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ERR: ${o.map(u=>`[${u.name}] ${u.err}`).join(", ")}`)};class EnvImpl{constructor(){this.wasm={},this.webgl={},this.logLevelInternal="warning"}set logLevel(n){if(n!==void 0){if(typeof n!="string"||["verbose","info","warning","error","fatal"].indexOf(n)===-1)throw new Error(`Unsupported logging level: ${n}`);this.logLevelInternal=n}}get logLevel(){return this.logLevelInternal}}const env$1=new EnvImpl,isBigInt64ArrayAvailable=typeof BigInt64Array<"u"&&typeof BigInt64Array.from=="function",isBigUint64ArrayAvailable=typeof BigUint64Array<"u"&&typeof BigUint64Array.from=="function",NUMERIC_TENSOR_TYPE_TO_TYPEDARRAY_MAP=new Map([["float32",Float32Array],["uint8",Uint8Array],["int8",Int8Array],["uint16",Uint16Array],["int16",Int16Array],["int32",Int32Array],["bool",Uint8Array],["float64",Float64Array],["uint32",Uint32Array]]),NUMERIC_TENSOR_TYPEDARRAY_TO_TYPE_MAP=new Map([[Float32Array,"float32"],[Uint8Array,"uint8"],[Int8Array,"int8"],[Uint16Array,"uint16"],[Int16Array,"int16"],[Int32Array,"int32"],[Float64Array,"float64"],[Uint32Array,"uint32"]]);isBigInt64ArrayAvailable&&(NUMERIC_TENSOR_TYPE_TO_TYPEDARRAY_MAP.set("int64",BigInt64Array),NUMERIC_TENSOR_TYPEDARRAY_TO_TYPE_MAP.set(BigInt64Array,"int64")),isBigUint64ArrayAvailable&&(NUMERIC_TENSOR_TYPE_TO_TYPEDARRAY_MAP.set("uint64",BigUint64Array),NUMERIC_TENSOR_TYPEDARRAY_TO_TYPE_MAP.set(BigUint64Array,"uint64"));const calculateSize=_=>{let n=1;for(let o=0;o<_.length;o++){const u=_[o];if(typeof u!="number"||!Number.isSafeInteger(u))throw new TypeError(`dims[${o}] must be an integer, got: ${u}`);if(u<0)throw new RangeError(`dims[${o}] must be a non-negative integer, got: ${u}`);n*=u}return n};let Tensor$2=class ut{constructor(n,o,u){let l,d,s;if(typeof n=="string")if(l=n,s=u,n==="string"){if(!Array.isArray(o))throw new TypeError("A string tensor's data must be a string array.");d=o}else{const 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bufferToTensor(n,o){if(n===void 0)throw new Error("Image buffer must be defined");if(o.height===void 0||o.width===void 0)throw new Error("Image height and width must be defined");const{height:u,width:l}=o,d=o.norm;let s,p;d===void 0||d.mean===void 0?s=255:s=d.mean,d===void 0||d.bias===void 0?p=0:p=d.bias;const f=o.bitmapFormat!==void 0?o.bitmapFormat:"RGBA",c=o.tensorFormat!==void 0&&o.tensorFormat!==void 0?o.tensorFormat:"RGB",a=u*l,t=c==="RGBA"?new Float32Array(a*4):new Float32Array(a*3);let e=4,r=0,i=1,h=2,g=3,m=0,b=a,y=a*2,w=-1;f==="RGB"&&(e=3,r=0,i=1,h=2,g=-1),c==="RGBA"?w=a*3:c==="RBG"?(m=0,y=a,b=a*2):c==="BGR"&&(y=0,b=a,m=a*2);for(let S=0;S<a;S++,r+=e,h+=e,i+=e,g+=e)t[m++]=(n[r]+p)/s,t[b++]=(n[i]+p)/s,t[y++]=(n[h]+p)/s,w!==-1&&g!==-1&&(t[w++]=(n[g]+p)/s);return c==="RGBA"?new ut("float32",t,[1,4,u,l]):new ut("float32",t,[1,3,u,l])}static async fromImage(n,o){const u=typeof HTMLImageElement<"u"&&n instanceof HTMLImageElement,l=typeof ImageData<"u"&&n instanceof ImageData,d=typeof 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i=0;i<e.floats.length;++i)r.float(e.floats[i]);r.ldelim()}if(e.ints!=null&&e.ints.length){for(r.uint32(66).fork(),i=0;i<e.ints.length;++i)r.int64(e.ints[i]);r.ldelim()}if(e.strings!=null&&e.strings.length)for(i=0;i<e.strings.length;++i)r.uint32(74).bytes(e.strings[i]);if(e.tensors!=null&&e.tensors.length)for(i=0;i<e.tensors.length;++i)a.onnx.TensorProto.encode(e.tensors[i],r.uint32(82).fork()).ldelim();if(e.graphs!=null&&e.graphs.length)for(i=0;i<e.graphs.length;++i)a.onnx.GraphProto.encode(e.graphs[i],r.uint32(90).fork()).ldelim();return e.docString!=null&&e.hasOwnProperty("docString")&&r.uint32(106).string(e.docString),e.type!=null&&e.hasOwnProperty("type")&&r.uint32(160).int32(e.type),e.refAttrName!=null&&e.hasOwnProperty("refAttrName")&&r.uint32(170).string(e.refAttrName),r},t.encodeDelimited=function(e,r){return this.encode(e,r).ldelim()},t.decode=function(e,r){e instanceof p||(e=p.create(e));for(var i=r===void 0?e.len:e.pos+r,h=new a.onnx.AttributeProto;e.pos<i;){var g=e.uint32();switch(g>>>3){case 1:h.name=e.string();break;case 21:h.refAttrName=e.string();break;case 13:h.docString=e.string();break;case 20:h.type=e.int32();break;case 2:h.f=e.float();break;case 3:h.i=e.int64();break;case 4:h.s=e.bytes();break;case 5:h.t=a.onnx.TensorProto.decode(e,e.uint32());break;case 6:h.g=a.onnx.GraphProto.decode(e,e.uint32());break;case 7:if(h.floats&&h.floats.length||(h.floats=[]),(7&g)==2)for(var m=e.uint32()+e.pos;e.pos<m;)h.floats.push(e.float());else h.floats.push(e.float());break;case 8:if(h.ints&&h.ints.length||(h.ints=[]),(7&g)==2)for(m=e.uint32()+e.pos;e.pos<m;)h.ints.push(e.int64());else h.ints.push(e.int64());break;case 9:h.strings&&h.strings.length||(h.strings=[]),h.strings.push(e.bytes());break;case 10:h.tensors&&h.tensors.length||(h.tensors=[]),h.tensors.push(a.onnx.TensorProto.decode(e,e.uint32()));break;case 11:h.graphs&&h.graphs.length||(h.graphs=[]),h.graphs.push(a.onnx.GraphProto.decode(e,e.uint32()));break;default:e.skipType(7&g)}}return h},t.decodeDelimited=function(e){return e instanceof p||(e=new p(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")&&!c.isString(e.name))return"name: string expected";if(e.refAttrName!=null&&e.hasOwnProperty("refAttrName")&&!c.isString(e.refAttrName))return"refAttrName: string expected";if(e.docString!=null&&e.hasOwnProperty("docString")&&!c.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")&&!(c.isInteger(e.i)||e.i&&c.isInteger(e.i.low)&&c.isInteger(e.i.high)))return"i: integer|Long expected";if(e.s!=null&&e.hasOwnProperty("s")&&!(e.s&&typeof e.s.length=="number"||c.isString(e.s)))return"s: buffer expected";if(e.t!=null&&e.hasOwnProperty("t")&&(i=a.onnx.TensorProto.verify(e.t)))return"t."+i;if(e.g!=null&&e.hasOwnProperty("g")&&(i=a.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(!(c.isInteger(e.ints[r])||e.ints[r]&&c.isInteger(e.ints[r].low)&&c.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"||c.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=a.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=a.onnx.GraphProto.verify(e.graphs[r]))return"graphs."+i}}return null},t.fromObject=function(e){if(e instanceof a.onnx.AttributeProto)return e;var r=new a.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&&(c.Long?(r.i=c.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 c.LongBits(e.i.low>>>0,e.i.high>>>0).toNumber())),e.s!=null&&(typeof e.s=="string"?c.base64.decode(e.s,r.s=c.newBuffer(c.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=a.onnx.TensorProto.fromObject(e.t)}if(e.g!=null){if(typeof e.g!="object")throw TypeError(".onnx.AttributeProto.g: object expected");r.g=a.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)c.Long?(r.ints[i]=c.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 c.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"?c.base64.decode(e.strings[i],r.strings[i]=c.newBuffer(c.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]=a.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]=a.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,c.Long){var h=new c.Long(0,0,!1);i.i=r.longs===String?h.toString():r.longs===Number?h.toNumber():h}else i.i=r.longs===String?"0":0;r.bytes===String?i.s="":(i.s=[],r.bytes!==Array&&(i.s=c.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?c.Long.prototype.toString.call(e.i):r.longs===Number?new c.LongBits(e.i.low>>>0,e.i.high>>>0).toNumber():e.i),e.s!=null&&e.hasOwnProperty("s")&&(i.s=r.bytes===String?c.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=a.onnx.TensorProto.toObject(e.t,r)),e.g!=null&&e.hasOwnProperty("g")&&(i.g=a.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?c.Long.prototype.toString.call(e.ints[g]):r.longs===Number?new c.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?c.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]=a.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]=a.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?a.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}(),d.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")&&a.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 p||(e=p.create(e));for(var i=r===void 0?e.len:e.pos+r,h=new a.onnx.ValueInfoProto;e.pos<i;){var g=e.uint32();switch(g>>>3){case 1:h.name=e.string();break;case 2:h.type=a.onnx.TypeProto.decode(e,e.uint32());break;case 3:h.docString=e.string();break;default:e.skipType(7&g)}}return h},t.decodeDelimited=function(e){return e instanceof p||(e=new p(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")&&!c.isString(e.name))return"name: string expected";if(e.type!=null&&e.hasOwnProperty("type")){var r=a.onnx.TypeProto.verify(e.type);if(r)return"type."+r}return e.docString!=null&&e.hasOwnProperty("docString")&&!c.isString(e.docString)?"docString: string expected":null},t.fromObject=function(e){if(e instanceof a.onnx.ValueInfoProto)return e;var r=new a.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=a.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=a.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}(),d.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=c.emptyArray,t.prototype.output=c.emptyArray,t.prototype.name="",t.prototype.opType="",t.prototype.domain="",t.prototype.attribute=c.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)a.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 p||(e=p.create(e));for(var i=r===void 0?e.len:e.pos+r,h=new a.onnx.NodeProto;e.pos<i;){var g=e.uint32();switch(g>>>3){case 1:h.input&&h.input.length||(h.input=[]),h.input.push(e.string());break;case 2:h.output&&h.output.length||(h.output=[]),h.output.push(e.string());break;case 3:h.name=e.string();break;case 4:h.opType=e.string();break;case 7:h.domain=e.string();break;case 5:h.attribute&&h.attribute.length||(h.attribute=[]),h.attribute.push(a.onnx.AttributeProto.decode(e,e.uint32()));break;case 6:h.docString=e.string();break;default:e.skipType(7&g)}}return h},t.decodeDelimited=function(e){return e instanceof p||(e=new p(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(!c.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(!c.isString(e.output[r]))return"output: string[] expected"}if(e.name!=null&&e.hasOwnProperty("name")&&!c.isString(e.name))return"name: string expected";if(e.opType!=null&&e.hasOwnProperty("opType")&&!c.isString(e.opType))return"opType: string expected";if(e.domain!=null&&e.hasOwnProperty("domain")&&!c.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=a.onnx.AttributeProto.verify(e.attribute[r]);if(i)return"attribute."+i}}return e.docString!=null&&e.hasOwnProperty("docString")&&!c.isString(e.docString)?"docString: string expected":null},t.fromObject=function(e){if(e instanceof a.onnx.NodeProto)return e;var r=new a.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]=a.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 h=0;h<e.input.length;++h)i.input[h]=e.input[h]}if(e.output&&e.output.length)for(i.output=[],h=0;h<e.output.length;++h)i.output[h]=e.output[h];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=[],h=0;h<e.attribute.length;++h)i.attribute[h]=a.onnx.AttributeProto.toObject(e.attribute[h],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}(),d.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=c.Long?c.Long.fromBits(0,0,!1):0,t.prototype.opsetImport=c.emptyArray,t.prototype.producerName="",t.prototype.producerVersion="",t.prototype.domain="",t.prototype.modelVersion=c.Long?c.Long.fromBits(0,0,!1):0,t.prototype.docString="",t.prototype.graph=null,t.prototype.metadataProps=c.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")&&a.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)a.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)a.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 p||(e=p.create(e));for(var i=r===void 0?e.len:e.pos+r,h=new a.onnx.ModelProto;e.pos<i;){var g=e.uint32();switch(g>>>3){case 1:h.irVersion=e.int64();break;case 8:h.opsetImport&&h.opsetImport.length||(h.opsetImport=[]),h.opsetImport.push(a.onnx.OperatorSetIdProto.decode(e,e.uint32()));break;case 2:h.producerName=e.string();break;case 3:h.producerVersion=e.string();break;case 4:h.domain=e.string();break;case 5:h.modelVersion=e.int64();break;case 6:h.docString=e.string();break;case 7:h.graph=a.onnx.GraphProto.decode(e,e.uint32());break;case 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a.onnx.ModelProto)return e;var r=new a.onnx.ModelProto;if(e.irVersion!=null&&(c.Long?(r.irVersion=c.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 c.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]=a.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&&(c.Long?(r.modelVersion=c.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 p||(e=p.create(e));for(var i=r===void 0?e.len:e.pos+r,h=new a.onnx.StringStringEntryProto;e.pos<i;){var g=e.uint32();switch(g>>>3){case 1:h.key=e.string();break;case 2:h.value=e.string();break;default:e.skipType(7&g)}}return h},t.decodeDelimited=function(e){return e instanceof p||(e=new p(e)),this.decode(e,e.uint32())},t.verify=function(e){return typeof e!="object"||e===null?"object expected":e.key!=null&&e.hasOwnProperty("key")&&!c.isString(e.key)?"key: string expected":e.value!=null&&e.hasOwnProperty("value")&&!c.isString(e.value)?"value: string expected":null},t.fromObject=function(e){if(e instanceof a.onnx.StringStringEntryProto)return e;var r=new a.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}(),d.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=c.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)a.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 p||(e=p.create(e));for(var i=r===void 0?e.len:e.pos+r,h=new a.onnx.TensorAnnotation;e.pos<i;){var g=e.uint32();switch(g>>>3){case 1:h.tensorName=e.string();break;case 2:h.quantParameterTensorNames&&h.quantParameterTensorNames.length||(h.quantParameterTensorNames=[]),h.quantParameterTensorNames.push(a.onnx.StringStringEntryProto.decode(e,e.uint32()));break;default:e.skipType(7&g)}}return h},t.decodeDelimited=function(e){return e instanceof p||(e=new p(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")&&!c.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=a.onnx.StringStringEntryProto.verify(e.quantParameterTensorNames[r]);if(i)return"quantParameterTensorNames."+i}}return null},t.fromObject=function(e){if(e instanceof a.onnx.TensorAnnotation)return e;var r=new a.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]=a.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 h=0;h<e.quantParameterTensorNames.length;++h)i.quantParameterTensorNames[h]=a.onnx.StringStringEntryProto.toObject(e.quantParameterTensorNames[h],r)}return i},t.prototype.toJSON=function(){return this.constructor.toObject(this,s.util.toJSONOptions)},t}(),d.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=c.emptyArray,t.prototype.name="",t.prototype.initializer=c.emptyArray,t.prototype.docString="",t.prototype.input=c.emptyArray,t.prototype.output=c.emptyArray,t.prototype.valueInfo=c.emptyArray,t.prototype.quantizationAnnotation=c.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)a.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)a.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)a.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)a.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)a.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)a.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 p||(e=p.create(e));for(var i=r===void 0?e.len:e.pos+r,h=new a.onnx.GraphProto;e.pos<i;){var g=e.uint32();switch(g>>>3){case 1:h.node&&h.node.length||(h.node=[]),h.node.push(a.onnx.NodeProto.decode(e,e.uint32()));break;case 2:h.name=e.string();break;case 5:h.initializer&&h.initializer.length||(h.initializer=[]),h.initializer.push(a.onnx.TensorProto.decode(e,e.uint32()));break;case 10:h.docString=e.string();break;case 11:h.input&&h.input.length||(h.input=[]),h.input.push(a.onnx.ValueInfoProto.decode(e,e.uint32()));break;case 12:h.output&&h.output.length||(h.output=[]),h.output.push(a.onnx.ValueInfoProto.decode(e,e.uint32()));break;case 13:h.valueInfo&&h.valueInfo.length||(h.valueInfo=[]),h.valueInfo.push(a.onnx.ValueInfoProto.decode(e,e.uint32()));break;case 14:h.quantizationAnnotation&&h.quantizationAnnotation.length||(h.quantizationAnnotation=[]),h.quantizationAnnotation.push(a.onnx.TensorAnnotation.decode(e,e.uint32()));break;default:e.skipType(7&g)}}return h},t.decodeDelimited=function(e){return e instanceof p||(e=new p(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=a.onnx.NodeProto.verify(e.node[r]))return"node."+i}if(e.name!=null&&e.hasOwnProperty("name")&&!c.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=a.onnx.TensorProto.verify(e.initializer[r]))return"initializer."+i}if(e.docString!=null&&e.hasOwnProperty("docString")&&!c.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=a.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=a.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=a.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=a.onnx.TensorAnnotation.verify(e.quantizationAnnotation[r]))return"quantizationAnnotation."+i}}return null},t.fromObject=function(e){if(e instanceof a.onnx.GraphProto)return e;var r=new a.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]=a.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]=a.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]=a.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]=a.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]=a.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]=a.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 h=0;h<e.node.length;++h)i.node[h]=a.onnx.NodeProto.toObject(e.node[h],r)}if(e.name!=null&&e.hasOwnProperty("name")&&(i.name=e.name),e.initializer&&e.initializer.length)for(i.initializer=[],h=0;h<e.initializer.length;++h)i.initializer[h]=a.onnx.TensorProto.toObject(e.initializer[h],r);if(e.docString!=null&&e.hasOwnProperty("docString")&&(i.docString=e.docString),e.input&&e.input.length)for(i.input=[],h=0;h<e.input.length;++h)i.input[h]=a.onnx.ValueInfoProto.toObject(e.input[h],r);if(e.output&&e.output.length)for(i.output=[],h=0;h<e.output.length;++h)i.output[h]=a.onnx.ValueInfoProto.toObject(e.output[h],r);if(e.valueInfo&&e.valueInfo.length)for(i.valueInfo=[],h=0;h<e.valueInfo.length;++h)i.valueInfo[h]=a.onnx.ValueInfoProto.toObject(e.valueInfo[h],r);if(e.quantizationAnnotation&&e.quantizationAnnotation.length)for(i.quantizationAnnotation=[],h=0;h<e.quantizationAnnotation.length;++h)i.quantizationAnnotation[h]=a.onnx.TensorAnnotation.toObject(e.quantizationAnnotation[h],r);return i},t.prototype.toJSON=function(){return this.constructor.toObject(this,s.util.toJSONOptions)},t}(),d.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=c.emptyArray,t.prototype.dataType=0,t.prototype.segment=null,t.prototype.floatData=c.emptyArray,t.prototype.int32Data=c.emptyArray,t.prototype.stringData=c.emptyArray,t.prototype.int64Data=c.emptyArray,t.prototype.name="",t.prototype.docString="",t.prototype.rawData=c.newBuffer([]),t.prototype.externalData=c.emptyArray,t.prototype.dataLocation=0,t.prototype.doubleData=c.emptyArray,t.prototype.uint64Data=c.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")&&a.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)a.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 p||(e=p.create(e));for(var i=r===void 0?e.len:e.pos+r,h=new a.onnx.TensorProto;e.pos<i;){var g=e.uint32();switch(g>>>3){case 1:if(h.dims&&h.dims.length||(h.dims=[]),(7&g)==2)for(var m=e.uint32()+e.pos;e.pos<m;)h.dims.push(e.int64());else h.dims.push(e.int64());break;case 2:h.dataType=e.int32();break;case 3:h.segment=a.onnx.TensorProto.Segment.decode(e,e.uint32());break;case 4:if(h.floatData&&h.floatData.length||(h.floatData=[]),(7&g)==2)for(m=e.uint32()+e.pos;e.pos<m;)h.floatData.push(e.float());else h.floatData.push(e.float());break;case 5:if(h.int32Data&&h.int32Data.length||(h.int32Data=[]),(7&g)==2)for(m=e.uint32()+e.pos;e.pos<m;)h.int32Data.push(e.int32());else h.int32Data.push(e.int32());break;case 6:h.stringData&&h.stringData.length||(h.stringData=[]),h.stringData.push(e.bytes());break;case 7:if(h.int64Data&&h.int64Data.length||(h.int64Data=[]),(7&g)==2)for(m=e.uint32()+e.pos;e.pos<m;)h.int64Data.push(e.int64());else h.int64Data.push(e.int64());break;case 8:h.name=e.string();break;case 12:h.docString=e.string();break;case 9:h.rawData=e.bytes();break;case 13:h.externalData&&h.externalData.length||(h.externalData=[]),h.externalData.push(a.onnx.StringStringEntryProto.decode(e,e.uint32()));break;case 14:h.dataLocation=e.int32();break;case 10:if(h.doubleData&&h.doubleData.length||(h.doubleData=[]),(7&g)==2)for(m=e.uint32()+e.pos;e.pos<m;)h.doubleData.push(e.double());else h.doubleData.push(e.double());break;case 11:if(h.uint64Data&&h.uint64Data.length||(h.uint64Data=[]),(7&g)==2)for(m=e.uint32()+e.pos;e.pos<m;)h.uint64Data.push(e.uint64());else h.uint64Data.push(e.uint64());break;default:e.skipType(7&g)}}return h},t.decodeDelimited=function(e){return e instanceof p||(e=new p(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(!(c.isInteger(e.dims[r])||e.dims[r]&&c.isInteger(e.dims[r].low)&&c.isInteger(e.dims[r].high)))return"dims: integer|Long[] expected"}if(e.dataType!=null&&e.hasOwnProperty("dataType")&&!c.isInteger(e.dataType))return"dataType: integer expected";if(e.segment!=null&&e.hasOwnProperty("segment")&&(i=a.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(!c.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"||c.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(!(c.isInteger(e.int64Data[r])||e.int64Data[r]&&c.isInteger(e.int64Data[r].low)&&c.isInteger(e.int64Data[r].high)))return"int64Data: integer|Long[] expected"}if(e.name!=null&&e.hasOwnProperty("name")&&!c.isString(e.name))return"name: string expected";if(e.docString!=null&&e.hasOwnProperty("docString")&&!c.isString(e.docString))return"docString: string expected";if(e.rawData!=null&&e.hasOwnProperty("rawData")&&!(e.rawData&&typeof e.rawData.length=="number"||c.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=a.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(!(c.isInteger(e.uint64Data[r])||e.uint64Data[r]&&c.isInteger(e.uint64Data[r].low)&&c.isInteger(e.uint64Data[r].high)))return"uint64Data: integer|Long[] expected"}return null},t.fromObject=function(e){if(e instanceof a.onnx.TensorProto)return e;var r=new a.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)c.Long?(r.dims[i]=c.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 c.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=a.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"?c.base64.decode(e.stringData[i],r.stringData[i]=c.newBuffer(c.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)c.Long?(r.int64Data[i]=c.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 c.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"?c.base64.decode(e.rawData,r.rawData=c.newBuffer(c.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]=a.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=c.newBuffer(i.rawData))),i.docString="",i.dataLocation=r.enums===String?"DEFAULT":0),e.dims&&e.dims.length){i.dims=[];for(var h=0;h<e.dims.length;++h)typeof e.dims[h]=="number"?i.dims[h]=r.longs===String?String(e.dims[h]):e.dims[h]:i.dims[h]=r.longs===String?c.Long.prototype.toString.call(e.dims[h]):r.longs===Number?new c.LongBits(e.dims[h].low>>>0,e.dims[h].high>>>0).toNumber():e.dims[h]}if(e.dataType!=null&&e.hasOwnProperty("dataType")&&(i.dataType=e.dataType),e.segment!=null&&e.hasOwnProperty("segment")&&(i.segment=a.onnx.TensorProto.Segment.toObject(e.segment,r)),e.floatData&&e.floatData.length)for(i.floatData=[],h=0;h<e.floatData.length;++h)i.floatData[h]=r.json&&!isFinite(e.floatData[h])?String(e.floatData[h]):e.floatData[h];if(e.int32Data&&e.int32Data.length)for(i.int32Data=[],h=0;h<e.int32Data.length;++h)i.int32Data[h]=e.int32Data[h];if(e.stringData&&e.stringData.length)for(i.stringData=[],h=0;h<e.stringData.length;++h)i.stringData[h]=r.bytes===String?c.base64.encode(e.stringData[h],0,e.stringData[h].length):r.bytes===Array?Array.prototype.slice.call(e.stringData[h]):e.stringData[h];if(e.int64Data&&e.int64Data.length)for(i.int64Data=[],h=0;h<e.int64Data.length;++h)typeof e.int64Data[h]=="number"?i.int64Data[h]=r.longs===String?String(e.int64Data[h]):e.int64Data[h]:i.int64Data[h]=r.longs===String?c.Long.prototype.toString.call(e.int64Data[h]):r.longs===Number?new c.LongBits(e.int64Data[h].low>>>0,e.int64Data[h].high>>>0).toNumber():e.int64Data[h];if(e.name!=null&&e.hasOwnProperty("name")&&(i.name=e.name),e.rawData!=null&&e.hasOwnProperty("rawData")&&(i.rawData=r.bytes===String?c.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=[],h=0;h<e.doubleData.length;++h)i.doubleData[h]=r.json&&!isFinite(e.doubleData[h])?String(e.doubleData[h]):e.doubleData[h];if(e.uint64Data&&e.uint64Data.length)for(i.uint64Data=[],h=0;h<e.uint64Data.length;++h)typeof e.uint64Data[h]=="number"?i.uint64Data[h]=r.longs===String?String(e.uint64Data[h]):e.uint64Data[h]:i.uint64Data[h]=r.longs===String?c.Long.prototype.toString.call(e.uint64Data[h]):r.longs===Number?new c.LongBits(e.uint64Data[h].low>>>0,e.uint64Data[h].high>>>0).toNumber(!0):e.uint64Data[h];if(e.docString!=null&&e.hasOwnProperty("docString")&&(i.docString=e.docString),e.externalData&&e.externalData.length)for(i.externalData=[],h=0;h<e.externalData.length;++h)i.externalData[h]=a.onnx.StringStringEntryProto.toObject(e.externalData[h],r);return e.dataLocation!=null&&e.hasOwnProperty("dataLocation")&&(i.dataLocation=r.enums===String?a.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),h=0;h<i.length;++h)r[i[h]]!=null&&(this[i[h]]=r[i[h]])}return e.prototype.begin=c.Long?c.Long.fromBits(0,0,!1):0,e.prototype.end=c.Long?c.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 p||(r=p.create(r));for(var h=i===void 0?r.len:r.pos+i,g=new a.onnx.TensorProto.Segment;r.pos<h;){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 p||(r=new p(r)),this.decode(r,r.uint32())},e.verify=function(r){return typeof r!="object"||r===null?"object expected":r.begin!=null&&r.hasOwnProperty("begin")&&!(c.isInteger(r.begin)||r.begin&&c.isInteger(r.begin.low)&&c.isInteger(r.begin.high))?"begin: integer|Long expected":r.end!=null&&r.hasOwnProperty("end")&&!(c.isInteger(r.end)||r.end&&c.isInteger(r.end.low)&&c.isInteger(r.end.high))?"end: integer|Long expected":null},e.fromObject=function(r){if(r instanceof a.onnx.TensorProto.Segment)return r;var i=new a.onnx.TensorProto.Segment;return r.begin!=null&&(c.Long?(i.begin=c.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 a=this.context.outputTextureLayout;return a.isPacked?this.getPackedOutputSamplingSnippet(a):this.getUnpackedOutputSamplingSnippet(a)}getPackedOutputSamplingSnippet(a){const t=a.unpackedShape,e=[a.width,a.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 h=`
      void setOutput(vec4 val) {
        ${(0,d.getGlsl)(this.context.glContext.version).output} = val;
      }
    `;return r.floatTextureSetRGBA=new l.GlslLibRoutine(h),r}getUnpackedOutputSamplingSnippet(a){const t=a.unpackedShape,e=[a.width,a.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 h=`
        void setOutput(float val) {
          ${(0,d.getGlsl)(this.context.glContext.version).output} = vec4(val, 0, 0, 0);
        }
    `;return r.floatTextureSetR=new l.GlslLibRoutine(h),r}getOutputScalarCoords(){return new l.GlslLibRoutine(`
      int getOutputCoords() {
        return 0;
      }
    `)}getOutputPacked1DCoords(a,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 * ${e[0]} + resTexRC.x);
        }
      `,new l.GlslLibRoutine(r))}getOutputPacked2DCoords(a,t){let e="";if(u.ArrayUtil.arraysEqual(a,t))return e=`
        ivec2 getOutputCoords() {
          return 2 * ivec2(TexCoords.xy * vec2(${t[0]}, ${t[1]}));
        }
      `,new l.GlslLibRoutine(e);const r=t,i=Math.ceil(a[1]/2);return e=`
        ivec2 getOutputCoords() {
          ivec2 resTexRC = ivec2(TexCoords.xy *
                                vec2(${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(a,t){const e=[t[0],t[1]],r=Math.ceil(a[2]/2),i=r*Math.ceil(a[1]/2),h=`
        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(h)}getOutputPackedNDCoords(a,t){const e=[t[0],t[1]],r=Math.ceil(a[a.length-1]/2),i=r*Math.ceil(a[a.length-2]/2);let h=i,g="",m="b, r, c";for(let y=2;y<a.length-1;y++)h*=a[a.length-y-1],g=`
      int b${y} = index / ${h};
      index -= b${y} * ${h};
    `+g,m=`b${y}, `+m;const b=`
      ivec${a.length} getOutputCoords() {
        ivec2 resTexRC = ivec2(TexCoords.xy *
                              vec2(${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${a.length}(${m});
      }
    `;return new l.GlslLibRoutine(b)}getOutputUnpacked1DCoords(a,t){const e=`
        int getOutputCoords() {
          ivec2 resTexRC = ivec2(TexCoords.xy *
                                vec2(${t[0]}, ${t[1]}));
          return resTexRC.y * ${t[0]} + resTexRC.x;
        }
      `;return new l.GlslLibRoutine(e)}getOutputUnpacked2DCoords(a,t){const e=`
        ivec2 getOutputCoords() {
          ivec2 resTexRC = ivec2(TexCoords.xy *
                                vec2(${t[0]}, ${t[1]}));
          int index = resTexRC.y * ${t[0]} + resTexRC.x;
          int r = index / ${a[1]};
          int c = index - r * ${a[1]};
          return ivec2(r, c);
        }
      `;return new l.GlslLibRoutine(e)}getOutputUnpacked3DCoords(a,t){let e="";const r=a.length;let i=null;r<2&&(i=[]),i=new Array(r-1),i[r-2]=a[r-1];for(let m=r-3;m>=0;--m)i[m]=i[m+1]*a[m+1];const h=["r","c","d"],g=i.map((m,b)=>`int ${h[b]} = index / ${m}; ${b===i.length-1?`int ${h[b+1]} = index - ${h[b]} * ${m}`:`index -= ${h[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(a,t){let e="";const r=a.length;let i=null;r<2&&(i=[]),i=new Array(r-1),i[r-2]=a[r-1];for(let m=r-3;m>=0;--m)i[m]=i[m+1]*a[m+1];const h=["r","c","d","d2"],g=i.map((m,b)=>`int ${h[b]} = index / ${m}; ${b===i.length-1?`int ${h[b+1]} = index - ${h[b]} * ${m}`:`index -= ${h[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(a,t){let e="";const r=a.length;let i=null;r<2&&(i=[]),i=new Array(r-1),i[r-2]=a[r-1];for(let m=r-3;m>=0;--m)i[m]=i[m+1]*a[m+1];const h=["r","c","d","d2","d3"],g=i.map((m,b)=>`int ${h[b]} = index / ${m}; ${b===i.length-1?`int ${h[b+1]} = index - ${h[b]} * ${m}`:`index -= ${h[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(a,t){let e="";const r=a.length;let i=null;r<2&&(i=[]),i=new Array(r-1),i[r-2]=a[r-1];for(let m=r-3;m>=0;--m)i[m]=i[m+1]*a[m+1];const h=["r","c","d","d2","d3","d4"],g=i.map((m,b)=>`int ${h[b]} = index / ${m}; ${b===i.length-1?`int ${h[b+1]} = index - ${h[b]} * ${m}`:`index -= ${h[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 a={};let t="uvFromFlat";a[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",a[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",a[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",a[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,d.getGlsl)(this.context.glContext.version);return a[t]=new l.GlslLibRoutine(`
        float sampleTexture(sampler2D textureSampler, vec2 uv) {
            return ${e.texture2D}(textureSampler, uv).r;
        }`),a}getInputsSamplingSnippets(){const a={},t=this.context.outputTextureLayout;return this.context.programInfo.inputNames.forEach((e,r)=>{const i=this.context.inputTextureLayouts[r],h=(0,p.generateShaderFuncNameFromInputSamplerName)(e);i.isPacked?a[h]=this.getPackedSamplerFromInput(h,e,i):a[h]=this.getUnpackedSamplerFromInput(h,e,i);const g=(0,p.generateShaderFuncNameFromInputSamplerNameAtOutCoords)(e);i.unpackedShape.length<=t.unpackedShape.length&&(i.isPacked?a[g]=this.getPackedSamplerAtOutputCoords(g,i,t,e):a[g]=this.getUnpackedSamplerAtOutputCoords(g,i,t,e))}),a}getPackedSamplerAtOutputCoords(a,t,e,r){const i=t.unpackedShape,h=e.unpackedShape,g=r,m=(0,p.generateShaderFuncNameFromInputSamplerName)(g),b=i.length,y=h.length,w=u.BroadcastUtil.getBroadcastDims(i,h),T=(0,p.getCoordsDataType)(y),S=y-b;let A;const O=(0,p.getGlChannels)();A=b===0?"":y<2&&w.length>=1?"coords = 0;":w.map(N=>`coords.${O[N+S]} = 0;`).join(`
`);let v="";v=y<2&&b>0?"coords":i.map((N,G)=>`coords.${O[G+S]}`).join(", ");let M="return outputValue;";const F=u.ShapeUtil.size(i)===1,R=u.ShapeUtil.size(h)===1;if(b!==1||F||R){if(F&&!R)M=y===1?`
          return vec4(outputValue.x, outputValue.x, 0., 0.);
        `:`
          return vec4(outputValue.x);
        `;else if(w.length){const N=b-2,G=b-1;w.indexOf(N)>-1&&w.indexOf(G)>-1?M="return vec4(outputValue.x);":w.indexOf(N)>-1?M="return vec4(outputValue.x, outputValue.y, outputValue.x, outputValue.y);":w.indexOf(G)>-1&&(M="return vec4(outputValue.xx, outputValue.zz);")}}else M=`
        return vec4(outputValue.xy, outputValue.xy);
      `;const $=`
      vec4 ${a}() {
        ${T} coords = getOutputCoords();
        
        int lastDim = coords.${O[y-1]};
        coords.${O[y-1]} = coords.${O[y-2]};
        coords.${O[y-2]} = lastDim;
      
        ${A}
        vec4 outputValue = ${m}(${v});
        ${M}
      }
    `;return new l.GlslLibRoutine($,["coordinates.getOutputCoords"])}getUnpackedSamplerAtOutputCoords(a,t,e,r){const i=[e.width,e.height],h=[t.width,t.height],g=t.unpackedShape.length,m=e.unpackedShape.length,b=t.unpackedShape,y=e.unpackedShape,w=(0,p.generateShaderFuncNameFromInputSamplerName)(r);if(g===m&&u.ArrayUtil.arraysEqual(h,i)){const R=`
          float ${a}() {
            return sampleTexture(${r}, TexCoords);
          }
        `;return new l.GlslLibRoutine(R,["coordinates.sampleTexture"])}const T=(0,p.getCoordsDataType)(m),S=u.BroadcastUtil.getBroadcastDims(b,y),A=m-g;let O;const v=(0,p.getGlChannels)();O=g===0?"":m<2&&S.length>=1?"coords = 0;":S.map(R=>`coords.${v[R+A]} = 0;`).join(`
`);let M="";M=m<2&&g>0?"coords":t.unpackedShape.map((R,$)=>`coords.${v[$+A]}`).join(", ");const F=`
        float ${a}() {
          ${T} coords = getOutputCoords();
          ${O}
          return ${w}(${M});
        }
      `;return new l.GlslLibRoutine(F,["coordinates.getOutputCoords"])}getPackedSamplerFromInput(a,t,e){switch(e.unpackedShape.length){case 0:return this.getPackedSamplerScalar(a,t);case 1:return this.getPackedSampler1D(a,t,e);case 2:return this.getPackedSampler2D(a,t,e);case 3:return this.getPackedSampler3D(a,t,e);default:return this.getPackedSamplerND(a,t,e)}}getUnpackedSamplerFromInput(a,t,e){const r=e.unpackedShape;switch(r.length){case 0:return this.getUnpackedSamplerScalar(a,t,e);case 1:return this.getUnpackedSampler1D(a,t,e);case 2:return this.getUnpackedSampler2D(a,t,e);case 3:return this.getUnpackedSampler3D(a,t,e);case 4:return this.getUnpackedSampler4D(a,t,e);case 5:return this.getUnpackedSampler5D(a,t,e);case 6:return this.getUnpackedSampler6D(a,t,e);default:throw new Error(`Unsupported dimension ${r.length}-D`)}}getPackedSamplerScalar(a,t){const e=`
          vec4 ${a}() {
            return ${(0,d.getGlsl)(this.context.glContext.version).texture2D}(${t}, halfCR);
          }
        `;return new l.GlslLibRoutine(e)}getPackedSampler1D(a,t,e){const r=[e.width,e.height],i=[r[1],r[0]],h=(0,d.getGlsl)(this.context.glContext.version),g=`vec4 ${a}(int index) {
      vec2 uv = packedUVfrom1D(
      ${i[0]}, ${i[1]}, index);
      return ${h.texture2D}(${t}, uv);
    }`;return new l.GlslLibRoutine(g,["coordinates.packedUVfrom1D"])}getPackedSampler2D(a,t,e){const r=e.unpackedShape,i=[e.width,e.height],h=(0,d.getGlsl)(this.context.glContext.version),g=i[0],m=i[1];if(i!=null&&u.ArrayUtil.arraysEqual(r,i)){const T=`vec4 ${a}(int row, int col) {
        vec2 uv = (vec2(col, row) + halfCR) / vec2(${m}.0, ${g}.0);
        return ${h.texture2D}(${t}, uv);
      }`;return new l.GlslLibRoutine(T)}const b=i,y=Math.ceil(r[1]/2),w=`vec4 ${a}(int row, int col) {
      vec2 uv = packedUVfrom2D(${b[1]}, ${b[0]}, ${y}, row, col);
      return ${h.texture2D}(${t}, uv);
    }`;return new l.GlslLibRoutine(w,["coordinates.packedUVfrom2D"])}getPackedSampler3D(a,t,e){const r=e.unpackedShape,i=[e.width,e.height],h=[i[0],i[1]],g=(0,d.getGlsl)(this.context.glContext.version);if(r[0]===1){const T=r.slice(1),S=[1,2],A=(0,p.squeezeInputShape)(r,T),O=["b","row","col"],v=JSON.parse(JSON.stringify(e));v.unpackedShape=A;const M=this.getPackedSamplerFromInput(a,t,v),F=`${M.routineBody}
      vec4 ${a}(int b, int row, int col) {
        return ${a}(${(0,p.getSqueezedParams)(O,S)});
      } `;return new l.GlslLibRoutine(F,M.dependencies)}const m=h[0],b=h[1],y=Math.ceil(r[2]/2),w=`vec4 ${a}(int b, int row, int col) {
      vec2 uv = packedUVfrom3D(
        ${b}, ${m}, ${y*Math.ceil(r[1]/2)}, ${y}, b, row, col);
      return ${g.texture2D}(${t}, uv);}`;return new l.GlslLibRoutine(w,["coordinates.packedUVfrom3D"])}getPackedSamplerND(a,t,e){const r=e.unpackedShape,i=r.length,h=[e.width,e.height],g=(0,d.getGlsl)(this.context.glContext.version),m=[h[0],h[1]],b=m[1],y=m[0],w=Math.ceil(r[i-1]/2);let T=w*Math.ceil(r[i-2]/2),S="int b, int row, int col",A=`b * ${T} + (row / 2) * ${w} + (col / 2)`;for(let v=2;v<i-1;v++)S=`int b${v}, `+S,T*=r[i-v-1],A=`b${v} * ${T} + `+A;const O=`vec4 ${a}(${S}) {
      int index = ${A};
      int texR = index / ${y};
      int texC = index - texR * ${y};
      vec2 uv = (vec2(texC, texR) + halfCR) / vec2(${y}, ${b});
      return ${g.texture2D}(${t}, uv);
    }`;return new l.GlslLibRoutine(O)}getUnpackedSamplerScalar(a,t,e){const[r,i]=[e.width,e.height];if(r===1&&i===1){const g=`
          float ${a}() {
            return sampleTexture(${t}, halfCR);
          }
        `;return new l.GlslLibRoutine(g,["coordinates.sampleTexture"])}const h=`
        float ${a}() {
          int offset_${t} = coordsToOffset(TexCoords, ${r}, ${i});
          vec2 uv = uvFromFlat(${r}, ${i}, offset_${t});
          return sampleTexture(${t}, uv);
        }
      `;return new l.GlslLibRoutine(h,["coordinates.uvFromFlat","coordinates.sampleTexture","coordinates.coordsToOffset"])}getUnpackedSampler1D(a,t,e){const r=e.width,i=e.height;if(i===1&&r===1){const g=`
        float ${a}(int index) {
          return sampleTexture(${t}, halfCR);
        }
      `;return new l.GlslLibRoutine(g,["coordinates.sampleTexture"])}if(i===1){const g=`
          float ${a}(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 ${a}(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 h=`
        float ${a}(int index) {
          vec2 uv = uvFromFlat(${r}, ${i}, index);
          return sampleTexture(${t}, uv);
        }
      `;return new l.GlslLibRoutine(h,["coordinates.uvFromFlat","coordinates.sampleTexture"])}getUnpackedSampler2D(a,t,e){const r=e.unpackedShape,i=[e.height,e.width];if(i!=null&&u.ArrayUtil.arraysEqual(r,i)){const T=`
          float ${a}(int row, int col) {
            vec2 uv = (vec2(row, col) + halfCR) / vec2(${i[1]}.0, ${i[0]}.0);
            return sampleTexture(${t}, uv);
          }
        `;return new l.GlslLibRoutine(T,["coordinates.sampleTexture"])}const{newShape:h,keptDims:g}=(0,s.squeezeShape)(r),m=h;if(m.length<r.length){const T=(0,p.squeezeInputShape)(r,m),S=JSON.parse(JSON.stringify(e));S.unpackedShape=T;const A=["col","row"],O=`
          ${this.getUnpackedSamplerFromInput(a,t,S).routineBody}
          float ${a}(int row, int col) {
            return ${a}(${(0,p.getSqueezedParams)(A,g)});
          }
        `;return new l.GlslLibRoutine(O,["coordinates.sampleTexture"])}const b=i[1],y=i[0];if(y===1){const T=`
          float ${a}(int row, int col) {
            int offset_${t} = coordsToOffset(TexCoords, ${b}, ${y});
            float index = dot(vec3(row, col, offset_${t}), vec3(${r[1]}, 1, 1));
            vec2 uv = vec2(0.5, (index + 0.5) / ${b}.0);
            return sampleTexture(${t}, uv);
          }
        `;return new l.GlslLibRoutine(T,["coordinates.sampleTexture","coordinates.coordsToOffset"])}if(b===1){const T=`
          float ${a}(int row, int col) {
            int offset_${t} = coordsToOffset(TexCoords, ${b}, ${y});
            float index = dot(vec3(row, col, offset_${t}), vec3(${r[1]}, 1, 1));
            vec2 uv = vec2((index + 0.5) / ${y}.0, 0.5);
            return sampleTexture(${t}, uv);
          }
        `;return new l.GlslLibRoutine(T,["coordinates.sampleTexture","coordinates.coordsToOffset"])}const w=`
        float ${a}(int row, int col) {
          int index = col * ${r[1]} + row;
          vec2 uv = uvFromFlat(${b}, ${y}, index);
          return sampleTexture(${t}, uv);
        }
      `;return new l.GlslLibRoutine(w,["coordinates.uvFromFlat","coordinates.sampleTexture","coordinates.coordsToOffset"])}getUnpackedSampler3D(a,t,e){const r=e.unpackedShape,i=r[1]*r[2],h=r[2],{newShape:g,keptDims:m}=(0,s.squeezeShape)(r),b=g;if(b.length<r.length){const w=(0,p.squeezeInputShape)(r,b),T=["batch","col","row"],S=JSON.parse(JSON.stringify(e));S.unpackedShape=w;const A=this.getUnpackedSamplerFromInput(a,t,S),O=m.reverse(),v=`
          ${A.routineBody}
          float ${a}(int batch, int row, int col) {
            return ${a}(${(0,p.getSqueezedParams)(T,O)});
          }
        `;return new l.GlslLibRoutine(v,A.dependencies)}const y=`
          float ${a}(int depth, int row, int col) {
            // Explicitly use integer operations as dot() only works on floats.
            int index = depth * ${i} + col * ${h} + row;
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        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)));
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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;
      }
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    ${this.getImports(f)}
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`)}}},5483:(_,n,o)=>{Object.defineProperty(n,"__esModule",{value:!0}),n.glslRegistry=void 0;const u=o(5107),l=o(7341),d=o(9894),s=o(2655),p=o(3891);n.glslRegistry={encoding:l.EncodingGlslLib,fragcolor:d.FragColorGlslLib,vec:p.VecGlslLib,shapeUtils:s.ShapeUtilsGlslLib,coordinates:u.CoordsGlslLib}},2655:(_,n,o)=>{Object.defineProperty(n,"__esModule",{value:!0}),n.ShapeUtilsGlslLib=void 0;const u=o(8520);class l extends u.GlslLib{constructor(s){super(s)}getFunctions(){return Object.assign(Object.assign(Object.assign(Object.assign(Object.assign({},this.bcastIndex()),this.bcastMatmulIndex()),this.offsetToIndices()),this.indicesToOffset()),this.incrementIndices())}getCustomTypes(){return{}}bcastIndex(){const s=this.context.outputTextureLayout.shape.length,p={};return this.context.programInfo.inputNames.forEach((f,c)=>{const a=this.context.inputTextureLayouts[c].unpackedShape;if(a.length<=s){const t=a.length,e=s-t,r=`bcastIndices_${f}`;let i="";for(let g=0;g<t;++g)i+=`
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        void ${r} (int bcastedIndices[${s}], out int realIndices[${t}]) {
          ${i}
        }
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          `;const h=`
        void ${r}(int bcastedIndices[${s}], out int realIndices[${t}]) {
          ${i}
          realIndices[${t-1}] = bcastedIndices[${s-1}];
          realIndices[${t-2}] = bcastedIndices[${s-2}];
        }
        `;p[r]=new u.GlslLibRoutine(h)}}),p}indicesToOffset(){const s={};return this.context.programInfo.inputNames.forEach((p,f)=>{const c=this.context.inputTextureLayouts[f].shape,a=this.context.inputTextureLayouts[f].strides,t=c.length;let e=`indicesToOffset_${p}`;s[e]=new u.GlslLibRoutine(l.indexToOffsetSingle(e,t,a)),e=`indicesToOffset_${p}_T`,s[e]=new u.GlslLibRoutine(l.indexToOffsetSingle(e,t,a.slice().reverse()))}),s}static indexToOffsetSingle(s,p,f){let c="";for(let a=p-1;a>=0;--a)c+=`
        offset += indices[${a}] * ${f[a]};
        `;return`
      int ${s}(int indices[${p}]) {
        int offset = 0;
        ${c}
        return offset;
      }
      `}offsetToIndices(){const s={};return this.context.programInfo.inputNames.forEach((p,f)=>{const c=this.context.inputTextureLayouts[f].shape,a=this.context.inputTextureLayouts[f].strides,t=c.length;let e=`offsetToIndices_${p}`;s[e]=new u.GlslLibRoutine(l.offsetToIndicesSingle(e,t,a)),e=`offsetToIndices_${p}_T`,s[e]=new u.GlslLibRoutine(l.offsetToIndicesSingle(e,t,a.slice().reverse()))}),s}static offsetToIndicesSingle(s,p,f){const c=[];for(let a=0;a<p-1;++a)c.push(`
      indices[${a}] = offset / ${f[a]};`),c.push(`
        offset -= indices[${a}] * ${f[a]};`);return c.push(`
      indices[${p-1}] = offset;`),`
      void ${s}(int offset, out int indices[${p}]) {
        ${c.join("")}
      }
      `}incrementIndices(){const s={};return this.context.programInfo.inputNames.forEach((p,f)=>{const c=this.context.inputTextureLayouts[f].shape,a=c.length,t=`incrementIndices_${p}`;let e="";for(let i=0;i<a;++i)e+=`
        shape[${i}] = ${c[i]};`;const r=`
        void ${t}(int axis, out int indices[${a}]) {
          int shape[${a}];
          ${e};
          for(int i = ${a} -1 ; i >= 0; --i) {
            if(i > axis) continue;
            indices[i] += 1;
            if(indices[i] < shape[i]) {
              break;
            }
            indices[i] = 0;
          }
        }
        `;s[t]=new u.GlslLibRoutine(r)}),s}}n.ShapeUtilsGlslLib=l},5060:(_,n)=>{Object.defineProperty(n,"__esModule",{value:!0}),n.getDefaultFragShaderMain=n.getFragShaderPreamble=n.getVertexShaderSource=n.getGlsl=void 0;const o={version:"",attribute:"attribute",varyingVertex:"varying",varyingFrag:"varying",texture2D:"texture2D",output:"gl_FragColor",outputDeclaration:""},u={version:"#version 300 es",attribute:"in",varyingVertex:"out",varyingFrag:"in",texture2D:"texture",output:"outputColor",outputDeclaration:"out vec4 outputColor;"};function l(d){return d===1?o:u}n.getGlsl=l,n.getVertexShaderSource=function(d){const s=l(d);return`${s.version}
      precision highp float;
      ${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(d){const s=l(d);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(d,s){return`
  void main() {
    int indices[${s}];
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    vec4 result = vec4(process(indices));
    ${l(d).output} = result;
  }
  `}},3891:(_,n,o)=>{Object.defineProperty(n,"__esModule",{value:!0}),n.VecGlslLib=void 0;const u=o(8520);class l extends u.GlslLib{constructor(s){super(s)}getCustomTypes(){return{}}getFunctions(){return Object.assign(Object.assign(Object.assign(Object.assign({},this.binaryVecFunctions()),this.copyVec()),this.setVecItem()),this.getVecItem())}binaryVecFunctions(){const s=this.context.outputTextureLayout.shape.length,p={add:"+=",sub:"-=",mul:"*=",div:"/="},f={};for(const c in p){const a=`${c}Vec`;let t="";for(let r=0;r<s;++r)t+=`
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        ${p}
      }
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        if(index < 0)
            index =${s} + index;
        if (index == 0)
            m[0] = value;
        `;for(let c=1;c<s-1;++c)p+=`
        else if (index == ${c})
            m[${c}] = value;
            `;p+=`
        else
            m[${s-1}] = value;
        `;const f=`
      void setVecItem(out int m[${s}], int index, int value) {
        ${p}
      }
        `;return{setVecItem:new u.GlslLibRoutine(f)}}getVecItem(){const s=this.context.outputTextureLayout.shape.length;let p=`
        if(index < 0)
            index = ${s} + index;
        if (index == 0)
            return m[0];
      `;for(let c=1;c<s-1;++c)p+=`
        else if (index == ${c})
            return m[${c}];
      `;p+=`
        else
            return m[${s-1}];
        `;const f=`
      int getVecItem(int m[${s}], int index) {
        ${p}
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      ${$.output} = result;
    }
    `;return{name:A.name,inputNames:["A","B"],inputTypes:[v,v],output:{dims:S[0].dims,type:O,textureType:v},shaderSource:N,hasMain:!0}};n.add=(T,S)=>[T.run(y(T,S,p()),S)],n.and=(T,S)=>[T.run(y(T,S,i(),"bool"),S)],n.div=(T,S)=>[T.run(y(T,S,f()),S)],n.equal=(T,S)=>[T.run(y(T,S,t(),"bool"),S)],n.greater=(T,S)=>[T.run(y(T,S,e(),"bool"),S)],n.less=(T,S)=>[T.run(y(T,S,r(),"bool"),S)],n.mul=(T,S)=>[T.run(y(T,S,c()),S)],n.or=(T,S)=>[T.run(y(T,S,h(),"bool"),S)],n.pow=(T,S)=>[T.run(y(T,S,m()),S)],n.pRelu=(T,S)=>[T.run(y(T,S,b()),S)],n.sub=(T,S)=>[T.run(y(T,S,a()),S)],n.xor=(T,S)=>[T.run(y(T,S,g(),"bool"),S)]},4196:(_,n,o)=>{Object.defineProperty(n,"__esModule",{value:!0}),n.parseCastAttributes=n.cast=void 0;const u=o(2517);n.cast=(d,s,p)=>(l(s),[d.cast(s[0],p)]),n.parseCastAttributes=d=>u.ProtoUtil.tensorDataTypeFromProto(d.attributes.getInt("to"));const l=d=>{if(!d||d.length!==1)throw new Error("Cast requires 1 input.");if(d[0].type==="string")throw new Error("Invalid input type.")}},1163:(_,n,o)=>{Object.defineProperty(n,"__esModule",{value:!0}),n.createPackedConcatProgramInfoLoader=void 0;const u=o(5060),l=o(2039),d=o(9390),s=o(2827);n.createPackedConcatProgramInfoLoader=(f,c,a)=>{const t=(e=c.length,r=a.cacheKey,{name:"Concat (packed)",inputNames:Array.from({length:e},(i,h)=>`X${h}`),inputTypes:Array(e).fill(l.TextureType.packed),cacheHint:r});var e,r;return Object.assign(Object.assign({},t),{get:()=>((i,h,g,m)=>{const b=g[0].dims.slice();if(m>=b.length||m<-1*b.length)throw new Error("axis specified for concat doesn't match input dimensionality");m<0&&(m=b.length+m);const y=b.slice(0);for(let X=1;X<g.length;X++){const Z=g[X].dims.slice();for(let ee=0;ee<b.length;ee++)if(ee===m)y[m]+=Z[ee];else if(b[ee]!==Z[ee])throw new Error("non concat dimensions must match")}const w=y.length,T=(0,s.getChannels)("coords",w),S=(0,d.getCoordsDataType)(w),A=(0,s.unpackFromChannel)(),O=g.map(X=>X.dims),v=(0,d.getGlChannels)(w),M=new Array(O.length-1);M[0]=O[0][m];for(let X=1;X<M.length;X++)M[X]=M[X-1]+O[X][m];const F=v[m],R=v.slice(-2),$=v.join();let N=`if (${F} < ${M[0]}) {
        return getChannel(
            getX0(${$}), vec2(${R.join()}));
        }`;for(let X=1;X<M.length;X++){const Z=M[X-1];N+=`
            if (${F} < ${M[X]}  && ${F} >= ${M[X-1]}) {
              return getChannel(
                getX${X}(${p(v,F,Z)}),
                vec2(${p(R,F,Z)}));
            }`}const G=M.length,D=M[M.length-1];N+=`
            return getChannel(
              getX${G}(${p(v,F,D)}),
              vec2(${p(R,F,D)}));`;const j=(0,u.getGlsl)(i.session.backend.glContext.version),Q=`
          ${A}
          float getValue(${v.map(X=>"int "+X)}) {
            ${N}
          }

          void main() {
            ${S} coords = getOutputCoords();
            int lastDim = coords.${v[w-1]};
            coords.${v[w-1]} = coords.${v[w-2]};
            coords.${v[w-2]} = lastDim;

