diff --git "a/assets/worker-d3671fec.js" "b/assets/worker-d3671fec.js"
deleted file mode 100644--- "a/assets/worker-d3671fec.js"
+++ /dev/null
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c},t.decodeDelimited=function(e){return e instanceof h||(e=new h(e)),this.decode(e,e.uint32())},t.verify=function(e){if(typeof e!="object"||e===null)return"object expected";if(e.name!=null&&e.hasOwnProperty("name")&&!a.isString(e.name))return"name: string expected";if(e.refAttrName!=null&&e.hasOwnProperty("refAttrName")&&!a.isString(e.refAttrName))return"refAttrName: string expected";if(e.docString!=null&&e.hasOwnProperty("docString")&&!a.isString(e.docString))return"docString: string expected";if(e.type!=null&&e.hasOwnProperty("type"))switch(e.type){default:return"type: enum value expected";case 0:case 1:case 2:case 3:case 4:case 5:case 6:case 7:case 8:case 9:case 10:}if(e.f!=null&&e.hasOwnProperty("f")&&typeof e.f!="number")return"f: number expected";if(e.i!=null&&e.hasOwnProperty("i")&&!(a.isInteger(e.i)||e.i&&a.isInteger(e.i.low)&&a.isInteger(e.i.high)))return"i: integer|Long expected";if(e.s!=null&&e.hasOwnProperty("s")&&!(e.s&&typeof e.s.length=="number"||a.isString(e.s)))return"s: buffer expected";if(e.t!=null&&e.hasOwnProperty("t")&&(i=o.onnx.TensorProto.verify(e.t)))return"t."+i;if(e.g!=null&&e.hasOwnProperty("g")&&(i=o.onnx.GraphProto.verify(e.g)))return"g."+i;if(e.floats!=null&&e.hasOwnProperty("floats")){if(!Array.isArray(e.floats))return"floats: array expected";for(var r=0;r<e.floats.length;++r)if(typeof e.floats[r]!="number")return"floats: number[] expected"}if(e.ints!=null&&e.hasOwnProperty("ints")){if(!Array.isArray(e.ints))return"ints: array expected";for(r=0;r<e.ints.length;++r)if(!(a.isInteger(e.ints[r])||e.ints[r]&&a.isInteger(e.ints[r].low)&&a.isInteger(e.ints[r].high)))return"ints: integer|Long[] expected"}if(e.strings!=null&&e.hasOwnProperty("strings")){if(!Array.isArray(e.strings))return"strings: array expected";for(r=0;r<e.strings.length;++r)if(!(e.strings[r]&&typeof e.strings[r].length=="number"||a.isString(e.strings[r])))return"strings: buffer[] expected"}if(e.tensors!=null&&e.hasOwnProperty("tensors")){if(!Array.isArray(e.tensors))return"tensors: array expected";for(r=0;r<e.tensors.length;++r)if(i=o.onnx.TensorProto.verify(e.tensors[r]))return"tensors."+i}if(e.graphs!=null&&e.hasOwnProperty("graphs")){if(!Array.isArray(e.graphs))return"graphs: array expected";for(r=0;r<e.graphs.length;++r){var i;if(i=o.onnx.GraphProto.verify(e.graphs[r]))return"graphs."+i}}return null},t.fromObject=function(e){if(e instanceof o.onnx.AttributeProto)return e;var r=new o.onnx.AttributeProto;switch(e.name!=null&&(r.name=String(e.name)),e.refAttrName!=null&&(r.refAttrName=String(e.refAttrName)),e.docString!=null&&(r.docString=String(e.docString)),e.type){case"UNDEFINED":case 0:r.type=0;break;case"FLOAT":case 1:r.type=1;break;case"INT":case 2:r.type=2;break;case"STRING":case 3:r.type=3;break;case"TENSOR":case 4:r.type=4;break;case"GRAPH":case 5:r.type=5;break;case"FLOATS":case 6:r.type=6;break;case"INTS":case 7:r.type=7;break;case"STRINGS":case 8:r.type=8;break;case"TENSORS":case 9:r.type=9;break;case"GRAPHS":case 10:r.type=10}if(e.f!=null&&(r.f=Number(e.f)),e.i!=null&&(a.Long?(r.i=a.Long.fromValue(e.i)).unsigned=!1:typeof e.i=="string"?r.i=parseInt(e.i,10):typeof e.i=="number"?r.i=e.i:typeof e.i=="object"&&(r.i=new a.LongBits(e.i.low>>>0,e.i.high>>>0).toNumber())),e.s!=null&&(typeof e.s=="string"?a.base64.decode(e.s,r.s=a.newBuffer(a.base64.length(e.s)),0):e.s.length&&(r.s=e.s)),e.t!=null){if(typeof e.t!="object")throw TypeError(".onnx.AttributeProto.t: object expected");r.t=o.onnx.TensorProto.fromObject(e.t)}if(e.g!=null){if(typeof e.g!="object")throw TypeError(".onnx.AttributeProto.g: object expected");r.g=o.onnx.GraphProto.fromObject(e.g)}if(e.floats){if(!Array.isArray(e.floats))throw TypeError(".onnx.AttributeProto.floats: array expected");r.floats=[];for(var i=0;i<e.floats.length;++i)r.floats[i]=Number(e.floats[i])}if(e.ints){if(!Array.isArray(e.ints))throw TypeError(".onnx.AttributeProto.ints: array expected");for(r.ints=[],i=0;i<e.ints.length;++i)a.Long?(r.ints[i]=a.Long.fromValue(e.ints[i])).unsigned=!1:typeof e.ints[i]=="string"?r.ints[i]=parseInt(e.ints[i],10):typeof e.ints[i]=="number"?r.ints[i]=e.ints[i]:typeof e.ints[i]=="object"&&(r.ints[i]=new a.LongBits(e.ints[i].low>>>0,e.ints[i].high>>>0).toNumber())}if(e.strings){if(!Array.isArray(e.strings))throw TypeError(".onnx.AttributeProto.strings: array expected");for(r.strings=[],i=0;i<e.strings.length;++i)typeof e.strings[i]=="string"?a.base64.decode(e.strings[i],r.strings[i]=a.newBuffer(a.base64.length(e.strings[i])),0):e.strings[i].length&&(r.strings[i]=e.strings[i])}if(e.tensors){if(!Array.isArray(e.tensors))throw TypeError(".onnx.AttributeProto.tensors: array expected");for(r.tensors=[],i=0;i<e.tensors.length;++i){if(typeof e.tensors[i]!="object")throw TypeError(".onnx.AttributeProto.tensors: object expected");r.tensors[i]=o.onnx.TensorProto.fromObject(e.tensors[i])}}if(e.graphs){if(!Array.isArray(e.graphs))throw TypeError(".onnx.AttributeProto.graphs: array expected");for(r.graphs=[],i=0;i<e.graphs.length;++i){if(typeof e.graphs[i]!="object")throw TypeError(".onnx.AttributeProto.graphs: object expected");r.graphs[i]=o.onnx.GraphProto.fromObject(e.graphs[i])}}return r},t.toObject=function(e,r){r||(r={});var i={};if((r.arrays||r.defaults)&&(i.floats=[],i.ints=[],i.strings=[],i.tensors=[],i.graphs=[]),r.defaults){if(i.name="",i.f=0,a.Long){var c=new a.Long(0,0,!1);i.i=r.longs===String?c.toString():r.longs===Number?c.toNumber():c}else i.i=r.longs===String?"0":0;r.bytes===String?i.s="":(i.s=[],r.bytes!==Array&&(i.s=a.newBuffer(i.s))),i.t=null,i.g=null,i.docString="",i.type=r.enums===String?"UNDEFINED":0,i.refAttrName=""}if(e.name!=null&&e.hasOwnProperty("name")&&(i.name=e.name),e.f!=null&&e.hasOwnProperty("f")&&(i.f=r.json&&!isFinite(e.f)?String(e.f):e.f),e.i!=null&&e.hasOwnProperty("i")&&(typeof e.i=="number"?i.i=r.longs===String?String(e.i):e.i:i.i=r.longs===String?a.Long.prototype.toString.call(e.i):r.longs===Number?new a.LongBits(e.i.low>>>0,e.i.high>>>0).toNumber():e.i),e.s!=null&&e.hasOwnProperty("s")&&(i.s=r.bytes===String?a.base64.encode(e.s,0,e.s.length):r.bytes===Array?Array.prototype.slice.call(e.s):e.s),e.t!=null&&e.hasOwnProperty("t")&&(i.t=o.onnx.TensorProto.toObject(e.t,r)),e.g!=null&&e.hasOwnProperty("g")&&(i.g=o.onnx.GraphProto.toObject(e.g,r)),e.floats&&e.floats.length){i.floats=[];for(var g=0;g<e.floats.length;++g)i.floats[g]=r.json&&!isFinite(e.floats[g])?String(e.floats[g]):e.floats[g]}if(e.ints&&e.ints.length)for(i.ints=[],g=0;g<e.ints.length;++g)typeof e.ints[g]=="number"?i.ints[g]=r.longs===String?String(e.ints[g]):e.ints[g]:i.ints[g]=r.longs===String?a.Long.prototype.toString.call(e.ints[g]):r.longs===Number?new a.LongBits(e.ints[g].low>>>0,e.ints[g].high>>>0).toNumber():e.ints[g];if(e.strings&&e.strings.length)for(i.strings=[],g=0;g<e.strings.length;++g)i.strings[g]=r.bytes===String?a.base64.encode(e.strings[g],0,e.strings[g].length):r.bytes===Array?Array.prototype.slice.call(e.strings[g]):e.strings[g];if(e.tensors&&e.tensors.length)for(i.tensors=[],g=0;g<e.tensors.length;++g)i.tensors[g]=o.onnx.TensorProto.toObject(e.tensors[g],r);if(e.graphs&&e.graphs.length)for(i.graphs=[],g=0;g<e.graphs.length;++g)i.graphs[g]=o.onnx.GraphProto.toObject(e.graphs[g],r);return e.docString!=null&&e.hasOwnProperty("docString")&&(i.docString=e.docString),e.type!=null&&e.hasOwnProperty("type")&&(i.type=r.enums===String?o.onnx.AttributeProto.AttributeType[e.type]:e.type),e.refAttrName!=null&&e.hasOwnProperty("refAttrName")&&(i.refAttrName=e.refAttrName),i},t.prototype.toJSON=function(){return this.constructor.toObject(this,s.util.toJSONOptions)},t.AttributeType=function(){var e={},r=Object.create(e);return r[e[0]="UNDEFINED"]=0,r[e[1]="FLOAT"]=1,r[e[2]="INT"]=2,r[e[3]="STRING"]=3,r[e[4]="TENSOR"]=4,r[e[5]="GRAPH"]=5,r[e[6]="FLOATS"]=6,r[e[7]="INTS"]=7,r[e[8]="STRINGS"]=8,r[e[9]="TENSORS"]=9,r[e[10]="GRAPHS"]=10,r}(),t}(),p.ValueInfoProto=function(){function t(e){if(e)for(var r=Object.keys(e),i=0;i<r.length;++i)e[r[i]]!=null&&(this[r[i]]=e[r[i]])}return t.prototype.name="",t.prototype.type=null,t.prototype.docString="",t.create=function(e){return new t(e)},t.encode=function(e,r){return r||(r=f.create()),e.name!=null&&e.hasOwnProperty("name")&&r.uint32(10).string(e.name),e.type!=null&&e.hasOwnProperty("type")&&o.onnx.TypeProto.encode(e.type,r.uint32(18).fork()).ldelim(),e.docString!=null&&e.hasOwnProperty("docString")&&r.uint32(26).string(e.docString),r},t.encodeDelimited=function(e,r){return this.encode(e,r).ldelim()},t.decode=function(e,r){e instanceof h||(e=h.create(e));for(var i=r===void 0?e.len:e.pos+r,c=new o.onnx.ValueInfoProto;e.pos<i;){var g=e.uint32();switch(g>>>3){case 1:c.name=e.string();break;case 2:c.type=o.onnx.TypeProto.decode(e,e.uint32());break;case 3:c.docString=e.string();break;default:e.skipType(7&g)}}return c},t.decodeDelimited=function(e){return e instanceof h||(e=new h(e)),this.decode(e,e.uint32())},t.verify=function(e){if(typeof e!="object"||e===null)return"object expected";if(e.name!=null&&e.hasOwnProperty("name")&&!a.isString(e.name))return"name: string expected";if(e.type!=null&&e.hasOwnProperty("type")){var r=o.onnx.TypeProto.verify(e.type);if(r)return"type."+r}return e.docString!=null&&e.hasOwnProperty("docString")&&!a.isString(e.docString)?"docString: string expected":null},t.fromObject=function(e){if(e instanceof o.onnx.ValueInfoProto)return e;var r=new o.onnx.ValueInfoProto;if(e.name!=null&&(r.name=String(e.name)),e.type!=null){if(typeof e.type!="object")throw TypeError(".onnx.ValueInfoProto.type: object expected");r.type=o.onnx.TypeProto.fromObject(e.type)}return e.docString!=null&&(r.docString=String(e.docString)),r},t.toObject=function(e,r){r||(r={});var i={};return r.defaults&&(i.name="",i.type=null,i.docString=""),e.name!=null&&e.hasOwnProperty("name")&&(i.name=e.name),e.type!=null&&e.hasOwnProperty("type")&&(i.type=o.onnx.TypeProto.toObject(e.type,r)),e.docString!=null&&e.hasOwnProperty("docString")&&(i.docString=e.docString),i},t.prototype.toJSON=function(){return this.constructor.toObject(this,s.util.toJSONOptions)},t}(),p.NodeProto=function(){function t(e){if(this.input=[],this.output=[],this.attribute=[],e)for(var r=Object.keys(e),i=0;i<r.length;++i)e[r[i]]!=null&&(this[r[i]]=e[r[i]])}return t.prototype.input=a.emptyArray,t.prototype.output=a.emptyArray,t.prototype.name="",t.prototype.opType="",t.prototype.domain="",t.prototype.attribute=a.emptyArray,t.prototype.docString="",t.create=function(e){return new t(e)},t.encode=function(e,r){if(r||(r=f.create()),e.input!=null&&e.input.length)for(var i=0;i<e.input.length;++i)r.uint32(10).string(e.input[i]);if(e.output!=null&&e.output.length)for(i=0;i<e.output.length;++i)r.uint32(18).string(e.output[i]);if(e.name!=null&&e.hasOwnProperty("name")&&r.uint32(26).string(e.name),e.opType!=null&&e.hasOwnProperty("opType")&&r.uint32(34).string(e.opType),e.attribute!=null&&e.attribute.length)for(i=0;i<e.attribute.length;++i)o.onnx.AttributeProto.encode(e.attribute[i],r.uint32(42).fork()).ldelim();return e.docString!=null&&e.hasOwnProperty("docString")&&r.uint32(50).string(e.docString),e.domain!=null&&e.hasOwnProperty("domain")&&r.uint32(58).string(e.domain),r},t.encodeDelimited=function(e,r){return this.encode(e,r).ldelim()},t.decode=function(e,r){e instanceof h||(e=h.create(e));for(var i=r===void 0?e.len:e.pos+r,c=new o.onnx.NodeProto;e.pos<i;){var g=e.uint32();switch(g>>>3){case 1:c.input&&c.input.length||(c.input=[]),c.input.push(e.string());break;case 2:c.output&&c.output.length||(c.output=[]),c.output.push(e.string());break;case 3:c.name=e.string();break;case 4:c.opType=e.string();break;case 7:c.domain=e.string();break;case 5:c.attribute&&c.attribute.length||(c.attribute=[]),c.attribute.push(o.onnx.AttributeProto.decode(e,e.uint32()));break;case 6:c.docString=e.string();break;default:e.skipType(7&g)}}return c},t.decodeDelimited=function(e){return e instanceof h||(e=new h(e)),this.decode(e,e.uint32())},t.verify=function(e){if(typeof e!="object"||e===null)return"object expected";if(e.input!=null&&e.hasOwnProperty("input")){if(!Array.isArray(e.input))return"input: array expected";for(var r=0;r<e.input.length;++r)if(!a.isString(e.input[r]))return"input: string[] expected"}if(e.output!=null&&e.hasOwnProperty("output")){if(!Array.isArray(e.output))return"output: array expected";for(r=0;r<e.output.length;++r)if(!a.isString(e.output[r]))return"output: string[] expected"}if(e.name!=null&&e.hasOwnProperty("name")&&!a.isString(e.name))return"name: string expected";if(e.opType!=null&&e.hasOwnProperty("opType")&&!a.isString(e.opType))return"opType: string expected";if(e.domain!=null&&e.hasOwnProperty("domain")&&!a.isString(e.domain))return"domain: string expected";if(e.attribute!=null&&e.hasOwnProperty("attribute")){if(!Array.isArray(e.attribute))return"attribute: array expected";for(r=0;r<e.attribute.length;++r){var i=o.onnx.AttributeProto.verify(e.attribute[r]);if(i)return"attribute."+i}}return e.docString!=null&&e.hasOwnProperty("docString")&&!a.isString(e.docString)?"docString: string expected":null},t.fromObject=function(e){if(e instanceof o.onnx.NodeProto)return e;var r=new o.onnx.NodeProto;if(e.input){if(!Array.isArray(e.input))throw TypeError(".onnx.NodeProto.input: array expected");r.input=[];for(var i=0;i<e.input.length;++i)r.input[i]=String(e.input[i])}if(e.output){if(!Array.isArray(e.output))throw TypeError(".onnx.NodeProto.output: array expected");for(r.output=[],i=0;i<e.output.length;++i)r.output[i]=String(e.output[i])}if(e.name!=null&&(r.name=String(e.name)),e.opType!=null&&(r.opType=String(e.opType)),e.domain!=null&&(r.domain=String(e.domain)),e.attribute){if(!Array.isArray(e.attribute))throw TypeError(".onnx.NodeProto.attribute: array expected");for(r.attribute=[],i=0;i<e.attribute.length;++i){if(typeof e.attribute[i]!="object")throw TypeError(".onnx.NodeProto.attribute: object expected");r.attribute[i]=o.onnx.AttributeProto.fromObject(e.attribute[i])}}return e.docString!=null&&(r.docString=String(e.docString)),r},t.toObject=function(e,r){r||(r={});var i={};if((r.arrays||r.defaults)&&(i.input=[],i.output=[],i.attribute=[]),r.defaults&&(i.name="",i.opType="",i.docString="",i.domain=""),e.input&&e.input.length){i.input=[];for(var c=0;c<e.input.length;++c)i.input[c]=e.input[c]}if(e.output&&e.output.length)for(i.output=[],c=0;c<e.output.length;++c)i.output[c]=e.output[c];if(e.name!=null&&e.hasOwnProperty("name")&&(i.name=e.name),e.opType!=null&&e.hasOwnProperty("opType")&&(i.opType=e.opType),e.attribute&&e.attribute.length)for(i.attribute=[],c=0;c<e.attribute.length;++c)i.attribute[c]=o.onnx.AttributeProto.toObject(e.attribute[c],r);return e.docString!=null&&e.hasOwnProperty("docString")&&(i.docString=e.docString),e.domain!=null&&e.hasOwnProperty("domain")&&(i.domain=e.domain),i},t.prototype.toJSON=function(){return this.constructor.toObject(this,s.util.toJSONOptions)},t}(),p.ModelProto=function(){function t(e){if(this.opsetImport=[],this.metadataProps=[],e)for(var r=Object.keys(e),i=0;i<r.length;++i)e[r[i]]!=null&&(this[r[i]]=e[r[i]])}return t.prototype.irVersion=a.Long?a.Long.fromBits(0,0,!1):0,t.prototype.opsetImport=a.emptyArray,t.prototype.producerName="",t.prototype.producerVersion="",t.prototype.domain="",t.prototype.modelVersion=a.Long?a.Long.fromBits(0,0,!1):0,t.prototype.docString="",t.prototype.graph=null,t.prototype.metadataProps=a.emptyArray,t.create=function(e){return new t(e)},t.encode=function(e,r){if(r||(r=f.create()),e.irVersion!=null&&e.hasOwnProperty("irVersion")&&r.uint32(8).int64(e.irVersion),e.producerName!=null&&e.hasOwnProperty("producerName")&&r.uint32(18).string(e.producerName),e.producerVersion!=null&&e.hasOwnProperty("producerVersion")&&r.uint32(26).string(e.producerVersion),e.domain!=null&&e.hasOwnProperty("domain")&&r.uint32(34).string(e.domain),e.modelVersion!=null&&e.hasOwnProperty("modelVersion")&&r.uint32(40).int64(e.modelVersion),e.docString!=null&&e.hasOwnProperty("docString")&&r.uint32(50).string(e.docString),e.graph!=null&&e.hasOwnProperty("graph")&&o.onnx.GraphProto.encode(e.graph,r.uint32(58).fork()).ldelim(),e.opsetImport!=null&&e.opsetImport.length)for(var i=0;i<e.opsetImport.length;++i)o.onnx.OperatorSetIdProto.encode(e.opsetImport[i],r.uint32(66).fork()).ldelim();if(e.metadataProps!=null&&e.metadataProps.length)for(i=0;i<e.metadataProps.length;++i)o.onnx.StringStringEntryProto.encode(e.metadataProps[i],r.uint32(114).fork()).ldelim();return r},t.encodeDelimited=function(e,r){return this.encode(e,r).ldelim()},t.decode=function(e,r){e instanceof h||(e=h.create(e));for(var i=r===void 0?e.len:e.pos+r,c=new o.onnx.ModelProto;e.pos<i;){var g=e.uint32();switch(g>>>3){case 1:c.irVersion=e.int64();break;case 8:c.opsetImport&&c.opsetImport.length||(c.opsetImport=[]),c.opsetImport.push(o.onnx.OperatorSetIdProto.decode(e,e.uint32()));break;case 2:c.producerName=e.string();break;case 3:c.producerVersion=e.string();break;case 4:c.domain=e.string();break;case 5:c.modelVersion=e.int64();break;case 6:c.docString=e.string();break;case 7:c.graph=o.onnx.GraphProto.decode(e,e.uint32());break;case 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o.onnx.ModelProto)return e;var r=new o.onnx.ModelProto;if(e.irVersion!=null&&(a.Long?(r.irVersion=a.Long.fromValue(e.irVersion)).unsigned=!1:typeof e.irVersion=="string"?r.irVersion=parseInt(e.irVersion,10):typeof e.irVersion=="number"?r.irVersion=e.irVersion:typeof e.irVersion=="object"&&(r.irVersion=new a.LongBits(e.irVersion.low>>>0,e.irVersion.high>>>0).toNumber())),e.opsetImport){if(!Array.isArray(e.opsetImport))throw TypeError(".onnx.ModelProto.opsetImport: array expected");r.opsetImport=[];for(var i=0;i<e.opsetImport.length;++i){if(typeof e.opsetImport[i]!="object")throw TypeError(".onnx.ModelProto.opsetImport: object expected");r.opsetImport[i]=o.onnx.OperatorSetIdProto.fromObject(e.opsetImport[i])}}if(e.producerName!=null&&(r.producerName=String(e.producerName)),e.producerVersion!=null&&(r.producerVersion=String(e.producerVersion)),e.domain!=null&&(r.domain=String(e.domain)),e.modelVersion!=null&&(a.Long?(r.modelVersion=a.Long.fromValue(e.modelVersion)).unsigned=!1:typeof 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r=Object.keys(e),i=0;i<r.length;++i)e[r[i]]!=null&&(this[r[i]]=e[r[i]])}return t.prototype.key="",t.prototype.value="",t.create=function(e){return new t(e)},t.encode=function(e,r){return r||(r=f.create()),e.key!=null&&e.hasOwnProperty("key")&&r.uint32(10).string(e.key),e.value!=null&&e.hasOwnProperty("value")&&r.uint32(18).string(e.value),r},t.encodeDelimited=function(e,r){return this.encode(e,r).ldelim()},t.decode=function(e,r){e instanceof h||(e=h.create(e));for(var i=r===void 0?e.len:e.pos+r,c=new o.onnx.StringStringEntryProto;e.pos<i;){var g=e.uint32();switch(g>>>3){case 1:c.key=e.string();break;case 2:c.value=e.string();break;default:e.skipType(7&g)}}return c},t.decodeDelimited=function(e){return e instanceof h||(e=new h(e)),this.decode(e,e.uint32())},t.verify=function(e){return typeof e!="object"||e===null?"object expected":e.key!=null&&e.hasOwnProperty("key")&&!a.isString(e.key)?"key: string expected":e.value!=null&&e.hasOwnProperty("value")&&!a.isString(e.value)?"value: string expected":null},t.fromObject=function(e){if(e instanceof o.onnx.StringStringEntryProto)return e;var r=new o.onnx.StringStringEntryProto;return e.key!=null&&(r.key=String(e.key)),e.value!=null&&(r.value=String(e.value)),r},t.toObject=function(e,r){r||(r={});var i={};return r.defaults&&(i.key="",i.value=""),e.key!=null&&e.hasOwnProperty("key")&&(i.key=e.key),e.value!=null&&e.hasOwnProperty("value")&&(i.value=e.value),i},t.prototype.toJSON=function(){return this.constructor.toObject(this,s.util.toJSONOptions)},t}(),p.TensorAnnotation=function(){function t(e){if(this.quantParameterTensorNames=[],e)for(var r=Object.keys(e),i=0;i<r.length;++i)e[r[i]]!=null&&(this[r[i]]=e[r[i]])}return t.prototype.tensorName="",t.prototype.quantParameterTensorNames=a.emptyArray,t.create=function(e){return new t(e)},t.encode=function(e,r){if(r||(r=f.create()),e.tensorName!=null&&e.hasOwnProperty("tensorName")&&r.uint32(10).string(e.tensorName),e.quantParameterTensorNames!=null&&e.quantParameterTensorNames.length)for(var i=0;i<e.quantParameterTensorNames.length;++i)o.onnx.StringStringEntryProto.encode(e.quantParameterTensorNames[i],r.uint32(18).fork()).ldelim();return r},t.encodeDelimited=function(e,r){return this.encode(e,r).ldelim()},t.decode=function(e,r){e instanceof h||(e=h.create(e));for(var i=r===void 0?e.len:e.pos+r,c=new o.onnx.TensorAnnotation;e.pos<i;){var g=e.uint32();switch(g>>>3){case 1:c.tensorName=e.string();break;case 2:c.quantParameterTensorNames&&c.quantParameterTensorNames.length||(c.quantParameterTensorNames=[]),c.quantParameterTensorNames.push(o.onnx.StringStringEntryProto.decode(e,e.uint32()));break;default:e.skipType(7&g)}}return c},t.decodeDelimited=function(e){return e instanceof h||(e=new h(e)),this.decode(e,e.uint32())},t.verify=function(e){if(typeof e!="object"||e===null)return"object expected";if(e.tensorName!=null&&e.hasOwnProperty("tensorName")&&!a.isString(e.tensorName))return"tensorName: string expected";if(e.quantParameterTensorNames!=null&&e.hasOwnProperty("quantParameterTensorNames")){if(!Array.isArray(e.quantParameterTensorNames))return"quantParameterTensorNames: array expected";for(var r=0;r<e.quantParameterTensorNames.length;++r){var i=o.onnx.StringStringEntryProto.verify(e.quantParameterTensorNames[r]);if(i)return"quantParameterTensorNames."+i}}return null},t.fromObject=function(e){if(e instanceof o.onnx.TensorAnnotation)return e;var r=new o.onnx.TensorAnnotation;if(e.tensorName!=null&&(r.tensorName=String(e.tensorName)),e.quantParameterTensorNames){if(!Array.isArray(e.quantParameterTensorNames))throw TypeError(".onnx.TensorAnnotation.quantParameterTensorNames: array expected");r.quantParameterTensorNames=[];for(var i=0;i<e.quantParameterTensorNames.length;++i){if(typeof e.quantParameterTensorNames[i]!="object")throw TypeError(".onnx.TensorAnnotation.quantParameterTensorNames: object expected");r.quantParameterTensorNames[i]=o.onnx.StringStringEntryProto.fromObject(e.quantParameterTensorNames[i])}}return r},t.toObject=function(e,r){r||(r={});var i={};if((r.arrays||r.defaults)&&(i.quantParameterTensorNames=[]),r.defaults&&(i.tensorName=""),e.tensorName!=null&&e.hasOwnProperty("tensorName")&&(i.tensorName=e.tensorName),e.quantParameterTensorNames&&e.quantParameterTensorNames.length){i.quantParameterTensorNames=[];for(var c=0;c<e.quantParameterTensorNames.length;++c)i.quantParameterTensorNames[c]=o.onnx.StringStringEntryProto.toObject(e.quantParameterTensorNames[c],r)}return i},t.prototype.toJSON=function(){return this.constructor.toObject(this,s.util.toJSONOptions)},t}(),p.GraphProto=function(){function t(e){if(this.node=[],this.initializer=[],this.input=[],this.output=[],this.valueInfo=[],this.quantizationAnnotation=[],e)for(var r=Object.keys(e),i=0;i<r.length;++i)e[r[i]]!=null&&(this[r[i]]=e[r[i]])}return t.prototype.node=a.emptyArray,t.prototype.name="",t.prototype.initializer=a.emptyArray,t.prototype.docString="",t.prototype.input=a.emptyArray,t.prototype.output=a.emptyArray,t.prototype.valueInfo=a.emptyArray,t.prototype.quantizationAnnotation=a.emptyArray,t.create=function(e){return new t(e)},t.encode=function(e,r){if(r||(r=f.create()),e.node!=null&&e.node.length)for(var i=0;i<e.node.length;++i)o.onnx.NodeProto.encode(e.node[i],r.uint32(10).fork()).ldelim();if(e.name!=null&&e.hasOwnProperty("name")&&r.uint32(18).string(e.name),e.initializer!=null&&e.initializer.length)for(i=0;i<e.initializer.length;++i)o.onnx.TensorProto.encode(e.initializer[i],r.uint32(42).fork()).ldelim();if(e.docString!=null&&e.hasOwnProperty("docString")&&r.uint32(82).string(e.docString),e.input!=null&&e.input.length)for(i=0;i<e.input.length;++i)o.onnx.ValueInfoProto.encode(e.input[i],r.uint32(90).fork()).ldelim();if(e.output!=null&&e.output.length)for(i=0;i<e.output.length;++i)o.onnx.ValueInfoProto.encode(e.output[i],r.uint32(98).fork()).ldelim();if(e.valueInfo!=null&&e.valueInfo.length)for(i=0;i<e.valueInfo.length;++i)o.onnx.ValueInfoProto.encode(e.valueInfo[i],r.uint32(106).fork()).ldelim();if(e.quantizationAnnotation!=null&&e.quantizationAnnotation.length)for(i=0;i<e.quantizationAnnotation.length;++i)o.onnx.TensorAnnotation.encode(e.quantizationAnnotation[i],r.uint32(114).fork()).ldelim();return r},t.encodeDelimited=function(e,r){return this.encode(e,r).ldelim()},t.decode=function(e,r){e instanceof h||(e=h.create(e));for(var i=r===void 0?e.len:e.pos+r,c=new o.onnx.GraphProto;e.pos<i;){var g=e.uint32();switch(g>>>3){case 1:c.node&&c.node.length||(c.node=[]),c.node.push(o.onnx.NodeProto.decode(e,e.uint32()));break;case 2:c.name=e.string();break;case 5:c.initializer&&c.initializer.length||(c.initializer=[]),c.initializer.push(o.onnx.TensorProto.decode(e,e.uint32()));break;case 10:c.docString=e.string();break;case 11:c.input&&c.input.length||(c.input=[]),c.input.push(o.onnx.ValueInfoProto.decode(e,e.uint32()));break;case 12:c.output&&c.output.length||(c.output=[]),c.output.push(o.onnx.ValueInfoProto.decode(e,e.uint32()));break;case 13:c.valueInfo&&c.valueInfo.length||(c.valueInfo=[]),c.valueInfo.push(o.onnx.ValueInfoProto.decode(e,e.uint32()));break;case 14:c.quantizationAnnotation&&c.quantizationAnnotation.length||(c.quantizationAnnotation=[]),c.quantizationAnnotation.push(o.onnx.TensorAnnotation.decode(e,e.uint32()));break;default:e.skipType(7&g)}}return c},t.decodeDelimited=function(e){return e instanceof h||(e=new h(e)),this.decode(e,e.uint32())},t.verify=function(e){if(typeof e!="object"||e===null)return"object expected";if(e.node!=null&&e.hasOwnProperty("node")){if(!Array.isArray(e.node))return"node: array expected";for(var r=0;r<e.node.length;++r)if(i=o.onnx.NodeProto.verify(e.node[r]))return"node."+i}if(e.name!=null&&e.hasOwnProperty("name")&&!a.isString(e.name))return"name: string expected";if(e.initializer!=null&&e.hasOwnProperty("initializer")){if(!Array.isArray(e.initializer))return"initializer: array expected";for(r=0;r<e.initializer.length;++r)if(i=o.onnx.TensorProto.verify(e.initializer[r]))return"initializer."+i}if(e.docString!=null&&e.hasOwnProperty("docString")&&!a.isString(e.docString))return"docString: string expected";if(e.input!=null&&e.hasOwnProperty("input")){if(!Array.isArray(e.input))return"input: array expected";for(r=0;r<e.input.length;++r)if(i=o.onnx.ValueInfoProto.verify(e.input[r]))return"input."+i}if(e.output!=null&&e.hasOwnProperty("output")){if(!Array.isArray(e.output))return"output: array expected";for(r=0;r<e.output.length;++r)if(i=o.onnx.ValueInfoProto.verify(e.output[r]))return"output."+i}if(e.valueInfo!=null&&e.hasOwnProperty("valueInfo")){if(!Array.isArray(e.valueInfo))return"valueInfo: array expected";for(r=0;r<e.valueInfo.length;++r)if(i=o.onnx.ValueInfoProto.verify(e.valueInfo[r]))return"valueInfo."