRow][inputCol] = mm_readB(batch,
        kStart + inputRow,
        globalCol + innerCol${t?", batchIndices":""});
    }
  }
  kStart = kStart + tileInner;
  workgroupBarrier();

  // Compute acc values for a single thread.
  var BCached : array<${n}, colPerThread>;
  for (var k = 0; k < tileInner; k = k + 1) {
    for (var inner = 0; inner < colPerThread; inner = inner + 1) {
      BCached[inner] = mm_Bsub[k][tileCol + inner];
    }

    for (var innerRow = 0; innerRow < rowPerThread; innerRow = innerRow + 1) {
      ${KP(o)}
      for (var innerCol = 0; innerCol < colPerThread; innerCol = innerCol + 1) {
        acc[innerRow][innerCol] = acc[innerRow][innerCol] + ACached * BCached[innerCol];
      }
    }
  }

  workgroupBarrier();
}

for (var innerRow = 0; innerRow < rowPerThread; innerRow = innerRow + 1) {
  for (var innerCol = 0; innerCol < colPerThread; innerCol = innerCol + 1) {
    mm_write(batch, globalRow + innerRow, globalCol + innerCol,
        acc[innerRow][innerCol]);
  }
}
`;return`
  var<workgroup> mm_Asub : array<array<${n}, ${f}>, ${h}>;
  var<workgroup> mm_Bsub : array<array<${n}, ${d}>, ${i}>;
  const rowPerThread = ${r[1]};
  const colPerThread = ${r[0]};
  const tileInner = ${i};

@compute @workgroup_size(${e[0]}, ${e[1]}, ${e[2]})
fn main(@builtin(local_invocation_id) localId : vec3<u32>,
        @builtin(global_invocation_id) globalId : vec3<u32>,
        @builtin(workgroup_id) workgroupId : vec3<u32>) {
    let batch = ${a?"0":"i32(globalId.z)"};
    ${t?`let batchIndices = ${t.offsetToIndices("u32(batch)")};`:""}
    let num_tiles = ${a?`${Math.ceil(s/i)}`:"(uniforms.dim_inner - 1) / tileInner + 1"};
    var kStart = ${a?`i32(globalId.z) * ${s}`:"0"};

    var acc : array<array<${n}, colPerThread>, rowPerThread>;
    ${T}
  }
`},XP=(r,e,n,t,o=!1)=>{let[i,a,s,u]=t,l=Ve(t[0].type.tensor);return`
    fn mm_readA(batch: i32, row: i32, colIn: i32, batchIndices: ${i.type.indices}) -> ${rt(r,l)} {
      var value = ${rt(r,l)}(0.0);
      let col = colIn * ${r};
      if(row < uniforms.dim_a_outer && col < uniforms.dim_inner)
      {
        var aIndices: ${a.type.indices};
        ${Ho("aIndices",a,a.rank-2,i.rank,"batchIndices")}
        ${a.indicesSet("aIndices",a.rank-2,"u32(row)")}
        ${a.indicesSet("aIndices",a.rank-1,"u32(colIn)")}
        value = ${a.getByIndices("aIndices")};
      }
      return value;
    }

    fn mm_readB(batch: i32, row: i32, colIn: i32, batchIndices: ${i.type.indices}) -> ${rt(r,l)} {
      var value = ${rt(r,l)}(0.0);
      let col = colIn * ${r};
      if(row < uniforms.dim_inner && col < uniforms.dim_b_outer)
      {
        var bIndices: ${s.type.indices};
        ${Ho("bIndices",s,s.rank-2,i.rank,"batchIndices")}
        ${s.indicesSet("bIndices",s.rank-2,"u32(row)")}
        ${s.indicesSet("bIndices",s.rank-1,"u32(colIn)")}
        value = ${s.getByIndices("bIndices")};
      }
      return value;
    }

    fn mm_write(batch: i32, row: i32, colIn: i32, valueIn: ${rt(r,l)}) {
      let col = colIn * ${r};
      if (row < uniforms.dim_a_outer && col < uniforms.dim_b_outer) {
        var value = valueIn;
        let coords = vec3<i32>(batch, row, colIn);
        ${e?`value = value + ${o?"bias[colIn]":`${rt(r,l)}(bias[row])`};`:""}
        ${n}
        ${u.setByIndices("vec3<u32>(coords)","value")}
      }
    }
    `},jo=(r,e,n,t,o=!1,i)=>{let a=r[0].dims,s=r[1].dims,u=a.slice(0,-2),l=s.slice(0,-2),d=t?t.slice(0,-2):n.slice(0,-2),f=C.size(d),h=a[a.length-2],g=a[a.length-1],b=s[s.length-1],_=g%4===0&&b%4===0,T=h<=8?[4,1,1]:[4,4,1],v=[8,8,1],x=[Math.ceil(b/v[0]/T[0]),Math.ceil(h/v[1]/T[1]),Math.ceil(f/v[2]/T[2])],I=_?4:1,$=[...u,h,g/I],O=$.length,E=[...l,g,b/I],L=E.length,R=[f,h,b/I],F=[{type:6,data:h},{type:6,data:b},{type:6,data:g}];Zt(e,F),F.push(...W(d,$,E));let q=["rank","rank"],Z=r.length>2;Z&&(F.push(...W(r[2].dims)),q.push("rank")),F.push(...W(R));let re=pe=>{let K=d.length,Oe=Ra("batchDims",r[0].dataType,K,1),fe=Ve(r[0].dataType),J=N("a",r[0].dataType,O,I),he=N("b",r[1].dataType,L,I),ee=G("result",r[0].dataType,R.length,I),Te=[J,he];if(Z){let z=o?I:1;Te.push(N("bias",r[2].dataType,r[2].dims.length,z))}let Ze=[{name:"dim_a_outer",type:"i32"},{name:"dim_b_outer",type:"i32"},{name:"dim_inner",type:"i32"}];Jt(e,Ze);let Se=Ve(ee.type.tensor),ae=Xt(e,ee.type.value,Se),U=XP(I,Z,ae,[Oe,J,he,ee],o);return`
  ${pe.registerUniforms(Ze).registerInternalVariables(Oe).declareVariables(...Te,ee)}
  ${U}
  ${_?Ac(T,v,fe,Oe):Oc(T,v,fe,Oe)}
                   `};return{name:"MatMul",shaderCache:{hint:`${T};${e.activation};${_};${o}`,inputDependencies:q},getRunData:()=>({outputs:[{dims:i?i(n):n,dataType:r[0].dataType}],dispatchGroup:{x:x[0],y:x[1],z:x[2]},programUniforms:F}),getShaderSource:re}}});var ZP,Ev,Cv=k(()=>{"use strict";le();Bn();ye();wr();Ua();Ov();ja();ZP=(r,e,n,t,o=!1,i,a=4,s=4,u=4,l="f32")=>{let d=q=>{switch(q){case 1:return"resData = x[xIndex];";case 3:return`resData = vec3<${l}>(x[xIndex], x[xIndex + 1], x[xIndex + 2]);`;case 4:return"resData = x[xIndex / 4];";default:throw new Error(`innerElementSize ${q} is not supported.`)}},f=q=>{switch(q){case 1:return"return w[row * i32(uniforms.w_shape[3]) + colIn];";case 4:return"return w[row * i32(uniforms.w_shape[3]) / 4 + colIn];";default:throw new Error(`innerElementSize ${q} is not supported.`)}},h=r?`
    let coord = vec4<i32>(batch, xRow, xCol, xCh);
    `:`
    let coord = vec4<i32>(batch, xCh, xRow, xCol);
    `,g=r?`
    let coords = vec4<i32>(
      batch,
      row / outWidth,
      row % outWidth,
      col);
    `:`
    let coords = vec4<i32>(
      batch,
      row,
      col / outWidth,
      col % outWidth);
    `,b=r?"i32(uniforms.x_shape[1])":"i32(uniforms.x_shape[2])",_=r?"i32(uniforms.x_shape[2])":"i32(uniforms.x_shape[3])",T=r?"row":"col",v=r?"col":"row",x=`
    let inChannels = i32(uniforms.w_shape[2]);
    let outWidth = ${r?"i32(uniforms.result_shape[2])":"i32(uniforms.result_shape[3])"};
    let outRow = ${T} / outWidth;
    let outCol = ${T} % outWidth;

    let WRow = ${v} / (i32(uniforms.w_shape[1]) * inChannels);
    let WCol = ${v} / inChannels % i32(uniforms.w_shape[1]);
    let xRow = outRow * uniforms.stride[0] + uniforms.dilation[0] * WRow - uniforms.pad[0];
    let xCol = outCol * uniforms.stride[1] + uniforms.dilation[1] * WCol - uniforms.pad[1];
    let xCh = ${v} % inChannels;
    var resData = ${rt(a,l)}(0.0);
    // The bounds checking is always needed since we use it to pad zero for
    // the 'same' padding type.
    if (xRow >= 0 && xRow < ${b} && xCol >= 0 && xCol < ${_}) {
      ${h}
      let xIndex = getIndexFromCoords4D(coord, vec4<i32>(uniforms.x_shape));
      ${d(a)}
    }
    return resData;`,I=r?e&&t?`
    let col = colIn * ${a};
    ${x}`:`
    let col = colIn * ${a};
    if (row < uniforms.dim_a_outer && col < uniforms.dim_inner) {
      ${x}
    }
    return ${rt(a,l)}(0.0);`:t&&n?`
    let col = colIn * ${a};
    ${x}`:`
    let col = colIn * ${a};
    if (row < uniforms.dim_inner && col < uniforms.dim_b_outer) {
      ${x}
    }
    return ${rt(a,l)}(0.0);`,$=r?t&&n?f(s):`
    let col = colIn * ${s};
    if (row < uniforms.dim_inner && col < uniforms.dim_b_outer) {
      ${f(s)}
    }
    return ${rt(s,l)}(0.0);`:`
    let col = colIn * ${s};
    if (row < uniforms.dim_inner && col < uniforms.dim_a_outer) {
      ${f(s)}
    }
    return ${rt(s,l)}(0.0);`,O=rt(u,l),E=r?rt(a,l):rt(s,l),L=r?rt(s,l):rt(a,l),R=Xt(i,O,l);return`
    fn mm_readA(batch: i32, row : i32, colIn : i32) -> ${E} {
      ${r?I:$}
    }

    fn mm_readB(batch: i32, row : i32, colIn : i32) -> ${L} {
      ${r?$:I}
    }

    fn mm_write(batch: i32, row : i32, colIn : i32, valueIn : ${O}) {
      let col = colIn * ${u};
      if (row < uniforms.dim_a_outer && col < uniforms.dim_b_outer)
      {
      var value = valueIn;
      let outWidth = ${r?"i32(uniforms.result_shape[2])":"i32(uniforms.result_shape[3])"};
      ${g}
      ${$v(o)}
      ${R}
      setOutputAtCoords(coords[0], coords[1], coords[2], coords[3], value);
      }
    }`},Ev=(r,e,n,t,o,i,a,s,u)=>{let l=e.format==="NHWC",d=l?r[0].dims[3]:r[0].dims[1],f=n[0],h=l?n[2]:n[3],g=l?n[1]:n[2],b=l?n[3]:n[1],_=l&&(d%4===0||d%3===0)&&b%4===0,T=l?b:h*g,v=l?h*g:b,x=[8,8,1],I=t<=8?[4,1,1]:[4,4,1],$=[Math.ceil(T/x[0]/I[0]),Math.ceil(v/x[1]/I[1]),Math.ceil(f/x[2]/I[2])];_e("verbose",()=>`[conv2d_mm_webgpu] dispatch = ${$}`);let O=_?l&&d%4!==0?3:4:1,E=x[1]*I[1],L=x[0]*I[0],R=Math.max(x[0]*O,x[1]),F=t%E===0,q=o%L===0,Z=i%R===0,re=_?[O,4,4]:[1,1,1],pe=[{type:6,data:t},{type:6,data:o},{type:6,data:i},{type:6,data:[e.pads[0],e.pads[1]]},{type:6,data:e.strides},{type:6,data:e.dilations}];Zt(e,pe),pe.push(...W(r[0].dims,r[1].dims));let K=["rank","rank"];a&&(pe.push(...W(r[2].dims)),K.push("rank")),pe.push(...W(n));let Oe=fe=>{let J=[{name:"dim_a_outer",type:"i32"},{name:"dim_b_outer",type:"i32"},{name:"dim_inner",type:"i32"},{name:"pad",type:"i32",length:2},{name:"stride",type:"i32",length:2},{name:"dilation",type:"i32",length:2}];Jt(e,J);let he=_?4:1,ee=Ve(r[0].dataType),Te=`
      fn setOutputAtIndex(flatIndex : i32, value : ${_?`vec4<${ee}>`:ee}) {
        result[flatIndex] = ${_?`vec4<${ee}>`:ee}(value);
      }
      fn setOutputAtCoords(d0 : i32, d1 : i32, d2 : i32, d3 : i32, value : ${_?`vec4<${ee}>`:ee}) {
        let flatIndex = getOutputIndexFromCoords(vec4<i32>(d0, d1, d2, d3));
        setOutputAtIndex(flatIndex ${_?"/ 4":""}, value);
      }`,Ze=N("x",r[0].dataType,r[0].dims.length,O===3?1:O),Se=N("w",r[1].dataType,r[1].dims.length,he),ae=[Ze,Se],U=G("result",r[0].dataType,n.length,he);if(a){let z=N("bias",r[2].dataType,r[2].dims.length,he);ae.push(z),Te+=`
        fn getBiasByOutputCoords(coords : vec4<i32>) -> ${_?`vec4<${ee}>`:ee} {
          return bias[coords.${l?"w":"y"}${_?"/ 4":""}];
        }`}return`
        ${Av("uniforms.result_strides")}
        //struct Uniforms { xShape : vec4<i32>, wShape : vec4<i32>, outShape : vec4<i32>,
        //  outShapeStrides: vec3<i32>, filterDims : vec2<i32>, pad : vec2<i32>, stride : vec2<i32>,
        //  dilation : vec2<i32>, dimAOuter : i32, dimBOuter : i32, dimInner : i32 };
        ${fe.registerUniforms(J).declareVariables(...ae,U)}
        ${Te}
        ${ZP(l,F,q,Z,a,e,re[0],re[1],re[2],ee)}
        ${_?Ac(I,x,ee,void 0,!l,R):Oc(I,x,ee,void 0,!l,R,!1,void 0,s)}`};return{name:"Conv2DMatMul",shaderCache:{hint:`${e.cacheKey};${O};${_};${F};${q};${Z};${E};${L};${R}`,inputDependencies:K},getRunData:()=>({outputs:[{dims:u?u(n):n,dataType:r[0].dataType}],dispatchGroup:{x:$[0],y:$[1],z:$[2]},programUniforms:pe}),getShaderSource:Oe}}});var JP,Dv,qa,YP,kv,QP,Nv,Lv,Rv=k(()=>{"use strict";le();Bn();ge();ye();wr();Ua();JP=r=>{let e=1;for(let n=0;n<r.length;n++)e*=r[n];return e},Dv=r=>typeof r=="number"?[r,r,r]:r,qa=(r,e)=>e<=1?r:r+(r-1)*(e-1),YP=(r,e,n,t=1)=>{let o=qa(e,t);return Math.floor((r[0]*(n-1)-n+o)/2)},kv=(r,e,n,t,o)=>{o==null&&(o=YP(r,e[0],t[0]));let i=[0,0,0,n];for(let a=0;a<3;a++)r[a]+2*o>=e[a]&&(i[a]=Math.trunc((r[a]-e[a]+2*o)/t[a]+1));return i},QP=(r,e,n,t,o,i,a,s,u,l)=>{let d,f,h,g;if(r==="VALID"&&(r=0),typeof r=="number"){d={top:r,bottom:r,left:r,right:r,front:r,back:r};let b=kv([e,n,t,1],[s,u,l],1,[o,i,a],r);f=b[0],h=b[1],g=b[2]}else if(Array.isArray(r)){if(!r.every((_,T,v)=>_===v[0]))throw Error(`Unsupported padding parameter: ${r}`);d={top:r[0],bottom:r[1],left:r[2],right:r[3],front:r[4],back:r[5]};let b=kv([e,n,t,1],[s,u,l],1,[o,i,a],r[0]);f=b[0],h=b[1],g=b[2]}else if(r==="SAME_UPPER"){f=Math.ceil(e/o),h=Math.ceil(n/i),g=Math.ceil(t/a);let b=(f-1)*o+s-e,_=(h-1)*i+u-n,T=(g-1)*a+l-t,v=Math.floor(b/2),x=b-v,I=Math.floor(_/2),$=_-I,O=Math.floor(T/2),E=T-O;d={top:I,bottom:$,left:O,right:E,front:v,back:x}}else throw Error(`Unknown padding parameter: ${r}`);return{padInfo:d,outDepth:f,outHeight:h,outWidth:g}},Nv=(r,e,n,t,o,i=!1,a="channelsLast")=>{let s,u,l,d,f;if(a==="channelsLast")[s,u,l,d,f]=r;else if(a==="channelsFirst")[s,f,u,l,d]=r;else throw new Error(`Unknown dataFormat ${a}`);let[h,,g,b,_]=e,[T,v,x]=Dv(n),[I,$,O]=Dv(t),E=qa(g,I),L=qa(b,$),R=qa(_,O),{padInfo:F,outDepth:q,outHeight:Z,outWidth:re}=QP(o,u,l,d,T,v,x,E,L,R),pe=i?h*f:h,K=[0,0,0,0,0];return a==="channelsFirst"?K=[s,pe,q,Z,re]:a==="channelsLast"&&(K=[s,q,Z,re,pe]),{batchSize:s,dataFormat:a,inDepth:u,inHeight:l,inWidth:d,inChannels:f,outDepth:q,outHeight:Z,outWidth:re,outChannels:pe,padInfo:F,strideDepth:T,strideHeight:v,strideWidth:x,filterDepth:g,filterHeight:b,filterWidth:_,effectiveFilterDepth:E,effectiveFilterHeight:L,effectiveFilterWidth:R,dilationDepth:I,dilationHeight:$,dilationWidth:O,inShape:r,outShape:K,filterShape:e}},Lv=(r,e,n,t,o,i)=>{let a=i==="channelsLast",s=a?r[0].dims[3]:r[0].dims[1],u=!1,l=[64,1,1],d={x:n.map((x,I)=>I)},f=[Math.ceil(JP(d.x.map(x=>n[x]))/l[0]),1,1];_e("verbose",()=>`[conv3d_naive_webgpu] dispatch = ${f}`);let h=u?a&&s%4!==0?3:4:1,g=C.size(n),b=[{type:12,data:g},{type:12,data:t},{type:12,data:o},{type:12,data:e.strides},{type:12,data:e.dilations}];Zt(e,b),b.push(...W(r[0].dims,r[1].dims));let _=["rank","rank"],T=r.length===3;T&&(b.push(...W(r[2].dims)),_.push("rank")),b.push(...W(n));let v=x=>{let I=[{name:"output_size",type:"u32"},{name:"filter_dims",type:"u32",length:t.length},{name:"pads",type:"u32",length:o.length},{name:"strides",type:"u32",length:e.strides.length},{name:"dilations",type:"u32",length:e.dilations.length}];Jt(e,I);let $=u?4:1,O=Ve(r[0].dataType),E=N("x",r[0].dataType,r[0].dims.length,h===3?1:h),L=N("W",r[1].dataType,r[1].dims.length,$),R=[E,L],F=G("result",r[0].dataType,n.length,$),q="";if(T){let pe=N("bias",r[2].dataType,r[2].dims.length,$);R.push(pe),q+=`
        fn getBiasByOutputCoords(coords : array<u32, 5>) -> ${u?`vec4<${O}>`:O} {
          return bias[${a?Q("coords",4,5):Q("coords",1,5)}${u?"/ 4":""}];
        }`}let Z=rt(h,O),re=Xt(e,Z,O);return`
            ${q}
            fn getX(d0 : u32, d1 : u32, d2 : u32, d3 : u32, d4 : u32) -> f32 {
              let aIndices = array<u32, 5>(d0, d1, d2, d3, d4);
              return ${E.getByIndices("aIndices")};
            }
            fn getW(d0 : u32, d1 : u32, d2 : u32, d3 : u32, d4 : u32) -> f32 {
              let aIndices = array<u32, 5>(d0, d1, d2, d3, d4);
              return ${L.getByIndices("aIndices")};
            }
          ${x.registerUniforms(I).declareVariables(...R,F)}
          ${x.mainStart()}
          ${x.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.output_size")}
              let coords = ${F.offsetToIndices("global_idx")};
              let batch = ${Q("coords",0,E.rank)};
              let d2 = ${a?Q("coords",E.rank-1,E.rank):Q("coords",1,E.rank)};
              let xFRCCorner = vec3<u32>(${a?Q("coords",1,E.rank):Q("coords",2,E.rank)},
              ${a?Q("coords",2,E.rank):Q("coords",3,E.rank)},
              ${a?Q("coords",3,E.rank):Q("coords",4,E.rank)}) * uniforms.strides - uniforms.pads;
              let xFCorner = xFRCCorner.x;
              let xRCorner = xFRCCorner.y;
              let xCCorner = xFRCCorner.z;
              let xShapeY = ${a?Q("uniforms.x_shape",1,E.rank):Q("uniforms.x_shape",2,E.rank)};
              let xShapeZ = ${a?Q("uniforms.x_shape",2,E.rank):Q("uniforms.x_shape",3,E.rank)};
              let xShapeW = ${a?Q("uniforms.x_shape",3,E.rank):Q("uniforms.x_shape",4,E.rank)};
              let xShapeU = ${a?Q("uniforms.x_shape",4,E.rank):Q("uniforms.x_shape",1,E.rank)};
              let inputDepthNearestVec4 = (xShapeU / 4) * 4;
              let inputDepthVec4Remainder = xShapeU % 4;

              var value = 0.0;
              for (var wF = 0u; wF < uniforms.filter_dims[0]; wF++) {
                let xF = xFCorner + wF * uniforms.dilations[0];
                if (xF < 0 || xF >= xShapeY) {
                  continue;
                }

                for (var wR = 0u; wR < uniforms.filter_dims[1]; wR++) {
                  let xR = xRCorner + wR * uniforms.dilations[1];
                  if (xR < 0 || xR >= xShapeZ) {
                    continue;
                  }

                  for (var wC = 0u; wC < uniforms.filter_dims[2]; wC++) {
                    let xC = xCCorner + wC * uniforms.dilations[2];
                    if (xC < 0 || xC >= xShapeW) {
                      continue;
                    }

                    for (var d1 = 0u; d1 < inputDepthNearestVec4; d1 += 4) {
                      ${a?`let xValues = vec4<f32>(
                               getX(batch, xF, xR, xC, d1),
                               getX(batch, xF, xR, xC, d1 + 1),
                               getX(batch, xF, xR, xC, d1 + 2),
                               getX(batch, xF, xR, xC, d1 + 3));
                            `:`let xValues = vec4<f32>(
                               getX(batch, d1, xF, xR, xC),
                               getX(batch, d1 + 1, xF, xR, xC),
                               getX(batch, d1 + 2, xF, xR, xC),
                               getX(batch, d1 + 3, xF, xR, xC));
                            `}
                            let wValues = vec4<f32>(
                              getW(d2, d1, wF, wR, wC),
                              getW(d2, d1 + 1, wF, wR, wC),
                              getW(d2, d1 + 2, wF, wR, wC),
                              getW(d2, d1 + 3, wF, wR, wC));
                      value += dot(xValues, wValues);
                    }
                    if (inputDepthVec4Remainder == 1) {
                        ${a?`value += getX(batch, xF, xR, xC, inputDepthNearestVec4)
                          * getW(d2, inputDepthNearestVec4, wF, wR, wC);`:`value += getX(batch, inputDepthNearestVec4, xF, xR, xC)
                          * getW(d2, inputDepthNearestVec4, wF, wR, wC);`}
                    } else if (inputDepthVec4Remainder == 2) {
                      ${a?`let xValues = vec2<f32>(
                        getX(batch, xF, xR, xC, inputDepthNearestVec4),
                        getX(batch, xF, xR, xC, inputDepthNearestVec4 + 1));
                      `:`let xValues = vec2<f32>(
                        getX(batch, inputDepthNearestVec4, xF, xR, xC),
                        getX(batch, inputDepthNearestVec4 + 1, xF, xR, xC));
                    `}
                    let wValues = vec2<f32>(
                      getW(d2, inputDepthNearestVec4, wF, wR, wC),
                      getW(d2, inputDepthNearestVec4 + 1, wF, wR, wC));
                      value += dot(xValues, wValues);
                    } else if (inputDepthVec4Remainder == 3) {
                      ${a?`let xValues = vec3<f32>(
                        getX(batch, xF, xR, xC, inputDepthNearestVec4),
                        getX(batch, xF, xR, xC, inputDepthNearestVec4 + 1),
                        getX(batch, xF, xR, xC, inputDepthNearestVec4 + 2));
                      `:`let xValues = vec3<f32>(
                        getX(batch, inputDepthNearestVec4, xF, xR, xC),
                        getX(batch, inputDepthNearestVec4 + 1, xF, xR, xC),
                        getX(batch, inputDepthNearestVec4 + 2, xF, xR, xC));
                    `}
                    let wValues = vec3<f32>(
                      getW(d2, inputDepthNearestVec4, wF, wR, wC),
                      getW(d2, inputDepthNearestVec4 + 1, wF, wR, wC),
                      getW(d2, inputDepthNearestVec4 + 2, wF, wR, wC));
                      value += dot(xValues, wValues);
                    }
                  }
                }
              }
              ${T?"value = value + getBiasByOutputCoords(coords)":""};
              ${re}
              result[global_idx] = f32(value);
          }`};return{name:"Conv3DNaive",shaderCache:{hint:`${e.cacheKey};${a};${h};${T}`,inputDependencies:_},getRunData:()=>({outputs:[{dims:n,dataType:r[0].dataType}],dispatchGroup:{x:f[0],y:f[1],z:f[2]},programUniforms:b}),getShaderSource:v}}});var zv,Mv,Bv=k(()=>{"use strict";le();ge();ye();wr();zv=(r,e,n,t)=>{let o=r.length>2,i=o?"value += b[output_channel];":"",a=r[0].dims,s=r[1].dims,u=e.format==="NHWC",l=u?n[3]:n[1],d=l/e.group,f=u&&d>=4?Ee(l):1,h=C.size(n)/f,g=[{type:12,data:h},{type:12,data:e.dilations},{type:12,data:[e.strides[0],e.strides[1]]},{type:12,data:[e.pads[0],e.pads[1]]},{type:12,data:d}];Zt(e,g),g.push(...W(a,[s[0],s[1],s[2],s[3]/f]));let b=o?["rank","rank","rank"]:["rank","rank"];g.push(...W([n[0],n[1],n[2],n[3]/f]));let _=T=>{let v=G("output",r[0].dataType,n.length,f),x=Ve(v.type.tensor),I=Xt(e,v.type.value,x),$=N("x",r[0].dataType,a.length),O=N("w",r[1].dataType,s.length,f),E=[$,O];o&&E.push(N("b",r[2].dataType,r[2].dims,f));let L=[{name:"output_size",type:"u32"},{name:"dilations",type:"u32",length:e.dilations.length},{name:"strides",type:"u32",length:2},{name:"pads",type:"u32",length:2},{name:"output_channels_per_group",type:"u32"}];Jt(e,L);let R=u?`
      for (var wHeight: u32 = 0u; wHeight < uniforms.w_shape[0]; wHeight++) {
        let xHeight = xRCCorner.x + wHeight * uniforms.dilations[0];

        if (xHeight < 0u || xHeight >= uniforms.x_shape[1]) {
          continue;
        }

        for (var wWidth: u32 = 0u; wWidth < uniforms.w_shape[1]; wWidth++) {
          let xWidth = xRCCorner.y + wWidth * uniforms.dilations[1];
          if (xWidth < 0u || xWidth >= uniforms.x_shape[2]) {
            continue;
          }

          for (var wInChannel: u32 = 0u; wInChannel < uniforms.w_shape[2]; wInChannel++) {
            let input_channel = in_channel_offset + wInChannel;
            let xVal = ${$.get("batch","xHeight","xWidth","input_channel")};
            let wVal = ${O.get("wHeight","wWidth","wInChannel","output_channel")};
            value += xVal * wVal;
          }
        }
      }
      `:`
      for (var wInChannel: u32 = 0u; wInChannel < uniforms.w_shape[1]; wInChannel++) {
        let input_channel = in_channel_offset + wInChannel;
        for (var wHeight: u32 = 0u; wHeight < uniforms.w_shape[2]; wHeight++) {
          let xHeight = xRCCorner.x + wHeight * uniforms.dilations[0];

          if (xHeight < 0u || xHeight >= uniforms.x_shape[2]) {
            continue;
          }

          for (var wWidth: u32 = 0u; wWidth < uniforms.w_shape[3]; wWidth++) {
            let xWidth = xRCCorner.y + wWidth * uniforms.dilations[1];
            if (xWidth < 0u || xWidth >= uniforms.x_shape[3]) {
              continue;
            }

            let xVal = ${$.get("batch","input_channel","xHeight","xWidth")};
            let wVal = ${O.get("output_channel","wInChannel","wHeight","wWidth")};
            value += xVal * wVal;
          }
        }
      }
      `;return`
  ${T.registerUniforms(L).declareVariables(...E,v)}