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

            ${T[w-1]} = ${T[w-1]} + 1;
            if (${T[w-1]} < ${y[w-1]}) {
              result.g = getValue(${T});
            }

            ${T[w-2]} = ${T[w-2]} + 1;
            if (${T[w-2]} < ${y[w-2]}) {
              result.a = getValue(${T});
            }

            ${T[w-1]} = ${T[w-1]} - 1;
            if (${T[w-2]} < ${y[w-2]} &&
                ${T[w-1]} < ${y[w-1]}) {
              result.b = getValue(${T});
            }
            ${j.output} = result;
          }
        `;return Object.assign(Object.assign({},h),{output:{dims:y,type:g[0].type,textureType:l.TextureType.packed},shaderSource:Q,hasMain:!0})})(f,t,c,a.axis)})};const p=(f,c,a)=>{const t=f.indexOf(c);return f.map((e,r)=>r===t?`${e} - ${a}`:e).join()}},2069:(_,n,o)=>{Object.defineProperty(n,"__esModule",{value:!0}),n.parseConcatAttributes=n.concat=void 0;const u=o(246),l=o(2039),d=o(1163);n.concat=(e,r,i)=>(t(r),e.session.pack&&r[0].dims.length>1?[e.run((0,d.createPackedConcatProgramInfoLoader)(e,r,i),r)]:[e.run(s(e,r,i),r)]);const s=(e,r,i)=>{const h=(g=r.length,m=i.cacheKey,{name:"Concat",inputNames:Array.from({length:g},(b,y)=>`X${y}`),inputTypes:Array(g).fill(l.TextureType.unpacked),cacheHint:m});var g,m;return Object.assign(Object.assign({},h),{get:()=>((b,y,w,T)=>{const S=w[0].dims.slice();if(T>=S.length||T<-1*S.length)throw new Error("axis specified for concat doesn't match input dimensionality");T<0&&(T=S.length+T);const A=S.slice(0);for(let $=1;$<w.length;$++){const N=w[$].dims.slice();for(let G=0;G<S.length;G++)if(G===T)A[T]+=N[G];else if(S[G]!==N[G])throw new Error("non concat dimensions must match")}const O=A.length,v=new Array(w.length);let M=0;for(let $=0;$<v.length;++$)M+=w[$].dims[T],v[$]=M;let F="";F=w.length<5?p(v):f(v);const R=`
        ${c(w.length,O)}
        ${a(v)}
        ${F}
        float process(int indices[${O}]) {
          int textureIndex = getTextureWhereDataResides (indices[${T}]);

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

          return fetchDataFromCorrectTexture(textureIndex, indices);
        }`;return Object.assign(Object.assign({},y),{output:{dims:A,type:w[0].type,textureType:l.TextureType.unpacked},shaderSource:R})})(0,h,r,i.axis)})},p=e=>`int getTextureWhereDataResides(int index) {
      ${e.map((r,i)=>`if(index<${r}) {return ${i};}
`).join("")}
    }`,f=e=>p(e),c=(e,r)=>{const i=[`float fetchDataFromCorrectTexture(int textureIndex, int indices[${r}]) {`];for(let h=0;h<e;++h)h===0?i.push(`	if (textureIndex == ${h}) { return _X${h}(indices); }`):h===e-1?i.push(`	else { return _X${h}(indices); }`):i.push(`	else if (textureIndex == ${h}) { return _X${h}(indices); }`);return i.push("	}"),i.join(`
`)},a=e=>{const r=["int getSizeInConcatAxisValueFromIndex(int index) {"];for(let i=0;i<e.length;++i)i===0?r.push(`	if (index == ${i}) { return ${e[i]}; }`):i===e.length-1?r.push(`	else { return ${e[i]}; }`):r.push(`	else if (index == ${i}) { return ${e[i]}; }`);return r.push("	}"),r.join(`
`)};n.parseConcatAttributes=e=>(0,u.createAttributeWithCacheKey)({axis:e.attributes.getInt("axis")});const t=e=>{if(!e||e.length<1)throw new Error("too few inputs");const r=e[0].type,i=e[0].dims.length;if(r==="string")throw new Error("string tensor is not supported yet");for(const h of e){if(h.type!==r)throw new Error("input tensors should be one type");if(h.dims.length!==i)throw new Error("input tensors should have the same shape")}}},4770:(_,n,o)=>{Object.defineProperty(n,"__esModule",{value:!0}),n.createUnpackedGroupedConvProgramInfoLoader=void 0;const u=o(6231),l=o(5060),d=o(2039),s=o(8138),p=o(2823);n.createUnpackedGroupedConvProgramInfoLoader=(f,c,a)=>{const t=(e=c.length>2,r=a.cacheKey,{name:"GroupedConv",inputNames:e?["X","W","Bias"]:["X","W"],inputTypes:e?[d.TextureType.unpacked,d.TextureType.unpacked,d.TextureType.unpacked]:[d.TextureType.unpacked,d.TextureType.unpacked],cacheHint:r});var e,r;return Object.assign(Object.assign({},t),{get:()=>((i,h,g,m)=>{const b=h.length>2?"value += getBias(output_channel);":"",y=h[0].dims.slice(),w=h[1].dims.slice(),T=w[0]/m.group;u.Logger.verbose("GroupedConv",`autpPad:${m.autoPad}, dilations:${m.dilations}, group:${m.group}, kernelShape:${m.kernelShape}, pads:${m.pads}, strides:${m.strides}`);const S=(0,s.calculateOutputShape)(y,w,m.dilations,m.pads,m.strides),A=(0,l.getGlsl)(i.session.backend.glContext.version),{activationFunction:O,applyActivation:v}=(0,p.getActivationSnippet)(m),M=`
  const ivec2 strides = ivec2(${m.strides[0]}, ${m.strides[1]});
  const ivec2 pads = ivec2(${m.pads[0]}, ${m.pads[1]});
  ${O}
  void main() {
    ivec4 coords = getOutputCoords();
    int batch = coords.x;
    int output_channel = coords.y;
    ivec2 xRCCorner = coords.zw * strides - pads;
    int group_id = output_channel / ${T};

    float value = 0.0;
    for (int wInChannel = 0; wInChannel < ${w[1]}; wInChannel++) {
      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 >= ${y[2]}) {
          continue;
        }

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

          float xVal = getX(batch, input_channel, xWidth, xHeight);
          float wVal = getW(output_channel, wInChannel, wWidth, wHeight);
          value += xVal*wVal;
        }
      }
    }
    ${b}
    ${v}
    ${A.output} = vec4(value, .0, .0, .0);
  }
`;return Object.assign(Object.assign({},g),{output:{dims:S,type:h[0].type,textureType:d.TextureType.unpacked},shaderSource:M,hasMain:!0})})(f,c,t,a)})}},1386:(_,n,o)=>{Object.defineProperty(n,"__esModule",{value:!0}),n.conv2DPacked=n.conv2DPackedPointwise=void 0;const u=o(8138),l=o(8555),d=o(708);n.conv2DPackedPointwise=(s,p,f)=>{const c=p[0].dims,a=p[1].dims,t=(0,u.calculateOutputShape)(c,a,f.dilations,f.pads,f.strides),e=s.reshapePacked(p[0],[c[1],c[2]*c[3]]),r=s.reshapePacked(p[1],[a[0],a[1]]),i=p.length>2?[r,e,p[2]]:[r,e],h=s.run((0,d.createPackedMatmulProgramInfoLoader)(s,i,f),i);return s.reshapePacked(h,t)},n.conv2DPacked=(s,p,f)=>{const c=p[0].dims,a=p[1].dims,t=(0,u.calculateOutputShape)(c,a,f.dilations,f.pads,f.strides),e=s.run((0,l.createPackedIm2ColProgramInfoLoader)(s,p[0],p[1],t,f),[p[0]]),r=s.reshapePacked(p[1],[a[0],a[1]*a[2]*a[3]]),i=p.length===3?[r,e,p[2]]:[r,e],h=s.run((0,d.createPackedMatmulProgramInfoLoader)(s,i,f),i);return s.reshapePacked(h,t)}},9663:(_,n,o)=>{Object.defineProperty(n,"__esModule",{value:!0}),n.parseConvTransposeAttributes=n.convTranspose=void 0;const u=o(246),l=o(5060),d=o(2039),s=o(2823),p=(r,i,h,g,m,b)=>(r-1)*i+h+(g-1)*m+1-b,f=(r,i,h,g,m)=>{const b=Math.floor(r/2);i==="SAME_UPPER"?(h[g]=b,h[m]=r-b):i==="SAME_LOWER"&&(h[g]=r-b,h[m]=b)};n.convTranspose=(r,i,h)=>(e(i,h),c(r,i,h));const c=(r,i,h)=>{const g=t(h,i);return[a(r,i,g)]},a=(r,i,h)=>r.run(((g,m,b)=>{const y=(w=m.length>2,T=b.cacheKey,{name:"ConvTranspose",inputNames:w?["X","W","B"]:["X","W"],inputTypes:w?[d.TextureType.unpacked,d.TextureType.unpacked,d.TextureType.unpacked]:[d.TextureType.unpacked,d.TextureType.unpacked],cacheHint:T});var w,T;return Object.assign(Object.assign({},y),{get:()=>((S,A,O,v)=>{const M=A.length>2?"getB(output_channel)":"0.0",F=A[0].dims,R=A[1].dims,$=R[1],N=R[0]/v.group,G=[A[0].dims[0],A[1].dims[1]*v.group,...v.outputShape],D=(0,l.getGlsl)(S.session.backend.glContext.version),{activationFunction:j,applyActivation:Q}=(0,s.getActivationSnippet)(v),X=`
  const ivec2 strides = ivec2(${v.strides[0]}, ${v.strides[1]});
  const ivec2 pads = ivec2(${v.pads[0]}, ${v.pads[1]});
  ${j}
  void main() {
    ivec4 coords = getOutputCoords();
    int batch = coords.x;
    int output_channel = coords.y;

    ivec2 loc = coords.zw + pads;

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

    float value = ${M};
    for (int inChannelOffset = 0; inChannelOffset < ${N}; inChannelOffset++) {
      int input_channel = group_id * ${N} + inChannelOffset;
      for (int wWOff = 0; wWOff < ${R[2]}; wWOff++) {
        for (int wHOff = 0; wHOff < ${R[3]}; wHOff++) {
          ivec2 wOff = ivec2(wWOff * ${v.dilations[0]}, wHOff * ${v.dilations[1]});
          ivec2 wLoc = loc - wOff;
          ivec2 wLocIn = wLoc / strides;
          if (
            wLocIn * strides == wLoc &&
            wLocIn.x >= 0 && wLocIn.x < ${F[2]} &&
            wLocIn.y >= 0 && wLocIn.y < ${F[3]}
          ) {
            float xVal = getX(batch, input_channel, wLocIn.y, wLocIn.x);
            float wVal = getW(input_channel, wOutChannel, wHOff, wWOff);
            value += xVal * wVal;
          }
        }
      }
    }
    ${Q}
    ${D.output} = vec4(value, .0, .0, .0);
  }
`;return Object.assign(Object.assign({},O),{output:{dims:G,type:A[0].type,textureType:d.TextureType.unpacked},shaderSource:X,hasMain:!0})})(g,m,y,b)})})(r,i,h),i),t=(r,i)=>{const h=r.kernelShape.slice();if(r.kernelShape.length===0)for(let y=2;y<i[1].dims.length;++y)h.push(i[1].dims[y]);const g=r.pads.slice(),m=r.outputShape.slice();((y,w,T,S,A,O,v,M)=>{const F=y.length-2,R=M.length===0;for(let $=0;$<F;++$){const N=R?y[$+2]*O[$]:M[$],G=p(y[$+2],O[$],A[$],w[$],T[$],N);f(G,S,A,$,$+F),R&&M.push(O[$]*(y[$+2]-1)+v[$]+(w[$]-1)*T[$]+1-A[$]-A[$+F])}})(i[0].dims,h,r.dilations,r.autoPad,g,r.strides,r.outputPadding,m);const b=Object.assign({},r);return Object.assign(b,{kernelShape:h,pads:g,outputShape:m,cacheKey:r.cacheKey}),b};n.parseConvTransposeAttributes=r=>{const i=r.attributes,h=(0,s.parseInternalActivationAttributes)(i),g=i.getString("auto_pad","NOTSET"),m=i.getInts("dilations",[1,1]),b=i.getInt("group",1),y=i.getInts("kernel_shape",[]),w=i.getInts("output_padding",[0,0]),T=i.getInts("output_shape",[]),S=i.getInts("pads",[0,0,0,0]),A=i.getInts("strides",[1,1]);return(0,u.createAttributeWithCacheKey)(Object.assign({autoPad:g,dilations:m,group:b,kernelShape:y,outputPadding:w,outputShape:T,pads:S,strides:A},h))};const e=(r,i)=>{if(!r||r.length!==2&&r.length!==3)throw new Error("Conv requires 2 or 3 inputs");if(r[0].dims.length!==4||r[1].dims.length!==4)throw new Error("currently only support 2-dimensional conv");if(r[0].dims[1]!==r[1].dims[0])throw new Error("FILTER_IN_CHANNEL should be equal to DATA_CHANNEL");const h=r[1].dims[1]*i.group;if(r.length===3&&(r[2].dims.length!==1||r[2].dims[0]!==h))throw new Error("invalid bias");const g=r[0].dims.length-2;if(i.dilations.length!==g)throw new Error(`dilations should be ${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:(_,n,o)=>{Object.defineProperty(n,"__esModule",{value:!0}),n.parseConvAttributes=n.conv=n.calculateOutputShape=void 0;const u=o(246),l=o(2517),d=o(4770),s=o(1386),p=o(9828),f=o(2823),c=o(3248),a=o(5623);n.calculateOutputShape=(g,m,b,y,w)=>{const T=g[0],S=g.slice(2),A=S.length,O=m[0],v=m.slice(2).map((F,R)=>F+(F-1)*(b[R]-1)),M=S.map((F,R)=>F+y[R]+y[R+A]).map((F,R)=>Math.floor((F-v[R]+w[R])/w[R]));return[T,O].concat(...M)},n.conv=(g,m,b)=>(h(m,b),t(g,m,b));const t=(g,m,b)=>{const y=i(b,m),w=g.session.pack,T=y.kernelShape[0]===1&&y.kernelShape[1]===1;return y.group>1?[g.run((0,d.createUnpackedGroupedConvProgramInfoLoader)(g,m,y),m)]:T&&w?[e(g,m,y)]:w&&m[0].dims.length===4&&m[0].dims[0]===1&&!T?[(0,s.conv2DPacked)(g,m,y)]:[r(g,m,y)]},e=(g,m,b)=>{const y=m[0].dims,w=m[1].dims,T=(0,n.calculateOutputShape)(y,w,b.dilations,b.pads,b.strides),S=g.reshapeUnpacked(m[0],[y[1],y[2]*y[3]]),A=g.reshapeUnpacked(m[1],[w[0],w[1]]),O=m.length>2?[A,S,m[2]]:[A,S],v=g.run((0,a.createMatmulProgramInfoLoader)(O,b),O);return g.reshapeUnpacked(v,T)},r=(g,m,b)=>{const y=m[0].dims,w=m[1].dims,T=(0,n.calculateOutputShape)(y,w,b.dilations,b.pads,b.strides),S=g.run((0,c.createIm2ColProgramInfoLoader)(g,m[0],m[1],T,b),[m[0]]),A=m.length===3?[S,m[1],m[2]]:[S,m[1]];return g.run((0,p.createDotProductProgramInfoLoader)(g,m,T,b),A)},i=(g,m)=>{const b=g.kernelShape.slice();if(g.kernelShape.length===0)for(let T=2;T<m[1].dims.length;++T)b.push(m[1].dims[T]);const y=g.pads.slice();l.PoolConvUtil.adjustPadsBasedOnAutoPad(m[0].dims,g.strides,g.dilations,b,y,g.autoPad);const w=Object.assign({},g);return Object.assign(w,{kernelShape:b,pads:y,cacheKey:g.cacheKey}),w};n.parseConvAttributes=g=>{const m=g.attributes,b=(0,f.parseInternalActivationAttributes)(m),y=m.getString("auto_pad","NOTSET"),w=m.getInts("dilations",[1,1]),T=m.getInt("group",1),S=m.getInts("kernel_shape",[]),A=m.getInts("pads",[0,0,0,0]),O=m.getInts("strides",[1,1]);return(0,u.createAttributeWithCacheKey)(Object.assign({autoPad:y,dilations:w,group:T,kernelShape:S,pads:A,strides:O},b))};const h=(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 ${2*b}D`);if(m.kernelShape.length!==0&&m.kernelShape.length!==g[1].dims.length-2)throw new Error("invalid kernel shape");if(g[0].type!=="float32"||g[1].type!=="float32")throw new Error("Conv input(X,W) should be float tensor");if(g.length===3&&g[2].type!=="float32")throw new Error("Conv input(bias) should be float tensor")}},5193:(_,n,o)=>{Object.defineProperty(n,"__esModule",{value:!0}),n.parseDepthToSpaceAttributes=n.depthToSpace=void 0;const u=o(3738);n.depthToSpace=(d,s,p)=>{l(s);const f=p.blocksize,c=f*f,a=p.mode==="DCR"?[0,3,4,1,5,2]:[0,1,4,2,5,3],t=p.mode==="DCR"?[s[0].dims[0],f,f,s[0].dims[1]/c,s[0].dims[2],s[0].dims[3]]:[s[0].dims[0],s[0].dims[1]/c,f,f,s[0].dims[2],s[0].dims[3]],e=d.reshapeUnpacked(s[0],t),r={perm:a,cacheKey:`${a}`},[i]=(0,u.transpose)(d,[e],r),h=[s[0].dims[0],s[0].dims[1]/c,s[0].dims[2]*f,s[0].dims[3]*f];return[d.reshapeUnpacked(i,h)]},n.parseDepthToSpaceAttributes=d=>{const s=d.attributes.getInt("blocksize");if(s<1)throw new Error(`blocksize must be >= 1, but got : ${s} for DepthToSpace`);const p=d.attributes.getString("mode","DCR");if(p!=="DCR"&&p!=="CRD")throw new Error(`unrecognized mode: ${p} for DepthToSpace`);return{mode:p,blocksize:s}};const l=d=>{if(d.length!==1)throw new Error(`DepthToSpace expect 1 inputs, but got ${d.length}`);if(d[0].type==="string"||d[0].dims.length!==4)throw new TypeError("DepthToSpace input should be a 4-D numeric tensor")}},9828:(_,n,o)=>{Object.defineProperty(n,"__esModule",{value:!0}),n.createDotProductProgramInfoLoader=void 0;const u=o(2517),l=o(5060),d=o(2039),s=o(2823),p=o(3248);n.createDotProductProgramInfoLoader=(f,c,a,t)=>{const e=((r,i)=>({name:"ConvDotProduct",inputNames:r?["Im2Col","K","B"]:["Im2Col","K"],inputTypes:r?[d.TextureType.unpacked,d.TextureType.packedLastDimension,d.TextureType.unpacked]:[d.TextureType.unpacked,d.TextureType.packedLastDimension],cacheKey:i.activationCacheKey}))(c.length>2,t);return Object.assign(Object.assign({},e),{get:()=>((r,i,h,g,m)=>{const b=h[0].dims,y=h[1].dims,w=[y[0],Math.ceil(b[1]*y[2]*y[3]/4)],T=(0,p.calculateIm2ColDims)(b,y,g),[S,A]=r.calculateTextureWidthAndHeight(w,d.TextureType.packedLastDimension),O=u.ShapeUtil.computeStrides(T),[v,M]=r.calculateTextureWidthAndHeight(T,d.TextureType.packedLastDimension),F=g.length,R=h.length<3?"0.0":"_B(b)",$=Math.ceil(b[1]*y[2]*y[3]/4),{activationFunction:N,applyActivation:G}=(0,s.getActivationSnippet)(m),D=(0,l.getGlsl)(r.session.backend.glContext.version),j=`
${N}
float process(int indices[${F}]) {
  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] * ${O[0]} + im2col[1] * ${O[1]} + im2col[2] * ${O[2]};
  int kernelOffset = indices[1] * ${w[1]};
  float value = ${R};
  for (int i = 0; i < ${$}; ++i) {
    vec2 im2colCoords = offsetToCoords(im2colOffset, ${v}, ${M});
    vec2 kernelCoords = offsetToCoords(kernelOffset, ${S}, ${A});
    value += dot(${D.texture2D}(Im2Col, im2colCoords), ${D.texture2D}(K, kernelCoords));
    ++im2colOffset;
    ++kernelOffset;
  }
  ${G}
  return value;
}`;return Object.assign(Object.assign({},i),{output:{dims:g,type:h[0].type,textureType:d.TextureType.unpacked},shaderSource:j})})(f,e,c,a,t)})}},7992:(_,n,o)=>{Object.defineProperty(n,"__esModule",{value:!0}),n.parseFlattenAttributes=n.flatten=void 0;const u=o(2517);n.flatten=(d,s,p)=>{l(s,p);const f=u.ShapeUtil.flattenShape(s[0].dims,p);return[d.reshapeUnpacked(s[0],f)]},n.parseFlattenAttributes=d=>d.attributes.getInt("axis",1);const l=(d,s)=>{if(!d||d.length!==1)throw new Error("Flatten requires 1 input.");const p=d[0].dims.length;if(p===0)throw new Error("scalar tensor is not supported.");if(s<-p||s>p)throw new Error("Invalid axis");if(d[0].type==="string")throw new Error("string tensor is not supported.")}},2823:(_,n,o)=>{Object.defineProperty(n,"__esModule",{value:!0}),n.parseInternalActivationAttributes=n.getActivationSnippet=void 0;const u=o(2517),l=o(4909);n.getActivationSnippet=function(d){let s;switch(d.activation){case"Relu":s=(0,l.glslRelu)();break;case"Sigmoid":s=(0,l.glslSigmoid)();break;case"Clip":s=(0,l.glslClip)(d.clipMin,d.clipMax);break;default:return{activationFunction:"",applyActivation:""}}const p=s.name;return{activationFunction:s.body,applyActivation:`value = ${p}_(value);`}},n.parseInternalActivationAttributes=d=>{const s=d.getString("activation","");if(s==="Clip"){const[p,f]=d.getFloats("activation_params",[u.MIN_CLIP,u.MAX_CLIP]);return{activation:s,clipMax:f,clipMin:p,activationCacheKey:`${s}:${p},${f}`}}return{activation:s,activationCacheKey:s}}},1253:(_,n,o)=>{Object.defineProperty(n,"__esModule",{value:!0}),n.parseGatherAttributes=n.gather=void 0;const u=o(246),l=o(782),d=o(2517),s=o(2039);n.gather=(a,t,e)=>(c(t,e.axis),[a.run(f(a,t,e),t)]),n.parseGatherAttributes=a=>(0,u.createAttributeWithCacheKey)({axis:a.attributes.getInt("axis",0)});const p={name:"Gather",inputNames:["A","B"],inputTypes:[s.TextureType.unpacked,s.TextureType.unpacked]},f=(a,t,e)=>{const r=Object.assign(Object.assign({},p),{cacheHint:e.cacheKey});return Object.assign(Object.assign({},r),{get:()=>((i,h,g,m)=>{const b=g[0].dims.slice(),y=g[1].dims.slice(),w=new Array(b.length+y.length-1);m=d.ShapeUtil.normalizeAxis(m,b.length);const T=[];for(let A=0;A<w.length;A++)A<m?(w[A]=b[A],T.push(`inputIdx[${A}] = outputIdx[${A}];`)):A<m+y.length?(w[A]=y[A-m],T.push(`indexDataIdx[${A-m}] = outputIdx[${A}];`)):(w[A]=b[A-y.length+1],T.push(`inputIdx[${A-y.length+1}] = outputIdx[${A}];`));const S=`
      float process(int outputIdx[${w.length||1}]) {
        int inputIdx[${b.length}];
        int indexDataIdx[${y.length||1}];
        indexDataIdx[0] = 0;
        ${T.join(`
        `)}
        int idx = int(_B(indexDataIdx));
        inputIdx[${m}] = idx < 0 ? idx + ${b[m]} : idx;
        return _A(inputIdx);
      }`;return Object.assign(Object.assign({},h),{output:{dims:w,type:g[0].type,textureType:s.TextureType.unpacked},shaderSource:S})})(0,r,t,e.axis)})},c=(a,t)=>{if(!a||a.length!==2)throw new Error("Gather requires 2 inputs.");const e=a[0].dims.length;if(e<1)throw new Error("Invalid input shape.");if(t<-e||t>e-1)throw new Error("Invalid axis.");if(l.NUMBER_TYPES.indexOf(a[0].type)===-1)throw new Error("Invaid input type.");if(a[1].type!=="int32"&&a[1].type!=="int16")throw new Error("Invaid input type.")}},4776:(_,n,o)=>{Object.defineProperty(n,"__esModule",{value:!0}),n.parseGemmAttributesV11=n.parseGemmAttributesV7=n.gemm=void 0;const u=o(246),l=o(2517),d=o(2039);n.gemm=(a,t,e)=>(c(t,e),[a.run(p(t,e),t)]);const s=(a,t)=>{const e=a.attributes.getInt("transA",0)!==0,r=a.attributes.getInt("transB",0)!==0,i=a.attributes.getFloat("alpha",1),h=a.attributes.getFloat("beta",1);return(0,u.createAttributeWithCacheKey)({transA:e,transB:r,alpha:i,beta:h,isOptionalC:t})};n.parseGemmAttributesV7=a=>s(a,!1),n.parseGemmAttributesV11=a=>s(a,!0);const p=(a,t)=>{const e={name:"Gemm",inputNames:a.length===3?["A","B","C"]:["A","B"],inputTypes:a.length===3?[d.TextureType.unpacked,d.TextureType.unpacked,d.TextureType.unpacked]:[d.TextureType.unpacked,d.TextureType.unpacked],key:t.cacheKey};return Object.assign(Object.assign({},e),{get:()=>f(e,a,t)})},f=(a,t,e)=>{const r=t[0].dims.slice(),i=t[1].dims.slice(),[h,g]=l.GemmUtil.getShapeOfGemmResult(r,e.transA,i,e.transB,t.length===3?t[2].dims:void 0),m=[h,g];if(!m)throw new Error("Can't use gemm on the given tensors");let b=r[r.length-1],y="";e.transA&&(b=r[0]),e.transA&&e.transB?y="value += _A_T(a) * _B_T(b);":e.transA&&!e.transB?y="value += _A_T(a) * _B(b);":!e.transA&&e.transB?y="value += _A(a) * _B_T(b);":e.transA||e.transB||(y="value += _A(a) * _B(b);");const w=m.length,T=`
      float process(int indices[${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;
              ${y}
          }

          value = value * alpha;
          ${t.length===3?"value += beta * _C(c);":""}
          return value;
      }`;return Object.assign(Object.assign({},a),{output:{dims:m,type:t[0].type,textureType:d.TextureType.unpacked},variables:[{name:"alpha",type:"float",data:e.alpha},{name:"beta",type:"float",data:e.beta}],shaderSource:T})},c=(a,t)=>{if(!a)throw new Error("Input is missing");if(t.isOptionalC&&(a.length<2||a.length>3))throw new Error("Invaid input shape.");if(!t.isOptionalC&&a.length!==3)throw new Error("Gemm requires 3 inputs");if(a.length===3&&a[2].dims.length!==1&&a[2].dims.length!==2)throw new Error("Invalid input shape of C");if(a[0].type!=="float32"&&a[0].type!=="float64"||a[1].type!=="float32"&&a[1].type!=="float64"||a.length===3&&a[2].type!=="float32"&&a[2].type!=="float64")throw new Error("Invalid input type.");if(a[0].type!==a[1].type||a.length===3&&a[0].type!==a[2].type)throw new Error("Input types are mismatched")}},8555:(_,n,o)=>{Object.defineProperty(n,"__esModule",{value:!0}),n.createPackedIm2ColProgramInfoLoader=void 0;const u=o(5060),l=o(2039),d=o(2827);n.createPackedIm2ColProgramInfoLoader=(s,p,f,c,a)=>{const t=(e=a.cacheKey,{name:"Im2Col (packed)",inputNames:["A"],inputTypes:[l.TextureType.packed],cacheHint:e});var e;return Object.assign(Object.assign({},t),{get:()=>((r,i,h,g,m,b)=>{const y=h.dims,w=g.dims,T=m.length,S=[w[1]*w[2]*w[3],m[2]*m[3]],A=w[2]*w[3],O=(0,d.unpackFromChannel)(),v=(0,u.getGlsl)(r.session.backend.glContext.version);let M="";for(let R=0;R<=1;R++)for(let $=0;$<=1;$++)M+=`
            blockIndex = rc.x + ${$};
            pos = rc.y + ${R};

            if(blockIndex < ${S[1]} && pos < ${S[0]}) {
              offsetY = int(blockIndex / (${m[T-1]})) * ${b.strides[0]} -
                ${b.pads[0]};
              d0 = offsetY + ${b.dilations[0]} * (imod(pos, ${A}) / ${w[2]});

              if(d0 < ${y[2]} && d0 >= 0) {
                offsetX = imod(blockIndex, ${m[T-1]}) * ${b.strides[1]} -
                  ${b.pads[1]};
                d1 = offsetX + ${b.dilations[1]} * imod(imod(pos, ${A}), ${w[2]});

                if(d1 < ${y[3]} && d1 >= 0) {

                  ch = int(float(pos)/ ${A}.);
                    innerDims = vec2(d0, d1);
                    result[${2*R+$}] = getChannel(
                      getA(0, ch, int(innerDims.x),
                      int(innerDims.y)), innerDims);
                }
              }
            }

          `;const F=`
      ${O}

      void main() {
        ivec2 rc = getOutputCoords();
          vec4 result = vec4(0.0);
          int blockIndex, pos, offsetY, d0, offsetX, d1, ch;
          vec2 innerDims;
          ${M}
          ${v.output} = result;
      }
            `;return Object.assign(Object.assign({},i),{output:{dims:S,type:h.type,textureType:l.TextureType.packed},shaderSource:F,hasMain:!0})})(s,t,p,f,c,a)})}},3248:(_,n,o)=>{Object.defineProperty(n,"__esModule",{value:!0}),n.calculateIm2ColDims=n.createIm2ColProgramInfoLoader=void 0;const u=o(2039);n.createIm2ColProgramInfoLoader=(l,d,s,p,f)=>{const c=(a=f.cacheKey,{name:"Im2Col",inputNames:["X"],inputTypes:[u.TextureType.unpacked],cacheHint:a});var a;return Object.assign(Object.assign({},c),{get:()=>((t,e,r,i,h,g)=>{const m=r.dims,b=i.dims,y=h.length,w=(0,n.calculateIm2ColDims)(m,b,h,4),T=`
        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 = ${g.pads[1]};
        const int KHKW = KH*KW;
        const int XCKHKW = XC * KHKW;
        const int outputChannels = 4;
        vec4 process(int indices[${y}]) {
          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:u.TextureType.packedLastDimension},shaderSource:T})})(0,c,d,s,p,f)})},n.calculateIm2ColDims=(l,d,s,p=4)=>[s[0],s[2],s[3],Math.ceil(l[1]*d[2]*d[3]/p)]},6572:(_,n,o)=>{Object.defineProperty(n,"__esModule",{value:!0}),n.parseImageScalerAttributes=n.imageScaler=void 0;const u=o(246),l=o(2039);n.imageScaler=(c,a,t)=>(f(a),[c.run(s(c,a,t),a)]),n.parseImageScalerAttributes=c=>{const a=c.attributes.getFloat("scale"),t=c.attributes.getFloats("bias");return(0,u.createAttributeWithCacheKey)({scale:a,bias:t})};const d={name:"ImageScaler",inputNames:["X"],inputTypes:[l.TextureType.unpacked]},s=(c,a,t)=>{const e=Object.assign(Object.assign({},d),{cacheHint:t.cacheKey});return Object.assign(Object.assign({},e),{get:()=>((r,i,h,g)=>{const m=h[0].dims.slice(),b=m.length,y=`
      ${p(g.bias.length)}
      float process(int indices[${b}]) {
        return _X(indices) * scale + getBias(bias, indices[1]);
      }`;return Object.assign(Object.assign({},i),{output:{dims:m,type:h[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:y})})(0,e,a,t)})},p=c=>{const a=[`float getBias(float bias[${c}], int channel) {`];for(let t=0;t<c;++t)t===0?a.push(`	if (channel == ${t}) { return bias[${t}]; }`):t===c-1?a.push(`	else { return bias[${t}]; }`):a.push(`	else if (channel == ${t}) { return bias[${t}]; }`);return a.push("	}"),a.join(`
`)},f=c=>{if(!c||c.length!==1)throw new Error("ImageScaler requires 1 input.");if(c[0].dims.length!==4)throw new Error("Invalid input shape.");if(c[0].type!=="float32"&&c[0].type!=="float64")throw new Error("Invalid input type.")}},3346:(_,n,o)=>{Object.defineProperty(n,"__esModule",{value:!0}),n.parseInstanceNormalizationAttributes=n.instanceNormalization=void 0;const u=o(5060),l=o(2039);n.instanceNormalization=(a,t,e)=>{c(t);const r=a.run(s(t[0]),t);return[a.run(f(a,t[0],e,r.dims),[t[0],r,t[1],t[2]])]},n.parseInstanceNormalizationAttributes=a=>a.attributes.getFloat("epsilon",1e-5);const d={name:"InstanceNormalization_MeanAndVariance",inputNames:["X"],inputTypes:[l.TextureType.unpacked]},s=a=>Object.assign(Object.assign({},d),{get:()=>((t,e)=>{const r=e.dims.slice(),i=r[1],h=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(${h});
        temp = 0.0;
        for(int a2=0; a2<${r[2]}; a2++) {
          a[2] = a2;
          for(int a3=0; a3<${r[3]}; a3++) {
            a[3] = a3;
            float x = _X(a);
            temp += (x - mean) * (x - mean);
          }
        }
        v.r = mean;
        v.g = temp / float(${h});

        return v;
      }`;return Object.assign(Object.assign({},t),{output:{dims:g,type:e.type,textureType:l.TextureType.packedLastDimension},shaderSource:m})})(d,a)}),p={name:"InstanceNormalization_ComputeOutput",inputNames:["X","MeanAndVariance","Scale","B"],inputTypes:[l.TextureType.unpacked,l.TextureType.packedLastDimension,l.TextureType.unpacked,l.TextureType.unpacked]},f=(a,t,e,r)=>{const i=Object.assign(Object.assign({},p),{cacheHint:`${e}`});return Object.assign(Object.assign({},i),{get:()=>((h,g,m,b,y)=>{const w=(0,u.getGlsl)(h.session.backend.glContext.version),[T,S]=h.calculateTextureWidthAndHeight(y,l.TextureType.packedLastDimension),[A,O]=[T/4,S],v=`
      vec4 get_MeanAndVariance(int[2] mv) {
        int offset = indicesToOffset_MeanAndVariance(mv);
        vec2 coords = offsetToCoords(offset, ${A}, ${O});
        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;
      }`;return Object.assign(Object.assign({},g),{output:{dims:m.dims,type:m.type,textureType:l.TextureType.unpacked},variables:[{name:"epsilon",type:"float",data:b}],shaderSource:v})})(a,i,t,e,r)})},c=a=>{if(!a||a.length!==3)throw new Error("InstanceNormalization requires 3 inputs.");const t=a[0],e=a[1],r=a[2];if(t.dims.length<3||e.dims.length!==1||r.dims.length!==1)throw new Error("Invalid input shape.");if(e.dims[0]!==t.dims[1]||r.dims[0]!==t.dims[1])throw new Error("Input shapes are mismatched.");if(t.type!=="float32"&&t.type!=="float64"||e.type!=="float32"&&e.type!=="float64"||r.type!=="float32"&&r.type!=="float64")throw new Error("Invalid input type.");if(a[0].dims.length!==4)throw new Error("Only support 4-D input shape.")}},708:(_,n,o)=>{Object.defineProperty(n,"__esModule",{value:!0}),n.createPackedMatmulProgramInfoLoader=void 0;const u=o(2517),l=o(5060),d=o(2039),s=o(9390),p=o(2823),f=o(5623);n.createPackedMatmulProgramInfoLoader=(c,a,t)=>{const e=(r=a.length>2,i=t.activationCacheKey,{name:"MatMul (packed)",inputNames:r?["A","B","Bias"]:["A","B"],inputTypes:r?[d.TextureType.packed,d.TextureType.packed,d.TextureType.packed]:[d.TextureType.packed,d.TextureType.packed],cacheHint:i});var r,i;return Object.assign(Object.assign({},e),{get:()=>((h,g,m,b)=>{const y=m.length>2,w=y?"value += getBiasForMatmul();":"",T=m[0].dims,S=m[1].dims,A=u.BroadcastUtil.calcShape(T,S,!0),O=!u.ShapeUtil.areEqual(m[0].dims,m[1].dims);if(!A)throw new Error("Can't use matmul on the given tensors");const v=T[T.length-1],M=Math.ceil(v/2),F=T.length,R=S.length,$=(0,l.getGlsl)(h.session.backend.glContext.version),N=(0,s.getCoordsDataType)(A.length),G=A.length,D=(0,s.getGlChannels)(),{activationFunction:j,applyActivation:Q}=(0,p.getActivationSnippet)(b),X=y?`${(0,f.getBiasForMatmul)(N,D,m[2].dims,A,!0)}`:"",Z=O?`${function(ve,oe,we,be){let ke=[],Le=[];const xe=we[0].dims,Fe=we[1].dims,Ce=xe.length,Pe=Fe.length,Oe=be.length,Be=Oe-Ce,Ge=Oe-Pe;ke=xe.map((Me,je)=>`coords.${oe[je+Be]}`),ke[Ce-1]="i*2",ke.join(", "),Le=Fe.map((Me,je)=>`coords.${oe[je+Ge]}`),Le[Pe-2]="i*2",Le.join(", ");const Ve=u.BroadcastUtil.getBroadcastDims(xe,be),Xe=u.BroadcastUtil.getBroadcastDims(Fe,be),Qe=Ve.map(Me=>`coords.${oe[Me+Be]} = 0;`).join(`
`),qe=Xe.map(Me=>`coords.${oe[Me+Ge]} = 0;`).join(`
`),Ue=`int lastDim = coords.${oe[Oe-1]};
  coords.${oe[Oe-1]} = coords.${oe[Oe-2]};
  coords.${oe[Oe-2]} = lastDim;`;return`
vec4 getAAtOutCoordsMatmul(int i) {
  ${ve} coords = getOutputCoords();
  ${Ue}
  ${Qe}
  vec4 outputValue = getA(${ke});
  return outputValue;
}

vec4 getBAtOutCoordsMatmul(int i) {
  ${ve} coords = getOutputCoords();
  ${Ue}
  ${qe}
  vec4 outputValue = getB(${Le});
  return outputValue;
}`}(N,D,m,A)}`:"",ee=O?"getAAtOutCoordsMatmul(i)":`getA(${function(ve,oe){let we="";for(let be=0;be<oe-2;be++)we+=`rc.${ve[be]}, `;return we+=`rc.${ve[oe-2]}, i*2`,we}(D,F)})`,ue=O?"getBAtOutCoordsMatmul(i)":`getB(${function(ve,oe){let we="";for(let be=0;be<oe-2;be++)we+=`rc.${ve[be]}, `;return we+=`i*2, rc.${ve[oe-1]}`,we}(D,R)})`,Ae=`
            ${Z}
            ${X}
            ${j}
            void main() {
              ${O?"":`${N} rc =
          getOutputCoords(); int lastDim = rc.${D[G-1]}; rc.${D[G-1]} =
          rc.${D[G-2]}; rc.${D[G-2]} = lastDim;
      `}

              vec4 value = vec4(0);
              for (int i = 0; i < ${M}; i++) {
                vec4 a = ${ee};
                vec4 b = ${ue};

                value += (a.rrbb * b.rgrg);
                value += (a.ggaa * b.baba);
              }
              ${w}
              ${Q}
              ${$.output} = value;
            }`;return Object.assign(Object.assign({},g),{output:{dims:A,type:m[0].type,textureType:d.TextureType.packed},shaderSource:Ae,hasMain:!0})})(c,e,a,t)})}},5623:(_,n,o)=>{Object.defineProperty(n,"__esModule",{value:!0}),n.getBiasForMatmul=n.createMatmulProgramInfoLoader=n.parseMatMulAttributes=n.matMul=void 0;const u=o(2517),l=o(2039),d=o(9390),s=o(2823),p=o(708);function f(t,e){const r=(i=t.length>2,h=e.activationCacheKey,{name:"MatMul",inputNames:i?["A","B","Bias"]:["A","B"],inputTypes:i?[l.TextureType.unpacked,l.TextureType.unpacked,l.TextureType.unpacked]:[l.TextureType.unpacked,l.TextureType.unpacked],cacheHint:h});var i,h;return Object.assign(Object.assign({},r),{get:()=>function(g,m,b){const y=m[0].dims,w=m[1].dims,T=u.BroadcastUtil.calcShape(y,w,!0);if(!T)throw new Error("Can't use matmul on the given tensors");const S=(0,d.getCoordsDataType)(T.length),A=(0,d.getGlChannels)(),{activationFunction:O,applyActivation:v}=(0,s.getActivationSnippet)(b),M=m.length>2,F=M?"value += getBiasForMatmul();":"",R=M?`${a(S,A,m[2].dims,T,!1)}`:"",$=T.length,N=y.length,G=w.length,D=`
    ${O}
    ${R}
    float process(int indices[${$}]) {
        int a[${N}];
        int b[${G}];
        bcastMatmulIndices_A(indices, a);
        bcastMatmulIndices_B(indices, b);

        float value;
        for (int k=0; k<${y[y.length-1]}; ++k) {
            a[${N-1}] = k;
            b[${G-2}] = k;
            value += _A(a) * _B(b);
        }
        ${F}
        ${v}
        return value;
    }`;return Object.assign(Object.assign({},g),{output:{dims:T,type:m[0].type,textureType:l.TextureType.unpacked},shaderSource:D})}(r,t,e)})}n.matMul=(t,e,r)=>(c(e),t.session.pack?[t.run((0,p.createPackedMatmulProgramInfoLoader)(t,e,r),e)]:[t.run(f(e,r),e)]),n.parseMatMulAttributes=t=>(0,s.parseInternalActivationAttributes)(t.attributes),n.createMatmulProgramInfoLoader=f;const c=t=>{if(!t||t.length!==2)throw new Error("MatMul requires 2 inputs.");if(t[0].dims[t[0].dims.length-1]!==t[1].dims[t[1].dims.length-2])throw new Error("shared dimension does not match.");if(t[0].type!=="float32"&&t[0].type!=="float64"||t[1].type!=="float32"&&t[1].type!=="float64")throw new Error("inputs should be float type");if(t[0].type!==t[1].type)throw new Error("inputs types should match")};function a(t,e,r,i,h){let g="";const m=r.length,b=i.length,y=b-m;g=b<2&&m>0?"coords":r.map((S,A)=>`coords.${e[A+y]}`).join(", ");const w=u.BroadcastUtil.getBroadcastDims(r,i).map(S=>`coords.${e[S+y]} = 0;`).join(`
`);let T="vec4(outputValue.xx, outputValue.yy)";return u.ShapeUtil.size(r)===1&&(T="vec4(outputValue.x)"),h?`
vec4 getBiasForMatmul() {
  ${t} coords = getOutputCoords();
  ${w}
  vec4 outputValue = getBias(${g});
  return ${T};
}`:`
float getBiasForMatmul() {
  ${t} coords = getOutputCoords();
  ${w}
  return getBias(coords.x);
}`}n.getBiasForMatmul=a},2403:(_,n,o)=>{Object.defineProperty(n,"__esModule",{value:!0}),n.createPackProgramInfoLoader=void 0;const u=o(5060),l=o(2039),d=o(9390),s=o(2827),p={name:"pack",inputNames:["A"],inputTypes:[l.TextureType.unpackedReversed]};n.createPackProgramInfoLoader=(f,c)=>Object.assign(Object.assign({},p),{get:()=>((a,t)=>{const e=(0,u.getGlsl)(a.session.backend.glContext.version),r=t.dims,i=r.length,h=t.dims.length,g=(0,d.getCoordsDataType)(h),m=(0,s.getChannels)("rc",h),b=(y=h,w=m,T=r[r.length-2],S=r[r.length-1],y===0||y===1?"":`
    int r = ${w[y-2]};
    int c = ${w[y-1]};
    int rp1 = ${w[y-2]} + 1;
    int cp1 = ${w[y-1]} + 1;
    bool rEdge = rp1 >= ${S};
    bool cEdge = cp1 >= ${T};
    `);var y,w,T,S;let A;A=i===0?[1,1]:i===1?[r[0],1]:[r[h-1],r[h-2]];const O=function(F,R,$){if(F===0)return"false";if(F===1)return`rc > ${R[0]}`;let N="";for(let G=F-2;G<F;G++)N+=`${$[G]} >= ${R[G-F+2]}`,G<F-1&&(N+="||");return N}(h,A,m),v=function(F,R){const $=F.length;if($===0)return"getA(), 0, 0, 0";if($===1)return`getA(rc),
            rc + 1 >= ${F[0]} ? 0. : getA(rc + 1),
            0, 0`;let N="";if($>2)for(let G=0;G<$-2;++G)N+=`${R[G]},`;return`getA(${N}r, c),
          rEdge ? 0. : getA(${N}rp1, c),
          cEdge ? 0. : getA(${N}r, cp1),
          rEdge || cEdge ? 0. : getA(${N}rp1, cp1)`}(r,m),M=`
        void main() {
          ${g} rc = getOutputCoords();

          if(${O}) {
            ${e.output} = vec4(0);
          } else {
            ${b}

