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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=b=>{let n=1;for(let a=0;a<b.length;a++){const u=b[a];if(typeof u!="number"||!Number.isSafeInteger(u))throw new TypeError(`dims[${a}] must be an integer, got: ${u}`);if(u<0)throw new RangeError(`dims[${a}] must be a non-negative integer, got: ${u}`);n*=u}return n};let Tensor$2=class st{constructor(n,a,u){let c,f,s;if(typeof n=="string")if(c=n,s=u,n==="string"){if(!Array.isArray(a))throw new TypeError("A string tensor's data must be a string array.");f=a}else{const 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bufferToTensor(n,a){if(n===void 0)throw new Error("Image buffer must be defined");if(a.height===void 0||a.width===void 0)throw new Error("Image height and width must be defined");const{height:u,width:c}=a,f=a.norm;let s,h;f===void 0||f.mean===void 0?s=255:s=f.mean,f===void 0||f.bias===void 0?h=0:h=f.bias;const p=a.bitmapFormat!==void 0?a.bitmapFormat:"RGBA",l=a.tensorFormat!==void 0&&a.tensorFormat!==void 0?a.tensorFormat:"RGB",o=u*c,t=l==="RGBA"?new Float32Array(o*4):new Float32Array(o*3);let e=4,r=0,i=1,d=2,g=3,m=0,_=o,y=o*2,T=-1;p==="RGB"&&(e=3,r=0,i=1,d=2,g=-1),l==="RGBA"?T=o*3:l==="RBG"?(m=0,y=o,_=o*2):l==="BGR"&&(y=0,_=o,m=o*2);for(let S=0;S<o;S++,r+=e,d+=e,i+=e,g+=e)t[m++]=(n[r]+h)/s,t[_++]=(n[i]+h)/s,t[y++]=(n[d]+h)/s,T!==-1&&g!==-1&&(t[T++]=(n[g]+h)/s);return l==="RGBA"?new st("float32",t,[1,4,u,c]):new st("float32",t,[1,3,u,c])}static async fromImage(n,a){const u=typeof HTMLImageElement<"u"&&n instanceof HTMLImageElement,c=typeof ImageData<"u"&&n instanceof ImageData,f=typeof 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g=e.uint32();switch(g>>>3){case 1:d.name=e.string();break;case 21:d.refAttrName=e.string();break;case 13:d.docString=e.string();break;case 20:d.type=e.int32();break;case 2:d.f=e.float();break;case 3:d.i=e.int64();break;case 4:d.s=e.bytes();break;case 5:d.t=o.onnx.TensorProto.decode(e,e.uint32());break;case 6:d.g=o.onnx.GraphProto.decode(e,e.uint32());break;case 7:if(d.floats&&d.floats.length||(d.floats=[]),(7&g)==2)for(var m=e.uint32()+e.pos;e.pos<m;)d.floats.push(e.float());else d.floats.push(e.float());break;case 8:if(d.ints&&d.ints.length||(d.ints=[]),(7&g)==2)for(m=e.uint32()+e.pos;e.pos<m;)d.ints.push(e.int64());else d.ints.push(e.int64());break;case 9:d.strings&&d.strings.length||(d.strings=[]),d.strings.push(e.bytes());break;case 10:d.tensors&&d.tensors.length||(d.tensors=[]),d.tensors.push(o.onnx.TensorProto.decode(e,e.uint32()));break;case 11:d.graphs&&d.graphs.length||(d.graphs=[]),d.graphs.push(o.onnx.GraphProto.decode(e,e.uint32()));break;default:e.skipType(7&g)}}return d},t.decodeDelimited=function(e){return e instanceof h||(e=new h(e)),this.decode(e,e.uint32())},t.verify=function(e){if(typeof e!="object"||e===null)return"object expected";if(e.name!=null&&e.hasOwnProperty("name")&&!l.isString(e.name))return"name: string expected";if(e.refAttrName!=null&&e.hasOwnProperty("refAttrName")&&!l.isString(e.refAttrName))return"refAttrName: string expected";if(e.docString!=null&&e.hasOwnProperty("docString")&&!l.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")&&!(l.isInteger(e.i)||e.i&&l.isInteger(e.i.low)&&l.isInteger(e.i.high)))return"i: integer|Long expected";if(e.s!=null&&e.hasOwnProperty("s")&&!(e.s&&typeof e.s.length=="number"||l.isString(e.s)))return"s: buffer expected";if(e.t!=null&&e.hasOwnProperty("t")&&(i=o.onnx.TensorProto.verify(e.t)))return"t."+i;if(e.g!=null&&e.hasOwnProperty("g")&&(i=o.onnx.GraphProto.verify(e.g)))return"g."+i;if(e.floats!=null&&e.hasOwnProperty("floats")){if(!Array.isArray(e.floats))return"floats: array expected";for(var r=0;r<e.floats.length;++r)if(typeof e.floats[r]!="number")return"floats: number[] expected"}if(e.ints!=null&&e.hasOwnProperty("ints")){if(!Array.isArray(e.ints))return"ints: array expected";for(r=0;r<e.ints.length;++r)if(!(l.isInteger(e.ints[r])||e.ints[r]&&l.isInteger(e.ints[r].low)&&l.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"||l.isString(e.strings[r])))return"strings: buffer[] expected"}if(e.tensors!=null&&e.hasOwnProperty("tensors")){if(!Array.isArray(e.tensors))return"tensors: array expected";for(r=0;r<e.tensors.length;++r)if(i=o.onnx.TensorProto.verify(e.tensors[r]))return"tensors."+i}if(e.graphs!=null&&e.hasOwnProperty("graphs")){if(!Array.isArray(e.graphs))return"graphs: array expected";for(r=0;r<e.graphs.length;++r){var i;if(i=o.onnx.GraphProto.verify(e.graphs[r]))return"graphs."+i}}return null},t.fromObject=function(e){if(e instanceof o.onnx.AttributeProto)return e;var r=new o.onnx.AttributeProto;switch(e.name!=null&&(r.name=String(e.name)),e.refAttrName!=null&&(r.refAttrName=String(e.refAttrName)),e.docString!=null&&(r.docString=String(e.docString)),e.type){case"UNDEFINED":case 0:r.type=0;break;case"FLOAT":case 1:r.type=1;break;case"INT":case 2:r.type=2;break;case"STRING":case 3:r.type=3;break;case"TENSOR":case 4:r.type=4;break;case"GRAPH":case 5:r.type=5;break;case"FLOATS":case 6:r.type=6;break;case"INTS":case 7:r.type=7;break;case"STRINGS":case 8:r.type=8;break;case"TENSORS":case 9:r.type=9;break;case"GRAPHS":case 10:r.type=10}if(e.f!=null&&(r.f=Number(e.f)),e.i!=null&&(l.Long?(r.i=l.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 l.LongBits(e.i.low>>>0,e.i.high>>>0).toNumber())),e.s!=null&&(typeof e.s=="string"?l.base64.decode(e.s,r.s=l.newBuffer(l.base64.length(e.s)),0):e.s.length&&(r.s=e.s)),e.t!=null){if(typeof e.t!="object")throw TypeError(".onnx.AttributeProto.t: object expected");r.t=o.onnx.TensorProto.fromObject(e.t)}if(e.g!=null){if(typeof e.g!="object")throw TypeError(".onnx.AttributeProto.g: object expected");r.g=o.onnx.GraphProto.fromObject(e.g)}if(e.floats){if(!Array.isArray(e.floats))throw TypeError(".onnx.AttributeProto.floats: array expected");r.floats=[];for(var i=0;i<e.floats.length;++i)r.floats[i]=Number(e.floats[i])}if(e.ints){if(!Array.isArray(e.ints))throw TypeError(".onnx.AttributeProto.ints: array expected");for(r.ints=[],i=0;i<e.ints.length;++i)l.Long?(r.ints[i]=l.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 l.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"?l.base64.decode(e.strings[i],r.strings[i]=l.newBuffer(l.base64.length(e.strings[i])),0):e.strings[i].length&&(r.strings[i]=e.strings[i])}if(e.tensors){if(!Array.isArray(e.tensors))throw TypeError(".onnx.AttributeProto.tensors: array expected");for(r.tensors=[],i=0;i<e.tensors.length;++i){if(typeof e.tensors[i]!="object")throw TypeError(".onnx.AttributeProto.tensors: object expected");r.tensors[i]=o.onnx.TensorProto.fromObject(e.tensors[i])}}if(e.graphs){if(!Array.isArray(e.graphs))throw TypeError(".onnx.AttributeProto.graphs: array expected");for(r.graphs=[],i=0;i<e.graphs.length;++i){if(typeof e.graphs[i]!="object")throw TypeError(".onnx.AttributeProto.graphs: object expected");r.graphs[i]=o.onnx.GraphProto.fromObject(e.graphs[i])}}return r},t.toObject=function(e,r){r||(r={});var i={};if((r.arrays||r.defaults)&&(i.floats=[],i.ints=[],i.strings=[],i.tensors=[],i.graphs=[]),r.defaults){if(i.name="",i.f=0,l.Long){var d=new l.Long(0,0,!1);i.i=r.longs===String?d.toString():r.longs===Number?d.toNumber():d}else i.i=r.longs===String?"0":0;r.bytes===String?i.s="":(i.s=[],r.bytes!==Array&&(i.s=l.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?l.Long.prototype.toString.call(e.i):r.longs===Number?new l.LongBits(e.i.low>>>0,e.i.high>>>0).toNumber():e.i),e.s!=null&&e.hasOwnProperty("s")&&(i.s=r.bytes===String?l.base64.encode(e.s,0,e.s.length):r.bytes===Array?Array.prototype.slice.call(e.s):e.s),e.t!=null&&e.hasOwnProperty("t")&&(i.t=o.onnx.TensorProto.toObject(e.t,r)),e.g!=null&&e.hasOwnProperty("g")&&(i.g=o.onnx.GraphProto.toObject(e.g,r)),e.floats&&e.floats.length){i.floats=[];for(var g=0;g<e.floats.length;++g)i.floats[g]=r.json&&!isFinite(e.floats[g])?String(e.floats[g]):e.floats[g]}if(e.ints&&e.ints.length)for(i.ints=[],g=0;g<e.ints.length;++g)typeof e.ints[g]=="number"?i.ints[g]=r.longs===String?String(e.ints[g]):e.ints[g]:i.ints[g]=r.longs===String?l.Long.prototype.toString.call(e.ints[g]):r.longs===Number?new l.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?l.base64.encode(e.strings[g],0,e.strings[g].length):r.bytes===Array?Array.prototype.slice.call(e.strings[g]):e.strings[g];if(e.tensors&&e.tensors.length)for(i.tensors=[],g=0;g<e.tensors.length;++g)i.tensors[g]=o.onnx.TensorProto.toObject(e.tensors[g],r);if(e.graphs&&e.graphs.length)for(i.graphs=[],g=0;g<e.graphs.length;++g)i.graphs[g]=o.onnx.GraphProto.toObject(e.graphs[g],r);return e.docString!=null&&e.hasOwnProperty("docString")&&(i.docString=e.docString),e.type!=null&&e.hasOwnProperty("type")&&(i.type=r.enums===String?o.onnx.AttributeProto.AttributeType[e.type]:e.type),e.refAttrName!=null&&e.hasOwnProperty("refAttrName")&&(i.refAttrName=e.refAttrName),i},t.prototype.toJSON=function(){return this.constructor.toObject(this,s.util.toJSONOptions)},t.AttributeType=function(){var e={},r=Object.create(e);return r[e[0]="UNDEFINED"]=0,r[e[1]="FLOAT"]=1,r[e[2]="INT"]=2,r[e[3]="STRING"]=3,r[e[4]="TENSOR"]=4,r[e[5]="GRAPH"]=5,r[e[6]="FLOATS"]=6,r[e[7]="INTS"]=7,r[e[8]="STRINGS"]=8,r[e[9]="TENSORS"]=9,r[e[10]="GRAPHS"]=10,r}(),t}(),f.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=p.create()),e.name!=null&&e.hasOwnProperty("name")&&r.uint32(10).string(e.name),e.type!=null&&e.hasOwnProperty("type")&&o.onnx.TypeProto.encode(e.type,r.uint32(18).fork()).ldelim(),e.docString!=null&&e.hasOwnProperty("docString")&&r.uint32(26).string(e.docString),r},t.encodeDelimited=function(e,r){return this.encode(e,r).ldelim()},t.decode=function(e,r){e instanceof h||(e=h.create(e));for(var i=r===void 0?e.len:e.pos+r,d=new o.onnx.ValueInfoProto;e.pos<i;){var g=e.uint32();switch(g>>>3){case 1:d.name=e.string();break;case 2:d.type=o.onnx.TypeProto.decode(e,e.uint32());break;case 3:d.docString=e.string();break;default:e.skipType(7&g)}}return d},t.decodeDelimited=function(e){return e instanceof h||(e=new h(e)),this.decode(e,e.uint32())},t.verify=function(e){if(typeof e!="object"||e===null)return"object expected";if(e.name!=null&&e.hasOwnProperty("name")&&!l.isString(e.name))return"name: string expected";if(e.type!=null&&e.hasOwnProperty("type")){var r=o.onnx.TypeProto.verify(e.type);if(r)return"type."+r}return e.docString!=null&&e.hasOwnProperty("docString")&&!l.isString(e.docString)?"docString: string expected":null},t.fromObject=function(e){if(e instanceof o.onnx.ValueInfoProto)return e;var r=new o.onnx.ValueInfoProto;if(e.name!=null&&(r.name=String(e.name)),e.type!=null){if(typeof e.type!="object")throw TypeError(".onnx.ValueInfoProto.type: object expected");r.type=o.onnx.TypeProto.fromObject(e.type)}return e.docString!=null&&(r.docString=String(e.docString)),r},t.toObject=function(e,r){r||(r={});var i={};return r.defaults&&(i.name="",i.type=null,i.docString=""),e.name!=null&&e.hasOwnProperty("name")&&(i.name=e.name),e.type!=null&&e.hasOwnProperty("type")&&(i.type=o.onnx.TypeProto.toObject(e.type,r)),e.docString!=null&&e.hasOwnProperty("docString")&&(i.docString=e.docString),i},t.prototype.toJSON=function(){return this.constructor.toObject(this,s.util.toJSONOptions)},t}(),f.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=l.emptyArray,t.prototype.output=l.emptyArray,t.prototype.name="",t.prototype.opType="",t.prototype.domain="",t.prototype.attribute=l.emptyArray,t.prototype.docString="",t.create=function(e){return new t(e)},t.encode=function(e,r){if(r||(r=p.create()),e.input!=null&&e.input.length)for(var i=0;i<e.input.length;++i)r.uint32(10).string(e.input[i]);if(e.output!=null&&e.output.length)for(i=0;i<e.output.length;++i)r.uint32(18).string(e.output[i]);if(e.name!=null&&e.hasOwnProperty("name")&&r.uint32(26).string(e.name),e.opType!=null&&e.hasOwnProperty("opType")&&r.uint32(34).string(e.opType),e.attribute!=null&&e.attribute.length)for(i=0;i<e.attribute.length;++i)o.onnx.AttributeProto.encode(e.attribute[i],r.uint32(42).fork()).ldelim();return e.docString!=null&&e.hasOwnProperty("docString")&&r.uint32(50).string(e.docString),e.domain!=null&&e.hasOwnProperty("domain")&&r.uint32(58).string(e.domain),r},t.encodeDelimited=function(e,r){return this.encode(e,r).ldelim()},t.decode=function(e,r){e instanceof h||(e=h.create(e));for(var i=r===void 0?e.len:e.pos+r,d=new o.onnx.NodeProto;e.pos<i;){var g=e.uint32();switch(g>>>3){case 1:d.input&&d.input.length||(d.input=[]),d.input.push(e.string());break;case 2:d.output&&d.output.length||(d.output=[]),d.output.push(e.string());break;case 3:d.name=e.string();break;case 4:d.opType=e.string();break;case 7:d.domain=e.string();break;case 5:d.attribute&&d.attribute.length||(d.attribute=[]),d.attribute.push(o.onnx.AttributeProto.decode(e,e.uint32()));break;case 6:d.docString=e.string();break;default:e.skipType(7&g)}}return d},t.decodeDelimited=function(e){return e instanceof h||(e=new h(e)),this.decode(e,e.uint32())},t.verify=function(e){if(typeof e!="object"||e===null)return"object expected";if(e.input!=null&&e.hasOwnProperty("input")){if(!Array.isArray(e.input))return"input: array expected";for(var r=0;r<e.input.length;++r)if(!l.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(!l.isString(e.output[r]))return"output: string[] expected"}if(e.name!=null&&e.hasOwnProperty("name")&&!l.isString(e.name))return"name: string expected";if(e.opType!=null&&e.hasOwnProperty("opType")&&!l.isString(e.opType))return"opType: string expected";if(e.domain!=null&&e.hasOwnProperty("domain")&&!l.isString(e.domain))return"domain: string expected";if(e.attribute!=null&&e.hasOwnProperty("attribute")){if(!Array.isArray(e.attribute))return"attribute: array expected";for(r=0;r<e.attribute.length;++r){var i=o.onnx.AttributeProto.verify(e.attribute[r]);if(i)return"attribute."+i}}return e.docString!=null&&e.hasOwnProperty("docString")&&!l.isString(e.docString)?"docString: string expected":null},t.fromObject=function(e){if(e instanceof o.onnx.NodeProto)return e;var r=new o.onnx.NodeProto;if(e.input){if(!Array.isArray(e.input))throw TypeError(".onnx.NodeProto.input: array expected");r.input=[];for(var i=0;i<e.input.length;++i)r.input[i]=String(e.input[i])}if(e.output){if(!Array.isArray(e.output))throw TypeError(".onnx.NodeProto.output: array expected");for(r.output=[],i=0;i<e.output.length;++i)r.output[i]=String(e.output[i])}if(e.name!=null&&(r.name=String(e.name)),e.opType!=null&&(r.opType=String(e.opType)),e.domain!=null&&(r.domain=String(e.domain)),e.attribute){if(!Array.isArray(e.attribute))throw TypeError(".onnx.NodeProto.attribute: array expected");for(r.attribute=[],i=0;i<e.attribute.length;++i){if(typeof e.attribute[i]!="object")throw TypeError(".onnx.NodeProto.attribute: object expected");r.attribute[i]=o.onnx.AttributeProto.fromObject(e.attribute[i])}}return e.docString!=null&&(r.docString=String(e.docString)),r},t.toObject=function(e,r){r||(r={});var i={};if((r.arrays||r.defaults)&&(i.input=[],i.output=[],i.attribute=[]),r.defaults&&(i.name="",i.opType="",i.docString="",i.domain=""),e.input&&e.input.length){i.input=[];for(var d=0;d<e.input.length;++d)i.input[d]=e.input[d]}if(e.output&&e.output.length)for(i.output=[],d=0;d<e.output.length;++d)i.output[d]=e.output[d];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=[],d=0;d<e.attribute.length;++d)i.attribute[d]=o.onnx.AttributeProto.toObject(e.attribute[d],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}(),f.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=l.Long?l.Long.fromBits(0,0,!1):0,t.prototype.opsetImport=l.emptyArray,t.prototype.producerName="",t.prototype.producerVersion="",t.prototype.domain="",t.prototype.modelVersion=l.Long?l.Long.fromBits(0,0,!1):0,t.prototype.docString="",t.prototype.graph=null,t.prototype.metadataProps=l.emptyArray,t.create=function(e){return new t(e)},t.encode=function(e,r){if(r||(r=p.create()),e.irVersion!=null&&e.hasOwnProperty("irVersion")&&r.uint32(8).int64(e.irVersion),e.producerName!=null&&e.hasOwnProperty("producerName")&&r.uint32(18).string(e.producerName),e.producerVersion!=null&&e.hasOwnProperty("producerVersion")&&r.uint32(26).string(e.producerVersion),e.domain!=null&&e.hasOwnProperty("domain")&&r.uint32(34).string(e.domain),e.modelVersion!=null&&e.hasOwnProperty("modelVersion")&&r.uint32(40).int64(e.modelVersion),e.docString!=null&&e.hasOwnProperty("docString")&&r.uint32(50).string(e.docString),e.graph!=null&&e.hasOwnProperty("graph")&&o.onnx.GraphProto.encode(e.graph,r.uint32(58).fork()).ldelim(),e.opsetImport!=null&&e.opsetImport.length)for(var i=0;i<e.opsetImport.length;++i)o.onnx.OperatorSetIdProto.encode(e.opsetImport[i],r.uint32(66).fork()).ldelim();if(e.metadataProps!=null&&e.metadataProps.length)for(i=0;i<e.metadataProps.length;++i)o.onnx.StringStringEntryProto.encode(e.metadataProps[i],r.uint32(114).fork()).ldelim();return r},t.encodeDelimited=function(e,r){return this.encode(e,r).ldelim()},t.decode=function(e,r){e instanceof h||(e=h.create(e));for(var i=r===void 0?e.len:e.pos+r,d=new o.onnx.ModelProto;e.pos<i;){var g=e.uint32();switch(g>>>3){case 1:d.irVersion=e.int64();break;case 8:d.opsetImport&&d.opsetImport.length||(d.opsetImport=[]),d.opsetImport.push(o.onnx.OperatorSetIdProto.decode(e,e.uint32()));break;case 2:d.producerName=e.string();break;case 3:d.producerVersion=e.string();break;case 4:d.domain=e.string();break;case 5:d.modelVersion=e.int64();break;case 6:d.docString=e.string();break;case 7:d.graph=o.onnx.GraphProto.decode(e,e.uint32());break;case 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o.onnx.ModelProto)return e;var r=new o.onnx.ModelProto;if(e.irVersion!=null&&(l.Long?(r.irVersion=l.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 l.LongBits(e.irVersion.low>>>0,e.irVersion.high>>>0).toNumber())),e.opsetImport){if(!Array.isArray(e.opsetImport))throw TypeError(".onnx.ModelProto.opsetImport: array expected");r.opsetImport=[];for(var i=0;i<e.opsetImport.length;++i){if(typeof e.opsetImport[i]!="object")throw TypeError(".onnx.ModelProto.opsetImport: object expected");r.opsetImport[i]=o.onnx.OperatorSetIdProto.fromObject(e.opsetImport[i])}}if(e.producerName!=null&&(r.producerName=String(e.producerName)),e.producerVersion!=null&&(r.producerVersion=String(e.producerVersion)),e.domain!=null&&(r.domain=String(e.domain)),e.modelVersion!=null&&(l.Long?(r.modelVersion=l.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=p.create()),e.key!=null&&e.hasOwnProperty("key")&&r.uint32(10).string(e.key),e.value!=null&&e.hasOwnProperty("value")&&r.uint32(18).string(e.value),r},t.encodeDelimited=function(e,r){return this.encode(e,r).ldelim()},t.decode=function(e,r){e instanceof h||(e=h.create(e));for(var i=r===void 0?e.len:e.pos+r,d=new o.onnx.StringStringEntryProto;e.pos<i;){var g=e.uint32();switch(g>>>3){case 1:d.key=e.string();break;case 2:d.value=e.string();break;default:e.skipType(7&g)}}return d},t.decodeDelimited=function(e){return e instanceof h||(e=new h(e)),this.decode(e,e.uint32())},t.verify=function(e){return typeof e!="object"||e===null?"object expected":e.key!=null&&e.hasOwnProperty("key")&&!l.isString(e.key)?"key: string expected":e.value!=null&&e.hasOwnProperty("value")&&!l.isString(e.value)?"value: string expected":null},t.fromObject=function(e){if(e instanceof o.onnx.StringStringEntryProto)return e;var r=new o.onnx.StringStringEntryProto;return e.key!=null&&(r.key=String(e.key)),e.value!=null&&(r.value=String(e.value)),r},t.toObject=function(e,r){r||(r={});var i={};return r.defaults&&(i.key="",i.value=""),e.key!=null&&e.hasOwnProperty("key")&&(i.key=e.key),e.value!=null&&e.hasOwnProperty("value")&&(i.value=e.value),i},t.prototype.toJSON=function(){return this.constructor.toObject(this,s.util.toJSONOptions)},t}(),f.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=l.emptyArray,t.create=function(e){return new t(e)},t.encode=function(e,r){if(r||(r=p.create()),e.tensorName!=null&&e.hasOwnProperty("tensorName")&&r.uint32(10).string(e.tensorName),e.quantParameterTensorNames!=null&&e.quantParameterTensorNames.length)for(var i=0;i<e.quantParameterTensorNames.length;++i)o.onnx.StringStringEntryProto.encode(e.quantParameterTensorNames[i],r.uint32(18).fork()).ldelim();return r},t.encodeDelimited=function(e,r){return this.encode(e,r).ldelim()},t.decode=function(e,r){e instanceof h||(e=h.create(e));for(var i=r===void 0?e.len:e.pos+r,d=new o.onnx.TensorAnnotation;e.pos<i;){var g=e.uint32();switch(g>>>3){case 1:d.tensorName=e.string();break;case 2:d.quantParameterTensorNames&&d.quantParameterTensorNames.length||(d.quantParameterTensorNames=[]),d.quantParameterTensorNames.push(o.onnx.StringStringEntryProto.decode(e,e.uint32()));break;default:e.skipType(7&g)}}return d},t.decodeDelimited=function(e){return e instanceof h||(e=new h(e)),this.decode(e,e.uint32())},t.verify=function(e){if(typeof e!="object"||e===null)return"object expected";if(e.tensorName!=null&&e.hasOwnProperty("tensorName")&&!l.isString(e.tensorName))return"tensorName: string expected";if(e.quantParameterTensorNames!=null&&e.hasOwnProperty("quantParameterTensorNames")){if(!Array.isArray(e.quantParameterTensorNames))return"quantParameterTensorNames: array expected";for(var r=0;r<e.quantParameterTensorNames.length;++r){var i=o.onnx.StringStringEntryProto.verify(e.quantParameterTensorNames[r]);if(i)return"quantParameterTensorNames."+i}}return null},t.fromObject=function(e){if(e instanceof o.onnx.TensorAnnotation)return e;var r=new o.onnx.TensorAnnotation;if(e.tensorName!=null&&(r.tensorName=String(e.tensorName)),e.quantParameterTensorNames){if(!Array.isArray(e.quantParameterTensorNames))throw TypeError(".onnx.TensorAnnotation.quantParameterTensorNames: array expected");r.quantParameterTensorNames=[];for(var i=0;i<e.quantParameterTensorNames.length;++i){if(typeof e.quantParameterTensorNames[i]!="object")throw TypeError(".onnx.TensorAnnotation.quantParameterTensorNames: object expected");r.quantParameterTensorNames[i]=o.onnx.StringStringEntryProto.fromObject(e.quantParameterTensorNames[i])}}return r},t.toObject=function(e,r){r||(r={});var i={};if((r.arrays||r.defaults)&&(i.quantParameterTensorNames=[]),r.defaults&&(i.tensorName=""),e.tensorName!=null&&e.hasOwnProperty("tensorName")&&(i.tensorName=e.tensorName),e.quantParameterTensorNames&&e.quantParameterTensorNames.length){i.quantParameterTensorNames=[];for(var d=0;d<e.quantParameterTensorNames.length;++d)i.quantParameterTensorNames[d]=o.onnx.StringStringEntryProto.toObject(e.quantParameterTensorNames[d],r)}return i},t.prototype.toJSON=function(){return this.constructor.toObject(this,s.util.toJSONOptions)},t}(),f.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=l.emptyArray,t.prototype.name="",t.prototype.initializer=l.emptyArray,t.prototype.docString="",t.prototype.input=l.emptyArray,t.prototype.output=l.emptyArray,t.prototype.valueInfo=l.emptyArray,t.prototype.quantizationAnnotation=l.emptyArray,t.create=function(e){return new t(e)},t.encode=function(e,r){if(r||(r=p.create()),e.node!=null&&e.node.length)for(var i=0;i<e.node.length;++i)o.onnx.NodeProto.encode(e.node[i],r.uint32(10).fork()).ldelim();if(e.name!=null&&e.hasOwnProperty("name")&&r.uint32(18).string(e.name),e.initializer!=null&&e.initializer.length)for(i=0;i<e.initializer.length;++i)o.onnx.TensorProto.encode(e.initializer[i],r.uint32(42).fork()).ldelim();if(e.docString!=null&&e.hasOwnProperty("docString")&&r.uint32(82).string(e.docString),e.input!=null&&e.input.length)for(i=0;i<e.input.length;++i)o.onnx.ValueInfoProto.encode(e.input[i],r.uint32(90).fork()).ldelim();if(e.output!=null&&e.output.length)for(i=0;i<e.output.length;++i)o.onnx.ValueInfoProto.encode(e.output[i],r.uint32(98).fork()).ldelim();if(e.valueInfo!=null&&e.valueInfo.length)for(i=0;i<e.valueInfo.length;++i)o.onnx.ValueInfoProto.encode(e.valueInfo[i],r.uint32(106).fork()).ldelim();if(e.quantizationAnnotation!=null&&e.quantizationAnnotation.length)for(i=0;i<e.quantizationAnnotation.length;++i)o.onnx.TensorAnnotation.encode(e.quantizationAnnotation[i],r.uint32(114).fork()).ldelim();return r},t.encodeDelimited=function(e,r){return this.encode(e,r).ldelim()},t.decode=function(e,r){e instanceof h||(e=h.create(e));for(var i=r===void 0?e.len:e.pos+r,d=new o.onnx.GraphProto;e.pos<i;){var g=e.uint32();switch(g>>>3){case 1:d.node&&d.node.length||(d.node=[]),d.node.push(o.onnx.NodeProto.decode(e,e.uint32()));break;case 2:d.name=e.string();break;case 5:d.initializer&&d.initializer.length||(d.initializer=[]),d.initializer.push(o.onnx.TensorProto.decode(e,e.uint32()));break;case 10:d.docString=e.string();break;case 11:d.input&&d.input.length||(d.input=[]),d.input.push(o.onnx.ValueInfoProto.decode(e,e.uint32()));break;case 12:d.output&&d.output.length||(d.output=[]),d.output.push(o.onnx.ValueInfoProto.decode(e,e.uint32()));break;case 13:d.valueInfo&&d.valueInfo.length||(d.valueInfo=[]),d.valueInfo.push(o.onnx.ValueInfoProto.decode(e,e.uint32()));break;case 14:d.quantizationAnnotation&&d.quantizationAnnotation.length||(d.quantizationAnnotation=[]),d.quantizationAnnotation.push(o.onnx.TensorAnnotation.decode(e,e.uint32()));break;default:e.skipType(7&g)}}return d},t.decodeDelimited=function(e){return e instanceof h||(e=new h(e)),this.decode(e,e.uint32())},t.verify=function(e){if(typeof e!="object"||e===null)return"object expected";if(e.node!=null&&e.hasOwnProperty("node")){if(!Array.isArray(e.node))return"node: array expected";for(var r=0;r<e.node.length;++r)if(i=o.onnx.NodeProto.verify(e.node[r]))return"node."+i}if(e.name!=null&&e.hasOwnProperty("name")&&!l.isString(e.name))return"name: string expected";if(e.initializer!=null&&e.hasOwnProperty("initializer")){if(!Array.isArray(e.initializer))return"initializer: array expected";for(r=0;r<e.initializer.length;++r)if(i=o.onnx.TensorProto.verify(e.initializer[r]))return"initializer."+i}if(e.docString!=null&&e.hasOwnProperty("docString")&&!l.isString(e.docString))return"docString: string expected";if(e.input!=null&&e.hasOwnProperty("input")){if(!Array.isArray(e.input))return"input: array expected";for(r=0;r<e.input.length;++r)if(i=o.onnx.ValueInfoProto.verify(e.input[r]))return"input."+i}if(e.output!=null&&e.hasOwnProperty("output")){if(!Array.isArray(e.output))return"output: array expected";for(r=0;r<e.output.length;++r)if(i=o.onnx.ValueInfoProto.verify(e.output[r]))return"output."+i}if(e.valueInfo!=null&&e.hasOwnProperty("valueInfo")){if(!Array.isArray(e.valueInfo))return"valueInfo: array expected";for(r=0;r<e.valueInfo.length;++r)if(i=o.onnx.ValueInfoProto.verify(e.valueInfo[r]))return"valueInfo."+i}if(e.quantizationAnnotation!=null&&e.hasOwnProperty("quantizationAnnotation")){if(!Array.isArray(e.quantizationAnnotation))return"quantizationAnnotation: array expected";for(r=0;r<e.quantizationAnnotation.length;++r){var i;if(i=o.onnx.TensorAnnotation.verify(e.quantizationAnnotation[r]))return"quantizationAnnotation."+i}}return null},t.fromObject=function(e){if(e instanceof o.onnx.GraphProto)return e;var r=new o.onnx.GraphProto;if(e.node){if(!Array.isArray(e.node))throw TypeError(".onnx.GraphProto.node: array expected");r.node=[];for(var i=0;i<e.node.length;++i){if(typeof e.node[i]!="object")throw TypeError(".onnx.GraphProto.node: object expected");r.node[i]=o.onnx.NodeProto.fromObject(e.node[i])}}if(e.name!=null&&(r.name=String(e.name)),e.initializer){if(!Array.isArray(e.initializer))throw TypeError(".onnx.GraphProto.initializer: array expected");for(r.initializer=[],i=0;i<e.initializer.length;++i){if(typeof e.initializer[i]!="object")throw TypeError(".onnx.GraphProto.initializer: object expected");r.initializer[i]=o.onnx.TensorProto.fromObject(e.initializer[i])}}if(e.docString!=null&&(r.docString=String(e.docString)),e.input){if(!Array.isArray(e.input))throw TypeError(".onnx.GraphProto.input: array expected");for(r.input=[],i=0;i<e.input.length;++i){if(typeof e.input[i]!="object")throw TypeError(".onnx.GraphProto.input: object expected");r.input[i]=o.onnx.ValueInfoProto.fromObject(e.input[i])}}if(e.output){if(!Array.isArray(e.output))throw TypeError(".onnx.GraphProto.output: array expected");for(r.output=[],i=0;i<e.output.length;++i){if(typeof e.output[i]!="object")throw TypeError(".onnx.GraphProto.output: object expected");r.output[i]=o.onnx.ValueInfoProto.fromObject(e.output[i])}}if(e.valueInfo){if(!Array.isArray(e.valueInfo))throw TypeError(".onnx.GraphProto.valueInfo: array expected");for(r.valueInfo=[],i=0;i<e.valueInfo.length;++i){if(typeof e.valueInfo[i]!="object")throw TypeError(".onnx.GraphProto.valueInfo: object expected");r.valueInfo[i]=o.onnx.ValueInfoProto.fromObject(e.valueInfo[i])}}if(e.quantizationAnnotation){if(!Array.isArray(e.quantizationAnnotation))throw TypeError(".onnx.GraphProto.quantizationAnnotation: array expected");for(r.quantizationAnnotation=[],i=0;i<e.quantizationAnnotation.length;++i){if(typeof e.quantizationAnnotation[i]!="object")throw TypeError(".onnx.GraphProto.quantizationAnnotation: object expected");r.quantizationAnnotation[i]=o.onnx.TensorAnnotation.fromObject(e.quantizationAnnotation[i])}}return r},t.toObject=function(e,r){r||(r={});var i={};if((r.arrays||r.defaults)&&(i.node=[],i.initializer=[],i.input=[],i.output=[],i.valueInfo=[],i.quantizationAnnotation=[]),r.defaults&&(i.name="",i.docString=""),e.node&&e.node.length){i.node=[];for(var d=0;d<e.node.length;++d)i.node[d]=o.onnx.NodeProto.toObject(e.node[d],r)}if(e.name!=null&&e.hasOwnProperty("name")&&(i.name=e.name),e.initializer&&e.initializer.length)for(i.initializer=[],d=0;d<e.initializer.length;++d)i.initializer[d]=o.onnx.TensorProto.toObject(e.initializer[d],r);if(e.docString!=null&&e.hasOwnProperty("docString")&&(i.docString=e.docString),e.input&&e.input.length)for(i.input=[],d=0;d<e.input.length;++d)i.input[d]=o.onnx.ValueInfoProto.toObject(e.input[d],r);if(e.output&&e.output.length)for(i.output=[],d=0;d<e.output.length;++d)i.output[d]=o.onnx.ValueInfoProto.toObject(e.output[d],r);if(e.valueInfo&&e.valueInfo.length)for(i.valueInfo=[],d=0;d<e.valueInfo.length;++d)i.valueInfo[d]=o.onnx.ValueInfoProto.toObject(e.valueInfo[d],r);if(e.quantizationAnnotation&&e.quantizationAnnotation.length)for(i.quantizationAnnotation=[],d=0;d<e.quantizationAnnotation.length;++d)i.quantizationAnnotation[d]=o.onnx.TensorAnnotation.toObject(e.quantizationAnnotation[d],r);return i},t.prototype.toJSON=function(){return this.constructor.toObject(this,s.util.toJSONOptions)},t}(),f.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=l.emptyArray,t.prototype.dataType=0,t.prototype.segment=null,t.prototype.floatData=l.emptyArray,t.prototype.int32Data=l.emptyArray,t.prototype.stringData=l.emptyArray,t.prototype.int64Data=l.emptyArray,t.prototype.name="",t.prototype.docString="",t.prototype.rawData=l.newBuffer([]),t.prototype.externalData=l.emptyArray,t.prototype.dataLocation=0,t.prototype.doubleData=l.emptyArray,t.prototype.uint64Data=l.emptyArray,t.create=function(e){return new t(e)},t.encode=function(e,r){if(r||(r=p.create()),e.dims!=null&&e.dims.length){r.uint32(10).fork();for(var i=0;i<e.dims.length;++i)r.int64(e.dims[i]);r.ldelim()}if(e.dataType!=null&&e.hasOwnProperty("dataType")&&r.uint32(16).int32(e.dataType),e.segment!=null&&e.hasOwnProperty("segment")&&o.onnx.TensorProto.Segment.encode(e.segment,r.uint32(26).fork()).ldelim(),e.floatData!=null&&e.floatData.length){for(r.uint32(34).fork(),i=0;i<e.floatData.length;++i)r.float(e.floatData[i]);r.ldelim()}if(e.int32Data!=null&&e.int32Data.length){for(r.uint32(42).fork(),i=0;i<e.int32Data.length;++i)r.int32(e.int32Data[i]);r.ldelim()}if(e.stringData!=null&&e.stringData.length)for(i=0;i<e.stringData.length;++i)r.uint32(50).bytes(e.stringData[i]);if(e.int64Data!=null&&e.int64Data.length){for(r.uint32(58).fork(),i=0;i<e.int64Data.length;++i)r.int64(e.int64Data[i]);r.ldelim()}if(e.name!=null&&e.hasOwnProperty("name")&&r.uint32(66).string(e.name),e.rawData!=null&&e.hasOwnProperty("rawData")&&r.uint32(74).bytes(e.rawData),e.doubleData!=null&&e.doubleData.length){for(r.uint32(82).fork(),i=0;i<e.doubleData.length;++i)r.double(e.doubleData[i]);r.ldelim()}if(e.uint64Data!=null&&e.uint64Data.length){for(r.uint32(90).fork(),i=0;i<e.uint64Data.length;++i)r.uint64(e.uint64Data[i]);r.ldelim()}if(e.docString!=null&&e.hasOwnProperty("docString")&&r.uint32(98).string(e.docString),e.externalData!=null&&e.externalData.length)for(i=0;i<e.externalData.length;++i)o.onnx.StringStringEntryProto.encode(e.externalData[i],r.uint32(106).fork()).ldelim();return e.dataLocation!=null&&e.hasOwnProperty("dataLocation")&&r.uint32(112).int32(e.dataLocation),r},t.encodeDelimited=function(e,r){return this.encode(e,r).ldelim()},t.decode=function(e,r){e instanceof h||(e=h.create(e));for(var i=r===void 0?e.len:e.pos+r,d=new o.onnx.TensorProto;e.pos<i;){var g=e.uint32();switch(g>>>3){case 1:if(d.dims&&d.dims.length||(d.dims=[]),(7&g)==2)for(var m=e.uint32()+e.pos;e.pos<m;)d.dims.push(e.int64());else d.dims.push(e.int64());break;case 2:d.dataType=e.int32();break;case 3:d.segment=o.onnx.TensorProto.Segment.decode(e,e.uint32());break;case 4:if(d.floatData&&d.floatData.length||(d.floatData=[]),(7&g)==2)for(m=e.uint32()+e.pos;e.pos<m;)d.floatData.push(e.float());else d.floatData.push(e.float());break;case 5:if(d.int32Data&&d.int32Data.length||(d.int32Data=[]),(7&g)==2)for(m=e.uint32()+e.pos;e.pos<m;)d.int32Data.push(e.int32());else d.int32Data.push(e.int32());break;case 6:d.stringData&&d.stringData.length||(d.stringData=[]),d.stringData.push(e.bytes());break;case 7:if(d.int64Data&&d.int64Data.length||(d.int64Data=[]),(7&g)==2)for(m=e.uint32()+e.pos;e.pos<m;)d.int64Data.push(e.int64());else d.int64Data.push(e.int64());break;case 8:d.name=e.string();break;case 12:d.docString=e.string();break;case 9:d.rawData=e.bytes();break;case 13:d.externalData&&d.externalData.length||(d.externalData=[]),d.externalData.push(o.onnx.StringStringEntryProto.decode(e,e.uint32()));break;case 14:d.dataLocation=e.int32();break;case 10:if(d.doubleData&&d.doubleData.length||(d.doubleData=[]),(7&g)==2)for(m=e.uint32()+e.pos;e.pos<m;)d.doubleData.push(e.double());else d.doubleData.push(e.double());break;case 11:if(d.uint64Data&&d.uint64Data.length||(d.uint64Data=[]),(7&g)==2)for(m=e.uint32()+e.pos;e.pos<m;)d.uint64Data.push(e.uint64());else d.uint64Data.push(e.uint64());break;default:e.skipType(7&g)}}return d},t.decodeDelimited=function(e){return e instanceof h||(e=new h(e)),this.decode(e,e.uint32())},t.verify=function(e){if(typeof e!="object"||e===null)return"object expected";if(e.dims!=null&&e.hasOwnProperty("dims")){if(!Array.isArray(e.dims))return"dims: array expected";for(var r=0;r<e.dims.length;++r)if(!(l.isInteger(e.dims[r])||e.dims[r]&&l.isInteger(e.dims[r].low)&&l.isInteger(e.dims[r].high)))return"dims: integer|Long[] expected"}if(e.dataType!=null&&e.hasOwnProperty("dataType")&&!l.isInteger(e.dataType))return"dataType: integer expected";if(e.segment!=null&&e.hasOwnProperty("segment")&&(i=o.onnx.TensorProto.Segment.verify(e.segment)))return"segment."+i;if(e.floatData!=null&&e.hasOwnProperty("floatData")){if(!Array.isArray(e.floatData))return"floatData: array expected";for(r=0;r<e.floatData.length;++r)if(typeof e.floatData[r]!="number")return"floatData: number[] expected"}if(e.int32Data!=null&&e.hasOwnProperty("int32Data")){if(!Array.isArray(e.int32Data))return"int32Data: array expected";for(r=0;r<e.int32Data.length;++r)if(!l.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"||l.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(!(l.isInteger(e.int64Data[r])||e.int64Data[r]&&l.isInteger(e.int64Data[r].low)&&l.isInteger(e.int64Data[r].high)))return"int64Data: integer|Long[] expected"}if(e.name!=null&&e.hasOwnProperty("name")&&!l.isString(e.name))return"name: string expected";if(e.docString!=null&&e.hasOwnProperty("docString")&&!l.isString(e.docString))return"docString: string expected";if(e.rawData!=null&&e.hasOwnProperty("rawData")&&!(e.rawData&&typeof e.rawData.length=="number"||l.isString(e.rawData)))return"rawData: buffer expected";if(e.externalData!=null&&e.hasOwnProperty("externalData")){if(!Array.isArray(e.externalData))return"externalData: array expected";for(r=0;r<e.externalData.length;++r){var i;if(i=o.onnx.StringStringEntryProto.verify(e.externalData[r]))return"externalData."+i}}if(e.dataLocation!=null&&e.hasOwnProperty("dataLocation"))switch(e.dataLocation){default:return"dataLocation: enum value expected";case 0:case 1:}if(e.doubleData!=null&&e.hasOwnProperty("doubleData")){if(!Array.isArray(e.doubleData))return"doubleData: array expected";for(r=0;r<e.doubleData.length;++r)if(typeof e.doubleData[r]!="number")return"doubleData: number[] expected"}if(e.uint64Data!=null&&e.hasOwnProperty("uint64Data")){if(!Array.isArray(e.uint64Data))return"uint64Data: array expected";for(r=0;r<e.uint64Data.length;++r)if(!(l.isInteger(e.uint64Data[r])||e.uint64Data[r]&&l.isInteger(e.uint64Data[r].low)&&l.isInteger(e.uint64Data[r].high)))return"uint64Data: integer|Long[] expected"}return null},t.fromObject=function(e){if(e instanceof o.onnx.TensorProto)return e;var r=new o.onnx.TensorProto;if(e.dims){if(!Array.isArray(e.dims))throw TypeError(".onnx.TensorProto.dims: array expected");r.dims=[];for(var i=0;i<e.dims.length;++i)l.Long?(r.dims[i]=l.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 l.LongBits(e.dims[i].low>>>0,e.dims[i].high>>>0).toNumber())}if(e.dataType!=null&&(r.dataType=0|e.dataType),e.segment!=null){if(typeof e.segment!="object")throw TypeError(".onnx.TensorProto.segment: object expected");r.segment=o.onnx.TensorProto.Segment.fromObject(e.segment)}if(e.floatData){if(!Array.isArray(e.floatData))throw TypeError(".onnx.TensorProto.floatData: array expected");for(r.floatData=[],i=0;i<e.floatData.length;++i)r.floatData[i]=Number(e.floatData[i])}if(e.int32Data){if(!Array.isArray(e.int32Data))throw TypeError(".onnx.TensorProto.int32Data: array expected");for(r.int32Data=[],i=0;i<e.int32Data.length;++i)r.int32Data[i]=0|e.int32Data[i]}if(e.stringData){if(!Array.isArray(e.stringData))throw TypeError(".onnx.TensorProto.stringData: array expected");for(r.stringData=[],i=0;i<e.stringData.length;++i)typeof e.stringData[i]=="string"?l.base64.decode(e.stringData[i],r.stringData[i]=l.newBuffer(l.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)l.Long?(r.int64Data[i]=l.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 l.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"?l.base64.decode(e.rawData,r.rawData=l.newBuffer(l.base64.length(e.rawData)),0):e.rawData.length&&(r.rawData=e.rawData)),e.externalData){if(!Array.isArray(e.externalData))throw TypeError(".onnx.TensorProto.externalData: array expected");for(r.externalData=[],i=0;i<e.externalData.length;++i){if(typeof e.externalData[i]!="object")throw TypeError(".onnx.TensorProto.externalData: object expected");r.externalData[i]=o.onnx.StringStringEntryProto.fromObject(e.externalData[i])}}switch(e.dataLocation){case"DEFAULT":case 0:r.dataLocation=0;break;case"EXTERNAL":case 1:r.dataLocation=1}if(e.doubleData){if(!Array.isArray(e.doubleData))throw TypeError(".onnx.TensorProto.doubleData: array 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i={};if((r.arrays||r.defaults)&&(i.dims=[],i.floatData=[],i.int32Data=[],i.stringData=[],i.int64Data=[],i.doubleData=[],i.uint64Data=[],i.externalData=[]),r.defaults&&(i.dataType=0,i.segment=null,i.name="",r.bytes===String?i.rawData="":(i.rawData=[],r.bytes!==Array&&(i.rawData=l.newBuffer(i.rawData))),i.docString="",i.dataLocation=r.enums===String?"DEFAULT":0),e.dims&&e.dims.length){i.dims=[];for(var d=0;d<e.dims.length;++d)typeof e.dims[d]=="number"?i.dims[d]=r.longs===String?String(e.dims[d]):e.dims[d]:i.dims[d]=r.longs===String?l.Long.prototype.toString.call(e.dims[d]):r.longs===Number?new l.LongBits(e.dims[d].low>>>0,e.dims[d].high>>>0).toNumber():e.dims[d]}if(e.dataType!=null&&e.hasOwnProperty("dataType")&&(i.dataType=e.dataType),e.segment!=null&&e.hasOwnProperty("segment")&&(i.segment=o.onnx.TensorProto.Segment.toObject(e.segment,r)),e.floatData&&e.floatData.length)for(i.floatData=[],d=0;d<e.floatData.length;++d)i.floatData[d]=r.json&&!isFinite(e.floatData[d])?String(e.floatData[d]):e.floatData[d];if(e.int32Data&&e.int32Data.length)for(i.int32Data=[],d=0;d<e.int32Data.length;++d)i.int32Data[d]=e.int32Data[d];if(e.stringData&&e.stringData.length)for(i.stringData=[],d=0;d<e.stringData.length;++d)i.stringData[d]=r.bytes===String?l.base64.encode(e.stringData[d],0,e.stringData[d].length):r.bytes===Array?Array.prototype.slice.call(e.stringData[d]):e.stringData[d];if(e.int64Data&&e.int64Data.length)for(i.int64Data=[],d=0;d<e.int64Data.length;++d)typeof e.int64Data[d]=="number"?i.int64Data[d]=r.longs===String?String(e.int64Data[d]):e.int64Data[d]:i.int64Data[d]=r.longs===String?l.Long.prototype.toString.call(e.int64Data[d]):r.longs===Number?new l.LongBits(e.int64Data[d].low>>>0,e.int64Data[d].high>>>0).toNumber():e.int64Data[d];if(e.name!=null&&e.hasOwnProperty("name")&&(i.name=e.name),e.rawData!=null&&e.hasOwnProperty("rawData")&&(i.rawData=r.bytes===String?l.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=[],d=0;d<e.doubleData.length;++d)i.doubleData[d]=r.json&&!isFinite(e.doubleData[d])?String(e.doubleData[d]):e.doubleData[d];if(e.uint64Data&&e.uint64Data.length)for(i.uint64Data=[],d=0;d<e.uint64Data.length;++d)typeof e.uint64Data[d]=="number"?i.uint64Data[d]=r.longs===String?String(e.uint64Data[d]):e.uint64Data[d]:i.uint64Data[d]=r.longs===String?l.Long.prototype.toString.call(e.uint64Data[d]):r.longs===Number?new l.LongBits(e.uint64Data[d].low>>>0,e.uint64Data[d].high>>>0).toNumber(!0):e.uint64Data[d];if(e.docString!=null&&e.hasOwnProperty("docString")&&(i.docString=e.docString),e.externalData&&e.externalData.length)for(i.externalData=[],d=0;d<e.externalData.length;++d)i.externalData[d]=o.onnx.StringStringEntryProto.toObject(e.externalData[d],r);return e.dataLocation!=null&&e.hasOwnProperty("dataLocation")&&(i.dataLocation=r.enums===String?o.onnx.TensorProto.DataLocation[e.dataLocation]:e.dataLocation),i},t.prototype.toJSON=function(){return this.constructor.toObject(this,s.util.toJSONOptions)},t.DataType=function(){var e={},r=Object.create(e);return r[e[0]="UNDEFINED"]=0,r[e[1]="FLOAT"]=1,r[e[2]="UINT8"]=2,r[e[3]="INT8"]=3,r[e[4]="UINT16"]=4,r[e[5]="INT16"]=5,r[e[6]="INT32"]=6,r[e[7]="INT64"]=7,r[e[8]="STRING"]=8,r[e[9]="BOOL"]=9,r[e[10]="FLOAT16"]=10,r[e[11]="DOUBLE"]=11,r[e[12]="UINT32"]=12,r[e[13]="UINT64"]=13,r[e[14]="COMPLEX64"]=14,r[e[15]="COMPLEX128"]=15,r[e[16]="BFLOAT16"]=16,r}(),t.Segment=function(){function e(r){if(r)for(var i=Object.keys(r),d=0;d<i.length;++d)r[i[d]]!=null&&(this[i[d]]=r[i[d]])}return e.prototype.begin=l.Long?l.Long.fromBits(0,0,!1):0,e.prototype.end=l.Long?l.Long.fromBits(0,0,!1):0,e.create=function(r){return new e(r)},e.encode=function(r,i){return i||(i=p.create()),r.begin!=null&&r.hasOwnProperty("begin")&&i.uint32(8).int64(r.begin),r.end!=null&&r.hasOwnProperty("end")&&i.uint32(16).int64(r.end),i},e.encodeDelimited=function(r,i){return this.encode(r,i).ldelim()},e.decode=function(r,i){r instanceof h||(r=h.create(r));for(var d=i===void 0?r.len:r.pos+i,g=new o.onnx.TensorProto.Segment;r.pos<d;){var m=r.uint32();switch(m>>>3){case 1:g.begin=r.int64();break;case 2:g.end=r.int64();break;default:r.skipType(7&m)}}return g},e.decodeDelimited=function(r){return r instanceof h||(r=new h(r)),this.decode(r,r.uint32())},e.verify=function(r){return typeof r!="object"||r===null?"object expected":r.begin!=null&&r.hasOwnProperty("begin")&&!(l.isInteger(r.begin)||r.begin&&l.isInteger(r.begin.low)&&l.isInteger(r.begin.high))?"begin: integer|Long expected":r.end!=null&&r.hasOwnProperty("end")&&!(l.isInteger(r.end)||r.end&&l.isInteger(r.end.low)&&l.isInteger(r.end.high))?"end: integer|Long expected":null},e.fromObject=function(r){if(r instanceof o.onnx.TensorProto.Segment)return r;var i=new o.onnx.TensorProto.Segment;return r.begin!=null&&(l.Long?(i.begin=l.Long.fromValue(r.begin)).unsigned=!1:typeof r.begin=="string"?i.begin=parseInt(r.begin,10):typeof r.begin=="number"?i.begin=r.begin:typeof r.begin=="object"&&(i.begin=new 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        return offset;
      }
      `)}}getOutputSamplingSnippet(){const o=this.context.outputTextureLayout;return o.isPacked?this.getPackedOutputSamplingSnippet(o):this.getUnpackedOutputSamplingSnippet(o)}getPackedOutputSamplingSnippet(o){const t=o.unpackedShape,e=[o.width,o.height],r={},i="getOutputCoords";switch(t.length){case 0:r[i]=this.getOutputScalarCoords();break;case 1:r[i]=this.getOutputPacked1DCoords(t,e);break;case 2:r[i]=this.getOutputPacked2DCoords(t,e);break;case 3:r[i]=this.getOutputPacked3DCoords(t,e);break;default:r[i]=this.getOutputPackedNDCoords(t,e)}const d=`
      void setOutput(vec4 val) {
        ${(0,f.getGlsl)(this.context.glContext.version).output} = val;
      }
    `;return r.floatTextureSetRGBA=new c.GlslLibRoutine(d),r}getUnpackedOutputSamplingSnippet(o){const t=o.unpackedShape,e=[o.width,o.height],r={},i="getOutputCoords";switch(t.length){case 0:r[i]=this.getOutputScalarCoords();break;case 1:r[i]=this.getOutputUnpacked1DCoords(t,e);break;case 2:r[i]=this.getOutputUnpacked2DCoords(t,e);break;case 3:r[i]=this.getOutputUnpacked3DCoords(t,e);break;case 4:r[i]=this.getOutputUnpacked4DCoords(t,e);break;case 5:r[i]=this.getOutputUnpacked5DCoords(t,e);break;case 6:r[i]=this.getOutputUnpacked6DCoords(t,e);break;default:throw new Error(`Unsupported output dimensionality: ${t.length}`)}const d=`
        void setOutput(float val) {
          ${(0,f.getGlsl)(this.context.glContext.version).output} = vec4(val, 0, 0, 0);
        }
    `;return r.floatTextureSetR=new c.GlslLibRoutine(d),r}getOutputScalarCoords(){return new c.GlslLibRoutine(`
      int getOutputCoords() {
        return 0;
      }
    `)}getOutputPacked1DCoords(o,t){const e=t;let r="";return e[0]===1?(r=`
          int getOutputCoords() {
            return 2 * int(TexCoords.y * ${e[1]}.0);
          }
        `,new c.GlslLibRoutine(r)):e[1]===1?(r=`
          int getOutputCoords() {
            return 2 * int(TexCoords.x * ${e[0]}.0);
          }
        `,new c.GlslLibRoutine(r)):(r=`
        int getOutputCoords() {
          ivec2 resTexRC = ivec2(TexCoords.xy *
                                 vec2(${e[0]}, ${e[1]}));
          return 2 * (resTexRC.y * ${e[0]} + resTexRC.x);
        }
      `,new c.GlslLibRoutine(r))}getOutputPacked2DCoords(o,t){let e="";if(u.ArrayUtil.arraysEqual(o,t))return e=`
        ivec2 getOutputCoords() {
          return 2 * ivec2(TexCoords.xy * vec2(${t[0]}, ${t[1]}));
        }
      `,new c.GlslLibRoutine(e);const r=t,i=Math.ceil(o[1]/2);return e=`
        ivec2 getOutputCoords() {
          ivec2 resTexRC = ivec2(TexCoords.xy *
                                vec2(${r[0]}, ${r[1]}));