  ${T.mainStart()}
    ${T.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.output_size")}

    let outputIndices = ${v.offsetToIndices("global_idx")};
    let batch: u32 = outputIndices[0];
    let output_channel: u32 = outputIndices[${u?3:1}];
    let xRCCorner: vec2<u32> = vec2<u32>(outputIndices[${u?1:2}], outputIndices[${u?2:3}]) * uniforms.strides - uniforms.pads;
    let group_id: u32 = output_channel * ${f} / uniforms.output_channels_per_group;
    var in_channel_offset = group_id * uniforms.w_shape[${u?2:1}];

    var value: ${v.type.value} = ${v.type.value}(0);
    ${R}
    ${i}
    ${I}
    ${v.setByOffset("global_idx","value")}
  }`};return{name:"GroupedConv",shaderCache:{hint:`${e.cacheKey}_${f}`,inputDependencies:b},getRunData:()=>({outputs:[{dims:t?t(n):n,dataType:r[0].dataType}],dispatchGroup:{x:Math.ceil(h/64)},programUniforms:g}),getShaderSource:_}},Mv=(r,e,n,t)=>{let o=r.length>2,i=Ee(n[3]),a=Ee(n[2]),s=C.size(n)/i/a,u=[r[0].dims[0],r[0].dims[1],r[0].dims[2],r[0].dims[3]/i],l=[r[1].dims[0],r[1].dims[1],r[1].dims[2],r[1].dims[3]/i],d=[n[0],n[1],n[2],n[3]/i],f=[{type:12,data:s},{type:6,data:[e.strides[0],e.strides[1]]},{type:6,data:[e.pads[0],e.pads[1]]}];Zt(e,f),f.push(...W(u,l,d));let h=(a-1)*e.strides[1]+l[1],g=b=>{let _=G("output",r[0].dataType,d.length,i),T=Ve(_.type.tensor),v=Xt(e,_.type.value,T),x=N("x",r[0].dataType,u.length,i),I=N("w",r[1].dataType,l.length,i),$=[x,I];o&&$.push(N("b",r[2].dataType,r[2].dims,i));let O=o?"value += b[output_channel];":"",E=[{name:"output_size",type:"u32"},{name:"strides",type:"i32",length:2},{name:"pads",type:"i32",length:2}];return Jt(e,E),`
  ${b.registerUniforms(E).declareVariables(...$,_)}
  ${b.mainStart()}
    ${b.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.output_size")}
    let width0 = uniforms.output_shape[3];
    let output_channel = global_idx % width0;
    var index1 = global_idx / width0;
    let width1 = uniforms.output_shape[2] / ${a}u;
    let col = (index1 % width1) * ${a}u;
    index1 = index1 / width1;
    let row = index1 % uniforms.output_shape[1];
    let batch = index1 / uniforms.output_shape[1];

    let x_corner = vec2<i32>(i32(row), i32(col)) * uniforms.strides - uniforms.pads;

    var x_vals: array<${x.type.value}, ${h}>;
    var values: array<${_.type.value}, ${a}>;
    let input_channel = output_channel;
    // Use constant instead of uniform can give better performance for w's height/width.
    for (var w_height: u32 = 0u; w_height < ${l[0]}; w_height++) {
      let x_height = x_corner.x + i32(w_height);
      if (x_height >= 0 && u32(x_height) < uniforms.x_shape[1]) {
        for (var i = 0; i < ${h}; i++) {
          let x_width = x_corner.y + i;
          if (x_width >= 0 && u32(x_width) < uniforms.x_shape[2]) {
            x_vals[i] = ${x.get("batch","u32(x_height)","u32(x_width)","input_channel")};
          } else {
            x_vals[i] = ${x.type.value}(0);
          }
        }
        for (var w_width: u32 = 0u; w_width < ${l[1]}; w_width++) {
          let w_val = ${I.get("w_height","w_width","0","output_channel")};
          for (var i = 0u; i < ${a}u; i++) {
            values[i] = fma(x_vals[i * u32(uniforms.strides[1]) + w_width], w_val, values[i]);
          }
        }
      }
    }

    for (var i = 0u; i < ${a}u; i++) {
      var value = values[i];
      ${O}
      ${v}
      ${_.set("batch","row","col + i","output_channel","value")};
    }
  }`};return{name:"GroupedConv-Vectorize",shaderCache:{hint:`${e.cacheKey};${i};${a};${h};${l[0]};${l[1]}`,inputDependencies:o?["rank","rank","type"]:["rank","rank"]},getRunData:()=>({outputs:[{dims:t?t(n):n,dataType:r[0].dataType}],dispatchGroup:{x:Math.ceil(s/64)},programUniforms:f}),getShaderSource:g}}});var eE,Pc,tE,Ec,Cc,Fv,nE,rE,Dc,Vv=k(()=>{"use strict";ge();Cv();Rv();ja();Bv();wr();Ha();Yn();eE=(r,e,n,t,o,i)=>{let a=r[0],s=r.slice(i?1:2,i?3:4),u=s.length,l=e[0],f=e.slice(2).map((b,_)=>b+(b-1)*(n[_]-1)),g=s.map((b,_)=>b+t[_]+t[_+u]).map((b,_)=>Math.floor((b-f[_]+o[_])/o[_]));return g.splice(0,0,a),g.splice(i?3:1,0,l),g},Pc=[2,3,1,0],tE=(r,e)=>{if(!r||r.length!==2&&r.length!==3)throw new Error("Conv requires 2 or 3 inputs");if(r[0].dims.length>5)throw new Error("greater than 5D is not supported");if(r[0].dims.length!==r[1].dims.length)throw new Error("filter does not have same dimension as input");let n=r[0].dims[e.format==="NHWC"?r[0].dims.length-1:1],t=r[1].dims[1]*e.group;if(n!==t)throw new Error("FILTER_IN_CHANNEL should be equal to DATA_CHANNEL");if(r.length===3&&(r[2].dims.length!==1||r[1].dims[0]!==r[2].dims[0]))throw new Error("invalid bias");let o=r[0].dims.length-2;if(e.dilations.length!==o)throw new Error(`dilations should be ${o}D`);if(e.strides.length!==o)throw new Error(`strides should be ${o}D`);if(e.pads.length!==o*2)throw new Error(`pads should be ${o*2}D`);if(e.kernelShape.length!==0&&e.kernelShape.length!==r[1].dims.length-2)throw new Error("invalid kernel shape")},Ec=(r,e)=>{let n=r.kernelShape.slice();n.length<e[1].dims.length-2&&n.push(...Array(e[1].dims.length-2-n.length).fill(0));for(let i=2;i<e[1].dims.length;++i)n[i-2]===0&&(n[i-2]=e[1].dims[i]);let t=r.pads.slice();Ur.adjustPadsBasedOnAutoPad(e[0].dims,r.strides,r.dilations,n,t,r.format==="NHWC",r.autoPad);let o=Object.assign({},r);return Object.assign(o,{kernelShape:n,pads:t}),o},Cc=r=>{let e=Ga(r),n=r.format,t=["NOTSET","VALID","SAME_UPPER","SAME_LOWER"][r.auto_pad],o=r.dilations,i=r.group,a=r.kernel_shape,s=r.pads,u=r.strides,l=r.w_is_const();return{autoPad:t,format:n,dilations:o,group:i,kernelShape:a,pads:s,strides:u,wIsConst:l,...e,cacheKey:`${r.format};${e.activation};`}},Fv=(r,e,n,t)=>{let o=n.format==="NHWC",i=eE(e[0].dims,e[1].dims,n.dilations,n.pads,n.strides,o);if(n.group!==1){let E=[e[0]];if(o){let R=r.kernelCustomData.wT??r.compute(at(e[1],Pc),{inputs:[1],outputs:[n.wIsConst?-2:-1]})[0];n.wIsConst&&!r.kernelCustomData.wT&&(r.kernelCustomData.wT=R),E.push(R)}else E.push(e[1]);e.length===3&&E.push(e[2]),!r.adapterInfo.isArchitecture("ampere")&&o&&e[1].dims[0]===n.group&&e[1].dims[1]===1&&n.dilations[0]===1&&n.dilations[1]===1?r.compute(Mv(E,n,i,t),{inputs:E}):r.compute(zv(E,n,i,t),{inputs:E});return}let a=e.length===3,s=e[0].dims[o?1:2],u=e[0].dims[o?2:3],l=e[0].dims[o?3:1],d=e[1].dims[2],f=e[1].dims[3],h=i[o?1:2],g=i[o?2:3],b=i[o?3:1],_=o&&d===s&&f===u&&n.pads[0]===0&&n.pads[1]===0;if(_||d===1&&f===1&&n.dilations[0]===1&&n.dilations[1]===1&&n.strides[0]===1&&n.strides[1]===1&&n.pads[0]===0&&n.pads[1]===0){let E=i[0],L,R,F,q=[];if(o){let pe=r.kernelCustomData.wT??r.compute(at(e[1],Pc),{inputs:[1],outputs:[n.wIsConst?-2:-1]})[0];if(n.wIsConst&&!r.kernelCustomData.wT&&(r.kernelCustomData.wT=pe),_){let K=s*u*l;L=e[0].reshape([1,E,K]),R=pe.reshape([1,K,b]),F=[1,E,b]}else L=e[0].reshape([E,s*u,l]),R=pe.reshape([1,l,b]),F=[E,h*g,b];q.push(L),q.push(R)}else L=e[0].reshape([E,l,s*u]),R=e[1].reshape([1,b,l]),F=[E,b,h*g],q.push(R),q.push(L);a&&q.push(e[2]);let Z=F[2],re=q[0].dims[q[0].dims.length-1];Z<8&&re<8?r.compute(Wa(q,n,i,F,o,t),{inputs:q}):r.compute(jo(q,n,i,F,o,t),{inputs:q});return}let T=!0,v=r.kernelCustomData.wT??r.compute(at(e[1],Pc),{inputs:[1],outputs:[n.wIsConst?-2:-1]})[0];n.wIsConst&&!r.kernelCustomData.wT&&(r.kernelCustomData.wT=v);let x=[e[0],v];a&&x.push(e[2]);let I=o?h*g:b,$=o?b:h*g,O=d*f*l;r.compute(Ev(x,n,i,I,$,O,a,T,t),{inputs:x})},nE=(r,e)=>{let n=e.format==="NHWC",t=[r.inputs[0].reshape(n?[r.inputs[0].dims[0],1,r.inputs[0].dims[1],r.inputs[0].dims[2]]:[r.inputs[0].dims[0],r.inputs[0].dims[1],1,r.inputs[0].dims[2]]),r.inputs[1].reshape([r.inputs[1].dims[0],r.inputs[1].dims[1],1,r.inputs[1].dims[2]])];r.inputs.length===3&&t.push(r.inputs[2]);let o=[0,e.pads[0],0,e.pads[1]],i=[1].concat(e.strides),a=[1].concat(e.dilations),s=[1].concat(e.kernelShape),u=Ec({...e,pads:o,strides:i,dilations:a,kernelShape:s},t);Fv(r,t,u,l=>n?[l[0],l[2],l[3]]:[l[0],l[1],l[3]])},rE=(r,e,n)=>{let t=n.format==="NHWC"?"channelsLast":"channelsFirst",o=Ec(n,e),i=n.autoPad==="NOTSET"?n.pads:n.autoPad,a=Nv(e[0].dims,e[1].dims,n.strides,n.dilations,i,!1,t);r.compute(Lv(e,o,a.outShape,[a.filterDepth,a.filterHeight,a.filterWidth],[a.padInfo.front,a.padInfo.top,a.padInfo.left],t))},Dc=(r,e)=>{if(tE(r.inputs,e),r.inputs[0].dims.length===3)nE(r,e);else if(r.inputs[0].dims.length===5)rE(r,r.inputs,e);else{let n=Ec(e,r.inputs);Fv(r,r.inputs,n)}}});var Gv,Uv=k(()=>{"use strict";le();Bn();ge();ye();Gv=(r,e,n)=>{let t=r.length>2,o=e.outputShape,i=e.format==="NHWC",a=e.group,s=r[1].dims,u=s[2]/a,l=s[3],d=i?Ee(u):1,f=i&&l===1&&u>=4,h=f?Math.floor(u/4)*4:Math.floor(u/d)*d,g=u-h,b=i?Ee(l):1,_=i?l===1?d:b:1,T=C.size(o)/b,v=[Math.ceil(T/64),1,1];_e("verbose",()=>`[conv2d_backprop_webgpu] dispatch = ${v}`);let x=["rank","rank"],I=[e.strides[0],e.strides[1]],$=[e.kernelShape[i?1:2],e.kernelShape[i?2:3]],O=[e.dilations[0],e.dilations[1]],E=[$[0]+(e.dilations[0]<=1?0:(e.kernelShape[i?1:2]-1)*(e.dilations[0]-1)),$[1]+(e.dilations[1]<=1?0:(e.kernelShape[i?2:3]-1)*(e.dilations[1]-1))],L=[E[0]-1-Math.floor((e.pads[0]+e.pads[2])/2),E[1]-1-Math.floor((e.pads[1]+e.pads[3])/2)],R=[{type:12,data:T},{type:12,data:I},{type:12,data:$},{type:12,data:O},{type:12,data:E},{type:6,data:L},{type:12,data:h},{type:12,data:u},{type:12,data:l},...W(r[0].dims,r[1].dims)];t&&(R.push(...W(r[2].dims)),x.push("rank")),R.push(...W(o));let F=q=>{let Z=[{name:"output_size",type:"u32"},{name:"strides",type:"u32",length:I.length},{name:"filter_dims",type:"u32",length:$.length},{name:"dilations",type:"u32",length:$.length},{name:"effective_filter_dims",type:"u32",length:E.length},{name:"pads",type:"i32",length:L.length},{name:"input_channels_per_group_int",type:"u32"},{name:"input_channels_per_group",type:"u32"},{name:"output_channels_per_group",type:"u32"}],re=Ve(r[0].dataType),pe=i?1:2,K=i?2:3,Oe=i?3:1,fe=N("W",r[1].dataType,r[1].dims.length,_),J=N("Dy",r[0].dataType,r[0].dims.length,d),he=[J,fe];t&&he.push(N("bias",r[2].dataType,[o[Oe]].length,b));let ee=G("result",r[0].dataType,o.length,b),Te=()=>{let ae="";if(f)d===4?ae+=`
        let xValue = ${J.getByOffset("x_offset")};
        let wValue = ${fe.getByOffset("w_offset")};
        dotProd = dotProd + dot(xValue, wValue);
        x_offset += 1u;
        w_offset += 1u;`:d===2?ae+=`
          dotProd = dotProd + dot(vec4<${re}>(${J.getByOffset("x_offset")}, ${J.getByOffset("x_offset + 1u")}), vec4<${re}>(${fe.getByOffset("w_offset")}, ${fe.getByOffset("w_offset + 1u")}));
          x_offset += 2u;
          w_offset += 2u;`:d===1&&(ae+=`
          dotProd = dotProd + dot(vec4<${re}>(${J.getByOffset("x_offset")}, ${J.getByOffset("x_offset + 1u")}, ${J.getByOffset("x_offset + 2u")}, ${J.getByOffset("x_offset + 3u")}), vec4<${re}>(${fe.getByOffset("w_offset")}, ${fe.getByOffset("w_offset + 1u")}, ${fe.getByOffset("w_offset + 2u")}, ${fe.getByOffset("w_offset + 3u")}));
          x_offset += 4u;
          w_offset += 4u;`);else if(ae+=`
                  let xValue = ${i?J.getByOffset(`${J.indicesToOffset(`${J.type.indices}(batch, idyR, idyC, inputChannel)`)} / ${d}`):J.get("batch","inputChannel","idyR","idyC")};
        `,d===1)ae+=`
          let w_offset = ${fe.indicesToOffset(`${fe.type.indices}(u32(wRPerm), u32(wCPerm), inputChannel, wOutChannel)`)};
          let wValue = ${fe.getByOffset(`w_offset / ${_}`)};
          dotProd = dotProd + xValue * wValue;`;else for(let U=0;U<d;U++)ae+=`
            let wValue${U} = ${fe.getByOffset(`${fe.indicesToOffset(`${fe.type.indices}(u32(wRPerm), u32(wCPerm), inputChannel + ${U}, wOutChannel)`)} / ${_}`)};
            dotProd = dotProd + xValue[${U}] * wValue${U};`;return ae},Ze=()=>{if(g===0)return"";if(!f)throw new Error(`packInputAs4 ${f} is not true.`);let ae="";if(d===1){ae+="dotProd = dotProd";for(let U=0;U<g;U++)ae+=`
            + ${J.getByOffset(`x_offset + ${U}`)} * ${fe.getByOffset(`w_offset + ${U}`)}`;ae+=";"}else if(d===2){if(g!==2)throw new Error(`Invalid inputChannelsRemainder ${g}.`);ae+=`
          let xValue = ${J.getByOffset("x_offset")};
          let wValue = ${fe.getByOffset("w_offset")};
          dotProd = dotProd + dot(xValue, wValue);`}return ae},Se=`
            let outputIndices = ${ee.offsetToIndices(`global_idx * ${b}`)};
            let batch = ${ee.indicesGet("outputIndices",0)};
            let d1 = ${ee.indicesGet("outputIndices",Oe)};
            let r = ${ee.indicesGet("outputIndices",pe)};
            let c = ${ee.indicesGet("outputIndices",K)};
            let dyCorner = vec2<i32>(i32(r), i32(c)) - uniforms.pads;
            let dyRCorner = dyCorner.x;
            let dyCCorner = dyCorner.y;
            let groupId = d1 / uniforms.output_channels_per_group;
            let wOutChannel = d1 - groupId * uniforms.output_channels_per_group;
            // Convolve dy(?, ?, d2) with w(:, :, d1, d2) to compute dx(xR, xC, d1).
            // ? = to be determined. : = across all values in that axis.
            var dotProd = ${ee.type.value}(0.0);
            var wR: u32 = 0;
            if (uniforms.dilations.x == 1) {
              // Minimum wR >= 0 that satisfies (dyRCorner + wR) % (uniforms.strides.x) == 0
              wR = u32(((dyRCorner + i32(uniforms.strides.x) - 1) / i32(uniforms.strides.x)) * i32(uniforms.strides.x) - dyRCorner);
            }
            for (; wR < uniforms.effective_filter_dims.x; wR = wR + 1) {
              if (wR % uniforms.dilations.x != 0) {
                continue;
              }
              let dyR = (${re}(dyRCorner) + ${re}(wR)) / ${re}(uniforms.strides[0]);
              let wRPerm = uniforms.filter_dims.x - 1 - wR / uniforms.dilations.x;
              if (dyR < 0.0 || dyR >= ${re}(uniforms.Dy_shape[${pe}]) || fract(dyR) > 0.0 ||
                  wRPerm < 0) {
                continue;
              }
              let idyR: u32 = u32(dyR);
              var wC: u32 = 0;
              if (uniforms.dilations.y == 1) {
                // Minimum wC >= 0 that satisfies (dyCCorner + wC) % (uniforms.strides.y) == 0
                wC = u32(((dyCCorner + i32(uniforms.strides.y) - 1) / i32(uniforms.strides.y)) * i32(uniforms.strides.y) - dyCCorner);
              }
              for (; wC < uniforms.effective_filter_dims.y; wC = wC + 1) {
                if (wC % uniforms.dilations.y != 0) {
                  continue;
                }
                let dyC = (${re}(dyCCorner) + ${re}(wC)) / ${re}(uniforms.strides.y);
                let wCPerm = uniforms.filter_dims.y - 1 - wC / uniforms.dilations.y;
                if (dyC < 0.0 || dyC >= ${re}(uniforms.Dy_shape[${K}]) ||
                    fract(dyC) > 0.0 || wCPerm < 0) {
                  continue;
                }
                let idyC: u32 = u32(dyC);
                var inputChannel = groupId * uniforms.input_channels_per_group;
                ${f?`
                var x_offset = ${J.indicesToOffset(`${J.type.indices}(batch, idyR, idyC, inputChannel)`)} / ${d};
                var w_offset = ${fe.indicesToOffset(`${fe.type.indices}(wRPerm, wCPerm, inputChannel, wOutChannel)`)} / ${_};
                  `:""}
                for (var d2: u32 = 0; d2 < uniforms.input_channels_per_group_int; d2 = d2 + ${f?4:d}) {
                  ${Te()}
                  inputChannel = inputChannel + ${f?4:d};
                }
                ${Ze()}
                wC = wC + uniforms.strides.y - 1;
              }
              wR = wR + uniforms.strides[0] - 1;
            }
            let value = dotProd${t?` + bias[d1 / ${b}]`:""};
            ${ee.setByOffset("global_idx","value")};
          `;return`
    ${q.registerUniforms(Z).declareVariables(...he,ee)}
      ${q.mainStart()}
      ${q.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.output_size")};
    ${Se}}`};return{name:"ConvTranspose2D",shaderCache:{hint:`${e.cacheKey};${d}${_}${b}${f}${g}`,inputDependencies:x},getRunData:()=>({dispatchGroup:{x:v[0],y:v[1],z:v[2]},outputs:[{dims:n?n(o):o,dataType:r[0].dataType}],programUniforms:R}),getShaderSource:F}}});var oE,iE,aE,Wv,Hv,sE,jv,uE,qv,Kv=k(()=>{"use strict";Uv();wr();Yn();oE=(r,e,n,t,o,i)=>(r-1)*e+n+(t-1)*o+1-i,iE=(r,e,n,t,o)=>{let i=Math.floor(r/2);e==="SAME_UPPER"?(n[t]=i,n[o]=r-i):e==="SAME_LOWER"&&(n[t]=r-i,n[o]=i)},aE=(r,e,n,t,o,i,a,s,u,l)=>{let d=r.length-2,f=l.length===0;u.length<d&&u.push(...Array(d-u.length).fill(0));let h=r[0],g=e[s?3:1]*o;for(let b=0,_=r.length-d-(s?1:0);b<d;++b,++_){let T=r[_],v=f?T*a[b]:l[b],x=oE(T,a[b],i[b],e[_],n[b],v);iE(x,t,i,b,b+d),f&&l.push(a[b]*(T-1)+u[b]+(e[_]-1)*n[b]+1-i[b]-i[b+d])}l.splice(0,0,h),l.splice(s?3:1,0,g)},Wv=(r,e)=>{let n=r.kernelShape.slice();if(r.kernelShape.length===0||r.kernelShape.reduce((f,h)=>f*h,1)===0){n.length=0;for(let f=2;f<e[1].dims.length;++f)n.push(e[1].dims[f])}let t=r.format==="NHWC";n.splice(0,0,e[1].dims[0]),n.splice(t?3:1,0,e[1].dims[1]);let o=r.pads.slice(),i=r.outputShape.slice(),a=r.outputPadding.slice(),s=e[0].dims,u=r.dilations.slice();if(u.reduce((f,h)=>f+h,0)===0){let f=e[0].dims.length-2;u=new Array(f).fill(1)}let l=r.strides.slice();if(l.reduce((f,h)=>f+h,0)===0){let f=e[0].dims.length-2;l=new Array(f).fill(1)}aE(s,n,u,r.autoPad,r.group,o,l,t,a,i);let d=Object.assign({},r);return Object.assign(d,{kernelShape:n,pads:o,outputPadding:a,outputShape:i,dilations:u,strides:l}),d},Hv=r=>{let e=Ga(r),n=r.format,t=["NOTSET","VALID","SAME_UPPER","SAME_LOWER"][typeof r.autoPad>"u"?0:r.autoPad],o=r.dilations,i=r.group,a=r.kernelShape,s=r.pads,u=r.strides,l=r.wIsConst(),d=r.outputPadding,f=r.outputShape;return{autoPad:t,format:n,dilations:o,group:i,kernelShape:a,outputPadding:d,outputShape:f,pads:s,strides:u,wIsConst:l,...e,cacheKey:`${r.format};${e.activation};`}},sE=(r,e)=>{if(!r||r.length!==2&&r.length!==3)throw new Error("Conv requires 2 or 3 inputs");if(r[0].dims.length!==4&&r[0].dims.length!==3)throw new Error("currently only support 2-dimensional conv");if(r[0].dims.length!==r[1].dims.length)throw new Error("filter does not have same dimension as input");let n=r[0].dims[e.format==="NHWC"?r[0].dims.length-1:1],t=r[1].dims[0];if(n!==t)throw new Error("FILTER_IN_CHANNEL should be equal to DATA_CHANNEL");let o=r[1].dims[1]*e.group;if(r.length===3&&(r[2].dims.length!==1||r[2].dims[0]!==o))throw new Error("invalid bias");let i=r[0].dims.length-2;if(e.dilations.reduce((d,f)=>d+f,0)>0&&e.dilations.length!==i)throw new Error(`dilations should be ${i}D`);if(e.strides.reduce((d,f)=>d+f,0)>0&&e.strides.length!==i)throw new Error(`strides should be ${i}D`);if(e.pads.reduce((d,f)=>d+f,0)>0&&e.pads.length!==i*2)throw new Error(`pads should be ${i*2}D`);if(e.outputPadding.length!==i&&e.outputPadding.length!==0)throw new Error(`output_padding should be ${i}D`);if(e.kernelShape.reduce((d,f)=>d+f,0)>0&&e.kernelShape.length!==0&&e.kernelShape.length!==r[1].dims.length-2)throw new Error("invalid kernel shape");if(e.outputShape.length!==0&&e.outputShape.length!==r[0].dims.length-2)throw new Error("invalid output shape")},jv=(r,e,n,t)=>{let o=r.kernelCustomData.wT??r.compute(at(e[1],[2,3,0,1]),{inputs:[1],outputs:[n.wIsConst?-2:-1]})[0];n.wIsConst&&!r.kernelCustomData.wT&&(r.kernelCustomData.wT=o);let i=[e[0],o];e.length===3&&i.push(e[2]),r.compute(Gv(i,n,t),{inputs:i})},uE=(r,e)=>{let n=e.format==="NHWC",t=[r.inputs[0].reshape(n?[r.inputs[0].dims[0],1,r.inputs[0].dims[1],r.inputs[0].dims[2]]:[r.inputs[0].dims[0],r.inputs[0].dims[1],1,r.inputs[0].dims[2]]),r.inputs[1].reshape([r.inputs[1].dims[0],r.inputs[1].dims[1],1,r.inputs[1].dims[2]])];r.inputs.length===3&&t.push(r.inputs[2]);let o=e.kernelShape;(o.length===0||o[0]===0)&&(o=[r.inputs[1].dims[2]]);let i=e.dilations;(i.length===0||i[0]===0)&&(i=[1]);let a=e.strides;(a.length===0||a[0]===0)&&(a=[1]);let s=e.pads;s.length===0&&(s=[0,0]),s=[0,s[0],0,s[1]],a=[1].concat(a),i=[1].concat(i),o=[1].concat(o);let u=e.outputPadding;u=[0].concat(u);let l=Wv({...e,pads:s,strides:a,dilations:i,kernelShape:o,outputPadding:u},t);jv(r,t,l,d=>n?[d[0],d[2],d[3]]:[d[0],d[1],d[3]])},qv=(r,e)=>{if(sE(r.inputs,e),r.inputs[0].dims.length===3)uE(r,e);else{let n=Wv(e,r.inputs);jv(r,r.inputs,n)}}});var lE,Xv,Zv,Jv=k(()=>{"use strict";le();ge();Xe();ye();lE=(r,e,n,t)=>{let o=C.size(e),i=e.length,a=N("input",r,i),s=G("output",r,i),u=n.dataType===6?n.getInt32Array()[0]:Number(n.getBigInt64Array()[0]),l=C.normalizeAxis(u,i),d=f=>{let h=` i32(${a.indicesGet("inputIndices","uniforms.axis")}) `,g=Q("uniforms.input_shape","uniforms.axis",i),b=t.reverse?h+(t.exclusive?" + 1":""):"0",_=t.reverse?g:h+(t.exclusive?"":" + 1");return`
                ${f.registerUniform("outputSize","u32").registerUniform("axis","u32").declareVariables(a,s)}
                ${f.mainStart()}
                  ${f.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.outputSize")}
                  var inputIndices = ${s.offsetToIndices("global_idx")};
                  var sum = ${s.type.value}(0);
                  let first : i32 = ${b};
                  let last : i32 = ${_};
                  for (var i : i32 = first; i < last; i++) {
                    ${a.indicesSet("inputIndices","uniforms.axis","u32(i)")};
                    sum = sum + ${a.getByIndices("inputIndices")};
                  }
                  ${s.setByOffset("global_idx","sum")};
                }`};return{name:"CumSum",shaderCache:{hint:t.cacheKey,inputDependencies:["rank"]},getRunData:()=>({outputs:[{dims:e,dataType:r}],dispatchGroup:{x:Math.ceil(o/64)},programUniforms:[{type:12,data:o},{type:12,data:l},...W(e,e)]}),getShaderSource:d}},Xv=(r,e)=>{let n=r.inputs[0].dims,t=r.inputs[0].dataType,o=r.inputs[1];r.compute(lE(t,n,o,e),{inputs:[0]})},Zv=r=>{let e=r.exclusive===1,n=r.reverse===1;return ce({exclusive:e,reverse:n})}});var cE,dE,pE,Yv,Qv,e0=k(()=>{"use strict";le();ge();Xe();ye();cE=r=>{if(!r||r.length!==1)throw new Error("DepthToSpace requires 1 input.");if(r[0].dims.length!==4)throw new Error("DepthToSpace requires 4D input.")},dE=(r,e,n,t)=>{let o=[];o.push(`fn perm(i: ${t.type.indices}) -> ${n.type.indices} {
    var a: ${n.type.indices};`);for(let i=0;i<e;++i)o.push(n.indicesSet("a",r[i],`i[${i}]`));return o.push("return a;}"),o.join(`
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  ${v.registerUniform("output_size","u32").declareVariables(b,_)}

