diff --git "a/assets/index-DPr5D1uq.js" "b/assets/index-DPr5D1uq.js"
deleted file mode 100644--- "a/assets/index-DPr5D1uq.js"
+++ /dev/null
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-* @license React
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a!=null&&(a.unstable_strictMode===!0&&(h=!0),a.identifierPrefix!==void 0&&(w=a.identifierPrefix),a.onUncaughtError!==void 0&&(S=a.onUncaughtError),a.onCaughtError!==void 0&&(C=a.onCaughtError),a.onRecoverableError!==void 0&&(L=a.onRecoverableError),a.unstable_transitionCallbacks!==void 0&&(V=a.unstable_transitionCallbacks),a.formState!==void 0&&(ie=a.formState)),r=Jd(t,1,!0,r,a??null,h,w,S,C,L,V,ie),r.context=ef(null),a=r.current,h=mn(),h=Qn(h),w=Pr(h),w.callback=null,kr(a,w,h),a=h,r.current.lanes=a,un(r,a),Yn(r),t[wn]=r.current,El(t),new Ka(r)},reactDomClient_production.version="19.1.0",reactDomClient_production}var hasRequiredClient;function requireClient(){if(hasRequiredClient)return client.exports;hasRequiredClient=1;function v(){if(!(typeof __REACT_DEVTOOLS_GLOBAL_HOOK__>"u"||typeof __REACT_DEVTOOLS_GLOBAL_HOOK__.checkDCE!="function"))try{__REACT_DEVTOOLS_GLOBAL_HOOK__.checkDCE(v)}catch(o){console.error(o)}}return v(),client.exports=requireReactDomClient_production(),client.exports}var clientExports=requireClient();function dispatchCallback(v,o){v&&v(o)}function reverseDictionary(v){return Object.fromEntries(Object.entries(v).map(([o,f])=>[f,o]))}const Callable=class{constructor(){let v=function(...o){return v._call(...o)};return Object.setPrototypeOf(v,new.target.prototype)}_call(...v){throw Error("Must implement _call method in subclass")}};function isTypedArray(v){var o,f,l;return((l=(f=(o=v==null?void 0:v.prototype)==null?void 0:o.__proto__)==null?void 0:f.constructor)==null?void 0:l.name)==="TypedArray"}function isIntegralNumber(v){return Number.isInteger(v)||typeof v=="bigint"}function exists(v){return v!=null}function calculateDimensions(v){const o=[];let f=v;for(;Array.isArray(f);)o.push(f.length),f=f[0];return o}function mergeArrays(...v){return Array.prototype.concat.apply([],v)}function calculateReflectOffset(v,o){return Math.abs((v+o)%(2*o)-o)}const sharp={},ONNX_NODE=Object.freeze(Object.defineProperty({__proto__:null,default:sharp},Symbol.toStringTag,{value:"Module"}));var ortWeb_min$1={exports:{}};const backends={},backendsSortedByPriority=[],registerBackend=(v,o,f)=>{if(o&&typeof o.init=="function"&&typeof o.createSessionHandler=="function"){const l=backends[v];if(l===void 0)backends[v]={backend:o,priority:f};else{if(l.priority>f)return;if(l.priority===f&&l.backend!==o)throw new Error(`cannot register backend "${v}" using priority ${f}`)}if(f>=0){const d=backendsSortedByPriority.indexOf(v);d!==-1&&backendsSortedByPriority.splice(d,1);for(let b=0;b<backendsSortedByPriority.length;b++)if(backends[backendsSortedByPriority[b]].priority<=f){backendsSortedByPriority.splice(b,0,v);return}backendsSortedByPriority.push(v)}return}throw new TypeError("not a valid backend")},resolveBackend=async v=>{const o=v.length===0?backendsSortedByPriority:v,f=[];for(const l of o){const d=backends[l];if(d){if(d.initialized)return d.backend;if(d.aborted)continue;const b=!!d.initPromise;try{return b||(d.initPromise=d.backend.init()),await d.initPromise,d.initialized=!0,d.backend}catch(c){b||f.push({name:l,err:c}),d.aborted=!0}finally{delete d.initPromise}}}throw new Error(`no available backend found. ERR: ${f.map(l=>`[${l.name}] ${l.err}`).join(", ")}`)};class EnvImpl{constructor(){this.wasm={},this.webgl={},this.logLevelInternal="warning"}set logLevel(o){if(o!==void 0){if(typeof o!="string"||["verbose","info","warning","error","fatal"].indexOf(o)===-1)throw new Error(`Unsupported logging level: ${o}`);this.logLevelInternal=o}}get logLevel(){return this.logLevelInternal}}const env$2=new EnvImpl,isBigInt64ArrayAvailable=typeof BigInt64Array<"u"&&typeof BigInt64Array.from=="function",isBigUint64ArrayAvailable=typeof BigUint64Array<"u"&&typeof BigUint64Array.from=="function",NUMERIC_TENSOR_TYPE_TO_TYPEDARRAY_MAP=new Map([["float32",Float32Array],["uint8",Uint8Array],["int8",Int8Array],["uint16",Uint16Array],["int16",Int16Array],["int32",Int32Array],["bool",Uint8Array],["float64",Float64Array],["uint32",Uint32Array]]),NUMERIC_TENSOR_TYPEDARRAY_TO_TYPE_MAP=new Map([[Float32Array,"float32"],[Uint8Array,"uint8"],[Int8Array,"int8"],[Uint16Array,"uint16"],[Int16Array,"int16"],[Int32Array,"int32"],[Float64Array,"float64"],[Uint32Array,"uint32"]]);isBigInt64ArrayAvailable&&(NUMERIC_TENSOR_TYPE_TO_TYPEDARRAY_MAP.set("int64",BigInt64Array),NUMERIC_TENSOR_TYPEDARRAY_TO_TYPE_MAP.set(BigInt64Array,"int64"));isBigUint64ArrayAvailable&&(NUMERIC_TENSOR_TYPE_TO_TYPEDARRAY_MAP.set("uint64",BigUint64Array),NUMERIC_TENSOR_TYPEDARRAY_TO_TYPE_MAP.set(BigUint64Array,"uint64"));const calculateSize=v=>{let o=1;for(let f=0;f<v.length;f++){const l=v[f];if(typeof l!="number"||!Number.isSafeInteger(l))throw new TypeError(`dims[${f}] must be an integer, got: ${l}`);if(l<0)throw new RangeError(`dims[${f}] must be a non-negative integer, got: ${l}`);o*=l}return o};let Tensor$2=class po{constructor(o,f,l){let d,b,c;if(typeof o=="string")if(d=o,c=l,o==="string"){if(!Array.isArray(f))throw new TypeError("A string tensor's data must be a string array.");b=f}else{const _=NUMERIC_TENSOR_TYPE_TO_TYPEDARRAY_MAP.get(o);if(_===void 0)throw new TypeError(`Unsupported tensor type: ${o}.`);if(Array.isArray(f))b=_.from(f);else if(f instanceof _)b=f;else throw new TypeError(`A ${d} tensor's data must be type of ${_}`)}else if(c=f,Array.isArray(o)){if(o.length===0)throw new TypeError("Tensor type cannot be inferred from an empty array.");const _=typeof o[0];if(_==="string")d="string",b=o;else if(_==="boolean")d="bool",b=Uint8Array.from(o);else throw new TypeError(`Invalid element type of data array: ${_}.`)}else{const _=NUMERIC_TENSOR_TYPEDARRAY_TO_TYPE_MAP.get(o.constructor);if(_===void 0)throw new TypeError(`Unsupported type for tensor data: ${o.constructor}.`);d=_,b=o}if(c===void 0)c=[b.length];else if(!Array.isArray(c))throw new TypeError("A tensor's dims must be a number array");const g=calculateSize(c);if(g!==b.length)throw new Error(`Tensor's size(${g}) does not match data length(${b.length}).`);this.dims=c,this.type=d,this.data=b,this.size=g}static bufferToTensor(o,f){if(o===void 0)throw new Error("Image buffer must be defined");if(f.height===void 0||f.width===void 0)throw new Error("Image height and width must be defined");const{height:l,width:d}=f,b=f.norm;let c,g;b===void 0||b.mean===void 0?c=255:c=b.mean,b===void 0||b.bias===void 0?g=0:g=b.bias;const _=f.bitmapFormat!==void 0?f.bitmapFormat:"RGBA",p=f.tensorFormat!==void 0&&f.tensorFormat!==void 0?f.tensorFormat:"RGB",u=l*d,n=p==="RGBA"?new Float32Array(u*4):new Float32Array(u*3);let e=4,i=0,s=1,m=2,y=3,x=0,T=u,E=u*2,P=-1;_==="RGB"&&(e=3,i=0,s=1,m=2,y=-1),p==="RGBA"?P=u*3:p==="RBG"?(x=0,E=u,T=u*2):p==="BGR"&&(E=0,T=u,x=u*2);for(let O=0;O<u;O++,i+=e,m+=e,s+=e,y+=e)n[x++]=(o[i]+g)/c,n[T++]=(o[s]+g)/c,n[E++]=(o[m]+g)/c,P!==-1&&y!==-1&&(n[P++]=(o[y]+g)/c);return p==="RGBA"?new po("float32",n,[1,4,l,d]):new po("float32",n,[1,3,l,d])}static async fromImage(o,f){const l=typeof HTMLImageElement<"u"&&o instanceof HTMLImageElement,d=typeof ImageData<"u"&&o instanceof ImageData,b=typeof ImageBitmap<"u"&&o instanceof ImageBitmap,c=typeof String<"u"&&(o instanceof String||typeof o=="string");let g,_={};if(l){const p=document.createElement("canvas"),u=p.getContext("2d");if(u!=null){let n=o.naturalHeight,e=o.naturalWidth;if(f!==void 0&&f.resizedHeight!==void 0&&f.resizedWidth!==void 0&&(n=f.resizedHeight,e=f.resizedWidth),f!==void 0){if(_=f,f.tensorFormat!==void 0)throw new Error("Image input config format must be RGBA for HTMLImageElement");if(_.tensorFormat="RGBA",f.height!==void 0&&f.height!==n)throw new Error("Image input config height doesn't match HTMLImageElement height");if(_.height=n,f.width!==void 0&&f.width!==e)throw new Error("Image input config width doesn't match HTMLImageElement width");_.width=e}else _.tensorFormat="RGBA",_.height=n,_.width=e;p.width=e,p.height=n,u.drawImage(o,0,0,e,n),g=u.getImageData(0,0,e,n).data}else throw new Error("Can not access image data")}else if(d){const p="RGBA";let u,n;if(f!==void 0&&f.resizedWidth!==void 0&&f.resizedHeight!==void 0?(u=f.resizedHeight,n=f.resizedWidth):(u=o.height,n=o.width),f!==void 0){if(_=f,f.bitmapFormat!==void 0&&f.bitmapFormat!==p)throw new Error("Image input config format must be RGBA for ImageData");_.bitmapFormat="RGBA"}else _.bitmapFormat="RGBA";if(_.height=u,_.width=n,f!==void 0){const e=document.createElement("canvas");e.width=n,e.height=u;const i=e.getContext("2d");if(i!=null)i.putImageData(o,0,0),g=i.getImageData(0,0,n,u).data;else throw new Error("Can not access image data")}else g=o.data}else if(b){if(f===void 0)throw new Error("Please provide image config with format for Imagebitmap");if(f.bitmapFormat!==void 0)throw new Error("Image input config format must be defined for ImageBitmap");const p=document.createElement("canvas").getContext("2d");if(p!=null){const u=o.height,n=o.width;if(p.drawImage(o,0,0,n,u),g=p.getImageData(0,0,n,u).data,f!==void 0){if(f.height!==void 0&&f.height!==u)throw new Error("Image input config height doesn't match ImageBitmap height");if(_.height=u,f.width!==void 0&&f.width!==n)throw new Error("Image input config width doesn't match ImageBitmap width");_.width=n}else _.height=u,_.width=n;return po.bufferToTensor(g,_)}else throw new Error("Can not access image data")}else{if(c)return new Promise((p,u)=>{const n=document.createElement("canvas"),e=n.getContext("2d");if(!o||!e)return u();const i=new Image;i.crossOrigin="Anonymous",i.src=o,i.onload=()=>{n.width=i.width,n.height=i.height,e.drawImage(i,0,0,n.width,n.height);const s=e.getImageData(0,0,n.width,n.height);if(f!==void 0){if(f.height!==void 0&&f.height!==n.height)throw new Error("Image input config height doesn't match ImageBitmap height");if(_.height=n.height,f.width!==void 0&&f.width!==n.width)throw new Error("Image input config width doesn't match ImageBitmap width");_.width=n.width}else _.height=n.height,_.width=n.width;p(po.bufferToTensor(s.data,_))}});throw new Error("Input data provided is not supported - aborted tensor creation")}if(g!==void 0)return po.bufferToTensor(g,_);throw new Error("Input data provided is not supported - aborted tensor creation")}toImageData(o){var f,l;const d=document.createElement("canvas").getContext("2d");let b;if(d!=null){const c=this.dims[3],g=this.dims[2],_=this.dims[1],p=o!==void 0&&o.format!==void 0?o.format:"RGB",u=o!==void 0&&((f=o.norm)===null||f===void 0?void 0:f.mean)!==void 0?o.norm.mean:255,n=o!==void 0&&((l=o.norm)===null||l===void 0?void 0:l.bias)!==void 0?o.norm.bias:0,e=g*c;if(o!==void 0){if(o.height!==void 0&&o.height!==g)throw new Error("Image output config height doesn't match tensor height");if(o.width!==void 0&&o.width!==c)throw new Error("Image output config width doesn't match tensor width");if(o.format!==void 0&&_===4&&o.format!=="RGBA"||_===3&&o.format!=="RGB"&&o.format!=="BGR")throw new Error("Tensor format doesn't match input tensor dims")}const i=4;let s=0,m=1,y=2,x=3,T=0,E=e,P=e*2,O=-1;p==="RGBA"?(T=0,E=e,P=e*2,O=e*3):p==="RGB"?(T=0,E=e,P=e*2):p==="RBG"&&(T=0,P=e,E=e*2),b=d.createImageData(c,g);for(let A=0;A<g*c;s+=i,m+=i,y+=i,x+=i,A++)b.data[s]=(this.data[T++]-n)*u,b.data[m]=(this.data[E++]-n)*u,b.data[y]=(this.data[P++]-n)*u,b.data[x]=O===-1?255:(this.data[O++]-n)*u}else throw new Error("Can not access image data");return b}reshape(o){return new po(this.type,this.data,o)}};const Tensor$1=Tensor$2;let InferenceSession$2=class df{constructor(o){this.handler=o}async run(o,f,l){const d={};let b={};if(typeof o!="object"||o===null||o instanceof Tensor$1||Array.isArray(o))throw new TypeError("'feeds' must be an object that use input names as keys and OnnxValue as corresponding values.");let c=!0;if(typeof f=="object"){if(f===null)throw new TypeError("Unexpected argument[1]: cannot be null.");if(f instanceof Tensor$1)throw new TypeError("'fetches' cannot be a Tensor");if(Array.isArray(f)){if(f.length===0)throw new TypeError("'fetches' cannot be an empty array.");c=!1;for(const p of f){if(typeof p!="string")throw new TypeError("'fetches' must be a string array or an object.");if(this.outputNames.indexOf(p)===-1)throw new RangeError(`'fetches' contains invalid output name: ${p}.`);d[p]=null}if(typeof l=="object"&&l!==null)b=l;else if(typeof l<"u")throw new TypeError("'options' must be an object.")}else{let p=!1;const u=Object.getOwnPropertyNames(f);for(const n of this.outputNames)if(u.indexOf(n)!==-1){const e=f[n];(e===null||e instanceof Tensor$1)&&(p=!0,c=!1,d[n]=e)}if(p){if(typeof l=="object"&&l!==null)b=l;else if(typeof l<"u")throw new TypeError("'options' must be an object.")}else b=f}}else if(typeof f<"u")throw new TypeError("Unexpected argument[1]: must be 'fetches' or 'options'.");for(const p of this.inputNames)if(typeof o[p]>"u")throw new Error(`input '${p}' is missing in 'feeds'.`);if(c)for(const p of this.outputNames)d[p]=null;const g=await this.handler.run(o,d,b),_={};for(const p in g)Object.hasOwnProperty.call(g,p)&&(_[p]=new Tensor$1(g[p].type,g[p].data,g[p].dims));return _}static async create(o,f,l,d){let b,c={};if(typeof o=="string"){if(b=o,typeof f=="object"&&f!==null)c=f;else if(typeof f<"u")throw new TypeError("'options' must be an object.")}else if(o instanceof Uint8Array){if(b=o,typeof f=="object"&&f!==null)c=f;else if(typeof f<"u")throw new TypeError("'options' must be an object.")}else if(o instanceof ArrayBuffer||typeof SharedArrayBuffer<"u"&&o instanceof SharedArrayBuffer){const p=o;let u=0,n=o.byteLength;if(typeof f=="object"&&f!==null)c=f;else if(typeof f=="number"){if(u=f,!Number.isSafeInteger(u))throw new RangeError("'byteOffset' must be an integer.");if(u<0||u>=p.byteLength)throw new RangeError(`'byteOffset' is out of range [0, ${p.byteLength}).`);if(n=o.byteLength-u,typeof l=="number"){if(n=l,!Number.isSafeInteger(n))throw new RangeError("'byteLength' must be an integer.");if(n<=0||u+n>p.byteLength)throw new RangeError(`'byteLength' is out of range (0, ${p.byteLength-u}].`);if(typeof d=="object"&&d!==null)c=d;else if(typeof d<"u")throw new TypeError("'options' must be an object.")}else if(typeof l<"u")throw new TypeError("'byteLength' must be a number.")}else if(typeof f<"u")throw new TypeError("'options' must be an object.");b=new Uint8Array(p,u,n)}else throw new TypeError("Unexpected argument[0]: must be 'path' or 'buffer'.");const g=(c.executionProviders||[]).map(p=>typeof p=="string"?p:p.name),_=await(await resolveBackend(g)).createSessionHandler(b,c);return new df(_)}startProfiling(){this.handler.startProfiling()}endProfiling(){this.handler.endProfiling()}get inputNames(){return this.handler.inputNames}get outputNames(){return this.handler.outputNames}};const InferenceSession$1=InferenceSession$2,lib=Object.freeze(Object.defineProperty({__proto__:null,InferenceSession:InferenceSession$1,Tensor:Tensor$1,env:env$2,registerBackend},Symbol.toStringTag,{value:"Module"})),require$$0=getAugmentedNamespace(lib);/*!
-* ONNX Runtime Web v1.14.0
-* Copyright (c) Microsoft Corporation. All rights reserved.
-* Licensed under the MIT License.
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k=this.high,$=this.low;return[k>>>24,k>>>16&255,k>>>8&255,255&k,$>>>24,$>>>16&255,$>>>8&255,255&$]},f.fromBytes=function(k,$,W){return W?f.fromBytesLE(k,$):f.fromBytesBE(k,$)},f.fromBytesLE=function(k,$){return new f(k[0]|k[1]<<8|k[2]<<16|k[3]<<24,k[4]|k[5]<<8|k[6]<<16|k[7]<<24,$)},f.fromBytesBE=function(k,$){return new f(k[4]<<24|k[5]<<16|k[6]<<8|k[7],k[0]<<24|k[1]<<16|k[2]<<8|k[3],$)}},1446:(v,o,f)=>{var l,d,b,c=f(2100),g=c.Reader,_=c.Writer,p=c.util,u=c.roots.default||(c.roots.default={});u.onnx=((b={}).Version=(l={},(d=Object.create(l))[l[0]="_START_VERSION"]=0,d[l[1]="IR_VERSION_2017_10_10"]=1,d[l[2]="IR_VERSION_2017_10_30"]=2,d[l[3]="IR_VERSION_2017_11_3"]=3,d[l[4]="IR_VERSION_2019_1_22"]=4,d[l[5]="IR_VERSION"]=5,d),b.AttributeProto=function(){function n(e){if(this.floats=[],this.ints=[],this.strings=[],this.tensors=[],this.graphs=[],e)for(var i=Object.keys(e),s=0;s<i.length;++s)e[i[s]]!=null&&(this[i[s]]=e[i[s]])}return n.prototype.name="",n.prototype.refAttrName="",n.prototype.docString="",n.prototype.type=0,n.prototype.f=0,n.prototype.i=p.Long?p.Long.fromBits(0,0,!1):0,n.prototype.s=p.newBuffer([]),n.prototype.t=null,n.prototype.g=null,n.prototype.floats=p.emptyArray,n.prototype.ints=p.emptyArray,n.prototype.strings=p.emptyArray,n.prototype.tensors=p.emptyArray,n.prototype.graphs=p.emptyArray,n.create=function(e){return new n(e)},n.encode=function(e,i){if(i||(i=_.create()),e.name!=null&&e.hasOwnProperty("name")&&i.uint32(10).string(e.name),e.f!=null&&e.hasOwnProperty("f")&&i.uint32(21).float(e.f),e.i!=null&&e.hasOwnProperty("i")&&i.uint32(24).int64(e.i),e.s!=null&&e.hasOwnProperty("s")&&i.uint32(34).bytes(e.s),e.t!=null&&e.hasOwnProperty("t")&&u.onnx.TensorProto.encode(e.t,i.uint32(42).fork()).ldelim(),e.g!=null&&e.hasOwnProperty("g")&&u.onnx.GraphProto.encode(e.g,i.uint32(50).fork()).ldelim(),e.floats!=null&&e.floats.length){i.uint32(58).fork();for(var s=0;s<e.floats.length;++s)i.float(e.floats[s]);i.ldelim()}if(e.ints!=null&&e.ints.length){for(i.uint32(66).fork(),s=0;s<e.ints.length;++s)i.int64(e.ints[s]);i.ldelim()}if(e.strings!=null&&e.strings.length)for(s=0;s<e.strings.length;++s)i.uint32(74).bytes(e.strings[s]);if(e.tensors!=null&&e.tensors.length)for(s=0;s<e.tensors.length;++s)u.onnx.TensorProto.encode(e.tensors[s],i.uint32(82).fork()).ldelim();if(e.graphs!=null&&e.graphs.length)for(s=0;s<e.graphs.length;++s)u.onnx.GraphProto.encode(e.graphs[s],i.uint32(90).fork()).ldelim();return e.docString!=null&&e.hasOwnProperty("docString")&&i.uint32(106).string(e.docString),e.type!=null&&e.hasOwnProperty("type")&&i.uint32(160).int32(e.type),e.refAttrName!=null&&e.hasOwnProperty("refAttrName")&&i.uint32(170).string(e.refAttrName),i},n.encodeDelimited=function(e,i){return this.encode(e,i).ldelim()},n.decode=function(e,i){e instanceof g||(e=g.create(e));for(var s=i===void 0?e.len:e.pos+i,m=new u.onnx.AttributeProto;e.pos<s;){var y=e.uint32();switch(y>>>3){case 1:m.name=e.string();break;case 21:m.refAttrName=e.string();break;case 13:m.docString=e.string();break;case 20:m.type=e.int32();break;case 2:m.f=e.float();break;case 3:m.i=e.int64();break;case 4:m.s=e.bytes();break;case 5:m.t=u.onnx.TensorProto.decode(e,e.uint32());break;case 6:m.g=u.onnx.GraphProto.decode(e,e.uint32());break;case 7:if(m.floats&&m.floats.length||(m.floats=[]),(7&y)==2)for(var x=e.uint32()+e.pos;e.pos<x;)m.floats.push(e.float());else m.floats.push(e.float());break;case 8:if(m.ints&&m.ints.length||(m.ints=[]),(7&y)==2)for(x=e.uint32()+e.pos;e.pos<x;)m.ints.push(e.int64());else m.ints.push(e.int64());break;case 9:m.strings&&m.strings.length||(m.strings=[]),m.strings.push(e.bytes());break;case 10:m.tensors&&m.tensors.length||(m.tensors=[]),m.tensors.push(u.onnx.TensorProto.decode(e,e.uint32()));break;case 11:m.graphs&&m.graphs.length||(m.graphs=[]),m.graphs.push(u.onnx.GraphProto.decode(e,e.uint32()));break;default:e.skipType(7&y)}}return m},n.decodeDelimited=function(e){return e instanceof g||(e=new g(e)),this.decode(e,e.uint32())},n.verify=function(e){if(typeof e!="object"||e===null)return"object expected";if(e.name!=null&&e.hasOwnProperty("name")&&!p.isString(e.name))return"name: string expected";if(e.refAttrName!=null&&e.hasOwnProperty("refAttrName")&&!p.isString(e.refAttrName))return"refAttrName: string expected";if(e.docString!=null&&e.hasOwnProperty("docString")&&!p.isString(e.docString))return"docString: string expected";if(e.type!=null&&e.hasOwnProperty("type"))switch(e.type){default:return"type: enum value expected";case 0:case 1:case 2:case 3:case 4:case 5:case 6:case 7:case 8:case 9:case 10:}if(e.f!=null&&e.hasOwnProperty("f")&&typeof e.f!="number")return"f: number expected";if(e.i!=null&&e.hasOwnProperty("i")&&!(p.isInteger(e.i)||e.i&&p.isInteger(e.i.low)&&p.isInteger(e.i.high)))return"i: integer|Long expected";if(e.s!=null&&e.hasOwnProperty("s")&&!(e.s&&typeof e.s.length=="number"||p.isString(e.s)))return"s: buffer expected";if(e.t!=null&&e.hasOwnProperty("t")&&(s=u.onnx.TensorProto.verify(e.t)))return"t."+s;if(e.g!=null&&e.hasOwnProperty("g")&&(s=u.onnx.GraphProto.verify(e.g)))return"g."+s;if(e.floats!=null&&e.hasOwnProperty("floats")){if(!Array.isArray(e.floats))return"floats: array expected";for(var i=0;i<e.floats.length;++i)if(typeof e.floats[i]!="number")return"floats: number[] expected"}if(e.ints!=null&&e.hasOwnProperty("ints")){if(!Array.isArray(e.ints))return"ints: array expected";for(i=0;i<e.ints.length;++i)if(!(p.isInteger(e.ints[i])||e.ints[i]&&p.isInteger(e.ints[i].low)&&p.isInteger(e.ints[i].high)))return"ints: integer|Long[] expected"}if(e.strings!=null&&e.hasOwnProperty("strings")){if(!Array.isArray(e.strings))return"strings: array expected";for(i=0;i<e.strings.length;++i)if(!(e.strings[i]&&typeof e.strings[i].length=="number"||p.isString(e.strings[i])))return"strings: buffer[] 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8:i.type=8;break;case"TENSORS":case 9:i.type=9;break;case"GRAPHS":case 10:i.type=10}if(e.f!=null&&(i.f=Number(e.f)),e.i!=null&&(p.Long?(i.i=p.Long.fromValue(e.i)).unsigned=!1:typeof e.i=="string"?i.i=parseInt(e.i,10):typeof e.i=="number"?i.i=e.i:typeof e.i=="object"&&(i.i=new p.LongBits(e.i.low>>>0,e.i.high>>>0).toNumber())),e.s!=null&&(typeof e.s=="string"?p.base64.decode(e.s,i.s=p.newBuffer(p.base64.length(e.s)),0):e.s.length&&(i.s=e.s)),e.t!=null){if(typeof e.t!="object")throw TypeError(".onnx.AttributeProto.t: object expected");i.t=u.onnx.TensorProto.fromObject(e.t)}if(e.g!=null){if(typeof e.g!="object")throw TypeError(".onnx.AttributeProto.g: object expected");i.g=u.onnx.GraphProto.fromObject(e.g)}if(e.floats){if(!Array.isArray(e.floats))throw TypeError(".onnx.AttributeProto.floats: array expected");i.floats=[];for(var s=0;s<e.floats.length;++s)i.floats[s]=Number(e.floats[s])}if(e.ints){if(!Array.isArray(e.ints))throw TypeError(".onnx.AttributeProto.ints: array 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TypeError(".onnx.AttributeProto.graphs: array expected");for(i.graphs=[],s=0;s<e.graphs.length;++s){if(typeof e.graphs[s]!="object")throw TypeError(".onnx.AttributeProto.graphs: object expected");i.graphs[s]=u.onnx.GraphProto.fromObject(e.graphs[s])}}return i},n.toObject=function(e,i){i||(i={});var s={};if((i.arrays||i.defaults)&&(s.floats=[],s.ints=[],s.strings=[],s.tensors=[],s.graphs=[]),i.defaults){if(s.name="",s.f=0,p.Long){var m=new p.Long(0,0,!1);s.i=i.longs===String?m.toString():i.longs===Number?m.toNumber():m}else s.i=i.longs===String?"0":0;i.bytes===String?s.s="":(s.s=[],i.bytes!==Array&&(s.s=p.newBuffer(s.s))),s.t=null,s.g=null,s.docString="",s.type=i.enums===String?"UNDEFINED":0,s.refAttrName=""}if(e.name!=null&&e.hasOwnProperty("name")&&(s.name=e.name),e.f!=null&&e.hasOwnProperty("f")&&(s.f=i.json&&!isFinite(e.f)?String(e.f):e.f),e.i!=null&&e.hasOwnProperty("i")&&(typeof 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p.LongBits(e.ints[y].low>>>0,e.ints[y].high>>>0).toNumber():e.ints[y];if(e.strings&&e.strings.length)for(s.strings=[],y=0;y<e.strings.length;++y)s.strings[y]=i.bytes===String?p.base64.encode(e.strings[y],0,e.strings[y].length):i.bytes===Array?Array.prototype.slice.call(e.strings[y]):e.strings[y];if(e.tensors&&e.tensors.length)for(s.tensors=[],y=0;y<e.tensors.length;++y)s.tensors[y]=u.onnx.TensorProto.toObject(e.tensors[y],i);if(e.graphs&&e.graphs.length)for(s.graphs=[],y=0;y<e.graphs.length;++y)s.graphs[y]=u.onnx.GraphProto.toObject(e.graphs[y],i);return e.docString!=null&&e.hasOwnProperty("docString")&&(s.docString=e.docString),e.type!=null&&e.hasOwnProperty("type")&&(s.type=i.enums===String?u.onnx.AttributeProto.AttributeType[e.type]:e.type),e.refAttrName!=null&&e.hasOwnProperty("refAttrName")&&(s.refAttrName=e.refAttrName),s},n.prototype.toJSON=function(){return this.constructor.toObject(this,c.util.toJSONOptions)},n.AttributeType=function(){var e={},i=Object.create(e);return i[e[0]="UNDEFINED"]=0,i[e[1]="FLOAT"]=1,i[e[2]="INT"]=2,i[e[3]="STRING"]=3,i[e[4]="TENSOR"]=4,i[e[5]="GRAPH"]=5,i[e[6]="FLOATS"]=6,i[e[7]="INTS"]=7,i[e[8]="STRINGS"]=8,i[e[9]="TENSORS"]=9,i[e[10]="GRAPHS"]=10,i}(),n}(),b.ValueInfoProto=function(){function n(e){if(e)for(var i=Object.keys(e),s=0;s<i.length;++s)e[i[s]]!=null&&(this[i[s]]=e[i[s]])}return n.prototype.name="",n.prototype.type=null,n.prototype.docString="",n.create=function(e){return new n(e)},n.encode=function(e,i){return i||(i=_.create()),e.name!=null&&e.hasOwnProperty("name")&&i.uint32(10).string(e.name),e.type!=null&&e.hasOwnProperty("type")&&u.onnx.TypeProto.encode(e.type,i.uint32(18).fork()).ldelim(),e.docString!=null&&e.hasOwnProperty("docString")&&i.uint32(26).string(e.docString),i},n.encodeDelimited=function(e,i){return this.encode(e,i).ldelim()},n.decode=function(e,i){e instanceof g||(e=g.create(e));for(var s=i===void 0?e.len:e.pos+i,m=new u.onnx.ValueInfoProto;e.pos<s;){var y=e.uint32();switch(y>>>3){case 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e.docString!=null&&(i.docString=String(e.docString)),i},n.toObject=function(e,i){i||(i={});var s={};return i.defaults&&(s.name="",s.type=null,s.docString=""),e.name!=null&&e.hasOwnProperty("name")&&(s.name=e.name),e.type!=null&&e.hasOwnProperty("type")&&(s.type=u.onnx.TypeProto.toObject(e.type,i)),e.docString!=null&&e.hasOwnProperty("docString")&&(s.docString=e.docString),s},n.prototype.toJSON=function(){return this.constructor.toObject(this,c.util.toJSONOptions)},n}(),b.NodeProto=function(){function n(e){if(this.input=[],this.output=[],this.attribute=[],e)for(var i=Object.keys(e),s=0;s<i.length;++s)e[i[s]]!=null&&(this[i[s]]=e[i[s]])}return n.prototype.input=p.emptyArray,n.prototype.output=p.emptyArray,n.prototype.name="",n.prototype.opType="",n.prototype.domain="",n.prototype.attribute=p.emptyArray,n.prototype.docString="",n.create=function(e){return new n(e)},n.encode=function(e,i){if(i||(i=_.create()),e.input!=null&&e.input.length)for(var s=0;s<e.input.length;++s)i.uint32(10).string(e.input[s]);if(e.output!=null&&e.output.length)for(s=0;s<e.output.length;++s)i.uint32(18).string(e.output[s]);if(e.name!=null&&e.hasOwnProperty("name")&&i.uint32(26).string(e.name),e.opType!=null&&e.hasOwnProperty("opType")&&i.uint32(34).string(e.opType),e.attribute!=null&&e.attribute.length)for(s=0;s<e.attribute.length;++s)u.onnx.AttributeProto.encode(e.attribute[s],i.uint32(42).fork()).ldelim();return e.docString!=null&&e.hasOwnProperty("docString")&&i.uint32(50).string(e.docString),e.domain!=null&&e.hasOwnProperty("domain")&&i.uint32(58).string(e.domain),i},n.encodeDelimited=function(e,i){return this.encode(e,i).ldelim()},n.decode=function(e,i){e instanceof g||(e=g.create(e));for(var s=i===void 0?e.len:e.pos+i,m=new u.onnx.NodeProto;e.pos<s;){var y=e.uint32();switch(y>>>3){case 1:m.input&&m.input.length||(m.input=[]),m.input.push(e.string());break;case 2:m.output&&m.output.length||(m.output=[]),m.output.push(e.string());break;case 3:m.name=e.string();break;case 4:m.opType=e.string();break;case 7:m.domain=e.string();break;case 5:m.attribute&&m.attribute.length||(m.attribute=[]),m.attribute.push(u.onnx.AttributeProto.decode(e,e.uint32()));break;case 6:m.docString=e.string();break;default:e.skipType(7&y)}}return m},n.decodeDelimited=function(e){return e instanceof g||(e=new g(e)),this.decode(e,e.uint32())},n.verify=function(e){if(typeof e!="object"||e===null)return"object expected";if(e.input!=null&&e.hasOwnProperty("input")){if(!Array.isArray(e.input))return"input: array expected";for(var i=0;i<e.input.length;++i)if(!p.isString(e.input[i]))return"input: string[] expected"}if(e.output!=null&&e.hasOwnProperty("output")){if(!Array.isArray(e.output))return"output: array expected";for(i=0;i<e.output.length;++i)if(!p.isString(e.output[i]))return"output: string[] expected"}if(e.name!=null&&e.hasOwnProperty("name")&&!p.isString(e.name))return"name: string expected";if(e.opType!=null&&e.hasOwnProperty("opType")&&!p.isString(e.opType))return"opType: string expected";if(e.domain!=null&&e.hasOwnProperty("domain")&&!p.isString(e.domain))return"domain: string expected";if(e.attribute!=null&&e.hasOwnProperty("attribute")){if(!Array.isArray(e.attribute))return"attribute: array expected";for(i=0;i<e.attribute.length;++i){var s=u.onnx.AttributeProto.verify(e.attribute[i]);if(s)return"attribute."+s}}return e.docString!=null&&e.hasOwnProperty("docString")&&!p.isString(e.docString)?"docString: string expected":null},n.fromObject=function(e){if(e instanceof u.onnx.NodeProto)return e;var i=new u.onnx.NodeProto;if(e.input){if(!Array.isArray(e.input))throw TypeError(".onnx.NodeProto.input: array expected");i.input=[];for(var s=0;s<e.input.length;++s)i.input[s]=String(e.input[s])}if(e.output){if(!Array.isArray(e.output))throw TypeError(".onnx.NodeProto.output: array expected");for(i.output=[],s=0;s<e.output.length;++s)i.output[s]=String(e.output[s])}if(e.name!=null&&(i.name=String(e.name)),e.opType!=null&&(i.opType=String(e.opType)),e.domain!=null&&(i.domain=String(e.domain)),e.attribute){if(!Array.isArray(e.attribute))throw TypeError(".onnx.NodeProto.attribute: array expected");for(i.attribute=[],s=0;s<e.attribute.length;++s){if(typeof e.attribute[s]!="object")throw TypeError(".onnx.NodeProto.attribute: object expected");i.attribute[s]=u.onnx.AttributeProto.fromObject(e.attribute[s])}}return e.docString!=null&&(i.docString=String(e.docString)),i},n.toObject=function(e,i){i||(i={});var s={};if((i.arrays||i.defaults)&&(s.input=[],s.output=[],s.attribute=[]),i.defaults&&(s.name="",s.opType="",s.docString="",s.domain=""),e.input&&e.input.length){s.input=[];for(var m=0;m<e.input.length;++m)s.input[m]=e.input[m]}if(e.output&&e.output.length)for(s.output=[],m=0;m<e.output.length;++m)s.output[m]=e.output[m];if(e.name!=null&&e.hasOwnProperty("name")&&(s.name=e.name),e.opType!=null&&e.hasOwnProperty("opType")&&(s.opType=e.opType),e.attribute&&e.attribute.length)for(s.attribute=[],m=0;m<e.attribute.length;++m)s.attribute[m]=u.onnx.AttributeProto.toObject(e.attribute[m],i);return e.docString!=null&&e.hasOwnProperty("docString")&&(s.docString=e.docString),e.domain!=null&&e.hasOwnProperty("domain")&&(s.domain=e.domain),s},n.prototype.toJSON=function(){return this.constructor.toObject(this,c.util.toJSONOptions)},n}(),b.ModelProto=function(){function n(e){if(this.opsetImport=[],this.metadataProps=[],e)for(var i=Object.keys(e),s=0;s<i.length;++s)e[i[s]]!=null&&(this[i[s]]=e[i[s]])}return n.prototype.irVersion=p.Long?p.Long.fromBits(0,0,!1):0,n.prototype.opsetImport=p.emptyArray,n.prototype.producerName="",n.prototype.producerVersion="",n.prototype.domain="",n.prototype.modelVersion=p.Long?p.Long.fromBits(0,0,!1):0,n.prototype.docString="",n.prototype.graph=null,n.prototype.metadataProps=p.emptyArray,n.create=function(e){return new n(e)},n.encode=function(e,i){if(i||(i=_.create()),e.irVersion!=null&&e.hasOwnProperty("irVersion")&&i.uint32(8).int64(e.irVersion),e.producerName!=null&&e.hasOwnProperty("producerName")&&i.uint32(18).string(e.producerName),e.producerVersion!=null&&e.hasOwnProperty("producerVersion")&&i.uint32(26).string(e.producerVersion),e.domain!=null&&e.hasOwnProperty("domain")&&i.uint32(34).string(e.domain),e.modelVersion!=null&&e.hasOwnProperty("modelVersion")&&i.uint32(40).int64(e.modelVersion),e.docString!=null&&e.hasOwnProperty("docString")&&i.uint32(50).string(e.docString),e.graph!=null&&e.hasOwnProperty("graph")&&u.onnx.GraphProto.encode(e.graph,i.uint32(58).fork()).ldelim(),e.opsetImport!=null&&e.opsetImport.length)for(var s=0;s<e.opsetImport.length;++s)u.onnx.OperatorSetIdProto.encode(e.opsetImport[s],i.uint32(66).fork()).ldelim();if(e.metadataProps!=null&&e.metadataProps.length)for(s=0;s<e.metadataProps.length;++s)u.onnx.StringStringEntryProto.encode(e.metadataProps[s],i.uint32(114).fork()).ldelim();return i},n.encodeDelimited=function(e,i){return this.encode(e,i).ldelim()},n.decode=function(e,i){e instanceof g||(e=g.create(e));for(var s=i===void 0?e.len:e.pos+i,m=new u.onnx.ModelProto;e.pos<s;){var y=e.uint32();switch(y>>>3){case 1:m.irVersion=e.int64();break;case 8:m.opsetImport&&m.opsetImport.length||(m.opsetImport=[]),m.opsetImport.push(u.onnx.OperatorSetIdProto.decode(e,e.uint32()));break;case 2:m.producerName=e.string();break;case 3:m.producerVersion=e.string();break;case 4:m.domain=e.string();break;case 5:m.modelVersion=e.int64();break;case 6:m.docString=e.string();break;case 7:m.graph=u.onnx.GraphProto.decode(e,e.uint32());break;case 14:m.metadataProps&&m.metadataProps.length||(m.metadataProps=[]),m.metadataProps.push(u.onnx.StringStringEntryProto.decode(e,e.uint32()));break;default:e.skipType(7&y)}}return m},n.decodeDelimited=function(e){return e instanceof g||(e=new g(e)),this.decode(e,e.uint32())},n.verify=function(e){if(typeof e!="object"||e===null)return"object expected";if(e.irVersion!=null&&e.hasOwnProperty("irVersion")&&!(p.isInteger(e.irVersion)||e.irVersion&&p.isInteger(e.irVersion.low)&&p.isInteger(e.irVersion.high)))return"irVersion: integer|Long expected";if(e.opsetImport!=null&&e.hasOwnProperty("opsetImport")){if(!Array.isArray(e.opsetImport))return"opsetImport: array expected";for(var i=0;i<e.opsetImport.length;++i)if(s=u.onnx.OperatorSetIdProto.verify(e.opsetImport[i]))return"opsetImport."+s}if(e.producerName!=null&&e.hasOwnProperty("producerName")&&!p.isString(e.producerName))return"producerName: string expected";if(e.producerVersion!=null&&e.hasOwnProperty("producerVersion")&&!p.isString(e.producerVersion))return"producerVersion: string expected";if(e.domain!=null&&e.hasOwnProperty("domain")&&!p.isString(e.domain))return"domain: string expected";if(e.modelVersion!=null&&e.hasOwnProperty("modelVersion")&&!(p.isInteger(e.modelVersion)||e.modelVersion&&p.isInteger(e.modelVersion.low)&&p.isInteger(e.modelVersion.high)))return"modelVersion: integer|Long expected";if(e.docString!=null&&e.hasOwnProperty("docString")&&!p.isString(e.docString))return"docString: string expected";if(e.graph!=null&&e.hasOwnProperty("graph")&&(s=u.onnx.GraphProto.verify(e.graph)))return"graph."+s;if(e.metadataProps!=null&&e.hasOwnProperty("metadataProps")){if(!Array.isArray(e.metadataProps))return"metadataProps: array expected";for(i=0;i<e.metadataProps.length;++i){var s;if(s=u.onnx.StringStringEntryProto.verify(e.metadataProps[i]))return"metadataProps."+s}}return null},n.fromObject=function(e){if(e instanceof u.onnx.ModelProto)return e;var i=new u.onnx.ModelProto;if(e.irVersion!=null&&(p.Long?(i.irVersion=p.Long.fromValue(e.irVersion)).unsigned=!1:typeof e.irVersion=="string"?i.irVersion=parseInt(e.irVersion,10):typeof e.irVersion=="number"?i.irVersion=e.irVersion:typeof e.irVersion=="object"&&(i.irVersion=new p.LongBits(e.irVersion.low>>>0,e.irVersion.high>>>0).toNumber())),e.opsetImport){if(!Array.isArray(e.opsetImport))throw TypeError(".onnx.ModelProto.opsetImport: array expected");i.opsetImport=[];for(var s=0;s<e.opsetImport.length;++s){if(typeof e.opsetImport[s]!="object")throw TypeError(".onnx.ModelProto.opsetImport: object expected");i.opsetImport[s]=u.onnx.OperatorSetIdProto.fromObject(e.opsetImport[s])}}if(e.producerName!=null&&(i.producerName=String(e.producerName)),e.producerVersion!=null&&(i.producerVersion=String(e.producerVersion)),e.domain!=null&&(i.domain=String(e.domain)),e.modelVersion!=null&&(p.Long?(i.modelVersion=p.Long.fromValue(e.modelVersion)).unsigned=!1:typeof e.modelVersion=="string"?i.modelVersion=parseInt(e.modelVersion,10):typeof e.modelVersion=="number"?i.modelVersion=e.modelVersion:typeof e.modelVersion=="object"&&(i.modelVersion=new p.LongBits(e.modelVersion.low>>>0,e.modelVersion.high>>>0).toNumber())),e.docString!=null&&(i.docString=String(e.docString)),e.graph!=null){if(typeof e.graph!="object")throw TypeError(".onnx.ModelProto.graph: object expected");i.graph=u.onnx.GraphProto.fromObject(e.graph)}if(e.metadataProps){if(!Array.isArray(e.metadataProps))throw TypeError(".onnx.ModelProto.metadataProps: array expected");for(i.metadataProps=[],s=0;s<e.metadataProps.length;++s){if(typeof e.metadataProps[s]!="object")throw TypeError(".onnx.ModelProto.metadataProps: object expected");i.metadataProps[s]=u.onnx.StringStringEntryProto.fromObject(e.metadataProps[s])}}return i},n.toObject=function(e,i){i||(i={});var s={};if((i.arrays||i.defaults)&&(s.opsetImport=[],s.metadataProps=[]),i.defaults){if(p.Long){var m=new p.Long(0,0,!1);s.irVersion=i.longs===String?m.toString():i.longs===Number?m.toNumber():m}else s.irVersion=i.longs===String?"0":0;s.producerName="",s.producerVersion="",s.domain="",p.Long?(m=new p.Long(0,0,!1),s.modelVersion=i.longs===String?m.toString():i.longs===Number?m.toNumber():m):s.modelVersion=i.longs===String?"0":0,s.docString="",s.graph=null}if(e.irVersion!=null&&e.hasOwnProperty("irVersion")&&(typeof e.irVersion=="number"?s.irVersion=i.longs===String?String(e.irVersion):e.irVersion:s.irVersion=i.longs===String?p.Long.prototype.toString.call(e.irVersion):i.longs===Number?new p.LongBits(e.irVersion.low>>>0,e.irVersion.high>>>0).toNumber():e.irVersion),e.producerName!=null&&e.hasOwnProperty("producerName")&&(s.producerName=e.producerName),e.producerVersion!=null&&e.hasOwnProperty("producerVersion")&&(s.producerVersion=e.producerVersion),e.domain!=null&&e.hasOwnProperty("domain")&&(s.domain=e.domain),e.modelVersion!=null&&e.hasOwnProperty("modelVersion")&&(typeof e.modelVersion=="number"?s.modelVersion=i.longs===String?String(e.modelVersion):e.modelVersion:s.modelVersion=i.longs===String?p.Long.prototype.toString.call(e.modelVersion):i.longs===Number?new p.LongBits(e.modelVersion.low>>>0,e.modelVersion.high>>>0).toNumber():e.modelVersion),e.docString!=null&&e.hasOwnProperty("docString")&&(s.docString=e.docString),e.graph!=null&&e.hasOwnProperty("graph")&&(s.graph=u.onnx.GraphProto.toObject(e.graph,i)),e.opsetImport&&e.opsetImport.length){s.opsetImport=[];for(var y=0;y<e.opsetImport.length;++y)s.opsetImport[y]=u.onnx.OperatorSetIdProto.toObject(e.opsetImport[y],i)}if(e.metadataProps&&e.metadataProps.length)for(s.metadataProps=[],y=0;y<e.metadataProps.length;++y)s.metadataProps[y]=u.onnx.StringStringEntryProto.toObject(e.metadataProps[y],i);return s},n.prototype.toJSON=function(){return this.constructor.toObject(this,c.util.toJSONOptions)},n}(),b.StringStringEntryProto=function(){function n(e){if(e)for(var i=Object.keys(e),s=0;s<i.length;++s)e[i[s]]!