          int index = resTexRC.y * ${r[0]} + resTexRC.x;

          // reverse r and c order for packed texture
          int r = imod(index, ${i}) * 2;
          int c = 2 * (index / ${i});

          return ivec2(r, c);
        }
      `,new c.GlslLibRoutine(e)}getOutputPacked3DCoords(o,t){const e=[t[0],t[1]],r=Math.ceil(o[2]/2),i=r*Math.ceil(o[1]/2),d=`
        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 c.GlslLibRoutine(d)}getOutputPackedNDCoords(o,t){const e=[t[0],t[1]],r=Math.ceil(o[o.length-1]/2),i=r*Math.ceil(o[o.length-2]/2);let d=i,g="",m="b, r, c";for(let y=2;y<o.length-1;y++)d*=o[o.length-y-1],g=`
      int b${y} = index / ${d};
      index -= b${y} * ${d};
    `+g,m=`b${y}, `+m;const _=`
      ivec${o.length} getOutputCoords() {
        ivec2 resTexRC = ivec2(TexCoords.xy *
                              vec2(${e[0]}, ${e[1]}));
        int index = resTexRC.y * ${e[0]} + resTexRC.x;

        ${g}

        int b = index / ${i};
        index -= b * ${i};

        // reverse r and c order for packed texture
        int r = imod(index, ${r}) * 2;
        int c = 2 * (index / ${r});

        return ivec${o.length}(${m});
      }
    `;return new c.GlslLibRoutine(_)}getOutputUnpacked1DCoords(o,t){const e=`
        int getOutputCoords() {
          ivec2 resTexRC = ivec2(TexCoords.xy *
                                vec2(${t[0]}, ${t[1]}));
          return resTexRC.y * ${t[0]} + resTexRC.x;
        }
      `;return new c.GlslLibRoutine(e)}getOutputUnpacked2DCoords(o,t){const e=`
        ivec2 getOutputCoords() {
          ivec2 resTexRC = ivec2(TexCoords.xy *
                                vec2(${t[0]}, ${t[1]}));
          int index = resTexRC.y * ${t[0]} + resTexRC.x;
          int r = index / ${o[1]};
          int c = index - r * ${o[1]};
          return ivec2(r, c);
        }
      `;return new c.GlslLibRoutine(e)}getOutputUnpacked3DCoords(o,t){let e="";const r=o.length;let i=null;r<2&&(i=[]),i=new Array(r-1),i[r-2]=o[r-1];for(let m=r-3;m>=0;--m)i[m]=i[m+1]*o[m+1];const d=["r","c","d"],g=i.map((m,_)=>`int ${d[_]} = index / ${m}; ${_===i.length-1?`int ${d[_+1]} = index - ${d[_]} * ${m}`:`index -= ${d[_]} * ${m}`};`).join("");return e=`
        ivec3 getOutputCoords() {
          ivec2 resTexRC = ivec2(TexCoords.xy *
                                vec2(${t[0]}, ${t[1]}));
          int index = resTexRC.y * ${t[0]} + resTexRC.x;
          ${g}
          return ivec3(r, c, d);
        }
      `,new c.GlslLibRoutine(e)}getOutputUnpacked4DCoords(o,t){let e="";const r=o.length;let i=null;r<2&&(i=[]),i=new Array(r-1),i[r-2]=o[r-1];for(let m=r-3;m>=0;--m)i[m]=i[m+1]*o[m+1];const d=["r","c","d","d2"],g=i.map((m,_)=>`int ${d[_]} = index / ${m}; ${_===i.length-1?`int ${d[_+1]} = index - ${d[_]} * ${m}`:`index -= ${d[_]} * ${m}`};`).join("");return e=`
      ivec4 getOutputCoords() {
          ivec2 resTexRC = ivec2(TexCoords.xy *
                                vec2(${t[0]}, ${t[1]}));
          int index = resTexRC.y * ${t[0]} + resTexRC.x;
          ${g}
          return ivec4(r, c, d, d2);
        }
      `,new c.GlslLibRoutine(e)}getOutputUnpacked5DCoords(o,t){let e="";const r=o.length;let i=null;r<2&&(i=[]),i=new Array(r-1),i[r-2]=o[r-1];for(let m=r-3;m>=0;--m)i[m]=i[m+1]*o[m+1];const d=["r","c","d","d2","d3"],g=i.map((m,_)=>`int ${d[_]} = index / ${m}; ${_===i.length-1?`int ${d[_+1]} = index - ${d[_]} * ${m}`:`index -= ${d[_]} * ${m}`};`).join("");return e=`
      ivec5 getOutputCoords() {
          ivec2 resTexRC = ivec2(TexCoords.xy *
                                vec2(${t[0]}, ${t[1]}));
          int index = resTexRC.y * ${t[0]} + resTexRC.x;
          ${g}
          return ivec5(r, c, d, d2, d3);
        }
      `,new c.GlslLibRoutine(e)}getOutputUnpacked6DCoords(o,t){let e="";const r=o.length;let i=null;r<2&&(i=[]),i=new Array(r-1),i[r-2]=o[r-1];for(let m=r-3;m>=0;--m)i[m]=i[m+1]*o[m+1];const d=["r","c","d","d2","d3","d4"],g=i.map((m,_)=>`int ${d[_]} = index / ${m}; ${_===i.length-1?`int ${d[_+1]} = index - ${d[_]} * ${m}`:`index -= ${d[_]} * ${m}`};`).join("");return e=`
     ivec6 getOutputCoords() {
         ivec2 resTexRC = ivec2(TexCoords.xy *
                               vec2(${t[0]}, ${t[1]}));
         int index = resTexRC.y * ${t[0]} + resTexRC.x;
         ${g}
         return ivec6(r, c, d, d2, d3, d4);
       }
     `,new c.GlslLibRoutine(e)}getCommonUtilFuncs(){const o={};let t="uvFromFlat";o[t]=new c.GlslLibRoutine(`
    vec2 uvFromFlat(int texNumR, int texNumC, int index) {
      int texC = index / texNumR;
      int texR = index - texC * texNumR;
      // TODO: swap texR, texC order in following function so row is corresponding to u and column is corresponding to
      //       v.
      return (vec2(texR, texC) + halfCR) / vec2(texNumR, texNumC);
    }
    `),t="packedUVfrom1D",o[t]=new c.GlslLibRoutine(`
      vec2 packedUVfrom1D(int texNumR, int texNumC, int index) {
        int texelIndex = index / 2;
        int texR = texelIndex / texNumC;
        int texC = texelIndex - texR * texNumC;
        return (vec2(texC, texR) + halfCR) / vec2(texNumC, texNumR);
      }
      `),t="packedUVfrom2D",o[t]=new c.GlslLibRoutine(`
      vec2 packedUVfrom2D(int texNumR, int texNumC, int texelsInLogicalRow, int row, int col) {
        int texelIndex = (row / 2) * texelsInLogicalRow + (col / 2);
        int texR = texelIndex / texNumC;
        int texC = texelIndex - texR * texNumC;
        return (vec2(texC, texR) + halfCR) / vec2(texNumC, texNumR);
      }
      `),t="packedUVfrom3D",o[t]=new c.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,f.getGlsl)(this.context.glContext.version);return o[t]=new c.GlslLibRoutine(`
        float sampleTexture(sampler2D textureSampler, vec2 uv) {
            return ${e.texture2D}(textureSampler, uv).r;
        }`),o}getInputsSamplingSnippets(){const o={},t=this.context.outputTextureLayout;return this.context.programInfo.inputNames.forEach((e,r)=>{const i=this.context.inputTextureLayouts[r],d=(0,h.generateShaderFuncNameFromInputSamplerName)(e);i.isPacked?o[d]=this.getPackedSamplerFromInput(d,e,i):o[d]=this.getUnpackedSamplerFromInput(d,e,i);const g=(0,h.generateShaderFuncNameFromInputSamplerNameAtOutCoords)(e);i.unpackedShape.length<=t.unpackedShape.length&&(i.isPacked?o[g]=this.getPackedSamplerAtOutputCoords(g,i,t,e):o[g]=this.getUnpackedSamplerAtOutputCoords(g,i,t,e))}),o}getPackedSamplerAtOutputCoords(o,t,e,r){const i=t.unpackedShape,d=e.unpackedShape,g=r,m=(0,h.generateShaderFuncNameFromInputSamplerName)(g),_=i.length,y=d.length,T=u.BroadcastUtil.getBroadcastDims(i,d),w=(0,h.getCoordsDataType)(y),S=y-_;let O;const E=(0,h.getGlChannels)();O=_===0?"":y<2&&T.length>=1?"coords = 0;":T.map(k=>`coords.${E[k+S]} = 0;`).join(`
`);let v="";v=y<2&&_>0?"coords":i.map((k,Y)=>`coords.${E[Y+S]}`).join(", ");let P="return outputValue;";const L=u.ShapeUtil.size(i)===1,V=u.ShapeUtil.size(d)===1;if(_!==1||L||V){if(L&&!V)P=y===1?`
          return vec4(outputValue.x, outputValue.x, 0., 0.);
        `:`
          return vec4(outputValue.x);
        `;else if(T.length){const k=_-2,Y=_-1;T.indexOf(k)>-1&&T.indexOf(Y)>-1?P="return vec4(outputValue.x);":T.indexOf(k)>-1?P="return vec4(outputValue.x, outputValue.y, outputValue.x, outputValue.y);":T.indexOf(Y)>-1&&(P="return vec4(outputValue.xx, outputValue.zz);")}}else P=`
        return vec4(outputValue.xy, outputValue.xy);
      `;const R=`
      vec4 ${o}() {
        ${w} coords = getOutputCoords();
        
        int lastDim = coords.${E[y-1]};
        coords.${E[y-1]} = coords.${E[y-2]};
        coords.${E[y-2]} = lastDim;
      
        ${O}
        vec4 outputValue = ${m}(${v});
        ${P}
      }
    `;return new c.GlslLibRoutine(R,["coordinates.getOutputCoords"])}getUnpackedSamplerAtOutputCoords(o,t,e,r){const i=[e.width,e.height],d=[t.width,t.height],g=t.unpackedShape.length,m=e.unpackedShape.length,_=t.unpackedShape,y=e.unpackedShape,T=(0,h.generateShaderFuncNameFromInputSamplerName)(r);if(g===m&&u.ArrayUtil.arraysEqual(d,i)){const V=`
          float ${o}() {
            return sampleTexture(${r}, TexCoords);
          }
        `;return new c.GlslLibRoutine(V,["coordinates.sampleTexture"])}const w=(0,h.getCoordsDataType)(m),S=u.BroadcastUtil.getBroadcastDims(_,y),O=m-g;let E;const v=(0,h.getGlChannels)();E=g===0?"":m<2&&S.length>=1?"coords = 0;":S.map(V=>`coords.${v[V+O]} = 0;`).join(`
`);let P="";P=m<2&&g>0?"coords":t.unpackedShape.map((V,R)=>`coords.${v[R+O]}`).join(", ");const L=`
        float ${o}() {
          ${w} coords = getOutputCoords();
          ${E}
          return ${T}(${P});
        }
      `;return new c.GlslLibRoutine(L,["coordinates.getOutputCoords"])}getPackedSamplerFromInput(o,t,e){switch(e.unpackedShape.length){case 0:return this.getPackedSamplerScalar(o,t);case 1:return this.getPackedSampler1D(o,t,e);case 2:return this.getPackedSampler2D(o,t,e);case 3:return this.getPackedSampler3D(o,t,e);default:return this.getPackedSamplerND(o,t,e)}}getUnpackedSamplerFromInput(o,t,e){const r=e.unpackedShape;switch(r.length){case 0:return this.getUnpackedSamplerScalar(o,t,e);case 1:return this.getUnpackedSampler1D(o,t,e);case 2:return this.getUnpackedSampler2D(o,t,e);case 3:return this.getUnpackedSampler3D(o,t,e);case 4:return this.getUnpackedSampler4D(o,t,e);case 5:return this.getUnpackedSampler5D(o,t,e);case 6:return this.getUnpackedSampler6D(o,t,e);default:throw new Error(`Unsupported dimension ${r.length}-D`)}}getPackedSamplerScalar(o,t){const e=`
          vec4 ${o}() {
            return ${(0,f.getGlsl)(this.context.glContext.version).texture2D}(${t}, halfCR);
          }
        `;return new c.GlslLibRoutine(e)}getPackedSampler1D(o,t,e){const r=[e.width,e.height],i=[r[1],r[0]],d=(0,f.getGlsl)(this.context.glContext.version),g=`vec4 ${o}(int index) {
      vec2 uv = packedUVfrom1D(
      ${i[0]}, ${i[1]}, index);
      return ${d.texture2D}(${t}, uv);
    }`;return new c.GlslLibRoutine(g,["coordinates.packedUVfrom1D"])}getPackedSampler2D(o,t,e){const r=e.unpackedShape,i=[e.width,e.height],d=(0,f.getGlsl)(this.context.glContext.version),g=i[0],m=i[1];if(i!=null&&u.ArrayUtil.arraysEqual(r,i)){const w=`vec4 ${o}(int row, int col) {
        vec2 uv = (vec2(col, row) + halfCR) / vec2(${m}.0, ${g}.0);
        return ${d.texture2D}(${t}, uv);
      }`;return new c.GlslLibRoutine(w)}const _=i,y=Math.ceil(r[1]/2),T=`vec4 ${o}(int row, int col) {
      vec2 uv = packedUVfrom2D(${_[1]}, ${_[0]}, ${y}, row, col);
      return ${d.texture2D}(${t}, uv);
    }`;return new c.GlslLibRoutine(T,["coordinates.packedUVfrom2D"])}getPackedSampler3D(o,t,e){const r=e.unpackedShape,i=[e.width,e.height],d=[i[0],i[1]],g=(0,f.getGlsl)(this.context.glContext.version);if(r[0]===1){const w=r.slice(1),S=[1,2],O=(0,h.squeezeInputShape)(r,w),E=["b","row","col"],v=JSON.parse(JSON.stringify(e));v.unpackedShape=O;const P=this.getPackedSamplerFromInput(o,t,v),L=`${P.routineBody}
      vec4 ${o}(int b, int row, int col) {
        return ${o}(${(0,h.getSqueezedParams)(E,S)});
      } `;return new c.GlslLibRoutine(L,P.dependencies)}const m=d[0],_=d[1],y=Math.ceil(r[2]/2),T=`vec4 ${o}(int b, int row, int col) {
      vec2 uv = packedUVfrom3D(
        ${_}, ${m}, ${y*Math.ceil(r[1]/2)}, ${y}, b, row, col);
      return ${g.texture2D}(${t}, uv);}`;return new c.GlslLibRoutine(T,["coordinates.packedUVfrom3D"])}getPackedSamplerND(o,t,e){const r=e.unpackedShape,i=r.length,d=[e.width,e.height],g=(0,f.getGlsl)(this.context.glContext.version),m=[d[0],d[1]],_=m[1],y=m[0],T=Math.ceil(r[i-1]/2);let w=T*Math.ceil(r[i-2]/2),S="int b, int row, int col",O=`b * ${w} + (row / 2) * ${T} + (col / 2)`;for(let v=2;v<i-1;v++)S=`int b${v}, `+S,w*=r[i-v-1],O=`b${v} * ${w} + `+O;const E=`vec4 ${o}(${S}) {
      int index = ${O};
      int texR = index / ${y};
      int texC = index - texR * ${y};
      vec2 uv = (vec2(texC, texR) + halfCR) / vec2(${y}, ${_});
      return ${g.texture2D}(${t}, uv);
    }`;return new c.GlslLibRoutine(E)}getUnpackedSamplerScalar(o,t,e){const[r,i]=[e.width,e.height];if(r===1&&i===1){const g=`
          float ${o}() {
            return sampleTexture(${t}, halfCR);
          }
        `;return new c.GlslLibRoutine(g,["coordinates.sampleTexture"])}const d=`
        float ${o}() {
          int offset_${t} = coordsToOffset(TexCoords, ${r}, ${i});
          vec2 uv = uvFromFlat(${r}, ${i}, offset_${t});
          return sampleTexture(${t}, uv);
        }
      `;return new c.GlslLibRoutine(d,["coordinates.uvFromFlat","coordinates.sampleTexture","coordinates.coordsToOffset"])}getUnpackedSampler1D(o,t,e){const r=e.width,i=e.height;if(i===1&&r===1){const g=`
        float ${o}(int index) {
          return sampleTexture(${t}, halfCR);
        }
      `;return new c.GlslLibRoutine(g,["coordinates.sampleTexture"])}if(i===1){const g=`
          float ${o}(int index) {
            vec2 uv = vec2((float(index) + 0.5) / ${r}.0, 0.5);
            return sampleTexture(${t}, uv);
          }
        `;return new c.GlslLibRoutine(g,["coordinates.sampleTexture"])}if(r===1){const g=`
          float ${o}(int index) {
            vec2 uv = vec2(0.5, (float(index) + 0.5) / ${i}.0);
            return sampleTexture(${t}, uv);
          }
        `;return new c.GlslLibRoutine(g,["coordinates.sampleTexture"])}const d=`
        float ${o}(int index) {
          vec2 uv = uvFromFlat(${r}, ${i}, index);
          return sampleTexture(${t}, uv);
        }
      `;return new c.GlslLibRoutine(d,["coordinates.uvFromFlat","coordinates.sampleTexture"])}getUnpackedSampler2D(o,t,e){const r=e.unpackedShape,i=[e.height,e.width];if(i!=null&&u.ArrayUtil.arraysEqual(r,i)){const w=`
          float ${o}(int row, int col) {
            vec2 uv = (vec2(row, col) + halfCR) / vec2(${i[1]}.0, ${i[0]}.0);
            return sampleTexture(${t}, uv);
          }
        `;return new c.GlslLibRoutine(w,["coordinates.sampleTexture"])}const{newShape:d,keptDims:g}=(0,s.squeezeShape)(r),m=d;if(m.length<r.length){const w=(0,h.squeezeInputShape)(r,m),S=JSON.parse(JSON.stringify(e));S.unpackedShape=w;const O=["col","row"],E=`
          ${this.getUnpackedSamplerFromInput(o,t,S).routineBody}
          float ${o}(int row, int col) {
            return ${o}(${(0,h.getSqueezedParams)(O,g)});
          }
        `;return new c.GlslLibRoutine(E,["coordinates.sampleTexture"])}const _=i[1],y=i[0];if(y===1){const w=`
          float ${o}(int row, int col) {
            int offset_${t} = coordsToOffset(TexCoords, ${_}, ${y});
            float index = dot(vec3(row, col, offset_${t}), vec3(${r[1]}, 1, 1));
            vec2 uv = vec2(0.5, (index + 0.5) / ${_}.0);
            return sampleTexture(${t}, uv);
          }
        `;return new c.GlslLibRoutine(w,["coordinates.sampleTexture","coordinates.coordsToOffset"])}if(_===1){const w=`
          float ${o}(int row, int col) {
            int offset_${t} = coordsToOffset(TexCoords, ${_}, ${y});
            float index = dot(vec3(row, col, offset_${t}), vec3(${r[1]}, 1, 1));
            vec2 uv = vec2((index + 0.5) / ${y}.0, 0.5);
            return sampleTexture(${t}, uv);
          }
        `;return new c.GlslLibRoutine(w,["coordinates.sampleTexture","coordinates.coordsToOffset"])}const T=`
        float ${o}(int row, int col) {
          int index = col * ${r[1]} + row;
          vec2 uv = uvFromFlat(${_}, ${y}, index);
          return sampleTexture(${t}, uv);
        }
      `;return new c.GlslLibRoutine(T,["coordinates.uvFromFlat","coordinates.sampleTexture","coordinates.coordsToOffset"])}getUnpackedSampler3D(o,t,e){const r=e.unpackedShape,i=r[1]*r[2],d=r[2],{newShape:g,keptDims:m}=(0,s.squeezeShape)(r),_=g;if(_.length<r.length){const T=(0,h.squeezeInputShape)(r,_),w=["batch","col","row"],S=JSON.parse(JSON.stringify(e));S.unpackedShape=T;const O=this.getUnpackedSamplerFromInput(o,t,S),E=m.reverse(),v=`
          ${O.routineBody}
          float ${o}(int batch, int row, int col) {
            return ${o}(${(0,h.getSqueezedParams)(w,E)});
          }
        `;return new c.GlslLibRoutine(v,O.dependencies)}const y=`
          float ${o}(int depth, int row, int col) {
            // Explicitly use integer operations as dot() only works on floats.
            int index = depth * ${i} + col * ${d} + row;
            vec2 uv = uvFromFlat(${e.width}, ${e.height}, index);
            return sampleTexture(${t}, uv);
          }
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          }
        `;return new c.GlslLibRoutine(E,["coordinates.sampleTexture","coordinates.uvFromFlat"])}const T=`
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          int index = row * ${m} + col * ${g} + depth * ${d} +
          depth3 * ${i} + depth2;
          vec2 uv = uvFromFlat(${e.width}, ${e.height}, index);
          return sampleTexture(${t}, uv);
        }
      `;return new c.GlslLibRoutine(T,["coordinates.sampleTexture","coordinates.uvFromFlat"])}getUnpackedSampler6D(o,t,e){const r=e.unpackedShape,i=r[5],d=r[4]*i,g=r[3]*d,m=r[2]*g,_=r[1]*m,{newShape:y,keptDims:T}=(0,s.squeezeShape)(r);if(y.length<r.length){const S=(0,h.squeezeInputShape)(r,y),O=["row","col","depth","depth2","depth3","depth4"],E=JSON.parse(JSON.stringify(e));E.unpackedShape=S;const v=`
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            float ${o}(int row, int col, int depth,
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              return ${o}(${(0,h.getSqueezedParams)(O,T)});
            }
          `;return new c.GlslLibRoutine(v,["coordinates.sampleTexture","coordinates.uvFromFlat"])}const w=`
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            vec2 uv = uvFromFlat(${e.width}, ${e.height}, index);
            return sampleTexture(${t}, uv);
          }
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        c[${m}] = offset / ${e[m]};`),d.push(`
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        c[${t-1}] = offset;`);const g=`
      void toVec(vec2 texCoords, out int c[${t}]) {
        int offset = coordsToOffset(texCoords, ${r}, ${i});
        ${d.join("")}
      }
      void toVec(int offset, out int c[${t}]) {
        ${d.join("")}
      }
    `;return{toVec:new c.GlslLibRoutine(g,["coordinates.coordsToOffset"])}}valueFrom(){const o={};return this.context.programInfo.inputNames.forEach((t,e)=>{const r=this.context.inputTextureLayouts[e],i=(r.unpackedShape.length>0?r.unpackedShape:r.shape).length;let d=`_${t}`;o[d]=new c.GlslLibRoutine(this.getValueFromSingle(t,i,r.width,r.height,!1),[`shapeUtils.indicesToOffset${d}`,"coordinates.offsetToCoords","fragcolor.getColorAsFloat"]),d+="_T",o[d]=new c.GlslLibRoutine(this.getValueFromSingle(t,i,r.width,r.height,!0),[`shapeUtils.indicesToOffset${d}`,"coordinates.offsetToCoords","fragcolor.getColorAsFloat"])}),o}getValueFromSingle(o,t,e,r,i){let d=`_${o}`;return i&&(d+="_T"),`
        float ${d}(int m[${t}]) {
          int offset = indicesToOffset${d}(m);
          vec2 coords = offsetToCoords(offset, ${e}, ${r});
          float value = getColorAsFloat(${(0,f.getGlsl)(this.context.glContext.version).texture2D}(${o}, coords));
          return value;
        }
        `}getPackedValueFrom(o,t,e,r,i){let d=`_${o}_Pack`;return i&&(d+="_T"),`
        vec4 ${d}(int m[${t}]) {
          int offset = indicesToOffset_${o}(m);
          vec2 coords = offsetToCoords(offset, ${e}, ${r});
          return ${(0,f.getGlsl)(this.context.glContext.version).texture2D}(${o}, coords);
        }
        `}}n.CoordsGlslLib=p},8520:(b,n)=>{var a;Object.defineProperty(n,"__esModule",{value:!0}),n.TopologicalSortGlslRoutines=n.GlslLibRoutineNode=n.GlslLibRoutine=n.GlslLib=n.GlslContext=n.FunctionType=void 0,(a=n.FunctionType||(n.FunctionType={}))[a.ValueBased=0]="ValueBased",a[a.Positional=1]="Positional",n.GlslContext=class{constructor(u,c,f,s){this.glContext=u,this.programInfo=c,this.inputTextureLayouts=f,this.outputTextureLayout=s}},n.GlslLib=class{constructor(u){this.context=u}},n.GlslLibRoutine=class{constructor(u,c){this.routineBody=u,this.dependencies=c}},n.GlslLibRoutineNode=class{constructor(u,c,f){this.name=u,this.dependencies=f||[],c&&(this.routineBody=c)}addDependency(u){u&&this.dependencies.push(u)}},n.TopologicalSortGlslRoutines=class{static returnOrderedNodes(u){if(!u||u.length===0)return[];if(u.length===1)return u;const c=new Set,f=new Set,s=new Array;return this.createOrderedNodes(u,c,f,s),s}static createOrderedNodes(u,c,f,s){for(let h=0;h<u.length;++h)this.dfsTraverse(u[h],c,f,s)}static dfsTraverse(u,c,f,s){if(!u||f.has(u.name))return;if(c.has(u.name))throw new Error("Cyclic dependency detected. Can't topologically sort routines needed for shader.");c.add(u.name);const h=u.dependencies;if(h&&h.length>0)for(let p=0;p<h.length;++p)this.dfsTraverse(h[p],c,f,s);s.push(u),f.add(u.name),c.delete(u.name)}}},7341:(b,n,a)=>{Object.defineProperty(n,"__esModule",{value:!0}),n.EncodingGlslLib=void 0;const u=a(8520);class c extends u.GlslLib{constructor(s){super(s)}getFunctions(){return Object.assign(Object.assign({},this.encodeFloat32()),this.decodeFloat32())}getCustomTypes(){return{}}encodeFloat32(){return{encode:new u.GlslLibRoutine(`highp vec4 encode(highp float f) {
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        `)}}encodeUint8(){const s=c.isLittleEndian()?"rgba.rgba=rgba.abgr;":"";return{encode:new u.GlslLibRoutine(`
      highp vec4 encode(highp float f) {
        highp float F = abs(f);
        highp float Sign = step(0.0,-f);
        highp float Exponent = floor(log2(F));
        highp float Mantissa = (exp2(- Exponent) * F);
        Exponent = floor(log2(F) + 127.0) + floor(log2(Mantissa));
        highp vec4 rgba;
        rgba[0] = 128.0 * Sign  + floor(Exponent*exp2(-1.0));
        rgba[1] = 128.0 * mod(Exponent,2.0) + mod(floor(Mantissa*128.0),128.0);
        rgba[2] = floor(mod(floor(Mantissa*exp2(23.0 -8.0)),exp2(8.0)));
        rgba[3] = floor(exp2(23.0)*mod(Mantissa,exp2(-15.0)));
        ${s}
        rgba = rgba / 255.0; // values need to be normalized to [0,1]
        return rgba;
    }
        `)}}decodeUint8(){const s=c.isLittleEndian()?"rgba.rgba=rgba.abgr;":"";return{decode:new u.GlslLibRoutine(`
        highp float decode(highp vec4 rgba) {
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          ${s}
          highp float Sign = 1.0 - step(128.0,rgba[0])*2.0;
          highp float Exponent = 2.0 * mod(rgba[0],128.0) + step(128.0,rgba[1]) - 127.0;
          highp float Mantissa = mod(rgba[1],128.0)*65536.0 + rgba[2]*256.0 +rgba[3] + float(0x800000);
          highp float Result =  Sign * exp2(Exponent) * (Mantissa * exp2(-23.0 ));
          return Result;
      }
        `)}}static isLittleEndian(){const s=new ArrayBuffer(4),h=new Uint32Array(s),p=new Uint8Array(s);if(h[0]=3735928559,p[0]===239)return!0;if(p[0]===222)return!1;throw new Error("unknown endianness")}}n.EncodingGlslLib=c},9894:(b,n,a)=>{Object.defineProperty(n,"__esModule",{value:!0}),n.FragColorGlslLib=void 0;const u=a(8520),c=a(5060);class f extends u.GlslLib{constructor(h){super(h)}getFunctions(){return Object.assign(Object.assign({},this.setFragColor()),this.getColorAsFloat())}getCustomTypes(){return{}}setFragColor(){const h=(0,c.getGlsl)(this.context.glContext.version);return{setFragColor:new u.GlslLibRoutine(`
        void setFragColor(float value) {
            ${h.output} = encode(value);
        }
        `,["encoding.encode"])}}getColorAsFloat(){return{getColorAsFloat:new u.GlslLibRoutine(`
        float getColorAsFloat(vec4 color) {
            return decode(color);
        }
        `,["encoding.decode"])}}}n.FragColorGlslLib=f},2848:(b,n)=>{Object.defineProperty(n,"__esModule",{value:!0}),n.replaceInlines=void 0;const a=/@inline[\s\n\r]+(\w+)[\s\n\r]+([0-9a-zA-Z_]+)\s*\(([^)]*)\)\s*{(([^}]|[\n\r])*)}/gm;n.replaceInlines=function(u){const c={};let f;for(;(f=a.exec(u))!==null;){const s=f[3].split(",").map(h=>{const p=h.trim().split(" ");return p&&p.length===2?{type:p[0],name:p[1]}:null}).filter(h=>h!==null);c[f[2]]={params:s,body:f[4]}}for(const s in c){const h="(\\w+)?\\s+([_0-9a-zA-Z]+)\\s+=\\s+__FUNC__\\((.*)\\)\\s*;".replace("__FUNC__",s),p=new RegExp(h,"gm");for(;(f=p.exec(u))!==null;){const l=f[1],o=f[2],t=f[3].split(","),e=l?`${l} ${o};`:"";let r=c[s].body,i="";c[s].params.forEach((g,m)=>{g&&(i+=`${g.type} ${g.name} = ${t[m]};
`)}),r=`${i}
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      ${e}
      {
        ${r}
      }
      `;u=u.replace(f[0],d)}}return u.replace(a,"")}},8879:(b,n,a)=>{Object.defineProperty(n,"__esModule",{value:!0}),n.GlslPreprocessor=void 0;const u=a(8520),c=a(2848),f=a(5483),s=a(5060);n.GlslPreprocessor=class{constructor(h,p,l,o){this.libs={},this.glslLibRoutineDependencyGraph={},this.context=new u.GlslContext(h,p,l,o),Object.keys(f.glslRegistry).forEach(e=>{const r=new f.glslRegistry[e](this.context);this.libs[e]=r});const t=this.glslLibRoutineDependencyGraph;for(const e in this.libs){const r=this.libs[e].getFunctions();for(const i in r){const d=e+"."+i;let g;t[d]?(g=t[d],g.routineBody=r[i].routineBody):(g=new u.GlslLibRoutineNode(d,r[i].routineBody),t[d]=g);const m=r[i].dependencies;if(m)for(let _=0;_<m.length;++_)if(t[m[_]])g.addDependency(t[m[_]]);else{const y=new u.GlslLibRoutineNode(m[_]);t[m[_]]=y,g.addDependency(y)}}}}preprocess(){const h=this.context.programInfo;let p=h.shaderSource;return this.context.programInfo.hasMain||(p=`${p}
      ${(0,s.getDefaultFragShaderMain)(this.context.glContext.version,this.context.outputTextureLayout.shape.length)}`),p=(0,c.replaceInlines)(p),`${(0,s.getFragShaderPreamble)(this.context.glContext.version)}
    ${this.getUniforms(h.inputNames,h.variables)}
    ${this.getImports(p)}
    ${p}`}getImports(h){const p=this.selectGlslLibRoutinesToBeIncluded(h);if(p.length===0)return"";let l="";for(let o=0;o<p.length;++o){if(!p[o].routineBody)throw new Error(`Missing body for the Glsl Library routine: ${p[o].name}`);l+=p[o].routineBody+`
`}return l}selectGlslLibRoutinesToBeIncluded(h){const p=[];return Object.keys(this.glslLibRoutineDependencyGraph).forEach(l=>{const o=l.split(".")[1];h.indexOf(o)!==-1&&p.push(this.glslLibRoutineDependencyGraph[l])}),u.TopologicalSortGlslRoutines.returnOrderedNodes(p)}getUniforms(h,p){const l=[];if(h)for(const o of h)l.push(`uniform sampler2D ${o};`);if(p)for(const o of p)l.push(`uniform ${o.type} ${o.name}${o.arrayLength?`[${o.arrayLength}]`:""};`);return l.join(`
`)}}},5483:(b,n,a)=>{Object.defineProperty(n,"__esModule",{value:!0}),n.glslRegistry=void 0;const u=a(5107),c=a(7341),f=a(9894),s=a(2655),h=a(3891);n.glslRegistry={encoding:c.EncodingGlslLib,fragcolor:f.FragColorGlslLib,vec:h.VecGlslLib,shapeUtils:s.ShapeUtilsGlslLib,coordinates:u.CoordsGlslLib}},2655:(b,n,a)=>{Object.defineProperty(n,"__esModule",{value:!0}),n.ShapeUtilsGlslLib=void 0;const u=a(8520);class c extends u.GlslLib{constructor(s){super(s)}getFunctions(){return Object.assign(Object.assign(Object.assign(Object.assign(Object.assign({},this.bcastIndex()),this.bcastMatmulIndex()),this.offsetToIndices()),this.indicesToOffset()),this.incrementIndices())}getCustomTypes(){return{}}bcastIndex(){const s=this.context.outputTextureLayout.shape.length,h={};return this.context.programInfo.inputNames.forEach((p,l)=>{const o=this.context.inputTextureLayouts[l].unpackedShape;if(o.length<=s){const t=o.length,e=s-t,r=`bcastIndices_${p}`;let i="";for(let g=0;g<t;++g)i+=`
          realIndices[${g}] = int( mod(float(bcastedIndices[${e+g}]), ${o[g]}.0) );
          `;const d=`
        void ${r} (int bcastedIndices[${s}], out int realIndices[${t}]) {
          ${i}
        }
        `;h[r]=new u.GlslLibRoutine(d)}}),h}bcastMatmulIndex(){const s=this.context.outputTextureLayout.shape.length,h={};return this.context.programInfo.inputNames.forEach((p,l)=>{const o=this.context.inputTextureLayouts[l].shape;if(!(o.length<2||o.length>s)){const t=o.length,e=s-t,r=`bcastMatmulIndices_${p}`;let i="";for(let g=0;g<t-2;++g)i+=`
          realIndices[${g}] = int( mod(float(bcastedIndices[${e+g}]), ${o[g]}.0) );
          `;const d=`
        void ${r}(int bcastedIndices[${s}], out int realIndices[${t}]) {
          ${i}
          realIndices[${t-1}] = bcastedIndices[${s-1}];
          realIndices[${t-2}] = bcastedIndices[${s-2}];
        }
        `;h[r]=new u.GlslLibRoutine(d)}}),h}indicesToOffset(){const s={};return this.context.programInfo.inputNames.forEach((h,p)=>{const l=this.context.inputTextureLayouts[p].shape,o=this.context.inputTextureLayouts[p].strides,t=l.length;let e=`indicesToOffset_${h}`;s[e]=new u.GlslLibRoutine(c.indexToOffsetSingle(e,t,o)),e=`indicesToOffset_${h}_T`,s[e]=new u.GlslLibRoutine(c.indexToOffsetSingle(e,t,o.slice().reverse()))}),s}static indexToOffsetSingle(s,h,p){let l="";for(let o=h-1;o>=0;--o)l+=`
        offset += indices[${o}] * ${p[o]};
        `;return`
      int ${s}(int indices[${h}]) {
        int offset = 0;
        ${l}
        return offset;
      }
      `}offsetToIndices(){const s={};return this.context.programInfo.inputNames.forEach((h,p)=>{const l=this.context.inputTextureLayouts[p].shape,o=this.context.inputTextureLayouts[p].strides,t=l.length;let e=`offsetToIndices_${h}`;s[e]=new u.GlslLibRoutine(c.offsetToIndicesSingle(e,t,o)),e=`offsetToIndices_${h}_T`,s[e]=new u.GlslLibRoutine(c.offsetToIndicesSingle(e,t,o.slice().reverse()))}),s}static offsetToIndicesSingle(s,h,p){const l=[];for(let o=0;o<h-1;++o)l.push(`
      indices[${o}] = offset / ${p[o]};`),l.push(`
        offset -= indices[${o}] * ${p[o]};`);return l.push(`
      indices[${h-1}] = offset;`),`
      void ${s}(int offset, out int indices[${h}]) {
        ${l.join("")}
      }
      `}incrementIndices(){const s={};return this.context.programInfo.inputNames.forEach((h,p)=>{const l=this.context.inputTextureLayouts[p].shape,o=l.length,t=`incrementIndices_${h}`;let e="";for(let i=0;i<o;++i)e+=`
        shape[${i}] = ${l[i]};`;const r=`
        void ${t}(int axis, out int indices[${o}]) {
          int shape[${o}];
          ${e};
          for(int i = ${o} -1 ; i >= 0; --i) {
            if(i > axis) continue;
            indices[i] += 1;
            if(indices[i] < shape[i]) {
              break;
            }
            indices[i] = 0;
          }
        }
        `;s[t]=new u.GlslLibRoutine(r)}),s}}n.ShapeUtilsGlslLib=c},5060:(b,n)=>{Object.defineProperty(n,"__esModule",{value:!0}),n.getDefaultFragShaderMain=n.getFragShaderPreamble=n.getVertexShaderSource=n.getGlsl=void 0;const a={version:"",attribute:"attribute",varyingVertex:"varying",varyingFrag:"varying",texture2D:"texture2D",output:"gl_FragColor",outputDeclaration:""},u={version:"#version 300 es",attribute:"in",varyingVertex:"out",varyingFrag:"in",texture2D:"texture",output:"outputColor",outputDeclaration:"out vec4 outputColor;"};function c(f){return f===1?a:u}n.getGlsl=c,n.getVertexShaderSource=function(f){const s=c(f);return`${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(f){const s=c(f);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(f,s){return`
  void main() {
    int indices[${s}];
    toVec(TexCoords, indices);
    vec4 result = vec4(process(indices));
    ${c(f).output} = result;
  }
  `}},3891:(b,n,a)=>{Object.defineProperty(n,"__esModule",{value:!0}),n.VecGlslLib=void 0;const u=a(8520);class c extends u.GlslLib{constructor(s){super(s)}getCustomTypes(){return{}}getFunctions(){return Object.assign(Object.assign(Object.assign(Object.assign({},this.binaryVecFunctions()),this.copyVec()),this.setVecItem()),this.getVecItem())}binaryVecFunctions(){const s=this.context.outputTextureLayout.shape.length,h={add:"+=",sub:"-=",mul:"*=",div:"/="},p={};for(const l in h){const o=`${l}Vec`;let t="";for(let r=0;r<s;++r)t+=`
          dest[${r}] ${h[l]} src[${r}];
          `;const e=`
        void ${o}(int src[${s}], out int dest[${s}]) {
          ${t}
        }
        `;p[o]=new u.GlslLibRoutine(e)}return p}copyVec(){const s=this.context.outputTextureLayout.shape.length;let h="";for(let l=0;l<s;++l)h+=`
        dest[${l}] = src[${l}];
        `;const p=`
      void copyVec(int src[${s}], out int dest[${s}]) {
        ${h}
      }
      `;return{copyVec:new u.GlslLibRoutine(p)}}setVecItem(){const s=this.context.outputTextureLayout.shape.length;let h=`
        if(index < 0)
            index =${s} + index;
        if (index == 0)
            m[0] = value;
        `;for(let l=1;l<s-1;++l)h+=`
        else if (index == ${l})
            m[${l}] = value;
            `;h+=`
        else
            m[${s-1}] = value;
        `;const p=`
      void setVecItem(out int m[${s}], int index, int value) {
        ${h}
      }
        `;return{setVecItem:new u.GlslLibRoutine(p)}}getVecItem(){const s=this.context.outputTextureLayout.shape.length;let h=`
        if(index < 0)
            index = ${s} + index;
        if (index == 0)
            return m[0];
      `;for(let l=1;l<s-1;++l)h+=`
        else if (index == ${l})
            return m[${l}];
      `;h+=`
        else
            return m[${s-1}];
        `;const p=`
      int getVecItem(int m[${s}], int index) {
        ${h}
      }
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  vec4 ${w}(vec4 v1, vec4 v2) {
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      ${R.output} = result;
    }
    `;return{name:O.name,inputNames:["A","B"],inputTypes:[v,v],output:{dims:S[0].dims,type:E,textureType:v},shaderSource:k,hasMain:!0}};n.add=(w,S)=>[w.run(y(w,S,h()),S)],n.and=(w,S)=>[w.run(y(w,S,i(),"bool"),S)],n.div=(w,S)=>[w.run(y(w,S,p()),S)],n.equal=(w,S)=>[w.run(y(w,S,t(),"bool"),S)],n.greater=(w,S)=>[w.run(y(w,S,e(),"bool"),S)],n.less=(w,S)=>[w.run(y(w,S,r(),"bool"),S)],n.mul=(w,S)=>[w.run(y(w,S,l()),S)],n.or=(w,S)=>[w.run(y(w,S,d(),"bool"),S)],n.pow=(w,S)=>[w.run(y(w,S,m()),S)],n.pRelu=(w,S)=>[w.run(y(w,S,_()),S)],n.sub=(w,S)=>[w.run(y(w,S,o()),S)],n.xor=(w,S)=>[w.run(y(w,S,g(),"bool"),S)]},4196:(b,n,a)=>{Object.defineProperty(n,"__esModule",{value:!0}),n.parseCastAttributes=n.cast=void 0;const u=a(2517);n.cast=(f,s,h)=>(c(s),[f.cast(s[0],h)]),n.parseCastAttributes=f=>u.ProtoUtil.tensorDataTypeFromProto(f.attributes.getInt("to"));const c=f=>{if(!f||f.length!==1)throw new Error("Cast requires 1 input.");if(f[0].type==="string")throw new Error("Invalid input type.")}},1163:(b,n,a)=>{Object.defineProperty(n,"__esModule",{value:!0}),n.createPackedConcatProgramInfoLoader=void 0;const u=a(5060),c=a(2039),f=a(9390),s=a(2827);n.createPackedConcatProgramInfoLoader=(p,l,o)=>{const t=(e=l.length,r=o.cacheKey,{name:"Concat (packed)",inputNames:Array.from({length:e},(i,d)=>`X${d}`),inputTypes:Array(e).fill(c.TextureType.packed),cacheHint:r});var e,r;return Object.assign(Object.assign({},t),{get:()=>((i,d,g,m)=>{const _=g[0].dims.slice();if(m>=_.length||m<-1*_.length)throw new Error("axis specified for concat doesn't match input dimensionality");m<0&&(m=_.length+m);const y=_.slice(0);for(let z=1;z<g.length;z++){const Z=g[z].dims.slice();for(let J=0;J<_.length;J++)if(J===m)y[m]+=Z[J];else if(_[J]!==Z[J])throw new Error("non concat dimensions must match")}const T=y.length,w=(0,s.getChannels)("coords",T),S=(0,f.getCoordsDataType)(T),O=(0,s.unpackFromChannel)(),E=g.map(z=>z.dims),v=(0,f.getGlChannels)(T),P=new Array(E.length-1);P[0]=E[0][m];for(let z=1;z<P.length;z++)P[z]=P[z-1]+E[z][m];const L=v[m],V=v.slice(-2),R=v.join();let k=`if (${L} < ${P[0]}) {
        return getChannel(
            getX0(${R}), vec2(${V.join()}));
        }`;for(let z=1;z<P.length;z++){const Z=P[z-1];k+=`
            if (${L} < ${P[z]}  && ${L} >= ${P[z-1]}) {
              return getChannel(
                getX${z}(${h(v,L,Z)}),
                vec2(${h(V,L,Z)}));
            }`}const Y=P.length,C=P[P.length-1];k+=`
            return getChannel(
              getX${Y}(${h(v,L,C)}),
              vec2(${h(V,L,C)}));`;const $=(0,u.getGlsl)(i.session.backend.glContext.version),X=`
          ${O}
          float getValue(${v.map(z=>"int "+z)}) {
            ${k}
          }

