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import*as e from"chrome://global/content/ml/ort.webgpu.mjs";var t={"#onnxruntime-webgpu":
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r=Object.freeze({Text:"Text",NumericLiteral:"NumericLiteral",BooleanLiteral:"BooleanLiteral",NullLiteral:"NullLiteral",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",Macro:"Macro",EndMacro:"EndMacro"}),o=Object.freeze({set:r.Set,for:r.For,in:r.In,is:r.Is,if:r.If,else:r.Else,endif:r.EndIf,elif:r.ElseIf,endfor:r.EndFor,and:r.And,or:r.Or,not:r.Not,"not 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!*** ./node_modules/onnxruntime-common/dist/esm/tensor-conversion.js ***!
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/*!*************************************************************************!*\
!*** ./node_modules/onnxruntime-common/dist/esm/tensor-factory-impl.js ***!
\*************************************************************************/(e,t,s)=>{s.r(t),s.d(t,{bufferToTensor:()=>o,tensorFromGpuBuffer:()=>i,tensorFromImage:()=>n,tensorFromMLTensor:()=>l,tensorFromPinnedBuffer:()=>c,tensorFromTexture:()=>a});var r=s(/*! ./tensor-impl.js */"./node_modules/onnxruntime-common/dist/esm/tensor-impl.js");const o=(e,t)=>{if(void 0===e)throw new Error("Image buffer must be defined");if(void 0===t.height||void 0===t.width)throw new Error("Image height and width must be defined");if("NHWC"===t.tensorLayout)throw new Error("NHWC Tensor layout is not supported yet");const{height:s,width:o}=t,n=t.norm??{mean:255,bias:0};let a,i;a="number"==typeof n.mean?[n.mean,n.mean,n.mean,n.mean]:[n.mean[0],n.mean[1],n.mean[2],n.mean[3]??255],i="number"==typeof n.bias?[n.bias,n.bias,n.bias,n.bias]:[n.bias[0],n.bias[1],n.bias[2],n.bias[3]??0];const l=void 0!==t.format?t.format:"RGBA",c=void 0!==t.tensorFormat&&void 0!==t.tensorFormat?t.tensorFormat:"RGB",d=s*o,u="RGBA"===c?new Float32Array(4*d):new Float32Array(3*d);let p=4,m=0,h=1,_=2,f=3,g=0,M=d,w=2*d,T=-1;"RGB"===l&&(p=3,m=0,h=1,_=2,f=-1),"RGBA"===c?T=3*d:"RBG"===c?(g=0,w=d,M=2*d):"BGR"===c&&(w=0,M=d,g=2*d);for(let t=0;t<d;t++,m+=p,_+=p,h+=p,f+=p)u[g++]=(e[m]+i[0])/a[0],u[M++]=(e[h]+i[1])/a[1],u[w++]=(e[_]+i[2])/a[2],-1!==T&&-1!==f&&(u[T++]=(e[f]+i[3])/a[3]);return"RGBA"===c?new r.Tensor("float32",u,[1,4,s,o]):new r.Tensor("float32",u,[1,3,s,o])},n=async(e,t)=>{const s="undefined"!=typeof HTMLImageElement&&e instanceof HTMLImageElement,r="undefined"!=typeof ImageData&&e instanceof ImageData,n="undefined"!=typeof ImageBitmap&&e instanceof ImageBitmap,a="string"==typeof e;let i,l=t??{};const c=()=>{if("undefined"!=typeof document)return document.createElement("canvas");if("undefined"!=typeof OffscreenCanvas)return new OffscreenCanvas(1,1);throw new Error("Canvas is not supported")},d=e=>"undefined"!=typeof HTMLCanvasElement&&e instanceof HTMLCanvasElement||e instanceof OffscreenCanvas?e.getContext("2d"):null;if(s){const s=c();s.width=e.width,s.height=e.height;const r=d(s);if(null==r)throw new Error("Can not access image data");{let s=e.height,o=e.width;if(void 0!==t&&void 0!==t.resizedHeight&&void 0!==t.resizedWidth&&(s=t.resizedHeight,o=t.resizedWidth),void 0!==t){if(l=t,void 0!==t.tensorFormat)throw new Error("Image input config format must be RGBA for HTMLImageElement");l.tensorFormat="RGBA",l.height=s,l.width=o}else l.tensorFormat="RGBA",l.height=s,l.width=o;r.drawImage(e,0,0),i=r.getImageData(0,0,o,s).data}}else{if(!r){if(n){if(void 0===t)throw new Error("Please provide image config with format for Imagebitmap");const s=c();s.width=e.width,s.height=e.height;const r=d(s);if(null!=r){const t=e.height,s=e.width;return r.drawImage(e,0,0,s,t),i=r.getImageData(0,0,s,t).data,l.height=t,l.width=s,o(i,l)}throw new Error("Can not access image data")}if(a)return new Promise(((t,s)=>{const r=c(),n=d(r);if(!e||!n)return s();const a=new Image;a.crossOrigin="Anonymous",a.src=e,a.onload=()=>{r.width=a.width,r.height=a.height,n.drawImage(a,0,0,r.width,r.height);const e=n.getImageData(0,0,r.width,r.height);l.height=r.height,l.width=r.width,t(o(e.data,l))}}));throw new Error("Input data provided is not supported - aborted tensor creation")}{let s,r;if(void 0!==t&&void 0!==t.resizedWidth&&void 0!==t.resizedHeight?(s=t.resizedHeight,r=t.resizedWidth):(s=e.height,r=e.width),void 0!==t&&(l=t),l.format="RGBA",l.height=s,l.width=r,void 0!==t){const t=c();t.width=r,t.height=s;const o=d(t);if(null==o)throw new Error("Can not access image data");o.putImageData(e,0,0),i=o.getImageData(0,0,r,s).data}else i=e.data}}if(void 0!==i)return o(i,l);throw new Error("Input data provided is not supported - aborted tensor creation")},a=(e,t)=>{const{width:s,height:o,download:n,dispose:a}=t,i=[1,o,s,4];return new r.Tensor({location:"texture",type:"float32",texture:e,dims:i,download:n,dispose:a})},i=(e,t)=>{const{dataType:s,dims:o,download:n,dispose:a}=t;return new r.Tensor({location:"gpu-buffer",type:s??"float32",gpuBuffer:e,dims:o,download:n,dispose:a})},l=(e,t)=>{const{dataType:s,dims:o,download:n,dispose:a}=t;return new r.Tensor({location:"ml-tensor",type:s??"float32",mlTensor:e,dims:o,download:n,dispose:a})},c=(e,t,s)=>new r.Tensor({location:"cpu-pinned",type:e,data:t,dims:s??[t.length]})},"./node_modules/onnxruntime-common/dist/esm/tensor-factory.js":
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!*** ./node_modules/onnxruntime-common/dist/esm/tensor-factory.js ***!
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!*** ./node_modules/onnxruntime-common/dist/esm/tensor-impl-type-mapping.js ***!
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!*** ./node_modules/onnxruntime-common/dist/esm/tensor-impl.js ***!
\*****************************************************************/(e,t,s)=>{s.r(t),s.d(t,{Tensor:()=>i});var r=s(/*! ./tensor-conversion-impl.js */"./node_modules/onnxruntime-common/dist/esm/tensor-conversion-impl.js"),o=s(/*! ./tensor-factory-impl.js */"./node_modules/onnxruntime-common/dist/esm/tensor-factory-impl.js"),n=s(/*! ./tensor-impl-type-mapping.js */"./node_modules/onnxruntime-common/dist/esm/tensor-impl-type-mapping.js"),a=s(/*! ./tensor-utils-impl.js */"./node_modules/onnxruntime-common/dist/esm/tensor-utils-impl.js");class i{constructor(e,t,s){let r,o;if((0,n.checkTypedArray)(),"object"==typeof e&&"location"in e)switch(this.dataLocation=e.location,r=e.type,o=e.dims,e.location){case"cpu-pinned":{const t=n.NUMERIC_TENSOR_TYPE_TO_TYPEDARRAY_MAP.get(r);if(!t)throw new TypeError(`unsupported type "${r}" to create tensor from pinned buffer`);if(!(e.data instanceof t))throw new TypeError(`buffer should be of type ${t.name}`);this.cpuData=e.data;break}case"texture":if("float32"!==r)throw new TypeError(`unsupported type "${r}" to create tensor from texture`);this.gpuTextureData=e.texture,this.downloader=e.download,this.disposer=e.dispose;break;case"gpu-buffer":if("float32"!==r&&"float16"!==r&&"int32"!==r&&"int64"!==r&&"uint32"!==r&&"uint8"!==r&&"bool"!==r&&"uint4"!==r&&"int4"!==r)throw new TypeError(`unsupported type "${r}" to create tensor from gpu buffer`);this.gpuBufferData=e.gpuBuffer,this.downloader=e.download,this.disposer=e.dispose;break;case"ml-tensor":if("float32"!==r&&"float16"!==r&&"int32"!==r&&"int64"!==r&&"uint32"!==r&&"uint64"!==r&&"int8"!==r&&"uint8"!==r&&"bool"!==r)throw new TypeError(`unsupported type "${r}" to create tensor from MLTensor`);this.mlTensorData=e.mlTensor,this.downloader=e.download,this.disposer=e.dispose;break;default:throw new Error(`Tensor constructor: unsupported location '${this.dataLocation}'`)}else{let a,i;if("string"==typeof e)if(r=e,i=s,"string"===e){if(!Array.isArray(t))throw new TypeError("A string tensor's data must be a string array.");a=t}else{const s=n.NUMERIC_TENSOR_TYPE_TO_TYPEDARRAY_MAP.get(e);if(void 0===s)throw new TypeError(`Unsupported tensor type: ${e}.`);if(Array.isArray(t)){if("float16"===e&&s===Uint16Array||"uint4"===e||"int4"===e)throw new TypeError(`Creating a ${e} tensor from number array is not supported. Please use ${s.name} as data.`);a="uint64"===e||"int64"===e?s.from(t,BigInt):s.from(t)}else if(t instanceof s)a=t;else{if(!(t instanceof Uint8ClampedArray))throw new TypeError(`A ${r} tensor's data must be type of ${s}`);if("uint8"!==e)throw new TypeError("A Uint8ClampedArray tensor's data must be type of uint8");a=Uint8Array.from(t)}}else if(i=t,Array.isArray(e)){if(0===e.length)throw new TypeError("Tensor type cannot be inferred from an empty array.");const t=typeof e[0];if("string"===t)r="string",a=e;else{if("boolean"!==t)throw new TypeError(`Invalid element type of data array: ${t}.`);r="bool",a=Uint8Array.from(e)}}else if(e instanceof Uint8ClampedArray)r="uint8",a=Uint8Array.from(e);else{const t=n.NUMERIC_TENSOR_TYPEDARRAY_TO_TYPE_MAP.get(e.constructor);if(void 0===t)throw new TypeError(`Unsupported type for tensor data: ${e.constructor}.`);r=t,a=e}if(void 0===i)i=[a.length];else if(!Array.isArray(i))throw new TypeError("A tensor's dims must be a number array");o=i,this.cpuData=a,this.dataLocation="cpu"}const i=(0,a.calculateSize)(o);if(this.cpuData&&i!==this.cpuData.length&&("uint4"!==r&&"int4"!==r||Math.ceil(i/2)!==this.cpuData.length))throw new Error(`Tensor's size(${i}) does not match data length(${this.cpuData.length}).`);this.type=r,this.dims=o,this.size=i}static async fromImage(e,t){return(0,o.tensorFromImage)(e,t)}static fromTexture(e,t){return(0,o.tensorFromTexture)(e,t)}static fromGpuBuffer(e,t){return(0,o.tensorFromGpuBuffer)(e,t)}static fromMLTensor(e,t){return(0,o.tensorFromMLTensor)(e,t)}static fromPinnedBuffer(e,t,s){return(0,o.tensorFromPinnedBuffer)(e,t,s)}toDataURL(e){return(0,r.tensorToDataURL)(this,e)}toImageData(e){return(0,r.tensorToImageData)(this,e)}get data(){if(this.ensureValid(),!this.cpuData)throw new Error("The data is not on CPU. Use `getData()` to download GPU data to CPU, or use `texture` or `gpuBuffer` property to access the GPU data directly.");return this.cpuData}get location(){return this.dataLocation}get texture(){if(this.ensureValid(),!this.gpuTextureData)throw new Error("The data is not stored as a WebGL texture.");return this.gpuTextureData}get gpuBuffer(){if(this.ensureValid(),!this.gpuBufferData)throw new Error("The data is not stored as a WebGPU buffer.");return this.gpuBufferData}get mlTensor(){if(this.ensureValid(),!this.mlTensorData)throw new Error("The data is not stored as a WebNN MLTensor.");return this.mlTensorData}async getData(e){switch(this.ensureValid(),this.dataLocation){case"cpu":case"cpu-pinned":return this.data;case"texture":case"gpu-buffer":case"ml-tensor":if(!this.downloader)throw new Error("The current tensor is not created with a specified data downloader.");if(this.isDownloading)throw new Error("The current tensor is being downloaded.");try{this.isDownloading=!0;const t=await this.downloader();return this.downloader=void 0,this.dataLocation="cpu",this.cpuData=t,e&&this.disposer&&(this.disposer(),this.disposer=void 0),t}finally{this.isDownloading=!1}default:throw new Error(`cannot get data from location: ${this.dataLocation}`)}}dispose(){if(this.isDownloading)throw new Error("The current tensor is being downloaded.");this.disposer&&(this.disposer(),this.disposer=void 0),this.cpuData=void 0,this.gpuTextureData=void 0,this.gpuBufferData=void 0,this.mlTensorData=void 0,this.downloader=void 0,this.isDownloading=void 0,this.dataLocation="none"}ensureValid(){if("none"===this.dataLocation)throw new Error("The tensor is disposed.")}reshape(e){if(this.ensureValid(),this.downloader||this.disposer)throw new Error("Cannot reshape a tensor that owns GPU resource.");return(0,a.tensorReshape)(this,e)}}},"./node_modules/onnxruntime-common/dist/esm/tensor-utils-impl.js":
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!*** ./node_modules/onnxruntime-common/dist/esm/tensor-utils-impl.js ***!
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/*!***************************************************************************!*\
!*** ./node_modules/onnxruntime-common/dist/esm/training-session-impl.js ***!
\***************************************************************************/(e,t,s)=>{s.r(t),s.d(t,{TrainingSession:()=>n});var r=s(/*! ./backend-impl.js */"./node_modules/onnxruntime-common/dist/esm/backend-impl.js"),o=s(/*! ./tensor.js */"./node_modules/onnxruntime-common/dist/esm/tensor.js");class n{constructor(e,t,s){this.handler=e,this.hasOptimizerModel=t,this.hasEvalModel=s}get trainingInputNames(){return this.handler.inputNames}get trainingOutputNames(){return this.handler.outputNames}get evalInputNames(){if(this.hasEvalModel)return this.handler.evalInputNames;throw new Error("This training session has no evalModel loaded.")}get evalOutputNames(){if(this.hasEvalModel)return this.handler.evalOutputNames;throw new Error("This training session has no evalModel loaded.")}static async create(e,t){const s=e.evalModel||"",o=e.optimizerModel||"",a=t||{},[i,l]=await(0,r.resolveBackendAndExecutionProviders)(a);if(i.createTrainingSessionHandler){const t=await i.createTrainingSessionHandler(e.checkpointState,e.trainModel,s,o,l);return new n(t,!!e.optimizerModel,!!e.evalModel)}throw new Error("Training backend could not be resolved. Make sure you're using the correct configuration & WebAssembly files.")}typeNarrowingForRunStep(e,t,s,r,n){const a={};let i={};if("object"!=typeof s||null===s||s instanceof o.Tensor||Array.isArray(s))throw new TypeError("'feeds' must be an object that use input names as keys and OnnxValue as corresponding values.");let l=!0;if("object"==typeof r){if(null===r)throw new TypeError("Unexpected argument[1]: cannot be null.");if(r instanceof o.Tensor)throw new TypeError("'fetches' cannot be a Tensor");if(Array.isArray(r)){if(0===r.length)throw new TypeError("'fetches' cannot be an empty array.");l=!1;for(const e of r){if("string"!=typeof e)throw new TypeError("'fetches' must be a string array or an object.");if(-1===t.indexOf(e))throw new RangeError(`'fetches' contains invalid output name: ${e}.`);a[e]=null}if("object"==typeof n&&null!==n)i=n;else if(void 0!==n)throw new TypeError("'options' must be an object.")}else{let e=!1;const s=Object.getOwnPropertyNames(r);for(const n of t)if(-1!==s.indexOf(n)){const t=r[n];(null===t||t instanceof o.Tensor)&&(e=!0,l=!1,a[n]=t)}if(e){if("object"==typeof n&&null!==n)i=n;else if(void 0!==n)throw new TypeError("'options' must be an object.")}else i=r}}else if(void 0!==r)throw new TypeError("Unexpected argument[1]: must be 'fetches' or 'options'.");for(const t of e)if(void 0===s[t])throw new Error(`input '${t}' is missing in 'feeds'.`);if(l)for(const e of t)a[e]=null;return[a,i]}convertHandlerReturnTypeToMapOfTensors(e){const t={};for(const s in e)if(Object.hasOwnProperty.call(e,s)){const r=e[s];r instanceof o.Tensor?t[s]=r:t[s]=new o.Tensor(r.type,r.data,r.dims)}return t}async lazyResetGrad(){await this.handler.lazyResetGrad()}async runTrainStep(e,t,s){const[r,o]=this.typeNarrowingForRunStep(this.trainingInputNames,this.trainingOutputNames,e,t,s),n=await this.handler.runTrainStep(e,r,o);return this.convertHandlerReturnTypeToMapOfTensors(n)}async runOptimizerStep(e){if(!this.hasOptimizerModel)throw new Error("This TrainingSession has no OptimizerModel loaded.");await this.handler.runOptimizerStep(e||{})}async runEvalStep(e,t,s){if(this.hasEvalModel){const[r,o]=this.typeNarrowingForRunStep(this.evalInputNames,this.evalOutputNames,e,t,s),n=await this.handler.runEvalStep(e,r,o);return this.convertHandlerReturnTypeToMapOfTensors(n)}throw new Error("This TrainingSession has no EvalModel loaded.")}async getParametersSize(e=!0){return this.handler.getParametersSize(e)}async loadParametersBuffer(e,t=!0){const s=await this.getParametersSize(t);if(e.length!==4*s)throw new Error("Size of the buffer passed into loadParametersBuffer must match the number of parameters in the model. Please use getParametersSize method to check.");return this.handler.loadParametersBuffer(e,t)}async getContiguousParameters(e=!0){return this.handler.getContiguousParameters(e)}async release(){return this.handler.dispose()}}},"./node_modules/onnxruntime-common/dist/esm/training-session.js":
/*!**********************************************************************!*\
!*** ./node_modules/onnxruntime-common/dist/esm/training-session.js ***!
\**********************************************************************/(e,t,s)=>{s.r(t),s.d(t,{TrainingSession:()=>r});const r=s(/*! ./training-session-impl.js */"./node_modules/onnxruntime-common/dist/esm/training-session-impl.js").TrainingSession},"./node_modules/onnxruntime-common/dist/esm/version.js":
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!*** ./node_modules/onnxruntime-common/dist/esm/version.js ***!
\*************************************************************/(e,t,s)=>{s.r(t),s.d(t,{version:()=>r});const r="1.20.1"},"./src/backends/onnx.js":
/*!******************************!*\
!*** ./src/backends/onnx.js ***!
\******************************/(e,t,s)=>{s.r(t),s.d(t,{Tensor:()=>n.Tensor,createInferenceSession:()=>h,deviceToExecutionProviders:()=>p,isONNXProxy:()=>g,isONNXTensor:()=>_});var r=s(/*! ../env.js */"./src/env.js"),o=s(/*! #onnxruntime-webgpu */"#onnxruntime-webgpu"),n=s(/*! onnxruntime-common */"./node_modules/onnxruntime-common/dist/esm/index.js");const a=Object.freeze({auto:null,gpu:null,cpu:"cpu",wasm:"wasm",webgpu:"webgpu",cuda:"cuda",dml:"dml",webnn:{name:"webnn",deviceType:"cpu"},"webnn-npu":{name:"webnn",deviceType:"npu"},"webnn-gpu":{name:"webnn",deviceType:"gpu"},"webnn-cpu":{name:"webnn",deviceType:"cpu"}}),i=[];let l,c;const d=Symbol.for("onnxruntime");if(d in globalThis)c=globalThis[d];else if(r.apis.IS_NODE_ENV){switch(c=null.default??null,process.platform){case"win32":i.push("dml");break;case"linux":"x64"===process.arch&&i.push("cuda")}i.push("cpu"),l=["cpu"]}else c=o,r.apis.IS_WEBNN_AVAILABLE&&i.push("webnn-npu","webnn-gpu","webnn-cpu","webnn"),r.apis.IS_WEBGPU_AVAILABLE&&i.push("webgpu"),i.push("wasm"),l=["wasm"];const u=c.InferenceSession;function p(e=null){if(!e)return l;switch(e){case"auto":return i;case"gpu":return i.filter((e=>["webgpu","cuda","dml","webnn-gpu"].includes(e)))}if(i.includes(e))return[a[e]??e];throw new Error(`Unsupported device: "${e}". Should be one of: ${i.join(", ")}.`)}let m=null;async function h(e,t,s){m&&await m;const r=u.create(e,t);m??=r;const o=await r;return o.config=s,o}function _(e){return e instanceof c.Tensor}const f=c?.env;function g(){return f?.wasm?.proxy}f?.wasm&&(f.wasm.wasmPaths=`https://cdn.jsdelivr.net/npm/@huggingface/transformers@${r.env.version}/dist/`,f.wasm.proxy=!1,"undefined"!=typeof crossOriginIsolated&&crossOriginIsolated||(f.wasm.numThreads=1)),f?.webgpu&&(f.webgpu.powerPreference="high-performance"),r.env.backends.onnx=f},"./src/base/feature_extraction_utils.js":
/*!**********************************************!*\
!*** ./src/base/feature_extraction_utils.js ***!
\**********************************************/(e,t,s)=>{s.r(t),s.d(t,{FeatureExtractor:()=>a,validate_audio_inputs:()=>i});var r=s(/*! ../utils/constants.js */"./src/utils/constants.js"),o=s(/*! ../utils/generic.js */"./src/utils/generic.js"),n=s(/*! ../utils/hub.js */"./src/utils/hub.js");class a extends o.Callable{constructor(e){super(),this.config=e}static async from_pretrained(e,t){return new this(await(0,n.getModelJSON)(e,r.FEATURE_EXTRACTOR_NAME,!0,t))}}function i(e,t){if(!(e instanceof Float32Array||e instanceof Float64Array))throw new Error(`${t} expects input to be a Float32Array or a Float64Array, but got ${e?.constructor?.name??typeof e} 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.`)}},"./src/base/image_processors_utils.js":
/*!********************************************!*\
!*** ./src/base/image_processors_utils.js ***!
\********************************************/(e,t,s)=>{s.r(t),s.d(t,{ImageProcessor:()=>w,post_process_instance_segmentation:()=>M,post_process_object_detection:()=>p,post_process_panoptic_segmentation:()=>g,post_process_semantic_segmentation:()=>m});var r=s(/*! ../utils/generic.js */"./src/utils/generic.js"),o=s(/*! ../utils/tensor.js */"./src/utils/tensor.js"),n=s(/*! ../utils/maths.js */"./src/utils/maths.js"),a=(s(/*! ../utils/image.js */"./src/utils/image.js"),s(/*! ../utils/core.js */"./src/utils/core.js")),i=s(/*! ../utils/hub.js */"./src/utils/hub.js"),l=s(/*! ../utils/constants.js */"./src/utils/constants.js");function c(e,t,s=0,r=null){const o=e/t;let a=(0,n.bankers_round)(o)*t;return null!==r&&a>r&&(a=Math.floor(o)*t),a<s&&(a=Math.ceil(o)*t),a}function d([e,t],s){return[Math.max(Math.floor(e/s),1)*s,Math.max(Math.floor(t/s),1)*s]}function u([e,t,s,r]){return[e-s/2,t-r/2,e+s/2,t+r/2]}function p(e,t=.5,s=null,r=!1){const o=e.logits,a=e.pred_boxes,[i,l,c]=o.dims;if(null!==s&&s.length!==i)throw Error("Make sure that you pass in as many target sizes as the batch dimension of the logits");let d=[];for(let e=0;e<i;++e){let i=null!==s?s[e]:null,p={boxes:[],classes:[],scores:[]},m=o[e],h=a[e];for(let e=0;e<l;++e){let s,o=m[e],a=[];if(r){s=o.sigmoid().data;for(let e=0;e<s.length;++e)s[e]>t&&a.push(e)}else{let e=(0,n.max)(o.data)[1];if(e===c-1)continue;if(s=(0,n.softmax)(o.data),s[e]<t)continue;a.push(e)}for(const t of a){let r=h[e].data;r=u(r),null!==i&&(r=r.map(((e,t)=>e*i[(t+1)%2]))),p.boxes.push(r),p.classes.push(t),p.scores.push(s[t])}}d.push(p)}return d}function m(e,t=null){const s=e.logits,r=s.dims[0];if(null!==t&&t.length!==r)throw Error("Make sure that you pass in as many target sizes as the batch dimension of the logits");const n=[];for(let e=0;e<r;++e){const r=null!==t?t[e]:null;let a=s[e];null!==r&&(a=(0,o.interpolate)(a,r,"bilinear",!1));const[i,l]=r??a.dims.slice(-2),c=new o.Tensor("int32",new Int32Array(i*l),[i,l]),d=a[0].data,u=c.data;for(let e=1;e<a.dims[0];++e){const t=a[e].data;for(let s=0;s<t.length;++s)t[s]>d[s]&&(d[s]=t[s],u[s]=e)}const p=new Array(a.dims[0]);for(let e=0;e<u.length;++e){const t=u[e];p[t]=t}const m=p.filter((e=>void 0!==e));n.push({segmentation:c,labels:m})}return n}function h(e,t,s,r){const o=[],a=[],i=[];for(let l=0;l<e.dims[0];++l){const c=e[l],d=t[l],u=(0,n.max)(c.data)[1];if(u===r)continue;const p=(0,n.softmax)(c.data)[u];p>s&&(o.push(d),a.push(p),i.push(u))}return[o,a,i]}function _(e,t,s,r=.5,o=.8){const n=[];let a=0,i=0;const l=t[s].data;for(let t=0;t<e.length;++t)e[t]===s&&(n.push(t),++a),l[t]>=r&&++i;let c=a>0&&i>0;if(c){c=a/i>o}return[c,n]}function f(e,t,s,r,n,a=null,i=null){const[l,c]=i??e[0].dims,d=new o.Tensor("int32",new Int32Array(l*c),[l,c]),u=[];if(null!==i)for(let t=0;t<e.length;++t)e[t]=(0,o.interpolate)(e[t],i,"bilinear",!1);const p=new Int32Array(e[0].data.length),m=new Float32Array(e[0].data.length);for(let s=0;s<e.length;++s){let r=t[s];const o=e[s].data;for(let e=0;e<o.length;++e)o[e]*=r,o[e]>m[e]&&(p[e]=s,m[e]=o[e])}let h=0;const f=d.data;for(let o=0;o<s.length;++o){const a=s[o],[i,l]=_(p,e,o,r,n);if(i){++h;for(const e of l)f[e]=h;u.push({id:h,label_id:a,score:t[o]})}}return[d,u]}function g(e,t=.5,s=.5,r=.8,n=null,a=null){null===n&&(console.warn("`label_ids_to_fuse` unset. No instance will be fused."),n=new Set);const i=e.class_queries_logits??e.logits,l=(e.masks_queries_logits??e.pred_masks).sigmoid();let[c,d,u]=i.dims;if(u-=1,null!==a&&a.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 e=0;e<c;++e){let c=null!==a?a[e]:null,d=i[e],m=l[e],[_,g,M]=h(d,m,t,u);if(0===M.length){let[e,t]=c??m.dims.slice(-2),s=new o.Tensor("int32",new Int32Array(e*t).fill(-1),[e,t]);p.push({segmentation:s,segments_info:[]});continue}let[w,T]=f(_,g,M,s,r,n,c);p.push({segmentation:w,segments_info:T})}return p}function M(e,t=.5,s=null){throw new Error("`post_process_instance_segmentation` is not yet implemented.")}class w extends r.Callable{constructor(e){super(),this.image_mean=e.image_mean??e.mean,this.image_std=e.image_std??e.std,this.resample=e.resample??2,this.do_rescale=e.do_rescale??!0,this.rescale_factor=e.rescale_factor??1/255,this.do_normalize=e.do_normalize,this.do_thumbnail=e.do_thumbnail,this.size=e.size??e.image_size,this.do_resize=e.do_resize??void 0!==this.size,this.size_divisibility=e.size_divisibility??e.size_divisor,this.do_center_crop=e.do_center_crop,this.crop_size=e.crop_size,this.do_convert_rgb=e.do_convert_rgb??!0,this.do_crop_margin=e.do_crop_margin,this.pad_size=e.pad_size,this.do_pad=e.do_pad,this.do_pad&&!this.pad_size&&this.size&&void 0!==this.size.width&&void 0!==this.size.height&&(this.pad_size=this.size),this.do_flip_channel_order=e.do_flip_channel_order??!1,this.config=e}async thumbnail(e,t,s=2){const r=e.height,o=e.width,n=t.height,a=t.width;let i=Math.min(r,n),l=Math.min(o,a);return i===r&&l===o?e:(r>o?l=Math.floor(o*i/r):o>r&&(i=Math.floor(r*l/o)),await e.resize(l,i,{resample:s}))}async crop_margin(e,t=200){const s=e.clone().grayscale(),r=(0,n.min)(s.data)[0],o=(0,n.max)(s.data)[0]-r;if(0===o)return e;const a=t/255;let i=s.width,l=s.height,c=0,d=0;const u=s.data;for(let e=0;e<s.height;++e){const t=e*s.width;for(let n=0;n<s.width;++n)(u[t+n]-r)/o<a&&(i=Math.min(i,n),l=Math.min(l,e),c=Math.max(c,n),d=Math.max(d,e))}return e=await e.crop([i,l,c,d])}pad_image(e,t,s,{mode:r="constant",center:o=!1,constant_values:n=0}={}){const[i,l,c]=t;let d,u;if("number"==typeof s?(d=s,u=s):(d=s.width,u=s.height),d!==l||u!==i){const s=new Float32Array(d*u*c);if(Array.isArray(n))for(let e=0;e<s.length;++e)s[e]=n[e%c];else 0!==n&&s.fill(n);const[p,m]=o?[Math.floor((d-l)/2),Math.floor((u-i)/2)]:[0,0];for(let t=0;t<i;++t){const r=(t+m)*d,o=t*l;for(let t=0;t<l;++t){const n=(r+t+p)*c,a=(o+t)*c;for(let t=0;t<c;++t)s[n+t]=e[a+t]}}if("symmetric"===r){if(o)throw new Error("`center` padding is not supported when `mode` is set to `symmetric`.");const t=i-1,r=l-1;for(let o=0;o<u;++o){const n=o*d,u=(0,a.calculateReflectOffset)(o,t)*l;for(let t=0;t<d;++t){if(o<i&&t<l)continue;const d=(n+t)*c,p=(u+(0,a.calculateReflectOffset)(t,r))*c;for(let t=0;t<c;++t)s[d+t]=e[p+t]}}}e=s,t=[u,d,c]}return[e,t]}rescale(e){for(let t=0;t<e.length;++t)e[t]=this.rescale_factor*e[t]}get_resize_output_image_size(e,t){const[s,r]=e.size;let o,n;if(this.do_thumbnail){const{height:e,width:s}=t;o=Math.min(e,s)}else Number.isInteger(t)?(o=t,n=this.config.max_size??o):void 0!==t&&(o=t.shortest_edge,n=t.longest_edge);if(void 0!==o||void 0!==n){const e=void 0===o?1:Math.max(o/s,o/r),t=s*e,a=r*e,i=void 0===n?1:Math.min(n/t,n/a);let l=Math.floor(Number((t*i).toFixed(2))),c=Math.floor(Number((a*i).toFixed(2)));return void 0!==this.size_divisibility&&([l,c]=d([l,c],this.size_divisibility)),[l,c]}if(void 0!==t&&void 0!==t.width&&void 0!==t.height){let e=t.width,o=t.height;if(this.config.keep_aspect_ratio&&this.config.ensure_multiple_of){let t=o/r,n=e/s;Math.abs(1-n)<Math.abs(1-t)?t=n:n=t,o=c(t*r,this.config.ensure_multiple_of),e=c(n*s,this.config.ensure_multiple_of)}return[e,o]}if(void 0!==this.size_divisibility)return d([s,r],this.size_divisibility);if(void 0!==t.min_pixels&&void 0!==t.max_pixels){const{min_pixels:e,max_pixels:o}=t;return function(e,t,s=28,r=3136,o=1003520){if(e<s||t<s)throw new Error(`height:${e} or width:${t} must be larger than factor:${s}`);if(Math.max(e,t)/Math.min(e,t)>200)throw new Error("absolute aspect ratio must be smaller than 200, got "+Math.max(e,t)/Math.min(e,t));let n=Math.round(e/s)*s,a=Math.round(t/s)*s;if(n*a>o){const r=Math.sqrt(e*t/o);n=Math.floor(e/r/s)*s,a=Math.floor(t/r/s)*s}else if(n*a<r){const o=Math.sqrt(r/(e*t));n=Math.ceil(e*o/s)*s,a=Math.ceil(t*o/s)*s}return[n,a]}(r,s,this.config.patch_size*this.config.merge_size,e,o)}throw new Error(`Could not resize image due to unsupported \`this.size\` option in config: ${JSON.stringify(t)}`)}async resize(e){const[t,s]=this.get_resize_output_image_size(e,this.size);return await e.resize(t,s,{resample:this.resample})}async preprocess(e,{do_normalize:t=null,do_pad:s=null,do_convert_rgb:r=null,do_convert_grayscale:n=null,do_flip_channel_order:a=null}={}){this.do_crop_margin&&(e=await this.crop_margin(e));const[i,l]=e.size;if(r??this.do_convert_rgb?e=e.rgb():n&&(e=e.grayscale()),this.do_resize&&(e=await this.resize(e)),this.do_thumbnail&&(e=await this.thumbnail(e,this.size,this.resample)),this.do_center_crop){let t,s;Number.isInteger(this.crop_size)?(t=this.crop_size,s=this.crop_size):(t=this.crop_size.width,s=this.crop_size.height),e=await e.center_crop(t,s)}const c=[e.height,e.width];let u=Float32Array.from(e.data),p=[e.height,e.width,e.channels];if(this.do_rescale&&this.rescale(u),t??this.do_normalize){let t=this.image_mean;Array.isArray(this.image_mean)||(t=new Array(e.channels).fill(t));let s=this.image_std;if(Array.isArray(this.image_std)||(s=new Array(e.channels).fill(t)),t.length!==e.channels||s.length!==e.channels)throw new Error(`When set to arrays, the length of \`image_mean\` (${t.length}) and \`image_std\` (${s.length}) must match the number of channels in the image (${e.channels}).`);for(let r=0;r<u.length;r+=e.channels)for(let o=0;o<e.channels;++o)u[r+o]=(u[r+o]-t[o])/s[o]}if(s??this.do_pad)if(this.pad_size){const t=this.pad_image(u,[e.height,e.width,e.channels],this.pad_size);[u,p]=t}else if(this.size_divisibility){const[e,t]=d([p[1],p[0]],this.size_divisibility);[u,p]=this.pad_image(u,p,{width:e,height:t})}if(a??this.do_flip_channel_order){if(3!==p[2])throw new Error("Flipping channel order is only supported for RGB images.");for(let e=0;e<u.length;e+=3){const t=u[e];u[e]=u[e+2],u[e+2]=t}}return{original_size:[l,i],reshaped_input_size:c,pixel_values:new o.Tensor("float32",u,p).permute(2,0,1)}}async _call(e,...t){Array.isArray(e)||(e=[e]);const s=await Promise.all(e.map((e=>this.preprocess(e))));return{pixel_values:(0,o.stack)(s.map((e=>e.pixel_values)),0),original_sizes:s.map((e=>e.original_size)),reshaped_input_sizes:s.map((e=>e.reshaped_input_size))}}static async from_pretrained(e,t){return new this(await(0,i.getModelJSON)(e,l.IMAGE_PROCESSOR_NAME,!0,t))}}},"./src/base/processing_utils.js":
/*!**************************************!*\
!*** ./src/base/processing_utils.js ***!