  ${dE(s,h,b,_)}

  ${v.mainStart()}
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    let indices = ${_.offsetToIndices("global_idx")};
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    ${_.setByOffset("global_idx",b.getByIndices("aIndices"))}
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            ${d.registerUniforms(u.map(O=>({name:`${n0(O)}`,type:"u32"}))).registerUniform("outputSize","u32").declareVariables(...i,s)}

            ${d.mainStart()}
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            var outputIndices = ${s.offsetToIndices("global_idx")};
            ${i.map((O,E)=>`var input${E}Indices: ${i[E].type.indices};`).join(`
`)}
            ${$.join(`
`)};
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          let offset${v} = ${h.broadcastedIndicesToOffset(`outputIndices${v}`,g)};
          let index${v} = offset${v} / 4u;
          let component${v} = offset${v} % 4u;
          ${T}[${v}] = ${x}(${h.getByOffset(`index${v}`)}[component${v}]);
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        let outputOffset = global_idx * ${s};
        var data = vec4<u32>(0);
        ${_("data",0,"u32")}
        ${_("data",1,"u32")}
        ${_("data",2,"u32")}
        ${_("data",3,"u32")}
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        ${g.setByOffset("global_idx","data")}
      }`;return`
    ${f.registerUniform("vec_size","u32").declareVariables(h,g)}
    ${f.mainStart()}
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    ${b}`},d=[{type:12,data:u},...W(e,t)];return{name:"Expand",shaderCache:{hint:`${t.length};${a}${s}`,inputDependencies:["rank"]},getShaderSource:l,getRunData:()=>({outputs:[{dims:t,dataType:r[0].dataType}],dispatchGroup:{x:Math.ceil(u/64)},programUniforms:d})}},s0=r=>{gE(r.inputs),r.compute(yE(r.inputs),{inputs:[0]})}});var _E,l0,c0=k(()=>{"use strict";le();ge();ye();Va();_E=r=>{let e=r[0].dataType,n=C.size(r[0].dims),t=C.size(r[1].dims),o=t%4===0,i=a=>{let s=N("x",e,[1],4),u=N("bias",e,[1],4),l=G("y",e,[1],4),d=[{name:"output_vec_size",type:"u32"},{name:"bias_size",type:"u32"}],f=g=>`
      let bias${g}_offset: u32 = (global_idx * 4 + ${g}) % uniforms.bias_size;
      let bias${g} = ${u.getByOffset(`bias${g}_offset / 4`)}[bias${g}_offset % 4];`,h=o?`
      let bias = ${u.getByOffset("global_idx % (uniforms.bias_size / 4)")};`:`${f(0)}${f(1)}${f(2)}${f(3)}
      let bias = ${s.type.value}(bias0, bias1, bias2, bias3);`;return`${a.registerUniforms(d).declareVariables(s,u,l)}

    ${Sc(it(e))}

    ${a.mainStart(Wr)}
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      let x = ${s.getByOffset("global_idx")};
      ${h}
      let x_in = x + bias;
      ${l.setByOffset("global_idx",$c("x_in"))}
    }`};return{name:"FastGeluWithBias",shaderCache:{hint:`${o}`,inputDependencies:["type","type"]},getShaderSource:i,getRunData:a=>({outputs:[{dims:a[0].dims,dataType:a[0].dataType}],programUniforms:[{type:12,data:Math.ceil(n/4)},{type:12,data:t}],dispatchGroup:{x:Math.ceil(n/Wr/4)}})}},l0=r=>{r.inputs.length<2||C.size(r.inputs[1].dims)===0?iv(r):r.compute(_E(r.inputs))}});var vE,wE,d0,p0,f0=k(()=>{"use strict";le();ge();Xe();ye();vE=r=>{if(!r||r.length!==2)throw new Error("Gather requires 2 inputs.")},wE=(r,e)=>{let n=r[0].dims,t=r[1].dims,o=n.length,i=C.normalizeAxis(e.axis,o),a=n.slice(0);a.splice(i,1,...t);let s=n[i],u=r[0].dataType===9?4:1,l=Math.ceil(C.size(a)/u),d=[{type:12,data:l},{type:6,data:s},{type:12,data:i},...W(r[0].dims,r[1].dims,a)],f=h=>{let g=N("data",r[0].dataType,r[0].dims.length,u),b=N("inputIndices",r[1].dataType,r[1].dims.length),_=G("output",r[0].dataType,a.length,u),T=x=>{let I=t.length,$=`var indicesIndices${x}  = ${b.type.indices}(0);`;for(let O=0;O<I;O++)$+=`${I>1?`indicesIndices${x}[${O}]`:`indicesIndices${x}`} = ${a.length>1?`outputIndices${x}[uniforms.axis + ${O}]`:`outputIndices${x}`};`;$+=`
          var idx${x} = ${b.getByIndices(`indicesIndices${x}`)};
          if (idx${x} < 0) {
            idx${x} = idx${x} + uniforms.axisDimLimit;
          }
          var dataIndices${x} : ${g.type.indices};
        `;for(let O=0,E=0;O<o;O++)O===i?($+=`${o>1?`dataIndices${x}[${O}]`:`dataIndices${x}`} = u32(idx${x});`,E+=I):($+=`${o>1?`dataIndices${x}[${O}]`:`dataIndices${x}`} = ${a.length>1?`outputIndices${x}[${E}]`:`outputIndices${x}`};`,E++);return $},v;if(r[0].dataType===9){let x=(I,$,O="")=>`
          let outputIndices${$} = ${_.offsetToIndices(`outputOffset + ${$}u`)};
          ${T($)};
          let offset${$} = ${g.indicesToOffset(`dataIndices${$}`)};
          let index${$} = offset${$} / 4u;
          let component${$} = offset${$} % 4u;
          ${I}[${$}] = ${O}(${g.getByOffset(`index${$}`)}[component${$}]);
        `;v=`
        let outputOffset = global_idx * ${u};
        var value = vec4<u32>(0);
        ${x("value",0,"u32")}
        ${x("value",1,"u32")}
        ${x("value",2,"u32")}
        ${x("value",3,"u32")}
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      `}else v=`
      let outputIndices = ${_.offsetToIndices("global_idx")};
      ${T("")};
      let value = ${g.getByIndices("dataIndices")};
      ${_.setByOffset("global_idx","value")};
      `;return`
      ${h.registerUniform("outputSize","u32").registerUniform("axisDimLimit","i32").registerUniform("axis","u32").declareVariables(g,b,_)}
      ${h.mainStart()}
        ${h.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.outputSize")}
        ${v}
      }`};return{name:"Gather",shaderCache:{hint:e.cacheKey,inputDependencies:["rank","rank"]},getRunData:()=>({outputs:[{dims:a,dataType:r[0].dataType}],dispatchGroup:{x:Math.ceil(l/64)},programUniforms:d}),getShaderSource:f}},d0=r=>ce({axis:r.axis}),p0=(r,e)=>{let n=r.inputs;vE(n),r.compute(wE(r.inputs,e))}});var xE,h0,m0,g0=k(()=>{"use strict";le();ge();ye();xE=(r,e,n,t,o,i,a,s,u)=>{let l=[{type:12,data:i},{type:12,data:t},{type:12,data:o},{type:12,data:n},{type:12,data:a},{type:12,data:s},{type:12,data:u}],d=[i];l.push(...W(e.dims,d));let f=h=>{let g=N("indices_data",e.dataType,e.dims.length),b=G("input_slice_offsets_data",12,1,1),_=[g,b],T=[{name:"output_size",type:"u32"},{name:"batch_dims",type:"u32"},{name:"input_dims",type:"u32",length:o.length},{name:"sizes_from_slice_dims_data",type:"u32",length:n.length},{name:"num_slices_per_batch",type:"u32"},{name:"input_batch_stride",type:"u32"},{name:"num_slice_dims",type:"u32"}];return`
  ${h.registerUniforms(T).declareVariables(..._)}
  ${h.mainStart()}
    ${h.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.output_size")}
    let batch_idx = global_idx / uniforms.num_slices_per_batch;
    let base_offset = batch_idx * uniforms.input_batch_stride;

    let slice_indices_base_offset = global_idx * uniforms.num_slice_dims;
    var relative_slice_offset = 0;
    for (var dim_idx = 0u; dim_idx < uniforms.num_slice_dims; dim_idx ++) {
      var index = i32(indices_data[dim_idx + slice_indices_base_offset].x);
      let input_dim_idx = uniforms.batch_dims + dim_idx;
      if (index < 0) {
        ${o.length===1?"index += i32(uniforms.input_dims);":"index += i32(uniforms.input_dims[input_dim_idx]);"}
      }
      ${n.length===1?"relative_slice_offset += index * i32(uniforms.sizes_from_slice_dims_data);":"relative_slice_offset += index * i32(uniforms.sizes_from_slice_dims_data[dim_idx]);"}
    }

    input_slice_offsets_data[global_idx] =  base_offset + u32(relative_slice_offset);
  }`};return r.compute({name:"computeSliceOffsets",shaderCache:{hint:`${o.length}_${n.length}`,inputDependencies:["rank"]},getRunData:()=>({outputs:[{dims:d,dataType:r.inputs[1].dataType}],dispatchGroup:{x:Math.ceil(i/64)},programUniforms:l}),getShaderSource:f},{inputs:[e],outputs:[-1]})[0]},h0=(r,e)=>{let n=r.inputs,t=n[0].dims,o=n[0].dataType,i=n[1].dims,a=i[i.length-1],s=C.sizeToDimension(i,i.length-1),u=C.sizeFromDimension(t,e.batchDims+a),l=C.sizeToDimension(t,e.batchDims),d=C.sizeFromDimension(t,e.batchDims),f=s/l,h=new Array(a),g=u;for(let $=0;$<a;++$)h[a-1-$]=g,g*=t[e.batchDims+a-1-$];let b=xE(r,n[1],h,e.batchDims,t,s,f,d,a),_=e.batchDims+a;if(_>t.length)throw new Error("last dimension of indices must not be larger than rank of input tensor");let T=i.slice(0,-1).concat(t.slice(_)),v=C.size(T),x=[{type:12,data:v},{type:12,data:u},...W(n[0].dims,b.dims,T)],I=$=>{let O=N("data",n[0].dataType,n[0].dims.length),E=N("slice_offsets",12,b.dims.length),L=G("output",n[0].dataType,T.length);return`
          ${$.registerUniform("output_size","u32").registerUniform("slice_size","u32").declareVariables(O,E,L)}
            ${$.mainStart()}
            ${$.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.output_size")}
          let slice_offset = slice_offsets[global_idx / uniforms.slice_size];
          output[global_idx] = data[u32(slice_offset) + global_idx % uniforms.slice_size];
        }`};r.compute({name:"GatherND",shaderCache:{hint:e.cacheKey,inputDependencies:["rank","rank"]},getRunData:()=>({outputs:[{dims:T,dataType:o}],dispatchGroup:{x:Math.ceil(v/64)},programUniforms:x}),getShaderSource:I},{inputs:[n[0],b]})},m0=r=>({batchDims:r.batch_dims,cacheKey:""})});var TE,IE,b0,y0,_0=k(()=>{"use strict";le();ge();Xe();ye();TE=(r,e)=>{if(r.length<3||r.length>4)throw new Error("GatherBlockQuantized requires 3 or 4 inputs.");let n=C.normalizeAxis(e.quantizeAxis,r[0].dims.length),t=e.blockSize,o=r[0],i=r[2],a=r.length===4?r[3]:void 0;if(i.dims.length!==o.dims.length||!o.dims.map((s,u)=>u===n?Math.ceil(s/t)===i.dims[u]:s===i.dims[u]).reduce((s,u)=>s&&u,!0))throw new Error("Scales must have the same rank as the input tensor and the dims should match except on gatherAxis.");if(a){if(a.dataType!==o.dataType)throw new Error("Zero point must have the same data type as the input tensor.");if(a.dims.length!==i.dims.length||!a.dims.map((s,u)=>s===i.dims[u]).reduce((s,u)=>s&&u,!0))throw new Error("Zero point must have the same rank as the input tensor and the dims should match except on quantizeAxis.")}},IE=(r,e)=>{let n=r[0].dims,t=r[1].dims,o=n.length,i=C.normalizeAxis(e.gatherAxis,o),a=C.normalizeAxis(e.quantizeAxis,o),s=n.slice(0);s.splice(i,1,...t);let u=C.size(s),l=r[2].dataType,f=r[0].dataType===22,h=[{type:12,data:u},{type:12,data:a},{type:12,data:i},{type:12,data:e.blockSize},...W(...r.map((b,_)=>b.dims),s)],g=b=>{let _=N("data",r[0].dataType,r[0].dims.length),T=N("inputIndices",r[1].dataType,r[1].dims.length),v=N("scales",r[2].dataType,r[2].dims.length),x=r.length>3?N("zeroPoint",r[3].dataType,r[3].dims.length):void 0,I=G("output",l,s.length),$=[_,T,v];x&&$.push(x);let O=[{name:"output_size",type:"u32"},{name:"quantize_axis",type:"u32"},{name:"gather_axis",type:"u32"},{name:"block_size",type:"u32"}];return`
        ${b.registerUniforms(O).declareVariables(...$,I)}
        ${b.mainStart()}
        let output_indices = ${I.offsetToIndices("global_idx")};
        var indices_indices = ${T.type.indices}(0);
        ${t.length>1?`
          for (var i: u32 = 0; i < ${t.length}; i++) {
            let index = ${I.indicesGet("output_indices","uniforms.gather_axis + i")};
            ${T.indicesSet("indices_indices","i","index")};
          }`:`indices_indices = ${I.indicesGet("output_indices","uniforms.gather_axis")};`};
        var data_indices = ${_.type.indices}(0);
        for (var i: u32 = 0; i < uniforms.gather_axis; i++) {
          let index = ${I.indicesGet("output_indices","i")};
          ${_.indicesSet("data_indices","i","index")};
        }
        var index_from_indices = ${T.getByIndices("indices_indices")};
        if (index_from_indices < 0) {
          index_from_indices += ${n[i]};
        }
        ${_.indicesSet("data_indices","uniforms.gather_axis","u32(index_from_indices)")};
        for (var i = uniforms.gather_axis + 1; i < ${s.length}; i++) {
          let index = ${I.indicesGet("output_indices",`i + ${t.length} - 1`)};
          ${_.indicesSet("data_indices","i","index")};
        }
        let data_offset = ${_.indicesToOffset("data_indices")};
        let data_index = data_offset % 8;
        // Convert 4-bit packed data to 8-bit packed data.
        let packed_4bit_quantized_data = ${_.getByOffset("data_offset / 8")};
        let packed_8bit_quantized_data = (packed_4bit_quantized_data >> (4 * (data_index % 2))) & 0x0f0f0f0f;
        let quantized_data_vec = ${f?"unpack4xI8":"unpack4xU8"}(u32(packed_8bit_quantized_data));
        let quantized_data = quantized_data_vec[data_index / 2];
        var scale_indices = data_indices;
        let quantize_axis_index = ${v.indicesGet("data_indices","uniforms.quantize_axis")} / uniforms.block_size;
        ${v.indicesSet("scale_indices","uniforms.quantize_axis","quantize_axis_index")};
        var scale = ${v.getByIndices("scale_indices")};
        ${x?`
              let zero_point_indices = scale_indices;
              let zero_point_offset = ${x.indicesToOffset("zero_point_indices")};
              let zero_point_index = zero_point_offset % 8;
              let packed_4bit_zero_points = ${x.getByOffset("zero_point_offset / 8")};
              let packed_8bit_zero_points = (packed_4bit_zero_points >> (4 * (zero_point_index % 2))) & 0x0f0f0f0f;
              let zero_point_vec = ${f?"unpack4xI8":"unpack4xU8"}(u32(packed_8bit_zero_points));
              let zero_point = zero_point_vec[zero_point_index / 2];`:"var zero_point = 0"};
        let dequantized_data = ${it(l)}(quantized_data - zero_point) * scale;
        ${I.setByOffset("global_idx","dequantized_data")};
    }`};return{name:"GatherBlockQuantized",shaderCache:{hint:`${e.cacheKey};${r.filter((b,_)=>_!==1).map(b=>b.dims.join("_")).join(";")}`,inputDependencies:Array.from({length:r.length},(b,_)=>"rank")},getRunData:()=>({outputs:[{dims:s,dataType:l}],dispatchGroup:{x:Math.ceil(u/64)},programUniforms:h}),getShaderSource:g}},b0=(r,e)=>{let n=r.inputs;TE(n,e),r.compute(IE(r.inputs,e))},y0=r=>ce({blockSize:r.blockSize,gatherAxis:r.gatherAxis,quantizeAxis:r.quantizeAxis})});var SE,$E,v0,w0,x0=k(()=>{"use strict";le();ge();Xe();ye();SE=r=>{if(!r||r.length!==2)throw new Error("GatherElements requires 2 inputs.");if(r[0].dims.length<1)throw new Error("GatherElements requires that the data input be rank >= 1.");if(r[0].dims.length!==r[1].dims.length)throw new Error(`GatherElements requires that the data input and
                     indices input tensors be of same rank.`)},$E=(r,e)=>{let n=r[0].dims,t=r[0].dataType,o=n.length,i=r[1].dims,a=r[1].dataType,s=C.normalizeAxis(e.axis,o),u=n[s],l=i.slice(0),d=C.size(l),f=N("input",t,o),h=N("indicesInput",a,i.length),g=G("output",t,l.length),b=[{type:12,data:d},{type:6,data:u},{type:12,data:s}];return b.push(...W(n,i,l)),{name:"GatherElements",shaderCache:{inputDependencies:["rank","rank"]},getRunData:()=>({outputs:[{dims:l,dataType:r[0].dataType}],dispatchGroup:{x:Math.ceil(d/64)},programUniforms:b}),getShaderSource:v=>`
      ${v.registerUniform("outputSize","u32").registerUniform("axisDimLimit","i32").registerUniform("axis","u32").declareVariables(f,h,g)}
      ${v.mainStart()}
      ${v.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.outputSize")}

      let outputIndices = ${g.offsetToIndices("global_idx")};

      var idx = ${h.getByOffset("global_idx")};
      if (idx < 0) {
        idx = idx + uniforms.axisDimLimit;
      }
      var inputIndices = ${f.type.indices}(outputIndices);
      ${f.indicesSet("inputIndices","uniforms.axis","u32(idx)")};
      let value = ${f.getByIndices("inputIndices")};

      ${g.setByOffset("global_idx","value")};
  }`}},v0=r=>ce({axis:r.axis}),w0=(r,e)=>{let n=r.inputs;SE(n),r.compute($E(r.inputs,e))}});var AE,OE,T0,I0,S0=k(()=>{"use strict";le();ge();ye();AE=r=>{if(!r)throw new Error("Input is missing");if(r.length<2||r.length>3)throw new Error("Invaid input number.");if(r.length===3&&r[2].dims.length>2)throw new Error("Invalid input shape of C");if(r[0].dataType!==r[1].dataType||r.length===3&&r[0].dataType!==r[2].dataType)throw new Error("Input types are mismatched")},OE=(r,e)=>{let n=r[0].dims.slice(),t=r[1].dims.slice(),[o,i,a]=Oa.getShapeOfGemmResult(n,e.transA,t,e.transB,r.length===3?r[2].dims:void 0),s=[o,i];if(!s)throw new Error("Can't use gemm on the given tensors");let u=16,l=Math.ceil(i/u),d=Math.ceil(o/u),f=!0,h=C.size(s),g=[{type:12,data:f?l:h},{type:12,data:o},{type:12,data:i},{type:12,data:a},{type:1,data:e.alpha},{type:1,data:e.beta}],b=["type","type"];r.length===3&&(g.push(...W(r[2].dims)),b.push("rank")),g.push(...W(s));let _=v=>{let x="";e.transA&&e.transB?x="value += a[k * uniforms.M + m] * b[n * uniforms.K + k];":e.transA&&!e.transB?x="value += a[k * uniforms.M + m] * b[k * uniforms.N + n];":!e.transA&&e.transB?x="value += a[m * uniforms.K + k] * b[n * uniforms.K + k];":!e.transA&&!e.transB&&(x="value += a[m * uniforms.K + k] * b[k * uniforms.N + n];");let I=e.alpha===1?"":"value *= uniforms.alpha;",$=N("a",r[0].dataType,r[0].dims),O=N("b",r[1].dataType,r[1].dims),E=$.type.value,L=null,R=[$,O];r.length===3&&(L=N("c",r[2].dataType,r[2].dims.length),R.push(L));let F=G("output",r[0].dataType,s.length);R.push(F);let q=[{name:"output_size",type:"u32"},{name:"M",type:"u32"},{name:"N",type:"u32"},{name:"K",type:"u32"},{name:"alpha",type:"f32"},{name:"beta",type:"f32"}];return`
  ${v.registerUniforms(q).declareVariables(...R)}

  ${v.mainStart()}
    ${v.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.output_size")}

    let m = global_idx / uniforms.N;
    let n = global_idx % uniforms.N;

    var value = ${E}(0);
    for (var k: u32 = 0u; k < uniforms.K; k++) {
      ${x}
    }

    ${I}
    ${L!=null?`let cOffset = ${L.broadcastedIndicesToOffset("vec2(m, n)",F)}; value += ${E}(uniforms.beta) * ${L.getByOffset("cOffset")};`:""}
    output[global_idx] = value;
  }`},T=v=>{let x=N("a",r[0].dataType,r[0].dims),I=N("b",r[1].dataType,r[1].dims),$=null,O=[x,I];r.length===3&&($=N("c",r[2].dataType,r[2].dims.length),O.push($));let E=G("output",r[0].dataType,s.length);O.push(E);let L=[{name:"num_tile_n",type:"u32"},{name:"M",type:"u32"},{name:"N",type:"u32"},{name:"K",type:"u32"},{name:"alpha",type:"f32"},{name:"beta",type:"f32"}],R="",F="";e.transA&&e.transB?(F=`
      var col = tile_row_start + local_id.x;
      var row = k_start + local_id.y;
      if (col < uniforms.M && row < uniforms.K) {
        tile_a[local_id.y][local_id.x] = a[row * uniforms.M + col];
      } else {
        tile_a[local_id.y][local_id.x] = ${x.type.value}(0);
      }

      col = k_start + local_id.x;
      row = tile_col_start + local_id.y;
      if (col < uniforms.K && row < uniforms.N) {
        tile_b[local_id.y][local_id.x] = b[row * uniforms.K + col];
      } else {
        tile_b[local_id.y][local_id.x] = ${I.type.value}(0);
      }
      `,R="value += tile_a[k][local_id.y] * tile_b[local_id.x][k];"):e.transA&&!e.transB?(F=`
      var col = tile_row_start + local_id.x;
      var row = k_start + local_id.y;
      if (col < uniforms.M && row < uniforms.K) {
        tile_a[local_id.y][local_id.x] = a[row * uniforms.M + col];
      } else {
        tile_a[local_id.y][local_id.x] = ${x.type.value}(0);
      }

      col = tile_col_start + local_id.x;
      row = k_start + local_id.y;
      if (col < uniforms.N && row < uniforms.K) {
        tile_b[local_id.y][local_id.x] = b[row * uniforms.N + col];
      } else {
        tile_b[local_id.y][local_id.x] = ${I.type.value}(0);
      }
      `,R="value += tile_a[k][local_id.y] * tile_b[k][local_id.x];"):!e.transA&&e.transB?(F=`
      var col = k_start + local_id.x;
      var row = tile_row_start + local_id.y;
      if (col < uniforms.K && row < uniforms.M) {
        tile_a[local_id.y][local_id.x] = a[row * uniforms.K + col];
      } else {
        tile_a[local_id.y][local_id.x] = ${x.type.value}(0);
      }

      col = k_start + local_id.x;
      row = tile_col_start + local_id.y;
      if (col < uniforms.K && row < uniforms.N) {
        tile_b[local_id.y][local_id.x] = b[row * uniforms.K + col];
      } else {
        tile_b[local_id.y][local_id.x] = ${I.type.value}(0);
      }
      `,R="value += tile_a[local_id.y][k] * tile_b[local_id.x][k];"):!e.transA&&!e.transB&&(F=`
      var col = k_start + local_id.x;
      var row = tile_row_start + local_id.y;
      if (col < uniforms.K && row < uniforms.M) {
        tile_a[local_id.y][local_id.x] = a[row * uniforms.K + col];
      } else {
        tile_a[local_id.y][local_id.x] = ${x.type.value}(0);
      }

      col = tile_col_start + local_id.x;
      row = k_start + local_id.y;
      if (col < uniforms.N && row < uniforms.K) {
        tile_b[local_id.y][local_id.x] = b[row * uniforms.N + col];
      } else {
        tile_b[local_id.y][local_id.x] = ${I.type.value}(0);
      }
      `,R="value += tile_a[local_id.y][k] * tile_b[k][local_id.x];");let q=e.alpha===1?"":"value *= uniforms.alpha;";return`
  ${v.registerUniforms(L).declareVariables(...O)}
  var<workgroup> tile_a: array<array<${x.type.storage}, ${u}>, ${u}>;
  var<workgroup> tile_b: array<array<${I.type.storage}, ${u}>, ${u}>;
  ${v.mainStart([u,u,1])}
    let tile_col_start = (workgroup_index % uniforms.num_tile_n) * ${u};
    let tile_row_start = (workgroup_index / uniforms.num_tile_n) * ${u};
    let num_tiles = (uniforms.K - 1) / ${u} + 1;
    var k_start = 0u;
    var value = ${E.type.value}(0);
    for (var t: u32 = 0u; t < num_tiles; t++) {
      ${F}
      k_start = k_start + ${u};
      workgroupBarrier();

      for (var k: u32 = 0u; k < ${u}; k++) {
        ${R}
      }
      workgroupBarrier();
    }

