diff --git "a/assets/index-D8jE9EXa.js" "b/assets/index-D8jE9EXa.js"
new file mode 100644--- /dev/null
+++ "b/assets/index-D8jE9EXa.js"
@@ -0,0 +1,1832 @@
+function __vite__mapDeps(indexes) {
+  if (!__vite__mapDeps.viteFileDeps) {
+    __vite__mapDeps.viteFileDeps = []
+  }
+  return indexes.map((i) => __vite__mapDeps.viteFileDeps[i])
+}
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i=1;i<t.length;++i)t[i]>e&&(e=t[i],o=i);return[Number(e),o]}function isPowerOfTwo(t){return t>0&&(t&t-1)===0}class P2FFT{constructor(e){if(this.size=e|0,this.size<=1||!isPowerOfTwo(this.size))throw new Error("FFT size must be a power of two larger than 1");this._csize=e<<1,this.table=new Float64Array(this.size*2);for(let i=0;i<this.table.length;i+=2){const u=Math.PI*i/this.size;this.table[i]=Math.cos(u),this.table[i+1]=-Math.sin(u)}let o=0;for(let i=1;this.size>i;i<<=1)++o;this._width=o%2===0?o-1:o,this._bitrev=new Int32Array(1<<this._width);for(let i=0;i<this._bitrev.length;++i){this._bitrev[i]=0;for(let u=0;u<this._width;u+=2){const n=this._width-u-2;this._bitrev[i]|=(i>>>u&3)<<n}}}createComplexArray(){return new Float64Array(this._csize)}fromComplexArray(e,o){const i=o||new Array(e.length>>>1);for(let u=0;u<e.length;u+=2)i[u>>>1]=e[u];return i}toComplexArray(e,o){const i=o||this.createComplexArray();for(let u=0;u<i.length;u+=2)i[u]=e[u>>>1],i[u+1]=0;return i}completeSpectrum(e){const o=this._csize,i=o>>>1;for(let u=2;u<i;u+=2)e[o-u]=e[u],e[o-u+1]=-e[u+1]}transform(e,o){if(e===o)throw new Error("Input and output buffers must be different");this._transform4(e,o,1)}realTransform(e,o){if(e===o)throw new Error("Input and output buffers must be different");this._realTransform4(e,o,1)}inverseTransform(e,o){if(e===o)throw new Error("Input and output buffers must be different");this._transform4(e,o,-1);for(let i=0;i<e.length;++i)e[i]/=this.size}_transform4(e,o,i){const u=this._csize;let s=1<<this._width,c=u/s<<1,d,r;const a=this._bitrev;if(c===4)for(d=0,r=0;d<u;d+=c,++r){const l=a[r];this._singleTransform2(o,e,d,l,s)}else for(d=0,r=0;d<u;d+=c,++r){const l=a[r];this._singleTransform4(o,e,d,l,s,i)}for(s>>=2;s>=2;s>>=2){c=u/s<<1;const l=c>>>2;for(d=0;d<u;d+=c){const h=d+l-1;for(let f=d,m=0;f<h;f+=2,m+=s){const g=f,b=g+l,v=b+l,T=v+l,E=e[g],M=e[g+1],F=e[b],Y=e[b+1],H=e[v],X=e[v+1],k=e[T],ne=e[T+1],Ce=this.table[m],Me=i*this.table[m+1],we=F*Ce-Y*Me,z=F*Me+Y*Ce,de=this.table[2*m],ie=i*this.table[2*m+1],re=H*de-X*ie,se=H*ie+X*de,V=this.table[3*m],ge=i*this.table[3*m+1],he=k*V-ne*ge,Ze=k*ge+ne*V,Qe=E+re,Be=M+se,je=E-re,Ue=M-se,Ne=we+he,Se=z+Ze,_e=i*(we-he),Le=i*(z-Ze);e[g]=Qe+Ne,e[g+1]=Be+Se,e[b]=je+Le,e[b+1]=Ue-_e,e[v]=Qe-Ne,e[v+1]=Be-Se,e[T]=je-Le,e[T+1]=Ue+_e}}}}_singleTransform2(e,o,i,u,n){const s=e[u],c=e[u+1],d=e[u+n],r=e[u+n+1];o[i]=s+d,o[i+1]=c+r,o[i+2]=s-d,o[i+3]=c-r}_singleTransform4(e,o,i,u,n,s){const c=n*2,d=n*3,r=e[u],a=e[u+1],l=e[u+n],h=e[u+n+1],f=e[u+c],m=e[u+c+1],g=e[u+d],b=e[u+d+1],v=r+f,T=a+m,E=r-f,M=a-m,F=l+g,Y=h+b,H=s*(l-g),X=s*(h-b);o[i]=v+F,o[i+1]=T+Y,o[i+2]=E+X,o[i+3]=M-H,o[i+4]=v-F,o[i+5]=T-Y,o[i+6]=E-X,o[i+7]=M+H}_realTransform4(e,o,i){const u=this._csize;let s=1<<this._width,c=u/s<<1,d,r;const a=this._bitrev;if(c===4)for(d=0,r=0;d<u;d+=c,++r){const l=a[r];this._singleRealTransform2(o,e,d,l>>>1,s>>>1)}else for(d=0,r=0;d<u;d+=c,++r){const l=a[r];this._singleRealTransform4(o,e,d,l>>>1,s>>>1,i)}for(s>>=2;s>=2;s>>=2){c=u/s<<1;const l=c>>>2;for(d=0;d<u;d+=c){const h=d+l-1;for(let f=d,m=0;f<h;f+=2,m+=s){const g=f,b=g+l,v=b+l,T=v+l,E=e[g],M=e[g+1],F=e[b],Y=e[b+1],H=e[v],X=e[v+1],k=e[T],ne=e[T+1],Ce=this.table[m],Me=i*this.table[m+1],we=F*Ce-Y*Me,z=F*Me+Y*Ce,de=this.table[2*m],ie=i*this.table[2*m+1],re=H*de-X*ie,se=H*ie+X*de,V=this.table[3*m],ge=i*this.table[3*m+1],he=k*V-ne*ge,Ze=k*ge+ne*V,Qe=E+re,Be=M+se,je=E-re,Ue=M-se,Ne=we+he,Se=z+Ze,_e=i*(we-he),Le=i*(z-Ze);e[g]=Qe+Ne,e[g+1]=Be+Se,e[b]=je+Le,e[b+1]=Ue-_e,e[v]=Qe-Ne,e[v+1]=Be-Se,e[T]=je-Le,e[T+1]=Ue+_e}}}}_singleRealTransform2(e,o,i,u,n){const s=e[u],c=e[u+n];o[i]=s+c,o[i+1]=0,o[i+2]=s-c,o[i+3]=0}_singleRealTransform4(e,o,i,u,n,s){const c=n*2,d=n*3,r=e[u],a=e[u+n],l=e[u+c],h=e[u+d],f=r+l,m=r-l,g=a+h,b=s*(a-h);o[i]=f+g,o[i+1]=0,o[i+2]=m,o[i+3]=-b,o[i+4]=f-g,o[i+5]=0,o[i+6]=m,o[i+7]=b}}class NP2FFT{constructor(e){const o=2*(e-1),i=2*(2*e-1),u=2**Math.ceil(Math.log2(i));this.bufferSize=u,this._a=o;const n=new Float64Array(i),s=new Float64Array(u);this._chirpBuffer=new Float64Array(u),this._buffer1=new Float64Array(u),this._buffer2=new Float64Array(u),this._outBuffer1=new Float64Array(u),this._outBuffer2=new Float64Array(u);const c=-2*Math.PI/e,d=Math.cos(c),r=Math.sin(c);for(let a=0;a<i>>1;++a){const l=(a+1-e)**2/2,h=Math.sqrt(d**2+r**2)**l,f=l*Math.atan2(r,d),m=2*a;n[m]=h*Math.cos(f),n[m+1]=h*Math.sin(f),s[m]=n[m],s[m+1]=-n[m+1]}this._slicedChirpBuffer=n.subarray(o,i),this._f=new P2FFT(u>>1),this._f.transform(this._chirpBuffer,s)}_transform(e,o,i){const u=this._buffer1,n=this._buffer2,s=this._outBuffer1,c=this._outBuffer2,d=this._chirpBuffer,r=this._slicedChirpBuffer,a=this._a;if(i)for(let l=0;l<r.length;l+=2){const 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Event("vite:preloadError",{cancelable:!0});if(s.payload=n,window.dispatchEvent(s),!s.defaultPrevented)throw n})};/*!
+ * ONNX Runtime Web v1.17.1
+ * Copyright (c) Microsoft Corporation. All rights reserved.
+ * Licensed under the MIT License.
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We(t)&&(t=t.toInt()),(t&=63)===0?this:t<32?le(this.low>>>t|this.high<<32-t,this.high>>>t,this.unsigned):t===32?le(this.high,0,this.unsigned):le(this.high>>>t-32,0,this.unsigned)},$.shru=$.shiftRightUnsigned,$.shr_u=$.shiftRightUnsigned,$.rotateLeft=function(t){var e;return We(t)&&(t=t.toInt()),(t&=63)===0?this:t===32?le(this.high,this.low,this.unsigned):t<32?(e=32-t,le(this.low<<t|this.high>>>e,this.high<<t|this.low>>>e,this.unsigned)):(t-=32,e=32-t,le(this.high<<t|this.low>>>e,this.low<<t|this.high>>>e,this.unsigned))},$.rotl=$.rotateLeft,$.rotateRight=function(t){var e;return We(t)&&(t=t.toInt()),(t&=63)===0?this:t===32?le(this.high,this.low,this.unsigned):t<32?(e=32-t,le(this.high<<e|this.low>>>t,this.low<<e|this.high>>>t,this.unsigned)):(t-=32,e=32-t,le(this.low<<e|this.high>>>t,this.high<<e|this.low>>>t,this.unsigned))},$.rotr=$.rotateRight,$.toSigned=function(){return this.unsigned?le(this.low,this.high,!1):this},$.toUnsigned=function(){return 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a=s.onnx.TensorProto.verify(r.t);if(a)return"t."+a}if(r.g!=null&&r.hasOwnProperty("g")){var a=s.onnx.GraphProto.verify(r.g);if(a)return"g."+a}if(r.sparseTensor!=null&&r.hasOwnProperty("sparseTensor")){var a=s.onnx.SparseTensorProto.verify(r.sparseTensor);if(a)return"sparseTensor."+a}if(r.tp!=null&&r.hasOwnProperty("tp")){var a=s.onnx.TypeProto.verify(r.tp);if(a)return"tp."+a}if(r.floats!=null&&r.hasOwnProperty("floats")){if(!Array.isArray(r.floats))return"floats: array expected";for(var l=0;l<r.floats.length;++l)if(typeof r.floats[l]!="number")return"floats: number[] expected"}if(r.ints!=null&&r.hasOwnProperty("ints")){if(!Array.isArray(r.ints))return"ints: array expected";for(var l=0;l<r.ints.length;++l)if(!n.isInteger(r.ints[l])&&!(r.ints[l]&&n.isInteger(r.ints[l].low)&&n.isInteger(r.ints[l].high)))return"ints: integer|Long[] expected"}if(r.strings!=null&&r.hasOwnProperty("strings")){if(!Array.isArray(r.strings))return"strings: array expected";for(var l=0;l<r.strings.length;++l)if(!(r.strings[l]&&typeof r.strings[l].length=="number"||n.isString(r.strings[l])))return"strings: buffer[] expected"}if(r.tensors!=null&&r.hasOwnProperty("tensors")){if(!Array.isArray(r.tensors))return"tensors: array expected";for(var l=0;l<r.tensors.length;++l){var a=s.onnx.TensorProto.verify(r.tensors[l]);if(a)return"tensors."+a}}if(r.graphs!=null&&r.hasOwnProperty("graphs")){if(!Array.isArray(r.graphs))return"graphs: array expected";for(var l=0;l<r.graphs.length;++l){var a=s.onnx.GraphProto.verify(r.graphs[l]);if(a)return"graphs."+a}}if(r.sparseTensors!=null&&r.hasOwnProperty("sparseTensors")){if(!Array.isArray(r.sparseTensors))return"sparseTensors: array expected";for(var l=0;l<r.sparseTensors.length;++l){var a=s.onnx.SparseTensorProto.verify(r.sparseTensors[l]);if(a)return"sparseTensors."+a}}if(r.typeProtos!=null&&r.hasOwnProperty("typeProtos")){if(!Array.isArray(r.typeProtos))return"typeProtos: array expected";for(var l=0;l<r.typeProtos.length;++l){var a=s.onnx.TypeProto.verify(r.typeProtos[l]);if(a)return"typeProtos."+a}}return null},d.fromObject=function(r){if(r instanceof s.onnx.AttributeProto)return r;var a=new s.onnx.AttributeProto;switch(r.name!=null&&(a.name=String(r.name)),r.refAttrName!=null&&(a.refAttrName=String(r.refAttrName)),r.docString!=null&&(a.docString=String(r.docString)),r.type){default:if(typeof r.type=="number"){a.type=r.type;break}break;case"UNDEFINED":case 0:a.type=0;break;case"FLOAT":case 1:a.type=1;break;case"INT":case 2:a.type=2;break;case"STRING":case 3:a.type=3;break;case"TENSOR":case 4:a.type=4;break;case"GRAPH":case 5:a.type=5;break;case"SPARSE_TENSOR":case 11:a.type=11;break;case"TYPE_PROTO":case 13:a.type=13;break;case"FLOATS":case 6:a.type=6;break;case"INTS":case 7:a.type=7;break;case"STRINGS":case 8:a.type=8;break;case"TENSORS":case 9:a.type=9;break;case"GRAPHS":case 10:a.type=10;break;case"SPARSE_TENSORS":case 12:a.type=12;break;case"TYPE_PROTOS":case 14:a.type=14;break}if(r.f!=null&&(a.f=Number(r.f)),r.i!=null&&(n.Long?(a.i=n.Long.fromValue(r.i)).unsigned=!1:typeof r.i=="string"?a.i=parseInt(r.i,10):typeof r.i=="number"?a.i=r.i:typeof r.i=="object"&&(a.i=new n.LongBits(r.i.low>>>0,r.i.high>>>0).toNumber())),r.s!=null&&(typeof r.s=="string"?n.base64.decode(r.s,a.s=n.newBuffer(n.base64.length(r.s)),0):r.s.length>=0&&(a.s=r.s)),r.t!=null){if(typeof r.t!="object")throw TypeError(".onnx.AttributeProto.t: object expected");a.t=s.onnx.TensorProto.fromObject(r.t)}if(r.g!=null){if(typeof r.g!="object")throw TypeError(".onnx.AttributeProto.g: object expected");a.g=s.onnx.GraphProto.fromObject(r.g)}if(r.sparseTensor!=null){if(typeof r.sparseTensor!="object")throw TypeError(".onnx.AttributeProto.sparseTensor: object expected");a.sparseTensor=s.onnx.SparseTensorProto.fromObject(r.sparseTensor)}if(r.tp!=null){if(typeof r.tp!="object")throw TypeError(".onnx.AttributeProto.tp: object expected");a.tp=s.onnx.TypeProto.fromObject(r.tp)}if(r.floats){if(!Array.isArray(r.floats))throw TypeError(".onnx.AttributeProto.floats: array expected");a.floats=[];for(var l=0;l<r.floats.length;++l)a.floats[l]=Number(r.floats[l])}if(r.ints){if(!Array.isArray(r.ints))throw TypeError(".onnx.AttributeProto.ints: array expected");a.ints=[];for(var l=0;l<r.ints.length;++l)n.Long?(a.ints[l]=n.Long.fromValue(r.ints[l])).unsigned=!1:typeof r.ints[l]=="string"?a.ints[l]=parseInt(r.ints[l],10):typeof r.ints[l]=="number"?a.ints[l]=r.ints[l]:typeof r.ints[l]=="object"&&(a.ints[l]=new n.LongBits(r.ints[l].low>>>0,r.ints[l].high>>>0).toNumber())}if(r.strings){if(!Array.isArray(r.strings))throw TypeError(".onnx.AttributeProto.strings: array expected");a.strings=[];for(var l=0;l<r.strings.length;++l)typeof r.strings[l]=="string"?n.base64.decode(r.strings[l],a.strings[l]=n.newBuffer(n.base64.length(r.strings[l])),0):r.strings[l].length>=0&&(a.strings[l]=r.strings[l])}if(r.tensors){if(!Array.isArray(r.tensors))throw TypeError(".onnx.AttributeProto.tensors: array expected");a.tensors=[];for(var l=0;l<r.tensors.length;++l){if(typeof r.tensors[l]!="object")throw TypeError(".onnx.AttributeProto.tensors: object expected");a.tensors[l]=s.onnx.TensorProto.fromObject(r.tensors[l])}}if(r.graphs){if(!Array.isArray(r.graphs))throw TypeError(".onnx.AttributeProto.graphs: array expected");a.graphs=[];for(var l=0;l<r.graphs.length;++l){if(typeof r.graphs[l]!="object")throw TypeError(".onnx.AttributeProto.graphs: object expected");a.graphs[l]=s.onnx.GraphProto.fromObject(r.graphs[l])}}if(r.sparseTensors){if(!Array.isArray(r.sparseTensors))throw TypeError(".onnx.AttributeProto.sparseTensors: array expected");a.sparseTensors=[];for(var l=0;l<r.sparseTensors.length;++l){if(typeof r.sparseTensors[l]!="object")throw TypeError(".onnx.AttributeProto.sparseTensors: object expected");a.sparseTensors[l]=s.onnx.SparseTensorProto.fromObject(r.sparseTensors[l])}}if(r.typeProtos){if(!Array.isArray(r.typeProtos))throw TypeError(".onnx.AttributeProto.typeProtos: array expected");a.typeProtos=[];for(var l=0;l<r.typeProtos.length;++l){if(typeof r.typeProtos[l]!="object")throw TypeError(".onnx.AttributeProto.typeProtos: object expected");a.typeProtos[l]=s.onnx.TypeProto.fromObject(r.typeProtos[l])}}return a},d.toObject=function(r,a){a||(a={});var l={};if((a.arrays||a.defaults)&&(l.floats=[],l.ints=[],l.strings=[],l.tensors=[],l.graphs=[],l.typeProtos=[],l.sparseTensors=[]),a.defaults){if(l.name="",l.f=0,n.Long){var h=new n.Long(0,0,!1);l.i=a.longs===String?h.toString():a.longs===Number?h.toNumber():h}else l.i=a.longs===String?"0":0;a.bytes===String?l.s="":(l.s=[],a.bytes!==Array&&(l.s=n.newBuffer(l.s))),l.t=null,l.g=null,l.docString="",l.tp=null,l.type=a.enums===String?"UNDEFINED":0,l.refAttrName="",l.sparseTensor=null}if(r.name!=null&&r.hasOwnProperty("name")&&(l.name=r.name),r.f!=null&&r.hasOwnProperty("f")&&(l.f=a.json&&!isFinite(r.f)?String(r.f):r.f),r.i!=null&&r.hasOwnProperty("i")&&(typeof r.i=="number"?l.i=a.longs===String?String(r.i):r.i:l.i=a.longs===String?n.Long.prototype.toString.call(r.i):a.longs===Number?new n.LongBits(r.i.low>>>0,r.i.high>>>0).toNumber():r.i),r.s!=null&&r.hasOwnProperty("s")&&(l.s=a.bytes===String?n.base64.encode(r.s,0,r.s.length):a.bytes===Array?Array.prototype.slice.call(r.s):r.s),r.t!=null&&r.hasOwnProperty("t")&&(l.t=s.onnx.TensorProto.toObject(r.t,a)),r.g!=null&&r.hasOwnProperty("g")&&(l.g=s.onnx.GraphProto.toObject(r.g,a)),r.floats&&r.floats.length){l.floats=[];for(var f=0;f<r.floats.length;++f)l.floats[f]=a.json&&!isFinite(r.floats[f])?String(r.floats[f]):r.floats[f]}if(r.ints&&r.ints.length){l.ints=[];for(var f=0;f<r.ints.length;++f)typeof r.ints[f]=="number"?l.ints[f]=a.longs===String?String(r.ints[f]):r.ints[f]:l.ints[f]=a.longs===String?n.Long.prototype.toString.call(r.ints[f]):a.longs===Number?new n.LongBits(r.ints[f].low>>>0,r.ints[f].high>>>0).toNumber():r.ints[f]}if(r.strings&&r.strings.length){l.strings=[];for(var f=0;f<r.strings.length;++f)l.strings[f]=a.bytes===String?n.base64.encode(r.strings[f],0,r.strings[f].length):a.bytes===Array?Array.prototype.slice.call(r.strings[f]):r.strings[f]}if(r.tensors&&r.tensors.length){l.tensors=[];for(var f=0;f<r.tensors.length;++f)l.tensors[f]=s.onnx.TensorProto.toObject(r.tensors[f],a)}if(r.graphs&&r.graphs.length){l.graphs=[];for(var f=0;f<r.graphs.length;++f)l.graphs[f]=s.onnx.GraphProto.toObject(r.graphs[f],a)}if(r.docString!=null&&r.hasOwnProperty("docString")&&(l.docString=r.docString),r.tp!=null&&r.hasOwnProperty("tp")&&(l.tp=s.onnx.TypeProto.toObject(r.tp,a)),r.typeProtos&&r.typeProtos.length){l.typeProtos=[];for(var f=0;f<r.typeProtos.length;++f)l.typeProtos[f]=s.onnx.TypeProto.toObject(r.typeProtos[f],a)}if(r.type!=null&&r.hasOwnProperty("type")&&(l.type=a.enums===String?s.onnx.AttributeProto.AttributeType[r.type]===void 0?r.type:s.onnx.AttributeProto.AttributeType[r.type]:r.type),r.refAttrName!=null&&r.hasOwnProperty("refAttrName")&&(l.refAttrName=r.refAttrName),r.sparseTensor!=null&&r.hasOwnProperty("sparseTensor")&&(l.sparseTensor=s.onnx.SparseTensorProto.toObject(r.sparseTensor,a)),r.sparseTensors&&r.sparseTensors.length){l.sparseTensors=[];for(var f=0;f<r.sparseTensors.length;++f)l.sparseTensors[f]=s.onnx.SparseTensorProto.toObject(r.sparseTensors[f],a)}return l},d.prototype.toJSON=function(){return this.constructor.toObject(this,o.util.toJSONOptions)},d.getTypeUrl=function(r){return r===void 0&&(r="type.googleapis.com"),r+"/onnx.AttributeProto"},d.AttributeType=function(){var r={},a=Object.create(r);return a[r[0]="UNDEFINED"]=0,a[r[1]="FLOAT"]=1,a[r[2]="INT"]=2,a[r[3]="STRING"]=3,a[r[4]="TENSOR"]=4,a[r[5]="GRAPH"]=5,a[r[11]="SPARSE_TENSOR"]=11,a[r[13]="TYPE_PROTO"]=13,a[r[6]="FLOATS"]=6,a[r[7]="INTS"]=7,a[r[8]="STRINGS"]=8,a[r[9]="TENSORS"]=9,a[r[10]="GRAPHS"]=10,a[r[12]="SPARSE_TENSORS"]=12,a[r[14]="TYPE_PROTOS"]=14,a}(),d}(),c.ValueInfoProto=function(){function d(r){if(r)for(var a=Object.keys(r),l=0;l<a.length;++l)r[a[l]]!=null&&(this[a[l]]=r[a[l]])}return d.prototype.name="",d.prototype.type=null,d.prototype.docString="",d.create=function(r){return new d(r)},d.encode=function(r,a){return a||(a=u.create()),r.name!=null&&Object.hasOwnProperty.call(r,"name")&&a.uint32(10).string(r.name),r.type!=null&&Object.hasOwnProperty.call(r,"type")&&s.onnx.TypeProto.encode(r.type,a.uint32(18).fork()).ldelim(),r.docString!=null&&Object.hasOwnProperty.call(r,"docString")&&a.uint32(26).string(r.docString),a},d.encodeDelimited=function(r,a){return this.encode(r,a).ldelim()},d.decode=function(r,a){r instanceof i||(r=i.create(r));for(var l=a===void 0?r.len:r.pos+a,h=new s.onnx.ValueInfoProto;r.pos<l;){var f=r.uint32();switch(f>>>3){case 1:{h.name=r.string();break}case 2:{h.type=s.onnx.TypeProto.decode(r,r.uint32());break}case 3:{h.docString=r.string();break}default:r.skipType(f&7);break}}return h},d.decodeDelimited=function(r){return r instanceof i||(r=new i(r)),this.decode(r,r.uint32())},d.verify=function(r){if(typeof r!="object"||r===null)return"object expected";if(r.name!=null&&r.hasOwnProperty("name")&&!n.isString(r.name))return"name: string expected";if(r.type!=null&&r.hasOwnProperty("type")){var a=s.onnx.TypeProto.verify(r.type);if(a)return"type."+a}return r.docString!=null&&r.hasOwnProperty("docString")&&!n.isString(r.docString)?"docString: string expected":null},d.fromObject=function(r){if(r instanceof s.onnx.ValueInfoProto)return r;var a=new s.onnx.ValueInfoProto;if(r.name!=null&&(a.name=String(r.name)),r.type!=null){if(typeof r.type!="object")throw TypeError(".onnx.ValueInfoProto.type: object expected");a.type=s.onnx.TypeProto.fromObject(r.type)}return r.docString!=null&&(a.docString=String(r.docString)),a},d.toObject=function(r,a){a||(a={});var l={};return a.defaults&&(l.name="",l.type=null,l.docString=""),r.name!=null&&r.hasOwnProperty("name")&&(l.name=r.name),r.type!=null&&r.hasOwnProperty("type")&&(l.type=s.onnx.TypeProto.toObject(r.type,a)),r.docString!=null&&r.hasOwnProperty("docString")&&(l.docString=r.docString),l},d.prototype.toJSON=function(){return this.constructor.toObject(this,o.util.toJSONOptions)},d.getTypeUrl=function(r){return r===void 0&&(r="type.googleapis.com"),r+"/onnx.ValueInfoProto"},d}(),c.NodeProto=function(){function d(r){if(this.input=[],this.output=[],this.attribute=[],r)for(var a=Object.keys(r),l=0;l<a.length;++l)r[a[l]]!=null&&(this[a[l]]=r[a[l]])}return d.prototype.input=n.emptyArray,d.prototype.output=n.emptyArray,d.prototype.name="",d.prototype.opType="",d.prototype.domain="",d.prototype.attribute=n.emptyArray,d.prototype.docString="",d.create=function(r){return new d(r)},d.encode=function(r,a){if(a||(a=u.create()),r.input!=null&&r.input.length)for(var l=0;l<r.input.length;++l)a.uint32(10).string(r.input[l]);if(r.output!=null&&r.output.length)for(var l=0;l<r.output.length;++l)a.uint32(18).string(r.output[l]);if(r.name!=null&&Object.hasOwnProperty.call(r,"name")&&a.uint32(26).string(r.name),r.opType!=null&&Object.hasOwnProperty.call(r,"opType")&&a.uint32(34).string(r.opType),r.attribute!=null&&r.attribute.length)for(var l=0;l<r.attribute.length;++l)s.onnx.AttributeProto.encode(r.attribute[l],a.uint32(42).fork()).ldelim();return r.docString!=null&&Object.hasOwnProperty.call(r,"docString")&&a.uint32(50).string(r.docString),r.domain!=null&&Object.hasOwnProperty.call(r,"domain")&&a.uint32(58).string(r.domain),a},d.encodeDelimited=function(r,a){return this.encode(r,a).ldelim()},d.decode=function(r,a){r instanceof i||(r=i.create(r));for(var l=a===void 0?r.len:r.pos+a,h=new s.onnx.NodeProto;r.pos<l;){var f=r.uint32();switch(f>>>3){case 1:{h.input&&h.input.length||(h.input=[]),h.input.push(r.string());break}case 2:{h.output&&h.output.length||(h.output=[]),h.output.push(r.string());break}case 3:{h.name=r.string();break}case 4:{h.opType=r.string();break}case 7:{h.domain=r.string();break}case 5:{h.attribute&&h.attribute.length||(h.attribute=[]),h.attribute.push(s.onnx.AttributeProto.decode(r,r.uint32()));break}case 6:{h.docString=r.string();break}default:r.skipType(f&7);break}}return h},d.decodeDelimited=function(r){return r instanceof i||(r=new i(r)),this.decode(r,r.uint32())},d.verify=function(r){if(typeof r!="object"||r===null)return"object expected";if(r.input!=null&&r.hasOwnProperty("input")){if(!Array.isArray(r.input))return"input: array expected";for(var a=0;a<r.input.length;++a)if(!n.isString(r.input[a]))return"input: string[] expected"}if(r.output!=null&&r.hasOwnProperty("output")){if(!Array.isArray(r.output))return"output: array expected";for(var a=0;a<r.output.length;++a)if(!n.isString(r.output[a]))return"output: string[] expected"}if(r.name!=null&&r.hasOwnProperty("name")&&!n.isString(r.name))return"name: string expected";if(r.opType!=null&&r.hasOwnProperty("opType")&&!n.isString(r.opType))return"opType: string expected";if(r.domain!=null&&r.hasOwnProperty("domain")&&!n.isString(r.domain))return"domain: string expected";if(r.attribute!=null&&r.hasOwnProperty("attribute")){if(!Array.isArray(r.attribute))return"attribute: array expected";for(var a=0;a<r.attribute.length;++a){var l=s.onnx.AttributeProto.verify(r.attribute[a]);if(l)return"attribute."+l}}return r.docString!=null&&r.hasOwnProperty("docString")&&!n.isString(r.docString)?"docString: string expected":null},d.fromObject=function(r){if(r instanceof s.onnx.NodeProto)return r;var a=new s.onnx.NodeProto;if(r.input){if(!Array.isArray(r.input))throw TypeError(".onnx.NodeProto.input: array expected");a.input=[];for(var l=0;l<r.input.length;++l)a.input[l]=String(r.input[l])}if(r.output){if(!Array.isArray(r.output))throw TypeError(".onnx.NodeProto.output: array expected");a.output=[];for(var l=0;l<r.output.length;++l)a.output[l]=String(r.output[l])}if(r.name!=null&&(a.name=String(r.name)),r.opType!=null&&(a.opType=String(r.opType)),r.domain!=null&&(a.domain=String(r.domain)),r.attribute){if(!Array.isArray(r.attribute))throw TypeError(".onnx.NodeProto.attribute: array expected");a.attribute=[];for(var l=0;l<r.attribute.length;++l){if(typeof r.attribute[l]!="object")throw TypeError(".onnx.NodeProto.attribute: object expected");a.attribute[l]=s.onnx.AttributeProto.fromObject(r.attribute[l])}}return r.docString!=null&&(a.docString=String(r.docString)),a},d.toObject=function(r,a){a||(a={});var l={};if((a.arrays||a.defaults)&&(l.input=[],l.output=[],l.attribute=[]),a.defaults&&(l.name="",l.opType="",l.docString="",l.domain=""),r.input&&r.input.length){l.input=[];for(var h=0;h<r.input.length;++h)l.input[h]=r.input[h]}if(r.output&&r.output.length){l.output=[];for(var h=0;h<r.output.length;++h)l.output[h]=r.output[h]}if(r.name!=null&&r.hasOwnProperty("name")&&(l.name=r.name),r.opType!=null&&r.hasOwnProperty("opType")&&(l.opType=r.opType),r.attribute&&r.attribute.length){l.attribute=[];for(var h=0;h<r.attribute.length;++h)l.attribute[h]=s.onnx.AttributeProto.toObject(r.attribute[h],a)}return r.docString!=null&&r.hasOwnProperty("docString")&&(l.docString=r.docString),r.domain!=null&&r.hasOwnProperty("domain")&&(l.domain=r.domain),l},d.prototype.toJSON=function(){return this.constructor.toObject(this,o.util.toJSONOptions)},d.getTypeUrl=function(r){return r===void 0&&(r="type.googleapis.com"),r+"/onnx.NodeProto"},d}(),c.TrainingInfoProto=function(){function d(r){if(this.initializationBinding=[],this.updateBinding=[],r)for(var a=Object.keys(r),l=0;l<a.length;++l)r[a[l]]!=null&&(this[a[l]]=r[a[l]])}return d.prototype.initialization=null,d.prototype.algorithm=null,d.prototype.initializationBinding=n.emptyArray,d.prototype.updateBinding=n.emptyArray,d.create=function(r){return new d(r)},d.encode=function(r,a){if(a||(a=u.create()),r.initialization!=null&&Object.hasOwnProperty.call(r,"initialization")&&s.onnx.GraphProto.encode(r.initialization,a.uint32(10).fork()).ldelim(),r.algorithm!=null&&Object.hasOwnProperty.call(r,"algorithm")&&s.onnx.GraphProto.encode(r.algorithm,a.uint32(18).fork()).ldelim(),r.initializationBinding!=null&&r.initializationBinding.length)for(var l=0;l<r.initializationBinding.length;++l)s.onnx.StringStringEntryProto.encode(r.initializationBinding[l],a.uint32(26).fork()).ldelim();if(r.updateBinding!=null&&r.updateBinding.length)for(var l=0;l<r.updateBinding.length;++l)s.onnx.StringStringEntryProto.encode(r.updateBinding[l],a.uint32(34).fork()).ldelim();return a},d.encodeDelimited=function(r,a){return this.encode(r,a).ldelim()},d.decode=function(r,a){r instanceof i||(r=i.create(r));for(var l=a===void 0?r.len:r.pos+a,h=new s.onnx.TrainingInfoProto;r.pos<l;){var f=r.uint32();switch(f>>>3){case 1:{h.initialization=s.onnx.GraphProto.decode(r,r.uint32());break}case 2:{h.algorithm=s.onnx.GraphProto.decode(r,r.uint32());break}case 3:{h.initializationBinding&&h.initializationBinding.length||(h.initializationBinding=[]),h.initializationBinding.push(s.onnx.StringStringEntryProto.decode(r,r.uint32()));break}case 4:{h.updateBinding&&h.updateBinding.length||(h.updateBinding=[]),h.updateBinding.push(s.onnx.StringStringEntryProto.decode(r,r.uint32()));break}default:r.skipType(f&7);break}}return h},d.decodeDelimited=function(r){return r instanceof i||(r=new i(r)),this.decode(r,r.uint32())},d.verify=function(r){if(typeof r!="object"||r===null)return"object expected";if(r.initialization!=null&&r.hasOwnProperty("initialization")){var a=s.onnx.GraphProto.verify(r.initialization);if(a)return"initialization."+a}if(r.algorithm!=null&&r.hasOwnProperty("algorithm")){var a=s.onnx.GraphProto.verify(r.algorithm);if(a)return"algorithm."+a}if(r.initializationBinding!=null&&r.hasOwnProperty("initializationBinding")){if(!Array.isArray(r.initializationBinding))return"initializationBinding: array expected";for(var l=0;l<r.initializationBinding.length;++l){var a=s.onnx.StringStringEntryProto.verify(r.initializationBinding[l]);if(a)return"initializationBinding."+a}}if(r.updateBinding!=null&&r.hasOwnProperty("updateBinding")){if(!Array.isArray(r.updateBinding))return"updateBinding: array expected";for(var l=0;l<r.updateBinding.length;++l){var a=s.onnx.StringStringEntryProto.verify(r.updateBinding[l]);if(a)return"updateBinding."+a}}return null},d.fromObject=function(r){if(r instanceof s.onnx.TrainingInfoProto)return r;var a=new s.onnx.TrainingInfoProto;if(r.initialization!=null){if(typeof r.initialization!="object")throw TypeError(".onnx.TrainingInfoProto.initialization: object expected");a.initialization=s.onnx.GraphProto.fromObject(r.initialization)}if(r.algorithm!=null){if(typeof r.algorithm!="object")throw TypeError(".onnx.TrainingInfoProto.algorithm: object expected");a.algorithm=s.onnx.GraphProto.fromObject(r.algorithm)}if(r.initializationBinding){if(!Array.isArray(r.initializationBinding))throw TypeError(".onnx.TrainingInfoProto.initializationBinding: array expected");a.initializationBinding=[];for(var l=0;l<r.initializationBinding.length;++l){if(typeof r.initializationBinding[l]!="object")throw TypeError(".onnx.TrainingInfoProto.initializationBinding: object expected");a.initializationBinding[l]=s.onnx.StringStringEntryProto.fromObject(r.initializationBinding[l])}}if(r.updateBinding){if(!Array.isArray(r.updateBinding))throw TypeError(".onnx.TrainingInfoProto.updateBinding: array expected");a.updateBinding=[];for(var l=0;l<r.updateBinding.length;++l){if(typeof r.updateBinding[l]!="object")throw TypeError(".onnx.TrainingInfoProto.updateBinding: object expected");a.updateBinding[l]=s.onnx.StringStringEntryProto.fromObject(r.updateBinding[l])}}return a},d.toObject=function(r,a){a||(a={});var l={};if((a.arrays||a.defaults)&&(l.initializationBinding=[],l.updateBinding=[]),a.defaults&&(l.initialization=null,l.algorithm=null),r.initialization!=null&&r.hasOwnProperty("initialization")&&(l.initialization=s.onnx.GraphProto.toObject(r.initialization,a)),r.algorithm!=null&&r.hasOwnProperty("algorithm")&&(l.algorithm=s.onnx.GraphProto.toObject(r.algorithm,a)),r.initializationBinding&&r.initializationBinding.length){l.initializationBinding=[];for(var h=0;h<r.initializationBinding.length;++h)l.initializationBinding[h]=s.onnx.StringStringEntryProto.toObject(r.initializationBinding[h],a)}if(r.updateBinding&&r.updateBinding.length){l.updateBinding=[];for(var h=0;h<r.updateBinding.length;++h)l.updateBinding[h]=s.onnx.StringStringEntryProto.toObject(r.updateBinding[h],a)}return l},d.prototype.toJSON=function(){return this.constructor.toObject(this,o.util.toJSONOptions)},d.getTypeUrl=function(r){return r===void 0&&(r="type.googleapis.com"),r+"/onnx.TrainingInfoProto"},d}(),c.ModelProto=function(){function d(r){if(this.opsetImport=[],this.metadataProps=[],this.trainingInfo=[],this.functions=[],r)for(var a=Object.keys(r),l=0;l<a.length;++l)r[a[l]]!=null&&(this[a[l]]=r[a[l]])}return d.prototype.irVersion=n.Long?n.Long.fromBits(0,0,!1):0,d.prototype.opsetImport=n.emptyArray,d.prototype.producerName="",d.prototype.producerVersion="",d.prototype.domain="",d.prototype.modelVersion=n.Long?n.Long.fromBits(0,0,!1):0,d.prototype.docString="",d.prototype.graph=null,d.prototype.metadataProps=n.emptyArray,d.prototype.trainingInfo=n.emptyArray,d.prototype.functions=n.emptyArray,d.create=function(r){return new d(r)},d.encode=function(r,a){if(a||(a=u.create()),r.irVersion!=null&&Object.hasOwnProperty.call(r,"irVersion")&&a.uint32(8).int64(r.irVersion),r.producerName!=null&&Object.hasOwnProperty.call(r,"producerName")&&a.uint32(18).string(r.producerName),r.producerVersion!=null&&Object.hasOwnProperty.call(r,"producerVersion")&&a.uint32(26).string(r.producerVersion),r.domain!=null&&Object.hasOwnProperty.call(r,"domain")&&a.uint32(34).string(r.domain),r.modelVersion!=null&&Object.hasOwnProperty.call(r,"modelVersion")&&a.uint32(40).int64(r.modelVersion),r.docString!=null&&Object.hasOwnProperty.call(r,"docString")&&a.uint32(50).string(r.docString),r.graph!=null&&Object.hasOwnProperty.call(r,"graph")&&s.onnx.GraphProto.encode(r.graph,a.uint32(58).fork()).ldelim(),r.opsetImport!=null&&r.opsetImport.length)for(var l=0;l<r.opsetImport.length;++l)s.onnx.OperatorSetIdProto.encode(r.opsetImport[l],a.uint32(66).fork()).ldelim();if(r.metadataProps!=null&&r.metadataProps.length)for(var l=0;l<r.metadataProps.length;++l)s.onnx.StringStringEntryProto.encode(r.metadataProps[l],a.uint32(114).fork()).ldelim();if(r.trainingInfo!=null&&r.trainingInfo.length)for(var l=0;l<r.trainingInfo.length;++l)s.onnx.TrainingInfoProto.encode(r.trainingInfo[l],a.uint32(162).fork()).ldelim();if(r.functions!=null&&r.functions.length)for(var l=0;l<r.functions.length;++l)s.onnx.FunctionProto.encode(r.functions[l],a.uint32(202).fork()).ldelim();return a},d.encodeDelimited=function(r,a){return this.encode(r,a).ldelim()},d.decode=function(r,a){r instanceof i||(r=i.create(r));for(var l=a===void 0?r.len:r.pos+a,h=new s.onnx.ModelProto;r.pos<l;){var f=r.uint32();switch(f>>>3){case 1:{h.irVersion=r.int64();break}case 8:{h.opsetImport&&h.opsetImport.length||(h.opsetImport=[]),h.opsetImport.push(s.onnx.OperatorSetIdProto.decode(r,r.uint32()));break}case 2:{h.producerName=r.string();break}case 3:{h.producerVersion=r.string();break}case 4:{h.domain=r.string();break}case 5:{h.modelVersion=r.int64();break}case 6:{h.docString=r.string();break}case 7:{h.graph=s.onnx.GraphProto.decode(r,r.uint32());break}case 14:{h.metadataProps&&h.metadataProps.length||(h.metadataProps=[]),h.metadataProps.push(s.onnx.StringStringEntryProto.decode(r,r.uint32()));break}case 20:{h.trainingInfo&&h.trainingInfo.length||(h.trainingInfo=[]),h.trainingInfo.push(s.onnx.TrainingInfoProto.decode(r,r.uint32()));break}case 25:{h.functions&&h.functions.length||(h.functions=[]),h.functions.push(s.onnx.FunctionProto.decode(r,r.uint32()));break}default:r.skipType(f&7);break}}return h},d.decodeDelimited=function(r){return r instanceof i||(r=new i(r)),this.decode(r,r.uint32())},d.verify=function(r){if(typeof r!="object"||r===null)return"object expected";if(r.irVersion!=null&&r.hasOwnProperty("irVersion")&&!n.isInteger(r.irVersion)&&!(r.irVersion&&n.isInteger(r.irVersion.low)&&n.isInteger(r.irVersion.high)))return"irVersion: integer|Long expected";if(r.opsetImport!=null&&r.hasOwnProperty("opsetImport")){if(!Array.isArray(r.opsetImport))return"opsetImport: array expected";for(var a=0;a<r.opsetImport.length;++a){var l=s.onnx.OperatorSetIdProto.verify(r.opsetImport[a]);if(l)return"opsetImport."+l}}if(r.producerName!=null&&r.hasOwnProperty("producerName")&&!n.isString(r.producerName))return"producerName: string expected";if(r.producerVersion!=null&&r.hasOwnProperty("producerVersion")&&!n.isString(r.producerVersion))return"producerVersion: string expected";if(r.domain!=null&&r.hasOwnProperty("domain")&&!n.isString(r.domain))return"domain: string expected";if(r.modelVersion!=null&&r.hasOwnProperty("modelVersion")&&!n.isInteger(r.modelVersion)&&!(r.modelVersion&&n.isInteger(r.modelVersion.low)&&n.isInteger(r.modelVersion.high)))return"modelVersion: integer|Long expected";if(r.docString!=null&&r.hasOwnProperty("docString")&&!n.isString(r.docString))return"docString: string expected";if(r.graph!=null&&r.hasOwnProperty("graph")){var l=s.onnx.GraphProto.verify(r.graph);if(l)return"graph."+l}if(r.metadataProps!=null&&r.hasOwnProperty("metadataProps")){if(!Array.isArray(r.metadataProps))return"metadataProps: array expected";for(var a=0;a<r.metadataProps.length;++a){var l=s.onnx.StringStringEntryProto.verify(r.metadataProps[a]);if(l)return"metadataProps."+l}}if(r.trainingInfo!=null&&r.hasOwnProperty("trainingInfo")){if(!Array.isArray(r.trainingInfo))return"trainingInfo: array expected";for(var a=0;a<r.trainingInfo.length;++a){var l=s.onnx.TrainingInfoProto.verify(r.trainingInfo[a]);if(l)return"trainingInfo."+l}}if(r.functions!=null&&r.hasOwnProperty("functions")){if(!Array.isArray(r.functions))return"functions: array expected";for(var a=0;a<r.functions.length;++a){var l=s.onnx.FunctionProto.verify(r.functions[a]);if(l)return"functions."+l}}return null},d.fromObject=function(r){if(r instanceof s.onnx.ModelProto)return r;var a=new s.onnx.ModelProto;if(r.irVersion!=null&&(n.Long?(a.irVersion=n.Long.fromValue(r.irVersion)).unsigned=!1:typeof r.irVersion=="string"?a.irVersion=parseInt(r.irVersion,10):typeof r.irVersion=="number"?a.irVersion=r.irVersion:typeof r.irVersion=="object"&&(a.irVersion=new n.LongBits(r.irVersion.low>>>0,r.irVersion.high>>>0).toNumber())),r.opsetImport){if(!Array.isArray(r.opsetImport))throw TypeError(".onnx.ModelProto.opsetImport: array expected");a.opsetImport=[];for(var l=0;l<r.opsetImport.length;++l){if(typeof r.opsetImport[l]!="object")throw TypeError(".onnx.ModelProto.opsetImport: object expected");a.opsetImport[l]=s.onnx.OperatorSetIdProto.fromObject(r.opsetImport[l])}}if(r.producerName!=null&&(a.producerName=String(r.producerName)),r.producerVersion!=null&&(a.producerVersion=String(r.producerVersion)),r.domain!=null&&(a.domain=String(r.domain)),r.modelVersion!=null&&(n.Long?(a.modelVersion=n.Long.fromValue(r.modelVersion)).unsigned=!1:typeof r.modelVersion=="string"?a.modelVersion=parseInt(r.modelVersion,10):typeof r.modelVersion=="number"?a.modelVersion=r.modelVersion:typeof r.modelVersion=="object"&&(a.modelVersion=new n.LongBits(r.modelVersion.low>>>0,r.modelVersion.high>>>0).toNumber())),r.docString!=null&&(a.docString=String(r.docString)),r.graph!=null){if(typeof r.graph!="object")throw TypeError(".onnx.ModelProto.graph: object expected");a.graph=s.onnx.GraphProto.fromObject(r.graph)}if(r.metadataProps){if(!Array.isArray(r.metadataProps))throw TypeError(".onnx.ModelProto.metadataProps: array expected");a.metadataProps=[];for(var l=0;l<r.metadataProps.length;++l){if(typeof r.metadataProps[l]!="object")throw TypeError(".onnx.ModelProto.metadataProps: object expected");a.metadataProps[l]=s.onnx.StringStringEntryProto.fromObject(r.metadataProps[l])}}if(r.trainingInfo){if(!Array.isArray(r.trainingInfo))throw TypeError(".onnx.ModelProto.trainingInfo: array expected");a.trainingInfo=[];for(var l=0;l<r.trainingInfo.length;++l){if(typeof r.trainingInfo[l]!="object")throw TypeError(".onnx.ModelProto.trainingInfo: object expected");a.trainingInfo[l]=s.onnx.TrainingInfoProto.fromObject(r.trainingInfo[l])}}if(r.functions){if(!Array.isArray(r.functions))throw TypeError(".onnx.ModelProto.functions: array expected");a.functions=[];for(var l=0;l<r.functions.length;++l){if(typeof r.functions[l]!="object")throw TypeError(".onnx.ModelProto.functions: object expected");a.functions[l]=s.onnx.FunctionProto.fromObject(r.functions[l])}}return a},d.toObject=function(r,a){a||(a={});var l={};if((a.arrays||a.defaults)&&(l.opsetImport=[],l.metadataProps=[],l.trainingInfo=[],l.functions=[]),a.defaults){if(n.Long){var h=new n.Long(0,0,!1);l.irVersion=a.longs===String?h.toString():a.longs===Number?h.toNumber():h}else l.irVersion=a.longs===String?"0":0;if(l.producerName="",l.producerVersion="",l.domain="",n.Long){var h=new n.Long(0,0,!1);l.modelVersion=a.longs===String?h.toString():a.longs===Number?h.toNumber():h}else l.modelVersion=a.longs===String?"0":0;l.docString="",l.graph=null}if(r.irVersion!=null&&r.hasOwnProperty("irVersion")&&(typeof r.irVersion=="number"?l.irVersion=a.longs===String?String(r.irVersion):r.irVersion:l.irVersion=a.longs===String?n.Long.prototype.toString.call(r.irVersion):a.longs===Number?new n.LongBits(r.irVersion.low>>>0,r.irVersion.high>>>0).toNumber():r.irVersion),r.producerName!=null&&r.hasOwnProperty("producerName")&&(l.producerName=r.producerName),r.producerVersion!=null&&r.hasOwnProperty("producerVersion")&&(l.producerVersion=r.producerVersion),r.domain!=null&&r.hasOwnProperty("domain")&&(l.domain=r.domain),r.modelVersion!=null&&r.hasOwnProperty("modelVersion")&&(typeof r.modelVersion=="number"?l.modelVersion=a.longs===String?String(r.modelVersion):r.modelVersion:l.modelVersion=a.longs===String?n.Long.prototype.toString.call(r.modelVersion):a.longs===Number?new n.LongBits(r.modelVersion.low>>>0,r.modelVersion.high>>>0).toNumber():r.modelVersion),r.docString!=null&&r.hasOwnProperty("docString")&&(l.docString=r.docString),r.graph!=null&&r.hasOwnProperty("graph")&&(l.graph=s.onnx.GraphProto.toObject(r.graph,a)),r.opsetImport&&r.opsetImport.length){l.opsetImport=[];for(var f=0;f<r.opsetImport.length;++f)l.opsetImport[f]=s.onnx.OperatorSetIdProto.toObject(r.opsetImport[f],a)}if(r.metadataProps&&r.metadataProps.length){l.metadataProps=[];for(var f=0;f<r.metadataProps.length;++f)l.metadataProps[f]=s.onnx.StringStringEntryProto.toObject(r.metadataProps[f],a)}if(r.trainingInfo&&r.trainingInfo.length){l.trainingInfo=[];for(var f=0;f<r.trainingInfo.length;++f)l.trainingInfo[f]=s.onnx.TrainingInfoProto.toObject(r.trainingInfo[f],a)}if(r.functions&&r.functions.length){l.functions=[];for(var f=0;f<r.functions.length;++f)l.functions[f]=s.onnx.FunctionProto.toObject(r.functions[f],a)}return l},d.prototype.toJSON=function(){return this.constructor.toObject(this,o.util.toJSONOptions)},d.getTypeUrl=function(r){return r===void 0&&(r="type.googleapis.com"),r+"/onnx.ModelProto"},d}(),c.StringStringEntryProto=function(){function d(r){if(r)for(var a=Object.keys(r),l=0;l<a.length;++l)r[a[l]]!=null&&(this[a[l]]=r[a[l]])}return d.prototype.key="",d.prototype.value="",d.create=function(r){return new d(r)},d.encode=function(r,a){return a||(a=u.create()),r.key!=null&&Object.hasOwnProperty.call(r,"key")&&a.uint32(10).string(r.key),r.value!=null&&Object.hasOwnProperty.call(r,"value")&&a.uint32(18).string(r.value),a},d.encodeDelimited=function(r,a){return this.encode(r,a).ldelim()},d.decode=function(r,a){r instanceof i||(r=i.create(r));for(var l=a===void 0?r.len:r.pos+a,h=new s.onnx.StringStringEntryProto;r.pos<l;){var f=r.uint32();switch(f>>>3){case 1:{h.key=r.string();break}case 2:{h.value=r.string();break}default:r.skipType(f&7);break}}return h},d.decodeDelimited=function(r){return r instanceof i||(r=new i(r)),this.decode(r,r.uint32())},d.verify=function(r){return typeof r!="object"||r===null?"object expected":r.key!=null&&r.hasOwnProperty("key")&&!n.isString(r.key)?"key: string expected":r.value!=null&&r.hasOwnProperty("value")&&!n.isString(r.value)?"value: string expected":null},d.fromObject=function(r){if(r instanceof s.onnx.StringStringEntryProto)return r;var a=new s.onnx.StringStringEntryProto;return r.key!=null&&(a.key=String(r.key)),r.value!=null&&(a.value=String(r.value)),a},d.toObject=function(r,a){a||(a={});var l={};return a.defaults&&(l.key="",l.value=""),r.key!=null&&r.hasOwnProperty("key")&&(l.key=r.key),r.value!=null&&r.hasOwnProperty("value")&&(l.value=r.value),l},d.prototype.toJSON=function(){return this.constructor.toObject(this,o.util.toJSONOptions)},d.getTypeUrl=function(r){return r===void 0&&(r="type.googleapis.com"),r+"/onnx.StringStringEntryProto"},d}(),c.TensorAnnotation=function(){function d(r){if(this.quantParameterTensorNames=[],r)for(var a=Object.keys(r),l=0;l<a.length;++l)r[a[l]]!=null&&(this[a[l]]=r[a[l]])}return d.prototype.tensorName="",d.prototype.quantParameterTensorNames=n.emptyArray,d.create=function(r){return new d(r)},d.encode=function(r,a){if(a||(a=u.create()),r.tensorName!=null&&Object.hasOwnProperty.call(r,"tensorName")&&a.uint32(10).string(r.tensorName),r.quantParameterTensorNames!=null&&r.quantParameterTensorNames.length)for(var l=0;l<r.quantParameterTensorNames.length;++l)s.onnx.StringStringEntryProto.encode(r.quantParameterTensorNames[l],a.uint32(18).fork()).ldelim();return a},d.encodeDelimited=function(r,a){return this.encode(r,a).ldelim()},d.decode=function(r,a){r instanceof i||(r=i.create(r));for(var l=a===void 0?r.len:r.pos+a,h=new s.onnx.TensorAnnotation;r.pos<l;){var f=r.uint32();switch(f>>>3){case 1:{h.tensorName=r.string();break}case 2:{h.quantParameterTensorNames&&h.quantParameterTensorNames.length||(h.quantParameterTensorNames=[]),h.quantParameterTensorNames.push(s.onnx.StringStringEntryProto.decode(r,r.uint32()));break}default:r.skipType(f&7);break}}return h},d.decodeDelimited=function(r){return r instanceof i||(r=new i(r)),this.decode(r,r.uint32())},d.verify=function(r){if(typeof r!="object"||r===null)return"object expected";if(r.tensorName!=null&&r.hasOwnProperty("tensorName")&&!n.isString(r.tensorName))return"tensorName: string expected";if(r.quantParameterTensorNames!=null&&r.hasOwnProperty("quantParameterTensorNames")){if(!Array.isArray(r.quantParameterTensorNames))return"quantParameterTensorNames: array expected";for(var a=0;a<r.quantParameterTensorNames.length;++a){var l=s.onnx.StringStringEntryProto.verify(r.quantParameterTensorNames[a]);if(l)return"quantParameterTensorNames."+l}}return null},d.fromObject=function(r){if(r instanceof s.onnx.TensorAnnotation)return r;var a=new s.onnx.TensorAnnotation;if(r.tensorName!=null&&(a.tensorName=String(r.tensorName)),r.quantParameterTensorNames){if(!Array.isArray(r.quantParameterTensorNames))throw TypeError(".onnx.TensorAnnotation.quantParameterTensorNames: array expected");a.quantParameterTensorNames=[];for(var l=0;l<r.quantParameterTensorNames.length;++l){if(typeof r.quantParameterTensorNames[l]!="object")throw TypeError(".onnx.TensorAnnotation.quantParameterTensorNames: object expected");a.quantParameterTensorNames[l]=s.onnx.StringStringEntryProto.fromObject(r.quantParameterTensorNames[l])}}return a},d.toObject=function(r,a){a||(a={});var l={};if((a.arrays||a.defaults)&&(l.quantParameterTensorNames=[]),a.defaults&&(l.tensorName=""),r.tensorName!=null&&r.hasOwnProperty("tensorName")&&(l.tensorName=r.tensorName),r.quantParameterTensorNames&&r.quantParameterTensorNames.length){l.quantParameterTensorNames=[];for(var h=0;h<r.quantParameterTensorNames.length;++h)l.quantParameterTensorNames[h]=s.onnx.StringStringEntryProto.toObject(r.quantParameterTensorNames[h],a)}return l},d.prototype.toJSON=function(){return this.constructor.toObject(this,o.util.toJSONOptions)},d.getTypeUrl=function(r){return r===void 0&&(r="type.googleapis.com"),r+"/onnx.TensorAnnotation"},d}(),c.GraphProto=function(){function d(r){if(this.node=[],this.initializer=[],this.sparseInitializer=[],this.input=[],this.output=[],this.valueInfo=[],this.quantizationAnnotation=[],r)for(var a=Object.keys(r),l=0;l<a.length;++l)r[a[l]]!