\**************************************/(e,t,s)=>{s.r(t),s.d(t,{Processor:()=>a});var r=s(/*! ../utils/constants.js */"./src/utils/constants.js"),o=s(/*! ../utils/generic.js */"./src/utils/generic.js"),n=s(/*! ../utils/hub.js */"./src/utils/hub.js");class a extends o.Callable{static classes=["image_processor_class","tokenizer_class","feature_extractor_class"];static uses_processor_config=!1;constructor(e,t){super(),this.config=e,this.components=t}get image_processor(){return this.components.image_processor}get tokenizer(){return this.components.tokenizer}get feature_extractor(){return this.components.feature_extractor}apply_chat_template(e,t={}){if(!this.tokenizer)throw new Error("Unable to apply chat template without a tokenizer.");return this.tokenizer.apply_chat_template(e,{tokenize:!1,...t})}batch_decode(...e){if(!this.tokenizer)throw new Error("Unable to decode without a tokenizer.");return this.tokenizer.batch_decode(...e)}async _call(e,...t){for(const s of[this.image_processor,this.feature_extractor,this.tokenizer])if(s)return s(e,...t);throw new Error("No image processor, feature extractor, or tokenizer found.")}static async from_pretrained(e,t){const[s,o]=await Promise.all([this.uses_processor_config?(0,n.getModelJSON)(e,r.PROCESSOR_NAME,!0,t):{},Promise.all(this.classes.filter((e=>e in this)).map((async s=>{const r=await this[s].from_pretrained(e,t);return[s.replace(/_class$/,""),r]}))).then(Object.fromEntries)]);return new this(s,o)}}},"./src/configs.js":
/*!************************!*\
!*** ./src/configs.js ***!
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s={};switch(e.model_type){case"llava":case"paligemma":case"florence2":case"llava_onevision":s=n(e.text_config);break;case"moondream1":s=n(e.phi_config);break;case"musicgen":s=n(e.decoder);break;case"multi_modality":s=n(e.language_config);break;case"gpt2":case"gptj":case"jais":case"codegen":case"gpt_bigcode":t.num_heads="n_head",t.num_layers="n_layer",t.hidden_size="n_embd";break;case"gpt_neox":case"stablelm":case"opt":case"phi":case"phi3":case"falcon":t.num_heads="num_attention_heads",t.num_layers="num_hidden_layers",t.hidden_size="hidden_size";break;case"llama":case"olmo":case"mobilellm":case"granite":case"cohere":case"mistral":case"starcoder2":case"qwen2":case"qwen2_vl":t.num_heads="num_key_value_heads",t.num_layers="num_hidden_layers",t.hidden_size="hidden_size",t.num_attention_heads="num_attention_heads";break;case"gemma":case"gemma2":t.num_heads="num_key_value_heads",t.num_layers="num_hidden_layers",t.dim_kv="head_dim";break;case"openelm":t.num_heads="num_kv_heads",t.num_layers="num_transformer_layers",t.dim_kv="head_dim";break;case"gpt_neo":case"donut-swin":t.num_heads="num_heads",t.num_layers="num_layers",t.hidden_size="hidden_size";break;case"bloom":t.num_heads="n_head",t.num_layers="n_layer",t.hidden_size="hidden_size";break;case"mpt":t.num_heads="n_heads",t.num_layers="n_layers",t.hidden_size="d_model";break;case"t5":case"mt5":case"longt5":t.num_decoder_layers="num_decoder_layers",t.num_decoder_heads="num_heads",t.decoder_dim_kv="d_kv",t.num_encoder_layers="num_layers",t.num_encoder_heads="num_heads",t.encoder_dim_kv="d_kv";break;case"bart":case"mbart":case"marian":case"whisper":case"m2m_100":case"blenderbot":case"blenderbot-small":case"florence2_language":t.num_decoder_layers="decoder_layers",t.num_decoder_heads="decoder_attention_heads",t.decoder_hidden_size="d_model",t.num_encoder_layers="encoder_layers",t.num_encoder_heads="encoder_attention_heads",t.encoder_hidden_size="d_model";break;case"speecht5":t.num_decoder_layers="decoder_layers",t.num_decoder_heads="decoder_attention_heads",t.decoder_hidden_size="hidden_size",t.num_encoder_layers="encoder_layers",t.num_encoder_heads="encoder_attention_heads",t.encoder_hidden_size="hidden_size";break;case"trocr":t.num_encoder_layers=t.num_decoder_layers="decoder_layers",t.num_encoder_heads=t.num_decoder_heads="decoder_attention_heads",t.encoder_hidden_size=t.decoder_hidden_size="d_model";break;case"musicgen_decoder":t.num_encoder_layers=t.num_decoder_layers="num_hidden_layers",t.num_encoder_heads=t.num_decoder_heads="num_attention_heads",t.encoder_hidden_size=t.decoder_hidden_size="hidden_size";break;case"vision-encoder-decoder":const o=n(e.decoder),a="num_decoder_layers"in o,i=(0,r.pick)(e,["model_type","is_encoder_decoder"]);return a?(i.num_decoder_layers=o.num_decoder_layers,i.num_decoder_heads=o.num_decoder_heads,i.decoder_hidden_size=o.decoder_hidden_size,i.num_encoder_layers=o.num_encoder_layers,i.num_encoder_heads=o.num_encoder_heads,i.encoder_hidden_size=o.encoder_hidden_size):(i.num_layers=o.num_layers,i.num_heads=o.num_heads,i.hidden_size=o.hidden_size),i}const o={...s,...(0,r.pick)(e,["model_type","multi_query","is_encoder_decoder"])};for(const s in t)o[s]=e[t[s]];return o}function a(e,{prefix:t="past_key_values",batch_size:s=1}={}){const r={},o=e.normalized_config;if(o.is_encoder_decoder&&"num_encoder_heads"in o&&"num_decoder_heads"in o){const e=o.encoder_dim_kv??o.encoder_hidden_size/o.num_encoder_heads,n=o.decoder_dim_kv??o.decoder_hidden_size/o.num_decoder_heads,a=[s,o.num_encoder_heads,0,e],i=[s,o.num_decoder_heads,0,n];for(let e=0;e<o.num_decoder_layers;++e)r[`${t}.${e}.encoder.key`]=a,r[`${t}.${e}.encoder.value`]=a,r[`${t}.${e}.decoder.key`]=i,r[`${t}.${e}.decoder.value`]=i}else{const e=o.num_heads,n=o.num_layers,a=o.dim_kv??o.hidden_size/(o.num_attention_heads??e);if("falcon"===o.model_type){const o=[s*e,0,a];for(let e=0;e<n;++e)r[`${t}.${e}.key`]=o,r[`${t}.${e}.value`]=o}else if(o.multi_query){const o=[s*e,0,2*a];for(let e=0;e<n;++e)r[`${t}.${e}.key_value`]=o}else if("bloom"===o.model_type){const o=[s*e,a,0],i=[s*e,0,a];for(let e=0;e<n;++e)r[`${t}.${e}.key`]=o,r[`${t}.${e}.value`]=i}else if("openelm"===o.model_type)for(let o=0;o<n;++o){const n=[s,e[o],0,a];r[`${t}.${o}.key`]=n,r[`${t}.${o}.value`]=n}else{const o=[s,e,0,a];for(let e=0;e<n;++e)r[`${t}.${e}.key`]=o,r[`${t}.${e}.value`]=o}}return r}class i{model_type=null;is_encoder_decoder=!1;max_position_embeddings;"transformers.js_config";constructor(e){Object.assign(this,e),this.normalized_config=n(this)}static async from_pretrained(e,{progress_callback:t=null,config:s=null,cache_dir:r=null,local_files_only:n=!1,revision:a="main"}={}){!s||s instanceof i||(s=new i(s));const l=s??await async function(e,t){return await(0,o.getModelJSON)(e,"config.json",!0,t)}(e,{progress_callback:t,config:s,cache_dir:r,local_files_only:n,revision:a});return new this(l)}}class l{static async from_pretrained(...e){return i.from_pretrained(...e)}}},"./src/env.js":
/*!********************!*\
!*** ./src/env.js ***!
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/*!***********************************************!*\
!*** ./src/generation/configuration_utils.js ***!
\***********************************************/(e,t,s)=>{s.r(t),s.d(t,{GenerationConfig:()=>o});var r=s(/*! ../utils/core.js */"./src/utils/core.js");class o{max_length=20;max_new_tokens=null;min_length=0;min_new_tokens=null;early_stopping=!1;max_time=null;do_sample=!1;num_beams=1;num_beam_groups=1;penalty_alpha=null;use_cache=!0;temperature=1;top_k=50;top_p=1;typical_p=1;epsilon_cutoff=0;eta_cutoff=0;diversity_penalty=0;repetition_penalty=1;encoder_repetition_penalty=1;length_penalty=1;no_repeat_ngram_size=0;bad_words_ids=null;force_words_ids=null;renormalize_logits=!1;constraints=null;forced_bos_token_id=null;forced_eos_token_id=null;remove_invalid_values=!1;exponential_decay_length_penalty=null;suppress_tokens=null;begin_suppress_tokens=null;forced_decoder_ids=null;guidance_scale=null;num_return_sequences=1;output_attentions=!1;output_hidden_states=!1;output_scores=!1;return_dict_in_generate=!1;pad_token_id=null;bos_token_id=null;eos_token_id=null;encoder_no_repeat_ngram_size=0;decoder_start_token_id=null;generation_kwargs={};constructor(e){Object.assign(this,(0,r.pick)(e,Object.getOwnPropertyNames(this)))}}},"./src/generation/logits_process.js":
/*!******************************************!*\
!*** ./src/generation/logits_process.js ***!
\******************************************/(e,t,s)=>{s.r(t),s.d(t,{ClassifierFreeGuidanceLogitsProcessor:()=>g,ForcedBOSTokenLogitsProcessor:()=>l,ForcedEOSTokenLogitsProcessor:()=>c,LogitsProcessor:()=>n,LogitsProcessorList:()=>i,LogitsWarper:()=>a,MinLengthLogitsProcessor:()=>h,MinNewTokensLengthLogitsProcessor:()=>_,NoBadWordsLogitsProcessor:()=>f,NoRepeatNGramLogitsProcessor:()=>p,RepetitionPenaltyLogitsProcessor:()=>m,SuppressTokensAtBeginLogitsProcessor:()=>d,TemperatureLogitsWarper:()=>M,TopKLogitsWarper:()=>T,TopPLogitsWarper:()=>w,WhisperTimeStampLogitsProcessor:()=>u});var r=s(/*! ../utils/generic.js */"./src/utils/generic.js"),o=(s(/*! ../utils/tensor.js */"./src/utils/tensor.js"),s(/*! ../utils/maths.js */"./src/utils/maths.js"));class n extends r.Callable{_call(e,t){throw Error("`_call` should be implemented in a subclass")}}class a extends r.Callable{_call(e,t){throw Error("`_call` should be implemented in a subclass")}}class i extends r.Callable{constructor(){super(),this.processors=[]}push(e){this.processors.push(e)}extend(e){this.processors.push(...e)}_call(e,t){let s=t;for(const t of this.processors)s=t(e,s);return s}[Symbol.iterator](){return this.processors.values()}}class l extends n{constructor(e){super(),this.bos_token_id=e}_call(e,t){for(let s=0;s<e.length;++s)if(1===e[s].length){const e=t[s].data;e.fill(-1/0),e[this.bos_token_id]=0}return t}}class c extends n{constructor(e,t){super(),this.max_length=e,this.eos_token_id=Array.isArray(t)?t:[t]}_call(e,t){for(let s=0;s<e.length;++s)if(e[s].length===this.max_length-1){const e=t[s].data;e.fill(-1/0);for(const t of this.eos_token_id)e[t]=0}return t}}class d extends n{constructor(e,t){super(),this.begin_suppress_tokens=e,this.begin_index=t}_call(e,t){for(let s=0;s<e.length;++s)if(e[s].length===this.begin_index){const e=t[s].data;for(const t of this.begin_suppress_tokens)e[t]=-1/0}return t}}class u extends n{constructor(e,t){super(),this.eos_token_id=Array.isArray(e.eos_token_id)?e.eos_token_id[0]:e.eos_token_id,this.no_timestamps_token_id=e.no_timestamps_token_id,this.timestamp_begin=this.no_timestamps_token_id+1,this.begin_index=t.length,t.at(-1)===this.no_timestamps_token_id&&(this.begin_index-=1),this.max_initial_timestamp_index=e.max_initial_timestamp_index}_call(e,t){for(let s=0;s<e.length;++s){const r=t[s].data;if(r[this.no_timestamps_token_id]=-1/0,e[s].length===this.begin_index-1){r.fill(-1/0),r[this.timestamp_begin]=0;continue}const n=e[s].slice(this.begin_index),a=n.length>=1&&n[n.length-1]>=this.timestamp_begin,i=n.length<2||n[n.length-2]>=this.timestamp_begin;if(a&&(i?r.subarray(this.timestamp_begin).fill(-1/0):r.subarray(0,this.eos_token_id).fill(-1/0)),e[s].length===this.begin_index&&null!==this.max_initial_timestamp_index){const e=this.timestamp_begin+this.max_initial_timestamp_index;r.subarray(e+1).fill(-1/0)}const l=(0,o.log_softmax)(r);Math.log(l.subarray(this.timestamp_begin).map(Math.exp).reduce(((e,t)=>e+t)))>(0,o.max)(l.subarray(0,this.timestamp_begin))[0]&&r.subarray(0,this.timestamp_begin).fill(-1/0)}return t}}class p extends n{constructor(e){super(),this.no_repeat_ngram_size=e}getNgrams(e){const t=e.length,s=[];for(let r=0;r<t+1-this.no_repeat_ngram_size;++r){const t=[];for(let s=0;s<this.no_repeat_ngram_size;++s)t.push(e[r+s]);s.push(t.map(Number))}const r=new Map;for(const e of s){const t=e.slice(0,e.length-1),s=JSON.stringify(t),o=r.get(s)??[];o.push(e[e.length-1]),r.set(s,o)}return r}getGeneratedNgrams(e,t){const s=t.slice(t.length+1-this.no_repeat_ngram_size,t.length);return e.get(JSON.stringify(s.map(Number)))??[]}calcBannedNgramTokens(e){const t=[];if(e.length+1<this.no_repeat_ngram_size)return t;{const t=this.getNgrams(e);return this.getGeneratedNgrams(t,e)}}_call(e,t){for(let s=0;s<e.length;++s){const r=t[s].data,o=this.calcBannedNgramTokens(e[s]);for(const e of o)r[e]=-1/0}return t}}class m extends n{constructor(e){super(),this.penalty=e}_call(e,t){for(let s=0;s<e.length;++s){const r=t[s].data;for(const t of e[s]){const e=Number(t);r[e]<0?r[e]*=this.penalty:r[e]/=this.penalty}}return t}}class h extends n{constructor(e,t){super(),this.min_length=e,this.eos_token_id=Array.isArray(t)?t:[t]}_call(e,t){for(let s=0;s<e.length;++s)if(e[s].length<this.min_length){const e=t[s].data;for(const t of this.eos_token_id)e[t]=-1/0}return t}}class _ extends n{constructor(e,t,s){super(),this.prompt_length_to_skip=e,this.min_new_tokens=t,this.eos_token_id=Array.isArray(s)?s:[s]}_call(e,t){for(let s=0;s<e.length;++s){if(e[s].length-this.prompt_length_to_skip<this.min_new_tokens){const e=t[s].data;for(const t of this.eos_token_id)e[t]=-1/0}}return t}}class f extends n{constructor(e,t){super(),this.bad_words_ids=e,this.eos_token_id=Array.isArray(t)?t:[t]}_call(e,t){for(let s=0;s<e.length;++s){const r=t[s].data,o=e[s];for(const e of this.bad_words_ids){let t=!0;for(let s=1;s<=e.length-1&&e.length<o.length;++s)if(e.at(-s-1)!=o.at(-s)){t=!1;break}t&&(r[e.at(-1)]=-1/0)}}return t}}class g extends n{constructor(e){if(super(),e<=1)throw new Error(`Require guidance scale >1 to use the classifier free guidance processor, got guidance scale ${e}.`);this.guidance_scale=e}_call(e,t){if(t.dims[0]!==2*e.length)throw new Error(`Logits should have twice the batch size of the input ids, the first half of batches corresponding to the conditional inputs, and the second half of batches corresponding to the unconditional inputs. Got batch size ${t.dims[0]} for the logits and ${e.length} for the input ids.`);const s=e.length,r=t.slice([0,s],null),o=t.slice([s,t.dims[0]],null);for(let e=0;e<o.data.length;++e)o.data[e]+=(r.data[e]-o.data[e])*this.guidance_scale;return o}}class M extends a{constructor(e){if(super(),"number"!=typeof e||e<=0){let t=`\`temperature\` (=${e}) must be a strictly positive float, otherwise your next token scores will be invalid.`;0===e&&(t+=" If you're looking for greedy decoding strategies, set `do_sample=false`.")}this.temperature=e}_call(e,t){const s=t.data;for(let e=0;e<s.length;++e)s[e]/=this.temperature;return t}}class w extends a{constructor(e,{filter_value:t=-1/0,min_tokens_to_keep:s=1}={}){if(super(),e<0||e>1)throw new Error(`\`top_p\` must be a float > 0 and < 1, but is ${e}`);if(!Number.isInteger(s)||s<1)throw new Error(`\`min_tokens_to_keep\` must be a positive integer, but is ${s}`);this.top_p=e,this.filter_value=t,this.min_tokens_to_keep=s}}class T extends a{constructor(e,{filter_value:t=-1/0,min_tokens_to_keep:s=1}={}){if(super(),!Number.isInteger(e)||e<0)throw new Error(`\`top_k\` must be a positive integer, but is ${e}`);this.top_k=Math.max(e,s),this.filter_value=t}}},"./src/generation/logits_sampler.js":
/*!******************************************!*\
!*** ./src/generation/logits_sampler.js ***!
\******************************************/(e,t,s)=>{s.r(t),s.d(t,{LogitsSampler:()=>a});var r=s(/*! ../utils/generic.js */"./src/utils/generic.js"),o=s(/*! ../utils/tensor.js */"./src/utils/tensor.js"),n=s(/*! ../utils/maths.js */"./src/utils/maths.js");s(/*! ../generation/configuration_utils.js */"./src/generation/configuration_utils.js");class a extends r.Callable{constructor(e){super(),this.generation_config=e}async _call(e){return this.sample(e)}async sample(e){throw Error("sample should be implemented in subclasses.")}getLogits(e,t){let s=e.dims.at(-1),r=e.data;if(-1===t)r=r.slice(-s);else{let e=t*s;r=r.slice(e,e+s)}return r}randomSelect(e){let t=0;for(let s=0;s<e.length;++s)t+=e[s];let s=Math.random()*t;for(let t=0;t<e.length;++t)if(s-=e[t],s<=0)return t;return 0}static getSampler(e){if(e.do_sample)return new l(e);if(e.num_beams>1)return new c(e);if(e.num_return_sequences>1)throw Error(`num_return_sequences has to be 1 when doing greedy search, but is ${e.num_return_sequences}.`);return new i(e)}}class i extends a{async sample(e){const t=(0,n.max)(e.data)[1];return[[BigInt(t),0]]}}class l extends a{async sample(e){let t=e.dims.at(-1);this.generation_config.top_k>0&&(t=Math.min(this.generation_config.top_k,t));const[s,r]=await(0,o.topk)(e,t),a=(0,n.softmax)(s.data);return Array.from({length:this.generation_config.num_beams},(()=>{const e=this.randomSelect(a);return[r.data[e],Math.log(a[e])]}))}}class c extends a{async sample(e){let t=e.dims.at(-1);this.generation_config.top_k>0&&(t=Math.min(this.generation_config.top_k,t));const[s,r]=await(0,o.topk)(e,t),a=(0,n.softmax)(s.data);return Array.from({length:this.generation_config.num_beams},((e,t)=>[r.data[t],Math.log(a[t])]))}}},"./src/generation/stopping_criteria.js":
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!*** ./src/generation/stopping_criteria.js ***!
\*********************************************/(e,t,s)=>{s.r(t),s.d(t,{EosTokenCriteria:()=>i,InterruptableStoppingCriteria:()=>l,MaxLengthCriteria:()=>a,StoppingCriteria:()=>o,StoppingCriteriaList:()=>n});var r=s(/*! ../utils/generic.js */"./src/utils/generic.js");class o extends r.Callable{_call(e,t){throw Error("StoppingCriteria needs to be subclassed")}}class n extends r.Callable{constructor(){super(),this.criteria=[]}push(e){this.criteria.push(e)}extend(e){e instanceof n?e=e.criteria:e instanceof o&&(e=[e]),this.criteria.push(...e)}_call(e,t){const s=new Array(e.length).fill(!1);for(const r of this.criteria){const o=r(e,t);for(let e=0;e<s.length;++e)s[e]||=o[e]}return s}[Symbol.iterator](){return this.criteria.values()}}class a extends o{constructor(e,t=null){super(),this.max_length=e,this.max_position_embeddings=t}_call(e){return e.map((e=>e.length>=this.max_length))}}class i extends o{constructor(e){super(),Array.isArray(e)||(e=[e]),this.eos_token_id=e}_call(e,t){return e.map((e=>{const t=e.at(-1);return this.eos_token_id.some((e=>t==e))}))}}class l extends o{constructor(){super(),this.interrupted=!1}interrupt(){this.interrupted=!0}reset(){this.interrupted=!1}_call(e,t){return new Array(e.length).fill(this.interrupted)}}},"./src/generation/streamers.js":
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!*** ./src/generation/streamers.js ***!
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!*** ./src/models.js ***!
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p.Tensor("bool",[e],[1])}async function B(e,t){let{encoder_outputs:s,input_ids:r,decoder_input_ids:o,...n}=t;if(!s){const r=(0,i.pick)(t,e.sessions.model.inputNames);s=(await O(e,r)).last_hidden_state}n.input_ids=o,n.encoder_hidden_states=s,e.sessions.decoder_model_merged.inputNames.includes("encoder_attention_mask")&&(n.encoder_attention_mask=t.attention_mask);return await D(e,n,!0)}async function O(e,t){const s=e.sessions.model,r=(0,i.pick)(t,s.inputNames);if(s.inputNames.includes("inputs_embeds")&&!r.inputs_embeds){if(!t.input_ids)throw new Error("Both `input_ids` and `inputs_embeds` are missing in the model inputs.");r.inputs_embeds=await e.encode_text({input_ids:t.input_ids})}return s.inputNames.includes("token_type_ids")&&!r.token_type_ids&&(r.token_type_ids=new p.Tensor("int64",new BigInt64Array(r.input_ids.data.length),r.input_ids.dims)),await z(s,r)}async function D(e,t,s=!1){const r=e.sessions[s?"decoder_model_merged":"model"],{past_key_values:o,...n}=t;r.inputNames.includes("use_cache_branch")&&(n.use_cache_branch=V(!!o)),r.inputNames.includes("position_ids")&&n.attention_mask&&!n.position_ids&&(n.position_ids=function(e,t=null){const{input_ids:s,inputs_embeds:r,attention_mask:o}=e,{data:n,dims:a}=G(o);let i=new p.Tensor("int64",n,a);if(t){const e=-(s??r).dims.at(1);i=i.slice(null,[e,null])}return i}(n,o)),e.addPastKeyValues(n,o);const a=(0,i.pick)(n,r.inputNames);return await z(r,a)}async function R(e,{input_ids:t=null,attention_mask:s=null,pixel_values:r=null,position_ids:o=null,inputs_embeds:n=null,past_key_values:a=null,generation_config:i=null,logits_processor:l=null,...c}){if(!n)if(n=await e.encode_text({input_ids:t,...c}),r&&1!==t.dims[1]){const o=await e.encode_image({pixel_values:r,...c});({inputs_embeds:n,attention_mask:s}=e._merge_input_ids_with_image_features({image_features:o,inputs_embeds:n,input_ids:t,attention_mask:s}))}else if(a&&r&&1===t.dims[1]){const e=t.dims[1],r=Object.values(a)[0].dims.at(-2);s=(0,p.cat)([(0,p.ones)([t.dims[0],r]),s.slice(null,[s.dims[1]-e,s.dims[1]])],1)}if(!o&&"qwen2_vl"===e.config.model_type){const{image_grid_thw:r,video_grid_thw:n}=c;[o]=e.get_rope_index(t,r,n,s)}return await D(e,{inputs_embeds:n,past_key_values:a,attention_mask:s,position_ids:o,generation_config:i,logits_processor:l},!0)}function G(e){const[t,s]=e.dims,r=e.data,o=new BigInt64Array(r.length);for(let e=0;e<t;++e){const t=e*s;let n=BigInt(0);for(let e=0;e<s;++e){const s=t+e;0n===r[s]?o[s]=BigInt(1):(o[s]=n,n+=r[s])}}return{data:o,dims:e.dims}}function q(e,t,s,r){if(s.past_key_values){const t=Object.values(s.past_key_values)[0].dims.at(-2),{input_ids:r,attention_mask:o}=s;if(o&&o.dims[1]>r.dims[1]);else if(t<r.dims[1])s.input_ids=r.slice(null,[t,null]);else if(null!=e.config.image_token_index&&r.data.some((t=>t==e.config.image_token_index))){const o=e.config.num_image_tokens;if(!o)throw new Error("`num_image_tokens` is missing in the model configuration.");const n=r.dims[1]-(t-o);s.input_ids=r.slice(null,[-n,null]),s.attention_mask=(0,p.ones)([1,t+n])}}return s}function W(e,t,s,r){return s.past_key_values&&(t=t.map((e=>[e.at(-1)]))),{...s,decoder_input_ids:N(t)}}function $(e,...t){return e.config.is_encoder_decoder?W(e,...t):q(e,...t)}function U(e,t,s,r){const o=!!s.past_key_values;if(null!==r.guidance_scale&&r.guidance_scale>1&&(o?s.input_ids=(0,p.cat)([s.input_ids,s.input_ids],0):(s.input_ids=(0,p.cat)([s.input_ids,(0,p.full_like)(s.input_ids,BigInt(r.pad_token_id))],0),s.attention_mask=(0,p.cat)([s.attention_mask,(0,p.full_like)(s.attention_mask,0n)],0))),!o&&s.pixel_values||(s.pixel_values=(0,p.full)([0,0,3,384,384],1)),o){const e=0,t=1,r=e>0?1:0,o=1;s.images_seq_mask=new p.Tensor("bool",new Array(e+t).fill(!0).fill(!1,0,t),[o,e+t]),s.images_emb_mask=new p.Tensor("bool",new Array(e).fill(!!r),[o,1,e])}return s}class Q extends a.Callable{main_input_name="input_ids";forward_params=["input_ids","attention_mask"];constructor(e,t,s){super(),this.config=e,this.sessions=t,this.configs=s;const r=E.get(this.constructor),o=S.get(r);switch(this.can_generate=!1,this._forward=null,this._prepare_inputs_for_generation=null,o){case k:this.can_generate=!0,this._forward=D,this._prepare_inputs_for_generation=q;break;case x:case P:case v:this.can_generate=!0,this._forward=B,this._prepare_inputs_for_generation=W;break;case b:this._forward=B;break;case F:this.can_generate=!0,this._forward=R,this._prepare_inputs_for_generation=$;break;case C:this.can_generate=!0,this._prepare_inputs_for_generation=U;break;default:this._forward=O}this.can_generate&&this.forward_params.push("past_key_values"),this.custom_config=this.config["transformers.js_config"]??{}}async dispose(){const e=[];for(const t of Object.values(this.sessions))t?.handler?.dispose&&e.push(t.handler.dispose());return await Promise.all(e)}static async from_pretrained(e,{progress_callback:t=null,config:s=null,cache_dir:o=null,local_files_only:n=!1,revision:a="main",model_file_name:i=null,subfolder:l="onnx",device:d=null,dtype:u=null,use_external_data_format:p=null,session_options:m={}}={}){let h={progress_callback:t,config:s,cache_dir:o,local_files_only:n,revision:a,model_file_name:i,subfolder:l,device:d,dtype:u,use_external_data_format:p,session_options:m};const _=E.get(this),f=S.get(_);let g;if(s=h.config=await r.AutoConfig.from_pretrained(e,h),f===k)g=await Promise.all([L(e,{model:h.model_file_name??"model"},h),I(e,{generation_config:"generation_config.json"},h)]);else if(f===x||f===P)g=await Promise.all([L(e,{model:"encoder_model",decoder_model_merged:"decoder_model_merged"},h),I(e,{generation_config:"generation_config.json"},h)]);else if(f===y)g=await Promise.all([L(e,{model:"vision_encoder",prompt_encoder_mask_decoder:"prompt_encoder_mask_decoder"},h)]);else if(f===b)g=await Promise.all([L(e,{model:"encoder_model",decoder_model_merged:"decoder_model_merged"},h)]);else if(f===F){const t={embed_tokens:"embed_tokens",vision_encoder:"vision_encoder",decoder_model_merged:"decoder_model_merged"};s.is_encoder_decoder&&(t.model="encoder_model"),g=await Promise.all([L(e,t,h),I(e,{generation_config:"generation_config.json"},h)])}else f===v?g=await Promise.all([L(e,{model:"text_encoder",decoder_model_merged:"decoder_model_merged",encodec_decode:"encodec_decode"},h),I(e,{generation_config:"generation_config.json"},h)]):f===C?g=await Promise.all([L(e,{prepare_inputs_embeds:"prepare_inputs_embeds",model:"language_model",lm_head:"lm_head",gen_head:"gen_head",gen_img_embeds:"gen_img_embeds",image_decode:"image_decode"},h),I(e,{generation_config:"generation_config.json"},h)]):(f!==T&&console.warn(`Model type for '${_??s?.model_type}' not found, assuming encoder-only architecture. Please report this at ${c.GITHUB_ISSUE_URL}.`),g=await Promise.all([L(e,{model:h.model_file_name??"model"},h)]));return new this(s,...g)}async _call(e){return await this.forward(e)}async forward(e){return await this._forward(this,e)}get generation_config(){return this.configs?.generation_config??null}_get_logits_warper(e){const t=new d.LogitsProcessorList;return null!==e.temperature&&1!==e.temperature&&t.push(new d.TemperatureLogitsWarper(e.temperature)),null!==e.top_k&&0!==e.top_k&&t.push(new d.TopKLogitsWarper(e.top_k)),null!==e.top_p&&e.top_p<1&&t.push(new d.TopPLogitsWarper(e.top_p)),t}_get_logits_processor(e,t,s=null){const r=new d.LogitsProcessorList;if(null!==e.repetition_penalty&&1!==e.repetition_penalty&&r.push(new d.RepetitionPenaltyLogitsProcessor(e.repetition_penalty)),null!==e.no_repeat_ngram_size&&e.no_repeat_ngram_size>0&&r.push(new d.NoRepeatNGramLogitsProcessor(e.no_repeat_ngram_size)),null!==e.bad_words_ids&&r.push(new d.NoBadWordsLogitsProcessor(e.bad_words_ids,e.eos_token_id)),null!==e.min_length&&null!==e.eos_token_id&&e.min_length>0&&r.push(new d.MinLengthLogitsProcessor(e.min_length,e.eos_token_id)),null!==e.min_new_tokens&&null!==e.eos_token_id&&e.min_new_tokens>0&&r.push(new d.MinNewTokensLengthLogitsProcessor(t,e.min_new_tokens,e.eos_token_id)),null!==e.forced_bos_token_id&&r.push(new d.ForcedBOSTokenLogitsProcessor(e.forced_bos_token_id)),null!==e.forced_eos_token_id&&r.push(new d.ForcedEOSTokenLogitsProcessor(e.max_length,e.forced_eos_token_id)),null!==e.begin_suppress_tokens){const s=t>1||null===e.forced_bos_token_id?t:t+1;r.push(new d.SuppressTokensAtBeginLogitsProcessor(e.begin_suppress_tokens,s))}return null!==e.guidance_scale&&e.guidance_scale>1&&r.push(new d.ClassifierFreeGuidanceLogitsProcessor(e.guidance_scale)),null!==s&&r.extend(s),r}_prepare_generation_config(e,t,s=u.GenerationConfig){const r={...this.config};for(const e of["decoder","generator","text_config"])e in r&&Object.assign(r,r[e]);const o=new s(r);return Object.assign(o,this.generation_config??{}),e&&Object.assign(o,e),t&&Object.assign(o,(0,i.pick)(t,Object.getOwnPropertyNames(o))),o}_get_stopping_criteria(e,t=null){const s=new _.StoppingCriteriaList;return null!==e.max_length&&s.push(new _.MaxLengthCriteria(e.max_length,this.config.max_position_embeddings??null)),null!==e.eos_token_id&&s.push(new _.EosTokenCriteria(e.eos_token_id)),t&&s.extend(t),s}_validate_model_class(){if(!this.can_generate){const e=[Wi,Xi,qi,Bi],t=E.get(this.constructor),s=new Set,r=this.config.model_type;for(const t of e){const e=t.get(r);e&&s.add(e[0])}let o=`The current model class (${t}) is not compatible with \`.generate()\`, as it doesn't have a language model head.`;throw s.size>0&&(o+=` Please use the following class instead: ${[...s].join(", ")}`),Error(o)}}prepare_inputs_for_generation(...e){return this._prepare_inputs_for_generation(this,...e)}_update_model_kwargs_for_generation({generated_input_ids:e,outputs:t,model_inputs:s,is_encoder_decoder:r}){return s.past_key_values=this.getPastKeyValues(t,s.past_key_values),s.input_ids=new p.Tensor("int64",e.flat(),[e.length,1]),r||(s.attention_mask=(0,p.cat)([s.attention_mask,(0,p.ones)([s.attention_mask.dims[0],1])],1)),s.position_ids=null,s}_prepare_model_inputs({inputs:e,bos_token_id:t,model_kwargs:s}){const r=(0,i.pick)(s,this.forward_params),o=this.main_input_name;if(o in r){if(e)throw new Error("`inputs`: {inputs}` were passed alongside {input_name} which is not allowed. Make sure to either pass {inputs} or {input_name}=...")