=null&&(this[i[s]]=e[i[s]])}return n.prototype.key="",n.prototype.value="",n.create=function(e){return new n(e)},n.encode=function(e,i){return i||(i=_.create()),e.key!=null&&e.hasOwnProperty("key")&&i.uint32(10).string(e.key),e.value!=null&&e.hasOwnProperty("value")&&i.uint32(18).string(e.value),i},n.encodeDelimited=function(e,i){return this.encode(e,i).ldelim()},n.decode=function(e,i){e instanceof g||(e=g.create(e));for(var s=i===void 0?e.len:e.pos+i,m=new u.onnx.StringStringEntryProto;e.pos<s;){var y=e.uint32();switch(y>>>3){case 1:m.key=e.string();break;case 2:m.value=e.string();break;default:e.skipType(7&y)}}return m},n.decodeDelimited=function(e){return e instanceof g||(e=new g(e)),this.decode(e,e.uint32())},n.verify=function(e){return typeof e!="object"||e===null?"object expected":e.key!=null&&e.hasOwnProperty("key")&&!p.isString(e.key)?"key: string expected":e.value!=null&&e.hasOwnProperty("value")&&!p.isString(e.value)?"value: string expected":null},n.fromObject=function(e){if(e instanceof u.onnx.StringStringEntryProto)return e;var i=new u.onnx.StringStringEntryProto;return e.key!=null&&(i.key=String(e.key)),e.value!=null&&(i.value=String(e.value)),i},n.toObject=function(e,i){i||(i={});var s={};return i.defaults&&(s.key="",s.value=""),e.key!=null&&e.hasOwnProperty("key")&&(s.key=e.key),e.value!=null&&e.hasOwnProperty("value")&&(s.value=e.value),s},n.prototype.toJSON=function(){return this.constructor.toObject(this,c.util.toJSONOptions)},n}(),b.TensorAnnotation=function(){function n(e){if(this.quantParameterTensorNames=[],e)for(var i=Object.keys(e),s=0;s<i.length;++s)e[i[s]]!=null&&(this[i[s]]=e[i[s]])}return n.prototype.tensorName="",n.prototype.quantParameterTensorNames=p.emptyArray,n.create=function(e){return new n(e)},n.encode=function(e,i){if(i||(i=_.create()),e.tensorName!=null&&e.hasOwnProperty("tensorName")&&i.uint32(10).string(e.tensorName),e.quantParameterTensorNames!=null&&e.quantParameterTensorNames.length)for(var s=0;s<e.quantParameterTensorNames.length;++s)u.onnx.StringStringEntryProto.encode(e.quantParameterTensorNames[s],i.uint32(18).fork()).ldelim();return i},n.encodeDelimited=function(e,i){return this.encode(e,i).ldelim()},n.decode=function(e,i){e instanceof g||(e=g.create(e));for(var s=i===void 0?e.len:e.pos+i,m=new u.onnx.TensorAnnotation;e.pos<s;){var y=e.uint32();switch(y>>>3){case 1:m.tensorName=e.string();break;case 2:m.quantParameterTensorNames&&m.quantParameterTensorNames.length||(m.quantParameterTensorNames=[]),m.quantParameterTensorNames.push(u.onnx.StringStringEntryProto.decode(e,e.uint32()));break;default:e.skipType(7&y)}}return m},n.decodeDelimited=function(e){return e instanceof g||(e=new g(e)),this.decode(e,e.uint32())},n.verify=function(e){if(typeof e!="object"||e===null)return"object expected";if(e.tensorName!=null&&e.hasOwnProperty("tensorName")&&!p.isString(e.tensorName))return"tensorName: string expected";if(e.quantParameterTensorNames!=null&&e.hasOwnProperty("quantParameterTensorNames")){if(!Array.isArray(e.quantParameterTensorNames))return"quantParameterTensorNames: array expected";for(var i=0;i<e.quantParameterTensorNames.length;++i){var s=u.onnx.StringStringEntryProto.verify(e.quantParameterTensorNames[i]);if(s)return"quantParameterTensorNames."+s}}return null},n.fromObject=function(e){if(e instanceof u.onnx.TensorAnnotation)return e;var i=new u.onnx.TensorAnnotation;if(e.tensorName!=null&&(i.tensorName=String(e.tensorName)),e.quantParameterTensorNames){if(!Array.isArray(e.quantParameterTensorNames))throw TypeError(".onnx.TensorAnnotation.quantParameterTensorNames: array expected");i.quantParameterTensorNames=[];for(var s=0;s<e.quantParameterTensorNames.length;++s){if(typeof e.quantParameterTensorNames[s]!="object")throw TypeError(".onnx.TensorAnnotation.quantParameterTensorNames: object expected");i.quantParameterTensorNames[s]=u.onnx.StringStringEntryProto.fromObject(e.quantParameterTensorNames[s])}}return i},n.toObject=function(e,i){i||(i={});var s={};if((i.arrays||i.defaults)&&(s.quantParameterTensorNames=[]),i.defaults&&(s.tensorName=""),e.tensorName!=null&&e.hasOwnProperty("tensorName")&&(s.tensorName=e.tensorName),e.quantParameterTensorNames&&e.quantParameterTensorNames.length){s.quantParameterTensorNames=[];for(var m=0;m<e.quantParameterTensorNames.length;++m)s.quantParameterTensorNames[m]=u.onnx.StringStringEntryProto.toObject(e.quantParameterTensorNames[m],i)}return s},n.prototype.toJSON=function(){return this.constructor.toObject(this,c.util.toJSONOptions)},n}(),b.GraphProto=function(){function n(e){if(this.node=[],this.initializer=[],this.input=[],this.output=[],this.valueInfo=[],this.quantizationAnnotation=[],e)for(var i=Object.keys(e),s=0;s<i.length;++s)e[i[s]]!=null&&(this[i[s]]=e[i[s]])}return n.prototype.node=p.emptyArray,n.prototype.name="",n.prototype.initializer=p.emptyArray,n.prototype.docString="",n.prototype.input=p.emptyArray,n.prototype.output=p.emptyArray,n.prototype.valueInfo=p.emptyArray,n.prototype.quantizationAnnotation=p.emptyArray,n.create=function(e){return new n(e)},n.encode=function(e,i){if(i||(i=_.create()),e.node!=null&&e.node.length)for(var s=0;s<e.node.length;++s)u.onnx.NodeProto.encode(e.node[s],i.uint32(10).fork()).ldelim();if(e.name!=null&&e.hasOwnProperty("name")&&i.uint32(18).string(e.name),e.initializer!=null&&e.initializer.length)for(s=0;s<e.initializer.length;++s)u.onnx.TensorProto.encode(e.initializer[s],i.uint32(42).fork()).ldelim();if(e.docString!=null&&e.hasOwnProperty("docString")&&i.uint32(82).string(e.docString),e.input!=null&&e.input.length)for(s=0;s<e.input.length;++s)u.onnx.ValueInfoProto.encode(e.input[s],i.uint32(90).fork()).ldelim();if(e.output!=null&&e.output.length)for(s=0;s<e.output.length;++s)u.onnx.ValueInfoProto.encode(e.output[s],i.uint32(98).fork()).ldelim();if(e.valueInfo!=null&&e.valueInfo.length)for(s=0;s<e.valueInfo.length;++s)u.onnx.ValueInfoProto.encode(e.valueInfo[s],i.uint32(106).fork()).ldelim();if(e.quantizationAnnotation!=null&&e.quantizationAnnotation.length)for(s=0;s<e.quantizationAnnotation.length;++s)u.onnx.TensorAnnotation.encode(e.quantizationAnnotation[s],i.uint32(114).fork()).ldelim();return i},n.encodeDelimited=function(e,i){return this.encode(e,i).ldelim()},n.decode=function(e,i){e instanceof g||(e=g.create(e));for(var s=i===void 0?e.len:e.pos+i,m=new u.onnx.GraphProto;e.pos<s;){var y=e.uint32();switch(y>>>3){case 1:m.node&&m.node.length||(m.node=[]),m.node.push(u.onnx.NodeProto.decode(e,e.uint32()));break;case 2:m.name=e.string();break;case 5:m.initializer&&m.initializer.length||(m.initializer=[]),m.initializer.push(u.onnx.TensorProto.decode(e,e.uint32()));break;case 10:m.docString=e.string();break;case 11:m.input&&m.input.length||(m.input=[]),m.input.push(u.onnx.ValueInfoProto.decode(e,e.uint32()));break;case 12:m.output&&m.output.length||(m.output=[]),m.output.push(u.onnx.ValueInfoProto.decode(e,e.uint32()));break;case 13:m.valueInfo&&m.valueInfo.length||(m.valueInfo=[]),m.valueInfo.push(u.onnx.ValueInfoProto.decode(e,e.uint32()));break;case 14:m.quantizationAnnotation&&m.quantizationAnnotation.length||(m.quantizationAnnotation=[]),m.quantizationAnnotation.push(u.onnx.TensorAnnotation.decode(e,e.uint32()));break;default:e.skipType(7&y)}}return m},n.decodeDelimited=function(e){return e instanceof g||(e=new g(e)),this.decode(e,e.uint32())},n.verify=function(e){if(typeof e!="object"||e===null)return"object expected";if(e.node!=null&&e.hasOwnProperty("node")){if(!Array.isArray(e.node))return"node: array expected";for(var i=0;i<e.node.length;++i)if(s=u.onnx.NodeProto.verify(e.node[i]))return"node."+s}if(e.name!=null&&e.hasOwnProperty("name")&&!p.isString(e.name))return"name: string expected";if(e.initializer!=null&&e.hasOwnProperty("initializer")){if(!Array.isArray(e.initializer))return"initializer: array expected";for(i=0;i<e.initializer.length;++i)if(s=u.onnx.TensorProto.verify(e.initializer[i]))return"initializer."+s}if(e.docString!=null&&e.hasOwnProperty("docString")&&!p.isString(e.docString))return"docString: string expected";if(e.input!=null&&e.hasOwnProperty("input")){if(!Array.isArray(e.input))return"input: array expected";for(i=0;i<e.input.length;++i)if(s=u.onnx.ValueInfoProto.verify(e.input[i]))return"input."+s}if(e.output!=null&&e.hasOwnProperty("output")){if(!Array.isArray(e.output))return"output: array expected";for(i=0;i<e.output.length;++i)if(s=u.onnx.ValueInfoProto.verify(e.output[i]))return"output."+s}if(e.valueInfo!=null&&e.hasOwnProperty("valueInfo")){if(!Array.isArray(e.valueInfo))return"valueInfo: array expected";for(i=0;i<e.valueInfo.length;++i)if(s=u.onnx.ValueInfoProto.verify(e.valueInfo[i]))return"valueInfo."+s}if(e.quantizationAnnotation!=null&&e.hasOwnProperty("quantizationAnnotation")){if(!Array.isArray(e.quantizationAnnotation))return"quantizationAnnotation: array expected";for(i=0;i<e.quantizationAnnotation.length;++i){var s;if(s=u.onnx.TensorAnnotation.verify(e.quantizationAnnotation[i]))return"quantizationAnnotation."+s}}return null},n.fromObject=function(e){if(e instanceof u.onnx.GraphProto)return e;var i=new u.onnx.GraphProto;if(e.node){if(!Array.isArray(e.node))throw TypeError(".onnx.GraphProto.node: array expected");i.node=[];for(var s=0;s<e.node.length;++s){if(typeof e.node[s]!="object")throw TypeError(".onnx.GraphProto.node: object expected");i.node[s]=u.onnx.NodeProto.fromObject(e.node[s])}}if(e.name!=null&&(i.name=String(e.name)),e.initializer){if(!Array.isArray(e.initializer))throw TypeError(".onnx.GraphProto.initializer: array expected");for(i.initializer=[],s=0;s<e.initializer.length;++s){if(typeof e.initializer[s]!="object")throw TypeError(".onnx.GraphProto.initializer: object expected");i.initializer[s]=u.onnx.TensorProto.fromObject(e.initializer[s])}}if(e.docString!=null&&(i.docString=String(e.docString)),e.input){if(!Array.isArray(e.input))throw TypeError(".onnx.GraphProto.input: array expected");for(i.input=[],s=0;s<e.input.length;++s){if(typeof e.input[s]!="object")throw TypeError(".onnx.GraphProto.input: object expected");i.input[s]=u.onnx.ValueInfoProto.fromObject(e.input[s])}}if(e.output){if(!Array.isArray(e.output))throw TypeError(".onnx.GraphProto.output: array expected");for(i.output=[],s=0;s<e.output.length;++s){if(typeof e.output[s]!="object")throw TypeError(".onnx.GraphProto.output: object expected");i.output[s]=u.onnx.ValueInfoProto.fromObject(e.output[s])}}if(e.valueInfo){if(!Array.isArray(e.valueInfo))throw TypeError(".onnx.GraphProto.valueInfo: array expected");for(i.valueInfo=[],s=0;s<e.valueInfo.length;++s){if(typeof e.valueInfo[s]!="object")throw TypeError(".onnx.GraphProto.valueInfo: object expected");i.valueInfo[s]=u.onnx.ValueInfoProto.fromObject(e.valueInfo[s])}}if(e.quantizationAnnotation){if(!Array.isArray(e.quantizationAnnotation))throw TypeError(".onnx.GraphProto.quantizationAnnotation: array expected");for(i.quantizationAnnotation=[],s=0;s<e.quantizationAnnotation.length;++s){if(typeof e.quantizationAnnotation[s]!="object")throw TypeError(".onnx.GraphProto.quantizationAnnotation: object expected");i.quantizationAnnotation[s]=u.onnx.TensorAnnotation.fromObject(e.quantizationAnnotation[s])}}return i},n.toObject=function(e,i){i||(i={});var s={};if((i.arrays||i.defaults)&&(s.node=[],s.initializer=[],s.input=[],s.output=[],s.valueInfo=[],s.quantizationAnnotation=[]),i.defaults&&(s.name="",s.docString=""),e.node&&e.node.length){s.node=[];for(var m=0;m<e.node.length;++m)s.node[m]=u.onnx.NodeProto.toObject(e.node[m],i)}if(e.name!=null&&e.hasOwnProperty("name")&&(s.name=e.name),e.initializer&&e.initializer.length)for(s.initializer=[],m=0;m<e.initializer.length;++m)s.initializer[m]=u.onnx.TensorProto.toObject(e.initializer[m],i);if(e.docString!=null&&e.hasOwnProperty("docString")&&(s.docString=e.docString),e.input&&e.input.length)for(s.input=[],m=0;m<e.input.length;++m)s.input[m]=u.onnx.ValueInfoProto.toObject(e.input[m],i);if(e.output&&e.output.length)for(s.output=[],m=0;m<e.output.length;++m)s.output[m]=u.onnx.ValueInfoProto.toObject(e.output[m],i);if(e.valueInfo&&e.valueInfo.length)for(s.valueInfo=[],m=0;m<e.valueInfo.length;++m)s.valueInfo[m]=u.onnx.ValueInfoProto.toObject(e.valueInfo[m],i);if(e.quantizationAnnotation&&e.quantizationAnnotation.length)for(s.quantizationAnnotation=[],m=0;m<e.quantizationAnnotation.length;++m)s.quantizationAnnotation[m]=u.onnx.TensorAnnotation.toObject(e.quantizationAnnotation[m],i);return s},n.prototype.toJSON=function(){return this.constructor.toObject(this,c.util.toJSONOptions)},n}(),b.TensorProto=function(){function n(e){if(this.dims=[],this.floatData=[],this.int32Data=[],this.stringData=[],this.int64Data=[],this.externalData=[],this.doubleData=[],this.uint64Data=[],e)for(var i=Object.keys(e),s=0;s<i.length;++s)e[i[s]]!=null&&(this[i[s]]=e[i[s]])}return n.prototype.dims=p.emptyArray,n.prototype.dataType=0,n.prototype.segment=null,n.prototype.floatData=p.emptyArray,n.prototype.int32Data=p.emptyArray,n.prototype.stringData=p.emptyArray,n.prototype.int64Data=p.emptyArray,n.prototype.name="",n.prototype.docString="",n.prototype.rawData=p.newBuffer([]),n.prototype.externalData=p.emptyArray,n.prototype.dataLocation=0,n.prototype.doubleData=p.emptyArray,n.prototype.uint64Data=p.emptyArray,n.create=function(e){return new n(e)},n.encode=function(e,i){if(i||(i=_.create()),e.dims!=null&&e.dims.length){i.uint32(10).fork();for(var s=0;s<e.dims.length;++s)i.int64(e.dims[s]);i.ldelim()}if(e.dataType!=null&&e.hasOwnProperty("dataType")&&i.uint32(16).int32(e.dataType),e.segment!=null&&e.hasOwnProperty("segment")&&u.onnx.TensorProto.Segment.encode(e.segment,i.uint32(26).fork()).ldelim(),e.floatData!=null&&e.floatData.length){for(i.uint32(34).fork(),s=0;s<e.floatData.length;++s)i.float(e.floatData[s]);i.ldelim()}if(e.int32Data!=null&&e.int32Data.length){for(i.uint32(42).fork(),s=0;s<e.int32Data.length;++s)i.int32(e.int32Data[s]);i.ldelim()}if(e.stringData!=null&&e.stringData.length)for(s=0;s<e.stringData.length;++s)i.uint32(50).bytes(e.stringData[s]);if(e.int64Data!=null&&e.int64Data.length){for(i.uint32(58).fork(),s=0;s<e.int64Data.length;++s)i.int64(e.int64Data[s]);i.ldelim()}if(e.name!=null&&e.hasOwnProperty("name")&&i.uint32(66).string(e.name),e.rawData!=null&&e.hasOwnProperty("rawData")&&i.uint32(74).bytes(e.rawData),e.doubleData!=null&&e.doubleData.length){for(i.uint32(82).fork(),s=0;s<e.doubleData.length;++s)i.double(e.doubleData[s]);i.ldelim()}if(e.uint64Data!=null&&e.uint64Data.length){for(i.uint32(90).fork(),s=0;s<e.uint64Data.length;++s)i.uint64(e.uint64Data[s]);i.ldelim()}if(e.docString!=null&&e.hasOwnProperty("docString")&&i.uint32(98).string(e.docString),e.externalData!=null&&e.externalData.length)for(s=0;s<e.externalData.length;++s)u.onnx.StringStringEntryProto.encode(e.externalData[s],i.uint32(106).fork()).ldelim();return e.dataLocation!=null&&e.hasOwnProperty("dataLocation")&&i.uint32(112).int32(e.dataLocation),i},n.encodeDelimited=function(e,i){return this.encode(e,i).ldelim()},n.decode=function(e,i){e instanceof g||(e=g.create(e));for(var s=i===void 0?e.len:e.pos+i,m=new u.onnx.TensorProto;e.pos<s;){var y=e.uint32();switch(y>>>3){case 1:if(m.dims&&m.dims.length||(m.dims=[]),(7&y)==2)for(var x=e.uint32()+e.pos;e.pos<x;)m.dims.push(e.int64());else m.dims.push(e.int64());break;case 2:m.dataType=e.int32();break;case 3:m.segment=u.onnx.TensorProto.Segment.decode(e,e.uint32());break;case 4:if(m.floatData&&m.floatData.length||(m.floatData=[]),(7&y)==2)for(x=e.uint32()+e.pos;e.pos<x;)m.floatData.push(e.float());else m.floatData.push(e.float());break;case 5:if(m.int32Data&&m.int32Data.length||(m.int32Data=[]),(7&y)==2)for(x=e.uint32()+e.pos;e.pos<x;)m.int32Data.push(e.int32());else m.int32Data.push(e.int32());break;case 6:m.stringData&&m.stringData.length||(m.stringData=[]),m.stringData.push(e.bytes());break;case 7:if(m.int64Data&&m.int64Data.length||(m.int64Data=[]),(7&y)==2)for(x=e.uint32()+e.pos;e.pos<x;)m.int64Data.push(e.int64());else m.int64Data.push(e.int64());break;case 8:m.name=e.string();break;case 12:m.docString=e.string();break;case 9:m.rawData=e.bytes();break;case 13:m.externalData&&m.externalData.length||(m.externalData=[]),m.externalData.push(u.onnx.StringStringEntryProto.decode(e,e.uint32()));break;case 14:m.dataLocation=e.int32();break;case 10:if(m.doubleData&&m.doubleData.length||(m.doubleData=[]),(7&y)==2)for(x=e.uint32()+e.pos;e.pos<x;)m.doubleData.push(e.double());else m.doubleData.push(e.double());break;case 11:if(m.uint64Data&&m.uint64Data.length||(m.uint64Data=[]),(7&y)==2)for(x=e.uint32()+e.pos;e.pos<x;)m.uint64Data.push(e.uint64());else m.uint64Data.push(e.uint64());break;default:e.skipType(7&y)}}return m},n.decodeDelimited=function(e){return e instanceof g||(e=new g(e)),this.decode(e,e.uint32())},n.verify=function(e){if(typeof e!="object"||e===null)return"object expected";if(e.dims!=null&&e.hasOwnProperty("dims")){if(!Array.isArray(e.dims))return"dims: array expected";for(var i=0;i<e.dims.length;++i)if(!(p.isInteger(e.dims[i])||e.dims[i]&&p.isInteger(e.dims[i].low)&&p.isInteger(e.dims[i].high)))return"dims: integer|Long[] expected"}if(e.dataType!=null&&e.hasOwnProperty("dataType")&&!p.isInteger(e.dataType))return"dataType: integer expected";if(e.segment!=null&&e.hasOwnProperty("segment")&&(s=u.onnx.TensorProto.Segment.verify(e.segment)))return"segment."+s;if(e.floatData!=null&&e.hasOwnProperty("floatData")){if(!Array.isArray(e.floatData))return"floatData: array expected";for(i=0;i<e.floatData.length;++i)if(typeof e.floatData[i]!="number")return"floatData: number[] expected"}if(e.int32Data!=null&&e.hasOwnProperty("int32Data")){if(!Array.isArray(e.int32Data))return"int32Data: array expected";for(i=0;i<e.int32Data.length;++i)if(!p.isInteger(e.int32Data[i]))return"int32Data: integer[] expected"}if(e.stringData!=null&&e.hasOwnProperty("stringData")){if(!Array.isArray(e.stringData))return"stringData: array expected";for(i=0;i<e.stringData.length;++i)if(!(e.stringData[i]&&typeof e.stringData[i].length=="number"||p.isString(e.stringData[i])))return"stringData: buffer[] expected"}if(e.int64Data!=null&&e.hasOwnProperty("int64Data")){if(!Array.isArray(e.int64Data))return"int64Data: array expected";for(i=0;i<e.int64Data.length;++i)if(!(p.isInteger(e.int64Data[i])||e.int64Data[i]&&p.isInteger(e.int64Data[i].low)&&p.isInteger(e.int64Data[i].high)))return"int64Data: integer|Long[] expected"}if(e.name!=null&&e.hasOwnProperty("name")&&!p.isString(e.name))return"name: string expected";if(e.docString!=null&&e.hasOwnProperty("docString")&&!p.isString(e.docString))return"docString: string expected";if(e.rawData!=null&&e.hasOwnProperty("rawData")&&!(e.rawData&&typeof e.rawData.length=="number"||p.isString(e.rawData)))return"rawData: buffer expected";if(e.externalData!=null&&e.hasOwnProperty("externalData")){if(!Array.isArray(e.externalData))return"externalData: array expected";for(i=0;i<e.externalData.length;++i){var s;if(s=u.onnx.StringStringEntryProto.verify(e.externalData[i]))return"externalData."+s}}if(e.dataLocation!=null&&e.hasOwnProperty("dataLocation"))switch(e.dataLocation){default:return"dataLocation: enum value expected";case 0:case 1:}if(e.doubleData!=null&&e.hasOwnProperty("doubleData")){if(!Array.isArray(e.doubleData))return"doubleData: array expected";for(i=0;i<e.doubleData.length;++i)if(typeof e.doubleData[i]!="number")return"doubleData: number[] expected"}if(e.uint64Data!=null&&e.hasOwnProperty("uint64Data")){if(!Array.isArray(e.uint64Data))return"uint64Data: array expected";for(i=0;i<e.uint64Data.length;++i)if(!(p.isInteger(e.uint64Data[i])||e.uint64Data[i]&&p.isInteger(e.uint64Data[i].low)&&p.isInteger(e.uint64Data[i].high)))return"uint64Data: integer|Long[] expected"}return null},n.fromObject=function(e){if(e instanceof u.onnx.TensorProto)return e;var i=new u.onnx.TensorProto;if(e.dims){if(!Array.isArray(e.dims))throw TypeError(".onnx.TensorProto.dims: array expected");i.dims=[];for(var s=0;s<e.dims.length;++s)p.Long?(i.dims[s]=p.Long.fromValue(e.dims[s])).unsigned=!1:typeof e.dims[s]=="string"?i.dims[s]=parseInt(e.dims[s],10):typeof e.dims[s]=="number"?i.dims[s]=e.dims[s]:typeof e.dims[s]=="object"&&(i.dims[s]=new p.LongBits(e.dims[s].low>>>0,e.dims[s].high>>>0).toNumber())}if(e.dataType!=null&&(i.dataType=0|e.dataType),e.segment!=null){if(typeof e.segment!="object")throw TypeError(".onnx.TensorProto.segment: object expected");i.segment=u.onnx.TensorProto.Segment.fromObject(e.segment)}if(e.floatData){if(!Array.isArray(e.floatData))throw TypeError(".onnx.TensorProto.floatData: array expected");for(i.floatData=[],s=0;s<e.floatData.length;++s)i.floatData[s]=Number(e.floatData[s])}if(e.int32Data){if(!Array.isArray(e.int32Data))throw TypeError(".onnx.TensorProto.int32Data: array expected");for(i.int32Data=[],s=0;s<e.int32Data.length;++s)i.int32Data[s]=0|e.int32Data[s]}if(e.stringData){if(!Array.isArray(e.stringData))throw TypeError(".onnx.TensorProto.stringData: array expected");for(i.stringData=[],s=0;s<e.stringData.length;++s)typeof e.stringData[s]=="string"?p.base64.decode(e.stringData[s],i.stringData[s]=p.newBuffer(p.base64.length(e.stringData[s])),0):e.stringData[s].length&&(i.stringData[s]=e.stringData[s])}if(e.int64Data){if(!Array.isArray(e.int64Data))throw TypeError(".onnx.TensorProto.int64Data: array expected");for(i.int64Data=[],s=0;s<e.int64Data.length;++s)p.Long?(i.int64Data[s]=p.Long.fromValue(e.int64Data[s])).unsigned=!1:typeof e.int64Data[s]=="string"?i.int64Data[s]=parseInt(e.int64Data[s],10):typeof e.int64Data[s]=="number"?i.int64Data[s]=e.int64Data[s]:typeof e.int64Data[s]=="object"&&(i.int64Data[s]=new p.LongBits(e.int64Data[s].low>>>0,e.int64Data[s].high>>>0).toNumber())}if(e.name!=null&&(i.name=String(e.name)),e.docString!=null&&(i.docString=String(e.docString)),e.rawData!=null&&(typeof e.rawData=="string"?p.base64.decode(e.rawData,i.rawData=p.newBuffer(p.base64.length(e.rawData)),0):e.rawData.length&&(i.rawData=e.rawData)),e.externalData){if(!Array.isArray(e.externalData))throw TypeError(".onnx.TensorProto.externalData: array expected");for(i.externalData=[],s=0;s<e.externalData.length;++s){if(typeof e.externalData[s]!="object")throw TypeError(".onnx.TensorProto.externalData: object expected");i.externalData[s]=u.onnx.StringStringEntryProto.fromObject(e.externalData[s])}}switch(e.dataLocation){case"DEFAULT":case 0:i.dataLocation=0;break;case"EXTERNAL":case 1:i.dataLocation=1}if(e.doubleData){if(!Array.isArray(e.doubleData))throw TypeError(".onnx.TensorProto.doubleData: array expected");for(i.doubleData=[],s=0;s<e.doubleData.length;++s)i.doubleData[s]=Number(e.doubleData[s])}if(e.uint64Data){if(!Array.isArray(e.uint64Data))throw TypeError(".onnx.TensorProto.uint64Data: array expected");for(i.uint64Data=[],s=0;s<e.uint64Data.length;++s)p.Long?(i.uint64Data[s]=p.Long.fromValue(e.uint64Data[s])).unsigned=!0:typeof e.uint64Data[s]=="string"?i.uint64Data[s]=parseInt(e.uint64Data[s],10):typeof e.uint64Data[s]=="number"?i.uint64Data[s]=e.uint64Data[s]:typeof e.uint64Data[s]=="object"&&(i.uint64Data[s]=new p.LongBits(e.uint64Data[s].low>>>0,e.uint64Data[s].high>>>0).toNumber(!0))}return i},n.toObject=function(e,i){i||(i={});var s={};if((i.arrays||i.defaults)&&(s.dims=[],s.floatData=[],s.int32Data=[],s.stringData=[],s.int64Data=[],s.doubleData=[],s.uint64Data=[],s.externalData=[]),i.defaults&&(s.dataType=0,s.segment=null,s.name="",i.bytes===String?s.rawData="":(s.rawData=[],i.bytes!==Array&&(s.rawData=p.newBuffer(s.rawData))),s.docString="",s.dataLocation=i.enums===String?"DEFAULT":0),e.dims&&e.dims.length){s.dims=[];for(var m=0;m<e.dims.length;++m)typeof e.dims[m]=="number"?s.dims[m]=i.longs===String?String(e.dims[m]):e.dims[m]:s.dims[m]=i.longs===String?p.Long.prototype.toString.call(e.dims[m]):i.longs===Number?new p.LongBits(e.dims[m].low>>>0,e.dims[m].high>>>0).toNumber():e.dims[m]}if(e.dataType!=null&&e.hasOwnProperty("dataType")&&(s.dataType=e.dataType),e.segment!=null&&e.hasOwnProperty("segment")&&(s.segment=u.onnx.TensorProto.Segment.toObject(e.segment,i)),e.floatData&&e.floatData.length)for(s.floatData=[],m=0;m<e.floatData.length;++m)s.floatData[m]=i.json&&!isFinite(e.floatData[m])?String(e.floatData[m]):e.floatData[m];if(e.int32Data&&e.int32Data.length)for(s.int32Data=[],m=0;m<e.int32Data.length;++m)s.int32Data[m]=e.int32Data[m];if(e.stringData&&e.stringData.length)for(s.stringData=[],m=0;m<e.stringData.length;++m)s.stringData[m]=i.bytes===String?p.base64.encode(e.stringData[m],0,e.stringData[m].length):i.bytes===Array?Array.prototype.slice.call(e.stringData[m]):e.stringData[m];if(e.int64Data&&e.int64Data.length)for(s.int64Data=[],m=0;m<e.int64Data.length;++m)typeof 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l.onnx.AttributeProto.AttributeType.FLOATS:return"floats";case l.onnx.AttributeProto.AttributeType.INTS:return"ints";case l.onnx.AttributeProto.AttributeType.STRINGS:return"strings";case l.onnx.AttributeProto.AttributeType.TENSORS:return"tensors";default:throw new Error(`attribute type is not supported yet: ${l.onnx.AttributeProto.AttributeType[n]}`)}}static getValue(u){const n=u instanceof l.onnx.AttributeProto?u.type:u.type();if(n===l.onnx.AttributeProto.AttributeType.GRAPH||n===l.onnx.AttributeProto.AttributeType.GRAPHS)throw new Error("graph attribute is not supported yet");const e=this.getValueNoCheck(u);if(n===l.onnx.AttributeProto.AttributeType.INT&&c.LongUtil.isLong(e))return c.LongUtil.longToNumber(e);if(n===l.onnx.AttributeProto.AttributeType.INTS){const i=e,s=new Array(i.length);for(let m=0;m<i.length;m++){const y=i[m];s[m]=c.LongUtil.longToNumber(y)}return s}if(n===l.onnx.AttributeProto.AttributeType.TENSOR)return u instanceof l.onnx.AttributeProto?b.Tensor.fromProto(e):b.Tensor.fromOrtTensor(e);if(n===l.onnx.AttributeProto.AttributeType.TENSORS){if(u instanceof l.onnx.AttributeProto)return e.map(i=>b.Tensor.fromProto(i));if(u instanceof g.Attribute)return e.map(i=>b.Tensor.fromOrtTensor(i))}if(n===l.onnx.AttributeProto.AttributeType.STRING&&u instanceof l.onnx.AttributeProto){const i=e;return(0,c.decodeUtf8String)(i)}return n===l.onnx.AttributeProto.AttributeType.STRINGS&&u instanceof l.onnx.AttributeProto?e.map(c.decodeUtf8String):e}static getValueNoCheck(u){return u instanceof l.onnx.AttributeProto?this.getValueNoCheckFromOnnxFormat(u):this.getValueNoCheckFromOrtFormat(u)}static getValueNoCheckFromOnnxFormat(u){switch(u.type){case l.onnx.AttributeProto.AttributeType.FLOAT:return u.f;case l.onnx.AttributeProto.AttributeType.INT:return u.i;case l.onnx.AttributeProto.AttributeType.STRING:return u.s;case l.onnx.AttributeProto.AttributeType.TENSOR:return u.t;case l.onnx.AttributeProto.AttributeType.GRAPH:return u.g;case l.onnx.AttributeProto.AttributeType.FLOATS:return u.floats;case l.onnx.AttributeProto.AttributeType.INTS:return u.ints;case l.onnx.AttributeProto.AttributeType.STRINGS:return u.strings;case l.onnx.AttributeProto.AttributeType.TENSORS:return u.tensors;case l.onnx.AttributeProto.AttributeType.GRAPHS:return u.graphs;default:throw new Error(`unsupported attribute type: ${l.onnx.AttributeProto.AttributeType[u.type]}`)}}static getValueNoCheckFromOrtFormat(u){switch(u.type()){case g.AttributeType.FLOAT:return u.f();case g.AttributeType.INT:return u.i();case g.AttributeType.STRING:return u.s();case g.AttributeType.TENSOR:return u.t();case g.AttributeType.GRAPH:return u.g();case g.AttributeType.FLOATS:return u.floatsArray();case g.AttributeType.INTS:{const n=[];for(let e=0;e<u.intsLength();e++)n.push(u.ints(e));return n}case g.AttributeType.STRINGS:{const n=[];for(let e=0;e<u.stringsLength();e++)n.push(u.strings(e));return n}case g.AttributeType.TENSORS:{const n=[];for(let e=0;e<u.tensorsLength();e++)n.push(u.tensors(e));return n}default:throw new Error(`unsupported attribute type: ${g.AttributeType[u.type()]}`)}}}o.Attribute=_},7091:(v,o,f)=>{Object.defineProperty(o,"__esModule",{value:!0}),o.resolveBackend=o.backend=void 0;const l=f(5038),d=new Map;async function b(c){const g=o.backend;if(g[c]!==void 0&&function(_){const p=_;return"initialize"in p&&typeof p.initialize=="function"&&"createSessionHandler"in p&&typeof p.createSessionHandler=="function"&&"dispose"in p&&typeof p.dispose=="function"}(g[c])){const _=g[c];let p=_.initialize();if(typeof p=="object"&&"then"in p&&(p=await p),p)return d.set(c,_),_}}o.backend={webgl:new l.WebGLBackend},o.resolveBackend=async function c(g){if(!g)return c(["webgl"]);{const _=typeof g=="string"?[g]:g;for(const p of _){const u=d.get(p);if(u)return u;const n=await b(p);if(n)return n}}throw new Error("no available backend to use")}},5038:(v,o,f)=>{Object.defineProperty(o,"__esModule",{value:!0}),o.WebGLBackend=void 0;const l=f(1670),d=f(6231),b=f(6416),c=f(7305);o.WebGLBackend=class{get contextId(){return l.env.webgl.contextId}set contextId(g){l.env.webgl.contextId=g}get matmulMaxBatchSize(){return l.env.webgl.matmulMaxBatchSize}set matmulMaxBatchSize(g){l.env.webgl.matmulMaxBatchSize=g}get textureCacheMode(){return l.env.webgl.textureCacheMode}set textureCacheMode(g){l.env.webgl.textureCacheMode=g}get pack(){return l.env.webgl.pack}set pack(g){l.env.webgl.pack=g}get async(){return l.env.webgl.async}set async(g){l.env.webgl.async=g}initialize(){try{return this.glContext=(0,c.createWebGLContext)(this.contextId),typeof this.matmulMaxBatchSize!="number"&&(this.matmulMaxBatchSize=16),typeof this.textureCacheMode!="string"&&(this.textureCacheMode="full"),typeof this.pack!="boolean"&&(this.pack=!1),typeof this.async!="boolean"&&(this.async=!1),d.Logger.setWithEnv(l.env),d.Logger.verbose("WebGLBackend",`Created WebGLContext: ${typeof this.glContext} with matmulMaxBatchSize: ${this.matmulMaxBatchSize}; textureCacheMode: ${this.textureCacheMode}; pack: ${this.pack}; async: ${this.async}.`),!0}catch(g){return d.Logger.warning("WebGLBackend",`Unable to initialize WebGLBackend. ${g}`),!1}}createSessionHandler(g){return new b.WebGLSessionHandler(this,g)}dispose(){this.glContext.dispose()}}},5107:(v,o,f)=>{Object.defineProperty(o,"__esModule",{value:!0}),o.CoordsGlslLib=void 0;const l=f(2517),d=f(8520),b=f(5060),c=f(7859),g=f(9390);class _ extends d.GlslLib{constructor(u){super(u)}getFunctions(){return Object.assign(Object.assign(Object.assign(Object.assign(Object.assign(Object.assign(Object.assign({},this.offsetToCoords()),this.coordsToOffset()),this.toVec()),this.valueFrom()),this.getCommonUtilFuncs()),this.getInputsSamplingSnippets()),this.getOutputSamplingSnippet())}getCustomTypes(){return{}}offsetToCoords(){return{offsetToCoords:new d.GlslLibRoutine(`
-      vec2 offsetToCoords(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(){return{coordsToOffset:new d.GlslLibRoutine(`
-      int coordsToOffset(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(){const u=this.context.outputTextureLayout;return u.isPacked?this.getPackedOutputSamplingSnippet(u):this.getUnpackedOutputSamplingSnippet(u)}getPackedOutputSamplingSnippet(u){const n=u.unpackedShape,e=[u.width,u.height],i={},s="getOutputCoords";switch(n.length){case 0:i[s]=this.getOutputScalarCoords();break;case 1:i[s]=this.getOutputPacked1DCoords(n,e);break;case 2:i[s]=this.getOutputPacked2DCoords(n,e);break;case 3:i[s]=this.getOutputPacked3DCoords(n,e);break;default:i[s]=this.getOutputPackedNDCoords(n,e)}const m=`
-      void setOutput(vec4 val) {
-        ${(0,b.getGlsl)(this.context.glContext.version).output} = val;
-      }
-    `;return i.floatTextureSetRGBA=new d.GlslLibRoutine(m),i}getUnpackedOutputSamplingSnippet(u){const n=u.unpackedShape,e=[u.width,u.height],i={},s="getOutputCoords";switch(n.length){case 0:i[s]=this.getOutputScalarCoords();break;case 1:i[s]=this.getOutputUnpacked1DCoords(n,e);break;case 2:i[s]=this.getOutputUnpacked2DCoords(n,e);break;case 3:i[s]=this.getOutputUnpacked3DCoords(n,e);break;case 4:i[s]=this.getOutputUnpacked4DCoords(n,e);break;case 5:i[s]=this.getOutputUnpacked5DCoords(n,e);break;case 6:i[s]=this.getOutputUnpacked6DCoords(n,e);break;default:throw new Error(`Unsupported output dimensionality: ${n.length}`)}const m=`
-        void setOutput(float val) {
-          ${(0,b.getGlsl)(this.context.glContext.version).output} = vec4(val, 0, 0, 0);
-        }
-    `;return i.floatTextureSetR=new d.GlslLibRoutine(m),i}getOutputScalarCoords(){return new d.GlslLibRoutine(`
-      int getOutputCoords() {
-        return 0;
-      }
-    `)}getOutputPacked1DCoords(u,n){const e=n;let i="";return e[0]===1?(i=`
-          int getOutputCoords() {
-            return 2 * int(TexCoords.y * ${e[1]}.0);
-          }
-        `,new d.GlslLibRoutine(i)):e[1]===1?(i=`
-          int getOutputCoords() {
-            return 2 * int(TexCoords.x * ${e[0]}.0);
-          }
-        `,new d.GlslLibRoutine(i)):(i=`
-        int getOutputCoords() {
-          ivec2 resTexRC = ivec2(TexCoords.xy *
-                                 vec2(${e[0]}, ${e[1]}));
-          return 2 * (resTexRC.y * ${e[0]} + resTexRC.x);
-        }
-      `,new d.GlslLibRoutine(i))}getOutputPacked2DCoords(u,n){let e="";if(l.ArrayUtil.arraysEqual(u,n))return e=`
-        ivec2 getOutputCoords() {
-          return 2 * ivec2(TexCoords.xy * vec2(${n[0]}, ${n[1]}));
-        }
-      `,new d.GlslLibRoutine(e);const i=n,s=Math.ceil(u[1]/2);return e=`
-        ivec2 getOutputCoords() {
-          ivec2 resTexRC = ivec2(TexCoords.xy *
-                                vec2(${i[0]}, ${i[1]}));
-
-          int index = resTexRC.y * ${i[0]} + resTexRC.x;
-
-          // reverse r and c order for packed texture
-          int r = imod(index, ${s}) * 2;
-          int c = 2 * (index / ${s});
-
-          return ivec2(r, c);
-        }
-      `,new d.GlslLibRoutine(e)}getOutputPacked3DCoords(u,n){const e=[n[0],n[1]],i=Math.ceil(u[2]/2),s=i*Math.ceil(u[1]/2),m=`
-        ivec3 getOutputCoords() {
-          ivec2 resTexRC = ivec2(TexCoords.xy *
-                                vec2(${e[0]}, ${e[1]}));
-          int index = resTexRC.y * ${e[0]} + resTexRC.x;
-
-          int b = index / ${s};
-          index -= b * ${s};
-
-          // reverse r and c order for packed texture
-          int r = imod(index, ${i}) * 2;
-          int c = 2 * (index / ${i});
-
-          return ivec3(b, r, c);
-        }
-      `;return new d.GlslLibRoutine(m)}getOutputPackedNDCoords(u,n){const e=[n[0],n[1]],i=Math.ceil(u[u.length-1]/2),s=i*Math.ceil(u[u.length-2]/2);let m=s,y="",x="b, r, c";for(let E=2;E<u.length-1;E++)m*=u[u.length-E-1],y=`
-      int b${E} = index / ${m};
-      index -= b${E} * ${m};
-    `+y,x=`b${E}, `+x;const T=`
-      ivec${u.length} getOutputCoords() {
-        ivec2 resTexRC = ivec2(TexCoords.xy *
-                              vec2(${e[0]}, ${e[1]}));
-        int index = resTexRC.y * ${e[0]} + resTexRC.x;
-
-        ${y}
-
-        int b = index / ${s};
-        index -= b * ${s};
-
-        // reverse r and c order for packed texture
-        int r = imod(index, ${i}) * 2;
-        int c = 2 * (index / ${i});
-
-        return ivec${u.length}(${x});
-      }
-    `;return new d.GlslLibRoutine(T)}getOutputUnpacked1DCoords(u,n){const e=`
-        int getOutputCoords() {
-          ivec2 resTexRC = ivec2(TexCoords.xy *
-                                vec2(${n[0]}, ${n[1]}));
-          return resTexRC.y * ${n[0]} + resTexRC.x;
-        }
-      `;return new d.GlslLibRoutine(e)}getOutputUnpacked2DCoords(u,n){const e=`
-        ivec2 getOutputCoords() {
-          ivec2 resTexRC = ivec2(TexCoords.xy *
-                                vec2(${n[0]}, ${n[1]}));
-          int index = resTexRC.y * ${n[0]} + resTexRC.x;
-          int r = index / ${u[1]};
-          int c = index - r * ${u[1]};
-          return ivec2(r, c);
-        }
-      `;return new d.GlslLibRoutine(e)}getOutputUnpacked3DCoords(u,n){let e="";const i=u.length;let s=null;i<2&&(s=[]),s=new Array(i-1),s[i-2]=u[i-1];for(let x=i-3;x>=0;--x)s[x]=s[x+1]*u[x+1];const m=["r","c","d"],y=s.map((x,T)=>`int ${m[T]} = index / ${x}; ${T===s.length-1?`int ${m[T+1]} = index - ${m[T]} * ${x}`:`index -= ${m[T]} * ${x}`};`).join("");return e=`
-        ivec3 getOutputCoords() {
-          ivec2 resTexRC = ivec2(TexCoords.xy *
-                                vec2(${n[0]}, ${n[1]}));
-          int index = resTexRC.y * ${n[0]} + resTexRC.x;
-          ${y}
-          return ivec3(r, c, d);
-        }
-      `,new d.GlslLibRoutine(e)}getOutputUnpacked4DCoords(u,n){let e="";const i=u.length;let s=null;i<2&&(s=[]),s=new Array(i-1),s[i-2]=u[i-1];for(let x=i-3;x>=0;--x)s[x]=s[x+1]*u[x+1];const m=["r","c","d","d2"],y=s.map((x,T)=>`int ${m[T]} = index / ${x}; ${T===s.length-1?`int ${m[T+1]} = index - ${m[T]} * ${x}`:`index -= ${m[T]} * ${x}`};`).join("");return e=`
-      ivec4 getOutputCoords() {
-          ivec2 resTexRC = ivec2(TexCoords.xy *
-                                vec2(${n[0]}, ${n[1]}));
-          int index = resTexRC.y * ${n[0]} + resTexRC.x;
-          ${y}
-          return ivec4(r, c, d, d2);
-        }
-      `,new d.GlslLibRoutine(e)}getOutputUnpacked5DCoords(u,n){let e="";const i=u.length;let s=null;i<2&&(s=[]),s=new Array(i-1),s[i-2]=u[i-1];for(let x=i-3;x>=0;--x)s[x]=s[x+1]*u[x+1];const m=["r","c","d","d2","d3"],y=s.map((x,T)=>`int ${m[T]} = index / ${x}; ${T===s.length-1?`int ${m[T+1]} = index - ${m[T]} * ${x}`:`index -= ${m[T]} * ${x}`};`).join("");return e=`
-      ivec5 getOutputCoords() {
-          ivec2 resTexRC = ivec2(TexCoords.xy *
-                                vec2(${n[0]}, ${n[1]}));
-          int index = resTexRC.y * ${n[0]} + resTexRC.x;
-          ${y}
-          return ivec5(r, c, d, d2, d3);
-        }
-      `,new d.GlslLibRoutine(e)}getOutputUnpacked6DCoords(u,n){let e="";const i=u.length;let s=null;i<2&&(s=[]),s=new Array(i-1),s[i-2]=u[i-1];for(let x=i-3;x>=0;--x)s[x]=s[x+1]*u[x+1];const m=["r","c","d","d2","d3","d4"],y=s.map((x,T)=>`int ${m[T]} = index / ${x}; ${T===s.length-1?`int ${m[T+1]} = index - ${m[T]} * ${x}`:`index -= ${m[T]} * ${x}`};`).join("");return e=`
-     ivec6 getOutputCoords() {
-         ivec2 resTexRC = ivec2(TexCoords.xy *
-                               vec2(${n[0]}, ${n[1]}));
-         int index = resTexRC.y * ${n[0]} + resTexRC.x;
-         ${y}
-         return ivec6(r, c, d, d2, d3, d4);
-       }
-     `,new d.GlslLibRoutine(e)}getCommonUtilFuncs(){const u={};let n="uvFromFlat";u[n]=new d.GlslLibRoutine(`
-    vec2 uvFromFlat(int texNumR, int texNumC, int index) {
-      int texC = index / texNumR;
-      int texR = index - texC * texNumR;
-      // TODO: swap texR, texC order in following function so row is corresponding to u and column is corresponding to
-      //       v.