=null&&(this[a[l]]=r[a[l]])}return d.prototype.node=n.emptyArray,d.prototype.name="",d.prototype.initializer=n.emptyArray,d.prototype.sparseInitializer=n.emptyArray,d.prototype.docString="",d.prototype.input=n.emptyArray,d.prototype.output=n.emptyArray,d.prototype.valueInfo=n.emptyArray,d.prototype.quantizationAnnotation=n.emptyArray,d.create=function(r){return new d(r)},d.encode=function(r,a){if(a||(a=u.create()),r.node!=null&&r.node.length)for(var l=0;l<r.node.length;++l)s.onnx.NodeProto.encode(r.node[l],a.uint32(10).fork()).ldelim();if(r.name!=null&&Object.hasOwnProperty.call(r,"name")&&a.uint32(18).string(r.name),r.initializer!=null&&r.initializer.length)for(var l=0;l<r.initializer.length;++l)s.onnx.TensorProto.encode(r.initializer[l],a.uint32(42).fork()).ldelim();if(r.docString!=null&&Object.hasOwnProperty.call(r,"docString")&&a.uint32(82).string(r.docString),r.input!=null&&r.input.length)for(var l=0;l<r.input.length;++l)s.onnx.ValueInfoProto.encode(r.input[l],a.uint32(90).fork()).ldelim();if(r.output!=null&&r.output.length)for(var l=0;l<r.output.length;++l)s.onnx.ValueInfoProto.encode(r.output[l],a.uint32(98).fork()).ldelim();if(r.valueInfo!=null&&r.valueInfo.length)for(var l=0;l<r.valueInfo.length;++l)s.onnx.ValueInfoProto.encode(r.valueInfo[l],a.uint32(106).fork()).ldelim();if(r.quantizationAnnotation!=null&&r.quantizationAnnotation.length)for(var l=0;l<r.quantizationAnnotation.length;++l)s.onnx.TensorAnnotation.encode(r.quantizationAnnotation[l],a.uint32(114).fork()).ldelim();if(r.sparseInitializer!=null&&r.sparseInitializer.length)for(var l=0;l<r.sparseInitializer.length;++l)s.onnx.SparseTensorProto.encode(r.sparseInitializer[l],a.uint32(122).fork()).ldelim();return a},d.encodeDelimited=function(r,a){return this.encode(r,a).ldelim()},d.decode=function(r,a){r instanceof i||(r=i.create(r));for(var l=a===void 0?r.len:r.pos+a,h=new s.onnx.GraphProto;r.pos<l;){var f=r.uint32();switch(f>>>3){case 1:{h.node&&h.node.length||(h.node=[]),h.node.push(s.onnx.NodeProto.decode(r,r.uint32()));break}case 2:{h.name=r.string();break}case 5:{h.initializer&&h.initializer.length||(h.initializer=[]),h.initializer.push(s.onnx.TensorProto.decode(r,r.uint32()));break}case 15:{h.sparseInitializer&&h.sparseInitializer.length||(h.sparseInitializer=[]),h.sparseInitializer.push(s.onnx.SparseTensorProto.decode(r,r.uint32()));break}case 10:{h.docString=r.string();break}case 11:{h.input&&h.input.length||(h.input=[]),h.input.push(s.onnx.ValueInfoProto.decode(r,r.uint32()));break}case 12:{h.output&&h.output.length||(h.output=[]),h.output.push(s.onnx.ValueInfoProto.decode(r,r.uint32()));break}case 13:{h.valueInfo&&h.valueInfo.length||(h.valueInfo=[]),h.valueInfo.push(s.onnx.ValueInfoProto.decode(r,r.uint32()));break}case 14:{h.quantizationAnnotation&&h.quantizationAnnotation.length||(h.quantizationAnnotation=[]),h.quantizationAnnotation.push(s.onnx.TensorAnnotation.decode(r,r.uint32()));break}default:r.skipType(f&7);break}}return h},d.decodeDelimited=function(r){return r instanceof i||(r=new i(r)),this.decode(r,r.uint32())},d.verify=function(r){if(typeof r!="object"||r===null)return"object expected";if(r.node!=null&&r.hasOwnProperty("node")){if(!Array.isArray(r.node))return"node: array expected";for(var a=0;a<r.node.length;++a){var l=s.onnx.NodeProto.verify(r.node[a]);if(l)return"node."+l}}if(r.name!=null&&r.hasOwnProperty("name")&&!n.isString(r.name))return"name: string expected";if(r.initializer!=null&&r.hasOwnProperty("initializer")){if(!Array.isArray(r.initializer))return"initializer: array expected";for(var a=0;a<r.initializer.length;++a){var l=s.onnx.TensorProto.verify(r.initializer[a]);if(l)return"initializer."+l}}if(r.sparseInitializer!=null&&r.hasOwnProperty("sparseInitializer")){if(!Array.isArray(r.sparseInitializer))return"sparseInitializer: array expected";for(var a=0;a<r.sparseInitializer.length;++a){var l=s.onnx.SparseTensorProto.verify(r.sparseInitializer[a]);if(l)return"sparseInitializer."+l}}if(r.docString!=null&&r.hasOwnProperty("docString")&&!n.isString(r.docString))return"docString: string expected";if(r.input!=null&&r.hasOwnProperty("input")){if(!Array.isArray(r.input))return"input: array expected";for(var a=0;a<r.input.length;++a){var l=s.onnx.ValueInfoProto.verify(r.input[a]);if(l)return"input."+l}}if(r.output!=null&&r.hasOwnProperty("output")){if(!Array.isArray(r.output))return"output: array expected";for(var a=0;a<r.output.length;++a){var l=s.onnx.ValueInfoProto.verify(r.output[a]);if(l)return"output."+l}}if(r.valueInfo!=null&&r.hasOwnProperty("valueInfo")){if(!Array.isArray(r.valueInfo))return"valueInfo: array expected";for(var a=0;a<r.valueInfo.length;++a){var l=s.onnx.ValueInfoProto.verify(r.valueInfo[a]);if(l)return"valueInfo."+l}}if(r.quantizationAnnotation!=null&&r.hasOwnProperty("quantizationAnnotation")){if(!Array.isArray(r.quantizationAnnotation))return"quantizationAnnotation: array expected";for(var a=0;a<r.quantizationAnnotation.length;++a){var l=s.onnx.TensorAnnotation.verify(r.quantizationAnnotation[a]);if(l)return"quantizationAnnotation."+l}}return null},d.fromObject=function(r){if(r instanceof s.onnx.GraphProto)return r;var a=new s.onnx.GraphProto;if(r.node){if(!Array.isArray(r.node))throw TypeError(".onnx.GraphProto.node: array expected");a.node=[];for(var l=0;l<r.node.length;++l){if(typeof r.node[l]!="object")throw TypeError(".onnx.GraphProto.node: object expected");a.node[l]=s.onnx.NodeProto.fromObject(r.node[l])}}if(r.name!=null&&(a.name=String(r.name)),r.initializer){if(!Array.isArray(r.initializer))throw TypeError(".onnx.GraphProto.initializer: array expected");a.initializer=[];for(var l=0;l<r.initializer.length;++l){if(typeof r.initializer[l]!="object")throw TypeError(".onnx.GraphProto.initializer: object expected");a.initializer[l]=s.onnx.TensorProto.fromObject(r.initializer[l])}}if(r.sparseInitializer){if(!Array.isArray(r.sparseInitializer))throw TypeError(".onnx.GraphProto.sparseInitializer: array expected");a.sparseInitializer=[];for(var l=0;l<r.sparseInitializer.length;++l){if(typeof r.sparseInitializer[l]!="object")throw TypeError(".onnx.GraphProto.sparseInitializer: object expected");a.sparseInitializer[l]=s.onnx.SparseTensorProto.fromObject(r.sparseInitializer[l])}}if(r.docString!=null&&(a.docString=String(r.docString)),r.input){if(!Array.isArray(r.input))throw TypeError(".onnx.GraphProto.input: array expected");a.input=[];for(var l=0;l<r.input.length;++l){if(typeof r.input[l]!="object")throw TypeError(".onnx.GraphProto.input: object expected");a.input[l]=s.onnx.ValueInfoProto.fromObject(r.input[l])}}if(r.output){if(!Array.isArray(r.output))throw TypeError(".onnx.GraphProto.output: array expected");a.output=[];for(var l=0;l<r.output.length;++l){if(typeof r.output[l]!="object")throw TypeError(".onnx.GraphProto.output: object expected");a.output[l]=s.onnx.ValueInfoProto.fromObject(r.output[l])}}if(r.valueInfo){if(!Array.isArray(r.valueInfo))throw TypeError(".onnx.GraphProto.valueInfo: array expected");a.valueInfo=[];for(var l=0;l<r.valueInfo.length;++l){if(typeof r.valueInfo[l]!="object")throw TypeError(".onnx.GraphProto.valueInfo: object expected");a.valueInfo[l]=s.onnx.ValueInfoProto.fromObject(r.valueInfo[l])}}if(r.quantizationAnnotation){if(!Array.isArray(r.quantizationAnnotation))throw TypeError(".onnx.GraphProto.quantizationAnnotation: array expected");a.quantizationAnnotation=[];for(var l=0;l<r.quantizationAnnotation.length;++l){if(typeof r.quantizationAnnotation[l]!="object")throw TypeError(".onnx.GraphProto.quantizationAnnotation: object expected");a.quantizationAnnotation[l]=s.onnx.TensorAnnotation.fromObject(r.quantizationAnnotation[l])}}return a},d.toObject=function(r,a){a||(a={});var l={};if((a.arrays||a.defaults)&&(l.node=[],l.initializer=[],l.input=[],l.output=[],l.valueInfo=[],l.quantizationAnnotation=[],l.sparseInitializer=[]),a.defaults&&(l.name="",l.docString=""),r.node&&r.node.length){l.node=[];for(var h=0;h<r.node.length;++h)l.node[h]=s.onnx.NodeProto.toObject(r.node[h],a)}if(r.name!=null&&r.hasOwnProperty("name")&&(l.name=r.name),r.initializer&&r.initializer.length){l.initializer=[];for(var h=0;h<r.initializer.length;++h)l.initializer[h]=s.onnx.TensorProto.toObject(r.initializer[h],a)}if(r.docString!=null&&r.hasOwnProperty("docString")&&(l.docString=r.docString),r.input&&r.input.length){l.input=[];for(var h=0;h<r.input.length;++h)l.input[h]=s.onnx.ValueInfoProto.toObject(r.input[h],a)}if(r.output&&r.output.length){l.output=[];for(var h=0;h<r.output.length;++h)l.output[h]=s.onnx.ValueInfoProto.toObject(r.output[h],a)}if(r.valueInfo&&r.valueInfo.length){l.valueInfo=[];for(var h=0;h<r.valueInfo.length;++h)l.valueInfo[h]=s.onnx.ValueInfoProto.toObject(r.valueInfo[h],a)}if(r.quantizationAnnotation&&r.quantizationAnnotation.length){l.quantizationAnnotation=[];for(var h=0;h<r.quantizationAnnotation.length;++h)l.quantizationAnnotation[h]=s.onnx.TensorAnnotation.toObject(r.quantizationAnnotation[h],a)}if(r.sparseInitializer&&r.sparseInitializer.length){l.sparseInitializer=[];for(var h=0;h<r.sparseInitializer.length;++h)l.sparseInitializer[h]=s.onnx.SparseTensorProto.toObject(r.sparseInitializer[h],a)}return l},d.prototype.toJSON=function(){return this.constructor.toObject(this,o.util.toJSONOptions)},d.getTypeUrl=function(r){return r===void 0&&(r="type.googleapis.com"),r+"/onnx.GraphProto"},d}(),c.TensorProto=function(){function d(r){if(this.dims=[],this.floatData=[],this.int32Data=[],this.stringData=[],this.int64Data=[],this.externalData=[],this.doubleData=[],this.uint64Data=[],r)for(var a=Object.keys(r),l=0;l<a.length;++l)r[a[l]]!=null&&(this[a[l]]=r[a[l]])}return d.prototype.dims=n.emptyArray,d.prototype.dataType=0,d.prototype.segment=null,d.prototype.floatData=n.emptyArray,d.prototype.int32Data=n.emptyArray,d.prototype.stringData=n.emptyArray,d.prototype.int64Data=n.emptyArray,d.prototype.name="",d.prototype.docString="",d.prototype.rawData=n.newBuffer([]),d.prototype.externalData=n.emptyArray,d.prototype.dataLocation=0,d.prototype.doubleData=n.emptyArray,d.prototype.uint64Data=n.emptyArray,d.create=function(r){return new d(r)},d.encode=function(r,a){if(a||(a=u.create()),r.dims!=null&&r.dims.length){a.uint32(10).fork();for(var l=0;l<r.dims.length;++l)a.int64(r.dims[l]);a.ldelim()}if(r.dataType!=null&&Object.hasOwnProperty.call(r,"dataType")&&a.uint32(16).int32(r.dataType),r.segment!=null&&Object.hasOwnProperty.call(r,"segment")&&s.onnx.TensorProto.Segment.encode(r.segment,a.uint32(26).fork()).ldelim(),r.floatData!=null&&r.floatData.length){a.uint32(34).fork();for(var l=0;l<r.floatData.length;++l)a.float(r.floatData[l]);a.ldelim()}if(r.int32Data!=null&&r.int32Data.length){a.uint32(42).fork();for(var l=0;l<r.int32Data.length;++l)a.int32(r.int32Data[l]);a.ldelim()}if(r.stringData!=null&&r.stringData.length)for(var l=0;l<r.stringData.length;++l)a.uint32(50).bytes(r.stringData[l]);if(r.int64Data!=null&&r.int64Data.length){a.uint32(58).fork();for(var l=0;l<r.int64Data.length;++l)a.int64(r.int64Data[l]);a.ldelim()}if(r.name!=null&&Object.hasOwnProperty.call(r,"name")&&a.uint32(66).string(r.name),r.rawData!=null&&Object.hasOwnProperty.call(r,"rawData")&&a.uint32(74).bytes(r.rawData),r.doubleData!=null&&r.doubleData.length){a.uint32(82).fork();for(var l=0;l<r.doubleData.length;++l)a.double(r.doubleData[l]);a.ldelim()}if(r.uint64Data!=null&&r.uint64Data.length){a.uint32(90).fork();for(var l=0;l<r.uint64Data.length;++l)a.uint64(r.uint64Data[l]);a.ldelim()}if(r.docString!=null&&Object.hasOwnProperty.call(r,"docString")&&a.uint32(98).string(r.docString),r.externalData!=null&&r.externalData.length)for(var l=0;l<r.externalData.length;++l)s.onnx.StringStringEntryProto.encode(r.externalData[l],a.uint32(106).fork()).ldelim();return r.dataLocation!=null&&Object.hasOwnProperty.call(r,"dataLocation")&&a.uint32(112).int32(r.dataLocation),a},d.encodeDelimited=function(r,a){return this.encode(r,a).ldelim()},d.decode=function(r,a){r instanceof i||(r=i.create(r));for(var l=a===void 0?r.len:r.pos+a,h=new s.onnx.TensorProto;r.pos<l;){var f=r.uint32();switch(f>>>3){case 1:{if(h.dims&&h.dims.length||(h.dims=[]),(f&7)===2)for(var m=r.uint32()+r.pos;r.pos<m;)h.dims.push(r.int64());else h.dims.push(r.int64());break}case 2:{h.dataType=r.int32();break}case 3:{h.segment=s.onnx.TensorProto.Segment.decode(r,r.uint32());break}case 4:{if(h.floatData&&h.floatData.length||(h.floatData=[]),(f&7)===2)for(var m=r.uint32()+r.pos;r.pos<m;)h.floatData.push(r.float());else h.floatData.push(r.float());break}case 5:{if(h.int32Data&&h.int32Data.length||(h.int32Data=[]),(f&7)===2)for(var m=r.uint32()+r.pos;r.pos<m;)h.int32Data.push(r.int32());else h.int32Data.push(r.int32());break}case 6:{h.stringData&&h.stringData.length||(h.stringData=[]),h.stringData.push(r.bytes());break}case 7:{if(h.int64Data&&h.int64Data.length||(h.int64Data=[]),(f&7)===2)for(var m=r.uint32()+r.pos;r.pos<m;)h.int64Data.push(r.int64());else h.int64Data.push(r.int64());break}case 8:{h.name=r.string();break}case 12:{h.docString=r.string();break}case 9:{h.rawData=r.bytes();break}case 13:{h.externalData&&h.externalData.length||(h.externalData=[]),h.externalData.push(s.onnx.StringStringEntryProto.decode(r,r.uint32()));break}case 14:{h.dataLocation=r.int32();break}case 10:{if(h.doubleData&&h.doubleData.length||(h.doubleData=[]),(f&7)===2)for(var m=r.uint32()+r.pos;r.pos<m;)h.doubleData.push(r.double());else h.doubleData.push(r.double());break}case 11:{if(h.uint64Data&&h.uint64Data.length||(h.uint64Data=[]),(f&7)===2)for(var m=r.uint32()+r.pos;r.pos<m;)h.uint64Data.push(r.uint64());else h.uint64Data.push(r.uint64());break}default:r.skipType(f&7);break}}return h},d.decodeDelimited=function(r){return r instanceof i||(r=new i(r)),this.decode(r,r.uint32())},d.verify=function(r){if(typeof r!="object"||r===null)return"object expected";if(r.dims!=null&&r.hasOwnProperty("dims")){if(!Array.isArray(r.dims))return"dims: array expected";for(var a=0;a<r.dims.length;++a)if(!n.isInteger(r.dims[a])&&!(r.dims[a]&&n.isInteger(r.dims[a].low)&&n.isInteger(r.dims[a].high)))return"dims: integer|Long[] expected"}if(r.dataType!=null&&r.hasOwnProperty("dataType")&&!n.isInteger(r.dataType))return"dataType: integer expected";if(r.segment!=null&&r.hasOwnProperty("segment")){var l=s.onnx.TensorProto.Segment.verify(r.segment);if(l)return"segment."+l}if(r.floatData!=null&&r.hasOwnProperty("floatData")){if(!Array.isArray(r.floatData))return"floatData: array expected";for(var a=0;a<r.floatData.length;++a)if(typeof r.floatData[a]!="number")return"floatData: number[] expected"}if(r.int32Data!=null&&r.hasOwnProperty("int32Data")){if(!Array.isArray(r.int32Data))return"int32Data: array expected";for(var a=0;a<r.int32Data.length;++a)if(!n.isInteger(r.int32Data[a]))return"int32Data: integer[] expected"}if(r.stringData!=null&&r.hasOwnProperty("stringData")){if(!Array.isArray(r.stringData))return"stringData: array expected";for(var a=0;a<r.stringData.length;++a)if(!(r.stringData[a]&&typeof r.stringData[a].length=="number"||n.isString(r.stringData[a])))return"stringData: buffer[] expected"}if(r.int64Data!=null&&r.hasOwnProperty("int64Data")){if(!Array.isArray(r.int64Data))return"int64Data: array expected";for(var a=0;a<r.int64Data.length;++a)if(!n.isInteger(r.int64Data[a])&&!(r.int64Data[a]&&n.isInteger(r.int64Data[a].low)&&n.isInteger(r.int64Data[a].high)))return"int64Data: integer|Long[] expected"}if(r.name!=null&&r.hasOwnProperty("name")&&!n.isString(r.name))return"name: string expected";if(r.docString!=null&&r.hasOwnProperty("docString")&&!n.isString(r.docString))return"docString: string expected";if(r.rawData!=null&&r.hasOwnProperty("rawData")&&!(r.rawData&&typeof r.rawData.length=="number"||n.isString(r.rawData)))return"rawData: buffer expected";if(r.externalData!=null&&r.hasOwnProperty("externalData")){if(!Array.isArray(r.externalData))return"externalData: array expected";for(var a=0;a<r.externalData.length;++a){var l=s.onnx.StringStringEntryProto.verify(r.externalData[a]);if(l)return"externalData."+l}}if(r.dataLocation!=null&&r.hasOwnProperty("dataLocation"))switch(r.dataLocation){default:return"dataLocation: enum value expected";case 0:case 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r[d[0]="EXPERIMENTAL"]=0,r[d[1]="STABLE"]=1,r}(),c.FunctionProto=function(){function d(r){if(this.input=[],this.output=[],this.attribute=[],this.attributeProto=[],this.node=[],this.opsetImport=[],r)for(var a=Object.keys(r),l=0;l<a.length;++l)r[a[l]]!=null&&(this[a[l]]=r[a[l]])}return d.prototype.name="",d.prototype.input=n.emptyArray,d.prototype.output=n.emptyArray,d.prototype.attribute=n.emptyArray,d.prototype.attributeProto=n.emptyArray,d.prototype.node=n.emptyArray,d.prototype.docString="",d.prototype.opsetImport=n.emptyArray,d.prototype.domain="",d.create=function(r){return new d(r)},d.encode=function(r,a){if(a||(a=u.create()),r.name!=null&&Object.hasOwnProperty.call(r,"name")&&a.uint32(10).string(r.name),r.input!=null&&r.input.length)for(var l=0;l<r.input.length;++l)a.uint32(34).string(r.input[l]);if(r.output!=null&&r.output.length)for(var l=0;l<r.output.length;++l)a.uint32(42).string(r.output[l]);if(r.attribute!=null&&r.attribute.length)for(var 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4:{h.input&&h.input.length||(h.input=[]),h.input.push(r.string());break}case 5:{h.output&&h.output.length||(h.output=[]),h.output.push(r.string());break}case 6:{h.attribute&&h.attribute.length||(h.attribute=[]),h.attribute.push(r.string());break}case 11:{h.attributeProto&&h.attributeProto.length||(h.attributeProto=[]),h.attributeProto.push(s.onnx.AttributeProto.decode(r,r.uint32()));break}case 7:{h.node&&h.node.length||(h.node=[]),h.node.push(s.onnx.NodeProto.decode(r,r.uint32()));break}case 8:{h.docString=r.string();break}case 9:{h.opsetImport&&h.opsetImport.length||(h.opsetImport=[]),h.opsetImport.push(s.onnx.OperatorSetIdProto.decode(r,r.uint32()));break}case 10:{h.domain=r.string();break}default:r.skipType(f&7);break}}return h},d.decodeDelimited=function(r){return r instanceof i||(r=new i(r)),this.decode(r,r.uint32())},d.verify=function(r){if(typeof r!="object"||r===null)return"object expected";if(r.name!=null&&r.hasOwnProperty("name")&&!n.isString(r.name))return"name: string expected";if(r.input!=null&&r.hasOwnProperty("input")){if(!Array.isArray(r.input))return"input: array expected";for(var a=0;a<r.input.length;++a)if(!n.isString(r.input[a]))return"input: string[] expected"}if(r.output!=null&&r.hasOwnProperty("output")){if(!Array.isArray(r.output))return"output: array expected";for(var a=0;a<r.output.length;++a)if(!n.isString(r.output[a]))return"output: string[] expected"}if(r.attribute!=null&&r.hasOwnProperty("attribute")){if(!Array.isArray(r.attribute))return"attribute: array expected";for(var a=0;a<r.attribute.length;++a)if(!n.isString(r.attribute[a]))return"attribute: string[] expected"}if(r.attributeProto!=null&&r.hasOwnProperty("attributeProto")){if(!Array.isArray(r.attributeProto))return"attributeProto: array expected";for(var a=0;a<r.attributeProto.length;++a){var l=s.onnx.AttributeProto.verify(r.attributeProto[a]);if(l)return"attributeProto."+l}}if(r.node!=null&&r.hasOwnProperty("node")){if(!Array.isArray(r.node))return"node: array expected";for(var a=0;a<r.node.length;++a){var l=s.onnx.NodeProto.verify(r.node[a]);if(l)return"node."+l}}if(r.docString!=null&&r.hasOwnProperty("docString")&&!n.isString(r.docString))return"docString: string expected";if(r.opsetImport!=null&&r.hasOwnProperty("opsetImport")){if(!Array.isArray(r.opsetImport))return"opsetImport: array expected";for(var a=0;a<r.opsetImport.length;++a){var l=s.onnx.OperatorSetIdProto.verify(r.opsetImport[a]);if(l)return"opsetImport."+l}}return r.domain!=null&&r.hasOwnProperty("domain")&&!n.isString(r.domain)?"domain: string expected":null},d.fromObject=function(r){if(r instanceof s.onnx.FunctionProto)return r;var a=new s.onnx.FunctionProto;if(r.name!=null&&(a.name=String(r.name)),r.input){if(!Array.isArray(r.input))throw TypeError(".onnx.FunctionProto.input: array expected");a.input=[];for(var l=0;l<r.input.length;++l)a.input[l]=String(r.input[l])}if(r.output){if(!Array.isArray(r.output))throw TypeError(".onnx.FunctionProto.output: array 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h=0;h<r.output.length;++h)l.output[h]=r.output[h]}if(r.attribute&&r.attribute.length){l.attribute=[];for(var h=0;h<r.attribute.length;++h)l.attribute[h]=r.attribute[h]}if(r.node&&r.node.length){l.node=[];for(var h=0;h<r.node.length;++h)l.node[h]=s.onnx.NodeProto.toObject(r.node[h],a)}if(r.docString!=null&&r.hasOwnProperty("docString")&&(l.docString=r.docString),r.opsetImport&&r.opsetImport.length){l.opsetImport=[];for(var h=0;h<r.opsetImport.length;++h)l.opsetImport[h]=s.onnx.OperatorSetIdProto.toObject(r.opsetImport[h],a)}if(r.domain!=null&&r.hasOwnProperty("domain")&&(l.domain=r.domain),r.attributeProto&&r.attributeProto.length){l.attributeProto=[];for(var h=0;h<r.attributeProto.length;++h)l.attributeProto[h]=s.onnx.AttributeProto.toObject(r.attributeProto[h],a)}return l},d.prototype.toJSON=function(){return this.constructor.toObject(this,o.util.toJSONOptions)},d.getTypeUrl=function(r){return r===void 0&&(r="type.googleapis.com"),r+"/onnx.FunctionProto"},d}(),c}(),e.exports=s});function Cr(t,e){if(!t)throw new Error(typeof e=="string"?e:e())}function sn(t){return new TextDecoder().decode(t)}var ye,cr,hi,Xe,Xn,Ve,nt,U,an,pr,dr,hr,fe=L(()=>{Wn(),ei(),ye=Er($r()),mr(),cr=class{static arraysEqual(t,e){if(t.length!==e.length)return!1;for(let o=0;o<t.length;o++)if(t[o]!==e[o])return!1;return!0}},hi=class{static preprocessInputShapes(t,e){let o=t.length===1?[1,t[0]]:t,i=e.length===1?[e[0],1]:e;return[o,i]}static postprocessOutputShape(t,e,o){e===1&&t.splice(t.length-2,1),o===1&&t.pop()}static calcMatMulShape(t,e){return t[1]!==e[0]?void 0:[t[0],e[1]]}},Xe=class Qt{static calcShape(e,o,i=!1){let u=e.length,n=o.length;if(u===0)return o;if(n===0)return e;let s=Math.max(e.length,o.length),c=new Array(s);if(i){if(u<2||n<2)return;let d=hi.calcMatMulShape([e[u-2],e[u-1]],[o[n-2],o[n-1]]);if(d===void 0)return;[c[s-2],c[s-1]]=d}for(let d=i?3:1;d<=s;d++){let r=u-d<0?1:e[u-d],a=n-d<0?1:o[n-d];if(r!==a&&r>1&&a>1)return;c[s-d]=Math.max(r,a)}return c}static index(e,o){let i=new Array(o.length);return Qt.fillIndex(e,o,i),i}static fillIndex(e,o,i){let u=e.length-o.length;for(let n=0;n<o.length;n++)i[n]=e[u+n]%o[n]}static calc(e,o,i,u,n){let s=Qt.calcShape(e.dims,o.dims);if(s){if(u&&!U.areEqual(s,e.dims))return;let c=U.size(s),d=u?e:new Fe(s,n||e.type);if(s.length===0)d.set([],i(e.get([]),o.get([])));else{let r=new Array(s.length),a=new Array(e.dims.length),l=new Array(o.dims.length),h=0,f=0,m=!1,g=!1;e.dims.length===0&&(h=e.get([]),m=!0),o.dims.length===0&&(f=o.get([]),g=!0);let b;for(let v=0;v<c;v++){b=v;for(let T=s.length-1;T>=0;T--)r[T]=b%s[T],b=Math.floor(b/s[T]);m||(Qt.fillIndex(r,e.dims,a),h=e.get(a)),g||(Qt.fillIndex(r,o.dims,l),f=o.get(l)),d.set(r,i(h,f))}}return d}}static isValidBroadcast(e,o){let i=e.length,u=o.length;if(i>u)return!1;for(let n=1;n<=i;n++)if(e[i-n]!==1&&e[i-n]!==o[u-n])return!1;return!0}static getBroadcastDims(e,o){let i=e.length,u=[];for(let n=0;n<i;n++){let s=i-1-n,c=e[s]||1;(o[o.length-1-n]||1)>1&&c===1&&u.unshift(s)}return u}},Xn=class{static getShapeOfGemmResult(t,e,o,i,u){if(t.length!==2||o.length!==2)throw new Error("shape need to be of size 2");let n,s,c;e?(n=t[1],s=t[0]):(n=t[0],s=t[1]);let d=-1;if(i?(c=o[0],d=1):(c=o[1],d=0),o[d]!==s)throw new Error("dimension mismatch");if(n<=0||c<=0||s<=0)throw new Error("invalid shape specified");if(u&&!Xe.isValidBroadcast(u,[n,c]))throw new Error("gemm: invalid bias shape for broadcast");return[n,c,s]}},Ve=class wn{static tensorDataTypeFromProto(e){switch(e){case ye.onnx.TensorProto.DataType.INT8:return"int8";case ye.onnx.TensorProto.DataType.UINT8:return"uint8";case ye.onnx.TensorProto.DataType.BOOL:return"bool";case ye.onnx.TensorProto.DataType.INT16:return"int16";case ye.onnx.TensorProto.DataType.UINT16:return"uint16";case ye.onnx.TensorProto.DataType.INT32:return"int32";case ye.onnx.TensorProto.DataType.UINT32:return"uint32";case 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ye.onnx.TensorProto.DataType.INT64;case"uint64":return ye.onnx.TensorProto.DataType.UINT64;default:throw new Error(`unsupported data type: ${e}`)}}static tensorDimsFromProto(e){return e.map(o=>zt.isLong(o)?o.toNumber():o)}static tensorValueTypeFromProto(e){return{tensorType:wn.tensorDataTypeFromProto(e.elemType),shape:{dims:wn.tensorDimsFromProto(e.shape.dim.map(o=>o.dimValue))}}}static tensorDimsFromORTFormat(e){let o=[];for(let i=0;i<e.dimsLength();i++)o.push(nt.longToNumber(e.dims(i)));return o}static tensorAttributesFromORTFormat(e){let o=[];for(let i=0;i<e.attributesLength();i++)o.push(e.attributes(i));return o}},nt=class{static longToNumber(t,e){return zt.isLong(t)?t.toNumber():t instanceof _.Long?zt.fromValue({low:t.low,high:t.high,unsigned:e??!1}).toNumber():t}static isLong(t){return zt.isLong(t)||t instanceof _.Long}},U=class Tt{static size(e){return Tt.getSizeFromDimensionRange(e,0,e.length)}static sizeFromDimension(e,o){if(o<0||o>e.length)throw new Error(`invalid dimension 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Input shape: [${e}] Output shape: [${o}]`);u[n]=c/s}else if(s!==c)throw new Error("reshapedDims and originalDims don't have matching sizes");return u}static sortBasedOnPerm(e,o){return o?o.map(i=>e[i]):e.slice().reverse()}static padShape(e,o){let i=e.length;return e.map((u,n)=>u+o[n]+o[n+i])}static areEqual(e,o){return e.length!==o.length?!1:e.every((i,u)=>i===o[u])}static validateDimsAndCalcSize(e){if(e.length>6)throw new TypeError("Only rank 0 to 6 is supported for tensor shape.");let o=1;for(let i of e){if(!Number.isInteger(i))throw new TypeError(`Invalid shape: ${i} is not an integer`);if(i<0||i>2147483647)throw new TypeError(`Invalid shape: length ${i} is not allowed`);o*=i}return o}static flattenShape(e,o){o<0&&(o+=e.length);let i=e.reduce((n,s)=>n*s,1),u=e.slice(o).reduce((n,s)=>n*s,1);return[i/u,u]}static squeezeShape(e,o){let i=new Array;o=Tt.normalizeAxes(o,e.length);for(let u=0;u<e.length;u++){let n=o.indexOf(u)>=0;if(n&&e[u]!==1)throw new Error("squeeze an axis of size different than 1");(o.length===0&&e[u]>1||o.length>0&&!n)&&i.push(e[u])}return i}static unsqueezeShape(e,o){let i=new Array(e.length+o.length);i.fill(0);for(let n=0;n<o.length;n++){let s=Tt.normalizeAxis(o[n],i.length);if(s>=i.length)throw new Error("'axes' has an out of range axis");if(i[s]!==0)throw new Error("'axes' has a duplicate axis");i[s]=1}let u=0;for(let n=0;n<i.length;n++)i[n]===0&&(i[n]=e[u++]);if(u!==e.length)throw new Error("the unsqueezed dimension could not be established");return i}},an=class Cs{static splitShape(e,o,i,u){if(i.length===0){if(!u)throw new Error("need to know number of outputs when the 'split' attribute is not specified");Cs.determineSplit(e[o],u,i)}let n=[],s=[0];for(let c=0;c<i.length;++c){c!==0&&s.push(s[c-1]+i[c-1]);let d=e.slice();d[o]=i[c],n.push(d)}return[n,s]}static determineSplit(e,o,i){if(e%o!==0)throw new Error("cannot split tensor to equal sized parts");for(let u=0;u<o;++u)i.push(e/o)}},pr=class er{static adjustPoolAttributes(e,o,i,u,n,s){if(!e&&i.length!==o.length-2)throw new Error("length of specified kernel shapes should be 2 less than length of input dimensions");if(e)for(let c=0;c<o.length-2;c++)c>=i.length?i.push(o[c+2]):i[c]=o[c+2];for(let c=0;c<i.length;c++)if(c<u.length){if(u[c]<0)throw new Error("strides should be greater than or equal to 1")}else u.push(1);for(let c=0;c<i.length;c++)if(c<n.length){if(n[c]<0)throw new Error("dilations should be greater than or equal to 1")}else n.push(1);for(let c=0;c<i.length*2;c++)if(c<s.length){if(s[c]<0)throw new Error("pad should be greater than or equal to 1")}else s.push(0);for(let c=0;c<i.length;c++){if(i[c]<=0)throw new Error("kernel shapes need to be greater than 0");if(s[c]>=i[c]||s[c+i.length]>=i[c])throw new Error("pads should be smaller than kernel")}}static adjustPadsBasedOnAutoPad(e,o,i,u,n,s){if(s){if(n.length!==2*(e.length-2))throw new Error("length of pads should be twice the length of data dimensions");if(o.length!==e.length-2)throw new Error("length of strides should be the length of data dimensions");if(u.length!==e.length-2)throw new Error("length of kernel shapes should be the length of data dimensions");for(let c=0;c<e.length-2;c++)er.adjustPadAndReturnShape(e[c+2],o[c],i[c],u[c],n,c,c+e.length-2,s)}}static computePoolOutputShape(e,o,i,u,n,s,c){if(o.length<=0)throw new Error("input shape must be of size greater than 0");let d=[o[0],o[1]];return er.computeShapeHelper(e,o,d,i,u,n,s,c),d}static computeConvOutputShape(e,o,i,u,n,s,c){if(e.length<=0||o.length<=0)throw new Error("invalid input tensor dims or invalid filter tensor dims");let d=[e[0],o[0]];return er.computeShapeHelper(!1,e,d,i,u,n,s,c),d}static computeShapeHelper(e,o,i,u,n,s,c,d){if(e)for(let r=0;r<o.length-2;r++)i.push(1);else for(let r=0;r<o.length-2;r++)i.push(er.adjustPadAndReturnShape(o[r+2],u[r],n[r],s[r],c,r,r+o.length-2,d))}static adjustPadAndReturnShape(e,o,i,u,n,s,c,d){let r=i*(u-1)+1;if(d&&d!=="NOTSET")switch(d){case"VALID":return n[s]=0,n[c]=0,Math.floor((e-r)/o+1);case"SAME_LOWER":case"SAME_UPPER":if(i!==1)throw new Error("Dilation not supported for SAME_UPPER or SAME_LOWER");{let a=((e+o-1)/o-1)*o+u-e;return n[s]=Math.floor(d==="SAME_LOWER"?(a+1)/2:a/2),n[c]=a-n[s],Math.floor((e+a-u)/o+1)}default:throw new Error("Unsupported AutoPad type")}else return Math.floor((e+n[s]+n[c]-r)/o+1)}},dr=-34028234663852886e22,hr=34028234663852886e22});function Vd(t){switch(t){case"bool":case"int8":case"uint8":return 1;case"int16":case"uint16":return 2;case"int32":case"uint32":case"float32":return 4;case"float64":return 8;default:throw new Error(`cannot calculate sizeof() on type ${t}`)}}function Fu(t){switch(t){case te.onnx.TensorProto.DataType.UINT8:case te.onnx.TensorProto.DataType.INT8:case te.onnx.TensorProto.DataType.BOOL:return 1;case te.onnx.TensorProto.DataType.UINT16:case te.onnx.TensorProto.DataType.INT16:return 2;case te.onnx.TensorProto.DataType.FLOAT:case te.onnx.TensorProto.DataType.INT32:case te.onnx.TensorProto.DataType.UINT32:return 4;case te.onnx.TensorProto.DataType.INT64:case te.onnx.TensorProto.DataType.DOUBLE:case te.onnx.TensorProto.DataType.UINT64:return 8;default:throw new Error(`cannot calculate sizeof() on type ${te.onnx.TensorProto.DataType[t]}`)}}function Wd(t,e){return new(ku(e))(t)}function ku(t){switch(t){case"bool":case"uint8":return Uint8Array;case"int8":return Int8Array;case"int16":return Int16Array;case"uint16":return Uint16Array;case"int32":return Int32Array;case"uint32":return Uint32Array;case"int64":return BigInt64Array;case"float32":return Float32Array;case"float64":return Float64Array;default:throw new Error("unspecified error")}}function bi(t,e){if(e===te.onnx.TensorProto.DataType.INT64||e===mi.TensorDataType.INT64){if(t.greaterThanOrEqual(2147483648)||t.lessThan(-2147483648))throw new TypeError("int64 is not supported")}else if(e===te.onnx.TensorProto.DataType.UINT32||e===mi.TensorDataType.UINT32||e===te.onnx.TensorProto.DataType.UINT64||e===mi.TensorDataType.UINT64){if(t.greaterThanOrEqual(4294967296)||t.lessThan(0))throw new TypeError("uint64 is not supported")}else throw new TypeError(`not a LONG type: ${te.onnx.TensorProto.DataType[e]}`);return t.toNumber()}function $u(t,e,o){switch(e){case te.onnx.TensorProto.DataType.BOOL:case te.onnx.TensorProto.DataType.UINT8:return t.getUint8(o);case te.onnx.TensorProto.DataType.INT8:return t.getInt8(o);case te.onnx.TensorProto.DataType.UINT16:return t.getUint16(o,!0);case te.onnx.TensorProto.DataType.INT16:return t.getInt16(o,!0);case te.onnx.TensorProto.DataType.FLOAT:return t.getFloat32(o,!0);case te.onnx.TensorProto.DataType.INT32:return t.getInt32(o,!0);case te.onnx.TensorProto.DataType.UINT32:return t.getUint32(o,!0);case te.onnx.TensorProto.DataType.INT64:return bi(zt.fromBits(t.getUint32(o,!0),t.getUint32(o+4,!0),!1),e);case te.onnx.TensorProto.DataType.DOUBLE:return t.getFloat64(o,!0);case te.onnx.TensorProto.DataType.UINT64:return bi(zt.fromBits(t.getUint32(o,!0),t.getUint32(o+4,!0),!0),e);default:throw new Error(`cannot read from DataView for type ${te.onnx.TensorProto.DataType[e]}`)}}var Cu,te,mi,Fe,mr=L(()=>{Cu=Er(_s()),ei(),tn(),te=Er($r()),fe(),mi=W.experimental.fbs,Fe=class qr{constructor(e,o,i,u,n,s=Cu.Guid.create()){this.dims=e,this.type=o,this.dataProvider=i,this.asyncDataProvider=u,this.cache=n,this.dataId=s,this.size=U.validateDimsAndCalcSize(e);let c=this.size,d=i===void 0&&u===void 0&&n===void 0;if(n!==void 0&&n.length!==c)throw new RangeError("Input dims doesn't match data length.");if(o==="string"){if(n!==void 0&&(!Array.isArray(n)||!n.every(r=>typeof r=="string")))throw new TypeError("cache should be a string array");d&&(this.cache=new Array(c))}else{if(n!==void 0){let r=ku(o);if(!(n instanceof r))throw new TypeError(`cache should be type ${r.name}`)}if(d){let r=new ArrayBuffer(c*Vd(o));this.cache=Wd(r,o)}}}get data(){if(this.cache===void 0){let e=this.dataProvider(this.dataId);if(e.length!==this.size)throw new Error("Length of data provided by the Data Provider is inconsistent with the dims of this Tensor.");this.cache=e}return this.cache}get stringData(){if(this.type!=="string")throw new TypeError("data type is not string");return this.data}get integerData(){switch(this.type){case"uint8":case"int8":case"uint16":case"int16":case"int32":case"uint32":case"bool":return this.data;default:throw new TypeError("data type is not integer (uint8, int8, uint16, int16, int32, uint32, bool)")}}get floatData(){switch(this.type){case"float32":case"float64":return this.data;default:throw new TypeError("data type is not float (float32, float64)")}}get numberData(){if(this.type!=="string")return this.data;throw new TypeError("type cannot be non-number (string)")}get(e){return this.data[U.indicesToOffset(e,this.strides)]}set(e,o){this.data[U.indicesToOffset(e,this.strides)]=o}async getData(){return this.cache===void 0&&(this.cache=await this.asyncDataProvider(this.dataId)),this.cache}get strides(){return this._strides||(this._strides=U.computeStrides(this.dims)),this._strides}static fromProto(e){if(!e)throw new Error("cannot construct Value from an empty tensor");let o=Ve.tensorDataTypeFromProto(e.dataType),i=Ve.tensorDimsFromProto(e.dims),u=new qr(i,o);if(o==="string")e.stringData.forEach((n,s)=>{u.data[s]=sn(n)});else if(e.rawData&&typeof e.rawData.byteLength=="number"&&e.rawData.byteLength>0){let n=u.data,s=new DataView(e.rawData.buffer,e.rawData.byteOffset,e.rawData.byteLength),c=Fu(e.dataType),d=e.rawData.byteLength/c;if(e.rawData.byteLength%c!==0)throw new Error("invalid buffer length");if(n.length!==d)throw new Error("buffer length mismatch");for(let r=0;r<d;r++){let a=$u(s,e.dataType,r*c);n[r]=a}}else{let n;switch(e.dataType){case te.onnx.TensorProto.DataType.FLOAT:n=e.floatData;break;case te.onnx.TensorProto.DataType.INT32:case te.onnx.TensorProto.DataType.INT16:case te.onnx.TensorProto.DataType.UINT16:case te.onnx.TensorProto.DataType.INT8:case te.onnx.TensorProto.DataType.UINT8:case te.onnx.TensorProto.DataType.BOOL:n=e.int32Data;break;case te.onnx.TensorProto.DataType.INT64:n=e.int64Data;break;case te.onnx.TensorProto.DataType.DOUBLE:n=e.doubleData;break;case te.onnx.TensorProto.DataType.UINT32:case te.onnx.TensorProto.DataType.UINT64:n=e.uint64Data;break;default:throw new Error("unspecific error")}if(n==null)throw new Error("failed to populate data from a tensorproto value");let s=u.data;if(s.length!==n.length)throw new Error("array length mismatch");for(let c=0;c<n.length;c++){let d=n[c];zt.isLong(d)?s[c]=bi(d,e.dataType):s[c]=d}}return u}static fromData(e,o,i){return new qr(o,i,void 0,void 0,e)}static fromOrtTensor(e){if(!e)throw new Error("cannot construct Value from an empty tensor");let o=Ve.tensorDimsFromORTFormat(e),i=Ve.tensorDataTypeFromProto(e.dataType()),u=new qr(o,i);if(i==="string")for(let n=0;n<e.stringDataLength();n++)u.data[n]=e.stringData(n);else if(e.rawDataArray()&&typeof e.rawDataLength()=="number"&&e.rawDataLength()>0){let n=u.data,s=new DataView(e.rawDataArray().buffer,e.rawDataArray().byteOffset,e.rawDataLength()),c=Fu(e.dataType()),d=e.rawDataLength()/c;if(e.rawDataLength()%c!==0)throw new Error("invalid buffer length");if(n.length!==d)throw new Error("buffer length mismatch");for(let r=0;r<d;r++){let a=$u(s,e.dataType(),r*c);n[r]=a}}return u}}});function q(t){return t===1?Hd:qd}function Bu(t){let e=q(t);return`${e.version}
+      precision highp float;
+      ${e.attribute} vec3 position;
+      ${e.attribute} vec2 textureCoord;
+
+      ${e.varyingVertex} vec2 TexCoords;
+
+      void main()
+      {
+          gl_Position = vec4(position, 1.0);
+          TexCoords = textureCoord;
+      }`}function Nu(t){let e=q(t);return`${e.version}
+    precision highp float;
+    precision highp int;
+    precision highp sampler2D;
+    ${e.varyingFrag} vec2 TexCoords;
+    ${e.outputDeclaration}
+    const vec2 halfCR = vec2(0.5, 0.5);
+
+    // Custom vector types to handle higher dimenalities.
+    struct ivec5
+    {
+      int x;
+      int y;
+      int z;
+      int w;
+      int u;
+    };
+
+    struct ivec6
+    {
+      int x;
+      int y;
+      int z;
+      int w;
+      int u;
+      int v;
+    };
+
+    int imod(int x, int y) {
+      return x - y * (x / y);
+    }
+
+    `}function Ru(t,e){let o=q(t);return`
+  void main() {
+    int indices[${e}];
+    toVec(TexCoords, indices);
+    vec4 result = vec4(process(indices));
+    ${o.output} = result;
+  }
+  `}var Hd,qd,xe=L(()=>{Hd={version:"",attribute:"attribute",varyingVertex:"varying",varyingFrag:"varying",texture2D:"texture2D",output:"gl_FragColor",outputDeclaration:""},qd={version:"#version 300 es",attribute:"in",varyingVertex:"out",varyingFrag:"in",texture2D:"texture",output:"outputColor",outputDeclaration:"out vec4 outputColor;"}}),ae=L(()=>{});async function gi(t,e=i=>0,o){return new Promise((i,u)=>{let n=0,s=()=>{if(t()){i();return}n++;let c=e(n);if(o!=null&&n>=o){u();return}setTimeout(s,c)};s()})}function Kn(t){return Cr(typeof t<"u"&&t.length!==0,()=>"empty string found for sampler name"),"get"+t.charAt(0).toUpperCase()+t.slice(1)}function Mu(t){return Cr(typeof t<"u"&&t.length!==0,()=>"empty string found for sampler name"),"get"+t.charAt(0).toUpperCase()+t.slice(1)+"AtOutCoords"}function kr(t,e){let o=JSON.parse(JSON.stringify(t));return o=e,o}function Br(t,e){return e.map(o=>t[o]).join(", ")}function Ke(t){if(t<=1)return"int";if(t===2)return"ivec2";if(t===3)return"ivec3";if(t===4)return"ivec4";if(t===5)return"ivec5";if(t===6)return"ivec6";throw Error(`GPU for rank ${t} is not yet supported`)}function St(t=6){return["x","y","z","w","u","v"].slice(0,t)}var Ft=L(()=>{fe()});function jd(t,e){return St(e).map(o=>`${t}.${o}`)}function Nr(t,e){return e===1?[t]:jd(t,e)}function $t(){return`
+    float getChannel(vec4 frag, int dim) {
+      int modCoord = imod(dim, 2);
+      return modCoord == 0 ? frag.r : frag.g;
+    }
+
+    float getChannel(vec4 frag, vec2 innerDims) {
+      vec2 modCoord = mod(innerDims, 2.);
+      return modCoord.x == 0. ?
+        (modCoord.y == 0. ? frag.r : frag.g) :
+        (modCoord.y == 0. ? frag.b : frag.a);
+    }
+  `}var br=L(()=>{Ft()});function Xd(t,e,o){if(t===0)return"false";if(t===1)return`rc > ${e[0]}`;let i="";for(let u=t-2;u<t;u++)i+=`${o[u]} >= ${e[u-t+2]}`,u<t-1&&(i+="||");return i}function Kd(t,e){let o=t.length;if(o===0)return"getA(), 0, 0, 0";if(o===1)return`getA(rc),
+            rc + 1 >= ${t[0]} ? 0. : getA(rc + 1),
+            0, 0`;let i="r, c",u="r, cp1",n="rp1, c",s="rp1, cp1",c="";if(o>2)for(let d=0;d<o-2;++d)c=c+`${e[d]},`;return`getA(${c}${i}),
+          rEdge ? 0. : getA(${c}${n}),
+          cEdge ? 0. : getA(${c}${u}),
+          rEdge || cEdge ? 0. : getA(${c}${s})`}function Jd(t,e,o,i){return t===0||t===1?"":`
+    int r = ${e[t-2]};
+    int c = ${e[t-1]};
+    int rp1 = ${e[t-2]} + 1;
+    int cp1 = ${e[t-1]} + 1;
+    bool rEdge = rp1 >= ${i};
+    bool cEdge = cp1 >= ${o};
+    `}var Gu,Yd,Uu,zu=L(()=>{xe(),ae(),Ft(),br(),Gu={name:"pack",inputNames:["A"],inputTypes:[1]},Yd=(t,e)=>{let o=q(t.session.backend.glContext.version),i=e.dims,u=i.length,n=e.dims.length,s=Ke(n),c=Nr("rc",n),d=Jd(n,c,i[i.length-2],i[i.length-1]),r;u===0?r=[1,1]:u===1?r=[i[0],1]:r=[i[n-1],i[n-2]];let a=Xd(n,r,c),l=Kd(i,c),h=`
+        void main() {
+          ${s} rc = getOutputCoords();
+
+          if(${a}) {
+            ${o.output} = vec4(0);
+          } else {
+            ${d}
+
+            ${o.output} = vec4(${l});
+          }
+        }
+      `;return{...Gu,hasMain:!0,output:{dims:e.dims,type:e.type,textureType:2},shaderSource:h}},Uu=(t,e)=>({...Gu,get:()=>Yd(t,e)})});function yi(t){if(t.length===0)return[1,1,1];let e=1;for(let o=0;o<t.length-2;++o)e*=t[o];return[e,t.length>1?t[t.length-2]:1,t[t.length-1]]}function Wu(t,e){let o=!1;return t.length===0||e.length===0?o=!0:t.length<2||e.length<2?o=t[t.length-1]===e[e.length-1]:o=t[t.length-1]===e[e.length-1]&&t[t.length-2]===e[e.length-2],o}function eh(t){let e=U.computeStrides(t),o=["b","r","c"],i="index";return`
+    ivec3 inputCoordsFromReshapedOutCoords(int index) {
+      ${e.map((u,n)=>{let s=`int ${o[n]} = ${i} / ${u}`,c=n===e.length-1?`int ${o[n+1]} = ${i} - ${o[n]} * ${u}`:`index -= ${o[n]} * ${u}`;return`${s}; ${c};`}).join("")}
+      return ivec3(b, r, c);
+    }
+  `}function th(t){let e=U.computeStrides(t);return`
+  int getFlattenedIndex(ivec3 coords) {
+    // reverse y, z order
+    return coords.x * ${e[0]} + coords.z * ${e[1]} + coords.y;
+  }
+`}var Zd,Qd,Vu,Hu=L(()=>{fe(),xe(),ae(),br(),Zd=t=>({name:"Reshape (packed)",inputTypes:[2],inputNames:["A"],cacheHint:`${t}`}),Qd=(t,e,o,i)=>{let u=e.dims,n=i,s="";for(let r=0;r<4;r++){let a="";switch(r){case 0:a="outputCoords = rc;";break;case 1:a="outputCoords = ivec3(rc.x, rc.y+1, rc.z);";break;case 2:a="outputCoords = ivec3(rc.x, rc.y, rc.z+1);";break;case 3:a="outputCoords = ivec3(rc.x, rc.y+1, rc.z+1);";break;default:throw new Error}s+=`
+        ${a}
+        ${r>0?"if(outputCoords.y < rows && outputCoords.z < cols){":""}
+          int flattenedIndex = getFlattenedIndex(outputCoords);
+
+          ivec3 inputRC = inputCoordsFromReshapedOutCoords(flattenedIndex);
+          vec2 innerDims = vec2(float(inputRC.y),float(inputRC.z));
+
+          result[${r}] = getChannel(getA(inputRC.x, inputRC.y, inputRC.z), innerDims);
+
+        ${r>0?"}":""}
+      `}let c=q(t.session.backend.glContext.version),d=`
+      ${eh(u)}
+      ${th(n)}
+      ${$t()}
+
+      void main() {
+        ivec3 rc = getOutputCoords();
+
+        vec4 result = vec4(0.0);
+
+        ivec3 outputCoords;
+        int rows = ${n[2]};
+        int cols = ${n[1]};
+
+        ${s}
+        ${c.output} = result;
+      }
+    `;return{...o,output:{dims:n,type:e.type,textureType:2},shaderSource:d,hasMain:!0}},Vu=(t,e,o)=>{let i=Zd(o);return{...i,get:()=>Qd(t,e,i,o)}}}),Ti,qu=L(()=>{xe(),ae(),Ti=(t,e)=>{let o=e.shape,i=q(t.session.backend.glContext.version),u=`
+    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 = ${i.texture2D}(X,TexCoords).r;
+      ${i.output} = encodeAsUint8(value);
+    }`,n={name:"Uint8Encode",inputTypes:[0],inputNames:["X"],output:{dims:o,type:e.tensor.type,textureType:3},shaderSource:u,hasMain:!0};return t.executeProgram(n,[e.tensor])}});function nh(t,e){if(t===1)return"rc";let o="";for(let i=0;i<t;i++)o+=e[i],i<t-1&&(o+=",");return o}var ju,rh,Yu,Xu=L(()=>{xe(),ae(),Ft(),br(),ju={name:"unpack",inputNames:["A"],inputTypes:[2]},rh=(t,e)=>{let o=e.dims.length,i=Nr("rc",o),u=i.slice(-2),n=Ke(o),s=$t(),c=e.dims.length===0?"":nh(o,i),d=o<=1?"rc":`vec2(${u.join(",")})`,r=q(t.session.backend.glContext.version),a=`
+    ${s}
+    void main() {
+      ${n} rc = getOutputCoords();
+
+       // Sample the texture with the coords to get the rgba channel value.