}else r[o]=e;return{inputs_tensor:r[o],model_inputs:r,model_input_name:o}}async _prepare_encoder_decoder_kwargs_for_generation({inputs_tensor:e,model_inputs:t,model_input_name:s,generation_config:r}){if(this.sessions.model.inputNames.includes("inputs_embeds")&&!t.inputs_embeds&&"_prepare_inputs_embeds"in this){const{input_ids:e,pixel_values:s,attention_mask:r,...o}=t,n=await this._prepare_inputs_embeds(t);t={...o,...(0,i.pick)(n,["inputs_embeds","attention_mask"])}}let{last_hidden_state:o}=await O(this,t);if(null!==r.guidance_scale&&r.guidance_scale>1)o=(0,p.cat)([o,(0,p.full_like)(o,0)],0),"attention_mask"in t&&(t.attention_mask=(0,p.cat)([t.attention_mask,(0,p.zeros_like)(t.attention_mask)],0));else if(t.decoder_input_ids){const e=N(t.decoder_input_ids).dims[0];if(e!==o.dims[0]){if(1!==o.dims[0])throw new Error(`The encoder outputs have a different batch size (${o.dims[0]}) than the decoder inputs (${e}).`);o=(0,p.cat)(Array.from({length:e},(()=>o)),0)}}return t.encoder_outputs=o,t}_prepare_decoder_input_ids_for_generation({batch_size:e,model_input_name:t,model_kwargs:s,decoder_start_token_id:r,bos_token_id:o,generation_config:n}){let{decoder_input_ids:a,...i}=s;if(!(a instanceof p.Tensor)){if(a)Array.isArray(a[0])||(a=Array.from({length:e},(()=>a)));else if(r??=o,"musicgen"===this.config.model_type)a=Array.from({length:e*this.config.decoder.num_codebooks},(()=>[r]));else if(Array.isArray(r)){if(r.length!==e)throw new Error(`\`decoder_start_token_id\` expcted to have length ${e} but got ${r.length}`);a=r}else a=Array.from({length:e},(()=>[r]));a=N(a)}return s.decoder_attention_mask=(0,p.ones_like)(a),{input_ids:a,model_inputs:i}}async generate({inputs:e=null,generation_config:t=null,logits_processor:s=null,stopping_criteria:r=null,streamer:o=null,...n}){this._validate_model_class(),t=this._prepare_generation_config(t,n);let{inputs_tensor:a,model_inputs:i,model_input_name:l}=this._prepare_model_inputs({inputs:e,model_kwargs:n});const c=this.config.is_encoder_decoder;let d;c&&("encoder_outputs"in i||(i=await this._prepare_encoder_decoder_kwargs_for_generation({inputs_tensor:a,model_inputs:i,model_input_name:l,generation_config:t}))),c?({input_ids:d,model_inputs:i}=this._prepare_decoder_input_ids_for_generation({batch_size:i[l].dims.at(0),model_input_name:l,model_kwargs:i,decoder_start_token_id:t.decoder_start_token_id,bos_token_id:t.bos_token_id,generation_config:t})):d=i[l];let u=d.dims.at(-1);null!==t.max_new_tokens&&(t.max_length=u+t.max_new_tokens);const m=this._get_logits_processor(t,u,s),h=this._get_stopping_criteria(t,r),_=i[l].dims.at(0),g=f.LogitsSampler.getSampler(t),M=new Array(_).fill(0),w=d.tolist();let T;o&&o.put(w);let b={};for(;;){if(i=this.prepare_inputs_for_generation(w,i,t),T=await this.forward(i),t.output_attentions&&t.return_dict_in_generate){const e=this.getAttentions(T);for(const t in e)t in b||(b[t]=[]),b[t].push(e[t])}const e=m(w,T.logits.slice(null,-1,null)),s=[];for(let t=0;t<e.dims.at(0);++t){const r=e[t],o=await g(r);for(const[e,r]of o){const o=BigInt(e);M[t]+=r,w[t].push(o),s.push([o]);break}}o&&o.put(s);if(h(w).every((e=>e)))break;i=this._update_model_kwargs_for_generation({generated_input_ids:s,outputs:T,model_inputs:i,is_encoder_decoder:c})}o&&o.end();const x=this.getPastKeyValues(T,i.past_key_values,!0),P=new p.Tensor("int64",w.flat(),[w.length,w[0].length]);if(t.return_dict_in_generate)return{sequences:P,past_key_values:x,...b};for(const e of Object.values(T))"gpu-buffer"===e.location&&e.dispose();return P}getPastKeyValues(e,t,s=!1){const r=Object.create(null);for(const o in e)if(o.startsWith("present")){const n=o.replace("present","past_key_values"),a=o.includes("encoder");if(r[n]=a&&t?t[n]:e[o],t&&(!a||s)){const e=t[n];"gpu-buffer"===e.location&&e.dispose()}}return r}getAttentions(e){const t={};for(const s of["cross_attentions","encoder_attentions","decoder_attentions"])for(const r in e)r.startsWith(s)&&(s in t||(t[s]=[]),t[s].push(e[r]));return t}addPastKeyValues(e,t){if(t)Object.assign(e,t);else{const t=this.sessions.decoder_model_merged??this.sessions.model,s=t?.config?.kv_cache_dtype??"float32",o="float16"===s?new Uint16Array:[],n=(e[this.main_input_name]??e.attention_mask).dims?.[0]??1,a=(0,r.getKeyValueShapes)(this.config,{batch_size:n});for(const t in a)e[t]=new p.Tensor(s,o,a[t])}}async encode_image({pixel_values:e}){const t=(await z(this.sessions.vision_encoder,{pixel_values:e})).image_features;return this.config.num_image_tokens||(console.warn(`The number of image tokens was not set in the model configuration. Setting it to the number of features detected by the vision encoder (${t.dims[1]}).`),this.config.num_image_tokens=t.dims[1]),t}async encode_text({input_ids:e}){return(await z(this.sessions.embed_tokens,{input_ids:e})).inputs_embeds}}class X{}class H extends X{constructor({last_hidden_state:e,hidden_states:t=null,attentions:s=null}){super(),this.last_hidden_state=e,this.hidden_states=t,this.attentions=s}}class J extends Q{}class Y extends J{}class K extends J{async _call(e){return new Jl(await super._call(e))}}class Z extends J{async _call(e){return new Ql(await super._call(e))}}class ee extends J{async _call(e){return new Hl(await super._call(e))}}class te extends J{async _call(e){return new Yl(await super._call(e))}}class se extends Q{}class re extends se{}class oe extends Q{}class ne extends oe{}class ae extends oe{async _call(e){return new Jl(await super._call(e))}}class ie extends oe{async _call(e){return new Ql(await super._call(e))}}class le extends oe{async _call(e){return new Hl(await super._call(e))}}class ce extends oe{async _call(e){return new Yl(await super._call(e))}}class de extends Q{}class ue extends de{}class pe extends de{async _call(e){return new Jl(await super._call(e))}}class me extends de{async _call(e){return new Ql(await super._call(e))}}class he extends de{async _call(e){return new Hl(await super._call(e))}}class _e extends de{async _call(e){return new Yl(await super._call(e))}}class fe extends Q{}class ge extends fe{}class Me extends fe{async _call(e){return new Jl(await super._call(e))}}class we extends fe{async _call(e){return new Ql(await super._call(e))}}class Te extends fe{async _call(e){return new Hl(await super._call(e))}}class be extends fe{async _call(e){return new Yl(await super._call(e))}}class xe extends Q{}class Pe extends xe{}class ke extends xe{async _call(e){return new Jl(await super._call(e))}}class ye extends xe{async _call(e){return new Ql(await super._call(e))}}class Fe extends xe{async _call(e){return new Hl(await super._call(e))}}class ve extends xe{async _call(e){return new Yl(await super._call(e))}}class Ce extends Q{}class Se extends Ce{}class Ae extends Ce{async _call(e){return new Jl(await super._call(e))}}class Ee extends Ce{async _call(e){return new Ql(await super._call(e))}}class Le extends Ce{async _call(e){return new Hl(await super._call(e))}}class Ie extends Ce{async _call(e){return new Yl(await super._call(e))}}class ze extends Q{}class je extends ze{}class Ne extends ze{async _call(e){return new Jl(await super._call(e))}}class Ve extends ze{async _call(e){return new Ql(await super._call(e))}}class Be extends ze{async _call(e){return new Hl(await super._call(e))}}class Oe extends ze{async _call(e){return new Yl(await super._call(e))}}class De extends Q{}class Re extends De{}class Ge extends De{async _call(e){return new Ql(await super._call(e))}}class qe extends De{async _call(e){return new Hl(await super._call(e))}}class We extends De{async _call(e){return new Yl(await super._call(e))}}class $e extends De{async _call(e){return new Jl(await super._call(e))}}class Ue extends Q{}class Qe extends Ue{}class Xe extends Ue{async _call(e){return new Jl(await super._call(e))}}class He extends Ue{async _call(e){return new Ql(await super._call(e))}}class Je extends Ue{async _call(e){return new Hl(await super._call(e))}}class Ye extends Q{}class Ke extends Ye{}class Ze extends Ye{async _call(e){return new Jl(await super._call(e))}}class et extends Ye{async _call(e){return new Ql(await super._call(e))}}class tt extends Ye{async _call(e){return new Yl(await super._call(e))}}class st extends Q{}class rt extends st{}class ot extends st{async _call(e){return new Jl(await super._call(e))}}class nt extends st{async _call(e){return new Ql(await super._call(e))}}class at extends st{async _call(e){return new Hl(await super._call(e))}}class it extends st{async _call(e){return new Yl(await super._call(e))}}class lt extends Q{}class ct extends lt{}class dt extends lt{async _call(e){return new Jl(await super._call(e))}}class ut extends lt{async _call(e){return new Ql(await super._call(e))}}class pt extends lt{async _call(e){return new Yl(await super._call(e))}}class mt extends Q{}class ht extends mt{}class _t extends mt{async _call(e){return new Ql(await super._call(e))}}class ft extends mt{async _call(e){return new Yl(await super._call(e))}}class gt extends mt{async _call(e){return new Jl(await super._call(e))}}class Mt extends Q{forward_params=["input_ids","attention_mask","encoder_outputs","decoder_input_ids","decoder_attention_mask","past_key_values"]}class wt extends Mt{}class Tt extends Mt{}class bt extends Q{}class xt extends bt{}class Pt extends bt{}class kt extends Q{}class yt extends kt{}class Ft extends kt{}class vt extends Q{}class Ct extends vt{}class St extends vt{}class At extends vt{async _call(e){return new Ql(await super._call(e))}}class Et extends Q{}class Lt extends Et{}class It extends Et{}class zt extends Et{async _call(e){return new Ql(await super._call(e))}}class jt extends Et{}class Nt extends Q{}class Vt extends Nt{}class Bt extends Nt{}class Ot extends Q{}class Dt extends Ot{}class Rt extends Ot{}class Gt extends Q{}class qt extends Gt{}class Wt extends Gt{async _call(e){return new Jl(await super._call(e))}}class $t extends Gt{async _call(e){return new Ql(await super._call(e))}}class Ut extends Gt{async _call(e){return new Hl(await super._call(e))}}class Qt extends Gt{async _call(e){return new Yl(await super._call(e))}}class Xt extends Q{}class Ht extends Xt{}class Jt extends Xt{async _call(e){return new Jl(await super._call(e))}}class Yt extends Xt{async _call(e){return new Ql(await super._call(e))}}class Kt extends Xt{async _call(e){return new Hl(await super._call(e))}}class Zt extends Xt{async _call(e){return new Yl(await super._call(e))}}class es extends Q{}class ts extends es{}class ss extends es{async _call(e){return new Jl(await super._call(e))}}class rs extends es{async _call(e){return new Ql(await super._call(e))}}class os extends es{async _call(e){return new Hl(await super._call(e))}}class ns extends es{async _call(e){return new Yl(await super._call(e))}}class as extends Q{}class is extends as{}class ls extends as{}class cs extends Q{requires_attention_mask=!1;main_input_name="input_features";forward_params=["input_features","attention_mask","decoder_input_ids","decoder_attention_mask","past_key_values"]}class ds extends cs{}class us extends cs{_prepare_generation_config(e,t){return super._prepare_generation_config(e,t,M.WhisperGenerationConfig)}_retrieve_init_tokens(e){const t=[e.decoder_start_token_id];let s=e.language;const r=e.task;if(e.is_multilingual){s||(console.warn("No language specified - defaulting to English (en)."),s="en");const o=`<|${(0,w.whisper_language_to_code)(s)}|>`;t.push(e.lang_to_id[o]),t.push(e.task_to_id[r??"transcribe"])}else if(s||r)throw new Error("Cannot specify `task` or `language` for an English-only model. If the model is intended to be multilingual, pass `is_multilingual=true` to generate, or update the generation config.");return!e.return_timestamps&&e.no_timestamps_token_id&&t.at(-1)!==e.no_timestamps_token_id?t.push(e.no_timestamps_token_id):e.return_timestamps&&t.at(-1)===e.no_timestamps_token_id&&(console.warn("<|notimestamps|> prompt token is removed from generation_config since `return_timestamps` is set to `true`."),t.pop()),t.filter((e=>null!=e))}async generate({inputs:e=null,generation_config:t=null,logits_processor:s=null,stopping_criteria:r=null,...o}){t=this._prepare_generation_config(t,o);const n=o.decoder_input_ids??this._retrieve_init_tokens(t);if(t.return_timestamps&&(s??=new d.LogitsProcessorList,s.push(new d.WhisperTimeStampLogitsProcessor(t,n))),t.begin_suppress_tokens&&(s??=new d.LogitsProcessorList,s.push(new d.SuppressTokensAtBeginLogitsProcessor(t.begin_suppress_tokens,n.length))),t.return_token_timestamps){if(!t.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.");"translate"===t.task&&console.warn("Token-level timestamps may not be reliable for task 'translate'."),t.output_attentions=!0,t.return_dict_in_generate=!0}const a=await super.generate({inputs:e,generation_config:t,logits_processor:s,decoder_input_ids:n,...o});return t.return_token_timestamps&&(a.token_timestamps=this._extract_token_timestamps(a,t.alignment_heads,t.num_frames)),a}_extract_token_timestamps(e,t,s=null,r=.02){if(!e.cross_attentions)throw new Error("Model outputs must contain cross attentions to extract timestamps. This is most likely because the model was not exported with `output_attentions=True`.");null==s&&console.warn("`num_frames` has not been set, meaning the entire audio will be analyzed. This may lead to inaccurate token-level timestamps for short audios (< 30 seconds).");let o=this.config.median_filter_width;void 0===o&&(console.warn("Model config has no `median_filter_width`, using default value of 7."),o=7);const n=e.cross_attentions,a=Array.from({length:this.config.decoder_layers},((e,t)=>(0,p.cat)(n.map((e=>e[t])),2))),l=(0,p.stack)(t.map((([e,t])=>{if(e>=a.length)throw new Error(`Layer index ${e} is out of bounds for cross attentions (length ${a.length}).`);return s?a[e].slice(null,t,null,[0,s]):a[e].slice(null,t)}))).transpose(1,0,2,3),[c,d]=(0,p.std_mean)(l,-2,0,!0),u=l.clone();for(let e=0;e<u.dims[0];++e){const t=u[e];for(let s=0;s<t.dims[0];++s){const r=t[s],n=c[e][s][0].data,a=d[e][s][0].data;for(let e=0;e<r.dims[0];++e){let t=r[e].data;for(let e=0;e<t.length;++e)t[e]=(t[e]-a[e])/n[e];t.set((0,h.medianFilter)(t,o))}}}const m=[(0,p.mean)(u,1)],_=e.sequences.dims,f=new p.Tensor("float32",new Float32Array(_[0]*_[1]),_);for(let e=0;e<_[0];++e){const t=m[e].neg().squeeze_(0),[s,o]=(0,h.dynamic_time_warping)(t.tolist()),n=Array.from({length:s.length-1},((e,t)=>s[t+1]-s[t])),a=(0,i.mergeArrays)([1],n).map((e=>!!e)),l=[];for(let e=0;e<a.length;++e)a[e]&&l.push(o[e]*r);f[e].data.set(l,1)}return f}}class ps extends Q{main_input_name="pixel_values";forward_params=["pixel_values","decoder_input_ids","encoder_hidden_states","past_key_values"]}class ms extends Q{forward_params=["input_ids","pixel_values","attention_mask","position_ids","past_key_values"]}class hs extends ms{_merge_input_ids_with_image_features({inputs_embeds:e,image_features:t,input_ids:s,attention_mask:r}){const o=this.config.image_token_index,n=s.tolist().map((e=>e.findIndex((e=>e==o)))),a=n.every((e=>-1===e)),i=n.every((e=>-1!==e));if(!a&&!i)throw new Error("Every input should contain either 0 or 1 image token.");if(a)return{inputs_embeds:e,attention_mask:r};const l=[],c=[];for(let s=0;s<n.length;++s){const o=n[s],a=e[s],i=t[s],d=r[s];l.push((0,p.cat)([a.slice([0,o]),i,a.slice([o+1,a.dims[0]])],0)),c.push((0,p.cat)([d.slice([0,o]),(0,p.ones)([i.dims[0]]),d.slice([o+1,d.dims[0]])],0))}return{inputs_embeds:(0,p.stack)(l,0),attention_mask:(0,p.stack)(c,0)}}}class _s extends hs{}class fs extends hs{}class gs extends Q{forward_params=["input_ids","inputs_embeds","attention_mask","pixel_values","encoder_outputs","decoder_input_ids","decoder_inputs_embeds","decoder_attention_mask","past_key_values"];main_input_name="inputs_embeds"}class Ms extends gs{_merge_input_ids_with_image_features({inputs_embeds:e,image_features:t,input_ids:s,attention_mask:r}){return{inputs_embeds:(0,p.cat)([t,e],1),attention_mask:(0,p.cat)([(0,p.ones)(t.dims.slice(0,2)),r],1)}}async _prepare_inputs_embeds({input_ids:e,pixel_values:t,inputs_embeds:s,attention_mask:r}){if(!e&&!t)throw new Error("Either `input_ids` or `pixel_values` should be provided.");let o,n;return e&&(o=await this.encode_text({input_ids:e})),t&&(n=await this.encode_image({pixel_values:t})),o&&n?({inputs_embeds:s,attention_mask:r}=this._merge_input_ids_with_image_features({inputs_embeds:o,image_features:n,input_ids:e,attention_mask:r})):s=o||n,{inputs_embeds:s,attention_mask:r}}async forward({input_ids:e,pixel_values:t,attention_mask:s,decoder_input_ids:r,decoder_attention_mask:o,encoder_outputs:n,past_key_values:a,inputs_embeds:i,decoder_inputs_embeds:l}){if(i||({inputs_embeds:i,attention_mask:s}=await this._prepare_inputs_embeds({input_ids:e,pixel_values:t,inputs_embeds:i,attention_mask:s})),!n){let{last_hidden_state:e}=await O(this,{inputs_embeds:i,attention_mask:s});n=e}if(!l){if(!r)throw new Error("Either `decoder_input_ids` or `decoder_inputs_embeds` should be provided.");l=await this.encode_text({input_ids:r})}const c={inputs_embeds:l,attention_mask:o,encoder_attention_mask:s,encoder_hidden_states:n,past_key_values:a};return await D(this,c,!0)}}class ws extends Q{}class Ts extends ws{}class bs extends ws{static async from_pretrained(e,t={}){return t.model_file_name??="text_model",super.from_pretrained(e,t)}}class xs extends ws{static async from_pretrained(e,t={}){return t.model_file_name??="text_model",super.from_pretrained(e,t)}}class Ps extends ws{static async from_pretrained(e,t={}){return t.model_file_name??="vision_model",super.from_pretrained(e,t)}}class ks extends ws{static async from_pretrained(e,t={}){return t.model_file_name??="vision_model",super.from_pretrained(e,t)}}class ys extends Q{}class Fs extends ys{}class vs extends ys{static async from_pretrained(e,t={}){return t.model_file_name??="text_model",super.from_pretrained(e,t)}}class Cs extends ws{static async from_pretrained(e,t={}){return t.model_file_name??="vision_model",super.from_pretrained(e,t)}}class Ss extends Q{}class As extends Ss{}class Es extends Q{}class Ls extends Es{async forward(e){const t=!e.input_ids,s=!e.pixel_values;if(t&&s)throw new Error("Either `input_ids` or `pixel_values` should be provided.");if(t&&(e.input_ids=(0,p.ones)([e.pixel_values.dims[0],1])),s){const{image_size:t}=this.config.vision_config;e.pixel_values=(0,p.full)([0,3,t,t],0)}const{text_embeddings:r,image_embeddings:o,l2norm_text_embeddings:n,l2norm_image_embeddings:a}=await super.forward(e),i={};return t||(i.text_embeddings=r,i.l2norm_text_embeddings=n),s||(i.image_embeddings=o,i.l2norm_image_embeddings=a),i}}class Is extends Es{static async from_pretrained(e,t={}){return t.model_file_name??="text_model",super.from_pretrained(e,t)}}class zs extends Es{static async from_pretrained(e,t={}){return t.model_file_name??="vision_model",super.from_pretrained(e,t)}}class js extends Q{}class Ns extends js{}class Vs extends js{}class Bs extends Q{}class Os extends Bs{}class Ds extends Bs{}class Rs extends Q{}class Gs extends Rs{}class qs extends Rs{}class Ws extends Q{}class $s extends Ws{}class Us extends Ws{}class Qs extends Q{}class Xs extends Qs{}class Hs extends Qs{}class Js extends Q{}class Ys extends Js{}class Ks extends Js{}class Zs extends Q{}class er extends Zs{}class tr extends Zs{}class sr extends Q{}class rr extends sr{}class or extends sr{}class nr extends Q{}class ar extends nr{}class ir extends nr{}class lr extends Q{}class cr extends lr{}class dr extends lr{}class ur extends Q{}class pr extends ur{}class mr extends ur{}class hr extends Q{}class _r extends hr{}class fr extends hr{}class gr extends Q{}class Mr extends gr{}class wr extends gr{}class Tr extends Q{}class br extends Tr{}class xr extends Tr{}class Pr extends Q{}class kr extends Pr{}class yr extends Pr{}class Fr extends Q{}class vr extends Fr{}class Cr extends Fr{}class Sr extends Q{}class Ar extends Sr{}class Er extends Sr{}class Lr extends Q{forward_params=["input_ids","attention_mask","position_ids","past_key_values","pixel_values","image_grid_thw"]}class Ir extends Lr{get_rope_index(e,t,s,r){const{vision_config:o,image_token_id:n,video_token_id:a,vision_start_token_id:i}=this.config,l=o.spatial_merge_size??2,c=[];if(t||s){let o=e.tolist();r||(r=(0,p.ones_like)(e));const d=r.tolist(),u=Array.from({length:3},(t=>Array.from({length:e.dims[0]},(t=>Array.from({length:e.dims[1]},(e=>1)))))),m=t?t.tolist():[],_=s?s.tolist():[];let f=0,g=0;for(let e=0;e<o.length;++e){const t=o[e].filter(((t,s)=>1==d[e][s])),s=t.reduce(((e,t,s)=>(t==i&&e.push(s),e)),[]).map((e=>t[e+1])),r=s.filter((e=>e==n)).length,p=s.filter((e=>e==a)).length;let M=[],w=0,T=r,b=p;for(let e=0;e<s.length;++e){const e=t.findIndex(((e,t)=>t>w&&e==n)),s=t.findIndex(((e,t)=>t>w&&e==a)),r=T>0&&-1!==e?e:t.length+1,o=b>0&&-1!==s?s:t.length+1;let i,c,d,u;r<o?([c,d,u]=m[f],++f,--T,i=r):([c,d,u]=_[g],++g,--b,i=o);const[p,x,P]=[Number(c),Math.floor(Number(d)/l),Math.floor(Number(u)/l)],k=i-w,y=M.length>0?(0,h.max)(M.at(-1))[0]+1:0;M.push(Array.from({length:3*k},((e,t)=>y+t%k)));const F=k+y,v=p*x*P,C=Array.from({length:v},((e,t)=>F+Math.floor(t/(x*P)))),S=Array.from({length:v},((e,t)=>F+Math.floor(t/P)%x)),A=Array.from({length:v},((e,t)=>F+t%P));M.push([C,S,A].flat()),w=i+v}if(w<t.length){const e=M.length>0?(0,h.max)(M.at(-1))[0]+1:0,s=t.length-w;M.push(Array.from({length:3*s},((t,r)=>e+r%s)))}const x=M.reduce(((e,t)=>e+t.length),0),P=new Array(x);let k=0;for(let e=0;e<3;++e)for(let t=0;t<M.length;++t){const s=M[t],r=s.length/3;for(let t=e*r;t<(e+1)*r;++t)P[k++]=s[t]}let y=0;const F=d[e];for(let t=0;t<F.length;++t)if(1==F[t]){for(let s=0;s<3;++s)u[s][e][t]=P[s*x/3+y];++y}const v=(0,h.max)(P)[0];c.push(v+1-o[e].length)}return[new p.Tensor("int64",u.flat(1/0),[3,e.dims[0],e.dims[1]]),new p.Tensor("int64",c,[c.length,1])]}if(r){const{data:e,dims:t}=G(r),s=BigInt64Array.from({length:3*e.length},((t,s)=>e[s%e.length])),o=Array.from({length:t[0]},((s,r)=>(0,h.max)(e.subarray(t[1]*r,t[1]*(r+1)))[0]+1+t[1]));return[new p.Tensor("int64",s,[3,...t]),new p.Tensor("int64",o,[o.length,1])]}{const[t,s]=e.dims,r=BigInt64Array.from({length:3*t*s},((e,r)=>BigInt(Math.floor(r%s/t))));return[new p.Tensor("int64",r,[3,...e.dims]),(0,p.zeros)([t,1])]}}async encode_image({pixel_values:e,image_grid_thw:t}){return(await z(this.sessions.vision_encoder,{pixel_values:e,grid_thw:t})).image_features}_merge_input_ids_with_image_features({inputs_embeds:e,image_features:t,input_ids:s,attention_mask:r}){const{image_token_id:o}=this.config,n=s.tolist().map((e=>e.reduce(((e,t,s)=>(t==o&&e.push(s),e)),[]))),a=n.reduce(((e,t)=>e+t.length),0),i=t.dims[0];if(a!==i)throw new Error(`Image features and image tokens do not match: tokens: ${a}, features ${i}`);let l=0;for(let s=0;s<n.length;++s){const r=n[s],o=e[s];for(let e=0;e<r.length;++e)o[r[e]].data.set(t[l++].data)}return{inputs_embeds:e,attention_mask:r}}prepare_inputs_for_generation(e,t,s){if(t.attention_mask&&!t.position_ids)if(t.past_key_values){t.pixel_values=null;const e=BigInt(Object.values(t.past_key_values)[0].dims.at(-2)),s=t.rope_deltas.map((t=>e+t));t.position_ids=(0,p.stack)([s,s,s],0)}else[t.position_ids,t.rope_deltas]=this.get_rope_index(t.input_ids,t.image_grid_thw,t.video_grid_thw,t.attention_mask);return t}}class zr extends Q{}class jr extends zr{}class Nr extends zr{}class Vr extends Q{}class Br extends Vr{}class Or extends Vr{}class Dr extends Q{}class Rr extends Dr{}class Gr extends Dr{}class qr extends Q{}class Wr extends qr{}class $r extends qr{}class Ur extends Q{}class Qr extends Ur{}class Xr extends Ur{}class Hr extends Q{}class Jr extends Hr{}class Yr extends Hr{async _call(e){return new Ql(await super._call(e))}}class Kr extends Q{}class Zr extends Kr{}class eo extends Q{}class to extends eo{}class so extends eo{async _call(e){return new Ql(await super._call(e))}}class ro extends Q{}class oo extends ro{}class no extends Q{}class ao extends no{}class io extends no{async _call(e){return new Ql(await super._call(e))}}class lo extends Q{}class co extends lo{}class uo extends Q{}class po extends uo{}class mo extends uo{async _call(e){return new Ql(await super._call(e))}}class ho extends Q{}class _o extends ho{async _call(e){return new ec(await super._call(e))}}class fo extends Q{}class go extends fo{}class Mo extends fo{async _call(e){return new Ql(await super._call(e))}}class wo extends Q{}class To extends wo{}class bo extends wo{async _call(e){return new Ql(await super._call(e))}}class xo extends Q{}class Po extends xo{}class ko extends xo{}class yo extends Q{}class Fo extends yo{}class vo extends yo{}class Co extends Q{}class So extends Co{}class Ao extends Co{async _call(e){return new Ql(await super._call(e))}}class Eo extends Q{}class Lo extends Eo{}class Io extends Eo{async _call(e){return new jo(await super._call(e))}}class zo extends Eo{async _call(e){return new No(await super._call(e))}}class jo extends X{constructor({logits:e,pred_boxes:t}){super(),this.logits=e,this.pred_boxes=t}}class No extends X{constructor({logits:e,pred_boxes:t,pred_masks:s}){super(),this.logits=e,this.pred_boxes=t,this.pred_masks=s}}class Vo extends Q{}class Bo extends Vo{}class Oo extends Vo{async _call(e){return new Do(await super._call(e))}}class Do extends X{constructor({logits:e,pred_boxes:t}){super(),this.logits=e,this.pred_boxes=t}}class Ro extends Q{}class Go extends Ro{}class qo extends Ro{async _call(e){return new Wo(await super._call(e))}}class Wo extends jo{}class $o extends Q{}class Uo extends $o{}class Qo extends $o{async _call(e){return new Ql(await super._call(e))}}class Xo extends Q{}class Ho extends Xo{}class Jo extends Xo{async _call(e){return new Ql(await super._call(e))}}class Yo extends Q{}class Ko extends Yo{}class Zo extends Yo{async _call(e){return new Ql(await super._call(e))}}class en extends Q{}class tn extends en{}class sn extends en{async _call(e){return new Ql(await super._call(e))}}class rn extends Q{}class on extends rn{}class nn extends rn{}class an extends Q{}class ln extends an{}class cn extends an{}class dn extends Q{}class un extends dn{}class pn extends Q{}class mn extends pn{}class hn extends pn{}class _n extends pn{}class fn extends Q{}class gn extends fn{}class Mn extends Q{}class wn extends Mn{}class Tn extends Mn{}class bn extends Q{}class xn extends bn{}class Pn extends bn{}class kn extends Q{}class yn extends kn{}class Fn extends Q{}class vn extends Fn{}class Cn extends Fn{async _call(e){return new Ql(await super._call(e))}}class Sn extends Q{}class An extends Sn{}class En extends Sn{async _call(e){return new Ql(await super._call(e))}}class Ln extends Q{}class In extends Ln{}class zn extends Ln{async _call(e){return new Ql(await super._call(e))}}class jn extends Q{}class Nn extends jn{}class Vn extends jn{async _call(e){return new Bn(await super._call(e))}}class Bn extends X{constructor({logits:e,pred_boxes:t}){super(),this.logits=e,this.pred_boxes=t}}class On extends Q{}class Dn extends On{async get_image_embeddings({pixel_values:e}){return await O(this,{pixel_values:e})}async forward(e){if(e.image_embeddings&&e.image_positional_embeddings||(e={...e,...await this.get_image_embeddings(e)}),!e.input_labels&&e.input_points){const t=e.input_points.dims.slice(0,-1),s=t.reduce(((e,t)=>e*t),1);e.input_labels=new p.Tensor("int64",new BigInt64Array(s).fill(1n),t)}const t={image_embeddings:e.image_embeddings,image_positional_embeddings:e.image_positional_embeddings};return e.input_points&&(t.input_points=e.input_points),e.input_labels&&(t.input_labels=e.input_labels),e.input_boxes&&(t.input_boxes=e.input_boxes),await z(this.sessions.prompt_encoder_mask_decoder,t)}async _call(e){return new Rn(await super._call(e))}}class Rn extends X{constructor({iou_scores:e,pred_masks:t}){super(),this.iou_scores=e,this.pred_masks=t}}class Gn extends Q{}class qn extends Gn{}class Wn extends Gn{}class $n extends Q{}class Un extends $n{}class Qn extends $n{}class Xn extends Q{}class Hn extends Xn{}class Jn extends Xn{async _call(e){return new Kl(await super._call(e))}}class Yn extends Xn{async _call(e){return new Ql(await super._call(e))}}class Kn extends Xn{async _call(e){return new Hl(await super._call(e))}}class Zn extends Q{}class ea extends Zn{}class ta extends Zn{async _call(e){return new Hl(await super._call(e))}}class sa extends Q{}class ra extends sa{}class oa extends Q{}class na extends oa{}class aa extends oa{async _call(e){return new Kl(await super._call(e))}}class ia extends oa{async _call(e){return new Ql(await super._call(e))}}class la extends Q{}class ca extends la{}class da extends la{async _call(e){return new Kl(await super._call(e))}}class ua extends la{async _call(e){return new Ql(await super._call(e))}}class pa extends la{async _call(e){return new Hl(await super._call(e))}}class ma extends Q{}class ha extends ma{}class _a extends ma{async _call(e){return new Kl(await super._call(e))}}class fa extends ma{async _call(e){return new Ql(await super._call(e))}}class ga extends Q{}class Ma extends Xn{}class wa extends Xn{async _call(e){return new Kl(await super._call(e))}}class Ta extends Xn{async _call(e){return new Ql(await super._call(e))}}class ba extends Q{}class xa extends ba{}class Pa extends ba{async _call(e){return new Kl(await super._call(e))}}class ka extends ba{async _call(e){return new Ql(await super._call(e))}}class ya extends ba{async _call(e){return new Xl(await super._call(e))}}class Fa extends ba{async _call(e){return new Hl(await super._call(e))}}class va extends Q{}class Ca extends va{}class Sa extends va{}class Aa extends va{async generate_speech(e,t,{threshold:s=.5,minlenratio:r=0,maxlenratio:o=20,vocoder:n=null}={}){const a={input_ids:e},{encoder_outputs:i,encoder_attention_mask:l}=await O(this,a),c=i.dims[1]/this.config.reduction_factor,d=Math.floor(c*o),u=Math.floor(c*r),m=this.config.num_mel_bins;let h=[],_=null,f=null,g=0;for(;;){++g;const e=V(!!f);let r;r=f?f.output_sequence_out:new p.Tensor("float32",new Float32Array(m),[1,1,m]);let o={use_cache_branch:e,output_sequence:r,encoder_attention_mask:l,speaker_embeddings:t,encoder_hidden_states:i};this.addPastKeyValues(o,_),f=await z(this.sessions.decoder_model_merged,o),_=this.getPastKeyValues(f,_);const{prob:n,spectrum:a}=f;if(h.push(a),g>=u&&(Array.from(n.data).filter((e=>e>=s)).length>0||g>=d))break}const M=(0,p.cat)(h),{waveform:w}=await z(n.sessions.model,{spectrogram:M});return{spectrogram:M,waveform:w}}}class Ea extends Q{main_input_name="spectrogram"}class La extends Q{}class Ia extends La{}class za extends Q{}class ja extends za{}class Na extends za{}class Va extends Q{}class Ba extends Va{}class Oa extends Va{}class Da extends Q{}class Ra extends Da{}class Ga extends Da{}class qa extends Q{}class Wa extends qa{}class $a extends qa{static async from_pretrained(e,t={}){return t.model_file_name??