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

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

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

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

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

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

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

    float value = 0.0;
    for (int wInChannel = 0; wInChannel < ${T[1]}; wInChannel++) {
      int input_channel = group_id * ${T[1]} + wInChannel;
      for (int wHeight = 0; wHeight < ${T[2]}; wHeight++) {
        int xHeight = xRCCorner.x + wHeight * ${m.dilations[0]};

        if (xHeight < 0 || xHeight >= ${y[2]}) {
          continue;
        }

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

    ivec2 loc = coords.zw + pads;

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

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

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

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

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

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

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

          `;const L=`
      ${E}

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

        return v;
      }`;return Object.assign(Object.assign({},t),{output:{dims:g,type:e.type,textureType:c.TextureType.packedLastDimension},shaderSource:m})})(f,o)}),h={name:"InstanceNormalization_ComputeOutput",inputNames:["X","MeanAndVariance","Scale","B"],inputTypes:[c.TextureType.unpacked,c.TextureType.packedLastDimension,c.TextureType.unpacked,c.TextureType.unpacked]},p=(o,t,e,r)=>{const i=Object.assign(Object.assign({},h),{cacheHint:`${e}`});return Object.assign(Object.assign({},i),{get:()=>((d,g,m,_,y)=>{const T=(0,u.getGlsl)(d.session.backend.glContext.version),[w,S]=d.calculateTextureWidthAndHeight(y,c.TextureType.packedLastDimension),[O,E]=[w/4,S],v=`
      vec4 get_MeanAndVariance(int[2] mv) {
        int offset = indicesToOffset_MeanAndVariance(mv);
        vec2 coords = offsetToCoords(offset, ${O}, ${E});
        return ${T.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:c.TextureType.unpacked},variables:[{name:"epsilon",type:"float",data:_}],shaderSource:v})})(o,i,t,e,r)})},l=o=>{if(!o||o.length!==3)throw new Error("InstanceNormalization requires 3 inputs.");const t=o[0],e=o[1],r=o[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(o[0].dims.length!==4)throw new Error("Only support 4-D input shape.")}},708:(b,n,a)=>{Object.defineProperty(n,"__esModule",{value:!0}),n.createPackedMatmulProgramInfoLoader=void 0;const u=a(2517),c=a(5060),f=a(2039),s=a(9390),h=a(2823),p=a(5623);n.createPackedMatmulProgramInfoLoader=(l,o,t)=>{const e=(r=o.length>2,i=t.activationCacheKey,{name:"MatMul (packed)",inputNames:r?["A","B","Bias"]:["A","B"],inputTypes:r?[f.TextureType.packed,f.TextureType.packed,f.TextureType.packed]:[f.TextureType.packed,f.TextureType.packed],cacheHint:i});var r,i;return Object.assign(Object.assign({},e),{get:()=>((d,g,m,_)=>{const y=m.length>2,T=y?"value += getBiasForMatmul();":"",w=m[0].dims,S=m[1].dims,O=u.BroadcastUtil.calcShape(w,S,!0),E=!u.ShapeUtil.areEqual(m[0].dims,m[1].dims);if(!O)throw new Error("Can't use matmul on the given tensors");const v=w[w.length-1],P=Math.ceil(v/2),L=w.length,V=S.length,R=(0,c.getGlsl)(d.session.backend.glContext.version),k=(0,s.getCoordsDataType)(O.length),Y=O.length,C=(0,s.getGlChannels)(),{activationFunction:$,applyActivation:X}=(0,h.getActivationSnippet)(_),z=y?`${(0,p.getBiasForMatmul)(k,C,m[2].dims,O,!0)}`:"",Z=E?`${function(Te,se,ye,be){let Ie=[],Le=[];const ve=ye[0].dims,Ne=ye[1].dims,Fe=ve.length,Me=Ne.length,Oe=be.length,Be=Oe-Fe,Ue=Oe-Me;Ie=ve.map((Ae,Re)=>`coords.${se[Re+Be]}`),Ie[Fe-1]="i*2",Ie.join(", "),Le=Ne.map((Ae,Re)=>`coords.${se[Re+Ue]}`),Le[Me-2]="i*2",Le.join(", ");const ze=u.BroadcastUtil.getBroadcastDims(ve,be),He=u.BroadcastUtil.getBroadcastDims(Ne,be),Ke=ze.map(Ae=>`coords.${se[Ae+Be]} = 0;`).join(`
`),Ge=He.map(Ae=>`coords.${se[Ae+Ue]} = 0;`).join(`
`),Ve=`int lastDim = coords.${se[Oe-1]};
  coords.${se[Oe-1]} = coords.${se[Oe-2]};
  coords.${se[Oe-2]} = lastDim;`;return`
vec4 getAAtOutCoordsMatmul(int i) {
  ${Te} coords = getOutputCoords();
  ${Ve}
  ${Ke}
  vec4 outputValue = getA(${Ie});
  return outputValue;
}

vec4 getBAtOutCoordsMatmul(int i) {
  ${Te} coords = getOutputCoords();
  ${Ve}
  ${Ge}
  vec4 outputValue = getB(${Le});
  return outputValue;
}`}(k,C,m,O)}`:"",J=E?"getAAtOutCoordsMatmul(i)":`getA(${function(Te,se){let ye="";for(let be=0;be<se-2;be++)ye+=`rc.${Te[be]}, `;return ye+=`rc.${Te[se-2]}, i*2`,ye}(C,L)})`,ue=E?"getBAtOutCoordsMatmul(i)":`getB(${function(Te,se){let ye="";for(let be=0;be<se-2;be++)ye+=`rc.${Te[be]}, `;return ye+=`i*2, rc.${Te[se-1]}`,ye}(C,V)})`,Se=`
            ${Z}
            ${z}
            ${$}
            void main() {
              ${E?"":`${k} rc =
          getOutputCoords(); int lastDim = rc.${C[Y-1]}; rc.${C[Y-1]} =
          rc.${C[Y-2]}; rc.${C[Y-2]} = lastDim;
      `}

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

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

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

          if(${E}) {
            ${e.output} = vec4(0);
          } else {
            ${_}

            ${e.output} = vec4(${v});
          }
        }
      `;return Object.assign(Object.assign({},h),{hasMain:!0,output:{dims:t.dims,type:t.type,textureType:c.TextureType.packed},shaderSource:P})})(p,l)})},2827:(b,n,a)=>{Object.defineProperty(n,"__esModule",{value:!0}),n.unpackFromChannel=n.getChannels=n.getVecChannels=void 0;const u=a(9390);function c(f,s){return(0,u.getGlChannels)(s).map(h=>`${f}.${h}`)}n.getVecChannels=c,n.getChannels=function(f,s){return s===1?[f]:c(f,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:(b,n,a)=>{Object.defineProperty(n,"__esModule",{value:!0}),n.parsePadAttributesV11=n.padV11=n.parsePadAttributesV2=n.padV2=void 0;const u=a(246),c=a(2517),f=a(5060),s=a(2039),h={name:"Pad",inputNames:["A"],inputTypes:[s.TextureType.unpacked]};n.padV2=(g,m,_)=>(o(m),[g.run(Object.assign(Object.assign({},h),{cacheHint:_.cacheKey,get:()=>l(g,m[0],_)}),m)]),n.parsePadAttributesV2=g=>{const m=g.attributes.getString("mode","constant"),_=g.attributes.getFloat("value",0),y=g.attributes.getInts("pads");return(0,u.createAttributeWithCacheKey)({mode:m,value:_,pads:y})},n.padV11=(g,m,_)=>{t(m);const y=p(g,m,_);return(0,n.padV2)(g,[m[0]],y)},n.parsePadAttributesV11=g=>g.attributes.getString("mode","constant");const p=(g,m,_)=>{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),T=m.length>=3?m[2].floatData[0]:0;return(0,u.createAttributeWithCacheKey)({mode:_,pads:y,value:T})},l=(g,m,_)=>{const y=c.ShapeUtil.padShape(m.dims.slice(),_.pads),T=y.length,w=`
      ${e(g,m,_)}
      float process(int[${T}] indices) {
          return padA(indices);
      }`;return{name:"Pad",inputNames:["A"],inputTypes:[s.TextureType.unpacked],output:{dims:y,type:m.type,textureType:s.TextureType.unpacked},shaderSource:w}},o=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,_)=>{const y=(0,f.getGlsl)(g.session.backend.glContext.version),[T,w]=g.calculateTextureWidthAndHeight(m.dims,s.TextureType.unpacked),S=c.ShapeUtil.computeStrides(m.dims);switch(_.mode){case"constant":return r(y,m.dims,S,T,w,_.pads,_.value);case"reflect":return i(y,m.dims,S,T,w,_.pads);case"edge":return d(y,m.dims,S,T,w,_.pads);default:throw new Error("Invalid mode")}},r=(g,m,_,y,T,w,S)=>{const O=m.length;let E="";for(let v=O-1;v>=0;--v)E+=`
        k = m[${v}] - ${w[v]};
        if (k < 0)  return constant;
        if (k >= ${m[v]}) return constant;
        offset += k * ${_[v]};
        `;return`
      float padA(int m[${O}]) {
        const float constant = float(${S});
        int offset = 0;
        int k = 0;
        ${E}
        vec2 coords = offsetToCoords(offset, ${y}, ${T});
        float value = getColorAsFloat(${g.texture2D}(A, coords));
        return value;
      }
      `},i=(g,m,_,y,T,w)=>{const S=m.length;let O="";for(let E=S-1;E>=0;--E)O+=`
        k = m[${E}] - ${w[E]};
        if (k < 0) { k = -k; }
        {
          const int _2n_1 = ${2*(m[E]-1)};
          k = int( mod( float(k), float(_2n_1) ) ) ;
          if(k >= ${m[E]}) { k = _2n_1 - k; }
        }
        offset += k * ${_[E]};
        `;return`
      float padA(int m[${S}]) {
        int offset = 0;
        int k = 0;
        ${O}
        vec2 coords = offsetToCoords(offset, ${y}, ${T});
        float value = getColorAsFloat(${g.texture2D}(A, coords));
        return value;
      }
      `},d=(g,m,_,y,T,w)=>{const S=m.length;let O="";for(let E=S-1;E>=0;--E)O+=`
        k = m[${E}] - ${w[E]};
        if (k < 0)  k = 0;
        if (k >= ${m[E]}) k = ${m[E]-1};
        offset += k * ${_[E]};
      `;return`
      float padA(int m[${S}]) {
        int offset = 0;
        int k = 0;
        ${O}
        vec2 coords = offsetToCoords(offset, ${y}, ${T});
        float value = getColorAsFloat(${g.texture2D}(A, coords));
        return value;
      }
      `}},2143:(b,n,a)=>{Object.defineProperty(n,"__esModule",{value:!0}),n.globalMaxPool=n.parseMaxPoolAttributes=n.maxPool=n.parseGlobalAveragePoolAttributes=n.globalAveragePool=n.parseAveragePoolAttributes=n.averagePool=void 0;const u=a(246),c=a(2517),f=a(2039);n.averagePool=(d,g,m)=>{t(g);const _={name:"AveragePool",inputNames:["X"],inputTypes:[f.TextureType.unpacked],cacheHint:m.cacheKey};return[d.run(Object.assign(Object.assign({},_),{get:()=>s(g,_,!1,m)}),g)]},n.parseAveragePoolAttributes=d=>{const g=d.attributes.getString("auto_pad","NOTSET"),m=d.attributes.getInt("ceil_mode",0),_=d.attributes.getInt("count_include_pad",0)!==0,y=d.attributes.getInts("kernel_shape"),T=d.attributes.getInts("strides",[]),w=d.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:_,kernelShape:y,strides:T,pads:w})};const s=(d,g,m,_)=>{const[y,T]=p(d,_,m),w=c.ShapeUtil.size(y.kernelShape);let S="";y.countIncludePad?S+=`value /= float(${w});`:S+=`value /= float(${w} - pad);`;const O=`
        ${e(d[0].dims,y,"value += _X(x);",S,"0.0")}
      `;return Object.assign(Object.assign({},g),{output:{dims:T,type:d[0].type,textureType:f.TextureType.unpacked},shaderSource:O})};n.globalAveragePool=(d,g,m)=>{t(g);const _={name:"GlobalAveragePool",inputNames:["X"],inputTypes:[f.TextureType.unpacked],cacheHint:`${m.countIncludePad}`};return[d.run(Object.assign(Object.assign({},_),{get:()=>s(g,_,!0,m)}),g)]},n.parseGlobalAveragePoolAttributes=d=>{const g=d.attributes.getInt("count_include_pad",0)!==0;return(0,u.createAttributeWithCacheKey)({autoPad:"",ceilMode:0,countIncludePad:g,kernelShape:[],strides:[],pads:[]})},n.maxPool=(d,g,m)=>{t(g);const _={name:"MaxPool",inputNames:["X"],inputTypes:[f.TextureType.unpacked],cacheHint:m.cacheKey};return[d.run(Object.assign(Object.assign({},_),{get:()=>h(g,_,!1,m)}),g)]},n.parseMaxPoolAttributes=d=>{const g=d.attributes.getString("auto_pad","NOTSET"),m=d.attributes.getInt("ceil_mode",0),_=d.attributes.getInts("kernel_shape"),y=d.attributes.getInts("strides",[]),T=d.attributes.getInts("pads",[]),w=d.attributes.getInt("storage_order",0),S=d.attributes.getInts("dilations",[]);if(w!==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:_,strides:y,pads:T,storageOrder:w,dilations:S})};const h=(d,g,m,_)=>{const[y,T]=p(d,_,m),w=`
      ${e(d[0].dims,y,`
      value = max(_X(x), value);
    `,"","-1e5")}
    `;return Object.assign(Object.assign({},g),{output:{dims:T,type:d[0].type,textureType:f.TextureType.unpacked},shaderSource:w})},p=(d,g,m)=>{const _=d[0].dims.slice(),y=Object.hasOwnProperty.call(g,"dilations"),T=g.kernelShape.slice(),w=g.strides.slice(),S=y?g.dilations.slice():[],O=g.pads.slice();c.PoolConvUtil.adjustPoolAttributes(m,_,T,w,S,O);const E=c.PoolConvUtil.computePoolOutputShape(m,_,w,S,T,O,g.autoPad),v=Object.assign({},g);return y?Object.assign(v,{kernelShape:T,strides:w,pads:O,dilations:S,cacheKey:g.cacheKey}):Object.assign(v,{kernelShape:T,strides:w,pads:O,cacheKey:g.cacheKey}),[v,E]},l={autoPad:"",ceilMode:0,countIncludePad:!1,kernelShape:[],strides:[],pads:[],storageOrder:0,dilations:[],cacheKey:""},o={name:"GlobalMaxPool",inputNames:["X"],inputTypes:[f.TextureType.unpacked]};n.globalMaxPool=(d,g)=>(t(g),[d.run(Object.assign(Object.assign({},o),{get:()=>h(g,o,!0,l)}),g)]);const t=d=>{if(!d||d.length!==1)throw new Error("Pool ops requires 1 input.");if(d[0].type!=="float32"&&d[0].type!=="float64")throw new Error("Invalid input type.")},e=(d,g,m,_,y)=>{const T=d.length;if(g.kernelShape.length<=2){const w=g.kernelShape[g.kernelShape.length-1],S=g.strides[g.strides.length-1],O=g.pads[g.pads.length/2-1],E=g.pads[g.pads.length-1],v=d[T-1];let P="",L="",V="";if(P=O+E!==0?`
          for (int i = 0; i < ${w}; i++) {
            x[${T} - 1] = indices[${T} - 1] * ${S} - ${O} + i;
            if (x[${T} - 1] < 0 || x[${T} - 1] >= ${v}) {
              pad++;
              continue;
            }
            ${m}
          }`:`
          for (int i = 0; i < ${w}; i++) {
            x[${T} - 1] = indices[${T} - 1] * ${S} - ${O} + i;
            ${m}
          }`,g.kernelShape.length===2){const R=g.kernelShape[g.kernelShape.length-2],k=g.strides[g.strides.length-2],Y=g.pads[g.pads.length/2-2],C=g.pads[g.pads.length-2],$=d[T-2];L=Y+C!==0?`
            for (int j = 0; j < ${R}; j++) {
              x[${T} - 2] = indices[${T} - 2] * ${k} - ${Y} + j;
              if (x[${T} - 2] < 0 || x[${T} - 2] >= ${$}) {
                pad+= ${w};
                continue;
              }
          `:`
            for (int j = 0; j < ${R}; j++) {
              x[${T} - 2] = indices[${T} - 2] * ${k} - ${Y} + j;
            `,V=`
          }
        `}return`
        float process(int indices[${T}]) {
          int x[${T}];
          copyVec(indices, x);

          float value = ${y};
          int pad = 0;
          ${L}
          ${P}
          ${V}
          ${_}
          return value;
        }
      `}{const w=c.ShapeUtil.size(g.kernelShape),S=c.ShapeUtil.computeStrides(g.kernelShape),O=S.length,E=g.pads.length,v=i(O),P=r(d,"inputDims"),L=r(g.pads,"pads"),V=r(S,"kernelStrides"),R=r(g.strides,"strides");let k="";return k=g.pads.reduce((Y,C)=>Y+C)?`
            if (x[j] >= inputDims[j] || x[j] < 0) {
              pad++;
              isPad = true;
              break;
            }
          }
          if (!isPad) {
            ${m}
          }`:`
          }
          ${m}
        `,`
        ${v}
        float process(int indices[${T}]) {
          int x[${T}];
          copyVec(indices, x);
          int offset[${O}];
          int pads[${E}];
          int inputDims[${T}];
          int kernelStrides[${O}];
          int strides[${O}];
          ${L}
          ${P}
          ${R}
          ${V}

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

          return value;
        }
      `}},r=(d,g)=>{let m="";for(let _=0;_<d.length;_++)m+=`
      ${g}[${_}] = ${d[_]};
    `;return m},i=d=>`
  void offsetToIndices(int offset, int[${d}] strides, out int[${d}] indices) {
    if (${d} == 0) {
      return;
    }
    for (int i = 0; i < ${d} - 1; ++i) {
      indices[i] = offset / strides[i];
      offset -= indices[i] * strides[i];
    }
    indices[${d} - 1] = offset;
  }`},4939:(b,n,a)=>{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=a(246),c=a(782),f=a(2517),s=a(2039),h=(o,t,e,r,i)=>{l(t);const d={name:r,inputNames:["A"],inputTypes:[s.TextureType.unpacked]};return[o.run(Object.assign(Object.assign({},d),{cacheHint:e.cacheKey,get:()=>p(o,t,e,r,i,d)}),t)]};n.parseReduceAttributes=o=>{const t=o.attributes.getInts("axes",[]),e=o.attributes.getInt("keepdims",1)===1;return(0,u.createAttributeWithCacheKey)({axes:t,keepDims:e})};const p=(o,t,e,r,i,d)=>{const g=[],m=t[0].dims.length||1,_=[],y=f.ShapeUtil.normalizeAxes(e.axes,t[0].dims.length),T=i(t,y);let w=T[1];for(let O=0;O<t[0].dims.length;O++)y.indexOf(O)>=0||y.length===0?(e.keepDims&&g.push(1),w=`
          for(int j${O} = 0; j${O} < ${t[0].dims[O]}; j${O}++) {
            inputIdx[${O}] = j${O};
            ${w}
          }`):(_.push(`inputIdx[${O}] = outputIdx[${g.length}];`),g.push(t[0].dims[O]));const S=`
      float process(int outputIdx[${g.length||1}]) {
        float value;                 // final result
        int inputIdx[${m}];      // addressing input data
        ${_.join(`
`)}
        ${T[0]}       // init ops for reduce max/min
        ${w}
        ${T[2]}       // final computation for reduce mean
        return value;
      }`;return Object.assign(Object.assign({},d),{output:{dims:g,type:t[0].type,textureType:s.TextureType.unpacked},shaderSource:S})},l=o=>{if(!o||o.length!==1)throw new Error("Reduce op requires 1 input.");if(c.NUMBER_TYPES.indexOf(o[0].type)===-1)throw new Error("Invalid input type.")};n.reduceSum=(o,t,e)=>h(o,t,e,"ReduceSum",()=>["value = 0.0;","value += _A(inputIdx);",""]),n.reduceMean=(o,t,e)=>h(o,t,e,"ReduceMean",(r,i)=>{let d=1;for(let g=0;g<r[0].dims.length;g++)(i.indexOf(g)>=0||i.length===0)&&(d*=r[0].dims[g]);return["value = 0.0;","value += _A(inputIdx);",`value /= ${d}.;`]}),n.reduceMax=(o,t,e)=>h(o,t,e,"ReduceMax",(r,i)=>{const d=[];for(let g=0;g<r[0].dims.length;g++)(i.indexOf(g)>=0||i.length===0)&&d.push(`inputIdx[${g}] = 0;`);return[`${d.join(`
`)}
value = _A(inputIdx);`,"value = max(value, _A(inputIdx));",""]}),n.reduceMin=(o,t,e)=>h(o,t,e,"ReduceMin",(r,i)=>{const d=[];for(let g=0;g<r[0].dims.length;g++)(i.indexOf(g)>=0||i.length===0)&&d.push(`inputIdx[${g}] = 0;`);return[`${d.join(`
`)}
value = _A(inputIdx);`,"value = min(value, _A(inputIdx));",""]}),n.reduceProd=(o,t,e)=>h(o,t,e,"ReduceProd",()=>["value = 1.0;","value *= _A(inputIdx);",""]),n.reduceLogSum=(o,t,e)=>h(o,t,e,"ReduceLogSum",()=>["value = 0.0;","value += _A(inputIdx);","value = log(value);"]),n.reduceLogSumSquare=(o,t,e)=>h(o,t,e,"ReduceLogSumSquare",()=>["float t; value = 0.0;","t = _A(inputIdx); value += t * t;",""])},7019:(b,n,a)=>{Object.defineProperty(n,"__esModule",{value:!0}),n.isReshapeCheap=n.processDims3D=n.createPackedReshape3DProgramInfoLoader=void 0;const u=a(2517),c=a(5060),f=a(2039),s=a(2827);n.createPackedReshape3DProgramInfoLoader=(h,p,l)=>{const o=(t=>({name:"Reshape (packed)",inputTypes:[f.TextureType.packed],inputNames:["A"],cacheHint:`${t}`}))(l);return Object.assign(Object.assign({},o),{get:()=>((t,e,r,i)=>{const d=e.dims,g=i;let m="";for(let T=0;T<4;T++){let w="";switch(T){case 0:w="outputCoords = rc;";break;case 1:w="outputCoords = ivec3(rc.x, rc.y+1, rc.z);";break;case 2:w="outputCoords = ivec3(rc.x, rc.y, rc.z+1);";break;case 3:w="outputCoords = ivec3(rc.x, rc.y+1, rc.z+1);";break;default:throw new Error}m+=`
        ${w}
        ${T>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[${T}] = getChannel(getA(inputRC.x, inputRC.y, inputRC.z), innerDims);