+       vec4 packedInput = getA(${c});
+
+       ${r.output} = vec4(getChannel(packedInput, ${d}), 0, 0, 0);
+     }
+   `;return{...ju,hasMain:!0,output:{dims:e.dims,type:e.type,textureType:0},shaderSource:a}},Yu=(t,e)=>({...ju,get:()=>rh(t,e)})}),Jn,un,Zn,ln=L(()=>{ut(),Jn=class{constructor(t,e=1){if(e===1)this.internalFormat=t.R32F,this.format=t.RED,this.textureType=t.FLOAT,this.channelSize=e;else if(e===4)this.internalFormat=t.RGBA32F,this.format=t.RGBA,this.textureType=t.FLOAT,this.channelSize=e;else throw new Error(`Invalid number of channels: ${e}`)}encode(t,e){let o,i;return t.constructor!==Float32Array&&(ce.warning("Encoder","data was not of type Float32; creating new Float32Array"),i=new Float32Array(t)),e*this.channelSize>t.length?(ce.warning("Encoder","Source data too small. 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throw new Error(`Invalid number of channels: ${e}`)}encode(t,e){return new Uint8Array(t.buffer,t.byteOffset,t.byteLength)}allocate(t){return new Uint8Array(t*this.channelSize)}decode(t,e){if(t instanceof Uint8Array)return t.subarray(0,e);throw new Error(`Invalid array type: ${t.constructor}`)}}}),fn,Ku,xi,Ju=L(()=>{fe(),ae(),fn=(t,e,o)=>{let i=o===0||o===1?1:4,u=o===2,n=o===1||o===2,s=o===4?e.length-1:void 0,c=o===4?e.map((d,r)=>r===e.length-1?d*4:d):void 0;return xi(t,e,i,c,{isPacked:u,reverseWH:n,breakAxis:s})},Ku=(t,e,o)=>{let i=fn(t,e,o);return[i.width,i.height]},xi=(t,e,o=1,i,u)=>{let n=!!(u&&u.isPacked),[s,c]=t.computeTextureWH(n&&i||e,u),d=e.length,r=e.slice(0);if(d===0&&(r=[1]),o===1)i=e;else if(n){if(o!==4)throw new Error("a packed texture must be 4-channel");i=e,d>0&&(r[d-1]=Math.ceil(r[d-1]/2)),d>1&&(r[d-2]=Math.ceil(r[d-2]/2))}else if(!i)throw new Error("Unpacked shape is needed when using channels > 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t.get=="function"?t.get():t,s=fn(this.session.layoutStrategy,n.output.dims,n.output.textureType),c=this.createTextureData(s,n.output.type);return u||(u=this.session.programManager.build(n,o,c),this.session.programManager.setArtifact(i,u)),this.runProgram(u,o,c),c}run(t,e){return this.executeProgram(t,e).tensor}runProgram(t,e,o){for(let i=0;i<e.length;++i)if(!!e[i].isPacked!=(t.programInfo.inputTypes[i]===2))throw new Error(`input[${i}] property packed inconsistent`);if(!!o.isPacked!=(t.programInfo.output.textureType===2))throw new Error("output property packed inconsistent");this.session.programManager.run(t,e,o)}getOrCreateTextureData(t,e){let o=this.getTextureData(t.dataId,e===2);if(!o&&(o=this.getTextureData(t.dataId,e!==2),o))return e===2?this.pack(o):this.unpack(o);if(!o){let i=fn(this.session.layoutStrategy,t.dims,e);if(e===4){let u=t.dims;if(u.length===4){let n=[u[0],Math.ceil(u[1]*u[2]*u[3]/4)],s=fn(this.session.layoutStrategy,n,e),c=t.numberData;if(u[1]*u[2]*u[3]%4!==0){let d=u[0],r=u[1]*u[2]*u[3],a=Math.ceil(r*1/4)*4,l=d*a;c=new Float32Array(l);for(let h=0;h<d;++h){let f=h*r,m=h*a+h%1*r;c.set(t.numberData.subarray(f,f+r),m)}}return this.createTextureData(s,t.type,c,t,1)}}if(e===2){let u=xi(this.session.layoutStrategy,t.dims,1,[],{reverseWH:!0}),n=this.createTextureData(u,t.type,t.numberData,t,1);o=this.pack(n)}else o=this.createTextureData(i,t.type,t.numberData,t,1)}return o}createTextureDataFromLayoutBindTensor(t,e,o,i){return this.createTextureData(t,e,o,i,1)}createTextureData(t,e,o,i,u){ce.verbose("InferenceHandler",`Creating TextureData: layout:[${JSON.stringify(t)}]`);let n=this.session.textureManager.createTextureFromLayout(e,t,o,u);return this.createTextureDataFromTexture(t,e,n,i)}reshapeUnpacked(t,e){let o=this.getOrCreateTextureData(t,0),i={channels:o.channels,height:o.height,width:o.width,shape:e.length!==0?e:[1],strides:U.computeStrides(e),unpackedShape:e};return this.createTextureDataFromTexture(i,t.type,o.texture).tensor}reshapePacked(t,e){let o=this.getOrCreateTextureData(t,2);if(Wu(t.dims,e)){let c={channels:o.channels,height:o.height,width:o.width,shape:e.length!==0?e:[1],strides:U.computeStrides(e),unpackedShape:e,isPacked:!0};return this.createTextureDataFromTexture(c,t.type,o.texture).tensor}let i=yi(t.dims),u=yi(e),n=this.reshapePacked(t,i),s=this.run(Vu(this,n,u),[n]);return this.reshapePacked(s,e)}cast(t,e){let o=this.getOrCreateTextureData(t,0);return this.createTextureDataFromTexture(o,e,o.texture).tensor}createTextureDataFromTexture(t,e,o,i,u){let n={...t,tensor:i||new Fe(t.unpackedShape,e,s=>this.readTexture(n),async s=>this.readTextureAsync(n),void 0,u),texture:o};return this.setTextureData(n.tensor.dataId,n,t.isPacked),n}getTextureData(t,e=!1){return this.session.isInitializer(t)?this.session.getTextureData(t,e):e?this.packedTextureDataCache.get(t):this.unpackedTextureDataCache.get(t)}setTextureData(t,e,o=!1){this.session.isInitializer(t)?this.session.setTextureData(t,e,o):(o?this.packedTextureDataCache:this.unpackedTextureDataCache).set(t,e)}isTextureLayoutCached(t,e=!1){return!!this.getTextureData(t.dataId,e)}dispose(){this.session.textureManager.clearActiveTextures(),this.packedTextureDataCache.forEach(t=>this.session.textureManager.releaseTexture(t)),this.packedTextureDataCache=new Map,this.unpackedTextureDataCache.forEach(t=>this.session.textureManager.releaseTexture(t)),this.unpackedTextureDataCache=new Map}readTexture(t){return t.isPacked?this.readTexture(this.unpack(t)):this.session.backend.glContext.isFloat32DownloadSupported?this.session.textureManager.readTexture(t,t.tensor.type,t.channels):this.session.textureManager.readUint8TextureAsFloat(Ti(this,t))}async readTextureAsync(t){return t.isPacked?this.readTextureAsync(this.unpack(t)):this.session.backend.glContext.isFloat32DownloadSupported?this.session.textureManager.readTextureAsync(t,t.tensor.type,t.channels):this.session.textureManager.readUint8TextureAsFloat(Ti(this,t))}pack(t){return this.executeProgram(Uu(this,t.tensor),[t.tensor])}unpack(t){return this.executeProgram(Yu(this,t.tensor),[t.tensor])}}}),vi,ee,Ge=L(()=>{vi=class{constructor(t){Object.assign(this,t)}get cacheKey(){return this.key||(this.key=Object.getOwnPropertyNames(this).sort().map(t=>`${this[t]}`).join(";")),this.key}},ee=t=>new vi(t)}),el,tl,rl,ah,sh,nl=L(()=>{Ge(),xe(),ae(),el={name:"BatchNormalization",inputNames:["A","Scale","B","Mean","Variance"],inputTypes:[0,0,0,0,0]},tl=(t,e,o)=>(sh(e),[t.run({...el,cacheHint:o.cacheKey,get:()=>ah(t,e,o)},e)]),rl=t=>{let e=t.attributes.getFloat("epsilon",1e-5),o=t.attributes.getFloat("momentum",.9),i=t.attributes.getInt("spatial",1);return ee({epsilon:e,momentum:o,spatial:i})},ah=(t,e,o)=>{let i=q(t.session.backend.glContext.version),u=e[0].dims.length,[n,s]=t.calculateTextureWidthAndHeight(e[1].dims,0),c=`
+  float process(int[${u}] indices) {
+    vec2 position = offsetToCoords(indices[1], ${n}, ${s});
+    float scale = getColorAsFloat(${i.texture2D}(Scale, position));
+    float mean = getColorAsFloat(${i.texture2D}(Mean, position));
+    float variance = getColorAsFloat(${i.texture2D}(Variance, position));
+    float b = getColorAsFloat(${i.texture2D}(B, position));
+
+    return scale * ( (_A(indices) - mean) / sqrt(variance + float(${o.epsilon})) ) + b;
+  }`;return{...el,output:{dims:e[0].dims,type:e[0].type,textureType:0},shaderSource:c}},sh=t=>{if(!t||t.length!==5)throw new Error("BatchNormalization requires 5 inputs.");let e=t[0],o=t[1],i=t[2],u=t[3],n=t[4];if(e.dims.length<3||o.dims.length!==1||i.dims.length!==1||u.dims.length!==1||n.dims.length!==1)throw new Error("invalid input shape.");if(o.dims[0]!==e.dims[1]||i.dims[0]!==e.dims[1]||u.dims[0]!==e.dims[1]||n.dims[0]!==e.dims[1])throw new Error("invalid input shape.");if(e.type!=="float32"&&e.type!=="float64"||o.type!=="float32"&&o.type!=="float64"||i.type!=="float32"&&i.type!=="float64"||u.type!=="float32"&&u.type!=="float64"||n.type!=="float32"&&n.type!=="float64")throw new Error("invalid input tensor types.")}}),eo,pt,R,cn,to,Vt=L(()=>{eo=class{constructor(t,e,o,i){this.glContext=t,this.programInfo=e,this.inputTextureLayouts=o,this.outputTextureLayout=i}},pt=class{constructor(t){this.context=t}},R=class{constructor(t,e){this.routineBody=t,this.dependencies=e}},cn=class{constructor(t,e,o){this.name=t,o?this.dependencies=o:this.dependencies=[],e&&(this.routineBody=e)}addDependency(t){t&&this.dependencies.push(t)}},to=class{static returnOrderedNodes(t){if(!t||t.length===0)return[];if(t.length===1)return t;let e=new Set,o=new Set,i=new Array;return this.createOrderedNodes(t,e,o,i),i}static createOrderedNodes(t,e,o,i){for(let u=0;u<t.length;++u)this.dfsTraverse(t[u],e,o,i)}static dfsTraverse(t,e,o,i){if(!t||o.has(t.name))return;if(e.has(t.name))throw new Error("Cyclic dependency detected. Can't topologically sort routines needed for shader.");e.add(t.name);let u=t.dependencies;if(u&&u.length>0)for(let n=0;n<u.length;++n)this.dfsTraverse(u[n],e,o,i);i.push(t),o.add(t.name),e.delete(t.name)}}});function lh(){let t="add_";return{body:`
+  float ${t}(float a, float b) {
+    return a + b;
+  }
+  vec4 ${t}(vec4 v1, vec4 v2) {
+    return v1 + v2;
+  }
+  `,name:t,type:0}}function fh(){let t="div_";return{body:`
+  float ${t}(float a, float b) {
+    return a / b;
+  }
+  vec4 ${t}(vec4 v1, vec4 v2) {
+    return v1 / v2;
+  }
+  `,name:t,type:0}}function ch(){let t="mul_";return{body:`
+  float ${t}(float a, float b) {
+    return a * b;
+  }
+  vec4 ${t}(vec4 v1, vec4 v2) {
+    return v1 * v2;
+  }
+  `,name:t,type:0}}function ph(){let t="sub_";return{body:`
+  float ${t}(float a, float b) {
+    return a - b;
+  }
+  vec4 ${t}(vec4 v1, vec4 v2) {
+    return v1 - v2;
+  }
+  `,name:t,type:0}}function dh(){let t="equal_";return{body:`
+  float ${t}(float a, float b) {
+    return float(a == b);
+  }
+  vec4 ${t}(vec4 v1, vec4 v2) {
+    return vec4(equal(v1, v2));
+  }
+  `,name:t,type:0}}function hh(){let t="greater_";return{body:`
+  float ${t}(float a, float b) {
+    return float(a > b);
+  }
+  vec4 ${t}(vec4 v1, vec4 v2) {
+    return vec4( v1.r > v2.r ,
+      v1.g > v2.g,
+      v1.b > v2.b,
+      v1.a > v2.a );
+  }
+  `,name:t,type:0}}function mh(){let t="less_";return{body:`
+  float ${t}(float a, float b) {
+    return float(a < b);
+  }
+  vec4 ${t}(vec4 v1, vec4 v2) {
+    return vec4( v1.r < v2.r ,
+                v1.g < v2.g,
+                v1.b < v2.b,
+                v1.a < v2.a );
+  }
+  `,name:t,type:0}}function bh(){let t="and_";return{body:`
+  float ${t}(float a, float b) {
+    return float( bool(a) && bool(b) );
+  }
+  vec4 ${t}(vec4 v1, vec4 v2) {
+    bvec4 b1 = bvec4(v1);
+    bvec4 b2 = bvec4(v2);
+    return vec4( b1.r && b2.r ,
+                b1.g && b2.g,
+                b1.b && b2.b,
+                b1.a && b2.a );
+  }
+  `,name:t,type:0}}function gh(){let t="or_";return{body:`
+  float ${t}(float a, float b) {
+    return float( bool(a) || bool(b) );
+  }
+  vec4 ${t}(vec4 v1, vec4 v2) {
+    bvec4 b1 = bvec4(v1);
+    bvec4 b2 = bvec4(v2);
+    return vec4( b1.r || b2.r ,
+                b1.g || b2.g,
+                b1.b || b2.b,
+                b1.a || b2.a );
+  }
+  `,name:t,type:0}}function yh(){let t="xor_";return{body:`
+  float ${t}(float a, float b) {
+    return float( bool(a) ^^ bool(b) );
+  }
+  vec4 ${t}(vec4 v1, vec4 v2) {
+    bvec4 b1 = bvec4(v1);
+    bvec4 b2 = bvec4(v2);
+    return vec4( b1.r ^^ b2.r ,
+                b1.g ^^ b2.g,
+                b1.b ^^ b2.b,
+                b1.a ^^ b2.a );
+  }
+  `,name:t,type:0}}function Th(){return vh("pow")}function xh(){let t="prelu_";return{body:`
+  float ${t}(float a, float b) {
+    return a < 0.0 ? a * b: a;
+  }
+  vec4 ${t}(vec4 v1, vec4 v2) {
+    return vec4(
+      v1.r < 0.0 ? v1.r * v2.r: v1.r,
+      v1.g < 0.0 ? v1.g * v2.g: v1.g,
+      v1.b < 0.0 ? v1.b * v2.b: v1.b,
+      v1.a < 0.0 ? v1.a * v2.a: v1.a
+      );
+  }
+  `,name:t,type:0}}function vh(t){let e=`${t}_`;return{body:`
+  float ${e}(float a, float b) {
+    return ${t}(a, b);
+  }
+  vec4 ${e}(vec4 v1, vec4 v2) {
+    return ${t}(v1, v2);
+  }
+  `,name:e,type:0}}var dt,wh,ol,il,al,sl,ul,ll,fl,cl,pl,dl,hl,ml,bl=L(()=>{fe(),Vt(),xe(),ae(),dt=(t,e,o,i=e[0].type,u)=>{let n=t.session.pack?2:0;return{name:o.name,inputNames:["A","B"],inputTypes:[n,n],cacheHint:u,get:()=>wh(t,e,o,i)}},wh=(t,e,o,i=e[0].type)=>{let u=t.session.pack?2:0,n=!U.areEqual(e[0].dims,e[1].dims),s=e[0].dims,c=t.session.pack;if(n){let a=Xe.calcShape(e[0].dims,e[1].dims,!1);if(!a)throw new Error("Can't perform binary op on the given tensors");s=a;let l=s.length,h=e[0].dims.length!==0?e[0].dims.length:1,f=e[1].dims.length!==0?e[1].dims.length:1,m=e[0].dims.length!==0?"bcastIndices_A(indices, aindices);":"aindices[0] = 0;",g=e[1].dims.length!==0?"bcastIndices_B(indices, bindices);":"bindices[0] = 0;",b=q(t.session.backend.glContext.version),v=c?`
+      ${o.body}
+      void main() {
+        vec4 a = getAAtOutCoords();
+        vec4 b = getBAtOutCoords();
+        vec4 result = ${o.name}(a, b);
+        ${b.output} = result;
+      }`:`
+      ${o.body}
+      float process(int indices[${l}]) {
+        int aindices[${h}];
+        int bindices[${f}];
+        ${m}
+        ${g}
+        return ${o.name}(_A(aindices), _B(bindices));
+      }`;return{name:o.name,inputNames:["A","B"],inputTypes:[u,u],output:{dims:s,type:i,textureType:u},shaderSource:v,hasMain:c}}let d=q(t.session.backend.glContext.version),r=`
+    ${o.body}
+    void main() {
+      vec4 v1 = ${d.texture2D}(A, TexCoords);
+      vec4 v2 = ${d.texture2D}(B, TexCoords);
+      vec4 result = ${o.name}(v1, v2);
+      ${d.output} = result;
+    }
+    `;return{name:o.name,inputNames:["A","B"],inputTypes:[u,u],output:{dims:e[0].dims,type:i,textureType:u},shaderSource:r,hasMain:!0}},ol=(t,e)=>[t.run(dt(t,e,lh()),e)],il=(t,e)=>[t.run(dt(t,e,bh(),"bool"),e)],al=(t,e)=>[t.run(dt(t,e,fh()),e)],sl=(t,e)=>[t.run(dt(t,e,dh(),"bool"),e)],ul=(t,e)=>[t.run(dt(t,e,hh(),"bool"),e)],ll=(t,e)=>[t.run(dt(t,e,mh(),"bool"),e)],fl=(t,e)=>[t.run(dt(t,e,ch()),e)],cl=(t,e)=>[t.run(dt(t,e,gh(),"bool"),e)],pl=(t,e)=>[t.run(dt(t,e,Th()),e)],dl=(t,e)=>[t.run(dt(t,e,xh()),e)],hl=(t,e)=>[t.run(dt(t,e,ph()),e)],ml=(t,e)=>[t.run(dt(t,e,yh(),"bool"),e)]}),gl,yl,Oh,Tl=L(()=>{fe(),gl=(t,e,o)=>(Oh(e),[t.cast(e[0],o)]),yl=t=>Ve.tensorDataTypeFromProto(t.attributes.getInt("to")),Oh=t=>{if(!t||t.length!==1)throw new Error("Cast requires 1 input.");if(t[0].type==="string")throw new Error("Invalid input type.")}}),Ih,Sh,xl,ro,vl=L(()=>{xe(),ae(),Ft(),br(),Ih=(t,e)=>({name:"Concat (packed)",inputNames:Array.from({length:t},(o,i)=>`X${i}`),inputTypes:Array(t).fill(2),cacheHint:e}),Sh=(t,e,o,i)=>{let u=o[0].dims.slice();if(i>=u.length||i<-1*u.length)throw new Error("axis specified for concat doesn't match input dimensionality");i<0&&(i=u.length+i);let n=u.slice(0);for(let F=1;F<o.length;F++){let Y=o[F].dims.slice();for(let H=0;H<u.length;H++)if(H===i)n[i]+=Y[H];else if(u[H]!==Y[H])throw new Error("non concat dimensions must match")}let s=n.length,c=Nr("coords",s),d=Ke(s),r=$t(),a=o.map(F=>F.dims),l=St(s),h=new Array(a.length-1);h[0]=a[0][i];for(let F=1;F<h.length;F++)h[F]=h[F-1]+a[F][i];let f=l[i],m=l.slice(-2),g=l.join(),b=`if (${f} < ${h[0]}) {
+        return getChannel(
+            getX0(${g}), vec2(${m.join()}));
+        }`;for(let F=1;F<h.length;F++){let Y=h[F-1];b+=`
+            if (${f} < ${h[F]}  && ${f} >= ${h[F-1]}) {
+              return getChannel(
+                getX${F}(${ro(l,f,Y)}),
+                vec2(${ro(m,f,Y)}));
+            }`}let v=h.length,T=h[h.length-1];b+=`
+            return getChannel(
+              getX${v}(${ro(l,f,T)}),
+              vec2(${ro(m,f,T)}));`;let E=q(t.session.backend.glContext.version),M=`
+          ${r}
+          float getValue(${l.map(F=>"int "+F)}) {
+            ${b}
+          }
+
+          void main() {
+            ${d} coords = getOutputCoords();
+            int lastDim = coords.${l[s-1]};
+            coords.${l[s-1]} = coords.${l[s-2]};
+            coords.${l[s-2]} = lastDim;
+
+            vec4 result = vec4(getValue(${c}), 0., 0., 0.);
+
+            ${c[s-1]} = ${c[s-1]} + 1;
+            if (${c[s-1]} < ${n[s-1]}) {
+              result.g = getValue(${c});
+            }
+
+            ${c[s-2]} = ${c[s-2]} + 1;
+            if (${c[s-2]} < ${n[s-2]}) {
+              result.a = getValue(${c});
+            }
+
+            ${c[s-1]} = ${c[s-1]} - 1;
+            if (${c[s-2]} < ${n[s-2]} &&
+                ${c[s-1]} < ${n[s-1]}) {
+              result.b = getValue(${c});
+            }
+            ${E.output} = result;
+          }
+        `;return{...e,output:{dims:n,type:o[0].type,textureType:2},shaderSource:M,hasMain:!0}},xl=(t,e,o)=>{let i=Ih(e.length,o.cacheKey);return{...i,get:()=>Sh(t,i,e,o.axis)}},ro=(t,e,o)=>{let i=t.indexOf(e);return t.map((u,n)=>n===i?`${u} - ${o}`:u).join()}}),wl,Ah,Ph,Eh,_l,Dh,Lh,Fh,Ol,$h,Il=L(()=>{Ge(),ae(),vl(),wl=(t,e,o)=>($h(e),t.session.pack&&e[0].dims.length>1?[t.run(xl(t,e,o),e)]:[t.run(Eh(t,e,o),e)]),Ah=(t,e)=>({name:"Concat",inputNames:Array.from({length:t},(o,i)=>`X${i}`),inputTypes:Array(t).fill(0),cacheHint:e}),Ph=(t,e,o,i)=>{let u=o[0].dims.slice();if(i>=u.length||i<-1*u.length)throw new Error("axis specified for concat doesn't match input dimensionality");i<0&&(i=u.length+i);let n=u.slice(0);for(let f=1;f<o.length;f++){let m=o[f].dims.slice();for(let g=0;g<u.length;g++)if(g===i)n[i]+=m[g];else if(u[g]!==m[g])throw new Error("non concat dimensions must match")}let s=n.length,c=new Array(o.length),d=0;for(let f=0;f<c.length;++f)d+=o[f].dims[i],c[f]=d;let r="";o.length<5?r=_l(c):r=Dh(c);let a=Lh(o.length,s),l=Fh(c),h=`
+        ${a}
+        ${l}
+        ${r}
+        float process(int indices[${s}]) {
+          int textureIndex = getTextureWhereDataResides (indices[${i}]);
+
+          if(textureIndex != 0) {
+            indices[${i}] = indices[${i}] - int(getSizeInConcatAxisValueFromIndex(textureIndex-int(1)));
+          }
+
+          return fetchDataFromCorrectTexture(textureIndex, indices);
+        }`;return{...e,output:{dims:n,type:o[0].type,textureType:0},shaderSource:h}},Eh=(t,e,o)=>{let i=Ah(e.length,o.cacheKey);return{...i,get:()=>Ph(t,i,e,o.axis)}},_l=t=>`int getTextureWhereDataResides(int index) {
+      ${t.map((e,o)=>`if(index<${e}) {return ${o};}
+`).join("")}
+    }`,Dh=t=>_l(t),Lh=(t,e)=>{let o=[`float fetchDataFromCorrectTexture(int textureIndex, int indices[${e}]) {`];for(let i=0;i<t;++i)i===0?o.push(`	if (textureIndex == ${i}) { return _X${i}(indices); }`):i===t-1?o.push(`	else { return _X${i}(indices); }`):o.push(`	else if (textureIndex == ${i}) { return _X${i}(indices); }`);return o.push("	}"),o.join(`
+`)},Fh=t=>{let e=["int getSizeInConcatAxisValueFromIndex(int index) {"];for(let o=0;o<t.length;++o)o===0?e.push(`	if (index == ${o}) { return ${t[o]}; }`):o===t.length-1?e.push(`	else { return ${t[o]}; }`):e.push(`	else if (index == ${o}) { return ${t[o]}; }`);return e.push("	}"),e.join(`
+`)},Ol=t=>ee({axis:t.attributes.getInt("axis")}),$h=t=>{if(!t||t.length<1)throw new Error("too few inputs");let e=t[0].type,o=t[0].dims.length;if(e==="string")throw new Error("string tensor is not supported yet");for(let i of t){if(i.type!==e)throw new Error("input tensors should be one type");if(i.dims.length!==o)throw new Error("input tensors should have the same shape")}}});function Ch(){return ht("abs")}function kh(){return ht("acos")}function Bh(){return ht("asin")}function Nh(){return ht("atan")}function Rh(){return ht("ceil")}function Mh(){return ht("cos")}function Gh(t){let e="elu";return{body:`
+  const float alpha = float(${t});
+
+  float ${e}_(float a) {
+    return a >= 0.0 ? a: (exp(a) - 1.0) * alpha;
+  }
+  vec4 ${e}_(vec4 v) {
+    return vec4(${e}_(v.x), ${e}_(v.y), ${e}_(v.z), ${e}_(v.w));
+  }
+  `,name:e,type:0}}function Uh(){return ht("exp")}function zh(){return ht("floor")}function wi(t,e){let o="clip";return{body:`
+  const float min = float(${t});
+  const float max = float(${e});
+
+  float ${o}_(float a) {
+    return clamp(a, min, max);
+  }
+  vec4 ${o}_(vec4 v) {
+    return clamp(v, min, max);
+  }
+  `,name:o,type:0}}function Vh(){let t="indentity";return{body:`
+  float ${t}_(float a) {
+    return a;
+  }
+  vec4 ${t}_(vec4 v) {
+    return v;
+  }
+  `,name:t,type:0}}function Wh(t){let e="leakyRelu";return{body:`
+  const float alpha = float(${t});
+
+  float ${e}_(float a) {
+    return a < 0.0 ? a * alpha : a;
+  }
+  vec4 ${e}_(vec4 v) {
+    return vec4(${e}_(v.x), ${e}_(v.y), ${e}_(v.z), ${e}_(v.w));
+  }
+  `,name:e,type:0}}function Hh(){return ht("log")}function qh(){let t="neg";return{body:`
+  float ${t}_(float a) {
+    return -a;
+  }
+  vec4 ${t}_(vec4 v) {
+    return -v;
+  }
+  `,name:t,type:0}}function jh(){let t="not";return{body:`
+  float ${t}_(float a) {
+    return float( ! bool(a) );
+  }
+  bool ${t}_(bool a) {
+    return !a;
+  }
+  vec4 ${t}_(vec4 v) {
+    return vec4(!bool(v.x), !bool(v.y), !bool(v.z), !bool(v.w));
+  }
+  bvec4 ${t}_(bvec4 v) {
+    return bvec4(!v.x, !v.y, !v.z, !v.w);
+  }
+  `,name:t,type:0}}function Yh(){return ht("sin")}function _i(){let t="relu";return{body:`
+  float ${t}_(float a) {
+    return max( a, 0.0 );
+  }
+  vec4 ${t}_(vec4 v) {
+    return max( v, 0.0 );
+  }
+  `,name:t,type:0}}function Oi(){let t="sigmoid";return{body:`
+  float ${t}_(float a) {
+    return 1.0 / (1.0 + exp(-a));
+  }
+  vec4 ${t}_(vec4 v) {
+    return 1.0 / (1.0 + exp(-v));
+  }
+  `,name:t,type:0}}function Xh(){return ht("sqrt")}function Kh(){return ht("tan")}function Jh(){let t="tanh";return{body:`
+  float ${t}_(float a) {
+    a = clamp(a, -10., 10.);
+    a = exp(2.*a);
+    return (a - 1.) / (a + 1.);
+  }
+  vec4 ${t}_(vec4 v) {
+    v = clamp(v, -10., 10.);
+    v = exp(2.*v);
+    return (v - 1.) / (v + 1.);
+  }
+  `,name:t,type:0}}function ht(t){return{body:`
+  float ${t}_(float a) {
+    return ${t}(a);
+  }
+  vec4 ${t}_(vec4 v) {
+    return ${t}(v);
+  }
+  `,name:t,type:0}}var Zh,Pe,Sl,Al,Pl,El,Ii,Dl,Ll,Qh,Fl,$l,Cl,kl,Bl,Nl,Si,Rl,Ml,Gl,Ul,zl,Vl,Wl,Hl,ql,jl,Yl,Ai=L(()=>{Ge(),fe(),Vt(),xe(),ae(),Zh=(t,e,o,i)=>{let u=t.session.pack?2:0,n=q(t.session.backend.glContext.version);return{...e,output:{dims:o.dims,type:o.type,textureType:u},shaderSource:`
+     ${i.body}
+     void main() {
+       vec4 v = ${n.texture2D}(A, TexCoords);
+       v = ${i.name}_(v);
+       ${n.output} = v;
+     }
+     `,hasMain:!0}},Pe=(t,e,o,i)=>{let u=t.session.pack?2:0,n={name:o.name,inputTypes:[u],inputNames:["A"],cacheHint:i};return{...n,get:()=>Zh(t,n,e,o)}},Sl=(t,e)=>[t.run(Pe(t,e[0],Ch()),e)],Al=(t,e)=>[t.run(Pe(t,e[0],kh()),e)],Pl=(t,e)=>[t.run(Pe(t,e[0],Bh()),e)],El=(t,e)=>[t.run(Pe(t,e[0],Nh()),e)],Ii=(t,e,o)=>[t.run(Pe(t,e[0],wi(o.min,o.max),o.cacheKey),e)],Dl=t=>ee({min:t.attributes.getFloat("min",dr),max:t.attributes.getFloat("max",hr)}),Ll=(t,e)=>{let o=Qh(t,e);return Ii(t,[e[0]],o)},Qh=(t,e)=>{if(e.length>=3&&(!t.session.isInitializer(e[1].dataId)||!t.session.isInitializer(e[2].dataId)))throw new Error("dynamic clip attributes are not allowed");let o=e.length>=3?e[1].numberData[0]:dr,i=e.length>=3?e[2].numberData[0]:hr;return ee({min:o,max:i})},Fl=(t,e)=>[t.run(Pe(t,e[0],Rh()),e)],$l=(t,e)=>[t.run(Pe(t,e[0],Mh()),e)],Cl=(t,e,o)=>[t.run(Pe(t,e[0],Gh(o.alpha),o.cacheKey),e)],kl=t=>ee({alpha:t.attributes.getFloat("alpha",1)}),Bl=(t,e)=>[t.run(Pe(t,e[0],Uh()),e)],Nl=(t,e)=>[t.run(Pe(t,e[0],zh()),e)],Si=(t,e)=>[t.run(Pe(t,e[0],Vh()),e)],Rl=(t,e,o)=>[t.run(Pe(t,e[0],Wh(o.alpha),o.cacheKey),e)],Ml=t=>ee({alpha:t.attributes.getFloat("alpha",.01)}),Gl=(t,e)=>[t.run(Pe(t,e[0],Hh()),e)],Ul=(t,e)=>[t.run(Pe(t,e[0],qh()),e)],zl=(t,e)=>[t.run(Pe(t,e[0],jh()),e)],Vl=(t,e)=>[t.run(Pe(t,e[0],_i()),e)],Wl=(t,e)=>[t.run(Pe(t,e[0],Oi()),e)],Hl=(t,e)=>[t.run(Pe(t,e[0],Yh()),e)],ql=(t,e)=>[t.run(Pe(t,e[0],Xh()),e)],jl=(t,e)=>[t.run(Pe(t,e[0],Kh()),e)],Yl=(t,e)=>[t.run(Pe(t,e[0],Jh()),e)]});function Ct(t){let e;switch(t.activation){case"Relu":e=_i();break;case"Sigmoid":e=Oi();break;case"Clip":e=wi(t.clipMin,t.clipMax);break;default:return{activationFunction:"",applyActivation:""}}let o=e.name,i=e.body,u=`value = ${o}_(value);`;return{activationFunction:i,applyActivation:u}}var Rr,gr=L(()=>{fe(),Ai(),Rr=t=>{let e=t.getString("activation","");if(e==="Clip"){let[o,i]=t.getFloats("activation_params",[dr,hr]);return{activation:e,clipMax:i,clipMin:o,activationCacheKey:`${e}:${o},${i}`}}return{activation:e,activationCacheKey:e}}}),tm,rm,Xl,Kl=L(()=>{ut(),xe(),ae(),no(),gr(),tm=(t,e)=>({name:"GroupedConv",inputNames:t?["X","W","Bias"]:["X","W"],inputTypes:t?[0,0,0]:[0,0],cacheHint:e}),rm=(t,e,o,i)=>{let u=e.length>2?"value += getBias(output_channel);":"",n=e[0].dims.slice(),s=e[1].dims.slice(),c=s[0]/i.group;ce.verbose("GroupedConv",`autpPad:${i.autoPad}, dilations:${i.dilations}, group:${i.group}, kernelShape:${i.kernelShape}, pads:${i.pads}, strides:${i.strides}`);let d=Mr(n,s,i.dilations,i.pads,i.strides),r=q(t.session.backend.glContext.version),{activationFunction:a,applyActivation:l}=Ct(i),h=`
+  const ivec2 strides = ivec2(${i.strides[0]}, ${i.strides[1]});
+  const ivec2 pads = ivec2(${i.pads[0]}, ${i.pads[1]});
+  ${a}
+  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 / ${c};
+
+    float value = 0.0;
+    for (int wInChannel = 0; wInChannel < ${s[1]}; wInChannel++) {
+      int input_channel = group_id * ${s[1]} + wInChannel;
+      for (int wHeight = 0; wHeight < ${s[2]}; wHeight++) {
+        int xHeight = xRCCorner.x + wHeight * ${i.dilations[0]};
+
+        if (xHeight < 0 || xHeight >= ${n[2]}) {
+          continue;
+        }
+
+        for (int wWidth = 0; wWidth < ${s[3]}; wWidth++) {
+          int xWidth = xRCCorner.y + wWidth * ${i.dilations[1]};
+          if (xWidth < 0 || xWidth >= ${n[3]}) {
+            continue;
+          }
+
+          float xVal = getX(batch, input_channel, xWidth, xHeight);
+          float wVal = getW(output_channel, wInChannel, wWidth, wHeight);
+          value += xVal*wVal;
+        }
+      }
+    }
+    ${u}
+    ${l}
+    ${r.output} = vec4(value, .0, .0, .0);
+  }
+`;return{...o,output:{dims:d,type:e[0].type,textureType:0},shaderSource:h,hasMain:!0}},Xl=(t,e,o)=>{let i=tm(e.length>2,o.cacheKey);return{...i,get:()=>rm(t,e,i,o)}}}),nm,om,Jl,Zl=L(()=>{xe(),ae(),br(),nm=t=>({name:"Im2Col (packed)",inputNames:["A"],inputTypes:[2],cacheHint:t}),om=(t,e,o,i,u,n)=>{let s=o.dims,c=i.dims,d=2,r=3,a=u.length,l=[c[1]*c[2]*c[3],u[2]*u[3]],h=c[2]*c[3],f=$t(),m=q(t.session.backend.glContext.version),g="";for(let v=0;v<=1;v++)for(let T=0;T<=1;T++)g+=`
+            blockIndex = rc.x + ${T};
+            pos = rc.y + ${v};
+
+            if(blockIndex < ${l[1]} && pos < ${l[0]}) {
+              offsetY = int(blockIndex / (${u[a-1]})) * ${n.strides[0]} -
+                ${n.pads[0]};
+              d0 = offsetY + ${n.dilations[0]} * (imod(pos, ${h}) / ${c[2]});
+
+              if(d0 < ${s[d]} && d0 >= 0) {
+                offsetX = imod(blockIndex, ${u[a-1]}) * ${n.strides[1]} -
+                  ${n.pads[1]};
+                d1 = offsetX + ${n.dilations[1]} * imod(imod(pos, ${h}), ${c[2]});
+
+                if(d1 < ${s[r]} && d1 >= 0) {
+
+                  ch = int(float(pos)/ ${h}.);
+                    innerDims = vec2(d0, d1);
+                    result[${v*2+T}] = getChannel(
+                      getA(0, ch, int(innerDims.x),
+                      int(innerDims.y)), innerDims);
+                }
+              }
+            }
+
+          `;let b=`
+      ${f}
+
+      void main() {
+        ivec2 rc = getOutputCoords();
+          vec4 result = vec4(0.0);
+          int blockIndex, pos, offsetY, d0, offsetX, d1, ch;
+          vec2 innerDims;
+          ${g}
+          ${m.output} = result;
+      }
+            `;return{...e,output:{dims:l,type:o.type,textureType:2},shaderSource:b,hasMain:!0}},Jl=(t,e,o,i,u)=>{let n=nm(u.cacheKey);return{...n,get:()=>om(t,n,e,o,i,u)}}});function am(t,e,o){let i=e[0].dims,u=e[1].dims,n=Xe.calcShape(i,u,!0);if(!n)throw new Error("Can't use matmul on the given tensors");let s=Ke(n.length),c=St(),{activationFunction:d,applyActivation:r}=Ct(o),a=e.length>2,l=a?"value += getBiasForMatmul();":"",h=a?`${Ei(s,c,e[2].dims,n,!1)}`:"",f=n.length,m=i.length,g=u.length,b=i[i.length-1],v=`
+    ${d}
+    ${h}
+    float process(int indices[${f}]) {
+        int a[${m}];
+        int b[${g}];
+        bcastMatmulIndices_A(indices, a);
+        bcastMatmulIndices_B(indices, b);
+
+        float value;
+        for (int k=0; k<${b}; ++k) {
+            a[${m-1}] = k;
+            b[${g-2}] = k;
+            value += _A(a) * _B(b);
+        }
+        ${l}
+        ${r}
+        return value;
+    }`;return{...t,output:{dims:n,type:e[0].type,textureType:0},shaderSource:v}}function Pi(t,e){let o=im(t.length>2,e.activationCacheKey);return{...o,get:()=>am(o,t,e)}}function Ei(t,e,o,i,u){let n="",s=o.length,c=i.length,d=c-s;c<2&&s>0?n="coords":n=o.map((h,f)=>`coords.${e[f+d]}`).join(", ");let r=Xe.getBroadcastDims(o,i).map(h=>`coords.${e[h+d]} = 0;`).join(`
+`),a=U.size(o)===1,l="vec4(outputValue.xx, outputValue.yy)";return a&&(l="vec4(outputValue.x)"),u?`
+vec4 getBiasForMatmul() {
+  ${t} coords = getOutputCoords();
+  ${r}
+  vec4 outputValue = getBias(${n});
+  return ${l};
+}`:`
+float getBiasForMatmul() {
+  ${t} coords = getOutputCoords();
+  ${r}
+  return getBias(coords.x);
+}`}var Ql,ef,im,sm,oo=L(()=>{fe(),ae(),Ft(),gr(),Di(),Ql=(t,e,o)=>(sm(e),t.session.pack?[t.run(io(t,e,o),e)]:[t.run(Pi(e,o),e)]),ef=t=>Rr(t.attributes),im=(t,e)=>({name:"MatMul",inputNames:t?["A","B","Bias"]:["A","B"],inputTypes:t?[0,0,0]:[0,0],cacheHint:e}),sm=t=>{if(!t||t.length!==2)throw new Error("MatMul requires 2 inputs.");if(t[0].dims[t[0].dims.length-1]!==t[1].dims[t[1].dims.length-2])throw new Error("shared dimension does not match.");if(t[0].type!=="float32"&&t[0].type!=="float64"||t[1].type!=="float32"&&t[1].type!=="float64")throw new Error("inputs should be float type");if(t[0].type!==t[1].type)throw new Error("inputs types should match")}});function fm(t,e,o,i){let u=[],n=[],s=o[0].dims,c=o[1].dims,d=s.length,r=c.length,a=i.length,l=a-d,h=a-r;u=s.map((T,E)=>`coords.${e[E+l]}`),u[d-1]="i*2",u.join(", "),n=c.map((T,E)=>`coords.${e[E+h]}`),n[r-2]="i*2",n.join(", ");let f=Xe.getBroadcastDims(s,i),m=Xe.getBroadcastDims(c,i),g=f.map(T=>`coords.${e[T+l]} = 0;`).join(`
+`),b=m.map(T=>`coords.${e[T+h]} = 0;`).join(`
+`),v=`int lastDim = coords.${e[a-1]};
+  coords.${e[a-1]} = coords.${e[a-2]};
+  coords.${e[a-2]} = lastDim;`;return`
+vec4 getAAtOutCoordsMatmul(int i) {
+  ${t} coords = getOutputCoords();
+  ${v}
+  ${g}
+  vec4 outputValue = getA(${u});
+  return outputValue;
+}
+
+vec4 getBAtOutCoordsMatmul(int i) {
+  ${t} coords = getOutputCoords();
+  ${v}
+  ${b}
+  vec4 outputValue = getB(${n});
+  return outputValue;
+}`}function cm(t,e){let o="";for(let i=0;i<e-2;i++)o+=`rc.${t[i]}, `;return o+=`rc.${t[e-2]}, i*2`,o}function pm(t,e){let o="";for(let i=0;i<e-2;i++)o+=`rc.${t[i]}, `;return o+=`i*2, rc.${t[e-1]}`,o}var um,lm,io,Di=L(()=>{fe(),xe(),ae(),Ft(),gr(),oo(),um=(t,e)=>({name:"MatMul (packed)",inputNames:t?["A","B","Bias"]:["A","B"],inputTypes:t?[2,2,2]:[2,2],cacheHint:e}),lm=(t,e,o,i)=>{let u=o.length>2,n=u?"value += getBiasForMatmul();":"",s=o[0].dims,c=o[1].dims,d=Xe.calcShape(s,c,!0),r=!U.areEqual(o[0].dims,o[1].dims);if(!d)throw new Error("Can't use matmul on the given tensors");let a=s[s.length-1],l=Math.ceil(a/2),h=s.length,f=c.length,m=q(t.session.backend.glContext.version),g=Ke(d.length),b=d.length,v=St(),{activationFunction:T,applyActivation:E}=Ct(i),M=u?`${Ei(g,v,o[2].dims,d,!0)}`:"",F=r?`${fm(g,v,o,d)}`:"",Y=r?"getAAtOutCoordsMatmul(i)":`getA(${cm(v,h)})`,H=r?"getBAtOutCoordsMatmul(i)":`getB(${pm(v,f)})`,X=r?"":`${g} rc =
+          getOutputCoords(); int lastDim = rc.${v[b-1]}; rc.${v[b-1]} =
+          rc.${v[b-2]}; rc.${v[b-2]} = lastDim;
+      `,k=`
+            ${F}
+            ${M}
+            ${T}
+            void main() {
+              ${X}
+
+              vec4 value = vec4(0);
+              for (int i = 0; i < ${l}; i++) {
+                vec4 a = ${Y};
+                vec4 b = ${H};
+
+                value += (a.rrbb * b.rgrg);
+                value += (a.ggaa * b.baba);
+              }
+              ${n}
+              ${E}
+              ${m.output} = value;
+            }`;return{...e,output:{dims:d,type:o[0].type,textureType:2},shaderSource:k,hasMain:!0}},io=(t,e,o)=>{let i=um(e.length>2,o.activationCacheKey);return{...i,get:()=>lm(t,i,e,o)}}}),tf,rf=L(()=>{no(),Zl(),Di(),tf=(t,e,o)=>{let i=e[0].dims,u=e[1].dims,n=Mr(i,u,o.dilations,o.pads,o.strides),s=t.run(Jl(t,e[0],e[1],n,o),[e[0]]),c=t.reshapePacked(e[1],[u[0],u[1]*u[2]*u[3]]),d=e.length===3?[c,s,e[2]]:[c,s],r=t.run(io(t,d,o),d);return t.reshapePacked(r,n)}}),dm,hm,nf,Li,Fi=L(()=>{ae(),dm=t=>({name:"Im2Col",inputNames:["X"],inputTypes:[0],cacheHint:t}),hm=(t,e,o,i,u,n)=>{let s=o.dims,c=i.dims,d=u.length,r=Li(s,c,u,4),a=`
+        const int XC = ${s[1]};
+        const int XH = ${s[2]};
+        const int XW = ${s[3]};
+        const int KH = ${n.kernelShape[0]};
+        const int KW = ${n.kernelShape[1]};
+        const int dilationH = ${n.dilations[0]};
+        const int dilationW = ${n.dilations[1]};
+        const int strideH = ${n.strides[0]};
+        const int strideW = ${n.strides[1]};
+        const int padH = ${n.pads[0]};
+        const int padW = ${n.pads[1]};
+        const int KHKW = KH*KW;
+        const int XCKHKW = XC * KHKW;
+        const int outputChannels = 4;
+        vec4 process(int indices[${d}]) {
+          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[${s.length}];
+              x[0] = b;
+              x[1] = patchC;
+              x[2] = xh2;
+              x[3] = xw2;
+              if(xh2 >= 0 &&
+                  xh2 < XH &&
+                  xw2 >= 0 &&
+                  xw2 < XW) {
+                value[i] = _X(x);
+              }
+            }
+            ++p;
+          }
+          return value;
+        }
+        `;return{...e,output:{dims:r,type:o.type,textureType:4},shaderSource:a}},nf=(t,e,o,i,u)=>{let n=dm(u.cacheKey);return{...n,get:()=>hm(t,n,e,o,i,u)}},Li=(t,e,o,i=4)=>[o[0],o[2],o[3],Math.ceil(t[1]*e[2]*e[3]/i)]}),mm,bm,of,af=L(()=>{fe(),xe(),ae(),gr(),Fi(),mm=(t,e)=>({name:"ConvDotProduct",inputNames:t?["Im2Col","K","B"]:["Im2Col","K"],inputTypes:t?[0,4,0]:[0,4],cacheKey:e.activationCacheKey}),bm=(t,e,o,i,u)=>{let n=o[0].dims,s=o[1].dims,c=[s[0],Math.ceil(n[1]*s[2]*s[3]/4)],d=Li(n,s,i),[r,a]=t.calculateTextureWidthAndHeight(c,4),l=U.computeStrides(d),[h,f]=t.calculateTextureWidthAndHeight(d,4),m=i.length,g=o.length<3?"0.0":"_B(b)",b=Math.ceil(n[1]*s[2]*s[3]/4),{activationFunction:v,applyActivation:T}=Ct(u),E=q(t.session.backend.glContext.version),M=`
+${v}
+float process(int indices[${m}]) {
+  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] * ${l[0]} + im2col[1] * ${l[1]} + im2col[2] * ${l[2]};
+  int kernelOffset = indices[1] * ${c[1]};
+  float value = ${g};
+  for (int i = 0; i < ${b}; ++i) {
+    vec2 im2colCoords = offsetToCoords(im2colOffset, ${h}, ${f});
+    vec2 kernelCoords = offsetToCoords(kernelOffset, ${r}, ${a});
+    value += dot(${E.texture2D}(Im2Col, im2colCoords), ${E.texture2D}(K, kernelCoords));
+    ++im2colOffset;
+    ++kernelOffset;
+  }
+  ${T}
+  return value;
+}`;return{...e,output:{dims:i,type:o[0].type,textureType:0},shaderSource:M}},of=(t,e,o,i)=>{let u=mm(e.length>2,i);return{...u,get:()=>bm(t,u,e,o,i)}}}),Mr,$i,gm,ym,Tm,xm,Ci,vm,no=L(()=>{Ge(),fe(),Kl(),rf(),af(),gr(),Fi(),oo(),Mr=(t,e,o,i,u)=>{let n=t[0],s=t.slice(2),c=s.length,d=e[0],r=e.slice(2).map((l,h)=>l+(l-1)*(o[h]-1)),a=s.map((l,h)=>l+i[h]+i[h+c]).map((l,h)=>Math.floor((l-r[h]+u[h])/u[h]));return[n,d].concat(...a)},$i=(t,e,o)=>(vm(e,o),gm(t,e,o)),gm=(t,e,o)=>{let i=xm(o,e),u=t.session.pack,n=i.kernelShape[0]===1&&i.kernelShape[1]===1;return i.group>1?[t.run(Xl(t,e,i),e)]:n&&u?[ym(t,e,i)]:u&&e[0].dims.length===4&&e[0].dims[0]===1&&!n?[tf(t,e,i)]:[Tm(t,e,i)]},ym=(t,e,o)=>{let i=e[0].dims,u=e[1].dims,n=Mr(i,u,o.dilations,o.pads,o.strides),s=t.reshapeUnpacked(e[0],[i[1],i[2]*i[3]]),c=t.reshapeUnpacked(e[1],[u[0],u[1]]),d=e.length>2?[c,s,e[2]]:[c,s],r=t.run(Pi(d,o),d);return t.reshapeUnpacked(r,n)},Tm=(t,e,o)=>{let i=e[0].dims,u=e[1].dims,n=Mr(i,u,o.dilations,o.pads,o.strides),s=t.run(nf(t,e[0],e[1],n,o),[e[0]]),c=e.length===3?[s,e[1],e[2]]:[s,e[1]];return t.run(of(t,e,n,o),c)},xm=(t,e)=>{let o=t.kernelShape.slice();if(t.kernelShape.length===0)for(let n=2;n<e[1].dims.length;++n)o.push(e[1].dims[n]);let i=t.pads.slice();pr.adjustPadsBasedOnAutoPad(e[0].dims,t.strides,t.dilations,o,i,t.autoPad);let u=Object.assign({},t);return Object.assign(u,{kernelShape:o,pads:i,cacheKey:t.cacheKey}),u},Ci=t=>{let e=t.attributes,o=Rr(e),i=e.getString("auto_pad","NOTSET"),u=e.getInts("dilations",[1,1]),n=e.getInt("group",1),s=e.getInts("kernel_shape",[]),c=e.getInts("pads",[0,0,0,0]),d=e.getInts("strides",[1,1]);return ee({autoPad:i,dilations:u,group:n,kernelShape:s,pads:c,strides:d,...o})},vm=(t,e)=>{if(!t||t.length!==2&&t.length!==3)throw new Error("Conv requires 2 or 3 inputs");if(t[0].dims.length!==4||t[1].dims.length!==4)throw new Error("currently only support 2-dimensional conv");let o=t[0].dims[1],i=t[1].dims[1]*e.group;if(o!==i)throw new Error("FILTER_IN_CHANNEL should be equal to DATA_CHANNEL");if(t.length===3&&(t[2].dims.length!==1||t[1].dims[0]!==t[2].dims[0]))throw new Error("invalid bias");let u=t[0].dims.length-2;if(e.dilations.length!==u)throw new Error(`dilations should be ${u}D`);if(e.strides.length!==u)throw new Error(`strides should be ${u}D`);if(e.pads.length!==u*2)throw new Error(`pads should be ${u*2}D`);if(e.kernelShape.length!==0&&e.kernelShape.length!==t[1].dims.length-2)throw new Error("invalid kernel shape");if(t[0].type!=="float32"||t[1].type!=="float32")throw new Error("Conv input(X,W) should be float tensor");if(t.length===3&&t[2].type!=="float32")throw new Error("Conv input(bias) should be float tensor")}}),wm,_m,Om,sf,Im,Sm,Am,Pm,Em,Dm,uf,Lm,lf=L(()=>{Ge(),xe(),ae(),gr(),wm=(t,e,o,i,u,n)=>(t-1)*e+o+(i-1)*u+1-n,_m=(t,e,o,i,u)=>{let n=Math.floor(t/2);e==="SAME_UPPER"?(o[i]=n,o[u]=t-n):e==="SAME_LOWER"&&(o[i]=t-n,o[u]=n)},Om=(t,e,o,i,u,n,s,c)=>{let d=t.length-2,r=c.length===0;for(let a=0;a<d;++a){let l=r?t[a+2]*n[a]:c[a],h=wm(t[a+2],n[a],u[a],e[a],o[a],l);_m(h,i,u,a,a+d),r&&c.push(n[a]*(t[a+2]-1)+s[a]+(e[a]-1)*o[a]+1-u[a]-u[a+d])}},sf=(t,e,o)=>(Lm(e,o),Im(t,e,o)),Im=(t,e,o)=>{let i=Dm(o,e);return[Em(t,e,i)]},Sm=(t,e)=>({name:"ConvTranspose",inputNames:t?["X","W","B"]:["X","W"],inputTypes:t?[0,0,0]:[0,0],cacheHint:e}),Am=(t,e,o,i)=>{let u=e.length>2?"getB(output_channel)":"0.0",n=e[0].dims,s=e[1].dims,c=s[1],d=s[0]/i.group,r=[e[0].dims[0],e[1].dims[1]*i.group,...i.outputShape],a=q(t.session.backend.glContext.version),{activationFunction:l,applyActivation:h}=Ct(i),f=`