-      return (vec2(texR, texC) + halfCR) / vec2(texNumR, texNumC);
-    }
-    `),n="packedUVfrom1D",u[n]=new d.GlslLibRoutine(`
-      vec2 packedUVfrom1D(int texNumR, int texNumC, int index) {
-        int texelIndex = index / 2;
-        int texR = texelIndex / texNumC;
-        int texC = texelIndex - texR * texNumC;
-        return (vec2(texC, texR) + halfCR) / vec2(texNumC, texNumR);
-      }
-      `),n="packedUVfrom2D",u[n]=new d.GlslLibRoutine(`
-      vec2 packedUVfrom2D(int texNumR, int texNumC, int texelsInLogicalRow, int row, int col) {
-        int texelIndex = (row / 2) * texelsInLogicalRow + (col / 2);
-        int texR = texelIndex / texNumC;
-        int texC = texelIndex - texR * texNumC;
-        return (vec2(texC, texR) + halfCR) / vec2(texNumC, texNumR);
-      }
-      `),n="packedUVfrom3D",u[n]=new d.GlslLibRoutine(`
-      vec2 packedUVfrom3D(int texNumR, int texNumC,
-          int texelsInBatch, int texelsInLogicalRow, int b,
-          int row, int col) {
-        int index = b * texelsInBatch + (row / 2) * texelsInLogicalRow + (col / 2);
-        int texR = index / texNumC;
-        int texC = index - texR * texNumC;
-        return (vec2(texC, texR) + halfCR) / vec2(texNumC, texNumR);
-      }
-      `),n="sampleTexture";const e=(0,b.getGlsl)(this.context.glContext.version);return u[n]=new d.GlslLibRoutine(`
-        float sampleTexture(sampler2D textureSampler, vec2 uv) {
-            return ${e.texture2D}(textureSampler, uv).r;
-        }`),u}getInputsSamplingSnippets(){const u={},n=this.context.outputTextureLayout;return this.context.programInfo.inputNames.forEach((e,i)=>{const s=this.context.inputTextureLayouts[i],m=(0,g.generateShaderFuncNameFromInputSamplerName)(e);s.isPacked?u[m]=this.getPackedSamplerFromInput(m,e,s):u[m]=this.getUnpackedSamplerFromInput(m,e,s);const y=(0,g.generateShaderFuncNameFromInputSamplerNameAtOutCoords)(e);s.unpackedShape.length<=n.unpackedShape.length&&(s.isPacked?u[y]=this.getPackedSamplerAtOutputCoords(y,s,n,e):u[y]=this.getUnpackedSamplerAtOutputCoords(y,s,n,e))}),u}getPackedSamplerAtOutputCoords(u,n,e,i){const s=n.unpackedShape,m=e.unpackedShape,y=i,x=(0,g.generateShaderFuncNameFromInputSamplerName)(y),T=s.length,E=m.length,P=l.BroadcastUtil.getBroadcastDims(s,m),O=(0,g.getCoordsDataType)(E),A=E-T;let D;const I=(0,g.getGlChannels)();D=T===0?"":E<2&&P.length>=1?"coords = 0;":P.map(q=>`coords.${I[q+A]} = 0;`).join(`
-`);let k="";k=E<2&&T>0?"coords":s.map((q,se)=>`coords.${I[se+A]}`).join(", ");let $="return outputValue;";const W=l.ShapeUtil.size(s)===1,X=l.ShapeUtil.size(m)===1;if(T!==1||W||X){if(W&&!X)$=E===1?`
-          return vec4(outputValue.x, outputValue.x, 0., 0.);
-        `:`
-          return vec4(outputValue.x);
-        `;else if(P.length){const q=T-2,se=T-1;P.indexOf(q)>-1&&P.indexOf(se)>-1?$="return vec4(outputValue.x);":P.indexOf(q)>-1?$="return vec4(outputValue.x, outputValue.y, outputValue.x, outputValue.y);":P.indexOf(se)>-1&&($="return vec4(outputValue.xx, outputValue.zz);")}}else $=`
-        return vec4(outputValue.xy, outputValue.xy);
-      `;const K=`
-      vec4 ${u}() {
-        ${O} coords = getOutputCoords();
-        
-        int lastDim = coords.${I[E-1]};
-        coords.${I[E-1]} = coords.${I[E-2]};
-        coords.${I[E-2]} = lastDim;
-      
-        ${D}
-        vec4 outputValue = ${x}(${k});
-        ${$}
-      }
-    `;return new d.GlslLibRoutine(K,["coordinates.getOutputCoords"])}getUnpackedSamplerAtOutputCoords(u,n,e,i){const s=[e.width,e.height],m=[n.width,n.height],y=n.unpackedShape.length,x=e.unpackedShape.length,T=n.unpackedShape,E=e.unpackedShape,P=(0,g.generateShaderFuncNameFromInputSamplerName)(i);if(y===x&&l.ArrayUtil.arraysEqual(m,s)){const X=`
-          float ${u}() {
-            return sampleTexture(${i}, TexCoords);
-          }
-        `;return new d.GlslLibRoutine(X,["coordinates.sampleTexture"])}const O=(0,g.getCoordsDataType)(x),A=l.BroadcastUtil.getBroadcastDims(T,E),D=x-y;let I;const k=(0,g.getGlChannels)();I=y===0?"":x<2&&A.length>=1?"coords = 0;":A.map(X=>`coords.${k[X+D]} = 0;`).join(`
-`);let $="";$=x<2&&y>0?"coords":n.unpackedShape.map((X,K)=>`coords.${k[K+D]}`).join(", ");const W=`
-        float ${u}() {
-          ${O} coords = getOutputCoords();
-          ${I}
-          return ${P}(${$});
-        }
-      `;return new d.GlslLibRoutine(W,["coordinates.getOutputCoords"])}getPackedSamplerFromInput(u,n,e){switch(e.unpackedShape.length){case 0:return this.getPackedSamplerScalar(u,n);case 1:return this.getPackedSampler1D(u,n,e);case 2:return this.getPackedSampler2D(u,n,e);case 3:return this.getPackedSampler3D(u,n,e);default:return this.getPackedSamplerND(u,n,e)}}getUnpackedSamplerFromInput(u,n,e){const i=e.unpackedShape;switch(i.length){case 0:return this.getUnpackedSamplerScalar(u,n,e);case 1:return this.getUnpackedSampler1D(u,n,e);case 2:return this.getUnpackedSampler2D(u,n,e);case 3:return this.getUnpackedSampler3D(u,n,e);case 4:return this.getUnpackedSampler4D(u,n,e);case 5:return this.getUnpackedSampler5D(u,n,e);case 6:return this.getUnpackedSampler6D(u,n,e);default:throw new Error(`Unsupported dimension ${i.length}-D`)}}getPackedSamplerScalar(u,n){const e=`
-          vec4 ${u}() {
-            return ${(0,b.getGlsl)(this.context.glContext.version).texture2D}(${n}, halfCR);
-          }
-        `;return new d.GlslLibRoutine(e)}getPackedSampler1D(u,n,e){const i=[e.width,e.height],s=[i[1],i[0]],m=(0,b.getGlsl)(this.context.glContext.version),y=`vec4 ${u}(int index) {
-      vec2 uv = packedUVfrom1D(
-      ${s[0]}, ${s[1]}, index);
-      return ${m.texture2D}(${n}, uv);
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-    float value = 0.0;
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-        for (int wWidth = 0; wWidth < ${P[3]}; wWidth++) {
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-
-    ivec2 loc = coords.zw + pads;
-
-    int group_id = output_channel / ${K};
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-
-    float value = ${$};
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-      for (int wWOff = 0; wWOff < ${X[2]}; wWOff++) {
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-            float xVal = getX(batch, input_channel, wLocIn.y, wLocIn.x);
-            float wVal = getW(input_channel, wOutChannel, wHOff, wWOff);
-            value += xVal * wVal;
-          }
-        }
-      }
-    }
-    ${fe}
-    ${F.output} = vec4(value, .0, .0, .0);
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-`;return Object.assign(Object.assign({},I),{output:{dims:se,type:D[0].type,textureType:b.TextureType.unpacked},shaderSource:ne,hasMain:!0})})(y,x,E,T)})})(i,s,m),s),n=(i,s)=>{const m=i.kernelShape.slice();if(i.kernelShape.length===0)for(let E=2;E<s[1].dims.length;++E)m.push(s[1].dims[E]);const y=i.pads.slice(),x=i.outputShape.slice();((E,P,O,A,D,I,k,$)=>{const W=E.length-2,X=$.length===0;for(let K=0;K<W;++K){const q=X?E[K+2]*I[K]:$[K],se=g(E[K+2],I[K],D[K],P[K],O[K],q);_(se,A,D,K,K+W),X&&$.push(I[K]*(E[K+2]-1)+k[K]+(P[K]-1)*O[K]+1-D[K]-D[K+W])}})(s[0].dims,m,i.dilations,i.autoPad,y,i.strides,i.outputPadding,x);const T=Object.assign({},i);return Object.assign(T,{kernelShape:m,pads:y,outputShape:x,cacheKey:i.cacheKey}),T};o.parseConvTransposeAttributes=i=>{const s=i.attributes,m=(0,c.parseInternalActivationAttributes)(s),y=s.getString("auto_pad","NOTSET"),x=s.getInts("dilations",[1,1]),T=s.getInt("group",1),E=s.getInts("kernel_shape",[]),P=s.getInts("output_padding",[0,0]),O=s.getInts("output_shape",[]),A=s.getInts("pads",[0,0,0,0]),D=s.getInts("strides",[1,1]);return(0,l.createAttributeWithCacheKey)(Object.assign({autoPad:y,dilations:x,group:T,kernelShape:E,outputPadding:P,outputShape:O,pads:A,strides:D},m))};const e=(i,s)=>{if(!i||i.length!==2&&i.length!==3)throw new Error("Conv requires 2 or 3 inputs");if(i[0].dims.length!==4||i[1].dims.length!==4)throw new Error("currently only support 2-dimensional conv");if(i[0].dims[1]!==i[1].dims[0])throw new Error("FILTER_IN_CHANNEL should be equal to DATA_CHANNEL");const m=i[1].dims[1]*s.group;if(i.length===3&&(i[2].dims.length!==1||i[2].dims[0]!==m))throw new Error("invalid bias");const y=i[0].dims.length-2;if(s.dilations.length!==y)throw new Error(`dilations should be ${y}D`);if(s.strides.length!==y)throw new Error(`strides should be ${y}D`);if(s.pads.length!==2*y)throw new Error(`pads should be ${2*y}D`);if(s.outputPadding.length!==y)throw new Error(`output_padding should be ${y}D`);if(s.kernelShape.length!==0&&s.kernelShape.length!==i[1].dims.length-2)throw new Error("invalid kernel shape");if(s.outputShape.length!==0&&s.outputShape.length!==i[0].dims.length-2)throw new Error("invalid output shape");if(i[0].type!=="float32"||i[1].type!=="float32")throw new Error("ConvTranspose input(X,W) should be float tensor");if(i.length===3&&i[2].type!=="float32")throw new Error("ConvTranspose input(bias) should be float tensor")}},8138:(v,o,f)=>{Object.defineProperty(o,"__esModule",{value:!0}),o.parseConvAttributes=o.conv=o.calculateOutputShape=void 0;const l=f(246),d=f(2517),b=f(4770),c=f(1386),g=f(9828),_=f(2823),p=f(3248),u=f(5623);o.calculateOutputShape=(y,x,T,E,P)=>{const O=y[0],A=y.slice(2),D=A.length,I=x[0],k=x.slice(2).map((W,X)=>W+(W-1)*(T[X]-1)),$=A.map((W,X)=>W+E[X]+E[X+D]).map((W,X)=>Math.floor((W-k[X]+P[X])/P[X]));return[O,I].concat(...$)},o.conv=(y,x,T)=>(m(x,T),n(y,x,T));const n=(y,x,T)=>{const E=s(T,x),P=y.session.pack,O=E.kernelShape[0]===1&&E.kernelShape[1]===1;return E.group>1?[y.run((0,b.createUnpackedGroupedConvProgramInfoLoader)(y,x,E),x)]:O&&P?[e(y,x,E)]:P&&x[0].dims.length===4&&x[0].dims[0]===1&&!O?[(0,c.conv2DPacked)(y,x,E)]:[i(y,x,E)]},e=(y,x,T)=>{const E=x[0].dims,P=x[1].dims,O=(0,o.calculateOutputShape)(E,P,T.dilations,T.pads,T.strides),A=y.reshapeUnpacked(x[0],[E[1],E[2]*E[3]]),D=y.reshapeUnpacked(x[1],[P[0],P[1]]),I=x.length>2?[D,A,x[2]]:[D,A],k=y.run((0,u.createMatmulProgramInfoLoader)(I,T),I);return y.reshapeUnpacked(k,O)},i=(y,x,T)=>{const E=x[0].dims,P=x[1].dims,O=(0,o.calculateOutputShape)(E,P,T.dilations,T.pads,T.strides),A=y.run((0,p.createIm2ColProgramInfoLoader)(y,x[0],x[1],O,T),[x[0]]),D=x.length===3?[A,x[1],x[2]]:[A,x[1]];return y.run((0,g.createDotProductProgramInfoLoader)(y,x,O,T),D)},s=(y,x)=>{const T=y.kernelShape.slice();if(y.kernelShape.length===0)for(let O=2;O<x[1].dims.length;++O)T.push(x[1].dims[O]);const E=y.pads.slice();d.PoolConvUtil.adjustPadsBasedOnAutoPad(x[0].dims,y.strides,y.dilations,T,E,y.autoPad);const P=Object.assign({},y);return Object.assign(P,{kernelShape:T,pads:E,cacheKey:y.cacheKey}),P};o.parseConvAttributes=y=>{const x=y.attributes,T=(0,_.parseInternalActivationAttributes)(x),E=x.getString("auto_pad","NOTSET"),P=x.getInts("dilations",[1,1]),O=x.getInt("group",1),A=x.getInts("kernel_shape",[]),D=x.getInts("pads",[0,0,0,0]),I=x.getInts("strides",[1,1]);return(0,l.createAttributeWithCacheKey)(Object.assign({autoPad:E,dilations:P,group:O,kernelShape:A,pads:D,strides:I},T))};const m=(y,x)=>{if(!y||y.length!==2&&y.length!==3)throw new Error("Conv requires 2 or 3 inputs");if(y[0].dims.length!==4||y[1].dims.length!==4)throw new Error("currently only support 2-dimensional conv");if(y[0].dims[1]!==y[1].dims[1]*x.group)throw new Error("FILTER_IN_CHANNEL should be equal to DATA_CHANNEL");if(y.length===3&&(y[2].dims.length!==1||y[1].dims[0]!==y[2].dims[0]))throw new Error("invalid bias");const T=y[0].dims.length-2;if(x.dilations.length!==T)throw new Error(`dilations should be ${T}D`);if(x.strides.length!==T)throw new Error(`strides should be ${T}D`);if(x.pads.length!==2*T)throw new Error(`pads should be ${2*T}D`);if(x.kernelShape.length!==0&&x.kernelShape.length!==y[1].dims.length-2)throw new Error("invalid kernel shape");if(y[0].type!=="float32"||y[1].type!=="float32")throw new Error("Conv input(X,W) should be float tensor");if(y.length===3&&y[2].type!=="float32")throw new Error("Conv input(bias) should be float tensor")}},5193:(v,o,f)=>{Object.defineProperty(o,"__esModule",{value:!0}),o.parseDepthToSpaceAttributes=o.depthToSpace=void 0;const l=f(3738);o.depthToSpace=(b,c,g)=>{d(c);const _=g.blocksize,p=_*_,u=g.mode==="DCR"?[0,3,4,1,5,2]:[0,1,4,2,5,3],n=g.mode==="DCR"?[c[0].dims[0],_,_,c[0].dims[1]/p,c[0].dims[2],c[0].dims[3]]:[c[0].dims[0],c[0].dims[1]/p,_,_,c[0].dims[2],c[0].dims[3]],e=b.reshapeUnpacked(c[0],n),i={perm:u,cacheKey:`${u}`},[s]=(0,l.transpose)(b,[e],i),m=[c[0].dims[0],c[0].dims[1]/p,c[0].dims[2]*_,c[0].dims[3]*_];return[b.reshapeUnpacked(s,m)]},o.parseDepthToSpaceAttributes=b=>{const c=b.attributes.getInt("blocksize");if(c<1)throw new Error(`blocksize must be >= 1, but got : ${c} for DepthToSpace`);const g=b.attributes.getString("mode","DCR");if(g!=="DCR"&&g!=="CRD")throw new Error(`unrecognized mode: ${g} for DepthToSpace`);return{mode:g,blocksize:c}};const d=b=>{if(b.length!==1)throw new Error(`DepthToSpace expect 1 inputs, but got ${b.length}`);if(b[0].type==="string"||b[0].dims.length!==4)throw new TypeError("DepthToSpace input should be a 4-D numeric tensor")}},9828:(v,o,f)=>{Object.defineProperty(o,"__esModule",{value:!0}),o.createDotProductProgramInfoLoader=void 0;const l=f(2517),d=f(5060),b=f(2039),c=f(2823),g=f(3248);o.createDotProductProgramInfoLoader=(_,p,u,n)=>{const e=((i,s)=>({name:"ConvDotProduct",inputNames:i?["Im2Col","K","B"]:["Im2Col","K"],inputTypes:i?[b.TextureType.unpacked,b.TextureType.packedLastDimension,b.TextureType.unpacked]:[b.TextureType.unpacked,b.TextureType.packedLastDimension],cacheKey:s.activationCacheKey}))(p.length>2,n);return Object.assign(Object.assign({},e),{get:()=>((i,s,m,y,x)=>{const T=m[0].dims,E=m[1].dims,P=[E[0],Math.ceil(T[1]*E[2]*E[3]/4)],O=(0,g.calculateIm2ColDims)(T,E,y),[A,D]=i.calculateTextureWidthAndHeight(P,b.TextureType.packedLastDimension),I=l.ShapeUtil.computeStrides(O),[k,$]=i.calculateTextureWidthAndHeight(O,b.TextureType.packedLastDimension),W=y.length,X=m.length<3?"0.0":"_B(b)",K=Math.ceil(T[1]*E[2]*E[3]/4),{activationFunction:q,applyActivation:se}=(0,c.getActivationSnippet)(x),F=(0,d.getGlsl)(i.session.backend.glContext.version),z=`
-${q}
-float process(int indices[${W}]) {
-  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] * ${I[0]} + im2col[1] * ${I[1]} + im2col[2] * ${I[2]};
-  int kernelOffset = indices[1] * ${P[1]};
-  float value = ${X};
-  for (int i = 0; i < ${K}; ++i) {
-    vec2 im2colCoords = offsetToCoords(im2colOffset, ${k}, ${$});
-    vec2 kernelCoords = offsetToCoords(kernelOffset, ${A}, ${D});
-    value += dot(${F.texture2D}(Im2Col, im2colCoords), ${F.texture2D}(K, kernelCoords));
-    ++im2colOffset;
-    ++kernelOffset;
-  }
-  ${se}
-  return value;
-}`;return Object.assign(Object.assign({},s),{output:{dims:y,type:m[0].type,textureType:b.TextureType.unpacked},shaderSource:z})})(_,e,p,u,n)})}},7992:(v,o,f)=>{Object.defineProperty(o,"__esModule",{value:!0}),o.parseFlattenAttributes=o.flatten=void 0;const l=f(2517);o.flatten=(b,c,g)=>{d(c,g);const _=l.ShapeUtil.flattenShape(c[0].dims,g);return[b.reshapeUnpacked(c[0],_)]},o.parseFlattenAttributes=b=>b.attributes.getInt("axis",1);const d=(b,c)=>{if(!b||b.length!==1)throw new Error("Flatten requires 1 input.");const g=b[0].dims.length;if(g===0)throw new Error("scalar tensor is not supported.");if(c<-g||c>g)throw new Error("Invalid axis");if(b[0].type==="string")throw new Error("string tensor is not supported.")}},2823:(v,o,f)=>{Object.defineProperty(o,"__esModule",{value:!0}),o.parseInternalActivationAttributes=o.getActivationSnippet=void 0;const l=f(2517),d=f(4909);o.getActivationSnippet=function(b){let c;switch(b.activation){case"Relu":c=(0,d.glslRelu)();break;case"Sigmoid":c=(0,d.glslSigmoid)();break;case"Clip":c=(0,d.glslClip)(b.clipMin,b.clipMax);break;default:return{activationFunction:"",applyActivation:""}}const g=c.name;return{activationFunction:c.body,applyActivation:`value = ${g}_(value);`}},o.parseInternalActivationAttributes=b=>{const c=b.getString("activation","");if(c==="Clip"){const[g,_]=b.getFloats("activation_params",[l.MIN_CLIP,l.MAX_CLIP]);return{activation:c,clipMax:_,clipMin:g,activationCacheKey:`${c}:${g},${_}`}}return{activation:c,activationCacheKey:c}}},1253:(v,o,f)=>{Object.defineProperty(o,"__esModule",{value:!0}),o.parseGatherAttributes=o.gather=void 0;const l=f(246),d=f(782),b=f(2517),c=f(2039);o.gather=(u,n,e)=>(p(n,e.axis),[u.run(_(u,n,e),n)]),o.parseGatherAttributes=u=>(0,l.createAttributeWithCacheKey)({axis:u.attributes.getInt("axis",0)});const g={name:"Gather",inputNames:["A","B"],inputTypes:[c.TextureType.unpacked,c.TextureType.unpacked]},_=(u,n,e)=>{const i=Object.assign(Object.assign({},g),{cacheHint:e.cacheKey});return Object.assign(Object.assign({},i),{get:()=>((s,m,y,x)=>{const T=y[0].dims.slice(),E=y[1].dims.slice(),P=new Array(T.length+E.length-1);x=b.ShapeUtil.normalizeAxis(x,T.length);const O=[];for(let D=0;D<P.length;D++)D<x?(P[D]=T[D],O.push(`inputIdx[${D}] = outputIdx[${D}];`)):D<x+E.length?(P[D]=E[D-x],O.push(`indexDataIdx[${D-x}] = outputIdx[${D}];`)):(P[D]=T[D-E.length+1],O.push(`inputIdx[${D-E.length+1}] = outputIdx[${D}];`));const A=`
-      float process(int outputIdx[${P.length||1}]) {
-        int inputIdx[${T.length}];
-        int indexDataIdx[${E.length||1}];
-        indexDataIdx[0] = 0;
-        ${O.join(`
-        `)}
-        int idx = int(_B(indexDataIdx));
-        inputIdx[${x}] = idx < 0 ? idx + ${T[x]} : idx;
-        return _A(inputIdx);
-      }`;return Object.assign(Object.assign({},m),{output:{dims:P,type:y[0].type,textureType:c.TextureType.unpacked},shaderSource:A})})(0,i,n,e.axis)})},p=(u,n)=>{if(!u||u.length!==2)throw new Error("Gather requires 2 inputs.");const e=u[0].dims.length;if(e<1)throw new Error("Invalid input shape.");if(n<-e||n>e-1)throw new Error("Invalid axis.");if(d.NUMBER_TYPES.indexOf(u[0].type)===-1)throw new Error("Invaid input type.");if(u[1].type!=="int32"&&u[1].type!=="int16")throw new Error("Invaid input type.")}},4776:(v,o,f)=>{Object.defineProperty(o,"__esModule",{value:!0}),o.parseGemmAttributesV11=o.parseGemmAttributesV7=o.gemm=void 0;const l=f(246),d=f(2517),b=f(2039);o.gemm=(u,n,e)=>(p(n,e),[u.run(g(n,e),n)]);const c=(u,n)=>{const e=u.attributes.getInt("transA",0)!==0,i=u.attributes.getInt("transB",0)!==0,s=u.attributes.getFloat("alpha",1),m=u.attributes.getFloat("beta",1);return(0,l.createAttributeWithCacheKey)({transA:e,transB:i,alpha:s,beta:m,isOptionalC:n})};o.parseGemmAttributesV7=u=>c(u,!1),o.parseGemmAttributesV11=u=>c(u,!0);const g=(u,n)=>{const e={name:"Gemm",inputNames:u.length===3?["A","B","C"]:["A","B"],inputTypes:u.length===3?[b.TextureType.unpacked,b.TextureType.unpacked,b.TextureType.unpacked]:[b.TextureType.unpacked,b.TextureType.unpacked],key:n.cacheKey};return Object.assign(Object.assign({},e),{get:()=>_(e,u,n)})},_=(u,n,e)=>{const i=n[0].dims.slice(),s=n[1].dims.slice(),[m,y]=d.GemmUtil.getShapeOfGemmResult(i,e.transA,s,e.transB,n.length===3?n[2].dims:void 0),x=[m,y];if(!x)throw new Error("Can't use gemm on the given tensors");let T=i[i.length-1],E="";e.transA&&(T=i[0]),e.transA&&e.transB?E="value += _A_T(a) * _B_T(b);":e.transA&&!e.transB?E="value += _A_T(a) * _B(b);":!e.transA&&e.transB?E="value += _A(a) * _B_T(b);":e.transA||e.transB||(E="value += _A(a) * _B(b);");const P=x.length,O=`
-      float process(int indices[${P}]) {
-          int a[${P}];
-          int b[${P}];
-          ${n.length===3?`int c[${n[2].dims.length}];`:""}
-
-          copyVec(indices, a);
-          copyVec(indices, b);
-          ${n.length===3?"bcastIndices_C(indices, c);":""}
-
-          float value = 0.0;
-          for (int k=0; k<${T}; ++k) {
-              a[${P-1}] = k;
-              b[${P-2}] = k;
-              ${E}
-          }
-
-          value = value * alpha;
-          ${n.length===3?"value += beta * _C(c);":""}
-          return value;
-      }`;return Object.assign(Object.assign({},u),{output:{dims:x,type:n[0].type,textureType:b.TextureType.unpacked},variables:[{name:"alpha",type:"float",data:e.alpha},{name:"beta",type:"float",data:e.beta}],shaderSource:O})},p=(u,n)=>{if(!u)throw new Error("Input is missing");if(n.isOptionalC&&(u.length<2||u.length>3))throw new Error("Invaid input shape.");if(!n.isOptionalC&&u.length!==3)throw new Error("Gemm requires 3 inputs");if(u.length===3&&u[2].dims.length!==1&&u[2].dims.length!==2)throw new Error("Invalid input shape of C");if(u[0].type!=="float32"&&u[0].type!=="float64"||u[1].type!=="float32"&&u[1].type!=="float64"||u.length===3&&u[2].type!=="float32"&&u[2].type!=="float64")throw new Error("Invalid input type.");if(u[0].type!==u[1].type||u.length===3&&u[0].type!==u[2].type)throw new Error("Input types are mismatched")}},8555:(v,o,f)=>{Object.defineProperty(o,"__esModule",{value:!0}),o.createPackedIm2ColProgramInfoLoader=void 0;const l=f(5060),d=f(2039),b=f(2827);o.createPackedIm2ColProgramInfoLoader=(c,g,_,p,u)=>{const n=(e=u.cacheKey,{name:"Im2Col (packed)",inputNames:["A"],inputTypes:[d.TextureType.packed],cacheHint:e});var e;return Object.assign(Object.assign({},n),{get:()=>((i,s,m,y,x,T)=>{const E=m.dims,P=y.dims,O=x.length,A=[P[1]*P[2]*P[3],x[2]*x[3]],D=P[2]*P[3],I=(0,b.unpackFromChannel)(),k=(0,l.getGlsl)(i.session.backend.glContext.version);let $="";for(let X=0;X<=1;X++)for(let K=0;K<=1;K++)$+=`
-            blockIndex = rc.x + ${K};
-            pos = rc.y + ${X};
-
-            if(blockIndex < ${A[1]} && pos < ${A[0]}) {
-              offsetY = int(blockIndex / (${x[O-1]})) * ${T.strides[0]} -
-                ${T.pads[0]};
-              d0 = offsetY + ${T.dilations[0]} * (imod(pos, ${D}) / ${P[2]});
-
-              if(d0 < ${E[2]} && d0 >= 0) {
-                offsetX = imod(blockIndex, ${x[O-1]}) * ${T.strides[1]} -
-                  ${T.pads[1]};
-                d1 = offsetX + ${T.dilations[1]} * imod(imod(pos, ${D}), ${P[2]});
-
-                if(d1 < ${E[3]} && d1 >= 0) {
-
-                  ch = int(float(pos)/ ${D}.);
-                    innerDims = vec2(d0, d1);
-                    result[${2*X+K}] = getChannel(
-                      getA(0, ch, int(innerDims.x),
-                      int(innerDims.y)), innerDims);
-                }
-              }
-            }
-
-          `;const W=`
-      ${I}
-
-      void main() {
-        ivec2 rc = getOutputCoords();
-          vec4 result = vec4(0.0);
-          int blockIndex, pos, offsetY, d0, offsetX, d1, ch;
-          vec2 innerDims;
-          ${$}
-          ${k.output} = result;
-      }
-            `;return Object.assign(Object.assign({},s),{output:{dims:A,type:m.type,textureType:d.TextureType.packed},shaderSource:W,hasMain:!0})})(c,n,g,_,p,u)})}},3248:(v,o,f)=>{Object.defineProperty(o,"__esModule",{value:!0}),o.calculateIm2ColDims=o.createIm2ColProgramInfoLoader=void 0;const l=f(2039);o.createIm2ColProgramInfoLoader=(d,b,c,g,_)=>{const p=(u=_.cacheKey,{name:"Im2Col",inputNames:["X"],inputTypes:[l.TextureType.unpacked],cacheHint:u});var u;return Object.assign(Object.assign({},p),{get:()=>((n,e,i,s,m,y)=>{const x=i.dims,T=s.dims,E=m.length,P=(0,o.calculateIm2ColDims)(x,T,m,4),O=`
-        const int XC = ${x[1]};
-        const int XH = ${x[2]};
-        const int XW = ${x[3]};
-        const int KH = ${y.kernelShape[0]};
-        const int KW = ${y.kernelShape[1]};
-        const int dilationH = ${y.dilations[0]};
-        const int dilationW = ${y.dilations[1]};
-        const int strideH = ${y.strides[0]};
-        const int strideW = ${y.strides[1]};
-        const int padH = ${y.pads[0]};
-        const int padW = ${y.pads[1]};
-        const int KHKW = KH*KW;
-        const int XCKHKW = XC * KHKW;
-        const int outputChannels = 4;
-        vec4 process(int indices[${E}]) {
-          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[${x.length}];
-              x[0] = b;
-              x[1] = patchC;
-              x[2] = xh2;
-              x[3] = xw2;
-              if(xh2 >= 0 &&
-                  xh2 < XH &&
-                  xw2 >= 0 &&
-                  xw2 < XW) {
-                value[i] = _X(x);
-              }
-            }
-            ++p;
-          }
-          return value;
-        }
-        `;return Object.assign(Object.assign({},e),{output:{dims:P,type:i.type,textureType:l.TextureType.packedLastDimension},shaderSource:O})})(0,p,b,c,g,_)})},o.calculateIm2ColDims=(d,b,c,g=4)=>[c[0],c[2],c[3],Math.ceil(d[1]*b[2]*b[3]/g)]},6572:(v,o,f)=>{Object.defineProperty(o,"__esModule",{value:!0}),o.parseImageScalerAttributes=o.imageScaler=void 0;const l=f(246),d=f(2039);o.imageScaler=(p,u,n)=>(_(u),[p.run(c(p,u,n),u)]),o.parseImageScalerAttributes=p=>{const u=p.attributes.getFloat("scale"),n=p.attributes.getFloats("bias");return(0,l.createAttributeWithCacheKey)({scale:u,bias:n})};const b={name:"ImageScaler",inputNames:["X"],inputTypes:[d.TextureType.unpacked]},c=(p,u,n)=>{const e=Object.assign(Object.assign({},b),{cacheHint:n.cacheKey});return Object.assign(Object.assign({},e),{get:()=>((i,s,m,y)=>{const x=m[0].dims.slice(),T=x.length,E=`
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-      float process(int[4] indices) {
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-        int sb[1];
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-  ${Rt}
-  ${_n}
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-vec4 getBAtOutCoordsMatmul(int i) {
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-              vec4 value = vec4(0);
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-                value += (a.rrbb * b.rgrg);
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-              ${fe}
-              ${K.output} = value;
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-    ${X}
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-        int a[${q}];
-        int b[${se}];
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-
-        float value;
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-            b[${se-2}] = k;
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-        }
-        ${W}
-        ${k}
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-`);let O="vec4(outputValue.xx, outputValue.yy)";return l.ShapeUtil.size(i)===1&&(O="vec4(outputValue.x)"),m?`
-vec4 getBiasForMatmul() {
-  ${n} coords = getOutputCoords();
-  ${P}
-  vec4 outputValue = getBias(${y});
-  return ${O};
-}`:`
-float getBiasForMatmul() {
-  ${n} coords = getOutputCoords();
-  ${P}
-  return getBias(coords.x);
-}`}o.getBiasForMatmul=u},2403:(v,o,f)=>{Object.defineProperty(o,"__esModule",{value:!0}),o.createPackProgramInfoLoader=void 0;const l=f(5060),d=f(2039),b=f(9390),c=f(2827),g={name:"pack",inputNames:["A"],inputTypes:[d.TextureType.unpackedReversed]};o.createPackProgramInfoLoader=(_,p)=>Object.assign(Object.assign({},g),{get:()=>((u,n)=>{const e=(0,l.getGlsl)(u.session.backend.glContext.version),i=n.dims,s=i.length,m=n.dims.length,y=(0,b.getCoordsDataType)(m),x=(0,c.getChannels)("rc",m),T=(E=m,P=x,O=i[i.length-2],A=i[i.length-1],E===0||E===1?"":`
-    int r = ${P[E-2]};
-    int c = ${P[E-1]};
-    int rp1 = ${P[E-2]} + 1;
-    int cp1 = ${P[E-1]} + 1;
-    bool rEdge = rp1 >= ${A};
-    bool cEdge = cp1 >= ${O};
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-            rc + 1 >= ${W[0]} ? 0. : getA(rc + 1),
-            0, 0`;let q="";if(K>2)for(let se=0;se<K-2;++se)q+=`${X[se]},`;return`getA(${q}r, c),
-          rEdge ? 0. : getA(${q}rp1, c),
-          cEdge ? 0. : getA(${q}r, cp1),
-          rEdge || cEdge ? 0. : getA(${q}rp1, cp1)`}(i,x),$=`
-        void main() {
-          ${y} rc = getOutputCoords();
-
-          if(${I}) {
-            ${e.output} = vec4(0);
-          } else {
-            ${T}
-
-            ${e.output} = vec4(${k});
-          }
-        }
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-    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. ?
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-        (modCoord.y == 0. ? frag.b : frag.a);
-    }
-  `}},2870:(v,o,f)=>{Object.defineProperty(o,"__esModule",{value:!0}),o.parsePadAttributesV11=o.padV11=o.parsePadAttributesV2=o.padV2=void 0;const l=f(246),d=f(2517),b=f(5060),c=f(2039),g={name:"Pad",inputNames:["A"],inputTypes:[c.TextureType.unpacked]};o.padV2=(y,x,T)=>(u(x),[y.run(Object.assign(Object.assign({},g),{cacheHint:T.cacheKey,get:()=>p(y,x[0],T)}),x)]),o.parsePadAttributesV2=y=>{const x=y.attributes.getString("mode","constant"),T=y.attributes.getFloat("value",0),E=y.attributes.getInts("pads");return(0,l.createAttributeWithCacheKey)({mode:x,value:T,pads:E})},o.padV11=(y,x,T)=>{n(x);const E=_(y,x,T);return(0,o.padV2)(y,[x[0]],E)},o.parsePadAttributesV11=y=>y.attributes.getString("mode","constant");const _=(y,x,T)=>{if(!y.session.isInitializer(x[1].dataId)||x.length>=3&&!y.session.isInitializer(x[2].dataId))throw new Error("dynamic pad attributes are not allowed");const E=Array.from(x[1].integerData),P=x.length>=3?x[2].floatData[0]:0;return(0,l.createAttributeWithCacheKey)({mode:T,pads:E,value:P})},p=(y,x,T)=>{const E=d.ShapeUtil.padShape(x.dims.slice(),T.pads),P=E.length,O=`
-      ${e(y,x,T)}
-      float process(int[${P}] indices) {
-          return padA(indices);
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-        k = m[${k}] - ${O[k]};
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-        offset += k * ${T[k]};
-        `;return`
-      float padA(int m[${D}]) {
-        const float constant = float(${A});
-        int offset = 0;
-        int k = 0;
-        ${I}
-        vec2 coords = offsetToCoords(offset, ${E}, ${P});
-        float value = getColorAsFloat(${y.texture2D}(A, coords));
-        return value;
-      }
-      `},s=(y,x,T,E,P,O)=>{const A=x.length;let D="";for(let I=A-1;I>=0;--I)D+=`
-        k = m[${I}] - ${O[I]};
-        if (k < 0) { k = -k; }
-        {
-          const int _2n_1 = ${2*(x[I]-1)};
-          k = int( mod( float(k), float(_2n_1) ) ) ;
-          if(k >= ${x[I]}) { k = _2n_1 - k; }
-        }
-        offset += k * ${T[I]};
-        `;return`
-      float padA(int m[${A}]) {
-        int offset = 0;
-        int k = 0;
-        ${D}
-        vec2 coords = offsetToCoords(offset, ${E}, ${P});
-        float value = getColorAsFloat(${y.texture2D}(A, coords));
-        return value;
-      }
-      `},m=(y,x,T,E,P,O)=>{const A=x.length;let D="";for(let I=A-1;I>=0;--I)D+=`
-        k = m[${I}] - ${O[I]};
-        if (k < 0)  k = 0;
-        if (k >= ${x[I]}) k = ${x[I]-1};
-        offset += k * ${T[I]};
-      `;return`
-      float padA(int m[${A}]) {
-        int offset = 0;
-        int k = 0;
-        ${D}
-        vec2 coords = offsetToCoords(offset, ${E}, ${P});
-        float value = getColorAsFloat(${y.texture2D}(A, coords));
-        return value;
-      }
-      `}},2143:(v,o,f)=>{Object.defineProperty(o,"__esModule",{value:!0}),o.globalMaxPool=o.parseMaxPoolAttributes=o.maxPool=o.parseGlobalAveragePoolAttributes=o.globalAveragePool=o.parseAveragePoolAttributes=o.averagePool=void 0;const l=f(246),d=f(2517),b=f(2039);o.averagePool=(m,y,x)=>{n(y);const T={name:"AveragePool",inputNames:["X"],inputTypes:[b.TextureType.unpacked],cacheHint:x.cacheKey};return[m.run(Object.assign(Object.assign({},T),{get:()=>c(y,T,!1,x)}),y)]},o.parseAveragePoolAttributes=m=>{const y=m.attributes.getString("auto_pad","NOTSET"),x=m.attributes.getInt("ceil_mode",0),T=m.attributes.getInt("count_include_pad",0)!==0,E=m.attributes.getInts("kernel_shape"),P=m.attributes.getInts("strides",[]),O=m.attributes.getInts("pads",[]);if(x!==0)throw new Error("using ceil() in shape computation is not yet supported for AveragePool");return(0,l.createAttributeWithCacheKey)({autoPad:y,ceilMode:x,countIncludePad:T,kernelShape:E,strides:P,pads:O})};const c=(m,y,x,T)=>{const[E,P]=_(m,T,x),O=d.ShapeUtil.size(E.kernelShape);let A="";E.countIncludePad?A+=`value /= float(${O});`:A+=`value /= float(${O} - pad);`;const D=`
-        ${e(m[0].dims,E,"value += _X(x);",A,"0.0")}
-      `;return Object.assign(Object.assign({},y),{output:{dims:P,type:m[0].type,textureType:b.TextureType.unpacked},shaderSource:D})};o.globalAveragePool=(m,y,x)=>{n(y);const T={name:"GlobalAveragePool",inputNames:["X"],inputTypes:[b.TextureType.unpacked],cacheHint:`${x.countIncludePad}`};return[m.run(Object.assign(Object.assign({},T),{get:()=>c(y,T,!0,x)}),y)]},o.parseGlobalAveragePoolAttributes=m=>{const y=m.attributes.getInt("count_include_pad",0)!==0;return(0,l.createAttributeWithCacheKey)({autoPad:"",ceilMode:0,countIncludePad:y,kernelShape:[],strides:[],pads:[]})},o.maxPool=(m,y,x)=>{n(y);const T={name:"MaxPool",inputNames:["X"],inputTypes:[b.TextureType.unpacked],cacheHint:x.cacheKey};return[m.run(Object.assign(Object.assign({},T),{get:()=>g(y,T,!1,x)}),y)]},o.parseMaxPoolAttributes=m=>{const y=m.attributes.getString("auto_pad","NOTSET"),x=m.attributes.getInt("ceil_mode",0),T=m.attributes.getInts("kernel_shape"),E=m.attributes.getInts("strides",[]),P=m.attributes.getInts("pads",[]),O=m.attributes.getInt("storage_order",0),A=m.attributes.getInts("dilations",[]);if(O!==0)throw new Error("column major storage order is not yet supported for MaxPool");if(x!==0)throw new Error("using ceil() in shape computation is not yet supported for MaxPool");return(0,l.createAttributeWithCacheKey)({autoPad:y,ceilMode:x,countIncludePad:!1,kernelShape:T,strides:E,pads:P,storageOrder:O,dilations:A})};const g=(m,y,x,T)=>{const[E,P]=_(m,T,x),O=`
-      ${e(m[0].dims,E,`
-      value = max(_X(x), value);
-    `,"","-1e5")}
-    `;return Object.assign(Object.assign({},y),{output:{dims:P,type:m[0].type,textureType:b.TextureType.unpacked},shaderSource:O})},_=(m,y,x)=>{const T=m[0].dims.slice(),E=Object.hasOwnProperty.call(y,"dilations"),P=y.kernelShape.slice(),O=y.strides.slice(),A=E?y.dilations.slice():[],D=y.pads.slice();d.PoolConvUtil.adjustPoolAttributes(x,T,P,O,A,D);const I=d.PoolConvUtil.computePoolOutputShape(x,T,O,A,P,D,y.autoPad),k=Object.assign({},y);return E?Object.assign(k,{kernelShape:P,strides:O,pads:D,dilations:A,cacheKey:y.cacheKey}):Object.assign(k,{kernelShape:P,strides:O,pads:D,cacheKey:y.cacheKey}),[k,I]},p={autoPad:"",ceilMode:0,countIncludePad:!1,kernelShape:[],strides:[],pads:[],storageOrder:0,dilations:[],cacheKey:""},u={name:"GlobalMaxPool",inputNames:["X"],inputTypes:[b.TextureType.unpacked]};o.globalMaxPool=(m,y)=>(n(y),[m.run(Object.assign(Object.assign({},u),{get:()=>g(y,u,!0,p)}),y)]);const n=m=>{if(!m||m.length!==1)throw new Error("Pool ops requires 1 input.");if(m[0].type!=="float32"&&m[0].type!=="float64")throw new Error("Invalid input type.")},e=(m,y,x,T,E)=>{const P=m.length;if(y.kernelShape.length<=2){const O=y.kernelShape[y.kernelShape.length-1],A=y.strides[y.strides.length-1],D=y.pads[y.pads.length/2-1],I=y.pads[y.pads.length-1],k=m[P-1];let $="",W="",X="";if($=D+I!==0?`
-          for (int i = 0; i < ${O}; i++) {
-            x[${P} - 1] = indices[${P} - 1] * ${A} - ${D} + i;
-            if (x[${P} - 1] < 0 || x[${P} - 1] >= ${k}) {
-              pad++;
-              continue;
-            }
-            ${x}
-          }`:`
-          for (int i = 0; i < ${O}; i++) {
-            x[${P} - 1] = indices[${P} - 1] * ${A} - ${D} + i;
-            ${x}
-          }`,y.kernelShape.length===2){const K=y.kernelShape[y.kernelShape.length-2],q=y.strides[y.strides.length-2],se=y.pads[y.pads.length/2-2],F=y.pads[y.pads.length-2],z=m[P-2];W=se+F!==0?`
-            for (int j = 0; j < ${K}; j++) {
-              x[${P} - 2] = indices[${P} - 2] * ${q} - ${se} + j;
-              if (x[${P} - 2] < 0 || x[${P} - 2] >= ${z}) {
-                pad+= ${O};
-                continue;
-              }
-          `:`
-            for (int j = 0; j < ${K}; j++) {
-              x[${P} - 2] = indices[${P} - 2] * ${q} - ${se} + j;
-            `,X=`
-          }
-        `}return`
-        float process(int indices[${P}]) {
-          int x[${P}];
-          copyVec(indices, x);
-
-          float value = ${E};
-          int pad = 0;
-          ${W}
-          ${$}
-          ${X}
-          ${T}
-          return value;
-        }
-      `}{const O=d.ShapeUtil.size(y.kernelShape),A=d.ShapeUtil.computeStrides(y.kernelShape),D=A.length,I=y.pads.length,k=s(D),$=i(m,"inputDims"),W=i(y.pads,"pads"),X=i(A,"kernelStrides"),K=i(y.strides,"strides");let q="";return q=y.pads.reduce((se,F)=>se+F)?`
-            if (x[j] >= inputDims[j] || x[j] < 0) {
-              pad++;
-              isPad = true;
-              break;
-            }
-          }
-          if (!isPad) {
-            ${x}
-          }`:`
-          }
-          ${x}
-        `,`
-        ${k}
-        float process(int indices[${P}]) {
-          int x[${P}];
-          copyVec(indices, x);
-          int offset[${D}];
-          int pads[${I}];
-          int inputDims[${P}];
-          int kernelStrides[${D}];
-          int strides[${D}];
-          ${W}
-          ${$}
-          ${K}
-          ${X}
-
-          float value = ${E};
-          int pad = 0;
-          bool isPad = false;
-          for (int i = 0; i < ${O}; i++) {
-            offsetToIndices(i, kernelStrides, offset);
-            isPad = false;
-            for (int j = ${P} - ${D}; j < ${P}; j++) {
-              x[j] = indices[j] * strides[j - ${P} + ${D}]
-                + offset[j - ${P} + ${D}] - pads[j - 2];
-              ${q}
-          }
-          ${T}
-
-          return value;
-        }
-      `}},i=(m,y)=>{let x="";for(let T=0;T<m.length;T++)x+=`
-      ${y}[${T}] = ${m[T]};
-    `;return x},s=m=>`
-  void offsetToIndices(int offset, int[${m}] strides, out int[${m}] indices) {
-    if (${m} == 0) {
-      return;
-    }
-    for (int i = 0; i < ${m} - 1; ++i) {
-      indices[i] = offset / strides[i];
-      offset -= indices[i] * strides[i];
-    }
-    indices[${m} - 1] = offset;
-  }`},4939:(v,o,f)=>{Object.defineProperty(o,"__esModule",{value:!0}),o.reduceLogSumSquare=o.reduceLogSum=o.reduceProd=o.reduceMin=o.reduceMax=o.reduceMean=o.reduceSum=o.parseReduceAttributes=void 0;const l=f(246),d=f(782),b=f(2517),c=f(2039),g=(u,n,e,i,s)=>{p(n);const m={name:i,inputNames:["A"],inputTypes:[c.TextureType.unpacked]};return[u.run(Object.assign(Object.assign({},m),{cacheHint:e.cacheKey,get:()=>_(u,n,e,i,s,m)}),n)]};o.parseReduceAttributes=u=>{const n=u.attributes.getInts("axes",[]),e=u.attributes.getInt("keepdims",1)===1;return(0,l.createAttributeWithCacheKey)({axes:n,keepDims:e})};const _=(u,n,e,i,s,m)=>{const y=[],x=n[0].dims.length||1,T=[],E=b.ShapeUtil.normalizeAxes(e.axes,n[0].dims.length),P=s(n,E);let O=P[1];for(let D=0;D<n[0].dims.length;D++)E.indexOf(D)>=0||E.length===0?(e.keepDims&&y.push(1),O=`
-          for(int j${D} = 0; j${D} < ${n[0].dims[D]}; j${D}++) {
-            inputIdx[${D}] = j${D};
-            ${O}
-          }`):(T.push(`inputIdx[${D}] = outputIdx[${y.length}];`),y.push(n[0].dims[D]));const A=`
-      float process(int outputIdx[${y.length||1}]) {
-        float value;                 // final result
-        int inputIdx[${x}];      // addressing input data
-        ${T.join(`
-`)}
-        ${P[0]}       // init ops for reduce max/min
-        ${O}
-        ${P[2]}       // final computation for reduce mean
-        return value;
-      }`;return Object.assign(Object.assign({},m),{output:{dims:y,type:n[0].type,textureType:c.TextureType.unpacked},shaderSource:A})},p=u=>{if(!u||u.length!==1)throw new Error("Reduce op requires 1 input.");if(d.NUMBER_TYPES.indexOf(u[0].type)===-1)throw new Error("Invalid input type.")};o.reduceSum=(u,n,e)=>g(u,n,e,"ReduceSum",()=>["value = 0.0;","value += _A(inputIdx);",""]),o.reduceMean=(u,n,e)=>g(u,n,e,"ReduceMean",(i,s)=>{let m=1;for(let y=0;y<i[0].dims.length;y++)(s.indexOf(y)>=0||s.length===0)&&(m*=i[0].dims[y]);return["value = 0.0;","value += _A(inputIdx);",`value /= ${m}.;`]}),o.reduceMax=(u,n,e)=>g(u,n,e,"ReduceMax",(i,s)=>{const m=[];for(let y=0;y<i[0].dims.length;y++)(s.indexOf(y)>=0||s.length===0)&&m.push(`inputIdx[${y}] = 0;`);return[`${m.join(`
-`)}
-value = _A(inputIdx);`,"value = max(value, _A(inputIdx));",""]}),o.reduceMin=(u,n,e)=>g(u,n,e,"ReduceMin",(i,s)=>{const m=[];for(let y=0;y<i[0].dims.length;y++)(s.indexOf(y)>=0||s.length===0)&&m.push(`inputIdx[${y}] = 0;`);return[`${m.join(`
-`)}
-value = _A(inputIdx);`,"value = min(value, _A(inputIdx));",""]}),o.reduceProd=(u,n,e)=>g(u,n,e,"ReduceProd",()=>["value = 1.0;","value *= _A(inputIdx);",""]),o.reduceLogSum=(u,n,e)=>g(u,n,e,"ReduceLogSum",()=>["value = 0.0;","value += _A(inputIdx);","value = log(value);"]),o.reduceLogSumSquare=(u,n,e)=>g(u,n,e,"ReduceLogSumSquare",()=>["float t; value = 0.0;","t = _A(inputIdx); value += t * t;",""])},7019:(v,o,f)=>{Object.defineProperty(o,"__esModule",{value:!0}),o.isReshapeCheap=o.processDims3D=o.createPackedReshape3DProgramInfoLoader=void 0;const l=f(2517),d=f(5060),b=f(2039),c=f(2827);o.createPackedReshape3DProgramInfoLoader=(g,_,p)=>{const u=(n=>({name:"Reshape (packed)",inputTypes:[b.TextureType.packed],inputNames:["A"],cacheHint:`${n}`}))(p);return Object.assign(Object.assign({},u),{get:()=>((n,e,i,s)=>{const m=e.dims,y=s;let x="";for(let P=0;P<4;P++){let O="";switch(P){case 0:O="outputCoords = rc;";break;case 1:O="outputCoords = ivec3(rc.x, rc.y+1, rc.z);";break;case 2:O="outputCoords = ivec3(rc.x, rc.y, rc.z+1);";break;case 3:O="outputCoords = ivec3(rc.x, rc.y+1, rc.z+1);";break;default:throw new Error}x+=`
-        ${O}
-        ${P>0?"if(outputCoords.y < rows && outputCoords.z < cols){":""}
-          int flattenedIndex = getFlattenedIndex(outputCoords);
-
-          ivec3 inputRC = inputCoordsFromReshapedOutCoords(flattenedIndex);
-          vec2 innerDims = vec2(float(inputRC.y),float(inputRC.z));
-
-          result[${P}] = getChannel(getA(inputRC.x, inputRC.y, inputRC.z), innerDims);
-
-        ${P>0?"}":""}
-      `}const T=(0,d.getGlsl)(n.session.backend.glContext.version),E=`
-      ${function(P){const O=l.ShapeUtil.computeStrides(P),A=["b","r","c"],D="index";return`
-    ivec3 inputCoordsFromReshapedOutCoords(int index) {
-      ${O.map((I,k)=>`int ${A[k]} = ${D} / ${I}; ${k===O.length-1?`int ${A[k+1]} = ${D} - ${A[k]} * ${I}`:`index -= ${A[k]} * ${I}`};`).join("")}
-      return ivec3(b, r, c);
-    }
-  `}(m)}
-      ${function(P){const O=l.ShapeUtil.computeStrides(P);return`