="text_model",super.from_pretrained(e,t)}}class Ua extends qa{static async from_pretrained(e,t={}){return t.model_file_name??="audio_model",super.from_pretrained(e,t)}}class Qa extends Q{}class Xa extends Qa{async _call(e){return new tc(await super._call(e))}}class Ha extends Q{}class Ja extends Ha{}class Ya extends Ha{}class Ka extends Ha{}class Za extends Q{}class ei extends Za{}class ti extends Za{}class si extends Q{}class ri extends si{}class oi extends si{async _call(e){return new Ql(await super._call(e))}}class ni extends Q{}class ai extends ni{}class ii extends ni{}class li extends Q{forward_params=["input_ids","attention_mask","encoder_outputs","decoder_input_ids","decoder_attention_mask","past_key_values"];_apply_and_filter_by_delay_pattern_mask(e){const[t,s]=e.dims,r=this.config.decoder.num_codebooks,o=s-r;let n=0;for(let t=0;t<e.size;++t){if(e.data[t]===this.config.decoder.pad_token_id)continue;const a=t%s-Math.floor(t/s)%r;a>0&&a<=o&&(e.data[n++]=e.data[t])}const a=Math.floor(t/r),i=n/(a*r);return new p.Tensor(e.type,e.data.slice(0,n),[a,r,i])}prepare_inputs_for_generation(e,t,s){let r=structuredClone(e);for(let e=0;e<r.length;++e)for(let t=0;t<r[e].length;++t)e%this.config.decoder.num_codebooks>=t&&(r[e][t]=BigInt(this.config.decoder.pad_token_id));null!==s.guidance_scale&&s.guidance_scale>1&&(r=r.concat(r));return super.prepare_inputs_for_generation(r,t,s)}async generate(e){const t=await super.generate(e),s=this._apply_and_filter_by_delay_pattern_mask(t).unsqueeze_(0),{audio_values:r}=await z(this.sessions.encodec_decode,{audio_codes:s});return r}}class ci extends Q{}class di extends ci{}class ui extends ci{async _call(e){return new Ql(await super._call(e))}}class pi extends Q{}class mi extends pi{}class hi extends pi{async _call(e){return new Ql(await super._call(e))}}class _i extends Q{}class fi extends _i{}class gi extends _i{async _call(e){return new Ql(await super._call(e))}}class Mi extends Q{}class wi extends Mi{}class Ti extends Mi{async _call(e){return new Ql(await super._call(e))}}class bi extends Q{}class xi extends bi{}class Pi extends Q{}class ki extends Pi{forward_params=["input_ids","pixel_values","images_seq_mask","images_emb_mask","attention_mask","position_ids","past_key_values"];constructor(...e){super(...e),this._generation_mode="text"}async forward(e){const t=this._generation_mode??"text";let s;if("text"!==t&&e.past_key_values){const t=this.sessions.gen_img_embeds,r=(0,i.pick)({image_ids:e.input_ids},t.inputNames);s=await z(t,r)}else{const t=this.sessions.prepare_inputs_embeds,r=(0,i.pick)(e,t.inputNames);s=await z(t,r)}const r={...e,...s},o=await D(this,r),n=this.sessions["text"===t?"lm_head":"gen_head"];if(!n)throw new Error(`Unable to find "${n}" generation head`);const a=await z(n,(0,i.pick)(o,n.inputNames));return{...s,...o,...a}}async generate(e){return this._generation_mode="text",super.generate(e)}async generate_images(e){this._generation_mode="image";const t=(e.inputs??e[this.main_input_name]).dims[1],s=(await super.generate(e)).slice(null,[t,null]),r=this.sessions.image_decode,{decoded_image:o}=await z(r,{generated_tokens:s}),n=o.add_(1).mul_(127.5).clamp_(0,255).to("uint8"),a=[];for(const e of n){const t=m.RawImage.fromTensor(e);a.push(t)}return a}}class yi extends X{constructor({char_logits:e,bpe_logits:t,wp_logits:s}){super(),this.char_logits=e,this.bpe_logits=t,this.wp_logits=s}get logits(){return[this.char_logits,this.bpe_logits,this.wp_logits]}}class Fi extends Q{}class vi extends Fi{async _call(e){return new yi(await super._call(e))}}class Ci extends Q{}class Si extends Ci{}class Ai extends Ci{}class Ei extends Q{}class Li extends Ei{}class Ii extends Ei{}class zi{static MODEL_CLASS_MAPPINGS=null;static BASE_IF_FAIL=!1;static async from_pretrained(e,{progress_callback:t=null,config:s=null,cache_dir:o=null,local_files_only:n=!1,revision:a="main",model_file_name:i=null,subfolder:l="onnx",device:c=null,dtype:d=null,use_external_data_format:u=null,session_options:p={}}={}){const m={progress_callback:t,config:s,cache_dir:o,local_files_only:n,revision:a,model_file_name:i,subfolder:l,device:c,dtype:d,use_external_data_format:u,session_options:p};if(m.config=await r.AutoConfig.from_pretrained(e,m),!this.MODEL_CLASS_MAPPINGS)throw new Error("`MODEL_CLASS_MAPPINGS` not implemented for this type of `AutoClass`: "+this.name);for(const t of this.MODEL_CLASS_MAPPINGS){const s=t.get(m.config.model_type);if(s)return await s[1].from_pretrained(e,m)}if(this.BASE_IF_FAIL)return console.warn(`Unknown model class "${m.config.model_type}", attempting to construct from base class.`),await Q.from_pretrained(e,m);throw Error(`Unsupported model type: ${m.config.model_type}`)}}const ji=new Map([["bert",["BertModel",Y]],["nomic_bert",["NomicBertModel",re]],["roformer",["RoFormerModel",ne]],["electra",["ElectraModel",ge]],["esm",["EsmModel",Qe]],["convbert",["ConvBertModel",ue]],["camembert",["CamembertModel",Pe]],["deberta",["DebertaModel",Se]],["deberta-v2",["DebertaV2Model",je]],["mpnet",["MPNetModel",rt]],["albert",["AlbertModel",ht]],["distilbert",["DistilBertModel",Re]],["roberta",["RobertaModel",qt]],["xlm",["XLMModel",Ht]],["xlm-roberta",["XLMRobertaModel",ts]],["clap",["ClapModel",Wa]],["clip",["CLIPModel",Ts]],["clipseg",["CLIPSegModel",Ns]],["chinese_clip",["ChineseCLIPModel",As]],["siglip",["SiglipModel",Fs]],["jina_clip",["JinaCLIPModel",Ls]],["mobilebert",["MobileBertModel",Ke]],["squeezebert",["SqueezeBertModel",ct]],["wav2vec2",["Wav2Vec2Model",Hn]],["wav2vec2-bert",["Wav2Vec2BertModel",ha]],["unispeech",["UniSpeechModel",na]],["unispeech-sat",["UniSpeechSatModel",ca]],["hubert",["HubertModel",Ma]],["wavlm",["WavLMModel",xa]],["audio-spectrogram-transformer",["ASTModel",is]],["vits",["VitsModel",Xa]],["pyannote",["PyAnnoteModel",ea]],["wespeaker-resnet",["WeSpeakerResNetModel",ra]],["detr",["DetrModel",Lo]],["rt_detr",["RTDetrModel",Bo]],["table-transformer",["TableTransformerModel",Go]],["vit",["ViTModel",Jr]],["pvt",["PvtModel",to]],["vit_msn",["ViTMSNModel",ao]],["vit_mae",["ViTMAEModel",oo]],["groupvit",["GroupViTModel",co]],["fastvit",["FastViTModel",po]],["mobilevit",["MobileViTModel",go]],["mobilevitv2",["MobileViTV2Model",To]],["owlvit",["OwlViTModel",Po]],["owlv2",["Owlv2Model",Fo]],["beit",["BeitModel",So]],["deit",["DeiTModel",Uo]],["hiera",["HieraModel",Ho]],["convnext",["ConvNextModel",vn]],["convnextv2",["ConvNextV2Model",An]],["dinov2",["Dinov2Model",In]],["resnet",["ResNetModel",Ko]],["swin",["SwinModel",tn]],["swin2sr",["Swin2SRModel",on]],["donut-swin",["DonutSwinModel",yn]],["yolos",["YolosModel",Nn]],["dpt",["DPTModel",ln]],["glpn",["GLPNModel",xn]],["hifigan",["SpeechT5HifiGan",Ea]],["efficientnet",["EfficientNetModel",ri]],["decision_transformer",["DecisionTransformerModel",xi]],["patchtst",["PatchTSTForPrediction",Si]],["patchtsmixer",["PatchTSMixerForPrediction",Li]],["mobilenet_v1",["MobileNetV1Model",di]],["mobilenet_v2",["MobileNetV2Model",mi]],["mobilenet_v3",["MobileNetV3Model",fi]],["mobilenet_v4",["MobileNetV4Model",wi]],["maskformer",["MaskFormerModel",wn]],["mgp-str",["MgpstrForSceneTextRecognition",vi]]]),Ni=new Map([["t5",["T5Model",wt]],["longt5",["LongT5Model",xt]],["mt5",["MT5Model",yt]],["bart",["BartModel",Ct]],["mbart",["MBartModel",Lt]],["marian",["MarianModel",qn]],["whisper",["WhisperModel",ds]],["m2m_100",["M2M100Model",Un]],["blenderbot",["BlenderbotModel",Vt]],["blenderbot-small",["BlenderbotSmallModel",Dt]]]),Vi=new Map([["bloom",["BloomModel",Rr]],["jais",["JAISModel",Gs]],["gpt2",["GPT2Model",Os]],["gptj",["GPTJModel",Ys]],["gpt_bigcode",["GPTBigCodeModel",er]],["gpt_neo",["GPTNeoModel",$s]],["gpt_neox",["GPTNeoXModel",Xs]],["codegen",["CodeGenModel",rr]],["llama",["LlamaModel",ar]],["olmo",["OlmoModel",pr]],["mobilellm",["MobileLLMModel",cr]],["granite",["GraniteModel",_r]],["cohere",["CohereModel",Mr]],["gemma",["GemmaModel",br]],["gemma2",["Gemma2Model",kr]],["openelm",["OpenELMModel",vr]],["qwen2",["Qwen2Model",Ar]],["phi",["PhiModel",jr]],["phi3",["Phi3Model",Br]],["mpt",["MptModel",Wr]],["opt",["OPTModel",Qr]],["mistral",["MistralModel",ja]],["starcoder2",["Starcoder2Model",Ba]],["falcon",["FalconModel",Ra]],["stablelm",["StableLmModel",ei]]]),Bi=new Map([["speecht5",["SpeechT5ForSpeechToText",Sa]],["whisper",["WhisperForConditionalGeneration",us]]]),Oi=new Map([["speecht5",["SpeechT5ForTextToSpeech",Aa]]]),Di=new Map([["vits",["VitsModel",Xa]],["musicgen",["MusicgenForConditionalGeneration",li]]]),Ri=new Map([["bert",["BertForSequenceClassification",Z]],["roformer",["RoFormerForSequenceClassification",ie]],["electra",["ElectraForSequenceClassification",we]],["esm",["EsmForSequenceClassification",He]],["convbert",["ConvBertForSequenceClassification",me]],["camembert",["CamembertForSequenceClassification",ye]],["deberta",["DebertaForSequenceClassification",Ee]],["deberta-v2",["DebertaV2ForSequenceClassification",Ve]],["mpnet",["MPNetForSequenceClassification",nt]],["albert",["AlbertForSequenceClassification",_t]],["distilbert",["DistilBertForSequenceClassification",Ge]],["roberta",["RobertaForSequenceClassification",$t]],["xlm",["XLMForSequenceClassification",Yt]],["xlm-roberta",["XLMRobertaForSequenceClassification",rs]],["bart",["BartForSequenceClassification",At]],["mbart",["MBartForSequenceClassification",zt]],["mobilebert",["MobileBertForSequenceClassification",et]],["squeezebert",["SqueezeBertForSequenceClassification",ut]]]),Gi=new Map([["bert",["BertForTokenClassification",ee]],["roformer",["RoFormerForTokenClassification",le]],["electra",["ElectraForTokenClassification",Te]],["esm",["EsmForTokenClassification",Je]],["convbert",["ConvBertForTokenClassification",he]],["camembert",["CamembertForTokenClassification",Fe]],["deberta",["DebertaForTokenClassification",Le]],["deberta-v2",["DebertaV2ForTokenClassification",Be]],["mpnet",["MPNetForTokenClassification",at]],["distilbert",["DistilBertForTokenClassification",qe]],["roberta",["RobertaForTokenClassification",Ut]],["xlm",["XLMForTokenClassification",Kt]],["xlm-roberta",["XLMRobertaForTokenClassification",os]]]),qi=new Map([["t5",["T5ForConditionalGeneration",Tt]],["longt5",["LongT5ForConditionalGeneration",Pt]],["mt5",["MT5ForConditionalGeneration",Ft]],["bart",["BartForConditionalGeneration",St]],["mbart",["MBartForConditionalGeneration",It]],["marian",["MarianMTModel",Wn]],["m2m_100",["M2M100ForConditionalGeneration",Qn]],["blenderbot",["BlenderbotForConditionalGeneration",Bt]],["blenderbot-small",["BlenderbotSmallForConditionalGeneration",Rt]]]),Wi=new Map([["bloom",["BloomForCausalLM",Gr]],["gpt2",["GPT2LMHeadModel",Ds]],["jais",["JAISLMHeadModel",qs]],["gptj",["GPTJForCausalLM",Ks]],["gpt_bigcode",["GPTBigCodeForCausalLM",tr]],["gpt_neo",["GPTNeoForCausalLM",Us]],["gpt_neox",["GPTNeoXForCausalLM",Hs]],["codegen",["CodeGenForCausalLM",or]],["llama",["LlamaForCausalLM",ir]],["olmo",["OlmoForCausalLM",mr]],["mobilellm",["MobileLLMForCausalLM",dr]],["granite",["GraniteForCausalLM",fr]],["cohere",["CohereForCausalLM",wr]],["gemma",["GemmaForCausalLM",xr]],["gemma2",["Gemma2ForCausalLM",yr]],["openelm",["OpenELMForCausalLM",Cr]],["qwen2",["Qwen2ForCausalLM",Er]],["phi",["PhiForCausalLM",Nr]],["phi3",["Phi3ForCausalLM",Or]],["mpt",["MptForCausalLM",$r]],["opt",["OPTForCausalLM",Xr]],["mbart",["MBartForCausalLM",jt]],["mistral",["MistralForCausalLM",Na]],["starcoder2",["Starcoder2ForCausalLM",Oa]],["falcon",["FalconForCausalLM",Ga]],["trocr",["TrOCRForCausalLM",Ia]],["stablelm",["StableLmForCausalLM",ti]]]),$i=new Map([["multi_modality",["MultiModalityCausalLM",ki]]]),Ui=new Map([["bert",["BertForMaskedLM",K]],["roformer",["RoFormerForMaskedLM",ae]],["electra",["ElectraForMaskedLM",Me]],["esm",["EsmForMaskedLM",Xe]],["convbert",["ConvBertForMaskedLM",pe]],["camembert",["CamembertForMaskedLM",ke]],["deberta",["DebertaForMaskedLM",Ae]],["deberta-v2",["DebertaV2ForMaskedLM",Ne]],["mpnet",["MPNetForMaskedLM",ot]],["albert",["AlbertForMaskedLM",gt]],["distilbert",["DistilBertForMaskedLM",$e]],["roberta",["RobertaForMaskedLM",Wt]],["xlm",["XLMWithLMHeadModel",Jt]],["xlm-roberta",["XLMRobertaForMaskedLM",ss]],["mobilebert",["MobileBertForMaskedLM",Ze]],["squeezebert",["SqueezeBertForMaskedLM",dt]]]),Qi=new Map([["bert",["BertForQuestionAnswering",te]],["roformer",["RoFormerForQuestionAnswering",ce]],["electra",["ElectraForQuestionAnswering",be]],["convbert",["ConvBertForQuestionAnswering",_e]],["camembert",["CamembertForQuestionAnswering",ve]],["deberta",["DebertaForQuestionAnswering",Ie]],["deberta-v2",["DebertaV2ForQuestionAnswering",Oe]],["mpnet",["MPNetForQuestionAnswering",it]],["albert",["AlbertForQuestionAnswering",ft]],["distilbert",["DistilBertForQuestionAnswering",We]],["roberta",["RobertaForQuestionAnswering",Qt]],["xlm",["XLMForQuestionAnswering",Zt]],["xlm-roberta",["XLMRobertaForQuestionAnswering",ns]],["mobilebert",["MobileBertForQuestionAnswering",tt]],["squeezebert",["SqueezeBertForQuestionAnswering",pt]]]),Xi=new Map([["vision-encoder-decoder",["VisionEncoderDecoderModel",ps]]]),Hi=new Map([["llava",["LlavaForConditionalGeneration",hs]],["llava_onevision",["LlavaOnevisionForConditionalGeneration",_s]],["moondream1",["Moondream1ForConditionalGeneration",fs]],["florence2",["Florence2ForConditionalGeneration",Ms]],["qwen2-vl",["Qwen2VLForConditionalGeneration",Ir]]]),Ji=new Map([["vision-encoder-decoder",["VisionEncoderDecoderModel",ps]]]),Yi=new Map([["vit",["ViTForImageClassification",Yr]],["pvt",["PvtForImageClassification",so]],["vit_msn",["ViTMSNForImageClassification",io]],["fastvit",["FastViTForImageClassification",mo]],["mobilevit",["MobileViTForImageClassification",Mo]],["mobilevitv2",["MobileViTV2ForImageClassification",bo]],["beit",["BeitForImageClassification",Ao]],["deit",["DeiTForImageClassification",Qo]],["hiera",["HieraForImageClassification",Jo]],["convnext",["ConvNextForImageClassification",Cn]],["convnextv2",["ConvNextV2ForImageClassification",En]],["dinov2",["Dinov2ForImageClassification",zn]],["resnet",["ResNetForImageClassification",Zo]],["swin",["SwinForImageClassification",sn]],["segformer",["SegformerForImageClassification",Ya]],["efficientnet",["EfficientNetForImageClassification",oi]],["mobilenet_v1",["MobileNetV1ForImageClassification",ui]],["mobilenet_v2",["MobileNetV2ForImageClassification",hi]],["mobilenet_v3",["MobileNetV3ForImageClassification",gi]],["mobilenet_v4",["MobileNetV4ForImageClassification",Ti]]]),Ki=new Map([["detr",["DetrForObjectDetection",Io]],["rt_detr",["RTDetrForObjectDetection",Oo]],["table-transformer",["TableTransformerForObjectDetection",qo]],["yolos",["YolosForObjectDetection",Vn]]]),Zi=new Map([["owlvit",["OwlViTForObjectDetection",ko]],["owlv2",["Owlv2ForObjectDetection",vo]]]),el=new Map([["detr",["DetrForSegmentation",zo]],["clipseg",["CLIPSegForImageSegmentation",Vs]]]),tl=new Map([["segformer",["SegformerForSemanticSegmentation",Ka]],["sapiens",["SapiensForSemanticSegmentation",mn]]]),sl=new Map([["detr",["DetrForSegmentation",zo]],["maskformer",["MaskFormerForInstanceSegmentation",Tn]]]),rl=new Map([["sam",["SamModel",Dn]]]),ol=new Map([["wav2vec2",["Wav2Vec2ForCTC",Jn]],["wav2vec2-bert",["Wav2Vec2BertForCTC",_a]],["unispeech",["UniSpeechForCTC",aa]],["unispeech-sat",["UniSpeechSatForCTC",da]],["wavlm",["WavLMForCTC",Pa]],["hubert",["HubertForCTC",wa]]]),nl=new Map([["wav2vec2",["Wav2Vec2ForSequenceClassification",Yn]],["wav2vec2-bert",["Wav2Vec2BertForSequenceClassification",fa]],["unispeech",["UniSpeechForSequenceClassification",ia]],["unispeech-sat",["UniSpeechSatForSequenceClassification",ua]],["wavlm",["WavLMForSequenceClassification",ka]],["hubert",["HubertForSequenceClassification",Ta]],["audio-spectrogram-transformer",["ASTForAudioClassification",ls]]]),al=new Map([["wavlm",["WavLMForXVector",ya]]]),il=new Map([["unispeech-sat",["UniSpeechSatForAudioFrameClassification",pa]],["wavlm",["WavLMForAudioFrameClassification",Fa]],["wav2vec2",["Wav2Vec2ForAudioFrameClassification",Kn]],["pyannote",["PyAnnoteForAudioFrameClassification",ta]]]),ll=new Map([["vitmatte",["VitMatteForImageMatting",_o]]]),cl=new Map([["patchtst",["PatchTSTForPrediction",Ai]],["patchtsmixer",["PatchTSMixerForPrediction",Ii]]]),dl=new Map([["swin2sr",["Swin2SRForImageSuperResolution",nn]]]),ul=new Map([["dpt",["DPTForDepthEstimation",cn]],["depth_anything",["DepthAnythingForDepthEstimation",un]],["glpn",["GLPNForDepthEstimation",Pn]],["sapiens",["SapiensForDepthEstimation",hn]],["depth_pro",["DepthProForDepthEstimation",gn]]]),pl=new Map([["sapiens",["SapiensForNormalEstimation",_n]]]),ml=new Map([["vitpose",["VitPoseForPoseEstimation",Zr]]]),hl=new Map([["clip",["CLIPVisionModelWithProjection",ks]],["siglip",["SiglipVisionModel",Cs]],["jina_clip",["JinaCLIPVisionModel",zs]]]),_l=[[ji,T],[Ni,b],[Vi,k],[Ri,T],[Gi,T],[qi,x],[Bi,x],[Wi,k],[$i,C],[Ui,T],[Qi,T],[Xi,P],[Hi,F],[Yi,T],[el,T],[sl,T],[tl,T],[ll,T],[cl,T],[dl,T],[ul,T],[pl,T],[ml,T],[Ki,T],[Zi,T],[rl,y],[ol,T],[nl,T],[Oi,x],[Di,T],[al,T],[il,T],[hl,T]];for(const[e,t]of _l)for(const[s,r]of e.values())S.set(s,t),E.set(r,s),A.set(s,r);const 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X{constructor({logits:e,past_key_values:t,encoder_outputs:s,decoder_attentions:r=null,cross_attentions:o=null}){super(),this.logits=e,this.past_key_values=t,this.encoder_outputs=s,this.decoder_attentions=r,this.cross_attentions=o}}class Ql extends X{constructor({logits:e}){super(),this.logits=e}}class Xl extends X{constructor({logits:e,embeddings:t}){super(),this.logits=e,this.embeddings=t}}class Hl extends X{constructor({logits:e}){super(),this.logits=e}}class Jl extends X{constructor({logits:e}){super(),this.logits=e}}class Yl extends X{constructor({start_logits:e,end_logits:t}){super(),this.start_logits=e,this.end_logits=t}}class Kl extends X{constructor({logits:e}){super(),this.logits=e}}class Zl extends X{constructor({logits:e,past_key_values:t}){super(),this.logits=e,this.past_key_values=t}}class ec extends X{constructor({alphas:e}){super(),this.alphas=e}}class tc extends 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\**********************************************/(e,t,s)=>{s.r(t),s.d(t,{WHISPER_LANGUAGE_MAPPING:()=>o,WHISPER_TO_LANGUAGE_CODE_MAPPING:()=>n,whisper_language_to_code:()=>a});const r=[["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"]],o=new Map(r),n=new Map([...r.map((([e,t])=>[t,e])),["burmese","my"],["valencian","ca"],["flemish","nl"],["haitian","ht"],["letzeburgesch","lb"],["pushto","ps"],["panjabi","pa"],["moldavian","ro"],["moldovan","ro"],["sinhalese","si"],["castilian","es"]]);function a(e){e=e.toLowerCase();let t=n.get(e);if(void 0===t){if(!o.has(e)){const t=2===e.length?o.keys():o.values();throw new Error(`Language "${e}" is not supported. Must be one of: ${JSON.stringify(t)}`)}t=e}return t}},"./src/models/whisper/feature_extraction_whisper.js":
/*!**********************************************************!*\
!*** ./src/models/whisper/feature_extraction_whisper.js ***!
\**********************************************************/(e,t,s)=>{s.r(t),s.d(t,{WhisperFeatureExtractor:()=>a});var r=s(/*! ../../base/feature_extraction_utils.js */"./src/base/feature_extraction_utils.js"),o=(s(/*! ../../utils/tensor.js */"./src/utils/tensor.js"),s(/*! ../../utils/audio.js */"./src/utils/audio.js")),n=s(/*! ../../utils/maths.js */"./src/utils/maths.js");class a extends r.FeatureExtractor{constructor(e){super(e),this.config.mel_filters??=(0,o.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=(0,o.window_function)(this.config.n_fft,"hann")}async _extract_fbank_features(e){const t=await(0,o.spectrogram)(e,this.window,this.config.n_fft,this.config.hop_length,{power:2,mel_filters:this.config.mel_filters,log_mel:"log10",max_num_frames:this.config.nb_max_frames}),s=t.data,r=(0,n.max)(s)[0];for(let e=0;e<s.length;++e)s[e]=(Math.max(s[e],r-8)+4)/4;return t}async _call(e){let t;(0,r.validate_audio_inputs)(e,"WhisperFeatureExtractor"),e.length>this.config.n_samples?(console.warn("Attempting to extract features for audio longer than 30 seconds. If using a pipeline to extract transcript from a long audio clip, remember to specify `chunk_length_s` and/or `stride_length_s`."),t=e.slice(0,this.config.n_samples)):(t=new Float32Array(this.config.n_samples),t.set(e));return{input_features:(await this._extract_fbank_features(t)).unsqueeze_(0)}}}},"./src/models/whisper/generation_whisper.js":
/*!**************************************************!*\
!*** ./src/models/whisper/generation_whisper.js ***!
\**************************************************/(e,t,s)=>{s.r(t),s.d(t,{WhisperGenerationConfig:()=>o});var r=s(/*! ../../generation/configuration_utils.js */"./src/generation/configuration_utils.js");class o extends r.GenerationConfig{return_timestamps=null;return_token_timestamps=null;num_frames=null;alignment_heads=null;task=null;language=null;no_timestamps_token_id=null;prompt_ids=null;is_multilingual=null;lang_to_id=null;task_to_id=null;max_initial_timestamp_index=1}},"./src/models/whisper/processing_whisper.js":
/*!**************************************************!*\
!*** ./src/models/whisper/processing_whisper.js ***!
\**************************************************/(e,t,s)=>{s.r(t),s.d(t,{WhisperProcessor:()=>a});var r=s(/*! ../auto/feature_extraction_auto.js */"./src/models/auto/feature_extraction_auto.js"),o=s(/*! ../../tokenizers.js */"./src/tokenizers.js"),n=s(/*! ../../base/processing_utils.js */"./src/base/processing_utils.js");class a extends n.Processor{static tokenizer_class=o.AutoTokenizer;static feature_extractor_class=r.AutoFeatureExtractor;async _call(e){return await this.feature_extractor(e)}}},"./src/models/yolos/image_processing_yolos.js":
/*!****************************************************!*\
!*** ./src/models/yolos/image_processing_yolos.js ***!
\****************************************************/(e,t,s)=>{s.r(t),s.d(t,{YolosFeatureExtractor:()=>n,YolosImageProcessor:()=>o});var r=s(/*! ../../base/image_processors_utils.js */"./src/base/image_processors_utils.js");class o extends r.ImageProcessor{post_process_object_detection(...e){return(0,r.post_process_object_detection)(...e)}}class n extends o{}},"./src/ops/registry.js":
/*!*****************************!*\
!*** ./src/ops/registry.js ***!
\*****************************/(e,t,s)=>{s.r(t),s.d(t,{TensorOpRegistry:()=>a});var r=s(/*! ../backends/onnx.js */"./src/backends/onnx.js"),o=s(/*! ../utils/tensor.js */"./src/utils/tensor.js");const n=async(e,t,s)=>{const n=await(0,r.createInferenceSession)(new Uint8Array(e),t);return async e=>{const t=Object.fromEntries(Object.entries(e).map((([e,t])=>[e,t.ort_tensor]))),r=await n.run(t);return Array.isArray(s)?s.map((e=>new o.Tensor(r[e]))):new o.Tensor(r[s])}};class a{static session_options={};static get bilinear_interpolate_4d(){return this._bilinear_interpolate_4d||(this._bilinear_interpolate_4d=n([8,9,18,0,58,128,1,10,40,10,1,120,10,0,10,0,10,1,115,18,1,121,34,6,82,101,115,105,122,101,42,17,10,4,109,111,100,101,34,6,108,105,110,101,97,114,160,1,3,18,1,114,90,31,10,1,120,18,26,10,24,8,1,18,20,10,3,18,1,98,10,3,18,1,99,10,3,18,1,104,10,3,18,1,119,90,15,10,1,115,18,10,10,8,8,7,18,4,10,2,8,4,98,31,10,1,121,18,26,10,24,8,1,18,20,10,3,18,1,98,10,3,18,1,99,10,3,18,1,104,10,3,18,1,119,66,2,16,20],this.session_options,"y")),this._bilinear_interpolate_4d}static get bicubic_interpolate_4d(){return this._bicubic_interpolate_4d||(this._bicubic_interpolate_4d=n([8,9,18,0,58,127,10,39,10,1,120,10,0,10,0,10,1,115,18,1,121,34,6,82,101,115,105,122,101,42,16,10,4,109,111,100,101,34,5,99,117,98,105,99,160,1,3,18,1,114,90,31,10,1,120,18,26,10,24,8,1,18,20,10,3,18,1,98,10,3,18,1,99,10,3,18,1,104,10,3,18,1,119,90,15,10,1,115,18,10,10,8,8,7,18,4,10,2,8,4,98,31,10,1,121,18,26,10,24,8,1,18,20,10,3,18,1,98,10,3,18,1,99,10,3,18,1,104,10,3,18,1,119,66,2,16,20],this.session_options,"y")),this._bicubic_interpolate_4d}static get matmul(){return this._matmul||(this._matmul=n([8,9,18,0,58,55,10,17,10,1,97,10,1,98,18,1,99,34,6,77,97,116,77,117,108,18,1,114,90,9,10,1,97,18,4,10,2,8,1,90,9,10,1,98,18,4,10,2,8,1,98,9,10,1,99,18,4,10,2,8,1,66,2,16,20],this.session_options,"c")),this._matmul}static get stft(){return this._stft||(this._stft=n([8,7,18,0,58,148,1,10,38,10,1,115,10,1,106,10,1,119,10,1,108,18,1,111,34,4,83,84,70,84,42,15,10,8,111,110,101,115,105,100,101,100,24,1,160,1,2,18,1,115,90,26,10,1,115,18,21,10,19,8,1,18,15,10,3,18,1,98,10,3,18,1,115,10,3,18,1,99,90,11,10,1,106,18,6,10,4,8,7,18,0,90,16,10,1,119,18,11,10,9,8,1,18,5,10,3,18,1,119,90,11,10,1,108,18,6,10,4,8,7,18,0,98,31,10,1,111,18,26,10,24,8,1,18,20,10,3,18,1,98,10,3,18,1,102,10,3,18,1,100,10,3,18,1,99,66,2,16,17],this.session_options,"o")),this._stft}static get rfft(){return this._rfft||(this._rfft=n([8,9,18,0,58,97,10,33,10,1,120,10,0,10,1,97,18,1,121,34,3,68,70,84,42,15,10,8,111,110,101,115,105,100,101,100,24,1,160,1,2,18,1,100,90,21,10,1,120,18,16,10,14,8,1,18,10,10,3,18,1,115,10,3,18,1,99,90,11,10,1,97,18,6,10,4,8,7,18,0,98,21,10,1,121,18,16,10,14,8,1,18,10,10,3,18,1,115,10,3,18,1,99,66,2,16,20],this.session_options,"y")),this._rfft}static get top_k(){return this._top_k||(this._top_k=n([8,10,18,0,58,73,10,18,10,1,120,10,1,107,18,1,118,18,1,105,34,4,84,111,112,75,18,1,116,90,9,10,1,120,18,4,10,2,8,1,90,15,10,1,107,18,10,10,8,8,7,18,4,10,2,8,1,98,9,10,1,118,18,4,10,2,8,1,98,9,10,1,105,18,4,10,2,8,7,66,2,16,21],this.session_options,["v","i"])),this._top_k}}},"./src/pipelines.js":
/*!**************************!*\
!*** ./src/pipelines.js ***!