+i}if(e.quantizationAnnotation!=null&&e.hasOwnProperty("quantizationAnnotation")){if(!Array.isArray(e.quantizationAnnotation))return"quantizationAnnotation: array expected";for(r=0;r<e.quantizationAnnotation.length;++r){var i;if(i=o.onnx.TensorAnnotation.verify(e.quantizationAnnotation[r]))return"quantizationAnnotation."+i}}return null},t.fromObject=function(e){if(e instanceof o.onnx.GraphProto)return e;var r=new o.onnx.GraphProto;if(e.node){if(!Array.isArray(e.node))throw TypeError(".onnx.GraphProto.node: array expected");r.node=[];for(var i=0;i<e.node.length;++i){if(typeof e.node[i]!="object")throw TypeError(".onnx.GraphProto.node: object expected");r.node[i]=o.onnx.NodeProto.fromObject(e.node[i])}}if(e.name!=null&&(r.name=String(e.name)),e.initializer){if(!Array.isArray(e.initializer))throw TypeError(".onnx.GraphProto.initializer: array expected");for(r.initializer=[],i=0;i<e.initializer.length;++i){if(typeof e.initializer[i]!="object")throw TypeError(".onnx.GraphProto.initializer: object expected");r.initializer[i]=o.onnx.TensorProto.fromObject(e.initializer[i])}}if(e.docString!=null&&(r.docString=String(e.docString)),e.input){if(!Array.isArray(e.input))throw TypeError(".onnx.GraphProto.input: array expected");for(r.input=[],i=0;i<e.input.length;++i){if(typeof e.input[i]!="object")throw TypeError(".onnx.GraphProto.input: object expected");r.input[i]=o.onnx.ValueInfoProto.fromObject(e.input[i])}}if(e.output){if(!Array.isArray(e.output))throw TypeError(".onnx.GraphProto.output: array expected");for(r.output=[],i=0;i<e.output.length;++i){if(typeof e.output[i]!="object")throw TypeError(".onnx.GraphProto.output: object expected");r.output[i]=o.onnx.ValueInfoProto.fromObject(e.output[i])}}if(e.valueInfo){if(!Array.isArray(e.valueInfo))throw TypeError(".onnx.GraphProto.valueInfo: array expected");for(r.valueInfo=[],i=0;i<e.valueInfo.length;++i){if(typeof e.valueInfo[i]!="object")throw TypeError(".onnx.GraphProto.valueInfo: object expected");r.valueInfo[i]=o.onnx.ValueInfoProto.fromObject(e.valueInfo[i])}}if(e.quantizationAnnotation){if(!Array.isArray(e.quantizationAnnotation))throw TypeError(".onnx.GraphProto.quantizationAnnotation: array expected");for(r.quantizationAnnotation=[],i=0;i<e.quantizationAnnotation.length;++i){if(typeof e.quantizationAnnotation[i]!="object")throw TypeError(".onnx.GraphProto.quantizationAnnotation: object expected");r.quantizationAnnotation[i]=o.onnx.TensorAnnotation.fromObject(e.quantizationAnnotation[i])}}return r},t.toObject=function(e,r){r||(r={});var i={};if((r.arrays||r.defaults)&&(i.node=[],i.initializer=[],i.input=[],i.output=[],i.valueInfo=[],i.quantizationAnnotation=[]),r.defaults&&(i.name="",i.docString=""),e.node&&e.node.length){i.node=[];for(var c=0;c<e.node.length;++c)i.node[c]=o.onnx.NodeProto.toObject(e.node[c],r)}if(e.name!=null&&e.hasOwnProperty("name")&&(i.name=e.name),e.initializer&&e.initializer.length)for(i.initializer=[],c=0;c<e.initializer.length;++c)i.initializer[c]=o.onnx.TensorProto.toObject(e.initializer[c],r);if(e.docString!=null&&e.hasOwnProperty("docString")&&(i.docString=e.docString),e.input&&e.input.length)for(i.input=[],c=0;c<e.input.length;++c)i.input[c]=o.onnx.ValueInfoProto.toObject(e.input[c],r);if(e.output&&e.output.length)for(i.output=[],c=0;c<e.output.length;++c)i.output[c]=o.onnx.ValueInfoProto.toObject(e.output[c],r);if(e.valueInfo&&e.valueInfo.length)for(i.valueInfo=[],c=0;c<e.valueInfo.length;++c)i.valueInfo[c]=o.onnx.ValueInfoProto.toObject(e.valueInfo[c],r);if(e.quantizationAnnotation&&e.quantizationAnnotation.length)for(i.quantizationAnnotation=[],c=0;c<e.quantizationAnnotation.length;++c)i.quantizationAnnotation[c]=o.onnx.TensorAnnotation.toObject(e.quantizationAnnotation[c],r);return i},t.prototype.toJSON=function(){return this.constructor.toObject(this,s.util.toJSONOptions)},t}(),p.TensorProto=function(){function t(e){if(this.dims=[],this.floatData=[],this.int32Data=[],this.stringData=[],this.int64Data=[],this.externalData=[],this.doubleData=[],this.uint64Data=[],e)for(var r=Object.keys(e),i=0;i<r.length;++i)e[r[i]]!=null&&(this[r[i]]=e[r[i]])}return t.prototype.dims=a.emptyArray,t.prototype.dataType=0,t.prototype.segment=null,t.prototype.floatData=a.emptyArray,t.prototype.int32Data=a.emptyArray,t.prototype.stringData=a.emptyArray,t.prototype.int64Data=a.emptyArray,t.prototype.name="",t.prototype.docString="",t.prototype.rawData=a.newBuffer([]),t.prototype.externalData=a.emptyArray,t.prototype.dataLocation=0,t.prototype.doubleData=a.emptyArray,t.prototype.uint64Data=a.emptyArray,t.create=function(e){return new t(e)},t.encode=function(e,r){if(r||(r=f.create()),e.dims!=null&&e.dims.length){r.uint32(10).fork();for(var i=0;i<e.dims.length;++i)r.int64(e.dims[i]);r.ldelim()}if(e.dataType!=null&&e.hasOwnProperty("dataType")&&r.uint32(16).int32(e.dataType),e.segment!=null&&e.hasOwnProperty("segment")&&o.onnx.TensorProto.Segment.encode(e.segment,r.uint32(26).fork()).ldelim(),e.floatData!=null&&e.floatData.length){for(r.uint32(34).fork(),i=0;i<e.floatData.length;++i)r.float(e.floatData[i]);r.ldelim()}if(e.int32Data!=null&&e.int32Data.length){for(r.uint32(42).fork(),i=0;i<e.int32Data.length;++i)r.int32(e.int32Data[i]);r.ldelim()}if(e.stringData!=null&&e.stringData.length)for(i=0;i<e.stringData.length;++i)r.uint32(50).bytes(e.stringData[i]);if(e.int64Data!=null&&e.int64Data.length){for(r.uint32(58).fork(),i=0;i<e.int64Data.length;++i)r.int64(e.int64Data[i]);r.ldelim()}if(e.name!=null&&e.hasOwnProperty("name")&&r.uint32(66).string(e.name),e.rawData!=null&&e.hasOwnProperty("rawData")&&r.uint32(74).bytes(e.rawData),e.doubleData!=null&&e.doubleData.length){for(r.uint32(82).fork(),i=0;i<e.doubleData.length;++i)r.double(e.doubleData[i]);r.ldelim()}if(e.uint64Data!=null&&e.uint64Data.length){for(r.uint32(90).fork(),i=0;i<e.uint64Data.length;++i)r.uint64(e.uint64Data[i]);r.ldelim()}if(e.docString!=null&&e.hasOwnProperty("docString")&&r.uint32(98).string(e.docString),e.externalData!=null&&e.externalData.length)for(i=0;i<e.externalData.length;++i)o.onnx.StringStringEntryProto.encode(e.externalData[i],r.uint32(106).fork()).ldelim();return e.dataLocation!=null&&e.hasOwnProperty("dataLocation")&&r.uint32(112).int32(e.dataLocation),r},t.encodeDelimited=function(e,r){return this.encode(e,r).ldelim()},t.decode=function(e,r){e instanceof h||(e=h.create(e));for(var i=r===void 0?e.len:e.pos+r,c=new o.onnx.TensorProto;e.pos<i;){var g=e.uint32();switch(g>>>3){case 1:if(c.dims&&c.dims.length||(c.dims=[]),(7&g)==2)for(var m=e.uint32()+e.pos;e.pos<m;)c.dims.push(e.int64());else c.dims.push(e.int64());break;case 2:c.dataType=e.int32();break;case 3:c.segment=o.onnx.TensorProto.Segment.decode(e,e.uint32());break;case 4:if(c.floatData&&c.floatData.length||(c.floatData=[]),(7&g)==2)for(m=e.uint32()+e.pos;e.pos<m;)c.floatData.push(e.float());else c.floatData.push(e.float());break;case 5:if(c.int32Data&&c.int32Data.length||(c.int32Data=[]),(7&g)==2)for(m=e.uint32()+e.pos;e.pos<m;)c.int32Data.push(e.int32());else c.int32Data.push(e.int32());break;case 6:c.stringData&&c.stringData.length||(c.stringData=[]),c.stringData.push(e.bytes());break;case 7:if(c.int64Data&&c.int64Data.length||(c.int64Data=[]),(7&g)==2)for(m=e.uint32()+e.pos;e.pos<m;)c.int64Data.push(e.int64());else c.int64Data.push(e.int64());break;case 8:c.name=e.string();break;case 12:c.docString=e.string();break;case 9:c.rawData=e.bytes();break;case 13:c.externalData&&c.externalData.length||(c.externalData=[]),c.externalData.push(o.onnx.StringStringEntryProto.decode(e,e.uint32()));break;case 14:c.dataLocation=e.int32();break;case 10:if(c.doubleData&&c.doubleData.length||(c.doubleData=[]),(7&g)==2)for(m=e.uint32()+e.pos;e.pos<m;)c.doubleData.push(e.double());else c.doubleData.push(e.double());break;case 11:if(c.uint64Data&&c.uint64Data.length||(c.uint64Data=[]),(7&g)==2)for(m=e.uint32()+e.pos;e.pos<m;)c.uint64Data.push(e.uint64());else c.uint64Data.push(e.uint64());break;default:e.skipType(7&g)}}return c},t.decodeDelimited=function(e){return e instanceof h||(e=new h(e)),this.decode(e,e.uint32())},t.verify=function(e){if(typeof e!="object"||e===null)return"object expected";if(e.dims!=null&&e.hasOwnProperty("dims")){if(!Array.isArray(e.dims))return"dims: array expected";for(var r=0;r<e.dims.length;++r)if(!(a.isInteger(e.dims[r])||e.dims[r]&&a.isInteger(e.dims[r].low)&&a.isInteger(e.dims[r].high)))return"dims: integer|Long[] expected"}if(e.dataType!=null&&e.hasOwnProperty("dataType")&&!a.isInteger(e.dataType))return"dataType: integer expected";if(e.segment!=null&&e.hasOwnProperty("segment")&&(i=o.onnx.TensorProto.Segment.verify(e.segment)))return"segment."+i;if(e.floatData!=null&&e.hasOwnProperty("floatData")){if(!Array.isArray(e.floatData))return"floatData: array expected";for(r=0;r<e.floatData.length;++r)if(typeof e.floatData[r]!="number")return"floatData: number[] expected"}if(e.int32Data!=null&&e.hasOwnProperty("int32Data")){if(!Array.isArray(e.int32Data))return"int32Data: array expected";for(r=0;r<e.int32Data.length;++r)if(!a.isInteger(e.int32Data[r]))return"int32Data: integer[] expected"}if(e.stringData!=null&&e.hasOwnProperty("stringData")){if(!Array.isArray(e.stringData))return"stringData: array expected";for(r=0;r<e.stringData.length;++r)if(!(e.stringData[r]&&typeof e.stringData[r].length=="number"||a.isString(e.stringData[r])))return"stringData: buffer[] expected"}if(e.int64Data!=null&&e.hasOwnProperty("int64Data")){if(!Array.isArray(e.int64Data))return"int64Data: array expected";for(r=0;r<e.int64Data.length;++r)if(!(a.isInteger(e.int64Data[r])||e.int64Data[r]&&a.isInteger(e.int64Data[r].low)&&a.isInteger(e.int64Data[r].high)))return"int64Data: integer|Long[] expected"}if(e.name!=null&&e.hasOwnProperty("name")&&!a.isString(e.name))return"name: string expected";if(e.docString!=null&&e.hasOwnProperty("docString")&&!a.isString(e.docString))return"docString: string expected";if(e.rawData!=null&&e.hasOwnProperty("rawData")&&!(e.rawData&&typeof e.rawData.length=="number"||a.isString(e.rawData)))return"rawData: buffer expected";if(e.externalData!=null&&e.hasOwnProperty("externalData")){if(!Array.isArray(e.externalData))return"externalData: array expected";for(r=0;r<e.externalData.length;++r){var i;if(i=o.onnx.StringStringEntryProto.verify(e.externalData[r]))return"externalData."+i}}if(e.dataLocation!=null&&e.hasOwnProperty("dataLocation"))switch(e.dataLocation){default:return"dataLocation: enum value expected";case 0:case 1:}if(e.doubleData!=null&&e.hasOwnProperty("doubleData")){if(!Array.isArray(e.doubleData))return"doubleData: array expected";for(r=0;r<e.doubleData.length;++r)if(typeof e.doubleData[r]!="number")return"doubleData: number[] expected"}if(e.uint64Data!=null&&e.hasOwnProperty("uint64Data")){if(!Array.isArray(e.uint64Data))return"uint64Data: array expected";for(r=0;r<e.uint64Data.length;++r)if(!(a.isInteger(e.uint64Data[r])||e.uint64Data[r]&&a.isInteger(e.uint64Data[r].low)&&a.isInteger(e.uint64Data[r].high)))return"uint64Data: integer|Long[] expected"}return null},t.fromObject=function(e){if(e instanceof o.onnx.TensorProto)return e;var r=new o.onnx.TensorProto;if(e.dims){if(!Array.isArray(e.dims))throw TypeError(".onnx.TensorProto.dims: array expected");r.dims=[];for(var i=0;i<e.dims.length;++i)a.Long?(r.dims[i]=a.Long.fromValue(e.dims[i])).unsigned=!1:typeof e.dims[i]=="string"?r.dims[i]=parseInt(e.dims[i],10):typeof e.dims[i]=="number"?r.dims[i]=e.dims[i]:typeof e.dims[i]=="object"&&(r.dims[i]=new a.LongBits(e.dims[i].low>>>0,e.dims[i].high>>>0).toNumber())}if(e.dataType!=null&&(r.dataType=0|e.dataType),e.segment!=null){if(typeof e.segment!="object")throw TypeError(".onnx.TensorProto.segment: object expected");r.segment=o.onnx.TensorProto.Segment.fromObject(e.segment)}if(e.floatData){if(!Array.isArray(e.floatData))throw TypeError(".onnx.TensorProto.floatData: array expected");for(r.floatData=[],i=0;i<e.floatData.length;++i)r.floatData[i]=Number(e.floatData[i])}if(e.int32Data){if(!Array.isArray(e.int32Data))throw TypeError(".onnx.TensorProto.int32Data: array expected");for(r.int32Data=[],i=0;i<e.int32Data.length;++i)r.int32Data[i]=0|e.int32Data[i]}if(e.stringData){if(!Array.isArray(e.stringData))throw TypeError(".onnx.TensorProto.stringData: array expected");for(r.stringData=[],i=0;i<e.stringData.length;++i)typeof e.stringData[i]=="string"?a.base64.decode(e.stringData[i],r.stringData[i]=a.newBuffer(a.base64.length(e.stringData[i])),0):e.stringData[i].length&&(r.stringData[i]=e.stringData[i])}if(e.int64Data){if(!Array.isArray(e.int64Data))throw TypeError(".onnx.TensorProto.int64Data: array expected");for(r.int64Data=[],i=0;i<e.int64Data.length;++i)a.Long?(r.int64Data[i]=a.Long.fromValue(e.int64Data[i])).unsigned=!1:typeof e.int64Data[i]=="string"?r.int64Data[i]=parseInt(e.int64Data[i],10):typeof e.int64Data[i]=="number"?r.int64Data[i]=e.int64Data[i]:typeof e.int64Data[i]=="object"&&(r.int64Data[i]=new a.LongBits(e.int64Data[i].low>>>0,e.int64Data[i].high>>>0).toNumber())}if(e.name!=null&&(r.name=String(e.name)),e.docString!=null&&(r.docString=String(e.docString)),e.rawData!=null&&(typeof e.rawData=="string"?a.base64.decode(e.rawData,r.rawData=a.newBuffer(a.base64.length(e.rawData)),0):e.rawData.length&&(r.rawData=e.rawData)),e.externalData){if(!Array.isArray(e.externalData))throw TypeError(".onnx.TensorProto.externalData: array expected");for(r.externalData=[],i=0;i<e.externalData.length;++i){if(typeof e.externalData[i]!="object")throw TypeError(".onnx.TensorProto.externalData: object expected");r.externalData[i]=o.onnx.StringStringEntryProto.fromObject(e.externalData[i])}}switch(e.dataLocation){case"DEFAULT":case 0:r.dataLocation=0;break;case"EXTERNAL":case 1:r.dataLocation=1}if(e.doubleData){if(!Array.isArray(e.doubleData))throw TypeError(".onnx.TensorProto.doubleData: array 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i={};if((r.arrays||r.defaults)&&(i.dims=[],i.floatData=[],i.int32Data=[],i.stringData=[],i.int64Data=[],i.doubleData=[],i.uint64Data=[],i.externalData=[]),r.defaults&&(i.dataType=0,i.segment=null,i.name="",r.bytes===String?i.rawData="":(i.rawData=[],r.bytes!==Array&&(i.rawData=a.newBuffer(i.rawData))),i.docString="",i.dataLocation=r.enums===String?"DEFAULT":0),e.dims&&e.dims.length){i.dims=[];for(var c=0;c<e.dims.length;++c)typeof e.dims[c]=="number"?i.dims[c]=r.longs===String?String(e.dims[c]):e.dims[c]:i.dims[c]=r.longs===String?a.Long.prototype.toString.call(e.dims[c]):r.longs===Number?new a.LongBits(e.dims[c].low>>>0,e.dims[c].high>>>0).toNumber():e.dims[c]}if(e.dataType!=null&&e.hasOwnProperty("dataType")&&(i.dataType=e.dataType),e.segment!=null&&e.hasOwnProperty("segment")&&(i.segment=o.onnx.TensorProto.Segment.toObject(e.segment,r)),e.floatData&&e.floatData.length)for(i.floatData=[],c=0;c<e.floatData.length;++c)i.floatData[c]=r.json&&!isFinite(e.floatData[c])?String(e.floatData[c]):e.floatData[c];if(e.int32Data&&e.int32Data.length)for(i.int32Data=[],c=0;c<e.int32Data.length;++c)i.int32Data[c]=e.int32Data[c];if(e.stringData&&e.stringData.length)for(i.stringData=[],c=0;c<e.stringData.length;++c)i.stringData[c]=r.bytes===String?a.base64.encode(e.stringData[c],0,e.stringData[c].length):r.bytes===Array?Array.prototype.slice.call(e.stringData[c]):e.stringData[c];if(e.int64Data&&e.int64Data.length)for(i.int64Data=[],c=0;c<e.int64Data.length;++c)typeof e.int64Data[c]=="number"?i.int64Data[c]=r.longs===String?String(e.int64Data[c]):e.int64Data[c]:i.int64Data[c]=r.longs===String?a.Long.prototype.toString.call(e.int64Data[c]):r.longs===Number?new a.LongBits(e.int64Data[c].low>>>0,e.int64Data[c].high>>>0).toNumber():e.int64Data[c];if(e.name!=null&&e.hasOwnProperty("name")&&(i.name=e.name),e.rawData!=null&&e.hasOwnProperty("rawData")&&(i.rawData=r.bytes===String?a.base64.encode(e.rawData,0,e.rawData.length):r.bytes===Array?Array.prototype.slice.call(e.rawData):e.rawData),e.doubleData&&e.doubleData.length)for(i.doubleData=[],c=0;c<e.doubleData.length;++c)i.doubleData[c]=r.json&&!isFinite(e.doubleData[c])?String(e.doubleData[c]):e.doubleData[c];if(e.uint64Data&&e.uint64Data.length)for(i.uint64Data=[],c=0;c<e.uint64Data.length;++c)typeof e.uint64Data[c]=="number"?i.uint64Data[c]=r.longs===String?String(e.uint64Data[c]):e.uint64Data[c]:i.uint64Data[c]=r.longs===String?a.Long.prototype.toString.call(e.uint64Data[c]):r.longs===Number?new a.LongBits(e.uint64Data[c].low>>>0,e.uint64Data[c].high>>>0).toNumber(!0):e.uint64Data[c];if(e.docString!=null&&e.hasOwnProperty("docString")&&(i.docString=e.docString),e.externalData&&e.externalData.length)for(i.externalData=[],c=0;c<e.externalData.length;++c)i.externalData[c]=o.onnx.StringStringEntryProto.toObject(e.externalData[c],r);return e.dataLocation!=null&&e.hasOwnProperty("dataLocation")&&(i.dataLocation=r.enums===String?o.onnx.TensorProto.DataLocation[e.dataLocation]:e.dataLocation),i},t.prototype.toJSON=function(){return this.constructor.toObject(this,s.util.toJSONOptions)},t.DataType=function(){var e={},r=Object.create(e);return r[e[0]="UNDEFINED"]=0,r[e[1]="FLOAT"]=1,r[e[2]="UINT8"]=2,r[e[3]="INT8"]=3,r[e[4]="UINT16"]=4,r[e[5]="INT16"]=5,r[e[6]="INT32"]=6,r[e[7]="INT64"]=7,r[e[8]="STRING"]=8,r[e[9]="BOOL"]=9,r[e[10]="FLOAT16"]=10,r[e[11]="DOUBLE"]=11,r[e[12]="UINT32"]=12,r[e[13]="UINT64"]=13,r[e[14]="COMPLEX64"]=14,r[e[15]="COMPLEX128"]=15,r[e[16]="BFLOAT16"]=16,r}(),t.Segment=function(){function e(r){if(r)for(var i=Object.keys(r),c=0;c<i.length;++c)r[i[c]]!=null&&(this[i[c]]=r[i[c]])}return e.prototype.begin=a.Long?a.Long.fromBits(0,0,!1):0,e.prototype.end=a.Long?a.Long.fromBits(0,0,!1):0,e.create=function(r){return new e(r)},e.encode=function(r,i){return i||(i=f.create()),r.begin!=null&&r.hasOwnProperty("begin")&&i.uint32(8).int64(r.begin),r.end!=null&&r.hasOwnProperty("end")&&i.uint32(16).int64(r.end),i},e.encodeDelimited=function(r,i){return this.encode(r,i).ldelim()},e.decode=function(r,i){r instanceof h||(r=h.create(r));for(var c=i===void 0?r.len:r.pos+i,g=new o.onnx.TensorProto.Segment;r.pos<c;){var m=r.uint32();switch(m>>>3){case 1:g.begin=r.int64();break;case 2:g.end=r.int64();break;default:r.skipType(7&m)}}return g},e.decodeDelimited=function(r){return r instanceof h||(r=new h(r)),this.decode(r,r.uint32())},e.verify=function(r){return typeof r!="object"||r===null?"object expected":r.begin!=null&&r.hasOwnProperty("begin")&&!(a.isInteger(r.begin)||r.begin&&a.isInteger(r.begin.low)&&a.isInteger(r.begin.high))?"begin: integer|Long expected":r.end!=null&&r.hasOwnProperty("end")&&!(a.isInteger(r.end)||r.end&&a.isInteger(r.end.low)&&a.isInteger(r.end.high))?"end: integer|Long expected":null},e.fromObject=function(r){if(r instanceof o.onnx.TensorProto.Segment)return r;var i=new o.onnx.TensorProto.Segment;return r.begin!=null&&(a.Long?(i.begin=a.Long.fromValue(r.begin)).unsigned=!1:typeof r.begin=="string"?i.begin=parseInt(r.begin,10):typeof r.begin=="number"?i.begin=r.begin:typeof r.begin=="object"&&(i.begin=new 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-        return offset;
-      }
-      `)}}getOutputSamplingSnippet(){const o=this.context.outputTextureLayout;return o.isPacked?this.getPackedOutputSamplingSnippet(o):this.getUnpackedOutputSamplingSnippet(o)}getPackedOutputSamplingSnippet(o){const t=o.unpackedShape,e=[o.width,o.height],r={},i="getOutputCoords";switch(t.length){case 0:r[i]=this.getOutputScalarCoords();break;case 1:r[i]=this.getOutputPacked1DCoords(t,e);break;case 2:r[i]=this.getOutputPacked2DCoords(t,e);break;case 3:r[i]=this.getOutputPacked3DCoords(t,e);break;default:r[i]=this.getOutputPackedNDCoords(t,e)}const c=`
-      void setOutput(vec4 val) {
-        ${(0,p.getGlsl)(this.context.glContext.version).output} = val;
-      }
-    `;return r.floatTextureSetRGBA=new l.GlslLibRoutine(c),r}getUnpackedOutputSamplingSnippet(o){const t=o.unpackedShape,e=[o.width,o.height],r={},i="getOutputCoords";switch(t.length){case 0:r[i]=this.getOutputScalarCoords();break;case 1:r[i]=this.getOutputUnpacked1DCoords(t,e);break;case 2:r[i]=this.getOutputUnpacked2DCoords(t,e);break;case 3:r[i]=this.getOutputUnpacked3DCoords(t,e);break;case 4:r[i]=this.getOutputUnpacked4DCoords(t,e);break;case 5:r[i]=this.getOutputUnpacked5DCoords(t,e);break;case 6:r[i]=this.getOutputUnpacked6DCoords(t,e);break;default:throw new Error(`Unsupported output dimensionality: ${t.length}`)}const c=`
-        void setOutput(float val) {
-          ${(0,p.getGlsl)(this.context.glContext.version).output} = vec4(val, 0, 0, 0);
-        }
-    `;return r.floatTextureSetR=new l.GlslLibRoutine(c),r}getOutputScalarCoords(){return new l.GlslLibRoutine(`
-      int getOutputCoords() {
-        return 0;
-      }
-    `)}getOutputPacked1DCoords(o,t){const e=t;let r="";return e[0]===1?(r=`
-          int getOutputCoords() {
-            return 2 * int(TexCoords.y * ${e[1]}.0);
-          }
-        `,new l.GlslLibRoutine(r)):e[1]===1?(r=`
-          int getOutputCoords() {
-            return 2 * int(TexCoords.x * ${e[0]}.0);
-          }
-        `,new l.GlslLibRoutine(r)):(r=`
-        int getOutputCoords() {
-          ivec2 resTexRC = ivec2(TexCoords.xy *
-                                 vec2(${e[0]}, ${e[1]}));
-          return 2 * (resTexRC.y * ${e[0]} + resTexRC.x);
-        }
-      `,new l.GlslLibRoutine(r))}getOutputPacked2DCoords(o,t){let e="";if(d.ArrayUtil.arraysEqual(o,t))return e=`
-        ivec2 getOutputCoords() {
-          return 2 * ivec2(TexCoords.xy * vec2(${t[0]}, ${t[1]}));
-        }
-      `,new l.GlslLibRoutine(e);const r=t,i=Math.ceil(o[1]/2);return e=`
-        ivec2 getOutputCoords() {
-          ivec2 resTexRC = ivec2(TexCoords.xy *
-                                vec2(${r[0]}, ${r[1]}));
-
-          int index = resTexRC.y * ${r[0]} + resTexRC.x;
-
-          // reverse r and c order for packed texture
-          int r = imod(index, ${i}) * 2;
-          int c = 2 * (index / ${i});
-
-          return ivec2(r, c);
-        }
-      `,new l.GlslLibRoutine(e)}getOutputPacked3DCoords(o,t){const e=[t[0],t[1]],r=Math.ceil(o[2]/2),i=r*Math.ceil(o[1]/2),c=`
-        ivec3 getOutputCoords() {
-          ivec2 resTexRC = ivec2(TexCoords.xy *
-                                vec2(${e[0]}, ${e[1]}));
-          int index = resTexRC.y * ${e[0]} + resTexRC.x;
-
-          int b = index / ${i};
-          index -= b * ${i};
-
-          // reverse r and c order for packed texture
-          int r = imod(index, ${r}) * 2;
-          int c = 2 * (index / ${r});
-
-          return ivec3(b, r, c);
-        }
-      `;return new l.GlslLibRoutine(c)}getOutputPackedNDCoords(o,t){const e=[t[0],t[1]],r=Math.ceil(o[o.length-1]/2),i=r*Math.ceil(o[o.length-2]/2);let c=i,g="",m="b, r, c";for(let _=2;_<o.length-1;_++)c*=o[o.length-_-1],g=`
-      int b${_} = index / ${c};
-      index -= b${_} * ${c};
-    `+g,m=`b${_}, `+m;const b=`
-      ivec${o.length} getOutputCoords() {
-        ivec2 resTexRC = ivec2(TexCoords.xy *
-                              vec2(${e[0]}, ${e[1]}));
-        int index = resTexRC.y * ${e[0]} + resTexRC.x;
-
-        ${g}
-
-        int b = index / ${i};
-        index -= b * ${i};
-
-        // reverse r and c order for packed texture
-        int r = imod(index, ${r}) * 2;
-        int c = 2 * (index / ${r});
-
-        return ivec${o.length}(${m});
-      }
-    `;return new l.GlslLibRoutine(b)}getOutputUnpacked1DCoords(o,t){const e=`
-        int getOutputCoords() {
-          ivec2 resTexRC = ivec2(TexCoords.xy *
-                                vec2(${t[0]}, ${t[1]}));
-          return resTexRC.y * ${t[0]} + resTexRC.x;
-        }
-      `;return new l.GlslLibRoutine(e)}getOutputUnpacked2DCoords(o,t){const e=`
-        ivec2 getOutputCoords() {
-          ivec2 resTexRC = ivec2(TexCoords.xy *
-                                vec2(${t[0]}, ${t[1]}));
-          int index = resTexRC.y * ${t[0]} + resTexRC.x;
-          int r = index / ${o[1]};
-          int c = index - r * ${o[1]};
-          return ivec2(r, c);
-        }
-      `;return new l.GlslLibRoutine(e)}getOutputUnpacked3DCoords(o,t){let e="";const r=o.length;let i=null;r<2&&(i=[]),i=new Array(r-1),i[r-2]=o[r-1];for(let m=r-3;m>=0;--m)i[m]=i[m+1]*o[m+1];const c=["r","c","d"],g=i.map((m,b)=>`int ${c[b]} = index / ${m}; ${b===i.length-1?`int ${c[b+1]} = index - ${c[b]} * ${m}`:`index -= ${c[b]} * ${m}`};`).join("");return e=`
-        ivec3 getOutputCoords() {
-          ivec2 resTexRC = ivec2(TexCoords.xy *
-                                vec2(${t[0]}, ${t[1]}));
-          int index = resTexRC.y * ${t[0]} + resTexRC.x;
-          ${g}
-          return ivec3(r, c, d);
-        }
-      `,new l.GlslLibRoutine(e)}getOutputUnpacked4DCoords(o,t){let e="";const r=o.length;let i=null;r<2&&(i=[]),i=new Array(r-1),i[r-2]=o[r-1];for(let m=r-3;m>=0;--m)i[m]=i[m+1]*o[m+1];const c=["r","c","d","d2"],g=i.map((m,b)=>`int ${c[b]} = index / ${m}; ${b===i.length-1?`int ${c[b+1]} = index - ${c[b]} * ${m}`:`index -= ${c[b]} * ${m}`};`).join("");return e=`
-      ivec4 getOutputCoords() {
-          ivec2 resTexRC = ivec2(TexCoords.xy *
-                                vec2(${t[0]}, ${t[1]}));
-          int index = resTexRC.y * ${t[0]} + resTexRC.x;
-          ${g}
-          return ivec4(r, c, d, d2);
-        }
-      `,new l.GlslLibRoutine(e)}getOutputUnpacked5DCoords(o,t){let e="";const r=o.length;let i=null;r<2&&(i=[]),i=new Array(r-1),i[r-2]=o[r-1];for(let m=r-3;m>=0;--m)i[m]=i[m+1]*o[m+1];const c=["r","c","d","d2","d3"],g=i.map((m,b)=>`int ${c[b]} = index / ${m}; ${b===i.length-1?`int ${c[b+1]} = index - ${c[b]} * ${m}`:`index -= ${c[b]} * ${m}`};`).join("");return e=`
-      ivec5 getOutputCoords() {
-          ivec2 resTexRC = ivec2(TexCoords.xy *
-                                vec2(${t[0]}, ${t[1]}));
-          int index = resTexRC.y * ${t[0]} + resTexRC.x;
-          ${g}
-          return ivec5(r, c, d, d2, d3);
-        }
-      `,new l.GlslLibRoutine(e)}getOutputUnpacked6DCoords(o,t){let e="";const r=o.length;let i=null;r<2&&(i=[]),i=new Array(r-1),i[r-2]=o[r-1];for(let m=r-3;m>=0;--m)i[m]=i[m+1]*o[m+1];const c=["r","c","d","d2","d3","d4"],g=i.map((m,b)=>`int ${c[b]} = index / ${m}; ${b===i.length-1?`int ${c[b+1]} = index - ${c[b]} * ${m}`:`index -= ${c[b]} * ${m}`};`).join("");return e=`
-     ivec6 getOutputCoords() {
-         ivec2 resTexRC = ivec2(TexCoords.xy *
-                               vec2(${t[0]}, ${t[1]}));
-         int index = resTexRC.y * ${t[0]} + resTexRC.x;
-         ${g}
-         return ivec6(r, c, d, d2, d3, d4);
-       }
-     `,new l.GlslLibRoutine(e)}getCommonUtilFuncs(){const o={};let t="uvFromFlat";o[t]=new l.GlslLibRoutine(`
-    vec2 uvFromFlat(int texNumR, int texNumC, int index) {
-      int texC = index / texNumR;
-      int texR = index - texC * texNumR;
-      // TODO: swap texR, texC order in following function so row is corresponding to u and column is corresponding to
-      //       v.