            ${e.output} = vec4(${v});
          }
        }
      `;return Object.assign(Object.assign({},p),{hasMain:!0,output:{dims:t.dims,type:t.type,textureType:l.TextureType.packed},shaderSource:M})})(f,c)})},2827:(_,n,o)=>{Object.defineProperty(n,"__esModule",{value:!0}),n.unpackFromChannel=n.getChannels=n.getVecChannels=void 0;const u=o(9390);function l(d,s){return(0,u.getGlChannels)(s).map(p=>`${d}.${p}`)}n.getVecChannels=l,n.getChannels=function(d,s){return s===1?[d]:l(d,s)},n.unpackFromChannel=function(){return`
    float getChannel(vec4 frag, int dim) {
      int modCoord = imod(dim, 2);
      return modCoord == 0 ? frag.r : frag.g;
    }

    float getChannel(vec4 frag, vec2 innerDims) {
      vec2 modCoord = mod(innerDims, 2.);
      return modCoord.x == 0. ?
        (modCoord.y == 0. ? frag.r : frag.g) :
        (modCoord.y == 0. ? frag.b : frag.a);
    }
  `}},2870:(_,n,o)=>{Object.defineProperty(n,"__esModule",{value:!0}),n.parsePadAttributesV11=n.padV11=n.parsePadAttributesV2=n.padV2=void 0;const u=o(246),l=o(2517),d=o(5060),s=o(2039),p={name:"Pad",inputNames:["A"],inputTypes:[s.TextureType.unpacked]};n.padV2=(g,m,b)=>(a(m),[g.run(Object.assign(Object.assign({},p),{cacheHint:b.cacheKey,get:()=>c(g,m[0],b)}),m)]),n.parsePadAttributesV2=g=>{const m=g.attributes.getString("mode","constant"),b=g.attributes.getFloat("value",0),y=g.attributes.getInts("pads");return(0,u.createAttributeWithCacheKey)({mode:m,value:b,pads:y})},n.padV11=(g,m,b)=>{t(m);const y=f(g,m,b);return(0,n.padV2)(g,[m[0]],y)},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 y=Array.from(m[1].integerData),w=m.length>=3?m[2].floatData[0]:0;return(0,u.createAttributeWithCacheKey)({mode:b,pads:y,value:w})},c=(g,m,b)=>{const y=l.ShapeUtil.padShape(m.dims.slice(),b.pads),w=y.length,T=`
      ${e(g,m,b)}
      float process(int[${w}] indices) {
          return padA(indices);
      }`;return{name:"Pad",inputNames:["A"],inputTypes:[s.TextureType.unpacked],output:{dims:y,type:m.type,textureType:s.TextureType.unpacked},shaderSource:T}},a=g=>{if(!g||g.length!==1)throw new Error("Pad requires 1 input");if(g[0].type!=="float32"&&g[0].type!=="float64")throw new Error("Invalid input type.")},t=g=>{if(!g||g.length!==2&&g.length!==3)throw new Error("Pad requires 2 or 3 inputs");if(g[1].type!=="int32")throw new Error("Invalid input type.");if(g.length>=3&&g[2].type==="string")throw new Error("Invalid input type.")},e=(g,m,b)=>{const y=(0,d.getGlsl)(g.session.backend.glContext.version),[w,T]=g.calculateTextureWidthAndHeight(m.dims,s.TextureType.unpacked),S=l.ShapeUtil.computeStrides(m.dims);switch(b.mode){case"constant":return r(y,m.dims,S,w,T,b.pads,b.value);case"reflect":return i(y,m.dims,S,w,T,b.pads);case"edge":return h(y,m.dims,S,w,T,b.pads);default:throw new Error("Invalid mode")}},r=(g,m,b,y,w,T,S)=>{const A=m.length;let O="";for(let v=A-1;v>=0;--v)O+=`
        k = m[${v}] - ${T[v]};
        if (k < 0)  return constant;
        if (k >= ${m[v]}) return constant;
        offset += k * ${b[v]};
        `;return`
      float padA(int m[${A}]) {
        const float constant = float(${S});
        int offset = 0;
        int k = 0;
        ${O}
        vec2 coords = offsetToCoords(offset, ${y}, ${w});
        float value = getColorAsFloat(${g.texture2D}(A, coords));
        return value;
      }
      `},i=(g,m,b,y,w,T)=>{const S=m.length;let A="";for(let O=S-1;O>=0;--O)A+=`
        k = m[${O}] - ${T[O]};
        if (k < 0) { k = -k; }
        {
          const int _2n_1 = ${2*(m[O]-1)};
          k = int( mod( float(k), float(_2n_1) ) ) ;
          if(k >= ${m[O]}) { k = _2n_1 - k; }
        }
        offset += k * ${b[O]};
        `;return`
      float padA(int m[${S}]) {
        int offset = 0;
        int k = 0;
        ${A}
        vec2 coords = offsetToCoords(offset, ${y}, ${w});
        float value = getColorAsFloat(${g.texture2D}(A, coords));
        return value;
      }
      `},h=(g,m,b,y,w,T)=>{const S=m.length;let A="";for(let O=S-1;O>=0;--O)A+=`
        k = m[${O}] - ${T[O]};
        if (k < 0)  k = 0;
        if (k >= ${m[O]}) k = ${m[O]-1};
        offset += k * ${b[O]};
      `;return`
      float padA(int m[${S}]) {
        int offset = 0;
        int k = 0;
        ${A}
        vec2 coords = offsetToCoords(offset, ${y}, ${w});
        float value = getColorAsFloat(${g.texture2D}(A, coords));
        return value;
      }
      `}},2143:(_,n,o)=>{Object.defineProperty(n,"__esModule",{value:!0}),n.globalMaxPool=n.parseMaxPoolAttributes=n.maxPool=n.parseGlobalAveragePoolAttributes=n.globalAveragePool=n.parseAveragePoolAttributes=n.averagePool=void 0;const u=o(246),l=o(2517),d=o(2039);n.averagePool=(h,g,m)=>{t(g);const b={name:"AveragePool",inputNames:["X"],inputTypes:[d.TextureType.unpacked],cacheHint:m.cacheKey};return[h.run(Object.assign(Object.assign({},b),{get:()=>s(g,b,!1,m)}),g)]},n.parseAveragePoolAttributes=h=>{const g=h.attributes.getString("auto_pad","NOTSET"),m=h.attributes.getInt("ceil_mode",0),b=h.attributes.getInt("count_include_pad",0)!==0,y=h.attributes.getInts("kernel_shape"),w=h.attributes.getInts("strides",[]),T=h.attributes.getInts("pads",[]);if(m!==0)throw new Error("using ceil() in shape computation is not yet supported for AveragePool");return(0,u.createAttributeWithCacheKey)({autoPad:g,ceilMode:m,countIncludePad:b,kernelShape:y,strides:w,pads:T})};const s=(h,g,m,b)=>{const[y,w]=f(h,b,m),T=l.ShapeUtil.size(y.kernelShape);let S="";y.countIncludePad?S+=`value /= float(${T});`:S+=`value /= float(${T} - pad);`;const A=`
        ${e(h[0].dims,y,"value += _X(x);",S,"0.0")}
      `;return Object.assign(Object.assign({},g),{output:{dims:w,type:h[0].type,textureType:d.TextureType.unpacked},shaderSource:A})};n.globalAveragePool=(h,g,m)=>{t(g);const b={name:"GlobalAveragePool",inputNames:["X"],inputTypes:[d.TextureType.unpacked],cacheHint:`${m.countIncludePad}`};return[h.run(Object.assign(Object.assign({},b),{get:()=>s(g,b,!0,m)}),g)]},n.parseGlobalAveragePoolAttributes=h=>{const g=h.attributes.getInt("count_include_pad",0)!==0;return(0,u.createAttributeWithCacheKey)({autoPad:"",ceilMode:0,countIncludePad:g,kernelShape:[],strides:[],pads:[]})},n.maxPool=(h,g,m)=>{t(g);const b={name:"MaxPool",inputNames:["X"],inputTypes:[d.TextureType.unpacked],cacheHint:m.cacheKey};return[h.run(Object.assign(Object.assign({},b),{get:()=>p(g,b,!1,m)}),g)]},n.parseMaxPoolAttributes=h=>{const g=h.attributes.getString("auto_pad","NOTSET"),m=h.attributes.getInt("ceil_mode",0),b=h.attributes.getInts("kernel_shape"),y=h.attributes.getInts("strides",[]),w=h.attributes.getInts("pads",[]),T=h.attributes.getInt("storage_order",0),S=h.attributes.getInts("dilations",[]);if(T!==0)throw new Error("column major storage order is not yet supported for MaxPool");if(m!==0)throw new Error("using ceil() in shape computation is not yet supported for MaxPool");return(0,u.createAttributeWithCacheKey)({autoPad:g,ceilMode:m,countIncludePad:!1,kernelShape:b,strides:y,pads:w,storageOrder:T,dilations:S})};const p=(h,g,m,b)=>{const[y,w]=f(h,b,m),T=`
      ${e(h[0].dims,y,`
      value = max(_X(x), value);
    `,"","-1e5")}
    `;return Object.assign(Object.assign({},g),{output:{dims:w,type:h[0].type,textureType:d.TextureType.unpacked},shaderSource:T})},f=(h,g,m)=>{const b=h[0].dims.slice(),y=Object.hasOwnProperty.call(g,"dilations"),w=g.kernelShape.slice(),T=g.strides.slice(),S=y?g.dilations.slice():[],A=g.pads.slice();l.PoolConvUtil.adjustPoolAttributes(m,b,w,T,S,A);const O=l.PoolConvUtil.computePoolOutputShape(m,b,T,S,w,A,g.autoPad),v=Object.assign({},g);return y?Object.assign(v,{kernelShape:w,strides:T,pads:A,dilations:S,cacheKey:g.cacheKey}):Object.assign(v,{kernelShape:w,strides:T,pads:A,cacheKey:g.cacheKey}),[v,O]},c={autoPad:"",ceilMode:0,countIncludePad:!1,kernelShape:[],strides:[],pads:[],storageOrder:0,dilations:[],cacheKey:""},a={name:"GlobalMaxPool",inputNames:["X"],inputTypes:[d.TextureType.unpacked]};n.globalMaxPool=(h,g)=>(t(g),[h.run(Object.assign(Object.assign({},a),{get:()=>p(g,a,!0,c)}),g)]);const t=h=>{if(!h||h.length!==1)throw new Error("Pool ops requires 1 input.");if(h[0].type!=="float32"&&h[0].type!=="float64")throw new Error("Invalid input type.")},e=(h,g,m,b,y)=>{const w=h.length;if(g.kernelShape.length<=2){const T=g.kernelShape[g.kernelShape.length-1],S=g.strides[g.strides.length-1],A=g.pads[g.pads.length/2-1],O=g.pads[g.pads.length-1],v=h[w-1];let M="",F="",R="";if(M=A+O!==0?`
          for (int i = 0; i < ${T}; i++) {
            x[${w} - 1] = indices[${w} - 1] * ${S} - ${A} + i;
            if (x[${w} - 1] < 0 || x[${w} - 1] >= ${v}) {
              pad++;
              continue;
            }
            ${m}
          }`:`
          for (int i = 0; i < ${T}; i++) {
            x[${w} - 1] = indices[${w} - 1] * ${S} - ${A} + i;
            ${m}
          }`,g.kernelShape.length===2){const $=g.kernelShape[g.kernelShape.length-2],N=g.strides[g.strides.length-2],G=g.pads[g.pads.length/2-2],D=g.pads[g.pads.length-2],j=h[w-2];F=G+D!==0?`
            for (int j = 0; j < ${$}; j++) {
              x[${w} - 2] = indices[${w} - 2] * ${N} - ${G} + j;
              if (x[${w} - 2] < 0 || x[${w} - 2] >= ${j}) {
                pad+= ${T};
                continue;
              }
          `:`
            for (int j = 0; j < ${$}; j++) {
              x[${w} - 2] = indices[${w} - 2] * ${N} - ${G} + j;
            `,R=`
          }
        `}return`
        float process(int indices[${w}]) {
          int x[${w}];
          copyVec(indices, x);

          float value = ${y};
          int pad = 0;
          ${F}
          ${M}
          ${R}
          ${b}
          return value;
        }
      `}{const T=l.ShapeUtil.size(g.kernelShape),S=l.ShapeUtil.computeStrides(g.kernelShape),A=S.length,O=g.pads.length,v=i(A),M=r(h,"inputDims"),F=r(g.pads,"pads"),R=r(S,"kernelStrides"),$=r(g.strides,"strides");let N="";return N=g.pads.reduce((G,D)=>G+D)?`
            if (x[j] >= inputDims[j] || x[j] < 0) {
              pad++;
              isPad = true;
              break;
            }
          }
          if (!isPad) {
            ${m}
          }`:`
          }
          ${m}
        `,`
        ${v}
        float process(int indices[${w}]) {
          int x[${w}];
          copyVec(indices, x);
          int offset[${A}];
          int pads[${O}];
          int inputDims[${w}];
          int kernelStrides[${A}];
          int strides[${A}];
          ${F}
          ${M}
          ${$}
          ${R}

          float value = ${y};
          int pad = 0;
          bool isPad = false;
          for (int i = 0; i < ${T}; i++) {
            offsetToIndices(i, kernelStrides, offset);
            isPad = false;
            for (int j = ${w} - ${A}; j < ${w}; j++) {
              x[j] = indices[j] * strides[j - ${w} + ${A}]
                + offset[j - ${w} + ${A}] - pads[j - 2];
              ${N}
          }
          ${b}

          return value;
        }
      `}},r=(h,g)=>{let m="";for(let b=0;b<h.length;b++)m+=`
      ${g}[${b}] = ${h[b]};
    `;return m},i=h=>`
  void offsetToIndices(int offset, int[${h}] strides, out int[${h}] indices) {
    if (${h} == 0) {
      return;
    }
    for (int i = 0; i < ${h} - 1; ++i) {
      indices[i] = offset / strides[i];
      offset -= indices[i] * strides[i];
    }
    indices[${h} - 1] = offset;
  }`},4939:(_,n,o)=>{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 u=o(246),l=o(782),d=o(2517),s=o(2039),p=(a,t,e,r,i)=>{c(t);const h={name:r,inputNames:["A"],inputTypes:[s.TextureType.unpacked]};return[a.run(Object.assign(Object.assign({},h),{cacheHint:e.cacheKey,get:()=>f(a,t,e,r,i,h)}),t)]};n.parseReduceAttributes=a=>{const t=a.attributes.getInts("axes",[]),e=a.attributes.getInt("keepdims",1)===1;return(0,u.createAttributeWithCacheKey)({axes:t,keepDims:e})};const f=(a,t,e,r,i,h)=>{const g=[],m=t[0].dims.length||1,b=[],y=d.ShapeUtil.normalizeAxes(e.axes,t[0].dims.length),w=i(t,y);let T=w[1];for(let A=0;A<t[0].dims.length;A++)y.indexOf(A)>=0||y.length===0?(e.keepDims&&g.push(1),T=`
          for(int j${A} = 0; j${A} < ${t[0].dims[A]}; j${A}++) {
            inputIdx[${A}] = j${A};
            ${T}
          }`):(b.push(`inputIdx[${A}] = outputIdx[${g.length}];`),g.push(t[0].dims[A]));const S=`
      float process(int outputIdx[${g.length||1}]) {
        float value;                 // final result
        int inputIdx[${m}];      // addressing input data
        ${b.join(`
`)}
        ${w[0]}       // init ops for reduce max/min
        ${T}
        ${w[2]}       // final computation for reduce mean
        return value;
      }`;return Object.assign(Object.assign({},h),{output:{dims:g,type:t[0].type,textureType:s.TextureType.unpacked},shaderSource:S})},c=a=>{if(!a||a.length!==1)throw new Error("Reduce op requires 1 input.");if(l.NUMBER_TYPES.indexOf(a[0].type)===-1)throw new Error("Invalid input type.")};n.reduceSum=(a,t,e)=>p(a,t,e,"ReduceSum",()=>["value = 0.0;","value += _A(inputIdx);",""]),n.reduceMean=(a,t,e)=>p(a,t,e,"ReduceMean",(r,i)=>{let h=1;for(let g=0;g<r[0].dims.length;g++)(i.indexOf(g)>=0||i.length===0)&&(h*=r[0].dims[g]);return["value = 0.0;","value += _A(inputIdx);",`value /= ${h}.;`]}),n.reduceMax=(a,t,e)=>p(a,t,e,"ReduceMax",(r,i)=>{const h=[];for(let g=0;g<r[0].dims.length;g++)(i.indexOf(g)>=0||i.length===0)&&h.push(`inputIdx[${g}] = 0;`);return[`${h.join(`
`)}
value = _A(inputIdx);`,"value = max(value, _A(inputIdx));",""]}),n.reduceMin=(a,t,e)=>p(a,t,e,"ReduceMin",(r,i)=>{const h=[];for(let g=0;g<r[0].dims.length;g++)(i.indexOf(g)>=0||i.length===0)&&h.push(`inputIdx[${g}] = 0;`);return[`${h.join(`
`)}
value = _A(inputIdx);`,"value = min(value, _A(inputIdx));",""]}),n.reduceProd=(a,t,e)=>p(a,t,e,"ReduceProd",()=>["value = 1.0;","value *= _A(inputIdx);",""]),n.reduceLogSum=(a,t,e)=>p(a,t,e,"ReduceLogSum",()=>["value = 0.0;","value += _A(inputIdx);","value = log(value);"]),n.reduceLogSumSquare=(a,t,e)=>p(a,t,e,"ReduceLogSumSquare",()=>["float t; value = 0.0;","t = _A(inputIdx); value += t * t;",""])},7019:(_,n,o)=>{Object.defineProperty(n,"__esModule",{value:!0}),n.isReshapeCheap=n.processDims3D=n.createPackedReshape3DProgramInfoLoader=void 0;const u=o(2517),l=o(5060),d=o(2039),s=o(2827);n.createPackedReshape3DProgramInfoLoader=(p,f,c)=>{const a=(t=>({name:"Reshape (packed)",inputTypes:[d.TextureType.packed],inputNames:["A"],cacheHint:`${t}`}))(c);return Object.assign(Object.assign({},a),{get:()=>((t,e,r,i)=>{const h=e.dims,g=i;let m="";for(let w=0;w<4;w++){let T="";switch(w){case 0:T="outputCoords = rc;";break;case 1:T="outputCoords = ivec3(rc.x, rc.y+1, rc.z);";break;case 2:T="outputCoords = ivec3(rc.x, rc.y, rc.z+1);";break;case 3:T="outputCoords = ivec3(rc.x, rc.y+1, rc.z+1);";break;default:throw new Error}m+=`
        ${T}
        ${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),y=`
      ${function(w){const T=u.ShapeUtil.computeStrides(w),S=["b","r","c"],A="index";return`
    ivec3 inputCoordsFromReshapedOutCoords(int index) {
      ${T.map((O,v)=>`int ${S[v]} = ${A} / ${O}; ${v===T.length-1?`int ${S[v+1]} = ${A} - ${S[v]} * ${O}`:`index -= ${S[v]} * ${O}`};`).join("")}
      return ivec3(b, r, c);
    }
  `}(h)}
      ${function(w){const T=u.ShapeUtil.computeStrides(w);return`
  int getFlattenedIndex(ivec3 coords) {
    // reverse y, z order
    return coords.x * ${T[0]} + coords.z * ${T[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:d.TextureType.packed},shaderSource:y,hasMain:!0})})(p,f,a,c)})},n.processDims3D=function(p){if(p.length===0)return[1,1,1];let f=1;for(let c=0;c<p.length-2;++c)f*=p[c];return[f,p.length>1?p[p.length-2]:1,p[p.length-1]]},n.isReshapeCheap=function(p,f){let c=!1;return c=p.length===0||f.length===0||(p.length<2||f.length<2?p[p.length-1]===f[f.length-1]:p[p.length-1]===f[f.length-1]&&p[p.length-2]===f[f.length-2]),c}},718:(_,n,o)=>{Object.defineProperty(n,"__esModule",{value:!0}),n.reshape=void 0;const u=o(2517);n.reshape=(l,d)=>{const s=u.ShapeUtil.calculateReshapedDims(d[0].dims,d[1].integerData);return l.session.pack?[l.reshapePacked(d[0],s)]:[l.reshapeUnpacked(d[0],s)]}},2268:(_,n,o)=>{Object.defineProperty(n,"__esModule",{value:!0}),n.parseResizeAttributesV11=n.parseResizeAttributesV10=n.resize=void 0;const u=o(5060),l=o(2039),d=o(9390),s=o(2827),p=o(9793),f={name:"Resize",inputNames:["A"],inputTypes:[l.TextureType.packed]};n.resize=(r,i,h)=>((0,p.validateInputs)(i,h),[r.run(Object.assign(Object.assign({},f),{cacheHint:h.cacheKey,get:()=>c(r,i,h)}),i)]),n.parseResizeAttributesV10=r=>(0,p.parseUpsampleAttributes)(r,10),n.parseResizeAttributesV11=r=>(0,p.parseUpsampleAttributes)(r,11);const c=(r,i,h)=>{const g=(0,u.getGlsl)(r.session.backend.glContext.version),[m,b]=a(i,h);if(m.every(N=>N===1)&&h.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 y=b.length;if(y<2)throw new Error(`output dimension should be at least 2, but got ${y}`);const w=b[y-2],T=b[y-1],S=i[0].dims;if(y!==S.length)throw new Error(`output dimension should match input ${S.length}, but got ${y}`);const A=S[y-2],O=S[y-1],v=m[y-2],M=m[y-1];let F="";if(h.mode!=="linear")throw new Error(`resize (packed) does not support mode: '${h.mode}'`);switch(h.coordinateTransformMode){case"asymmetric":F=`
                    vec4 getSourceFracIndex(ivec4 coords) {
                        return vec4(coords) / scaleWHWH;
                    }
                `;break;case"half_pixel":F=`
                    vec4 getSourceFracIndex(ivec4 coords) {
                        return (vec4(coords) + 0.5) / scaleWHWH - 0.5;
                    }
                `;break;case"pytorch_half_pixel":F=`
                    vec4 getSourceFracIndex(ivec4 coords) {
                        vec4 fcoords = vec4(coords);
                        return vec4(
                            ${T}.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,
                            ${T}.0 > 1.0 ? (fcoords.z + 0.5) / scaleWHWH.z - 0.5 : 0.0,
                            ${w}.0 > 1.0 ? (fcoords.w + 0.5) / scaleWHWH.w - 0.5 : 0.0
                          );
                    }
                `;break;case"align_corners":F=`
                    vec4 getSourceFracIndex(ivec4 coords) {
                        vec4 resized = vec4(${T}.0 - 1.0, ${w}.0 - 1.0, ${T}.0 - 1.0,
                            ${w}.0 - 1.0);
                        vec4 original = vec4(${O}.0 - 1.0, ${A}.0 - 1.0, ${O}.0 - 1.0,
                            ${A}.0 - 1.0);
                        vec4 new_scale = original / resized;
                        return vec4(coords) * new_scale;
                    }
                `;break;default:throw new Error(`resize (packed) does not support coordinateTransformMode:                                 '${h.coordinateTransformMode}'`)}const R=(0,d.getCoordsDataType)(y),$=`
            const vec2 inputWH = vec2(${A}.0, ${O}.0);
            const vec4 scaleWHWH = vec4(float(${v}), float(${M}), float(${v}), float(${M}));
            ${(0,s.unpackFromChannel)()}
            ${F}
            float getAValue(int x10, int r, int c, int d) {
                return getChannel(getA(x10, r, c, d), vec2(c, d));
            }
            void main() {
                ${R} 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 < ${T-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);

                ${g.output} = vec4(newValue);
            }
        `;return Object.assign(Object.assign({},f),{output:{dims:b,type:i[0].type,textureType:l.TextureType.packed},hasMain:!0,shaderSource:$})},a=(r,i)=>{const h=r[0].dims;let g,m=i.scales;if(m.length===0){const y=r[i.scalesInputIdx];if(y&&y.size!==0){if(r[i.sizesInputIdx])throw new Error("Only one of scales or sizes must be provided as input.");m=t(y,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,h,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||h.map((y,w)=>Math.floor(y*m[w]));return[m,b]},t=(r,i,h)=>{const g=Array.from(r.floatData);return(0,p.scalesValidation)(g,i,h),g},e=(r,i,h,g)=>{const m=i.length,b=new Array(m);for(let y=0,w=m;y<w;y++)if(i[y]===0){if(r[y]!==0)throw new Error("Input dim is zero but required output dim is non-zero.");b[y]=1}else b[y]=r[y]/i[y];return(0,p.scalesValidation)(b,h,g),b}},8117:(_,n,o)=>{Object.defineProperty(n,"__esModule",{value:!0}),n.shape=void 0;const u=o(9162);n.shape=(d,s)=>(l(s),[new u.Tensor([s[0].dims.length],"int32",void 0,void 0,new Int32Array(s[0].dims))]);const l=d=>{if(!d||d.length!==1)throw new Error("Shape requires 1 input.")}},2278:(_,n,o)=>{Object.defineProperty(n,"__esModule",{value:!0}),n.sliceV10=n.parseSliceAttributes=n.slice=void 0;const u=o(246),l=o(782),d=o(2517),s=o(2039),p={name:"Slice",inputNames:["A"],inputTypes:[s.TextureType.unpacked]};n.slice=(e,r,i)=>(c(r),[e.run(Object.assign(Object.assign({},p),{cacheHint:i.cacheKey,get:()=>f(e,r[0],i)}),r)]),n.parseSliceAttributes=e=>{const r=e.attributes.getInts("starts"),i=e.attributes.getInts("ends"),h=e.attributes.getInts("axes",[]);return(0,u.createAttributeWithCacheKey)({starts:r,ends:i,axes:h})};const f=(e,r,i)=>{const h=i.axes.length===0?r.dims.slice(0).map((S,A)=>A):i.axes,g=d.ShapeUtil.normalizeAxes(h,r.dims.length),m=i.starts.map((S,A)=>S>r.dims[g[A]]-1?r.dims[g[A]]:d.ShapeUtil.normalizeAxis(S,r.dims[g[A]])),b=i.ends.map((S,A)=>S>r.dims[g[A]]-1?r.dims[g[A]]:d.ShapeUtil.normalizeAxis(S,r.dims[g[A]])),y=r.dims.slice(),w=[];for(let S=0;S<g.length;S++)y[g[S]]=b[S]-m[S],m[S]>0&&w.push(`outputIdx[${g[S]}] += ${m[S]};`);const T=`
      float process(int outputIdx[${y.length}]) {
        ${w.join(`
      `)}
        return _A(outputIdx);
      }`;return Object.assign(Object.assign({},p),{output:{dims:y,type:r.type,textureType:s.TextureType.unpacked},shaderSource:T})},c=e=>{if(!e||e.length!==1)throw new Error("Slice requires 1 input.");if(l.NUMBER_TYPES.indexOf(e[0].type)===-1)throw new Error("Invalid input type.")};n.sliceV10=(e,r)=>{t(r);const i=a(e,r);return[e.run(Object.assign(Object.assign({},p),{cacheHint:i.cacheKey,get:()=>f(e,r[0],i)}),[r[0]])]};const a=(e,r)=>{if(!e.session.isInitializer(r[1].dataId)||!e.session.isInitializer(r[2].dataId)||r.length>=4&&!e.session.isInitializer(r[3].dataId)||r.length>=5&&!e.session.isInitializer(r[4].dataId))throw new Error("dynamic slice attributes are not allowed");if(r.length>=5&&r[4].integerData.some(m=>m!==1))throw new Error("currently non-1 steps is not supported for Slice");const i=Array.from(r[1].integerData),h=Array.from(r[2].integerData),g=r.length>=4?Array.from(r[3].integerData):[];return{starts:i,ends:h,axes:g,cacheKey:`${g};${i};${h}`}},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:(_,n,o)=>{Object.defineProperty(n,"__esModule",{value:!0}),n.softmaxV13=n.parseSoftmaxAttributesV13=n.parseSoftmaxAttributes=n.softmax=void 0;const u=o(246),l=o(2517),d=o(5060),s=o(2039),p=o(3738),f={name:"SoftmaxComputeMax",inputNames:["A"],inputTypes:[s.TextureType.unpacked]},c={name:"SoftmaxComputeScale",inputNames:["A","Max"],inputTypes:[s.TextureType.unpacked,s.TextureType.unpacked]},a={name:"SoftMax",inputNames:["A","Max","Norm"],inputTypes:[s.TextureType.unpacked,s.TextureType.unpacked,s.TextureType.unpacked]};n.softmax=(g,m,b)=>{h(m);const y=m[0].dims.slice(),w=l.ShapeUtil.normalizeAxis(b.axis,y.length),T=l.ShapeUtil.sizeToDimension(y,w),S=l.ShapeUtil.sizeFromDimension(y,w);return t(g,m,b,T,S)},n.parseSoftmaxAttributes=g=>(0,u.createAttributeWithCacheKey)({axis:g.attributes.getInt("axis",1)}),n.parseSoftmaxAttributesV13=g=>(0,u.createAttributeWithCacheKey)({axis:g.attributes.getInt("axis",-1)}),n.softmaxV13=(g,m,b)=>{h(m);const y=m[0].dims.slice(),w=l.ShapeUtil.normalizeAxis(b.axis,y.length),T=y.length,S=w!==T-1,A=[];let O,v=[],M=[];S&&(v=Array.from({length:T}).map((N,G)=>G),v[w]=T-1,v[T-1]=w,v.map(N=>A.push(y[N])),O=(0,u.createAttributeWithCacheKey)({perm:v}),M=(0,p.transpose)(g,m,O));const F=S?l.ShapeUtil.sizeToDimension(A,T-1):l.ShapeUtil.sizeToDimension(y,T-1),R=S?l.ShapeUtil.sizeFromDimension(A,T-1):l.ShapeUtil.sizeFromDimension(y,T-1),$=t(g,S?M:m,b,F,R);return S?(0,p.transpose)(g,$,O):$};const t=(g,m,b,y,w)=>{const T=e(g,m[0],y,w,[y]),S=g.run(Object.assign(Object.assign({},f),{cacheHint:b.cacheKey,get:()=>T}),m),A=r(g,m[0],y,w,T.output.dims,[y]),O=g.run(Object.assign(Object.assign({},c),{cacheHint:b.cacheKey,get:()=>A}),[m[0],S]),v=i(g,m[0],y,w,T.output.dims,A.output.dims);return[g.run(Object.assign(Object.assign({},a),{cacheHint:b.cacheKey,get:()=>v}),[m[0],S,O])]},e=(g,m,b,y,w)=>{const[T,S]=g.calculateTextureWidthAndHeight(m.dims,s.TextureType.unpacked),A=w.length;if(b<1||y<1)throw new Error("Logical row count N and feature count D must be greater than or equal to 1");if(w.length!==1)throw new Error("Dimensionality of the output should be 1");if(w[0]!==b)throw new Error("Shape of the output should be equal to logical row count");const O=(0,d.getGlsl)(g.session.backend.glContext.version),v=`
      float process(int[${A}] indices) {
        int logical_row_start_offset = indices[0] * ${y};

        float max = getColorAsFloat(${O.texture2D}(A, offsetToCoords(logical_row_start_offset, ${T},
        ${S} )));
        for(int i=1; i<${y}; ++i)
        {
          float current = getColorAsFloat(${O.texture2D}(A, offsetToCoords(logical_row_start_offset + i,
            ${T}, ${S})));
          if(current > max)
          max = current;
        }

        return max;
      }`;return Object.assign(Object.assign({},f),{output:{dims:w,type:m.type,textureType:s.TextureType.unpacked},shaderSource:v})},r=(g,m,b,y,w,T)=>{const[S,A]=g.calculateTextureWidthAndHeight(m.dims,s.TextureType.unpacked),O=T.length;if(b<1||y<1)throw new Error("Logical row count N and feature count D must be greater than or equal to 1");if(T.length!==1)throw new Error("Dimensionality of the output should be 1");if(T[0]!==b)throw new Error("Shape of the output should be equal to logical row count");if(w.length!==1)throw new Error("Dimensionality of the intermediate results should be 1");if(w[0]!==b)throw new Error("Shape of the intermediate results should be equal to logical row count");const v=`
      float process(int[${O}] indices) {
        int logical_row_start_offset = indices[0] * ${y};

        float norm_factor = 0.0;
        float max = _Max(indices);
        for(int i=0; i<${y}; ++i)
        {
          norm_factor += exp(getColorAsFloat(${(0,d.getGlsl)(g.session.backend.glContext.version).texture2D}(A, offsetToCoords(logical_row_start_offset + i,
            ${S}, ${A}))) - max);
        }

        return norm_factor;
      }`;return Object.assign(Object.assign({},c),{output:{dims:T,type:m.type,textureType:s.TextureType.unpacked},shaderSource:v})},i=(g,m,b,y,w,T)=>{const[S,A]=g.calculateTextureWidthAndHeight(m.dims,s.TextureType.unpacked),O=m.dims.length;if(b<1||y<1)throw new Error("Logical row count N and feature count D must be greater than or equal to 1");if(w.length!==1||T.length!==1)throw new Error("Dimensionality of the intermediate results should be 1");if(w[0]!==b||T[0]!==b)throw new Error("Shape of the intermediate results should be equal to logical row count");const v=`
      float process(int[${O}] indices) {

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

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

      float norm_factor = _Norm(logical_row_index);

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

      return exp(_A(indices) - _Max(logical_row_index)) / norm_factor;
    }`;return Object.assign(Object.assign({},a),{output:{dims:m.dims,type:m.type,textureType:s.TextureType.unpacked},shaderSource:v})},h=g=>{if(!g||g.length!==1)throw new Error("Softmax requires 1 input.");if(g[0].type!=="float32"&&g[0].type!=="float64")throw new Error("Invalid input type")}},5975:(_,n,o)=>{Object.defineProperty(n,"__esModule",{value:!0}),n.parseSplitAttributes=n.split=void 0;const u=o(246),l=o(2517),d=o(2039),s={name:"Split",inputNames:["A"],inputTypes:[d.TextureType.unpacked]};n.split=(a,t,e)=>{c(t);const r=l.ShapeUtil.normalizeAxis(e.axis,t[0].dims.length),i=p(a,t,r,e),h=[];for(let g=0;g<i;++g)h.push(a.run(Object.assign(Object.assign({},s),{cacheHint:`${e.cacheKey};${g}`,get:()=>f(a,t[0],e,r,g)}),t));return h},n.parseSplitAttributes=a=>{const t=a.attributes.getInt("axis",0),e=a.attributes.getInts("split",[]),r=a.outputs.length;return(0,u.createAttributeWithCacheKey)({axis:t,split:e,numOutputs:r})};const p=(a,t,e,r)=>{const[,i]=l.SplitUtil.splitShape(t[0].dims,e,r.split,r.numOutputs);return i.length},f=(a,t,e,r,i)=>{const[h,g]=l.SplitUtil.splitShape(t.dims,r,e.split,e.numOutputs),m=g[i],b=h[i],y=`
      float process(int indices[${b.length}]) {
        indices[${r}] += ${m};
        return _A(indices);
      }
    `;return Object.assign(Object.assign({},s),{cacheHint:`${e.cacheKey}:${i}`,output:{dims:b,type:t.type,textureType:d.TextureType.unpacked},shaderSource:y})},c=a=>{if(!a||a.length!==1)throw new Error("Split requires one input.");if(a[0].type!=="int8"&&a[0].type!=="uint8"&&a[0].type!=="int16"&&a[0].type!=="uint16"&&a[0].type!=="int32"&&a[0].type!=="uint32"&&a[0].type!=="float32"&&a[0].type!=="float64"&&a[0].type!=="bool")throw new Error("Invalid input type.")}},3933:(_,n,o)=>{Object.defineProperty(n,"__esModule",{value:!0}),n.parseSqueezeAttributes=n.squeezeV13=n.squeeze=void 0;const u=o(2517);n.squeeze=(s,p,f)=>{l(p);const c=u.ShapeUtil.squeezeShape(p[0].dims,f);return[s.reshapeUnpacked(p[0],c)]},n.squeezeV13=(s,p)=>(d(p),(0,n.squeeze)(s,[p[0]],Array.from(p[1].integerData))),n.parseSqueezeAttributes=s=>s.attributes.getInts("axes");const l=s=>{if(!s||s.length!==1)throw new Error("Squeeze requires 1 input.");if(s[0].type==="string")throw new Error("invalid input tensor types.")},d=s=>{if(!s||s.length!==2)throw new Error("Squeeze requires 2 inputs.");if(s[1].type!=="int32")throw new Error("Invalid input type.")}},6558:(_,n,o)=>{Object.defineProperty(n,"__esModule",{value:!0}),n.sum=void 0;const u=o(5060),l=o(2039);n.sum=(p,f)=>{s(f);const c={name:"Sum",inputNames:f.map((a,t)=>`X${t}`),inputTypes:new Array(f.length).fill(l.TextureType.unpacked)};return[p.run(Object.assign(Object.assign({},c),{get:()=>d(p,f,c)}),f)]};const d=(p,f,c)=>{const a=(0,u.getGlsl)(p.session.backend.glContext.version),t=f[0].dims.slice(),e=`
      void main() {
        vec4 result = ${f.map((r,i)=>`${a.texture2D}(X${i},TexCoords)`).join(" + ")};
        ${a.output} = result;
      }
    `;return Object.assign(Object.assign({},c),{output:{dims:t,type:f[0].type,textureType:l.TextureType.unpacked},hasMain:!0,shaderSource:e})},s=p=>{if(!p||p.length===0)throw new Error("Sum requires inputs.");const f=p[0].dims.length;for(let c=1;c<p.length;c++){if(f!==p[c].dims.length)throw new Error("Input shapes are mismatched.");for(let a=0;a<f;a++)if(p[0].dims[a]!==p[c].dims[a])throw new Error("Input shapes are not matched.")}if(p[0].type!=="float32"&&p[0].type!=="float64")throw new Error("Invalid input type.");for(let c=1;c<p.length;c++)if(p[0].type!==p[c].type)throw new Error("Input types are not matched.")}},5723:(_,n,o)=>{Object.defineProperty(n,"__esModule",{value:!0}),n.tile=void 0;const u=o(782),l=o(2039);n.tile=(p,f)=>{s(f);const c={name:"Tile",inputNames:["A"],inputTypes:[l.TextureType.unpacked]};return[p.run(Object.assign(Object.assign({},c),{get:()=>d(p,f,c)}),f)]};const d=(p,f,c)=>{const a=f[0].dims.slice(),t=new Array(a.length),e=[];for(let h=0;h<a.length;h++)t[h]=a[h]*f[1].numberData[h],e.push(`inputIdx[${h}] = int(mod(float(outputIdx[${h}]), ${a[h]}.));`);const r=t.length,i=`
      float process(int outputIdx[${r}]) {
        int inputIdx[${r}];
        ${e.join(`
`)}
        return _A(inputIdx);
      }
    `;return Object.assign(Object.assign({},c),{output:{dims:t,type:f[0].type,textureType:l.TextureType.unpacked},shaderSource:i})},s=p=>{if(!p||p.length!==2)throw new Error("Tile requires 2 input.");if(p[1].dims.length!==1)throw new Error("The second input shape must 1 dimension.");if(p[1].dims[0]!==p[0].dims.length)throw new Error("Invalid input shape.");if(u.NUMBER_TYPES.indexOf(p[0].type)===-1)throw new Error("Invalid input type.");if(p[1].type!=="int32"&&p[1].type!=="int16")throw new Error("Invalid repeat type.")}},3738:(_,n,o)=>{Object.defineProperty(n,"__esModule",{value:!0}),n.parseTransposeAttributes=n.transpose=void 0;const u=o(246),l=o(2517),d=o(2039),s={name:"Transpose",inputNames:["A"],inputTypes:[d.TextureType.unpacked]};n.transpose=(e,r,i)=>(t(r),[e.run(Object.assign(Object.assign({},s),{cacheHint:i.cacheKey,get:()=>p(e,r[0],i.perm)}),r)]),n.parseTransposeAttributes=e=>(0,u.createAttributeWithCacheKey)({perm:e.attributes.getInts("perm",[])});const p=(e,r,i)=>{const h=r.dims;i=f(h,i);const g=c(h,i),m=h.length,b=`
      ${a("perm",i,m)}
      float process(int indices[${m}]) {
        int a[${m}];
        perm(a, indices);
        return _A(a);
      }`;return Object.assign(Object.assign({},s),{output:{dims:g,type:r.type,textureType:d.TextureType.unpacked},shaderSource:b})},f=(e,r)=>(r&&r.length!==e.length&&(r=[...e.keys()].reverse()),r),c=(e,r)=>(r=f(e,r),l.ShapeUtil.sortBasedOnPerm(e,r)),a=(e,r,i)=>{const h=[];h.push(`void ${e}(out int a[${i}], int src[${i}]) {`);for(let g=0;g<i;++g)h.push(`	a[${r[g]}]=src[${g}];`);return h.push("	}"),h.join(`
`)},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:(_,n,o)=>{Object.defineProperty(n,"__esModule",{value:!0}),n.encodeAsUint8=void 0;const u=o(5060),l=o(2039);n.encodeAsUint8=(d,s)=>{const p=s.shape,f=(0,u.getGlsl)(d.session.backend.glContext.version),c=`
    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;
      ${f.output} = encodeAsUint8(value);
    }`,a={name:"Uint8Encode",inputTypes:[l.TextureType.unpacked],inputNames:["X"],output:{dims:p,type:s.tensor.type,textureType:l.TextureType.downloadUint8AsFloat},shaderSource:c,hasMain:!0};return d.executeProgram(a,[s.tensor])}},4909:(_,n,o)=>{Object.defineProperty(n,"__esModule",{value:!0}),n.tanh=n.tan=n.sqrt=n.sin=n.sigmoid=n.relu=n.not=n.neg=n.log=n.parseLeakyReluAttributes=n.leakyRelu=n.identity=n.floor=n.exp=n.parseEluAttributes=n.elu=n.cos=n.ceil=n.clipV11=n.parseClipAttributes=n.clip=n.atan=n.asin=n.acos=n.abs=n.glslTanh=n.glslTan=n.glslSqrt=n.glslSigmoid=n.glslRelu=n.glslSin=n.glslNot=n.glslNeg=n.glslLog=n.glslLeakyRelu=n.glslIdentity=n.glslClip=n.glslFloor=n.glslExp=n.glslElu=n.glslCos=n.glslCeil=n.glslAtan=n.glslAsin=n.glslAcos=n.glslAbs=void 0;const u=o(246),l=o(2517),d=o(8520),s=o(5060),p=o(2039);function f(){return $("abs")}function c(){return $("acos")}function a(){return $("asin")}function t(){return $("atan")}function e(){return $("ceil")}function r(){return $("cos")}function i(D){const j="elu";return{body:`
  const float alpha = float(${D});

  float ${j}_(float a) {
    return a >= 0.0 ? a: (exp(a) - 1.0) * alpha;
  }
  vec4 ${j}_(vec4 v) {
    return vec4(${j}_(v.x), ${j}_(v.y), ${j}_(v.z), ${j}_(v.w));
  }
  `,name:j,type:d.FunctionType.ValueBased}}function h(){return $("exp")}function g(){return $("floor")}function m(D,j){const Q="clip";return{body:`
  const float min = float(${D});
  const float max = float(${j});

  float ${Q}_(float a) {
    return clamp(a, min, max);
  }
  vec4 ${Q}_(vec4 v) {
    return clamp(v, min, max);
  }
  `,name:Q,type:d.FunctionType.ValueBased}}function b(){const D="indentity";return{body:`
  float ${D}_(float a) {
    return a;
  }
  vec4 ${D}_(vec4 v) {
    return v;
  }
  `,name:D,type:d.FunctionType.ValueBased}}function y(D){const j="leakyRelu";return{body:`
  const float alpha = float(${D});

  float ${j}_(float a) {
    return a < 0.0 ? a * alpha : a;
  }
  vec4 ${j}_(vec4 v) {
    return vec4(${j}_(v.x), ${j}_(v.y), ${j}_(v.z), ${j}_(v.w));
  }
  `,name:j,type:d.FunctionType.ValueBased}}function w(){return $("log")}function T(){const D="neg";return{body:`
  float ${D}_(float a) {
    return -a;
  }
  vec4 ${D}_(vec4 v) {
    return -v;
  }
  `,name:D,type:d.FunctionType.ValueBased}}function S(){const D="not";return{body:`
  float ${D}_(float a) {
    return float( ! bool(a) );
  }
  bool ${D}_(bool a) {
    return !a;
  }
  vec4 ${D}_(vec4 v) {
    return vec4(!bool(v.x), !bool(v.y), !bool(v.z), !bool(v.w));
  }
  bvec4 ${D}_(bvec4 v) {
    return bvec4(!v.x, !v.y, !v.z, !v.w);
  }
  `,name:D,type:d.FunctionType.ValueBased}}function A(){return $("sin")}function O(){const D="relu";return{body:`
  float ${D}_(float a) {
    return max( a, 0.0 );
  }
  vec4 ${D}_(vec4 v) {
    return max( v, 0.0 );
  }
  `,name:D,type:d.FunctionType.ValueBased}}function v(){const D="sigmoid";return{body:`
  float ${D}_(float a) {
    return 1.0 / (1.0 + exp(-a));
  }
  vec4 ${D}_(vec4 v) {
    return 1.0 / (1.0 + exp(-v));
  }
  `,name:D,type:d.FunctionType.ValueBased}}function M(){return $("sqrt")}function F(){return $("tan")}function R(){const D="tanh";return{body:`
  float ${D}_(float a) {
    a = clamp(a, -10., 10.);
    a = exp(2.*a);
    return (a - 1.) / (a + 1.);
  }
  vec4 ${D}_(vec4 v) {
    v = clamp(v, -10., 10.);
    v = exp(2.*v);
    return (v - 1.) / (v + 1.);
  }
  `,name:D,type:d.FunctionType.ValueBased}}function $(D){return{body:`
  float ${D}_(float a) {
    return ${D}(a);
  }
  vec4 ${D}_(vec4 v) {
    return ${D}(v);
  }
  `,name:D,type:d.FunctionType.ValueBased}}n.glslAbs=f,n.glslAcos=c,n.glslAsin=a,n.glslAtan=t,n.glslCeil=e,n.glslCos=r,n.glslElu=i,n.glslExp=h,n.glslFloor=g,n.glslClip=m,n.glslIdentity=b,n.glslLeakyRelu=y,n.glslLog=w,n.glslNeg=T,n.glslNot=S,n.glslSin=A,n.glslRelu=O,n.glslSigmoid=v,n.glslSqrt=M,n.glslTan=F,n.glslTanh=R;const N=(D,j,Q,X)=>{const Z=D.session.pack?p.TextureType.packed:p.TextureType.unpacked,ee={name:Q.name,inputTypes:[Z],inputNames:["A"],cacheHint:X};return Object.assign(Object.assign({},ee),{get:()=>((ue,Ae,ve,oe)=>{const we=ue.session.pack?p.TextureType.packed:p.TextureType.unpacked,be=(0,s.getGlsl)(ue.session.backend.glContext.version);return Object.assign(Object.assign({},Ae),{output:{dims:ve.dims,type:ve.type,textureType:we},shaderSource:`
     ${oe.body}
     void main() {
       vec4 v = ${be.texture2D}(A, TexCoords);
       v = ${oe.name}_(v);
       ${be.output} = v;
     }
     `,hasMain:!0})})(D,ee,j,Q)})};n.abs=(D,j)=>[D.run(N(D,j[0],f()),j)],n.acos=(D,j)=>[D.run(N(D,j[0],c()),j)],n.asin=(D,j)=>[D.run(N(D,j[0],a()),j)],n.atan=(D,j)=>[D.run(N(D,j[0],t()),j)],n.clip=(D,j,Q)=>[D.run(N(D,j[0],m(Q.min,Q.max),Q.cacheKey),j)],n.parseClipAttributes=D=>(0,u.createAttributeWithCacheKey)({min:D.attributes.getFloat("min",l.MIN_CLIP),max:D.attributes.getFloat("max",l.MAX_CLIP)}),n.clipV11=(D,j)=>{const Q=G(D,j);return(0,n.clip)(D,[j[0]],Q)};const G=(D,j)=>{if(j.length>=3&&(!D.session.isInitializer(j[1].dataId)||!D.session.isInitializer(j[2].dataId)))throw new Error("dynamic clip attributes are not allowed");const Q=j.length>=3?j[1].numberData[0]:l.MIN_CLIP,X=j.length>=3?j[2].numberData[0]:l.MAX_CLIP;return(0,u.createAttributeWithCacheKey)({min:Q,max:X})};n.ceil=(D,j)=>[D.run(N(D,j[0],e()),j)],n.cos=(D,j)=>[D.run(N(D,j[0],r()),j)],n.elu=(D,j,Q)=>[D.run(N(D,j[0],i(Q.alpha),Q.cacheKey),j)],n.parseEluAttributes=D=>(0,u.createAttributeWithCacheKey)({alpha:D.attributes.getFloat("alpha",1)}),n.exp=(D,j)=>[D.run(N(D,j[0],h()),j)],n.floor=(D,j)=>[D.run(N(D,j[0],g()),j)],n.identity=(D,j)=>[D.run(N(D,j[0],b()),j)],n.leakyRelu=(D,j,Q)=>[D.run(N(D,j[0],y(Q.alpha),Q.cacheKey),j)],n.parseLeakyReluAttributes=D=>(0,u.createAttributeWithCacheKey)({alpha:D.attributes.getFloat("alpha",.01)}),n.log=(D,j)=>[D.run(N(D,j[0],w()),j)],n.neg=(D,j)=>[D.run(N(D,j[0],T()),j)],n.not=(D,j)=>[D.run(N(D,j[0],S()),j)],n.relu=(D,j)=>[D.run(N(D,j[0],O()),j)],n.sigmoid=(D,j)=>[D.run(N(D,j[0],v()),j)],n.sin=(D,j)=>[D.run(N(D,j[0],A()),j)],n.sqrt=(D,j)=>[D.run(N(D,j[0],M()),j)],n.tan=(D,j)=>[D.run(N(D,j[0],F()),j)],n.tanh=(D,j)=>[D.run(N(D,j[0],R()),j)]},5611:(_,n,o)=>{Object.defineProperty(n,"__esModule",{value:!0}),n.createUnpackProgramInfoLoader=n.createUnpackProgramInfo=void 0;const u=o(5060),l=o(2039),d=o(9390),s=o(2827),p={name:"unpack",inputNames:["A"],inputTypes:[l.TextureType.packed]};n.createUnpackProgramInfo=(f,c)=>{const a=c.dims.length,t=(0,s.getChannels)("rc",a),e=t.slice(-2),r=(0,d.getCoordsDataType)(a),i=(0,s.unpackFromChannel)(),h=c.dims.length===0?"":function(b,y){if(b===1)return"rc";let w="";for(let T=0;T<b;T++)w+=y[T],T<b-1&&(w+=",");return w}(a,t),g=a<=1?"rc":`vec2(${e.join(",")})`,m=`
    ${i}
    void main() {
      ${r} rc = getOutputCoords();