\**************************/(e,t,s)=>{s.r(t),s.d(t,{AudioClassificationPipeline:()=>C,AutomaticSpeechRecognitionPipeline:()=>A,DepthEstimationPipeline:()=>D,DocumentQuestionAnsweringPipeline:()=>V,FeatureExtractionPipeline:()=>F,FillMaskPipeline:()=>w,ImageClassificationPipeline:()=>L,ImageFeatureExtractionPipeline:()=>v,ImageSegmentationPipeline:()=>I,ImageToImagePipeline:()=>O,ImageToTextPipeline:()=>E,ObjectDetectionPipeline:()=>j,Pipeline:()=>_,QuestionAnsweringPipeline:()=>M,SummarizationPipeline:()=>b,Text2TextGenerationPipeline:()=>T,TextClassificationPipeline:()=>f,TextGenerationPipeline:()=>k,TextToAudioPipeline:()=>B,TokenClassificationPipeline:()=>g,TranslationPipeline:()=>x,ZeroShotAudioClassificationPipeline:()=>S,ZeroShotClassificationPipeline:()=>y,ZeroShotImageClassificationPipeline:()=>z,ZeroShotObjectDetectionPipeline:()=>N,pipeline:()=>q});var r=s(/*! ./tokenizers.js */"./src/tokenizers.js"),o=s(/*! ./models.js */"./src/models.js"),n=s(/*! ./models/auto/processing_auto.js */"./src/models/auto/processing_auto.js"),a=(s(/*! ./base/processing_utils.js */"./src/base/processing_utils.js"),s(/*! ./utils/generic.js */"./src/utils/generic.js")),i=s(/*! ./utils/core.js */"./src/utils/core.js"),l=s(/*! ./utils/maths.js */"./src/utils/maths.js"),c=s(/*! ./utils/audio.js */"./src/utils/audio.js"),d=s(/*! ./utils/tensor.js */"./src/utils/tensor.js"),u=s(/*! ./utils/image.js */"./src/utils/image.js");async function p(e){return Array.isArray(e)||(e=[e]),await Promise.all(e.map((e=>u.RawImage.read(e))))}async function m(e,t){return Array.isArray(e)||(e=[e]),await Promise.all(e.map((e=>"string"==typeof e||e instanceof URL?(0,c.read_audio)(e,t):e instanceof Float64Array?new Float32Array(e):e)))}function h(e,t){t&&(e=e.map((e=>0|e)));const[s,r,o,n]=e;return{xmin:s,ymin:r,xmax:o,ymax:n}}class _ extends a.Callable{constructor({task:e,model:t,tokenizer:s=null,processor:r=null}){super(),this.task=e,this.model=t,this.tokenizer=s,this.processor=r}async dispose(){await this.model.dispose()}}class f extends _{constructor(e){super(e)}async _call(e,{top_k:t=1}={}){const s=this.tokenizer(e,{padding:!0,truncation:!0}),r=await this.model(s),o="multi_label_classification"===this.model.config.problem_type?e=>e.sigmoid():e=>new d.Tensor("float32",(0,l.softmax)(e.data),e.dims),n=this.model.config.id2label,a=[];for(const e of r.logits){const s=o(e),r=await(0,d.topk)(s,t),i=r[0].tolist(),l=r[1].tolist().map(((e,t)=>({label:n?n[e]:`LABEL_${e}`,score:i[t]})));1===t?a.push(...l):a.push(l)}return Array.isArray(e)||1===t?a:a[0]}}class g extends _{constructor(e){super(e)}async _call(e,{ignore_labels:t=["O"]}={}){const s=Array.isArray(e),r=this.tokenizer(s?e:[e],{padding:!0,truncation:!0}),o=(await this.model(r)).logits,n=this.model.config.id2label,a=[];for(let e=0;e<o.dims[0];++e){const s=r.input_ids[e],i=o[e],c=[];for(let e=0;e<i.dims[0];++e){const r=i[e],o=(0,l.max)(r.data)[1],a=n?n[o]:`LABEL_${o}`;if(t.includes(a))continue;const d=this.tokenizer.decode([s[e].item()],{skip_special_tokens:!0});if(""===d)continue;const u=(0,l.softmax)(r.data);c.push({entity:a,score:u[o],index:e,word:d})}a.push(c)}return s?a:a[0]}}class M extends _{constructor(e){super(e)}async _call(e,t,{top_k:s=1}={}){const r=this.tokenizer(e,{text_pair:t,padding:!0,truncation:!0}),{start_logits:o,end_logits:n}=await this.model(r),a=r.input_ids.tolist(),c=r.attention_mask.tolist(),d=this.tokenizer.all_special_ids,u=[];for(let e=0;e<o.dims[0];++e){const t=a[e],r=t.findIndex((e=>e==this.tokenizer.sep_token_id)),p=(c[e].map(((e,s)=>1==e&&(0===s||s>r&&-1===d.findIndex((e=>e==t[s]))))),o[e].tolist()),m=n[e].tolist();for(let s=1;s<p.length;++s)(0==c[e]||s<=r||-1!==d.findIndex((e=>e==t[s])))&&(p[s]=-1/0,m[s]=-1/0);const h=(0,l.softmax)(p).map(((e,t)=>[e,t])),_=(0,l.softmax)(m).map(((e,t)=>[e,t]));h[0][0]=0,_[0][0]=0;const f=(0,i.product)(h,_).filter((e=>e[0][1]<=e[1][1])).map((e=>[e[0][1],e[1][1],e[0][0]*e[1][0]])).sort(((e,t)=>t[2]-e[2]));for(let e=0;e<Math.min(f.length,s);++e){const[s,r,o]=f[e],n=t.slice(s,r+1),a=this.tokenizer.decode(n,{skip_special_tokens:!0});u.push({answer:a,score:o})}}return 1===s?u[0]:u}}class w extends _{constructor(e){super(e)}async _call(e,{top_k:t=5}={}){const s=this.tokenizer(e,{padding:!0,truncation:!0}),{logits:r}=await this.model(s),o=[],n=s.input_ids.tolist();for(let e=0;e<n.length;++e){const s=n[e],a=s.findIndex((e=>e==this.tokenizer.mask_token_id));if(-1===a)throw Error(`Mask token (${this.tokenizer.mask_token}) not found in text.`);const i=r[e][a],c=await(0,d.topk)(new d.Tensor("float32",(0,l.softmax)(i.data),i.dims),t),u=c[0].tolist(),p=c[1].tolist();o.push(p.map(((e,t)=>{const r=s.slice();return r[a]=e,{score:u[t],token:Number(e),token_str:this.tokenizer.model.vocab[e],sequence:this.tokenizer.decode(r,{skip_special_tokens:!0})}})))}return Array.isArray(e)?o:o[0]}}class T extends _{_key="generated_text";constructor(e){super(e)}async _call(e,t={}){Array.isArray(e)||(e=[e]),this.model.config.prefix&&(e=e.map((e=>this.model.config.prefix+e)));const s=this.model.config.task_specific_params;s&&s[this.task]&&s[this.task].prefix&&(e=e.map((e=>s[this.task].prefix+e)));const r=this.tokenizer,o={padding:!0,truncation:!0};let n;n=this instanceof x&&"_build_translation_inputs"in r?r._build_translation_inputs(e,o,t):r(e,o);const a=await this.model.generate({...n,...t});return r.batch_decode(a,{skip_special_tokens:!0}).map((e=>({[this._key]:e})))}}class b extends T{_key="summary_text";constructor(e){super(e)}}class x extends T{_key="translation_text";constructor(e){super(e)}}function P(e){return Array.isArray(e)&&e.every((e=>"role"in e&&"content"in e))}class k extends _{constructor(e){super(e)}async _call(e,t={}){let s,r=!1,o=!1;if("string"==typeof e)s=e=[e];else if(Array.isArray(e)&&e.every((e=>"string"==typeof e)))r=!0,s=e;else{if(P(e))e=[e];else{if(!Array.isArray(e)||!e.every(P))throw new Error("Input must be a string, an array of strings, a Chat, or an array of Chats");r=!0}o=!0,s=e.map((e=>this.tokenizer.apply_chat_template(e,{tokenize:!1,add_generation_prompt:!0})))}const n=t.add_special_tokens??!1,a=!o&&(t.return_full_text??!0);this.tokenizer.padding_side="left";const i=this.tokenizer(s,{add_special_tokens:n,padding:!0,truncation:!0}),l=await this.model.generate({...i,...t}),c=this.tokenizer.batch_decode(l,{skip_special_tokens:!0});let d;!a&&i.input_ids.dims.at(-1)>0&&(d=this.tokenizer.batch_decode(i.input_ids,{skip_special_tokens:!0}).map((e=>e.length)));const u=Array.from({length:e.length},(e=>[]));for(let t=0;t<c.length;++t){const s=Math.floor(t/l.dims[0]*e.length);d&&(c[t]=c[t].slice(d[s])),u[s].push({generated_text:o?[...e[s],{role:"assistant",content:c[t]}]:c[t]})}return r||1!==u.length?u:u[0]}}class y extends _{constructor(e){super(e),this.label2id=Object.fromEntries(Object.entries(this.model.config.label2id).map((([e,t])=>[e.toLowerCase(),t]))),this.entailment_id=this.label2id.entailment,void 0===this.entailment_id&&(console.warn("Could not find 'entailment' in label2id mapping. Using 2 as entailment_id."),this.entailment_id=2),this.contradiction_id=this.label2id.contradiction??this.label2id.not_entailment,void 0===this.contradiction_id&&(console.warn("Could not find 'contradiction' in label2id mapping. Using 0 as contradiction_id."),this.contradiction_id=0)}async _call(e,t,{hypothesis_template:s="This example is {}.",multi_label:r=!1}={}){const o=Array.isArray(e);o||(e=[e]),Array.isArray(t)||(t=[t]);const n=t.map((e=>s.replace("{}",e))),a=r||1===t.length,i=[];for(const s of e){const e=[];for(const t of n){const r=this.tokenizer(s,{text_pair:t,padding:!0,truncation:!0}),o=await this.model(r);a?e.push([o.logits.data[this.contradiction_id],o.logits.data[this.entailment_id]]):e.push(o.logits.data[this.entailment_id])}const r=(a?e.map((e=>(0,l.softmax)(e)[1])):(0,l.softmax)(e)).map(((e,t)=>[e,t])).sort(((e,t)=>t[0]-e[0]));i.push({sequence:s,labels:r.map((e=>t[e[1]])),scores:r.map((e=>e[0]))})}return o?i:i[0]}}class F extends _{constructor(e){super(e)}async _call(e,{pooling:t="none",normalize:s=!1,quantize:r=!1,precision:o="binary"}={}){const n=this.tokenizer(e,{padding:!0,truncation:!0}),a=await this.model(n);let i=a.last_hidden_state??a.logits??a.token_embeddings;if("none"===t);else if("mean"===t)i=(0,d.mean_pooling)(i,n.attention_mask);else{if("cls"!==t)throw Error(`Pooling method '${t}' not supported.`);i=i.slice(null,0)}return s&&(i=i.normalize(2,-1)),r&&(i=(0,d.quantize_embeddings)(i,o)),i}}class v extends _{constructor(e){super(e)}async _call(e,{pool:t=null}={}){const s=await p(e),{pixel_values:r}=await this.processor(s),o=await this.model({pixel_values:r});let n;if(t){if(!("pooler_output"in o))throw Error("No pooled output was returned. Make sure the model has a 'pooler' layer when using the 'pool' option.");n=o.pooler_output}else n=o.last_hidden_state??o.logits??o.image_embeds;return n}}class C extends _{constructor(e){super(e)}async _call(e,{top_k:t=5}={}){const s=this.processor.feature_extractor.config.sampling_rate,r=await m(e,s),o=this.model.config.id2label,n=[];for(const e of r){const s=await this.processor(e),r=(await this.model(s)).logits[0],a=await(0,d.topk)(new d.Tensor("float32",(0,l.softmax)(r.data),r.dims),t),i=a[0].tolist(),c=a[1].tolist().map(((e,t)=>({label:o?o[e]:`LABEL_${e}`,score:i[t]})));n.push(c)}return Array.isArray(e)?n:n[0]}}class S extends _{constructor(e){super(e)}async _call(e,t,{hypothesis_template:s="This is a sound of {}."}={}){const r=!Array.isArray(e);r&&(e=[e]);const o=t.map((e=>s.replace("{}",e))),n=this.tokenizer(o,{padding:!0,truncation:!0}),a=this.processor.feature_extractor.config.sampling_rate,i=await m(e,a),c=[];for(const e of i){const s=await this.processor(e),r=await this.model({...n,...s}),o=(0,l.softmax)(r.logits_per_audio.data);c.push([...o].map(((e,s)=>({score:e,label:t[s]}))))}return r?c[0]:c}}class A extends _{constructor(e){super(e)}async _call(e,t={}){switch(this.model.config.model_type){case"whisper":return this._call_whisper(e,t);case"wav2vec2":case"wav2vec2-bert":case"unispeech":case"unispeech-sat":case"hubert":return this._call_wav2vec2(e,t);default:throw new Error(`AutomaticSpeechRecognitionPipeline does not support model type '${this.model.config.model_type}'.`)}}async _call_wav2vec2(e,t){t.language&&console.warn('`language` parameter is not yet supported for `wav2vec2` models, defaulting to "English".'),t.task&&console.warn('`task` parameter is not yet supported for `wav2vec2` models, defaulting to "transcribe".');const s=!Array.isArray(e);s&&(e=[e]);const r=this.processor.feature_extractor.config.sampling_rate,o=await m(e,r),n=[];for(const e of o){const t=await this.processor(e),s=(await this.model(t)).logits[0],r=[];for(const e of s)r.push((0,l.max)(e.data)[1]);const o=this.tokenizer.decode(r);n.push({text:o})}return s?n[0]:n}async _call_whisper(e,t){const s=t.return_timestamps??!1,r=t.chunk_length_s??0,o=t.force_full_sequences??!1;let n=t.stride_length_s??null;const a={...t};"word"===s&&(a.return_token_timestamps=!0,a.return_timestamps=!1);const i=!Array.isArray(e);i&&(e=[e]);const c=this.processor.feature_extractor.config.chunk_length/this.model.config.max_source_positions,d=this.processor.feature_extractor.config.hop_length,u=this.processor.feature_extractor.config.sampling_rate,p=await m(e,u),h=[];for(const e of p){let t=[];if(r>0){if(null===n)n=r/6;else if(r<=n)throw Error("`chunk_length_s` must be larger than `stride_length_s`.");const s=u*r,o=u*n,a=s-2*o;let i=0;for(;;){const r=i+s,n=e.subarray(i,r),l=await this.processor(n),c=0===i,d=r>=e.length;if(t.push({stride:[n.length,c?0:o,d?0:o],input_features:l.input_features,is_last:d}),d)break;i+=a}}else t=[{stride:[e.length,0,0],input_features:(await this.processor(e)).input_features,is_last:!0}];for(const e of t){a.num_frames=Math.floor(e.stride[0]/d);const t=await this.model.generate({inputs:e.input_features,...a});"word"===s?(e.tokens=t.sequences.tolist()[0],e.token_timestamps=t.token_timestamps.tolist()[0].map((e=>(0,l.round)(e,2)))):e.tokens=t[0].tolist(),e.stride=e.stride.map((e=>e/u))}const[i,p]=this.tokenizer._decode_asr(t,{time_precision:c,return_timestamps:s,force_full_sequences:o});h.push({text:i,...p})}return i?h[0]:h}}class E extends _{constructor(e){super(e)}async _call(e,t={}){const s=Array.isArray(e),r=await p(e),{pixel_values:o}=await this.processor(r),n=[];for(const e of o){e.dims=[1,...e.dims];const s=await this.model.generate({inputs:e,...t}),r=this.tokenizer.batch_decode(s,{skip_special_tokens:!0}).map((e=>({generated_text:e.trim()})));n.push(r)}return s?n:n[0]}}class L extends _{constructor(e){super(e)}async _call(e,{top_k:t=5}={}){const s=await p(e),{pixel_values:r}=await this.processor(s),o=await this.model({pixel_values:r}),n=this.model.config.id2label,a=[];for(const e of o.logits){const s=await(0,d.topk)(new d.Tensor("float32",(0,l.softmax)(e.data),e.dims),t),r=s[0].tolist(),o=s[1].tolist().map(((e,t)=>({label:n?n[e]:`LABEL_${e}`,score:r[t]})));a.push(o)}return Array.isArray(e)?a:a[0]}}class I extends _{constructor(e){super(e),this.subtasks_mapping={panoptic:"post_process_panoptic_segmentation",instance:"post_process_instance_segmentation",semantic:"post_process_semantic_segmentation"}}async _call(e,{threshold:t=.5,mask_threshold:s=.5,overlap_mask_area_threshold:r=.8,label_ids_to_fuse:o=null,target_sizes:n=null,subtask:a=null}={}){if(Array.isArray(e)&&1!==e.length)throw Error("Image segmentation pipeline currently only supports a batch size of 1.");const i=await p(e),l=i.map((e=>[e.height,e.width])),{pixel_values:c,pixel_mask:d}=await this.processor(i),m=await this.model({pixel_values:c,pixel_mask:d});let h=null;if(null!==a)h=this.subtasks_mapping[a];else for(let[e,t]of Object.entries(this.subtasks_mapping))if(t in this.processor.image_processor){h=this.processor.image_processor[t].bind(this.processor.image_processor),a=e;break}const _=this.model.config.id2label,f=[];if("panoptic"===a||"instance"===a){const e=h(m,t,s,r,o,n??l)[0],a=e.segmentation;for(const t of e.segments_info){const e=new Uint8ClampedArray(a.data.length);for(let s=0;s<a.data.length;++s)a.data[s]===t.id&&(e[s]=255);const s=new u.RawImage(e,a.dims[1],a.dims[0],1);f.push({score:t.score,label:_[t.label_id],mask:s})}}else{if("semantic"!==a)throw Error(`Subtask ${a} not supported.`);{const{segmentation:e,labels:t}=h(m,n??l)[0];for(const s of t){const t=new Uint8ClampedArray(e.data.length);for(let r=0;r<e.data.length;++r)e.data[r]===s&&(t[r]=255);const r=new u.RawImage(t,e.dims[1],e.dims[0],1);f.push({score:null,label:_[s],mask:r})}}}return f}}class z extends _{constructor(e){super(e)}async _call(e,t,{hypothesis_template:s="This is a photo of {}"}={}){const r=Array.isArray(e),o=await p(e),n=t.map((e=>s.replace("{}",e))),a=this.tokenizer(n,{padding:"siglip"!==this.model.config.model_type||"max_length",truncation:!0}),{pixel_values:i}=await this.processor(o),c=await this.model({...a,pixel_values:i}),d="siglip"===this.model.config.model_type?e=>e.sigmoid().data:e=>(0,l.softmax)(e.data),u=[];for(const e of c.logits_per_image){const s=[...d(e)].map(((e,s)=>({score:e,label:t[s]})));s.sort(((e,t)=>t.score-e.score)),u.push(s)}return r?u:u[0]}}class j extends _{constructor(e){super(e)}async _call(e,{threshold:t=.9,percentage:s=!1}={}){const r=Array.isArray(e);if(r&&1!==e.length)throw Error("Object detection pipeline currently only supports a batch size of 1.");const o=await p(e),n=s?null:o.map((e=>[e.height,e.width])),{pixel_values:a,pixel_mask:i}=await this.processor(o),l=await this.model({pixel_values:a,pixel_mask:i}),c=this.processor.image_processor.post_process_object_detection(l,t,n),d=this.model.config.id2label,u=c.map((e=>e.boxes.map(((t,r)=>({score:e.scores[r],label:d[e.classes[r]],box:h(t,!s)})))));return r?u:u[0]}}class N extends _{constructor(e){super(e)}async _call(e,t,{threshold:s=.1,top_k:r=null,percentage:o=!1}={}){const n=Array.isArray(e),a=await p(e),i=this.tokenizer(t,{padding:!0,truncation:!0}),l=await this.processor(a),c=[];for(let e=0;e<a.length;++e){const n=a[e],d=o?null:[[n.height,n.width]],u=l.pixel_values[e].unsqueeze_(0),p=await this.model({...i,pixel_values:u}),m=this.processor.image_processor.post_process_object_detection(p,s,d,!0)[0];let _=m.boxes.map(((e,s)=>({score:m.scores[s],label:t[m.classes[s]],box:h(e,!o)}))).sort(((e,t)=>t.score-e.score));null!==r&&(_=_.slice(0,r)),c.push(_)}return n?c:c[0]}}class V extends _{constructor(e){super(e)}async _call(e,t,s={}){const r=(await p(e))[0],{pixel_values:o}=await this.processor(r),n=`<s_docvqa><s_question>${t}</s_question><s_answer>`,a=this.tokenizer(n,{add_special_tokens:!1,padding:!0,truncation:!0}).input_ids,i=await this.model.generate({inputs:o,max_length:this.model.config.decoder.max_position_embeddings,decoder_input_ids:a,...s}),l=this.tokenizer.batch_decode(i)[0].match(/<s_answer>(.*?)<\/s_answer>/);let c=null;return l&&l.length>=2&&(c=l[1].trim()),[{answer:c}]}}class B extends _{DEFAULT_VOCODER_ID="Xenova/speecht5_hifigan";constructor(e){super(e),this.vocoder=e.vocoder??null}async _call(e,{speaker_embeddings:t=null}={}){return this.processor?this._call_text_to_spectrogram(e,{speaker_embeddings:t}):this._call_text_to_waveform(e)}async _call_text_to_waveform(e){const t=this.tokenizer(e,{padding:!0,truncation:!0}),{waveform:s}=await this.model(t),r=this.model.config.sampling_rate;return{audio:s.data,sampling_rate:r}}async _call_text_to_spectrogram(e,{speaker_embeddings:t}){if(this.vocoder||(console.log("No vocoder specified, using default HifiGan vocoder."),this.vocoder=await o.AutoModel.from_pretrained(this.DEFAULT_VOCODER_ID,{dtype:"fp32"})),("string"==typeof t||t instanceof URL)&&(t=new Float32Array(await(await fetch(t)).arrayBuffer())),t instanceof Float32Array)t=new d.Tensor("float32",t,[1,t.length]);else if(!(t instanceof d.Tensor))throw new Error("Speaker embeddings must be a `Tensor`, `Float32Array`, `string`, or `URL`.");const{input_ids:s}=this.tokenizer(e,{padding:!0,truncation:!0}),{waveform:r}=await this.model.generate_speech(s,t,{vocoder:this.vocoder}),n=this.processor.feature_extractor.config.sampling_rate;return{audio:r.data,sampling_rate:n}}}class O extends _{constructor(e){super(e)}async _call(e){const t=await p(e),s=await this.processor(t),r=await this.model(s),o=[];for(const e of r.reconstruction){const t=e.squeeze().clamp_(0,1).mul_(255).round_().to("uint8");o.push(u.RawImage.fromTensor(t))}return o.length>1?o:o[0]}}class D extends _{constructor(e){super(e)}async _call(e){const t=await p(e),s=await this.processor(t),{predicted_depth:r}=await this.model(s),o=[];for(let e=0;e<t.length;++e){const s=(0,d.interpolate)(r[e],t[e].size.reverse(),"bilinear",!1),n=s.mul_(255/(0,l.max)(s.data)[0]).to("uint8");o.push({predicted_depth:r[e],depth:u.RawImage.fromTensor(n)})}return o.length>1?o:o[0]}}const R=Object.freeze({"text-classification":{tokenizer:r.AutoTokenizer,pipeline:f,model:o.AutoModelForSequenceClassification,default:{model:"Xenova/distilbert-base-uncased-finetuned-sst-2-english"},type:"text"},"token-classification":{tokenizer:r.AutoTokenizer,pipeline:g,model:o.AutoModelForTokenClassification,default:{model:"Xenova/bert-base-multilingual-cased-ner-hrl"},type:"text"},"question-answering":{tokenizer:r.AutoTokenizer,pipeline:M,model:o.AutoModelForQuestionAnswering,default:{model:"Xenova/distilbert-base-cased-distilled-squad"},type:"text"},"fill-mask":{tokenizer:r.AutoTokenizer,pipeline:w,model:o.AutoModelForMaskedLM,default:{model:"Xenova/bert-base-uncased"},type:"text"},summarization:{tokenizer:r.AutoTokenizer,pipeline:b,model:o.AutoModelForSeq2SeqLM,default:{model:"Xenova/distilbart-cnn-6-6"},type:"text"},translation:{tokenizer:r.AutoTokenizer,pipeline:x,model:o.AutoModelForSeq2SeqLM,default:{model:"Xenova/t5-small"},type:"text"},"text2text-generation":{tokenizer:r.AutoTokenizer,pipeline:T,model:o.AutoModelForSeq2SeqLM,default:{model:"Xenova/flan-t5-small"},type:"text"},"text-generation":{tokenizer:r.AutoTokenizer,pipeline:k,model:o.AutoModelForCausalLM,default:{model:"Xenova/gpt2"},type:"text"},"zero-shot-classification":{tokenizer:r.AutoTokenizer,pipeline:y,model:o.AutoModelForSequenceClassification,default:{model:"Xenova/distilbert-base-uncased-mnli"},type:"text"},"audio-classification":{pipeline:C,model:o.AutoModelForAudioClassification,processor:n.AutoProcessor,default:{model:"Xenova/wav2vec2-base-superb-ks"},type:"audio"},"zero-shot-audio-classification":{tokenizer:r.AutoTokenizer,pipeline:S,model:o.AutoModel,processor:n.AutoProcessor,default:{model:"Xenova/clap-htsat-unfused"},type:"multimodal"},"automatic-speech-recognition":{tokenizer:r.AutoTokenizer,pipeline:A,model:[o.AutoModelForSpeechSeq2Seq,o.AutoModelForCTC],processor:n.AutoProcessor,default:{model:"Xenova/whisper-tiny.en"},type:"multimodal"},"text-to-audio":{tokenizer:r.AutoTokenizer,pipeline:B,model:[o.AutoModelForTextToWaveform,o.AutoModelForTextToSpectrogram],processor:[n.AutoProcessor,null],default:{model:"Xenova/speecht5_tts"},type:"text"},"image-to-text":{tokenizer:r.AutoTokenizer,pipeline:E,model:o.AutoModelForVision2Seq,processor:n.AutoProcessor,default:{model:"Xenova/vit-gpt2-image-captioning"},type:"multimodal"},"image-classification":{pipeline:L,model:o.AutoModelForImageClassification,processor:n.AutoProcessor,default:{model:"Xenova/vit-base-patch16-224"},type:"multimodal"},"image-segmentation":{pipeline:I,model:[o.AutoModelForImageSegmentation,o.AutoModelForSemanticSegmentation,o.AutoModelForUniversalSegmentation],processor:n.AutoProcessor,default:{model:"Xenova/detr-resnet-50-panoptic"},type:"multimodal"},"zero-shot-image-classification":{tokenizer:r.AutoTokenizer,pipeline:z,model:o.AutoModel,processor:n.AutoProcessor,default:{model:"Xenova/clip-vit-base-patch32"},type:"multimodal"},"object-detection":{pipeline:j,model:o.AutoModelForObjectDetection,processor:n.AutoProcessor,default:{model:"Xenova/detr-resnet-50"},type:"multimodal"},"zero-shot-object-detection":{tokenizer:r.AutoTokenizer,pipeline:N,model:o.AutoModelForZeroShotObjectDetection,processor:n.AutoProcessor,default:{model:"Xenova/owlvit-base-patch32"},type:"multimodal"},"document-question-answering":{tokenizer:r.AutoTokenizer,pipeline:V,model:o.AutoModelForDocumentQuestionAnswering,processor:n.AutoProcessor,default:{model:"Xenova/donut-base-finetuned-docvqa"},type:"multimodal"},"image-to-image":{pipeline:O,model:o.AutoModelForImageToImage,processor:n.AutoProcessor,default:{model:"Xenova/swin2SR-classical-sr-x2-64"},type:"image"},"depth-estimation":{pipeline:D,model:o.AutoModelForDepthEstimation,processor:n.AutoProcessor,default:{model:"Xenova/dpt-large"},type:"image"},"feature-extraction":{tokenizer:r.AutoTokenizer,pipeline:F,model:o.AutoModel,default:{model:"Xenova/all-MiniLM-L6-v2"},type:"text"},"image-feature-extraction":{processor:n.AutoProcessor,pipeline:v,model:[o.AutoModelForImageFeatureExtraction,o.AutoModel],default:{model:"Xenova/vit-base-patch16-224-in21k"},type:"image"}}),G=Object.freeze({"sentiment-analysis":"text-classification",ner:"token-classification",asr:"automatic-speech-recognition","text-to-speech":"text-to-audio",embeddings:"feature-extraction"});async function q(e,t=null,{progress_callback:s=null,config:r=null,cache_dir:o=null,local_files_only:n=!1,revision:a="main",device:l=null,dtype:c=null,model_file_name:d=null,session_options:u={}}={}){e=G[e]??e;const p=R[e.split("_",1)[0]];if(!p)throw Error(`Unsupported pipeline: ${e}. 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!*** ./src/tokenizers.js ***!