        ${T>0?"}":""}
      `}const _=(0,c.getGlsl)(t.session.backend.glContext.version),y=`
      ${function(T){const w=u.ShapeUtil.computeStrides(T),S=["b","r","c"],O="index";return`
    ivec3 inputCoordsFromReshapedOutCoords(int index) {
      ${w.map((E,v)=>`int ${S[v]} = ${O} / ${E}; ${v===w.length-1?`int ${S[v+1]} = ${O} - ${S[v]} * ${E}`:`index -= ${S[v]} * ${E}`};`).join("")}
      return ivec3(b, r, c);
    }
  `}(d)}
      ${function(T){const w=u.ShapeUtil.computeStrides(T);return`
  int getFlattenedIndex(ivec3 coords) {
    // reverse y, z order
    return coords.x * ${w[0]} + coords.z * ${w[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}
        ${_.output} = result;
      }
    `;return Object.assign(Object.assign({},r),{output:{dims:g,type:e.type,textureType:f.TextureType.packed},shaderSource:y,hasMain:!0})})(h,p,o,l)})},n.processDims3D=function(h){if(h.length===0)return[1,1,1];let p=1;for(let l=0;l<h.length-2;++l)p*=h[l];return[p,h.length>1?h[h.length-2]:1,h[h.length-1]]},n.isReshapeCheap=function(h,p){let l=!1;return l=h.length===0||p.length===0||(h.length<2||p.length<2?h[h.length-1]===p[p.length-1]:h[h.length-1]===p[p.length-1]&&h[h.length-2]===p[p.length-2]),l}},718:(b,n,a)=>{Object.defineProperty(n,"__esModule",{value:!0}),n.reshape=void 0;const u=a(2517);n.reshape=(c,f)=>{const s=u.ShapeUtil.calculateReshapedDims(f[0].dims,f[1].integerData);return c.session.pack?[c.reshapePacked(f[0],s)]:[c.reshapeUnpacked(f[0],s)]}},2268:(b,n,a)=>{Object.defineProperty(n,"__esModule",{value:!0}),n.parseResizeAttributesV11=n.parseResizeAttributesV10=n.resize=void 0;const u=a(5060),c=a(2039),f=a(9390),s=a(2827),h=a(9793),p={name:"Resize",inputNames:["A"],inputTypes:[c.TextureType.packed]};n.resize=(r,i,d)=>((0,h.validateInputs)(i,d),[r.run(Object.assign(Object.assign({},p),{cacheHint:d.cacheKey,get:()=>l(r,i,d)}),i)]),n.parseResizeAttributesV10=r=>(0,h.parseUpsampleAttributes)(r,10),n.parseResizeAttributesV11=r=>(0,h.parseUpsampleAttributes)(r,11);const l=(r,i,d)=>{const g=(0,u.getGlsl)(r.session.backend.glContext.version),[m,_]=o(i,d);if(m.every(k=>k===1)&&d.coordinateTransformMode!=="tf_crop_and_resize")return Object.assign(Object.assign({},p),{output:{dims:_,type:i[0].type,textureType:c.TextureType.packed},hasMain:!0,shaderSource:`void main() {
                    vec4 v = ${g.texture2D}(X, TexCoords);
                    ${g.output} = v;
                }`});const y=_.length;if(y<2)throw new Error(`output dimension should be at least 2, but got ${y}`);const T=_[y-2],w=_[y-1],S=i[0].dims;if(y!==S.length)throw new Error(`output dimension should match input ${S.length}, but got ${y}`);const O=S[y-2],E=S[y-1],v=m[y-2],P=m[y-1];let L="";if(d.mode!=="linear")throw new Error(`resize (packed) does not support mode: '${d.mode}'`);switch(d.coordinateTransformMode){case"asymmetric":L=`
                    vec4 getSourceFracIndex(ivec4 coords) {
                        return vec4(coords) / scaleWHWH;
                    }
                `;break;case"half_pixel":L=`
                    vec4 getSourceFracIndex(ivec4 coords) {
                        return (vec4(coords) + 0.5) / scaleWHWH - 0.5;
                    }
                `;break;case"pytorch_half_pixel":L=`
                    vec4 getSourceFracIndex(ivec4 coords) {
                        vec4 fcoords = vec4(coords);
                        return vec4(
                            ${w}.0 > 1.0 ? (fcoords.x + 0.5) / scaleWHWH.x - 0.5 : 0.0,
                            ${T}.0 > 1.0 ? (fcoords.y + 0.5) / scaleWHWH.y - 0.5 : 0.0,
                            ${w}.0 > 1.0 ? (fcoords.z + 0.5) / scaleWHWH.z - 0.5 : 0.0,
                            ${T}.0 > 1.0 ? (fcoords.w + 0.5) / scaleWHWH.w - 0.5 : 0.0
                          );
                    }
                `;break;case"align_corners":L=`
                    vec4 getSourceFracIndex(ivec4 coords) {
                        vec4 resized = vec4(${w}.0 - 1.0, ${T}.0 - 1.0, ${w}.0 - 1.0,
                            ${T}.0 - 1.0);
                        vec4 original = vec4(${E}.0 - 1.0, ${O}.0 - 1.0, ${E}.0 - 1.0,
                            ${O}.0 - 1.0);
                        vec4 new_scale = original / resized;
                        return vec4(coords) * new_scale;
                    }
                `;break;default:throw new Error(`resize (packed) does not support coordinateTransformMode:                                 '${d.coordinateTransformMode}'`)}const V=(0,f.getCoordsDataType)(y),R=`
            const vec2 inputWH = vec2(${O}.0, ${E}.0);
            const vec4 scaleWHWH = vec4(float(${v}), float(${P}), float(${v}), float(${P}));
            ${(0,s.unpackFromChannel)()}
            ${L}
            float getAValue(int x10, int r, int c, int d) {
                return getChannel(getA(x10, r, c, d), vec2(c, d));
            }
            void main() {
                ${V} 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 < ${T-1};
                bool hasNextCol = rc.z < ${w-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({},p),{output:{dims:_,type:i[0].type,textureType:c.TextureType.packed},hasMain:!0,shaderSource:R})},o=(r,i)=>{const d=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 T=r[i.sizesInputIdx];if(!T||T.size===0)throw new Error("Either scales or sizes MUST be provided as input.");g=Array.from(T.integerData),m=e(g,d,i.mode,i.isResize)}}else if(r[i.sizesInputIdx])throw new Error("Only one of scales or sizes must be provided as input.");const _=g||d.map((y,T)=>Math.floor(y*m[T]));return[m,_]},t=(r,i,d)=>{const g=Array.from(r.floatData);return(0,h.scalesValidation)(g,i,d),g},e=(r,i,d,g)=>{const m=i.length,_=new Array(m);for(let y=0,T=m;y<T;y++)if(i[y]===0){if(r[y]!==0)throw new Error("Input dim is zero but required output dim is non-zero.");_[y]=1}else _[y]=r[y]/i[y];return(0,h.scalesValidation)(_,d,g),_}},8117:(b,n,a)=>{Object.defineProperty(n,"__esModule",{value:!0}),n.shape=void 0;const u=a(9162);n.shape=(f,s)=>(c(s),[new u.Tensor([s[0].dims.length],"int32",void 0,void 0,new Int32Array(s[0].dims))]);const c=f=>{if(!f||f.length!==1)throw new Error("Shape requires 1 input.")}},2278:(b,n,a)=>{Object.defineProperty(n,"__esModule",{value:!0}),n.sliceV10=n.parseSliceAttributes=n.slice=void 0;const u=a(246),c=a(782),f=a(2517),s=a(2039),h={name:"Slice",inputNames:["A"],inputTypes:[s.TextureType.unpacked]};n.slice=(e,r,i)=>(l(r),[e.run(Object.assign(Object.assign({},h),{cacheHint:i.cacheKey,get:()=>p(e,r[0],i)}),r)]),n.parseSliceAttributes=e=>{const r=e.attributes.getInts("starts"),i=e.attributes.getInts("ends"),d=e.attributes.getInts("axes",[]);return(0,u.createAttributeWithCacheKey)({starts:r,ends:i,axes:d})};const p=(e,r,i)=>{const d=i.axes.length===0?r.dims.slice(0).map((S,O)=>O):i.axes,g=f.ShapeUtil.normalizeAxes(d,r.dims.length),m=i.starts.map((S,O)=>S>r.dims[g[O]]-1?r.dims[g[O]]:f.ShapeUtil.normalizeAxis(S,r.dims[g[O]])),_=i.ends.map((S,O)=>S>r.dims[g[O]]-1?r.dims[g[O]]:f.ShapeUtil.normalizeAxis(S,r.dims[g[O]])),y=r.dims.slice(),T=[];for(let S=0;S<g.length;S++)y[g[S]]=_[S]-m[S],m[S]>0&&T.push(`outputIdx[${g[S]}] += ${m[S]};`);const w=`
      float process(int outputIdx[${y.length}]) {
        ${T.join(`
      `)}
        return _A(outputIdx);
      }`;return Object.assign(Object.assign({},h),{output:{dims:y,type:r.type,textureType:s.TextureType.unpacked},shaderSource:w})},l=e=>{if(!e||e.length!==1)throw new Error("Slice requires 1 input.");if(c.NUMBER_TYPES.indexOf(e[0].type)===-1)throw new Error("Invalid input type.")};n.sliceV10=(e,r)=>{t(r);const i=o(e,r);return[e.run(Object.assign(Object.assign({},h),{cacheHint:i.cacheKey,get:()=>p(e,r[0],i)}),[r[0]])]};const o=(e,r)=>{if(!e.session.isInitializer(r[1].dataId)||!e.session.isInitializer(r[2].dataId)||r.length>=4&&!e.session.isInitializer(r[3].dataId)||r.length>=5&&!e.session.isInitializer(r[4].dataId))throw new Error("dynamic slice attributes are not allowed");if(r.length>=5&&r[4].integerData.some(m=>m!==1))throw new Error("currently non-1 steps is not supported for Slice");const i=Array.from(r[1].integerData),d=Array.from(r[2].integerData),g=r.length>=4?Array.from(r[3].integerData):[];return{starts:i,ends:d,axes:g,cacheKey:`${g};${i};${d}`}},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:(b,n,a)=>{Object.defineProperty(n,"__esModule",{value:!0}),n.softmaxV13=n.parseSoftmaxAttributesV13=n.parseSoftmaxAttributes=n.softmax=void 0;const u=a(246),c=a(2517),f=a(5060),s=a(2039),h=a(3738),p={name:"SoftmaxComputeMax",inputNames:["A"],inputTypes:[s.TextureType.unpacked]},l={name:"SoftmaxComputeScale",inputNames:["A","Max"],inputTypes:[s.TextureType.unpacked,s.TextureType.unpacked]},o={name:"SoftMax",inputNames:["A","Max","Norm"],inputTypes:[s.TextureType.unpacked,s.TextureType.unpacked,s.TextureType.unpacked]};n.softmax=(g,m,_)=>{d(m);const y=m[0].dims.slice(),T=c.ShapeUtil.normalizeAxis(_.axis,y.length),w=c.ShapeUtil.sizeToDimension(y,T),S=c.ShapeUtil.sizeFromDimension(y,T);return t(g,m,_,w,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,_)=>{d(m);const y=m[0].dims.slice(),T=c.ShapeUtil.normalizeAxis(_.axis,y.length),w=y.length,S=T!==w-1,O=[];let E,v=[],P=[];S&&(v=Array.from({length:w}).map((k,Y)=>Y),v[T]=w-1,v[w-1]=T,v.map(k=>O.push(y[k])),E=(0,u.createAttributeWithCacheKey)({perm:v}),P=(0,h.transpose)(g,m,E));const L=S?c.ShapeUtil.sizeToDimension(O,w-1):c.ShapeUtil.sizeToDimension(y,w-1),V=S?c.ShapeUtil.sizeFromDimension(O,w-1):c.ShapeUtil.sizeFromDimension(y,w-1),R=t(g,S?P:m,_,L,V);return S?(0,h.transpose)(g,R,E):R};const t=(g,m,_,y,T)=>{const w=e(g,m[0],y,T,[y]),S=g.run(Object.assign(Object.assign({},p),{cacheHint:_.cacheKey,get:()=>w}),m),O=r(g,m[0],y,T,w.output.dims,[y]),E=g.run(Object.assign(Object.assign({},l),{cacheHint:_.cacheKey,get:()=>O}),[m[0],S]),v=i(g,m[0],y,T,w.output.dims,O.output.dims);return[g.run(Object.assign(Object.assign({},o),{cacheHint:_.cacheKey,get:()=>v}),[m[0],S,E])]},e=(g,m,_,y,T)=>{const[w,S]=g.calculateTextureWidthAndHeight(m.dims,s.TextureType.unpacked),O=T.length;if(_<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]!==_)throw new Error("Shape of the output should be equal to logical row count");const E=(0,f.getGlsl)(g.session.backend.glContext.version),v=`
      float process(int[${O}] indices) {
        int logical_row_start_offset = indices[0] * ${y};

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

        return max;
      }`;return Object.assign(Object.assign({},p),{output:{dims:T,type:m.type,textureType:s.TextureType.unpacked},shaderSource:v})},r=(g,m,_,y,T,w)=>{const[S,O]=g.calculateTextureWidthAndHeight(m.dims,s.TextureType.unpacked),E=w.length;if(_<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]!==_)throw new Error("Shape of the output should be equal to logical row count");if(T.length!==1)throw new Error("Dimensionality of the intermediate results should be 1");if(T[0]!==_)throw new Error("Shape of the intermediate results should be equal to logical row count");const v=`
      float process(int[${E}] 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,f.getGlsl)(g.session.backend.glContext.version).texture2D}(A, offsetToCoords(logical_row_start_offset + i,
            ${S}, ${O}))) - max);
        }

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

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

      //determine the logical row for this index
      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({},o),{output:{dims:m.dims,type:m.type,textureType:s.TextureType.unpacked},shaderSource:v})},d=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:(b,n,a)=>{Object.defineProperty(n,"__esModule",{value:!0}),n.parseSplitAttributes=n.split=void 0;const u=a(246),c=a(2517),f=a(2039),s={name:"Split",inputNames:["A"],inputTypes:[f.TextureType.unpacked]};n.split=(o,t,e)=>{l(t);const r=c.ShapeUtil.normalizeAxis(e.axis,t[0].dims.length),i=h(o,t,r,e),d=[];for(let g=0;g<i;++g)d.push(o.run(Object.assign(Object.assign({},s),{cacheHint:`${e.cacheKey};${g}`,get:()=>p(o,t[0],e,r,g)}),t));return d},n.parseSplitAttributes=o=>{const t=o.attributes.getInt("axis",0),e=o.attributes.getInts("split",[]),r=o.outputs.length;return(0,u.createAttributeWithCacheKey)({axis:t,split:e,numOutputs:r})};const h=(o,t,e,r)=>{const[,i]=c.SplitUtil.splitShape(t[0].dims,e,r.split,r.numOutputs);return i.length},p=(o,t,e,r,i)=>{const[d,g]=c.SplitUtil.splitShape(t.dims,r,e.split,e.numOutputs),m=g[i],_=d[i],y=`
      float process(int indices[${_.length}]) {
        indices[${r}] += ${m};
        return _A(indices);
      }
    `;return Object.assign(Object.assign({},s),{cacheHint:`${e.cacheKey}:${i}`,output:{dims:_,type:t.type,textureType:f.TextureType.unpacked},shaderSource:y})},l=o=>{if(!o||o.length!==1)throw new Error("Split requires one input.");if(o[0].type!=="int8"&&o[0].type!=="uint8"&&o[0].type!=="int16"&&o[0].type!=="uint16"&&o[0].type!=="int32"&&o[0].type!=="uint32"&&o[0].type!=="float32"&&o[0].type!=="float64"&&o[0].type!=="bool")throw new Error("Invalid input type.")}},3933:(b,n,a)=>{Object.defineProperty(n,"__esModule",{value:!0}),n.parseSqueezeAttributes=n.squeezeV13=n.squeeze=void 0;const u=a(2517);n.squeeze=(s,h,p)=>{c(h);const l=u.ShapeUtil.squeezeShape(h[0].dims,p);return[s.reshapeUnpacked(h[0],l)]},n.squeezeV13=(s,h)=>(f(h),(0,n.squeeze)(s,[h[0]],Array.from(h[1].integerData))),n.parseSqueezeAttributes=s=>s.attributes.getInts("axes");const c=s=>{if(!s||s.length!==1)throw new Error("Squeeze requires 1 input.");if(s[0].type==="string")throw new Error("invalid input tensor types.")},f=s=>{if(!s||s.length!==2)throw new Error("Squeeze requires 2 inputs.");if(s[1].type!=="int32")throw new Error("Invalid input type.")}},6558:(b,n,a)=>{Object.defineProperty(n,"__esModule",{value:!0}),n.sum=void 0;const u=a(5060),c=a(2039);n.sum=(h,p)=>{s(p);const l={name:"Sum",inputNames:p.map((o,t)=>`X${t}`),inputTypes:new Array(p.length).fill(c.TextureType.unpacked)};return[h.run(Object.assign(Object.assign({},l),{get:()=>f(h,p,l)}),p)]};const f=(h,p,l)=>{const o=(0,u.getGlsl)(h.session.backend.glContext.version),t=p[0].dims.slice(),e=`
      void main() {
        vec4 result = ${p.map((r,i)=>`${o.texture2D}(X${i},TexCoords)`).join(" + ")};
        ${o.output} = result;
      }
    `;return Object.assign(Object.assign({},l),{output:{dims:t,type:p[0].type,textureType:c.TextureType.unpacked},hasMain:!0,shaderSource:e})},s=h=>{if(!h||h.length===0)throw new Error("Sum requires inputs.");const p=h[0].dims.length;for(let l=1;l<h.length;l++){if(p!==h[l].dims.length)throw new Error("Input shapes are mismatched.");for(let o=0;o<p;o++)if(h[0].dims[o]!==h[l].dims[o])throw new Error("Input shapes are not matched.")}if(h[0].type!=="float32"&&h[0].type!=="float64")throw new Error("Invalid input type.");for(let l=1;l<h.length;l++)if(h[0].type!==h[l].type)throw new Error("Input types are not matched.")}},5723:(b,n,a)=>{Object.defineProperty(n,"__esModule",{value:!0}),n.tile=void 0;const u=a(782),c=a(2039);n.tile=(h,p)=>{s(p);const l={name:"Tile",inputNames:["A"],inputTypes:[c.TextureType.unpacked]};return[h.run(Object.assign(Object.assign({},l),{get:()=>f(h,p,l)}),p)]};const f=(h,p,l)=>{const o=p[0].dims.slice(),t=new Array(o.length),e=[];for(let d=0;d<o.length;d++)t[d]=o[d]*p[1].numberData[d],e.push(`inputIdx[${d}] = int(mod(float(outputIdx[${d}]), ${o[d]}.));`);const r=t.length,i=`
      float process(int outputIdx[${r}]) {
        int inputIdx[${r}];
        ${e.join(`
`)}
        return _A(inputIdx);
      }
    `;return Object.assign(Object.assign({},l),{output:{dims:t,type:p[0].type,textureType:c.TextureType.unpacked},shaderSource:i})},s=h=>{if(!h||h.length!==2)throw new Error("Tile requires 2 input.");if(h[1].dims.length!==1)throw new Error("The second input shape must 1 dimension.");if(h[1].dims[0]!==h[0].dims.length)throw new Error("Invalid input shape.");if(u.NUMBER_TYPES.indexOf(h[0].type)===-1)throw new Error("Invalid input type.");if(h[1].type!=="int32"&&h[1].type!=="int16")throw new Error("Invalid repeat type.")}},3738:(b,n,a)=>{Object.defineProperty(n,"__esModule",{value:!0}),n.parseTransposeAttributes=n.transpose=void 0;const u=a(246),c=a(2517),f=a(2039),s={name:"Transpose",inputNames:["A"],inputTypes:[f.TextureType.unpacked]};n.transpose=(e,r,i)=>(t(r),[e.run(Object.assign(Object.assign({},s),{cacheHint:i.cacheKey,get:()=>h(e,r[0],i.perm)}),r)]),n.parseTransposeAttributes=e=>(0,u.createAttributeWithCacheKey)({perm:e.attributes.getInts("perm",[])});const h=(e,r,i)=>{const d=r.dims;i=p(d,i);const g=l(d,i),m=d.length,_=`
      ${o("perm",i,m)}
      float process(int indices[${m}]) {
        int a[${m}];
        perm(a, indices);
        return _A(a);
      }`;return Object.assign(Object.assign({},s),{output:{dims:g,type:r.type,textureType:f.TextureType.unpacked},shaderSource:_})},p=(e,r)=>(r&&r.length!==e.length&&(r=[...e.keys()].reverse()),r),l=(e,r)=>(r=p(e,r),c.ShapeUtil.sortBasedOnPerm(e,r)),o=(e,r,i)=>{const d=[];d.push(`void ${e}(out int a[${i}], int src[${i}]) {`);for(let g=0;g<i;++g)d.push(`	a[${r[g]}]=src[${g}];`);return d.push("	}"),d.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:(b,n,a)=>{Object.defineProperty(n,"__esModule",{value:!0}),n.encodeAsUint8=void 0;const u=a(5060),c=a(2039);n.encodeAsUint8=(f,s)=>{const h=s.shape,p=(0,u.getGlsl)(f.session.backend.glContext.version),l=`
    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 = ${p.texture2D}(X,TexCoords).r;
      ${p.output} = encodeAsUint8(value);
    }`,o={name:"Uint8Encode",inputTypes:[c.TextureType.unpacked],inputNames:["X"],output:{dims:h,type:s.tensor.type,textureType:c.TextureType.downloadUint8AsFloat},shaderSource:l,hasMain:!0};return f.executeProgram(o,[s.tensor])}},4909:(b,n,a)=>{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=a(246),c=a(2517),f=a(8520),s=a(5060),h=a(2039);function p(){return R("abs")}function l(){return R("acos")}function o(){return R("asin")}function t(){return R("atan")}function e(){return R("ceil")}function r(){return R("cos")}function i(C){const $="elu";return{body:`
  const float alpha = float(${C});

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

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

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

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

      ${T}

      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?`
    ${w}
    float process(int indices[4]) {
      int input_index = 0;
      int output_index = coordsToOffset(TexCoords, ${d}, ${g});

      ${T}

      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 == (${l[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]);
    }`:`
    ${w}
    float process(int indices[2]) {
      int input_index = 0;
      int output_index = coordsToOffset(TexCoords, ${d}, ${g});