       // Sample the texture with the coords to get the rgba channel value.
       vec4 packedInput = getA(${h});

       ${(0,u.getGlsl)(f.session.backend.glContext.version).output} = vec4(getChannel(packedInput, ${g}), 0, 0, 0);
     }
   `;return Object.assign(Object.assign({},p),{hasMain:!0,output:{dims:c.dims,type:c.type,textureType:l.TextureType.unpacked},shaderSource:m})},n.createUnpackProgramInfoLoader=(f,c)=>Object.assign(Object.assign({},p),{get:()=>(0,n.createUnpackProgramInfo)(f,c)})},8428:(_,n,o)=>{Object.defineProperty(n,"__esModule",{value:!0}),n.parseUnsqueezeAttributes=n.unsqueezeV13=n.unsqueeze=void 0;const u=o(2517);n.unsqueeze=(s,p,f)=>{l(p);const c=u.ShapeUtil.unsqueezeShape(p[0].dims,f);return[s.reshapeUnpacked(p[0],c)]},n.unsqueezeV13=(s,p)=>(d(p),(0,n.unsqueeze)(s,[p[0]],Array.from(p[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.")},d=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:(_,n,o)=>{Object.defineProperty(n,"__esModule",{value:!0}),n.scalesValidation=n.validateInputs=n.parseUpsampleAttributes=n.parseUpsampleAttributesV9=n.parseUpsampleAttributesV7=n.upsample=void 0;const u=o(246),l=o(5060),d=o(2039),s={name:"Upsample",inputNames:["X"],inputTypes:[d.TextureType.unpacked]};n.upsample=(f,c,a)=>((0,n.validateInputs)(c,a),[f.run(Object.assign(Object.assign({},s),{cacheHint:a.cacheKey,get:()=>p(f,c,a)}),c)]),n.parseUpsampleAttributesV7=f=>(0,n.parseUpsampleAttributes)(f,7),n.parseUpsampleAttributesV9=f=>(0,n.parseUpsampleAttributes)(f,9),n.parseUpsampleAttributes=(f,c)=>{const a=c>=10,t=f.attributes.getString("mode","nearest");if(t!=="nearest"&&t!=="linear"&&(c<11||t!=="cubic"))throw new Error(`unrecognized mode: ${t}`);let e=[];c<9&&(e=f.attributes.getFloats("scales"),(0,n.scalesValidation)(e,t,a));const r=f.attributes.getFloat("extrapolation_value",0),i=c>10?f.attributes.getString("coordinate_transformation_mode","half_pixel"):"asymmetric";if(["asymmetric","pytorch_half_pixel","tf_half_pixel_for_nn","align_corners","tf_crop_and_resize","half_pixel"].indexOf(i)===-1)throw new Error(`coordinate_transform_mode '${i}' is not supported`);const h=i==="tf_crop_and_resize",g=h,m=t==="nearest"&&c>=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 '${m}' is not supported`);const b=f.attributes.getFloat("cubic_coeff_a",-.75),y=f.attributes.getInt("exclude_outside",0)!==0;if(y&&t!=="cubic")throw new Error("exclude_outside can be set to 1 only when mode is CUBIC.");const w=c<11||t==="nearest"&&i==="asymmetric"&&m==="floor";let T=0,S=0,A=0;return c>10?f.inputs.length>2?(T=1,S=2,A=3):(S=1,A=2):c===9&&(S=1),(0,u.createAttributeWithCacheKey)({opset:c,isResize:a,mode:t,scales:e,extrapolationValue:r,coordinateTransformMode:i,useExtrapolation:g,needRoiInput:h,nearestMode:m,cubicCoefficientA:b,excludeOutside:y,useNearest2xOptimization:w,roiInputIdx:T,scalesInputIdx:S,sizesInputIdx:A})};const p=(f,c,a)=>{const t=(0,l.getGlsl)(f.session.backend.glContext.version),[e,r]=f.calculateTextureWidthAndHeight(c[0].dims,d.TextureType.unpacked),i=c[0].dims.map((A,O)=>Math.floor(A*a.scales[O])),[h,g]=f.calculateTextureWidthAndHeight(i,d.TextureType.unpacked),m=i.length,b=new Array(m),y=new Array(m);let w=`
      int output_pitches[${m}];
      int input_pitches[${m}];
      `;for(let A=m-1;A>=0;A--)b[A]=A===m-1?1:b[A+1]*i[A+1],y[A]=A===m-1?1:y[A+1]*c[0].dims[A+1],w+=`
        output_pitches[${A}] = ${b[A]};
        input_pitches[${A}] = ${y[A]};
        `;const T=`
      float getInputFloat(int index) {
        vec2 coords = offsetToCoords(index, ${e}, ${r});
        float value = getColorAsFloat(${t.texture2D}(X, coords));
        return value;
      }
      `,S=a.mode==="nearest"?`
    ${T}
    float process(int indices[${m}]) {
      int input_index = 0;
      int output_index = coordsToOffset(TexCoords, ${h}, ${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?`
    ${T}
    float process(int indices[4]) {
      int input_index = 0;
      int output_index = coordsToOffset(TexCoords, ${h}, ${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 == (${c[0].dims[2]} - 1)) {
        // It's the end in dimension 2
        x01 = x00;
        end_of_dim2 = true;
      } else {
        x01 = getInputFloat(input_index + input_pitches[2]);
      }

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

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

      ${w}

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

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

      input_index = index_of_input_dim0 * input_pitches[0] + index_of_input_dim1;