+  const ivec2 strides = ivec2(${i.strides[0]}, ${i.strides[1]});
+  const ivec2 pads = ivec2(${i.pads[0]}, ${i.pads[1]});
+  ${l}
+  void main() {
+    ivec4 coords = getOutputCoords();
+    int batch = coords.x;
+    int output_channel = coords.y;
+
+    ivec2 loc = coords.zw + pads;
+
+    int group_id = output_channel / ${c};
+    int wOutChannel = output_channel - group_id * ${c};
+
+    float value = ${u};
+    for (int inChannelOffset = 0; inChannelOffset < ${d}; inChannelOffset++) {
+      int input_channel = group_id * ${d} + inChannelOffset;
+      for (int wWOff = 0; wWOff < ${s[2]}; wWOff++) {
+        for (int wHOff = 0; wHOff < ${s[3]}; wHOff++) {
+          ivec2 wOff = ivec2(wWOff * ${i.dilations[0]}, wHOff * ${i.dilations[1]});
+          ivec2 wLoc = loc - wOff;
+          ivec2 wLocIn = wLoc / strides;
+          if (
+            wLocIn * strides == wLoc &&
+            wLocIn.x >= 0 && wLocIn.x < ${n[2]} &&
+            wLocIn.y >= 0 && wLocIn.y < ${n[3]}
+          ) {
+            float xVal = getX(batch, input_channel, wLocIn.y, wLocIn.x);
+            float wVal = getW(input_channel, wOutChannel, wHOff, wWOff);
+            value += xVal * wVal;
+          }
+        }
+      }
+    }
+    ${h}
+    ${a.output} = vec4(value, .0, .0, .0);
+  }
+`;return{...o,output:{dims:r,type:e[0].type,textureType:0},shaderSource:f,hasMain:!0}},Pm=(t,e,o)=>{let i=Sm(e.length>2,o.cacheKey);return{...i,get:()=>Am(t,e,i,o)}},Em=(t,e,o)=>t.run(Pm(t,e,o),e),Dm=(t,e)=>{let o=t.kernelShape.slice();if(t.kernelShape.length===0)for(let c=2;c<e[1].dims.length;++c)o.push(e[1].dims[c]);let i=t.pads.slice(),u=t.outputShape.slice(),n=e[0].dims;Om(n,o,t.dilations,t.autoPad,i,t.strides,t.outputPadding,u);let s=Object.assign({},t);return Object.assign(s,{kernelShape:o,pads:i,outputShape:u,cacheKey:t.cacheKey}),s},uf=t=>{let e=t.attributes,o=Rr(e),i=e.getString("auto_pad","NOTSET"),u=e.getInts("dilations",[1,1]),n=e.getInt("group",1),s=e.getInts("kernel_shape",[]),c=e.getInts("output_padding",[0,0]),d=e.getInts("output_shape",[]),r=e.getInts("pads",[0,0,0,0]),a=e.getInts("strides",[1,1]);return ee({autoPad:i,dilations:u,group:n,kernelShape:s,outputPadding:c,outputShape:d,pads:r,strides:a,...o})},Lm=(t,e)=>{if(!t||t.length!==2&&t.length!==3)throw new Error("Conv requires 2 or 3 inputs");if(t[0].dims.length!==4||t[1].dims.length!==4)throw new Error("currently only support 2-dimensional conv");let o=t[0].dims[1],i=t[1].dims[0];if(o!==i)throw new Error("FILTER_IN_CHANNEL should be equal to DATA_CHANNEL");let u=t[1].dims[1]*e.group;if(t.length===3&&(t[2].dims.length!==1||t[2].dims[0]!==u))throw new Error("invalid bias");let n=t[0].dims.length-2;if(e.dilations.length!==n)throw new Error(`dilations should be ${n}D`);if(e.strides.length!==n)throw new Error(`strides should be ${n}D`);if(e.pads.length!==n*2)throw new Error(`pads should be ${n*2}D`);if(e.outputPadding.length!==n)throw new Error(`output_padding should be ${n}D`);if(e.kernelShape.length!==0&&e.kernelShape.length!==t[1].dims.length-2)throw new Error("invalid kernel shape");if(e.outputShape.length!==0&&e.outputShape.length!==t[0].dims.length-2)throw new Error("invalid output shape");if(t[0].type!=="float32"||t[1].type!=="float32")throw new Error("ConvTranspose input(X,W) should be float tensor");if(t.length===3&&t[2].type!=="float32")throw new Error("ConvTranspose input(bias) should be float tensor")}}),ff,yr,cf,Fm,pf,$m,Cm,km,ao=L(()=>{Ge(),fe(),ae(),ff={name:"Transpose",inputNames:["A"],inputTypes:[0]},yr=(t,e,o)=>(km(e),[t.run({...ff,cacheHint:o.cacheKey,get:()=>Fm(t,e[0],o.perm)},e)]),cf=t=>ee({perm:t.attributes.getInts("perm",[])}),Fm=(t,e,o)=>{let i=e.dims;o=pf(i,o);let u=$m(i,o),n=i.length,s=`
+      ${Cm("perm",o,n)}
+      float process(int indices[${n}]) {
+        int a[${n}];
+        perm(a, indices);
+        return _A(a);
+      }`;return{...ff,output:{dims:u,type:e.type,textureType:0},shaderSource:s}},pf=(t,e)=>(e&&e.length!==t.length&&(e=[...t.keys()].reverse()),e),$m=(t,e)=>(e=pf(t,e),U.sortBasedOnPerm(t,e)),Cm=(t,e,o)=>{let i=[];i.push(`void ${t}(out int a[${o}], int src[${o}]) {`);for(let u=0;u<o;++u)i.push(`	a[${e[u]}]=src[${u}];`);return i.push("	}"),i.join(`
+`)},km=t=>{if(!t||t.length!==1)throw new Error("Transpose requires 1 input.");if(t[0].type!=="float32"&&t[0].type!=="float64")throw new Error("input should be float tensor")}}),df,hf,Bm,mf=L(()=>{ao(),df=(t,e,o)=>{Bm(e);let i=o.blocksize,u=i*i,n=o.mode==="DCR"?[0,3,4,1,5,2]:[0,1,4,2,5,3],s=o.mode==="DCR"?[e[0].dims[0],i,i,e[0].dims[1]/u,e[0].dims[2],e[0].dims[3]]:[e[0].dims[0],e[0].dims[1]/u,i,i,e[0].dims[2],e[0].dims[3]],c=t.reshapeUnpacked(e[0],s),d={perm:n,cacheKey:`${n}`},[r]=yr(t,[c],d),a=[e[0].dims[0],e[0].dims[1]/u,e[0].dims[2]*i,e[0].dims[3]*i];return[t.reshapeUnpacked(r,a)]},hf=t=>{let e=t.attributes.getInt("blocksize");if(e<1)throw new Error(`blocksize must be >= 1, but got : ${e} for DepthToSpace`);let o=t.attributes.getString("mode","DCR");if(o!=="DCR"&&o!=="CRD")throw new Error(`unrecognized mode: ${o} for DepthToSpace`);return{mode:o,blocksize:e}},Bm=t=>{if(t.length!==1)throw new Error(`DepthToSpace expect 1 inputs, but got ${t.length}`);if(t[0].type==="string"||t[0].dims.length!==4)throw new TypeError("DepthToSpace input should be a 4-D numeric tensor")}}),bf,gf,Nm,yf=L(()=>{fe(),bf=(t,e,o)=>{Nm(e,o);let i=U.flattenShape(e[0].dims,o);return[t.reshapeUnpacked(e[0],i)]},gf=t=>t.attributes.getInt("axis",1),Nm=(t,e)=>{if(!t||t.length!==1)throw new Error("Flatten requires 1 input.");let o=t[0].dims.length;if(o===0)throw new Error("scalar tensor is not supported.");if(e<-o||e>o)throw new Error("Invalid axis");if(t[0].type==="string")throw new Error("string tensor is not supported.")}}),Jt,pn=L(()=>{Jt=["float32","float64","int32","int16","int8","uint16","uint32","uint8"]}),Tf,xf,Rm,Mm,Gm,Um,vf=L(()=>{Ge(),pn(),fe(),ae(),Tf=(t,e,o)=>(Um(e,o.axis),[t.run(Gm(t,e,o),e)]),xf=t=>ee({axis:t.attributes.getInt("axis",0)}),Rm={name:"Gather",inputNames:["A","B"],inputTypes:[0,0]},Mm=(t,e,o,i)=>{let u=o[0].dims.slice(),n=o[1].dims.slice(),s=new Array(u.length+n.length-1);i=U.normalizeAxis(i,u.length);let c=[];for(let h=0;h<s.length;h++)h<i?(s[h]=u[h],c.push(`inputIdx[${h}] = outputIdx[${h}];`)):h<i+n.length?(s[h]=n[h-i],c.push(`indexDataIdx[${h-i}] = outputIdx[${h}];`)):(s[h]=u[h-n.length+1],c.push(`inputIdx[${h-n.length+1}] = outputIdx[${h}];`));let d=s.length||1,r=u.length,a=n.length||1,l=`
+      float process(int outputIdx[${d}]) {
+        int inputIdx[${r}];
+        int indexDataIdx[${a}];
+        indexDataIdx[0] = 0;
+        ${c.join(`
+        `)}
+        int idx = int(_B(indexDataIdx));
+        inputIdx[${i}] = idx < 0 ? idx + ${u[i]} : idx;
+        return _A(inputIdx);
+      }`;return{...e,output:{dims:s,type:o[0].type,textureType:0},shaderSource:l}},Gm=(t,e,o)=>{let i={...Rm,cacheHint:o.cacheKey};return{...i,get:()=>Mm(t,i,e,o.axis)}},Um=(t,e)=>{if(!t||t.length!==2)throw new Error("Gather requires 2 inputs.");let o=t[0].dims.length;if(o<1)throw new Error("Invalid input shape.");if(e<-o||e>o-1)throw new Error("Invalid axis.");if(Jt.indexOf(t[0].type)===-1)throw new Error("Invaid input type.");if(t[1].type!=="int32"&&t[1].type!=="int16")throw new Error("Invaid input type.")}}),ki,wf,_f,Of,zm,Vm,Wm,If=L(()=>{Ge(),fe(),ae(),ki=(t,e,o)=>(Wm(e,o),[t.run(zm(e,o),e)]),wf=(t,e)=>{let o=t.attributes.getInt("transA",0)!==0,i=t.attributes.getInt("transB",0)!==0,u=t.attributes.getFloat("alpha",1),n=t.attributes.getFloat("beta",1);return ee({transA:o,transB:i,alpha:u,beta:n,isOptionalC:e})},_f=t=>wf(t,!1),Of=t=>wf(t,!0),zm=(t,e)=>{let o={name:"Gemm",inputNames:t.length===3?["A","B","C"]:["A","B"],inputTypes:t.length===3?[0,0,0]:[0,0],key:e.cacheKey};return{...o,get:()=>Vm(o,t,e)}},Vm=(t,e,o)=>{let i=e[0].dims.slice(),u=e[1].dims.slice(),[n,s]=Xn.getShapeOfGemmResult(i,o.transA,u,o.transB,e.length===3?e[2].dims:void 0),c=[n,s];if(!c)throw new Error("Can't use gemm on the given tensors");let d=i[i.length-1],r="";o.transA&&(d=i[0]),o.transA&&o.transB?r="value += _A_T(a) * _B_T(b);":o.transA&&!o.transB?r="value += _A_T(a) * _B(b);":!o.transA&&o.transB?r="value += _A(a) * _B_T(b);":!o.transA&&!o.transB&&(r="value += _A(a) * _B(b);");let a=c.length,l=e.length===3?`int c[${e[2].dims.length}];`:"",h=e.length===3?"bcastIndices_C(indices, c);":"",f=e.length===3?"value += beta * _C(c);":"",m=`
+      float process(int indices[${a}]) {
+          int a[${a}];
+          int b[${a}];
+          ${l}
+
+          copyVec(indices, a);
+          copyVec(indices, b);
+          ${h}
+
+          float value = 0.0;
+          for (int k=0; k<${d}; ++k) {
+              a[${a-1}] = k;
+              b[${a-2}] = k;
+              ${r}
+          }
+
+          value = value * alpha;
+          ${f}
+          return value;
+      }`;return{...t,output:{dims:c,type:e[0].type,textureType:0},variables:[{name:"alpha",type:"float",data:o.alpha},{name:"beta",type:"float",data:o.beta}],shaderSource:m}},Wm=(t,e)=>{if(!t)throw new Error("Input is missing");if(e.isOptionalC&&(t.length<2||t.length>3))throw new Error("Invaid input shape.");if(!e.isOptionalC&&t.length!==3)throw new Error("Gemm requires 3 inputs");if(t.length===3&&t[2].dims.length!==1&&t[2].dims.length!==2)throw new Error("Invalid input shape of C");if(t[0].type!=="float32"&&t[0].type!=="float64"||t[1].type!=="float32"&&t[1].type!=="float64"||t.length===3&&t[2].type!=="float32"&&t[2].type!=="float64")throw new Error("Invalid input type.");if(t[0].type!==t[1].type||t.length===3&&t[0].type!==t[2].type)throw new Error("Input types are mismatched")}}),Sf,Af,Hm,qm,jm,Ym,Xm,Pf=L(()=>{Ge(),ae(),Sf=(t,e,o)=>(Xm(e),[t.run(jm(t,e,o),e)]),Af=t=>{let e=t.attributes.getFloat("scale"),o=t.attributes.getFloats("bias");return ee({scale:e,bias:o})},Hm={name:"ImageScaler",inputNames:["X"],inputTypes:[0]},qm=(t,e,o,i)=>{let u=o[0].dims.slice(),n=u.length,s=`
+      ${Ym(i.bias.length)}
+      float process(int indices[${n}]) {
+        return _X(indices) * scale + getBias(bias, indices[1]);
+      }`;return{...e,output:{dims:u,type:o[0].type,textureType:0},variables:[{name:"bias",type:"float",arrayLength:i.bias.length,data:i.bias},{name:"scale",type:"float",data:i.scale}],shaderSource:s}},jm=(t,e,o)=>{let i={...Hm,cacheHint:o.cacheKey};return{...i,get:()=>qm(t,i,e,o)}},Ym=t=>{let e=[`float getBias(float bias[${t}], int channel) {`];for(let o=0;o<t;++o)o===0?e.push(`	if (channel == ${o}) { return bias[${o}]; }`):o===t-1?e.push(`	else { return bias[${o}]; }`):e.push(`	else if (channel == ${o}) { return bias[${o}]; }`);return e.push("	}"),e.join(`
+`)},Xm=t=>{if(!t||t.length!==1)throw new Error("ImageScaler requires 1 input.");if(t[0].dims.length!==4)throw new Error("Invalid input shape.");if(t[0].type!=="float32"&&t[0].type!=="float64")throw new Error("Invalid input type.")}}),Df,Lf,Ef,Km,Jm,Zm,Qm,eb,tb,Ff=L(()=>{xe(),ae(),Df=(t,e,o)=>{tb(e);let i=t.run(Jm(e[0]),e);return[t.run(eb(t,e[0],o,i.dims),[e[0],i,e[1],e[2]])]},Lf=t=>t.attributes.getFloat("epsilon",1e-5),Ef={name:"InstanceNormalization_MeanAndVariance",inputNames:["X"],inputTypes:[0]},Km=(t,e)=>{let o=e.dims.slice(),i=o[1],u=o[2]*o[3],n=[o[0],i],s=`
+      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<${o[2]}; a2++) {
+          a[2] = a2;
+          for(int a3=0; a3<${o[3]}; a3++) {
+            a[3] = a3;
+            float x = _X(a);
+            temp += x;
+          }
+        }
+        float mean = temp / float(${u});
+        temp = 0.0;
+        for(int a2=0; a2<${o[2]}; a2++) {
+          a[2] = a2;
+          for(int a3=0; a3<${o[3]}; a3++) {
+            a[3] = a3;
+            float x = _X(a);
+            temp += (x - mean) * (x - mean);
+          }
+        }
+        v.r = mean;
+        v.g = temp / float(${u});
+
+        return v;
+      }`;return{...t,output:{dims:n,type:e.type,textureType:4},shaderSource:s}},Jm=t=>({...Ef,get:()=>Km(Ef,t)}),Zm={name:"InstanceNormalization_ComputeOutput",inputNames:["X","MeanAndVariance","Scale","B"],inputTypes:[0,4,0,0]},Qm=(t,e,o,i,u)=>{let n=q(t.session.backend.glContext.version),[s,c]=t.calculateTextureWidthAndHeight(u,4),[d,r]=[s/4,c],a=`
+      vec4 get_MeanAndVariance(int[2] mv) {
+        int offset = indicesToOffset_MeanAndVariance(mv);
+        vec2 coords = offsetToCoords(offset, ${d}, ${r});
+        return ${n.texture2D}(MeanAndVariance, coords);
+      }
+
+      float process(int[4] indices) {
+        int mv[2];
+        mv[0] = indices[0];
+        mv[1] = indices[1];
+        vec4 mean_and_variance = get_MeanAndVariance(mv);
+        float mean = mean_and_variance.r;
+        float variance = mean_and_variance.g;
+
+        int sb[1];
+        sb[0] = indices[1];
+        float scale = _Scale(sb);
+        float b = _B(sb);
+
+        return scale * (_X(indices) - mean) / sqrt(variance + epsilon) + b;
+      }`;return{...e,output:{dims:o.dims,type:o.type,textureType:0},variables:[{name:"epsilon",type:"float",data:i}],shaderSource:a}},eb=(t,e,o,i)=>{let u={...Zm,cacheHint:`${o}`};return{...u,get:()=>Qm(t,u,e,o,i)}},tb=t=>{if(!t||t.length!==3)throw new Error("InstanceNormalization requires 3 inputs.");let e=t[0],o=t[1],i=t[2];if(e.dims.length<3||o.dims.length!==1||i.dims.length!==1)throw new Error("Invalid input shape.");if(o.dims[0]!==e.dims[1]||i.dims[0]!==e.dims[1])throw new Error("Input shapes are mismatched.");if(e.type!=="float32"&&e.type!=="float64"||o.type!=="float32"&&o.type!=="float64"||i.type!=="float32"&&i.type!=="float64")throw new Error("Invalid input type.");if(t[0].dims.length!==4)throw new Error("Only support 4-D input shape.")}});function rb(t,e){let o=t[0].dims[1],i=t[0].dims.length,u=-Math.floor((e.size-1)/2),n=Math.ceil((e.size-1)/2),s=`float(${e.alpha}) / float(${e.size})`,c=`float(${e.bias})`,d=`float(${e.beta})`,r=`
+    float process(int indices[${i}]) {
+        int c = indices[1];
+        float x = _X(indices);
+        float square_sum = 0.0;
+
+        for (int i = ${u}; i <= ${n}; i++) {
+          int idx = c + i;
+          if (c >= 0 && c < ${o}) {
+            indices[1] = idx;
+            float j = _X(indices);
+            square_sum += j * j;
+          }
+        }
+        return x / pow(${c} + ${s} * square_sum, ${d});
+    }`;return{...kf,cacheHint:e.cacheKey,output:{dims:t[0].dims,type:t[0].type,textureType:0},shaderSource:r}}function nb(t,e){return{...kf,cacheHint:e.cacheKey,get:()=>rb(t,e)}}var $f,Cf,kf,ob,Bf=L(()=>{Ge(),ae(),$f=(t,e,o)=>(ob(e),[t.run(nb(e,o),e)]),Cf=t=>{let e=t.attributes.getFloat("alpha",1e-4),o=t.attributes.getFloat("beta",.75),i=t.attributes.getFloat("bias",1),u=t.attributes.getInt("size");return ee({alpha:e,beta:o,bias:i,size:u})},kf={name:"LRN",inputNames:["X"],inputTypes:[0]},ob=t=>{if(!t||t.length!==1)throw new Error("LRN requires 1 input.");if(t[0].dims.length!==4)throw new Error('currently only support LRN for input with "NCHW" format');if(t[0].type!=="float32")throw new Error("input should be float type")}}),ib,Bi,Nf,Rf,Mf,ab,sb,ub,lb,fb,cb,pb,db,Gf=L(()=>{Ge(),fe(),xe(),ae(),ib={name:"Pad",inputNames:["A"],inputTypes:[0]},Bi=(t,e,o)=>(ub(e),[t.run({...ib,cacheHint:o.cacheKey,get:()=>sb(t,e[0],o)},e)]),Nf=t=>{let e=t.attributes.getString("mode","constant"),o=t.attributes.getFloat("value",0),i=t.attributes.getInts("pads");return ee({mode:e,value:o,pads:i})},Rf=(t,e,o)=>{lb(e);let i=ab(t,e,o);return Bi(t,[e[0]],i)},Mf=t=>t.attributes.getString("mode","constant"),ab=(t,e,o)=>{if(!t.session.isInitializer(e[1].dataId)||e.length>=3&&!t.session.isInitializer(e[2].dataId))throw new Error("dynamic pad attributes are not allowed");let i=Array.from(e[1].integerData),u=e.length>=3?e[2].floatData[0]:0;return ee({mode:o,pads:i,value:u})},sb=(t,e,o)=>{let i=U.padShape(e.dims.slice(),o.pads),u=i.length,n=`
+      ${fb(t,e,o)}
+      float process(int[${u}] indices) {
+          return padA(indices);
+      }`;return{name:"Pad",inputNames:["A"],inputTypes:[0],output:{dims:i,type:e.type,textureType:0},shaderSource:n}},ub=t=>{if(!t||t.length!==1)throw new Error("Pad requires 1 input");if(t[0].type!=="float32"&&t[0].type!=="float64")throw new Error("Invalid input type.")},lb=t=>{if(!t||t.length!==2&&t.length!==3)throw new Error("Pad requires 2 or 3 inputs");if(t[1].type!=="int32")throw new Error("Invalid input type.");if(t.length>=3&&t[2].type==="string")throw new Error("Invalid input type.")},fb=(t,e,o)=>{let i=q(t.session.backend.glContext.version),[u,n]=t.calculateTextureWidthAndHeight(e.dims,0),s=U.computeStrides(e.dims);switch(o.mode){case"constant":return cb(i,e.dims,s,u,n,o.pads,o.value);case"reflect":return pb(i,e.dims,s,u,n,o.pads);case"edge":return db(i,e.dims,s,u,n,o.pads);default:throw new Error("Invalid mode")}},cb=(t,e,o,i,u,n,s)=>{let c=e.length,d="";for(let r=c-1;r>=0;--r)d+=`
+        k = m[${r}] - ${n[r]};
+        if (k < 0)  return constant;
+        if (k >= ${e[r]}) return constant;
+        offset += k * ${o[r]};
+        `;return`
+      float padA(int m[${c}]) {
+        const float constant = float(${s});
+        int offset = 0;
+        int k = 0;
+        ${d}
+        vec2 coords = offsetToCoords(offset, ${i}, ${u});
+        float value = getColorAsFloat(${t.texture2D}(A, coords));
+        return value;
+      }
+      `},pb=(t,e,o,i,u,n)=>{let s=e.length,c="";for(let d=s-1;d>=0;--d)c+=`
+        k = m[${d}] - ${n[d]};
+        if (k < 0) { k = -k; }
+        {
+          const int _2n_1 = ${2*(e[d]-1)};
+          k = int( mod( float(k), float(_2n_1) ) ) ;
+          if(k >= ${e[d]}) { k = _2n_1 - k; }
+        }
+        offset += k * ${o[d]};
+        `;return`
+      float padA(int m[${s}]) {
+        int offset = 0;
+        int k = 0;
+        ${c}
+        vec2 coords = offsetToCoords(offset, ${i}, ${u});
+        float value = getColorAsFloat(${t.texture2D}(A, coords));
+        return value;
+      }
+      `},db=(t,e,o,i,u,n)=>{let s=e.length,c="";for(let d=s-1;d>=0;--d)c+=`
+        k = m[${d}] - ${n[d]};
+        if (k < 0)  k = 0;
+        if (k >= ${e[d]}) k = ${e[d]-1};
+        offset += k * ${o[d]};
+      `;return`
+      float padA(int m[${s}]) {
+        int offset = 0;
+        int k = 0;
+        ${c}
+        vec2 coords = offsetToCoords(offset, ${i}, ${u});
+        float value = getColorAsFloat(${t.texture2D}(A, coords));
+        return value;
+      }
+      `}}),zf,Vf,Wf,Hf,qf,jf,Yf,Xf,Kf,hb,Uf,Jf,uo,Zf,so,mb,Qf=L(()=>{Ge(),fe(),ae(),zf=(t,e,o)=>{uo(e);let i={name:"AveragePool",inputNames:["X"],inputTypes:[0],cacheHint:o.cacheKey};return[t.run({...i,get:()=>Wf(e,i,!1,o)},e)]},Vf=t=>{let e=t.attributes.getString("auto_pad","NOTSET"),o=t.attributes.getInt("ceil_mode",0),i=t.attributes.getInt("count_include_pad",0)!==0,u=t.attributes.getInts("kernel_shape"),n=t.attributes.getInts("strides",[]),s=t.attributes.getInts("pads",[]);if(o!==0)throw new Error("using ceil() in shape computation is not yet supported for AveragePool");return ee({autoPad:e,ceilMode:o,countIncludePad:i,kernelShape:u,strides:n,pads:s})},Wf=(t,e,o,i)=>{let[u,n]=Kf(t,i,o),s=U.size(u.kernelShape),c="value += _X(x);",d="";u.countIncludePad?d+=`value /= float(${s});`:d+=`value /= float(${s} - pad);`;let r=`
+        ${Zf(t[0].dims,u,c,d,"0.0")}
+      `;return{...e,output:{dims:n,type:t[0].type,textureType:0},shaderSource:r}},Hf=(t,e,o)=>{uo(e);let i={name:"GlobalAveragePool",inputNames:["X"],inputTypes:[0],cacheHint:`${o.countIncludePad}`};return[t.run({...i,get:()=>Wf(e,i,!0,o)},e)]},qf=t=>{let e=t.attributes.getInt("count_include_pad",0)!==0;return ee({autoPad:"",ceilMode:0,countIncludePad:e,kernelShape:[],strides:[],pads:[]})},jf=(t,e,o)=>{uo(e);let i={name:"MaxPool",inputNames:["X"],inputTypes:[0],cacheHint:o.cacheKey};return[t.run({...i,get:()=>Xf(e,i,!1,o)},e)]},Yf=t=>{let e=t.attributes.getString("auto_pad","NOTSET"),o=t.attributes.getInt("ceil_mode",0),i=t.attributes.getInts("kernel_shape"),u=t.attributes.getInts("strides",[]),n=t.attributes.getInts("pads",[]),s=t.attributes.getInt("storage_order",0),c=t.attributes.getInts("dilations",[]);if(s!==0)throw new Error("column major storage order is not yet supported for MaxPool");if(o!==0)throw new Error("using ceil() in shape computation is not yet supported for MaxPool");return ee({autoPad:e,ceilMode:o,countIncludePad:!1,kernelShape:i,strides:u,pads:n,storageOrder:s,dilations:c})},Xf=(t,e,o,i)=>{let[u,n]=Kf(t,i,o),s=`
+      value = max(_X(x), value);
+    `,c="",d=`
+      ${Zf(t[0].dims,u,s,c,"-1e5")}
+    `;return{...e,output:{dims:n,type:t[0].type,textureType:0},shaderSource:d}},Kf=(t,e,o)=>{let i=t[0].dims.slice(),u=Object.hasOwnProperty.call(e,"dilations"),n=e.kernelShape.slice(),s=e.strides.slice(),c=u?e.dilations.slice():[],d=e.pads.slice();pr.adjustPoolAttributes(o,i,n,s,c,d);let r=pr.computePoolOutputShape(o,i,s,c,n,d,e.autoPad),a=Object.assign({},e);return u?Object.assign(a,{kernelShape:n,strides:s,pads:d,dilations:c,cacheKey:e.cacheKey}):Object.assign(a,{kernelShape:n,strides:s,pads:d,cacheKey:e.cacheKey}),[a,r]},hb={autoPad:"",ceilMode:0,countIncludePad:!1,kernelShape:[],strides:[],pads:[],storageOrder:0,dilations:[],cacheKey:""},Uf={name:"GlobalMaxPool",inputNames:["X"],inputTypes:[0]},Jf=(t,e)=>(uo(e),[t.run({...Uf,get:()=>Xf(e,Uf,!0,hb)},e)]),uo=t=>{if(!t||t.length!==1)throw new Error("Pool ops requires 1 input.");if(t[0].type!=="float32"&&t[0].type!=="float64")throw new Error("Invalid input type.")},Zf=(t,e,o,i,u)=>{let n=t.length;if(e.kernelShape.length<=2){let s=e.kernelShape[e.kernelShape.length-1],c=e.strides[e.strides.length-1],d=e.pads[e.pads.length/2-1],r=e.pads[e.pads.length-1],a=t[n-1],l="",h="",f="";if(d+r!==0?l=`
+          for (int i = 0; i < ${s}; i++) {
+            x[${n} - 1] = indices[${n} - 1] * ${c} - ${d} + i;
+            if (x[${n} - 1] < 0 || x[${n} - 1] >= ${a}) {
+              pad++;
+              continue;
+            }
+            ${o}
+          }`:l=`
+          for (int i = 0; i < ${s}; i++) {
+            x[${n} - 1] = indices[${n} - 1] * ${c} - ${d} + i;
+            ${o}
+          }`,e.kernelShape.length===2){let m=e.kernelShape[e.kernelShape.length-2],g=e.strides[e.strides.length-2],b=e.pads[e.pads.length/2-2],v=e.pads[e.pads.length-2],T=t[n-2];b+v!==0?h=`
+            for (int j = 0; j < ${m}; j++) {
+              x[${n} - 2] = indices[${n} - 2] * ${g} - ${b} + j;
+              if (x[${n} - 2] < 0 || x[${n} - 2] >= ${T}) {
+                pad+= ${s};
+                continue;
+              }
+          `:h=`
+            for (int j = 0; j < ${m}; j++) {
+              x[${n} - 2] = indices[${n} - 2] * ${g} - ${b} + j;
+            `,f=`
+          }
+        `}return`
+        float process(int indices[${n}]) {
+          int x[${n}];
+          copyVec(indices, x);
+
+          float value = ${u};
+          int pad = 0;
+          ${h}
+          ${l}
+          ${f}
+          ${i}
+          return value;
+        }
+      `}else{let s=U.size(e.kernelShape),c=U.computeStrides(e.kernelShape),d=c.length,r=e.pads.length,a=mb(d),l=so(t,"inputDims"),h=so(e.pads,"pads"),f=so(c,"kernelStrides"),m=so(e.strides,"strides"),g=e.pads.reduce((v,T)=>v+T),b="";return g?b=`
+            if (x[j] >= inputDims[j] || x[j] < 0) {
+              pad++;
+              isPad = true;
+              break;
+            }
+          }
+          if (!isPad) {
+            ${o}
+          }`:b=`
+          }
+          ${o}
+        `,`
+        ${a}
+        float process(int indices[${n}]) {
+          int x[${n}];
+          copyVec(indices, x);
+          int offset[${d}];
+          int pads[${r}];
+          int inputDims[${n}];
+          int kernelStrides[${d}];
+          int strides[${d}];
+          ${h}
+          ${l}
+          ${m}
+          ${f}
+
+          float value = ${u};
+          int pad = 0;
+          bool isPad = false;
+          for (int i = 0; i < ${s}; i++) {
+            offsetToIndices(i, kernelStrides, offset);
+            isPad = false;
+            for (int j = ${n} - ${d}; j < ${n}; j++) {
+              x[j] = indices[j] * strides[j - ${n} + ${d}]
+                + offset[j - ${n} + ${d}] - pads[j - 2];
+              ${b}
+          }
+          ${i}
+
+          return value;
+        }
+      `}},so=(t,e)=>{let o="";for(let i=0;i<t.length;i++)o+=`
+      ${e}[${i}] = ${t[i]};
+    `;return o},mb=t=>`
+  void offsetToIndices(int offset, int[${t}] strides, out int[${t}] indices) {
+    if (${t} == 0) {
+      return;
+    }
+    for (int i = 0; i < ${t} - 1; ++i) {
+      indices[i] = offset / strides[i];
+      offset -= indices[i] * strides[i];
+    }
+    indices[${t} - 1] = offset;
+  }`}),Tr,Zt,bb,gb,ec,tc,rc,nc,oc,ic,ac,sc=L(()=>{Ge(),pn(),fe(),ae(),Tr=(t,e,o,i,u)=>{gb(e);let n={name:i,inputNames:["A"],inputTypes:[0]};return[t.run({...n,cacheHint:o.cacheKey,get:()=>bb(t,e,o,i,u,n)},e)]},Zt=t=>{let e=t.attributes.getInts("axes",[]),o=t.attributes.getInt("keepdims",1)===1;return ee({axes:e,keepDims:o})},bb=(t,e,o,i,u,n)=>{let s=[],c=e[0].dims.length||1,d=[],r=U.normalizeAxes(o.axes,e[0].dims.length),a=u(e,r),l=a[1];for(let f=0;f<e[0].dims.length;f++)r.indexOf(f)>=0||r.length===0?(o.keepDims&&s.push(1),l=`
+          for(int j${f} = 0; j${f} < ${e[0].dims[f]}; j${f}++) {
+            inputIdx[${f}] = j${f};
+            ${l}
+          }`):(d.push(`inputIdx[${f}] = outputIdx[${s.length}];`),s.push(e[0].dims[f]));let h=`
+      float process(int outputIdx[${s.length||1}]) {
+        float value;                 // final result
+        int inputIdx[${c}];      // addressing input data
+        ${d.join(`
+`)}
+        ${a[0]}       // init ops for reduce max/min
+        ${l}
+        ${a[2]}       // final computation for reduce mean
+        return value;
+      }`;return{...n,output:{dims:s,type:e[0].type,textureType:0},shaderSource:h}},gb=t=>{if(!t||t.length!==1)throw new Error("Reduce op requires 1 input.");if(Jt.indexOf(t[0].type)===-1)throw new Error("Invalid input type.")},ec=(t,e,o)=>Tr(t,e,o,"ReduceSum",()=>["value = 0.0;","value += _A(inputIdx);",""]),tc=(t,e,o)=>Tr(t,e,o,"ReduceMean",(i,u)=>{let n=1;for(let s=0;s<i[0].dims.length;s++)(u.indexOf(s)>=0||u.length===0)&&(n*=i[0].dims[s]);return["value = 0.0;","value += _A(inputIdx);",`value /= ${n}.;`]}),rc=(t,e,o)=>Tr(t,e,o,"ReduceMax",(i,u)=>{let n=[];for(let s=0;s<i[0].dims.length;s++)(u.indexOf(s)>=0||u.length===0)&&n.push(`inputIdx[${s}] = 0;`);return[`${n.join(`
+`)}
+value = _A(inputIdx);`,"value = max(value, _A(inputIdx));",""]}),nc=(t,e,o)=>Tr(t,e,o,"ReduceMin",(i,u)=>{let n=[];for(let s=0;s<i[0].dims.length;s++)(u.indexOf(s)>=0||u.length===0)&&n.push(`inputIdx[${s}] = 0;`);return[`${n.join(`
+`)}
+value = _A(inputIdx);`,"value = min(value, _A(inputIdx));",""]}),oc=(t,e,o)=>Tr(t,e,o,"ReduceProd",()=>["value = 1.0;","value *= _A(inputIdx);",""]),ic=(t,e,o)=>Tr(t,e,o,"ReduceLogSum",()=>["value = 0.0;","value += _A(inputIdx);","value = log(value);"]),ac=(t,e,o)=>Tr(t,e,o,"ReduceLogSumSquare",()=>["float t; value = 0.0;","t = _A(inputIdx); value += t * t;",""])}),uc,lc=L(()=>{fe(),uc=(t,e)=>{let o=U.calculateReshapedDims(e[0].dims,e[1].integerData);return t.session.pack?[t.reshapePacked(e[0],o)]:[t.reshapeUnpacked(e[0],o)]}}),fc,Ni,cc,pc,dn,yb,Ri,lo,Mi=L(()=>{Ge(),xe(),ae(),fc={name:"Upsample",inputNames:["X"],inputTypes:[0]},Ni=(t,e,o)=>(Ri(e,o),[t.run({...fc,cacheHint:o.cacheKey,get:()=>yb(t,e,o)},e)]),cc=t=>dn(t,7),pc=t=>dn(t,9),dn=(t,e)=>{let o=e>=10,i=t.attributes.getString("mode","nearest");if(i!=="nearest"&&i!=="linear"&&(e<11||i!=="cubic"))throw new Error(`unrecognized mode: ${i}`);let u=[];e<9&&(u=t.attributes.getFloats("scales"),lo(u,i,o));let n=t.attributes.getFloat("extrapolation_value",0),s=e>10?t.attributes.getString("coordinate_transformation_mode","half_pixel"):"asymmetric";if(["asymmetric","pytorch_half_pixel","tf_half_pixel_for_nn","align_corners","tf_crop_and_resize","half_pixel"].indexOf(s)===-1)throw new Error(`coordinate_transform_mode '${s}' is not supported`);let c=s==="tf_crop_and_resize",d=c,r=i==="nearest"&&e>=11?t.attributes.getString("nearest_mode","round_prefer_floor"):"";if(["round_prefer_floor","round_prefer_ceil","floor","ceil",""].indexOf(r)===-1)throw new Error(`nearest_mode '${r}' is not supported`);let a=t.attributes.getFloat("cubic_coeff_a",-.75),l=t.attributes.getInt("exclude_outside",0)!==0;if(l&&i!=="cubic")throw new Error("exclude_outside can be set to 1 only when mode is CUBIC.");let h=e<11?!0:i==="nearest"&&s==="asymmetric"&&r==="floor",f=0,m=0,g=0;return e>10?t.inputs.length>2?(f=1,m=2,g=3):(m=1,g=2):e===9&&(m=1),ee({opset:e,isResize:o,mode:i,scales:u,extrapolationValue:n,coordinateTransformMode:s,useExtrapolation:d,needRoiInput:c,nearestMode:r,cubicCoefficientA:a,excludeOutside:l,useNearest2xOptimization:h,roiInputIdx:f,scalesInputIdx:m,sizesInputIdx:g})},yb=(t,e,o)=>{let i=q(t.session.backend.glContext.version),[u,n]=t.calculateTextureWidthAndHeight(e[0].dims,0),s=e[0].dims.map((g,b)=>Math.floor(g*o.scales[b])),[c,d]=t.calculateTextureWidthAndHeight(s,0),r=s.length,a=new Array(r),l=new Array(r),h=`
+      int output_pitches[${r}];
+      int input_pitches[${r}];
+      `;for(let g=r-1;g>=0;g--)a[g]=g===r-1?1:a[g+1]*s[g+1],l[g]=g===r-1?1:l[g+1]*e[0].dims[g+1],h+=`
+        output_pitches[${g}] = ${a[g]};
+        input_pitches[${g}] = ${l[g]};
+        `;let f=`
+      float getInputFloat(int index) {
+        vec2 coords = offsetToCoords(index, ${u}, ${n});
+        float value = getColorAsFloat(${i.texture2D}(X, coords));
+        return value;
+      }
+      `,m=o.mode==="nearest"?`
+    ${f}
+    float process(int indices[${r}]) {
+      int input_index = 0;
+      int output_index = coordsToOffset(TexCoords, ${c}, ${d});
+
+      ${h}
+
+      int d, m;
+      for (int dim = 0; dim < ${r}; ++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);
+    }`:r===4?`
+    ${f}
+    float process(int indices[4]) {
+      int input_index = 0;
+      int output_index = coordsToOffset(TexCoords, ${c}, ${d});
+
+      ${h}
+
+      int m;
+      int index_of_dim0, index_of_dim1, index_of_dim2, index_of_dim3;
+      index_of_dim0 = output_index / output_pitches[0];
+      m = output_index - index_of_dim0 * output_pitches[0];
+      index_of_dim1 = m / output_pitches[1];
+      m = m - index_of_dim1 * output_pitches[1];
+      index_of_dim2 = m / output_pitches[2];
+      m = m - index_of_dim2 * output_pitches[2];
+      index_of_dim3 = m;
+
+      int index_of_input_dim2, index_of_input_dim3, x_offset, y_offset;
+      index_of_input_dim2 = index_of_dim2 / scales[2];
+      y_offset = index_of_dim2 - index_of_input_dim2 * scales[2];
+      index_of_input_dim3 = index_of_dim3 / scales[3];
+      x_offset = index_of_dim3 - index_of_input_dim3 * scales[3];
+
+      input_index = index_of_dim0 * input_pitches[0] +
+            index_of_dim1 * input_pitches[1] +
+            index_of_input_dim2 * input_pitches[2] +
+            index_of_input_dim3;
+
+      float x00 = getInputFloat(input_index);
+      float x10, x01, x11;
+
+      bool end_of_dim2 = false;
+      if (index_of_input_dim2 == (${e[0].dims[2]} - 1)) {
+        // It's the end in dimension 2
+        x01 = x00;
+        end_of_dim2 = true;
+      } else {
+        x01 = getInputFloat(input_index + input_pitches[2]);
+      }
+
+      if (index_of_input_dim3 == (input_pitches[2] - 1)) {
+        // It's the end in dimension 3
+        x10 = x00;
+        x11 = x01;
+      }
+      else {
+        x10 = getInputFloat(input_index + 1);
+        x11 = end_of_dim2 ? x10 : getInputFloat(input_index + input_pitches[2] + 1);
+      }
+
+      float y0 = x00 + float(y_offset) * (x01 - x00) / float(scales[2]);
+      float y1 = x10 + float(y_offset) * (x11 - x10) / float(scales[2]);
+      return y0 + float(x_offset) * (y1 - y0) / float(scales[3]);
+    }`:`
+    ${f}
+    float process(int indices[2]) {
+      int input_index = 0;
+      int output_index = coordsToOffset(TexCoords, ${c}, ${d});
+
+      ${h}
+
+      int m;
+      int index_of_dim0, index_of_dim1;
+      index_of_dim0 = output_index / output_pitches[0];
+      m = output_index - index_of_dim0 * output_pitches[0];
+      index_of_dim1 = m;
+
+      int index_of_input_dim0, index_of_input_dim1, x_offset, y_offset;
+      index_of_input_dim0 = index_of_dim0 / scales[0];
+      y_offset = index_of_dim0 - index_of_input_dim0 * scales[0];
+      index_of_input_dim1 = index_of_dim1 / scales[1];
+      x_offset = index_of_dim1 - index_of_input_dim1 * scales[1];
+
+      input_index = index_of_input_dim0 * input_pitches[0] + index_of_input_dim1;
+
+      float x00 = getInputFloat(input_index);
+      float x10, x01, x11;
+
+      bool end_of_dim0 = false;
+      if (index_of_input_dim0 == (${e[0].dims[0]} - 1)) {
+        // It's the end in dimension 0
+        x01 = x00;
+        end_of_dim0 = true;
+      } else {
+        x01 = getInputFloat(input_index + input_pitches[0]);
+      }
+
+      if (index_of_input_dim1 == (input_pitches[0] - 1)) {
+        // It's the end in dimension 1
+        x10 = x00;
+        x11 = x01;
+      }
+      else {
+        x10 = getInputFloat(input_index + 1);
+        x11 = end_of_dim0 ? x10 : getInputFloat(input_index + input_pitches[0] + 1);
+      }
+
+      float y0 = x00 + float(y_offset) * (x01 - x00) / float(scales[0]);
+      float y1 = x10 + float(y_offset) * (x11 - x10) / float(scales[0]);
+      return y0 + float(x_offset) * (y1 - y0) / float(scales[1]);
+    }`;return{...fc,output:{dims:s,type:e[0].type,textureType:0},shaderSource:m,variables:[{name:"scales",type:"int",arrayLength:o.scales.length,data:o.scales.map(g=>Math.ceil(g))}]}},Ri=(t,e)=>{if(!t||e.opset<9&&t.length!==1||e.opset>=9&&e.opset<11&&t.length!==2||e.opset>=11&&t.length<2)throw new Error("invalid inputs.");if(e.scales.length>0&&t[0].dims.length!==e.scales.length)throw new Error("Invalid input shape.");if(t[0].type==="string")throw new Error("Invalid input tensor types.")},lo=(t,e,o)=>{if(o){for(let i of t)if(i<=0)throw new Error("Scale value should be greater than 0.")}else for(let i of t)if(i<1)throw new Error("Scale value should be greater than or equal to 1.");if((e==="linear"||e==="cubic")&&t.length!==2&&(t.length!==4||t[0]!==1||t[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.`)}}),Gi,Ui,dc,hc,Tb,xb,vb,wb,mc=L(()=>{xe(),ae(),Ft(),br(),Mi(),Gi={name:"Resize",inputNames:["A"],inputTypes:[2]},Ui=(t,e,o)=>(Ri(e,o),[t.run({...Gi,cacheHint:o.cacheKey,get:()=>Tb(t,e,o)},e)]),dc=t=>dn(t,10),hc=t=>dn(t,11),Tb=(t,e,o)=>{let i=q(t.session.backend.glContext.version),[u,n]=xb(e,o);if(u.every(T=>T===1)&&o.coordinateTransformMode!=="tf_crop_and_resize")return{...Gi,output:{dims:n,type:e[0].type,textureType:2},hasMain:!0,shaderSource:`void main() {
+                    vec4 v = ${i.texture2D}(X, TexCoords);
+                    ${i.output} = v;
+                }`};let s=n.length;if(s<2)throw new Error(`output dimension should be at least 2, but got ${s}`);let c=n[s-2],d=n[s-1],r=e[0].dims;if(s!==r.length)throw new Error(`output dimension should match input ${r.length}, but got ${s}`);let a=r[s-2],l=r[s-1],h=u[s-2],f=u[s-1],m="";if(o.mode!=="linear")throw new Error(`resize (packed) does not support mode: '${o.mode}'`);switch(o.coordinateTransformMode){case"asymmetric":m=`
+                    vec4 getSourceFracIndex(ivec4 coords) {
+                        return vec4(coords) / scaleWHWH;
+                    }
+                `;break;case"half_pixel":m=`
+                    vec4 getSourceFracIndex(ivec4 coords) {
+                        return (vec4(coords) + 0.5) / scaleWHWH - 0.5;