-  int getFlattenedIndex(ivec3 coords) {
-    // reverse y, z order
-    return coords.x * ${O[0]} + coords.z * ${O[1]} + coords.y;
-  }
-`}(y)}
-      ${(0,c.unpackFromChannel)()}
-
-      void main() {
-        ivec3 rc = getOutputCoords();
-
-        vec4 result = vec4(0.0);
-
-        ivec3 outputCoords;
-        int rows = ${y[2]};
-        int cols = ${y[1]};
-
-        ${x}
-        ${T.output} = result;
-      }
-    `;return Object.assign(Object.assign({},i),{output:{dims:y,type:e.type,textureType:b.TextureType.packed},shaderSource:E,hasMain:!0})})(g,_,u,p)})},o.processDims3D=function(g){if(g.length===0)return[1,1,1];let _=1;for(let p=0;p<g.length-2;++p)_*=g[p];return[_,g.length>1?g[g.length-2]:1,g[g.length-1]]},o.isReshapeCheap=function(g,_){let p=!1;return p=g.length===0||_.length===0||(g.length<2||_.length<2?g[g.length-1]===_[_.length-1]:g[g.length-1]===_[_.length-1]&&g[g.length-2]===_[_.length-2]),p}},718:(v,o,f)=>{Object.defineProperty(o,"__esModule",{value:!0}),o.reshape=void 0;const l=f(2517);o.reshape=(d,b)=>{const c=l.ShapeUtil.calculateReshapedDims(b[0].dims,b[1].integerData);return d.session.pack?[d.reshapePacked(b[0],c)]:[d.reshapeUnpacked(b[0],c)]}},2268:(v,o,f)=>{Object.defineProperty(o,"__esModule",{value:!0}),o.parseResizeAttributesV11=o.parseResizeAttributesV10=o.resize=void 0;const l=f(5060),d=f(2039),b=f(9390),c=f(2827),g=f(9793),_={name:"Resize",inputNames:["A"],inputTypes:[d.TextureType.packed]};o.resize=(i,s,m)=>((0,g.validateInputs)(s,m),[i.run(Object.assign(Object.assign({},_),{cacheHint:m.cacheKey,get:()=>p(i,s,m)}),s)]),o.parseResizeAttributesV10=i=>(0,g.parseUpsampleAttributes)(i,10),o.parseResizeAttributesV11=i=>(0,g.parseUpsampleAttributes)(i,11);const p=(i,s,m)=>{const y=(0,l.getGlsl)(i.session.backend.glContext.version),[x,T]=u(s,m);if(x.every(q=>q===1)&&m.coordinateTransformMode!=="tf_crop_and_resize")return Object.assign(Object.assign({},_),{output:{dims:T,type:s[0].type,textureType:d.TextureType.packed},hasMain:!0,shaderSource:`void main() {
-                    vec4 v = ${y.texture2D}(X, TexCoords);
-                    ${y.output} = v;
-                }`});const E=T.length;if(E<2)throw new Error(`output dimension should be at least 2, but got ${E}`);const P=T[E-2],O=T[E-1],A=s[0].dims;if(E!==A.length)throw new Error(`output dimension should match input ${A.length}, but got ${E}`);const D=A[E-2],I=A[E-1],k=x[E-2],$=x[E-1];let W="";if(m.mode!=="linear")throw new Error(`resize (packed) does not support mode: '${m.mode}'`);switch(m.coordinateTransformMode){case"asymmetric":W=`
-                    vec4 getSourceFracIndex(ivec4 coords) {
-                        return vec4(coords) / scaleWHWH;
-                    }
-                `;break;case"half_pixel":W=`
-                    vec4 getSourceFracIndex(ivec4 coords) {
-                        return (vec4(coords) + 0.5) / scaleWHWH - 0.5;
-                    }
-                `;break;case"pytorch_half_pixel":W=`
-                    vec4 getSourceFracIndex(ivec4 coords) {
-                        vec4 fcoords = vec4(coords);
-                        return vec4(
-                            ${O}.0 > 1.0 ? (fcoords.x + 0.5) / scaleWHWH.x - 0.5 : 0.0,
-                            ${P}.0 > 1.0 ? (fcoords.y + 0.5) / scaleWHWH.y - 0.5 : 0.0,
-                            ${O}.0 > 1.0 ? (fcoords.z + 0.5) / scaleWHWH.z - 0.5 : 0.0,
-                            ${P}.0 > 1.0 ? (fcoords.w + 0.5) / scaleWHWH.w - 0.5 : 0.0
-                          );
-                    }
-                `;break;case"align_corners":W=`
-                    vec4 getSourceFracIndex(ivec4 coords) {
-                        vec4 resized = vec4(${O}.0 - 1.0, ${P}.0 - 1.0, ${O}.0 - 1.0,
-                            ${P}.0 - 1.0);
-                        vec4 original = vec4(${I}.0 - 1.0, ${D}.0 - 1.0, ${I}.0 - 1.0,
-                            ${D}.0 - 1.0);
-                        vec4 new_scale = original / resized;
-                        return vec4(coords) * new_scale;
-                    }
-                `;break;default:throw new Error(`resize (packed) does not support coordinateTransformMode:                                 '${m.coordinateTransformMode}'`)}const X=(0,b.getCoordsDataType)(E),K=`
-            const vec2 inputWH = vec2(${D}.0, ${I}.0);
-            const vec4 scaleWHWH = vec4(float(${k}), float(${$}), float(${k}), float(${$}));
-            ${(0,c.unpackFromChannel)()}
-            ${W}
-            float getAValue(int x10, int r, int c, int d) {
-                return getChannel(getA(x10, r, c, d), vec2(c, d));
-            }
-            void main() {
-                ${X} 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 < ${P-1};
-                bool hasNextCol = rc.z < ${O-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);
-
-                ${y.output} = vec4(newValue);
-            }
-        `;return Object.assign(Object.assign({},_),{output:{dims:T,type:s[0].type,textureType:d.TextureType.packed},hasMain:!0,shaderSource:K})},u=(i,s)=>{const m=i[0].dims;let y,x=s.scales;if(x.length===0){const E=i[s.scalesInputIdx];if(E&&E.size!==0){if(i[s.sizesInputIdx])throw new Error("Only one of scales or sizes must be provided as input.");x=n(E,s.mode,s.isResize)}else{const P=i[s.sizesInputIdx];if(!P||P.size===0)throw new Error("Either scales or sizes MUST be provided as input.");y=Array.from(P.integerData),x=e(y,m,s.mode,s.isResize)}}else if(i[s.sizesInputIdx])throw new Error("Only one of scales or sizes must be provided as input.");const T=y||m.map((E,P)=>Math.floor(E*x[P]));return[x,T]},n=(i,s,m)=>{const y=Array.from(i.floatData);return(0,g.scalesValidation)(y,s,m),y},e=(i,s,m,y)=>{const x=s.length,T=new Array(x);for(let E=0,P=x;E<P;E++)if(s[E]===0){if(i[E]!==0)throw new Error("Input dim is zero but required output dim is non-zero.");T[E]=1}else T[E]=i[E]/s[E];return(0,g.scalesValidation)(T,m,y),T}},8117:(v,o,f)=>{Object.defineProperty(o,"__esModule",{value:!0}),o.shape=void 0;const l=f(9162);o.shape=(b,c)=>(d(c),[new l.Tensor([c[0].dims.length],"int32",void 0,void 0,new Int32Array(c[0].dims))]);const d=b=>{if(!b||b.length!==1)throw new Error("Shape requires 1 input.")}},2278:(v,o,f)=>{Object.defineProperty(o,"__esModule",{value:!0}),o.sliceV10=o.parseSliceAttributes=o.slice=void 0;const l=f(246),d=f(782),b=f(2517),c=f(2039),g={name:"Slice",inputNames:["A"],inputTypes:[c.TextureType.unpacked]};o.slice=(e,i,s)=>(p(i),[e.run(Object.assign(Object.assign({},g),{cacheHint:s.cacheKey,get:()=>_(e,i[0],s)}),i)]),o.parseSliceAttributes=e=>{const i=e.attributes.getInts("starts"),s=e.attributes.getInts("ends"),m=e.attributes.getInts("axes",[]);return(0,l.createAttributeWithCacheKey)({starts:i,ends:s,axes:m})};const _=(e,i,s)=>{const m=s.axes.length===0?i.dims.slice(0).map((A,D)=>D):s.axes,y=b.ShapeUtil.normalizeAxes(m,i.dims.length),x=s.starts.map((A,D)=>A>i.dims[y[D]]-1?i.dims[y[D]]:b.ShapeUtil.normalizeAxis(A,i.dims[y[D]])),T=s.ends.map((A,D)=>A>i.dims[y[D]]-1?i.dims[y[D]]:b.ShapeUtil.normalizeAxis(A,i.dims[y[D]])),E=i.dims.slice(),P=[];for(let A=0;A<y.length;A++)E[y[A]]=T[A]-x[A],x[A]>0&&P.push(`outputIdx[${y[A]}] += ${x[A]};`);const O=`
-      float process(int outputIdx[${E.length}]) {
-        ${P.join(`
-      `)}
-        return _A(outputIdx);
-      }`;return Object.assign(Object.assign({},g),{output:{dims:E,type:i.type,textureType:c.TextureType.unpacked},shaderSource:O})},p=e=>{if(!e||e.length!==1)throw new Error("Slice requires 1 input.");if(d.NUMBER_TYPES.indexOf(e[0].type)===-1)throw new Error("Invalid input type.")};o.sliceV10=(e,i)=>{n(i);const s=u(e,i);return[e.run(Object.assign(Object.assign({},g),{cacheHint:s.cacheKey,get:()=>_(e,i[0],s)}),[i[0]])]};const u=(e,i)=>{if(!e.session.isInitializer(i[1].dataId)||!e.session.isInitializer(i[2].dataId)||i.length>=4&&!e.session.isInitializer(i[3].dataId)||i.length>=5&&!e.session.isInitializer(i[4].dataId))throw new Error("dynamic slice attributes are not allowed");if(i.length>=5&&i[4].integerData.some(x=>x!==1))throw new Error("currently non-1 steps is not supported for Slice");const s=Array.from(i[1].integerData),m=Array.from(i[2].integerData),y=i.length>=4?Array.from(i[3].integerData):[];return{starts:s,ends:m,axes:y,cacheKey:`${y};${s};${m}`}},n=e=>{if(!e||e.length<3||e.length>5)throw new Error("Invalid input number.");if(e[1].type!=="int32"||e[1].dims.length!==1)throw new Error("Invalid input type.");if(e[2].type!=="int32"||e[2].dims.length!==1)throw new Error("Invalid input type.");if(e.length>=4&&(e[3].type!=="int32"||e[3].dims.length!==1))throw new Error("Invalid input type.");if(e.length>=5&&(e[4].type!=="int32"||e[4].dims.length!==1))throw new Error("Invalid input type.")}},5524:(v,o,f)=>{Object.defineProperty(o,"__esModule",{value:!0}),o.softmaxV13=o.parseSoftmaxAttributesV13=o.parseSoftmaxAttributes=o.softmax=void 0;const l=f(246),d=f(2517),b=f(5060),c=f(2039),g=f(3738),_={name:"SoftmaxComputeMax",inputNames:["A"],inputTypes:[c.TextureType.unpacked]},p={name:"SoftmaxComputeScale",inputNames:["A","Max"],inputTypes:[c.TextureType.unpacked,c.TextureType.unpacked]},u={name:"SoftMax",inputNames:["A","Max","Norm"],inputTypes:[c.TextureType.unpacked,c.TextureType.unpacked,c.TextureType.unpacked]};o.softmax=(y,x,T)=>{m(x);const E=x[0].dims.slice(),P=d.ShapeUtil.normalizeAxis(T.axis,E.length),O=d.ShapeUtil.sizeToDimension(E,P),A=d.ShapeUtil.sizeFromDimension(E,P);return n(y,x,T,O,A)},o.parseSoftmaxAttributes=y=>(0,l.createAttributeWithCacheKey)({axis:y.attributes.getInt("axis",1)}),o.parseSoftmaxAttributesV13=y=>(0,l.createAttributeWithCacheKey)({axis:y.attributes.getInt("axis",-1)}),o.softmaxV13=(y,x,T)=>{m(x);const E=x[0].dims.slice(),P=d.ShapeUtil.normalizeAxis(T.axis,E.length),O=E.length,A=P!==O-1,D=[];let I,k=[],$=[];A&&(k=Array.from({length:O}).map((q,se)=>se),k[P]=O-1,k[O-1]=P,k.map(q=>D.push(E[q])),I=(0,l.createAttributeWithCacheKey)({perm:k}),$=(0,g.transpose)(y,x,I));const W=A?d.ShapeUtil.sizeToDimension(D,O-1):d.ShapeUtil.sizeToDimension(E,O-1),X=A?d.ShapeUtil.sizeFromDimension(D,O-1):d.ShapeUtil.sizeFromDimension(E,O-1),K=n(y,A?$:x,T,W,X);return A?(0,g.transpose)(y,K,I):K};const n=(y,x,T,E,P)=>{const O=e(y,x[0],E,P,[E]),A=y.run(Object.assign(Object.assign({},_),{cacheHint:T.cacheKey,get:()=>O}),x),D=i(y,x[0],E,P,O.output.dims,[E]),I=y.run(Object.assign(Object.assign({},p),{cacheHint:T.cacheKey,get:()=>D}),[x[0],A]),k=s(y,x[0],E,P,O.output.dims,D.output.dims);return[y.run(Object.assign(Object.assign({},u),{cacheHint:T.cacheKey,get:()=>k}),[x[0],A,I])]},e=(y,x,T,E,P)=>{const[O,A]=y.calculateTextureWidthAndHeight(x.dims,c.TextureType.unpacked),D=P.length;if(T<1||E<1)throw new Error("Logical row count N and feature count D must be greater than or equal to 1");if(P.length!==1)throw new Error("Dimensionality of the output should be 1");if(P[0]!==T)throw new Error("Shape of the output should be equal to logical row count");const I=(0,b.getGlsl)(y.session.backend.glContext.version),k=`
-      float process(int[${D}] indices) {
-        int logical_row_start_offset = indices[0] * ${E};
-
-        float max = getColorAsFloat(${I.texture2D}(A, offsetToCoords(logical_row_start_offset, ${O},
-        ${A} )));
-        for(int i=1; i<${E}; ++i)
-        {
-          float current = getColorAsFloat(${I.texture2D}(A, offsetToCoords(logical_row_start_offset + i,
-            ${O}, ${A})));
-          if(current > max)
-          max = current;
-        }
-
-        return max;
-      }`;return Object.assign(Object.assign({},_),{output:{dims:P,type:x.type,textureType:c.TextureType.unpacked},shaderSource:k})},i=(y,x,T,E,P,O)=>{const[A,D]=y.calculateTextureWidthAndHeight(x.dims,c.TextureType.unpacked),I=O.length;if(T<1||E<1)throw new Error("Logical row count N and feature count D must be greater than or equal to 1");if(O.length!==1)throw new Error("Dimensionality of the output should be 1");if(O[0]!==T)throw new Error("Shape of the output should be equal to logical row count");if(P.length!==1)throw new Error("Dimensionality of the intermediate results should be 1");if(P[0]!==T)throw new Error("Shape of the intermediate results should be equal to logical row count");const k=`
-      float process(int[${I}] indices) {
-        int logical_row_start_offset = indices[0] * ${E};
-
-        float norm_factor = 0.0;
-        float max = _Max(indices);
-        for(int i=0; i<${E}; ++i)
-        {
-          norm_factor += exp(getColorAsFloat(${(0,b.getGlsl)(y.session.backend.glContext.version).texture2D}(A, offsetToCoords(logical_row_start_offset + i,
-            ${A}, ${D}))) - max);
-        }
-
-        return norm_factor;
-      }`;return Object.assign(Object.assign({},p),{output:{dims:O,type:x.type,textureType:c.TextureType.unpacked},shaderSource:k})},s=(y,x,T,E,P,O)=>{const[A,D]=y.calculateTextureWidthAndHeight(x.dims,c.TextureType.unpacked),I=x.dims.length;if(T<1||E<1)throw new Error("Logical row count N and feature count D must be greater than or equal to 1");if(P.length!==1||O.length!==1)throw new Error("Dimensionality of the intermediate results should be 1");if(P[0]!==T||O[0]!==T)throw new Error("Shape of the intermediate results should be equal to logical row count");const k=`
-      float process(int[${I}] indices) {
-
-      // get offset of current logical tensor index from the 2-D texture coordinates (TexCoords)
-      int offset = coordsToOffset(TexCoords, ${A}, ${D});
-
-      //determine the logical row for this index
-      int logical_row_index[1];
-      logical_row_index[0] = offset / ${E};
-
-      float norm_factor = _Norm(logical_row_index);
-
-      // avoid possible division by 0
-      // if norm_facor is 0, all elements are zero
-      // if so, return 0
-      if(norm_factor == 0.0)
-        return 0.0;
-
-      return exp(_A(indices) - _Max(logical_row_index)) / norm_factor;
-    }`;return Object.assign(Object.assign({},u),{output:{dims:x.dims,type:x.type,textureType:c.TextureType.unpacked},shaderSource:k})},m=y=>{if(!y||y.length!==1)throw new Error("Softmax requires 1 input.");if(y[0].type!=="float32"&&y[0].type!=="float64")throw new Error("Invalid input type")}},5975:(v,o,f)=>{Object.defineProperty(o,"__esModule",{value:!0}),o.parseSplitAttributes=o.split=void 0;const l=f(246),d=f(2517),b=f(2039),c={name:"Split",inputNames:["A"],inputTypes:[b.TextureType.unpacked]};o.split=(u,n,e)=>{p(n);const i=d.ShapeUtil.normalizeAxis(e.axis,n[0].dims.length),s=g(u,n,i,e),m=[];for(let y=0;y<s;++y)m.push(u.run(Object.assign(Object.assign({},c),{cacheHint:`${e.cacheKey};${y}`,get:()=>_(u,n[0],e,i,y)}),n));return m},o.parseSplitAttributes=u=>{const n=u.attributes.getInt("axis",0),e=u.attributes.getInts("split",[]),i=u.outputs.length;return(0,l.createAttributeWithCacheKey)({axis:n,split:e,numOutputs:i})};const g=(u,n,e,i)=>{const[,s]=d.SplitUtil.splitShape(n[0].dims,e,i.split,i.numOutputs);return s.length},_=(u,n,e,i,s)=>{const[m,y]=d.SplitUtil.splitShape(n.dims,i,e.split,e.numOutputs),x=y[s],T=m[s],E=`
-      float process(int indices[${T.length}]) {
-        indices[${i}] += ${x};
-        return _A(indices);
-      }
-    `;return Object.assign(Object.assign({},c),{cacheHint:`${e.cacheKey}:${s}`,output:{dims:T,type:n.type,textureType:b.TextureType.unpacked},shaderSource:E})},p=u=>{if(!u||u.length!==1)throw new Error("Split requires one input.");if(u[0].type!=="int8"&&u[0].type!=="uint8"&&u[0].type!=="int16"&&u[0].type!=="uint16"&&u[0].type!=="int32"&&u[0].type!=="uint32"&&u[0].type!=="float32"&&u[0].type!=="float64"&&u[0].type!=="bool")throw new Error("Invalid input type.")}},3933:(v,o,f)=>{Object.defineProperty(o,"__esModule",{value:!0}),o.parseSqueezeAttributes=o.squeezeV13=o.squeeze=void 0;const l=f(2517);o.squeeze=(c,g,_)=>{d(g);const p=l.ShapeUtil.squeezeShape(g[0].dims,_);return[c.reshapeUnpacked(g[0],p)]},o.squeezeV13=(c,g)=>(b(g),(0,o.squeeze)(c,[g[0]],Array.from(g[1].integerData))),o.parseSqueezeAttributes=c=>c.attributes.getInts("axes");const d=c=>{if(!c||c.length!==1)throw new Error("Squeeze requires 1 input.");if(c[0].type==="string")throw new Error("invalid input tensor types.")},b=c=>{if(!c||c.length!==2)throw new Error("Squeeze requires 2 inputs.");if(c[1].type!=="int32")throw new Error("Invalid input type.")}},6558:(v,o,f)=>{Object.defineProperty(o,"__esModule",{value:!0}),o.sum=void 0;const l=f(5060),d=f(2039);o.sum=(g,_)=>{c(_);const p={name:"Sum",inputNames:_.map((u,n)=>`X${n}`),inputTypes:new Array(_.length).fill(d.TextureType.unpacked)};return[g.run(Object.assign(Object.assign({},p),{get:()=>b(g,_,p)}),_)]};const b=(g,_,p)=>{const u=(0,l.getGlsl)(g.session.backend.glContext.version),n=_[0].dims.slice(),e=`
-      void main() {
-        vec4 result = ${_.map((i,s)=>`${u.texture2D}(X${s},TexCoords)`).join(" + ")};
-        ${u.output} = result;
-      }
-    `;return Object.assign(Object.assign({},p),{output:{dims:n,type:_[0].type,textureType:d.TextureType.unpacked},hasMain:!0,shaderSource:e})},c=g=>{if(!g||g.length===0)throw new Error("Sum requires inputs.");const _=g[0].dims.length;for(let p=1;p<g.length;p++){if(_!==g[p].dims.length)throw new Error("Input shapes are mismatched.");for(let u=0;u<_;u++)if(g[0].dims[u]!==g[p].dims[u])throw new Error("Input shapes are not matched.")}if(g[0].type!=="float32"&&g[0].type!=="float64")throw new Error("Invalid input type.");for(let p=1;p<g.length;p++)if(g[0].type!==g[p].type)throw new Error("Input types are not matched.")}},5723:(v,o,f)=>{Object.defineProperty(o,"__esModule",{value:!0}),o.tile=void 0;const l=f(782),d=f(2039);o.tile=(g,_)=>{c(_);const p={name:"Tile",inputNames:["A"],inputTypes:[d.TextureType.unpacked]};return[g.run(Object.assign(Object.assign({},p),{get:()=>b(g,_,p)}),_)]};const b=(g,_,p)=>{const u=_[0].dims.slice(),n=new Array(u.length),e=[];for(let m=0;m<u.length;m++)n[m]=u[m]*_[1].numberData[m],e.push(`inputIdx[${m}] = int(mod(float(outputIdx[${m}]), ${u[m]}.));`);const i=n.length,s=`
-      float process(int outputIdx[${i}]) {
-        int inputIdx[${i}];
-        ${e.join(`
-`)}
-        return _A(inputIdx);
-      }
-    `;return Object.assign(Object.assign({},p),{output:{dims:n,type:_[0].type,textureType:d.TextureType.unpacked},shaderSource:s})},c=g=>{if(!g||g.length!==2)throw new Error("Tile requires 2 input.");if(g[1].dims.length!==1)throw new Error("The second input shape must 1 dimension.");if(g[1].dims[0]!==g[0].dims.length)throw new Error("Invalid input shape.");if(l.NUMBER_TYPES.indexOf(g[0].type)===-1)throw new Error("Invalid input type.");if(g[1].type!=="int32"&&g[1].type!=="int16")throw new Error("Invalid repeat type.")}},3738:(v,o,f)=>{Object.defineProperty(o,"__esModule",{value:!0}),o.parseTransposeAttributes=o.transpose=void 0;const l=f(246),d=f(2517),b=f(2039),c={name:"Transpose",inputNames:["A"],inputTypes:[b.TextureType.unpacked]};o.transpose=(e,i,s)=>(n(i),[e.run(Object.assign(Object.assign({},c),{cacheHint:s.cacheKey,get:()=>g(e,i[0],s.perm)}),i)]),o.parseTransposeAttributes=e=>(0,l.createAttributeWithCacheKey)({perm:e.attributes.getInts("perm",[])});const g=(e,i,s)=>{const m=i.dims;s=_(m,s);const y=p(m,s),x=m.length,T=`
-      ${u("perm",s,x)}
-      float process(int indices[${x}]) {
-        int a[${x}];
-        perm(a, indices);
-        return _A(a);
-      }`;return Object.assign(Object.assign({},c),{output:{dims:y,type:i.type,textureType:b.TextureType.unpacked},shaderSource:T})},_=(e,i)=>(i&&i.length!==e.length&&(i=[...e.keys()].reverse()),i),p=(e,i)=>(i=_(e,i),d.ShapeUtil.sortBasedOnPerm(e,i)),u=(e,i,s)=>{const m=[];m.push(`void ${e}(out int a[${s}], int src[${s}]) {`);for(let y=0;y<s;++y)m.push(`	a[${i[y]}]=src[${y}];`);return m.push("	}"),m.join(`
-`)},n=e=>{if(!e||e.length!==1)throw new Error("Transpose requires 1 input.");if(e[0].type!=="float32"&&e[0].type!=="float64")throw new Error("input should be float tensor")}},8710:(v,o,f)=>{Object.defineProperty(o,"__esModule",{value:!0}),o.encodeAsUint8=void 0;const l=f(5060),d=f(2039);o.encodeAsUint8=(b,c)=>{const g=c.shape,_=(0,l.getGlsl)(b.session.backend.glContext.version),p=`
-    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 = ${_.texture2D}(X,TexCoords).r;
-      ${_.output} = encodeAsUint8(value);
-    }`,u={name:"Uint8Encode",inputTypes:[d.TextureType.unpacked],inputNames:["X"],output:{dims:g,type:c.tensor.type,textureType:d.TextureType.downloadUint8AsFloat},shaderSource:p,hasMain:!0};return b.executeProgram(u,[c.tensor])}},4909:(v,o,f)=>{Object.defineProperty(o,"__esModule",{value:!0}),o.tanh=o.tan=o.sqrt=o.sin=o.sigmoid=o.relu=o.not=o.neg=o.log=o.parseLeakyReluAttributes=o.leakyRelu=o.identity=o.floor=o.exp=o.parseEluAttributes=o.elu=o.cos=o.ceil=o.clipV11=o.parseClipAttributes=o.clip=o.atan=o.asin=o.acos=o.abs=o.glslTanh=o.glslTan=o.glslSqrt=o.glslSigmoid=o.glslRelu=o.glslSin=o.glslNot=o.glslNeg=o.glslLog=o.glslLeakyRelu=o.glslIdentity=o.glslClip=o.glslFloor=o.glslExp=o.glslElu=o.glslCos=o.glslCeil=o.glslAtan=o.glslAsin=o.glslAcos=o.glslAbs=void 0;const l=f(246),d=f(2517),b=f(8520),c=f(5060),g=f(2039);function _(){return K("abs")}function p(){return K("acos")}function u(){return K("asin")}function n(){return K("atan")}function e(){return K("ceil")}function i(){return K("cos")}function s(F){const z="elu";return{body:`
-  const float alpha = float(${F});
-
-  float ${z}_(float a) {
-    return a >= 0.0 ? a: (exp(a) - 1.0) * alpha;
-  }
-  vec4 ${z}_(vec4 v) {
-    return vec4(${z}_(v.x), ${z}_(v.y), ${z}_(v.z), ${z}_(v.w));
-  }
-  `,name:z,type:b.FunctionType.ValueBased}}function m(){return K("exp")}function y(){return K("floor")}function x(F,z){const fe="clip";return{body:`
-  const float min = float(${F});
-  const float max = float(${z});
-
-  float ${fe}_(float a) {
-    return clamp(a, min, max);
-  }
-  vec4 ${fe}_(vec4 v) {
-    return clamp(v, min, max);
-  }
-  `,name:fe,type:b.FunctionType.ValueBased}}function T(){const F="indentity";return{body:`
-  float ${F}_(float a) {
-    return a;
-  }
-  vec4 ${F}_(vec4 v) {
-    return v;
-  }
-  `,name:F,type:b.FunctionType.ValueBased}}function E(F){const z="leakyRelu";return{body:`
-  const float alpha = float(${F});
-
-  float ${z}_(float a) {
-    return a < 0.0 ? a * alpha : a;
-  }
-  vec4 ${z}_(vec4 v) {
-    return vec4(${z}_(v.x), ${z}_(v.y), ${z}_(v.z), ${z}_(v.w));
-  }
-  `,name:z,type:b.FunctionType.ValueBased}}function P(){return K("log")}function O(){const F="neg";return{body:`
-  float ${F}_(float a) {
-    return -a;
-  }
-  vec4 ${F}_(vec4 v) {
-    return -v;
-  }
-  `,name:F,type:b.FunctionType.ValueBased}}function A(){const F="not";return{body:`
-  float ${F}_(float a) {
-    return float( ! bool(a) );
-  }
-  bool ${F}_(bool a) {
-    return !a;
-  }
-  vec4 ${F}_(vec4 v) {
-    return vec4(!bool(v.x), !bool(v.y), !bool(v.z), !bool(v.w));
-  }
-  bvec4 ${F}_(bvec4 v) {
-    return bvec4(!v.x, !v.y, !v.z, !v.w);
-  }
-  `,name:F,type:b.FunctionType.ValueBased}}function D(){return K("sin")}function I(){const F="relu";return{body:`
-  float ${F}_(float a) {
-    return max( a, 0.0 );
-  }
-  vec4 ${F}_(vec4 v) {
-    return max( v, 0.0 );
-  }
-  `,name:F,type:b.FunctionType.ValueBased}}function k(){const F="sigmoid";return{body:`
-  float ${F}_(float a) {
-    return 1.0 / (1.0 + exp(-a));
-  }
-  vec4 ${F}_(vec4 v) {
-    return 1.0 / (1.0 + exp(-v));
-  }
-  `,name:F,type:b.FunctionType.ValueBased}}function $(){return K("sqrt")}function W(){return K("tan")}function X(){const F="tanh";return{body:`
-  float ${F}_(float a) {
-    a = clamp(a, -10., 10.);
-    a = exp(2.*a);
-    return (a - 1.) / (a + 1.);
-  }
-  vec4 ${F}_(vec4 v) {
-    v = clamp(v, -10., 10.);
-    v = exp(2.*v);
-    return (v - 1.) / (v + 1.);
-  }
-  `,name:F,type:b.FunctionType.ValueBased}}function K(F){return{body:`
-  float ${F}_(float a) {
-    return ${F}(a);
-  }
-  vec4 ${F}_(vec4 v) {
-    return ${F}(v);
-  }
-  `,name:F,type:b.FunctionType.ValueBased}}o.glslAbs=_,o.glslAcos=p,o.glslAsin=u,o.glslAtan=n,o.glslCeil=e,o.glslCos=i,o.glslElu=s,o.glslExp=m,o.glslFloor=y,o.glslClip=x,o.glslIdentity=T,o.glslLeakyRelu=E,o.glslLog=P,o.glslNeg=O,o.glslNot=A,o.glslSin=D,o.glslRelu=I,o.glslSigmoid=k,o.glslSqrt=$,o.glslTan=W,o.glslTanh=X;const q=(F,z,fe,ne)=>{const B=F.session.pack?g.TextureType.packed:g.TextureType.unpacked,Q={name:fe.name,inputTypes:[B],inputNames:["A"],cacheHint:ne};return Object.assign(Object.assign({},Q),{get:()=>((re,ue,_e,ve)=>{const we=re.session.pack?g.TextureType.packed:g.TextureType.unpacked,ke=(0,c.getGlsl)(re.session.backend.glContext.version);return Object.assign(Object.assign({},ue),{output:{dims:_e.dims,type:_e.type,textureType:we},shaderSource:`
-     ${ve.body}
-     void main() {
-       vec4 v = ${ke.texture2D}(A, TexCoords);
-       v = ${ve.name}_(v);
-       ${ke.output} = v;
-     }
-     `,hasMain:!0})})(F,Q,z,fe)})};o.abs=(F,z)=>[F.run(q(F,z[0],_()),z)],o.acos=(F,z)=>[F.run(q(F,z[0],p()),z)],o.asin=(F,z)=>[F.run(q(F,z[0],u()),z)],o.atan=(F,z)=>[F.run(q(F,z[0],n()),z)],o.clip=(F,z,fe)=>[F.run(q(F,z[0],x(fe.min,fe.max),fe.cacheKey),z)],o.parseClipAttributes=F=>(0,l.createAttributeWithCacheKey)({min:F.attributes.getFloat("min",d.MIN_CLIP),max:F.attributes.getFloat("max",d.MAX_CLIP)}),o.clipV11=(F,z)=>{const fe=se(F,z);return(0,o.clip)(F,[z[0]],fe)};const se=(F,z)=>{if(z.length>=3&&(!F.session.isInitializer(z[1].dataId)||!F.session.isInitializer(z[2].dataId)))throw new Error("dynamic clip attributes are not allowed");const fe=z.length>=3?z[1].numberData[0]:d.MIN_CLIP,ne=z.length>=3?z[2].numberData[0]:d.MAX_CLIP;return(0,l.createAttributeWithCacheKey)({min:fe,max:ne})};o.ceil=(F,z)=>[F.run(q(F,z[0],e()),z)],o.cos=(F,z)=>[F.run(q(F,z[0],i()),z)],o.elu=(F,z,fe)=>[F.run(q(F,z[0],s(fe.alpha),fe.cacheKey),z)],o.parseEluAttributes=F=>(0,l.createAttributeWithCacheKey)({alpha:F.attributes.getFloat("alpha",1)}),o.exp=(F,z)=>[F.run(q(F,z[0],m()),z)],o.floor=(F,z)=>[F.run(q(F,z[0],y()),z)],o.identity=(F,z)=>[F.run(q(F,z[0],T()),z)],o.leakyRelu=(F,z,fe)=>[F.run(q(F,z[0],E(fe.alpha),fe.cacheKey),z)],o.parseLeakyReluAttributes=F=>(0,l.createAttributeWithCacheKey)({alpha:F.attributes.getFloat("alpha",.01)}),o.log=(F,z)=>[F.run(q(F,z[0],P()),z)],o.neg=(F,z)=>[F.run(q(F,z[0],O()),z)],o.not=(F,z)=>[F.run(q(F,z[0],A()),z)],o.relu=(F,z)=>[F.run(q(F,z[0],I()),z)],o.sigmoid=(F,z)=>[F.run(q(F,z[0],k()),z)],o.sin=(F,z)=>[F.run(q(F,z[0],D()),z)],o.sqrt=(F,z)=>[F.run(q(F,z[0],$()),z)],o.tan=(F,z)=>[F.run(q(F,z[0],W()),z)],o.tanh=(F,z)=>[F.run(q(F,z[0],X()),z)]},5611:(v,o,f)=>{Object.defineProperty(o,"__esModule",{value:!0}),o.createUnpackProgramInfoLoader=o.createUnpackProgramInfo=void 0;const l=f(5060),d=f(2039),b=f(9390),c=f(2827),g={name:"unpack",inputNames:["A"],inputTypes:[d.TextureType.packed]};o.createUnpackProgramInfo=(_,p)=>{const u=p.dims.length,n=(0,c.getChannels)("rc",u),e=n.slice(-2),i=(0,b.getCoordsDataType)(u),s=(0,c.unpackFromChannel)(),m=p.dims.length===0?"":function(T,E){if(T===1)return"rc";let P="";for(let O=0;O<T;O++)P+=E[O],O<T-1&&(P+=",");return P}(u,n),y=u<=1?"rc":`vec2(${e.join(",")})`,x=`
-    ${s}
-    void main() {
-      ${i} rc = getOutputCoords();
-
-       // Sample the texture with the coords to get the rgba channel value.
-       vec4 packedInput = getA(${m});
-
-       ${(0,l.getGlsl)(_.session.backend.glContext.version).output} = vec4(getChannel(packedInput, ${y}), 0, 0, 0);
-     }
-   `;return Object.assign(Object.assign({},g),{hasMain:!0,output:{dims:p.dims,type:p.type,textureType:d.TextureType.unpacked},shaderSource:x})},o.createUnpackProgramInfoLoader=(_,p)=>Object.assign(Object.assign({},g),{get:()=>(0,o.createUnpackProgramInfo)(_,p)})},8428:(v,o,f)=>{Object.defineProperty(o,"__esModule",{value:!0}),o.parseUnsqueezeAttributes=o.unsqueezeV13=o.unsqueeze=void 0;const l=f(2517);o.unsqueeze=(c,g,_)=>{d(g);const p=l.ShapeUtil.unsqueezeShape(g[0].dims,_);return[c.reshapeUnpacked(g[0],p)]},o.unsqueezeV13=(c,g)=>(b(g),(0,o.unsqueeze)(c,[g[0]],Array.from(g[1].integerData))),o.parseUnsqueezeAttributes=c=>c.attributes.getInts("axes");const d=c=>{if(!c||c.length!==1)throw new Error("Unsqueeze requires 1 input.");if(c[0].type==="string")throw new Error("invalid input tensor types.")},b=c=>{if(!c||c.length!==2)throw new Error("Unsqueeze requires 2 inputs.");if(c[1].type!=="int32")throw new Error("Invalid input type.")}},9793:(v,o,f)=>{Object.defineProperty(o,"__esModule",{value:!0}),o.scalesValidation=o.validateInputs=o.parseUpsampleAttributes=o.parseUpsampleAttributesV9=o.parseUpsampleAttributesV7=o.upsample=void 0;const l=f(246),d=f(5060),b=f(2039),c={name:"Upsample",inputNames:["X"],inputTypes:[b.TextureType.unpacked]};o.upsample=(_,p,u)=>((0,o.validateInputs)(p,u),[_.run(Object.assign(Object.assign({},c),{cacheHint:u.cacheKey,get:()=>g(_,p,u)}),p)]),o.parseUpsampleAttributesV7=_=>(0,o.parseUpsampleAttributes)(_,7),o.parseUpsampleAttributesV9=_=>(0,o.parseUpsampleAttributes)(_,9),o.parseUpsampleAttributes=(_,p)=>{const u=p>=10,n=_.attributes.getString("mode","nearest");if(n!=="nearest"&&n!=="linear"&&(p<11||n!=="cubic"))throw new Error(`unrecognized mode: ${n}`);let e=[];p<9&&(e=_.attributes.getFloats("scales"),(0,o.scalesValidation)(e,n,u));const i=_.attributes.getFloat("extrapolation_value",0),s=p>10?_.attributes.getString("coordinate_transformation_mode","half_pixel"):"asymmetric";if(["asymmetric","pytorch_half_pixel","tf_half_pixel_for_nn","align_corners","tf_crop_and_resize","half_pixel"].indexOf(s)===-1)throw new Error(`coordinate_transform_mode '${s}' is not supported`);const m=s==="tf_crop_and_resize",y=m,x=n==="nearest"&&p>=11?_.attributes.getString("nearest_mode","round_prefer_floor"):"";if(["round_prefer_floor","round_prefer_ceil","floor","ceil",""].indexOf(x)===-1)throw new Error(`nearest_mode '${x}' is not supported`);const T=_.attributes.getFloat("cubic_coeff_a",-.75),E=_.attributes.getInt("exclude_outside",0)!==0;if(E&&n!=="cubic")throw new Error("exclude_outside can be set to 1 only when mode is CUBIC.");const P=p<11||n==="nearest"&&s==="asymmetric"&&x==="floor";let O=0,A=0,D=0;return p>10?_.inputs.length>2?(O=1,A=2,D=3):(A=1,D=2):p===9&&(A=1),(0,l.createAttributeWithCacheKey)({opset:p,isResize:u,mode:n,scales:e,extrapolationValue:i,coordinateTransformMode:s,useExtrapolation:y,needRoiInput:m,nearestMode:x,cubicCoefficientA:T,excludeOutside:E,useNearest2xOptimization:P,roiInputIdx:O,scalesInputIdx:A,sizesInputIdx:D})};const g=(_,p,u)=>{const n=(0,d.getGlsl)(_.session.backend.glContext.version),[e,i]=_.calculateTextureWidthAndHeight(p[0].dims,b.TextureType.unpacked),s=p[0].dims.map((D,I)=>Math.floor(D*u.scales[I])),[m,y]=_.calculateTextureWidthAndHeight(s,b.TextureType.unpacked),x=s.length,T=new Array(x),E=new Array(x);let P=`
-      int output_pitches[${x}];
-      int input_pitches[${x}];
-      `;for(let D=x-1;D>=0;D--)T[D]=D===x-1?1:T[D+1]*s[D+1],E[D]=D===x-1?1:E[D+1]*p[0].dims[D+1],P+=`
-        output_pitches[${D}] = ${T[D]};
-        input_pitches[${D}] = ${E[D]};
-        `;const O=`
-      float getInputFloat(int index) {
-        vec2 coords = offsetToCoords(index, ${e}, ${i});
-        float value = getColorAsFloat(${n.texture2D}(X, coords));
-        return value;
-      }
-      `,A=u.mode==="nearest"?`
-    ${O}
-    float process(int indices[${x}]) {
-      int input_index = 0;
-      int output_index = coordsToOffset(TexCoords, ${m}, ${y});
-
-      ${P}
-
-      int d, m;
-      for (int dim = 0; dim < ${x}; ++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);
-    }`:x===4?`
-    ${O}
-    float process(int indices[4]) {
-      int input_index = 0;
-      int output_index = coordsToOffset(TexCoords, ${m}, ${y});
-
-      ${P}
-
-      int m;
-      int index_of_dim0, index_of_dim1, index_of_dim2, index_of_dim3;
-      index_of_dim0 = output_index / output_pitches[0];
-      m = output_index - index_of_dim0 * output_pitches[0];
-      index_of_dim1 = m / output_pitches[1];
-      m = m - index_of_dim1 * output_pitches[1];
-      index_of_dim2 = m / output_pitches[2];
-      m = m - index_of_dim2 * output_pitches[2];
-      index_of_dim3 = m;
-
-      int index_of_input_dim2, index_of_input_dim3, x_offset, y_offset;
-      index_of_input_dim2 = index_of_dim2 / scales[2];
-      y_offset = index_of_dim2 - index_of_input_dim2 * scales[2];
-      index_of_input_dim3 = index_of_dim3 / scales[3];
-      x_offset = index_of_dim3 - index_of_input_dim3 * scales[3];
-
-      input_index = index_of_dim0 * input_pitches[0] +
-            index_of_dim1 * input_pitches[1] +
-            index_of_input_dim2 * input_pitches[2] +
-            index_of_input_dim3;
-
-      float x00 = getInputFloat(input_index);
-      float x10, x01, x11;
-
-      bool end_of_dim2 = false;
-      if (index_of_input_dim2 == (${p[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]);
-    }`:`
-    ${O}
-    float process(int indices[2]) {
-      int input_index = 0;
-      int output_index = coordsToOffset(TexCoords, ${m}, ${y});
-
-      ${P}
-
-      int m;
-      int index_of_dim0, index_of_dim1;
-      index_of_dim0 = output_index / output_pitches[0];
-      m = output_index - index_of_dim0 * output_pitches[0];
-      index_of_dim1 = m;
-
-      int index_of_input_dim0, index_of_input_dim1, x_offset, y_offset;
-      index_of_input_dim0 = index_of_dim0 / scales[0];
-      y_offset = index_of_dim0 - index_of_input_dim0 * scales[0];
-      index_of_input_dim1 = index_of_dim1 / scales[1];
-      x_offset = index_of_dim1 - index_of_input_dim1 * scales[1];
-
-      input_index = index_of_input_dim0 * input_pitches[0] + index_of_input_dim1;
-
-      float x00 = getInputFloat(input_index);
-      float x10, x01, x11;
-
-      bool end_of_dim0 = false;
-      if (index_of_input_dim0 == (${p[0].dims[0]} - 1)) {
-        // It's the end in dimension 0
-        x01 = x00;
-        end_of_dim0 = true;
-      } else {
-        x01 = getInputFloat(input_index + input_pitches[0]);
-      }
-
-      if (index_of_input_dim1 == (input_pitches[0] - 1)) {
-        // It's the end in dimension 1
-        x10 = x00;
-        x11 = x01;
-      }
-      else {
-        x10 = getInputFloat(input_index + 1);
-        x11 = end_of_dim0 ? x10 : getInputFloat(input_index + input_pitches[0] + 1);
-      }
-
-      float y0 = x00 + float(y_offset) * (x01 - x00) / float(scales[0]);
-      float y1 = x10 + float(y_offset) * (x11 - x10) / float(scales[0]);
-      return y0 + float(x_offset) * (y1 - y0) / float(scales[1]);
-    }`;return Object.assign(Object.assign({},c),{output:{dims:s,type:p[0].type,textureType:b.TextureType.unpacked},shaderSource:A,variables:[{name:"scales",type:"int",arrayLength:u.scales.length,data:u.scales.map(D=>Math.ceil(D))}]})};o.validateInputs=(_,p)=>{if(!_||p.opset<9&&_.length!==1||p.opset>=9&&p.opset<11&&_.length!==2||p.opset>=11&&_.length<2)throw new Error("invalid inputs.");if(p.scales.length>0&&_[0].dims.length!==p.scales.length)throw new Error("Invalid input shape.");if(_[0].type==="string")throw new Error("Invalid input tensor types.")},o.scalesValidation=(_,p,u)=>{if(u){for(const n of _)if(n<=0)throw new Error("Scale value should be greater than 0.")}else for(const n of _)if(n<1)throw new Error("Scale value should be greater than or equal to 1.");if(!(p!=="linear"&&p!=="cubic"||_.length===2||_.length===4&&_[0]===1&&_[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 ${u?"Resize":"Upsample"} opeartor.`)}},1958:(v,o,f)=>{Object.defineProperty(o,"__esModule",{value:!0}),o.ProgramManager=void 0;const l=f(1670),d=f(6231),b=f(8879),c=f(5060);o.ProgramManager=class{constructor(g,_,p){this.profiler=g,this.glContext=_,this.textureLayoutStrategy=p,this.repo=new Map,this.attributesBound=!1}getArtifact(g){return this.repo.get(g)}setArtifact(g,_){this.repo.set(g,_)}run(g,_,p){var u;this.profiler.event("op",`ProgramManager.run ${(u=g.programInfo.name)!==null&&u!==void 0?u:"unknown kernel"}`,()=>{var n;const e=this.glContext.gl,i=g.program;e.useProgram(i);try{this.bindOutput(p),this.attributesBound||this.bindAttributes(g.attribLocations),this.bindUniforms(g.uniformLocations,(n=g.programInfo.variables)!==null&&n!==void 0?n:[],_)}catch(s){throw d.Logger.error("ProgramManager",g.programInfo.shaderSource),s}this.profiler.event("backend","GlContext.draw()",()=>{this.glContext.draw()})},this.glContext)}dispose(){this.vertexShader&&this.glContext.deleteShader(this.vertexShader),this.repo.forEach(g=>this.glContext.deleteProgram(g.program))}build(g,_,p){return this.profiler.event("backend","ProgramManager.build",()=>{const u=new b.GlslPreprocessor(this.glContext,g,_,p),n=u.preprocess(),e=this.compile(n);return{programInfo:g,program:e,uniformLocations:this.getUniformLocations(e,u.context.programInfo.inputNames,u.context.programInfo.variables),attribLocations:this.getAttribLocations(e)}})}compile(g){if(!this.vertexShader){d.Logger.verbose("ProrgramManager","Compiling and caching Vertex shader for the first time");const u=(0,c.getVertexShaderSource)(this.glContext.version);this.vertexShader=this.glContext.compileShader(u,this.glContext.gl.VERTEX_SHADER)}l.env.debug&&d.Logger.verbose("ProrgramManager",`FragShader:
-${g}