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

      bool end_of_dim0 = false;
      if (index_of_input_dim0 == (${c[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:c[0].type,textureType:d.TextureType.unpacked},shaderSource:S,variables:[{name:"scales",type:"int",arrayLength:a.scales.length,data:a.scales.map(A=>Math.ceil(A))}]})};n.validateInputs=(f,c)=>{if(!f||c.opset<9&&f.length!==1||c.opset>=9&&c.opset<11&&f.length!==2||c.opset>=11&&f.length<2)throw new Error("invalid inputs.");if(c.scales.length>0&&f[0].dims.length!==c.scales.length)throw new Error("Invalid input shape.");if(f[0].type==="string")throw new Error("Invalid input tensor types.")},n.scalesValidation=(f,c,a)=>{if(a){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(!(c!=="linear"&&c!=="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 ${a?"Resize":"Upsample"} opeartor.`)}},1958:(_,n,o)=>{Object.defineProperty(n,"__esModule",{value:!0}),n.ProgramManager=void 0;const u=o(1670),l=o(6231),d=o(8879),s=o(5060);n.ProgramManager=class{constructor(p,f,c){this.profiler=p,this.glContext=f,this.textureLayoutStrategy=c,this.repo=new Map,this.attributesBound=!1}getArtifact(p){return this.repo.get(p)}setArtifact(p,f){this.repo.set(p,f)}run(p,f,c){var a;this.profiler.event("op",`ProgramManager.run ${(a=p.programInfo.name)!==null&&a!==void 0?a:"unknown kernel"}`,()=>{var t;const e=this.glContext.gl,r=p.program;e.useProgram(r);try{this.bindOutput(c),this.attributesBound||this.bindAttributes(p.attribLocations),this.bindUniforms(p.uniformLocations,(t=p.programInfo.variables)!==null&&t!==void 0?t:[],f)}catch(i){throw l.Logger.error("ProgramManager",p.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(p=>this.glContext.deleteProgram(p.program))}build(p,f,c){return this.profiler.event("backend","ProgramManager.build",()=>{const a=new d.GlslPreprocessor(this.glContext,p,f,c),t=a.preprocess(),e=this.compile(t);return{programInfo:p,program:e,uniformLocations:this.getUniformLocations(e,a.context.programInfo.inputNames,a.context.programInfo.variables),attribLocations:this.getAttribLocations(e)}})}compile(p){if(!this.vertexShader){l.Logger.verbose("ProrgramManager","Compiling and caching Vertex shader for the first time");const a=(0,s.getVertexShaderSource)(this.glContext.version);this.vertexShader=this.glContext.compileShader(a,this.glContext.gl.VERTEX_SHADER)}u.env.debug&&l.Logger.verbose("ProrgramManager",`FragShader:
${p}
`);const f=this.glContext.compileShader(p,this.glContext.gl.FRAGMENT_SHADER),c=this.glContext.createProgram(this.vertexShader,f);return this.glContext.deleteShader(f),c}bindOutput(p){const f=p.width,c=p.height;l.Logger.verbose("ProrgramManager",`Binding output texture to Framebuffer: w/h=${f}/${c}, shape=${p.shape}, type=${p.tensor.type}`),this.glContext.attachFramebuffer(p.texture,f,c)}bindAttributes(p){const f=p.position,c=p.textureCoord;this.glContext.setVertexAttributes(f,c),this.attributesBound=!0}bindUniforms(p,f,c){var a;const t=this.glContext.gl;let e=0;for(const{name:r,type:i,location:h,arrayLength:g}of p){const m=(a=f.find(b=>b.name===r))===null||a===void 0?void 0:a.data;if(i!=="sampler2D"&&!m)throw new Error(`variable '${r}' does not have data defined in program info`);switch(i){case"sampler2D":this.bindTexture(c[e],h,e),e++;break;case"float":g?t.uniform1fv(h,m):t.uniform1f(h,m);break;case"int":g?t.uniform1iv(h,m):t.uniform1i(h,m);break;default:throw new Error(`Uniform not implemented: ${i}`)}}}bindTexture(p,f,c){this.glContext.bindTextureToUniform(p.texture,c,f)}getAttribLocations(p){return{position:this.getAttribLocation(p,"position"),textureCoord:this.getAttribLocation(p,"textureCoord")}}getUniformLocations(p,f,c){const a=[];if(f)for(const t of f)a.push({name:t,type:"sampler2D",location:this.getUniformLocation(p,t)});if(c)for(const t of c)a.push(Object.assign(Object.assign({},t),{location:this.getUniformLocation(p,t.name)}));return a}getUniformLocation(p,f){const c=this.glContext.gl.getUniformLocation(p,f);if(c===null)throw new Error(`Uniform ${f} not found.`);return c}getAttribLocation(p,f){return this.glContext.gl.getAttribLocation(p,f)}}},6416:(_,n,o)=>{Object.defineProperty(n,"__esModule",{value:!0}),n.WebGLSessionHandler=void 0;const u=o(6231),l=o(1047),d=o(8316),s=o(1640),p=o(1958),f=o(7859),c=o(5702);n.WebGLSessionHandler=class{constructor(a,t){this.backend=a,this.context=t,this.layoutStrategy=new f.PreferLogicalStrategy(a.glContext.maxTextureSize),this.programManager=new p.ProgramManager(this.context.profiler,a.glContext,this.layoutStrategy),this.textureManager=new c.TextureManager(a.glContext,this.layoutStrategy,this.context.profiler,{reuseTextures:a.textureCacheMode==="full"}),this.packedTextureDataCache=new Map,this.unpackedTextureDataCache=new Map,this.pack=a.pack,this.pack2unpackMap=new Map,this.unpack2packMap=new Map}createInferenceHandler(){return new d.WebGLInferenceHandler(this)}onGraphInitialized(a){const t=a.getValues().filter(e=>e.from===-1&&e.tensor).map(e=>e.tensor.dataId);this.initializers=new Set(t)}isInitializer(a){return!!this.initializers&&this.initializers.has(a)}addInitializer(a){this.initializers.add(a)}getTextureData(a,t){return t?this.packedTextureDataCache.get(a):this.unpackedTextureDataCache.get(a)}setTextureData(a,t,e=!1){u.Logger.verbose("WebGLSessionHandler","Storing Texture data in cache"),e?this.packedTextureDataCache.set(a,t):this.unpackedTextureDataCache.set(a,t)}dispose(){this.programManager.dispose(),this.textureManager.clearActiveTextures(),this.packedTextureDataCache.forEach(a=>this.textureManager.releaseTexture(a,!0)),this.packedTextureDataCache=new Map,this.unpackedTextureDataCache.forEach(a=>this.textureManager.releaseTexture(a,!0)),this.unpackedTextureDataCache=new Map}resolve(a,t,e){const r=(0,l.resolveOperator)(a,t,s.WEBGL_OP_RESOLVE_RULES);return{impl:r.opImpl,context:r.opInit?r.opInit(a,e):a}}}},7769:(_,n,o)=>{Object.defineProperty(n,"__esModule",{value:!0}),n.Uint8DataEncoder=n.RGBAFloatDataEncoder=n.RedFloat32DataEncoder=void 0;const u=o(6231);n.RedFloat32DataEncoder=class{constructor(l,d=1){if(d===1)this.internalFormat=l.R32F,this.format=l.RED,this.textureType=l.FLOAT,this.channelSize=d;else{if(d!==4)throw new Error(`Invalid number of channels: ${d}`);this.internalFormat=l.RGBA32F,this.format=l.RGBA,this.textureType=l.FLOAT,this.channelSize=d}}encode(l,d){let s,p;return l.constructor!==Float32Array&&(u.Logger.warning("Encoder","data was not of type Float32; creating new Float32Array"),p=new Float32Array(l)),d*this.channelSize>l.length?(u.Logger.warning("Encoder","Source data too small. Allocating larger array"),p=l,s=this.allocate(d*this.channelSize),p.forEach((f,c)=>s[c]=f)):(p=l,s=p),s}allocate(l){return new Float32Array(4*l)}decode(l,d){return this.channelSize===1?l.filter((s,p)=>p%4==0).subarray(0,d):l.subarray(0,d)}},n.RGBAFloatDataEncoder=class{constructor(l,d=1,s){if(d!==1&&d!==4)throw new Error(`Invalid number of channels: ${d}`);this.internalFormat=l.RGBA,this.format=l.RGBA,this.channelSize=d,this.textureType=s||l.FLOAT}encode(l,d){let s=l;return this.channelSize===1&&(u.Logger.verbose("Encoder","Exploding into a larger array"),s=this.allocate(d),l.forEach((p,f)=>s[4*f]=p)),s}allocate(l){return new Float32Array(4*l)}decode(l,d){return this.channelSize===1?l.filter((s,p)=>p%4==0).subarray(0,d):l.subarray(0,d)}},n.Uint8DataEncoder=class{constructor(l,d=1){if(this.channelSize=4,d===1)this.internalFormat=l.ALPHA,this.format=l.ALPHA,this.textureType=l.UNSIGNED_BYTE,this.channelSize=d;else{if(d!==4)throw new Error(`Invalid number of channels: ${d}`);this.internalFormat=l.RGBA,this.format=l.RGBA,this.textureType=l.UNSIGNED_BYTE,this.channelSize=d}}encode(l,d){return new Uint8Array(l.buffer,l.byteOffset,l.byteLength)}allocate(l){return new Uint8Array(l*this.channelSize)}decode(l,d){if(l instanceof Uint8Array)return l.subarray(0,d);throw new Error(`Invalid array type: ${l.constructor}`)}}},7859:(_,n,o)=>{Object.defineProperty(n,"__esModule",{value:!0}),n.getBatchDim=n.sizeToSquarishShape=n.getRowsCols=n.sizeFromShape=n.isInt=n.parseAxisParam=n.squeezeShape=n.PreferLogicalStrategy=n.AlwaysKeepOriginalSizeStrategy=void 0;const u=o(6231),l=o(2517);function d(a,t){const e=[],r=[],i=t!=null&&Array.isArray(t)&&t.length===0,h=t==null||i?null:s(t,a).sort();let g=0;for(let m=0;m<a.length;++m){if(h!=null){if(h[g]===m&&a[m]!==1)throw new Error(`Can't squeeze axis ${m} since its dim '${a[m]}' is not 1`);(h[g]==null||h[g]>m)&&a[m]===1&&(e.push(a[m]),r.push(m)),h[g]<=m&&g++}a[m]!==1&&(e.push(a[m]),r.push(m))}return{newShape:e,keptDims:r}}function s(a,t){const e=t.length;return a=a==null?t.map((r,i)=>i):[].concat(a),(0,l.assert)(a.every(r=>r>=-e&&r<e),()=>`All values in axis param must be in range [-${e}, ${e}) but got axis ${a}`),(0,l.assert)(a.every(p),()=>`All values in axis param must be integers but got axis ${a}`),a.map(r=>r<0?e+r:r)}function p(a){return a%1==0}function f(a){if(a.length===0)return 1;let t=a[0];for(let e=1;e<a.length;e++)t*=a[e];return t}function c(a){const t=Math.ceil(Math.sqrt(a));return[t,Math.ceil(a/t)]}n.AlwaysKeepOriginalSizeStrategy=class{constructor(a){this.maxTextureSize=a}computeTextureWH(a,t){if(a.length===0)return[1,1];const e=this.maxTextureSize;if(t&&t.breakAxis!==void 0){const h=t.breakAxis>=a.length?1:a.slice(t.breakAxis).reduce((m,b)=>m*b),g=t.breakAxis<=0?1:a.slice(0,t.breakAxis).reduce((m,b)=>m*b);if(!(h>e||g>e))return[h,g];u.Logger.verbose("TextureLayout",`Given width/height preferences were unattainable: shape:${a}, breakAxis:${t.breakAxis}`)}const r=a.reduce((h,g)=>h*g);let i=Math.floor(Math.sqrt(r));for(;i<e&&i<r&&r%i!=0;i++);if(i>=e||r%i!=0)throw new Error(`The given dimensions are outside this GPU's boundaries: ${a}`);return[i,r/i]}},n.PreferLogicalStrategy=class{constructor(a){this.maxTextureSize=a}computeTextureWH(a,t){const e=this.computeTexture(a,t);return t&&t.isPacked&&(e[0]/=2,e[1]/=2),t&&t.reverseWH?[e[1],e[0]]:e}computeTexture(a,t){const e=t&&t.isPacked;if(a.length===0)return e?[2,2]:[1,1];let r=this.maxTextureSize;if(t&&t.breakAxis!==void 0){const g=t.breakAxis>=a.length?1:a.slice(t.breakAxis).reduce((b,y)=>b*y),m=t.breakAxis<=0?1:a.slice(0,t.breakAxis).reduce((b,y)=>b*y);if(!(g>r||m>r))return[g,m];u.Logger.verbose("TextureLayout",`Given width/height preferences were unattainable: shape:${a}, breakAxis:${t.breakAxis}`)}let i=a.slice(0);e&&(r*=2,i=i.map((g,m)=>m>=i.length-2?i[m]%2==0?i[m]:i[m]+1:i[m]),i.length===1&&(i=[2,i[0]])),i.length!==2&&(i=d(i).newShape);const h=f(i);return i.length<=1&&h<=r?[1,h]:i.length===2&&i[0]<=r&&i[1]<=r?i:i.length===3&&i[0]*i[1]<=r&&i[2]<=r?[i[0]*i[1],i[2]]:i.length===3&&i[0]<=r&&i[1]*i[2]<=r?[i[0],i[1]*i[2]]:i.length===4&&i[0]*i[1]*i[2]<=r&&i[3]<=r?[i[0]*i[1]*i[2],i[3]]:i.length===4&&i[0]<=r&&i[1]*i[2]*i[3]<=r?[i[0],i[1]*i[2]*i[3]]:e?c(h/4).map(g=>2*g):c(h)}},n.squeezeShape=d,n.parseAxisParam=s,n.isInt=p,n.sizeFromShape=f,n.getRowsCols=function(a){if(a.length===0)throw Error("Cannot get rows and columns of an empty shape array.");return[a.length>1?a[a.length-2]:1,a[a.length-1]]},n.sizeToSquarishShape=c,n.getBatchDim=function(a,t=2){return f(a.slice(0,a.length-t))}},4057:(_,n,o)=>{Object.defineProperty(n,"__esModule",{value:!0}),n.createTextureLayoutFromShape=n.calculateTextureWidthAndHeight=n.createTextureLayoutFromTextureType=void 0;const u=o(2517),l=o(2039);n.createTextureLayoutFromTextureType=(d,s,p)=>{const f=p===l.TextureType.unpacked||p===l.TextureType.unpackedReversed?1:4,c=p===l.TextureType.packed,a=p===l.TextureType.unpackedReversed||p===l.TextureType.packed,t=p===l.TextureType.packedLastDimension?s.length-1:void 0,e=p===l.TextureType.packedLastDimension?s.map((r,i)=>i===s.length-1?4*r:r):void 0;return(0,n.createTextureLayoutFromShape)(d,s,f,e,{isPacked:c,reverseWH:a,breakAxis:t})},n.calculateTextureWidthAndHeight=(d,s,p)=>{const f=(0,n.createTextureLayoutFromTextureType)(d,s,p);return[f.width,f.height]},n.createTextureLayoutFromShape=(d,s,p=1,f,c)=>{const a=!(!c||!c.isPacked),[t,e]=d.computeTextureWH(a&&f||s,c),r=s.length;let i=s.slice(0);if(r===0&&(i=[1]),p===1)f=s;else if(a){if(p!==4)throw new Error("a packed texture must be 4-channel");f=s,r>0&&(i[r-1]=Math.ceil(i[r-1]/2)),r>1&&(i[r-2]=Math.ceil(i[r-2]/2))}else if(!f)throw new Error("Unpacked shape is needed when using channels > 1");return{width:t,height:e,channels:p,isPacked:a,shape:i,strides:u.ShapeUtil.computeStrides(i),unpackedShape:f,reversedWH:c&&c.reverseWH}}},5702:(_,n,o)=>{Object.defineProperty(n,"__esModule",{value:!0}),n.TextureManager=void 0;const u=o(6231);n.TextureManager=class{constructor(l,d,s,p){this.glContext=l,this.layoutStrategy=d,this.profiler=s,this.config=p,this.pendingRead=new Map,p.reuseTextures&&(this.inUseTextures=new Map,this.idleTextures=new Map,this.textureLookup=new Map)}createTextureFromLayout(l,d,s,p){const f=this.toEncoderType(l),c=this.glContext.getEncoder(f,d.channels||1,p);if(d.isPacked&&p===1)throw new Error("not implemented");const a=d.width,t=d.height;let e,r;if(this.config.reuseTextures){e=`${a}x${t}_${c.format}_${c.internalFormat}_${c.textureType}`,r=this.inUseTextures.get(e),r||(r=[],this.inUseTextures.set(e,r));const h=this.idleTextures.get(e);if(h&&h.length>0){const g=h.pop();return r.push(g),p===1&&this.glContext.updateTexture(g,a,t,c,this.toTextureData(l,s)),g}}u.Logger.verbose("TextureManager",`Creating new texture of size ${d.width}x${d.height}`);const i=this.glContext.allocateTexture(a,t,c,this.toTextureData(l,s));return this.config.reuseTextures&&(r.push(i),this.textureLookup.set(i,e)),i}readTexture(l,d,s){return s||(s=1),this.profiler.event("backend","TextureManager.readTexture",()=>{const p=l.shape.reduce((c,a)=>c*a)*s,f=this.glContext.readTexture(l.texture,l.width,l.height,p,this.toEncoderType(d),s);return this.toTensorData(d,f)})}async readTextureAsync(l,d,s){const p=l.tensor.dataId;if(s||(s=1),this.pendingRead.has(p)){const f=this.pendingRead.get(p);return new Promise(c=>f==null?void 0:f.push(c))}return 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Shader source:
${a}`);return r}deleteShader(a){this.gl.deleteShader(a)}bindTextureToUniform(a,t,e){const r=this.gl;r.activeTexture(r.TEXTURE0+t),this.checkError(),r.bindTexture(r.TEXTURE_2D,a),this.checkError(),r.uniform1i(e,t),this.checkError()}draw(){this.gl.drawArrays(this.gl.TRIANGLE_STRIP,0,4),this.checkError()}checkError(){if(s.env.debug){const a=this.gl,t=a.getError();let e="";switch(t){case a.NO_ERROR:return;case a.INVALID_ENUM:e="INVALID_ENUM";break;case a.INVALID_VALUE:e="INVALID_VALUE";break;case a.INVALID_OPERATION:e="INVALID_OPERATION";break;case a.INVALID_FRAMEBUFFER_OPERATION:e="INVALID_FRAMEBUFFER_OPERATION";break;case a.OUT_OF_MEMORY:e="OUT_OF_MEMORY";break;case a.CONTEXT_LOST_WEBGL:e="CONTEXT_LOST_WEBGL";break;default:e=`Unknown WebGL Error: ${t.toString(16)}`}throw new Error(e)}}deleteTexture(a){this.gl.deleteTexture(a)}deleteProgram(a){this.gl.deleteProgram(a)}getEncoder(a,t,e=0){if(this.version===2)return new p.RedFloat32DataEncoder(this.gl,t);switch(a){case"float":return e===1||this.isRenderFloat32Supported?new p.RGBAFloatDataEncoder(this.gl,t):new p.RGBAFloatDataEncoder(this.gl,t,this.textureHalfFloatExtension.HALF_FLOAT_OES);case"int":throw new Error("not implemented");case"byte":return new p.Uint8DataEncoder(this.gl,t);default:throw new Error(`Invalid dataType: ${a}`)}}clearActiveTextures(){const a=this.gl;for(let t=0;t<this.maxTextureImageUnits;++t)a.activeTexture(a.TEXTURE0+t),a.bindTexture(a.TEXTURE_2D,null)}dispose(){if(this.disposed)return;const a=this.gl;a.bindFramebuffer(a.FRAMEBUFFER,null),a.deleteFramebuffer(this.framebuffer),a.bindBuffer(a.ARRAY_BUFFER,null),a.deleteBuffer(this.vertexbuffer),a.bindBuffer(a.ELEMENT_ARRAY_BUFFER,null),a.finish(),this.disposed=!0}createDefaultGeometry(){return new Float32Array([-1,1,0,0,1,-1,-1,0,0,0,1,1,0,1,1,1,-1,0,1,0])}createVertexbuffer(){const a=this.gl,t=a.createBuffer();if(!t)throw new Error("createBuffer() returned null");const e=this.createDefaultGeometry();return a.bindBuffer(a.ARRAY_BUFFER,t),a.bufferData(a.ARRAY_BUFFER,e,a.STATIC_DRAW),this.checkError(),t}createFramebuffer(){const a=this.gl.createFramebuffer();if(!a)throw new Error("createFramebuffer returned null");return a}queryVitalParameters(){const a=this.gl;if(this.isFloatTextureAttachableToFrameBuffer=this.checkFloatTextureAttachableToFrameBuffer(),this.isRenderFloat32Supported=this.checkRenderFloat32(),this.isFloat32DownloadSupported=this.checkFloat32Download(),this.version===1&&!this.textureHalfFloatExtension&&!this.isRenderFloat32Supported)throw new Error("both float32 and float16 TextureType are not supported");this.isBlendSupported=!this.isRenderFloat32Supported||this.checkFloat32Blend(),this.maxTextureSize=a.getParameter(a.MAX_TEXTURE_SIZE),this.maxTextureImageUnits=a.getParameter(a.MAX_TEXTURE_IMAGE_UNITS),this.version}getExtensions(){this.version===2?(this.colorBufferFloatExtension=this.gl.getExtension("EXT_color_buffer_float"),this.disjointTimerQueryWebgl2Extension=this.gl.getExtension("EXT_disjoint_timer_query_webgl2")):(this.textureFloatExtension=this.gl.getExtension("OES_texture_float"),this.textureHalfFloatExtension=this.gl.getExtension("OES_texture_half_float"))}checkFloatTextureAttachableToFrameBuffer(){const a=this.gl,t=a.createTexture();a.bindTexture(a.TEXTURE_2D,t);const e=this.version===2?a.RGBA32F:a.RGBA;a.texImage2D(a.TEXTURE_2D,0,e,1,1,0,a.RGBA,a.FLOAT,null);const r=a.createFramebuffer();a.bindFramebuffer(a.FRAMEBUFFER,r),a.framebufferTexture2D(a.FRAMEBUFFER,a.COLOR_ATTACHMENT0,a.TEXTURE_2D,t,0);const i=a.checkFramebufferStatus(a.FRAMEBUFFER)===a.FRAMEBUFFER_COMPLETE;return a.bindTexture(a.TEXTURE_2D,null),a.bindFramebuffer(a.FRAMEBUFFER,null),a.deleteTexture(t),a.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 a=this.gl;let t,e,r,i,h;try{t=a.createTexture(),e=a.createFramebuffer(),a.bindTexture(a.TEXTURE_2D,t);const g=this.version===2?a.RGBA32F:a.RGBA;return a.texImage2D(a.TEXTURE_2D,0,g,1,1,0,a.RGBA,a.FLOAT,null),a.bindFramebuffer(a.FRAMEBUFFER,e),a.framebufferTexture2D(a.FRAMEBUFFER,a.COLOR_ATTACHMENT0,a.TEXTURE_2D,t,0),a.enable(a.BLEND),r=a.createShader(a.VERTEX_SHADER),!!r&&(a.shaderSource(r,"void main(){}"),a.compileShader(r),i=a.createShader(a.FRAGMENT_SHADER),!!i&&(a.shaderSource(i,"precision highp float;void main(){gl_FragColor=vec4(0.5);}"),a.compileShader(i),h=a.createProgram(),!!h&&(a.attachShader(h,r),a.attachShader(h,i),a.linkProgram(h),a.useProgram(h),a.drawArrays(a.POINTS,0,1),a.getError()===a.NO_ERROR)))}finally{a.disable(a.BLEND),h&&a.deleteProgram(h),r&&a.deleteShader(r),i&&a.deleteShader(i),e&&(a.bindFramebuffer(a.FRAMEBUFFER,null),a.deleteFramebuffer(e)),t&&(a.bindTexture(a.TEXTURE_2D,null),a.deleteTexture(t))}}beginTimer(){if(this.version===2&&this.disjointTimerQueryWebgl2Extension){const a=this.gl,t=this.disjointTimerQueryWebgl2Extension,e=a.createQuery();return a.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 a=this.gl,t=this.disjointTimerQueryWebgl2Extension;a.endQuery(t.TIME_ELAPSED_EXT)}}isTimerResultAvailable(a){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(a,r.QUERY_RESULT_AVAILABLE),e=r.getParameter(i.GPU_DISJOINT_EXT)}return t&&!e}getTimerResult(a){let t=0;if(this.version!==2)throw new Error("WebGL1 profiling currently not supported");{const e=this.gl;t=e.getQueryParameter(a,e.QUERY_RESULT),e.deleteQuery(a)}return t/1e6}async waitForQueryAndGetTime(a){return await(0,f.repeatedTry)(()=>this.isTimerResultAvailable(a)),this.getTimerResult(a)}async createAndWaitForFence(){const a=this.createFence(this.gl);return this.pollFence(a)}createFence(a){let t;const e=a,r=e.fenceSync(e.SYNC_GPU_COMMANDS_COMPLETE,0);return a.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(a){return new Promise(t=>{this.addItemToPoll(()=>a.isFencePassed(),()=>t())})}pollItems(){const a=c(this.itemsToPoll.map(t=>t.isDoneFn));for(let t=0;t<=a;++t){const{resolveFn:e}=this.itemsToPoll[t];e()}this.itemsToPoll=this.itemsToPoll.slice(a+1)}async addItemToPoll(a,t){this.itemsToPoll.push({isDoneFn:a,resolveFn:t}),this.itemsToPoll.length>1||await(0,f.repeatedTry)(()=>(this.pollItems(),this.itemsToPoll.length===0))}}},1036:(_,n,o)=>{Object.defineProperty(n,"__esModule",{value:!0}),n.ExecutionPlan=void 0;const u=o(6231);class l{constructor(s,p){this.op=s,this.node=p}}n.ExecutionPlan=class{constructor(d,s,p){this.graph=d,this.profiler=p,this.initialize(s)}initialize(d){this.profiler.event("session","ExecutionPlan.initialize",()=>{const s=this.graph.getNodes();if(s.length!==d.length)throw new Error("The size of nodes and OPs do not match.");this._ops=d.map((p,f)=>new l(p,s[f])),this.reset(),this._starter=[],this._ops.forEach((p,f)=>{let c=!0;for(const a of p.node.inputs)if(!this._values[a]&&this.graph.getInputIndices().indexOf(a)===-1){c=!1;break}c&&this._starter.push(f)})})}reset(){this._values=this.graph.getValues().map(d=>d.tensor)}async execute(d,s){return this.profiler.event("session","ExecutionPlan.execute",async()=>{this.reset();const p=d.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,h)=>{const g=f[h];this._values[g]=i});const c=this._starter.slice(0),a=this.graph.getValues(),t=this.graph.getNodes();let e=0;for(;e<c.length;){const i=c[e++],h=this._ops[i],g=h.node.inputs.map(w=>this._values[w]);if(g.indexOf(void 0)!==-1)throw new Error(`unresolved input detected: op: ${h.node}`);const m=g;u.Logger.verbose("ExecPlan",`Runing op:${h.node.name} (${m.map((w,T)=>`'${h.node.inputs[T]}': ${w.type}[${w.dims.join(",")}]`).join(", ")})`);const b=await this.profiler.event("node",h.node.name,async()=>h.op.impl(p,m,h.op.context));if(b.length!==h.node.outputs.length)throw new Error("the size of output does not match model definition.");b.forEach((w,T)=>{const S=h.node.outputs[T];if(this._values[S])throw new Error(`output [${S}] already has value: op:${h.node.name}`);this._values[S]=w});const y=new Set;b.forEach((w,T)=>{const S=h.node.outputs[T];for(const A of a[S].to){const O=t[A];let v=!0;for(const M of O.inputs)if(!this._values[M]){v=!1;break}v&&y.add(A)}}),c.push(...y)}const r=[];for(let i=0;i<this.graph.getOutputIndices().length;i++){const h=this.graph.getOutputIndices()[i],g=this._values[h];if(g===void 0)throw new Error(`required output [${h}] does not have value`);h===0?await g.getData():g.data,r.push(g)}return u.Logger.verbose("ExecPlan","disposing of inferenceHandler"),p.dispose(),r})}}},7070:(_,n,o)=>{Object.defineProperty(n,"__esModule",{value:!0}),n.Graph=void 0;const u=o(1446),l=o(7778),d=o(9395),s=o(9162),p=o(2517);var f=d.onnxruntime.experimental.fbs;n.Graph={from:(e,r)=>new t(e,r)};class c{constructor(r){this._from=void 0,this._to=[],this.tensor=void 0,this.type=void 0,r&&(this.type=p.ProtoUtil.tensorValueTypeFromProto(r.type.tensorType))}get from(){return this._from}get to(){return this._to}}class a{constructor(r,i){r instanceof u.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 c(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 y=new c;y.type={shape:{dims:p.ProtoUtil.tensorDimsFromProto(m.dims)},tensorType:p.ProtoUtil.tensorDataTypeFromProto(m.dataType)},b=this._allData.push(y)-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: ${m.name}`);const b=this._allData.push(new c(m))-1;i.set(m.name,b),this._allOutputIndices.push(b),this._allOutputNames.push(m.name)}if(!r.node)throw new Error("missing information in graph: node");for(const m of r.node){if(!m.name)for(let y=0;;y++){const w=`unnamed_${m.opType}_${y}`;if(!h.has(w)){m.name=w;break}}if(h.has(m.name))throw new Error(`duplicated node name: ${m.name}`);const b=this._nodes.push(new a(m))-1;h.set(m.name,b)}for(let m=0;m<this._nodes.length;m++){const b=this._nodes[m],y=r.node[m];if(!y.output)throw new Error(`missing output for node: ${y.name}`);for(const w of y.output){let T=i.get(w);if(T===void 0&&(T=this._allData.push(new c)-1,i.set(w,T)),b.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=m,y.opType==="Constant"){if(!y.attribute||y.attribute.length!==1||!y.attribute[0].t)throw new Error("missing attributes or missing tensor value in attributes for this Constant operator");if(!y.output||y.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[T]._from=-1,this._allData[T].tensor=s.Tensor.fromProto(y.attribute[0].t)}}}for(let m=0;m<this._nodes.length;m++){const b=this._nodes[m],y=r.node[m];if(!y.input)throw new Error(`missing input for node: ${y.name}`);for(const w of y.input){const T=i.get(w);if(T===void 0){if(w===""&&y.input.length===3&&y.opType==="Resize")continue;throw new Error(`unrecognized input '${w}' for node: ${y.name}`)}b.inputs.push(T),this._allData[T]._to.push(m)}}return!0}buildGraphFromOrtFormat(r){var i,h,g;const m=new Map;this._allData=[],this._allInputIndices=[],this._allInputNames=[],this._allOutputIndices=[],this._allOutputNames=[],this._nodes=[];const b=new Map,y=[];for(let w=0;w<r.inputsLength();w++){const T=r.inputs(w);if(m.has(T))throw new Error(`duplicated input name: ${T}`);for(let S=0;S<r.nodeArgsLength();S++)if(((i=r.nodeArgs(S))===null||i===void 0?void 0:i.name())===T){const A=new c;if(((g=(h=r.nodeArgs(S))===null||h===void 0?void 0:h.type())===null||g===void 0?void 0:g.valueType())!==f.TypeInfoValue.tensor_type)throw new Error("Unexpected value type for the nodeArg.");const O=r.nodeArgs(S).type().value(new f.TensorTypeAndShape),v=p.ProtoUtil.tensorDataTypeFromProto(O.elemType()),M=O.shape(),F=[];for(let $=0;$<M.dimLength();$++)F.push(p.LongUtil.longToNumber(M.dim($).value().dimValue()));A.type={shape:{dims:F},tensorType:v};const R=this._allData.push(A)-1;m.set(T,R),y.push(T)}}for(let w=0;w<r.initializersLength();w++){const T=r.initializers(w);let S=m.get(T.name());if(S===void 0){const A=new c,O=p.ProtoUtil.tensorDimsFromORTFormat(T),v=p.ProtoUtil.tensorDataTypeFromProto(T.dataType());A.type={shape:{dims:O},tensorType:v},S=this._allData.push(A)-1,m.set(T.name(),S)}this._allData[S]._from=-1,this._allData[S].tensor=s.Tensor.fromOrtTensor(T)}for(let w=0;w<this._allData.length;w++)this._allData[w].tensor||(this._allInputIndices.push(w),this._allInputNames.push(y[w]));for(let w=0;w<r.outputsLength();w++){const T=r.outputs(w);if(m.has(T))throw new Error(`duplicated output name: ${T}`);const S=this._allData.push(new c)-1;m.set(T,S),this._allOutputIndices.push(S),this._allOutputNames.push(T)}if(!r.nodes)throw new Error("missing information in graph: node");for(let w=0;w<r.nodesLength();w++){const T=r.nodes(w);let S=T.name();if(!S)for(let O=0;S=`unnamed_${T.opType()}_${O}`,b.has(S);O++);if(b.has(S))throw new Error(`duplicated node name: ${S}`);const A=this._nodes.push(new a(T,S))-1;b.set(S,A)}for(let w=0;w<this._nodes.length;w++){const T=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 A=0;A<(S==null?void 0:S.outputsLength());A++){const O=S==null?void 0:S.outputs(A);let v=m.get(O);if(v===void 0&&(v=this._allData.push(new c)-1,m.set(O,v)),T.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=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");T.outputs.pop(),T.executeNode=!1,this._allData[v]._from=-1,this._allData[v].tensor=s.Tensor.fromOrtTensor(S.attributes(0).t())}}}for(let w=0;w<this._nodes.length;w++){const T=this._nodes[w],S=r.nodes(w);if(S.inputsLength()===0)throw new Error(`missing input for node: ${S.name}`);for(let A=0;A<S.inputsLength();A++){const O=S.inputs(A),v=m.get(O);if(v===void 0)throw new Error(`unrecognized input '${O}' for node: ${S.name()}`);T.inputs.push(v),this._allData[v]._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),h=new Array(this._nodes.length).fill("white");for(;i.length>0;){const g=i.pop();h[g]==="gray"?h[g]="black":(i.push(g),h[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(y=>{if(h[y]==="gray")throw new Error("model graph is cyclic");h[y]==="white"&&i.push(y)})}))}}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(h=>{const g=this._allData[h]._to.indexOf(i+r);g!==-1&&(this._allData[h]._to[g]=i)}),this._nodes[i].outputs.forEach(h=>{this._allData[h]._from&&this._allData[h]._from===i+r&&(this._allData[h]._from=i)})):(r++,this._nodes[i].outputs.forEach(h=>{this._allData[h]._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 h=-1;this._allData[i].from!==void 0&&this._allData[i].from!==-1?(h=this._nodes[this._allData[i].from].outputs.indexOf(i+r),h!==-1&&(this._nodes[this._allData[i].from].outputs[h]=i)):(h=this._allInputIndices.indexOf(i+r),h!==-1&&(this._allInputIndices[h]=i)),this._allData[i].to.forEach(g=>{h=this._nodes[g].inputs.indexOf(i+r),h!==-1&&(this._nodes[g].inputs[h]=i)}),this._allData[i].to.length===0&&(h=this._allOutputIndices.indexOf(i+r),h!==-1&&(this._allOutputIndices[h]=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 h=i.inputs[0],g=i.outputs[0],m=this._allData[g].to,b=this._allData[h].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[h].to.splice(b,1),this._allData[g]._to=[];const y=this._allOutputIndices.indexOf(g);if(y!==-1&&(this._allOutputIndices[y]=h),m&&m.length>0)for(const w of m){const T=this._nodes[w].inputs.indexOf(g);if(T===-1)throw new Error("The Node object doesn't have the output Value in it's 'inputs' property ");this._nodes[w].inputs[T]=h,this._allData[h].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. 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startInputsVector(t,e){t.startVector(4,e,4)}static addOutputs(t,e){t.addFieldOffset(6,e,0)}static createOutputsVector(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 startOutputsVector(t,e){t.startVector(4,e,4)}static addSparseInitializers(t,e){t.addFieldOffset(7,e,0)}static createSparseInitializersVector(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 startSparseInitializersVector(t,e){t.startVector(4,e,4)}static endGraph(t){return t.endObject()}static createGraph(t,e,r,i,h,g,m,b,y){return c.startGraph(t),c.addInitializers(t,e),c.addNodeArgs(t,r),c.addNodes(t,i),c.addMaxNodeIndex(t,h),c.addNodeEdges(t,g),c.addInputs(t,m),c.addOutputs(t,b),c.addSparseInitializers(t,y),c.endGraph(t)}}f.Graph=c})(p.fbs||(p.fbs={}))})(s.experimental||(s.experimental={}))}(n.onnxruntime||(n.onnxruntime={})),function(s){(function(p){(function(f){class c{constructor(){this.bb=null,this.bb_pos=0}__init(t,e){return this.bb_pos=t,this.bb=e,this}static getRootAsModel(t,e){return(e||new c).__init(t.readInt32(t.position())+t.position(),t)}static getSizePrefixedRootAsModel(t,e){return t.setPosition(t.position()+u.flatbuffers.SIZE_PREFIX_LENGTH),(e||new c).__init(t.readInt32(t.position())+t.position(),t)}irVersion(){let t=this.bb.__offset(this.bb_pos,4);return t?this.bb.readInt64(this.bb_pos+t):this.bb.createLong(0,0)}opsetImport(t,e){let r=this.bb.__offset(this.bb_pos,6);return r?(e||new s.experimental.fbs.OperatorSetId).__init(this.bb.__indirect(this.bb.__vector(this.bb_pos+r)+4*t),this.bb):null}opsetImportLength(){let t=this.bb.__offset(this.bb_pos,6);return t?this.bb.__vector_len(this.bb_pos+t):0}producerName(t){let e=this.bb.__offset(this.bb_pos,8);return e?this.bb.__string(this.bb_pos+e,t):null}producerVersion(t){let e=this.bb.__offset(this.bb_pos,10);return e?this.bb.__string(this.bb_pos+e,t):null}domain(t){let 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,h,g,m,b,y,w){return c.startModel(t),c.addIrVersion(t,e),c.addOpsetImport(t,r),c.addProducerName(t,i),c.addProducerVersion(t,h),c.addDomain(t,g),c.addModelVersion(t,m),c.addDocString(t,b),c.addGraph(t,y),c.addGraphDocString(t,w),c.endModel(t)}}f.Model=c})(p.fbs||(p.fbs={}))})(s.experimental||(s.experimental={}))}(n.onnxruntime||(n.onnxruntime={})),function(s){(function(p){(function(f){class c{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 c).__init(t.readInt32(t.position())+t.position(),t)}static getSizePrefixedRootAsKernelCreateInfos(t,e){return t.setPosition(t.position()+u.flatbuffers.SIZE_PREFIX_LENGTH),(e||new c).__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 c.startKernelCreateInfos(t),c.addNodeIndices(t,e),c.addKernelDefHashes(t,r),c.endKernelCreateInfos(t)}}f.KernelCreateInfos=c})(p.fbs||(p.fbs={}))})(s.experimental||(s.experimental={}))}(n.onnxruntime||(n.onnxruntime={})),function(s){(function(p){(function(f){class c{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 c).__init(t.readInt32(t.position())+t.position(),t)}static getSizePrefixedRootAsSubGraphSessionState(t,e){return t.setPosition(t.position()+u.flatbuffers.SIZE_PREFIX_LENGTH),(e||new c).__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 c.startSubGraphSessionState(t),c.addGraphId(t,e),c.addSessionState(t,r),c.endSubGraphSessionState(t)}}f.SubGraphSessionState=c})(p.fbs||(p.fbs={}))})(s.experimental||(s.experimental={}))}(n.onnxruntime||(n.onnxruntime={})),function(s){(function(p){(function(f){class c{constructor(){this.bb=null,this.bb_pos=0}__init(t,e){return this.bb_pos=t,this.bb=e,this}static getRootAsSessionState(t,e){return(e||new c).__init(t.readInt32(t.position())+t.position(),t)}static getSizePrefixedRootAsSessionState(t,e){return t.setPosition(t.position()+u.flatbuffers.SIZE_PREFIX_LENGTH),(e||new c).__init(t.readInt32(t.position())+t.position(),t)}kernels(t){let e=this.bb.__offset(this.bb_pos,4);return e?(t||new s.experimental.fbs.KernelCreateInfos).__init(this.bb.__indirect(this.bb_pos+e),this.bb):null}subGraphSessionStates(t,e){let r=this.bb.__offset(this.bb_pos,6);return r?(e||new s.experimental.fbs.SubGraphSessionState).__init(this.bb.__indirect(this.bb.__vector(this.bb_pos+r)+4*t),this.bb):null}subGraphSessionStatesLength(){let t=this.bb.__offset(this.bb_pos,6);return t?this.bb.__vector_len(this.bb_pos+t):0}static startSessionState(t){t.startObject(2)}static addKernels(t,e){t.addFieldOffset(0,e,0)}static addSubGraphSessionStates(t,e){t.addFieldOffset(1,e,0)}static createSubGraphSessionStatesVector(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 startSubGraphSessionStatesVector(t,e){t.startVector(4,e,4)}static endSessionState(t){return t.endObject()}static createSessionState(t,e,r){return c.startSessionState(t),c.addKernels(t,e),c.addSubGraphSessionStates(t,r),c.endSessionState(t)}}f.SessionState=c})(p.fbs||(p.fbs={}))})(s.experimental||(s.experimental={}))}(n.onnxruntime||(n.onnxruntime={})),function(s){(function(p){(function(f){class c{constructor(){this.bb=null,this.bb_pos=0}__init(t,e){return this.bb_pos=t,this.bb=e,this}static getRootAsInferenceSession(t,e){return(e||new c).__init(t.readInt32(t.position())+t.position(),t)}static getSizePrefixedRootAsInferenceSession(t,e){return t.setPosition(t.position()+u.flatbuffers.SIZE_PREFIX_LENGTH),(e||new c).__init(t.readInt32(t.position())+t.position(),t)}static bufferHasIdentifier(t){return t.__has_identifier("ORTM")}ortVersion(t){let 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 c.startInferenceSession(t),c.addOrtVersion(t,e),c.addModel(t,r),c.addSessionState(t,i),c.endInferenceSession(t)}}f.InferenceSession=c})(p.fbs||(p.fbs={}))})(s.experimental||(s.experimental={}))}(n.onnxruntime||(n.onnxruntime={}))},7448:(_,n,o)=>{Object.defineProperty(n,"__esModule",{value:!0}),n.OnnxjsSessionHandler=void 0;const u=o(1670),l=o(9162);n.OnnxjsSessionHandler=class{constructor(d){this.session=d,this.inputNames=this.session.inputNames,this.outputNames=this.session.outputNames}async dispose(){}async run(d,s,p){const f=new Map;for(const t in d)if(Object.hasOwnProperty.call(d,t)){const e=d[t];f.set(t,new l.Tensor(e.dims,e.type,void 0,void 0,e.data))}const c=await this.session.run(f),a={};return c.forEach((t,e)=>{a[e]=new u.Tensor(t.type,t.data,t.dims)}),a}startProfiling(){this.session.startProfiling()}endProfiling(){this.session.endProfiling()}}},6919:(_,n,o)=>{Object.defineProperty(n,"__esModule",{value:!0}),n.Session=void 0;const u=o(7067),l=o(1296),d=o(7091),s=o(1036),p=o(6231),f=o(2644);n.Session=class{constructor(c={}){this._initialized=!1,this.backendHint=c.backendHint,this.profiler=p.Profiler.create(c.profiler),this.context={profiler:this.profiler,graphInputTypes:[],graphInputDims:[]}}get inputNames(){return this._model.graph.getInputNames()}get outputNames(){return this._model.graph.getOutputNames()}startProfiling(){this.profiler.start()}endProfiling(){this.profiler.stop()}async loadModel(c,a,t){await this.profiler.event("session","Session.loadModel",async()=>{const e=await(0,d.resolveBackend)(this.backendHint);if(this.sessionHandler=e.createSessionHandler(this.context),this._model=new f.Model,typeof c=="string"){const r=c.endsWith(".ort");if(typeof fetch>"u"){const i=await(0,l.promisify)(u.readFile)(c);this.initialize(i,r)}else{const i=await fetch(c),h=await i.arrayBuffer();this.initialize(new Uint8Array(h),r)}}else if(ArrayBuffer.isView(c))this.initialize(c);else{const r=new Uint8Array(c,a||0,t||c.byteLength);this.initialize(r)}})}initialize(c,a){if(this._initialized)throw new Error("already initialized");this.profiler.event("session","Session.initialize",()=>{const t=this.sessionHandler.transformGraph?this.sessionHandler:void 0;this._model.load(c,t,a),this.sessionHandler.onGraphInitialized&&this.sessionHandler.onGraphInitialized(this._model.graph),this.initializeOps(this._model.graph),this._executionPlan=new s.ExecutionPlan(this._model.graph,this._ops,this.profiler)}),this._initialized=!0}async run(c){if(!this._initialized)throw new Error("session not initialized yet");return this.profiler.event("session","Session.run",async()=>{const a=this.normalizeAndValidateInputs(c),t=await this._executionPlan.execute(this.sessionHandler,a);return this.createOutput(t)})}normalizeAndValidateInputs(c){const a=this._model.graph.getInputNames();if(Array.isArray(c)){if(c.length!==a.length)throw new Error(`incorrect input array length: expected ${a.length} but got ${c.length}`)}else{if(c.size!==a.length)throw new Error(`incorrect input map size: expected ${a.length} but got ${c.size}`);const t=new Array(c.size);let e=0;for(let r=0;r<a.length;++r){const i=c.get(a[r]);if(!i)throw new Error(`missing input tensor for: '${name}'`);t[e++]=i}c=t}if(this.context.graphInputTypes&&this.context.graphInputTypes.length!==0&&this.context.graphInputDims&&this.context.graphInputDims.length!==0)this.validateInputTensorDims(this.context.graphInputDims,c,!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 h=e[t[i]];r[i]=h.type.shape.dims,this.context.graphInputTypes.push(h.type.tensorType),this.context.graphInputDims.push(c[i].dims)}this.validateInputTensorDims(r,c,!0)}return this.validateInputTensorTypes(this.context.graphInputTypes,c),c}validateInputTensorTypes(c,a){for(let t=0;t<a.length;t++){const e=c[t],r=a[t].type;if(e!==r)throw new Error(`input tensor[${t}] check failed: expected type '${e}' but got ${r}`)}}validateInputTensorDims(c,a,t){for(let e=0;e<a.length;e++){const r=c[e],i=a[e].dims;if(!this.compareTensorDims(r,i,t))throw new Error(`input tensor[${e}] check failed: expected shape '[${r.join(",")}]' but got [${i.join(",")}]`)}}compareTensorDims(c,a,t){if(c.length!==a.length)return!1;for(let e=0;e<c.length;++e)if(c[e]!==a[e]&&(!t||c[e]!==0))return!1;return!0}createOutput(c){const a=this._model.graph.getOutputNames();if(c.length!==a.length)throw new Error("expected number of outputs do not match number of generated outputs");const t=new Map;for(let e=0;e<a.length;++e)t.set(a[e],c[e]);return t}initializeOps(c){const a=c.getNodes();this._ops=new Array(a.length);for(let t=0;t<a.length;t++)this._ops[t]=this.sessionHandler.resolve(a[t],this._model.opsets,c)}}},9162:function(_,n,o){var u=this&&this.__importDefault||function(h){return h&&h.__esModule?h:{default:h}};Object.defineProperty(n,"__esModule",{value:!0}),n.Tensor=void 0;const l=o(3442),d=u(o(3720)),s=o(1446),p=o(9395),f=o(2517);var c=p.onnxruntime.experimental.fbs;class a{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,y,w,T=l.Guid.create()){this.dims=g,this.type=m,this.dataProvider=b,this.asyncDataProvider=y,this.cache=w,this.dataId=T,this.size=f.ShapeUtil.validateDimsAndCalcSize(g);const S=this.size,A=b===void 0&&y===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(O=>typeof O=="string")))throw new TypeError("cache should be a string array");A&&(this.cache=new Array(S))}else{if(w!==void 0){const O=e(m);if(!(w instanceof O))throw new TypeError(`cache should be type ${O.name}`)}if(A){const O=new ArrayBuffer(S*function(v){switch(v){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 ${v}`)}}(m));this.cache=function(v,M){return new(e(M))(v)}(O,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),y=new a(b,m);if(m==="string")g.stringData.forEach((w,T)=>{y.data[T]=(0,f.decodeUtf8String)(w)});else if(g.rawData&&typeof g.rawData.byteLength=="number"&&g.rawData.byteLength>0){const w=y.data,T=new DataView(g.rawData.buffer,g.rawData.byteOffset,g.rawData.byteLength),S=t(g.dataType),A=g.rawData.byteLength/S;if(g.rawData.byteLength%S!=0)throw new Error("invalid buffer length");if(w.length!==A)throw new Error("buffer length mismatch");for(let O=0;O<A;O++){const v=i(T,g.dataType,O*S);w[O]=v}}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 T=y.data;if(T.length!==w.length)throw new Error("array length mismatch");for(let S=0;S<w.length;S++){const A=w[S];d.default.isLong(A)?T[S]=r(A,g.dataType):T[S]=A}}return y}static fromData(g,m,b){return new a(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()),y=new a(m,b);if(b==="string")for(let w=0;w<g.stringDataLength();w++)y.data[w]=g.stringData(w);else if(g.rawDataArray()&&typeof g.rawDataLength()=="number"&&g.rawDataLength()>0){const w=y.data,T=new DataView(g.rawDataArray().buffer,g.rawDataArray().byteOffset,g.rawDataLength()),S=t(g.dataType()),A=g.rawDataLength()/S;if(g.rawDataLength()%S!=0)throw new Error("invalid buffer length");if(w.length!==A)throw new Error("buffer length mismatch");for(let O=0;O<A;O++){const v=i(T,g.dataType(),O*S);w[O]=v}}return y}}function t(h){switch(h){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[h]}`)}}function e(h){switch(h){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(h,g){if(g===s.onnx.TensorProto.DataType.INT64||g===c.TensorDataType.INT64){if(h.greaterThanOrEqual(2147483648)||h.lessThan(-2147483648))throw new TypeError("int64 is not supported")}else{if(g!==s.onnx.TensorProto.DataType.UINT32&&g!==c.TensorDataType.UINT32&&g!==s.onnx.TensorProto.DataType.UINT64&&g!==c.TensorDataType.UINT64)throw new TypeError(`not a LONG type: ${s.onnx.TensorProto.DataType[g]}`);if(h.greaterThanOrEqual(4294967296)||h.lessThan(0))throw new TypeError("uint64 is not supported")}return h.toNumber()}function i(h,g,m){switch(g){case s.onnx.TensorProto.DataType.BOOL:case s.onnx.TensorProto.DataType.UINT8:return h.getUint8(m);case s.onnx.TensorProto.DataType.INT8:return h.getInt8(m);case s.onnx.TensorProto.DataType.UINT16:return h.getUint16(m,!0);case s.onnx.TensorProto.DataType.INT16:return h.getInt16(m,!0);case s.onnx.TensorProto.DataType.FLOAT:return h.getFloat32(m,!0);case s.onnx.TensorProto.DataType.INT32:return h.getInt32(m,!0);case s.onnx.TensorProto.DataType.UINT32:return h.getUint32(m,!0);case s.onnx.TensorProto.DataType.INT64:return r(d.default.fromBits(h.getUint32(m,!0),h.getUint32(m+4,!0),!1),g);case s.onnx.TensorProto.DataType.DOUBLE:return h.getFloat64(m,!0);case s.onnx.TensorProto.DataType.UINT64:return r(d.default.fromBits(h.getUint32(m,!0),h.getUint32(m+4,!0),!0),g);default:throw new Error(`cannot read from DataView for type ${s.onnx.TensorProto.DataType[g]}`)}}n.Tensor=a},2517:function(_,n,o){var u=this&&this.__importDefault||function(g){return g&&g.__esModule?g:{default:g}};Object.defineProperty(n,"__esModule",{value:!0}),n.decodeUtf8String=n.MAX_CLIP=n.MIN_CLIP=n.PoolConvUtil=n.ReduceUtil=n.SplitUtil=n.MathUtil=n.ShapeUtil=n.LongUtil=n.ProtoUtil=n.GemmUtil=n.arrayCopyHelper=n.BroadcastUtil=n.MatMulUtil=n.ArrayUtil=n.assert=n.checkInputsShape=void 0;const l=o(5686),d=u(o(3720)),s=o(1446),p=o(9162);n.checkInputsShape=function(g,...m){if(!g||g.length!==m.length)return!1;for(let b=0;b<g.length;b++)if(!g[b].dims||g[b].dims.length!==m[b])return!1;return!0},n.assert=function(g,m){if(!g)throw new Error(typeof m=="string"?m:m())},n.ArrayUtil=class{static arraysEqual(g,m){if(g.length!==m.length)return!1;for(let b=0;b<g.length;b++)if(g[b]!==m[b])return!1;return!0}};class f{static preprocessInputShapes(m,b){return[m.length===1?[1,m[0]]:m,b.length===1?[b[0],1]:b]}static postprocessOutputShape(m,b,y){b===1&&m.splice(m.length-2,1),y===1&&m.pop()}static calcMatMulShape(m,b){return m[1]!==b[0]?void 0:[m[0],b[1]]}}n.MatMulUtil=f;class c{static calcShape(m,b,y=!1){const w=m.length,T=b.length;if(w===0)return b;if(T===0)return m;const S=Math.max(m.length,b.length),A=new Array(S);if(y){if(w<2||T<2)return;const O=f.calcMatMulShape([m[w-2],m[w-1]],[b[T-2],b[T-1]]);if(O===void 0)return;[A[S-2],A[S-1]]=O}for(let O=y?3:1;O<=S;O++){const v=w-O<0?1:m[w-O],M=T-O<0?1:b[T-O];if(v!==M&&v>1&&M>1)return;A[S-O]=Math.max(v,M)}return A}static index(m,b){const y=new Array(b.length);return c.fillIndex(m,b,y),y}static fillIndex(m,b,y){const w=m.length-b.length;for(let T=0;T<b.length;T++)y[T]=m[w+T]%b[T]}static calc(m,b,y,w,T){const S=c.calcShape(m.dims,b.dims);if(S){if(w&&!e.areEqual(S,m.dims))return;const A=e.size(S),O=w?m:new p.Tensor(S,T||m.type);if(S.length===0)O.set([],y(m.get([]),b.get([])));else{const v=new Array(S.length),M=new Array(m.dims.length),F=new Array(b.dims.length);let R,$=0,N=0,G=!1,D=!1;m.dims.length===0&&($=m.get([]),G=!0),b.dims.length===0&&(N=b.get([]),D=!0);for(let j=0;j<A;j++){R=j;for(let Q=S.length-1;Q>=0;Q--)v[Q]=R%S[Q],R=Math.floor(R/S[Q]);G||(c.fillIndex(v,m.dims,M),$=m.get(M)),D||(c.fillIndex(v,b.dims,F),N=b.get(F)),O.set(v,y($,N))}}return O}}static isValidBroadcast(m,b){const y=m.length,w=b.length;if(y>w)return!1;for(let T=1;T<=y;T++)if(m[y-T]!==1&&m[y-T]!==b[w-T])return!1;return!0}static getBroadcastDims(m,b){const y=m.length,w=[];for(let T=0;T<y;T++){const S=y-1-T,A=m[S]||1;(b[b.length-1-T]||1)>1&&A===1&&w.unshift(S)}return w}}n.BroadcastUtil=c,n.arrayCopyHelper=function(g,m,b,y,w){if(y<0||y>=m.length)throw new Error("sourceIndex out of bounds");if(b<0||b>=g.length)throw new Error("targetIndex out of bounds");if(y+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 T=0;T<w;T++)g[b+T]=m[y+T]},n.GemmUtil=class{static getShapeOfGemmResult(g,m,b,y,w){if(g.length!==2||b.length!==2)throw new Error("shape need to be of size 2");let T,S,A;m?(T=g[1],S=g[0]):(T=g[0],S=g[1]);let O=-1;if(y?(A=b[0],O=1):(A=b[1],O=0),b[O]!==S)throw new Error("dimension mismatch");if(T<=0||A<=0||S<=0)throw new Error("invalid shape specified");if(w&&!c.isValidBroadcast(w,[T,A]))throw new Error("gemm: invalid bias shape for broadcast");return[T,A,S]}};class a{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=>d.default.isLong(b)?b.toNumber():b)}static tensorValueTypeFromProto(m){return{tensorType:a.tensorDataTypeFromProto(m.elemType),shape:{dims:a.tensorDimsFromProto(m.shape.dim.map(b=>b.dimValue))}}}static tensorDimsFromORTFormat(m){const b=[];for(let y=0;y<m.dimsLength();y++)b.push(t.longToNumber(m.dims(y)));return b}static tensorAttributesFromORTFormat(m){const b=[];for(let y=0;y<m.attributesLength();y++)b.push(m.attributes(y));return b}}n.ProtoUtil=a;class t{static longToNumber(m,b){return d.default.isLong(m)?m.toNumber():m instanceof l.flatbuffers.Long?d.default.fromValue({low:m.low,high:m.high,unsigned:b!=null&&b}).toNumber():m}static isLong(m){return d.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 ${m.length} dimensions.`);return e.getSizeFromDimensionRange(m,0,b)}static getSizeFromDimensionRange(m,b,y){let w=1;for(let T=b;T<y;T++){if(m[T]<=0)throw new Error("cannot get valid size from specified dimension range. Most likely the range contains 0 or negative values in them.");w*=m[T]}return w}static computeStrides(m){const b=m.length;if(b===0)return[];if(b===1)return[1];const y=new Array(b);y[b-1]=1,y[b-2]=m[b-1];for(let w=b-3;w>=0;--w)y[w]=y[w+1]*m[w+1];return y}static transpose(m){return m.slice().reverse()}static indicesToOffset(m,b,y){y===void 0&&(y=m.length);let w=0;for(let T=0;T<y;++T)w+=b[T]*m[T];return w}static offsetToIndices(m,b){const y=b.length;if(y===0)return[];if(y===1)return[m*b[0]];const w=new Array(b.length);for(let T=0;T<w.length-1;++T)w[T]=Math.floor(m/b[T]),m-=w[T]*b[T];return w[w.length-1]=m,w}static normalizeAxis(m,b){if(m<-b&&m>=b)throw new Error("unsupported axis for this operation.");return m<0?m+b:m}static normalizeAxes(m,b){return m.map(y=>this.normalizeAxis(y,b))}static incrementIndex(m,b,y){if(b.length===0||m.length===0)throw new Error("Index incrementing unsupported for scalar Tensor");if(y===void 0)y=b.length;else if(y<=0||y>b.length)throw new Error("Incorrect axis to increment on");for(let w=y-1;w>=0&&(m[w]++,!(m[w]<b[w]));--w)m[w]=0}static calculateReshapedDims(m,b){if(b.length===0){if(m.length===0||e.size(m)===1)return[];throw new Error("cannot reshape to a scalar Tensor")}const y=b.length,w=new Array(y);let T=-1,S=1;for(let O=0;O<y;O++){if(b[O]<-1)throw new Error("a dimension in shape hints cannot be less than -1");if(b[O]===-1){if(T!==-1)throw new Error("at most one dimension in shape hints can be -1");T=O}else{if(b[O]===0){if(O>=m.length)throw new Error("the dimension with value zero exceeds the dimension size of the input tensor");w[O]=m[O]}else w[O]=b[O];S*=w[O]}}const A=e.size(m);if(T!==-1){if(A%S!=0)throw new Error(`the input tensor cannot be reshaped to the requested shape. Input shape: [${m}] Output shape: [${b}]`);w[T]=A/S}else if(S!