    ${q}
    let m = tile_row_start + local_id.y;
    let n = tile_col_start + local_id.x;
    ${$!=null?`let cOffset = ${$.broadcastedIndicesToOffset("vec2(m, n)",E)}; value += ${E.type.value}(uniforms.beta) * ${$.getByOffset("cOffset")};`:""}
    if (m < uniforms.M && n < uniforms.N) {
      output[m * uniforms.N + n] = value;
    }
  }`};return f?{name:"GemmShared",shaderCache:{hint:`${e.cacheKey}`,inputDependencies:b},getRunData:()=>({outputs:[{dims:s,dataType:r[0].dataType}],dispatchGroup:{x:l*d},programUniforms:g}),getShaderSource:T}:{name:"Gemm",shaderCache:{hint:`${e.cacheKey}`,inputDependencies:b},getRunData:()=>({outputs:[{dims:s,dataType:r[0].dataType}],dispatchGroup:{x:Math.ceil(h/64)},programUniforms:g}),getShaderSource:_}},T0=r=>{let e=r.transA,n=r.transB,t=r.alpha,o=r.beta;return{transA:e,transB:n,alpha:t,beta:o,cacheKey:`${r.transA};${r.transB};${r.alpha===1}`}},I0=(r,e)=>{AE(r.inputs),r.compute(OE(r.inputs,e))}});var Qn,xr,fo,ho,PE,EE,CE,DE,kE,NE,LE,RE,$0,A0,O0=k(()=>{"use strict";le();ge();Xe();ye();[Qn,xr,fo,ho]=[0,1,2,3],PE=r=>{if(r[0].dims.length!==4)throw new Error("only 4-D tensor is supported.");if(r[0].dims.length!==r[1].dims.length)throw new Error("input dimensions must be equal to grid dimensions");if(r[0].dims.length-2!==r[1].dims[r[1].dims.length-1])throw new Error(`last dimension of grid must be equal to ${r[0].dims.length-2}`);if(r[0].dims[0]!==r[1].dims[0])throw new Error("grid batch size must match input batch size")},EE=`
  fn gs_get_cubic_coeffs(x: f32) -> vec4<f32> {
    let cubic_alpha = -0.75f;
    let x_abs = abs(x);
    var coeffs: vec4<f32>;
    coeffs[0] = (((cubic_alpha * (x_abs + 1) - 5 * cubic_alpha) * (x_abs + 1) + 8 * cubic_alpha) * (x_abs + 1) - 4 * cubic_alpha);
    coeffs[1] = (((cubic_alpha + 2) * x_abs - (cubic_alpha + 3)) * x_abs * x_abs + 1);
    coeffs[2] = (((cubic_alpha + 2) * (1 - x_abs) - (cubic_alpha + 3)) * (1 - x_abs) * (1 - x_abs) + 1);
    coeffs[3] = (((cubic_alpha * (2 - x_abs) - 5 * cubic_alpha) * (2 - x_abs) + 8 * cubic_alpha) * (2 - x_abs) - 4 * cubic_alpha);
    return coeffs;
  }
`,CE=r=>`
  fn gs_bicubic_interpolate(p: mat4x4<${r}>, x: f32, y: f32) -> ${r} {
    var v: vec4<f32>;
    var coeffs = gs_get_cubic_coeffs(x);
    for (var i = 0; i < 4; i++) {
      v[i] = coeffs[0] * p[i][0] + coeffs[1] * p[i][1] + coeffs[2] * p[i][2] + coeffs[3] * p[i][3];
    }
    coeffs = gs_get_cubic_coeffs(y);
    let pixel = ${r}(coeffs[0] * v[0] + coeffs[1] * v[1] + coeffs[2] * v[2] + coeffs[3] * v[3]);
    return pixel;
  }
`,DE=r=>`
  fn gs_denormalize(n: f32, length: i32) -> f32 {
    ${r.alignCorners===0?`
    // alignCorners: false => [-1, 1] to [-0.5, length - 0.5]
    return ((n + 1.0) * f32(length) - 1.0) / 2.0;
    `:`
    // alignCorners: true => [-1, 1] to [0, length - 1]
    return (n + 1.0) / 2.0 * (f32(length - 1));
    `}
  }
`,kE=r=>`
  ${r.paddingMode==="reflection"?`
      fn gs_reflect(x: i32, x_min: f32, x_max: f32) -> u32 {
        var dx = 0.0;
        var fx = f32(x);
        let range = x_max - x_min;
        if (fx < x_min) {
          dx = x_min - fx;
          let n = u32(dx / range);
          let r = dx - f32(n) * range;
          if (n % 2 == 0) {
            fx = x_min + r;
          } else {
            fx = x_max - r;
          }
        } else if (fx > x_max) {
          dx = fx - x_max;
          let n = u32(dx / range);
          let r = dx - f32(n) * range;
          if (n % 2 == 0) {
            fx = x_max - r;
          } else {
            fx = x_min + r;
          }
        }
        return u32(fx);
      }`:""}
`,NE=(r,e,n)=>`
  fn pixel_at_grid(r: i32, c: i32, H: i32, W: i32, batch: u32, channel: u32, border: vec4<f32>) -> ${e} {
     var pixel = ${e}(0);
     var indices = vec4<u32>(0);
     indices[${Qn}] = batch;
     indices[${xr}] = channel;`+(()=>{switch(n.paddingMode){case"zeros":return`
          if (r >= 0 && r < H && c >=0 && c < W) {
            indices[${fo}] = u32(r);
            indices[${ho}] = u32(c);
          } else {
            return ${e}(0);
          }
        `;case"border":return`
          indices[${fo}] = u32(clamp(r, 0, H - 1));
          indices[${ho}] = u32(clamp(c, 0, W - 1));
        `;case"reflection":return`
          indices[${fo}] = gs_reflect(r, border[1], border[3]);
          indices[${ho}] = gs_reflect(c, border[0], border[2]);
        `;default:throw new Error(`padding mode ${n.paddingMode} is not supported`)}})()+`
    return ${r.getByIndices("indices")};
  }
`,LE=(r,e,n)=>(()=>{switch(n.mode){case"nearest":return`
          let result = pixel_at_grid(i32(round(y)), i32(round(x)), H_in, W_in, indices[${Qn}], indices[${xr}], border);
        `;case"bilinear":return`
          let x1 = i32(floor(x));
          let y1 = i32(floor(y));
          let x2 = x1 + 1;
          let y2 = y1 + 1;

          let p11 = pixel_at_grid(y1, x1, H_in, W_in, indices[${Qn}], indices[${xr}], border);
          let p12 = pixel_at_grid(y1, x2, H_in, W_in, indices[${Qn}], indices[${xr}], border);
          let p21 = pixel_at_grid(y2, x1, H_in, W_in, indices[${Qn}], indices[${xr}], border);
          let p22 = pixel_at_grid(y2, x2, H_in, W_in, indices[${Qn}], indices[${xr}], border);

          let dx2 = ${e}(f32(x2) - x);
          let dx1 = ${e}(x - f32(x1));
          let dy2 = ${e}(f32(y2) - y);
          let dy1 = ${e}(y - f32(y1));
          let result = dy2 * (dx2 * p11 + dx1 * p12) + dy1 * (dx2 * p21 + dx1 * p22);
        `;case"bicubic":return`
          let x0 = i32(floor(x)) - 1;
          let y0 = i32(floor(y)) - 1;
          var p: mat4x4<${e}>;
          for (var h = 0; h < 4; h++) {
            for (var w = 0; w < 4; w++) {
              p[h][w] = pixel_at_grid(h + y0, w + x0, H_in, W_in, indices[${Qn}], indices[${xr}], border);
            }
          }

          let dx = x - f32(x0 + 1);
          let dy = y - f32(y0 + 1);
          let result = gs_bicubic_interpolate(p, dx, dy);
        `;default:throw new Error(`mode ${n.mode} is not supported`)}})()+`${r.setByOffset("global_idx","result")}`,RE=(r,e)=>{let n=N("x",r[0].dataType,r[0].dims.length),t=[r[1].dims[0],r[1].dims[1],r[1].dims[2]],o=N("grid",r[1].dataType,t.length,2),i=[r[0].dims[0],r[0].dims[1],r[1].dims[1],r[1].dims[2]];e.format==="NHWC"&&(i=[r[0].dims[0],r[1].dims[1],r[1].dims[2],r[0].dims[3]],[Qn,xr,fo,ho]=[0,3,1,2]);let a=G("output",r[0].dataType,i.length),s=n.type.value,u=C.size(i),l=[{type:12,data:u},...W(r[0].dims,t,i)],d=f=>`
  ${f.registerUniform("output_size","u32").declareVariables(n,o,a)}
  ${EE}
  ${CE(s)}
  ${DE(e)}
  ${kE(e)}
  ${NE(n,s,e)}

  ${f.mainStart()}
    ${f.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.output_size")}
      let H_in = i32(uniforms.x_shape[${fo}]);
      let W_in = i32(uniforms.x_shape[${ho}]);

      ${e.alignCorners===0?`
      let x_min = -0.5;
      let x_max = f32(W_in) - 0.5;
      let y_min = -0.5;
      let y_max = f32(H_in) - 0.5;
      `:`
      let x_min = 0.0;
      let x_max = f32(W_in) - 1.0;
      let y_min = 0.0;
      let y_max = f32(H_in) - 1.0;
      `};
      let border = vec4<f32>(x_min, y_min, x_max, y_max);

      let indices = ${a.offsetToIndices("global_idx")};
      var grid_indices = vec3<u32>(indices[${Qn}], indices[${fo}], indices[${ho}]);
      let nxy = ${o.getByIndices("grid_indices")};
      var x = gs_denormalize(f32(nxy[0]), W_in);
      var y = gs_denormalize(f32(nxy[1]), H_in);

      ${LE(a,s,e)}
  }`;return{name:"GridSample",shaderCache:{hint:`${e.cacheKey}`,inputDependencies:["type","type"]},getRunData:f=>{let h=C.size(i);return{outputs:[{dims:i,dataType:f[0].dataType}],dispatchGroup:{x:Math.ceil(h/64)},programUniforms:l}},getShaderSource:d}},$0=(r,e)=>{PE(r.inputs),r.compute(RE(r.inputs,e))},A0=r=>ce({alignCorners:r.align_corners,mode:r.mode,paddingMode:r.padding_mode,format:r.format})});var Tt,BE,E0,P0,FE,qo,C0,Rc=k(()=>{"use strict";le();ge();Xe();Na();Ba();ye();Yn();Tt=(r,e)=>r.length>e&&r[e].dims.length>0?r[e]:void 0,BE=(r,e)=>{let n=r[0],t=Tt(r,1),o=Tt(r,2),i=Tt(r,3),a=Tt(r,4),s=Tt(r,5),u=Tt(r,6),l=Tt(r,7);if(n.dims.length!==3&&n.dims.length!==5)throw new Error("Input query is expected to have 3 or 5 dimensions");let d=n.dims[0],f=n.dims[1],h=n.dims.length===3?n.dims[2]:e.numHeads*n.dims[4],g=f,b=0,_=0,T=Math.floor(h/e.numHeads);if(u&&l&&C.size(u.dims)&&C.size(l.dims)){if(u.dims.length!==4)throw new Error('Input "past_key" is expected to have 4 dimensions');if(u.dims[0]!==d||u.dims[1]!==e.numHeads||u.dims[3]!==T)throw new Error('Input "past_key" shape (batch_size, num_heads, past_sequence_length, head_size)');if(l.dims[0]!==d||l.dims[1]!==e.numHeads||l.dims[3]!==T)throw new Error('Input "past_value" shape (batch_size, num_heads, past_sequence_length, head_size)');if(u.dims[2]!==l.dims[2])throw new Error('Input "past_key" and "past_value" shall have same dim 2 (past_sequence_length)');if(l.dims.length!==4)throw new Error('Input "past_value" is expected to have 4 dimensions');b=u.dims[2],_=u.dims[2]}else if(u&&C.size(u.dims)||l&&C.size(l.dims))throw new Error('Input "past_key" and "past_value" shall be both present or both absent');let v;if(t&&C.size(t.dims)>0){if(n.dims.length!==3)throw new Error('Input "query" is expected to have 3 dimensions when key is given');if(t.dims.length<3||t.dims.length>5)throw new Error('Input "key" is expected to have 3, 4, or 5 dimensions');if(n.dims[0]!==t.dims[0])throw new Error('Input "query" and "key" shall have same dim 0 (batch size)');if(t.dims.length===3){if(t.dims[2]!==n.dims[2])throw new Error('Input "query" and "key" shall have same dim 2 (hidden_size)');v=2,g=t.dims[1]}else if(t.dims.length===5){if(t.dims[2]!==e.numHeads||t.dims[3]!==2||t.dims[4]!==T)throw new Error('Expect "key" shape (batch_size, kv_sequence_length, num_heads, 2, head_size) for packed kv');if(o)throw new Error('Expect "value" be none when "key" has packed kv format.');v=5,g=t.dims[1]}else{if(t.dims[1]!==e.numHeads||t.dims[3]!==T)throw new Error('Expect "key" shape (batch_size, num_heads, kv_sequence_length, head_size) for past_key');v=0,g=t.dims[2]}}else{if(n.dims.length!==5)throw new Error('Input "query" is expected to have 5 dimensions when key is empty');if(n.dims[2]!==e.numHeads||n.dims[3]!==3)throw new Error('Expect "query" shape (batch_size, kv_sequence_length, num_heads, 3, head_size) for packed kv');v=3}if(i&&C.size(i.dims)>0){if(i.dims.length!==1)throw new Error('Input "bias" is expected to have 1 dimension');if(t&&t.dims.length===5&&t.dims[3]===2)throw new Error("bias is not allowed for packed kv.")}let x=b+g,I=0;if(a&&C.size(a.dims)>0){I=8;let L=a.dims;throw L.length===1?L[0]===d?I=1:L[0]===3*d+2&&(I=3):L.length===2&&L[0]===d&&L[1]===x&&(I=5),I===8?new Error('Input "key_padding_mask" shape shall be (batch_size) or (batch_size, total_sequence_length)'):new Error("Mask not supported")}let $=!1,O=h;if(o&&C.size(o.dims)>0){if(o.dims.length!==3&&o.dims.length!==4)throw new Error('Input "value" is expected to have 3 or 4 dimensions');if(n.dims[0]!==o.dims[0])throw new Error('Input "query" and "value" shall have same dim 0 (batch_size)');if(o.dims.length===3){if(g!==o.dims[1])throw new Error('Input "key" and "value" shall have the same dim 1 (kv_sequence_length)');O=o.dims[2]}else{if(g!==o.dims[2])throw new Error('Input "key" and "value" shall have the same dim 2 (kv_sequence_length)');O=o.dims[1]*o.dims[3],$=!0}}let E=!1;if(a&&C.size(a.dims)>0)throw new Error("Key padding mask is not supported");if(s&&C.size(s.dims)>0){if(s.dims.length!==4)throw new Error('Input "attention_bias" is expected to have 4 dimensions');if(s.dims[0]!==d||s.dims[1]!==e.numHeads||s.dims[2]!==f||s.dims[3]!==x)throw new Error('Expect "attention_bias" shape (batch_size, num_heads, sequence_length, total_sequence_length)')}return{batchSize:d,sequenceLength:f,pastSequenceLength:b,kvSequenceLength:g,totalSequenceLength:x,maxSequenceLength:_,inputHiddenSize:0,hiddenSize:h,vHiddenSize:O,headSize:T,vHeadSize:Math.floor(O/e.numHeads),numHeads:e.numHeads,isUnidirectional:!1,pastPresentShareBuffer:!1,maskFilterValue:e.maskFilterValue,maskType:I,scale:e.scale,broadcastResPosBias:E,passPastInKv:$,qkvFormat:v}},E0=r=>ce({...r}),P0=ce({perm:[0,2,1,3]}),FE=(r,e,n,t,o,i,a)=>{let s=[t,o,i],u=C.size(s),l=[{type:12,data:u},{type:12,data:a},{type:12,data:i}],d=f=>{let h=G("qkv_with_bias",e.dataType,s),g=N("qkv",e.dataType,s),b=N("bias",n.dataType,s),_=[{name:"output_size",type:"u32"},{name:"bias_offset",type:"u32"},{name:"hidden_size",type:"u32"}];return`
  ${f.registerUniforms(_).declareVariables(g,b,h)}
  ${f.mainStart()}
    ${f.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.output_size")}
    let bias_offset_idx = (global_idx % uniforms.hidden_size) + uniforms.bias_offset;

    qkv_with_bias[global_idx] = qkv[global_idx] + bias[bias_offset_idx];
  }`};return r.compute({name:"MultiHeadAttentionAddBias",shaderCache:{inputDependencies:["type","type"]},getRunData:()=>({outputs:[{dims:s,dataType:e.dataType,gpuDataType:0}],dispatchGroup:{x:Math.ceil(u/64)},programUniforms:l}),getShaderSource:d},{inputs:[e,n],outputs:[-1]})[0]},qo=(r,e,n,t,o,i,a,s)=>{let u=i;if(a&&C.size(a.dims)>0){if(t===1)throw new Error("AddBiasReshape is not implemented. Please export your model with packed QKV or KV");return u=FE(r,i,a,e,t,n*o,s),u=u.reshape([e,t,n,o]),n===1||t===1?u:r.compute(at(u,P0.perm),{inputs:[u],outputs:[-1]})[0]}else return i.dims.length===3&&(u=i.reshape([e,t,n,o])),n===1||t===1?u:r.compute(at(u,P0.perm),{inputs:[u],outputs:[-1]})[0]},C0=(r,e)=>{let n=BE(r.inputs,e),t=r.inputs[0],o=Tt(r.inputs,1),i=Tt(r.inputs,2),a=Tt(r.inputs,3),s=Tt(r.inputs,4),u=Tt(r.inputs,5),l=Tt(r.inputs,6),d=Tt(r.inputs,7);if(t.dims.length===5)throw new Error("Packed QKV is not implemented");if(o?.dims.length===5)throw new Error("Packed KV is not implemented");let f=o&&i&&o.dims.length===4&&i.dims.length===4,h=qo(r,n.batchSize,n.numHeads,n.sequenceLength,n.headSize,t,a,0);if(f)return po(r,h,o,i,s,void 0,l,d,u,n);if(!o||!i)throw new Error("key and value must be provided");let g=qo(r,n.batchSize,n.numHeads,n.kvSequenceLength,n.headSize,o,a,n.hiddenSize),b=qo(r,n.batchSize,n.numHeads,n.kvSequenceLength,n.vHeadSize,i,a,2*n.hiddenSize);po(r,h,g,b,s,void 0,l,d,u,n)}});var VE,GE,UE,WE,zc,D0,k0,Mc=k(()=>{"use strict";le();ge();Xe();ye();VE=r=>{if(!r||r.length<1)throw new Error("too few inputs")},GE=(r,e)=>{let n=[],t=e.numOutputs;return r[1].dims[0]>0&&(r[1].getBigInt64Array().forEach(o=>n.push(Number(o))),t=n.length),ce({numOutputs:t,axis:e.axis,splitSizes:n})},UE=r=>`
fn calculateOutputIndex(index: u32) -> u32 {
    for (var i: u32 = 0u; i < ${r}u; i += 1u ) {
    if (index < ${Q("uniforms.size_in_split_axis","i",r)}) {
        return i;
    }
    }
    return ${r}u;
}`,WE=r=>{let e=r.length,n=[];for(let t=0;t<e;++t){let o=r[t].setByIndices("indices","input[global_idx]");e===1?n.push(o):t===0?n.push(`if (output_number == ${t}u) { ${o} }`):t===e-1?n.push(`else { ${o} }`):n.push(`else if (output_number == ${t}) { ${o} }`)}return`
      fn writeBufferData(output_number: u32, indices: ${r[0].type.indices}, global_idx: u32) {
        ${n.join(`
`)}
      }`},zc=(r,e)=>{let n=r[0].dims,t=C.size(n),o=r[0].dataType,i=C.normalizeAxis(e.axis,n.length),a=new Array(e.numOutputs),s=N("input",o,n.length),u=new Array(e.numOutputs),l=[],d=[],f=0,h=[{type:12,data:t}];for(let b=0;b<e.numOutputs;b++){f+=e.splitSizes[b],u[b]=f;let _=n.slice();_[i]=e.splitSizes[b],d.push(_),a[b]=G(`output${b}`,o,_.length),l.push({dims:d[b],dataType:r[0].dataType})}h.push({type:12,data:u},...W(n,...d));let g=b=>`
  ${b.registerUniform("input_size","u32").registerUniform("size_in_split_axis","u32",u.length).declareVariables(s,...a)}
  ${UE(u.length)}
  ${WE(a)}

  ${b.mainStart()}
    ${b.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.input_size")}

    var indices = ${s.offsetToIndices("global_idx")};
    var index = ${s.indicesGet("indices",i)};
    let output_number = calculateOutputIndex(index);
    if (output_number != 0) {
      index -= ${Q("uniforms.size_in_split_axis","output_number - 1u",u.length)};
      ${s.indicesSet("indices",i,"index")};
    }
    writeBufferData(output_number, indices, global_idx);
  }`;return{name:"Split",shaderCache:{hint:e.cacheKey,inputDependencies:["rank"]},getShaderSource:g,getRunData:()=>({outputs:l,dispatchGroup:{x:Math.ceil(t/64)},programUniforms:h})}},D0=(r,e)=>{VE(r.inputs);let n=r.inputs.length===1?e:GE(r.inputs,e);r.compute(zc(r.inputs,n),{inputs:[0]})},k0=r=>{let e=r.axis,n=r.splitSizes,t=r.numOutputs<0?n.length:r.numOutputs;if(t!==n.length)throw new Error("numOutputs and splitSizes length must be equal");return ce({axis:e,numOutputs:t,splitSizes:n})}});var HE,Xa,N0,Bc=k(()=>{"use strict";le();ge();Xe();ye();HE=(r,e)=>{let[n,t,o,i]=r,{numHeads:a,rotaryEmbeddingDim:s}=e;if(n.dims.length!==3&&n.dims.length!==4)throw new Error(`Input 'x' is expected to have 3 or 4 dimensions, got ${n.dims.length}`);if(!C.areEqual(t.dims,[])&&!C.areEqual(t.dims,[1])&&t.dims.length!==2)throw new Error(`Input 'position_ids' is expected to have 0, 1, or 2 dimensions, got ${t.dims.length}`);if(o.dims.length!==2)throw new Error(`Input 'cos_cache' is expected to have 2 dimensions, got ${o.dims.length}`);if(i.dims.length!==2)throw new Error(`Input 'sin_cache' is expected to have 2 dimensions, got ${i.dims.length}`);if(!C.areEqual(o.dims,i.dims))throw new Error("Inputs 'cos_cache' and 'sin_cache' are expected to have the same shape");if(s>0&&a===0)throw new Error("num_heads must be provided if rotary_embedding_dim is specified");let u=n.dims[0],l=n.dims[n.dims.length-2],d=o.dims[0],f=C.sizeFromDimension(n.dims,1)/l,h=s===0?o.dims[1]*2:f/a;if(s>h)throw new Error("rotary_embedding_dim must be less than or equal to head_size");if(t.dims.length===2){if(u!==t.dims[0])throw new Error(`Input 'position_ids' dimension 0 should be of size batch_size, got ${t.dims[0]}`);if(l!==t.dims[1])throw new Error(`Input 'position_ids' dimension 1 should be of size sequence_length, got ${t.dims[1]}`)}if(h/2!==o.dims[1]&&s/2!==o.dims[1])throw new Error(`Input 'cos_cache' dimension 1 should be same as head_size / 2 or rotary_embedding_dim / 2, got ${o.dims[1]}`);if(l>d)throw new Error("Updating cos_cache and sin_cache in RotaryEmbedding is not currently supported")},Xa=(r,e)=>{let{interleaved:n,numHeads:t,rotaryEmbeddingDim:o,scale:i}=e,a=r[0].dims[0],s=C.sizeFromDimension(r[0].dims,1),u=r[0].dims[r[0].dims.length-2],l=s/u,d=r[2].dims[1],f=o===0?d*2:l/t,h=new Array(a,u,l/f,f-d),g=C.computeStrides(h),b=[{type:1,data:i},{type:12,data:h},{type:12,data:g},...r[0].dims.length===3?new Array({type:12,data:[s,l,f,1]}):[],...r[0].dims.length===4?new Array({type:12,data:[s,f,u*f,1]}):[],...W(r[0].dims,r[1].dims,r[2].dims,r[3].dims,r[0].dims)],_=T=>{let v=N("input",r[0].dataType,r[0].dims.length),x=N("position_ids",r[1].dataType,r[1].dims.length),I=N("cos_cache",r[2].dataType,r[2].dims.length),$=N("sin_cache",r[3].dataType,r[3].dims.length),O=G("output",r[0].dataType,r[0].dims.length);return T.registerUniforms([{name:"scale",type:"f32"},{name:"global_shape",type:"u32",length:h.length},{name:"global_strides",type:"u32",length:g.length},{name:"input_output_strides",type:"u32",length:g.length}]),`
        ${T.declareVariables(v,x,I,$,O)}