-      return (vec2(texR, texC) + halfCR) / vec2(texNumR, texNumC);
-    }
-    `),t="packedUVfrom1D",o[t]=new l.GlslLibRoutine(`
-      vec2 packedUVfrom1D(int texNumR, int texNumC, int index) {
-        int texelIndex = index / 2;
-        int texR = texelIndex / texNumC;
-        int texC = texelIndex - texR * texNumC;
-        return (vec2(texC, texR) + halfCR) / vec2(texNumC, texNumR);
-      }
-      `),t="packedUVfrom2D",o[t]=new l.GlslLibRoutine(`
-      vec2 packedUVfrom2D(int texNumR, int texNumC, int texelsInLogicalRow, int row, int col) {
-        int texelIndex = (row / 2) * texelsInLogicalRow + (col / 2);
-        int texR = texelIndex / texNumC;
-        int texC = texelIndex - texR * texNumC;
-        return (vec2(texC, texR) + halfCR) / vec2(texNumC, texNumR);
-      }
-      `),t="packedUVfrom3D",o[t]=new l.GlslLibRoutine(`
-      vec2 packedUVfrom3D(int texNumR, int texNumC,
-          int texelsInBatch, int texelsInLogicalRow, int b,
-          int row, int col) {
-        int index = b * texelsInBatch + (row / 2) * texelsInLogicalRow + (col / 2);
-        int texR = index / texNumC;
-        int texC = index - texR * texNumC;
-        return (vec2(texC, texR) + halfCR) / vec2(texNumC, texNumR);
-      }
-      `),t="sampleTexture";const e=(0,p.getGlsl)(this.context.glContext.version);return o[t]=new l.GlslLibRoutine(`
-        float sampleTexture(sampler2D textureSampler, vec2 uv) {
-            return ${e.texture2D}(textureSampler, uv).r;
-        }`),o}getInputsSamplingSnippets(){const o={},t=this.context.outputTextureLayout;return this.context.programInfo.inputNames.forEach((e,r)=>{const i=this.context.inputTextureLayouts[r],c=(0,h.generateShaderFuncNameFromInputSamplerName)(e);i.isPacked?o[c]=this.getPackedSamplerFromInput(c,e,i):o[c]=this.getUnpackedSamplerFromInput(c,e,i);const g=(0,h.generateShaderFuncNameFromInputSamplerNameAtOutCoords)(e);i.unpackedShape.length<=t.unpackedShape.length&&(i.isPacked?o[g]=this.getPackedSamplerAtOutputCoords(g,i,t,e):o[g]=this.getUnpackedSamplerAtOutputCoords(g,i,t,e))}),o}getPackedSamplerAtOutputCoords(o,t,e,r){const i=t.unpackedShape,c=e.unpackedShape,g=r,m=(0,h.generateShaderFuncNameFromInputSamplerName)(g),b=i.length,_=c.length,w=d.BroadcastUtil.getBroadcastDims(i,c),v=(0,h.getCoordsDataType)(_),S=_-b;let O;const E=(0,h.getGlChannels)();O=b===0?"":_<2&&w.length>=1?"coords = 0;":w.map(N=>`coords.${E[N+S]} = 0;`).join(`
-`);let T="";T=_<2&&b>0?"coords":i.map((N,H)=>`coords.${E[H+S]}`).join(", ");let I="return outputValue;";const C=d.ShapeUtil.size(i)===1,B=d.ShapeUtil.size(c)===1;if(b!==1||C||B){if(C&&!B)I=_===1?`
-          return vec4(outputValue.x, outputValue.x, 0., 0.);
-        `:`
-          return vec4(outputValue.x);
-        `;else if(w.length){const N=b-2,H=b-1;w.indexOf(N)>-1&&w.indexOf(H)>-1?I="return vec4(outputValue.x);":w.indexOf(N)>-1?I="return vec4(outputValue.x, outputValue.y, outputValue.x, outputValue.y);":w.indexOf(H)>-1&&(I="return vec4(outputValue.xx, outputValue.zz);")}}else I=`
-        return vec4(outputValue.xy, outputValue.xy);
-      `;const F=`
-      vec4 ${o}() {
-        ${v} coords = getOutputCoords();
-        
-        int lastDim = coords.${E[_-1]};
-        coords.${E[_-1]} = coords.${E[_-2]};
-        coords.${E[_-2]} = lastDim;
-      
-        ${O}
-        vec4 outputValue = ${m}(${T});
-        ${I}
-      }
-    `;return new l.GlslLibRoutine(F,["coordinates.getOutputCoords"])}getUnpackedSamplerAtOutputCoords(o,t,e,r){const i=[e.width,e.height],c=[t.width,t.height],g=t.unpackedShape.length,m=e.unpackedShape.length,b=t.unpackedShape,_=e.unpackedShape,w=(0,h.generateShaderFuncNameFromInputSamplerName)(r);if(g===m&&d.ArrayUtil.arraysEqual(c,i)){const B=`
-          float ${o}() {
-            return sampleTexture(${r}, TexCoords);
-          }
-        `;return new l.GlslLibRoutine(B,["coordinates.sampleTexture"])}const v=(0,h.getCoordsDataType)(m),S=d.BroadcastUtil.getBroadcastDims(b,_),O=m-g;let E;const T=(0,h.getGlChannels)();E=g===0?"":m<2&&S.length>=1?"coords = 0;":S.map(B=>`coords.${T[B+O]} = 0;`).join(`
-`);let I="";I=m<2&&g>0?"coords":t.unpackedShape.map((B,F)=>`coords.${T[F+O]}`).join(", ");const C=`
-        float ${o}() {
-          ${v} coords = getOutputCoords();
-          ${E}
-          return ${w}(${I});
-        }
-      `;return new l.GlslLibRoutine(C,["coordinates.getOutputCoords"])}getPackedSamplerFromInput(o,t,e){switch(e.unpackedShape.length){case 0:return this.getPackedSamplerScalar(o,t);case 1:return this.getPackedSampler1D(o,t,e);case 2:return this.getPackedSampler2D(o,t,e);case 3:return this.getPackedSampler3D(o,t,e);default:return this.getPackedSamplerND(o,t,e)}}getUnpackedSamplerFromInput(o,t,e){const r=e.unpackedShape;switch(r.length){case 0:return this.getUnpackedSamplerScalar(o,t,e);case 1:return this.getUnpackedSampler1D(o,t,e);case 2:return this.getUnpackedSampler2D(o,t,e);case 3:return this.getUnpackedSampler3D(o,t,e);case 4:return this.getUnpackedSampler4D(o,t,e);case 5:return this.getUnpackedSampler5D(o,t,e);case 6:return this.getUnpackedSampler6D(o,t,e);default:throw new Error(`Unsupported dimension ${r.length}-D`)}}getPackedSamplerScalar(o,t){const e=`
-          vec4 ${o}() {
-            return ${(0,p.getGlsl)(this.context.glContext.version).texture2D}(${t}, halfCR);
-          }
-        `;return new l.GlslLibRoutine(e)}getPackedSampler1D(o,t,e){const r=[e.width,e.height],i=[r[1],r[0]],c=(0,p.getGlsl)(this.context.glContext.version),g=`vec4 ${o}(int index) {
-      vec2 uv = packedUVfrom1D(
-      ${i[0]}, ${i[1]}, index);
-      return ${c.texture2D}(${t}, uv);
-    }`;return new l.GlslLibRoutine(g,["coordinates.packedUVfrom1D"])}getPackedSampler2D(o,t,e){const r=e.unpackedShape,i=[e.width,e.height],c=(0,p.getGlsl)(this.context.glContext.version),g=i[0],m=i[1];if(i!=null&&d.ArrayUtil.arraysEqual(r,i)){const v=`vec4 ${o}(int row, int col) {
-        vec2 uv = (vec2(col, row) + halfCR) / vec2(${m}.0, ${g}.0);
-        return ${c.texture2D}(${t}, uv);
-      }`;return new l.GlslLibRoutine(v)}const b=i,_=Math.ceil(r[1]/2),w=`vec4 ${o}(int row, int col) {
-      vec2 uv = packedUVfrom2D(${b[1]}, ${b[0]}, ${_}, row, col);
-      return ${c.texture2D}(${t}, uv);
-    }`;return new l.GlslLibRoutine(w,["coordinates.packedUVfrom2D"])}getPackedSampler3D(o,t,e){const r=e.unpackedShape,i=[e.width,e.height],c=[i[0],i[1]],g=(0,p.getGlsl)(this.context.glContext.version);if(r[0]===1){const v=r.slice(1),S=[1,2],O=(0,h.squeezeInputShape)(r,v),E=["b","row","col"],T=JSON.parse(JSON.stringify(e));T.unpackedShape=O;const I=this.getPackedSamplerFromInput(o,t,T),C=`${I.routineBody}
-      vec4 ${o}(int b, int row, int col) {
-        return ${o}(${(0,h.getSqueezedParams)(E,S)});
-      } `;return new l.GlslLibRoutine(C,I.dependencies)}const m=c[0],b=c[1],_=Math.ceil(r[2]/2),w=`vec4 ${o}(int b, int row, int col) {
-      vec2 uv = packedUVfrom3D(
-        ${b}, ${m}, ${_*Math.ceil(r[1]/2)}, ${_}, b, row, col);
-      return ${g.texture2D}(${t}, uv);}`;return new l.GlslLibRoutine(w,["coordinates.packedUVfrom3D"])}getPackedSamplerND(o,t,e){const r=e.unpackedShape,i=r.length,c=[e.width,e.height],g=(0,p.getGlsl)(this.context.glContext.version),m=[c[0],c[1]],b=m[1],_=m[0],w=Math.ceil(r[i-1]/2);let v=w*Math.ceil(r[i-2]/2),S="int b, int row, int col",O=`b * ${v} + (row / 2) * ${w} + (col / 2)`;for(let T=2;T<i-1;T++)S=`int b${T}, `+S,v*=r[i-T-1],O=`b${T} * ${v} + `+O;const E=`vec4 ${o}(${S}) {
-      int index = ${O};
-      int texR = index / ${_};
-      int texC = index - texR * ${_};
-      vec2 uv = (vec2(texC, texR) + halfCR) / vec2(${_}, ${b});
-      return ${g.texture2D}(${t}, uv);
-    }`;return new l.GlslLibRoutine(E)}getUnpackedSamplerScalar(o,t,e){const[r,i]=[e.width,e.height];if(r===1&&i===1){const g=`
-          float ${o}() {
-            return sampleTexture(${t}, halfCR);
-          }
-        `;return new l.GlslLibRoutine(g,["coordinates.sampleTexture"])}const c=`
-        float ${o}() {
-          int offset_${t} = coordsToOffset(TexCoords, ${r}, ${i});
-          vec2 uv = uvFromFlat(${r}, ${i}, offset_${t});
-          return sampleTexture(${t}, uv);
-        }
-      `;return new l.GlslLibRoutine(c,["coordinates.uvFromFlat","coordinates.sampleTexture","coordinates.coordsToOffset"])}getUnpackedSampler1D(o,t,e){const r=e.width,i=e.height;if(i===1&&r===1){const g=`
-        float ${o}(int index) {
-          return sampleTexture(${t}, halfCR);
-        }
-      `;return new l.GlslLibRoutine(g,["coordinates.sampleTexture"])}if(i===1){const g=`
-          float ${o}(int index) {
-            vec2 uv = vec2((float(index) + 0.5) / ${r}.0, 0.5);
-            return sampleTexture(${t}, uv);
-          }
-        `;return new l.GlslLibRoutine(g,["coordinates.sampleTexture"])}if(r===1){const g=`
-          float ${o}(int index) {
-            vec2 uv = vec2(0.5, (float(index) + 0.5) / ${i}.0);
-            return sampleTexture(${t}, uv);
-          }
-        `;return new l.GlslLibRoutine(g,["coordinates.sampleTexture"])}const c=`
-        float ${o}(int index) {
-          vec2 uv = uvFromFlat(${r}, ${i}, index);
-          return sampleTexture(${t}, uv);
-        }
-      `;return new l.GlslLibRoutine(c,["coordinates.uvFromFlat","coordinates.sampleTexture"])}getUnpackedSampler2D(o,t,e){const r=e.unpackedShape,i=[e.height,e.width];if(i!=null&&d.ArrayUtil.arraysEqual(r,i)){const v=`
-          float ${o}(int row, int col) {
-            vec2 uv = (vec2(row, col) + halfCR) / vec2(${i[1]}.0, ${i[0]}.0);
-            return sampleTexture(${t}, uv);
-          }
-        `;return new l.GlslLibRoutine(v,["coordinates.sampleTexture"])}const{newShape:c,keptDims:g}=(0,s.squeezeShape)(r),m=c;if(m.length<r.length){const v=(0,h.squeezeInputShape)(r,m),S=JSON.parse(JSON.stringify(e));S.unpackedShape=v;const O=["col","row"],E=`
-          ${this.getUnpackedSamplerFromInput(o,t,S).routineBody}
-          float ${o}(int row, int col) {
-            return ${o}(${(0,h.getSqueezedParams)(O,g)});
-          }
-        `;return new l.GlslLibRoutine(E,["coordinates.sampleTexture"])}const b=i[1],_=i[0];if(_===1){const v=`
-          float ${o}(int row, int col) {
-            int offset_${t} = coordsToOffset(TexCoords, ${b}, ${_});
-            float index = dot(vec3(row, col, offset_${t}), vec3(${r[1]}, 1, 1));
-            vec2 uv = vec2(0.5, (index + 0.5) / ${b}.0);
-            return sampleTexture(${t}, uv);
-          }
-        `;return new l.GlslLibRoutine(v,["coordinates.sampleTexture","coordinates.coordsToOffset"])}if(b===1){const v=`
-          float ${o}(int row, int col) {
-            int offset_${t} = coordsToOffset(TexCoords, ${b}, ${_});
-            float index = dot(vec3(row, col, offset_${t}), vec3(${r[1]}, 1, 1));
-            vec2 uv = vec2((index + 0.5) / ${_}.0, 0.5);
-            return sampleTexture(${t}, uv);
-          }
-        `;return new l.GlslLibRoutine(v,["coordinates.sampleTexture","coordinates.coordsToOffset"])}const w=`
-        float ${o}(int row, int col) {
-          int index = col * ${r[1]} + row;
-          vec2 uv = uvFromFlat(${b}, ${_}, index);
-          return sampleTexture(${t}, uv);
-        }
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-        highp float Exponent = floor(log2(F));
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-        rgba[1] = 128.0 * mod(Exponent,2.0) + mod(floor(Mantissa*128.0),128.0);
-        rgba[2] = floor(mod(floor(Mantissa*exp2(23.0 -8.0)),exp2(8.0)));
-        rgba[3] = floor(exp2(23.0)*mod(Mantissa,exp2(-15.0)));
-        ${s}
-        rgba = rgba / 255.0; // values need to be normalized to [0,1]
-        return rgba;
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-          highp float Sign = 1.0 - step(128.0,rgba[0])*2.0;
-          highp float Exponent = 2.0 * mod(rgba[0],128.0) + step(128.0,rgba[1]) - 127.0;
-          highp float Mantissa = mod(rgba[1],128.0)*65536.0 + rgba[2]*256.0 +rgba[3] + float(0x800000);
-          highp float Result =  Sign * exp2(Exponent) * (Mantissa * exp2(-23.0 ));
-          return Result;
-      }
-        `)}}static isLittleEndian(){const s=new ArrayBuffer(4),h=new Uint32Array(s),f=new Uint8Array(s);if(h[0]=3735928559,f[0]===239)return!0;if(f[0]===222)return!1;throw new Error("unknown endianness")}}n.EncodingGlslLib=l},9894:(y,n,u)=>{Object.defineProperty(n,"__esModule",{value:!0}),n.FragColorGlslLib=void 0;const d=u(8520),l=u(5060);class p extends d.GlslLib{constructor(h){super(h)}getFunctions(){return Object.assign(Object.assign({},this.setFragColor()),this.getColorAsFloat())}getCustomTypes(){return{}}setFragColor(){const h=(0,l.getGlsl)(this.context.glContext.version);return{setFragColor:new d.GlslLibRoutine(`
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-      {
-        ${r}
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-          `;const c=`
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-          ${i}
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-          `;const c=`
-        void ${r}(int bcastedIndices[${s}], out int realIndices[${t}]) {
-          ${i}
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-        }
-        `;h[r]=new d.GlslLibRoutine(c)}}),h}indicesToOffset(){const s={};return this.context.programInfo.inputNames.forEach((h,f)=>{const a=this.context.inputTextureLayouts[f].shape,o=this.context.inputTextureLayouts[f].strides,t=a.length;let e=`indicesToOffset_${h}`;s[e]=new d.GlslLibRoutine(l.indexToOffsetSingle(e,t,o)),e=`indicesToOffset_${h}_T`,s[e]=new d.GlslLibRoutine(l.indexToOffsetSingle(e,t,o.slice().reverse()))}),s}static indexToOffsetSingle(s,h,f){let a="";for(let o=h-1;o>=0;--o)a+=`
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-      int ${s}(int indices[${h}]) {
-        int offset = 0;
-        ${a}
-        return offset;
-      }
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-      void ${s}(int offset, out int indices[${h}]) {
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-      }
-      `}incrementIndices(){const s={};return this.context.programInfo.inputNames.forEach((h,f)=>{const a=this.context.inputTextureLayouts[f].shape,o=a.length,t=`incrementIndices_${h}`;let e="";for(let i=0;i<o;++i)e+=`
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-        void ${t}(int axis, out int indices[${o}]) {
-          int shape[${o}];
-          ${e};
-          for(int i = ${o} -1 ; i >= 0; --i) {
-            if(i > axis) continue;
-            indices[i] += 1;
-            if(indices[i] < shape[i]) {
-              break;
-            }
-            indices[i] = 0;
-          }
-        }
-        `;s[t]=new d.GlslLibRoutine(r)}),s}}n.ShapeUtilsGlslLib=l},5060:(y,n)=>{Object.defineProperty(n,"__esModule",{value:!0}),n.getDefaultFragShaderMain=n.getFragShaderPreamble=n.getVertexShaderSource=n.getGlsl=void 0;const u={version:"",attribute:"attribute",varyingVertex:"varying",varyingFrag:"varying",texture2D:"texture2D",output:"gl_FragColor",outputDeclaration:""},d={version:"#version 300 es",attribute:"in",varyingVertex:"out",varyingFrag:"in",texture2D:"texture",output:"outputColor",outputDeclaration:"out vec4 outputColor;"};function l(p){return p===1?u:d}n.getGlsl=l,n.getVertexShaderSource=function(p){const s=l(p);return`${s.version}
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-      ${s.attribute} vec3 position;
-      ${s.attribute} vec2 textureCoord;
-
-      ${s.varyingVertex} vec2 TexCoords;
-
-      void main()
-      {
-          gl_Position = vec4(position, 1.0);
-          TexCoords = textureCoord;
-      }`},n.getFragShaderPreamble=function(p){const s=l(p);return`${s.version}
-    precision highp float;
-    precision highp int;
-    precision highp sampler2D;
-    ${s.varyingFrag} vec2 TexCoords;
-    ${s.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);
-    }
-
-    `},n.getDefaultFragShaderMain=function(p,s){return`
-  void main() {
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-    ${l(p).output} = result;
-  }
-  `}},3891:(y,n,u)=>{Object.defineProperty(n,"__esModule",{value:!0}),n.VecGlslLib=void 0;const d=u(8520);class l extends d.GlslLib{constructor(s){super(s)}getCustomTypes(){return{}}getFunctions(){return Object.assign(Object.assign(Object.assign(Object.assign({},this.binaryVecFunctions()),this.copyVec()),this.setVecItem()),this.getVecItem())}binaryVecFunctions(){const s=this.context.outputTextureLayout.shape.length,h={add:"+=",sub:"-=",mul:"*=",div:"/="},f={};for(const a in h){const o=`${a}Vec`;let t="";for(let r=0;r<s;++r)t+=`
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-            index =${s} + index;
-        if (index == 0)
-            m[0] = value;
-        `;for(let a=1;a<s-1;++a)h+=`
-        else if (index == ${a})
-            m[${a}] = value;
-            `;h+=`
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-            return m[0];
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-              return getChannel(
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-          float getValue(${T.map(X=>"int "+X)}) {
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-          }
-
-          void main() {
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-            int lastDim = coords.${T[w-1]};
-            coords.${T[w-1]} = coords.${T[w-2]};
-            coords.${T[w-2]} = lastDim;
-
-            vec4 result = vec4(getValue(${v}), 0., 0., 0.);
-
-            ${v[w-1]} = ${v[w-1]} + 1;
-            if (${v[w-1]} < ${_[w-1]}) {
-              result.g = getValue(${v});
-            }
-
-            ${v[w-2]} = ${v[w-2]} + 1;
-            if (${v[w-2]} < ${_[w-2]}) {
-              result.a = getValue(${v});
-            }
-
-            ${v[w-1]} = ${v[w-1]} - 1;
-            if (${v[w-2]} < ${_[w-2]} &&
-                ${v[w-1]} < ${_[w-1]}) {
-              result.b = getValue(${v});
-            }
-            ${z.output} = result;
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-          if(textureIndex != 0) {
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-  const ivec2 pads = ivec2(${m.pads[0]}, ${m.pads[1]});
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-    int batch = coords.x;
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-    int group_id = output_channel / ${v};
-
-    float value = 0.0;
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-      int input_channel = group_id * ${w[1]} + wInChannel;
-      for (int wHeight = 0; wHeight < ${w[2]}; wHeight++) {
-        int xHeight = xRCCorner.x + wHeight * ${m.dilations[0]};
-
-        if (xHeight < 0 || xHeight >= ${_[2]}) {
-          continue;
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-
-        for (int wWidth = 0; wWidth < ${w[3]}; wWidth++) {
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-          if (xWidth < 0 || xWidth >= ${_[3]}) {
-            continue;
-          }
-
-          float xVal = getX(batch, input_channel, xWidth, xHeight);
-          float wVal = getW(output_channel, wInChannel, wWidth, wHeight);
-          value += xVal*wVal;
-        }
-      }
-    }
-    ${b}
-    ${T}
-    ${O.output} = vec4(value, .0, .0, .0);
-  }
-`;return Object.assign(Object.assign({},g),{output:{dims:S,type:c[0].type,textureType:p.TextureType.unpacked},shaderSource:I,hasMain:!0})})(f,a,t,o)})}},1386:(y,n,u)=>{Object.defineProperty(n,"__esModule",{value:!0}),n.conv2DPacked=n.conv2DPackedPointwise=void 0;const d=u(8138),l=u(8555),p=u(708);n.conv2DPackedPointwise=(s,h,f)=>{const a=h[0].dims,o=h[1].dims,t=(0,d.calculateOutputShape)(a,o,f.dilations,f.pads,f.strides),e=s.reshapePacked(h[0],[a[1],a[2]*a[3]]),r=s.reshapePacked(h[1],[o[0],o[1]]),i=h.length>2?[r,e,h[2]]:[r,e],c=s.run((0,p.createPackedMatmulProgramInfoLoader)(s,i,f),i);return s.reshapePacked(c,t)},n.conv2DPacked=(s,h,f)=>{const a=h[0].dims,o=h[1].dims,t=(0,d.calculateOutputShape)(a,o,f.dilations,f.pads,f.strides),e=s.run((0,l.createPackedIm2ColProgramInfoLoader)(s,h[0],h[1],t,f),[h[0]]),r=s.reshapePacked(h[1],[o[0],o[1]*o[2]*o[3]]),i=h.length===3?[r,e,h[2]]:[r,e],c=s.run((0,p.createPackedMatmulProgramInfoLoader)(s,i,f),i);return s.reshapePacked(c,t)}},9663:(y,n,u)=>{Object.defineProperty(n,"__esModule",{value:!0}),n.parseConvTransposeAttributes=n.convTranspose=void 0;const d=u(246),l=u(5060),p=u(2039),s=u(2823),h=(r,i,c,g,m,b)=>(r-1)*i+c+(g-1)*m+1-b,f=(r,i,c,g,m)=>{const b=Math.floor(r/2);i==="SAME_UPPER"?(c[g]=b,c[m]=r-b):i==="SAME_LOWER"&&(c[g]=r-b,c[m]=b)};n.convTranspose=(r,i,c)=>(e(i,c),a(r,i,c));const a=(r,i,c)=>{const g=t(c,i);return[o(r,i,g)]},o=(r,i,c)=>r.run(((g,m,b)=>{const _=(w=m.length>2,v=b.cacheKey,{name:"ConvTranspose",inputNames:w?["X","W","B"]:["X","W"],inputTypes:w?[p.TextureType.unpacked,p.TextureType.unpacked,p.TextureType.unpacked]:[p.TextureType.unpacked,p.TextureType.unpacked],cacheHint:v});var w,v;return Object.assign(Object.assign({},_),{get:()=>((S,O,E,T)=>{const I=O.length>2?"getB(output_channel)":"0.0",C=O[0].dims,B=O[1].dims,F=B[1],N=B[0]/T.group,H=[O[0].dims[0],O[1].dims[1]*T.group,...T.outputShape],$=(0,l.getGlsl)(S.session.backend.glContext.version),{activationFunction:z,applyActivation:J}=(0,s.getActivationSnippet)(T),X=`
-  const ivec2 strides = ivec2(${T.strides[0]}, ${T.strides[1]});
-  const ivec2 pads = ivec2(${T.pads[0]}, ${T.pads[1]});
-  ${z}
-  void main() {
-    ivec4 coords = getOutputCoords();
-    int batch = coords.x;
-    int output_channel = coords.y;
-
-    ivec2 loc = coords.zw + pads;
-
-    int group_id = output_channel / ${F};
-    int wOutChannel = output_channel - group_id * ${F};
-
-    float value = ${I};
-    for (int inChannelOffset = 0; inChannelOffset < ${N}; inChannelOffset++) {
-      int input_channel = group_id * ${N} + inChannelOffset;
-      for (int wWOff = 0; wWOff < ${B[2]}; wWOff++) {
-        for (int wHOff = 0; wHOff < ${B[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 < ${C[2]} &&
-            wLocIn.y >= 0 && wLocIn.y < ${C[3]}
-          ) {
-            float xVal = getX(batch, input_channel, wLocIn.y, wLocIn.x);
-            float wVal = getW(input_channel, wOutChannel, wHOff, wWOff);
-            value += xVal * wVal;
-          }
-        }
-      }
-    }
-    ${J}
-    ${$.output} = vec4(value, .0, .0, .0);
-  }
-`;return Object.assign(Object.assign({},E),{output:{dims:H,type:O[0].type,textureType:p.TextureType.unpacked},shaderSource:X,hasMain:!0})})(g,m,_,b)})})(r,i,c),i),t=(r,i)=>{const c=r.kernelShape.slice();if(r.kernelShape.length===0)for(let _=2;_<i[1].dims.length;++_)c.push(i[1].dims[_]);const g=r.pads.slice(),m=r.outputShape.slice();((_,w,v,S,O,E,T,I)=>{const C=_.length-2,B=I.length===0;for(let F=0;F<C;++F){const N=B?_[F+2]*E[F]:I[F],H=h(_[F+2],E[F],O[F],w[F],v[F],N);f(H,S,O,F,F+C),B&&I.push(E[F]*(_[F+2]-1)+T[F]+(w[F]-1)*v[F]+1-O[F]-O[F+C])}})(i[0].dims,c,r.dilations,r.autoPad,g,r.strides,r.outputPadding,m);const b=Object.assign({},r);return Object.assign(b,{kernelShape:c,pads:g,outputShape:m,cacheKey:r.cacheKey}),b};n.parseConvTransposeAttributes=r=>{const 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${g}D`);if(i.strides.length!==g)throw new Error(`strides should be ${g}D`);if(i.pads.length!==2*g)throw new Error(`pads should be ${2*g}D`);if(i.outputPadding.length!==g)throw new Error(`output_padding should be ${g}D`);if(i.kernelShape.length!==0&&i.kernelShape.length!==r[1].dims.length-2)throw new Error("invalid kernel shape");if(i.outputShape.length!==0&&i.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")}},8138:(y,n,u)=>{Object.defineProperty(n,"__esModule",{value:!0}),n.parseConvAttributes=n.conv=n.calculateOutputShape=void 0;const d=u(246),l=u(2517),p=u(4770),s=u(1386),h=u(9828),f=u(2823),a=u(3248),o=u(5623);n.calculateOutputShape=(g,m,b,_,w)=>{const v=g[0],S=g.slice(2),O=S.length,E=m[0],T=m.slice(2).map((C,B)=>C+(C-1)*(b[B]-1)),I=S.map((C,B)=>C+_[B]+_[B+O]).map((C,B)=>Math.floor((C-T[B]+w[B])/w[B]));return[v,E].concat(...I)},n.conv=(g,m,b)=>(c(m,b),t(g,m,b));const t=(g,m,b)=>{const _=i(b,m),w=g.session.pack,v=_.kernelShape[0]===1&&_.kernelShape[1]===1;return _.group>1?[g.run((0,p.createUnpackedGroupedConvProgramInfoLoader)(g,m,_),m)]:v&&w?[e(g,m,_)]:w&&m[0].dims.length===4&&m[0].dims[0]===1&&!v?[(0,s.conv2DPacked)(g,m,_)]:[r(g,m,_)]},e=(g,m,b)=>{const _=m[0].dims,w=m[1].dims,v=(0,n.calculateOutputShape)(_,w,b.dilations,b.pads,b.strides),S=g.reshapeUnpacked(m[0],[_[1],_[2]*_[3]]),O=g.reshapeUnpacked(m[1],[w[0],w[1]]),E=m.length>2?[O,S,m[2]]:[O,S],T=g.run((0,o.createMatmulProgramInfoLoader)(E,b),E);return g.reshapeUnpacked(T,v)},r=(g,m,b)=>{const _=m[0].dims,w=m[1].dims,v=(0,n.calculateOutputShape)(_,w,b.dilations,b.pads,b.strides),S=g.run((0,a.createIm2ColProgramInfoLoader)(g,m[0],m[1],v,b),[m[0]]),O=m.length===3?[S,m[1],m[2]]:[S,m[1]];return g.run((0,h.createDotProductProgramInfoLoader)(g,m,v,b),O)},i=(g,m)=>{const b=g.kernelShape.slice();if(g.kernelShape.length===0)for(let v=2;v<m[1].dims.length;++v)b.push(m[1].dims[v]);const _=g.pads.slice();l.PoolConvUtil.adjustPadsBasedOnAutoPad(m[0].dims,g.strides,g.dilations,b,_,g.autoPad);const w=Object.assign({},g);return Object.assign(w,{kernelShape:b,pads:_,cacheKey:g.cacheKey}),w};n.parseConvAttributes=g=>{const m=g.attributes,b=(0,f.parseInternalActivationAttributes)(m),_=m.getString("auto_pad","NOTSET"),w=m.getInts("dilations",[1,1]),v=m.getInt("group",1),S=m.getInts("kernel_shape",[]),O=m.getInts("pads",[0,0,0,0]),E=m.getInts("strides",[1,1]);return(0,d.createAttributeWithCacheKey)(Object.assign({autoPad:_,dilations:w,group:v,kernelShape:S,pads:O,strides:E},b))};const c=(g,m)=>{if(!g||g.length!==2&&g.length!==3)throw new Error("Conv requires 2 or 3 inputs");if(g[0].dims.length!==4||g[1].dims.length!==4)throw new Error("currently only support 2-dimensional conv");if(g[0].dims[1]!==g[1].dims[1]*m.group)throw new Error("FILTER_IN_CHANNEL should be equal to DATA_CHANNEL");if(g.length===3&&(g[2].dims.length!==1||g[1].dims[0]!==g[2].dims[0]))throw new Error("invalid bias");const b=g[0].dims.length-2;if(m.dilations.length!==b)throw new Error(`dilations should be ${b}D`);if(m.strides.length!==b)throw new Error(`strides should be ${b}D`);if(m.pads.length!==2*b)throw new Error(`pads should be ${2*b}D`);if(m.kernelShape.length!==0&&m.kernelShape.length!==g[1].dims.length-2)throw new Error("invalid kernel shape");if(g[0].type!=="float32"||g[1].type!=="float32")throw new Error("Conv input(X,W) should be float tensor");if(g.length===3&&g[2].type!=="float32")throw new Error("Conv input(bias) should be float tensor")}},5193:(y,n,u)=>{Object.defineProperty(n,"__esModule",{value:!0}),n.parseDepthToSpaceAttributes=n.depthToSpace=void 0;const d=u(3738);n.depthToSpace=(p,s,h)=>{l(s);const f=h.blocksize,a=f*f,o=h.mode==="DCR"?[0,3,4,1,5,2]:[0,1,4,2,5,3],t=h.mode==="DCR"?[s[0].dims[0],f,f,s[0].dims[1]/a,s[0].dims[2],s[0].dims[3]]:[s[0].dims[0],s[0].dims[1]/a,f,f,s[0].dims[2],s[0].dims[3]],e=p.reshapeUnpacked(s[0],t),r={perm:o,cacheKey:`${o}`},[i]=(0,d.transpose)(p,[e],r),c=[s[0].dims[0],s[0].dims[1]/a,s[0].dims[2]*f,s[0].dims[3]*f];return[p.reshapeUnpacked(i,c)]},n.parseDepthToSpaceAttributes=p=>{const s=p.attributes.getInt("blocksize");if(s<1)throw new Error(`blocksize must be >= 1, but got : ${s} for DepthToSpace`);const h=p.attributes.getString("mode","DCR");if(h!=="DCR"&&h!=="CRD")throw new Error(`unrecognized mode: ${h} for DepthToSpace`);return{mode:h,blocksize:s}};const l=p=>{if(p.length!==1)throw new Error(`DepthToSpace expect 1 inputs, but got ${p.length}`);if(p[0].type==="string"||p[0].dims.length!==4)throw new TypeError("DepthToSpace input should be a 4-D numeric tensor")}},9828:(y,n,u)=>{Object.defineProperty(n,"__esModule",{value:!0}),n.createDotProductProgramInfoLoader=void 0;const d=u(2517),l=u(5060),p=u(2039),s=u(2823),h=u(3248);n.createDotProductProgramInfoLoader=(f,a,o,t)=>{const e=((r,i)=>({name:"ConvDotProduct",inputNames:r?["Im2Col","K","B"]:["Im2Col","K"],inputTypes:r?[p.TextureType.unpacked,p.TextureType.packedLastDimension,p.TextureType.unpacked]:[p.TextureType.unpacked,p.TextureType.packedLastDimension],cacheKey:i.activationCacheKey}))(a.length>2,t);return Object.assign(Object.assign({},e),{get:()=>((r,i,c,g,m)=>{const b=c[0].dims,_=c[1].dims,w=[_[0],Math.ceil(b[1]*_[2]*_[3]/4)],v=(0,h.calculateIm2ColDims)(b,_,g),[S,O]=r.calculateTextureWidthAndHeight(w,p.TextureType.packedLastDimension),E=d.ShapeUtil.computeStrides(v),[T,I]=r.calculateTextureWidthAndHeight(v,p.TextureType.packedLastDimension),C=g.length,B=c.length<3?"0.0":"_B(b)",F=Math.ceil(b[1]*_[2]*_[3]/4),{activationFunction:N,applyActivation:H}=(0,s.getActivationSnippet)(m),$=(0,l.getGlsl)(r.session.backend.glContext.version),z=`
-${N}
-float process(int indices[${C}]) {
-  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] * ${E[0]} + im2col[1] * ${E[1]} + im2col[2] * ${E[2]};
-  int kernelOffset = indices[1] * ${w[1]};
-  float value = ${B};
-  for (int i = 0; i < ${F}; ++i) {
-    vec2 im2colCoords = offsetToCoords(im2colOffset, ${T}, ${I});
-    vec2 kernelCoords = offsetToCoords(kernelOffset, ${S}, ${O});
-    value += dot(${$.texture2D}(Im2Col, im2colCoords), ${$.texture2D}(K, kernelCoords));
-    ++im2colOffset;
-    ++kernelOffset;
-  }
-  ${H}
-  return value;
-}`;return Object.assign(Object.assign({},i),{output:{dims:g,type:c[0].type,textureType:p.TextureType.unpacked},shaderSource:z})})(f,e,a,o,t)})}},7992:(y,n,u)=>{Object.defineProperty(n,"__esModule",{value:!0}),n.parseFlattenAttributes=n.flatten=void 0;const d=u(2517);n.flatten=(p,s,h)=>{l(s,h);const f=d.ShapeUtil.flattenShape(s[0].dims,h);return[p.reshapeUnpacked(s[0],f)]},n.parseFlattenAttributes=p=>p.attributes.getInt("axis",1);const l=(p,s)=>{if(!p||p.length!==1)throw new Error("Flatten requires 1 input.");const h=p[0].dims.length;if(h===0)throw new Error("scalar tensor is not supported.");if(s<-h||s>h)throw new Error("Invalid axis");if(p[0].type==="string")throw new Error("string tensor is not supported.")}},2823:(y,n,u)=>{Object.defineProperty(n,"__esModule",{value:!0}),n.parseInternalActivationAttributes=n.getActivationSnippet=void 0;const d=u(2517),l=u(4909);n.getActivationSnippet=function(p){let s;switch(p.activation){case"Relu":s=(0,l.glslRelu)();break;case"Sigmoid":s=(0,l.glslSigmoid)();break;case"Clip":s=(0,l.glslClip)(p.clipMin,p.clipMax);break;default:return{activationFunction:"",applyActivation:""}}const h=s.name;return{activationFunction:s.body,applyActivation:`value = ${h}_(value);`}},n.parseInternalActivationAttributes=p=>{const s=p.getString("activation","");if(s==="Clip"){const[h,f]=p.getFloats("activation_params",[d.MIN_CLIP,d.MAX_CLIP]);return{activation:s,clipMax:f,clipMin:h,activationCacheKey:`${s}:${h},${f}`}}return{activation:s,activationCacheKey:s}}},1253:(y,n,u)=>{Object.defineProperty(n,"__esModule",{value:!0}),n.parseGatherAttributes=n.gather=void 0;const d=u(246),l=u(782),p=u(2517),s=u(2039);n.gather=(o,t,e)=>(a(t,e.axis),[o.run(f(o,t,e),t)]),n.parseGatherAttributes=o=>(0,d.createAttributeWithCacheKey)({axis:o.attributes.getInt("axis",0)});const h={name:"Gather",inputNames:["A","B"],inputTypes:[s.TextureType.unpacked,s.TextureType.unpacked]},f=(o,t,e)=>{const r=Object.assign(Object.assign({},h),{cacheHint:e.cacheKey});return Object.assign(Object.assign({},r),{get:()=>((i,c,g,m)=>{const b=g[0].dims.slice(),_=g[1].dims.slice(),w=new Array(b.length+_.length-1);m=p.ShapeUtil.normalizeAxis(m,b.length);const v=[];for(let O=0;O<w.length;O++)O<m?(w[O]=b[O],v.push(`inputIdx[${O}] = outputIdx[${O}];`)):O<m+_.length?(w[O]=_[O-m],v.push(`indexDataIdx[${O-m}] = outputIdx[${O}];`)):(w[O]=b[O-_.length+1],v.push(`inputIdx[${O-_.length+1}] = outputIdx[${O}];`));const S=`
-      float process(int outputIdx[${w.length||1}]) {
-        int inputIdx[${b.length}];
-        int indexDataIdx[${_.length||1}];
-        indexDataIdx[0] = 0;
-        ${v.join(`
-        `)}
-        int idx = int(_B(indexDataIdx));
-        inputIdx[${m}] = idx < 0 ? idx + ${b[m]} : idx;
-        return _A(inputIdx);
-      }`;return Object.assign(Object.assign({},c),{output:{dims:w,type:g[0].type,textureType:s.TextureType.unpacked},shaderSource:S})})(0,r,t,e.axis)})},a=(o,t)=>{if(!o||o.length!==2)throw new Error("Gather requires 2 inputs.");const e=o[0].dims.length;if(e<1)throw new Error("Invalid input shape.");if(t<-e||t>e-1)throw new Error("Invalid axis.");if(l.NUMBER_TYPES.indexOf(o[0].type)===-1)throw new Error("Invaid input type.");if(o[1].type!=="int32"&&o[1].type!=="int16")throw new Error("Invaid input type.")}},4776:(y,n,u)=>{Object.defineProperty(n,"__esModule",{value:!0}),n.parseGemmAttributesV11=n.parseGemmAttributesV7=n.gemm=void 0;const d=u(246),l=u(2517),p=u(2039);n.gemm=(o,t,e)=>(a(t,e),[o.run(h(t,e),t)]);const s=(o,t)=>{const e=o.attributes.getInt("transA",0)!==0,r=o.attributes.getInt("transB",0)!==0,i=o.attributes.getFloat("alpha",1),c=o.attributes.getFloat("beta",1);return(0,d.createAttributeWithCacheKey)({transA:e,transB:r,alpha:i,beta:c,isOptionalC:t})};n.parseGemmAttributesV7=o=>s(o,!1),n.parseGemmAttributesV11=o=>s(o,!0);const h=(o,t)=>{const e={name:"Gemm",inputNames:o.length===3?["A","B","C"]:["A","B"],inputTypes:o.length===3?[p.TextureType.unpacked,p.TextureType.unpacked,p.TextureType.unpacked]:[p.TextureType.unpacked,p.TextureType.unpacked],key:t.cacheKey};return Object.assign(Object.assign({},e),{get:()=>f(e,o,t)})},f=(o,t,e)=>{const r=t[0].dims.slice(),i=t[1].dims.slice(),[c,g]=l.GemmUtil.getShapeOfGemmResult(r,e.transA,i,e.transB,t.length===3?t[2].dims:void 0),m=[c,g];if(!m)throw new Error("Can't use gemm on the given tensors");let b=r[r.length-1],_="";e.transA&&(b=r[0]),e.transA&&e.transB?_="value += _A_T(a) * _B_T(b);":e.transA&&!e.transB?_="value += _A_T(a) * _B(b);":!e.transA&&e.transB?_="value += _A(a) * _B_T(b);":e.transA||e.transB||(_="value += _A(a) * _B(b);");const w=m.length,v=`