-`);const _=this.glContext.compileShader(g,this.glContext.gl.FRAGMENT_SHADER),p=this.glContext.createProgram(this.vertexShader,_);return this.glContext.deleteShader(_),p}bindOutput(g){const _=g.width,p=g.height;d.Logger.verbose("ProrgramManager",`Binding output texture to Framebuffer: w/h=${_}/${p}, shape=${g.shape}, type=${g.tensor.type}`),this.glContext.attachFramebuffer(g.texture,_,p)}bindAttributes(g){const _=g.position,p=g.textureCoord;this.glContext.setVertexAttributes(_,p),this.attributesBound=!0}bindUniforms(g,_,p){var u;const n=this.glContext.gl;let e=0;for(const{name:i,type:s,location:m,arrayLength:y}of g){const x=(u=_.find(T=>T.name===i))===null||u===void 0?void 0:u.data;if(s!=="sampler2D"&&!x)throw new Error(`variable '${i}' does not have data defined in program info`);switch(s){case"sampler2D":this.bindTexture(p[e],m,e),e++;break;case"float":y?n.uniform1fv(m,x):n.uniform1f(m,x);break;case"int":y?n.uniform1iv(m,x):n.uniform1i(m,x);break;default:throw new Error(`Uniform not implemented: ${s}`)}}}bindTexture(g,_,p){this.glContext.bindTextureToUniform(g.texture,p,_)}getAttribLocations(g){return{position:this.getAttribLocation(g,"position"),textureCoord:this.getAttribLocation(g,"textureCoord")}}getUniformLocations(g,_,p){const u=[];if(_)for(const n of _)u.push({name:n,type:"sampler2D",location:this.getUniformLocation(g,n)});if(p)for(const n of p)u.push(Object.assign(Object.assign({},n),{location:this.getUniformLocation(g,n.name)}));return u}getUniformLocation(g,_){const p=this.glContext.gl.getUniformLocation(g,_);if(p===null)throw new Error(`Uniform ${_} not found.`);return p}getAttribLocation(g,_){return this.glContext.gl.getAttribLocation(g,_)}}},6416:(v,o,f)=>{Object.defineProperty(o,"__esModule",{value:!0}),o.WebGLSessionHandler=void 0;const l=f(6231),d=f(1047),b=f(8316),c=f(1640),g=f(1958),_=f(7859),p=f(5702);o.WebGLSessionHandler=class{constructor(u,n){this.backend=u,this.context=n,this.layoutStrategy=new _.PreferLogicalStrategy(u.glContext.maxTextureSize),this.programManager=new g.ProgramManager(this.context.profiler,u.glContext,this.layoutStrategy),this.textureManager=new p.TextureManager(u.glContext,this.layoutStrategy,this.context.profiler,{reuseTextures:u.textureCacheMode==="full"}),this.packedTextureDataCache=new Map,this.unpackedTextureDataCache=new Map,this.pack=u.pack,this.pack2unpackMap=new Map,this.unpack2packMap=new Map}createInferenceHandler(){return new b.WebGLInferenceHandler(this)}onGraphInitialized(u){const n=u.getValues().filter(e=>e.from===-1&&e.tensor).map(e=>e.tensor.dataId);this.initializers=new Set(n)}isInitializer(u){return!!this.initializers&&this.initializers.has(u)}addInitializer(u){this.initializers.add(u)}getTextureData(u,n){return n?this.packedTextureDataCache.get(u):this.unpackedTextureDataCache.get(u)}setTextureData(u,n,e=!1){l.Logger.verbose("WebGLSessionHandler","Storing Texture data in cache"),e?this.packedTextureDataCache.set(u,n):this.unpackedTextureDataCache.set(u,n)}dispose(){this.programManager.dispose(),this.textureManager.clearActiveTextures(),this.packedTextureDataCache.forEach(u=>this.textureManager.releaseTexture(u,!0)),this.packedTextureDataCache=new Map,this.unpackedTextureDataCache.forEach(u=>this.textureManager.releaseTexture(u,!0)),this.unpackedTextureDataCache=new Map}resolve(u,n,e){const i=(0,d.resolveOperator)(u,n,c.WEBGL_OP_RESOLVE_RULES);return{impl:i.opImpl,context:i.opInit?i.opInit(u,e):u}}}},7769:(v,o,f)=>{Object.defineProperty(o,"__esModule",{value:!0}),o.Uint8DataEncoder=o.RGBAFloatDataEncoder=o.RedFloat32DataEncoder=void 0;const l=f(6231);o.RedFloat32DataEncoder=class{constructor(d,b=1){if(b===1)this.internalFormat=d.R32F,this.format=d.RED,this.textureType=d.FLOAT,this.channelSize=b;else{if(b!==4)throw new Error(`Invalid number of channels: ${b}`);this.internalFormat=d.RGBA32F,this.format=d.RGBA,this.textureType=d.FLOAT,this.channelSize=b}}encode(d,b){let c,g;return d.constructor!==Float32Array&&(l.Logger.warning("Encoder","data was not of type Float32; creating new Float32Array"),g=new Float32Array(d)),b*this.channelSize>d.length?(l.Logger.warning("Encoder","Source data too small. 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width/height preferences were unattainable: shape:${u}, breakAxis:${n.breakAxis}`)}let s=u.slice(0);e&&(i*=2,s=s.map((y,x)=>x>=s.length-2?s[x]%2==0?s[x]:s[x]+1:s[x]),s.length===1&&(s=[2,s[0]])),s.length!==2&&(s=b(s).newShape);const m=_(s);return s.length<=1&&m<=i?[1,m]:s.length===2&&s[0]<=i&&s[1]<=i?s:s.length===3&&s[0]*s[1]<=i&&s[2]<=i?[s[0]*s[1],s[2]]:s.length===3&&s[0]<=i&&s[1]*s[2]<=i?[s[0],s[1]*s[2]]:s.length===4&&s[0]*s[1]*s[2]<=i&&s[3]<=i?[s[0]*s[1]*s[2],s[3]]:s.length===4&&s[0]<=i&&s[1]*s[2]*s[3]<=i?[s[0],s[1]*s[2]*s[3]]:e?p(m/4).map(y=>2*y):p(m)}},o.squeezeShape=b,o.parseAxisParam=c,o.isInt=g,o.sizeFromShape=_,o.getRowsCols=function(u){if(u.length===0)throw Error("Cannot get rows and columns of an empty shape array.");return[u.length>1?u[u.length-2]:1,u[u.length-1]]},o.sizeToSquarishShape=p,o.getBatchDim=function(u,n=2){return _(u.slice(0,u.length-n))}},4057:(v,o,f)=>{Object.defineProperty(o,"__esModule",{value:!0}),o.createTextureLayoutFromShape=o.calculateTextureWidthAndHeight=o.createTextureLayoutFromTextureType=void 0;const l=f(2517),d=f(2039);o.createTextureLayoutFromTextureType=(b,c,g)=>{const _=g===d.TextureType.unpacked||g===d.TextureType.unpackedReversed?1:4,p=g===d.TextureType.packed,u=g===d.TextureType.unpackedReversed||g===d.TextureType.packed,n=g===d.TextureType.packedLastDimension?c.length-1:void 0,e=g===d.TextureType.packedLastDimension?c.map((i,s)=>s===c.length-1?4*i:i):void 0;return(0,o.createTextureLayoutFromShape)(b,c,_,e,{isPacked:p,reverseWH:u,breakAxis:n})},o.calculateTextureWidthAndHeight=(b,c,g)=>{const _=(0,o.createTextureLayoutFromTextureType)(b,c,g);return[_.width,_.height]},o.createTextureLayoutFromShape=(b,c,g=1,_,p)=>{const u=!(!p||!p.isPacked),[n,e]=b.computeTextureWH(u&&_||c,p),i=c.length;let s=c.slice(0);if(i===0&&(s=[1]),g===1)_=c;else if(u){if(g!==4)throw new Error("a packed texture must be 4-channel");_=c,i>0&&(s[i-1]=Math.ceil(s[i-1]/2)),i>1&&(s[i-2]=Math.ceil(s[i-2]/2))}else if(!_)throw new Error("Unpacked shape is needed when using channels > 1");return{width:n,height:e,channels:g,isPacked:u,shape:s,strides:l.ShapeUtil.computeStrides(s),unpackedShape:_,reversedWH:p&&p.reverseWH}}},5702:(v,o,f)=>{Object.defineProperty(o,"__esModule",{value:!0}),o.TextureManager=void 0;const l=f(6231);o.TextureManager=class{constructor(d,b,c,g){this.glContext=d,this.layoutStrategy=b,this.profiler=c,this.config=g,this.pendingRead=new Map,g.reuseTextures&&(this.inUseTextures=new Map,this.idleTextures=new Map,this.textureLookup=new Map)}createTextureFromLayout(d,b,c,g){const _=this.toEncoderType(d),p=this.glContext.getEncoder(_,b.channels||1,g);if(b.isPacked&&g===1)throw new Error("not implemented");const u=b.width,n=b.height;let e,i;if(this.config.reuseTextures){e=`${u}x${n}_${p.format}_${p.internalFormat}_${p.textureType}`,i=this.inUseTextures.get(e),i||(i=[],this.inUseTextures.set(e,i));const m=this.idleTextures.get(e);if(m&&m.length>0){const y=m.pop();return i.push(y),g===1&&this.glContext.updateTexture(y,u,n,p,this.toTextureData(d,c)),y}}l.Logger.verbose("TextureManager",`Creating new texture of size ${b.width}x${b.height}`);const s=this.glContext.allocateTexture(u,n,p,this.toTextureData(d,c));return this.config.reuseTextures&&(i.push(s),this.textureLookup.set(s,e)),s}readTexture(d,b,c){return c||(c=1),this.profiler.event("backend","TextureManager.readTexture",()=>{const g=d.shape.reduce((p,u)=>p*u)*c,_=this.glContext.readTexture(d.texture,d.width,d.height,g,this.toEncoderType(b),c);return this.toTensorData(b,_)})}async readTextureAsync(d,b,c){const g=d.tensor.dataId;if(c||(c=1),this.pendingRead.has(g)){const _=this.pendingRead.get(g);return new Promise(p=>_==null?void 0:_.push(p))}return 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Error: ${n}`)}throw new Error("WebGL is not supported")}o.createWebGLContext=function g(_){let p;_&&_!=="webgl2"||!("webgl2"in b)?_&&_!=="webgl"||!("webgl"in b)||(p=b.webgl):p=b.webgl2,p=p||c(_),_=_||p.version===1?"webgl":"webgl2";const u=p.gl;return b[_]=p,u.isContextLost()?(delete b[_],g(_)):(u.disable(u.DEPTH_TEST),u.disable(u.STENCIL_TEST),u.disable(u.BLEND),u.disable(u.DITHER),u.disable(u.POLYGON_OFFSET_FILL),u.disable(u.SAMPLE_COVERAGE),u.enable(u.SCISSOR_TEST),u.enable(u.CULL_FACE),u.cullFace(u.BACK),p)},o.createNewWebGLContext=c},1713:function(v,o,f){var l=this&&this.__createBinding||(Object.create?function(u,n,e,i){i===void 0&&(i=e);var s=Object.getOwnPropertyDescriptor(n,e);s&&!("get"in s?!n.__esModule:s.writable||s.configurable)||(s={enumerable:!0,get:function(){return n[e]}}),Object.defineProperty(u,i,s)}:function(u,n,e,i){i===void 0&&(i=e),u[i]=n[e]}),d=this&&this.__setModuleDefault||(Object.create?function(u,n){Object.defineProperty(u,"default",{enumerable:!0,value:n})}:function(u,n){u.default=n}),b=this&&this.__importStar||function(u){if(u&&u.__esModule)return u;var n={};if(u!=null)for(var e in u)e!=="default"&&Object.prototype.hasOwnProperty.call(u,e)&&l(n,u,e);return d(n,u),n};Object.defineProperty(o,"__esModule",{value:!0}),o.WebGLContext=o.linearSearchLastTrue=void 0;const c=f(1670),g=b(f(7769)),_=f(9390);function p(u){let n=0;for(;n<u.length&&u[n]();++n);return n-1}o.linearSearchLastTrue=p,o.WebGLContext=class{constructor(u,n){this.frameBufferBound=!1,this.itemsToPoll=[],this.gl=u,this.version=n,this.getExtensions(),this.vertexbuffer=this.createVertexbuffer(),this.framebuffer=this.createFramebuffer(),this.queryVitalParameters()}allocateTexture(u,n,e,i){const s=this.gl,m=s.createTexture();s.bindTexture(s.TEXTURE_2D,m),s.texParameteri(s.TEXTURE_2D,s.TEXTURE_MIN_FILTER,s.NEAREST),s.texParameteri(s.TEXTURE_2D,s.TEXTURE_MAG_FILTER,s.NEAREST),s.texParameteri(s.TEXTURE_2D,s.TEXTURE_WRAP_S,s.CLAMP_TO_EDGE),s.texParameteri(s.TEXTURE_2D,s.TEXTURE_WRAP_T,s.CLAMP_TO_EDGE);const y=i?e.encode(i,u*n):null;return s.texImage2D(s.TEXTURE_2D,0,e.internalFormat,u,n,0,e.format,e.textureType,y),this.checkError(),m}updateTexture(u,n,e,i,s){const m=this.gl;m.bindTexture(m.TEXTURE_2D,u);const y=i.encode(s,n*e);m.texSubImage2D(m.TEXTURE_2D,0,0,0,n,e,i.format,i.textureType,y),this.checkError()}attachFramebuffer(u,n,e){const i=this.gl;i.bindTexture(i.TEXTURE_2D,u),i.bindFramebuffer(i.FRAMEBUFFER,this.framebuffer),i.framebufferTexture2D(i.FRAMEBUFFER,i.COLOR_ATTACHMENT0,i.TEXTURE_2D,u,0),this.checkError(),i.viewport(0,0,n,e),i.scissor(0,0,n,e)}readTexture(u,n,e,i,s,m){const y=this.gl;m||(m=1),this.frameBufferBound||this.attachFramebuffer(u,n,e);const x=this.getEncoder(s,m),T=x.allocate(n*e);return y.bindTexture(y.TEXTURE_2D,u),y.framebufferTexture2D(y.FRAMEBUFFER,y.COLOR_ATTACHMENT0,y.TEXTURE_2D,u,0),y.readPixels(0,0,n,e,y.RGBA,x.textureType,T),this.checkError(),x.decode(T,i)}isFramebufferReady(){return!0}getActiveTexture(){const u=this.gl;return"TEXTURE"+(u.getParameter(this.gl.ACTIVE_TEXTURE)-u.TEXTURE0)}getTextureBinding(){return this.gl.getParameter(this.gl.TEXTURE_BINDING_2D)}getFramebufferBinding(){return this.gl.getParameter(this.gl.FRAMEBUFFER_BINDING)}setVertexAttributes(u,n){const e=this.gl;e.vertexAttribPointer(u,3,e.FLOAT,!1,20,0),e.enableVertexAttribArray(u),n!==-1&&(e.vertexAttribPointer(n,2,e.FLOAT,!1,20,12),e.enableVertexAttribArray(n)),this.checkError()}createProgram(u,n){const e=this.gl,i=e.createProgram();return e.attachShader(i,u),e.attachShader(i,n),e.linkProgram(i),i}compileShader(u,n){const e=this.gl,i=e.createShader(n);if(!i)throw new Error(`createShader() returned null with type ${n}`);if(e.shaderSource(i,u),e.compileShader(i),e.getShaderParameter(i,e.COMPILE_STATUS)===!1)throw new Error(`Failed to compile shader: ${e.getShaderInfoLog(i)}
-Shader source:
-${u}`);return i}deleteShader(u){this.gl.deleteShader(u)}bindTextureToUniform(u,n,e){const i=this.gl;i.activeTexture(i.TEXTURE0+n),this.checkError(),i.bindTexture(i.TEXTURE_2D,u),this.checkError(),i.uniform1i(e,n),this.checkError()}draw(){this.gl.drawArrays(this.gl.TRIANGLE_STRIP,0,4),this.checkError()}checkError(){if(c.env.debug){const u=this.gl,n=u.getError();let e="";switch(n){case u.NO_ERROR:return;case u.INVALID_ENUM:e="INVALID_ENUM";break;case u.INVALID_VALUE:e="INVALID_VALUE";break;case u.INVALID_OPERATION:e="INVALID_OPERATION";break;case u.INVALID_FRAMEBUFFER_OPERATION:e="INVALID_FRAMEBUFFER_OPERATION";break;case u.OUT_OF_MEMORY:e="OUT_OF_MEMORY";break;case u.CONTEXT_LOST_WEBGL:e="CONTEXT_LOST_WEBGL";break;default:e=`Unknown WebGL Error: ${n.toString(16)}`}throw new Error(e)}}deleteTexture(u){this.gl.deleteTexture(u)}deleteProgram(u){this.gl.deleteProgram(u)}getEncoder(u,n,e=0){if(this.version===2)return new g.RedFloat32DataEncoder(this.gl,n);switch(u){case"float":return e===1||this.isRenderFloat32Supported?new g.RGBAFloatDataEncoder(this.gl,n):new g.RGBAFloatDataEncoder(this.gl,n,this.textureHalfFloatExtension.HALF_FLOAT_OES);case"int":throw new Error("not implemented");case"byte":return new g.Uint8DataEncoder(this.gl,n);default:throw new Error(`Invalid dataType: ${u}`)}}clearActiveTextures(){const u=this.gl;for(let n=0;n<this.maxTextureImageUnits;++n)u.activeTexture(u.TEXTURE0+n),u.bindTexture(u.TEXTURE_2D,null)}dispose(){if(this.disposed)return;const u=this.gl;u.bindFramebuffer(u.FRAMEBUFFER,null),u.deleteFramebuffer(this.framebuffer),u.bindBuffer(u.ARRAY_BUFFER,null),u.deleteBuffer(this.vertexbuffer),u.bindBuffer(u.ELEMENT_ARRAY_BUFFER,null),u.finish(),this.disposed=!0}createDefaultGeometry(){return new Float32Array([-1,1,0,0,1,-1,-1,0,0,0,1,1,0,1,1,1,-1,0,1,0])}createVertexbuffer(){const u=this.gl,n=u.createBuffer();if(!n)throw new Error("createBuffer() returned null");const e=this.createDefaultGeometry();return u.bindBuffer(u.ARRAY_BUFFER,n),u.bufferData(u.ARRAY_BUFFER,e,u.STATIC_DRAW),this.checkError(),n}createFramebuffer(){const u=this.gl.createFramebuffer();if(!u)throw new Error("createFramebuffer returned null");return u}queryVitalParameters(){const u=this.gl;if(this.isFloatTextureAttachableToFrameBuffer=this.checkFloatTextureAttachableToFrameBuffer(),this.isRenderFloat32Supported=this.checkRenderFloat32(),this.isFloat32DownloadSupported=this.checkFloat32Download(),this.version===1&&!this.textureHalfFloatExtension&&!this.isRenderFloat32Supported)throw new Error("both float32 and float16 TextureType are not supported");this.isBlendSupported=!this.isRenderFloat32Supported||this.checkFloat32Blend(),this.maxTextureSize=u.getParameter(u.MAX_TEXTURE_SIZE),this.maxTextureImageUnits=u.getParameter(u.MAX_TEXTURE_IMAGE_UNITS),this.version}getExtensions(){this.version===2?(this.colorBufferFloatExtension=this.gl.getExtension("EXT_color_buffer_float"),this.disjointTimerQueryWebgl2Extension=this.gl.getExtension("EXT_disjoint_timer_query_webgl2")):(this.textureFloatExtension=this.gl.getExtension("OES_texture_float"),this.textureHalfFloatExtension=this.gl.getExtension("OES_texture_half_float"))}checkFloatTextureAttachableToFrameBuffer(){const u=this.gl,n=u.createTexture();u.bindTexture(u.TEXTURE_2D,n);const e=this.version===2?u.RGBA32F:u.RGBA;u.texImage2D(u.TEXTURE_2D,0,e,1,1,0,u.RGBA,u.FLOAT,null);const i=u.createFramebuffer();u.bindFramebuffer(u.FRAMEBUFFER,i),u.framebufferTexture2D(u.FRAMEBUFFER,u.COLOR_ATTACHMENT0,u.TEXTURE_2D,n,0);const s=u.checkFramebufferStatus(u.FRAMEBUFFER)===u.FRAMEBUFFER_COMPLETE;return u.bindTexture(u.TEXTURE_2D,null),u.bindFramebuffer(u.FRAMEBUFFER,null),u.deleteTexture(n),u.deleteFramebuffer(i),s}checkRenderFloat32(){if(this.version===2){if(!this.colorBufferFloatExtension)return!1}else if(!this.textureFloatExtension)return!1;return this.isFloatTextureAttachableToFrameBuffer}checkFloat32Download(){if(this.version===2){if(!this.colorBufferFloatExtension)return!1}else if(!this.textureFloatExtension||!this.gl.getExtension("WEBGL_color_buffer_float"))return!1;return this.isFloatTextureAttachableToFrameBuffer}checkFloat32Blend(){const u=this.gl;let n,e,i,s,m;try{n=u.createTexture(),e=u.createFramebuffer(),u.bindTexture(u.TEXTURE_2D,n);const y=this.version===2?u.RGBA32F:u.RGBA;return u.texImage2D(u.TEXTURE_2D,0,y,1,1,0,u.RGBA,u.FLOAT,null),u.bindFramebuffer(u.FRAMEBUFFER,e),u.framebufferTexture2D(u.FRAMEBUFFER,u.COLOR_ATTACHMENT0,u.TEXTURE_2D,n,0),u.enable(u.BLEND),i=u.createShader(u.VERTEX_SHADER),!!i&&(u.shaderSource(i,"void main(){}"),u.compileShader(i),s=u.createShader(u.FRAGMENT_SHADER),!!s&&(u.shaderSource(s,"precision highp float;void main(){gl_FragColor=vec4(0.5);}"),u.compileShader(s),m=u.createProgram(),!!m&&(u.attachShader(m,i),u.attachShader(m,s),u.linkProgram(m),u.useProgram(m),u.drawArrays(u.POINTS,0,1),u.getError()===u.NO_ERROR)))}finally{u.disable(u.BLEND),m&&u.deleteProgram(m),i&&u.deleteShader(i),s&&u.deleteShader(s),e&&(u.bindFramebuffer(u.FRAMEBUFFER,null),u.deleteFramebuffer(e)),n&&(u.bindTexture(u.TEXTURE_2D,null),u.deleteTexture(n))}}beginTimer(){if(this.version===2&&this.disjointTimerQueryWebgl2Extension){const u=this.gl,n=this.disjointTimerQueryWebgl2Extension,e=u.createQuery();return u.beginQuery(n.TIME_ELAPSED_EXT,e),e}throw new Error("WebGL1 profiling currently not supported.")}endTimer(){if(this.version!==2||!this.disjointTimerQueryWebgl2Extension)throw new Error("WebGL1 profiling currently not supported");{const u=this.gl,n=this.disjointTimerQueryWebgl2Extension;u.endQuery(n.TIME_ELAPSED_EXT)}}isTimerResultAvailable(u){let n=!1,e=!1;if(this.version!==2||!this.disjointTimerQueryWebgl2Extension)throw new Error("WebGL1 profiling currently not supported");{const i=this.gl,s=this.disjointTimerQueryWebgl2Extension;n=i.getQueryParameter(u,i.QUERY_RESULT_AVAILABLE),e=i.getParameter(s.GPU_DISJOINT_EXT)}return n&&!e}getTimerResult(u){let n=0;if(this.version!==2)throw new Error("WebGL1 profiling currently not supported");{const e=this.gl;n=e.getQueryParameter(u,e.QUERY_RESULT),e.deleteQuery(u)}return n/1e6}async waitForQueryAndGetTime(u){return await(0,_.repeatedTry)(()=>this.isTimerResultAvailable(u)),this.getTimerResult(u)}async createAndWaitForFence(){const u=this.createFence(this.gl);return this.pollFence(u)}createFence(u){let n;const e=u,i=e.fenceSync(e.SYNC_GPU_COMMANDS_COMPLETE,0);return u.flush(),n=i===null?()=>!0:()=>{const s=e.clientWaitSync(i,0,0);return s===e.ALREADY_SIGNALED||s===e.CONDITION_SATISFIED},{query:i,isFencePassed:n}}async pollFence(u){return new Promise(n=>{this.addItemToPoll(()=>u.isFencePassed(),()=>n())})}pollItems(){const u=p(this.itemsToPoll.map(n=>n.isDoneFn));for(let n=0;n<=u;++n){const{resolveFn:e}=this.itemsToPoll[n];e()}this.itemsToPoll=this.itemsToPoll.slice(u+1)}async addItemToPoll(u,n){this.itemsToPoll.push({isDoneFn:u,resolveFn:n}),this.itemsToPoll.length>1||await(0,_.repeatedTry)(()=>(this.pollItems(),this.itemsToPoll.length===0))}}},1036:(v,o,f)=>{Object.defineProperty(o,"__esModule",{value:!0}),o.ExecutionPlan=void 0;const l=f(6231);class d{constructor(c,g){this.op=c,this.node=g}}o.ExecutionPlan=class{constructor(b,c,g){this.graph=b,this.profiler=g,this.initialize(c)}initialize(b){this.profiler.event("session","ExecutionPlan.initialize",()=>{const c=this.graph.getNodes();if(c.length!==b.length)throw new Error("The size of nodes and OPs do not match.");this._ops=b.map((g,_)=>new d(g,c[_])),this.reset(),this._starter=[],this._ops.forEach((g,_)=>{let p=!0;for(const u of g.node.inputs)if(!this._values[u]&&this.graph.getInputIndices().indexOf(u)===-1){p=!1;break}p&&this._starter.push(_)})})}reset(){this._values=this.graph.getValues().map(b=>b.tensor)}async execute(b,c){return this.profiler.event("session","ExecutionPlan.execute",async()=>{this.reset();const g=b.createInferenceHandler(),_=this.graph.getInputIndices();if(c.length!==_.length)throw new Error(`number of input tensors don't match the number of inputs to the model: actual: ${c.length} expected: ${_.length}`);c.forEach((s,m)=>{const y=_[m];this._values[y]=s});const p=this._starter.slice(0),u=this.graph.getValues(),n=this.graph.getNodes();let e=0;for(;e<p.length;){const s=p[e++],m=this._ops[s],y=m.node.inputs.map(P=>this._values[P]);if(y.indexOf(void 0)!==-1)throw new Error(`unresolved input detected: op: ${m.node}`);const x=y;l.Logger.verbose("ExecPlan",`Runing op:${m.node.name} (${x.map((P,O)=>`'${m.node.inputs[O]}': ${P.type}[${P.dims.join(",")}]`).join(", ")})`);const T=await this.profiler.event("node",m.node.name,async()=>m.op.impl(g,x,m.op.context));if(T.length!==m.node.outputs.length)throw new Error("the size of output does not match model definition.");T.forEach((P,O)=>{const A=m.node.outputs[O];if(this._values[A])throw new Error(`output [${A}] already has value: op:${m.node.name}`);this._values[A]=P});const E=new Set;T.forEach((P,O)=>{const A=m.node.outputs[O];for(const D of u[A].to){const I=n[D];let k=!0;for(const $ of I.inputs)if(!this._values[$]){k=!1;break}k&&E.add(D)}}),p.push(...E)}const i=[];for(let s=0;s<this.graph.getOutputIndices().length;s++){const m=this.graph.getOutputIndices()[s],y=this._values[m];if(y===void 0)throw new Error(`required output [${m}] does not have value`);m===0?await y.getData():y.data,i.push(y)}return l.Logger.verbose("ExecPlan","disposing of inferenceHandler"),g.dispose(),i})}}},7070:(v,o,f)=>{Object.defineProperty(o,"__esModule",{value:!0}),o.Graph=void 0;const l=f(1446),d=f(7778),b=f(9395),c=f(9162),g=f(2517);var _=b.onnxruntime.experimental.fbs;o.Graph={from:(e,i)=>new n(e,i)};class p{constructor(i){this._from=void 0,this._to=[],this.tensor=void 0,this.type=void 0,i&&(this.type=g.ProtoUtil.tensorValueTypeFromProto(i.type.tensorType))}get from(){return this._from}get to(){return this._to}}class u{constructor(i,s){i instanceof l.onnx.NodeProto?(this.name=i.name,this.opType=i.opType,this.attributes=new d.Attribute(i.attribute)):i instanceof _.Node&&(this.name=s??i.name(),this.opType=i.opType(),this.attributes=new d.Attribute(g.ProtoUtil.tensorAttributesFromORTFormat(i))),this.inputs=[],this.outputs=[],this.executeNode=!0}}class n{constructor(i,s){if(!i)throw new TypeError("graph is empty");this.buildGraph(i),this.transformGraph(s),this.checkIsAcyclic()}getInputIndices(){return this._allInputIndices}getInputNames(){return this._allInputNames}getOutputIndices(){return this._allOutputIndices}getOutputNames(){return this._allOutputNames}getValues(){return this._allData}getNodes(){return this._nodes}buildGraph(i){if(i instanceof l.onnx.GraphProto)this.buildGraphFromOnnxFormat(i);else{if(!(i instanceof _.Graph))throw new TypeError("Graph type is not supported.");this.buildGraphFromOrtFormat(i)}}buildGraphFromOnnxFormat(i){const s=new Map;this._allData=[],this._allInputIndices=[],this._allInputNames=[],this._allOutputIndices=[],this._allOutputNames=[],this._nodes=[];const m=new Map;if(!i.input)throw new Error("missing information in graph: input");const y=[];for(const x of i.input){if(s.has(x.name))throw new Error(`duplicated input name: ${x.name}`);const T=this._allData.push(new p(x))-1;s.set(x.name,T),y.push(x.name)}if(!i.initializer)throw new Error("missing information in graph: initializer");for(const x of i.initializer){let T=s.get(x.name);if(T===void 0){const E=new p;E.type={shape:{dims:g.ProtoUtil.tensorDimsFromProto(x.dims)},tensorType:g.ProtoUtil.tensorDataTypeFromProto(x.dataType)},T=this._allData.push(E)-1,s.set(x.name,T)}this._allData[T]._from=-1,this._allData[T].tensor=c.Tensor.fromProto(x)}for(let x=0;x<this._allData.length;x++)this._allData[x].tensor||(this._allInputIndices.push(x),this._allInputNames.push(y[x]));if(!i.output)throw new Error("missing information in graph: output");for(const x of i.output){if(s.has(x.name))throw new Error(`duplicated output name: ${x.name}`);const T=this._allData.push(new p(x))-1;s.set(x.name,T),this._allOutputIndices.push(T),this._allOutputNames.push(x.name)}if(!i.node)throw new Error("missing information in graph: node");for(const x of i.node){if(!x.name)for(let E=0;;E++){const P=`unnamed_${x.opType}_${E}`;if(!m.has(P)){x.name=P;break}}if(m.has(x.name))throw new Error(`duplicated node name: ${x.name}`);const T=this._nodes.push(new u(x))-1;m.set(x.name,T)}for(let x=0;x<this._nodes.length;x++){const T=this._nodes[x],E=i.node[x];if(!E.output)throw new Error(`missing output for node: ${E.name}`);for(const P of E.output){let O=s.get(P);if(O===void 0&&(O=this._allData.push(new p)-1,s.set(P,O)),T.outputs.push(O),this._allData[O]._from!==void 0)throw new Error(`multiple nodes output to one data value: ${O}`);if(this._allData[O]._from=x,E.opType==="Constant"){if(!E.attribute||E.attribute.length!==1||!E.attribute[0].t)throw new Error("missing attributes or missing tensor value in attributes for this Constant operator");if(!E.output||E.output.length!==1)throw new Error("missing output or incorrect number of outputs for this Constant operator");T.outputs.pop(),T.executeNode=!1,this._allData[O]._from=-1,this._allData[O].tensor=c.Tensor.fromProto(E.attribute[0].t)}}}for(let x=0;x<this._nodes.length;x++){const T=this._nodes[x],E=i.node[x];if(!E.input)throw new Error(`missing input for node: ${E.name}`);for(const P of E.input){const O=s.get(P);if(O===void 0){if(P===""&&E.input.length===3&&E.opType==="Resize")continue;throw new Error(`unrecognized input '${P}' for node: ${E.name}`)}T.inputs.push(O),this._allData[O]._to.push(x)}}return!0}buildGraphFromOrtFormat(i){var s,m,y;const x=new Map;this._allData=[],this._allInputIndices=[],this._allInputNames=[],this._allOutputIndices=[],this._allOutputNames=[],this._nodes=[];const T=new Map,E=[];for(let P=0;P<i.inputsLength();P++){const O=i.inputs(P);if(x.has(O))throw new Error(`duplicated input name: ${O}`);for(let A=0;A<i.nodeArgsLength();A++)if(((s=i.nodeArgs(A))===null||s===void 0?void 0:s.name())===O){const D=new p;if(((y=(m=i.nodeArgs(A))===null||m===void 0?void 0:m.type())===null||y===void 0?void 0:y.valueType())!==_.TypeInfoValue.tensor_type)throw new Error("Unexpected value type for the nodeArg.");const I=i.nodeArgs(A).type().value(new _.TensorTypeAndShape),k=g.ProtoUtil.tensorDataTypeFromProto(I.elemType()),$=I.shape(),W=[];for(let K=0;K<$.dimLength();K++)W.push(g.LongUtil.longToNumber($.dim(K).value().dimValue()));D.type={shape:{dims:W},tensorType:k};const X=this._allData.push(D)-1;x.set(O,X),E.push(O)}}for(let P=0;P<i.initializersLength();P++){const O=i.initializers(P);let A=x.get(O.name());if(A===void 0){const D=new p,I=g.ProtoUtil.tensorDimsFromORTFormat(O),k=g.ProtoUtil.tensorDataTypeFromProto(O.dataType());D.type={shape:{dims:I},tensorType:k},A=this._allData.push(D)-1,x.set(O.name(),A)}this._allData[A]._from=-1,this._allData[A].tensor=c.Tensor.fromOrtTensor(O)}for(let P=0;P<this._allData.length;P++)this._allData[P].tensor||(this._allInputIndices.push(P),this._allInputNames.push(E[P]));for(let P=0;P<i.outputsLength();P++){const O=i.outputs(P);if(x.has(O))throw new Error(`duplicated output name: ${O}`);const A=this._allData.push(new p)-1;x.set(O,A),this._allOutputIndices.push(A),this._allOutputNames.push(O)}if(!i.nodes)throw new Error("missing information in graph: node");for(let P=0;P<i.nodesLength();P++){const O=i.nodes(P);let A=O.name();if(!A)for(let I=0;A=`unnamed_${O.opType()}_${I}`,T.has(A);I++);if(T.has(A))throw new Error(`duplicated node name: ${A}`);const D=this._nodes.push(new u(O,A))-1;T.set(A,D)}for(let P=0;P<this._nodes.length;P++){const O=this._nodes[P],A=i.nodes(P);if(A==null)throw new Error(`No node exists at index ${P}`);if((A==null?void 0:A.outputsLength())===0)throw new Error(`missing output for node: ${A.name}`);for(let D=0;D<(A==null?void 0:A.outputsLength());D++){const I=A==null?void 0:A.outputs(D);let k=x.get(I);if(k===void 0&&(k=this._allData.push(new p)-1,x.set(I,k)),O.outputs.push(k),this._allData[k]._from!==void 0)throw new Error(`multiple nodes output to one data value: ${k}`);if(this._allData[k]._from=P,A.opType()==="Constant"){if(A.attributesLength()!==1||!A.attributes(0).t())throw new Error("missing attributes or missing tensor value in attributes for this Constant operator");if(A.outputsLength()!==1)throw new Error("missing output or incorrect number of outputs for this Constant operator");O.outputs.pop(),O.executeNode=!1,this._allData[k]._from=-1,this._allData[k].tensor=c.Tensor.fromOrtTensor(A.attributes(0).t())}}}for(let P=0;P<this._nodes.length;P++){const O=this._nodes[P],A=i.nodes(P);if(A.inputsLength()===0)throw new Error(`missing input for node: ${A.name}`);for(let D=0;D<A.inputsLength();D++){const I=A.inputs(D),k=x.get(I);if(k===void 0)throw new Error(`unrecognized input '${I}' for node: ${A.name()}`);O.inputs.push(k),this._allData[k]._to.push(P)}}}checkIsAcyclic(){const i=new Set;this._allInputIndices.forEach(y=>{this._allData[y]._to.forEach(x=>{i.add(x)})});const s=Array.from(i),m=new 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startInputsVector(n,e){n.startVector(4,e,4)}static addOutputs(n,e){n.addFieldOffset(6,e,0)}static createOutputsVector(n,e){n.startVector(4,e.length,4);for(let i=e.length-1;i>=0;i--)n.addOffset(e[i]);return n.endVector()}static startOutputsVector(n,e){n.startVector(4,e,4)}static addSparseInitializers(n,e){n.addFieldOffset(7,e,0)}static createSparseInitializersVector(n,e){n.startVector(4,e.length,4);for(let i=e.length-1;i>=0;i--)n.addOffset(e[i]);return n.endVector()}static startSparseInitializersVector(n,e){n.startVector(4,e,4)}static endGraph(n){return n.endObject()}static createGraph(n,e,i,s,m,y,x,T,E){return p.startGraph(n),p.addInitializers(n,e),p.addNodeArgs(n,i),p.addNodes(n,s),p.addMaxNodeIndex(n,m),p.addNodeEdges(n,y),p.addInputs(n,x),p.addOutputs(n,T),p.addSparseInitializers(n,E),p.endGraph(n)}}_.Graph=p})(g.fbs||(g.fbs={}))})(c.experimental||(c.experimental={}))}(o.onnxruntime||(o.onnxruntime={})),function(c){(function(g){(function(_){class p{constructor(){this.bb=null,this.bb_pos=0}__init(n,e){return this.bb_pos=n,this.bb=e,this}static getRootAsModel(n,e){return(e||new p).__init(n.readInt32(n.position())+n.position(),n)}static getSizePrefixedRootAsModel(n,e){return n.setPosition(n.position()+l.flatbuffers.SIZE_PREFIX_LENGTH),(e||new p).__init(n.readInt32(n.position())+n.position(),n)}irVersion(){let n=this.bb.__offset(this.bb_pos,4);return n?this.bb.readInt64(this.bb_pos+n):this.bb.createLong(0,0)}opsetImport(n,e){let i=this.bb.__offset(this.bb_pos,6);return i?(e||new c.experimental.fbs.OperatorSetId).__init(this.bb.__indirect(this.bb.__vector(this.bb_pos+i)+4*n),this.bb):null}opsetImportLength(){let n=this.bb.__offset(this.bb_pos,6);return n?this.bb.__vector_len(this.bb_pos+n):0}producerName(n){let e=this.bb.__offset(this.bb_pos,8);return e?this.bb.__string(this.bb_pos+e,n):null}producerVersion(n){let e=this.bb.__offset(this.bb_pos,10);return e?this.bb.__string(this.bb_pos+e,n):null}domain(n){let e=this.bb.__offset(this.bb_pos,12);return e?this.bb.__string(this.bb_pos+e,n):null}modelVersion(){let n=this.bb.__offset(this.bb_pos,14);return n?this.bb.readInt64(this.bb_pos+n):this.bb.createLong(0,0)}docString(n){let e=this.bb.__offset(this.bb_pos,16);return e?this.bb.__string(this.bb_pos+e,n):null}graph(n){let e=this.bb.__offset(this.bb_pos,18);return e?(n||new c.experimental.fbs.Graph).__init(this.bb.__indirect(this.bb_pos+e),this.bb):null}graphDocString(n){let e=this.bb.__offset(this.bb_pos,20);return e?this.bb.__string(this.bb_pos+e,n):null}static startModel(n){n.startObject(9)}static addIrVersion(n,e){n.addFieldInt64(0,e,n.createLong(0,0))}static addOpsetImport(n,e){n.addFieldOffset(1,e,0)}static createOpsetImportVector(n,e){n.startVector(4,e.length,4);for(let i=e.length-1;i>=0;i--)n.addOffset(e[i]);return n.endVector()}static startOpsetImportVector(n,e){n.startVector(4,e,4)}static addProducerName(n,e){n.addFieldOffset(2,e,0)}static 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createNodeIndicesVector(n,e){n.startVector(4,e.length,4);for(let i=e.length-1;i>=0;i--)n.addInt32(e[i]);return n.endVector()}static startNodeIndicesVector(n,e){n.startVector(4,e,4)}static addKernelDefHashes(n,e){n.addFieldOffset(1,e,0)}static createKernelDefHashesVector(n,e){n.startVector(8,e.length,8);for(let i=e.length-1;i>=0;i--)n.addInt64(e[i]);return n.endVector()}static startKernelDefHashesVector(n,e){n.startVector(8,e,8)}static endKernelCreateInfos(n){return n.endObject()}static createKernelCreateInfos(n,e,i){return p.startKernelCreateInfos(n),p.addNodeIndices(n,e),p.addKernelDefHashes(n,i),p.endKernelCreateInfos(n)}}_.KernelCreateInfos=p})(g.fbs||(g.fbs={}))})(c.experimental||(c.experimental={}))}(o.onnxruntime||(o.onnxruntime={})),function(c){(function(g){(function(_){class p{constructor(){this.bb=null,this.bb_pos=0}__init(n,e){return this.bb_pos=n,this.bb=e,this}static getRootAsSubGraphSessionState(n,e){return(e||new p).__init(n.readInt32(n.position())+n.position(),n)}static 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createSubGraphSessionStatesVector(n,e){n.startVector(4,e.length,4);for(let i=e.length-1;i>=0;i--)n.addOffset(e[i]);return n.endVector()}static startSubGraphSessionStatesVector(n,e){n.startVector(4,e,4)}static endSessionState(n){return n.endObject()}static createSessionState(n,e,i){return p.startSessionState(n),p.addKernels(n,e),p.addSubGraphSessionStates(n,i),p.endSessionState(n)}}_.SessionState=p})(g.fbs||(g.fbs={}))})(c.experimental||(c.experimental={}))}(o.onnxruntime||(o.onnxruntime={})),function(c){(function(g){(function(_){class p{constructor(){this.bb=null,this.bb_pos=0}__init(n,e){return this.bb_pos=n,this.bb=e,this}static getRootAsInferenceSession(n,e){return(e||new p).__init(n.readInt32(n.position())+n.position(),n)}static getSizePrefixedRootAsInferenceSession(n,e){return n.setPosition(n.position()+l.flatbuffers.SIZE_PREFIX_LENGTH),(e||new p).__init(n.readInt32(n.position())+n.position(),n)}static bufferHasIdentifier(n){return n.__has_identifier("ORTM")}ortVersion(n){let e=this.bb.__offset(this.bb_pos,4);return e?this.bb.__string(this.bb_pos+e,n):null}model(n){let e=this.bb.__offset(this.bb_pos,6);return e?(n||new c.experimental.fbs.Model).__init(this.bb.__indirect(this.bb_pos+e),this.bb):null}sessionState(n){let e=this.bb.__offset(this.bb_pos,8);return e?(n||new c.experimental.fbs.SessionState).__init(this.bb.__indirect(this.bb_pos+e),this.bb):null}static startInferenceSession(n){n.startObject(3)}static addOrtVersion(n,e){n.addFieldOffset(0,e,0)}static addModel(n,e){n.addFieldOffset(1,e,0)}static addSessionState(n,e){n.addFieldOffset(2,e,0)}static endInferenceSession(n){return n.endObject()}static finishInferenceSessionBuffer(n,e){n.finish(e,"ORTM")}static finishSizePrefixedInferenceSessionBuffer(n,e){n.finish(e,"ORTM",!0)}static createInferenceSession(n,e,i,s){return p.startInferenceSession(n),p.addOrtVersion(n,e),p.addModel(n,i),p.addSessionState(n,s),p.endInferenceSession(n)}}_.InferenceSession=p})(g.fbs||(g.fbs={}))})(c.experimental||(c.experimental={}))}(o.onnxruntime||(o.onnxruntime={}))},7448:(v,o,f)=>{Object.defineProperty(o,"__esModule",{value:!0}),o.OnnxjsSessionHandler=void 0;const l=f(1670),d=f(9162);o.OnnxjsSessionHandler=class{constructor(b){this.session=b,this.inputNames=this.session.inputNames,this.outputNames=this.session.outputNames}async dispose(){}async run(b,c,g){const _=new Map;for(const n in b)if(Object.hasOwnProperty.call(b,n)){const e=b[n];_.set(n,new d.Tensor(e.dims,e.type,void 0,void 0,e.data))}const p=await this.session.run(_),u={};return p.forEach((n,e)=>{u[e]=new l.Tensor(n.type,n.data,n.dims)}),u}startProfiling(){this.session.startProfiling()}endProfiling(){this.session.endProfiling()}}},6919:(v,o,f)=>{Object.defineProperty(o,"__esModule",{value:!0}),o.Session=void 0;const l=f(7067),d=f(1296),b=f(7091),c=f(1036),g=f(6231),_=f(2644);o.Session=class{constructor(p={}){this._initialized=!1,this.backendHint=p.backendHint,this.profiler=g.Profiler.create(p.profiler),this.context={profiler:this.profiler,graphInputTypes:[],graphInputDims:[]}}get inputNames(){return this._model.graph.getInputNames()}get outputNames(){return this._model.graph.getOutputNames()}startProfiling(){this.profiler.start()}endProfiling(){this.profiler.stop()}async loadModel(p,u,n){await this.profiler.event("session","Session.loadModel",async()=>{const e=await(0,b.resolveBackend)(this.backendHint);if(this.sessionHandler=e.createSessionHandler(this.context),this._model=new _.Model,typeof p=="string"){const i=p.endsWith(".ort");if(typeof fetch>"u"){const s=await(0,d.promisify)(l.readFile)(p);this.initialize(s,i)}else{const s=await fetch(p),m=await s.arrayBuffer();this.initialize(new Uint8Array(m),i)}}else if(ArrayBuffer.isView(p))this.initialize(p);else{const i=new Uint8Array(p,u||0,n||p.byteLength);this.initialize(i)}})}initialize(p,u){if(this._initialized)throw new Error("already initialized");this.profiler.event("session","Session.initialize",()=>{const n=this.sessionHandler.transformGraph?this.sessionHandler:void 0;this._model.load(p,n,u),this.sessionHandler.onGraphInitialized&&this.sessionHandler.onGraphInitialized(this._model.graph),this.initializeOps(this._model.graph),this._executionPlan=new c.ExecutionPlan(this._model.graph,this._ops,this.profiler)}),this._initialized=!0}async run(p){if(!this._initialized)throw new Error("session not initialized yet");return this.profiler.event("session","Session.run",async()=>{const u=this.normalizeAndValidateInputs(p),n=await this._executionPlan.execute(this.sessionHandler,u);return this.createOutput(n)})}normalizeAndValidateInputs(p){const u=this._model.graph.getInputNames();if(Array.isArray(p)){if(p.length!==u.length)throw new Error(`incorrect input array length: expected ${u.length} but got ${p.length}`)}else{if(p.size!==u.length)throw new Error(`incorrect input map size: expected ${u.length} but got ${p.size}`);const n=new Array(p.size);let e=0;for(let i=0;i<u.length;++i){const s=p.get(u[i]);if(!s)throw new Error(`missing input tensor for: '${name}'`);n[e++]=s}p=n}if(this.context.graphInputTypes&&this.context.graphInputTypes.length!==0&&this.context.graphInputDims&&this.context.graphInputDims.length!==0)this.validateInputTensorDims(this.context.graphInputDims,p,!1);else{const n=this._model.graph.getInputIndices(),e=this._model.graph.getValues(),i=new Array(n.length);for(let s=0;s<n.length;++s){const m=e[n[s]];i[s]=m.type.shape.dims,this.context.graphInputTypes.push(m.type.tensorType),this.context.graphInputDims.push(p[s].dims)}this.validateInputTensorDims(i,p,!0)}return this.validateInputTensorTypes(this.context.graphInputTypes,p),p}validateInputTensorTypes(p,u){for(let n=0;n<u.length;n++){const e=p[n],i=u[n].type;if(e!==i)throw new Error(`input tensor[${n}] check failed: expected type '${e}' but got ${i}`)}}validateInputTensorDims(p,u,n){for(let e=0;e<u.length;e++){const i=p[e],s=u[e].dims;if(!this.compareTensorDims(i,s,n))throw new Error(`input tensor[${e}] check failed: expected shape '[${i.join(",")}]' but got [${s.join(",")}]`)}}compareTensorDims(p,u,n){if(p.length!==u.length)return!1;for(let e=0;e<p.length;++e)if(p[e]!==u[e]&&(!n||p[e]!==0))return!1;return!0}createOutput(p){const u=this._model.graph.getOutputNames();if(p.length!==u.length)throw new Error("expected number of outputs do not match number of generated outputs");const n=new Map;for(let e=0;e<u.length;++e)n.set(u[e],p[e]);return n}initializeOps(p){const u=p.getNodes();this._ops=new Array(u.length);for(let n=0;n<u.length;n++)this._ops[n]=this.sessionHandler.resolve(u[n],this._model.opsets,p)}}},9162:function(v,o,f){var l=this&&this.__importDefault||function(m){return m&&m.__esModule?m:{default:m}};Object.defineProperty(o,"__esModule",{value:!0}),o.Tensor=void 0;const d=f(3442),b=l(f(3720)),c=f(1446),g=f(9395),_=f(2517);var p=g.onnxruntime.experimental.fbs;class u{get data(){if(this.cache===void 0){const y=this.dataProvider(this.dataId);if(y.length!==this.size)throw new Error("Length of data provided by the Data Provider is inconsistent with the dims of this Tensor.");this.cache=y}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(y){return this.data[_.ShapeUtil.indicesToOffset(y,this.strides)]}set(y,x){this.data[_.ShapeUtil.indicesToOffset(y,this.strides)]=x}async getData(){return this.cache===void 0&&(this.cache=await this.asyncDataProvider(this.dataId)),this.cache}get strides(){return this._strides||(this._strides=_.ShapeUtil.computeStrides(this.dims)),this._strides}constructor(y,x,T,E,P,O=d.Guid.create()){this.dims=y,this.type=x,this.dataProvider=T,this.asyncDataProvider=E,this.cache=P,this.dataId=O,this.size=_.ShapeUtil.validateDimsAndCalcSize(y);const A=this.size,D=T===void 0&&E===void 0&&P===void 0;if(P!==void 0&&P.length!==A)throw new RangeError("Input dims doesn't match data length.");if(x==="string"){if(!(P===void 0||Array.isArray(P)&&P.every(I=>typeof I=="string")))throw new TypeError("cache should be a string array");D&&(this.cache=new Array(A))}else{if(P!==void 0){const I=e(x);if(!(P instanceof I))throw new TypeError(`cache should be type ${I.name}`)}if(D){const I=new ArrayBuffer(A*function(k){switch(k){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 ${k}`)}}(x));this.cache=function(k,$){return new(e($))(k)}(I,x)}}}static fromProto(y){if(!y)throw new Error("cannot construct Value from an empty tensor");const x=_.ProtoUtil.tensorDataTypeFromProto(y.dataType),T=_.ProtoUtil.tensorDimsFromProto(y.dims),E=new u(T,x);if(x==="string")y.stringData.forEach((P,O)=>{E.data[O]=(0,_.decodeUtf8String)(P)});else if(y.rawData&&typeof y.rawData.byteLength=="number"&&y.rawData.byteLength>0){const P=E.data,O=new DataView(y.rawData.buffer,y.rawData.byteOffset,y.rawData.byteLength),A=n(y.dataType),D=y.rawData.byteLength/A;if(y.rawData.byteLength%A!=0)throw new Error("invalid buffer length");if(P.length!==D)throw new Error("buffer length mismatch");for(let I=0;I<D;I++){const k=s(O,y.dataType,I*A);P[I]=k}}else{let P;switch(y.dataType){case c.onnx.TensorProto.DataType.FLOAT:P=y.floatData;break;case c.onnx.TensorProto.DataType.INT32:case c.onnx.TensorProto.DataType.INT16:case c.onnx.TensorProto.DataType.UINT16:case c.onnx.TensorProto.DataType.INT8:case c.onnx.TensorProto.DataType.UINT8:case c.onnx.TensorProto.DataType.BOOL:P=y.int32Data;break;case c.onnx.TensorProto.DataType.INT64:P=y.int64Data;break;case c.onnx.TensorProto.DataType.DOUBLE:P=y.doubleData;break;case c.onnx.TensorProto.DataType.UINT32:case c.onnx.TensorProto.DataType.UINT64:P=y.uint64Data;break;default:throw new Error("unspecific error")}if(P==null)throw new Error("failed to populate data from a tensorproto value");const O=E.data;if(O.length!==P.length)throw new Error("array length mismatch");for(let A=0;A<P.length;A++){const D=P[A];b.default.isLong(D)?O[A]=i(D,y.dataType):O[A]=D}}return E}static fromData(y,x,T){return new u(x,T,void 0,void 0,y)}static fromOrtTensor(y){if(!y)throw new Error("cannot construct Value from an empty tensor");const x=_.ProtoUtil.tensorDimsFromORTFormat(y),T=_.ProtoUtil.tensorDataTypeFromProto(y.dataType()),E=new u(x,T);if(T==="string")for(let P=0;P<y.stringDataLength();P++)E.data[P]=y.stringData(P);else if(y.rawDataArray()&&typeof y.rawDataLength()=="number"&&y.rawDataLength()>0){const P=E.data,O=new DataView(y.rawDataArray().buffer,y.rawDataArray().byteOffset,y.rawDataLength()),A=n(y.dataType()),D=y.rawDataLength()/A;if(y.rawDataLength()%A!=0)throw new Error("invalid buffer length");if(P.length!==D)throw new Error("buffer length mismatch");for(let I=0;I<D;I++){const k=s(O,y.dataType(),I*A);P[I]=k}}return E}}function n(m){switch(m){case c.onnx.TensorProto.DataType.UINT8:case c.onnx.TensorProto.DataType.INT8:case c.onnx.TensorProto.DataType.BOOL:return 1;case c.onnx.TensorProto.DataType.UINT16:case c.onnx.TensorProto.DataType.INT16:return 2;case c.onnx.TensorProto.DataType.FLOAT:case c.onnx.TensorProto.DataType.INT32:case c.onnx.TensorProto.DataType.UINT32:return 4;case c.onnx.TensorProto.DataType.INT64:case c.onnx.TensorProto.DataType.DOUBLE:case c.onnx.TensorProto.DataType.UINT64:return 8;default:throw new Error(`cannot calculate sizeof() on type ${c.onnx.TensorProto.DataType[m]}`)}}function e(m){switch(m){case"bool":case"uint8":return Uint8Array;case"int8":return Int8Array;case"int16":return Int16Array;case"uint16":return Uint16Array;case"int32":return Int32Array;case"uint32":return Uint32Array;case"float32":return Float32Array;case"float64":return Float64Array;default:throw new Error("unspecified error")}}function i(m,y){if(y===c.onnx.TensorProto.DataType.INT64||y===p.TensorDataType.INT64){if(m.greaterThanOrEqual(2147483648)||m.lessThan(-2147483648))throw new TypeError("int64 is not supported")}else{if(y!==c.onnx.TensorProto.DataType.UINT32&&y!==p.TensorDataType.UINT32&&y!==c.onnx.TensorProto.DataType.UINT64&&y!==p.TensorDataType.UINT64)throw new TypeError(`not a LONG type: ${c.onnx.TensorProto.DataType[y]}`);if(m.greaterThanOrEqual(4294967296)||m.lessThan(0))throw new TypeError("uint64 is not supported")}return m.toNumber()}function s(m,y,x){switch(y){case c.onnx.TensorProto.DataType.BOOL:case c.onnx.TensorProto.DataType.UINT8:return m.getUint8(x);case c.onnx.TensorProto.DataType.INT8:return m.getInt8(x);case c.onnx.TensorProto.DataType.UINT16:return m.getUint16(x,!0);case c.onnx.TensorProto.DataType.INT16:return m.getInt16(x,!0);case c.onnx.TensorProto.DataType.FLOAT:return m.getFloat32(x,!0);case c.onnx.TensorProto.DataType.INT32:return m.getInt32(x,!0);case c.onnx.TensorProto.DataType.UINT32:return m.getUint32(x,!0);case c.onnx.TensorProto.DataType.INT64:return i(b.default.fromBits(m.getUint32(x,!0),m.getUint32(x+4,!0),!1),y);case c.onnx.TensorProto.DataType.DOUBLE:return m.getFloat64(x,!0);case c.onnx.TensorProto.DataType.UINT64:return i(b.default.fromBits(m.getUint32(x,!0),m.getUint32(x+4,!0),!0),y);default:throw new Error(`cannot read from DataView for type ${c.onnx.TensorProto.DataType[y]}`)}}o.Tensor=u},2517:function(v,o,f){var l=this&&this.__importDefault||function(y){return y&&y.__esModule?y:{default:y}};Object.defineProperty(o,"__esModule",{value:!0}),o.decodeUtf8String=o.MAX_CLIP=o.MIN_CLIP=o.PoolConvUtil=o.ReduceUtil=o.SplitUtil=o.MathUtil=o.ShapeUtil=o.LongUtil=o.ProtoUtil=o.GemmUtil=o.arrayCopyHelper=o.BroadcastUtil=o.MatMulUtil=o.ArrayUtil=o.assert=o.checkInputsShape=void 0;const d=f(5686),b=l(f(3720)),c=f(1446),g=f(9162);o.checkInputsShape=function(y,...x){if(!y||y.length!==x.length)return!1;for(let T=0;T<y.length;T++)if(!y[T].dims||y[T].dims.length!==x[T])return!1;return!0},o.assert=function(y,x){if(!y)throw new Error(typeof x=="string"?x:x())},o.ArrayUtil=class{static arraysEqual(y,x){if(y.length!==x.length)return!1;for(let T=0;T<y.length;T++)if(y[T]!==x[T])return!1;return!0}};class _{static preprocessInputShapes(x,T){return[x.length===1?[1,x[0]]:x,T.length===1?[T[0],1]:T]}static postprocessOutputShape(x,T,E){T===1&&x.splice(x.length-2,1),E===1&&x.pop()}static calcMatMulShape(x,T){return x[1]!==T[0]?void 0:[x[0],T[1]]}}o.MatMulUtil=_;class p{static calcShape(x,T,E=!1){const P=x.length,O=T.length;if(P===0)return T;if(O===0)return x;const A=Math.max(x.length,T.length),D=new Array(A);if(E){if(P<2||O<2)return;const I=_.calcMatMulShape([x[P-2],x[P-1]],[T[O-2],T[O-1]]);if(I===void 0)return;[D[A-2],D[A-1]]=I}for(let I=E?3:1;I<=A;I++){const k=P-I<0?1:x[P-I],$=O-I<0?1:T[O-I];if(k!