        ${T.mainStart(Wr)}
          let half_rotary_emb_dim = uniforms.${I.name}_shape[1];
          let bsnh = global_idx / uniforms.global_strides % uniforms.global_shape;
          let size = uniforms.global_shape[0] * uniforms.global_strides[0];
          ${T.guardAgainstOutOfBoundsWorkgroupSizes("size")}

          if (bsnh[3] < half_rotary_emb_dim) {
            let position_ids_idx =
                ${x.broadcastedIndicesToOffset("bsnh.xy",G("",x.type.tensor,2))};
            let position_id =
                u32(${x.getByOffset("position_ids_idx")}) + select(0, bsnh[1], position_ids_idx == 0);
            let i = dot(bsnh, uniforms.input_output_strides) + select(0, bsnh[3], ${n});
            let j = i + select(half_rotary_emb_dim, 1, ${n});
            let re = ${v.getByOffset("i")} * ${I.get("position_id","bsnh[3]")} -
                ${v.getByOffset("j")} * ${$.get("position_id","bsnh[3]")};
            ${O.setByOffset("i","re")}
            let im = ${v.getByOffset("i")} * ${$.get("position_id","bsnh[3]")} +
                ${v.getByOffset("j")} * ${I.get("position_id","bsnh[3]")};
            ${O.setByOffset("j","im")}
          } else {
            let k = dot(bsnh, uniforms.input_output_strides) + half_rotary_emb_dim;
            ${O.setByOffset("k",v.getByOffset("k"))}
          }
        }`};return{name:"RotaryEmbedding",shaderCache:{hint:ce({interleaved:n}).cacheKey,inputDependencies:["rank","rank","rank","rank"]},getShaderSource:_,getRunData:()=>({outputs:[{dims:r[0].dims,dataType:r[0].dataType}],dispatchGroup:{x:Math.ceil(C.size(h)/Wr)},programUniforms:b})}},N0=(r,e)=>{HE(r.inputs,e),r.compute(Xa(r.inputs,e))}});var jE,qE,L0,KE,R0,z0=k(()=>{"use strict";Xe();le();Ba();Rc();Mc();Yn();Bc();ye();jE=(r,e)=>{if(e.doRotary&&r.length<=7)throw new Error("cos_cache and sin_cache inputs are required if do_rotary is specified");let n=r[0],t=r[1],o=r[2],i=r[3],a=r[4];if(e.doRotary!==0&&r.length<=7)throw new Error("cos_cast and sin_cache are expected if do_rotary attribute is non-zero");if(e.localWindowSize!==-1)throw new Error("Local attention is not supported");if(e.softcap!==0)throw new Error("Softcap is not supported");if(e.rotaryInterleaved!==0)throw new Error("Rotary interleaved is not supported");if(e.smoothSoftmax)throw new Error("Smooth softmax is not supported");if(n.dims.length!==3&&n.dims.length!==5)throw new Error("Input query is expected to have 3 or 5 dimensions");let s=!1,u=n.dims[0],l=n.dims[1],d=n.dims.length===3?s?n.dims[2]/3:n.dims[2]:e.numHeads*n.dims[4],f=l,h=0,g=!t||t.dims.length===0,b=Math.floor(g?d/(e.numHeads+2*e.kvNumHeads):d/e.numHeads);g&&(d=b*e.numHeads);let _=i&&i.dims.length!==0,T=a&&a.dims.length!==0;if(_&&i.dims.length===4&&i.dims[0]===u&&i.dims[1]!==e.kvNumHeads&&i.dims[2]===e.kvNumHeads&&i.dims[3]===b)throw new Error("BSNH pastKey/pastValue is not supported");if(_&&T){if(i.dims.length!==4)throw new Error('Input "past_key" is expected to have 4 dimensions');if(a.dims.length!==4)throw new Error('Input "past_value" is expected to have 4 dimensions');h=i.dims[2]}else if(_||T)throw new Error('Input "past_key" and "past_value" shall be both present or both absent');let x=1;if(t&&t.dims.length>0){if(n.dims.length!==3)throw new Error('Input "query" is expected to have 3 dimensions when key is given');if(t.dims.length<3||t.dims.length>5)throw new Error('Input "key" is expected to have 3, 4, or 5 dimensions');if(n.dims[0]!==t.dims[0])throw new Error('Input "query" and "key" shall have same dim 0 (batch size)');if(t.dims.length===3){if(n.dims[2]%t.dims[2]!==0)throw new Error('Dimension 2 of "query" should be a multiple of "key"');f=t.dims[1]}else if(t.dims.length===5){if(t.dims[2]!==e.numHeads||t.dims[3]!==2||t.dims[4]!==b)throw new Error('Expect "key" shape (batch_size, kv_sequence_length, num_heads, 2, head_size) for packed kv');if(o)throw new Error('Expect "value" be none when "key" has packed kv format.');f=t.dims[1]}else{if(t.dims[1]!==e.numHeads||t.dims[3]!==b)throw new Error('Expect "key" shape (batch_size, num_heads, kv_sequence_length, head_size) for past_key');f=t.dims[2]}}else{if(n.dims.length!==3&&n.dims.length!==5)throw new Error('Input "query" is expected to have 3 or 5 dimensions when key is empty');if(n.dims.length===5&&(n.dims[2]!==e.numHeads||n.dims[3]!==3))throw new Error('Expect "query" shape (batch_size, kv_sequence_length, num_heads, 3, head_size) for packed kv');x=3}let I=0,$=!1,O=e.kvNumHeads?b*e.kvNumHeads:d;if(o&&o.dims.length>0){if(o.dims.length!==3&&o.dims.length!==4)throw new Error('Input "value" is expected to have 3 or 4 dimensions');if(n.dims[0]!==o.dims[0])throw new Error('Input "query" and "value" shall have same dim 0 (batch_size)');if(o.dims.length===3){if(f!==o.dims[1])throw new Error('Input "key" and "value" shall have the same dim 1 (kv_sequence_length)');O=o.dims[2]}else{if(f!==o.dims[2])throw new Error('Input "past_key" and "past_value" shall have the same dim 2 (kv_sequence_length)');O=o.dims[1]*o.dims[3],$=!0}}let E=r.length>4?r[5]:void 0;if(E&&E.dims.length!==1&&E.dims[0]!==u)throw new Error('Input "seqlens" is expected to have 1 dimension and the same dim 0 as batch_size');return{batchSize:u,sequenceLength:l,pastSequenceLength:h,kvSequenceLength:f,totalSequenceLength:-1,maxSequenceLength:-1,inputHiddenSize:0,hiddenSize:d,vHiddenSize:O,headSize:b,vHeadSize:Math.floor(O/e.kvNumHeads),numHeads:e.numHeads,kvNumHeads:e.kvNumHeads,nReps:e.numHeads/e.kvNumHeads,pastPresentShareBuffer:!1,maskType:I,scale:e.scale,broadcastResPosBias:!1,passPastInKv:$,qkvFormat:x}},qE=ce({perm:[0,2,1,3]}),L0=(r,e,n)=>{let t=e,o=n.kvNumHeads;return e.dims.length===3&&n.kvSequenceLength!==0&&(t=e.reshape([n.batchSize,n.kvSequenceLength,o,n.headSize]),t=r.compute(at(t,qE.perm),{inputs:[t],outputs:[-1]})[0]),t},KE=(r,e,n,t)=>{let o=7,i=["type","type"],a=[r*e],s=r*e,u=[{type:12,data:s},{type:12,data:e},{type:12,data:r}],l=d=>{let f=N("seq_lens",n.dataType,n.dims),h=N("total_seq_lens",t.dataType,t.dims),g=G("pos_ids",o,a),b=[{name:"output_size",type:"u32"},{name:"sequence_length",type:"u32"},{name:"batch_size",type:"u32"}];return`
  ${d.registerUniforms(b).declareVariables(f,h,g)}
  ${d.mainStart()}
    ${d.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.output_size")}
    let total_sequence_length = u32(${h.getByOffset("0")});
    let is_subsequent_prompt = uniforms.sequence_length > 1 && uniforms.sequence_length != total_sequence_length;
    let is_first_prompt = !is_subsequent_prompt && uniforms.sequence_length == total_sequence_length;
    let batch_idx = global_idx / uniforms.sequence_length;
    let sequence_idx = i32(global_idx % uniforms.sequence_length);
    var pos_id: i32 = 0;
    let seqlen = ${f.getByOffset("batch_idx")};
    let total_seqlen = seqlen + 1;
    if (is_first_prompt) {
      if (sequence_idx < total_seqlen) {
        pos_id = sequence_idx;
      } else {
        pos_id = 1;
      }
      ${g.setByOffset("global_idx","pos_id")}
    } else if (is_subsequent_prompt) {
      let past_seqlen = total_seqlen - i32(uniforms.sequence_length);
      if (past_seqlen + sequence_idx < total_seqlen) {
        pos_id = past_seqlen + sequence_idx;
      } else {
        pos_id = 1;
      }
      ${g.setByOffset("global_idx","pos_id")}
    } else if (global_idx < uniforms.batch_size) {
      ${g.setByOffset("global_idx","seqlen")}
    };
  }
  `};return{name:"GeneratePositionIds",shaderCache:{hint:`${r};${e}`,inputDependencies:i},getRunData:()=>({outputs:[{dims:a,dataType:o}],dispatchGroup:{x:Math.ceil(s/64)},programUniforms:u}),getShaderSource:l}},R0=(r,e)=>{let n=jE(r.inputs,e);if(r.inputs[0].dims.length===5)throw new Error("Packed QKV is not implemented");if(r.inputs[1]?.dims.length===5)throw new Error("Packed KV is not implemented");let t=r.inputs[0],o=r.inputs[1]&&r.inputs[1].dims.length>0?r.inputs[1]:void 0,i=r.inputs[2]&&r.inputs[2].dims.length>0?r.inputs[2]:void 0,a=r.inputs[3]&&r.inputs[3].dims.length!==0?r.inputs[3]:void 0,s=r.inputs[4]&&r.inputs[4].dims.length!==0?r.inputs[4]:void 0,u=r.inputs.length>4?r.inputs[5]:void 0,l=r.inputs.length>5?r.inputs[6]:void 0,d=n.kvNumHeads?n.kvNumHeads:n.numHeads,f=ce({axis:2,numOutputs:3,splitSizes:[n.numHeads*n.headSize,d*n.headSize,d*n.headSize]}),[h,g,b]=!o&&!i?r.compute(zc([t],f),{inputs:[t],outputs:[-1,-1,-1]}):[t,o,i],_,T;if(e.doRotary){let $=r.compute(KE(n.batchSize,n.sequenceLength,u,l),{inputs:[u,l],outputs:[-1]})[0],O=r.inputs[7],E=r.inputs[8],L=ce({interleaved:e.rotaryInterleaved!==0,numHeads:n.numHeads,rotaryEmbeddingDim:0,scale:e.scale}),R=[h,$,O,E],F=[-1];_=r.compute(Xa(R,L),{inputs:R,outputs:F})[0],R.splice(0,1,g);let q=ce({interleaved:e.rotaryInterleaved!==0,numHeads:n.kvNumHeads,rotaryEmbeddingDim:0,scale:e.scale});T=r.compute(Xa(R,q),{inputs:R,outputs:F})[0]}let v=qo(r,n.batchSize,n.numHeads,n.sequenceLength,n.headSize,e.doRotary?_:h,void 0,0),x=L0(r,e.doRotary?T:g,n),I=L0(r,b,n);po(r,v,x,I,void 0,void 0,a,s,void 0,n,u,l)}});var M0,XE,ZE,B0,F0=k(()=>{"use strict";le();ge();Yn();ye();M0=(r,e,n,t,o,i,a,s)=>{let u=Ee(i),l=u===1?"f32":`vec${u}f`,d=u===1?"vec2f":`mat2x${u}f`,f=o*a,h=64;f===1&&(h=256);let g=[o,a,i/u],b=[o,a,2],_=["rank","type","type"],T=[];T.push(...W(g,b));let v=x=>{let I=N("x",e.dataType,3,u),$=N("scale",n.dataType,n.dims),O=N("bias",t.dataType,t.dims),E=G("output",1,3,2),L=[I,$,O,E];return`
  var<workgroup> workgroup_shared : array<${d}, ${h}>;
  const workgroup_size = ${h}u;
  ${x.declareVariables(...L)}
  ${x.mainStart(h)}
    let batch = workgroup_index / uniforms.x_shape[1];
    let channel = workgroup_index % uniforms.x_shape[1];
    let hight = uniforms.x_shape[2];
    // initialize workgroup memory
    var sum = ${l}(0);
    var squared_sum = ${l}(0);
    for (var h = local_idx; h < hight; h += workgroup_size) {
      let value = ${l}(${I.get("batch","channel","h")});
      sum += value;
      squared_sum += value * value;
    }
    workgroup_shared[local_idx] = ${d}(sum, squared_sum);
    workgroupBarrier();

    for (var currSize = workgroup_size >> 1;  currSize > 0; currSize = currSize >> 1) {
      if (local_idx < currSize) {
        workgroup_shared[local_idx] = workgroup_shared[local_idx] + workgroup_shared[local_idx + currSize];
      }
      workgroupBarrier();
    }
    if (local_idx == 0) {
      let sum_final = ${Kt("workgroup_shared[0][0]",u)} / f32(hight * ${u});
      let squared_sum_final = ${Kt("workgroup_shared[0][1]",u)} / f32(hight * ${u});

      let inv_std_dev = inverseSqrt(squared_sum_final - sum_final * sum_final + f32(${s}));
      let channel_scale = inv_std_dev * f32(scale[channel]);
      let channel_shift = f32(bias[channel]) - sum_final * channel_scale;
      output[workgroup_index] = vec2f(channel_scale, channel_shift);
    }
  }`};return r.compute({name:"InstanceNormComputeChannelScaleShift",shaderCache:{hint:`${u};${s};${h}`,inputDependencies:_},getRunData:()=>({outputs:[{dims:b,dataType:1}],dispatchGroup:{x:f},programUniforms:T}),getShaderSource:v},{inputs:[e,n,t],outputs:[-1]})[0]},XE=(r,e,n)=>{let t=e[0].dims,o=t,i=2,a=t[0],s=t[1],u=C.sizeFromDimension(t,i),l=Ee(u),d=C.size(o)/l,f=M0(r,e[0],e[1],e[2],a,u,s,n.epsilon),h=[a,s,u/l],g=[a,s],b=["type","none"],_=T=>{let v=N("x",e[0].dataType,h.length,l),x=N("scale_shift",1,g.length,2),I=G("output",e[0].dataType,h.length,l),$=[v,x,I];return`
  ${T.registerUniform("output_size","u32").declareVariables(...$)}
  ${T.mainStart()}
  ${T.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.output_size")}
      let outputIndices = ${I.offsetToIndices("global_idx")};
      let batch = outputIndices[0];
      let channel = outputIndices[1];
      let scale_shift = ${x.getByIndices("vec2<u32>(batch, channel)")};
      let value = ${v.getByOffset("global_idx")} * ${I.type.value}(scale_shift.x) + ${I.type.value}(scale_shift.y);
      ${I.setByOffset("global_idx","value")};
  }`};r.compute({name:"InstanceNormalization",shaderCache:{hint:`${l}`,inputDependencies:b},getRunData:()=>({outputs:[{dims:o,dataType:e[0].dataType}],dispatchGroup:{x:Math.ceil(d/64)},programUniforms:[{type:12,data:d},...W(h,g,h)]}),getShaderSource:_},{inputs:[e[0],f]})},ZE=(r,e,n)=>{let t=e[0].dims,o=t,i=t[0],a=t[t.length-1],s=C.sizeFromDimension(t,1)/a,u=Ee(a),l=C.size(o)/u,d=[{type:12,data:s},{type:12,data:Math.floor(a/u)}],f=["type","type"],h=!1,g=[0,t.length-1];for(let v=0;v<t.length-2;v++)h=h||t[v+1]!==1,g.push(v+1);h=h&&t[t.length-1]!==1;let b=h?r.compute(at(r.inputs[0],g),{inputs:[r.inputs[0]],outputs:[-1]})[0]:r.inputs[0].reshape(Array.from({length:t.length},(v,x)=>t[g[x]])),_=M0(r,b,e[1],e[2],i,s,a,n.epsilon),T=v=>{let x=Ve(e[0].dataType),I=u===1?"vec2f":`mat${u}x2f`,$=L=>{let R=L===0?"x":"y",F=u===1?"f32":`vec${u}f`;switch(u){case 1:return`${x}(${F}(scale.${R}))`;case 2:return`vec2<${x}>(${F}(scale[0].${R}, scale[1].${R}))`;case 4:return`vec4<${x}>(${F}(scale[0].${R}, scale[1].${R}, scale[2].${R}, scale[3].${R}))`;default:throw new Error(`Not supported compoents ${u}`)}},O=N("input",e[0].dataType,e[0].dims,u),E=G("output",e[0].dataType,o,u);return`
  @group(0) @binding(0) var<storage, read> input : array<${O.type.storage}>;
  @group(0) @binding(1) var<storage, read> scale_input : array<${I}>;
  @group(0) @binding(2) var<storage, read_write> output : array<${E.type.storage}>;
  struct Uniforms {H: u32, C : u32};
  @group(0) @binding(3) var<uniform> uniforms: Uniforms;

  ${v.mainStart()}
    let current_image_number = global_idx / (uniforms.C * uniforms.H);
    let current_channel_number = global_idx % uniforms.C;

    let scale_offset = current_image_number * uniforms.C + current_channel_number;
    let scale = scale_input[scale_offset];
    output[global_idx] = fma(input[global_idx], ${$(0)}, ${$(1)});
  }`};r.compute({name:"InstanceNormalizationNHWC",shaderCache:{hint:`${u}`,inputDependencies:f},getRunData:()=>({outputs:[{dims:o,dataType:e[0].dataType}],dispatchGroup:{x:Math.ceil(l/64)},programUniforms:d}),getShaderSource:T},{inputs:[e[0],_]})},B0=(r,e)=>{e.format==="NHWC"?ZE(r,r.inputs,e):XE(r,r.inputs,e)}});var JE,YE,V0,G0=k(()=>{"use strict";le();ge();ye();JE=r=>{if(!r||r.length<2)throw new Error("layerNorm requires at least 2 inputs.")},YE=(r,e,n)=>{let t=e.simplified,o=r[0].dims,i=r[1],a=!t&&r[2],s=o,u=C.normalizeAxis(e.axis,o.length),l=C.sizeToDimension(o,u),d=C.sizeFromDimension(o,u),f=C.size(i.dims),h=a?C.size(a.dims):0;if(f!==d||a&&h!==d)throw new Error(`Size of X.shape()[axis:] == ${d}.
       Size of scale and bias (if provided) must match this.
       Got scale size of ${f} and bias size of ${h}`);let g=[];for(let O=0;O<o.length;++O)O<u?g.push(o[O]):g.push(1);let b=Ee(d),_=["type","type"],T=[{type:12,data:l},{type:1,data:d},{type:12,data:Math.floor(d/b)},{type:1,data:e.epsilon}];a&&_.push("type");let v=n>1,x=n>2,I=O=>{let E=Ve(r[0].dataType),L=[N("x",r[0].dataType,r[0].dims,b),N("scale",i.dataType,i.dims,b)];a&&L.push(N("bias",a.dataType,a.dims,b)),L.push(G("output",r[0].dataType,s,b)),v&&L.push(G("mean_data_output",1,g)),x&&L.push(G("inv_std_output",1,g));let R=[{name:"norm_count",type:"u32"},{name:"norm_size",type:"f32"},{name:"norm_size_vectorized",type:"u32"},{name:"epsilon",type:"f32"}];return`
  ${O.registerUniforms(R).declareVariables(...L)}
  ${O.mainStart()}
    ${O.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.norm_count")}
    let offset = global_idx * uniforms.norm_size_vectorized;
    var mean_vector = ${wc("f32",b)};
    var mean_square_vector = ${wc("f32",b)};

    for (var h: u32 = 0u; h < uniforms.norm_size_vectorized; h++) {
      let value = ${Hr(E,b,"x[h + offset]")};
      mean_vector += value;
      mean_square_vector += value * value;
    }
    let mean = ${Kt("mean_vector",b)} / uniforms.norm_size;
    let inv_std_dev = inverseSqrt(${Kt("mean_square_vector",b)} / uniforms.norm_size ${t?"":"- mean * mean"} + uniforms.epsilon);

    for (var j: u32 = 0; j < uniforms.norm_size_vectorized; j++) {
      let f32input = ${Hr(E,b,"x[j + offset]")};
      let f32scale = ${Hr(E,b,"scale[j]")};
      output[j + offset] = ${L[0].type.value}((f32input ${t?"":"- mean"}) * inv_std_dev * f32scale
        ${a?`+ ${Hr(E,b,"bias[j]")}`:""}
      );
    }

    ${v?"mean_data_output[global_idx] = mean":""};
    ${x?"inv_std_output[global_idx] = inv_std_dev":""};
  }`},$=[{dims:s,dataType:r[0].dataType}];return v&&$.push({dims:g,dataType:1}),x&&$.push({dims:g,dataType:1}),{name:"LayerNormalization",shaderCache:{hint:`${b};${n};${t}`,inputDependencies:_},getRunData:()=>({outputs:$,dispatchGroup:{x:Math.ceil(l/64)},programUniforms:T}),getShaderSource:I}},V0=(r,e)=>{JE(r.inputs),r.compute(YE(r.inputs,e,r.outputCount))}});var QE,U0,W0=k(()=>{"use strict";ge();Ha();ja();QE=r=>{if(!r||r.length!==2)throw new Error("MatMul requires 2 inputs.");if(r[0].dims[r[0].dims.length-1]!==r[1].dims[r[1].dims.length-2])throw new Error("shared dimension does not match.")},U0=r=>{QE(r.inputs);let e=Fn.calcShape(r.inputs[0].dims,r.inputs[1].dims,!0);if(!e)throw new Error("Can't use matmul on the given tensors");let n=e[e.length-1],t=r.inputs[0].dims[r.inputs[0].dims.length-1];if(n<8&&t<8)r.compute(Wa(r.inputs,{activation:""},e));else{let o=e[e.length-2],i=C.size(r.inputs[0].dims.slice(0,-2)),a=C.size(r.inputs[1].dims.slice(0,-2));if(i!==1&&o===1&&a===1){let s=r.inputs[0].reshape([1,i,t]),u=r.inputs[1].reshape([1,t,n]),l=[1,i,n],d=[s,u];r.compute(jo(d,{activation:""},e,l),{inputs:d})}else r.compute(jo(r.inputs,{activation:""},e))}}});var eC,tC,nC,H0,j0,q0=k(()=>{"use strict";le();ge();Xe();ye();eC=(r,e)=>{if(r.length<3||r.length>4)throw new Error("MatMulNBits requires 3 or 4 inputs");let n=r[0],t=n.dims.length;if(n.dims[t-1]!==e.k)throw new Error("The last dim of input shape does not match the k value");let o=Math.floor((e.k+e.blockSize-1)/e.blockSize),i=e.blockSize/8*e.bits,a=r[1];if(!C.areEqual(a.dims,[e.n,o,i]))throw new Error("The second inputs must be 3D tensor with shape N X nBlocksPerCol X blobSize");let u=r[2].dims;if(C.size(u)!==e.n*o)throw new Error("scales input size error.");if(r.length===4){let d=r[3].dims,f=e.n*(e.bits===8?o:Math.floor((o*e.bits+7)/8));if(C.size(d)!==f)throw new Error("zeroPoints input size error.")}},tC=(r,e)=>{let n=r[0].dims,t=n.length,o=n[t-2],i=e.k,a=e.n,s=n.slice(0,t-2),u=C.size(s),d=r[1].dims[2]/4,f=r[0].dataType,h=Ee(e.k),g=Ee(d),b=Ee(a),_=s.concat([o,a]),T=o>1&&a/b%2===0?2:1,v=C.size(_)/b/T,x=64,I=[],$=[u,o,i/h],O=C.convertShape(r[1].dims).slice();O.splice(-1,1,d/g),I.push(...W($)),I.push(...W(O)),I.push(...W(r[2].dims)),r.length===4&&I.push(...W(C.convertShape(r[3].dims)));let E=[u,o,a/b];I.push(...W(E));let L=R=>{let F=$.length,q=N("a",r[0].dataType,F,h),Z=N("b",12,O.length,g),re=N("scales",r[2].dataType,r[2].dims.length),pe=[q,Z,re],K=r.length===4?N("zero_points",12,r[3].dims.length):void 0;K&&pe.push(K);let Oe=E.length,fe=G("output",r[0].dataType,Oe,b),J=Ve(r[0].dataType),he=(()=>{switch(h){case 1:return`array<${J}, 8>`;case 2:return`mat4x2<${J}>`;case 4:return`mat2x4<${J}>`;default:throw new Error(`${h}-component is not supported.`)}})(),ee=()=>{let Se=`
          // reuse a data
            var input_offset = ${q.indicesToOffset(`${q.type.indices}(batch, row, word_offset)`)};
            var a_data: ${he};
            for (var j: u32 = 0; j < ${8/h}; j++) {
              a_data[j] = ${q.getByOffset("input_offset")};
              input_offset++;
            }
          `;for(let ae=0;ae<b*T;ae++)Se+=`
            b_value = ${g===1?`b${ae}_data`:`b${ae}_data[i]`};
            b_value_lower = unpack4xU8(b_value & b_mask);
            b_value_upper = unpack4xU8((b_value >> 4) & b_mask);
            b_quantized_values = ${he}(${Array.from({length:4},(U,z)=>`${J}(b_value_lower[${z}]), ${J}(b_value_upper[${z}])`).join(", ")});
            b_dequantized_values = ${h===1?`${he}(${Array.from({length:8},(U,z)=>`(b_quantized_values[${z}] - ${K?`zero_point${ae}`:"zero_point"}) * scale${ae}`).join(", ")});`:`(b_quantized_values - ${he}(${Array(8).fill(`${K?`zero_point${ae}`:"zero_point"}`).join(",")})) * scale${ae};`};
            workgroup_shared[local_id.x * ${T} + ${Math.floor(ae/b)}]${b>1?`[${ae%b}]`:""} += ${Array.from({length:8/h},(U,z)=>`${h===1?`a_data[${z}] * b_dequantized_values[${z}]`:`dot(a_data[${z}], b_dequantized_values[${z}])`}`).join(" + ")};
          `;return Se},Te=()=>{let Se=`
            var col_index = col * ${b};
            ${K?`
            let zero_point_bytes_per_col = (nBlocksPerCol + 1) / 2;
            var zero_point_byte_count: u32;
            var zero_point_word_index: u32;
            var zero_point_byte_offset: u32;
            let zero_point_nibble_offset: u32 = block & 0x1u;
            var zero_point_bits_offset: u32;
            var zero_point_word: u32;`:`
            // The default zero point is 8 for unsigned 4-bit quantization.
            let zero_point = ${J}(8);`}
            `;for(let ae=0;ae<b*T;ae++)Se+=`
            let scale${ae} = ${re.getByOffset("col_index * nBlocksPerCol + block")};
            ${K?`
            zero_point_byte_count = col_index * zero_point_bytes_per_col + (block >> 0x1u);
            zero_point_word_index = zero_point_byte_count >> 0x2u;
            zero_point_byte_offset = zero_point_byte_count & 0x3u;
            zero_point_bits_offset = (zero_point_byte_offset << 3) + (zero_point_nibble_offset << 2);
            zero_point_word = ${K.getByOffset("zero_point_word_index")} >> zero_point_bits_offset;
            let zero_point${ae} = ${J}((zero_point_word) & 0xFu);`:""}
            col_index += 1;`;return Se},Ze=()=>{let Se=`col_index = col * ${b};`;for(let ae=0;ae<b*T;ae++)Se+=`
            let b${ae}_data = ${Z.getByIndices(`${Z.type.indices}(col_index, block, word)`)};
            col_index += 1;`;return Se+=`
            var b_value: u32;
            let b_mask: u32 = 0x0F0F0F0Fu;
            var b_value_lower: vec4<u32>;
            var b_value_upper: vec4<u32>;
            var b_quantized_values: ${he};
            var b_dequantized_values: ${he};`,Se};return`
        var<workgroup> workgroup_shared: array<${fe.type.value}, ${T*x}>;
        ${R.declareVariables(...pe,fe)}
        ${R.mainStart([x,1,1])}
          let output_indices = ${fe.offsetToIndices(`(global_idx / ${x}) * ${T}`)};
          let col = output_indices[2];
          let row = output_indices[1];
          let batch = output_indices[0];
          let nBlocksPerCol = uniforms.b_shape[1];

          for (var block = local_id.x; block < nBlocksPerCol; block += ${x}) {
            //process one block
            var word_offset: u32 = block * ${e.blockSize/h};
            ${Te()}
            for (var word: u32 = 0; word < ${d}; word += ${g}) {
              ${Ze()}
              for (var i: u32 = 0; i < ${g}; i++) {
                ${ee()}
                word_offset += ${8/h};
              }
            }
          }
          workgroupBarrier();

          if (local_id.x < ${T}) {
            var output_value: ${fe.type.value} = ${fe.type.value}(0);
            var workgroup_shared_offset: u32 = local_id.x;
            for (var b: u32 = 0u; b < ${x}u; b++) {
              output_value += workgroup_shared[workgroup_shared_offset];
              workgroup_shared_offset += ${T};
            }
            ${fe.setByIndices(`${fe.type.indices}(batch, row, col + local_id.x)`,"output_value")};
          }
        }`};return{name:"MatMulNBits",shaderCache:{hint:`${e.blockSize};${e.bits};${h};${g};${b};${T};${x}`,inputDependencies:Array(r.length).fill("rank")},getRunData:()=>({outputs:[{dims:_,dataType:f}],dispatchGroup:{x:v},programUniforms:I}),getShaderSource:L}},nC=(r,e)=>{let n=r[0].dims,t=n.length,o=n[t-2],i=e.k,a=e.n,s=n.slice(0,t-2),u=C.size(s),d=r[1].dims[2]/4,f=r[0].dataType,h=Ee(e.k),g=Ee(d),b=s.concat([o,a]),_=128,T=a%8===0?8:a%4===0?4:1,v=_/T,x=v*g*8,I=x/h,$=x/e.blockSize,O=C.size(b)/T,E=[],L=[u,o,i/h],R=C.convertShape(r[1].dims).slice();R.splice(-1,1,d/g),E.push(...W(L)),E.push(...W(R)),E.push(...W(r[2].dims)),r.length===4&&E.push(...W(C.convertShape(r[3].dims)));let F=[u,o,a];E.push(...W(F));let q=Z=>{let re=L.length,pe=N("a",r[0].dataType,re,h),K=N("b",12,R.length,g),Oe=N("scales",r[2].dataType,r[2].dims.length),fe=[pe,K,Oe],J=r.length===4?N("zero_points",12,r[3].dims.length):void 0;J&&fe.push(J);let he=F.length,ee=G("output",r[0].dataType,he),Te=Ve(r[0].dataType),Ze=()=>{switch(h){case 1:return`
          let a_data0 = vec4<${Te}>(sub_a[word_offset], sub_a[word_offset + 1], sub_a[word_offset + 2], sub_a[word_offset + 3]);
          let a_data1 = vec4<${Te}>(sub_a[word_offset + 4], sub_a[word_offset + 5], sub_a[word_offset + 6], sub_a[word_offset + 7]);`;case 2:return`
          let a_data0 = vec4<${Te}>(sub_a[word_offset], sub_a[word_offset + 1]);
          let a_data1 = vec4<${Te}>(sub_a[word_offset + 2], sub_a[word_offset + 3]);`;case 4:return`
          let a_data0 = sub_a[word_offset];
          let a_data1 = sub_a[word_offset + 1];`;default:throw new Error(`${h}-component is not supported.`)}};return`
        var<workgroup> sub_a: array<${pe.type.value}, ${I}>;
        var<workgroup> inter_results: array<array<${ee.type.value}, ${v}>, ${T}>;
        ${Z.declareVariables(...fe,ee)}
        ${Z.mainStart([v,T,1])}
          let output_indices = ${ee.offsetToIndices(`workgroup_index * ${T}`)};
          let col = output_indices[2];
          let row = output_indices[1];
          let batch = output_indices[0];
          let n_blocks_per_col = uniforms.b_shape[1];
          let num_tiles =  (n_blocks_per_col - 1) / ${$} + 1;