+                    }
+                `;break;case"pytorch_half_pixel":m=`
+                    vec4 getSourceFracIndex(ivec4 coords) {
+                        vec4 fcoords = vec4(coords);
+                        return vec4(
+                            ${d}.0 > 1.0 ? (fcoords.x + 0.5) / scaleWHWH.x - 0.5 : 0.0,
+                            ${c}.0 > 1.0 ? (fcoords.y + 0.5) / scaleWHWH.y - 0.5 : 0.0,
+                            ${d}.0 > 1.0 ? (fcoords.z + 0.5) / scaleWHWH.z - 0.5 : 0.0,
+                            ${c}.0 > 1.0 ? (fcoords.w + 0.5) / scaleWHWH.w - 0.5 : 0.0
+                          );
+                    }
+                `;break;case"align_corners":m=`
+                    vec4 getSourceFracIndex(ivec4 coords) {
+                        vec4 resized = vec4(${d}.0 - 1.0, ${c}.0 - 1.0, ${d}.0 - 1.0,
+                            ${c}.0 - 1.0);
+                        vec4 original = vec4(${l}.0 - 1.0, ${a}.0 - 1.0, ${l}.0 - 1.0,
+                            ${a}.0 - 1.0);
+                        vec4 new_scale = original / resized;
+                        return vec4(coords) * new_scale;
+                    }
+                `;break;default:throw new Error(`resize (packed) does not support coordinateTransformMode:                                 '${o.coordinateTransformMode}'`)}let g=Ke(s),b=$t(),v=`
+            const vec2 inputWH = vec2(${a}.0, ${l}.0);
+            const vec4 scaleWHWH = vec4(float(${h}), float(${f}), float(${h}), float(${f}));
+            ${b}
+            ${m}
+            float getAValue(int x10, int r, int c, int d) {
+                return getChannel(getA(x10, r, c, d), vec2(c, d));
+            }
+            void main() {
+                ${g} 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 < ${c-1};
+                bool hasNextCol = rc.z < ${d-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);
+
+                ${i.output} = vec4(newValue);
+            }
+        `;return{...Gi,output:{dims:n,type:e[0].type,textureType:2},hasMain:!0,shaderSource:v}},xb=(t,e)=>{let o=t[0].dims,i=e.scales,u;if(i.length===0){let s=t[e.scalesInputIdx];if(s&&s.size!==0){if(t[e.sizesInputIdx])throw new Error("Only one of scales or sizes must be provided as input.");i=vb(s,e.mode,e.isResize)}else{let c=t[e.sizesInputIdx];if(!c||c.size===0)throw new Error("Either scales or sizes MUST be provided as input.");u=Array.from(c.integerData),i=wb(u,o,e.mode,e.isResize)}}else if(t[e.sizesInputIdx])throw new Error("Only one of scales or sizes must be provided as input.");let n=u||o.map((s,c)=>Math.floor(s*i[c]));return[i,n]},vb=(t,e,o)=>{let i=Array.from(t.floatData);return lo(i,e,o),i},wb=(t,e,o,i)=>{let u=e.length,n=new Array(u);for(let s=0,c=u;s<c;s++)if(e[s]===0){if(t[s]!==0)throw new Error("Input dim is zero but required output dim is non-zero.");n[s]=1}else n[s]=t[s]/e[s];return lo(n,o,i),n}}),bc,_b,gc=L(()=>{mr(),bc=(t,e)=>(_b(e),[new Fe([e[0].dims.length],"int32",void 0,void 0,new Int32Array(e[0].dims))]),_b=t=>{if(!t||t.length!==1)throw new Error("Shape requires 1 input.")}}),zi,yc,Tc,xc,Ob,vc,Ib,Sb,wc=L(()=>{Ge(),pn(),fe(),ae(),zi={name:"Slice",inputNames:["A"],inputTypes:[0]},yc=(t,e,o)=>(Ob(e),[t.run({...zi,cacheHint:o.cacheKey,get:()=>xc(t,e[0],o)},e)]),Tc=t=>{let e=t.attributes.getInts("starts"),o=t.attributes.getInts("ends"),i=t.attributes.getInts("axes",[]);return ee({starts:e,ends:o,axes:i})},xc=(t,e,o)=>{let i=o.axes.length===0?e.dims.slice(0).map((a,l)=>l):o.axes,u=U.normalizeAxes(i,e.dims.length),n=o.starts.map((a,l)=>a>e.dims[u[l]]-1?e.dims[u[l]]:U.normalizeAxis(a,e.dims[u[l]])),s=o.ends.map((a,l)=>a>e.dims[u[l]]-1?e.dims[u[l]]:U.normalizeAxis(a,e.dims[u[l]])),c=e.dims.slice(),d=[];for(let a=0;a<u.length;a++)c[u[a]]=s[a]-n[a],n[a]>0&&d.push(`outputIdx[${u[a]}] += ${n[a]};`);let r=`
+      float process(int outputIdx[${c.length}]) {
+        ${d.join(`
+      `)}
+        return _A(outputIdx);
+      }`;return{...zi,output:{dims:c,type:e.type,textureType:0},shaderSource:r}},Ob=t=>{if(!t||t.length!==1)throw new Error("Slice requires 1 input.");if(Jt.indexOf(t[0].type)===-1)throw new Error("Invalid input type.")},vc=(t,e)=>{Sb(e);let o=Ib(t,e);return[t.run({...zi,cacheHint:o.cacheKey,get:()=>xc(t,e[0],o)},[e[0]])]},Ib=(t,e)=>{if(!t.session.isInitializer(e[1].dataId)||!t.session.isInitializer(e[2].dataId)||e.length>=4&&!t.session.isInitializer(e[3].dataId)||e.length>=5&&!t.session.isInitializer(e[4].dataId))throw new Error("dynamic slice attributes are not allowed");if(e.length>=5&&e[4].integerData.some(s=>s!==1))throw new Error("currently non-1 steps is not supported for Slice");let o=Array.from(e[1].integerData),i=Array.from(e[2].integerData),u=e.length>=4?Array.from(e[3].integerData):[],n=`${u};${o};${i}`;return{starts:o,ends:i,axes:u,cacheKey:n}},Sb=t=>{if(!t||t.length<3||t.length>5)throw new Error("Invalid input number.");if(t[1].type!=="int32"||t[1].dims.length!==1)throw new Error("Invalid input type.");if(t[2].type!=="int32"||t[2].dims.length!==1)throw new Error("Invalid input type.");if(t.length>=4&&(t[3].type!=="int32"||t[3].dims.length!==1))throw new Error("Invalid input type.");if(t.length>=5&&(t[4].type!=="int32"||t[4].dims.length!==1))throw new Error("Invalid input type.")}}),_c,Oc,Ic,Sc,Ac,Pc,Ec,Dc,Ab,Pb,Eb,Lc,Fc=L(()=>{Ge(),fe(),xe(),ae(),ao(),_c={name:"SoftmaxComputeMax",inputNames:["A"],inputTypes:[0]},Oc={name:"SoftmaxComputeScale",inputNames:["A","Max"],inputTypes:[0,0]},Ic={name:"SoftMax",inputNames:["A","Max","Norm"],inputTypes:[0,0,0]},Sc=(t,e,o)=>{Lc(e);let i=e[0].dims.slice(),u=U.normalizeAxis(o.axis,i.length),n=U.sizeToDimension(i,u),s=U.sizeFromDimension(i,u);return Dc(t,e,o,n,s)},Ac=t=>ee({axis:t.attributes.getInt("axis",1)}),Pc=t=>ee({axis:t.attributes.getInt("axis",-1)}),Ec=(t,e,o)=>{Lc(e);let i=e[0].dims.slice(),u=U.normalizeAxis(o.axis,i.length),n=i.length,s=u!==n-1,c=[],d=[],r=[],a;s&&(d=Array.from({length:n}).map((m,g)=>g),d[u]=n-1,d[n-1]=u,d.map(m=>c.push(i[m])),a=ee({perm:d}),r=yr(t,e,a));let l=s?U.sizeToDimension(c,n-1):U.sizeToDimension(i,n-1),h=s?U.sizeFromDimension(c,n-1):U.sizeFromDimension(i,n-1),f=Dc(t,s?r:e,o,l,h);return s?yr(t,f,a):f},Dc=(t,e,o,i,u)=>{let n=Ab(t,e[0],i,u,[i]),s=t.run({..._c,cacheHint:o.cacheKey,get:()=>n},e),c=Pb(t,e[0],i,u,n.output.dims,[i]),d=t.run({...Oc,cacheHint:o.cacheKey,get:()=>c},[e[0],s]),r=Eb(t,e[0],i,u,n.output.dims,c.output.dims);return[t.run({...Ic,cacheHint:o.cacheKey,get:()=>r},[e[0],s,d])]},Ab=(t,e,o,i,u)=>{let[n,s]=t.calculateTextureWidthAndHeight(e.dims,0),c=u.length;if(o<1||i<1)throw new Error("Logical row count N and feature count D must be greater than or equal to 1");if(u.length!==1)throw new Error("Dimensionality of the output should be 1");if(u[0]!==o)throw new Error("Shape of the output should be equal to logical row count");let d=q(t.session.backend.glContext.version),r=`
+      float process(int[${c}] indices) {
+        int logical_row_start_offset = indices[0] * ${i};
+
+        float max = getColorAsFloat(${d.texture2D}(A, offsetToCoords(logical_row_start_offset, ${n},
+        ${s} )));
+        for(int i=1; i<${i}; ++i)
+        {
+          float current = getColorAsFloat(${d.texture2D}(A, offsetToCoords(logical_row_start_offset + i,
+            ${n}, ${s})));
+          if(current > max)
+          max = current;
+        }
+
+        return max;
+      }`;return{..._c,output:{dims:u,type:e.type,textureType:0},shaderSource:r}},Pb=(t,e,o,i,u,n)=>{let[s,c]=t.calculateTextureWidthAndHeight(e.dims,0),d=n.length;if(o<1||i<1)throw new Error("Logical row count N and feature count D must be greater than or equal to 1");if(n.length!==1)throw new Error("Dimensionality of the output should be 1");if(n[0]!==o)throw new Error("Shape of the output should be equal to logical row count");if(u.length!==1)throw new Error("Dimensionality of the intermediate results should be 1");if(u[0]!==o)throw new Error("Shape of the intermediate results should be equal to logical row count");let r=q(t.session.backend.glContext.version),a=`
+      float process(int[${d}] indices) {
+        int logical_row_start_offset = indices[0] * ${i};
+
+        float norm_factor = 0.0;
+        float max = _Max(indices);
+        for(int i=0; i<${i}; ++i)
+        {
+          norm_factor += exp(getColorAsFloat(${r.texture2D}(A, offsetToCoords(logical_row_start_offset + i,
+            ${s}, ${c}))) - max);
+        }
+
+        return norm_factor;
+      }`;return{...Oc,output:{dims:n,type:e.type,textureType:0},shaderSource:a}},Eb=(t,e,o,i,u,n)=>{let[s,c]=t.calculateTextureWidthAndHeight(e.dims,0),d=e.dims.length;if(o<1||i<1)throw new Error("Logical row count N and feature count D must be greater than or equal to 1");if(u.length!==1||n.length!==1)throw new Error("Dimensionality of the intermediate results should be 1");if(u[0]!==o||n[0]!==o)throw new Error("Shape of the intermediate results should be equal to logical row count");let r=`
+      float process(int[${d}] indices) {
+
+      // get offset of current logical tensor index from the 2-D texture coordinates (TexCoords)
+      int offset = coordsToOffset(TexCoords, ${s}, ${c});
+
+      //determine the logical row for this index
+      int logical_row_index[1];
+      logical_row_index[0] = offset / ${i};
+
+      float norm_factor = _Norm(logical_row_index);
+
+      // avoid possible division by 0
+      // if norm_facor is 0, all elements are zero
+      // if so, return 0
+      if(norm_factor == 0.0)
+        return 0.0;
+
+      return exp(_A(indices) - _Max(logical_row_index)) / norm_factor;
+    }`;return{...Ic,output:{dims:e.dims,type:e.type,textureType:0},shaderSource:r}},Lc=t=>{if(!t||t.length!==1)throw new Error("Softmax requires 1 input.");if(t[0].type!=="float32"&&t[0].type!=="float64")throw new Error("Invalid input type")}}),$c,Cc,kc,Db,Lb,Fb,Bc=L(()=>{Ge(),fe(),ae(),$c={name:"Split",inputNames:["A"],inputTypes:[0]},Cc=(t,e,o)=>{Fb(e);let i=U.normalizeAxis(o.axis,e[0].dims.length),u=Db(t,e,i,o),n=[];for(let s=0;s<u;++s)n.push(t.run({...$c,cacheHint:`${o.cacheKey};${s}`,get:()=>Lb(t,e[0],o,i,s)},e));return n},kc=t=>{let e=t.attributes.getInt("axis",0),o=t.attributes.getInts("split",[]),i=t.outputs.length;return ee({axis:e,split:o,numOutputs:i})},Db=(t,e,o,i)=>{let[,u]=an.splitShape(e[0].dims,o,i.split,i.numOutputs);return u.length},Lb=(t,e,o,i,u)=>{let[n,s]=an.splitShape(e.dims,i,o.split,o.numOutputs),c=s[u],d=n[u],r=`
+      float process(int indices[${d.length}]) {
+        indices[${i}] += ${c};
+        return _A(indices);
+      }
+    `;return{...$c,cacheHint:`${o.cacheKey}:${u}`,output:{dims:d,type:e.type,textureType:0},shaderSource:r}},Fb=t=>{if(!t||t.length!==1)throw new Error("Split requires one input.");if(t[0].type!=="int8"&&t[0].type!=="uint8"&&t[0].type!=="int16"&&t[0].type!=="uint16"&&t[0].type!=="int32"&&t[0].type!=="uint32"&&t[0].type!=="float32"&&t[0].type!=="float64"&&t[0].type!=="bool")throw new Error("Invalid input type.")}}),Vi,Nc,Rc,$b,Cb,Mc=L(()=>{fe(),Vi=(t,e,o)=>{$b(e);let i=U.squeezeShape(e[0].dims,o);return[t.reshapeUnpacked(e[0],i)]},Nc=(t,e)=>(Cb(e),Vi(t,[e[0]],Array.from(e[1].integerData))),Rc=t=>t.attributes.getInts("axes"),$b=t=>{if(!t||t.length!==1)throw new Error("Squeeze requires 1 input.");if(t[0].type==="string")throw new Error("invalid input tensor types.")},Cb=t=>{if(!t||t.length!==2)throw new Error("Squeeze requires 2 inputs.");if(t[1].type!=="int32")throw new Error("Invalid input type.")}}),Gc,kb,Bb,Uc=L(()=>{xe(),ae(),Gc=(t,e)=>{Bb(e);let o={name:"Sum",inputNames:e.map((i,u)=>`X${u}`),inputTypes:new Array(e.length).fill(0)};return[t.run({...o,get:()=>kb(t,e,o)},e)]},kb=(t,e,o)=>{let i=q(t.session.backend.glContext.version),u=e[0].dims.slice(),n=`
+      void main() {
+        vec4 result = ${e.map((s,c)=>`${i.texture2D}(X${c},TexCoords)`).join(" + ")};
+        ${i.output} = result;
+      }
+    `;return{...o,output:{dims:u,type:e[0].type,textureType:0},hasMain:!0,shaderSource:n}},Bb=t=>{if(!t||t.length===0)throw new Error("Sum requires inputs.");let e=t[0].dims.length;for(let o=1;o<t.length;o++){if(e!==t[o].dims.length)throw new Error("Input shapes are mismatched.");for(let i=0;i<e;i++)if(t[0].dims[i]!==t[o].dims[i])throw new Error("Input shapes are not matched.")}if(t[0].type!=="float32"&&t[0].type!=="float64")throw new Error("Invalid input type.");for(let o=1;o<t.length;o++)if(t[0].type!==t[o].type)throw new Error("Input types are not matched.")}}),zc,Nb,Rb,Vc=L(()=>{pn(),ae(),zc=(t,e)=>{Rb(e);let o={name:"Tile",inputNames:["A"],inputTypes:[0]};return[t.run({...o,get:()=>Nb(t,e,o)},e)]},Nb=(t,e,o)=>{let i=e[0].dims.slice(),u=new Array(i.length),n=[];for(let d=0;d<i.length;d++)u[d]=i[d]*e[1].numberData[d],n.push(`inputIdx[${d}] = int(mod(float(outputIdx[${d}]), ${i[d]}.));`);let s=u.length,c=`
+      float process(int outputIdx[${s}]) {
+        int inputIdx[${s}];
+        ${n.join(`
+`)}
+        return _A(inputIdx);
+      }
+    `;return{...o,output:{dims:u,type:e[0].type,textureType:0},shaderSource:c}},Rb=t=>{if(!t||t.length!==2)throw new Error("Tile requires 2 input.");if(t[1].dims.length!==1)throw new Error("The second input shape must 1 dimension.");if(t[1].dims[0]!==t[0].dims.length)throw new Error("Invalid input shape.");if(Jt.indexOf(t[0].type)===-1)throw new Error("Invalid input type.");if(t[1].type!=="int32"&&t[1].type!=="int16")throw new Error("Invalid repeat type.")}}),Wi,Wc,Hc,Mb,Gb,qc=L(()=>{fe(),Wi=(t,e,o)=>{Mb(e);let i=U.unsqueezeShape(e[0].dims,o);return[t.reshapeUnpacked(e[0],i)]},Wc=(t,e)=>(Gb(e),Wi(t,[e[0]],Array.from(e[1].integerData))),Hc=t=>t.attributes.getInts("axes"),Mb=t=>{if(!t||t.length!==1)throw new Error("Unsqueeze requires 1 input.");if(t[0].type==="string")throw new Error("invalid input tensor types.")},Gb=t=>{if(!t||t.length!==2)throw new Error("Unsqueeze requires 2 inputs.");if(t[1].type!=="int32")throw new Error("Invalid input type.")}}),jc,Yc=L(()=>{nl(),bl(),Tl(),Il(),no(),lf(),mf(),yf(),vf(),If(),Pf(),Ff(),Bf(),oo(),Gf(),Qf(),sc(),lc(),mc(),gc(),wc(),Fc(),Bc(),Mc(),Uc(),Vc(),ao(),Ai(),qc(),Mi(),jc=[["Abs","","6+",Sl],["Acos","","7+",Al],["Add","","7+",ol],["And","","7+",il],["Asin","","7+",Pl],["Atan","","7+",El],["AveragePool","","7+",zf,Vf],["BatchNormalization","","7+",tl,rl],["Cast","","6+",gl,yl],["Ceil","","6+",Fl],["Clip","","6-10",Ii,Dl],["Clip","","11+",Ll],["Concat","","4+",wl,Ol],["Conv","","1+",$i,Ci],["ConvTranspose","","1+",sf,uf],["Cos","","7+",$l],["Div","","7+",al],["Dropout","","7+",Si],["DepthToSpace","","1+",df,hf],["Equal","","7+",sl],["Elu","","6+",Cl,kl],["Exp","","6+",Bl],["Flatten","","1+",bf,gf],["Floor","","6+",Nl],["FusedConv","com.microsoft","1+",$i,Ci],["Gather","","1+",Tf,xf],["Gemm","","7-10",ki,_f],["Gemm","","11+",ki,Of],["GlobalAveragePool","","1+",Hf,qf],["GlobalMaxPool","","1+",Jf],["Greater","","7+",ul],["Identity","","1+",Si],["ImageScaler","","1+",Sf,Af],["InstanceNormalization","","6+",Df,Lf],["LeakyRelu","","6+",Rl,Ml],["Less","","7+",ll],["LRN","","1+",$f,Cf],["Log","","6+",Gl],["MatMul","","1+",Ql,ef],["MaxPool","","1+",jf,Yf],["Mul","","7+",fl],["Neg","","6+",Ul],["Not","","1+",zl],["Or","","7+",cl],["Pad","","2-10",Bi,Nf],["Pad","","11+",Rf,Mf],["Pow","","7+",pl],["PRelu","","7+",dl],["ReduceLogSum","","1+",ic,Zt],["ReduceMax","","1+",rc,Zt],["ReduceMean","","1+",tc,Zt],["ReduceMin","","1+",nc,Zt],["ReduceProd","","1+",oc,Zt],["ReduceSum","","1-12",ec,Zt],["ReduceSumSquare","","1+",ac,Zt],["Relu","","6+",Vl],["Reshape","","5+",uc],["Resize","","10",Ui,dc],["Resize","","11+",Ui,hc],["Shape","","1+",bc],["Sigmoid","","6+",Wl],["Sin","","7+",Hl],["Slice","","10+",vc],["Slice","","1-9",yc,Tc],["Softmax","","1-12",Sc,Ac],["Softmax","","13+",Ec,Pc],["Split","","2-12",Cc,kc],["Sqrt","","6+",ql],["Squeeze","","1-12",Vi,Rc],["Squeeze","","13+",Nc],["Sub","","7+",hl],["Sum","","6+",Gc],["Tan","","7+",jl],["Tanh","","6+",Yl],["Tile","","6+",zc],["Transpose","","1+",yr,cf],["Upsample","","7-8",Ni,cc],["Upsample","","9",Ni,pc],["Unsqueeze","","1-12",Wi,Hc],["Unsqueeze","","13+",Wc],["Xor","","7+",ml]]});function Kc(t){let e={},o;for(;(o=Xc.exec(t))!==null;){let i=o[3].split(",").map(u=>{let n=u.trim().split(" ");return n&&n.length===2?{type:n[0],name:n[1]}:null}).filter(u=>u!==null);e[o[2]]={params:i,body:o[4]}}for(let i in e){let u=Ub.replace("__FUNC__",i),n=new RegExp(u,"gm");for(;(o=n.exec(t))!==null;){let s=o[1],c=o[2],d=o[3].split(","),r=s?`${s} ${c};`:"",a=e[i].body,l="";e[i].params.forEach((f,m)=>{f&&(l+=`${f.type} ${f.name} = ${d[m]};
+`)}),a=`${l}
+ ${a}`,a=a.replace("return",`${c} = `);let h=`
+      ${r}
+      {
+        ${a}
+      }
+      `;t=t.replace(o[0],h)}}return t=t.replace(Xc,""),t}var Xc,Ub,Jc=L(()=>{Xc=/@inline[\s\n\r]+(\w+)[\s\n\r]+([0-9a-zA-Z_]+)\s*\(([^)]*)\)\s*{(([^}]|[\n\r])*)}/gm,Ub="(\\w+)?\\s+([_0-9a-zA-Z]+)\\s+=\\s+__FUNC__\\((.*)\\)\\s*;"});function Gr(t,e){let o=[],i=[],u=e!=null&&Array.isArray(e)&&e.length===0,n=e==null||u?null:zb(e,t).sort(),s=0;for(let c=0;c<t.length;++c){if(n!=null){if(n[s]===c&&t[c]!==1)throw new Error(`Can't squeeze axis ${c} since its dim '${t[c]}' is not 1`);(n[s]==null||n[s]>c)&&t[c]===1&&(o.push(t[c]),i.push(c)),n[s]<=c&&s++}t[c]!==1&&(o.push(t[c]),i.push(c))}return{newShape:o,keptDims:i}}function zb(t,e){let o=e.length;return t=t==null?e.map((i,u)=>u):[].concat(t),Cr(t.every(i=>i>=-o&&i<o),()=>`All values in axis param must be in range [-${o}, ${o}) but got axis ${t}`),Cr(t.every(Vb),()=>`All values in axis param must be integers but got axis ${t}`),t.map(i=>i<0?o+i:i)}function Vb(t){return t%1===0}function Wb(t){if(t.length===0)return 1;let e=t[0];for(let o=1;o<t.length;o++)e*=t[o];return e}function Zc(t){let e=Math.ceil(Math.sqrt(t));return[e,Math.ceil(t/e)]}var fo,Hi=L(()=>{ut(),fe(),fo=class{constructor(t){this.maxTextureSize=t}computeTextureWH(t,e){let o=this.computeTexture(t,e);return e&&e.isPacked&&(o[0]/=2,o[1]/=2),e&&e.reverseWH?[o[1],o[0]]:o}computeTexture(t,e){let o=e&&e.isPacked;if(t.length===0)return o?[2,2]:[1,1];let i=this.maxTextureSize;if(e&&e.breakAxis!==void 0){let s=e.breakAxis>=t.length?1:t.slice(e.breakAxis).reduce((d,r)=>d*r),c=e.breakAxis<=0?1:t.slice(0,e.breakAxis).reduce((d,r)=>d*r);if(s>i||c>i)ce.verbose("TextureLayout",`Given width/height preferences were unattainable: shape:${t}, breakAxis:${e.breakAxis}`);else return[s,c]}let u=t.slice(0);o&&(i=i*2,u=u.map((s,c)=>c>=u.length-2?u[c]%2===0?u[c]:u[c]+1:u[c]),u.length===1&&(u=[2,u[0]])),u.length!==2&&(u=Gr(u).newShape);let n=Wb(u);return u.length<=1&&n<=i?[1,n]:u.length===2&&u[0]<=i&&u[1]<=i?u:u.length===3&&u[0]*u[1]<=i&&u[2]<=i?[u[0]*u[1],u[2]]:u.length===3&&u[0]<=i&&u[1]*u[2]<=i?[u[0],u[1]*u[2]]:u.length===4&&u[0]*u[1]*u[2]<=i&&u[3]<=i?[u[0]*u[1]*u[2],u[3]]:u.length===4&&u[0]<=i&&u[1]*u[2]*u[3]<=i?[u[0],u[1]*u[2]*u[3]]:o?Zc(n/4).map(s=>s*2):Zc(n)}}}),co,Qc=L(()=>{fe(),Vt(),xe(),Hi(),Ft(),co=class extends pt{constructor(t){super(t)}getFunctions(){return{...this.offsetToCoords(),...this.coordsToOffset(),...this.toVec(),...this.valueFrom(),...this.getCommonUtilFuncs(),...this.getInputsSamplingSnippets(),...this.getOutputSamplingSnippet()}}getCustomTypes(){return{}}offsetToCoords(){let t="offsetToCoords";return{offsetToCoords:new R(`
+      vec2 ${t}(int offset, int width, int height) {
+        int t = offset / width;
+        int s = offset - t*width;
+        vec2 coords = (vec2(s,t) + vec2(0.5,0.5)) / vec2(width, height);
+        return coords;
+      }
+      `)}}coordsToOffset(){let t="coordsToOffset";return{coordsToOffset:new R(`
+      int ${t}(vec2 coords, int width, int height) {
+        float s = coords.s * float(width);
+        float t = coords.t * float(height);
+        int offset = int(t) * width + int(s);
+        return offset;
+      }
+      `)}}getOutputSamplingSnippet(){let t=this.context.outputTextureLayout;return t.isPacked?this.getPackedOutputSamplingSnippet(t):this.getUnpackedOutputSamplingSnippet(t)}getPackedOutputSamplingSnippet(t){let e=t.unpackedShape,o=[t.width,t.height],i={},u="getOutputCoords";switch(e.length){case 0:i[u]=this.getOutputScalarCoords();break;case 1:i[u]=this.getOutputPacked1DCoords(e,o);break;case 2:i[u]=this.getOutputPacked2DCoords(e,o);break;case 3:i[u]=this.getOutputPacked3DCoords(e,o);break;default:i[u]=this.getOutputPackedNDCoords(e,o)}let n=`
+      void setOutput(vec4 val) {
+        ${q(this.context.glContext.version).output} = val;
+      }
+    `,s="floatTextureSetRGBA";return i[s]=new R(n),i}getUnpackedOutputSamplingSnippet(t){let e=t.unpackedShape,o=[t.width,t.height],i={},u="getOutputCoords";switch(e.length){case 0:i[u]=this.getOutputScalarCoords();break;case 1:i[u]=this.getOutputUnpacked1DCoords(e,o);break;case 2:i[u]=this.getOutputUnpacked2DCoords(e,o);break;case 3:i[u]=this.getOutputUnpacked3DCoords(e,o);break;case 4:i[u]=this.getOutputUnpacked4DCoords(e,o);break;case 5:i[u]=this.getOutputUnpacked5DCoords(e,o);break;case 6:i[u]=this.getOutputUnpacked6DCoords(e,o);break;default:throw new Error(`Unsupported output dimensionality: ${e.length}`)}let n=`
+        void setOutput(float val) {
+          ${q(this.context.glContext.version).output} = vec4(val, 0, 0, 0);
+        }
+    `,s="floatTextureSetR";return i[s]=new R(n),i}getOutputScalarCoords(){return new R(`
+      int getOutputCoords() {
+        return 0;
+      }
+    `)}getOutputPacked1DCoords(t,e){let o=e,i="";return o[0]===1?(i=`
+          int getOutputCoords() {
+            return 2 * int(TexCoords.y * ${o[1]}.0);
+          }
+        `,new R(i)):o[1]===1?(i=`
+          int getOutputCoords() {
+            return 2 * int(TexCoords.x * ${o[0]}.0);
+          }
+        `,new R(i)):(i=`
+        int getOutputCoords() {
+          ivec2 resTexRC = ivec2(TexCoords.xy *
+                                 vec2(${o[0]}, ${o[1]}));
+          return 2 * (resTexRC.y * ${o[0]} + resTexRC.x);
+        }
+      `,new R(i))}getOutputPacked2DCoords(t,e){let o="";if(cr.arraysEqual(t,e))return o=`
+        ivec2 getOutputCoords() {
+          return 2 * ivec2(TexCoords.xy * vec2(${e[0]}, ${e[1]}));
+        }
+      `,new R(o);let i=e,u=Math.ceil(t[1]/2);return o=`
+        ivec2 getOutputCoords() {
+          ivec2 resTexRC = ivec2(TexCoords.xy *
+                                vec2(${i[0]}, ${i[1]}));
+
+          int index = resTexRC.y * ${i[0]} + resTexRC.x;
+
+          // reverse r and c order for packed texture
+          int r = imod(index, ${u}) * 2;
+          int c = 2 * (index / ${u});
+
+          return ivec2(r, c);
+        }
+      `,new R(o)}getOutputPacked3DCoords(t,e){let o=[e[0],e[1]],i=Math.ceil(t[2]/2),u=i*Math.ceil(t[1]/2),n=`
+        ivec3 getOutputCoords() {
+          ivec2 resTexRC = ivec2(TexCoords.xy *
+                                vec2(${o[0]}, ${o[1]}));
+          int index = resTexRC.y * ${o[0]} + resTexRC.x;
+
+          int b = index / ${u};
+          index -= b * ${u};
+
+          // reverse r and c order for packed texture
+          int r = imod(index, ${i}) * 2;
+          int c = 2 * (index / ${i});
+
+          return ivec3(b, r, c);
+        }
+      `;return new R(n)}getOutputPackedNDCoords(t,e){let o=[e[0],e[1]],i=Math.ceil(t[t.length-1]/2),u=i*Math.ceil(t[t.length-2]/2),n=u,s="",c="b, r, c";for(let r=2;r<t.length-1;r++)n*=t[t.length-r-1],s=`
+      int b${r} = index / ${n};
+      index -= b${r} * ${n};
+    `+s,c=`b${r}, `+c;let d=`
+      ivec${t.length} getOutputCoords() {
+        ivec2 resTexRC = ivec2(TexCoords.xy *
+                              vec2(${o[0]}, ${o[1]}));
+        int index = resTexRC.y * ${o[0]} + resTexRC.x;
+
+        ${s}
+
+        int b = index / ${u};
+        index -= b * ${u};
+
+        // reverse r and c order for packed texture
+        int r = imod(index, ${i}) * 2;
+        int c = 2 * (index / ${i});
+
+        return ivec${t.length}(${c});
+      }
+    `;return new R(d)}getOutputUnpacked1DCoords(t,e){let o=`
+        int getOutputCoords() {
+          ivec2 resTexRC = ivec2(TexCoords.xy *
+                                vec2(${e[0]}, ${e[1]}));
+          return resTexRC.y * ${e[0]} + resTexRC.x;
+        }
+      `;return new R(o)}getOutputUnpacked2DCoords(t,e){let o=`
+        ivec2 getOutputCoords() {
+          ivec2 resTexRC = ivec2(TexCoords.xy *
+                                vec2(${e[0]}, ${e[1]}));
+          int index = resTexRC.y * ${e[0]} + resTexRC.x;
+          int r = index / ${t[1]};
+          int c = index - r * ${t[1]};
+          return ivec2(r, c);
+        }
+      `;return new R(o)}getOutputUnpacked3DCoords(t,e){let o="",i=t.length,u=null;i<2&&(u=[]),u=new Array(i-1),u[i-2]=t[i-1];for(let c=i-3;c>=0;--c)u[c]=u[c+1]*t[c+1];let n=["r","c","d"],s=u.map((c,d)=>{let r=`int ${n[d]} = index / ${c}`,a=d===u.length-1?`int ${n[d+1]} = index - ${n[d]} * ${c}`:`index -= ${n[d]} * ${c}`;return`${r}; ${a};`}).join("");return o=`
+        ivec3 getOutputCoords() {
+          ivec2 resTexRC = ivec2(TexCoords.xy *
+                                vec2(${e[0]}, ${e[1]}));
+          int index = resTexRC.y * ${e[0]} + resTexRC.x;
+          ${s}
+          return ivec3(r, c, d);
+        }
+      `,new R(o)}getOutputUnpacked4DCoords(t,e){let o="",i=t.length,u=null;i<2&&(u=[]),u=new Array(i-1),u[i-2]=t[i-1];for(let c=i-3;c>=0;--c)u[c]=u[c+1]*t[c+1];let n=["r","c","d","d2"],s=u.map((c,d)=>{let r=`int ${n[d]} = index / ${c}`,a=d===u.length-1?`int ${n[d+1]} = index - ${n[d]} * ${c}`:`index -= ${n[d]} * ${c}`;return`${r}; ${a};`}).join("");return o=`
+      ivec4 getOutputCoords() {
+          ivec2 resTexRC = ivec2(TexCoords.xy *
+                                vec2(${e[0]}, ${e[1]}));
+          int index = resTexRC.y * ${e[0]} + resTexRC.x;
+          ${s}
+          return ivec4(r, c, d, d2);
+        }
+      `,new R(o)}getOutputUnpacked5DCoords(t,e){let o="",i=t.length,u=null;i<2&&(u=[]),u=new Array(i-1),u[i-2]=t[i-1];for(let c=i-3;c>=0;--c)u[c]=u[c+1]*t[c+1];let n=["r","c","d","d2","d3"],s=u.map((c,d)=>{let r=`int ${n[d]} = index / ${c}`,a=d===u.length-1?`int ${n[d+1]} = index - ${n[d]} * ${c}`:`index -= ${n[d]} * ${c}`;return`${r}; ${a};`}).join("");return o=`
+      ivec5 getOutputCoords() {
+          ivec2 resTexRC = ivec2(TexCoords.xy *
+                                vec2(${e[0]}, ${e[1]}));
+          int index = resTexRC.y * ${e[0]} + resTexRC.x;
+          ${s}
+          return ivec5(r, c, d, d2, d3);
+        }
+      `,new R(o)}getOutputUnpacked6DCoords(t,e){let o="",i=t.length,u=null;i<2&&(u=[]),u=new Array(i-1),u[i-2]=t[i-1];for(let c=i-3;c>=0;--c)u[c]=u[c+1]*t[c+1];let n=["r","c","d","d2","d3","d4"],s=u.map((c,d)=>{let r=`int ${n[d]} = index / ${c}`,a=d===u.length-1?`int ${n[d+1]} = index - ${n[d]} * ${c}`:`index -= ${n[d]} * ${c}`;return`${r}; ${a};`}).join("");return o=`
+     ivec6 getOutputCoords() {
+         ivec2 resTexRC = ivec2(TexCoords.xy *
+                               vec2(${e[0]}, ${e[1]}));
+         int index = resTexRC.y * ${e[0]} + resTexRC.x;
+         ${s}
+         return ivec6(r, c, d, d2, d3, d4);
+       }
+     `,new R(o)}getCommonUtilFuncs(){let t={},e="uvFromFlat";t[e]=new R(`
+    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);
+    }
+    `),e="packedUVfrom1D",t[e]=new R(`
+      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);
+      }
+      `),e="packedUVfrom2D",t[e]=new R(`
+      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);
+      }
+      `),e="packedUVfrom3D",t[e]=new R(`
+      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);
+      }
+      `),e="sampleTexture";let o=q(this.context.glContext.version);return t[e]=new R(`
+        float sampleTexture(sampler2D textureSampler, vec2 uv) {
+            return ${o.texture2D}(textureSampler, uv).r;
+        }`),t}getInputsSamplingSnippets(){let t={},e=this.context.outputTextureLayout;return this.context.programInfo.inputNames.forEach((o,i)=>{let u=this.context.inputTextureLayouts[i],n=Kn(o);u.isPacked?t[n]=this.getPackedSamplerFromInput(n,o,u):t[n]=this.getUnpackedSamplerFromInput(n,o,u);let s=Mu(o);u.unpackedShape.length<=e.unpackedShape.length&&(u.isPacked?t[s]=this.getPackedSamplerAtOutputCoords(s,u,e,o):t[s]=this.getUnpackedSamplerAtOutputCoords(s,u,e,o))}),t}getPackedSamplerAtOutputCoords(t,e,o,i){let u=e.unpackedShape,n=o.unpackedShape,s=Kn(i),c=u.length,d=n.length,r=Xe.getBroadcastDims(u,n),a=Ke(d),l=d-c,h,f=St();c===0?h="":d<2&&r.length>=1?h="coords = 0;":h=r.map(M=>`coords.${f[M+l]} = 0;`).join(`
+`);let m="";d<2&&c>0?m="coords":m=u.map((M,F)=>`coords.${f[F+l]}`).join(", ");let g="return outputValue;",b=U.size(u)===1,v=U.size(n)===1;if(c===1&&!b&&!v)g=`
+        return vec4(outputValue.xy, outputValue.xy);
+      `;else if(b&&!v)d===1?g=`
+          return vec4(outputValue.x, outputValue.x, 0., 0.);
+        `:g=`
+          return vec4(outputValue.x);
+        `;else if(r.length){let M=c-2,F=c-1;r.indexOf(M)>-1&&r.indexOf(F)>-1?g="return vec4(outputValue.x);":r.indexOf(M)>-1?g="return vec4(outputValue.x, outputValue.y, outputValue.x, outputValue.y);":r.indexOf(F)>-1&&(g="return vec4(outputValue.xx, outputValue.zz);")}let T=`
+        int lastDim = coords.${f[d-1]};
+        coords.${f[d-1]} = coords.${f[d-2]};
+        coords.${f[d-2]} = lastDim;
+      `,E=`
+      vec4 ${t}() {
+        ${a} coords = getOutputCoords();
+        ${T}
+        ${h}
+        vec4 outputValue = ${s}(${m});
+        ${g}
+      }
+    `;return new R(E,["coordinates.getOutputCoords"])}getUnpackedSamplerAtOutputCoords(t,e,o,i){let u=[o.width,o.height],n=[e.width,e.height],s=e.unpackedShape.length,c=o.unpackedShape.length,d=e.unpackedShape,r=o.unpackedShape,a=Kn(i);if(s===c&&cr.arraysEqual(n,u)){let T=`
+          float ${t}() {
+            return sampleTexture(${i}, TexCoords);
+          }
+        `;return new R(T,["coordinates.sampleTexture"])}let l=Ke(c),h=Xe.getBroadcastDims(d,r),f=c-s,m,g=St();s===0?m="":c<2&&h.length>=1?m="coords = 0;":m=h.map(T=>`coords.${g[T+f]} = 0;`).join(`
+`);let b="";c<2&&s>0?b="coords":b=e.unpackedShape.map((T,E)=>`coords.${g[E+f]}`).join(", ");let v=`
+        float ${t}() {
+          ${l} coords = getOutputCoords();
+          ${m}
+          return ${a}(${b});
+        }
+      `;return new R(v,["coordinates.getOutputCoords"])}getPackedSamplerFromInput(t,e,o){switch(o.unpackedShape.length){case 0:return this.getPackedSamplerScalar(t,e);case 1:return this.getPackedSampler1D(t,e,o);case 2:return this.getPackedSampler2D(t,e,o);case 3:return this.getPackedSampler3D(t,e,o);default:return this.getPackedSamplerND(t,e,o)}}getUnpackedSamplerFromInput(t,e,o){let i=o.unpackedShape;switch(i.length){case 0:return this.getUnpackedSamplerScalar(t,e,o);case 1:return this.getUnpackedSampler1D(t,e,o);case 2:return this.getUnpackedSampler2D(t,e,o);case 3:return this.getUnpackedSampler3D(t,e,o);case 4:return this.getUnpackedSampler4D(t,e,o);case 5:return this.getUnpackedSampler5D(t,e,o);case 6:return this.getUnpackedSampler6D(t,e,o);default:throw new Error(`Unsupported dimension ${i.length}-D`)}}getPackedSamplerScalar(t,e){let o=q(this.context.glContext.version),i=`
+          vec4 ${t}() {
+            return ${o.texture2D}(${e}, halfCR);
+          }
+        `;return new R(i)}getPackedSampler1D(t,e,o){let i=[o.width,o.height],u=[i[1],i[0]],n=q(this.context.glContext.version),s=`vec4 ${t}(int index) {
+      vec2 uv = packedUVfrom1D(
+      ${u[0]}, ${u[1]}, index);
+      return ${n.texture2D}(${e}, uv);
+    }`;return new R(s,["coordinates.packedUVfrom1D"])}getPackedSampler2D(t,e,o){let i=o.unpackedShape,u=[o.width,o.height],n=q(this.context.glContext.version),s=u[0],c=u[1];if(u!=null&&cr.arraysEqual(i,u)){let l=`vec4 ${t}(int row, int col) {
+        vec2 uv = (vec2(col, row) + halfCR) / vec2(${c}.0, ${s}.0);
+        return ${n.texture2D}(${e}, uv);
+      }`;return new R(l)}let d=u,r=Math.ceil(i[1]/2),a=`vec4 ${t}(int row, int col) {
+      vec2 uv = packedUVfrom2D(${d[1]}, ${d[0]}, ${r}, row, col);
+      return ${n.texture2D}(${e}, uv);
+    }`;return new R(a,["coordinates.packedUVfrom2D"])}getPackedSampler3D(t,e,o){let i=o.unpackedShape,u=[o.width,o.height],n=[u[0],u[1]],s=q(this.context.glContext.version);if(i[0]===1){let h=i.slice(1),f=[1,2],m=kr(i,h),g=["b","row","col"],b=JSON.parse(JSON.stringify(o));b.unpackedShape=m;let v=this.getPackedSamplerFromInput(t,e,b),T=`${v.routineBody}
+      vec4 ${t}(int b, int row, int col) {
+        return ${t}(${Br(g,f)});
+      } `;return new R(T,v.dependencies)}let c=n[0],d=n[1],r=Math.ceil(i[2]/2),a=r*Math.ceil(i[1]/2),l=`vec4 ${t}(int b, int row, int col) {
+      vec2 uv = packedUVfrom3D(
+        ${d}, ${c}, ${a}, ${r}, b, row, col);
+      return ${s.texture2D}(${e}, uv);}`;return new R(l,["coordinates.packedUVfrom3D"])}getPackedSamplerND(t,e,o){let i=o.unpackedShape,u=i.length,n=[o.width,o.height],s=q(this.context.glContext.version),c=[n[0],n[1]],d=c[1],r=c[0],a=Math.ceil(i[u-1]/2),l=a*Math.ceil(i[u-2]/2),h="int b, int row, int col",f=`b * ${l} + (row / 2) * ${a} + (col / 2)`;for(let g=2;g<u-1;g++)h=`int b${g}, `+h,l*=i[u-g-1],f=`b${g} * ${l} + `+f;let m=`vec4 ${t}(${h}) {
+      int index = ${f};
+      int texR = index / ${r};
+      int texC = index - texR * ${r};
+      vec2 uv = (vec2(texC, texR) + halfCR) / vec2(${r}, ${d});
+      return ${s.texture2D}(${e}, uv);
+    }`;return new R(m)}getUnpackedSamplerScalar(t,e,o){let[i,u]=[o.width,o.height];if(i===1&&u===1){let s=`
+          float ${t}() {
+            return sampleTexture(${e}, halfCR);
+          }
+        `;return new R(s,["coordinates.sampleTexture"])}let n=`
+        float ${t}() {
+          int offset_${e} = coordsToOffset(TexCoords, ${i}, ${u});
+          vec2 uv = uvFromFlat(${i}, ${u}, offset_${e});
+          return sampleTexture(${e}, uv);
+        }
+      `;return new R(n,["coordinates.uvFromFlat","coordinates.sampleTexture","coordinates.coordsToOffset"])}getUnpackedSampler1D(t,e,o){let i=o.width,u=o.height;if(u===1&&i===1){let s=`
+        float ${t}(int index) {
+          return sampleTexture(${e}, halfCR);
+        }
+      `;return new R(s,["coordinates.sampleTexture"])}if(u===1){let s=`
+          float ${t}(int index) {
+            vec2 uv = vec2((float(index) + 0.5) / ${i}.0, 0.5);
+            return sampleTexture(${e}, uv);
+          }
+        `;return new R(s,["coordinates.sampleTexture"])}if(i===1){let s=`
+          float ${t}(int index) {
+            vec2 uv = vec2(0.5, (float(index) + 0.5) / ${u}.0);
+            return sampleTexture(${e}, uv);
+          }
+        `;return new R(s,["coordinates.sampleTexture"])}let n=`
+        float ${t}(int index) {
+          vec2 uv = uvFromFlat(${i}, ${u}, index);
+          return sampleTexture(${e}, uv);
+        }
+      `;return new R(n,["coordinates.uvFromFlat","coordinates.sampleTexture"])}getUnpackedSampler2D(t,e,o){let i=o.unpackedShape,u=[o.height,o.width];if(u!=null&&cr.arraysEqual(i,u)){let l=u[1],h=u[0],f=`
+          float ${t}(int row, int col) {
+            vec2 uv = (vec2(row, col) + halfCR) / vec2(${l}.0, ${h}.0);
+            return sampleTexture(${e}, uv);
+          }
+        `;return new R(f,["coordinates.sampleTexture"])}let{newShape:n,keptDims:s}=Gr(i),c=n;if(c.length<i.length){let l=kr(i,c),h=JSON.parse(JSON.stringify(o));h.unpackedShape=l;let f=["col","row"],m=`
+          ${this.getUnpackedSamplerFromInput(t,e,h).routineBody}
+          float ${t}(int row, int col) {
+            return ${t}(${Br(f,s)});
+          }
+        `;return new R(m,["coordinates.sampleTexture"])}let d=u[1],r=u[0];if(r===1){let l=`
+          float ${t}(int row, int col) {
+            int offset_${e} = coordsToOffset(TexCoords, ${d}, ${r});
+            float index = dot(vec3(row, col, offset_${e}), vec3(${i[1]}, 1, 1));
+            vec2 uv = vec2(0.5, (index + 0.5) / ${d}.0);
+            return sampleTexture(${e}, uv);
+          }
+        `;return new R(l,["coordinates.sampleTexture","coordinates.coordsToOffset"])}if(d===1){let l=`
+          float ${t}(int row, int col) {
+            int offset_${e} = coordsToOffset(TexCoords, ${d}, ${r});
+            float index = dot(vec3(row, col, offset_${e}), vec3(${i[1]}, 1, 1));
+            vec2 uv = vec2((index + 0.5) / ${r}.0, 0.5);
+            return sampleTexture(${e}, uv);
+          }
+        `;return new R(l,["coordinates.sampleTexture","coordinates.coordsToOffset"])}let a=`
+        float ${t}(int row, int col) {
+          int index = col * ${i[1]} + row;
+          vec2 uv = uvFromFlat(${d}, ${r}, index);
+          return sampleTexture(${e}, uv);
+        }
+      `;return new R(a,["coordinates.uvFromFlat","coordinates.sampleTexture","coordinates.coordsToOffset"])}getUnpackedSampler3D(t,e,o){let i=o.unpackedShape,u=i[1]*i[2],n=i[2],{newShape:s,keptDims:c}=Gr(i),d=s;if(d.length<i.length){let h=kr(i,d),f=["batch","col","row"],m=JSON.parse(JSON.stringify(o));m.unpackedShape=h;let g=this.getUnpackedSamplerFromInput(t,e,m),b=c.reverse(),v=`
+          ${g.routineBody}
+          float ${t}(int batch, int row, int col) {
+            return ${t}(${Br(f,b)});
+          }
+        `;return new R(v,g.dependencies)}let r=o.width,a=o.height,l=`
+          float ${t}(int depth, int row, int col) {
+            // Explicitly use integer operations as dot() only works on floats.