-      float process(int indices[${w}]) {
-          int a[${w}];
-          int b[${w}];
-          ${t.length===3?`int c[${t[2].dims.length}];`:""}
-
-          copyVec(indices, a);
-          copyVec(indices, b);
-          ${t.length===3?"bcastIndices_C(indices, c);":""}
-
-          float value = 0.0;
-          for (int k=0; k<${b}; ++k) {
-              a[${w-1}] = k;
-              b[${w-2}] = k;
-              ${_}
-          }
-
-          value = value * alpha;
-          ${t.length===3?"value += beta * _C(c);":""}
-          return value;
-      }`;return Object.assign(Object.assign({},o),{output:{dims:m,type:t[0].type,textureType:p.TextureType.unpacked},variables:[{name:"alpha",type:"float",data:e.alpha},{name:"beta",type:"float",data:e.beta}],shaderSource:v})},a=(o,t)=>{if(!o)throw new Error("Input is missing");if(t.isOptionalC&&(o.length<2||o.length>3))throw new Error("Invaid input shape.");if(!t.isOptionalC&&o.length!==3)throw new Error("Gemm requires 3 inputs");if(o.length===3&&o[2].dims.length!==1&&o[2].dims.length!==2)throw new Error("Invalid input shape of C");if(o[0].type!=="float32"&&o[0].type!=="float64"||o[1].type!=="float32"&&o[1].type!=="float64"||o.length===3&&o[2].type!=="float32"&&o[2].type!=="float64")throw new Error("Invalid input type.");if(o[0].type!==o[1].type||o.length===3&&o[0].type!==o[2].type)throw new Error("Input types are mismatched")}},8555:(y,n,u)=>{Object.defineProperty(n,"__esModule",{value:!0}),n.createPackedIm2ColProgramInfoLoader=void 0;const d=u(5060),l=u(2039),p=u(2827);n.createPackedIm2ColProgramInfoLoader=(s,h,f,a,o)=>{const t=(e=o.cacheKey,{name:"Im2Col (packed)",inputNames:["A"],inputTypes:[l.TextureType.packed],cacheHint:e});var e;return Object.assign(Object.assign({},t),{get:()=>((r,i,c,g,m,b)=>{const _=c.dims,w=g.dims,v=m.length,S=[w[1]*w[2]*w[3],m[2]*m[3]],O=w[2]*w[3],E=(0,p.unpackFromChannel)(),T=(0,d.getGlsl)(r.session.backend.glContext.version);let I="";for(let B=0;B<=1;B++)for(let F=0;F<=1;F++)I+=`
-            blockIndex = rc.x + ${F};
-            pos = rc.y + ${B};
-
-            if(blockIndex < ${S[1]} && pos < ${S[0]}) {
-              offsetY = int(blockIndex / (${m[v-1]})) * ${b.strides[0]} -
-                ${b.pads[0]};
-              d0 = offsetY + ${b.dilations[0]} * (imod(pos, ${O}) / ${w[2]});
-
-              if(d0 < ${_[2]} && d0 >= 0) {
-                offsetX = imod(blockIndex, ${m[v-1]}) * ${b.strides[1]} -
-                  ${b.pads[1]};
-                d1 = offsetX + ${b.dilations[1]} * imod(imod(pos, ${O}), ${w[2]});
-
-                if(d1 < ${_[3]} && d1 >= 0) {
-
-                  ch = int(float(pos)/ ${O}.);
-                    innerDims = vec2(d0, d1);
-                    result[${2*B+F}] = getChannel(
-                      getA(0, ch, int(innerDims.x),
-                      int(innerDims.y)), innerDims);
-                }
-              }
-            }
-
-          `;const C=`
-      ${E}
-
-      void main() {
-        ivec2 rc = getOutputCoords();
-          vec4 result = vec4(0.0);
-          int blockIndex, pos, offsetY, d0, offsetX, d1, ch;
-          vec2 innerDims;
-          ${I}
-          ${T.output} = result;
-      }
-            `;return Object.assign(Object.assign({},i),{output:{dims:S,type:c.type,textureType:l.TextureType.packed},shaderSource:C,hasMain:!0})})(s,t,h,f,a,o)})}},3248:(y,n,u)=>{Object.defineProperty(n,"__esModule",{value:!0}),n.calculateIm2ColDims=n.createIm2ColProgramInfoLoader=void 0;const d=u(2039);n.createIm2ColProgramInfoLoader=(l,p,s,h,f)=>{const a=(o=f.cacheKey,{name:"Im2Col",inputNames:["X"],inputTypes:[d.TextureType.unpacked],cacheHint:o});var o;return Object.assign(Object.assign({},a),{get:()=>((t,e,r,i,c,g)=>{const m=r.dims,b=i.dims,_=c.length,w=(0,n.calculateIm2ColDims)(m,b,c,4),v=`
-        const int XC = ${m[1]};
-        const int XH = ${m[2]};
-        const int XW = ${m[3]};
-        const int KH = ${g.kernelShape[0]};
-        const int KW = ${g.kernelShape[1]};
-        const int dilationH = ${g.dilations[0]};
-        const int dilationW = ${g.dilations[1]};
-        const int strideH = ${g.strides[0]};
-        const int strideW = ${g.strides[1]};
-        const int padH = ${g.pads[0]};
-        const int padW = ${g.pads[1]};
-        const int KHKW = KH*KW;
-        const int XCKHKW = XC * KHKW;
-        const int outputChannels = 4;
-        vec4 process(int indices[${_}]) {
-          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[${m.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 Object.assign(Object.assign({},e),{output:{dims:w,type:r.type,textureType:d.TextureType.packedLastDimension},shaderSource:v})})(0,a,p,s,h,f)})},n.calculateIm2ColDims=(l,p,s,h=4)=>[s[0],s[2],s[3],Math.ceil(l[1]*p[2]*p[3]/h)]},6572:(y,n,u)=>{Object.defineProperty(n,"__esModule",{value:!0}),n.parseImageScalerAttributes=n.imageScaler=void 0;const d=u(246),l=u(2039);n.imageScaler=(a,o,t)=>(f(o),[a.run(s(a,o,t),o)]),n.parseImageScalerAttributes=a=>{const o=a.attributes.getFloat("scale"),t=a.attributes.getFloats("bias");return(0,d.createAttributeWithCacheKey)({scale:o,bias:t})};const p={name:"ImageScaler",inputNames:["X"],inputTypes:[l.TextureType.unpacked]},s=(a,o,t)=>{const e=Object.assign(Object.assign({},p),{cacheHint:t.cacheKey});return Object.assign(Object.assign({},e),{get:()=>((r,i,c,g)=>{const m=c[0].dims.slice(),b=m.length,_=`
-      ${h(g.bias.length)}
-      float process(int indices[${b}]) {
-        return _X(indices) * scale + getBias(bias, indices[1]);
-      }`;return Object.assign(Object.assign({},i),{output:{dims:m,type:c[0].type,textureType:l.TextureType.unpacked},variables:[{name:"bias",type:"float",arrayLength:g.bias.length,data:g.bias},{name:"scale",type:"float",data:g.scale}],shaderSource:_})})(0,e,o,t)})},h=a=>{const o=[`float getBias(float bias[${a}], int channel) {`];for(let t=0;t<a;++t)t===0?o.push(`	if (channel == ${t}) { return bias[${t}]; }`):t===a-1?o.push(`	else { return bias[${t}]; }`):o.push(`	else if (channel == ${t}) { return bias[${t}]; }`);return o.push("	}"),o.join(`
-`)},f=a=>{if(!a||a.length!==1)throw new Error("ImageScaler requires 1 input.");if(a[0].dims.length!==4)throw new Error("Invalid input shape.");if(a[0].type!=="float32"&&a[0].type!=="float64")throw new Error("Invalid input type.")}},3346:(y,n,u)=>{Object.defineProperty(n,"__esModule",{value:!0}),n.parseInstanceNormalizationAttributes=n.instanceNormalization=void 0;const d=u(5060),l=u(2039);n.instanceNormalization=(o,t,e)=>{a(t);const r=o.run(s(t[0]),t);return[o.run(f(o,t[0],e,r.dims),[t[0],r,t[1],t[2]])]},n.parseInstanceNormalizationAttributes=o=>o.attributes.getFloat("epsilon",1e-5);const p={name:"InstanceNormalization_MeanAndVariance",inputNames:["X"],inputTypes:[l.TextureType.unpacked]},s=o=>Object.assign(Object.assign({},p),{get:()=>((t,e)=>{const r=e.dims.slice(),i=r[1],c=r[2]*r[3],g=[r[0],i],m=`
-      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<${r[2]}; a2++) {
-          a[2] = a2;
-          for(int a3=0; a3<${r[3]}; a3++) {
-            a[3] = a3;
-            float x = _X(a);
-            temp += x;
-          }
-        }
-        float mean = temp / float(${c});
-        temp = 0.0;
-        for(int a2=0; a2<${r[2]}; a2++) {
-          a[2] = a2;
-          for(int a3=0; a3<${r[3]}; a3++) {
-            a[3] = a3;
-            float x = _X(a);
-            temp += (x - mean) * (x - mean);
-          }
-        }
-        v.r = mean;
-        v.g = temp / float(${c});
-
-        return v;
-      }`;return Object.assign(Object.assign({},t),{output:{dims:g,type:e.type,textureType:l.TextureType.packedLastDimension},shaderSource:m})})(p,o)}),h={name:"InstanceNormalization_ComputeOutput",inputNames:["X","MeanAndVariance","Scale","B"],inputTypes:[l.TextureType.unpacked,l.TextureType.packedLastDimension,l.TextureType.unpacked,l.TextureType.unpacked]},f=(o,t,e,r)=>{const i=Object.assign(Object.assign({},h),{cacheHint:`${e}`});return Object.assign(Object.assign({},i),{get:()=>((c,g,m,b,_)=>{const w=(0,d.getGlsl)(c.session.backend.glContext.version),[v,S]=c.calculateTextureWidthAndHeight(_,l.TextureType.packedLastDimension),[O,E]=[v/4,S],T=`
-      vec4 get_MeanAndVariance(int[2] mv) {
-        int offset = indicesToOffset_MeanAndVariance(mv);
-        vec2 coords = offsetToCoords(offset, ${O}, ${E});
-        return ${w.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;
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-        float value;
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-    int c = ${w[_-1]};
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-
-          if(${E}) {
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-            ${b}
-
-            ${e.output} = vec4(${T});
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-    }
-
-    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);
-    }
-  `}},2870:(y,n,u)=>{Object.defineProperty(n,"__esModule",{value:!0}),n.parsePadAttributesV11=n.padV11=n.parsePadAttributesV2=n.padV2=void 0;const d=u(246),l=u(2517),p=u(5060),s=u(2039),h={name:"Pad",inputNames:["A"],inputTypes:[s.TextureType.unpacked]};n.padV2=(g,m,b)=>(o(m),[g.run(Object.assign(Object.assign({},h),{cacheHint:b.cacheKey,get:()=>a(g,m[0],b)}),m)]),n.parsePadAttributesV2=g=>{const m=g.attributes.getString("mode","constant"),b=g.attributes.getFloat("value",0),_=g.attributes.getInts("pads");return(0,d.createAttributeWithCacheKey)({mode:m,value:b,pads:_})},n.padV11=(g,m,b)=>{t(m);const _=f(g,m,b);return(0,n.padV2)(g,[m[0]],_)},n.parsePadAttributesV11=g=>g.attributes.getString("mode","constant");const f=(g,m,b)=>{if(!g.session.isInitializer(m[1].dataId)||m.length>=3&&!g.session.isInitializer(m[2].dataId))throw new Error("dynamic pad attributes are not allowed");const _=Array.from(m[1].integerData),w=m.length>=3?m[2].floatData[0]:0;return(0,d.createAttributeWithCacheKey)({mode:b,pads:_,value:w})},a=(g,m,b)=>{const _=l.ShapeUtil.padShape(m.dims.slice(),b.pads),w=_.length,v=`
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-        {
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-          for (int i = 0; i < ${v}; i++) {
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-            for (int j = 0; j < ${F}; j++) {
-              x[${w} - 2] = indices[${w} - 2] * ${N} - ${H} + j;
-              if (x[${w} - 2] < 0 || x[${w} - 2] >= ${z}) {
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-                continue;
-              }
-          `:`
-            for (int j = 0; j < ${F}; j++) {
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-            `,B=`
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-        `}return`
-        float process(int indices[${w}]) {
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-
-          float value = ${_};
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-          ${I}
-          ${B}
-          ${b}
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-            if (x[j] >= inputDims[j] || x[j] < 0) {
-              pad++;
-              isPad = true;
-              break;
-            }
-          }
-          if (!isPad) {
-            ${m}
-          }`:`
-          }
-          ${m}
-        `,`
-        ${T}
-        float process(int indices[${w}]) {
-          int x[${w}];
-          copyVec(indices, x);
-          int offset[${O}];
-          int pads[${E}];
-          int inputDims[${w}];
-          int kernelStrides[${O}];
-          int strides[${O}];
-          ${C}
-          ${I}
-          ${F}
-          ${B}
-
-          float value = ${_};
-          int pad = 0;
-          bool isPad = false;
-          for (int i = 0; i < ${v}; i++) {
-            offsetToIndices(i, kernelStrides, offset);
-            isPad = false;
-            for (int j = ${w} - ${O}; j < ${w}; j++) {
-              x[j] = indices[j] * strides[j - ${w} + ${O}]
-                + offset[j - ${w} + ${O}] - pads[j - 2];
-              ${N}
-          }
-          ${b}
-
-          return value;
-        }
-      `}},r=(c,g)=>{let m="";for(let b=0;b<c.length;b++)m+=`
-      ${g}[${b}] = ${c[b]};
-    `;return m},i=c=>`
-  void offsetToIndices(int offset, int[${c}] strides, out int[${c}] indices) {
-    if (${c} == 0) {
-      return;
-    }
-    for (int i = 0; i < ${c} - 1; ++i) {
-      indices[i] = offset / strides[i];
-      offset -= indices[i] * strides[i];
-    }
-    indices[${c} - 1] = offset;
-  }`},4939:(y,n,u)=>{Object.defineProperty(n,"__esModule",{value:!0}),n.reduceLogSumSquare=n.reduceLogSum=n.reduceProd=n.reduceMin=n.reduceMax=n.reduceMean=n.reduceSum=n.parseReduceAttributes=void 0;const d=u(246),l=u(782),p=u(2517),s=u(2039),h=(o,t,e,r,i)=>{a(t);const c={name:r,inputNames:["A"],inputTypes:[s.TextureType.unpacked]};return[o.run(Object.assign(Object.assign({},c),{cacheHint:e.cacheKey,get:()=>f(o,t,e,r,i,c)}),t)]};n.parseReduceAttributes=o=>{const t=o.attributes.getInts("axes",[]),e=o.attributes.getInt("keepdims",1)===1;return(0,d.createAttributeWithCacheKey)({axes:t,keepDims:e})};const f=(o,t,e,r,i,c)=>{const g=[],m=t[0].dims.length||1,b=[],_=p.ShapeUtil.normalizeAxes(e.axes,t[0].dims.length),w=i(t,_);let v=w[1];for(let O=0;O<t[0].dims.length;O++)_.indexOf(O)>=0||_.length===0?(e.keepDims&&g.push(1),v=`
-          for(int j${O} = 0; j${O} < ${t[0].dims[O]}; j${O}++) {
-            inputIdx[${O}] = j${O};
-            ${v}
-          }`):(b.push(`inputIdx[${O}] = outputIdx[${g.length}];`),g.push(t[0].dims[O]));const S=`
-      float process(int outputIdx[${g.length||1}]) {
-        float value;                 // final result
-        int inputIdx[${m}];      // addressing input data
-        ${b.join(`
-`)}
-        ${w[0]}       // init ops for reduce max/min
-        ${v}
-        ${w[2]}       // final computation for reduce mean
-        return value;
-      }`;return Object.assign(Object.assign({},c),{output:{dims:g,type:t[0].type,textureType:s.TextureType.unpacked},shaderSource:S})},a=o=>{if(!o||o.length!==1)throw new Error("Reduce op requires 1 input.");if(l.NUMBER_TYPES.indexOf(o[0].type)===-1)throw new Error("Invalid input type.")};n.reduceSum=(o,t,e)=>h(o,t,e,"ReduceSum",()=>["value = 0.0;","value += _A(inputIdx);",""]),n.reduceMean=(o,t,e)=>h(o,t,e,"ReduceMean",(r,i)=>{let c=1;for(let g=0;g<r[0].dims.length;g++)(i.indexOf(g)>=0||i.length===0)&&(c*=r[0].dims[g]);return["value = 0.0;","value += _A(inputIdx);",`value /= ${c}.;`]}),n.reduceMax=(o,t,e)=>h(o,t,e,"ReduceMax",(r,i)=>{const c=[];for(let g=0;g<r[0].dims.length;g++)(i.indexOf(g)>=0||i.length===0)&&c.push(`inputIdx[${g}] = 0;`);return[`${c.join(`
-`)}
-value = _A(inputIdx);`,"value = max(value, _A(inputIdx));",""]}),n.reduceMin=(o,t,e)=>h(o,t,e,"ReduceMin",(r,i)=>{const c=[];for(let g=0;g<r[0].dims.length;g++)(i.indexOf(g)>=0||i.length===0)&&c.push(`inputIdx[${g}] = 0;`);return[`${c.join(`
-`)}
-value = _A(inputIdx);`,"value = min(value, _A(inputIdx));",""]}),n.reduceProd=(o,t,e)=>h(o,t,e,"ReduceProd",()=>["value = 1.0;","value *= _A(inputIdx);",""]),n.reduceLogSum=(o,t,e)=>h(o,t,e,"ReduceLogSum",()=>["value = 0.0;","value += _A(inputIdx);","value = log(value);"]),n.reduceLogSumSquare=(o,t,e)=>h(o,t,e,"ReduceLogSumSquare",()=>["float t; value = 0.0;","t = _A(inputIdx); value += t * t;",""])},7019:(y,n,u)=>{Object.defineProperty(n,"__esModule",{value:!0}),n.isReshapeCheap=n.processDims3D=n.createPackedReshape3DProgramInfoLoader=void 0;const d=u(2517),l=u(5060),p=u(2039),s=u(2827);n.createPackedReshape3DProgramInfoLoader=(h,f,a)=>{const o=(t=>({name:"Reshape (packed)",inputTypes:[p.TextureType.packed],inputNames:["A"],cacheHint:`${t}`}))(a);return Object.assign(Object.assign({},o),{get:()=>((t,e,r,i)=>{const c=e.dims,g=i;let m="";for(let w=0;w<4;w++){let v="";switch(w){case 0:v="outputCoords = rc;";break;case 1:v="outputCoords = ivec3(rc.x, rc.y+1, rc.z);";break;case 2:v="outputCoords = ivec3(rc.x, rc.y, rc.z+1);";break;case 3:v="outputCoords = ivec3(rc.x, rc.y+1, rc.z+1);";break;default:throw new Error}m+=`
-        ${v}
-        ${w>0?"if(outputCoords.y < rows && outputCoords.z < cols){":""}
-          int flattenedIndex = getFlattenedIndex(outputCoords);
-
-          ivec3 inputRC = inputCoordsFromReshapedOutCoords(flattenedIndex);
-          vec2 innerDims = vec2(float(inputRC.y),float(inputRC.z));
-
-          result[${w}] = getChannel(getA(inputRC.x, inputRC.y, inputRC.z), innerDims);
-
-        ${w>0?"}":""}
-      `}const b=(0,l.getGlsl)(t.session.backend.glContext.version),_=`
-      ${function(w){const v=d.ShapeUtil.computeStrides(w),S=["b","r","c"],O="index";return`
-    ivec3 inputCoordsFromReshapedOutCoords(int index) {
-      ${v.map((E,T)=>`int ${S[T]} = ${O} / ${E}; ${T===v.length-1?`int ${S[T+1]} = ${O} - ${S[T]} * ${E}`:`index -= ${S[T]} * ${E}`};`).join("")}
-      return ivec3(b, r, c);
-    }
-  `}(c)}
-      ${function(w){const v=d.ShapeUtil.computeStrides(w);return`
-  int getFlattenedIndex(ivec3 coords) {
-    // reverse y, z order
-    return coords.x * ${v[0]} + coords.z * ${v[1]} + coords.y;
-  }
-`}(g)}
-      ${(0,s.unpackFromChannel)()}
-
-      void main() {
-        ivec3 rc = getOutputCoords();
-
-        vec4 result = vec4(0.0);
-
-        ivec3 outputCoords;
-        int rows = ${g[2]};
-        int cols = ${g[1]};
-
-        ${m}
-        ${b.output} = result;
-      }
-    `;return Object.assign(Object.assign({},r),{output:{dims:g,type:e.type,textureType:p.TextureType.packed},shaderSource:_,hasMain:!0})})(h,f,o,a)})},n.processDims3D=function(h){if(h.length===0)return[1,1,1];let f=1;for(let a=0;a<h.length-2;++a)f*=h[a];return[f,h.length>1?h[h.length-2]:1,h[h.length-1]]},n.isReshapeCheap=function(h,f){let a=!1;return a=h.length===0||f.length===0||(h.length<2||f.length<2?h[h.length-1]===f[f.length-1]:h[h.length-1]===f[f.length-1]&&h[h.length-2]===f[f.length-2]),a}},718:(y,n,u)=>{Object.defineProperty(n,"__esModule",{value:!0}),n.reshape=void 0;const d=u(2517);n.reshape=(l,p)=>{const s=d.ShapeUtil.calculateReshapedDims(p[0].dims,p[1].integerData);return l.session.pack?[l.reshapePacked(p[0],s)]:[l.reshapeUnpacked(p[0],s)]}},2268:(y,n,u)=>{Object.defineProperty(n,"__esModule",{value:!0}),n.parseResizeAttributesV11=n.parseResizeAttributesV10=n.resize=void 0;const d=u(5060),l=u(2039),p=u(9390),s=u(2827),h=u(9793),f={name:"Resize",inputNames:["A"],inputTypes:[l.TextureType.packed]};n.resize=(r,i,c)=>((0,h.validateInputs)(i,c),[r.run(Object.assign(Object.assign({},f),{cacheHint:c.cacheKey,get:()=>a(r,i,c)}),i)]),n.parseResizeAttributesV10=r=>(0,h.parseUpsampleAttributes)(r,10),n.parseResizeAttributesV11=r=>(0,h.parseUpsampleAttributes)(r,11);const a=(r,i,c)=>{const g=(0,d.getGlsl)(r.session.backend.glContext.version),[m,b]=o(i,c);if(m.every(N=>N===1)&&c.coordinateTransformMode!=="tf_crop_and_resize")return Object.assign(Object.assign({},f),{output:{dims:b,type:i[0].type,textureType:l.TextureType.packed},hasMain:!0,shaderSource:`void main() {
-                    vec4 v = ${g.texture2D}(X, TexCoords);
-                    ${g.output} = v;
-                }`});const _=b.length;if(_<2)throw new Error(`output dimension should be at least 2, but got ${_}`);const w=b[_-2],v=b[_-1],S=i[0].dims;if(_!==S.length)throw new Error(`output dimension should match input ${S.length}, but got ${_}`);const O=S[_-2],E=S[_-1],T=m[_-2],I=m[_-1];let C="";if(c.mode!=="linear")throw new Error(`resize (packed) does not support mode: '${c.mode}'`);switch(c.coordinateTransformMode){case"asymmetric":C=`
-                    vec4 getSourceFracIndex(ivec4 coords) {
-                        return vec4(coords) / scaleWHWH;
-                    }
-                `;break;case"half_pixel":C=`
-                    vec4 getSourceFracIndex(ivec4 coords) {
-                        return (vec4(coords) + 0.5) / scaleWHWH - 0.5;
-                    }
-                `;break;case"pytorch_half_pixel":C=`
-                    vec4 getSourceFracIndex(ivec4 coords) {
-                        vec4 fcoords = vec4(coords);
-                        return vec4(
-                            ${v}.0 > 1.0 ? (fcoords.x + 0.5) / scaleWHWH.x - 0.5 : 0.0,
-                            ${w}.0 > 1.0 ? (fcoords.y + 0.5) / scaleWHWH.y - 0.5 : 0.0,
-                            ${v}.0 > 1.0 ? (fcoords.z + 0.5) / scaleWHWH.z - 0.5 : 0.0,
-                            ${w}.0 > 1.0 ? (fcoords.w + 0.5) / scaleWHWH.w - 0.5 : 0.0
-                          );
-                    }
-                `;break;case"align_corners":C=`
-                    vec4 getSourceFracIndex(ivec4 coords) {
-                        vec4 resized = vec4(${v}.0 - 1.0, ${w}.0 - 1.0, ${v}.0 - 1.0,
-                            ${w}.0 - 1.0);
-                        vec4 original = vec4(${E}.0 - 1.0, ${O}.0 - 1.0, ${E}.0 - 1.0,
-                            ${O}.0 - 1.0);
-                        vec4 new_scale = original / resized;
-                        return vec4(coords) * new_scale;
-                    }
-                `;break;default:throw new Error(`resize (packed) does not support coordinateTransformMode:                                 '${c.coordinateTransformMode}'`)}const B=(0,p.getCoordsDataType)(_),F=`
-            const vec2 inputWH = vec2(${O}.0, ${E}.0);
-            const vec4 scaleWHWH = vec4(float(${T}), float(${I}), float(${T}), float(${I}));
-            ${(0,s.unpackFromChannel)()}
-            ${C}
-            float getAValue(int x10, int r, int c, int d) {
-                return getChannel(getA(x10, r, c, d), vec2(c, d));
-            }
-            void main() {
-                ${B} 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 < ${w-1};
-                bool hasNextCol = rc.z < ${v-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);
-
-                ${g.output} = vec4(newValue);
-            }
-        `;return Object.assign(Object.assign({},f),{output:{dims:b,type:i[0].type,textureType:l.TextureType.packed},hasMain:!0,shaderSource:F})},o=(r,i)=>{const c=r[0].dims;let g,m=i.scales;if(m.length===0){const _=r[i.scalesInputIdx];if(_&&_.size!==0){if(r[i.sizesInputIdx])throw new Error("Only one of scales or sizes must be provided as input.");m=t(_,i.mode,i.isResize)}else{const w=r[i.sizesInputIdx];if(!w||w.size===0)throw new Error("Either scales or sizes MUST be provided as input.");g=Array.from(w.integerData),m=e(g,c,i.mode,i.isResize)}}else if(r[i.sizesInputIdx])throw new Error("Only one of scales or sizes must be provided as input.");const b=g||c.map((_,w)=>Math.floor(_*m[w]));return[m,b]},t=(r,i,c)=>{const g=Array.from(r.floatData);return(0,h.scalesValidation)(g,i,c),g},e=(r,i,c,g)=>{const m=i.length,b=new Array(m);for(let _=0,w=m;_<w;_++)if(i[_]===0){if(r[_]!==0)throw new Error("Input dim is zero but required output dim is non-zero.");b[_]=1}else b[_]=r[_]/i[_];return(0,h.scalesValidation)(b,c,g),b}},8117:(y,n,u)=>{Object.defineProperty(n,"__esModule",{value:!0}),n.shape=void 0;const d=u(9162);n.shape=(p,s)=>(l(s),[new d.Tensor([s[0].dims.length],"int32",void 0,void 0,new Int32Array(s[0].dims))]);const l=p=>{if(!p||p.length!==1)throw new Error("Shape requires 1 input.")}},2278:(y,n,u)=>{Object.defineProperty(n,"__esModule",{value:!0}),n.sliceV10=n.parseSliceAttributes=n.slice=void 0;const d=u(246),l=u(782),p=u(2517),s=u(2039),h={name:"Slice",inputNames:["A"],inputTypes:[s.TextureType.unpacked]};n.slice=(e,r,i)=>(a(r),[e.run(Object.assign(Object.assign({},h),{cacheHint:i.cacheKey,get:()=>f(e,r[0],i)}),r)]),n.parseSliceAttributes=e=>{const r=e.attributes.getInts("starts"),i=e.attributes.getInts("ends"),c=e.attributes.getInts("axes",[]);return(0,d.createAttributeWithCacheKey)({starts:r,ends:i,axes:c})};const f=(e,r,i)=>{const c=i.axes.length===0?r.dims.slice(0).map((S,O)=>O):i.axes,g=p.ShapeUtil.normalizeAxes(c,r.dims.length),m=i.starts.map((S,O)=>S>r.dims[g[O]]-1?r.dims[g[O]]:p.ShapeUtil.normalizeAxis(S,r.dims[g[O]])),b=i.ends.map((S,O)=>S>r.dims[g[O]]-1?r.dims[g[O]]:p.ShapeUtil.normalizeAxis(S,r.dims[g[O]])),_=r.dims.slice(),w=[];for(let S=0;S<g.length;S++)_[g[S]]=b[S]-m[S],m[S]>0&&w.push(`outputIdx[${g[S]}] += ${m[S]};`);const v=`
-      float process(int outputIdx[${_.length}]) {
-        ${w.join(`
-      `)}
-        return _A(outputIdx);
-      }`;return Object.assign(Object.assign({},h),{output:{dims:_,type:r.type,textureType:s.TextureType.unpacked},shaderSource:v})},a=e=>{if(!e||e.length!==1)throw new Error("Slice requires 1 input.");if(l.NUMBER_TYPES.indexOf(e[0].type)===-1)throw new Error("Invalid input type.")};n.sliceV10=(e,r)=>{t(r);const i=o(e,r);return[e.run(Object.assign(Object.assign({},h),{cacheHint:i.cacheKey,get:()=>f(e,r[0],i)}),[r[0]])]};const o=(e,r)=>{if(!e.session.isInitializer(r[1].dataId)||!e.session.isInitializer(r[2].dataId)||r.length>=4&&!e.session.isInitializer(r[3].dataId)||r.length>=5&&!e.session.isInitializer(r[4].dataId))throw new Error("dynamic slice attributes are not allowed");if(r.length>=5&&r[4].integerData.some(m=>m!==1))throw new Error("currently non-1 steps is not supported for Slice");const i=Array.from(r[1].integerData),c=Array.from(r[2].integerData),g=r.length>=4?Array.from(r[3].integerData):[];return{starts:i,ends:c,axes:g,cacheKey:`${g};${i};${c}`}},t=e=>{if(!e||e.length<3||e.length>5)throw new Error("Invalid input number.");if(e[1].type!=="int32"||e[1].dims.length!==1)throw new Error("Invalid input type.");if(e[2].type!=="int32"||e[2].dims.length!==1)throw new Error("Invalid input type.");if(e.length>=4&&(e[3].type!=="int32"||e[3].dims.length!==1))throw new Error("Invalid input type.");if(e.length>=5&&(e[4].type!=="int32"||e[4].dims.length!==1))throw new Error("Invalid input type.")}},5524:(y,n,u)=>{Object.defineProperty(n,"__esModule",{value:!0}),n.softmaxV13=n.parseSoftmaxAttributesV13=n.parseSoftmaxAttributes=n.softmax=void 0;const d=u(246),l=u(2517),p=u(5060),s=u(2039),h=u(3738),f={name:"SoftmaxComputeMax",inputNames:["A"],inputTypes:[s.TextureType.unpacked]},a={name:"SoftmaxComputeScale",inputNames:["A","Max"],inputTypes:[s.TextureType.unpacked,s.TextureType.unpacked]},o={name:"SoftMax",inputNames:["A","Max","Norm"],inputTypes:[s.TextureType.unpacked,s.TextureType.unpacked,s.TextureType.unpacked]};n.softmax=(g,m,b)=>{c(m);const _=m[0].dims.slice(),w=l.ShapeUtil.normalizeAxis(b.axis,_.length),v=l.ShapeUtil.sizeToDimension(_,w),S=l.ShapeUtil.sizeFromDimension(_,w);return t(g,m,b,v,S)},n.parseSoftmaxAttributes=g=>(0,d.createAttributeWithCacheKey)({axis:g.attributes.getInt("axis",1)}),n.parseSoftmaxAttributesV13=g=>(0,d.createAttributeWithCacheKey)({axis:g.attributes.getInt("axis",-1)}),n.softmaxV13=(g,m,b)=>{c(m);const _=m[0].dims.slice(),w=l.ShapeUtil.normalizeAxis(b.axis,_.length),v=_.length,S=w!==v-1,O=[];let E,T=[],I=[];S&&(T=Array.from({length:v}).map((N,H)=>H),T[w]=v-1,T[v-1]=w,T.map(N=>O.push(_[N])),E=(0,d.createAttributeWithCacheKey)({perm:T}),I=(0,h.transpose)(g,m,E));const C=S?l.ShapeUtil.sizeToDimension(O,v-1):l.ShapeUtil.sizeToDimension(_,v-1),B=S?l.ShapeUtil.sizeFromDimension(O,v-1):l.ShapeUtil.sizeFromDimension(_,v-1),F=t(g,S?I:m,b,C,B);return S?(0,h.transpose)(g,F,E):F};const t=(g,m,b,_,w)=>{const v=e(g,m[0],_,w,[_]),S=g.run(Object.assign(Object.assign({},f),{cacheHint:b.cacheKey,get:()=>v}),m),O=r(g,m[0],_,w,v.output.dims,[_]),E=g.run(Object.assign(Object.assign({},a),{cacheHint:b.cacheKey,get:()=>O}),[m[0],S]),T=i(g,m[0],_,w,v.output.dims,O.output.dims);return[g.run(Object.assign(Object.assign({},o),{cacheHint:b.cacheKey,get:()=>T}),[m[0],S,E])]},e=(g,m,b,_,w)=>{const[v,S]=g.calculateTextureWidthAndHeight(m.dims,s.TextureType.unpacked),O=w.length;if(b<1||_<1)throw new Error("Logical row count N and feature count D must be greater than or equal to 1");if(w.length!==1)throw new Error("Dimensionality of the output should be 1");if(w[0]!==b)throw new Error("Shape of the output should be equal to logical row count");const E=(0,p.getGlsl)(g.session.backend.glContext.version),T=`
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-        float max = getColorAsFloat(${E.texture2D}(A, offsetToCoords(logical_row_start_offset, ${v},
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-        return max;
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-        int logical_row_start_offset = indices[0] * ${_};
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-        float norm_factor = 0.0;
-        float max = _Max(indices);
-        for(int i=0; i<${_}; ++i)
-        {
-          norm_factor += exp(getColorAsFloat(${(0,p.getGlsl)(g.session.backend.glContext.version).texture2D}(A, offsetToCoords(logical_row_start_offset + i,
-            ${S}, ${O}))) - max);
-        }
-
-        return norm_factor;
-      }`;return Object.assign(Object.assign({},a),{output:{dims:v,type:m.type,textureType:s.TextureType.unpacked},shaderSource:T})},i=(g,m,b,_,w,v)=>{const[S,O]=g.calculateTextureWidthAndHeight(m.dims,s.TextureType.unpacked),E=m.dims.length;if(b<1||_<1)throw new Error("Logical row count N and feature count D must be greater than or equal to 1");if(w.length!==1||v.length!==1)throw new Error("Dimensionality of the intermediate results should be 1");if(w[0]!==b||v[0]!==b)throw new Error("Shape of the intermediate results should be equal to logical row count");const T=`
-      float process(int[${E}] indices) {
-
-      // get offset of current logical tensor index from the 2-D texture coordinates (TexCoords)
-      int offset = coordsToOffset(TexCoords, ${S}, ${O});
-
-      //determine the logical row for this index
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-      float norm_factor = _Norm(logical_row_index);
-
-      // avoid possible division by 0
-      // if norm_facor is 0, all elements are zero
-      // if so, return 0
-      if(norm_factor == 0.0)
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-      return exp(_A(indices) - _Max(logical_row_index)) / norm_factor;
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-      float process(int indices[${b.length}]) {
-        indices[${r}] += ${m};
-        return _A(indices);
-      }
-    `;return Object.assign(Object.assign({},s),{cacheHint:`${e.cacheKey}:${i}`,output:{dims:b,type:t.type,textureType:p.TextureType.unpacked},shaderSource:_})},a=o=>{if(!o||o.length!==1)throw new Error("Split requires one input.");if(o[0].type!=="int8"&&o[0].type!=="uint8"&&o[0].type!=="int16"&&o[0].type!=="uint16"&&o[0].type!=="int32"&&o[0].type!=="uint32"&&o[0].type!=="float32"&&o[0].type!=="float64"&&o[0].type!=="bool")throw new Error("Invalid input type.")}},3933:(y,n,u)=>{Object.defineProperty(n,"__esModule",{value:!0}),n.parseSqueezeAttributes=n.squeezeV13=n.squeeze=void 0;const d=u(2517);n.squeeze=(s,h,f)=>{l(h);const a=d.ShapeUtil.squeezeShape(h[0].dims,f);return[s.reshapeUnpacked(h[0],a)]},n.squeezeV13=(s,h)=>(p(h),(0,n.squeeze)(s,[h[0]],Array.from(h[1].integerData))),n.parseSqueezeAttributes=s=>s.attributes.getInts("axes");const l=s=>{if(!s||s.length!==1)throw new Error("Squeeze requires 1 input.");if(s[0].type==="string")throw new Error("invalid input tensor types.")},p=s=>{if(!s||s.length!==2)throw new Error("Squeeze requires 2 inputs.");if(s[1].type!=="int32")throw new Error("Invalid input type.")}},6558:(y,n,u)=>{Object.defineProperty(n,"__esModule",{value:!0}),n.sum=void 0;const d=u(5060),l=u(2039);n.sum=(h,f)=>{s(f);const a={name:"Sum",inputNames:f.map((o,t)=>`X${t}`),inputTypes:new Array(f.length).fill(l.TextureType.unpacked)};return[h.run(Object.assign(Object.assign({},a),{get:()=>p(h,f,a)}),f)]};const p=(h,f,a)=>{const o=(0,d.getGlsl)(h.session.backend.glContext.version),t=f[0].dims.slice(),e=`
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-        int inputIdx[${r}];
-        ${e.join(`
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-        return _A(inputIdx);
-      }
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-      ${o("perm",i,m)}
-      float process(int indices[${m}]) {
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-`)},t=e=>{if(!e||e.length!==1)throw new Error("Transpose requires 1 input.");if(e[0].type!=="float32"&&e[0].type!=="float64")throw new Error("input should be float tensor")}},8710:(y,n,u)=>{Object.defineProperty(n,"__esModule",{value:!0}),n.encodeAsUint8=void 0;const d=u(5060),l=u(2039);n.encodeAsUint8=(p,s)=>{const h=s.shape,f=(0,d.getGlsl)(p.session.backend.glContext.version),a=`
-    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 = ${f.texture2D}(X,TexCoords).r;
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-  float ${z}_(float a) {
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-  `,name:$,type:p.FunctionType.ValueBased}}function T(){const $="sigmoid";return{body:`
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-  `,name:$,type:p.FunctionType.ValueBased}}function I(){return F("sqrt")}function C(){return F("tan")}function B(){const $="tanh";return{body:`
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-    ${i}
-    void main() {
-      ${r} rc = getOutputCoords();
-
-       // Sample the texture with the coords to get the rgba channel value.