==A)throw new Error("reshapedDims and originalDims don't have matching sizes");return w}static sortBasedOnPerm(m,b){return b?b.map(y=>m[y]):m.slice().reverse()}static padShape(m,b){const y=m.length;return m.map((w,T)=>w+b[T]+b[T+y])}static areEqual(m,b){return m.length===b.length&&m.every((y,w)=>y===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 y of m){if(!Number.isInteger(y))throw new TypeError(`Invalid shape: ${y} is not an integer`);if(y<0||y>2147483647)throw new TypeError(`Invalid shape: length ${y} is not allowed`);b*=y}return b}static flattenShape(m,b){b<0&&(b+=m.length);const y=m.reduce((T,S)=>T*S,1),w=m.slice(b).reduce((T,S)=>T*S,1);return[y/w,w]}static squeezeShape(m,b){const y=new Array;b=e.normalizeAxes(b,m.length);for(let w=0;w<m.length;w++){const T=b.indexOf(w)>=0;if(T&&m[w]!==1)throw new Error("squeeze an axis of size different than 1");(b.length===0&&m[w]>1||b.length>0&&!T)&&y.push(m[w])}return y}static unsqueezeShape(m,b){const y=new Array(m.length+b.length);y.fill(0);for(let T=0;T<b.length;T++){const S=e.normalizeAxis(b[T],y.length);if(S>=y.length)throw new Error("'axes' has an out of range axis");if(y[S]!==0)throw new Error("'axes' has a duplicate axis");y[S]=1}let w=0;for(let T=0;T<y.length;T++)y[T]===0&&(y[T]=m[w++]);if(w!==m.length)throw new Error("the unsqueezed dimension could not be established");return y}}n.ShapeUtil=e,n.MathUtil=class{static sqr(g,m,b,y,w){if(y<0||y>=m.length)throw new Error("sourceIndex out of bounds");if(b<0||b>=g.length)throw new Error("targetIndex out of bounds");if(y+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 T=0;T<w;T++)g[b+T]+=Math.pow(m[y+T],2)}static axpy(g,m,b,y,w,T){if(y<0||y>=m.length)throw new Error("sourceIndex out of bounds");if(b<0||b>=g.length)throw new Error("targetIndex out of bounds");if(y+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]+=T*m[y+S]}static powx(g,m,b,y,w,T){if(y<0||y>=m.length)throw new Error("sourceIndex out of bounds");if(b<0||b>=g.length)throw new Error("targetIndex out of bounds");if(y+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[y+S],T)}static mul(g,m,b,y,w){if(y<0||y>=m.length)throw new Error("sourceIndex out of bounds");if(b<0||b>=g.length)throw new Error("targetIndex out of bounds");if(y+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 T=0;T<w;T++)g[b+T]=m[y+T]*g[b+T]}};class r{static splitShape(m,b,y,w){if(y.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,y)}const T=[],S=[0];for(let A=0;A<y.length;++A){A!==0&&S.push(S[A-1]+y[A-1]);const O=m.slice();O[b]=y[A],T.push(O)}return[T,S]}static determineSplit(m,b,y){if(m%b!=0)throw new Error("cannot split tensor to equal sized parts");for(let w=0;w<b;++w)y.push(m/b)}}n.SplitUtil=r;class i{static calcReduce(m,b,y,w,T){const S=m.dims.slice(0);b.length===0&&S.forEach(($,N)=>b.push(N));const A=i.calcReduceShape(S,b,!0),O=e.size(A),v=new p.Tensor(A,m.type),M=e.computeStrides(A),F=e.computeStrides(S),R=new Array(S.length);for(let $=0;$<O;$++){const N=e.offsetToIndices($,M);c.fillIndex(N,S,R),v.set(N,i.calcReduceByAxis(m.numberData,b,S,0,e.indicesToOffset(R,F),w,T))}return y?v:new p.Tensor(i.calcReduceShape(S,b,y),v.type,void 0,void 0,v.data,v.dataId)}static calcReduceByAxis(m,b,y,w,T,S,A){let O=0;if(w>=b.length)return S(m[T]);const v=b[w],M=v>=y.length?1:e.size(y.slice(v+1));for(let F=0;F<y[v];F++)O=F===0?i.calcReduceByAxis(m,b,y,w+1,T,S,A):A(O,i.calcReduceByAxis(m,b,y,w+1,T,S,A)),T+=M;return O}static calcReduceShape(m,b,y){const w=m.slice();for(let T=0;T<b.length;T++)w[b[T]]=y?1:0;return w.filter(T=>T!==0)}}n.ReduceUtil=i;class h{static adjustPoolAttributes(m,b,y,w,T,S){if(!m&&y.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 A=0;A<b.length-2;A++)A>=y.length?y.push(b[A+2]):y[A]=b[A+2];for(let A=0;A<y.length;A++)if(A<w.length){if(w[A]<0)throw new Error("strides should be greater than or equal to 1")}else w.push(1);for(let A=0;A<y.length;A++)if(A<T.length){if(T[A]<0)throw new Error("dilations should be greater than or equal to 1")}else T.push(1);for(let A=0;A<2*y.length;A++)if(A<S.length){if(S[A]<0)throw new Error("pad should be greater than or equal to 1")}else S.push(0);for(let A=0;A<y.length;A++){if(y[A]<=0)throw new Error("kernel shapes need to be greater than 0");if(S[A]>=y[A]||S[A+y.length]>=y[A])throw new Error("pads should be smaller than kernel")}}static adjustPadsBasedOnAutoPad(m,b,y,w,T,S){if(S){if(T.length!==2*(m.length-2))throw new Error("length of pads should be twice the length of data dimensions");if(b.length!==m.length-2)throw new Error("length of strides should be the length of data dimensions");if(w.length!==m.length-2)throw new Error("length of kernel shapes should be the length of data dimensions");for(let A=0;A<m.length-2;A++)h.adjustPadAndReturnShape(m[A+2],b[A],y[A],w[A],T,A,A+m.length-2,S)}}static computePoolOutputShape(m,b,y,w,T,S,A){if(b.length<=0)throw new Error("input shape must be of size greater than 0");const O=[b[0],b[1]];return h.computeShapeHelper(m,b,O,y,w,T,S,A),O}static computeConvOutputShape(m,b,y,w,T,S,A){if(m.length<=0||b.length<=0)throw new Error("invalid input tensor dims or invalid filter tensor dims");const O=[m[0],b[0]];return h.computeShapeHelper(!1,m,O,y,w,T,S,A),O}static computeShapeHelper(m,b,y,w,T,S,A,O){if(m)for(let v=0;v<b.length-2;v++)y.push(1);else for(let v=0;v<b.length-2;v++)y.push(h.adjustPadAndReturnShape(b[v+2],w[v],T[v],S[v],A,v,v+b.length-2,O))}static adjustPadAndReturnShape(m,b,y,w,T,S,A,O){const v=y*(w-1)+1;if(!O||O==="NOTSET")return Math.floor((m+T[S]+T[A]-v)/b+1);switch(O){case"VALID":return T[S]=0,T[A]=0,Math.floor((m-v)/b+1);case"SAME_LOWER":case"SAME_UPPER":if(y!==1)throw new Error("Dilation not supported for SAME_UPPER or SAME_LOWER");{const M=((m+b-1)/b-1)*b+w-m;return T[S]=Math.floor(O==="SAME_LOWER"?(M+1)/2:M/2),T[A]=M-T[S],Math.floor((m+M-w)/b+1)}default:throw new Error("Unsupported AutoPad type")}}}n.PoolConvUtil=h,n.MIN_CLIP=-34028234663852886e22,n.MAX_CLIP=34028234663852886e22,n.decodeUtf8String=function(g){return new TextDecoder().decode(g)}},7967:(_,n)=>{Object.defineProperty(n,"__esModule",{value:!0}),n.iterateExtraOptions=void 0,n.iterateExtraOptions=(o,u,l,d)=>{if(typeof 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env={backends:{onnx:onnx_env,tfjs:{}},__dirname,version:VERSION,allowRemoteModels:!0,remoteHost:"https://huggingface.co/",remotePathTemplate:"{model}/resolve/{revision}/",allowLocalModels:!0,localModelPath,useFS:FS_AVAILABLE,useBrowserCache:WEB_CACHE_AVAILABLE,useFSCache:FS_AVAILABLE,cacheDir:DEFAULT_CACHE_DIR,useCustomCache:!1,customCache:null};function isEmpty(_){return Object.keys(_).length===0}globalThis.ReadableStream||(globalThis.ReadableStream=sharp.ReadableStream);class FileResponse{constructor(n){le(this,"_CONTENT_TYPE_MAP",{txt:"text/plain",html:"text/html",css:"text/css",js:"text/javascript",json:"application/json",png:"image/png",jpg:"image/jpeg",jpeg:"image/jpeg",gif:"image/gif"});if(this.filePath=n,this.headers=new Headers,this.exists=sharp.existsSync(n),this.exists){this.status=200,this.statusText="OK";let o=sharp.statSync(n);this.headers.set("content-length",o.size.toString()),this.updateContentType();let u=this;this.body=new ReadableStream({start(l){u.arrayBuffer().then(d=>{l.enqueue(new Uint8Array(d)),l.close()})}})}else this.status=404,this.statusText="Not Found",this.body=null}updateContentType(){const n=this.filePath.toString().split(".").pop().toLowerCase();this.headers.set("content-type",this._CONTENT_TYPE_MAP[n]??"application/octet-stream")}clone(){let n=new FileResponse(this.filePath);return n.exists=this.exists,n.status=this.status,n.statusText=this.statusText,n.headers=new Headers(this.headers),n}async arrayBuffer(){return(await sharp.promises.readFile(this.filePath)).buffer}async blob(){const n=await sharp.promises.readFile(this.filePath);return new Blob([n],{type:this.headers.get("content-type")})}async text(){return await sharp.promises.readFile(this.filePath,"utf8")}async json(){return JSON.parse(await this.text())}}function isValidHttpUrl(_,n=null){let o;try{o=new URL(_)}catch{return!1}return n&&!n.includes(o.hostname)?!1:o.protocol==="http:"||o.protocol==="https:"}async function 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getFile(p),a=p}catch(g){console.warn(`Unable to load from local path "${p}": "${g}"`)}if(r===void 0||r.status===404){if(u.local_files_only||!env.allowRemoteModels){if(o)throw Error(`\`local_files_only=true\` or \`env.allowRemoteModels=false\` and file was not found locally at "${p}".`);return null}if(r=await getFile(f),r.status!==200)return handleError(r.status,f,o);a=t}e=l&&typeof Response<"u"&&r instanceof Response&&r.status===200}dispatchCallback(u.progress_callback,{status:"download",name:_,file:n});const i=await readResponse(r,h=>{dispatchCallback(u.progress_callback,{status:"progress",...h,name:_,file:n})});return e&&a&&await l.match(a)===void 0&&await l.put(a,new Response(i,{headers:r.headers})).catch(h=>{console.warn(`Unable to add response to browser cache: ${h}.`)}),dispatchCallback(u.progress_callback,{status:"done",name:_,file:n}),i}async function getModelJSON(_,n,o=!0,u={}){let l=await getModelFile(_,n,o,u);if(l===null)return{};let s=new TextDecoder("utf-8").decode(l);return JSON.parse(s)}async function readResponse(_,n){const o=_.headers.get("Content-Length");o===null&&console.warn("Unable to determine content-length from response headers. Will expand buffer when needed.");let u=parseInt(o??"0"),l=new Uint8Array(u),d=0;const s=_.body.getReader();async function p(){const{done:f,value:c}=await s.read();if(f)return;let a=d+c.length;if(a>u){u=a;let e=new Uint8Array(u);e.set(l),l=e}l.set(c,d),d=a;const t=d/u*100;return n({progress:t,loaded:d,total:u}),p()}return await p(),l}function pathJoin(..._){return _=_.map((n,o)=>(o&&(n=n.replace(new RegExp("^/"),"")),o!==_.length-1&&(n=n.replace(new RegExp("/$"),"")),n)),_.join("/")}function interpolate_data(_,[n,o,u],[l,d],s="bilinear",p=!1){const f=d/u,c=l/o,a=new _.constructor(l*d*n),t=o*u,e=l*d;for(let r=0;r<l;++r)for(let i=0;i<d;++i){const h=r*d+i,g=(i+.5)/f-.5,m=(r+.5)/c-.5;let b=Math.floor(g),y=Math.floor(m);const w=Math.min(b+1,u-1),T=Math.min(y+1,o-1);b=Math.max(b,0),y=Math.max(y,0);const S=g-b,A=m-y,O=(1-S)*(1-A),v=S*(1-A),M=(1-S)*A,F=S*A,R=y*u,$=T*u,N=R+b,G=R+w,D=$+b,j=$+w;for(let Q=0;Q<n;++Q){const X=Q*t;a[Q*e+h]=O*_[X+N]+v*_[X+G]+M*_[X+D]+F*_[X+j]}}return a}function transpose_data(_,n,o){const u=new Array(o.length),l=new Array(o.length);for(let p=o.length-1,f=1;p>=0;--p)l[p]=f,u[p]=n[o[p]],f*=u[p];const d=o.map((p,f)=>l[o.indexOf(f)]),s=new _.constructor(_.length);for(let p=0;p<_.length;++p){let f=0;for(let c=n.length-1,a=p;c>=0;--c)f+=a%n[c]*d[c],a=Math.floor(a/n[c]);s[f]=_[p]}return[s,u]}function softmax(_){const n=max(_)[0],o=_.map(d=>Math.exp(d-n)),u=o.reduce((d,s)=>d+s,0);return o.map(d=>d/u)}function log_softmax(_){return softmax(_).map(u=>Math.log(u))}function getTopItems(_,n=0){return _=Array.from(_).map((o,u)=>[u,o]).sort((o,u)=>u[1]-o[1]),n>0&&(_=_.slice(0,n)),_}function min(_){if(_.length===0)throw Error("Array must not be empty");let n=_[0],o=0;for(let u=1;u<_.length;++u)_[u]<n&&(n=_[u],o=u);return[n,o]}function max(_){if(_.length===0)throw Error("Array must not be empty");let n=_[0],o=0;for(let u=1;u<_.length;++u)_[u]>n&&(n=_[u],o=u);return[n,o]}function rfftfreq(_,n=1){if(!Number.isInteger(_))throw new TypeError(`n should be an integer, but ${_} given.`);const o=1/(_*n),u=Math.floor(_/2)+1,l=new Array(u);for(let d=0;d<u;++d)l[d]=d*o;return l}class FFT{constructor(n){if(this.size=n|0,this.size<=1||this.size&this.size-1)throw new Error("FFT size must be a power of two and bigger than 1");this._csize=n<<1,this.table=new Float32Array(this.size*2);for(let u=0;u<this.table.length;u+=2){const l=Math.PI*u/this.size;this.table[u]=Math.cos(l),this.table[u+1]=-Math.sin(l)}let o=0;for(let u=1;this.size>u;u<<=1)++o;this._width=o%2===0?o-1:o,this._bitrev=new Int32Array(1<<this._width);for(let u=0;u<this._bitrev.length;++u){this._bitrev[u]=0;for(let l=0;l<this._width;l+=2){const d=this._width-l-2;this._bitrev[u]|=(u>>>l&3)<<d}}}createComplexArray(){return new Float32Array(this._csize)}fromComplexArray(n,o){const u=o||new Array(n.length>>>1);for(let l=0;l<n.length;l+=2)u[l>>>1]=n[l];return u}toComplexArray(n,o){const u=o||this.createComplexArray();for(let l=0;l<u.length;l+=2)u[l]=n[l>>>1],u[l+1]=0;return u}completeSpectrum(n){const o=this._csize,u=o>>>1;for(let l=2;l<u;l+=2)n[o-l]=n[l],n[o-l+1]=-n[l+1]}transform(n,o){if(n===o)throw new Error("Input and output buffers must be different");this._transform4(n,o,1)}realTransform(n,o){if(n===o)throw new Error("Input and output buffers must be different");this._realTransform4(n,o,1)}inverseTransform(n,o){if(n===o)throw new Error("Input and output buffers must be different");this._transform4(n,o,-1);for(let u=0;u<n.length;++u)n[u]/=this.size}_transform4(n,o,u){const l=this._csize;let s=1<<this._width,p=l/s<<1,f,c,a=this._bitrev;if(p===4)for(f=0,c=0;f<l;f+=p,++c){const t=a[c];this._singleTransform2(o,n,f,t,s)}else for(f=0,c=0;f<l;f+=p,++c){const t=a[c];this._singleTransform4(o,n,f,t,s,u)}for(s>>=2;s>=2;s>>=2){p=l/s<<1;let t=p>>>2;for(f=0;f<l;f+=p){let e=f+t;for(let r=f,i=0;r<e;r+=2,i+=s){const h=r,g=h+t,m=g+t,b=m+t,y=n[h],w=n[h+1],T=n[g],S=n[g+1],A=n[m],O=n[m+1],v=n[b],M=n[b+1],F=this.table[i],R=u*this.table[i+1],$=T*F-S*R,N=T*R+S*F,G=this.table[2*i],D=u*this.table[2*i+1],j=A*G-O*D,Q=A*D+O*G,X=this.table[3*i],Z=u*this.table[3*i+1],ee=v*X-M*Z,ue=v*Z+M*X,Ae=y+j,ve=w+Q,oe=y-j,we=w-Q,be=$+ee,ke=N+ue,Le=u*($-ee),xe=u*(N-ue);n[h]=Ae+be,n[h+1]=ve+ke,n[g]=oe+xe,n[g+1]=we-Le,n[m]=Ae-be,n[m+1]=ve-ke,n[b]=oe-xe,n[b+1]=we+Le}}}}_singleTransform2(n,o,u,l,d){const s=n[l],p=n[l+1],f=n[l+d],c=n[l+d+1];o[u]=s+f,o[u+1]=p+c,o[u+2]=s-f,o[u+3]=p-c}_singleTransform4(n,o,u,l,d,s){const p=d*2,f=d*3,c=n[l],a=n[l+1],t=n[l+d],e=n[l+d+1],r=n[l+p],i=n[l+p+1],h=n[l+f],g=n[l+f+1],m=c+r,b=a+i,y=c-r,w=a-i,T=t+h,S=e+g,A=s*(t-h),O=s*(e-g);o[u]=m+T,o[u+1]=b+S,o[u+2]=y+O,o[u+3]=w-A,o[u+4]=m-T,o[u+5]=b-S,o[u+6]=y-O,o[u+7]=w+A}_realTransform4(n,o,u){const l=this._csize;let s=1<<this._width,p=l/s<<1;var f,c,a=this._bitrev;if(p===4)for(f=0,c=0;f<l;f+=p,++c){const t=a[c];this._singleRealTransform2(o,n,f,t>>>1,s>>>1)}else for(f=0,c=0;f<l;f+=p,++c){const t=a[c];this._singleRealTransform4(o,n,f,t>>>1,s>>>1,u)}for(s>>=2;s>=2;s>>=2){p=l/s<<1;const t=p>>>1,e=t>>>1,r=e>>>1;for(f=0;f<l;f+=p)for(let i=0,h=0;i<=r;i+=2,h+=s){const g=f+i,m=g+e,b=m+e,y=b+e,w=n[g],T=n[g+1],S=n[m],A=n[m+1],O=n[b],v=n[b+1],M=n[y],F=n[y+1],R=this.table[h],$=u*this.table[h+1],N=S*R-A*$,G=S*$+A*R,D=this.table[2*h],j=u*this.table[2*h+1],Q=O*D-v*j,X=O*j+v*D,Z=this.table[3*h],ee=u*this.table[3*h+1],ue=M*Z-F*ee,Ae=M*ee+F*Z,ve=w+Q,oe=T+X,we=w-Q,be=T-X,ke=N+ue,Le=G+Ae,xe=u*(N-ue),Fe=u*(G-Ae);if(n[g]=ve+ke,n[g+1]=oe+Le,n[m]=we+Fe,n[m+1]=be-xe,i===0){n[b]=ve-ke,n[b+1]=oe-Le;continue}if(i===r)continue;const Ce=f+e-i,Pe=f+t-i;n[Ce]=we+-u*Fe,n[Ce+1]=-be-u*xe,n[Pe]=ve+-u*ke,n[Pe+1]=-oe+u*Le}}}_singleRealTransform2(n,o,u,l,d){const s=n[l],p=n[l+d];o[u]=s+p,o[u+1]=0,o[u+2]=s-p,o[u+3]=0}_singleRealTransform4(n,o,u,l,d,s){const p=d*2,f=d*3,c=n[l],a=n[l+d],t=n[l+p],e=n[l+f],r=c+t,i=c-t,h=a+e,g=s*(a-e);o[u]=r+h,o[u+1]=0,o[u+2]=i,o[u+3]=-g,o[u+4]=r-h,o[u+5]=0,o[u+6]=i,o[u+7]=g}}function medianFilter(_,n){if(n%2===0||n<=0)throw new Error("Window size must be a positive odd number");const o=new _.constructor(_.length),u=new _.constructor(n),l=Math.floor(n/2);for(let d=0;d<_.length;++d){let s=0;for(let p=-l;p<=l;++p){let f=d+p;f<0?f=Math.abs(f):f>=_.length&&(f=2*(_.length-1)-f),u[s++]=_[f]}u.sort(),o[d]=u[l]}return o}function round(_,n){const o=Math.pow(10,n);return Math.round(_*o)/o}const ONNXTensor$1=ONNX.Tensor;class Tensor extends ONNXTensor$1{constructor(...n){return n[0]instanceof ONNX.Tensor?super(n[0].type,n[0].data,n[0].dims):super(...n),new Proxy(this,{get:(o,u)=>{if(typeof u=="string"){let l=Number(u);if(Number.isInteger(l))return o._getitem(l)}return o[u]},set:(o,u,l)=>o[u]=l})}*[Symbol.iterator](){const[n,...o]=this.dims;if(o.length>0){const u=o.reduce((l,d)=>l*d);for(let l=0;l<n;++l)yield this._subarray(l,u,o)}else yield*this.data}_getitem(n){const[o,...u]=this.dims;if(n=safeIndex(n,o),u.length>0){const l=u.reduce((d,s)=>d*s);return this._subarray(n,l,u)}else return new Tensor(this.type,[this.data[n]],u)}indexOf(n){for(let o=0;o<this.data.length;++o)if(this.data[o]==n)return o;return-1}_subarray(n,o,u){let l=this.data.subarray(n*o,(n+1)*o);return new Tensor(this.type,l,u)}item(){if(this.data.length!==1)throw new Error(`a Tensor with ${this.data.length} elements cannot be converted to Scalar`);return this.data[0]}tolist(){return reshape(this.data,this.dims)}sigmoid(){return this.clone().sigmoid_()}sigmoid_(){for(let n=0;n<this.data.length;++n)this.data[n]=1/(1+Math.exp(-this.data[n]));return this}clone(){return new Tensor(this.type,this.data.slice(),this.dims.slice())}slice(...n){let o=[],u=[];for(let f=0;f<this.dims.length;++f){let c=n[f];if(c==null)u.push([0,this.dims[f]]),o.push(this.dims[f]);else if(typeof c=="number")c=safeIndex(c,this.dims[f],f),u.push([c,c+1]);else if(Array.isArray(c)&&c.length===2){if(c[0]>c[1])throw new Error(`Invalid slice: ${c}`);let a=[Math.max(c[0],0),Math.min(c[1],this.dims[f])];u.push(a),o.push(a[1]-a[0])}else throw new Error(`Invalid slice: ${c}`)}let l=u.map(([f,c])=>c-f),d=l.reduce((f,c)=>f*c),s=new this.data.constructor(d);const p=this.stride();for(let f=0;f<d;++f){let c=0;for(let a=l.length-1,t=f;a>=0;--a){const e=l[a];c+=(t%e+u[a][0])*p[a],t=Math.floor(t/e)}s[f]=this.data[c]}return new Tensor(this.type,s,o)}transpose(...n){return transpose(this,n)}sum(n=null,o=!1){return this.norm(1,n,o)}norm(n="fro",o=null,u=!1){if(n==="fro")n=2;else if(typeof n=="string")throw Error(`Unsupported norm: ${n}`);if(o===null){let s=this.data.reduce((p,f)=>p+f**n,0)**(1/n);return new Tensor(this.type,[s],[])}o=safeIndex(o,this.dims.length);const l=this.dims.slice();l[o]=1;const d=new this.data.constructor(this.data.length/this.dims[o]);for(let s=0;s<this.data.length;++s){let p=0;for(let f=this.dims.length-1,c=s,a=1;f>=0;--f){const t=this.dims[f];if(f!==o){const e=c%t;p+=e*a,a*=l[f]}c=Math.floor(c/t)}d[p]+=this.data[s]**n}if(n!==1)for(let s=0;s<d.length;++s)d[s]=d[s]**(1/n);return u||l.splice(o,1),new Tensor(this.type,d,l)}normalize_(n=2,o=1){o=safeIndex(o,this.dims.length);const u=this.norm(n,o,!0);for(let l=0;l<this.data.length;++l){let d=0;for(let s=this.dims.length-1,p=l,f=1;s>=0;--s){const c=this.dims[s];if(s!==o){const a=p%c;d+=a*f,f*=this.dims[s]}p=Math.floor(p/c)}this.data[l]/=u.data[d]}return this}normalize(n=2,o=1){return this.clone().normalize_(n,o)}stride(){return dimsToStride(this.dims)}squeeze(n=null){return new Tensor(this.type,this.data,calc_squeeze_dims(this.dims,n))}squeeze_(n=null){return this.dims=calc_squeeze_dims(this.dims,n),this}unsqueeze(n=null){return new Tensor(this.type,this.data,calc_unsqueeze_dims(this.dims,n))}unsqueeze_(n=null){return this.dims=calc_unsqueeze_dims(this.dims,n),this}flatten_(n=0,o=-1){o=(o+this.dims.length)%this.dims.length;let u=this.dims.slice(0,n),l=this.dims.slice(n,o+1),d=this.dims.slice(o+1);return this.dims=[...u,l.reduce((s,p)=>s*p,1),...d],this}flatten(n=0,o=-1){return this.clone().flatten_(n,o)}view(...n){let o=-1;for(let u=0;u<n.length;++u)if(n[u]===-1){if(o!==-1)throw new Error("Only one dimension can be inferred");o=u}if(o!==-1){const u=n.reduce((l,d,s)=>s!==o?l*d:l,1);n[o]=this.data.length/u}return new Tensor(this.type,this.data,n)}neg_(){for(let n=0;n<this.data.length;++n)this.data[n]=-this.data[n];return this}neg(){return this.clone().neg_()}}function reshape(_,n){const o=_.length,u=n.reduce((d,s)=>d*s);if(o!==u)throw Error(`cannot reshape array of size ${o} into shape (${n})`);let l=_;for(let d=n.length-1;d>=0;d--)l=l.reduce((s,p)=>{let f=s[s.length-1];return f.length<n[d]?f.push(p):s.push([p]),s},[[]]);return l[0]}function transpose(_,n){const[o,u]=transpose_data(_.data,_.dims,n);return new Tensor(_.type,o,u)}function interpolate(_,[n,o],u="bilinear",l=!1){const d=_.dims.at(-3)??1,s=_.dims.at(-2),p=_.dims.at(-1);let f=interpolate_data(_.data,[d,s,p],[n,o],u,l);return new Tensor(_.type,f,[d,n,o])}function mean_pooling(_,n){let o=[_.dims[0],_.dims[2]],u=new _.data.constructor(o[0]*o[1]),[l,d,s]=_.dims,p=0;for(let f=0;f<l;++f){let c=f*s*d;for(let a=0;a<s;++a){let t=0,e=0,r=f*d,i=c+a;for(let g=0;g<d;++g){let m=Number(n.data[r+g]);e+=m,t+=_.data[i+g*s]*m}let h=t/e;u[p++]=h}}return new Tensor(_.type,u,o)}function calc_squeeze_dims(_,n){return _=_.slice(),n===null?_=_.filter(o=>o!==1):typeof n=="number"?_[n]===1&&_.splice(n,1):Array.isArray(n)&&(_=_.filter((o,u)=>o!==1||!n.includes(u))),_}function calc_unsqueeze_dims(_,n){return n=safeIndex(n,_.length+1),_=_.slice(),_.splice(n,0,1),_}function safeIndex(_,n,o=null){if(_<-n||_>=n)throw new Error(`IndexError: index ${_} is out of bounds for dimension${o===null?"":" "+o} with size ${n}`);return _<0&&(_=(_%n+n)%n),_}function cat(_,n=0){n=safeIndex(n,_[0].dims.length);const o=_[0].dims.slice();o[n]=_.reduce((s,p)=>s+p.dims[n],0);const u=o.reduce((s,p)=>s*p,1),l=new _[0].data.constructor(u),d=_[0].type;if(n===0){let s=0;for(let p of _)l.set(p.data,s),s+=p.data.length}else{let s=0;for(let p=0;p<_.length;++p){let f=_[p];for(let c=0;c<f.data.length;++c){let a=0;for(let t=f.dims.length-1,e=c,r=1;t>=0;--t){const i=f.dims[t];let h=e%i;t===n&&(h+=s),a+=h*r,r*=o[t],e=Math.floor(e/i)}l[a]=f.data[c]}s+=f.dims[n]}}return new Tensor(d,l,o)}function stack(_,n=0){return cat(_.map(o=>o.unsqueeze(n)),n)}function std_mean(_,n=null,o=1,u=!1){if(n===null){const c=_.data.reduce((r,i)=>r+i,0)/_.data.length,a=Math.sqrt(_.data.reduce((r,i)=>r+(i-c)**2,0)/(_.data.length-o)),t=new Tensor(_.type,[c],[]);return[new Tensor(_.type,[a],[]),t]}n=safeIndex(n,_.dims.length);const l=mean(_,n,u),d=_.dims.slice();d[n]=1;const s=new _.data.constructor(_.data.length/_.dims[n]);for(let f=0;f<_.data.length;++f){let c=0;for(let a=_.dims.length-1,t=f,e=1;a>=0;--a){const r=_.dims[a];if(a!==n){const i=t%r;c+=i*e,e*=d[a]}t=Math.floor(t/r)}s[c]+=(_.data[f]-l.data[c])**2}for(let f=0;f<s.length;++f)s[f]=Math.sqrt(s[f]/(_.dims[n]-o));return u||d.splice(n,1),[new Tensor(_.type,s,d),l]}function mean(_,n=null,o=!1){if(n===null){let d=_.data.reduce((s,p)=>s+p,0);return new Tensor(_.type,[d/_.data.length],[])}n=safeIndex(n,_.dims.length);const u=_.dims.slice();u[n]=1;const l=new _.data.constructor(_.data.length/_.dims[n]);for(let d=0;d<_.data.length;++d){let s=0;for(let p=_.dims.length-1,f=d,c=1;p>=0;--p){const a=_.dims[p];if(p!==n){const t=f%a;s+=t*c,c*=u[p]}f=Math.floor(f/a)}l[s]+=_.data[d]}if(_.dims[n]!==1)for(let d=0;d<l.length;++d)l[d]=l[d]/_.dims[n];return o||u.splice(n,1),new Tensor(_.type,l,u)}function dynamicTimeWarping(_){const[n,o]=_.dims,u=[n+1,o+1],l=new Tensor("float32",new Float32Array(u[0]*u[1]).fill(1/0),u),d=new Tensor("float32",new Float32Array(u[0]*u[1]).fill(-1),u);l[0].data[0]=0;for(let a=1;a<o+1;++a)for(let t=1;t<n+1;++t){const e=l[t-1][a-1].item(),r=l[t-1][a].item(),i=l[t][a-1].item();let h,g;e<r&&e<i?(h=e,g=0):r<e&&r<i?(h=r,g=1):(h=i,g=2),l[t].data[a]=_[t-1][a-1].item()+h,d[t].data[a]=g}let s=n,p=o;d.data.fill(2,0,u[1]);for(let a=0;a<u[0];++a)d[a].data[0]=1;let f=[],c=[];for(;s>0||p>0;)switch(f.push(s-1),c.push(p-1),d[s][p].item()){case 0:--s,--p;break;case 1:--s;break;case 2:--p;break;default:throw new Error(`Internal error in dynamic time warping. Unexpected trace[${s}, ${p}]. Please file a bug report.`)}return f.reverse(),c.reverse(),[f,c]}function dimsToStride(_){const n=new Array(_.length);for(let o=_.length-1,u=1;o>=0;--o)n[o]=u,u*=_[o];return n}function ones(_){const n=_.reduce((o,u)=>o*u,1);return new Tensor("int64",new BigInt64Array(n).fill(1n),_)}function ones_like(_){return ones(_.dims)}class PriorityQueue{constructor(n=(o,u)=>o>u){this._heap=[],this._comparator=n}get size(){return this._heap.length}isEmpty(){return this.size===0}peek(){return this._heap[0]}push(...n){return this.extend(n)}extend(n){for(const o of n)this._heap.push(o),this._siftUp();return this.size}pop(){const n=this.peek(),o=this.size-1;return o>0&&this._swap(0,o),this._heap.pop(),this._siftDown(),n}replace(n){const o=this.peek();return this._heap[0]=n,this._siftDown(),o}_parent(n){return(n+1>>>1)-1}_left(n){return(n<<1)+1}_right(n){return n+1<<1}_greater(n,o){return this._comparator(this._heap[n],this._heap[o])}_swap(n,o){const u=this._heap[n];this._heap[n]=this._heap[o],this._heap[o]=u}_siftUp(){let n=this.size-1;for(;n>0&&this._greater(n,this._parent(n));)this._swap(n,this._parent(n)),n=this._parent(n)}_siftDown(){let n=0;for(;this._left(n)<this.size&&this._greater(this._left(n),n)||this._right(n)<this.size&&this._greater(this._right(n),n);){const o=this._right(n)<this.size&&this._greater(this._right(n),this._left(n))?this._right(n):this._left(n);this._swap(n,o),n=o}}}class CharTrie{constructor(){this.root=CharTrieNode.default()}extend(n){for(let o of n)this.push(o)}push(n){let o=this.root;for(let u of n){let l=o.children.get(u);l===void 0&&(l=CharTrieNode.default(),o.children.set(u,l)),o=l}o.isLeaf=!0}*commonPrefixSearch(n){let o=this.root,u="";for(let l=0;l<n.length&&o!==void 0;++l){const d=n[l];u+=d,o=o.children.get(d),o!==void 0&&o.isLeaf&&(yield u)}}}class CharTrieNode{constructor(n,o){this.isLeaf=n,this.children=o}static default(){return new CharTrieNode(!1,new Map)}}class TokenLattice{constructor(n,o,u){this.sentence=n,this.len=n.length,this.bosTokenId=o,this.eosTokenId=u,this.nodes=[],this.beginNodes=Array.from({length:this.len+1},()=>[]),this.endNodes=Array.from({length:this.len+1},()=>[]);const l=new TokenLatticeNode(this.bosTokenId,0,0,0,0),d=new TokenLatticeNode(this.eosTokenId,1,this.len,0,0);this.nodes.push(l.clone()),this.nodes.push(d.clone()),this.beginNodes[this.len].push(d),this.endNodes[0].push(l)}insert(n,o,u,l){const d=this.nodes.length,s=new TokenLatticeNode(l,d,n,o,u);this.beginNodes[n].push(s),this.endNodes[n+o].push(s),this.nodes.push(s)}viterbi(){const n=this.len;let o=0;for(;o<=n;){if(this.beginNodes[o].length==0)return[];for(let p of this.beginNodes[o]){p.prev=null;let f=0,c=null;for(let a of this.endNodes[o]){const t=a.backtraceScore+p.score;(c===null||t>f)&&(c=a.clone(),f=t)}if(c!==null)p.prev=c,p.backtraceScore=f;else return[]}++o}const u=[],d=this.beginNodes[n][0].prev;if(d===null)return[];let s=d.clone();for(;s.prev!==null;)u.push(s.clone()),s=s.clone().prev.clone();return u.reverse(),u}piece(n){return this.sentence.slice(n.pos,n.pos+n.length)}tokens(){return this.viterbi().map(o=>this.piece(o))}tokenIds(){return this.viterbi().map(o=>o.tokenId)}}class TokenLatticeNode{constructor(n,o,u,l,d){this.tokenId=n,this.nodeId=o,this.pos=u,this.length=l,this.score=d,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}}async function loadTokenizer(_,n){return await Promise.all([getModelJSON(_,"tokenizer.json",!0,n),getModelJSON(_,"tokenizer_config.json",!0,n)])}function createPattern(_,n=!0){return _.Regex!==void 0?new RegExp(n?_.Regex:`(${_.Regex})`,"gu"):_.String!==void 0?_.String:(console.warn("Unknown pattern type:",_),null)}function objectToMap(_){return new Map(Object.entries(_))}function clean_up_tokenization(_){return _.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 remove_accents(_){return _.replace(/[\u0300-\u036f]/g,"")}function lowercase_and_remove_accent(_){return remove_accents(_.toLowerCase())}function fuse(_,n){let o=[],u=0;for(;u<_.length;){if(o.push(_[u]),_[u]!==n){++u;continue}for(;u<_.length&&_[u]===n;)++u}return o}function whitespace_split(_){return _.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=this.config.fuse_unk??!1}static fromConfig(n,...o){switch(n.type){case"WordPiece":return new WordPieceTokenizer(n);case"Unigram":return new Unigram(n,...o);case"BPE":return new BPE(n);default:if(n.vocab)return new LegacyTokenizerModel(n,...o);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 o=n.map(u=>this.tokens_to_ids.get(u)??this.unk_token_id);return this.fuse_unk&&(o=fuse(o,this.unk_token_id)),o}convert_ids_to_tokens(n){return n.map(o=>this.vocab[o]??this.unk_token)}}class WordPieceTokenizer extends TokenizerModel{constructor(n){super(n),this.tokens_to_ids=objectToMap(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[o,u]of this.tokens_to_ids)this.vocab[u]=o}encode(n){let o=[];for(let u of n){let l=[...u],d=!1,s=0,p=[];for(;s<l.length;){let f=l.length,c=null;for(;s<f;){let a=l.slice(s,f).join("");if(s>0&&(a=this.config.continuing_subword_prefix+a),this.tokens_to_ids.has(a)){c=a;break}--f}if(c===null){d=!0;break}p.push(c),s=f}d?o.push(this.unk_token):o.push(...p)}return o}}class Unigram extends TokenizerModel{constructor(n,o){super(n);const u=n.vocab.length;this.vocab=new Array(u),this.scores=new Array(u);for(let l=0;l<u;++l){const d=n.vocab[l];this.vocab[l]=d[0],this.scores[l]=d[1]}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,d)=>[l,d])),this.bosToken=" ",this.bosTokenId=this.tokens_to_ids.get(this.bosToken),this.eosToken=o.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 o=n.sentence,u=o.length;let l=0;for(;l<u;){let s=!1;for(let p of this.trie.commonPrefixSearch(o.slice(l))){const f=this.tokens_to_ids.get(p),c=this.scores[f],a=p.length;n.insert(l,a,c,f),!s&&a===1&&(s=!0)}s||n.insert(l,1,this.unkScore,this.unk_token_id),l+=1}}tokenize(n){const o=new TokenLattice(n,this.bosTokenId,this.eosTokenId);return this.populateNodes(o),o.tokens()}encode(n){let o=[];for(let u of n){const l=this.tokenize(u);o.push(...l)}return o}}const BYTES_TO_UNICODE=(()=>{const _=[...Array.from({length:"~".charCodeAt(0)-"!".charCodeAt(0)+1},(l,d)=>d+"!".charCodeAt(0)),...Array.from({length:"¬".charCodeAt(0)-"¡".charCodeAt(0)+1},(l,d)=>d+"¡".charCodeAt(0)),...Array.from({length:"ÿ".charCodeAt(0)-"®".charCodeAt(0)+1},(l,d)=>d+"®".charCodeAt(0))];let n=_.slice(),o=0;for(let l=0;l<256;++l)_.includes(l)||(_.push(l),n.push(256+o),o+=1);let u=n.map(l=>String.fromCharCode(l));return Object.fromEntries(_.map((l,d)=>[l,u[d]]))})(),UNICODE_TO_BYTES=reverseDictionary(BYTES_TO_UNICODE);class BPE extends TokenizerModel{constructor(n){super(n),this.BPE_SPLIT_TOKEN=" ",this.tokens_to_ids=objectToMap(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[o,u]of this.tokens_to_ids)this.vocab[u]=o;this.bpe_ranks=new Map(n.merges.map((o,u)=>[o,u])),this.merges=n.merges.map(o=>o.split(this.BPE_SPLIT_TOKEN)),this.end_of_word_suffix=n.end_of_word_suffix,this.continuing_subword_suffix=n.continuing_subword_suffix??null,this.byte_fallback=this.config.byte_fallback??!1,this.byte_fallback&&(this.text_encoder=new TextEncoder),this.cache=new Map}bpe(n){if(n.length===0)return[];const o=this.cache.get(n);if(o!==void 0)return o;const u=Array.from(n);this.end_of_word_suffix&&(u[u.length-1]+=this.end_of_word_suffix);let l=[];if(u.length>1){const d=new PriorityQueue((f,c)=>f.score<c.score);let s={token:u[0],bias:0,prev:null,next:null},p=s;for(let f=1;f<u.length;++f){const c={bias:f/u.length,token:u[f],prev:p,next:null};p.next=c,this._add_node(d,p),p=c}for(;!d.isEmpty();){const f=d.pop();if(f.deleted||!f.next||f.next.deleted)continue;if(f.deleted=!0,f.next.deleted=!0,f.prev){const a={...f.prev};f.prev.deleted=!0,f.prev=a,a.prev?a.prev.next=a:s=a}const c={token:f.token+f.next.token,bias:f.bias,prev:f.prev,next:f.next.next};c.prev?(c.prev.next=c,this._add_node(d,c.prev)):s=c,c.next&&(c.next.prev=c,this._add_node(d,c))}for(let f=s;f!==null;f=f.next)l.push(f.token)}else l=u;if(this.continuing_subword_suffix)for(let d=0;d<l.length-1;++d)l[d]+=this.continuing_subword_suffix;return this.cache.set(n,l),l}_add_node(n,o){const u=this.bpe_ranks.get(o.token+this.BPE_SPLIT_TOKEN+o.next.token);u!==void 0&&(o.score=u+o.bias,n.push(o))}encode(n){let o=[];for(let u of n){let l=this.bpe(u);for(let d of l)this.tokens_to_ids.has(d)?o.push(d):this.byte_fallback?o.push(...Array.from(this.text_encoder.encode(d)).map(s=>`<0x${s.toString(16).toUpperCase().padStart(2,"0")}>`)):o.push(this.unk_token)}return o}}class LegacyTokenizerModel extends TokenizerModel{constructor(n,o){super(n),this.tokens_to_ids=objectToMap(o.target_lang?n.vocab[o.target_lang]:n.vocab),this.bos_token=o.bos_token,this.bos_token_id=this.tokens_to_ids.get(this.bos_token),this.eos_token=o.eos_token,this.eos_token_id=this.tokens_to_ids.get(this.eos_token),this.pad_token=o.pad_token,this.pad_token_id=this.tokens_to_ids.get(this.pad_token),this.unk_token=o.unk_token,this.unk_token_id=this.tokens_to_ids.get(this.unk_token),this.vocab=new Array(this.tokens_to_ids.size);for(const[u,l]of this.tokens_to_ids)this.vocab[l]=u}encode(n){return n}}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"Strip":return new StripNormalizer(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 o=createPattern(this.config.pattern);return o===null||(n=n.replaceAll(o,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 StripNormalizer extends Normalizer{normalize(n){return this.config.strip_left&&this.config.strip_right?n=n.trim():(this.config.strip_left&&(n=n.trimStart()),this.config.strip_right&&(n=n.trimEnd())),n}}class StripAccents extends Normalizer{normalize(n){return n=remove_accents(n),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(o=>Normalizer.fromConfig(o))}normalize(n){return this.normalizers.reduce((o,u)=>u.normalize(o),n)}}class BertNormalizer extends Normalizer{_tokenize_chinese_chars(n){let o=[];for(let u=0;u<n.length;++u){let l=n[u],d=l.charCodeAt(0);this._is_chinese_char(d)?(o.push(" "),o.push(l),o.push(" ")):o.push(l)}return o.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);case"Replace":return new ReplacePreTokenizer(n);default:throw new Error(`Unknown PreTokenizer type: ${n.type}`)}}pre_tokenize_text(n){throw Error("pre_tokenize_text should be implemented in subclass.")}pre_tokenize(n){let o=[];return Array.isArray(n)?o=n.map(u=>this.pre_tokenize_text(u)):o=this.pre_tokenize_text(n),o.flat()}_call(n){return this.pre_tokenize(n)}}class BertPreTokenizer extends PreTokenizer{constructor(n){super(),this.pattern=new RegExp(`[^\\s${PUNCTUATION_REGEX}]+|[${PUNCTUATION_REGEX}]`,"gu")}pre_tokenize_text(n){return n.trim().match(this.pattern)||[]}}class ByteLevelPreTokenizer extends PreTokenizer{constructor(n){super(),this.config=n,this.add_prefix_space=this.config.add_prefix_space,this.trim_offsets=this.config.trim_offsets,this.use_regex=this.config.use_regex??!0,this.pattern=/'s|'t|'re|'ve|'m|'ll|'d| ?\p{L}+| ?\p{N}+| ?[^\s\p{L}\p{N}]+|\s+(?!\S)|\s+/gu,this.byte_encoder=BYTES_TO_UNICODE,this.text_encoder=new TextEncoder}pre_tokenize_text(n){return this.add_prefix_space&&!n.startsWith(" ")&&(n=" "+n),(this.use_regex?n.match(this.pattern)||[]:[n]).map(u=>Array.from(this.text_encoder.encode(u),l=>this.byte_encoder[l]).join(""))}}class SplitPreTokenizer extends PreTokenizer{constructor(n){super(),this.config=n,this.pattern=createPattern(this.config.pattern,this.config.invert)}pre_tokenize_text(n){return this.pattern===null?[]:this.config.invert?n.match(this.pattern)||[]:n.split(this.pattern).filter(o=>o)}}class PunctuationPreTokenizer extends PreTokenizer{constructor(n){super(),this.config=n,this.pattern=new RegExp(`[^${PUNCTUATION_REGEX}]+|[${PUNCTUATION_REGEX}]+`,"gu")}pre_tokenize_text(n){return n.match(this.pattern)||[]}}class DigitsPreTokenizer extends PreTokenizer{constructor(n){super(),this.config=n;const o=`[^\\d]+|\\d${this.config.individual_digits?"":"+"}`;this.pattern=new RegExp(o,"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);case"BertProcessing":return new BertProcessing(n);default:throw new Error(`Unknown PostProcessor type: ${n.type}`)}}post_process(n,...o){throw Error("post_process should be implemented in subclass.")}_call(n,...o){return this.post_process(n,...o)}}class BertProcessing extends PostProcessor{constructor(n){super(n),this.cls=n.cls[0],this.sep=n.sep[0]}post_process(n,o=null){return n=mergeArrays([this.cls],n,[this.sep]),o!==null&&(n=mergeArrays(n,[this.sep],o,[this.sep])),n}}class RobertaProcessing extends BertProcessing{}class TemplateProcessing extends PostProcessor{constructor(n){super(n),this.single=n.single,this.pair=n.pair}post_process(n,o=null){let u=o===null?this.single:this.pair,l=[];for(let d of u)"SpecialToken"in d?l.push(d.SpecialToken.id):"Sequence"in d&&(d.Sequence.id==="A"?l=mergeArrays(l,n):d.Sequence.id==="B"&&(l=mergeArrays(l,o)));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);case"CTC":return new CTCDecoder(n);case"BPEDecoder":return new BPEDecoder(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{decode_chain(n){let o=createPattern(this.config.pattern);return o===null?n:n.map(u=>u.replaceAll(o,this.config.content))}}class ByteFallback extends Decoder{constructor(n){super(n),this.text_decoder=new TextDecoder}decode_chain(n){let o=[],u=[];for(let l of n){let d=null;if(l.length===6&&l.startsWith("<0x")&&l.endsWith(">")){let s=parseInt(l.slice(3,5),16);isNaN(s)||(d=s)}if(d!==null)u.push(d);else{if(u.length>0){let s=this.text_decoder.decode(Uint8Array.from(u));o.push(s),u=[]}o.push(l)}}if(u.length>0){let l=this.text_decoder.decode(Uint8Array.from(u));o.push(l),u=[]}return o}}class FuseDecoder extends Decoder{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(o=>{let u=0;for(let d=0;d<this.start&&o[d]===this.content;++d){u=d+1;continue}let l=o.length;for(let d=0;d<this.stop;++d){const s=o.length-d-1;if(o[s]===this.content){l=s;continue}else break}return o.slice(u,l)})}}class WordPieceDecoder extends Decoder{constructor(n){super(n),this.cleanup=n.cleanup}decode_chain(n){return n.map((o,u)=>(u!==0&&(o.startsWith(this.config.prefix)?o=o.replace(this.config.prefix,""):o=" "+o),this.cleanup&&(o=clean_up_tokenization(o)),o))}}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 o=n.join(""),u=new Uint8Array([...o].map(d=>this.byte_decoder[d]));return this.text_decoder.decode(u)}decode_chain(n){let o=[],u=[];for(let l of n)this.added_tokens.includes(l)?(u.length>0&&(o.push(this.convert_tokens_to_string(u)),u=[]),o.push(l)):u.push(l);return u.length>0&&o.push(this.convert_tokens_to_string(u)),o}}class CTCDecoder extends Decoder{constructor(n){super(n),this.pad_token=this.config.pad_token,this.word_delimiter_token=this.config.word_delimiter_token,this.cleanup=this.config.cleanup}convert_tokens_to_string(n){if(n.length===0)return"";let o=[n[0]];for(let d=1;d<n.length;++d)n[d]!==o.at(-1)&&o.push(n[d]);let l=o.filter(d=>d!==this.pad_token).join("");return this.cleanup&&(l=clean_up_tokenization(l).replaceAll(this.word_delimiter_token," ").trim()),l}decode_chain(n){return[this.convert_tokens_to_string(n)]}}class DecoderSequence extends Decoder{constructor(n){super(n),this.decoders=n.decoders.map(o=>Decoder.fromConfig(o))}decode_chain(n){return this.decoders.reduce((o,u)=>u.decode_chain(o),n)}}class BPEDecoder extends Decoder{constructor(n){super(n),this.suffix=this.config.suffix}decode_chain(n){return n.map((o,u)=>o.replaceAll(this.suffix,u===n.length-1?"":" "))}}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 o=[];for(let u of n){let l=u.replaceAll(" ",this.strRep);this.addPrefixSpace&&!l.startsWith(this.replacement)&&(l=this.strRep+l),o.push(l)}return o}}class MetaspaceDecoder extends Decoder{constructor(n){super(n),this.addPrefixSpace=n.add_prefix_space,this.replacement=n.replacement}decode_chain(n){let o=[];for(let u=0;u<n.length;++u){let l=n[u].replaceAll(this.replacement," ");this.addPrefixSpace&&u==0&&l.startsWith(" ")&&(l=l.substring(1)),o.push(l)}return o}}class Precompiled extends Normalizer{constructor(n){super(n),this.charsmap=n.precompiled_charsmap}normalize(n){return n=n.replace(/[\u0001-\u0008\u000B\u000E-\u001F\u007F\u008F\u009F]/gm,""),n=n.replace(/[\u0009\u000A\u000C\u000D\u1680\u200B\u200C\u200E\u200F\u2028\u2029\u2581\uFEFF\uFFFD]/gm," "),n.includes("～")?n=n.split("～").map(u=>u.normalize("NFKC")).join("～"):n=n.normalize("NFKC"),n}}class PreTokenizerSequence extends PreTokenizer{constructor(n){super(),this.tokenizers=n.pretokenizers.map(o=>PreTokenizer.fromConfig(o))}pre_tokenize_text(n){return typeof n=="string"&&(n=[n]),this.tokenizers.reduce((o,u)=>u.pre_tokenize(o),n)}}class WhitespaceSplit extends PreTokenizer{constructor(n){super()}pre_tokenize_text(n){return whitespace_split(n)}}class ReplacePreTokenizer extends PreTokenizer{constructor(n){super(),this.config=n,this.pattern=createPattern(this.config.pattern),this.content=this.config.content}pre_tokenize_text(n){return this.pattern===null?[n]:[n.replaceAll(this.pattern,this.config.content)]}}class PreTrainedTokenizer extends Callable{constructor(n,o){super(),this.normalizer=Normalizer.fromConfig(n.normalizer),this.pre_tokenizer=PreTokenizer.fromConfig(n.pre_tokenizer),this.model=TokenizerModel.fromConfig(n.model,o),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 u of n.added_tokens){let l=u.id,d=u.content;this.added_tokens.push(d),this.model.tokens_to_ids.set(d,l),this.model.vocab[l]=d,u.special&&(this.special_tokens.push(d),this.all_special_ids.push(l))}this.special_tokens.push(...o.additional_special_tokens??[]),this.special_tokens=[...new Set(this.special_tokens)],this.decoder.added_tokens=this.added_tokens,this.added_tokens_regex=this.added_tokens.length>0?new RegExp("("+this.added_tokens.map(escapeRegExp).join("|")+")"):null,this.mask_token=this.getToken(o,"mask_token"),this.mask_token_id=this.model.tokens_to_ids.get(this.mask_token),this.pad_token=this.getToken(o,"pad_token","eos_token"),this.pad_token_id=this.model.tokens_to_ids.get(this.pad_token),this.sep_token=this.getToken(o,"sep_token"),this.sep_token_id=this.model.tokens_to_ids.get(this.sep_token),this.model_max_length=o.model_max_length,this.remove_space=o.remove_space,this.clean_up_tokenization_spaces=o.clean_up_tokenization_spaces??!0,this.do_lowercase_and_remove_accent=o.do_lowercase_and_remove_accent??!1,this.padding_side="right"}getToken(n,...o){for(let u of o){let l=n[u];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:o=null,config:u=null,cache_dir:l=null,local_files_only:d=!1,revision:s="main"}={}){let p=await loadTokenizer(n,{progress_callback:o,config:u,cache_dir:l,local_files_only:d,revision:s});return new this(...p)}prepare_model_inputs(n){return n}_call(n,{text_pair:o=null,add_special_tokens:u=!0,padding:l=!1,truncation:d=null,max_length:s=null,return_tensor:p=!0}={}){let f;if(Array.isArray(n)){if(n.length===0)throw Error("text array must be non-empty");if(o!==null){if(Array.isArray(o)){if(n.length!==o.length)throw Error("text and text_pair must have the same length")}else throw Error("text_pair must also be an array");f=n.map((e,r)=>this.encode(e,o[r],{add_special_tokens:u}))}else f=n.map(e=>this.encode(e,null,{add_special_tokens:u}))}else{if(n===null)throw Error("text may not be null");if(Array.isArray(o))throw Error("When specifying `text_pair`, since `text` is a string, `text_pair` must also be a string (i.e., not an array).");f=[this.encode(n,o,{add_special_tokens:u})]}let c=max(f.map(e=>e.length))[0];s===null&&(s=c),s=Math.min(s,this.model_max_length);let a=[];if(l||d)for(let e=0;e<f.length;++e)if(f[e].length===s){a.push(new Array(f[e].length).fill(1));continue}else if(f[e].length>s)d&&(f[e]=f[e].slice(0,s)),a.push(new Array(f[e].length).fill(1));else if(l){let r=s-f[e].length;this.padding_side==="right"?(a.push(new Array(f[e].length).fill(1).concat(new Array(r).fill(0))),f[e].push(...new Array(r).fill(this.pad_token_id))):(a.push(new Array(r).fill(0).concat(new Array(f[e].length).fill(1))),f[e].unshift(...new Array(r).fill(this.pad_token_id)))}else a.push(new Array(f[e].length).fill(1));else a=f.map(e=>new Array(e.length).fill(1));if(p){if(!(l&&d)&&f.some(r=>r.length!==f[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 e=[f.length,f[0].length];f=new Tensor("int64",BigInt64Array.from(f.flat().map(BigInt)),e),a=new Tensor("int64",BigInt64Array.from(a.flat().map(BigInt)),e)}else Array.isArray(n)||(f=f[0],a=a[0]);let t={input_ids:f,attention_mask:a};return t=this.prepare_model_inputs(t),t}_encode_text(n){return n===null?null:(this.added_tokens_regex?n.split(this.added_tokens_regex).filter(l=>l):[n]).map(l=>{if(this.added_tokens.includes(l))return l;{this.remove_space===!0&&(l=l.trim().split(/\s+/).join(" ")),this.do_lowercase_and_remove_accent&&(l=lowercase_and_remove_accent(l)),this.normalizer!==null&&(l=this.normalizer(l));let d=this.pre_tokenizer!==null?this.pre_tokenizer(l):[l];return this.model(d)}}).flat()}encode(n,o=null,{add_special_tokens:u=!0}={}){let l=this._encode_text(n),d=this._encode_text(o),s=this.post_processor!==null&&u?this.post_processor(l,d):mergeArrays(l??[],d??[]);return this.model.convert_tokens_to_ids(s)}batch_decode(n,o={}){return n.map(u=>this.decode(u,o))}decode(n,o={}){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,o)}decode_single(n,{skip_special_tokens:o=!1,clean_up_tokenization_spaces:u=null}){let l=this.model.convert_ids_to_tokens(n);o&&(l=l.filter(s=>!this.special_tokens.includes(s)));let d=this.decoder(l);return this.decoder.end_of_word_suffix&&(d=d.replaceAll(this.decoder.end_of_word_suffix," "),o&&(d=d.trim())),(u??this.clean_up_tokenization_spaces)&&(d=clean_up_tokenization(d)),d}}function add_token_types(_){if(_.input_ids instanceof Tensor)_.token_type_ids=new Tensor("int64",new BigInt64Array(_.input_ids.data.length),_.input_ids.dims);else if(Array.isArray(_.input_ids))Array.isArray(_.input_ids[0])?_.token_type_ids=_.input_ids.map(n=>new Array(n.length).fill(0)):_.token_type_ids=new Array(_.input_ids.length).fill(0);else throw new Error("Input ids must be a Tensor or an Array");return _}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 DebertaTokenizer extends PreTrainedTokenizer{prepare_model_inputs(n){return add_token_types(n)}}class DebertaV2Tokenizer extends PreTrainedTokenizer{prepare_model_inputs(n){return add_token_types(n)}}class HerbertTokenizer extends PreTrainedTokenizer{prepare_model_inputs(n){return add_token_types(n)}}class DistilBertTokenizer extends PreTrainedTokenizer{}class CamembertTokenizer extends PreTrainedTokenizer{}class XLMTokenizer extends PreTrainedTokenizer{constructor(n,o){super(n,o),console.warn('WARNING: `XLMTokenizer` is not yet supported by Hugging Face\'s "fast" tokenizers library. Therefore, you may experience slightly inaccurate results.')}prepare_model_inputs(n){return add_token_types(n)}}class T5Tokenizer extends PreTrainedTokenizer{}class GPT2Tokenizer extends PreTrainedTokenizer{}class BartTokenizer extends PreTrainedTokenizer{}class MBartTokenizer extends PreTrainedTokenizer{constructor(n,o){super(n,o),this.languageRegex=/^[a-z]{2}_[A-Z]{2}$/,this.language_codes=this.special_tokens.filter(u=>this.languageRegex.test(u)),this.lang_to_token=u=>u}_build_translation_inputs(n,o,u){return _build_translation_inputs(this,n,o,u)}}class MBart50Tokenizer extends MBartTokenizer{}class RobertaTokenizer extends PreTrainedTokenizer{}class BloomTokenizer extends PreTrainedTokenizer{constructor(n,o){var d,s;const u=".,!?…。，、।۔،",l=(s=(d=n.pre_tokenizer)==null?void 0:d.pretokenizers[0])==null?void 0:s.pattern;l&&l.Regex===` ?[^(\\s|[${u}])]+`&&(l.Regex=` ?[^\\s${u}]+`),super(n,o)}}class LlamaTokenizer extends PreTrainedTokenizer{}class CodeLlamaTokenizer extends PreTrainedTokenizer{}class XLMRobertaTokenizer extends PreTrainedTokenizer{}class MPNetTokenizer extends PreTrainedTokenizer{}class FalconTokenizer extends PreTrainedTokenizer{}class GPTNeoXTokenizer extends PreTrainedTokenizer{}function _build_translation_inputs(_,n,o,u){if(!("language_codes"in _)||!Array.isArray(_.language_codes))throw new Error("Tokenizer must have `language_codes` attribute set and it should be an array of language ids.");if(!("languageRegex"in _)||!(_.languageRegex instanceof RegExp))throw new Error("Tokenizer must have `languageRegex` attribute set and it should be a regular expression.");if(!("lang_to_token"in _)||typeof _.lang_to_token!="function")throw new Error("Tokenizer must have `lang_to_token` attribute set and it should be a function.");const l=u.src_lang,d=u.tgt_lang;if(!_.language_codes.includes(d))throw new Error(`Target language code "${d}" is not valid. Must be one of: {${_.language_codes.join(", ")}}`);if(l!==void 0){if(!_.language_codes.includes(l))throw new Error(`Source language code "${l}" is not valid. Must be one of: {${_.language_codes.join(", ")}}`);for(let s of _.post_processor.config.single)if("SpecialToken"in s&&_.languageRegex.test(s.SpecialToken.id)){s.SpecialToken.id=_.lang_to_token(l);break}}return u.forced_bos_token_id=_.model.convert_tokens_to_ids([_.lang_to_token(d)])[0],_._call(n,o)}class NllbTokenizer extends PreTrainedTokenizer{constructor(n,o){super(n,o),this.languageRegex=/^[a-z]{3}_[A-Z][a-z]{3}$/,this.language_codes=this.special_tokens.filter(u=>this.languageRegex.test(u)),this.lang_to_token=u=>u}_build_translation_inputs(n,o,u){return _build_translation_inputs(this,n,o,u)}}class M2M100Tokenizer extends PreTrainedTokenizer{constructor(n,o){super(n,o),this.languageRegex=/^__[a-z]{2,3}__$/,this.language_codes=this.special_tokens.filter(u=>this.languageRegex.test(u)).map(u=>u.slice(2,-2)),this.lang_to_token=u=>`__${u}__`}_build_translation_inputs(n,o,u){return _build_translation_inputs(this,n,o,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(([_,n])=>[n,_]),["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:o=!1,return_language:u=!1,time_precision:l=null,force_full_sequences:d=!0}={}){if(l===null)throw Error("Must specify time_precision");let s=null;const p=o==="word";function f(){return{language:s,timestamp:[null,null],text:""}}const c=[];let a=f(),t=0;const e=this.model.convert_tokens_to_ids(["<|notimestamps|>"])[0]+1;let r=[],i=[],h=!1,g=null;const m=new Set(this.all_special_ids);for(let w of n){const T=w.tokens,S=p?w.token_timestamps:null;let A=null,O=e;if("stride"in w){const[F,R,$]=w.stride;if(t-=R,g=F-$,R&&(O=R/l+e),$)for(let N=T.length-1;N>=0;--N){const G=T[N];if(G>=e){if(A!