==$&&k>1&&$>1)return;D[A-I]=Math.max(k,$)}return D}static index(x,T){const E=new Array(T.length);return p.fillIndex(x,T,E),E}static fillIndex(x,T,E){const P=x.length-T.length;for(let O=0;O<T.length;O++)E[O]=x[P+O]%T[O]}static calc(x,T,E,P,O){const A=p.calcShape(x.dims,T.dims);if(A){if(P&&!e.areEqual(A,x.dims))return;const D=e.size(A),I=P?x:new g.Tensor(A,O||x.type);if(A.length===0)I.set([],E(x.get([]),T.get([])));else{const k=new Array(A.length),$=new Array(x.dims.length),W=new Array(T.dims.length);let X,K=0,q=0,se=!1,F=!1;x.dims.length===0&&(K=x.get([]),se=!0),T.dims.length===0&&(q=T.get([]),F=!0);for(let z=0;z<D;z++){X=z;for(let fe=A.length-1;fe>=0;fe--)k[fe]=X%A[fe],X=Math.floor(X/A[fe]);se||(p.fillIndex(k,x.dims,$),K=x.get($)),F||(p.fillIndex(k,T.dims,W),q=T.get(W)),I.set(k,E(K,q))}}return I}}static isValidBroadcast(x,T){const E=x.length,P=T.length;if(E>P)return!1;for(let O=1;O<=E;O++)if(x[E-O]!==1&&x[E-O]!==T[P-O])return!1;return!0}static getBroadcastDims(x,T){const E=x.length,P=[];for(let O=0;O<E;O++){const A=E-1-O,D=x[A]||1;(T[T.length-1-O]||1)>1&&D===1&&P.unshift(A)}return P}}o.BroadcastUtil=p,o.arrayCopyHelper=function(y,x,T,E,P){if(E<0||E>=x.length)throw new Error("sourceIndex out of bounds");if(T<0||T>=y.length)throw new Error("targetIndex out of bounds");if(E+P>x.length)throw new Error("source indices to be copied are outside bounds");if(T+P>y.length)throw new Error("target array is too small to hold result");for(let O=0;O<P;O++)y[T+O]=x[E+O]},o.GemmUtil=class{static getShapeOfGemmResult(y,x,T,E,P){if(y.length!==2||T.length!==2)throw new Error("shape need to be of size 2");let O,A,D;x?(O=y[1],A=y[0]):(O=y[0],A=y[1]);let I=-1;if(E?(D=T[0],I=1):(D=T[1],I=0),T[I]!==A)throw new Error("dimension mismatch");if(O<=0||D<=0||A<=0)throw new Error("invalid shape specified");if(P&&!p.isValidBroadcast(P,[O,D]))throw new Error("gemm: invalid bias shape for broadcast");return[O,D,A]}};class u{static tensorDataTypeFromProto(x){switch(x){case c.onnx.TensorProto.DataType.INT8:return"int8";case c.onnx.TensorProto.DataType.UINT8:return"uint8";case c.onnx.TensorProto.DataType.BOOL:return"bool";case c.onnx.TensorProto.DataType.INT16:return"int16";case c.onnx.TensorProto.DataType.UINT16:return"uint16";case c.onnx.TensorProto.DataType.INT32:return"int32";case c.onnx.TensorProto.DataType.UINT32:return"uint32";case c.onnx.TensorProto.DataType.FLOAT:return"float32";case c.onnx.TensorProto.DataType.DOUBLE:return"float64";case c.onnx.TensorProto.DataType.STRING:return"string";case c.onnx.TensorProto.DataType.INT64:return"int32";case c.onnx.TensorProto.DataType.UINT64:return"uint32";default:throw new Error(`unsupported data type: ${c.onnx.TensorProto.DataType[x]}`)}}static tensorDataTypeStringToEnum(x){switch(x){case"int8":return c.onnx.TensorProto.DataType.INT8;case"uint8":return c.onnx.TensorProto.DataType.UINT8;case"bool":return c.onnx.TensorProto.DataType.BOOL;case"int16":return c.onnx.TensorProto.DataType.INT16;case"uint16":return c.onnx.TensorProto.DataType.UINT16;case"int32":return c.onnx.TensorProto.DataType.INT32;case"uint32":return c.onnx.TensorProto.DataType.UINT32;case"float32":return c.onnx.TensorProto.DataType.FLOAT;case"float64":return c.onnx.TensorProto.DataType.DOUBLE;case"string":return c.onnx.TensorProto.DataType.STRING;case"int64":return c.onnx.TensorProto.DataType.INT64;case"uint64":return c.onnx.TensorProto.DataType.UINT64;default:throw new Error(`unsupported data type: ${x}`)}}static tensorDimsFromProto(x){return x.map(T=>b.default.isLong(T)?T.toNumber():T)}static tensorValueTypeFromProto(x){return{tensorType:u.tensorDataTypeFromProto(x.elemType),shape:{dims:u.tensorDimsFromProto(x.shape.dim.map(T=>T.dimValue))}}}static tensorDimsFromORTFormat(x){const T=[];for(let E=0;E<x.dimsLength();E++)T.push(n.longToNumber(x.dims(E)));return T}static tensorAttributesFromORTFormat(x){const T=[];for(let E=0;E<x.attributesLength();E++)T.push(x.attributes(E));return T}}o.ProtoUtil=u;class n{static longToNumber(x,T){return b.default.isLong(x)?x.toNumber():x instanceof d.flatbuffers.Long?b.default.fromValue({low:x.low,high:x.high,unsigned:T!=null&&T}).toNumber():x}static isLong(x){return b.default.isLong(x)||x instanceof d.flatbuffers.Long}}o.LongUtil=n;class e{static size(x){return e.getSizeFromDimensionRange(x,0,x.length)}static sizeFromDimension(x,T){if(T<0||T>x.length)throw new Error(`invalid dimension of ${T} for sizeFromDimension as Tensor has ${x.length} dimensions.`);return e.getSizeFromDimensionRange(x,T,x.length)}static sizeToDimension(x,T){if(T<0||T>x.length)throw new Error(`invalid dimension of ${T} for sizeToDimension as Tensor has ${x.length} dimensions.`);return e.getSizeFromDimensionRange(x,0,T)}static getSizeFromDimensionRange(x,T,E){let P=1;for(let O=T;O<E;O++){if(x[O]<=0)throw new Error("cannot get valid size from specified dimension range. 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env$1={version:VERSION,remoteHost:"https://huggingface.co/",remotePathTemplate:"{model}/resolve/{revision}/",localModelPath,useFS:FS_AVAILABLE,useBrowserCache:WEB_CACHE_AVAILABLE,useFSCache:FS_AVAILABLE,cacheDir:DEFAULT_CACHE_DIR};function isEmpty(v){return Object.keys(v).length===0}var define_process_env_default={};class FileResponse{constructor(o){if(Ht(this,"_CONTENT_TYPE_MAP",{txt:"text/plain",html:"text/html",css:"text/css",js:"text/javascript",json:"application/json",png:"image/png",jpg:"image/jpeg",jpeg:"image/jpeg",gif:"image/gif"}),this.filePath=o,this.headers=new Headers,this.exists=sharp.existsSync(o),this.exists){this.status=200,this.statusText="OK";let f=sharp.statSync(o);this.headers.set("content-length",f.size.toString()),this.updateContentType();let l=this;this.body=new ReadableStream({start(d){l.arrayBuffer().then(b=>{d.enqueue(new Uint8Array(b)),d.close()})}})}else this.status=404,this.statusText="Not Found",this.body=null}updateContentType(){const 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f=!!(define_process_env_default!=null&&define_process_env_default.TESTING_REMOTELY),l=env$1.version,d=new Headers;if(d.set("User-Agent",`transformers.js/${l}; is_ci/${f};`),isValidUrl(v,["http:","https:"],["huggingface.co","hf.co"])){const b=(define_process_env_default==null?void 0:define_process_env_default.HF_TOKEN)??(define_process_env_default==null?void 0:define_process_env_default.HF_ACCESS_TOKEN);b&&d.set("Authorization",`Bearer ${b}`)}return fetch(v,{headers:d})}else return fetch(v)}const ERROR_MAPPING={400:"Bad request error occurred while trying to load file",401:"Unauthorized access to file",403:"Forbidden access to file",404:"Could not locate file",408:"Request timeout error occurred while trying to load file",500:"Internal server error error occurred while trying to load file",502:"Bad gateway error occurred while trying to load file",503:"Service unavailable error occurred while trying to load file",504:"Gateway timeout error occurred while trying to load file"};function 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P2FFT{constructor(o){if(this.size=o|0,this.size<=1||!isPowerOfTwo(this.size))throw new Error("FFT size must be a power of two larger than 1");this._csize=o<<1,this.table=new Float64Array(this.size*2);for(let l=0;l<this.table.length;l+=2){const d=Math.PI*l/this.size;this.table[l]=Math.cos(d),this.table[l+1]=-Math.sin(d)}let f=0;for(let l=1;this.size>l;l<<=1)++f;this._width=f%2===0?f-1:f,this._bitrev=new Int32Array(1<<this._width);for(let l=0;l<this._bitrev.length;++l){this._bitrev[l]=0;for(let d=0;d<this._width;d+=2){const b=this._width-d-2;this._bitrev[l]|=(l>>>d&3)<<b}}}createComplexArray(){return new Float64Array(this._csize)}fromComplexArray(o,f){const l=f||new Array(o.length>>>1);for(let d=0;d<o.length;d+=2)l[d>>>1]=o[d];return l}toComplexArray(o,f){const l=f||this.createComplexArray();for(let d=0;d<l.length;d+=2)l[d]=o[d>>>1],l[d+1]=0;return l}transform(o,f){if(o===f)throw new Error("Input and output buffers must be different");this._transform4(o,f,1)}realTransform(o,f){if(o===f)throw new Error("Input and output buffers must be different");this._realTransform4(o,f,1)}inverseTransform(o,f){if(o===f)throw new Error("Input and output buffers must be different");this._transform4(o,f,-1);for(let l=0;l<o.length;++l)o[l]/=this.size}_transform4(o,f,l){const d=this._csize;let b=1<<this._width,c=d/b<<1,g,_;const p=this._bitrev;if(c===4)for(g=0,_=0;g<d;g+=c,++_){const n=p[_];this._singleTransform2(f,o,g,n,b)}else for(g=0,_=0;g<d;g+=c,++_){const n=p[_];this._singleTransform4(f,o,g,n,b,l)}const u=this.table;for(b>>=2;b>=2;b>>=2){c=d/b<<1;const n=c>>>2;for(g=0;g<d;g+=c){const e=g+n-1;for(let i=g,s=0;i<e;i+=2,s+=b){const m=i,y=m+n,x=y+n,T=x+n,E=o[m],P=o[m+1],O=o[y],A=o[y+1],D=o[x],I=o[x+1],k=o[T],$=o[T+1],W=u[s],X=l*u[s+1],K=O*W-A*X,q=O*X+A*W,se=u[2*s],F=l*u[2*s+1],z=D*se-I*F,fe=D*F+I*se,ne=u[3*s],B=l*u[3*s+1],Q=k*ne-$*B,re=k*B+$*ne,ue=E+z,_e=P+fe,ve=E-z,we=P-fe,ke=K+Q,Me=q+re,tt=l*(K-Q),Je=l*(q-re);o[m]=ue+ke,o[m+1]=_e+Me,o[y]=ve+Je,o[y+1]=we-tt,o[x]=ue-ke,o[x+1]=_e-Me,o[T]=ve-Je,o[T+1]=we+tt}}}}_singleTransform2(o,f,l,d,b){const c=o[d],g=o[d+1],_=o[d+b],p=o[d+b+1];f[l]=c+_,f[l+1]=g+p,f[l+2]=c-_,f[l+3]=g-p}_singleTransform4(o,f,l,d,b,c){const g=b*2,_=b*3,p=o[d],u=o[d+1],n=o[d+b],e=o[d+b+1],i=o[d+g],s=o[d+g+1],m=o[d+_],y=o[d+_+1],x=p+i,T=u+s,E=p-i,P=u-s,O=n+m,A=e+y,D=c*(n-m),I=c*(e-y);f[l]=x+O,f[l+1]=T+A,f[l+2]=E+I,f[l+3]=P-D,f[l+4]=x-O,f[l+5]=T-A,f[l+6]=E-I,f[l+7]=P+D}_realTransform4(o,f,l){const d=this._csize;let b=1<<this._width,c=d/b<<1,g,_;const p=this._bitrev;if(c===4)for(g=0,_=0;g<d;g+=c,++_){const e=p[_];this._singleRealTransform2(f,o,g,e>>>1,b>>>1)}else for(g=0,_=0;g<d;g+=c,++_){const e=p[_];this._singleRealTransform4(f,o,g,e>>>1,b>>>1,l)}const u=this.table;for(b>>=2;b>=2;b>>=2){c=d/b<<1;const e=c>>>1,i=e>>>1,s=i>>>1;for(g=0;g<d;g+=c)for(let m=0,y=0;m<=s;m+=2,y+=b){const x=g+m,T=x+i,E=T+i,P=E+i,O=o[x],A=o[x+1],D=o[T],I=o[T+1],k=o[E],$=o[E+1],W=o[P],X=o[P+1],K=O,q=A,se=u[y],F=l*u[y+1],z=D*se-I*F,fe=D*F+I*se,ne=u[2*y],B=l*u[2*y+1],Q=k*ne-$*B,re=k*B+$*ne,ue=u[3*y],_e=l*u[3*y+1],ve=W*ue-X*_e,we=W*_e+X*ue,ke=K+Q,Me=q+re,tt=K-Q,Je=q-re,ot=z+ve,Ue=fe+we,Qe=l*(z-ve),nt=l*(fe-we);if(o[x]=ke+ot,o[x+1]=Me+Ue,o[T]=tt+nt,o[T+1]=Je-Qe,m===0){o[E]=ke-ot,o[E+1]=Me-Ue;continue}if(m===s)continue;const Lt=g+i-m,zt=g+e-m;o[Lt]=tt-l*nt,o[Lt+1]=-Je-l*Qe,o[zt]=ke-l*ot,o[zt+1]=-Me+l*Ue}}const n=d>>>1;for(let e=2;e<n;e+=2)o[d-e]=o[e],o[d-e+1]=-o[e+1]}_singleRealTransform2(o,f,l,d,b){const c=o[d],g=o[d+b];f[l]=c+g,f[l+1]=0,f[l+2]=c-g,f[l+3]=0}_singleRealTransform4(o,f,l,d,b,c){const g=b*2,_=b*3,p=o[d],u=o[d+b],n=o[d+g],e=o[d+_],i=p+n,s=p-n,m=u+e,y=c*(u-e);f[l]=i+m,f[l+1]=0,f[l+2]=s,f[l+3]=-y,f[l+4]=i-m,f[l+5]=0,f[l+6]=s,f[l+7]=y}}class NP2FFT{constructor(o){const f=2*(o-1),l=2*(2*o-1),d=2**Math.ceil(Math.log2(l));this.bufferSize=d,this._a=f;const b=new Float64Array(l),c=new Float64Array(d);this._chirpBuffer=new Float64Array(d),this._buffer1=new Float64Array(d),this._buffer2=new Float64Array(d),this._outBuffer1=new Float64Array(d),this._outBuffer2=new Float64Array(d);const g=-2*Math.PI/o,_=Math.cos(g),p=Math.sin(g);for(let u=0;u<l>>1;++u){const n=(u+1-o)**2/2,e=Math.sqrt(_**2+p**2)**n,i=n*Math.atan2(p,_),s=2*u;b[s]=e*Math.cos(i),b[s+1]=e*Math.sin(i),c[s]=b[s],c[s+1]=-b[s+1]}this._slicedChirpBuffer=b.subarray(f,l),this._f=new P2FFT(d>>1),this._f.transform(this._chirpBuffer,c)}_transform(o,f,l){const d=this._buffer1,b=this._buffer2,c=this._outBuffer1,g=this._outBuffer2,_=this._chirpBuffer,p=this._slicedChirpBuffer,u=this._a;if(l)for(let n=0;n<p.length;n+=2){const e=n+1,i=n>>1,s=f[i];d[n]=s*p[n],d[e]=s*p[e]}else for(let n=0;n<p.length;n+=2){const e=n+1;d[n]=f[n]*p[n]-f[e]*p[e],d[e]=f[n]*p[e]+f[e]*p[n]}this._f.transform(c,d);for(let n=0;n<_.length;n+=2){const e=n+1;b[n]=c[n]*_[n]-c[e]*_[e],b[e]=c[n]*_[e]+c[e]*_[n]}this._f.inverseTransform(g,b);for(let n=0;n<g.length;n+=2){const e=g[n+u],i=g[n+u+1],s=p[n],m=p[n+1];o[n]=e*s-i*m,o[n+1]=e*m+i*s}}transform(o,f){this._transform(o,f,!1)}realTransform(o,f){this._transform(o,f,!0)}}class FFT{constructor(o){this.fft_length=o,this.isPowerOfTwo=isPowerOfTwo(o),this.isPowerOfTwo?(this.fft=new P2FFT(o),this.outputBufferSize=2*o):(this.fft=new NP2FFT(o),this.outputBufferSize=this.fft.bufferSize)}realTransform(o,f){this.fft.realTransform(o,f)}transform(o,f){this.fft.transform(o,f)}}function medianFilter(v,o){if(o%2===0||o<=0)throw new Error("Window size must be a positive odd number");const f=new v.constructor(v.length),l=new v.constructor(o),d=Math.floor(o/2);for(let b=0;b<v.length;++b){let c=0;for(let g=-d;g<=d;++g){let _=b+g;_<0?_=Math.abs(_):_>=v.length&&(_=2*(v.length-1)-_),l[c++]=v[_]}l.sort(),f[b]=l[d]}return f}function bankers_round(v){const o=Math.round(v);return Math.abs(v)%1===.5?o%2===0?o:o-1:o}const DataTypeMap=Object.freeze({float32:Float32Array,float64:Float64Array,string:Array,int8:Int8Array,uint8:Uint8Array,int16:Int16Array,uint16:Uint16Array,int32:Int32Array,uint32:Uint32Array,int64:BigInt64Array,uint64:BigUint64Array,bool:Uint8Array}),ONNXTensor$1=ONNX.Tensor;class Tensor{constructor(...o){return Ht(this,"dims"),Ht(this,"type"),Ht(this,"data"),Ht(this,"size"),o[0]instanceof ONNXTensor$1?Object.assign(this,o[0]):Object.assign(this,new ONNXTensor$1(o[0],o[1],o[2])),new Proxy(this,{get:(f,l)=>{if(typeof l=="string"){let d=Number(l);if(Number.isInteger(d))return f._getitem(d)}return f[l]},set:(f,l,d)=>f[l]=d})}*[Symbol.iterator](){const[o,...f]=this.dims;if(f.length>0){const l=f.reduce((d,b)=>d*b);for(let d=0;d<o;++d)yield this._subarray(d,l,f)}else yield*this.data}_getitem(o){const[f,...l]=this.dims;if(o=safeIndex(o,f),l.length>0){const d=l.reduce((b,c)=>b*c);return this._subarray(o,d,l)}else return new Tensor(this.type,[this.data[o]],l)}indexOf(o){for(let f=0;f<this.data.length;++f)if(this.data[f]==o)return f;return-1}_subarray(o,f,l){const d=o*f,b=(o+1)*f,c="subarray"in this.data?this.data.subarray(d,b):this.data.slice(d,b);return new Tensor(this.type,c,l)}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 o=0;o<this.data.length;++o)this.data[o]=1/(1+Math.exp(-this.data[o]));return this}mul(o){return this.clone().mul_(o)}mul_(o){for(let f=0;f<this.data.length;++f)this.data[f]*=o;return this}add(o){return this.clone().add_(o)}add_(o){for(let f=0;f<this.data.length;++f)this.data[f]+=o;return this}clone(){return new Tensor(this.type,this.data.slice(),this.dims.slice())}slice(...o){let f=[],l=[];for(let _=0;_<this.dims.length;++_){let p=o[_];if(p==null)l.push([0,this.dims[_]]),f.push(this.dims[_]);else if(typeof p=="number")p=safeIndex(p,this.dims[_],_),l.push([p,p+1]);else if(Array.isArray(p)&&p.length===2){if(p[0]>p[1])throw new Error(`Invalid slice: ${p}`);let u=[Math.max(p[0],0),Math.min(p[1],this.dims[_])];l.push(u),f.push(u[1]-u[0])}else throw new Error(`Invalid slice: ${p}`)}let d=l.map(([_,p])=>p-_),b=d.reduce((_,p)=>_*p),c=new this.data.constructor(b);const g=this.stride();for(let _=0;_<b;++_){let p=0;for(let u=d.length-1,n=_;u>=0;--u){const e=d[u];p+=(n%e+l[u][0])*g[u],n=Math.floor(n/e)}c[_]=this.data[p]}return new Tensor(this.type,c,f)}permute(...o){return permute(this,o)}transpose(...o){return this.permute(...o)}sum(o=null,f=!1){return this.norm(1,o,f)}norm(o="fro",f=null,l=!1){if(o==="fro")o=2;else if(typeof o=="string")throw Error(`Unsupported norm: ${o}`);if(f===null){let c=this.data.reduce((g,_)=>g+_**o,0)**(1/o);return new Tensor(this.type,[c],[])}f=safeIndex(f,this.dims.length);const d=this.dims.slice();d[f]=1;const b=new this.data.constructor(this.data.length/this.dims[f]);for(let c=0;c<this.data.length;++c){let g=0;for(let _=this.dims.length-1,p=c,u=1;_>=0;--_){const n=this.dims[_];if(_!==f){const e=p%n;g+=e*u,u*=d[_]}p=Math.floor(p/n)}b[g]+=this.data[c]**o}if(o!==1)for(let c=0;c<b.length;++c)b[c]=b[c]**(1/o);return l||d.splice(f,1),new Tensor(this.type,b,d)}normalize_(o=2,f=1){f=safeIndex(f,this.dims.length);const l=this.norm(o,f,!0);for(let d=0;d<this.data.length;++d){let b=0;for(let c=this.dims.length-1,g=d,_=1;c>=0;--c){const p=this.dims[c];if(c!==f){const u=g%p;b+=u*_,_*=this.dims[c]}g=Math.floor(g/p)}this.data[d]/=l.data[b]}return this}normalize(o=2,f=1){return this.clone().normalize_(o,f)}stride(){return dimsToStride(this.dims)}squeeze(o=null){return new Tensor(this.type,this.data,calc_squeeze_dims(this.dims,o))}squeeze_(o=null){return this.dims=calc_squeeze_dims(this.dims,o),this}unsqueeze(o=null){return new Tensor(this.type,this.data,calc_unsqueeze_dims(this.dims,o))}unsqueeze_(o=null){return this.dims=calc_unsqueeze_dims(this.dims,o),this}flatten_(o=0,f=-1){f=(f+this.dims.length)%this.dims.length;let l=this.dims.slice(0,o),d=this.dims.slice(o,f+1),b=this.dims.slice(f+1);return this.dims=[...l,d.reduce((c,g)=>c*g,1),...b],this}flatten(o=0,f=-1){return this.clone().flatten_(o,f)}view(...o){let f=-1;for(let l=0;l<o.length;++l)if(o[l]===-1){if(f!==-1)throw new Error("Only one dimension can be inferred");f=l}if(f!==-1){const l=o.reduce((d,b,c)=>c!==f?d*b:d,1);o[f]=this.data.length/l}return new Tensor(this.type,this.data,o)}neg_(){for(let o=0;o<this.data.length;++o)this.data[o]=-this.data[o];return this}neg(){return this.clone().neg_()}clamp_(o,f){for(let l=0;l<this.data.length;++l)this.data[l]=Math.min(Math.max(this.data[l],o),f);return this}clamp(o,f){return this.clone().clamp_(o,f)}round_(){for(let o=0;o<this.data.length;++o)this.data[o]=Math.round(this.data[o]);return this}round(){return this.clone().round_()}to(o){if(this.type===o)return this;if(!DataTypeMap.hasOwnProperty(o))throw new Error(`Unsupported type: ${o}`);return new Tensor(o,DataTypeMap[o].from(this.data),this.dims)}}function reshape(v,o){const f=v.length,l=o.reduce((b,c)=>b*c);if(f!==l)throw Error(`cannot reshape array of size ${f} into shape (${o})`);let d=v;for(let b=o.length-1;b>=0;b--)d=d.reduce((c,g)=>{let _=c[c.length-1];return _.length<o[b]?_.push(g):c.push([g]),c},[[]]);return d[0]}function permute(v,o){const[f,l]=permute_data(v.data,v.dims,o);return new Tensor(v.type,f,l)}function interpolate(v,[o,f],l="bilinear",d=!1){const b=v.dims.at(-3)??1,c=v.dims.at(-2),g=v.dims.at(-1);let _=interpolate_data(v.data,[b,c,g],[o,f],l,d);return new Tensor(v.type,_,[b,o,f])}function calc_squeeze_dims(v,o){return v=v.slice(),o===null?v=v.filter(f=>f!==1):typeof o=="number"?v[o]===1&&v.splice(o,1):Array.isArray(o)&&(v=v.filter((f,l)=>f!==1||!o.includes(l))),v}function calc_unsqueeze_dims(v,o){return o=safeIndex(o,v.length+1),v=v.slice(),v.splice(o,0,1),v}function safeIndex(v,o,f=null){if(v<-o||v>=o)throw new Error(`IndexError: index ${v} is out of bounds for dimension${f===null?"":" "+f} with size ${o}`);return v<0&&(v=(v%o+o)%o),v}function cat(v,o=0){o=safeIndex(o,v[0].dims.length);const f=v[0].dims.slice();f[o]=v.reduce((c,g)=>c+g.dims[o],0);const l=f.reduce((c,g)=>c*g,1),d=new v[0].data.constructor(l),b=v[0].type;if(o===0){let c=0;for(let g of v)d.set(g.data,c),c+=g.data.length}else{let c=0;for(let g=0;g<v.length;++g){let _=v[g];for(let p=0;p<_.data.length;++p){let u=0;for(let n=_.dims.length-1,e=p,i=1;n>=0;--n){const s=_.dims[n];let m=e%s;n===o&&(m+=c),u+=m*i,i*=f[n],e=Math.floor(e/s)}d[u]=_.data[p]}c+=_.dims[o]}}return new Tensor(b,d,f)}function stack(v,o=0){return cat(v.map(f=>f.unsqueeze(o)),o)}function std_mean(v,o=null,f=1,l=!1){if(o===null){const g=v.data.reduce((u,n)=>u+n,0)/v.data.length,_=Math.sqrt(v.data.reduce((u,n)=>u+(n-g)**2,0)/(v.data.length-f)),p=new Tensor(v.type,[g],[]);return[new Tensor(v.type,[_],[]),p]}o=safeIndex(o,v.dims.length);const d=mean(v,o,l),b=v.dims.slice();b[o]=1;const c=new v.data.constructor(v.data.length/v.dims[o]);for(let g=0;g<v.data.length;++g){let _=0;for(let p=v.dims.length-1,u=g,n=1;p>=0;--p){const e=v.dims[p];if(p!==o){const i=u%e;_+=i*n,n*=b[p]}u=Math.floor(u/e)}c[_]+=(v.data[g]-d.data[_])**2}for(let g=0;g<c.length;++g)c[g]=Math.sqrt(c[g]/(v.dims[o]-f));return l||b.splice(o,1),[new Tensor(v.type,c,b),d]}function mean(v,o=null,f=!1){if(o===null){let b=v.data.reduce((c,g)=>c+g,0);return new Tensor(v.type,[b/v.data.length],[])}o=safeIndex(o,v.dims.length);const l=v.dims.slice();l[o]=1;const d=new v.data.constructor(v.data.length/v.dims[o]);for(let b=0;b<v.data.length;++b){let c=0;for(let g=v.dims.length-1,_=b,p=1;g>=0;--g){const u=v.dims[g];if(g!==o){const n=_%u;c+=n*p,p*=l[g]}_=Math.floor(_/u)}d[c]+=v.data[b]}if(v.dims[o]!==1)for(let b=0;b<d.length;++b)d[b]=d[b]/v.dims[o];return f||l.splice(o,1),new Tensor(v.type,d,l)}function dynamicTimeWarping(v){const[o,f]=v.dims,l=[o+1,f+1],d=new Tensor("float32",new Float32Array(l[0]*l[1]).fill(1/0),l),b=new Tensor("float32",new Float32Array(l[0]*l[1]).fill(-1),l);d[0].data[0]=0;for(let u=1;u<f+1;++u)for(let n=1;n<o+1;++n){const e=d[n-1][u-1].item(),i=d[n-1][u].item(),s=d[n][u-1].item();let m,y;e<i&&e<s?(m=e,y=0):i<e&&i<s?(m=i,y=1):(m=s,y=2),d[n].data[u]=v[n-1][u-1].item()+m,b[n].data[u]=y}let c=o,g=f;b.data.fill(2,0,l[1]);for(let u=0;u<l[0];++u)b[u].data[0]=1;let _=[],p=[];for(;c>0||g>0;)switch(_.push(c-1),p.push(g-1),b[c][g].item()){case 0:--c,--g;break;case 1:--c;break;case 2:--g;break;default:throw new Error(`Internal error in dynamic time warping. Unexpected trace[${c}, ${g}]. Please file a bug report.`)}return _.reverse(),p.reverse(),[_,p]}function dimsToStride(v){const o=new Array(v.length);for(let f=v.length-1,l=1;f>=0;--f)o[f]=l,l*=v[f];return o}function ones(v){const o=v.reduce((f,l)=>f*l,1);return new Tensor("int64",new BigInt64Array(o).fill(1n),v)}function ones_like(v){return ones(v.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 v=[...Array.from({length:94},(d,b)=>b+33),...Array.from({length:12},(d,b)=>b+161),...Array.from({length:82},(d,b)=>b+174)],o=v.slice();let f=0;for(let d=0;d<256;++d)v.includes(d)||(v.push(d),o.push(256+f),f+=1);const l=o.map(d=>String.fromCharCode(d));return Object.fromEntries(v.map((d,b)=>[d,l[b]]))})();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);new Map([...WHISPER_LANGUAGES.map(([v,o])=>[o,v]),["burmese","my"],["valencian","ca"],["flemish","nl"],["haitian","ht"],["letzeburgesch","lb"],["pushto","ps"],["panjabi","pa"],["moldavian","ro"],["moldovan","ro"],["sinhalese","si"],["castilian","es"]]);async function loadConfig(v,o){return await getModelJSON(v,"config.json",!0,o)}class PretrainedConfig{constructor(o){this.model_type=null,this.is_encoder_decoder=!1,Object.assign(this,o)}static async from_pretrained(o,{progress_callback:f=null,config:l=null,cache_dir:d=null,local_files_only:b=!1,revision:c="main"}={}){let g=l??await loadConfig(o,{progress_callback:f,cache_dir:d,local_files_only:b,revision:c});return new this(g)}}class AutoConfig{static async from_pretrained(...o){return PretrainedConfig.from_pretrained(...o)}}class LogitsProcessorList extends Callable{constructor(){super(),this.processors=[]}push(o){this.processors.push(o)}extend(o){this.processors.push(...o)}_call(o,f){for(let l of f)this.processors.forEach(d=>d(o,l))}[Symbol.iterator](){return this.processors.values()}}class LogitsProcessor extends Callable{_call(o,f){throw Error("`_call` should be implemented in a subclass")}}class ForceTokensLogitsProcessor extends LogitsProcessor{constructor(o){super(),this.force_token_map=Object.fromEntries(o??[])}_call(o,f){let l=this.force_token_map[o.length];return exists(l)&&(f.data.fill(-1/0),f.data[l]=0),f}}class ForcedBOSTokenLogitsProcessor extends LogitsProcessor{constructor(o){super(),this.bos_token_id=o}_call(o,f){return o.length===1&&(f.data.fill(-1/0),f.data[this.bos_token_id]=0),f}}class ForcedEOSTokenLogitsProcessor extends LogitsProcessor{constructor(o,f){super(),this.max_length=o,this.forced_eos_token_id=f}_call(o,f){}}class SuppressTokensAtBeginLogitsProcessor extends LogitsProcessor{constructor(o,f){super(),this.begin_suppress_tokens=o,this.begin_index=f}_call(o,f){if(o.length===this.begin_index)for(let l of this.begin_suppress_tokens)f.data[l]=-1/0;return f}}class WhisperTimeStampLogitsProcessor extends LogitsProcessor{constructor(o){super(),this.eos_token_id=o.eos_token_id,this.no_timestamps_token_id=o.no_timestamps_token_id,this.timestamp_begin=this.no_timestamps_token_id+1,this.begin_index=(o.forced_decoder_ids||[]).length+2,o.forced_decoder_ids.slice(-1)[0][1]===this.no_timestamps_token_id&&(this.begin_index-=1),this.max_initial_timestamp_index=o.max_initial_timestamp_index}_call(o,f){const l=f.data;if(l[this.no_timestamps_token_id]=-1/0,o.length===this.begin_index-1)return l.fill(-1/0),l[this.timestamp_begin]=0,f;const d=o.slice(this.begin_index),b=d.length>=1&&d[d.length-1]>=this.timestamp_begin,c=d.length<2||d[d.length-2]>=this.timestamp_begin;if(b&&(c?l.subarray(this.timestamp_begin).fill(-1/0):l.subarray(0,this.eos_token_id).fill(-1/0)),o.length===this.begin_index&&this.max_initial_timestamp_index!==null){const u=this.timestamp_begin+this.max_initial_timestamp_index;l.subarray(u+1).fill(-1/0)}const g=log_softmax(l),_=Math.log(g.subarray(this.timestamp_begin).map(Math.exp).reduce((u,n)=>u+n)),p=max(g.subarray(0,this.timestamp_begin))[0];return _>p&&l.subarray(0,this.timestamp_begin).fill(-1/0),f}}class NoRepeatNGramLogitsProcessor extends LogitsProcessor{constructor(o){super(),this.no_repeat_ngram_size=o}getNgrams(o){const f=o.length,l=[];for(let b=0;b<f+1-this.no_repeat_ngram_size;++b){const c=[];for(let g=0;g<this.no_repeat_ngram_size;++g)c.push(o[b+g]);l.push(c)}const d=new Map;for(const b of l){const c=b.slice(0,b.length-1),g=JSON.stringify(c),_=d.get(g)??[];_.push(b[b.length-1]),d.set(g,_)}return d}getGeneratedNgrams(o,f){const l=f.slice(f.length+1-this.no_repeat_ngram_size,f.length);return o.get(JSON.stringify(l))??[]}calcBannedNgramTokens(o){const f=[];if(o.length+1<this.no_repeat_ngram_size)return f;{const l=this.getNgrams(o);return this.getGeneratedNgrams(l,o)}}_call(o,f){const l=this.calcBannedNgramTokens(o);for(const d of l)f.data[d]=-1/0;return f}}class RepetitionPenaltyLogitsProcessor extends LogitsProcessor{constructor(o){super(),this.penalty=o}_call(o,f){for(const l of o)f.data[l]<0?f.data[l]*=this.penalty:f.data[l]/=this.penalty;return f}}class MinLengthLogitsProcessor extends LogitsProcessor{constructor(o,f){super(),this.min_length=o,this.eos_token_id=Array.isArray(f)?f:[f]}_call(o,f){if(o.length<this.min_length)for(const l of this.eos_token_id)f.data[l]=-1/0;return f}}class MinNewTokensLengthLogitsProcessor extends LogitsProcessor{constructor(o,f,l){super(),this.prompt_length_to_skip=o,this.min_new_tokens=f,this.eos_token_id=Array.isArray(l)?l:[l]}_call(o,f){if(o.length-this.prompt_length_to_skip<this.min_new_tokens)for(const l of this.eos_token_id)f.data[l]=-1/0;return f}}class NoBadWordsLogitsProcessor extends LogitsProcessor{constructor(o,f){super(),this.bad_words_ids=o,this.eos_token_id=Array.isArray(f)?f:[f]}_call(o,f){for(const l of this.bad_words_ids){let d=!0;for(let b=1;b<=l.length-1&&l.length<o.length;++b)if(l.at(-b-1)!==o.at(-b)){d=!1;break}d&&(f.data[l.at(-1)]=-1/0)}return f}}const GenerationConfig=class{constructor(v={}){this.max_length=v.max_length??20,this.max_new_tokens=v.max_new_tokens??null,this.min_length=v.min_length??0,this.min_new_tokens=v.min_new_tokens??null,this.early_stopping=v.early_stopping??!1,this.max_time=v.max_time??null,this.do_sample=v.do_sample??!1,this.num_beams=v.num_beams??1,this.num_beam_groups=v.num_beam_groups??1,this.penalty_alpha=v.penalty_alpha??null,this.use_cache=v.use_cache??!0,this.temperature=v.temperature??1,this.top_k=v.top_k??50,this.top_p=v.top_p??1,this.typical_p=v.typical_p??1,this.epsilon_cutoff=v.epsilon_cutoff??0,this.eta_cutoff=v.eta_cutoff??0,this.diversity_penalty=v.diversity_penalty??0,this.repetition_penalty=v.repetition_penalty??1,this.encoder_repetition_penalty=v.encoder_repetition_penalty??1,this.length_penalty=v.length_penalty??1,this.no_repeat_ngram_size=v.no_repeat_ngram_size??0,this.bad_words_ids=v.bad_words_ids??null,this.force_words_ids=v.force_words_ids??null,this.renormalize_logits=v.renormalize_logits??!1,this.constraints=v.constraints??null,this.forced_bos_token_id=v.forced_bos_token_id??null,this.forced_eos_token_id=v.forced_eos_token_id??null,this.remove_invalid_values=v.remove_invalid_values??!1,this.exponential_decay_length_penalty=v.exponential_decay_length_penalty??null,this.suppress_tokens=v.suppress_tokens??null,this.begin_suppress_tokens=v.begin_suppress_tokens??null,this.forced_decoder_ids=v.forced_decoder_ids??null,this.num_return_sequences=v.num_return_sequences??1,this.output_attentions=v.output_attentions??!1,this.output_hidden_states=v.output_hidden_states??!1,this.output_scores=v.output_scores??!1,this.return_dict_in_generate=v.return_dict_in_generate??!1,this.pad_token_id=v.pad_token_id??null,this.bos_token_id=v.bos_token_id??null,this.eos_token_id=v.eos_token_id??null,this.encoder_no_repeat_ngram_size=v.encoder_no_repeat_ngram_size??0,this.decoder_start_token_id=v.decoder_start_token_id??null,this.generation_kwargs=v.generation_kwargs??{}}};class Sampler extends Callable{constructor(o){super(),this.generation_config=o}_call(o,f=-1){return this.sample(o,f)}sample(o,f){throw Error("sample should be implemented in subclasses.")}getLogits(o,f){let l=o.dims.at(-1),d=o.data;if(f===-1)d=d.slice(-l);else{let b=f*l;d=d.slice(b,b+l)}return this.generation_config.temperature>0&&(d=d.map(b=>b/this.generation_config.temperature)),d}randomSelect(o){let f=o.reduce((d,b)=>d+b,0),l=Math.random()*f;for(let d=0;d<o.length;++d)if(l-=o[d],l<=0)return d;return 0}static getSampler(o){if(o.do_sample)return new MultinomialSampler(o);if(o.num_beams>1)return new BeamSearchSampler(o);if(o.num_return_sequences>1)throw Error(`num_return_sequences has to be 1 when doing greedy search, but is ${o.num_return_sequences}.`);return new GreedySampler(o)}}class GreedySampler extends Sampler{sample(o,f=-1){let l=this.getLogits(o,f);return[[max(l)[1],0]]}}class MultinomialSampler extends Sampler{sample(o,f=-1){let l=o.dims.at(-1);this.generation_config.top_k>0&&(l=Math.min(this.generation_config.top_k,l));const d=this.getLogits(o,f),b=getTopItems(d,l),c=softmax(b.map(g=>g[1]));return Array.from({length:this.generation_config.num_beams},()=>{const g=this.randomSelect(c);return[b[g][0],Math.log(c[g])]})}}class BeamSearchSampler extends Sampler{sample(o,f=-1){let l=o.dims.at(-1);this.generation_config.top_k>0&&(l=Math.min(this.generation_config.top_k,l));const d=this.getLogits(o,f),b=getTopItems(d,l),c=softmax(b.map(g=>g[1]));return Array.from({length:this.generation_config.num_beams},(g,_)=>[b[_][0],Math.log(c[_])])}}const{InferenceSession,Tensor:ONNXTensor,env}=ONNX,MODEL_TYPES={EncoderOnly:0,EncoderDecoder:1,Seq2Seq:2,Vision2Seq:3,DecoderOnly:4,MaskGeneration:5},MODEL_TYPE_MAPPING=new Map,MODEL_NAME_TO_CLASS_MAPPING=new Map,MODEL_CLASS_TO_NAME_MAPPING=new Map;async function constructSession(v,o,f){let l=`onnx/${o}${f.quantized?"_quantized":""}.onnx`,d=await getModelFile(v,l,!0,f);try{return await InferenceSession.create(d,{executionProviders})}catch(b){if(executionProviders.length===1&&executionProviders[0]==="wasm")throw b;return console.warn(b),console.warn("Something went wrong during model construction (most likely a missing operation). Using `wasm` as a fallback. "),await InferenceSession.create(d,{executionProviders:["wasm"]})}}function validateInputs(v,o){const f=Object.create(null),l=[];for(const c of v.inputNames){const g=o[c];if(!(g instanceof Tensor)){l.push(c);continue}f[c]=env.wasm.proxy?g.clone():g}if(l.length>0)throw new Error(`An error occurred during model execution: "Missing the following inputs: ${l.join(", ")}.`);const d=Object.keys(o).length,b=v.inputNames.length;if(d>b){let c=Object.keys(o).filter(g=>!v.inputNames.includes(g));console.warn(`WARNING: Too many inputs were provided (${d} > ${b}). The following inputs will be ignored: "${c.join(", ")}".`)}return f}async function sessionRun(v,o){const f=validateInputs(v,o);try{let l=await v.run(f);return l=replaceTensors(l),l}catch(l){throw console.error(`An error occurred during model execution: "${l}".`),console.error("Inputs given to model:",f),l}}function replaceTensors(v){for(let o in v)v[o]instanceof ONNXTensor?v[o]=new Tensor(v[o]):typeof v[o]=="object"&&replaceTensors(v[o]);return v}function toI64Tensor(v){if(v instanceof Tensor)return v;if(v.length===0)throw Error("items must be non-empty");if(Array.isArray(v[0])){if(v.some(o=>o.length!==v[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(v.flat().map(o=>BigInt(o))),[v.length,v[0].length])}else return new Tensor("int64",BigInt64Array.from(v.map(o=>BigInt(o))),[1,v.length])}function prepareAttentionMask(v,o){let f=v.config.pad_token_id??null,l=v.config.eos_token_id??null;isIntegralNumber(l)&&(l=[l]);let d=o.indexOf(f)!==-1,b=l===null||!l.includes(f);if(d&&b){let c=BigInt64Array.from(o.data.map(g=>g!=f));return new Tensor("int64",c,o.dims)}else return ones_like(o)}function preparePositionIds(v,o,f){if(!v.inputNames.includes("position_ids"))return;const l=new BigInt64Array(o.attention_mask.data.length);for(let d=0;d<o.attention_mask.dims[0];++d){let b=d*o.attention_mask.dims[1],c=BigInt(0);for(let g=0;g<o.attention_mask.dims[1];++g){const _=b+g;o.attention_mask.data[_]===0n?l[_]=BigInt(1):(l[_]=c,c+=o.attention_mask.data[_])}}o.position_ids=new Tensor("int64",l,o.attention_mask.dims),f&&(o.position_ids=o.position_ids.slice(null,-1).unsqueeze_(-1))}function boolTensor(v){return new Tensor("bool",[v],[1])}async function seq2seqForward(v,o){let{encoder_outputs:f,past_key_values:l}=o;f||(f=(await encoderForward(v,o)).last_hidden_state);let d={input_ids:o.decoder_input_ids,encoder_hidden_states:f};const b=!!l;v.decoder_merged_session.inputNames.includes("use_cache_branch")&&(d.use_cache_branch=boolTensor(b)),v.decoder_merged_session.inputNames.includes("encoder_attention_mask")&&(d.encoder_attention_mask=o.attention_mask),preparePositionIds(v.decoder_merged_session,d,b),v.addPastKeyValues(d,l);const c=await sessionRun(v.decoder_merged_session,d);let g=c.logits;l=v.getPastKeyValues(c,l);const _=v.getAttentions(c);return new Seq2SeqLMOutput({logits:g,past_key_values:l,encoder_outputs:f,..._})}function seq2seqStartBeams(v,o,f,l){let d=[],b=0;const c=v.requires_attention_mask??!0;let g=f.decoder_input_ids??f.decoder_start_token_id??f.bos_token_id??f.eos_token_id;g instanceof Tensor?g=g.tolist().flat():Array.isArray(g)||(g=[g]);for(let _ of o){_.dims=[1,..._.dims];let p={inputs:_,encoder_outputs:null,prev_model_outputs:null,output_token_ids:g,done:!1,score:0,id:b++};c&&(p.attention_mask=prepareAttentionMask(v,_)),d.push(p)}return d}async function seq2seqRunBeam(v,o){var f;const l=v.main_input_name;let d=o.output_token_ids;o.prev_model_outputs&&(d=d.slice(-1));let b={[l]:o.inputs,decoder_input_ids:toI64Tensor(d),encoder_outputs:o.encoder_outputs,past_key_values:(f=o.prev_model_outputs)==null?void 0:f.past_key_values};o.attention_mask&&(b.attention_mask=o.attention_mask);let c=await v.forward(b);return o.prev_model_outputs=c,o.encoder_outputs=c.encoder_outputs,c}function seq2seqUpdatebeam(v,o){v.output_token_ids=[...v.output_token_ids,o]}async function encoderForward(v,o){const f=Object.create(null);for(const l of v.session.inputNames)f[l]=o[l];return v.session.inputNames.includes("token_type_ids")&&!f.token_type_ids&&(f.token_type_ids=new Tensor("int64",new BigInt64Array(f.input_ids.data.length),f.input_ids.dims)),await sessionRun(v.session,f)}async function decoderForward(v,o){let{input_ids:f,past_key_values:l,attention_mask:d}=o,b={input_ids:f,attention_mask:d??prepareAttentionMask(v,f)};const c=!!l;v.session.inputNames.includes("use_cache_branch")&&(b.use_cache_branch=boolTensor(c)),preparePositionIds(v.session,b,c),v.addPastKeyValues(b,l);let g=await sessionRun(v.session,b),_=g.logits;return l=v.getPastKeyValues(g,l),{logits:_,past_key_values:l}}function decoderStartBeams(v,o,f,l,d){let b=[],c=0;for(let g of o){let _=g.tolist().map(Number);g.dims=[1,...g.dims];let p;d?(p=d[c],p.dims=[1,...p.dims]):p=prepareAttentionMask(v,g);let u={input:g,model_input_ids:g,attention_mask:p,prev_model_outputs:null,output_token_ids:_,num_output_tokens:l,done:!1,score:0,id:c++};b.push(u)}return b}async function decoderRunBeam(v,o){var f;let l=new BigInt64Array(o.output_token_ids.length).fill(1n),d={input_ids:o.model_input_ids,attention_mask:new Tensor("int64",l,[1,l.length]),past_key_values:(f=o.prev_model_outputs)==null?void 0:f.past_key_values},b=await v.forward(d);return o.prev_model_outputs=b,b}function decoderUpdatebeam(v,o){v.output_token_ids=[...v.output_token_ids,o],v.model_input_ids=new Tensor("int64",[BigInt(o)],[1,1])}class PreTrainedModel extends Callable{constructor(o,f){super(),Ht(this,"main_input_name","input_ids"),this.config=o,this.session=f;const l=MODEL_CLASS_TO_NAME_MAPPING.get(this.constructor),d=MODEL_TYPE_MAPPING.get(l);this.can_generate=!1,this._runBeam=null,this._getStartBeams=null,this._updateBeam=null,this._forward=null,d===MODEL_TYPES.DecoderOnly?(this.can_generate=!0,this._runBeam=decoderRunBeam,this._getStartBeams=decoderStartBeams,this._updateBeam=decoderUpdatebeam,this._forward=decoderForward):d===MODEL_TYPES.Seq2Seq||d===MODEL_TYPES.Vision2Seq?(this.can_generate=!0,this._runBeam=seq2seqRunBeam,this._getStartBeams=seq2seqStartBeams,this._updateBeam=seq2seqUpdatebeam,this._forward=seq2seqForward):d===MODEL_TYPES.EncoderDecoder?this._forward=encoderForward:this._forward=encoderForward}async dispose(){const o=[];for(let f of Object.keys(this)){const l=this[f];l instanceof InferenceSession&&o.push(l.handler.dispose())}return await Promise.all(o)}static async from_pretrained(o,{quantized:f=!0,progress_callback:l=null,config:d=null,cache_dir:b=null,local_files_only:c=!1,revision:g="main",model_file_name:_=null}={}){let p={quantized:f,progress_callback:l,config:d,cache_dir:b,local_files_only:c,revision:g,model_file_name:_};const u=MODEL_CLASS_TO_NAME_MAPPING.get(this),n=MODEL_TYPE_MAPPING.get(u);let e;return n===MODEL_TYPES.DecoderOnly?e=await Promise.all([AutoConfig.from_pretrained(o,p),constructSession(o,p.model_file_name??"decoder_model_merged",p),getModelJSON(o,"generation_config.json",!1,p)]):n===MODEL_TYPES.Seq2Seq||n===MODEL_TYPES.Vision2Seq?e=await Promise.all([AutoConfig.from_pretrained(o,p),constructSession(o,"encoder_model",p),constructSession(o,"decoder_model_merged",p),getModelJSON(o,"generation_config.json",!1,p)]):n===MODEL_TYPES.MaskGeneration?e=await Promise.all([AutoConfig.from_pretrained(o,p),constructSession(o,"vision_encoder",p),constructSession(o,"prompt_encoder_mask_decoder",p)]):n===MODEL_TYPES.EncoderDecoder?e=await Promise.all([AutoConfig.from_pretrained(o,p),constructSession(o,"encoder_model",p),constructSession(o,"decoder_model_merged",p)]):(n!==MODEL_TYPES.EncoderOnly&&console.warn(`Model type for '${u??(d==null?void 0:d.model_type)}' not found, assuming encoder-only architecture. Please report this at https://github.com/xenova/transformers.js/issues/new/choose.`),e=await Promise.all([AutoConfig.from_pretrained(o,p),constructSession(o,p.model_file_name??"model",p)])),new this(...e)}async _call(o){return await this.forward(o)}async forward(o){return await this._forward(this,o)}_get_logits_processor(o,f,l=null){const d=new LogitsProcessorList;if(o.repetition_penalty!==null&&o.repetition_penalty!==1&&d.push(new RepetitionPenaltyLogitsProcessor(o.repetition_penalty)),o.no_repeat_ngram_size!==null&&o.no_repeat_ngram_size>0&&d.push(new NoRepeatNGramLogitsProcessor(o.no_repeat_ngram_size)),o.bad_words_ids!==null&&d.push(new NoBadWordsLogitsProcessor(o.bad_words_ids,o.eos_token_id)),o.min_length!==null&&o.eos_token_id!==null&&o.min_length>0&&d.push(new MinLengthLogitsProcessor(o.min_length,o.eos_token_id)),o.min_new_tokens!==null&&o.eos_token_id!==null&&o.min_new_tokens>0&&d.push(new MinNewTokensLengthLogitsProcessor(f,o.min_new_tokens,o.eos_token_id)),o.forced_bos_token_id!==null&&d.push(new ForcedBOSTokenLogitsProcessor(o.forced_bos_token_id)),o.forced_eos_token_id!==null&&d.push(new ForcedEOSTokenLogitsProcessor(o.max_length,o.forced_eos_token_id)),o.begin_suppress_tokens!==null){let b=f>1||o.forced_bos_token_id===null?f:f+1;o.forced_decoder_ids!==null&&(b+=o.forced_decoder_ids[o.forced_decoder_ids.length-1][0]),d.push(new SuppressTokensAtBeginLogitsProcessor(o.begin_suppress_tokens,b))}return o.forced_decoder_ids!==null&&d.push(new ForceTokensLogitsProcessor(o.forced_decoder_ids)),l!==null&&d.extend(l),d}_get_generation_config(o){let f=new GenerationConfig(this.config);return"generation_config"in this&&Object.assign(f,this.generation_config),o!==null&&Object.assign(f,o),f}async generate(o,f=null,l=null,{inputs_attention_mask:d=null}={}){if(!this.can_generate){let m=`The current model class (${MODEL_CLASS_TO_NAME_MAPPING.get(this.constructor)}) is not compatible with \`.generate()\`, as it doesn't have a language model head.`;const y=this.config.model_type,x=MODEL_WITH_LM_HEAD_MAPPING_NAMES.get(y)??MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING_NAMES.get(y)??MODEL_FOR_SPEECH_SEQ_2_SEQ_MAPPING_NAMES.get(y)??MODEL_FOR_VISION_2_SEQ_MAPPING_NAMES.get(y);throw x&&(m+=` Please use the following class instead: '${x[0]}'`),Error(m)}if(!(o instanceof Tensor)&&!isTypedArray(o)&&!Array.isArray(o))throw Error(`\`inputs\` must be a Tensor, TypedArray, or Array, but is "${o.constructor.name}".`);let b;if(this.config.is_encoder_decoder)b=0;else if(b=o instanceof Tensor?o.dims.at(-1):o.length,b===0)throw Error("Must supply a non-empty array of input token ids.");f=this._get_generation_config(f),l=l??new LogitsProcessorList,l=this._get_logits_processor(f,b,l);let c=f.eos_token_id;c!