-       vec4 packedInput = getA(${c});
-
-       ${(0,d.getGlsl)(f.session.backend.glContext.version).output} = vec4(getChannel(packedInput, ${g}), 0, 0, 0);
-     }
-   `;return Object.assign(Object.assign({},h),{hasMain:!0,output:{dims:a.dims,type:a.type,textureType:l.TextureType.unpacked},shaderSource:m})},n.createUnpackProgramInfoLoader=(f,a)=>Object.assign(Object.assign({},h),{get:()=>(0,n.createUnpackProgramInfo)(f,a)})},8428:(y,n,u)=>{Object.defineProperty(n,"__esModule",{value:!0}),n.parseUnsqueezeAttributes=n.unsqueezeV13=n.unsqueeze=void 0;const d=u(2517);n.unsqueeze=(s,h,f)=>{l(h);const a=d.ShapeUtil.unsqueezeShape(h[0].dims,f);return[s.reshapeUnpacked(h[0],a)]},n.unsqueezeV13=(s,h)=>(p(h),(0,n.unsqueeze)(s,[h[0]],Array.from(h[1].integerData))),n.parseUnsqueezeAttributes=s=>s.attributes.getInts("axes");const l=s=>{if(!s||s.length!==1)throw new Error("Unsqueeze requires 1 input.");if(s[0].type==="string")throw new Error("invalid input tensor types.")},p=s=>{if(!s||s.length!==2)throw new Error("Unsqueeze requires 2 inputs.");if(s[1].type!=="int32")throw new Error("Invalid input type.")}},9793:(y,n,u)=>{Object.defineProperty(n,"__esModule",{value:!0}),n.scalesValidation=n.validateInputs=n.parseUpsampleAttributes=n.parseUpsampleAttributesV9=n.parseUpsampleAttributesV7=n.upsample=void 0;const d=u(246),l=u(5060),p=u(2039),s={name:"Upsample",inputNames:["X"],inputTypes:[p.TextureType.unpacked]};n.upsample=(f,a,o)=>((0,n.validateInputs)(a,o),[f.run(Object.assign(Object.assign({},s),{cacheHint:o.cacheKey,get:()=>h(f,a,o)}),a)]),n.parseUpsampleAttributesV7=f=>(0,n.parseUpsampleAttributes)(f,7),n.parseUpsampleAttributesV9=f=>(0,n.parseUpsampleAttributes)(f,9),n.parseUpsampleAttributes=(f,a)=>{const o=a>=10,t=f.attributes.getString("mode","nearest");if(t!=="nearest"&&t!=="linear"&&(a<11||t!=="cubic"))throw new Error(`unrecognized mode: ${t}`);let e=[];a<9&&(e=f.attributes.getFloats("scales"),(0,n.scalesValidation)(e,t,o));const r=f.attributes.getFloat("extrapolation_value",0),i=a>10?f.attributes.getString("coordinate_transformation_mode","half_pixel"):"asymmetric";if(["asymmetric","pytorch_half_pixel","tf_half_pixel_for_nn","align_corners","tf_crop_and_resize","half_pixel"].indexOf(i)===-1)throw new Error(`coordinate_transform_mode '${i}' is not supported`);const c=i==="tf_crop_and_resize",g=c,m=t==="nearest"&&a>=11?f.attributes.getString("nearest_mode","round_prefer_floor"):"";if(["round_prefer_floor","round_prefer_ceil","floor","ceil",""].indexOf(m)===-1)throw new Error(`nearest_mode '${m}' is not supported`);const b=f.attributes.getFloat("cubic_coeff_a",-.75),_=f.attributes.getInt("exclude_outside",0)!==0;if(_&&t!=="cubic")throw new Error("exclude_outside can be set to 1 only when mode is CUBIC.");const w=a<11||t==="nearest"&&i==="asymmetric"&&m==="floor";let v=0,S=0,O=0;return a>10?f.inputs.length>2?(v=1,S=2,O=3):(S=1,O=2):a===9&&(S=1),(0,d.createAttributeWithCacheKey)({opset:a,isResize:o,mode:t,scales:e,extrapolationValue:r,coordinateTransformMode:i,useExtrapolation:g,needRoiInput:c,nearestMode:m,cubicCoefficientA:b,excludeOutside:_,useNearest2xOptimization:w,roiInputIdx:v,scalesInputIdx:S,sizesInputIdx:O})};const h=(f,a,o)=>{const t=(0,l.getGlsl)(f.session.backend.glContext.version),[e,r]=f.calculateTextureWidthAndHeight(a[0].dims,p.TextureType.unpacked),i=a[0].dims.map((O,E)=>Math.floor(O*o.scales[E])),[c,g]=f.calculateTextureWidthAndHeight(i,p.TextureType.unpacked),m=i.length,b=new Array(m),_=new Array(m);let w=`
-      int output_pitches[${m}];
-      int input_pitches[${m}];
-      `;for(let O=m-1;O>=0;O--)b[O]=O===m-1?1:b[O+1]*i[O+1],_[O]=O===m-1?1:_[O+1]*a[0].dims[O+1],w+=`
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-        input_pitches[${O}] = ${_[O]};
-        `;const v=`
-      float getInputFloat(int index) {
-        vec2 coords = offsetToCoords(index, ${e}, ${r});
-        float value = getColorAsFloat(${t.texture2D}(X, coords));
-        return value;
-      }
-      `,S=o.mode==="nearest"?`
-    ${v}
-    float process(int indices[${m}]) {
-      int input_index = 0;
-      int output_index = coordsToOffset(TexCoords, ${c}, ${g});
-
-      ${w}
-
-      int d, m;
-      for (int dim = 0; dim < ${m}; ++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);
-    }`:m===4?`
-    ${v}
-    float process(int indices[4]) {
-      int input_index = 0;
-      int output_index = coordsToOffset(TexCoords, ${c}, ${g});
-
-      ${w}
-
-      int m;
-      int index_of_dim0, index_of_dim1, index_of_dim2, index_of_dim3;
-      index_of_dim0 = output_index / output_pitches[0];
-      m = output_index - index_of_dim0 * output_pitches[0];
-      index_of_dim1 = m / output_pitches[1];
-      m = m - index_of_dim1 * output_pitches[1];
-      index_of_dim2 = m / output_pitches[2];
-      m = m - index_of_dim2 * output_pitches[2];
-      index_of_dim3 = m;
-
-      int index_of_input_dim2, index_of_input_dim3, x_offset, y_offset;
-      index_of_input_dim2 = index_of_dim2 / scales[2];
-      y_offset = index_of_dim2 - index_of_input_dim2 * scales[2];
-      index_of_input_dim3 = index_of_dim3 / scales[3];
-      x_offset = index_of_dim3 - index_of_input_dim3 * scales[3];
-
-      input_index = index_of_dim0 * input_pitches[0] +
-            index_of_dim1 * input_pitches[1] +
-            index_of_input_dim2 * input_pitches[2] +
-            index_of_input_dim3;
-
-      float x00 = getInputFloat(input_index);
-      float x10, x01, x11;
-
-      bool end_of_dim2 = false;
-      if (index_of_input_dim2 == (${a[0].dims[2]} - 1)) {
-        // It's the end in dimension 2
-        x01 = x00;
-        end_of_dim2 = true;
-      } else {
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-      }
-
-      if (index_of_input_dim3 == (input_pitches[2] - 1)) {
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-        x11 = x01;
-      }
-      else {
-        x10 = getInputFloat(input_index + 1);
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-      }
-
-      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]);
-    }`:`
-    ${v}
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-      int input_index = 0;
-      int output_index = coordsToOffset(TexCoords, ${c}, ${g});
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-      ${w}
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-      int m;
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-      index_of_dim1 = m;
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-      int index_of_input_dim0, index_of_input_dim1, x_offset, y_offset;
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-      y_offset = index_of_dim0 - index_of_input_dim0 * scales[0];
-      index_of_input_dim1 = index_of_dim1 / scales[1];
-      x_offset = index_of_dim1 - index_of_input_dim1 * scales[1];
-
-      input_index = index_of_input_dim0 * input_pitches[0] + index_of_input_dim1;
-
-      float x00 = getInputFloat(input_index);
-      float x10, x01, x11;
-
-      bool end_of_dim0 = false;
-      if (index_of_input_dim0 == (${a[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 Object.assign(Object.assign({},s),{output:{dims:i,type:a[0].type,textureType:p.TextureType.unpacked},shaderSource:S,variables:[{name:"scales",type:"int",arrayLength:o.scales.length,data:o.scales.map(O=>Math.ceil(O))}]})};n.validateInputs=(f,a)=>{if(!f||a.opset<9&&f.length!==1||a.opset>=9&&a.opset<11&&f.length!==2||a.opset>=11&&f.length<2)throw new Error("invalid inputs.");if(a.scales.length>0&&f[0].dims.length!==a.scales.length)throw new Error("Invalid input shape.");if(f[0].type==="string")throw new Error("Invalid input tensor types.")},n.scalesValidation=(f,a,o)=>{if(o){for(const t of f)if(t<=0)throw new Error("Scale value should be greater than 0.")}else for(const t of f)if(t<1)throw new Error("Scale value should be greater than or equal to 1.");if(!(a!=="linear"&&a!=="cubic"||f.length===2||f.length===4&&f[0]===1&&f[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 ${o?"Resize":"Upsample"} opeartor.`)}},1958:(y,n,u)=>{Object.defineProperty(n,"__esModule",{value:!0}),n.ProgramManager=void 0;const d=u(1670),l=u(6231),p=u(8879),s=u(5060);n.ProgramManager=class{constructor(h,f,a){this.profiler=h,this.glContext=f,this.textureLayoutStrategy=a,this.repo=new Map,this.attributesBound=!1}getArtifact(h){return this.repo.get(h)}setArtifact(h,f){this.repo.set(h,f)}run(h,f,a){var o;this.profiler.event("op",`ProgramManager.run ${(o=h.programInfo.name)!==null&&o!==void 0?o:"unknown kernel"}`,()=>{var t;const e=this.glContext.gl,r=h.program;e.useProgram(r);try{this.bindOutput(a),this.attributesBound||this.bindAttributes(h.attribLocations),this.bindUniforms(h.uniformLocations,(t=h.programInfo.variables)!==null&&t!==void 0?t:[],f)}catch(i){throw l.Logger.error("ProgramManager",h.programInfo.shaderSource),i}this.profiler.event("backend","GlContext.draw()",()=>{this.glContext.draw()})},this.glContext)}dispose(){this.vertexShader&&this.glContext.deleteShader(this.vertexShader),this.repo.forEach(h=>this.glContext.deleteProgram(h.program))}build(h,f,a){return this.profiler.event("backend","ProgramManager.build",()=>{const o=new p.GlslPreprocessor(this.glContext,h,f,a),t=o.preprocess(),e=this.compile(t);return{programInfo:h,program:e,uniformLocations:this.getUniformLocations(e,o.context.programInfo.inputNames,o.context.programInfo.variables),attribLocations:this.getAttribLocations(e)}})}compile(h){if(!this.vertexShader){l.Logger.verbose("ProrgramManager","Compiling and caching Vertex shader for the first time");const o=(0,s.getVertexShaderSource)(this.glContext.version);this.vertexShader=this.glContext.compileShader(o,this.glContext.gl.VERTEX_SHADER)}d.env.debug&&l.Logger.verbose("ProrgramManager",`FragShader:
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i=o.checkFramebufferStatus(o.FRAMEBUFFER)===o.FRAMEBUFFER_COMPLETE;return o.bindTexture(o.TEXTURE_2D,null),o.bindFramebuffer(o.FRAMEBUFFER,null),o.deleteTexture(t),o.deleteFramebuffer(r),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(){const o=this.gl;let t,e,r,i,c;try{t=o.createTexture(),e=o.createFramebuffer(),o.bindTexture(o.TEXTURE_2D,t);const g=this.version===2?o.RGBA32F:o.RGBA;return o.texImage2D(o.TEXTURE_2D,0,g,1,1,0,o.RGBA,o.FLOAT,null),o.bindFramebuffer(o.FRAMEBUFFER,e),o.framebufferTexture2D(o.FRAMEBUFFER,o.COLOR_ATTACHMENT0,o.TEXTURE_2D,t,0),o.enable(o.BLEND),r=o.createShader(o.VERTEX_SHADER),!!r&&(o.shaderSource(r,"void main(){}"),o.compileShader(r),i=o.createShader(o.FRAGMENT_SHADER),!!i&&(o.shaderSource(i,"precision highp float;void main(){gl_FragColor=vec4(0.5);}"),o.compileShader(i),c=o.createProgram(),!!c&&(o.attachShader(c,r),o.attachShader(c,i),o.linkProgram(c),o.useProgram(c),o.drawArrays(o.POINTS,0,1),o.getError()===o.NO_ERROR)))}finally{o.disable(o.BLEND),c&&o.deleteProgram(c),r&&o.deleteShader(r),i&&o.deleteShader(i),e&&(o.bindFramebuffer(o.FRAMEBUFFER,null),o.deleteFramebuffer(e)),t&&(o.bindTexture(o.TEXTURE_2D,null),o.deleteTexture(t))}}beginTimer(){if(this.version===2&&this.disjointTimerQueryWebgl2Extension){const o=this.gl,t=this.disjointTimerQueryWebgl2Extension,e=o.createQuery();return o.beginQuery(t.TIME_ELAPSED_EXT,e),e}throw new Error("WebGL1 profiling currently not supported.")}endTimer(){if(this.version!==2||!this.disjointTimerQueryWebgl2Extension)throw new Error("WebGL1 profiling currently not supported");{const o=this.gl,t=this.disjointTimerQueryWebgl2Extension;o.endQuery(t.TIME_ELAPSED_EXT)}}isTimerResultAvailable(o){let t=!1,e=!1;if(this.version!==2||!this.disjointTimerQueryWebgl2Extension)throw new Error("WebGL1 profiling currently not supported");{const r=this.gl,i=this.disjointTimerQueryWebgl2Extension;t=r.getQueryParameter(o,r.QUERY_RESULT_AVAILABLE),e=r.getParameter(i.GPU_DISJOINT_EXT)}return t&&!e}getTimerResult(o){let t=0;if(this.version!==2)throw new Error("WebGL1 profiling currently not supported");{const e=this.gl;t=e.getQueryParameter(o,e.QUERY_RESULT),e.deleteQuery(o)}return t/1e6}async waitForQueryAndGetTime(o){return await(0,f.repeatedTry)(()=>this.isTimerResultAvailable(o)),this.getTimerResult(o)}async createAndWaitForFence(){const o=this.createFence(this.gl);return this.pollFence(o)}createFence(o){let t;const e=o,r=e.fenceSync(e.SYNC_GPU_COMMANDS_COMPLETE,0);return o.flush(),t=r===null?()=>!0:()=>{const i=e.clientWaitSync(r,0,0);return i===e.ALREADY_SIGNALED||i===e.CONDITION_SATISFIED},{query:r,isFencePassed:t}}async pollFence(o){return new Promise(t=>{this.addItemToPoll(()=>o.isFencePassed(),()=>t())})}pollItems(){const o=a(this.itemsToPoll.map(t=>t.isDoneFn));for(let t=0;t<=o;++t){const{resolveFn:e}=this.itemsToPoll[t];e()}this.itemsToPoll=this.itemsToPoll.slice(o+1)}async addItemToPoll(o,t){this.itemsToPoll.push({isDoneFn:o,resolveFn:t}),this.itemsToPoll.length>1||await(0,f.repeatedTry)(()=>(this.pollItems(),this.itemsToPoll.length===0))}}},1036:(y,n,u)=>{Object.defineProperty(n,"__esModule",{value:!0}),n.ExecutionPlan=void 0;const d=u(6231);class l{constructor(s,h){this.op=s,this.node=h}}n.ExecutionPlan=class{constructor(p,s,h){this.graph=p,this.profiler=h,this.initialize(s)}initialize(p){this.profiler.event("session","ExecutionPlan.initialize",()=>{const s=this.graph.getNodes();if(s.length!==p.length)throw new Error("The size of nodes and OPs do not match.");this._ops=p.map((h,f)=>new l(h,s[f])),this.reset(),this._starter=[],this._ops.forEach((h,f)=>{let a=!0;for(const o of h.node.inputs)if(!this._values[o]&&this.graph.getInputIndices().indexOf(o)===-1){a=!1;break}a&&this._starter.push(f)})})}reset(){this._values=this.graph.getValues().map(p=>p.tensor)}async execute(p,s){return this.profiler.event("session","ExecutionPlan.execute",async()=>{this.reset();const h=p.createInferenceHandler(),f=this.graph.getInputIndices();if(s.length!==f.length)throw new Error(`number of input tensors don't match the number of inputs to the model: actual: ${s.length} expected: ${f.length}`);s.forEach((i,c)=>{const g=f[c];this._values[g]=i});const a=this._starter.slice(0),o=this.graph.getValues(),t=this.graph.getNodes();let e=0;for(;e<a.length;){const i=a[e++],c=this._ops[i],g=c.node.inputs.map(w=>this._values[w]);if(g.indexOf(void 0)!==-1)throw new Error(`unresolved input detected: op: ${c.node}`);const m=g;d.Logger.verbose("ExecPlan",`Runing op:${c.node.name} (${m.map((w,v)=>`'${c.node.inputs[v]}': ${w.type}[${w.dims.join(",")}]`).join(", ")})`);const b=await this.profiler.event("node",c.node.name,async()=>c.op.impl(h,m,c.op.context));if(b.length!==c.node.outputs.length)throw new Error("the size of output does not match model definition.");b.forEach((w,v)=>{const S=c.node.outputs[v];if(this._values[S])throw new Error(`output [${S}] already has value: op:${c.node.name}`);this._values[S]=w});const _=new Set;b.forEach((w,v)=>{const S=c.node.outputs[v];for(const O of o[S].to){const E=t[O];let T=!0;for(const I of E.inputs)if(!this._values[I]){T=!1;break}T&&_.add(O)}}),a.push(..._)}const r=[];for(let i=0;i<this.graph.getOutputIndices().length;i++){const c=this.graph.getOutputIndices()[i],g=this._values[c];if(g===void 0)throw new Error(`required output [${c}] does not have value`);c===0?await g.getData():g.data,r.push(g)}return d.Logger.verbose("ExecPlan","disposing of inferenceHandler"),h.dispose(),r})}}},7070:(y,n,u)=>{Object.defineProperty(n,"__esModule",{value:!0}),n.Graph=void 0;const d=u(1446),l=u(7778),p=u(9395),s=u(9162),h=u(2517);var f=p.onnxruntime.experimental.fbs;n.Graph={from:(e,r)=>new t(e,r)};class a{constructor(r){this._from=void 0,this._to=[],this.tensor=void 0,this.type=void 0,r&&(this.type=h.ProtoUtil.tensorValueTypeFromProto(r.type.tensorType))}get from(){return this._from}get to(){return this._to}}class o{constructor(r,i){r instanceof d.onnx.NodeProto?(this.name=r.name,this.opType=r.opType,this.attributes=new l.Attribute(r.attribute)):r instanceof f.Node&&(this.name=i??r.name(),this.opType=r.opType(),this.attributes=new 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r.input){if(i.has(m.name))throw new Error(`duplicated input name: ${m.name}`);const b=this._allData.push(new a(m))-1;i.set(m.name,b),g.push(m.name)}if(!r.initializer)throw new Error("missing information in graph: initializer");for(const m of r.initializer){let b=i.get(m.name);if(b===void 0){const _=new a;_.type={shape:{dims:h.ProtoUtil.tensorDimsFromProto(m.dims)},tensorType:h.ProtoUtil.tensorDataTypeFromProto(m.dataType)},b=this._allData.push(_)-1,i.set(m.name,b)}this._allData[b]._from=-1,this._allData[b].tensor=s.Tensor.fromProto(m)}for(let m=0;m<this._allData.length;m++)this._allData[m].tensor||(this._allInputIndices.push(m),this._allInputNames.push(g[m]));if(!r.output)throw new Error("missing information in graph: output");for(const m of r.output){if(i.has(m.name))throw new Error(`duplicated output name: ${m.name}`);const b=this._allData.push(new a(m))-1;i.set(m.name,b),this._allOutputIndices.push(b),this._allOutputNames.push(m.name)}if(!r.node)throw new Error("missing information in graph: node");for(const m of r.node){if(!m.name)for(let _=0;;_++){const w=`unnamed_${m.opType}_${_}`;if(!c.has(w)){m.name=w;break}}if(c.has(m.name))throw new Error(`duplicated node name: ${m.name}`);const b=this._nodes.push(new o(m))-1;c.set(m.name,b)}for(let m=0;m<this._nodes.length;m++){const b=this._nodes[m],_=r.node[m];if(!_.output)throw new Error(`missing output for node: ${_.name}`);for(const w of _.output){let v=i.get(w);if(v===void 0&&(v=this._allData.push(new a)-1,i.set(w,v)),b.outputs.push(v),this._allData[v]._from!==void 0)throw new Error(`multiple nodes output to one data value: ${v}`);if(this._allData[v]._from=m,_.opType==="Constant"){if(!_.attribute||_.attribute.length!==1||!_.attribute[0].t)throw new Error("missing attributes or missing tensor value in attributes for this Constant operator");if(!_.output||_.output.length!==1)throw new Error("missing output or incorrect number of outputs for this Constant operator");b.outputs.pop(),b.executeNode=!1,this._allData[v]._from=-1,this._allData[v].tensor=s.Tensor.fromProto(_.attribute[0].t)}}}for(let m=0;m<this._nodes.length;m++){const b=this._nodes[m],_=r.node[m];if(!_.input)throw new Error(`missing input for node: ${_.name}`);for(const w of _.input){const v=i.get(w);if(v===void 0){if(w===""&&_.input.length===3&&_.opType==="Resize")continue;throw new Error(`unrecognized input '${w}' for node: ${_.name}`)}b.inputs.push(v),this._allData[v]._to.push(m)}}return!0}buildGraphFromOrtFormat(r){var i,c,g;const m=new Map;this._allData=[],this._allInputIndices=[],this._allInputNames=[],this._allOutputIndices=[],this._allOutputNames=[],this._nodes=[];const b=new Map,_=[];for(let w=0;w<r.inputsLength();w++){const v=r.inputs(w);if(m.has(v))throw new Error(`duplicated input name: ${v}`);for(let S=0;S<r.nodeArgsLength();S++)if(((i=r.nodeArgs(S))===null||i===void 0?void 0:i.name())===v){const O=new a;if(((g=(c=r.nodeArgs(S))===null||c===void 0?void 0:c.type())===null||g===void 0?void 0:g.valueType())!==f.TypeInfoValue.tensor_type)throw new Error("Unexpected value type for the nodeArg.");const E=r.nodeArgs(S).type().value(new f.TensorTypeAndShape),T=h.ProtoUtil.tensorDataTypeFromProto(E.elemType()),I=E.shape(),C=[];for(let F=0;F<I.dimLength();F++)C.push(h.LongUtil.longToNumber(I.dim(F).value().dimValue()));O.type={shape:{dims:C},tensorType:T};const B=this._allData.push(O)-1;m.set(v,B),_.push(v)}}for(let w=0;w<r.initializersLength();w++){const v=r.initializers(w);let S=m.get(v.name());if(S===void 0){const O=new a,E=h.ProtoUtil.tensorDimsFromORTFormat(v),T=h.ProtoUtil.tensorDataTypeFromProto(v.dataType());O.type={shape:{dims:E},tensorType:T},S=this._allData.push(O)-1,m.set(v.name(),S)}this._allData[S]._from=-1,this._allData[S].tensor=s.Tensor.fromOrtTensor(v)}for(let w=0;w<this._allData.length;w++)this._allData[w].tensor||(this._allInputIndices.push(w),this._allInputNames.push(_[w]));for(let w=0;w<r.outputsLength();w++){const v=r.outputs(w);if(m.has(v))throw new Error(`duplicated output name: ${v}`);const S=this._allData.push(new a)-1;m.set(v,S),this._allOutputIndices.push(S),this._allOutputNames.push(v)}if(!r.nodes)throw new Error("missing information in graph: node");for(let w=0;w<r.nodesLength();w++){const v=r.nodes(w);let S=v.name();if(!S)for(let E=0;S=`unnamed_${v.opType()}_${E}`,b.has(S);E++);if(b.has(S))throw new Error(`duplicated node name: ${S}`);const O=this._nodes.push(new o(v,S))-1;b.set(S,O)}for(let w=0;w<this._nodes.length;w++){const v=this._nodes[w],S=r.nodes(w);if(S==null)throw new Error(`No node exists at index ${w}`);if((S==null?void 0:S.outputsLength())===0)throw new Error(`missing output for node: ${S.name}`);for(let O=0;O<(S==null?void 0:S.outputsLength());O++){const E=S==null?void 0:S.outputs(O);let T=m.get(E);if(T===void 0&&(T=this._allData.push(new a)-1,m.set(E,T)),v.outputs.push(T),this._allData[T]._from!==void 0)throw new Error(`multiple nodes output to one data value: ${T}`);if(this._allData[T]._from=w,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");v.outputs.pop(),v.executeNode=!1,this._allData[T]._from=-1,this._allData[T].tensor=s.Tensor.fromOrtTensor(S.attributes(0).t())}}}for(let w=0;w<this._nodes.length;w++){const v=this._nodes[w],S=r.nodes(w);if(S.inputsLength()===0)throw new Error(`missing input for node: ${S.name}`);for(let O=0;O<S.inputsLength();O++){const E=S.inputs(O),T=m.get(E);if(T===void 0)throw new Error(`unrecognized input '${E}' for node: ${S.name()}`);v.inputs.push(T),this._allData[T]._to.push(w)}}}checkIsAcyclic(){const r=new Set;this._allInputIndices.forEach(g=>{this._allData[g]._to.forEach(m=>{r.add(m)})});const i=Array.from(r),c=new Array(this._nodes.length).fill("white");for(;i.length>0;){const g=i.pop();c[g]==="gray"?c[g]="black":(i.push(g),c[g]="gray",this._nodes[g].outputs.forEach(m=>{const b=this._allData[m];if(b.tensor!==void 0)throw new Error("node outputs should not be initialized");if(b._from!==g)throw new Error("from property of the Value object doesn't match index of Node being processed");b._to.forEach(_=>{if(c[_]==="gray")throw new Error("model graph is cyclic");c[_]==="white"&&i.push(_)})}))}}transformGraph(r){this.removeAllIdentityNodes(),this.removeAllDropoutNodes(),this.fuseConvActivationNodes(),r&&r.transformGraph(this),this.finalizeGraph()}finalizeGraph(){let r=0;for(let i=0;i<this._nodes.length;i++)this._nodes[i].executeNode?r>0&&(this._nodes[i].inputs.forEach(c=>{const g=this._allData[c]._to.indexOf(i+r);g!==-1&&(this._allData[c]._to[g]=i)}),this._nodes[i].outputs.forEach(c=>{this._allData[c]._from&&this._allData[c]._from===i+r&&(this._allData[c]._from=i)})):(r++,this._nodes[i].outputs.forEach(c=>{this._allData[c]._from=-2}),this._nodes.splice(i,1),i--);r=0;for(let i=0;i<this._allData.length;i++)if(this._allData[i].from!==-2||this._allOutputIndices.indexOf(i+r)!==-1){if(r>0){let c=-1;this._allData[i].from!==void 0&&this._allData[i].from!==-1?(c=this._nodes[this._allData[i].from].outputs.indexOf(i+r),c!==-1&&(this._nodes[this._allData[i].from].outputs[c]=i)):(c=this._allInputIndices.indexOf(i+r),c!==-1&&(this._allInputIndices[c]=i)),this._allData[i].to.forEach(g=>{c=this._nodes[g].inputs.indexOf(i+r),c!==-1&&(this._nodes[g].inputs[c]=i)}),this._allData[i].to.length===0&&(c=this._allOutputIndices.indexOf(i+r),c!==-1&&(this._allOutputIndices[c]=i))}}else r++,this._allData.splice(i,1),i--}deleteNode(r){const i=this._nodes[r];if(i.outputs.length>1){for(let w=1;w<i.outputs.length;w++)if(this._allData[i.outputs[w]].to.length>0)throw new Error("Node deletion with more than one output connected to other nodes is not supported. ")}i.executeNode=!1;const c=i.inputs[0],g=i.outputs[0],m=this._allData[g].to,b=this._allData[c].to.indexOf(r);if(b===-1)throw new Error("The Value object doesn't have the current Node in it's 'to' property ");this._allData[c].to.splice(b,1),this._allData[g]._to=[];const _=this._allOutputIndices.indexOf(g);if(_!==-1&&(this._allOutputIndices[_]=c),m&&m.length>0)for(const w of m){const v=this._nodes[w].inputs.indexOf(g);if(v===-1)throw new Error("The Node object doesn't have the output Value in it's 'inputs' property ");this._nodes[w].inputs[v]=c,this._allData[c].to.push(w)}}removeAllDropoutNodes(){let r=0;for(const i of this._nodes){if(i.opType==="Dropout"){if(i.inputs.length!==1)throw new Error("Dropout nodes should only contain one input. ");if(i.outputs.length!==1&&i.outputs.length!==2)throw new Error("Dropout nodes should contain either 1 or 2 output(s)");if(i.outputs.length===2&&this._allData[i.outputs[1]]._to.length!==0)throw new Error("Dropout nodes's second output should not be referenced by other nodes");this.deleteNode(r)}r++}}removeAllIdentityNodes(){let r=0;for(const i of this._nodes)i.opType==="Identity"&&this.deleteNode(r),r++}isActivation(r){switch(r.opType){case"Relu":case"Sigmoid":case"Clip":return!0;default:return!1}}fuseConvActivationNodes(){for(const r of this._nodes)if(r.opType==="Conv"){const i=this._allData[r.outputs[0]]._to;if(i.length===1&&this.isActivation(this._nodes[i[0]])){const 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e=this.bb.__offset(this.bb_pos,12);return e?this.bb.__string(this.bb_pos+e,t):null}modelVersion(){let t=this.bb.__offset(this.bb_pos,14);return t?this.bb.readInt64(this.bb_pos+t):this.bb.createLong(0,0)}docString(t){let e=this.bb.__offset(this.bb_pos,16);return e?this.bb.__string(this.bb_pos+e,t):null}graph(t){let e=this.bb.__offset(this.bb_pos,18);return e?(t||new s.experimental.fbs.Graph).__init(this.bb.__indirect(this.bb_pos+e),this.bb):null}graphDocString(t){let e=this.bb.__offset(this.bb_pos,20);return e?this.bb.__string(this.bb_pos+e,t):null}static startModel(t){t.startObject(9)}static addIrVersion(t,e){t.addFieldInt64(0,e,t.createLong(0,0))}static addOpsetImport(t,e){t.addFieldOffset(1,e,0)}static createOpsetImportVector(t,e){t.startVector(4,e.length,4);for(let r=e.length-1;r>=0;r--)t.addOffset(e[r]);return t.endVector()}static startOpsetImportVector(t,e){t.startVector(4,e,4)}static addProducerName(t,e){t.addFieldOffset(2,e,0)}static addProducerVersion(t,e){t.addFieldOffset(3,e,0)}static addDomain(t,e){t.addFieldOffset(4,e,0)}static addModelVersion(t,e){t.addFieldInt64(5,e,t.createLong(0,0))}static addDocString(t,e){t.addFieldOffset(6,e,0)}static addGraph(t,e){t.addFieldOffset(7,e,0)}static addGraphDocString(t,e){t.addFieldOffset(8,e,0)}static endModel(t){return t.endObject()}static createModel(t,e,r,i,c,g,m,b,_,w){return a.startModel(t),a.addIrVersion(t,e),a.addOpsetImport(t,r),a.addProducerName(t,i),a.addProducerVersion(t,c),a.addDomain(t,g),a.addModelVersion(t,m),a.addDocString(t,b),a.addGraph(t,_),a.addGraphDocString(t,w),a.endModel(t)}}f.Model=a})(h.fbs||(h.fbs={}))})(s.experimental||(s.experimental={}))}(n.onnxruntime||(n.onnxruntime={})),function(s){(function(h){(function(f){class a{constructor(){this.bb=null,this.bb_pos=0}__init(t,e){return this.bb_pos=t,this.bb=e,this}static getRootAsKernelCreateInfos(t,e){return(e||new a).__init(t.readInt32(t.position())+t.position(),t)}static getSizePrefixedRootAsKernelCreateInfos(t,e){return t.setPosition(t.position()+d.flatbuffers.SIZE_PREFIX_LENGTH),(e||new a).__init(t.readInt32(t.position())+t.position(),t)}nodeIndices(t){let e=this.bb.__offset(this.bb_pos,4);return e?this.bb.readUint32(this.bb.__vector(this.bb_pos+e)+4*t):0}nodeIndicesLength(){let t=this.bb.__offset(this.bb_pos,4);return t?this.bb.__vector_len(this.bb_pos+t):0}nodeIndicesArray(){let t=this.bb.__offset(this.bb_pos,4);return t?new Uint32Array(this.bb.bytes().buffer,this.bb.bytes().byteOffset+this.bb.__vector(this.bb_pos+t),this.bb.__vector_len(this.bb_pos+t)):null}kernelDefHashes(t){let e=this.bb.__offset(this.bb_pos,6);return e?this.bb.readUint64(this.bb.__vector(this.bb_pos+e)+8*t):this.bb.createLong(0,0)}kernelDefHashesLength(){let t=this.bb.__offset(this.bb_pos,6);return t?this.bb.__vector_len(this.bb_pos+t):0}static startKernelCreateInfos(t){t.startObject(2)}static addNodeIndices(t,e){t.addFieldOffset(0,e,0)}static createNodeIndicesVector(t,e){t.startVector(4,e.length,4);for(let r=e.length-1;r>=0;r--)t.addInt32(e[r]);return t.endVector()}static startNodeIndicesVector(t,e){t.startVector(4,e,4)}static addKernelDefHashes(t,e){t.addFieldOffset(1,e,0)}static createKernelDefHashesVector(t,e){t.startVector(8,e.length,8);for(let r=e.length-1;r>=0;r--)t.addInt64(e[r]);return t.endVector()}static startKernelDefHashesVector(t,e){t.startVector(8,e,8)}static endKernelCreateInfos(t){return t.endObject()}static createKernelCreateInfos(t,e,r){return a.startKernelCreateInfos(t),a.addNodeIndices(t,e),a.addKernelDefHashes(t,r),a.endKernelCreateInfos(t)}}f.KernelCreateInfos=a})(h.fbs||(h.fbs={}))})(s.experimental||(s.experimental={}))}(n.onnxruntime||(n.onnxruntime={})),function(s){(function(h){(function(f){class a{constructor(){this.bb=null,this.bb_pos=0}__init(t,e){return this.bb_pos=t,this.bb=e,this}static getRootAsSubGraphSessionState(t,e){return(e||new a).__init(t.readInt32(t.position())+t.position(),t)}static getSizePrefixedRootAsSubGraphSessionState(t,e){return t.setPosition(t.position()+d.flatbuffers.SIZE_PREFIX_LENGTH),(e||new a).__init(t.readInt32(t.position())+t.position(),t)}graphId(t){let e=this.bb.__offset(this.bb_pos,4);return e?this.bb.__string(this.bb_pos+e,t):null}sessionState(t){let e=this.bb.__offset(this.bb_pos,6);return e?(t||new s.experimental.fbs.SessionState).__init(this.bb.__indirect(this.bb_pos+e),this.bb):null}static startSubGraphSessionState(t){t.startObject(2)}static addGraphId(t,e){t.addFieldOffset(0,e,0)}static addSessionState(t,e){t.addFieldOffset(1,e,0)}static endSubGraphSessionState(t){let e=t.endObject();return t.requiredField(e,4),e}static createSubGraphSessionState(t,e,r){return a.startSubGraphSessionState(t),a.addGraphId(t,e),a.addSessionState(t,r),a.endSubGraphSessionState(t)}}f.SubGraphSessionState=a})(h.fbs||(h.fbs={}))})(s.experimental||(s.experimental={}))}(n.onnxruntime||(n.onnxruntime={})),function(s){(function(h){(function(f){class 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e=this.bb.__offset(this.bb_pos,4);return e?this.bb.__string(this.bb_pos+e,t):null}model(t){let e=this.bb.__offset(this.bb_pos,6);return e?(t||new s.experimental.fbs.Model).__init(this.bb.__indirect(this.bb_pos+e),this.bb):null}sessionState(t){let e=this.bb.__offset(this.bb_pos,8);return e?(t||new s.experimental.fbs.SessionState).__init(this.bb.__indirect(this.bb_pos+e),this.bb):null}static startInferenceSession(t){t.startObject(3)}static addOrtVersion(t,e){t.addFieldOffset(0,e,0)}static addModel(t,e){t.addFieldOffset(1,e,0)}static addSessionState(t,e){t.addFieldOffset(2,e,0)}static endInferenceSession(t){return t.endObject()}static finishInferenceSessionBuffer(t,e){t.finish(e,"ORTM")}static finishSizePrefixedInferenceSessionBuffer(t,e){t.finish(e,"ORTM",!0)}static createInferenceSession(t,e,r,i){return a.startInferenceSession(t),a.addOrtVersion(t,e),a.addModel(t,r),a.addSessionState(t,i),a.endInferenceSession(t)}}f.InferenceSession=a})(h.fbs||(h.fbs={}))})(s.experimental||(s.experimental={}))}(n.onnxruntime||(n.onnxruntime={}))},7448:(y,n,u)=>{Object.defineProperty(n,"__esModule",{value:!0}),n.OnnxjsSessionHandler=void 0;const d=u(1670),l=u(9162);n.OnnxjsSessionHandler=class{constructor(p){this.session=p,this.inputNames=this.session.inputNames,this.outputNames=this.session.outputNames}async dispose(){}async run(p,s,h){const f=new Map;for(const t in p)if(Object.hasOwnProperty.call(p,t)){const e=p[t];f.set(t,new l.Tensor(e.dims,e.type,void 0,void 0,e.data))}const a=await this.session.run(f),o={};return a.forEach((t,e)=>{o[e]=new d.Tensor(t.type,t.data,t.dims)}),o}startProfiling(){this.session.startProfiling()}endProfiling(){this.session.endProfiling()}}},6919:(y,n,u)=>{Object.defineProperty(n,"__esModule",{value:!0}),n.Session=void 0;const 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${a.length}`)}else{if(a.size!==o.length)throw new Error(`incorrect input map size: expected ${o.length} but got ${a.size}`);const t=new Array(a.size);let e=0;for(let r=0;r<o.length;++r){const i=a.get(o[r]);if(!i)throw new Error(`missing input tensor for: '${name}'`);t[e++]=i}a=t}if(this.context.graphInputTypes&&this.context.graphInputTypes.length!==0&&this.context.graphInputDims&&this.context.graphInputDims.length!==0)this.validateInputTensorDims(this.context.graphInputDims,a,!1);else{const t=this._model.graph.getInputIndices(),e=this._model.graph.getValues(),r=new Array(t.length);for(let i=0;i<t.length;++i){const c=e[t[i]];r[i]=c.type.shape.dims,this.context.graphInputTypes.push(c.type.tensorType),this.context.graphInputDims.push(a[i].dims)}this.validateInputTensorDims(r,a,!0)}return this.validateInputTensorTypes(this.context.graphInputTypes,a),a}validateInputTensorTypes(a,o){for(let t=0;t<o.length;t++){const e=a[t],r=o[t].type;if(e!