==null&&(G-e)*l<g)break;A=G}}}let v=[],M=[];for(let F=0;F<T.length;++F){const R=T[F];if(m.has(R)){const $=this.decode([R]),N=WHISPER_LANGUAGE_MAPPING.get($.slice(2,-2));if(N!==void 0){if(s!==null&&N!==s&&!o){r.push(v);const G=this.findLongestCommonSequence(r)[0],D=this.decode(G);a.text=D,c.push(a),r=[],v=[],a=f()}s=a.language=N}}else if(R>=e){const $=(R-e)*l+t,N=round($,2);if(A!==null&&R>=A)h=!0;else if(h||r.length>0&&R<O)h=!1;else if(a.timestamp[0]===null)a.timestamp[0]=N;else if(N!==a.timestamp[0]){a.timestamp[1]=N,r.push(v),p&&i.push(M);const[G,D]=this.findLongestCommonSequence(r,i),j=this.decode(G);a.text=j,p&&(a.words=this.collateWordTimestamps(G,D,s)),c.push(a),r=[],v=[],i=[],M=[],a=f()}}else if(v.push(R),p){let $=round(S[F]+t,2),N;F+1<S.length?N=round(S[F+1]+t,2):N=null,M.push([$,N])}}if("stride"in w){const[F,R,$]=w.stride;t+=F-$}v.length>0?(r.push(v),p&&i.push(M)):r.every(F=>F.length===0)&&(a=f(),r=[],v=[],i=[],M=[])}if(r.length>0){if(d&&o)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,T]=this.findLongestCommonSequence(r,i),S=this.decode(w);a.text=S,p&&(a.words=this.collateWordTimestamps(w,T,s)),c.push(a)}let b=Object.create(null);const y=c.map(w=>w.text).join("");if(o||u){for(let w=0;w<c.length;++w){const T=c[w];o||delete T.timestamp,u||delete T.language}if(p){let w=[];for(let T of c)for(let S of T.words)w.push(S);b={chunks:w}}else b={chunks:c}}return[y,b]}findLongestCommonSequence(n,o=null){let u=n[0],l=u.length,d=[];const s=Array.isArray(o)&&o.length>0;let p=s?[]:null,f=s?o[0]:null;for(let c=1;c<n.length;++c){const a=n[c];let t=0,e=[l,l,0,0];const r=a.length;for(let w=1;w<l+r;++w){const T=w/1e4,S=Math.max(0,l-w),A=Math.min(l,l+r-w),O=u.slice(S,A),v=Math.max(0,w-l),M=Math.min(r,w),F=a.slice(v,M);if(O.length!==F.length)throw new Error("There is a bug within whisper `decode_asr` function, please report it. Dropping to prevent bad inference.");const R=O.filter((N,G)=>N===F[G]).length,$=R/w+T;R>1&&$>t&&(t=$,e=[S,A,v,M])}const[i,h,g,m]=e,b=Math.floor((h+i)/2),y=Math.floor((m+g)/2);d.push(...u.slice(0,b)),u=a.slice(y),l=u.length,s&&(p.push(...f.slice(0,b)),f=o[c].slice(y))}return d.push(...u),s?(p.push(...f),[d,p]):[d,[]]}collateWordTimestamps(n,o,u){let[l,d,s]=this.combineTokensIntoWords(n,u),p=[];for(let f=0;f<l.length;++f){const c=s[f];p.push({text:l[f],timestamp:[o[c.at(0)][0],o[c.at(-1)][1]]})}return p}combineTokensIntoWords(n,o,u=`"'“¡¿([{-`,l=`"'.。,，!！?？:：”)]}、`){o=o??"english";let d,s,p;return["chinese","japanese","thai","lao","myanmar"].includes(o)?[d,s,p]=this.splitTokensOnUnicode(n):[d,s,p]=this.splitTokensOnSpaces(n),this.mergePunctuations(d,s,p,u,l)}decode(n,o){let u;return o&&o.decode_with_timestamps?u=this.decodeWithTimestamps(n,o):u=super.decode(n,o),u}decodeWithTimestamps(n,o){const u=(o==null?void 0:o.time_precision)??.02,l=Array.from(this.all_special_ids).at(-1)+1;let d=[[]];for(let s of n)if(s>=l){let p=(s-l)*u;p=round(p,2),d.push(`<|${p}|>`),d.push([])}else d[d.length-1].push(s);return d=d.map(s=>typeof s=="string"?s:super.decode(s,o)),d.join("")}splitTokensOnUnicode(n){const o=this.decode(n,{decode_with_timestamps:!0}),u="�";let l=[],d=[],s=[],p=[],f=[],c=0;for(let a=0;a<n.length;++a){const t=n[a];p.push(t),f.push(a);const e=this.decode(p,{decode_with_timestamps:!0});(!e.includes(u)||o[c+e.indexOf(u)]===u)&&(l.push(e),d.push(p),s.push(f),p=[],f=[],c+=e.length)}return[l,d,s]}splitTokensOnSpaces(n){let[o,u,l]=this.splitTokensOnUnicode(n),d=[],s=[],p=[];const f=new RegExp(`^[${PUNCTUATION_REGEX}]$`,"gu");for(let c=0;c<o.length;++c){const a=o[c],t=u[c],e=l[c],r=t[0]>=this.model.tokens_to_ids.get("<|endoftext|>"),i=a.startsWith(" "),h=a.trim(),g=f.test(h);if(r||i||g||d.length===0)d.push(a),s.push(t),p.push(e);else{const m=d.length-1;d[m]+=a,s[m].push(...t),p[m].push(...e)}}return[d,s,p]}mergePunctuations(n,o,u,l,d){let s=structuredClone(n),p=structuredClone(o),f=structuredClone(u),c=s.length-2,a=s.length-1;for(;c>=0;)s[c].startsWith(" ")&&l.includes(s[c].trim())?(s[a]=s[c]+s[a],p[a]=mergeArrays(p[c],p[a]),f[a]=mergeArrays(f[c],f[a]),s[c]="",p[c]=[],f[c]=[]):a=c,--c;for(c=0,a=1;a<s.length;)!s[c].endsWith(" ")&&d.includes(s[a])?(s[c]+=s[a],p[c]=mergeArrays(p[c],p[a]),f[c]=mergeArrays(f[c],f[a]),s[a]="",p[a]=[],f[a]=[]):c=a,++a;return[s.filter(t=>t),p.filter(t=>t.length>0),f.filter(t=>t.length>0)]}get_decoder_prompt_ids({language:n=null,task:o=null,no_timestamps:u=!0}={}){let l=[];if(n){n=n.toLowerCase();let d=WHISPER_TO_LANGUAGE_CODE_MAPPING.get(n);if(d===void 0)if(WHISPER_LANGUAGE_MAPPING.has(n))d=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: ${JSON.stringify(f)}`)}let s=this.model.tokens_to_ids.get(`<|${d}|>`);if(s===void 0)throw new Error(`Unable to find language "${d}" in model vocabulary. Please report this issue at https://github.com/xenova/transformers.js/issues/new/choose.`);l.push(s)}else l.push(null);if(o){if(o=o.toLowerCase(),o!=="transcribe"&&o!=="translate")throw new Error(`Task "${o}" is not supported. Must be one of: ["transcribe", "translate"]`);let d=this.model.tokens_to_ids.get(`<|${o}|>`);if(d===void 0)throw new Error(`Unable to find task "${o}" in model vocabulary. Please report this issue at https://github.com/xenova/transformers.js/issues/new/choose.`);l.push(d)}else l.push(null);if(u){let d=this.model.tokens_to_ids.get("<|notimestamps|>");if(d===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(d)}return l.map((d,s)=>[s+1,d]).filter(d=>d[1]!==null)}}class CodeGenTokenizer extends PreTrainedTokenizer{}class CLIPTokenizer extends PreTrainedTokenizer{}class MarianTokenizer extends PreTrainedTokenizer{constructor(n,o){super(n,o),this.languageRegex=/^(>>\w+<<)\s*/g,this.supported_language_codes=this.model.vocab.filter(u=>this.languageRegex.test(u)),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[o,...u]=n.trim().split(this.languageRegex);if(u.length===0)return super._encode_text(o);if(u.length===2){let[l,d]=u;return this.supported_language_codes.includes(l)||console.warn(`Unsupported language code "${l}" detected, which may lead to unexpected behavior. Should be one of: ${JSON.stringify(this.supported_language_codes)}`),mergeArrays([l],super._encode_text(d))}}}class Wav2Vec2CTCTokenizer extends PreTrainedTokenizer{}class BlenderbotTokenizer extends PreTrainedTokenizer{}class BlenderbotSmallTokenizer extends PreTrainedTokenizer{}class SpeechT5Tokenizer extends PreTrainedTokenizer{}class AutoTokenizer{static async from_pretrained(n,{quantized:o=!0,progress_callback:u=null,config:l=null,cache_dir:d=null,local_files_only:s=!1,revision:p="main"}={}){let[f,c]=await loadTokenizer(n,{quantized:o,progress_callback:u,config:l,cache_dir:d,local_files_only:s,revision:p}),a=c.tokenizer_class.replace(/Fast$/,""),t=this.TOKENIZER_CLASS_MAPPING[a];return t||(console.warn(`Unknown tokenizer class "${a}", attempting to construct from base class.`),t=PreTrainedTokenizer),new t(f,c)}}le(AutoTokenizer,"TOKENIZER_CLASS_MAPPING",{T5Tokenizer,DistilBertTokenizer,CamembertTokenizer,DebertaTokenizer,DebertaV2Tokenizer,BertTokenizer,HerbertTokenizer,XLMTokenizer,MobileBertTokenizer,SqueezeBertTokenizer,AlbertTokenizer,GPT2Tokenizer,BartTokenizer,MBartTokenizer,MBart50Tokenizer,RobertaTokenizer,WhisperTokenizer,CodeGenTokenizer,CLIPTokenizer,MarianTokenizer,BloomTokenizer,NllbTokenizer,M2M100Tokenizer,LlamaTokenizer,CodeLlamaTokenizer,XLMRobertaTokenizer,MPNetTokenizer,FalconTokenizer,GPTNeoXTokenizer,Wav2Vec2CTCTokenizer,BlenderbotTokenizer,BlenderbotSmallTokenizer,SpeechT5Tokenizer,PreTrainedTokenizer});async function loadConfig(_,n){return await getModelJSON(_,"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:o=null,config:u=null,cache_dir:l=null,local_files_only:d=!1,revision:s="main"}={}){let p=u??await loadConfig(n,{progress_callback:o,config:u,cache_dir:l,local_files_only:d,revision:s});return new this(p)}}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,o){for(let u of o)this.processors.forEach(l=>l(n,u))}[Symbol.iterator](){return this.processors.values()}}class LogitsProcessor extends Callable{_call(n,o){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,o){let u=this.force_token_map[n.length];return exists(u)&&(o.data.fill(-1/0),o.data[u]=0),o}}class ForcedBOSTokenLogitsProcessor extends LogitsProcessor{constructor(n){super(),this.bos_token_id=n}_call(n,o){return n.length===1&&(o.data.fill(-1/0),o.data[this.bos_token_id]=0),o}}class ForcedEOSTokenLogitsProcessor extends LogitsProcessor{constructor(n,o){super(),this.max_length=n,this.forced_eos_token_id=o}_call(n,o){}}class SuppressTokensAtBeginLogitsProcessor extends LogitsProcessor{constructor(n,o){super(),this.begin_suppress_tokens=n,this.begin_index=o}_call(n,o){if(n.length===this.begin_index)for(let u of this.begin_suppress_tokens)o.data[u]=-1/0;return o}}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,o){if(o.data[this.no_timestamps_token_id]=-1/0,n.length===this.begin_index-1)return o.data.fill(-1/0),o.data[this.timestamp_begin]=0,o;const u=n.slice(this.begin_index),l=u.length>=1&&u[u.length-1]>=this.timestamp_begin,d=u.length<2||u[u.length-2]>=this.timestamp_begin;if(l&&(d?o.data.subarray(this.timestamp_begin).fill(-1/0):o.data.subarray(0,this.eos_token_id).fill(-1/0)),n.length===this.begin_index&&this.max_initial_timestamp_index!==null){const c=this.timestamp_begin+this.max_initial_timestamp_index;o.data.subarray(c+1).fill(-1/0)}const s=log_softmax(o.data),p=Math.log(s.subarray(this.timestamp_begin).map(Math.exp).reduce((c,a)=>c+a)),f=max(s.subarray(0,this.timestamp_begin))[0];return p>f&&o.data.subarray(0,this.timestamp_begin).fill(-1/0),o}}class NoRepeatNGramLogitsProcessor extends LogitsProcessor{constructor(n){super(),this.no_repeat_ngram_size=n}getNgrams(n){const o=n.length,u=[];for(let d=0;d<o+1-this.no_repeat_ngram_size;++d){const s=[];for(let p=0;p<this.no_repeat_ngram_size;++p)s.push(n[d+p]);u.push(s)}const l=new Map;for(const d of u){const s=d.slice(0,d.length-1),p=JSON.stringify(s),f=l.get(p)??[];f.push(d[d.length-1]),l.set(p,f)}return l}getGeneratedNgrams(n,o){const u=o.slice(o.length+1-this.no_repeat_ngram_size,o.length);return n.get(JSON.stringify(u))??[]}calcBannedNgramTokens(n){const o=[];if(n.length+1<this.no_repeat_ngram_size)return o;{const u=this.getNgrams(n);return this.getGeneratedNgrams(u,n)}}_call(n,o){const u=this.calcBannedNgramTokens(n);for(const l of u)o.data[l]=-1/0;return o}}class RepetitionPenaltyLogitsProcessor extends LogitsProcessor{constructor(n){super(),this.penalty=n}_call(n,o){for(const u of n)o.data[u]<0?o.data[u]*=this.penalty:o.data[u]/=this.penalty;return o}}class MinLengthLogitsProcessor extends LogitsProcessor{constructor(n,o){super(),this.min_length=n,this.eos_token_id=Array.isArray(o)?o:[o]}_call(n,o){if(n.length<this.min_length)for(const u of this.eos_token_id)o.data[u]=-1/0;return o}}class MinNewTokensLengthLogitsProcessor extends LogitsProcessor{constructor(n,o,u){super(),this.prompt_length_to_skip=n,this.min_new_tokens=o,this.eos_token_id=Array.isArray(u)?u:[u]}_call(n,o){if(n.length-this.prompt_length_to_skip<this.min_new_tokens)for(const l of this.eos_token_id)o.data[l]=-1/0;return o}}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,o=-1){return this.sample(n,o)}sample(n,o){throw Error("sample should be implemented in subclasses.")}getLogits(n,o){let u=n.dims.at(-1),l=n.data;if(o===-1)l=l.slice(-u);else{let d=o*u;l=l.slice(d,d+u)}return this.generation_config.temperature>0&&(l=l.map(d=>d/this.generation_config.temperature)),l}randomSelect(n){let o=n.reduce((l,d)=>l+d,0),u=Math.random()*o;for(let l=0;l<n.length;++l)if(u-=n[l],u<=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 ${n.num_return_sequences}.`);return new GreedySampler(n)}}class GreedySampler extends Sampler{sample(n,o=-1){let u=this.getLogits(n,o);return[[max(u)[1],0]]}}class MultinomialSampler extends Sampler{sample(n,o=-1){let u=n.dims.at(-1);this.generation_config.top_k>0&&(u=Math.min(this.generation_config.top_k,u));const l=this.getLogits(n,o),d=getTopItems(l,u),s=softmax(d.map(p=>p[1]));return Array.from({length:this.generation_config.num_beams},()=>{const p=this.randomSelect(s);return[d[p][0],Math.log(s[p])]})}}class BeamSearchSampler extends Sampler{sample(n,o=-1){let u=n.dims.at(-1);this.generation_config.top_k>0&&(u=Math.min(this.generation_config.top_k,u));const l=this.getLogits(n,o),d=getTopItems(l,u),s=softmax(d.map(p=>p[1]));return Array.from({length:this.generation_config.num_beams},(p,f)=>[d[f][0],Math.log(s[f])])}}const{InferenceSession,Tensor:ONNXTensor}=ONNX,MODEL_TYPES={EncoderOnly:0,EncoderDecoder:1,Seq2Seq:2,Vision2Seq:3,DecoderOnly:4},MODEL_TYPE_MAPPING=new Map,MODEL_NAME_TO_CLASS_MAPPING=new Map,MODEL_CLASS_TO_NAME_MAPPING=new Map;async function constructSession(_,n,o){let u=`onnx/${n}${o.quantized?"_quantized":""}.onnx`,l=await getModelFile(_,u,!0,o);try{return await InferenceSession.create(l,{executionProviders})}catch(d){if(executionProviders.length===1&&executionProviders[0]==="wasm")throw d;return console.warn(d),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(_,n){const o={},u=[];for(let s of _.inputNames)n[s]===void 0?u.push(s):o[s]=n[s];if(u.length>0)throw new Error(`An error occurred during model execution: "Missing the following inputs: ${u.join(", ")}.`);const l=Object.keys(n).length,d=_.inputNames.length;if(l>d){let s=Object.keys(n).filter(p=>!_.inputNames.includes(p));console.warn(`WARNING: Too many inputs were provided (${l} > ${d}). The following inputs will be ignored: "${s.join(", ")}".`)}return o}async function sessionRun(_,n){const o=await validateInputs(_,n);try{let u=await _.run(o);return u=replaceTensors(u),u}catch(u){throw console.error(`An error occurred during model execution: "${u}".`),console.error("Inputs given to model:",o),u}}function replaceTensors(_){for(let n in _)_[n]instanceof ONNXTensor?_[n]=new Tensor(_[n]):typeof _[n]=="object"&&replaceTensors(_[n]);return _}function toI64Tensor(_){if(_ instanceof Tensor)return _;if(_.length===0)throw Error("items must be non-empty");if(Array.isArray(_[0])){if(_.some(n=>n.length!==_[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(_.flat().map(n=>BigInt(n))),[_.length,_[0].length])}else return new Tensor("int64",BigInt64Array.from(_.map(n=>BigInt(n))),[1,_.length])}function prepareAttentionMask(_,n){let o=_.config.pad_token_id??null,u=_.config.eos_token_id??null;isIntegralNumber(u)&&(u=[u]);let l=n.indexOf(o)!==-1,d=u===null||!u.includes(o);if(l&&d){let s=BigInt64Array.from(n.data.map(p=>p!=o));return new Tensor("int64",s,n.dims)}else return ones_like(n)}function boolTensor(_){return new Tensor("bool",[_],[1])}async function seq2seqForward(_,n){let{encoder_outputs:o,past_key_values:u}=n;o||(o=(await encoderForward(_,n)).last_hidden_state);let l={input_ids:n.decoder_input_ids,encoder_hidden_states:o,use_cache_branch:boolTensor(!!u)};_.decoder_merged_session.inputNames.includes("encoder_attention_mask")&&(l.encoder_attention_mask=n.attention_mask),_.addPastKeyValues(l,u);const d=await sessionRun(_.decoder_merged_session,l);let s=d.logits;u=_.getPastKeyValues(d,u);const p=_.getAttentions(d);return new Seq2SeqLMOutput({logits:s,past_key_values:u,encoder_outputs:o,...p})}function seq2seqStartBeams(_,n,o,u){let l=[],d=0;const s=_.requires_attention_mask??!0;let p=o.decoder_input_ids??o.decoder_start_token_id??o.bos_token_id??o.eos_token_id;p instanceof Tensor?p=p.tolist().flat():Array.isArray(p)||(p=[p]);for(let f of n){f.dims=[1,...f.dims];let c={inputs:f,encoder_outputs:null,prev_model_outputs:null,output_token_ids:p,done:!1,score:0,id:d++};s&&(c.attention_mask=prepareAttentionMask(_,f)),l.push(c)}return l}async function seq2seqRunBeam(_,n){var s;const o=_.main_input_name;let u=n.output_token_ids;n.prev_model_outputs&&(u=u.slice(-1));let l={[o]:n.inputs,decoder_input_ids:toI64Tensor(u),encoder_outputs:n.encoder_outputs,past_key_values:(s=n.prev_model_outputs)==null?void 0:s.past_key_values};n.attention_mask&&(l.attention_mask=n.attention_mask);let d=await _.forward(l);return n.prev_model_outputs=d,n.encoder_outputs=d.encoder_outputs,d}function seq2seqUpdatebeam(_,n){_.output_token_ids=[..._.output_token_ids,n]}async function encoderForward(_,n){let o={};for(let u of _.session.inputNames)o[u]=n[u];return await sessionRun(_.session,o)}async function decoderForward(_,n){let{input_ids:o,past_key_values:u,attention_mask:l}=n,d={input_ids:o,attention_mask:l??prepareAttentionMask(_,o),use_cache_branch:boolTensor(!!u)};_.addPastKeyValues(d,u);let s=await sessionRun(_.session,d),p=s.logits;return u=_.getPastKeyValues(s,u),{logits:p,past_key_values:u}}function decoderStartBeams(_,n,o,u,l){let d=[],s=0;for(let p of n){let f=p.tolist().map(Number);p.dims=[1,...p.dims];let c;l?(c=l[s],c.dims=[1,...c.dims]):c=prepareAttentionMask(_,p);let a={input:p,model_input_ids:p,attention_mask:c,prev_model_outputs:null,output_token_ids:f,num_output_tokens:u,done:!1,score:0,id:s++};d.push(a)}return d}async function decoderRunBeam(_,n){var d;let o=new BigInt64Array(n.output_token_ids.length).fill(1n),u={input_ids:n.model_input_ids,attention_mask:new Tensor("int64",o,[1,o.length]),past_key_values:(d=n.prev_model_outputs)==null?void 0:d.past_key_values},l=await _.forward(u);return n.prev_model_outputs=l,l}function decoderUpdatebeam(_,n){_.output_token_ids=[..._.output_token_ids,n],_.model_input_ids=new Tensor("int64",[BigInt(n)],[1,1])}class PreTrainedModel extends Callable{constructor(o,u){super();le(this,"main_input_name","input_ids");this.config=o,this.session=u;const l=MODEL_CLASS_TO_NAME_MAPPING.get(this.constructor),d=MODEL_TYPE_MAPPING.get(l);this.can_generate=!1,this._runBeam=null,this._getStartBeams=null,this._updateBeam=null,this._forward=null,d===MODEL_TYPES.DecoderOnly?(this.can_generate=!0,this._runBeam=decoderRunBeam,this._getStartBeams=decoderStartBeams,this._updateBeam=decoderUpdatebeam,this._forward=decoderForward):d===MODEL_TYPES.Seq2Seq||d===MODEL_TYPES.Vision2Seq?(this.can_generate=!0,this._runBeam=seq2seqRunBeam,this._getStartBeams=seq2seqStartBeams,this._updateBeam=seq2seqUpdatebeam,this._forward=seq2seqForward):d===MODEL_TYPES.EncoderDecoder?this._forward=encoderForward:this._forward=encoderForward}async dispose(){let o=[];for(let u of Object.keys(this)){let l=this[u];l instanceof InferenceSession&&o.push(l.handler.dispose())}return await Promise.all(o)}static async from_pretrained(o,{quantized:u=!0,progress_callback:l=null,config:d=null,cache_dir:s=null,local_files_only:p=!1,revision:f="main",model_file_name:c=null}={}){let a={quantized:u,progress_callback:l,config:d,cache_dir:s,local_files_only:p,revision:f,model_file_name:c};const t=MODEL_CLASS_TO_NAME_MAPPING.get(this),e=MODEL_TYPE_MAPPING.get(t);let r;return e===MODEL_TYPES.DecoderOnly?r=await Promise.all([AutoConfig.from_pretrained(o,a),constructSession(o,a.model_file_name??"decoder_model_merged",a),getModelJSON(o,"generation_config.json",!1,a)]):e===MODEL_TYPES.Seq2Seq||e===MODEL_TYPES.Vision2Seq?r=await Promise.all([AutoConfig.from_pretrained(o,a),constructSession(o,"encoder_model",a),constructSession(o,"decoder_model_merged",a),getModelJSON(o,"generation_config.json",!1,a)]):e===MODEL_TYPES.EncoderDecoder?r=await Promise.all([AutoConfig.from_pretrained(o,a),constructSession(o,"encoder_model",a),constructSession(o,"decoder_model_merged",a)]):(e!==MODEL_TYPES.EncoderOnly&&console.warn(`Model type for '${t}' not found, assuming encoder-only architecture. Please report this at https://github.com/xenova/transformers.js/issues/new/choose.`),r=await Promise.all([AutoConfig.from_pretrained(o,a),constructSession(o,a.model_file_name??"model",a)])),new this(...r)}async _call(o){return await this.forward(o)}async forward(o){return await this._forward(this,o)}_get_logits_processor(o,u,l=null){const d=new LogitsProcessorList;if(o.repetition_penalty!==null&&o.repetition_penalty!==1&&d.push(new RepetitionPenaltyLogitsProcessor(o.repetition_penalty)),o.no_repeat_ngram_size!==null&&o.no_repeat_ngram_size>0&&d.push(new NoRepeatNGramLogitsProcessor(o.no_repeat_ngram_size)),o.min_length!==null&&o.eos_token_id!==null&&o.min_length>0&&d.push(new MinLengthLogitsProcessor(o.min_length,o.eos_token_id)),o.min_new_tokens!==null&&o.eos_token_id!==null&&o.min_new_tokens>0&&d.push(new MinNewTokensLengthLogitsProcessor(u,o.min_new_tokens,o.eos_token_id)),o.forced_bos_token_id!==null&&d.push(new ForcedBOSTokenLogitsProcessor(o.forced_bos_token_id)),o.forced_eos_token_id!==null&&d.push(new ForcedEOSTokenLogitsProcessor(o.max_length,o.forced_eos_token_id)),o.begin_suppress_tokens!==null){let s=u>1||o.forced_bos_token_id===null?u:u+1;o.forced_decoder_ids!==null&&(s+=o.forced_decoder_ids[o.forced_decoder_ids.length-1][0]),d.push(new SuppressTokensAtBeginLogitsProcessor(o.begin_suppress_tokens,s))}return o.forced_decoder_ids!==null&&d.push(new ForceTokensLogitsProcessor(o.forced_decoder_ids)),l!==null&&d.extend(l),d}_get_generation_config(o){let u=new GenerationConfig(this.config);return"generation_config"in this&&Object.assign(u,this.generation_config),o!==null&&Object.assign(u,o),u}async generate(o,u=null,l=null,{inputs_attention_mask:d=null}={}){if(!this.can_generate){let m=`The current model class (${MODEL_CLASS_TO_NAME_MAPPING.get(this.constructor)}) is not compatible with \`.generate()\`, as it doesn't have a language model head.`;const b=this.config.model_type,y=MODEL_WITH_LM_HEAD_MAPPING_NAMES.get(b)??MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING_NAMES.get(b)??MODEL_FOR_SPEECH_SEQ_2_SEQ_MAPPING_NAMES.get(b)??MODEL_FOR_VISION_2_SEQ_MAPPING_NAMES.get(b);throw y&&(m+=` Please use the following class instead: '${y[0]}'`),Error(m)}if(!(o instanceof Tensor)&&!isTypedArray(o)&&!Array.isArray(o))throw Error(`\`inputs\` must be a Tensor, TypedArray, or Array, but is "${o.constructor.name}".`);let s;if(this.config.is_encoder_decoder)s=0;else if(s=o instanceof Tensor?o.dims.at(-1):o.length,s===0)throw Error("Must supply a non-empty array of input token ids.");u=this._get_generation_config(u),l=l??new LogitsProcessorList,l=this._get_logits_processor(u,s,l);let p=u.eos_token_id;p!==null&&!Array.isArray(p)&&(p=[p]);let f=1;const c=f+(u.max_new_tokens??1/0),a=Number.isInteger(u.max_length)&&(u.max_new_tokens??null)===null;let t=Sampler.getSampler(u),e=this.getStartBeams(o,u,f,d);for(;e.some(g=>!g.done)&&f<c;){let g=[];for(let m of e){if(m.done){g.push(m);continue}if(a&&m.output_token_ids.length>=u.max_length){m.done=!0,g.push(m);continue}let b=await this.runBeam(m);u.output_attentions&&this.addAttentionsToBeam(m,b),u.output_scores;let y=b.logits.slice(null,-1,null);l(m.output_token_ids,y);let w=t(y);for(let[T,S]of w){let A={...m};this.updateBeam(A,T),A.score+=S,p&&p.includes(T)&&(A.done=!0),g.push(A)}}++f,g=this.groupBeams(g).map(m=>m.sort((b,y)=>y.score-b.score).slice(0,u.num_beams)),e=g.flat(),u.callback_function&&u.callback_function(e)}const r=this.groupBeams(e),i=g=>r.map(m=>u.num_return_sequences>1?m.slice(0,u.num_return_sequences).map(b=>b[g]):[m[0][g]]).flat(),h=i("output_token_ids");if(u.return_dict_in_generate){const g=i("decoder_attentions"),m=i("cross_attentions");return{sequences:h,decoder_attentions:g,cross_attentions:m}}else return h}addAttentionsToBeam(o,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`.");o.cross_attentions||(o.cross_attentions=[]),o.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`.");o.decoder_attentions||(o.decoder_attentions=[]),o.decoder_attentions.push(u.decoder_attentions)}groupBeams(o){const u=Object.create(null);for(const l of o)u[l.id]===void 0?u[l.id]=[l]:u[l.id].push(l);return Object.values(u)}getPastKeyValues(o,u){const l=Object.create(null);for(const d in o)if(d.startsWith("present")){let s=d.replace("present","past_key_values");u&&d.includes("encoder")?l[s]=u[s]:l[s]=o[d]}return l}getAttentions(o){const u=Object.create(null);for(const l of["cross_attentions","decoder_attentions"]){const d=[];for(const s in o)if(s.startsWith(l)){const p=s.split(".").pop();d[p]=o[s]}u[l]=d}return u}addPastKeyValues(o,u){if(u)Object.assign(o,u);else if(this.config.is_encoder_decoder&&(this.add_encoder_pkv??!0)){let l=[1,this.num_encoder_heads,0,this.encoder_dim_kv],d=[1,this.num_decoder_heads,0,this.decoder_dim_kv];for(let s=0;s<this.num_decoder_layers;++s)o[`past_key_values.${s}.encoder.key`]=new Tensor("float32",[],l),o[`past_key_values.${s}.encoder.value`]=new Tensor("float32",[],l),o[`past_key_values.${s}.decoder.key`]=new Tensor("float32",[],d),o[`past_key_values.${s}.decoder.value`]=new Tensor("float32",[],d)}else if(this.config.multi_query){let l=[1,0,2*this.dim_kv];for(let d=0;d<this.num_layers;++d)o[`past_key_values.${d}.key_value`]=new Tensor("float32",[],l)}else if(this.config.model_type==="bloom"){let l=[1*this.num_heads,this.dim_kv,0],d=[1*this.num_heads,0,this.dim_kv];for(let s=0;s<this.num_layers;++s)o[`past_key_values.${s}.key`]=new Tensor("float32",[],l),o[`past_key_values.${s}.value`]=new Tensor("float32",[],d)}else{let l=[1,this.num_heads,0,this.dim_kv];for(let d=0;d<this.num_layers;++d)o[`past_key_values.${d}.key`]=new Tensor("float32",[],l),o[`past_key_values.${d}.value`]=new Tensor("float32",[],l)}}getStartBeams(o,u,l,d){return this._getStartBeams(this,o,u,l,d)}async runBeam(o){return await this._runBeam(this,o)}updateBeam(o,u){return this._updateBeam(o,u)}}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 CamembertPreTrainedModel extends PreTrainedModel{}class CamembertModel extends CamembertPreTrainedModel{}class CamembertForMaskedLM extends CamembertPreTrainedModel{async _call(n){return new MaskedLMOutput(await super._call(n))}}class CamembertForSequenceClassification extends CamembertPreTrainedModel{async _call(n){return new SequenceClassifierOutput(await super._call(n))}}class CamembertForTokenClassification extends CamembertPreTrainedModel{async _call(n){return new TokenClassifierOutput(await super._call(n))}}class CamembertForQuestionAnswering extends CamembertPreTrainedModel{async _call(n){return new QuestionAnsweringModelOutput(await super._call(n))}}class DebertaPreTrainedModel extends PreTrainedModel{}class DebertaModel extends DebertaPreTrainedModel{}class DebertaForMaskedLM extends DebertaPreTrainedModel{async _call(n){return new MaskedLMOutput(await super._call(n))}}class DebertaForSequenceClassification extends DebertaPreTrainedModel{async _call(n){return new SequenceClassifierOutput(await super._call(n))}}class DebertaForTokenClassification extends DebertaPreTrainedModel{async _call(n){return new TokenClassifierOutput(await super._call(n))}}class DebertaForQuestionAnswering extends DebertaPreTrainedModel{async _call(n){return new QuestionAnsweringModelOutput(await super._call(n))}}class DebertaV2PreTrainedModel extends PreTrainedModel{}class DebertaV2Model extends DebertaV2PreTrainedModel{}class DebertaV2ForMaskedLM extends DebertaV2PreTrainedModel{async _call(n){return new MaskedLMOutput(await super._call(n))}}class DebertaV2ForSequenceClassification extends DebertaV2PreTrainedModel{async _call(n){return new SequenceClassifierOutput(await super._call(n))}}class DebertaV2ForTokenClassification extends DebertaV2PreTrainedModel{async _call(n){return new TokenClassifierOutput(await super._call(n))}}class DebertaV2ForQuestionAnswering extends DebertaV2PreTrainedModel{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{}class T5ForConditionalGeneration extends T5PreTrainedModel{constructor(n,o,u,l){super(n,o),this.decoder_merged_session=u,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}}class LongT5PreTrainedModel extends PreTrainedModel{}class LongT5Model extends LongT5PreTrainedModel{}class LongT5ForConditionalGeneration extends LongT5PreTrainedModel{constructor(n,o,u,l){super(n,o),this.decoder_merged_session=u,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}}class MT5PreTrainedModel extends PreTrainedModel{}class MT5Model extends MT5PreTrainedModel{}class MT5ForConditionalGeneration extends MT5PreTrainedModel{constructor(n,o,u,l){super(n,o),this.decoder_merged_session=u,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}}class BartPretrainedModel extends PreTrainedModel{}class BartModel extends BartPretrainedModel{}class BartForConditionalGeneration extends BartPretrainedModel{constructor(n,o,u,l){super(n,o),this.decoder_merged_session=u,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}}class BartForSequenceClassification extends BartPretrainedModel{async _call(n){return new SequenceClassifierOutput(await super._call(n))}}class MBartPreTrainedModel extends PreTrainedModel{}class MBartModel extends MBartPreTrainedModel{}class MBartForConditionalGeneration extends MBartPreTrainedModel{constructor(n,o,u,l){super(n,o),this.decoder_merged_session=u,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}}class MBartForSequenceClassification extends MBartPreTrainedModel{async _call(n){return new SequenceClassifierOutput(await super._call(n))}}class MBartForCausalLM extends MBartPreTrainedModel{constructor(n,o,u){super(n,o),this.generation_config=u,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}}class BlenderbotPreTrainedModel extends PreTrainedModel{}class BlenderbotModel extends BlenderbotPreTrainedModel{}class BlenderbotForConditionalGeneration extends BlenderbotPreTrainedModel{constructor(n,o,u,l){super(n,o),this.decoder_merged_session=u,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}}class BlenderbotSmallPreTrainedModel extends PreTrainedModel{}class BlenderbotSmallModel extends BlenderbotSmallPreTrainedModel{}class BlenderbotSmallForConditionalGeneration extends BlenderbotSmallPreTrainedModel{constructor(n,o,u,l){super(n,o),this.decoder_merged_session=u,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}}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 XLMPreTrainedModel extends PreTrainedModel{}class XLMModel extends XLMPreTrainedModel{}class XLMWithLMHeadModel extends XLMPreTrainedModel{async _call(n){return new MaskedLMOutput(await super._call(n))}}class XLMForSequenceClassification extends XLMPreTrainedModel{async _call(n){return new SequenceClassifierOutput(await super._call(n))}}class XLMForTokenClassification extends XLMPreTrainedModel{async _call(n){return new TokenClassifierOutput(await super._call(n))}}class XLMForQuestionAnswering extends XLMPreTrainedModel{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{}class WhisperForConditionalGeneration extends WhisperPreTrainedModel{constructor(o,u,l,d){super(o,u);le(this,"requires_attention_mask",!1);le(this,"main_input_name","input_features");this.decoder_merged_session=l,this.generation_config=d,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(o,u=null,l=null){if(u=this._get_generation_config(u),u.return_timestamps??(u.return_timestamps=!1),u.return_timestamps&&(l=[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 d=await super.generate(o,u,l);return u.return_token_timestamps&&u.alignment_heads&&(d.token_timestamps=this._extract_token_timestamps(d,u.alignment_heads,u.num_frames)),d}_extract_token_timestamps(o,u,l=null,d=.02){if(!o.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 s=this.config.median_filter_width;s===void 0&&(console.warn("Model config has no `median_filter_width`, using default value of 7."),s=7);const p=o.cross_attentions.map(a=>{let t=Array.from({length:this.config.decoder_layers},(m,b)=>cat(a.map(y=>y[b]),2)),e=stack(u.map(([m,b])=>l?t[m].slice(null,b,null,[0,l]):t[m].slice(null,b)));e=e.transpose(1,0,2,3);let[r,i]=std_mean(e,-2,0,!0),h=e.clone();for(let m=0;m<h.dims[0];++m){let b=h[m];for(let y=0;y<b.dims[0];++y){let w=b[y];const T=r[m][y][0],S=i[m][y][0];for(let A=0;A<w.dims[0];++A){let O=w[A];for(let v=0;v<O.data.length;++v)O.data[v]=(O.data[v]-S.data[v])/T.data[v];O.data.set(medianFilter(O.data,s))}}}return mean(h,1)}),f=[o.sequences.length,o.sequences[0].length],c=new Tensor("float32",new Float32Array(f[0]*f[1]),f);for(let a=0;a<f[0];++a){const t=p[a].neg().squeeze_(0);let[e,r]=dynamicTimeWarping(t),i=Array.from({length:e.length-1},(m,b)=>e[b+1]-e[b]),h=mergeArrays([1],i).map(m=>!!m),g=[];for(let m=0;m<h.length;++m)h[m]&&g.push(r[m]*d);c[a].data.set(g,1)}return c}}class VisionEncoderDecoderModel extends PreTrainedModel{constructor(o,u,l,d){super(o,u);le(this,"main_input_name","pixel_values");this.decoder_merged_session=l,this.generation_config=d;const s=this.config.encoder,p=this.config.decoder,f=s.model_type;(MODEL_MAPPING_NAMES_ENCODER_ONLY.get(f)??MODEL_MAPPING_NAMES_ENCODER_DECODER.get(f))||console.warn(`Model type for encoder '${f}' not found, assuming encoder-only architecture. Please report this at https://github.com/xenova/transformers.js/issues/new/choose.`);const a=MODEL_WITH_LM_HEAD_MAPPING_NAMES.get(p.model_type);if(!a)throw new Error(`Unable to construct \`VisionEncoderDecoder\` due to unsupported decoder: "${this.config.decoder.model_type}"`);const t=a[1],e=new t(p,l,d);this.add_encoder_pkv="num_decoder_layers"in e,this.add_encoder_pkv?(this.num_decoder_layers=e.num_decoder_layers,this.num_decoder_heads=e.num_decoder_heads,this.decoder_dim_kv=e.decoder_dim_kv,this.num_encoder_layers=e.num_encoder_layers,this.num_encoder_heads=e.num_encoder_heads,this.encoder_dim_kv=e.encoder_dim_kv):(this.num_layers=e.num_layers,this.num_heads=e.num_heads,this.dim_kv=e.dim_kv)}}class CLIPPreTrainedModel extends PreTrainedModel{}class CLIPModel extends CLIPPreTrainedModel{}class CLIPTextModelWithProjection extends CLIPPreTrainedModel{static async from_pretrained(n,o={}){return o.model_file_name??(o.model_file_name="text_model"),super.from_pretrained(n,o)}}class CLIPVisionModelWithProjection extends CLIPPreTrainedModel{static async from_pretrained(n,o={}){return o.model_file_name??(o.model_file_name="vision_model"),super.from_pretrained(n,o)}}class GPT2PreTrainedModel extends PreTrainedModel{constructor(n,o,u){super(n,o),this.generation_config=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{}class GPT2LMHeadModel extends GPT2PreTrainedModel{}class GPTNeoPreTrainedModel extends PreTrainedModel{constructor(n,o,u){super(n,o),this.generation_config=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{}class GPTNeoForCausalLM extends GPTNeoPreTrainedModel{}class GPTNeoXPreTrainedModel extends PreTrainedModel{constructor(n,o,u){super(n,o),this.generation_config=u,this.config.pad_token_id=this.config.eos_token_id,this.num_heads=this.config.num_attention_heads,this.num_layers=this.config.num_hidden_layers,this.dim_kv=this.config.hidden_size/this.num_heads}}class GPTNeoXModel extends GPTNeoXPreTrainedModel{}class GPTNeoXForCausalLM extends GPTNeoXPreTrainedModel{}class GPTJPreTrainedModel extends PreTrainedModel{constructor(n,o,u){super(n,o),this.generation_config=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 GPTJModel extends GPTJPreTrainedModel{}class GPTJForCausalLM extends GPTJPreTrainedModel{}class GPTBigCodePreTrainedModel extends PreTrainedModel{constructor(n,o,u){super(n,o),this.generation_config=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{}class GPTBigCodeForCausalLM extends GPTBigCodePreTrainedModel{}class CodeGenPreTrainedModel extends PreTrainedModel{constructor(n,o,u){super(n,o),this.generation_config=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{}class CodeGenForCausalLM extends CodeGenPreTrainedModel{}class LlamaPreTrainedModel extends PreTrainedModel{constructor(n,o,u){super(n,o),this.generation_config=u,this.config.pad_token_id=this.config.eos_token_id,this.num_heads=this.config.num_attention_heads,this.num_layers=this.config.num_hidden_layers,this.dim_kv=this.config.hidden_size/this.num_heads}}class LlamaModel extends LlamaPreTrainedModel{}class LlamaForCausalLM extends LlamaPreTrainedModel{}class BloomPreTrainedModel extends PreTrainedModel{constructor(n,o,u){super(n,o),this.generation_config=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.hidden_size/this.num_heads}}class BloomModel extends BloomPreTrainedModel{}class BloomForCausalLM extends BloomPreTrainedModel{}class MptPreTrainedModel extends PreTrainedModel{constructor(n,o,u){super(n,o),this.generation_config=u,this.config.pad_token_id=this.config.eos_token_id,this.num_heads=this.config.n_heads,this.num_layers=this.config.n_layers,this.dim_kv=this.config.d_model/this.num_heads}}class MptModel extends MptPreTrainedModel{}class MptForCausalLM extends MptPreTrainedModel{}class OPTPreTrainedModel extends PreTrainedModel{constructor(n,o,u){super(n,o),this.generation_config=u,this.config.pad_token_id=this.config.eos_token_id,this.num_heads=this.config.num_attention_heads,this.num_layers=this.config.num_hidden_layers,this.dim_kv=this.config.hidden_size/this.num_heads}}class OPTModel extends OPTPreTrainedModel{}class OPTForCausalLM extends OPTPreTrainedModel{}class ViTPreTrainedModel extends PreTrainedModel{}class ViTModel extends ViTPreTrainedModel{}class ViTForImageClassification extends ViTPreTrainedModel{async _call(n){return new SequenceClassifierOutput(await super._call(n))}}class MobileViTPreTrainedModel extends PreTrainedModel{}class MobileViTModel extends MobileViTPreTrainedModel{}class MobileViTForImageClassification extends MobileViTPreTrainedModel{async _call(n){return new SequenceClassifierOutput(await super._call(n))}}class BeitPreTrainedModel extends PreTrainedModel{}class BeitModel extends BeitPreTrainedModel{}class BeitForImageClassification extends BeitPreTrainedModel{async _call(n){return new SequenceClassifierOutput(await super._call(n))}}class DetrPreTrainedModel extends PreTrainedModel{}class DetrModel extends DetrPreTrainedModel{}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:o}){super(),this.logits=n,this.pred_boxes=o}}class DetrSegmentationOutput extends ModelOutput{constructor({logits:n,pred_boxes:o,pred_masks:u}){super(),this.logits=n,this.pred_boxes=o,this.pred_masks=u}}class DeiTPreTrainedModel extends PreTrainedModel{}class DeiTModel extends DeiTPreTrainedModel{}class DeiTForImageClassification extends DeiTPreTrainedModel{async _call(n){return new SequenceClassifierOutput(await super._call(n))}}class ResNetPreTrainedModel extends PreTrainedModel{}class ResNetModel extends ResNetPreTrainedModel{}class ResNetForImageClassification extends ResNetPreTrainedModel{async _call(n){return new SequenceClassifierOutput(await super._call(n))}}class SwinPreTrainedModel extends PreTrainedModel{}class SwinModel extends SwinPreTrainedModel{}class SwinForImageClassification extends SwinPreTrainedModel{async _call(n){return new SequenceClassifierOutput(await super._call(n))}}class DonutSwinPreTrainedModel extends PreTrainedModel{}class DonutSwinModel extends DonutSwinPreTrainedModel{}class YolosPreTrainedModel extends PreTrainedModel{}class YolosModel extends YolosPreTrainedModel{}class YolosForObjectDetection extends YolosPreTrainedModel{async _call(n){return new YolosObjectDetectionOutput(await super._call(n))}}class YolosObjectDetectionOutput extends ModelOutput{constructor({logits:n,pred_boxes:o}){super(),this.logits=n,this.pred_boxes=o}}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:o}){super(),this.iou_scores=n,this.pred_masks=o}}class MarianPreTrainedModel extends PreTrainedModel{}class MarianModel extends MarianPreTrainedModel{}class MarianMTModel extends MarianPreTrainedModel{constructor(n,o,u,l){super(n,o),this.decoder_merged_session=u,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}}class M2M100PreTrainedModel extends PreTrainedModel{}class M2M100Model extends M2M100PreTrainedModel{}class M2M100ForConditionalGeneration extends M2M100PreTrainedModel{constructor(n,o,u,l){super(n,o),this.decoder_merged_session=u,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}}class Wav2Vec2PreTrainedModel extends PreTrainedModel{}class Wav2Vec2Model extends Wav2Vec2PreTrainedModel{}class Wav2Vec2ForCTC extends Wav2Vec2PreTrainedModel{async _call(n){return new CausalLMOutput(await super._call(n))}}class Wav2Vec2ForSequenceClassification extends Wav2Vec2PreTrainedModel{async _call(n){return new SequenceClassifierOutput(await super._call(n))}}class WavLMPreTrainedModel extends PreTrainedModel{}class WavLMModel extends WavLMPreTrainedModel{}class WavLMForCTC extends WavLMPreTrainedModel{async _call(n){return new CausalLMOutput(await super._call(n))}}class WavLMForSequenceClassification extends WavLMPreTrainedModel{async _call(n){return new SequenceClassifierOutput(await super._call(n))}}class SpeechT5PreTrainedModel extends PreTrainedModel{}class SpeechT5ForSpeechToText extends SpeechT5PreTrainedModel{}class SpeechT5ForTextToSpeech extends SpeechT5PreTrainedModel{constructor(n,o,u,l){super(n,o),this.decoder_merged_session=u,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.hidden_size/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.hidden_size/this.num_encoder_heads}async generate_speech(n,o,{threshold:u=.5,minlenratio:l=0,maxlenratio:d=20,vocoder:s=null}={}){const p={input_ids:n},{encoder_outputs:f,encoder_attention_mask:c}=await encoderForward(this,p),a=f.dims[1]/this.config.reduction_factor,t=Math.floor(a*d),e=Math.floor(a*l),r=this.config.num_mel_bins;let i=[],h=null,g=null,m=0;for(;;){++m;const w=boolTensor(!!g);let T;g?T=g.output_sequence_out:T=new Tensor("float32",new Float32Array(r),[1,1,r]);let S={use_cache_branch:w,output_sequence:T,encoder_attention_mask:c,speaker_embeddings:o,encoder_hidden_states:f};this.addPastKeyValues(S,h),g=await sessionRun(this.decoder_merged_session,S),h=this.getPastKeyValues(g,h);const{prob:A,spectrum:O}=g;if(i.push(O),m>=e&&(Array.from(A.data).filter(v=>v>=u).length>0||m>=t))break}const b=cat(i),{waveform:y}=await sessionRun(s.session,{spectrogram:b});return{spectrogram:b,waveform:y}}}class SpeechT5HifiGan extends PreTrainedModel{constructor(){super(...arguments);le(this,"main_input_name","spectrogram")}}class PretrainedMixin{static async from_pretrained(n,{quantized:o=!0,progress_callback:u=null,config:l=null,cache_dir:d=null,local_files_only:s=!1,revision:p="main",model_file_name:f=null}={}){let c={quantized:o,progress_callback:u,config:l,cache_dir:d,local_files_only:s,revision:p,model_file_name:f};if(l=await AutoConfig.from_pretrained(n,c),c.config||(c.config=l),!this.MODEL_CLASS_MAPPINGS)throw new Error("`MODEL_CLASS_MAPPINGS` not implemented for this type of `AutoClass`: "+this.name);for(let a of this.MODEL_CLASS_MAPPINGS){const t=a.get(l.model_type);if(t)return await t[1].from_pretrained(n,c)}if(this.BASE_IF_FAIL)return console.warn(`Unknown model class "${l.model_type}", attempting to construct from base class.