+            int index = depth * ${u} + col * ${n} + row;
+            vec2 uv = uvFromFlat(${r}, ${a}, index);
+            return sampleTexture(${e}, uv);
+          }
+      `;return new R(l,["coordinates.uvFromFlat","coordinates.sampleTexture","coordinates.coordsToOffset"])}getUnpackedSampler4D(t,e,o){let i=o.unpackedShape,u=i[3],n=i[2]*u,s=i[1]*n,c=o.width,d=o.height,r=`
+        float ${t}(int row, int col, int depth, int depth2) {
+          int index = row * ${s} + col * ${n} +
+              depth2 * ${u} + depth;
+          vec2 uv = uvFromFlat(${c}, ${d}, index);
+          return sampleTexture(${e}, uv);
+        }
+      `;return new R(r,["coordinates.uvFromFlat","coordinates.sampleTexture"])}getUnpackedSampler5D(t,e,o){let i=o.unpackedShape,u=i[4],n=i[3]*u,s=i[2]*n,c=i[1]*s,{newShape:d,keptDims:r}=Gr(i);if(d.length<i.length){let f=kr(i,d),m=["row","col","depth","depth2","depth3"],g=JSON.parse(JSON.stringify(o));g.unpackedShape=f;let b=`
+          ${this.getUnpackedSamplerFromInput(t,e,g).routineBody}
+          float ${t}(int row, int col, int depth, int depth2, int depth3) {
+            return ${t}(${Br(m,r)});
+          }
+        `;return new R(b,["coordinates.sampleTexture","coordinates.uvFromFlat"])}let a=o.width,l=o.height,h=`
+        float ${t}(int row, int col, int depth, int depth2, int depth3) {
+          int index = row * ${c} + col * ${s} + depth * ${n} +
+          depth3 * ${u} + depth2;
+          vec2 uv = uvFromFlat(${a}, ${l}, index);
+          return sampleTexture(${e}, uv);
+        }
+      `;return new R(h,["coordinates.sampleTexture","coordinates.uvFromFlat"])}getUnpackedSampler6D(t,e,o){let i=o.unpackedShape,u=i[5],n=i[4]*u,s=i[3]*n,c=i[2]*s,d=i[1]*c,{newShape:r,keptDims:a}=Gr(i);if(r.length<i.length){let m=kr(i,r),g=["row","col","depth","depth2","depth3","depth4"],b=JSON.parse(JSON.stringify(o));b.unpackedShape=m;let v=`
+            ${this.getUnpackedSamplerFromInput(t,e,b).routineBody}
+            float ${t}(int row, int col, int depth,
+              int depth2, int depth3, int depth4) {
+              return ${t}(${Br(g,a)});
+            }
+          `;return new R(v,["coordinates.sampleTexture","coordinates.uvFromFlat"])}let l=o.width,h=o.height,f=`
+          float ${t}(int row, int col, int depth,
+            int depth2, int depth3, int depth4) {
+            int index = row * ${d} + col * ${c} + depth * ${s} +
+            depth2 * ${n} + depth3 * ${u} + depth4;
+            vec2 uv = uvFromFlat(${l}, ${h}, index);
+            return sampleTexture(${e}, uv);
+          }
+        `;return new R(f,["coordinates.uvFromFlat","coordinates.sampleTexture","coordinates.coordsToOffset"])}toVec(){let t=this.context.outputTextureLayout,e=t.shape.length,o=t.strides,i=t.width,u=t.height,n=[];for(let c=0;c<e-1;++c)n.push(`
+        c[${c}] = offset / ${o[c]};`),n.push(`
+        offset -= c[${c}] * ${o[c]};`);n.push(`
+        c[${e-1}] = offset;`);let s=`
+      void toVec(vec2 texCoords, out int c[${e}]) {
+        int offset = coordsToOffset(texCoords, ${i}, ${u});
+        ${n.join("")}
+      }
+      void toVec(int offset, out int c[${e}]) {
+        ${n.join("")}
+      }
+    `;return{toVec:new R(s,["coordinates.coordsToOffset"])}}valueFrom(){let t={};return this.context.programInfo.inputNames.forEach((e,o)=>{let i=this.context.inputTextureLayouts[o],u=(i.unpackedShape.length>0?i.unpackedShape:i.shape).length,n=`_${e}`;t[n]=new R(this.getValueFromSingle(e,u,i.width,i.height,!1),[`shapeUtils.indicesToOffset${n}`,"coordinates.offsetToCoords","fragcolor.getColorAsFloat"]),n=n+"_T",t[n]=new R(this.getValueFromSingle(e,u,i.width,i.height,!0),[`shapeUtils.indicesToOffset${n}`,"coordinates.offsetToCoords","fragcolor.getColorAsFloat"])}),t}getValueFromSingle(t,e,o,i,u){let n=`_${t}`;u&&(n=n+"_T");let s=q(this.context.glContext.version);return`
+        float ${n}(int m[${e}]) {
+          int offset = indicesToOffset${n}(m);
+          vec2 coords = offsetToCoords(offset, ${o}, ${i});
+          float value = getColorAsFloat(${s.texture2D}(${t}, coords));
+          return value;
+        }
+        `}getPackedValueFrom(t,e,o,i,u){let n=`_${t}_Pack`;u&&(n=n+"_T");let s=q(this.context.glContext.version);return`
+        vec4 ${n}(int m[${e}]) {
+          int offset = indicesToOffset_${t}(m);
+          vec2 coords = offsetToCoords(offset, ${o}, ${i});
+          return ${s.texture2D}(${t}, coords);
+        }
+        `}}}),po,ep=L(()=>{Vt(),po=class vn extends pt{constructor(e){super(e)}getFunctions(){return{...this.encodeFloat32(),...this.decodeFloat32()}}getCustomTypes(){return{}}encodeFloat32(){return{encode:new R(`highp vec4 encode(highp float f) {
+        return vec4(f, 0.0, 0.0, 0.0);
+      }
+        `)}}decodeFloat32(){return{decode:new R(`highp float decode(highp vec4 rgba) {
+        return rgba.r;
+      }
+        `)}}encodeUint8(){let e=vn.isLittleEndian()?"rgba.rgba=rgba.abgr;":"";return{encode:new R(`
+      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)));
+        ${e}
+        rgba = rgba / 255.0; // values need to be normalized to [0,1]
+        return rgba;
+    }
+        `)}}decodeUint8(){let e=vn.isLittleEndian()?"rgba.rgba=rgba.abgr;":"";return{decode:new R(`
+        highp float decode(highp vec4 rgba) {
+          rgba = rgba * 255.0; // values need to be de-normalized from [0,1] to [0,255]
+          ${e}
+          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(){let e=new ArrayBuffer(4),o=new Uint32Array(e),i=new Uint8Array(e);if(o[0]=3735928559,i[0]===239)return!0;if(i[0]===222)return!1;throw new Error("unknown endianness")}}}),ho,tp=L(()=>{Vt(),xe(),ho=class extends pt{constructor(t){super(t)}getFunctions(){return{...this.setFragColor(),...this.getColorAsFloat()}}getCustomTypes(){return{}}setFragColor(){let t=q(this.context.glContext.version);return{setFragColor:new R(`
+        void setFragColor(float value) {
+            ${t.output} = encode(value);
+        }
+        `,["encoding.encode"])}}getColorAsFloat(){return{getColorAsFloat:new R(`
+        float getColorAsFloat(vec4 color) {
+            return decode(color);
+        }
+        `,["encoding.decode"])}}}}),mo,rp=L(()=>{Vt(),mo=class tr extends pt{constructor(e){super(e)}getFunctions(){return{...this.bcastIndex(),...this.bcastMatmulIndex(),...this.offsetToIndices(),...this.indicesToOffset(),...this.incrementIndices()}}getCustomTypes(){return{}}bcastIndex(){let e=this.context.outputTextureLayout.shape.length,o={};return this.context.programInfo.inputNames.forEach((i,u)=>{let n=this.context.inputTextureLayouts[u].unpackedShape;if(n.length<=e){let s=n.length,c=e-s,d=`bcastIndices_${i}`,r="";for(let l=0;l<s;++l)r+=`
+          realIndices[${l}] = int( mod(float(bcastedIndices[${c+l}]), ${n[l]}.0) );
+          `;let a=`
+        void ${d} (int bcastedIndices[${e}], out int realIndices[${s}]) {
+          ${r}
+        }
+        `;o[d]=new R(a)}}),o}bcastMatmulIndex(){let e=this.context.outputTextureLayout.shape.length,o={};return this.context.programInfo.inputNames.forEach((i,u)=>{let n=this.context.inputTextureLayouts[u].shape;if(!(n.length<2||n.length>e)){let s=n.length,c=e-s,d=`bcastMatmulIndices_${i}`,r="";for(let l=0;l<s-2;++l)r+=`
+          realIndices[${l}] = int( mod(float(bcastedIndices[${c+l}]), ${n[l]}.0) );
+          `;let a=`
+        void ${d}(int bcastedIndices[${e}], out int realIndices[${s}]) {
+          ${r}
+          realIndices[${s-1}] = bcastedIndices[${e-1}];
+          realIndices[${s-2}] = bcastedIndices[${e-2}];
+        }
+        `;o[d]=new R(a)}}),o}indicesToOffset(){let e={};return this.context.programInfo.inputNames.forEach((o,i)=>{let u=this.context.inputTextureLayouts[i].shape,n=this.context.inputTextureLayouts[i].strides,s=u.length,c=`indicesToOffset_${o}`;e[c]=new R(tr.indexToOffsetSingle(c,s,n)),c=`indicesToOffset_${o}_T`,e[c]=new R(tr.indexToOffsetSingle(c,s,n.slice().reverse()))}),e}static indexToOffsetSingle(e,o,i){let u="";for(let n=o-1;n>=0;--n)u+=`
+        offset += indices[${n}] * ${i[n]};
+        `;return`
+      int ${e}(int indices[${o}]) {
+        int offset = 0;
+        ${u}
+        return offset;
+      }
+      `}offsetToIndices(){let e={};return this.context.programInfo.inputNames.forEach((o,i)=>{let u=this.context.inputTextureLayouts[i].shape,n=this.context.inputTextureLayouts[i].strides,s=u.length,c=`offsetToIndices_${o}`;e[c]=new R(tr.offsetToIndicesSingle(c,s,n)),c=`offsetToIndices_${o}_T`,e[c]=new R(tr.offsetToIndicesSingle(c,s,n.slice().reverse()))}),e}static offsetToIndicesSingle(e,o,i){let u=[];for(let n=0;n<o-1;++n)u.push(`
+      indices[${n}] = offset / ${i[n]};`),u.push(`
+        offset -= indices[${n}] * ${i[n]};`);return u.push(`
+      indices[${o-1}] = offset;`),`
+      void ${e}(int offset, out int indices[${o}]) {
+        ${u.join("")}
+      }
+      `}incrementIndices(){let e={};return this.context.programInfo.inputNames.forEach((o,i)=>{let u=this.context.inputTextureLayouts[i].shape,n=u.length,s=`incrementIndices_${o}`,c="";for(let r=0;r<n;++r)c+=`
+        shape[${r}] = ${u[r]};`;let d=`
+        void ${s}(int axis, out int indices[${n}]) {
+          int shape[${n}];
+          ${c};
+          for(int i = ${n} -1 ; i >= 0; --i) {
+            if(i > axis) continue;
+            indices[i] += 1;
+            if(indices[i] < shape[i]) {
+              break;
+            }
+            indices[i] = 0;
+          }
+        }
+        `;e[s]=new R(d)}),e}}}),bo,np=L(()=>{Vt(),bo=class extends pt{constructor(t){super(t)}getCustomTypes(){return{}}getFunctions(){return{...this.binaryVecFunctions(),...this.copyVec(),...this.setVecItem(),...this.getVecItem()}}binaryVecFunctions(){let t=this.context.outputTextureLayout.shape.length,e={add:"+=",sub:"-=",mul:"*=",div:"/="},o={};for(let i in e){let u=`${i}Vec`,n="";for(let c=0;c<t;++c)n+=`
+          dest[${c}] ${e[i]} src[${c}];
+          `;let s=`
+        void ${u}(int src[${t}], out int dest[${t}]) {
+          ${n}
+        }
+        `;o[u]=new R(s)}return o}copyVec(){let t=this.context.outputTextureLayout.shape.length,e="";for(let i=0;i<t;++i)e+=`
+        dest[${i}] = src[${i}];
+        `;let o=`
+      void copyVec(int src[${t}], out int dest[${t}]) {
+        ${e}
+      }
+      `;return{copyVec:new R(o)}}setVecItem(){let t=this.context.outputTextureLayout.shape.length,e=`
+        if(index < 0)
+            index =${t} + index;
+        if (index == 0)
+            m[0] = value;
+        `;for(let i=1;i<t-1;++i)e+=`
+        else if (index == ${i})
+            m[${i}] = value;
+            `;e+=`
+        else
+            m[${t-1}] = value;
+        `;let o=`
+      void setVecItem(out int m[${t}], int index, int value) {
+        ${e}
+      }
+        `;return{setVecItem:new R(o)}}getVecItem(){let t=this.context.outputTextureLayout.shape.length,e=`
+        if(index < 0)
+            index = ${t} + index;
+        if (index == 0)
+            return m[0];
+      `;for(let i=1;i<t-1;++i)e+=`
+        else if (index == ${i})
+            return m[${i}];
+      `;e+=`
+        else
+            return m[${t-1}];
+        `;let o=`
+      int getVecItem(int m[${t}], int index) {
+        ${e}
+      }
+    `;return{getVecItem:new R(o)}}}}),qi,op=L(()=>{Qc(),ep(),tp(),rp(),np(),qi={encoding:po,fragcolor:ho,vec:bo,shapeUtils:mo,coordinates:co}}),go,ip=L(()=>{Vt(),Jc(),op(),xe(),go=class{constructor(t,e,o,i){this.libs={},this.glslLibRoutineDependencyGraph={},this.context=new eo(t,e,o,i),Object.keys(qi).forEach(n=>{let s=new qi[n](this.context);this.libs[n]=s});let u=this.glslLibRoutineDependencyGraph;for(let n in this.libs){let s=this.libs[n].getFunctions();for(let c in s){let d=n+"."+c,r;u[d]?(r=u[d],r.routineBody=s[c].routineBody):(r=new cn(d,s[c].routineBody),u[d]=r);let a=s[c].dependencies;if(a)for(let l=0;l<a.length;++l)if(u[a[l]])r.addDependency(u[a[l]]);else{let h=new cn(a[l]);u[a[l]]=h,r.addDependency(h)}}}}preprocess(){let t=this.context.programInfo,e=t.shaderSource;return this.context.programInfo.hasMain||(e=`${e}
+      ${Ru(this.context.glContext.version,this.context.outputTextureLayout.shape.length)}`),e=Kc(e),`${Nu(this.context.glContext.version)}
+    ${this.getUniforms(t.inputNames,t.variables)}
+    ${this.getImports(e)}
+    ${e}`}getImports(t){let e=this.selectGlslLibRoutinesToBeIncluded(t);if(e.length===0)return"";let o="";for(let i=0;i<e.length;++i)if(e[i].routineBody)o+=e[i].routineBody+`
+`;else throw new Error(`Missing body for the Glsl Library routine: ${e[i].name}`);return o}selectGlslLibRoutinesToBeIncluded(t){let e=[];return Object.keys(this.glslLibRoutineDependencyGraph).forEach(o=>{let i=o.split(".")[1];t.indexOf(i)!==-1&&e.push(this.glslLibRoutineDependencyGraph[o])}),to.returnOrderedNodes(e)}getUniforms(t,e){let o=[];if(t)for(let i of t)o.push(`uniform sampler2D ${i};`);if(e)for(let i of e)o.push(`uniform ${i.type} ${i.name}${i.arrayLength?`[${i.arrayLength}]`:""};`);return o.join(`
+`)}}}),yo,ap=L(()=>{vt(),ut(),ip(),xe(),yo=class{constructor(t,e,o){this.profiler=t,this.glContext=e,this.textureLayoutStrategy=o,this.repo=new Map,this.attributesBound=!1}getArtifact(t){return this.repo.get(t)}setArtifact(t,e){this.repo.set(t,e)}run(t,e,o){this.profiler.event("op",`ProgramManager.run ${t.programInfo.name??"unknown kernel"}`,()=>{let i=this.glContext.gl,u=t.program;i.useProgram(u);try{this.bindOutput(o),this.attributesBound||this.bindAttributes(t.attribLocations),this.bindUniforms(t.uniformLocations,t.programInfo.variables??[],e)}catch(n){throw ce.error("ProgramManager",t.programInfo.shaderSource),n}this.profiler.event("backend","GlContext.draw()",()=>{this.glContext.draw()})},this.glContext)}dispose(){this.vertexShader&&this.glContext.deleteShader(this.vertexShader),this.repo.forEach(t=>this.glContext.deleteProgram(t.program))}build(t,e,o){return this.profiler.event("backend","ProgramManager.build",()=>{let i=new go(this.glContext,t,e,o),u=i.preprocess(),n=this.compile(u);return{programInfo:t,program:n,uniformLocations:this.getUniformLocations(n,i.context.programInfo.inputNames,i.context.programInfo.variables),attribLocations:this.getAttribLocations(n)}})}compile(t){if(!this.vertexShader){ce.verbose("ProrgramManager","Compiling and caching Vertex shader for the first time");let i=Bu(this.glContext.version);this.vertexShader=this.glContext.compileShader(i,this.glContext.gl.VERTEX_SHADER)}Z.debug&&ce.verbose("ProrgramManager",`FragShader:
+${t}
+`);let e=this.glContext.compileShader(t,this.glContext.gl.FRAGMENT_SHADER),o=this.glContext.createProgram(this.vertexShader,e);return this.glContext.deleteShader(e),o}bindOutput(t){let e=t.width,o=t.height;ce.verbose("ProrgramManager",`Binding output texture to Framebuffer: w/h=${e}/${o}, shape=${t.shape}, type=${t.tensor.type}`),this.glContext.attachFramebuffer(t.texture,e,o)}bindAttributes(t){let e=t.position,o=t.textureCoord;this.glContext.setVertexAttributes(e,o),this.attributesBound=!0}bindUniforms(t,e,o){let i=this.glContext.gl,u=0;for(let{name:n,type:s,location:c,arrayLength:d}of t){let r=e.find(a=>a.name===n)?.data;if(s!=="sampler2D"&&!r)throw new Error(`variable '${n}' does not have data defined in program info`);switch(s){case"sampler2D":this.bindTexture(o[u],c,u),u++;break;case"float":d?i.uniform1fv(c,r):i.uniform1f(c,r);break;case"int":d?i.uniform1iv(c,r):i.uniform1i(c,r);break;default:throw new Error(`Uniform not implemented: ${s}`)}}}bindTexture(t,e,o){this.glContext.bindTextureToUniform(t.texture,o,e)}getAttribLocations(t){return{position:this.getAttribLocation(t,"position"),textureCoord:this.getAttribLocation(t,"textureCoord")}}getUniformLocations(t,e,o){let i=[];if(e)for(let u of e)i.push({name:u,type:"sampler2D",location:this.getUniformLocation(t,u)});if(o)for(let u of o)i.push({...u,location:this.getUniformLocation(t,u.name)});return i}getUniformLocation(t,e){let o=this.glContext.gl.getUniformLocation(t,e);if(o===null)throw new Error(`Uniform ${e} not found.`);return o}getAttribLocation(t,e){return this.glContext.gl.getAttribLocation(t,e)}}}),To,sp=L(()=>{ut(),ln(),To=class{constructor(t,e,o,i){this.glContext=t,this.layoutStrategy=e,this.profiler=o,this.config=i,this.pendingRead=new Map,i.reuseTextures&&(this.inUseTextures=new Map,this.idleTextures=new Map,this.textureLookup=new Map)}createTextureFromLayout(t,e,o,i){let u=this.toEncoderType(t),n=this.glContext.getEncoder(u,e.channels||1,i);if(e.isPacked&&i===1)throw new Error("not implemented");let s=e.width,c=e.height,d,r;if(this.config.reuseTextures){d=`${s}x${c}_${n.format}_${n.internalFormat}_${n.textureType}`,r=this.inUseTextures.get(d),r||(r=[],this.inUseTextures.set(d,r));let l=this.idleTextures.get(d);if(l&&l.length>0){let h=l.pop();return r.push(h),i===1&&this.glContext.updateTexture(h,s,c,n,this.toTextureData(t,o)),h}}ce.verbose("TextureManager",`Creating new texture of size ${e.width}x${e.height}`);let a=this.glContext.allocateTexture(s,c,n,this.toTextureData(t,o));return this.config.reuseTextures&&(r.push(a),this.textureLookup.set(a,d)),a}readTexture(t,e,o){return o||(o=1),this.profiler.event("backend","TextureManager.readTexture",()=>{let i=t.shape.reduce((n,s)=>n*s)*o,u=this.glContext.readTexture(t.texture,t.width,t.height,i,this.toEncoderType(e),o);return this.toTensorData(e,u)})}async readTextureAsync(t,e,o){let i=t.tensor.dataId;if(o||(o=1),this.pendingRead.has(i)){let u=this.pendingRead.get(i);return new Promise(n=>u?.push(n))}return this.profiler.event("backend","TextureManager.readTextureAsync",async()=>{this.pendingRead.set(i,[]);let u=t.shape.reduce((d,r)=>d*r)*o;await this.glContext.createAndWaitForFence();let n=this.glContext.readTexture(t.texture,t.width,t.height,u,this.toEncoderType(e),o),s=this.toTensorData(e,n),c=this.pendingRead.get(i);return this.pendingRead.delete(i),c?.forEach(d=>d(s)),s})}readUint8TextureAsFloat(t){return this.profiler.event("backend","TextureManager.readUint8TextureAsFloat",()=>{let e=t.shape.reduce((i,u)=>i*u),o=this.glContext.readTexture(t.texture,t.width,t.height,e*4,"byte",4);return new Float32Array(o.buffer,o.byteOffset,e)})}releaseTexture(t,e){let o;if(this.config.reuseTextures&&(o=this.textureLookup.get(t.texture),o)){e&&this.textureLookup.delete(o);let i=this.inUseTextures.get(o);if(i){let u=i.indexOf(t.texture);if(u!==-1){i.splice(u,1);let n=this.idleTextures.get(o);n||(n=[],this.idleTextures.set(o,n)),n.push(t.texture)}}}(!o||e)&&(ce.verbose("TextureManager",`Deleting texture of size ${t.width}x${t.height}`),this.glContext.deleteTexture(t.texture))}toTensorData(t,e){switch(t){case"int16":return e instanceof Int16Array?e:Int16Array.from(e);case"int32":return e instanceof Int32Array?e:Int32Array.from(e);case"int8":return e instanceof Int8Array?e:Int8Array.from(e);case"uint16":return e instanceof Uint16Array?e:Uint16Array.from(e);case"uint32":return e instanceof Uint32Array?e:Uint32Array.from(e);case"uint8":case"bool":return e instanceof Uint8Array?e:Uint8Array.from(e);case"float32":return e instanceof Float32Array?e:Float32Array.from(e);case"float64":return e instanceof Float64Array?e:Float64Array.from(e);default:throw new Error(`TensorData type ${t} is not supported`)}}toTextureData(t,e){if(e)return e instanceof Float32Array?e:new Float32Array(e)}toEncoderType(t){return"float"}clearActiveTextures(){this.glContext.clearActiveTextures()}}}),xo,up=L(()=>{ut(),ws(),Qu(),Yc(),ap(),Hi(),sp(),xo=class{constructor(t,e){this.backend=t,this.context=e,this.layoutStrategy=new fo(t.glContext.maxTextureSize),this.programManager=new yo(this.context.profiler,t.glContext,this.layoutStrategy),this.textureManager=new To(t.glContext,this.layoutStrategy,this.context.profiler,{reuseTextures:t.textureCacheMode==="full"}),this.packedTextureDataCache=new Map,this.unpackedTextureDataCache=new Map,this.pack=t.pack,this.pack2unpackMap=new Map,this.unpack2packMap=new Map}createInferenceHandler(){return new Qn(this)}onGraphInitialized(t){let e=t.getValues().filter(o=>o.from===-1&&o.tensor).map(o=>o.tensor.dataId);this.initializers=new Set(e)}isInitializer(t){return this.initializers?this.initializers.has(t):!1}addInitializer(t){this.initializers.add(t)}getTextureData(t,e){return 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e.floats;case oe.onnx.AttributeProto.AttributeType.INTS:return e.ints;case oe.onnx.AttributeProto.AttributeType.STRINGS:return e.strings;case oe.onnx.AttributeProto.AttributeType.TENSORS:return e.tensors;case oe.onnx.AttributeProto.AttributeType.GRAPHS:return e.graphs;default:throw new Error(`unsupported attribute type: ${oe.onnx.AttributeProto.AttributeType[e.type]}`)}}static getValueNoCheckFromOrtFormat(e){switch(e.type()){case mt.AttributeType.FLOAT:return e.f();case mt.AttributeType.INT:return e.i();case mt.AttributeType.STRING:return e.s();case mt.AttributeType.TENSOR:return e.t();case mt.AttributeType.GRAPH:return e.g();case mt.AttributeType.FLOATS:return e.floatsArray();case mt.AttributeType.INTS:{let o=[];for(let i=0;i<e.intsLength();i++)o.push(e.ints(i));return o}case mt.AttributeType.STRINGS:{let o=[];for(let i=0;i<e.stringsLength();i++)o.push(e.strings(i));return o}case mt.AttributeType.TENSORS:{let o=[];for(let i=0;i<e.tensorsLength();i++)o.push(e.tensors(i));return 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this._allOutputIndices}getOutputNames(){return this._allOutputNames}getValues(){return this._allData}getNodes(){return this._nodes}buildGraph(t){if(t instanceof Ji.onnx.GraphProto)this.buildGraphFromOnnxFormat(t);else if(t instanceof _o.Graph)this.buildGraphFromOrtFormat(t);else throw new TypeError("Graph type is not supported.")}buildGraphFromOnnxFormat(t){let e=new Map;this._allData=[],this._allInputIndices=[],this._allInputNames=[],this._allOutputIndices=[],this._allOutputNames=[],this._nodes=[];let o=new Map;if(!t.input)throw new Error("missing information in graph: input");let i=[];for(let u of t.input){if(e.has(u.name))throw new Error(`duplicated input name: ${u.name}`);let n=this._allData.push(new kt(u))-1;e.set(u.name,n),i.push(u.name)}if(!t.initializer)throw new Error("missing information in graph: initializer");for(let u of t.initializer){let n=e.get(u.name);if(n===void 0){let s=new kt;s.type={shape:{dims:Ve.tensorDimsFromProto(u.dims)},tensorType:Ve.tensorDataTypeFromProto(u.dataType)},n=this._allData.push(s)-1,e.set(u.name,n)}this._allData[n]._from=-1,this._allData[n].tensor=Fe.fromProto(u)}for(let u=0;u<this._allData.length;u++)this._allData[u].tensor||(this._allInputIndices.push(u),this._allInputNames.push(i[u]));if(!t.output)throw new Error("missing information in graph: output");for(let u of t.output){if(e.has(u.name))throw new Error(`duplicated output name: ${u.name}`);let n=this._allData.push(new kt(u))-1;e.set(u.name,n),this._allOutputIndices.push(n),this._allOutputNames.push(u.name)}if(!t.node)throw new Error("missing information in graph: node");for(let u of t.node){if(!u.name)for(let s=0;;s++){let c=`unnamed_${u.opType}_${s}`;if(!o.has(c)){u.name=c;break}}if(o.has(u.name))throw new Error(`duplicated node name: ${u.name}`);let n=this._nodes.push(new Oo(u))-1;o.set(u.name,n)}for(let u=0;u<this._nodes.length;u++){let n=this._nodes[u],s=t.node[u];if(!s.output)throw new Error(`missing output for node: ${s.name}`);for(let c of s.output){let d=e.get(c);if(typeof d>"u"&&(d=this._allData.push(new kt)-1,e.set(c,d)),n.outputs.push(d),this._allData[d]._from!==void 0)throw new Error(`multiple nodes output to one data value: ${d}`);if(this._allData[d]._from=u,s.opType==="Constant"){if(!s.attribute||s.attribute.length!==1||!s.attribute[0].t)throw new Error("missing attributes or missing tensor value in attributes for this Constant operator");if(!s.output||s.output.length!==1)throw new Error("missing output or incorrect number of outputs for this Constant operator");n.outputs.pop(),n.executeNode=!1,this._allData[d]._from=-1,this._allData[d].tensor=Fe.fromProto(s.attribute[0].t)}}}for(let u=0;u<this._nodes.length;u++){let n=this._nodes[u],s=t.node[u];if(!s.input)throw new Error(`missing input for node: ${s.name}`);for(let c of s.input){let d=e.get(c);if(typeof d>"u"){if(c===""&&(s.input.length===3||s.input.length===4)&&s.opType==="Resize")continue;throw new Error(`unrecognized input '${c}' for node: ${s.name}`)}n.inputs.push(d),this._allData[d]._to.push(u)}}return!0}buildGraphFromOrtFormat(t){let e=new Map;this._allData=[],this._allInputIndices=[],this._allInputNames=[],this._allOutputIndices=[],this._allOutputNames=[],this._nodes=[];let o=new Map,i=[];for(let u=0;u<t.inputsLength();u++){let n=t.inputs(u);if(e.has(n))throw new Error(`duplicated input name: ${n}`);for(let s=0;s<t.nodeArgsLength();s++)if(t.nodeArgs(s)?.name()===n){let c=new kt;if(t.nodeArgs(s)?.type()?.valueType()!==_o.TypeInfoValue.tensor_type)throw new Error("Unexpected value type for the nodeArg.");let d=t.nodeArgs(s).type().value(new _o.TensorTypeAndShape),r=Ve.tensorDataTypeFromProto(d.elemType()),a=d.shape(),l=[];for(let f=0;f<a.dimLength();f++)l.push(nt.longToNumber(a.dim(f).value().dimValue()));c.type={shape:{dims:l},tensorType:r};let h=this._allData.push(c)-1;e.set(n,h),i.push(n)}}for(let u=0;u<t.initializersLength();u++){let n=t.initializers(u),s=e.get(n.name());if(s===void 0){let c=new kt,d=Ve.tensorDimsFromORTFormat(n),r=Ve.tensorDataTypeFromProto(n.dataType());c.type={shape:{dims:d},tensorType:r},s=this._allData.push(c)-1,e.set(n.name(),s)}this._allData[s]._from=-1,this._allData[s].tensor=Fe.fromOrtTensor(n)}for(let u=0;u<this._allData.length;u++)this._allData[u].tensor||(this._allInputIndices.push(u),this._allInputNames.push(i[u]));for(let u=0;u<t.outputsLength();u++){let n=t.outputs(u);if(e.has(n))throw new Error(`duplicated output name: ${n}`);let s=this._allData.push(new kt)-1;e.set(n,s),this._allOutputIndices.push(s),this._allOutputNames.push(n)}if(!t.nodes)throw new Error("missing information in graph: node");for(let u=0;u<t.nodesLength();u++){let n=t.nodes(u),s=n.name();if(!s)for(let d=0;s=`unnamed_${n.opType()}_${d}`,!!o.has(s);d++);if(o.has(s))throw new Error(`duplicated node name: ${s}`);let c=this._nodes.push(new Oo(n,s))-1;o.set(s,c)}for(let u=0;u<this._nodes.length;u++){let n=this._nodes[u],s=t.nodes(u);if(s==null)throw new Error(`No node exists at index ${u}`);if(s?.outputsLength()===0)throw new Error(`missing output for node: ${s.name}`);for(let c=0;c<s?.outputsLength();c++){let d=s?.outputs(c),r=e.get(d);if(typeof r>"u"&&(r=this._allData.push(new kt)-1,e.set(d,r)),n.outputs.push(r),this._allData[r]._from!==void 0)throw new Error(`multiple nodes output to one data value: ${r}`);if(this._allData[r]._from=u,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");n.outputs.pop(),n.executeNode=!1,this._allData[r]._from=-1,this._allData[r].tensor=Fe.fromOrtTensor(s.attributes(0).t())}}}for(let u=0;u<this._nodes.length;u++){let n=this._nodes[u],s=t.nodes(u);if(s.inputsLength()===0)throw new Error(`missing input for node: ${s.name}`);for(let c=0;c<s.inputsLength();c++){let d=s.inputs(c),r=e.get(d);if(typeof r>"u")throw new Error(`unrecognized input '${d}' for node: ${s.name()}`);n.inputs.push(r),this._allData[r]._to.push(u)}}}checkIsAcyclic(){let t=new Set;this._allInputIndices.forEach(i=>{this._allData[i]._to.forEach(u=>{t.add(u)})});let e=Array.from(t),o=new Array(this._nodes.length).fill("white");for(;e.length>0;){let i=e.pop();o[i]==="gray"?o[i]="black":(e.push(i),o[i]="gray",this._nodes[i].outputs.forEach(u=>{let n=this._allData[u];if(typeof n.tensor<"u")throw new Error("node outputs should not be initialized");if(n._from!==i)throw new Error("from property of the Value object doesn't match index of Node being processed");n._to.forEach(s=>{if(o[s]==="gray")throw new Error("model graph is cyclic");o[s]==="white"&&e.push(s)})}))}}transformGraph(t){this.removeAllIdentityNodes(),this.removeAllDropoutNodes(),this.fuseConvActivationNodes(),t&&t.transformGraph(this),this.finalizeGraph()}finalizeGraph(){let t=0,e=new Array(this._nodes.length,0),o=0;for(let i=0;i<this._nodes.length;i++)e[i]=o,this._nodes[i].executeNode?(o!==i&&(this._nodes[o]=this._nodes[i]),o++):this._nodes[i].outputs.forEach(u=>{this._allData[u]._from=-2});this._nodes.splice(o,this._nodes.length-o);for(let i=0;i<this._allData.length;i++){let u=this._allData[i];u._from!==void 0&&u._from!==-1&&u._from!==-2&&(u._from=e[u._from]);for(let n=0;n<u._to.length;n++)if(u._to[n]>=0)u._to[n]=e[u._to[n]];else throw new Error("Trying to update a removed node")}t=0;for(let i=0;i<this._allData.length;i++){if(this._allData[i].from===-2&&this._allOutputIndices.indexOf(i+t)===-1){t++,this._allData.splice(i,1),i--;continue}if(t>0){let u=-1;this._allData[i].from!==void 0&&this._allData[i].from!==-1?(u=this._nodes[this._allData[i].from].outputs.indexOf(i+t),u!==-1&&(this._nodes[this._allData[i].from].outputs[u]=i)):(u=this._allInputIndices.indexOf(i+t),u!==-1&&(this._allInputIndices[u]=i)),this._allData[i].to.forEach(n=>{u=this._nodes[n].inputs.indexOf(i+t),u!==-1&&(this._nodes[n].inputs[u]=i)}),this._allData[i].to.length===0&&(u=this._allOutputIndices.indexOf(i+t),u!==-1&&(this._allOutputIndices[u]=i))}}}deleteNode(t){let e=this._nodes[t];if(e.outputs.length>1){for(let s=1;s<e.outputs.length;s++)if(this._allData[e.outputs[s]].to.length>0)throw new Error("Node deletion with more than one output connected to other nodes is not supported. ")}e.executeNode=!1;let o=e.inputs[0],i=e.outputs[0],u=this._allData[i].to;for(let s=0;s<e.inputs.length;s++){let c=this._allData[e.inputs[s]].to.indexOf(t);if(c===-1)throw new Error("The Value object doesn't have the current Node in it's 'to' property ");this._allData[e.inputs[s]].to.splice(c,1)}this._allData[i]._to=[];let n=this._allOutputIndices.indexOf(i);if(n!==-1&&(this._allOutputIndices[n]=o),u&&u.length>0)for(let s of u){let c=this._nodes[s].inputs.indexOf(i);if(c===-1)throw new Error("The Node object doesn't have the output Value in it's 'inputs' property ");this._nodes[s].inputs[c]=o,this._allData[o].to.push(s)}}removeAllDropoutNodes(){let t=0;for(let e of this._nodes){if(e.opType==="Dropout"){if(e.inputs.length!==1)throw new Error("Dropout nodes should only contain one input. ");if(e.outputs.length!==1&&e.outputs.length!==2)throw new Error("Dropout nodes should contain either 1 or 2 output(s)");if(e.outputs.length===2&&this._allData[e.outputs[1]]._to.length!==0)throw new Error("Dropout nodes's second output should not be referenced by other nodes");this.deleteNode(t)}t++}}removeAllIdentityNodes(){let t=0;for(let e of this._nodes)e.opType==="Identity"&&this.deleteNode(t),t++}isActivation(t){switch(t.opType){case"Relu":case"Sigmoid":case"Clip":return!0;default:return!1}}fuseConvActivationNodes(){for(let t of this._nodes)if(t.opType==="Conv"){let e=this._allData[t.outputs[0]]._to;if(e.length===1&&this.isActivation(this._nodes[e[0]])){let o=this._nodes[e[0]];if(o.opType==="Clip")if(o.inputs.length===1)try{t.attributes.set("activation_params","floats",[o.attributes.getFloat("min"),o.attributes.getFloat("max")])}catch{t.attributes.set("activation_params","floats",[dr,hr])}else if(o.inputs.length>=3&&this._allData[o.inputs[1]].tensor!==void 0&&this._allData[o.inputs[2]].tensor!==void 0)t.attributes.set("activation_params","floats",[this._allData[o.inputs[1]].tensor.floatData[0],this._allData[o.inputs[2]].tensor.floatData[0]]);else continue;t.attributes.set("activation","string",o.opType),this.deleteNode(e[0])}}}}}),yp,Jb,Io,Tp=L(()=>{Wn(),gp(),tn(),yp=Er($r()),fe(),Jb=W.experimental.fbs,Io=class{constructor(){}load(t,e,o){if(!o)try{this.loadFromOnnxFormat(t,e);return}catch(i){if(o!==void 0)throw i}this.loadFromOrtFormat(t,e)}loadFromOnnxFormat(t,e){let o=yp.onnx.ModelProto.decode(t);if(nt.longToNumber(o.irVersion)<3)throw new Error("only support ONNX model with IR_VERSION>=3");this._opsets=o.opsetImport.map(i=>({domain:i.domain,version:nt.longToNumber(i.version)})),this._graph=Zi.from(o.graph,e)}loadFromOrtFormat(t,e){let o=new _.ByteBuffer(t),i=Jb.InferenceSession.getRootAsInferenceSession(o).model();if(nt.longToNumber(i.irVersion())<3)throw new Error("only support ONNX model with IR_VERSION>=3");this._opsets=[];for(let 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0)(t);this.initialize(n,u)}else{let n=await(await fetch(t)).arrayBuffer();this.initialize(new Uint8Array(n),u)}}else if(ArrayBuffer.isView(t))this.initialize(t);else{let u=new Uint8Array(t,e||0,o||t.byteLength);this.initialize(u)}})}initialize(t,e){if(this._initialized)throw new Error("already initialized");this.profiler.event("session","Session.initialize",()=>{let o=this.sessionHandler.transformGraph?this.sessionHandler:void 0;this._model.load(t,o,e),this.sessionHandler.onGraphInitialized&&this.sessionHandler.onGraphInitialized(this._model.graph),this.initializeOps(this._model.graph),this._executionPlan=new wo(this._model.graph,this._ops,this.profiler)}),this._initialized=!0}async run(t){if(!this._initialized)throw new Error("session not initialized yet");return this.profiler.event("session","Session.run",async()=>{let e=this.normalizeAndValidateInputs(t),o=await this._executionPlan.execute(this.sessionHandler,e);return this.createOutput(o)})}normalizeAndValidateInputs(t){let 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Bundled license information:
+
+long/index.js:
+  (**
+   * @license
+   * Copyright 2009 The Closure Library Authors
+   * Copyright 2020 Daniel Wirtz / The long.js Authors.
+   *
+   * Licensed under the Apache License, Version 2.0 (the "License");
+   * you may not use this file except in compliance with the License.
+   * You may obtain a copy of the License at
+   *
+   *     http://www.apache.org/licenses/LICENSE-2.0
+   *
+   * Unless required by applicable law or agreed to in writing, software
+   * distributed under the License is distributed on an "AS IS" BASIS,
+   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+   * See the License for the specific language governing permissions and
+   * limitations under the License.
+   *
+   * SPDX-License-Identifier: Apache-2.0
+   *)
+*/const ONNX_WEB=Object.freeze(Object.defineProperty({__proto__:null,get InferenceSession(){return Sd},get TRACE(){return cs},get TRACE_FUNC_BEGIN(){return ar},get TRACE_FUNC_END(){return sr},get Tensor(){return Re},get TrainingSession(){return Pd},default:zO,get env(){return Z},get registerBackend(){return or}},Symbol.toStringTag,{value:"Module"})),tensorToDataURL=(t,e)=>{const o=typeof document<"u"?document.createElement("canvas"):new OffscreenCanvas(1,1);o.width=t.dims[3],o.height=t.dims[2];const i=o.getContext("2d");if(i!=null){let u,n;e?.tensorLayout!==void 0&&e.tensorLayout==="NHWC"?(u=t.dims[2],n=t.dims[3]):(u=t.dims[3],n=t.dims[2]);const s=e?.format!==void 0?e.format:"RGB",c=e?.norm;let d,r;c===void 0||c.mean===void 0?d=[255,255,255,255]:typeof c.mean=="number"?d=[c.mean,c.mean,c.mean,c.mean]:(d=[c.mean[0],c.mean[1],c.mean[2],0],c.mean[3]!==void 0&&(d[3]=c.mean[3])),c===void 0||c.bias===void 0?r=[0,0,0,0]:typeof c.bias=="number"?r=[c.bias,c.bias,c.bias,c.bias]:(r=[c.bias[0],c.bias[1],c.bias[2],0],c.bias[3]!==void 0&&(r[3]=c.bias[3]));const a=n*u;let l=0,h=a,f=a*2,m=-1;s==="RGBA"?(l=0,h=a,f=a*2,m=a*3):s==="RGB"?(l=0,h=a,f=a*2):s==="RBG"&&(l=0,f=a,h=a*2);for(let g=0;g<n;g++)for(let b=0;b<u;b++){const v=(t.data[l++]-r[0])*d[0],T=(t.data[h++]-r[1])*d[1],E=(t.data[f++]-r[2])*d[2],M=m===-1?255:(t.data[m++]-r[3])*d[3];i.fillStyle="rgba("+v+","+T+","+E+","+M+")",i.fillRect(b,g,1,1)}if("toDataURL"in o)return o.toDataURL();throw new Error("toDataURL is not supported")}else throw new Error("Can not access image data")},tensorToImageData=(t,e)=>{const o=typeof document<"u"?document.createElement("canvas").getContext("2d"):new OffscreenCanvas(1,1).getContext("2d");let i;if(o!=null){let u,n,s;e?.tensorLayout!==void 0&&e.tensorLayout==="NHWC"?(u=t.dims[2],n=t.dims[1],s=t.dims[3]):(u=t.dims[3],n=t.dims[2],s=t.dims[1]);const c=e!==void 0&&e.format!==void 0?e.format:"RGB",d=e?.norm;let r,a;d===void 0||d.mean===void 0?r=[255,255,255,255]:typeof d.mean=="number"?r=[d.mean,d.mean,d.mean,d.mean]:(r=[d.mean[0],d.mean[1],d.mean[2],255],d.mean[3]!==void 0&&(r[3]=d.mean[3])),d===void 0||d.bias===void 0?a=[0,0,0,0]:typeof d.bias=="number"?a=[d.bias,d.bias,d.bias,d.bias]:(a=[d.bias[0],d.bias[1],d.bias[2],0],d.bias[3]!==void 0&&(a[3]=d.bias[3]));const l=n*u;if(e!==void 0&&(e.format!==void 0&&s===4&&e.format!=="RGBA"||s===3&&e.format!=="RGB"&&e.format!=="BGR"))throw new Error("Tensor format doesn't match input tensor dims");const h=4;let f=0,m=1,g=2,b=3,v=0,T=l,E=l*2,M=-1;c==="RGBA"?(v=0,T=l,E=l*2,M=l*3):c==="RGB"?(v=0,T=l,E=l*2):c==="RBG"&&(v=0,E=l,T=l*2),i=o.createImageData(u,n);for(let F=0;F<n*u;f+=h,m+=h,g+=h,b+=h,F++)i.data[f]=(t.data[v++]-a[0])*r[0],i.data[m]=(t.data[T++]-a[1])*r[1],i.data[g]=(t.data[E++]-a[2])*r[2],i.data[b]=M===-1?255:(t.data[M++]-a[3])*r[3]}else throw new Error("Can not access image data");return i},bufferToTensor=(t,e)=>{if(t===void 0)throw new Error("Image buffer must be defined");if(e.height===void 0||e.width===void 0)throw new Error("Image height and width must be defined");if(e.tensorLayout==="NHWC")throw new Error("NHWC Tensor layout is not supported yet");const{height:o,width:i}=e,u=e.norm??{mean:255,bias:0};let n,s;typeof u.mean=="number"?n=[u.mean,u.mean,u.mean,u.mean]:n=[u.mean[0],u.mean[1],u.mean[2],u.mean[3]??255],typeof u.bias=="number"?s=[u.bias,u.bias,u.bias,u.bias]:s=[u.bias[0],u.bias[1],u.bias[2],u.bias[3]??0];const c=e.format!==void 0?e.format:"RGBA",d=e.tensorFormat!==void 0&&e.tensorFormat!==void 0?e.tensorFormat:"RGB",r=o*i,a=d==="RGBA"?new Float32Array(r*4):new Float32Array(r*3);let l=4,h=0,f=1,m=2,g=3,b=0,v=r,T=r*2,E=-1;c==="RGB"&&(l=3,h=0,f=1,m=2,g=-1),d==="RGBA"?E=r*3:d==="RBG"?(b=0,T=r,v=r*2):d==="BGR"&&(T=0,v=r,b=r*2);for(let F=0;F<r;F++,h+=l,m+=l,f+=l,g+=l)a[b++]=(t[h]+s[0])/n[0],a[v++]=(t[f]+s[1])/n[1],a[T++]=(t[m]+s[2])/n[2],E!==-1&&g!==-1&&(a[E++]=(t[g]+s[3])/n[3]);return d==="RGBA"?new Tensor$2("float32",a,[1,4,o,i]):new Tensor$2("float32",a,[1,3,o,i])},tensorFromImage=async(t,e)=>{const o=typeof HTMLImageElement<"u"&&t instanceof HTMLImageElement,i=typeof ImageData<"u"&&t instanceof ImageData,u=typeof ImageBitmap<"u"&&t instanceof ImageBitmap,n=typeof t=="string";let s,c=e??{};const d=()=>{if(typeof document<"u")return document.createElement("canvas");if(typeof OffscreenCanvas<"u")return new OffscreenCanvas(1,1);throw new Error("Canvas is not supported")},r=a=>a instanceof HTMLCanvasElement||a instanceof OffscreenCanvas?a.getContext("2d"):null;if(o){const a=d();a.width=t.width,a.height=t.height;const l=r(a);if(l!=null){let h=t.height,f=t.width;if(e!==void 0&&e.resizedHeight!==void 0&&e.resizedWidth!==void 0&&(h=e.resizedHeight,f=e.resizedWidth),e!==void 0){if(c=e,e.tensorFormat!==void 0)throw new Error("Image input config format must be RGBA for HTMLImageElement");c.tensorFormat="RGBA",c.height=h,c.width=f}else c.tensorFormat="RGBA",c.height=h,c.width=f;l.drawImage(t,0,0),s=l.getImageData(0,0,f,h).data}else throw new Error("Can not access image data")}else if(i){let a,l;if(e!==void 0&&e.resizedWidth!==void 0&&e.resizedHeight!==void 0?(a=e.resizedHeight,l=e.resizedWidth):(a=t.height,l=t.width),e!==void 0&&(c=e),c.format="RGBA",c.height=a,c.width=l,e!==void 0){const h=d();h.width=l,h.height=a;const f=r(h);if(f!=null)f.putImageData(t,0,0),s=f.getImageData(0,0,l,a).data;else throw new Error("Can not access image data")}else s=t.data}else if(u){if(e===void 0)throw new Error("Please provide image config with format for Imagebitmap");const a=d();a.width=t.width,a.height=t.height;const l=r(a);if(l!=null){const h=t.height,f=t.width;return l.drawImage(t,0,0,f,h),s=l.getImageData(0,0,f,h).data,c.height=h,c.width=f,bufferToTensor(s,c)}else throw new Error("Can not access image data")}else{if(n)return new Promise((a,l)=>{const h=d(),f=r(h);if(!t||!f)return l();const m=new Image;m.crossOrigin="Anonymous",m.src=t,m.onload=()=>{h.width=m.width,h.height=m.height,f.drawImage(m,0,0,h.width,h.height);const g=f.getImageData(0,0,h.width,h.height);c.height=h.height,c.width=h.width,a(bufferToTensor(g.data,c))}});throw new Error("Input data provided is not supported - aborted tensor creation")}if(s!==void 0)return bufferToTensor(s,c);throw new Error("Input data provided is not supported - aborted tensor creation")},tensorFromTexture=(t,e)=>{const{width:o,height:i,download:u,dispose:n}=e,s=[1,i,o,4];return new Tensor$2({location:"texture",type:"float32",texture:t,dims:s,download:u,dispose:n})},tensorFromGpuBuffer=(t,e)=>{const{dataType:o,dims:i,download:u,dispose:n}=e;return new Tensor$2({location:"gpu-buffer",type:o??"float32",gpuBuffer:t,dims:i,download:u,dispose:n})},tensorFromPinnedBuffer=(t,e,o)=>new Tensor$2({location:"cpu-pinned",type:t,data:e,dims:o??[e.length]}),NUMERIC_TENSOR_TYPE_TO_TYPEDARRAY_MAP=new Map([["float32",Float32Array],["uint8",Uint8Array],["int8",Int8Array],["uint16",Uint16Array],["float16",Uint16Array],["int16",Int16Array],["int32",Int32Array],["bool",Uint8Array],["float64",Float64Array],["uint32",Uint32Array]]),NUMERIC_TENSOR_TYPEDARRAY_TO_TYPE_MAP=new Map([[Float32Array,"float32"],[Uint8Array,"uint8"],[Int8Array,"int8"],[Uint16Array,"uint16"],[Int16Array,"int16"],[Int32Array,"int32"],[Float64Array,"float64"],[Uint32Array,"uint32"]]);let isBigIntChecked=!1;const checkBigInt=()=>{if(!isBigIntChecked){isBigIntChecked=!0;const t=typeof BigInt64Array<"u"&&typeof BigInt64Array.from=="function",e=typeof BigUint64Array<"u"&&typeof BigUint64Array.from=="function";t&&(NUMERIC_TENSOR_TYPE_TO_TYPEDARRAY_MAP.set("int64",BigInt64Array),NUMERIC_TENSOR_TYPEDARRAY_TO_TYPE_MAP.set(BigInt64Array,"int64")),e&&(NUMERIC_TENSOR_TYPE_TO_TYPEDARRAY_MAP.set("uint64",BigUint64Array),NUMERIC_TENSOR_TYPEDARRAY_TO_TYPE_MAP.set(BigUint64Array,"uint64"))}},calculateSize=t=>{let e=1;for(let o=0;o<t.length;o++){const i=t[o];if(typeof i!="number"||!Number.isSafeInteger(i))throw new TypeError(`dims[${o}] must be an integer, got: ${i}`);if(i<0)throw new RangeError(`dims[${o}] must be a non-negative integer, got: ${i}`);e*=i}return e},tensorReshape=(t,e)=>{switch(t.location){case"cpu":return new Tensor$2(t.type,t.data,e);case"cpu-pinned":return new Tensor$2({location:"cpu-pinned",data:t.data,type:t.type,dims:e});case"texture":return new Tensor$2({location:"texture",texture:t.texture,type:t.type,dims:e});case"gpu-buffer":return new Tensor$2({location:"gpu-buffer",gpuBuffer:t.gpuBuffer,type:t.type,dims:e});default:throw new Error(`tensorReshape: tensor location ${t.location} is not supported`)}};let Tensor$2=class{constructor(e,o,i){checkBigInt();let u,n;if(typeof e=="object"&&"location"in e)switch(this.dataLocation=e.location,u=e.type,n=e.dims,e.location){case"cpu-pinned":{const c=NUMERIC_TENSOR_TYPE_TO_TYPEDARRAY_MAP.get(u);if(!c)throw new TypeError(`unsupported type "${u}" to create tensor from pinned buffer`);if(!(e.data instanceof c))throw new TypeError(`buffer should be of type ${c.name}`);this.cpuData=e.data;break}case"texture":{if(u!=="float32")throw new TypeError(`unsupported type "${u}" to create tensor from texture`);this.gpuTextureData=e.texture,this.downloader=e.download,this.disposer=e.dispose;break}case"gpu-buffer":{if(u!=="float32"&&u!=="float16"&&u!=="int32"&&u!=="int64"&&u!=="uint32"&&u!=="bool")throw new TypeError(`unsupported type "${u}" to create tensor from gpu buffer`);this.gpuBufferData=e.gpuBuffer,this.downloader=e.download,this.disposer=e.dispose;break}default:throw new Error(`Tensor constructor: unsupported location '${this.dataLocation}'`)}else{let c,d;if(typeof e=="string")if(u=e,d=i,e==="string"){if(!Array.isArray(o))throw new TypeError("A string tensor's data must be a string array.");c=o}else{const r=NUMERIC_TENSOR_TYPE_TO_TYPEDARRAY_MAP.get(e);if(r===void 0)throw new TypeError(`Unsupported tensor type: ${e}.`);if(Array.isArray(o)){if(e==="float16")throw new TypeError("Creating a float16 tensor from number array is not supported. Please use Uint16Array as data.");e==="uint64"||e==="int64"?c=r.from(o,BigInt):c=r.from(o)}else if(o instanceof r)c=o;else throw new TypeError(`A ${u} tensor's data must be type of ${r}`)}else if(d=o,Array.isArray(e)){if(e.length===0)throw new TypeError("Tensor type cannot be inferred from an empty array.");const r=typeof e[0];if(r==="string")u="string",c=e;else if(r==="boolean")u="bool",c=Uint8Array.from(e);else throw new TypeError(`Invalid element type of data array: ${r}.`)}else{const r=NUMERIC_TENSOR_TYPEDARRAY_TO_TYPE_MAP.get(e.constructor);if(r===void 0)throw new TypeError(`Unsupported type for tensor data: ${e.constructor}.`);u=r,c=e}if(d===void 0)d=[c.length];else if(!Array.isArray(d))throw new TypeError("A tensor's dims must be a number array");n=d,this.cpuData=c,this.dataLocation="cpu"}const s=calculateSize(n);if(this.cpuData&&s!==this.cpuData.length)throw new Error(`Tensor's size(${s}) does not match data length(${this.cpuData.length}).`);this.type=u,this.dims=n,this.size=s}static async fromImage(e,o){return tensorFromImage(e,o)}static fromTexture(e,o){return tensorFromTexture(e,o)}static fromGpuBuffer(e,o){return tensorFromGpuBuffer(e,o)}static fromPinnedBuffer(e,o,i){return tensorFromPinnedBuffer(e,o,i)}toDataURL(e){return tensorToDataURL(this,e)}toImageData(e){return tensorToImageData(this,e)}get data(){if(this.ensureValid(),!this.cpuData)throw new Error("The data is not on CPU. Use `getData()` to download GPU data to CPU, or use `texture` or `gpuBuffer` property to access the GPU data directly.");return this.cpuData}get location(){return this.dataLocation}get texture(){if(this.ensureValid(),!this.gpuTextureData)throw new Error("The data is not stored as a WebGL texture.");return this.gpuTextureData}get gpuBuffer(){if(this.ensureValid(),!this.gpuBufferData)throw new Error("The data is not stored as a WebGPU buffer.");return this.gpuBufferData}async getData(e){switch(this.ensureValid(),this.dataLocation){case"cpu":case"cpu-pinned":return this.data;case"texture":case"gpu-buffer":{if(!this.downloader)throw new Error("The current tensor is not created with a specified data downloader.");if(this.isDownloading)throw new Error("The current tensor is being downloaded.");try{this.isDownloading=!0;const o=await this.downloader();return this.downloader=void 0,this.dataLocation="cpu",this.cpuData=o,e&&this.disposer&&(this.disposer(),this.disposer=void 0),o}finally{this.isDownloading=!1}}default:throw new Error(`cannot get data from location: ${this.dataLocation}`)}}dispose(){if(this.isDownloading)throw new Error("The current tensor is being downloaded.");this.disposer&&(this.disposer(),this.disposer=void 0),this.cpuData=void 0,this.gpuTextureData=void 0,this.gpuBufferData=void 0,this.downloader=void 0,this.isDownloading=void 0,this.dataLocation="none"}ensureValid(){if(this.dataLocation==="none")throw new Error("The tensor is disposed.")}reshape(e){if(this.ensureValid(),this.downloader||this.disposer)throw new Error("Cannot reshape a tensor that owns GPU resource.");return tensorReshape(this,e)}};const Tensor$1=Tensor$2;let ONNX;const WEBGPU_AVAILABLE=typeof navigator<"u"&&"gpu"in navigator,USE_ONNXRUNTIME_NODE=typeof process<"u"&&process?.release?.name==="node",ONNX_MODULES=new Map;USE_ONNXRUNTIME_NODE?(ONNX=sharp??ONNX_NODE,ONNX_MODULES.set("node",ONNX)):(ONNX=zO??ONNX_WEB,ONNX_MODULES.set("web",ONNX));async function createInferenceSession(t,e){let o,i;if(USE_ONNXRUNTIME_NODE){const s=ONNX_MODULES.get("node");i=s.InferenceSession,o=["cpu"],Object.assign(s.env,env.backends.onnx)}else if(WEBGPU_AVAILABLE&&env.experimental.useWebGPU){let s=ONNX_MODULES.get("webgpu");s===void 0&&(s=await __vitePreload(()=>import("./ort.webgpu.min-BaaO-cT5.js"),__vite__mapDeps([])),ONNX_MODULES.set("webgpu",s)),i=s.InferenceSession,o=["webgpu","wasm"],Object.assign(s.env,env.backends.onnx)}else{const s=ONNX_MODULES.get("web");i=s.InferenceSession,o=["wasm"],Object.assign(s.env,env.backends.onnx)}const u={executionProviders:o,...e};return await i.create(t,u)}function isONNXTensor(t){for(const e of ONNX_MODULES.values())if(t instanceof e.Tensor)return!0;return!1}const ONNX_ENV=ONNX?.env;ONNX_ENV&&ONNX_ENV.wasm&&(ONNX_ENV.wasm.wasmPaths=RUNNING_LOCALLY?sharp.join(env.__dirname,"/dist/"):`https://cdn.jsdelivr.net/npm/@xenova/transformers@${env.version}/dist/`,typeof navigator<"u"&&/iP(hone|od|ad).+16_4.