==r)throw new Error(`input tensor[${t}] check 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l=u(3442),p=d(u(3720)),s=u(1446),h=u(9395),f=u(2517);var a=h.onnxruntime.experimental.fbs;class o{get data(){if(this.cache===void 0){const g=this.dataProvider(this.dataId);if(g.length!==this.size)throw new Error("Length of data provided by the Data Provider is inconsistent with the dims of this Tensor.");this.cache=g}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(g){return this.data[f.ShapeUtil.indicesToOffset(g,this.strides)]}set(g,m){this.data[f.ShapeUtil.indicesToOffset(g,this.strides)]=m}async getData(){return this.cache===void 0&&(this.cache=await this.asyncDataProvider(this.dataId)),this.cache}get strides(){return this._strides||(this._strides=f.ShapeUtil.computeStrides(this.dims)),this._strides}constructor(g,m,b,_,w,v=l.Guid.create()){this.dims=g,this.type=m,this.dataProvider=b,this.asyncDataProvider=_,this.cache=w,this.dataId=v,this.size=f.ShapeUtil.validateDimsAndCalcSize(g);const S=this.size,O=b===void 0&&_===void 0&&w===void 0;if(w!==void 0&&w.length!==S)throw new RangeError("Input dims doesn't match data length.");if(m==="string"){if(!(w===void 0||Array.isArray(w)&&w.every(E=>typeof E=="string")))throw new TypeError("cache should be a string array");O&&(this.cache=new Array(S))}else{if(w!==void 0){const E=e(m);if(!(w instanceof E))throw new TypeError(`cache should be type ${E.name}`)}if(O){const E=new ArrayBuffer(S*function(T){switch(T){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 ${T}`)}}(m));this.cache=function(T,I){return new(e(I))(T)}(E,m)}}}static fromProto(g){if(!g)throw new Error("cannot construct Value from an empty tensor");const m=f.ProtoUtil.tensorDataTypeFromProto(g.dataType),b=f.ProtoUtil.tensorDimsFromProto(g.dims),_=new o(b,m);if(m==="string")g.stringData.forEach((w,v)=>{_.data[v]=(0,f.decodeUtf8String)(w)});else if(g.rawData&&typeof g.rawData.byteLength=="number"&&g.rawData.byteLength>0){const w=_.data,v=new DataView(g.rawData.buffer,g.rawData.byteOffset,g.rawData.byteLength),S=t(g.dataType),O=g.rawData.byteLength/S;if(g.rawData.byteLength%S!=0)throw new Error("invalid buffer length");if(w.length!==O)throw new Error("buffer length mismatch");for(let E=0;E<O;E++){const T=i(v,g.dataType,E*S);w[E]=T}}else{let w;switch(g.dataType){case s.onnx.TensorProto.DataType.FLOAT:w=g.floatData;break;case s.onnx.TensorProto.DataType.INT32:case s.onnx.TensorProto.DataType.INT16:case s.onnx.TensorProto.DataType.UINT16:case s.onnx.TensorProto.DataType.INT8:case s.onnx.TensorProto.DataType.UINT8:case s.onnx.TensorProto.DataType.BOOL:w=g.int32Data;break;case s.onnx.TensorProto.DataType.INT64:w=g.int64Data;break;case s.onnx.TensorProto.DataType.DOUBLE:w=g.doubleData;break;case s.onnx.TensorProto.DataType.UINT32:case s.onnx.TensorProto.DataType.UINT64:w=g.uint64Data;break;default:throw new Error("unspecific error")}if(w==null)throw new Error("failed to populate data from a tensorproto value");const v=_.data;if(v.length!==w.length)throw new Error("array length mismatch");for(let S=0;S<w.length;S++){const O=w[S];p.default.isLong(O)?v[S]=r(O,g.dataType):v[S]=O}}return _}static fromData(g,m,b){return new o(m,b,void 0,void 0,g)}static fromOrtTensor(g){if(!g)throw new Error("cannot construct Value from an empty tensor");const m=f.ProtoUtil.tensorDimsFromORTFormat(g),b=f.ProtoUtil.tensorDataTypeFromProto(g.dataType()),_=new o(m,b);if(b==="string")for(let w=0;w<g.stringDataLength();w++)_.data[w]=g.stringData(w);else if(g.rawDataArray()&&typeof g.rawDataLength()=="number"&&g.rawDataLength()>0){const w=_.data,v=new DataView(g.rawDataArray().buffer,g.rawDataArray().byteOffset,g.rawDataLength()),S=t(g.dataType()),O=g.rawDataLength()/S;if(g.rawDataLength()%S!=0)throw new Error("invalid buffer length");if(w.length!==O)throw new Error("buffer length mismatch");for(let E=0;E<O;E++){const T=i(v,g.dataType(),E*S);w[E]=T}}return _}}function t(c){switch(c){case s.onnx.TensorProto.DataType.UINT8:case s.onnx.TensorProto.DataType.INT8:case s.onnx.TensorProto.DataType.BOOL:return 1;case s.onnx.TensorProto.DataType.UINT16:case s.onnx.TensorProto.DataType.INT16:return 2;case s.onnx.TensorProto.DataType.FLOAT:case s.onnx.TensorProto.DataType.INT32:case s.onnx.TensorProto.DataType.UINT32:return 4;case s.onnx.TensorProto.DataType.INT64:case s.onnx.TensorProto.DataType.DOUBLE:case s.onnx.TensorProto.DataType.UINT64:return 8;default:throw new Error(`cannot calculate sizeof() on type ${s.onnx.TensorProto.DataType[c]}`)}}function e(c){switch(c){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"float32":return Float32Array;case"float64":return Float64Array;default:throw new Error("unspecified error")}}function r(c,g){if(g===s.onnx.TensorProto.DataType.INT64||g===a.TensorDataType.INT64){if(c.greaterThanOrEqual(2147483648)||c.lessThan(-2147483648))throw new TypeError("int64 is not supported")}else{if(g!==s.onnx.TensorProto.DataType.UINT32&&g!==a.TensorDataType.UINT32&&g!==s.onnx.TensorProto.DataType.UINT64&&g!==a.TensorDataType.UINT64)throw new TypeError(`not a LONG type: ${s.onnx.TensorProto.DataType[g]}`);if(c.greaterThanOrEqual(4294967296)||c.lessThan(0))throw new TypeError("uint64 is not supported")}return c.toNumber()}function i(c,g,m){switch(g){case s.onnx.TensorProto.DataType.BOOL:case s.onnx.TensorProto.DataType.UINT8:return c.getUint8(m);case s.onnx.TensorProto.DataType.INT8:return c.getInt8(m);case s.onnx.TensorProto.DataType.UINT16:return c.getUint16(m,!0);case s.onnx.TensorProto.DataType.INT16:return c.getInt16(m,!0);case s.onnx.TensorProto.DataType.FLOAT:return c.getFloat32(m,!0);case s.onnx.TensorProto.DataType.INT32:return c.getInt32(m,!0);case s.onnx.TensorProto.DataType.UINT32:return c.getUint32(m,!0);case s.onnx.TensorProto.DataType.INT64:return r(p.default.fromBits(c.getUint32(m,!0),c.getUint32(m+4,!0),!1),g);case s.onnx.TensorProto.DataType.DOUBLE:return c.getFloat64(m,!0);case s.onnx.TensorProto.DataType.UINT64:return r(p.default.fromBits(c.getUint32(m,!0),c.getUint32(m+4,!0),!0),g);default:throw new Error(`cannot read from DataView for type ${s.onnx.TensorProto.DataType[g]}`)}}n.Tensor=o},2517:function(y,n,u){var d=this&&this.__importDefault||function(g){return g&&g.__esModule?g:{default:g}};Object.defineProperty(n,"__esModule",{value:!0}),n.decodeUtf8String=n.MAX_CLIP=n.MIN_CLIP=n.PoolConvUtil=n.ReduceUtil=n.SplitUtil=n.MathUtil=n.ShapeUtil=n.LongUtil=n.ProtoUtil=n.GemmUtil=n.arrayCopyHelper=n.BroadcastUtil=n.MatMulUtil=n.ArrayUtil=n.assert=n.checkInputsShape=void 0;const l=u(5686),p=d(u(3720)),s=u(1446),h=u(9162);n.checkInputsShape=function(g,...m){if(!g||g.length!==m.length)return!1;for(let b=0;b<g.length;b++)if(!g[b].dims||g[b].dims.length!==m[b])return!1;return!0},n.assert=function(g,m){if(!g)throw new Error(typeof m=="string"?m:m())},n.ArrayUtil=class{static arraysEqual(g,m){if(g.length!==m.length)return!1;for(let b=0;b<g.length;b++)if(g[b]!==m[b])return!1;return!0}};class f{static preprocessInputShapes(m,b){return[m.length===1?[1,m[0]]:m,b.length===1?[b[0],1]:b]}static postprocessOutputShape(m,b,_){b===1&&m.splice(m.length-2,1),_===1&&m.pop()}static calcMatMulShape(m,b){return m[1]!==b[0]?void 0:[m[0],b[1]]}}n.MatMulUtil=f;class a{static calcShape(m,b,_=!1){const w=m.length,v=b.length;if(w===0)return b;if(v===0)return m;const S=Math.max(m.length,b.length),O=new Array(S);if(_){if(w<2||v<2)return;const E=f.calcMatMulShape([m[w-2],m[w-1]],[b[v-2],b[v-1]]);if(E===void 0)return;[O[S-2],O[S-1]]=E}for(let E=_?3:1;E<=S;E++){const T=w-E<0?1:m[w-E],I=v-E<0?1:b[v-E];if(T!==I&&T>1&&I>1)return;O[S-E]=Math.max(T,I)}return O}static index(m,b){const _=new Array(b.length);return a.fillIndex(m,b,_),_}static fillIndex(m,b,_){const w=m.length-b.length;for(let v=0;v<b.length;v++)_[v]=m[w+v]%b[v]}static calc(m,b,_,w,v){const S=a.calcShape(m.dims,b.dims);if(S){if(w&&!e.areEqual(S,m.dims))return;const O=e.size(S),E=w?m:new h.Tensor(S,v||m.type);if(S.length===0)E.set([],_(m.get([]),b.get([])));else{const T=new Array(S.length),I=new Array(m.dims.length),C=new Array(b.dims.length);let B,F=0,N=0,H=!1,$=!1;m.dims.length===0&&(F=m.get([]),H=!0),b.dims.length===0&&(N=b.get([]),$=!0);for(let z=0;z<O;z++){B=z;for(let J=S.length-1;J>=0;J--)T[J]=B%S[J],B=Math.floor(B/S[J]);H||(a.fillIndex(T,m.dims,I),F=m.get(I)),$||(a.fillIndex(T,b.dims,C),N=b.get(C)),E.set(T,_(F,N))}}return E}}static isValidBroadcast(m,b){const _=m.length,w=b.length;if(_>w)return!1;for(let v=1;v<=_;v++)if(m[_-v]!==1&&m[_-v]!==b[w-v])return!1;return!0}static getBroadcastDims(m,b){const _=m.length,w=[];for(let v=0;v<_;v++){const S=_-1-v,O=m[S]||1;(b[b.length-1-v]||1)>1&&O===1&&w.unshift(S)}return w}}n.BroadcastUtil=a,n.arrayCopyHelper=function(g,m,b,_,w){if(_<0||_>=m.length)throw new Error("sourceIndex out of bounds");if(b<0||b>=g.length)throw new Error("targetIndex out of bounds");if(_+w>m.length)throw new Error("source indices to be copied are outside bounds");if(b+w>g.length)throw new Error("target array is too small to hold result");for(let v=0;v<w;v++)g[b+v]=m[_+v]},n.GemmUtil=class{static getShapeOfGemmResult(g,m,b,_,w){if(g.length!==2||b.length!==2)throw new Error("shape need to be of size 2");let v,S,O;m?(v=g[1],S=g[0]):(v=g[0],S=g[1]);let E=-1;if(_?(O=b[0],E=1):(O=b[1],E=0),b[E]!==S)throw new Error("dimension mismatch");if(v<=0||O<=0||S<=0)throw new Error("invalid shape specified");if(w&&!a.isValidBroadcast(w,[v,O]))throw new Error("gemm: invalid bias shape for broadcast");return[v,O,S]}};class o{static tensorDataTypeFromProto(m){switch(m){case s.onnx.TensorProto.DataType.INT8:return"int8";case s.onnx.TensorProto.DataType.UINT8:return"uint8";case s.onnx.TensorProto.DataType.BOOL:return"bool";case s.onnx.TensorProto.DataType.INT16:return"int16";case s.onnx.TensorProto.DataType.UINT16:return"uint16";case s.onnx.TensorProto.DataType.INT32:return"int32";case s.onnx.TensorProto.DataType.UINT32:return"uint32";case s.onnx.TensorProto.DataType.FLOAT:return"float32";case s.onnx.TensorProto.DataType.DOUBLE:return"float64";case s.onnx.TensorProto.DataType.STRING:return"string";case s.onnx.TensorProto.DataType.INT64:return"int32";case s.onnx.TensorProto.DataType.UINT64:return"uint32";default:throw new Error(`unsupported data type: ${s.onnx.TensorProto.DataType[m]}`)}}static tensorDataTypeStringToEnum(m){switch(m){case"int8":return s.onnx.TensorProto.DataType.INT8;case"uint8":return s.onnx.TensorProto.DataType.UINT8;case"bool":return s.onnx.TensorProto.DataType.BOOL;case"int16":return s.onnx.TensorProto.DataType.INT16;case"uint16":return s.onnx.TensorProto.DataType.UINT16;case"int32":return s.onnx.TensorProto.DataType.INT32;case"uint32":return s.onnx.TensorProto.DataType.UINT32;case"float32":return s.onnx.TensorProto.DataType.FLOAT;case"float64":return s.onnx.TensorProto.DataType.DOUBLE;case"string":return s.onnx.TensorProto.DataType.STRING;case"int64":return s.onnx.TensorProto.DataType.INT64;case"uint64":return s.onnx.TensorProto.DataType.UINT64;default:throw new Error(`unsupported data type: ${m}`)}}static tensorDimsFromProto(m){return m.map(b=>p.default.isLong(b)?b.toNumber():b)}static tensorValueTypeFromProto(m){return{tensorType:o.tensorDataTypeFromProto(m.elemType),shape:{dims:o.tensorDimsFromProto(m.shape.dim.map(b=>b.dimValue))}}}static tensorDimsFromORTFormat(m){const b=[];for(let _=0;_<m.dimsLength();_++)b.push(t.longToNumber(m.dims(_)));return b}static tensorAttributesFromORTFormat(m){const b=[];for(let _=0;_<m.attributesLength();_++)b.push(m.attributes(_));return b}}n.ProtoUtil=o;class t{static longToNumber(m,b){return p.default.isLong(m)?m.toNumber():m instanceof l.flatbuffers.Long?p.default.fromValue({low:m.low,high:m.high,unsigned:b!=null&&b}).toNumber():m}static isLong(m){return p.default.isLong(m)||m instanceof l.flatbuffers.Long}}n.LongUtil=t;class e{static size(m){return e.getSizeFromDimensionRange(m,0,m.length)}static sizeFromDimension(m,b){if(b<0||b>m.length)throw new Error(`invalid dimension of ${b} for sizeFromDimension as Tensor has ${m.length} dimensions.`);return e.getSizeFromDimensionRange(m,b,m.length)}static sizeToDimension(m,b){if(b<0||b>m.length)throw new Error(`invalid dimension of ${b} for sizeToDimension as Tensor has ${m.length} dimensions.`);return e.getSizeFromDimensionRange(m,0,b)}static getSizeFromDimensionRange(m,b,_){let w=1;for(let v=b;v<_;v++){if(m[v]<=0)throw new Error("cannot get valid size from specified dimension range. Most likely the range contains 0 or negative values in them.");w*=m[v]}return w}static computeStrides(m){const b=m.length;if(b===0)return[];if(b===1)return[1];const _=new Array(b);_[b-1]=1,_[b-2]=m[b-1];for(let w=b-3;w>=0;--w)_[w]=_[w+1]*m[w+1];return _}static transpose(m){return m.slice().reverse()}static indicesToOffset(m,b,_){_===void 0&&(_=m.length);let w=0;for(let v=0;v<_;++v)w+=b[v]*m[v];return w}static offsetToIndices(m,b){const _=b.length;if(_===0)return[];if(_===1)return[m*b[0]];const w=new Array(b.length);for(let v=0;v<w.length-1;++v)w[v]=Math.floor(m/b[v]),m-=w[v]*b[v];return w[w.length-1]=m,w}static normalizeAxis(m,b){if(m<-b&&m>=b)throw new Error("unsupported axis for this operation.");return m<0?m+b:m}static normalizeAxes(m,b){return m.map(_=>this.normalizeAxis(_,b))}static incrementIndex(m,b,_){if(b.length===0||m.length===0)throw new Error("Index incrementing unsupported for scalar Tensor");if(_===void 0)_=b.length;else if(_<=0||_>b.length)throw new Error("Incorrect axis to increment on");for(let w=_-1;w>=0&&(m[w]++,!(m[w]<b[w]));--w)m[w]=0}static calculateReshapedDims(m,b){if(b.length===0){if(m.length===0||e.size(m)===1)return[];throw new Error("cannot reshape to a scalar Tensor")}const _=b.length,w=new Array(_);let v=-1,S=1;for(let E=0;E<_;E++){if(b[E]<-1)throw new Error("a dimension in shape hints cannot be less than -1");if(b[E]===-1){if(v!==-1)throw new Error("at most one dimension in shape hints can be -1");v=E}else{if(b[E]===0){if(E>=m.length)throw new Error("the dimension with value zero exceeds the dimension size of the input tensor");w[E]=m[E]}else w[E]=b[E];S*=w[E]}}const O=e.size(m);if(v!==-1){if(O%S!=0)throw new Error(`the input tensor cannot be reshaped to the requested shape. Input shape: [${m}] Output shape: [${b}]`);w[v]=O/S}else if(S!==O)throw new Error("reshapedDims and originalDims don't have matching sizes");return w}static sortBasedOnPerm(m,b){return b?b.map(_=>m[_]):m.slice().reverse()}static padShape(m,b){const _=m.length;return m.map((w,v)=>w+b[v]+b[v+_])}static areEqual(m,b){return m.length===b.length&&m.every((_,w)=>_===b[w])}static validateDimsAndCalcSize(m){if(m.length>6)throw new TypeError("Only rank 0 to 6 is supported for tensor shape.");let b=1;for(const _ of m){if(!Number.isInteger(_))throw new TypeError(`Invalid shape: ${_} is not an integer`);if(_<0||_>2147483647)throw new TypeError(`Invalid shape: length ${_} is not allowed`);b*=_}return b}static flattenShape(m,b){b<0&&(b+=m.length);const _=m.reduce((v,S)=>v*S,1),w=m.slice(b).reduce((v,S)=>v*S,1);return[_/w,w]}static squeezeShape(m,b){const _=new Array;b=e.normalizeAxes(b,m.length);for(let w=0;w<m.length;w++){const v=b.indexOf(w)>=0;if(v&&m[w]!==1)throw new Error("squeeze an axis of size different than 1");(b.length===0&&m[w]>1||b.length>0&&!v)&&_.push(m[w])}return _}static unsqueezeShape(m,b){const _=new Array(m.length+b.length);_.fill(0);for(let v=0;v<b.length;v++){const S=e.normalizeAxis(b[v],_.length);if(S>=_.length)throw new Error("'axes' has an out of range axis");if(_[S]!==0)throw new Error("'axes' has a duplicate axis");_[S]=1}let w=0;for(let v=0;v<_.length;v++)_[v]===0&&(_[v]=m[w++]);if(w!==m.length)throw new Error("the unsqueezed dimension could not be established");return _}}n.ShapeUtil=e,n.MathUtil=class{static sqr(g,m,b,_,w){if(_<0||_>=m.length)throw new Error("sourceIndex out of bounds");if(b<0||b>=g.length)throw new Error("targetIndex out of bounds");if(_+w>m.length)throw new Error("source indices to be copied are outside bounds");if(b+w>g.length)throw new Error("target array is too small to hold result");for(let v=0;v<w;v++)g[b+v]+=Math.pow(m[_+v],2)}static axpy(g,m,b,_,w,v){if(_<0||_>=m.length)throw new Error("sourceIndex out of bounds");if(b<0||b>=g.length)throw new Error("targetIndex out of bounds");if(_+w>m.length)throw new Error("source indices to be copied are outside bounds");if(b+w>g.length)throw new Error("target array is too small to hold result");for(let S=0;S<w;S++)g[b+S]+=v*m[_+S]}static powx(g,m,b,_,w,v){if(_<0||_>=m.length)throw new Error("sourceIndex out of bounds");if(b<0||b>=g.length)throw new Error("targetIndex out of bounds");if(_+w>m.length)throw new Error("source indices to be copied are outside bounds");if(b+w>g.length)throw new Error("target array is too small to hold result");for(let S=0;S<w;S++)g[b+S]=Math.pow(m[_+S],v)}static mul(g,m,b,_,w){if(_<0||_>=m.length)throw new Error("sourceIndex out of bounds");if(b<0||b>=g.length)throw new Error("targetIndex out of bounds");if(_+w>m.length)throw new Error("source indices to be copied are outside bounds");if(b+w>g.length)throw new Error("target array is too small to hold result");for(let v=0;v<w;v++)g[b+v]=m[_+v]*g[b+v]}};class r{static splitShape(m,b,_,w){if(_.length===0){if(!w)throw new Error("need to know number of outputs when the 'split' attribute is not specified");r.determineSplit(m[b],w,_)}const v=[],S=[0];for(let O=0;O<_.length;++O){O!==0&&S.push(S[O-1]+_[O-1]);const E=m.slice();E[b]=_[O],v.push(E)}return[v,S]}static determineSplit(m,b,_){if(m%b!=0)throw new Error("cannot split tensor to equal sized parts");for(let w=0;w<b;++w)_.push(m/b)}}n.SplitUtil=r;class i{static calcReduce(m,b,_,w,v){const S=m.dims.slice(0);b.length===0&&S.forEach((F,N)=>b.push(N));const O=i.calcReduceShape(S,b,!0),E=e.size(O),T=new h.Tensor(O,m.type),I=e.computeStrides(O),C=e.computeStrides(S),B=new Array(S.length);for(let F=0;F<E;F++){const N=e.offsetToIndices(F,I);a.fillIndex(N,S,B),T.set(N,i.calcReduceByAxis(m.numberData,b,S,0,e.indicesToOffset(B,C),w,v))}return _?T:new h.Tensor(i.calcReduceShape(S,b,_),T.type,void 0,void 0,T.data,T.dataId)}static calcReduceByAxis(m,b,_,w,v,S,O){let E=0;if(w>=b.length)return S(m[v]);const T=b[w],I=T>=_.length?1:e.size(_.slice(T+1));for(let C=0;C<_[T];C++)E=C===0?i.calcReduceByAxis(m,b,_,w+1,v,S,O):O(E,i.calcReduceByAxis(m,b,_,w+1,v,S,O)),v+=I;return E}static calcReduceShape(m,b,_){const w=m.slice();for(let v=0;v<b.length;v++)w[b[v]]=_?1:0;return w.filter(v=>v!==0)}}n.ReduceUtil=i;class c{static adjustPoolAttributes(m,b,_,w,v,S){if(!m&&_.length!==b.length-2)throw new Error("length of specified kernel shapes should be 2 less than length of input dimensions");if(m)for(let O=0;O<b.length-2;O++)O>=_.length?_.push(b[O+2]):_[O]=b[O+2];for(let O=0;O<_.length;O++)if(O<w.length){if(w[O]<0)throw new Error("strides should be greater than or equal to 1")}else w.push(1);for(let O=0;O<_.length;O++)if(O<v.length){if(v[O]<0)throw new Error("dilations should be greater than or equal to 1")}else v.push(1);for(let O=0;O<2*_.length;O++)if(O<S.length){if(S[O]<0)throw new Error("pad should be greater than or equal to 1")}else S.push(0);for(let O=0;O<_.length;O++){if(_[O]<=0)throw new 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Unexpected trace[${s}, ${h}]. Please file a bug report.`)}return f.reverse(),a.reverse(),[f,a]}function dimsToStride(y){const n=new Array(y.length);for(let u=y.length-1,d=1;u>=0;--u)n[u]=d,d*=y[u];return n}async function loadTokenizer(y,n){return await Promise.all([getModelJSON(y,"tokenizer.json",!0,n),getModelJSON(y,"tokenizer_config.json",!0,n)])}function createPattern(y,n=!0){return y.Regex?new RegExp(n?y.Regex:`(${y.Regex})`,"gu"):y.String?y.String:(console.warn("Unknown pattern type:",y),null)}function clean_up_tokenization(y){return y.replace(/ \./g,".").replace(/ \?/g,"?").replace(/ \!/g,"!").replace(/ ,/g,",").replace(/ \' /g,"'").replace(/ n\'t/g,"n't").replace(/ \'m/g,"'m").replace(/ \'s/g,"'s").replace(/ \'ve/g,"'ve").replace(/ \'re/g,"'re")}function fuse(y,n){let u=[],d=0;for(;d<y.length;){if(u.push(y[d]),y[d]!==n){++d;continue}for(;d<y.length&&y[d]===n;)++d}return u}function whitespace_split(y){return y.match(/\S+/g)||[]}const PUNCTUATION_REGEX="\\p{P}\\u0021-\\u002F\\u003A-\\u0040\\u005B-\\u0060\\u007B-\\u007E";class TokenizerModel extends Callable{constructor(n){super(),this.config=n,this.vocab=[],this.tokens_to_ids=new Map,this.unk_token_id=void 0,this.unk_token=void 0,this.end_of_word_suffix=void 0,this.fuse_unk=!1}static fromConfig(n,...u){switch(n.type){case"WordPiece":return new WordPieceTokenizer(n);case"Unigram":return new Unigram(n,...u);case"BPE":return new BPE(n,...u);default:throw new Error(`Unknown TokenizerModel type: ${n.type}`)}}_call(n){return this.encode(n)}encode(n){throw Error("encode should be implemented in subclass.")}convert_tokens_to_ids(n){let u=n.map(d=>this.tokens_to_ids.get(d)??this.unk_token_id);return this.fuse_unk&&(u=fuse(u,this.unk_token_id)),u}convert_ids_to_tokens(n){return n.map(u=>this.vocab[u]??this.unk_token)}}class WordPieceTokenizer extends TokenizerModel{constructor(n){super(n),this.tokens_to_ids=n.vocab,this.unk_token_id=this.tokens_to_ids.get(n.unk_token),this.unk_token=n.unk_token,this.vocab=new Array(this.tokens_to_ids.size);for(const[u,d]of this.tokens_to_ids)this.vocab[d]=u}encode(n){let u=[];for(let d of n){let l=[...d],p=!1,s=0,h=[];for(;s<l.length;){let f=l.length,a=null;for(;s<f;){let o=l.slice(s,f).join("");if(s>0&&(o=this.config.continuing_subword_prefix+o),this.tokens_to_ids.has(o)){a=o;break}--f}if(a===null){p=!0;break}h.push(a),s=f}p?u.push(this.unk_token):u.push(...h)}return u}}class Unigram extends TokenizerModel{constructor(n,u){super(n),this.vocab=new Array(n.vocab.size),this.scores=new Array(n.vocab.size);let d=0;n.vocab.forEach((l,p)=>{this.vocab[d]=p,this.scores[d]=l,++d}),this.unk_token_id=n.unk_id,this.unk_token=this.vocab[n.unk_id],this.tokens_to_ids=new Map(this.vocab.map((l,p)=>[l,p])),this.bosToken=" ",this.bosTokenId=this.tokens_to_ids.get(this.bosToken),this.eosToken=u.eos_token,this.eosTokenId=this.tokens_to_ids.get(this.eosToken),this.unkToken=this.vocab[this.unk_token_id],this.minScore=min(this.scores)[0],this.unkScore=this.minScore-10,this.scores[this.unk_token_id]=this.unkScore,this.trie=new CharTrie,this.trie.extend(this.vocab),this.fuse_unk=!0}populateNodes(n){const u=n.sentence,d=u.length;let l=0;for(;l<d;){let s=!1;for(let h of this.trie.commonPrefixSearch(u.slice(l))){const f=this.tokens_to_ids.get(h),a=this.scores[f],o=h.length;n.insert(l,o,a,f),!s&&o===1&&(s=!0)}s||n.insert(l,1,this.unkScore,this.unk_token_id),l+=1}}tokenize(n){const u=new TokenLattice(n,this.bosTokenId,this.eosTokenId);return this.populateNodes(u),u.tokens()}encode(n){let u=[];for(let d of n){const l=this.tokenize(d);u.push(...l)}return u}}const BYTES_TO_UNICODE=(()=>{const y=[...Array.from({length:"~".charCodeAt(0)-"!".charCodeAt(0)+1},(l,p)=>p+"!".charCodeAt(0)),...Array.from({length:"¬".charCodeAt(0)-"¡".charCodeAt(0)+1},(l,p)=>p+"¡".charCodeAt(0)),...Array.from({length:"ÿ".charCodeAt(0)-"®".charCodeAt(0)+1},(l,p)=>p+"®".charCodeAt(0))];let n=y.slice(),u=0;for(let l=0;l<256;++l)y.includes(l)||(y.push(l),n.push(256+u),u+=1);let d=n.map(l=>String.fromCharCode(l));return Object.fromEntries(y.map((l,p)=>[l,d[p]]))})(),UNICODE_TO_BYTES=reverseDictionary(BYTES_TO_UNICODE);class BPE extends TokenizerModel{constructor(n){super(n),this.BPE_SPLIT_TOKEN=" ",this.tokens_to_ids=n.vocab,this.unk_token_id=this.tokens_to_ids.get(n.unk_token),this.unk_token=n.unk_token,this.vocab=new Array(this.tokens_to_ids.size);for(const[u,d]of this.tokens_to_ids)this.vocab[d]=u;this.bpe_ranks=new Map(n.merges.map((u,d)=>[u,d])),this.merges=n.merges.map(u=>u.split(this.BPE_SPLIT_TOKEN)),this.end_of_word_suffix=n.end_of_word_suffix,this.byte_fallback=this.config.byte_fallback??!1,this.byte_fallback&&(this.text_encoder=new TextEncoder),this.cache=Object.create(null),this.fuse_unk??(this.fuse_unk=this.config.fuse_unk)}get_pairs(n){let u=new Set,d=n[0];for(let l=1;l<n.length;++l){let p=n[l];u.add(d+this.BPE_SPLIT_TOKEN+p),d=p}return u}_get_best_bigram(n){let u,d=1/0;for(let l of n){let p=this.bpe_ranks.get(l)??1/0;p<d&&(u=l,d=p)}return u}bpe(n){if(n in this.cache)return this.cache[n];let u=Array.from(n);this.end_of_word_suffix&&(u[u.length-1]+=this.end_of_word_suffix);let d=this.get_pairs(u);if(!d.size)return this.end_of_word_suffix&&(n+=this.end_of_word_suffix),n;for(;;){let p=this._get_best_bigram(d);if(!this.bpe_ranks.has(p))break;let[s,h]=p.split(this.BPE_SPLIT_TOKEN),f=[],a=0,o=-1;for(;a<u.length;){if(o=u.indexOf(s,a),o===-1){for(let t=a;t<u.length;++t)f.push(u[t]);break}for(let t=a;t<o;++t)f.push(u[t]);a=o,u[a]===s&&a<u.length-1&&u[a+1]===h?(f.push(s+h),a+=2):(f.push(u[a]),a+=1)}if(u=f,u.length===1)break;d=this.get_pairs(u)}let l=u.join(this.BPE_SPLIT_TOKEN);return this.cache[n]=l,l}encode(n){let u=[];for(let d of n){let l=this.bpe(d).split(this.BPE_SPLIT_TOKEN);for(let p of l)this.tokens_to_ids.has(p)?u.push(p):this.byte_fallback?u.push(...Array.from(this.text_encoder.encode(p)).map(s=>`<0x${s.toString(16).toUpperCase().padStart(2,"0")}>`)):u.push(this.unk_token)}return u}}class Normalizer extends Callable{constructor(n){super(),this.config=n}static fromConfig(n){if(n===null)return null;switch(n.type){case"BertNormalizer":return new BertNormalizer(n);case"Precompiled":return new Precompiled(n);case"Sequence":return new NormalizerSequence(n);case"Replace":return new Replace(n);case"NFC":return new NFC(n);case"NFKD":return new NFKD(n);case"StripAccents":return new StripAccents(n);case"Lowercase":return new Lowercase(n);case"Prepend":return new Prepend(n);default:throw new Error(`Unknown Normalizer type: ${n.type}`)}}normalize(n){throw Error("normalize should be implemented in subclass.")}_call(n){return this.normalize(n)}}class Replace extends Normalizer{normalize(n){let u=createPattern(this.config.pattern);return u===null||(n=n.replaceAll(u,this.config.content)),n}}class NFC extends Normalizer{normalize(n){return n=n.normalize("NFC"),n}}class NFKD extends Normalizer{normalize(n){return n=n.normalize("NFKD"),n}}class StripAccents extends Normalizer{normalize(n){return n=n.replace(/[\u0300-\u036f]/g,""),n}}class Lowercase extends Normalizer{normalize(n){return n=n.toLowerCase(),n}}class Prepend extends Normalizer{normalize(n){return n=this.config.prepend+n,n}}class NormalizerSequence extends Normalizer{constructor(n){super(n),this.normalizers=n.normalizers.map(u=>Normalizer.fromConfig(u))}normalize(n){return this.normalizers.reduce((u,d)=>d.normalize(u),n)}}class BertNormalizer extends Normalizer{_tokenize_chinese_chars(n){let u=[];for(let d=0;d<n.length;++d){let l=n[d],p=l.charCodeAt(0);this._is_chinese_char(p)?(u.push(" "),u.push(l),u.push(" ")):u.push(l)}return u.join("")}_is_chinese_char(n){return n>=19968&&n<=40959||n>=13312&&n<=19903||n>=131072&&n<=173791||n>=173824&&n<=177983||n>=177984&&n<=178207||n>=178208&&n<=183983||n>=63744&&n<=64255||n>=194560&&n<=195103}stripAccents(n){return n.normalize("NFD").replace(/[\u0300-\u036f]/g,"")}normalize(n){return this.config.handle_chinese_chars&&(n=this._tokenize_chinese_chars(n)),this.config.lowercase?(n=n.toLowerCase(),this.config.strip_accents!==!1&&(n=this.stripAccents(n))):this.config.strip_accents&&(n=this.stripAccents(n)),n}}class PreTokenizer extends Callable{static fromConfig(n){if(n===null)return null;switch(n.type){case"BertPreTokenizer":return new BertPreTokenizer(n);case"Sequence":return new PreTokenizerSequence(n);case"WhitespaceSplit":return new WhitespaceSplit(n);case"Metaspace":return new MetaspacePreTokenizer(n);case"ByteLevel":return new ByteLevelPreTokenizer(n);case"Split":return new SplitPreTokenizer(n);case"Punctuation":return new PunctuationPreTokenizer(n);case"Digits":return new DigitsPreTokenizer(n);default:throw new Error(`Unknown PreTokenizer type: ${n.type}`)}}pre_tokenize_text(n){throw Error("pre_tokenize_text should be implemented in subclass.")}pre_tokenize(n){let u=[];return Array.isArray(n)?u=n.map(d=>this.pre_tokenize_text(d)):u=this.pre_tokenize_text(n),u.flat()}_call(n){return this.pre_tokenize(n)}}class BertPreTokenizer extends PreTokenizer{constructor(n){super(),this.pattern=new RegExp(`[^\\s${PUNCTUATION_REGEX}]+|[${PUNCTUATION_REGEX}]`,"gu")}pre_tokenize_text(n){return n.trim().match(this.pattern)||[]}}class ByteLevelPreTokenizer extends PreTokenizer{constructor(n){super(),this.config=n,this.add_prefix_space=this.config.add_prefix_space,this.trim_offsets=this.config.trim_offsets,this.use_regex=this.config.use_regex??!0,this.pattern=/'s|'t|'re|'ve|'m|'ll|'d| ?\p{L}+| ?\p{N}+| ?[^\s\p{L}\p{N}]+|\s+(?!\S)|\s+/gu,this.byte_encoder=BYTES_TO_UNICODE,this.text_encoder=new TextEncoder}pre_tokenize_text(n){return(this.use_regex?n.match(this.pattern)||[]:[n]).map(d=>(this.add_prefix_space&&!d.startsWith(" ")&&(d=" "+d),d=Array.from(this.text_encoder.encode(d),l=>this.byte_encoder[l]).join(""),d))}}class SplitPreTokenizer extends PreTokenizer{constructor(n){super(),this.config=n,this.pattern=createPattern(this.config.pattern,this.config.invert)}pre_tokenize_text(n){return this.pattern===null?[]:this.config.invert?n.match(this.pattern)||[]:n.split(this.pattern).filter(u=>u)}}class PunctuationPreTokenizer extends PreTokenizer{constructor(n){super(),this.config=n,this.pattern=new RegExp(`[^${PUNCTUATION_REGEX}]+|[${PUNCTUATION_REGEX}]+`,"gu")}pre_tokenize_text(n){return n.match(this.pattern)||[]}}class DigitsPreTokenizer extends PreTokenizer{constructor(n){super(),this.config=n;const u=`[^\\d]+|\\d${this.config.individual_digits?"":"+"}`;this.pattern=new RegExp(u,"gu")}pre_tokenize_text(n){return n.match(this.pattern)||[]}}class PostProcessor extends Callable{constructor(n){super(),this.config=n}static fromConfig(n){if(n===null)return null;switch(n.type){case"TemplateProcessing":return new TemplateProcessing(n);case"ByteLevel":return new ByteLevelPostProcessor(n);case"RobertaProcessing":return new RobertaProcessing(n);default:throw new Error(`Unknown PostProcessor type: ${n.type}`)}}post_process(n,...u){throw Error("post_process should be implemented in subclass.")}_call(n,...u){return this.post_process(n,...u)}}class RobertaProcessing extends PostProcessor{constructor(n){super(n),this.cls=n.cls[0],this.sep=n.sep[0]}post_process(n,u=null){return n=mergeArrays([this.cls],n,[this.sep]),u!==null&&(n=mergeArrays(n,[this.sep],u,[this.sep])),n}}class TemplateProcessing extends PostProcessor{constructor(n){super(n),this.single=n.single,this.pair=n.pair}post_process(n,u=null){let d=u===null?this.single:this.pair,l=[];for(let p of d)"SpecialToken"in p?l.push(p.SpecialToken.id):"Sequence"in p&&(p.Sequence.id==="A"?l=mergeArrays(l,n):p.Sequence.id==="B"&&(l=mergeArrays(l,u)));return l}}class ByteLevelPostProcessor extends PostProcessor{post_process(n){return n}}class Decoder extends Callable{constructor(n){super(),this.config=n,this.added_tokens=[],this.end_of_word_suffix=null,this.trim_offsets=n.trim_offsets}static fromConfig(n){switch(n.type){case"WordPiece":return new WordPieceDecoder(n);case"Metaspace":return new MetaspaceDecoder(n);case"ByteLevel":return new ByteLevelDecoder(n);case"Replace":return new ReplaceDecoder(n);case"ByteFallback":return new ByteFallback(n);case"Fuse":return new FuseDecoder(n);case"Strip":return new StripDecoder(n);case"Sequence":return new DecoderSequence(n);default:throw new Error(`Unknown Decoder type: ${n.type}`)}}_call(n){return this.decode(n)}decode(n){return this.decode_chain(n).join("")}decode_chain(n){throw Error("`decode_chain` should be implemented in subclass.")}}class ReplaceDecoder extends Decoder{constructor(n){super(n)}decode_chain(n){let u=createPattern(this.config.pattern);return u===null?n:n.map(d=>d.replaceAll(u,this.config.content))}}class ByteFallback extends Decoder{constructor(n){super(n),this.text_decoder=new TextDecoder}decode_chain(n){let u=[],d=[];for(let l of n){let p=null;if(l.length===6&&l.startsWith("<0x")&&l.endsWith(">")){let s=parseInt(l.slice(3,5),16);isNaN(s)||(p=s)}if(p!==null)d.push(p);else{if(d.length>0){let s=this.text_decoder.decode(Uint8Array.from(d));u.push(s),d=[]}u.push(l)}}if(d.length>0){let l=this.text_decoder.decode(Uint8Array.from(d));u.push(l),d=[]}return u}}class FuseDecoder extends Decoder{constructor(n){super(n)}decode_chain(n){return[n.join("")]}}class StripDecoder extends Decoder{constructor(n){super(n),this.content=this.config.content,this.start=this.config.start,this.stop=this.config.stop}decode_chain(n){return n.map(u=>{let d=0;for(let p=0;p<this.start&&u[p]===this.content;++p){d=p+1;continue}let l=u.length;for(let p=0;p<this.stop;++p){const s=u.length-p-1;if(u[s]===this.content){l=s;continue}else break}return u.slice(d,l)})}}class WordPieceDecoder extends Decoder{constructor(n){super(n),this.cleanup=n.cleanup}decode_chain(n){return n.map((u,d)=>(d!