      ${T}

      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 == (${l[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:l[0].type,textureType:f.TextureType.unpacked},shaderSource:S,variables:[{name:"scales",type:"int",arrayLength:o.scales.length,data:o.scales.map(O=>Math.ceil(O))}]})};n.validateInputs=(p,l)=>{if(!p||l.opset<9&&p.length!==1||l.opset>=9&&l.opset<11&&p.length!==2||l.opset>=11&&p.length<2)throw new Error("invalid inputs.");if(l.scales.length>0&&p[0].dims.length!==l.scales.length)throw new Error("Invalid input shape.");if(p[0].type==="string")throw new Error("Invalid input tensor types.")},n.scalesValidation=(p,l,o)=>{if(o){for(const t of p)if(t<=0)throw new Error("Scale value should be greater than 0.")}else for(const t of p)if(t<1)throw new Error("Scale value should be greater than or equal to 1.");if(!(l!=="linear"&&l!=="cubic"||p.length===2||p.length===4&&p[0]===1&&p[1]===1))throw new Error(`'Linear' mode and 'Cubic' mode only support 2-D inputs ('Bilinear', 'Bicubic')         or 4-D inputs with the corresponding outermost 2 scale values being 1         in the ${o?"Resize":"Upsample"} opeartor.`)}},1958:(b,n,a)=>{Object.defineProperty(n,"__esModule",{value:!0}),n.ProgramManager=void 0;const u=a(1670),c=a(6231),f=a(8879),s=a(5060);n.ProgramManager=class{constructor(h,p,l){this.profiler=h,this.glContext=p,this.textureLayoutStrategy=l,this.repo=new Map,this.attributesBound=!1}getArtifact(h){return this.repo.get(h)}setArtifact(h,p){this.repo.set(h,p)}run(h,p,l){var o;this.profiler.event("op",`ProgramManager.run ${(o=h.programInfo.name)!==null&&o!==void 0?o:"unknown kernel"}`,()=>{var t;const e=this.glContext.gl,r=h.program;e.useProgram(r);try{this.bindOutput(l),this.attributesBound||this.bindAttributes(h.attribLocations),this.bindUniforms(h.uniformLocations,(t=h.programInfo.variables)!==null&&t!==void 0?t:[],p)}catch(i){throw c.Logger.error("ProgramManager",h.programInfo.shaderSource),i}this.profiler.event("backend","GlContext.draw()",()=>{this.glContext.draw()})},this.glContext)}dispose(){this.vertexShader&&this.glContext.deleteShader(this.vertexShader),this.repo.forEach(h=>this.glContext.deleteProgram(h.program))}build(h,p,l){return this.profiler.event("backend","ProgramManager.build",()=>{const o=new f.GlslPreprocessor(this.glContext,h,p,l),t=o.preprocess(),e=this.compile(t);return{programInfo:h,program:e,uniformLocations:this.getUniformLocations(e,o.context.programInfo.inputNames,o.context.programInfo.variables),attribLocations:this.getAttribLocations(e)}})}compile(h){if(!this.vertexShader){c.Logger.verbose("ProrgramManager","Compiling and caching Vertex shader for the first time");const o=(0,s.getVertexShaderSource)(this.glContext.version);this.vertexShader=this.glContext.compileShader(o,this.glContext.gl.VERTEX_SHADER)}u.env.debug&&c.Logger.verbose("ProrgramManager",`FragShader:
${h}
`);const p=this.glContext.compileShader(h,this.glContext.gl.FRAGMENT_SHADER),l=this.glContext.createProgram(this.vertexShader,p);return this.glContext.deleteShader(p),l}bindOutput(h){const p=h.width,l=h.height;c.Logger.verbose("ProrgramManager",`Binding output texture to Framebuffer: w/h=${p}/${l}, shape=${h.shape}, type=${h.tensor.type}`),this.glContext.attachFramebuffer(h.texture,p,l)}bindAttributes(h){const p=h.position,l=h.textureCoord;this.glContext.setVertexAttributes(p,l),this.attributesBound=!0}bindUniforms(h,p,l){var o;const t=this.glContext.gl;let e=0;for(const{name:r,type:i,location:d,arrayLength:g}of h){const m=(o=p.find(_=>_.name===r))===null||o===void 0?void 0:o.data;if(i!=="sampler2D"&&!m)throw new Error(`variable '${r}' does not have data defined in program info`);switch(i){case"sampler2D":this.bindTexture(l[e],d,e),e++;break;case"float":g?t.uniform1fv(d,m):t.uniform1f(d,m);break;case"int":g?t.uniform1iv(d,m):t.uniform1i(d,m);break;default:throw new Error(`Uniform not implemented: ${i}`)}}}bindTexture(h,p,l){this.glContext.bindTextureToUniform(h.texture,l,p)}getAttribLocations(h){return{position:this.getAttribLocation(h,"position"),textureCoord:this.getAttribLocation(h,"textureCoord")}}getUniformLocations(h,p,l){const o=[];if(p)for(const t of p)o.push({name:t,type:"sampler2D",location:this.getUniformLocation(h,t)});if(l)for(const t of l)o.push(Object.assign(Object.assign({},t),{location:this.getUniformLocation(h,t.name)}));return o}getUniformLocation(h,p){const l=this.glContext.gl.getUniformLocation(h,p);if(l===null)throw new Error(`Uniform ${p} not found.`);return l}getAttribLocation(h,p){return this.glContext.gl.getAttribLocation(h,p)}}},6416:(b,n,a)=>{Object.defineProperty(n,"__esModule",{value:!0}),n.WebGLSessionHandler=void 0;const u=a(6231),c=a(1047),f=a(8316),s=a(1640),h=a(1958),p=a(7859),l=a(5702);n.WebGLSessionHandler=class{constructor(o,t){this.backend=o,this.context=t,this.layoutStrategy=new p.PreferLogicalStrategy(o.glContext.maxTextureSize),this.programManager=new h.ProgramManager(this.context.profiler,o.glContext,this.layoutStrategy),this.textureManager=new l.TextureManager(o.glContext,this.layoutStrategy,this.context.profiler,{reuseTextures:o.textureCacheMode==="full"}),this.packedTextureDataCache=new Map,this.unpackedTextureDataCache=new Map,this.pack=o.pack,this.pack2unpackMap=new Map,this.unpack2packMap=new Map}createInferenceHandler(){return new f.WebGLInferenceHandler(this)}onGraphInitialized(o){const t=o.getValues().filter(e=>e.from===-1&&e.tensor).map(e=>e.tensor.dataId);this.initializers=new Set(t)}isInitializer(o){return!!this.initializers&&this.initializers.has(o)}addInitializer(o){this.initializers.add(o)}getTextureData(o,t){return t?this.packedTextureDataCache.get(o):this.unpackedTextureDataCache.get(o)}setTextureData(o,t,e=!1){u.Logger.verbose("WebGLSessionHandler","Storing Texture data in cache"),e?this.packedTextureDataCache.set(o,t):this.unpackedTextureDataCache.set(o,t)}dispose(){this.programManager.dispose(),this.textureManager.clearActiveTextures(),this.packedTextureDataCache.forEach(o=>this.textureManager.releaseTexture(o,!0)),this.packedTextureDataCache=new Map,this.unpackedTextureDataCache.forEach(o=>this.textureManager.releaseTexture(o,!0)),this.unpackedTextureDataCache=new Map}resolve(o,t,e){const r=(0,c.resolveOperator)(o,t,s.WEBGL_OP_RESOLVE_RULES);return{impl:r.opImpl,context:r.opInit?r.opInit(o,e):o}}}},7769:(b,n,a)=>{Object.defineProperty(n,"__esModule",{value:!0}),n.Uint8DataEncoder=n.RGBAFloatDataEncoder=n.RedFloat32DataEncoder=void 0;const u=a(6231);n.RedFloat32DataEncoder=class{constructor(c,f=1){if(f===1)this.internalFormat=c.R32F,this.format=c.RED,this.textureType=c.FLOAT,this.channelSize=f;else{if(f!==4)throw new Error(`Invalid number of channels: ${f}`);this.internalFormat=c.RGBA32F,this.format=c.RGBA,this.textureType=c.FLOAT,this.channelSize=f}}encode(c,f){let s,h;return c.constructor!==Float32Array&&(u.Logger.warning("Encoder","data was not of type Float32; creating new Float32Array"),h=new Float32Array(c)),f*this.channelSize>c.length?(u.Logger.warning("Encoder","Source data too small. Allocating larger array"),h=c,s=this.allocate(f*this.channelSize),h.forEach((p,l)=>s[l]=p)):(h=c,s=h),s}allocate(c){return new Float32Array(4*c)}decode(c,f){return this.channelSize===1?c.filter((s,h)=>h%4==0).subarray(0,f):c.subarray(0,f)}},n.RGBAFloatDataEncoder=class{constructor(c,f=1,s){if(f!==1&&f!==4)throw new Error(`Invalid number of channels: ${f}`);this.internalFormat=c.RGBA,this.format=c.RGBA,this.channelSize=f,this.textureType=s||c.FLOAT}encode(c,f){let s=c;return this.channelSize===1&&(u.Logger.verbose("Encoder","Exploding into a larger array"),s=this.allocate(f),c.forEach((h,p)=>s[4*p]=h)),s}allocate(c){return new Float32Array(4*c)}decode(c,f){return this.channelSize===1?c.filter((s,h)=>h%4==0).subarray(0,f):c.subarray(0,f)}},n.Uint8DataEncoder=class{constructor(c,f=1){if(this.channelSize=4,f===1)this.internalFormat=c.ALPHA,this.format=c.ALPHA,this.textureType=c.UNSIGNED_BYTE,this.channelSize=f;else{if(f!==4)throw new Error(`Invalid number of channels: ${f}`);this.internalFormat=c.RGBA,this.format=c.RGBA,this.textureType=c.UNSIGNED_BYTE,this.channelSize=f}}encode(c,f){return new Uint8Array(c.buffer,c.byteOffset,c.byteLength)}allocate(c){return new Uint8Array(c*this.channelSize)}decode(c,f){if(c instanceof Uint8Array)return c.subarray(0,f);throw new Error(`Invalid array type: ${c.constructor}`)}}},7859:(b,n,a)=>{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=a(6231),c=a(2517);function f(o,t){const e=[],r=[],i=t!=null&&Array.isArray(t)&&t.length===0,d=t==null||i?null:s(t,o).sort();let g=0;for(let m=0;m<o.length;++m){if(d!=null){if(d[g]===m&&o[m]!==1)throw new Error(`Can't squeeze axis ${m} since its dim '${o[m]}' is not 1`);(d[g]==null||d[g]>m)&&o[m]===1&&(e.push(o[m]),r.push(m)),d[g]<=m&&g++}o[m]!==1&&(e.push(o[m]),r.push(m))}return{newShape:e,keptDims:r}}function s(o,t){const e=t.length;return o=o==null?t.map((r,i)=>i):[].concat(o),(0,c.assert)(o.every(r=>r>=-e&&r<e),()=>`All values in axis param must be in range [-${e}, ${e}) but got axis ${o}`),(0,c.assert)(o.every(h),()=>`All values in axis param must be integers but got axis ${o}`),o.map(r=>r<0?e+r:r)}function h(o){return o%1==0}function p(o){if(o.length===0)return 1;let t=o[0];for(let e=1;e<o.length;e++)t*=o[e];return t}function l(o){const t=Math.ceil(Math.sqrt(o));return[t,Math.ceil(o/t)]}n.AlwaysKeepOriginalSizeStrategy=class{constructor(o){this.maxTextureSize=o}computeTextureWH(o,t){if(o.length===0)return[1,1];const e=this.maxTextureSize;if(t&&t.breakAxis!==void 0){const d=t.breakAxis>=o.length?1:o.slice(t.breakAxis).reduce((m,_)=>m*_),g=t.breakAxis<=0?1:o.slice(0,t.breakAxis).reduce((m,_)=>m*_);if(!(d>e||g>e))return[d,g];u.Logger.verbose("TextureLayout",`Given width/height preferences were unattainable: shape:${o}, breakAxis:${t.breakAxis}`)}const r=o.reduce((d,g)=>d*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: ${o}`);return[i,r/i]}},n.PreferLogicalStrategy=class{constructor(o){this.maxTextureSize=o}computeTextureWH(o,t){const e=this.computeTexture(o,t);return t&&t.isPacked&&(e[0]/=2,e[1]/=2),t&&t.reverseWH?[e[1],e[0]]:e}computeTexture(o,t){const e=t&&t.isPacked;if(o.length===0)return e?[2,2]:[1,1];let r=this.maxTextureSize;if(t&&t.breakAxis!==void 0){const g=t.breakAxis>=o.length?1:o.slice(t.breakAxis).reduce((_,y)=>_*y),m=t.breakAxis<=0?1:o.slice(0,t.breakAxis).reduce((_,y)=>_*y);if(!(g>r||m>r))return[g,m];u.Logger.verbose("TextureLayout",`Given 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Shader source:
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e===1||this.isRenderFloat32Supported?new h.RGBAFloatDataEncoder(this.gl,t):new h.RGBAFloatDataEncoder(this.gl,t,this.textureHalfFloatExtension.HALF_FLOAT_OES);case"int":throw new Error("not implemented");case"byte":return new h.Uint8DataEncoder(this.gl,t);default:throw new Error(`Invalid dataType: ${o}`)}}clearActiveTextures(){const o=this.gl;for(let t=0;t<this.maxTextureImageUnits;++t)o.activeTexture(o.TEXTURE0+t),o.bindTexture(o.TEXTURE_2D,null)}dispose(){if(this.disposed)return;const o=this.gl;o.bindFramebuffer(o.FRAMEBUFFER,null),o.deleteFramebuffer(this.framebuffer),o.bindBuffer(o.ARRAY_BUFFER,null),o.deleteBuffer(this.vertexbuffer),o.bindBuffer(o.ELEMENT_ARRAY_BUFFER,null),o.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 o=this.gl,t=o.createBuffer();if(!t)throw new Error("createBuffer() returned null");const e=this.createDefaultGeometry();return o.bindBuffer(o.ARRAY_BUFFER,t),o.bufferData(o.ARRAY_BUFFER,e,o.STATIC_DRAW),this.checkError(),t}createFramebuffer(){const o=this.gl.createFramebuffer();if(!o)throw new Error("createFramebuffer returned null");return o}queryVitalParameters(){const o=this.gl;if(this.isFloatTextureAttachableToFrameBuffer=this.checkFloatTextureAttachableToFrameBuffer(),this.isRenderFloat32Supported=this.checkRenderFloat32(),this.isFloat32DownloadSupported=this.checkFloat32Download(),this.version===1&&!this.textureHalfFloatExtension&&!this.isRenderFloat32Supported)throw new Error("both float32 and float16 TextureType are not 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i=o.checkFramebufferStatus(o.FRAMEBUFFER)===o.FRAMEBUFFER_COMPLETE;return o.bindTexture(o.TEXTURE_2D,null),o.bindFramebuffer(o.FRAMEBUFFER,null),o.deleteTexture(t),o.deleteFramebuffer(r),i}checkRenderFloat32(){if(this.version===2){if(!this.colorBufferFloatExtension)return!1}else if(!this.textureFloatExtension)return!1;return this.isFloatTextureAttachableToFrameBuffer}checkFloat32Download(){if(this.version===2){if(!this.colorBufferFloatExtension)return!1}else if(!this.textureFloatExtension||!this.gl.getExtension("WEBGL_color_buffer_float"))return!1;return this.isFloatTextureAttachableToFrameBuffer}checkFloat32Blend(){const o=this.gl;let t,e,r,i,d;try{t=o.createTexture(),e=o.createFramebuffer(),o.bindTexture(o.TEXTURE_2D,t);const g=this.version===2?o.RGBA32F:o.RGBA;return o.texImage2D(o.TEXTURE_2D,0,g,1,1,0,o.RGBA,o.FLOAT,null),o.bindFramebuffer(o.FRAMEBUFFER,e),o.framebufferTexture2D(o.FRAMEBUFFER,o.COLOR_ATTACHMENT0,o.TEXTURE_2D,t,0),o.enable(o.BLEND),r=o.createShader(o.VERTEX_SHADER),!!r&&(o.shaderSource(r,"void main(){}"),o.compileShader(r),i=o.createShader(o.FRAGMENT_SHADER),!!i&&(o.shaderSource(i,"precision highp float;void main(){gl_FragColor=vec4(0.5);}"),o.compileShader(i),d=o.createProgram(),!!d&&(o.attachShader(d,r),o.attachShader(d,i),o.linkProgram(d),o.useProgram(d),o.drawArrays(o.POINTS,0,1),o.getError()===o.NO_ERROR)))}finally{o.disable(o.BLEND),d&&o.deleteProgram(d),r&&o.deleteShader(r),i&&o.deleteShader(i),e&&(o.bindFramebuffer(o.FRAMEBUFFER,null),o.deleteFramebuffer(e)),t&&(o.bindTexture(o.TEXTURE_2D,null),o.deleteTexture(t))}}beginTimer(){if(this.version===2&&this.disjointTimerQueryWebgl2Extension){const o=this.gl,t=this.disjointTimerQueryWebgl2Extension,e=o.createQuery();return o.beginQuery(t.TIME_ELAPSED_EXT,e),e}throw new Error("WebGL1 profiling currently not supported.")}endTimer(){if(this.version!==2||!this.disjointTimerQueryWebgl2Extension)throw new Error("WebGL1 profiling currently not supported");{const o=this.gl,t=this.disjointTimerQueryWebgl2Extension;o.endQuery(t.TIME_ELAPSED_EXT)}}isTimerResultAvailable(o){let t=!1,e=!1;if(this.version!==2||!this.disjointTimerQueryWebgl2Extension)throw new Error("WebGL1 profiling currently not supported");{const r=this.gl,i=this.disjointTimerQueryWebgl2Extension;t=r.getQueryParameter(o,r.QUERY_RESULT_AVAILABLE),e=r.getParameter(i.GPU_DISJOINT_EXT)}return t&&!e}getTimerResult(o){let t=0;if(this.version!==2)throw new Error("WebGL1 profiling currently not supported");{const e=this.gl;t=e.getQueryParameter(o,e.QUERY_RESULT),e.deleteQuery(o)}return t/1e6}async waitForQueryAndGetTime(o){return await(0,p.repeatedTry)(()=>this.isTimerResultAvailable(o)),this.getTimerResult(o)}async createAndWaitForFence(){const o=this.createFence(this.gl);return this.pollFence(o)}createFence(o){let t;const e=o,r=e.fenceSync(e.SYNC_GPU_COMMANDS_COMPLETE,0);return o.flush(),t=r===null?()=>!0:()=>{const i=e.clientWaitSync(r,0,0);return i===e.ALREADY_SIGNALED||i===e.CONDITION_SATISFIED},{query:r,isFencePassed:t}}async pollFence(o){return new Promise(t=>{this.addItemToPoll(()=>o.isFencePassed(),()=>t())})}pollItems(){const o=l(this.itemsToPoll.map(t=>t.isDoneFn));for(let t=0;t<=o;++t){const{resolveFn:e}=this.itemsToPoll[t];e()}this.itemsToPoll=this.itemsToPoll.slice(o+1)}async addItemToPoll(o,t){this.itemsToPoll.push({isDoneFn:o,resolveFn:t}),this.itemsToPoll.length>1||await(0,p.repeatedTry)(()=>(this.pollItems(),this.itemsToPoll.length===0))}}},1036:(b,n,a)=>{Object.defineProperty(n,"__esModule",{value:!0}),n.ExecutionPlan=void 0;const u=a(6231);class c{constructor(s,h){this.op=s,this.node=h}}n.ExecutionPlan=class{constructor(f,s,h){this.graph=f,this.profiler=h,this.initialize(s)}initialize(f){this.profiler.event("session","ExecutionPlan.initialize",()=>{const s=this.graph.getNodes();if(s.length!==f.length)throw new Error("The size of nodes and OPs do not match.");this._ops=f.map((h,p)=>new c(h,s[p])),this.reset(),this._starter=[],this._ops.forEach((h,p)=>{let l=!0;for(const o of h.node.inputs)if(!this._values[o]&&this.graph.getInputIndices().indexOf(o)===-1){l=!1;break}l&&this._starter.push(p)})})}reset(){this._values=this.graph.getValues().map(f=>f.tensor)}async execute(f,s){return this.profiler.event("session","ExecutionPlan.execute",async()=>{this.reset();const h=f.createInferenceHandler(),p=this.graph.getInputIndices();if(s.length!==p.length)throw new Error(`number of input tensors don't match the number of inputs to the model: actual: ${s.length} expected: ${p.length}`);s.forEach((i,d)=>{const g=p[d];this._values[g]=i});const l=this._starter.slice(0),o=this.graph.getValues(),t=this.graph.getNodes();let e=0;for(;e<l.length;){const i=l[e++],d=this._ops[i],g=d.node.inputs.map(T=>this._values[T]);if(g.indexOf(void 0)!==-1)throw new Error(`unresolved input detected: op: ${d.node}`);const m=g;u.Logger.verbose("ExecPlan",`Runing op:${d.node.name} (${m.map((T,w)=>`'${d.node.inputs[w]}': ${T.type}[${T.dims.join(",")}]`).join(", ")})`);const _=await this.profiler.event("node",d.node.name,async()=>d.op.impl(h,m,d.op.context));if(_.length!==d.node.outputs.length)throw new Error("the size of output does not match model definition.");_.forEach((T,w)=>{const S=d.node.outputs[w];if(this._values[S])throw new Error(`output [${S}] already has value: op:${d.node.name}`);this._values[S]=T});const y=new Set;_.forEach((T,w)=>{const S=d.node.outputs[w];for(const O of o[S].to){const E=t[O];let v=!0;for(const P of E.inputs)if(!this._values[P]){v=!1;break}v&&y.add(O)}}),l.push(...y)}const r=[];for(let i=0;i<this.graph.getOutputIndices().length;i++){const d=this.graph.getOutputIndices()[i],g=this._values[d];if(g===void 0)throw new Error(`required output [${d}] does not have value`);d===0?await g.getData():g.data,r.push(g)}return u.Logger.verbose("ExecPlan","disposing of inferenceHandler"),h.dispose(),r})}}},7070:(b,n,a)=>{Object.defineProperty(n,"__esModule",{value:!0}),n.Graph=void 0;const u=a(1446),c=a(7778),f=a(9395),s=a(9162),h=a(2517);var p=f.onnxruntime.experimental.fbs;n.Graph={from:(e,r)=>new t(e,r)};class l{constructor(r){this._from=void 0,this._to=[],this.tensor=void 0,this.type=void 0,r&&(this.type=h.ProtoUtil.tensorValueTypeFromProto(r.type.tensorType))}get from(){return this._from}get to(){return this._to}}class o{constructor(r,i){r instanceof u.onnx.NodeProto?(this.name=r.name,this.opType=r.opType,this.attributes=new c.Attribute(r.attribute)):r instanceof p.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 _=this._allData.push(new l(m))-1;i.set(m.name,_),g.push(m.name)}if(!r.initializer)throw new Error("missing information in graph: initializer");for(const m of r.initializer){let _=i.get(m.name);if(_===void 0){const y=new l;y.type={shape:{dims:h.ProtoUtil.tensorDimsFromProto(m.dims)},tensorType:h.ProtoUtil.tensorDataTypeFromProto(m.dataType)},_=this._allData.push(y)-1,i.set(m.name,_)}this._allData[_]._from=-1,this._allData[_].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: ${m.name}`);const _=this._allData.push(new l(m))-1;i.set(m.name,_),this._allOutputIndices.push(_),this._allOutputNames.push(m.name)}if(!r.node)throw new Error("missing information in graph: node");for(const m of r.node){if(!m.name)for(let y=0;;y++){const T=`unnamed_${m.opType}_${y}`;if(!d.has(T)){m.name=T;break}}if(d.has(m.name))throw new Error(`duplicated node name: ${m.name}`);const _=this._nodes.push(new o(m))-1;d.set(m.name,_)}for(let m=0;m<this._nodes.length;m++){const _=this._nodes[m],y=r.node[m];if(!y.output)throw new Error(`missing output for node: ${y.name}`);for(const T of y.output){let w=i.get(T);if(w===void 0&&(w=this._allData.push(new l)-1,i.set(T,w)),_.outputs.push(w),this._allData[w]._from!==void 0)throw new Error(`multiple nodes output to one data value: ${w}`);if(this._allData[w]._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");_.outputs.pop(),_.executeNode=!1,this._allData[w]._from=-1,this._allData[w].tensor=s.Tensor.fromProto(y.attribute[0].t)}}}for(let m=0;m<this._nodes.length;m++){const _=this._nodes[m],y=r.node[m];if(!y.input)throw new Error(`missing input for node: ${y.name}`);for(const T of y.input){const w=i.get(T);if(w===void 0){if(T===""&&y.input.length===3&&y.opType==="Resize")continue;throw new Error(`unrecognized input '${T}' for node: ${y.name}`)}_.inputs.push(w),this._allData[w]._to.push(m)}}return!0}buildGraphFromOrtFormat(r){var i,d,g;const m=new Map;this._allData=[],this._allInputIndices=[],this._allInputNames=[],this._allOutputIndices=[],this._allOutputNames=[],this._nodes=[];const _=new Map,y=[];for(let T=0;T<r.inputsLength();T++){const w=r.inputs(T);if(m.has(w))throw new Error(`duplicated input name: ${w}`);for(let S=0;S<r.nodeArgsLength();S++)if(((i=r.nodeArgs(S))===null||i===void 0?void 0:i.name())===w){const O=new l;if(((g=(d=r.nodeArgs(S))===null||d===void 0?void 0:d.type())===null||g===void 0?void 0:g.valueType())!==p.TypeInfoValue.tensor_type)throw new Error("Unexpected value type for the nodeArg.");const E=r.nodeArgs(S).type().value(new p.TensorTypeAndShape),v=h.ProtoUtil.tensorDataTypeFromProto(E.elemType()),P=E.shape(),L=[];for(let R=0;R<P.dimLength();R++)L.push(h.LongUtil.longToNumber(P.dim(R).value().dimValue()));O.type={shape:{dims:L},tensorType:v};const V=this._allData.push(O)-1;m.set(w,V),y.push(w)}}for(let T=0;T<r.initializersLength();T++){const w=r.initializers(T);let S=m.get(w.name());if(S===void 0){const O=new l,E=h.ProtoUtil.tensorDimsFromORTFormat(w),v=h.ProtoUtil.tensorDataTypeFromProto(w.dataType());O.type={shape:{dims:E},tensorType:v},S=this._allData.push(O)-1,m.set(w.name(),S)}this._allData[S]._from=-1,this._allData[S].tensor=s.Tensor.fromOrtTensor(w)}for(let T=0;T<this._allData.length;T++)this._allData[T].tensor||(this._allInputIndices.push(T),this._allInputNames.push(y[T]));for(let T=0;T<r.outputsLength();T++){const w=r.outputs(T);if(m.has(w))throw new Error(`duplicated output name: ${w}`);const S=this._allData.push(new l)-1;m.set(w,S),this._allOutputIndices.push(S),this._allOutputNames.push(w)}if(!r.nodes)throw new Error("missing information in graph: node");for(let T=0;T<r.nodesLength();T++){const w=r.nodes(T);let S=w.name();if(!S)for(let E=0;S=`unnamed_${w.opType()}_${E}`,_.has(S);E++);if(_.has(S))throw new Error(`duplicated node name: ${S}`);const O=this._nodes.push(new o(w,S))-1;_.set(S,O)}for(let T=0;T<this._nodes.length;T++){const w=this._nodes[T],S=r.nodes(T);if(S==null)throw new Error(`No node exists at index ${T}`);if(S?.outputsLength()===0)throw new Error(`missing output for node: ${S.name}`);for(let O=0;O<S?.outputsLength();O++){const E=S?.outputs(O);let v=m.get(E);if(v===void 0&&(v=this._allData.push(new l)-1,m.set(E,v)),w.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=T,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");w.outputs.pop(),w.executeNode=!1,this._allData[v]._from=-1,this._allData[v].tensor=s.Tensor.fromOrtTensor(S.attributes(0).t())}}}for(let T=0;T<this._nodes.length;T++){const w=this._nodes[T],S=r.nodes(T);if(S.inputsLength()===0)throw new Error(`missing input for node: ${S.name}`);for(let O=0;O<S.inputsLength();O++){const E=S.inputs(O),v=m.get(E);if(v===void 0)throw new Error(`unrecognized input '${E}' for node: ${S.name()}`);w.inputs.push(v),this._allData[v]._to.push(T)}}}checkIsAcyclic(){const r=new Set;this._allInputIndices.forEach(g=>{this._allData[g]._to.forEach(m=>{r.add(m)})});const i=Array.from(r),d=new Array(this._nodes.length).fill("white");for(;i.length>0;){const g=i.pop();d[g]==="gray"?d[g]="black":(i.push(g),d[g]="gray",this._nodes[g].outputs.forEach(m=>{const _=this._allData[m];if(_.tensor!==void 0)throw new Error("node outputs should not be initialized");if(_._from!==g)throw new Error("from property of the Value object doesn't match index of Node being processed");_._to.forEach(y=>{if(d[y]==="gray")throw new Error("model graph is cyclic");d[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(d=>{const g=this._allData[d]._to.indexOf(i+r);g!==-1&&(this._allData[d]._to[g]=i)}),this._nodes[i].outputs.forEach(d=>{this._allData[d]._from&&this._allData[d]._from===i+r&&(this._allData[d]._from=i)})):(r++,this._nodes[i].outputs.forEach(d=>{this._allData[d]._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 d=-1;this._allData[i].from!==void 0&&this._allData[i].from!==-1?(d=this._nodes[this._allData[i].from].outputs.indexOf(i+r),d!==-1&&(this._nodes[this._allData[i].from].outputs[d]=i)):(d=this._allInputIndices.indexOf(i+r),d!==-1&&(this._allInputIndices[d]=i)),this._allData[i].to.forEach(g=>{d=this._nodes[g].inputs.indexOf(i+r),d!==-1&&(this._nodes[g].inputs[d]=i)}),this._allData[i].to.length===0&&(d=this._allOutputIndices.indexOf(i+r),d!==-1&&(this._allOutputIndices[d]=i))}}else r++,this._allData.splice(i,1),i--}deleteNode(r){const i=this._nodes[r];if(i.outputs.length>1){for(let T=1;T<i.outputs.length;T++)if(this._allData[i.outputs[T]].to.length>0)throw new Error("Node deletion with more than one output connected to other nodes is not supported. ")}i.executeNode=!1;const d=i.inputs[0],g=i.outputs[0],m=this._allData[g].to,_=this._allData[d].to.indexOf(r);if(_===-1)throw new Error("The Value object doesn't have the current Node in it's 'to' property ");this._allData[d].to.splice(_,1),this._allData[g]._to=[];const y=this._allOutputIndices.indexOf(g);if(y!==-1&&(this._allOutputIndices[y]=d),m&&m.length>0)for(const T of m){const w=this._nodes[T].inputs.indexOf(g);if(w===-1)throw new Error("The Node object doesn't have the output Value in it's 'inputs' property ");this._nodes[T].inputs[w]=d,this._allData[d].to.push(T)}}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,d,g,m,_,y){return l.startGraph(t),l.addInitializers(t,e),l.addNodeArgs(t,r),l.addNodes(t,i),l.addMaxNodeIndex(t,d),l.addNodeEdges(t,g),l.addInputs(t,m),l.addOutputs(t,_),l.addSparseInitializers(t,y),l.endGraph(t)}}p.Graph=l})(h.fbs||(h.fbs={}))})(s.experimental||(s.experimental={}))}(n.onnxruntime||(n.onnxruntime={})),function(s){(function(h){(function(p){class l{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 l).__init(t.readInt32(t.position())+t.position(),t)}static getSizePrefixedRootAsModel(t,e){return t.setPosition(t.position()+u.flatbuffers.SIZE_PREFIX_LENGTH),(e||new l).__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,d,g,m,_,y,T){return l.startModel(t),l.addIrVersion(t,e),l.addOpsetImport(t,r),l.addProducerName(t,i),l.addProducerVersion(t,d),l.addDomain(t,g),l.addModelVersion(t,m),l.addDocString(t,_),l.addGraph(t,y),l.addGraphDocString(t,T),l.endModel(t)}}p.Model=l})(h.fbs||(h.fbs={}))})(s.experimental||(s.experimental={}))}(n.onnxruntime||(n.onnxruntime={})),function(s){(function(h){(function(p){class l{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 l).__init(t.readInt32(t.position())+t.position(),t)}static getSizePrefixedRootAsKernelCreateInfos(t,e){return t.setPosition(t.position()+u.flatbuffers.SIZE_PREFIX_LENGTH),(e||new l).__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 l.startKernelCreateInfos(t),l.addNodeIndices(t,e),l.addKernelDefHashes(t,r),l.endKernelCreateInfos(t)}}p.KernelCreateInfos=l})(h.fbs||(h.fbs={}))})(s.experimental||(s.experimental={}))}(n.onnxruntime||(n.onnxruntime={})),function(s){(function(h){(function(p){class l{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 l).__init(t.readInt32(t.position())+t.position(),t)}static getSizePrefixedRootAsSubGraphSessionState(t,e){return t.setPosition(t.position()+u.flatbuffers.SIZE_PREFIX_LENGTH),(e||new l).__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 l.startSubGraphSessionState(t),l.addGraphId(t,e),l.addSessionState(t,r),l.endSubGraphSessionState(t)}}p.SubGraphSessionState=l})(h.fbs||(h.fbs={}))})(s.experimental||(s.experimental={}))}(n.onnxruntime||(n.onnxruntime={})),function(s){(function(h){(function(p){class l{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 l).__init(t.readInt32(t.position())+t.position(),t)}static getSizePrefixedRootAsSessionState(t,e){return t.setPosition(t.position()+u.flatbuffers.SIZE_PREFIX_LENGTH),(e||new l).__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 l.startSessionState(t),l.addKernels(t,e),l.addSubGraphSessionStates(t,r),l.endSessionState(t)}}p.SessionState=l})(h.fbs||(h.fbs={}))})(s.experimental||(s.experimental={}))}(n.onnxruntime||(n.onnxruntime={})),function(s){(function(h){(function(p){class l{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 l).__init(t.readInt32(t.position())+t.position(),t)}static getSizePrefixedRootAsInferenceSession(t,e){return t.setPosition(t.position()+u.flatbuffers.SIZE_PREFIX_LENGTH),(e||new l).__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 l.startInferenceSession(t),l.addOrtVersion(t,e),l.addModel(t,r),l.addSessionState(t,i),l.endInferenceSession(t)}}p.InferenceSession=l})(h.fbs||(h.fbs={}))})(s.experimental||(s.experimental={}))}(n.onnxruntime||(n.onnxruntime={}))},7448:(b,n,a)=>{Object.defineProperty(n,"__esModule",{value:!0}),n.OnnxjsSessionHandler=void 0;const u=a(1670),c=a(9162);n.OnnxjsSessionHandler=class{constructor(f){this.session=f,this.inputNames=this.session.inputNames,this.outputNames=this.session.outputNames}async dispose(){}async run(f,s,h){const p=new Map;for(const t in f)if(Object.hasOwnProperty.call(f,t)){const e=f[t];p.set(t,new c.Tensor(e.dims,e.type,void 0,void 0,e.data))}const l=await this.session.run(p),o={};return l.forEach((t,e)=>{o[e]=new u.Tensor(t.type,t.data,t.dims)}),o}startProfiling(){this.session.startProfiling()}endProfiling(){this.session.endProfiling()}}},6919:(b,n,a)=>{Object.defineProperty(n,"__esModule",{value:!0}),n.Session=void 0;const u=a(7067),c=a(1296),f=a(7091),s=a(1036),h=a(6231),p=a(2644);n.Session=class{constructor(l={}){this._initialized=!1,this.backendHint=l.backendHint,this.profiler=h.Profiler.create(l.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(l,o,t){await this.profiler.event("session","Session.loadModel",async()=>{const e=await(0,f.resolveBackend)(this.backendHint);if(this.sessionHandler=e.createSessionHandler(this.context),this._model=new p.Model,typeof l=="string"){const r=l.endsWith(".ort");if(typeof fetch>"u"){const i=await(0,c.promisify)(u.readFile)(l);this.initialize(i,r)}else{const i=await fetch(l),d=await i.arrayBuffer();this.initialize(new Uint8Array(d),r)}}else if(ArrayBuffer.isView(l))this.initialize(l);else{const r=new Uint8Array(l,o||0,t||l.byteLength);this.initialize(r)}})}initialize(l,o){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(l,t,o),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(l){if(!this._initialized)throw new Error("session not initialized yet");return this.profiler.event("session","Session.run",async()=>{const o=this.normalizeAndValidateInputs(l),t=await this._executionPlan.execute(this.sessionHandler,o);return this.createOutput(t)})}normalizeAndValidateInputs(l){const o=this._model.graph.getInputNames();if(Array.isArray(l)){if(l.length!==o.length)throw new Error(`incorrect input array length: expected ${o.length} but got ${l.length}`)}else{if(l.size!==o.length)throw new Error(`incorrect input map size: expected ${o.length} but got ${l.size}`);const t=new Array(l.size);let e=0;for(let r=0;r<o.length;++r){const i=l.get(o[r]);if(!i)throw new Error(`missing input tensor for: '${name}'`);t[e++]=i}l=t}if(this.context.graphInputTypes&&this.context.graphInputTypes.length!==0&&this.context.graphInputDims&&this.context.graphInputDims.length!==0)this.validateInputTensorDims(this.context.graphInputDims,l,!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 d=e[t[i]];r[i]=d.type.shape.dims,this.context.graphInputTypes.push(d.type.tensorType),this.context.graphInputDims.push(l[i].dims)}this.validateInputTensorDims(r,l,!0)}return this.validateInputTensorTypes(this.context.graphInputTypes,l),l}validateInputTensorTypes(l,o){for(let t=0;t<o.length;t++){const e=l[t],r=o[t].type;if(e!==r)throw new Error(`input tensor[${t}] check failed: expected type '${e}' but got ${r}`)}}validateInputTensorDims(l,o,t){for(let e=0;e<o.length;e++){const r=l[e],i=o[e].dims;if(!this.compareTensorDims(r,i,t))throw new Error(`input tensor[${e}] check failed: expected shape '[${r.join(",")}]' but got [${i.join(",")}]`)}}compareTensorDims(l,o,t){if(l.length!==o.length)return!1;for(let e=0;e<l.length;++e)if(l[e]!==o[e]&&(!t||l[e]!==0))return!1;return!0}createOutput(l){const o=this._model.graph.getOutputNames();if(l.length!==o.length)throw new Error("expected number of outputs do not match number of generated outputs");const t=new Map;for(let e=0;e<o.length;++e)t.set(o[e],l[e]);return t}initializeOps(l){const o=l.getNodes();this._ops=new Array(o.length);for(let t=0;t<o.length;t++)this._ops[t]=this.sessionHandler.resolve(o[t],this._model.opsets,l)}}},9162:function(b,n,a){var u=this&&this.__importDefault||function(d){return d&&d.__esModule?d:{default:d}};Object.defineProperty(n,"__esModule",{value:!0}),n.Tensor=void 0;const c=a(3442),f=u(a(3720)),s=a(1446),h=a(9395),p=a(2517);var l=h.onnxruntime.experimental.fbs;class o{get data(){if(this.cache===void 0){const g=this.dataProvider(this.dataId);if(g.length!==this.size)throw new Error("Length of data provided by the Data Provider is inconsistent with the dims of this Tensor.");this.cache=g}return this.cache}get stringData(){if(this.type!=="string")throw new TypeError("data type is not string");return this.data}get integerData(){switch(this.type){case"uint8":case"int8":case"uint16":case"int16":case"int32":case"uint32":case"bool":return this.data;default:throw new TypeError("data type is not integer (uint8, int8, uint16, int16, int32, uint32, bool)")}}get floatData(){switch(this.type){case"float32":case"float64":return this.data;default:throw new TypeError("data type is not float (float32, float64)")}}get numberData(){if(this.type!=="string")return this.data;throw new TypeError("type cannot be non-number (string)")}get(g){return this.data[p.ShapeUtil.indicesToOffset(g,this.strides)]}set(g,m){this.data[p.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=p.ShapeUtil.computeStrides(this.dims)),this._strides}constructor(g,m,_,y,T,w=c.Guid.create()){this.dims=g,this.type=m,this.dataProvider=_,this.asyncDataProvider=y,this.cache=T,this.dataId=w,this.size=p.ShapeUtil.validateDimsAndCalcSize(g);const S=this.size,O=_===void 0&&y===void 0&&T===void 0;if(T!==void 0&&T.length!==S)throw new RangeError("Input dims doesn't match data length.");if(m==="string"){if(!(T===void 0||Array.isArray(T)&&T.every(E=>typeof E=="string")))throw new TypeError("cache should be a string array");O&&(this.cache=new Array(S))}else{if(T!==void 0){const E=e(m);if(!(T instanceof E))throw new TypeError(`cache should be type ${E.name}`)}if(O){const E=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,P){return new(e(P))(v)}(E,m)}}}static fromProto(g){if(!g)throw new Error("cannot construct Value from an empty tensor");const m=p.ProtoUtil.tensorDataTypeFromProto(g.dataType),_=p.ProtoUtil.tensorDimsFromProto(g.dims),y=new o(_,m);if(m==="string")g.stringData.forEach((T,w)=>{y.data[w]=(0,p.decodeUtf8String)(T)});else if(g.rawData&&typeof g.rawData.byteLength=="number"&&g.rawData.byteLength>0){const T=y.data,w=new DataView(g.rawData.buffer,g.rawData.byteOffset,g.rawData.byteLength),S=t(g.dataType),O=g.rawData.byteLength/S;if(g.rawData.byteLength%S!=0)throw new Error("invalid buffer length");if(T.length!==O)throw new Error("buffer length mismatch");for(let E=0;E<O;E++){const v=i(w,g.dataType,E*S);T[E]=v}}else{let T;switch(g.dataType){case s.onnx.TensorProto.DataType.FLOAT:T=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:T=g.int32Data;break;case s.onnx.TensorProto.DataType.INT64:T=g.int64Data;break;case s.onnx.TensorProto.DataType.DOUBLE:T=g.doubleData;break;case s.onnx.TensorProto.DataType.UINT32:case s.onnx.TensorProto.DataType.UINT64:T=g.uint64Data;break;default:throw new Error("unspecific error")}if(T==null)throw new Error("failed to populate data from a tensorproto value");const w=y.data;if(w.length!==T.length)throw new Error("array length mismatch");for(let S=0;S<T.length;S++){const O=T[S];f.default.isLong(O)?w[S]=r(O,g.dataType):w[S]=O}}return y}static fromData(g,m,_){return new o(m,_,void 0,void 0,g)}static fromOrtTensor(g){if(!g)throw new Error("cannot construct Value from an empty tensor");const m=p.ProtoUtil.tensorDimsFromORTFormat(g),_=p.ProtoUtil.tensorDataTypeFromProto(g.dataType()),y=new o(m,_);if(_==="string")for(let T=0;T<g.stringDataLength();T++)y.data[T]=g.stringData(T);else if(g.rawDataArray()&&typeof g.rawDataLength()=="number"&&g.rawDataLength()>0){const T=y.data,w=new DataView(g.rawDataArray().buffer,g.rawDataArray().byteOffset,g.rawDataLength()),S=t(g.dataType()),O=g.rawDataLength()/S;if(g.rawDataLength()%S!=0)throw new Error("invalid buffer length");if(T.length!==O)throw new Error("buffer length mismatch");for(let E=0;E<O;E++){const v=i(w,g.dataType(),E*S);T[E]=v}}return y}}function t(d){switch(d){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[d]}`)}}function e(d){switch(d){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(d,g){if(g===s.onnx.TensorProto.DataType.INT64||g===l.TensorDataType.INT64){if(d.greaterThanOrEqual(2147483648)||d.lessThan(-2147483648))throw new TypeError("int64 is not supported")}else{if(g!==s.onnx.TensorProto.DataType.UINT32&&g!==l.TensorDataType.UINT32&&g!==s.onnx.TensorProto.DataType.UINT64&&g!==l.TensorDataType.UINT64)throw new TypeError(`not a LONG type: ${s.onnx.TensorProto.DataType[g]}`);if(d.greaterThanOrEqual(4294967296)||d.lessThan(0))throw new TypeError("uint64 is not supported")}return d.toNumber()}function i(d,g,m){switch(g){case s.onnx.TensorProto.DataType.BOOL:case s.onnx.TensorProto.DataType.UINT8:return d.getUint8(m);case s.onnx.TensorProto.DataType.INT8:return d.getInt8(m);case s.onnx.TensorProto.DataType.UINT16:return d.getUint16(m,!0);case s.onnx.TensorProto.DataType.INT16:return d.getInt16(m,!0);case s.onnx.TensorProto.DataType.FLOAT:return d.getFloat32(m,!0);case s.onnx.TensorProto.DataType.INT32:return d.getInt32(m,!0);case s.onnx.TensorProto.DataType.UINT32:return d.getUint32(m,!0);case s.onnx.TensorProto.DataType.INT64:return r(f.default.fromBits(d.getUint32(m,!0),d.getUint32(m+4,!0),!1),g);case s.onnx.TensorProto.DataType.DOUBLE:return d.getFloat64(m,!0);case s.onnx.TensorProto.DataType.UINT64:return r(f.default.fromBits(d.getUint32(m,!0),d.getUint32(m+4,!0),!0),g);default:throw new Error(`cannot read from DataView for type ${s.onnx.TensorProto.DataType[g]}`)}}n.Tensor=o},2517:function(b,n,a){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 c=a(5686),f=u(a(3720)),s=a(1446),h=a(9162);n.checkInputsShape=function(g,...m){if(!g||g.length!==m.length)return!1;for(let _=0;_<g.length;_++)if(!g[_].dims||g[_].dims.length!==m[_])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 _=0;_<g.length;_++)if(g[_]!==m[_])return!1;return!0}};class p{static preprocessInputShapes(m,_){return[m.length===1?[1,m[0]]:m,_.length===1?[_[0],1]:_]}static postprocessOutputShape(m,_,y){_===1&&m.splice(m.length-2,1),y===1&&m.pop()}static calcMatMulShape(m,_){return m[1]!==_[0]?void 0:[m[0],_[1]]}}n.MatMulUtil=p;class l{static calcShape(m,_,y=!1){const T=m.length,w=_.length;if(T===0)return _;if(w===0)return m;const S=Math.max(m.length,_.length),O=new Array(S);if(y){if(T<2||w<2)return;const E=p.calcMatMulShape([m[T-2],m[T-1]],[_[w-2],_[w-1]]);if(E===void 0)return;[O[S-2],O[S-1]]=E}for(let E=y?3:1;E<=S;E++){const v=T-E<0?1:m[T-E],P=w-E<0?1:_[w-E];if(v!