+AppleWebKit/.test(navigator.userAgent)&&(ONNX_ENV.wasm.simd=!1),ONNX_ENV.wasm.proxy=!0);function isONNXProxy(){return ONNX_ENV.wasm.proxy}env.backends.onnx=ONNX_ENV;const DataTypeMap=Object.freeze({float32:Float32Array,float16:Uint16Array,float64:Float64Array,string:Array,int8:Int8Array,uint8:Uint8Array,int16:Int16Array,uint16:Uint16Array,int32:Int32Array,uint32:Uint32Array,int64:BigInt64Array,uint64:BigUint64Array,bool:Uint8Array});class Tensor{get dims(){return this.ort_tensor.dims}set dims(e){this.ort_tensor.dims=e}get type(){return this.ort_tensor.type}get data(){return this.ort_tensor.data}get size(){return this.ort_tensor.size}ort_tensor;constructor(...e){return isONNXTensor(e[0])?this.ort_tensor=e[0]:this.ort_tensor=new Tensor$1(e[0],e[1],e[2]),new Proxy(this,{get:(o,i)=>{if(typeof i=="string"){let u=Number(i);if(Number.isInteger(u))return o._getitem(u)}return o[i]},set:(o,i,u)=>o[i]=u})}dispose(){this.ort_tensor.dispose()}*[Symbol.iterator](){const[e,...o]=this.dims;if(o.length>0){const i=o.reduce((u,n)=>u*n);for(let u=0;u<e;++u)yield this._subarray(u,i,o)}else yield*this.data}_getitem(e){const[o,...i]=this.dims;if(e=safeIndex(e,o),i.length>0){const u=i.reduce((n,s)=>n*s);return this._subarray(e,u,i)}else return new Tensor(this.type,[this.data[e]],i)}indexOf(e){for(let o=0;o<this.data.length;++o)if(this.data[o]==e)return o;return-1}_subarray(e,o,i){const u=e*o,n=(e+1)*o,s="subarray"in this.data?this.data.subarray(u,n):this.data.slice(u,n);return new Tensor(this.type,s,i)}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 e=0;e<this.data.length;++e)this.data[e]=1/(1+Math.exp(-this.data[e]));return this}mul(e){return this.clone().mul_(e)}mul_(e){for(let o=0;o<this.data.length;++o)this.data[o]*=e;return this}add(e){return this.clone().add_(e)}add_(e){for(let o=0;o<this.data.length;++o)this.data[o]+=e;return this}clone(){return new Tensor(this.type,this.data.slice(),this.dims.slice())}slice(...e){let o=[],i=[];for(let d=0;d<this.dims.length;++d){let r=e[d];if(r==null)i.push([0,this.dims[d]]),o.push(this.dims[d]);else if(typeof r=="number")r=safeIndex(r,this.dims[d],d),i.push([r,r+1]);else if(Array.isArray(r)&&r.length===2){if(r[0]>r[1])throw new Error(`Invalid slice: ${r}`);let a=[Math.max(r[0],0),Math.min(r[1],this.dims[d])];i.push(a),o.push(a[1]-a[0])}else throw new Error(`Invalid slice: ${r}`)}let u=i.map(([d,r])=>r-d),n=u.reduce((d,r)=>d*r),s=new this.data.constructor(n);const c=this.stride();for(let d=0;d<n;++d){let r=0;for(let a=u.length-1,l=d;a>=0;--a){const h=u[a];r+=(l%h+i[a][0])*c[a],l=Math.floor(l/h)}s[d]=this.data[r]}return new Tensor(this.type,s,o)}transpose(...e){return transpose(this,e)}sum(e=null,o=!1){return this.norm(1,e,o)}norm(e="fro",o=null,i=!1){if(e==="fro")e=2;else if(typeof e=="string")throw Error(`Unsupported norm: ${e}`);if(o===null){let s=this.data.reduce((c,d)=>c+d**e,0)**(1/e);return new Tensor(this.type,[s],[])}o=safeIndex(o,this.dims.length);const u=this.dims.slice();u[o]=1;const n=new this.data.constructor(this.data.length/this.dims[o]);for(let s=0;s<this.data.length;++s){let c=0;for(let d=this.dims.length-1,r=s,a=1;d>=0;--d){const l=this.dims[d];if(d!==o){const h=r%l;c+=h*a,a*=u[d]}r=Math.floor(r/l)}n[c]+=this.data[s]**e}if(e!==1)for(let s=0;s<n.length;++s)n[s]=n[s]**(1/e);return i||u.splice(o,1),new Tensor(this.type,n,u)}normalize_(e=2,o=1){o=safeIndex(o,this.dims.length);const i=this.norm(e,o,!0);for(let u=0;u<this.data.length;++u){let n=0;for(let s=this.dims.length-1,c=u,d=1;s>=0;--s){const r=this.dims[s];if(s!==o){const a=c%r;n+=a*d,d*=this.dims[s]}c=Math.floor(c/r)}this.data[u]/=i.data[n]}return this}normalize(e=2,o=1){return this.clone().normalize_(e,o)}stride(){return dimsToStride(this.dims)}squeeze(e=null){return new Tensor(this.type,this.data,calc_squeeze_dims(this.dims,e))}squeeze_(e=null){return this.dims=calc_squeeze_dims(this.dims,e),this}unsqueeze(e=null){return new Tensor(this.type,this.data,calc_unsqueeze_dims(this.dims,e))}unsqueeze_(e=null){return this.dims=calc_unsqueeze_dims(this.dims,e),this}flatten_(e=0,o=-1){o=(o+this.dims.length)%this.dims.length;let i=this.dims.slice(0,e),u=this.dims.slice(e,o+1),n=this.dims.slice(o+1);return this.dims=[...i,u.reduce((s,c)=>s*c,1),...n],this}flatten(e=0,o=-1){return this.clone().flatten_(e,o)}view(...e){let o=-1;for(let i=0;i<e.length;++i)if(e[i]===-1){if(o!==-1)throw new Error("Only one dimension can be inferred");o=i}if(o!==-1){const i=e.reduce((u,n,s)=>s!==o?u*n:u,1);e[o]=this.data.length/i}return new Tensor(this.type,this.data,e)}neg_(){for(let e=0;e<this.data.length;++e)this.data[e]=-this.data[e];return this}neg(){return this.clone().neg_()}clamp_(e,o){for(let i=0;i<this.data.length;++i)this.data[i]=Math.min(Math.max(this.data[i],e),o);return this}clamp(e,o){return this.clone().clamp_(e,o)}round_(){for(let e=0;e<this.data.length;++e)this.data[e]=Math.round(this.data[e]);return this}round(){return this.clone().round_()}to(e){if(this.type===e)return this;if(!DataTypeMap.hasOwnProperty(e))throw new Error(`Unsupported type: ${e}`);return new Tensor(e,DataTypeMap[e].from(this.data),this.dims)}}function reshape(t,e){const o=t.length,i=e.reduce((n,s)=>n*s);if(o!==i)throw Error(`cannot reshape array of size ${o} into shape (${e})`);let u=t;for(let n=e.length-1;n>=0;n--)u=u.reduce((s,c)=>{let d=s[s.length-1];return d.length<e[n]?d.push(c):s.push([c]),s},[[]]);return u[0]}function transpose(t,e){const[o,i]=transpose_data(t.data,t.dims,e);return new Tensor(t.type,o,i)}function interpolate(t,[e,o],i="bilinear",u=!1){const n=t.dims.at(-3)??1,s=t.dims.at(-2),c=t.dims.at(-1);let d=interpolate_data(t.data,[n,s,c],[e,o],i,u);return new Tensor(t.type,d,[n,e,o])}function calc_squeeze_dims(t,e){return t=t.slice(),e===null?t=t.filter(o=>o!==1):typeof e=="number"?t[e]===1&&t.splice(e,1):Array.isArray(e)&&(t=t.filter((o,i)=>o!==1||!e.includes(i))),t}function calc_unsqueeze_dims(t,e){return e=safeIndex(e,t.length+1),t=t.slice(),t.splice(e,0,1),t}function safeIndex(t,e,o=null){if(t<-e||t>=e)throw new Error(`IndexError: index ${t} is out of bounds for dimension${o===null?"":" "+o} with size ${e}`);return t<0&&(t=(t%e+e)%e),t}function cat(t,e=0){e=safeIndex(e,t[0].dims.length);const o=t[0].dims.slice();o[e]=t.reduce((s,c)=>s+c.dims[e],0);const i=o.reduce((s,c)=>s*c,1),u=new t[0].data.constructor(i),n=t[0].type;if(e===0){let s=0;for(let c of t)u.set(c.data,s),s+=c.data.length}else{let s=0;for(let c=0;c<t.length;++c){let d=t[c];for(let r=0;r<d.data.length;++r){let a=0;for(let l=d.dims.length-1,h=r,f=1;l>=0;--l){const m=d.dims[l];let g=h%m;l===e&&(g+=s),a+=g*f,f*=o[l],h=Math.floor(h/m)}u[a]=d.data[r]}s+=d.dims[e]}}return new Tensor(n,u,o)}function stack(t,e=0){return cat(t.map(o=>o.unsqueeze(e)),e)}function std_mean(t,e=null,o=1,i=!1){if(e===null){const r=t.data.reduce((f,m)=>f+m,0)/t.data.length,a=Math.sqrt(t.data.reduce((f,m)=>f+(m-r)**2,0)/(t.data.length-o)),l=new Tensor(t.type,[r],[]);return[new Tensor(t.type,[a],[]),l]}e=safeIndex(e,t.dims.length);const u=mean(t,e,i),n=t.dims.slice();n[e]=1;const s=new t.data.constructor(t.data.length/t.dims[e]);for(let d=0;d<t.data.length;++d){let r=0;for(let a=t.dims.length-1,l=d,h=1;a>=0;--a){const f=t.dims[a];if(a!==e){const m=l%f;r+=m*h,h*=n[a]}l=Math.floor(l/f)}s[r]+=(t.data[d]-u.data[r])**2}for(let d=0;d<s.length;++d)s[d]=Math.sqrt(s[d]/(t.dims[e]-o));return i||n.splice(e,1),[new Tensor(t.type,s,n),u]}function mean(t,e=null,o=!1){if(e===null){let n=t.data.reduce((s,c)=>s+c,0);return new Tensor(t.type,[n/t.data.length],[])}e=safeIndex(e,t.dims.length);const i=t.dims.slice();i[e]=1;const u=new t.data.constructor(t.data.length/t.dims[e]);for(let n=0;n<t.data.length;++n){let s=0;for(let c=t.dims.length-1,d=n,r=1;c>=0;--c){const a=t.dims[c];if(c!==e){const l=d%a;s+=l*r,r*=i[c]}d=Math.floor(d/a)}u[s]+=t.data[n]}if(t.dims[e]!==1)for(let n=0;n<u.length;++n)u[n]=u[n]/t.dims[e];return o||i.splice(e,1),new Tensor(t.type,u,i)}function dynamicTimeWarping(t){const[e,o]=t.dims,i=[e+1,o+1],u=new Tensor("float32",new Float32Array(i[0]*i[1]).fill(1/0),i),n=new Tensor("float32",new Float32Array(i[0]*i[1]).fill(-1),i);u[0].data[0]=0;for(let a=1;a<o+1;++a)for(let l=1;l<e+1;++l){const h=u[l-1][a-1].item(),f=u[l-1][a].item(),m=u[l][a-1].item();let g,b;h<f&&h<m?(g=h,b=0):f<h&&f<m?(g=f,b=1):(g=m,b=2),u[l].data[a]=t[l-1][a-1].item()+g,n[l].data[a]=b}let s=e,c=o;n.data.fill(2,0,i[1]);for(let a=0;a<i[0];++a)n[a].data[0]=1;let d=[],r=[];for(;s>0||c>0;)switch(d.push(s-1),r.push(c-1),n[s][c].item()){case 0:--s,--c;break;case 1:--s;break;case 2:--c;break;default:throw new Error(`Internal error in dynamic time warping. Unexpected trace[${s}, ${c}]. Please file a bug report.`)}return d.reverse(),r.reverse(),[d,r]}function dimsToStride(t){const e=new Array(t.length);for(let o=t.length-1,i=1;o>=0;--o)e[o]=i,i*=t[o];return e}function ones(t){const e=t.reduce((o,i)=>o*i,1);return new Tensor("int64",new BigInt64Array(e).fill(1n),t)}function ones_like(t){return ones(t.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",OpenCurlyBracket:"OpenCurlyBracket",CloseCurlyBracket:"CloseCurlyBracket",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",Is:"Is",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,is:TOKEN_TYPES.Is,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.OpenCurlyBracket,TOKEN_TYPES.CloseCurlyBracket,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 t=[...Array.from({length:94},(u,n)=>n+33),...Array.from({length:12},(u,n)=>n+161),...Array.from({length:82},(u,n)=>n+174)],e=t.slice();let o=0;for(let u=0;u<256;++u)t.includes(u)||(t.push(u),e.push(256+o),o+=1);const i=e.map(u=>String.fromCharCode(u));return Object.fromEntries(t.map((u,n)=>[u,i[n]]))})();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(t,e){return await getModelJSON(t,"config.json",!0,e)}class PretrainedConfig{constructor(e){this.model_type=null,this.is_encoder_decoder=!1,Object.assign(this,e)}static async from_pretrained(e,{progress_callback:o=null,config:i=null,cache_dir:u=null,local_files_only:n=!1,revision:s="main"}={}){let c=i??await loadConfig(e,{progress_callback:o,config:i,cache_dir:u,local_files_only:n,revision:s});return new this(c)}}class AutoConfig{static async from_pretrained(...e){return PretrainedConfig.from_pretrained(...e)}}class LogitsProcessorList extends Callable{constructor(){super(),this.processors=[]}push(e){this.processors.push(e)}extend(e){this.processors.push(...e)}_call(e,o){for(let i of o)this.processors.forEach(u=>u(e,i))}[Symbol.iterator](){return this.processors.values()}}class LogitsProcessor extends Callable{_call(e,o){throw Error("`_call` should be implemented in a subclass")}}class ForceTokensLogitsProcessor extends LogitsProcessor{constructor(e){super(),this.force_token_map=Object.fromEntries(e??[])}_call(e,o){let i=this.force_token_map[e.length];return exists(i)&&(o.data.fill(-1/0),o.data[i]=0),o}}class ForcedBOSTokenLogitsProcessor extends LogitsProcessor{constructor(e){super(),this.bos_token_id=e}_call(e,o){return e.length===1&&(o.data.fill(-1/0),o.data[this.bos_token_id]=0),o}}class ForcedEOSTokenLogitsProcessor extends LogitsProcessor{constructor(e,o){super(),this.max_length=e,this.forced_eos_token_id=o}_call(e,o){}}class SuppressTokensAtBeginLogitsProcessor extends LogitsProcessor{constructor(e,o){super(),this.begin_suppress_tokens=e,this.begin_index=o}_call(e,o){if(e.length===this.begin_index)for(let i of this.begin_suppress_tokens)o.data[i]=-1/0;return o}}class WhisperTimeStampLogitsProcessor extends LogitsProcessor{constructor(e){super(),this.eos_token_id=e.eos_token_id,this.no_timestamps_token_id=e.no_timestamps_token_id,this.timestamp_begin=this.no_timestamps_token_id+1,this.begin_index=(e.forced_decoder_ids||[]).length+2,e.forced_decoder_ids.slice(-1)[0][1]===this.no_timestamps_token_id&&(this.begin_index-=1),this.max_initial_timestamp_index=e.max_initial_timestamp_index}_call(e,o){const i=o.data;if(i[this.no_timestamps_token_id]=-1/0,e.length===this.begin_index-1)return i.fill(-1/0),i[this.timestamp_begin]=0,o;const u=e.slice(this.begin_index),n=u.length>=1&&u[u.length-1]>=this.timestamp_begin,s=u.length<2||u[u.length-2]>=this.timestamp_begin;if(n&&(s?i.subarray(this.timestamp_begin).fill(-1/0):i.subarray(0,this.eos_token_id).fill(-1/0)),e.length===this.begin_index&&this.max_initial_timestamp_index!==null){const a=this.timestamp_begin+this.max_initial_timestamp_index;i.subarray(a+1).fill(-1/0)}const c=log_softmax(i),d=Math.log(c.subarray(this.timestamp_begin).map(Math.exp).reduce((a,l)=>a+l)),r=max(c.subarray(0,this.timestamp_begin))[0];return d>r&&i.subarray(0,this.timestamp_begin).fill(-1/0),o}}class NoRepeatNGramLogitsProcessor extends LogitsProcessor{constructor(e){super(),this.no_repeat_ngram_size=e}getNgrams(e){const o=e.length,i=[];for(let n=0;n<o+1-this.no_repeat_ngram_size;++n){const s=[];for(let c=0;c<this.no_repeat_ngram_size;++c)s.push(e[n+c]);i.push(s)}const u=new Map;for(const n of i){const s=n.slice(0,n.length-1),c=JSON.stringify(s),d=u.get(c)??[];d.push(n[n.length-1]),u.set(c,d)}return u}getGeneratedNgrams(e,o){const i=o.slice(o.length+1-this.no_repeat_ngram_size,o.length);return e.get(JSON.stringify(i))??[]}calcBannedNgramTokens(e){const o=[];if(e.length+1<this.no_repeat_ngram_size)return o;{const i=this.getNgrams(e);return this.getGeneratedNgrams(i,e)}}_call(e,o){const i=this.calcBannedNgramTokens(e);for(const u of i)o.data[u]=-1/0;return o}}class RepetitionPenaltyLogitsProcessor extends LogitsProcessor{constructor(e){super(),this.penalty=e}_call(e,o){for(const i of e)o.data[i]<0?o.data[i]*=this.penalty:o.data[i]/=this.penalty;return o}}class MinLengthLogitsProcessor extends LogitsProcessor{constructor(e,o){super(),this.min_length=e,this.eos_token_id=Array.isArray(o)?o:[o]}_call(e,o){if(e.length<this.min_length)for(const i of this.eos_token_id)o.data[i]=-1/0;return o}}class MinNewTokensLengthLogitsProcessor extends LogitsProcessor{constructor(e,o,i){super(),this.prompt_length_to_skip=e,this.min_new_tokens=o,this.eos_token_id=Array.isArray(i)?i:[i]}_call(e,o){if(e.length-this.prompt_length_to_skip<this.min_new_tokens)for(const u of this.eos_token_id)o.data[u]=-1/0;return o}}class NoBadWordsLogitsProcessor extends LogitsProcessor{constructor(e,o){super(),this.bad_words_ids=e,this.eos_token_id=Array.isArray(o)?o:[o]}_call(e,o){for(const i of this.bad_words_ids){let u=!0;for(let n=1;n<=i.length-1&&i.length<e.length;++n)if(i.at(-n-1)!==e.at(-n)){u=!1;break}u&&(o.data[i.at(-1)]=-1/0)}return o}}const GenerationConfig=class{constructor(t={}){this.max_length=t.max_length??20,this.max_new_tokens=t.max_new_tokens??null,this.min_length=t.min_length??0,this.min_new_tokens=t.min_new_tokens??null,this.early_stopping=t.early_stopping??!1,this.max_time=t.max_time??null,this.do_sample=t.do_sample??!1,this.num_beams=t.num_beams??1,this.num_beam_groups=t.num_beam_groups??1,this.penalty_alpha=t.penalty_alpha??null,this.use_cache=t.use_cache??!0,this.temperature=t.temperature??1,this.top_k=t.top_k??50,this.top_p=t.top_p??1,this.typical_p=t.typical_p??1,this.epsilon_cutoff=t.epsilon_cutoff??0,this.eta_cutoff=t.eta_cutoff??0,this.diversity_penalty=t.diversity_penalty??0,this.repetition_penalty=t.repetition_penalty??1,this.encoder_repetition_penalty=t.encoder_repetition_penalty??1,this.length_penalty=t.length_penalty??1,this.no_repeat_ngram_size=t.no_repeat_ngram_size??0,this.bad_words_ids=t.bad_words_ids??null,this.force_words_ids=t.force_words_ids??null,this.renormalize_logits=t.renormalize_logits??!1,this.constraints=t.constraints??null,this.forced_bos_token_id=t.forced_bos_token_id??null,this.forced_eos_token_id=t.forced_eos_token_id??null,this.remove_invalid_values=t.remove_invalid_values??!1,this.exponential_decay_length_penalty=t.exponential_decay_length_penalty??null,this.suppress_tokens=t.suppress_tokens??null,this.begin_suppress_tokens=t.begin_suppress_tokens??null,this.forced_decoder_ids=t.forced_decoder_ids??null,this.num_return_sequences=t.num_return_sequences??1,this.output_attentions=t.output_attentions??!1,this.output_hidden_states=t.output_hidden_states??!1,this.output_scores=t.output_scores??!1,this.return_dict_in_generate=t.return_dict_in_generate??!1,this.pad_token_id=t.pad_token_id??null,this.bos_token_id=t.bos_token_id??null,this.eos_token_id=t.eos_token_id??null,this.encoder_no_repeat_ngram_size=t.encoder_no_repeat_ngram_size??0,this.decoder_start_token_id=t.decoder_start_token_id??null,this.generation_kwargs=t.generation_kwargs??{}}};class Sampler extends Callable{constructor(e){super(),this.generation_config=e}_call(e,o=-1){return this.sample(e,o)}sample(e,o){throw Error("sample should be implemented in subclasses.")}getLogits(e,o){let i=e.dims.at(-1),u=e.data;if(o===-1)u=u.slice(-i);else{let n=o*i;u=u.slice(n,n+i)}return this.generation_config.temperature>0&&(u=u.map(n=>n/this.generation_config.temperature)),u}randomSelect(e){let o=e.reduce((u,n)=>u+n,0),i=Math.random()*o;for(let u=0;u<e.length;++u)if(i-=e[u],i<=0)return u;return 0}static getSampler(e){if(e.do_sample)return new MultinomialSampler(e);if(e.num_beams>1)return new BeamSearchSampler(e);if(e.num_return_sequences>1)throw Error(`num_return_sequences has to be 1 when doing greedy search, but is ${e.num_return_sequences}.`);return new GreedySampler(e)}}class GreedySampler extends Sampler{sample(e,o=-1){let i=this.getLogits(e,o);return[[max(i)[1],0]]}}class MultinomialSampler extends Sampler{sample(e,o=-1){let i=e.dims.at(-1);this.generation_config.top_k>0&&(i=Math.min(this.generation_config.top_k,i));const u=this.getLogits(e,o),n=getTopItems(u,i),s=softmax(n.map(c=>c[1]));return Array.from({length:this.generation_config.num_beams},()=>{const c=this.randomSelect(s);return[n[c][0],Math.log(s[c])]})}}class BeamSearchSampler extends Sampler{sample(e,o=-1){let i=e.dims.at(-1);this.generation_config.top_k>0&&(i=Math.min(this.generation_config.top_k,i));const u=this.getLogits(e,o),n=getTopItems(u,i),s=softmax(n.map(c=>c[1]));return Array.from({length:this.generation_config.num_beams},(c,d)=>[n[d][0],Math.log(s[d])])}}const MODEL_TYPES={EncoderOnly:0,EncoderDecoder:1,Seq2Seq:2,Vision2Seq:3,DecoderOnly:4,MaskGeneration:5},MODEL_TYPE_MAPPING=new Map,MODEL_NAME_TO_CLASS_MAPPING=new Map,MODEL_CLASS_TO_NAME_MAPPING=new Map;async function constructSession(t,e,o){const i=`onnx/${e}${o.quantized?"_quantized":""}.onnx`,u=await getModelFile(t,i,!0,o),n=o.session_options??{};if(n.externalData!==void 0)for(let s=0;s<n.externalData.length;s++){const c=n.externalData[s];if(typeof c.data=="string"){const d=await getModelFile(t,c.data,!0,o);c.data=d}}return await createInferenceSession(u,n)}function validateInputs(t,e){const o=Object.create(null),i=[];for(const s of t.inputNames){const c=e[s];if(!(c instanceof Tensor)){i.push(s);continue}o[s]=isONNXProxy()?c.clone():c}if(i.length>0)throw new Error(`An error occurred during model execution: "Missing the following inputs: ${i.join(", ")}.`);const u=Object.keys(e).length,n=t.inputNames.length;if(u>n){let s=Object.keys(e).filter(c=>!t.inputNames.includes(c));console.warn(`WARNING: Too many inputs were provided (${u} > ${n}). The following inputs will be ignored: "${s.join(", ")}".`)}return o}async function sessionRun(t,e){const o=validateInputs(t,e);try{const i=Object.fromEntries(Object.entries(o).map(([n,s])=>[n,s.ort_tensor]));let u=await t.run(i);u=replaceTensors(u);for(const[n,s]of Object.entries(o))n.startsWith("past_key_values")&&s.dispose();return u}catch(i){throw console.error(`An error occurred during model execution: "${i}".`),console.error("Inputs given to model:",o),i}}function replaceTensors(t){for(let e in t)isONNXTensor(t[e])?t[e]=new Tensor(t[e]):typeof t[e]=="object"&&replaceTensors(t[e]);return t}function toI64Tensor(t){if(t instanceof Tensor)return t;if(t.length===0)throw Error("items must be non-empty");if(Array.isArray(t[0])){if(t.some(e=>e.length!==t[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(t.flat().map(e=>BigInt(e))),[t.length,t[0].length])}else return new Tensor("int64",BigInt64Array.from(t.map(e=>BigInt(e))),[1,t.length])}function prepareAttentionMask(t,e){let o=t.config.pad_token_id??null,i=t.config.eos_token_id??null;isIntegralNumber(i)&&(i=[i]);let u=e.indexOf(o)!==-1,n=i===null||!i.includes(o);if(u&&n){let s=BigInt64Array.from(e.data.map(c=>c!=o));return new Tensor("int64",s,e.dims)}else return ones_like(e)}function preparePositionIds(t,e,o){if(!t.inputNames.includes("position_ids"))return;const i=new BigInt64Array(e.attention_mask.data.length);for(let u=0;u<e.attention_mask.dims[0];++u){let n=u*e.attention_mask.dims[1],s=BigInt(0);for(let c=0;c<e.attention_mask.dims[1];++c){const d=n+c;e.attention_mask.data[d]===0n?i[d]=BigInt(1):(i[d]=s,s+=e.attention_mask.data[d])}}e.position_ids=new Tensor("int64",i,e.attention_mask.dims),o&&(e.position_ids=e.position_ids.slice(null,-1).unsqueeze_(-1))}function boolTensor(t){return new Tensor("bool",[t],[1])}async function seq2seqForward(t,e){let{encoder_outputs:o,past_key_values:i}=e;o||(o=(await encoderForward(t,e)).last_hidden_state);let u={input_ids:e.decoder_input_ids,encoder_hidden_states:o};const n=!!i;t.decoder_merged_session.inputNames.includes("use_cache_branch")&&(u.use_cache_branch=boolTensor(n)),t.decoder_merged_session.inputNames.includes("encoder_attention_mask")&&(u.encoder_attention_mask=e.attention_mask),preparePositionIds(t.decoder_merged_session,u,n),t.addPastKeyValues(u,i);const s=await sessionRun(t.decoder_merged_session,u);let c=s.logits;i=t.getPastKeyValues(s,i);const d=t.getAttentions(s);return new Seq2SeqLMOutput({logits:c,past_key_values:i,encoder_outputs:o,...d})}function seq2seqStartBeams(t,e,o,i){let u=[],n=0;const s=t.requires_attention_mask??!0;let c=o.decoder_input_ids??o.decoder_start_token_id??o.bos_token_id??o.eos_token_id;c instanceof Tensor?c=c.tolist().flat():Array.isArray(c)||(c=[c]);for(let d of e){d.dims=[1,...d.dims];let r={inputs:d,encoder_outputs:null,prev_model_outputs:null,output_token_ids:c,done:!1,score:0,id:n++};s&&(r.attention_mask=prepareAttentionMask(t,d)),u.push(r)}return u}async function seq2seqRunBeam(t,e){const o=t.main_input_name;let i=e.output_token_ids;e.prev_model_outputs&&(i=i.slice(-1));let u={[o]:e.inputs,decoder_input_ids:toI64Tensor(i),encoder_outputs:e.encoder_outputs,past_key_values:e.prev_model_outputs?.past_key_values};e.attention_mask&&(u.attention_mask=e.attention_mask);let n=await t.forward(u);return e.prev_model_outputs=n,e.encoder_outputs=n.encoder_outputs,n}function seq2seqUpdatebeam(t,e){t.output_token_ids=[...t.output_token_ids,e]}async function encoderForward(t,e){const o=Object.create(null);for(const i of t.session.inputNames)o[i]=e[i];return t.session.inputNames.includes("token_type_ids")&&!o.token_type_ids&&(o.token_type_ids=new Tensor("int64",new BigInt64Array(o.input_ids.data.length),o.input_ids.dims)),await sessionRun(t.session,o)}async function decoderForward(t,e){let{input_ids:o,past_key_values:i,attention_mask:u}=e,n={input_ids:o,attention_mask:u??prepareAttentionMask(t,o)};const s=!!i;t.session.inputNames.includes("use_cache_branch")&&(n.use_cache_branch=boolTensor(s)),preparePositionIds(t.session,n,s),t.addPastKeyValues(n,i);let c=await sessionRun(t.session,n),d=c.logits;return i=t.getPastKeyValues(c,i),{logits:d,past_key_values:i}}function decoderStartBeams(t,e,o,i,u){let n=[],s=0;for(let c of e){let d=c.tolist().map(Number);c.dims=[1,...c.dims];let r;u?(r=u[s],r.dims=[1,...r.dims]):r=prepareAttentionMask(t,c);let a={input:c,model_input_ids:c,attention_mask:r,prev_model_outputs:null,output_token_ids:d,num_output_tokens:i,done:!1,score:0,id:s++};n.push(a)}return n}async function decoderRunBeam(t,e){let o=new BigInt64Array(e.output_token_ids.length).fill(1n),i={input_ids:e.model_input_ids,attention_mask:new Tensor("int64",o,[1,o.length]),past_key_values:e.prev_model_outputs?.past_key_values},u=await t.forward(i);return e.prev_model_outputs=u,u}function decoderUpdatebeam(t,e){t.output_token_ids=[...t.output_token_ids,e],t.model_input_ids=new Tensor("int64",[BigInt(e)],[1,1])}class PreTrainedModel extends Callable{main_input_name="input_ids";constructor(e,o){super(),this.config=e,this.session=o;const i=MODEL_CLASS_TO_NAME_MAPPING.get(this.constructor),u=MODEL_TYPE_MAPPING.get(i);this.can_generate=!1,this._runBeam=null,this._getStartBeams=null,this._updateBeam=null,this._forward=null,u===MODEL_TYPES.DecoderOnly?(this.can_generate=!0,this._runBeam=decoderRunBeam,this._getStartBeams=decoderStartBeams,this._updateBeam=decoderUpdatebeam,this._forward=decoderForward):u===MODEL_TYPES.Seq2Seq||u===MODEL_TYPES.Vision2Seq?(this.can_generate=!0,this._runBeam=seq2seqRunBeam,this._getStartBeams=seq2seqStartBeams,this._updateBeam=seq2seqUpdatebeam,this._forward=seq2seqForward):u===MODEL_TYPES.EncoderDecoder?this._forward=encoderForward:this._forward=encoderForward}async dispose(){const e=[];for(let o of Object.keys(this)){let i=this[o];i?.handler?.dispose!==void 0&&e.push(i.handler.dispose())}return await Promise.all(e)}static async from_pretrained(e,{quantized:o=!0,progress_callback:i=null,config:u=null,cache_dir:n=null,local_files_only:s=!1,revision:c="main",model_file_name:d=null,session_options:r={}}={}){let a={quantized:o,progress_callback:i,config:u,cache_dir:n,local_files_only:s,revision:c,model_file_name:d,session_options:r};const l=MODEL_CLASS_TO_NAME_MAPPING.get(this),h=MODEL_TYPE_MAPPING.get(l);let f;return h===MODEL_TYPES.DecoderOnly?f=await Promise.all([AutoConfig.from_pretrained(e,a),constructSession(e,a.model_file_name??"decoder_model_merged",a),getModelJSON(e,"generation_config.json",!1,a)]):h===MODEL_TYPES.Seq2Seq||h===MODEL_TYPES.Vision2Seq?f=await Promise.all([AutoConfig.from_pretrained(e,a),constructSession(e,"encoder_model",a),constructSession(e,"decoder_model_merged",a),getModelJSON(e,"generation_config.json",!1,a)]):h===MODEL_TYPES.MaskGeneration?f=await Promise.all([AutoConfig.from_pretrained(e,a),constructSession(e,"vision_encoder",a),constructSession(e,"prompt_encoder_mask_decoder",a)]):h===MODEL_TYPES.EncoderDecoder?f=await Promise.all([AutoConfig.from_pretrained(e,a),constructSession(e,"encoder_model",a),constructSession(e,"decoder_model_merged",a)]):(h!==MODEL_TYPES.EncoderOnly&&console.warn(`Model type for '${l??u?.model_type}' not found, assuming encoder-only architecture. Please report this at https://github.com/xenova/transformers.js/issues/new/choose.`),f=await Promise.all([AutoConfig.from_pretrained(e,a),constructSession(e,a.model_file_name??"model",a)])),new this(...f)}async _call(e){return await this.forward(e)}async forward(e){return await this._forward(this,e)}_get_logits_processor(e,o,i=null){const u=new LogitsProcessorList;if(e.repetition_penalty!==null&&e.repetition_penalty!==1&&u.push(new RepetitionPenaltyLogitsProcessor(e.repetition_penalty)),e.no_repeat_ngram_size!==null&&e.no_repeat_ngram_size>0&&u.push(new NoRepeatNGramLogitsProcessor(e.no_repeat_ngram_size)),e.bad_words_ids!==null&&u.push(new NoBadWordsLogitsProcessor(e.bad_words_ids,e.eos_token_id)),e.min_length!==null&&e.eos_token_id!==null&&e.min_length>0&&u.push(new MinLengthLogitsProcessor(e.min_length,e.eos_token_id)),e.min_new_tokens!==null&&e.eos_token_id!==null&&e.min_new_tokens>0&&u.push(new MinNewTokensLengthLogitsProcessor(o,e.min_new_tokens,e.eos_token_id)),e.forced_bos_token_id!==null&&u.push(new ForcedBOSTokenLogitsProcessor(e.forced_bos_token_id)),e.forced_eos_token_id!==null&&u.push(new ForcedEOSTokenLogitsProcessor(e.max_length,e.forced_eos_token_id)),e.begin_suppress_tokens!==null){let n=o>1||e.forced_bos_token_id===null?o:o+1;e.forced_decoder_ids!==null&&(n+=e.forced_decoder_ids[e.forced_decoder_ids.length-1][0]),u.push(new SuppressTokensAtBeginLogitsProcessor(e.begin_suppress_tokens,n))}return e.forced_decoder_ids!==null&&u.push(new ForceTokensLogitsProcessor(e.forced_decoder_ids)),i!==null&&u.extend(i),u}_get_generation_config(e){let o=new GenerationConfig(this.config);return"generation_config"in this&&Object.assign(o,this.generation_config),e!==null&&Object.assign(o,e),o}async generate(e,o=null,i=null,{inputs_attention_mask:u=null}={}){if(!this.can_generate){let b=`The current model class (${MODEL_CLASS_TO_NAME_MAPPING.get(this.constructor)}) is not compatible with \`.generate()\`, as it doesn't have a language model head.`;const v=this.config.model_type,T=MODEL_WITH_LM_HEAD_MAPPING_NAMES.get(v)??MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING_NAMES.get(v)??MODEL_FOR_SPEECH_SEQ_2_SEQ_MAPPING_NAMES.get(v)??MODEL_FOR_VISION_2_SEQ_MAPPING_NAMES.get(v);throw T&&(b+=` Please use the following class instead: '${T[0]}'`),Error(b)}if(!(e instanceof Tensor)&&!isTypedArray(e)&&!Array.isArray(e))throw Error(`\`inputs\` must be a Tensor, TypedArray, or Array, but is "${e.constructor.name}".`);let n;if(this.config.is_encoder_decoder)n=0;else if(n=e instanceof Tensor?e.dims.at(-1):e.length,n===0)throw Error("Must supply a non-empty array of input token ids.");o=this._get_generation_config(o),i=i??new LogitsProcessorList,i=this._get_logits_processor(o,n,i);let s=o.eos_token_id;s!==null&&!Array.isArray(s)&&(s=[s]);let c=1;const d=c+(o.max_new_tokens??1/0),r=Number.isInteger(o.max_length)&&(o.max_new_tokens??null)===null;let a=Sampler.getSampler(o),l=this.getStartBeams(e,o,c,u);for(;l.some(g=>!g.done)&&c<d;){let g=[];for(let b of l){if(b.done){g.push(b);continue}if(r&&b.output_token_ids.length>=o.max_length){b.done=!0,g.push(b);continue}let v=await this.runBeam(b);o.output_attentions&&this.addAttentionsToBeam(b,v),o.output_scores;let T=v.logits.slice(null,-1,null);i(b.output_token_ids,T);let E=a(T);for(let[M,F]of E){let Y={...b};this.updateBeam(Y,M),Y.score+=F,s&&s.includes(M)&&(Y.done=!0),g.push(Y)}}++c,g=this.groupBeams(g).map(b=>b.sort((v,T)=>T.score-v.score).slice(0,o.num_beams)),l=g.flat(),o.callback_function&&o.callback_function(l)}const h=this.groupBeams(l),f=g=>h.map(b=>o.num_return_sequences>1?b.slice(0,o.num_return_sequences).map(v=>v[g]):[b[0][g]]).flat(),m=f("output_token_ids");if(o.return_dict_in_generate){const g=f("decoder_attentions"),b=f("cross_attentions");return{sequences:m,decoder_attentions:g,cross_attentions:b}}else return m}addAttentionsToBeam(e,o){if(this.config.is_encoder_decoder){if(!o.cross_attentions||o.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`.");e.cross_attentions||(e.cross_attentions=[]),e.cross_attentions.push(o.cross_attentions)}if(!o.decoder_attentions||o.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`.");e.decoder_attentions||(e.decoder_attentions=[]),e.decoder_attentions.push(o.decoder_attentions)}groupBeams(e){const o=Object.create(null);for(const i of e)o[i.id]===void 0?o[i.id]=[i]:o[i.id].push(i);return Object.values(o)}getPastKeyValues(e,o){const i=Object.create(null);for(const u in e)if(u.startsWith("present")){let n=u.replace("present","past_key_values");o&&u.includes("encoder")?i[n]=o[n]:i[n]=e[u]}return i}getAttentions(e){const o=Object.create(null);for(const i of["cross_attentions","decoder_attentions"]){const u=[];for(const n in e)if(n.startsWith(i)){const s=n.split(".").pop();u[s]=e[n]}o[i]=u}return o}addPastKeyValues(e,o){if(o)Object.assign(e,o);else{const u=this.config.precision||"float32",n=u==="float16"?new Uint16Array:[];if(this.config.is_encoder_decoder&&(this.add_encoder_pkv??!0)){let s=[1,this.num_encoder_heads,0,this.encoder_dim_kv],c=[1,this.num_decoder_heads,0,this.decoder_dim_kv];for(let d=0;d<this.num_decoder_layers;++d)e[`past_key_values.${d}.encoder.key`]=new Tensor(u,n,s),e[`past_key_values.${d}.encoder.value`]=new Tensor(u,n,s),e[`past_key_values.${d}.decoder.key`]=new Tensor(u,n,c),e[`past_key_values.${d}.decoder.value`]=new Tensor(u,n,c)}else if(this.config.model_type==="falcon"){let s=[1*this.num_heads,0,this.dim_kv];for(let c=0;c<this.num_layers;++c)e[`past_key_values.${c}.key`]=new Tensor(u,n,s),e[`past_key_values.${c}.value`]=new Tensor(u,n,s)}else if(this.config.multi_query){let s=[1*this.num_heads,0,2*this.dim_kv];for(let c=0;c<this.num_layers;++c)e[`past_key_values.${c}.key_value`]=new Tensor(u,n,s)}else if(this.config.model_type==="bloom"){let s=[1*this.num_heads,this.dim_kv,0],c=[1*this.num_heads,0,this.dim_kv];for(let d=0;d<this.num_layers;++d)e[`past_key_values.${d}.key`]=new Tensor(u,n,s),e[`past_key_values.${d}.value`]=new Tensor(u,n,c)}else{let s=[1,this.num_heads,0,this.dim_kv];for(let c=0;c<this.num_layers;++c)e[`past_key_values.${c}.key`]=new Tensor(u,n,s),e[`past_key_values.${c}.value`]=new Tensor(u,n,s)}}}getStartBeams(e,o,i,u){return this._getStartBeams(this,e,o,i,u)}async runBeam(e){return await this._runBeam(this,e)}updateBeam(e,o){return this._updateBeam(e,o)}}class ModelOutput{}class BertPreTrainedModel extends PreTrainedModel{}class BertModel extends BertPreTrainedModel{}class BertForMaskedLM extends BertPreTrainedModel{async _call(e){return new MaskedLMOutput(await super._call(e))}}class BertForSequenceClassification extends BertPreTrainedModel{async _call(e){return new SequenceClassifierOutput(await super._call(e))}}class BertForTokenClassification extends BertPreTrainedModel{async _call(e){return new TokenClassifierOutput(await super._call(e))}}class BertForQuestionAnswering extends BertPreTrainedModel{async _call(e){return new QuestionAnsweringModelOutput(await super._call(e))}}class NomicBertPreTrainedModel extends PreTrainedModel{}class NomicBertModel extends NomicBertPreTrainedModel{}class RoFormerPreTrainedModel extends PreTrainedModel{}class RoFormerModel extends RoFormerPreTrainedModel{}class RoFormerForMaskedLM extends RoFormerPreTrainedModel{async _call(e){return new MaskedLMOutput(await super._call(e))}}class RoFormerForSequenceClassification extends RoFormerPreTrainedModel{async _call(e){return new SequenceClassifierOutput(await super._call(e))}}class RoFormerForTokenClassification extends RoFormerPreTrainedModel{async _call(e){return new TokenClassifierOutput(await super._call(e))}}class RoFormerForQuestionAnswering extends RoFormerPreTrainedModel{async _call(e){return new QuestionAnsweringModelOutput(await super._call(e))}}class ConvBertPreTrainedModel extends PreTrainedModel{}class ConvBertModel extends ConvBertPreTrainedModel{}class ConvBertForMaskedLM extends ConvBertPreTrainedModel{async _call(e){return new MaskedLMOutput(await super._call(e))}}class ConvBertForSequenceClassification extends ConvBertPreTrainedModel{async _call(e){return new SequenceClassifierOutput(await super._call(e))}}class ConvBertForTokenClassification extends ConvBertPreTrainedModel{async _call(e){return new TokenClassifierOutput(await super._call(e))}}class ConvBertForQuestionAnswering extends ConvBertPreTrainedModel{async _call(e){return new QuestionAnsweringModelOutput(await super._call(e))}}class ElectraPreTrainedModel extends PreTrainedModel{}class ElectraModel extends ElectraPreTrainedModel{}class ElectraForMaskedLM extends ElectraPreTrainedModel{async _call(e){return new MaskedLMOutput(await super._call(e))}}class ElectraForSequenceClassification extends ElectraPreTrainedModel{async _call(e){return new SequenceClassifierOutput(await super._call(e))}}class ElectraForTokenClassification extends ElectraPreTrainedModel{async _call(e){return new TokenClassifierOutput(await super._call(e))}}class ElectraForQuestionAnswering extends ElectraPreTrainedModel{async _call(e){return new QuestionAnsweringModelOutput(await super._call(e))}}class CamembertPreTrainedModel extends PreTrainedModel{}class CamembertModel extends CamembertPreTrainedModel{}class CamembertForMaskedLM extends CamembertPreTrainedModel{async _call(e){return new MaskedLMOutput(await super._call(e))}}class CamembertForSequenceClassification extends CamembertPreTrainedModel{async _call(e){return new SequenceClassifierOutput(await super._call(e))}}class CamembertForTokenClassification extends CamembertPreTrainedModel{async _call(e){return new TokenClassifierOutput(await super._call(e))}}class CamembertForQuestionAnswering extends CamembertPreTrainedModel{async _call(e){return new QuestionAnsweringModelOutput(await super._call(e))}}class DebertaPreTrainedModel extends PreTrainedModel{}class DebertaModel extends DebertaPreTrainedModel{}class DebertaForMaskedLM extends DebertaPreTrainedModel{async _call(e){return new MaskedLMOutput(await super._call(e))}}class DebertaForSequenceClassification extends DebertaPreTrainedModel{async _call(e){return new SequenceClassifierOutput(await super._call(e))}}class DebertaForTokenClassification extends DebertaPreTrainedModel{async _call(e){return new TokenClassifierOutput(await super._call(e))}}class DebertaForQuestionAnswering extends DebertaPreTrainedModel{async _call(e){return new QuestionAnsweringModelOutput(await super._call(e))}}class DebertaV2PreTrainedModel extends PreTrainedModel{}class DebertaV2Model extends DebertaV2PreTrainedModel{}class DebertaV2ForMaskedLM extends DebertaV2PreTrainedModel{async _call(e){return new MaskedLMOutput(await super._call(e))}}class DebertaV2ForSequenceClassification extends DebertaV2PreTrainedModel{async _call(e){return new SequenceClassifierOutput(await super._call(e))}}class DebertaV2ForTokenClassification extends DebertaV2PreTrainedModel{async _call(e){return new TokenClassifierOutput(await super._call(e))}}class DebertaV2ForQuestionAnswering extends DebertaV2PreTrainedModel{async _call(e){return new QuestionAnsweringModelOutput(await super._call(e))}}class DistilBertPreTrainedModel extends PreTrainedModel{}class DistilBertModel extends DistilBertPreTrainedModel{}class DistilBertForSequenceClassification extends DistilBertPreTrainedModel{async _call(e){return new SequenceClassifierOutput(await super._call(e))}}class DistilBertForTokenClassification extends DistilBertPreTrainedModel{async _call(e){return new TokenClassifierOutput(await super._call(e))}}class DistilBertForQuestionAnswering extends DistilBertPreTrainedModel{async _call(e){return new QuestionAnsweringModelOutput(await super._call(e))}}class DistilBertForMaskedLM extends DistilBertPreTrainedModel{async _call(e){return new MaskedLMOutput(await super._call(e))}}class EsmPreTrainedModel extends PreTrainedModel{}class EsmModel extends EsmPreTrainedModel{}class EsmForMaskedLM extends EsmPreTrainedModel{async _call(e){return new MaskedLMOutput(await super._call(e))}}class EsmForSequenceClassification extends EsmPreTrainedModel{async _call(e){return new SequenceClassifierOutput(await super._call(e))}}class EsmForTokenClassification extends EsmPreTrainedModel{async _call(e){return new TokenClassifierOutput(await super._call(e))}}class MobileBertPreTrainedModel extends PreTrainedModel{}class MobileBertModel extends MobileBertPreTrainedModel{}class MobileBertForMaskedLM extends MobileBertPreTrainedModel{async _call(e){return new MaskedLMOutput(await super._call(e))}}class MobileBertForSequenceClassification extends MobileBertPreTrainedModel{async _call(e){return new SequenceClassifierOutput(await super._call(e))}}class MobileBertForQuestionAnswering extends MobileBertPreTrainedModel{async _call(e){return new QuestionAnsweringModelOutput(await super._call(e))}}class MPNetPreTrainedModel extends PreTrainedModel{}class MPNetModel extends MPNetPreTrainedModel{}class MPNetForMaskedLM extends MPNetPreTrainedModel{async _call(e){return new MaskedLMOutput(await super._call(e))}}class MPNetForSequenceClassification extends MPNetPreTrainedModel{async _call(e){return new SequenceClassifierOutput(await super._call(e))}}class MPNetForTokenClassification extends MPNetPreTrainedModel{async _call(e){return new TokenClassifierOutput(await super._call(e))}}class MPNetForQuestionAnswering extends MPNetPreTrainedModel{async _call(e){return new QuestionAnsweringModelOutput(await super._call(e))}}class SqueezeBertPreTrainedModel extends PreTrainedModel{}class SqueezeBertModel extends SqueezeBertPreTrainedModel{}class SqueezeBertForMaskedLM extends SqueezeBertPreTrainedModel{async _call(e){return new MaskedLMOutput(await super._call(e))}}class SqueezeBertForSequenceClassification extends SqueezeBertPreTrainedModel{async _call(e){return new SequenceClassifierOutput(await super._call(e))}}class SqueezeBertForQuestionAnswering extends SqueezeBertPreTrainedModel{async _call(e){return new QuestionAnsweringModelOutput(await super._call(e))}}class AlbertPreTrainedModel extends PreTrainedModel{}class AlbertModel extends AlbertPreTrainedModel{}class AlbertForSequenceClassification extends AlbertPreTrainedModel{async _call(e){return new SequenceClassifierOutput(await super._call(e))}}class AlbertForQuestionAnswering extends AlbertPreTrainedModel{async _call(e){return new QuestionAnsweringModelOutput(await super._call(e))}}class AlbertForMaskedLM extends AlbertPreTrainedModel{async _call(e){return new MaskedLMOutput(await super._call(e))}}class T5PreTrainedModel extends PreTrainedModel{}class T5Model extends T5PreTrainedModel{}class T5ForConditionalGeneration extends T5PreTrainedModel{constructor(e,o,i,u){super(e,o),this.decoder_merged_session=i,this.generation_config=u,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(e,o,i,u){super(e,o),this.decoder_merged_session=i,this.generation_config=u,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(e,o,i,u){super(e,o),this.decoder_merged_session=i,this.generation_config=u,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(e,o,i,u){super(e,o),this.decoder_merged_session=i,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 BartForSequenceClassification extends BartPretrainedModel{async _call(e){return new SequenceClassifierOutput(await super._call(e))}}class MBartPreTrainedModel extends PreTrainedModel{}class MBartModel extends MBartPreTrainedModel{}class MBartForConditionalGeneration extends MBartPreTrainedModel{constructor(e,o,i,u){super(e,o),this.decoder_merged_session=i,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 MBartForSequenceClassification extends MBartPreTrainedModel{async _call(e){return new SequenceClassifierOutput(await super._call(e))}}class MBartForCausalLM extends MBartPreTrainedModel{constructor(e,o,i){super(e,o),this.generation_config=i,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(e,o,i,u){super(e,o),this.decoder_merged_session=i,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 BlenderbotSmallPreTrainedModel extends PreTrainedModel{}class BlenderbotSmallModel extends BlenderbotSmallPreTrainedModel{}class BlenderbotSmallForConditionalGeneration extends BlenderbotSmallPreTrainedModel{constructor(e,o,i,u){super(e,o),this.decoder_merged_session=i,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 RobertaPreTrainedModel extends PreTrainedModel{}class RobertaModel extends RobertaPreTrainedModel{}class RobertaForMaskedLM extends RobertaPreTrainedModel{async _call(e){return new MaskedLMOutput(await super._call(e))}}class RobertaForSequenceClassification extends RobertaPreTrainedModel{async _call(e){return new SequenceClassifierOutput(await super._call(e))}}class RobertaForTokenClassification extends RobertaPreTrainedModel{async _call(e){return new TokenClassifierOutput(await super._call(e))}}class RobertaForQuestionAnswering extends RobertaPreTrainedModel{async _call(e){return new QuestionAnsweringModelOutput(await super._call(e))}}class XLMPreTrainedModel extends PreTrainedModel{}class XLMModel extends XLMPreTrainedModel{}class XLMWithLMHeadModel extends XLMPreTrainedModel{async _call(e){return new MaskedLMOutput(await super._call(e))}}class XLMForSequenceClassification extends XLMPreTrainedModel{async _call(e){return new SequenceClassifierOutput(await super._call(e))}}class XLMForTokenClassification extends XLMPreTrainedModel{async _call(e){return new TokenClassifierOutput(await super._call(e))}}class XLMForQuestionAnswering extends XLMPreTrainedModel{async _call(e){return new QuestionAnsweringModelOutput(await super._call(e))}}class XLMRobertaPreTrainedModel extends PreTrainedModel{}class XLMRobertaModel extends XLMRobertaPreTrainedModel{}class XLMRobertaForMaskedLM extends XLMRobertaPreTrainedModel{async _call(e){return new MaskedLMOutput(await super._call(e))}}class XLMRobertaForSequenceClassification extends XLMRobertaPreTrainedModel{async _call(e){return new SequenceClassifierOutput(await super._call(e))}}class XLMRobertaForTokenClassification extends XLMRobertaPreTrainedModel{async _call(e){return new TokenClassifierOutput(await super._call(e))}}class XLMRobertaForQuestionAnswering extends XLMRobertaPreTrainedModel{async _call(e){return new QuestionAnsweringModelOutput(await super._call(e))}}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(e,o,i,u){super(e,o),this.decoder_merged_session=i,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}async generate(e,o=null,i=null){if(o=this._get_generation_config(o),o.return_timestamps??=!1,o.return_timestamps&&(i=[new WhisperTimeStampLogitsProcessor(o)]),o.return_token_timestamps&&(o.output_attentions=!0,o.return_dict_in_generate=!0,o.task==="translate"&&console.warn("Token-level timestamps may not be reliable for task 'translate'."),!o.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 u=await super.generate(e,o,i);return o.return_token_timestamps&&o.alignment_heads&&(u.token_timestamps=this._extract_token_timestamps(u,o.alignment_heads,o.num_frames)),u}_extract_token_timestamps(e,o,i=null,u=.02){if(!e.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 n=this.config.median_filter_width;n===void 0&&(console.warn("Model config has no `median_filter_width`, using default value of 7."),n=7);const s=e.cross_attentions.map(r=>{let a=Array.from({length:this.config.decoder_layers},(b,v)=>cat(r.map(T=>T[v]),2)),l=stack(o.map(([b,v])=>i?a[b].slice(null,v,null,[0,i]):a[b].slice(null,v)));l=l.transpose(1,0,2,3);let[h,f]=std_mean(l,-2,0,!0),m=l.clone();for(let b=0;b<m.dims[0];++b){let v=m[b];for(let T=0;T<v.dims[0];++T){let E=v[T];const M=h[b][T][0],F=f[b][T][0];for(let Y=0;Y<E.dims[0];++Y){let H=E[Y];for(let X=0;X<H.data.length;++X)H.data[X]=(H.data[X]-F.data[X])/M.data[X];H.data.set(medianFilter(H.data,n))}}}return mean(m,1)}),c=[e.sequences.length,e.sequences[0].length],d=new Tensor("float32",new Float32Array(c[0]*c[1]),c);for(let r=0;r<c[0];++r){const a=s[r].neg().squeeze_(0);let[l,h]=dynamicTimeWarping(a),f=Array.from({length:l.length-1},(b,v)=>l[v+1]-l[v]),m=mergeArrays([1],f).map(b=>!!b),g=[];for(let b=0;b<m.length;++b)m[b]&&g.push(h[b]*u);d[r].data.set(g,1)}return d}}class VisionEncoderDecoderModel extends PreTrainedModel{main_input_name="pixel_values";constructor(e,o,i,u){super(e,o),this.decoder_merged_session=i,this.generation_config=u;const n=this.config.encoder,s=this.config.decoder,c=n.model_type;(MODEL_MAPPING_NAMES_ENCODER_ONLY.get(c)??MODEL_MAPPING_NAMES_ENCODER_DECODER.get(c))||console.warn(`Model type for encoder '${c}' not found, assuming encoder-only architecture. Please report this at https://github.com/xenova/transformers.js/issues/new/choose.`);const r=MODEL_WITH_LM_HEAD_MAPPING_NAMES.get(s.model_type);if(!r)throw new Error(`Unable to construct \`VisionEncoderDecoder\` due to unsupported decoder: "${this.config.decoder.model_type}"`);const a=r[1],l=new a(s,i,u);this.add_encoder_pkv="num_decoder_layers"in l,this.add_encoder_pkv?(this.num_decoder_layers=l.num_decoder_layers,this.num_decoder_heads=l.num_decoder_heads,this.decoder_dim_kv=l.decoder_dim_kv,this.num_encoder_layers=l.num_encoder_layers,this.num_encoder_heads=l.num_encoder_heads,this.encoder_dim_kv=l.encoder_dim_kv):(this.num_layers=l.num_layers,this.num_heads=l.num_heads,this.dim_kv=l.dim_kv)}}class CLIPPreTrainedModel extends PreTrainedModel{}class CLIPModel extends CLIPPreTrainedModel{}class CLIPTextModelWithProjection extends CLIPPreTrainedModel{static async from_pretrained(e,o={}){return o.model_file_name??="text_model",super.from_pretrained(e,o)}}class CLIPVisionModelWithProjection extends CLIPPreTrainedModel{static async from_pretrained(e,o={}){return o.model_file_name??="vision_model",super.from_pretrained(e,o)}}class SiglipPreTrainedModel extends PreTrainedModel{}class SiglipModel extends SiglipPreTrainedModel{}class SiglipTextModel extends SiglipPreTrainedModel{static async from_pretrained(e,o={}){return o.model_file_name??="text_model",super.from_pretrained(e,o)}}class SiglipVisionModel extends CLIPPreTrainedModel{static async from_pretrained(e,o={}){return o.model_file_name??