==0&&(u.startsWith(this.config.prefix)?u=u.replace(this.config.prefix,""):u=" "+u),this.cleanup&&(u=clean_up_tokenization(u)),u))}}class ByteLevelDecoder extends Decoder{constructor(n){super(n),this.byte_decoder=UNICODE_TO_BYTES,this.text_decoder=new TextDecoder("utf-8",{fatal:!1,ignoreBOM:!0}),this.end_of_word_suffix=null}convert_tokens_to_string(n){let u=n.join(""),d=new Uint8Array([...u].map(p=>this.byte_decoder[p]));return this.text_decoder.decode(d)}decode_chain(n){let u=[],d=[];for(let l of n)this.added_tokens.includes(l)?(d.length>0&&(u.push(this.convert_tokens_to_string(d)),d=[]),u.push(l)):d.push(l);return d.length>0&&u.push(this.convert_tokens_to_string(d)),u}}class DecoderSequence extends Decoder{constructor(n){super(n),this.decoders=n.decoders.map(u=>Decoder.fromConfig(u))}decode_chain(n){return this.decoders.reduce((u,d)=>d.decode_chain(u),n)}}class MetaspacePreTokenizer extends PreTokenizer{constructor(n){super(),this.addPrefixSpace=n.add_prefix_space,this.replacement=n.replacement,this.strRep=n.str_rep||this.replacement}pre_tokenize(n){typeof n=="string"&&(n=n.trimStart().split(/\s+/));const u=[];for(let d of n){let l=d.replaceAll(" ",this.strRep);this.addPrefixSpace&&!l.startsWith(this.replacement)&&(l=this.strRep+l),u.push(l)}return u}}class MetaspaceDecoder extends Decoder{constructor(n){super(n),this.addPrefixSpace=n.add_prefix_space,this.replacement=n.replacement}decode_chain(n){let u=[];for(let d=0;d<n.length;++d){let l=n[d].replaceAll(this.replacement," ");this.addPrefixSpace&&d==0&&l.startsWith(" ")&&(l=l.substring(1)),u.push(l)}return u}}class Precompiled extends Normalizer{constructor(n){super(n),this.charsmap=n.precompiled_charsmap}normalize(n){return n=n.normalize("NFKC"),n}}class PreTokenizerSequence extends PreTokenizer{constructor(n){super(),this.tokenizers=n.pretokenizers.map(u=>PreTokenizer.fromConfig(u))}pre_tokenize_text(n){return typeof n=="string"&&(n=[n]),this.tokenizers.reduce((u,d)=>d.pre_tokenize(u),n)}}class WhitespaceSplit extends PreTokenizer{constructor(n){super()}pre_tokenize_text(n){return whitespace_split(n)}}class PreTrainedTokenizer extends Callable{constructor(n,u){super(),this.normalizer=Normalizer.fromConfig(n.normalizer),this.pre_tokenizer=PreTokenizer.fromConfig(n.pre_tokenizer),n.model.vocab&&(Array.isArray(n.model.vocab)||(n.model.vocab=Object.entries(n.model.vocab)),n.model.vocab=new Map(n.model.vocab)),this.model=TokenizerModel.fromConfig(n.model,u),this.post_processor=PostProcessor.fromConfig(n.post_processor),this.decoder=Decoder.fromConfig(n.decoder),this.decoder.end_of_word_suffix=this.model.end_of_word_suffix,this.special_tokens=[],this.all_special_ids=[],this.added_tokens=[];for(let d of n.added_tokens){let l=d.id,p=d.content;this.added_tokens.push(p),this.model.tokens_to_ids.set(p,l),this.model.vocab[l]=p,d.special&&(this.special_tokens.push(p),this.all_special_ids.push(l))}this.decoder.added_tokens=this.added_tokens,this.added_tokens_regex=new RegExp("("+this.added_tokens.map(escapeRegExp).join("|")+")"),this.mask_token=this.getToken(u,"mask_token"),this.mask_token_id=this.model.tokens_to_ids.get(this.mask_token),this.pad_token=this.getToken(u,"pad_token","eos_token"),this.pad_token_id=this.model.tokens_to_ids.get(this.pad_token),this.sep_token=this.getToken(u,"sep_token"),this.sep_token_id=this.model.tokens_to_ids.get(this.sep_token),this.model_max_length=u.model_max_length,this.remove_space=u.remove_space,this.clean_up_tokenization_spaces=u.clean_up_tokenization_spaces??!0,this.padding_side="right"}getToken(n,...u){for(let d of u){let l=n[d];if(l)if(typeof l=="object"){if(l.__type==="AddedToken")return l.content;throw Error(`Unknown token: ${l}`)}else return l}return null}static async from_pretrained(n,{progress_callback:u=null,config:d=null,cache_dir:l=null,local_files_only:p=!1,revision:s="main"}={}){let h=await loadTokenizer(n,{progress_callback:u,config:d,cache_dir:l,local_files_only:p,revision:s});return new this(...h)}prepare_model_inputs(n){return n}_call(n,{text_pair:u=null,padding:d=!1,truncation:l=null,max_length:p=null,return_tensor:s=!0}={}){let h;if(Array.isArray(n)){if(n.length===0)throw Error("text array must be non-empty");if(u!==null){if(Array.isArray(u)){if(n.length!==u.length)throw Error("text and text_pair must have the same length")}else throw Error("text_pair must also be an array");h=n.map((t,e)=>this.encode(t,u[e]))}else h=n.map(t=>this.encode(t))}else{if(n===null)throw Error("text may not be null");if(Array.isArray(u))throw Error("When specifying `text_pair`, since `text` is a string, `text_pair` must also be a string (i.e., not an array).");h=[this.encode(n,u)]}let f=max(h.map(t=>t.length))[0];p===null&&(p=f),p=Math.min(p,this.model_max_length);let a=[];if(d||l)for(let t=0;t<h.length;++t)if(h[t].length===p){a.push(new Array(h[t].length).fill(1));continue}else if(h[t].length>p)l&&(h[t]=h[t].slice(0,p)),a.push(new Array(h[t].length).fill(1));else if(d){let e=p-h[t].length;this.padding_side==="right"?(a.push(new Array(h[t].length).fill(1).concat(new Array(e).fill(0))),h[t].push(...new Array(e).fill(this.pad_token_id))):(a.push(new Array(e).fill(0).concat(new Array(h[t].length).fill(1))),h[t].unshift(...new Array(e).fill(this.pad_token_id)))}else a.push(new Array(h[t].length).fill(1));else a=h.map(t=>new Array(t.length).fill(1));if(s){if(!(d&&l)&&h.some(e=>e.length!==h[0].length))throw Error("Unable to create tensor, you should probably activate truncation and/or padding with 'padding=true' and 'truncation=true' to have batched tensors with the same length.");let t=[h.length,h[0].length];h=new Tensor("int64",BigInt64Array.from(h.flat().map(BigInt)),t),a=new Tensor("int64",BigInt64Array.from(a.flat().map(BigInt)),t)}else Array.isArray(n)||(h=h[0],a=a[0]);let o={input_ids:h,attention_mask:a};return o=this.prepare_model_inputs(o),o}_encode_text(n){return n===null?null:n.split(this.added_tokens_regex).filter(l=>l).map(l=>{if(this.added_tokens.includes(l))return l;{this.remove_space===!0&&(l=l.trim().split(/\s+/).join(" ")),this.normalizer!==null&&(l=this.normalizer(l));let p=this.pre_tokenizer!==null?this.pre_tokenizer(l):[l];return this.model(p)}}).flat()}encode(n,u=null){let d=this._encode_text(n),l=this._encode_text(u),p=this.post_processor!==null?this.post_processor(d,l):mergeArrays(d??[],l??[]);return this.model.convert_tokens_to_ids(p)}batch_decode(n,u={}){return n.map(d=>this.decode(d,u))}decode(n,u={}){if(!Array.isArray(n)||n.length===0||!isIntegralNumber(n[0]))throw Error("token_ids must be a non-empty array of integers.");return this.decode_single(n,u)}decode_single(n,{skip_special_tokens:u=!1,clean_up_tokenization_spaces:d=null}){let l=this.model.convert_ids_to_tokens(n);u&&(l=l.filter(s=>!this.special_tokens.includes(s)));let p=this.decoder(l);return this.decoder.end_of_word_suffix&&(p=p.replaceAll(this.decoder.end_of_word_suffix," "),u&&(p=p.trim())),(d??this.clean_up_tokenization_spaces)&&(p=clean_up_tokenization(p)),p}}function add_token_types(y){if(y.input_ids instanceof Tensor)y.token_type_ids=new Tensor("int64",new BigInt64Array(y.input_ids.data.length),y.input_ids.dims);else if(Array.isArray(y.input_ids))Array.isArray(y.input_ids[0])?y.token_type_ids=y.input_ids.map(n=>new Array(n.length).fill(0)):y.token_type_ids=new Array(y.input_ids.length).fill(0);else throw new Error("Input ids must be a Tensor or an Array");return y}class BertTokenizer extends PreTrainedTokenizer{prepare_model_inputs(n){return add_token_types(n)}}class AlbertTokenizer extends PreTrainedTokenizer{prepare_model_inputs(n){return add_token_types(n)}}class MobileBertTokenizer extends PreTrainedTokenizer{prepare_model_inputs(n){return add_token_types(n)}}class SqueezeBertTokenizer extends PreTrainedTokenizer{prepare_model_inputs(n){return add_token_types(n)}}class DistilBertTokenizer extends PreTrainedTokenizer{}class T5Tokenizer extends PreTrainedTokenizer{}class GPT2Tokenizer extends PreTrainedTokenizer{}class BartTokenizer extends PreTrainedTokenizer{}class RobertaTokenizer extends PreTrainedTokenizer{}class BloomTokenizer extends PreTrainedTokenizer{}class LlamaTokenizer extends PreTrainedTokenizer{}class XLMRobertaTokenizer extends PreTrainedTokenizer{}class MPNetTokenizer extends PreTrainedTokenizer{}class FalconTokenizer extends PreTrainedTokenizer{prepare_model_inputs(n){return add_token_types(n)}}class GPTNeoXTokenizer extends PreTrainedTokenizer{}class NllbTokenizer extends PreTrainedTokenizer{constructor(n,u){super(n,u),this.languageRegex=/^[a-z]{3}_[A-Z][a-z]{3}$/,this.language_codes=this.special_tokens.filter(d=>this.languageRegex.test(d))}_build_translation_inputs(n,u,d){if(!this.language_codes.includes(d.tgt_lang))throw new Error(`Target language code "${d.tgt_lang}" is not valid. Must be one of: {${this.language_codes.join(", ")}}`);if(d.src_lang!==void 0){if(!this.language_codes.includes(d.src_lang))throw new Error(`Source language code "${d.src_lang}" is not valid. Must be one of: {${this.language_codes.join(", ")}}`);for(let l of this.post_processor.config.single)if("SpecialToken"in l&&this.languageRegex.test(l.SpecialToken.id)){l.SpecialToken.id=d.src_lang;break}}return d.forced_bos_token_id=this.model.convert_tokens_to_ids([d.tgt_lang])[0],this._call(n,u)}}const WHISPER_LANGUAGES=[["en","english"],["zh","chinese"],["de","german"],["es","spanish"],["ru","russian"],["ko","korean"],["fr","french"],["ja","japanese"],["pt","portuguese"],["tr","turkish"],["pl","polish"],["ca","catalan"],["nl","dutch"],["ar","arabic"],["sv","swedish"],["it","italian"],["id","indonesian"],["hi","hindi"],["fi","finnish"],["vi","vietnamese"],["he","hebrew"],["uk","ukrainian"],["el","greek"],["ms","malay"],["cs","czech"],["ro","romanian"],["da","danish"],["hu","hungarian"],["ta","tamil"],["no","norwegian"],["th","thai"],["ur","urdu"],["hr","croatian"],["bg","bulgarian"],["lt","lithuanian"],["la","latin"],["mi","maori"],["ml","malayalam"],["cy","welsh"],["sk","slovak"],["te","telugu"],["fa","persian"],["lv","latvian"],["bn","bengali"],["sr","serbian"],["az","azerbaijani"],["sl","slovenian"],["kn","kannada"],["et","estonian"],["mk","macedonian"],["br","breton"],["eu","basque"],["is","icelandic"],["hy","armenian"],["ne","nepali"],["mn","mongolian"],["bs","bosnian"],["kk","kazakh"],["sq","albanian"],["sw","swahili"],["gl","galician"],["mr","marathi"],["pa","punjabi"],["si","sinhala"],["km","khmer"],["sn","shona"],["yo","yoruba"],["so","somali"],["af","afrikaans"],["oc","occitan"],["ka","georgian"],["be","belarusian"],["tg","tajik"],["sd","sindhi"],["gu","gujarati"],["am","amharic"],["yi","yiddish"],["lo","lao"],["uz","uzbek"],["fo","faroese"],["ht","haitian creole"],["ps","pashto"],["tk","turkmen"],["nn","nynorsk"],["mt","maltese"],["sa","sanskrit"],["lb","luxembourgish"],["my","myanmar"],["bo","tibetan"],["tl","tagalog"],["mg","malagasy"],["as","assamese"],["tt","tatar"],["haw","hawaiian"],["ln","lingala"],["ha","hausa"],["ba","bashkir"],["jw","javanese"],["su","sundanese"]],WHISPER_LANGUAGE_MAPPING=new Map(WHISPER_LANGUAGES),WHISPER_TO_LANGUAGE_CODE_MAPPING=new Map([...WHISPER_LANGUAGES.map(([y,n])=>[n,y]),["burmese","my"],["valencian","ca"],["flemish","nl"],["haitian","ht"],["letzeburgesch","lb"],["pushto","ps"],["panjabi","pa"],["moldavian","ro"],["moldovan","ro"],["sinhalese","si"],["castilian","es"]]);class WhisperTokenizer extends PreTrainedTokenizer{_decode_asr(n,{return_timestamps:u=!1,return_language:d=!1,time_precision:l=null,force_full_sequences:p=!0}={}){if(l===null)throw Error("Must specify time_precision");let s=null;const h=u==="word";function f(){return{language:s,timestamp:[null,null],text:""}}const a=[];let o=f(),t=0;const e=this.model.convert_tokens_to_ids(["<|notimestamps|>"])[0]+1;let r=[],i=[],c=!1,g=null;const m=new Set(this.all_special_ids);for(let w of n){const v=w.tokens,S=h?w.token_timestamps:null;let O=null,E=e;if("stride"in w){const[C,B,F]=w.stride;if(t-=B,g=C-F,B&&(E=B/l+e),F)for(let N=v.length-1;N>=0;--N){const H=v[N];if(H>=e){if(O!==null&&(H-e)*l<g)break;O=H}}}let T=[],I=[];for(let C=0;C<v.length;++C){const B=v[C];if(m.has(B)){const F=this.decode([B]);if(F[0]==="["&&F[F.length-1]==="]"){const N=WHISPER_LANGUAGE_MAPPING.get(F.slice(1,-1));if(N!==void 0){if(s!==null&&N!==s&&!u){r.push(T);const H=this.findLongestCommonSequence(r)[0],$=this.decode(H);o.text=$,a.push(o),r=[],T=[],o=f()}s=o.language=N}}}else if(B>=e){const F=(B-e)*l+t,N=round(F,2);if(O!==null&&B>=O)c=!0;else if(c||r.length>0&&B<E)c=!1;else if(o.timestamp[0]===null)o.timestamp[0]=N;else if(N!==o.timestamp[0]){o.timestamp[1]=N,r.push(T),h&&i.push(I);const[H,$]=this.findLongestCommonSequence(r,i),z=this.decode(H);o.text=z,h&&(o.words=this.collateWordTimestamps(H,$,s)),a.push(o),r=[],T=[],i=[],I=[],o=f()}}else if(T.push(B),h){let F=round(S[C]+t,2),N;C+1<S.length?N=round(S[C+1]+t,2):N=null,I.push([F,N])}}if("stride"in w){const[C,B,F]=w.stride;t+=C-F}T.length>0?(r.push(T),h&&i.push(I)):r.every(C=>C.length===0)&&(o=f(),r=[],T=[],i=[],I=[])}if(r.length>0){if(p&&u)throw new Error("Whisper did not predict an ending timestamp, which can happen if audio is cut off in the middle of a word. Also make sure WhisperTimeStampLogitsProcessor was used during generation.");const[w,v]=this.findLongestCommonSequence(r,i),S=this.decode(w);o.text=S,h&&(o.words=this.collateWordTimestamps(w,v,s)),a.push(o)}let b=Object.create(null);const _=a.map(w=>w.text).join("");if(u||d){for(let w=0;w<a.length;++w){const v=a[w];u||delete v.timestamp,d||delete v.language}if(h){let w=[];for(let v of a)for(let S of v.words)w.push(S);b={chunks:w}}else b={chunks:a}}return[_,b]}findLongestCommonSequence(n,u=null){let d=n[0],l=d.length,p=[];const s=Array.isArray(u)&&u.length>0;let h=s?[]:null,f=s?u[0]:null;for(let a=1;a<n.length;++a){const o=n[a];let t=0,e=[l,l,0,0];const r=o.length;for(let w=1;w<l+r;++w){const v=w/1e4,S=Math.max(0,l-w),O=Math.min(l,l+r-w),E=d.slice(S,O),T=Math.max(0,w-l),I=Math.min(r,w),C=o.slice(T,I);if(E.length!==C.length)throw new Error("There is a bug within whisper `decode_asr` function, please report it. Dropping to prevent bad inference.");const B=E.filter((N,H)=>N===C[H]).length,F=B/w+v;B>1&&F>t&&(t=F,e=[S,O,T,I])}const[i,c,g,m]=e,b=Math.floor((c+i)/2),_=Math.floor((m+g)/2);p.push(...d.slice(0,b)),d=o.slice(_),l=d.length,s&&(h.push(...f.slice(0,b)),f=u[a].slice(_))}return p.push(...d),s?(h.push(...f),[p,h]):[p,[]]}collateWordTimestamps(n,u,d){let[l,p,s]=this.combineTokensIntoWords(n,d),h=[];for(let f=0;f<l.length;++f){const a=s[f];h.push({text:l[f],timestamp:[u[a.at(0)][0],u[a.at(-1)][1]]})}return h}combineTokensIntoWords(n,u,d=`"'“¡¿([{-`,l=`"'.。,，!！?？:：”)]}、`){u=u??"english";let p,s,h;return["chinese","japanese","thai","lao","myanmar"].includes(u)?[p,s,h]=this.splitTokensOnUnicode(n):[p,s,h]=this.splitTokensOnSpaces(n),this.mergePunctuations(p,s,h,d,l)}decode(n,u){let d;return u&&u.decode_with_timestamps?d=this.decodeWithTimestamps(n,u):d=super.decode(n,u),d}decodeWithTimestamps(n,u){const d=(u==null?void 0:u.time_precision)??.02,l=Array.from(this.all_special_ids).at(-1)+1;let p=[[]];for(let s of n)if(s>=l){let h=(s-l)*d;h=round(h,2),p.push(`<|${h}|>`),p.push([])}else p[p.length-1].push(s);return p=p.map(s=>typeof s=="string"?s:super.decode(s,u)),p.join("")}splitTokensOnUnicode(n){const u=this.decode(n,{decode_with_timestamps:!0}),d="�";let l=[],p=[],s=[],h=[],f=[],a=0;for(let o=0;o<n.length;++o){const t=n[o];h.push(t),f.push(o);const e=this.decode(h,{decode_with_timestamps:!0});(!e.includes(d)||u[a+e.indexOf(d)]===d)&&(l.push(e),p.push(h),s.push(f),h=[],f=[],a+=e.length)}return[l,p,s]}splitTokensOnSpaces(n){let[u,d,l]=this.splitTokensOnUnicode(n),p=[],s=[],h=[];const f=new RegExp(`[${PUNCTUATION_REGEX}]`);for(let a=0;a<u.length;++a){const o=u[a],t=d[a],e=l[a],r=t[0]>=this.model.tokens_to_ids.get("<|endoftext|>"),i=o.startsWith(" "),c=o.trim(),g=f.test(c);if(r||i||g||p.length===0)p.push(o),s.push(t),h.push(e);else{const m=p.length-1;p[m]+=o,s[m].push(...t),h[m].push(...e)}}return[p,s,h]}mergePunctuations(n,u,d,l,p){let s=structuredClone(n),h=structuredClone(u),f=structuredClone(d),a=s.length-2,o=s.length-1;for(;a>=0;)s[a].startsWith(" ")&&l.includes(s[a].trim())?(s[o]=s[a]+s[o],h[o]=mergeArrays(h[a],h[o]),f[o]=mergeArrays(f[a],f[o]),s[a]="",h[a]=[],f[a]=[]):o=a,--a;for(a=0,o=1;o<s.length;)!s[a].endsWith(" ")&&p.includes(s[o])?(s[a]+=s[o],h[a]=mergeArrays(h[a],h[o]),f[a]=mergeArrays(f[a],f[o]),s[o]="",h[o]=[],f[o]=[]):a=o,++o;return[s.filter(t=>t),h.filter(t=>t.length>0),f.filter(t=>t.length>0)]}get_decoder_prompt_ids({language:n=null,task:u=null,no_timestamps:d=!0}={}){let l=[];if(n){n=n.toLowerCase();let p=WHISPER_TO_LANGUAGE_CODE_MAPPING.get(n);if(p===void 0)if(WHISPER_LANGUAGE_MAPPING.has(n))p=n;else{const f=n.length===2?WHISPER_LANGUAGE_MAPPING.keys():WHISPER_LANGUAGE_MAPPING.values();throw new Error(`Language "${n}" is not supported. Must be one of: ${JSON.stringify(f)}`)}let s=this.model.tokens_to_ids.get(`<|${p}|>`);if(s===void 0)throw new Error(`Unable to find language "${p}" in model vocabulary. Please report this issue at https://github.com/xenova/transformers.js/issues/new/choose.`);l.push(s)}else l.push(null);if(u){if(u=u.toLowerCase(),u!=="transcribe"&&u!=="translate")throw new Error(`Task "${u}" is not supported. Must be one of: ["transcribe", "translate"]`);let p=this.model.tokens_to_ids.get(`<|${u}|>`);if(p===void 0)throw new Error(`Unable to find task "${u}" in model vocabulary. Please report this issue at https://github.com/xenova/transformers.js/issues/new/choose.`);l.push(p)}else l.push(null);if(d){let p=this.model.tokens_to_ids.get("<|notimestamps|>");if(p===void 0)throw new Error('Unable to find "<|notimestamps|>" in model vocabulary. Please report this issue at https://github.com/xenova/transformers.js/issues/new/choose.');l.push(p)}return l.map((p,s)=>[s+1,p]).filter(p=>p[1]!==null)}}class CodeGenTokenizer extends PreTrainedTokenizer{}class CLIPTokenizer extends PreTrainedTokenizer{}class MarianTokenizer extends PreTrainedTokenizer{constructor(n,u){super(n,u),this.languageRegex=/^(>>\w+<<)\s*/g,this.supported_language_codes=this.model.vocab.filter(d=>this.languageRegex.test(d)),console.warn('WARNING: `MarianTokenizer` is not yet supported by Hugging Face\'s "fast" tokenizers library. Therefore, you may experience slightly inaccurate results.')}_encode_text(n){if(n===null)return null;let[u,...d]=n.trim().split(this.languageRegex);if(d.length===0)return super._encode_text(u);if(d.length===2){let[l,p]=d;return this.supported_language_codes.includes(l)||console.warn(`Unsupported language code "${l}" detected, which may lead to unexpected behavior. Should be one of: ${JSON.stringify(this.supported_language_codes)}`),mergeArrays([l],super._encode_text(p))}}}class CharTrie{constructor(){this.root=CharTrieNode.default()}extend(n){for(let u of n)this.push(u)}push(n){let u=this.root;for(let d of n){let l=u.children.get(d);l===void 0&&(l=CharTrieNode.default(),u.children.set(d,l)),u=l}u.isLeaf=!0}*commonPrefixSearch(n){let u=this.root,d="";for(let l=0;l<n.length&&u!==void 0;++l){const p=n[l];d+=p,u=u.children.get(p),u!==void 0&&u.isLeaf&&(yield d)}}}class CharTrieNode{constructor(n,u){this.isLeaf=n,this.children=u}static default(){return new CharTrieNode(!1,new Map)}}class TokenLattice{constructor(n,u,d){this.sentence=n,this.len=n.length,this.bosTokenId=u,this.eosTokenId=d,this.nodes=[],this.beginNodes=new Array(this.len+1),this.endNodes=new Array(this.len+1);for(let s=0;s<this.len+1;++s)this.beginNodes[s]=[],this.endNodes[s]=[];const l=new TokenLatticeNode(this.bosTokenId,0,0,0,0),p=new TokenLatticeNode(this.eosTokenId,1,this.len,0,0);this.nodes.push(l.clone()),this.nodes.push(p.clone()),this.beginNodes[this.len].push(p),this.endNodes[0].push(l)}insert(n,u,d,l){const p=this.nodes.length,s=new TokenLatticeNode(l,p,n,u,d);this.beginNodes[n].push(s),this.endNodes[n+u].push(s),this.nodes.push(s)}viterbi(){const n=this.len;let u=0;for(;u<=n;){if(this.beginNodes[u].length==0)return[];for(let h of this.beginNodes[u]){h.prev=null;let f=0,a=null;for(let o of this.endNodes[u]){const t=o.backtraceScore+h.score;(a===null||t>f)&&(a=o.clone(),f=t)}if(a!==null)h.prev=a,h.backtraceScore=f;else return[]}++u}const d=[],p=this.beginNodes[n][0].prev;if(p===null)return[];let s=p.clone();for(;s.prev!==null;)d.push(s.clone()),s=s.clone().prev.clone();return d.reverse(),d}piece(n){return this.sentence.slice(n.pos,n.pos+n.length)}tokens(){return this.viterbi().map(u=>this.piece(u))}tokenIds(){return this.viterbi().map(u=>u.tokenId)}}class TokenLatticeNode{constructor(n,u,d,l,p){this.tokenId=n,this.nodeId=u,this.pos=d,this.length=l,this.score=p,this.prev=null,this.backtraceScore=0}clone(){const n=new TokenLatticeNode(this.tokenId,this.nodeId,this.pos,this.length,this.score);return n.prev=this.prev,n.backtraceScore=this.backtraceScore,n}}class AutoTokenizer{static async from_pretrained(n,{quantized:u=!0,progress_callback:d=null,config:l=null,cache_dir:p=null,local_files_only:s=!1,revision:h="main"}={}){let[f,a]=await loadTokenizer(n,{quantized:u,progress_callback:d,config:l,cache_dir:p,local_files_only:s,revision:h}),o=a.tokenizer_class.replace(/Fast$/,""),t=this.TOKENIZER_CLASS_MAPPING[o];return t||(console.warn(`Unknown tokenizer class "${o}", attempting to construct from base class.`),t=PreTrainedTokenizer),new t(f,a)}}jt(AutoTokenizer,"TOKENIZER_CLASS_MAPPING",{T5Tokenizer,DistilBertTokenizer,BertTokenizer,MobileBertTokenizer,SqueezeBertTokenizer,AlbertTokenizer,GPT2Tokenizer,BartTokenizer,RobertaTokenizer,WhisperTokenizer,CodeGenTokenizer,CLIPTokenizer,MarianTokenizer,BloomTokenizer,NllbTokenizer,LlamaTokenizer,XLMRobertaTokenizer,MPNetTokenizer,FalconTokenizer,GPTNeoXTokenizer,PreTrainedTokenizer});async function loadConfig(y,n){return await getModelJSON(y,"config.json",!0,n)}class PretrainedConfig{constructor(n){this.model_type=null,this.is_encoder_decoder=!1,Object.assign(this,n)}static async from_pretrained(n,{progress_callback:u=null,config:d=null,cache_dir:l=null,local_files_only:p=!1,revision:s="main"}={}){let h=d??await loadConfig(n,{progress_callback:u,config:d,cache_dir:l,local_files_only:p,revision:s});return new this(h)}}class AutoConfig{static async from_pretrained(...n){return PretrainedConfig.from_pretrained(...n)}}class LogitsProcessorList extends Callable{constructor(){super(),this.processors=[]}push(n){this.processors.push(n)}extend(n){this.processors.push(...n)}_call(n,u){for(let d of u)this.processors.forEach(l=>l(n,d))}[Symbol.iterator](){return this.processors.values()}}class LogitsProcessor extends Callable{_call(n,u){throw Error("`_call` should be implemented in a subclass")}}class ForceTokensLogitsProcessor extends LogitsProcessor{constructor(n){super(),this.force_token_map=Object.fromEntries(n??[])}_call(n,u){let d=this.force_token_map[n.length];return exists(d)&&(u.data.fill(-1/0),u.data[d]=0),u}}class ForcedBOSTokenLogitsProcessor extends LogitsProcessor{constructor(n){super(),this.bos_token_id=n}_call(n,u){return n.length===1&&(u.data.fill(-1/0),u.data[this.bos_token_id]=0),u}}class ForcedEOSTokenLogitsProcessor extends LogitsProcessor{constructor(n,u){super(),this.max_length=n,this.forced_eos_token_id=u}_call(n,u){}}class SuppressTokensAtBeginLogitsProcessor extends LogitsProcessor{constructor(n,u){super(),this.begin_suppress_tokens=n,this.begin_index=u}_call(n,u){if(n.length===this.begin_index)for(let d of this.begin_suppress_tokens)u.data[d]=-1/0;return u}}class WhisperTimeStampLogitsProcessor extends LogitsProcessor{constructor(n){super(),this.eos_token_id=n.eos_token_id,this.no_timestamps_token_id=n.no_timestamps_token_id,this.timestamp_begin=this.no_timestamps_token_id+1,this.begin_index=(n.forced_decoder_ids||[]).length+2,n.forced_decoder_ids.slice(-1)[0][1]===this.no_timestamps_token_id&&(this.begin_index-=1),this.max_initial_timestamp_index=n.max_initial_timestamp_index}_call(n,u){if(u.data[this.no_timestamps_token_id]=-1/0,n.length===this.begin_index-1)return u.data.fill(-1/0),u.data[this.timestamp_begin]=0,u;const d=n.slice(this.begin_index),l=d.length>=1&&d[d.length-1]>=this.timestamp_begin,p=d.length<2||d[d.length-2]>=this.timestamp_begin;if(l&&(p?u.data.subarray(this.timestamp_begin).fill(-1/0):u.data.subarray(0,this.eos_token_id).fill(-1/0)),n.length===this.begin_index&&this.max_initial_timestamp_index!==null){const a=this.timestamp_begin+this.max_initial_timestamp_index;u.data.subarray(a+1).fill(-1/0)}const s=log_softmax(u.data),h=Math.log(s.subarray(this.timestamp_begin).map(Math.exp).reduce((a,o)=>a+o)),f=max(s.subarray(0,this.timestamp_begin))[0];return h>f&&u.data.subarray(0,this.timestamp_begin).fill(-1/0),u}}class NoRepeatNGramLogitsProcessor extends LogitsProcessor{constructor(n){super(),this.no_repeat_ngram_size=n}getNgrams(n){const u=n.length,d=[];for(let p=0;p<u+1-this.no_repeat_ngram_size;++p){const s=[];for(let h=0;h<this.no_repeat_ngram_size;++h)s.push(n[p+h]);d.push(s)}const l=new Map;for(const p of d){const s=p.slice(0,p.length-1),h=JSON.stringify(s),f=l.get(h)??[];f.push(p[p.length-1]),l.set(h,f)}return l}getGeneratedNgrams(n,u){const d=u.slice(u.length+1-this.no_repeat_ngram_size,u.length);return n.get(JSON.stringify(d))??[]}calcBannedNgramTokens(n){const u=[];if(n.length+1<this.no_repeat_ngram_size)return u;{const d=this.getNgrams(n);return this.getGeneratedNgrams(d,n)}}_call(n,u){const d=this.calcBannedNgramTokens(n);for(const l of d)u.data[l]=-1/0;return u}}class RepetitionPenaltyLogitsProcessor extends LogitsProcessor{constructor(n){super(),this.penalty=n}_call(n,u){for(const d of n)u.data[d]<0?u.data[d]*=this.penalty:u.data[d]/=this.penalty;return u}}class GenerationConfig{constructor(n={}){this.max_length=n.max_length??20,this.max_new_tokens=n.max_new_tokens??null,this.min_length=n.min_length??0,this.min_new_tokens=n.min_new_tokens??null,this.early_stopping=n.early_stopping??!1,this.max_time=n.max_time??null,this.do_sample=n.do_sample??!1,this.num_beams=n.num_beams??1,this.num_beam_groups=n.num_beam_groups??1,this.penalty_alpha=n.penalty_alpha??null,this.use_cache=n.use_cache??!0,this.temperature=n.temperature??1,this.top_k=n.top_k??50,this.top_p=n.top_p??1,this.typical_p=n.typical_p??1,this.epsilon_cutoff=n.epsilon_cutoff??0,this.eta_cutoff=n.eta_cutoff??0,this.diversity_penalty=n.diversity_penalty??0,this.repetition_penalty=n.repetition_penalty??1,this.encoder_repetition_penalty=n.encoder_repetition_penalty??1,this.length_penalty=n.length_penalty??1,this.no_repeat_ngram_size=n.no_repeat_ngram_size??0,this.bad_words_ids=n.bad_words_ids??null,this.force_words_ids=n.force_words_ids??null,this.renormalize_logits=n.renormalize_logits??!1,this.constraints=n.constraints??null,this.forced_bos_token_id=n.forced_bos_token_id??null,this.forced_eos_token_id=n.forced_eos_token_id??null,this.remove_invalid_values=n.remove_invalid_values??!1,this.exponential_decay_length_penalty=n.exponential_decay_length_penalty??null,this.suppress_tokens=n.suppress_tokens??null,this.begin_suppress_tokens=n.begin_suppress_tokens??null,this.forced_decoder_ids=n.forced_decoder_ids??null,this.num_return_sequences=n.num_return_sequences??1,this.output_attentions=n.output_attentions??!1,this.output_hidden_states=n.output_hidden_states??!1,this.output_scores=n.output_scores??!1,this.return_dict_in_generate=n.return_dict_in_generate??!1,this.pad_token_id=n.pad_token_id??null,this.bos_token_id=n.bos_token_id??null,this.eos_token_id=n.eos_token_id??null,this.encoder_no_repeat_ngram_size=n.encoder_no_repeat_ngram_size??0,this.decoder_start_token_id=n.decoder_start_token_id??null,this.generation_kwargs=n.generation_kwargs??{}}}class Sampler extends Callable{constructor(n){super(),this.generation_config=n}_call(n,u=-1){return this.sample(n,u)}sample(n,u){throw Error("sample should be implemented in subclasses.")}getLogits(n,u){let d=n.dims.at(-1),l=n.data;if(u===-1)l=l.slice(-d);else{let p=u*d;l=l.slice(p,p+d)}return this.generation_config.temperature>0&&(l=l.map(p=>p/this.generation_config.temperature)),l}randomSelect(n){let u=n.reduce((l,p)=>l+p,0),d=Math.random()*u;for(let l=0;l<n.length;++l)if(d-=n[l],d<=0)return l;return 0}static getSampler(n){if(n.do_sample)return new MultinomialSampler(n);if(n.num_beams>1)return new BeamSearchSampler(n);if(n.num_return_sequences>1)throw Error(`num_return_sequences has to be 1 when doing greedy search, but is ${n.num_return_sequences}.`);return new GreedySampler(n)}}class GreedySampler extends Sampler{sample(n,u=-1){let d=this.getLogits(n,u);return[[max(d)[1],0]]}}class MultinomialSampler extends Sampler{sample(n,u=-1){let d=n.dims.at(-1);this.generation_config.top_k>0&&(d=Math.min(this.generation_config.top_k,d));const l=this.getLogits(n,u),p=getTopItems(l,d),s=softmax(p.map(h=>h[1]));return Array.from({length:this.generation_config.num_beams},()=>{const h=this.randomSelect(s);return[p[h][0],Math.log(s[h])]})}}class BeamSearchSampler extends Sampler{sample(n,u=-1){let d=n.dims.at(-1);this.generation_config.top_k>0&&(d=Math.min(this.generation_config.top_k,d));const l=this.getLogits(n,u),p=getTopItems(l,d),s=softmax(p.map(h=>h[1]));return Array.from({length:this.generation_config.num_beams},(h,f)=>[p[f][0],Math.log(s[f])])}}const{InferenceSession,Tensor:ONNXTensor}=ONNX;class ModelType{}class EncoderOnlyModelType extends ModelType{}class EncoderDecoderModelType extends ModelType{}class Seq2SeqModelType extends EncoderDecoderModelType{}class DecoderOnlyModelType extends ModelType{}const MODEL_TYPE_MAPPING=new Map([["CLIPTextModelWithProjection",EncoderOnlyModelType],["CLIPVisionModelWithProjection",EncoderOnlyModelType]]);async function forward(y,n){return MODEL_TYPE_MAPPING.get(y.constructor.name)===DecoderOnlyModelType?await decoderForward(y,n):await encoderForward(y,n)}async function constructSession(y,n,u){let d=`onnx/${n}${u.quantized?"_quantized":""}.onnx`,l=await getModelFile(y,d,!0,u);try{return await InferenceSession.create(l,{executionProviders})}catch(p){if(executionProviders.length===1&&executionProviders[0]==="wasm")throw p;return console.warn(p),console.warn("Something went wrong during model construction (most likely a missing operation). Using `wasm` as a fallback. "),await InferenceSession.create(l,{executionProviders:["wasm"]})}}async function validateInputs(y,n){const u={},d=[];for(let s of y.inputNames)n[s]===void 0?d.push(s):u[s]=n[s];if(d.length>0)throw new Error(`An error occurred during model execution: "Missing the following inputs: ${d.join(", ")}.`);const l=Object.keys(n).length,p=y.inputNames.length;if(l>p){let s=Object.keys(n).filter(h=>!y.inputNames.includes(h));console.warn(`WARNING: Too many inputs were provided (${l} > ${p}). The following inputs will be ignored: "${s.join(", ")}".`)}return u}async function sessionRun(y,n){const u=await validateInputs(y,n);try{let d=await y.run(u);return d=replaceTensors(d),d}catch(d){throw console.error(`An error occurred during model execution: "${d}".`),console.error("Inputs given to model:",u),d}}function replaceTensors(y){for(let n in y)y[n]instanceof ONNXTensor?y[n]=new Tensor(y[n]):typeof y[n]=="object"&&replaceTensors(y[n]);return y}function toI64Tensor(y){if(y instanceof Tensor)return y;if(y.length===0)throw Error("items must be non-empty");if(Array.isArray(y[0])){if(y.some(n=>n.length!==y[0].length))throw Error("Unable to create tensor, you should probably activate truncation and/or padding with 'padding=True' and/or 'truncation=True' to have batched tensors with the same length.");return new Tensor("int64",BigInt64Array.from(y.flat().map(n=>BigInt(n))),[y.length,y[0].length])}else return new Tensor("int64",BigInt64Array.from(y.map(n=>BigInt(n))),[1,y.length])}function prepareAttentionMask(y,n){let u=y.config.pad_token_id??null,d=y.config.eos_token_id??null;isIntegralNumber(d)&&(d=[d]);let l=n.indexOf(u)!==-1,p=d===null||!d.includes(u);if(l&&p){let s=BigInt64Array.from(n.data.map(h=>h!