==P&&v>1&&P>1)return;O[S-E]=Math.max(v,P)}return O}static index(m,_){const y=new Array(_.length);return l.fillIndex(m,_,y),y}static fillIndex(m,_,y){const T=m.length-_.length;for(let w=0;w<_.length;w++)y[w]=m[T+w]%_[w]}static calc(m,_,y,T,w){const S=l.calcShape(m.dims,_.dims);if(S){if(T&&!e.areEqual(S,m.dims))return;const O=e.size(S),E=T?m:new h.Tensor(S,w||m.type);if(S.length===0)E.set([],y(m.get([]),_.get([])));else{const v=new Array(S.length),P=new Array(m.dims.length),L=new Array(_.dims.length);let V,R=0,k=0,Y=!1,C=!1;m.dims.length===0&&(R=m.get([]),Y=!0),_.dims.length===0&&(k=_.get([]),C=!0);for(let $=0;$<O;$++){V=$;for(let X=S.length-1;X>=0;X--)v[X]=V%S[X],V=Math.floor(V/S[X]);Y||(l.fillIndex(v,m.dims,P),R=m.get(P)),C||(l.fillIndex(v,_.dims,L),k=_.get(L)),E.set(v,y(R,k))}}return E}}static isValidBroadcast(m,_){const y=m.length,T=_.length;if(y>T)return!1;for(let w=1;w<=y;w++)if(m[y-w]!==1&&m[y-w]!==_[T-w])return!1;return!0}static getBroadcastDims(m,_){const y=m.length,T=[];for(let w=0;w<y;w++){const S=y-1-w,O=m[S]||1;(_[_.length-1-w]||1)>1&&O===1&&T.unshift(S)}return T}}n.BroadcastUtil=l,n.arrayCopyHelper=function(g,m,_,y,T){if(y<0||y>=m.length)throw new Error("sourceIndex out of bounds");if(_<0||_>=g.length)throw new Error("targetIndex out of bounds");if(y+T>m.length)throw new Error("source indices to be copied are outside bounds");if(_+T>g.length)throw new Error("target array is too small to hold result");for(let w=0;w<T;w++)g[_+w]=m[y+w]},n.GemmUtil=class{static getShapeOfGemmResult(g,m,_,y,T){if(g.length!==2||_.length!==2)throw new Error("shape need to be of size 2");let w,S,O;m?(w=g[1],S=g[0]):(w=g[0],S=g[1]);let E=-1;if(y?(O=_[0],E=1):(O=_[1],E=0),_[E]!==S)throw new Error("dimension mismatch");if(w<=0||O<=0||S<=0)throw new Error("invalid shape specified");if(T&&!l.isValidBroadcast(T,[w,O]))throw new Error("gemm: invalid bias shape for broadcast");return[w,O,S]}};class o{static tensorDataTypeFromProto(m){switch(m){case s.onnx.TensorProto.DataType.INT8:return"int8";case s.onnx.TensorProto.DataType.UINT8:return"uint8";case s.onnx.TensorProto.DataType.BOOL:return"bool";case s.onnx.TensorProto.DataType.INT16:return"int16";case s.onnx.TensorProto.DataType.UINT16:return"uint16";case s.onnx.TensorProto.DataType.INT32:return"int32";case s.onnx.TensorProto.DataType.UINT32:return"uint32";case s.onnx.TensorProto.DataType.FLOAT:return"float32";case s.onnx.TensorProto.DataType.DOUBLE:return"float64";case s.onnx.TensorProto.DataType.STRING:return"string";case s.onnx.TensorProto.DataType.INT64:return"int32";case s.onnx.TensorProto.DataType.UINT64:return"uint32";default:throw new Error(`unsupported data type: ${s.onnx.TensorProto.DataType[m]}`)}}static tensorDataTypeStringToEnum(m){switch(m){case"int8":return s.onnx.TensorProto.DataType.INT8;case"uint8":return s.onnx.TensorProto.DataType.UINT8;case"bool":return s.onnx.TensorProto.DataType.BOOL;case"int16":return s.onnx.TensorProto.DataType.INT16;case"uint16":return s.onnx.TensorProto.DataType.UINT16;case"int32":return s.onnx.TensorProto.DataType.INT32;case"uint32":return s.onnx.TensorProto.DataType.UINT32;case"float32":return s.onnx.TensorProto.DataType.FLOAT;case"float64":return s.onnx.TensorProto.DataType.DOUBLE;case"string":return s.onnx.TensorProto.DataType.STRING;case"int64":return s.onnx.TensorProto.DataType.INT64;case"uint64":return s.onnx.TensorProto.DataType.UINT64;default:throw new Error(`unsupported data type: ${m}`)}}static tensorDimsFromProto(m){return m.map(_=>f.default.isLong(_)?_.toNumber():_)}static tensorValueTypeFromProto(m){return{tensorType:o.tensorDataTypeFromProto(m.elemType),shape:{dims:o.tensorDimsFromProto(m.shape.dim.map(_=>_.dimValue))}}}static tensorDimsFromORTFormat(m){const _=[];for(let y=0;y<m.dimsLength();y++)_.push(t.longToNumber(m.dims(y)));return _}static tensorAttributesFromORTFormat(m){const _=[];for(let y=0;y<m.attributesLength();y++)_.push(m.attributes(y));return _}}n.ProtoUtil=o;class t{static longToNumber(m,_){return f.default.isLong(m)?m.toNumber():m instanceof c.flatbuffers.Long?f.default.fromValue({low:m.low,high:m.high,unsigned:_!=null&&_}).toNumber():m}static isLong(m){return f.default.isLong(m)||m instanceof c.flatbuffers.Long}}n.LongUtil=t;class e{static size(m){return e.getSizeFromDimensionRange(m,0,m.length)}static sizeFromDimension(m,_){if(_<0||_>m.length)throw new Error(`invalid dimension of ${_} for sizeFromDimension as Tensor has ${m.length} dimensions.`);return e.getSizeFromDimensionRange(m,_,m.length)}static sizeToDimension(m,_){if(_<0||_>m.length)throw new Error(`invalid dimension of ${_} for sizeToDimension as Tensor has ${m.length} dimensions.`);return e.getSizeFromDimensionRange(m,0,_)}static getSizeFromDimensionRange(m,_,y){let T=1;for(let w=_;w<y;w++){if(m[w]<=0)throw new Error("cannot get valid size from specified dimension range. Most likely the range contains 0 or negative values in them.");T*=m[w]}return T}static computeStrides(m){const _=m.length;if(_===0)return[];if(_===1)return[1];const y=new Array(_);y[_-1]=1,y[_-2]=m[_-1];for(let T=_-3;T>=0;--T)y[T]=y[T+1]*m[T+1];return y}static transpose(m){return m.slice().reverse()}static indicesToOffset(m,_,y){y===void 0&&(y=m.length);let T=0;for(let w=0;w<y;++w)T+=_[w]*m[w];return T}static offsetToIndices(m,_){const y=_.length;if(y===0)return[];if(y===1)return[m*_[0]];const T=new Array(_.length);for(let w=0;w<T.length-1;++w)T[w]=Math.floor(m/_[w]),m-=T[w]*_[w];return T[T.length-1]=m,T}static normalizeAxis(m,_){if(m<-_&&m>=_)throw new Error("unsupported axis for this operation.");return m<0?m+_:m}static normalizeAxes(m,_){return m.map(y=>this.normalizeAxis(y,_))}static incrementIndex(m,_,y){if(_.length===0||m.length===0)throw new Error("Index incrementing unsupported for scalar Tensor");if(y===void 0)y=_.length;else if(y<=0||y>_.length)throw new Error("Incorrect axis to increment on");for(let T=y-1;T>=0&&(m[T]++,!(m[T]<_[T]));--T)m[T]=0}static calculateReshapedDims(m,_){if(_.length===0){if(m.length===0||e.size(m)===1)return[];throw new Error("cannot reshape to a scalar Tensor")}const y=_.length,T=new Array(y);let w=-1,S=1;for(let E=0;E<y;E++){if(_[E]<-1)throw new Error("a dimension in shape hints cannot be less than -1");if(_[E]===-1){if(w!==-1)throw new Error("at most one dimension in shape hints can be -1");w=E}else{if(_[E]===0){if(E>=m.length)throw new Error("the dimension with value zero exceeds the dimension size of the input tensor");T[E]=m[E]}else T[E]=_[E];S*=T[E]}}const O=e.size(m);if(w!==-1){if(O%S!=0)throw new Error(`the input tensor cannot be reshaped to the requested shape. Input shape: [${m}] Output shape: [${_}]`);T[w]=O/S}else if(S!==O)throw new Error("reshapedDims and originalDims don't have matching sizes");return T}static sortBasedOnPerm(m,_){return _?_.map(y=>m[y]):m.slice().reverse()}static padShape(m,_){const y=m.length;return m.map((T,w)=>T+_[w]+_[w+y])}static areEqual(m,_){return m.length===_.length&&m.every((y,T)=>y===_[T])}static validateDimsAndCalcSize(m){if(m.length>6)throw new TypeError("Only rank 0 to 6 is supported for tensor shape.");let _=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`);_*=y}return _}static flattenShape(m,_){_<0&&(_+=m.length);const y=m.reduce((w,S)=>w*S,1),T=m.slice(_).reduce((w,S)=>w*S,1);return[y/T,T]}static squeezeShape(m,_){const y=new Array;_=e.normalizeAxes(_,m.length);for(let T=0;T<m.length;T++){const w=_.indexOf(T)>=0;if(w&&m[T]!==1)throw new Error("squeeze an axis of size different than 1");(_.length===0&&m[T]>1||_.length>0&&!w)&&y.push(m[T])}return y}static unsqueezeShape(m,_){const y=new Array(m.length+_.length);y.fill(0);for(let w=0;w<_.length;w++){const S=e.normalizeAxis(_[w],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 T=0;for(let w=0;w<y.length;w++)y[w]===0&&(y[w]=m[T++]);if(T!==m.length)throw new Error("the unsqueezed dimension could not be established");return y}}n.ShapeUtil=e,n.MathUtil=class{static sqr(g,m,_,y,T){if(y<0||y>=m.length)throw new Error("sourceIndex out of bounds");if(_<0||_>=g.length)throw new Error("targetIndex out of bounds");if(y+T>m.length)throw new Error("source indices to be copied are outside bounds");if(_+T>g.length)throw new Error("target array is too small to hold result");for(let w=0;w<T;w++)g[_+w]+=Math.pow(m[y+w],2)}static axpy(g,m,_,y,T,w){if(y<0||y>=m.length)throw new Error("sourceIndex out of bounds");if(_<0||_>=g.length)throw new Error("targetIndex out of bounds");if(y+T>m.length)throw new Error("source indices to be copied are outside bounds");if(_+T>g.length)throw new Error("target array is too small to hold result");for(let S=0;S<T;S++)g[_+S]+=w*m[y+S]}static powx(g,m,_,y,T,w){if(y<0||y>=m.length)throw new Error("sourceIndex out of bounds");if(_<0||_>=g.length)throw new Error("targetIndex out of bounds");if(y+T>m.length)throw new Error("source indices to be copied are outside bounds");if(_+T>g.length)throw new Error("target array is too small to hold result");for(let S=0;S<T;S++)g[_+S]=Math.pow(m[y+S],w)}static mul(g,m,_,y,T){if(y<0||y>=m.length)throw new Error("sourceIndex out of bounds");if(_<0||_>=g.length)throw new Error("targetIndex out of bounds");if(y+T>m.length)throw new Error("source indices to be copied are outside bounds");if(_+T>g.length)throw new Error("target array is too small to hold result");for(let w=0;w<T;w++)g[_+w]=m[y+w]*g[_+w]}};class r{static splitShape(m,_,y,T){if(y.length===0){if(!T)throw new Error("need to know number of outputs when the 'split' attribute is not specified");r.determineSplit(m[_],T,y)}const w=[],S=[0];for(let O=0;O<y.length;++O){O!==0&&S.push(S[O-1]+y[O-1]);const E=m.slice();E[_]=y[O],w.push(E)}return[w,S]}static determineSplit(m,_,y){if(m%_!=0)throw new Error("cannot split tensor to equal sized parts");for(let T=0;T<_;++T)y.push(m/_)}}n.SplitUtil=r;class i{static calcReduce(m,_,y,T,w){const S=m.dims.slice(0);_.length===0&&S.forEach((R,k)=>_.push(k));const O=i.calcReduceShape(S,_,!0),E=e.size(O),v=new h.Tensor(O,m.type),P=e.computeStrides(O),L=e.computeStrides(S),V=new Array(S.length);for(let R=0;R<E;R++){const k=e.offsetToIndices(R,P);l.fillIndex(k,S,V),v.set(k,i.calcReduceByAxis(m.numberData,_,S,0,e.indicesToOffset(V,L),T,w))}return y?v:new h.Tensor(i.calcReduceShape(S,_,y),v.type,void 0,void 0,v.data,v.dataId)}static calcReduceByAxis(m,_,y,T,w,S,O){let E=0;if(T>=_.length)return S(m[w]);const v=_[T],P=v>=y.length?1:e.size(y.slice(v+1));for(let L=0;L<y[v];L++)E=L===0?i.calcReduceByAxis(m,_,y,T+1,w,S,O):O(E,i.calcReduceByAxis(m,_,y,T+1,w,S,O)),w+=P;return E}static calcReduceShape(m,_,y){const T=m.slice();for(let w=0;w<_.length;w++)T[_[w]]=y?1:0;return T.filter(w=>w!==0)}}n.ReduceUtil=i;class d{static adjustPoolAttributes(m,_,y,T,w,S){if(!m&&y.length!==_.length-2)throw new Error("length of specified kernel shapes should be 2 less than length of input dimensions");if(m)for(let O=0;O<_.length-2;O++)O>=y.length?y.push(_[O+2]):y[O]=_[O+2];for(let O=0;O<y.length;O++)if(O<T.length){if(T[O]<0)throw new Error("strides should be greater than or equal to 1")}else T.push(1);for(let O=0;O<y.length;O++)if(O<w.length){if(w[O]<0)throw new Error("dilations should be greater than or equal to 1")}else w.push(1);for(let O=0;O<2*y.length;O++)if(O<S.length){if(S[O]<0)throw new Error("pad should be greater than or equal to 1")}else S.push(0);for(let O=0;O<y.length;O++){if(y[O]<=0)throw new Error("kernel shapes need to be greater than 0");if(S[O]>=y[O]||S[O+y.length]>=y[O])throw new Error("pads should be smaller than kernel")}}static adjustPadsBasedOnAutoPad(m,_,y,T,w,S){if(S){if(w.length!==2*(m.length-2))throw new Error("length of pads should be twice the length of data dimensions");if(_.length!==m.length-2)throw new Error("length of strides should be the length of data dimensions");if(T.length!==m.length-2)throw new Error("length of kernel shapes should be the length of data dimensions");for(let O=0;O<m.length-2;O++)d.adjustPadAndReturnShape(m[O+2],_[O],y[O],T[O],w,O,O+m.length-2,S)}}static computePoolOutputShape(m,_,y,T,w,S,O){if(_.length<=0)throw new Error("input shape must be of size greater than 0");const E=[_[0],_[1]];return d.computeShapeHelper(m,_,E,y,T,w,S,O),E}static computeConvOutputShape(m,_,y,T,w,S,O){if(m.length<=0||_.length<=0)throw new Error("invalid input tensor dims or invalid filter tensor dims");const E=[m[0],_[0]];return d.computeShapeHelper(!1,m,E,y,T,w,S,O),E}static computeShapeHelper(m,_,y,T,w,S,O,E){if(m)for(let v=0;v<_.length-2;v++)y.push(1);else for(let v=0;v<_.length-2;v++)y.push(d.adjustPadAndReturnShape(_[v+2],T[v],w[v],S[v],O,v,v+_.length-2,E))}static adjustPadAndReturnShape(m,_,y,T,w,S,O,E){const v=y*(T-1)+1;if(!E||E==="NOTSET")return Math.floor((m+w[S]+w[O]-v)/_+1);switch(E){case"VALID":return w[S]=0,w[O]=0,Math.floor((m-v)/_+1);case"SAME_LOWER":case"SAME_UPPER":if(y!==1)throw new Error("Dilation not supported for SAME_UPPER or SAME_LOWER");{const P=((m+_-1)/_-1)*_+T-m;return w[S]=Math.floor(E==="SAME_LOWER"?(P+1)/2:P/2),w[O]=P-w[S],Math.floor((m+P-T)/_+1)}default:throw new Error("Unsupported AutoPad type")}}}n.PoolConvUtil=d,n.MIN_CLIP=-34028234663852886e22,n.MAX_CLIP=34028234663852886e22,n.decodeUtf8String=function(g){return new TextDecoder().decode(g)}},7967:(b,n)=>{Object.defineProperty(n,"__esModule",{value:!0}),n.iterateExtraOptions=void 0,n.iterateExtraOptions=(a,u,c,f)=>{if(typeof 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load a remote file from: ${s}.`)}else try{r=await getFile(h),o=h}catch(_){console.warn(`Unable to load from local path "${h}": "${_}"`)}if(r===void 0||r.status===404){if(u.local_files_only||!env$1.allowRemoteModels){if(a)throw Error(`\`local_files_only=true\` or \`env.allowRemoteModels=false\` and file was not found locally at "${h}".`);return null}if(r=await getFile(p),r.status!==200)return handleError(r.status,p,a);o=t}e=c&&typeof Response<"u"&&r instanceof Response&&r.status===200}dispatchCallback(u.progress_callback,{status:"download",name:b,file:n});const d={status:"progress",name:b,file:n};let g;return u.progress_callback?i&&typeof navigator<"u"&&/firefox/i.test(navigator.userAgent)?(g=new Uint8Array(await r.arrayBuffer()),dispatchCallback(u.progress_callback,{...d,progress:100,loaded:g.length,total:g.length})):g=await readResponse(r,m=>{dispatchCallback(u.progress_callback,{...d,...m})}):g=new Uint8Array(await r.arrayBuffer()),e&&o&&await c.match(o)===void 0&&await c.put(o,new Response(g,{headers:r.headers})).catch(m=>{console.warn(`Unable to add response to browser cache: ${m}.`)}),dispatchCallback(u.progress_callback,{status:"done",name:b,file:n}),g}async function getModelJSON(b,n,a=!0,u={}){let c=await getModelFile(b,n,a,u);if(c===null)return{};let s=new TextDecoder("utf-8").decode(c);return JSON.parse(s)}async function readResponse(b,n){const a=b.headers.get("Content-Length");a===null&&console.warn("Unable to determine content-length from response headers. Will expand buffer when needed.");let u=parseInt(a??"0"),c=new Uint8Array(u),f=0;const s=b.body.getReader();async function h(){const{done:p,value:l}=await s.read();if(p)return;let o=f+l.length;if(o>u){u=o;let e=new Uint8Array(u);e.set(c),c=e}c.set(l,f),f=o;const t=f/u*100;return n({progress:t,loaded:f,total:u}),h()}return await h(),c}function pathJoin(...b){return b=b.map((n,a)=>(a&&(n=n.replace(new RegExp("^/"),"")),a!==b.length-1&&(n=n.replace(new RegExp("/$"),"")),n)),b.join("/")}function interpolate_data(b,[n,a,u],[c,f],s="bilinear",h=!1){const p=f/u,l=c/a,o=new b.constructor(c*f*n),t=a*u,e=c*f;for(let r=0;r<c;++r)for(let i=0;i<f;++i){const d=r*f+i,g=(i+.5)/p-.5,m=(r+.5)/l-.5;let _=Math.floor(g),y=Math.floor(m);const T=Math.min(_+1,u-1),w=Math.min(y+1,a-1);_=Math.max(_,0),y=Math.max(y,0);const S=g-_,O=m-y,E=(1-S)*(1-O),v=S*(1-O),P=(1-S)*O,L=S*O,V=y*u,R=w*u,k=V+_,Y=V+T,C=R+_,$=R+T;for(let X=0;X<n;++X){const z=X*t;o[X*e+d]=E*b[z+k]+v*b[z+Y]+P*b[z+C]+L*b[z+$]}}return o}function transpose_data(b,n,a){const u=new Array(a.length),c=new Array(a.length);for(let h=a.length-1,p=1;h>=0;--h)c[h]=p,u[h]=n[a[h]],p*=u[h];const f=a.map((h,p)=>c[a.indexOf(p)]),s=new b.constructor(b.length);for(let h=0;h<b.length;++h){let p=0;for(let l=n.length-1,o=h;l>=0;--l)p+=o%n[l]*f[l],o=Math.floor(o/n[l]);s[p]=b[h]}return[s,u]}function softmax(b){const n=max(b)[0],a=b.map(f=>Math.exp(f-n)),u=a.reduce((f,s)=>f+s,0);return a.map(f=>f/u)}function log_softmax(b){return softmax(b).map(u=>Math.log(u))}function getTopItems(b,n=0){return b=Array.from(b).map((a,u)=>[u,a]).sort((a,u)=>u[1]-a[1]),n!==null&&n>0&&(b=b.slice(0,n)),b}function min(b){if(b.length===0)throw Error("Array must not be empty");let n=b[0],a=0;for(let u=1;u<b.length;++u)b[u]<n&&(n=b[u],a=u);return[n,a]}function max(b){if(b.length===0)throw Error("Array must not be empty");let n=b[0],a=0;for(let u=1;u<b.length;++u)b[u]>n&&(n=b[u],a=u);return[Number(n),a]}function isPowerOfTwo(b){return b>0&&(b&b-1)===0}class P2FFT{constructor(n){if(this.size=n|0,this.size<=1||!isPowerOfTwo(this.size))throw new Error("FFT size must be a power of two larger than 1");this._csize=n<<1,this.table=new Float64Array(this.size*2);for(let u=0;u<this.table.length;u+=2){const c=Math.PI*u/this.size;this.table[u]=Math.cos(c),this.table[u+1]=-Math.sin(c)}let a=0;for(let u=1;this.size>u;u<<=1)++a;this._width=a%2===0?a-1:a,this._bitrev=new Int32Array(1<<this._width);for(let u=0;u<this._bitrev.length;++u){this._bitrev[u]=0;for(let c=0;c<this._width;c+=2){const f=this._width-c-2;this._bitrev[u]|=(u>>>c&3)<<f}}}createComplexArray(){return new Float64Array(this._csize)}fromComplexArray(n,a){const u=a||new Array(n.length>>>1);for(let c=0;c<n.length;c+=2)u[c>>>1]=n[c];return u}toComplexArray(n,a){const u=a||this.createComplexArray();for(let c=0;c<u.length;c+=2)u[c]=n[c>>>1],u[c+1]=0;return u}completeSpectrum(n){const a=this._csize,u=a>>>1;for(let c=2;c<u;c+=2)n[a-c]=n[c],n[a-c+1]=-n[c+1]}transform(n,a){if(n===a)throw new Error("Input and output buffers must be different");this._transform4(n,a,1)}realTransform(n,a){if(n===a)throw new Error("Input and output buffers must be different");this._realTransform4(n,a,1)}inverseTransform(n,a){if(n===a)throw new Error("Input and output buffers must be different");this._transform4(n,a,-1);for(let u=0;u<n.length;++u)n[u]/=this.size}_transform4(n,a,u){const c=this._csize;let s=1<<this._width,h=c/s<<1,p,l;const o=this._bitrev;if(h===4)for(p=0,l=0;p<c;p+=h,++l){const t=o[l];this._singleTransform2(a,n,p,t,s)}else for(p=0,l=0;p<c;p+=h,++l){const t=o[l];this._singleTransform4(a,n,p,t,s,u)}for(s>>=2;s>=2;s>>=2){h=c/s<<1;const t=h>>>2;for(p=0;p<c;p+=h){const e=p+t-1;for(let r=p,i=0;r<e;r+=2,i+=s){const d=r,g=d+t,m=g+t,_=m+t,y=n[d],T=n[d+1],w=n[g],S=n[g+1],O=n[m],E=n[m+1],v=n[_],P=n[_+1],L=this.table[i],V=u*this.table[i+1],R=w*L-S*V,k=w*V+S*L,Y=this.table[2*i],C=u*this.table[2*i+1],$=O*Y-E*C,X=O*C+E*Y,z=this.table[3*i],Z=u*this.table[3*i+1],J=v*z-P*Z,ue=v*Z+P*z,Se=y+$,Te=T+X,se=y-$,ye=T-X,be=R+J,Ie=k+ue,Le=u*(R-J),ve=u*(k-ue);n[d]=Se+be,n[d+1]=Te+Ie,n[g]=se+ve,n[g+1]=ye-Le,n[m]=Se-be,n[m+1]=Te-Ie,n[_]=se-ve,n[_+1]=ye+Le}}}}_singleTransform2(n,a,u,c,f){const s=n[c],h=n[c+1],p=n[c+f],l=n[c+f+1];a[u]=s+p,a[u+1]=h+l,a[u+2]=s-p,a[u+3]=h-l}_singleTransform4(n,a,u,c,f,s){const h=f*2,p=f*3,l=n[c],o=n[c+1],t=n[c+f],e=n[c+f+1],r=n[c+h],i=n[c+h+1],d=n[c+p],g=n[c+p+1],m=l+r,_=o+i,y=l-r,T=o-i,w=t+d,S=e+g,O=s*(t-d),E=s*(e-g);a[u]=m+w,a[u+1]=_+S,a[u+2]=y+E,a[u+3]=T-O,a[u+4]=m-w,a[u+5]=_-S,a[u+6]=y-E,a[u+7]=T+O}_realTransform4(n,a,u){const c=this._csize;let s=1<<this._width,h=c/s<<1,p,l;const o=this._bitrev;if(h===4)for(p=0,l=0;p<c;p+=h,++l){const t=o[l];this._singleRealTransform2(a,n,p,t>>>1,s>>>1)}else for(p=0,l=0;p<c;p+=h,++l){const t=o[l];this._singleRealTransform4(a,n,p,t>>>1,s>>>1,u)}for(s>>=2;s>=2;s>>=2){h=c/s<<1;const t=h>>>2;for(p=0;p<c;p+=h){const e=p+t-1;for(let r=p,i=0;r<e;r+=2,i+=s){const d=r,g=d+t,m=g+t,_=m+t,y=n[d],T=n[d+1],w=n[g],S=n[g+1],O=n[m],E=n[m+1],v=n[_],P=n[_+1],L=this.table[i],V=u*this.table[i+1],R=w*L-S*V,k=w*V+S*L,Y=this.table[2*i],C=u*this.table[2*i+1],$=O*Y-E*C,X=O*C+E*Y,z=this.table[3*i],Z=u*this.table[3*i+1],J=v*z-P*Z,ue=v*Z+P*z,Se=y+$,Te=T+X,se=y-$,ye=T-X,be=R+J,Ie=k+ue,Le=u*(R-J),ve=u*(k-ue);n[d]=Se+be,n[d+1]=Te+Ie,n[g]=se+ve,n[g+1]=ye-Le,n[m]=Se-be,n[m+1]=Te-Ie,n[_]=se-ve,n[_+1]=ye+Le}}}}_singleRealTransform2(n,a,u,c,f){const s=n[c],h=n[c+f];a[u]=s+h,a[u+1]=0,a[u+2]=s-h,a[u+3]=0}_singleRealTransform4(n,a,u,c,f,s){const h=f*2,p=f*3,l=n[c],o=n[c+f],t=n[c+h],e=n[c+p],r=l+t,i=l-t,d=o+e,g=s*(o-e);a[u]=r+d,a[u+1]=0,a[u+2]=i,a[u+3]=-g,a[u+4]=r-d,a[u+5]=0,a[u+6]=i,a[u+7]=g}}class NP2FFT{constructor(n){const a=2*(n-1),u=2*(2*n-1),c=2**Math.ceil(Math.log2(u));this.bufferSize=c,this._a=a;const f=new Float64Array(u),s=new Float64Array(c);this._chirpBuffer=new Float64Array(c),this._buffer1=new Float64Array(c),this._buffer2=new Float64Array(c),this._outBuffer1=new Float64Array(c),this._outBuffer2=new Float64Array(c);const h=-2*Math.PI/n,p=Math.cos(h),l=Math.sin(h);for(let o=0;o<u>>1;++o){const t=(o+1-n)**2/2,e=Math.sqrt(p**2+l**2)**t,r=t*Math.atan2(l,p),i=2*o;f[i]=e*Math.cos(r),f[i+1]=e*Math.sin(r),s[i]=f[i],s[i+1]=-f[i+1]}this._slicedChirpBuffer=f.subarray(a,u),this._f=new P2FFT(c>>1),this._f.transform(this._chirpBuffer,s)}_transform(n,a,u){const c=this._buffer1,f=this._buffer2,s=this._outBuffer1,h=this._outBuffer2,p=this._chirpBuffer,l=this._slicedChirpBuffer,o=this._a;if(u)for(let t=0;t<l.length;t+=2){const e=t+1,r=t>>1,i=a[r];c[t]=i*l[t],c[e]=i*l[e]}else for(let t=0;t<l.length;t+=2){const e=t+1;c[t]=a[t]*l[t]-a[e]*l[e],c[e]=a[t]*l[e]+a[e]*l[t]}this._f.transform(s,c);for(let t=0;t<p.length;t+=2){const e=t+1;f[t]=s[t]*p[t]-s[e]*p[e],f[e]=s[t]*p[e]+s[e]*p[t]}this._f.inverseTransform(h,f);for(let t=0;t<h.length;t+=2){const e=h[t+o],r=h[t+o+1],i=l[t],d=l[t+1];n[t]=e*i-r*d,n[t+1]=e*d+r*i}}transform(n,a){this._transform(n,a,!1)}realTransform(n,a){this._transform(n,a,!0)}}class FFT{constructor(n){this.fft_length=n,this.isPowerOfTwo=isPowerOfTwo(n),this.isPowerOfTwo?(this.fft=new P2FFT(n),this.outputBufferSize=2*n):(this.fft=new NP2FFT(n),this.outputBufferSize=this.fft.bufferSize)}realTransform(n,a){this.fft.realTransform(n,a)}transform(n,a){this.fft.transform(n,a)}}function medianFilter(b,n){if(n%2===0||n<=0)throw new Error("Window size must be a positive odd number");const a=new b.constructor(b.length),u=new b.constructor(n),c=Math.floor(n/2);for(let f=0;f<b.length;++f){let s=0;for(let h=-c;h<=c;++h){let p=f+h;p<0?p=Math.abs(p):p>=b.length&&(p=2*(b.length-1)-p),u[s++]=b[p]}u.sort(),a[f]=u[c]}return a}const DataTypeMap=Object.freeze({float32:Float32Array,float64:Float64Array,string:Array,int8:Int8Array,uint8:Uint8Array,int16:Int16Array,uint16:Uint16Array,int32:Int32Array,uint32:Uint32Array,int64:BigInt64Array,uint64:BigUint64Array,bool:Uint8Array}),ONNXTensor$1=ONNX.Tensor;class Tensor{dims;type;data;size;constructor(...n){return n[0]instanceof ONNXTensor$1?Object.assign(this,n[0]):Object.assign(this,new ONNXTensor$1(n[0],n[1],n[2])),new Proxy(this,{get:(a,u)=>{if(typeof u=="string"){let c=Number(u);if(Number.isInteger(c))return a._getitem(c)}return a[u]},set:(a,u,c)=>a[u]=c})}*[Symbol.iterator](){const[n,...a]=this.dims;if(a.length>0){const u=a.reduce((c,f)=>c*f);for(let c=0;c<n;++c)yield this._subarray(c,u,a)}else yield*this.data}_getitem(n){const[a,...u]=this.dims;if(n=safeIndex(n,a),u.length>0){const c=u.reduce((f,s)=>f*s);return this._subarray(n,c,u)}else return new Tensor(this.type,[this.data[n]],u)}indexOf(n){for(let a=0;a<this.data.length;++a)if(this.data[a]==n)return a;return-1}_subarray(n,a,u){const c=n*a,f=(n+1)*a,s="subarray"in this.data?this.data.subarray(c,f):this.data.slice(c,f);return new Tensor(this.type,s,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}mul(n){return this.clone().mul_(n)}mul_(n){for(let a=0;a<this.data.length;++a)this.data[a]*=n;return this}add(n){return this.clone().add_(n)}add_(n){for(let a=0;a<this.data.length;++a)this.data[a]+=n;return this}clone(){return new Tensor(this.type,this.data.slice(),this.dims.slice())}slice(...n){let a=[],u=[];for(let p=0;p<this.dims.length;++p){let l=n[p];if(l==null)u.push([0,this.dims[p]]),a.push(this.dims[p]);else if(typeof l=="number")l=safeIndex(l,this.dims[p],p),u.push([l,l+1]);else if(Array.isArray(l)&&l.length===2){if(l[0]>l[1])throw new Error(`Invalid slice: ${l}`);let o=[Math.max(l[0],0),Math.min(l[1],this.dims[p])];u.push(o),a.push(o[1]-o[0])}else throw new Error(`Invalid slice: ${l}`)}let c=u.map(([p,l])=>l-p),f=c.reduce((p,l)=>p*l),s=new this.data.constructor(f);const h=this.stride();for(let p=0;p<f;++p){let l=0;for(let o=c.length-1,t=p;o>=0;--o){const e=c[o];l+=(t%e+u[o][0])*h[o],t=Math.floor(t/e)}s[p]=this.data[l]}return new Tensor(this.type,s,a)}transpose(...n){return transpose(this,n)}sum(n=null,a=!1){return this.norm(1,n,a)}norm(n="fro",a=null,u=!1){if(n==="fro")n=2;else if(typeof n=="string")throw Error(`Unsupported norm: ${n}`);if(a===null){let s=this.data.reduce((h,p)=>h+p**n,0)**(1/n);return new Tensor(this.type,[s],[])}a=safeIndex(a,this.dims.length);const c=this.dims.slice();c[a]=1;const f=new this.data.constructor(this.data.length/this.dims[a]);for(let s=0;s<this.data.length;++s){let h=0;for(let p=this.dims.length-1,l=s,o=1;p>=0;--p){const t=this.dims[p];if(p!==a){const e=l%t;h+=e*o,o*=c[p]}l=Math.floor(l/t)}f[h]+=this.data[s]**n}if(n!==1)for(let s=0;s<f.length;++s)f[s]=f[s]**(1/n);return u||c.splice(a,1),new Tensor(this.type,f,c)}normalize_(n=2,a=1){a=safeIndex(a,this.dims.length);const u=this.norm(n,a,!0);for(let c=0;c<this.data.length;++c){let f=0;for(let s=this.dims.length-1,h=c,p=1;s>=0;--s){const l=this.dims[s];if(s!==a){const o=h%l;f+=o*p,p*=this.dims[s]}h=Math.floor(h/l)}this.data[c]/=u.data[f]}return this}normalize(n=2,a=1){return this.clone().normalize_(n,a)}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,a=-1){a=(a+this.dims.length)%this.dims.length;let u=this.dims.slice(0,n),c=this.dims.slice(n,a+1),f=this.dims.slice(a+1);return this.dims=[...u,c.reduce((s,h)=>s*h,1),...f],this}flatten(n=0,a=-1){return this.clone().flatten_(n,a)}view(...n){let a=-1;for(let u=0;u<n.length;++u)if(n[u]===-1){if(a!==-1)throw new Error("Only one dimension can be inferred");a=u}if(a!==-1){const u=n.reduce((c,f,s)=>s!==a?c*f:c,1);n[a]=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_()}clamp_(n,a){for(let u=0;u<this.data.length;++u)this.data[u]=Math.min(Math.max(this.data[u],n),a);return this}clamp(n,a){return this.clone().clamp_(n,a)}round_(){for(let n=0;n<this.data.length;++n)this.data[n]=Math.round(this.data[n]);return this}round(){return this.clone().round_()}to(n){if(this.type===n)return this;if(!DataTypeMap.hasOwnProperty(n))throw new Error(`Unsupported type: ${n}`);return new Tensor(n,DataTypeMap[n].from(this.data),this.dims)}}function reshape(b,n){const a=b.length,u=n.reduce((f,s)=>f*s);if(a!==u)throw Error(`cannot reshape array of size ${a} into shape (${n})`);let c=b;for(let f=n.length-1;f>=0;f--)c=c.reduce((s,h)=>{let p=s[s.length-1];return p.length<n[f]?p.push(h):s.push([h]),s},[[]]);return c[0]}function transpose(b,n){const[a,u]=transpose_data(b.data,b.dims,n);return new Tensor(b.type,a,u)}function interpolate(b,[n,a],u="bilinear",c=!1){const f=b.dims.at(-3)??1,s=b.dims.at(-2),h=b.dims.at(-1);let p=interpolate_data(b.data,[f,s,h],[n,a],u,c);return new Tensor(b.type,p,[f,n,a])}function calc_squeeze_dims(b,n){return b=b.slice(),n===null?b=b.filter(a=>a!==1):typeof n=="number"?b[n]===1&&b.splice(n,1):Array.isArray(n)&&(b=b.filter((a,u)=>a!==1||!n.includes(u))),b}function calc_unsqueeze_dims(b,n){return n=safeIndex(n,b.length+1),b=b.slice(),b.splice(n,0,1),b}function safeIndex(b,n,a=null){if(b<-n||b>=n)throw new Error(`IndexError: index ${b} is out of bounds for dimension${a===null?"":" "+a} with size ${n}`);return b<0&&(b=(b%n+n)%n),b}function cat(b,n=0){n=safeIndex(n,b[0].dims.length);const a=b[0].dims.slice();a[n]=b.reduce((s,h)=>s+h.dims[n],0);const u=a.reduce((s,h)=>s*h,1),c=new b[0].data.constructor(u),f=b[0].type;if(n===0){let s=0;for(let h of b)c.set(h.data,s),s+=h.data.length}else{let s=0;for(let h=0;h<b.length;++h){let p=b[h];for(let l=0;l<p.data.length;++l){let o=0;for(let t=p.dims.length-1,e=l,r=1;t>=0;--t){const i=p.dims[t];let d=e%i;t===n&&(d+=s),o+=d*r,r*=a[t],e=Math.floor(e/i)}c[o]=p.data[l]}s+=p.dims[n]}}return new Tensor(f,c,a)}function stack(b,n=0){return cat(b.map(a=>a.unsqueeze(n)),n)}function std_mean(b,n=null,a=1,u=!1){if(n===null){const l=b.data.reduce((r,i)=>r+i,0)/b.data.length,o=Math.sqrt(b.data.reduce((r,i)=>r+(i-l)**2,0)/(b.data.length-a)),t=new Tensor(b.type,[l],[]);return[new Tensor(b.type,[o],[]),t]}n=safeIndex(n,b.dims.length);const c=mean(b,n,u),f=b.dims.slice();f[n]=1;const s=new b.data.constructor(b.data.length/b.dims[n]);for(let p=0;p<b.data.length;++p){let l=0;for(let o=b.dims.length-1,t=p,e=1;o>=0;--o){const r=b.dims[o];if(o!==n){const i=t%r;l+=i*e,e*=f[o]}t=Math.floor(t/r)}s[l]+=(b.data[p]-c.data[l])**2}for(let p=0;p<s.length;++p)s[p]=Math.sqrt(s[p]/(b.dims[n]-a));return u||f.splice(n,1),[new Tensor(b.type,s,f),c]}function mean(b,n=null,a=!1){if(n===null){let f=b.data.reduce((s,h)=>s+h,0);return new Tensor(b.type,[f/b.data.length],[])}n=safeIndex(n,b.dims.length);const u=b.dims.slice();u[n]=1;const c=new b.data.constructor(b.data.length/b.dims[n]);for(let f=0;f<b.data.length;++f){let s=0;for(let h=b.dims.length-1,p=f,l=1;h>=0;--h){const o=b.dims[h];if(h!==n){const t=p%o;s+=t*l,l*=u[h]}p=Math.floor(p/o)}c[s]+=b.data[f]}if(b.dims[n]!==1)for(let f=0;f<c.length;++f)c[f]=c[f]/b.dims[n];return a||u.splice(n,1),new Tensor(b.type,c,u)}function dynamicTimeWarping(b){const[n,a]=b.dims,u=[n+1,a+1],c=new Tensor("float32",new Float32Array(u[0]*u[1]).fill(1/0),u),f=new Tensor("float32",new Float32Array(u[0]*u[1]).fill(-1),u);c[0].data[0]=0;for(let o=1;o<a+1;++o)for(let t=1;t<n+1;++t){const e=c[t-1][o-1].item(),r=c[t-1][o].item(),i=c[t][o-1].item();let d,g;e<r&&e<i?(d=e,g=0):r<e&&r<i?(d=r,g=1):(d=i,g=2),c[t].data[o]=b[t-1][o-1].item()+d,f[t].data[o]=g}let s=n,h=a;f.data.fill(2,0,u[1]);for(let o=0;o<u[0];++o)f[o].data[0]=1;let p=[],l=[];for(;s>0||h>0;)switch(p.push(s-1),l.push(h-1),f[s][h].item()){case 0:--s,--h;break;case 1:--s;break;case 2:--h;break;default:throw new Error(`Internal error in dynamic time warping. Unexpected trace[${s}, ${h}]. Please file a bug report.`)}return p.reverse(),l.reverse(),[p,l]}function dimsToStride(b){const n=new Array(b.length);for(let a=b.length-1,u=1;a>=0;--a)n[a]=u,u*=b[a];return n}function ones(b){const n=b.reduce((a,u)=>a*u,1);return new Tensor("int64",new BigInt64Array(n).fill(1n),b)}function ones_like(b){return ones(b.dims)}var TOKEN_TYPES=Object.freeze({Text:"Text",NumericLiteral:"NumericLiteral",BooleanLiteral:"BooleanLiteral",StringLiteral:"StringLiteral",Identifier:"Identifier",Equals:"Equals",OpenParen:"OpenParen",CloseParen:"CloseParen",OpenStatement:"OpenStatement",CloseStatement:"CloseStatement",OpenExpression:"OpenExpression",CloseExpression:"CloseExpression",OpenSquareBracket:"OpenSquareBracket",CloseSquareBracket:"CloseSquareBracket",Comma:"Comma",Dot:"Dot",Colon:"Colon",Pipe:"Pipe",CallOperator:"CallOperator",AdditiveBinaryOperator:"AdditiveBinaryOperator",MultiplicativeBinaryOperator:"MultiplicativeBinaryOperator",ComparisonBinaryOperator:"ComparisonBinaryOperator",UnaryOperator:"UnaryOperator",Set:"Set",If:"If",For:"For",In:"In",NotIn:"NotIn",Else:"Else",EndIf:"EndIf",ElseIf:"ElseIf",EndFor:"EndFor",And:"And",Or:"Or",Not:"UnaryOperator"});Object.freeze({set:TOKEN_TYPES.Set,for:TOKEN_TYPES.For,in:TOKEN_TYPES.In,if:TOKEN_TYPES.If,else:TOKEN_TYPES.Else,endif:TOKEN_TYPES.EndIf,elif:TOKEN_TYPES.ElseIf,endfor:TOKEN_TYPES.EndFor,and:TOKEN_TYPES.And,or:TOKEN_TYPES.Or,not:TOKEN_TYPES.Not,"not in":TOKEN_TYPES.NotIn,true:TOKEN_TYPES.BooleanLiteral,false:TOKEN_TYPES.BooleanLiteral});TOKEN_TYPES.OpenStatement,TOKEN_TYPES.CloseStatement,TOKEN_TYPES.OpenExpression,TOKEN_TYPES.CloseExpression,TOKEN_TYPES.OpenParen,TOKEN_TYPES.CloseParen,TOKEN_TYPES.OpenSquareBracket,TOKEN_TYPES.CloseSquareBracket,TOKEN_TYPES.Comma,TOKEN_TYPES.Dot,TOKEN_TYPES.Colon,TOKEN_TYPES.Pipe,TOKEN_TYPES.ComparisonBinaryOperator,TOKEN_TYPES.ComparisonBinaryOperator,TOKEN_TYPES.ComparisonBinaryOperator,TOKEN_TYPES.ComparisonBinaryOperator,TOKEN_TYPES.ComparisonBinaryOperator,TOKEN_TYPES.ComparisonBinaryOperator,TOKEN_TYPES.AdditiveBinaryOperator,TOKEN_TYPES.AdditiveBinaryOperator,TOKEN_TYPES.MultiplicativeBinaryOperator,TOKEN_TYPES.MultiplicativeBinaryOperator,TOKEN_TYPES.MultiplicativeBinaryOperator,TOKEN_TYPES.Equals;const BYTES_TO_UNICODE=(()=>{const b=[...Array.from({length:94},(c,f)=>f+33),...Array.from({length:12},(c,f)=>f+161),...Array.from({length:82},(c,f)=>f+174)],n=b.slice();let a=0;for(let c=0;c<256;++c)b.includes(c)||(b.push(c),n.push(256+a),a+=1);const u=n.map(c=>String.fromCharCode(c));return Object.fromEntries(b.map((c,f)=>[c,u[f]]))})();reverseDictionary(BYTES_TO_UNICODE);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"]];new Map(WHISPER_LANGUAGES);async function loadConfig(b,n){return await getModelJSON(b,"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:a=null,config:u=null,cache_dir:c=null,local_files_only:f=!1,revision:s="main"}={}){let h=u??await loadConfig(n,{progress_callback:a,config:u,cache_dir:c,local_files_only:f,revision:s});return new this(h)}}class AutoConfig{static async from_pretrained(...n){return PretrainedConfig.from_pretrained(...n)}}class LogitsProcessorList extends Callable{constructor(){super(),this.processors=[]}push(n){this.processors.push(n)}extend(n){this.processors.push(...n)}_call(n,a){for(let u of a)this.processors.forEach(c=>c(n,u))}[Symbol.iterator](){return this.processors.values()}}class LogitsProcessor extends Callable{_call(n,a){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,a){let u=this.force_token_map[n.length];return exists(u)&&(a.data.fill(-1/0),a.data[u]=0),a}}class ForcedBOSTokenLogitsProcessor extends LogitsProcessor{constructor(n){super(),this.bos_token_id=n}_call(n,a){return n.length===1&&(a.data.fill(-1/0),a.data[this.bos_token_id]=0),a}}class ForcedEOSTokenLogitsProcessor extends LogitsProcessor{constructor(n,a){super(),this.max_length=n,this.forced_eos_token_id=a}_call(n,a){}}class SuppressTokensAtBeginLogitsProcessor extends LogitsProcessor{constructor(n,a){super(),this.begin_suppress_tokens=n,this.begin_index=a}_call(n,a){if(n.length===this.begin_index)for(let u of this.begin_suppress_tokens)a.data[u]=-1/0;return a}}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,a){const u=a.data;if(u[this.no_timestamps_token_id]=-1/0,n.length===this.begin_index-1)return u.fill(-1/0),u[this.timestamp_begin]=0,a;const c=n.slice(this.begin_index),f=c.length>=1&&c[c.length-1]>=this.timestamp_begin,s=c.length<2||c[c.length-2]>=this.timestamp_begin;if(f&&(s?u.subarray(this.timestamp_begin).fill(-1/0):u.subarray(0,this.eos_token_id).fill(-1/0)),n.length===this.begin_index&&this.max_initial_timestamp_index!==null){const o=this.timestamp_begin+this.max_initial_timestamp_index;u.subarray(o+1).fill(-1/0)}const h=log_softmax(u),p=Math.log(h.subarray(this.timestamp_begin).map(Math.exp).reduce((o,t)=>o+t)),l=max(h.subarray(0,this.timestamp_begin))[0];return p>l&&u.subarray(0,this.timestamp_begin).fill(-1/0),a}}class NoRepeatNGramLogitsProcessor extends LogitsProcessor{constructor(n){super(),this.no_repeat_ngram_size=n}getNgrams(n){const a=n.length,u=[];for(let f=0;f<a+1-this.no_repeat_ngram_size;++f){const s=[];for(let h=0;h<this.no_repeat_ngram_size;++h)s.push(n[f+h]);u.push(s)}const c=new Map;for(const f of u){const s=f.slice(0,f.length-1),h=JSON.stringify(s),p=c.get(h)??[];p.push(f[f.length-1]),c.set(h,p)}return c}getGeneratedNgrams(n,a){const u=a.slice(a.length+1-this.no_repeat_ngram_size,a.length);return n.get(JSON.stringify(u))??[]}calcBannedNgramTokens(n){const a=[];if(n.length+1<this.no_repeat_ngram_size)return a;{const u=this.getNgrams(n);return this.getGeneratedNgrams(u,n)}}_call(n,a){const u=this.calcBannedNgramTokens(n);for(const c of u)a.data[c]=-1/0;return a}}class RepetitionPenaltyLogitsProcessor extends LogitsProcessor{constructor(n){super(),this.penalty=n}_call(n,a){for(const u of n)a.data[u]<0?a.data[u]*=this.penalty:a.data[u]/=this.penalty;return a}}class MinLengthLogitsProcessor extends LogitsProcessor{constructor(n,a){super(),this.min_length=n,this.eos_token_id=Array.isArray(a)?a:[a]}_call(n,a){if(n.length<this.min_length)for(const u of this.eos_token_id)a.data[u]=-1/0;return a}}class MinNewTokensLengthLogitsProcessor extends LogitsProcessor{constructor(n,a,u){super(),this.prompt_length_to_skip=n,this.min_new_tokens=a,this.eos_token_id=Array.isArray(u)?u:[u]}_call(n,a){if(n.length-this.prompt_length_to_skip<this.min_new_tokens)for(const c of this.eos_token_id)a.data[c]=-1/0;return a}}class NoBadWordsLogitsProcessor extends LogitsProcessor{constructor(n,a){super(),this.bad_words_ids=n,this.eos_token_id=Array.isArray(a)?a:[a]}_call(n,a){for(const u of this.bad_words_ids){let c=!0;for(let f=1;f<=u.length-1&&u.length<n.length;++f)if(u.at(-f-1)!==n.at(-f)){c=!1;break}c&&(a.data[u.at(-1)]=-1/0)}return a}}const GenerationConfig=class{constructor(b={}){this.max_length=b.max_length??20,this.max_new_tokens=b.max_new_tokens??null,this.min_length=b.min_length??0,this.min_new_tokens=b.min_new_tokens??null,this.early_stopping=b.early_stopping??!1,this.max_time=b.max_time??null,this.do_sample=b.do_sample??!1,this.num_beams=b.num_beams??1,this.num_beam_groups=b.num_beam_groups??1,this.penalty_alpha=b.penalty_alpha??null,this.use_cache=b.use_cache??!0,this.temperature=b.temperature??1,this.top_k=b.top_k??50,this.top_p=b.top_p??1,this.typical_p=b.typical_p??1,this.epsilon_cutoff=b.epsilon_cutoff??0,this.eta_cutoff=b.eta_cutoff??0,this.diversity_penalty=b.diversity_penalty??0,this.repetition_penalty=b.repetition_penalty??1,this.encoder_repetition_penalty=b.encoder_repetition_penalty??1,this.length_penalty=b.length_penalty??1,this.no_repeat_ngram_size=b.no_repeat_ngram_size??0,this.bad_words_ids=b.bad_words_ids??null,this.force_words_ids=b.force_words_ids??null,this.renormalize_logits=b.renormalize_logits??!1,this.constraints=b.constraints??null,this.forced_bos_token_id=b.forced_bos_token_id??null,this.forced_eos_token_id=b.forced_eos_token_id??null,this.remove_invalid_values=b.remove_invalid_values??!1,this.exponential_decay_length_penalty=b.exponential_decay_length_penalty??null,this.suppress_tokens=b.suppress_tokens??null,this.begin_suppress_tokens=b.begin_suppress_tokens??null,this.forced_decoder_ids=b.forced_decoder_ids??null,this.num_return_sequences=b.num_return_sequences??1,this.output_attentions=b.output_attentions??!1,this.output_hidden_states=b.output_hidden_states??!1,this.output_scores=b.output_scores??!1,this.return_dict_in_generate=b.return_dict_in_generate??!1,this.pad_token_id=b.pad_token_id??null,this.bos_token_id=b.bos_token_id??null,this.eos_token_id=b.eos_token_id??null,this.encoder_no_repeat_ngram_size=b.encoder_no_repeat_ngram_size??0,this.decoder_start_token_id=b.decoder_start_token_id??null,this.generation_kwargs=b.generation_kwargs??{}}};class Sampler extends Callable{constructor(n){super(),this.generation_config=n}_call(n,a=-1){return this.sample(n,a)}sample(n,a){throw Error("sample should be implemented in subclasses.")