\***************************/(e,t,s)=>{s.r(t),s.d(t,{AlbertTokenizer:()=>xe,AutoTokenizer:()=>ht,BartTokenizer:()=>Ne,BertTokenizer:()=>be,BlenderbotSmallTokenizer:()=>lt,BlenderbotTokenizer:()=>it,BloomTokenizer:()=>De,CLIPTokenizer:()=>rt,CamembertTokenizer:()=>Ee,CodeGenTokenizer:()=>st,CodeLlamaTokenizer:()=>qe,CohereTokenizer:()=>pt,ConvBertTokenizer:()=>Ce,DebertaTokenizer:()=>ye,DebertaV2Tokenizer:()=>Fe,DistilBertTokenizer:()=>Ae,ElectraTokenizer:()=>Ie,EsmTokenizer:()=>Xe,FalconTokenizer:()=>Ue,GPT2Tokenizer:()=>je,GPTNeoXTokenizer:()=>Qe,GemmaTokenizer:()=>Je,Grok1Tokenizer:()=>Ye,HerbertTokenizer:()=>ve,LlamaTokenizer:()=>Ge,M2M100Tokenizer:()=>et,MBart50Tokenizer:()=>Be,MBartTokenizer:()=>Ve,MPNetTokenizer:()=>$e,MarianTokenizer:()=>nt,MgpstrTokenizer:()=>mt,MobileBertTokenizer:()=>Pe,NllbTokenizer:()=>Ze,NougatTokenizer:()=>dt,PreTrainedTokenizer:()=>Te,Qwen2Tokenizer:()=>He,RoFormerTokenizer:()=>Se,RobertaTokenizer:()=>Oe,SiglipTokenizer:()=>ot,SpeechT5Tokenizer:()=>ct,SqueezeBertTokenizer:()=>ke,T5Tokenizer:()=>ze,TokenizerModel:()=>P,VitsTokenizer:()=>ut,Wav2Vec2CTCTokenizer:()=>at,WhisperTokenizer:()=>tt,XLMRobertaTokenizer:()=>We,XLMTokenizer:()=>Le,is_chinese_char:()=>g});var r=s(/*! ./utils/generic.js */"./src/utils/generic.js"),o=s(/*! ./utils/core.js */"./src/utils/core.js"),n=s(/*! ./utils/hub.js */"./src/utils/hub.js"),a=s(/*! ./utils/maths.js */"./src/utils/maths.js"),i=s(/*! ./utils/tensor.js */"./src/utils/tensor.js"),l=s(/*! ./utils/data-structures.js */"./src/utils/data-structures.js"),c=s(/*! @huggingface/jinja */"./node_modules/@huggingface/jinja/dist/index.js"),d=s(/*! ./models/whisper/common_whisper.js */"./src/models/whisper/common_whisper.js");s(/*! ./utils/constants.js */"./src/utils/constants.js");async function u(e,t){const s=await Promise.all([(0,n.getModelJSON)(e,"tokenizer.json",!0,t),(0,n.getModelJSON)(e,"tokenizer_config.json",!0,t)]);return null!==t.legacy&&(s[1].legacy=t.legacy),s}function p(e,t=!0){if(void 0!==e.Regex){let t=e.Regex.replace(/\\([#&~])/g,"$1");for(const[e,s]of b)t=t.replaceAll(e,s);return new RegExp(t,"gu")}if(void 0!==e.String){const s=(0,o.escapeRegExp)(e.String);return new RegExp(t?s:`(${s})`,"gu")}return console.warn("Unknown pattern type:",e),null}function m(e){return new Map(Object.entries(e))}function h(e){const t=e.dims;switch(t.length){case 1:return e.tolist();case 2:if(1!==t[0])throw new Error("Unable to decode tensor with `batch size !== 1`. Use `tokenizer.batch_decode(...)` for batched inputs.");return e.tolist()[0];default:throw new Error(`Expected tensor to have 1-2 dimensions, got ${t.length}.`)}}function _(e){return e.replace(/ \./g,".").replace(/ \?/g,"?").replace(/ \!/g,"!").replace(/ ,/g,",").replace(/ \' /g,"'").replace(/ n\'t/g,"n't").replace(/ \'m/g,"'m").replace(/ \'s/g,"'s").replace(/ \'ve/g,"'ve").replace(/ \'re/g,"'re")}function f(e){return e.replace(/\p{M}/gu,"")}function g(e){return e>=19968&&e<=40959||e>=13312&&e<=19903||e>=131072&&e<=173791||e>=173824&&e<=177983||e>=177984&&e<=178207||e>=178208&&e<=183983||e>=63744&&e<=64255||e>=194560&&e<=195103}const M="\\p{P}\\u0021-\\u002F\\u003A-\\u0040\\u005B-\\u0060\\u007B-\\u007E",w=new RegExp(`^[${M}]+$`,"gu"),T=".,!?…。，、।۔،",b=new Map([["(?i:'s|'t|'re|'ve|'m|'ll|'d)","(?:'([sS]|[tT]|[rR][eE]|[vV][eE]|[mM]|[lL][lL]|[dD]))"],[` ?[^(\\s|[${T}])]+`,` ?[^\\s${T}]+`]]);class x{constructor(e){this.content=e.content,this.id=e.id,this.single_word=e.single_word??!1,this.lstrip=e.lstrip??!1,this.rstrip=e.rstrip??!1,this.special=e.special??!1,this.normalized=e.normalized??null}}class P extends r.Callable{constructor(e){super(),this.config=e,this.vocab=[],this.tokens_to_ids=new Map,this.unk_token_id=void 0,this.unk_token=void 0,this.end_of_word_suffix=void 0,this.fuse_unk=this.config.fuse_unk??!1}static fromConfig(e,...t){switch(e.type){case"WordPiece":return new k(e);case"Unigram":return new y(e,...t);case"BPE":return new C(e);default:if(e.vocab)return Array.isArray(e.vocab)?new y(e,...t):new S(e,...t);throw new Error(`Unknown TokenizerModel type: ${e.type}`)}}_call(e){return e=this.encode(e),this.fuse_unk&&(e=function(e,t,s){const r=[];let o=0;for(;o<e.length;)if(r.push(e[o]),(t.get(e[o])??s)===s)for(;++o<e.length&&(t.get(e[o])??s)===s;)t.get(r.at(-1))!==s&&(r[r.length-1]+=e[o]);else++o;return r}(e,this.tokens_to_ids,this.unk_token_id)),e}encode(e){throw Error("encode should be implemented in subclass.")}convert_tokens_to_ids(e){return e.map((e=>this.tokens_to_ids.get(e)??this.unk_token_id))}convert_ids_to_tokens(e){return e.map((e=>this.vocab[e]??this.unk_token))}}class k extends P{constructor(e){super(e),this.tokens_to_ids=m(e.vocab),this.unk_token_id=this.tokens_to_ids.get(e.unk_token),this.unk_token=e.unk_token,this.max_input_chars_per_word=e.max_input_chars_per_word??100,this.vocab=new Array(this.tokens_to_ids.size);for(const[e,t]of this.tokens_to_ids)this.vocab[t]=e}encode(e){const t=[];for(const s of e){const e=[...s];if(e.length>this.max_input_chars_per_word){t.push(this.unk_token);continue}let r=!1,o=0;const n=[];for(;o<e.length;){let t=e.length,s=null;for(;o<t;){let r=e.slice(o,t).join("");if(o>0&&(r=this.config.continuing_subword_prefix+r),this.tokens_to_ids.has(r)){s=r;break}--t}if(null===s){r=!0;break}n.push(s),o=t}r?t.push(this.unk_token):t.push(...n)}return t}}class y extends P{constructor(e,t){super(e);const s=e.vocab.length;this.vocab=new Array(s),this.scores=new Array(s);for(let t=0;t<s;++t){const s=e.vocab[t];this.vocab[t]=s[0],this.scores[t]=s[1]}this.unk_token_id=e.unk_id,this.unk_token=this.vocab[e.unk_id],this.tokens_to_ids=new Map(this.vocab.map(((e,t)=>[e,t]))),this.bos_token=" ",this.bos_token_id=this.tokens_to_ids.get(this.bos_token),this.eos_token=t.eos_token,this.eos_token_id=this.tokens_to_ids.get(this.eos_token),this.unk_token=this.vocab[this.unk_token_id],this.minScore=(0,a.min)(this.scores)[0],this.unk_score=this.minScore-10,this.scores[this.unk_token_id]=this.unk_score,this.trie=new l.CharTrie,this.trie.extend(this.vocab),this.fuse_unk=!0}populateNodes(e){const t=e.chars;let s=0;for(;s<t.length;){let r=!1;const n=[],a=t.slice(s).join(""),i=this.trie.commonPrefixSearch(a);for(const t of i){n.push(t);const a=this.tokens_to_ids.get(t),i=this.scores[a],l=(0,o.len)(t);e.insert(s,l,i,a),r||1!==l||(r=!0)}r||e.insert(s,1,this.unk_score,this.unk_token_id),s+=1}}tokenize(e){const t=new l.TokenLattice(e,this.bos_token_id,this.eos_token_id);return this.populateNodes(t),t.tokens()}encode(e){const t=[];for(const s of e){const e=this.tokenize(s);t.push(...e)}return t}}const F=(()=>{const e=[...Array.from({length:"~".charCodeAt(0)-"!".charCodeAt(0)+1},((e,t)=>t+"!".charCodeAt(0))),...Array.from({length:"¬".charCodeAt(0)-"¡".charCodeAt(0)+1},((e,t)=>t+"¡".charCodeAt(0))),...Array.from({length:"ÿ".charCodeAt(0)-"®".charCodeAt(0)+1},((e,t)=>t+"®".charCodeAt(0)))],t=e.slice();let s=0;for(let r=0;r<256;++r)e.includes(r)||(e.push(r),t.push(256+s),s+=1);const r=t.map((e=>String.fromCharCode(e)));return Object.fromEntries(e.map(((e,t)=>[e,r[t]])))})(),v=(0,o.reverseDictionary)(F);class C extends P{constructor(e){super(e),this.tokens_to_ids=m(e.vocab),this.unk_token_id=this.tokens_to_ids.get(e.unk_token),this.unk_token=e.unk_token,this.vocab=new Array(this.tokens_to_ids.size);for(const[e,t]of this.tokens_to_ids)this.vocab[t]=e;const t=Array.isArray(e.merges[0]);this.merges=t?e.merges:e.merges.map((e=>e.split(" ",2))),this.bpe_ranks=new Map(this.merges.map(((e,t)=>[JSON.stringify(e),t]))),this.end_of_word_suffix=e.end_of_word_suffix,this.continuing_subword_suffix=e.continuing_subword_suffix??null,this.byte_fallback=this.config.byte_fallback??!1,this.byte_fallback&&(this.text_encoder=new TextEncoder),this.ignore_merges=this.config.ignore_merges??!1,this.cache=new Map}bpe(e){if(0===e.length)return[];const t=this.cache.get(e);if(void 0!==t)return t;const s=Array.from(e);this.end_of_word_suffix&&(s[s.length-1]+=this.end_of_word_suffix);let r=[];if(s.length>1){const e=new l.PriorityQueue(((e,t)=>e.score<t.score));let t={token:s[0],bias:0,prev:null,next:null},o=t;for(let t=1;t<s.length;++t){const r={bias:t/s.length,token:s[t],prev:o,next:null};o.next=r,this._add_node(e,o),o=r}for(;!e.isEmpty();){const s=e.pop();if(s.deleted||!s.next||s.next.deleted)continue;if(s.deleted=!0,s.next.deleted=!0,s.prev){const e={...s.prev};s.prev.deleted=!0,s.prev=e,e.prev?e.prev.next=e:t=e}const r={token:s.token+s.next.token,bias:s.bias,prev:s.prev,next:s.next.next};r.prev?(r.prev.next=r,this._add_node(e,r.prev)):t=r,r.next&&(r.next.prev=r,this._add_node(e,r))}for(let e=t;null!==e;e=e.next)r.push(e.token)}else r=s;if(this.continuing_subword_suffix)for(let e=0;e<r.length-1;++e)r[e]+=this.continuing_subword_suffix;return this.cache.set(e,r),r}_add_node(e,t){const s=this.bpe_ranks.get(JSON.stringify([t.token,t.next.token]));void 0!==s&&(t.score=s+t.bias,e.push(t))}encode(e){const t=[];for(const s of e){if(this.ignore_merges&&this.tokens_to_ids.has(s)){t.push(s);continue}const e=this.bpe(s);for(const s of e)if(this.tokens_to_ids.has(s))t.push(s);else if(this.byte_fallback){const e=Array.from(this.text_encoder.encode(s)).map((e=>`<0x${e.toString(16).toUpperCase().padStart(2,"0")}>`));e.every((e=>this.tokens_to_ids.has(e)))?t.push(...e):t.push(this.unk_token)}else t.push(this.unk_token)}return t}}class S extends P{constructor(e,t){super(e),this.tokens_to_ids=m(t.target_lang?e.vocab[t.target_lang]:e.vocab),this.bos_token=t.bos_token,this.bos_token_id=this.tokens_to_ids.get(this.bos_token),this.eos_token=t.eos_token,this.eos_token_id=this.tokens_to_ids.get(this.eos_token),this.pad_token=t.pad_token,this.pad_token_id=this.tokens_to_ids.get(this.pad_token),this.unk_token=t.unk_token,this.unk_token_id=this.tokens_to_ids.get(this.unk_token),this.vocab=new Array(this.tokens_to_ids.size);for(const[e,t]of this.tokens_to_ids)this.vocab[t]=e}encode(e){return e}}class A extends r.Callable{constructor(e){super(),this.config=e}static fromConfig(e){if(null===e)return null;switch(e.type){case"BertNormalizer":return new D(e);case"Precompiled":return new pe(e);case"Sequence":return new O(e);case"Replace":return new E(e);case"NFC":return new L(e);case"NFKC":return new I(e);case"NFKD":return new z(e);case"Strip":return new j(e);case"StripAccents":return new N(e);case"Lowercase":return new V(e);case"Prepend":return new B(e);default:throw new Error(`Unknown Normalizer type: ${e.type}`)}}normalize(e){throw Error("normalize should be implemented in subclass.")}_call(e){return this.normalize(e)}}class E extends A{normalize(e){const t=p(this.config.pattern);return null===t?e:e.replaceAll(t,this.config.content)}}class L extends A{normalize(e){return e=e.normalize("NFC")}}class I extends A{normalize(e){return e=e.normalize("NFKC")}}class z extends A{normalize(e){return e=e.normalize("NFKD")}}class j extends A{normalize(e){return this.config.strip_left&&this.config.strip_right?e=e.trim():(this.config.strip_left&&(e=e.trimStart()),this.config.strip_right&&(e=e.trimEnd())),e}}class N extends A{normalize(e){return e=f(e)}}class V extends A{normalize(e){return e=e.toLowerCase()}}class B extends A{normalize(e){return e=this.config.prepend+e}}class O extends A{constructor(e){super(e),this.normalizers=e.normalizers.map((e=>A.fromConfig(e)))}normalize(e){return this.normalizers.reduce(((e,t)=>t.normalize(e)),e)}}class D extends A{_tokenize_chinese_chars(e){const t=[];for(let s=0;s<e.length;++s){const r=e[s];g(r.charCodeAt(0))?(t.push(" "),t.push(r),t.push(" ")):t.push(r)}return t.join("")}stripAccents(e){return e.normalize("NFD").replace(/\p{Mn}/gu,"")}_is_control(e){switch(e){case"\t":case"\n":case"\r":return!1;default:return/^\p{Cc}|\p{Cf}|\p{Co}|\p{Cs}$/u.test(e)}}_clean_text(e){const t=[];for(const s of e){const e=s.charCodeAt(0);0===e||65533===e||this._is_control(s)||(/^\s$/.test(s)?t.push(" "):t.push(s))}return t.join("")}normalize(e){return this.config.clean_text&&(e=this._clean_text(e)),this.config.handle_chinese_chars&&(e=this._tokenize_chinese_chars(e)),this.config.lowercase?(e=e.toLowerCase(),!1!==this.config.strip_accents&&(e=this.stripAccents(e))):this.config.strip_accents&&(e=this.stripAccents(e)),e}}class R extends r.Callable{static fromConfig(e){if(null===e)return null;switch(e.type){case"BertPreTokenizer":return new G(e);case"Sequence":return new me(e);case"Whitespace":return new he(e);case"WhitespaceSplit":return new _e(e);case"Metaspace":return new de(e);case"ByteLevel":return new q(e);case"Split":return new W(e);case"Punctuation":return new $(e);case"Digits":return new U(e);case"Replace":return new fe(e);default:throw new Error(`Unknown PreTokenizer type: ${e.type}`)}}pre_tokenize_text(e,t){throw Error("pre_tokenize_text should be implemented in subclass.")}pre_tokenize(e,t){return(Array.isArray(e)?e.map((e=>this.pre_tokenize_text(e,t))):this.pre_tokenize_text(e,t)).flat()}_call(e,t){return this.pre_tokenize(e,t)}}class G extends R{constructor(e){super(),this.pattern=new RegExp(`[^\\s${M}]+|[${M}]`,"gu")}pre_tokenize_text(e,t){return e.trim().match(this.pattern)||[]}}class q extends R{constructor(e){super(),this.config=e,this.add_prefix_space=this.config.add_prefix_space,this.trim_offsets=this.config.trim_offsets,this.use_regex=this.config.use_regex??!0,this.pattern=/'s|'t|'re|'ve|'m|'ll|'d| ?\p{L}+| ?\p{N}+| ?[^\s\p{L}\p{N}]+|\s+(?!\S)|\s+/gu,this.byte_encoder=F,this.text_encoder=new TextEncoder}pre_tokenize_text(e,t){this.add_prefix_space&&!e.startsWith(" ")&&(e=" "+e);return(this.use_regex?e.match(this.pattern)||[]:[e]).map((e=>Array.from(this.text_encoder.encode(e),(e=>this.byte_encoder[e])).join("")))}}class W extends R{constructor(e){super(),this.config=e,this.pattern=p(this.config.pattern,this.config.invert)}pre_tokenize_text(e,t){return null===this.pattern?[]:this.config.invert?e.match(this.pattern)||[]:"removed"===this.config.behavior?.toLowerCase()?e.split(this.pattern).filter((e=>e)):function(e,t){const s=[];let r=0;for(const o of e.matchAll(t)){const t=o[0];r<o.index&&s.push(e.slice(r,o.index)),t.length>0&&s.push(t),r=o.index+t.length}return r<e.length&&s.push(e.slice(r)),s}(e,this.pattern)}}class $ extends R{constructor(e){super(),this.config=e,this.pattern=new RegExp(`[^${M}]+|[${M}]+`,"gu")}pre_tokenize_text(e,t){return e.match(this.pattern)||[]}}class U extends R{constructor(e){super(),this.config=e;const t="[^\\d]+|\\d"+(this.config.individual_digits?"":"+");this.pattern=new RegExp(t,"gu")}pre_tokenize_text(e,t){return e.match(this.pattern)||[]}}class Q extends r.Callable{constructor(e){super(),this.config=e}static fromConfig(e){if(null===e)return null;switch(e.type){case"TemplateProcessing":return new J(e);case"ByteLevel":return new Y(e);case"RobertaProcessing":return new H(e);case"BertProcessing":return new X(e);case"Sequence":return new K(e);default:throw new Error(`Unknown PostProcessor type: ${e.type}`)}}post_process(e,...t){throw Error("post_process should be implemented in subclass.")}_call(e,...t){return this.post_process(e,...t)}}class X extends Q{constructor(e){super(e),this.cls=e.cls[0],this.sep=e.sep[0]}post_process(e,t=null,{add_special_tokens:s=!0}={}){s&&(e=(0,o.mergeArrays)([this.cls],e,[this.sep]));let r=new Array(e.length).fill(0);if(null!==t){const n=s&&this instanceof H?[this.sep]:[],a=s?[this.sep]:[];e=(0,o.mergeArrays)(e,n,t,a),r=(0,o.mergeArrays)(r,new Array(t.length+n.length+a.length).fill(1))}return{tokens:e,token_type_ids:r}}}class H extends X{}class J extends Q{constructor(e){super(e),this.single=e.single,this.pair=e.pair}post_process(e,t=null,{add_special_tokens:s=!0}={}){const r=null===t?this.single:this.pair;let n=[],a=[];for(const i of r)"SpecialToken"in i?s&&(n.push(i.SpecialToken.id),a.push(i.SpecialToken.type_id)):"Sequence"in i&&("A"===i.Sequence.id?(n=(0,o.mergeArrays)(n,e),a=(0,o.mergeArrays)(a,new Array(e.length).fill(i.Sequence.type_id))):"B"===i.Sequence.id&&(n=(0,o.mergeArrays)(n,t),a=(0,o.mergeArrays)(a,new Array(t.length).fill(i.Sequence.type_id))));return{tokens:n,token_type_ids:a}}}class Y extends Q{post_process(e,t=null){return t&&(e=(0,o.mergeArrays)(e,t)),{tokens:e}}}class K extends Q{constructor(e){super(e),this.processors=e.processors.map((e=>Q.fromConfig(e)))}post_process(e,t=null,s={}){let r;for(const o of this.processors)if(o instanceof Y){if(e=o.post_process(e).tokens,t){t=o.post_process(t).tokens}}else{const n=o.post_process(e,t,s);e=n.tokens,r=n.token_type_ids}return{tokens:e,token_type_ids:r}}}class Z extends r.Callable{constructor(e){super(),this.config=e,this.added_tokens=[],this.end_of_word_suffix=null,this.trim_offsets=e.trim_offsets}static fromConfig(e){if(null===e)return null;switch(e.type){case"WordPiece":return new oe(e);case"Metaspace":return new ue(e);case"ByteLevel":return new ne(e);case"Replace":return new ee(e);case"ByteFallback":return new te(e);case"Fuse":return new se(e);case"Strip":return new re(e);case"Sequence":return new ie(e);case"CTC":return new ae(e);case"BPEDecoder":return new le(e);default:throw new Error(`Unknown Decoder type: ${e.type}`)}}_call(e){return this.decode(e)}decode(e){return this.decode_chain(e).join("")}decode_chain(e){throw Error("`decode_chain` should be implemented in subclass.")}}class ee extends Z{decode_chain(e){const t=p(this.config.pattern);return null===t?e:e.map((e=>e.replaceAll(t,this.config.content)))}}class te extends Z{constructor(e){super(e),this.text_decoder=new TextDecoder}decode_chain(e){const t=[];let s=[];for(const r of e){let e=null;if(6===r.length&&r.startsWith("<0x")&&r.endsWith(">")){const t=parseInt(r.slice(3,5),16);isNaN(t)||(e=t)}if(null!==e)s.push(e);else{if(s.length>0){const e=this.text_decoder.decode(Uint8Array.from(s));t.push(e),s=[]}t.push(r)}}if(s.length>0){const e=this.text_decoder.decode(Uint8Array.from(s));t.push(e),s=[]}return t}}class se extends Z{decode_chain(e){return[e.join("")]}}class re extends Z{constructor(e){super(e),this.content=this.config.content,this.start=this.config.start,this.stop=this.config.stop}decode_chain(e){return e.map((e=>{let t=0;for(let s=0;s<this.start&&e[s]===this.content;++s)t=s+1;let s=e.length;for(let t=0;t<this.stop;++t){const r=e.length-t-1;if(e[r]!==this.content)break;s=r}return e.slice(t,s)}))}}class oe extends Z{constructor(e){super(e),this.cleanup=e.cleanup}decode_chain(e){return e.map(((e,t)=>(0!==t&&(e=e.startsWith(this.config.prefix)?e.replace(this.config.prefix,""):" "+e),this.cleanup&&(e=_(e)),e)))}}class ne extends Z{constructor(e){super(e),this.byte_decoder=v,this.text_decoder=new TextDecoder("utf-8",{fatal:!1,ignoreBOM:!0}),this.end_of_word_suffix=null}convert_tokens_to_string(e){const t=e.join(""),s=new Uint8Array([...t].map((e=>this.byte_decoder[e])));return this.text_decoder.decode(s)}decode_chain(e){const t=[];let s=[];for(const r of e)void 0!==this.added_tokens.find((e=>e.content===r))?(s.length>0&&(t.push(this.convert_tokens_to_string(s)),s=[]),t.push(r)):s.push(r);return s.length>0&&t.push(this.convert_tokens_to_string(s)),t}}class ae extends Z{constructor(e){super(e),this.pad_token=this.config.pad_token,this.word_delimiter_token=this.config.word_delimiter_token,this.cleanup=this.config.cleanup}convert_tokens_to_string(e){if(0===e.length)return"";const t=[e[0]];for(let s=1;s<e.length;++s)e[s]!==t.at(-1)&&t.push(e[s]);let s=t.filter((e=>e!==this.pad_token)).join("");return this.cleanup&&(s=_(s).replaceAll(this.word_delimiter_token," ").trim()),s}decode_chain(e){return[this.convert_tokens_to_string(e)]}}class ie extends Z{constructor(e){super(e),this.decoders=e.decoders.map((e=>Z.fromConfig(e)))}decode_chain(e){return this.decoders.reduce(((e,t)=>t.decode_chain(e)),e)}}class le extends Z{constructor(e){super(e),this.suffix=this.config.suffix}decode_chain(e){return e.map(((t,s)=>t.replaceAll(this.suffix,s===e.length-1?"":" ")))}}class ce extends Z{decode_chain(e){let t="";for(let s=1;s<e.length;s+=2)t+=e[s];return[t]}}class de extends R{constructor(e){super(),this.addPrefixSpace=e.add_prefix_space,this.replacement=e.replacement,this.strRep=e.str_rep||this.replacement,this.prepend_scheme=e.prepend_scheme??"always"}pre_tokenize_text(e,{section_index:t}={}){let s=e.replaceAll(" ",this.strRep);return this.addPrefixSpace&&!s.startsWith(this.replacement)&&("always"===this.prepend_scheme||"first"===this.prepend_scheme&&0===t)&&(s=this.strRep+s),[s]}}class ue extends Z{constructor(e){super(e),this.addPrefixSpace=e.add_prefix_space,this.replacement=e.replacement}decode_chain(e){const t=[];for(let s=0;s<e.length;++s){let r=e[s].replaceAll(this.replacement," ");this.addPrefixSpace&&0==s&&r.startsWith(" ")&&(r=r.substring(1)),t.push(r)}return t}}class pe extends A{constructor(e){super(e),this.charsmap=e.precompiled_charsmap}normalize(e){if((e=(e=e.replace(/[\u0001-\u0008\u000B\u000E-\u001F\u007F\u008F\u009F]/gm,"")).replace(/[\u0009\u000A\u000C\u000D\u00A0\u1680\u2000-\u200F\u2028\u2029\u202F\u205F\u2581\u3000\uFEFF\uFFFD]/gm," ")).includes("～")){const t=e.split("～");e=t.map((e=>e.normalize("NFKC"))).join("～")}else e=e.normalize("NFKC");return e}}class me extends R{constructor(e){super(),this.tokenizers=e.pretokenizers.map((e=>R.fromConfig(e)))}pre_tokenize_text(e,t){return this.tokenizers.reduce(((e,s)=>s.pre_tokenize(e,t)),[e])}}class he extends R{constructor(e){super()}pre_tokenize_text(e,t){return e.match(/\w+|[^\w\s]+/g)||[]}}class _e extends R{constructor(e){super()}pre_tokenize_text(e,t){return function(e){return e.match(/\S+/g)||[]}(e)}}class fe extends R{constructor(e){super(),this.config=e,this.pattern=p(this.config.pattern),this.content=this.config.content}pre_tokenize_text(e,t){return null===this.pattern?[e]:[e.replaceAll(this.pattern,this.config.content)]}}const ge=["bos_token","eos_token","unk_token","sep_token","pad_token","cls_token","mask_token"];function Me(e,t,s,r){for(const n of Object.keys(e)){const a=t-e[n].length,i=s(n),l=new Array(a).fill(i);e[n]="right"===r?(0,o.mergeArrays)(e[n],l):(0,o.mergeArrays)(l,e[n])}}function we(e,t){for(const s of Object.keys(e))e[s].length=t}class Te extends r.Callable{return_token_type_ids=!1;padding_side="right";constructor(e,t){super(),this._tokenizer_config=t,this.normalizer=A.fromConfig(e.normalizer),this.pre_tokenizer=R.fromConfig(e.pre_tokenizer),this.model=P.fromConfig(e.model,t),this.post_processor=Q.fromConfig(e.post_processor),this.decoder=Z.fromConfig(e.decoder),this.special_tokens=[],this.all_special_ids=[],this.added_tokens=[];for(const t of e.added_tokens){const e=new x(t);this.added_tokens.push(e),this.model.tokens_to_ids.set(e.content,e.id),this.model.vocab[e.id]=e.content,e.special&&(this.special_tokens.push(e.content),this.all_special_ids.push(e.id))}if(this.additional_special_tokens=t.additional_special_tokens??[],this.special_tokens.push(...this.additional_special_tokens),this.special_tokens=[...new Set(this.special_tokens)],this.decoder&&(this.decoder.added_tokens=this.added_tokens,this.decoder.end_of_word_suffix=this.model.end_of_word_suffix),this.added_tokens_regex=this.added_tokens.length>0?new RegExp(this.added_tokens.slice().sort(((e,t)=>t.content.length-e.content.length)).map((e=>`${e.lstrip?"\\s*":""}(${(0,o.escapeRegExp)(e.content)})${e.rstrip?"\\s*":""}`)).join("|")):null,this.mask_token=this.getToken("mask_token"),this.mask_token_id=this.model.tokens_to_ids.get(this.mask_token),this.pad_token=this.getToken("pad_token","eos_token"),this.pad_token_id=this.model.tokens_to_ids.get(this.pad_token),this.sep_token=this.getToken("sep_token"),this.sep_token_id=this.model.tokens_to_ids.get(this.sep_token),this.unk_token=this.getToken("unk_token"),this.unk_token_id=this.model.tokens_to_ids.get(this.unk_token),this.model_max_length=t.model_max_length,this.remove_space=t.remove_space,this.clean_up_tokenization_spaces=t.clean_up_tokenization_spaces??!0,this.do_lowercase_and_remove_accent=t.do_lowercase_and_remove_accent??!1,t.padding_side&&(this.padding_side=t.padding_side),this.legacy=!1,this.chat_template=t.chat_template??null,Array.isArray(this.chat_template)){const e=Object.create(null);for(const{name:t,template:s}of this.chat_template){if("string"!=typeof t||"string"!=typeof s)throw new Error('Chat template must be a list of objects with "name" and "template" properties');e[t]=s}this.chat_template=e}this._compiled_template_cache=new Map}getToken(...e){for(const t of e){const e=this._tokenizer_config[t];if(e){if("object"==typeof e){if("AddedToken"===e.__type)return e.content;throw Error(`Unknown token: ${e}`)}return e}}return null}static async from_pretrained(e,{progress_callback:t=null,config:s=null,cache_dir:r=null,local_files_only:o=!1,revision:n="main",legacy:a=null}={}){return new this(...await u(e,{progress_callback:t,config:s,cache_dir:r,local_files_only:o,revision:n,legacy:a}))}_call(e,{text_pair:t=null,add_special_tokens:s=!0,padding:r=!1,truncation:o=null,max_length:n=null,return_tensor:l=!0,return_token_type_ids:c=null}={}){const d=Array.isArray(e);let u;if(d){if(0===e.length)throw Error("text array must be non-empty");if(null!==t){if(!Array.isArray(t))throw Error("text_pair must also be an array");if(e.length!==t.length)throw Error("text and text_pair must have the same length");u=e.map(((e,r)=>this._encode_plus(e,{text_pair:t[r],add_special_tokens:s,return_token_type_ids:c})))}else u=e.map((e=>this._encode_plus(e,{add_special_tokens:s,return_token_type_ids:c})))}else{if(null==e)throw Error("text may not be null or undefined");if(Array.isArray(t))throw Error("When specifying `text_pair`, since `text` is a string, `text_pair` must also be a string (i.e., not an array).");u=[this._encode_plus(e,{text_pair:t,add_special_tokens:s,return_token_type_ids:c})]}if(null===n?n="max_length"===r?this.model_max_length:(0,a.max)(u.map((e=>e.input_ids.length)))[0]:o||console.warn("Truncation was not explicitly activated but `max_length` is provided a specific value, please use `truncation=true` to explicitly truncate examples to max length."),n=Math.min(n,this.model_max_length??1/0),r||o)for(let e=0;e<u.length;++e)u[e].input_ids.length!==n&&(u[e].input_ids.length>n?o&&we(u[e],n):r&&Me(u[e],n,(e=>"input_ids"===e?this.pad_token_id:0),this.padding_side));const p={};if(l){if((!r||!o)&&u.some((e=>{for(const t of Object.keys(e))if(e[t].length!==u[0][t]?.length)return!0;return!1})))throw Error("Unable to create tensor, you should probably activate truncation and/or padding with 'padding=true' and 'truncation=true' to have batched tensors with the same length.");const e=[u.length,u[0].input_ids.length];for(const t of Object.keys(u[0]))p[t]=new i.Tensor("int64",BigInt64Array.from(u.flatMap((e=>e[t])).map(BigInt)),e)}else{for(const e of Object.keys(u[0]))p[e]=u.map((t=>t[e]));if(!d)for(const e of Object.keys(p))p[e]=p[e][0]}return p}_encode_text(e){if(null===e)return null;const t=(this.added_tokens_regex?e.split(this.added_tokens_regex).filter((e=>e)):[e]).map(((e,t)=>{if(void 0!==this.added_tokens.find((t=>t.content===e)))return e;{if(!0===this.remove_space&&(e=e.trim().split(/\s+/).join(" ")),this.do_lowercase_and_remove_accent&&(e=function(e){return f(e.toLowerCase())}(e)),null!==this.normalizer&&(e=this.normalizer(e)),0===e.length)return[];const s=null!==this.pre_tokenizer?this.pre_tokenizer(e,{section_index:t}):[e];return this.model(s)}})).flat();return t}_encode_plus(e,{text_pair:t=null,add_special_tokens:s=!0,return_token_type_ids:r=null}={}){const{tokens:o,token_type_ids:n}=this._tokenize_helper(e,{pair:t,add_special_tokens:s}),a=this.model.convert_tokens_to_ids(o),i={input_ids:a,attention_mask:new Array(a.length).fill(1)};return(r??this.return_token_type_ids)&&n&&(i.token_type_ids=n),i}_tokenize_helper(e,{pair:t=null,add_special_tokens:s=!1}={}){const r=this._encode_text(e),n=this._encode_text(t);return this.post_processor?this.post_processor(r,n,{add_special_tokens:s}):{tokens:(0,o.mergeArrays)(r??[],n??[])}}tokenize(e,{pair:t=null,add_special_tokens:s=!1}={}){return this._tokenize_helper(e,{pair:t,add_special_tokens:s}).tokens}encode(e,{text_pair:t=null,add_special_tokens:s=!0,return_token_type_ids:r=null}={}){return this._encode_plus(e,{text_pair:t,add_special_tokens:s,return_token_type_ids:r}).input_ids}batch_decode(e,t={}){return e instanceof i.Tensor&&(e=e.tolist()),e.map((e=>this.decode(e,t)))}decode(e,t={}){if(e instanceof i.Tensor&&(e=h(e)),!Array.isArray(e)||0===e.length||!(0,o.isIntegralNumber)(e[0]))throw Error("token_ids must be a non-empty array of integers.");return this.decode_single(e,t)}decode_single(e,{skip_special_tokens:t=!1,clean_up_tokenization_spaces:s=null}){let r=this.model.convert_ids_to_tokens(e);t&&(r=r.filter((e=>!this.special_tokens.includes(e))));let o=this.decoder?this.decoder(r):r.join(" ");return this.decoder&&this.decoder.end_of_word_suffix&&(o=o.replaceAll(this.decoder.end_of_word_suffix," "),t&&(o=o.trim())),(s??this.clean_up_tokenization_spaces)&&(o=_(o)),o}get_chat_template({chat_template:e=null,tools:t=null}={}){if(this.chat_template&&"object"==typeof this.chat_template){const s=this.chat_template;if(null!==e&&Object.hasOwn(s,e))e=s[e];else if(null===e)if(null!==t&&"tool_use"in s)e=s.tool_use;else{if(!("default"in s))throw Error(`This model has multiple chat templates with no default specified! Please either pass a chat template or the name of the template you wish to use to the 'chat_template' argument. Available template names are ${Object.keys(s).sort()}.`);e=s.default}}else if(null===e){if(!this.chat_template)throw Error("Cannot use apply_chat_template() because tokenizer.chat_template is not set and no template argument was passed! For information about writing templates and setting the tokenizer.chat_template attribute, please see the documentation at https://huggingface.co/docs/transformers/main/en/chat_templating");e=this.chat_template}return e}apply_chat_template(e,{tools:t=null,documents:s=null,chat_template:r=null,add_generation_prompt:o=!1,tokenize:n=!0,padding:a=!1,truncation:i=!1,max_length:l=null,return_tensor:d=!0,return_dict:u=!1,tokenizer_kwargs:p={},...m}={}){if("string"!=typeof(r=this.get_chat_template({chat_template:r,tools:t})))throw Error("chat_template must be a string, but got "+typeof r);let h=this._compiled_template_cache.get(r);void 0===h&&(h=new c.Template(r),this._compiled_template_cache.set(r,h));const _=Object.create(null);for(const e of ge){const t=this.getToken(e);t&&(_[e]=t)}const f=h.render({messages:e,add_generation_prompt:o,tools:t,documents:s,..._,...m});if(n){const e=this._call(f,{add_special_tokens:!1,padding:a,truncation:i,max_length:l,return_tensor:d,...p});return u?e:e.input_ids}return f}}class be extends Te{return_token_type_ids=!0}class xe extends Te{return_token_type_ids=!0}class Pe extends Te{return_token_type_ids=!0}class ke extends Te{return_token_type_ids=!0}class ye extends Te{return_token_type_ids=!0}class Fe extends Te{return_token_type_ids=!0}class ve extends Te{return_token_type_ids=!0}class Ce extends Te{return_token_type_ids=!0}class Se extends Te{return_token_type_ids=!0}class Ae extends Te{}class Ee extends Te{}class Le extends Te{return_token_type_ids=!0;constructor(e,t){super(e,t),console.warn('WARNING: `XLMTokenizer` is not yet supported by Hugging Face\'s "fast" tokenizers library. Therefore, you may experience slightly inaccurate results.')}}class Ie extends Te{return_token_type_ids=!0}class ze extends Te{}class je extends Te{}class Ne extends Te{}class Ve extends Te{constructor(e,t){super(e,t),this.languageRegex=/^[a-z]{2}_[A-Z]{2}$/,this.language_codes=this.special_tokens.filter((e=>this.languageRegex.test(e))),this.lang_to_token=e=>e}_build_translation_inputs(e,t,s){return Ke(this,e,t,s)}}class Be extends Ve{}class Oe extends Te{}class De extends Te{}const Re="▁";class Ge extends Te{padding_side="left";constructor(e,t){super(e,t),this.legacy=t.legacy??!0,this.legacy||(this.normalizer=null,this.pre_tokenizer=new de({replacement:Re,add_prefix_space:!0,prepend_scheme:"first"}))}_encode_text(e){if(null===e)return null;if(this.legacy||0===e.length)return super._encode_text(e);let t=super._encode_text(Re+e.replaceAll(Re," "));return t.length>1&&t[0]===Re&&this.special_tokens.includes(t[1])&&(t=t.slice(1)),t}}class qe extends Te{}class We extends Te{}class $e extends Te{}class Ue extends Te{}class Qe extends Te{}class Xe extends Te{}class He extends Te{}class Je extends Te{}class Ye extends Te{}function Ke(e,t,s,r){if(!("language_codes"in e)||!Array.isArray(e.language_codes))throw new Error("Tokenizer must have `language_codes` attribute set and it should be an array of language ids.");if(!("languageRegex"in e&&e.languageRegex instanceof RegExp))throw new Error("Tokenizer must have `languageRegex` attribute set and it should be a regular expression.");if(!("lang_to_token"in e)||"function"!=typeof e.lang_to_token)throw new Error("Tokenizer must have `lang_to_token` attribute set and it should be a function.");const o=r.src_lang,n=r.tgt_lang;if(!e.language_codes.includes(n))throw new Error(`Target language code "${n}" is not valid. Must be one of: {${e.language_codes.join(", ")}}`);if(void 0!==o){if(!e.language_codes.includes(o))throw new Error(`Source language code "${o}" is not valid. Must be one of: {${e.language_codes.join(", ")}}`);for(const t of e.post_processor.config.single)if("SpecialToken"in t&&e.languageRegex.test(t.SpecialToken.id)){t.SpecialToken.id=e.lang_to_token(o);break}}return r.forced_bos_token_id=e.model.convert_tokens_to_ids([e.lang_to_token(n)])[0],e._call(t,s)}class Ze extends Te{constructor(e,t){super(e,t),this.languageRegex=/^[a-z]{3}_[A-Z][a-z]{3}$/,this.language_codes=this.special_tokens.filter((e=>this.languageRegex.test(e))),this.lang_to_token=e=>e}_build_translation_inputs(e,t,s){return Ke(this,e,t,s)}}class et extends Te{constructor(e,t){super(e,t),this.languageRegex=/^__[a-z]{2,3}__$/,this.language_codes=this.special_tokens.filter((e=>this.languageRegex.test(e))).map((e=>e.slice(2,-2))),this.lang_to_token=e=>`__${e}__`}_build_translation_inputs(e,t,s){return Ke(this,e,t,s)}}class tt extends Te{get timestamp_begin(){return this.model.convert_tokens_to_ids(["<|notimestamps|>"])[0]+1}_decode_asr(e,{return_timestamps:t=!1,return_language:s=!1,time_precision:r=null,force_full_sequences:o=!0}={}){if(null===r)throw Error("Must specify time_precision");let n=null;const i="word"===t;function l(){return{language:n,timestamp:[null,null],text:""}}const c=[];let u=l(),p=0;const m=this.timestamp_begin;let h=[],_=[],f=!1,g=null;const M=new Set(this.all_special_ids);for(const s of e){const e=s.tokens,o=i?s.token_timestamps:null;let T=null,b=m;if("stride"in s){const[t,o,n]=s.stride;if(p-=o,g=t-n,o&&(b=o/r+m),n)for(let t=e.length-1;t>=0;--t){const s=Number(e[t]);if(s>=m){if(null!==T&&(s-m)*r<g)break;T=s}}}let x=[],P=[];for(let s=0;s<e.length;++s){const g=Number(e[s]);if(M.has(g)){const e=this.decode([g]),s=d.WHISPER_LANGUAGE_MAPPING.get(e.slice(2,-2));if(void 0!==s){if(null!==n&&s!==n&&!t){h.push(x);const e=this.findLongestCommonSequence(h)[0],t=this.decode(e);u.text=t,c.push(u),h=[],x=[],u=l()}n=u.language=s}}else if(g>=m){const e=(g-m)*r+p,t=(0,a.round)(e,2);if(null!==T&&g>=T)f=!0;else if(f||h.length>0&&g<b)f=!1;else if(null===u.timestamp[0])u.timestamp[0]=t;else if(t===u.timestamp[0]);else{u.timestamp[1]=t,h.push(x),i&&_.push(P);const[e,s]=this.findLongestCommonSequence(h,_),r=this.decode(e);u.text=r,i&&(u.words=this.collateWordTimestamps(e,s,n)),c.push(u),h=[],x=[],_=[],P=[],u=l()}}else if(x.push(g),i){let e,t=(0,a.round)(o[s]+p,2);if(s+1<o.length){e=(0,a.round)(o[s+1]+p,2);const n=this.decode([g]);w.test(n)&&(e=(0,a.round)(Math.min(t+r,e),2))}else e=null;P.push([t,e])}}if("stride"in s){const[e,t,r]=s.stride;p+=e-r}x.length>0?(h.push(x),i&&_.push(P)):h.every((e=>0===e.length))&&(u=l(),h=[],x=[],_=[],P=[])}if(h.length>0){if(o&&t)throw new Error("Whisper did not predict an ending timestamp, which can happen if audio is cut off in the middle of a word. Also make sure WhisperTimeStampLogitsProcessor was used during generation.");const[e,s]=this.findLongestCommonSequence(h,_),r=this.decode(e);u.text=r,i&&(u.words=this.collateWordTimestamps(e,s,n)),c.push(u)}let T=Object.create(null);const b=c.map((e=>e.text)).join("");if(t||s){for(let e=0;e<c.length;++e){const r=c[e];t||delete r.timestamp,s||delete r.language}if(i){const e=[];for(const t of c)for(const s of t.words)e.push(s);T={chunks:e}}else T={chunks:c}}return[b,T]}findLongestCommonSequence(e,t=null){let s=e[0],r=s.length,o=[];const n=Array.isArray(t)&&t.length>0;let a=n?[]:null,i=n?t[0]:null;for(let l=1;l<e.length;++l){const c=e[l];let d=0,u=[r,r,0,0];const p=c.length;for(let e=1;e<r+p;++e){const o=Math.max(0,r-e),a=Math.min(r,r+p-e),m=s.slice(o,a),h=Math.max(0,e-r),_=Math.min(p,e),f=c.slice(h,_);if(m.length!==f.length)throw new Error("There is a bug within whisper `decode_asr` function, please report it. Dropping to prevent bad inference.");let g;g=n?m.filter(((e,s)=>e===f[s]&&i[o+s]<=t[l][h+s])).length:m.filter(((e,t)=>e===f[t])).length;const M=g/e+e/1e4;g>1&&M>d&&(d=M,u=[o,a,h,_])}const[m,h,_,f]=u,g=Math.floor((h+m)/2),M=Math.floor((f+_)/2);o.push(...s.slice(0,g)),s=c.slice(M),r=s.length,n&&(a.push(...i.slice(0,g)),i=t[l].slice(M))}return o.push(...s),n?(a.push(...i),[o,a]):[o,[]]}collateWordTimestamps(e,t,s){const[r,o,n]=this.combineTokensIntoWords(e,s),a=[];for(let e=0;e<r.length;++e){const s=n[e];a.push({text:r[e],timestamp:[t[s.at(0)][0],t[s.at(-1)][1]]})}return a}combineTokensIntoWords(e,t,s="\"'“¡¿([{-",r="\"'.。,，!！?？:：”)]}、"){let o,n,a;return["chinese","japanese","thai","lao","myanmar"].includes(t=t??"english")?[o,n,a]=this.splitTokensOnUnicode(e):[o,n,a]=this.splitTokensOnSpaces(e),this.mergePunctuations(o,n,a,s,r)}decode(e,t){let s;return t?.decode_with_timestamps?(e instanceof i.Tensor&&(e=h(e)),s=this.decodeWithTimestamps(e,t)):s=super.decode(e,t),s}decodeWithTimestamps(e,t){const s=t?.time_precision??.02,r=Array.from(this.all_special_ids).at(-1)+1;let o=[[]];for(let t of e)if(t=Number(t),t>=r){const e=((t-r)*s).toFixed(2);o.push(`<|${e}|>`),o.push([])}else o[o.length-1].push(t);return o=o.map((e=>"string"==typeof e?e:super.decode(e,t))),o.join("")}splitTokensOnUnicode(e){const t=this.decode(e,{decode_with_timestamps:!0}),s=[],r=[],o=[];let n=[],a=[],i=0;for(let l=0;l<e.length;++l){const c=e[l];n.push(c),a.push(l);const d=this.decode(n,{decode_with_timestamps:!0});d.includes("�")&&"�"!==t[i+d.indexOf("�")]||(s.push(d),r.push(n),o.push(a),n=[],a=[],i+=d.length)}return[s,r,o]}splitTokensOnSpaces(e){const[t,s,r]=this.splitTokensOnUnicode(e),o=[],n=[],a=[],i=new RegExp(`^[${M}]$`,"gu");for(let e=0;e<t.length;++e){const l=t[e],c=s[e],d=r[e],u=c[0]>=this.model.tokens_to_ids.get("<|endoftext|>"),p=l.startsWith(" "),m=l.trim(),h=i.test(m);if(u||p||h||0===o.length)o.push(l),n.push(c),a.push(d);else{const e=o.length-1;o[e]+=l,n[e].push(...c),a[e].push(...d)}}return[o,n,a]}mergePunctuations(e,t,s,r,n){const a=structuredClone(e),i=structuredClone(t),l=structuredClone(s);let c=a.length-2,d=a.length-1;for(;c>=0;)a[c].startsWith(" ")&&r.includes(a[c].trim())?(a[d]=a[c]+a[d],i[d]=(0,o.mergeArrays)(i[c],i[d]),l[d]=(0,o.mergeArrays)(l[c],l[d]),a[c]="",i[c]=[],l[c]=[]):d=c,--c;for(c=0,d=1;d<a.length;)!a[c].endsWith(" ")&&n.includes(a[d])?(a[c]+=a[d],i[c]=(0,o.mergeArrays)(i[c],i[d]),l[c]=(0,o.mergeArrays)(l[c],l[d]),a[d]="",i[d]=[],l[d]=[]):c=d,++d;return[a.filter((e=>e)),i.filter((e=>e.length>0)),l.filter((e=>e.length>0))]}}class st extends Te{}class rt extends Te{}class ot extends Te{}class nt extends Te{constructor(e,t){super(e,t),this.languageRegex=/^(>>\w+<<)\s*/g,this.supported_language_codes=this.model.vocab.filter((e=>this.languageRegex.test(e))),console.warn('WARNING: `MarianTokenizer` is not yet supported by Hugging Face\'s "fast" tokenizers library. Therefore, you may experience slightly inaccurate results.')}_encode_text(e){if(null===e)return null;const[t,...s]=e.trim().split(this.languageRegex);if(0===s.length)return super._encode_text(t);if(2===s.length){const[e,t]=s;return this.supported_language_codes.includes(e)||console.warn(`Unsupported language code "${e}" detected, which may lead to unexpected behavior. Should be one of: ${JSON.stringify(this.supported_language_codes)}`),(0,o.mergeArrays)([e],super._encode_text(t))}}}class at extends Te{}class it extends Te{}class lt extends Te{}class ct extends Te{}class dt extends Te{}class ut extends Te{constructor(e,t){super(e,t),this.decoder=new ce({})}}class pt extends Te{}class mt extends Te{}class ht{static TOKENIZER_CLASS_MAPPING={T5Tokenizer:ze,DistilBertTokenizer:Ae,CamembertTokenizer:Ee,DebertaTokenizer:ye,DebertaV2Tokenizer:Fe,BertTokenizer:be,HerbertTokenizer:ve,ConvBertTokenizer:Ce,RoFormerTokenizer:Se,XLMTokenizer:Le,ElectraTokenizer:Ie,MobileBertTokenizer:Pe,SqueezeBertTokenizer:ke,AlbertTokenizer:xe,GPT2Tokenizer:je,BartTokenizer:Ne,MBartTokenizer:Ve,MBart50Tokenizer:Be,RobertaTokenizer:Oe,WhisperTokenizer:tt,CodeGenTokenizer:st,CLIPTokenizer:rt,SiglipTokenizer:ot,MarianTokenizer:nt,BloomTokenizer:De,NllbTokenizer:Ze,M2M100Tokenizer:et,LlamaTokenizer:Ge,CodeLlamaTokenizer:qe,XLMRobertaTokenizer:We,MPNetTokenizer:$e,FalconTokenizer:Ue,GPTNeoXTokenizer:Qe,EsmTokenizer:Xe,Wav2Vec2CTCTokenizer:at,BlenderbotTokenizer:it,BlenderbotSmallTokenizer:lt,SpeechT5Tokenizer:ct,NougatTokenizer:dt,VitsTokenizer:ut,Qwen2Tokenizer:He,GemmaTokenizer:Je,Grok1Tokenizer:Ye,CohereTokenizer:pt,MgpstrTokenizer:mt,PreTrainedTokenizer:Te};static async from_pretrained(e,{progress_callback:t=null,config:s=null,cache_dir:r=null,local_files_only:o=!1,revision:n="main",legacy:a=null}={}){const[i,l]=await u(e,{progress_callback:t,config:s,cache_dir:r,local_files_only:o,revision:n,legacy:a}),c=l.tokenizer_class?.replace(/Fast$/,"")??"PreTrainedTokenizer";let d=this.TOKENIZER_CLASS_MAPPING[c];return d||(console.warn(`Unknown tokenizer class "${c}", attempting to construct from base class.`),d=Te),new d(i,l)}}},"./src/utils/audio.js":
/*!****************************!*\
!*** ./src/utils/audio.js ***!