          // Loop over shared dimension.
          for (var tile: u32 = 0; tile < num_tiles; tile += 1) {
            let a_col_start = tile * ${I};
            // load one tile A data into shared memory.
            for (var a_offset = local_idx; a_offset < ${I}; a_offset += ${_})
            {
              let a_col = a_col_start + a_offset;
              if (a_col < uniforms.a_shape[2])
              {
                sub_a[a_offset] = ${pe.getByIndices(`${pe.type.indices}(batch, row, a_col)`)};
              } else {
                sub_a[a_offset] = ${pe.type.value}(0);
              }
            }
            workgroupBarrier();

            // each thread process one block
            let b_row = col + local_id.y;
            let block = tile * ${$} + local_id.x;
            ${J?`
            let zero_point_bytes_per_col = (n_blocks_per_col + 1) / 2;
            let zero_point_byte_count = b_row * zero_point_bytes_per_col + (block >> 0x1u);
            let zero_point_word_index = zero_point_byte_count >> 0x2u;
            let zero_point_byte_offset = zero_point_byte_count & 0x3u;
            let zero_point_nibble_offset: u32 = block & 0x1u;
            let zero_point_bits_offset = (zero_point_byte_offset << 3) + (zero_point_nibble_offset << 2);
            let zero_point_word = ${J.getByOffset("zero_point_word_index")} >> zero_point_bits_offset;
            let zero_point = ${Te}((zero_point_word) & 0xFu);`:`
            // The default zero point is 8 for unsigned 4-bit quantization.
            let zero_point = ${Te}(8);`}
            let scale = ${Oe.getByOffset("b_row * n_blocks_per_col + block")};
            let b_data = ${K.getByIndices(`${K.type.indices}(b_row, block, 0)`)};
            var word_offset = local_id.x * ${e.blockSize/h};
            for (var i: u32 = 0; i < ${g}; i++) {
              ${Ze()}
              let b_value = ${g===1?"b_data":"b_data[i]"};
              let b_value_lower = unpack4xU8(b_value & 0x0F0F0F0Fu);
              let b_value_upper = unpack4xU8((b_value >> 4) & 0x0F0F0F0Fu);
              let b_quantized_values = mat2x4<${Te}>(${Array.from({length:4},(Se,ae)=>`${Te}(b_value_lower[${ae}]), ${Te}(b_value_upper[${ae}])`).join(", ")});
              let b_dequantized_values = (b_quantized_values - mat2x4<${Te}>(${Array(8).fill("zero_point").join(",")})) * scale;
              inter_results[local_id.y][local_id.x] += ${Array.from({length:2},(Se,ae)=>`${`dot(a_data${ae}, b_dequantized_values[${ae}])`}`).join(" + ")};
              word_offset += ${8/h};
            }
            workgroupBarrier();
          }

          if (local_idx < ${T}) {
            var output_value: ${ee.type.value} = ${ee.type.value}(0);
            for (var b = 0u; b < ${v}; b++) {
              output_value += inter_results[local_idx][b];
            }
            if (col + local_idx < uniforms.output_shape[2])
            {
              ${ee.setByIndices(`${ee.type.indices}(batch, row, col + local_idx)`,"output_value")}
            }
          }
        }`};return{name:"BlockwiseMatMulNBits32",shaderCache:{hint:`${e.blockSize};${h};${g};${v};${T}`,inputDependencies:Array(r.length).fill("rank")},getRunData:()=>({outputs:[{dims:b,dataType:f}],dispatchGroup:{x:O},programUniforms:E}),getShaderSource:q}},H0=(r,e)=>{eC(r.inputs,e),e.blockSize===32&&r.adapterInfo.isVendor("intel")&&r.adapterInfo.isArchitecture("gen-12lp")?r.compute(nC(r.inputs,e)):r.compute(tC(r.inputs,e))},j0=r=>ce(r)});var rC,oC,iC,aC,sC,uC,lC,cC,K0,X0=k(()=>{"use strict";le();ge();ye();rC=r=>{if(!r||r.length<1)throw new Error("Too few inputs");if(r[0].dataType!==1&&r[0].dataType!==10)throw new Error("Input type must be float or float16.");if(r.length>=2){let e=r[0].dims.length*2===r[1].dims[0];if(r.length===4&&(e=r[3].dims[0]*2===r[1].dims[0]),!e)throw new Error("The pads should be a 1D tensor of shape [2 * input_rank] or [2 * num_axes].")}},oC=(r,e,n)=>{let t="";for(let o=e-1;o>=0;--o)t+=`
            k = i32(${r.indicesGet("indices",o)}) - ${Q("uniforms.pads",o,n)};
            if (k < 0) {
              break;
            }
            if (k >= i32(${Q("uniforms.x_shape",o,e)})) {
              break;
            }
            offset += k * i32(${Q("uniforms.x_strides",o,e)});
        `;return`
          value = ${r.type.value}(uniforms.constant_value);
          for (var i = 0; i < 1; i++) {
            var offset = 0;
            var k = 0;
            ${t}
            value = x[offset];
          }
      `},iC=(r,e,n)=>{let t="";for(let o=e-1;o>=0;--o)t+=`
                k = i32(${r.indicesGet("indices",o)}) - ${Q("uniforms.pads",o,n)};
                if (k < 0) {
                  k = -k;
                }
                {
                  let _2n_1 = 2 * (i32(${Q("uniforms.x_shape",o,e)}) - 1);
                  k = k % _2n_1;
                  if(k >= i32(${Q("uniforms.x_shape",o,e)})) {
                    k = _2n_1 - k;
                  }
                }
                offset += k * i32(${Q("uniforms.x_strides",o,e)});
            `;return`
              var offset = 0;
              var k = 0;
              ${t}
              value = x[offset];
          `},aC=(r,e,n)=>{let t="";for(let o=e-1;o>=0;--o)t+=`
                k = i32(${r.indicesGet("indices",o)}) - ${Q("uniforms.pads",o,n)};
                if (k < 0) {
                  k = 0;
                }
                if (k >= i32(${Q("uniforms.x_shape",o,e)})) {
                  k = i32(${Q("uniforms.x_shape",o,e)}) - 1;
                }
                offset += k * i32(${Q("uniforms.x_strides",o,e)});
            `;return`
              var offset = 0;
              var k = 0;
              ${t}
              value = x[offset];
          `},sC=(r,e,n)=>{let t="";for(let o=e-1;o>=0;--o)t+=`
                k = i32(${r.indicesGet("indices",o)}) - ${Q("uniforms.pads",o,n)};
                if (k < 0)  {
                  k += i32(${Q("uniforms.x_shape",o,e)}]);
                }
                if (k >= i32(${Q("uniforms.x_shape",o,e)})) {
                  k -= i32(${Q("uniforms.x_shape",o,e)});
                }
                offset += k * i32(${Q("uniforms.x_strides",o,e)});
            `;return`
              var offset = 0;
              var k = 0;
              ${t}
              value = x[offset];
          `},uC=(r,e,n)=>{switch(n.mode){case 0:return oC(r,e,n.pads.length);case 1:return iC(r,e,n.pads.length);case 2:return aC(r,e,n.pads.length);case 3:return sC(r,e,n.pads.length);default:throw new Error("Invalid mode")}},lC=(r,e)=>{let n=C.padShape(r[0].dims.slice(),e.pads),t=r[0].dims,o=C.size(n),i=[{type:12,data:o},{type:6,data:e.pads}],a=r.length>=3&&r[2].data;e.mode===0&&i.push({type:a?r[2].dataType:1,data:e.value}),i.push(...W(r[0].dims,n));let s=["rank"],u=l=>{let d=G("output",r[0].dataType,n.length),f=N("x",r[0].dataType,t.length),h=f.type.value,g=uC(d,t.length,e),b=[{name:"output_size",type:"u32"},{name:"pads",type:"i32",length:e.pads.length}];return e.mode===0&&b.push({name:"constant_value",type:a?h:"f32"}),`
            ${l.registerUniforms(b).declareVariables(f,d)}
            ${l.mainStart()}
            ${l.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.output_size")}

            let indices = ${d.offsetToIndices("global_idx")};

            var value = ${h}(0);
            ${g}
            output[global_idx] = value;
        }`};return{name:"Pad",shaderCache:{hint:`${e.mode}${a}`,inputDependencies:s},getRunData:()=>({outputs:[{dims:n,dataType:r[0].dataType}],dispatchGroup:{x:Math.ceil(C.size(n)/64)},programUniforms:i}),getShaderSource:u}},cC=(r,e)=>{if(r.length>1){let n=r[1].getBigInt64Array(),t=r.length>=3&&r[2].data?r[2].dataType===10?r[2].getUint16Array()[0]:r[2].getFloat32Array()[0]:0,o=r[0].dims.length,i=new Int32Array(2*o).fill(0);if(r.length>=4){let s=r[3].getBigInt64Array();for(let u=0;u<s.length;u++)i[Number(s[u])]=Number(n[u]),i[Number(s[u])+o]=Number(n[u+s.length])}else n.forEach((s,u)=>i[Number(u)]=Number(s));let a=[];return i.forEach(s=>a.push(s)),{mode:e.mode,value:t,pads:a}}else return e},K0=(r,e)=>{rC(r.inputs);let n=cC(r.inputs,e);r.compute(lC(r.inputs,n),{inputs:[0]})}});var Za,Z0,J0,Y0,Q0,dC,pC,ew,tw,nw,rw,ow,iw,aw,sw,uw,lw,cw,dw,pw=k(()=>{"use strict";pt();le();ge();ye();Za=r=>{if(me.webgpu.validateInputContent&&(!r||r.length!==1))throw new Error("Pool ops requires 1 input.")},Z0=(r,e,n)=>{let t=e.format==="NHWC",o=r.dims.slice();t&&o.splice(1,0,o.pop());let i=Object.hasOwnProperty.call(e,"dilations"),a=e.kernelShape.slice(),s=e.strides.slice(),u=i?e.dilations.slice():[],l=e.pads.slice();Ur.adjustPoolAttributes(n,o,a,s,u,l);let d=Ur.computePoolOutputShape(n,o,s,u,a,l,e.autoPad),f=Object.assign({},e);i?Object.assign(f,{kernelShape:a,strides:s,pads:l,dilations:u,cacheKey:e.cacheKey}):Object.assign(f,{kernelShape:a,strides:s,pads:l,cacheKey:e.cacheKey});let h=d.slice();return h.push(h.splice(1,1)[0]),[f,t?h:d]},J0=(r,e)=>{let n=e.format==="NHWC",t=C.size(r),o=C.size(e.kernelShape),i=[{type:12,data:t},{type:12,data:o}],a=[{name:"outputSize",type:"u32"},{name:"kernelSize",type:"u32"}];if(e.kernelShape.length<=2){let s=e.kernelShape[e.kernelShape.length-1],u=e.strides[e.strides.length-1],l=e.pads[e.pads.length/2-1],d=e.pads[e.pads.length-1],f=!!(l+d);i.push({type:12,data:s},{type:12,data:u},{type:12,data:l},{type:12,data:d}),a.push({name:"kw",type:"u32"},{name:"sw",type:"u32"},{name:"pwStart",type:"u32"},{name:"pwEnd",type:"u32"});let h=!1;if(e.kernelShape.length===2){let g=e.kernelShape[e.kernelShape.length-2],b=e.strides[e.strides.length-2],_=e.pads[e.pads.length/2-2],T=e.pads[e.pads.length-2];h=!!(_+T),i.push({type:12,data:g},{type:12,data:b},{type:12,data:_},{type:12,data:T}),a.push({name:"kh",type:"u32"},{name:"sh",type:"u32"},{name:"phStart",type:"u32"},{name:"phEnd",type:"u32"})}return[i,a,!0,f,h]}else{if(n)throw new Error("Pooling with kernelShape.length > 2 is not supported for NHWC format.");let s=C.computeStrides(e.kernelShape);i.push({type:12,data:s},{type:12,data:e.pads},{type:12,data:e.strides}),a.push({name:"kernelStrides",type:"u32",length:s.length},{name:"pads",type:"u32",length:e.pads.length},{name:"strides",type:"u32",length:e.strides.length});let u=e.pads.reduce((l,d)=>l+d);return[i,a,!!u,!1,!1]}},Y0=(r,e,n,t,o,i,a,s,u,l,d,f)=>{let h=o.format==="NHWC",g=e.type.value,b=G("output",e.type.tensor,t);if(o.kernelShape.length<=2){let _="",T="",v="",x=n-(h?2:1);if(d?_=`
                for (var i: u32 = 0u; i < uniforms.kw; i++) {
                  xIndices[${x}] = indices[${x}] * uniforms.sw - uniforms.pwStart + i;
                  if (xIndices[${x}] < 0 || xIndices[${x}]
                      >= uniforms.x_shape[${x}]) {
                    pad++;
                    continue;
                  }
                  let x_val = x[${e.indicesToOffset("xIndices")}];
                  ${i}
                }`:_=`
                for (var i: u32 = 0u; i < uniforms.kw; i++) {
                  xIndices[${x}] = indices[${x}] * uniforms.sw - uniforms.pwStart + i;
                  let x_val = x[${e.indicesToOffset("xIndices")}];
                  ${i}
                }`,o.kernelShape.length===2){let $=n-(h?3:2);f?T=`
                for (var j: u32 = 0u; j < uniforms.kh; j++) {
                  xIndices[${$}] = indices[${$}] * uniforms.sh - uniforms.phStart + j;
                  if (xIndices[${$}] < 0 || xIndices[${$}] >= uniforms.x_shape[${$}]) {
                    pad += i32(uniforms.kw);
                    continue;
                  }
              `:T=`
                for (var j: u32 = 0u; j < uniforms.kh; j++) {
                  xIndices[${$}] = indices[${$}] * uniforms.sh - uniforms.phStart + j;
                `,v=`
              }
            `}return`
            ${r.registerUniforms(u).declareVariables(e,b)}

            ${r.mainStart()}
              ${r.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.outputSize")}

              let indices = ${b.offsetToIndices("global_idx")};
              var xIndices = ${b.offsetToIndices("global_idx")};

              var value = ${g}(${s});
              var pad = 0;
              ${T}
              ${_}
              ${v}
              ${a}

              output[global_idx] = value;
            }`}else{if(h)throw new Error("Pooling with kernelShape.length > 2 is not supported for NHWC format.");let _=o.kernelShape.length,T=o.pads.length,v="";return l?v=`
                if (xIndices[j] >= uniforms.x_shape[j]) {
                  pad++;
                  isPad = true;
                  break;
                }
              }
              if (!isPad) {
                let x_val = x[${e.indicesToOffset("xIndices")}];
                ${i}
              }`:v=`
              }
              let x_val = x[${e.indicesToOffset("xIndices")}];
              ${i}
            `,`
            ${r.registerUniforms(u).declareVariables(e,b)}

            ${r.mainStart()}
              ${r.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.outputSize")}
              let indices = ${b.offsetToIndices("global_idx")};
              var xIndices = ${b.offsetToIndices("global_idx")};

              var offsets: array<u32, ${_}>;

              var value = ${g}(${s});
              var pad = 0;
              var isPad = false;

              for (var i: u32 = 0u; i < uniforms.kernelSize; i++) {
                var offset = i;
                for (var j = 0u; j < ${_-1}u; j++) {
                  offsets[j] = offset / ${Q("uniforms.kernelStrides","j",_)};
                  offset -= offsets[j] * ${Q("uniforms.kernelStrides","j",_)};
                }
                offsets[${_-1}] = offset;

                isPad = false;
                for (var j = ${n-_}u; j < ${n}u; j++) {
                  xIndices[j] = indices[j] * ${Q("uniforms.strides",`j - ${n-_}u`,_)}
                    + offsets[j - ${n-_}u] - ${Q("uniforms.pads","j - 2u",T)};
                  ${v}
              }
              ${a}

              output[global_idx] = value;
            }`}},Q0=r=>`${r.format};${r.ceilMode};${r.autoPad};${r.kernelShape.length}`,dC=r=>`${Q0(r)};${r.countIncludePad}`,pC=r=>`${Q0(r)};${r.storageOrder};${r.dilations}`,ew=r=>({format:r.format,autoPad:["NOTSET","VALID","SAME_UPPER","SAME_LOWER"][r.auto_pad],ceilMode:r.ceil_mode,kernelShape:r.kernel_shape,strides:r.strides,pads:r.pads}),tw=(r,e,n,t)=>{let[o,i]=Z0(e,t,n),a=N("x",e.dataType,e.dims.length),s=a.type.value,u="value += x_val;",l="";o.countIncludePad?l+=`value /= ${s}(uniforms.kernelSize);`:l+=`value /= ${s}(i32(uniforms.kernelSize) - pad);`;let[d,f,h,g,b]=J0(i,o);d.push(...W(e.dims,i));let _=["rank"];return{name:r,shaderCache:{hint:`${t.cacheKey};${h};${g};${b}`,inputDependencies:_},getRunData:()=>({outputs:[{dims:i,dataType:e.dataType}],dispatchGroup:{x:Math.ceil(C.size(i)/64)},programUniforms:d}),getShaderSource:T=>Y0(T,a,e.dims.length,i.length,o,u,l,0,f,h,g,b)}},nw=r=>{let e=r.count_include_pad!==0,n=ew(r);if(n.ceilMode!==0)throw new Error("using ceil() in shape computation is not yet supported for AveragePool");let t={countIncludePad:e,...n,cacheKey:""};return{...t,cacheKey:dC(t)}},rw=(r,e)=>{Za(r.inputs),r.compute(tw("AveragePool",r.inputs[0],!1,e))},ow={autoPad:"",ceilMode:0,countIncludePad:!1,kernelShape:[],strides:[],pads:[],storageOrder:0,dilations:[]},iw=r=>{let e=r.format;return{format:e,...ow,cacheKey:e}},aw=(r,e)=>{Za(r.inputs),r.compute(tw("GlobalAveragePool",r.inputs[0],!0,e))},sw=(r,e,n,t)=>{let[o,i]=Z0(e,t,n),a=`
      value = max(x_val, value);
    `,s="",u=N("x",e.dataType,e.dims.length),l=["rank"],[d,f,h,g,b]=J0(i,o);return d.push(...W(e.dims,i)),{name:r,shaderCache:{hint:`${t.cacheKey};${h};${g};${b}`,inputDependencies:l},getRunData:()=>({outputs:[{dims:i,dataType:e.dataType}],dispatchGroup:{x:Math.ceil(C.size(i)/64)},programUniforms:d}),getShaderSource:_=>Y0(_,u,e.dims.length,i.length,o,a,s,e.dataType===10?-65504:-1e5,f,h,g,b)}},uw=(r,e)=>{Za(r.inputs),r.compute(sw("MaxPool",r.inputs[0],!1,e))},lw=r=>{let e=r.storage_order,n=r.dilations,t=ew(r);if(e!==0)throw new Error("column major storage order is not yet supported for MaxPool");if(t.ceilMode!==0)throw new Error("using ceil() in shape computation is not yet supported for MaxPool");let o={storageOrder:e,dilations:n,...t,cacheKey:""};return{...o,cacheKey:pC(o)}},cw=r=>{let e=r.format;return{format:e,...ow,cacheKey:e}},dw=(r,e)=>{Za(r.inputs),r.compute(sw("GlobalMaxPool",r.inputs[0],!0,e))}});var hC,mC,fw,hw,mw=k(()=>{"use strict";le();ge();Xe();ye();hC=(r,e)=>{if(r.length<2||r.length>3)throw new Error("DequantizeLinear requires 2 or 3 inputs.");if(r.length===3&&r[1].dims===r[2].dims)throw new Error("x-scale and x-zero-point must have the same shape.");if(r.length===3&&r[0].dataType!==r[2].dataType)throw new Error("x and x-zero-point must have the same data type.");if(r[0].dataType===6&&r.length>2)throw new Error("In the case of dequantizing int32 there is no zero point.");if(r[1].dims.length!==0&&r[1].dims.length!==1&&r[1].dims.length!==r[0].dims.length)throw new Error("scale input must be a scalar, a 1D tensor, or have the same rank as the input tensor.");if(r.length>2){if(r[0].dataType!==r[2].dataType)throw new Error("x and x-zero-point must have the same data type.");if(r[1].dims.length!==r[2].dims.length)throw new Error("scale and zero-point inputs must have the same rank.");if(!r[1].dims.map((n,t)=>n===r[2].dims[t]).reduce((n,t)=>n&&t,!0))throw new Error("scale and zero-point inputs must have the same shape.")}if(e.blockSize>0){if(r[1].dims.length===0||r[1].dims.length===1&&r[1].dims[0]===1)throw new Error("blockSize must be set only for block quantization.");if(!r[1].dims.map((o,i)=>i===e.axis||o===r[0].dims[i]).reduce((o,i)=>o&&i,!0))throw new Error("For block qunatization, scale input shape to match the input shape except for the axis");if(r[1].dims.length!==r[0].dims.length)throw new Error("For block qunatization the scale input rank must be the same as the x rank.");let n=r[0].dims[e.axis],t=r[1].dims[e.axis];if(e.blockSize<Math.ceil(n/t)||e.blockSize>Math.ceil(n/(t-1)-1))throw new Error("blockSize must be with in the range [ceil(dI / Si), ceil(dI / (Si - 1) - 1)].")}},mC=(r,e)=>{let n=C.normalizeAxis(e.axis,r[0].dims.length),t=r[0].dataType,o=t===3,i=r[0].dims,a=r[1].dataType,s=C.size(i),u=t===3||t===2,l=u?[Math.ceil(C.size(r[0].dims)/4)]:r[0].dims,d=r[1].dims,f=r.length>2?r[2]:void 0,h=f?u?[Math.ceil(C.size(f.dims)/4)]:f.dims:void 0,g=d.length===0||d.length===1&&d[0]===1,b=g===!1&&d.length===1,_=Ee(s),T=g&&(!u||_===4),v=T?_:1,x=T&&!u?_:1,I=N("input",u?12:t,l.length,x),$=N("scale",a,d.length),O=f?N("zero_point",u?12:t,h.length):void 0,E=G("output",a,i.length,v),L=[I,$];O&&L.push(O);let R=[l,d];f&&R.push(h);let F=[{type:12,data:s/v},{type:12,data:n},{type:12,data:e.blockSize},...W(...R,i)],q=Z=>{let re=[{name:"output_size",type:"u32"},{name:"axis",type:"u32"},{name:"block_size",type:"u32"}];return`
      ${Z.registerUniforms(re).declareVariables(...L,E)}
      ${Z.mainStart()}
          ${Z.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.output_size")}
          let output_indices = ${E.offsetToIndices("global_idx")};

          // Set input x
          ${u?`
            let input = ${I.getByOffset("global_idx / 4")};
            let x_vec = ${o?"unpack4xI8(input)":"unpack4xU8(input)"};
            let x_value = ${v===1?"x_vec[global_idx % 4]":"x_vec"};`:`let x_value = ${I.getByOffset("global_idx")};`};

          // Set scale input
          ${g?`let scale_value= ${$.getByOffset("0")}`:b?`
            let scale_index = ${E.indicesGet("output_indices","uniforms.axis")};
            let scale_value= ${$.getByOffset("scale_index")};`:`
            var scale_indices: ${$.type.indices} = output_indices;
            let index = ${$.indicesGet("scale_indices","uniforms.axis")} / uniforms.block_size;
            ${$.indicesSet("scale_indices","uniforms.axis","index")};
            let scale_value= ${$.getByIndices("scale_indices")};`};