}getLogits(n,a){let u=n.dims.at(-1),c=n.data;if(a===-1)c=c.slice(-u);else{let f=a*u;c=c.slice(f,f+u)}return this.generation_config.temperature>0&&(c=c.map(f=>f/this.generation_config.temperature)),c}randomSelect(n){let a=n.reduce((c,f)=>c+f,0),u=Math.random()*a;for(let c=0;c<n.length;++c)if(u-=n[c],u<=0)return c;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 ${n.num_return_sequences}.`);return new GreedySampler(n)}}class GreedySampler extends Sampler{sample(n,a=-1){let u=this.getLogits(n,a);return[[max(u)[1],0]]}}class MultinomialSampler extends Sampler{sample(n,a=-1){let u=n.dims.at(-1);this.generation_config.top_k>0&&(u=Math.min(this.generation_config.top_k,u));const c=this.getLogits(n,a),f=getTopItems(c,u),s=softmax(f.map(h=>h[1]));return Array.from({length:this.generation_config.num_beams},()=>{const h=this.randomSelect(s);return[f[h][0],Math.log(s[h])]})}}class BeamSearchSampler extends Sampler{sample(n,a=-1){let u=n.dims.at(-1);this.generation_config.top_k>0&&(u=Math.min(this.generation_config.top_k,u));const c=this.getLogits(n,a),f=getTopItems(c,u),s=softmax(f.map(h=>h[1]));return Array.from({length:this.generation_config.num_beams},(h,p)=>[f[p][0],Math.log(s[p])])}}const{InferenceSession,Tensor:ONNXTensor,env}=ONNX,MODEL_TYPES={EncoderOnly:0,EncoderDecoder:1,Seq2Seq:2,Vision2Seq:3,DecoderOnly:4,MaskGeneration:5},MODEL_TYPE_MAPPING=new Map,MODEL_NAME_TO_CLASS_MAPPING=new Map,MODEL_CLASS_TO_NAME_MAPPING=new Map;async function constructSession(b,n,a){let u=`onnx/${n}${a.quantized?"_quantized":""}.onnx`,c=await getModelFile(b,u,!0,a);try{return await InferenceSession.create(c,{executionProviders})}catch(f){if(executionProviders.length===1&&executionProviders[0]==="wasm")throw f;return console.warn(f),console.warn("Something went wrong during model construction (most likely a missing operation). Using `wasm` as a fallback. "),await InferenceSession.create(c,{executionProviders:["wasm"]})}}function validateInputs(b,n){const a=Object.create(null),u=[];for(const s of b.inputNames){const h=n[s];if(!(h instanceof Tensor)){u.push(s);continue}a[s]=env.wasm.proxy?h.clone():h}if(u.length>0)throw new Error(`An error occurred during model execution: "Missing the following inputs: ${u.join(", ")}.`);const c=Object.keys(n).length,f=b.inputNames.length;if(c>f){let s=Object.keys(n).filter(h=>!b.inputNames.includes(h));console.warn(`WARNING: Too many inputs were provided (${c} > ${f}). The following inputs will be ignored: "${s.join(", ")}".`)}return a}async function sessionRun(b,n){const a=validateInputs(b,n);try{let u=await b.run(a);return u=replaceTensors(u),u}catch(u){throw console.error(`An error occurred during model execution: "${u}".`),console.error("Inputs given to model:",a),u}}function replaceTensors(b){for(let n in b)b[n]instanceof ONNXTensor?b[n]=new Tensor(b[n]):typeof b[n]=="object"&&replaceTensors(b[n]);return b}function toI64Tensor(b){if(b instanceof Tensor)return b;if(b.length===0)throw Error("items must be non-empty");if(Array.isArray(b[0])){if(b.some(n=>n.length!==b[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(b.flat().map(n=>BigInt(n))),[b.length,b[0].length])}else return new Tensor("int64",BigInt64Array.from(b.map(n=>BigInt(n))),[1,b.length])}function prepareAttentionMask(b,n){let a=b.config.pad_token_id??null,u=b.config.eos_token_id??null;isIntegralNumber(u)&&(u=[u]);let c=n.indexOf(a)!==-1,f=u===null||!u.includes(a);if(c&&f){let s=BigInt64Array.from(n.data.map(h=>h!=a));return new Tensor("int64",s,n.dims)}else return ones_like(n)}function preparePositionIds(b,n,a){if(!b.inputNames.includes("position_ids"))return;const u=new BigInt64Array(n.attention_mask.data.length);for(let c=0;c<n.attention_mask.dims[0];++c){let f=c*n.attention_mask.dims[1],s=BigInt(0);for(let h=0;h<n.attention_mask.dims[1];++h){const p=f+h;n.attention_mask.data[p]===0n?u[p]=BigInt(1):(u[p]=s,s+=n.attention_mask.data[p])}}n.position_ids=new Tensor("int64",u,n.attention_mask.dims),a&&(n.position_ids=n.position_ids.slice(null,-1).unsqueeze_(-1))}function boolTensor(b){return new Tensor("bool",[b],[1])}async function seq2seqForward(b,n){let{encoder_outputs:a,past_key_values:u}=n;a||(a=(await encoderForward(b,n)).last_hidden_state);let c={input_ids:n.decoder_input_ids,encoder_hidden_states:a};const f=!!u;b.decoder_merged_session.inputNames.includes("use_cache_branch")&&(c.use_cache_branch=boolTensor(f)),b.decoder_merged_session.inputNames.includes("encoder_attention_mask")&&(c.encoder_attention_mask=n.attention_mask),preparePositionIds(b.decoder_merged_session,c,f),b.addPastKeyValues(c,u);const s=await sessionRun(b.decoder_merged_session,c);let h=s.logits;u=b.getPastKeyValues(s,u);const p=b.getAttentions(s);return new Seq2SeqLMOutput({logits:h,past_key_values:u,encoder_outputs:a,...p})}function seq2seqStartBeams(b,n,a,u){let c=[],f=0;const s=b.requires_attention_mask??!0;let h=a.decoder_input_ids??a.decoder_start_token_id??a.bos_token_id??a.eos_token_id;h instanceof Tensor?h=h.tolist().flat():Array.isArray(h)||(h=[h]);for(let p of n){p.dims=[1,...p.dims];let l={inputs:p,encoder_outputs:null,prev_model_outputs:null,output_token_ids:h,done:!1,score:0,id:f++};s&&(l.attention_mask=prepareAttentionMask(b,p)),c.push(l)}return c}async function seq2seqRunBeam(b,n){const a=b.main_input_name;let u=n.output_token_ids;n.prev_model_outputs&&(u=u.slice(-1));let c={[a]:n.inputs,decoder_input_ids:toI64Tensor(u),encoder_outputs:n.encoder_outputs,past_key_values:n.prev_model_outputs?.past_key_values};n.attention_mask&&(c.attention_mask=n.attention_mask);let f=await b.forward(c);return n.prev_model_outputs=f,n.encoder_outputs=f.encoder_outputs,f}function seq2seqUpdatebeam(b,n){b.output_token_ids=[...b.output_token_ids,n]}async function encoderForward(b,n){const a=Object.create(null);for(const u of b.session.inputNames)a[u]=n[u];return b.session.inputNames.includes("token_type_ids")&&!a.token_type_ids&&(a.token_type_ids=new Tensor("int64",new BigInt64Array(a.input_ids.data.length),a.input_ids.dims)),await sessionRun(b.session,a)}async function decoderForward(b,n){let{input_ids:a,past_key_values:u,attention_mask:c}=n,f={input_ids:a,attention_mask:c??prepareAttentionMask(b,a)};const s=!!u;b.session.inputNames.includes("use_cache_branch")&&(f.use_cache_branch=boolTensor(s)),preparePositionIds(b.session,f,s),b.addPastKeyValues(f,u);let h=await sessionRun(b.session,f),p=h.logits;return u=b.getPastKeyValues(h,u),{logits:p,past_key_values:u}}function decoderStartBeams(b,n,a,u,c){let f=[],s=0;for(let h of n){let p=h.tolist().map(Number);h.dims=[1,...h.dims];let l;c?(l=c[s],l.dims=[1,...l.dims]):l=prepareAttentionMask(b,h);let o={input:h,model_input_ids:h,attention_mask:l,prev_model_outputs:null,output_token_ids:p,num_output_tokens:u,done:!1,score:0,id:s++};f.push(o)}return f}async function decoderRunBeam(b,n){let a=new BigInt64Array(n.output_token_ids.length).fill(1n),u={input_ids:n.model_input_ids,attention_mask:new Tensor("int64",a,[1,a.length]),past_key_values:n.prev_model_outputs?.past_key_values},c=await b.forward(u);return n.prev_model_outputs=c,c}function decoderUpdatebeam(b,n){b.output_token_ids=[...b.output_token_ids,n],b.model_input_ids=new Tensor("int64",[BigInt(n)],[1,1])}class PreTrainedModel extends Callable{main_input_name="input_ids";constructor(n,a){super(),this.config=n,this.session=a;const u=MODEL_CLASS_TO_NAME_MAPPING.get(this.constructor),c=MODEL_TYPE_MAPPING.get(u);this.can_generate=!1,this._runBeam=null,this._getStartBeams=null,this._updateBeam=null,this._forward=null,c===MODEL_TYPES.DecoderOnly?(this.can_generate=!0,this._runBeam=decoderRunBeam,this._getStartBeams=decoderStartBeams,this._updateBeam=decoderUpdatebeam,this._forward=decoderForward):c===MODEL_TYPES.Seq2Seq||c===MODEL_TYPES.Vision2Seq?(this.can_generate=!0,this._runBeam=seq2seqRunBeam,this._getStartBeams=seq2seqStartBeams,this._updateBeam=seq2seqUpdatebeam,this._forward=seq2seqForward):c===MODEL_TYPES.EncoderDecoder?this._forward=encoderForward:this._forward=encoderForward}async dispose(){const n=[];for(let a of Object.keys(this)){const u=this[a];u instanceof InferenceSession&&n.push(u.handler.dispose())}return await Promise.all(n)}static async from_pretrained(n,{quantized:a=!0,progress_callback:u=null,config:c=null,cache_dir:f=null,local_files_only:s=!1,revision:h="main",model_file_name:p=null}={}){let l={quantized:a,progress_callback:u,config:c,cache_dir:f,local_files_only:s,revision:h,model_file_name:p};const o=MODEL_CLASS_TO_NAME_MAPPING.get(this),t=MODEL_TYPE_MAPPING.get(o);let e;return t===MODEL_TYPES.DecoderOnly?e=await Promise.all([AutoConfig.from_pretrained(n,l),constructSession(n,l.model_file_name??"decoder_model_merged",l),getModelJSON(n,"generation_config.json",!1,l)]):t===MODEL_TYPES.Seq2Seq||t===MODEL_TYPES.Vision2Seq?e=await Promise.all([AutoConfig.from_pretrained(n,l),constructSession(n,"encoder_model",l),constructSession(n,"decoder_model_merged",l),getModelJSON(n,"generation_config.json",!1,l)]):t===MODEL_TYPES.MaskGeneration?e=await Promise.all([AutoConfig.from_pretrained(n,l),constructSession(n,"vision_encoder",l),constructSession(n,"prompt_encoder_mask_decoder",l)]):t===MODEL_TYPES.EncoderDecoder?e=await Promise.all([AutoConfig.from_pretrained(n,l),constructSession(n,"encoder_model",l),constructSession(n,"decoder_model_merged",l)]):(t!==MODEL_TYPES.EncoderOnly&&console.warn(`Model type for '${o??c?.model_type}' not found, assuming encoder-only architecture. Please report this at https://github.com/xenova/transformers.js/issues/new/choose.`),e=await Promise.all([AutoConfig.from_pretrained(n,l),constructSession(n,l.model_file_name??"model",l)])),new this(...e)}async _call(n){return await this.forward(n)}async forward(n){return await this._forward(this,n)}_get_logits_processor(n,a,u=null){const c=new LogitsProcessorList;if(n.repetition_penalty!==null&&n.repetition_penalty!==1&&c.push(new RepetitionPenaltyLogitsProcessor(n.repetition_penalty)),n.no_repeat_ngram_size!==null&&n.no_repeat_ngram_size>0&&c.push(new NoRepeatNGramLogitsProcessor(n.no_repeat_ngram_size)),n.bad_words_ids!==null&&c.push(new NoBadWordsLogitsProcessor(n.bad_words_ids,n.eos_token_id)),n.min_length!==null&&n.eos_token_id!==null&&n.min_length>0&&c.push(new MinLengthLogitsProcessor(n.min_length,n.eos_token_id)),n.min_new_tokens!==null&&n.eos_token_id!==null&&n.min_new_tokens>0&&c.push(new MinNewTokensLengthLogitsProcessor(a,n.min_new_tokens,n.eos_token_id)),n.forced_bos_token_id!==null&&c.push(new ForcedBOSTokenLogitsProcessor(n.forced_bos_token_id)),n.forced_eos_token_id!==null&&c.push(new ForcedEOSTokenLogitsProcessor(n.max_length,n.forced_eos_token_id)),n.begin_suppress_tokens!==null){let f=a>1||n.forced_bos_token_id===null?a:a+1;n.forced_decoder_ids!==null&&(f+=n.forced_decoder_ids[n.forced_decoder_ids.length-1][0]),c.push(new SuppressTokensAtBeginLogitsProcessor(n.begin_suppress_tokens,f))}return n.forced_decoder_ids!==null&&c.push(new ForceTokensLogitsProcessor(n.forced_decoder_ids)),u!==null&&c.extend(u),c}_get_generation_config(n){let a=new GenerationConfig(this.config);return"generation_config"in this&&Object.assign(a,this.generation_config),n!==null&&Object.assign(a,n),a}async generate(n,a=null,u=null,{inputs_attention_mask:c=null}={}){if(!this.can_generate){let g=`The current model class (${MODEL_CLASS_TO_NAME_MAPPING.get(this.constructor)}) is not compatible with \`.generate()\`, as it doesn't have a language model head.`;const m=this.config.model_type,_=MODEL_WITH_LM_HEAD_MAPPING_NAMES.get(m)??MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING_NAMES.get(m)??MODEL_FOR_SPEECH_SEQ_2_SEQ_MAPPING_NAMES.get(m)??MODEL_FOR_VISION_2_SEQ_MAPPING_NAMES.get(m);throw _&&(g+=` Please use the following class instead: '${_[0]}'`),Error(g)}if(!(n instanceof Tensor)&&!isTypedArray(n)&&!Array.isArray(n))throw Error(`\`inputs\` must be a Tensor, TypedArray, or Array, but is "${n.constructor.name}".`);let f;if(this.config.is_encoder_decoder)f=0;else if(f=n instanceof Tensor?n.dims.at(-1):n.length,f===0)throw Error("Must supply a non-empty array of input token ids.");a=this._get_generation_config(a),u=u??new LogitsProcessorList,u=this._get_logits_processor(a,f,u);let s=a.eos_token_id;s!==null&&!Array.isArray(s)&&(s=[s]);let h=1;const p=h+(a.max_new_tokens??1/0),l=Number.isInteger(a.max_length)&&(a.max_new_tokens??null)===null;let o=Sampler.getSampler(a),t=this.getStartBeams(n,a,h,c);for(;t.some(d=>!d.done)&&h<p;){let d=[];for(let g of t){if(g.done){d.push(g);continue}if(l&&g.output_token_ids.length>=a.max_length){g.done=!0,d.push(g);continue}let m=await this.runBeam(g);a.output_attentions&&this.addAttentionsToBeam(g,m),a.output_scores;let _=m.logits.slice(null,-1,null);u(g.output_token_ids,_);let y=o(_);for(let[T,w]of y){let S={...g};this.updateBeam(S,T),S.score+=w,s&&s.includes(T)&&(S.done=!0),d.push(S)}}++h,d=this.groupBeams(d).map(g=>g.sort((m,_)=>_.score-m.score).slice(0,a.num_beams)),t=d.flat(),a.callback_function&&a.callback_function(t)}const e=this.groupBeams(t),r=d=>e.map(g=>a.num_return_sequences>1?g.slice(0,a.num_return_sequences).map(m=>m[d]):[g[0][d]]).flat(),i=r("output_token_ids");if(a.return_dict_in_generate){const d=r("decoder_attentions"),g=r("cross_attentions");return{sequences:i,decoder_attentions:d,cross_attentions:g}}else return i}addAttentionsToBeam(n,a){if(this.config.is_encoder_decoder){if(!a.cross_attentions||a.cross_attentions.length===0)throw Error("`output_attentions` is true, but the model did not produce cross-attentions. This is most likely because the model was not exported with `output_attentions=True`.");n.cross_attentions||(n.cross_attentions=[]),n.cross_attentions.push(a.cross_attentions)}if(!a.decoder_attentions||a.decoder_attentions.length===0)throw Error("`output_attentions` is true, but the model did not produce decoder-attentions. This is most likely because the model was not exported with `output_attentions=True`.");n.decoder_attentions||(n.decoder_attentions=[]),n.decoder_attentions.push(a.decoder_attentions)}groupBeams(n){const a=Object.create(null);for(const u of n)a[u.id]===void 0?a[u.id]=[u]:a[u.id].push(u);return Object.values(a)}getPastKeyValues(n,a){const u=Object.create(null);for(const c in n)if(c.startsWith("present")){let f=c.replace("present","past_key_values");a&&c.includes("encoder")?u[f]=a[f]:u[f]=n[c]}return u}getAttentions(n){const a=Object.create(null);for(const u of["cross_attentions","decoder_attentions"]){const c=[];for(const f in n)if(f.startsWith(u)){const s=f.split(".").pop();c[s]=n[f]}a[u]=c}return a}addPastKeyValues(n,a){if(a)Object.assign(n,a);else if(this.config.is_encoder_decoder&&(this.add_encoder_pkv??!0)){let c=[1,this.num_encoder_heads,0,this.encoder_dim_kv],f=[1,this.num_decoder_heads,0,this.decoder_dim_kv];for(let s=0;s<this.num_decoder_layers;++s)n[`past_key_values.${s}.encoder.key`]=new Tensor("float32",[],c),n[`past_key_values.${s}.encoder.value`]=new Tensor("float32",[],c),n[`past_key_values.${s}.decoder.key`]=new Tensor("float32",[],f),n[`past_key_values.${s}.decoder.value`]=new Tensor("float32",[],f)}else if(this.config.model_type==="falcon"){let c=[1*this.num_heads,0,this.dim_kv];for(let f=0;f<this.num_layers;++f)n[`past_key_values.${f}.key`]=new Tensor("float32",[],c),n[`past_key_values.${f}.value`]=new Tensor("float32",[],c)}else if(this.config.multi_query){let c=[1*this.num_heads,0,2*this.dim_kv];for(let f=0;f<this.num_layers;++f)n[`past_key_values.${f}.key_value`]=new Tensor("float32",[],c)}else if(this.config.model_type==="bloom"){let c=[1*this.num_heads,this.dim_kv,0],f=[1*this.num_heads,0,this.dim_kv];for(let s=0;s<this.num_layers;++s)n[`past_key_values.${s}.key`]=new Tensor("float32",[],c),n[`past_key_values.${s}.value`]=new Tensor("float32",[],f)}else{let c=[1,this.num_heads,0,this.dim_kv];for(let f=0;f<this.num_layers;++f)n[`past_key_values.${f}.key`]=new Tensor("float32",[],c),n[`past_key_values.${f}.value`]=new Tensor("float32",[],c)}}getStartBeams(n,a,u,c){return this._getStartBeams(this,n,a,u,c)}async runBeam(n){return await this._runBeam(this,n)}updateBeam(n,a){return this._updateBeam(n,a)}}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 RoFormerPreTrainedModel extends PreTrainedModel{}class RoFormerModel extends RoFormerPreTrainedModel{}class RoFormerForMaskedLM extends RoFormerPreTrainedModel{async _call(n){return new MaskedLMOutput(await super._call(n))}}class RoFormerForSequenceClassification extends RoFormerPreTrainedModel{async _call(n){return new SequenceClassifierOutput(await super._call(n))}}class RoFormerForTokenClassification extends RoFormerPreTrainedModel{async _call(n){return new TokenClassifierOutput(await super._call(n))}}class RoFormerForQuestionAnswering extends RoFormerPreTrainedModel{async _call(n){return new QuestionAnsweringModelOutput(await super._call(n))}}class ConvBertPreTrainedModel extends PreTrainedModel{}class ConvBertModel extends ConvBertPreTrainedModel{}class ConvBertForMaskedLM extends ConvBertPreTrainedModel{async _call(n){return new MaskedLMOutput(await super._call(n))}}class ConvBertForSequenceClassification extends ConvBertPreTrainedModel{async _call(n){return new SequenceClassifierOutput(await super._call(n))}}class ConvBertForTokenClassification extends ConvBertPreTrainedModel{async _call(n){return new TokenClassifierOutput(await super._call(n))}}class ConvBertForQuestionAnswering extends ConvBertPreTrainedModel{async _call(n){return new QuestionAnsweringModelOutput(await super._call(n))}}class ElectraPreTrainedModel extends PreTrainedModel{}class ElectraModel extends ElectraPreTrainedModel{}class ElectraForMaskedLM extends ElectraPreTrainedModel{async _call(n){return new MaskedLMOutput(await super._call(n))}}class ElectraForSequenceClassification extends ElectraPreTrainedModel{async _call(n){return new SequenceClassifierOutput(await super._call(n))}}class ElectraForTokenClassification extends ElectraPreTrainedModel{async _call(n){return new TokenClassifierOutput(await super._call(n))}}class ElectraForQuestionAnswering extends ElectraPreTrainedModel{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 EsmPreTrainedModel extends PreTrainedModel{}class EsmModel extends EsmPreTrainedModel{}class EsmForMaskedLM extends EsmPreTrainedModel{async _call(n){return new MaskedLMOutput(await super._call(n))}}class EsmForSequenceClassification extends EsmPreTrainedModel{async _call(n){return new SequenceClassifierOutput(await super._call(n))}}class EsmForTokenClassification extends EsmPreTrainedModel{async _call(n){return new TokenClassifierOutput(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,a,u,c){super(n,a),this.decoder_merged_session=u,this.generation_config=c,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,a,u,c){super(n,a),this.decoder_merged_session=u,this.generation_config=c,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,a,u,c){super(n,a),this.decoder_merged_session=u,this.generation_config=c,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,a,u,c){super(n,a),this.decoder_merged_session=u,this.generation_config=c,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,a,u,c){super(n,a),this.decoder_merged_session=u,this.generation_config=c,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,a,u){super(n,a),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,a,u,c){super(n,a),this.decoder_merged_session=u,this.generation_config=c,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,a,u,c){super(n,a),this.decoder_merged_session=u,this.generation_config=c,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 ASTPreTrainedModel extends PreTrainedModel{}class ASTModel extends ASTPreTrainedModel{}class ASTForAudioClassification extends ASTPreTrainedModel{}class WhisperPreTrainedModel extends PreTrainedModel{}class WhisperModel extends WhisperPreTrainedModel{}class WhisperForConditionalGeneration extends WhisperPreTrainedModel{requires_attention_mask=!1;main_input_name="input_features";constructor(n,a,u,c){super(n,a),this.decoder_merged_session=u,this.generation_config=c,this.num_decoder_layers=this.config.decoder_layers,this.num_decoder_heads=this.config.decoder_attention_heads,this.decoder_dim_kv=this.config.d_model/this.num_decoder_heads,this.num_encoder_layers=this.config.encoder_layers,this.num_encoder_heads=this.config.encoder_attention_heads,this.encoder_dim_kv=this.config.d_model/this.num_encoder_heads}async generate(n,a=null,u=null){if(a=this._get_generation_config(a),a.return_timestamps??=!1,a.return_timestamps&&(u=[new WhisperTimeStampLogitsProcessor(a)]),a.return_token_timestamps&&(a.output_attentions=!0,a.return_dict_in_generate=!0,a.task==="translate"&&console.warn("Token-level timestamps may not be reliable for task 'translate'."),!a.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 c=await super.generate(n,a,u);return a.return_token_timestamps&&a.alignment_heads&&(c.token_timestamps=this._extract_token_timestamps(c,a.alignment_heads,a.num_frames)),c}_extract_token_timestamps(n,a,u=null,c=.02){if(!n.cross_attentions)throw new Error("Model outputs must contain cross attentions to extract timestamps. This is most likely because the model was not exported with `output_attentions=True`.");let f=this.config.median_filter_width;f===void 0&&(console.warn("Model config has no `median_filter_width`, using default value of 7."),f=7);const s=n.cross_attentions.map(l=>{let o=Array.from({length:this.config.decoder_layers},(g,m)=>cat(l.map(_=>_[m]),2)),t=stack(a.map(([g,m])=>u?o[g].slice(null,m,null,[0,u]):o[g].slice(null,m)));t=t.transpose(1,0,2,3);let[e,r]=std_mean(t,-2,0,!0),i=t.clone();for(let g=0;g<i.dims[0];++g){let m=i[g];for(let _=0;_<m.dims[0];++_){let y=m[_];const T=e[g][_][0],w=r[g][_][0];for(let S=0;S<y.dims[0];++S){let O=y[S];for(let E=0;E<O.data.length;++E)O.data[E]=(O.data[E]-w.data[E])/T.data[E];O.data.set(medianFilter(O.data,f))}}}return mean(i,1)}),h=[n.sequences.length,n.sequences[0].length],p=new Tensor("float32",new Float32Array(h[0]*h[1]),h);for(let l=0;l<h[0];++l){const o=s[l].neg().squeeze_(0);let[t,e]=dynamicTimeWarping(o),r=Array.from({length:t.length-1},(g,m)=>t[m+1]-t[m]),i=mergeArrays([1],r).map(g=>!!g),d=[];for(let g=0;g<i.length;++g)i[g]&&d.push(e[g]*c);p[l].data.set(d,1)}return p}}class VisionEncoderDecoderModel extends PreTrainedModel{main_input_name="pixel_values";constructor(n,a,u,c){super(n,a),this.decoder_merged_session=u,this.generation_config=c;const f=this.config.encoder,s=this.config.decoder,h=f.model_type;(MODEL_MAPPING_NAMES_ENCODER_ONLY.get(h)??MODEL_MAPPING_NAMES_ENCODER_DECODER.get(h))||console.warn(`Model type for encoder '${h}' not found, assuming encoder-only architecture. Please report this at https://github.com/xenova/transformers.js/issues/new/choose.`);const l=MODEL_WITH_LM_HEAD_MAPPING_NAMES.get(s.model_type);if(!l)throw new Error(`Unable to construct \`VisionEncoderDecoder\` due to unsupported decoder: "${this.config.decoder.model_type}"`);const o=l[1],t=new o(s,u,c);this.add_encoder_pkv="num_decoder_layers"in t,this.add_encoder_pkv?(this.num_decoder_layers=t.num_decoder_layers,this.num_decoder_heads=t.num_decoder_heads,this.decoder_dim_kv=t.decoder_dim_kv,this.num_encoder_layers=t.num_encoder_layers,this.num_encoder_heads=t.num_encoder_heads,this.encoder_dim_kv=t.encoder_dim_kv):(this.num_layers=t.num_layers,this.num_heads=t.num_heads,this.dim_kv=t.dim_kv)}}class CLIPPreTrainedModel extends PreTrainedModel{}class CLIPModel extends CLIPPreTrainedModel{}class CLIPTextModelWithProjection extends CLIPPreTrainedModel{static async from_pretrained(n,a={}){return a.model_file_name??="text_model",super.from_pretrained(n,a)}}class CLIPVisionModelWithProjection extends CLIPPreTrainedModel{static async from_pretrained(n,a={}){return a.model_file_name??="vision_model",super.from_pretrained(n,a)}}class SiglipPreTrainedModel extends PreTrainedModel{}class SiglipModel extends SiglipPreTrainedModel{}class SiglipTextModel extends SiglipPreTrainedModel{static async from_pretrained(n,a={}){return a.model_file_name??="text_model",super.from_pretrained(n,a)}}class SiglipVisionModel extends CLIPPreTrainedModel{static async from_pretrained(n,a={}){return a.model_file_name??="vision_model",super.from_pretrained(n,a)}}class ChineseCLIPPreTrainedModel extends PreTrainedModel{}class ChineseCLIPModel extends ChineseCLIPPreTrainedModel{}class CLIPSegPreTrainedModel extends PreTrainedModel{}class CLIPSegModel extends CLIPSegPreTrainedModel{}class CLIPSegForImageSegmentation extends CLIPSegPreTrainedModel{}class GPT2PreTrainedModel extends PreTrainedModel{constructor(n,a,u){super(n,a),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,a,u){super(n,a),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,a,u){super(n,a),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,a,u){super(n,a),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,a,u){super(n,a),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,a,u){super(n,a),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,a,u){super(n,a),this.generation_config=u,this.config.pad_token_id=this.config.eos_token_id,this.num_heads=this.config.num_key_value_heads??this.config.num_attention_heads,this.num_layers=this.config.num_hidden_layers,this.dim_kv=this.config.hidden_size/this.config.num_attention_heads}}class LlamaModel extends LlamaPreTrainedModel{}class LlamaForCausalLM extends LlamaPreTrainedModel{}class Qwen2PreTrainedModel extends PreTrainedModel{constructor(n,a,u){super(n,a),this.generation_config=u,this.config.pad_token_id=this.config.eos_token_id,this.num_heads=this.config.num_key_value_heads??this.config.num_attention_heads,this.num_layers=this.config.num_hidden_layers,this.dim_kv=this.config.hidden_size/this.config.num_attention_heads}}class Qwen2Model extends Qwen2PreTrainedModel{}class Qwen2ForCausalLM extends Qwen2PreTrainedModel{}class PhiPreTrainedModel extends PreTrainedModel{constructor(n,a,u){super(n,a),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 PhiModel extends PhiPreTrainedModel{}class PhiForCausalLM extends PhiPreTrainedModel{}class BloomPreTrainedModel extends PreTrainedModel{constructor(n,a,u){super(n,a),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,a,u){super(n,a),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,a,u){super(n,a),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 VitMattePreTrainedModel extends PreTrainedModel{}class VitMatteForImageMatting extends VitMattePreTrainedModel{async _call(n){return new ImageMattingOutput(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 OwlViTPreTrainedModel extends PreTrainedModel{}class OwlViTModel extends OwlViTPreTrainedModel{}class OwlViTForObjectDetection extends OwlViTPreTrainedModel{}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:a}){super(),this.logits=n,this.pred_boxes=a}}class DetrSegmentationOutput extends ModelOutput{constructor({logits:n,pred_boxes:a,pred_masks:u}){super(),this.logits=n,this.pred_boxes=a,this.pred_masks=u}}class TableTransformerPreTrainedModel extends PreTrainedModel{}class TableTransformerModel extends TableTransformerPreTrainedModel{}class TableTransformerForObjectDetection extends TableTransformerPreTrainedModel{async _call(n){return new TableTransformerObjectDetectionOutput(await super._call(n))}}class TableTransformerObjectDetectionOutput extends DetrObjectDetectionOutput{}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 Swin2SRPreTrainedModel extends PreTrainedModel{}class Swin2SRModel extends Swin2SRPreTrainedModel{}class Swin2SRForImageSuperResolution extends Swin2SRPreTrainedModel{}class DPTPreTrainedModel extends PreTrainedModel{}class DPTModel extends DPTPreTrainedModel{}class DPTForDepthEstimation extends DPTPreTrainedModel{}class DepthAnythingPreTrainedModel extends PreTrainedModel{}class DepthAnythingForDepthEstimation extends DepthAnythingPreTrainedModel{}class GLPNPreTrainedModel extends PreTrainedModel{}class GLPNModel extends GLPNPreTrainedModel{}class GLPNForDepthEstimation extends GLPNPreTrainedModel{}class DonutSwinPreTrainedModel extends PreTrainedModel{}class DonutSwinModel extends DonutSwinPreTrainedModel{}class ConvNextPreTrainedModel extends PreTrainedModel{}class ConvNextModel extends ConvNextPreTrainedModel{}class ConvNextForImageClassification extends ConvNextPreTrainedModel{async _call(n){return new SequenceClassifierOutput(await super._call(n))}}class ConvNextV2PreTrainedModel extends PreTrainedModel{}class ConvNextV2Model extends ConvNextV2PreTrainedModel{}class ConvNextV2ForImageClassification extends ConvNextV2PreTrainedModel{async _call(n){return new SequenceClassifierOutput(await super._call(n))}}class Dinov2PreTrainedModel extends PreTrainedModel{}class Dinov2Model extends Dinov2PreTrainedModel{}class Dinov2ForImageClassification extends Dinov2PreTrainedModel{async _call(n){return new SequenceClassifierOutput(await super._call(n))}}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:a}){super(),this.logits=n,this.pred_boxes=a}}class SamPreTrainedModel extends PreTrainedModel{}class SamModel extends SamPreTrainedModel{constructor(n,a,u){super(n,a),this.prompt_encoder_mask_decoder=u}async get_image_embeddings({pixel_values:n}){return await encoderForward(this,{pixel_values:n})}async forward(n){if((!n.image_embeddings||!n.image_positional_embeddings)&&(n={...n,...await this.get_image_embeddings(n)}),!n.input_labels){const a=n.input_points.dims.slice(0,-1),u=a.reduce((c,f)=>c*f,1);n.input_labels=new Tensor("int64",new BigInt64Array(u).fill(1n),a)}return await sessionRun(this.prompt_encoder_mask_decoder,{input_points:n.input_points,input_labels:n.input_labels,image_embeddings:n.image_embeddings,image_positional_embeddings:n.image_positional_embeddings})}async _call(n){return new SamImageSegmentationOutput(await super._call(n))}}class SamImageSegmentationOutput extends ModelOutput{constructor({iou_scores:n,pred_masks:a}){super(),this.iou_scores=n,this.pred_masks=a}}class MarianPreTrainedModel extends PreTrainedModel{}class MarianModel extends MarianPreTrainedModel{}class MarianMTModel extends MarianPreTrainedModel{constructor(n,a,u,c){super(n,a),this.decoder_merged_session=u,this.generation_config=c,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,a,u,c){super(n,a),this.decoder_merged_session=u,this.generation_config=c,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 Wav2Vec2BertPreTrainedModel extends PreTrainedModel{}class Wav2Vec2BertModel extends Wav2Vec2BertPreTrainedModel{}class Wav2Vec2BertForCTC extends Wav2Vec2BertPreTrainedModel{async _call(n){return new CausalLMOutput(await super._call(n))}}class Wav2Vec2BertForSequenceClassification extends Wav2Vec2BertPreTrainedModel{async _call(n){return new SequenceClassifierOutput(await super._call(n))}}class HubertModel extends Wav2Vec2PreTrainedModel{}class HubertForCTC extends Wav2Vec2PreTrainedModel{async _call(n){return new CausalLMOutput(await super._call(n))}}class HubertForSequenceClassification 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,a,u,c){super(n,a),this.decoder_merged_session=u,this.generation_config=c,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,a,{threshold:u=.5,minlenratio:c=0,maxlenratio:f=20,vocoder:s=null}={}){const h={input_ids:n},{encoder_outputs:p,encoder_attention_mask:l}=await encoderForward(this,h),o=p.dims[1]/this.config.reduction_factor,t=Math.floor(o*f),e=Math.floor(o*c),r=this.config.num_mel_bins;let i=[],d=null,g=null,m=0;for(;;){++m;const T=boolTensor(!!g);let w;g?w=g.output_sequence_out:w=new Tensor("float32",new Float32Array(r),[1,1,r]);let S={use_cache_branch:T,output_sequence:w,encoder_attention_mask:l,speaker_embeddings:a,encoder_hidden_states:p};this.addPastKeyValues(S,d),g=await sessionRun(this.decoder_merged_session,S),d=this.getPastKeyValues(g,d);const{prob:O,spectrum:E}=g;if(i.push(E),m>=e&&(Array.from(O.data).filter(v=>v>=u).length>0||m>=t))break}const _=cat(i),{waveform:y}=await sessionRun(s.session,{spectrogram:_});return{spectrogram:_,waveform:y}}}class SpeechT5HifiGan extends PreTrainedModel{main_input_name="spectrogram"}class TrOCRPreTrainedModel extends PreTrainedModel{constructor(n,a,u){super(n,a),this.generation_config=u,this.config.pad_token_id=this.config.eos_token_id,this.num_encoder_layers=this.num_decoder_layers=this.config.decoder_layers,this.num_encoder_heads=this.num_decoder_heads=this.config.decoder_attention_heads,this.encoder_dim_kv=this.decoder_dim_kv=this.config.d_model/this.num_decoder_heads}}class TrOCRForCausalLM extends TrOCRPreTrainedModel{}class MistralPreTrainedModel extends PreTrainedModel{constructor(n,a,u){super(n,a),this.generation_config=u,this.config.pad_token_id=this.config.eos_token_id,this.num_heads=this.config.num_key_value_heads,this.num_layers=this.config.num_hidden_layers,this.dim_kv=this.config.hidden_size/this.config.num_attention_heads}}class MistralModel extends MistralPreTrainedModel{}class MistralForCausalLM extends MistralPreTrainedModel{}class FalconPreTrainedModel extends PreTrainedModel{constructor(n,a,u){super(n,a),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.config.num_attention_heads}}class FalconModel extends FalconPreTrainedModel{}class FalconForCausalLM extends FalconPreTrainedModel{}class ClapPreTrainedModel extends PreTrainedModel{}class ClapModel extends ClapPreTrainedModel{}class ClapTextModelWithProjection extends ClapPreTrainedModel{static async from_pretrained(n,a={}){return a.model_file_name??="text_model",super.from_pretrained(n,a)}}class ClapAudioModelWithProjection extends ClapPreTrainedModel{static async from_pretrained(n,a={}){return a.model_file_name??="audio_model",super.from_pretrained(n,a)}}class VitsPreTrainedModel extends PreTrainedModel{}class VitsModel extends VitsPreTrainedModel{async _call(n){return new VitsModelOutput(await super._call(n))}}class SegformerPreTrainedModel extends PreTrainedModel{}class SegformerForImageClassification extends SegformerPreTrainedModel{}class SegformerForSemanticSegmentation extends SegformerPreTrainedModel{}class PretrainedMixin{static MODEL_CLASS_MAPPINGS=null;static BASE_IF_FAIL=!1;static async from_pretrained(n,{quantized:a=!0,progress_callback:u=null,config:c=null,cache_dir:f=null,local_files_only:s=!1,revision:h="main",model_file_name:p=null}={}){let l={quantized:a,progress_callback:u,config:c,cache_dir:f,local_files_only:s,revision:h,model_file_name:p};if(c=await AutoConfig.from_pretrained(n,l),l.config||(l.config=c),!this.MODEL_CLASS_MAPPINGS)throw new Error("`MODEL_CLASS_MAPPINGS` not implemented for this type of `AutoClass`: "+this.name);for(let o of this.MODEL_CLASS_MAPPINGS){const t=o.get(c.model_type);if(t)return await t[1].from_pretrained(n,l)}if(this.BASE_IF_FAIL)return console.warn(`Unknown model class "${c.model_type}", attempting to construct from base class.