\****************************/(e,t,s)=>{s.r(t),s.d(t,{hamming:()=>d,hanning:()=>c,mel_filter_bank:()=>_,read_audio:()=>i,spectrogram:()=>g,window_function:()=>M});var r=s(/*! ./hub.js */"./src/utils/hub.js"),o=s(/*! ./maths.js */"./src/utils/maths.js"),n=s(/*! ./core.js */"./src/utils/core.js"),a=s(/*! ./tensor.js */"./src/utils/tensor.js");async function i(e,t){if("undefined"==typeof AudioContext)throw Error("Unable to load audio from path/URL since `AudioContext` is not available in your environment. Instead, audio data should be passed directly to the pipeline/processor. For more information and some example code, see https://huggingface.co/docs/transformers.js/guides/node-audio-processing.");const s=await(await(0,r.getFile)(e)).arrayBuffer(),o=new AudioContext({sampleRate:t});void 0===t&&console.warn(`No sampling rate provided, using default of ${o.sampleRate}Hz.`);const n=await o.decodeAudioData(s);let a;if(2===n.numberOfChannels){const e=Math.sqrt(2),t=n.getChannelData(0),s=n.getChannelData(1);a=new Float32Array(t.length);for(let r=0;r<n.length;++r)a[r]=e*(t[r]+s[r])/2}else a=n.getChannelData(0);return a}function l(e,t){if(e<1)return new Float64Array;if(1===e)return new Float64Array([1]);const s=1-t,r=2*Math.PI/(e-1),o=new Float64Array(e);for(let n=0;n<e;++n)o[n]=t-s*Math.cos(n*r);return o}function c(e){return l(e,.5)}function d(e){return l(e,.54)}const u={htk:e=>2595*Math.log10(1+e/700),kaldi:e=>1127*Math.log(1+e/700),slaney:(e,t=1e3,s=15,r=27/Math.log(6.4))=>e>=t?s+Math.log(e/t)*r:3*e/200};function p(e,t="htk"){const s=u[t];if(!s)throw new Error('mel_scale should be one of "htk", "slaney" or "kaldi".');return"number"==typeof e?s(e):e.map((e=>s(e)))}const m={htk:e=>700*(10**(e/2595)-1),kaldi:e=>700*(Math.exp(e/1127)-1),slaney:(e,t=1e3,s=15,r=Math.log(6.4)/27)=>e>=s?t*Math.exp(r*(e-s)):200*e/3};function h(e,t,s){const r=(t-e)/(s-1);return Float64Array.from({length:s},((t,s)=>e+r*s))}function _(e,t,s,r,o,n=null,a="htk",i=!1){if(null!==n&&"slaney"!==n)throw new Error('norm must be one of null or "slaney"');const l=h(p(s,a),p(r,a),t+2);let c,d=function(e,t="htk"){const s=m[t];if(!s)throw new Error('mel_scale should be one of "htk", "slaney" or "kaldi".');return"number"==typeof e?s(e):e.map((e=>s(e)))}(l,a);if(i){const t=o/(2*e);c=p(Float64Array.from({length:e},((e,s)=>s*t)),a),d=l}else c=h(0,Math.floor(o/2),e);const u=function(e,t){const s=Float64Array.from({length:t.length-1},((e,s)=>t[s+1]-t[s])),r=Array.from({length:e.length},(()=>new Array(t.length)));for(let s=0;s<e.length;++s){const o=r[s];for(let r=0;r<t.length;++r)o[r]=t[r]-e[s]}const o=t.length-2,n=Array.from({length:o},(()=>new Array(e.length)));for(let t=0;t<e.length;++t){const e=r[t];for(let r=0;r<o;++r){const o=-e[r]/s[r],a=e[r+2]/s[r+1];n[r][t]=Math.max(0,Math.min(o,a))}}return n}(c,d);if(null!==n&&"slaney"===n)for(let s=0;s<t;++s){const t=u[s],r=2/(d[s+2]-d[s]);for(let s=0;s<e;++s)t[s]*=r}return u}function f(e,t,s,r,n){if(s<=0)throw new Error("reference must be greater than zero");if(r<=0)throw new Error("min_value must be greater than zero");s=Math.max(r,s);const a=Math.log10(s);for(let s=0;s<e.length;++s)e[s]=t*Math.log10(Math.max(r,e[s])-a);if(null!==n){if(n<=0)throw new Error("db_range must be greater than zero");const t=(0,o.max)(e)[0]-n;for(let s=0;s<e.length;++s)e[s]=Math.max(e[s],t)}return e}async function g(e,t,s,r,{fft_length:i=null,power:l=1,center:c=!0,pad_mode:d="reflect",onesided:u=!0,preemphasis:p=null,mel_filters:m=null,mel_floor:h=1e-10,log_mel:_=null,reference:g=1,min_value:M=1e-10,db_range:w=null,remove_dc_offset:T=null,min_num_frames:b=null,max_num_frames:x=null,do_pad:P=!0,transpose:k=!1}={}){const y=t.length;if(null===i&&(i=s),s>i)throw Error(`frame_length (${s}) may not be larger than fft_length (${i})`);if(y!==s)throw new Error(`Length of the window (${y}) must equal frame_length (${s})`);if(r<=0)throw new Error("hop_length must be greater than zero");if(null===l&&null!==m)throw new Error("You have provided `mel_filters` but `power` is `None`. Mel spectrogram computation is not yet supported for complex-valued spectrogram. 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Got '${_}'`)}}return j}function M(e,t,{periodic:s=!0,frame_length:r=null,center:o=!0}={}){const n=s?e+1:e;let a;switch(t){case"boxcar":a=new Float64Array(n).fill(1);break;case"hann":case"hann_window":a=c(n);break;case"hamming":a=d(n);break;case"povey":a=c(n).map((e=>Math.pow(e,.85)));break;default:throw new Error(`Unknown window type ${t}.`)}if(s&&(a=a.subarray(0,e)),null===r)return a;if(e>r)throw new Error(`Length of the window (${e}) may not be larger than frame_length (${r})`);return a}},"./src/utils/constants.js":
/*!********************************!*\
!*** ./src/utils/constants.js ***!
\********************************/(e,t,s)=>{s.r(t),s.d(t,{CHAT_TEMPLATE_NAME:()=>l,CONFIG_NAME:()=>o,FEATURE_EXTRACTOR_NAME:()=>n,GENERATION_CONFIG_NAME:()=>c,GITHUB_ISSUE_URL:()=>r,IMAGE_PROCESSOR_NAME:()=>a,PROCESSOR_NAME:()=>i});const r="https://github.com/huggingface/transformers.js/issues/new/choose",o="config.json",n="preprocessor_config.json",a=n,i="processor_config.json",l="chat_template.json",c="generation_config.json"},"./src/utils/core.js":
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!*** ./src/utils/core.js ***!
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/*!**************************************!*\
!*** ./src/utils/data-structures.js ***!
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!*** ./src/utils/devices.js ***!
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!*** ./src/utils/dtypes.js ***!
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!*** ./src/utils/generic.js ***!
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!*** ./src/utils/hub.js ***!
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/*!****************************!*\
!*** ./src/utils/image.js ***!
\****************************/(e,t,s)=>{s.r(t),s.d(t,{RawImage:()=>_});var r=s(/*! ./core.js */"./src/utils/core.js"),o=s(/*! ./hub.js */"./src/utils/hub.js"),n=s(/*! ../env.js */"./src/env.js"),a=s(/*! ./tensor.js */"./src/utils/tensor.js"),i=s(/*! sharp */"?2b25");const l="undefined"!=typeof self,c=l&&"DedicatedWorkerGlobalScope"===self.constructor.name;let d,u,p;if(l)d=(e,t)=>{if(!self.OffscreenCanvas)throw new Error("OffscreenCanvas not supported by this browser.");return new self.OffscreenCanvas(e,t)},p=self.createImageBitmap,u=self.ImageData;else{if(!i)throw new Error("Unable to load image processing library.");p=async e=>{const t=(await e.metadata()).channels,{data:s,info:r}=await e.rotate().raw().toBuffer({resolveWithObject:!0}),o=new _(new Uint8ClampedArray(s),r.width,r.height,r.channels);return void 0!==t&&t!==r.channels&&o.convert(t),o}}const m={0:"nearest",1:"lanczos",2:"bilinear",3:"bicubic",4:"box",5:"hamming"},h=new Map([["png","image/png"],["jpg","image/jpeg"],["jpeg","image/jpeg"],["gif","image/gif"]]);class _{constructor(e,t,s,r){this.data=e,this.width=t,this.height=s,this.channels=r}get size(){return[this.width,this.height]}static async read(e){if(e instanceof _)return e;if("string"==typeof e||e instanceof URL)return await this.fromURL(e);throw new Error("Unsupported input type: "+typeof e)}static fromCanvas(e){if(!l)throw new Error("fromCanvas() is only supported in browser environments.");const t=e.getContext("2d").getImageData(0,0,e.width,e.height).data;return new _(t,e.width,e.height,4)}static async fromURL(e){const t=await(0,o.getFile)(e);if(200!==t.status)throw new Error(`Unable to read image from "${e}" (${t.status} ${t.statusText})`);const s=await t.blob();return this.fromBlob(s)}static async fromBlob(e){if(l){const t=await p(e),s=d(t.width,t.height).getContext("2d");return s.drawImage(t,0,0),new this(s.getImageData(0,0,t.width,t.height).data,t.width,t.height,4)}{const t=i(await e.arrayBuffer());return await p(t)}}static fromTensor(e,t="CHW"){if(3!==e.dims.length)throw new Error(`Tensor should have 3 dimensions, but has ${e.dims.length} dimensions.`);if("CHW"===t)e=e.transpose(1,2,0);else if("HWC"!==t)throw new Error(`Unsupported channel format: ${t}`);if(!(e.data instanceof Uint8ClampedArray||e.data instanceof Uint8Array))throw new Error(`Unsupported tensor type: ${e.type}`);switch(e.dims[2]){case 1:case 2:case 3:case 4:return new _(e.data,e.dims[1],e.dims[0],e.dims[2]);default:throw new Error(`Unsupported number of channels: ${e.dims[2]}`)}}grayscale(){if(1===this.channels)return this;const e=new Uint8ClampedArray(this.width*this.height*1);switch(this.channels){case 3:case 4:for(let t=0,s=0;t<this.data.length;t+=this.channels){const r=this.data[t],o=this.data[t+1],n=this.data[t+2];e[s++]=Math.round(.2989*r+.587*o+.114*n)}break;default:throw new Error(`Conversion failed due to unsupported number of channels: ${this.channels}`)}return this._update(e,this.width,this.height,1)}rgb(){if(3===this.channels)return this;const e=new Uint8ClampedArray(this.width*this.height*3);switch(this.channels){case 1:for(let t=0,s=0;t<this.data.length;++t)e[s++]=this.data[t],e[s++]=this.data[t],e[s++]=this.data[t];break;case 4:for(let t=0,s=0;t<this.data.length;t+=4)e[s++]=this.data[t],e[s++]=this.data[t+1],e[s++]=this.data[t+2];break;default:throw new Error(`Conversion failed due to unsupported number of channels: ${this.channels}`)}return this._update(e,this.width,this.height,3)}rgba(){if(4===this.channels)return this;const e=new Uint8ClampedArray(this.width*this.height*4);switch(this.channels){case 1:for(let t=0,s=0;t<this.data.length;++t)e[s++]=this.data[t],e[s++]=this.data[t],e[s++]=this.data[t],e[s++]=255;break;case 3:for(let t=0,s=0;t<this.data.length;t+=3)e[s++]=this.data[t],e[s++]=this.data[t+1],e[s++]=this.data[t+2],e[s++]=255;break;default:throw new Error(`Conversion failed due to unsupported number of channels: ${this.channels}`)}return this._update(e,this.width,this.height,4)}async resize(e,t,{resample:s=2}={}){if(this.width===e&&this.height===t)return this;let o=m[s]??s;const n=(0,r.isNullishDimension)(e),a=(0,r.isNullishDimension)(t);if(n&&a)return this;if(n?e=t/this.height*this.width:a&&(t=e/this.width*this.height),l){const s=this.channels,r=this.toCanvas(),o=d(e,t).getContext("2d");o.drawImage(r,0,0,e,t);return new _(o.getImageData(0,0,e,t).data,e,t,4).convert(s)}{let s=this.toSharp();switch(o){case"box":case"hamming":"box"!==o&&"hamming"!==o||(console.warn(`Resampling method ${o} is not yet supported. Using bilinear instead.`),o="bilinear");case"nearest":case"bilinear":case"bicubic":s=s.affine([e/this.width,0,0,t/this.height],{interpolator:o});break;case"lanczos":s=s.resize({width:e,height:t,fit:"fill",kernel:"lanczos3"});break;default:throw new Error(`Resampling method ${o} is not supported.`)}return await p(s)}}async pad([e,t,s,r]){if(e=Math.max(e,0),t=Math.max(t,0),s=Math.max(s,0),r=Math.max(r,0),0===e&&0===t&&0===s&&0===r)return this;if(l){const o=this.channels,n=this.toCanvas(),a=this.width+e+t,i=this.height+s+r,l=d(a,i).getContext("2d");l.drawImage(n,0,0,this.width,this.height,e,s,this.width,this.height);return new _(l.getImageData(0,0,a,i).data,a,i,4).convert(o)}{const o=this.toSharp().extend({left:e,right:t,top:s,bottom:r});return await p(o)}}async crop([e,t,s,r]){if(e=Math.max(e,0),t=Math.max(t,0),s=Math.min(s,this.width-1),r=Math.min(r,this.height-1),0===e&&0===t&&s===this.width-1&&r===this.height-1)return this;const o=s-e+1,n=r-t+1;if(l){const s=this.channels,r=this.toCanvas(),a=d(o,n).getContext("2d");a.drawImage(r,e,t,o,n,0,0,o,n);return new _(a.getImageData(0,0,o,n).data,o,n,4).convert(s)}{const s=this.toSharp().extract({left:e,top:t,width:o,height:n});return await p(s)}}async center_crop(e,t){if(this.width===e&&this.height===t)return this;const s=(this.width-e)/2,r=(this.height-t)/2;if(l){const o=this.channels,n=this.toCanvas(),a=d(e,t).getContext("2d");let i=0,l=0,c=0,u=0;s>=0?i=s:c=-s,r>=0?l=r:u=-r,a.drawImage(n,i,l,e,t,c,u,e,t);return new _(a.getImageData(0,0,e,t).data,e,t,4).convert(o)}{let o=this.toSharp();if(s>=0&&r>=0)o=o.extract({left:Math.floor(s),top:Math.floor(r),width:e,height:t});else if(s<=0&&r<=0){const n=Math.floor(-r),a=Math.floor(-s);o=o.extend({top:n,left:a,right:e-this.width-a,bottom:t-this.height-n})}else{let n=[0,0],a=0;r<0?(n[0]=Math.floor(-r),n[1]=t-this.height-n[0]):a=Math.floor(r);let i=[0,0],l=0;s<0?(i[0]=Math.floor(-s),i[1]=e-this.width-i[0]):l=Math.floor(s),o=o.extend({top:n[0],bottom:n[1],left:i[0],right:i[1]}).extract({left:l,top:a,width:e,height:t})}return await p(o)}}async toBlob(e="image/png",t=1){if(!l)throw new Error("toBlob() is only supported in browser environments.");const s=this.toCanvas();return await s.convertToBlob({type:e,quality:t})}toTensor(e="CHW"){let t=new a.Tensor("uint8",new Uint8Array(this.data),[this.height,this.width,this.channels]);if("HWC"===e);else{if("CHW"!==e)throw new Error(`Unsupported channel format: ${e}`);t=t.permute(2,0,1)}return t}toCanvas(){if(!l)throw new Error("toCanvas() is only supported in browser environments.");const e=this.clone().rgba(),t=d(e.width,e.height),s=new u(e.data,e.width,e.height);return t.getContext("2d").putImageData(s,0,0),t}split(){const{data:e,width:t,height:s,channels:r}=this,o=e.constructor,n=e.length/r,a=Array.from({length:r},(()=>new o(n)));for(let t=0;t<n;++t){const s=r*t;for(let o=0;o<r;++o)a[o][t]=e[s+o]}return a.map((e=>new _(e,t,s,1)))}_update(e,t,s,r=null){return this.data=e,this.width=t,this.height=s,null!==r&&(this.channels=r),this}clone(){return new _(this.data.slice(),this.width,this.height,this.channels)}convert(e){if(this.channels===e)return this;switch(e){case 1:this.grayscale();break;case 3:this.rgb();break;case 4:this.rgba();break;default:throw new Error(`Conversion failed due to unsupported number of channels: ${this.channels}`)}return this}async save(e){if(!l){if(n.env.useFS){const t=this.toSharp();return await t.toFile(e)}throw new Error("Unable to save the image because filesystem is disabled in this environment.")}{if(c)throw new Error("Unable to save an image from a Web Worker.");const t=e.split(".").pop().toLowerCase(),s=h.get(t)??"image/png",r=await this.toBlob(s),o=URL.createObjectURL(r),n=document.createElement("a");n.href=o,n.download=e,n.click(),n.remove()}}toSharp(){if(l)throw new Error("toSharp() is only supported in server-side environments.");return i(this.data,{raw:{width:this.width,height:this.height,channels:this.channels}})}}},"./src/utils/maths.js":
/*!****************************!*\
!*** ./src/utils/maths.js ***!
\****************************/(e,t,s)=>{function r(e,[t,s,r],[o,n],a="bilinear",i=!1){const l=n/r,c=o/s,d=new e.constructor(o*n*t),u=s*r,p=o*n;for(let a=0;a<o;++a)for(let o=0;o<n;++o){const i=a*n+o,m=(o+.5)/l-.5,h=(a+.5)/c-.5;let _=Math.floor(m),f=Math.floor(h);const g=Math.min(_+1,r-1),M=Math.min(f+1,s-1);_=Math.max(_,0),f=Math.max(f,0);const w=m-_,T=h-f,b=(1-w)*(1-T),x=w*(1-T),P=(1-w)*T,k=w*T,y=f*r,F=M*r,v=y+_,C=y+g,S=F+_,A=F+g;for(let s=0;s<t;++s){const t=s*u;d[s*p+i]=b*e[t+v]+x*e[t+C]+P*e[t+S]+k*e[t+A]}}return d}function o(e,t,s){const r=new Array(s.length),o=new Array(s.length);for(let e=s.length-1,n=1;e>=0;--e)o[e]=n,r[e]=t[s[e]],n*=r[e];const n=s.map(((e,t)=>o[s.indexOf(t)])),a=new e.constructor(e.length);for(let s=0;s<e.length;++s){let r=0;for(let e=t.length-1,o=s;e>=0;--e)r+=o%t[e]*n[e],o=Math.floor(o/t[e]);a[r]=e[s]}return[a,r]}function n(e){const t=u(e)[0],s=e.map((e=>Math.exp(e-t))),r=s.reduce(((e,t)=>e+t),0);return s.map((e=>e/r))}function a(e){const t=u(e)[0];let s=0;for(let r=0;r<e.length;++r)s+=Math.exp(e[r]-t);const r=Math.log(s);return e.map((e=>e-t-r))}function i(e,t){let s=0;for(let r=0;r<e.length;++r)s+=e[r]*t[r];return s}function l(e,t){return i(e,t)/(c(e)*c(t))}function c(e){return Math.sqrt(e.reduce(((e,t)=>e+t*t),0))}function d(e){if(0===e.length)throw Error("Array must not be empty");let t=e[0],s=0;for(let r=1;r<e.length;++r)e[r]<t&&(t=e[r],s=r);return[t,s]}function u(e){if(0===e.length)throw Error("Array must not be empty");let t=e[0],s=0;for(let r=1;r<e.length;++r)e[r]>t&&(t=e[r],s=r);return[Number(t),s]}function p(e){return e>0&&!(e&e-1)}s.r(t),s.d(t,{FFT:()=>_,bankers_round:()=>M,cos_sim:()=>l,dot:()=>i,dynamic_time_warping:()=>w,interpolate_data:()=>r,log_softmax:()=>a,magnitude:()=>c,max:()=>u,medianFilter:()=>f,min:()=>d,permute_data:()=>o,round:()=>g,softmax:()=>n});class m{constructor(e){if(this.size=0|e,this.size<=1||!p(this.size))throw new Error("FFT size must be a power of two larger than 1");this._csize=e<<1,this.table=new Float64Array(2*this.size);for(let e=0;e<this.table.length;e+=2){const t=Math.PI*e/this.size;this.table[e]=Math.cos(t),this.table[e+1]=-Math.sin(t)}let t=0;for(let e=1;this.size>e;e<<=1)++t;this._width=t%2==0?t-1:t,this._bitrev=new Int32Array(1<<this._width);for(let e=0;e<this._bitrev.length;++e){this._bitrev[e]=0;for(let t=0;t<this._width;t+=2){const s=this._width-t-2;this._bitrev[e]|=(e>>>t&3)<<s}}}createComplexArray(){return new Float64Array(this._csize)}fromComplexArray(e,t){const s=t||new Array(e.length>>>1);for(let t=0;t<e.length;t+=2)s[t>>>1]=e[t];return s}toComplexArray(e,t){const s=t||this.createComplexArray();for(let t=0;t<s.length;t+=2)s[t]=e[t>>>1],s[t+1]=0;return s}transform(e,t){if(e===t)throw new Error("Input and output buffers must be different");this._transform4(e,t,1)}realTransform(e,t){if(e===t)throw new Error("Input and output buffers must be different");this._realTransform4(e,t,1)}inverseTransform(e,t){if(e===t)throw new Error("Input and output buffers must be different");this._transform4(e,t,-1);for(let t=0;t<e.length;++t)e[t]/=this.size}_transform4(e,t,s){const r=this._csize;let o,n,a=1<<this._width,i=r/a<<1;const l=this._bitrev;if(4===i)for(o=0,n=0;o<r;o+=i,++n){const s=l[n];this._singleTransform2(t,e,o,s,a)}else for(o=0,n=0;o<r;o+=i,++n){const r=l[n];this._singleTransform4(t,e,o,r,a,s)}const c=this.table;for(a>>=2;a>=2;a>>=2){i=r/a<<1;const t=i>>>2;for(o=0;o<r;o+=i){const r=o+t-1;for(let n=o,i=0;n<r;n+=2,i+=a){const r=n,o=r+t,a=o+t,l=a+t,d=e[r],u=e[r+1],p=e[o],m=e[o+1],h=e[a],_=e[a+1],f=e[l],g=e[l+1],M=c[i],w=s*c[i+1],T=p*M-m*w,b=p*w+m*M,x=c[2*i],P=s*c[2*i+1],k=h*x-_*P,y=h*P+_*x,F=c[3*i],v=s*c[3*i+1],C=f*F-g*v,S=f*v+g*F,A=d+k,E=u+y,L=d-k,I=u-y,z=T+C,j=b+S,N=s*(T-C),V=s*(b-S);e[r]=A+z,e[r+1]=E+j,e[o]=L+V,e[o+1]=I-N,e[a]=A-z,e[a+1]=E-j,e[l]=L-V,e[l+1]=I+N}}}}_singleTransform2(e,t,s,r,o){const n=e[r],a=e[r+1],i=e[r+o],l=e[r+o+1];t[s]=n+i,t[s+1]=a+l,t[s+2]=n-i,t[s+3]=a-l}_singleTransform4(e,t,s,r,o,n){const a=2*o,i=3*o,l=e[r],c=e[r+1],d=e[r+o],u=e[r+o+1],p=e[r+a],m=e[r+a+1],h=e[r+i],_=e[r+i+1],f=l+p,g=c+m,M=l-p,w=c-m,T=d+h,b=u+_,x=n*(d-h),P=n*(u-_);t[s]=f+T,t[s+1]=g+b,t[s+2]=M+P,t[s+3]=w-x,t[s+4]=f-T,t[s+5]=g-b,t[s+6]=M-P,t[s+7]=w+x}_realTransform4(e,t,s){const r=this._csize;let o,n,a=1<<this._width,i=r/a<<1;const l=this._bitrev;if(4===i)for(o=0,n=0;o<r;o+=i,++n){const s=l[n];this._singleRealTransform2(t,e,o,s>>>1,a>>>1)}else for(o=0,n=0;o<r;o+=i,++n){const r=l[n];this._singleRealTransform4(t,e,o,r>>>1,a>>>1,s)}const c=this.table;for(a>>=2;a>=2;a>>=2){i=r/a<<1;const t=i>>>1,n=t>>>1,l=n>>>1;for(o=0;o<r;o+=i)for(let r=0,i=0;r<=l;r+=2,i+=a){const a=o+r,d=a+n,u=d+n,p=u+n,m=e[a],h=e[a+1],_=e[d],f=e[d+1],g=e[u],M=e[u+1],w=e[p],T=e[p+1],b=m,x=h,P=c[i],k=s*c[i+1],y=_*P-f*k,F=_*k+f*P,v=c[2*i],C=s*c[2*i+1],S=g*v-M*C,A=g*C+M*v,E=c[3*i],L=s*c[3*i+1],I=w*E-T*L,z=w*L+T*E,j=b+S,N=x+A,V=b-S,B=x-A,O=y+I,D=F+z,R=s*(y-I),G=s*(F-z);if(e[a]=j+O,e[a+1]=N+D,e[d]=V+G,e[d+1]=B-R,0===r){e[u]=j-O,e[u+1]=N-D;continue}if(r===l)continue;const q=o+n-r,W=o+t-r;e[q]=V-s*G,e[q+1]=-B-s*R,e[W]=j-s*O,e[W+1]=s*D-N}}const d=r>>>1;for(let t=2;t<d;t+=2)e[r-t]=e[t],e[r-t+1]=-e[t+1]}_singleRealTransform2(e,t,s,r,o){const n=e[r],a=e[r+o];t[s]=n+a,t[s+1]=0,t[s+2]=n-a,t[s+3]=0}_singleRealTransform4(e,t,s,r,o,n){const a=2*o,i=3*o,l=e[r],c=e[r+o],d=e[r+a],u=e[r+i],p=l+d,m=l-d,h=c+u,_=n*(c-u);t[s]=p+h,t[s+1]=0,t[s+2]=m,t[s+3]=-_,t[s+4]=p-h,t[s+5]=0,t[s+6]=m,t[s+7]=_}}class h{constructor(e){const t=2*(e-1),s=2*(2*e-1),r=2**Math.ceil(Math.log2(s));this.bufferSize=r,this._a=t;const o=new Float64Array(s),n=new Float64Array(r);this._chirpBuffer=new Float64Array(r),this._buffer1=new Float64Array(r),this._buffer2=new Float64Array(r),this._outBuffer1=new Float64Array(r),this._outBuffer2=new Float64Array(r);const a=-2*Math.PI/e,i=Math.cos(a),l=Math.sin(a);for(let t=0;t<s>>1;++t){const s=(t+1-e)**2/2,r=Math.sqrt(i**2+l**2)**s,a=s*Math.atan2(l,i),c=2*t;o[c]=r*Math.cos(a),o[c+1]=r*Math.sin(a),n[c]=o[c],n[c+1]=-o[c+1]}this._slicedChirpBuffer=o.subarray(t,s),this._f=new m(r>>1),this._f.transform(this._chirpBuffer,n)}_transform(e,t,s){const r=this._buffer1,o=this._buffer2,n=this._outBuffer1,a=this._outBuffer2,i=this._chirpBuffer,l=this._slicedChirpBuffer,c=this._a;if(s)for(let e=0;e<l.length;e+=2){const s=e+1,o=t[e>>1];r[e]=o*l[e],r[s]=o*l[s]}else for(let e=0;e<l.length;e+=2){const s=e+1;r[e]=t[e]*l[e]-t[s]*l[s],r[s]=t[e]*l[s]+t[s]*l[e]}this._f.transform(n,r);for(let e=0;e<i.length;e+=2){const t=e+1;o[e]=n[e]*i[e]-n[t]*i[t],o[t]=n[e]*i[t]+n[t]*i[e]}this._f.inverseTransform(a,o);for(let t=0;t<a.length;t+=2){const s=a[t+c],r=a[t+c+1],o=l[t],n=l[t+1];e[t]=s*o-r*n,e[t+1]=s*n+r*o}}transform(e,t){this._transform(e,t,!1)}realTransform(e,t){this._transform(e,t,!0)}}class _{constructor(e){this.fft_length=e,this.isPowerOfTwo=p(e),this.isPowerOfTwo?(this.fft=new m(e),this.outputBufferSize=2*e):(this.fft=new h(e),this.outputBufferSize=this.fft.bufferSize)}realTransform(e,t){this.fft.realTransform(e,t)}transform(e,t){this.fft.transform(e,t)}}function f(e,t){if(t%2==0||t<=0)throw new Error("Window size must be a positive odd number");const s=new e.constructor(e.length),r=new e.constructor(t),o=Math.floor(t/2);for(let t=0;t<e.length;++t){let n=0;for(let s=-o;s<=o;++s){let o=t+s;o<0?o=Math.abs(o):o>=e.length&&(o=2*(e.length-1)-o),r[n++]=e[o]}r.sort(),s[t]=r[o]}return s}function g(e,t){const s=Math.pow(10,t);return Math.round(e*s)/s}function M(e){const t=Math.round(e);return Math.abs(e)%1==.5?t%2==0?t:t-1:t}function w(e){const t=e.length,s=e[0].length,r=[t+1,s+1],o=Array.from({length:r[0]},(()=>Array(r[1]).fill(1/0)));o[0][0]=0;const n=Array.from({length:r[0]},(()=>Array(r[1]).fill(-1)));for(let t=1;t<r[1];++t)for(let s=1;s<r[0];++s){const r=o[s-1][t-1],a=o[s-1][t],i=o[s][t-1];let l,c;r<a&&r<i?(l=r,c=0):a<r&&a<i?(l=a,c=1):(l=i,c=2),o[s][t]=e[s-1][t-1]+l,n[s][t]=c}for(let e=0;e<r[1];++e)n[0][e]=2;for(let e=0;e<r[0];++e)n[e][0]=1;let a=t,i=s,l=[],c=[];for(;a>0||i>0;)switch(l.push(a-1),c.push(i-1),n[a][i]){case 0:--a,--i;break;case 1:--a;break;case 2:--i;break;default:throw new Error(`Internal error in dynamic time warping. Unexpected trace[${a}, ${i}]. Please file a bug report.`)}return l.reverse(),c.reverse(),[l,c]}},"./src/utils/tensor.js":
/*!*****************************!*\
!*** ./src/utils/tensor.js ***!