==null&&!Array.isArray(c)&&(c=[c]);let g=1;const _=g+(f.max_new_tokens??1/0),p=Number.isInteger(f.max_length)&&(f.max_new_tokens??null)===null;let u=Sampler.getSampler(f),n=this.getStartBeams(o,f,g,d);for(;n.some(m=>!m.done)&&g<_;){let m=[];for(let y of n){if(y.done){m.push(y);continue}if(p&&y.output_token_ids.length>=f.max_length){y.done=!0,m.push(y);continue}let x=await this.runBeam(y);f.output_attentions&&this.addAttentionsToBeam(y,x),f.output_scores;let T=x.logits.slice(null,-1,null);l(y.output_token_ids,T);let E=u(T);for(let[P,O]of E){let A={...y};this.updateBeam(A,P),A.score+=O,c&&c.includes(P)&&(A.done=!0),m.push(A)}}++g,m=this.groupBeams(m).map(y=>y.sort((x,T)=>T.score-x.score).slice(0,f.num_beams)),n=m.flat(),f.callback_function&&f.callback_function(n)}const e=this.groupBeams(n),i=m=>e.map(y=>f.num_return_sequences>1?y.slice(0,f.num_return_sequences).map(x=>x[m]):[y[0][m]]).flat(),s=i("output_token_ids");if(f.return_dict_in_generate){const m=i("decoder_attentions"),y=i("cross_attentions");return{sequences:s,decoder_attentions:m,cross_attentions:y}}else return s}addAttentionsToBeam(o,f){if(this.config.is_encoder_decoder){if(!f.cross_attentions||f.cross_attentions.length===0)throw Error("`output_attentions` is true, but the model did not produce cross-attentions. This is most likely because the model was not exported with `output_attentions=True`.");o.cross_attentions||(o.cross_attentions=[]),o.cross_attentions.push(f.cross_attentions)}if(!f.decoder_attentions||f.decoder_attentions.length===0)throw Error("`output_attentions` is true, but the model did not produce decoder-attentions. This is most likely because the model was not exported with `output_attentions=True`.");o.decoder_attentions||(o.decoder_attentions=[]),o.decoder_attentions.push(f.decoder_attentions)}groupBeams(o){const f=Object.create(null);for(const l of o)f[l.id]===void 0?f[l.id]=[l]:f[l.id].push(l);return Object.values(f)}getPastKeyValues(o,f){const l=Object.create(null);for(const d in o)if(d.startsWith("present")){let b=d.replace("present","past_key_values");f&&d.includes("encoder")?l[b]=f[b]:l[b]=o[d]}return l}getAttentions(o){const f=Object.create(null);for(const l of["cross_attentions","decoder_attentions"]){const d=[];for(const b in o)if(b.startsWith(l)){const c=b.split(".").pop();d[c]=o[b]}f[l]=d}return f}addPastKeyValues(o,f){if(f)Object.assign(o,f);else if(this.config.is_encoder_decoder&&(this.add_encoder_pkv??!0)){let l=[1,this.num_encoder_heads,0,this.encoder_dim_kv],d=[1,this.num_decoder_heads,0,this.decoder_dim_kv];for(let b=0;b<this.num_decoder_layers;++b)o[`past_key_values.${b}.encoder.key`]=new Tensor("float32",[],l),o[`past_key_values.${b}.encoder.value`]=new Tensor("float32",[],l),o[`past_key_values.${b}.decoder.key`]=new Tensor("float32",[],d),o[`past_key_values.${b}.decoder.value`]=new Tensor("float32",[],d)}else if(this.config.model_type==="falcon"){let l=[1*this.num_heads,0,this.dim_kv];for(let d=0;d<this.num_layers;++d)o[`past_key_values.${d}.key`]=new Tensor("float32",[],l),o[`past_key_values.${d}.value`]=new Tensor("float32",[],l)}else if(this.config.multi_query){let l=[1*this.num_heads,0,2*this.dim_kv];for(let d=0;d<this.num_layers;++d)o[`past_key_values.${d}.key_value`]=new Tensor("float32",[],l)}else if(this.config.model_type==="bloom"){let l=[1*this.num_heads,this.dim_kv,0],d=[1*this.num_heads,0,this.dim_kv];for(let b=0;b<this.num_layers;++b)o[`past_key_values.${b}.key`]=new Tensor("float32",[],l),o[`past_key_values.${b}.value`]=new Tensor("float32",[],d)}else{let l=[1,this.num_heads,0,this.dim_kv];for(let d=0;d<this.num_layers;++d)o[`past_key_values.${d}.key`]=new Tensor("float32",[],l),o[`past_key_values.${d}.value`]=new Tensor("float32",[],l)}}getStartBeams(o,f,l,d){return this._getStartBeams(this,o,f,l,d)}async runBeam(o){return await this._runBeam(this,o)}updateBeam(o,f){return this._updateBeam(o,f)}}class ModelOutput{}class BertPreTrainedModel extends PreTrainedModel{}class BertModel extends BertPreTrainedModel{}class BertForMaskedLM extends BertPreTrainedModel{async _call(o){return new MaskedLMOutput(await super._call(o))}}class BertForSequenceClassification extends BertPreTrainedModel{async _call(o){return new SequenceClassifierOutput(await super._call(o))}}class BertForTokenClassification extends BertPreTrainedModel{async _call(o){return new TokenClassifierOutput(await super._call(o))}}class BertForQuestionAnswering extends BertPreTrainedModel{async _call(o){return new QuestionAnsweringModelOutput(await super._call(o))}}class NomicBertPreTrainedModel extends PreTrainedModel{}class NomicBertModel extends NomicBertPreTrainedModel{}class RoFormerPreTrainedModel extends PreTrainedModel{}class RoFormerModel extends RoFormerPreTrainedModel{}class RoFormerForMaskedLM extends RoFormerPreTrainedModel{async _call(o){return new MaskedLMOutput(await super._call(o))}}class RoFormerForSequenceClassification extends RoFormerPreTrainedModel{async _call(o){return new SequenceClassifierOutput(await super._call(o))}}class RoFormerForTokenClassification extends RoFormerPreTrainedModel{async _call(o){return new TokenClassifierOutput(await super._call(o))}}class RoFormerForQuestionAnswering extends RoFormerPreTrainedModel{async _call(o){return new QuestionAnsweringModelOutput(await super._call(o))}}class ConvBertPreTrainedModel extends PreTrainedModel{}class ConvBertModel extends ConvBertPreTrainedModel{}class ConvBertForMaskedLM extends ConvBertPreTrainedModel{async _call(o){return new MaskedLMOutput(await super._call(o))}}class ConvBertForSequenceClassification extends ConvBertPreTrainedModel{async _call(o){return new SequenceClassifierOutput(await super._call(o))}}class ConvBertForTokenClassification extends ConvBertPreTrainedModel{async _call(o){return new TokenClassifierOutput(await super._call(o))}}class ConvBertForQuestionAnswering extends ConvBertPreTrainedModel{async _call(o){return new QuestionAnsweringModelOutput(await super._call(o))}}class ElectraPreTrainedModel extends PreTrainedModel{}class ElectraModel extends ElectraPreTrainedModel{}class ElectraForMaskedLM extends ElectraPreTrainedModel{async _call(o){return new MaskedLMOutput(await super._call(o))}}class ElectraForSequenceClassification extends ElectraPreTrainedModel{async _call(o){return new SequenceClassifierOutput(await super._call(o))}}class ElectraForTokenClassification extends ElectraPreTrainedModel{async _call(o){return new TokenClassifierOutput(await super._call(o))}}class ElectraForQuestionAnswering extends ElectraPreTrainedModel{async _call(o){return new QuestionAnsweringModelOutput(await super._call(o))}}class CamembertPreTrainedModel extends PreTrainedModel{}class CamembertModel extends CamembertPreTrainedModel{}class CamembertForMaskedLM extends CamembertPreTrainedModel{async _call(o){return new MaskedLMOutput(await super._call(o))}}class CamembertForSequenceClassification extends CamembertPreTrainedModel{async _call(o){return new SequenceClassifierOutput(await super._call(o))}}class CamembertForTokenClassification extends CamembertPreTrainedModel{async _call(o){return new TokenClassifierOutput(await super._call(o))}}class CamembertForQuestionAnswering extends CamembertPreTrainedModel{async _call(o){return new QuestionAnsweringModelOutput(await super._call(o))}}class DebertaPreTrainedModel extends PreTrainedModel{}class DebertaModel extends DebertaPreTrainedModel{}class DebertaForMaskedLM extends DebertaPreTrainedModel{async _call(o){return new MaskedLMOutput(await super._call(o))}}class DebertaForSequenceClassification extends DebertaPreTrainedModel{async _call(o){return new SequenceClassifierOutput(await super._call(o))}}class DebertaForTokenClassification extends DebertaPreTrainedModel{async _call(o){return new TokenClassifierOutput(await super._call(o))}}class DebertaForQuestionAnswering extends DebertaPreTrainedModel{async _call(o){return new QuestionAnsweringModelOutput(await super._call(o))}}class DebertaV2PreTrainedModel extends PreTrainedModel{}class DebertaV2Model extends DebertaV2PreTrainedModel{}class DebertaV2ForMaskedLM extends DebertaV2PreTrainedModel{async _call(o){return new MaskedLMOutput(await super._call(o))}}class DebertaV2ForSequenceClassification extends DebertaV2PreTrainedModel{async _call(o){return new SequenceClassifierOutput(await super._call(o))}}class DebertaV2ForTokenClassification extends DebertaV2PreTrainedModel{async _call(o){return new TokenClassifierOutput(await super._call(o))}}class DebertaV2ForQuestionAnswering extends DebertaV2PreTrainedModel{async _call(o){return new QuestionAnsweringModelOutput(await super._call(o))}}class DistilBertPreTrainedModel extends PreTrainedModel{}class DistilBertModel extends DistilBertPreTrainedModel{}class DistilBertForSequenceClassification extends DistilBertPreTrainedModel{async _call(o){return new SequenceClassifierOutput(await super._call(o))}}class DistilBertForTokenClassification extends DistilBertPreTrainedModel{async _call(o){return new TokenClassifierOutput(await super._call(o))}}class DistilBertForQuestionAnswering extends DistilBertPreTrainedModel{async _call(o){return new QuestionAnsweringModelOutput(await super._call(o))}}class DistilBertForMaskedLM extends DistilBertPreTrainedModel{async _call(o){return new MaskedLMOutput(await super._call(o))}}class EsmPreTrainedModel extends PreTrainedModel{}class EsmModel extends EsmPreTrainedModel{}class EsmForMaskedLM extends EsmPreTrainedModel{async _call(o){return new MaskedLMOutput(await super._call(o))}}class EsmForSequenceClassification extends EsmPreTrainedModel{async _call(o){return new SequenceClassifierOutput(await super._call(o))}}class EsmForTokenClassification extends EsmPreTrainedModel{async _call(o){return new TokenClassifierOutput(await super._call(o))}}class MobileBertPreTrainedModel extends PreTrainedModel{}class MobileBertModel extends MobileBertPreTrainedModel{}class MobileBertForMaskedLM extends MobileBertPreTrainedModel{async _call(o){return new MaskedLMOutput(await super._call(o))}}class MobileBertForSequenceClassification extends MobileBertPreTrainedModel{async _call(o){return new SequenceClassifierOutput(await super._call(o))}}class MobileBertForQuestionAnswering extends MobileBertPreTrainedModel{async _call(o){return new QuestionAnsweringModelOutput(await super._call(o))}}class MPNetPreTrainedModel extends PreTrainedModel{}class MPNetModel extends MPNetPreTrainedModel{}class MPNetForMaskedLM extends MPNetPreTrainedModel{async _call(o){return new MaskedLMOutput(await super._call(o))}}class MPNetForSequenceClassification extends MPNetPreTrainedModel{async _call(o){return new SequenceClassifierOutput(await super._call(o))}}class MPNetForTokenClassification extends MPNetPreTrainedModel{async _call(o){return new TokenClassifierOutput(await super._call(o))}}class MPNetForQuestionAnswering extends MPNetPreTrainedModel{async _call(o){return new QuestionAnsweringModelOutput(await super._call(o))}}class SqueezeBertPreTrainedModel extends PreTrainedModel{}class SqueezeBertModel extends SqueezeBertPreTrainedModel{}class SqueezeBertForMaskedLM extends SqueezeBertPreTrainedModel{async _call(o){return new MaskedLMOutput(await super._call(o))}}class SqueezeBertForSequenceClassification extends SqueezeBertPreTrainedModel{async _call(o){return new SequenceClassifierOutput(await super._call(o))}}class SqueezeBertForQuestionAnswering extends SqueezeBertPreTrainedModel{async _call(o){return new QuestionAnsweringModelOutput(await super._call(o))}}class AlbertPreTrainedModel extends PreTrainedModel{}class AlbertModel extends AlbertPreTrainedModel{}class AlbertForSequenceClassification extends AlbertPreTrainedModel{async _call(o){return new SequenceClassifierOutput(await super._call(o))}}class AlbertForQuestionAnswering extends AlbertPreTrainedModel{async _call(o){return new QuestionAnsweringModelOutput(await super._call(o))}}class AlbertForMaskedLM extends AlbertPreTrainedModel{async _call(o){return new MaskedLMOutput(await super._call(o))}}class T5PreTrainedModel extends PreTrainedModel{}class T5Model extends T5PreTrainedModel{}class T5ForConditionalGeneration extends T5PreTrainedModel{constructor(o,f,l,d){super(o,f),this.decoder_merged_session=l,this.generation_config=d,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(o,f,l,d){super(o,f),this.decoder_merged_session=l,this.generation_config=d,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(o,f,l,d){super(o,f),this.decoder_merged_session=l,this.generation_config=d,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(o,f,l,d){super(o,f),this.decoder_merged_session=l,this.generation_config=d,this.num_decoder_layers=this.config.decoder_layers,this.num_decoder_heads=this.config.decoder_attention_heads,this.decoder_dim_kv=this.config.d_model/this.num_decoder_heads,this.num_encoder_layers=this.config.encoder_layers,this.num_encoder_heads=this.config.encoder_attention_heads,this.encoder_dim_kv=this.config.d_model/this.num_encoder_heads}}class BartForSequenceClassification extends BartPretrainedModel{async _call(o){return new SequenceClassifierOutput(await super._call(o))}}class MBartPreTrainedModel extends PreTrainedModel{}class MBartModel extends MBartPreTrainedModel{}class MBartForConditionalGeneration extends MBartPreTrainedModel{constructor(o,f,l,d){super(o,f),this.decoder_merged_session=l,this.generation_config=d,this.num_decoder_layers=this.config.decoder_layers,this.num_decoder_heads=this.config.decoder_attention_heads,this.decoder_dim_kv=this.config.d_model/this.num_decoder_heads,this.num_encoder_layers=this.config.encoder_layers,this.num_encoder_heads=this.config.encoder_attention_heads,this.encoder_dim_kv=this.config.d_model/this.num_encoder_heads}}class MBartForSequenceClassification extends MBartPreTrainedModel{async _call(o){return new SequenceClassifierOutput(await super._call(o))}}class MBartForCausalLM extends MBartPreTrainedModel{constructor(o,f,l){super(o,f),this.generation_config=l,this.num_decoder_layers=this.config.decoder_layers,this.num_decoder_heads=this.config.decoder_attention_heads,this.decoder_dim_kv=this.config.d_model/this.num_decoder_heads,this.num_encoder_layers=this.config.encoder_layers,this.num_encoder_heads=this.config.encoder_attention_heads,this.encoder_dim_kv=this.config.d_model/this.num_encoder_heads}}class BlenderbotPreTrainedModel extends PreTrainedModel{}class BlenderbotModel extends BlenderbotPreTrainedModel{}class BlenderbotForConditionalGeneration extends BlenderbotPreTrainedModel{constructor(o,f,l,d){super(o,f),this.decoder_merged_session=l,this.generation_config=d,this.num_decoder_layers=this.config.decoder_layers,this.num_decoder_heads=this.config.decoder_attention_heads,this.decoder_dim_kv=this.config.d_model/this.num_decoder_heads,this.num_encoder_layers=this.config.encoder_layers,this.num_encoder_heads=this.config.encoder_attention_heads,this.encoder_dim_kv=this.config.d_model/this.num_encoder_heads}}class BlenderbotSmallPreTrainedModel extends PreTrainedModel{}class BlenderbotSmallModel extends BlenderbotSmallPreTrainedModel{}class BlenderbotSmallForConditionalGeneration extends BlenderbotSmallPreTrainedModel{constructor(o,f,l,d){super(o,f),this.decoder_merged_session=l,this.generation_config=d,this.num_decoder_layers=this.config.decoder_layers,this.num_decoder_heads=this.config.decoder_attention_heads,this.decoder_dim_kv=this.config.d_model/this.num_decoder_heads,this.num_encoder_layers=this.config.encoder_layers,this.num_encoder_heads=this.config.encoder_attention_heads,this.encoder_dim_kv=this.config.d_model/this.num_encoder_heads}}class RobertaPreTrainedModel extends PreTrainedModel{}class RobertaModel extends RobertaPreTrainedModel{}class RobertaForMaskedLM extends RobertaPreTrainedModel{async _call(o){return new MaskedLMOutput(await super._call(o))}}class RobertaForSequenceClassification extends RobertaPreTrainedModel{async _call(o){return new SequenceClassifierOutput(await super._call(o))}}class RobertaForTokenClassification extends RobertaPreTrainedModel{async _call(o){return new TokenClassifierOutput(await super._call(o))}}class RobertaForQuestionAnswering extends RobertaPreTrainedModel{async _call(o){return new QuestionAnsweringModelOutput(await super._call(o))}}class XLMPreTrainedModel extends PreTrainedModel{}class XLMModel extends XLMPreTrainedModel{}class XLMWithLMHeadModel extends XLMPreTrainedModel{async _call(o){return new MaskedLMOutput(await super._call(o))}}class XLMForSequenceClassification extends XLMPreTrainedModel{async _call(o){return new SequenceClassifierOutput(await super._call(o))}}class XLMForTokenClassification extends XLMPreTrainedModel{async _call(o){return new TokenClassifierOutput(await super._call(o))}}class XLMForQuestionAnswering extends XLMPreTrainedModel{async _call(o){return new QuestionAnsweringModelOutput(await super._call(o))}}class XLMRobertaPreTrainedModel extends PreTrainedModel{}class XLMRobertaModel extends XLMRobertaPreTrainedModel{}class XLMRobertaForMaskedLM extends XLMRobertaPreTrainedModel{async _call(o){return new MaskedLMOutput(await super._call(o))}}class XLMRobertaForSequenceClassification extends XLMRobertaPreTrainedModel{async _call(o){return new SequenceClassifierOutput(await super._call(o))}}class XLMRobertaForTokenClassification extends XLMRobertaPreTrainedModel{async _call(o){return new TokenClassifierOutput(await super._call(o))}}class XLMRobertaForQuestionAnswering extends XLMRobertaPreTrainedModel{async _call(o){return new QuestionAnsweringModelOutput(await super._call(o))}}class ASTPreTrainedModel extends PreTrainedModel{}class ASTModel extends ASTPreTrainedModel{}class ASTForAudioClassification extends ASTPreTrainedModel{}class WhisperPreTrainedModel extends PreTrainedModel{}class WhisperModel extends WhisperPreTrainedModel{}class WhisperForConditionalGeneration extends WhisperPreTrainedModel{constructor(o,f,l,d){super(o,f),Ht(this,"requires_attention_mask",!1),Ht(this,"main_input_name","input_features"),this.decoder_merged_session=l,this.generation_config=d,this.num_decoder_layers=this.config.decoder_layers,this.num_decoder_heads=this.config.decoder_attention_heads,this.decoder_dim_kv=this.config.d_model/this.num_decoder_heads,this.num_encoder_layers=this.config.encoder_layers,this.num_encoder_heads=this.config.encoder_attention_heads,this.encoder_dim_kv=this.config.d_model/this.num_encoder_heads}async generate(o,f=null,l=null){if(f=this._get_generation_config(f),f.return_timestamps??(f.return_timestamps=!1),f.return_timestamps&&(l=[new WhisperTimeStampLogitsProcessor(f)]),f.return_token_timestamps&&(f.output_attentions=!0,f.return_dict_in_generate=!0,f.task==="translate"&&console.warn("Token-level timestamps may not be reliable for task 'translate'."),!f.alignment_heads))throw new Error("Model generation config has no `alignment_heads`, token-level timestamps not available. See https://gist.github.com/hollance/42e32852f24243b748ae6bc1f985b13a on how to add this property to the generation config.");const d=await super.generate(o,f,l);return f.return_token_timestamps&&f.alignment_heads&&(d.token_timestamps=this._extract_token_timestamps(d,f.alignment_heads,f.num_frames)),d}_extract_token_timestamps(o,f,l=null,d=.02){if(!o.cross_attentions)throw new Error("Model outputs must contain cross attentions to extract timestamps. This is most likely because the model was not exported with `output_attentions=True`.");let b=this.config.median_filter_width;b===void 0&&(console.warn("Model config has no `median_filter_width`, using default value of 7."),b=7);const c=o.cross_attentions.map(p=>{let u=Array.from({length:this.config.decoder_layers},(m,y)=>cat(p.map(x=>x[y]),2)),n=stack(f.map(([m,y])=>l?u[m].slice(null,y,null,[0,l]):u[m].slice(null,y)));n=n.transpose(1,0,2,3);let[e,i]=std_mean(n,-2,0,!0),s=n.clone();for(let m=0;m<s.dims[0];++m){let y=s[m];for(let x=0;x<y.dims[0];++x){let T=y[x];const E=e[m][x][0],P=i[m][x][0];for(let O=0;O<T.dims[0];++O){let A=T[O];for(let D=0;D<A.data.length;++D)A.data[D]=(A.data[D]-P.data[D])/E.data[D];A.data.set(medianFilter(A.data,b))}}}return mean(s,1)}),g=[o.sequences.length,o.sequences[0].length],_=new Tensor("float32",new Float32Array(g[0]*g[1]),g);for(let p=0;p<g[0];++p){const u=c[p].neg().squeeze_(0);let[n,e]=dynamicTimeWarping(u),i=Array.from({length:n.length-1},(y,x)=>n[x+1]-n[x]),s=mergeArrays([1],i).map(y=>!!y),m=[];for(let y=0;y<s.length;++y)s[y]&&m.push(e[y]*d);_[p].data.set(m,1)}return _}}class VisionEncoderDecoderModel extends PreTrainedModel{constructor(o,f,l,d){super(o,f),Ht(this,"main_input_name","pixel_values"),this.decoder_merged_session=l,this.generation_config=d;const b=this.config.encoder,c=this.config.decoder,g=b.model_type;(MODEL_MAPPING_NAMES_ENCODER_ONLY.get(g)??MODEL_MAPPING_NAMES_ENCODER_DECODER.get(g))||console.warn(`Model type for encoder '${g}' not found, assuming encoder-only architecture. Please report this at https://github.com/xenova/transformers.js/issues/new/choose.`);const _=MODEL_WITH_LM_HEAD_MAPPING_NAMES.get(c.model_type);if(!_)throw new Error(`Unable to construct \`VisionEncoderDecoder\` due to unsupported decoder: "${this.config.decoder.model_type}"`);const p=_[1],u=new p(c,l,d);this.add_encoder_pkv="num_decoder_layers"in u,this.add_encoder_pkv?(this.num_decoder_layers=u.num_decoder_layers,this.num_decoder_heads=u.num_decoder_heads,this.decoder_dim_kv=u.decoder_dim_kv,this.num_encoder_layers=u.num_encoder_layers,this.num_encoder_heads=u.num_encoder_heads,this.encoder_dim_kv=u.encoder_dim_kv):(this.num_layers=u.num_layers,this.num_heads=u.num_heads,this.dim_kv=u.dim_kv)}}class CLIPPreTrainedModel extends PreTrainedModel{}class CLIPModel extends CLIPPreTrainedModel{}class CLIPTextModelWithProjection extends CLIPPreTrainedModel{static async from_pretrained(o,f={}){return f.model_file_name??(f.model_file_name="text_model"),super.from_pretrained(o,f)}}class CLIPVisionModelWithProjection extends CLIPPreTrainedModel{static async from_pretrained(o,f={}){return f.model_file_name??(f.model_file_name="vision_model"),super.from_pretrained(o,f)}}class SiglipPreTrainedModel extends PreTrainedModel{}class SiglipModel extends SiglipPreTrainedModel{}class SiglipTextModel extends SiglipPreTrainedModel{static async from_pretrained(o,f={}){return f.model_file_name??(f.model_file_name="text_model"),super.from_pretrained(o,f)}}class SiglipVisionModel extends CLIPPreTrainedModel{static async from_pretrained(o,f={}){return f.model_file_name??(f.model_file_name="vision_model"),super.from_pretrained(o,f)}}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(o,f,l){super(o,f),this.generation_config=l,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(o,f,l){super(o,f),this.generation_config=l,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(o,f,l){super(o,f),this.generation_config=l,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(o,f,l){super(o,f),this.generation_config=l,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(o,f,l){super(o,f),this.generation_config=l,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(o,f,l){super(o,f),this.generation_config=l,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(o,f,l){super(o,f),this.generation_config=l,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(o,f,l){super(o,f),this.generation_config=l,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(o,f,l){super(o,f),this.generation_config=l,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(o,f,l){super(o,f),this.generation_config=l,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(o,f,l){super(o,f),this.generation_config=l,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(o,f,l){super(o,f),this.generation_config=l,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(o){return new SequenceClassifierOutput(await super._call(o))}}class FastViTPreTrainedModel extends PreTrainedModel{}class FastViTModel extends FastViTPreTrainedModel{}class FastViTForImageClassification extends FastViTPreTrainedModel{async _call(o){return new SequenceClassifierOutput(await super._call(o))}}class VitMattePreTrainedModel extends PreTrainedModel{}class VitMatteForImageMatting extends VitMattePreTrainedModel{async _call(o){return new ImageMattingOutput(await super._call(o))}}class MobileViTPreTrainedModel extends PreTrainedModel{}class MobileViTModel extends MobileViTPreTrainedModel{}class MobileViTForImageClassification extends MobileViTPreTrainedModel{async _call(o){return new SequenceClassifierOutput(await super._call(o))}}class MobileViTV2PreTrainedModel extends PreTrainedModel{}class MobileViTV2Model extends MobileViTV2PreTrainedModel{}class MobileViTV2ForImageClassification extends MobileViTV2PreTrainedModel{async _call(o){return new SequenceClassifierOutput(await super._call(o))}}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(o){return new SequenceClassifierOutput(await super._call(o))}}class DetrPreTrainedModel extends PreTrainedModel{}class DetrModel extends DetrPreTrainedModel{}class DetrForObjectDetection extends DetrPreTrainedModel{async _call(o){return new DetrObjectDetectionOutput(await super._call(o))}}class DetrForSegmentation extends DetrPreTrainedModel{async _call(o){return new DetrSegmentationOutput(await super._call(o))}}class DetrObjectDetectionOutput extends ModelOutput{constructor({logits:o,pred_boxes:f}){super(),this.logits=o,this.pred_boxes=f}}class DetrSegmentationOutput extends ModelOutput{constructor({logits:o,pred_boxes:f,pred_masks:l}){super(),this.logits=o,this.pred_boxes=f,this.pred_masks=l}}class TableTransformerPreTrainedModel extends PreTrainedModel{}class TableTransformerModel extends TableTransformerPreTrainedModel{}class TableTransformerForObjectDetection extends TableTransformerPreTrainedModel{async _call(o){return new TableTransformerObjectDetectionOutput(await super._call(o))}}class TableTransformerObjectDetectionOutput extends DetrObjectDetectionOutput{}class DeiTPreTrainedModel extends PreTrainedModel{}class DeiTModel extends DeiTPreTrainedModel{}class DeiTForImageClassification extends DeiTPreTrainedModel{async _call(o){return new SequenceClassifierOutput(await super._call(o))}}class ResNetPreTrainedModel extends PreTrainedModel{}class ResNetModel extends ResNetPreTrainedModel{}class ResNetForImageClassification extends ResNetPreTrainedModel{async _call(o){return new SequenceClassifierOutput(await super._call(o))}}class SwinPreTrainedModel extends PreTrainedModel{}class SwinModel extends SwinPreTrainedModel{}class SwinForImageClassification extends SwinPreTrainedModel{async _call(o){return new SequenceClassifierOutput(await super._call(o))}}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(o){return new SequenceClassifierOutput(await super._call(o))}}class ConvNextV2PreTrainedModel extends PreTrainedModel{}class ConvNextV2Model extends ConvNextV2PreTrainedModel{}class ConvNextV2ForImageClassification extends ConvNextV2PreTrainedModel{async _call(o){return new SequenceClassifierOutput(await super._call(o))}}class Dinov2PreTrainedModel extends PreTrainedModel{}class Dinov2Model extends Dinov2PreTrainedModel{}class Dinov2ForImageClassification extends Dinov2PreTrainedModel{async _call(o){return new SequenceClassifierOutput(await super._call(o))}}class YolosPreTrainedModel extends PreTrainedModel{}class YolosModel extends YolosPreTrainedModel{}class YolosForObjectDetection extends YolosPreTrainedModel{async _call(o){return new YolosObjectDetectionOutput(await super._call(o))}}class YolosObjectDetectionOutput extends ModelOutput{constructor({logits:o,pred_boxes:f}){super(),this.logits=o,this.pred_boxes=f}}class SamPreTrainedModel extends PreTrainedModel{}class SamModel extends SamPreTrainedModel{constructor(o,f,l){super(o,f),this.prompt_encoder_mask_decoder=l}async get_image_embeddings({pixel_values:o}){return await encoderForward(this,{pixel_values:o})}async forward(o){if((!o.image_embeddings||!o.image_positional_embeddings)&&(o={...o,...await this.get_image_embeddings(o)}),!o.input_labels){const f=o.input_points.dims.slice(0,-1),l=f.reduce((d,b)=>d*b,1);o.input_labels=new Tensor("int64",new BigInt64Array(l).fill(1n),f)}return await sessionRun(this.prompt_encoder_mask_decoder,{input_points:o.input_points,input_labels:o.input_labels,image_embeddings:o.image_embeddings,image_positional_embeddings:o.image_positional_embeddings})}async _call(o){return new SamImageSegmentationOutput(await super._call(o))}}class SamImageSegmentationOutput extends ModelOutput{constructor({iou_scores:o,pred_masks:f}){super(),this.iou_scores=o,this.pred_masks=f}}class MarianPreTrainedModel extends PreTrainedModel{}class MarianModel extends MarianPreTrainedModel{}class MarianMTModel extends MarianPreTrainedModel{constructor(o,f,l,d){super(o,f),this.decoder_merged_session=l,this.generation_config=d,this.num_decoder_layers=this.config.decoder_layers,this.num_decoder_heads=this.config.decoder_attention_heads,this.decoder_dim_kv=this.config.d_model/this.num_decoder_heads,this.num_encoder_layers=this.config.encoder_layers,this.num_encoder_heads=this.config.encoder_attention_heads,this.encoder_dim_kv=this.config.d_model/this.num_encoder_heads}}class M2M100PreTrainedModel extends PreTrainedModel{}class M2M100Model extends M2M100PreTrainedModel{}class M2M100ForConditionalGeneration extends M2M100PreTrainedModel{constructor(o,f,l,d){super(o,f),this.decoder_merged_session=l,this.generation_config=d,this.num_decoder_layers=this.config.decoder_layers,this.num_decoder_heads=this.config.decoder_attention_heads,this.decoder_dim_kv=this.config.d_model/this.num_decoder_heads,this.num_encoder_layers=this.config.encoder_layers,this.num_encoder_heads=this.config.encoder_attention_heads,this.encoder_dim_kv=this.config.d_model/this.num_encoder_heads}}class Wav2Vec2PreTrainedModel extends PreTrainedModel{}class Wav2Vec2Model extends Wav2Vec2PreTrainedModel{}class Wav2Vec2ForCTC extends Wav2Vec2PreTrainedModel{async _call(o){return new CausalLMOutput(await super._call(o))}}class Wav2Vec2ForSequenceClassification extends Wav2Vec2PreTrainedModel{async _call(o){return new SequenceClassifierOutput(await super._call(o))}}class Wav2Vec2ForAudioFrameClassification extends Wav2Vec2PreTrainedModel{async _call(o){return new TokenClassifierOutput(await super._call(o))}}class UniSpeechPreTrainedModel extends PreTrainedModel{}class UniSpeechModel extends UniSpeechPreTrainedModel{}class UniSpeechForCTC extends UniSpeechPreTrainedModel{async _call(o){return new CausalLMOutput(await super._call(o))}}class UniSpeechForSequenceClassification extends UniSpeechPreTrainedModel{async _call(o){return new SequenceClassifierOutput(await super._call(o))}}class UniSpeechSatPreTrainedModel extends PreTrainedModel{}class UniSpeechSatModel extends UniSpeechSatPreTrainedModel{}class UniSpeechSatForCTC extends UniSpeechSatPreTrainedModel{async _call(o){return new CausalLMOutput(await super._call(o))}}class UniSpeechSatForSequenceClassification extends UniSpeechSatPreTrainedModel{async _call(o){return new SequenceClassifierOutput(await super._call(o))}}class UniSpeechSatForAudioFrameClassification extends UniSpeechSatPreTrainedModel{async _call(o){return new TokenClassifierOutput(await super._call(o))}}class Wav2Vec2BertPreTrainedModel extends PreTrainedModel{}class Wav2Vec2BertModel extends Wav2Vec2BertPreTrainedModel{}class Wav2Vec2BertForCTC extends Wav2Vec2BertPreTrainedModel{async _call(o){return new CausalLMOutput(await super._call(o))}}class Wav2Vec2BertForSequenceClassification extends Wav2Vec2BertPreTrainedModel{async _call(o){return new SequenceClassifierOutput(await super._call(o))}}class HubertModel extends Wav2Vec2PreTrainedModel{}class HubertForCTC extends Wav2Vec2PreTrainedModel{async _call(o){return new CausalLMOutput(await super._call(o))}}class HubertForSequenceClassification extends Wav2Vec2PreTrainedModel{async _call(o){return new SequenceClassifierOutput(await super._call(o))}}class WavLMPreTrainedModel extends PreTrainedModel{}class WavLMModel extends WavLMPreTrainedModel{}class WavLMForCTC extends WavLMPreTrainedModel{async _call(o){return new CausalLMOutput(await super._call(o))}}class WavLMForSequenceClassification extends WavLMPreTrainedModel{async _call(o){return new SequenceClassifierOutput(await super._call(o))}}class WavLMForXVector extends WavLMPreTrainedModel{async _call(o){return new XVectorOutput(await super._call(o))}}class WavLMForAudioFrameClassification extends WavLMPreTrainedModel{async _call(o){return new TokenClassifierOutput(await super._call(o))}}class SpeechT5PreTrainedModel extends PreTrainedModel{}class SpeechT5ForSpeechToText extends SpeechT5PreTrainedModel{}class SpeechT5ForTextToSpeech extends SpeechT5PreTrainedModel{constructor(o,f,l,d){super(o,f),this.decoder_merged_session=l,this.generation_config=d,this.num_decoder_layers=this.config.decoder_layers,this.num_decoder_heads=this.config.decoder_attention_heads,this.decoder_dim_kv=this.config.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(o,f,{threshold:l=.5,minlenratio:d=0,maxlenratio:b=20,vocoder:c=null}={}){const g={input_ids:o},{encoder_outputs:_,encoder_attention_mask:p}=await encoderForward(this,g),u=_.dims[1]/this.config.reduction_factor,n=Math.floor(u*b),e=Math.floor(u*d),i=this.config.num_mel_bins;let s=[],m=null,y=null,x=0;for(;;){++x;const P=boolTensor(!!y);let O;y?O=y.output_sequence_out:O=new Tensor("float32",new Float32Array(i),[1,1,i]);let A={use_cache_branch:P,output_sequence:O,encoder_attention_mask:p,speaker_embeddings:f,encoder_hidden_states:_};this.addPastKeyValues(A,m),y=await sessionRun(this.decoder_merged_session,A),m=this.getPastKeyValues(y,m);const{prob:D,spectrum:I}=y;if(s.push(I),x>=e&&(Array.from(D.data).filter(k=>k>=l).length>0||x>=n))break}const T=cat(s),{waveform:E}=await sessionRun(c.session,{spectrogram:T});return{spectrogram:T,waveform:E}}}class SpeechT5HifiGan extends PreTrainedModel{constructor(){super(...arguments),Ht(this,"main_input_name","spectrogram")}}class TrOCRPreTrainedModel extends PreTrainedModel{constructor(o,f,l){super(o,f),this.generation_config=l,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(o,f,l){super(o,f),this.generation_config=l,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(o,f,l){super(o,f),this.generation_config=l,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(o,f,l){super(o,f),this.generation_config=l,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(o,f={}){return f.model_file_name??(f.model_file_name="text_model"),super.from_pretrained(o,f)}}class ClapAudioModelWithProjection extends ClapPreTrainedModel{static async from_pretrained(o,f={}){return f.model_file_name??(f.model_file_name="audio_model"),super.from_pretrained(o,f)}}class VitsPreTrainedModel extends PreTrainedModel{}class VitsModel extends VitsPreTrainedModel{async _call(o){return new VitsModelOutput(await super._call(o))}}class SegformerPreTrainedModel extends PreTrainedModel{}class SegformerForImageClassification extends SegformerPreTrainedModel{}class SegformerForSemanticSegmentation extends SegformerPreTrainedModel{}class StableLmPreTrainedModel extends PreTrainedModel{constructor(o,f,l){super(o,f),this.generation_config=l,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 EfficientNetPreTrainedModel extends PreTrainedModel{}class EfficientNetModel extends EfficientNetPreTrainedModel{}class EfficientNetForImageClassification extends EfficientNetPreTrainedModel{async _call(o){return new SequenceClassifierOutput(await super._call(o))}}class PretrainedMixin{static async from_pretrained(o,{quantized:f=!0,progress_callback:l=null,config:d=null,cache_dir:b=null,local_files_only:c=!1,revision:g="main",model_file_name:_=null}={}){let p={quantized:f,progress_callback:l,config:d,cache_dir:b,local_files_only:c,revision:g,model_file_name:_};if(d=await AutoConfig.from_pretrained(o,p),p.config||(p.config=d),!this.MODEL_CLASS_MAPPINGS)throw new Error("`MODEL_CLASS_MAPPINGS` not implemented for this type of `AutoClass`: "+this.name);for(let u of this.MODEL_CLASS_MAPPINGS){const n=u.get(d.model_type);if(n)return await n[1].from_pretrained(o,p)}if(this.BASE_IF_FAIL)return console.warn(`Unknown model class "${d.model_type}", attempting to construct from base class.`),await PreTrainedModel.from_pretrained(o,p);throw Error(`Unsupported model type: ${d.model_type}`)}}Ht(PretrainedMixin,"MODEL_CLASS_MAPPINGS",null),Ht(PretrainedMixin,"BASE_IF_FAIL",!1);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]],["fastvit",["FastViTModel",FastViTModel]],["mobilevit",["MobileViTModel",MobileViTModel]],["mobilevitv2",["MobileViTV2Model",MobileViTV2Model]],["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]],["efficientnet",["EfficientNetModel",EfficientNetModel]]]),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]],["fastvit",["FastViTForImageClassification",FastViTForImageClassification]],["mobilevit",["MobileViTForImageClassification",MobileViTForImageClassification]],["mobilevitv2",["MobileViTV2ForImageClassification",MobileViTV2ForImageClassification]],["beit",["BeitForImageClassification",BeitForImageClassification]],["deit",["DeiTForImageClassification",DeiTForImageClassification]],["convnext",["ConvNextForImageClassification",ConvNextForImageClassification]],["convnextv2",["ConvNextV2ForImageClassification",ConvNextV2ForImageClassification]],["dinov2",["Dinov2ForImageClassification",Dinov2ForImageClassification]],["resnet",["ResNetForImageClassification",ResNetForImageClassification]],["swin",["SwinForImageClassification",SwinForImageClassification]],["segformer",["SegformerForImageClassification",SegformerForImageClassification]],["efficientnet",["EfficientNetForImageClassification",EfficientNetForImageClassification]]]),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_FOR_IMAGE_FEATURE_EXTRACTION_MAPPING_NAMES=new Map([["clip",["CLIPVisionModelWithProjection",CLIPVisionModelWithProjection]],["siglip",["SiglipVisionModel",SiglipVisionModel]]]),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],[MODEL_FOR_IMAGE_FEATURE_EXTRACTION_MAPPING_NAMES,MODEL_TYPES.EncoderOnly]];for(const[v,o]of MODEL_CLASS_TYPE_MAPPING)for(const[f,l]of v.values())MODEL_TYPE_MAPPING.set(f,o),MODEL_CLASS_TO_NAME_MAPPING.set(l,f),MODEL_NAME_TO_CLASS_MAPPING.set(f,l);const CUSTOM_MAPPING=[["CLIPTextModelWithProjection",CLIPTextModelWithProjection,MODEL_TYPES.EncoderOnly],["SiglipTextModel",SiglipTextModel,MODEL_TYPES.EncoderOnly],["ClapTextModelWithProjection",ClapTextModelWithProjection,MODEL_TYPES.EncoderOnly],["ClapAudioModelWithProjection",ClapAudioModelWithProjection,MODEL_TYPES.EncoderOnly]];for(const[v,o,f]of CUSTOM_MAPPING)MODEL_TYPE_MAPPING.set(v,f),MODEL_CLASS_TO_NAME_MAPPING.set(o,v),MODEL_NAME_TO_CLASS_MAPPING.set(v,o);class AutoModel extends PretrainedMixin{}Ht(AutoModel,"MODEL_CLASS_MAPPINGS",MODEL_CLASS_TYPE_MAPPING.map(v=>v[0])),Ht(AutoModel,"BASE_IF_FAIL",!0);class Seq2SeqLMOutput extends ModelOutput{constructor({logits:o,past_key_values:f,encoder_outputs:l,decoder_attentions:d=null,cross_attentions:b=null}){super(),this.logits=o,this.past_key_values=f,this.encoder_outputs=l,this.decoder_attentions=d,this.cross_attentions=b}}class SequenceClassifierOutput extends ModelOutput{constructor({logits:o}){super(),this.logits=o}}class XVectorOutput extends ModelOutput{constructor({logits:o,embeddings:f}){super(),this.logits=o,this.embeddings=f}}class TokenClassifierOutput extends ModelOutput{constructor({logits:o}){super(),this.logits=o}}class MaskedLMOutput extends ModelOutput{constructor({logits:o}){super(),this.logits=o}}class QuestionAnsweringModelOutput extends ModelOutput{constructor({start_logits:o,end_logits:f}){super(),this.start_logits=o,this.end_logits=f}}class CausalLMOutput extends ModelOutput{constructor({logits:o}){super(),this.logits=o}}class ImageMattingOutput extends ModelOutput{constructor({alphas:o}){super(),this.alphas=o}}class VitsModelOutput extends ModelOutput{constructor({waveform:o,spectrogram:f}){super(),this.waveform=o,this.spectrogram=f}}const BROWSER_ENV=typeof self<"u";if(!BROWSER_ENV&&!sharp)throw new Error("Unable to load image processing library.");function hanning(v){if(v<1)return new Float64Array;if(v===1)return new Float64Array([1]);const o=v-1,f=Math.PI/o,l=new Float64Array(v);for(let d=0;d<v;++d){const b=2*d-o;l[d]=.5+.5*Math.cos(f*b)}return l}const HERTZ_TO_MEL_MAPPING={htk:v=>2595*Math.log10(1+v/700),kaldi:v=>1127*Math.log(1+v/700),slaney:(v,o=1e3,f=15,l=27/Math.log(6.4))=>v>=o?f+Math.log(v/o)*l:3*v/200};function hertz_to_mel(v,o="htk"){const f=HERTZ_TO_MEL_MAPPING[o];if(!f)throw new Error('mel_scale should be one of "htk", "slaney" or "kaldi".');return typeof v=="number"?f(v):v.map(l=>f(l))}const MEL_TO_HERTZ_MAPPING={htk:v=>700*(10**(v/2595)-1),kaldi:v=>700*(Math.exp(v/1127)-1),slaney:(v,o=1e3,f=15,l=Math.log(6.4)/27)=>v>=f?o*Math.exp(l*(v-f)):200*v/3};function mel_to_hertz(v,o="htk"){const f=MEL_TO_HERTZ_MAPPING[o];if(!f)throw new Error('mel_scale should be one of "htk", "slaney" or "kaldi".');return typeof v=="number"?f(v):v.map(l=>f(l))}function _create_triangular_filter_bank(v,o){const f=Float64Array.from({length:o.length-1},(c,g)=>o[g+1]-o[g]),l=Array.from({length:v.length},()=>new Array(o.length));for(let c=0;c<v.length;++c){const g=l[c];for(let _=0;_<o.length;++_)g[_]=o[_]-v[c]}const d=o.length-2,b=Array.from({length:d},()=>new Array(v.length));for(let c=0;c<v.length;++c){const g=l[c];for(let _=0;_<d;++_){const p=-g[_]/f[_],u=g[_+2]/f[_+1];b[_][c]=Math.max(0,Math.min(p,u))}}return b}function linspace(v,o,f){const l=(o-v)/(f-1);return Float64Array.from({length:f},(d,b)=>v+l*b)}function mel_filter_bank(v,o,f,l,d,b=null,c="htk",g=!1){if(b!==null&&b!=="slaney")throw new Error('norm must be one of null or "slaney"');const _=hertz_to_mel(f,c),p=hertz_to_mel(l,c),u=linspace(_,p,o+2);let n=mel_to_hertz(u,c),e;if(g){const s=d/(v*2);e=hertz_to_mel(Float64Array.from({length:v},(m,y)=>y*s),c),n=u}else e=linspace(0,Math.floor(d/2),v);const i=_create_triangular_filter_bank(e,n);if(b!==null&&b==="slaney")for(let s=0;s<o;++s){const m=i[s],y=2/(n[s+2]-n[s]);for(let x=0;x<v;++x)m[x]*=y}return i}function padReflect(v,o,f){const l=new v.constructor(v.length+o+f),d=v.length-1;for(let b=0;b<v.length;++b)l[o+b]=v[b];for(let b=1;b<=o;++b)l[o-b]=v[calculateReflectOffset(b,d)];for(let b=1;b<=f;++b)l[d+o+b]=v[calculateReflectOffset(d-b,d)];return l}function _db_conversion_helper(v,o,f,l,d){if(f<=0)throw new Error("reference must be greater than zero");if(l<=0)throw new Error("min_value must be greater than zero");f=Math.max(l,f);const b=Math.log10(f);for(let c=0;c<v.length;++c)v[c]=o*Math.log10(Math.max(l,v[c])-b);if(d!==null){if(d<=0)throw new Error("db_range must be greater than zero");const c=max(v)[0]-d;for(let g=0;g<v.length;++g)v[g]=Math.max(v[g],c)}return v}function amplitude_to_db(v,o=1,f=1e-5,l=null){return _db_conversion_helper(v,20,o,f,l)}function power_to_db(v,o=1,f=1e-10,l=null){return _db_conversion_helper(v,10,o,f,l)}function spectrogram(v,o,f,l,{fft_length:d=null,power:b=1,center:c=!0,pad_mode:g="reflect",onesided:_=!0,preemphasis:p=null,mel_filters:u=null,mel_floor:n=1e-10,log_mel:e=null,reference:i=1,min_value:s=1e-10,db_range:m=null,remove_dc_offset:y=null,max_num_frames:x=null,do_pad:T=!0,transpose:E=!1}={}){const P=o.length;if(d===null&&(d=f),f>d)throw Error(`frame_length (${f}) may not be larger than fft_length (${d})`);if(P!