`),await PreTrainedModel.from_pretrained(n,c);throw Error(`Unsupported model type: ${l.model_type}`)}}le(PretrainedMixin,"MODEL_CLASS_MAPPINGS",null),le(PretrainedMixin,"BASE_IF_FAIL",!1);const MODEL_MAPPING_NAMES_ENCODER_ONLY=new Map([["bert",["BertModel",BertModel]],["camembert",["CamembertModel",CamembertModel]],["deberta",["DebertaModel",DebertaModel]],["deberta-v2",["DebertaV2Model",DebertaV2Model]],["mpnet",["MPNetModel",MPNetModel]],["albert",["AlbertModel",AlbertModel]],["distilbert",["DistilBertModel",DistilBertModel]],["roberta",["RobertaModel",RobertaModel]],["xlm",["XLMModel",XLMModel]],["xlm-roberta",["XLMRobertaModel",XLMRobertaModel]],["clip",["CLIPModel",CLIPModel]],["mobilebert",["MobileBertModel",MobileBertModel]],["squeezebert",["SqueezeBertModel",SqueezeBertModel]],["wav2vec2",["Wav2Vec2Model",Wav2Vec2Model]],["wavlm",["WavLMModel",WavLMModel]],["detr",["DetrModel",DetrModel]],["vit",["ViTModel",ViTModel]],["mobilevit",["MobileViTModel",MobileViTModel]],["beit",["BeitModel",BeitModel]],["deit",["DeiTModel",DeiTModel]],["resnet",["ResNetModel",ResNetModel]],["swin",["SwinModel",SwinModel]],["donut-swin",["DonutSwinModel",DonutSwinModel]],["yolos",["YolosModel",YolosModel]],["hifigan",["SpeechT5HifiGan",SpeechT5HifiGan]],["sam",["SamModel",SamModel]]]),MODEL_MAPPING_NAMES_ENCODER_DECODER=new Map([["t5",["T5Model",T5Model]],["longt5",["LongT5Model",LongT5Model]],["mt5",["MT5Model",MT5Model]],["bart",["BartModel",BartModel]],["mbart",["MBartModel",MBartModel]],["marian",["MarianModel",MarianModel]],["whisper",["WhisperModel",WhisperModel]],["m2m_100",["M2M100Model",M2M100Model]],["blenderbot",["BlenderbotModel",BlenderbotModel]],["blenderbot-small",["BlenderbotSmallModel",BlenderbotSmallModel]]]),MODEL_MAPPING_NAMES_DECODER_ONLY=new Map([["bloom",["BloomModel",BloomModel]],["gpt2",["GPT2Model",GPT2Model]],["gptj",["GPTJModel",GPTJModel]],["gpt_bigcode",["GPTBigCodeModel",GPTBigCodeModel]],["gpt_neo",["GPTNeoModel",GPTNeoModel]],["gpt_neox",["GPTNeoXModel",GPTNeoXModel]],["codegen",["CodeGenModel",CodeGenModel]],["llama",["LlamaModel",LlamaModel]],["mpt",["MptModel",MptModel]],["opt",["OPTModel",OPTModel]]]),MODEL_FOR_SPEECH_SEQ_2_SEQ_MAPPING_NAMES=new Map([["speecht5",["SpeechT5ForSpeechToText",SpeechT5ForSpeechToText]],["whisper",["WhisperForConditionalGeneration",WhisperForConditionalGeneration]]]),MODEL_FOR_TEXT_TO_SPECTROGRAM_MAPPING_NAMES=new Map([["speecht5",["SpeechT5ForTextToSpeech",SpeechT5ForTextToSpeech]]]),MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING_NAMES=new Map([["bert",["BertForSequenceClassification",BertForSequenceClassification]],["camembert",["CamembertForSequenceClassification",CamembertForSequenceClassification]],["deberta",["DebertaForSequenceClassification",DebertaForSequenceClassification]],["deberta-v2",["DebertaV2ForSequenceClassification",DebertaV2ForSequenceClassification]],["mpnet",["MPNetForSequenceClassification",MPNetForSequenceClassification]],["albert",["AlbertForSequenceClassification",AlbertForSequenceClassification]],["distilbert",["DistilBertForSequenceClassification",DistilBertForSequenceClassification]],["roberta",["RobertaForSequenceClassification",RobertaForSequenceClassification]],["xlm",["XLMForSequenceClassification",XLMForSequenceClassification]],["xlm-roberta",["XLMRobertaForSequenceClassification",XLMRobertaForSequenceClassification]],["bart",["BartForSequenceClassification",BartForSequenceClassification]],["mbart",["MBartForSequenceClassification",MBartForSequenceClassification]],["mobilebert",["MobileBertForSequenceClassification",MobileBertForSequenceClassification]],["squeezebert",["SqueezeBertForSequenceClassification",SqueezeBertForSequenceClassification]]]),MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING_NAMES=new Map([["bert",["BertForTokenClassification",BertForTokenClassification]],["camembert",["CamembertForTokenClassification",CamembertForTokenClassification]],["deberta",["DebertaForTokenClassification",DebertaForTokenClassification]],["deberta-v2",["DebertaV2ForTokenClassification",DebertaV2ForTokenClassification]],["mpnet",["MPNetForTokenClassification",MPNetForTokenClassification]],["distilbert",["DistilBertForTokenClassification",DistilBertForTokenClassification]],["roberta",["RobertaForTokenClassification",RobertaForTokenClassification]],["xlm",["XLMForTokenClassification",XLMForTokenClassification]],["xlm-roberta",["XLMRobertaForTokenClassification",XLMRobertaForTokenClassification]]]),MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING_NAMES=new Map([["t5",["T5ForConditionalGeneration",T5ForConditionalGeneration]],["longt5",["LongT5ForConditionalGeneration",LongT5ForConditionalGeneration]],["mt5",["MT5ForConditionalGeneration",MT5ForConditionalGeneration]],["bart",["BartForConditionalGeneration",BartForConditionalGeneration]],["mbart",["MBartForConditionalGeneration",MBartForConditionalGeneration]],["marian",["MarianMTModel",MarianMTModel]],["m2m_100",["M2M100ForConditionalGeneration",M2M100ForConditionalGeneration]],["blenderbot",["BlenderbotForConditionalGeneration",BlenderbotForConditionalGeneration]],["blenderbot-small",["BlenderbotSmallForConditionalGeneration",BlenderbotSmallForConditionalGeneration]]]),MODEL_WITH_LM_HEAD_MAPPING_NAMES=new Map([["bloom",["BloomForCausalLM",BloomForCausalLM]],["gpt2",["GPT2LMHeadModel",GPT2LMHeadModel]],["gptj",["GPTJForCausalLM",GPTJForCausalLM]],["gpt_bigcode",["GPTBigCodeForCausalLM",GPTBigCodeForCausalLM]],["gpt_neo",["GPTNeoForCausalLM",GPTNeoForCausalLM]],["gpt_neox",["GPTNeoXForCausalLM",GPTNeoXForCausalLM]],["codegen",["CodeGenForCausalLM",CodeGenForCausalLM]],["llama",["LlamaForCausalLM",LlamaForCausalLM]],["mpt",["MptForCausalLM",MptForCausalLM]],["opt",["OPTForCausalLM",OPTForCausalLM]],["mbart",["MBartForCausalLM",MBartForCausalLM]]]),MODEL_FOR_MASKED_LM_MAPPING_NAMES=new Map([["bert",["BertForMaskedLM",BertForMaskedLM]],["camembert",["CamembertForMaskedLM",CamembertForMaskedLM]],["deberta",["DebertaForMaskedLM",DebertaForMaskedLM]],["deberta-v2",["DebertaV2ForMaskedLM",DebertaV2ForMaskedLM]],["mpnet",["MPNetForMaskedLM",MPNetForMaskedLM]],["albert",["AlbertForMaskedLM",AlbertForMaskedLM]],["distilbert",["DistilBertForMaskedLM",DistilBertForMaskedLM]],["roberta",["RobertaForMaskedLM",RobertaForMaskedLM]],["xlm",["XLMWithLMHeadModel",XLMWithLMHeadModel]],["xlm-roberta",["XLMRobertaForMaskedLM",XLMRobertaForMaskedLM]],["mobilebert",["MobileBertForMaskedLM",MobileBertForMaskedLM]],["squeezebert",["SqueezeBertForMaskedLM",SqueezeBertForMaskedLM]]]),MODEL_FOR_QUESTION_ANSWERING_MAPPING_NAMES=new Map([["bert",["BertForQuestionAnswering",BertForQuestionAnswering]],["camembert",["CamembertForQuestionAnswering",CamembertForQuestionAnswering]],["deberta",["DebertaForQuestionAnswering",DebertaForQuestionAnswering]],["deberta-v2",["DebertaV2ForQuestionAnswering",DebertaV2ForQuestionAnswering]],["mpnet",["MPNetForQuestionAnswering",MPNetForQuestionAnswering]],["albert",["AlbertForQuestionAnswering",AlbertForQuestionAnswering]],["distilbert",["DistilBertForQuestionAnswering",DistilBertForQuestionAnswering]],["roberta",["RobertaForQuestionAnswering",RobertaForQuestionAnswering]],["xlm",["XLMForQuestionAnswering",XLMForQuestionAnswering]],["xlm-roberta",["XLMRobertaForQuestionAnswering",XLMRobertaForQuestionAnswering]],["mobilebert",["MobileBertForQuestionAnswering",MobileBertForQuestionAnswering]],["squeezebert",["SqueezeBertForQuestionAnswering",SqueezeBertForQuestionAnswering]]]),MODEL_FOR_VISION_2_SEQ_MAPPING_NAMES=new Map([["vision-encoder-decoder",["VisionEncoderDecoderModel",VisionEncoderDecoderModel]]]),MODEL_FOR_DOCUMENT_QUESTION_ANSWERING_MAPPING_NAMES=new Map([["vision-encoder-decoder",["VisionEncoderDecoderModel",VisionEncoderDecoderModel]]]),MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING_NAMES=new Map([["vit",["ViTForImageClassification",ViTForImageClassification]],["mobilevit",["MobileViTForImageClassification",MobileViTForImageClassification]],["beit",["BeitForImageClassification",BeitForImageClassification]],["deit",["DeiTForImageClassification",DeiTForImageClassification]],["resnet",["ResNetForImageClassification",ResNetForImageClassification]],["swin",["SwinForImageClassification",SwinForImageClassification]]]),MODEL_FOR_OBJECT_DETECTION_MAPPING_NAMES=new Map([["detr",["DetrForObjectDetection",DetrForObjectDetection]],["yolos",["YolosForObjectDetection",YolosForObjectDetection]]]),MODEL_FOR_IMAGE_SEGMENTATION_MAPPING_NAMES=new Map([["detr",["DetrForSegmentation",DetrForSegmentation]]]),MODEL_FOR_MASK_GENERATION_MAPPING_NAMES=new Map([["sam",["SamModel",SamModel]]]),MODEL_FOR_CTC_MAPPING_NAMES=new Map([["wav2vec2",["Wav2Vec2ForCTC",Wav2Vec2ForCTC]],["wavlm",["WavLMForCTC",WavLMForCTC]]]),MODEL_FOR_AUDIO_CLASSIFICATION_MAPPING_NAMES=new Map([["wav2vec2",["Wav2Vec2ForSequenceClassification",Wav2Vec2ForSequenceClassification]],["wavlm",["WavLMForSequenceClassification",WavLMForSequenceClassification]]]),MODEL_CLASS_TYPE_MAPPING=[[MODEL_MAPPING_NAMES_ENCODER_ONLY,MODEL_TYPES.EncoderOnly],[MODEL_MAPPING_NAMES_ENCODER_DECODER,MODEL_TYPES.EncoderDecoder],[MODEL_MAPPING_NAMES_DECODER_ONLY,MODEL_TYPES.DecoderOnly],[MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING_NAMES,MODEL_TYPES.EncoderOnly],[MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING_NAMES,MODEL_TYPES.EncoderOnly],[MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING_NAMES,MODEL_TYPES.Seq2Seq],[MODEL_FOR_SPEECH_SEQ_2_SEQ_MAPPING_NAMES,MODEL_TYPES.Seq2Seq],[MODEL_WITH_LM_HEAD_MAPPING_NAMES,MODEL_TYPES.DecoderOnly],[MODEL_FOR_MASKED_LM_MAPPING_NAMES,MODEL_TYPES.EncoderOnly],[MODEL_FOR_QUESTION_ANSWERING_MAPPING_NAMES,MODEL_TYPES.EncoderOnly],[MODEL_FOR_VISION_2_SEQ_MAPPING_NAMES,MODEL_TYPES.Vision2Seq],[MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING_NAMES,MODEL_TYPES.EncoderOnly],[MODEL_FOR_IMAGE_SEGMENTATION_MAPPING_NAMES,MODEL_TYPES.EncoderOnly],[MODEL_FOR_OBJECT_DETECTION_MAPPING_NAMES,MODEL_TYPES.EncoderOnly],[MODEL_FOR_MASK_GENERATION_MAPPING_NAMES,MODEL_TYPES.EncoderOnly],[MODEL_FOR_CTC_MAPPING_NAMES,MODEL_TYPES.EncoderOnly],[MODEL_FOR_AUDIO_CLASSIFICATION_MAPPING_NAMES,MODEL_TYPES.EncoderOnly],[MODEL_FOR_TEXT_TO_SPECTROGRAM_MAPPING_NAMES,MODEL_TYPES.Seq2Seq]];for(const[_,n]of MODEL_CLASS_TYPE_MAPPING)for(const[o,u]of _.values())MODEL_TYPE_MAPPING.set(o,n),MODEL_CLASS_TO_NAME_MAPPING.set(u,o),MODEL_NAME_TO_CLASS_MAPPING.set(o,u);const CUSTOM_MAPPING=[["CLIPTextModelWithProjection",CLIPTextModelWithProjection,MODEL_TYPES.EncoderOnly],["CLIPVisionModelWithProjection",CLIPVisionModelWithProjection,MODEL_TYPES.EncoderOnly]];for(const[_,n,o]of CUSTOM_MAPPING)MODEL_TYPE_MAPPING.set(_,o),MODEL_CLASS_TO_NAME_MAPPING.set(n,_),MODEL_NAME_TO_CLASS_MAPPING.set(_,n);class AutoModel extends PretrainedMixin{}le(AutoModel,"MODEL_CLASS_MAPPINGS",[MODEL_MAPPING_NAMES_ENCODER_ONLY,MODEL_MAPPING_NAMES_ENCODER_DECODER,MODEL_MAPPING_NAMES_DECODER_ONLY]),le(AutoModel,"BASE_IF_FAIL",!0);class AutoModelForSequenceClassification extends PretrainedMixin{}le(AutoModelForSequenceClassification,"MODEL_CLASS_MAPPINGS",[MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING_NAMES]);class AutoModelForTokenClassification extends PretrainedMixin{}le(AutoModelForTokenClassification,"MODEL_CLASS_MAPPINGS",[MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING_NAMES]);class AutoModelForSeq2SeqLM extends PretrainedMixin{}le(AutoModelForSeq2SeqLM,"MODEL_CLASS_MAPPINGS",[MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING_NAMES]);class AutoModelForSpeechSeq2Seq extends PretrainedMixin{}le(AutoModelForSpeechSeq2Seq,"MODEL_CLASS_MAPPINGS",[MODEL_FOR_SPEECH_SEQ_2_SEQ_MAPPING_NAMES]);class AutoModelForTextToSpectrogram extends PretrainedMixin{}le(AutoModelForTextToSpectrogram,"MODEL_CLASS_MAPPINGS",[MODEL_FOR_TEXT_TO_SPECTROGRAM_MAPPING_NAMES]);class AutoModelForCausalLM extends PretrainedMixin{}le(AutoModelForCausalLM,"MODEL_CLASS_MAPPINGS",[MODEL_WITH_LM_HEAD_MAPPING_NAMES]);class AutoModelForMaskedLM extends PretrainedMixin{}le(AutoModelForMaskedLM,"MODEL_CLASS_MAPPINGS",[MODEL_FOR_MASKED_LM_MAPPING_NAMES]);class AutoModelForQuestionAnswering extends PretrainedMixin{}le(AutoModelForQuestionAnswering,"MODEL_CLASS_MAPPINGS",[MODEL_FOR_QUESTION_ANSWERING_MAPPING_NAMES]);class AutoModelForVision2Seq extends PretrainedMixin{}le(AutoModelForVision2Seq,"MODEL_CLASS_MAPPINGS",[MODEL_FOR_VISION_2_SEQ_MAPPING_NAMES]);class AutoModelForImageClassification extends PretrainedMixin{}le(AutoModelForImageClassification,"MODEL_CLASS_MAPPINGS",[MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING_NAMES]);class AutoModelForImageSegmentation extends PretrainedMixin{}le(AutoModelForImageSegmentation,"MODEL_CLASS_MAPPINGS",[MODEL_FOR_IMAGE_SEGMENTATION_MAPPING_NAMES]);class AutoModelForObjectDetection extends PretrainedMixin{}le(AutoModelForObjectDetection,"MODEL_CLASS_MAPPINGS",[MODEL_FOR_OBJECT_DETECTION_MAPPING_NAMES]);class AutoModelForCTC extends PretrainedMixin{}le(AutoModelForCTC,"MODEL_CLASS_MAPPINGS",[MODEL_FOR_CTC_MAPPING_NAMES]);class AutoModelForAudioClassification extends PretrainedMixin{}le(AutoModelForAudioClassification,"MODEL_CLASS_MAPPINGS",[MODEL_FOR_AUDIO_CLASSIFICATION_MAPPING_NAMES]);class AutoModelForDocumentQuestionAnswering extends PretrainedMixin{}le(AutoModelForDocumentQuestionAnswering,"MODEL_CLASS_MAPPINGS",[MODEL_FOR_DOCUMENT_QUESTION_ANSWERING_MAPPING_NAMES]);class Seq2SeqLMOutput extends ModelOutput{constructor({logits:n,past_key_values:o,encoder_outputs:u,decoder_attentions:l=null,cross_attentions:d=null}){super(),this.logits=n,this.past_key_values=o,this.encoder_outputs=u,this.decoder_attentions=l,this.cross_attentions=d}}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:o}){super(),this.start_logits=n,this.end_logits=o}}class CausalLMOutput extends ModelOutput{constructor({logits:n}){super(),this.logits=n}}const BROWSER_ENV=typeof self<"u";let createCanvasFunction,ImageDataClass,loadImageFunction;if(BROWSER_ENV)createCanvasFunction=(_,n)=>{if(!self.OffscreenCanvas)throw new Error("OffscreenCanvas not supported by this browser.");return new self.OffscreenCanvas(_,n)},loadImageFunction=self.createImageBitmap,ImageDataClass=self.ImageData;else if(sharp)loadImageFunction=async _=>{const o=(await _.metadata()).channels;let{data:u,info:l}=await _.raw().toBuffer({resolveWithObject:!0});const d=new RawImage(new Uint8ClampedArray(u),l.width,l.height,l.channels);return o!==void 0&&o!==l.channels&&d.convert(o),d};else throw new Error("Unable to load image processing library.");const RESAMPLING_MAPPING={0:"nearest",1:"lanczos",2:"bilinear",3:"bicubic",4:"box",5:"hamming"};class RawImage{constructor(n,o,u,l){le(this,"_CONTENT_TYPE_MAP",{png:"image/png",jpg:"image/jpeg",jpeg:"image/jpeg",gif:"image/gif"});this.data=n,this.width=o,this.height=u,this.channels=l}static async read(n){if(n instanceof RawImage)return n;if(isString(n)||n instanceof URL)return await this.fromURL(n);throw new Error(`Unsupported input type: ${typeof n}`)}static async fromURL(n){let o=await getFile(n);if(o.status!==200)throw new Error(`Unable to read image from "${n}" (${o.status} ${o.statusText})`);let u=await o.blob();return this.fromBlob(u)}static async fromBlob(n){if(BROWSER_ENV){let o=await loadImageFunction(n);const u=createCanvasFunction(o.width,o.height).getContext("2d");return u.drawImage(o,0,0),new this(u.getImageData(0,0,o.width,o.height).data,o.width,o.height,4)}else{let o=sharp(await n.arrayBuffer());return await loadImageFunction(o)}}grayscale(){if(this.channels===1)return this;let n=new Uint8ClampedArray(this.width*this.height*1);switch(this.channels){case 3:case 4:for(let o=0,u=0;o<this.data.length;o+=this.channels){const l=this.data[o],d=this.data[o+1],s=this.data[o+2];n[u++]=Math.round(.2989*l+.587*d+.114*s)}break;default:throw new Error(`Conversion failed due to unsupported number of channels: ${this.channels}`)}return this._update(n,this.width,this.height,1)}rgb(){if(this.channels===3)return this;let n=new Uint8ClampedArray(this.width*this.height*3);switch(this.channels){case 1:for(let o=0,u=0;o<this.data.length;++o)n[u++]=this.data[o],n[u++]=this.data[o],n[u++]=this.data[o];break;case 4:for(let o=0,u=0;o<this.data.length;o+=4)n[u++]=this.data[o],n[u++]=this.data[o+1],n[u++]=this.data[o+2];break;default:throw new Error(`Conversion failed due to unsupported number of channels: ${this.channels}`)}return this._update(n,this.width,this.height,3)}rgba(){if(this.channels===4)return this;let n=new Uint8ClampedArray(this.width*this.height*4);switch(this.channels){case 1:for(let o=0,u=0;o<this.data.length;++o)n[u++]=this.data[o],n[u++]=this.data[o],n[u++]=this.data[o],n[u++]=255;break;case 3:for(let o=0,u=0;o<this.data.length;o+=3)n[u++]=this.data[o],n[u++]=this.data[o+1],n[u++]=this.data[o+2],n[u++]=255;break;default:throw new Error(`Conversion failed due to unsupported number of channels: ${this.channels}`)}return this._update(n,this.width,this.height,4)}async resize(n,o,{resample:u=2}={}){let l=RESAMPLING_MAPPING[u]??u;if(BROWSER_ENV){let d=this.channels,s=this.toCanvas();const p=createCanvasFunction(n,o).getContext("2d");return p.drawImage(s,0,0,n,o),new RawImage(p.getImageData(0,0,n,o).data,n,o,4).convert(d)}else{let d=this.toSharp();switch(l){case"box":case"hamming":(l==="box"||l==="hamming")&&(console.warn(`Resampling method ${l} is not yet supported. Using bilinear instead.`),l="bilinear");case"nearest":case"bilinear":case"bicubic":d=d.affine([n/this.width,0,0,o/this.height],{interpolator:l});break;case"lanczos":d=d.resize({width:n,height:o,fit:"fill",kernel:"lanczos3"});break;default:throw new Error(`Resampling method ${l} is not supported.`)}return await loadImageFunction(d)}}async pad([n,o,u,l]){if(n=Math.max(n,0),o=Math.max(o,0),u=Math.max(u,0),l=Math.max(l,0),n===0&&o===0&&u===0&&l===0)return this;if(BROWSER_ENV){let d=this.channels,s=this.toCanvas(),p=this.width+n+o,f=this.height+u+l;const c=createCanvasFunction(p,f).getContext("2d");return c.drawImage(s,0,0,this.width,this.height,n,u,p,f),new RawImage(c.getImageData(0,0,p,f).data,p,f,4).convert(d)}else{let d=this.toSharp().extend({left:n,right:o,top:u,bottom:l});return await loadImageFunction(d)}}async center_crop(n,o){if(this.width===n&&this.height===o)return this;let u=(this.width-n)/2,l=(this.height-o)/2;if(BROWSER_ENV){let d=this.channels,s=this.toCanvas();const p=createCanvasFunction(n,o).getContext("2d");let f=0,c=0,a=0,t=0;return u>=0?f=u:a=-u,l>=0?c=l:t=-l,p.drawImage(s,f,c,n,o,a,t,n,o),new RawImage(p.getImageData(0,0,n,o).data,n,o,4).convert(d)}else{let d=this.toSharp();if(u>=0&&l>=0)d=d.extract({left:Math.floor(u),top:Math.floor(l),width:n,height:o});else if(u<=0&&l<=0){let s=Math.floor(-l),p=Math.floor(-u);d=d.extend({top:s,left:p,right:n-this.width-p,bottom:o-this.height-s})}else{let s=[0,0],p=0;l<0?(s[0]=Math.floor(-l),s[1]=o-this.height-s[0]):p=Math.floor(l);let f=[0,0],c=0;u<0?(f[0]=Math.floor(-u),f[1]=n-this.width-f[0]):c=Math.floor(u),d=d.extend({top:s[0],bottom:s[1],left:f[0],right:f[1]}).extract({left:c,top:p,width:n,height:o})}return await loadImageFunction(d)}}toCanvas(){if(!BROWSER_ENV)throw new Error("toCanvas() is only supported in browser environments.");let n=this.clone().rgba(),o=createCanvasFunction(n.width,n.height),u=new ImageDataClass(n.data,n.width,n.height);return o.getContext("2d").putImageData(u,0,0),o}_update(n,o,u,l=null){return this.data=n,this.width=o,this.height=u,l!==null&&(this.channels=l),this}clone(){return new RawImage(this.data.slice(),this.width,this.height,this.channels)}convert(n){if(this.channels===n)return this;switch(n){case 1:this.grayscale();break;case 3:this.rgb();break;case 4:this.rgba();break;default:throw new Error(`Conversion failed due to unsupported number of channels: ${this.channels}`)}return this}save(n){if(BROWSER_ENV){const o=n.split(".").pop().toLowerCase(),u=this._CONTENT_TYPE_MAP[o]??"image/png",d=this.toCanvas().toDataURL(u),s=document.createElement("a");s.href=d,s.download=n,s.click(),s.remove()}else if(env.useFS)this.toSharp().toFile(n);else throw new Error("Unable to save the image because filesystem is disabled in this environment.")}toSharp(){if(BROWSER_ENV)throw new Error("toSharp() is only supported in server-side environments.");return sharp(this.data,{raw:{width:this.width,height:this.height,channels:this.channels}})}}async function read_audio(_,n){if(typeof AudioContext>"u")throw Error("Unable to load audio from path/URL since `AudioContext` is not available in your environment. Instead, audio data should be passed directly to the pipeline/processor. For more information and some example code, see https://huggingface.co/docs/transformers.js/guides/node-audio-processing.");const o=await(await getFile(_)).arrayBuffer(),u=new AudioContext({sampleRate:n});typeof n>"u"&&console.warn(`No sampling rate provided, using default of ${u.sampleRate}Hz.`);const l=await u.decodeAudioData(o);let d;if(l.numberOfChannels===2){const s=Math.sqrt(2);let p=l.getChannelData(0),f=l.getChannelData(1);d=new Float32Array(p.length);for(let c=0;c<l.length;++c)d[c]=s*(p[c]+f[c])/2}else d=l.getChannelData(0);return d}function getMelFilters(_,n,o=128){o=Math.floor(o);const u=Math.floor(1+n/2),l=new Array(o),d=rfftfreq(n,1/_),s=0,c=(45.245640471924965-s)/(o+1),a=0,t=200/3,e=new Array(o+2),r=1e3,i=(r-a)/t,h=Math.log(6.4)/27,g=new Array(e.length);for(let b=0;b<e.length;++b){const y=b*c+s;y>=i?e[b]=r*Math.exp(h*(y-i)):e[b]=a+t*y,g[b]=d.map(w=>e[b]-w)}const m=e.slice(1).map((b,y)=>1/(b-e[y]));for(let b=0;b<l.length;++b){l[b]=new Array(u);const y=m[b],w=m[b+1],T=g[b],S=g[b+2],A=2/(e[b+2]-e[b]);for(let O=0;O<l[b].length;++O){const v=-T[O]*y,M=S[O]*w;l[b][O]=Math.max(0,Math.min(v,M))*A}}return l}function center_to_corners_format([_,n,o,u]){return[_-o/2,n-u/2,_+o/2,n+u/2]}function post_process_object_detection(_,n=.5,o=null){const u=_.logits,l=_.pred_boxes,[d,s,p]=u.dims;if(o!==null&&o.length!==d)throw Error("Make sure that you pass in as many target sizes as the batch dimension of the logits");let f=[];for(let c=0;c<d;++c){let a=o!==null?o[c]:null,t={boxes:[],classes:[],scores:[]},e=u[c],r=l[c];for(let i=0;i<s;++i){let h=e[i],g=max(h.data)[1];if(g===p-1)continue;let b=softmax(h.data)[g];if(b>n){let y=r[i].data;y=center_to_corners_format(y),a!==null&&(y=y.map((w,T)=>w*a[(T+1)%2])),t.boxes.push(y),t.classes.push(g),t.scores.push(b)}}f.push(t)}return f}class FeatureExtractor extends Callable{constructor(n){super(),this.config=n}}class ImageFeatureExtractor extends FeatureExtractor{constructor(n){super(n),this.image_mean=this.config.image_mean,this.image_std=this.config.image_std,this.resample=this.config.resample??2,this.do_rescale=this.config.do_rescale??!0,this.rescale_factor=this.config.rescale_factor??1/255,this.do_normalize=this.config.do_normalize,this.do_resize=this.config.do_resize,this.do_thumbnail=this.config.do_thumbnail,this.size=this.config.size,this.do_center_crop=this.config.do_center_crop,this.crop_size=this.config.crop_size,this.do_convert_rgb=this.config.do_convert_rgb??!0,this.pad_size=this.config.pad_size,this.do_pad=this.config.do_pad,this.do_pad&&!this.pad_size&&this.size.width!==void 0&&this.size.height!==void 0&&(this.pad_size=this.size)}async thumbnail(n,o,u=2){const l=n.height,d=n.width,s=o.height,p=o.width;let f=Math.min(l,s),c=Math.min(d,p);return f===l&&c===d?n:(l>d?c=Math.floor(d*f/l):d>l&&(f=Math.floor(l*c/d)),await n.resize(c,f,{resample:u}))}async preprocess(n){this.do_convert_rgb&&(n=n.rgb());const o=n.width,u=n.height;if(this.do_resize){let c,a;if(this.do_thumbnail){const{height:t,width:e}=this.size;c=Math.min(t,e)}else Number.isInteger(this.size)?(c=this.size,a=this.config.max_size??c):(c=this.size.shortest_edge,a=this.size.longest_edge);if(c!==void 0||a!==void 0){const t=c===void 0?1:Math.max(c/o,c/u),e=o*t,r=u*t,i=a===void 0?1:Math.min(a/e,a/r),h=Math.floor(Number((e*i).toFixed(2))),g=Math.floor(Number((r*i).toFixed(2)));n=await n.resize(h,g,{resample:this.resample})}else if(this.size.width!==void 0&&this.size.height!==void 0)n=await n.resize(this.size.width,this.size.height,{resample:this.resample});else throw new Error(`Could not resize image due to unsupported \`this.size\` option in config: ${JSON.stringify(this.size)}`)}if(this.do_thumbnail&&(n=await this.thumbnail(n,this.size,this.resample)),this.do_center_crop){let c,a;Number.isInteger(this.crop_size)?(c=this.crop_size,a=this.crop_size):(c=this.crop_size.width,a=this.crop_size.height),n=await n.center_crop(c,a)}let l=[n.height,n.width];if(this.do_pad&&this.pad_size){let c=0,a=this.pad_size.width-n.width,t=0,e=this.pad_size.height-n.height;n=await n.pad([c,a,t,e])}const d=Float32Array.from(n.data);if(this.do_rescale)for(let c=0;c<d.length;++c)d[c]=this.rescale_factor*d[c];if(this.do_normalize){let c=this.image_mean;Array.isArray(this.image_mean)||(c=new Array(n.channels).fill(c));let a=this.image_std;if(Array.isArray(this.image_std)||(a=new Array(n.channels).fill(c)),c.length!==n.channels||a.length!==n.channels)throw new Error(`When set to arrays, the length of \`image_mean\` (${c.length}) and \`image_std\` (${a.length}) must match the number of channels in the image (${n.channels}).`);for(let t=0;t<d.length;t+=n.channels)for(let e=0;e<n.channels;++e)d[t+e]=(d[t+e]-this.image_mean[e])/this.image_std[e]}let s=[n.height,n.width,n.channels],p=new Tensor("float32",d,s),f=transpose(p,[2,0,1]);return{original_size:[u,o],reshaped_input_size:l,pixel_values:f}}async _call(n,...o){Array.isArray(n)||(n=[n]);const u=await Promise.all(n.map(d=>this.preprocess(d)));return u.forEach(d=>d.pixel_values.dims=[1,...d.pixel_values.dims]),{pixel_values:cat(u.map(d=>d.pixel_values)),original_sizes:u.map(d=>d.original_size),reshaped_input_sizes:u.map(d=>d.reshaped_input_size)}}}class ConvNextFeatureExtractor extends ImageFeatureExtractor{}class ViTFeatureExtractor extends ImageFeatureExtractor{}class MobileViTFeatureExtractor extends ImageFeatureExtractor{}class DeiTFeatureExtractor extends ImageFeatureExtractor{}class BeitFeatureExtractor extends ImageFeatureExtractor{}class DonutFeatureExtractor extends ImageFeatureExtractor{}class DetrFeatureExtractor extends ImageFeatureExtractor{async _call(n){const o=await super._call(n),u=[o.pixel_values.dims[0],64,64],l=new Tensor("int64",new BigInt64Array(u.reduce((d,s)=>d*s)).fill(1n),u);return{...o,pixel_mask:l}}post_process_object_detection(...n){return post_process_object_detection(...n)}remove_low_and_no_objects(n,o,u,l){let d=[],s=[],p=[];for(let f=0;f<n.dims[0];++f){let c=n[f],a=o[f],t=max(c.data)[1];if(t===l)continue;let r=softmax(c.data)[t];r>u&&(d.push(a),s.push(r),p.push(t))}return[d,s,p]}check_segment_validity(n,o,u,l=.5,d=.8){let s=[],p=0,f=0;for(let a=0;a<n.length;++a)n[a]===u&&(s.push(a),++p),o[u].data[a]>=l&&++f;let c=p>0&&f>0;return c&&(c=p/f>d),[c,s]}compute_segments(n,o,u,l,d,s=null,p=null){let[f,c]=p??n[0].dims,a=new Tensor("int32",new Int32Array(f*c),[f,c]),t=[];if(p!==null)for(let h=0;h<n.length;++h)n[h]=interpolate(n[h],p,"bilinear",!1);let e=new Int32Array(n[0].data.length),r=new Float32Array(n[0].data.length);for(let h=0;h<n.length;++h){let g=o[h];for(let m=0;m<n[h].data.length;++m)n[h].data[m]*=g,n[h].data[m]>r[m]&&(e[m]=h,r[m]=n[h].data[m])}let i=0;for(let h=0;h<u.length;++h){let g=u[h],[m,b]=this.check_segment_validity(e,n,h,l,d);if(m){++i;for(let y of b)a.data[y]=i;t.push({id:i,label_id:g,score:o[h]})}}return[a,t]}post_process_panoptic_segmentation(n,o=.5,u=.5,l=.8,d=null,s=null){d===null&&(console.warn("`label_ids_to_fuse` unset. No instance will be fused."),d=new Set);const p=n.logits,c=n.pred_masks.sigmoid();let[a,t,e]=p.dims;if(e-=1,s!==null&&s.length!==a)throw Error("Make sure that you pass in as many target sizes as the batch dimension of the logits");let r=[];for(let i=0;i<a;++i){let h=s!==null?s[i]:null,g=p[i],m=c[i],[b,y,w]=this.remove_low_and_no_objects(g,m,o,e);if(w.length===0){let[A,O]=h??m.dims.slice(-2),v=new Tensor("int32",new Int32Array(A*O).fill(-1),[A,O]);r.push({segmentation:v,segments_info:[]});continue}let[T,S]=this.compute_segments(b,y,w,u,l,d,h);r.push({segmentation:T,segments_info:S})}return r}post_process_instance_segmentation(){throw Error("Not implemented yet")}}class YolosFeatureExtractor extends ImageFeatureExtractor{post_process_object_detection(...n){return post_process_object_detection(...n)}}class SamImageProcessor extends ImageFeatureExtractor{async _call(n,o){let{pixel_values:u,original_sizes:l,reshaped_input_sizes:d}=await super._call(n),s=calculateDimensions(o);if(s.length===3)s=[1,...s],o=[o];else if(s.length!==4)throw Error("The input_points must be a 4D tensor of shape `batch_size`, `point_batch_size`, `nb_points_per_image`, `2`.");for(let f=0;f<o.length;++f){let c=l[f],a=d[f],t=[a[0]/c[0],a[1]/c[1]];for(let e=0;e<o[f].length;++e)for(let r=0;r<o[f][e].length;++r)for(let i=0;i<o[f][e][r].length;++i)o[f][e][r][i]*=t[i]}let p=new Tensor("int64",BigInt64Array.from(o.flat(1/0).map(f=>BigInt(Math.round(f)))),s);return{pixel_values:u,original_sizes:l,reshaped_input_sizes:d,input_points:p}}post_process_masks(n,o,u,{mask_threshold:l=0,binarize:d=!0,pad_size:s=null}={}){let p=[];s=s??this.pad_size;let f=[s.height,s.width];for(let c=0;c<o.length;++c){let a=o[c],t=u[c],e=n[c],r=[];for(let h=0;h<e.dims[0];++h){let g=e[h],m=interpolate(g,f,"bilinear",!1);m=m.slice(null,[0,t[0]],[0,t[1]]),m=interpolate(e,a,"bilinear",!1),d&&(m=new Tensor("bool",Array.from(m.data).map(b=>b>l),m.dims)),m.dims=[1,...m.dims],r.push(m)}let i=cat(r);p.push(i)}return p}}class WhisperFeatureExtractor extends FeatureExtractor{constructor(n){var o;super(n),(o=this.config).mel_filters??(o.mel_filters=getMelFilters(this.config.sampling_rate,this.config.n_fft,this.config.feature_size))}calcOffset(n,o){return Math.abs((n+o)%(2*o)-o)}padReflect(n,o,u){const l=new Float32Array(n.length+o+u),d=n.length-1;for(let s=0;s<n.length;++s)l[o+s]=n[s];for(let s=1;s<=o;++s)l[o-s]=n[this.calcOffset(s,d)];for(let s=1;s<=u;++s)l[d+o+s]=n[this.calcOffset(d-s,d)];return l}stft(n,o){const u=this.config.n_fft,l=2*(u-1),d=2*(2*u-1),s=2**Math.ceil(Math.log2(d)),p=u+2,f=new Float32Array(p*n.length),c=new Float32Array(d),a=new Float32Array(s),t=new Float32Array(s),e=new Float32Array(s),r=new Float32Array(s),i=new Float32Array(s),h=new Float32Array(s),g=-2*Math.PI/u,m=Math.cos(g),b=Math.sin(g);for(let T=0;T<d>>1;++T){const S=(T+1-u)**2/2,A=Math.sqrt(m**2+b**2)**S,O=S*Math.atan2(b,m);let v=2*T;c[v]=A*Math.cos(O),c[v+1]=A*Math.sin(O),a[v]=c[v],a[v+1]=-c[v+1]}const y=c.subarray(l,d),w=new FFT(s>>1);w.transform(r,a);for(let T=0;T<n.length;++T){const S=n[T];for(let O=0;O<y.length;O+=2){const v=O+1,M=O>>1,F=S[M]*o[M];t[O]=F*y[O],t[v]=F*y[v]}w.transform(i,t);for(let O=0;O<r.length;O+=2){const v=O+1;e[O]=i[O]*r[O]-i[v]*r[v],e[v]=i[O]*r[v]+i[v]*r[O]}w.inverseTransform(h,e);const A=T*p;for(let O=0;O<p;O+=2){const v=h[O+l],M=h[O+l+1],F=y[O],R=y[O+1],$=A+O;f[$]=v*F-M*R,f[$+1]=v*R+M*F}}return{data:f,dims:[n.length,p]}}fram_wave(n,o=!0){const u=[],l=Math.floor((this.config.n_fft-1)/2)+1,d=n.length;for(let s=0;s<d+1;s+=this.config.hop_length){let p;if(o){let f=s>l?s-l:0,c=s<d-l?s+l:d;p=n.subarray(f,c),f===0?p=this.padReflect(p,-s+l,0):c===d&&(p=this.padReflect(p,0,s-d+l))}else{p=new Float32Array(this.config.n_fft);const f=n.subarray(s,s+this.config.n_fft);f.length<this.config.n_fft?(p.set(f),p.fill(0,f.length,this.config.n_fft)):p=f}u.push(p)}return u}hanning(n){if(n<1)return[];if(n===1)return[1];const o=n-1,u=new Float32Array(o);for(let l=0;l<o;++l){const d=2*l-n+1;u[l]=.5+.5*Math.cos(Math.PI*d/o)}return u}_extract_fbank_features(n){const o=new Float32Array(this.config.n_samples);o.set(n);const u=this.hanning(this.config.n_fft+1),l=this.fram_wave(o),d=this.stft(l,u),s=d.data,p=d.dims[0]-1,f=d.dims[1]>>1,c=new Float32Array(p*f);for(let m=0;m<p;++m)for(let b=0;b<f;++b){let y=m*f+b,w=y<<1,T=s[w]**2+s[w+1]**2;c[y]=T}const a=this.config.mel_filters,t=a.length,e=new Float32Array(t*p);let r=0;for(let m=0;m<t;++m){const b=a[m];for(let y=0;y<p;++y){let w=0;for(let T=0;T<f;++T)w+=b[T]*c[y*f+T];e[r++]=w}}const i=1e-10,h=new Float32Array(e.length);let g=0;for(let m=0;m<e.length;++m){const b=Math.max(i,e[m]),y=Math.log10(b);h[m]=y,g=Math.max(y,g)}for(let m=0;m<h.length;++m)h[m]=Math.max(h[m],g-8),h[m]=(h[m]+4)/4;return{data:h,dims:[t,p]}}async _call(n){var l;if(!(n instanceof Float32Array||n instanceof Float64Array))throw new Error(`WhisperFeatureExtractor expects input to be a Float32Array or a Float64Array, but got ${((l=n==null?void 0:n.constructor)==null?void 0:l.name)??typeof n} instead.If using the feature extractor directly, remember to use \`read_audio(url, sampling_rate)\` to obtain the raw audio data of the file/url.`);n.length>this.config.n_samples&&console.warn("Attempting to extract features for audio longer than 30 seconds. If using a pipeline to extract transcript from a long audio clip, remember to specify `chunk_length_s` and/or `stride_length_s`.");let o=n.slice(0,this.config.n_samples),u=this._extract_fbank_features(o);return{input_features:new Tensor("float32",u.data,[1,...u.dims])}}}class Wav2Vec2FeatureExtractor extends FeatureExtractor{_zero_mean_unit_var_norm(n){const u=n.reduce((d,s)=>d+s,0)/n.length,l=n.reduce((d,s)=>d+(s-u)**2,0)/n.length;return n.map(d=>(d-u)/Math.sqrt(l+1e-7))}async _call(n){var l;if(!(n instanceof Float32Array||n instanceof Float64Array))throw new Error(`Wav2Vec2FeatureExtractor expects input to be a Float32Array or a Float64Array, but got ${((l=n==null?void 0:n.constructor)==null?void 0:l.name)??typeof n} instead.If using the feature extractor directly, remember to use \`read_audio(url, sampling_rate)\` to obtain the raw audio data of the file/url.`);n instanceof Float64Array&&(n=new Float32Array(n));let o=n;this.config.do_normalize&&(o=this._zero_mean_unit_var_norm(o));const u=[1,o.length];return{input_values:new Tensor("float32",o,u),attention_mask:new Tensor("int64",new BigInt64Array(o.length).fill(1n),u)}}}class SpeechT5FeatureExtractor extends FeatureExtractor{}class Processor extends Callable{constructor(n){super(),this.feature_extractor=n}async _call(n,...o){return await this.feature_extractor(n)}}class SamProcessor extends Processor{async _call(n,o){return await this.feature_extractor(n,o)}post_process_masks(...n){return this.feature_extractor.post_process_masks(...n)}}class WhisperProcessor extends Processor{async _call(n){return await this.feature_extractor(n)}}class Wav2Vec2ProcessorWithLM extends Processor{async _call(n){return await this.feature_extractor(n)}}class SpeechT5Processor extends Processor{async _call(n){return await this.feature_extractor(n)}}class AutoProcessor{static async from_pretrained(n,{progress_callback:o=null,config:u=null,cache_dir:l=null,local_files_only:d=!1,revision:s="main"}={}){let p=u??await getModelJSON(n,"preprocessor_config.json",!0,{progress_callback:o,config:u,cache_dir:l,local_files_only:d,revision:s}),f=p.feature_extractor_type??p.image_processor_type,c=this.FEATURE_EXTRACTOR_CLASS_MAPPING[f];if(!c)if(p.size!==void 0)console.warn(`Feature extractor type "${f}" not found, assuming ImageFeatureExtractor due to size parameter in config.`),c=ImageFeatureExtractor;else throw new Error(`Unknown Feature Extractor type: ${f}`);let a=this.PROCESSOR_CLASS_MAPPING[p.processor_class]??Processor,t=new c(p);return new a(t)}}le(AutoProcessor,"FEATURE_EXTRACTOR_CLASS_MAPPING",{WhisperFeatureExtractor,ViTFeatureExtractor,MobileViTFeatureExtractor,ConvNextFeatureExtractor,BeitFeatureExtractor,DeiTFeatureExtractor,DetrFeatureExtractor,YolosFeatureExtractor,DonutFeatureExtractor,SamImageProcessor,Wav2Vec2FeatureExtractor,SpeechT5FeatureExtractor}),le(AutoProcessor,"PROCESSOR_CLASS_MAPPING",{WhisperProcessor,Wav2Vec2ProcessorWithLM,SamProcessor,SpeechT5Processor});async function prepareImages(_){return Array.isArray(_)||(_=[_]),_=await Promise.all(_.map(n=>RawImage.read(n))),_}class Pipeline extends Callable{constructor({task:n,model:o,tokenizer:u=null,processor:l=null}){super(),this.task=n,this.model=o,this.tokenizer=u,this.processor=l}async dispose(){await this.model.dispose()}async _call(n,...o){let u=this.tokenizer(n,{padding:!0,truncation:!0}),l=await this.model(u);return[u,l]}}class TextClassificationPipeline extends Pipeline{async _call(n,{topk:o=1}={}){let u=this.model.config.problem_type==="multi_label_classification"?f=>f.sigmoid().data:f=>softmax(f.data),[l,d]=await super._call(n),s=this.model.config.id2label,p=[];for(let f of d.logits){let c=u(f),t=getTopItems(c,o).map(function(e){return{label:s[e[0]],score:e[1]}});o===1?p.push(...t):p.push(t)}return Array.isArray(n)||o===1?p:p[0]}}class TokenClassificationPipeline extends Pipeline{async _call(n,{ignore_labels:o=["O"]}={}){let u=Array.isArray(n);u||(n=[n]);let l=this.tokenizer,[d,s]=await super._call(n),p=s.logits,f=this.model.config.id2label,c=[];for(let a=0;a<p.dims[0];++a){let t=d.input_ids[a],e=p[a],r=[];for(let i=0;i<e.dims[0];++i){let h=e[i],g=max(h.data)[1],m=f[g];if(o.includes(m))continue;let b=l.decode([t[i].item()],{skip_special_tokens:!0});if(b==="")continue;let y=softmax(h.data);r.push({entity:m,score:y[g],index:i,word:b,start:null,end:null})}c.push(r)}return u?c:c[0]}}class QuestionAnsweringPipeline extends Pipeline{async _call(n,o,{topk:u=1}={}){let l=this.tokenizer(n,{text_pair:o,padding:!0,truncation:!0}),d=await this.model(l),s=[];for(let p=0;p<d.start_logits.dims[0];++p){let f=l.input_ids[p],c=f.indexOf(this.tokenizer.sep_token_id),a=Array.from(softmax(d.start_logits[p].data)).map((r,i)=>[r,i]).filter(r=>r[1]>c),t=Array.from(softmax(d.end_logits[p].data)).map((r,i)=>[r,i]).filter(r=>r[1]>c),e=product(a,t).filter(r=>r[0][1]<=r[1][1]).map(r=>[r[0][1],r[1][1],r[0][0]*r[1][0]]).sort((r,i)=>i[2]-r[2]);for(let r=0;r<Math.min(e.length,u);++r){let[i,h,g]=e[r],m=[...f].slice(i,h+1),b=this.tokenizer.decode(m,{skip_special_tokens:!0});s.push({answer:b,score:g})}}return u===1?s[0]:s}}class FillMaskPipeline extends Pipeline{async _call(n,{topk:o=5}={}){let[u,l]=await super._call(n),d=this.tokenizer,s=[];for(let p=0;p<u.input_ids.dims[0];++p){let f=u.input_ids[p],c=f.indexOf(this.tokenizer.mask_token_id);if(c===-1)throw Error(`Mask token (${d.mask_token}) not found in text.`);let t=l.logits[p][c],e=getTopItems(softmax(t.data),o);s.push(e.map(r=>{let i=[...f];return i[c]=r[0],{score:r[1],token:r[0],token_str:d.model.vocab[r[0]],sequence:d.decode(i,{skip_special_tokens:!0})}}))}return Array.isArray(n)?s:s[0]}}class Text2TextGenerationPipeline extends Pipeline{constructor(){super(...arguments);le(this,"_key",null)}async _call(o,u={}){Array.isArray(o)||(o=[o]),this.model.config.prefix&&(o=o.map(c=>this.model.config.prefix+c));let l=this.model.config.task_specific_params;l&&l[this.task]&&l[this.task].prefix&&(o=o.map(c=>l[this.task].prefix+c));let d={padding:!0,truncation:!0},s;this instanceof TranslationPipeline&&"_build_translation_inputs"in this.tokenizer?s=this.tokenizer._build_translation_inputs(o,d,u).input_ids:s=this.tokenizer(o,d).input_ids;let p=await this.model.generate(s,u),f=this.tokenizer.batch_decode(p,{skip_special_tokens:!0});return this._key!==null&&(f=f.map(c=>this._key===null?c:{[this._key]:c})),f}}class SummarizationPipeline extends Text2TextGenerationPipeline{constructor(){super(...arguments);le(this,"_key","summary_text")}}class TranslationPipeline extends Text2TextGenerationPipeline{constructor(){super(...arguments);le(this,"_key","translation_text")}}class TextGenerationPipeline extends Pipeline{async _call(n,o={}){let u=typeof n=="string"||n instanceof String;u&&(n=[n]),this.tokenizer.padding_side="left";let l=this.tokenizer(n,{padding:!0,truncation:!0}),d=l.input_ids,s=l.attention_mask,p=await this.model.generate(d,o,null,{inputs_attention_mask:s});const f=this.tokenizer.batch_decode(p,{skip_special_tokens:!0}),c=Array.from({length:n.length},a=>[]);for(let a=0;a<f.length;++a){const t=Math.floor(a/p.length*n.length);c[t].push({generated_text:f[a]})}return u&&c.length===1?c[0]:c}}class ZeroShotClassificationPipeline extends Pipeline{constructor(n){super(n),this.label2id=Object.fromEntries(Object.entries(this.model.config.label2id).map(([o,u])=>[o.toLowerCase(),u])),this.entailment_id=this.label2id.entailment,this.entailment_id===void 0&&(console.warn("Could not find 'entailment' in label2id mapping. Using 2 as entailment_id."),this.entailment_id=2),this.contradiction_id=this.label2id.contradiction??this.label2id.not_entailment,this.contradiction_id===void 0&&(console.warn("Could not find 'contradiction' in label2id mapping. Using 0 as contradiction_id."),this.contradiction_id=0)}async _call(n,o,{hypothesis_template:u="This example is {}.",multi_label:l=!1}={}){let d=Array.isArray(n);d||(n=[n]),Array.isArray(o)||(o=[o]);let s=o.map(c=>u.replace("{}",c)),p=l||o.length===1,f=[];for(let c of n){let a=[];for(let r of s){let i=this.tokenizer(c,{text_pair:r,padding:!0,truncation:!0}),h=await this.model(i);p?a.push([h.logits.data[this.contradiction_id],h.logits.data[this.entailment_id]]):a.push(h.logits.data[this.entailment_id])}let t;p?t=a.map(r=>softmax(r)[1]):t=softmax(a);let e=t.map((r,i)=>[r,i]).sort((r,i)=>i[0]-r[0]);f.push({sequence:c,labels:e.map(r=>o[r[1]]),scores:e.map(r=>r[0])})}return d?f:f[0]}}class FeatureExtractionPipeline extends Pipeline{async _call(n,{pooling:o="none",normalize:u=!1}={}){let[l,d]=await super._call(n),s=d.last_hidden_state??d.logits;if(o!=="none")if(o==="mean")s=mean_pooling(s,l.attention_mask);else throw Error(`Pooling method '${o}' not supported.`);return u&&(s=s.normalize(2,-1)),s}}class AudioClassificationPipeline extends Pipeline{constructor(n){super(n)}async _preprocess(n,o){return isString(n)&&(n=await read_audio(n,o)),n}async _call(n,{topk:o=5}={}){let u=!Array.isArray(n);u&&(n=[n]);const l=this.model.config.id2label,d=this.processor.feature_extractor.config.sampling_rate;let s=[];for(let p of n){p=await this._preprocess(p,d);const f=await this.processor(p),a=(await this.model(f)).logits[0];let e=getTopItems(softmax(a.data),o).map(function(r){return{label:l[r[0]],score:r[1]}});o===1?s.push(...e):s.push(e)}return!u||o===1?s:s[0]}}class AutomaticSpeechRecognitionPipeline extends Pipeline{constructor(n){super(n)}async _preprocess(n,o){return isString(n)&&(n=await read_audio(n,o)),n}async _call(n,o={}){switch(this.model.config.model_type){case"whisper":return this._call_whisper(n,o);case"wav2vec2":return this._call_wav2vec2(n,o);default:throw new Error(`AutomaticSpeechRecognitionPipeline does not support model type '${this.model.config.model_type}'.`)}}async _call_wav2vec2(n,o={}){o.language&&console.warn('`language` parameter is not yet supported for `wav2vec2` models, defaulting to "English".'),o.task&&console.warn('`task` parameter is not yet supported for `wav2vec2` models, defaulting to "transcribe".');let u=!Array.isArray(n);u&&(n=[n]);const l=this.processor.feature_extractor.config.sampling_rate;let d=[];for(let s of n){s=await this._preprocess(s,l);const p=await this.processor(s),c=(await this.model(p)).logits[0],a=[];for(let e of c)a.push(max(e.data)[1]);const t=this.tokenizer.decode(a);d.push({text:t})}return u?d[0]:d}async _call_whisper(n,o={}){let u=o.return_timestamps??!1,l=o.chunk_length_s??0,d=o.stride_length_s??null,s=o.chunk_callback??null,p=o.force_full_sequences??!1;u==="word"&&(o.return_token_timestamps=!0);let f=pop(o,"language",null),c=pop(o,"task",null);if(f||c||u){if(o.forced_decoder_ids)throw new Error("Cannot specify `language`/`task`/`return_timestamps` and `forced_decoder_ids` at the same time.");let h=this.tokenizer.get_decoder_prompt_ids({language:f,task:c,no_timestamps:!u});h.length>0&&(o.forced_decoder_ids=h)}let a=!Array.isArray(n);a&&(n=[n]);const t=this.processor.feature_extractor.config.sampling_rate,e=this.processor.feature_extractor.config.chunk_length/this.model.config.max_source_positions,r=this.processor.feature_extractor.config.hop_length;let i=[];for(let h of n){h=await this._preprocess(h,t);let g=[];if(l>0){if(d===null)d=l/6;else if(l<=d)throw Error("`chunk_length_s` must be larger than `stride_length_s`.");const y=t*l,w=t*d,T=y-2*w;let S=0;for(;S<h.length;){let A=h.subarray(S,S+y),O=await this.processor(A),v=S===0,M=S+T>=h.length;g.push({stride:[A.length,v?0:w,M?0:w],input_features:O.input_features,is_last:M}),S+=T}}else g=[{stride:[h.length,0,0],input_features:(await this.processor(h)).input_features,is_last:!0}];for(let y of g){o.num_frames=Math.floor(y.stride[0]/r);let w=await this.model.generate(y.input_features,o);u==="word"?(y.tokens=w.sequences[0],y.token_timestamps=w.token_timestamps.tolist()[0].map(T=>round(T,2))):y.tokens=w[0],y.stride=y.stride.map(T=>T/t),s!==null&&s(y)}let[m,b]=this.tokenizer._decode_asr(g,{time_precision:e,return_timestamps:u,force_full_sequences:p});i.push({text:m,...b})}return a?i[0]:i}}class ImageToTextPipeline extends Pipeline{constructor(n){super(n)}async _call(n,o={}){let u=Array.isArray(n);n=await prepareImages(n);let{pixel_values:l}=await this.processor(n),d=[];for(let s of l){s.dims=[1,...s.dims];let p=await this.model.generate(s,o),f=this.tokenizer.batch_decode(p,{skip_special_tokens:!0}).map(c=>({generated_text:c.trim()}));d.push(f)}return u?d:d[0]}}class ImageClassificationPipeline extends Pipeline{constructor(n){super(n)}async _call(n,{topk:o=1}={}){let u=Array.isArray(n);n=await prepareImages(n);let{pixel_values:l}=await this.processor(n),d=await this.model({pixel_values:l}),s=this.model.config.id2label,p=[];for(let f of d.logits){let a=getTopItems(softmax(f.data),o).map(function(t){return{label:s[t[0]],score:t[1]}});o===1?p.push(...a):p.push(a)}return u||o===1?p:p[0]}}class ImageSegmentationPipeline extends Pipeline{constructor(n){super(n),this.subtasks_mapping={panoptic:"post_process_panoptic_segmentation",instance:"post_process_instance_segmentation",semantic:"post_process_semantic_segmentation"}}async _call(n,{threshold:o=.5,mask_threshold:u=.5,overlap_mask_area_threshold:l=.8,label_ids_to_fuse:d=null,target_sizes:s=null,subtask:p=null}={}){if(Array.isArray(n)&&n.length!==1)throw Error("Image segmentation pipeline currently only supports a batch size of 1.");n=await prepareImages(n);let c=n.map(h=>[h.height,h.width]),{pixel_values:a,pixel_mask:t}=await this.processor(n),e=await this.model({pixel_values:a,pixel_mask:t}),r=null;if(p!==null)r=this.subtasks_mapping[p];else for(let[h,g]of Object.entries(this.subtasks_mapping))if(g in this.processor.feature_extractor){r=this.processor.feature_extractor[g].bind(this.processor.feature_extractor),p=h;break}let i=[];if(p==="panoptic"||p==="instance"){let h=r(e,o,u,l,d,s??c)[0],g=h.segmentation,m=this.model.config.id2label;for(let b of h.segments_info){let y=new Uint8ClampedArray(g.data.length);for(let T=0;T<g.data.length;++T)g.data[T]===b.id&&(y[T]=255);let w=new RawImage(y,g.dims[1],g.dims[0],1);i.push({score:b.score,label:m[b.label_id],mask:w})}}else throw Error(p==="semantic"?"semantic segmentation not yet supported.":`Subtask ${p} not supported.`);return i}}class ZeroShotImageClassificationPipeline extends Pipeline{constructor(n){super(n)}async _call(n,o,{hypothesis_template:u="This is a photo of {}"}={}){let l=Array.isArray(n);n=await prepareImages(n);let d=o.map(a=>u.replace("{}",a)),s=this.tokenizer(d,{padding:!0,truncation:!0}),{pixel_values:p}=await this.processor(n),f=await this.model({...s,pixel_values:p}),c=[];for(let a of f.logits_per_image){let t=softmax(a.data);c.push([...t].map((e,r)=>({score:e,label:o[r]})))}return l?c:c[0]}}class ObjectDetectionPipeline extends Pipeline{constructor(n){super(n)}async _call(n,{threshold:o=.9,percentage:u=!1}={}){let l=Array.isArray(n);if(l&&n.length!==1)throw Error("Object detection pipeline currently only supports a batch size of 1.");n=await prepareImages(n);let d=u?null:n.map(e=>[e.height,e.width]),{pixel_values:s,pixel_mask:p}=await this.processor(n),f=await this.model({pixel_values:s,pixel_mask:p}),c=this.processor.feature_extractor.post_process_object_detection(f,o,d),a=this.model.config.id2label;const t=c.map(e=>e.boxes.map((r,i)=>({score:e.scores[i],label:a[e.classes[i]],box:this._get_bounding_box(r,!u)})));return l?t:t[0]}_get_bounding_box(n,o){o&&(n=n.map(p=>p|0));const[u,l,d,s]=n;return{xmin:u,ymin:l,xmax:d,ymax:s}}}class DocumentQuestionAnsweringPipeline extends Pipeline{constructor(n){super(n)}async _call(n,o,u={}){n=(await prepareImages(n))[0];const{pixel_values:l}=await this.processor(n),d=`<s_docvqa><s_question>${o}</s_question><s_answer>`,s=this.tokenizer(d,{add_special_tokens:!1,padding:!0,truncation:!0}).input_ids,p=await this.model.generate(l,{...u,decoder_input_ids:s,max_length:this.model.config.decoder.max_position_embeddings}),c=this.tokenizer.batch_decode(p)[0].match(/<s_answer>(.*?)<\/s_answer>/);let a=null;return c&&c.length>=2&&(a=c[1].trim()),[{answer:a}]}}class TextToAudioPipeline extends Pipeline{constructor(o){super(o);le(this,"DEFAULT_VOCODER_ID","Xenova/speecht5_hifigan");this.vocoder=o.vocoder??null}async _call(o,{speaker_embeddings:u=null}={}){if(this.vocoder||(console.log("No vocoder specified, using default HifiGan vocoder."),this.vocoder=await AutoModel.from_pretrained(this.DEFAULT_VOCODER_ID,{quantized:!1})),(typeof u=="string"||u instanceof URL)&&(u=new Float32Array(await(await fetch(u)).arrayBuffer())),u instanceof Float32Array)u=new Tensor("float32",u,[1,u.length]);else if(!(u instanceof Tensor))throw new Error("Speaker embeddings must be a `Tensor`, `Float32Array`, `string`, or `URL`.");const{input_ids:l}=this.tokenizer(o,{padding:!0,truncation:!0}),{waveform:d}=await this.model.generate_speech(l,u,{vocoder:this.vocoder}),s=this.processor.feature_extractor.config.sampling_rate;return{audio:d.data,sampling_rate:s}}}const SUPPORTED_TASKS={"text-classification":{tokenizer:AutoTokenizer,pipeline:TextClassificationPipeline,model:AutoModelForSequenceClassification,default:{model:"Xenova/distilbert-base-uncased-finetuned-sst-2-english"},type:"text"},"token-classification":{tokenizer:AutoTokenizer,pipeline:TokenClassificationPipeline,model:AutoModelForTokenClassification,default:{model:"Xenova/bert-base-multilingual-cased-ner-hrl"},type:"text"},"question-answering":{tokenizer:AutoTokenizer,pipeline:QuestionAnsweringPipeline,model:AutoModelForQuestionAnswering,default:{model:"Xenova/distilbert-base-cased-distilled-squad"},type:"text"},"fill-mask":{tokenizer:AutoTokenizer,pipeline:FillMaskPipeline,model:AutoModelForMaskedLM,default:{model:"Xenova/bert-base-uncased"},type:"text"},summarization:{tokenizer:AutoTokenizer,pipeline:SummarizationPipeline,model:AutoModelForSeq2SeqLM,default:{model:"Xenova/distilbart-cnn-6-6"},type:"text"},translation:{tokenizer:AutoTokenizer,pipeline:TranslationPipeline,model:AutoModelForSeq2SeqLM,default:{model:"Xenova/t5-small"},type:"text"},"text2text-generation":{tokenizer:AutoTokenizer,pipeline:Text2TextGenerationPipeline,model:AutoModelForSeq2SeqLM,default:{model:"Xenova/flan-t5-small"},type:"text"},"text-generation":{tokenizer:AutoTokenizer,pipeline:TextGenerationPipeline,model:AutoModelForCausalLM,default:{model:"Xenova/gpt2"},type:"text"},"zero-shot-classification":{tokenizer:AutoTokenizer,pipeline:ZeroShotClassificationPipeline,model:AutoModelForSequenceClassification,default:{model:"Xenova/distilbert-base-uncased-mnli"},type:"text"},"audio-classification":{pipeline:AudioClassificationPipeline,model:AutoModelForAudioClassification,processor:AutoProcessor,default:{model:"Xenova/wav2vec2-base-superb-ks"},type:"audio"},"automatic-speech-recognition":{tokenizer:AutoTokenizer,pipeline:AutomaticSpeechRecognitionPipeline,model:[AutoModelForSpeechSeq2Seq,AutoModelForCTC],processor:AutoProcessor,default:{model:"Xenova/whisper-tiny.en"},type:"multimodal"},"text-to-audio":{tokenizer:AutoTokenizer,pipeline:TextToAudioPipeline,model:[AutoModelForTextToSpectrogram],processor:AutoProcessor,default:{model:"Xenova/speecht5_tts"},type:"text"},"image-to-text":{tokenizer:AutoTokenizer,pipeline:ImageToTextPipeline,model:AutoModelForVision2Seq,processor:AutoProcessor,default:{model:"Xenova/vit-gpt2-image-captioning"},type:"multimodal"},"image-classification":{pipeline:ImageClassificationPipeline,model:AutoModelForImageClassification,processor:AutoProcessor,default:{model:"Xenova/vit-base-patch16-224"},type:"multimodal"},"image-segmentation":{pipeline:ImageSegmentationPipeline,model:AutoModelForImageSegmentation,processor:AutoProcessor,default:{model:"Xenova/detr-resnet-50-panoptic"},type:"multimodal"},"zero-shot-image-classification":{tokenizer:AutoTokenizer,pipeline:ZeroShotImageClassificationPipeline,model:AutoModel,processor:AutoProcessor,default:{model:"Xenova/clip-vit-base-patch32"},type:"multimodal"},"object-detection":{pipeline:ObjectDetectionPipeline,model:AutoModelForObjectDetection,processor:AutoProcessor,default:{model:"Xenova/detr-resnet-50"},type:"multimodal"},"document-question-answering":{tokenizer:AutoTokenizer,pipeline:DocumentQuestionAnsweringPipeline,model:AutoModelForDocumentQuestionAnswering,processor:AutoProcessor,default:{model:"Xenova/donut-base-finetuned-docvqa"},type:"multimodal"},"feature-extraction":{tokenizer:AutoTokenizer,pipeline:FeatureExtractionPipeline,model:AutoModel,default:{model:"Xenova/all-MiniLM-L6-v2"},type:"text"}},TASK_ALIASES={"sentiment-analysis":"text-classification",ner:"token-classification",vqa:"visual-question-answering",asr:"automatic-speech-recognition","text-to-speech":"text-to-audio",embeddings:"feature-extraction"};async function pipeline(_,n=null,{quantized:o=!0,progress_callback:u=null,config:l=null,cache_dir:d=null,local_files_only:s=!1,revision:p="main"}={}){_=TASK_ALIASES[_]??_;let f=SUPPORTED_TASKS[_.split("_",1)[0]];if(!f)throw Error(`Unsupported pipeline: ${_}. Must be one of [${Object.keys(SUPPORTED_TASKS)}]`);n||(n=f.default.model,console.log(`No model specified. Using default model: "${n}".`));let c={quantized:o,progress_callback:u,config:l,cache_dir:d,local_files_only:s,revision:p};const a=new Map([["tokenizer",f.tokenizer],["model",f.model],["processor",f.processor]]);let t=await loadItems(a,n,c);t.task=_,dispatchCallback(u,{status:"ready",task:_,model:n});let e=f.pipeline;return new e(t)}async function loadItems(_,n,o){const u=Object.create(null),l=[];for(let[d,s]of _.entries()){if(!s)continue;let p;Array.isArray(s)?p=new Promise(async(f,c)=>{let a;for(let t of s)try{f(await t.from_pretrained(n,o));return}catch(e){a=e}c(a)}):p=s.from_pretrained(n,o),u[d]=p,l.push(p)}await Promise.all(l);for(let[d,s]of Object.entries(u))u[d]=await s;return u}env.allowLocalModels=!1;class PipelineFactory{constructor(n,o,u){this.tokenizer=n,this.model=o,this.quantized=u}static async getInstance(n=null){return this.instance===null&&(this.instance=pipeline(this.task,this.model,{quantized:this.quantized,progress_callback:n,revision:this.model.includes("/whisper-large")?"no_attentions":"main"})),this.instance}}le(PipelineFactory,"task",null),le(PipelineFactory,"model",null),le(PipelineFactory,"quantized",null),le(PipelineFactory,"instance",null),self.addEventListener("message",async _=>{const n=_.data;let o=await transcribe(n.audio,n.model,n.multilingual,n.quantized,n.subtask,n.language);o!==null&&self.postMessage({status:"complete",task:"automatic-speech-recognition",data:o})});class AutomaticSpeechRecognitionPipelineFactory extends PipelineFactory{}le(AutomaticSpeechRecognitionPipelineFactory,"task","automatic-speech-recognition"),le(AutomaticSpeechRecognitionPipelineFactory,"model",null),le(AutomaticSpeechRecognitionPipelineFactory,"quantized",null);const transcribe=async(_,n,o,u,l,d)=>{const s=n.startsWith("distil-whisper/");let p=n;!s&&!o&&(p+="");const f=AutomaticSpeechRecognitionPipelineFactory;(f.model!==p||f.quantized!==u)&&(f.model=p,f.quantized=u,f.instance!==null&&((await f.getInstance()).dispose(),f.instance=null));let c=await f.getInstance(h=>{self.postMessage(h)});const a=c.processor.feature_extractor.config.chunk_length/c.model.config.max_source_positions;let t=[{tokens:[],finalised:!1}];function e(h){let g=t[t.length-1];Object.assign(g,h),g.finalised=!0,h.is_last||t.push({tokens:[],finalised:!1})}function r(h){let g=t[t.length-1];g.tokens=[...h[0].output_token_ids];let m=c.tokenizer._decode_asr(t,{time_precision:a,return_timestamps:!0,force_full_sequences:!1});self.postMessage({status:"update",task:"automatic-speech-recognition",data:m})}return await c(_,{top_k:0,do_sample:!1,chunk_length_s:s?20:30,stride_length_s:s?3:5,language:d,task:l,return_timestamps:!0,force_full_sequences:!1,callback_function:r,chunk_callback:e}).catch(h=>(self.postMessage({status:"error",task:"automatic-speech-recognition",data:h}),null))}})();