`),await PreTrainedModel.from_pretrained(n,l);throw Error(`Unsupported model type: ${c.model_type}`)}}const MODEL_MAPPING_NAMES_ENCODER_ONLY=new Map([["bert",["BertModel",BertModel]],["roformer",["RoFormerModel",RoFormerModel]],["electra",["ElectraModel",ElectraModel]],["esm",["EsmModel",EsmModel]],["convbert",["ConvBertModel",ConvBertModel]],["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]],["clap",["ClapModel",ClapModel]],["clip",["CLIPModel",CLIPModel]],["clipseg",["CLIPSegModel",CLIPSegModel]],["chinese_clip",["ChineseCLIPModel",ChineseCLIPModel]],["siglip",["SiglipModel",SiglipModel]],["mobilebert",["MobileBertModel",MobileBertModel]],["squeezebert",["SqueezeBertModel",SqueezeBertModel]],["wav2vec2",["Wav2Vec2Model",Wav2Vec2Model]],["wav2vec2-bert",["Wav2Vec2BertModel",Wav2Vec2BertModel]],["hubert",["HubertModel",HubertModel]],["wavlm",["WavLMModel",WavLMModel]],["audio-spectrogram-transformer",["ASTModel",ASTModel]],["vits",["VitsModel",VitsModel]],["detr",["DetrModel",DetrModel]],["table-transformer",["TableTransformerModel",TableTransformerModel]],["vit",["ViTModel",ViTModel]],["mobilevit",["MobileViTModel",MobileViTModel]],["owlvit",["OwlViTModel",OwlViTModel]],["beit",["BeitModel",BeitModel]],["deit",["DeiTModel",DeiTModel]],["convnext",["ConvNextModel",ConvNextModel]],["convnextv2",["ConvNextV2Model",ConvNextV2Model]],["dinov2",["Dinov2Model",Dinov2Model]],["resnet",["ResNetModel",ResNetModel]],["swin",["SwinModel",SwinModel]],["swin2sr",["Swin2SRModel",Swin2SRModel]],["donut-swin",["DonutSwinModel",DonutSwinModel]],["yolos",["YolosModel",YolosModel]],["dpt",["DPTModel",DPTModel]],["glpn",["GLPNModel",GLPNModel]],["hifigan",["SpeechT5HifiGan",SpeechT5HifiGan]]]),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]],["qwen2",["Qwen2Model",Qwen2Model]],["phi",["PhiModel",PhiModel]],["mpt",["MptModel",MptModel]],["opt",["OPTModel",OPTModel]],["mistral",["MistralModel",MistralModel]],["falcon",["FalconModel",FalconModel]]]),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_TEXT_TO_WAVEFORM_MAPPING_NAMES=new Map([["vits",["VitsModel",VitsModel]]]),MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING_NAMES=new Map([["bert",["BertForSequenceClassification",BertForSequenceClassification]],["roformer",["RoFormerForSequenceClassification",RoFormerForSequenceClassification]],["electra",["ElectraForSequenceClassification",ElectraForSequenceClassification]],["esm",["EsmForSequenceClassification",EsmForSequenceClassification]],["convbert",["ConvBertForSequenceClassification",ConvBertForSequenceClassification]],["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]],["roformer",["RoFormerForTokenClassification",RoFormerForTokenClassification]],["electra",["ElectraForTokenClassification",ElectraForTokenClassification]],["esm",["EsmForTokenClassification",EsmForTokenClassification]],["convbert",["ConvBertForTokenClassification",ConvBertForTokenClassification]],["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]],["qwen2",["Qwen2ForCausalLM",Qwen2ForCausalLM]],["phi",["PhiForCausalLM",PhiForCausalLM]],["mpt",["MptForCausalLM",MptForCausalLM]],["opt",["OPTForCausalLM",OPTForCausalLM]],["mbart",["MBartForCausalLM",MBartForCausalLM]],["mistral",["MistralForCausalLM",MistralForCausalLM]],["falcon",["FalconForCausalLM",FalconForCausalLM]],["trocr",["TrOCRForCausalLM",TrOCRForCausalLM]]]),MODEL_FOR_MASKED_LM_MAPPING_NAMES=new Map([["bert",["BertForMaskedLM",BertForMaskedLM]],["roformer",["RoFormerForMaskedLM",RoFormerForMaskedLM]],["electra",["ElectraForMaskedLM",ElectraForMaskedLM]],["esm",["EsmForMaskedLM",EsmForMaskedLM]],["convbert",["ConvBertForMaskedLM",ConvBertForMaskedLM]],["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]],["roformer",["RoFormerForQuestionAnswering",RoFormerForQuestionAnswering]],["electra",["ElectraForQuestionAnswering",ElectraForQuestionAnswering]],["convbert",["ConvBertForQuestionAnswering",ConvBertForQuestionAnswering]],["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_IMAGE_CLASSIFICATION_MAPPING_NAMES=new Map([["vit",["ViTForImageClassification",ViTForImageClassification]],["mobilevit",["MobileViTForImageClassification",MobileViTForImageClassification]],["beit",["BeitForImageClassification",BeitForImageClassification]],["deit",["DeiTForImageClassification",DeiTForImageClassification]],["convnext",["ConvNextForImageClassification",ConvNextForImageClassification]],["convnextv2",["ConvNextV2ForImageClassification",ConvNextV2ForImageClassification]],["dinov2",["Dinov2ForImageClassification",Dinov2ForImageClassification]],["resnet",["ResNetForImageClassification",ResNetForImageClassification]],["swin",["SwinForImageClassification",SwinForImageClassification]],["segformer",["SegformerForImageClassification",SegformerForImageClassification]]]),MODEL_FOR_OBJECT_DETECTION_MAPPING_NAMES=new Map([["detr",["DetrForObjectDetection",DetrForObjectDetection]],["table-transformer",["TableTransformerForObjectDetection",TableTransformerForObjectDetection]],["yolos",["YolosForObjectDetection",YolosForObjectDetection]]]),MODEL_FOR_ZERO_SHOT_OBJECT_DETECTION_MAPPING_NAMES=new Map([["owlvit",["OwlViTForObjectDetection",OwlViTForObjectDetection]]]),MODEL_FOR_IMAGE_SEGMENTATION_MAPPING_NAMES=new Map([["detr",["DetrForSegmentation",DetrForSegmentation]],["clipseg",["CLIPSegForImageSegmentation",CLIPSegForImageSegmentation]]]),MODEL_FOR_SEMANTIC_SEGMENTATION_MAPPING_NAMES=new Map([["segformer",["SegformerForSemanticSegmentation",SegformerForSemanticSegmentation]]]),MODEL_FOR_MASK_GENERATION_MAPPING_NAMES=new Map([["sam",["SamModel",SamModel]]]),MODEL_FOR_CTC_MAPPING_NAMES=new Map([["wav2vec2",["Wav2Vec2ForCTC",Wav2Vec2ForCTC]],["wav2vec2-bert",["Wav2Vec2BertForCTC",Wav2Vec2BertForCTC]],["wavlm",["WavLMForCTC",WavLMForCTC]],["hubert",["HubertForCTC",HubertForCTC]]]),MODEL_FOR_AUDIO_CLASSIFICATION_MAPPING_NAMES=new Map([["wav2vec2",["Wav2Vec2ForSequenceClassification",Wav2Vec2ForSequenceClassification]],["wav2vec2-bert",["Wav2Vec2BertForSequenceClassification",Wav2Vec2BertForSequenceClassification]],["wavlm",["WavLMForSequenceClassification",WavLMForSequenceClassification]],["hubert",["HubertForSequenceClassification",HubertForSequenceClassification]],["audio-spectrogram-transformer",["ASTForAudioClassification",ASTForAudioClassification]]]),MODEL_FOR_IMAGE_MATTING_MAPPING_NAMES=new Map([["vitmatte",["VitMatteForImageMatting",VitMatteForImageMatting]]]),MODEL_FOR_IMAGE_TO_IMAGE_MAPPING_NAMES=new Map([["swin2sr",["Swin2SRForImageSuperResolution",Swin2SRForImageSuperResolution]]]),MODEL_FOR_DEPTH_ESTIMATION_MAPPING_NAMES=new Map([["dpt",["DPTForDepthEstimation",DPTForDepthEstimation]],["depth_anything",["DepthAnythingForDepthEstimation",DepthAnythingForDepthEstimation]],["glpn",["GLPNForDepthEstimation",GLPNForDepthEstimation]]]),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_SEMANTIC_SEGMENTATION_MAPPING_NAMES,MODEL_TYPES.EncoderOnly],[MODEL_FOR_IMAGE_MATTING_MAPPING_NAMES,MODEL_TYPES.EncoderOnly],[MODEL_FOR_IMAGE_TO_IMAGE_MAPPING_NAMES,MODEL_TYPES.EncoderOnly],[MODEL_FOR_DEPTH_ESTIMATION_MAPPING_NAMES,MODEL_TYPES.EncoderOnly],[MODEL_FOR_OBJECT_DETECTION_MAPPING_NAMES,MODEL_TYPES.EncoderOnly],[MODEL_FOR_ZERO_SHOT_OBJECT_DETECTION_MAPPING_NAMES,MODEL_TYPES.EncoderOnly],[MODEL_FOR_MASK_GENERATION_MAPPING_NAMES,MODEL_TYPES.MaskGeneration],[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],[MODEL_FOR_TEXT_TO_WAVEFORM_MAPPING_NAMES,MODEL_TYPES.EncoderOnly]];for(const[b,n]of MODEL_CLASS_TYPE_MAPPING)for(const[a,u]of b.values())MODEL_TYPE_MAPPING.set(a,n),MODEL_CLASS_TO_NAME_MAPPING.set(u,a),MODEL_NAME_TO_CLASS_MAPPING.set(a,u);const CUSTOM_MAPPING=[["CLIPTextModelWithProjection",CLIPTextModelWithProjection,MODEL_TYPES.EncoderOnly],["CLIPVisionModelWithProjection",CLIPVisionModelWithProjection,MODEL_TYPES.EncoderOnly],["SiglipTextModel",SiglipTextModel,MODEL_TYPES.EncoderOnly],["SiglipVisionModel",SiglipVisionModel,MODEL_TYPES.EncoderOnly],["ClapTextModelWithProjection",ClapTextModelWithProjection,MODEL_TYPES.EncoderOnly],["ClapAudioModelWithProjection",ClapAudioModelWithProjection,MODEL_TYPES.EncoderOnly]];for(const[b,n,a]of CUSTOM_MAPPING)MODEL_TYPE_MAPPING.set(b,a),MODEL_CLASS_TO_NAME_MAPPING.set(n,b),MODEL_NAME_TO_CLASS_MAPPING.set(b,n);class AutoModel extends PretrainedMixin{static MODEL_CLASS_MAPPINGS=MODEL_CLASS_TYPE_MAPPING.map(n=>n[0]);static BASE_IF_FAIL=!0}class Seq2SeqLMOutput extends ModelOutput{constructor({logits:n,past_key_values:a,encoder_outputs:u,decoder_attentions:c=null,cross_attentions:f=null}){super(),this.logits=n,this.past_key_values=a,this.encoder_outputs=u,this.decoder_attentions=c,this.cross_attentions=f}}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:a}){super(),this.start_logits=n,this.end_logits=a}}class CausalLMOutput extends ModelOutput{constructor({logits:n}){super(),this.logits=n}}class ImageMattingOutput extends ModelOutput{constructor({alphas:n}){super(),this.alphas=n}}class VitsModelOutput extends ModelOutput{constructor({waveform:n,spectrogram:a}){super(),this.waveform=n,this.spectrogram=a}}const BROWSER_ENV=typeof self<"u",WEBWORKER_ENV=BROWSER_ENV&&self.constructor.name==="DedicatedWorkerGlobalScope";let createCanvasFunction,ImageDataClass,loadImageFunction;if(BROWSER_ENV)createCanvasFunction=(b,n)=>{if(!self.OffscreenCanvas)throw new Error("OffscreenCanvas not supported by this browser.");return new self.OffscreenCanvas(b,n)},loadImageFunction=self.createImageBitmap,ImageDataClass=self.ImageData;else if(sharp)loadImageFunction=async b=>{const a=(await b.metadata()).channels;let{data:u,info:c}=await b.raw().toBuffer({resolveWithObject:!0});const f=new RawImage(new Uint8ClampedArray(u),c.width,c.height,c.channels);return a!==void 0&&a!==c.channels&&f.convert(a),f};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"},CONTENT_TYPE_MAP=new Map([["png","image/png"],["jpg","image/jpeg"],["jpeg","image/jpeg"],["gif","image/gif"]]);class RawImage{constructor(n,a,u,c){this.data=n,this.width=a,this.height=u,this.channels=c}get size(){return[this.width,this.height]}static async read(n){if(n instanceof RawImage)return n;if(typeof n=="string"||n instanceof URL)return await this.fromURL(n);throw new Error(`Unsupported input type: ${typeof n}`)}static async fromURL(n){let a=await getFile(n);if(a.status!==200)throw new Error(`Unable to read image from "${n}" (${a.status} ${a.statusText})`);let u=await a.blob();return this.fromBlob(u)}static async fromBlob(n){if(BROWSER_ENV){let a=await loadImageFunction(n);const u=createCanvasFunction(a.width,a.height).getContext("2d");return u.drawImage(a,0,0),new this(u.getImageData(0,0,a.width,a.height).data,a.width,a.height,4)}else{let a=sharp(await n.arrayBuffer());return await loadImageFunction(a)}}static fromTensor(n,a="CHW"){if(n.dims.length!==3)throw new Error(`Tensor should have 3 dimensions, but has ${n.dims.length} dimensions.`);if(a==="CHW")n=n.transpose(1,2,0);else if(a!=="HWC")throw new Error(`Unsupported channel format: ${a}`);if(!(n.data instanceof Uint8ClampedArray||n.data instanceof Uint8Array))throw new Error(`Unsupported tensor type: ${n.type}`);switch(n.dims[2]){case 1:case 2:case 3:case 4:return new RawImage(n.data,n.dims[1],n.dims[0],n.dims[2]);default:throw new Error(`Unsupported number of channels: ${n.dims[2]}`)}}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 a=0,u=0;a<this.data.length;a+=this.channels){const c=this.data[a],f=this.data[a+1],s=this.data[a+2];n[u++]=Math.round(.2989*c+.587*f+.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 a=0,u=0;a<this.data.length;++a)n[u++]=this.data[a],n[u++]=this.data[a],n[u++]=this.data[a];break;case 4:for(let a=0,u=0;a<this.data.length;a+=4)n[u++]=this.data[a],n[u++]=this.data[a+1],n[u++]=this.data[a+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 a=0,u=0;a<this.data.length;++a)n[u++]=this.data[a],n[u++]=this.data[a],n[u++]=this.data[a],n[u++]=255;break;case 3:for(let a=0,u=0;a<this.data.length;a+=3)n[u++]=this.data[a],n[u++]=this.data[a+1],n[u++]=this.data[a+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,a,{resample:u=2}={}){let c=RESAMPLING_MAPPING[u]??u;if(BROWSER_ENV){let f=this.channels,s=this.toCanvas();const h=createCanvasFunction(n,a).getContext("2d");return h.drawImage(s,0,0,n,a),new RawImage(h.getImageData(0,0,n,a).data,n,a,4).convert(f)}else{let f=this.toSharp();switch(c){case"box":case"hamming":(c==="box"||c==="hamming")&&(console.warn(`Resampling method ${c} is not yet supported. Using bilinear instead.`),c="bilinear");case"nearest":case"bilinear":case"bicubic":f=f.affine([n/this.width,0,0,a/this.height],{interpolator:c});break;case"lanczos":f=f.resize({width:n,height:a,fit:"fill",kernel:"lanczos3"});break;default:throw new Error(`Resampling method ${c} is not supported.`)}return await loadImageFunction(f)}}async pad([n,a,u,c]){if(n=Math.max(n,0),a=Math.max(a,0),u=Math.max(u,0),c=Math.max(c,0),n===0&&a===0&&u===0&&c===0)return this;if(BROWSER_ENV){let f=this.channels,s=this.toCanvas(),h=this.width+n+a,p=this.height+u+c;const l=createCanvasFunction(h,p).getContext("2d");return l.drawImage(s,0,0,this.width,this.height,n,u,h,p),new RawImage(l.getImageData(0,0,h,p).data,h,p,4).convert(f)}else{let f=this.toSharp().extend({left:n,right:a,top:u,bottom:c});return await loadImageFunction(f)}}async crop([n,a,u,c]){if(n=Math.max(n,0),a=Math.max(a,0),u=Math.min(u,this.width-1),c=Math.min(c,this.height-1),n===0&&a===0&&u===this.width-1&&c===this.height-1)return this;const f=u-n+1,s=c-a+1;if(BROWSER_ENV){const h=this.channels,p=this.toCanvas(),l=createCanvasFunction(f,s).getContext("2d");return l.drawImage(p,n,a,f,s,0,0,f,s),new RawImage(l.getImageData(0,0,f,s).data,f,s,4).convert(h)}else{const h=this.toSharp().extract({left:n,top:a,width:f,height:s});return await loadImageFunction(h)}}async center_crop(n,a){if(this.width===n&&this.height===a)return this;let u=(this.width-n)/2,c=(this.height-a)/2;if(BROWSER_ENV){let f=this.channels,s=this.toCanvas();const h=createCanvasFunction(n,a).getContext("2d");let p=0,l=0,o=0,t=0;return u>=0?p=u:o=-u,c>=0?l=c:t=-c,h.drawImage(s,p,l,n,a,o,t,n,a),new RawImage(h.getImageData(0,0,n,a).data,n,a,4).convert(f)}else{let f=this.toSharp();if(u>=0&&c>=0)f=f.extract({left:Math.floor(u),top:Math.floor(c),width:n,height:a});else if(u<=0&&c<=0){let s=Math.floor(-c),h=Math.floor(-u);f=f.extend({top:s,left:h,right:n-this.width-h,bottom:a-this.height-s})}else{let s=[0,0],h=0;c<0?(s[0]=Math.floor(-c),s[1]=a-this.height-s[0]):h=Math.floor(c);let p=[0,0],l=0;u<0?(p[0]=Math.floor(-u),p[1]=n-this.width-p[0]):l=Math.floor(u),f=f.extend({top:s[0],bottom:s[1],left:p[0],right:p[1]}).extract({left:l,top:h,width:n,height:a})}return await loadImageFunction(f)}}async toBlob(n="image/png",a=1){if(!BROWSER_ENV)throw new Error("toBlob() is only supported in browser environments.");return await this.toCanvas().convertToBlob({type:n,quality:a})}toCanvas(){if(!BROWSER_ENV)throw new Error("toCanvas() is only supported in browser environments.");let n=this.clone().rgba(),a=createCanvasFunction(n.width,n.height),u=new ImageDataClass(n.data,n.width,n.height);return a.getContext("2d").putImageData(u,0,0),a}_update(n,a,u,c=null){return this.data=n,this.width=a,this.height=u,c!==null&&(this.channels=c),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}async save(n){if(BROWSER_ENV){if(WEBWORKER_ENV)throw new Error("Unable to save an image from a Web Worker.");const a=n.split(".").pop().toLowerCase(),u=CONTENT_TYPE_MAP.get(a)??"image/png",c=await this.toBlob(u),f=URL.createObjectURL(c),s=document.createElement("a");s.href=f,s.download=n,s.click(),s.remove()}else{if(env$1.useFS)return await this.toSharp().toFile(n);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}})}}function hanning(b){if(b<1)return new Float64Array;if(b===1)return new Float64Array([1]);const n=b-1,a=Math.PI/n,u=new Float64Array(b);for(let c=0;c<b;++c){const f=2*c-n;u[c]=.5+.5*Math.cos(a*f)}return u}const HERTZ_TO_MEL_MAPPING={htk:b=>2595*Math.log10(1+b/700),kaldi:b=>1127*Math.log(1+b/700),slaney:(b,n=1e3,a=15,u=27/Math.log(6.4))=>b>=n?a+Math.log(b/n)*u:3*b/200};function hertz_to_mel(b,n="htk"){const a=HERTZ_TO_MEL_MAPPING[n];if(!a)throw new Error('mel_scale should be one of "htk", "slaney" or "kaldi".');return typeof b=="number"?a(b):b.map(u=>a(u))}const MEL_TO_HERTZ_MAPPING={htk:b=>700*(10**(b/2595)-1),kaldi:b=>700*(Math.exp(b/1127)-1),slaney:(b,n=1e3,a=15,u=Math.log(6.4)/27)=>b>=a?n*Math.exp(u*(b-a)):200*b/3};function mel_to_hertz(b,n="htk"){const a=MEL_TO_HERTZ_MAPPING[n];if(!a)throw new Error('mel_scale should be one of "htk", "slaney" or "kaldi".');return typeof b=="number"?a(b):b.map(u=>a(u))}function _create_triangular_filter_bank(b,n){const a=Float64Array.from({length:n.length-1},(s,h)=>n[h+1]-n[h]),u=Array.from({length:b.length},()=>new Array(n.length));for(let s=0;s<b.length;++s){const h=u[s];for(let p=0;p<n.length;++p)h[p]=n[p]-b[s]}const c=n.length-2,f=Array.from({length:c},()=>new Array(b.length));for(let s=0;s<b.length;++s){const h=u[s];for(let p=0;p<c;++p){const l=-h[p]/a[p],o=h[p+2]/a[p+1];f[p][s]=Math.max(0,Math.min(l,o))}}return f}function linspace(b,n,a){const u=(n-b)/(a-1);return Float64Array.from({length:a},(c,f)=>b+u*f)}function mel_filter_bank(b,n,a,u,c,f=null,s="htk",h=!1){if(f!==null&&f!=="slaney")throw new Error('norm must be one of null or "slaney"');const p=hertz_to_mel(a,s),l=hertz_to_mel(u,s),o=linspace(p,l,n+2);let t=mel_to_hertz(o,s),e;if(h){const i=c/(b*2);e=hertz_to_mel(Float64Array.from({length:b},(d,g)=>g*i),s),t=o}else e=linspace(0,Math.floor(c/2),b);const r=_create_triangular_filter_bank(e,t);if(f!==null&&f==="slaney")for(let i=0;i<n;++i){const d=r[i],g=2/(t[i+2]-t[i]);for(let m=0;m<b;++m)d[m]*=g}return r}function padReflect(b,n,a){const u=new b.constructor(b.length+n+a),c=b.length-1;for(let f=0;f<b.length;++f)u[n+f]=b[f];for(let f=1;f<=n;++f)u[n-f]=b[calculateReflectOffset(f,c)];for(let f=1;f<=a;++f)u[c+n+f]=b[calculateReflectOffset(c-f,c)];return u}function _db_conversion_helper(b,n,a,u,c){if(a<=0)throw new Error("reference must be greater than zero");if(u<=0)throw new Error("min_value must be greater than zero");a=Math.max(u,a);const f=Math.log10(a);for(let s=0;s<b.length;++s)b[s]=n*Math.log10(Math.max(u,b[s])-f);if(c!==null){if(c<=0)throw new Error("db_range must be greater than zero");const s=max(b)[0]-c;for(let h=0;h<b.length;++h)b[h]=Math.max(b[h],s)}return b}function amplitude_to_db(b,n=1,a=1e-5,u=null){return _db_conversion_helper(b,20,n,a,u)}function power_to_db(b,n=1,a=1e-10,u=null){return _db_conversion_helper(b,10,n,a,u)}function spectrogram(b,n,a,u,{fft_length:c=null,power:f=1,center:s=!0,pad_mode:h="reflect",onesided:p=!0,preemphasis:l=null,mel_filters:o=null,mel_floor:t=1e-10,log_mel:e=null,reference:r=1,min_value:i=1e-10,db_range:d=null,remove_dc_offset:g=null,max_num_frames:m=null,do_pad:_=!0,transpose:y=!1}={}){const T=n.length;if(c===null&&(c=a),a>c)throw Error(`frame_length (${a}) may not be larger than fft_length (${c})`);if(T!==a)throw new Error(`Length of the window (${T}) must equal frame_length (${a})`);if(u<=0)throw new Error("hop_length must be greater than zero");if(s){if(h!=="reflect")throw new Error(`pad_mode="${h}" not implemented yet.`);const C=Math.floor((c-1)/2)+1;b=padReflect(b,C,C)}const w=Math.floor(1+Math.floor((b.length-a)/u)),S=p?Math.floor(c/2)+1:c;let O=w,E=w;m!==null&&(m>w?_&&(E=m):E=O=m);const v=new FFT(c),P=new Float64Array(c),L=new Float64Array(v.outputBufferSize),V=new Array(O);for(let C=0;C<O;++C){const $=C*u;for(let z=0;z<a;++z)P[z]=b[$+z];if(g){let z=0;for(let J=0;J<a;++J)z+=P[J];const Z=z/a;for(let J=0;J<a;++J)P[J]-=Z}if(l!==null){for(let z=a-1;z>=1;--z)P[z]-=l*P[z-1];P[0]*=1-l}for(let z=0;z<n.length;++z)P[z]*=n[z];v.realTransform(L,P);const X=new Array(S);for(let z=0;z<X.length;++z){const Z=z<<1;X[z]=L[Z]**2+L[Z+1]**2}V[C]=X}if(f!==null&&f!==2){const C=2/f;for(let $=0;$<V.length;++$){const X=V[$];for(let z=0;z<X.length;++z)X[z]**=C}}const R=o.length,k=new Float32Array(R*E),Y=y?[E,R]:[R,E];for(let C=0;C<R;++C){const $=o[C];for(let X=0;X<O;++X){const z=V[X];let Z=0;for(let J=0;J<S;++J)Z+=$[J]*z[J];k[y?X*R+C:C*O+X]=Math.max(t,Z)}}if(f!==null&&e!==null){const C=Math.min(k.length,O*R);switch(e){case"log":for(let $=0;$<C;++$)k[$]=Math.log(k[$]);break;case"log10":for(let $=0;$<C;++$)k[$]=Math.log10(k[$]);break;case"dB":if(f===1)amplitude_to_db(k,r,i,d);else if(f===2)power_to_db(k,r,i,d);else throw new Error(`Cannot use log_mel option '${e}' with power ${f}`);break;default:throw new Error(`log_mel must be one of null, 'log', 'log10' or 'dB'. Got '${e}'`)}}return{data:k,dims:Y}}function window_function(b,n,{periodic:a=!0,frame_length:u=null,center:c=!0}={}){const f=a?b+1:b;let s;switch(n){case"boxcar":s=new Float64Array(f).fill(1);break;case"hann":case"hann_window":s=hanning(f);break;case"povey":s=hanning(f).map(h=>Math.pow(h,.85));break;default:throw new Error(`Unknown window type ${n}.`)}if(a&&(s=s.subarray(0,b)),u===null)return s;if(b>u)throw new Error(`Length of the window (${b}) may not be larger than frame_length (${u})`);return s}function center_to_corners_format([b,n,a,u]){return[b-a/2,n-u/2,b+a/2,n+u/2]}function post_process_object_detection(b,n=.5,a=null,u=!1){const c=b.logits,f=b.pred_boxes,[s,h,p]=c.dims;if(a!==null&&a.length!==s)throw Error("Make sure that you pass in as many target sizes as the batch dimension of the logits");let l=[];for(let o=0;o<s;++o){let t=a!==null?a[o]:null,e={boxes:[],classes:[],scores:[]},r=c[o],i=f[o];for(let d=0;d<h;++d){let g=r[d],m=[],_;if(u){_=g.sigmoid().data;for(let y=0;y<_.length;++y)_[y]>n&&m.push(y)}else{let y=max(g.data)[1];if(y===p-1)continue;m.push(y),_=softmax(g.data)}for(const y of m){let T=i[d].data;T=center_to_corners_format(T),t!==null&&(T=T.map((w,S)=>w*t[(S+1)%2])),e.boxes.push(T),e.classes.push(y),e.scores.push(_[y])}}l.push(e)}return l}function validate_audio_inputs(b,n){if(!(b instanceof Float32Array||b instanceof Float64Array))throw new Error(`${n} expects input to be a Float32Array or a Float64Array, but got ${b?.constructor?.name??typeof b} 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.`)}function constraint_to_multiple_of(b,n,a=0,u=null){let c=Math.round(b/n)*n;return u!==null&&c>u&&(c=Math.floor(b/n)*n),c<a&&(c=Math.ceil(b/n)*n),c}function enforce_size_divisibility([b,n],a){return[Math.floor(b/a)*a,Math.floor(n/a)*a]}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.config.mean,this.image_std=this.config.image_std??this.config.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.size_divisibility=this.config.size_divisibility??this.config.size_divisor,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.do_crop_margin=this.config.do_crop_margin,this.pad_size=this.config.pad_size,this.do_pad=this.config.do_pad,this.do_pad&&!this.pad_size&&this.size&&this.size.width!==void 0&&this.size.height!==void 0&&(this.pad_size=this.size)}async thumbnail(n,a,u=2){const c=n.height,f=n.width,s=a.height,h=a.width;let p=Math.min(c,s),l=Math.min(f,h);return p===c&&l===f?n:(c>f?l=Math.floor(f*p/c):f>c&&(p=Math.floor(c*l/f)),await n.resize(l,p,{resample:u}))}async crop_margin(n,a=200){const u=n.clone().grayscale(),c=min(u.data)[0],s=max(u.data)[0]-c;if(s===0)return n;const h=a/255;let p=u.width,l=u.height,o=0,t=0;for(let e=0;e<u.height;++e){const r=e*u.width;for(let i=0;i<u.width;++i)(u.data[r+i]-c)/s<h&&(p=Math.min(p,i),l=Math.min(l,e),o=Math.max(o,i),t=Math.max(t,e))}return n=await n.crop([p,l,o,t]),n}pad_image(n,a,u,{mode:c="constant",center:f=!1,constant_values:s=0}={}){const[h,p,l]=a;let o,t;if(typeof u=="number"?(o=u,t=u):(o=u.width,t=u.height),o!==h||t!==p){const e=new Float32Array(o*t*l);if(Array.isArray(s))for(let d=0;d<e.length;++d)e[d]=s[d%l];else s!==0&&e.fill(s);const[r,i]=f?[Math.floor((o-h)/2),Math.floor((t-p)/2)]:[0,0];for(let d=0;d<p;++d){const g=(d+i)*o,m=d*h;for(let _=0;_<h;++_){const y=(g+_+r)*l,T=(m+_)*l;for(let w=0;w<l;++w)e[y+w]=n[T+w]}}if(c==="symmetric"){if(f)throw new Error("`center` padding is not supported when `mode` is set to `symmetric`.");const d=p-1,g=h-1;for(let m=0;m<t;++m){const _=m*o,y=calculateReflectOffset(m,d)*h;for(let T=0;T<o;++T){if(m<p&&T<h)continue;const w=(_+T)*l,S=(y+calculateReflectOffset(T,g))*l;for(let O=0;O<l;++O)e[w+O]=n[S+O]}}}n=e,a=[t,o,l]}return[n,a]}rescale(n){for(let a=0;a<n.length;++a)n[a]=this.rescale_factor*n[a]}get_resize_output_image_size(n,a){const[u,c]=n.size;let f,s;if(this.do_thumbnail){const{height:h,width:p}=a;f=Math.min(h,p)}else Number.isInteger(a)?(f=a,s=this.config.max_size??f):a!==void 0&&(f=a.shortest_edge,s=a.longest_edge);if(f!==void 0||s!==void 0){const h=f===void 0?1:Math.max(f/u,f/c),p=u*h,l=c*h,o=s===void 0?1:Math.min(s/p,s/l);let t=Math.floor(Number((p*o).toFixed(2))),e=Math.floor(Number((l*o).toFixed(2)));return this.size_divisibility!==void 0&&([t,e]=enforce_size_divisibility([t,e],this.size_divisibility)),[t,e]}else if(a!==void 0&&a.width!==void 0&&a.height!==void 0){let h=a.width,p=a.height;if(this.config.keep_aspect_ratio&&this.config.ensure_multiple_of){let l=a.height/c,o=a.width/u;Math.abs(1-o)<Math.abs(1-l)?l=o:o=l,p=constraint_to_multiple_of(l*c,this.config.ensure_multiple_of),h=constraint_to_multiple_of(o*u,this.config.ensure_multiple_of)}return[h,p]}else{if(this.size_divisibility!==void 0)return enforce_size_divisibility([u,c],this.size_divisibility);throw new Error(`Could not resize image due to unsupported \`this.size\` option in config: ${JSON.stringify(a)}`)}}async resize(n){const[a,u]=this.get_resize_output_image_size(n,this.size);return await n.resize(a,u,{resample:this.resample})}async preprocess(n,{do_normalize:a=null,do_pad:u=null,do_convert_rgb:c=null,do_convert_grayscale:f=null}={}){this.do_crop_margin&&(n=await this.crop_margin(n));const[s,h]=n.size;if(c??this.do_convert_rgb?n=n.rgb():f&&(n=n.grayscale()),this.do_resize&&(n=await this.resize(n)),this.do_thumbnail&&(n=await this.thumbnail(n,this.size,this.resample)),this.do_center_crop){let r,i;Number.isInteger(this.crop_size)?(r=this.crop_size,i=this.crop_size):(r=this.crop_size.width,i=this.crop_size.height),n=await n.center_crop(r,i)}const p=[n.height,n.width];let l=Float32Array.from(n.data),o=[n.height,n.width,n.channels];if(this.do_rescale&&this.rescale(l),a??this.do_normalize){let r=this.image_mean;Array.isArray(this.image_mean)||(r=new Array(n.channels).fill(r));let i=this.image_std;if(Array.isArray(this.image_std)||(i=new Array(n.channels).fill(r)),r.length!==n.channels||i.length!==n.channels)throw new Error(`When set to arrays, the length of \`image_mean\` (${r.length}) and \`image_std\` (${i.length}) must match the number of channels in the image (${n.channels}).`);for(let d=0;d<l.length;d+=n.channels)for(let g=0;g<n.channels;++g)l[d+g]=(l[d+g]-this.image_mean[g])/this.image_std[g]}(u??(this.do_pad&&this.pad_size))&&([l,o]=this.pad_image(l,[n.width,n.height,n.channels],this.pad_size));const t=new Tensor("float32",l,o),e=transpose(t,[2,0,1]);return{original_size:[h,s],reshaped_input_size:p,pixel_values:e}}async _call(n,...a){Array.isArray(n)||(n=[n]);const u=await Promise.all(n.map(f=>this.preprocess(f)));return{pixel_values:stack(u.map(f=>f.pixel_values),0),original_sizes:u.map(f=>f.original_size),reshaped_input_sizes:u.map(f=>f.reshaped_input_size)}}}class SegformerFeatureExtractor extends ImageFeatureExtractor{post_process_semantic_segmentation(n,a=null){const u=n.logits,c=u.dims[0];if(a!==null&&a.length!==c)throw Error("Make sure that you pass in as many target sizes as the batch dimension of the logits");const f=[];for(let s=0;s<c;++s){const h=a!==null?a[s]:null;let p=u[s];h!==null&&(p=interpolate(p,h,"bilinear",!1));const[l,o]=h??p.dims.slice(-2),t=new Tensor("int32",new Int32Array(l*o),[l,o]),e=p[0].data;for(let g=1;g<p.dims[0];++g){const m=p[g].data;for(let _=0;_<m.length;++_)m[_]>e[_]&&(e[_]=m[_],t.data[_]=g)}const r=new Array(p.dims[0]),i=t.data;for(let g=0;g<i.length;++g){const m=i[g];r[m]=m}const d=r.filter(g=>g!==void 0);f.push({segmentation:t,labels:d})}return f}}class DPTImageProcessor extends ImageFeatureExtractor{}class BitImageProcessor extends ImageFeatureExtractor{}class DPTFeatureExtractor extends ImageFeatureExtractor{}class GLPNFeatureExtractor extends ImageFeatureExtractor{}class CLIPFeatureExtractor extends ImageFeatureExtractor{}class ChineseCLIPFeatureExtractor extends ImageFeatureExtractor{}class SiglipImageProcessor extends ImageFeatureExtractor{}class ConvNextFeatureExtractor extends ImageFeatureExtractor{constructor(n){super(n),this.crop_pct=this.config.crop_pct??224/256}async resize(n){const a=this.size?.shortest_edge;if(a===void 0)throw new Error("Size dictionary must contain 'shortest_edge' key.");if(a<384){const u=Math.floor(a/this.crop_pct),[c,f]=this.get_resize_output_image_size(n,{shortest_edge:u});n=await n.resize(c,f,{resample:this.resample}),n=await n.center_crop(a,a)}else n=await n.resize(a,a,{resample:this.resample});return n}}class ConvNextImageProcessor extends ConvNextFeatureExtractor{}class ViTFeatureExtractor extends ImageFeatureExtractor{}class ViTImageProcessor extends ImageFeatureExtractor{}class MobileViTFeatureExtractor extends ImageFeatureExtractor{}class OwlViTFeatureExtractor extends ImageFeatureExtractor{post_process_object_detection(...n){return post_process_object_detection(...n)}}class DeiTFeatureExtractor extends ImageFeatureExtractor{}class BeitFeatureExtractor extends ImageFeatureExtractor{}class DonutFeatureExtractor extends ImageFeatureExtractor{pad_image(n,a,u,c={}){const[f,s,h]=a;let p=this.image_mean;Array.isArray(this.image_mean)||(p=new Array(h).fill(p));let l=this.image_std;Array.isArray(l)||(l=new Array(h).fill(p));const o=p.map((t,e)=>-t/this.image_std[e]);return super.pad_image(n,a,u,{center:!0,constant_values:o,...c})}}class NougatImageProcessor extends DonutFeatureExtractor{}class DetrFeatureExtractor extends ImageFeatureExtractor{async _call(n){const a=await super._call(n),u=[a.pixel_values.dims[0],64,64],c=new Tensor("int64",new BigInt64Array(u.reduce((f,s)=>f*s)).fill(1n),u);return{...a,pixel_mask:c}}post_process_object_detection(...n){return post_process_object_detection(...n)}remove_low_and_no_objects(n,a,u,c){let f=[],s=[],h=[];for(let p=0;p<n.dims[0];++p){let l=n[p],o=a[p],t=max(l.data)[1];if(t===c)continue;let r=softmax(l.data)[t];r>u&&(f.push(o),s.push(r),h.push(t))}return[f,s,h]}check_segment_validity(n,a,u,c=.5,f=.8){let s=[],h=0,p=0;for(let o=0;o<n.length;++o)n[o]===u&&(s.push(o),++h),a[u].data[o]>=c&&++p;let l=h>0&&p>0;return l&&(l=h/p>f),[l,s]}compute_segments(n,a,u,c,f,s=null,h=null){let[p,l]=h??n[0].dims,o=new Tensor("int32",new Int32Array(p*l),[p,l]),t=[];if(h!==null)for(let d=0;d<n.length;++d)n[d]=interpolate(n[d],h,"bilinear",!1);let e=new Int32Array(n[0].data.length),r=new Float32Array(n[0].data.length);for(let d=0;d<n.length;++d){let g=a[d];for(let m=0;m<n[d].data.length;++m)n[d].data[m]*=g,n[d].data[m]>r[m]&&(e[m]=d,r[m]=n[d].data[m])}let i=0;for(let d=0;d<u.length;++d){let g=u[d],[m,_]=this.check_segment_validity(e,n,d,c,f);if(m){++i;for(let y of _)o.data[y]=i;t.push({id:i,label_id:g,score:a[d]})}}return[o,t]}post_process_panoptic_segmentation(n,a=.5,u=.5,c=.8,f=null,s=null){f===null&&(console.warn("`label_ids_to_fuse` unset. No instance will be fused."),f=new Set);const h=n.logits,l=n.pred_masks.sigmoid();let[o,t,e]=h.dims;if(e-=1,s!==null&&s.length!==o)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<o;++i){let d=s!==null?s[i]:null,g=h[i],m=l[i],[_,y,T]=this.remove_low_and_no_objects(g,m,a,e);if(T.length===0){let[O,E]=d??m.dims.slice(-2),v=new Tensor("int32",new Int32Array(O*E).fill(-1),[O,E]);r.push({segmentation:v,segments_info:[]});continue}let[w,S]=this.compute_segments(_,y,T,u,c,f,d);r.push({segmentation:w,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{reshape_input_points(n,a,u){n=structuredClone(n);let c=calculateDimensions(n);if(c.length===3)c=[1,...c],n=[n];else if(c.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<n.length;++f){let s=a[f],h=u[f],p=[h[0]/s[0],h[1]/s[1]];for(let l=0;l<n[f].length;++l)for(let o=0;o<n[f][l].length;++o)for(let t=0;t<n[f][l][o].length;++t)n[f][l][o][t]*=p[t]}return new Tensor("float32",Float32Array.from(n.flat(1/0)),c)}add_input_labels(n,a){let u=calculateDimensions(n);if(u.length===2)u=[1,...u],n=[n];else if(u.length!==3)throw Error("The input_points must be a 4D tensor of shape `batch_size`, `point_batch_size`, `nb_points_per_image`, `2`.");if(u.some((c,f)=>c!==a.dims[f]))throw Error(`The first ${u.length} dimensions of 'input_points' and 'input_labels' must be the same.`);return new Tensor("int64",n.flat(1/0).map(BigInt),u)}async _call(n,a=null,u=null){const c=await super._call(n);if(a&&(c.input_points=this.reshape_input_points(a,c.original_sizes,c.reshaped_input_sizes)),u){if(!c.input_points)throw Error("`input_points` must be provided if `input_labels` are provided.");c.input_labels=this.add_input_labels(u,c.input_points)}return c}post_process_masks(n,a,u,{mask_threshold:c=0,binarize:f=!0,pad_size:s=null}={}){const h=[];s=s??this.pad_size;const p=[s.height,s.width];for(let l=0;l<a.length;++l){const o=a[l],t=u[l],e=n[l],r=[];for(let i=0;i<e.dims[0];++i){const d=e[i];let g=interpolate(d,p,"bilinear",!1);if(g=g.slice(null,[0,t[0]],[0,t[1]]),g=interpolate(g,o,"bilinear",!1),f){const m=new Uint8Array(g.data.length);for(let _=0;_<g.data.length;++_)g.data[_]>c&&(m[_]=1);g=new Tensor("bool",m,g.dims)}r.push(g)}h.push(stack(r))}return h}}class Swin2SRImageProcessor extends ImageFeatureExtractor{pad_image(n,a,u,c={}){const[f,s,h]=a;return super.pad_image(n,a,{width:f+(u-f%u)%u,height:s+(u-s%u)%u},{mode:"symmetric",center:!1,constant_values:-1,...c})}}class VitMatteImageProcessor extends ImageFeatureExtractor{async _call(n,a){Array.isArray(n)||(n=[n]),Array.isArray(a)||(a=[a]);const u=await Promise.all(n.map(s=>this.preprocess(s))),c=await Promise.all(a.map(s=>this.preprocess(s,{do_normalize:!1,do_convert_rgb:!1,do_convert_grayscale:!0})));return{pixel_values:stack(u.map((s,h)=>cat([s.pixel_values,c[h].pixel_values],0)),0),original_sizes:u.map(s=>s.original_size),reshaped_input_sizes:u.map(s=>s.reshaped_input_size)}}}class WhisperFeatureExtractor extends FeatureExtractor{constructor(n){super(n),this.config.mel_filters??=mel_filter_bank(Math.floor(1+this.config.n_fft/2),this.config.feature_size,0,8e3,this.config.sampling_rate,"slaney","slaney"),this.window=window_function(this.config.n_fft,"hann")}_extract_fbank_features(n){const{data:a,dims:u}=spectrogram(n,this.window,this.config.n_fft,this.config.hop_length,{power:2,mel_filters:this.config.mel_filters,log_mel:"log10",max_num_frames:this.config.nb_max_frames}),c=max(a)[0];for(let f=0;f<a.length;++f)a[f]=(Math.max(a[f],c-8)+4)/4;return{data:a,dims:u}}async _call(n){validate_audio_inputs(n,"WhisperFeatureExtractor");let a;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`."),a=n.slice(0,this.config.n_samples)):(a=new Float32Array(this.config.n_samples),a.set(n));const{data:u,dims:c}=this._extract_fbank_features(a);return{input_features:new Tensor("float32",u,[1,...c])}}}class Wav2Vec2FeatureExtractor extends FeatureExtractor{_zero_mean_unit_var_norm(n){const u=n.reduce((f,s)=>f+s,0)/n.length,c=n.reduce((f,s)=>f+(s-u)**2,0)/n.length;return n.map(f=>(f-u)/Math.sqrt(c+1e-7))}async _call(n){validate_audio_inputs(n,"Wav2Vec2FeatureExtractor"),n instanceof Float64Array&&(n=new Float32Array(n));let a=n;this.config.do_normalize&&(a=this._zero_mean_unit_var_norm(a));const u=[1,a.length];return{input_values:new Tensor("float32",a,u),attention_mask:new Tensor("int64",new BigInt64Array(a.length).fill(1n),u)}}}class SeamlessM4TFeatureExtractor extends FeatureExtractor{constructor(n){super(n);const a=this.config.sampling_rate,u=mel_filter_bank(256,this.config.num_mel_bins,20,Math.floor(a/2),a,null,"kaldi",!0);for(let c=0;c<u.length;++c)u[c].push(0);this.mel_filters=u,this.window=window_function(400,"povey",{periodic:!1})}_extract_fbank_features(n,a){return n=n.map(u=>u*32768),spectrogram(n,this.window,400,160,{fft_length:512,power:2,center:!1,preemphasis:.97,mel_filters:this.mel_filters,log_mel:"log",mel_floor:1192092955078125e-22,remove_dc_offset:!0,max_num_frames:a,transpose:!0})}async _call(n,{padding:a=!0,pad_to_multiple_of:u=2,do_normalize_per_mel_bins:c=!0,return_attention_mask:f=!0}={}){validate_audio_inputs(n,"SeamlessM4TFeatureExtractor");let s=this._extract_fbank_features(n,this.config.max_length);if(c){const[i,d]=s.dims;for(let g=0;g<d;++g){let m=0;for(let w=0;w<i;++w)m+=s.data[w*d+g];const _=m/i;let y=0;for(let w=0;w<i;++w)y+=(s.data[w*d+g]-_)**2;y/=i-1;const T=Math.sqrt(y+1e-7);for(let w=0;w<i;++w){const S=w*d+g;s.data[S]=(s.data[S]-_)/T}}}let h;if(a){const[i,d]=s.dims,g=i%u;if(g>0){const m=new Float32Array(d*(i+g));m.set(s.data),m.fill(this.config.padding_value,s.data.length);const _=i+g;s={data:m,dims:[_,d]},f&&(h=new Tensor("int64",new BigInt64Array(_),[1,_]),h.data.fill(1n,0,i))}}const[p,l]=s.dims,o=this.config.stride;if(p%o!==0)throw new Error(`The number of frames (${p}) must be a multiple of the stride (${o}).`);const e=new Tensor("float32",s.data,s.dims).view(1,Math.floor(p/o),l*o),r={input_features:e};if(f){const i=e.dims[1],d=new Tensor("int64",new BigInt64Array(i),[1,i]);if(h)for(let g=1,m=0;g<p;g+=o,++m)d.data[m]=h.data[g];else d.data.fill(1n);r.attention_mask=d}return r}}class ASTFeatureExtractor extends FeatureExtractor{constructor(n){super(n);const a=this.config.sampling_rate,u=mel_filter_bank(256,this.config.num_mel_bins,20,Math.floor(a/2),a,null,"kaldi",!0);for(let c=0;c<u.length;++c)u[c].push(0);this.mel_filters=u,this.window=window_function(400,"hann",{periodic:!1}),this.mean=this.config.mean,this.std=this.config.std}_extract_fbank_features(n,a){return spectrogram(n,this.window,400,160,{fft_length:512,power:2,center:!1,preemphasis:.97,mel_filters:this.mel_filters,log_mel:"log",mel_floor:1192092955078125e-22,remove_dc_offset:!0,max_num_frames:a,transpose:!0})}async _call(n){validate_audio_inputs(n,"ASTFeatureExtractor");const a=this._extract_fbank_features(n,this.config.max_length);if(this.config.do_normalize){const u=this.std*2;for(let c=0;c<a.data.length;++c)a.data[c]=(a.data[c]-this.mean)/u}return{input_values:new Tensor("float32",a.data,[1,...a.dims])}}}class ClapFeatureExtractor extends FeatureExtractor{constructor(n){super(n),this.mel_filters=mel_filter_bank(this.config.nb_frequency_bins,this.config.feature_size,this.config.frequency_min,this.config.frequency_max,this.config.sampling_rate,null,"htk"),this.mel_filters_slaney=mel_filter_bank(this.config.nb_frequency_bins,this.config.feature_size,this.config.frequency_min,this.config.frequency_max,this.config.sampling_rate,"slaney","slaney"),this.window=window_function(this.config.fft_window_size,"hann")}_get_input_mel(n,a,u,c){let f,s=!1;const h=n.length-a;if(h>0)if(u==="rand_trunc"){s=!0;const p=Math.floor(Math.random()*(h+1));n=n.subarray(p,p+a),f=this._extract_fbank_features(n,this.mel_filters_slaney,this.config.nb_max_samples),f.dims=[1,...f.dims]}else throw new Error(`Truncation strategy "${u}" not implemented`);else{if(h<0){let p=new Float64Array(a);if(p.set(n),c==="repeat")for(let l=n.length;l<a;l+=n.length)p.set(n.subarray(0,Math.min(n.length,a-l)),l);else if(c==="repeatpad")for(let l=n.length;l<-h;l+=n.length)p.set(n,l);n=p}if(u==="fusion")throw new Error(`Truncation strategy "${u}" not implemented`);f=this._extract_fbank_features(n,this.mel_filters_slaney,this.config.nb_max_samples),f.dims=[1,...f.dims]}return{...f,longer:s}}_extract_fbank_features(n,a,u=null){return spectrogram(n,this.window,this.config.fft_window_size,this.config.hop_length,{power:2,mel_filters:a,log_mel:"dB",max_num_frames:u,do_pad:!1,transpose:!0})}async _call(n,{max_length:a=null}={}){validate_audio_inputs(n,"ClapFeatureExtractor");const u=this._get_input_mel(n,a??this.config.nb_max_samples,this.config.truncation,this.config.padding);return{input_features:new Tensor("float32",u.data,[1,...u.dims])}}}class SpeechT5FeatureExtractor extends FeatureExtractor{}class Processor extends Callable{constructor(n){super(),this.feature_extractor=n}async _call(n,...a){return await this.feature_extractor(n,...a)}}class SamProcessor extends Processor{async _call(...n){return await this.feature_extractor(...n)}post_process_masks(...n){return this.feature_extractor.post_process_masks(...n)}reshape_input_points(...n){return this.feature_extractor.reshape_input_points(...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 OwlViTProcessor extends Processor{}class AutoProcessor{static FEATURE_EXTRACTOR_CLASS_MAPPING={ImageFeatureExtractor,WhisperFeatureExtractor,ViTFeatureExtractor,MobileViTFeatureExtractor,OwlViTFeatureExtractor,CLIPFeatureExtractor,ChineseCLIPFeatureExtractor,SiglipImageProcessor,ConvNextFeatureExtractor,ConvNextImageProcessor,SegformerFeatureExtractor,BitImageProcessor,DPTImageProcessor,DPTFeatureExtractor,GLPNFeatureExtractor,BeitFeatureExtractor,DeiTFeatureExtractor,DetrFeatureExtractor,YolosFeatureExtractor,DonutFeatureExtractor,NougatImageProcessor,ViTImageProcessor,VitMatteImageProcessor,SamImageProcessor,Swin2SRImageProcessor,Wav2Vec2FeatureExtractor,SeamlessM4TFeatureExtractor,SpeechT5FeatureExtractor,ASTFeatureExtractor,ClapFeatureExtractor};static PROCESSOR_CLASS_MAPPING={WhisperProcessor,Wav2Vec2ProcessorWithLM,SamProcessor,SpeechT5Processor,OwlViTProcessor};static async from_pretrained(n,{progress_callback:a=null,config:u=null,cache_dir:c=null,local_files_only:f=!1,revision:s="main"}={}){let h=u??await getModelJSON(n,"preprocessor_config.json",!0,{progress_callback:a,config:u,cache_dir:c,local_files_only:f,revision:s}),p=h.feature_extractor_type??h.image_processor_type,l=this.FEATURE_EXTRACTOR_CLASS_MAPPING[p];if(!l)if(h.size!==void 0)console.warn(`Feature extractor type "${p}" not found, assuming ImageFeatureExtractor due to size parameter in config.`),l=ImageFeatureExtractor;else throw new Error(`Unknown Feature Extractor type: ${p}`);let o=this.PROCESSOR_CLASS_MAPPING[h.processor_class]??Processor,t=new l(h);return new o(t)}}env$1.allowLocalModels=!1;env$1.backends.onnx.wasm.proxy=!0;const EXAMPLE_URL="https://images.pexels.com/photos/5965592/pexels-photo-5965592.jpeg?auto=compress&cs=tinysrgb&w=1024",status=document.getElementById("status"),fileUpload=document.getElementById("upload"),imageContainer=document.getElementById("container"),example=document.getElementById("example");status.textContent="Loading model...";const model=await AutoModel.from_pretrained("briaai/RMBG-1.4",{config:{model_type:"custom"},revision:"refs/pr/3"}),processor=await AutoProcessor.from_pretrained("briaai/RMBG-1.4",{config:{do_normalize:!0,do_pad:!1,do_rescale:!0,do_resize:!0,image_mean:[.5,.5,.5],feature_extractor_type:"ImageFeatureExtractor",image_std:[1,1,1],resample:2,rescale_factor:.00392156862745098,size:{width:1024,height:1024}}});status.textContent="Ready";example.addEventListener("click",b=>{b.preventDefault(),predict(EXAMPLE_URL)});fileUpload.addEventListener("change",function(b){const n=b.target.files[0];if(!n)return;const a=new FileReader;a.onload=u=>predict(u.target.result),a.readAsDataURL(n)});async function predict(b){imageContainer.innerHTML="",imageContainer.style.backgroundImage=`url(${b})`,status.textContent="Analysing...";const n=await RawImage.fromURL(b),{pixel_values:a}=await processor(n),{output:u}=await model({input:a}),c=await RawImage.fromTensor(u[0].mul(255).to("uint8")).resize(n.width,n.height),f=document.createElement("canvas");f.width=n.width,f.height=n.height;const s=f.getContext("2d");s.drawImage(n.toCanvas(),0,0);const h=s.getImageData(0,0,n.width,n.height);for(let p=0;p<c.data.length;++p)h.data[4*p+3]=c.data[p];s.putImageData(h,0,0),imageContainer.append(f),imageContainer.style.removeProperty("background-image"),imageContainer.style.background='url("data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAABAAAAAQBAMAAADt3eJSAAAAAXNSR0IArs4c6QAAAARnQU1BAACxjwv8YQUAAAAGUExURb+/v////5nD/3QAAAAJcEhZcwAADsMAAA7DAcdvqGQAAAAUSURBVBjTYwABQSCglEENMxgYGAAynwRB8BEAgQAAAABJRU5ErkJggg==")',status.textContent="Done!"}