==f)throw new Error(`Length of the window (${P}) must equal frame_length (${f})`);if(l<=0)throw new Error("hop_length must be greater than zero");if(b===null&&u!==null)throw new Error("You have provided `mel_filters` but `power` is `None`. Mel spectrogram computation is not yet supported for complex-valued spectrogram. Specify `power` to fix this issue.");if(c){if(g!=="reflect")throw new Error(`pad_mode="${g}" not implemented yet.`);const F=Math.floor((d-1)/2)+1;v=padReflect(v,F,F)}const O=Math.floor(1+Math.floor((v.length-f)/l)),A=_?Math.floor(d/2)+1:d;let D=O,I=O;x!==null&&(x>O?T&&(I=x):I=D=x);const k=new FFT(d),$=new Float64Array(d),W=new Float64Array(k.outputBufferSize),X=new Array(D);for(let F=0;F<D;++F){const z=F*l;for(let ne=0;ne<f;++ne)$[ne]=v[z+ne];if(y){let ne=0;for(let Q=0;Q<f;++Q)ne+=$[Q];const B=ne/f;for(let Q=0;Q<f;++Q)$[Q]-=B}if(p!==null){for(let ne=f-1;ne>=1;--ne)$[ne]-=p*$[ne-1];$[0]*=1-p}for(let ne=0;ne<o.length;++ne)$[ne]*=o[ne];k.realTransform(W,$);const fe=new Array(A);for(let ne=0;ne<fe.length;++ne){const B=ne<<1;fe[ne]=W[B]**2+W[B+1]**2}X[F]=fe}if(b!==null&&b!==2){const F=2/b;for(let z=0;z<X.length;++z){const fe=X[z];for(let ne=0;ne<fe.length;++ne)fe[ne]**=F}}const K=u.length,q=new Float32Array(K*I),se=E?[I,K]:[K,I];for(let F=0;F<K;++F){const z=u[F];for(let fe=0;fe<D;++fe){const ne=X[fe];let B=0;for(let Q=0;Q<A;++Q)B+=z[Q]*ne[Q];q[E?fe*K+F:F*D+fe]=Math.max(n,B)}}if(b!==null&&e!==null){const F=Math.min(q.length,D*K);switch(e){case"log":for(let z=0;z<F;++z)q[z]=Math.log(q[z]);break;case"log10":for(let z=0;z<F;++z)q[z]=Math.log10(q[z]);break;case"dB":if(b===1)amplitude_to_db(q,i,s,m);else if(b===2)power_to_db(q,i,s,m);else throw new Error(`Cannot use log_mel option '${e}' with power ${b}`);break;default:throw new Error(`log_mel must be one of null, 'log', 'log10' or 'dB'. Got '${e}'`)}}return{data:q,dims:se}}function window_function(v,o,{periodic:f=!0,frame_length:l=null,center:d=!0}={}){const b=f?v+1:v;let c;switch(o){case"boxcar":c=new Float64Array(b).fill(1);break;case"hann":case"hann_window":c=hanning(b);break;case"povey":c=hanning(b).map(g=>Math.pow(g,.85));break;default:throw new Error(`Unknown window type ${o}.`)}if(f&&(c=c.subarray(0,v)),l===null)return c;if(v>l)throw new Error(`Length of the window (${v}) may not be larger than frame_length (${l})`);return c}function center_to_corners_format([v,o,f,l]){return[v-f/2,o-l/2,v+f/2,o+l/2]}function post_process_object_detection(v,o=.5,f=null,l=!1){const d=v.logits,b=v.pred_boxes,[c,g,_]=d.dims;if(f!==null&&f.length!==c)throw Error("Make sure that you pass in as many target sizes as the batch dimension of the logits");let p=[];for(let u=0;u<c;++u){let n=f!==null?f[u]:null,e={boxes:[],classes:[],scores:[]},i=d[u],s=b[u];for(let m=0;m<g;++m){let y=i[m],x=[],T;if(l){T=y.sigmoid().data;for(let E=0;E<T.length;++E)T[E]>o&&x.push(E)}else{let E=max(y.data)[1];if(E===_-1)continue;x.push(E),T=softmax(y.data)}for(const E of x){let P=s[m].data;P=center_to_corners_format(P),n!==null&&(P=P.map((O,A)=>O*n[(A+1)%2])),e.boxes.push(P),e.classes.push(E),e.scores.push(T[E])}}p.push(e)}return p}function validate_audio_inputs(v,o){var f;if(!(v instanceof Float32Array||v instanceof Float64Array))throw new Error(`${o} expects input to be a Float32Array or a Float64Array, but got ${((f=v==null?void 0:v.constructor)==null?void 0:f.name)??typeof v} 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(v,o,f=0,l=null){const d=v/o;let b=bankers_round(d)*o;return l!==null&&b>l&&(b=Math.floor(d)*o),b<f&&(b=Math.ceil(d)*o),b}function enforce_size_divisibility([v,o],f){return[Math.max(Math.floor(v/f),1)*f,Math.max(Math.floor(o/f),1)*f]}class FeatureExtractor extends Callable{constructor(o){super(),this.config=o}}class ImageFeatureExtractor extends FeatureExtractor{constructor(o){super(o),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),this.do_flip_channel_order=this.config.do_flip_channel_order??!1}async thumbnail(o,f,l=2){const d=o.height,b=o.width,c=f.height,g=f.width;let _=Math.min(d,c),p=Math.min(b,g);return _===d&&p===b?o:(d>b?p=Math.floor(b*_/d):b>d&&(_=Math.floor(d*p/b)),await o.resize(p,_,{resample:l}))}async crop_margin(o,f=200){const l=o.clone().grayscale(),d=min(l.data)[0],b=max(l.data)[0]-d;if(b===0)return o;const c=f/255;let g=l.width,_=l.height,p=0,u=0;for(let n=0;n<l.height;++n){const e=n*l.width;for(let i=0;i<l.width;++i)(l.data[e+i]-d)/b<c&&(g=Math.min(g,i),_=Math.min(_,n),p=Math.max(p,i),u=Math.max(u,n))}return o=await o.crop([g,_,p,u]),o}pad_image(o,f,l,{mode:d="constant",center:b=!1,constant_values:c=0}={}){const[g,_,p]=f;let u,n;if(typeof l=="number"?(u=l,n=l):(u=l.width,n=l.height),u!==_||n!==g){const e=new Float32Array(u*n*p);if(Array.isArray(c))for(let m=0;m<e.length;++m)e[m]=c[m%p];else c!==0&&e.fill(c);const[i,s]=b?[Math.floor((u-_)/2),Math.floor((n-g)/2)]:[0,0];for(let m=0;m<g;++m){const y=(m+s)*u,x=m*_;for(let T=0;T<_;++T){const E=(y+T+i)*p,P=(x+T)*p;for(let O=0;O<p;++O)e[E+O]=o[P+O]}}if(d==="symmetric"){if(b)throw new Error("`center` padding is not supported when `mode` is set to `symmetric`.");const m=g-1,y=_-1;for(let x=0;x<n;++x){const T=x*u,E=calculateReflectOffset(x,m)*_;for(let P=0;P<u;++P){if(x<g&&P<_)continue;const O=(T+P)*p,A=(E+calculateReflectOffset(P,y))*p;for(let D=0;D<p;++D)e[O+D]=o[A+D]}}}o=e,f=[n,u,p]}return[o,f]}rescale(o){for(let f=0;f<o.length;++f)o[f]=this.rescale_factor*o[f]}get_resize_output_image_size(o,f){const[l,d]=o.size;let b,c;if(this.do_thumbnail){const{height:g,width:_}=f;b=Math.min(g,_)}else Number.isInteger(f)?(b=f,c=this.config.max_size??b):f!==void 0&&(b=f.shortest_edge,c=f.longest_edge);if(b!==void 0||c!==void 0){const g=b===void 0?1:Math.max(b/l,b/d),_=l*g,p=d*g,u=c===void 0?1:Math.min(c/_,c/p);let n=Math.floor(Number((_*u).toFixed(2))),e=Math.floor(Number((p*u).toFixed(2)));return this.size_divisibility!==void 0&&([n,e]=enforce_size_divisibility([n,e],this.size_divisibility)),[n,e]}else if(f!==void 0&&f.width!==void 0&&f.height!==void 0){let g=f.width,_=f.height;if(this.config.keep_aspect_ratio&&this.config.ensure_multiple_of){let p=_/d,u=g/l;Math.abs(1-u)<Math.abs(1-p)?p=u:u=p,_=constraint_to_multiple_of(p*d,this.config.ensure_multiple_of),g=constraint_to_multiple_of(u*l,this.config.ensure_multiple_of)}return[g,_]}else{if(this.size_divisibility!==void 0)return enforce_size_divisibility([l,d],this.size_divisibility);throw new Error(`Could not resize image due to unsupported \`this.size\` option in config: ${JSON.stringify(f)}`)}}async resize(o){const[f,l]=this.get_resize_output_image_size(o,this.size);return await o.resize(f,l,{resample:this.resample})}async preprocess(o,{do_normalize:f=null,do_pad:l=null,do_convert_rgb:d=null,do_convert_grayscale:b=null,do_flip_channel_order:c=null}={}){this.do_crop_margin&&(o=await this.crop_margin(o));const[g,_]=o.size;if(d??this.do_convert_rgb?o=o.rgb():b&&(o=o.grayscale()),this.do_resize&&(o=await this.resize(o)),this.do_thumbnail&&(o=await this.thumbnail(o,this.size,this.resample)),this.do_center_crop){let i,s;Number.isInteger(this.crop_size)?(i=this.crop_size,s=this.crop_size):(i=this.crop_size.width,s=this.crop_size.height),o=await o.center_crop(i,s)}const p=[o.height,o.width];let u=Float32Array.from(o.data),n=[o.height,o.width,o.channels];if(this.do_rescale&&this.rescale(u),f??this.do_normalize){let i=this.image_mean;Array.isArray(this.image_mean)||(i=new Array(o.channels).fill(i));let s=this.image_std;if(Array.isArray(this.image_std)||(s=new Array(o.channels).fill(i)),i.length!==o.channels||s.length!==o.channels)throw new Error(`When set to arrays, the length of \`image_mean\` (${i.length}) and \`image_std\` (${s.length}) must match the number of channels in the image (${o.channels}).`);for(let m=0;m<u.length;m+=o.channels)for(let y=0;y<o.channels;++y)u[m+y]=(u[m+y]-i[y])/s[y]}if(l??this.do_pad){if(this.pad_size)[u,n]=this.pad_image(u,[o.height,o.width,o.channels],this.pad_size);else if(this.size_divisibility){const[i,s]=enforce_size_divisibility([n[1],n[0]],this.size_divisibility);[u,n]=this.pad_image(u,n,{width:i,height:s})}}if(c??this.do_flip_channel_order){if(n[2]!==3)throw new Error("Flipping channel order is only supported for RGB images.");for(let i=0;i<u.length;i+=3){const s=u[i];u[i]=u[i+2],u[i+2]=s}}const e=new Tensor("float32",u,n).permute(2,0,1);return{original_size:[_,g],reshaped_input_size:p,pixel_values:e}}async _call(o,...f){Array.isArray(o)||(o=[o]);const l=await Promise.all(o.map(d=>this.preprocess(d)));return{pixel_values:stack(l.map(d=>d.pixel_values),0),original_sizes:l.map(d=>d.original_size),reshaped_input_sizes:l.map(d=>d.reshaped_input_size)}}}class SegformerFeatureExtractor extends ImageFeatureExtractor{post_process_semantic_segmentation(o,f=null){const l=o.logits,d=l.dims[0];if(f!==null&&f.length!==d)throw Error("Make sure that you pass in as many target sizes as the batch dimension of the logits");const b=[];for(let c=0;c<d;++c){const g=f!==null?f[c]:null;let _=l[c];g!==null&&(_=interpolate(_,g,"bilinear",!1));const[p,u]=g??_.dims.slice(-2),n=new Tensor("int32",new Int32Array(p*u),[p,u]),e=_[0].data;for(let y=1;y<_.dims[0];++y){const x=_[y].data;for(let T=0;T<x.length;++T)x[T]>e[T]&&(e[T]=x[T],n.data[T]=y)}const i=new Array(_.dims[0]),s=n.data;for(let y=0;y<s.length;++y){const x=s[y];i[x]=x}const m=i.filter(y=>y!==void 0);b.push({segmentation:n,labels:m})}return b}}class DPTFeatureExtractor extends ImageFeatureExtractor{}class DPTImageProcessor extends DPTFeatureExtractor{}class BitImageProcessor extends ImageFeatureExtractor{}class GLPNFeatureExtractor extends ImageFeatureExtractor{}class CLIPFeatureExtractor extends ImageFeatureExtractor{}class ChineseCLIPFeatureExtractor extends ImageFeatureExtractor{}class SiglipImageProcessor extends ImageFeatureExtractor{}class ConvNextFeatureExtractor extends ImageFeatureExtractor{constructor(o){super(o),this.crop_pct=this.config.crop_pct??224/256}async resize(o){var f;const l=(f=this.size)==null?void 0:f.shortest_edge;if(l===void 0)throw new Error("Size dictionary must contain 'shortest_edge' key.");if(l<384){const d=Math.floor(l/this.crop_pct),[b,c]=this.get_resize_output_image_size(o,{shortest_edge:d});o=await o.resize(b,c,{resample:this.resample}),o=await o.center_crop(l,l)}else o=await o.resize(l,l,{resample:this.resample});return o}}class ConvNextImageProcessor extends ConvNextFeatureExtractor{}class ViTFeatureExtractor extends ImageFeatureExtractor{}class ViTImageProcessor extends ImageFeatureExtractor{}class EfficientNetImageProcessor extends ImageFeatureExtractor{constructor(o){super(o),this.include_top=this.config.include_top??!0,this.include_top&&(this.image_std=this.image_std.map(f=>f*f))}}class MobileViTFeatureExtractor extends ImageFeatureExtractor{}class MobileViTImageProcessor extends MobileViTFeatureExtractor{}class OwlViTFeatureExtractor extends ImageFeatureExtractor{post_process_object_detection(...o){return post_process_object_detection(...o)}}class Owlv2ImageProcessor extends OwlViTFeatureExtractor{}class DeiTFeatureExtractor extends ImageFeatureExtractor{}class BeitFeatureExtractor extends ImageFeatureExtractor{}class DonutFeatureExtractor extends ImageFeatureExtractor{pad_image(o,f,l,d={}){const[b,c,g]=f;let _=this.image_mean;Array.isArray(this.image_mean)||(_=new Array(g).fill(_));let p=this.image_std;Array.isArray(p)||(p=new Array(g).fill(_));const u=_.map((n,e)=>-n/p[e]);return super.pad_image(o,f,l,{center:!0,constant_values:u,...d})}}class NougatImageProcessor extends DonutFeatureExtractor{}class DetrFeatureExtractor extends ImageFeatureExtractor{async _call(o){const f=await super._call(o),l=[f.pixel_values.dims[0],64,64],d=new Tensor("int64",new BigInt64Array(l.reduce((b,c)=>b*c)).fill(1n),l);return{...f,pixel_mask:d}}post_process_object_detection(...o){return post_process_object_detection(...o)}remove_low_and_no_objects(o,f,l,d){let b=[],c=[],g=[];for(let _=0;_<o.dims[0];++_){let p=o[_],u=f[_],n=max(p.data)[1];if(n===d)continue;let e=softmax(p.data)[n];e>l&&(b.push(u),c.push(e),g.push(n))}return[b,c,g]}check_segment_validity(o,f,l,d=.5,b=.8){let c=[],g=0,_=0;for(let u=0;u<o.length;++u)o[u]===l&&(c.push(u),++g),f[l].data[u]>=d&&++_;let p=g>0&&_>0;return p&&(p=g/_>b),[p,c]}compute_segments(o,f,l,d,b,c=null,g=null){let[_,p]=g??o[0].dims,u=new Tensor("int32",new Int32Array(_*p),[_,p]),n=[];if(g!==null)for(let m=0;m<o.length;++m)o[m]=interpolate(o[m],g,"bilinear",!1);let e=new Int32Array(o[0].data.length),i=new Float32Array(o[0].data.length);for(let m=0;m<o.length;++m){let y=f[m];for(let x=0;x<o[m].data.length;++x)o[m].data[x]*=y,o[m].data[x]>i[x]&&(e[x]=m,i[x]=o[m].data[x])}let s=0;for(let m=0;m<l.length;++m){let y=l[m],[x,T]=this.check_segment_validity(e,o,m,d,b);if(x){++s;for(let E of T)u.data[E]=s;n.push({id:s,label_id:y,score:f[m]})}}return[u,n]}post_process_panoptic_segmentation(o,f=.5,l=.5,d=.8,b=null,c=null){b===null&&(console.warn("`label_ids_to_fuse` unset. No instance will be fused."),b=new Set);const g=o.logits,_=o.pred_masks.sigmoid();let[p,u,n]=g.dims;if(n-=1,c!==null&&c.length!==p)throw Error("Make sure that you pass in as many target sizes as the batch dimension of the logits");let e=[];for(let i=0;i<p;++i){let s=c!==null?c[i]:null,m=g[i],y=_[i],[x,T,E]=this.remove_low_and_no_objects(m,y,f,n);if(E.length===0){let[A,D]=s??y.dims.slice(-2),I=new Tensor("int32",new Int32Array(A*D).fill(-1),[A,D]);e.push({segmentation:I,segments_info:[]});continue}let[P,O]=this.compute_segments(x,T,E,l,d,b,s);e.push({segmentation:P,segments_info:O})}return e}post_process_instance_segmentation(){throw Error("Not implemented yet")}}class YolosFeatureExtractor extends ImageFeatureExtractor{post_process_object_detection(...o){return post_process_object_detection(...o)}}class SamImageProcessor extends ImageFeatureExtractor{reshape_input_points(o,f,l){o=structuredClone(o);let d=calculateDimensions(o);if(d.length===3)d=[1,...d],o=[o];else if(d.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 b=0;b<o.length;++b){let c=f[b],g=l[b],_=[g[0]/c[0],g[1]/c[1]];for(let p=0;p<o[b].length;++p)for(let u=0;u<o[b][p].length;++u)for(let n=0;n<o[b][p][u].length;++n)o[b][p][u][n]*=_[n]}return new Tensor("float32",Float32Array.from(o.flat(1/0)),d)}add_input_labels(o,f){let l=calculateDimensions(o);if(l.length===2)l=[1,...l],o=[o];else if(l.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(l.some((d,b)=>d!==f.dims[b]))throw Error(`The first ${l.length} dimensions of 'input_points' and 'input_labels' must be the same.`);return new Tensor("int64",o.flat(1/0).map(BigInt),l)}async _call(o,f=null,l=null){const d=await super._call(o);if(f&&(d.input_points=this.reshape_input_points(f,d.original_sizes,d.reshaped_input_sizes)),l){if(!d.input_points)throw Error("`input_points` must be provided if `input_labels` are provided.");d.input_labels=this.add_input_labels(l,d.input_points)}return d}post_process_masks(o,f,l,{mask_threshold:d=0,binarize:b=!0,pad_size:c=null}={}){const g=[];c=c??this.pad_size;const _=[c.height,c.width];for(let p=0;p<f.length;++p){const u=f[p],n=l[p],e=o[p],i=[];for(let s=0;s<e.dims[0];++s){const m=e[s];let y=interpolate(m,_,"bilinear",!1);if(y=y.slice(null,[0,n[0]],[0,n[1]]),y=interpolate(y,u,"bilinear",!1),b){const x=new Uint8Array(y.data.length);for(let T=0;T<y.data.length;++T)y.data[T]>d&&(x[T]=1);y=new Tensor("bool",x,y.dims)}i.push(y)}g.push(stack(i))}return g}}class Swin2SRImageProcessor extends ImageFeatureExtractor{pad_image(o,f,l,d={}){const[b,c,g]=f;return super.pad_image(o,f,{width:c+(l-c%l)%l,height:b+(l-b%l)%l},{mode:"symmetric",center:!1,constant_values:-1,...d})}}class VitMatteImageProcessor extends ImageFeatureExtractor{async _call(o,f){Array.isArray(o)||(o=[o]),Array.isArray(f)||(f=[f]);const l=await Promise.all(o.map(b=>this.preprocess(b))),d=await Promise.all(f.map(b=>this.preprocess(b,{do_normalize:!1,do_convert_rgb:!1,do_convert_grayscale:!0})));return{pixel_values:stack(l.map((b,c)=>cat([b.pixel_values,d[c].pixel_values],0)),0),original_sizes:l.map(b=>b.original_size),reshaped_input_sizes:l.map(b=>b.reshaped_input_size)}}}class WhisperFeatureExtractor extends FeatureExtractor{constructor(o){var f;super(o),(f=this.config).mel_filters??(f.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(o){const{data:f,dims:l}=spectrogram(o,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}),d=max(f)[0];for(let b=0;b<f.length;++b)f[b]=(Math.max(f[b],d-8)+4)/4;return{data:f,dims:l}}async _call(o){validate_audio_inputs(o,"WhisperFeatureExtractor");let f;o.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`."),f=o.slice(0,this.config.n_samples)):(f=new Float32Array(this.config.n_samples),f.set(o));const{data:l,dims:d}=this._extract_fbank_features(f);return{input_features:new Tensor("float32",l,[1,...d])}}}class Wav2Vec2FeatureExtractor extends FeatureExtractor{_zero_mean_unit_var_norm(o){const f=o.reduce((d,b)=>d+b,0)/o.length,l=o.reduce((d,b)=>d+(b-f)**2,0)/o.length;return o.map(d=>(d-f)/Math.sqrt(l+1e-7))}async _call(o){validate_audio_inputs(o,"Wav2Vec2FeatureExtractor"),o instanceof Float64Array&&(o=new Float32Array(o));let f=o;this.config.do_normalize&&(f=this._zero_mean_unit_var_norm(f));const l=[1,f.length];return{input_values:new Tensor("float32",f,l),attention_mask:new Tensor("int64",new BigInt64Array(f.length).fill(1n),l)}}}class SeamlessM4TFeatureExtractor extends FeatureExtractor{constructor(o){super(o);const f=this.config.sampling_rate,l=mel_filter_bank(256,this.config.num_mel_bins,20,Math.floor(f/2),f,null,"kaldi",!0);for(let d=0;d<l.length;++d)l[d].push(0);this.mel_filters=l,this.window=window_function(400,"povey",{periodic:!1})}_extract_fbank_features(o,f){return o=o.map(l=>l*32768),spectrogram(o,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:f,transpose:!0})}async _call(o,{padding:f=!0,pad_to_multiple_of:l=2,do_normalize_per_mel_bins:d=!0,return_attention_mask:b=!0}={}){validate_audio_inputs(o,"SeamlessM4TFeatureExtractor");let c=this._extract_fbank_features(o,this.config.max_length);if(d){const[i,s]=c.dims;for(let m=0;m<s;++m){let y=0;for(let P=0;P<i;++P)y+=c.data[P*s+m];const x=y/i;let T=0;for(let P=0;P<i;++P)T+=(c.data[P*s+m]-x)**2;T/=i-1;const E=Math.sqrt(T+1e-7);for(let P=0;P<i;++P){const O=P*s+m;c.data[O]=(c.data[O]-x)/E}}}let g;if(f){const[i,s]=c.dims,m=i%l;if(m>0){const y=new Float32Array(s*(i+m));y.set(c.data),y.fill(this.config.padding_value,c.data.length);const x=i+m;c={data:y,dims:[x,s]},b&&(g=new Tensor("int64",new BigInt64Array(x),[1,x]),g.data.fill(1n,0,i))}}const[_,p]=c.dims,u=this.config.stride;if(_%u!==0)throw new Error(`The number of frames (${_}) must be a multiple of the stride (${u}).`);const n=new Tensor("float32",c.data,c.dims).view(1,Math.floor(_/u),p*u),e={input_features:n};if(b){const i=n.dims[1],s=new Tensor("int64",new BigInt64Array(i),[1,i]);if(g)for(let m=1,y=0;m<_;m+=u,++y)s.data[y]=g.data[m];else s.data.fill(1n);e.attention_mask=s}return e}}class ASTFeatureExtractor extends FeatureExtractor{constructor(o){super(o);const f=this.config.sampling_rate,l=mel_filter_bank(256,this.config.num_mel_bins,20,Math.floor(f/2),f,null,"kaldi",!0);for(let d=0;d<l.length;++d)l[d].push(0);this.mel_filters=l,this.window=window_function(400,"hann",{periodic:!1}),this.mean=this.config.mean,this.std=this.config.std}_extract_fbank_features(o,f){return spectrogram(o,this.window,400,160,{fft_length:512,power:2,center:!1,preemphasis:.97,mel_filters:this.mel_filters,log_mel:"log",mel_floor:1192092955078125e-22,remove_dc_offset:!0,max_num_frames:f,transpose:!0})}async _call(o){validate_audio_inputs(o,"ASTFeatureExtractor");const f=this._extract_fbank_features(o,this.config.max_length);if(this.config.do_normalize){const l=this.std*2;for(let d=0;d<f.data.length;++d)f.data[d]=(f.data[d]-this.mean)/l}return{input_values:new Tensor("float32",f.data,[1,...f.dims])}}}class ClapFeatureExtractor extends FeatureExtractor{constructor(o){super(o),this.mel_filters=mel_filter_bank(this.config.nb_frequency_bins,this.config.feature_size,this.config.frequency_min,this.config.frequency_max,this.config.sampling_rate,null,"htk"),this.mel_filters_slaney=mel_filter_bank(this.config.nb_frequency_bins,this.config.feature_size,this.config.frequency_min,this.config.frequency_max,this.config.sampling_rate,"slaney","slaney"),this.window=window_function(this.config.fft_window_size,"hann")}_get_input_mel(o,f,l,d){let b,c=!1;const g=o.length-f;if(g>0)if(l==="rand_trunc"){c=!0;const _=Math.floor(Math.random()*(g+1));o=o.subarray(_,_+f),b=this._extract_fbank_features(o,this.mel_filters_slaney,this.config.nb_max_samples),b.dims=[1,...b.dims]}else throw new Error(`Truncation strategy "${l}" not implemented`);else{if(g<0){let _=new Float64Array(f);if(_.set(o),d==="repeat")for(let p=o.length;p<f;p+=o.length)_.set(o.subarray(0,Math.min(o.length,f-p)),p);else if(d==="repeatpad")for(let p=o.length;p<-g;p+=o.length)_.set(o,p);o=_}if(l==="fusion")throw new Error(`Truncation strategy "${l}" not implemented`);b=this._extract_fbank_features(o,this.mel_filters_slaney,this.config.nb_max_samples),b.dims=[1,...b.dims]}return{...b,longer:c}}_extract_fbank_features(o,f,l=null){return spectrogram(o,this.window,this.config.fft_window_size,this.config.hop_length,{power:2,mel_filters:f,log_mel:"dB",max_num_frames:l,do_pad:!1,transpose:!0})}async _call(o,{max_length:f=null}={}){validate_audio_inputs(o,"ClapFeatureExtractor");const l=this._get_input_mel(o,f??this.config.nb_max_samples,this.config.truncation,this.config.padding);return{input_features:new Tensor("float32",l.data,[1,...l.dims])}}}class SpeechT5FeatureExtractor extends FeatureExtractor{}class Processor extends Callable{constructor(o){super(),this.feature_extractor=o}async _call(o,...f){return await this.feature_extractor(o,...f)}}class SamProcessor extends Processor{async _call(...o){return await this.feature_extractor(...o)}post_process_masks(...o){return this.feature_extractor.post_process_masks(...o)}reshape_input_points(...o){return this.feature_extractor.reshape_input_points(...o)}}class WhisperProcessor extends Processor{async _call(o){return await this.feature_extractor(o)}}class Wav2Vec2ProcessorWithLM extends Processor{async _call(o){return await this.feature_extractor(o)}}class SpeechT5Processor extends Processor{async _call(o){return await this.feature_extractor(o)}}class OwlViTProcessor extends Processor{}class AutoProcessor{static async from_pretrained(o,{progress_callback:f=null,config:l=null,cache_dir:d=null,local_files_only:b=!1,revision:c="main"}={}){let g=l??await getModelJSON(o,"preprocessor_config.json",!0,{progress_callback:f,cache_dir:d,local_files_only:b,revision:c}),_=g.feature_extractor_type??g.image_processor_type,p=this.FEATURE_EXTRACTOR_CLASS_MAPPING[_];if(!p)if(g.size!==void 0)console.warn(`Feature extractor type "${_}" not found, assuming ImageFeatureExtractor due to size parameter in config.`),p=ImageFeatureExtractor;else throw new Error(`Unknown Feature Extractor type: ${_}`);let u=this.PROCESSOR_CLASS_MAPPING[g.processor_class]??Processor,n=new p(g);return new u(n)}}Ht(AutoProcessor,"FEATURE_EXTRACTOR_CLASS_MAPPING",{ImageFeatureExtractor,WhisperFeatureExtractor,ViTFeatureExtractor,MobileViTFeatureExtractor,MobileViTImageProcessor,OwlViTFeatureExtractor,Owlv2ImageProcessor,CLIPFeatureExtractor,ChineseCLIPFeatureExtractor,SiglipImageProcessor,ConvNextFeatureExtractor,ConvNextImageProcessor,SegformerFeatureExtractor,BitImageProcessor,DPTImageProcessor,DPTFeatureExtractor,GLPNFeatureExtractor,BeitFeatureExtractor,DeiTFeatureExtractor,DetrFeatureExtractor,YolosFeatureExtractor,DonutFeatureExtractor,NougatImageProcessor,EfficientNetImageProcessor,ViTImageProcessor,VitMatteImageProcessor,SamImageProcessor,Swin2SRImageProcessor,Wav2Vec2FeatureExtractor,SeamlessM4TFeatureExtractor,SpeechT5FeatureExtractor,ASTFeatureExtractor,ClapFeatureExtractor}),Ht(AutoProcessor,"PROCESSOR_CLASS_MAPPING",{WhisperProcessor,Wav2Vec2ProcessorWithLM,SamProcessor,SpeechT5Processor,OwlViTProcessor});class CLAPProcessor{constructor(){this.model=null,this.processor=null,this.defaultLabels=["speech","music","singing","guitar","piano","drums","violin","trumpet","saxophone","flute","classical music","rock music","pop music","jazz","electronic music","ambient","nature sounds","rain","wind","ocean waves","birds chirping","dog barking","cat meowing","car engine","traffic","footsteps","door closing","applause","laughter","crying","coughing","sneezing","telephone ringing","alarm clock","typing","water running","fire crackling","thunder","helicopter","airplane","train","motorcycle","bell ringing","whistle","horn","siren","explosion","gunshot","silence","noise","distortion"]}async initialize(){if(!(this.model&&this.processor))try{this.processor=await AutoProcessor.from_pretrained("Xenova/clap-htsat-unfused"),this.model=await ClapAudioModelWithProjection.from_pretrained("Xenova/clap-htsat-unfused"),console.log("CLAP model loaded successfully")}catch(o){throw console.error("Failed to load CLAP model:",o),o}}async processAudio(o){(!this.model||!this.processor)&&await this.initialize();try{const f=await this.preprocessAudio(o),l=await this.processor(f),d=await this.model.get_audio_features(l),b=await this.processor.text(this.defaultLabels),c=await this.model.get_text_features(b),g=await this.calculateSimilarities(d,c);return this.getTopTags(g,5)}catch(f){throw console.error("Error processing audio:",f),f}}async preprocessAudio(o){let f;if(o.numberOfChannels>1){f=new Float32Array(o.length);for(let d=0;d<o.length;d++){let b=0;for(let c=0;c<o.numberOfChannels;c++)b+=o.getChannelData(c)[d];f[d]=b/o.numberOfChannels}}else f=o.getChannelData(0);const l=48e3;return o.sampleRate!==l&&(f=await this.resampleAudio(f,o.sampleRate,l)),f}async resampleAudio(o,f,l){const d=f/l,b=Math.round(o.length/d),c=new Float32Array(b);for(let g=0;g<b;g++){const _=g*d,p=Math.floor(_),u=Math.min(p+1,o.length-1),n=_-p;c[g]=o[p]*(1-n)+o[u]*n}return c}async calculateSimilarities(o,f){const l=o.data,d=[];for(let b=0;b<this.defaultLabels.length;b++){const c=f.data.slice(b*l.length,(b+1)*l.length),g=this.cosineSimilarity(l,c);d.push(g)}return d}cosineSimilarity(o,f){let l=0,d=0,b=0;for(let c=0;c<o.length;c++)l+=o[c]*f[c],d+=o[c]*o[c],b+=f[c]*f[c];return l/(Math.sqrt(d)*Math.sqrt(b))}getTopTags(o,f=5){return this.defaultLabels.map((l,d)=>({label:l,confidence:Math.max(0,o[d])})).sort((l,d)=>d.confidence-l.confidence).slice(0,f)}async fileToAudioBuffer(o){const f=await o.arrayBuffer();return await new(window.AudioContext||window.webkitAudioContext)().decodeAudioData(f)}}class UserFeedbackStore{constructor(){this.dbName="ClipTaggerDB",this.version=1,this.db=null}async initialize(){return new Promise((o,f)=>{const l=indexedDB.open(this.dbName,this.version);l.onerror=()=>f(l.error),l.onsuccess=()=>{this.db=l.result,o()},l.onupgradeneeded=d=>{const b=d.target.result;if(b.objectStoreNames.contains("audioFeedback")||b.createObjectStore("audioFeedback",{keyPath:"id",autoIncrement:!0}).createIndex("timestamp","timestamp",{unique:!1}),!b.objectStoreNames.contains("tagFeedback")){const c=b.createObjectStore("tagFeedback",{keyPath:"id",autoIncrement:!0});c.createIndex("tag","tag",{unique:!1}),c.createIndex("timestamp","timestamp",{unique:!1})}b.objectStoreNames.contains("customTags")||b.createObjectStore("customTags",{keyPath:"tag"}).createIndex("usage","usage",{unique:!1})}})}async saveAudioFeedback(o,f,l,d){this.db||await this.initialize();const b=this.db.transaction(["audioFeedback"],"readwrite").objectStore("audioFeedback"),c={audioHash:o,originalTags:f,correctedTags:l,audioFeatures:d,timestamp:Date.now()};return new Promise((g,_)=>{const p=b.add(c);p.onsuccess=()=>g(p.result),p.onerror=()=>_(p.error)})}async saveTagFeedback(o,f,l){this.db||await this.initialize();const d=this.db.transaction(["tagFeedback"],"readwrite").objectStore("tagFeedback"),b={tag:o,feedback:f,audioHash:l,timestamp:Date.now()};return new Promise((c,g)=>{const _=d.add(b);_.onsuccess=()=>c(_.result),_.onerror=()=>g(_.error)})}async saveCustomTag(o){this.db||await this.initialize();const f=this.db.transaction(["customTags"],"readwrite").objectStore("customTags");return new Promise((l,d)=>{const b=f.get(o);b.onsuccess=()=>{const c=b.result,g=c?{...c,usage:c.usage+1}:{tag:o,usage:1,timestamp:Date.now()},_=f.put(g);_.onsuccess=()=>l(_.result),_.onerror=()=>d(_.error)},b.onerror=()=>d(b.error)})}async getCustomTags(o=20){this.db||await this.initialize();const f=this.db.transaction(["customTags"],"readonly").objectStore("customTags").index("usage");return new Promise((l,d)=>{const b=f.openCursor(null,"prev"),c=[];b.onsuccess=g=>{const _=g.target.result;_&&c.length<o?(c.push(_.value),_.continue()):l(c)},b.onerror=()=>d(b.error)})}async getTagFeedback(o=null){this.db||await this.initialize();const f=this.db.transaction(["tagFeedback"],"readonly").objectStore("tagFeedback");return new Promise((l,d)=>{let b;o?b=f.index("tag").getAll(o):b=f.getAll(),b.onsuccess=()=>l(b.result),b.onerror=()=>d(b.error)})}async getAudioFeedback(o=100){this.db||await this.initialize();const f=this.db.transaction(["audioFeedback"],"readonly").objectStore("audioFeedback").index("timestamp");return new Promise((l,d)=>{const b=f.openCursor(null,"prev"),c=[];b.onsuccess=g=>{const _=g.target.result;_&&c.length<o?(c.push(_.value),_.continue()):l(c)},b.onerror=()=>d(b.error)})}async hashAudioFile(o){const f=await o.arrayBuffer(),l=await crypto.subtle.digest("SHA-256",f);return Array.from(new Uint8Array(l)).map(d=>d.toString(16).padStart(2,"0")).join("").substring(0,16)}async clearAllData(){this.db||await this.initialize();const o=this.db.transaction(["audioFeedback","tagFeedback","customTags"],"readwrite");await Promise.all([new Promise((f,l)=>{const d=o.objectStore("audioFeedback").clear();d.onsuccess=()=>f(),d.onerror=()=>l(d.error)}),new Promise((f,l)=>{const d=o.objectStore("tagFeedback").clear();d.onsuccess=()=>f(),d.onerror=()=>l(d.error)}),new Promise((f,l)=>{const d=o.objectStore("customTags").clear();d.onsuccess=()=>f(),d.onerror=()=>l(d.error)})])}}class LocalClassifier{constructor(){this.weights=new Map,this.biases=new Map,this.learningRate=.01,this.featureDim=512,this.isInitialized=!1}initialize(o=512){this.featureDim=o,this.isInitialized=!0}trainOnFeedback(o,f,l){this.isInitialized||this.initialize();let d;switch(l){case"positive":d=1;break;case"negative":d=0;break;case"custom":d=1;break;default:return}this.weights.has(f)||(this.weights.set(f,new Array(this.featureDim).fill(0).map(()=>(Math.random()-.5)*.01)),this.biases.set(f,0));const b=this.weights.get(f),c=this.biases.get(f);let g=c;for(let p=0;p<o.length;p++)g+=b[p]*o[p];const _=1/(1+Math.exp(-g))-d;for(let p=0;p<o.length;p++)b[p]-=this.learningRate*_*o[p];this.biases.set(f,c-this.learningRate*_),this.weights.set(f,b)}predict(o,f){if(!this.weights.has(f))return null;const l=this.weights.get(f);let d=this.biases.get(f);for(let b=0;b<Math.min(o.length,l.length);b++)d+=l[b]*o[b];return 1/(1+Math.exp(-d))}predictAll(o,f){const l=[];for(const d of f){const b=this.predict(o,d);b!==null&&l.push({tag:d,confidence:b})}return l.sort((d,b)=>b.confidence-d.confidence)}retrainOnBatch(o){for(const f of o)if(f.audioFeatures&&f.correctedTags){const l=this.extractSimpleFeatures(f.audioFeatures);for(const d of f.correctedTags)this.trainOnFeedback(l,d.tag,d.feedback)}}extractSimpleFeatures(o){const f=new Array(this.featureDim).fill(0);if(o){f[0]=o.duration/60,f[1]=o.sampleRate/48e3,f[2]=o.numberOfChannels;const l=this.simpleHash(JSON.stringify(o));for(let d=3;d<this.featureDim;d++)f[d]=this.seededRandom(l+d)*.1}return f}simpleHash(o){let f=0;for(let l=0;l<o.length;l++){const d=o.charCodeAt(l);f=(f<<5)-f+d,f=f&f}return Math.abs(f)}seededRandom(o){const f=Math.sin(o)*1e4;return f-Math.floor(f)}saveModel(){const o={weights:Object.fromEntries(this.weights),biases:Object.fromEntries(this.biases),featureDim:this.featureDim,learningRate:this.learningRate};localStorage.setItem("clipTaggerModel",JSON.stringify(o))}loadModel(){const o=localStorage.getItem("clipTaggerModel");if(o)try{const f=JSON.parse(o);return this.weights=new Map(Object.entries(f.weights)),this.biases=new Map(Object.entries(f.biases)),this.featureDim=f.featureDim||512,this.learningRate=f.learningRate||.01,this.isInitialized=!0,!0}catch(f){console.error("Error loading model:",f)}return!1}getModelStats(){return{trainedTags:this.weights.size,featureDim:this.featureDim,learningRate:this.learningRate,tags:Array.from(this.weights.keys())}}clearModel(){this.weights.clear(),this.biases.clear(),localStorage.removeItem("clipTaggerModel")}}function App(){var v;const[o,f]=reactExports.useState(null),[l,d]=reactExports.useState(!1),[b,c]=reactExports.useState(!1),[g,_]=reactExports.useState([]),[p,u]=reactExports.useState(null),[n,e]=reactExports.useState([]),[i,s]=reactExports.useState(""),[m,y]=reactExports.useState(null),[x,T]=reactExports.useState(null),E=reactExports.useRef(null),P=reactExports.useRef(null),O=reactExports.useRef([]),A=reactExports.useRef(null),D=reactExports.useRef(null),I=reactExports.useRef(null);reactExports.useEffect(()=>{(async()=>(D.current=new UserFeedbackStore,await D.current.initialize(),I.current=new LocalClassifier,I.current.loadModel(),k()))()},[]);const k=async()=>{try{const re=await D.current.getCustomTags();e(re.map(ue=>ue.tag))}catch(re){console.error("Error loading custom tags:",re)}},$=re=>{const ue=re.target.files[0];ue&&ue.type.startsWith("audio/")&&(f(ue),se(ue))},W=re=>{re.preventDefault();const ue=re.dataTransfer.files[0];ue&&ue.type.startsWith("audio/")&&(f(ue),se(ue))},X=re=>{re.preventDefault()},K=async()=>{try{const re=await navigator.mediaDevices.getUserMedia({audio:!0});P.current=new MediaRecorder(re),O.current=[],P.current.ondataavailable=ue=>{O.current.push(ue.data)},P.current.onstop=()=>{const ue=new Blob(O.current,{type:"audio/wav"}),_e=new File([ue],"recording.wav",{type:"audio/wav"});f(_e),se(_e),re.getTracks().forEach(ve=>ve.stop())},P.current.start(),d(!0)}catch(re){console.error("Error accessing microphone:",re)}},q=()=>{P.current&&l&&(P.current.stop(),d(!1))},se=async re=>{c(!0),_([]),u(null);try{A.current||(A.current=new CLAPProcessor);const ue=await D.current.hashAudioFile(re);y(ue);const _e=await A.current.fileToAudioBuffer(re),ve=await A.current.processAudio(_e),we={sampleRate:_e.sampleRate,duration:_e.duration,numberOfChannels:_e.numberOfChannels};T(we);let ke=ve.map(Me=>({...Me,userFeedback:null}));if(I.current){const Me=I.current.extractSimpleFeatures(we),tt=[...ve.map(Ue=>Ue.label),...n],Je=I.current.predictAll(Me,tt),ot=new Map;for(const Ue of ve)ot.set(Ue.label,{...Ue,source:"clap"});for(const Ue of Je)if(ot.has(Ue.tag)){const Qe=ot.get(Ue.tag);Qe.confidence=(Qe.confidence+Ue.confidence)/2,Qe.source="blended"}else Ue.confidence>.6&&ot.set(Ue.tag,{label:Ue.tag,confidence:Ue.confidence,source:"local",userFeedback:null});ke=Array.from(ot.values()).sort((Ue,Qe)=>Qe.confidence-Ue.confidence).slice(0,8)}_(ke)}catch(ue){console.error("Error processing audio:",ue),u("Failed to process audio. Using fallback tags."),_([{label:"audio",confidence:.9,userFeedback:null},{label:"sound",confidence:.8,userFeedback:null},{label:"recording",confidence:.7,userFeedback:null}])}finally{c(!1)}},F=async(re,ue)=>{const _e=[...g];_e[re].userFeedback=ue,_(_e);try{if(await D.current.saveTagFeedback(_e[re].label,ue,m),I.current&&x){const ve=I.current.extractSimpleFeatures(x);I.current.trainOnFeedback(ve,_e[re].label,ue),I.current.saveModel()}}catch(ve){console.error("Error saving tag feedback:",ve)}},z=async()=>{if(!i.trim())return;const re={label:i.trim(),confidence:1,userFeedback:"custom",isCustom:!0,source:"custom"};_(ue=>[...ue,re]);try{if(await D.current.saveCustomTag(i.trim()),await D.current.saveTagFeedback(i.trim(),"custom",m),I.current&&x){const ue=I.current.extractSimpleFeatures(x);I.current.trainOnFeedback(ue,i.trim(),"custom"),I.current.saveModel()}k()}catch(ue){console.error("Error saving custom tag:",ue)}s("")},fe=re=>{re.key==="Enter"&&z()},ne=async()=>{var re;try{const ue=(re=I.current)==null?void 0:re.getModelStats(),_e=await D.current.getAudioFeedback(),ve=await D.current.getCustomTags(),we={modelStats:ue,feedbackData:_e.slice(0,50),customTags:ve,exportDate:new Date().toISOString(),version:"1.0"},ke=new Blob([JSON.stringify(we,null,2)],{type:"application/json"}),Me=URL.createObjectURL(ke),tt=document.createElement("a");tt.href=Me,tt.download=`clip-tagger-model-${Date.now()}.json`,document.body.appendChild(tt),tt.click(),document.body.removeChild(tt),URL.revokeObjectURL(Me)}catch(ue){console.error("Error exporting model:",ue),u("Failed to export model")}},B=()=>{if(g.length===0)return;const re={audioFile:(o==null?void 0:o.name)||"recorded-audio",audioHash:m,timestamp:new Date().toISOString(),tags:g.map(we=>({label:we.label,confidence:we.confidence,source:we.source||"clap",userFeedback:we.userFeedback}))},ue=new Blob([JSON.stringify(re,null,2)],{type:"application/json"}),_e=URL.createObjectURL(ue),ve=document.createElement("a");ve.href=_e,ve.download=`tags-${(o==null?void 0:o.name)||"audio"}-${Date.now()}.json`,document.body.appendChild(ve),ve.click(),document.body.removeChild(ve),URL.revokeObjectURL(_e)},Q=async()=>{var re;if(confirm("Are you sure you want to clear all training data? This cannot be undone."))try{await D.current.clearAllData(),(re=I.current)==null||re.clearModel(),e([]),_([]),f(null),u(null)}catch(ue){console.error("Error clearing data:",ue),u("Failed to clear data")}};return jsxRuntimeExports.jsxs("div",{className:"app",children:[jsxRuntimeExports.jsxs("header",{children:[jsxRuntimeExports.jsx("h1",{children:"🎵 clip-tagger"}),jsxRuntimeExports.jsx("p",{children:"Custom audio tagging in the browser"})]}),jsxRuntimeExports.jsxs("main",{children:[jsxRuntimeExports.jsxs("div",{className:"upload-area",onDrop:W,onDragOver:X,onClick:()=>{var re;return(re=E.current)==null?void 0:re.click()},children:[jsxRuntimeExports.jsx("input",{ref:E,type:"file",accept:"audio/*",onChange:$,hidden:!0}),jsxRuntimeExports.jsx("div",{className:"upload-content",children:o?jsxRuntimeExports.jsxs("div",{children:[jsxRuntimeExports.jsxs("p",{children:["📁 ",o.name]}),jsxRuntimeExports.jsx("audio",{controls:!0,src:URL.createObjectURL(o)})]}):jsxRuntimeExports.jsxs("div",{children:[jsxRuntimeExports.jsx("p",{children:"🎵 Drop an audio file here or click to upload"}),jsxRuntimeExports.jsx("p",{children:"Supports WAV, MP3, and other audio formats"})]})})]}),jsxRuntimeExports.jsx("div",{className:"controls",children:jsxRuntimeExports.jsx("button",{onClick:l?q:K,className:l?"recording":"",children:l?"⏹️ Stop Recording":"🎤 Record Audio"})}),b&&jsxRuntimeExports.jsx("div",{className:"loading",children:jsxRuntimeExports.jsx("p",{children:"🧠 Analyzing audio with CLAP model..."})}),p&&jsxRuntimeExports.jsx("div",{className:"error",children:jsxRuntimeExports.jsxs("p",{children:["⚠️ ",p]})}),g.length>0&&jsxRuntimeExports.jsxs("div",{className:"tags-section",children:[jsxRuntimeExports.jsx("h3",{children:"Generated Tags"}),jsxRuntimeExports.jsx("div",{className:"tags",children:g.map((re,ue)=>jsxRuntimeExports.jsxs("div",{className:`tag-item ${re.userFeedback?"has-feedback":""}`,children:[jsxRuntimeExports.jsxs("span",{className:`tag ${re.isCustom?"custom":""} ${re.userFeedback==="negative"?"negative":""} ${re.source||"clap"}`,children:[re.label," (",Math.round(re.confidence*100),"%)",re.source==="local"&&jsxRuntimeExports.jsx("span",{className:"source-indicator",children:"🧠"}),re.source==="blended"&&jsxRuntimeExports.jsx("span",{className:"source-indicator",children:"⚡"}),re.source==="custom"&&jsxRuntimeExports.jsx("span",{className:"source-indicator",children:"✨"})]}),!re.isCustom&&jsxRuntimeExports.jsxs("div",{className:"tag-controls",children:[jsxRuntimeExports.jsx("button",{onClick:()=>F(ue,"positive"),className:`feedback-btn ${re.userFeedback==="positive"?"active":""}`,title:"Good tag",children:"✓"}),jsxRuntimeExports.jsx("button",{onClick:()=>F(ue,"negative"),className:`feedback-btn ${re.userFeedback==="negative"?"active":""}`,title:"Bad tag",children:"✗"})]})]},ue))}),jsxRuntimeExports.jsxs("div",{className:"add-tag",children:[jsxRuntimeExports.jsx("input",{type:"text",value:i,onChange:re=>s(re.target.value),onKeyPress:fe,placeholder:"Add custom tag...",className:"tag-input"}),jsxRuntimeExports.jsx("button",{onClick:z,className:"add-tag-btn",children:"Add Tag"})]}),n.length>0&&jsxRuntimeExports.jsxs("div",{className:"frequent-tags",children:[jsxRuntimeExports.jsx("h4",{children:"Frequent Tags:"}),jsxRuntimeExports.jsx("div",{className:"frequent-tag-list",children:n.slice(0,10).map((re,ue)=>jsxRuntimeExports.jsx("button",{onClick:()=>s(re),className:"frequent-tag",children:re},ue))})]})]}),(g.length>0||n.length>0)&&jsxRuntimeExports.jsxs("div",{className:"export-section",children:[jsxRuntimeExports.jsx("h3",{children:"Export & Management"}),jsxRuntimeExports.jsxs("div",{className:"export-controls",children:[g.length>0&&jsxRuntimeExports.jsx("button",{onClick:B,className:"export-btn",children:"📁 Export Current Tags"}),((v=I.current)==null?void 0:v.getModelStats().trainedTags)>0&&jsxRuntimeExports.jsx("button",{onClick:ne,className:"export-btn",children:"🧠 Export Trained Model"}),jsxRuntimeExports.jsx("button",{onClick:Q,className:"clear-btn",children:"🗑️ Clear All Data"})]}),I.current&&jsxRuntimeExports.jsxs("div",{className:"model-stats",children:[jsxRuntimeExports.jsxs("p",{children:["Trained tags: ",I.current.getModelStats().trainedTags]}),jsxRuntimeExports.jsxs("p",{children:["Custom tags: ",n.length]})]})]})]}),jsxRuntimeExports.jsx("footer",{children:jsxRuntimeExports.jsxs("p",{children:["Powered by ",jsxRuntimeExports.jsx("a",{href:"https://github.com/xenova/transformers.js",target:"_blank",rel:"noopener",children:"Transformers.js"})," "," • CLAP model: ",jsxRuntimeExports.jsx("a",{href:"https://huggingface.co/Xenova/clap-htsat-unfused",target:"_blank",rel:"noopener",children:"Xenova/clap-htsat-unfused"})," "," • Everything runs locally in your browser"]})})]})}clientExports.createRoot(document.getElementById("root")).render(jsxRuntimeExports.jsx(reactExports.StrictMode,{children:jsxRuntimeExports.jsx(App,{})}));