="vision_model",super.from_pretrained(e,o)}}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(e,o,i){super(e,o),this.generation_config=i,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(e,o,i){super(e,o),this.generation_config=i,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(e,o,i){super(e,o),this.generation_config=i,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(e,o,i){super(e,o),this.generation_config=i,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(e,o,i){super(e,o),this.generation_config=i,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(e,o,i){super(e,o),this.generation_config=i,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(e,o,i){super(e,o),this.generation_config=i,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(e,o,i){super(e,o),this.generation_config=i,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(e,o,i){super(e,o),this.generation_config=i,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(e,o,i){super(e,o),this.generation_config=i,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(e,o,i){super(e,o),this.generation_config=i,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(e,o,i){super(e,o),this.generation_config=i,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(e){return new SequenceClassifierOutput(await super._call(e))}}class VitMattePreTrainedModel extends PreTrainedModel{}class VitMatteForImageMatting extends VitMattePreTrainedModel{async _call(e){return new ImageMattingOutput(await super._call(e))}}class MobileViTPreTrainedModel extends PreTrainedModel{}class MobileViTModel extends MobileViTPreTrainedModel{}class MobileViTForImageClassification extends MobileViTPreTrainedModel{async _call(e){return new SequenceClassifierOutput(await super._call(e))}}class OwlViTPreTrainedModel extends PreTrainedModel{}class OwlViTModel extends OwlViTPreTrainedModel{}class OwlViTForObjectDetection extends OwlViTPreTrainedModel{}class Owlv2PreTrainedModel extends PreTrainedModel{}class Owlv2Model extends Owlv2PreTrainedModel{}class Owlv2ForObjectDetection extends Owlv2PreTrainedModel{}class BeitPreTrainedModel extends PreTrainedModel{}class BeitModel extends BeitPreTrainedModel{}class BeitForImageClassification extends BeitPreTrainedModel{async _call(e){return new SequenceClassifierOutput(await super._call(e))}}class DetrPreTrainedModel extends PreTrainedModel{}class DetrModel extends DetrPreTrainedModel{}class DetrForObjectDetection extends DetrPreTrainedModel{async _call(e){return new DetrObjectDetectionOutput(await super._call(e))}}class DetrForSegmentation extends DetrPreTrainedModel{async _call(e){return new DetrSegmentationOutput(await super._call(e))}}class DetrObjectDetectionOutput extends ModelOutput{constructor({logits:e,pred_boxes:o}){super(),this.logits=e,this.pred_boxes=o}}class DetrSegmentationOutput extends ModelOutput{constructor({logits:e,pred_boxes:o,pred_masks:i}){super(),this.logits=e,this.pred_boxes=o,this.pred_masks=i}}class TableTransformerPreTrainedModel extends PreTrainedModel{}class TableTransformerModel extends TableTransformerPreTrainedModel{}class TableTransformerForObjectDetection extends TableTransformerPreTrainedModel{async _call(e){return new TableTransformerObjectDetectionOutput(await super._call(e))}}class TableTransformerObjectDetectionOutput extends DetrObjectDetectionOutput{}class DeiTPreTrainedModel extends PreTrainedModel{}class DeiTModel extends DeiTPreTrainedModel{}class DeiTForImageClassification extends DeiTPreTrainedModel{async _call(e){return new SequenceClassifierOutput(await super._call(e))}}class ResNetPreTrainedModel extends PreTrainedModel{}class ResNetModel extends ResNetPreTrainedModel{}class ResNetForImageClassification extends ResNetPreTrainedModel{async _call(e){return new SequenceClassifierOutput(await super._call(e))}}class SwinPreTrainedModel extends PreTrainedModel{}class SwinModel extends SwinPreTrainedModel{}class SwinForImageClassification extends SwinPreTrainedModel{async _call(e){return new SequenceClassifierOutput(await super._call(e))}}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(e){return new SequenceClassifierOutput(await super._call(e))}}class ConvNextV2PreTrainedModel extends PreTrainedModel{}class ConvNextV2Model extends ConvNextV2PreTrainedModel{}class ConvNextV2ForImageClassification extends ConvNextV2PreTrainedModel{async _call(e){return new SequenceClassifierOutput(await super._call(e))}}class Dinov2PreTrainedModel extends PreTrainedModel{}class Dinov2Model extends Dinov2PreTrainedModel{}class Dinov2ForImageClassification extends Dinov2PreTrainedModel{async _call(e){return new SequenceClassifierOutput(await super._call(e))}}class YolosPreTrainedModel extends PreTrainedModel{}class YolosModel extends YolosPreTrainedModel{}class YolosForObjectDetection extends YolosPreTrainedModel{async _call(e){return new YolosObjectDetectionOutput(await super._call(e))}}class YolosObjectDetectionOutput extends ModelOutput{constructor({logits:e,pred_boxes:o}){super(),this.logits=e,this.pred_boxes=o}}class SamPreTrainedModel extends PreTrainedModel{}class SamModel extends SamPreTrainedModel{constructor(e,o,i){super(e,o),this.prompt_encoder_mask_decoder=i}async get_image_embeddings({pixel_values:e}){return await encoderForward(this,{pixel_values:e})}async forward(e){if((!e.image_embeddings||!e.image_positional_embeddings)&&(e={...e,...await this.get_image_embeddings(e)}),!e.input_labels){const o=e.input_points.dims.slice(0,-1),i=o.reduce((u,n)=>u*n,1);e.input_labels=new Tensor("int64",new BigInt64Array(i).fill(1n),o)}return await sessionRun(this.prompt_encoder_mask_decoder,{input_points:e.input_points,input_labels:e.input_labels,image_embeddings:e.image_embeddings,image_positional_embeddings:e.image_positional_embeddings})}async _call(e){return new SamImageSegmentationOutput(await super._call(e))}}class SamImageSegmentationOutput extends ModelOutput{constructor({iou_scores:e,pred_masks:o}){super(),this.iou_scores=e,this.pred_masks=o}}class MarianPreTrainedModel extends PreTrainedModel{}class MarianModel extends MarianPreTrainedModel{}class MarianMTModel extends MarianPreTrainedModel{constructor(e,o,i,u){super(e,o),this.decoder_merged_session=i,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 M2M100PreTrainedModel extends PreTrainedModel{}class M2M100Model extends M2M100PreTrainedModel{}class M2M100ForConditionalGeneration extends M2M100PreTrainedModel{constructor(e,o,i,u){super(e,o),this.decoder_merged_session=i,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 Wav2Vec2PreTrainedModel extends PreTrainedModel{}class Wav2Vec2Model extends Wav2Vec2PreTrainedModel{}class Wav2Vec2ForCTC extends Wav2Vec2PreTrainedModel{async _call(e){return new CausalLMOutput(await super._call(e))}}class Wav2Vec2ForSequenceClassification extends Wav2Vec2PreTrainedModel{async _call(e){return new SequenceClassifierOutput(await super._call(e))}}class Wav2Vec2ForAudioFrameClassification extends Wav2Vec2PreTrainedModel{async _call(e){return new TokenClassifierOutput(await super._call(e))}}class UniSpeechPreTrainedModel extends PreTrainedModel{}class UniSpeechModel extends UniSpeechPreTrainedModel{}class UniSpeechForCTC extends UniSpeechPreTrainedModel{async _call(e){return new CausalLMOutput(await super._call(e))}}class UniSpeechForSequenceClassification extends UniSpeechPreTrainedModel{async _call(e){return new SequenceClassifierOutput(await super._call(e))}}class UniSpeechSatPreTrainedModel extends PreTrainedModel{}class UniSpeechSatModel extends UniSpeechSatPreTrainedModel{}class UniSpeechSatForCTC extends UniSpeechSatPreTrainedModel{async _call(e){return new CausalLMOutput(await super._call(e))}}class UniSpeechSatForSequenceClassification extends UniSpeechSatPreTrainedModel{async _call(e){return new SequenceClassifierOutput(await super._call(e))}}class UniSpeechSatForAudioFrameClassification extends UniSpeechSatPreTrainedModel{async _call(e){return new TokenClassifierOutput(await super._call(e))}}class Wav2Vec2BertPreTrainedModel extends PreTrainedModel{}class Wav2Vec2BertModel extends Wav2Vec2BertPreTrainedModel{}class Wav2Vec2BertForCTC extends Wav2Vec2BertPreTrainedModel{async _call(e){return new CausalLMOutput(await super._call(e))}}class Wav2Vec2BertForSequenceClassification extends Wav2Vec2BertPreTrainedModel{async _call(e){return new SequenceClassifierOutput(await super._call(e))}}class HubertModel extends Wav2Vec2PreTrainedModel{}class HubertForCTC extends Wav2Vec2PreTrainedModel{async _call(e){return new CausalLMOutput(await super._call(e))}}class HubertForSequenceClassification extends Wav2Vec2PreTrainedModel{async _call(e){return new SequenceClassifierOutput(await super._call(e))}}class WavLMPreTrainedModel extends PreTrainedModel{}class WavLMModel extends WavLMPreTrainedModel{}class WavLMForCTC extends WavLMPreTrainedModel{async _call(e){return new CausalLMOutput(await super._call(e))}}class WavLMForSequenceClassification extends WavLMPreTrainedModel{async _call(e){return new SequenceClassifierOutput(await super._call(e))}}class WavLMForXVector extends WavLMPreTrainedModel{async _call(e){return new XVectorOutput(await super._call(e))}}class WavLMForAudioFrameClassification extends WavLMPreTrainedModel{async _call(e){return new TokenClassifierOutput(await super._call(e))}}class SpeechT5PreTrainedModel extends PreTrainedModel{}class SpeechT5ForSpeechToText extends SpeechT5PreTrainedModel{}class SpeechT5ForTextToSpeech extends SpeechT5PreTrainedModel{constructor(e,o,i,u){super(e,o),this.decoder_merged_session=i,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.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(e,o,{threshold:i=.5,minlenratio:u=0,maxlenratio:n=20,vocoder:s=null}={}){const c={input_ids:e},{encoder_outputs:d,encoder_attention_mask:r}=await encoderForward(this,c),a=d.dims[1]/this.config.reduction_factor,l=Math.floor(a*n),h=Math.floor(a*u),f=this.config.num_mel_bins;let m=[],g=null,b=null,v=0;for(;;){++v;const M=boolTensor(!!b);let F;b?F=b.output_sequence_out:F=new Tensor("float32",new Float32Array(f),[1,1,f]);let Y={use_cache_branch:M,output_sequence:F,encoder_attention_mask:r,speaker_embeddings:o,encoder_hidden_states:d};this.addPastKeyValues(Y,g),b=await sessionRun(this.decoder_merged_session,Y),g=this.getPastKeyValues(b,g);const{prob:H,spectrum:X}=b;if(m.push(X),v>=h&&(Array.from(H.data).filter(k=>k>=i).length>0||v>=l))break}const T=cat(m),{waveform:E}=await sessionRun(s.session,{spectrogram:T});return{spectrogram:T,waveform:E}}}class SpeechT5HifiGan extends PreTrainedModel{main_input_name="spectrogram"}class TrOCRPreTrainedModel extends PreTrainedModel{constructor(e,o,i){super(e,o),this.generation_config=i,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(e,o,i){super(e,o),this.generation_config=i,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 Starcoder2PreTrainedModel extends PreTrainedModel{constructor(e,o,i){super(e,o),this.generation_config=i,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 Starcoder2Model extends Starcoder2PreTrainedModel{}class Starcoder2ForCausalLM extends Starcoder2PreTrainedModel{}class FalconPreTrainedModel extends PreTrainedModel{constructor(e,o,i){super(e,o),this.generation_config=i,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(e,o={}){return o.model_file_name??="text_model",super.from_pretrained(e,o)}}class ClapAudioModelWithProjection extends ClapPreTrainedModel{static async from_pretrained(e,o={}){return o.model_file_name??="audio_model",super.from_pretrained(e,o)}}class VitsPreTrainedModel extends PreTrainedModel{}class VitsModel extends VitsPreTrainedModel{async _call(e){return new VitsModelOutput(await super._call(e))}}class SegformerPreTrainedModel extends PreTrainedModel{}class SegformerForImageClassification extends SegformerPreTrainedModel{}class SegformerForSemanticSegmentation extends SegformerPreTrainedModel{}class StableLmPreTrainedModel extends PreTrainedModel{constructor(e,o,i){super(e,o),this.generation_config=i,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 StableLmForCausalLM extends StableLmPreTrainedModel{}class PretrainedMixin{static MODEL_CLASS_MAPPINGS=null;static BASE_IF_FAIL=!1;static async from_pretrained(e,{quantized:o=!0,progress_callback:i=null,config:u=null,cache_dir:n=null,local_files_only:s=!1,revision:c="main",model_file_name:d=null,session_options:r={}}={}){let a={quantized:o,progress_callback:i,config:u,cache_dir:n,local_files_only:s,revision:c,model_file_name:d,session_options:r};if(u=await AutoConfig.from_pretrained(e,a),a.config||(a.config=u),!this.MODEL_CLASS_MAPPINGS)throw new Error("`MODEL_CLASS_MAPPINGS` not implemented for this type of `AutoClass`: "+this.name);for(let l of this.MODEL_CLASS_MAPPINGS){const h=l.get(u.model_type);if(h)return await h[1].from_pretrained(e,a)}if(this.BASE_IF_FAIL)return console.warn(`Unknown model class "${u.model_type}", attempting to construct from base class.`),await PreTrainedModel.from_pretrained(e,a);throw Error(`Unsupported model type: ${u.model_type}`)}}const MODEL_MAPPING_NAMES_ENCODER_ONLY=new Map([["bert",["BertModel",BertModel]],["nomic_bert",["NomicBertModel",NomicBertModel]],["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]],["unispeech",["UniSpeechModel",UniSpeechModel]],["unispeech-sat",["UniSpeechSatModel",UniSpeechSatModel]],["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]],["owlv2",["Owlv2Model",Owlv2Model]],["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]],["starcoder2",["Starcoder2Model",Starcoder2Model]],["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]],["starcoder2",["Starcoder2ForCausalLM",Starcoder2ForCausalLM]],["falcon",["FalconForCausalLM",FalconForCausalLM]],["trocr",["TrOCRForCausalLM",TrOCRForCausalLM]],["stablelm",["StableLmForCausalLM",StableLmForCausalLM]]]),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]],["owlv2",["Owlv2ForObjectDetection",Owlv2ForObjectDetection]]]),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]],["unispeech",["UniSpeechForCTC",UniSpeechForCTC]],["unispeech-sat",["UniSpeechSatForCTC",UniSpeechSatForCTC]],["wavlm",["WavLMForCTC",WavLMForCTC]],["hubert",["HubertForCTC",HubertForCTC]]]),MODEL_FOR_AUDIO_CLASSIFICATION_MAPPING_NAMES=new Map([["wav2vec2",["Wav2Vec2ForSequenceClassification",Wav2Vec2ForSequenceClassification]],["wav2vec2-bert",["Wav2Vec2BertForSequenceClassification",Wav2Vec2BertForSequenceClassification]],["unispeech",["UniSpeechForSequenceClassification",UniSpeechForSequenceClassification]],["unispeech-sat",["UniSpeechSatForSequenceClassification",UniSpeechSatForSequenceClassification]],["wavlm",["WavLMForSequenceClassification",WavLMForSequenceClassification]],["hubert",["HubertForSequenceClassification",HubertForSequenceClassification]],["audio-spectrogram-transformer",["ASTForAudioClassification",ASTForAudioClassification]]]),MODEL_FOR_AUDIO_XVECTOR_MAPPING_NAMES=new Map([["wavlm",["WavLMForXVector",WavLMForXVector]]]),MODEL_FOR_AUDIO_FRAME_CLASSIFICATION_MAPPING_NAMES=new Map([["unispeech-sat",["UniSpeechSatForAudioFrameClassification",UniSpeechSatForAudioFrameClassification]],["wavlm",["WavLMForAudioFrameClassification",WavLMForAudioFrameClassification]],["wav2vec2",["Wav2Vec2ForAudioFrameClassification",Wav2Vec2ForAudioFrameClassification]]]),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],[MODEL_FOR_AUDIO_XVECTOR_MAPPING_NAMES,MODEL_TYPES.EncoderOnly],[MODEL_FOR_AUDIO_FRAME_CLASSIFICATION_MAPPING_NAMES,MODEL_TYPES.EncoderOnly]];for(const[t,e]of MODEL_CLASS_TYPE_MAPPING)for(const[o,i]of t.values())MODEL_TYPE_MAPPING.set(o,e),MODEL_CLASS_TO_NAME_MAPPING.set(i,o),MODEL_NAME_TO_CLASS_MAPPING.set(o,i);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[t,e,o]of CUSTOM_MAPPING)MODEL_TYPE_MAPPING.set(t,o),MODEL_CLASS_TO_NAME_MAPPING.set(e,t),MODEL_NAME_TO_CLASS_MAPPING.set(t,e);class AutoModel extends PretrainedMixin{static MODEL_CLASS_MAPPINGS=MODEL_CLASS_TYPE_MAPPING.map(e=>e[0]);static BASE_IF_FAIL=!0}class Seq2SeqLMOutput extends ModelOutput{constructor({logits:e,past_key_values:o,encoder_outputs:i,decoder_attentions:u=null,cross_attentions:n=null}){super(),this.logits=e,this.past_key_values=o,this.encoder_outputs=i,this.decoder_attentions=u,this.cross_attentions=n}}class SequenceClassifierOutput extends ModelOutput{constructor({logits:e}){super(),this.logits=e}}class XVectorOutput extends ModelOutput{constructor({logits:e,embeddings:o}){super(),this.logits=e,this.embeddings=o}}class TokenClassifierOutput extends ModelOutput{constructor({logits:e}){super(),this.logits=e}}class MaskedLMOutput extends ModelOutput{constructor({logits:e}){super(),this.logits=e}}class QuestionAnsweringModelOutput extends ModelOutput{constructor({start_logits:e,end_logits:o}){super(),this.start_logits=e,this.end_logits=o}}class CausalLMOutput extends ModelOutput{constructor({logits:e}){super(),this.logits=e}}class ImageMattingOutput extends ModelOutput{constructor({alphas:e}){super(),this.alphas=e}}class VitsModelOutput extends ModelOutput{constructor({waveform:e,spectrogram:o}){super(),this.waveform=e,this.spectrogram=o}}const BROWSER_ENV=typeof self<"u",WEBWORKER_ENV=BROWSER_ENV&&self.constructor.name==="DedicatedWorkerGlobalScope";let createCanvasFunction,ImageDataClass,loadImageFunction;if(BROWSER_ENV)createCanvasFunction=(t,e)=>{if(!self.OffscreenCanvas)throw new Error("OffscreenCanvas not supported by this browser.");return new self.OffscreenCanvas(t,e)},loadImageFunction=self.createImageBitmap,ImageDataClass=self.ImageData;else if(sharp)loadImageFunction=async t=>{const o=(await t.metadata()).channels,{data:i,info:u}=await t.raw().toBuffer({resolveWithObject:!0}),n=new RawImage(new Uint8ClampedArray(i),u.width,u.height,u.channels);return o!==void 0&&o!==u.channels&&n.convert(o),n};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(e,o,i,u){this.data=e,this.width=o,this.height=i,this.channels=u}get size(){return[this.width,this.height]}static async read(e){if(e instanceof RawImage)return e;if(typeof e=="string"||e instanceof URL)return await this.fromURL(e);throw new Error(`Unsupported input type: ${typeof e}`)}static async fromURL(e){const o=await getFile(e);if(o.status!==200)throw new Error(`Unable to read image from "${e}" (${o.status} ${o.statusText})`);const i=await o.blob();return this.fromBlob(i)}static async fromBlob(e){if(BROWSER_ENV){const o=await loadImageFunction(e),i=createCanvasFunction(o.width,o.height).getContext("2d");return i.drawImage(o,0,0),new this(i.getImageData(0,0,o.width,o.height).data,o.width,o.height,4)}else{const o=sharp(await e.arrayBuffer());return await loadImageFunction(o)}}static fromTensor(e,o="CHW"){if(e.dims.length!==3)throw new Error(`Tensor should have 3 dimensions, but has ${e.dims.length} dimensions.`);if(o==="CHW")e=e.transpose(1,2,0);else if(o!=="HWC")throw new Error(`Unsupported channel format: ${o}`);if(!(e.data instanceof Uint8ClampedArray||e.data instanceof Uint8Array))throw new Error(`Unsupported tensor type: ${e.type}`);switch(e.dims[2]){case 1:case 2:case 3:case 4:return new RawImage(e.data,e.dims[1],e.dims[0],e.dims[2]);default:throw new Error(`Unsupported number of channels: ${e.dims[2]}`)}}grayscale(){if(this.channels===1)return this;const e=new Uint8ClampedArray(this.width*this.height*1);switch(this.channels){case 3:case 4:for(let o=0,i=0;o<this.data.length;o+=this.channels){const u=this.data[o],n=this.data[o+1],s=this.data[o+2];e[i++]=Math.round(.2989*u+.587*n+.114*s)}break;default:throw new Error(`Conversion failed due to unsupported number of channels: ${this.channels}`)}return this._update(e,this.width,this.height,1)}rgb(){if(this.channels===3)return this;const e=new Uint8ClampedArray(this.width*this.height*3);switch(this.channels){case 1:for(let o=0,i=0;o<this.data.length;++o)e[i++]=this.data[o],e[i++]=this.data[o],e[i++]=this.data[o];break;case 4:for(let o=0,i=0;o<this.data.length;o+=4)e[i++]=this.data[o],e[i++]=this.data[o+1],e[i++]=this.data[o+2];break;default:throw new Error(`Conversion failed due to unsupported number of channels: ${this.channels}`)}return this._update(e,this.width,this.height,3)}rgba(){if(this.channels===4)return this;const e=new Uint8ClampedArray(this.width*this.height*4);switch(this.channels){case 1:for(let o=0,i=0;o<this.data.length;++o)e[i++]=this.data[o],e[i++]=this.data[o],e[i++]=this.data[o],e[i++]=255;break;case 3:for(let o=0,i=0;o<this.data.length;o+=3)e[i++]=this.data[o],e[i++]=this.data[o+1],e[i++]=this.data[o+2],e[i++]=255;break;default:throw new Error(`Conversion failed due to unsupported number of channels: ${this.channels}`)}return this._update(e,this.width,this.height,4)}async resize(e,o,{resample:i=2}={}){let u=RESAMPLING_MAPPING[i]??i;if(BROWSER_ENV){const n=this.channels,s=this.toCanvas(),c=createCanvasFunction(e,o).getContext("2d");return c.drawImage(s,0,0,e,o),new RawImage(c.getImageData(0,0,e,o).data,e,o,4).convert(n)}else{let n=this.toSharp();switch(u){case"box":case"hamming":(u==="box"||u==="hamming")&&(console.warn(`Resampling method ${u} is not yet supported. Using bilinear instead.`),u="bilinear");case"nearest":case"bilinear":case"bicubic":n=n.affine([e/this.width,0,0,o/this.height],{interpolator:u});break;case"lanczos":n=n.resize({width:e,height:o,fit:"fill",kernel:"lanczos3"});break;default:throw new Error(`Resampling method ${u} is not supported.`)}return await loadImageFunction(n)}}async pad([e,o,i,u]){if(e=Math.max(e,0),o=Math.max(o,0),i=Math.max(i,0),u=Math.max(u,0),e===0&&o===0&&i===0&&u===0)return this;if(BROWSER_ENV){const n=this.channels,s=this.toCanvas(),c=this.width+e+o,d=this.height+i+u,r=createCanvasFunction(c,d).getContext("2d");return r.drawImage(s,0,0,this.width,this.height,e,i,c,d),new RawImage(r.getImageData(0,0,c,d).data,c,d,4).convert(n)}else{const n=this.toSharp().extend({left:e,right:o,top:i,bottom:u});return await loadImageFunction(n)}}async crop([e,o,i,u]){if(e=Math.max(e,0),o=Math.max(o,0),i=Math.min(i,this.width-1),u=Math.min(u,this.height-1),e===0&&o===0&&i===this.width-1&&u===this.height-1)return this;const n=i-e+1,s=u-o+1;if(BROWSER_ENV){const c=this.channels,d=this.toCanvas(),r=createCanvasFunction(n,s).getContext("2d");return r.drawImage(d,e,o,n,s,0,0,n,s),new RawImage(r.getImageData(0,0,n,s).data,n,s,4).convert(c)}else{const c=this.toSharp().extract({left:e,top:o,width:n,height:s});return await loadImageFunction(c)}}async center_crop(e,o){if(this.width===e&&this.height===o)return this;const i=(this.width-e)/2,u=(this.height-o)/2;if(BROWSER_ENV){const n=this.channels,s=this.toCanvas(),c=createCanvasFunction(e,o).getContext("2d");let d=0,r=0,a=0,l=0;return i>=0?d=i:a=-i,u>=0?r=u:l=-u,c.drawImage(s,d,r,e,o,a,l,e,o),new RawImage(c.getImageData(0,0,e,o).data,e,o,4).convert(n)}else{let n=this.toSharp();if(i>=0&&u>=0)n=n.extract({left:Math.floor(i),top:Math.floor(u),width:e,height:o});else if(i<=0&&u<=0){const s=Math.floor(-u),c=Math.floor(-i);n=n.extend({top:s,left:c,right:e-this.width-c,bottom:o-this.height-s})}else{let s=[0,0],c=0;u<0?(s[0]=Math.floor(-u),s[1]=o-this.height-s[0]):c=Math.floor(u);let d=[0,0],r=0;i<0?(d[0]=Math.floor(-i),d[1]=e-this.width-d[0]):r=Math.floor(i),n=n.extend({top:s[0],bottom:s[1],left:d[0],right:d[1]}).extract({left:r,top:c,width:e,height:o})}return await loadImageFunction(n)}}async toBlob(e="image/png",o=1){if(!BROWSER_ENV)throw new Error("toBlob() is only supported in browser environments.");return await this.toCanvas().convertToBlob({type:e,quality:o})}toCanvas(){if(!BROWSER_ENV)throw new Error("toCanvas() is only supported in browser environments.");const e=this.clone().rgba(),o=createCanvasFunction(e.width,e.height),i=new ImageDataClass(e.data,e.width,e.height);return o.getContext("2d").putImageData(i,0,0),o}_update(e,o,i,u=null){return this.data=e,this.width=o,this.height=i,u!==null&&(this.channels=u),this}clone(){return new RawImage(this.data.slice(),this.width,this.height,this.channels)}convert(e){if(this.channels===e)return this;switch(e){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(e){if(BROWSER_ENV){if(WEBWORKER_ENV)throw new Error("Unable to save an image from a Web Worker.");const o=e.split(".").pop().toLowerCase(),i=CONTENT_TYPE_MAP.get(o)??"image/png",u=await this.toBlob(i),n=URL.createObjectURL(u),s=document.createElement("a");s.href=n,s.download=e,s.click(),s.remove()}else{if(env.useFS)return await this.toSharp().toFile(e);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(t){if(t<1)return new Float64Array;if(t===1)return new Float64Array([1]);const e=t-1,o=Math.PI/e,i=new Float64Array(t);for(let u=0;u<t;++u){const n=2*u-e;i[u]=.5+.5*Math.cos(o*n)}return i}const HERTZ_TO_MEL_MAPPING={htk:t=>2595*Math.log10(1+t/700),kaldi:t=>1127*Math.log(1+t/700),slaney:(t,e=1e3,o=15,i=27/Math.log(6.4))=>t>=e?o+Math.log(t/e)*i:3*t/200};function hertz_to_mel(t,e="htk"){const o=HERTZ_TO_MEL_MAPPING[e];if(!o)throw new Error('mel_scale should be one of "htk", "slaney" or "kaldi".');return typeof t=="number"?o(t):t.map(i=>o(i))}const MEL_TO_HERTZ_MAPPING={htk:t=>700*(10**(t/2595)-1),kaldi:t=>700*(Math.exp(t/1127)-1),slaney:(t,e=1e3,o=15,i=Math.log(6.4)/27)=>t>=o?e*Math.exp(i*(t-o)):200*t/3};function mel_to_hertz(t,e="htk"){const o=MEL_TO_HERTZ_MAPPING[e];if(!o)throw new Error('mel_scale should be one of "htk", "slaney" or "kaldi".');return typeof t=="number"?o(t):t.map(i=>o(i))}function _create_triangular_filter_bank(t,e){const o=Float64Array.from({length:e.length-1},(s,c)=>e[c+1]-e[c]),i=Array.from({length:t.length},()=>new Array(e.length));for(let s=0;s<t.length;++s){const c=i[s];for(let d=0;d<e.length;++d)c[d]=e[d]-t[s]}const u=e.length-2,n=Array.from({length:u},()=>new Array(t.length));for(let s=0;s<t.length;++s){const c=i[s];for(let d=0;d<u;++d){const r=-c[d]/o[d],a=c[d+2]/o[d+1];n[d][s]=Math.max(0,Math.min(r,a))}}return n}function linspace(t,e,o){const i=(e-t)/(o-1);return Float64Array.from({length:o},(u,n)=>t+i*n)}function mel_filter_bank(t,e,o,i,u,n=null,s="htk",c=!1){if(n!==null&&n!=="slaney")throw new Error('norm must be one of null or "slaney"');const d=hertz_to_mel(o,s),r=hertz_to_mel(i,s),a=linspace(d,r,e+2);let l=mel_to_hertz(a,s),h;if(c){const m=u/(t*2);h=hertz_to_mel(Float64Array.from({length:t},(g,b)=>b*m),s),l=a}else h=linspace(0,Math.floor(u/2),t);const f=_create_triangular_filter_bank(h,l);if(n!==null&&n==="slaney")for(let m=0;m<e;++m){const g=f[m],b=2/(l[m+2]-l[m]);for(let v=0;v<t;++v)g[v]*=b}return f}function padReflect(t,e,o){const i=new t.constructor(t.length+e+o),u=t.length-1;for(let n=0;n<t.length;++n)i[e+n]=t[n];for(let n=1;n<=e;++n)i[e-n]=t[calculateReflectOffset(n,u)];for(let n=1;n<=o;++n)i[u+e+n]=t[calculateReflectOffset(u-n,u)];return i}function _db_conversion_helper(t,e,o,i,u){if(o<=0)throw new Error("reference must be greater than zero");if(i<=0)throw new Error("min_value must be greater than zero");o=Math.max(i,o);const n=Math.log10(o);for(let s=0;s<t.length;++s)t[s]=e*Math.log10(Math.max(i,t[s])-n);if(u!==null){if(u<=0)throw new Error("db_range must be greater than zero");const s=max(t)[0]-u;for(let c=0;c<t.length;++c)t[c]=Math.max(t[c],s)}return t}function amplitude_to_db(t,e=1,o=1e-5,i=null){return _db_conversion_helper(t,20,e,o,i)}function power_to_db(t,e=1,o=1e-10,i=null){return _db_conversion_helper(t,10,e,o,i)}function spectrogram(t,e,o,i,{fft_length:u=null,power:n=1,center:s=!0,pad_mode:c="reflect",onesided:d=!0,preemphasis:r=null,mel_filters:a=null,mel_floor:l=1e-10,log_mel:h=null,reference:f=1,min_value:m=1e-10,db_range:g=null,remove_dc_offset:b=null,max_num_frames:v=null,do_pad:T=!0,transpose:E=!1}={}){const M=e.length;if(u===null&&(u=o),o>u)throw Error(`frame_length (${o}) may not be larger than fft_length (${u})`);if(M!==o)throw new Error(`Length of the window (${M}) must equal frame_length (${o})`);if(i<=0)throw new Error("hop_length must be greater than zero");if(s){if(c!=="reflect")throw new Error(`pad_mode="${c}" not implemented yet.`);const ie=Math.floor((u-1)/2)+1;t=padReflect(t,ie,ie)}const F=Math.floor(1+Math.floor((t.length-o)/i)),Y=d?Math.floor(u/2)+1:u;let H=F,X=F;v!==null&&(v>F?T&&(X=v):X=H=v);const k=new FFT(u),ne=new Float64Array(u),Ce=new Float64Array(k.outputBufferSize),Me=new Array(H);for(let ie=0;ie<H;++ie){const re=ie*i;for(let V=0;V<o;++V)ne[V]=t[re+V];if(b){let V=0;for(let he=0;he<o;++he)V+=ne[he];const ge=V/o;for(let he=0;he<o;++he)ne[he]-=ge}if(r!==null){for(let V=o-1;V>=1;--V)ne[V]-=r*ne[V-1];ne[0]*=1-r}for(let V=0;V<e.length;++V)ne[V]*=e[V];k.realTransform(Ce,ne);const se=new Array(Y);for(let V=0;V<se.length;++V){const ge=V<<1;se[V]=Ce[ge]**2+Ce[ge+1]**2}Me[ie]=se}if(n!==null&&n!==2){const ie=2/n;for(let re=0;re<Me.length;++re){const se=Me[re];for(let V=0;V<se.length;++V)se[V]**=ie}}const we=a.length,z=new Float32Array(we*X),de=E?[X,we]:[we,X];for(let ie=0;ie<we;++ie){const re=a[ie];for(let se=0;se<H;++se){const V=Me[se];let ge=0;for(let he=0;he<Y;++he)ge+=re[he]*V[he];z[E?se*we+ie:ie*H+se]=Math.max(l,ge)}}if(n!==null&&h!==null){const ie=Math.min(z.length,H*we);switch(h){case"log":for(let re=0;re<ie;++re)z[re]=Math.log(z[re]);break;case"log10":for(let re=0;re<ie;++re)z[re]=Math.log10(z[re]);break;case"dB":if(n===1)amplitude_to_db(z,f,m,g);else if(n===2)power_to_db(z,f,m,g);else throw new Error(`Cannot use log_mel option '${h}' with power ${n}`);break;default:throw new Error(`log_mel must be one of null, 'log', 'log10' or 'dB'. Got '${h}'`)}}return{data:z,dims:de}}function window_function(t,e,{periodic:o=!0,frame_length:i=null,center:u=!0}={}){const n=o?t+1:t;let s;switch(e){case"boxcar":s=new Float64Array(n).fill(1);break;case"hann":case"hann_window":s=hanning(n);break;case"povey":s=hanning(n).map(c=>Math.pow(c,.85));break;default:throw new Error(`Unknown window type ${e}.`)}if(o&&(s=s.subarray(0,t)),i===null)return s;if(t>i)throw new Error(`Length of the window (${t}) may not be larger than frame_length (${i})`);return s}function center_to_corners_format([t,e,o,i]){return[t-o/2,e-i/2,t+o/2,e+i/2]}function post_process_object_detection(t,e=.5,o=null,i=!1){const u=t.logits,n=t.pred_boxes,[s,c,d]=u.dims;if(o!==null&&o.length!==s)throw Error("Make sure that you pass in as many target sizes as the batch dimension of the logits");let r=[];for(let a=0;a<s;++a){let l=o!==null?o[a]:null,h={boxes:[],classes:[],scores:[]},f=u[a],m=n[a];for(let g=0;g<c;++g){let b=f[g],v=[],T;if(i){T=b.sigmoid().data;for(let E=0;E<T.length;++E)T[E]>e&&v.push(E)}else{let E=max(b.data)[1];if(E===d-1)continue;v.push(E),T=softmax(b.data)}for(const E of v){let M=m[g].data;M=center_to_corners_format(M),l!==null&&(M=M.map((F,Y)=>F*l[(Y+1)%2])),h.boxes.push(M),h.classes.push(E),h.scores.push(T[E])}}r.push(h)}return r}function validate_audio_inputs(t,e){if(!(t instanceof Float32Array||t instanceof Float64Array))throw new Error(`${e} expects input to be a Float32Array or a Float64Array, but got ${t?.constructor?.name??typeof t} 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(t,e,o=0,i=null){let u=Math.round(t/e)*e;return i!==null&&u>i&&(u=Math.floor(t/e)*e),u<o&&(u=Math.ceil(t/e)*e),u}function enforce_size_divisibility([t,e],o){return[Math.floor(t/o)*o,Math.floor(e/o)*o]}class FeatureExtractor extends Callable{constructor(e){super(),this.config=e}}class ImageFeatureExtractor extends FeatureExtractor{constructor(e){super(e),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(e,o,i=2){const u=e.height,n=e.width,s=o.height,c=o.width;let d=Math.min(u,s),r=Math.min(n,c);return d===u&&r===n?e:(u>n?r=Math.floor(n*d/u):n>u&&(d=Math.floor(u*r/n)),await e.resize(r,d,{resample:i}))}async crop_margin(e,o=200){const i=e.clone().grayscale(),u=min(i.data)[0],s=max(i.data)[0]-u;if(s===0)return e;const c=o/255;let d=i.width,r=i.height,a=0,l=0;for(let h=0;h<i.height;++h){const f=h*i.width;for(let m=0;m<i.width;++m)(i.data[f+m]-u)/s<c&&(d=Math.min(d,m),r=Math.min(r,h),a=Math.max(a,m),l=Math.max(l,h))}return e=await e.crop([d,r,a,l]),e}pad_image(e,o,i,{mode:u="constant",center:n=!1,constant_values:s=0}={}){const[c,d,r]=o;let a,l;if(typeof i=="number"?(a=i,l=i):(a=i.width,l=i.height),a!==c||l!==d){const h=new Float32Array(a*l*r);if(Array.isArray(s))for(let g=0;g<h.length;++g)h[g]=s[g%r];else s!==0&&h.fill(s);const[f,m]=n?[Math.floor((a-c)/2),Math.floor((l-d)/2)]:[0,0];for(let g=0;g<d;++g){const b=(g+m)*a,v=g*c;for(let T=0;T<c;++T){const E=(b+T+f)*r,M=(v+T)*r;for(let F=0;F<r;++F)h[E+F]=e[M+F]}}if(u==="symmetric"){if(n)throw new Error("`center` padding is not supported when `mode` is set to `symmetric`.");const g=d-1,b=c-1;for(let v=0;v<l;++v){const T=v*a,E=calculateReflectOffset(v,g)*c;for(let M=0;M<a;++M){if(v<d&&M<c)continue;const F=(T+M)*r,Y=(E+calculateReflectOffset(M,b))*r;for(let H=0;H<r;++H)h[F+H]=e[Y+H]}}}e=h,o=[l,a,r]}return[e,o]}rescale(e){for(let o=0;o<e.length;++o)e[o]=this.rescale_factor*e[o]}get_resize_output_image_size(e,o){const[i,u]=e.size;let n,s;if(this.do_thumbnail){const{height:c,width:d}=o;n=Math.min(c,d)}else Number.isInteger(o)?(n=o,s=this.config.max_size??n):o!==void 0&&(n=o.shortest_edge,s=o.longest_edge);if(n!==void 0||s!==void 0){const c=n===void 0?1:Math.max(n/i,n/u),d=i*c,r=u*c,a=s===void 0?1:Math.min(s/d,s/r);let l=Math.floor(Number((d*a).toFixed(2))),h=Math.floor(Number((r*a).toFixed(2)));return this.size_divisibility!==void 0&&([l,h]=enforce_size_divisibility([l,h],this.size_divisibility)),[l,h]}else if(o!==void 0&&o.width!==void 0&&o.height!==void 0){let c=o.width,d=o.height;if(this.config.keep_aspect_ratio&&this.config.ensure_multiple_of){let r=o.height/u,a=o.width/i;Math.abs(1-a)<Math.abs(1-r)?r=a:a=r,d=constraint_to_multiple_of(r*u,this.config.ensure_multiple_of),c=constraint_to_multiple_of(a*i,this.config.ensure_multiple_of)}return[c,d]}else{if(this.size_divisibility!==void 0)return enforce_size_divisibility([i,u],this.size_divisibility);throw new Error(`Could not resize image due to unsupported \`this.size\` option in config: ${JSON.stringify(o)}`)}}async resize(e){const[o,i]=this.get_resize_output_image_size(e,this.size);return await e.resize(o,i,{resample:this.resample})}async preprocess(e,{do_normalize:o=null,do_pad:i=null,do_convert_rgb:u=null,do_convert_grayscale:n=null}={}){this.do_crop_margin&&(e=await this.crop_margin(e));const[s,c]=e.size;if(u??this.do_convert_rgb?e=e.rgb():n&&(e=e.grayscale()),this.do_resize&&(e=await this.resize(e)),this.do_thumbnail&&(e=await this.thumbnail(e,this.size,this.resample)),this.do_center_crop){let f,m;Number.isInteger(this.crop_size)?(f=this.crop_size,m=this.crop_size):(f=this.crop_size.width,m=this.crop_size.height),e=await e.center_crop(f,m)}const d=[e.height,e.width];let r=Float32Array.from(e.data),a=[e.height,e.width,e.channels];if(this.do_rescale&&this.rescale(r),o??this.do_normalize){let f=this.image_mean;Array.isArray(this.image_mean)||(f=new Array(e.channels).fill(f));let m=this.image_std;if(Array.isArray(this.image_std)||(m=new Array(e.channels).fill(f)),f.length!==e.channels||m.length!==e.channels)throw new Error(`When set to arrays, the length of \`image_mean\` (${f.length}) and \`image_std\` (${m.length}) must match the number of channels in the image (${e.channels}).`);for(let g=0;g<r.length;g+=e.channels)for(let b=0;b<e.channels;++b)r[g+b]=(r[g+b]-this.image_mean[b])/this.image_std[b]}if(this.do_pad&&this.pad_size){const f=new Float32Array(this.pad_size.width*this.pad_size.height*e.channels);for(let m=0;m<e.height;++m){const g=m*this.pad_size.width,b=m*e.width;for(let v=0;v<e.width;++v){const T=(g+v)*e.channels,E=(b+v)*e.channels;for(let M=0;M<e.channels;++M)f[T+M]=r[E+M]}}r=f,a=[this.pad_size.height,this.pad_size.width,e.channels]}const l=new Tensor("float32",r,a),h=transpose(l,[2,0,1]);return{original_size:[c,s],reshaped_input_size:d,pixel_values:h}}async _call(e,...o){Array.isArray(e)||(e=[e]);const i=await Promise.all(e.map(n=>this.preprocess(n)));return{pixel_values:stack(i.map(n=>n.pixel_values),0),original_sizes:i.map(n=>n.original_size),reshaped_input_sizes:i.map(n=>n.reshaped_input_size)}}}class SegformerFeatureExtractor extends ImageFeatureExtractor{post_process_semantic_segmentation(e,o=null){const i=e.logits,u=i.dims[0];if(o!==null&&o.length!==u)throw Error("Make sure that you pass in as many target sizes as the batch dimension of the logits");const n=[];for(let s=0;s<u;++s){const c=o!==null?o[s]:null;let d=i[s];c!==null&&(d=interpolate(d,c,"bilinear",!1));const[r,a]=c??d.dims.slice(-2),l=new Tensor("int32",new Int32Array(r*a),[r,a]),h=d[0].data;for(let b=1;b<d.dims[0];++b){const v=d[b].data;for(let T=0;T<v.length;++T)v[T]>h[T]&&(h[T]=v[T],l.data[T]=b)}const f=new Array(d.dims[0]),m=l.data;for(let b=0;b<m.length;++b){const v=m[b];f[v]=v}const g=f.filter(b=>b!==void 0);n.push({segmentation:l,labels:g})}return n}}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(e){super(e),this.crop_pct=this.config.crop_pct??224/256}async resize(e){const o=this.size?.shortest_edge;if(o===void 0)throw new Error("Size dictionary must contain 'shortest_edge' key.");if(o<384){const i=Math.floor(o/this.crop_pct),[u,n]=this.get_resize_output_image_size(e,{shortest_edge:i});e=await e.resize(u,n,{resample:this.resample}),e=await e.center_crop(o,o)}else e=await e.resize(o,o,{resample:this.resample});return e}}class ConvNextImageProcessor extends ConvNextFeatureExtractor{}class ViTFeatureExtractor extends ImageFeatureExtractor{}class ViTImageProcessor extends ImageFeatureExtractor{}class MobileViTFeatureExtractor extends ImageFeatureExtractor{}class OwlViTFeatureExtractor extends ImageFeatureExtractor{post_process_object_detection(...e){return post_process_object_detection(...e)}}class Owlv2ImageProcessor extends OwlViTFeatureExtractor{}class DeiTFeatureExtractor extends ImageFeatureExtractor{}class BeitFeatureExtractor extends ImageFeatureExtractor{}class DonutFeatureExtractor extends ImageFeatureExtractor{pad_image(e,o,i,u={}){const[n,s,c]=o;let d=this.image_mean;Array.isArray(this.image_mean)||(d=new Array(c).fill(d));let r=this.image_std;Array.isArray(r)||(r=new Array(c).fill(d));const a=d.map((l,h)=>-l/this.image_std[h]);return super.pad_image(e,o,i,{center:!0,constant_values:a,...u})}}class NougatImageProcessor extends DonutFeatureExtractor{}class DetrFeatureExtractor extends ImageFeatureExtractor{async _call(e){const o=await super._call(e),i=[o.pixel_values.dims[0],64,64],u=new Tensor("int64",new BigInt64Array(i.reduce((n,s)=>n*s)).fill(1n),i);return{...o,pixel_mask:u}}post_process_object_detection(...e){return post_process_object_detection(...e)}remove_low_and_no_objects(e,o,i,u){let n=[],s=[],c=[];for(let d=0;d<e.dims[0];++d){let r=e[d],a=o[d],l=max(r.data)[1];if(l===u)continue;let f=softmax(r.data)[l];f>i&&(n.push(a),s.push(f),c.push(l))}return[n,s,c]}check_segment_validity(e,o,i,u=.5,n=.8){let s=[],c=0,d=0;for(let a=0;a<e.length;++a)e[a]===i&&(s.push(a),++c),o[i].data[a]>=u&&++d;let r=c>0&&d>0;return r&&(r=c/d>n),[r,s]}compute_segments(e,o,i,u,n,s=null,c=null){let[d,r]=c??e[0].dims,a=new Tensor("int32",new Int32Array(d*r),[d,r]),l=[];if(c!==null)for(let g=0;g<e.length;++g)e[g]=interpolate(e[g],c,"bilinear",!1);let h=new Int32Array(e[0].data.length),f=new Float32Array(e[0].data.length);for(let g=0;g<e.length;++g){let b=o[g];for(let v=0;v<e[g].data.length;++v)e[g].data[v]*=b,e[g].data[v]>f[v]&&(h[v]=g,f[v]=e[g].data[v])}let m=0;for(let g=0;g<i.length;++g){let b=i[g],[v,T]=this.check_segment_validity(h,e,g,u,n);if(v){++m;for(let E of T)a.data[E]=m;l.push({id:m,label_id:b,score:o[g]})}}return[a,l]}post_process_panoptic_segmentation(e,o=.5,i=.5,u=.8,n=null,s=null){n===null&&(console.warn("`label_ids_to_fuse` unset. No instance will be fused."),n=new Set);const c=e.logits,r=e.pred_masks.sigmoid();let[a,l,h]=c.dims;if(h-=1,s!==null&&s.length!==a)throw Error("Make sure that you pass in as many target sizes as the batch dimension of the logits");let f=[];for(let m=0;m<a;++m){let g=s!==null?s[m]:null,b=c[m],v=r[m],[T,E,M]=this.remove_low_and_no_objects(b,v,o,h);if(M.length===0){let[H,X]=g??v.dims.slice(-2),k=new Tensor("int32",new Int32Array(H*X).fill(-1),[H,X]);f.push({segmentation:k,segments_info:[]});continue}let[F,Y]=this.compute_segments(T,E,M,i,u,n,g);f.push({segmentation:F,segments_info:Y})}return f}post_process_instance_segmentation(){throw Error("Not implemented yet")}}class YolosFeatureExtractor extends ImageFeatureExtractor{post_process_object_detection(...e){return post_process_object_detection(...e)}}class SamImageProcessor extends ImageFeatureExtractor{reshape_input_points(e,o,i){e=structuredClone(e);let u=calculateDimensions(e);if(u.length===3)u=[1,...u],e=[e];else if(u.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 n=0;n<e.length;++n){let s=o[n],c=i[n],d=[c[0]/s[0],c[1]/s[1]];for(let r=0;r<e[n].length;++r)for(let a=0;a<e[n][r].length;++a)for(let l=0;l<e[n][r][a].length;++l)e[n][r][a][l]*=d[l]}return new Tensor("float32",Float32Array.from(e.flat(1/0)),u)}add_input_labels(e,o){let i=calculateDimensions(e);if(i.length===2)i=[1,...i],e=[e];else if(i.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(i.some((u,n)=>u!==o.dims[n]))throw Error(`The first ${i.length} dimensions of 'input_points' and 'input_labels' must be the same.`);return new Tensor("int64",e.flat(1/0).map(BigInt),i)}async _call(e,o=null,i=null){const u=await super._call(e);if(o&&(u.input_points=this.reshape_input_points(o,u.original_sizes,u.reshaped_input_sizes)),i){if(!u.input_points)throw Error("`input_points` must be provided if `input_labels` are provided.");u.input_labels=this.add_input_labels(i,u.input_points)}return u}post_process_masks(e,o,i,{mask_threshold:u=0,binarize:n=!0,pad_size:s=null}={}){const c=[];s=s??this.pad_size;const d=[s.height,s.width];for(let r=0;r<o.length;++r){const a=o[r],l=i[r],h=e[r],f=[];for(let m=0;m<h.dims[0];++m){const g=h[m];let b=interpolate(g,d,"bilinear",!1);if(b=b.slice(null,[0,l[0]],[0,l[1]]),b=interpolate(b,a,"bilinear",!1),n){const v=new Uint8Array(b.data.length);for(let T=0;T<b.data.length;++T)b.data[T]>u&&(v[T]=1);b=new Tensor("bool",v,b.dims)}f.push(b)}c.push(stack(f))}return c}generate_crop_boxes(e,o,{crop_n_layers:i=0,overlap_ratio:u=512/1500,points_per_crop:n=32,crop_n_points_downscale_factor:s=1}={}){}}class Swin2SRImageProcessor extends ImageFeatureExtractor{pad_image(e,o,i,u={}){const[n,s,c]=o;return super.pad_image(e,o,{width:n+(i-n%i)%i,height:s+(i-s%i)%i},{mode:"symmetric",center:!1,constant_values:-1,...u})}}class VitMatteImageProcessor extends ImageFeatureExtractor{async _call(e,o){Array.isArray(e)||(e=[e]),Array.isArray(o)||(o=[o]);const i=await Promise.all(e.map(s=>this.preprocess(s))),u=await Promise.all(o.map(s=>this.preprocess(s,{do_normalize:!1,do_convert_rgb:!1,do_convert_grayscale:!0})));return{pixel_values:stack(i.map((s,c)=>cat([s.pixel_values,u[c].pixel_values],0)),0),original_sizes:i.map(s=>s.original_size),reshaped_input_sizes:i.map(s=>s.reshaped_input_size)}}}class WhisperFeatureExtractor extends FeatureExtractor{constructor(e){super(e),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(e){const{data:o,dims:i}=spectrogram(e,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}),u=max(o)[0];for(let n=0;n<o.length;++n)o[n]=(Math.max(o[n],u-8)+4)/4;return{data:o,dims:i}}async _call(e){validate_audio_inputs(e,"WhisperFeatureExtractor");let o;e.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`."),o=e.slice(0,this.config.n_samples)):(o=new Float32Array(this.config.n_samples),o.set(e));const{data:i,dims:u}=this._extract_fbank_features(o);return{input_features:new Tensor("float32",i,[1,...u])}}}class Wav2Vec2FeatureExtractor extends FeatureExtractor{_zero_mean_unit_var_norm(e){const i=e.reduce((n,s)=>n+s,0)/e.length,u=e.reduce((n,s)=>n+(s-i)**2,0)/e.length;return e.map(n=>(n-i)/Math.sqrt(u+1e-7))}async _call(e){validate_audio_inputs(e,"Wav2Vec2FeatureExtractor"),e instanceof Float64Array&&(e=new Float32Array(e));let o=e;this.config.do_normalize&&(o=this._zero_mean_unit_var_norm(o));const i=[1,o.length];return{input_values:new Tensor("float32",o,i),attention_mask:new Tensor("int64",new BigInt64Array(o.length).fill(1n),i)}}}class SeamlessM4TFeatureExtractor extends FeatureExtractor{constructor(e){super(e);const o=this.config.sampling_rate,i=mel_filter_bank(256,this.config.num_mel_bins,20,Math.floor(o/2),o,null,"kaldi",!0);for(let u=0;u<i.length;++u)i[u].push(0);this.mel_filters=i,this.window=window_function(400,"povey",{periodic:!1})}_extract_fbank_features(e,o){return e=e.map(i=>i*32768),spectrogram(e,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:o,transpose:!0})}async _call(e,{padding:o=!0,pad_to_multiple_of:i=2,do_normalize_per_mel_bins:u=!0,return_attention_mask:n=!0}={}){validate_audio_inputs(e,"SeamlessM4TFeatureExtractor");let s=this._extract_fbank_features(e,this.config.max_length);if(u){const[m,g]=s.dims;for(let b=0;b<g;++b){let v=0;for(let F=0;F<m;++F)v+=s.data[F*g+b];const T=v/m;let E=0;for(let F=0;F<m;++F)E+=(s.data[F*g+b]-T)**2;E/=m-1;const M=Math.sqrt(E+1e-7);for(let F=0;F<m;++F){const Y=F*g+b;s.data[Y]=(s.data[Y]-T)/M}}}let c;if(o){const[m,g]=s.dims,b=m%i;if(b>0){const v=new Float32Array(g*(m+b));v.set(s.data),v.fill(this.config.padding_value,s.data.length);const T=m+b;s={data:v,dims:[T,g]},n&&(c=new Tensor("int64",new BigInt64Array(T),[1,T]),c.data.fill(1n,0,m))}}const[d,r]=s.dims,a=this.config.stride;if(d%a!==0)throw new Error(`The number of frames (${d}) must be a multiple of the stride (${a}).`);const h=new Tensor("float32",s.data,s.dims).view(1,Math.floor(d/a),r*a),f={input_features:h};if(n){const m=h.dims[1],g=new Tensor("int64",new BigInt64Array(m),[1,m]);if(c)for(let b=1,v=0;b<d;b+=a,++v)g.data[v]=c.data[b];else g.data.fill(1n);f.attention_mask=g}return f}}class ASTFeatureExtractor extends FeatureExtractor{constructor(e){super(e);const o=this.config.sampling_rate,i=mel_filter_bank(256,this.config.num_mel_bins,20,Math.floor(o/2),o,null,"kaldi",!0);for(let u=0;u<i.length;++u)i[u].push(0);this.mel_filters=i,this.window=window_function(400,"hann",{periodic:!1}),this.mean=this.config.mean,this.std=this.config.std}_extract_fbank_features(e,o){return spectrogram(e,this.window,400,160,{fft_length:512,power:2,center:!1,preemphasis:.97,mel_filters:this.mel_filters,log_mel:"log",mel_floor:1192092955078125e-22,remove_dc_offset:!0,max_num_frames:o,transpose:!0})}async _call(e){validate_audio_inputs(e,"ASTFeatureExtractor");const o=this._extract_fbank_features(e,this.config.max_length);if(this.config.do_normalize){const i=this.std*2;for(let u=0;u<o.data.length;++u)o.data[u]=(o.data[u]-this.mean)/i}return{input_values:new Tensor("float32",o.data,[1,...o.dims])}}}class ClapFeatureExtractor extends FeatureExtractor{constructor(e){super(e),this.mel_filters=mel_filter_bank(this.config.nb_frequency_bins,this.config.feature_size,this.config.frequency_min,this.config.frequency_max,this.config.sampling_rate,null,"htk"),this.mel_filters_slaney=mel_filter_bank(this.config.nb_frequency_bins,this.config.feature_size,this.config.frequency_min,this.config.frequency_max,this.config.sampling_rate,"slaney","slaney"),this.window=window_function(this.config.fft_window_size,"hann")}_get_input_mel(e,o,i,u){let n,s=!1;const c=e.length-o;if(c>0)if(i==="rand_trunc"){s=!0;const d=Math.floor(Math.random()*(c+1));e=e.subarray(d,d+o),n=this._extract_fbank_features(e,this.mel_filters_slaney,this.config.nb_max_samples),n.dims=[1,...n.dims]}else throw new Error(`Truncation strategy "${i}" not implemented`);else{if(c<0){let d=new Float64Array(o);if(d.set(e),u==="repeat")for(let r=e.length;r<o;r+=e.length)d.set(e.subarray(0,Math.min(e.length,o-r)),r);else if(u==="repeatpad")for(let r=e.length;r<-c;r+=e.length)d.set(e,r);e=d}if(i==="fusion")throw new Error(`Truncation strategy "${i}" not implemented`);n=this._extract_fbank_features(e,this.mel_filters_slaney,this.config.nb_max_samples),n.dims=[1,...n.dims]}return{...n,longer:s}}_extract_fbank_features(e,o,i=null){return spectrogram(e,this.window,this.config.fft_window_size,this.config.hop_length,{power:2,mel_filters:o,log_mel:"dB",max_num_frames:i,do_pad:!1,transpose:!0})}async _call(e,{max_length:o=null}={}){validate_audio_inputs(e,"ClapFeatureExtractor");const i=this._get_input_mel(e,o??this.config.nb_max_samples,this.config.truncation,this.config.padding);return{input_features:new Tensor("float32",i.data,[1,...i.dims])}}}class SpeechT5FeatureExtractor extends FeatureExtractor{}class Processor extends Callable{constructor(e){super(),this.feature_extractor=e}async _call(e,...o){return await this.feature_extractor(e,...o)}}class SamProcessor extends Processor{async _call(...e){return await this.feature_extractor(...e)}post_process_masks(...e){return this.feature_extractor.post_process_masks(...e)}reshape_input_points(...e){return this.feature_extractor.reshape_input_points(...e)}}class WhisperProcessor extends Processor{async _call(e){return await this.feature_extractor(e)}}class Wav2Vec2ProcessorWithLM extends Processor{async _call(e){return await this.feature_extractor(e)}}class SpeechT5Processor extends Processor{async _call(e){return await this.feature_extractor(e)}}class OwlViTProcessor extends Processor{}class AutoProcessor{static FEATURE_EXTRACTOR_CLASS_MAPPING={ImageFeatureExtractor,WhisperFeatureExtractor,ViTFeatureExtractor,MobileViTFeatureExtractor,OwlViTFeatureExtractor,Owlv2ImageProcessor,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(e,{progress_callback:o=null,config:i=null,cache_dir:u=null,local_files_only:n=!1,revision:s="main"}={}){let c=i??await getModelJSON(e,"preprocessor_config.json",!0,{progress_callback:o,config:i,cache_dir:u,local_files_only:n,revision:s}),d=c.feature_extractor_type??c.image_processor_type,r=this.FEATURE_EXTRACTOR_CLASS_MAPPING[d];if(!r)if(c.size!==void 0)console.warn(`Feature extractor type "${d}" not found, assuming ImageFeatureExtractor due to size parameter in config.`),r=ImageFeatureExtractor;else throw new Error(`Unknown Feature Extractor type: ${d}`);let a=this.PROCESSOR_CLASS_MAPPING[c.processor_class]??Processor,l=new r(c);return new a(l)}}env.backends.onnx.wasm.proxy=!0;env.backends.onnx.wasm.wasmPaths="https://cdn.jsdelivr.net/npm/onnxruntime-web@1.17.1/dist/";env.backends.onnx.wasm.numThreads=1;env.experimental.useWebGPU=!0;const status=document.getElementById("status"),container=document.getElementById("container"),canvas=document.getElementById("canvas"),outputCanvas=document.getElementById("output-canvas"),video=document.getElementById("video"),sizeSlider=document.getElementById("size"),sizeLabel=document.getElementById("size-value");status.textContent="Loading model...";const model_id="Xenova/modnet";let model;try{model=await AutoModel.from_pretrained(model_id,{quantized:!1})}catch(t){throw status.textContent=t.message,alert(t.message),t}const processor=await AutoProcessor.from_pretrained(model_id);let size=256;processor.feature_extractor.size={shortest_edge:size};sizeSlider.addEventListener("input",()=>{size=Number(sizeSlider.value),processor.feature_extractor.size={shortest_edge:size},sizeLabel.textContent=size});sizeSlider.disabled=!1;status.textContent="Ready";let isProcessing=!1,previousTime;const context=canvas.getContext("2d",{willReadFrequently:!0}),outputContext=outputCanvas.getContext("2d",{willReadFrequently:!0});function updateCanvas(){const{width:t,height:e}=canvas;isProcessing||(isProcessing=!0,async function(){context.drawImage(video,0,0,t,e);const o=context.getImageData(0,0,t,e),i=new RawImage(o.data,t,e,4),u=await processor(i),{output:n}=await model({input:u.pixel_values}),s=await RawImage.fromTensor(n[0].mul(255).to("uint8")).resize(t,e),c=o;for(let d=0;d<s.data.length;++d)c.data[4*d+3]=s.data[d];if(outputContext.putImageData(c,0,0),previousTime!==void 0){const d=1e3/(performance.now()-previousTime);status.textContent=`FPS: ${d.toFixed(2)}`}previousTime=performance.now(),isProcessing=!1}()),window.requestAnimationFrame(updateCanvas)}navigator.mediaDevices.getUserMedia({video:!0}).then(t=>{video.srcObject=t,video.play();const e=t.getVideoTracks()[0],{width:o,height:i}=e.getSettings();video.width=outputCanvas.width=canvas.width=o,video.height=outputCanvas.height=canvas.height=i;const u=o/i,[n,s]=u>720/405?[720,720/u]:[405*u,405];container.style.width=`${n}px`,container.style.height=`${s}px`,setTimeout(updateCanvas,10)}).catch(t=>{alert(t)});