          // Set zero-point input
          ${O?g?u?`
                let zero_point_input = ${O.getByOffset("0")};
                let zero_point_vec =  ${o?"unpack4xI8(zero_point_input)":"unpack4xU8(zero_point_input)"};
                let zero_point_value= zero_point_vec[0]`:`let zero_point_value = ${O.getByOffset("0")}`:b?u?`
                let zero_point_index = ${E.indicesGet("output_indices","uniforms.axis")};
                let zero_point_input = ${O.getByOffset("zero_point_index / 4")};
                let zero_point_vec =  ${o?"unpack4xI8(zero_point_input)":"unpack4xU8(zero_point_input)"};
                let zero_point_value = zero_point_vec[zero_point_index % 4]`:`
                let zero_point_index = ${E.indicesGet("output_indices","uniforms.axis")};
                let zero_point_value = ${O.getByOffset("zero_point_index")};`:u?`
                let zero_point_offset = ${$.indicesToOffset("scale_indices")};
                let zero_point_input = ${O.getByOffset("zero_point_offset / 4")};
                let zero_point_vec = ${o?"unpack4xI8(zero_point_input)":"unpack4xU8(zero_point_input)"};
                let zero_point_value = zero_point_vec[zero_point_offset % 4];`:`let zero_point_value = ${O.getByIndices("scale_indices")};`:`let zero_point_value = ${u?o?"i32":"u32":I.type.value}(0);`};
      // Compute and write output
      ${E.setByOffset("global_idx",`${E.type.value}(x_value - zero_point_value) * scale_value`)};
      }`};return{name:"DequantizeLinear",shaderCache:{hint:e.cacheKey,inputDependencies:O?["rank","rank","rank"]:["rank","rank"]},getShaderSource:q,getRunData:()=>({outputs:[{dims:i,dataType:a}],dispatchGroup:{x:Math.ceil(s/v/64),y:1,z:1},programUniforms:F})}},fw=(r,e)=>{hC(r.inputs,e),r.compute(mC(r.inputs,e))},hw=r=>ce({axis:r.axis,blockSize:r.blockSize})});var gC,bC,gw,bw=k(()=>{"use strict";pt();le();ye();gC=(r,e,n)=>{let t=r===e,o=r<e&&n<0,i=r>e&&n>0;if(t||o||i)throw new Error("Range these inputs' contents are invalid.")},bC=(r,e,n,t)=>{let o=Math.abs(Math.ceil((e-r)/n)),i=[o],a=o,s=[{type:12,data:a},{type:t,data:r},{type:t,data:n},...W(i)],u=l=>{let d=G("output",t,i.length),f=d.type.value,h=[{name:"outputSize",type:"u32"},{name:"start",type:f},{name:"delta",type:f}];return`
        ${l.registerUniforms(h).declareVariables(d)}
        ${l.mainStart()}
        ${l.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.outputSize")}
        output[global_idx] = uniforms.start + ${f}(global_idx) * uniforms.delta;
      }`};return{name:"Range",shaderCache:{hint:`${t}`},getShaderSource:u,getRunData:()=>({outputs:[{dims:i,dataType:t}],dispatchGroup:{x:Math.ceil(a/64)},programUniforms:s})}},gw=r=>{let e=0,n=0,t=0;r.inputs[0].dataType===6?(e=r.inputs[0].getInt32Array()[0],n=r.inputs[1].getInt32Array()[0],t=r.inputs[2].getInt32Array()[0]):r.inputs[0].dataType===1&&(e=r.inputs[0].getFloat32Array()[0],n=r.inputs[1].getFloat32Array()[0],t=r.inputs[2].getFloat32Array()[0]),me.webgpu.validateInputContent&&gC(e,n,t),r.compute(bC(e,n,t,r.inputs[0].dataType),{inputs:[]})}});var yC,_C,yw,_w,vw=k(()=>{"use strict";le();ge();Xe();ye();yC=(r,e,n,t)=>{if(r!=="none"&&t!=="i32"&&t!=="u32"&&t!=="f32")throw new Error(`Input ${t} is not supported with reduction ${r}.`);let o=`{
                var oldValue = 0;
                loop {
                  let newValueF32 =`,i=`;
                  let newValue = bitcast<i32>(newValueF32);
                  let res = atomicCompareExchangeWeak(&${e}, oldValue, newValue);
                  if res.exchanged {
                    break;
                  }
                  oldValue = res.old_value;
                }
              }`;switch(r){case"none":return`${e}=${n};`;case"add":return t==="i32"||t==="u32"?`atomicAdd(&${e}, bitcast<${t}>(${n}));`:`
              ${o}bitcast<${t}>(oldValue) + (${n})${i}`;case"max":return t==="i32"||t==="u32"?`atomicMax(&${e}, bitcast<${t}>(${n}));`:`
                ${o}max(bitcast<f32>(oldValue), (${n}))${i}`;case"min":return t==="i32"||t==="u32"?`atomicMin(&${e}, bitcast<${t}>(${n}));`:`${o}min(bitcast<${t}>(oldValue), (${n}))${i}`;case"mul":return`${o}(bitcast<${t}>(oldValue) * (${n}))${i}`;default:throw new Error(`Reduction ${r} is not supported.`)}},_C=(r,e)=>{let n=r[0].dims,t=r[1].dims,o=n,i=1,a=Math.ceil(C.sizeToDimension(t,t.length-1)/i),s=t[t.length-1],u=C.sizeFromDimension(n,s),l=[{type:12,data:a},{type:12,data:s},{type:12,data:u},...W(r[1].dims,r[2].dims,o)],d=f=>{let h=N("indices",r[1].dataType,r[1].dims.length),g=N("updates",r[2].dataType,r[2].dims.length,i),b=e.reduction!=="none"&&e.reduction!==""?jy("output",r[0].dataType,o.length):G("output",r[0].dataType,o.length,i);return`
      ${f.registerUniform("output_size","u32").registerUniform("last_index_dimension","u32").registerUniform("num_updates_elements","u32").declareVariables(h,g,b)}
      ${f.mainStart()}
        ${f.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.output_size")}
  var data_offset = 0u;
  let indices_start = uniforms.last_index_dimension * global_idx;
  let indices_end = indices_start + uniforms.last_index_dimension;
  for (var i = indices_start; i < indices_end; i++) {
    var index = i32(indices[i].x);
    ${r[0].dims.length===1?`
    let element_count_dim = uniforms.output_strides;
    let dim_value = uniforms.output_shape;`:`
    let element_count_dim = uniforms.output_strides[i - indices_start];
    let dim_value = uniforms.output_shape[i - indices_start];`}
    if (index >= 0) {
      if (index >= i32(dim_value)) {
        index = i32(dim_value - 1);
      }
    } else {
      if (index < -i32(dim_value)) {
        index = 0;
      } else {
        index += i32(dim_value);
      }
    }
    data_offset += u32((u32(index) * element_count_dim));
  }

  for (var i = 0u; i < uniforms.num_updates_elements; i++) {
    let value = updates[uniforms.num_updates_elements * global_idx + i];
    ${yC(e.reduction,"output[data_offset + i]","value",b.type.value)}
  }

      }`};return{name:"ScatterND",shaderCache:{hint:`${e.cacheKey}_${e.reduction}`,inputDependencies:["rank","rank"]},getRunData:()=>({outputs:[{dims:o,dataType:r[0].dataType}],dispatchGroup:{x:Math.ceil(a/64)},programUniforms:l}),getShaderSource:d}},yw=r=>ce({reduction:r.reduction}),_w=(r,e)=>{r.compute(_C(r.inputs,e),{inputs:[r.inputs[1],r.inputs[2]],outputs:[]})}});var vC,wC,xC,ww,TC,IC,SC,$C,AC,OC,PC,EC,xw,CC,DC,kC,NC,LC,Tw,Iw,Sw=k(()=>{"use strict";le();ge();Xe();ye();vC=(r,e)=>{if(r.every(n=>n>0||(()=>{throw new Error("Resize requires scales input values to be positive")})),r.length>0){if(e.mode==="linear"){if(!(r.length===2||r.length===3||r.length===4&&r[0]===1&&r[1]===1||r.length===4&&r[0]===1&&r[3]===1||r.length===5&&r[0]===1&&r[1]===1))throw new Error(`For linear mode, Resize requires scales to be 2D, 3D, 4D with either two outermost or one innermost and
            one outermost scale values equal to 1, or 5D with two outermost scale values equal to 1`)}else if(e.mode==="cubic"&&!(r.length===2||r.length===4&&r[0]===1&&r[1]===1||r.length===4&&r[0]===1&&r[3]===1))throw new Error("Resize requires scales input size to be 2 or 4 for cubic mode")}},wC=(r,e,n)=>{e.every(o=>o>=0&&o<n||(()=>{throw new Error("Resize requires axes input values to be positive and less than rank")}));let t=new Array(n).fill(1);return e.forEach((o,i)=>t[o]=r[i]),t},xC=(r,e,n,t,o,i)=>{let[a,s,u]=n>10?[1,2,3]:[-1,r.length>1?1:-1,-1],l=r[0].dims.length;if(a>0&&r.length>a&&r[a].dims.length>0)r[a].getFloat32Array().forEach(d=>i.push(d));else if(e.coordinateTransformMode==="tf_crop_and_resize")throw new Error("Resize requires RoI input to be specified when coordinateTransformMode is tfCropAndResize");if(s>0&&r.length>s&&r[s].dims.length===1&&r[s].dims[0]>0){if(r[s].getFloat32Array().forEach(d=>t.push(d)),t.length!==0&&t.length!==l&&n>=18&&t.length!==e.axes.length)throw new Error("Resize requires scales input size to be same as input rank or axes size for opset 18 and up");vC(t,e),e.axes.length>0&&wC(t,e.axes,l).forEach((d,f)=>t[f]=d)}if(u>0&&r.length>u&&r[u].dims.length===1&&r[u].dims[0]>0&&(r[u].getBigInt64Array().forEach(d=>o.push(Number(d))),o.length!==0&&o.length!==l&&n>=18&&o.length!==e.axes.length))throw new Error("Resize requires sizes input size to be same as input rank or axes size for opset 18 and up");if(e.axes.length>0){if(t.length!==0&&t.length!==e.axes.length)throw new Error('Resize requires "scales" input size to be of axes rank when axes attributes is specified');if(o.length!==0&&o.length!==e.axes.length)throw new Error('Resize requires "sizes" input size to be of rank axes rank when axes attributes is specified')}if(typeof t<"u"&&typeof o<"u"&&t.length>0&&o.length>l)throw new Error("Resize requires only of scales or sizes to be specified")},ww=(r,e,n,t)=>`
  // The whole part and the fractional part are calculated separately due to inaccuracy of floating
  // point division. As an example, f32(21) / f32(7) may evaluate to 2.99... instead of 3, causing an
  // offset-by-one error later in floor().
  let big = (${r}) * (${e});
  let whole = ${t}(big / (${n}));
  let fract = ${t}(big % (${n})) / ${t}(${n});
  return whole + fract;
`,TC=(r,e)=>`fn getOriginalCoordinateFromResizedCoordinate(xResized: u32, xScale: f32, lengthResized: u32,
     lengthOriginal: u32, roiStart: f32, roiEnd: f32) -> ${e} { `+(()=>{switch(r){case"asymmetric":return`
          if (xScale < 1.0 || floor(xScale) != xScale) {
            return ${e}(xResized) / ${e}(xScale);
          } else {
            ${ww("xResized","lengthOriginal","lengthResized",e)}
          }
        `;case"pytorch_half_pixel":return`if (lengthResized > 1) {
                    return (${e}(xResized) + 0.5) / ${e}(xScale) - 0.5;
                  } else {
                    return 0.0;
                  }`;case"tf_half_pixel_for_nn":return`return (${e}(xResized) + 0.5) / ${e}(xScale);`;case"align_corners":return`if (lengthResized == 1) {
                    return 0.0;
                  } else {
                    ${ww("xResized","lengthOriginal - 1","lengthResized - 1",e)}
                  }`;case"tf_crop_and_resize":return`if (lengthResized > 1) {
                    return ${e}(roiStart) * ${e}(lengthOriginal - 1) +
                        (${e}(xResized) * ${e}(roiEnd - roiStart) * ${e}(lengthOriginal - 1)) /
                        ${e}(lengthResized - 1);
                  } else {
                    return 0.5 * ${e}(roiStart + roiEnd) * ${e}(lengthOriginal - 1);
                  }`;case"half_pixel_symmetric":return`const outputWidth = ${e}xScale * ${e}(lengthResized);
                  const adjustment = ${e}(lengthResized) / outputWidth;
                  const center = ${e}(lengthOriginal) / 2;
                  const offset = center * (1 - adjustment);
                  return offset + ((${e}(xResized) + 0.5) / ${e}(xScale)) - 0.5;`;case"half_pixel":return`return ((${e}(xResized) + 0.5) / ${e}(xScale)) - 0.5;`;default:throw new Error(`Coordinate transform mode ${r} is not supported`)}})()+"}",IC=(r,e,n)=>`fn getNearestPixelFromOriginal(xOriginal: ${n}, isDownSample: bool) -> ${n} {`+(()=>{switch(r){case"round_prefer_ceil":return"if (fract(xOriginal) == 0.5) {             return ceil(xOriginal);           } else {             return round(xOriginal);           }";case"floor":return"return floor(xOriginal);";case"ceil":return"return ceil(xOriginal);";case"round_prefer_floor":return"if (fract(xOriginal) == 0.5) {                     return floor(xOriginal);                   } else {                     return round(xOriginal);                   }";case"simple":default:if(e<11)return"if (isDownSample)                     {                       return ceil(xOriginal);                     } else {                       return xOriginal;                     }";throw new Error(`Nearest mode ${r} is not supported`)}})()+"}",SC=(r,e,n)=>{let t=new Array(n).fill(0).concat(new Array(n).fill(1)),o=r.length===0?t:r.slice();return e.length>0?(e.forEach((i,a)=>{t[i]=o[a],t[a+n]=o[e.length+a]}),t):o},$C=(r,e,n,t)=>{let o=[];if(n.length>0)if(t.length>0){if(r.forEach(i=>o.push(i)),Math.max(...t)>r.length)throw new Error("axes is out of bound");t.forEach((i,a)=>o[i]=n[a])}else n.forEach(i=>o.push(i));else{if(e.length===0)throw new Error("Resize requires either scales or sizes.");o=r.map((i,a)=>Math.round(i*e[a]))}return o},AC=(r,e,n)=>{let t=(()=>{switch(n.keepAspectRatioPolicy){case"not_larger":return n.axes.length>0?Math.min(...n.axes.map(i=>e[i]),Number.MAX_VALUE):Math.min(...e,Number.MAX_VALUE);case"not_smaller":return n.axes.length>0?Math.max(...n.axes.map(i=>e[i]),Number.MIN_VALUE):Math.max(...e,Number.MIN_VALUE);default:throw new Error(`Keep aspect ratio policy ${n.keepAspectRatioPolicy} is not supported`)}})();e.fill(1,0,e.length);let o=r.slice();return n.axes.length>0?(n.axes.forEach(i=>e[i]=t),n.axes.forEach(i=>o[i]=Math.round(r[i]*e[i]))):(e.fill(t,0,e.length),o.forEach((i,a)=>o[a]=Math.round(i*e[a]))),o},OC=(r,e,n,t,o)=>`
    fn calculateOriginalIndicesFromOutputIndices(output_indices: ${r.type.indices}) -> array<${r.type.value}, ${n.length}> {
      var original_indices: array<${r.type.value}, ${n.length}>;
      for (var i:u32 = 0; i < ${n.length}; i++) {
        var output_index = ${r.indicesGet("output_indices","i")};
        var scale = ${Q("uniforms.scales","i",t)};
        var roi_low = ${Q("uniforms.roi","i",o)};
        var roi_hi = ${Q("uniforms.roi",`i + ${e.length}`,o)};
        if (scale == 1.0) {
          original_indices[i] = ${r.type.value}(output_index);
        } else {
          var input_shape_i = ${Q("uniforms.input_shape","i",e.length)};
          var output_shape_i = ${Q("uniforms.output_shape","i",n.length)};
          original_indices[i] = getOriginalCoordinateFromResizedCoordinate(output_index, scale, output_shape_i,
                                                                           input_shape_i, roi_low, roi_hi);
        }
      }
      return original_indices;
    }`,PC=(r,e,n,t,o,i,a)=>`
    fn calculateInputIndicesFromOutputIndices(output_indices: ${e.type.indices}) -> ${r.type.indices} {
      var input_indices: ${r.type.indices};
      for (var i:u32 = 0; i < ${t.length}; i++) {
        var output_index = ${e.indicesGet("output_indices","i")};
        var input_index: u32;
        var scale = ${Q("uniforms.scales","i",o)};
        if (scale == 1.0) {
          input_index = output_index;
        } else {
          var roi_low = ${Q("uniforms.roi","i",i)};
          var roi_hi = ${Q("uniforms.roi",`i + ${n.length}`,i)};
          var input_shape_i = ${Q("uniforms.input_shape","i",n.length)};
          var output_shape_i = ${Q("uniforms.output_shape","i",t.length)};
          var original_idx = getOriginalCoordinateFromResizedCoordinate(output_index, scale, output_shape_i,
                                                                        input_shape_i, roi_low, roi_hi);
          if (!${a} || (original_idx >= 0 && original_idx < ${e.type.value}(input_shape_i))) {
            if (original_idx < 0) {
              input_index = 0;
            } else if (original_idx > ${e.type.value}(input_shape_i - 1)) {
              input_index = input_shape_i - 1;
            } else {
              input_index = u32(getNearestPixelFromOriginal(original_idx, scale < 1));
            }
          } else {
            input_index = u32(original_idx);
          }
        }
        ${r.indicesSet("input_indices","i","input_index")}
      }
      return input_indices;
    }`,EC=(r,e)=>`
    fn checkInputIndices(input_indices: ${r.type.indices}) -> bool {
      for (var i:u32 = 0; i < ${e.length}; i++) {
        var input_index = ${r.indicesGet("input_indices","i")};
        if (input_index < 0 || input_index >= ${Q("uniforms.input_shape","i",e.length)}) {
          return false;
        }
      }
      return true;
    }`,xw=(r,e,n,t)=>r.rank>t?`
    ${r.indicesSet("input_indices",e,"channel")};
    ${r.indicesSet("input_indices",n,"batch")};
`:"",CC=(r,e,n,t,o)=>{let[a,s,u,l]=n.length===2?[-1,0,1,-1]:[0,2,3,1],d=r.type.value;return`
    fn getInputValue(batch: u32, channel: u32, row: u32, col: u32) -> ${d} {
      var input_indices: ${r.type.indices};
      ${r.indicesSet("input_indices",s,`max(0, min(row, ${n[s]} - 1))`)};
      ${r.indicesSet("input_indices",u,`max(0, min(col, ${n[u]} - 1))`)};
      ${xw(r,l,a,2)}
      return ${r.getByIndices("input_indices")};
    }

    fn bilinearInterpolation(output_indices: ${e.type.indices}) -> ${d} {
      var originalIndices = calculateOriginalIndicesFromOutputIndices(output_indices);
      var row:${d} = originalIndices[${s}];
      var col:${d} = originalIndices[${u}];
      ${t?`if (row < 0 || row > (${n[s]} - 1) || col < 0 || col > (${n[u]} - 1)) {
        return ${o};
      }`:""};
      row = max(0, min(row, ${n[s]} - 1));
      col = max(0, min(col, ${n[u]} - 1));
      var row1: u32 = u32(row);
      var col1: u32 = u32(col);
      var row2: u32 = u32(row + 1);
      var col2: u32 = u32(col + 1);
      var channel: u32 = ${n.length>2?`u32(originalIndices[${l}])`:"0"};
      var batch: u32 =  ${n.length>2?`u32(originalIndices[${a}])`:"0"};
      var x11: ${d} = getInputValue(batch, channel, row1, col1);
      var x12: ${d} = getInputValue(batch, channel, row1, col2);
      var x21: ${d} = getInputValue(batch, channel, row2, col1);
      var x22: ${d} = getInputValue(batch, channel, row2, col2);
      var dx1: ${d} = abs(row - ${d}(row1));
      var dx2: ${d} = abs(${d}(row2) - row);
      var dy1: ${d} = abs(col - ${d}(col1));
      var dy2: ${d} = abs(${d}(col2) - col);
      if (row1 == row2) {
        dx1 = 0.5;
        dx2 = 0.5;
      }
      if (col1 == col2) {
        dy1 = 0.5;
        dy2 = 0.5;
      }
      return (x11 * dx2 * dy2 + x12 * dx2 * dy1 + x21 * dx1 * dy2 + x22 * dx1 * dy1);
    }`},DC=(r,e,n,t,o,i,a,s,u,l)=>{let d=n.length===2,f=!0,[h,g]=d?[0,1]:f?[2,3]:[1,2],b=r.type.value,_=T=>{let v=T===h?"row":"col";return`
      fn ${v}CubicInterpolation(input_indices: ${r.type.indices}, output_indices: ${e.type.indices}) -> ${b} {
        var output_index = ${e.indicesGet("output_indices",T)};
        var originalIdx: ${b} = getOriginalCoordinateFromResizedCoordinate(output_index, ${o[T]},
        ${t[T]}, ${n[T]}, ${i[T]}, ${i[T]} + ${n.length});
        var fractOriginalIdx: ${b} = originalIdx - floor(originalIdx);
        var coefs = getCubicInterpolationCoefs(fractOriginalIdx);

        if (${s} && (originalIdx < 0 || originalIdx > (${n[T]} - 1))) {
          return ${u};
        }
        var data: array<${b}, 4> = array<${b}, 4>(0.0, 0.0, 0.0, 0.0);
        for (var i: i32 = -1; i < 3; i++) {
          var ${v}: ${b} = originalIdx + ${b}(i);
          if (${v} < 0 || ${v} >= ${n[T]}) {
            ${l?`coefs[i + 1] = 0.0;
                        continue;`:s?`return ${u};`:`${v} = max(0, min(${v}, ${n[T]} - 1));`};
          }
        var input_indices_copy: ${r.type.indices} = input_indices;
          ${r.indicesSet("input_indices_copy",T,`u32(${v})`)};
          data[i + 1] = ${T===h?r.getByIndices("input_indices_copy"):"rowCubicInterpolation(input_indices_copy, output_indices)"};
        }
        return cubicInterpolation1D(data, coefs);
      }`};return`
    ${_(h)};
    ${_(g)};
  fn getCubicInterpolationCoefs(s: ${b}) -> array<${b}, 4> {
    var absS = abs(s);
    var coeffs: array<${b}, 4> = array<${b}, 4>(0.0, 0.0, 0.0, 0.0);
    var oneMinusAbsS: ${b} = 1.0 - absS;
    var twoMinusAbsS: ${b} = 2.0 - absS;
    var onePlusAbsS: ${b} = 1.0 + absS;
    coeffs[0] = ((${a} * onePlusAbsS - 5 * ${a}) * onePlusAbsS + 8 * ${a}) * onePlusAbsS - 4 * ${a};
    coeffs[1] = ((${a} + 2) * absS - (${a} + 3)) * absS * absS + 1;
    coeffs[2] = ((${a} + 2) * oneMinusAbsS - (${a} + 3)) * oneMinusAbsS * oneMinusAbsS + 1;
    coeffs[3] = ((${a} * twoMinusAbsS - 5 * ${a}) * twoMinusAbsS + 8 * ${a}) * twoMinusAbsS - 4 * ${a};
    return coeffs;
  }

  fn cubicInterpolation1D(x: array<${b}, 4>, coefs: array<${b}, 4>) -> ${b} {
    var coefsSum: ${b} = coefs[0] + coefs[1] + coefs[2] + coefs[3];
    return (x[0] * coefs[0] + x[1] * coefs[1]+ x[2] * coefs[2]+ x[3] * coefs[3]) / coefsSum;
  }

  fn bicubicInterpolation(output_indices: ${e.type.indices}) -> ${b} {
    var input_indices: ${r.type.indices} = output_indices;
    return colCubicInterpolation(input_indices, output_indices);
  }
    `},kC=(r,e,n,t,o)=>{let[a,s,u,l,d]=n.length===3?[-1,0,1,2,-1]:[0,2,3,4,1],f=r.type.value;return`
    fn getInputValue(batch: u32, channel: u32, depth:u32, height: u32, width: u32) -> ${f} {
      var input_indices: ${r.type.indices};
      ${r.indicesSet("input_indices",s,`max(0, min(depth, ${n[s]} - 1))`)};
      ${r.indicesSet("input_indices",u,`max(0, min(height, ${n[u]} - 1))`)};
      ${r.indicesSet("input_indices",l,`max(0, min(width, ${n[l]} - 1))`)};
      ${xw(r,d,a,3)}
      return ${r.getByIndices("input_indices")};
    }

    fn trilinearInterpolation(output_indices: ${e.type.indices}) -> ${f} {
      var originalIndices = calculateOriginalIndicesFromOutputIndices(output_indices);
      var depth:${f} = originalIndices[${s}];
      var height:${f} = originalIndices[${u}];
      var width:${f} = originalIndices[${l}];
      ${t?`if (depth < 0 || depth > (${n[s]} - 1) || height < 0 || height > (${n[u]} - 1) || width < 0 || (width > ${n[l]} - 1)) {
      return ${o};
        }`:""};

    depth = max(0, min(depth, ${n[s]} - 1));
      height = max(0, min(height, ${n[u]} - 1));
      width = max(0, min(width, ${n[l]} - 1));
      var depth1: u32 = u32(depth);
      var height1: u32 = u32(height);
      var width1: u32 = u32(width);
      var depth2: u32 = u32(depth + 1);
      var height2: u32 = u32(height + 1);
      var width2: u32 = u32(width + 1);
      var channel: u32 = ${n.length>3?`u32(originalIndices[${d}])`:"0"};
      var batch: u32 =  ${n.length>3?`u32(originalIndices[${a}])`:"0"};

      var x111: ${f} = getInputValue(batch, channel, depth1, height1, width1);
      var x112: ${f} = getInputValue(batch, channel, depth1, height1, width2);
      var x121: ${f} = getInputValue(batch, channel, depth1, height2, width1);
      var x122: ${f} = getInputValue(batch, channel, depth1, height2, width2);
      var x211: ${f} = getInputValue(batch, channel, depth2, height1, width1);
      var x212: ${f} = getInputValue(batch, channel, depth2, height1, width2);
      var x221: ${f} = getInputValue(batch, channel, depth2, height2, width1);
      var x222: ${f} = getInputValue(batch, channel, depth2, height2, width2);
      var dx1: ${f} = abs(depth - ${f}(depth1));
      var dx2: ${f} = abs(${f}(depth2) - depth);
      var dy1: ${f} = abs(height - ${f}(height1));
      var dy2: ${f} = abs(${f}(height2) - height);
      var dz1: ${f} = abs(width - ${f}(width1));
      var dz2: ${f} = abs(${f}(width2) - width);
      if (depth1 == depth2) {
        dx1 = 0.5;
        dx2 = 0.5;
      }
      if (height1 == height2) {
        dy1 = 0.5;
        dy2 = 0.5;
      }
      if (width1 == width2) {
        dz1 = 0.5;
        dz2 = 0.5;
      }
      return (x111 * dx2 * dy2 * dz2 + x112 * dx2 * dy2 * dz1 + x121 * dx2 * dy1 *dz2 + x122 * dx2 * dy1 * dz1 +
              x211 * dx1 * dy2 * dz2 + x212 * dx1 * dy2 * dz1 + x221 * dx1 * dy1 *dz2 + x222 * dx1 * dy1 * dz1);
    }`},NC=(r,e,n,t,o,i)=>{let a=r.dims,s=SC(i,e.axes,a.length),u=$C(a,t,o,e.axes),l=t.slice();t.length===0&&(l=a.map((x,I)=>x===0?1:u[I]/x),e.keepAspectRatioPolicy!=="stretch"&&(u=AC(a,l,e)));let d=G("output",r.dataType,u.length),f=N("input",r.dataType,a.length),h=C.size(u),g=a.length===u.length&&a.every((x,I)=>x===u[I]),b=e.coordinateTransformMode==="tf_crop_and_resize",_=e.extrapolationValue,T=f.type.value,v=x=>`
      ${g?"":`
      ${TC(e.coordinateTransformMode,T)};
      ${(()=>{switch(e.mode){case"nearest":return`
              ${EC(f,a)};
              ${IC(e.nearestMode,n,T)};
              ${PC(f,d,a,u,l.length,s.length,b)};
              `;case"linear":return`
              ${OC(d,a,u,l.length,s.length)};
              ${(()=>{if(a.length===2||a.length===4)return`${CC(f,d,a,b,_)}`;if(a.length===3||a.length===5)return`${kC(f,d,a,b,_)}`;throw Error("Linear mode only supports input dims 2, 3, 4 and 5 are supported in linear mode.")})()};
            `;case"cubic":return`
            ${(()=>{if(a.length===2||a.length===4)return`${DC(f,d,a,u,l,s,e.cubicCoeffA,b,e.extrapolationValue,e.excludeOutside)}`;throw Error("Cubic mode only supports input dims 2 and 4 are supported in linear mode.")})()};
            `;default:throw Error("Invalid resize mode")}})()};
      `}
      ${x.registerUniform("output_size","u32").registerUniform("scales","f32",l.length).registerUniform("roi","f32",s.length).declareVariables(f,d)}
      ${x.mainStart()}
        ${x.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.output_size")}
        ${g?"output[global_idx] = input[global_idx];":`
        let output_indices = ${d.offsetToIndices("global_idx")};
        var input_indices: ${f.type.indices};
        ${(()=>{switch(e.mode){case"nearest":return`input_indices = calculateInputIndicesFromOutputIndices(output_indices);
                if (checkInputIndices(input_indices)) {
                  output[global_idx] = ${f.getByIndices("input_indices")};
                } else {
                  output[global_idx] = ${e.extrapolationValue};
                }`;case"linear":return`output[global_idx] = ${a.length===2||a.length===4?"bilinearInterpolation":"trilinearInterpolation"}(output_indices);`;case"cubic":return"output[global_idx] = bicubicInterpolation(output_indices);";default:throw Error(`Unsupported resize mode: ${e.mode}`)}})()};
`}
      }`;return{name:"Resize",shaderCache:{hint:`${e.cacheKey}|${n}|${l.length>0?e.mode==="cubic"?l:l.length:""}|${o.length>0?o:""}|${s.length>0?s:""}|${g}|${e.mode==="nearest"?a.length:a}`,inputDependencies:["rank"]},getShaderSource:v,getRunData:()=>({outputs:[{dims:u,dataType:r.dataType}],dispatchGroup:{x:Math.ceil(h/64)},programUniforms:[{type:12,data:h},{type:1,data:l},{type:1,data:s},...W(a,u)]})}},LC=r=>{let e=r.customDataBuffer;return new Uint32Array(e,e.byteOffset,1)[0]},Tw=(r,e)=>{let n=[],t=[],o=[],i=LC(r);if(e.antialias!==0)throw Error("Only default value (0) for Antialias attribute is supported");xC(r.inputs,e,i,n,t,o),r.compute(NC(r.inputs[0],e,i,n,t,o),{inputs:[0]})},Iw=r=>{let e=r.antialias,n=r.axes,t=r.coordinateTransformMode,o=r.cubicCoeffA,i=r.excludeOutside!==0,a=r.extrapolationValue,s=r.keepAspectRatioPolicy,u=r.mode,l=r.nearestMode===""?"simple":r.nearestMode;return ce({antialias:e,axes:n,coordinateTransformMode:t,cubicCoeffA:o,excludeOutside:i,extrapolationValue:a,keepAspectRatioPolicy:s,mode:u,nearestMode:l})}});var RC,zC,$w,Aw=k(()=>{"use strict";le();ge();ye();RC=r=>{if(!r||r.length<3)throw new Error("layerNorm requires at least 3 inputs.");let e=r[0],n=r[1],t=r[2];if(e.dataType!==n.dataType||e.dataType!==t.dataType)throw new Error("All inputs must have the same data type");if(e.dims.length!==3&&e.dims.length!==2)throw new Error("Input must be 2D or 3D");if(n.dims.length!==3&&n.dims.length!==2)throw new Error("Skip must be 2D or 3D");let o=e.dims[e.dims.length-1],i=e.dims[e.dims.length-2];if(n.dims[n.dims.length-1]!==o)throw new Error("Skip must have the same hidden size as input");if(n.dims[n.dims.length-2]!==i)throw new Error("Skip must have the same sequence length as input");if(t.dims.length!==1)throw new Error("Gamma must be 1D");if(t.dims[t.dims.length-1]!==o)throw new Error("Gamma must have the same hidden size as input");if(r.length>3){let a=r[3];if(a.dims.length!==1)throw new Error("Beta must be 1D");if(a.dims[a.dims.length-1]!==o)throw new Error("Beta must have the same hidden size as input")}if(r.length>4){let a=r[4];if(a.dims.length!==1)throw new Error("Bias must be 1D");if(a.dims[a.dims.length-1]!==o)throw new Error("Bias must have the same hidden size as input")}},zC=(r,e,n,t)=>{let o=e.simplified,i=r[0].dims,a=C.size(i),s=i,u=a,l=i.slice(-1)[0],d=t?i.slice(0,-1).concat(1):[],f=!o&&r.length>3,h=r.length>4,g=t&&n>1,b=t&&n>2,_=n>3,T=64,v=Ee(l),x=[{type:12,data:u},{type:12,data:v},{type:12,data:l},{type:1,data:e.epsilon}],I=O=>{let E=[{name:"output_size",type:"u32"},{name:"components",type:"u32"},{name:"hidden_size",type:"u32"},{name:"epsilon",type:"f32"}],L=[N("x",r[0].dataType,r[0].dims,v),N("skip",r[1].dataType,r[1].dims,v),N("gamma",r[2].dataType,r[2].dims,v)];f&&L.push(N("beta",r[3].dataType,r[3].dims,v)),h&&L.push(N("bias",r[4].dataType,r[4].dims,v)),L.push(G("output",r[0].dataType,s,v)),g&&L.push(G("mean_output",1,d)),b&&L.push(G("inv_std_output",1,d)),_&&L.push(G("input_skip_bias_sum",r[0].dataType,s,v));let R=Ve(r[0].dataType),F=Ve(1,v);return`