\*****************************/(e,t,s)=>{s.r(t),s.d(t,{Tensor:()=>i,cat:()=>w,full:()=>k,full_like:()=>y,interpolate:()=>c,interpolate_4d:()=>d,layer_norm:()=>_,matmul:()=>u,mean:()=>x,mean_pooling:()=>h,ones:()=>F,ones_like:()=>v,permute:()=>l,quantize_embeddings:()=>A,rfft:()=>p,stack:()=>T,std_mean:()=>b,topk:()=>m,zeros:()=>C,zeros_like:()=>S});var r=s(/*! ./maths.js */"./src/utils/maths.js"),o=s(/*! ../backends/onnx.js */"./src/backends/onnx.js"),n=s(/*! ../ops/registry.js */"./src/ops/registry.js");const a=Object.freeze({float32:Float32Array,float16:Uint16Array,float64:Float64Array,string:Array,int8:Int8Array,uint8:Uint8Array,int16:Int16Array,uint16:Uint16Array,int32:Int32Array,uint32:Uint32Array,int64:BigInt64Array,uint64:BigUint64Array,bool:Uint8Array});class i{get dims(){return this.ort_tensor.dims}set dims(e){this.ort_tensor.dims=e}get type(){return this.ort_tensor.type}get data(){return this.ort_tensor.data}get size(){return this.ort_tensor.size}get location(){return this.ort_tensor.location}ort_tensor;constructor(...e){return(0,o.isONNXTensor)(e[0])?this.ort_tensor=e[0]:this.ort_tensor=new o.Tensor(e[0],e[1],e[2]),new Proxy(this,{get:(e,t)=>{if("string"==typeof t){let s=Number(t);if(Number.isInteger(s))return e._getitem(s)}return e[t]},set:(e,t,s)=>e[t]=s})}dispose(){this.ort_tensor.dispose()}*[Symbol.iterator](){const[e,...t]=this.dims;if(t.length>0){const s=t.reduce(((e,t)=>e*t));for(let r=0;r<e;++r)yield this._subarray(r,s,t)}else yield*this.data}_getitem(e){const[t,...s]=this.dims;if(e=M(e,t),s.length>0){const t=s.reduce(((e,t)=>e*t));return this._subarray(e,t,s)}return new i(this.type,[this.data[e]],s)}indexOf(e){const t=this.data;for(let s=0;s<t.length;++s)if(t[s]==e)return s;return-1}_subarray(e,t,s){const r=e*t,o=(e+1)*t,n="subarray"in this.data?this.data.subarray(r,o):this.data.slice(r,o);return new i(this.type,n,s)}item(){const e=this.data;if(1!==e.length)throw new Error(`a Tensor with ${e.length} elements cannot be converted to Scalar`);return e[0]}tolist(){return function(e,t){const s=e.length,r=t.reduce(((e,t)=>e*t));if(s!==r)throw Error(`cannot reshape array of size ${s} into shape (${t})`);let o=e;for(let e=t.length-1;e>=0;e--)o=o.reduce(((s,r)=>{let o=s[s.length-1];return o.length<t[e]?o.push(r):s.push([r]),s}),[[]]);return o[0]}(this.data,this.dims)}sigmoid(){return this.clone().sigmoid_()}sigmoid_(){const e=this.data;for(let t=0;t<e.length;++t)e[t]=1/(1+Math.exp(-e[t]));return this}map(e){return this.clone().map_(e)}map_(e){const t=this.data;for(let s=0;s<t.length;++s)t[s]=e(t[s],s,t);return this}mul(e){return this.clone().mul_(e)}mul_(e){const t=this.data;for(let s=0;s<t.length;++s)t[s]*=e;return this}div(e){return this.clone().div_(e)}div_(e){const t=this.data;for(let s=0;s<t.length;++s)t[s]/=e;return this}add(e){return this.clone().add_(e)}add_(e){const t=this.data;for(let s=0;s<t.length;++s)t[s]+=e;return this}sub(e){return this.clone().sub_(e)}sub_(e){const t=this.data;for(let s=0;s<t.length;++s)t[s]-=e;return this}clone(){return new i(this.type,this.data.slice(),this.dims.slice())}slice(...e){const t=[],s=[];for(let r=0;r<this.dims.length;++r){let o=e[r];if(null==o)s.push([0,this.dims[r]]),t.push(this.dims[r]);else if("number"==typeof o)o=M(o,this.dims[r],r),s.push([o,o+1]);else{if(!Array.isArray(o)||2!==o.length)throw new Error(`Invalid slice: ${o}`);{let[e,n]=o;if(e=null===e?0:M(e,this.dims[r],r,!1),n=null===n?this.dims[r]:M(n,this.dims[r],r,!1),e>n)throw new Error(`Invalid slice: ${o}`);const a=[Math.max(e,0),Math.min(n,this.dims[r])];s.push(a),t.push(a[1]-a[0])}}}const r=s.map((([e,t])=>t-e)),o=r.reduce(((e,t)=>e*t)),n=this.data,a=new n.constructor(o),l=this.stride();for(let e=0;e<o;++e){let t=0;for(let o=r.length-1,n=e;o>=0;--o){const e=r[o];t+=(n%e+s[o][0])*l[o],n=Math.floor(n/e)}a[e]=n[t]}return new i(this.type,a,t)}permute(...e){return l(this,e)}transpose(...e){return this.permute(...e)}sum(e=null,t=!1){return this.norm(1,e,t)}norm(e="fro",t=null,s=!1){if("fro"===e)e=2;else if("string"==typeof e)throw Error(`Unsupported norm: ${e}`);const r=this.data;if(null===t){let t=r.reduce(((t,s)=>t+s**e),0)**(1/e);return new i(this.type,[t],[])}t=M(t,this.dims.length);const o=this.dims.slice();o[t]=1;const n=new r.constructor(r.length/this.dims[t]);for(let s=0;s<r.length;++s){let a=0;for(let e=this.dims.length-1,r=s,n=1;e>=0;--e){const s=this.dims[e];if(e!==t){a+=r%s*n,n*=o[e]}r=Math.floor(r/s)}n[a]+=r[s]**e}if(1!==e)for(let t=0;t<n.length;++t)n[t]=n[t]**(1/e);return s||o.splice(t,1),new i(this.type,n,o)}normalize_(e=2,t=1){t=M(t,this.dims.length);const s=this.norm(e,t,!0),r=this.data,o=s.data;for(let e=0;e<r.length;++e){let s=0;for(let r=this.dims.length-1,o=e,n=1;r>=0;--r){const e=this.dims[r];if(r!==t){s+=o%e*n,n*=this.dims[r]}o=Math.floor(o/e)}r[e]/=o[s]}return this}normalize(e=2,t=1){return this.clone().normalize_(e,t)}stride(){return function(e){const t=new Array(e.length);for(let 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as ASTFeatureExtractor,v as ASTForAudioClassification,C as ASTModel,S as ASTPreTrainedModel,A as AlbertForMaskedLM,E as AlbertForQuestionAnswering,L as AlbertForSequenceClassification,I as AlbertModel,z as AlbertPreTrainedModel,j as AlbertTokenizer,N as AudioClassificationPipeline,V as AutoConfig,B as AutoFeatureExtractor,O as AutoImageProcessor,D as AutoModel,R as AutoModelForAudioClassification,G as AutoModelForAudioFrameClassification,q as AutoModelForCTC,W as AutoModelForCausalLM,$ as AutoModelForDepthEstimation,U as AutoModelForDocumentQuestionAnswering,Q as AutoModelForImageClassification,X as AutoModelForImageFeatureExtraction,H as AutoModelForImageMatting,J as AutoModelForImageSegmentation,Y as AutoModelForImageToImage,K as AutoModelForMaskGeneration,Z as AutoModelForMaskedLM,ee as AutoModelForNormalEstimation,te as AutoModelForObjectDetection,se as AutoModelForPoseEstimation,re as AutoModelForQuestionAnswering,oe as AutoModelForSemanticSegmentation,ne as AutoModelForSeq2SeqLM,ae as AutoModelForSequenceClassification,ie as AutoModelForSpeechSeq2Seq,le as AutoModelForTextToSpectrogram,ce as AutoModelForTextToWaveform,de as AutoModelForTokenClassification,ue as AutoModelForUniversalSegmentation,pe as AutoModelForVision2Seq,me as AutoModelForXVector,he as AutoModelForZeroShotObjectDetection,_e as AutoProcessor,fe as AutoTokenizer,ge as AutomaticSpeechRecognitionPipeline,Me as BartForConditionalGeneration,we as BartForSequenceClassification,Te as BartModel,be as BartPretrainedModel,xe as BartTokenizer,Pe as BaseModelOutput,ke as BaseStreamer,ye as BeitFeatureExtractor,Fe as BeitForImageClassification,ve as BeitModel,Ce as BeitPreTrainedModel,Se as BertForMaskedLM,Ae as BertForQuestionAnswering,Ee as BertForSequenceClassification,Le as BertForTokenClassification,Ie as BertModel,ze as BertPreTrainedModel,je as BertTokenizer,Ne as BitImageProcessor,Ve as BlenderbotForConditionalGeneration,Be as BlenderbotModel,Oe as BlenderbotPreTrainedModel,De as BlenderbotSmallForConditionalGeneration,Re as BlenderbotSmallModel,Ge as BlenderbotSmallPreTrainedModel,qe as BlenderbotSmallTokenizer,We as BlenderbotTokenizer,$e as BloomForCausalLM,Ue as BloomModel,Qe as BloomPreTrainedModel,Xe as BloomTokenizer,He as CLIPFeatureExtractor,Je as CLIPImageProcessor,Ye as CLIPModel,Ke as CLIPPreTrainedModel,Ze as CLIPSegForImageSegmentation,et as CLIPSegModel,tt as CLIPSegPreTrainedModel,st as CLIPTextModel,rt as CLIPTextModelWithProjection,ot as CLIPTokenizer,nt as CLIPVisionModel,at as CLIPVisionModelWithProjection,it as CamembertForMaskedLM,lt as CamembertForQuestionAnswering,ct as CamembertForSequenceClassification,dt as CamembertForTokenClassification,ut as CamembertModel,pt as CamembertPreTrainedModel,mt as CamembertTokenizer,ht as CausalLMOutput,_t as CausalLMOutputWithPast,ft as ChineseCLIPFeatureExtractor,gt as ChineseCLIPModel,Mt as ChineseCLIPPreTrainedModel,wt as ClapAudioModelWithProjection,Tt as ClapFeatureExtractor,bt as ClapModel,xt as ClapPreTrainedModel,Pt as ClapTextModelWithProjection,kt as ClassifierFreeGuidanceLogitsProcessor,yt as CodeGenForCausalLM,Ft as CodeGenModel,vt as CodeGenPreTrainedModel,Ct as CodeGenTokenizer,St as CodeLlamaTokenizer,At as CohereForCausalLM,Et as CohereModel,Lt as CoherePreTrainedModel,It as CohereTokenizer,zt as ConvBertForMaskedLM,jt as ConvBertForQuestionAnswering,Nt as ConvBertForSequenceClassification,Vt as ConvBertForTokenClassification,Bt as ConvBertModel,Ot as ConvBertPreTrainedModel,Dt as ConvBertTokenizer,Rt as ConvNextFeatureExtractor,Gt as ConvNextForImageClassification,qt as ConvNextImageProcessor,Wt as ConvNextModel,$t as ConvNextPreTrainedModel,Ut as ConvNextV2ForImageClassification,Qt as ConvNextV2Model,Xt as ConvNextV2PreTrainedModel,Ht as DPTFeatureExtractor,Jt as DPTForDepthEstimation,Yt as DPTImageProcessor,Kt as DPTModel,Zt as DPTPreTrainedModel,es as DebertaForMaskedLM,ts as DebertaForQuestionAnswering,ss as DebertaForSequenceClassification,rs as DebertaForTokenClassification,os as DebertaModel,ns as DebertaPreTrainedModel,as as DebertaTokenizer,is as DebertaV2ForMaskedLM,ls as DebertaV2ForQuestionAnswering,cs as DebertaV2ForSequenceClassification,ds as DebertaV2ForTokenClassification,us as DebertaV2Model,ps as DebertaV2PreTrainedModel,ms as DebertaV2Tokenizer,hs as DecisionTransformerModel,_s as DecisionTransformerPreTrainedModel,fs as DeiTFeatureExtractor,gs as DeiTForImageClassification,Ms as DeiTImageProcessor,ws as DeiTModel,Ts as DeiTPreTrainedModel,bs as DepthAnythingForDepthEstimation,xs as DepthAnythingPreTrainedModel,Ps as DepthEstimationPipeline,ks as DepthProForDepthEstimation,ys as DepthProPreTrainedModel,Fs as DetrFeatureExtractor,vs as DetrForObjectDetection,Cs as DetrForSegmentation,Ss as DetrImageProcessor,As as DetrModel,Es as DetrObjectDetectionOutput,Ls as DetrPreTrainedModel,Is as DetrSegmentationOutput,zs as Dinov2ForImageClassification,js as Dinov2Model,Ns as Dinov2PreTrainedModel,Vs as DistilBertForMaskedLM,Bs as DistilBertForQuestionAnswering,Os as DistilBertForSequenceClassification,Ds as DistilBertForTokenClassification,Rs as DistilBertModel,Gs as DistilBertPreTrainedModel,qs as DistilBertTokenizer,Ws as DocumentQuestionAnsweringPipeline,$s as DonutFeatureExtractor,Us as DonutImageProcessor,Qs as DonutSwinModel,Xs as DonutSwinPreTrainedModel,Hs as EfficientNetForImageClassification,Js as EfficientNetImageProcessor,Ys as EfficientNetModel,Ks as EfficientNetPreTrainedModel,Zs as ElectraForMaskedLM,er as ElectraForQuestionAnswering,tr as ElectraForSequenceClassification,sr as ElectraForTokenClassification,rr as ElectraModel,or as ElectraPreTrainedModel,nr as ElectraTokenizer,ar as EosTokenCriteria,ir as EsmForMaskedLM,lr as EsmForSequenceClassification,cr as EsmForTokenClassification,dr as EsmModel,ur as EsmPreTrainedModel,pr as EsmTokenizer,mr as FFT,hr as FalconForCausalLM,_r as FalconModel,fr as FalconPreTrainedModel,gr as FalconTokenizer,Mr as FastViTForImageClassification,wr as FastViTModel,Tr as FastViTPreTrainedModel,br as FeatureExtractionPipeline,xr as FeatureExtractor,Pr as FillMaskPipeline,kr as Florence2ForConditionalGeneration,yr as Florence2PreTrainedModel,Fr as Florence2Processor,vr as ForcedBOSTokenLogitsProcessor,Cr as ForcedEOSTokenLogitsProcessor,Sr as GLPNFeatureExtractor,Ar as GLPNForDepthEstimation,Er as GLPNModel,Lr as GLPNPreTrainedModel,Ir as GPT2LMHeadModel,zr as GPT2Model,jr as GPT2PreTrainedModel,Nr as GPT2Tokenizer,Vr as GPTBigCodeForCausalLM,Br as GPTBigCodeModel,Or as GPTBigCodePreTrainedModel,Dr as GPTJForCausalLM,Rr as GPTJModel,Gr as GPTJPreTrainedModel,qr as GPTNeoForCausalLM,Wr as GPTNeoModel,$r as GPTNeoPreTrainedModel,Ur as GPTNeoXForCausalLM,Qr as GPTNeoXModel,Xr as GPTNeoXPreTrainedModel,Hr as GPTNeoXTokenizer,Jr as Gemma2ForCausalLM,Yr as Gemma2Model,Kr as Gemma2PreTrainedModel,Zr as GemmaForCausalLM,eo as GemmaModel,to as GemmaPreTrainedModel,so as GemmaTokenizer,ro as GraniteForCausalLM,oo as GraniteModel,no as GranitePreTrainedModel,ao as Grok1Tokenizer,io as GroupViTModel,lo as GroupViTPreTrainedModel,co as HerbertTokenizer,uo as HieraForImageClassification,po as HieraModel,mo as HieraPreTrainedModel,ho as HubertForCTC,_o as HubertForSequenceClassification,fo as HubertModel,go as HubertPreTrainedModel,Mo as ImageClassificationPipeline,wo as ImageFeatureExtractionPipeline,To as ImageFeatureExtractor,bo as ImageMattingOutput,xo as ImageProcessor,Po as ImageSegmentationPipeline,ko as ImageToImagePipeline,yo as ImageToTextPipeline,Fo as InterruptableStoppingCriteria,vo as JAISLMHeadModel,Co as JAISModel,So as JAISPreTrainedModel,Ao as JinaCLIPImageProcessor,Eo as JinaCLIPModel,Lo as JinaCLIPPreTrainedModel,Io as JinaCLIPProcessor,zo as JinaCLIPTextModel,jo as JinaCLIPVisionModel,No as LlamaForCausalLM,Vo as LlamaModel,Bo as LlamaPreTrainedModel,Oo as LlamaTokenizer,Do as LlavaForConditionalGeneration,Ro as LlavaOnevisionForConditionalGeneration,Go as LlavaOnevisionImageProcessor,qo as LlavaPreTrainedModel,Wo as LogitsProcessor,$o as LogitsProcessorList,Uo as LogitsWarper,Qo as LongT5ForConditionalGeneration,Xo as LongT5Model,Ho as LongT5PreTrainedModel,Jo as M2M100ForConditionalGeneration,Yo as M2M100Model,Ko as M2M100PreTrainedModel,Zo as M2M100Tokenizer,en as MBart50Tokenizer,tn as MBartForCausalLM,sn as MBartForConditionalGeneration,rn as MBartForSequenceClassification,on as MBartModel,nn as MBartPreTrainedModel,an as MBartTokenizer,ln as MPNetForMaskedLM,cn as MPNetForQuestionAnswering,dn as MPNetForSequenceClassification,un as MPNetForTokenClassification,pn as MPNetModel,mn as MPNetPreTrainedModel,hn as MPNetTokenizer,_n as MT5ForConditionalGeneration,fn as MT5Model,gn as MT5PreTrainedModel,Mn as MarianMTModel,wn as MarianModel,Tn as MarianPreTrainedModel,bn as MarianTokenizer,xn as Mask2FormerImageProcessor,Pn as MaskFormerFeatureExtractor,kn as MaskFormerForInstanceSegmentation,yn as MaskFormerImageProcessor,Fn as MaskFormerModel,vn as MaskFormerPreTrainedModel,Cn as MaskedLMOutput,Sn as MaxLengthCriteria,An as MgpstrForSceneTextRecognition,En as MgpstrModelOutput,Ln as MgpstrPreTrainedModel,In as MgpstrProcessor,zn as MgpstrTokenizer,jn as MinLengthLogitsProcessor,Nn as MinNewTokensLengthLogitsProcessor,Vn as MistralForCausalLM,Bn as MistralModel,On as MistralPreTrainedModel,Dn as MobileBertForMaskedLM,Rn as MobileBertForQuestionAnswering,Gn as MobileBertForSequenceClassification,qn as MobileBertModel,Wn as MobileBertPreTrainedModel,$n as MobileBertTokenizer,Un as MobileLLMForCausalLM,Qn as MobileLLMModel,Xn as MobileLLMPreTrainedModel,Hn as MobileNetV1FeatureExtractor,Jn as MobileNetV1ForImageClassification,Yn as MobileNetV1ImageProcessor,Kn as MobileNetV1Model,Zn as MobileNetV1PreTrainedModel,ea as MobileNetV2FeatureExtractor,ta as MobileNetV2ForImageClassification,sa as MobileNetV2ImageProcessor,ra as MobileNetV2Model,oa as MobileNetV2PreTrainedModel,na as MobileNetV3FeatureExtractor,aa as MobileNetV3ForImageClassification,ia as MobileNetV3ImageProcessor,la as MobileNetV3Model,ca as MobileNetV3PreTrainedModel,da as MobileNetV4FeatureExtractor,ua as MobileNetV4ForImageClassification,pa as MobileNetV4ImageProcessor,ma as MobileNetV4Model,ha as MobileNetV4PreTrainedModel,_a as MobileViTFeatureExtractor,fa as MobileViTForImageClassification,ga as MobileViTImageProcessor,Ma as MobileViTModel,wa as MobileViTPreTrainedModel,Ta as MobileViTV2ForImageClassification,ba as MobileViTV2Model,xa as MobileViTV2PreTrainedModel,Pa as ModelOutput,ka as Moondream1ForConditionalGeneration,ya as MptForCausalLM,Fa as MptModel,va as MptPreTrainedModel,Ca as MultiModalityCausalLM,Sa as MultiModalityPreTrainedModel,Aa as MusicgenForCausalLM,Ea as MusicgenForConditionalGeneration,La as MusicgenModel,Ia as MusicgenPreTrainedModel,za as NllbTokenizer,ja as NoBadWordsLogitsProcessor,Na as NoRepeatNGramLogitsProcessor,Va as NomicBertModel,Ba as NomicBertPreTrainedModel,Oa as NougatImageProcessor,Da as NougatTokenizer,Ra as OPTForCausalLM,Ga as OPTModel,qa as OPTPreTrainedModel,Wa as ObjectDetectionPipeline,$a as OlmoForCausalLM,Ua as OlmoModel,Qa as OlmoPreTrainedModel,Xa as OpenELMForCausalLM,Ha as OpenELMModel,Ja as OpenELMPreTrainedModel,Ya as OwlViTFeatureExtractor,Ka as OwlViTForObjectDetection,Za as OwlViTImageProcessor,ei as OwlViTModel,ti as OwlViTPreTrainedModel,si as OwlViTProcessor,ri as Owlv2ForObjectDetection,oi as Owlv2ImageProcessor,ni as Owlv2Model,ai as Owlv2PreTrainedModel,ii as PatchTSMixerForPrediction,li as PatchTSMixerModel,ci as PatchTSMixerPreTrainedModel,di as PatchTSTForPrediction,ui as PatchTSTModel,pi as PatchTSTPreTrainedModel,mi as Phi3ForCausalLM,hi as Phi3Model,_i as Phi3PreTrainedModel,fi as PhiForCausalLM,gi as PhiModel,Mi as PhiPreTrainedModel,wi as Pipeline,Ti as PreTrainedModel,bi as PreTrainedTokenizer,xi as PretrainedConfig,Pi as PretrainedMixin,ki as Processor,yi as PvtForImageClassification,Fi as PvtImageProcessor,vi as PvtModel,Ci as PvtPreTrainedModel,Si as PyAnnoteFeatureExtractor,Ai as PyAnnoteForAudioFrameClassification,Ei as PyAnnoteModel,Li as PyAnnotePreTrainedModel,Ii as PyAnnoteProcessor,zi as QuestionAnsweringModelOutput,ji as QuestionAnsweringPipeline,Ni as Qwen2ForCausalLM,Vi as Qwen2Model,Bi as Qwen2PreTrainedModel,Oi as Qwen2Tokenizer,Di as Qwen2VLForConditionalGeneration,Ri as Qwen2VLImageProcessor,Gi as Qwen2VLPreTrainedModel,qi as Qwen2VLProcessor,Wi as RTDetrForObjectDetection,$i as RTDetrImageProcessor,Ui as RTDetrModel,Qi as RTDetrObjectDetectionOutput,Xi as RTDetrPreTrainedModel,Hi as RawImage,Ji as RepetitionPenaltyLogitsProcessor,Yi as ResNetForImageClassification,Ki as ResNetModel,Zi as ResNetPreTrainedModel,el as RoFormerForMaskedLM,tl as RoFormerForQuestionAnswering,sl as RoFormerForSequenceClassification,rl as RoFormerForTokenClassification,ol as RoFormerModel,nl as RoFormerPreTrainedModel,al as RoFormerTokenizer,il as RobertaForMaskedLM,ll as RobertaForQuestionAnswering,cl as RobertaForSequenceClassification,dl as RobertaForTokenClassification,ul as RobertaModel,pl as RobertaPreTrainedModel,ml as RobertaTokenizer,hl as SamImageProcessor,_l as SamImageSegmentationOutput,fl as SamModel,gl as SamPreTrainedModel,Ml as SamProcessor,wl as SapiensForDepthEstimation,Tl as SapiensForNormalEstimation,bl as SapiensForSemanticSegmentation,xl as SapiensPreTrainedModel,Pl as SeamlessM4TFeatureExtractor,kl as SegformerFeatureExtractor,yl as SegformerForImageClassification,Fl as SegformerForSemanticSegmentation,vl as SegformerImageProcessor,Cl as SegformerModel,Sl as SegformerPreTrainedModel,Al as Seq2SeqLMOutput,El as SequenceClassifierOutput,Ll as SiglipImageProcessor,Il as SiglipModel,zl as SiglipPreTrainedModel,jl as SiglipTextModel,Nl as SiglipTokenizer,Vl as SiglipVisionModel,Bl as SpeechT5FeatureExtractor,Ol as SpeechT5ForSpeechToText,Dl as SpeechT5ForTextToSpeech,Rl as SpeechT5HifiGan,Gl as SpeechT5Model,ql as SpeechT5PreTrainedModel,Wl as SpeechT5Processor,$l as SpeechT5Tokenizer,Ul as SqueezeBertForMaskedLM,Ql as SqueezeBertForQuestionAnswering,Xl as SqueezeBertForSequenceClassification,Hl as SqueezeBertModel,Jl as SqueezeBertPreTrainedModel,Yl as SqueezeBertTokenizer,Kl as StableLmForCausalLM,Zl as StableLmModel,ec as StableLmPreTrainedModel,tc as Starcoder2ForCausalLM,sc as Starcoder2Model,rc as Starcoder2PreTrainedModel,oc as StoppingCriteria,nc as StoppingCriteriaList,ac as SummarizationPipeline,ic as SuppressTokensAtBeginLogitsProcessor,lc as Swin2SRForImageSuperResolution,cc as Swin2SRImageProcessor,dc as Swin2SRModel,uc as Swin2SRPreTrainedModel,pc as SwinForImageClassification,mc as SwinModel,hc as SwinPreTrainedModel,_c as T5ForConditionalGeneration,fc as T5Model,gc as T5PreTrainedModel,Mc as T5Tokenizer,wc as TableTransformerForObjectDetection,Tc as TableTransformerModel,bc as TableTransformerObjectDetectionOutput,xc as TableTransformerPreTrainedModel,Pc as TemperatureLogitsWarper,kc as Tensor,yc as Text2TextGenerationPipeline,Fc as TextClassificationPipeline,vc as TextGenerationPipeline,Cc as TextStreamer,Sc as TextToAudioPipeline,Ac as TokenClassificationPipeline,Ec as TokenClassifierOutput,Lc as TokenizerModel,Ic as TopKLogitsWarper,zc as TopPLogitsWarper,jc as TrOCRForCausalLM,Nc as TrOCRPreTrainedModel,Vc as TranslationPipeline,Bc as UniSpeechForCTC,Oc as UniSpeechForSequenceClassification,Dc as UniSpeechModel,Rc as UniSpeechPreTrainedModel,Gc as UniSpeechSatForAudioFrameClassification,qc as UniSpeechSatForCTC,Wc as UniSpeechSatForSequenceClassification,$c as UniSpeechSatModel,Uc as UniSpeechSatPreTrainedModel,Qc as VLChatProcessor,Xc as VLMImageProcessor,Hc as ViTFeatureExtractor,Jc as ViTForImageClassification,Yc as ViTImageProcessor,Kc as ViTMAEModel,Zc as ViTMAEPreTrainedModel,ed as ViTMSNForImageClassification,td as ViTMSNModel,sd as ViTMSNPreTrainedModel,rd as ViTModel,od as ViTPreTrainedModel,nd as VisionEncoderDecoderModel,ad as VitMatteForImageMatting,id as VitMatteImageProcessor,ld as VitMattePreTrainedModel,cd as VitPoseForPoseEstimation,dd as VitPoseImageProcessor,ud as VitPosePreTrainedModel,pd as VitsModel,md as VitsModelOutput,hd as VitsPreTrainedModel,_d as VitsTokenizer,fd as Wav2Vec2BertForCTC,gd as Wav2Vec2BertForSequenceClassification,Md as Wav2Vec2BertModel,wd as Wav2Vec2BertPreTrainedModel,Td as Wav2Vec2CTCTokenizer,bd as Wav2Vec2FeatureExtractor,xd as Wav2Vec2ForAudioFrameClassification,Pd as Wav2Vec2ForCTC,kd as Wav2Vec2ForSequenceClassification,yd as Wav2Vec2Model,Fd as Wav2Vec2PreTrainedModel,vd as Wav2Vec2ProcessorWithLM,Cd as WavLMForAudioFrameClassification,Sd as WavLMForCTC,Ad as WavLMForSequenceClassification,Ed as WavLMForXVector,Ld as WavLMModel,Id as WavLMPreTrainedModel,zd as WeSpeakerFeatureExtractor,jd as WeSpeakerResNetModel,Nd as WeSpeakerResNetPreTrainedModel,Vd as WhisperFeatureExtractor,Bd as WhisperForConditionalGeneration,Od as WhisperModel,Dd as WhisperPreTrainedModel,Rd as WhisperProcessor,Gd as WhisperTextStreamer,qd as WhisperTimeStampLogitsProcessor,Wd as WhisperTokenizer,$d as XLMForQuestionAnswering,Ud as XLMForSequenceClassification,Qd as XLMForTokenClassification,Xd as XLMModel,Hd as XLMPreTrainedModel,Jd as XLMRobertaForMaskedLM,Yd as XLMRobertaForQuestionAnswering,Kd as XLMRobertaForSequenceClassification,Zd as XLMRobertaForTokenClassification,eu as XLMRobertaModel,tu as XLMRobertaPreTrainedModel,su as XLMRobertaTokenizer,ru as XLMTokenizer,ou as XLMWithLMHeadModel,nu as XVectorOutput,au as YolosFeatureExtractor,iu as YolosForObjectDetection,lu as YolosImageProcessor,cu as YolosModel,du as YolosObjectDetectionOutput,uu as YolosPreTrainedModel,pu as ZeroShotAudioClassificationPipeline,mu as ZeroShotClassificationPipeline,hu as ZeroShotImageClassificationPipeline,_u as ZeroShotObjectDetectionPipeline,fu as bankers_round,gu as cat,Mu as cos_sim,wu as dot,Tu as dynamic_time_warping,bu as env,xu as full,Pu as full_like,ku as getKeyValueShapes,yu as hamming,Fu as hanning,vu as interpolate,Cu as interpolate_4d,Su as interpolate_data,Au as is_chinese_char,Eu as layer_norm,Lu as log_softmax,Iu as magnitude,zu as matmul,ju as max,Nu as mean,Vu as mean_pooling,Bu as medianFilter,Ou as mel_filter_bank,Du as min,Ru as ones,Gu as ones_like,qu as permute,Wu as permute_data,$u as pipeline,Uu as quantize_embeddings,Qu as read_audio,Xu as rfft,Hu as round,Ju as softmax,Yu as spectrogram,Ku as stack,Zu as std_mean,ep as topk,tp as window_function,sp as zeros,rp as zeros_like};
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