=u));return new Tensor("int64",s,n.dims)}else return new Tensor("int64",new BigInt64Array(n.data.length).fill(1n),n.dims)}function boolTensor(y){return new Tensor("bool",[y],[1])}async function seq2seqForward(y,n,{add_decoder_pkv:u=!0}={}){let{encoder_outputs:d,past_key_values:l}=n;d||(d=(await encoderForward(y,n)).last_hidden_state);let p={input_ids:n.decoder_input_ids,encoder_hidden_states:d,use_cache_branch:boolTensor(!!l)};y.decoder_merged_session.inputNames.includes("encoder_attention_mask")&&(p.encoder_attention_mask=n.attention_mask),y.addPastKeyValues(p,l,u);const s=await sessionRun(y.decoder_merged_session,p);let h=s.logits;l=y.getPastKeyValues(s,l);const f=y.getAttentions(s);return new Seq2SeqLMOutput({logits:h,past_key_values:l,encoder_outputs:d,...f})}function seq2seqStartBeams(y,n,u,d=!0){let l=[],p=0,s=y.config.decoder_start_token_id;Array.isArray(s)||(s=[s]);for(let h of n){h.dims=[1,...h.dims];let f={inputs:h,encoder_outputs:null,prev_model_outputs:null,output_token_ids:s,done:!1,score:0,id:p++};d&&(f.attention_mask=prepareAttentionMask(y,h)),l.push(f)}return l}async function seq2seqRunBeam(y,n,{input_name:u="input_ids"}={}){var p;let d={[u]:n.inputs,decoder_input_ids:toI64Tensor(n.output_token_ids.slice(-1)),encoder_outputs:n.encoder_outputs,past_key_values:(p=n.prev_model_outputs)==null?void 0:p.past_key_values};n.attention_mask&&(d.attention_mask=n.attention_mask);let l=await y.forward(d);return n.prev_model_outputs=l,n.encoder_outputs=l.encoder_outputs,l}async function encoderForward(y,n){let u={};for(let d of y.session.inputNames)u[d]=n[d];return await sessionRun(y.session,u)}async function decoderForward(y,n){let{input_ids:u,past_key_values:d,attention_mask:l}=n,p={input_ids:u,attention_mask:l??prepareAttentionMask(y,u),use_cache_branch:boolTensor(d!==null)};y.addPastKeyValues(p,d);let s=await sessionRun(y.session,p),h=s.logits;return d=y.getPastKeyValues(s,d),{logits:h,past_key_values:d}}function decoderStartBeams(y,n,u,d){let l=[],p=0;for(let s of n){let h=s.tolist().map(Number);s.dims=[1,...s.dims];let f;d?(f=d[p],f.dims=[1,...f.dims]):f=prepareAttentionMask(y,s);let a={input:s,model_input_ids:s,attention_mask:f,prev_model_outputs:null,output_token_ids:h,num_output_tokens:u,done:!1,score:0,id:p++};l.push(a)}return l}async function decoderRunBeam(y,n){var p;let u=new BigInt64Array(n.output_token_ids.length).fill(1n),d={input_ids:n.model_input_ids,attention_mask:new Tensor("int64",u,[1,u.length]),past_key_values:(p=n.prev_model_outputs)==null?void 0:p.past_key_values},l=await y.forward(d);return n.prev_model_outputs=l,l}function decoderUpdatebeam(y,n){y.output_token_ids=[...y.output_token_ids,n],y.model_input_ids=new Tensor("int64",[BigInt(n)],[1,1])}class PreTrainedModel extends Callable{constructor(n,u){super(),this.config=n,this.session=u}async dispose(){let n=[];for(let u of Object.keys(this)){let d=this[u];d instanceof InferenceSession&&n.push(d.handler.dispose())}return await Promise.all(n)}static async from_pretrained(n,{quantized:u=!0,progress_callback:d=null,config:l=null,cache_dir:p=null,local_files_only:s=!1,revision:h="main",model_file_name:f=null}={}){let a={quantized:u,progress_callback:d,config:l,cache_dir:p,local_files_only:s,revision:h,model_file_name:f},o=MODEL_TYPE_MAPPING.get(this.name),t;if(o===DecoderOnlyModelType)t=await Promise.all([AutoConfig.from_pretrained(n,a),constructSession(n,a.model_file_name??"decoder_model_merged",a)]);else if(o===Seq2SeqModelType)t=await Promise.all([AutoConfig.from_pretrained(n,a),constructSession(n,"encoder_model",a),constructSession(n,"decoder_model_merged",a),getModelJSON(n,"generation_config.json",!1,a)]);else if(o===EncoderDecoderModelType)t=await Promise.all([AutoConfig.from_pretrained(n,a),constructSession(n,"encoder_model",a),constructSession(n,"decoder_model_merged",a)]);else if(o===EncoderOnlyModelType)t=await Promise.all([AutoConfig.from_pretrained(n,a),constructSession(n,a.model_file_name??"model",a)]);else throw console.warn("Malformed class definition.",this),Error(`Unable to load model: ${n}. Please report this bug at https://github.com/xenova/transformers.js/issues/new/choose.`);return new this(...t)}async _call(n){return await this.forward(n)}async forward(n){return await forward(this,n)}_get_logits_processor(n,u,d=null){const l=new LogitsProcessorList;if(n.repetition_penalty!==null&&n.repetition_penalty!==1&&l.push(new RepetitionPenaltyLogitsProcessor(n.repetition_penalty)),n.no_repeat_ngram_size!==null&&n.no_repeat_ngram_size>0&&l.push(new NoRepeatNGramLogitsProcessor(n.no_repeat_ngram_size)),n.forced_bos_token_id!==null&&l.push(new ForcedBOSTokenLogitsProcessor(n.forced_bos_token_id)),n.forced_eos_token_id!==null&&l.push(new ForcedEOSTokenLogitsProcessor(n.max_length,n.forced_eos_token_id)),n.begin_suppress_tokens!==null){let p=u>1||n.forced_bos_token_id===null?u:u+1;n.forced_decoder_ids!==null&&(p+=n.forced_decoder_ids[n.forced_decoder_ids.length-1][0]),l.push(new SuppressTokensAtBeginLogitsProcessor(n.begin_suppress_tokens,p))}return n.forced_decoder_ids!==null&&l.push(new ForceTokensLogitsProcessor(n.forced_decoder_ids)),d!==null&&l.extend(d),l}_get_generation_config(n){let u=new GenerationConfig;return"generation_config"in this&&Object.assign(u,this.generation_config),n!==null&&Object.assign(u,n),u}async generate(n,u=null,d=null,{inputs_attention_mask:l=null}={}){if(!(n instanceof Tensor)&&!isTypedArray(n)&&!Array.isArray(n))throw Error(`\`inputs\` must be a Tensor, TypedArray, or Array, but is "${n.constructor.name}".`);let p;if(this.config.is_encoder_decoder)p=0;else if(p=n instanceof Tensor?n.dims[0]:n.length,p===0)throw Error("Must supply a non-empty array of input token ids.");u=this._get_generation_config(u),d=d??new LogitsProcessorList,d=this._get_logits_processor(u,p,d);let s=1;const h=s+(u.max_new_tokens??1/0),f=Number.isInteger(u.max_length)&&(u.max_new_tokens??null)===null;let a=Sampler.getSampler(u),o=this.getStartBeams(n,s,l);for(;o.some(i=>!i.done)&&s<h;){let i=[];for(let c of o){if(c.done){i.push(c);continue}if(f&&c.output_token_ids.length>=u.max_length){c.done=!0,i.push(c);continue}let g=await this.runBeam(c);u.output_attentions&&this.addAttentionsToBeam(c,g),u.output_scores;let m=g.logits.slice(null,-1,null);d(c.output_token_ids,m);let b=a(m);for(let[_,w]of b){let v={...c};this.updateBeam(v,_),v.score+=w,_===this.config.eos_token_id&&(v.done=!0),i.push(v)}}++s,i=this.groupBeams(i).map(c=>c.sort((g,m)=>m.score-g.score).slice(0,u.num_beams)),o=i.flat(),u.callback_function&&u.callback_function(o)}const t=this.groupBeams(o),e=i=>t.map(c=>u.num_return_sequences>1?c.slice(0,u.num_return_sequences).map(g=>g[i]):[c[0][i]]).flat(),r=e("output_token_ids");if(u.return_dict_in_generate){const i=e("decoder_attentions"),c=e("cross_attentions");return{sequences:r,decoder_attentions:i,cross_attentions:c}}else return r}addAttentionsToBeam(n,u){if(this.config.is_encoder_decoder){if(!u.cross_attentions||u.cross_attentions.length===0)throw Error("`output_attentions` is true, but the model did not produce cross-attentions. This is most likely because the model was not exported with `output_attentions=True`.");n.cross_attentions||(n.cross_attentions=[]),n.cross_attentions.push(u.cross_attentions)}if(!u.decoder_attentions||u.decoder_attentions.length===0)throw Error("`output_attentions` is true, but the model did not produce decoder-attentions. This is most likely because the model was not exported with `output_attentions=True`.");n.decoder_attentions||(n.decoder_attentions=[]),n.decoder_attentions.push(u.decoder_attentions)}groupBeams(n){const u=Object.create(null);for(const d of n)u[d.id]===void 0?u[d.id]=[d]:u[d.id].push(d);return Object.values(u)}getPastKeyValues(n,u){const d=Object.create(null);for(const l in n)if(l.startsWith("present")){let p=l.replace("present","past_key_values");u&&l.includes("encoder")?d[p]=u[p]:d[p]=n[l]}return d}getAttentions(n){const u=Object.create(null);for(const d of["cross_attentions","decoder_attentions"]){const l=[];for(const p in n)if(p.startsWith(d)){const s=p.split(".").pop();l[s]=n[p]}u[d]=l}return u}addPastKeyValues(n,u,d=!1){if(u)Object.assign(n,u);else if(d){let l=[1,this.num_encoder_heads,0,this.encoder_dim_kv];for(let s=0;s<this.num_encoder_layers;++s)n[`past_key_values.${s}.encoder.key`]=new Tensor("float32",[],l),n[`past_key_values.${s}.encoder.value`]=new Tensor("float32",[],l);let p=[1,this.num_decoder_heads,0,this.decoder_dim_kv];for(let s=0;s<this.num_decoder_layers;++s)n[`past_key_values.${s}.decoder.key`]=new Tensor("float32",[],p),n[`past_key_values.${s}.decoder.value`]=new Tensor("float32",[],p)}else if(this.config.multi_query){let l=[1,0,2*this.dim_kv];for(let p=0;p<this.num_layers;++p)n[`past_key_values.${p}.key_value`]=new Tensor("float32",[],l)}else{let l=[1,this.num_heads,0,this.dim_kv];for(let p=0;p<this.num_layers;++p)n[`past_key_values.${p}.key`]=new Tensor("float32",[],l),n[`past_key_values.${p}.value`]=new Tensor("float32",[],l)}}}class ModelOutput{}class BertPreTrainedModel extends PreTrainedModel{}class BertModel extends BertPreTrainedModel{}class BertForMaskedLM extends BertPreTrainedModel{async _call(n){return new MaskedLMOutput(await super._call(n))}}class BertForSequenceClassification extends BertPreTrainedModel{async _call(n){return new SequenceClassifierOutput(await super._call(n))}}class BertForTokenClassification extends BertPreTrainedModel{async _call(n){return new TokenClassifierOutput(await super._call(n))}}class BertForQuestionAnswering extends BertPreTrainedModel{async _call(n){return new QuestionAnsweringModelOutput(await super._call(n))}}class DistilBertPreTrainedModel extends PreTrainedModel{}class DistilBertModel extends DistilBertPreTrainedModel{}class DistilBertForSequenceClassification extends DistilBertPreTrainedModel{async _call(n){return new SequenceClassifierOutput(await super._call(n))}}class DistilBertForTokenClassification extends DistilBertPreTrainedModel{async _call(n){return new TokenClassifierOutput(await super._call(n))}}class DistilBertForQuestionAnswering extends DistilBertPreTrainedModel{async _call(n){return new QuestionAnsweringModelOutput(await super._call(n))}}class DistilBertForMaskedLM extends DistilBertPreTrainedModel{async _call(n){return new MaskedLMOutput(await super._call(n))}}class MobileBertPreTrainedModel extends PreTrainedModel{}class MobileBertModel extends MobileBertPreTrainedModel{}class MobileBertForMaskedLM extends MobileBertPreTrainedModel{async _call(n){return new MaskedLMOutput(await super._call(n))}}class MobileBertForSequenceClassification extends MobileBertPreTrainedModel{async _call(n){return new SequenceClassifierOutput(await super._call(n))}}class MobileBertForQuestionAnswering extends MobileBertPreTrainedModel{async _call(n){return new QuestionAnsweringModelOutput(await super._call(n))}}class MPNetPreTrainedModel extends PreTrainedModel{}class MPNetModel extends MPNetPreTrainedModel{}class MPNetForMaskedLM extends MPNetPreTrainedModel{async _call(n){return new MaskedLMOutput(await super._call(n))}}class MPNetForSequenceClassification extends MPNetPreTrainedModel{async _call(n){return new SequenceClassifierOutput(await super._call(n))}}class MPNetForTokenClassification extends MPNetPreTrainedModel{async _call(n){return new TokenClassifierOutput(await super._call(n))}}class MPNetForQuestionAnswering extends MPNetPreTrainedModel{async _call(n){return new QuestionAnsweringModelOutput(await super._call(n))}}class SqueezeBertPreTrainedModel extends PreTrainedModel{}class SqueezeBertModel extends SqueezeBertPreTrainedModel{}class SqueezeBertForMaskedLM extends SqueezeBertPreTrainedModel{async _call(n){return new MaskedLMOutput(await super._call(n))}}class SqueezeBertForSequenceClassification extends SqueezeBertPreTrainedModel{async _call(n){return new SequenceClassifierOutput(await super._call(n))}}class SqueezeBertForQuestionAnswering extends SqueezeBertPreTrainedModel{async _call(n){return new QuestionAnsweringModelOutput(await super._call(n))}}class AlbertPreTrainedModel extends PreTrainedModel{}class AlbertModel extends AlbertPreTrainedModel{}class AlbertForSequenceClassification extends AlbertPreTrainedModel{async _call(n){return new SequenceClassifierOutput(await super._call(n))}}class AlbertForQuestionAnswering extends AlbertPreTrainedModel{async _call(n){return new QuestionAnsweringModelOutput(await super._call(n))}}class AlbertForMaskedLM extends AlbertPreTrainedModel{async _call(n){return new MaskedLMOutput(await super._call(n))}}class T5PreTrainedModel extends PreTrainedModel{}class T5Model extends T5PreTrainedModel{async generate(...n){throw Error("The current model class (T5Model) is not compatible with `.generate()`, as it doesn't have a language model head. Please use one of the following classes instead: {'T5ForConditionalGeneration'}")}}class T5ForConditionalGeneration extends T5PreTrainedModel{constructor(n,u,d,l){super(n,u),this.decoder_merged_session=d,this.generation_config=l,this.num_decoder_layers=this.config.num_decoder_layers,this.num_decoder_heads=this.config.num_heads,this.decoder_dim_kv=this.config.d_kv,this.num_encoder_layers=this.config.num_layers,this.num_encoder_heads=this.config.num_heads,this.encoder_dim_kv=this.config.d_kv}getStartBeams(n,u,...d){return seq2seqStartBeams(this,n)}async runBeam(n){return await seq2seqRunBeam(this,n)}updateBeam(n,u){n.output_token_ids=[...n.output_token_ids,u]}async forward(n){return await seq2seqForward(this,n)}}class MT5PreTrainedModel extends PreTrainedModel{}class MT5Model extends MT5PreTrainedModel{async generate(...n){throw Error("The current model class (MT5Model) is not compatible with `.generate()`, as it doesn't have a language model head. Please use one of the following classes instead: {'MT5ForConditionalGeneration'}")}}class MT5ForConditionalGeneration extends MT5PreTrainedModel{constructor(n,u,d,l){super(n,u),this.decoder_merged_session=d,this.generation_config=l,this.num_decoder_layers=this.config.num_decoder_layers,this.num_decoder_heads=this.config.num_heads,this.decoder_dim_kv=this.config.d_kv,this.num_encoder_layers=this.config.num_layers,this.num_encoder_heads=this.config.num_heads,this.encoder_dim_kv=this.config.d_kv}getStartBeams(n,u,...d){return seq2seqStartBeams(this,n)}async runBeam(n){return await seq2seqRunBeam(this,n)}updateBeam(n,u){n.output_token_ids=[...n.output_token_ids,u]}async forward(n){return await seq2seqForward(this,n)}}class BartPretrainedModel extends PreTrainedModel{}class BartModel extends BartPretrainedModel{async generate(...n){throw Error("The current model class (BartModel) is not compatible with `.generate()`, as it doesn't have a language model head. Please use one of the following classes instead: {'BartForConditionalGeneration'}")}}class BartForConditionalGeneration extends BartPretrainedModel{constructor(n,u,d,l){super(n,u),this.decoder_merged_session=d,this.generation_config=l,this.num_decoder_layers=this.config.decoder_layers,this.num_decoder_heads=this.config.decoder_attention_heads,this.decoder_dim_kv=this.config.d_model/this.num_decoder_heads,this.num_encoder_layers=this.config.encoder_layers,this.num_encoder_heads=this.config.encoder_attention_heads,this.encoder_dim_kv=this.config.d_model/this.num_encoder_heads}getStartBeams(n,u,...d){return seq2seqStartBeams(this,n)}async runBeam(n){return await seq2seqRunBeam(this,n)}updateBeam(n,u){n.output_token_ids=[...n.output_token_ids,u]}async forward(n){return await seq2seqForward(this,n)}}class BartForSequenceClassification extends BartPretrainedModel{async _call(n){return new SequenceClassifierOutput(await super._call(n))}}class RobertaPreTrainedModel extends PreTrainedModel{}class RobertaModel extends RobertaPreTrainedModel{}class RobertaForMaskedLM extends RobertaPreTrainedModel{async _call(n){return new MaskedLMOutput(await super._call(n))}}class RobertaForSequenceClassification extends RobertaPreTrainedModel{async _call(n){return new SequenceClassifierOutput(await super._call(n))}}class RobertaForTokenClassification extends RobertaPreTrainedModel{async _call(n){return new TokenClassifierOutput(await super._call(n))}}class RobertaForQuestionAnswering extends RobertaPreTrainedModel{async _call(n){return new QuestionAnsweringModelOutput(await super._call(n))}}class XLMRobertaPreTrainedModel extends PreTrainedModel{}class XLMRobertaModel extends XLMRobertaPreTrainedModel{}class XLMRobertaForMaskedLM extends XLMRobertaPreTrainedModel{async _call(n){return new MaskedLMOutput(await super._call(n))}}class XLMRobertaForSequenceClassification extends XLMRobertaPreTrainedModel{async _call(n){return new SequenceClassifierOutput(await super._call(n))}}class XLMRobertaForTokenClassification extends XLMRobertaPreTrainedModel{async _call(n){return new TokenClassifierOutput(await super._call(n))}}class XLMRobertaForQuestionAnswering extends XLMRobertaPreTrainedModel{async _call(n){return new QuestionAnsweringModelOutput(await super._call(n))}}class WhisperPreTrainedModel extends PreTrainedModel{}class WhisperModel extends WhisperPreTrainedModel{async generate(...n){throw Error("The current model class (WhisperModel) is not compatible with `.generate()`, as it doesn't have a language model head. Please use one of the following classes instead: {'WhisperForConditionalGeneration'}")}}class WhisperForConditionalGeneration extends WhisperPreTrainedModel{constructor(n,u,d,l){super(n,u),this.decoder_merged_session=d,this.generation_config=l,this.num_decoder_layers=this.config.decoder_layers,this.num_decoder_heads=this.config.decoder_attention_heads,this.decoder_dim_kv=this.config.d_model/this.num_decoder_heads,this.num_encoder_layers=this.config.encoder_layers,this.num_encoder_heads=this.config.encoder_attention_heads,this.encoder_dim_kv=this.config.d_model/this.num_encoder_heads}async generate(n,u=null,d=null){if(u=this._get_generation_config(u),u.return_timestamps??(u.return_timestamps=!1),u.return_timestamps&&(d=[new WhisperTimeStampLogitsProcessor(u)]),u.return_token_timestamps&&(u.output_attentions=!0,u.return_dict_in_generate=!0,u.task==="translate"&&console.warn("Token-level timestamps may not be reliable for task 'translate'."),!u.alignment_heads))throw new Error("Model generation config has no `alignment_heads`, token-level timestamps not available. See https://gist.github.com/hollance/42e32852f24243b748ae6bc1f985b13a on how to add this property to the generation config.");const l=await super.generate(n,u,d);return u.return_token_timestamps&&u.alignment_heads&&(l.token_timestamps=this._extract_token_timestamps(l,u.alignment_heads)),l}getStartBeams(n,u,...d){return seq2seqStartBeams(this,n,u,!1)}async runBeam(n){return await seq2seqRunBeam(this,n,{input_name:"input_features"})}updateBeam(n,u){n.output_token_ids=[...n.output_token_ids,u]}async forward(n){return await seq2seqForward(this,n)}_extract_token_timestamps(n,u,d=.02){if(!n.cross_attentions)throw new Error("Model outputs must contain cross attentions to extract timestamps. This is most likely because the model was not exported with `output_attentions=True`.");let l=this.config.median_filter_width;l===void 0&&(console.warn("Model config has no `median_filter_width`, using default value of 7."),l=7);const p=n.cross_attentions.map(f=>{let a=Array.from({length:this.config.decoder_layers},(c,g)=>cat(f.map(m=>m[g]),2)),o=stack(u.map(([c,g])=>a[c].slice(null,g)));o=o.transpose(1,0,2,3);let[t,e]=std_mean(o,-2,0,!0),r=o.clone();for(let c=0;c<r.dims[0];++c){let g=r[c];for(let m=0;m<g.dims[0];++m){let b=g[m];const _=t[c][m][0],w=e[c][m][0];for(let v=0;v<b.dims[0];++v){let S=b[v];for(let O=0;O<S.data.length;++O)S.data[O]=(S.data[O]-w.data[O])/_.data[O];S.data.set(medianFilter(S.data,l))}}}return mean(r,1)}),s=[n.sequences.length,n.sequences[0].length],h=new Tensor("float32",new Float32Array(s[0]*s[1]),s);for(let f=0;f<s[0];++f){const a=p[f].neg().squeeze_(0);let[o,t]=dynamicTimeWarping(a),e=Array.from({length:o.length-1},(c,g)=>o[g+1]-o[g]),r=mergeArrays([1],e).map(c=>!!c),i=[];for(let c=0;c<r.length;++c)r[c]&&i.push(t[c]*d);h[f].data.set(i,1)}return h}}class VisionEncoderDecoderModel extends PreTrainedModel{constructor(n,u,d){super(n,u),this.decoder_merged_session=d,this.num_layers=this.config.decoder.n_layer,this.num_heads=this.config.decoder.n_head,this.dim_kv=this.config.decoder.n_embd/this.num_heads}getStartBeams(n,u,...d){return seq2seqStartBeams(this,n)}async runBeam(n){return seq2seqRunBeam(this,n,{input_name:"pixel_values"})}updateBeam(n,u){n.output_token_ids=[...n.output_token_ids,u]}async forward(n){return await seq2seqForward(this,n,{add_decoder_pkv:!1})}}class CLIPPreTrainedModel extends PreTrainedModel{}class CLIPModel extends CLIPPreTrainedModel{}class GPT2PreTrainedModel extends PreTrainedModel{constructor(n,u){super(n,u),this.config.pad_token_id=this.config.eos_token_id,this.num_heads=this.config.n_head,this.num_layers=this.config.n_layer,this.dim_kv=this.config.n_embd/this.num_heads}}class GPT2Model extends GPT2PreTrainedModel{async generate(...n){throw Error("The current model class (GPT2Model) is not compatible with `.generate()`, as it doesn't have a language model head. Please use one of the following classes instead: {'GPT2LMHeadModel'}")}}class GPT2LMHeadModel extends GPT2PreTrainedModel{getStartBeams(n,u,d){return decoderStartBeams(this,n,u,d)}async runBeam(n){return await decoderRunBeam(this,n)}updateBeam(n,u){return decoderUpdatebeam(n,u)}async forward(n){return await decoderForward(this,n)}}class GPTNeoPreTrainedModel extends PreTrainedModel{constructor(n,u){super(n,u),this.config.pad_token_id=this.config.eos_token_id,this.num_heads=this.config.num_heads,this.num_layers=this.config.num_layers,this.dim_kv=this.config.hidden_size/this.num_heads}}class GPTNeoModel extends GPTNeoPreTrainedModel{async generate(...n){throw Error("The current model class (GPTNeoModel) is not compatible with `.generate()`, as it doesn't have a language model head. Please use one of the following classes instead: {'GPTNeoForCausalLM'}")}}class GPTNeoForCausalLM extends GPTNeoPreTrainedModel{getStartBeams(n,u,d){return decoderStartBeams(this,n,u,d)}async runBeam(n){return await decoderRunBeam(this,n)}updateBeam(n,u){return decoderUpdatebeam(n,u)}async forward(n){return await decoderForward(this,n)}}class GPTBigCodePreTrainedModel extends PreTrainedModel{constructor(n,u){super(n,u),this.config.pad_token_id=this.config.eos_token_id,this.num_heads=this.config.n_head,this.num_layers=this.config.n_layer,this.dim_kv=this.config.n_embd/this.num_heads}}class GPTBigCodeModel extends GPTBigCodePreTrainedModel{async generate(...n){throw Error("The current model class (GPTBigCodeModel) is not compatible with `.generate()`, as it doesn't have a language model head. Please use one of the following classes instead: {'GPTBigCodeForCausalLM'}")}}class GPTBigCodeForCausalLM extends GPTBigCodePreTrainedModel{getStartBeams(n,u,d){return decoderStartBeams(this,n,u,d)}async runBeam(n){return await decoderRunBeam(this,n)}updateBeam(n,u){return decoderUpdatebeam(n,u)}async forward(n){return await decoderForward(this,n)}}class CodeGenPreTrainedModel extends PreTrainedModel{constructor(n,u){super(n,u),this.config.pad_token_id=this.config.eos_token_id,this.num_heads=this.config.n_head,this.num_layers=this.config.n_layer,this.dim_kv=this.config.n_embd/this.num_heads}}class CodeGenModel extends CodeGenPreTrainedModel{async generate(...n){throw Error("The current model class (CodeGenModel) is not compatible with `.generate()`, as it doesn't have a language model head. Please use one of the following classes instead: {'CodeGenForCausalLM'}")}}class CodeGenForCausalLM extends CodeGenPreTrainedModel{getStartBeams(n,u,d){return decoderStartBeams(this,n,u,d)}async runBeam(n){return await decoderRunBeam(this,n)}updateBeam(n,u){return decoderUpdatebeam(n,u)}async forward(n){return await decoderForward(this,n)}}class ViTPreTrainedModel extends PreTrainedModel{}class ViTForImageClassification extends ViTPreTrainedModel{async _call(n){return new SequenceClassifierOutput(await super._call(n))}}class MobileViTPreTrainedModel extends PreTrainedModel{}class MobileViTForImageClassification extends MobileViTPreTrainedModel{async _call(n){return new SequenceClassifierOutput(await super._call(n))}}class DetrPreTrainedModel extends PreTrainedModel{}class DetrForObjectDetection extends DetrPreTrainedModel{async _call(n){return new DetrObjectDetectionOutput(await super._call(n))}}class DetrForSegmentation extends DetrPreTrainedModel{async _call(n){return new DetrSegmentationOutput(await super._call(n))}}class DetrObjectDetectionOutput extends ModelOutput{constructor({logits:n,pred_boxes:u}){super(),this.logits=n,this.pred_boxes=u}}class DetrSegmentationOutput extends ModelOutput{constructor({logits:n,pred_boxes:u,pred_masks:d}){super(),this.logits=n,this.pred_boxes=u,this.pred_masks=d}}class SamPreTrainedModel extends PreTrainedModel{}class SamModel extends SamPreTrainedModel{async _call(n){return new SamImageSegmentationOutput(await super._call(n))}}class SamImageSegmentationOutput extends ModelOutput{constructor({iou_scores:n,pred_masks:u}){super(),this.iou_scores=n,this.pred_masks=u}}class MarianPreTrainedModel extends PreTrainedModel{}class MarianModel extends MarianPreTrainedModel{async generate(...n){throw Error("The current model class (MarianModel) is not compatible with `.generate()`, as it doesn't have a language model head. Please use one of the following classes instead: {'MarianMTModel'}")}}class MarianMTModel extends MarianPreTrainedModel{constructor(n,u,d,l){super(n,u),this.decoder_merged_session=d,this.generation_config=l,this.num_decoder_layers=this.config.decoder_layers,this.num_decoder_heads=this.config.decoder_attention_heads,this.decoder_dim_kv=this.config.d_model/this.num_decoder_heads,this.num_encoder_layers=this.config.encoder_layers,this.num_encoder_heads=this.config.encoder_attention_heads,this.encoder_dim_kv=this.config.d_model/this.num_encoder_heads}getStartBeams(n,u,...d){return seq2seqStartBeams(this,n)}async runBeam(n){return await seq2seqRunBeam(this,n)}updateBeam(n,u){n.output_token_ids=[...n.output_token_ids,u]}async forward(n){return await seq2seqForward(this,n)}}class M2M100PreTrainedModel extends PreTrainedModel{}class M2M100Model extends M2M100PreTrainedModel{async generate(...n){throw Error("The current model class (M2M100Model) is not compatible with `.generate()`, as it doesn't have a language model head. Please use one of the following classes instead: {'M2M100ForConditionalGeneration'}")}}class M2M100ForConditionalGeneration extends M2M100PreTrainedModel{constructor(n,u,d,l){super(n,u),this.decoder_merged_session=d,this.generation_config=l,this.num_decoder_layers=this.config.decoder_layers,this.num_decoder_heads=this.config.decoder_attention_heads,this.decoder_dim_kv=this.config.d_model/this.num_decoder_heads,this.num_encoder_layers=this.config.encoder_layers,this.num_encoder_heads=this.config.encoder_attention_heads,this.encoder_dim_kv=this.config.d_model/this.num_encoder_heads}getStartBeams(n,u,...d){return seq2seqStartBeams(this,n)}async runBeam(n){return await seq2seqRunBeam(this,n)}updateBeam(n,u){n.output_token_ids=[...n.output_token_ids,u]}async forward(n){return await seq2seqForward(this,n)}}const MODEL_MAPPING_NAMES_ENCODER_ONLY=new Map([["bert",BertModel],["mpnet",MPNetModel],["albert",AlbertModel],["distilbert",DistilBertModel],["roberta",RobertaModel],["xlm-roberta",XLMRobertaModel],["clip",CLIPModel],["mobilebert",MobileBertModel],["squeezebert",SqueezeBertModel],["sam",SamModel]]),MODEL_MAPPING_NAMES_ENCODER_DECODER=new Map([["t5",T5Model],["mt5",MT5Model],["bart",BartModel],["marian",MarianModel],["whisper",WhisperModel],["m2m_100",M2M100Model]]),MODEL_MAPPING_NAMES_DECODER_ONLY=new Map([["gpt2",GPT2Model],["gpt_bigcode",GPTBigCodeModel],["gpt_neo",GPTNeoModel],["codegen",CodeGenModel]]),MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING_NAMES=new Map([["bert",BertForSequenceClassification],["mpnet",MPNetForSequenceClassification],["albert",AlbertForSequenceClassification],["distilbert",DistilBertForSequenceClassification],["roberta",RobertaForSequenceClassification],["xlm-roberta",XLMRobertaForSequenceClassification],["bart",BartForSequenceClassification],["mobilebert",MobileBertForSequenceClassification],["squeezebert",SqueezeBertForSequenceClassification]]),MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING_NAMES=new Map([["bert",BertForTokenClassification],["mpnet",MPNetForTokenClassification],["distilbert",DistilBertForTokenClassification],["roberta",RobertaForTokenClassification],["xlm-roberta",XLMRobertaForTokenClassification]]),MODEL_FOR_SEQ_2_SEQ_MAPPING_NAMES=new Map([["t5",T5ForConditionalGeneration],["mt5",MT5ForConditionalGeneration],["bart",BartForConditionalGeneration],["whisper",WhisperForConditionalGeneration],["marian",MarianMTModel],["m2m_100",M2M100ForConditionalGeneration]]),MODEL_WITH_LM_HEAD_MAPPING_NAMES=new Map([["gpt2",GPT2LMHeadModel],["gpt_bigcode",GPTBigCodeForCausalLM],["gpt_neo",GPTNeoForCausalLM],["codegen",CodeGenForCausalLM]]),MODEL_FOR_MASKED_LM_MAPPING_NAMES=new Map([["bert",BertForMaskedLM],["mpnet",MPNetForMaskedLM],["albert",AlbertForMaskedLM],["distilbert",DistilBertForMaskedLM],["roberta",RobertaForMaskedLM],["xlm-roberta",XLMRobertaForMaskedLM],["mobilebert",MobileBertForMaskedLM],["squeezebert",SqueezeBertForMaskedLM]]),MODEL_FOR_QUESTION_ANSWERING_MAPPING_NAMES=new Map([["bert",BertForQuestionAnswering],["mpnet",MPNetForQuestionAnswering],["albert",AlbertForQuestionAnswering],["distilbert",DistilBertForQuestionAnswering],["roberta",RobertaForQuestionAnswering],["xlm-roberta",XLMRobertaForQuestionAnswering],["mobilebert",MobileBertForQuestionAnswering],["squeezebert",SqueezeBertForQuestionAnswering]]),MODEL_FOR_VISION_2_SEQ_MAPPING_NAMES=new Map([["vision-encoder-decoder",VisionEncoderDecoderModel]]),MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING_NAMES=new Map([["vit",ViTForImageClassification],["mobilevit",MobileViTForImageClassification]]),MODEL_FOR_OBJECT_DETECTION_MAPPING_NAMES=new Map([["detr",DetrForObjectDetection]]),MODEL_FOR_IMAGE_SEGMENTATION_MAPPING_NAMES=new Map([["detr",DetrForSegmentation]]),MODEL_FOR_MASK_GENERATION_MAPPING_NAMES=new Map([["sam",SamModel]]),MODEL_CLASS_TYPE_MAPPING=[[MODEL_MAPPING_NAMES_ENCODER_ONLY,EncoderOnlyModelType],[MODEL_MAPPING_NAMES_ENCODER_DECODER,EncoderDecoderModelType],[MODEL_MAPPING_NAMES_DECODER_ONLY,DecoderOnlyModelType],[MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING_NAMES,EncoderOnlyModelType],[MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING_NAMES,EncoderOnlyModelType],[MODEL_FOR_SEQ_2_SEQ_MAPPING_NAMES,Seq2SeqModelType],[MODEL_WITH_LM_HEAD_MAPPING_NAMES,DecoderOnlyModelType],[MODEL_FOR_MASKED_LM_MAPPING_NAMES,EncoderOnlyModelType],[MODEL_FOR_QUESTION_ANSWERING_MAPPING_NAMES,EncoderOnlyModelType],[MODEL_FOR_VISION_2_SEQ_MAPPING_NAMES,EncoderDecoderModelType],[MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING_NAMES,EncoderOnlyModelType],[MODEL_FOR_IMAGE_SEGMENTATION_MAPPING_NAMES,EncoderOnlyModelType],[MODEL_FOR_OBJECT_DETECTION_MAPPING_NAMES,EncoderOnlyModelType],[MODEL_FOR_MASK_GENERATION_MAPPING_NAMES,EncoderOnlyModelType]];for(const[y,n]of MODEL_CLASS_TYPE_MAPPING)for(const u of y.values())MODEL_TYPE_MAPPING.set(u.name,n);class Seq2SeqLMOutput extends ModelOutput{constructor({logits:n,past_key_values:u,encoder_outputs:d,decoder_attentions:l=null,cross_attentions:p=null}){super(),this.logits=n,this.past_key_values=u,this.encoder_outputs=d,this.decoder_attentions=l,this.cross_attentions=p}}class SequenceClassifierOutput extends ModelOutput{constructor({logits:n}){super(),this.logits=n}}class TokenClassifierOutput extends ModelOutput{constructor({logits:n}){super(),this.logits=n}}class MaskedLMOutput extends ModelOutput{constructor({logits:n}){super(),this.logits=n}}class QuestionAnsweringModelOutput extends ModelOutput{constructor({start_logits:n,end_logits:u}){super(),this.start_logits=n,this.end_logits=u}}const BROWSER_ENV=typeof self<"u";if(!BROWSER_ENV){if(!sharp)throw new Error("Unable to load image processing library.")}env.allowLocalModels=!1;const TOKENIZER_MAPPINGS=new Map;self.addEventListener("message",async y=>{let n=TOKENIZER_MAPPINGS.get(y.data.model_id);n||(n=AutoTokenizer.from_pretrained(y.data.model_id),TOKENIZER_MAPPINGS.set(y.data.model_id,new Promise(a=>{n.then(o=>{switch(o.constructor.name){case"LlamaTokenizer":o.decoder.decoders.pop();break;case"T5Tokenizer":o.decoder.addPrefixSpace=!1;break}a(o)})})));const u=await n,d=y.data.text,l=performance.now(),p=u.encode(d),s=performance.now();console.log("[INFO]",`Tokenized ${d.length} characters in ${(s-l).toFixed(2)}ms`);let h=p.map(a=>u.decode([a])),f=[];switch(u.constructor.name){case"BertTokenizer":f=h.map((a,o)=>o===0||a.startsWith("##")?0:8),h=h.map(a=>a.replace("##",""));break;case"T5Tokenizer":h.length>0&&h.length!==" "&&(h[0]=h[0].replace(/^ /,""));break}self.postMessage({token_ids:p,decoded:h,margins:f})})})();