      ${O.registerUniforms(E).declareVariables(...L)}
      var<workgroup> sum_shared : array<${F}, ${T}>;
      var<workgroup> sum_squared_shared : array<${F}, ${T}>;

      ${O.mainStart([T,1,1])}
        let ix = local_id.x;
        let iy = global_id.x / ${T};

        let hidden_size_vectorized: u32 = uniforms.hidden_size / uniforms.components;
        var stride = hidden_size_vectorized / ${T};
        let offset = ix * stride + iy * hidden_size_vectorized;
        let offset1d = stride * ix;
        if (ix == ${T-1}) {
          stride = hidden_size_vectorized - stride * ix;
        }
        for (var i: u32 = 0; i < stride; i++) {
          let skip_value = skip[offset + i];
          let bias_value = ${h?"bias[offset1d + i]":R+"(0.0)"};
          let input_value = x[offset + i];
          let value = input_value + skip_value + bias_value;
          ${_?"input_skip_bias_sum[offset + i] = value;":""}
          output[offset + i] = value;
          let f32_value = ${Hr(R,v,"value")};
          sum_shared[ix] += f32_value;
          sum_squared_shared[ix] += f32_value * f32_value;
        }
        workgroupBarrier();

        var reduce_size : u32 = ${T};
        for (var curr_size = reduce_size >> 1;  curr_size > 0; curr_size = reduce_size >> 1) {
          reduce_size = curr_size + (reduce_size & 1);
          if (ix < curr_size) {
            sum_shared[ix] += sum_shared[ix + reduce_size];
            sum_squared_shared[ix] += sum_squared_shared[ix + reduce_size];
          }
          workgroupBarrier();
        }

        let sum = sum_shared[0];
        let square_sum = sum_squared_shared[0];
        let mean = ${Kt("sum",v)} / f32(uniforms.hidden_size);
        let inv_std_dev = inverseSqrt(${Kt("square_sum",v)} / f32(uniforms.hidden_size) ${o?"":"- mean * mean"} + uniforms.epsilon);
        ${g?"mean_output[global_idx] = mean;":""}
        ${b?"inv_std_output[global_idx] = inv_std_dev;":""}

        for (var i: u32 = 0; i < stride; i++) {
          output[offset + i] = (output[offset + i] ${o?"":`- ${R}(mean)`}) *
            ${R}(inv_std_dev) * gamma[offset1d + i]
            ${f?"+ beta[offset1d + i]":""};
        }
      }`},$=[{dims:s,dataType:r[0].dataType}];return n>1&&$.push({dims:d,dataType:1}),n>2&&$.push({dims:d,dataType:1}),n>3&&$.push({dims:i,dataType:r[0].dataType}),{name:"SkipLayerNormalization",shaderCache:{hint:`${v};${g};${b};${_}`,inputDependencies:r.map((O,E)=>"type")},getShaderSource:I,getRunData:()=>({outputs:$,dispatchGroup:{x:Math.ceil(u/l)},programUniforms:x})}},$w=(r,e)=>{RC(r.inputs);let t=[0];r.outputCount>1&&t.push(-3),r.outputCount>2&&t.push(-3),r.outputCount>3&&t.push(3),r.compute(zC(r.inputs,e,r.outputCount,!1),{outputs:t})}});var MC,Ja,BC,Ow,FC,VC,Pw,Ew,Cw=k(()=>{"use strict";le();ge();Xe();ye();MC=(r,e)=>{if(!r||r.length<1)throw new Error("too few inputs");if(e.axes.length!==0){if(e.axes.length!==e.starts.length||e.axes.length!==e.ends.length)throw new Error("axes, starts and ends must have the same length")}else if(e.starts.length!==e.ends.length)throw new Error("starts and ends must have the same length");r.slice(1).forEach((n,t)=>{if(r[t+1].dataType!==6&&r[t+1].dataType!==7)throw new Error(`Input ${t} must be an array of int32 or int64`)})},Ja=(r,e)=>{let n=[];if(r.length>e)if(r[e].dataType===7)r[e].getBigInt64Array().forEach(t=>n.push(Number(t)));else if(r[e].dataType===6)r[e].getInt32Array().forEach(t=>n.push(Number(t)));else throw new Error(`Input ${e} must be an array of int32 or int64`);return n},BC=(r,e)=>{if(r.length>1){let n=Ja(r,1),t=Ja(r,2),o=Ja(r,3);return o.length===0&&(o=[...Array(r[0].dims.length).keys()]),ce({starts:n,ends:t,axes:o})}else return e},Ow=(r,e,n,t,o)=>{let i=r;return r<0&&(i+=n[t[e]]),o[e]<0?Math.max(0,Math.min(i,n[t[e]]-1)):Math.max(0,Math.min(i,n[t[e]]))},FC=(r,e,n)=>`fn calculateInputIndices(output_indices: ${e.type.indices}) -> ${r.type.indices} {
          var input_indices: ${r.type.indices};
          var carry = 0u;
          for (var i = ${n.length-1}; i >= 0; i--) {
            let input_shape_i = ${Q("uniforms.input_shape","i",n.length)};
            let steps_i = ${Q("uniforms.steps","i",n.length)};
            let signs_i = ${Q("uniforms.signs","i",n.length)};
            let starts_i = ${Q("uniforms.starts","i",n.length)};
            var output_index = ${e.indicesGet("output_indices","i")};
            var input_index = output_index * steps_i + starts_i + carry;
            carry = input_index / input_shape_i;
            input_index = input_index % input_shape_i;
            if (signs_i < 0) {
              input_index = input_shape_i - input_index - 1u + starts_i;
            }
            ${r.indicesSet("input_indices","i","input_index")};
          }
          return input_indices;
      }`,VC=(r,e)=>{let n=r[0].dims,t=C.size(n),o=e.axes.length>0?C.normalizeAxes(e.axes,n.length):[...Array(n.length).keys()],i=Ja(r,4);i.forEach(v=>v!==0||(()=>{throw new Error("step cannot be 0")})),i.length===0&&(i=Array(o.length).fill(1));let a=e.starts.map((v,x)=>Ow(v,x,n,o,i)),s=e.ends.map((v,x)=>Ow(v,x,n,o,i));if(o.length!==a.length||o.length!==s.length)throw new Error("start, ends and axes should have the same number of elements");if(o.length!==n.length)for(let v=0;v<n.length;++v)o.includes(v)||(a.splice(v,0,0),s.splice(v,0,n[v]),i.splice(v,0,1));let u=i.map(v=>Math.sign(v));i.forEach((v,x,I)=>{if(v<0){let $=(s[x]-a[x])/v,O=a[x],E=O+$*i[x];a[x]=E,s[x]=O,I[x]=-v}});let l=n.slice(0);o.forEach((v,x)=>{l[v]=Math.ceil((s[v]-a[v])/i[v])});let d={dims:l,dataType:r[0].dataType},f=G("output",r[0].dataType,l.length),h=N("input",r[0].dataType,r[0].dims.length),g=C.size(l),b=[{name:"outputSize",type:"u32"},{name:"starts",type:"u32",length:a.length},{name:"signs",type:"i32",length:u.length},{name:"steps",type:"u32",length:i.length}],_=[{type:12,data:g},{type:12,data:a},{type:6,data:u},{type:12,data:i},...W(r[0].dims,l)],T=v=>`
      ${v.registerUniforms(b).declareVariables(h,f)}
        ${FC(h,f,n)}
        ${v.mainStart()}
          ${v.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.outputSize")}
          let output_indices = ${f.offsetToIndices("global_idx")};
          let input_indices = calculateInputIndices(output_indices);
          ${f.setByOffset("global_idx",h.getByIndices("input_indices"))}
      }`;return{name:"Slice",shaderCache:{hint:`${u.length}_${a.length}_${i.length}`,inputDependencies:["rank"]},getShaderSource:T,getRunData:()=>({outputs:[d],dispatchGroup:{x:Math.ceil(t/64)},programUniforms:_})}},Pw=(r,e)=>{MC(r.inputs,e);let n=BC(r.inputs,e);r.compute(VC(r.inputs,n),{inputs:[0]})},Ew=r=>{let e=r.starts,n=r.ends,t=r.axes;return ce({starts:e,ends:n,axes:t})}});var GC,UC,Dw,kw,Nw=k(()=>{"use strict";le();ge();Xe();Yn();ye();GC=r=>{if(!r||r.length!==1)throw new Error("Softmax op requires 1 input.")},UC=(r,e)=>{let n=r.inputs[0],t=n.dims,o=C.size(t),i=t.length,a=C.normalizeAxis(e.axis,i),s=a<t.length-1,u,l=[];s?(l=Array.from({length:i},(L,R)=>R),l[a]=i-1,l[i-1]=a,u=r.compute(at(n,l),{inputs:[n],outputs:[-1]})[0]):u=n;let d=u.dims,f=d[i-1],h=o/f,g=Ee(f),b=f/g,_=64;h===1&&(_=256);let T=(L,R)=>R===4?`max(max(${L}.x, ${L}.y), max(${L}.z, ${L}.w))`:R===2?`max(${L}.x, ${L}.y)`:R===3?`max(max(${L}.x, ${L}.y), ${L}.z)`:L,v=N("x",u.dataType,u.dims,g),x=G("result",u.dataType,u.dims,g),I=v.type.value,$=Ve(u.dataType)==="f32"?`var threadMax = ${I}(-3.402823e+38f);`:`var threadMax = ${I}(-65504.0h);`,O=L=>`
      var<workgroup> rowMaxShared : ${I};
      var<workgroup> rowSumShared : ${I};
      var<workgroup> threadShared : array<${I}, ${_}>;

      fn getValue(row: i32, col: i32, row_stride: i32) -> ${I} {
        let index = row * row_stride + col;
        return x[index];
      }

      fn setValue(row: i32, col: i32, row_stride: i32, value: ${I}) {
        let index = row * row_stride + col;
        result[index] = value;
      }
      ${L.registerUniform("packedCols","i32").declareVariables(v,x)}
      ${L.mainStart(_)}
        let gindex = i32(global_idx);
        let lindex = i32(local_idx);
        const wg = ${_};
        let row = gindex / wg;
        let cols = uniforms.packedCols;
        let row_stride : i32 = uniforms.packedCols;

        // find the rows max
        ${$}
        for (var col = lindex; col < cols; col += wg) {
          let value = getValue(row, col, row_stride);
          threadMax = max(threadMax, value);
        }
        if (lindex < cols) {
          threadShared[lindex] = threadMax;
        }
        workgroupBarrier();

        var reduceSize = min(cols, wg);
        for (var currSize = reduceSize >> 1;  currSize > 0; currSize = reduceSize >> 1) {
          reduceSize = currSize + (reduceSize & 1);
          if (lindex < currSize) {
            threadShared[lindex] = max(threadShared[lindex], threadShared[lindex + reduceSize]);
          }
          workgroupBarrier();
        }
        if (lindex == 0) {
          rowMaxShared = ${I}(${T("threadShared[0]",g)});
        }
        workgroupBarrier();

        // find the rows sum
        var threadSum = ${I}(0.0);
        for (var col = lindex; col < cols; col += wg) {
          let subExp = exp(getValue(row, col, row_stride) - rowMaxShared);
          threadSum += subExp;
        }
        threadShared[lindex] = threadSum;
        workgroupBarrier();

        for (var currSize = wg >> 1;  currSize > 0; currSize = currSize >> 1) {
          if (lindex < currSize) {
            threadShared[lindex] = threadShared[lindex] + threadShared[lindex + currSize];
          }
          workgroupBarrier();
        }
        if (lindex == 0) {
          rowSumShared = ${I}(${Kt("threadShared[0]",g)});
        }
        workgroupBarrier();

        // calculate final value for each element in the row
        for (var col = lindex; col < cols; col += wg) {
          var value = exp(getValue(row, col, row_stride) - rowMaxShared) / rowSumShared;
          // max operation protects against NaN since all values should be >=0
          value = max(value, ${I}(0.0));
          setValue(row, col, row_stride, value);
        }
      }`,E=r.compute({name:"Softmax",shaderCache:{hint:`${g};${_}`,inputDependencies:["type"]},getRunData:()=>({outputs:[{dims:d,dataType:u.dataType}],dispatchGroup:{x:h},programUniforms:[{type:6,data:b}]}),getShaderSource:O},{inputs:[u],outputs:[s?-1:0]})[0];s&&r.compute(at(E,l),{inputs:[E]})},Dw=(r,e)=>{GC(r.inputs),UC(r,e)},kw=r=>ce({axis:r.axis})});var Lw,WC,HC,jC,Rw,zw=k(()=>{"use strict";le();ge();ye();Lw=r=>Array.from(r.getBigInt64Array(),Number),WC=r=>{if(!r||r.length!==2)throw new Error("Tile requires 2 inputs.");if(r[0].dataType!==1&&r[0].dataType!==10&&r[0].dataType!==6&&r[0].dataType!==12)throw new Error("Tile only support float, float16, int32, and uint32 data types");if(r[1].dataType!==7)throw new Error("Tile `repeats` input should be of int64 data type");if(r[1].dims.length!==1)throw new Error("Tile `repeats` input should be 1-D");if(Lw(r[1]).length!==r[0].dims.length)throw new Error("Tile `repeats` input should have same number of elements as rank of input data tensor")},HC=(r,e)=>{let n=[];for(let t=0;t<r.length;++t)n.push(r[t]*e[t]);return n},jC=(r,e)=>{let n=r[0].dims,t=e??Lw(r[1]),o=HC(n,t),i=C.size(o),a=r[0].dataType,s=N("input",a,n.length),u=G("output",a,o.length),l=d=>`
      const inputShape = ${s.indices(...n)};
      ${d.registerUniform("output_size","u32").declareVariables(s,u)}
      ${d.mainStart()}
      ${d.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.output_size")}
      let output_indices = ${u.offsetToIndices("global_idx")};
      var input_indices: ${s.type.indices};
      for (var i = 0; i < ${n.length}; i++) {
        let input_dim_i = ${s.indicesGet("uniforms.input_shape","i")};
        let input_dim_value = ${u.indicesGet("output_indices","i")}  % input_dim_i;

        ${s.indicesSet("input_indices","i","input_dim_value")}
      }
      ${u.setByOffset("global_idx",s.getByIndices("input_indices"))}
    }`;return{name:"Tile",shaderCache:{hint:`${t}`,inputDependencies:["rank"]},getRunData:()=>({outputs:[{dims:o,dataType:r[0].dataType}],dispatchGroup:{x:Math.ceil(i/64)},programUniforms:[{type:12,data:i},...W(r[0].dims,o)]}),getShaderSource:l}},Rw=r=>{WC(r.inputs),r.compute(jC(r.inputs),{inputs:[0]})}});var qC,KC,Mw,Bw=k(()=>{"use strict";le();ge();ye();qC=(r,e,n,t,o)=>{let i=G("output_data",o,n.length,4),a=N("a_data",e[1].dataType,e[1].dims.length,4),s=N("b_data",e[2].dataType,e[2].dims.length,4),u=N("c_data",e[0].dataType,e[0].dims.length,4),l,d=(f,h,g)=>`select(${h}, ${f}, ${g})`;if(!t)l=i.setByOffset("global_idx",d(a.getByOffset("global_idx"),s.getByOffset("global_idx"),u.getByOffset("global_idx")));else{let f=(h,g,b="")=>{let _=`a_data[index_a${g}][component_a${g}]`,T=`b_data[index_b${g}][component_b${g}]`,v=`bool(c_data[index_c${g}] & (0xffu << (component_c${g} * 8)))`;return`
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this.env.trace>"u"?this.env.wasm.trace:this.env.trace))&&(this.device.features.has("chromium-experimental-timestamp-query-inside-passes")?this.queryType="inside-passes":this.device.features.has("timestamp-query")&&(this.queryType="at-passes"),this.queryType!=="none"&&typeof this.querySet>"u"&&(this.querySet=this.device.createQuerySet({type:"timestamp",count:this.maxDispatchNumber*2}),this.queryResolveBuffer=this.device.createBuffer({size:this.maxDispatchNumber*2*8,usage:GPUBufferUsage.COPY_SRC|GPUBufferUsage.QUERY_RESOLVE})))}captureBegin(){_e("info","captureBegin"),this.capturedCommandList.get(this.currentSessionId)||this.capturedCommandList.set(this.currentSessionId,[]),this.capturedPendingKernels.get(this.currentSessionId)||this.capturedPendingKernels.set(this.currentSessionId,[]),this.flush(),this.sessionStatus="capturing"}captureEnd(){_e("info","captureEnd"),this.flush(),this.sessionStatus="default"}replay(){_e("info","replay"),this.sessionStatus="replaying";let 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i=this.getComputePassEncoder(),a=e[o];this.writeTimestamp(this.pendingDispatchNumber*2),i.setPipeline(a.computePipeline),i.setBindGroup(0,a.bindGroup),i.dispatchWorkgroups(...a.dispatchGroup),this.writeTimestamp(this.pendingDispatchNumber*2+1),this.pendingDispatchNumber++,this.queryType!=="none"&&this.pendingKernels.push(n[o]),(this.pendingDispatchNumber>=this.maxDispatchNumber||this.queryType==="at-passes")&&this.endComputePass(),this.pendingDispatchNumber>=this.maxDispatchNumber&&this.flush()}this.flush(),this.sessionStatus="default"}onCreateSession(){this.gpuDataManager.onCreateSession()}onReleaseSession(e){this.unregisterBuffers(e),this.capturedCommandList.has(e)&&this.capturedCommandList.delete(e),this.capturedPendingKernels.has(e)&&this.capturedPendingKernels.delete(e),this.gpuDataManager.onReleaseSession(e)}onRunStart(e){this.currentSessionId=e,this.setQueryType()}}});var Hw={};$r(Hw,{init:()=>JC});var Ko,Gc,JC,jw=k(()=>{"use strict";le();Bn();ge();Fy();Ko=class 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o={type:"init-wasm",in:me};!o.in.wasm.wasmPaths&&(n||ic)&&(o.in.wasm.wasmPaths={wasm:new URL("ort-wasm-simd-threaded.jsep.wasm",import.meta.url).href}),Ft.postMessage(o),Qa=n}catch(o){e(o)}},e)});try{await ha(me.wasm),await ma(me),es=!0}catch(r){throw ts=!0,r}finally{Xo=!1}}},Zw=async r=>{if(qr())return go(),new Promise((e,n)=>{mo("init-ep",[e,n]);let t={type:"init-ep",in:{epName:r,env:me}};Ft.postMessage(t)});await ga(me,r)},Jw=async r=>qr()?(go(),new Promise((e,n)=>{mo("copy-from",[e,n]);let t={type:"copy-from",in:{buffer:r}};Ft.postMessage(t,[r.buffer])})):Fo(r),Yw=async(r,e)=>{if(qr()){if(e?.preferredOutputLocation)throw new Error('session option "preferredOutputLocation" is not supported for proxy.');return go(),new Promise((n,t)=>{mo("create",[n,t]);let o={type:"create",in:{model:r,options:{...e}}},i=[];r instanceof Uint8Array&&i.push(r.buffer),Ft.postMessage(o,i)})}else return ba(r,e)},Qw=async r=>{if(qr())return go(),new Promise((e,n)=>{mo("release",[e,n]);let t={type:"release",in:r};Ft.postMessage(t)});ya(r)},ex=async(r,e,n,t,o,i)=>{if(qr()){if(n.some(a=>a[3]!=="cpu"))throw new Error("input tensor on GPU is not supported for proxy.");if(o.some(a=>a))throw new Error("pre-allocated output tensor is not supported for proxy.");return go(),new Promise((a,s)=>{mo("run",[a,s]);let u=n,l={type:"run",in:{sessionId:r,inputIndices:e,inputs:u,outputIndices:t,options:i}};Ft.postMessage(l,wa(u))})}else return _a(r,e,n,t,o,i)},tx=async r=>{if(qr())return go(),new Promise((e,n)=>{mo("end-profiling",[e,n]);let t={type:"end-profiling",in:r};Ft.postMessage(t)});va(r)}});var nx,n3,ns,rx=k(()=>{"use strict";pt();Hc();le();pa();lc();nx=(r,e)=>{switch(r.location){case"cpu":return[r.type,r.dims,r.data,"cpu"];case"gpu-buffer":return[r.type,r.dims,{gpuBuffer:r.gpuBuffer},"gpu-buffer"];case"ml-tensor":return[r.type,r.dims,{mlTensor:r.mlTensor},"ml-tensor"];default:throw new Error(`invalid data location: ${r.location} for ${e()}`)}},n3=r=>{switch(r[3]){case"cpu":return new St(r[0],r[2],r[1]);case"gpu-buffer":{let e=r[0];if(!Sa(e))throw new Error(`not supported data type: ${e} for deserializing GPU tensor`);let{gpuBuffer:n,download:t,dispose:o}=r[2];return St.fromGpuBuffer(n,{dataType:e,dims:r[1],download:t,dispose:o})}case"ml-tensor":{let e=r[0];if(!$a(e))throw new Error(`not supported data type: ${e} for deserializing MLTensor tensor`);let{mlTensor:n,download:t,dispose:o}=r[2];return St.fromMLTensor(n,{dataType:e,dims:r[1],download:t,dispose:o})}default:throw new Error(`invalid data location: ${r[3]}`)}},ns=class{async fetchModelAndCopyToWasmMemory(e){return Jw(await Uo(e))}async loadModel(e,n){$t();let t;typeof e=="string"?t=await this.fetchModelAndCopyToWasmMemory(e):t=e,[this.sessionId,this.inputNames,this.outputNames,this.inputMetadata,this.outputMetadata]=await Yw(t,n),yt()}async dispose(){return Qw(this.sessionId)}async run(e,n,t){$t();let o=[],i=[];Object.entries(e).forEach(h=>{let g=h[0],b=h[1],_=this.inputNames.indexOf(g);if(_===-1)throw new Error(`invalid input '${g}'`);o.push(b),i.push(_)});let a=[],s=[];Object.entries(n).forEach(h=>{let g=h[0],b=h[1],_=this.outputNames.indexOf(g);if(_===-1)throw new Error(`invalid output '${g}'`);a.push(b),s.push(_)});let u=o.map((h,g)=>nx(h,()=>`input "${this.inputNames[i[g]]}"`)),l=a.map((h,g)=>h?nx(h,()=>`output "${this.outputNames[s[g]]}"`):null),d=await ex(this.sessionId,i,u,s,l,t),f={};for(let h=0;h<d.length;h++)f[this.outputNames[s[h]]]=a[h]??n3(d[h]);return yt(),f}startProfiling(){}endProfiling(){tx(this.sessionId)}}});var ix={};$r(ix,{OnnxruntimeWebAssemblyBackend:()=>rs,initializeFlags:()=>ox,wasmBackend:()=>r3});var ox,rs,r3,ax=k(()=>{"use strict";pt();Hc();rx();ox=()=>{(typeof me.wasm.initTimeout!="number"||me.wasm.initTimeout<0)&&(me.wasm.initTimeout=0);let r=me.wasm.simd;if(typeof r!="boolean"&&r!==void 0&&r!=="fixed"&&r!=="relaxed"&&(console.warn(`Property "env.wasm.simd" is set to unknown value "${r}". Reset it to \`false\` and ignore SIMD feature checking.`),me.wasm.simd=!1),typeof me.wasm.proxy!="boolean"&&(me.wasm.proxy=!1),typeof me.wasm.trace!="boolean"&&(me.wasm.trace=!1),typeof me.wasm.numThreads!="number"||!Number.isInteger(me.wasm.numThreads)||me.wasm.numThreads<=0)if(typeof self<"u"&&!self.crossOriginIsolated)me.wasm.numThreads=1;else{let e=typeof navigator>"u"?Os("node:os").cpus().length:navigator.hardwareConcurrency;me.wasm.numThreads=Math.min(4,Math.ceil((e||1)/2))}},rs=class{async init(e){ox(),await Xw(),await Zw(e)}async createInferenceSessionHandler(e,n){let t=new ns;return await t.loadModel(e,n),t}},r3=new rs});pt();pt();pt();var Dp="1.23.2";var zq=Ns;{let r=(cy(),Jr(ly)).onnxjsBackend;sr("webgl",r,-10)}{let r=(ax(),Jr(ix)).wasmBackend;sr("webgpu",r,5),sr("webnn",r,5),sr("cpu",r,10),sr("wasm",r,10)}Object.defineProperty(me.versions,"web",{value:Dp,enumerable:!0});export{LT as InferenceSession,li as TRACE,ur as TRACE_EVENT_BEGIN,lr as TRACE_EVENT_END,$t as TRACE_FUNC_BEGIN,yt as TRACE_FUNC_END,St as Tensor,zq as default,me as env,sr as registerBackend};
/**
 * @license
 * Copyright 2021 Google LLC. All Rights Reserved.
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 * =============================================================================
 */
/**
 * @license
 * Copyright 2020 Google LLC. All Rights Reserved.
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 * =============================================================================
 */
/**
 * @license
 * Copyright 2019 Google LLC. All Rights Reserved.
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 * =============================================================================
 */
/*! Bundled license information:

long/index.js:
  (**
   * @license
   * Copyright 2009 The Closure Library Authors
   * Copyright 2020 Daniel Wirtz / The long.js Authors.
   *
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   *     http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   *
   * SPDX-License-Identifier: Apache-2.0
   *)
*/
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